Joël, Karen Martine Mathilde

Keywords: Omics, Biogeochemistry, Functional traits, Plankton, Biogeography

Marine planktonic organisms play a crucial role in trophic networks, global biogeochemical cycles, and climate regulation. Biogeochemical models simulate planktonic ecosystems dynamics to understand and predict climate change. In most biogeochemical models, planktonic diversity is implemented either through plankton functional types (PFT), i.e. theoretical entities grouping planktonic organisms according to shared functional capacities (e.g., calcifiers, nitrogen fixers or silicifiers), or functional traits, i.e. morphological, physiological or phenological features measurable at the individual level that effect growth, reproduction or survival (e.g. feeding modes, production of toxins or body size). These methods imply an a priori and restricted choice of the considered types or traits of planktonic organisms, potentially leading to oversimplified representations of planktonic diversity in models. Unprecedented amounts of meta-omics data on marine planktonic communities were recently collected at global scales, calling for the use of data-driven methodologies to determine and quantify the potential and realized functional traits of planktonic organisms in-situ. My objective in this thesis was therefore to determine how to use meta-omics data to quantify the distribution of functional traits in the environment. In a first part, I present how omics data can be used to describe and quantify specific, a priori selected traits in the global ocean. A particular focus is made on two functional traits: mixotrophy, from which the genomic basis is poorly known, and dimethyl sulfide (DMS) production, from which the genomic basis is well understood. I show how metabarcoding data on one hand and functional genomic markers on the other hand allow to decipher the biogeography of functional traits, identifying limits and advantages of the two types of data. In a second part, I present an approach allowing to detect putative protein families in metagenomics data that can be associated with functional traits, without any a priori choice of functional traits of interest. By quantifying the response of these emergent clusters to physico-chemical gradients in the global ocean, I show how this approach could allow to predict the functional composition of planktonic communities from environmental data in the near future. Finally, I use my results to discuss the potential of meta-omics data as a means of realistically representing the diversity of planktonic communities in biogeochemical models.

Résumé

Les organismes planctoniques marins jouent un rôle crucial dans les réseaux trophiques, les cycles biogéochimiques globaux et la régulation du climat. Les modèles biogéochimiques simule la dynamique des écosystèmes planctoniques pour comprendre et prédire le changement climatique. Dans la plupart de ces modèles, la diversité planctonique est représentée soit sous forme de types fonctionnels planctoniques (PFT), i.e. par des entités théoriques classant les organismes planctoniques selon leurs capacités fonctionnelles (e.g. organismes calcifiants, fixateurs d'azote, ou silicifiants), ou bien de traits fonctionnels, i.e. des caractéristiques morphologiques, physiologiques ou phénologiques mesurables au niveau individuel qui affectent la croissance, la reproduction ou la survie (e.g. modes trophiques, production de toxines ou taille corporelle). Un choix a priori et restreint des types planctoniques ou traits fonctionnels considérés est donc nécessaire, pouvant conduire à des représentations simplifiées de la diversité planctoniques dans les modèles. Des quantités inédites de données méta-omiques sur les communautés planctoniques ont récemment été collectées à l'échelle de l'océan global, appelant à l'utilisation de méthodes permettant de déterminer et quantifier les traits fonctionnels potentiels et réalisés des organismes planctoniques à partir de ces données in-situ. Mon objectif au cours de cette thèse fut donc de déterminer comment utiliser les données méta-omiques afin de quantifier la distribution de traits fonctionnels dans l'environnement. Dans une première partie, je présente comment les données omiques peuvent être utilisées pour décrire et quantifier dans l'océan global des traits spécifiques, choisis a priori. Deux traits fonctionnels sont utilisés en exemple: la mixotrophie, dont la base génomique est mal connue, et la production de diméthylsulfure (DMS), dont la base génomique est relativement bien étudiée. Je montre comment les données de métabarcoding d'une part et des marqueurs génomiques fonctionnels d'autre part permettent de décrire la biogéographie des traits fonctionnels, en identifiant les limites et les avantages des deux types de données. Dans une deuxième partie, je présente une approche permettant de faire émerger des familles protéiques putatives pouvant être associés à des traits fonctionnels au sein des données de métagénomique, sans choix a priori de traits fonctionnels d'intérêt. En quantifiant la réponse de ces familles aux gradients physico-chimiques dans l'océan global, je montre comment cette approche pourrait permettre de prédire la composition fonctionnelle des communautés planctoniques à partir de données environnementales dans un avenir proche. Enfin, j'utilise mes résultats pour discuter du potentiel des données méta-omiques comme moyen de représenter de manière réaliste la diversité des communautés planctoniques dans les modèles biogéochimiques.

Mots-clefs:

Omiques; Biogéochimie; Traits Fonctionnels; Plancton; Biogéographie.

L'homme et la mer

-Range of body size and abundance in logarithmic scales for viruses, prokaryotes, unicellular eukaryotes (commonly called protists) and multicellular eukaryotes (including metazoans and macro-algae).

A scale of macroscopic references was added to help apprehend the extent of size diversity among planktonic organisms. Size and abundance ranges were extracted from Pesant et al. (2015), but the size scale of pluricellular eukaryotes was extended to include larger organisms like jellyfish or sargass that can reach over a meter in length.

The word plankton is derived from the ancient Greek πλαγκτoζ ("planktos"), meaning wanderer. It was used for the first time by Viktor Hensen at the end of the XIXth century, to describe the animals and plants floating in the see [START_REF] Hensen | Über die Bestimmung des Planktons oder des im Meere treibenden Materials an Pflanzen und Thieren[END_REF]. The term plankton now regroups all the living organisms that can not swim against the currents, in oppo-

The bacterioplankton gather planktonic prokaryotic cells, whose abundance reaches about 10 30 in the ocean subsurface [START_REF] Whitman | Prokaryotes: The unseen majority[END_REF]. It includes a vast diversity of organisms spanning across two domains of life (Figure 1.2).

The bacterioplankton includes heterotrophic prokaryotes (see Box 1), that are responsible for the recycling of organic matter into inorganic nutrients in the ocean [START_REF] Ducklow | The bacterial component of the oceanic euphotic zone[END_REF]. It also includes small autotrophic prokaryotes (see Box 1), like Prochlorococcus, which was estimated to be the most abundant photosynthetic organism on earth [START_REF] Partensky | Prochlorococcus, a Marine Photosynthetic Prokaryote of Global Significance[END_REF]. Autotrophic species of the bacterioplankton are sometimes found in symbiosis (see Box 1) with eukaryotic organisms, for example the cyanobacteria Synechococcus is found in symbiosis with dinoflagellate genus like Ornithocercus. Such ecological relationships between planktonic organisms play key roles in marine ecosystems' dynamics [START_REF] Worden | Rethinking the marine carbon cycle: Factoring in the multifarious lifestyles of microbes[END_REF] (Figure 1.3).

Among planktonic eukaryotes, the most abundant group of organisms is usually considered to be the copepods, crustaceans that are sometimes even described as the most abundant multicellular animal on earth [START_REF] Bron | Observing copepods through a genomic lens[END_REF]. Marine planktonic unicellular eukaryotes (or protists) constitute the majority of lineages across the eukaryotic tree of life [START_REF] Worden | Rethinking the marine carbon cycle: Factoring in the multifarious lifestyles of microbes[END_REF] (Figure 1.2). Historically, planktonic eukaryotes have been divided

in two compartments based on their trophic regime. Phytoplankton corresponds to photosynthetic organisms, in opposition to the zooplankton, corresponding to heterotrophic organisms. Phytoplanktonic organisms are mostly unicellular and represent only 1% of the photosynthetic biomass on earth, but are responsible for 45% of the global net pri-

-Phylogenetic diversity of prokaryotic (A) and eukaryotic (B) marine plankton. (A)

Maximum-likelihood phylogenetic tree based on 16 concatenated single-copy phylogenetic markers extracted from 2,631 re-assembled genomes of prokaryotic plankton [START_REF] Tully | The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans[END_REF]. These genomes were assembled from 234 marine metagenomic samples from the Tara Oceans expedition (details on metagenome-assembled genomes are available in section 1.3.2). The black nodes on branches indicate bootstrap values >0.75, the color of polygons on branches extremities is coding for the phyla while their size is proportional to the number of genomes associated to the corresponding branch. Figure from [START_REF] Tully | The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans[END_REF]. (B) Diversity tree of eukaryotic plankton synthesizing information from morphological, phylogenetic (comparisons between a few marker genes from a large diversity of organisms) and phylogenomic (comparisons between parts of genomes or even full genomes of a large diversity of organisms) criteria. Seven "supergroups" are highlighted in colour, and pictures on the sides illustrate the morphological diversity of eukaryotes. Clockwise from right: archaeplastids (rhodophytes, chlorophytes, streptophytes); amoebozoa (tubulinids, arcellinids, mycetozoans); opisthokonts (fungus, microsporidians, choanoflagellates, cnidarians, bilaterians); excavates (parabasalians, oxymonads, euglenids); rhizaria (acantharians, foraminiferans, chlorarachniophytes); alveolates (ciliates, dinoflagellates); stramenopiles (labyrinthulids, synurophytes, diatoms, phaeophytes, actinophryids); unassigned [cryptomonads, katablepharids, haptophytes]. Figure from [START_REF] Worden | Rethinking the marine carbon cycle: Factoring in the multifarious lifestyles of microbes[END_REF].

mary production (see Box 1, Field et al. (1998); [START_REF] Falkowski | The Evolution of Modern Eukaryotic Phytoplankton[END_REF]). Zooplankton includes a wide diversity of organisms, from unicellular heterotrophs to jellyfish, passing by small crustaceans and planktonic larvae of nektonic organisms. They play a key role in marine ecosystems as grazers of the phytoplankton (Figure 1.3), and hence as secondary producers in marine food webs (See Box 1, Calbet et al. (2001)). However, this dichotomy modeled on the one observed between embryophytes and metazoans in terrestrial ecosystems is only poorly representing the diversity of trophic modes found among marine planktonic organisms [START_REF] Flynn | Misuse of the phytoplankton-zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types[END_REF]. Indeed, many marine unicellular eukaryotes are able to feed both through photosynthesis and by predating other organisms [START_REF] Stoecker | Mixotrophy in the Marine Plankton[END_REF]. These widely abundant organisms blurring the line between autotrophy and heterotrophy are called mixotrophs (see Box 1, Flynn et al. (2013)), and their hybrid position in the food chain constitutes a good example of the large diversity of ecological roles achieved by planktonic organisms (Figure 1.3).

Grazing of eukaryotes

Grazing of prokaryotes

Parasitism

Primary production Secondary production

Primary production

Second. production Remineralization Figure 1.3 -Ecological and biogeochemical roles in marine planktonic ecosystems. Protists from 4 size ranges and 4 trophic regimes are depicted, with their ecological interactions represented by arrows. Phototrophs and mixotrophs fix atmospheric CO 2 into organic matter through photosynthesis ( primary production), before being grazed on by heterotrophs ( secondary production, red arrows).

The organic matter then travels through higher levels of the food web, which are not represented in this diagram. Parasitoids (see Box 1) and viruses, which are not depicted here, can cause the death of their hosts (blue dotted arrows), which leads to a release of organic matter and an increase of available nutrients in the water column. Bacteria are represented as a unique simplified group in this diagram, but contain both phototrophic and heterotrophic organisms. Bacteria notably have the important role of remineralizing organic matter back to inorganic nutrients and carbon dioxide, which are then available for primary production. Figure modified from [START_REF] Caron | Probing the evolution, ecology and physiology of marine protists using transcriptomics[END_REF].

The component of biodiversity that dictates the number of ecological functions achieved in an ecosystem is often described as functional diversity [START_REF] Tilman | The Influence of Functional Diversity and Composition on Ecosystem Processes[END_REF], while the term taxonomic diversity is used to describe the biodiversity in terms of distinct 1.1 The role of marine plankton diversity in global biogeochemical cycles taxa/species, and the term phylogenetic diversity to illustrate the evolutive divergence between taxa/species [START_REF] Naeem | The Functions of Biological Diversity in an Age of Extinction[END_REF]. Similar metrics can be computed for these three facets of diversity, like the functional,taxonomic or phylogenetic richness (i.e. how many functions, species or lineages are present ?) and evenness (i.e. are there dominant functions, species or lineages ?) of a community (Mason et al., 2005;[START_REF] Cadotte | Phylogenetic diversity metrics for ecological communities: integrating species richness, abundance and evolutionary history[END_REF]. These various facets of diversity are sometimes coupled [START_REF] Galand | A strong link between marine microbial community composition and function challenges the idea of functional redundancy[END_REF], as functional capacities of organisms are shaped by their evolutive history, but a single taxonomic or phylogenetic group can also have a great diversity of ecological functions.

Dinoflagellates for example, are a functionally diverse lineage of planktonic unicellular eukaryotes that include autotrophic, mixotrophic, symbiotic or even parasitic organisms, some of which producing toxins [START_REF] Bibliography Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF]). In this thesis, I will mainly focus on the links between the functional diversity of marine plankton and global biogeochemical cycles, which are driven by planktonic organisms.

Marine biogeochemical cycles driven by planktonic communities

The physiology of planktonic organisms and their ecological interactions have multiple impacts on global biogeochemical cycles, notably including the carbon, nitrogen, phosphorus, sulfur or iron cycles. I will now detail the role of planktonic organisms in some of these elemental cycles, and how it highlights the necessity to take planktonic communities into account when studying climate.

The carbon cycle

The ocean contains about 39,000 PgC (1 PgC = 10 15 grams of carbon), which is more than 46 times the amount of carbon present in the atmosphere, and about 10 times the one found in terrestrial soils, permafrost and vegetation [START_REF] Ciais | Carbon and Other Biogeochemical Cycles[END_REF][START_REF] Quéré | Global Carbon Budget 2018[END_REF]. Carbon stocks in the ocean are mainly distributed in two pools: the dissolved inorganic carbon (DIC, about 38,000 PgC), which corresponds to carbon dioxide, carbonic acid, bicarbonate and carbonate ions, and the dissolved organic carbon (DOC, about 700

PgC) [START_REF] Ciais | Carbon and Other Biogeochemical Cycles[END_REF][START_REF] Quéré | Global Carbon Budget 2018[END_REF]. The pool of carbon corresponding to living organisms represents 3 PgC [START_REF] Ciais | Carbon and Other Biogeochemical Cycles[END_REF][START_REF] Siegenthaler | Atmospheric carbon dioxide and the ocean[END_REF].

The majority of the ocean carbon pool is located in intermediate and deep waters, where about 98% of the DIC is stored [START_REF] Ciais | Carbon and Other Biogeochemical Cycles[END_REF][START_REF] Siegenthaler | Atmospheric carbon dioxide and the ocean[END_REF].

By absorbing and stocking carbon, oceans take up to a third of anthropogenic carbon emissions, hence mitigating climate change impacts on the biosphere [START_REF] Siegenthaler | Atmospheric carbon dioxide and the ocean[END_REF]. It is then essential to understand how carbon fluxes operate in the Chapter 1. General introduction ocean to be able to predict the impacts of climate change.

Fluxes of carbon within the ocean are governed by four main processes, described as carbon pumps: the solubility pump, the biological pump (also called the soft tissue pump), the carbonate pump and the microbial pump [START_REF] Siegenthaler | Atmospheric carbon dioxide and the ocean[END_REF] The biological carbon pump is depicted in the green kernel, while the microbial carbon pump is represented by the yellow scheme on the right. Atmospheric carbon is either sequestered through the export of particulate organic matter (POM) produced by the biological carbon pump, or the creation of refractory dissolved organic matter (RDOM) produced by the microbial carbon pump. Diagram modified from [START_REF] Jiao | Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean[END_REF].

The solubility pump is an abiotic process, where atmospheric carbon is chemically absorbed by oceanic waters before reaching the deep layers of the ocean through thermohaline circulation, where it can stay more than 1000 years before reaching surface again [START_REF] Volk | Ocean Carbon Pumps: Analysis of Relative Strengths and Efficiencies in Ocean-Driven Atmospheric CO2 Changes[END_REF]. Sometimes called the physical carbon pump, the solubility pump is very different from the biological, carbonate and microbial ones, which are all governed by biotic processes.

The biological carbon pump starts with the fixation of dissolved inorganic carbon by autotrophic organisms through primary production, i.e. the synthesis of organic matter from inorganic carbon (Figure 1.4). The primary production of marine planktonic organisms leads to the net fixation of 45-50 Gt C per year, which is comparable to the 45-68 Gt C fixed per year by terrestrial plants [START_REF] Longhurst | An estimate of global primary production in the ocean from satellite radiometer data[END_REF][START_REF] Chavez | Marine Primary Production in Relation to Climate Variability and Change[END_REF]. The produced organic matter is then transferred along food webs, and exported towards ocean's 1.1 The role of marine plankton diversity in global biogeochemical cycles depths through the sedimentation of organisms' dead bodies and fecal pellets [START_REF] Ducklow | Upper ocean carbon export and the biological pump[END_REF][START_REF] Legendre | The microbial carbon pump concept: Potential biogeochemical significance in the globally changing ocean[END_REF] (Figure 1.4). When the organic matter reaches the ocean floor, it can stay sequestrated on geological time scales, i.e. up to millions of years [START_REF] Ducklow | Upper ocean carbon export and the biological pump[END_REF][START_REF] Herndl | Microbial control of the dark end of the biological pump[END_REF][START_REF] Legendre | The microbial carbon pump concept: Potential biogeochemical significance in the globally changing ocean[END_REF]. The biological carbon pump is estimated to cause the sinking of between 0.3 and 0.7 PgC per year at a 2000 m depth, which represents between 0.6 and 1.3% of the organic carbon produced by primary production.

The carbonate pump also refers to the sinking of biological matter towards the ocean floor, this time under the form of carbonate shells produced by calcifying planktonic organisms, such as coccolithophores or forams [START_REF] Volk | Ocean Carbon Pumps: Analysis of Relative Strengths and Efficiencies in Ocean-Driven Atmospheric CO2 Changes[END_REF]. But the magnitude of the carbonate pump is harder to estimate than the one of the biological pump, as the production of carbonate shells leads to a release of CO 2 in the surrounding waters, and eventually to the atmosphere [START_REF] Legendre | The microbial carbon pump concept: Potential biogeochemical significance in the globally changing ocean[END_REF]. The carbonate pump is even sometimes called the carbonate counter-pump [START_REF] Legendre | The microbial carbon pump concept: Potential biogeochemical significance in the globally changing ocean[END_REF].

The microbial carbon pump differs from the three other pumps as it does not correspond to a vertical flux of carbon. Instead, the microbial carbon pump includes all biotic reactions allowing to switch from the most labile forms of DOC, which are short-lived (hours to days) and accessible to micro-organisms for decomposition, to the most refractory forms of DOC, which are long lived (20 to 40,000 years) and resistant to microbial decomposition [START_REF] Legendre | The microbial carbon pump concept: Potential biogeochemical significance in the globally changing ocean[END_REF] (Figure 1.4). Since the decomposition of DOC by microbes leads to a production of CO 2 through a reaction called remineralization, the amounts of labile and refractory DOC directly influence oceanic carbon stocks [START_REF] Jiao | Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean[END_REF][START_REF] Legendre | The microbial carbon pump concept: Potential biogeochemical significance in the globally changing ocean[END_REF]. Hence, biological processes leading to the production of refractory DOC from labile DOC can be seen as carbon sequestration processes, analog to the three other carbon pumps.

I showed how biogenic fluxes of carbon from the atmosphere to the ocean are mainly driven by primary production. Marine primary production can be limited by multiple factors, notably nitrogen, phosphorus and trace elements like iron. I will now illustrate how planktonic communities play important roles in the biogeochemical cycles of these limiting substrates.

The nitrogen and phosphorus cycles

In the 1930s, Alfred Clarence Redfield identified that multiple elements were present in near constant ratio both in phytoplanktonic cells and in the marine water, giving his name to the famous Redfield ratio [START_REF] Redfield | James Johnstone memorial volume[END_REF]. More precisely, he identified the Chapter 1. General introduction carbon:nitrogen:phosphorus ratio to always be near 106:16:1, before new observations showed that this ratio could slightly change depending on phytoplankton species and environmental conditions [START_REF] Redfield | James Johnstone memorial volume[END_REF][START_REF] Martiny | Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean[END_REF]. Redfield also observed that when nitrogen or phosphorus was depleted, it was always also the case for the other, making it hard to identify which element was the most limiting one for primary production [START_REF] Redfield | James Johnstone memorial volume[END_REF]. This observation led to debates between oceanographers, with most geologists identifying phosphate as the limiting nutrient, while most biologists argued for nitrogen being the main limiter of primary productivity [START_REF] Gruber | The Dynamics of the Marine Nitrogen Cycle and its Influence on Atmospheric CO2 Variations[END_REF][START_REF] Tyrrell | The relative influences of nitrogen and phosphorus on oceanic primary production[END_REF].

Since the beginning of the century, phosphorus impact on carbon fluxes is considered to be influential on geological time scales, while nitrogen's impact on primary production is considered to be more immediate [START_REF] Gruber | The Dynamics of the Marine Nitrogen Cycle and its Influence on Atmospheric CO2 Variations[END_REF]. This is mostly explained by the fact that phosphorus enters oceanic waters only through abiotic processes [START_REF] Baturin | Phosphorus Cycle in the Ocean[END_REF]. River runoffs constitute about 80% of the total phosphorus supply into oceans, the rest being provided by volcanism, coastal abrasion, atmospheric precipitations, glacier erosion and groundwater discharge [START_REF] Baturin | Phosphorus Cycle in the Ocean[END_REF]. Inorganic phosphorus then either sediments or is assimilated by phytoplankton, and stays trapped in the Ocean for long time scales, ranging from 10,000 to 270,000 years [START_REF] Baturin | Phosphorus Cycle in the Ocean[END_REF]. On the opposite, marine nitrogen fluxes are mostly biologically driven [START_REF] Gruber | The Dynamics of the Marine Nitrogen Cycle and its Influence on Atmospheric CO2 Variations[END_REF][START_REF] Baturin | Phosphorus Cycle in the Ocean[END_REF].

Nitrogen is found in 5 relatively stable forms in the ocean (which is more than most other elements), and is used to synthesize structural elements of living cells and produce their metabolic energy [START_REF] Gruber | The Dynamics of the Marine Nitrogen Cycle and its Influence on Atmospheric CO2 Variations[END_REF]. These forms are dinitrogen (N 2 ), ammonium (NH 4 + ), nitrate (NO 3 -), nitrite (NO 2 -) and nitrous oxide (N 2 O), to which we can add all organic compounds containing nitrogen, like urea for instance [START_REF] Gruber | The Dynamics of the Marine Nitrogen Cycle and its Influence on Atmospheric CO2 Variations[END_REF]. Switches between all these different forms can be operated through oxidation-reduction reactions, mostly biologically regulated [START_REF] Gruber | The Dynamics of the Marine Nitrogen Cycle and its Influence on Atmospheric CO2 Variations[END_REF]. However, the most abundant chemical form of nitrogen, N 2 , can not be assimilated by most organisms. Indeed, the assimilation of N 2 , or nitrogen fixation, can only be achieved by organisms called diazotrophs (Figure 1.5). Until recently, only some Cyanobacteria genera like Trichodesmium were known to be diazotrophs, but multiple uncultured planktonic taxa like the Cyanobacteria UCYN-A or some Proteobacteria and Planctomycetes were identified as nitrogen fixers thanks to their genomic signature (Zehr, 2011a;[START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF]) (more on that in section 1.3.2.2). Non-diazotrophic phytoplankton can only absorb bioavailable nitrogen (i.e. not dinitrogen), mainly under the form of ammonium, which is released in large quantities by bacteria through remineralization and requires a very low energetic cost to be absorbed [START_REF] Gruber | The Dynamics of the Marine Nitrogen Cycle and its Influence on Atmospheric CO2 Variations[END_REF]. Most phytoplankton can also assimilate nitrate, nitrite and urea through 1.1 The role of marine plankton diversity in global biogeochemical cycles enzymes reducing oxidized nitrogen to ammonium, but under higher energetic costs (Zehr, 2011a).

Processes closing the nitrogen cycle by allowing the release of N 2 back to the atmosphere are also biologically mediated. Two main metabolic reactions allow the dinitrogen release to the atmosphere: denitrification and anaerobic ammonia oxydation, or anammox [START_REF] Gruber | The Dynamics of the Marine Nitrogen Cycle and its Influence on Atmospheric CO2 Variations[END_REF]Zehr, 2011a). These reductive processes occur almost only in regions where oxygen concentrations are very low, e.g. oxygen minimum zones (OMZ), benthic sediments or hydrothermal vents.

INT RODUCT ION

T he oceans are a central feature of the biosphere, with biogeochemical links to the land and atmosphere. Because the oceans cover almost three quarters of the Earth's surface, the chemical reactionswithintheoceans,bothbioticandabiotic,haveprofoundeffectsonthegascompositionof theatmosphere. T hemicroorganismsin seawater maintain thefertilityof theoceansbycatalyzing reactions that provide nutrients for growth of higher organisms and result in production and consumption of greenhouse gases. M any key variables control these ecosystem properties, and nitrogen (N ), the fourth most abundant element (after hydrogen, oxygen, and carbon) in organic matter, is one of them. T he N cycle is a critical component of the biogeochemical cycles of the water column of theocean (Figure1 -3to+5. It iscommonlyfoundasamineor amidegroupsinorganicmatter but isreadilyoxidized or reduced and, thus, hasan additional significancein marinesystemsasboth an electron acceptor and donor for energy metabolism. It isthiscomplexity in microbial metabolism that resultsin theformation and consumption of different chemical formsinvolving N atoms, and thiscomplexitydrivesthebiogeochemical cycleof N in thesea(and on Earth, in general). Clearly, N isacentral nutrient for terrestrial andaquaticsystems (Vitousek & Howarth 1991) Conceptual diagram highlighting and comparing themajor nitrogen-cycle componentsin (a) thetypical oceanic water column to that in (b) oxygen minimumzones. T heoxidation of ammoniumto nitrateiscalled nitrification but includes the processes of ammonia oxidation and nitrite oxidation, catalyzed by different microorganisms. Abbreviations: DN RA, dissimilatory nitrate reduction to ammonia; PN , particulate nitrogen.

198 Zehr • Kudela A diagram of the oceanic sulfur cycle is depicted in the red panel. Please note that this diagram focuses on the dynamics of dimethyl sulfide (DMS) and dimethylsulfoniopropionate (DMSP), and eludes all geologic parts of the marine sulfur cycle. The sulfur input to the ocean comes mostly from river runoffs, here indicated as weathering. The production of DMSP and DMS relies then exclusively on planktonic organisms. The produced DMS is ventilated to the atmosphere, which provokes the creation and densification of clouds. See section 1.1.2.3. The two left panels are extracted from Zehr (2011a), while the sulfur panel was inspired by [START_REF] Alcolombri | Identification of the algal dimethyl sulfide-releasing enzyme: A missing link in the marine sulfur cycle[END_REF].

The marine nitrogen cycle is then largely dominated by biologically driven processes (Figure 1.5). Many of these processes have only recently been identified (Zehr, 2011a), and we still can not culture most diazotrophs to study their metabolism in details. This makes it difficult, even now, to close the global nitrogen budget [START_REF] Gruber | Consistent patterns of nitrogen fixation identified in the ocean[END_REF]. Most estimates identify low nutrient areas like subtropical gyres to be concentrating nitrogen fixation (Wang et al., 2019a). In these waters, nitrogen fixation seems to be the main driver of primary production.

It was then proposed that nitrogen should be considered the proximate limiting nutrient in oceanic systems, or local limiting nutrient, while phosphorus should be considered as the ultimate limiting nutrient, limiting the total system productivity over longer time scale [START_REF] Tyrrell | The relative influences of nitrogen and phosphorus on oceanic primary production[END_REF]. Still, in oligotrophic areas like the Sargasso Sea, phosphate concentrations are sufficiently low for phosphate to be the locally limiting nutrient for primary production [START_REF] Wu | Phosphate Depletion in the Western North Atlantic Ocean[END_REF]. There are also zones of the ocean where both nitrogen and phosphate levels are high, while chlorophyll levels still remain low. In these high nutrient low chlorophyll (HNLC) zones, trace elements like iron become the main limiting factors of primary production [START_REF] Borcard | Mesoscale Iron Enrichment Experiments 1993-2005: Synthesis and Future Directions[END_REF].

When the conditions are well adapted to primary production, i.e. light and high concentrations of the limiting nutrient are available, it triggers blooms of phototrophic organisms [START_REF] Borcard | Mesoscale Iron Enrichment Experiments 1993-2005: Synthesis and Future Directions[END_REF]. These dense populations of planktonic organisms not only affect the carbon, nitrogen or phosphorus cycles, but also many other important biogeochemical cycles. Among these cycles, the sulfur cycle is one of the most influential on the climate.

Dimethyl sulfide (DMS) production by planktonic organisms and its climatic impact

Oceanic dimethyl sulfide (DMS) is the first natural source of sulfur to the atmosphere [START_REF] Charlson | Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate[END_REF][START_REF] Shaw | Bio-controlled thermostasis involving the sulfur cycle[END_REF][START_REF] Simó | Coupled dynamics of dimethylsulfoniopropionate and dimethylsulfide cycling and the microbial food web in surface waters of the North Atlantic[END_REF]. It plays an important role in marine ecology, by attracting large marine predators like fishes, but also marine birds and mammals, which seem to use it to detect planktonic blooms [START_REF] Charlson | Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate[END_REF]. It is also very influential on earth climate, by facilitating the formation and condensation of clouds, thus significantly modifying the planet albedo [START_REF] Alcolombri | Identification of the algal dimethyl sulfide-releasing enzyme: A missing link in the marine sulfur cycle[END_REF][START_REF] Charlson | Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate[END_REF][START_REF] Shaw | Bio-controlled thermostasis involving the sulfur cycle[END_REF][START_REF] Simó | Coupled dynamics of dimethylsulfoniopropionate and dimethylsulfide cycling and the microbial food web in surface waters of the North Atlantic[END_REF]. The precursor of DMS, dimethylsulfoniopropionate (DMSP), is exclusively produced by phytoplanktonic organisms (Simó et al., 2002) (Figure 1.5). Part of the DMSP is cleaved into DMS directly by phytoplankton lineages bearing DMSP lyases, notably blooming taxa like Phaeocystis [START_REF] Schoemann | Phaeocystis blooms in the global ocean and their controlling mechanisms: a review[END_REF] or Emiliania huxleyi [START_REF] Alcolombri | Identification of the algal dimethyl sulfide-releasing enzyme: A missing link in the marine sulfur cycle[END_REF]. The rest is released into the water column where it becomes available to bacterioplankton [START_REF] Levine | Environmental, biochemical and genetic drivers of DMSP degradation and DMS production in the Sargasso Sea[END_REF][START_REF] Simó | Coupled dynamics of dimethylsulfoniopropionate and dimethylsulfide cycling and the microbial food web in surface waters of the North Atlantic[END_REF]) (Figure 1.5). Bacteria can either demethylate DMSP to produce carbon and reduced sulfur compounds, which does not lead to the production of DMS, or cleave DMSP to produce an easily accessible 3-Carbon compound and volatile DMS [START_REF] Levine | Environmental, biochemical and genetic drivers of DMSP degradation and DMS production in the Sargasso Sea[END_REF].

1.2 Functional types and traits to represent marine plankton diversity in biogeochemical models

Both pathways are present in diverse bacterial lineages, including alphaproteobacteria, betaproteobacteria, gammaproteobacteria and epsilonproteobacteria, but are particularly found in the Rhodobacterales order, including the abundant Roseobacter genus [START_REF] Curson | Catabolism of dimethylsulphoniopropionate: microorganisms, enzymes and genes[END_REF]. Eukaryotic and prokaryotic planktonic organisms are then responsible for the release of DMS to the atmosphere, which impacts the ecology, the geochemistry and the climate of marine ecosystems.

I reviewed evidences that planktonic organisms are the primary and secondary producers in the ocean, and that their abilities to recycle organic matter, produce carbonate shells, fix nitrogen, intake phosphorus, or build energy on sulfur compounds are all directly affecting earth climate. But how can this functional diversity be taken into account when it comes to modeling the functioning of marine ecosystems? The use of mathematical models (see Box 2) to theoretically represent planktonic communities has been common since the early works of [START_REF] Fleming | The Control of Diatom Populations by Grazing[END_REF], Riley (1946) and [START_REF] Steele | Plant production in the northern North Sea[END_REF]. The first goals of such models were to better understand the drivers of seasonal variability in planktonic communities, first by simulating the prey-predator dynamics of Chapter 1. General introduction diatoms and zooplankton through a Lotka-Volterra (see Box 2) type approach [START_REF] Fleming | The Control of Diatom Populations by Grazing[END_REF], then by representing the annual dynamics of phytoplankton concentrations in response to environmental factors like the light intensity or the nutrient limitation (Riley, 1946). To obtain a more realistic model of the phytoplankton annual dynamics, Steele (1958) added 2 state variables (see Box 2) interacting with phytoplankton populations:

one following the dynamics of zooplankton populations and one corresponding to nutrients concentration.

Box 2: Theoretical ecology

Model: In this thesis, I will use the term 'model' to refer to mathematical models, which are mathematical representations of systems. In theoretical ecology, mathematical models are used to represent ecological systems, from simple prey-predator dynamics to global ecosystems. Such models allow to increase our understanding of the represented systems by allowing to test hypotheses on their global functioning, and sometimes predict their behavior.

State variables:

Variables that define the current state of the modeled system. Examples of state variables for ecosystem models are the biomass or population size of different groups of organisms, like phytoplankton and zooplankton.

Parameters: Values that define the modeled system. Unlike state variable, parameters values stay fixed independently of the state of the system. Examples of parameters for ecosystem models are population carrying capacities, maximum growth rates, predation rates, etc… The step of defining parameters values is called parameterization.

Differential equations:

Equations linking one or more function(s) to their derivative(s). Differential equations are used in ecosystem models to depict the dynamics of state variables over time. The differential equations of ecosystem models often can not be resolved analytically, and have to be solved through numerical approximations. Such approximations are computationally greedy, and responsible for most of the computing costs in ecological models.

Lotka-Volterra type model: Predator-prey model proposed independently by Alfred Lotka and Vito Volterra in the early 1900s.

Originally representing two state-variables, prey and predator numbers, and using only 4 parameters: the prey growth rate, the predation rate, the efficiency of predation and the predator mortality rate.

Ocean circulation models: Physical models describing the physical and thermodynamical processes of the global Ocean.

Model coupling:

The act of linking together two independent models. Two types of model coupling exist: offline coupling where outputs of one model are used as inputs in a second model, and online coupling, or full coupling, where feedbacks between the state variables of both models are defined.

From there, models simulating (1990). This model simulated the dynamics of 7 state variables: phytoplankton, zooplankton, bacteria, nitrate, ammonium, dissolved organic nitrogen and detritus. It was 1.2 Functional types and traits to represent marine plankton diversity in biogeochemical models coupled with a simplified vertical model of the ocean mixed layer circulation, and allowed to obtain quantitative estimates of seasonal nitrogen fluxes in the ocean [START_REF] Fasham | A nitrogen-based model of plankton dynamics in the oceanic mixed layer[END_REF][START_REF] Fasham | Ecosystem behavior at Bermuda Station "S" and ocean weather station "India": A general circulation model and observational analysis[END_REF][START_REF] Sarmiento | A seasonal three-dimensional ecosystem model of nitrogen cycling in the North Atlantic Euphotic Zone[END_REF].

The reduction of planktonic diversity to one, two or three state variables quickly raised the question of the potential oversimplification of biological interactions in biogeochemical models, already evoked by Riley in the 1940s [START_REF] Anderson | Progress in marine ecosystem modelling and the "unreasonable effectiveness of mathematics[END_REF]Riley, 1946). Indeed, can we hope for realistic model predictions without simulating the dynamics of key biogeochemical actors like diazotrophs, calcifiers or remineralizing prokaryotes [START_REF] Doney | Major challenges confronting marine biogeochemical modeling[END_REF]? Of course, the question became even more itching with the progress in biological knowledge about planktonic communities, and the parallel increase in computing power allowing for the inclusion of more and more state variables into models. In the 2000s, the first global 3-dimensional biogeochemical models including significantly more planktonic diversity than NPZD models came out [START_REF] Moore | An intermediate complexity marine ecosystem model for the global domain[END_REF][START_REF] Aumont | An ecosystem model of the global ocean including Fe, Si, P colimitations[END_REF][START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF][START_REF] Kishi | NEMURO-a lower trophic level model for the North Pacific marine ecosystem[END_REF]. These models relied on the concept of Plankton Functional Types (PFTs).

Plankton functional types and their use in biogeochemical modeling

In the early 90s, the concept of plant functional type was introduced in terrestrial plant ecology to group plants depending on their functional response to light and water availability [START_REF] Smith | A theory of the spatial and temporal dynamics of plant communities[END_REF][START_REF] Smith | Plant Functional Types[END_REF]. The same concept of functional types was evoked for planktonic organisms at that time, notably to describe the different size fractions of zooplankton and their different biogeochemical impacts (i.e. larger zooplankton grazing on larger preys, and their bigger fecal pellets sinking faster, enhancing export rates) [START_REF] Armstrong | Trophic Resolution[END_REF]. However, it is only in the seminal paper of Le [START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF] that the first operational 3D biogeochemical model explicitly relying on plankton functional types (PFTs) came out. If the paper by Le [START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF] was the first to clearly use the term of plankton functional type, a few anterior models had already been using multiple functional groups of plankton. It is notably the case of [START_REF] Moore | An intermediate complexity marine ecosystem model for the global domain[END_REF], who simulated the dynamics of carbon, nitrogen, phosphorus, iron, calcium carbonate and chlorophyll in 3 functional types of phytoplankton (small phytoplankton, diazotrophs and diatoms) and 1 type of zooplankton. This biogeochemical model was the first attempt at representing multiple plankton functional types and multiple limiting nutrients at global scale, but it was coupled to a grid model of ocean circulation which did not include horizontal transport (Moore et al., 2001a). The first biogeochemical model of similar complexity to use a 3D dynamic ocean circulation model was the one presented in [START_REF] Aumont | An ecosystem model of the global ocean including Fe, Si, P colimitations[END_REF], simulating the dynamics of carbon, phosphate, silicate and iron in two functional types of phytoplankton (small phytoplankton and diatoms) and two functional types of zooplankton (microzooplankton and mesozooplankton) (Figure 1.7). [START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF]. From there, the use of plankton functional types in biogeochemical models became the norm, with the number of PFTs varying from 4 or 5 to sometimes hundreds depending on the models [START_REF] Sinha | Comparison of the emergent behavior of a complex ecosystem model in two ocean general circulation models[END_REF][START_REF] Aumont | PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies[END_REF]Follows et al., 2007;Ward and Follows, 1.2 Functional types and traits to represent marine plankton diversity in biogeochemical models 2016; [START_REF] Lévy | Phytoplankton diversity and community structure affected by oceanic dispersal and mesoscale turbulence: Dispersal Impact on Plankton Diversity[END_REF]. [START_REF] Flynn | Misuse of the phytoplankton-zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types[END_REF][START_REF] Worden | Rethinking the marine carbon cycle: Factoring in the multifarious lifestyles of microbes[END_REF], created during my master 2 internship to illustrate how the PFT approach could allow detailed representations of planktonic functional diversity. Here, 21 PFTs are represented in colored boxes, yellow arrows correspond to light uptake, blue arrows to nutrient uptake, black arrows to grazing and red arrows to organic matter transfers through sinking and remineralization. The phytoplankton PFTs written in yellow correspond to potential constitutive mixotrophs, i.e. phagotrophic algae. NC mixotrophs stands for non-constitutive mixotrophs, i.e. mixotrophs that acquire the ability to achieve photosynthesis through "stealing" chloroplasts to their preys or symbiosis. To my knowledge, no current biogeochemical model include that level of functional diversity, as even the [START_REF] Dutkiewicz | Dimensions of marine phytoplankton diversity[END_REF] model, which has 350 PFTs, does not include parasitic and symbiotic relationships. Abreviations: PAR = photosynthetically active radiation, Euka = eukaryotes, Proka = prokaryotic, Prod = producers, Phyto = phytoplanctonic, Zoo = zooplanktonic.

Since its advent, the PFT approach has been criticized multiple times for its lack of ecological justifications [START_REF] Anderson | Plankton functional type modelling: running before we can walk[END_REF][START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF]. Indeed, one of the underlying assumptions behind the PFT concept is that each PFT can be modeled with a single set of parameters. But if we take the calcifiers as example, it should regroup both autotrophic calcifiers like coccolithophores (typically 5 to 100 microns in size) and heterotrophic calcifiers like forams or ostracods (both around 1 mm in size, sometimes up to several cm), Chapter 1. General introduction which play drastically distinct roles in the food webs. Hence, grouping together such a wide diversity of organisms asks the question of how to define the right set of parameters to model their growth in response of environmental factors [START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF].

A second caveat of the PFT approach lies in the a priori choice of the functional types included in models, which is left to the modeler. This can lead to the exclusion of groups of organisms like mixotrophs, which are absent from the vast majority of PFT models despite their global ecological influence [START_REF] Flynn | Misuse of the phytoplankton-zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types[END_REF][START_REF] Stoecker | Mixotrophy in the Marine Plankton[END_REF]Caron, 2016a).

Adding more and more PFTs to existing models could in theory resolve these issues, provided that in situ or experimental data are available to parameterize them. But it would push towards the production of increasingly complex, harder to interpret models, and we would need an unreachable number of PFTs to hope to exhaustively represent plankton diversity [START_REF] Frede Thingstad | Stepwise building of plankton functional type (PFT) models: A feasible route to complex models?[END_REF]. This way, a trade off has to be made between the quality of diversity representation in models and their complexity [START_REF] Frede Thingstad | Stepwise building of plankton functional type (PFT) models: A feasible route to complex models?[END_REF]. To better merge the biological aspects of plankton ecology with the theoretical frameworks of biogeochemical modeling, it was then proposed to switch towards approaches focused on the phenotypes of individuals, rather than functional groups or types [START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF][START_REF] Allen | Linking physiology to ecology: towards a new generation of plankton models[END_REF].

The functional trait approach and its use in biogeochemical modeling

Concepts and definitions

During my PhD, I participated in a review of the use of functional-trait based approaches in aquatic ecology (Martini et al., under review, full version of the paper available in Appendix A). In this review, I created an interactive mental map of functional traits commonly used in aquatic ecology (Figure 1.9). I was also in charge of writing the paragraph on links between trait-based approaches and omics data (see section 1.4). Here, I will present few of the most important points and definitions of the trait-based approach, focusing on the contribution of functional trait trade-offs theory in biogeochemical modeling, focusing on how the trait-based approach can contribute to improving the representation of planktonic organisms in marine biogeochemical models.

Like the concept of functional types, the concepts of traits and functional traits emerged from terrestrial ecology (McGill et al., 2006). These terms were widely used in the literature in the past 20 years, sometimes to describe different concepts (Violle et al., 2007). Here, we will refer to the definitions given in Violle et al. (2007) :

1.2 Functional types and traits to represent marine plankton diversity in biogeochemical models

• A trait is any morphological, physiological or phenological feature measurable at the individual level, from the cell to the whole-organism level, without reference to the environment or any other level of organization.

• A functional trait is any trait that impacts fitness (i.e. reproductive success) indirectly via its effects on growth, reproduction and survival.

Among functional traits, we can further differentiate potential traits, which are described from the literature, usually at the species level, and ideally covering a large variety of environmental conditions, from realized traits, actually measured in situ or in the laboratory (Reu et al., 2011). Litchman and Klausmeier (2008)). This typology focuses on the key functional traits that transcend taxonomic peculiarities of the different aquatic ecosystems. This typology do not explicitly include two functional traits on which I focused during my PhD: mixotrophy and DMS production. I added orange stars next to traits of the typology that are associated with mixotrophy, and blue stars next to ones associated with DMS production (see main text for further explanations on these associations).

The interactive mental map version of this typology is under the form of a network in which blue nodes correspond to the different ecological functions depicted as columns here, while orange nodes correspond to the different trait types depicted as lines. Each trait of the typology is then represented as a grey node, and is linked to the ecological functions and trait type nodes corresponding to its classification in the typology. On this interactive mental map, it is possible to hover the mouse over a node to highlight its connections, and a simple click on a trait node will open a bibliography search about this trait in aquatic ecology studies, while a maintained click on any node will allow you to move it around.

Examples of functional traits for phyto-and zooplanktonic organisms are depicted in Figure 1.9. In this thesis, i will notably focus on two functional traits, mixotrophy and DMS production, which are not explicitly represented in Figure 1.9. Mixotrophy can be considered as implicitly included as a feeding mode, but also as a combination of traits such as the photosynthesis ability and the ability to feed through phagotrophy, or as a gradient of food preferences between full autotrophy and full heterotrophy (Figure 1.9; [START_REF] Berge | Modeling succession of key resource-harvesting traits of mixotrophic plankton[END_REF]). DMS production is a harder trait to classify in a typology, as its beneficial impact on fitness still remains unclear [START_REF] Levine | Environmental, biochemical and genetic drivers of DMSP degradation and DMS production in the Sargasso Sea[END_REF]. Some proposed that the impact of DMS atmospheric release increases local winds and currents, thus increasing the dissemination potential of DMS producers, others hypothesized that DMS acted as a protection against harmful UV radiation, and finally DMS production was identified as a way to create biomass in conditions of high DMSP production by phytoplankton and low requirements in sulfur in DMS producing prokaryotes [START_REF] Simó | Production of atmospheric sulfur by oceanic plankton: biogeochemical, ecological and evolutionary links[END_REF][START_REF] Levine | Environmental, biochemical and genetic drivers of DMSP degradation and DMS production in the Sargasso Sea[END_REF].

The concepts of functional types and functional traits overlap in the sense that PFT are defined according to the functional traits of organisms, e.g. the calcifiers PFT regroups all organisms capable of producing a carbonate shell, which is a morphological and physiological feature measurable at the individual level that impacts fitness via its effect on survival. Some models even blend the concepts of functional traits and PFT, like the DARWIN model presented in Follows et al. (2007), where hundreds of PFT are randomly created from a set of functional traits. The created PFTs are then competing against each others, and only the fittest survive, allowing for the emergence of adapted functional strategies in different zones of the Ocean (Follows et al., 2007). But soon after the apparition of the first global PFT models, a new approach proposed to use functional traits to mechanistically link phytoplankton cellular-level physiology to ecosystem-level community patterns (Litchman et al., 2007). At the basis of this approach lied the concept of trade-offs between functional traits.

Traits trade-offs

Traits related to growth, reproduction and survival are often quantitatively correlated, and these correlations (or anti-correlations), described as "trade-offs", can provide a continuous view of ecological strategies among planktonic organisms (Litchman et al., 2007).

For example, a strong positive correlation exists between the maximum nutrient uptake and the cell volume (Figure 1.10), or the half-saturation constant for nutrient uptake and the cell volume (Edwards et al., 2012). These trade-offs allow to define a continuous gradient of nutrient uptake strategies, for example opposing groups like diatoms with high cell volume, maximum nutrient uptake rate and half-saturation constant for nutri-1.2 Functional types and traits to represent marine plankton diversity in biogeochemical models more comprehensive sampling of natural communities, including endpoints of the size spectrum, from picoplankton to giant diatoms.

Between-taxon differences-We find strong differences between taxa in average nutrient trait values (F igs. 3-6; Table 2). A t the same time, few between-taxon differences remain when controlling for cell volume (Table 2), indicating that taxonomic variation in nutrient utilization traits is largely driven by size variation. Considering raw trait values (not corrected for volume), among marine species differences in scaled uptake affinities suggest that the relatively small haptophytes and prymnesiophytes should be better competitors under P limitation, whereas the relatively large dinoflagellates and raphidophytes should be poorer competitors (F ig. 7C). The same trends occur for scaled N affinity, although there are no data for chlorophytes (F ig. 7D). A mong freshwater species, the data for scaled P affinity suggest that cyanobacteria tend to be better competitors, whereas the desmids are poor competitors, with diatoms and non-desmid chlorophytes in between (F ig. 5C). There are fewer data for scaled N affinity among freshwater species, but the present patterns suggest that cyanobacteria may be poorer competitors for nitrate (F ig. 5D); however, only two cyanobacteria are present in this analysis, and one of these species can fix nitrogen, which may compensate for low nitrate affinity.

There is significant variation in mean m max bet (Table 2), with most variation due to the fact th marine species, diatoms have high growth rat to their cell volume, whereas among freshwate cyanobacteria have low growth rates relative to volume (F ig. 3C,D). However, because of the small size of cyanobacteria cells, their raw growt still relatively high among freshwater species, o (Fig. 3B).

Q N

min : Q P min ratios-Our analysis of the struc ratio supports prior work showing broad variat ratio across phytoplankton (Rhee and Goth K lausmeier et al. 2004). We also find that Q N m greater on average for freshwater species. I f the of this ratio is driven by therelativeavailability o the difference between habitats may be due prevalenceof P limitation in freshwater systems ( 2007). A lternatively, if the evolution of this ratio by allocation to resource acquisition proteins vs. (K lausmeier et al. 2004), higher ratios among species may indicatea greater tendency for nutrie limitation in freshwater systems, and a greater ten near-maximal growth rates in marine systems et al. 1979). Reviews in L &O fewer than two species. Therefore, the following groups were compared for freshwater species: cyanobacteria, diatoms, desmids, and non-desmid chlorophytes (desmids were only present for the phosphate traits). F or the marine species, the following groups were compared: diatoms, dinoflagellates, chlorophytes, haptophytes, and raphidophytes. We also used t-tests to compare mean trait values for freshwater and marine diatoms, because diatoms from both environments were relatively well represented.

Comparison of scaling exponents to theoretic tions-Theobserved scalingexponentsof nutrient

We tested for correlations between the primary nutrient utilization traits, which could indicate physiological trait constraints or correlated selection pressures. Because most traits are correlated with volume, we also tested for partial correlations that controlled for the combined effect of volume on each pair of traits. Correlations between the compositetraits arethesubject of a separatemanuscript, in which we use techniques for multivariate analysis in the presence of missing data to better estimate the correlations between cell volume, uptake affinity, and scaled uptake affinity for N and P (Edwards et al. 2011).

Results

Trait-volume scaling-All six of the primary nutrient traits tend to increase with cell volume (Fig. 1). Four of these traits have no significant difference in slope between freshwater and marinespecies: V N max , Q N min , V P max , and Q P min (Table 1; Fig. 1). In contrast, K N and K P both have significantly different slopes for freshwater vs. marine species, with freshwater species exhibiting a steeper slope for K N , but exhibiting no significant relationship with volume for K P (Fig. 1B,E). Of the four composite nutrient traits, P affinity, N affinity, and scaled N affinity all have slopes that are indistinguishable for freshwater and marine species. Both P affinity and N affinity increase with cell volume, whereas scaled N affinity decreases with cell volume (F ig. 2A,B,D). Scaled P affinity exhibits significantly different slopes for freshwater and marine species, with marine species showing a negative relationship with volume, and freshwater species showing no significant relationship (Fig. 2C). M aximum growth rate tends to declinewith cell volume(F ig. 3A), and marinespecies show a significantly shallower slope than freshwater species (Table 1).

Between-taxon trait differences-m max differs significantly between taxa for both freshwater and marine species, with and without controlling for volume (Table 2; Fig. 3C,D). Because of the greater amount of data for m max , we also tested for differences in the allometric exponent between taxa; SM A slopes did not differ by taxon for freshwater (likelihood ratio 5 6.46, p 5 0.09) or marine species (likelihood ratio 5 0.58, p 5 0.96). F or marinespecies, after controllingfor volume, mean maximum Fig. 1. Scaling relationships between primary nutrient utilization traits and cell volume. All variables arelog 10 -transformed. M arine species are larger open circles, freshwater species are smaller filled circles. L ines plotted are from the SM A regressions in Table 1. I f there is no significant difference in slopes between marine and freshwater species, as reported in Table 1, a single solid line is plotted. If the slopes differ significantly, a dotted line is plotted for the marine species and a dashed line is plotted for the freshwater species. I f there is no significant correlation between the variables, no line is plotted. component parameters V max , K, and Q min . V max tends to increase more steeply with volume than K, resulting in an increasein cell-specific uptakeaffinity with volume(Fig. 2). When uptake affinity is scaled by Q min , this results in a decrease in scaled uptake affinity with increasing volume, because the scaling exponents of V max and Q min tend to be similar. The exception to this trend is scaled P affinity for freshwater species, which does not decline with increasing volume; this may bedueto thefact that K P does not decline with volume.

Potential constraints among nutrient utilization traits-A raw correlation analysis reveals a preponderanceof positive correlations among nutrient traits (Table 3). These positive correlations may partially constrain the evolution of competitive ability, because for a given nutrient, a positive correlation between V max and K, or between V max and Q min , constrains the evolution of equilibrium competitive ability, whereas a positive correlation between K and Q min has the opposite effect (L itchman et al. 2007). However, if these trait correlations are driven solely by cell size, then the only relevant constraints for trait evolution are those that will constrain size evolution, i.e., the trait-volume scaling parameters (L itchman et al. 2009). Our partial correlation analysis, which tested for trait correlations after controlling for the effect of cell volume, yielded equivocal results. This analysis yielded only positive significant correlations between nutrient traits, with the exception of K P vs. K N for fr the raw correla significant corre were strongly c (Table 3). It th correlation anal correlated effect error for either plasticity in cell volume relation correlation from between a trait a to the negative traits (Table 3).

However, som represent constr negative correla species is unlike tribute to a trad phosphate amon partial correlati lution of high s (for marine sp freshwater speci simultaneous ev both nutrients(f for freshwater Therefore, these ent uptake, which can be related to the "velocity" or "r" strategy in theoretical ecology (Margalef, 1978), to ones like haptophytes (including coccolithophores) with low cell volume, maximum nutrient uptake rate, and half-saturation constant for nutrient uptake (Litchman et al., 2007;Edwards et al., 2012), which can be related to the "affinity" or "K" strategy (Figure 1.10). This way, by using continuous quantitative relationships between traits, it is possible to create a multidimensional trait space in which different trait combinations are available, corresponding to different phenotypes and ecological strategies [START_REF] Lamanna | Functional trait space and the latitudinal diversity gradient[END_REF]. Trait-based models typically use trade-offs between traits to define individual-level processes such as mortality, nutrient uptake rates, or metabolic costs (Kiørboe et al., 2018). By including the influence of the environmental conditions on trade offs, it is then possible to define optimal traits and trait distributions on large geographical scales, which links trait-based models to global bigeochemical ones (Kiørboe et al., 2018).

The DARWIN model that I evoked earlier uses known trade-offs between functional traits to shape a limited trait-space to draw the random PFTs from, and avoid the creation of super-dominant PFTs overrunning the ecosystem. Still, it differs from trait-based models for it does not take into account the inter-individual variations in traits in each randomly created PFT.

By focusing on continuous quantitative relationships between measurable individual features, trait-based approaches allow to incorporate the notions of plasticity and phenotypic variability between species and individual organisms (Violle et al., 2012). This key biological aspect is often absent from classic PFT modeling, where single sets of parameters are used for each PFT, and could help introduce acclimation in biogeochemical models [START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF]. Moreover, trade-offs between traits have not only been described in phytoplankton, but also in zooplankton (Litchman et al., 2013) and microbial populations [START_REF] Litchman | Microbial resource utilization traits and trade-offs: implications for community structure, functioning, and biogeochemical impacts at present and in the future[END_REF], offering a way to describe most actors of planktonic communities.

However, trade-offs between functional traits are: (1) often not applicable to all organisms and/or all environments (i.e. exceptions exist to almost every trade-offs), ( 2) especially challenging to assess because a lot of data are required to define a trade-off as a general rule, and (3) difficult to compare and combine (how to prioritize the impact of different trade-offs on fitness ?) [START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF]. Trait-based approaches in general also suffer from the drawback of a priori choosing the functional traits included in the model, as already evoked in 1.2.2. Yet, the recent wealth in omics data is now changing our vision of planktonic diversity, which could help resolve some of these drawbacks.

Emergence of omics data to study planktonic diversity 1.3.1 Omics data and their application to plankton communities

In 1977, Frederick Sanger, Steve Nicklen and Alan Coulson presented the first rapid and reliable DNA sequencing method [START_REF] Sanger | DNA sequencing with chain-terminating inhibitors[END_REF]. Only 15 years later, the sequencing of ribosomal RNA from environmental samples allowed for the first time to detect Archaea in coastal marine waters [START_REF] Delong | Archaea in coastal marine environments[END_REF] and in the open waters of the Pacific ocean [START_REF] Fuhrman | Novel major archaebacterial group from marine plankton[END_REF]). Archaea were thought to only live in extreme environments at the time, and this is an early example of how progresses made in DNA sequencing facilitated the sampling and analysis of full planktonic communities by bypassing the tedious morphological identification of species in complex samples. Only a few years later, such advances gave birth to the fields of metagenomics (See Box 3, [START_REF] Riesenfeld | Metagenomics: Genomic Analysis of Microbial Communities[END_REF]; [START_REF] Tringe | Metagenomics: DNA sequencing of environmental samples[END_REF]; [START_REF] Venter | Environmental Genome Shotgun Sequencing of the Sargasso Sea[END_REF]).

Before the end of the century, Sanger sequencers were used at long-term oceanographic 1.3 Emergence of omics data to study planktonic diversity time-series like BATS to monitor seasonal changes in planktonic functional and taxonomic diversity [START_REF] Giovannoni | Twenty-five Years of Omics at BATS[END_REF]. The acquired ability to quickly analyze full planktonic communities notably led Craig Venter and his team to launch the Global Ocean Sampling, a large scale oceanographic cruise inspired by the circumglobal naturalist expeditions of the XIXth century (e.g. the Challenger expedition, 1872-1876) [START_REF] Venter | Environmental Genome Shotgun Sequencing of the Sargasso Sea[END_REF][START_REF] Rusch | The Sorcerer II Global Ocean Sampling Expedition: Northwest Atlantic through Eastern Tropical Pacific[END_REF]. This expedition started in 2003, it used Sanger sequencing, and did not include metatranscriptomics data (See Box 3, [START_REF] Venter | Environmental Genome Shotgun Sequencing of the Sargasso Sea[END_REF]; [START_REF] Rusch | The Sorcerer II Global Ocean Sampling Expedition: Northwest Atlantic through Eastern Tropical Pacific[END_REF]). Metatranscriptomics: Study of the RNA sequencing data of one or multiple communities of organisms in their environment.

Box 3: Sequencing data

Barcoding: Study of one or multiple molecular markers sequenced after a targeted PCR amplification of the corresponding DNA or RNA region(s). Proxy for detecting and quantifying taxa. Was achieved with Sanger sequencing in the late 90s, and sometimes still is.

Metabarcoding: Barcoding of one or multiple communities of organisms in their environment. The most commonly used markers are hyper-variable regions (labelled V1 to V9) of the16S ribosomal RNA which is universal among prokaryotes and of the 18S ribosomal RNA which is universal among eukaryotes.

Operational taxonomic unit (OTU):

Cluster of sequences grouped by similarity, used as a proxy of species.

Reads: Sequences resulting from HTS.

In between 1996 and 2015, while the costs were divided by at least 250,000 over the same time period [START_REF] Reuter | High-Throughput Sequencing Technologies[END_REF]. HTS methods became the reference for producing sequencing data in planktonic ecology, both for local studies including long term timeseries [START_REF] Gilbert | The Taxonomic and 157 BIBLIOGRAPHY Functional Diversity of Microbes at a Temperate Coastal Site: A 'Multi-Omic' Study of Seasonal and Diel Temporal Variation[END_REF][START_REF] Galand | A strong link between marine microbial community composition and function challenges the idea of functional redundancy[END_REF][START_REF] Arsenieff | Diversity and dynamics of relevant nanoplanktonic diatoms in the Western English Channel[END_REF], and for larger spatial scale studies (Sunagawa et al., 2015;[START_REF] Acinas | Metabolic Architecture of the Deep Ocean Microbiome[END_REF]Kopf et al., 2015). The most popular method from the 2nd generation of HTS, Illumina sequencing, used to produced relatively short reads, i.e. around 150 base pairs.

In this way, Sanger sequencing, ensuring routinely the collection of 500 base pairs sequences, is still used today, especially when targeting specific genes [START_REF] Levine | Environmental, biochemical and genetic drivers of DMSP degradation and DMS production in the Sargasso Sea[END_REF].

Since 2015, a 3rd generation of HTS methods were developed with the aim of produc-Chapter 1. General introduction ing long reads of multiple thousands of base pairs, while keeping the cost and speed advantages of 2nd generation sequencing [START_REF] Giordano | De novo yeast genome assemblies from MinION, PacBio and MiSeq platforms[END_REF]. For now, uses of 3rd generation sequencing methods in planktonic ecology are quite scarce, but might become the new standard in the next decade (Lombard et al., 2019). [START_REF] Pesant | Open science resources for the discovery and analysis of Tara Oceans data[END_REF].

Following the footsteps of the Global Ocean Sampling, other large-scale cruises were launched in the 2000s and 2010s, this time taking advantage of the HTS to include metatranscriptomics (see Box 3) and cover more locations and depths of sampling (Pesant 1.3 Emergence of omics data to study planktonic diversity et al., 2015;[START_REF] Acinas | Metabolic Architecture of the Deep Ocean Microbiome[END_REF]. Examples of such cruises are the Malaspina expedition in 2010-2011 [START_REF] Duarte | Seafaring in the 21st century: the Malaspina 2010 circumnavigation expedition[END_REF][START_REF] Acinas | Metabolic Architecture of the Deep Ocean Microbiome[END_REF], and the Tara expeditions, including Tara Arctic in [START_REF] Marlin | BIOTIC -Biological Traits Information Catalogue[END_REF]-2008, Tara Oceans and Tara Oceans Polar Circle in 2009[START_REF] Li | Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM[END_REF], and Tara Pacific in 2016-2018 (Figure 1.11;[START_REF] Karsenti | A Holistic Approach to Marine Eco-Systems Biology[END_REF]; [START_REF] Planes | The Tara Pacific expedition-A pan-ecosystemic approach of the "-omics" complexity of coral reef holobionts across the Pacific Ocean[END_REF]. By sampling plankton communities along with their physico-chemical environment, such expeditions offered the first opportunities to study the links between the environmental conditions and planktonic taxonomic and functional diversity in the global ocean (de Vargas et al., 2015;Sunagawa et al., 2015). The philosophy behind the Tara expeditions was described as holistic [START_REF] Karsenti | A Holistic Approach to Marine Eco-Systems Biology[END_REF], as it involved meta-omics, but also physico-chemical measurements, and other methods like automatic underwater imaging, allowing to study the entire planktonic communities from viruses to pluricellular organisms (see Figure 1.1;Figure 1.11). A complementary approach to global scale plankton sampling was proposed by the Ocean Sampling Day (OSD) consortium, who organized the sampling of more than 300 meta-omics samples on the same day (June 21st 2014), all across the global ocean and with a unified protocol (Kopf et al., 2015). Finally, initiatives like the international census of marine microbes (ICoMM) approach the idea of global scale sampling by merging and homogenizing multiple independent datasets and making them available on easily accessible platforms [START_REF] Amaral-Zettler | A global census of marine microbes[END_REF]. In the case of ICoMM, datasets come from omics but also environmental data, mass spectrometry or lipid structures data, with the goal to provide an atlas of marine unicellular organisms and their physiology [START_REF] Amaral-Zettler | A global census of marine microbes[END_REF].

An unprecedented amount of meta-omics data has then been sampled in the last 20 years, but it would not be useful without reference databases. Metabarcodes and/or

Operational Taxonomic Units (OTUs, see Box 3) need to be confronted to taxonomic annotation databases such as PR2 (Guillou et al., 2013) or SILVA [START_REF] Quast | The SILVA ribosomal RNA gene database project: improved data processing and web-based tools[END_REF] to be annotated to a lineages. Similarly, genes and transcripts obtained from metagenomics and metatranscriptomics need to be confronted to reference databases such as KEGG [START_REF] Aramaki | KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold[END_REF], or EggNOG [START_REF] Huerta-Cepas | eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences[END_REF] to be associated with a function and/or an organism. Such reference databases play a key role for the analysis of functional traits in planktonic communities through meta-omics: functional annotation databases allow to make direct links between genes and metabolic pathways, some of which can be associated with functional traits, while taxonomic databases can be coupled with trait databases that link taxa to their potential functional traits. In the next section, I will review some concrete examples of how meta-omics data changed our understanding of planktonic ecosystems, notably illustrating the influence of reference databases. Metabarcodes are associated to taxa, through the use of taxonomic databases as PR2 for eukaryotes (Guillou et al., 2013) or SILVA for prokaryotes [START_REF] Quast | The SILVA ribosomal RNA gene database project: improved data processing and web-based tools[END_REF]. The identified taxa can then be associated with potential traits through manual trait annotation or the use of trait databases, such as Traitbank which includes traits of lineages from the whole tree of life [START_REF] Parr | TraitBank: Practical semantics for organism attribute data[END_REF], or the functional traits of marine protists database (Ramond et al., 2018) (see Table 2 of Martini et al. in appendix A for a thorough list of trait databases). Genes and transcripts sequenced through metagenomics and metatranscriptomics can be associated to taxa and functions through databases like eggNOG [START_REF] Huerta-Cepas | eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences[END_REF], metagenomics giving access to potential traits while metatranscriptomics allowing to measure realized traits.

Planktonic functional and taxonomic diversity through the lens of meta-omics data

This section will be organized around three axes: how omics data (1) pushed us to re-scale our view of planktonic organisms' taxonomic and functional diversity, (2) provided new insights in plankton-mediated biogeochemical cycles, and (3) allowed for the reconsideration of long overlooked groups of organisms.

Quantifying taxonomic and functional diversity

As evoked in 1.3.1, one of the first contributions of omics data to plankton ecology was the discovery of archaeal 16S rDNA in open water and coastal samples [START_REF] Fuhrman | Novel major archaebacterial group from marine plankton[END_REF][START_REF] Delong | Archaea in coastal marine environments[END_REF]. More recently, by analyzing about 1.7 million V9 regions of 18S rDNA sequences from 334 size-fractionated plankton samples of the Tara Oceans expedition, de Vargas et al. (2015) were able to detect about 110,000 OTUs, when only 11,200 species of eukaryotic plankton had been formally described morphologically at the time, according to these authors. About one third of these OTUs could not be associated to any known eukaryotic group (de Vargas et al., 2015). In parallel, Sunagawa et al. (2015) detected 1.3 Emergence of omics data to study planktonic diversity 35,650 prokaryotic OTU using nearly full 16S rDNA (assembled from metagetagenomic reads) of 243 size-fractionated samples of the same expedition, from which 7% could not be annotated at the phylum level. By analyzing together these 16S and 18S datasets, [START_REF] Ibarbalz | Global Trends in Marine Plankton Diversity across Kingdoms of Life[END_REF] recently showed that the taxonomic diversity of planktonic Bacteria, Archaea and Eukaryota was higher at low latitudes, near the Equator, than at polar locations, like for larger marine organisms and for terrestrial ecosystems [START_REF] Hillebrand | On the Generality of the Latitudinal Diversity Gradient[END_REF][START_REF] Rombouts | Global latitudinal variations in marine copepod diversity and environmental factors[END_REF].

In addition to metabarcoding data, the recent advent of metagenome-assembled genomes (MAGs) largely participated in increasing our knowledge about the taxonomic diversity of planktonic organisms (n.b. MAGs are also called metagenomic species). MAGs are nearcomplete genomes assembled from DNA fragments coming from metagenomes, without using reference genomes [START_REF] Nielsen | Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes[END_REF]. Instead, quality-filtered reads are assembled into contigs, and contigs reaching a minimum length (typically more than a few thousand base-pairs) are then binned together according to metrics like their abundance profiles across metagenomes (i.e. contigs with similar distribution across the samples)

and their GC content (percentage of bases that are either guanine or cytosine), allowing to identify the groups of contigs (or "bins") coming from the same population of a single lineage [START_REF] Nielsen | Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes[END_REF][START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF][START_REF] Parks | Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life[END_REF][START_REF] Tully | The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans[END_REF].

The assembly of each bin can then give a more-or-less complete MAG (usually MAGs with completion estimated below 50% are discarded, completion being estimated based on the presence of sets of marker genes [START_REF] Parks | CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes[END_REF]), which can be taxonomically and functionally annotated through comparisons with reference genomes. Although the presence of a unique lineage/organism in the resulting MAG remains hypothetical (e.g. phylogeneticaly distinct organisms in symbiosis could for example be binned together due to high correlations in their abundances), tools like the manual refinement of bins and the computation of contamination percentages estimating the amount of badly binned contigs allow for determining high quality MAGs that can serve as proxies for taxonomic entities, in a way similar to OTUs [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF]. They bring the advantage of giving access to near complete genomes instead of only ribosomal DNA, but also of not using primers and including unannotated sequences. In only the last three years, [START_REF] Acinas | Metabolic Architecture of the Deep Ocean Microbiome[END_REF]. Finally, [START_REF] Vorobev | Transcriptome reconstruction and functional analysis of eukaryotic marine plankton communities via high-throughput metagenomics and metatranscriptomics[END_REF] proposed a similar Chapter 1. General introduction approach to MAGs binning, but this time aiming at building transcriptomes. They were able to detect about 12,000 groups of co-abundant genes in 365 metagenomes, among which 924 were identified as metagenomic based transcriptomes (MGTs), for they contained more than 500 unigenes (i.e. groups of transcripts coming from a unique gene) [START_REF] Vorobev | Transcriptome reconstruction and functional analysis of eukaryotic marine plankton communities via high-throughput metagenomics and metatranscriptomics[END_REF]. The taxonomic assignation of unigenes among each MGT were then used to define their taxonomy and estimate their phylogenetic homogeneity. These

MAGs do not only expand our view of taxonomic diversity among planktonic organisms (Figure 1.2A), but also in the tree of life, with notably 11 potential new phyla detected in the 76 bathypelagic MAGs only [START_REF] Acinas | Metabolic Architecture of the Deep Ocean Microbiome[END_REF].

Omics data also allowed to quantify the genetic diversity of plankton communities, and to link it with functional diversity through the analysis of present and expressed genes in hundreds of metagenomes and metatranscriptomes (Sunagawa et al., 2015;[START_REF] Acinas | Metabolic Architecture of the Deep Ocean Microbiome[END_REF][START_REF] Delong | Community Genomics Among Stratified Microbial Assemblages in the Ocean's Interior[END_REF][START_REF] Louca | Decoupling function and taxonomy in the global ocean microbiome[END_REF][START_REF] Galand | A strong link between marine microbial community composition and function challenges the idea of functional redundancy[END_REF]. Notably, a catalog of 47 million non-redundant genes was recently issued using 370 metagenomes of the prokaryotes-enriched size fractions from Tara Oceans and Polar Circle expeditions (Salazar et al., 2019). Comparing them with the gene available in reference databases, 39% of the genes in this catalog could not be annotated with a biological function, highlighting the gap remaining in our functional understanding of plankton genetic diversity. Similarly, 4.03 million genes were detected in 58 bathypelagic metagenomes, from which 71% had not been detected in global surveys of the upper ocean [START_REF] Acinas | Metabolic Architecture of the Deep Ocean Microbiome[END_REF]Sunagawa et al., 2015).

Thanks to such catalogs, it is now possible to quantify a part of the functional diversity of planktonic communities corresponding to genes with annotated functions, and notably to investigate its link with environmental conditions. This way, temperature was identified as the main driver of genomic functional diversity in prokaryotic plankton communities of the open ocean (Salazar et al., 2019). The quantification of both taxonomic and functional diversity of plankton communities through omics data also allowed to investigate the links between the two facets of plankton diversity on large spatial and time scales (Sunagawa et al., 2015;[START_REF] Louca | Decoupling function and taxonomy in the global ocean microbiome[END_REF][START_REF] Galand | A strong link between marine microbial community composition and function challenges the idea of functional redundancy[END_REF]. This led to contrasting results, as the two facets of diversity were identified as decoupled when using the Tara Oceans data [START_REF] Louca | Decoupling function and taxonomy in the global ocean microbiome[END_REF]Sunagawa et al., 2015), but were tightly correlated in a coastal time-series station in the northwestern Mediterranean Sea [START_REF] Galand | A strong link between marine microbial community composition and function challenges the idea of functional redundancy[END_REF]. Finally, recent results based on a multi-omics analysis (i.e., blending metagenomics and metatranscriptomics) showed that changes in gene expression rates across prokaryotic planktonic communities with similar taxonomic and genetic composition could significantly shape their functional activity (Salazar et al., 2019), adding a new 1.3 Emergence of omics data to study planktonic diversity layer of complexity to the global picture.

Omics data have led to the discovery of uncharted branches in the tree of life [START_REF] Rinke | Insights into the phylogeny and coding potential of microbial dark matter[END_REF] and of millions of novel genes coding for proteins of unknown biological functions, sometimes issued from organisms of unknown phylogenetic lineages (Salazar et al., 2019;[START_REF] Acinas | Metabolic Architecture of the Deep Ocean Microbiome[END_REF]. This fraction of uncharacterized data has been described as the microbial dark matter, or more recently the dark side of omics, or the twilight zone, to avoid using the poor comparison with astrophysics' dark matter, which is theoretically predicted to exist but experimentally undetectable, pretty much the inverse from microbial dark matter [START_REF] Lobb | Remote homology and the functions of metagenomic dark matter[END_REF] [START_REF] Fondi | Every Gene Is Everywhere but the Environment Selects": Global Geolocalization of Gene Sharing in Environmental Samples through Network Analysis[END_REF] are divided in four categories depending on their taxonomic annotation (lines, based on the RAIphy algorithm [START_REF] Nalbantoglu | RAIphy: Phylogenetic classification of metagenomics samples using iterative refinement of relative abundance index profiles[END_REF]) and functional annotation (columns, based on the Pfam database [START_REF] Bateman | The Pfam protein families database[END_REF]). The yellow background indicates sequences with known taxonomic and functional annotations, while the grey background indicates proteins from unknown lineages and with unknown function, hence corresponding to microbial dark matter. The number of sequences in each category is indicated, as well as the corresponding percentage, and a pie-chart indicating the distribution of the sequences across different ecosystem types. Proteins found in hosts ( e.g. human gut microbiome) dominate the known/known category, while proteins from aquatic environments dominate the microbial dark matter category. For this graph, the minimum percentage of identity used for taxonomic annotation was 85% while the alignment e-value cut-off for functional annotations was of 0.1. Figure modified from [START_REF] Bernard | Microbial Dark Matter Investigations: How Microbial Studies Transform Biological Knowledge and Empirically Sketch a Logic of Scientific Discovery[END_REF].

Omics-based functional insights into biogeochemical cycles

As seen in section 1.1.2.2, the nitrogen cycle is largely driven by biological processes.

Until the 1990s, the filamentous cyanobacteria Trichodesmium was considered to be the only significant diazotroph [START_REF] Zehr | Nitrogen Cycle of the Open Ocean: From Genes to Ecosystems[END_REF]. Nitrogenase, the enzyme responsible for nitrogen fixation, is composed of two parts, one of which is encoded by the nifH gene Zehr (2011b). By amplifying nifH sequences in environmental samples, [START_REF] Zehr | New nitrogen-fixing microorganisms detected in oligotrophic oceans by amplification of nitrogenase (nifH) genes[END_REF] detected nitrogenase genes in multiple cyanobacteria, but also in gamma and alpha Proteobacteria. Hence, by using omics data, [START_REF] Zehr | New nitrogen-fixing microorganisms detected in oligotrophic oceans by amplification of nitrogenase (nifH) genes[END_REF] [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF]. 6 of these MAGs were annotated as Proteobacteria, while the 3 others were detected as Planctomycetes, providing the first evidence of diazotrophy among this taxonomic group [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF]. Even more surprisingly, these 9 MAGs appeared as very abundant in the surface ocean, corresponding to up to 0.3% of all the sequences of a full metagenome in the Pacific Ocean (Figure 1.14).

In addition to the discovery of new diazotrophs, omics data also changed our understanding of the nitrification pathways in the global ocean [START_REF] Zehr | Nitrogen Cycle of the Open Ocean: From Genes to Ecosystems[END_REF]. Indeed, [START_REF] Venter | Environmental Genome Shotgun Sequencing of the Sargasso Sea[END_REF] found ammonia monooxygenase genes putatively coming from Crenarchaea in the samples from the global ocean survey, the archeal clade identified as abundant in the open and coastal ocean by [START_REF] Fuhrman | Novel major archaebacterial group from marine plankton[END_REF] and [START_REF] Delong | Archaea in coastal marine environments[END_REF].

This enzyme, responsible for the oxidation of ammonium into nitrite and nitrate, or nitrification (see section 1.1.2.2), was then considered to be only present in Bacteria like

Nitrospira [START_REF] Zehr | Nitrogen Cycle of the Open Ocean: From Genes to Ecosystems[END_REF]. Using targeted amplification of ammonia monooxygenase genes in environmental samples, [START_REF] Francis | Ubiquity and diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean[END_REF] then confirmed that ammoniaoxydizing archaea were ubiquitous and significantly abundant.

Omics data also greatly improved our understanding of the sulfur cycle [START_REF] Moran | Genomic Insights into Bacterial DMSP Transformations[END_REF]. The first enzyme identified as involved in DMSP demethylation, dmdA, was described in 2006, followed a year after by the first one involved in DMSP cleavage [START_REF] Howard | Bacterial Taxa That Limit Sulfur Flux from the Ocean[END_REF][START_REF] Todd | Structural and Regulatory Genes Required to Make the Gas Dimethyl Sulfide in Bacteria[END_REF]. Only five years after that, 4 additional enzymes involved in the demethylation pathway and 10 additional enzymes involved in the cleavage pathway were described [START_REF] Moran | Genomic Insights into Bacterial DMSP Transformations[END_REF]. Metagenomics data showed that the dmdA enzyme 1.3 Emergence of omics data to study planktonic diversity UCYN-A1 was abundant throughout the tropics and subtropics. However, the distribution of UCYN-A2 was much broader, with relatively high abundances not only in tropical and subtropical watersbut alsoin temperatewatersextendingfar intohigher latitudes. Populating all oceans between the Arctic and Antarctic circles, the unicellular cyanobacteria group A has a cosmopolitan distribution contrasting with Trichodesmium, whose distribution and activity is generally limited to tropical and subtropical waters with temperatures above 20 °C (refs 20,29). Given the abundance of the UCYN-A-haptophyteassociations, their geographically widespread distribution (also ref. Fig. 3 | Abundance of nitrogen-fixing populations of Planctomycetes and Proteobacteria in the surface ocean. Top: boxplots display the square-rootnormalized cumulative relative distribution of the Planctomycetes (n = 2) and Proteobacteria (n = 7) HBDs in 93 metagenomes corresponding to 12 marine geographic regions (*assuming that each litre in the surface ocean contains 0.5 billion archaeal and bacterial cells 83 ). Boxes represent the first quartile, median and third quartile of distribution values, and whiskers of 1.5 × interquartile range. Bottom: maps show the niche partitioning of HBD-06, HBD-07, HBD-08 and HBD-09 at the surface of four oceans and two seas (61 metagenomes from surface samples). Fig. 4 | Relative abundance of the TARA Oceans nifHgenes in the context of reference collections and amplicons. Violin plots summarizing the average mean coverage of nifH genes retrieved in this study, nifH reference databases 46,47 and nifH amplicon sequences from a large-scale survey 39 across 93 TARA Oceans metagenomes using a competitive read recruitment strategy. The 18 nifH genes retrieved in this study were separated into two groups ('HBD genomes' and 'Orphan genes' for which we only have a scaffold) and compared to a database of nifH gene sequences. For each gene sequence, the coverage values were corrected by excluding nucleotide positions with coverage in the 1st and 4th quartiles to minimize the effect of non-specific mapping. in samples from three datasets: ICoMM, OSD and Tara Oceans (See Section 1.3.1 for quick description of these datasets). Figure from [START_REF] Martínez-Pérez | The small unicellular diazotrophic symbiont, UCYN-A, is a key player in the marine nitrogen cycle[END_REF]. (C) Maps of the sequence abundance (in % of total sample reads) of 4 diazotrophic MAGs in 61 metagenomes from the surface ocean. The two maps on the left correspond to Proteobacteria MAGs while the two maps on the right correspond to Planctomycetes MAGs. MAGs names begin with the acronym HBD, standing for heterotrophic bacterial diazotroph. Figure from [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF].

was extremely abundant, and could be harbored by more than 50% of bacterioplankton cells in the surface Ocean [START_REF] Moran | Genomic Insights into Bacterial DMSP Transformations[END_REF][START_REF] Delong | Community Genomics Among Stratified Microbial Assemblages in the Ocean's Interior[END_REF][START_REF] Rusch | The Sorcerer II Global Ocean Sampling Expedition: Northwest Atlantic through Eastern Tropical Pacific[END_REF].

At the time, metagenomics also showed that cleavage-related enzymes were two orders of magnitude less abundant than demethylation-related ones, suggesting that some cleavage genes remained unknown, or that the cleavage of DMSP into DMS by bacteria might not be as important as previously thought [START_REF] Moran | Genomic Insights into Bacterial DMSP Transformations[END_REF]. Since then, two key enzymes responsible for DMSP cleavage have been identified: the dddK enzyme was identified as cleaving DMSP into DMS through a previously unknown pathway in the very abundant Pelagibacter, while the Alma1 enzyme was the first ever eukaryotic DMS-releasing enzyme identified, and was detected in many lineages of haptophytes and dinoflagellates (Alcolom-Chapter 1. General introduction bri et al., 2015). This way, missing links in the sulfur cycle highlighted by metagenomics data were resolved through targeted, biochemical extraction and purification approaches. Oceanic Rhizaria are protists: ingle-celled and some colonial rganisms that are eukaryotic, eaning that they contain nuclei nd other membrane-bound rganelles. The Rhizaria were rmerly thought to be phyloenetically related to the much maller and better known amoeae, because both groups feed y capturing and engulfing prey ith extensions of their cytolasm called pseudopodia. Howver, the Rhizaria can produce omplex pseudopodial networks at attain sizes of more than a entimetre. Some species can ven form cylindrical colonies pproximately 1 cm in diameter nd greater than 1 m in length 3 . These pseudopodial networks, nd the intricate mineral skeletal structures of pal (SiO 2 ), celestite (SrSO 4 ) or calcite (CaCO 3 ) at many Rhizaria form, distinguish them om amoebae, as does DNA-sequence inforation. The supergroup Rhizaria was devised more than a decade ago to contain these morphologically complex forms, and their smaller amoebic cousins have been placed among several eukaryotic supergroups in modern phylogenetic schemes 4 .

The large oceanic Rhizaria entangle and engulf a wide range of prey in their pseudopodial networks 5 . Many species dwelling in the upper ocean also possess symbiotic algae 6 , which can contribute significantly to host nutrition and to total primary production in the ocean 7 . This nutritional versatility makes amoeboid Rhizaria well adapted for life in the vast stretches of oligo trophic (nutrient-poor) waters of the open ocean.

The renowned nineteenthcentury German scientist and artist Ernst Haeckel immortalized these species in drawings that captured their elegance and complexity (Fig. 1). Much of the material for Haeckel's drawings came from samples returned by the Challenger expedition of 1872-76, a circumnavigation of the planet that laid the foundation for modern oceano graphy 8 . Yet, although the Rhizaria are valued by palaeontologists for climate reconstructions based on the fossil shell assemblages left by some of these species in deep ocean sediments, they have received only scant attention from biologists.

One of the reasons for their anonymity to oceanographers is the delicate morphologies of living specimens. These structures deteriorate badly as a result of the methods and preservatives that have routinely been used for collection and species identification. Some species contain no skeletal material, and in plankton samples their remains are often not recognizable. Substantial abundances of Rhizaria were detected by divers in the open ocean more than two decades ago 9,10 , and are visible in earlier underwater images 11 . However, truly global surveys have never been conducted.

The Tara Oceans project has begun to The diversity of morphology across 3 species of Acantharea (up: Lithoptera fenestra, middle: Amphilonche elongata, down: unidentified), all bearing photosynthetic symbionts. Collozoum inerme is a Collodaria species, a group of Radiolaria forming colonies, and of which every described species bears photosynthetic endosymbionts [START_REF] Biard | Biogeography and diversity of Collodaria (Radiolaria) in the global ocean[END_REF]. Acanthareans are rhizarians with celestite skeletons, living in symbiosis with the ubiquist haptophyte Phaeocystis. Haeckel illustrations extracted from Caron (2016b), pictures taken from [START_REF] Decelle | An original mode of symbiosis in open ocean plankton[END_REF].

The advent of meta-omics data did not only lead to the discovery of novel biogeochemically impactful organisms, but also to the identification of well-known but yet overlooked lineages as key actors of the global ocean biogeochemistry. This is particularly well illustrated by the Rhizaria supergroup. This supergroup of unicellular and sometimes colonial eukaryotes has been known since the XIXth century, as they were described by Ernst

Haeckel from samples of the Challenger expedition (1872-1876) (Figure 1.15). Rhizaria use complex pseudopodial networks to feed on preys, and harbor skeletal structures made of opal (SiO 2 ), celestite (SrSO 4 ) or calcite (CaCO 3 ) (Caron, 2016b). Many species of the Rhizaria supergroup are also known for bearing photosynthetic endo-or ectosymbionts (Figure 1.15), and the question of their impact on global primary productivity was already asked in the 1990s [START_REF] Caron | Primary productivity by symbiont-bearing planktonic sarcodines (Acantharia, Radiolaria, Foraminifera) in surface waters near Bermuda[END_REF]. However, the amount of biological knowledge about these organisms remained very scarce until the last decade, mainly because their delicate skeletal structures often do not resist to sampling and preservation methods, while they remain impossible to maintain alive in culture (Caron, 2016b). Despite earlier evidences of their high abundance derived mostly from open ocean diving observations [START_REF] Swanberg | The trophic role of colonial Radiolaria in oligotrophic oceanic environments1,2[END_REF][START_REF] Michaels | Planktonic sarcodines (Acantharia, Radiolaria, Foraminifera) in surface waters near Bermuda: abundance, biomass and vertical flux[END_REF], no global survey of their abundance and diversity had been conducted before the advent of meta-omics (Caron, 2016b). Also, they are absent of all major PFT models (e.g. they were not mentioned in Le [START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF], [START_REF] Aumont | An ecosystem model of the global ocean including Fe, Si, P colimitations[END_REF], or Follows et al. (2007)).

In 2012, omics data allowed to describe a widespread symbiotic relationship between

Phaeocystis and the rhizarian group of Acantharia [START_REF] Decelle | An original mode of symbiosis in open ocean plankton[END_REF]. This symbiosis was identified through the amplification of the 18S and 28S rDNA of isolated specimens from Acantharia, which allowed to characterize their photosymbionts as Phaeocystis, a ubiquitous haptophyte genus found free-living (i.e. not in symbiosis) from poles to tropics, which had yet never been identified in symbiotic relationships [START_REF] Decelle | An original mode of symbiosis in open ocean plankton[END_REF].

The description of this original mode of symbiosis was soon followed by the discovery of Rhizaria as the second most abundant lineage in Tara Oceans 18S metabarcoding data, just after the Opisthokonta (de Vargas et al., 2015). In-situ imaging data also collected during the Tara Oceans expedition estimated that rhizarians might constitute up to 5.2% of the total oceanic biota carbon reservoir, confirming their greatly underestimated abundance, and asking the question of their impact on global primary production (Biard et al., 2016).

A co-occurrence based analysis of the Tara Oceans metabarcoding samples identified a group of 49 eukaryotic OTUs to be particularly correlated to carbon export in the global ocean, 5 of which were annotated as rhizarians [START_REF] Guidi | Plankton networks driving carbon export in the oligotrophic ocean[END_REF]. Among the 5 OTUs identified as having the most influence on carbon export, two were annotated as Collodaria, a photosymbiotic group of Rhizaria (Figure 1.15A). Collodarians can either be solitary or colonials, they live in symbiosis with micro-algae of the Brandtodinium genus, and some species harbour an opal skeleton, while others stay "naked" [START_REF] Biard | Biogeography and diversity of Collodaria (Radiolaria) in the global ocean[END_REF].

They contributed to 82% of the rhizarian 18S rDNA sequence sampled during the Tara Oceans cruise [START_REF] Biard | Biogeography and diversity of Collodaria (Radiolaria) in the global ocean[END_REF]. These first insights into the ecological impacts of rhizarians, obtained through the analysis of Tara Oceans data, were confirmed by the identification of Rhizaria as important contributors to carbon export in the California Current Chapter 1. General introduction Ecosystem [START_REF] Gutierrez-Rodriguez | High contribution of Rhizaria (Radiolaria) to vertical BIBLIOGRAPHY export in the California Current Ecosystem revealed by DNA metabarcoding[END_REF]. This discovery relied on the metabarcoding analysis of sediment traps, i.e. oceanographic tools collecting sinking particles in the water column, in which Radiolaria (a branch of Rhizaria, including Collodaria and Acantharea) contributed to up to 90% of the 18S rDNA reads (Figure 1.16). Omics data then showed that rhizarians exhibit unique symbiotic relationships [START_REF] Decelle | An original mode of symbiosis in open ocean plankton[END_REF], are ubiquitous and globally abundant [START_REF] Biard | Biogeography and diversity of Collodaria (Radiolaria) in the global ocean[END_REF], and have a significant impact on carbon export [START_REF] Guidi | Plankton networks driving carbon export in the oligotrophic ocean[END_REF][START_REF] Gutierrez-Rodriguez | High contribution of Rhizaria (Radiolaria) to vertical BIBLIOGRAPHY export in the California Current Ecosystem revealed by DNA metabarcoding[END_REF][START_REF] Stoecker | Acquired phototrophy in aquatic protists[END_REF].

These results, combined to the recent evidences of the essential role of Rhizaria in the global silica cycle [START_REF] Biard | The Significance of Giant Phaeodarians (Rhizaria) to Biogenic Silica Export in the California Current Ecosystem[END_REF][START_REF] Monferrer | Estimating Biogenic Silica Production of Rhizaria in the Global Ocean[END_REF] are highlighting the necessity of their inclusion in biogeochemical models. Samples were taken from the water column (a), from biologically fixed sediment traps (b, fixation with formaldehyde to minimize decomposition and consumption of organic matter), and from live sediment traps (c, no biological fixation). Radiolaria contributed to 12% of the total sequence number in the water column, 88% in fixed traps, and 9.6% in live traps. The increase of Radiolaria abundance in fixed traps compared to the water column samples demonstrate their high contribution to carbon export through sedimentation. The decrease of Radiolaria abundance in live traps was probably due to selective consumption by copepods, heterotrophic nanoflagellates, or phaeodarians in the traps, and supposes a rapid remineralization of organic matter associated with Radiolaria.

Using omics data to bridge the gap between observed and modeled diversity 1.4.1 Improving marine biogeochemical models using omics data

As shown in sections 1.2.2 and 1.2.3, traditional modeling approaches based on the representation of exchanges between a few ecosystem components (i.e., nutrients, phytoplankton, zooplankton and detritus, or plankton functional types) have been criticized for their lack of ecological justifications [START_REF] Anderson | Plankton functional type modelling: running before we can walk[END_REF][START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF]. The proposition 1.4 Using omics data to bridge the gap between observed and modeled diversity of using omics data to improve the representation of planktonic diversity in biogeochemical models was raised very soon after the publication of the first results from the Global Ocean Sampling circumglobal cruise [START_REF] Venter | Environmental Genome Shotgun Sequencing of the Sargasso Sea[END_REF][START_REF] Rusch | The Sorcerer II Global Ocean Sampling Expedition: Northwest Atlantic through Eastern Tropical Pacific[END_REF][START_REF] Holling | The components of predation as revealed by a study of small-mammal predation of the European pine sawfly[END_REF]. Even though the amount of published meta-omics data was very scarce at the time compared to what is available today, [START_REF] Holling | The components of predation as revealed by a study of small-mammal predation of the European pine sawfly[END_REF] 2007) also insisted on the fact that traditional modeling approaches did not account for adaptation and evolution of planktonic organisms, which could be problematic when trying to predict the effects of climate change, and to often not explicitly represent the bacterioplankton, despite the experimental evidence of their ecological importance.

Meta-omics data then quickly appeared as an opportunity to inform traditional models

(1) on the geographical distribution of organisms and metabolisms in the global ocean,

(2) on the response of such organisms and metabolisms to environmental conditions, and

(3) on potential new ecological theories and fundamental laws that could emerge from the unprecedented quantity of data available [START_REF] Holling | The components of predation as revealed by a study of small-mammal predation of the European pine sawfly[END_REF][START_REF] Allen | Linking physiology to ecology: towards a new generation of plankton models[END_REF]Mock et al., 2016). By providing information at the gene level, omics data theoretically allow for a switch from species-based or trait-based models towards metabolism-based or gene-centric models, notably inspired by systems biology, and the physics of complex systems [START_REF] Reed | Gene-centric approach to integrating environmental genomics and biogeochemical models[END_REF][START_REF] Toseland | The impact of temperature on marine phytoplankton resource allocation and metabolism[END_REF][START_REF] D'alelio | Modelling the complexity of plankton communities exploiting omics potential: From present challenges to an integrative pipeline[END_REF]Follows and Dutkiewicz, 2011).

Omics-based metabolic modeling

Metabolism-based models rely on metabolic networks, which consist in a conceptual reconstruction of the metabolic pathways occurring in a (meta)genome, based on the functional annotation of its genes [START_REF] Grossart | Linking metagenomics to aquatic microbial ecology and biogeochemical cycles[END_REF][START_REF] Budinich | A multi-objective constraintbased approach for modeling genome-scale microbial ecosystems[END_REF]. In these networks, nodes correspond to metabolites, and arrows to metabolic reactions, driven by enzymes [START_REF] Steuer | Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth[END_REF]Budinich et al., 2017) (Figure 1.17). A metabolic network can be summarized in a stoichiometric matrix, in which columns correspond to reactions, lines correspond to metabolites, and each coefficient S ij correspond to the stoichiometric coefficient of the metabolite M i in the reaction R j (Figure 1.17) [START_REF] Budinich | A multi-objective constraintbased approach for modeling genome-scale microbial ecosystems[END_REF]. Given a set of inputs to the network (e.g. a concentration of nutrients), and under the hypothesis that the rate of formation of internal metabolites is equal to their rate Then, assuming stationary conditions, the mass balance constraint is applied, resulting in relationships between intracellular f uxes: the feasible f ux cone. A particular solution can be identif ed by applying optimization criteria, such as the optimization of biomass yield (BOF). The optimal solution is not necessarily unique.

Fig. 6. The principles of FBA: a simple example. The starting point is a reaction network (upper left panel) encoded as a stoichiometric matrix (upper right panel). Given a certain maximal utilization that satisf es the mass balance constraint and results in an optimal yield of biomass formation, as predicted by the biomass objective function, BOF (lower left panel), the problem can be cast into a linear optimization problem and is solved using standard methods of linear programming. A solution for the simple example is provided in the lower right panel. In general, the solution is not unique.

Fig. 5. The principles of fux balance analysis (FBA) as a two-stage constraint optimization: initially, all fux values are constraint by individual upper and lower bounds. Then, assuming stationary conditions, the mass balance constraint is applied, resulting in relationships between intracellular f uxes: the feasible f ux cone. A particular solution can be identif ed by applying optimization criteria, such as the optimization of biomass yield (BOF). The optimal solution is not necessarily unique.

Fig. 6. The principles of FBA: a simple example. The starting point is a reaction network (upper left panel) encoded as a stoichiometric matrix (upper right panel). Given a certain maximal utilization that satisf es the mass balance constraint and results in an optimal yield of biomass formation, as predicted by the biomass objective function, BOF (lower left panel), the problem can be cast into a linear optimization problem and is solved using standard methods of linear programming. A solution for the simple example is provided in the lower right panel. In general, the solution is not unique.

Fig. 5. The principles of fux balance analysis (FBA) as a two-stage constraint optimization: initially, all fux values are constraint by individual upper and lower bounds. Then, assuming stationary conditions, the mass balance constraint is applied, resulting in relationships between intracellular f uxes: the feasible f ux cone. A particular solution can be identif ed by applying optimization criteria, such as the optimization of biomass yield (BOF). The optimal solution is not necessarily unique.

Fig. 6. The principles of FBA: a simple example. The starting point is a reaction network (upper left panel) encoded as a stoichiometric matrix (upper right panel). Given a certain maximal utilization that satisf es the mass balance constraint and results in an optimal yield of biomass formation, as predicted by the biomass objective function, BOF (lower left panel), the problem can be cast into a linear optimization problem and is solved using standard methods of linear programming. A solution for the simple example is provided in the lower right panel. In general, the solution is not unique. Then, assuming stationary conditions, the mass balance constraint is applied, resulting in relationships between intracellular f uxes: the feasible f ux cone. A particular solution can be identif ed by applying optimization criteria, such as the optimization of biomass yield (BOF). The optimal solution is not necessarily unique.

Fig. 6. The principles of FBA: a simple example. The starting point is a reaction network (upper left panel) encoded as a stoichiometric matrix (upper right panel). Given a certain maximal utilization that satisf es the mass balance constraint and results in an optimal yield of biomass formation, as predicted by the biomass objective function, BOF (lower left panel), the problem can be cast into a linear optimization problem and is solved using standard methods of linear programming. A solution for the simple example is provided in the lower right panel. In general, the solution is not unique. . The principles of fux balance analysis (FBA) as a two-stage constraint optimization: initially, all fux values are constraint by ual upper and lower bounds. Then, assuming stationary conditions, the mass balance constraint is applied, resulting in nships between intracellular f uxes: the feasible f ux cone. A particular solution can be identif ed by applying optimization criteria, as the optimization of biomass yield (BOF). The optimal solution is not necessarily unique.

Models of cyanobacteria | 2267

. The principles of FBA: a simple example. The starting point is a reaction network (upper left panel) encoded as a stoichiometric (upper right panel). Given a certain maximal utilization that satisf es the mass balance constraint and results in an optimal yield of ss formation, as predicted by the biomass objective function, BOF (lower left panel), the problem can be cast into a linear ization problem and is solved using standard methods of linear programming. A solution for the simple example is provided in the right panel. In general, the solution is not unique.

Models of cyanobacteria | 2267

Fig. 5. The principles of fux balance analysis (FBA) as a two-stage constraint optimization: initially, all fux values are constraint by individual upper and lower bounds. Then, assuming stationary conditions, the mass balance constraint is applied, resulting in relationships between intracellular f uxes: the feasible f ux cone. A particular solution can be identif ed by applying optimization criteria, such as the optimization of biomass yield (BOF). The optimal solution is not necessarily unique.

Fig. 6. The principles of FBA: a simple example. The starting point is a reaction network (upper left panel) encoded as a stoichiometric matrix (upper right panel). Given a certain maximal utilization that satisf es the mass balance constraint and results in an optimal yield of biomass formation, as predicted by the biomass objective function, BOF (lower left panel), the problem can be cast into a linear optimization problem and is solved using standard methods of linear programming. A solution for the simple example is provided in the lower right panel. In general, the solution is not unique. Here, one optimal solution is presented, but most of the time multiple optimal solutions exist. In these cases, other cellular objectives can be optimized in addition to biomass, like minimizing the total amount of fluxes or the thermodynamic costs of reactions. Figure modified from [START_REF] Steuer | Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth[END_REF].

Models of cyanobacteria | 2267

of consumption, the stoichiometric matrix can be used to compute a constrained space of possible values for an objective function, like biomass production or growth rate [START_REF] Budinich | A multi-objective constraintbased approach for modeling genome-scale microbial ecosystems[END_REF][START_REF] Steuer | Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth[END_REF]. The optimal value for the objective function can then be obtained by solving a linear optimization problem, and corresponds to the value maximizing the "fitness" (i.e. the reproductive success) of the modeled organism given the conditions [START_REF] Budinich | A multi-objective constraintbased approach for modeling genome-scale microbial ecosystems[END_REF]Steuer et al., 2012) (Figure 1.17). This approach is called flux balance analysis (FBA), and rely on constraint-based models. Metabolic networks then allow for the mechanistic modeling of intra-cellular processes, and can even be extended to model metabolisms at community scales [START_REF] Budinich | A multi-objective constraintbased approach for modeling genome-scale microbial ecosystems[END_REF].

1.4 Using omics data to bridge the gap between observed and modeled diversity Moreover, tools are now available for the automatic reconstruction of metabolic networks [START_REF] Konwar | MetaPathways v2.5: quantitative functional, taxonomic and usability improvements[END_REF][START_REF] Kanehisa | New approach for understanding genome variations in KEGG[END_REF], opening the door to their application on full communities [START_REF] Budinich | A multi-objective constraintbased approach for modeling genome-scale microbial ecosystems[END_REF][START_REF] D'alelio | Modelling the complexity of plankton communities exploiting omics potential: From present challenges to an integrative pipeline[END_REF].

But to be included in metabolic networks, genes require a functional annotation [START_REF] Grossart | Linking metagenomics to aquatic microbial ecology and biogeochemical cycles[END_REF], when a significant part of omics data remain poorly understood on a functional level (Section 1.3.2.1). [START_REF] Reed | Gene-centric approach to integrating environmental genomics and biogeochemical models[END_REF] wrote that "although insightful for laboratory studies, [the metabolic network approach] is infeasible for use in conjunction with environmental genomics data because the majority of microbes are uncultured and their metabolic networks are thus unknown". In only 5 years, this statement became questionable with the advent of high quality MAGs for uncultured organisms, but illustrates well the necessity to have high quality genomics data and functional annotations for the metabolism-based approach to work. Metabolic networks have also been criticized for their struggle to capture temporal changes in metabolisms, like the switch between phototrophic metabolism during the day and storage-based metabolism at night in Cyanobacteria for example, despite the excellent quality of genomics data available [START_REF] Steuer | Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth[END_REF]. But recent advances in constraint-based modeling techniques proved that metabolic diurnal cycles could be derived from metabolic networks [START_REF] Reimers | Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth[END_REF]. Finally, flux balance analysis analysis have yet been rarely applied on eukaryotic organisms, at least compared to prokaryotic organisms, mainly due to the compartmentalization of eukaryotic cells, which adds a layer of complexity to the picture [START_REF] Niklas | Metabolic flux analysis in eukaryotes[END_REF]. It is why the metabolism-based approach has not yet been applied to a full biogeochemical model, despite its great potential for a mechanistic modeling of planktonic organisms. Instead, all of the omics-informed biogeochemical models published in the last decade relied on a gene-centric approach.

Gene-centric approach for biogeochemical modeling

In gene-centric models, organisms are grouped according to a few genes pre-selected for their metabolic function and usually referred to as functional genes [START_REF] Reed | Gene-centric approach to integrating environmental genomics and biogeochemical models[END_REF]Louca et al., 2016a). For example, the first published gene-centric biogeochemical model used 8 functional genes involved in nitrogen cycling as state variables: amoA for aerobic ammonia oxidation, hzo for anaerobic ammonium oxidation, nor for aerobic nitrite oxidation, dsr for sulfate reduction, nap for sulfide oxidation coupled to nitrate reduction, sox for aerobic sulfide oxidation, narG for nitrate reduction, nirK for nitrite reduction, nrf for dissimilatory nitrite reduction to ammonium and cox for aerobic respiration [START_REF] Reed | Gene-centric approach to integrating environmental genomics and biogeochemical models[END_REF]. The model described the rate of each gene production as dependant on nutrients and concentrations of reaction inhibitors, while the concentration of nutrients Chapter 1. General introduction was in turn impacted by the abundance of the different functional genes [START_REF] Reed | Gene-centric approach to integrating environmental genomics and biogeochemical models[END_REF]. It was applied to a 1 dimension (vertical) section of the Arabian Sea, allowing to reproduce observed patterns of oxygen, ammonium, nitrate and nitrite concentrations, but also observed sequence abundances of functional genes [START_REF] Reed | Gene-centric approach to integrating environmental genomics and biogeochemical models[END_REF]. A similar approach was proposed two years later, this time modeling carbon, sulfur and nitrogen cycles in a Canadian fjord through the modeling of 6 functional genes (Louca et al., 2016a).

This model improved the biological realism of gene-centric approaches by not only modeling the DNA concentration of functional genes, but also their transcription from DNA to mRNA, allowing for a more mechanistic representation of enzyme production, and a better justification for comparing model outputs with metatranscriptomics data (Louca et al., 2016a).

These early attempts led to the publication of GENOME, the first 3D (i.e. longitude, latitude and depth) gene-centric biogeochemical model, including 20 prokaryotic gene functional groups (Coles et al., 2017), and from which the functioning is schematized in Figure 1.18. The model structure of GENOME was different from the ones of Reed et al. ( 2014) and Louca et al. (2016a), as instead of only following genes concentrations as state variables, it used the abundance of organisms bearing different randomly assigned genomes and transcriptomes (Figure 1.18, the terms genomes and transcriptomes referring here to the composition in functional genes and their predicted expressions). Inspired by emergent trait-based models such as DARWIN (Follows et al., 2007) (see section 1.2.3), GENOME creates random organisms to which are assigned functional genes, before being included in a global circulation model where only the fittest organisms are conserved, allowing for the emergence of the most adapted communities at each model run (Figure 1.18). It led to the observation that across different simulation runs, the community-level metabolic rates were similar in the same geographic areas, independently of the fact that the modeled organisms were different (Coles et al., 2017). The authors then proposed that genomic composition of planktonic communities in the model had more influence on biogeochemistry than the genetic composition of the individual organisms (Coles et al., 2017).

This model-based hypothesis illustrates how gene-centric approaches allow to draw conclusions on functional diversity and its impact on biogeochemistry, where traditional PFT modeling would have struggled to decouple function from taxonomy.

Gene-centric approaches have also proven useful for confronting model outputs to experimental data [START_REF] Reed | Gene-centric approach to integrating environmental genomics and biogeochemical models[END_REF]Louca et al., 2016a;Coles et al., 2017). Indeed, by modeling DNA and mRNA concentrations, gene-centric models allow for direct comparisons with metagenomics and metatranscriptomics datasets (Figure 1.18). Despite these improvements, all the gene-centric models published so far fail at answering to Hood's Marine ecosystem models have advanced to incorporate metabolic pathways discovered with genomic sequencing, but direct comparisons between models and "omics" data are lacking. We developed a model that directly simulates metagenomes and metatranscriptomes for comparison with observations. Model microbes were randomly assigned genes for specialized functions, and communities of 68 species were simulated in the Atlantic Ocean. Unfit organisms were replaced, and the model self-organized to develop community genomes and transcriptomes. Emergent communities from simulations that were initialized with different cohorts of randomly generated microbes all produced realistic vertical and horizontal ocean nutrient, genome, and transcriptome gradients. Thus, the library of gene functions available to the community, rather than the distribution of functions among specific organisms, drove community assembly and biogeochemical gradients in the model ocean.

G enomic technologies that elucidate the links between microbes and biochemistry have revolutionized our understanding of marine ecology and the cycling of nitrogen, carbon, and other elements (1)(2)(3)(4)(5). However, the models used to predict regional and global ocean biogeochemistry and the microbes mediating their variability do not simulate quantities that can bedirectly compared with genomic data. In deep chemosynthetic environments, simulating the genes of each individual species has proven fruitful ( 6); however, for the vast diversity of microbes in the open ocean, most of which are uncultured, this approach is impractical. Hence, wedeveloped theGenome-based EmergeNt Ocean Microbial Ecosystem(GENOME) model, in which organisms with genomes and transcriptomes are simulated and compared with "omics" data. The model was inspired by trait-based emergent phytoplankton community models ( 7), but we took a different approach to developing model microbes with diverse metabolic potential. The model addresses how the distribution of metabolic functions among members of microbial communities influences ocean biogeochemistry (8), asking whether communities with distinctly different types of organisms but common metabolic ca-pabilities create similar marine biogeochemical gradients over space and time.

Constructing model communities

The GENOME model was incorporated into a general circulation model of the tropical and subtropical Atlantic Ocean to provide flow fields and environmental conditions for the model com-munities. To construct model communities, organisms were first randomly assigned sizes (1 to 2000 m m) spanning the range from bacteria to large phytoplankton. General substrate uptake affinity, growth rate, mortality, and sinking parameters wereestablished, which varied with size on the basis of observations and theory (fig. S1 and table S2) (9-11). These allometric equations described functional responses for all organisms as a function of their size, but the functional responses were only applied when an organism carried a gene relevant to the response. For example, functional responses to light were not invoked for organisms lacking a light-harvesting gene. The model was not especially sensitive to the slope of these scaling relationships because the cumulative impact of the traits associated with each gene led to high variance in the scaling relationships, consistent with observations of natural populations (fig. S1).

Each organism was then randomly assigned adegreeof complexity(number of genesselected from 20 gene functional groups; table S3). Here, we use the term "gene" to refer to a single marker gene used to identify a biochemical pathway that might involvemany coordinating genes. To avoid generating "super" microbes with all gene combinations simultaneously, the model penalized complexity by linearly reducing maximum potential growth rate as the gene number increased. This accords with the hypothesis that microbes with smaller genomes require less carbon and other limiting nutrients to produce a new cell (12) but are unable to maintain as many specialist gene functions (13), although in practice this may not always be the case ( 14). In the concerns evoked at the beginning of this section [START_REF] Holling | The components of predation as revealed by a study of small-mammal predation of the European pine sawfly[END_REF]. Indeed, these models require to a priori select a set of functional genes from which the function has to be well known (e.g light-harvesting genes, nitrification genes, nitrogen fixation genes,...), exactly like PFT need to be a priori selected in traditional PFT models. This way, the gene centric approach was only applied to well known pathways, mostly present in prokaryotic organisms, and unrepresentative of the observed functional diversity of planktonic organisms [START_REF] Reed | Gene-centric approach to integrating environmental genomics and biogeochemical models[END_REF]Louca et al., 2016a;Coles et al., 2017).

The emergence of metabolism-based and gene-centric approaches have provided a theoretical framework that allow for the use of genes as structural components of biogeochemical models (Mock et al., 2016;Stec et al., 2017;[START_REF] D'alelio | Modelling the complexity of plankton communities exploiting omics potential: From present challenges to an integrative pipeline[END_REF][START_REF] Grossart | Linking metagenomics to aquatic microbial ecology and biogeochemical cycles[END_REF][START_REF] Reed | Gene-centric approach to integrating environmental genomics and biogeochemical models[END_REF]Louca et al., 2016a;Coles et al., 2017). The gene-centric approach has even been proven to be applicable at the scale of the entire Atlantic Ocean (Coles et al., 2017). However, it has yet failed at increasing the diversity representation in biogeochemical models, by only focusing on small numbers of functional genes (a maximum of 20 in Coles et al. (2017)) and only prokaryotic metabolic pathways. This highlights the current need for data-driven methods allowing for the automatic detection and quantification of functional genes of biogeochemical and ecological importance from meta-omics data.

How to use omics data to improve planktonic diversity representation in biogeochemical models ?

In this introduction, I have reviewed how the functionally and taxonomically diverse planktonic communities impact global biogeochemical cycles. I have then presented how these planktonic communities are currently represented in biogeochemical models, highlighting the gap between observed and modeled planktonic diversity. [START_REF] Fasham | A nitrogen-based model of plankton dynamics in the oceanic mixed layer[END_REF], [START_REF] Aumont | An ecosystem model of the global ocean including Fe, Si, P colimitations[END_REF], [START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF], Follows et al. (2007) and Coles et al. (2017). NPZD stands for nutrient, phytoplankton, zooplankton and detritus models (see section 1.2.1)

-Gradient of planktonic functional and taxonomic diversity implemented in biogeochemical models, with examples of key models discussed in this introduction

, PFT stands for plankton functional types models (see section 1.2.2), while omics-based models refer to gene-centric and metabolism-based approaches, as well as to potential new theoretical frameworks involving omics data and potentially allowing for a more realistic representation of planktonic diversity in biogeochemical models (see section 1.4).

In particular, I highlighted the need for data-driven methods allowing to define model structural components from observational data, to avoid a priori choices of the model 1.5 How to use omics data to improve planktonic diversity representation in biogeochemical models ? PFTs, traits, genes or metabolic pathways. I demonstrated the promises carried by omics data to tackle this issue, but identified multiple limitations emerging from their current use in theoretical frameworks, notably the bias towards cultivated organisms and welldescribed pathways in gene-centric and metabolic models, preventing us to take full advantage of the richness of omics datasets. It led me to consider 3 main research questions, which drove the analyses presented in the following chapters of this thesis.

1. Can we use meta-omics data to detect functional traits from which the genomic basis is poorly known?

2. Can we use meta-omics data to predict the distribution of functional traits/genes in the environment through statistical modeling?

3. Can the abundance and distribution of functional traits/genes be quantified in meta-omics data without any a priori choice of focal functions and/or species? I will try to bring answers to these questions in the following parts and chapters of this manuscript:

In Part I, entitled From genes to functional traits in the global ocean: the mixotrophy and DMS production case studies, I will bring answers to questions 1. and 2. using two a priori chosen functional traits as case studies: mixotrophy, from which the genomic basis is poorly known, and DMS production, from which the metabolism is well described. This first part will be composed of two chapters: Chapter 2 will focus on how metabarcoding can be used to describe the distribution of functional traits, using mixotrophy as an illustration. In section 1.2.2 of this introduction, I evoked how mixotrophic protists are often absent from biogeochemical models, despite growing evidence of their biogeochemical importance. Here, I propose the first ever omics-based assessment of mixotrophic protists abundance in the global ocean, showing their ubiquity and unveiling some interesting characteristics of their biogeography. This chapter will be mainly composed of a manuscript entitled Mixotrophic protists display contrasted biogeographies in the global ocean, published in the ISME Journal in 2018, of which I am first author.

Chapter 3 will focus on how genomics and transcriptomics data can be used to investigate the genomic basis of functional traits in planktonic lineages, using both mixotrophy and DMS production as illustrations. The chapter will also expose how genomic markers of functional traits can be used to derive quantitative predictions of metabolic functions, through preliminary results on the analysis of the global distribution of genomic markers of DMS production.

Part II, entitled Data-driven approaches to identify and quantify the functional composition of planktonic communities, will mainly be focused on questions 2. and 3., as I will present a method allowing to use omics data to quantify gene functional groups in the global ocean without a priori. This part will be composed of one chapter:

Chapter 4 will describe a data-driven method to identify gene functional groups in meta-omics datasets, quantify their abundance, and study their response to environmental gradients. The method was tested on more than 800 prokaryotic MAGs, and machine learning techniques were used to try to predict the abundance of their gene functional groups from the environmental context. This chapter will be mainly composed of a manuscript entitled Towards omics-based predictions of planktonic functional composition from environmental data, which I have submitted as first-author to Nature Communications on the 24th of April 2020, and is currently under review.

Chapter 5 will consist in a general discussion, in which I will summarize the advantages and limits of omics-driven approaches to increase diversity in biogeochemical models.

I will also comment on the potential combination of omics data with other data types, such as high-throughput imaging, to improve our theoretical understanding of planktonic ecosystems. Finally, I will question if we should keep working at the functional-trait level, switch exclusively to gene-centric level, or maybe even try to come up with a new conceptual framework to improve planktonic diversity representation in biogeochemical models. Metabarcodes can be associated to taxonomic lineages through annotation databases such as PR2 (Guillou et al., 2013), and the number of metabarcodes associated to each lineage is approximately proportional to its biomass [START_REF] Biard | Biogeography and diversity of Collodaria (Radiolaria) in the global ocean[END_REF], more on this in the next section). A functional annotation can then allow to link lineages with functional traits, based on the literature and the knowledge of experts. The abundance of metabarcodes associated to each trait can then be computed.

Part

In the introduction, I highlighted how omics data had been included in theoretical frameworks with a strong bias towards well described metabolic pathways. One of the scientific questions that I asked was can we use meta-omics data to detect functional traits from which the genomic basis is poorly known?. The first step towards the integration of functional traits in models through omics data is to find ways to detect them in-situ, and hopefully quantify their presence in samples. Such a quantification would allow to explore the distribution of the detected trait, identify potential trade-offs with other traits, and determine its affinity for specific environmental conditions, which are key elements for the implementation of the trait in a modeling framework. The silver bullet for detecting and quantifying a trait in the environment through omics data is the access to functional genomic markers, i.e. one or multiple genes directly linked and/or responsible of the or- [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF].

Figure 2.2 -The different types of mixotrophy, or mixotypes. Constitutive mixotrophs have an innate ability to fix carbon through photosynthesis, they are sometimes called 'algae that eat'. Non-constitutive mixotrophs do not have an innate ability to fix carbon, and can be subdivided in three subgroups depending on how they acquire this ability. Generalist non-constitutive mixotrophs (GNCM) can steal the chloroplasts from any of their preys, while plastidic specialist non-constitutive mixotrophs (pSNCM) can steal the chloroplasts from some specific preys. Finally endosymbiotic specialist non-constitutive mixotrophs (eSNCM) bear photosynthtic symbionts. Pictures from

Mixotrophy is a functional trait that has long been overlooked, but is now considered to be present in the majority of protistan lineages [START_REF] Flynn | Misuse of the phytoplankton-zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types[END_REF]. Still, the physiology and 2.1 Prelude ecology of mixotrophs remain poorly known, in part because as most protistan lineages, they remain challenging to culture in labs [START_REF] Flynn | Misuse of the phytoplankton-zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types[END_REF]. The first description of the global biogeography of mixotrophs was published through a review of morphological identification data in 2017, at the beginning of my PhD [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF]. At the time, no study had investigated mixotrophy in meta-omics data, despite their clear ecological significance [START_REF] Mitra | The role of mixotrophic protists in the biological carbon pump[END_REF] and the need for information about their abundance in the open ocean [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF]. This lack of meta-omics based studies focusing on mixotrophs can notably be explained by the absence of genomic markers of mixotrophy, i.e. genes that could allow to detect mixotrophy in metagenomics and/or metatranscriptomics samples. This absence can notably be explained by the fact that multiple types of mixotrophy exist, each corresponding to organisms with distinct physiologies and behaviors (Detailed in figure 2.2), making it hard to identify specific metabolic pathways associated with this trait. Mixotrophy then appeared as a perfect case study for testing metabarcoding as an alternative to functional genomic markers, and my goal was to investigate the biogeography of mixotrophic protists in the global ocean through meta-omics data, providing the first ever list of metabarcodes corresponding to mixotrophic lineages.

The rest of this chapter will consist in a manuscript entitled Mixotrophic protists display contrasted biogeographies in the global ocean, published in the ISME journal in January 2019, and on which I am first author.

Mixotrophic protists display contrasted biogeographies in the global ocean

Authors: Emile Faure 1,2 , Fabrice Not 3 , Anne-Sophie Benoiston 2 , Karine Labadie 4 , Lucie endo-symbiotic specialist non-constitutive mixotrophs (eSNCM), and plastidic specialist nonconstitutive mixotrophs (pSNCM). Mixotrophy appeared ubiquitous, and the distributions of the four mixotypes were analyzed to identify the abiotic factors shaping their biogeographies. Kleptoplastidic mixotrophs (GNCM and pSNCM) were detected in new zones compared to previous morphological studies. Constitutive and non-constitutive mixotrophs had similar ranges of distributions. Most lineages were evenly found in the samples, yet some of them displayed strongly contrasted distributions, both across and within mixotypes. Particularly divergent biogeographies were found within endo-symbiotic mixotrophs, depending on the ability to form colonies or the mode of symbiosis. We showed how metabarcoding can be used in a complementary way with previous morphological observations to study the biogeography of mixotrophic protists and to identify key drivers of their biogeography.

Introduction

Marine unicellular eukaryotes, or protists, have a tremendous range of life styles, sizes and forms [START_REF] Caron | Marine Protistan Diversity[END_REF], showing a taxonomic and functional diversity that remains hard to define (de Vargas et al., 2015;[START_REF] Pawlowski | CBOL Protist Working Group: Barcoding Eukaryotic Richness beyond the Animal, Plant, and Fungal Kingdoms[END_REF]. This variety of organisms is having an impact on major biogeochemical cycles such as carbon, oxygen, nitrogen, sulfur, silica, or iron, while being at the base of marine trophic networks [START_REF] Caron | Probing the evolution, ecology and physiology of marine protists using transcriptomics[END_REF][START_REF] Keeling | Marine Protists Are Not Just Big Bacteria[END_REF]Caron, 2016a;[START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF][START_REF] Amacher | Molecular approach to determine contributions of the protist community to particle flux[END_REF]. Hence, they are key actors of the global functioning of the ocean.

Historically, marine protists have been classified into two groups depending on their trophic strategy: the photosynthetic plankton (phytoplankton) and the heterotrophic plankton (zooplankton).

It is now clear that mixotrophy, i.e., the ability to combine autotrophy and heterotrophy, has been largely underestimated and is commonly found in planktonic protists (Caron, 2016a;[START_REF] Stoecker | Mixotrophy in the Marine Plankton[END_REF][START_REF] Flynn | Misuse of the phytoplankton-zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types[END_REF][START_REF] Selosse | Mixotrophy everywhere on land and in water: the grand écart hypothesis[END_REF]. Instead of a dichotomy between two trophic types, their trophic regime should be regarded as a continuum between full phototrophy and full heterotrophy, with species from many planktonic lineages lying between these two extremes [START_REF] Flynn | Misuse of the phytoplankton-zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types[END_REF]. [START_REF] Mitra | Defining Planktonic Protist Functional Groups on Mechanisms for Energy and Nutrient Acquisition: Incorporation of Diverse Mixotrophic Strategies[END_REF] have proposed a classification of marine mixotrophic protists into four functional groups, or mixotypes. The constitutive mixotrophs, or CM, are photosynthetic organisms that are capable of phagotrophy, also called "phytoplankton that eat" [START_REF] Mitra | Defining Planktonic Protist Functional Groups on Mechanisms for Energy and Nutrient Acquisition: Incorporation of Diverse Mixotrophic Strategies[END_REF]. They include most mixotrophic nanoflagellates (e.g., Prymnesium parvum, Karlodinium micrum). On the opposite, the non-constitutive mixotrophs, or "photosynthetic zooplankton", are heterotrophic organisms that have developed the ability to acquire energy through photosynthesis [START_REF] Stoecker | Mixotrophy in the Marine Plankton[END_REF]. This ability can be acquired in three different ways: the generalist non-constitutive mixotrophs (GNCM) steal the chloroplasts of their prey, such as most plastid-retaining oligotrich ciliates (e.g., Laboea strobila), the plastidic specialist non-constitutive mixotrophs (pSNCM) steal the chloroplasts of a specific type of prey (e.g., Mesodinium rubrum or Dinophysis spp.), and finally the endo-symbiotic specialist non-constitutive mixotrophs (eSNCM) are bearing photosynthetically active endo-symbionts (most mixotrophic Rhizaria from Collodaria, Acantharea, Polycystinea, and Foraminifera, as well as dinoflagellates like Noctiluca scintillans).

As drivers of biogeochemical cycles in the global ocean, and particularly of the biological carbon pump [START_REF] Keeling | Marine Protists Are Not Just Big Bacteria[END_REF][START_REF] Ducklow | Upper ocean carbon export and the biological pump[END_REF][START_REF] Guidi | Plankton networks driving carbon export in the oligotrophic ocean[END_REF], marine protists are a key part of ocean biogeochemical models (Le [START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF][START_REF] Aumont | PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies[END_REF]Follows et al., 2007;[START_REF] Reed | Gene-centric approach to integrating environmental genomics and biogeochemical models[END_REF]. However, physiological details of mixotrophic energy acquisition strategies have only been studied in a restricted number of lineages [START_REF] Stoecker | Mixotrophy in the Marine Plankton[END_REF][START_REF] Johnson | Acquired Phototrophy in Ciliates: A Review of Cellular Interactions and Structural Adaptations[END_REF][START_REF] Stoecker | Acquired phototrophy in aquatic protists[END_REF]. They appear to be quite complex and greatly differ across mixotypes, which makes mixotrophy hard to include in a simple model structure [START_REF] Flynn | Building the "perfect beast": modelling mixotrophic plankton[END_REF][START_REF] Ward | Marine mixotrophy increases trophic transfer efficiency, mean organism size, and vertical carbon flux[END_REF][START_REF] Berge | Modeling succession of key resource-harvesting traits of mixotrophic plankton[END_REF][START_REF] Ghyoot | Modeling Plankton Mixotrophy: A Mechanistic Model Consistent with the Shuter-Type Biochemical Approach[END_REF][START_REF] Ward | Biophysical Aspects of Resource Acquisition and Competition in Algal Mixotrophs[END_REF]. Hence at this time, mixotrophy is not included in most biogeochemical models, neglecting the amount of carbon fixed by non-constitutive mixotrophs through photosynthesis, and missing the population dynamics of photosynthetically active constitutive mixotrophs that can still grow under nutrient limitation [START_REF] Ghyoot | Modeling Plankton Mixotrophy: A Mechanistic Model Consistent with the Shuter-Type Biochemical Approach[END_REF][START_REF] Mitra | The role of mixotrophic protists in the biological carbon pump[END_REF]. This is most probably skewing climatic models Chapter 2. Metabarcoding as a tool to decipher the biogeography of functional traits predictions [START_REF] Mitra | Defining Planktonic Protist Functional Groups on Mechanisms for Energy and Nutrient Acquisition: Incorporation of Diverse Mixotrophic Strategies[END_REF][START_REF] Mitra | The role of mixotrophic protists in the biological carbon pump[END_REF], as well as our ability to understand and prevent future effects of global change.

A better understanding of the environmental diversity of marine mixotrophic protists, as well as a description of the abiotic factors driving their biogeography at global scale are still needed, in particular to integrate them in biogeochemical models. [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF] attempted to tackle this problem by reviewing about 110,000 morphological identification records of a set of more than 60 mixotrophic protists species in the ocean, taken from the Ocean Biogeographic Information System (OBIS) database. They found distinctive patterns in the biogeography of the three different nonconstitutive mixotypes (GNCM, pSNCM, and eSNCM), highlighting the need to better understand such diverging distributions [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF]. Environmental molecular biodiversity surveys through metabarcoding have been widely used in the past fifteen years to decipher planktonic taxonomic diversity (de Vargas et al., 2015;[START_REF] Stoeck | Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water[END_REF][START_REF] Bik | Sequencing our way towards understanding global eukaryotic biodiversity[END_REF][START_REF] Bittner | Diversity patterns of uncultured Haptophytes unravelled by pyrosequencing in Naples Bay[END_REF]. Here, we exploited the global Tara Oceans datasets [START_REF] Karsenti | A Holistic Approach to Marine Eco-Systems Biology[END_REF][START_REF] Bibliography Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF][START_REF] Pesant | Open science resources for the discovery and analysis of Tara Oceans data[END_REF], and identified 133 mixotrophic lineages, that we classified into the four mixotypes defined by [START_REF] Mitra | Defining Planktonic Protist Functional Groups on Mechanisms for Energy and Nutrient Acquisition: Incorporation of Diverse Mixotrophic Strategies[END_REF]. This first ever set of mixotrophic metabarcodes allowed us to investigate the global biogeography of both constitutive and non-constitutive mixotrophs, in relation with in-situ abiotic measurements. We tested (i) if new information on marine mixotrophic protists distribution can be gained in comparison with previous morphological identifications [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF]; (ii) if the constitutive mixotrophs, which are not addressed in [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF], and the non-constitutive mixotrophs diverge in terms of biogeography; (iii) if the study of diversity and abundance of environmental metabarcodes could lead to the definition of key environmental factors shaping mixotrophic communities.

Materials and methods

Samples collection and dataset creation

Metabarcoding datasets from the worldwide Tara Oceans sampling campaigns that took place between 2009 and 2013 [START_REF] Karsenti | A Holistic Approach to Marine Eco-Systems Biology[END_REF][START_REF] Pesant | Open science resources for the discovery and analysis of Tara Oceans data[END_REF] (data published in open access at the European Nucleotide Archive under project accession number PRJEB6610) were investigated.

We analyzed 659 samples from 122 distinct stations, and for each sample, the V9-18S ribosomal DNA region was sequenced through Illumina HiSeq [START_REF] Bibliography Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF]. Assembled and filtered V9 metabarcodes (cf. details in de Vargas et al. (de Vargas et al., 2015)) were assigned to the lowest taxonomic rank possible via the Protist Ribosomal Reference (PR2) database (Guillou et al., 2013). To limit false positives, we chose to only analyze the metabarcodes (i.e., unique versions of V9 sequences) for which the assignment to a reference sequence had been achieved with a similarity of 95% or higher. This represents 65% of the total dataset in terms of metabarcodes and 84% in terms of total sequences. Our dataset involved 1,492,912,215 sequences, distributed into 4,099,567 metabarcodes assigned to 5071 different taxonomic assignations, going from species to kingdom level precision.

Defining a set of mixotrophic organisms

Among these 5071 taxonomic assignations, we searched for mixotrophic protist lineages, taking into account the four mixotypes described by [START_REF] Mitra | Defining Planktonic Protist Functional Groups on Mechanisms for Energy and Nutrient Acquisition: Incorporation of Diverse Mixotrophic Strategies[END_REF]: constitutive mixotrophs (CM), generalist non-constitutive mixotrophs (GNCM), endo-symbiotic specialist nonconstitutive mixotrophs (eSNCM), and plastidic specialist non-constitutive mixotrophs (pSNCM). We used the table S2 from Leles et al. [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF], which is referencing 71 species or genera belonging to three non-constitutive mixotypes (GNCM, pSNCM, and eSNCM), as well as multiple other sources coming from the recent literature on mixotrophy (Caron, 2016a;[START_REF] Stoecker | Mixotrophy in the Marine Plankton[END_REF][START_REF] Flynn | Misuse of the phytoplankton-zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types[END_REF][START_REF] Mitra | Defining Planktonic Protist Functional Groups on Mechanisms for Energy and Nutrient Acquisition: Incorporation of Diverse Mixotrophic Strategies[END_REF][START_REF] Esteban | Mixotrophy in Ciliates[END_REF][START_REF] Granéli | The ecophysiology and bloom dynamics of Prymnesium spp[END_REF][START_REF] Liu | A Time line of the Environmental Genetics of the Haptophytes[END_REF][START_REF] Hansen | Direct evidence for symbiont sequestration in the marine red tide ciliate Mesodinium rubrum[END_REF][START_REF] Agatha | Pelagostrobilidium neptuni (Montagnes and Taylor, 1994) and Strombidium biarmatum nov. spec. (Ciliophora, Oligotrichea): phylogenetic position inferred from morphology, ontogenesis, and gene sequence data[END_REF][START_REF] Jones | Mixotrophy in marine species of Chrysochromulina (Prymnesiophyceae): ingestion and digestion of a small green flagellate[END_REF][START_REF] Johnsen | Ecophysiology, bio-optics and toxicity of the ichtyotoxic Chrysochromulina leadbeateri (Prymnesiophyceae)[END_REF][START_REF] Rhodes | Morphology and growth characteristics of Chrysochromulina species (Haptophyceae = Prymnesiophyceae) isolated from New Zealand coastal waters[END_REF][START_REF] Hemleben | Test morphology, organic layers and chamber formation of the planktonic foraminifer Globorotalia menardii (d'Orbigny)[END_REF][START_REF] Fehrenbacher | Observations of living non-spinose planktic foraminifers Neogloboquadrina dutertrei and N. pachyderma from specimens grown in culture[END_REF][START_REF] Spero | Photosynthesis in the symbiotic planktonic foraminifer Orbulina universa, and its potential contribution to oceanic primary productivity[END_REF][START_REF] Faber | Algal-foraminiferal symbiosis in the planktonic foraminifer Globigerinella aequilateralis; II, Effects of two symbiont species on foraminiferal growth and longevity[END_REF][START_REF] Kuile | In situ growth rate experiments on the symbiont-bearing foraminifera Amphistegina lobifera and Amphisorus hemprichii[END_REF][START_REF] Biard | Biogeography and diversity of Collodaria (Radiolaria) in the global ocean[END_REF], and inputs from mixotrophic protists' taxonomy specialists (cf. Acknowledgments section). Within the 5071 taxonomic assignations of variable precisions, we identified 5 GNCM, 9 pSNCM, 77 eSNCM, and 42 CM lineages (detailed list available publicly under the https://doi.org/10.6084/m9.figshare.6715754, and all metabarcodes were tagged with their mixotypes in the PR2 database). Among these 133 taxonomic assignations that we will call "lineages", 92 were defined at the species level, 119 at the genus level, and the last 14 at higher taxonomic levels where mixotrophy is always present (mostly eSNCM groups like Collodaria). In the Chrysophyceae family, metabarcodes assigned to clades B2, E, G, H, and I were included even though we couldn't find a general proof that all species included in these clades have mixotrophic capabilities. However, if we exclude the photolithophic Synurophyceae and genera like Paraphysomonas and Spumella, which we did, a vast majority of Chrysophyceae are considered mixotrophic [START_REF] Flynn | Misuse of the phytoplankton-zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types[END_REF]. The final dataset included 318 054 metabarcodes assigned to the 133 mixotrophic lineages selected, as well as their sequence abundance in 659 samples (table available publicly under the https://doi.org/10.6084/m9.figshare.6715754).

Environmental dataset

We built a corresponding contextual dataset using the environmental variables available in the PANGAEA repository from the Tara Oceans expeditions [START_REF] Pesant | Open science resources for the discovery and analysis of Tara Oceans data[END_REF][START_REF] Ardyna | Environmental context of all samples from the Tara Oceans Expedition[END_REF].

The 235 environmental variables available were a priori reduced to 84, keeping only one version of each variable that was calculated twice or more using different tools, units and/or formulas.

For example, the daily photosynthetically active radiations (PAR) were measured using 10 different approaches, some using in situ sensors, others using satellite observations combined with equations based on the diffuse attenuation coefficient [START_REF] Morel | Examining the consistency of products derived from various ocean color sensors in open ocean (Case 1) waters in the perspective of a multi-sensor approach[END_REF]. For similar reasons, among the conductivity, the temperature, and the salinity, only the last two were kept. Then, among the 83 remaining variables, only the ones recorded for at least half of the samples were kept. We obtained a table composed of 57 different environmental variables (Available publicly under the DOI 10.6084/m9.figshare.6715754). In this table, 8.67% of the values were missing. This table contained environmental context for 658 of our 659 samples, lacking environmental information only for the DCM of station number 120. This sample was then removed for the Chapter 2. Metabarcoding as a tool to decipher the biogeography of functional traits statistical analysis.

Distribution and diversity of mixotrophic protists

For each mixotype, the number of metabarcodes, the total sequence abundance and the mean sequence abundance by metabarcode was computed (Table 2.1). Also, we measured each metabarcode's station occupancy, i.e., the number of stations in which it was found, and station evenness, i.e., the homogeneity of its distribution among the stations in which it was detected (Fig. 2.4). Diversity of mixotrophic protists was investigated through mixotype-specific metabarcode richness per station (Table 2.1). As the number of samples taken per station can impact the abundance and diversity of detected metabarcodes, richness was computed only at stations for which the maximum number of eight samples were available (40 stations over 122).

Table 2.1 -Detailed number of lineages found for each mixotype, as well as the number of metabarcodes, the corresponding total sequence counts over all stations, the mean sequence abundance by metabarcode, and mean metabarcode richness. The richness was computed as the number of different metabarcodes present at each station. It was calculated for each mixotype and means are indicated in the fifth line. Absences correspond to the number of stations in which no sequences were detected for the corresponding mixotype. CM constitutive mixotrophs, GNCM generalist non-constitutive mixotrophs, eSNCM endo-symbiotic specialist non-constitutive mixotrophs, pSNCM plastidic specialist non-constitutive mixotrophs. a The mean indicated here was calculated using only stations having the maximum number of samples (see main text)

Global biogeography of mixotrophic protists

Two statistical analyses were performed to investigate mixotrophic protists biogeography. One at the metabarcode level, and one at the lineage level, i.e., merging the sequence abundance of metabarcodes sharing the same taxonomical assignation. The metabarcodes abundance table was composed of 318,054 rows/metabarcodes, and 659 columns/samples, whereas the lineage abundance table was composed of 133 rows/lineages and 659 columns/samples (both datasets are available publicly at https://doi.org/10.6084/m9.figshare.6715754).

The two redundancy analyses led to very similar conclusions, but the biogeography of lineages appeared easier to visually represent and interpret than the one of metabarcodes. Hence, we will only present the lineage-based analysis here, before presenting and discussing the methods used and results obtained for the metabarcodes level analysis in a separate section (Section 2.2.5).

62

2.2 Mixotrophic protists display contrasted biogeographies in the global ocean Our statistical model was designed to identify lineages (or metabarcodes) with contrasted biogeographies, and relate their presence to the environmental context. We normalized the sequence counts from the lineage abundance matrix using a Hellinger transformation [START_REF] Legendre | Numerical Ecology[END_REF]. We used the environmental dataset and the mixotrophic lineages' abundance matrix as explanatory and response matrices, respectively, to conduct a redundancy analysis (RDA) (Legendre and [START_REF] Legendre | Numerical Ecology[END_REF]. Since redundancy analyses (RDA) cannot handle missing values in the explanatory dataset, we replaced missing environmental variables by their means across all samples, to keep all the environmental variables in the analysis. This option was selected over a joint modelling approach,because of a too high collinearity among some variables, and difficulties to define a multivariate distribution fitting the whole dataset. Environmental variables were then centered prior to the analysis. For that, we made a species pre-selection using Escoufier's vectors [START_REF] Escoufier | Le Traitement des Variables Vectorielles[END_REF], which allowed to keep only the 62 most significant mixotrophic lineages. This method selects lineages according to a principal component analysis (PCA), sorting them based on their correlation to the principal axes. We then used a maximum model (Y X) and a null model (Y 1) to conduct a two directional stepwise model selection based on the Akaike information criterion (AIC) [START_REF] Borcard | Mesoscale Iron Enrichment Experiments 1993-2005: Synthesis and Future Directions[END_REF]. The resulting model contained 28 response variables, among which five were qualitative (filter size, biogeographical province sensu Longhurst [START_REF] Longhurst | Ecological Geography of the Sea[END_REF], season, season moment,i.e. early, middle or late, and sampling depth, i.e. surface or DCM), and 23 quantitative. The latter included: longitude, bathymetry, distance to coast, mixed layer depth, euphotic zone depth, oxygen maximum depth, ammonium at 5m, temperature, silica, oxygen, chlorophyll a, daylight duration, absorption coefficient of colored dissolved organic matter (ac-CDOM), calcite saturation state, Okubo-Weiss parameter, PO4, CO3, HCO3, photosynthetically active radiations (PAR), salinity, maximum Lyapunov exponent, optical beam attenuation coefficient at 660 nm and beam attenuation coefficient of particles. Analyses and graphs were realized with the R software version 3.4.3 (R Core Team, 2019). All scripts are available on GitHub platform (https://github.com/upmcgenomics/MixoBioGeo).

Results

Global distribution and diversity of marine mixotrophic protists

Mixotrophic protists metabarcodes were detected in all the 659 samples with a total sequence abundance of 89,951,866, representing 12.56% of the total sequence abundance in the 659 samples studied. They represented a mean of 12.64% of the total sequence abundance per sample, with a maximum of 96.96% and a minimum of 0.01%. To avoid any potential overestimation of mixotrophic lineages presence in the following results, we marked all records of less than a hundred sequences as questionable. We found both eSNCM and CM in each of the 122 stations studied (Table 2.1 and Fig. 2.3). In only two occasions the number of sequences belonging to CM was questionable, at stations for which only one sample was sequenced. GNCM were found absent in only two stations and their presence was questionable in 39 stations (Fig. 2.3). pSNCM were absent at five stations (three in the Indian Ocean, and two in the Pacific Ocean) and detected Stations in which no sequence was found were marked as absent, ones with less than 100 sequences marked as questionable. Each Longhurst biogeographical provinces [START_REF] Longhurst | Ecological Geography of the Sea[END_REF] is colored in the background if more than 100 sequences are detected in at least one of its stations.

with questionable presence in 54 additional stations, which were mostly located in the central Pacific and the Indian Ocean (Fig. 2.3). We found significant amounts of sequences corresponding to GNCM in the Central Pacific, Southern subtropical Atlantic, and Indian Ocean. The presence of GNCM in these areas has not yet been recorded through morphological identifications during field expeditions [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF]. Also, we detected more than 100 sequences of pSNCM metabarcodes at 11 stations belonging to biogeographical provinces in which no morphological identifications had been published [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF][START_REF] Longhurst | Ecological Geography of the Sea[END_REF], mostly in offshore areas of the Atlantic and Pacific Ocean (Fig. 2.3). The mean evenness of mixotrophic metabarcodes across stations was of 0.87, and 82.3% of the metabarcodes had a station evenness above 0.5 (Fig. 2.4). Station occupancy varied a lot depending on the metabarcodes, with a high density of rare metabarcodes leading to a mean of 5.14 stations over a maximum of 122, and a standard deviation of 7.7. However, three eSNCM metabarcodes were found in all the 122 stations, and three CM metabarcodes were detected in 121 stations. The maximum occupancy for a GNCM metabarcode was of 111 stations, while 92 stations was the maximum for a pSNCM metabarcode.

CM and GNCM metabarcodes showed a strong tendency towards high evenness values (Fig. 2.4, means of 0.90 and 0.95, respectively), even for the most sequence abundant metabarcodes. Many eSNCM metabarcodes had high evenness values, but below average values were detected for the 64 2.2 Mixotrophic protists display contrasted biogeographies in the global ocean most abundant ones (Fig. 2.4, global mean of 0.87). pSNCM metabarcodes had a similar mean of evenness values (0.87), but a different distribution compared to other mixotypes (Fig. 2.4). Among the 50 most abundant metabarcodes, 43 corresponded to Collodaria lineages, 47 were eSNCM and 3 were CM, all three assigned to Gonyaulax polygramma. GNCM and pSNCM metabarcodes had homogeneously low sequence abundances (Fig. 2.4 and Table 2.1).

Figure 2.4 -Sequence abundance, occupancy, and spatial evenness of each mixotrophic metabarcode across sampled stations. Each metabarcode is plotted as a bubble, with its station occupancy, i.e., the number of stations in which it was found, and its station evenness, i.e., the homogeneity of its distribution among the stations in which it was detected, as coordinates. Violin plots were drawn for each mixotype on both the x and y axes. The size of each bubble is scaled to the sequence abundance found globally for the corresponding metabarcode.

Main factors affecting the biogeography of mixotrophic protists

The redundancy analysis helped to investigate further the environmental variables responsible for the mixotrophic protists' biogeography. The 62 lineages selected with the Escoufier's vector method corresponded to 20 CM, 34 eSNCM, 3 GNCM, and 5 pSNCM. Even after selection, a significant part of the lineages did not show any response to environmental data in their distribution (Fig. 2.5, e.g., 19 of the 62 lineages were found between -0.01 and 0.01 on both RDA1 and RDA2). The adjusted R-squared of the RDA was of 34.89% (41.43% unadjusted), with 24.01% of variance explained on the two first axes (Fig. 2.5). The first RDA axis (14.96%) marks an opposition between samples from oligotrophic waters with low productivity (RDA1 > 0) and samples from eutrophic and productive water masses (RDA1 < 0). This axis is negatively correlated to chlorophyll concentration, particles density, ammonium concentration, absorption coefficient of colored dissolved organic matter (acCDOM), duration of daylight, silica, CO3, oxygen, and PO4 Among the 20 CM lineages, seven clearly emerged from the redundancy analysis (Fig. eSNCMs can be divided in three groups in the RDA space. The first group (RDA1 < 0) corresponds to eSNCM species dominating rich and productive environments. It includes mainly Acantharia and Spumellaria species. The second group (RDA1 > 0) dominates oligotrophic environments, and includes multiple Collodaria as well as one Dinophyceae genus (Ornithocercus). Within this group, Ornithocercus spp. is found mainly in coastal subtropical environments (RDA2 < 0), as opposed to Sphaerozoum punctatum that is found mainly in offshore subpolar regions (RDA2 > 0). Siphonosphaera cyathina lies between these two trends as it is found only in oligotrophic samples, but is not influenced by temperature or bathymetry (Figs. 2.5 and 2.6). The third group corresponds to the eSNCM lineages that can be interpreted as distributed homogeneously in regards of the environmental data we are using (e.g., lineages with the shortest arrows in Fig.

2.5

). These notably include the 12 Foraminifera lineages present in the RDA. Looking at filters centroids in the RDA space (Fig. 2.5), we can suppose that eSNCM lineages dominating eutrophic systems (RDA1 < 0) are smaller in size than those dominating oligotrophic ones (RDA1 > 0).

Out of the five pSNCM included in the RDA, only Mesodinium rubrum, the most abundant one, is distinctively represented in the RDA space. This suggests that the other pSNCM have homoge-Chapter 2. Metabarcoding as a tool to decipher the biogeography of functional traits neous distributions in response to our environmental variables. Mesodinium rubrum dominates eutrophic environments, independently from the bathymetry or the temperature (RDA1 < 0, RDA2 ≈ 0). We find a similar pattern for GNCM, with only Pseudotontonia simplicidens well represented in the RDA space out of the three species included in the analysis. Like M. rubrum, Pseudotontonia simplicidens is the most abundant species in its group and it is mainly found in eutrophic waters (RDA1 < 0).

Discussion

Mixotrophy occurs everywhere in the global ocean

Our metabarcoding survey confirms that marine mixotrophic protists are ubiquitous in the global ocean [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF], possibly extending the known range of distribution of two mixotypes (Figs. 2.3 and 2.4). Mixotrophic organisms represented more than 12% of the sequences in the complete Tara Oceans metabarcoding dataset, showing that they should not be understated.

We found contrasted biogeographies among metabarcodes and their corresponding lineages, both within and across mixotypes (Figs. 2.4, 2.5, 2.6 and Section 2.2.5). We found constitutive mixotrophs (CM) and endo-symbiotic specialist non-constitutive mixotrophs (eSNCM) metabarcodes at all the 122 stations included in this global study (Table 2.1 and Fig. 2.4), verifying that these two mixotypes are the most abundant in the ocean [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF]. This dominance of eSNCM and CM in our data is also linked to the relatively high number of metabarcodes available for these two mixotypes in databases. Using 1360 generalist non-constitutive mixotrophs (GNCM) metabarcodes corresponding to only five lineages, we detected them in ten biogeographical provinces [START_REF] Longhurst | Ecological Geography of the Sea[END_REF] where no morphological identification had been recorded before [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF]. GNCM metabarcodes had consistently high evenness values, and some had station occupancy records comparable to the most abundant eSNCM and CM metabarcodes (Fig.

2.4

). These results support the hypothesis of a globally ubiquitous distribution of GNCM. Plastidic specialist non-constitutive mixotrophs (pSNCM) were found in five provinces in which no record existed so far from morphological identification field studies [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF]. However, these observations were often in a questionable range in terms of sequence abundance (Fig. 2.3), and the overall distribution of pSNCM in our data appears as very concordant with morphological observations [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF]. pSNCM metabarcodes had dominantly low station evenness values, which again supports the conclusions of [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF] that identified pSNCM as highly seasonal and spatially restricted in their distribution.

While building our set of mixotrophic lineages, some widespread and potentially mixotrophic genera did not appear, such as Ceratium spp., Tontonia spp., Amphisolenia spp., Triposolenia spp., or Citharistes spp., mainly because of a poor representation in the PR2 database. Also, we decided to only consider metabarcodes with more than 95% similarity to a reference sequence.

This threshold could be too selective for some species and not enough for some others, as single similarity threshold are hardly efficient when studying whole eukaryotic populations [START_REF] Wu | Taxonomic Resolutions Based on 18S rRNA Genes: A Case Study of Subclass Copepoda[END_REF][START_REF] Brown | Divergence thresholds and divergent biodiversity estimates: can metabarcoding reliably describe zooplankton communities?[END_REF]. For example, some species appeared with low sequence abundance in 68 2.2 Mixotrophic protists display contrasted biogeographies in the global ocean the data even though they couldnot have been sampled, such as three lacustrine species, e.g., Poteriospumella lacustris. Considering these biases and the sometimes relatively low sequence counts (marked as questionable in Fig. 2.3), some of the new GNCM and pSNCM records we observed should be considered with care, as they could be over-estimated or even sometimes artefactual. However, the low number of lineages found for these two mixotypes in PR2 and in our dataset are leading us to think that we were unable to capture the whole GNCM and pSNCM communities. This supposes a global underestimation of GNCM and pSNCM abundances in our results.

Tara Oceans metabarcoding dataset is built on snapshot samples taken irregularly during a 3year cruise, hence allowing no proper seasonal variations investigations. However, morphological identifications of mixotrophic protists revealed seasonal variations in their abundance, with Mesodinium biomass blooming in spring in coastal seas for example [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF]. As metabarcoding datasets have been successfully applied on time series to detect species successions across gradients of time and space [START_REF] Egge | 454 pyrosequencing to describe microbial eukaryotic community composition, diversity and relative abundance: a test for marine haptophytes[END_REF][START_REF] Gilbert | The Taxonomic and 157 BIBLIOGRAPHY Functional Diversity of Microbes at a Temperate Coastal Site: A 'Multi-Omic' Study of Seasonal and Diel Temporal Variation[END_REF][START_REF] Delong | Community Genomics Among Stratified Microbial Assemblages in the Ocean's Interior[END_REF], it would be interesting to similarly investigate seasonal trends in mixotrophic communities. Our set of mixotrophic lineages and metabarcodes being publicly available, our method will be applicable to any other metabarcoding dataset, including time series. It will also be open to inputs and updates from the global scientific community.

The contrasted biogeographies of marine mixotypes

Constitutive mixotrophs

Constitutive mixotrophs (CM) have very diverse feeding behaviors, with some species requiring phototrophy to grow, others phagotrophy, and some being obligate mixotrophs [START_REF] Stoecker | Acquired phototrophy in aquatic protists[END_REF]. They were described in all waters of the global ocean [START_REF] Arenovski | Mixotrophic nanoplankton in oligotrophic surface waters of the Sargasso Sea may employ phagotrophy to obtain major nutrients[END_REF][START_REF] Safi | Mixotrophic and heterotrophic nanoflagellate grazing in the convergence zone east of New Zealand[END_REF][START_REF] Moorthi | Mixotrophy: a widespread and important ecological strategy for planktonic and sea-ice nanoflagellates in the Ross Sea, Antarctica[END_REF][START_REF] Unrein | Dinobryon faculiferum (Chrysophyta) in coastal Mediterranean seawater: presence and grazing impact on bacteria[END_REF][START_REF] Sanders | Bacterivory by phototrophic picoplankton and nanoplankton in Arctic waters[END_REF]. We found them distributed in a range of conditions almost as wide as non-constitutive mixotrophs (Figs. 2.3 and 2.5). Among highly abundant lineages, most were dominantly found in eutrophic and shallow habitats. However, a few dinoflagellates were found to be highly dominant in oligotrophic, subtropical waters, showing how wide of a range of conditions constitutive mixotrophs can grow in (Fig. 2.5). This illustrates how mixotrophy can allow organisms to dominate ecosystems even when environmental conditions are poorly adapted to purely phototrophic or heterotrophic organisms. When taken explicitly into account in biogeochemical models, marine mixotrophs increase carbon export by up to 30% [START_REF] Ward | Marine mixotrophy increases trophic transfer efficiency, mean organism size, and vertical carbon flux[END_REF]. Hence, their global ubiquity supposes that the carbon export of the biological carbon pump could be underestimated in both oligotrophic and eutrophic areas [START_REF] Mitra | Defining Planktonic Protist Functional Groups on Mechanisms for Energy and Nutrient Acquisition: Incorporation of Diverse Mixotrophic Strategies[END_REF].

Plastidic specialist and generalist non-constitutive mixotrophs (pSNCM and GNCM)

Like [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF], we found pSNCM and GNCM to have quite similar biogeographies (Fig.

2.5, Section 2.2.5). Sequence abundance of most of the metabarcodes for these two mixotypes was homogeneously low ( No species-level barcode is available in the PR2 database for the Tontonia genus, and only one can be found for Pseudotontonia and Laboea genera, even though morphological records of these GNCM are numerous [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF]. Experiments using meso-and microcosms combined with individual counts and morphological identification have found that GNCM ciliates can represent up to half of the individuals in ciliate communities of the photic zone [START_REF] Mitra | Defining Planktonic Protist Functional Groups on Mechanisms for Energy and Nutrient Acquisition: Incorporation of Diverse Mixotrophic Strategies[END_REF][START_REF] Calbet | Effects of light availability on mixotrophy and microzooplankton grazing in an oligotrophic plankton food web: Evidences from a mesocosm study in Eastern Mediterranean waters[END_REF][START_REF] Dolan | Costs, benefits and characteristics of mixotrophy in marine oligotrichs[END_REF]. A proportion we would have trouble to reach with the five lineages we were able to consider, knowing that there are 8686 different ciliate lineages available in PR2. This highlights the urgent need for supplementing 18S reference databases for mixotrophic ciliates.

Endo-symbiotic specialist non-constitutive mixotrophs (eSNCM)

Endo-symbiotic specialist non-constitutive mixotrophs (eSNCM) is by far the most widespread and abundant non-constitutive mixotype in the global ocean (Figs. 2.3 and 2.4) [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF][START_REF] Biard | Biogeography and diversity of Collodaria (Radiolaria) in the global ocean[END_REF][START_REF] Decelle | An original mode of symbiosis in open ocean plankton[END_REF]. Their biogeography stands out, with a lot of highly abundant ubiquitous lineages, and some other specialized towards certain types of ecosystems (Fig. 2.5). They represent 95.7% of the sequence counts in our study and correspond to 90.7%

of the metabarcodes (Table 2.1), which highlights their abundance and diversity. The very high number of rDNA copies present in Rhizaria orders such as Collodaria [START_REF] Biard | Biogeography and diversity of Collodaria (Radiolaria) in the global ocean[END_REF] might lead the eSNCM to appear more abundant in metabarcoding datasets than they ecologically are.

However, in oligotrophic open oceans the Rhizaria biomass is estimated to be equivalent to that of all other mesozooplankton (Biard et al., 2016), and positively correlated to the carbon export [START_REF] Guidi | Plankton networks driving carbon export in the oligotrophic ocean[END_REF], showing how ecologically important they can be.

Investigating the divergent biogeographies of Collodaria and Acantharia

Collodaria are living either as solitary large cells or as colonies [START_REF] Biard | Biogeography and diversity of Collodaria (Radiolaria) in the global ocean[END_REF], which explains why they are predominantly found in macro-sized (180-2000 µm) filter samples (Fig. 2.5). All described Collodaria species so far harbor photosynthetic endo-symbionts, mostly identified as the dinoflagellate species Brandtodinium nutricula [START_REF] Biard | Biogeography and diversity of Collodaria (Radiolaria) in the global ocean[END_REF][START_REF] Probert | Brandtodinium gen. nov. and B. nutricula comb. Nov. (Dinophyceae), a dinoflagellate commonly found in symbiosis with polycystine radiolarians[END_REF]. These dinoflagellates are able to get in and out of their symbiotic state, which implies a light and/or reversible effect of the Collodarian host on its symbiont metabolism [START_REF] Probert | Brandtodinium gen. nov. and B. nutricula comb. Nov. (Dinophyceae), a dinoflagellate commonly found in symbiosis with polycystine radiolarians[END_REF]. Based on the same metabarcoding dataset, Collodaria were described as particularly abundant and diverse

in the oligotrophic open ocean [START_REF] Biard | Biogeography and diversity of Collodaria (Radiolaria) in the global ocean[END_REF]. In our results, Collodaria dominate oligotrophic, relatively deep waters (Figs. 2.5 and 2.6a). These Collodaria appear opposed to another set of Rhizaria (Acantharia and Spumellaria) linked to eutrophic and shallow waters (Figs. 2.5 and 2.6b, Section 2.2.5). Acantharia are found ubiquitously in the global ocean, but display particularly high sequence abundances in some specific regions [START_REF] Decelle | An original mode of symbiosis in open ocean plankton[END_REF]. Mixotrophic Acantharia live in symbiosis with the cosmopolitan haptophyte Phaeocystis, which is highly abundant and ecologically active in its free-living phase [START_REF] Decelle | An original mode of symbiosis in open ocean plankton[END_REF]. Unlike the one of Collodaria, this symbiosis is irreversible: an algal symbiont can not go back to its free-living phase [START_REF] Decelle | An original mode of symbiosis in open ocean plankton[END_REF]. Our results suppose that these specific symbiotic modes could enable Acantharia [START_REF] Decelle | An original mode of symbiosis in open ocean plankton[END_REF]. Experiments and modeling studies should help to investigate the contribution of this assumption, comparing food acquisition capacity and growth rates of free individuals versus in colony.

Towards an integration of mixotrophic diversity into marine ecosystem models

The future of marine communities' modeling lies in the integration of omics datasets into modeling frameworks [START_REF] Reed | Gene-centric approach to integrating environmental genomics and biogeochemical models[END_REF]Stec et al., 2017;[START_REF] Dick | Embracing the mantra of modellers and synthesizing omics, experiments and models[END_REF]Mock et al., 2016;Coles et al., 2017).

The use of metabolic networks and gene-centric methods has already shown very promising results in modeling prokaryotic ecological dynamics [START_REF] Reed | Gene-centric approach to integrating environmental genomics and biogeochemical models[END_REF]Coles et al., 2017). However, eukaryotic metabolic complexity makes these methods hard to apply on protists for now, and we still lack a universal theoretical framework on how to integrate such methods into concrete modeling (Stec et al., 2017). Mixotrophic protists are physiologically complex, and their feeding behavior can vary drastically on short time scales [START_REF] Stoecker | Mixotrophy in the Marine Plankton[END_REF]. It will then take a few more years of comparative genomics and transcriptomics studies before being able to model their physiology with purely gene-based approaches. Still, mechanistic models of mixotrophy exist and are quite complex [START_REF] Flynn | Misuse of the phytoplankton-zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types[END_REF][START_REF] Ghyoot | Modeling Plankton Mixotrophy: A Mechanistic Model Consistent with the Shuter-Type Biochemical Approach[END_REF], even if the one from [START_REF] Ghyoot | Modeling Plankton Mixotrophy: A Mechanistic Model Consistent with the Shuter-Type Biochemical Approach[END_REF] could be implemented in a global biogeochemical model [START_REF] Shuter | A model of physiological adaptation in unicellular algae[END_REF]. Most models make the choice to represent either one or two (NCM and CM) types of organisms able to play the role of all mixotypes depending on parameterization. However, no global agreement has been reached

on to what extent the different mixotypes should be modeled. This is mainly due to a lack of quantitative and comparative data on the global impact of grazing and carbon fixation by the different mixotypes [START_REF] Millette | Hidden in plain sight: The importance of cryptic interactions in marine plankton[END_REF]. With our study, we show how meta-omics data can be used to identify groups of organisms distributed differently in response to the environment. It also allows the identification of ecological traits and environmental factors potentially responsible for these divergences. This information can be used to identify key species or lineages, and design controlled experiments with variations of targeted environmental factors to produce the quantitative data needed by modelers. Considering our results, we propose that host-symbiont dynamics of eSNCM should be investigated as a trait playing a potential role on Rhizaria ability to thrive in oligotrophic conditions. Particularly, the mechanisms behind holobiont formation and its potential reversibility could play major roles on eSNCM carbon fixation in various nutrient conditions. Future experiments comparing responses of Collodaria and Acantharia holobionts to different stresses in terms of grazing and carbon fixation could lead to a better understanding of the physiological differences between their two modes of symbiosis. Also, our results suggest that the metabolic flexibility of CM should allow this mixotype to grow in almost any conditions, with individual species probably spanning continuously between complete autotrophy and complete heterotrophy. The risk is then to create a "perfect beast" mixotroph dominating all systems [START_REF] Flynn | Building the "perfect beast": modelling mixotrophic plankton[END_REF]. To avoid that, we need more comparative data on grazing and carbon Chapter 2. Metabarcoding as a tool to decipher the biogeography of functional traits fixation of obligate phototrophs versus obligate heterotrophs in response to nutrient depletion and environmental fluctuation. Here again, meta-omics data could help to identify candidates for efficient experiment designs. Finally, the small number of lineages of GNCM and pSNCM in our study makes it hard to come up with strongly supported conclusions on whether they should be differentiated in models or not. They seem to share similar biogeographies using snapshot data (Fig. 2.5, Section 2.2.5), but considering that they have different abilities for conserving stolen chloroplasts over time, it might not be the case when looking at a time series analysis [START_REF] Stoecker | Acquired phototrophy in aquatic protists[END_REF][START_REF] Johnson | Retention of transcriptionally active cryptophyte nuclei by the ciliate Myrionecta rubra[END_REF][START_REF] Schoener | Plastid retention, use, and replacement in a kleptoplastidic ciliate[END_REF].

Our study uses meta-omics data to investigate the global distribution and biogeography of mixotrophic protists in the ocean. Our results, currently based on metabarcoding data, complement morphological records and will be complemented in the near future by metagenomics and metatranscriptomics studies. The latter will allow to distinguish the protists with mixotrophic capabilities from the ones with ongoing mixotrophic activity. This could lead to quantitative estimations of mixotrophic rates in environmental samples, allowing a sharpened study of mixotrophic protists ecology in the global ocean. It could also lead to a metabolic description of complex processes like kleptoplasty and endo-symbiosis, hence facilitating the modeling of mixo-trophic behaviors and its incorporation in ocean biogeo-chemical models.

Supplementary: Metabarcodes level redundancy analysis (RDA) Methods

Starting with 318 054 metabarcodes in our dataset, we had to use strong selection thresholds to build a parsimonious redundancy analysis model. The Escoufier's vector method is quite robust and only asks for one threshold definition in order to select variables [START_REF] Escoufier | Le Traitement des Variables Vectorielles[END_REF], and hence was used when working at the lineage level. However, the escouf function implemented in the R package pastecs (https://github.com/phgrosjean/pastecs) is not adapted to large datasets, and we couldn't apply the same method at the V9 metabarcode level. Instead, we selected metabarcodes based on rarity and variance of their abundance profiles. First, we only kept the 272 471 metabarcodes appearing in more than one station. Then, we arbitrarily selected the metabarcodes with a variance greater than 0.0001 in their Hellinger transformed abundance profiles. These 363 metabarcodes represented 0.1% of the total dataset in terms of metabarcodes, but 21.1% in terms of sequence abundance.

The environmental variables were modified following the same steps as for the lineages level RDA. Similarly, model selection was run using a two directional AIC-based stepwise selections.

The resulting model contained 5 qualitative response variables (filter, biogeographical province [START_REF] Longhurst | Ecological Geography of the Sea[END_REF], Ocean region, season moment and depth), as well as 37 quantitative response variables (CO3, HCO3, carbon flux, carbon total, density, PAR, 5m depth NO2, surface NO2, daylight duration, bathymetry, surface NO3, 5m depth NO3, salinity, iron, moon phase, acCDOM, longitude, distance to coast, chlorophyll A, latitude, SST gradient, water residence time, calcite and aragonite saturation states, Lyapunov exponent, nitracline depth, ammonium, temperature, fluorescence, mixed-layer depth, oxygen, depth of oxygen maximum as well as 5 different scatter-ing coefficients measuring particular density).

Figure 2.7 -Visualization of the metabarcodes involved in our statistical analyses. The two plots correspond to the bubble plot already presented in figure 2. On the left, bubbles corresponding to metabarcodes that were not included in the lineage RDA are blurred. On the right, bubbles corresponding to metabarcodes that were not included in the metabarcode RDA are blurred.

Results

The 363 metabarcodes selected corresponded to 92 CM, 257 eSNCM, 5 GNCM and 9 pSNCM (Fig- The second RDA axis (4.9%) is opposing subpolar samples (RDA2<0) to subtropical ones (RDA2>0).

The axis is negatively correlated to the density, latitude, iron concentrations, salinity, CO3, oxygen, longitude, HCO3 and total carbon. It is positively correlated to temperature, NO2, NO3, bathymetry, photosynthetically active radiations (PAR), day length and ammonium.

Among the 92 CM metabarcodes, only a few clearly emerged from the redundancy analysis (Figure 2.8) and showed distinct biogeographies related to environmental variables. Three metabarcodes assigned to Gonyaulax polygramma, a Dinophyceae belonging to the Gonyaulacales order, were found in oligotrophic waters (RDA1<0, RDA2 0). Metabarcodes assigned to the Dinophyceae Fragilidium mexicanum and Alexandrium tamarense were also found in this area of the triplot.

The other well represented CM metabarcodes (assigned to a few Chrysophyceae, a couple of Chrysochromulina species and a Dinophyceae from the Kareniaceae family, Karlodinium micrum)

were found in productive water masses (RDA1>0).

eSNCMs can be divided in three groups in the RDA space. The first group (RDA1>0, RDA2 0) dominates oligotrophic environments, and includes multiple Collodaria metabarcodes as well as a few metabarcodes assigned to a Dinophyceae genus (Ornithocercus). Within this group, metabarcodes can be found both in subtropical environments (RDA2>0), and in subpolar regions (RDA2<0). Some lineages like Acrosphaera spp. can even be dominant in the two conditions,

showing intra-lineage variation in their biogeography. The third group corresponds to the eSNCM metabarcodes that are badly represented in the space of the redundancy analysis (e.g. represented with the shortest arrows in Figure 2.8), but that can be interpreted as distributed homogeneously in regards of the environmental data we are using. These notably include all the Foraminifera metabarcodes present in the RDA. Looking at filters centroids in the RDA space (Figure 2.8), we can suppose that mixotrophic organisms dominating eutrophic systems (RDA1>0) are smaller in size than those dominating oligotrophic ones (RDA1<0).

Mixotrophic protists display contrasted biogeographies in the global ocean

Only two pSNCM metabarcodes appeared well represented in the RDA triplot, both corresponding to Mesodinium rubrum.

Out of the five GNCM metabarcodes included in the analysis, only two were well represented in the RDA space, both assigned to Pseudotontonia simplicidens. These metabarcodes were mainly found in eutrophic waters (RDA1>0).

Discussion

The results from the metabarcode RDA are very concordant with the ones obtained through the lineage level analysis presented in the main manuscript. Indeed, if the selected environmental variables were slightly different, the global organization of the RDA space was similar in the two analyses. The mixotypes were distributed very similarly in regard of environmental variables when comparing lineage and metabarcode level analyses. There are evident similarities between the distribution of metabarcodes on Figure 2.8, and the one of their corresponding lineages on Figure 2.5. This way, the two analyses conducted led to extremely similar discussion points and conclusions.

Building a redundancy analysis model at the metabarcode level allows to investigate intra-species, intra-genus, intra-families and intra-order variabilities in terms of biogeography. Theses variabilities can't be observed when aggregating metabarcodes into broader lineages, where for example all undefined Collodaria taxa are pooled together. A good example of this problem is the Acrosphaera spp. case. On Figure 2.7, we observe that a few of the most abundant metabarcodes belonging to eSNCM lineages were not selected in the lineage RDA. These metabarcodes are all assigned to Acrosphaera spp. and Collodaria_X lineages, and can display different biogeographies (e.g. the three Acrosphaera spp. metabarcodes represented on Figure 2.8). By merging sequence abundances of metabarcodes with different biogeographical patterns, we attenuate their statistical signal, and it could explain why Acrosphaera spp. was not selected in our lineage RDA.

However, the overall intra-lineage variability seemed to be very limited to Collodaria lineages. Also, the opposite effect could be spotted when comparing our analyses. For example, three metabarcodes of Gonyaulax polygramma were found in our metabarcode level RDA, with arrows pointing in the exact same direction (Figure 2.8). Hence, the low intra-lineage variability led to information redundancy in the metabarcode RDA representation. In our lineage based RDA, we had 3 species of dinoflagellates with similar biogeographies selected and well represented: Gonyaulax polygramma, Fragilidium mexicanum and Alexandrium tamarense (Figure 2.5), giving more visual impact to the statistical analysis. Moreover, to build an interpretable metabarcode-level RDA, we could only include in the model 0.1% of the mixotrophic metabarcodes found in the Tara Oceans dataset, focusing only on the most abundant ones (Figure 2.7). It highlights the difficulty of selecting only ecologically interesting and non-redundant metabarcodes out of a complete omics dataset. Operational Taxonomical Units (OTUs) can help to answer this issue, but they can also make things worse, especially when constructed using a single similarity threshold for a whole complex eukaryotic population [START_REF] Brown | Divergence thresholds and divergent biodiversity estimates: can metabarcoding reliably describe zooplankton communities?[END_REF]. During this project, we constructed 97%, 99%, and one difference OTUs using multiple algorithms, but always found contradictions between our original taxonomical assignations and the obtained metabarcode clusters. The algo-Chapter 2. Metabarcoding as a tool to decipher the biogeography of functional traits rithms we used were the usearch software [START_REF] Edgar | Usearch. Technical report[END_REF] (functions -cluster_otus and -cluster_fast)

and the swarm software [START_REF] Mahé | Swarm v2: highlyscalable and high-resolution amplicon clustering[END_REF] (using the d=1 parameter: one base pair difference between sequences).

All analyses and graphs were realized with the R software version 3.4.3 (R Core Team, 2019), using the packages vegan version 2.4-5 [START_REF] Oksanen | vegan: Community Ecology Package[END_REF] and ggplot2 version 2.2.1 [START_REF] Wickham | ggplot2: Elegant Graphics for Data Analysis[END_REF]. For the functions implying randomness, the char2seed function from package TeachingDemos v2.10 (Snow, 2016) was used setting "Faure" as a seed. All scripts are available on GitHub (https://github.com/upmcgenomics/MixoBioGeo).

the Tara Oceans expedition, we thank the commitment of the CNRS (in particular, Groupement Foundation, the Tara schooner and its captains and crew. We are also grateful to the French Ministry of Foreign Affairs for supporting the expedition and to the countries who graciously granted sampling permissions. Tara Oceans would not exist without continuous support from 23 institutes (http://oceans.taraexpeditions.org).

Conclusion: Going further than metabarcoding

In the concluding remarks of the article presented in this chapter, I evoked how metagenomics and metatranscriptomics studies should in a near future complement morphological and metabarcodingbased observations. For that, one of the strategy should be to define a set of functional genomic markers of mixotrophy, which would allow to quantify mixotrophs and their activity in metagenomics and metatranscriptomics samples. In the next chapter, I will review some of the methods available to detect such markers, and how they could help to build links between genes and functional traits.

Chapter 3

Detecting functional traits in meta-omics data through the use of genomic markers

In chapter 2, I showed how metabarcoding could be used to decipher the biogeography of mixotrophs, for which no functional genomic markers are available yet. I evoked the limits linked to such a use of metabarcoding, like quantification biases due to copy number variations, the necessity to annotate traits to taxonomic databases or the fact that metabarcodes can not reflect the level of realization of a trait (e.g. mixotrophic species can be detected through metabarcoding but it can not give any insights on their realized mixotrophic activity at the time of sampling). The identification of genomic markers of functional traits avoids most of these limitations, as such markers are detectable in metagenomes and metatranscriptomes, can be detected in known and unknown species as long as their sequence is conserved across distinct lineages, and can be related to measures of a trait realization. But the identification of such functional genomics markers from lab experiments and environmental data is often challenging, and multiple traits of biogeochemical importance such as mixotrophy, size, or reproduction strategy still lack genomic markers. In this chapter, I will first illustrate how the access to a known functional genomic marker allows for the quantitative exploration of a functional trait biogeography with more precision than metabarcoding, using the demethylation of DMSP as an example. Then, I will review the available methods for identifying genomic markers of functional traits with poorly known genomic basis, and illustrate them with a case study aiming at detecting markers of mixotrophy in dinoflagellates. Finally, I will evoke the important challenge of mining for functional traits in taxonomically and/or functionally unannotated data, and some recent progresses made considering this issue. This chapter will include preliminary results partly derived from the work of two masters students I supervised during spring 2019: Nina Guerin a 1st year I supervised during 6 weeks, and Aurélie Pham, a 2nd year master student I supervised during 6 months. Nina focused on genomic markers of DMS production whereas Aurélie investigated dinoflagellate transcriptomes looking for genomic markers of mixotrophy.

Genomic markers of functional traits: simple versus complex traits

In medicine and agronomy, simple traits are defined as being inherited through Mendelian transmission patterns (trait value defined by the dominant allele of a gene inherited from the parents, whom each transmit only one allele to the descendant), while complex traits do not conform to such patterns (due to factors like incomplete dominance, polygenic or polyallelic interactions) [START_REF] Peltonen | Use of population isolates for mapping complex traits[END_REF][START_REF] Sonah | Identification of loci governing eight agronomic traits using a GBS-GWAS approach and validation by QTL mapping in soya bean[END_REF][START_REF] Zak | Genetic control of complex traits, with a focus on reproduction in pigs[END_REF]. But in prokaryotic and eukaryotic micro-organisms, even traits that are encoded by one single gene with a simple allelic-dominance scheme can be transmitted horizontally (i.e. by an other organisms with no parental link) [START_REF] Koonin | Horizontal Gene Transfer in Prokaryotes: Quantification and Classification[END_REF][START_REF] Keeling | Horizontal gene transfer in eukaryotic evolution[END_REF], questioning the applicability of this definition to the planktonic world. This is why I will classify traits as simple or complex based on the complexity of their genomic basis rather than their inheritance patterns. I will consider simple functional traits as encoded by one gene, or a few genes corresponding to enzymes involved in a single metabolic pathway (e.g. the production of DMS through the cleavage of DMSP which can be achieved by at least 8 enzymes, or nitrogen fixation which is encoded by the nifH genes), and complex traits as coded by multiple genes distributed at different loci and taking part in distinct metabolic pathways (e.g. body or cell size, and most reproduction and behavioral traits). In the following section, I will focus on methods aiming at the discovery of genomic markers of simple functional traits.

Identifying markers of simple functional traits

State of the art: biochemical extractions and genome manipulations

Genomic markers of simple functional traits can be studied through targeted wet-lab experiments, like biochemical extraction approaches or genome manipulation techniques (e.g. gene knock-outs or the move of DNA fragments into host strains). For example, nitrogenase, the enzyme responsible for dinitrogen fixation in microorganisms, was first described through biochemical extractions and analyses in the 60s, long before the omics era [START_REF] Eady | Nitrogenase[END_REF][START_REF] Hardy | Biological Nitrogen Fixation[END_REF]).

The purification of nitrogenase from about 20 prokaryotic organisms allowed to describe the enzyme structure as highly conserved across diazotrophic species [START_REF] Chatt | Recent advances in the chemistry of nitrogen fixation[END_REF][START_REF] Zehr | Nitrogenase gene diversity and microbial community structure: a cross-system comparison[END_REF]. Thanks to this observation, the hypothesis of an evolutionarily conserved nitrogenase protein complex was proposed, and the hybridization of genomes from different diazotrophic bacteria allowed to identify and sequence nifH genes [START_REF] Mevarech | Nucleotide sequence of a cyanobacterial nifH gene coding for nitrogenase reductase[END_REF][START_REF] Ruvkun | Interspecies homology of nitrogenase genes[END_REF].

Then, the omics era allowed to detect new globally abundant diazotrophs using nifH sequences (See section 1.3.2.2).

The production of DMS by planktonic bacteria is an other example of a simple functional trait related to plankton ecology with relatively well studied genomic markers. As evoked in section 1.1.2.3, eubacteria can either demethylate DMSP, which does not lead to the production of DMS, or cleave DMSP, which leads to the production of DMS [START_REF] Moran | Genomic Insights into Bacterial DMSP Transformations[END_REF]. The two pathways being 78 3.2 Identifying markers of simple functional traits concurrent, the choice between the two is often described as the DMS "bacterial switch" [START_REF] Levine | Environmental, biochemical and genetic drivers of DMSP degradation and DMS production in the Sargasso Sea[END_REF]. The enzyme starting the pathway leading to DMSP demethylation was identified through the integration of transposons in the DMSP demethylating bacteria Silicibacter pomeroyi [START_REF] Howard | Bacterial Taxa That Limit Sulfur Flux from the Ocean[END_REF]. A mutant was identified as unable to demethylate DMSP, and the position of its transposon allowed for the identification of the dmdA enzyme, which is now estimated to be present in up to 40% of bacterioplankton cells in the open ocean [START_REF] Moran | Genomic Insights into Bacterial DMSP Transformations[END_REF]. Genes coding for the cleavage of DMSP into DMS were also identified through genome manipulation:

DNA fragments from DMS producing bacteria were introduced in non-DMS producing hosts (e.g.

E. coli)

, and the detection of DMS production in hosts allowed for the identification of the DNA fragments responsible for DMS production [START_REF] Todd | Structural and Regulatory Genes Required to Make the Gas Dimethyl Sulfide in Bacteria[END_REF].

The experiments presented in this section would be very unlikely to work for identifying the genomic basis of complex traits. Hence, most studies investigating complex traits rely on statistical analyses of genome and transcriptome content across multiple organisms with known traits. I will present such methods in the next section, but first, I will illustrate how the access to a genomic marker for a functional trait can help to decipher its response to environmental gradients.

A concrete example: exploring the biogeography of the dmdA enzyme

Introduction

By allowing the demethylation of DMSP in marine eubacteria, dmdA plays a key role in the regulation of the sulfur cycle [START_REF] Howard | Bacterial Taxa That Limit Sulfur Flux from the Ocean[END_REF][START_REF] Levine | Environmental, biochemical and genetic drivers of DMSP degradation and DMS production in the Sargasso Sea[END_REF]. dmdA transcription rates can be directly related to the state of the bacterial switch between DMSP demethylation (not leading to DMS production) and DMSP cleavage into DMS [START_REF] Levine | Environmental, biochemical and genetic drivers of DMSP degradation and DMS production in the Sargasso Sea[END_REF]. A study investigating dmdA transcription rates at the Bermuda Atlantic Time Series (BATS) over a 10 months period identified that high temperature and UV-A dose could lead to more DMSP cleavage into DMS, while colder temperature led to more DMSP demethylation [START_REF] Levine | Environmental, biochemical and genetic drivers of DMSP degradation and DMS production in the Sargasso Sea[END_REF]). These transcription rates were obtained from qPCR using adapted primers [START_REF] Levine | Environmental, biochemical and genetic drivers of DMSP degradation and DMS production in the Sargasso Sea[END_REF]. In spite of the referencing of dmdA in functional annotation databases such as KEGG [START_REF] Aramaki | KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold[END_REF]Salazar et al., 2019), these findings are yet to be confirmed by a global scale study. Salazar et al. (2019) demonstrated that the transcriptomic abundance of dmdA was negatively correlated to the ones of assimilatory sulfate reduction marker genes, using the Tara Oceans and Tara Polar Circle datasets, and proposed that the DMSP demethylation pathway could be concurrent with assimilatory sulfate reduction pathways for sulfur integration in the metabolism. However they did not relate these observations to particular environmental conditions or to transcriptomic abundances of DMSP cleavage enzymes. Here, the same global scale meta-omics and metadata from the Tara expeditions will be used, but this time to focus on the biogeography of dmdA, and identify the main abiotic drivers of its expression at global scale.

Material & methods

The 2019)). This catalog was functionally annotated using BlastKOALA [START_REF] Aramaki | KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold[END_REF] and eggNOG-mapper [START_REF] Huerta-Cepas | Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper[END_REF].

Metagenomic and metatranscriptomic abundance profiles were determined for each sample by mapping the quality-filtered metagenomics and metatanscriptomics reads to the OM-RGC.v2 catalog, and normalizing the mapped read counts by the median abundance of 10 universal singlecopy phylogenetic marker genes (see details in Salazar et al. (2019)). Finally, the profiles were converted to variance-stabilized integer counts by dividing each profile by its maximum value, multiplying the result by 10 9 , and applying a log 2 transformation. These normalized abundances were used to compute metagenomic and metatranscriptomic profiles at the gene level but also at the functional level (grouping genes according to their KEGG or eggNOG functional annotations).

Among the 187 metatranscriptomes and 370 metagenomes, 129 came from the same samples, i.e.

collected at the same location and depth using the same size fraction. For these 129 samples, an expression profile was computed as the difference between the log 2 -transformed metatranscriptomic and metagenomic profiles (available at https://www.ocean-microbiome.org). Here, I focused on the 129 metagenomic, metatranscriptomic and expression profiles available for the K17486 KEGG ortholog group, corresponding to the dmdA enzyme.

The environmental context of the 129 samples, was retrieved from https://www.ocean-microbiome. For further statistical analysis, this environmental dataset was scaled, centered, and consequently value as environmental drivers of dmdA abundance; total carbon, NO 2 /NO 3 and NO 3 because they showed too high colinearity with other variables; and PAR because it had more than 50% of missing values (92 NAs over the 129 samples). Each of the remaining missing values in the dataset was replaced by the mean of the concerned variable in the 5 nearest samples in terms of environmental profile.

All of these operations were achieved using the PreProcess command from the caret package [START_REF] Kuhn | Building Predictive Models in R Using the caret Package[END_REF] in R version 3.5.3 (R Core Team, 2019), through options center, scale, knnImpute, corr and nzv. The R code for this project is available at https://github.com/EmileFaure/DmdA.

Results & Discussion

Distribution of the dmdA enzyme in the global ocean The first axis of the RDA opposes samples taken at high depths (RDA>1) from samples taken closer to the surface (RDA<1) (Figure 3.3). It confirms that gene abundance, gene expression and especially transcript abundance are all higher in surface samples. This seems coherent considering that DMSP is produced by phytoplanktonic organisms, mostly found in the euphotic zone [START_REF] Moran | Genomic Insights into Bacterial DMSP Transformations[END_REF], and that mesopelagic samples exhibited very low transcript abundance (Figre 3.1). The second axis of the RDA opposes samples from subtropical, warm waters (RDA2>0), and samples from subpolar, cold and dense waters (RDA2<0) (Figure 3.3). The RDA confirms that gene abundance is higher in subtropical waters, and identify it as only poorly correlated to gene expression, which is mostly high in chlorophyll rich, oxygenated waters (Figure 3.3). The mismatch between gene abundance and expression could be explained by the presence of organisms that are able to demethylate DMSP but are rather using the cleavage pathway in warmer conditions.

Chapter 3. Detecting functional traits in meta-omics data through the use of genomic markers Again, this is coherent with the results from [START_REF] Levine | Environmental, biochemical and genetic drivers of DMSP degradation and DMS production in the Sargasso Sea[END_REF], whom identified the bacterial switch to be in favor of the demethylation pathway over the cleavage one in cold waters, and our results confirm the applicability of their findings at the global scale. The high adjusted R 2 value suggested that dmdA abundance was strongly linked to the environmental context, which led me to explore its predictability from environmental variables using a machine learning approach.

Predictions of dmdA abundance from the environmental context

The richness of meta-omics data offers the potential to predict the distribution of functional traits via their genomic markers in the environment through statistical modeling (Tang and Cassar, 2019). The large quantities of data collected by global meta-omics datasets allows for the construction of large training sets, i.e. subsets of data that need to include a wide enough range of conditions to be representative of the global dataset, so that statistical models can be trained on them, while retaining a part of the samples as test sets, which are used to compare models predictions with observations and test the models performance. Reaching this ability of predicting the abundance for a large set of traits would (1) help attaining an unprecedented level of precision in our knowledge of the abiotic drivers of functional diversity in planktonic communities, ( 2) provide quantitative insights to improve the construction and validation of biogeochemical models, through the identification of general ecological laws governing functions distribution in the global ocean.

Here, to test the predictability of dmdA expression, genes and transcripts abundances from environmental data, elastic net regressions were used, i.e. a combination of lasso and ridge regressions, allowing to penalize uninformative predictors by shrinking their regression coefficient towards 0. Unlike random forest regressions or neural networks, this method only uses linear relationships between the interest variable and predictors to produce predictions, which is less likely to lead to overfitting (i.e. an over-adaptation of the model to the training set leading to poor capacities of predictions over additional data). Independent elastic net regressions were computed for each of the abundance and expression profiles: one with gene abundance as the interest variable, one with transcript abundance and one with gene expression. For each elastic net regression, environmental variables were used as predictors, with categorical variables coded as dummy variables (i.e. columns of 0 or 1 for each categorical level). Training sets corresponding to 105 samples (i.e. 80% of the data) were randomly selected. Elastic net regression models were trained on these training sets with a 3 times repeated 10 fold cross-validation process. Regularization (λ, or penalty coefficient) and mixing (α, the mix level between a Lasso approach and a Ridge one, leading to different penalizations of coefficients) parameters, were optimized for each regression model by selecting the pair of parameters minimizing cross validation error across all possible combinations of 10 random values of α and λ (higher numbers of combinations were tested, not leading to better R 2 ). Finally, I used the best models selected after cross validation and parameters optimization to predict gene abundance, transcript abundance and gene expression values from the test sets (i.e. 20% of the samples that were not selected in training sets). Models performance at predicting dmdA abundance and expression was measured by computing R 2 value 3.2 Identifying markers of simple functional traits from comparisons between test set observations and model predictions.

-4 -3 -2 -4 -3 -2

Metagenomic abundance observations

Metagenomic abundance predictions

-6 -5 -4 -3 -2 -1 -6 -4 -2

Metatranscriptomics abundance observations

Metatranscriptomics abundance predictions

-3 -2 -1 0 -5 -4 -3 -2 -1 0 1

Gene expression observations

Gene expression predictions

Metatranscriptomics abundance observations Metagenomics abundance observations Gene expression observations Metagenomics abundance predictions Metatranscriptomics abundance observations

Gene expression observations

A B C -2 -3 -4 -4 -3 -2 -1 -2 -3 -4 -5 -6 -6 -4 -2 -5 -4 -3 -2 -1 0 -3 -2 -1 0 R 2 = 69% R 2 = 68% R 2 = 74% Figure 3.

-Comparison of observations from the test sets and predictions from the elastic net regressions on (A) gene abundance, (B) transcript abundance and (C) gene expression. Red lines indicate perfect predictions (y=x). Blue lines are simple linear regression lines, with the corresponding 95% confidence interval indicated in grey. The R 2 values indicated in red are the one of each elastic net regression model.

The three models led to predictions of dmdA abundance and expression with R 2 values comprised between 68 and 74% (Figure 3.4).

In the gene abundance elastic net regression, 13 environmental variables had non-zero regression coefficients, with the 5 most influential ones being: Ocean region Southern Ocean (-0.76), polar (-0.32), temperature (0.26), nominal depth (-0.23) and chlorophyll A (0.14). These results confirm the observations from the RDA, identifying metagenomic abundance of dmdA as higher in warm, subtropical waters.

In the transcript abundance elastic net regression, 26 variables had non-zero coefficients, with the 5 most influential being: mesopelagic layer (-0.89), Ocean region [MS] Mediterranean Sea (-0.79), mixed water layer (-0.66), Ocean region [SO] Southern Ocean (0.52) and nominal depth (-0.45).

Here again, observations from the RDA are confirmed, with depth appearing as the most influential factor on transcript abundance. The elastic net regression identifies the Mediterranean Sea as a zone of particularly low dmdA transcript abundance, but this should be taken with precaution as only two samples from the Mediterranean sea are present in the dataset.

In the gene expression elastic net regression, 25 variables had non-zero coefficients, with the 5 most influential being: Ocean region [MS] Mediterranean Sea (-0.94), Ocean region [SO] Southern Ocean (0.60), oxygen (0.46), mesopelagic layer (-0.42) and nominal depth (-0.35). Gene expression then appears particularly high in the Southern Ocean, and low in the Mediterranean Sea. The positive influence of oxygen on gene expression supposed from the RDA results is confirmed, as is the negative correlation with depth.

Using 129 samples from the global ocean, I was able to produce predictions of dmdA gene abundance, transcript abundance, and expression from the environmental context, with R 2 above 65%.

Chapter 3. Detecting functional traits in meta-omics data through the use of genomic markers One obvious way of increasing the statistical power of our models would be the addition of more metagenomes and metatranscriptomes to the dataset, allowing to increase the size of the training and test sets. For example, the fact that only two samples were issued from the Mediterranean Sea and 3 from the Southern Ocean limit the capacities to draw general conclusions on these areas despite their significant influence as predictors. In the future it would then be interesting to reproduce this approach on other global datasets like the Ocean Sampling Day (Kopf et al., 2015) or Malaspina [START_REF] Duarte | Seafaring in the 21st century: the Malaspina 2010 circumnavigation expedition[END_REF]. An elegant way to confirm the predictive abilities of models such as the ones presented here would be to use samples from an expedition as a training set, and the ones from an other expedition as the test set. The principal difficulty in this case would be to homogenize the values of gene and transcript abundances across datasets to be able to compare them. Applying the same normalization steps across datasets to maintain homogeneity would be quite trivial, but discrepancies in sequencing methods and/or size fractions might be hard to deal with.

By investigating the Tara Oceans and Tara Polar Circle datasets, the principal environmental drivers of a single genomic marker coding for DMSP demethylation were identified in the global ocean. Gene and transcript abundances of dmdA were correlated, but governed by different drivers: gene abundance was mainly linked with temperature, while transcript abundance was mostly explained by depth (Figure 3.3). This could be explained by the presence of organisms bearing the dmdA enzyme at high depth, were DMSP is unavailable, making the transcription of the demethylation enzyme useless and/or avoided. This genomic signal could notably be due to the presence of genetic material from dead organisms sinking in the water column, which do not appear in transcriptomic samples [START_REF] Singh | Metagenomics: Concept, methodology, ecological inference and recent advances[END_REF]. These discrepancies led the gene expression measures to be quite poorly correlated with gene abundance, and oxygen appeared as one of their most important drivers. Machine learning allowed to predict dmdA gene abundance, transcript abundance and gene expression from the environmental context with a good accuracy (Figure 3.4), demonstrating how the activity of this pathway with a strong influence on the sulfur cycle could be determined from purely physico-chemical data. It is particularly interesting to note that the best predictors of the different elastic net regressions were identified either as variables that are fixed through time (e.g. depth or Ocean region), or as routinely measured variables for which high quantities of data are available at the global scale (e.g. temperature, oxygen, chlorophyll A).

These variables being already described in the majority of biogeochemical models, the results here suggest the potential of predicting DMSP demethylation at global scale using data issued from models and observations, allowing to target the use of such predictions as inputs or validation tools in biogeochemical models.

Perspectives

In the next 6 months (starting in October 2020), I plan to reproduce this approach on other marker genes linked to the sulfur cycle, and especially on DMSP lyases, which cleave DMSP to DMS [START_REF] Moran | Genomic Insights into Bacterial DMSP Transformations[END_REF]. The access to both dmdA and DMSP lyases abundances would allow to quantitatively describe the state of the bacterial switch at a global scale for the first time. But the 86 3.3 Exploring the genomic basis of complex functional traits cleavage pathway is trickier to study than the demethylation one, as seven different DMSP lyases have been described in prokaryotes, and only one (dddL) corresponds to a KEGG ortholog group (K16953). Moreover, this KEGG ortholog group is absent from the Tara Oceans and Polar circle functional profiles. Nina Guérin's internship aimed at overcoming some of the difficulties encountered when studying DMSP lyases in global scale datasets, by ( 1) constituting a database of available DMSP lyase sequences, (2) using an alignment algorithm (Nina used Diamond, [START_REF] Buchfink | Fast and sensitive protein alignment using DIAMOND[END_REF]) to find matches to the database in environmental samples. The database she computed should allow to extract DMSP lyases gene abundance, transcript abundance and gene expression in the near future using the same Tara dataset as described in the study presented in this section. This will allow to provide the first meta-omics based study of the environmental factors governing the DMSP bacterial switch, including both the demethylation and the cleavage pathways.

Exploring the genomic basis of complex functional traits

State of the art: linkage, association methods and comparative transcriptomics

The investigation of the links between genotypes and complex traits constitutes a whole field of research, mainly driven by medical and agronomic studies (Members of the Complex Trait Consortium, 2003;[START_REF] Visscher | 10 Years of GWAS Discovery: Biology, Function, and Translation[END_REF]. The goal of such studies is often to identify portions of genomes, genes or sets of genes associated with multigenic traits like plant height and weight in agronomy [START_REF] Sonah | Identification of loci governing eight agronomic traits using a GBS-GWAS approach and validation by QTL mapping in soya bean[END_REF], disease susceptibility in medicine (Members of the Complex Trait Consortium, 2003), and less frequently with behavioral or life-history traits in planktonic ecology [START_REF] Routtu | An SNP-based second-generation genetic map of Daphnia magna and its application to QTL analysis of phenotypic traits[END_REF]. These studies aim at finding statistical links between compositional variations in genomes (often focusing on single nucleotide polymorphisms, or SNPs) and functional traits. This can be achieved by comparing the genetic variants of segregating biparental populations of organisms over multiple generations, to identify quantitative trait loci (QTL) linked with variations in trait values across individuals (Members of the Complex Trait Consortium, 2003; [START_REF] Sonah | Identification of loci governing eight agronomic traits using a GBS-GWAS approach and validation by QTL mapping in soya bean[END_REF].

For example, the genomic basis of sediment browsing in the planktonic freshwater crustacean Daphnia magna was investigated through the genotyping of 185 F2 (i.e. second generation of offspring) recombinant individuals obtained through in-lab culture and breeding [START_REF] Arbore | Ecological genetics of sediment browsing behaviour in a planktonic crustacean[END_REF]. The Daphnia genus are considered as keystone species in many ponds and lake ecosystems, and Daphnia magna and Daphnia pulex are often used as model organisms [START_REF] Czypionka | The genetic architecture underlying diapause termination in a planktonic crustacean[END_REF]). An SNP-based genetic map of the D. magna genome was released in 2014 Routtu et al., which led to the identification of markers for different sediment browsing strategies [START_REF] Arbore | Ecological genetics of sediment browsing behaviour in a planktonic crustacean[END_REF], but also of markers of diapause termination (diapause being a dormancy phase in the life cycle of many invertebrates to avoid unfavourable conditions) [START_REF] Czypionka | The genetic architecture underlying diapause termination in a planktonic crustacean[END_REF].

In addition to QTL mapping, it is also possible to compare whole genomes of numerous organisms Chapter 3. Detecting functional traits in meta-omics data through the use of genomic markers from which trait values are measured, to identify statistically significant associations between portions of genomes and trait values, in which case we talk about genome wide association studies, or GWAS [START_REF] Visscher | 10 Years of GWAS Discovery: Biology, Function, and Translation[END_REF]. QTL and GWAS approaches can even be used in conjunction, with QTL allowing to validate and quantify the influence of candidate genes identified through GWAS [START_REF] Sonah | Identification of loci governing eight agronomic traits using a GBS-GWAS approach and validation by QTL mapping in soya bean[END_REF]. GWAS has notably been used to identify markers of infectivity in Pasteuria ramosa, a model bacterial pathogen of D. magna [START_REF] Andras | Genomewide association analysis identifies a genetic basis of infectivity in a model bacterial pathogen[END_REF]. Despite their ability to decipher the genomic basis of complex functional traits, these approaches remain exclusively applied to model planktonic organisms, for which genomes of multiple individuals are available. A first reason is that QTL methods are based on vertical heredity of traits, and recombination events between generations (i.e. the exchange of genetic material between organisms leading to the production of offsprings with different traits than their parents). These methods are centered and developed on eukaryotic multicellular organisms (Metazoa, Plants), and seem quite unadapted for studying prokaryotes, in which horizontal transfers and clonal reproduction are common. Also, both QTL and GWAS methods rely on the genotyping of numerous single individuals with known trait values and/or known demographic history, often implying intensive culture and the use of reference omic sequences to accurately detect SNPs [START_REF] Visscher | 10 Years of GWAS Discovery: Biology, Function, and Translation[END_REF]. Thus, studies focusing on planktonic functional traits rely so far mainly on comparative transcriptomics.

Comparative transcriptomics studies compare populations of the same species or strain exposed to varying abiotic and/or biotic conditions, with the aim to identify up-regulated and down-regulated genes [START_REF] Caron | Probing the evolution, ecology and physiology of marine protists using transcriptomics[END_REF][START_REF] Marchetti | Comparative metatranscriptomics identifies molecular bases for the physiological responses of phytoplankton to varying iron availability[END_REF][START_REF] Mckie-Krisberg | Evaluation of Mixotrophy-Associated Gene Expression in Two Species of Polar Marine Algae[END_REF][START_REF] Liu | Gene expression characterizes different nutritional strategies among three mixotrophic protists[END_REF][START_REF] Lv | Transcriptomic analysis reveals the mechanism on the response of Chlorococcum sp. GD to glucose concentration in mixotrophic cultivation[END_REF]. For example, experiments of mixotrophic algae (here two prasinophytes, Micromonas Similarly, transcriptomes of three mixotrophic protists (the haptophyte Prymnesium parvum and two chrysophytes: Dinobryon sp. and Ochromonas sp.) have been sequenced across gradients of light [START_REF] Liu | Gene expression characterizes different nutritional strategies among three mixotrophic protists[END_REF], allowing to identify potential marker genes of phagotrophy in mixotrophic lineages. Finally, a genome-wide transcriptomic analysis of the marine diatom Thalassiosira pseudonana in different conditions (combinations of silicon limitation, nitrogen limitation and iron limitation) allowed to identify genes involved in the biogenic production of the silica cell-wall structures typical of diatoms [START_REF] Mock | Whole-genome expression profiling of the marine diatom Thalassiosira pseudonana identifies genes involved in silicon bioprocesses[END_REF].

But comparative transcriptomics do not solve all the issues evoked earlier, as sequencing comparable transcriptomes across a gradient of trait values requires the focal species to be cultivable.

Also, the potential markers detected through comparative transcriptomics need to be investigated of functional diversity in dinoflagellates. They based their approach on a sequence similarity network built from 46 transcriptomes of dinoflagellates strains from 28 distinct genera. A sequence similarity network (SSN) is a graph in which nodes are proteins, and links represent the similarity (i.e. the percentage of common amino acids) and coverage (i.e. the length of overlap between two proteins relative to total protein length) between each pair of proteins (Figure 3.5). SSNs are composed of singletons, i.e. proteins that are not linked to any other protein in the network, and connected components (CCs), i.e. subgraphs in which nodes are directly or indirectly (i.e. through other nodes) connected together, but disconnected from the rest of the network (Figure 3.5). The first step to build an SSN is then to compute pairwise similarity and coverage measures on a set of sequences, often through an all against all alignment (e.g. using Diamond [START_REF] Buchfink | Fast and sensitive protein alignment using DIAMOND[END_REF] Chapter 3. Detecting functional traits in meta-omics data through the use of genomic markers or BWA-MEM [START_REF] Li | Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM[END_REF]. By applying stringent thresholds on the obtained similarity and coverage measures to build the links of the network, one can create a SSN composed of connected components potentially sharing similar functions, and assimilable to protein families [START_REF] Atkinson | Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies[END_REF][START_REF] Cheng | Sequence similarity network reveals the imprints of major diversification events in the evolution of microbial life[END_REF][START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF][START_REF] Lopez | Highly divergent ancient gene families in metagenomic samples are compatible with additional divisions of life[END_REF]. Statistics can then be computed from the network, such as connected components number, sizes, density (number of observed connections relative to the number of potential connections), or functional and taxonomical homogeneity which can be derived from the functional and taxonomic annotations of the proteins of the network. In [START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF], each sequence has been taxonomically and functionally annotated, but also tagged with the functional traits of its organism of origin: mixotrophy, toxicity, kleptoplasty, symbiosis, parasitism, or DMSP production. The sorting of connected components allowed to identify CCs coming exclusively from toxic organisms, or symbiotic ones for example, which makes them good candidates as potential genomic markers of functional traits. 5 connected components were only composed of proteins from toxic dinoflagellate species, corresponding to 49 protein coding domains among which none were functionally annotated in the Gene Ontology. In this study, the annotation of mixotrophy was limited to 'yes' or 'no', with no distinction of the types of mixotrophy, and connected components associated with mixotrophic species were not investigated in details. Hence, the results of [START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF] suppose that a more particular focus on mixotrophy using the same data might allow to discover markers of mixotrophy in dinoflagellates, and maybe even extend them to other mixotrophic lineages available in MMETSP.

From January to June 2019, I supervised Aurélie Pham for her Master 2 internship, which focused on finding markers of mixotrophy in dinoflagellates. The preliminary results that I will present in the next sections are mainly derived from her work.

A concrete example: Markers of mixotrophy in dinoflagellate transcriptomes

Introduction

During the past decade, the historic dichotomy classifying planktonic unicellular eukaryotes as either phytoplankton or zooplankton has been replaced by a new vision based on a distribution of protists along a continuum from full autotrophy to full heterotrophy, in which most of the planktonic organisms display mixotrophic abilities [START_REF] Flynn | Misuse of the phytoplankton-zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types[END_REF][START_REF] Mitra | Defining Planktonic Protist Functional Groups on Mechanisms for Energy and Nutrient Acquisition: Incorporation of Diverse Mixotrophic Strategies[END_REF][START_REF] Stoecker | Mixotrophy in the Marine Plankton[END_REF]Caron, 2016a). This led to multiple efforts aiming at better understanding the biogeography of mixotrophs, and trying to identify their potential affinity with particular environmental conditions [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF][START_REF] Leles | Sampling bias misrepresents the biogeographical significance of constitutive mixotrophs across global oceans[END_REF]Faure et al., 2019). These studies have either used morphological identification data [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF][START_REF] Leles | Sampling bias misrepresents the biogeographical significance of constitutive mixotrophs across global oceans[END_REF] or metabarcoding data (Faure et al., 2019), all identifying mixotrophs as ubiquitous and abundant, but also highlighting strong limitations in their approaches. The main identified limit lies in the necessity of using databases of mixotrophic species and/or the available literature to define the focal set of mixotrophic lineages, while the lack of routine protocols to measure mixotrophic capacities led to misconceptions about the trophic mode of most primary producers in the global ocean [START_REF] Leles | Sampling bias misrepresents the biogeographical significance of constitutive mixotrophs across global oceans[END_REF]. It led these studies to 90 3.3 Exploring the genomic basis of complex functional traits focus on a maximum of 133 mixotrophic lineages, when more than 4300 species of phytoplankton are morphologically described, and OTU-based estimates suppose a 3 to 8 times higher number (de Vargas et al., 2015). There is then a strong discrepancy between the statement that most protists display mixotrophic capacities [START_REF] Flynn | Misuse of the phytoplankton-zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types[END_REF] and the number of lineages that are included in biogeographical studies [START_REF] Leles | Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance[END_REF][START_REF] Leles | Sampling bias misrepresents the biogeographical significance of constitutive mixotrophs across global oceans[END_REF]Faure et al., 2019). The discovery of genomic markers of mixotrophy would tackle this issue by allowing to directly detect and potentially quantify mixotrophic activities in metagenomes and metatranscriptomes, which are now available at global scales (de Vargas et al., 2015;[START_REF] Ibarbalz | Global Trends in Marine Plankton Diversity across Kingdoms of Life[END_REF][START_REF] Richter | Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems[END_REF].

The search for genomic markers of mixotrophy has mainly focused on constitutive mixotrophy, i.e.

the ability to eat through phagotrophy in inherently photosynthetic organisms, essentially through comparative transcriptomics approaches [START_REF] Mckie-Krisberg | Evaluation of Mixotrophy-Associated Gene Expression in Two Species of Polar Marine Algae[END_REF][START_REF] Liu | Gene expression characterizes different nutritional strategies among three mixotrophic protists[END_REF]. Genes upregulated during the assimilation of prey chloroplasts in the plastidic-specialist non-constitutive mixotroph dinoflagellate Nusuttodinium aeruginosum were also identified earlier this year [START_REF] Onuma | Changes in the transcriptome, ploidy, and optimal light intensity of a cryptomonad upon integration into a kleptoplastic dinoflagellate[END_REF]. But none of the genes identified as upregulated in these studies seems to establish as a good candidate for the detection of mixotrophy in environmental samples. In parallel, Burns et al. ( 2018) proposed a gene-based predictive model of phagotrophy and photosynthesis using complete genomes of 35 eukaryotic lineages. Their approach consisted in identifying protein clusters based on sequence similarity that were enriched in organisms either capable of phagotrophy or photosynthesis, allowing them to identify a set of 474 proteins associated with phagocytosis, and one of 243 proteins associated with photosynthesis [START_REF] Burns | Gene-based predictive models of trophic modes suggest Asgard archaea are not phagocytotic[END_REF]. Using these protein sets, they were able to correctly predict constitutive mixotrophy in the haptophyte Prymnesium parvum, and the absence of mixotrophy in the strictly phototrophic diatom Phaeodactylum tricornutum [START_REF] Burns | Gene-based predictive models of trophic modes suggest Asgard archaea are not phagocytotic[END_REF]. These results demonstrate the potential lying in the use of annotated databases of whole-genomes to identify genomic markers of mixotrophy, but remains limited for further application on marine mixotrophic plankton by the presence of only 4 genomes of marine planktonic organisms among the 35 reference genomes selected for the study. This low representation of marine plankton among the references is explained by the fact that [START_REF] Burns | Gene-based predictive models of trophic modes suggest Asgard archaea are not phagocytotic[END_REF] mainly aimed at detecting phagocytose and photosynthetis marker genes in archaea, and not in protists.

A study also using similarity-based protein clusters to identify genomic markers of metabolic functions focused on the Alveolata lineage of dinoflagellates [START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF]. These protists are known for their high functional diversity, as the lineage include strict autotrophs (e.g. Pelagodinium beii), constitutive mixotrophs (i.e. phagotrophs that display an innate capability to achieve photosynthesis, e.g. P. parvum), non-constitutive mixotrophs (i.e. heterotrophs that acquire photosynthetic capacity through the stealing of chloroplasts from any prey -generalists -, specific preys -plastidic-specialists -or the bearing of endosymbionts -endo-symbiotic specialists -, e.g.

the plastidic specialist non-constitutive mixotroph Dinophysis acuminata), and strict heterotrophs (e.g. Polykrikos kofoidii) [START_REF] Jeong | Growth, feeding and ecological roles of the mixotrophic and heterotrophic dinoflagellates in marine planktonic food webs[END_REF][START_REF] Mitra | Defining Planktonic Protist Functional Groups on Mechanisms for Energy and Nutrient Acquisition: Incorporation of Diverse Mixotrophic Strategies[END_REF]. As I evoked in section 3.3. 1, Meng et al. (2018) used 46 dinoflagellates transcriptomes to compute a sequence similarity network, in which they were able to retrieve connected components corresponding to highly similar clusters of proteins, potentially coding for the same functions [START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF][START_REF] Atkinson | Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies[END_REF];

91

Chapter 3. Detecting functional traits in meta-omics data through the use of genomic markers [START_REF] Lopez | Highly divergent ancient gene families in metagenomic samples are compatible with additional divisions of life[END_REF]. They were then able to identify the clusters only composed of proteins from toxic or mixotrophic dinoflagellate species, constituting putative markers of the corresponding functions [START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF]. However, they annotated mixotrophy without differentiating the different types of mixotrophy (i.e. constitutive versus non-constitutive), and did not focus on the functional annotations in clusters identified as putative markers of mixotrophy.

Here, I will use a dataset of 47 dinoflagellate transcriptomes including 19 from mixotrophic species to try to detect protein clusters that could serve as markers of mixotrophy in dinoflagellates. 2014)), before being aligned in all versus all mode using the DIAMOND software to retrieve similarity and coverage statistics for each transcripts pair [START_REF] Buchfink | Fast and sensitive protein alignment using DIAMOND[END_REF]. As only one transcriptome was added to the dataset in comparison to [START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF], the same thresholds were used to perform this analysis: edges with a similarity higher than 60% and a coverage of more than 80% were conserved in order to build the SSN using the R (R Core Team, 2019) package igraph [START_REF] Csardi | The igraph software package for complex network research[END_REF]. [START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF] selected these parameters to maximize the number of connected components with more than 30 vertices and the number of connected components involving a unique functional annotation.

Material & methods

Results and discussion

The SSN was composed of 2,901,054 proteins, including 728,916 singletons (25.1%) and 304,026 connected components, ranging from 2 to 43,480 proteins in size (Table 3.1). In comparison,

92

3.3 Exploring the genomic basis of complex functional traits connected components ranging from 2 to 1600 proteins in size. The important differences in numbers of singletons and maximum size of connected components clearly questions our choice of using the same thresholds as [START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF]. Indeed, even though only one transcriptome was added to the SSN, it ranked second in number of proteins in connected components, and significantly changed the structure of the network, notably leading to the creation of 4 gigantic connected components of more than 10,000 proteins each. These gigantic connected components included proteins from all the 47 transcriptomes, and their functional homogeneity was poor: e.g. more than 40 distinct functional annotations were found in the biggest CC. This indicates that the thresholds of similarity and coverage used were probably too low and led to the construction of chimeric connected components composed of functionally and evolutionary unrelated proteins.

The important effect of the addition of the A. minutum transcriptome on the network structure can be explained by its important size (110,667 proteins, which makes it the fifth biggest in the data set), and its high quality. Indeed, this transcriptome was obtained from the separate sequencing of 18 strains of A. minutum (Le [START_REF] Gac | Evolutionary processes and cellular functions underlying divergence in Alexandrium minutum[END_REF], and contained more than 10 times more protein coding domains than the A. minutum transcriptome already available in the MMETSP collection (which contained 10,572 proteins). Considering that transcriptomes corresponding to different strains of the same species were included separately in the dataset (in [START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF], transcriptomes from different strains of the same species were kept separated when their numbers of reads were sufficient to create independent high-quality 1 transcriptomes, e.g. two strains of Brandtodinium nutricula and of Kryptoperidinium foliaceum), implementing the sequence similarity network with 18 separated transcriptomes corresponding to each strains of A. minutum might lead to better results. As evoked earlier, the impact of this added transcriptome on the network structure could also be mitigated by a modification of the similarity and coverage thresholds, but Chapter 3. Detecting functional traits in meta-omics data through the use of genomic markers increasing these thresholds would lead to the creation and exclusion of singletons that were taken in account in [START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF], and the break of interesting connected components identified as putative markers into multiple smaller ones. To avoid this, it could be interesting to reproduce the study using a community detection algorithm, like Louvain [START_REF] Blondel | Fast unfolding of communities in large networks[END_REF], which allow to detect communities of highly connected nodes in large networks. This way, large connected components can be subdivided in smaller communities [START_REF] Watson | The Methodology Behind Network Thinking: Graphs to Analyze Microbial Complexity and Evolution[END_REF]. The Louvain algorithm has the advantage of being fast, and to not rely on parameter choices by the user [START_REF] Blondel | Fast unfolding of communities in large networks[END_REF], when other algorithms like MCL ask the user to choose parameter values that are not trivial to define [START_REF] Watson | The Methodology Behind Network Thinking: Graphs to Analyze Microbial Complexity and Evolution[END_REF].

Table 3.2 -Composition of the 4 connected components identified as potential markers of mixotrophy in dinoflagellates. Species with names in green are constitutive mixotrophs while Dinophysis acuminata, indicated in red, is a plastidic-specialist non-constitutive mixotroph. Functional annotations are from InterProScan v5.24-63.0 [START_REF] Jones | InterProScan 5: genome-scale protein function classification[END_REF].

In our SSN, 56,791 connected components were only composed of proteins from constitutive mixotrophs, corresponding to almost a fifth of the total number of CCs (Table 3.1). These connected components had a mean size of 2.7 proteins, and showed low taxonomic richness (i.e.

low number of distinct species represented in the CC), with a maximum of 7 mixotrophic species found in the same connected component (over 18 mixotrophic species in the dataset). The four 94

3.3 Exploring the genomic basis of complex functional traits connected components associated only to mixotrophic species that had the highest species richness were selected as the best candidates for being potential markers of mixotrophy (Table 3.2).

Two of them were composed of 12 proteins, one of 13 and one of 15. One had proteins from 7 different constitutive mixotrophs, two others from 6 and one last component blended proteins from 5 constitutive and 1 plastidic-specialist non-constitutive mixotrophs (Table 3.2 The fourth CC identified as a potential marker of mixotrophy blended 15 proteins from 4 different orders, Gonyaulacales, Gymnodiniales, Prorocentrales and Dinophysiales (Table 3.2). It is less likely for the proteins of this connected component to be limited to phylogenetic markers than it was for the three other candidate CCs, because these four orders belong to a large clade also including the Peridiniales, Suessiales and Noctilucales orders (Figure 3.6), from which no proteins appear in the CC. It is however legitimate to question the credibility of a genomic marker that would detect both constitutive and plastidic-specialist non-constitutive mixotrophy, as organisms from the two trophic modes differ strongly in terms of physiology [START_REF] Mitra | Defining Planktonic Protist Functional Groups on Mechanisms for Energy and Nutrient Acquisition: Incorporation of Diverse Mixotrophic Strategies[END_REF]. For example, this CC could not be a marker of kleptoplasty, which can not be performed by constitutive mixoitrophs.

However, both types of mixotrophic organisms share the ability to eat through phagocytosis, and this CC could then be tested as a marker of phagotrophy in dinoflagellates. Here again, all the 15 proteins from the connected component were 'known unknowns', i.e. they were found in the InterProScan database [START_REF] Jones | InterProScan 5: genome-scale protein function classification[END_REF], but could not be associated to a biological function.

They could then constitute interesting proteins to target for wet lab experiments focusing on phagocytosis, to check for their influence on mixotrophic abilities in dinoflagellates.

Despite the fact that mixotrophic abilities were associated with more than a hundred transcriptomes of MMETSP through a literature review, no information were available in the metadata of these transcriptomes indicating the mode of feeding of the cultivated organisms. Considering that constitutive mixotrophs often have varying feeding behaviors, adapting their rate of mixotrophy to their environment [START_REF] Mckie-Krisberg | Evaluation of Mixotrophy-Associated Gene Expression in Two Species of Polar Marine Algae[END_REF][START_REF] Liu | Gene expression characterizes different nutritional strategies among three mixotrophic protists[END_REF][START_REF] Lv | Transcriptomic analysis reveals the mechanism on the response of Chlorococcum sp. GD to glucose concentration in mixotrophic cultivation[END_REF], it is difficult to tell whether the transcriptomes of mixotrophic species in the MMETSP database should carry markers of mixotrophy or not. This way, even with an improved version of the SSN, and eventually the addition of more transcriptomes to the 798 ones that were annotated, the potential detection pSNCM CM Non-mixo of mixotrophy markers would still be questionable regarding the data used. This illustrates well how the fact that mixotrophy is a continuum between autotrophy and heterotrophy hardens the analysis of its genomic basis.

Conclusion

The more reliable way to find markers of mixotrophy would be to reproduce a SSN-based approach using transcriptomes of mixotrophic organisms issued from comparative transcriptomics studies et al., 2018;[START_REF] Liu | Gene expression characterizes different nutritional strategies among three mixotrophic protists[END_REF][START_REF] Lv | Transcriptomic analysis reveals the mechanism on the response of Chlorococcum sp. GD to glucose concentration in mixotrophic cultivation[END_REF]. By building a sequence similarity network using transcriptomes coming out of such experimental designs, and by analyzing it through the lens of metadata indicating rates of phagotrophy and photosynthesis, it should be possible to find connected components grouping proteins from multiple species in the same feeding behavior state.

It could allow to expand from species or strain-specific putative markers detected by comparative transcriptomics to higher trophic levels ones. The presence of such markers could then be tested in other, less studied mixotrophic species, or in environmental metagenomics and metatranscriptomics samples.

Next challenge: linking unknown functions and uncultivated organisms to functional traits

Until now, I have only presented approaches based either on wet lab investigations of specific organisms, or on the statistical investigation of large datasets of full genomes/transcriptomes.

This often implies that the focal organisms can be cultivated in labs. However, up to 99% of microbial species remain impossible to cultivate [START_REF] Rappé | The Uncultured Microbial Majority[END_REF][START_REF] Rinke | Insights into the phylogeny and coding potential of microbial dark matter[END_REF][START_REF] Watson | The Methodology Behind Network Thinking: Graphs to Analyze Microbial Complexity and Evolution[END_REF][START_REF] Mangot | Accessing the genomic information of unculturable oceanic picoeukaryotes by combining multiple single cells[END_REF], so one important challenge is to associate functional traits to poorly known and not yet culturable organisms.

As illustrated in section 3.2.2, the mining of genomic markers in metagenomes and metatranscriptomes can allow to draw quantitative hypotheses on the distribution of functional traits without distinguishing cultivated and uncultivated organisms. In a more organism-centered way, the recent identification of hundreds of MAGs from the Tara Oceans metagenomics data allowed to detect nitrogen fixation genes in abundant yet uncultivated Planctomycetes and Proteobacteria (See section 1.3.2.2, Delmont et al. (2018)), genomes of uncultured picoeukaryotes and giant viruses were retrieved from targeted single-cell genomics [START_REF] Mangot | Accessing the genomic information of unculturable oceanic picoeukaryotes by combining multiple single cells[END_REF][START_REF] Needham | Targeted metagenomic recovery of four divergent viruses reveals shared and distinctive characteristics of giant viruses of marine eukaryotes[END_REF], and transcriptomes of uncultured eukaryotes were determined from metagenomic samples [START_REF] Vorobev | Transcriptome reconstruction and functional analysis of eukaryotic marine plankton communities via high-throughput metagenomics and metatranscriptomics[END_REF]. Similarly, [START_REF] Lannes | Carbon Fixation by Marine Ultrasmall Prokaryotes[END_REF] were able to detect carbon fixation pathways in marine ultrasmall prokaryotes, without even relying on the assembly of genomes or transcriptomes. Instead, they filtered sequences from metagenomes of the viral size fraction from Tara Oceans, in order to only keep sequences affiliated to prokaryotes. This way, they identified ultrasmall prokaryotes to collectively harbor (i.e. without proof of presence in a single genome) the dicarboxylate/4-hydroxybutyrate pathway and the 4-hydroxybutyrate pathway, which are both energy efficient pathways leading to autotrophic carbon fixation [START_REF] Lannes | Carbon Fixation by Marine Ultrasmall Prokaryotes[END_REF].

Still, 40 to 60% of the open reading frames (ORF) detected in microbiome analyses are of unknown

97

Chapter 3. Detecting functional traits in meta-omics data through the use of genomic markers function [START_REF] Vanni | Light into the darkness: Unifying the known and unknown coding sequence space in microbiome analyses[END_REF][START_REF] Bernard | Microbial Dark Matter Investigations: How Microbial Studies Transform Biological Knowledge and Empirically Sketch a Logic of Scientific Discovery[END_REF]. To explore the function of such ORFs, one method is to identify their remote homologs, also called distant homologs, or transitive homologs [START_REF] Lopez | Highly divergent ancient gene families in metagenomic samples are compatible with additional divisions of life[END_REF][START_REF] Watson | The Methodology Behind Network Thinking: Graphs to Analyze Microbial Complexity and Evolution[END_REF]. The idea behind this method is to find proteins in environmental samples that are indirectly homologous to proteins from functional annotation databases, e.g. an unannotated homolog to a protein that has a match in functional annotation databases [START_REF] Watson | The Methodology Behind Network Thinking: Graphs to Analyze Microbial Complexity and Evolution[END_REF]. [START_REF] Lobb | Remote homology and the functions of metagenomic dark matter[END_REF] found 15.3% of the 484,121 ORFs analyzed in their study to be distant homologs of structurally characterized proteins, and were able to identify hundreds of novel enzymes. Very recently, a new database and tool called AGNOSTOS came out, that references known and unknown genes as clusters based on their sequence similarity, allowing to very rapidly identify distant homologs of query proteins [START_REF] Vanni | Light into the darkness: Unifying the known and unknown coding sequence space in microbiome analyses[END_REF]; more on this in the general discussion).

These are examples of the methods available to decipher the functional potential of uncultivated organisms and functionally unannotated genes. However, in matters of functional traits, only traits with well known genomic markers can be detected in meta-omics samples and in MAGs.

We can hope that in a near future, comparative transcriptomics, methods like GWAS and wet lab experiments will allow to better describe the genomic basis of complex traits like cell size or mixotrophy. Only then, investigating these multigenic markers in environmental samples and uncultivated organisms will be possible. This is why I decided to test a different approach for the next part of my thesis, trying to focus on detecting functional clusters of genes of interest without any a priori selection based on their functional annotations.

Part II

Data-driven approaches to identify and quantify the functional composition of planktonic communities 99 Chapter 4

Towards omics-based predictions of planktonic functional composition from environmental data

Prelude

In the introduction, I highlighted how current biogeochemical modeling approaches did not allow to define model structural components from observational data, and always relied on a priori choices of the model PFTs, traits, genes or metabolic pathways. During the first part of my thesis, I did not address this particular issue, as I focused my attention on two particular traits, namely mixotrophy and DMS production. As presented in chapter 3, the investigation of a priori chosen functional traits often implies to rely on genomic markers detected in cultivated organisms. I presented how meta-omics data led to the detection of such markers in unexpected taxa, and how they allowed to better understand the biogeography of functional traits independently from taxonomical assignations. But such studies do not take full advantage of the richness of metaomics data, as they focus on one or a few a priori selected genes and discard the rest, including functionally unannotated genes, even though full metagenomes and metatranscriptomes contain information on the functional potential of planktonic communities as a whole [START_REF] Vanni | Light into the darkness: Unifying the known and unknown coding sequence space in microbiome analyses[END_REF].

In the second part of my thesis, my goal was then to design an approach to extract functionally homogeneous clusters of proteins from meta-omics data without any a priori based on their functional and/or taxonomic annotation (Figure 4.1). One of the main objectives was to be able to compute the abundance of each cluster in the environment, to be able to describe and understand their biogeography. It allowed me to identify and confirm the main drivers of functional composition in planktonic prokaryotic communities, but also to highlight proteins, functions and MAGs particularly associated with environmental gradients in the global ocean.

In the following section, I will present a data-driven method applicable to any set of sequences, reflect the similarity and coverage between each pair of proteins. In this toy example, the sequence similarity network is composed of 4 connected components, or groups of nodes connected together directly or indirectly, and disconnected form the rest of the network. Each connected component is defined as a protein functional cluster, and examples of taxonomic and functional annotations are indicated to illustrate the kind of information that can be retrieved from the approach. The abundance of each protein functional cluster can be computed in environmental metagenomes, through the mapping of environmental reads to each protein. These abundances can finally be statistically related to the environmental context.

proteins. I decided to apply this method on the 885 prokaryotic MAGs produced by [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF], which were manually curated and appeared to me as more reliable than fully-automatically binned ones (e.g. [START_REF] Parks | Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life[END_REF] or [START_REF] Tully | The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans[END_REF]). The same approach could in theory be applied to larger datasets, and even to full Tara Oceans gene catalogs, but computational limitations and the necessity to benchmark the approach with a more easy to handle dataset pushed me to first focus on the MAGs. They appeared as good candidates as they mostly correspond to uncultivated organisms, with poorly described functional potentials despite high abundances across the global ocean.

The rest of this chapter will consist in a manuscript entitled Towards omics-based predictions of planktonic functional composition from environmental data, currently undergoing modifications after a first round of revisions in Nature Communications.

Towards omics-based predictions of planktonic functional composition from environmental data

Authors: Emile Faure 1,2 , Sakina-Dorothée Ayata Abstract: Marine microbes play a crucial role in climate regulation, biogeochemical cycles, and trophic networks. Unprecedented amounts of data on planktonic communities were recently collected, sparking a need for innovative data-driven methodologies to quantify and predict their ecosystemic functions. We reanalysed 885 marine prokaryotic metagenome-assembled genomes through a network-based approach and detected 233,756 protein functional clusters, from which 15% were functionally unannotated. We investigated all clusters' distributions across the global ocean through machine learning, identifying biogeographical provinces as the best predictors of protein functional clusters' abundance. The abundances of 2,444 clusters were predictable from the environmental context, including 183 functionally unannotated clusters. We analyzed the biogeography of these 2,444 clusters, identifying the Mediterranean Sea as an outlier in terms of protein functional clusters composition. Applicable to any set of sequences, our approach constitutes a step towards quantitative predictions of functional composition from the environmental context.

Introduction

Planktonic organisms play an essential role in biogeochemical cycles through the capture and export of carbon into the deep ocean, nitrogen fixation, remineralization of organic matter, or the production of dimethyl-sulfur, hence impacting global climate [START_REF] Falkowski | Biogeochemical Controls and Feedbacks on Ocean Primary Production[END_REF][START_REF] Guidi | Plankton networks driving carbon export in the oligotrophic ocean[END_REF][START_REF] Whitman | Prokaryotes: The unseen majority[END_REF][START_REF] Ferrera | Prokaryotic functional gene diversity in the sunlit ocean: Stumbling in the dark[END_REF]Sunagawa et al., 2015). The understanding and modeling of such biogeochemical functions is key for predicting the global functioning of oceanic ecosystems, and especially their response to climate change (Le [START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF]Litchman et al., 2015a;Follows et al., 2007). These biogeochemical functions are usually modeled by simulating the dynamics of plankton functional types (PFT) that are theoretical entities grouping planktonic organisms according to shared functional capacities (e.g. calcifiers, nitrogen fixers or silicifiers) (Le [START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF]. This approach allows to incorporate the functional diversity of marine plankton into biogeochemical models (Follows et al., 2007;[START_REF] Aumont | PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies[END_REF]Coles et al., 2017;[START_REF] Leles | Evaluation of the complexity and performance of marine planktonic trophic models[END_REF], but often relies on a priori and restricted choices of the considered types of planktonic organisms and of their physiological rates or parameters [START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF]. For example, prokaryotic organisms are often lacking an explicit representation in global PFT models [START_REF] Aumont | PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies[END_REF][START_REF] Leles | Evaluation of the complexity and performance of marine planktonic trophic models[END_REF], even though more than 1030 prokaryotic cells inhabit the 103 Chapter 4. Towards omics-based predictions of planktonic functional composition from environmental data ocean's subsurface [START_REF] Whitman | Prokaryotes: The unseen majority[END_REF]. To tackle this issue, recent works proposed to switch towards data-driven modeling of planktonic communities and their impact on the environment, notably through the use of high-throughput sequencing data (Coles et al., 2017;Mock et al., 2016;Louca et al., 2016a).

Next generation sequencing technologies have led to significant advances in the knowledge of the taxonomic and functional diversity of planktonic organisms (Sunagawa et al., 2015;[START_REF] Louca | Decoupling function and taxonomy in the global ocean microbiome[END_REF]. Bioinformatics workflows allow the assembly of metagenome-assembled genomes (MAGs), which are near-complete genomes retrieved from DNA fragments coming from environmentally sequenced individuals of one or a few closely related populations [START_REF] Parks | Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life[END_REF][START_REF] Tully | The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans[END_REF][START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF][START_REF] Nielsen | Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes[END_REF]. MAGs can be taxonomically annotated using multi marker gene approaches, and organism-level functional profiles can be drawn from their genomic content [START_REF] Parks | Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life[END_REF][START_REF] Tully | The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans[END_REF][START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF]. Reads from environmental meta-omics datasets can also be mapped to their reconstructed sequences to obtain abundance measurements both at MAG and single protein level [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF]Salazar et al., 2019). MAGs can be considered as representative of the genetic potential of natural populations, hence allowing to retrieve genomes of cultivable, uncultivable or even unknown species present in the environment. They constitute a promising tool for investigating as a whole the functional potential of known and unknown planktonic life forms.

Recently, a genomics-based model revealed that the gene content of planktonic communities is more relatable to biogeochemical gradients than taxonomic content (Coles et al., 2017). In another study, omics data were used to quantitatively estimate global nitrogen fixers abundance through machine learning algorithms (Tang and Cassar, 2019). It illustrates how quantitative, data-driven biogeochemical models can be built from global omics datasets. However, these studies focused only on a relatively small number of well-described genes (e.g. nif or amtB genes, involved in dinitrogen and ammonium fixation, respectively) (Coles et al., 2017;Tang and Cassar, 2019), far from exploiting the rich functional diversity observed in omic samples. This way, the large proportion of unknown sequences detected in environmental meta-omics datasets, that is to say the open reading frames (ORFs) which can not be linked to any biological functions (usually around 40% for prokaryotes, and about 50% for eukaryotes), is as yet untapped [START_REF] Ferrera | Prokaryotic functional gene diversity in the sunlit ocean: Stumbling in the dark[END_REF]Sunagawa et al., 2015;Salazar et al., 2019;de Vargas et al., 2015;[START_REF] Acinas | Metabolic Architecture of the Deep Ocean Microbiome[END_REF][START_REF] Carradec | A global ocean atlas of eukaryotic genes[END_REF]. Besides, many meta-omics studies have either focused on semi-quantitative diversity and interactions surveys at global scales (de Vargas et al., 2015;Lima-Mendez et al., 2015), on specific taxonomic groups (e.g. Collodaria [START_REF] Biard | Biogeography and diversity of Collodaria (Radiolaria) in the global ocean[END_REF]) or on particular biological functions (such as nitrogen fixation or mixotrophy [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF]Tang and Cassar, 2019;Faure et al., 2019)). A recent study has grouped protein sequences of marine planktonic prokaryotes according to their annotated metabolic pathways to investigate their differential abundance and expression, mainly focusing on pre-selected biogeochemical functions such as photosynthesis or nitrogen fixation (Salazar et al., 2019). By investigating the response of biogeochemistryrelated protein groups to environmental conditions, significant differences in terms of presence and expression were identified between polar and non-polar areas, and between mesopelagic and surface depths (Salazar et al., 2019). These results highlight the potential of function-clustering 104 4.2 Towards omics-based predictions of planktonic functional composition from environmental data based approaches for deciphering global ocean biogeochemistry, but could be further extended by skipping any sequence pre-selection step requiring database-dependent metabolic pathways annotations.

In this study, we followed a similar approach while avoiding any a priori choices of particular genes or metabolic pathways. We used 51 quantitative and qualitative environmental variables to detect both known and unknown protein clusters that are sensitive to environmental gradients.

We re-analysed 885 high quality MAGs from marine prokaryotic plankton belonging to the Bacteria (n=820) and Archaea (n=65) domains, assembled by [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF] using 93 Tara Oceans picoplanktonic metagenomes from the surface of the global ocean. With these almost 2 million sequences, we built functional clusters of proteins using a sequence similarity network, i.e. a graph in which nodes are protein sequences, and edges represent the similarity and coverage between each pair of sequences [START_REF] Atkinson | Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies[END_REF][START_REF] Forster | Testing ecological theories with sequence similarity networks: marine ciliates exhibit similar geographic dispersal patterns as multicellular organisms[END_REF][START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF][START_REF] Bittner | Some considerations for analyzing biodiversity using integrative metagenomics and gene networks[END_REF][START_REF] Lopez | Highly divergent ancient gene families in metagenomic samples are compatible with additional divisions of life[END_REF]. Such approaches allow for the construction of sequence clusters putatively homogenous in function [START_REF] Atkinson | Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies[END_REF], and were recently used to investigate the genomic basis of functional diversity in prokaryotes [START_REF] Cheng | Sequence similarity network reveals the imprints of major diversification events in the evolution of microbial life[END_REF], in a lineage of eukaryotes [START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF], or in natural microbial communities [START_REF] Lopez | Highly divergent ancient gene families in metagenomic samples are compatible with additional divisions of life[END_REF].

Particularly, we are here interested in knowing if the abundance of some protein clusters could be predicted from environmental data in the oceanic ecosystem. For example, is the distribution of biogeochemistry-related protein clusters more sensitive to environmental gradients than the one of other clusters? We thus explored the biogeography of environment-related protein clusters in light of their potential functional and / or taxonomic annotation, in order to identify the ones being specific to certain environmental conditions, such as oligotrophic or particularly cold waters.

We introduce here a data-driven, large-scale, fast and automatable approach, potentially applicable to any set of environmental sequences, which involves (1) the network-based construction of sequence clusters, putatively homogeneous in function, (2) the functional annotation of these clusters, (3) the calculation of environmental abundance values for each of these protein clusters through environmental reads re-mapping, and (4) the description of statistical relationships between cluster abundances and environmental gradients through machine learning and constrained ordination methods. We then present the first biogeographical analysis of known and unknown prokaryotic protein functional clusters identified as sensitive to environmental gradients in the global ocean, with no a priori choice of specific functions or taxa.

Results

From sequence similarity network to protein functional clusters

We analysed the 1,914,171 proteins from 885 prokaryotic MAGs from marine plankton, recovered from 12 geographically bound assemblies of metagenomic sets corresponding to a total of 93 Tara Oceans samples [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF]. 39.6% of the MAGs' proteins (757,457) were involved in our sequence similarity network, i.e. they had at least one similarity relationship with another protein that satisfied the chosen threshold of 80% similarity and 80% coverage (see Methods).

105

Chapter 4. Towards omics-based predictions of planktonic functional composition from environmental data 51.1% of the network proteins could be annotated to 4,922 unique molecular function IDs in the KEGG database [START_REF] Aramaki | KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold[END_REF], associated with 327 distinct metabolic pathways (a full list of these pathways is displayed in Table S1, available in appendix C). 85.2% of the network proteins were annotated to 17,009 eggNOG functional descriptions [START_REF] Huerta-Cepas | Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper[END_REF], 2016).

Table 4.1 -Metrics computed on the 233,756 protein functional clusters (PFC) from the sequence similarity network of MAGs proteins. Functional scores are based on the functional annotation of

MAGs proteins, with a functional homogeneity score of 1 meaning that all proteins in a PFC share the same annotation, while a score of 0 indicates that all proteins have different annotations (see Methods for details). By "unknown proteins" we refer both to sequences with no match in databases (KEGG and/or eggNOG) and to sequences existing in databases but with no functional and/or taxonomic annotation. Taxonomy scores are based on taxonomic annotations of MAGs available from Delmont et al. 2018. This way, the 6,367 PFCs with only proteins from MAGs unannotated at the phylum level were only composed of proteins coming from the 45 Bacteria MAGs of unidentified phylum. Detailed functional and taxonomic annotations for each protein sequence are available online, as well as detailed sizes and functional/taxonomy scores for each PFC (see Data availability section).

The sequence similarity network involved 233,756 connected components (CCs), i.e. groups of nodes (here proteins) connected together by at least one path and disconnected from the rest of the network. According to KEGG and eggNOG databases, 15.3% and 48.5% of the CCs remained without any functional annotation (i.e. all sequences from the CC were unmatched in the databases, or had a match but were not yet linked to any biological function, Table 1), and 14.8% were functionally unannotated for both databases. We ranked the functional homogeneity of CCs involving at least one functional annotation from 0 (all annotations in the CC are different) to 1 (all annotations in the CC are the same), and found mean homogeneity scores of 0.99 over 1 for KEGG annotations and 0.94 over 1 for eggNOG ones (see Methods for score calculation details). Only 88 (0.04%) CCs had an homogeneity score below 0.5 in both annotation databases, all with sizes below 5 proteins. 177 PFCs (0.07%) had a score below 0.8 in both databases, all under 12 proteins in size. These CCs were kept in the analysis while tagged as poorly homogenous. We thereafter 106 4.2 Towards omics-based predictions of planktonic functional composition from environmental data considered each CC as a protein functional cluster (PFC), numeroted from #1 to #233,756.

To check for the influence of taxonomic relationships between the MAGs on our PFCs, we computed different metrics based on MAGs taxonomic annotations provided by [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF] (Table 1). This taxonomic annotation based on 43 single-copy core genes allowed to annotate 100% of the MAGs at the domain level, and 95% of the MAGs at the phylum level, the remaining 5% corresponding to Bacteria MAGs of unidentified phyla [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF]. Only 1,330 PFCs (0.6%) mixed proteins from the Archaea and Bacteria domains. PFCs were very homogeneous at the phylum level, then the homogeneity decreased at lower taxonomic rank, meaning that PFCs studied here were generally not specific from a single class, order, family, genus or MAG (Table 1). 7,834 PFCs (3.4%) were only composed of proteins with no functional annotation in KEGG and eggNOG databases, and no taxonomic annotation under the phylum level. Their sizes ranged from 2 to 30 proteins (mean of 2.62). Their 20,552 proteins came from Euryarcheota MAGs (12,458; 60.6%), Bacteria MAGs of unidentified phylum (2,742; 13.3%), Candidatus Marinimicrobia MAGs (2,451; 11.9%), Proteobacteria MAGs (1,528; 7.4%), Acidobacteria MAGs (1,031; 5%), Verrucomicrobia MAGs (103; 0.5%), Planctomycetes MAGs (89; 0.4%), Bacteroidetes MAGs (79; 0.4%), Chloroflexi MAGs (59; 0.3%) and Candidate Phyla Radiation MAGs (12; 0.05%). We hereafter considered these functionally and taxonomically unknown PFCs as "microbial dark matter" [START_REF] Rinke | Insights into the phylogeny and coding potential of microbial dark matter[END_REF][START_REF] Bernard | Microbial Dark Matter Investigations: How Microbial Studies Transform Biological Knowledge and Empirically Sketch a Logic of Scientific Discovery[END_REF]. Their nucleotidic sequences are available in separate supplementary files (see Data availability section). The abundance of microbial dark matter PFCs was significantly different from the abundance of other PFCs in 85 samples over 93 (Wilcoxon test, p-value <0.05). The median abundance of microbial dark matter PFCs was higher than the one of other PFCs in 36 of these 85 samples, and lower in the 49 others. Further details on dark matter PFCs' abundance are available in the Detection of the rare biosphere section.

Identification of protein functional clusters highly related to environmental gradients

To identify the PFCs that responded the most to environmental gradients, we first selected the 228,914 clusters with non-zero variance abundance profiles (i.e. at least 10% of distinct abundance values across all samples, and less than a 95 to 5 ratio between the most and the second most observed abundance value). We then built random forest regression models for each of these 228,914 clusters. We used the sequence abundances as response variables, or labels, and 51 environmental variables as explanatory variables (see Methods for details of model training and tuning). About a fifth of the random forest regression models showed a clear statistical signal: 44,653 models (19.5%) had R 2 values over 0.25, corresponding to PFCs linked to environmental conditions, and 2,444 (1.1%) had values over 0.5 (Figure 4.2A), corresponding to PFCs highly linked to environmental gradients. The mean R 2 value over all models was 0.09, with a maximum of 0.91 (Figure 4.2A). Longhurst biogeographical provinces [START_REF] Longhurst | Ecological Geography of the Sea[END_REF] were detected as the most important predictor in 90,235 models (39.4%), and were in the top 3 most important predictors in 166,639 models (72.8%) (Figure 4.2B). Among models with biogeographical provinces as the best predictor, the mean R 2 reached 0.15. Temperature was in the top 3 most important

Table 4.2 -Metrics computed on the 2,444 protein functional clusters detected as particularly linked to environmental gradients (hlePFCs). Functional scores are based on the functional annotation of

MAGs proteins, with a functional homogeneity score of 1 meaning that all proteins in a PFC share the same annotation, while a score of 0 indicates that all proteins have different annotations (See Methods). By "unknown proteins" we refer both to sequences with no match in databases (KEGG and/or eggNOG) and to sequences existing in databases but with no functional and/or taxonomic annotation. Taxonomy scores are based on taxonomic annotations of MAGs from [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF] predictors in 11,856 models (5.2%). Models with temperature as the best predictor had a mean R 2 of 0.29, which is the highest value of all quantitative variables. We focused on the 2,444 PFCs associated with models showing R 2 values over 0.5, hereafter called "hlePFCs" for highly linked to environmental conditions protein functional clusters. 207 KEGG pathways were associated with the 2,444 hlePFCs, i.e. 63% of the pathways identified on the full network were detected in hlePFCs (c.f. Table S1 in Appendix C for a detailed list). Among commonly detected pathways (arbitrary threshold of at least 1000 detections in the similarity sequence network, more details in the Links between models R 2 and their associated metabolic pathways section), the 5 most conserved pathways after selection were methane metabolism (2.63% of the PFCs associated to this pathway were hlePFCs), ribosome (2.43%), carbon fixation in photosynthetic organisms (2.28%), carbon fixation pathways in prokaryotes (2.01%) and pentose phosphate pathway (1.99%).

The functional homogeneity of the 2,444 hlePFCs was similar to the one of the total 233,756 PFCs (Table 1,Table 2). In parallel, proportions of functionally unannotated hlePFCs were lower and the proportion of hlePFCs only composed of unannotated proteins in both functional databases was divided by two (from 14.8% to 7.5%).

The proportions of hlePFCs homogenous at the phylum, class and order level were comparable to 108 4.2 Towards omics-based predictions of planktonic functional composition from environmental data the one of the total PFCs and superior or equal to 70% (Table 1, Table 2). The trend differed at the family and genus level, where only 84.4% and 29.6% of the hlePFCs with at least one protein annotated were associated to a single taxonomic annotation, while this proportion was of 95.3% and 91.9% in the total PFCs (Table 1,Table 2). This way, hlePFCs tended to mix more taxa at the family and genus levels than the rest of the PFCs, while retaining a high functional homogeneity.

The proportion of taxonomically unannotated hlePFCs was lower than the one of total PFCs at the phylum, class and order levels, but was higher at the family and genus levels (Table 1, Table 2). 24 of the 7,834 PFCs (i.e. 0.3%) tagged as microbial dark matter (i.e. PFCs without any functional annotation and without taxonomic information under the phylum level) were selected among the 2,444 hlePFCs. These 24 hlePFCs corresponded to 49 proteins belonging to 12 unique MAGs, annotated as Proteobacteria (n = 5), Euryarchaeota (n = 3), Candidatus Marinimicrobia (n = 2), and Bacteria unannotated at Phylum level (n = 2).

Global biogeography of the protein functional clusters highly linked to environmental gradients

The canonical correspondence analysis (CCA) achieved on the 2,444 hlePFCs to investigate their biogeographical repartition had an R 2 value of 72.9%, and was significant (p-value < 0.001).

The first axis (20.62% of explained variance) opposed warm and oligotrophic waters (CCA1>0) to cold and nutrient rich waters (CCA1<0) (Figure 4.3). The second axis (17.03%) mostly opposed samples from the Mediterranean Sea (CCA2>0) to the rest of the samples. At the exception of Mediterranean samples, samples from geographically close biogeographical provinces appeared close to each other in the CCA space, with samples from the Southern Ocean and the Atlantic

-Canonical correspondence analysis (CCA) on abundances of the 2,444 protein functional clusters highly linked to environmental variables (hlePFCs). hlePFCs are represented as black dots, quantitative environmental variables as arrows, and samples as circles colored according to their biogeographical province (correspondence between 4 letters codes used here and full biogeographical provinces names, as well as descriptions of all other environmental variables are displayed in Table S2, available in Appendix C). For simplification issues, other qualitative variables (Season moment and Ocean region) were not represented. On the right and upper panels, density plots are represented along each axis, illustrating the density of functionally annotated and unannotated hlePFCs based on KEGG annotations ( i.e. functionally annotated hlePFCs contain at least one functionally annotated protein; functionally unannotated hlePFCs contain only functionally unannotated proteins)

. The mean hlePFC density was of 0.27 along CCA1 (standard deviation = 0.34,maximum = 1.38),and 0.21 along CCA2 (standard deviation = 0.23,maximum = 0.94). The mean difference in density between functionally annotated and unannotated hlePFCs along CCA1 was of 0.06 (standard deviation = 0.07, maximum = 0.28). The mean density difference between annotated and unannotated hlePFCs along CCA2 was 0.04 (standard deviation = 0.03, maximum = 0.13). Similar observations were done using eggNOG annotations densities (Figure 4.4). Positions of all functionally and/or taxonomically unannotated hlePFCs in the CCA space, the most important drivers of their abundance according to random forest models, the nucleotidic sequences of their proteins and their MAGs of origins are all publicly accessible (link in Data availability section). Pathways associated to cold and rich waters are included in blue convex hulls, while the ones associated to mediterranean samples are in red convex hulls. We also selected three pathways linked to inter-organisms interactions, in orange hulls, and three pathways related to biogeochemical functions in green hulls. Finally, two black convex hulls englobing all functional unknown PFCs were represented, one for KEGG annotations and another for eggNOG ones.

Detection of the rare biosphere

By defining microbial dark matter protein functional clusters (PFCs) as made of proteins functionally unannotated and taxonomically unannotated under the phylum level, we selected quite abundant PFCs, probably missing most of the " rare biosphere " [START_REF] Lynch | Ecology and exploration of the rare biosphere[END_REF] (See main text). To check for the impact of our choice of definition, we investigated the abundance of microbial dark matter PFCs using other thresholds of taxonomic annotation to define microbial dark matter.w 12,895 PFCs were only composed of proteins with no functional annotation in KEGG and eggNOG databases, and had no taxonomic annotation under the class level. The abundance of these 12,895 PFCs was significantly different from the one of the rest of PFCs in 88 samples over 93 (Wilcoxon test, p-value <0.05). Their median abundance was lower in 32 of these 88 samples, and higher in the 56 others. 20,166 PFCs were only composed of proteins with no functional annotation in KEGG and eggNOG databases, and had no taxonomic annotation under the order level. The abundance of these 20,166 PFCs was significantly different from the one of the rest of PFCs in 84 samples over 93 (Wilcoxon test, p-value <0.05). Their median abundance was lower in 65 of these 84 samples, and higher in the 19 others. 32,737 PFCs were only composed of proteins with no functional annotation in KEGG and eggNOG databases, and had no taxonomic annotation under the family level. The abundance of these 32,737 PFCs was significantly different from the one of the rest of PFCs in all samples (Wilcoxon test, p-value <0.05), and their median abundance was lower in all samples but one, a surface sample from the Indian Ocean (TARA_064). This way, step by step considering PFCs taxonomically unannotated under the class, order and family level as microbial dark matter led to a decrease of median abundance of microbial dark matter PFCs at each step. It then seems that the " rare biosphere " was better detected when including unidentified lineages of known class, order or family, than when using only unidentified lineages of known phylum. As stated in the main text, this can be explained by the lack of knowledge about the abundants Archaea and Candidatus Marinimicrobia phyla.

Although, we could also argue that using MAGs might not be the best way to observe the " rare biosphere ". Indeed, the binning of contigs into MAGs relying mainly on co-abundance profiles [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF][START_REF] Parks | Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life[END_REF][START_REF] Tully | The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans[END_REF] , it is likely that organisms with very low and erratic abundances over samples could be missed in the binning steps.

Links between models R2 and their associated metabolic pathways

The 228,914 random forest produced models with R 2 values ranging from -0.38 to 0.91. We investigated the mean R 2 values of models grouped by their associated metabolic pathways. The maximum mean value of 30% was obtained for the calcium signaling pathway, but only 1 model was associated to it. Among pathways associated to at least 100 proteins, values ranged from Figure 4.9 -(A) Relationships between normalized sequence abundance and temperature for 6 selected protein functional clusters highly linked to the environment (hlePFCs) that were only composed of functionally unannotated sequences. The three graphs on the left, in purple, correspond to the three hlePFCs functionally unannotated in both KEGG and eggNOG databases that had the lowest positions on the first axis of the canonical correspondence analysis (CCA1) (cold and nutrient rich waters). The three graphs in the middle, in green, correspond to the three hlePFCs functionally unannotated in both KEGG and eggNOG databases that had the highest positions on CCA1 (oligotrophic and warm waters). (B) Relationships between normalized sequence abundance and location of sampling, whether in the Mediterranean Sea or not, for 3 microbial dark matter hlePFCs (only functionally unannotated sequences and no taxonomic annotations under the phylum level). These 3 hlePFCs had the highest positions among microbial dark matter PFCs on the second axis of the canonical correspondence analysis (CCA2) (correlated to Mediterranean samples).

5.6% to 17.1%, showing that no common pathway could be associated to particularly high R 2 values. 16 negative values were observed, mostly corresponding to human-related metabolic pathways, e.g. rheumatoid arthritis, cocaine addiction, thyroid hormone signaling pathway, or pancreatic secretion. We then investigated the metabolic pathways associated to PFCs highly linked to environmental gradients (hlePFCs), to check if some pathways were more associated to hlePFCs than others. 121 pathways were not detected at all in hlePFCs (Table S1 in appendix C).

The most selected pathway was biosynthesis of vancomycin group antibiotics , with 4.65% of its associated PFCs being hlePFCs. But this pathway was associated to only 43 PFCs in total, and 2 of them only were hlePFCs. Similarly, other rare pathways appeared as well selcted even though they did not make much sense for planktonic communities, like prostate cancer (66 associated PFCs, 4.54% in hlePFCs) or cardiac muscle contraction (182 associated PFCs, 4.40% in hlePFCs). This is why we decided to focus on more abundant pathways only. A threshold of 200 associated PFCs was too low, as pathways like Parkinson disease (associated to 215 PFCs) or Alzheimer 4.2 Towards omics-based predictions of planktonic functional composition from environmental data disease (associated to 371 PFCs were still among the most selected pathways). At a threshold of 500 associated PFCs, we had 98 unique pathways, with still some unrelated to prokaryotic communities like thermogenesis (591 associated PFCs, 2.71% in hlePFCs) or pathways in cancer (555 associated PFCs, 1.26% in hlePFCs). But results were already close to the ones presented in the main text (using a threshold of 1000 associated PFCs), with methane metabolism , ribosome , carbon fixation in photosynthetic organisms and pentose phosphate pathway in the top 10 of most selected pathways among hlePFCS. Keeping a threshold of 500 associated pathways, we could even add the photosynthesis pathway to this list, which was the 7th most selected pathway (641 associated pathways, 2.18% in hlePFCs). Using a threshold of 1000 associated pathways as presented in the main text, we were able to focus only on the 62 most common pathways, which were all making sense in a planktonic ecology context.

Discussion

Functional composition of prokaryotic plankton communities is driven by interactions between multiple environmental factors rather than by single variables

Building statistical models including 51 environmental variables to test for their effect on protein functional clusters (PFCs) abundance, we were able to quantify the impact of each variable both globally and in each individual random forest model. Our results hence give access to the most influential predictors of 228,914 protein functional cluster's abundances (see Data availability section), while pushing towards a consideration of other variables in addition to temperature and oxygen when studying prokaryotic communities functional composition. Indeed, water temperature is commonly presented as the most influential determinant of the taxonomic and functional composition of prokaryotic communities (Sunagawa et al., 2015;Salazar et al., 2019;[START_REF] Delmont | The global biogeography of amino acid variants within a single SAR11 population is governed by natural selection[END_REF]. Here, we found temperature to be one of the best quantitative predictors of PFC abundance. Still, it was determined as less important than other quantitative variables like iron, carbonate or oxygen concentrations, but also salinity or bathymetry. However, when temperature was the most important variable in a model, it highly increased abundance predictions accuracy (mean R 2 values three times higher than the one over all models), showing how influential this variable is on at least some ecosystemic functions. Among all environmental predictors, Longhurst biogeographical provinces [START_REF] Longhurst | Ecological Geography of the Sea[END_REF] were by far the most important variable in our random forest models, and were well distinguished on the canonical correspondence analysis (CCA) triplot (Figure 4.3). Longhurst provinces represent homogeneous areas both in terms of physico-chemical and ecological conditions [START_REF] Longhurst | Ecological Geography of the Sea[END_REF]. Here we thus suggest that functional composition is more impacted by interactions between multiple variables, than by one or a few variables like it has been previously suggested (Sunagawa et al., 2015;Salazar et al., 2019). This result adds to a similar observation made by a recent global biogeographical analysis of planktonic communities, finding Longhurst biogeographical provinces to match the distribution of viruses, bacteria and eukaryotes smaller than 20 µm [START_REF] Richter | Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems[END_REF]. Sampling depth had 117 Chapter 4. Towards omics-based predictions of planktonic functional composition from environmental data among the lowest impacts on our regression models, confirming the weak differences in functional composition between surface and deep chlorophyll maximum samples of picoplankton (Salazar et al., 2019).

Identifying protein functional clusters and metabolic pathways associated with particular environmental conditions

The identification of biogeographical provinces as best predictors of PFC's abundance can be interpreted as a consequence of the Baas Becking hypothesis 'everything is everywhere, but the environment selects' [START_REF] Wit | Everything is everywhere, but, the environment selects'; what did Baas Becking and Beijerinck really say[END_REF][START_REF] Fondi | Every Gene Is Everywhere but the Environment Selects": Global Geolocalization of Gene Sharing in Environmental Samples through Network Analysis[END_REF], which implies that all microbes are potentially ubiquitous, but dominant taxa depend on the environmental niche. A precedent study observed this pattern at the proteic level, by comparing protein families sampled in different ecosystems such as sea water, sludge water or soils, and showing that the ecosystem type had more impact on protein families composition than geographical distance [START_REF] Fondi | Every Gene Is Everywhere but the Environment Selects": Global Geolocalization of Gene Sharing in Environmental Samples through Network Analysis[END_REF]. Similarly, by identifying Longhurst biogeographical provinces to be the best predictors of PFCs abundance, we verify that environmental niches are the most determinant drivers of marine prokaryotic communities functional composition. However, the fact that 80.5% of our PFCs showed poor responses to environmental conditions challenges the extent of applicability of the Baas Becking hypothesis at the proteic level, at least within a single ecosystem. It could be explained by the high decoupling observed between functional diversity and taxonomic diversity among marine prokaryotic communities [START_REF] Louca | Decoupling function and taxonomy in the global ocean microbiome[END_REF]. Indeed, functional redundancy among prokaryotes can lead to stable functional diversity even with high taxonomic variability [START_REF] Louca | High taxonomic variability despite stable functional structure across microbial communities[END_REF]. In our analysis, we chose to focus on changes in communities functions because it could lead to more stable abundance measures than when relying on taxonomic entities, and provide thus more valuable information on the ecosystem functioning and associated biogeochemical functions [START_REF] Louca | Decoupling function and taxonomy in the global ocean microbiome[END_REF]Salazar et al., 2019;[START_REF] Louca | High taxonomic variability despite stable functional structure across microbial communities[END_REF].

Still, 19.5% of the PFCs were linked to environmental gradients, and 1.1% showed very strong responses to particular environmental conditions. Among these 2,444 PFCs identified as highly linked to environmental gradients (hlePFCs), we observed a clear distinction between the ones associated with polar nutrient-rich waters and those abundant in tropical nutrient-poor ones, which is coherent with classical observations in marine ecology (Faure et al., 2019;[START_REF] Ibarbalz | Global Trends in Marine Plankton Diversity across Kingdoms of Life[END_REF]. Metabolic pathways like DNA replication or mismatch repair could be associated with eutrophic conditions and colder waters (Figure 4.5), potentially reflecting the higher growth potential and metabolic activity of micro-organisms in the eutrophic conditions of the polar summer [START_REF] Alonso-Sáez | Winter-to-summer changes in the composition and single-cell activity of nearsurface Arctic prokaryotes[END_REF]. No particular metabolic pathway could be associated with warm and oligotrophic waters, but proteins from two classes of Cyanobacteria were overrepresented in hlePFCs abundant in these waters, which may reflect that cyanobacteria are particularly abundant in tropical, nutrient-poor waters [START_REF] Flombaum | Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus[END_REF]. Among commonly observed pathways, methane metabolism, carbon fixation in photosynthetic organisms and carbon fixation pathways in prokaryotes were three of the 4 most selected pathways in hlePFCs, and were correlated to a wide range of physico-chemical conditions (Figure 4.7). Hence, such key biogeochemical func-118 4.2 Towards omics-based predictions of planktonic functional composition from environmental data tions are quite ubiquitously present in the global ocean, but can be achieved by different actors and protein families depending on the environmental conditions. One exception was the nitrogen metabolism pathway, which was mainly associated with Pacific and North Atlantic samples, corroborating results obtained with the same set of MAGs [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF].

More surprisingly, we identified Mediterranean samples as clear outliers, with an important part of hlePFCs showing higher abundances in Mediterranean samples than elsewhere. These samples were notably characterized by a relative over-abundance of metabolic functions like carotenoid biosynthesis and flagellar assembly, and an over-abundance of proteins from MAGs of the Candidatus Marinimicrobia phylum, which is only composed of poorly known and yet uncultivable bacteria of potentially high biogeochemical impact [START_REF] Hawley | Diverse Marinimicrobia bacteria may mediate coupled biogeochemical cycles along eco-thermodynamic gradients[END_REF]. Our strongest hypothesis to explain these patterns lies in the fact that the Mediterranean Sea is a semi-enclosed sea that experienced multiple isolation and colonization events [START_REF] Patarnello | Pillars of Hercules: is the Atlantic-Mediterranean transition a phylogeographical break?[END_REF]. For some pelagic species, the strait of Gibraltar constitutes a phylogeographic barrier causing genetic contrasts between Atlantic and Mediterranean populations [START_REF] Patarnello | Pillars of Hercules: is the Atlantic-Mediterranean transition a phylogeographical break?[END_REF][START_REF] Lowe | A legacy of contrasting spatial genetic structure on either side of the Atlantic-Mediterranean transition zone in a marine protist[END_REF]. Here, we identified most Atlantic samples to be closer to Pacific ones than to Mediterranean ones in terms of hlePFCs composition, at the exception of one which came from the mouth of the strait of Gibraltar. Also, the Mediterranean Sea was the only biogeographical zone exhibiting a strong over-abundance of locally assembled proteins. This way, hlePFCs overabundant in Mediterranean samples shared only very few links with proteins from MAGs of other assemblies in our sequence similarity network, highlighting their functional and taxonomical originality. We then propose that the strait of Gibraltar and the Suez canal could shape the genetic and functional structure of some planktonic prokaryotic populations, as it is observed in some eukaryotic species [START_REF] Patarnello | Pillars of Hercules: is the Atlantic-Mediterranean transition a phylogeographical break?[END_REF][START_REF] Lowe | A legacy of contrasting spatial genetic structure on either side of the Atlantic-Mediterranean transition zone in a marine protist[END_REF].

Mining the unknown to identify potential key organisms and proteins

The main originality of our approach is its ability to take into account both annotated and unannotated sequences. It enables the identification of PFCs composed of functionally unannotated sequences, of taxonomically unannotated sequences, and of both, leading here to the inclusion of at least 15% more proteins than methods excluding functionally unannotated sequences. By including 7,834 PFCs corresponding to 20,552 protein sequences that could not be annotated under the phylum level nor to a biological function, we propose an original way to highlight the response of microbial dark matter abundance to environmental gradients. While a previous study estimated that the inclusion of microbial dark matter sequences could increase by up to 58% the amount of analysed sequences [START_REF] Bernard | Microbial Dark Matter Investigations: How Microbial Studies Transform Biological Knowledge and Empirically Sketch a Logic of Scientific Discovery[END_REF], we provide here a pragmatic bioinformatic pipeline which helps to extend our knowledge in environmental microbiology.

It is often proposed that most of the unidentified microbial diversity could come from rare organisms, described as the 'rare biosphere', and which are considered as diversity reservoirs able to respond rapidly to environmental changes [START_REF] Logares | Patterns of Rare and Abundant Marine Microbial Eukaryotes[END_REF][START_REF] Lynch | Ecology and exploration of the rare biosphere[END_REF]. Our results partly corroborate this theory (see Detection of the rare biosphere), but we also found the 7,834 microbial dark matter PFCs to be relatively overabundant in 41% of our samples. This can 119 Chapter 4. Towards omics-based predictions of planktonic functional composition from environmental data be explained by the fact that 72.5% of the proteins from our microbial dark matter PFCs came from Candidatus Marinimicrobia and Euryarcheota MAGs, two yet poorly studied and uncultivable phyla identified as highly abundant in the global ocean, and potentially impacting biogeochemistry [START_REF] Hawley | Diverse Marinimicrobia bacteria may mediate coupled biogeochemical cycles along eco-thermodynamic gradients[END_REF][START_REF] Santoro | Planktonic Marine Archaea[END_REF][START_REF] Francis | Ubiquity and diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean[END_REF].

Our analysis allowed to describe the biogeography of 183 functionally unannotated hlePFCs, which might participate in metabolic pathways involved in functional responses to peculiar environmental conditions. They included 24 microbial dark matter hlePFCs, mainly found in Mediterranean samples and related to Candidatus Marinimicrobia MAGs. Our method being applicable to any set of sequences, we predict that an accumulation of similar results on multiple datasets will help identify recurrent unannotated protein clusters linked to specific environmental niches [START_REF] Wyman | A most wanted list of conserved microbial protein families with no known domains[END_REF]. It could further help targeting wet lab studies towards the description of unknown proteins particularly adapted to specific conditions, like subtropical nutrient-poor waters or oxygen minimum zones [START_REF] Hawley | Diverse Marinimicrobia bacteria may mediate coupled biogeochemical cycles along eco-thermodynamic gradients[END_REF]. However, functionally unannotated hlePFCs sometimes contained proteins from only one MAG, and in this case their response to environmental gradients could be a reflection of the global abundance of this MAG instead of a real functional level response. We then advise future wet lab investigations to mainly select PFCs involving proteins from different MAGs. To pave the way for such further analyses, we have provided all nucleotide sequences for each microbial dark matter PFC, as well as the statistics associated with their response to environmental gradients (see Data availability).

Towards more global quantitative studies of meta-omics at the function level

Statistical models in this study were based on the abundances of each PFC in 93 metagenomic samples. For each random forest model, 75% of the samples (i.e. 70 samples) were used as a training set. Even though each model was run 10 times on 10 distinct training sets, it remains a relatively low amount of samples to do abundance predictions and extrapolations at the global ocean scale (as a way of comparison, 181 samples allowed to predict diatoms abundance from environmental data in a chinese river [START_REF] Shin | The Prediction of Diatom Abundance by Comparison of Various Machine Learning Methods[END_REF]). Hence, machine learning models were not used to provide extrapolated predictions in this study, but to detect protein functional clusters highly linked to environmental gradients and the main drivers of their biogeography. However, as more and more omics datasets are collected in the global ocean (Salazar et al., 2019;[START_REF] Acinas | Metabolic Architecture of the Deep Ocean Microbiome[END_REF][START_REF] Vorobev | Transcriptome reconstruction and functional analysis of eukaryotic marine plankton communities via high-throughput metagenomics and metatranscriptomics[END_REF][START_REF] Planes | The Tara Pacific expedition-A pan-ecosystemic approach of the "-omics" complexity of coral reef holobionts across the Pacific Ocean[END_REF], we assume that similar approaches could be conducted with much more samples in the near future, which should increase models' performances. By using less than 100 samples, we were nonetheless able to obtain 2,444 models with R 2 values over 0.5. It highlights the potential of such quantitative approaches for predicting the abundance of key protein families in the global ocean. Moreover, our dataset was only composed of metagenomics samples, when it is hypothesized that a big part of prokaryotic communities response to environmental change comes from variations in gene expression [START_REF] Moran | Sizing up metatranscriptomics[END_REF].

This assumption was recently disputed (Salazar et al., 2019), but applying our method to metatranscriptomes in the future would allow to use the environmental context to predict protein 120 4.2 Towards omics-based predictions of planktonic functional composition from environmental data expressions instead of metagenome sequence abundances, which could help improve the accuracy of models predictions.

In the future, biogeochemical modeling should benefit from our ability to quantify and predict biological functions using environmental and omics data (Coles et al., 2017;Mock et al., 2016;Stec et al., 2017;Tang and Cassar, 2019). Through our quantitative and data-driven analysis, we have shown one illustration of how metagenomics data can be used without a priori choices of taxon or metabolic function to (1) identify key environmental drivers of planktonic communities functional composition, such as biogeographical provinces, (2) detect potential key protein families and organisms with original biogeographies and (3) investigate the microbial dark matter response to environmental fluctuations. We have then paved the way for more quantitative analysis taking advantage of the richness of global omics datasets, both at the functional and taxonomic level, which should in the long term increase our ability to better predict future global climate.

Methods

Samples collection and metagenome-assembled genomes (MAGs)

We focused our study on the 885 non-eukaryotic MAGs made publicly available [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF]. The whole bioinformatic workflow designed to build these MAGs, as well as all the links leading to the fasta files and Anvi'o profiles for each MAG can be found at http://merenlab.org/ data/tara-oceans-mags/. These MAGs were built from 93 Tara Oceans metagenomes retrieved from 61 surface samples and 32 deep chlorophyll maximum samples collected worldwide in the global ocean, using a size filter targeting free-living microorganisms (0.2-3 µm). Original metagenomes are available under the European Bioinformatics Institute (EBI) repository with project ID ERP001736. To date, the work achieved by [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF] constitutes the only database of manually refined MAGs constructed using the Tara Oceans project data. Automated binning efforts provided larger numbers of MAGs and focused on multiple size fractions [START_REF] Tully | The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans[END_REF], but are subject to higher binning errors, causing sometimes obvious contigs misplacement (as discussed here: https://bjtully.github.io/posts/2018/10/re-visiting-tmed-mags/).

Further information on the MAGs' genomic features, such as their completion or GC content, can be found in the supplementary table 5 of [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF]

Gene detection and quantification

Prodigal v2.6.3 [START_REF] Hyatt | Prodigal: prokaryotic gene recognition and translation initiation site identification[END_REF] was run to retrieve the nucleotide and protein sequences of each detected gene for each of the 885 MAGs. By concatenation, one nucleotide and one protein fasta files were finally created, containing each in total 1,914,171 sequences. The nucleotide sequences were then used for the mapping and quantification step (hereafter developed) whereas the protein sequences were used for building the sequence similarity network (cf. next paragraph).

The nucleotide file was used as an index to quantify the MAGs' genes abundance in the 93 metagenomes used by Delmont et al. for the MAGs binning process [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF]. For this,

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Chapter 4. Towards omics-based predictions of planktonic functional composition from environmental data we mapped metagenome reads to the MAGs gene catalog using the quant function from Salmon v.0.11.3 [START_REF] Patro | Salmon provides fast and bias-aware quantification of transcript expression[END_REF] in quasi-mapping mode, with the following parameters '-libType A -meta -incompatPrior 0.0 -seqBias -gcBias -biasSpeedSamp'. To normalize the obtained read counts, we divided them by the gene length, and by the total of sequenced reads per sample, then multiplied them by 10e9. The obtained value is analogous to an RNA-seq transcripts per million value (TPM), except that TPM calculation is based on the total amount of reads that mapped to the transcripts index, while we used here the total amount of reads that has been sequenced in each sample (e.g. mapped + unmapped). In fact, the underlying assumption behind TPM and other RNA-seq orientated normalizations is that all compared samples should come from similar tissues, hence displaying a comparable number of mapped reads, which is incompatible with environmental metagenomics. Indeed, Tara Oceans samples contain variable quantities of biological matter coming from different sampling in the global ocean, leading them to have very variable amounts of total sequenced and mapped reads. Typically, if a sample has a high total number of sequenced reads but a low number of mapped reads, it will still display high abundance values for the few mapping reads when using the classic TPM normalization, while it would not be the case with our method.

Building a Sequence Similarity Network (SSN) from 885 prokaryotic MAGs

A Sequence Similarity Network (SSN) is a graph object in which vertices correspond to sequences and edges represent the similarity and coverage between pairs of sequences [START_REF] Atkinson | Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies[END_REF][START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF][START_REF] Lopez | Highly divergent ancient gene families in metagenomic samples are compatible with additional divisions of life[END_REF][START_REF] Rizzolo | A widely distributed diheme enzyme from Burkholderia that displays an atypically stable bis -Fe(IV) state[END_REF]. Diamond v0.8.22 was used in blastp mode to compute the percentage of similarity between every pair of proteins detected in the MAGs, using options '-e 1e-3 -p 30 -sensitive'. A sequence similarity network was built with the diamond output using 80% identity and 80% coverage threshold. This coverage threshold is commonly used in SSN studies [START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF][START_REF] Lopez | Highly divergent ancient gene families in metagenomic samples are compatible with additional divisions of life[END_REF][START_REF] Rizzolo | A widely distributed diheme enzyme from Burkholderia that displays an atypically stable bis -Fe(IV) state[END_REF] and we also tested 4 other similarity thresholds: 70%, 75%, 85% and 90%. We selected the intermediary 80% identity threshold to minimize the amount of singletons, while maximizing the functional homogeneity between linked proteins.

Extracting, annotating and quantifying protein functional clusters in the Sequence Similarity Network (SSN)

A SSN is made of singletons (vertice or sequence without any homology with other sequences) and connected components (subgraphs composed of at least two vertices disconnected from the rest of the network). In our case, a connected component (CC) corresponds to a group of at least two protein sequences that are linked together (directly or via neighbors), and that have no link with other groups of sequences in the SSN. We assume that the proteins contained in a CC potentially share a similar molecular function [START_REF] Forster | Testing ecological theories with sequence similarity networks: marine ciliates exhibit similar geographic dispersal patterns as multicellular organisms[END_REF][START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF][START_REF] Atkinson | Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies[END_REF][START_REF] Rizzolo | A widely distributed diheme enzyme from Burkholderia that displays an atypically stable bis -Fe(IV) state[END_REF]. The term "protein family" is often used to describe such clusters of homologous proteins, but as this term is related to the description of evolutionary relationships, 122 4.2 Towards omics-based predictions of planktonic functional composition from environmental data we here prefer the use of protein functional clusters (PFC).

Our SSN was composed of 233,756 protein functional clusters, including 757,457 proteins (i.e. 1,156,714 singletons were excluded from the analysis). These proteins were functionally annotated using eggNOG mapper v4. 5.1 (Huerta-Cepas et al., 2016[START_REF] Dick | Embracing the mantra of modellers and synthesizing omics, experiments and models[END_REF] and KofamScan [START_REF] Aramaki | KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold[END_REF]. EggNOG emapper was run using the diamond mode and the -no_annot flag. It produced a table containing seed orthologous sequences for 677,684 of our proteins (89.5%), the rest of them not being similar enough from any sequence in the eggNOG database. The annotation phase was then launched on these 677,684 proteins, using the seed orthologous sequences table as input to the emapper function, and the -annotate_hits_table flag. We obtain an annotation table with GO IDs, KEGG IDs, and eggNOG descriptions. KoFamScan was launched with default options and -mapper flag. The KEGG API was then used to retrieve KEGG pathways ID and descriptions for each KEGG ID identified by KoFamScan in our protein catalog. To assess for the functional homogeneity in our protein functional clusters, we computed an homogeneity score F hom :

N annot > 1 ⇒ F hom = 1 -N annot N prot N annot == 1 ⇒ F hom = 1
With N annot the number of unique annotation terms found in the PFC (either KEGG IDs or eggNOG terms), and N prot the number of proteins in the PFC.

As multiple eggNOG terms can exist for similar functions (e.g. 'UBA-ThiF-type NAD FAD binding protein' and 'UBA-THIF-type NAD FAD binding'), they can lead to artifactually low homogeneity scores. For this reason, protein functional clusters with low homogeneity scores obtained with the EggNOG database were tagged as poorly homogeneous but were kept in the analysis.

Statistics on functionally unannotated PFCs presented in Table 1 andTable 2 include both (1) query sequences that did not match to any reference in public databases, and (2) query sequences that match to one or multiple references in public databases, but could not yet be associated to any biological function.

To assess taxonomic diversity in our PFCs, we used the taxonomic annotation of the 885 MAGs provided by [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF]. This taxonomic annotation was inferred from 43 single-copy core genes through a combined use of CheckM [START_REF] Parks | CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes[END_REF], RAST [START_REF] Aziz | The RAST Server: Rapid Annotations using Subsystems Technology[END_REF] and manual BLAST searches (See [START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF] for further details).

We computed a mean abundance for each protein functional cluster in each of the 93 metagenomes, using relative protein abundances (see Gene detection and quantification). We obtained an abundance table composed of 233,756 rows, corresponding to protein functional clusters, and 93 columns, corresponding to the 93 Tara Oceans metagenomes used in the study.

Environmental dataset

For each of the 93 Tara Oceans metagenomes, we retrieved the environmental context from Faure et al. (https://figshare.com/articles/Data _ MixoBioGeo _ Faure _ et _ al _ 2018/6715754) 2019. To complete this environmental dataset, we added 10 climatology variables retrieved from the World Ocean Atlas [START_REF] Boyer | The World Ocean Atlas 2018: Improvements and Uses of Climatological Mean Fields[END_REF]: temperature, salinity, density, conductivity, dissolved oxygen, percent oxygen saturation, apparent oxygen utilization, silicate, phosphate and nitrate. For tem-

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Chapter 4. Towards omics-based predictions of planktonic functional composition from environmental data perature, salinity and conductivity we retrieved the mean and the mean seasonal anomaly at each sampling point (precision of 1°) over the 2005-2012 period. Only the mean was retrieved for density. For the 6 other variables, we retrieved the mean and the mean seasonal anomaly at each sampling point (precision of 1°) over all available years. In total, we obtained 74 environmental variables, that we reduced to 51 by getting rid of near zero variance variables and too highly correlated ones, using options "nzv" and "corr" from the preProcess function of the caret package [START_REF] Kuhn | Building Predictive Models in R Using the caret Package[END_REF] in R (R Core Team, 2019). A detailed description of these variables is available in Table S2 (Appendix C). We then scaled and centered the 51 selected environmental variables, and used a k-nearest neighbours approach to replace NA values (6.6% of the data) by the mean of the concerned variable in the 5 nearest samples in terms of global environmental profile (knnImpute option from caret's preProcess function [START_REF] Kuhn | Building Predictive Models in R Using the caret Package[END_REF]).

Identification of protein functional clusters varying along environmental gradients

Among the 233,756 protein functional clusters, we detected 4,842 (2,1%) clusters with near zero variance using caret preProcess function [START_REF] Kuhn | Building Predictive Models in R Using the caret Package[END_REF], i.e. they had less than 10% of abundance values across all samples that were distinct, and a ratio between the most common abundance value and the second most common one that was higher than 95 to 5. These clusters were removed from further statistical analysis. We built a random forest regression model for each of the remaining 228,914 protein functional clusters, using the environmental variables as predictors of cluster relative abundance. To suppress eventual biases linked to over/underfitting due to training set selection, each model was launched 10 times using 10 different training sets built using 75% of the 93 samples available. For each iteration, i.e., for each pair of training set and protein functional cluster, 500 trees were built. For each model, we computed the mean prediction error over the 10 iterations, as well as the mean R 2 , and the mean rank of importance in the model for each environmental predictor. The mean R 2 was used to discriminate protein functional clusters following significant environmental gradients from the ones showing no response to the environmental context. Specifically, we considered every model with R 2 values over the arbitrary threshold of 0.5 to be very tightly linked to environmental gradients. Different thresholds ranging from 0.25 to 0.75 were tried, thresholds higher than 0.5 tended to select a few hundreds of PFCs, mainly the ones overabundant in the Mediterranean Sea, while too low thresholds tended to diminish the R 2 value and readability of the canonical correspondence analysis (cf next section).

All random forest models were launched using the randomForest R package [START_REF] Liaw | Classification and Regression by randomForest[END_REF].

Biogeography of protein functional clusters (PFCs) linked to environmental gradients

We used a canonical correspondence analysis (CCA) to describe in a more integrated way the relationships between PFCs and environmental variables. The CCA used the relative abundance 124 4.2 Towards omics-based predictions of planktonic functional composition from environmental data table of all PFCs linked to environmental gradients (mean R 2 of random forest regressions > 0.5) as response variables, and 13 selected environmental variables as explanatory variables: biogeographical province, ocean region, season moment (i.e. early / middle / late), temperature, depth of the euphotic zone, longitude, maximum sampling depth, optical backscattering coefficient at 470nm, depth of the O2 minimum, calcite saturation state, fluorescence, NO3, chlorophyll A.

The 13 environmental variables were selected through a backward and forward stepwise selection based on the AIC criterion [START_REF] Legendre | Numerical Ecology[END_REF].

Using positions of PFCs in the two first dimensions of the CCA space (37.65% of variance), we computed a barycenter position for each metabolic pathway detected among PFCs (Figure 4.6).

Similarly, we computed barycenters for phyla, classes and genomic assemblies in the CCA space (Figure 4.9,4.7,4.8). We also represented pathways distributions along each CCA axis using boxplots, to help identify pathways that were the most characteristic of certain environmental conditions (then represented in

Chapter 5

General discussion and perspectives 5.1 Detecting and quantifying functional traits using metaomics data

Summary of the principal results

Throughout this thesis, I demonstrated that the richness of meta-omics data allows to decipher trait/environment associations, providing new insights on functional traits biogeographies. I achieved this using 3 main methods: the annotation of metabarcoding data to detect a priori selected traits in chapter 2, the quantification of a priori selected genomic markers in chapter 3, and the quantification of gene functional clusters without any a priori choice of function or species in chapter 4. All these methods were applied on data from the Tara Oceans project, but remain applicable to any other meta-omics datasets.

In the introduction, I raised 3 scientific issues that served as the guidelines of my thesis work:

1. Can we use meta-omics data to detect functional traits from which the genomic basis is poorly known?

In chapter 2, I showed how metabarcoding data allowed to detect mixotrophs in the global ocean, despite the absence of genomic markers of mixotrophy. The results of this chapter demonstrate that metabarcoding data can help to study the biogeography of any functional trait, as long as a manual or database-based annotation of traits to metabarcodes is possible. However, this approach comes with quantitative limitations inherent to metabarcoding data, on which I will come back later in this discussion (Section 5.1.2.1). This is why in chapter 3, I presented how network-based approaches combined to wet lab investigations might allow to detect genomic markers of functional traits. But the limited results obtained when trying to identify genomic markers of mixotrophy during my thesis combined to the few available in the literature so far tend to show that we might not be ready yet for the quantification of some key complex traits in meta-omics data (See section 5.1.2.2).

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Chapter 5. General discussion and perspectives 2. Can we use meta-omics data to predict the distribution of functional traits/genes in the environment through statistical modeling?

Overall, the results presented in this thesis show meta-omics data as an efficient tool to quantitatively describe the global distribution of plankton functional traits. In Chapter 2, I showed how metabarcoding data could be used to decipher the biogeography of plankton functional traits, by providing the first ever omics-based biogeography of mixotrophic protists. In chapter 3, I showed how machine learning methods could be used to predict the gene abundance, transcript abundance and gene expression of an enzyme with a key biogeochemical role, only using environmental data as predictors. In chapter 4, I showed that similar methods could be applied on hundred thousands of gene functional groups in just a few days of computing, allowing to automatically identify gene functional groups responding tightly to environmental gradients. Around 80% of the gene functional groups were only poorly predictable from the environment, thus this second question remain open for many genes and functions. Still, I provided in this thesis some clear evidence of the possibility to use machine learning to model the response of gene functional groups to environmental fluctuations.

Can the abundance of gene functional groups be quantified in meta-omics data without any a priori choice of focal functions and/or species?

One of the main criticisms made to plankton functional types and gene functional groups approaches lies in the necessity to a priori define functional types or groups without any guarantee to represent the actual functional diversity of natural planktonic communities.

Focusing on the search for specific, pre-defined traits in meta-omics data seemed to reproduce the same flaw. Hence in chapter 4 I presented a network-based approach allowing to define gene functional groups without any a priori choice of function or species. Through this approach I showed that it was possible to make gene functional groups emerge from meta-omics samples, and to quantify their abundance in the global ocean.

I will now highlight the remaining challenges regarding the detection and quantification of functional traits in meta-omics data. Then, in the second part of this discussion, I will provide some insights and ideas on how the richness of meta-omics data could be used to inform new kinds of trait-based models.

Main challenges remaining to detect and quantify functional traits in meta-omics data

The metabarcoding approach to study functional traits and its limits

Metabarcoding offers a relatively affordable and direct access to lists of present lineages in metaomics samples, and remain widely used in biodiversity surveys of natural planktonic communities [START_REF] Santoferrara | Current practice in plankton metabarcoding: optimization and error management[END_REF]. In Chapter 2, I manually annotated the mixotypes of 133 lineages, which allowed to study their distribution in the global ocean. A similar approach was conducted by 128 5.1 Detecting and quantifying functional traits using meta-omics data Ramond et al. (2018), who annotated a wider set of 30 biological traits to 2,007 taxonomic lineages detected in metabarcoding data obtained from 277 water samples taken off the coastal North-East Atlantic Ocean. This annotation allowed to (i) identify 6 functional groups corresponding to lineages sharing similar traits, and (ii) quantify the functional diversity of coastal protistan communities, identifying it as coupled to taxonomic diversity (Ramond et al., 2019). These results and the ones presented in Chapter 2 both show the annotation of traits to lineages identified in metabarcoding data as a promising tool to move towards more realistic diversity representations in trait-based studies.

The main difficulty in applying this methodology lies in the annotation of potential traits to metabarcodes and/or OTUs, which is time-consuming, and prone to oversights and mistakes.

However, this drawback might become less and less problematic in the years to come, as metabarcoding databases such as EukRef (Del Campo et al., 2018) and PR2 (Guillou et al., 2013) start to include potential functional trait information at the species level. The mixotrophy annotation provided in chapter 2 should notably appear in the next PR2 database iteration, and Ramond et al. (2018) recently published a metabarcoding database including the annotation of 30 functional traits to more than 2000 protistan lineages (i.e. maximum and minimum size, kleptoplastidy, coloniality, production of DMS,...). Integrating information from the many available trait databases (a complete list of trait databases including traits from marine and freshwater species is available in table 2 from Martini et al., in appendix A), which are mostly not yet related to metabarcodes but only to taxonomic references, should allow for an increase in the amount of annotated traits in metabarcoding databases. Although these trait databases might help to automatize the metabarcode to trait annotation process, they are unfortunately marred with multiple flaws. The first one is a clear bias in species representation, with for example only 9% of all UK demersal (i.e. living near or on the bottom) marine fauna annotated with 8 fundamental functional traits in online databases (body size, diet, feeding method, reproductive timing, fecundity, larval dispersal, adult dispersal and longevity), with a clear difference in annotations between fish (median of 6 annotated traits) and invertebrates (median of 1 annotated trait, with body size being the most frequently annotated one) (Tyler et al., 2012). The second main flaw of trait databases lies in the lack of a common vocabulary across the different fields using trait-based approaches, and in the heterogeneity of data formats and units [START_REF] Schneider | Towards an ecological trait-data standard[END_REF], see also paragraph 2.1 and 2.2 in appendix A), making it difficult to compare and use multiple databases. Finally, metadata in trait databases often poorly describe the methods and conditions in which the traits were measured.

But all these flaws are starting to be tackled by international initiatives like the open traits network (Gallagher et al., 2019), and some of the major problems in using metabarcoding data to quantify functional traits might rather come from the omics data themselves than from the databases.

The ability of metabarcoding to give a quantitatively accurate view of planktonic community is quite debated [START_REF] Lamb | How quantitative is metabarcoding: A meta-analytical approach[END_REF]. In the past ten years, some studies showed strong relationships between biomass and metabarcoding abundance [START_REF] Hirai | Effects of plankton net characteristics on metagenetic community analysis of metazoan zooplankton in a coastal marine ecosystem[END_REF][START_REF] Lindeque | Next Generation Sequencing Reveals the Hidden Diversity of Zooplankton Assemblages[END_REF], but others highlighted biases due to sequencing errors and to sometimes important rDNA copy number variations across and even within species (e.g. radiolarians tend to have very high rDNA copy numbers which could cause them to be overabundant in metabarcoding samples, Biard et al. Chapter 5. General discussion and perspectives (2017); [START_REF] Decelle | Intracellular Diversity of the V4 and V9 Regions of the 18S rRNA in Marine Protists (Radiolarians) Assessed by High-Throughput Sequencing[END_REF]). Recently, a meta-analysis assessed the ability of metabarcoding data to quantitatively estimate biomass using the data from 22 different projects, and found the metabarcode abundance to be loosely related to biomass (slope of 0.52 in a linear regression), but with very high variability (+/-0.34 variance in slope) [START_REF] Lamb | How quantitative is metabarcoding: A meta-analytical approach[END_REF]. In addition to this quite poor quantitative representation of diversity, it is important to note that metabarcode abundance can only reflect biomass, and not the realization of functional traits. For example, metabarcoding data can only tell whether lineages known to be capable of mixotrophy are present, but can not give any indication on whether the lineages were feeding through mixotrophy at the moment of sampling.

Other potential biases linked to metabarcoding that are commonly discussed in the literature include false positives (detection of a lineage that was not present in the sample) due to contamination or phylogenetically mixed OTUs, and false negatives (non-detection of a present lineage), due to low abundance, incomplete extractions, primer mismatches or incomplete reference databases [START_REF] Santoferrara | Current practice in plankton metabarcoding: optimization and error management[END_REF]. Hence, metabarcoding can only give a biased estimate of species biomass, and only for species that are present in metabarcodes databases, while metagenomics and metatranscriptomics offer access to gene and transcript abundance, which are potentially translatable to trait realization (see Chapter 3). However, we have also shown that for complex traits like mixotrophy, the way towards detection of genomic markers could be difficult.

Tackling the need for more genomic markers of functional traits

In chapter 3, I showed that enzymes coding for a simple functional trait could be quantified in meta-omics data, and linked to environmental gradients. The dmdA enzyme was a good example to display this, as it is very well conserved across organisms, and has a well known role in DMSP catabolism [START_REF] Curson | Catabolism of dimethylsulphoniopropionate: microorganisms, enzymes and genes[END_REF], giving it the potential to be quantitatively linked to a concrete functional trait. Similarly, the biology of the dinitrogen fixing nifH enzyme is well known, and its abundance in meta-omics data was recently used to build a data-driven model of global diazotrophs abundance (Tang and Cassar, 2019). These results illustrate well the benefits of having access to well defined genomic markers for functional traits.

Trait-centred annotation tools

Recently, a new annotation tool called DRAM (Distilled and Refined Anotation of Metabolism) was designed to specifically detect genomic markers of metabolic traits in genomes and metagenomes [START_REF] Shaffer | DRAM for distilling microbial metabolism to automate the curation of microbiome function[END_REF]. Crossing the functional annotations from 5 databases (Figure 5.1), this tool automatically gives access to the list of metabolic traits in a genome or metagenome, along with the FASTA files of the corresponding marker genes (Figure 5.1). The emergence of such traitcentred annotation tools should be of great help for the detection of functional traits in meta-omics data, allowing results from data-driven statistical models, like the one focusing on diazotrophs presented in Tang and Cassar (2019) or the ones presented in Chapter 3, to be obtained on more functional traits.

However, as I have exposed in chapter 3, genomic markers of complex traits are hard to detect 2020). The user is asked to provide FASTA files of assembly-derived contigs, either binned into genome-resolved data ( e.g. MAGs or isolate genomes) or unbinned ( e.g. metagenome contigs). The user can also input taxonomic annotations and genome or MAGs completion files into DRAM, which will then be included in output files. Prodigal [START_REF] Hyatt | Prodigal: prokaryotic gene recognition and translation initiation site identification[END_REF] is used to call genes in the input file(s), before 5 databases are used to annotate them. Users can also add any annotation database of their choice. In parallel, tRNA are detected using tRNAscan-SE [START_REF] Lowe | tRNAscan-SE On-line: integrating search and context for analysis of transfer RNA genes[END_REF], as well as 5S, 16S and 23S rRNA fragments using barrnap (https://github.com/tseemann/barrnap). All these data are then compiled into three files: the 'raw' file corresponds to the 'classic' output one would obtain from an annotation software, the 'distillate' file contains pathway-centric annotation and genome statistics, and the 'product' file is an html document indicating metabolic pathway coverage, electron transport chain component completion and presence/absence of specific functions associated to functional traits ( e.g. methanogenesis). and remain under-investigated. They are also hard to quantify considering that multiple loci and genes are involved. Indeed, even if a panel of genes from multiple loci are identified as markers of a specific trait, should we use the mean or median transcript abundance over all loci as a proxy for trait realization? Or maybe build a statistical model to link loci abundance to trait value? These questions remain open, and would need high quantities of data from specifically targeted wet-lab studies in order to be answered.

High-throughput measurements of traits

We thus need to improve our ability to sequence meta-omics samples in controlled environments (e.g. microcosms or mesocosms) with high numbers of replicates to obtain accurate quantitative relationships between traits and omics content over time and changing conditions [START_REF] Faust | Towards a Better Understanding of Microbial Community Dynamics through High-Throughput Cultivation and Data Integration[END_REF]. High-throughput cultivation, i.e. simultaneous and automated cultivation of tens of microbial communities through minibioreactor arrays [START_REF] Auchtung | Cultivation of stable, reproducible microbial communities from different fecal donors using minibioreactor arrays (MBRAs)[END_REF] or microfluidic flow 131 Chapter 5. General discussion and perspectives cells [START_REF] Pousti | Microfluidic bioanalytical flow cells for biofilm studies: a review[END_REF], was successfully applied on mock communities of prokaryotes from the human gut, leading to accurate, data driven models of microbial interactions [START_REF] Venturelli | Deciphering microbial interactions in synthetic human gut microbiome communities[END_REF][START_REF] Hekstra | Contingency and Statistical Laws in Replicate Microbial Closed Ecosystems[END_REF]. In parallel, recent progresses in microfluidic greatly improved our capacity to sort cells from aqueous environment based on their physiological properties, leading to the emergence of omics datasets sequenced from targeted single planktonic cells sorted in-situ [START_REF] Needham | Targeted metagenomic recovery of four divergent viruses reveals shared and distinctive characteristics of giant viruses of marine eukaryotes[END_REF]. Through high-throughput, in-lab sorting, it is possible to sort 200 to 500 cells per hour according to their functional properties based on the use of isotopic labels on selected elements [START_REF] Lee | An automated Raman-based platform for the sorting of live cells by functional properties[END_REF]. Such high-throughput cell sorting techniques, associated with high-throughput cultivation and measurement of trait values should improve our ability to build omics datasets with high number of replicates, allowing to decipher the genomic basis of multiple complex functional traits such as mixotrophy, body size, body shape, ability to remineralize organic matter or coloniality. But such approaches will remain biased by the lack of inclusion of yet unculturable organisms.

What about unculturable organisms?

Usually between 40 and 60% of sequences from metagenomes can not be linked to a function or species, mostly issued from unculturable organisms [START_REF] Bernard | Microbial Dark Matter Investigations: How Microbial Studies Transform Biological Knowledge and Empirically Sketch a Logic of Scientific Discovery[END_REF][START_REF] Vanni | Light into the darkness: Unifying the known and unknown coding sequence space in microbiome analyses[END_REF]. In chapter 4, I did not discard the unknown sequences, and even particularly focused my attention on them, using a sequence similarity network to group them with known and other unknown sequences into protein functional clusters. Sequence similarity networks were used to directly infer functions of unknown sequences, but it now appears evident that more will be needed to decipher the globality of the dark side of omics [START_REF] Arnold | Directed Evolution: Bringing New Chemistry to Life[END_REF][START_REF] Vanni | Light into the darkness: Unifying the known and unknown coding sequence space in microbiome analyses[END_REF]. Very recently, a new bioinformatic tool was proposed with the aim to build a link between known and unknown genes from meta-omics data (Figure 5.2, Vanni et al. (2020)). Their approach combines gene clustering based on similarity and the use of metadata like co-expression, phylogenetic relatedness, biogeographical distribution or wet-lab experimental knowledge (Figure 5.2). In 1,749 microbial metagenomes from marine and human environment where only 44% of the genes could be annotated through Pfam, AGNOSTOS was able to annotate 70% of them, while identifying key lineages responsible for most of the remaining unknown part of genes, e.g. the newly described Cand. Riflebacteria and Cand. Patescibacteria [START_REF] Vanni | Light into the darkness: Unifying the known and unknown coding sequence space in microbiome analyses[END_REF]. This shows how the accumulation of data and observations on cultivated organisms and uncultivated organisms can be combined into frameworks allowing to make sens of the unknown, and ultimately get a better grasp of planktonic functional diversity.

We might not be ready yet for the use of omics data to implement complex traits in mechanistically realist biogeochemical models, or even to make accurate quantitative predictions of complex traits realization. But the results of this thesis and the promising works that I have discussed in this section emphasize the important progresses made in this domain in the past decade. Putting a strong focus on the biology and genomics of complex traits, which should also help to better understand the dark side of omics, appears essential to better understand and model functional diversity. In parallel, we must bridge the gap between observations and biogeochemical models, to be able to better integrate our knowledge on diversity into modeling frames. [START_REF] Vanni | Light into the darkness: Unifying the known and unknown coding sequence space in microbiome analyses[END_REF]. The idea is to bridge the known coding sequences with unknown coding sequences using sequence composition, domain architecture and remote homology ( i.e. indirect homology) to build a database (DB) of gene clusters (GC). The clusters are then complemented by meta-data such as levels of expression in metatranscriptomes, abundance in metagenomes, phylogenetic origin or environmental range to diffuse (or not) functional and/or taxonomic annotations from known sequences to unknown sequences in each cluster. Overall, the pipeline uses as many information as we have on the known coding sequence space to draw information on the unknown coding sequence space.

How to bridge the gap between observations and biogeochemical models ?

In the introduction of this thesis, I highlighted the gap between observed (i.e. measured in-situ) and modeled functional diversity of plankton communities and its potential impacts on model predictions. In this section, I will present a few leads on how to bridge this gap using meta-omics data.

Using omics data to validate trait-based models

Trait-based models are hard to confront with most types of in-situ observations, as traits transcend taxonomy to focus on function, which is hard to measure at community-level. New types of data such as meta-omics, but also high-throughput imaging and satellite imagery now offer chances to make in-situ measures at the trait-level in planktonic communities (e.g. dmdA measures of Chapter 3, or automated detection of planktonic organisms attributes through machine learning treatment of in-situ images [START_REF] Luo | Automated plankton image analysis using convolutional neural networks[END_REF]). But most of these data come from cruises in which samples are taken in 'snapshots', i.e. at one point in space and time, while global biogeochemical models are usually run over at the scale of decades (e.g. 20 years runs for Coles et al. (2017), 10 years runs for Follows et al. (2007)). This discrepancy between the time scales of observations 133 Chapter 5. General discussion and perspectives and models makes it hard to compare direct measures with model outputs. Coles et al. (2017) managed to do it, taking advantage of using gene functional groups as model agents to compare the abundance of two well known genes in their model and in meta-omics samples. But the approach remained limited in the sense that the model had for goal to give a broad overview of prokaryotic plankton functional diversity over the whole Amazon river plume region, while the scarce number of snapshot meta-omics samples available for model validation only covered a very small spatio-temporal range (each sample corresponding to a maximal temporal range of a few days spent in a space of a few square kilometers). Hence, it appears to me that the best way to validate model outputs with omics data would be to derive general statistical relationships from observations, similar to general ecological laws, that could then be used as guidelines to check model outputs, instead of 'point to point' comparisons.

Chapters 3 and 4 illustrate well how the richness of meta-omics datasets can allow to derive strong statistical relationships from observations at global scale. Such strong relationships, whether it be between markers of different traits or between markers of a functional trait and the environment, offer a chance to better validate model outputs with observations. Indeed, the verification of the observed relationship in the model could serve as a kind of validation. To go further, we could imagine using correlative models based on these relationships, inspired from the niche modelling approach, that could give access to global maps of traits distribution on spatio-temporal scales similar to classic biogeochemical models (Figure 5.3). For example, the study by Tang and Cassar (2019) provide such a data-driven global scale model of diazotrophs abundance, and could help to validate the outputs of biogeochemical models including diazotrophs, and even help to identify the zones of good and poor performances from both models.

Using omics data to model individual functional traits

Trait-based models rely on the description of trade-offs between functional traits, that define the parameters of individual-level processes such as mortality, or the acquisition and allocation of resources (Kiørboe et al., 2018). Such trade-offs are often defined empirically, thus on limited numbers of species, and would benefit from more mechanistic descriptions, allowing for a more general applicability (Kiørboe et al., 2018). Omics data could help to better define trade-offs in two ways: (i) by providing more general empirical evidence of trade-offs than wet-lab experiments through global scale sampling, and (ii) by allowing to compute metabolic networks leading to the identification of trade-offs in a mechanistic manner, through metabolic modeling.

In this thesis, I mainly focused on building statistical relationships between functional marker genes and the environment, but similar approaches can be used to decipher links between genes responsible for different functions. For example, Salazar et al. (2019) identified dmdA transcript abundance to be significantly anti-correlated to cysN and cysD transcript abundances, two enzymes involved in the assimilatory sulfate reduction pathway. This supposes a trade-off between the use of sulfate and DMSP as a source for sulfur in prokaryotes (Salazar et al., 2019). Hence, when marker genes are available for different traits, meta-omics data offer the chance to test for trade-offs between them using publicly available, large scale datasets. Figure 5.3 -Concept of correlative models, illustrated with the dmdA metagenomics sequence abundance predictions obtained in chapter 3. Observations from oceanographic cruises, satellite imaging, remotely operated underwater vehicles, and global circulation models can be used to obtain global maps of environmental variables. These data can then be used as predictors of sequence abundance for specific genes or organisms through machine learning algorithms or bayesian network approaches. The statistical model used as an illustration here was presented in chapter 3, and allowed to predict dmdA sequence abundance in metagenomics samples using 5 variables: Ocean region, is the sample from polar or non polar area, temperature, depth and chlorophyll a. chapter The illustration used for global scale predictions was taken from [START_REF] Sonnewald | Elucidating ecological complexity: Unsupervised learning determines global marine eco-provinces[END_REF].

Models using genome-scale metabolic reconstruction include flux-balance analysis models [START_REF] Steuer | Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth[END_REF][START_REF] Budinich | A multi-objective constraintbased approach for modeling genome-scale microbial ecosystems[END_REF], as presented in the introduction, and more recently a new generation of resource allocation models [START_REF] Reimers | Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth[END_REF][START_REF] Sharma | Modelling microbial communities using biochemical resource allocation analysis[END_REF]. These two types of models are similar in the sense that they are based on genome-scale reconstruction of metabolic networks, and the evolutionary optimality principle, i.e. the assumption that all metabolic fluxes are set to obtain the maximal growth rate, or maximal fitness [START_REF] Sharma | Modelling microbial communities using biochemical resource allocation analysis[END_REF]. Resource allocation models are based on the idea that for a cell in a given environmental condition, the acquired resources can be allocated either to non-enzymatic components, or to the translation of catalysing enzymes, which will define the rates of metabolic reactions in the cell [START_REF] Sharma | Modelling microbial communities using biochemical resource allocation analysis[END_REF]. All reactions modeled in the cell are then bounded by the availability of the corresponding catalyzing enzyme, which is defined by the amount of resources spent in the translation of the enzyme. The model can then be optimized in order to select the resource allocation option that leads to the maximal growth rate [START_REF] Reimers | Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth[END_REF][START_REF] Sharma | Modelling microbial communities using biochemical resource allocation analysis[END_REF]. The modeled cells can adopt different allocation strategies depending on the environmental conditions, and such strategies 'automatically' emerge from the model structure.

Resource allocation models can thus predict the physiological behaviour of bacterial cells in dif-135 Chapter 5. General discussion and perspectives ferent environments [START_REF] Sharma | Modelling microbial communities using biochemical resource allocation analysis[END_REF], in the presence of multiple competing nutrients [START_REF] Wang | Growth strategy of microbes on mixed carbon sources[END_REF], and model outputs allow to identify intra-cellular trade-offs leading to the choice of one strategy or the other [START_REF] Sharma | Modelling microbial communities using biochemical resource allocation analysis[END_REF][START_REF] Reimers | Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth[END_REF].

= can be obtained from metabolic modeling Figure 5.4 Kiørboe et al. (2018). The figure depicts how individual-level processes can be derived from traits through the definition of trade-offs between traits. Large scale community-level functional attributes can then be defined as a function of the available traits and the environmental context. Yellow boxes and arrows were added to the original figure to highlight where metabolic modeling could be of help. Please note that the modifications made on this figure only apply to well known unicellular organisms, at least for now (see main text). Metabolic modeling allows to detect resource acquisition traits from genomic content, and to derive all related metabolic costs and affinities through mechanistically realist modelling of intra-cellular trade-offs. Most defence traits, like the production of toxin, or the building of siliceous or calcareous shells can be derived from genomic contents. However, their influence on mortality does not depend exclusively on metabolism, but mainly on the impact of such defence strategies on ecological interactions like predation, which can hardly be derived from metabolic modeling. In most metabolic models, cell size and density are fixed, thus cell size was not highlighted as derivable from metabolic modeling here. Nevertheless, the trade offs linked with the impact of different cell sizes on metabolic costs and nutrient affinities can already be explored through metabolic modeling. Metabolic modeling could then help to define a data-driven set of optimal traits, and the corresponding trait distribution. Kiørboe et al. (2018) proposed that three categories of functional traits were essential to any traitbased approach: resource acquisition traits, defense traits, and size-related traits (Figure 5.4). [START_REF] Sharma | Modelling microbial communities using biochemical resource allocation analysis[END_REF] needs not only the list and nucleotidic composition of each catalyzing enzyme of the modeled organism, but also quantitative information on their activity. While the authors advocate that "reasonable estimates for all required parameters exist" [START_REF] Sharma | Modelling microbial communities using biochemical resource allocation analysis[END_REF], it might not be the case for unicellular eukaryotes, and it is obviously not the case for multicellular organisms, for which these types of approaches have not yet been adapted. It then appears necessary to focus on better understanding the genomic and metabolic basis of functional diversity for these promising modeling approaches to become more widely applicable, through approaches like the ones presented in chapter 3 and 4, or the creation and general use of tools like AGNOSTOS [START_REF] Vanni | Light into the darkness: Unifying the known and unknown coding sequence space in microbiome analyses[END_REF].

-The principles of trait-based approaches, and what metabolic modeling can bring to the table, modified from

I have discussed here the potential contributions of omics data to the modeling of functional traits.

But as shown in the introduction, most of the global biogeochemical models including explicit representations of planktonic diversity rely on functional entities such as plankton functional types or gene functional groups. In the next paragraph, I will discuss how omics and meta-omics data might be used to better represent plankton functional diversity in such models.

Using omics data to improve plankton functional diversity representation in models

Until now, plankton functional type (PFT) models have always been built through a priori selections of the model agents, often with a strong focus on their biogeochemical impacts, rather than their biological and ecological attributes [START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF]. The gene functional groups approach allowed to bypass the association that often remain between PFTs and taxonomy, but remain for now based on an a priori selection of the modeled gene groups (Coles et al., 2017). In theory, the advent of omics data offer ways of including a more realistic view of plankton functional diversity in ecosystem models, through the detection of in-situ functional traits and metabolic functions (Chapter 2 and 3) or even gene functional groups (Chapter 4), but also through the automatic biogeochemical characterization of genomes [START_REF] Shaffer | DRAM for distilling microbial metabolism to automate the curation of microbiome function[END_REF], and the functional characterization of uncultivated fractions of planktonic communities [START_REF] Vanni | Light into the darkness: Unifying the known and unknown coding sequence space in microbiome analyses[END_REF]. It potentially offers the opportunity to build plankton ecosystem models with data-driven definition of agents. But increasing diversity and agent number rhymes with increasing model complexity, and trait-based approaches were introduced to reduce complexity compared to species-based approaches, when the original PFT models with 10 to 15 PFT were already deemed as too complex [START_REF] Frede Thingstad | Stepwise building of plankton functional type (PFT) models: A feasible route to complex models?[END_REF]. Here I will discuss some ideas on how omics data could be used in a reasonable way to better represent planktonic diversity within biogeochemical models.

In 'classic' PFT models, each PFT is represented by a single differential equation. This equation represent the sum of source terms (e.g. reproduction, growth), minus the sum of sink terms (e.g. mortality, predation). Such equations include the definition of a growth rate, often dependent on parameters like maximum growth rate, temperature dependence of growth, and for autotrophs light dependence of growth [START_REF] Aumont | An ecosystem model of the global ocean including Fe, Si, P colimitations[END_REF]. Such parameters are derived from wet-lab experiments based on one or a few species deemed as representative of the concerned PFT (Le [START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF]. Hence, when a proposition of adding more diversity (e.g. more variables, i.e. more PFTs) into biogeochemical models is made, the parameterization is automatically raised as problematic. In parallel, the fact that parameters in PFT models remain quite heavily biased towards culturable organisms is quite problematic as well, and the idea of generalizing such parameters across a whole group of taxonomically diverse organisms (e.g. the proto-zooplankton PFT in Le [START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF], which notably includes cilliates and dinoflagellates) is biologically doubtful [START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF].

Deriving parameters from genome-scale metabolic networks

Already evoked in the precedent section, genome-scale reconstruction of metabolic networks and their associated modeling approaches offer the possibility to derive environment dependent metabolic rates directly from omics data [START_REF] Budinich | A multi-objective constraintbased approach for modeling genome-scale microbial ecosystems[END_REF][START_REF] Steuer | Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth[END_REF][START_REF] Sharma | Modelling microbial communities using biochemical resource allocation analysis[END_REF][START_REF] Reimers | Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth[END_REF]. Hence, in a more classic modeling context, relying on metabolic modeling for the parameterization of growth rates, nutrient exchange rates and biogeochemistry-related rates seems possible. Of course, it would probably only be applicable on prokaryotes and simple traits for now (e.g. DMS production or diazotrophy, See precedent section). Still, the advent of MAGs theoretically allows to apply genome-scale reconstruction of metabolic networks on a great diversity of plankton organisms, and the combination of such networks with innovative annotation tools like DRAM [START_REF] Shaffer | DRAM for distilling microbial metabolism to automate the curation of microbiome function[END_REF] or AGNOSTOS [START_REF] Vanni | Light into the darkness: Unifying the known and unknown coding sequence space in microbiome analyses[END_REF] could be used in conjunction to automatically derive the sets of parameters representing the physiology of a very wide diversity of organisms. In fact, considering the poor representation of prokaryotes in many PFT models (e.g. no prokaryotes explicitly included in [START_REF] Aumont | An ecosystem model of the global ocean including Fe, Si, P colimitations[END_REF], where the remineralization rate varies only with depth), even a small step towards more datadriven prokaryotic diversity representation could be significant [START_REF] Coles | Approaches and Challenges for Linking Marine Biogeochemical Models with the "Omics" Revolution[END_REF], notably concerning remineralization estimates [START_REF] Miki | Immigration of prokaryotes to local environments enhances remineralization efficiency of sinking particles: a metacommunity model[END_REF].

Using network-based approaches to define ecological interactions

PFT models also include parameters and functional response curves (e.g., Holling type [START_REF] Holling | The components of predation as revealed by a study of small-mammal predation of the European pine sawfly[END_REF][START_REF] Gentleman | Functional responses for zooplankton feeding on multiple resources: a review of assumptions and biological dynamics[END_REF]) representing ecological interactions between the different functional types, mostly focusing on predation in current models, but that could ideally include more complex interactions like parasitism and symbiosis as well [START_REF] Worden | Rethinking the marine carbon cycle: Factoring in the multifarious lifestyles of microbes[END_REF]. Reconstructions of food webs in models depend mostly on parameters such as maximum grazing rates and preference for different kinds and sizes of preys, which are hard to measure and thus often broadly estimated from wet-lab experiments and allometric relationships [START_REF] Aumont | An ecosystem model of the global ocean including Fe, Si, P colimitations[END_REF][START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF].

The definition of such parameters can become particularly problematic when the number of PFTs increase. One way to limit this problem is the use of allometric relationships to define sizedependant parameters (e.g. Ward et al. (2012)). However, such models still rely on the classical PFT approach.

Meta-omics data could help to resolve this issue through the use of co-occurrence networks and machine learning. Co-occurrence networks are graphs in which nodes correspond to biological entities (e.g. lineages, OTUs or amplicon sequence variants), and links to a measure reflecting the co-occurrence between them (Figure 5.5). The construction of such networks in the light of the environmental context can lead to the identification of significant positive and negative associations between biological identities, which can be interpreted as ecological interactions (Faust and 138 5.2 How to bridge the gap between observations and biogeochemical models ? [START_REF] Faust | Microbial interactions: from networks to models[END_REF]Lima-Mendez et al., 2015;[START_REF] Berry | Deciphering microbial interactions and detecting keystone species with co-occurrence networks[END_REF]. For example, a co-occurrence network based on metabarcoding and metagenomics data from Tara Oceans allowed to draw hypothetic ecological interactions between more than 9,000 taxa, including some unknown symbiotic relations which were validated in parallel by imagery data (Lima-Mendez et al., 2015). But interpretations of interactions are not straight forward, and a positive interaction could be interpreted as cross-feeding, co-aggregation in biofilms or niche overlap among other possibilities, while a negative interaction could be explained by a predator-prey relationship, but also amensalism, competition, etc. [START_REF] Faust | Microbial interactions: from networks to models[END_REF]. Overall, the inference of ecological interactions from co-occurrence networks is based on the hypothesis that ecological interactions are the first driver of species occurrences, which is of course debatable [START_REF] Faust | Microbial interactions: from networks to models[END_REF][START_REF] Freilich | Species co-occurrence networks: Can they reveal trophic and nontrophic interactions in ecological communities?[END_REF]. Hence, multiple studies aimed at testing the ability of co-occurrence networks to detect real non-trophic and trophic interactions. [START_REF] Berry | Deciphering microbial interactions and detecting keystone species with co-occurrence networks[END_REF] used a simulated microbial community to build a co-occurrence network, and showed that it was able to retrieve ecological interactions when following some guide rules. These rules included for instance: the removal of infrequent taxa, the inclusion of more than 25 samples (and as many as possible), only coming from similar environments (at least from the same Chapter 5. General discussion and perspectives ecosystem type), or the use of corrections on relative abundances to avoid apparent correlations.

One important flaw of this study is that they assumed all species of the simulated community to be efficiently sampled and quantified, which is probably often not the case in 'real' data. [START_REF] Freilich | Species co-occurrence networks: Can they reveal trophic and nontrophic interactions in ecological communities?[END_REF] used a different approach, as they relied on a heavily-studied ecological interaction network derived from long-term observations as reference, and tested the ability of co-occurrence data to retrieve the full network. They found a very poor overlap between links of the 'real' network (from which the quality and completeness is difficult to assess) and the one derived from the co-occurrence network [START_REF] Freilich | Species co-occurrence networks: Can they reveal trophic and nontrophic interactions in ecological communities?[END_REF]. However, their construction of the co-occurrence network appeared quite poor: they did not remove infrequent species, used only presence/absence instead of absolute or even corrected relative abundance, and most importantly included only the tide level as an environmental factor despite using samples from 49 different sites, sampled unevenly and possibly in different seasons (not precised in the manuscript) during a 15 years period [START_REF] Freilich | Species co-occurrence networks: Can they reveal trophic and nontrophic interactions in ecological communities?[END_REF]. It is then still difficult to estimate whether co-occurrence networks can or can not reflect ecological interactions. But here again, genome-scale reconstruction of metabolic networks might be of help. Indeed, [START_REF] Freilich | Competitive and cooperative metabolic interactions in bacterial communities[END_REF] 1 demonstrated that metabolic models offered the opportunity to predict competition and cooperation between prokaryotes. They used 118 genome-scale metabolic networks, and predicted the metabolic interactions for each of the 6903 corresponding species pairs based on their metabolic needs, using methods described in [START_REF] Stolyar | Metabolic modeling of a mutualistic microbial community[END_REF]. Thus, co-occurrence networks ability to retrieve ecological interactions could be tremendously increased by using full genomes or MAGs abundance instead of metabarcoding data or presence/absence. It would allow to integrate information from metabolic modeling and full genome trait-centered annotations into the classic network inference frameworks.

How many plankton groups should be included ?

Meta-omics data could then improve planktonic diversity representation into models by providing the ability to make data-driven choices of model actors, and by facilitating the parameterization steps. But even in a case where the parameterization of a 'realistic' diversity of PFTs would not be problematic, the model complexity would cause (i) computational power issues and (ii) difficulties to understand the model behaviour, making it difficult to use the model as a tool for answering ecological questions. It then appears absolutely necessary for any biogeochemical model including a data-driven description of planktonic diversity to include an aggregation step, like illustrated in Figure 5.6. The idea would be to start with complete genomes, MAGs or MGTs, and combine information from annotation tools, metabolic models, co-occurence networks and omics-based biogeographies to detect genomes homogeneous in function and sharing similar niches, that could then be aggregated into functional types. Biochemical resource allocation models allow to aggregate metabolic networks into simplified, coarse-grained networks, which could help to obtain parameters at the aggregated functional group level [START_REF] Sharma | Modelling microbial communities using biochemical resource allocation analysis[END_REF]. Algorithms to automatically reduce metabolic networks to coarse-grained models also exist [START_REF] Erdrich | An algorithm for the reduction of genome-scale metabolic network models to meaningful core models[END_REF]. Of course, the method for aggregating the functional groups based on the blending of such different types of information would not be straight forward. I would advocate Game theory / adaptive dynamics Figure 5.6 -Conceptual scheme of a solution for integrating multiple types of data and models to improve the representation of planktonic diversity in models. This scheme focuses on near complete genomes and transcriptomes, which can be obtained from culture but also from meta-omics data for metagenome-assembled genomes (MAGs) and metagenomics-based transcriptomes (MGTs). Each genome and/or transcriptome can be functionally annotated, notably via tools specifically targeting biogeochemical functions like the recent DRAM algorithm [START_REF] Shaffer | DRAM for distilling microbial metabolism to automate the curation of microbiome function[END_REF]. Metabolic rates can be extracted from them through metabolic network reconstruction, or resource allocation models like the biochemical resource allocation model proposed by [START_REF] Sharma | Modelling microbial communities using biochemical resource allocation analysis[END_REF]. Co-occurrence networks can be used on the genomes and transcriptomes abundance to determine potential antagonistic or mutualistic interactions between them (Lima-Mendez et al., 2015). Finally, I demonstrated in this thesis how statistical analyses and machine learning could help to decipher the functional and taxonomic response to environmental gradients. This information could serve as a way to aggregate genomes and transcriptomes that are sharing similar metabolims, ecological interactions and response to environment through multivariate network analysis (see main text). To my knowledge, this has not yet been done. Such aggregated genomes and transcriptomes would be analog to functional groups, and could be integrated into different types of models, PFT like models but also correlative models like presented in figure 5.7 (replacing gene clusters), and models involving adaptive dynamics to simulate evolutive adaptation.

META-OMICS

for the use of hierarchical classifiers, unsupervised clustering and similarity networks for each separated type of information. For example, functional similarity scores could be computed based on the number of complete KEGG pathways shared between MAGs, and these scores could be used to compute a functional similarity network of MAGs from which functional clusters could be derived (using connected components as in Chapter 4, or using Louvain clustering for example).

In parallel, clusters of MAGs with similar response to the environmental context could be obtained from an unsupervised clustering of their coordinates in an RDA multidimensional space. This step could be followed by a comparison of the genome clusters found for each information type, with the aim to detect MAGs that appear in the same cluster for multiple information types. Ideally, depending on the thresholds applied during the aggregation step, the model obtained could have a more or less coarse representation of diversity, enabling to answer a variety of different ecological questions, but always with a data-driven start.

Challenges and limits

The modeling approach proposed in this section would have flaws like the heavy computational power needed, the probably heavy dependence on the data used as inputs, on the tools used for the pre-aggregation steps, and on the methods used for the aggregation step itself. As discussed in the precedent paragraph, the detection of ecological interactions through co-occurrence networks is still heavily debated and should definitely be improved. Also, after discussing with experts of metabolic network modeling about the current state of the field, it appears that such an approach could maybe be applicable on prokaryotes, focusing on well known metabolic reactions, but prob-

ably not yet on eukaryotes. Considering the recent advances presented in this discussion and the only recent first applications of systems biology approaches in planktonic ecology, I believe that we can hope for important progresses in the domains of genome-scale metabolic modeling and network-based community ecology in the years to come, which will be key for integrating realistic planktonic diversity into biogeochemical models. But integrating such a realistic diversity in 'classic' PFT modeling frameworks would not allow to take full advantage of the available data, as it would lead to a 'fixed' representation of biodiversity, not including any representation of acclimation or evolutive adaptation in biogeochemical models [START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF]. Considering that many biogeochemical models are used over long time scale for climate predictions [START_REF] Ciais | Carbon and Other Biogeochemical Cycles[END_REF], the integration of multiple modeling types seems essential for a better understanding of the role of microbial diversity in future oceans [START_REF] Song | Mathematical Modeling of Microbial Community Dynamics: A Methodological Review[END_REF][START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF][START_REF] D'alelio | Modelling the complexity of plankton communities exploiting omics potential: From present challenges to an integrative pipeline[END_REF].

Integrating multiple types of models to understand the role of microbial diversity in biogeochemical cycles

The idea of integrating multiple types of modeling was proposed in most review papers focusing on the future of biogeochemical models [START_REF] Song | Mathematical Modeling of Microbial Community Dynamics: A Methodological Review[END_REF][START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF][START_REF] D'alelio | Modelling the complexity of plankton communities exploiting omics potential: From present challenges to an integrative pipeline[END_REF][START_REF] Allen | Linking physiology to ecology: towards a new generation of plankton models[END_REF]. The four most frequently evoked types of models are: trait-based models, Lotka-Volterra derived models (PFT models being mixed between trait-based and generalized Lotka-Volterra models), correlative/regression-based/species distribution models, and game theory/adaptive dynamics models. The results presented in this thesis notably pushes towards the creation of biogeochemical models inspired from correlative models (Figure 5.7).

Chapters 3 and 4 focused on the predictability of functional gene clusters abundance from the environmental context, providing some encouraging results. Tang and Cassar (2019) provided other compelling evidence of the possibility to use omics and environmental data to predict the abundance of functional gene clusters. What lacks from both this study and my results is a way to quantitatively link functional genes to biogeochemically meaningful trait realization measures.

A recent study focusing on plant traits was able to achieve this through a bayesian network approach [START_REF] Guadagno | Use of transcriptomic data to inform biophysical models via Bayesian networks[END_REF]. Their approach relied on transcriptomic data from controlled experiments that were coupled to measures of three traits in specimens of the globally cultivated crop Brassica rapa: assimilation rate, photosystem efficiency and stomatal conductance. Traits measures and leaf tissue samples for transcriptomic analysis were taken every four hours during In-Situ meta-omics 2020), [START_REF] Guidi | Plankton networks driving carbon export in the oligotrophic ocean[END_REF] and [START_REF] Steuer | Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth[END_REF]. a period of 48h, sequencing was achieved using Illumina HiSeq and reads were aligned to a previsouly published reference genome of B. rapa (Wang et al., 2011). These coupled measures were used to build a bayesian network, i.e. a graph where nodes are variables (here including both trait measures and transcript abundances), and links are their conditional dependencies. Such a network then describes the probabilistic relationships between transcript abundances and realized traits. The network built in [START_REF] Guadagno | Use of transcriptomic data to inform biophysical models via Bayesian networks[END_REF] was able to predict trait realization from the abundance of transcripts, while capturing the uncertainty of both the biological system and the measures. Such an approach could be used on planktonic organisms, especially with the recent progresses in microfluidics and high-throughput cultivation [START_REF] Needham | Targeted metagenomic recovery of four divergent viruses reveals shared and distinctive characteristics of giant viruses of marine eukaryotes[END_REF][START_REF] Lee | An automated Raman-based platform for the sorting of live cells by functional properties[END_REF][START_REF] Faust | Towards a Better Understanding of Microbial Community Dynamics through High-Throughput Cultivation and Data Integration[END_REF]. The combination of such an approach with metabolic networks modeling, machine learning, and the methods used in this thesis could allow for the construction of probabilistic biogeochemical models including a biological layer (e.g. the Gene clusters layer in Figure 5.7). This biological layer could be removed to directly try to predict the trait realization from the environment, as in Tang et al. (2019), where they attempted to predict nitrogen fixation rate from environmental conditions without predicting diazotrophs abundance. But the presence of a biological layer allows (i) to confront model outputs to observations from meta-omics datasets in order to control model performance, and (ii) to take into account the positive and negative Chapter 5. General discussion and perspectives correlations between gene clusters to predict the trait output, which allows to include some kind of biological feedback in the model and could greatly improve its performance. Such models would be reasonable in terms of computational power, especially compared to models based on hundreds of differential equations. They would allow to take advantage of all the richness of the data available, as there is no need to pre-select markers or to use aggregation steps. In fact, such a method could even include gene clusters of unknown functions, if they appear as statistically linked to a trait and/or environmental conditions. Genomic markers of both simple and complex traits might then emerge from the construction of such models. Finally, this approach would allow to take into account the effects of environmental variations on trait realization, and to estimate the uncertainty of model outputs at every step, which is not doable with a classic 'PFT' model. This approach would then be quite complementary to more classic trait-based and PFT approaches.

An important drawback of such an approach resides in the absence of explicit representations of ecological interactions such as grazing, and in the absence of representation of population stocks, which can be important for decision makers. Also, many technical challenges regarding the construction of such models remain. Notably, in order to derive trait values from transcriptomic composition on a significant set of different traits, we would need large amounts of data from specifically designed wet-labs experiments, which do not exist for now. Our results of Chapter 4 also suppose that some functional genes might not be well predicted by the environment, at least through the metagenomics data that we used. More studies using similar approaches should then be conducted before focusing our efforts on the actual modeling process.

Among the four types of models that I evoked at the beginning of this section, adaptive dynamics models are the only ones that I have not yet discussed. Multiple clear evidences exist of rapid adaptation (e.g. inter-generational change (evolution) to inherited traits involving changes to the DNA sequence ;[START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF]) of planktonic organisms to environmental changes, and the inclusion of this strong adaptive ability in climatic models is a key challenge [START_REF] Mock | Evolutionary genomics of the cold-adapted diatom Fragilariopsis cylindrus[END_REF][START_REF] Ward | Considering the Role of Adaptive Evolution in Models of the Ocean and Climate System[END_REF][START_REF] Cermeño | Marine Primary Productivity Is Driven by a Selection Effect[END_REF][START_REF] Sauterey | When everything is not everywhere but species evolve: an alternative method to model adaptive properties of marine ecosystems[END_REF]. As for now, state-of-the-art global biogeochemical models do not take into account the adaptive potential of planktonic organisms [START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF][START_REF] Ward | Considering the Role of Adaptive Evolution in Models of the Ocean and Climate System[END_REF]. Indeed, despite existing efforts to account for acclimation, or reversible intra-generational change through changes in expression of inherited traits [START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF], such as the use of varying stoichiometric ratios to simulate photo-acclimation [START_REF] Ayata | Phytoplankton growth formulation in marine ecosystem models: Should we take into account photo-acclimation and variable stoichiometry in oligotrophic areas[END_REF], very few models manage to include proper adaptation in their formulation. This is notably due to the fact that most biogeochemical models do not model individuals, and so the inheritance of traits is not explicitly modeled [START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF]. Some methods manage to bypass that, notably by allowing for the variation of parameters to maximize growth rate in response to environmental changes [START_REF] Toseland | The impact of temperature on marine phytoplankton resource allocation and metabolism[END_REF], but such approaches allow for large jumps in trait values and reversibility of changes, which are characteristics of acclimation more than adaptation [START_REF] Flynn | Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling[END_REF]. The adaptive dynamics theory provides a clear modeling framework for the explicit representation of adaptation in ecological models [START_REF] Kisdi | Adaptive Dynamics in Allele Space: Evolution of Genetic Polymorphism by Small Mutations in a Heterogeneous Environment[END_REF].

Adaptive dynamics are based on a simple idea: a resident population with fixed traits and a 5.3 Perspectives fixed growth rate is invaded by a mutant, with slightly different parameters. If the mutant has a positive invasion fitness, he invades the population, and becomes representative of the new resident population. The environment being shaped by the traits of the resident population, the invasion fitness of the mutant will depend on its per-capita growth rate in the conditions given by the traits of the resident population [START_REF] Kisdi | Adaptive Dynamics in Allele Space: Evolution of Genetic Polymorphism by Small Mutations in a Heterogeneous Environment[END_REF][START_REF] Sauterey | When everything is not everywhere but species evolve: an alternative method to model adaptive properties of marine ecosystems[END_REF]. In simple systems, the invasion fitness can be obtained through analytical resolution, but this is not doable in biogeochemical models with many state variables [START_REF] Sauterey | When everything is not everywhere but species evolve: an alternative method to model adaptive properties of marine ecosystems[END_REF]. The method can still be applied to biogeochemical models through the numerical resolution of mutant's growth rate [START_REF] Sauterey | When everything is not everywhere but species evolve: an alternative method to model adaptive properties of marine ecosystems[END_REF][START_REF] Sauterey | The Implications of Eco-Evolutionary Processes for the Emergence of Marine Plankton Community Biogeography[END_REF].

A similar approach could be conducted using resource allocation models. [START_REF] Toseland | The impact of temperature on marine phytoplankton resource allocation and metabolism[END_REF] showed how such models allowed to predict growth rates in specific environments, and using a similar model in an adaptive dynamics context should be feasible. The main difference between [START_REF] Toseland | The impact of temperature on marine phytoplankton resource allocation and metabolism[END_REF] and an adaptative dynamics approach is that in [START_REF] Toseland | The impact of temperature on marine phytoplankton resource allocation and metabolism[END_REF], the assumption is that 'everything is everywhere' and so the parameters giving the optimal growth rate are always selected, while in adaptive dynamics, the system must be at steady state with fixed parameters when the mutant arrives, and the success of the mutant is entirely dependant on who was there before him [START_REF] Sauterey | When everything is not everywhere but species evolve: an alternative method to model adaptive properties of marine ecosystems[END_REF]. In a model like the one presented in Figure 5.6, with data-driven metabolic networks of organisms serving as the basis for parameterization, we could even imagine modeling the appearance of mutant by introducing random mutations at the genome level.

Perspectives

During most of my PhD, I tried to build links between meta-omics data, functional traits and the environmental context. If my initial goal was to find new ways of doing 'concrete' biogeochemical modeling, I then realized that multiple methods for the integration of sequencing data into models already existed, and that the limiting factors were actually more often on the biological side of things, where a significant part of unknown remains. Hence, I focused on the statistical exploration of the functional and taxonomic composition of planktonic communities, trying to disentangle and organize the complex richness of biological data, a step that I consider as key for later improving diversity representation in biogeochemical models. Thus, as a post-doc, I will keep working on large scale meta-omics data, this time focusing on prokaryotes from the Antarctic, sampled during the Antarctic circumglobal expedition (ACE, https://spi-ace-expedition.ch/).

In particular, I hope to take advantage from the multiple datasets available from the expedition (e.g. meta-omics datasets focusing on viruses, prokaryotes, and protists are available, as well as metadata on the environmental context of sampling including detailed measures of primary production and trace metals) to dig into the linking between genomic composition and trait realization (upper part of the green box in Figure 5.7). Actually, I have indicated in Figure 5.7 that this step should take place in labs, but I hope to be able to show that inter-disciplinary cruises on highly-equipped boats like ACE provide sufficient lab power to focus on this question in-situ.

A.1.1 Introduction

The aquatic realm encompasses very diverse environments from freshwater ponds, lakes, and rivers to estuaries, salt marshes, mangroves, coasts, continental shelves, deep-seas, marginal seas, and open ocean areas. It plays a major role in the Earth's climate system and supplies important ecosystem services for human populations [START_REF] Grizzetti | Assessing water ecosystem services for water resource management[END_REF]). Yet, different aquatic ecosystems are still studied by distinct scientific communities that have limited interactions with each other, as illustrated by the tendency to train graduate students independently, to publish in different journals and to attend distinct conferences (with a few exceptions, such as the Association for the Sciences of Limnology and Oceanography (ASLO) and its conferences and journals, including Limnology and Oceanography). Freshwater and marine ecosystems even belong to different Sustainable Development Goals for the United Nations, with one dedicated to the marine environment (#14: Life below water), and another to terrestrial systems including freshwater (#15: Life on land) (United Nations 2015).

Ecology seeks to understand interactions between organisms and the environment, as well as to identify general rules that elucidate the functioning of ecosystems, to ultimately improve our ability to predict ecosystem changes [START_REF] Loreau | Linking biodiversity and ecosystems: towards a unifying ecological theory[END_REF]. In both freshwater and marine environments, and for both pelagic and benthic habitats, the crucial questions remain the same [START_REF] Heino | A comparative analysis of metacommunity types in the freshwater realm[END_REF]:

(1) What are the processes that control the structure and functioning of aquatic ecosystems? (2) What ecological patterns emerge at various spatio-temporal scales, and what are their key drivers?

(3) How will aquatic organisms respond to increasing anthropogenic pressures? Some efforts have been made to integrate aquatic ecology for planktonic (Margalef 1978;[START_REF] Hecky | Nutrient limitation of phytoplankton in freshwater and marine environments: A review of recent evidence on the effects of enrichment[END_REF][START_REF] Leibold | Biodiversity in metacommunities: Plankton as complex adaptive systems?[END_REF]Litchman and Klausmeier 2008) and benthic (Mermillod-Blondin and Rosenberg 2006) studies. Despite recent efforts to bring the communities together (e.g. the AQUASHIFT and DynaTrait projects priority programmes of the German Research Foundation, or such as the bi-annual Trait workshop https://www.traitspace.com/ including limnologists, benthic ecologists, terrestrial ecologists), a unified framework for addressing ecological questions in pelagic and benthic habitats of both environments has been slow to develop. A recent review highlights the potential of trait-based ecology for studying aquatic ecosystems and the need for collaborative approaches among aquatic ecologists was emphasized [START_REF] Kremer | Realizing the potential of trait-based aquatic ecology: New tools and collaborative approaches: Challenges of trait-based aquatic ecology[END_REF]). In addition to bridging the gap between freshwater and marine studies, there is a crucial need to integrate planktonic and benthic studies, especially because of the strong coupling between these two habitats [START_REF] Griffiths | The importance of benthic-pelagic coupling for marine ecosystem functioning in a changing world[END_REF]. The present synthesis proposes a practical framework to address these needs. Indeed, trait-based approaches, defined in ecological research as any method that focuses on individual properties of organisms (so-called traits) rather than species, could provide this common framework (McGill et al. 2006;[START_REF] Kremer | Realizing the potential of trait-based aquatic ecology: New tools and collaborative approaches: Challenges of trait-based aquatic ecology[END_REF]). These approaches emerged from terrestrial ecology when attributes at the individual level, initially used to describe ecosystem function based on elements common to multiple species, were considered to gather individuals into functional groups (i.e., "plant functional types") based on their physical, phylogenetic and phenological characteristics, rather than on their taxonomy (e.g. species). Trait-based models of aquatic ecosystems can be traced back to the pioneering work of Riley in the 1940's (Riley 1946), who modelled the phytoplankton bloom dynamics in the North Atlantic focusing on the main physiological and biological characteristics of phytoplankton as a group. Since earlier attempts to classify phytoplankton by "life-forms" [START_REF] Sournia | Form and Function in Marine Phytoplankton[END_REF][START_REF] Reynolds | Functional morphology and the adaptive strategies of freshwater phytoplankton[END_REF]), a similar approach was applied to identify functional groups for freshwater benthic macrofauna (Usseglio-Polatera et al. 2000, 2001), marine benthos [START_REF] Rigolet | Benthic control freaks: Effects of the tubiculous amphipod Haploops nirae on the specific diversity and functional structure of benthic communities[END_REF], benthic algae [START_REF] Tapolczai | Trait-based ecological classifications for benthic algae: review and perspectives[END_REF], submerged plants [START_REF] Willby | Attribute-based classification of European hydrophytes and its relationship to habitat utilization[END_REF][START_REF] Lukács | Carbon forms, nutrients and water velocity filter hydrophyte and riverbank species differently: A trait-based study[END_REF], or marine zooplankton [START_REF] Benedetti | Identifying copepod functional groups from species functional traits[END_REF]. The underlying assumption is that functional grouping would make it easier to link community ecology to biogeochemical processes and biodiversity to ecosystem functioning [START_REF] Naeem | Disentangling biodiversity effects on ecosystem functioning: deriving solutions to a seemingly insurmountable problem[END_REF]. Through the study of functional diversity and functional traits, these approaches allow for the quantitative assessment of community or ecosystem resistance or resilience to changes through functional redundancy [START_REF] Lavorel | Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail[END_REF]McGill et al. 2006) which could potentially enhance generality and predictability in future projections of ecosystem function and service provision than the species-centred or taxonomic approaches [START_REF] Levine | A trail map for trait-based studies[END_REF].

In aquatic ecology alone, more than 2,476 articles were published between 1991 and 2018 using the terms "functional trait" or "trait-based" (see Supplementary Information). The percentage of in situ (e.g. realized traits) or inferred from the literature (e.g. potential traits). Realized traits are ultimately one of the sources for potential traits found in databases (see section A. 1.3.1). To establish a unified framework and avoid subjectivity in these definitions, we recommend the use of the definitions that focus on the individual level only. These are the ones proposed by Violle et al. (2007), by Litchman and Klausmeier (2008) and by Reu et al. (2011) are summarized in Table A.1.

Since it is the diversity of organismal functions that structures communities and eventually ecosystems, trait-based approaches should rather refer to "functional traits" (sensu Violle et al. 2007: Any trait that impacts fitness indirectly via its effects on growth, reproduction and survival) than to "traits" and should in fact be called functional trait-based approaches. Functional traits have been further divided into four types: life history traits, morphological traits, physiological traits, and behavioral/mobility traits (Litchman and Klausmeier 2008;Litchman et al. 2013Litchman et al. , 2015;;[START_REF] Desrosiers | Assessing anthropogenic pressure in the St. Lawrence River using traits of benthic macroinvertebrates[END_REF]. The term "ecological traits" has also been used in the context of "functional traits" to describe the environmental preference of the organisms, especially for benthic ones (e.g. [START_REF] Desrosiers | Assessing anthropogenic pressure in the St. Lawrence River using traits of benthic macroinvertebrates[END_REF]). Where "ecological traits" refer to ecological or environmental preferences of organisms, they should rather be called physiological traits (e.g. salinity preference/tolerance) or behavioral traits (e.g. relationship with the substrate). In contrast, ecological traits referring to taxonomic information, sampling location or habitat features (e.g. depth, substratum type) should neither be considered as functional traits nor as traits.

A.1.2.2 Functional traits as a common framework beyond taxonomy to transcend ecosystems

Functional traits provide a "common currency across biological organizational levels and taxonomic groups" [START_REF] Violle | The emergence and promise of functional biogeography[END_REF], beyond taxonomic variation and geographic or ecosystemic A.1 Functional trait-based approaches as a common framework for aquatic ecologists toplankton (Litchman and Klausmeier 2008) and zooplankton (Litchman et al. 2013;Brun et al. 2016) but now incorporates new elements proposed for marine benthic ecosystems [START_REF] Degen | Trait-based approaches in rapidly changing ecosystems: A roadmap to the future polar oceans[END_REF]. Moreover, in the typologies proposed by Litchman and colleagues, functional traits are classified in one of four types (morphological, physiological, behavioral and life-history) and associated to three main ecological functions (resource acquisition, reproduction and predator avoidance for phytoplankton; feeding, growth/reproduction, and survival for zooplankton). Here we propose to separate growth and reproduction into two distinct columns (Figure A.1). Compared to earlier typologies, ours identifies key functional traits that can be used for multicompartmental studies because they transcend the taxonomic specificities of the different aquatic ecosystems [START_REF] Salguero-Gómez | Delivering the promises of trait-based approaches to the needs of demographic approaches, and vice versa[END_REF]. For instance it includes some traits that have been disregarded so far in studies focusing on only one compartment. These traits are: water content, color, breeding type, life-cycle, life span, diapause, reproduction strategy, salinity preference/tolerance, chemical compounds for mating or detecting congeners, diet/food preference, allochemical compounds, dissemination potential, substrate relation (plankton/benthos, including substrate specific relation for benthos), ecosystem engineering, including bioturbation/irrigation for benthos, and finally perception/production of sounds. Most of the 40 functional traits presented in this typology can be estimated quantitatively [START_REF] Costello | Biological and ecological traits of marine species[END_REF], making them good candidates for comparative studies. In addition, a dynamic representation of this typology is proposed as an online mental map (http://doi.org/10.5281/zenodo.3635898) which links to associated research articles. This mental map is not only a different way to represent the functional traits proposed in

A.1.2.3 Estimating functional diversity from functional traits

Traits are useful tools to quantify not only the functional biogeography of a system or organism, but also the diversity of a system, its functional redundancy, and/or its likely resilience to perturbations. Those traits that have been measured at the individual level, or estimated for each species of a given community, can be used to estimate trait-based Shannon diversity (Usseglio-Polatera et al. 2000) or Rao's quadratic entropy indices [START_REF] Rao | Diversity and dissimilarity coefficients: A unified approach[END_REF]. Functional diversity (FD) and its various dimensions, such as functional richness, functional divergence, or functional evenness (Mason et al. 2005;[START_REF] Ricotta | A note on functional diversity measures[END_REF]) can further be quantified, either using dendrogram-based metrics (e.g. [START_REF] Petchey | Dendrograms and measuring functional diversity[END_REF][START_REF] Mouchet | Towards a consensus for calculating dendrogram-based functional diversity indices[END_REF], or from the definition of a functional space (e.g. [START_REF] Villéger | New Multidimensional Functional Diversity Indices for a Multifaceted Framework in Functional Ecology[END_REF][START_REF] Laliberté | A distance-based framework for measuring functional diversity from multiple traits[END_REF] Several indices taking explicitly into account intraspecific trait variability were also proposed (e.g. Bello et al. 2011;[START_REF] Carmona | 1 Functional trait-based approaches as a common framework for aquatic ecologists[END_REF].

Functional beta diversity can be estimated too, including through the more classical Biological Trait Analysis (BTA) (e.g. [START_REF] Bremner | Methods for describing ecological functioning of marine benthic assemblages using biological traits analysis (BTA)[END_REF][START_REF] Beauchard | The use of multiple biological traits in marine community ecology and its potential in ecological indicator development[END_REF]). To aid ecologists in finding their way among the many functional diversity metrics, several guides were published about their definition and use [START_REF] Schleuter | A user's guide to functional diversity indices[END_REF][START_REF] Mason | A guide for using functional diversity indices to reveal changes in assembly processes along ecological gradients[END_REF][START_REF] Mouillot | A functional approach reveals community responses to disturbances[END_REF][START_REF] Carmona | 1 Functional trait-based approaches as a common framework for aquatic ecologists[END_REF] In recent years, numerous open access databases recording functional traits have been developed to document traits included in existing databases (Table A.2). This diversity of databases gathers trait information not only for widely studied traits (e.g. body size or feeding strategy), but also for less common traits or for those that are more difficult to measure (e.g. age at first reproduction, migration mode, or nutrient affinities). Some large trait databases were published online and open access (e.g. [START_REF] Herring | Systematic distribution of bioluminescence in living organisms[END_REF][START_REF] Barnett | Functional diversity of crustacean zooplankton communities: towards a trait-based classification[END_REF][START_REF] Benedetti | Mediterranean copepods' functional traits[END_REF][START_REF] Hébert | A meta-analysis of zooplankton functional traits influencing ecosystem function[END_REF][START_REF] Degen | The Arctic Traits Database -a repository of Arctic benthic invertebrate traits[END_REF], thus allowing for follow-up studies that compare and merge trait data across taxa, species and environments. In some instances (e.g. freshwater invertebrates) published databases rapidly became foundational for environmental assessment procedures (e.g. [START_REF] Mondy | A new macroinvertebratebased multimetric index (I2M2) to evaluate ecological quality of French wadeable streams fulfilling the WFD demands: A taxonomical and trait approach[END_REF][START_REF] Mondy | Using conditional tree forests and life history traits to assess specific risks of stream degradation under multiple pressure scenario[END_REF][START_REF] Larras | Assessing anthropogenic pressures on streams: A random forest approach based on benthic diatom communities[END_REF]. The main caveat of these FTBAs A.1 Functional trait-based approaches as a common framework for aquatic ecologists Because the number of images stored on acoustic and imaging systems is limited, and even short deployment times lead to considerable data volumes, the development of artificial intelligence (AI) techniques such as machine learning, deep learning recognition and classification has been a crucial tipping point in the extraction of traits from these large datasets [START_REF] Villon | Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG+SVM Methods[END_REF]Maps et al. 2019). Bigger storage capacity, standardized learning sets for machine learning combined with the automatized pre-processing of data directly in autonomous sampling instruments are already under development and will be an asset for the future of functional traits quantification by imaging.

A.1.3.3 Omics techniques for FTBAs

Another opportunity for automatic measurements of functional traits has emerged from the recent rise of high-throughput sequencing techniques (HTS, also called NGS, for Next Generation Sequencing, or "-omics" in the broader sense). These techniques provide fast and relatively cheap nucleic acid sequencing and have opened new perspectives for investigating the structure and functioning of aquatic communities, both in marine [START_REF] Raes | Toward molecular trait-based ecology through integration of biogeochemical, geographical and metagenomic data[END_REF]Sunagawa et al. 2015;Mock et al. 2016) and freshwater systems [START_REF] Chonova | Benthic Diatom Communities in an Alpine River Impacted by Waste Water Treatment Effluents as Revealed Using DNA Metabarcoding[END_REF]. Methods based on DNA or RNA sequencing can be used for large-scale studies of environmental samples, investigating water samples in which any nucleic acid that is present can theoretically be retrieved.

For FTBAs, the identification of targeted DNA sequences (or metabarcoding; [START_REF] Bucklin | DNA Barcoding of Marine Metazoa[END_REF][START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF]) can be used as a first step for fast and automatic taxon recognition, prior to the attribution of traits to the respective taxa using trait databases (Figure A.2C;Table A.2). This was recently done to describe the biogeography of mixotrophic traits of marine protists at global scale (Faure et al. 2019), or to estimate the functional diversity of coastal protist communities (Ramond et al. 2019). In freshwater systems, metabarcoding of benthic diatoms was used to assess the water quality status of rivers [START_REF] Vasselon | Assessing ecological status with diatoms DNA metabarcoding: Scaling-up on a WFD monitoring network (Mayotte island, France)[END_REF]) and metabarcoding was combined with text-mining or phylogenetic inference of ecological profiles and traits for biomonitoring [START_REF] Keck | Boosting DNA metabarcoding for biomonitoring with phylogenetic estimation of operational taxonomic units' ecological profiles[END_REF][START_REF] Compson | Chapter Two -Linking DNA Metabarcoding and Text Mining to Create Network-Based Biomonitoring Tools: A Case Study on Boreal Wetland Macroinvertebrate Communities[END_REF]). Yet, metabarcoding is inherently biased in multiple ways, such as its lack of quantitative link between the number of copies of barcodes (targeted DNA sequences) and the biomass or abundance distribution of organisms, the risk of gene amplification from dead material (not currently influencing ecosystem function), or the use of universal barcodes that may not be adapted to distinguish taxa for all lineages (e.g. [START_REF] Deiner | Environmental DNA metabarcoding: Transforming how we survey animal and plant communities[END_REF]. However, the main obstacle to using metabarcoding data for FTBAs is the low number of taxa for which barcodes have been documented (in addition to the low number of taxa for which trait information is available). This limitation precludes a full assessment of ecosystem structure from metabarcoding (e.g. de Vargas et al. 2015;[START_REF] Bescot | Global patterns of pelagic dinoflagellate diversity across protist size classes unveiled by metabarcoding[END_REF]. Thus, a strong effort remains to be made to supplement existing genomic databases with more taxonomically-referenced sequences and trait information to allow the metabarcoding-based monitoring of aquatic functional traits (e.g. Ramond et al. 2018;PR2: Guillou et al. 2013;EukRef: del Campo et al. 2018;Diat.barcode: Rimet et al. 2019).

Beyond metabarcoding, -omics approaches are of particular interest to identify or measure functional traits linked to metabolic pathways (e.g. photosynthesis, nitrification, diazotrophy, cal-In addition to studying spatial patterns, traits can be used to study the temporal variation of functional diversity and how aquatic organisms respond to increasing global changes from anthropogenic pressures in the context of biomonitoring.

A.1.4.3 Trait response to global changes

Temporal dynamics of traits and their response to climate change FTBAs can be used to estimate the temporal response of aquatic organisms and ecosystems to environmental forcing (Figure A.3.3). For example, functional traits have been shown to explain community structure and seasonal dynamics of marine phytoplankton (Edwards et al. 2013b). It is also possible to combine classical data sets, and especially time-series of species abundance, with trait databases described at the species level (see section A. 1.3.1) to apply a FTBA to in situ observations and/or monitoring datasets previously collected. In such reanalyses, key traits could be targeted (see section A. 1.4.1) to compare their temporal changes, identify tipping points, and reveal trade-offs among traits. Hence, FTBAs offer novel perspectives for a posteriori (re)analysis of historical or long term monitoring data for the study of climate change and its impact on communities and ecosystem functioning [START_REF] Pomerleau | Evaluation of functional trait diversity for marine zooplankton communities in the Northeast subarctic Pacific Ocean[END_REF][START_REF] Abonyi | Functional richness outperforms taxonomic richness in predicting ecosystem functioning in natural phytoplankton communities[END_REF][START_REF] Floury | Climatic and trophic processes drive long-term[END_REF].

Marine ecologists have long since used FTBAs to study the impact of climate change on aquatic ecosystems (c.f. purple cluster in Supplementary Figure A.4). For example, numerous marine studies explored the response of individual size to climate change (e.g. [START_REF] Schmidt | Biogeography and evolution of body size in marine plankton[END_REF][START_REF] Genner | Body size-dependent responses of a marine fish assemblage to climate change and fishing over a century-long scale[END_REF][START_REF] Finkel | Phytoplankton in a changing world: cell size and elemental stoichiometry[END_REF], showing that ocean warming is likely to cause a shift towards a larger contribution of smaller organisms to total biomass. Freshwater ecology could benefit from this experience, but currently, two main challenges can be pointed out for both freshwater and marine systems: the identification of links between functional traits and climate-change related variables (e.g. acidification and temperature increase in oceans, rivers and lakes; increase of freshwater shortage/scarcity in small streams) but also the deconvolution of the effects of multiple stressors on marine ecosystems [START_REF] Mouillot | A functional approach reveals community responses to disturbances[END_REF]. The joint pressure of multiple simultaneous stressors makes the identification of relationships between stressors and functional traits even more complicated, since interactions (e.g. synergism, antagonism, additivity, or inhibition) need to be taken into account. Under such conditions, monitoring functional traits of various types (Figure A.1) may prove useful to disentangle these complex interactions.

Impact of climate change on functional diversity

The study of functional diversity may also reveal functional redundancies at the community scale, which may have implications for ecosystem responses to climate change. As a consequence, because functional groups gather together individuals belonging to different species, the loss of a given species with a particular function does not necessarily mean that such function will be lost at higher ecological scale. Indeed, some ecosystems were shown to be insensitive to species loss because multiple species share similar functional roles (mixotrophy, nutrient uptake or requirements), or some species only make a small contribution to the ecosystem processes (Hooper et al.

206

A.1 Functional trait-based approaches as a common framework for aquatic ecologists 2005). Recently, it was suggested that climate change may have minor impacts on marine zooplankton functional diversity, due to strong functional redundancy [START_REF] Benedetti | The Seasonal and Inter-Annual Fluctuations of Plankton Abundance and Community Structure in a North Atlantic Marine Protected Area[END_REF]. Conversely, climate change may have contrasting impacts on stream fishes [START_REF] Buisson | Contrasted impacts of climate change on stream fish assemblages along an environmental gradient[END_REF] or decrease their functional diversity [START_REF] Buisson | Toward a loss of functional diversity in stream fish assemblages under climate change[END_REF]. By combining climate change scenario modeling with species distribution modeling and functional trait databases, the impact of climate change on the functional diversity of aquatic ecosystems can be assessed at broader scales and across biological compartments and ecosystem boundaries. For aquatic insects, such a combined modeling-FTBA study revealed the spatial patterns of vulnerability to climate change, which also opens opportunities for biomonitoring [START_REF] Conti | A trait-based approach to assess the vulnerability of European aquatic insects to climate change[END_REF]. However, limitations remain in the use of trait-based approaches for the assessment of the effects of multiple stressors in the context of climate change, as emphasized recently by [START_REF] Hamilton | Limitations of trait-based approaches for stressor assessment: The case of freshwater invertebrates and climate drivers[END_REF] for freshwater invertebrates. These authors pointed out the need to better account for trait redundancy, to better define the appropriate spatial scales for trait applicability and to progress towards the quantification of categorical traits.

Trait-based biomonitoring

Traditionally, the ecological health or "good environmental status" of aquatic ecosystems has been assessed in terms of species composition or relative abundance/biomass of specific indicators, initially within the context of the European Water Framework Directive (WFD-2000/60/CE). However, trait-based approaches offer new opportunities for the monitoring of aquatic ecosystems [START_REF] Culp | Incorporating traits in aquatic biomonitoring to enhance causal diagnosis and prediction[END_REF], since they can provide new tools that transcend taxonomical denomination, directly related to ecological functions, and exploit the traits available in open databases (Usseglio-Polatera et al. 2000;[START_REF] Baird | Toward a knowledge infrastructure for traits-based ecological risk assessment[END_REF], see also Table A.2). To date, trait-based biomonitoring has been mainly applied to freshwater ecosystems (cf. the corresponding cluster in Supplementary Figure A.4). Indeed, the links between traits of organisms and natural environmental variables (e.g. pH, flow velocity) or even anthropogenic pressures (e.g. nutrient or organic matter contamination) have been explored for decades by freshwater ecologists. More specifically, biomonitoring studies put a strong emphasis on the definition and the attribution of traits to taxa such as freshwater benthic macroinvertebrates (Usseglio-Polatera et al. 2000;[START_REF] Menezes | Beyond taxonomy: a review of macroinvertebrate trait-based community descriptors as tools for freshwater biomonitoring[END_REF]), benthic diatoms (Van Dam et al. 1994;[START_REF] Passy | Diatom ecological guilds display distinct and predictable behavior along nutrient and disturbance gradients in running waters[END_REF]) and phytoplankton [START_REF] Reynolds | Towards a functional classification of the freshwater phytoplankton[END_REF]. As a consequence, the last versions of several biological indices for stream monitoring are mainly based on functional traits (e.g. I2M2 in [START_REF] Mondy | A new macroinvertebratebased multimetric index (I2M2) to evaluate ecological quality of French wadeable streams fulfilling the WFD demands: A taxonomical and trait approach[END_REF], BDI in Coste et al. 2009). Within the context of lake monitoring, FTBAs mainly investigated the abundance and the seasonal variability of phytoplanktonic functional groups, as they are known to respond to nutrient concentrations [START_REF] St-Gelais | Limited functional responses of plankton food webs in northern lakes following diamond mining[END_REF][START_REF] Huang | Seasonal succession of phytoplankton functional groups in a reservoir in central China[END_REF]. Furthermore, the traits of macroinvertebrates (e.g. reproduction mode, size) and diatoms (e.g. auto-ecological guilds, life form) are now used in ecotoxicological and ecological models to identify the probability that chemical and/or land use related pressures impair natural communities [START_REF] Mondy | Using conditional tree forests and life history traits to assess specific risks of stream degradation under multiple pressure scenario[END_REF][START_REF] Larras | Assessing anthropogenic pressures on streams: A random forest approach based on benthic diatom communities[END_REF]

Figure 1 -

 1 Figure 1 -Picture taken by Joana Roussillon on board of the Atalante during the MOOSE 2018 expedition, on which I had the great pleasure to participate.

Figure 2 -

 2 Figure 2 -100 music albums that marked my PhD. The list is organized in 4 tier-lists, from extremely heavy rotations to reasonably heavy rotations. In each tier-list, no particular order is respected.

3. 2 . 2 A

 22 concrete example: exploring the biogeography of the dmdA enzyme 3.2.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2.2 Material & methods . . . . . . . . . . . . . . . . . . . . . . . 3.2.2.3 Results & Discussion . . . . . . . . . . . . . . . . . . . . . . 3.2.2.4 Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . .3.3 Exploring the genomic basis of complex functional traits . . . . . . . . . . . 3.3.1 State of the art: linkage, association methods and comparative transcriptomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 A concrete example: Markers of mixotrophy in dinoflagellate transcriptomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2.2 Material & methods . . . . . . . . . . . . . . . . . . . . . . . 3.3.2.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . 3.3.2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Next challenge: linking unknown functions and uncultivated organisms to functional traits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 4.2 Towards omics-based predictions of planktonic functional composition from environmental data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Figure 1 . 1

 11 Figure 1.1 -Range of body size and abundance in logarithmic scales for viruses, prokaryotes, unicellular eukaryotes (commonly called protists) and multicellular eukaryotes (including metazoans and macro-algae). A scale of macroscopic references was added to help apprehend the extent of size diversity among planktonic organisms. Size and abundance ranges were extracted fromPesant et al. (2015), but the size scale of pluricellular eukaryotes was extended to include larger organisms like jellyfish or sargass that can reach over a meter in length.

1. 1 AFigure 1 . 2

 112 Figure 1.2 -Phylogenetic diversity of prokaryotic (A) and eukaryotic (B) marine plankton. (A)Maximum-likelihood phylogenetic tree based on 16 concatenated single-copy phylogenetic markers extracted from 2,631 re-assembled genomes of prokaryotic plankton[START_REF] Tully | The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans[END_REF]. These genomes were assembled from 234 marine metagenomic samples from the Tara Oceans expedition (details on metagenome-assembled genomes are available in section 1.3.2). The black nodes on branches indicate bootstrap values >0.75, the color of polygons on branches extremities is coding for the phyla while their size is proportional to the number of genomes associated to the corresponding branch. Figure from Tully et al. (2018). (B) Diversity tree of eukaryotic plankton synthesizing information from morphological, phylogenetic (comparisons between a few marker genes from a large diversity of organisms) and phylogenomic (comparisons between parts of genomes or even full genomes of a large diversity of organisms) criteria.Seven "supergroups" are highlighted in colour, and pictures on the sides illustrate the morphological diversity of eukaryotes. Clockwise from right: archaeplastids (rhodophytes, chlorophytes, streptophytes); amoebozoa(tubulinids, arcellinids, mycetozoans); opisthokonts (fungus, microsporidians, choanoflagellates, cnidarians, bilaterians); excavates(parabasalians, oxymonads, euglenids); rhizaria (acantharians, foraminiferans, chlorarachniophytes); alveolates (ciliates, dinoflagellates); stramenopiles(labyrinthulids, synurophytes, diatoms, phaeophytes, actinophryids); unassigned [cryptomonads, katablepharids, haptophytes]. Figure from[START_REF] Worden | Rethinking the marine carbon cycle: Factoring in the multifarious lifestyles of microbes[END_REF].

Figure 1

 1 Figure 1.4 -Planktonic organisms interact to fix carbon on geological time scales in the ocean.The biological carbon pump is depicted in the green kernel, while the microbial carbon pump is represented by the yellow scheme on the right. Atmospheric carbon is either sequestered through the export of particulate organic matter (POM) produced by the biological carbon pump, or the creation of refractory dissolved organic matter (RDOM) produced by the microbial carbon pump. Diagram modified from[START_REF] Jiao | Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean[END_REF].

  Figure 1

Figure 1 . 5 -

 15 Figure 1.5 -The roles of planktonic organisms in the nitrogen and sulfur cycles. Depth, temperature and oxygen profiles are indicated in the left panel. A diagram of the open ocean nitrogen cycle is depicted in the blue panel (see section 1.1.2.2 for a detailed description). The upwelling and advection of NO 3 -and the sink of particulate nitrogen (PN) are the only processes in the diagram that are not achieved through biological processes by planktonic organisms. This cycle would be different in an oxygen minimum zone (OMZ), in which oxygen is almost absent around 200m-1000m depths.A diagram of the oceanic sulfur cycle is depicted in the red panel. Please note that this diagram focuses on the dynamics of dimethyl sulfide (DMS) and dimethylsulfoniopropionate (DMSP), and eludes all geologic parts of the marine sulfur cycle. The sulfur input to the ocean comes mostly from river runoffs, here indicated as weathering. The production of DMSP and DMS relies then exclusively on planktonic organisms. The produced DMS is ventilated to the atmosphere, which provokes the creation and densification of clouds. See section1.1.2.3. The two left panels are extracted fromZehr (2011a), while the sulfur panel was inspired by[START_REF] Alcolombri | Identification of the algal dimethyl sulfide-releasing enzyme: A missing link in the marine sulfur cycle[END_REF].

Figure 1 . 6 -

 16 Figure 1.6 -Structure of a nutrient, phytoplankton, zooplankton and detritus model (NPZD model). State variables are represented as colored boxes. N stands for nutrients, P for phytoplankton, Z for zooplankton and D for detritus. Matter fluxes are represented by arrows. The differential equations represent the dynamics of each state variable over time.

  the dynamics of nutrients, phytoplankton, zooplankton and detritus, or NPZD models, became the "go-to" tool for modeling plankton communities dynamics (Gentleman, 2002) (Figure 1.6). The limitations (or even absence) of computer power first restricted these models to simulations in 0 or 1 dimension (i.e. across depth in the water column). But with the progress in computer power, it became possible in the late eighties to come up with basin scale and then global scale models of planktonic dynamics. The idea behind such models was to couple (see model coupling in Box 2) the ecological models simulating planktonic interactions with physical models of ocean circulation (see Box 2), offering the ability to simulate the processes influencing biogeochemical cycles on important temporal and spatial scales. Hence, these coupled models were called biogeochemical models, and one of the first and most famous was the one of Fasham et al.

Figure 1 .

 1 Figure 1.7 -Structure of the PISCES-version 2 model, re-drawn from Aumont et al. (2015). State variables are represented as colored boxes, with a color code similar as Figure 1.6. In this version of PISCES, 4 PFTs are represented: nanophytoplankton, autotrophic silicifiers (diatoms), microzooplankton and mesozooplankton. Acronyms : TALK = total alkalinity; PAR = photosynthetically active radiations; Chl = Chlorophyll; POM = Particulate organic matter; DOM = Dissolved Organic Matter.

Figure 1

 1 Figure1.8 -Structure of a complex plankton functional type (PFT) model including overlooked trophic modes like mixotrophy and parasitism[START_REF] Flynn | Misuse of the phytoplankton-zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types[END_REF][START_REF] Worden | Rethinking the marine carbon cycle: Factoring in the multifarious lifestyles of microbes[END_REF], created during my master 2 internship to illustrate how the PFT approach could allow detailed representations of planktonic functional diversity. Here, 21 PFTs are represented in colored boxes, yellow arrows correspond to light uptake, blue arrows to nutrient uptake, black arrows to grazing and red arrows to organic matter transfers through sinking and remineralization. The phytoplankton PFTs written in yellow correspond to potential constitutive mixotrophs, i.e. phagotrophic algae. NC mixotrophs stands for non-constitutive mixotrophs, i.e. mixotrophs that acquire the ability to achieve photosynthesis through "stealing" chloroplasts to their preys or symbiosis. To my knowledge, no current biogeochemical model include that level of functional diversity, as even the[START_REF] Dutkiewicz | Dimensions of marine phytoplankton diversity[END_REF] model, which has 350 PFTs, does not include parasitic and symbiotic relationships. Abreviations: PAR = photosynthetically active radiation, Euka = eukaryotes, Proka = prokaryotic, Prod = producers, Phyto = phytoplanctonic, Zoo = zooplanktonic.

  Figure 1.9 -Unified typology of aquatic functional traits, modified from Martini et al. (Appendix A). I made an interactive mental map of this topology, available at https://github.com/EmileFaure/ AquaticFunctionalTraitsMap. The functional traits commonly used in trait-based approaches in aquatic ecology are compiled in a common typology, where they are classified by type and ecological function (as inLitchman and Klausmeier (2008)). This typology focuses on the key functional traits that transcend taxonomic peculiarities of the different aquatic ecosystems. This typology do not explicitly include two functional traits on which I focused during my PhD: mixotrophy and DMS production. I added orange stars next to traits of the typology that are associated with mixotrophy, and blue stars next to ones associated with DMS production (see main text for further explanations on these associations). The interactive mental map version of this typology is under the form of a network in which blue nodes correspond to the different ecological functions depicted as columns here, while orange nodes correspond to the different trait types depicted as lines. Each trait of the typology is then represented as a grey node, and is linked to the ecological functions and trait type nodes corresponding to its classification in the typology. On this interactive mental map, it is possible to hover the mouse over a node to highlight its connections, and a simple click on a trait node will open a bibliography search about this trait in aquatic ecology studies, while a maintained click on any node will allow you to move it around.

Fig. 4 .

 4 Fig. 4. Between-taxon variation in primary nutrient traits for freshwater species. All variables are log 10 -transformed taxon, mean trait valueand mean volumeareplotted, 6 1 SE. TheSM A fits from Fig. 1 areplotted as dotted lines. cyan, cya diat, diatoms; chlor, non-desmid chlorophytes; desm, desmids. (A) V N max , (B) K N , (C) Q N min , (D) V P max , (E) K P , (F) Q P min .

  Fig. 1. Scaling relationships between primary nutrient utilization traits and cell volume. All variables arelog 10 -transformed. M arine species are larger open circles, freshwater species are smaller filled circles. L ines plotted are from the SM A regressions in Table1. I f there is no significant difference in slopes between marine and freshwater species, as reported in Table1, a single solid line is plotted. If the slopes differ significantly, a dotted line is plotted for the marine species and a dashed line is plotted for the freshwater species. I f there is no significant correlation between the variables, no line is plotted. (A) V N max , (B) K N , (C) Q N min , (D) V P max , (E) K P , (F) Q P min .

Fig. 6 .Figure 1 .

 61 Fig. 6. Between-taxon variation in primary nutrient traits for marine species. All v mean trait value and mean volume are plotted, 6 1 SE. The SM A fits from F ig. 1 are diatoms; chlor, chlorophytes; dino, dinoflagellates; raph. raphidophytes. (A) V N max , (B)

  the mid-2000s, DNA sequencing of environmental samples became an even more common method in plankton ecology with the advent of high-throughput sequencing methods (See Box 3, Riesenfeld et al. (2004); Tringe and Rubin (2005)). HTS allowed to multiply the quantity of data sequenced per day by 500,000

Figure 1 .

 1 Figure 1.11 -(A) Sampling route and stations of the Tara Oceans and Tara Oceans Polar Circle campaigns. (B) Spatial representation and chronology of the sampling events conducted during a 24-48h station. The color code indicates the depth of the sampling event (surface water in red, deep chlorophyll layer in green, mesopelagic zone in blue, and other fixed depths in white for day deployments and grey for night deployments). The black dotted line indicates the deployment of an Argo drifter, used to follow the water mass during sampling. Figures from[START_REF] Pesant | Open science resources for the discovery and analysis of Tara Oceans data[END_REF].

Figure 1 .

 1 Figure1.12 -Use of High-Throughput Sequencing (HTS) techniques to identify or measure functional traits of aquatic organisms, fromMartini et al. (in review, Appendix A). B: Barcoding, G: Genomics, T: Transcriptomic. Sequencing can be done at the community scale (meta-omics) or at the individual scale after manual or automatic sorting. Metabarcodes are associated to taxa, through the use of taxonomic databases as PR2 for eukaryotes(Guillou et al., 2013) or SILVA for prokaryotes[START_REF] Quast | The SILVA ribosomal RNA gene database project: improved data processing and web-based tools[END_REF]. The identified taxa can then be associated with potential traits through manual trait annotation or the use of trait databases, such as Traitbank which includes traits of lineages from the whole tree of life[START_REF] Parr | TraitBank: Practical semantics for organism attribute data[END_REF], or the functional traits of marine protists database(Ramond et al., 2018) (see Table2of Martini et al. in appendix A for a thorough list of trait databases). Genes and transcripts sequenced through metagenomics and metatranscriptomics can be associated to taxa and functions through databases like eggNOG[START_REF] Huerta-Cepas | eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences[END_REF], metagenomics giving access to potential traits while metatranscriptomics allowing to measure realized traits.

Figure 4 |

 4 Figure 4 | Distribution of UCYN-A in the world's oceans. a, Abundance of UCYN-A as determined by nifH-qPCR (squares: data from ref. 7; circles: unpublished data; see Methods). b,c, Percentage of UCYN-A1(b) and UCYN-A2 (c) associated 16S rDNA sequences in amplicon-sequencing and metagenomic data sets (see Supplementary Fig. 7 and Supplementary Table2for metadata).

Figure 1 .

 1 Figure 1.14 -Global abundance of diazotrophs unveiled by omics data. (A,B) Percentage of 16SrDNA reads annotated to UCYN-A1 (A) and UCYN-A2 (B) in samples from three datasets: ICoMM, OSD and Tara Oceans (See Section 1.3.1 for quick description of these datasets). Figure from[START_REF] Martínez-Pérez | The small unicellular diazotrophic symbiont, UCYN-A, is a key player in the marine nitrogen cycle[END_REF]. (C) Maps of the sequence abundance (in % of total sample reads) of 4 diazotrophic MAGs in 61 metagenomes from the surface ocean. The two maps on the left correspond to Proteobacteria MAGs while the two maps on the right correspond to Planctomycetes MAGs. MAGs names begin with the acronym HBD, standing for heterotrophic bacterial diazotroph. Figure from[START_REF] Delmont | Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes[END_REF].

Figure 1 |Figure 1 .

 11 Figure 1 | Abundant plankton. These illustrations by Ernst Haeckel were drawn from samples collected by the oceanic Challenger expedition of 1872-76. They depict the colony shape, central capsule structure and symbiotic algae in the colonial plankton Collozoum inerme, which belongs to the supergroup Rhizaria. Biard et al. 1 and Guidi et al. 2 present analysis of data collected from the Tara Oceans expedition that reveals an unexpectedly large abundance of Rhizaria in the ocean, and implicates these organisms in the vital export of carbon from upper ocean layers to the deep ocean.

Fig. 6

 6 Fig.6Mean percentage of 18S rDNA reads affiliated to protistan taxonomic groups (classes and orders mainly) in water-column, fixedand live-trap samples across different sampling cycles. The area represents the mean percentage of reads affiliated to each protistan group (only groups with >1% mean contribution are labeled). Color codes, as in Fig.5, represent the taxonomic affiliation, mainly at the division level

Figure 1 .

 1 Figure 1.16 -The influence of Radiolaria on carbon export. Mean percentage of 18S rDNA reads affiliated to different plankton taxonomic groups in samples from the California Current Ecosystem. Samples were taken from the water column (a), from biologically fixed sediment traps (b, fixation with formaldehyde to minimize decomposition and consumption of organic matter), and from live sediment traps (c, no biological fixation). Radiolaria contributed to 12% of the total sequence number in the water column, 88% in fixed traps, and 9.6% in live traps. The increase of Radiolaria abundance in fixed traps compared to the water column samples demonstrate their high contribution to carbon export through sedimentation. The decrease of Radiolaria abundance in live traps was probably due to selective consumption by copepods, heterotrophic nanoflagellates, or phaeodarians in the traps, and supposes a rapid remineralization of organic matter associated with Radiolaria.

Fig. 5 .

 5 Fig.5. The principles of fux balance analysis (FBA) as a two-stage constraint optimization: initially, all fux values are constraint by individual upper and lower bounds. Then, assuming stationary conditions, the mass balance constraint is applied, resulting in relationships between intracellular f uxes: the feasible f ux cone. A particular solution can be identif ed by applying optimization criteria, such as the optimization of biomass yield (BOF). The optimal solution is not necessarily unique.

Fig. 5 .

 5 Fig.5. The principles of fux balance analysis (FBA) as a two-stage constraint optimization: initially, all fux values are constraint by individual upper and lower bounds. Then, assuming stationary conditions, the mass balance constraint is applied, resulting in relationships between intracellular f uxes: the feasible f ux cone. A particular solution can be identif ed by applying optimization criteria, such as the optimization of biomass yield (BOF). The optimal solution is not necessarily unique.

Fig. 5 .Fig. 6 .

 56 Fig.5. The principles of fux balance analysis (FBA) as a two-stage constraint optimization: in individual upper and lower bounds. Then, assuming stationary conditions, the mass balance c relationships between intracellular f uxes: the feasible f ux cone. A particular solution can be id such as the optimization of biomass yield (BOF). The optimal solution is not necessarily unique

Figure 1 . 17 -

 117 Figure 1.17 -Example of the metabolic network modeling process. A simple metabolic network is represented in the upper left, along with the corresponding stoichiometric matrix. The depicted metabolic pathway leads to the production of biomass through fluxes of three metabolites, P1, P2 and L, which are represented in a 3 dimensional space in the upper right. By initializing the flux of the first reaction r1 (here from G to M1) to 1.0, assuming steady state conditions and satisfying mass conservation constraint, it is possible to determine a space of solutions for the values of the P1, P2 and L fluxes (middle right graph), each leading to a value of biomass production. The biomass production can then be optimized in this space of solutions, as presented in the bottom graphs.Here, one optimal solution is presented, but most of the time multiple optimal solutions exist. In these cases, other cellular objectives can be optimized in addition to biomass, like minimizing the total amount of fluxes or the thermodynamic costs of reactions. Figure modified from[START_REF] Steuer | Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth[END_REF].

1. 4

 4 Using omics data to bridge the gap between observed and modeled diversity . Coles, 1 * M. R. Stukel, 2 M . T. Brooks, 1 A. Burd, 3 B. C. Crump, 4 M . A. M oran, 3 J . H. Paul, 5 B. M . Satinsky, 3 P. L . Y ager, 3 B. L . Zielinski, 4 R. R. H ood 1

Fig. 1 .Figure 1 .

 11 Fig. 1. Organism replacement over time in the GENOME model. The first seven rows are substrates that are constant over the model run. The next 68 rows delineate organisms by color. Color change denotes replacement of an organism.

  Figure 1.19 -Gradient of planktonic functional and taxonomic diversity implemented in biogeochemical models, with examples of key models discussed in this introduction:[START_REF] Fasham | A nitrogen-based model of plankton dynamics in the oceanic mixed layer[END_REF],[START_REF] Aumont | An ecosystem model of the global ocean including Fe, Si, P colimitations[END_REF],[START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF],Follows et al. (2007) andColes et al. (2017). NPZD stands for nutrient, phytoplankton, zooplankton and detritus models (see section1.2.1), PFT stands for plankton functional types models (see section1.2.2), while omics-based models refer to gene-centric and metabolism-based approaches, as well as to potential new theoretical frameworks involving omics data and potentially allowing for a more realistic representation of planktonic diversity in biogeochemical models (see section1.4).

Figure 2 . 1 -

 21 Figure 2.1 -Metabarcoding as an alternative to functional genomic markers for the detection of functional traits with poorly known genomic basis. Metabarcodes can be associated to taxonomic lineages through annotation databases such as PR2(Guillou et al., 2013), and the number of metabarcodes associated to each lineage is approximately proportional to its biomass[START_REF] Biard | Biogeography and diversity of Collodaria (Radiolaria) in the global ocean[END_REF], more on this in the next section). A functional annotation can then allow to link lineages with functional traits, based on the literature and the knowledge of experts. The abundance of metabarcodes associated to each trait can then be computed.

  ganism's functional trait, and of which the genomic and transcriptomic abundances can be used as a proxy of the trait presence and realization. But the detection and quantification of traits that are governed by poorly known or even unknown molecular mechanisms remains problematic (more on this issue in Chapter 3). During the first year of my thesis, I thus explored how metabarcoding could provide an alternative to functional genomic markers for the detection of traits with poorly known genomic basis, relying mostly on the manual annotation of functional traits to taxonomic lineages (Figure2.1). In particular, I focused my work on the functional trait of mixotrophy.

Bittner 2 *

 2 , Sakina-Dorothée Ayata 1,2 * . 1-Sorbonne Université, CNRS, Laboratoire d'océanographie de Villefranche, LOV, 06230 Villefranchesur-Mer, France 2-Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum national d'Histoire naturelle, CNRS, Sorbonne Université, EPHE, CP 50, 57 rue Cuvier, 75005 Paris, France 3-Sorbonne Université, CNRS, UMR7144 Adaptation and Diversity in Marine Environment (AD2M) Laboratory, Ecology of Marine Plankton team, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France 4-Genoscope, Institut de biologie François-Jacob, Commissariat à l'Energie Atomique (CEA), Evry, France, 91057 Evry, France 5-Authors contributed equally to this work Abstract: Mixotrophy, or the ability to acquire carbon from both auto-and heterotrophy, is a widespread ecological trait in marine protists. Using a metabarcoding dataset of marine plankton from the global ocean, 318,054 mixotrophic metabarcodes represented by 89,951,866 sequences and belonging to 133 taxonomic lineages were identified and classified into four mixotrophic functional types: constitutive mixotrophs (CM), generalist non-constitutive mixotrophs (GNCM),

Figure 2 .

 2 Figure 2.3 -Global distribution of mixotypes from metabarcoding data. Maps showing for each station the proportion of sequences (in %) belonging to each mixotype over the total number of mixotrophic sequences.Stations in which no sequence was found were marked as absent, ones with less than 100 sequences marked as questionable. Each Longhurst biogeographical provinces[START_REF] Longhurst | Ecological Geography of the Sea[END_REF] is colored in the background if more than 100 sequences are detected in at least one of its stations.

Figure 2 . 6 -

 26 Figure 2.6 -Contrasted global distributions of metabarcodes corresponding to two eSNCM lineages. Maps of Hellinger-transformed sequence count abundances for metabarcodes assigned to the Collodaria Siphonosphaera cyathina (a) and the Acantharia Acanthrometridae F3 spp. (b). These two lineages are opposed on the first RDA axis (Fig. 2.5 and 2.8). Size and color both illustrate abundance for better readability. Ellipses were drawn to highlight high abundance zones, and reveal the differences in lineages distribution.

ure 2 .

 2 7). Even after selection, a significant part of the metabarcodes did not show any response to environmental data in their distribution (Figure2.8 in supplementary materials). The adjusted R-squared of the RDA model was of 31.1% (versus 34.89% in the lineage level RDA), with 10.7% of variance explained by the two first axes (versus 24.01% in the lineage level RDA). The first RDA axis (5.8%) marks an opposition between samples from oligotrophic waters with low productivity (RDA1<0) and samples from eutrophic and productive water masses (RDA1>0). This axis is positively correlated to chlorophyll concentrations, carbon flux, CO3, ammonium concentration, absorption coefficient of colored dissolved organic matter (acCDOM), gradient of sea surface temperature as well as to 5 different coefficients measuring the particle density of the water. It is negatively correlated to deep nitracline, deep mixed layer, as well as to deep oxygen maximum.

Figure 2 .

 2 Figure 2.8 -Impact of environmental variables on the distribution of marine mixotrophs. Triplot of the redundancy analysis (RDA) computed on the 363 selected metabarcodes, after model selection, see Section 2.2.5 for details on the methods used. The adjusted R-squared of the analysis is of 31.1%. Each grey dot corresponds to a sample, i.e. one filter at one depth at one station. The blue dashed arrows correspond to the quantitative environmental variables. Abbreviations are as follows: MLD = mixed layer depth, O 2 MaxD = O 2 maximum depth, c_660nm = optical beam attenuation coefficient at 660 nm, c_part = beam attenuation coefficient of particles, c_470nm = optical beam attenuation coefficient at 470 nm, acCDOM = absorption coefficient of colored dissolved organic matter, SST Gradient = Sea surface temperature gradient. Plain arrows correspond to mixotrophic metabarcodes, colors indicating mixotypes. The qualitative variable indicating filter sizes is represented through orange centroids. For more readability, we did not represent all quantitative and qualitative variables included in the model.

de

  Recherche GDR3280), European Molecular Biology Laboratory (EMBL), Genoscope/CEA, VIB, Stazione Zoologica Anton Dohrn, UNIMIB, Fund for Scientific Research-Flanders, Rega Institute, KU Leuven, The French Ministry of Research. We also thank the support and commitment of Agnès b. and Etienne Bourgois, the Veolia Environment Foundation, Région Bretagne, Lorient Agglomération, World Courier, Illumina, the EDF Foundation, FRB, the Prince Albert II de Monaco

  dataset from Tara Oceans and Tara Polar circle included 187 metatranscriptomes and 370 metagenomes sampled from 126 globally distributed sampling stations. These samples were obtained from the surface (5-10m), the deep chlorophyll maximum(DCM, and the mesopelagic layer (200-1000m), and corresponded to prokaryote and virus enriched size fractions (0.22-1.6 µm and 0.22-3 µm). 9 epipelagic samples could not be classified as surface nor DCM, and were annotated as mixed layer(25-200m). Metagenomics and metatranscriptomics data were obtained through Illumina sequencing following the protocols described in[START_REF] Pesant | Open science resources for the discovery and analysis of Tara Oceans data[END_REF] and[START_REF] Bibliography Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF]. Metagenomic reads were quality-filtered, assembled, gene-coding sequences were predicted and dereplicated which led a set of 46,775,154 non-redundant genes catalog, named the OM-RGC.v2 (available at https://www.ocean-microbiome.org; detailed methods inSalazar et al. (

org.

  It corresponded to 37 variables: ID of the sample, station label, sampling layer (surface, deep chlorophyll maximum, mesopelagic or mixed layer), sample located in the polar or non polar area, upper threshold of the size fraction (1.6 or 3 µm), date of sampling, latitude, longitude, nominal depth (in meters), ocean region, temperature, oxygen, chlorophyll A, total carbon, salinity, sea surface temperature gradient, fluorescence, CO 3 , HCO 3 , water density, PO 4 , NO 3 , Si, Photosynthetically active radiation (PAR), Alkalinity, Ammonium at 5m depth, Depth of the mixed layer, Lyapunov, NO 2 , Depth of the O 2 minimum, NO 2 /NO 3 , Nitracline, depth of the maximum Brunt-Vaisala frequency (which is a proxy for the depth of the mixed layer), iron at 5 meter depth, depth of the O 2 maximum, Okubo Weiss parameter (values below/above 0 indicate that the sample is inside/outside an eddy) and water residence time.

Figure 3 . 1 -Figure 3 . 2 -Figure 3

 31323 Figure3.1 -dmdA gene versus transcript abundance. Each dot corresponds to one of the 129 samples (a sample corresponding to a station and a given depth layer, indicated by the point shape). Abundances are normalized, but the log transformation was not applied here to keep positive abundances. Dots were colored according to gene expression (reflecting the ratio between log2-transformed transcript abundance and gene abundance).

  polaris and Pyramimonas tychotreta) in different nutrient conditions allows to compare transcriptomes of algae eating through phagotrophy (i.e. low nutrients conditions) with transcriptomes of photosynthetically active ones (i.e. high nutrients conditions)[START_REF] Mckie-Krisberg | Evaluation of Mixotrophy-Associated Gene Expression in Two Species of Polar Marine Algae[END_REF].

  Figure 3.5 -Building a sequence similarity network. Pairwise values of similarity and coverage are computed on a set of sequences, here open reading frames(ORF) sequences as the focus is on proteins, represented as circles with colors coding for their taxonomy. The sequence similarity network is then built by linking together all the pairs of proteins that satisfy a certain similarity and coverage threshold. The network is composed of singletons, i.e. proteins that did not match to any other in the network with the selected thresholds, and connected components, i.e. subgraphs in which at least one path allows to directly or indirectly connect two proteins. Each protein in the network can be associated with a functional and/or taxonomic annotation, which then allows to investigate the links between similarity, function and taxonomy at the scale of the full network, or in connected components. Examples of possible annotations at the connected component level are given here for illustrative purposes.

  [START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF] study. One transcriptome of Alexandrium minutum that was extracted from Le[START_REF] Gac | Evolutionary processes and cellular functions underlying divergence in Alexandrium minutum[END_REF] was finally added to the dataset, constituting the only dinoflagellate transcriptome of our analysis that was not already included in[START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF]. Within the 798 microbial eukaryotic transcriptomes, I identified 105 transcriptomes of constitutive mixotrophs (from 23 different genera), 1 of generalist non-constitutive mixotroph, 4 of plastidic-specialist non-constitutive mixotrophs, and 3 of endo-symbiotic non-constitutive mixotrophs (full list available in Appendix B). Among the 47 dinoflagellate transcriptomes corresponding to 43 distinct species from 27 genera, 18 came from constitutive mixotrophs and 1 came from a plastidic-specialist non-constitutive mixotroph (D. acuminata). The methods described in[START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF] were then used to build a sequence similarity network (SSN) of the 47 transcriptomes: protein coding domains were detected and functionally annotated throughTransDecoder (v5.5.0, Haas et al. (2013)) and InterProScan (v5.24-63.0,Jones et al. (

  Figure 3.6). The three CCs identifed here could then constitute markers of constitutive mixotrophy at the level of this clade. But they could also be phylogenetic markers of this clade with no functional relation to mixotrophy. The 3 CCs did not contain any proteins from the 11 transcriptomes of non-mixotrophic species belonging to the Gonyaulacales/Peridiniales/Prorocentrales clade in our dataset, but they also did not include proteins from some transcriptomes of mixotrophic Peridiniales (e.g. Scrippsiella trochoidea) and Gonyaulacales (e.g. Ceratium fusus) (Table3.2, Figure 3.6). It is then hard to conclude with certainty on the potential of these CCs as markers of constitutive mixotrophy without further wet-lab explorations (as evoked in section 3.1), especially since they could not be associated with any function in reference databases.

Figure 3 . 6 -

 36 Figure3.6 -Maximum-likelihood phylogeny of dinoflagellates inferred using 1043 orthologous protein sets issued from 47 transcriptomes of dinoflagellates, modified from[START_REF] Stephens | Core genes in diverse dinoflagellate lineages include a wealth of conserved dark genes with unknown functions[END_REF]. Bootstrap support values are indicated at each split. Branch length is based on the rate of substitution per site. Colored dots were added next to every species that was represented in our sequence similarity network (strains were not taken into account except for the Symbiodinium genus, in which species could not be attributed). Red dots indicate plastidic-specialist non-constitutive mixotrophs (pSNCM), green dots indicate constitutive mixotrophs (CM) and blue dots indicate species for which no proofs of mixotrophy were found in the literature.

  at a fine scale (i.e. including species sharing similar mixotrophic behaviours, and exposing them 96 3.4 Next challenge: linking unknown functions and uncultivated organisms to functional traits to similar sets of conditions). The goal should be to produce quantitative data that would allow to sort connected components/transcript families based on expression level, implying the need for replicates (this analysis showed the impact of sequencing depth) in different conditions favoring certain trophic modes. Studies are already showing the way by sequencing transcriptomes of mixotrophic species along gradients of lights and nutrients, giving us access to transcriptomes of mixotrophs with varying levels of photosynthetic and phagotrophic capacity (McKie-Krisberg

Figure 4 . 1 -

 41 Figure4.1 -Analyzing the biogeography of functionally homogeneous protein clusters obtained from metagenome-assembled genomes. Nearly 2 millions of proteins from 885 prokaryotic MAGs are assembled into a sequence similarity network, i.e. a graph in which nodes are proteins and links reflect the similarity and coverage between each pair of proteins. In this toy example, the sequence similarity network is composed of 4 connected components, or groups of nodes connected together directly or indirectly, and disconnected form the rest of the network. Each connected component is defined as a protein functional cluster, and examples of taxonomic and functional annotations are indicated to illustrate the kind of information that can be retrieved from the approach. The abundance of each protein functional cluster can be computed in environmental metagenomes, through the mapping of environmental reads to each protein. These abundances can finally be statistically related to the environmental context.

Figure 4

 4 Figure 4.2 -(A) Boxplot distribution of R 2 values of all the 228,914 random forest models. Each point corresponds to a model, the y axis corresponding to the R 2 values, and the x axis position being randomized for visual representation. The mean R 2 value over all models was of 0.09, with a maximum of 0.91. (B) Rank of importance of each environmental variable in models with positive R 2 values. Ranks were attributed from 1 for the most important to 51 for the least important variable in each model.

Figure 4 .

 4 Figure 4.3 -Canonical correspondence analysis (CCA) on abundances of the 2,444 protein functional clusters highly linked to environmental variables (hlePFCs). hlePFCs are represented as black dots, quantitative environmental variables as arrows, and samples as circles colored according to their biogeographical province (correspondence between 4 letters codes used here and full biogeographical provinces names, as well as descriptions of all other environmental variables are displayed in TableS2, available in Appendix C). For simplification issues, other qualitative variables (Season moment and Ocean region) were not represented. On the right and upper panels, density plots are represented along each axis, illustrating the density of functionally annotated and unannotated hlePFCs based on KEGG annotations ( i.e. functionally annotated hlePFCs contain at least one functionally annotated protein; functionally unannotated hlePFCs contain only functionally unannotated proteins). The mean hlePFC density was of 0.27 along CCA1 (standard deviation = 0.34, maximum = 1.38), and 0.21 along CCA2 (standard deviation = 0.23, maximum = 0.94). The mean difference in density between functionally annotated and unannotated hlePFCs along CCA1 was of 0.06 (standard deviation = 0.07, maximum = 0.28). The mean density difference between annotated and unannotated hlePFCs along CCA2 was 0.04 (standard deviation = 0.03, maximum = 0.13). Similar observations were done using eggNOG annotations densities (Figure4.4).

  zones on the left, Pacific Ocean in the middle and Indian Ocean on the right (Figure 4.3). The two closest samples from the Mediterranean ones in the CCA space were from the closest Atlantic station to the strait of Gibraltar (station TARA_004) at the entrance of the Mediterranean Sea, and the second closest Red Sea station to the Suez canal mouth (station TARA_032) (Figure 4.3). Combining the CCA results with the functional annotation of hlePFCs, we identified several metabolic pathways enriched in particular environmental conditions (Figure 4.5). Carotenoid biosynthesis was enriched in the Mediterranean Sea (CCA2>0), as well as flagellar assembly and oxidative phosphorylation pathways, while mismatch repair and DNA replication pathways were enriched in nutrient rich, cold waters (Figure 4.5; Figure 4.7). Pathways related to biogeochemical functions (e.g. carbon fixation pathways in prokaryotes or methane metabolism) or linked to ecological interactions between organisms (e.g. biosynthesis of antibiotics, quorum sensing or ABC 4.2 Towards omics-based predictions of planktonic functional composition from environmental data

Figure 4

 4 Figure 4.4 -Density plots illustrating the proportions of functional known and unknown hlePFCs along CCA1 and CCA2 axes, based on eggNOG annotations ( i.e. a known hlePFC corresponds to a protein cluster in which at least one sequence has an eggNOG assignment; an unknown hlePFC corresponds to a protein cluster without any eggNOG assignment). The mean hlePFC density was of 0.26 along CCA1 (standard deviation = 0.32, maximum = 1.27), and 0.21 along CCA2 (standard deviation = 0.21, maximum = 0.82). The mean difference in density between functional known and unknown hlePFCs along CCA1 was of -0.0006 (standard deviation = 0.1, maximum = 0.34). The mean density difference between known and unknown hlePFCs along CCA2 was 0.03 (standard deviation = 0.1, maximum = 0.33).

Figure 4 . 5 -

 45 Figure 4.5 -Barycenters of the positions of protein functional clusters highly linked to the environment (hlePFCs) associated to 6 selected metabolic pathways and to microbial dark matter in the canonical correspondence analysis (CCA) space. These barycenters were selected for their peripheral positions in the CCA space. Pathways with barycenters in shades of pink were related to cold rich waters only, while those with barycenters in shades of orange were mostly associated to Mediterranean samples. The barycenter of microbial dark matter hlePFCs ( i.e. hlePFCs without functional annotation and taxonomical assignment below the phylum level) was represented in black, and was strongly associated to Mediterranean samples. Error bars correspond to the standard deviations of hlePFCs positions on x and y axes. Size of barycenters represent their number of associated hlePFCs, with the exact corresponding values written in white in each barycenter. Colored arrows indicate the environmental conditions associated with the different zones of the CCA space (See Figure 4.3).

Figure 4 . 6 -

 46 Figure 4.6 -Distribution in the canonical correspondence analysis (CCA) space of the barycenters of protein functional clusters highly linked to the environment (hlePFCs) associated to particular taxa: (A) 4 selected phylum and (B) 6 selected classes. These taxa were selected because they had the most peripheral barycenters' positions in the CCA space. Error bars correspond to the standard deviations of hlePFCs positions on CCA1 and CCA2 axes for each taxa. The size of barycenters represents the number of associated hlePFCs for each taxa, with the exact corresponding values written in white in each barycenter. Colored arrows indicate the environmental conditions associated with the different zones of the CCA space (c.f. Figure 4.3).

Figure 4

 4 Figure 4.7 -Convex hulls englobing all hlePFCs associated to different pathways in the CCA twodimensional space. On each graph, black dots represent hlePFCs that are not associated to the focal pathway, while yellow dots represent hlePFCs containing at least one sequence associated to the focal pathway. Convex hulls were drawn in different colors depending on the type of pathway.Pathways associated to cold and rich waters are included in blue convex hulls, while the ones associated to mediterranean samples are in red convex hulls. We also selected three pathways linked to inter-organisms interactions, in orange hulls, and three pathways related to biogeochemical functions in green hulls. Finally, two black convex hulls englobing all functional unknown PFCs were represented, one for KEGG annotations and another for eggNOG ones.

Figure 4

 4 Figure 4.8 -Barycenters of the protein functional clusters highly linked to environmental gradients (hlePFCs) from 5 selected assemblies in the canonical correspondence analysis (CCA) space. These assemblies were selected because they had the most peripheral barycenters' positions in the CCA space. Error bars correspond to the standard deviations of hlePFC positions on CCA1 and CCA2 axis for each assembly. Size of barycenters represent the number of associated hlePFCs for each assembly, with the exact corresponding values written in white in each barycenter, except for the Red Sea assembly for which it is written in red next to the barycenter. Colored arrows indicate the environmental conditions associated with the different zones of the CCA space (See Figure 4.3).

  Figure 4.6). Finally, convex hulls englobing all PFCs associated to a pathway were drawn for a selection of pathways corresponding to (1) pathways enriched in cold and eutrophic waters, (2) pathways enriched in Mediterranean samples, (3) pathways linked to inter-organisms interactions, (4) pathways associated to biogeochemical functions, and (5) pathways composed of only unknown sequences (Figure 4.7).

Figure 5 . 1 -

 51 Figure 5.1 -Towards trait-centred annotation algorithms: computational workflow of the DRAM (Distilled and Refined Annotation of Metabolism) algorithm, extracted from[START_REF] Shaffer | DRAM for distilling microbial metabolism to automate the curation of microbiome function[END_REF]. The user is asked to provide FASTA files of assembly-derived contigs, either binned into genome-resolved data ( e.g. MAGs or isolate genomes) or unbinned ( e.g. metagenome contigs). The user can also input taxonomic annotations and genome or MAGs completion files into DRAM, which will then be included in output files. Prodigal[START_REF] Hyatt | Prodigal: prokaryotic gene recognition and translation initiation site identification[END_REF] is used to call genes in the input file(s), before 5 databases are used to annotate them. Users can also add any annotation database of their choice. In parallel, tRNA are detected using tRNAscan-SE[START_REF] Lowe | tRNAscan-SE On-line: integrating search and context for analysis of transfer RNA genes[END_REF], as well as 5S, 16S and 23S rRNA fragments using barrnap (https://github.com/tseemann/barrnap). All these data are then compiled into three files: the 'raw' file corresponds to the 'classic' output one would obtain from an annotation software, the 'distillate' file contains pathway-centric annotation and genome statistics, and the 'product' file is an html document indicating metabolic pathway coverage, electron transport chain component completion and presence/absence of specific functions associated to functional traits ( e.g. methanogenesis).

Figure 5 . 2 -

 52 Figure 5.2 -Computational workflow of AGNOSTOS, a tool for shedding light on the dark side of omics, from[START_REF] Vanni | Light into the darkness: Unifying the known and unknown coding sequence space in microbiome analyses[END_REF]. The idea is to bridge the known coding sequences with unknown coding sequences using sequence composition, domain architecture and remote homology ( i.e. indirect homology) to build a database (DB) of gene clusters (GC). The clusters are then complemented by meta-data such as levels of expression in metatranscriptomes, abundance in metagenomes, phylogenetic origin or environmental range to diffuse (or not) functional and/or taxonomic annotations from known sequences to unknown sequences in each cluster. Overall, the pipeline uses as many information as we have on the known coding sequence space to draw information on the unknown coding sequence space.

  134

5. 2

 2 How to bridge the gap between observations and biogeochemical models ?

Figure 5 .

 5 Figure 5.4 summarizes what metabolic models, i.e. models based on genome-scale metabolic reconstruction, including flux-balance analysis and resource allocation models, can bring to such classic trade-based approaches. But as stated in the introduction, such models are heavily limited by data availability. For example, the biochemical resource allocation model approach presented by Sharma and Steuer (2019) needs not only the list and nucleotidic composition of each catalyzing

Figure 5 . 5 -

 55 Figure 5.5 -Co-occurrence networks to decipher ecological interactions in microbial populations, extracted from[START_REF] Faust | Microbial interactions: from networks to models[END_REF]. (a) Co-occurence networks are built from presence/absence or abundance matrices, with biological entities in rows, and samples in columns. Adding environmental factors in rows along with biological identities allows to better distinguish in the network if interactions are due to similar responses to environmental gradients, or to 'true' direct or indirect ecological interactions. (b) From the incidence or abundance matrix, it is possible to base the construction of the network on a matrix of similarity or dissimilarity, but the use of multiple regressions is preferable as it allows to directly detect associations between more than two biological entities at the time. Cross-validated penalized regressions (similar to the ones conducted on dmdA data in Chapter 3) are often preferred to classic regressions to avoid over-fitting. Species with the highest co-occurrence are given the highest score. (c) The scoring step is repeated multiple times on randomized data, to obtain a Gaussian of score distribution. It allows to compute for each relationship the probability of obtaining a score equal or higher to the observed one by chance, or p-value. (d) Pairs of biological entities with p-value inferior to a selected threshold are drawn as links to form the co-occurrence network. Link width and color can reflect the strength and sign of the relationship.

  142

5. 2

 2 How to bridge the gap between observations and biogeochemical models ?

Figure 5 .

 5 Figure 5.7 -Linking environmental context to biogeochemical (BGC) outputs through gene predictions. In-situ meta-omics and environmental data allow to link the environmental context with genomic and/or transcriptomic abundance. Here I propose to treat these abundance at the functional gene cluster level but it could also be possible to use non-clustered sequence abundance.Wet lab experiments allow to link genomic and transcriptomic abundances to biogeochemical fluxes (BGC output). The dotted arrow indicates the potential use of genomic and transcriptomic abundance of some functional gene clusters to predict the one of other related clusters in the case of clusters with significantly correlated or anti-correlated abundances. This could improve predictions while including some kind of ecological interactions in the model ( e.g. gene clusters that come fromantagonistic species should be anti-correlated in co-occurrence networks, and would not be predicted in high proportions in the same samples). The environmental context could be obtained from a general circulation model ( cf figure5.3), and the biogeochemical outputs could in turn influence elemental fluxes in the model, creating a correlative biogeochemical model. Illustrations were taken from[START_REF] Guadagno | Use of transcriptomic data to inform biophysical models via Bayesian networks[END_REF],[START_REF] Guidi | Plankton networks driving carbon export in the oligotrophic ocean[END_REF] and[START_REF] Steuer | Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth[END_REF].

  Figure A.1, but it also provides a dynamic visual representation. It can serve as a pedagogical tool for teaching purposes and as a basis to identify trade-offs between related traits. Further work could initiate a globally shared ontology for aquatic traits, for instance as part of Open Biological and Biomedical Ontology (OBO) Foundry (http://obofoundry.org/).

Figure A. 2 -

 2 Figure A.2 -Main methods to study traits. A: Use of empirical studies to measure realized traits (in laboratory or in situ) or to estimate potential traits from the literature. The abundance frequency the modalities a given trait can be used to code trait profile by taxon using fuzzy coding and thereby inform trait databases. B: Use of imaging and acoustic techniques to identify or measure functional traits of aquatic organisms from sampling, images/sound recording, features extraction to databases (ZooVis picture has been kindly provided by H. Bi). C: Use of sequencing techniques to identify or measure functional traits. Sequencing can be done at the community scale (meta-omics) or at the individual scale after manual or automatic sorting. (Meta)B: (Meta)Barcoding, (Meta)G: (Meta)Genomics, (Meta)T: (Meta)Transcriptomic.

KNOWN KNOWN UNKNOWN UNKNOWN Ecosystem type

	GBE
	; Rinke et al. (2013); Vanni et al. (2020); Figure 1.13). The
	dark side of omics is present in all types of ecosystems, but is particularly significant in aquatic metagenomes, where the share of sequences of unknown function and taxonomy can reach up to 60% (Figure 1.13). I will now present some examples of how the func-tional characterization of unknown genes and lineages unveiled by meta-omics data led to discoveries of key planktonic actors in global biogeochemical cycles. Aquatic Air Bioremediation Biotransformation Other Host Soil Sludge waste Waste water Figure 1.13 -Microbial dark matter across metagenomes from different ecosystem types. Genes inferred from the reads of 339 environmental metagenomes	Downloaded from https://academic.oup.com/gbe/article-abstract/10/3/707/4840377 by guest on 24 Februar

Table 2

 2 for metadata).

				acific cean				tlantic cean	Indian Oce		outhern cea		Red e	editerranea ea
	umulative distributio	o nine HBD	. . . .		3 m o											,000 cell er tre
				P	P	N	N	ANE	AW	E	I	I				E	ED
		vera e:	.2	.	. 7	. 7	. 5	.	.	.	.	7	.	.	7	.	5
								6			1
		HBD-06							HBD-08
		Phylum Proteobacteria				0.0% 0.1% 0.2% 0.3%	Phylum Planctomycetes				0% 0.02% 0.04% 0.06%
	4	HBD-07 Phylum Proteobacteria			0%	0.01% 0.02% 0.03% 0.05%	NATURE MICROBIOLOGY | VOL 1| NOVEMBER 201 6 | www.nature.com/ naturemicrobiology HBD-09 0% 0.04% 0.08% 0.12% 0.16% Phylum Planctomycetes
						©201 6 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
	metagenomic sequencing data to investigate the diversity of	primer targeting the nifH gene 39,49,50 . Although these findings
	nifH genes without primer bias, which led to the identifica-	substantiate the previous observations madethrough PCR ampli-
	tion of a mismatch in nifH4, a widely used degenerate PCR	con surveys regarding the diversity of HBDs in the surface of the

1.3.2.3 Rehabilitating overlooked groups of planktonic organisms

	AV I D A . CA R O N
	o you know the name a n d e v o l u t i o n a r y affiliation of any of the ost conspicuous groups of ingle-celled organisms in the orld's oceans? Did you guess he Rhizaria, or one of the ore fami liar groups of plank-on that make up this super-roup, such as the Radiolaria, cantharia or Foraminifera? If ou didn't, you're not alone -ntil recently, neither did the vast ajority of biological oceano-raphers. Biard et al. 1 report on age 504 of this issue that the bundance and biomass of these nigmatic species in the ocean re much greater than previously ecognized. In addition, Guidi t al. 2 (page 465) reveal the extent f the Rhizaria's involvement in he export of carbon from the tmosphere to the ocean depths.

Sphaerozoum punctatum Collozoum inerme Collodaria Siphonosphaera cyathina Ornithocercus spp. Chrysophyceae clade G spp Gonyaulax polygramma Fragilidium mexicanum Alexandrium tamarense

  concentration, as well as longitude. It is positively correlated to bathymetry, deep euphotic zone, deep oxygen maximum, deep mixed layer, as well as to the distance to coast. The second RDA axis (9.05%) is opposing offshore and subpolar samples (RDA2 > 0) to coastal and subtropical ones (RDA2 < 0). The axis is positively correlated to the depth of the mixed layer, the dis-tance to coast, the bathymetry, high maximum Lyapunov exponents as well as high concentrations of PO4, oxygen, CO3 and silica. It is negatively correlated to temperature, salinity, and photosynthetically active radiations (PAR).

		0.5
					MLD Bathymetry
	RDA2 (9.05%)	EuphZoneD O 2 MaxD Distance to coast Temperature Lyapunov PAR HCO3 Oxygen CO3 acCDOM PO4 c_660 c_part Si ChlorophyllA Longitude Okubo Weiss Calcite Sat.St. Day length NH4 + Chrysochromulina acantha P. simplicidens Mesodinium rubrum Acanthometridae F3 Acantharea Other Chrysophyceae clade H spp. 0.0 Karlodinium micrum Amphibelone spp. Acantharea & Spumellaria
					Salinity
			Mixotype
			CM
			eSNCM
		-0.5	GNCM pSNCM
			Filter size (μm)
			0.8-5
			5-20
			20-180
			180-2000
			-0.50	-0.25	0.00	0.25
					RDA1 (14.96%)
					O2MaxD O2
	maximum depth, EuphzoneD euphotic zone depth, PAR photo-synthetically active radiations, Cal-
	cite Sat. St. Calcite Saturation State, c_660 optical beam attenuation coefficient at 660 nm, c_part
	beam attenuation coefficient of particles, acCDOM absorption coefficient of colored dissolved organic
	matter. Plain arrows correspond to mixo-trophic lineages, colors indicating mixotypes. For more
	readability, we do not represent all qualitative variables included in the model. That is why only the
	filter centroids are appearing, even though the sampling depth, season, season moment, i.e., early,
	middle or late, and bio-geographical province were used in the RDA calculation

Figure 2.5 -Impact of environmental variables on the distribution of marine mixotrophs. Triplot of the redundancy analysis (RDA) computed on the 62 Escoufier-selected lineages, after model selection.

The adjusted R-squared of the analysis is of 34.

89% (41.43% unadjusted)

. Each gray dot corresponds to a sample, i.e., one filter at one depth at one station. The blue dashed arrows correspond to the quantitative envir-onmental variables. Abbreviations: MLD mixed layer depth,

Table 2 .

 2 1), but the two most abundant species, Mesodinium rubrum (pSNCM) and Pseudotontonia simplicidens (GNCM), were found mostly in coastal and eutrophic waters, consistently withLeles et al.'s 2017 morphological observations (Fig. 2.5, Section 2.2.5).

  2.2 Mixotrophic protists display contrasted biogeographies in the global ocean and Collodaria to dominate different ecosystems (Figs. 2.5 and 2.6). Moreover, living in colonies as Collodaria could help to dominate oligotrophic systems, e.g., by accumulating more food and nutrients through their gelatinous extra-cellular matrix

Table 3 .

 3 1 -Metrics of the dinoflagellates transcriptomes sequence similarity network. CC stands for connected component, CM for constitutive mixotroph.

		Number of CCs (%)	Proteins in CCs (%)
	Total CCs	304,026 (100%)	2,172,138 (100%)
	CCs with at least one sequence from a CM	143,676 (47.2%)	803,736 (36.9%)
	CCs with at least one sequence from a pSNCM	15,212 (5.0%)	33,346 (3.1%)
	CCs with at least one sequence
	from A. minutum (Le Gac et al.,	46,473 (15.3%)	107,672 (4.9%)
	2016)
	CCs with at least one sequence from the MMETSP A. minutum	6,357 (2.1%)	10,190 (0.5%)
	CCs only composed of sequences from CMs	56,791 (18.7%)	153,340 (7%)
	CCs only composed of sequences from mixotrophs	60,864 (20%)	163,310 (7.5%)
	the SSN built in Meng et al. (2018) (without the A. minutum transcriptome from Le Gac et al.
	(2016)) was composed of 2,790,387 proteins including 1,514,476 singletons (54.3%) and 350,267

  1,2 * , Lucie Bittner 2 * .

	1-Sorbonne Université, CNRS, Laboratoire d'océanographie de Villefranche, LOV, 06230 Villefranche-
	sur-Mer, France
	2-Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum national d'Histoire naturelle,
	CNRS, Sorbonne Université, EPHE, CP 50, 57 rue Cuvier, 75005 Paris, France
	* Authors contributed equally

Table A . 1 -

 A1 Main definitions related to traits in aquatic trait-based studies.

  even in multiple stressor scenarios. Combined with statistical modeling, traits also allow deriving stressor-specific models to assess environmental quality (e.g. focusing on invertebrates inhabiting large rivers, Desrosiers et al. 2019). M. J., Jeong, H. J., Lee, K. H., Jang, S. H., Kim, J. H., & Kim, K. Y. (2015). Mixotrophy in the nematocyst-taeniocyst complex-bearing phototrophic dinoflagellate Polykrikos hartmannii. Harmful Algae, 49, 124-134. Du Yoo, Y., Park, J. Y., Song, J. Y., Kim, S. T., Lee, S. H., ... & Yih, W. H. (2005). Lee, K. H., Du Yoo, Y., Kang, N. S., Song, J. Y., Kim, T. H., ... & Potvin, E. (2018). Effects of light intensity, temperature, and salinity on the growth and ingestion rates of the red-tide mixotrophic dinoflagellate Paragymnodinium shiwhaense. Harmful Algae, 80, 46-54. H. J., Du Yoo, Y., Park, J. Y., Song, J. Y., Kim, S. T., Lee, S. H., ... & Yih, W. H. (2005). Feeding by phototrophic red-tide dinoflagellates: five species newly revealed and six species previously known to be mixotrophic. Aquatic microbial ecology, 40(2), 133-150. Grazing by Karenia brevis on Synechococcus enhances its growth rate and may help to sustain blooms. Aquatic Microbial Ecology, 55(1), 17-30.

	Supergroup	Phylum	Class	Genus	Species	NCBI ID	ENA ID	Mixotrophy Mixotype Reference
										Legrand, C., & Carlsson, P. (1998). Uptake of high
										molecular weight dextran by the dinoflagellate
										Alexandrium catenella. Aquatic microbial ecology,
	Alveolata	Dinoflagellata	Dinophyceae	Alexandrium	catenella	2925 SRR1296704	Yes	CM	16(1), 81-86.
	Alveolata Lee, Alveolata Dinoflagellata Dinophyceae Alexandrium monilatum 311494 SRR1296897 Yes CM Dinoflagellata Dinophyceae Alexandrium monilatum 311494 SRR1296895 Yes CM
	Alveolata	Dinoflagellata	Dinophyceae	Alexandrium	monilatum	311494 SRR1296896	Yes	CM
	Alveolata	Dinoflagellata	Dinophyceae	Alexandrium	monilatum	311494 SRR1296898	Yes	CM
	Alveolata Alveolata Alveolata	Dinoflagellata Dinoflagellata Dinoflagellata	Dinophyceae Dinophyceae Dinophyceae Filosa-	Alexandrium Alexandrium Alexandrium	temarense temarense temarense	2926 SRR1300222 2926 SRR1300221 2926 SRR1296766	Yes Yes Yes	CM CM CM	Jeong, H. J., Feeding by phototrophic red-tide dinoflagellates:
	five species newly revealed and six species previously known to be mixotrophic. Aquatic Jeong, H. J., Alveolata Alveolata Dinoflagellata Dinophyceae Alexandrium temarense 2926 SRR1296765 Yes Rhizaria Cercozoa Chlorarachnea Bigelowiella natans 227086 SRR1300359 Yes CM CM Alveolata Dinoflagellata Dinophyceae Amphidinium carterae 2961 SRR1294392 Yes CM Filosa-Dinoflagellata Dinophyceae Amphidinium carterae 2961 SRR1294391 Yes Rhizaria Cercozoa Chlorarachnea Bigelowiella natans 227086 SRR1300357 Yes CM CM Alveolata Dinoflagellata Dinophyceae Amphidinium carterae 2961 SRR1296757 Yes Filosa-CM Alveolata Dinoflagellata Dinophyceae Amphidinium carterae 2961 SRR1296758 Yes Rhizaria Cercozoa Chlorarachnea Bigelowiella natans 227086 SRR1296871 Yes CM CM Alveolata Dinoflagellata Dinophyceae Amphidinium carterae 2961 SRR1294393 Yes Filosa-CM Alveolata Dinoflagellata Dinophyceae Amphidinium carterae 2961 SRR1294394 Yes Rhizaria Cercozoa Chlorarachnea Bigelowiella natans 227086 SRR1300358 Yes CM CM Alveolata Dinoflagellata Dinophyceae Amphidinium massartii 160604 SRR1296892 Yes Filosa-Meng, A., Corre, E., Probert, I., Gutierrez-Rhizaria Cercozoa Chlorarachnea Bigelowiella natans 227086 Yes CM Rodriguez, A., Siano, R., Annamale, A., ... & Not, Stramenopiles Ochrophyta Chrysophyceae Dinobryon sp 98059 SRR1296865 Yes CM Kamjunke, N., Henrichs, T., & Gaedke, U. (2006). F. (2018). Analysis of the genomic basis of Phosphorus gain by bacterivory promotes the Stramenopiles Ochrophyta Chrysophyceae Dinobryon sp 98059 SRR1294384 Yes CM functional diversity in dinoflagellates using a mixotrophic flagellate Dinobryon spp. during re-transcriptome-based sequence similarity network. oligotrophication. Journal of plankton research, Molecular ecology, 27(10), 2365-2380. 29(1), 39-46. Stramenopiles Ochrophyta Chrysophyceae Dinobryon sp 98059 SRR1296864 Yes CM CM Alveolata Dinoflagellata Dinophyceae Ceratium fusus 2916 SRR1300300 Yes CM Stramenopiles Ochrophyta Chrysophyceae Ochromonas sp 2985 SRR1300383 Yes CM Maranger, R., Bird, D. F., & Price, N. M. (1998). Baek, S. H., Shimode, S., & Kikuchi, T. (2007). Iron acquisition by photosynthetic marine Stramenopiles Ochrophyta Chrysophyceae Ochromonas sp 2985 SRR1300317 Yes CM Reproductive ecology of the dominant phytoplankton from ingested bacteria. Nature, Stramenopiles Ochrophyta Chrysophyceae Ochromonas sp 420556 SRR1296863 Yes CM dinoflagellate, Ceratium fusus, in coastal area of Sagami Bay, Japan. Journal of Oceanography, 396(6708), 248. Stramenopiles Ochrophyta Chrysophyceae Ochromonas sp 420556 SRR1296767 Yes CM
	Alveolata Alveolata Stramenopiles Stramenopiles Stramenopiles Stramenopiles	Dinoflagellata Dinoflagellata Ochrophyta Ochrophyta Ochrophyta Ochrophyta	Dinophyceae Dinophyceae Raphidophyceae Raphidophyceae Raphidophyceae Raphidophyceae	Ceratium Gonyaulax Chattonella Chattonella Chattonella Chattonella	fusus spinifera subsalsa subsalsa subsalsa subsalsa	2916 SRR1300301 66791 SRR1300518 44440 SRR1300238 44440 SRR1300239 44440 SRR1300240 44440 SRR1300237	Yes Yes Yes Yes Yes Yes	CM CM CM CM CM CM	63(1), 35-45. Stoecker, D. K. (1999). Mixotrophy among Dinoflagellates 1. Journal of Eukaryotic Microbiology, 46(4), 397-401. Jeong, H. J. (2011). Mixotrophy in Red Tide Algae Raphidophytes 1. Journal of Eukaryotic Microbiology, 58(3), 215-222.
	Alveolata Stramenopiles Stramenopiles Stramenopiles Jeong, Alveolata Dinoflagellata Dinophyceae Gymnodinium catenatum 39447 SRR1296705 Yes CM Ochrophyta Raphidophyceae Fibrocapsa japonica 94617 SRR1300377 Yes CM Jeong, H. J. (2011). Mixotrophy in Red Tide Algae Raphidophytes 1. Journal of Eukaryotic Microbiology, 58(3), 215-222. Ochrophyta Raphidophyceae Heterosigma akashiwo 2829 SRR1296775 Yes CM Ochrophyta Raphidophyceae Heterosigma akashiwo 2829 SRR1296777 Yes CM Dinoflagellata Dinophyceae Heterocapsa rotundata 89963 SRR1296810 Yes CM Stramenopiles Ochrophyta Raphidophyceae Heterosigma akashiwo 2829 SRR1296797 Yes CM Jeong, H. J. (2011). Mixotrophy in Red Tide Algae Stramenopiles Ochrophyta Raphidophyceae Heterosigma akashiwo 2829 SRR1296798 Yes CM Raphidophytes 1. Journal of Eukaryotic Stramenopiles Ochrophyta Raphidophyceae Heterosigma akashiwo 2829 SRR1296776 Yes CM Microbiology, 58(3), 215-222. Alveolata Dinoflagellata Dinophyceae Heterocapsa triquetra 66468 SRR1296978 Yes CM Legrand, C., Graneli, E., & Carlsson, P. (1998). Induced phagotrophy in the photosynthetic dinoflagellate Heterocapsa triquetra. Aquatic Stramenopiles Ochrophyta Raphidophyceae Heterosigma akashiwo 2829 SRR1296915 Yes CM Jeong, H. J. (2011). Mixotrophy in Red Tide Algae Stramenopiles Ochrophyta Raphidophyceae Heterosigma akashiwo 536046 SRR1296856 Yes CM Raphidophytes 1. Journal of Eukaryotic Stramenopiles Ochrophyta Raphidophyceae Heterosigma akashiwo 2829 SRR1296916 Yes CM Microbiology, 58(3), 215-222. Stramenopiles Ochrophyta Raphidophyceae Heterosigma akashiwo 536046 SRR1296857 Yes CM Microbial Ecology, 15(1), 65-75. Alveolata Dinoflagellata Dinophyceae Karenia brevis 156230 SRR1296744 Yes Stramenopiles Ochrophyta Raphidophyceae Heterosigma akashiwo 536046 SRR1296858 Yes CM CM Alveolata Dinoflagellata Dinophyceae Karenia brevis 156230 SRR1296952 Yes Stramenopiles Ochrophyta Raphidophyceae Heterosigma akashiwo 2829 SRR1296914 Yes CM CM Alveolata Dinoflagellata Dinophyceae Karenia brevis 156230 SRR1296851 Yes Stramenopiles Ochrophyta Raphidophyceae Heterosigma akashiwo 2829 SRR1296913 Yes CM CM Alveolata Dinoflagellata Dinophyceae Karenia brevis 156230 SRR1296748 Yes Stramenopiles Ochrophyta Raphidophyceae Heterosigma akashiwo 2829 SRR1296799 Yes CM CM Alveolata Dinoflagellata Dinophyceae Karenia brevis 156230 SRR1296749 Yes CM Stramenopiles Ochrophyta Raphidophyceae Heterosigma akashiwo 536046 SRR1296859 Yes CM
	Alveolata	Dinoflagellata	Dinophyceae	Karenia	brevis	156230 SRR1296750	Yes	CM
	Alveolata	Dinoflagellata	Dinophyceae	Karenia	brevis	156230 SRR1296852	Yes	CM
	Alveolata	Dinoflagellata	Dinophyceae	Karenia	brevis	156230 SRR1296743	Yes	CM
	Alveolata	Dinoflagellata	Dinophyceae	Karenia	brevis	156230 SRR1296853	Yes	CM
	Alveolata	Dinoflagellata	Dinophyceae	Karenia	brevis	156230 SRR1296854	Yes	CM
	Alveolata	Dinoflagellata	Dinophyceae	Karenia	brevis	156230 SRR1296714	Yes	CM
	Alveolata	Dinoflagellata	Dinophyceae	Karenia	brevis	156230 SRR1296712	Yes	CM
	Alveolata Calbet, A.Alveolata Dinoflagellata Dinophyceae Karlodinium micrum 407301 SRR1300327 Yes CM Dinoflagellata Dinophyceae Karlodinium micrum 407301 SRR1300325 Yes CM
	Alveolata	Dinoflagellata	Dinophyceae	Karlodinium	micrum	407301 SRR1300326	Yes	CM

Glibert, P. M., Burkholder, J. M., Kana, T. M., Alexander, J.,

Skelton, H., & Shilling, C. (2009)

. , Bertos, M., Fuentes-Grünewald, C., Alacid, E., Figueroa, R., Renom, B., & Garcés, E. (

2011

). Intraspecific variability in Karlodinium veneficum: growth rates, mixotrophy, and lipid

Table S1

 S1 Appendix C. Supplementary tables[START_REF] Faure | Biogeographical province Qualitative [ARAB] Northwest Arabian Sea Upwelling Province (MRGID: 21475) Biogeographical province of the sampling station, sensu Longhurst. The 4 letter code between brackets were used on Figure 3[END_REF] 250 Appendix C. Supplementary tables[START_REF] Faure | Biogeographical province Qualitative [ARAB] Northwest Arabian Sea Upwelling Province (MRGID: 21475) Biogeographical province of the sampling station, sensu Longhurst. The 4 letter code between brackets were used on Figure 3[END_REF]

	Histidine metabolism Inositol phosphate metabolism Insulin signaling pathway Calcium signaling pathway mRNA surveillance pathway Thyroid hormone signaling pathway		936 318 112 3 32 1		2.02991452991453 1.57232704402516 1.28205128205128 0.892857142857143 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Pathway description Isoquinoline alkaloid biosynthesis Synthesis and degradation of ketone bodies Pathways in cancer Sphingolipid metabolism cAMP signaling pathway mTOR signaling pathway Tight junction	Total occurrences 99 510 555 345 9 10 21	Occurrences	Percentage of occurrences 2.02020202020202 1.56862745098039 1.26126126126126 0.869565217391304 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Carbon fixation pathways in prokaryotes HIF-1 signaling pathway Tropane, piperidine and pyridine alkaloid biosynthesis Glutathione metabolism Carbohydrate digestion and absorption Naphthalene degradation Toxoplasmosis		2092 511 238 1160 5 25 31	among hlePFCs	among hlePFCs 2.00764818355641 1.56555772994129 1.26050420168067 0.862068965517241 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Biosynthesis of vancomycin group antibiotics Pentose phosphate pathway Quorum sensing Insulin resistance Ferroptosis Cell cycle Neomycin, kanamycin and gentamicin biosynthesis Vibrio cholerae infection		43 1358 3451 159 119 12 20 8		4.65116279069767 1.98821796759941 1.56476383656911 1.25786163522013 0.840336134453782 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Antigen processing and presentation Geraniol degradation Phenylalanine, tyrosine and tryptophan biosynthesis Fluorobenzoate degradation Meiosis -yeast Cholesterol metabolism Neuroactive ligand-receptor interaction Viral myocarditis		66 458 1866 160 126 18 18 31		4.54545454545455 1.96506550218341 1.55412647374062 1.25 0.793650793650794 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Estrogen signaling pathway African trypanosomiasis Biosynthesis of antibiotics Purine metabolism Other glycan degradation Choline metabolism in cancer N-Glycan biosynthesis Wnt signaling pathway		66 51 13874 3695 126 8 42 6		4.54545454545455 1.96078431372549 1.5496612368459 1.24492557510149 0.793650793650794 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	IL-17 signaling pathway Citrate cycle (TCA cycle) Aminoacyl-tRNA biosynthesis FoxO signaling pathway Phosphotransferase system (PTS) Cholinergic synapse Nitrotoluene degradation Yersinia infection		66 1499 2650 81 127 3 48 2		4.54545454545455 1.9346230820547 1.54716981132075 1.23456790123457 0.78740157480315 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Progesterone-mediated oocyte maturation Biosynthesis of various secondary metabolites -part 2 Alanine, aspartate and glutamate metabolism Nicotinate and nicotinamide metabolism Chemical carcinogenesis Chronic myeloid leukemia Non-homologous end-joining Zeatin biosynthesis		66 104 1817 1215 387 2 20 83		4.54545454545455 1.92307692307692 1.5410016510732 1.23456790123457 0.775193798449612 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Prostate cancer MAPK signaling pathway -fly Longevity regulating pathway Longevity regulating pathway -worm Hepatocellular carcinoma Circadian rhythm Nonribosomal peptide structures		66 52 65 742 389 4 17		4.54545454545455 1.92307692307692 1.53846153846154 1.21293800539084 0.77120822622108 Not detected among hlePFCs Not detected among hlePFCs
	Th17 cell differentiation Prodigiosin biosynthesis Microbial metabolism in diverse environments Vitamin B6 metabolism Thyroid hormone synthesis Cocaine addiction Notch signaling pathway		66 574 12774 497 130 7 2		4.54545454545455 1.91637630662021 1.51870987944262 1.20724346076459 0.769230769230769 Not detected among hlePFCs Not detected among hlePFCs
	Cardiac muscle contraction Pertussis Butanoate metabolism Glycosaminoglycan degradation Metabolism of xenobiotics by cytochrome P450 Colorectal cancer Osteoclast differentiation		182 53 1913 250 396 31 9		4.3956043956044 1.88679245283019 1.51594354417146 1.2 0.757575757575758 Not detected among hlePFCs Not detected among hlePFCs
	Phenylpropanoid biosynthesis Viral carcinogenesis Glycolysis / Gluconeogenesis Ubiquinone and other terpenoid-quinone biosynthesis Biofilm formation -Vibrio cholerae Cutin, suberine and wax biosynthesis p53 signaling pathway		167 159 1848 669 926 10 31		4.19161676646707 1.88679245283019 1.51515151515152 1.19581464872945 0.755939524838013 Not detected among hlePFCs Not detected among hlePFCs
	Linoleic acid metabolism Cationic antimicrobial peptide (CAMP) resistance Peroxisome Homologous recombination Ascorbate and aldarate metabolism Cytosolic DNA-sensing pathway Pancreatic cancer		24 533 530 1840 269 1 5		4.16666666666667 1.87617260787993 1.50943396226415 1.19565217391304 0.743494423791822 Not detected among hlePFCs Not detected among hlePFCs
	Parkinson disease D-Arginine and D-ornithine metabolism Fatty acid metabolism Fluid shear stress and atherosclerosis Drug metabolism -cytochrome P450 Dopaminergic synapse Pancreatic secretion		215 54 2455 671 409 8 4		3.72093023255814 1.85185185185185 1.5071283095723 1.19225037257824 0.733496332518337 Not detected among hlePFCs Not detected among hlePFCs
	Glycosphingolipid biosynthesis -ganglio series Fructose and mannose metabolism Fatty acid degradation Primary immunodeficiency Bacterial chemotaxis ECM-receptor interaction Parathyroid hormone synthesis, secretion and action		27 925 1067 85 846 59 16		3.7037037037037 1.83783783783784 1.49953139643861 1.17647058823529 0.709219858156028 Not detected among hlePFCs Not detected among hlePFCs
	Non-alcoholic fatty liver disease (NAFLD) Biofilm formation -Pseudomonas aeruginosa Valine, leucine and isoleucine degradation Aminobenzoate degradation Lysosome EGFR tyrosine kinase inhibitor resistance Penicillin and cephalosporin biosynthesis		225 765 1738 851 293 10 81		3.55555555555556 1.83006535947712 1.49597238204833 1.17508813160987 0.68259385665529 Not detected among hlePFCs Not detected among hlePFCs
	Apoptosis -fly Propanoate metabolism Chlorocyclohexane and chlorobenzene degradation Necroptosis Platinum drug resistance Endocytosis Phagosome		118 1921 268 341 454 5 6		3.38983050847458 1.82196772514315 1.49253731343284 1.17302052785924 0.66079295154185 Not detected among hlePFCs Not detected among hlePFCs
	Other types of O-glycan biosynthesis Carbon metabolism Folate biosynthesis Pantothenate and CoA biosynthesis Phosphonate and phosphinate metabolism Epstein-Barr virus infection Phospholipase D signaling pathway		30 6538 1883 1450 154 31 5		3.33333333333333 1.82012847965739 1.48698884758364 1.17241379310345 0.649350649350649 Not detected among hlePFCs Not detected among hlePFCs
	Huntington disease Ethylbenzene degradation Sulfur relay system Lysine biosynthesis Toluene degradation Ether lipid metabolism Photosynthesis -antenna proteins		276 55 876 1112 171 29 17		3.26086956521739 1.81818181818182 1.48401826484018 1.16906474820144 0.584795321637427 Not detected among hlePFCs Not detected among hlePFCs
	Alzheimer disease Oxidative phosphorylation Fatty acid biosynthesis Riboflavin metabolism Glutamatergic synapse Fanconi anemia pathway Polycyclic aromatic hydrocarbon degradation		371 2874 1550 601 172 21 106		3.23450134770889 1.80932498260264 1.48387096774194 1.16472545757072 0.581395348837209 Not detected among hlePFCs Not detected among hlePFCs
	Salmonella infection Biofilm formation -Escherichia coli Lipopolysaccharide biosynthesis Pyrimidine metabolism GABAergic synapse Fatty acid elongation Primary bile acid biosynthesis		124 613 1281 2181 184 1 48		3.2258064516129 1.79445350734095 1.4832162373146 1.14626318202659 0.543478260869565 Not detected among hlePFCs Not detected among hlePFCs
	PI3K-Akt signaling pathway Vancomycin resistance Phenylalanine metabolism Benzoate degradation Biosynthesis of unsaturated fatty acids Fc gamma R-mediated phagocytosis Prion diseases		94 448 1417 1225 195 5 6		3.19148936170213 1.78571428571429 1.48200423429781 1.14285714285714 0.512820512820513 Not detected among hlePFCs Not detected among hlePFCs
	Phenazine biosynthesis Amino sugar and nucleotide sugar metabolism Terpenoid backbone biosynthesis Caprolactam degradation RNA transport Flavone and flavonol biosynthesis Prolactin signaling pathway		190 1738 1284 526 206 3 21		3.15789473684211 1.78365937859609 1.4797507788162 1.14068441064639 0.485436893203883 Not detected among hlePFCs Not detected among hlePFCs
	Various types of N-glycan biosynthesis Porphyrin and chlorophyll metabolism Type I diabetes mellitus Longevity regulating pathway -multiple species Taurine and hypotaurine metabolism Flavonoid biosynthesis Proteasome		32 1907 68 263 332 34 41		3.125 1.78290508652333 1.47058823529412 1.14068441064639 0.301204819277108 Not detected among hlePFCs Not detected among hlePFCs
	RNA polymerase Glycosphingolipid biosynthesis -globo and isoglobo series Monobactam biosynthesis Cyanoamino acid metabolism Degradation of aromatic compounds Furfural degradation Protein digestion and absorption	329 57 479 357 701 25 19		3.03951367781155 1.75438596491228 1.46137787056367 1.12044817927171 0.285306704707561 Not detected among hlePFCs Not detected among hlePFCs
	RNA degradation Antifolate resistance Atrazine degradation Alcoholism Glycosylphosphatidylinositol (GPI)-anchor biosynthesis Proteoglycans in cancer		912 411 90 33 1 95		1.75438596491228 1.45985401459854 1.11111111111111 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Biosynthesis of amino acids Nitrogen metabolism Glucosinolate biosynthesis Amoebiasis GnRH signaling pathway Proximal tubule bicarbonate reclamation		8454 685 182 8 5 41		1.75065057960729 1.45985401459854 1.0989010989011 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Central carbon metabolism in cancer Pyruvate metabolism Glycerolipid metabolism Amphetamine addiction Hepatitis B Ras signaling pathway		345 2551 643 7 43 5		1.73913043478261 1.45041160329283 1.08864696734059 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	2-Oxocarboxylic acid metabolism Flagellar assembly Pentose and glucuronate interconversions Amyotrophic lateral sclerosis (ALS) Hepatitis C Renin-angiotensin system		1788 1534 559 50 43 37		1.7337807606264 1.43415906127771 1.07334525939177 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	AMPK signaling pathway Metabolic pathways Lipoic acid metabolism Apelin signaling pathway Herpes simplex virus 1 infection Renin secretion		176 45830 190 10 42 7		1.70454545454545 1.42264891992145 1.05263157894737 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	beta-Alanine metabolism Cysteine and methionine metabolism Streptomycin biosynthesis Apoptosis Human cytomegalovirus infection Retinol metabolism		647 2115 382 51 31 13		1.70015455950541 1.41843971631206 1.04712041884817 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Cell cycle -Caulobacter Glycerophospholipid metabolism PPAR signaling pathway Apoptosis -multiple species Human immunodeficiency virus 1 infection Retrograde endocannabinoid signaling		1194 1135 383 31 31 32		1.6750418760469 1.40969162995595 1.0443864229765 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Biotin metabolism Limonene and pinene degradation DNA replication Arabinogalactan biosynthesis -Mycobacterium Human T-cell leukemia virus 1 infection Rheumatoid arthritis		1016 357 1437 46 20 9		1.67322834645669 1.40056022408964 1.04384133611691 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Tuberculosis Arginine biosynthesis Starch and sucrose metabolism Arachidonic acid metabolism Hypertrophic cardiomyopathy (HCM) Ribosome biogenesis in eukaryotes		180 1179 576 107 7 221		1.66666666666667 1.35708227311281 1.04166666666667 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Two-component system Bacterial secretion system Sulfur metabolism Autophagy -animal Influenza A RIG-I-like receptor signaling pathway		3963 1916 1065 11 43 4		1.66540499621499 1.35699373695198 1.03286384976526 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Peptidoglycan biosynthesis Glycine, serine and threonine metabolism Steroid hormone biosynthesis Autophagy -yeast Insect hormone biosynthesis Secondary bile acid biosynthesis		1350 2656 291 75 13 22		1.62962962962963 1.35542168674699 1.03092783505155 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Cushing syndrome One carbon pool by folate Arginine and proline metabolism Bacterial invasion of epithelial cells Isoflavonoid biosynthesis Serotonergic synapse		62 1041 1659 6 2 7		1.61290322580645 1.34486071085495 1.02471368294153 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Polyketide sugar unit biosynthesis Drug metabolism -other enzymes Galactose metabolism Basal transcription factors Kaposi sarcoma-associated herpesvirus infection Sesquiterpenoid and triterpenoid biosynthesis		186 967 491 16 31 33		1.61290322580645 1.34436401240951 1.0183299389002 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Renal cell carcinoma Dioxin degradation Phosphatidylinositol signaling system Betalain biosynthesis Leishmaniasis Small cell lung cancer		62 75 198 12 3 31		1.61290322580645 1.33333333333333 1.01010101010101 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Biosynthesis of secondary metabolites Lysine degradation Xylene degradation Bile secretion Lipoarabinomannan (LAM) biosynthesis Sphingolipid signaling pathway		18860 1126 100 9 32 17		1.60657476139979 1.33214920071048 1.00 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Tryptophan metabolism Protein export Selenocompound metabolism Biosynthesis of enediyne antibiotics Mannose type O-glycan biosynthesis Staphylococcus aureus infection		1623 1660 724 15 1 3		1.60197165742452 1.32530120481928 0.966850828729282 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Biosynthesis of ansamycins Carbapenem biosynthesis D-Alanine metabolism Biosynthesis of siderophore group nonribosomal peptides MAPK signaling pathway -plant Staurosporine biosynthesis	126 151 211 25 128 24		1.58730158730159 1.32450331125828 0.947867298578199 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Glyoxylate and dicarboxylate metabolism Nucleotide excision repair Tyrosine metabolism Biosynthesis of type II polyketide products MAPK signaling pathway -yeast Steroid biosynthesis		2525 908 535 17 13 29		1.58415841584158 1.3215859030837 0.934579439252336 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Valine, leucine and isoleucine biosynthesis Base excision repair Mismatch repair Biosynthesis of various secondary metabolites -part 3 Measles Steroid degradation		1074 1081 1519 2 42 61		1.5828677839851 1.29509713228492 0.921658986175115 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	ABC transporters Styrene degradation Adipocytokine signaling pathway Bisphenol degradation MicroRNAs in cancer Stilbenoid, diarylheptanoid and gingerol biosynthesis		4189 542 112 9 28 34		1.57555502506565 1.29151291512915 0.892857142857143 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
	Glucagon signaling pathway Thiamine metabolism D-Glutamine and D-glutamate metabolism Bladder cancer Mineral absorption Tetracycline biosynthesis		381 700 336 6 13 3		1.5748031496063 1.28571428571429 0.892857142857143 Not detected among hlePFCs Not detected among hlePFCs Not detected among hlePFCs
					247 249 251
		2 4 5 6 7 8

1.3 Emergence of omics data to study planktonic diversity

in Meng et al. (2018), high-quality transcriptomes are defined as having more than 30,000 transcripts, with 50% of the whole transcriptome in transcripts longer than 400 base pairs and read re-mapping rate over 50%

Shiri Freilich, first author of Freilich et al. (2011), and Mara Freilich, first author of[START_REF] Freilich | Species co-occurrence networks: Can they reveal trophic and nontrophic interactions in ecological communities?[END_REF] are different persons.

Acknowledgements Acknowledgements

We would like to particularly thank Stéphane Pesant and Stéphane Audic for their work on making Tara Oceans datasets available. We also thank John Dolan (CNRS, LOV, Villefranche-sur-mer, France), Miguel Mendez-Sandin (Sorbonne Université, Station Biologique de Roscoff, France), and Wei-Ting Chen (National Taiwan Ocean University, Taiwan) for their essential help during the construction of the mixotrophic lineages set. We also thank Florentin Constancias for his help on the metabarcodes clustering tests conducted. Finally, we thank the three anonymous reviewers for their very constructive comments. This article is contribution number #84 of Tara Oceans. For

Ackowledgements

We would like to particularly thank Loïs Maignien, Olivier Aumont and Eric Pelletier for the insightful discussions concerning this study. We also thank the Meren Lab and all the people involved in Tara Oceans for producing the data we used, and making them publicly available.

We would also like to thank all participants of the GOBITMAP and GREENOCEAN workshops for their useful advice. Finally, EF would like to thank Raphaël Berthier and Jean-Olivier Irisson for their advice concerning machine learning, as well as François Duchenne, Elise Kerdoncuff, Benoît Pérez and Eric Bapteste for their helpful comments.

Acknowledgements

This paper emerged from informal discussions about trait-based ecology between aquatic ecologists, especially through visit of SDA at TAKUVIK, ISYEB and LIEC during her sabbatical periods.

SM was funded by a postdoctoral grant from Sorbonne Université while SDA sabbaticals were funded by Sorbonne Université and CNRS. The authors declare no conflict of interests.

Funding

This work was funded mainly by our salary as French State agents and therefore by French taxpayers' taxes. EF acknowledges a 3-year Ph.D. grant from the "Interface Pour le Vivant" (IPV) doctoral program of Sorbonne Universite. SDA acknowledges the CNRS for her two sabbatical years as visiting researcher at ISYEB in 2018-2020. Additional support was provided by the Institut des Sciences du Calcul et des Données (ISCD) of Sorbonne Université through the support of the sponsored junior team FORMAL (From ObseRving to Modeling oceAn Life). EF acknowledges

Data availability

Instructions on how to build or download the MAGs used in this study are available at http: //merenlab.org/data/tara-oceans-mags/. All other data used in this study are available at https: //figshare.com/s/b33fc72a62db44b7192f. All bash and R codes necessary to reproduce our analysis are available at https://github.com/EmileFaure/MAGsProteinFunctionalClusters.

Ongoing work

Functional traits of marine macrophytes

European marine macrophytes, including seaweeds [START_REF] Jänes | Functional traits of marine macrophytes predict primary production E. Carrington[END_REF] Functional traits (morphology, ecophysiology) and ecological information for 68 species. https://www.datadr yad.org/resource/d oi:10.5061/dryad.9 64pf/1

POLYTRAITS

Marine polychaetes (Faulwetter et al. 2014) 47 traits describing morphological, behavioural, physiological, life-history characteristics, as well as the environmental preferences, for a total of 27198 trait records for 952 species. http://polytraits.life watchgreece.eu/ The Arctic Traits Database Marine organisms from the Arctic (Degen and Faulwetter 2019) Traits for 478 species-level taxa. https://www.univie. ac.at/arctictraits/tea m WoRMS Marine Species Traits portal Marine species (WoRMS Editorial Board 2019) Provides 10 traits that have been prioritized within EMODnet Biology , as part of the World Register of Marine Species (WoRMS). http://www.marines pecies.org/traits/ind ex.php Functional traits of marine protists Marine protists, including fungi. (Ramond et al. 2018) Provides 30 functional traits for 2,007 taxonomic references associated to V4 18S rDNA sequences. https://doi.org/10.1 7882/51662 COPEPEDIA/ COPEPOD Marine plankton (O'Brien 2014) Database of plankton taxa distribution maps, photographs, biometric traits, and genetic markers. https://www.st.nmfs .noaa.gov/copepod /documentation/co ntact-us.html Data accessibility Code and resource for the bibliographic network (Supplementary Figure A.4) is available at: https: //github.com/severine13/Biblio-Functional-traits. The mental map presenting the functional trait typology is available online at http://doi.org/10.5281/zenodo.3635898.

Competing interest

Authors declare no conflict of interest.

Authors contributions

EF, SDA and LB conceived the study. EF processed and analyzed the data, with inputs from SDA and LB. EF, SDA and LB wrote the manuscript.

Conclusion: bridging the gap between observations and modeling

The approach presented in this chapter and the preliminary results of section 3.2.2 both show promising results of gene or protein functional clusters quantification and prediction using metaomics and environmental data. While section 3.2.2 focused on the a priori selected dmdA gene, I presented in this chapter a method which enables to create protein functional clusters without a priori, and analyze their biogeography. I showed how such protein functional clusters could be related to functional traits and to taxonomy, the two of them not being completely decoupled by this approach. Indeed, with the similarity and coverage thresholds that I used I showed that metabolic functions could be associated to multiple distinct protein functional clusters, which could correspond to proteins coding for the same function in distinct phylogenetic groups. This specificity of our approach could be seen as a drawback compared to other options like the creation of clusters based uniquely on functional annotation (e.g. by Kegg IDs like in Salazar et al. (2019)), which theoretically allow to entirely decouple function from taxonomy (e.g. all sequences with the same KEGG ID are found in the same cluster independently from their taxonomy in Salazar et al. (2019)). However, these other approaches (1) do not include functional unknowns (or in separate analysis) and ( 2) are heavily dependant on the used database, whereas our approach includes unknowns and only depends on the similarity and coverage thresholds used in the sequence similarity network, for which multiple sets of values can easily be compared, producing functional clusters of different granularities. I believe it makes our approach more inclusive, and thus more adapted to answer diverse questions by varying the thresholds of similarity and coverage to push towards a decoupling of taxonomy and function (low thresholds), a coupling of both (high thresholds), or an intermediate solution. In the following general discussion, I will come back on the findings presented in chapters 2, 3 and 4, discussing their relevance for trait-based approaches and particularly trait-based models, discussing their limits and proposing future ways of improvements. I will propose ideas on how to concretely integrate these data-driven approaches in trait-based modeling studies.

Chapter 5. General discussion and perspectives Data from the cruise notably include in-situ mass spectrometry measures of net primary production, which I hope to relate to prokaryotic metagenomes. Other data types could actually allow to investigate this question, like in-situ high throughput imaging which gives information on traits such as shape, size, defense structure, coloniality or transparency (Martini et al., under review, Appendix A), or like satellite imagery, which is starting to be used to automatically quantify plankton functional groups at the ocean surface through machine learning [START_REF] El Hourany | A neural-based bioregionalization of the Mediterranean Sea using satellite and Argo-float records[END_REF].

Building links between these different kinds of high-throughput data is the first step towards their common integration into modeling frameworks, which I believe will be the future of biogeochemical modeling. those publications relative to the total ones published in freshwater and marine ecology (using those terms as keywords in Web of Science) has increased over time. This emergent and still increasing area of research in aquatic ecology has been the topic of several recent reviews, which summarize the state of the knowledge with regard to specific taxonomic or trophic groups, or traits (Litchman and Klausmeier 2008;Litchman et al. 2013;[START_REF] Nock | Functional Traits[END_REF][START_REF] Meunier | From Elements to Function: Toward Unifying Ecological Stoichiometry and Trait-Based Ecology[END_REF][START_REF] Hébert | Linking zooplankton communities to ecosystem functioning: toward an effect-trait framework[END_REF][START_REF] Kremer | Realizing the potential of trait-based aquatic ecology: New tools and collaborative approaches: Challenges of trait-based aquatic ecology[END_REF][START_REF] Beauchard | The use of multiple biological traits in marine community ecology and its potential in ecological indicator development[END_REF][START_REF] Degen | Trait-based approaches in rapidly changing ecosystems: A roadmap to the future polar oceans[END_REF]Kiørboe et al. 2018). Previous studies focused either on one species [START_REF] Pardo | Explaining variation in life-history traits: growth rate, size, and fecundity in a marine snail across an environmental gradient lacking predators[END_REF], on one taxonomic group of organisms (e.g. crustaceans in [START_REF] Hébert | A meta-analysis of zooplankton functional traits influencing ecosystem function[END_REF][START_REF] Hébert | Linking zooplankton communities to ecosystem functioning: toward an effect-trait framework[END_REF], on one compartment of the ecosystem (e.g. pelagic primary producers in Litchman and Klausmeier (2008); benthic primary producers in [START_REF] Tapolczai | Trait-based ecological classifications for benthic algae: review and perspectives[END_REF]; zooplankton in Litchman et al. (2013) and [START_REF] Hébert | Linking zooplankton communities to ecosystem functioning: toward an effect-trait framework[END_REF]; stream fish in [START_REF] Frimpong | Trait-based approaches in the analysis of stream fish communities[END_REF]), on a particular ecosystem (e.g. oceans in [START_REF] Barton | The biogeography of marine plankton traits[END_REF] and Kiørboe et al. (2018) marine benthos in [START_REF] Degen | Trait-based approaches in rapidly changing ecosystems: A roadmap to the future polar oceans[END_REF]; running water benthos in [START_REF] Statzner | Can biological invertebrate traits resolve effects of multiple stressors on running water ecosystems?[END_REF]) or even on a single type of trait (e.g. size in [START_REF] Andersen | The theoretical foundations for size spectrum models of fish communities J. Baum[END_REF] or stoichiometric traits in [START_REF] Meunier | From Elements to Function: Toward Unifying Ecological Stoichiometry and Trait-Based Ecology[END_REF]. A network analysis of key words associated with the aquatic trait-based literature highlights differences between studies, both in the terminology used to characterize traits and in the application of trait-based approaches in studies of freshwater and marine systems (Figure A.4).

The goal of this review is to facilitate exchanges of FTBAs and their products across different aquatic fields. To do so, we propose: 1) A table compiling the main definitions of traits that are commonly used in trait-based studies, in addition to recommendations for using a common and unambiguous vocabulary, 2) A unified typology of 40 aquatic functional traits that could be used in multicompartment studies (including several biological compartments, or different habitats e.g. sediment and water), 3) A summary of existing databases that contain information on functional traits, 4) A review of traditional and emerging methods for estimating and using traits of aquatic organisms, and 5) The main challenges that aquatic ecologists can now address using FTBAs and that should inspire future studies.

A.1.2 Trait definition and aquatic trait description

The term "trait" depicts specific attributes of an individual that are both inherent and characteristic to its nature. However, as highlighted by our literature survey (Supplementary Figure A.4 and A.5, see also Supplementary Information), this term is used in multiple contexts to describe a diverse set of attributes such as: "physiological traits", "functional traits", "life history traits", "biological traits", "ecological traits", "response traits", "effect traits", "behavioral traits", etc (see Table A.1).

To avoid misunderstandings, clear definitions of these concepts are needed (Violle et al. 2007).

A.1.2.1 Adopting common definitions for aquatic FTBAs

Trait definitions vary between scientific communities, from the individual organism (e.g. lifehistory traits) to the population (e.g. demographic traits), community (e.g. response traits) and the ecosystem scale (e.g. effect traits; [START_REF] Hébert | Linking zooplankton communities to ecosystem functioning: toward an effect-trait framework[END_REF]. Traits can also be directly measured peculiarities. Firstly, functional trait-based ecology describes emergent properties related to ecosystem functioning, without necessarily having to explicitly identify the organisms at a given taxonomic level. Secondly, FTBAs in aquatic ecology can account for a continuous degree of plasticity in the trait expressed [START_REF] Chevenet | A fuzzy coding approach for the analysis of long-term ecological data[END_REF], thus allowing for a better quantification of intra-specific variability (see also A.1.4.1). Moreover, phenotypic plasticity can result in substantial intra-specific variation [START_REF] Des Roches | The ecological importance of intraspecific variation[END_REF], with clonal differences in plasticity. For instance, many aquatic species can exhibit a high degree of morphological plasticity in response to different environmental cues. Zooplankters such as Daphnia can form elongated carapaces (e.g. longer tailspines or helmets) in response to strong predation [START_REF] O'brien | Helmets and Invisible Armor: Structures Reducing Predation from Tactile and Visual Planktivores[END_REF][START_REF] Lüning | Phenotypic plasticity of Daphnia pulex in the presence of invertebrate predators: morphological and life history responses[END_REF][START_REF] Swaffar | Spines of Daphnia lumholtzi create feeding difficulties for juvenile bluegill sunfish (Lepomis macrochirus)[END_REF] while the freshwater phytoplankters Desmodesmus can increase the size of their colonies to avoid mortality from numerous grazers [START_REF] Lürling | Phenotypic plasticity in the green algae Desmodesmus and Scenedesmus with special reference to the induction of defensive morphology[END_REF]. These are examples among a vast amount of abilities for phenotypic plasticity that can in and of themselves be seen as functional trait of the organisms that possess this flexibility [START_REF] Barnett | Functional diversity of crustacean zooplankton communities: towards a trait-based classification[END_REF][START_REF] Weithoff | Measures and Approaches in Trait-Based Phytoplankton Community Ecology -From Freshwater to Marine Ecosystems[END_REF]. Intra-specific variability can be substantial in aquatic organisms (e.g. Sanford and Kelly 2011), and can impact community and ecosystem dynamics similarly to inter-specific trait variability [START_REF] Des Roches | The ecological importance of intraspecific variation[END_REF][START_REF] Raffard | The community and ecosystem consequences of intraspecific diversity: a meta-analysis[END_REF]. Within the climate context, understanding the drivers and link between intra-and inter-specific trait variability is another argument for the use of FTBAs instead of species-centered approaches (Violle et al. 2012). To go further towards a unified framework, we propose a common typology of functional traits for aquatic organisms (Figure A.1). It not only follows what was previously proposed for phy-al. 2016;[START_REF] Schmera | Functional diversity: a review of methodology and current knowledge in freshwater macroinvertebrate research[END_REF][START_REF] Legras | Functional richness: Overview of indices and underlying concepts[END_REF]). Many of these indices are sensitive to the number and the type of traits that are considered (e.g. [START_REF] Legras | Assessing functional diversity: the influence of the number of the functional traits[END_REF], as well as to the species richness of the communities, meaning that the comparison of sites with different richness levels would require using comparable indices that are unbiased by species richness and trait selection.

A.1.3 Estimating and using traits: tools and limits for studying functional traits Several observational methods, both used in situ as well as in vitro, allow for the quantification or identification of functional traits; but they are predominantly used in either oceanographical or limnological applications, not both. Currently available methods to measure or estimate traits include classical trait measurements (laboratory and field), imaging and acoustic techniques, as well as molecular sequencing (-omics). These methods will be described in the following sections and opportunities for sharing between scientific communities will be outlined.

A.1.3.1 Empirical studies of traits as a source for trait databases

The investigation of functional traits has been largely based on empirical studies. Such studies rely on three complementary approaches that can be described by: 1) measurements of traits in situ, 2) measurements of traits under controlled laboratory conditions, and 3) metadata analyses of databases and literature (Figure A.2A). The metadata approach has been undoubtedly the most developed across aquatic ecosystems [START_REF] Degen | Trait-based approaches in rapidly changing ecosystems: A roadmap to the future polar oceans[END_REF]Kiørboe et al. 2018) and the literature has been the basis of a number of reviews describing functional traits. For example, in freshwater ecology, Kolkwitz and Marsson (1909) pioneered a compilation of types of organisms in relation to the presence of various pollution levels. In marine ecosystems, metadata compilations allowed mapping of key traits of marine copepods at a global scale and evaluation of their relationships with environmental conditions (Brun et al. 2016b;[START_REF] Benedetti | Do functional groups of planktonic copepods differ in their ecological niches?[END_REF]. One effective way to merge functional traits with taxa, based on a variety of sources and literature, is the fuzzy coding procedure (e.g. [START_REF] Chevenet | A fuzzy coding approach for the analysis of long-term ecological data[END_REF]. In functional trait-based approaches, the fuzzy coding uses positive scores to describe the affinity of a species for the different categories of a given trait, e.g. using "0", "1", "2" and "3" for species exhibiting respectively "no", "weak", "moderate" and "strong" link with a given trait category [START_REF] Chevenet | A fuzzy coding approach for the analysis of long-term ecological data[END_REF]Usseglio-Polatera et al. 2000). When a trait applies to a subset of the different stages of the species life cycle (egg, larva, pupa, and adult), the relative duration of each stage is considered in assigning appropriate scores to the different categories of this trait. To standardize the description of species attributes, trait category scores are converted into a relative abundance distribution so that the sum of the trait category scores for an individual trait and a given taxon equals one. This technique of coding is robust enough to compensate for different types and levels of information available for different taxa.

Table A.2 -Online databases documenting functional traits of aquatic organisms.

Databases without a primary focus on traits, but that also provide trait information, are included. This list is available at https://github.com/severine13/FonctionalTrait _ databases.

Name of the database

Taxonomic groups of interest and habitats

Reference

Brief description Web link

Traitbank -Encyclopedia of Life

All taxa across the tree of life, including marine and freshwater organisms (Parr et

EMODnet Biology database

European seaweeds [START_REF] Robuchon | Towards a seaweed trait database for European species[END_REF] Functional traits (morphology, life history, ecophysiology) and ecological information (incl. biogeography) for the 1800 seaweed species listed in Europe.

Trait database for marine copepods

Marine pelagic copepods [START_REF] Brun | A trait database for marine copepods[END_REF]) [START_REF] Troudet | Taxonomic bias in biodiversity data and societal preferences[END_REF]) restrict the application of trait databases at the community scale and remain a limiting factor for the integration of FTBAs into macroecology (Tyler et al. 2012;[START_REF] Borgy | Sensitivity of community-level trait-environment relationships to data representativeness: A test for functional biogeography[END_REF]). However, the main limit so far to provide and share trait data remains the lack of an ecological standard for data [START_REF] Schneider | Towards an Ecological Trait-data Standard[END_REF]. Attempts to increase unification are currently emerging on various fronts such as the terminology of traits (e.g. [START_REF] Schmera | A proposed unified terminology of species traits in stream ecology[END_REF] for stream ecology), the cross-taxa compatibility of functional traits (e.g. Therefore, their manipulation requires strong computational abilities [START_REF] Durden | Integrating "Big Data" into Aquatic Ecology: Challenges and Opportunities[END_REF]). As a result, aquatic research is evolving into a more biostatistical-and bioinformatical-based field, enabling the extraction of large-scale information on traits and putting to full use taxonomic surveys recorded over time. Despite this, naturalist taxonomic knowledge per se remains critical and future challenges in ecology will undoubtedly benefit from a combination of modern functional trait-based approaches and a modern integrative taxonomic knowledge.

The traits documented by these databases originate from direct measurements of realized traits in the laboratory or in situ (Figure A.2A). Laboratory experiments allow for the quantification of functional traits of model species within a large range of controlled environmental conditions.

They provide a well-constrained system, both in physical variables and species content, to measure functional traits at the individual level. However, they are often limited to a few cultured species that do not necessarily reflect the actual functional diversity and complexity of whole ecosystems, as should FTBAs do. One of the few examples of lab-measured traits tested the existence of trade-offs across many phytoplankton species between maximum growth rate, competitive ability for phosphorus acquisition, and ability to store phosphorus (Edwards et al. 2013a).

In recent years, innovative instruments and tools have become available to measure in situ new functional traits. They include imaging and genomics tools that have the potential to provide a comprehensive picture of aquatic ecosystem composition, structure and function. Their implementation should greatly help advance the use of functional traits in aquatic studies.

A.1.3.2 Imaging and acoustic techniques

Imaging systems are best suited for the quantification of morphological traits, such as size, transparency, bioluminescence or shape (Forest et al. 2012;[START_REF] Barton | The biogeography of marine plankton traits[END_REF]Fontana et al. 2014;[START_REF] Andersen | The theoretical foundations for size spectrum models of fish communities J. Baum[END_REF]), but also for the estimation of some behavioral (e.g. motility or substrate re-

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A.1 Functional trait-based approaches as a common framework for aquatic ecologists lationships), life-history or physiological traits (Table A traits from pico-to macro-scales (e.g. [START_REF] Culverhouse | Automatic image analysis of plankton: future perspectives[END_REF][START_REF] Stemmann | Global zoogeography of fragile macrozooplankton in the upper 100-1000 m inferred from the underwater video profiler[END_REF][START_REF] Sieracki | Proceedings of OceanObs'09: Sustained Ocean A.1 Functional trait-based approaches as a common framework for aquatic ecologists Observations and Information for Society[END_REF]Biard et al. 2016). To date, imaging tools have mostly been used by marine ecologists (Table A.3), in both benthic and pelagic ecosystems, with only a few implementations in freshwater environments (e.g. [START_REF] Althaus | A Standardised Vocabulary for Identifying Benthic Biota and Substrata from Underwater Imagery: The CATAMI Classification Scheme J. Hewitt[END_REF][START_REF] González-Rivero | Scaling up Ecological Measurements of Coral Reefs Using Semi-Automated Field Image Collection and Analysis[END_REF]. This is mainly due to the large amount of particles, the higher turbidity and the relatively smaller size of the crustacean zooplankton in freshwater ecosystems. Benthic imaging tools include baited, unbaited, towed, autonomous-and diver-operated systems [START_REF] Matabos | Highfrequency study of epibenthic megafaunal community dynamics in Barkley Canyon: A multidisciplinary approach using the NEPTUNE Canada network[END_REF][START_REF] De Juan | Standardising the assessment of Functional Integrity in benthic ecosystems[END_REF][START_REF] Mérillet | Using underwater video to assess megabenthic community vulnerability to trawling in the Grande Vasière (Bay of Biscay)[END_REF], while pelagic ones are mainly in situ or bench-top systems. Since the turbidity and obstacles in benthic, coastal or river ecosystems strongly modify optical characteristics, systems with external light are more commonly used to efficiently capture morphological traits of aquatic organisms.

Table A.3 -Examples of instruments for imaging and acoustic assessment, used for trait description and quantification in aquatic ecosystems.

A major advantage of imaging systems is their variable degree of invasiveness during observation.

Imaging systems can analyse discrete measurement of water samples (living or fixed samples), but they can also acquire in situ continuous records on living organisms. For instance, imaging techniques applied to marine plankton revealed that the abundance of the most fragile organisms (such as gelatinous zooplankton, Rhizaria, etc.) has been underestimated for a long time using traditional observation techniques (e.g. Biard et al. 2016), as they tend to break when collected using plankton nets [START_REF] Stemmann | Global zoogeography of fragile macrozooplankton in the upper 100-1000 m inferred from the underwater video profiler[END_REF]. The use of in situ imaging systems also provides information on poorly studied traits, such as transparency and water content of gelatinous organisms. For benthic systems, imaging techniques provide non-intrusive and non-destructive methods that can be valuable to assess endangered habitats and/or marine protected areas and to collect information on the distribution of large over-dispersed epifaunal species inadequately sampled by traditional gears like grabs (e.g. [START_REF] Althaus | A Standardised Vocabulary for Identifying Benthic Biota and Substrata from Underwater Imagery: The CATAMI Classification Scheme J. Hewitt[END_REF].

In addition to classical imaging, acoustic methods (passive and active) are also tools of increasing importance to quantify particular functional traits. Acoustic Doppler current profilers (ADCPs)

have been successfully used in lakes to capture diel migration behavior in larger planktonic species such as the insect larval predators of zooplankton (e.g. Chaoborus; [START_REF] Lorke | Acoustic observations of zooplankton in lakes using a Doppler current profiler[END_REF].

Hydrophone recordings can be used to record sound emissions by the organisms themselves.

The sounds produced by freshwater organisms represent a highly overlooked trait and such trait recordings might provide relevant non-invasive tools to monitor the complexity and changes in aquatic communities. In a literature survey, [START_REF] Desjonquères | Ecologie et diversité acoustique des milieux aquatiques: exploration en milieux tempérés[END_REF] showed that at least 271 freshwater species amongst French aquatic fauna (89% insects, but also fish and crustaceans) produce sounds. Using continuous underwater recordings with hydrophones, it was shown that the acoustic diversity of ponds and floodplain water bodies reflects the taxonomic diversity of aquatic communities [START_REF] Desjonquères | Acoustic communities reflects lateral hydrological connectivity in riverine floodplain similarly to macroinvertebrate communities[END_REF]. Similarly, sound production by benthic invertebrates in the bay of Brest (France) was used to describe the soundscape and assess the ecological status of maerl beds [START_REF] Coquereau | Sound production and associated behaviours of benthic invertebrates from a coastal habitat in the north-east Atlantic[END_REF].

One of the main caveats of imaging methods for FTBAs is that imaging tools have a low resolution below a certain size (most of these tools are of limited accuracy below a size limit of 200 µm for zooplankton, and 30-40 µm for phytoplankton, see Table A.3), and may not allow for a reliable analysis of smaller size fractions, often associated with detrital matter or particles with a lack of discernible morphological differences. This limit is especially true for organisms without hard structures such as naked dinoflagellates or aloricate ciliates. However, imaging and acoustic methods generate high frequency and automated datasets at large spatial scales, with some of them recorded by inter-calibrated instruments, which allow for their comparison and combination in space and time (e.g. UVP for marine plankton; Table A.3). These data are also suitable for the validation of trait-based marine ecosystem models (Kiørboe et al. 2018) and new ecological questions have been addressed by combining both recent imaging techniques and FTBAs [START_REF] Schmid | Lipid load triggers migration to diapause in Arctic Calanus copepods-insights from underwater imaging[END_REF]). New opportunities using imaging and acoustics include the evaluation of feeding behaviors and network associations [START_REF] Choy | Deep pelagic food web structure as revealed by in situ feeding observations[END_REF], filtration rates and carbon fluxes [START_REF] Katija | From the surface to the seafloor: How giant larvaceans transport microplastics into the deep sea[END_REF]) and migration patterns of zooplankton (Benoit-Bird and Lawson 2016). cification, etc.), using either (meta-)genomic or (meta-)transcriptomic approaches (Figure A.2C).

When combined with databases like KEGG [START_REF] Kanehisa | KEGG: Kyoto Encyclopedia of Genes and Genomes[END_REF], which includes the genes (for genomics/transcriptomics), proteins (for proteomics) and metabolites (for metabolomics) implied in a specific pathway, -omics approaches open up the possibility of monitoring functional traits (defined at the individual level) across different levels of biological organisation (from organisms to communities). For example, approaches that report the expression level of genes, proteins and metabolites are increasingly used in ecotoxicology to assess functional traits (e.g. photosynthesis, chemical degradation) in response to stressor(s) via targeted approaches (e.g. q-PCR on pre-identified candidate genes, [START_REF] Pesce | Response of a diuron-degrading community to diuron exposure assessed by real-time quantitative PCR monitoring of phenylurea hydrolase A and B encoding genes[END_REF][START_REF] Moisset | Genetic and physiological responses of three freshwater diatoms to realistic diuron exposures[END_REF]. Although it is still very challenging to relate -omics data to functional traits (Stec et al. 2017), the identification of certain genes coding for particular metabolic or physiological traits (e.g. iron uptake, nitrogen fixation) may help to directly link ecosystem structure to ecosystem functions (Mock et al. 2016), while taking into account the majority of organisms that in fact cannot be classified based on their morphological characteristics (e.g. picophytoplankton), and/or cannot be captured by imaging methods due to their small size or behavior. For instance, using metagenomic data, Farrell et al.

(2018) created a machine-learning algorithm that can predict values of 65 phenotypic traits with more than 90% accuracy, thus allowing the investigation of the functional profiles of 660 uncultured marine prokaryotes based only on their metagenomically-assembled genomes or MAGs (i.e.

genomes putatively reconstructed from metagenomics data). This very promising method cannot yet be applied to eukaryotes, as relating genes to potential traits in eukaryotes remains much more challenging than for prokaryotes (Sunagawa et al. 2015;Salazar et al. 2019). However, transcriptomics techniques were successfully used to estimate putative traits for marine protists using sequence similarity network-based approaches ( toxicity and symbiosis for dinoflagellates; [START_REF] Meng | Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network[END_REF]. For pluricellular organisms, many challenges remain for the application of such methods in FTBAs, especially because of the large size of their genomes and because reference genomes are lacking (hence, the function of their DNA or RNA remains unknown). Yet, the use of transcriptomics approaches seem promising for these organisms (e.g. [START_REF] Lenz | De Novo Assembly of a Transcriptome for Calanus finmarchicus (Crustacea, Copepoda) -The Dominant Zooplankter of the North Atlantic Ocean[END_REF][START_REF] Blanco-Bercial | A transcriptomic resource for the northern krill Meganyctiphanes norvegica based on a short-term temperature exposure experiment[END_REF].

Substantial progress remains to be made before aquatic ecologists can fully exploit -omics information using a FTBA. This includes the design of new methods to estimate the quantitative aspects of -omics information, but also to decipher the large quantity of sequences that cannot be assigned to any taxon in an environmental sample, and to circumvent the low proportion of genomic functional annotation (especially for eukaryotes). However, ongoing and future -omics studies may allow skipping taxonomic assignation and even the identification of gene functions as an intermediary between ecosystem composition and function. Such studies would fully contribute to FTBAs of aquatic ecosystems by targeting the -omics signature of relevant functional traits (Mock et al. 2016;Stec et al. 2017).

Another application would be the use of -omics data to develop a new generation of trait-based models (Mock et al. 2016;Stec et al. 2017;Coles et al. 2017). Metatranscriptomic data could be used to identify physiological traits of phytoplankton, combined with a mechanistic model of the phytoplankton cell, and used to construct a trait-based global marine ecosystem model (Mock

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A.1 Functional trait-based approaches as a common framework for aquatic ecologists et al. 2016). Emergent communities of marine microbes (from bacteria to phytoplankton) have already been predicted by directly simulating their metagenomes and metatranscriptomes (Coles et al. 2017). In summary, the idea of improving ecosystem models using -omics is not new [START_REF] Hood | Pelagic functional group modeling: Progress, challenges and prospects[END_REF]), but FTBAs could constitute the common framework needed for next-generation ecosystem modellers, observers, molecular biologists, and ecologists working in limnology and oceanography. This would advance our ecological understanding of aquatic ecosystems and the links between ecosystem structure, function and ecosystem services or bioindicators relevant for ecosystem monitoring and management. Using either empirical studies, imaging/acoustics, or -omics, both quantitative and qualitative traits can be estimated. One advantage of qualitative traits is that they do not have to be measured using the same instruments, and can be more easily described across compartments and realms.

For quantitative traits, metrics and indices, one challenge is to be able to compare trait-based functional diversity among studies. In the next section we will focus on such traits that can be shared between ecological compartments and we will describe new opportunities in aquatic ecology to highlight spatio-temporal patterns, study anthropogenic impacts and better describe trophic interactions between plankton species (Figure A.3).

A.1.4 Future opportunities for aquatic FTBAs

A.1.4.1 Going further towards a trait-based aquatic ecology by identifying key traits

Documenting key traits in multi-compartment studies

Given that the main power of FTBAs is to transcend both taxonomy and realms, trait-based ecological studies could result in a common set of ecological rules and theoretical principles that could be applied to multiple systems (e.g. benthos and pelagos, including plankton and fish). Following our framework, aquatic functional traits could be described at various spatiotemporal scales for both benthic and planktonic organisms, for instance taking benthic-pelagic coupling into account. To do so, we recommend a closer collaboration among aquatic ecologists, including process-oriented projects and comparative studies of freshwater and marine systems, focusing on the aquatic functional traits that we have identified and on their links to ecosystem functions (Figure A.1). In addition to morphological traits that are relatively easy to identify and to measure (such as size, shape, cellularity, defences and colour), priority traits to be investigated should also include: 1) life history traits such as voltinism (number of generation or breeding per year), life cycle, life span, type of reproduction, and reproduction strategy, 2) physiological traits such as photosynthesis ability, diet, feeding mode, salinity preference, and trophic regime, and 3) behavioral traits like motility, dispersal potential, and substrate relation. Indeed, among all the traits identified in . Such statistical approaches using these continuous traits can easily be applied to multi-compartment studies, (for example using transparency to describe gelatinous ecological patterns).

Documenting the trade-offs between key traits

Multi-compartmental studies that aggregate effects across species and trophic levels, hence taking into account the network structure of a community or the food-web structure of an ecosystem, would also enable a better understanding and quantification of the trade-offs occurring between two or multiple traits. Trade-offs, which result from the inherent metabolic, energetic or behavioral costs associated with each expressed trait, provide the fundamental basis to understand species coexistence and the trait composition of communities [START_REF] Ehrlich | Trait-fitness relationships determine how trade-off shapes affect species coexistence[END_REF]. In particular, the competition-colonization trade-off is a major mechanism for biodiversity maintenance [START_REF] Tilman | Competition and Biodiversity in Spatially Structured Habitats[END_REF][START_REF] Muthukrishnan | Trait plasticity alters the range of possible coexistence conditions in a competition-colonisation trade-off[END_REF][START_REF] Ehrlich | The shape of a defense-growth trade-off governs seasonal trait dynamics in natural phytoplankton[END_REF]. Strong competitors able to exclude other species in any given habitat are often slow dispersers. In contrast, poor competitors are often strong colonizers, able to easily disperse and find unoccupied niches. A trade-off between resource acquisition and survival (or predation vulnerability) was reported for zooplankton: organisms that feed using feeding currents, increase their risk of being detected by predators that are sensitive to flow disturbances [START_REF] Kiørboe | Shifts in Mass Scaling of Respiration, Feeding, and Growth Rates across Life-Form Transitions in Marine Pelagic Organisms[END_REF]. Unexpected trade-offs can often explain the relative mismatch between expected and observed individual traits in aquatic communities along gradients of anthropogenic pressure, complexifying the trait-based diagnostic of water bodies [START_REF] Resh | Theoretical habitat templets, species traits, and species richness: a synthesis of long-term ecological research on the Upper Rhône River in the context of concurrently developed ecological theory[END_REF]Mondy and Usseglio-Polatera 2014;[START_REF] Desrosiers | Assessing anthropogenic pressure in the St. Lawrence River using traits of benthic macroinvertebrates[END_REF]. Indeed, the success of a species in adverse conditions might be due to a particularly effective adaptation without the need

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A.1 Functional trait-based approaches as a common framework for aquatic ecologists for further adaptive traits. Moreover, investing in a given adaptation can leave fewer resources available for the investment in another adaptation. Species of various lineages (e.g. different phyla in invertebrate assemblages) may also solve the same ecological constraint with different adaptations.

Trade-offs have been globally recognised as a central component of trait-based approaches in aquatic ecology [START_REF] Resh | Theoretical habitat templets, species traits, and species richness: a synthesis of long-term ecological research on the Upper Rhône River in the context of concurrently developed ecological theory[END_REF][START_REF] Kremer | Realizing the potential of trait-based aquatic ecology: New tools and collaborative approaches: Challenges of trait-based aquatic ecology[END_REF] especially in plankton ecology (Litchman et al. 2007(Litchman et al. , 2013(Litchman et al. , 2015;;Litchman and Klausmeier 2008;[START_REF] Hébert | Linking zooplankton communities to ecosystem functioning: toward an effect-trait framework[END_REF]Kiørboe et al. 2018;[START_REF] Ehrlich | The shape of a defense-growth trade-off governs seasonal trait dynamics in natural phytoplankton[END_REF]. In benthic studies, there has been a clear lack of work that considers simultaneously several traits relative to what has been done in studies on marine plankton (e.g. Litchman et al. 2013) and in freshwater ecology [START_REF] Verberk | Applying life-history strategies for freshwater macroinvertebrates to lentic waters[END_REF]). As a case in point, the term trade-off is not mentioned in the recent review on benthic traits by [START_REF] Degen | Trait-based approaches in rapidly changing ecosystems: A roadmap to the future polar oceans[END_REF].

More studies are needed to explore trade-offs among traits across compartments and realms in order to identify the rules governing the links between traits, trade-offs, community structure and function. To accomplish this, researchers will have to put effort on measuring multiple traits, focusing on those related to resource acquisition, growth, storage and predation avoidance (i. To further explore trait relationships, aquatic ecologists may be inspired by what has been done in terrestrial plant ecology: the identification of so-called trait syndromes. Trait syndromes are relationships between traits that are defined by fundamental trade-offs amongst taxa that determine their ecological roles in ecosystems. The classic example in plant ecology is the "leaf economics spectrum" that characterizes taxa according to the speed at which they are able to take up nutrients and invest in leaf biomass (Wright et al. 2004). In this vein, some work was done with aquatic organisms by considering trade-offs amongst lotic insects (e.g. Poff et al. 2006), fishes (e.g. [START_REF] Winemiller | Functional traits, convergent evolution, and periodic tables of niches[END_REF] and phytoplankton (e.g. Edwards et al. 2013a). By considering trait syndromes, FTBAs are likely to better predict competitive outcomes as well as distributions of traits across environmental gradients. We thus encourage the aquatic ecology community to engage with the vast array of accessible trait databases provided in Table A.2 and to take the next steps to characterize trait syndromes across the different groups of aquatic organisms.

Documenting the variability of key traits

Finally, more attention should be given to document the variability of all key traits at all organisational levels, i.e. at the community scale, between individuals in a given population (i.e. intra-specific variability; [START_REF] Raffard | The community and ecosystem consequences of intraspecific diversity: a meta-analysis[END_REF], but also for one individual throughout its lifespan (i.e. ontogenic variability; e.g. Zhao et al. 2014). Indeed, with the exception of a few studies (e.g. Maps et al. 2014b;[START_REF] Banas | Traits controlling body size in copepods: separating general constraints from species-specific strategies[END_REF], intra-specific variability of traits is rarely taken into account, mainly because of a lack of empirical information on this variability. For example, the ability to engage fully in autotrophy or to add in heterotrophic feeding is a characteristic of mixotrophic phytoplankton taxa. By characterizing the conditions under which one or the other condition is utilized by a taxon, we can begin to characterize intra-specific variability. Therefore, the question of the scale of variation of functional traits, both at community and population scales, and its impact on ecosystem structure and functioning should be further explored, especially with the use of new methodological development to measure traits (see section A.1.3). Trait-based models could also be used (see review on trait-based modeling in Kiørboe et al. 2018) to quantify the impact of environmental changes on the intra-and inter-specific variability of functional traits (e.g. lipid content and size of copepods, [START_REF] Renaud | Pelagic food-webs in a changing Arctic: a trait-based perspective suggests a mode of resilience[END_REF], and to assess the variation of peculiar traits along environmental gradients (Edwards et al. 2012).

Identifying key traits common in limnology and oceanography and their trade-offs, syndromes, and variability, will allow aquatic ecologists to better address central ecological questions, including understanding: 1) the spatial patterns of functional diversity and its drivers, 2) the effects of environmental and anthropogenic pressures on ecosystem structure and functioning, and 3) the interactions among organisms and associated food web organisation and dynamics. For each of these main opportunities, we will briefly describe what has been done to date and then identify potential ways to advance the field of aquatic ecology using FTBAs.

A.1.4.2 New opportunities emerging from the study of the spatial distribution of aquatic traits

The

description of aquatic trait biogeography

To date, trait biogeography has been studied for a few compartments in marine ecosystems, such as marine plankton [START_REF] Barton | The biogeography of marine plankton traits[END_REF] Based on the biogeography of some key traits (e.g. size, feeding strategy), aquatic ecologists can now relate functional traits to environmental conditions and identify general rules governing trait diversity distribution. For instance, the description of key traits of marine copepods (body size, offspring size and myelination) has highlighted latitudinal global patterns in trait biogeography.

These patterns are in agreement with the temperature-size rule and have unveiled relationships between these traits and environmental conditions, such as water column transparency, but also

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A.1 Functional trait-based approaches as a common framework for aquatic ecologists biotic conditions, such as chlorophyll seasonality or phytoplankton size (Brun et al. 2016a). More recently, the study of taxonomic and functional diversity of fish communities between two different regions (Caribbean and Great Barrier Reef) and among three habitats (coral reef, seagrass, and mangrove) revealed that traits and functional groups varied among habitats, whereas taxonomic composition varied between regions [START_REF] Hemingson | Biogeographic patterns in major marine realms: function not taxonomy unites fish assemblages in reef, seagrass and mangrove systems[END_REF]. Similar relationships should now be tested across ecosystems, geographical regions and trophic levels to verify whether these findings can be generalized to other aquatic organisms/ecosystems (Figure A.3.2). The trait databases now available for many groups of aquatic organisms (see Table A.2) should provide relevant information to explore this direction.

Using traits for revealing hidden community assembly rules at various spatial scales

Based on the spatial description of functional traits, hypotheses underlying community assembly rules can also be tested and community composition can be predicted [START_REF] Cadotte | Predicting communities from functional traits[END_REF].

For example, the description of physiological and behavioral traits of dragonfly larvae in various lakes recently suggested that traits can drive species distribution and community assembly, through the direct impact of physiological and behavioral traits (activity rate and burst swimming speed) on foraging and predator avoidance behavior [START_REF] Start | Physiology underlies the assembly of ecological communities[END_REF]). The traits considered in this study were driven by two biomolecules, the expression of which could predict more than 80% of the variation in dragonfly community structure across lakes, and which were involved in the interactions between the dragonfly larvae and their fish predators. Measurements made by new observational methods such as metabolomics, transcriptomics (see section A. 1.3.3)

From trait biogeography to spatial variation in functional diversity

Traits that are shared among compartments could also be used to describe the spatial variability of functional diversity [START_REF] Petchey | Functional diversity: back to basics and looking forward[END_REF]. Among the metrics that were proposed to measure functional diversity and its different dimensions (see section A.1.2.3), aquatic ecologists have to adopt common metrics for comparative studies. Based on these common metrics, the spatial variation of the functional diversity of aquatic communities could be estimated across environments and in multi-compartment studies. For example, the functional diversity of macrophytes was described along a water depth gradient in a freshwater lake [START_REF] Fu | Functional traits composition predict macrophytes community productivity along a water depth gradient in a freshwater lake[END_REF]): future studies could cover similar environmental gradients in both freshwater and marine environments (e.g. rivers, estuaries, coasts, islands, etc.) and also include other organisms and higher trophic levels, both benthic and pelagic, to test whether the resulting spatial patterns of functional diversity can be generalized. The spatial distribution of traits and functional diversity could also be used to identify functional diversity hotspots and propose protected areas for a trait-based conservation.

The diversity of functional traits is indeed correlated to both taxonomic diversity (e.g.

Both the European Water Framework Directive and the European Marine Strategy Framework

Directive require the estimation of the biological status of aquatic ecosystems from the evaluation of each compartment (benthic diatoms, macrophytes and macroalgae, benthic macroinvertebrates, phytoplankton, zooplankton, and fish) independently (e.g. [START_REF] Birk | Three hundred ways to assess Europe's surface waters: An almost complete overview of biological methods to implement the Water Framework Directive[END_REF]. Universal and standardized trait-based indices for biomonitoring should now cover all compartments (Borja et al. 2010). To this end, freshwater ecologists, who have a greater experience in multi-compartment monitoring [START_REF] Lainé | A Multicompartment Approach -Diatoms, Macrophytes, Benthic Macroinvertebrates and Fish -To Assess the Impact of Toxic Industrial A.1 Functional trait-based approaches as a common framework for aquatic ecologists Releases on a Small French River[END_REF], could inspire marine ecologists, who are more used to focus on one compartment only (e.g. benthos, plankton, or fish).

Trait-based ecotoxicology

Besides the policy frameworks, FTBAs can also be used in ecotoxicology to highlight the impact of various stressors (e.g. organic synthetics products) on aquatic ecosystems [START_REF] Baird | Using biological traits to predict species sensitivity to toxic substances[END_REF]. In fact, trait-based ecological risk assessments have been proposed as the new frontier in ecotoxicology [START_REF] Baird | Trait-based ecological risk assessment (TERA): The new frontier?[END_REF][START_REF] Rubach | Framework for traits-based assessment in ecotoxicology[END_REF]. In freshwater systems, diatom traits such as life form (e.g. colonial, solitary) or affinities to water quality have already been linked to pesticides contamination [START_REF] Roubeix | Variations of periphytic diatom sensitivity to the herbicide diuron and relation to species distribution in a contamination gradient: implications for biomonitoring[END_REF]. The deformation of their silicified exoskeleton (teratology) has also been considered as a morphological trait that can inform on organisms exposure to heavy metals or pesticides [START_REF] Lavoie | Diatom teratologies as biomarkers of contamination: Are all deformities ecologically meaningful?[END_REF] Ultimately, this could expand biomonitoring approaches beyond traditional taxonomically based assessments.

A.1.4.4 Scaling up from functional traits to community structure and ecosystem functions

Finally, FTBAs could be used to explore trophic interactions and food webs [START_REF] Reiss | Emerging horizons in biodiversity and ecosystem functioning research[END_REF]).

Indeed, several traits directly reflect trophic interactions (e.g. diet, size, stoichiometric traits) and can be used to better understand food web structure and dynamics (Figure A.3.4). However, scaling-up from individual traits to populations, communities, and ecosystems requires taking trait variation at multiple intermediate organisation scales into account (e.g. population, metapopulation, and community scales; [START_REF] Gibert | Chapter One -Scaling-up Trait Variation from Individuals to Ecosystems[END_REF]. Rather than considering a collection of traits independently, one approach is to analyse how these traits influence or reveal the biotic interactions and trophic structure of aquatic communities. To do so, the numerous traits that are directly related to the way consumers interact with their prey (e.g. diet, feeding modes, motility, and perception of sounds) or the way prey interact with their predators (e.g. toxin production, bioluminescence, migration) are emphasized in the following subparts.

Body size as a major functional trait driving trophic interactions

The functional trait of body/cell size plays a particularly important role and is often referred to as "a master trait". Size influences most of the ecological, physiological and behavioral functions of organisms due to metabolic laws, underpinning trophic position and interactions that are especially influenced by relative prey and predator sizes [START_REF] Weitz | Size and scaling of predator-prey dynamics[END_REF][START_REF] Conley | Mammoth grazers on the ocean's minuteness: a review of selective feeding using mucous meshes[END_REF]. Size or morphological characteristics can potentially be measured directly using imaging methods (see section A.1.3.2), and could be used to infer trophic relationships. Predator traits (i.e., body/cell size and motility type) may also be responsible for the body-size architecture of natural food webs in freshwater, marine and terrestrial ecosystems [START_REF] Brose | Predator traits determine food-web architecture across ecosystems[END_REF]. At large spatial scales, body size and prey selection were shown to be modified by climate change and therefore to strongly impact food webs and ecosystem functions in return (Hoegh-Guldberg and Bruno 2010; Sheridan and Bickford 2011). For example, ocean warming was associated with a reduction in copepod body size, which may impact upper trophic levels and ultimately fisheries [START_REF] Beaugrand | Marine biodiversity, ecosystem functioning, and carbon cycles[END_REF], but see also [START_REF] Renaud | Pelagic food-webs in a changing Arctic: a trait-based perspective suggests a mode of resilience[END_REF]. More general laws between size, trophic interactions and environmental variables could be tested in future trait-based studies, especially by taking advantage of automatic morphological measurements (including but not restricted to size) through imaging. More specifically, direct observations of predator-prey interactions and associated traits could be performed by combining imaging [START_REF] Choy | Deep pelagic food web structure as revealed by in situ feeding observations[END_REF][START_REF] Ohman | A sea of tentacles: optically discernible traits resolved from planktonic organisms in situ[END_REF] with gut content and/or faeces analysis based on taxonomic and/or -omics description, such tools being complementary and sometimes even more informative than the stable isotope methods that have been traditionally used so far (e.g. [START_REF] Majdi | There's no harm in having too much: A comprehensive toolbox of methods in trophic ecology[END_REF].

Including stoichiometric traits to study trophic interactions

In addition to body size, stoichiometric traits are highly promising for integrating FTBAs into food web models and to bridge the gap between community structure and ecosystem functioning [START_REF] Meunier | From Elements to Function: Toward Unifying Ecological Stoichiometry and Trait-Based Ecology[END_REF]. Because all organisms are composed of the same major elements (e.g. C, N, and P), their balance not only reflects nutrient cycling in the ecosystem but also food web topologies. Quantifying stoichiometric traits across taxonomic and trophic groups allows the depiction of trophic interactions. In food web approaches, trophic position is associated with significant changes in C:N:P ratios, as well as altered isotope ratios due to selective uptake. As an example, heterotrophs are generally relatively less rich in carbon than autotrophs [START_REF] Hessen | Carbon Sequestration in Ecosystems: The Role of Stoichiometry[END_REF][START_REF] Persson | To be or not to be what you eat: regulation of stoichiometric homeostasis among autotrophs and heterotrophs[END_REF]). However, while stoichiometric composition and variation have been quantified for some species in different taxonomic groups (e.g. some plants, marine bacteria or plankton), there is still a lack of knowledge of the C:N:P ratios and their variations for numerous taxa, including higher-level consumers (e.g. [START_REF] Frost | Ecological stoichiometry of trophic interactions in the benthos: Understanding the role of C:N:P ratios in lentic and lotic habitats[END_REF][START_REF] Frost | Threshold elemental ratios of carbon and phosphorus in aquatic consumers[END_REF]. Stoichiometric gradients may also inform on some specific traits such as growth rate, food preferences, nutrient acquisition and on some life history traits type such as fecundity, or even genome and cell size (see review in [START_REF] Carnicer | Global biodiversity, stoichiometry and ecosystem function responses to human-induced C-N-P imbalances[END_REF]. Indeed, stoichiometric ratios have the advantage of being directly related to organismal growth rates, which are central life history traits. The "growth rate hypothesis" demonstrates that rapidly growing organisms commonly have low biomass C:P and N:P ratios. This observation is explained by a high demand for P-rich ribosomal RNA, but also by the shorter lifespan of faster growing organisms, which prevents large investments into reserve structures [START_REF] Elser | Organism Size, Life History, and N:P Stoichiometry[END_REF][START_REF] Elser | Growth rate-stoichiometry couplings in diverse biota[END_REF]. Consumers stoichiometry, in addition to metabolic characteristics, also gives important information on consumers driven nutrient recycling [START_REF] Allen | Towards an integration of ecological stoichiometry and the metabolic theory of ecology to better understand nutrient cycling[END_REF]. Better documenting the stoichiometric ratios of aquatic organisms in existing trait databases would help to identify their drivers and thus improve our understanding of the impact of stoichiometric traits on food web dynamics and ecosystem functioning.

From aquatic functional traits to global biogeochemical cycles

Finally, studying aquatic food webs following a FTBA should improve predictions of nutrient and carbon fluxes at the ecosystem scale [START_REF] Vanni | Predicting nutrient excretion of aquatic animals with metabolic ecology and ecological stoichiometry: a global synthesis[END_REF]. For example, trait-based models of food webs could be constructed to infer trophic interactions influencing ecosystem stocks and fluxes [START_REF] Woodward | Quantification and Resolution of a Complex, Size-Structured Food Web[END_REF]. In addition to size and stoichiometry, several other functional traits could be taken into account in these models, such as predator foraging and prey vulnerability traits [START_REF] Boukal | Trait-and size-based descriptions of trophic links in freshwater food webs: current status and perspectives[END_REF]). To do so, one promising pathway is to increase the exploitation of trait databases.

For example, global datasets of marine plankton abundances and biomass were recently coupled with a trait-based model used to predict dominant feeding strategies in pelagic ambush predators and to estimate the effects of these feeding traits on energy and biomass transfer efficiency [START_REF] Prowe | Biogeography of zooplankton feeding strategy[END_REF]. For fish, diets and trophic strategies can be predicted from their functional traits A.1 Functional trait-based approaches as a common framework for aquatic ecologists [START_REF] Albouy | Predicting trophic guild and diet overlap from functional traits: statistics, opportunities and limitations for marine ecology[END_REF]. This approach could be extended to other aquatic organisms. Scaling-up from individual traits to food web dynamics should ultimately contribute to better understand the response of aquatic ecosystems to environmental changes in terms of biogeochemical cycling, ultimately improving long-term prediction of ecosystem dynamics and feedback mechanisms to climate.

A.1.5 Conclusions

The main goal of FTBAs is to improve our understanding of the links between community structure, ecosystem function and ecosystem service provision. The main advantages of such approaches come from the definition of traits at the individual level. Indeed, this allows for the direct measurement of the functional traits of any organism without an additional step of taxonomic assignment that may be time-consuming. This can also provide access to universal ecological rules (transcending trophic levels and ecosystems). On the contrary, FTBAs would not be adapted to study population dynamics that require taxonomic description at the species level, nor to directly estimate bulk properties of the communities (which would require summing of individual-level information). For these reasons, the description and quantification of functional traits provide a common basis across diverse ecological fields, from ecophysiology to community and ecosystem ecology, via population and evolutionary biology. Yet, distinct questions and methods are often specific to each identified habitat (i.e. benthic and pelagic) or even to each biological compartment (i.e. invertebrates and diatoms for the freshwater benthic habitat). Here, we proposed functional trait-based pathways across multiple ecological components. As a first step, we: i) homogenized the terminology used in FTBAs and provided a common typology for aquatic functional traits that can be used across various aquatic systems and for multi-compartment studies, ii) listed the currently available databases dedicated to (aquatic) functional traits, iii) described classical and emerging methods for estimating traits of marine and freshwater organisms, and iv) highlighted some key traits that could be used for multi-compartment and trans-ecosystem studies. Establishing such a common ground among aquatic ecologists is required to further encourage and stimulate collaborative research across disciplines. The next step would be to create a common ontology dedicated to FTBAs, such as the Open Traits Network initiative (Gallagher et al. 2019), in order to improve the sharing of trait information in databases.

The recent methodologies we described offer new opportunities to study traits at various scales, from -omic sequences to whole-ecosystem approaches and biogeochemical cycles. Imaging, -omics and modeling tools are amongst the most promising emerging approaches to work with traits across the tree of life. We propose extending discussions within aquatic ecologists, including freshwater, marine, benthic and pelagic fields, to better share expertise in these tools, thereby improving our knowledge on potential and realized functional traits. With these methodologies, FTBAs provide promising foundations for the development of integrated frameworks that combine ecological theories with empirical knowledge across scales. Authors contribution statement SM, SDA and ML proposed the initial idea and wrote the original draft. FL, AB and EF contributed to writing some subsections of the original draft. EF performed the mental map. All authors contributed to and reviewed the different portions and versions of the manuscript according to their respective scientific expertise.

Supplementary information

A database of 2,476 publications related to trait-based approaches was extracted from a search 2010). To create the network, data were cleaned using a thesaurus file, merging keywords into more general terms and homogenizing them (removing plurals, merging synonyms). For example "trait plasticity", "trait evolution", "trait ecology", "trait adaptation", "quantitative trait", "biological trait approach" are closely related terms and were merged into "trait". This step is important to increase the overlap between publications. The analysis method relied upon an incidence

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A.1 Functional trait-based approaches as a common framework for aquatic ecologists matrix of 13,337 keywords, appearing in at least 26 publications. Based on this threshold, 100 top-keywords of the highest co-occurrence were identified. A network was built based on cooccurrence calculations (i.e. nodes correspond to keywords, edges correspond to the strength of the links between keywords) and for its representation the diameter of the corresponding circle was plotted as proportional to their total occurrence in the 2,476 publications. The association strength method (also called proximity index, for computation see van Eck and [START_REF] Van Eck | How to normalize cooccurrence data? An analysis of some well-known similarity measures[END_REF] was used to normalize the strength of the links between keywords. There were 3 clusters (blue, orange and purple), identified as minimizing the distance between nodes (for the weighted and parameterized variant of modularity-based clustering method used for bibliographic networks, see [START_REF] Waltman | A unified approach to mapping and clustering of bibliometric networks[END_REF], with a minimum size defined as 20 keywords for easier interpretation.

Trait-based modeling

Trait-based ecological models can simulate functional traits as a continuum of trait values according to particular trade-offs (Follows and Dutkiewicz 2011). In such models, the numerical analog of an "individual" is the numerical entity defined by a unique vector of parameters that constitute its numerical traits. These models then simulate the response of individual phenotypic plasticity to environmental forcing and frequently rely on optimization for maximizing individual fitness [START_REF] Smith | Optimality-based modeling of planktonic organisms[END_REF]. When parameters can vary to represent genotypic variability, these models can also be used to model trait adaptation and evolution (such as temperature optimum trait of phytoplankton growth, e.g. [START_REF] Grimaud | Modelling the effect of temperature on phytoplankton growth across the global ocean[END_REF]. Most of these trait-based models focused on protists, as it may be easier to describe in a mechanistic way the functional traits and associated trade-offs of unicellular organisms [START_REF] Merico | A trait-based approach for downscaling complexity in plankton ecosystem models[END_REF]Follows and Dutkiewicz 2011).

However, trait-based models were also developed for multicellular organisms such as copepods [START_REF] Prowe | Biogeography of zooplankton feeding strategy[END_REF] or even fish (e.g. [START_REF] Andersen | The theoretical foundations for size spectrum models of fish communities J. Baum[END_REF]. In these models, the traits that are the most commonly simulated are morphological and/or physiological traits, in particular traits that relate to size and allometric relationships (e.g. [START_REF] Hartvig | Food web framework for size-structured populations[END_REF]Acevedo-Trejos et al. 2015;[START_REF] Andersen | The theoretical foundations for size spectrum models of fish communities J. Baum[END_REF][START_REF] Blanchard | From Bacteria to Whales: Using Functional Size Spectra to Model Marine Ecosystems[END_REF]) and/or to resource acquisition, including assimilation rates (e.g. [START_REF] Fiksen | Trait-based models of nutrient uptake in microbes extend the Michaelis-Menten framework[END_REF] or light-harvesting vs nutrient-harvesting investment for protists (with usually a focus on pigment-related traits, e.g. (Litchman et al. 2007;[START_REF] Hickman | Modelling the effects of chromatic adaptation on phytoplankton community structure in the oligotrophic ocean[END_REF] or more recently on mixotrophy (e.g. [START_REF] Chakraborty | Trophic Strategies of Unicellular Plankton[END_REF][START_REF] Leles | Modelling mixotrophic functional diversity and implications for ecosystem function[END_REF].

Some models have also focused on modeling life history traits, such as copepod dormancy [START_REF] Maps | A metabolic approach to dormancy in pelagic copepods helps explaining inter-and intra-specific variability in life-history strategies[END_REF] or bivalve fecundity and age at maturity (Sarà et al. 2013). Another type of traitbased model relies on traits to define functional groups whose dynamics are explicitly simulated, such as the Plankton Ecology Group (PEG) models [START_REF] Sommer | 1 Functional trait-based approaches as a common framework for aquatic ecologists[END_REF][START_REF] Sommer | Beyond the Plankton Ecology Group (PEG) Model: Mechanisms Driving Plankton Succession[END_REF] or the Plankton Functional Type (PFT) models [START_REF] Le Quere | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF][START_REF] Hood | Pelagic functional group modeling: Progress, challenges and prospects[END_REF]. In these models, traits are usually fixed parameters (e.g. size, nutrient assimilation rates, trophic regime) that are used in a mechanistic approach of ecological and physiological processes relying on trade-offs between the fundamental functions of the organisms (e.g. [START_REF] Smith | Leaving misleading legacies behind in plankton ecosystem modelling[END_REF]. Functional types based on size, feeding modes and ecosystem engineering (bioturbation) have also been used for modeling benthic fauna (e.g. [START_REF] Chardy | Carbon flows in a subtidal fine sand community from the western English Channel: a simulation analysis[END_REF][START_REF] Rosenberg | Marine benthic faunal successional stages and related sedimentary activity[END_REF][START_REF] Alexandridis | Qualitative modelling of functional relationships in marine benthic communities[END_REF]. Dynamic Energy Budget (DEB) models [START_REF] Nisbet | From molecules to ecosystems through dynamic energy budget models[END_REF] could also be seen as trait-based models that rely on the DEB theory [START_REF] Kooijman | Dynamic Energy and Mass Budgets in Biological Systems[END_REF] and its trade-offs among energy allocation.

Several models actually borrow from both approaches by defining, for example, a few groups of organisms for which life-cycle strategies or peculiar trade-offs are hardwired, while allometric relationships to the size master trait allow for inter-individual or inter-specific variability within the functional groups (e.g. Ward et al. 2012). Finally, statistical trait-based models (involving statistical relationships rather than differential equations based on mechanistic assumptions) have also been developed, for instance for relating functional traits to body trace metal concentrations in freshwater invertebrates [START_REF] Peter | Modeling whole body trace metal concentrations in aquatic invertebrate communities: A trait-based approach[END_REF] . Trait-based models were used for simulating the distribution of traits at the community scale [START_REF] Andersen | Asymptotic Size Determines Species Abundance in the Marine Size Spectrum[END_REF], emergent trait biogeography (Follows et al. 2007;[START_REF] Record | Emergent copepod communities in an adaptive trait-structured model[END_REF], size spectrum (e.g. [START_REF] Andersen | The theoretical foundations for size spectrum models of fish communities J. Baum[END_REF], competition among species and/or seasonal dynamics [START_REF] Merico | A trait-based approach for downscaling complexity in plankton ecosystem models[END_REF][START_REF] Terseleer | Trait-based representation of diatom functional diversity in a plankton functional type model of the eutrophied southern North Sea[END_REF][START_REF] Leblanc | Nanoplanktonic diatoms are globally overlooked but play a role in spring blooms and carbon export[END_REF]), impact of traits on biogeochemical cycles [START_REF] Sommer | 1 Functional trait-based approaches as a common framework for aquatic ecologists[END_REF]Coles et al. 2017), or impact of human pressures such as fishing on trait and trophic structure [START_REF] Andersen | Damped trophic cascades driven by fishing in model marine ecosystems[END_REF]. Traitbased models can also be used for estimating unknown traits, for instance from phylogeny [START_REF] Bruggeman | A Phylogenetic Approach to the Estimation of Phytoplankton Traits1[END_REF] or for estimating the inter-and intra-specific variability of traits [START_REF] Maps | A metabolic approach to dormancy in pelagic copepods helps explaining inter-and intra-specific variability in life-history strategies[END_REF]. A description of some examples of trait-based models developed in marine ecology can be found in Kiørboe et al. (2018), with a special focus on optimality-based resource acquisition in unicellular plankton and size-based trophic dynamics of fish communities. To our knowledge, mechanistic trait-based models developed for benthic organisms remain relatively scarce (e.g. Sarà et al. 2013) and have considered size, ecosystem engineering, adult motility, fecundity and dispersal. A few statistical trait-based models have also been developed for these organisms (e.g. [START_REF] Peter | Modeling whole body trace metal concentrations in aquatic invertebrate communities: A trait-based approach[END_REF]. With the exception of a few studies (e.g. [START_REF] Maps | A metabolic approach to dormancy in pelagic copepods helps explaining inter-and intra-specific variability in life-history strategies[END_REF][START_REF] Banas | Traits controlling body size in copepods: separating general constraints from species-specific strategies[END_REF], the intra-specific variability of the traits is poorly or not taken into account, mainly because of the lack of empirical information on this variability. A first perspective for trait-based modeling could then be to tackle this question of inter-individual variation of traits and its impact on ecosystem structure and functioning. Trait-based models can for instance be used to quantify the impact of environmental changes on the intra-and inter-specific variability of functional traits (e.g. inter-individual variability of lipid content and inter-specific variability of size of Arctic copepods in the Barents Sea, [START_REF] Renaud | Pelagic food-webs in a changing Arctic: a trait-based perspective suggests a mode of resilience[END_REF]. A second perspective would be the development of a new generation of trait-based models using -omics data (Mock et al. 2016;Stec et al. 2017;Coles et al. 2017). Metatranscriptomic data could for instance be used to identify physiological traits of phytoplankton, combined to a mechanistic model of the phytoplankton cell, and used to construct a trait-based global marine ecosystem model (Mock et al. 2016). Emergent communities of marine microbes (from bacteria to phytoplankton) have also been predicted by directly simulating their metagenomes and metatranscriptomes (Coles et al. 2017). Such models could then be used for estimating functional trait plasticity across ecosystems. The idea of improving ecosystem models using -omics is not new [START_REF] Hood | Pelagic functional group modeling: Progress, challenges and prospects[END_REF]), but functional trait-based approaches could constitute the common framework needed for modellers, observers, molecular biologists, and ecologists working in limnology and oceanography.

Supplementary references (not cited in main text)

Acevedo-Trejos, E., G. Brandt, J. Bruggeman, and A. Merico. 2015. Mechanisms shaping size structure and functional diversity of phytoplankton communities in the ocean. Sci. Rep. 5: 8918. The "freshwater", "river" and "stream" ecosystems drive the first cluster as well as "invertebrate" organisms, while the second cluster refers to "marine" ecosystems with "fish", "zooplankton" and "phytoplankton" organisms. These clusters highlight different ecological questions either linked to "indicators" and "land-use" (cluster 1 in orange) or to "food webs" and "climate-change" (cluster 2 in purple). The third cluster (in blue) is mainly associated with theoretical ecology, grouping "biodiversity", "community", "ecology", "ecological functioning" and "pattern" showing numerous links rather evenly distributed between freshwater and marine ecosystem studies. This third cluster also refers to "sediment" or "organic matter", two components transcending both limnology and oceanography. "microbiology" and "technology" (Figure A.4).

Supplementary Figures

transcriptomes of mixotrophic protists

Taxonomy and access numbers for each of the 113 transcriptomes of mixotrophic species identified among the 798 used in Chapter 3.