Jean-François Humbert

Ça y est, c'est la fin d'une belle aventure. Les microorganismes y auront joué un rôle prépondérant, mais il faut bien le dire, sans des macroorganismes de qualité autour de moi, ces quelques années n'auraient pas été les mêmes...

your crazyness, for your curiosity for all living beings, and for being such a good friend. Guillaume, pas seulement mon co-bureau mais aussi mon coloc, apparemment, se voir tous les jours au labo ne suffisait pas... Merci pour ta gentillesse, pour ta bonne humeur, pour le poivre de sapin et bien sûr pour tes plats trop pimentés. Merci à Jodie, pour toutes tes folles aventures qui auront bien rythmé nos journées, mais surtout pour ton enthousiasme et ton soutien.

Merci à Maria, pour le chocolat, pour le réconfort, pour les repas du midi dans le jardin. Cette fin de thèse n'aurait vraiment pas été la même sans ta présence et tes attentions quotidiennes. Merci d'avoir pris soin de moi.

Merci à tout le reste de la d(r)eem team ! Thomas, merci pour les jeux de mots, l'inventivité, les gadgets improbables et les qualités de producteur/réalisateur. Tout ça va bien me manquer. Fabian, pour les "C'est la vie". Guifré, merci pour nous avoir accueillis dans ton bureau au début de la thèse et pour ta présence tout du long. Rafael (Rrrafouu), pour ton aide à nos débuts avant que tu ne nous abandonnes et pour ta gentillesse ! Miguel, merci pour ton enthousiasme de tous les jours. Julien, merci pour toutes les discussions, scientifiques ou non et pour le partage de tes réflexions. Merci à Laura, Philippe, Naoji, Ana, Jolien. Merci à Brittany, Jazmin et Feriel, bon courage pour vos thèses, kut pour m'avoir emmenée au fin fond de la Sibérie pour une mission incroyable et dépaysante. Merci mille fois de m'avoir donné une telle opportunité, je n'aurais jamais pensé dans ma vie avoir l'occasion de couper des m² d'herbe aux ciseaux assise sur des polygones.

Merci à Jean-Nicolas, Laurent et Cybill pour m'avoir encadrée pendant mon stage de master 2. Je n'aurais pu espérer mieux comme stage de recherche : du terrain, des manips, de l'analyse de données et de la motivation pour rédiger mon premier article. Cependant, pas du tout merci à Laurent pour la déformation, maintenant je vois des cymbalaria partout et tout le temps.

Vielen Dank Bernd! You welcomed me during 3 months in Magdeburg, and it was a great experience, professionally and humanely.

Mille mercis à Maman pour croire en moi depuis toute petite, me motiver toujours un peu plus et bien sûr pour toutes les relectures attentives, à Pap's pour tes idées farfelues, à Leslie (le yang de mon yin) pour la vie parisienne et les sorties culturelles entre poule poule, à ma Mamie pour les confitures et la couture, à ma Grand-mère pour ses "c'est comme ça", à mon Grand-père, à Marie pour les hérissons et les conseils lectures, à Béné pour sponsoriser ma vaisselle, à Simon (la colocation fut brêve, mais intense), Didi, Mat, Mélou d'être des germains si chouettes, à Vincent pour le bateau. A Saïf pour le tofu aux olives et le sticky rice (promis, pas que pour ça). A Laurent et Elisabeth pour les repas à Gif, les coings et votre présence. A Bruno pour sa merveilleuse statuette de Dodo. Merci à Joëlle et Marc pour les expos et les séjours sur Ré. Merci à Francis pour toutes les vacances et à Izia pour les dessins.

Merci à tous les copains sans qui la vie serait quand même drôlement moins chouette ! A ma Nenni si précieuse qui me supporte depuis 15 ans. A ma Astrid (ou Astroud pour les intimes), la meilleure des dentistes, pour les moments rennais passés et à venir. A mes acolytes princesses et à nos week-ends entre copines : Clémence, pour ta bonne humeur h24, le vélo/vin, les soirées-douche à Moulins, les couronnes de fleurs et les plus belles vacances (liste clairement non exhaustive); Laure, mon double, pour les post-it, les calendriers de l'avent, la passion pour Pio, le soutien indéfectible et tant d'autres choses. A ma Manon, merci pour ta présence quotidienne (coucou copine !), pour le serpent dans la botte, pour Margaux et pour toutes les aventures qui arrivent prochainement... A Lisa, pour la collocation à Massy, les licornes, toutes les soirées jeux et pour ta folie, surtout. A Baptou et Laure, les deux Mours, pour l'accueil toujours trop bien, les appels pour dire des blagues et le parrainage de Bobby. A la pitchou et associés, Roro (et Denis), Lolo (les pousse-Doline me manquent presque), Aline (trop hâte de venir dans ton château bb) et Coup-coup (la meilleure créatrice de sauces). A Lucas et ses cuillères, Constant (dommage qu'on puisse pas imiter Camille à l'écrit), Jéroums et tous les copains/copines de l'ENGEES. Au super her'eau de l'ombre pour sa subtilité, à Thomas et Emilie, à Céline, à Théo et son bob, à Pierre. A Justine, parce que je sais que tu n'es jamais bien loin de moi.

Thanks to Siri, and I hope we will see each other in Berlin! Aux copains du lycée, Charles et ses leçons d'architecture de nuit, Gaëlle, Arielle, Laurent, et soon to be Dr. Foni. Merci à Christoph, François, Philippe et Prunelle pour m'avoir permis de reprendre la musique de la meilleure façon qui soit.

Pour terminer, un très grand merci à Maxime. Merci d'être toujours là à mes côtés pour me motiver et croire en moi. Merci de ton aide inestimable sur R, pour avoir toujours plein d'idées à proposer. Merci d'accepter toutes mes lubies sans me juger. Merci pour les macarons et la cuisine en général, les plantes, les vehiculo longo, les montées et descentes d'escaliers, les soirées jeux, ta gentillesse, ta curiosité, ton humour et j'en passe. En résumé... merci pour la vie ensemble ! Et bien évidemment, je remercie chaleureusement Par Jupiter, les coloriages et les dinosaures. Since the mid-seventeenth, microbial communities have been explored in a large range of environments. The first observations of microorganisms in the nature were only descriptive and resulted from serendipity and were conducted by two scientists. In 1674, Antoni van Leeuwenhoek (1632Leeuwenhoek ( -1723)), a draper from Delft, built a hand-made microscope to investigate the quality of its fabrics [START_REF] Corliss | Three centuries of protozoology: a brief tribute to its founding father, a. van leeuwenhoek of delft[END_REF]. Fascinated by his observations, he investigated various environments, in particular freshwater ponds, and was the first to describe protists, that is microbial eukaryotes. At that time, they were named "animalculum", from the Latin "tiny animal". In France, Louis Joblot (1645-1723) a mathematician, built microscopes and published drawings of his observations in 1718 and in several publications later (see Figure 1.1, [START_REF] Lechevalier | Louis joblot and his microscopes[END_REF].

The microorganisms observed at that time were among the largest ones, and nowadays we know that they are covering an important size fraction, ranging from 0.2 to 100 µm [START_REF] Azam | Microbial structuring of marine ecosystems[END_REF]. From this period, the fascination for microorganisms, also called the "unseen majority" did not stop growing. Indeed, to decipher the diversity of microbial communities, it is essential to investigate all types of environments. Therefore, even if aquatic systems have been among the first ones investigated because water is crucial for life, various types of ecosystems have been and are investigated nowadays (e.g. soil, digestive track, extreme environments). Aquatic ecosystems are still widely studied, because of their major importance at different levels. At the global scale, aquatic ecosystems are important reservoirs of liquid water. They are known to regulate climate, and to have a role in biogeochemical cycles [START_REF] Worden | Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes[END_REF][START_REF] Cavicchioli | Scientists' warning to humanity: microorganisms and climate change[END_REF]. For instance, oceanic investigations revealed that 50% of the photosynthesis on Earth is conducted in marine waters [START_REF] Seymour | Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships[END_REF]. Aquatic ecosystems offer various types of habitat, and harbour a wide diversity of life forms, from mammals to bacteria. At a local and anthropogenic scale, they provide diverse ecosystem services. Among others, they are source of drinking water, of food through the fishes, and allow the irrigation of crops. Water-bodies also constitute commercial paths, as well as recreational areas. However, the study of aquatic environments is challenging, as they are constituted of a wide variety of systems, including for instance marine coastal areas, wide lakes, deep ocean, rivers, or small ponds.

To understand the functioning of ecosystems, it is important to describe the kind of organisms that occur, to decipher the spatial distribution and temporal dynamics of organisms, and to apprehend their interactions with other organisms (biotic interactions) and their environment (abiotic interactions). This is what I addressed in my thesis, by focusing on microorganisms of the three domains of life in Lake Baikal and in small freshwater ecosystems. In this introduction, I first depict the diversity of aquatic ecosystems on Earth. Then, I expose the importance of microorganisms in these environments, in terms of diversity and function. Finally, I present the different factors influencing microbial communities, as well as the importance of considering aquatic ecosystems as dynamic environments, that present both spatial and temporal variations.

Overview of aquatic ecosystems dynamics

Three quarters of the surface of Earth, also called Blue Planet, are covered by water. Marine systems contain 97% of water, while the remaining 3% is terrestrial and defined as freshwater. Water on Earth is constantly renewed through the hydrological cycles and circulates from one reservoir to another. The study of marine systems is the domain of oceanography, from the Greek word "okeanos" [START_REF] Pinet | Invitation to oceanography[END_REF]. Ocean exploration started with the Greeks by 4000 BC, but first expeditions to investigate marine systems and more particularly marine plankton were conducted by Charles Darwin (1809Darwin ( -1882)). Indeed, the expeditions on the Beagle and the Challenger (1835,(1872)(1873)(1874)(1875)(1876) allowed him to discover radiolarians and diatoms. Oceanography covers the investigation of the whole marine volume, from surface water to deep sea and hydrothermal vents. The study of inland freshwater systems is called limnology, from the Greek word "limne" meaning the lake. This term was proposed by F.A. Forel in 1904 following his work on Lake Geneva [START_REF] Forel | The global nitrogen cycle in the twenty-first century[END_REF]. It covers the investigation of all freshwater systems, both lentic (i.e. stagnant) and lotic (i.e. running).

Characteristic of aquatic ecosystems

Two of the major differences between marine and inland freshwater systems are the salinity and the nutrient state. Marine environments cover up to 70% of Earth surface, and they are characterized by their salinity, which is relatively stable around 35on average [START_REF] Madsen | Flash: fast length adjustment of short reads to improve genome assemblies[END_REF].

They have a weak nutrient load, and are thus mainly oligotrophic except in up-welling zones where the ecosystem productivity is high [START_REF] Madsen | Flash: fast length adjustment of short reads to improve genome assemblies[END_REF]. Freshwater systems are defined by the USGS (United States Geological Survey) as a water system containing less than 1000 mg/L of dissolved solids. In comparison to marine systems, freshwater systems are more diversified and they present a wide range of nutrient states, from oligotrophic to highly eutrophic waters.

Freshwaters can be divided into lentic and lotic ecosystems. In addition, they can be divided into size-categories (e.g. "small" or "large"), but are not accurate because there is an environmental continuum of freshwater ecosystems sizes [START_REF] Wellborn | Mechanisms creating community structure across a freshwater habitat gradient[END_REF]. Freshwater ecosystems are also classified based on their lifespan. For instance, lentic ecosystems range from small ephemeral ponds generated after a rain event to large and profound lakes formed millions years ago [START_REF] Wellborn | Mechanisms creating community structure across a freshwater habitat gradient[END_REF]. Traditionally, lakes are opposed to ponds, even if their definitions remain unclear. Indeed, some authors limit the ponds to water-bodies with a surface ≤ 0.001 km 2 [START_REF] Downing | Emerging global role of small lakes and ponds: little things mean a lot[END_REF], while the European Pond Conservation Network (EPCN) goes up to 0.02 km 2 width and a maximal depth of 8 m [START_REF] Indermuehle | Pond conservation in europe: The european pond conservation network (epcn)[END_REF].

Therefore, the definition of pond covers a large variety of ecosystems, from the small puddle created after the rain to larger permanent water-bodies [START_REF] Biggs | The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers[END_REF]. In the case of lotic systems, small ecosystems correspond to small tributaries and brooks, whereas large ecosystems correspond to rivers presenting a wide floodplain. As for ponds, there is no clear definition of small streams. They can be defined as the small order streams, according to the Strahler's classification [START_REF] Strahler | Quantitative analysis of watershed geomorphology[END_REF][START_REF] Moss | Widespread dominance of kinetoplastids and unexpected presence of diplonemids in deep freshwater lakes[END_REF]. For instance, a stream with no tributaries, that is of order 1, is a small stream. Their width is also often used to classify them. The small lotic systems are generally defined as having a maximal width of 1.5 m [START_REF] Biggs | The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers[END_REF].

Spatial changes

Within aquatic ecosystems, multiples types of habitats are present due to structural spatial variations. Indeed, there are differences in horizontal and vertical dimensions, leading for instance to changes in exposure to light, temperature [START_REF] Lewis | ) Encyclopedia of Inland Waters[END_REF][START_REF] Madsen | Flash: fast length adjustment of short reads to improve genome assemblies[END_REF][START_REF] Brönmark | The biology of lakes and ponds[END_REF]. In lotic systems, habitat variations are observed from upstream to downstream. The environmental conditions evolve from the source to the sea, as described by the River Continuum Concept [START_REF] Vannote | The river continuum concept[END_REF]. Along the water course, there is a progressive shift of abiotic conditions, communities and therefore of functioning of the ecosystem [START_REF] Vannote | The river continuum concept[END_REF]. For instance, the quotient between production and respiration evolves with the stream order. Also, the water quality tends to decline along the water course, leading to a spatial eutrophication of the system [START_REF] Vannote | The river continuum concept[END_REF]. These variations result in changes in the composition of microbial communities [START_REF] Zancarini | Deciphering biodiversity and interactions between bacteria and microeukaryotes within epilithic biofilms from the loue river, france[END_REF].

Similarly, for marine and lentic systems, there are horizontal spatial differences, from littoral to pelagic areas. These ecosystems can be divided into two to three horizontal zones (see e.g. Figure 1.2 adapted from [START_REF] Madsen | Flash: fast length adjustment of short reads to improve genome assemblies[END_REF]. The littoral zone corresponds to the shallowest area. It is influenced by the shore and the light goes through the whole water column. This zone is often characterized by a wide biodiversity and hosts rooted aquatic plants [START_REF] Brönmark | The biology of lakes and ponds[END_REF]. In marine systems, an intermediate zone exists between the littoral and pelagic zones, called the neritic zone which covers the continental shelf. Finally, the pelagic zone corresponds to the offshore areas, which is also called limnetic zone for freshwater systems. Compared to the pelagic zone, the littoral area is more subject to perturbations. Therefore, microbial communities vary along this horizontal gradient [START_REF] Obertegger | Temporal variability of bacterioplankton is habitat driven[END_REF].

Finally, ecosystems also change from the surface to the bottom. On this vertical axis, the light is the main factor delimiting the different zones (see Figure 1.2), since it is determinant in the photosynthetic process [START_REF] Sarmiento | Ocean biogeochemical dynamics[END_REF]. The light penetration segregates two main areas, which are called the euphotic and the aphotic zones. The euphotic zone is the area where photosynthesis can occur [START_REF] Lewis | ) Encyclopedia of Inland Waters[END_REF]. The aphotic area is the depth under which the light is ≤ 1% of the surface irradiance [START_REF] Lewis | ) Encyclopedia of Inland Waters[END_REF][START_REF] Pinet | Invitation to oceanography[END_REF]. Therefore, its depth varies greatly depending on water masses. In addition to the light stratification, four depthlevels can be identified in aquatic systems [START_REF] Lewis | ) Encyclopedia of Inland Waters[END_REF][START_REF] Moss | Widespread dominance of kinetoplastids and unexpected presence of diplonemids in deep freshwater lakes[END_REF][START_REF] Kurt | Depth distribution of microbial diversity in lakes[END_REF]. First, the neuston layer that corresponds to the air-water interface is an area of nutrients accumulation [START_REF] Kurt | Depth distribution of microbial diversity in lakes[END_REF]. Then, the water column, that corresponds to the global volume of water filling the marine system or lake, from the surface to the benthic zone. Finally, at the bottom of the ecosystems are found the benthic zone and the sediments. The benthic zone is the interface between the water column and the lacustrine sediments. The functioning of small freshwater systems is however quite different from what is described above, because of their small water volume and shallower depth. In these systems, the light can often reach the sediments leading to the absence of an aphotic area, as observed by [START_REF] Forel | The global nitrogen cycle in the twenty-first century[END_REF][START_REF] Oertli | Conservation and monitoring of pond biodiversity: introduction[END_REF]. Hence, primary production can occur in the whole water column, which is not happening in larger lakes. However, light penetration can be reduced by resuspension of sediments due to the mixing induced by winds [START_REF] Blottière | Effects of mixing on the pelagic food web in shallow lakes[END_REF]. [START_REF] Madsen | Flash: fast length adjustment of short reads to improve genome assemblies[END_REF] and [START_REF] Brönmark | The biology of lakes and ponds[END_REF].

Temporal changes

Aquatic ecosystems can be characterized by their temporal dynamics, at different scales. Shorttime changes in ecosystems can be observed yearly, following seasonal changes. In lakes, over the course of a year, the water column often experiences several phases of mixing and periods of stratification [START_REF] Jr | A revised classification of lakes based on mixing[END_REF]. Some lakes show a seasonal vertical zonation [START_REF] Lewis | ) Encyclopedia of Inland Waters[END_REF][START_REF] Brönmark | The biology of lakes and ponds[END_REF]. In temperate regions, the dimictic lakes are the most common.

They alternate between periods of stratification and ater column mixing twice a year. This inter-annual variability affects the communities inhabiting the ecosystems, with for instance the mixing of microorganisms from the different depth layers and the resuspension of cells from the sediments [START_REF] Shade | The influence of habitat heterogeneity on freshwater bacterial community composition and dynamics[END_REF][START_REF] Linz | Bacterial community composition and dynamics spanning five years in freshwater bog lakes[END_REF]. In small freshwater ecosystems, the small buffering capacity results in a fast response to any environmental perturbation or allochtonous input [START_REF] Hurst | Understanding aquatic microbial communities[END_REF]. For instance, inputs from surrounding terrestrial ecosystems can induce disruptions which will quickly modify water chemistry and communities. Therefore, it leads to continuous temporal changes of the conditions. So, small freshwater ecosystems follow local and regional weather conditions [START_REF] Blottière | Effects of mixing on the pelagic food web in shallow lakes[END_REF]. Moreover, small freshwater ecosystems are characterized by a very mobile water column. During the year, they experience strong variability in their hydrological regime, resulting in significant variations of their depth, extreme variations corresponding to a temporarily dryness of the ecosystem. If the dryness state is recurrent, the ecosystem is then classified as "temporary". Temporary streams represent on average more than 60% of the entire waterways [START_REF] Mcdonough | Temporary streams: the hydrology, geography, and ecology of non-perennially flowing waters[END_REF]. In small freshwater ecosystems, the water depth remains low throughout the year, which results in an absence of thermal stratification, contrary to what is observed in lakes. However, recent studies revealed that daytime stratifications can occur, combined to nocturnal mixing [START_REF] Andersen | Extreme diel dissolved oxygen and carbon cycles in shallow vegetated lakes[END_REF][START_REF] Martinsen | Water temperature dynamics and the prevalence of daytime stratification in small temperate shallow lakes[END_REF]. This impacts the distribution of oxygen in the ecosystem on very short scales, that may lead to oxygen depletion at the bottom of small freshwater ecosystems [START_REF] Briée | Archaeal and bacterial community composition of sediment and plankton from a suboxic freshwater pond[END_REF][START_REF] Andersen | Extreme diel dissolved oxygen and carbon cycles in shallow vegetated lakes[END_REF]. Further investigations should be conducted in the various small ecosystems to better decipher their mixing functioning, in order to avoid misinterpretations [START_REF] Biggs | The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers[END_REF].

Long-time changes are also observed in aquatic ecosystems, especially because freshwater ecosystems have a given life time. Indeed, lakes are created by different natural processes (e.g. tectonic, withdrawal of ice, volcanic; [START_REF] Brönmark | The biology of lakes and ponds[END_REF]. During their life time, lentic freshwater ecosystems are shrinking, going through ecological succession [START_REF] Carpenter | Submersed vegetation: an internal factor in lake ecosystem succession[END_REF]. Similarly to the spatial eutrophication in lotic systems, a temporal eutrophication is observed in lentic systems [START_REF] Anneville | Long-term study (1974-1998) of seasonal changes in the phytoplankton in lake geneva: a multi-table approach[END_REF]. This phenomenon is often enhanced by human activities [START_REF] Moss | Widespread dominance of kinetoplastids and unexpected presence of diplonemids in deep freshwater lakes[END_REF]. The successional stages of the freshwater ecosystems and their filling by sediments can be observed easily in small freshwater ecosystems, because they occur much faster than in larger systems. Indeed, if ponds are not properly managed, they can fill up and quickly become terrestrial ecosystems [START_REF] Angélibert | Seasonal variations in the physical and chemical characteristics of ponds: implications for biodiversity conservation[END_REF].

Hence, aquatic ecosystems are dynamic at different time scales. To assess their functioning and understand the dynamics of the microbial communities inhabiting them, it is therefore important to take into account multiple time scales. In this thesis, I will focus on small ecosystems that have a lifespan long enough to allow a survey of several years, typically 10 to 20 years (see the ecosystem presentation in Figure 2.2 and Table 2.1).

Functional specificities of small freshwater ecosystems

For a long time, small freshwater ecosystems have been left apart in the scientific literature. Indeed, ponds were considered to be of much less interest than lakes, and small streams appeared to be of negligible interest with respect to downstream reaches [START_REF] De Meester | Ponds and pools as model systems in conservation biology, ecology and evolutionary biology[END_REF][START_REF] Moore | Progress towards understanding the structure, function, and ecological significance of small stream channels and their riparian zones[END_REF][START_REF] Larned | Emerging concepts in temporary-river ecology[END_REF]. Indeed, they were considered as less important in the ecosystems because of their small size, or considered as small versions of bigger ecosystems [START_REF] Céréghino | The ecology of european ponds: defining the characteristics of a neglected freshwater habitat[END_REF]. Also, it has been hypothesized that the ecosystem size was positively correlated to biological richness, which made small ecosystems less interesting to study than larger ones [START_REF] Reche | Does ecosystem size determine aquatic bacterial richness?[END_REF]. However, the link between microbial diversity and ecosystem size is controverted, and the few studies dedicated to microbial communities in small freshwater ecosystems revealed a high diversity despite their small ecosystem size [START_REF] Lindström | Does ecosystem size determine aquatic bacterial richness? comment[END_REF][START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF][START_REF] Teittinen | Testing the theory of island biogeography for microorganisms patterns for spring diatoms[END_REF]. Therefore, there is nowadays a regain of interest for this type of ecosystem in the limnological field, because of their high frequency, diversity, and major ecological importance (see e.g. [START_REF] Gomi | Understanding processes and downstream linkages of headwater systems: headwaters differ from downstream reaches by their close coupling to hillslope processes, more temporal and spatial variation, and their need for different means of protection from land use[END_REF][START_REF] Søndergaard | Pond or lake: does it make any difference?[END_REF][START_REF] Downing | Emerging global role of small lakes and ponds: little things mean a lot[END_REF][START_REF] Biggs | The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers[END_REF][START_REF] Mullins | Big things come in small packages: why limnologists should care about small ponds[END_REF][START_REF] Pätzig | How much information do we gain from multiple-year sampling in natural pond research?[END_REF]. [START_REF] Downing | Emerging global role of small lakes and ponds: little things mean a lot[END_REF] estimated that ponds with a surface ≤ 0.001 km 2 represent an amount of 3.2×10 9 waterbodies for a total surface of 0.8 billion of km 2 . Most of the microbial studies on small freshwater ecosystems have been conducted on aquaculture ponds, because of the economic importance for fish production. For instance, the impact of cyanobacterial blooms is studied with the aim to reduce fish mortality; and microbial communities are investigated to improve water quality [START_REF] Sevrin-Reyssac | Cyanobacteria in fish ponds[END_REF][START_REF] Liu | Comparative analysis of microbial community structure in the ponds with different aquaculture model and fish by high-throughput sequencing[END_REF]. There is therefore a lack of studies in small freshwater ecosystems in natural environments.

Small freshwater ecosystems have a clear ecological importance. As they are numerous, they harbour a wide diversity of environmental conditions and localization (e.g. rural, forest or urbanized areas). Most of them are still weakly impacted by humans and therefore constitute preservation areas for various species [START_REF] Cantonati | Crenic habitats, hotspots for freshwater biodiversity conservation: toward an understanding of their ecology[END_REF][START_REF] Biggs | The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers[END_REF]. A wide range of species inhabits small freshwater ecosystems, either lentic or lotic. Even if fishes are often absent, ponds and streams harbour diverse communities of invertebrates, amphibians, or microorganisms, among others [START_REF] Céréghino | The ecology of european ponds: defining the characteristics of a neglected freshwater habitat[END_REF][START_REF] Downing | Emerging global role of small lakes and ponds: little things mean a lot[END_REF][START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF][START_REF] Teittinen | Testing the theory of island biogeography for microorganisms patterns for spring diatoms[END_REF][START_REF] Semlitsch | Intermediate pond sizes contain the highest density, richness, and diversity of pond-breeding amphibians[END_REF]. According to [START_REF] Biggs | The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers[END_REF], ponds are of major importance for endangered species preservation. Moreover, they play a significant role not only for water species but also for terrestrial species. For instance, they act as drinking areas for mammals, and they have a positive impact on pollinator populations [START_REF] Stewart | Ecosystem services across the aquatic-terrestrial boundary: Linking ponds to pollination[END_REF].

Moreover, small freshwater ecosystems play an important role in the global cycling of carbon and methane [START_REF] Downing | Emerging global role of small lakes and ponds: little things mean a lot[END_REF]. It has been shown that the carbon dioxide and the methane concentration in water are decreasing with increasing lake size [START_REF] Holgerson | Large contribution to inland water co 2 and ch 4 emissions from very small ponds[END_REF].

However, despite their small size, ponds have been identified as systems emitting but also sequestering carbon dioxide. They are also emitting methane, with an amount corresponding to 40% of the aquatic continental methane emission [START_REF] Abnizova | Small ponds with major impact: The relevance of ponds and lakes in permafrost landscapes to carbon dioxide emissions[END_REF][START_REF] Holgerson | Large contribution to inland water co 2 and ch 4 emissions from very small ponds[END_REF][START_REF] Yvon-Durocher | Long-term warming amplifies shifts in the carbon cycle of experimental ponds[END_REF]. Therefore, better understanding their functioning is of major importance in the context of global climate change.

Finally, small freshwater ecosystems are also an important actor of ecosystem services for humans [START_REF] Biggs | The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers[END_REF]. Ponds can be considered as potential water supply sources. Many man-made ponds were constructed with the objective of storing water, but we can also see them as preserved areas for drinking water. They can act as reservoirs of water and as retainers of the running water after rains, contributing to the global limitation of water-flow in the catchment area. Their ability to reduce pollutant loads has also been investigated, as well are their role of nutrient retention areas [START_REF] Mallin | Pollutant removal efficacy of three wet detention ponds[END_REF][START_REF] Gomi | Understanding processes and downstream linkages of headwater systems: headwaters differ from downstream reaches by their close coupling to hillslope processes, more temporal and spatial variation, and their need for different means of protection from land use[END_REF][START_REF] Cheng | Biogeochemical hotspots: Role of small water bodies in landscape nutrient processing[END_REF]. Small streams also play a major role in the global freshwater cycle as they are flowing in the direction of higher order streams, rivers, but also lentic systems [START_REF] Biggs | The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers[END_REF]. Finally, they can have a strong cultural impact. Man-created ponds can relate a part of the region history, in particular of the agricultural development. Ponds can be created for recreational purposes, in particular next to or within urbanized area. They are also used as material to teach to students or to vulgarize freshwater biodiversity and ecology [START_REF] Mullins | Big things come in small packages: why limnologists should care about small ponds[END_REF]. Nowadays, and for all the above reasons, conservation programs encourage preservation of small freshwater ecosystems [START_REF] Angélibert | Seasonal variations in the physical and chemical characteristics of ponds: implications for biodiversity conservation[END_REF][START_REF] Oertli | Pond conservation: from science to practice[END_REF][START_REF] Oertli | Freshwater biodiversity conservation: The role of artificial ponds in the 21st century[END_REF][START_REF] Hill | New policy directions for global pond conservation[END_REF]. This concerns also the preservation of micro-diversity, with the particular case of the Krauthügel pond in Austria. In 2012, this temporary pond and its buffer area have been declared as "Natural Monument for Single-celled Organisms", to protect its microbial diversity [START_REF] Cotterill | Conservation of protists: the krauthügel pond in austria[END_REF].

Microorganisms are key components of aquatic ecosystems

Microorganisms are ubiquitous in a large amount of ecosystems, where they are taking part in multiple processes within the systems [START_REF] Worden | Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes[END_REF]. Since Woese et al. work in 1990 on the phylogenetic marker of life, they are distributed in a phylogenetic tree of life composed of three branches, corresponding to the following domains: bacteria, archaea, and eukaryota (see Figure 1.3). Between these three domains, microorganisms share common ecological features (e.g. small cell size, wide and diverse populations), but they also present strong differences, in physiology, metabolic capacities, or trophic behaviour for instance (review by [START_REF] Massana | Eukaryotic versus prokaryotic marine picoplankton ecology[END_REF]. Therefore, it is important to focus on all the microbial diversity to investigate the functioning of aquatic ecosystems. In this thesis, I will focus on the microbial communities of the three domains of life living in freshwater ecosystems.

Wide microbial diversity in freshwater

Since the beginning of microbiology, microbial diversity has been investigated in various types of environments using a wide range of methods, including observation, culture, sequencing and metagenomic. Today, massive-sequencing technologies (also referred as next-generation sequencing, NGS) such as metabarcoding allow us to explore a large proportion of the microbial world, including the detection of new taxa [START_REF] Hug | A new view of the tree of life[END_REF]. These sequencing techniques are generating a large number of sequences, which are then regrouped into Operational Taxonomic Units (OTUs), or Amplicon Sequence Variants (ASVs), both being a proxy for species. However, among the entire microbial biodiversity detected by molecular approaches, a large proportion of the microorganisms remains uncultivated, which results in a lack of knowledge about their implication in the ecosystems.

A large diversity of microorganisms has been detected in lakes and river, for both prokaryotic and eukaryotic communities [START_REF] Zwart | Typical freshwater bacteria: an analysis of available 16s rrna gene sequences from plankton of lakes and rivers[END_REF]Newton et al., 2011;[START_REF] Pernthaler | A 'rare biosphere' microorganism contributes to sulfate reduction in a peatland[END_REF][START_REF] Debroas | Overview of freshwater microbial eukaryotes diversity: a first analysis of publicly available metabarcoding data[END_REF]. By comparing the bacterial communities at the surface of marine and freshwater systems, [START_REF] Barbera | Global phylogenetic community structure and 𝛽-diversity patterns in surface bacterioplankton metacommunities[END_REF] have found that inland waters harbour more diverse communities. However, microbial eukaryotes, also called protists, are less studied in the literature [START_REF] Caron | Protists are microbes too: a perspective[END_REF][START_REF] Debroas | Overview of freshwater microbial eukaryotes diversity: a first analysis of publicly available metabarcoding data[END_REF]. Moreover, most of the studies have been conducted in lakes, and we are still missing a lot of knowledge on the diversity in smaller ecosystems [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF][START_REF] Biggs | The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers[END_REF].

Structure of microbial communities

Microbial communities are typically separated into two categories defined by their abundance (see Figure 1.4 from [START_REF] Massana | Distribution and abundance of uncultured heterotrophic flagellates in the world oceans[END_REF]. A small part of the taxa is dominating the community.

They are well adapted to the conditions in the ecosystem and are therefore in an optimal growth situation. On the other side, as they are numerous, they are more likely prone to predation, parasitism, and viral lysis. Indeed, abundant microorganisms are more likely to be controlled by top-down processes, following the "Killing the winner hypothesis" [START_REF] Thingstad | Elements of a theory for the mechanisms controlling abundance, diversity, and biogeochemical role of lytic bacterial viruses in aquatic systems[END_REF][START_REF] Pernthaler | Predation on prokaryotes in the water column and its ecological implications[END_REF]. The "rare biosphere", defined by [START_REF] Sogin | Microbial diversity in the deep sea and the underexplored "rare biosphere[END_REF], is constituted of microorganisms occurring at very low frequency and rarely detected. Among those rare taxa, some can become punctually highly abundant and are therefore defined as "conditionally rare taxa" [START_REF] Shade | Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity[END_REF]. The other taxa constitute a seed bank and can be inactive or dormant [START_REF] Stoeck | A molecular approach to identify active microbes in environmental eukaryote clone libraries[END_REF][START_REF] Lennon | Microbial seed banks: the ecological and evolutionary implications of dormancy[END_REF][START_REF] Simon | Resilience of freshwater communities of small microbial eukaryotes undergoing severe drought events[END_REF]. Rare taxa have also an important role in the ecosystem functioning, as in the biogeochemical cycles of nitrogen and sulphur for instance [START_REF] Pernthaler | A 'rare biosphere' microorganism contributes to sulfate reduction in a peatland[END_REF]. The community assembly and the maintenance of a high diversity in ecosystems are still under investigation [START_REF] Langenheder | Factors influencing aquatic and terrestrial bacterial community assembly[END_REF]. However, four main processes have been defined as the drivers shaping microbial communities, i.e. selection, dispersal, ecological drift, and speciation [START_REF] Vellend | Conceptual synthesis in community ecology[END_REF]. Selection, also called "species sorting", is the variation of reproductive success across individuals and species due to biotic and abiotic pressures [START_REF] Stegen | Quantifying community assembly processes and identifying features that impose them[END_REF][START_REF] Langenheder | Factors influencing aquatic and terrestrial bacterial community assembly[END_REF]. Therefore, selection has long been considered as one of the most important processes structuring community composition. Dispersal corresponds to the movement of microorganisms across space, and it is a passive process [START_REF] Nemergut | Patterns and processes of microbial community assembly[END_REF]. The size of the cells can induce a limitation of the potential for dispersion, as well as the ability to transit into a cyst formation [START_REF] Logares | Disentangling the mechanisms shaping the surface ocean microbiota[END_REF]. Ecological drift corresponds to the random changes in the relative abundance of microorganisms [START_REF] Logares | Contrasting prevalence of selection and drift in the community structuring of bacteria and microbial eukaryotes[END_REF]. It is a stochastic process. Finally, speciation is the generation of new species in an environment [START_REF] Vellend | Conceptual synthesis in community ecology[END_REF][START_REF] Logares | Contrasting prevalence of selection and drift in the community structuring of bacteria and microbial eukaryotes[END_REF].

The origin of the immigration and the dispersal in freshwater ecosystems is still poorly known. Some studies indicate that inlet inflow of lakes plays an important role, whereas atmospheric deposition, sediment resuspension, and precipitation are not effective sources of immigration [START_REF] Jones | Species-sorting may explain an apparent minimal effect of immigration on freshwater bacterial community dynamics[END_REF][START_REF] Comte | Contribution of different bacterial dispersal sources to lakes: population and community effects in different seasons[END_REF]. However, these conclusions could vary depending on the ecosystem studied. Indeed, the success of an immigration process depends both on the ability the invader has to invade and on the ability the local community has to resist to invasion. For instance, the immigration of new species can be easier during disturbance events. Further studies are still needed to better identify the dispersal mechanisms [START_REF] Lindström | Local and regional factors influencing bacterial community assembly[END_REF][START_REF] Kinnunen | Stochastic processes govern invasion success in microbial communities when the invader is phylogenetically close to resident bacteria[END_REF][START_REF] Langenheder | Factors influencing aquatic and terrestrial bacterial community assembly[END_REF].

Overview of eukaryotic and prokaryotic phyla in freshwaters

In ecosystems, the most abundant microorganisms are often affiliated to a restricted group of phyla. These phyla are often the taxa always found in the considered type of ecosystems. Here, I will present several prokaryotic and eukaryotic phyla that are classically detected in freshwater environments, and give insight into their ecology.

Five major bacterial phyla are often detected at high abundances in freshwater systems, the proteobacteria, the actinobacteria, the bacteroidetes, the cyanobacteria, and the verrucomicrobia [START_REF] Zwart | Typical freshwater bacteria: an analysis of available 16s rrna gene sequences from plankton of lakes and rivers[END_REF]Newton et al., 2011;[START_REF] Salcher | Same same but different: ecological niche partitioning of planktonic freshwater prokaryotes[END_REF][START_REF] Madigan | Brock Biology of Microorganisms[END_REF][START_REF] Kurt | Depth distribution of microbial diversity in lakes[END_REF].

In general, archaea have a low abundance in freshwater environment [START_REF] Kirchman | Processes in microbial ecology[END_REF]. Among them, members of the thaumarcheota are found to be as the most abundant [START_REF] Salcher | Same same but different: ecological niche partitioning of planktonic freshwater prokaryotes[END_REF].

Proteobacteria is the most diverse phylum and is composed of gram-negative bacteria. It is divided into six classes, i.e. the alpha-, beta-, delta-, epsilon-, gamma-, and zetaproteobacteria; the most abundant in freshwater being the alpha-, beta-, and gammaproteobacteria [START_REF] Zwart | Typical freshwater bacteria: an analysis of available 16s rrna gene sequences from plankton of lakes and rivers[END_REF]. All classes of proteobacteria differ in terms of metabolism. Some members of the proteobacteria are phototrophs and therefore use the light as energy source. This concerns the orders rhizobiales, rhodobacterales, rhodospirillales (alphaproteobacteria), rhodocyclales (betaproteobacteria), and chromatiales (gammaproteobacteria) [START_REF] Imhoff | Photosynthesis is widely distributed among proteobacteria as demonstrated by the phylogeny of puflm reaction center proteins[END_REF]. Alphaand betaproteobacteria are usually abundant and ubiquitous in freshwater systems [START_REF] Salcher | Same same but different: ecological niche partitioning of planktonic freshwater prokaryotes[END_REF]. Alphaproteobacteria are obligate of facultative aerobes, and they have optimal growth rates in oligotrophic environments [START_REF] Madigan | Brock Biology of Microorganisms[END_REF]. In majority, they are obligate or facultative aerobes. The microorganisms associated to betaproteobacteria are in general fast growing, and are in majority chimioorganotrophs and lithoautotrophs (Newton et al., 2011;[START_REF] Madigan | Brock Biology of Microorganisms[END_REF]. Gammaproteobacteria represent the largest and most diverse class of proteobacteria. At the same time, they present a large diversity of metabolisms and ecology, with members that can be phototrophic, chemoorganotrophic, or chemolithotrophic. Because of these ecological differences between the members of the proteobacteria, I will consider in this thesisproteobacteria at the class level rather than at the phyla level. Actinobacteria are gram-positive bacteria that can contribute up to 50% of the epilimnion communities in lakes (Newton et al., 2011). They are small size bacteria, most of them being chemoorganotrophic.

Bacteroidetes are gram-negative bacteria, which feature a wide metabolic diversity [START_REF] Madigan | Brock Biology of Microorganisms[END_REF]. Most of them are saccharolytic, with aerobic or fermentative metabolisms. They are known to be efficient in the degradation of humic material. Cyanobacteria are a group of oxygenic phototrophic bacteria, using CO 2 as carbon source. They are highly diverse in terms of morphology and ecology. Some of the members of cyanobacteria can produce cyanotoxins, which can be harmful for the environment [START_REF] Wood | Toxic benthic freshwater cyanobacterial proliferations: Challenges and solutions for enhancing knowledge and improving monitoring and mitigation[END_REF]. Verrucomicrobia are all aerobic or facultatively aerobic bacteria which can ferment sugars [START_REF] Madigan | Brock Biology of Microorganisms[END_REF]. They are known to be symbiotic with microbial eukaryotes.

In eukaryotic communities, the superphyla alveolata, cryptophyta, and the phylum stramenopiles are the protists classically dominating freshwater communities [START_REF] Debroas | Overview of freshwater microbial eukaryotes diversity: a first analysis of publicly available metabarcoding data[END_REF].

The alveolates are characterized by the presence of cytoplasmic sacs located under the cell membrane, called "alveoli" [START_REF] Madigan | Brock Biology of Microorganisms[END_REF]. Among the alveolata, various lifestyles are represented, such as predation, phototrophy, and parasitism [START_REF] Adl | The revised classification of eukaryotes[END_REF]. Two phyla are particularly abundant in freshwater, the ciliates and the dinoflagellates. The major characteristic of ciliates is the cilia at the surface of their membrane [START_REF] Adl | The revised classification of eukaryotes[END_REF][START_REF] Madigan | Brock Biology of Microorganisms[END_REF]. They are major predators in freshwater systems and their cilia are involved in food capture [START_REF] Pernthaler | Predation on prokaryotes in the water column and its ecological implications[END_REF]. They fed in majority on algae, detritus, and protozoans. The dinoflagellates have classically two long flagellates. They are photosynthetic, mixotrophs, or heterotrophs and present a wide diversity in terms of size and shape [START_REF] Kirchman | Processes in microbial ecology[END_REF]. They are also characterized by the ability to form resting cysts [START_REF] Bravo | Towards an ecological understanding of dinoflagellate cyst functions[END_REF]. The group of the stramenopiles constitute one of the most diversified eukaryotic group. It is composed of a majority of phototrophs and heterotrophs. In the phototrophic stramenopiles, the diatoms (Bacillariophyta) are among the most abundant algae in aquatic systems. Finally, cryptophytes are in majority phototrophs, but some of them can also be parasites or endosymbionts.

Among the non-abundant phyla in freshwater ecosystems, some have nevertheless an important role in ecosystem functioning (see section 1.2.1.1). However, we are still missing the role of a large amount of taxa in freshwater ecosystems. For instance, MArine STramenopiles (MAST) represent an eukaryotic phylum, initially detected in marine systems and composed of 19 classes [START_REF] Massana | Phylogenetic and ecological analysis of novel marine stramenopiles[END_REF][START_REF] Massana | Exploring the uncultured microeukaryote majority in the oceans: reevaluation of ribogroups within stramenopiles[END_REF]. These organisms were first thought to be only found in marine systems, but then several lineages have also been detected in freshwaters [START_REF] Simon | Complex communities of small protists and unexpected occurrence of typical marine lineages in shallow freshwater systems[END_REF]. This type of marine-freshwater transitions is rare, which makes their study interesting [START_REF] Cabello-Yeves | Marine-freshwater prokaryotic transitions require extensive changes in the predicted proteome[END_REF]. Moreover, they are known to be mostly heterotrophic and to act as major predators in marine systems [START_REF] Massana | Exploring the uncultured microeukaryote majority in the oceans: reevaluation of ribogroups within stramenopiles[END_REF]. However, we do not know if they have the same role and ecological importance in freshwater systems as in marine environments. Similarly, we are missing of information of the role and function of microorganisms affiliated to the Candidate Phyla Radiation (CPR). The CPR is a recently discovered prokaryotic clade that includes 74 potentially phylum-level lineages and represents up to 25% of the bacterial diversity [START_REF] Hug | A new view of the tree of life[END_REF]Castelle et al., 2018;Castelle and Banfield, 2018). For now, no members of the CPR have a cultivate representative, implying that their ecology is poorly known [START_REF] Hug | A new view of the tree of life[END_REF]. CPR members have been discovered in a wide range of ecosystems and are characterized by a small genome, which limits their biosynthetic capacities [START_REF] Brown | Unusual biology across a group comprising more than 15% of domain bacteria[END_REF]. This underlines that CPR members are probably symbionts of other microbes [START_REF] Nelson | The reduced genomes of parcubacteria (od1) contain signatures of a symbiotic lifestyle[END_REF]. Therefore, they might represent a significant part of the parasitic load in the ecosystems and some of them have already been identified as parasites, e.g. Saccharibacteria or Dojkabacteria (Castelle and Banfield, 2018). In terms of ecosystem functioning, the Parcubacteria super-phyla has been identified as involved in the nitrogen cycle by conducting denitrification [START_REF] Danczak | Members of the candidate phyla radiation are functionally differentiated by carbon-and nitrogen-cycling capabilities[END_REF]. Hence, exploring the diversity and the temporal variation of microbial eukaryotes and prokaryotes in small freshwater ecosystem can help to elucidate new aspects of the ecology of poorly known taxa.

Implications of microorganisms in ecosystem functioning

Microorganisms are key players in the ecosystems. They are involved in the aquatic food web through the microbial loop and are also major actors of all biogeochemical cycles [START_REF] Worden | Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes[END_REF].

Microbial loop

The microbial loop describes the microbial path of the aquatic food web and is included in the cycle of the dissolved organic matter (DOM) (see Figure 1.5, [START_REF] Azam | The ecological role of water-column microbes in the sea[END_REF][START_REF] Pinet | Invitation to oceanography[END_REF][START_REF] Worden | Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes[END_REF]. These processes have been first described in marine environments, but they are also occurring in freshwater ecosystems [START_REF] Sherr | Role of microbes in pelagic food webs: a revised concept[END_REF].

There are four major steps in the microbial loop (see Figure 1.5, [START_REF] Worden | Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes[END_REF].

Briefly, the phytoplankton, i.e. the primary producers, releases great amounts of DOM. Then, heterotrophic bacteria use a large proportion of this DOM as food source. These bacteria are mainly predated by heterotrophic flagellates and finally, these latter are grazed by ciliates and are preys of zooplankton. This predation allows to recycle nutrients such as carbon, nitrogen, and phosphorus. The microbial loop allows to incorporate DOM to higher trophic levels through heterotrophic bacterial activity [START_REF] Azam | The ecological role of water-column microbes in the sea[END_REF]. Indeed, a large part of the DOM produced by the phytoplankton is only accessible to heterotrophic bacteria and archaea [START_REF] Azam | Microbial structuring of marine ecosystems[END_REF]. Viruses are an integral part of the microbial loop and have a major effect on prokaryotic cells [START_REF] Fenchel | The microbial loop-25 years later[END_REF][START_REF] Worden | Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes[END_REF]. Indeed, cell viral lysis also contributes to the pool of DOM available in the aquatic environment. Moreover, viruses regulate microbial biomass and composition.

Biogeochemical cycles

Biogeochemical cycles correspond to the circulation of substance (e.g. nutrients) in the biosphere [START_REF] Bolle | The natural environment and the biogeochemical cycles[END_REF]. For all living organisms, six major elements (i.e. H, C, N, O, S, and P) are necessary to run the cellular machinery and to produce carbohydrates, proteins, nucleic acids [START_REF] Falkowski | The microbial engines that drive earth's biogeochemical cycles[END_REF]. Microorganisms are actively involved in the cycles of these elements, and act in the transformation and mineralization of the organic matter (Del [START_REF] Giorgio | Bacterial growth efficiency in natural aquatic systems[END_REF]. They are essential for the functioning of ecosystems, in particular for macro-organisms [START_REF] Falkowski | The microbial engines that drive earth's biogeochemical cycles[END_REF]. Indeed, microorganisms are able to transform nutrients, which make them available for other organisms. For instance, the only way to make nitrogen available for organisms such as plants is through the reduction of atmospheric N 2 to ammonia (nitrogen fixation, [START_REF] Van Der Heijden | The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems[END_REF][START_REF] Falkowski | The microbial engines that drive earth's biogeochemical cycles[END_REF].

Examples of the implication of microorganisms in three cycles

Carbon and oxygen Carbon and oxygen cycles are tightly linked: oxygen is stored in diverse organic compounds as well as in CO 2 . Carbon is present in reduced forms, such as methane (CH 4 ) and organic carbon. It is also present in oxidized forms, such as carbon dioxide and inorganic carbon (limestone). In the carbon cycle, the principal way to synthesize organic carbon is through photosynthesis and chimiosynthesis. Therefore, photosynthetic organisms constitute the basis of the carbon cycle. For instance, it has been shown that picophytoplankton (i.e. microorganisms smaller than 3 µm performing photosynthesis) is highly involved in the carbon dioxide fixation in oceans [START_REF] Jardillier | Significant co 2 fixation by small prymnesiophytes in the subtropical and tropical northeast atlantic ocean[END_REF]. Globally, the phytoplankton represents up to 50% of the global photosynthesis on Earth, that is of the carbon fixation [START_REF] Seymour | Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships[END_REF].

Nitrogen Microorganisms are major players in the four processes of the nitrogen cycle [START_REF] Falkowski | The microbial engines that drive earth's biogeochemical cycles[END_REF][START_REF] Forel | The global nitrogen cycle in the twenty-first century[END_REF]. First, they are involved in nitrogen fixation by converting the atmospheric N 2 into ammonia [START_REF] Madigan | Brock Biology of Microorganisms[END_REF]. Then, they are able to assimilate ammonia and transform it into organic compounds. Finally, aerobic organisms conduct the nitrification by oxidizing ammonia into nitrates, whereas anaerobic bacteria conduct denitrification by transforming nitrate into N 2 [START_REF] Kuenen | Anammox bacteria: from discovery to application[END_REF].

How to elucidate ecosystem functioning?

One of the major objectives of microbial ecology is to understand how ecosystems are functioning. Several steps are necessary to progress in the resolution of this global question:

1. Describe community composition and structuration.

To understand an ecosystem, we first have to figure out which or what type of organisms inhabits it. This step can also allow the detection of previously undiscovered taxa.

2. Identify the abiotic factors influencing community structuration.

Microbial communities can be influenced by a wide range of physico-chemical parameters.

Exploring the links between communities and their environments will help us to understand how communities are structured. Indeed, these abiotic parameters constitute the bottom-up controls of community composition.

3. Decipher the interactions between the members of the communities.

Environmental conditions cannot completely explain community composition because organisms are in constant interactions with each other. Understanding the principal relationships between organisms will therefore help us to identify the role of the different members of the community. Microbial interactions consist in top-down (e.g. predation)

and sideways (e.g. competition) controls driving community composition.

Moreover, these three steps have to be integrated in a global context. Indeed, studying an ecosystem at one time point or one spatial location is not enough to decipher microbial communities diversity and complexity. For instance, temporal surveys can help to identify bottom-up and top-down factors shaping the community composition [START_REF] Muylaert | Relationship between bacterial community composition and bottom-up versus top-down variables in four eutrophic shallow lakes[END_REF].

Therefore, to study an ecosystem and to be able to identify the ecological processes within, it is important to focus on various spatio-temporal scales.

Abiotic factors influence microorganisms community composition

The role played by a large set of abiotic parameters has been investigated over the years and proves to impact the microbial community composition. A wide range of factors (e.g. temperature, nutrients availability) can govern the communities but it is difficult, time consuming, and expensive to measure them all. Classically, we focus on the environmental conditions as well as on resources and nutrient availability [START_REF] Madigan | Brock Biology of Microorganisms[END_REF]. The environmental conditions are often resumed to the temperature, the pH, the dissolved oxygen, the light, and the osmotic conditions. The nutrient availability refers to the concentrations of the diverse chemical forms of carbon, nitrogen, phosphorus, and other macronutrients. These various forms reflect the different phases of biogeochemical cycles, which allow to have a global picture of the processes.

It can also include the concentration of micronutrients, such as copper or zinc. The characterisation and study of these parameters help to define environmental gradients that can provide a more suitable environment for certain organisms than to others. In general, the trophic state index of the ecosystems, i.e. oligotrophic, mesotrophic, or eutrophic one, can play a role in the composition of microbial communities [START_REF] Smith | Eutrophication of freshwater and coastal marine ecosystems a global problem[END_REF][START_REF] Yannarell | Temporal patterns in bacterial communities in three temperate lakes of different trophic status[END_REF][START_REF] Monchamp | Homogenization of lake cyanobacterial communities over a century of climate change and eutrophication[END_REF].

Temperature is often depicted as the most important factor influencing aquatic community composition and distribution, for both macro and micro-organisms [START_REF] Crump | Synchrony and seasonality in bacterioplankton communities of two temperate rivers[END_REF][START_REF] Gilbert | Defining seasonal marine microbial community dynamics[END_REF][START_REF] Brönmark | The biology of lakes and ponds[END_REF]. Temperature influences microbial richness [START_REF] Fuhrman | A latitudinal diversity gradient in planktonic marine bacteria[END_REF][START_REF] Kirchman | Processes in microbial ecology[END_REF]. Its effect on communities can vary depending on the ecosystems, as well as on the organisms considered. For instance, in a surface marine study by [START_REF] Logares | Disentangling the mechanisms shaping the surface ocean microbiota[END_REF], temperature was found to be the most important measured parameter impacting the selection of prokaryotes, but it had a smaller effect on picoeukaryotes. Furthermore, temperature is also a structuring factor at the global scale. Indeed, it has been shown that in oceans, temperature is partly responsible of the decrease of bacterial diversity at the poles compared to lower latitudes [START_REF] Sunagawa | Structure and function of the global ocean microbiome[END_REF]. In freshwater systems, temperatures have an indirect effect on communities, trough the water mixing processes [START_REF] Brönmark | The biology of lakes and ponds[END_REF]. Indeed, variations in water temperature induce stratification-mixing dynamics in the water-column. Therefore, it leads to disturbances in the ecosystems, resulting in habitat heterogeneity and changes in the microbial communities [START_REF] Shade | The influence of habitat heterogeneity on freshwater bacterial community composition and dynamics[END_REF].

In temperate freshwater aquatic habitat, the pH value usually varies between 6 and 9 (Kirchman, 2018). In freshwater systems, pH often reflects the functioning of the catchment area [START_REF] Brönmark | The biology of lakes and ponds[END_REF]. Indeed, pH depends on the hydrology and geology of the surrounding environment. It can change after the input of acidifying substances (e.g. acidic rain, [START_REF] Schindler | Effects of acid rain on freshwater ecosystems[END_REF] and depends on ecosystem productivity. The pH gives information on the equilibrium relationship between carbon dioxide and bicarbonate in the ecosystem [START_REF] Brönmark | The biology of lakes and ponds[END_REF].

The oxygen level depends on the diffusion from the atmosphere as well as on the release of photosynthetic organisms [START_REF] Moss | Widespread dominance of kinetoplastids and unexpected presence of diplonemids in deep freshwater lakes[END_REF]. Furthermore, an increase of temperature alters the concentration of dissolved oxygen as oxygen is more soluble in cold water [START_REF] Brönmark | The biology of lakes and ponds[END_REF]). The oxygen level can impact biological processes, like for instance the denitrification which requires anaerobic conditions [START_REF] Lam | Microbial nitrogen cycling processes in oxygen minimum zones[END_REF]. In ecosystems, oxygen is required for aerobic respiration which concerns almost all eukaryotic microorganisms, as well as macroorganisms. Therefore, variations of the level of dissolved oxygen can impact the development of microorganisms. For instance, microorganism that are adapted to oxygen-depleted environments such as CPR will benefit from a decrease in dissolved oxygen concentration (Castelle et al., 2018). In eutrophic systems, both high and low levels of oxygen are observed. High levels of oxygen can be observed during the day, due to the high photosynthetic activity of the phytoplankton [START_REF] Paerl | Mitigating the expansion of harmful algal blooms across the freshwater-to-marine continuum Parada AE, Fuhrman JA (2017) Marine archaeal dynamics and interactions with the microbial community over 5 years from surface to seafloor[END_REF]. However, eutrophication induces a global reduction of oxygen in freshwater ecosystems, especially in the hypolimnion [START_REF] Foley | Long-term changes in oxygen depletion in a small temperate lake: effects of climate change and eutrophication[END_REF]. This oxygen depletion phenomenon is caused by the respiration and the decomposition of the algal blooms [START_REF] Graneli | Harmful algal blooms of allelopathic microalgal species: the role of eutrophication[END_REF]. Therefore, eutrophication and the changes in oxygen concentration can induce perturbations in microbial community composition [START_REF] Filstrup | Cyanobacteria dominance influences resource use efficiency and community turnover in phytoplankton and zooplankton communities[END_REF].

Light availability (in terms of quality and quantity) can be of major importance for microbial communities. First, the diurnal cycles of dark and light phases impact microbial communities for the primary production, as light is crucial for photosynthetic organisms [START_REF] Trench-Fiol | Metatranscriptomics from a small aquatic system: Microeukaryotic community functions through the diurnal cycle[END_REF]. It also impacts indirectly other communities, such as bacteria using the dissolved organic matter released in ecosystems by phytoplankton [START_REF] Worden | Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes[END_REF]. Therefore, microbial communities are changing on a daily basis, which highlights the need for selecting the proper temporal scale depending on the research question. Intensity of light also affects microbial communities, with for instance Chrysophyceae that seems to be associated with higher light levels in marine systems [START_REF] Kirkham | Basinscale distribution patterns of photosynthetic picoeukaryotes along an atlantic meridional transect[END_REF]. Seasonal variations of light intensity can therefore change community composition. Indeed, light intensity has been depicted as a factor regulating the germination of cyanobacteria and diatoms and therefore their recruitment from the sediment to the freshwater communities [START_REF] Rengefors | Factors regulating the recruitment of cyanobacterial and eukaryotic phytoplankton from littoral and profundal sediments[END_REF].

The conductivity of a medium reflects its ability to conduct electricity. It is tightly linked to salinity, that is the concentration of dissolved salts in water. It is also an indicator of the osmotic conditions in the aquatic environment. An increase of conductivity can benefit to salinity tolerant species. For instance, the conductivity has been shown to be a driver of diatoms community composition [START_REF] Aarnio | High diatom species turnover in a baltic sea rock pool metacommunity[END_REF]. Finally, salinity has been identified as a major factor of community dissimilarities in a wide range of environments [START_REF] Lozupone | Global patterns in bacterial diversity[END_REF].

The concentration of nutrients in freshwater ecosystems is partly linked to the catchment area, to the bedrock type, as well as to the plants inhabiting the ecosystem and its banks. Microbial communities in lakes are tightly linked to the concentration of nutrients, as proven for instance by studies of bacteria in oligotrophic lakes [START_REF] Logue | Freshwater bacterioplankton richness in oligotrophic lakes depends on nutrient availability rather than on species-area relationships[END_REF]. Among the nutrients, phosphorous is often depicted as the most limiting factors in lakes, mainly because of its low solubility in water [START_REF] Tiessen | Phosphorus in the global environment[END_REF]. Therefore, it is an important environmental driver of phytoplankton composition, because it generates competition between microorganisms [START_REF] Vadstein | Heterotrophic, planktonic bacteria and cycling of phosphorus[END_REF][START_REF] Salcher | Modulation of microbial predator-prey dynamics by phosphorus availability: growth patterns and survival strategies of bacterial phylogenetic clades[END_REF].

Interactions between microorganisms shape communities

Abiotic factors are drivers of microbial community composition. However, they are not sufficient to explain the entire community variability, even with a large panel of parameters took into account [START_REF] Muylaert | Relationship between bacterial community composition and bottom-up versus top-down variables in four eutrophic shallow lakes[END_REF][START_REF] Lidicker | Determinants of community structure in the global plankton interactome[END_REF]. Therefore, it is also necessary to focus on biotic interactions within the organisms in ecosystems.

Microbial interactions are very diverse and happen within and between the three domains of life. However, they are difficult to study and researchers face a wide range of challenges.

Interactions between microorganisms are difficult to reproduce in vitro. It is due to the fact that less than 1% of the microbial diversity is cultivated, the rest constituting the "uncultured microbial majority" [START_REF] Rappé | The uncultured microbial majority[END_REF][START_REF] Hug | Sizing up the uncultured microbial majority[END_REF]. Microbial interactions are also complex to investigate through direct observations because of their small size and the abundance of microbial cells. Indeed, a tremendous number of interactions can take place at the same time between the members of a single community, making experimental identifications difficult [START_REF] Deutschmann | Disentangling environmental effects in microbial association networks. BMC Microbiome Diamond JM[END_REF]. Finally, we can consider that, even if we could observe interactions in vitro, there would be no guaranty that they would be the same in environment.

Despite these difficulties, interactions between microorganisms have been studied for a long time and, nowadays, co-occurrence studies help to better decipher the complexity of the relationship within communities.

A variety of interactions between microorganisms

There is a large range of interactions types between microorganisms, which can benefit or impair the involved organisms (see Figure 1.6; [START_REF] Faust | Microbial interactions: from networks to models[END_REF][START_REF] Karimi | Microbial diversity and ecological networks as indicators of environmental quality[END_REF]. They can be defined as antagonist or beneficial, they can have different strength, and they can involve specialist or generalist organisms. Interactions result in positive, negative, or neutral effects for the protagonists [START_REF] Lidicker | Determinants of community structure in the global plankton interactome[END_REF]. Moreover, the interactions between two organisms can vary over time, depending on the environmental conditions [START_REF] Hoek | Resource availability modulates the cooperative and competitive nature of a microbial cross-feeding mutualism[END_REF].

Figure 1.6 Possible biological interactions and their effects between microorganisms. The interaction can result in a positive (+), negative (-), or neutral (0) effect for each protagonist [START_REF] Faust | Microbial interactions: from networks to models[END_REF][START_REF] Lidicker | Determinants of community structure in the global plankton interactome[END_REF]. Illustration of the interactions in the case of metabolic exchange are provided.

Dark green pentagons are for toxic compounds [START_REF] Cavaliere | Cooperation in microbial communities and their biotechnological applications[END_REF][START_REF] Karimi | Microbial diversity and ecological networks as indicators of environmental quality[END_REF].

Beneficial interactions There are two types of beneficial interactions, commensalism and mutualism [START_REF] Karimi | Microbial diversity and ecological networks as indicators of environmental quality[END_REF]. Commensalism interactions benefit only to one of the involved organisms. The other organism is not affected by the interaction. In the case of an exchange of metabolites, commensalism is also called cross-feeding [START_REF] Faust | Microbial interactions: from networks to models[END_REF]. For instance, commensalism corresponds to the release of dissolved organic matter by phytoplankton which benefits to bacteria [START_REF] Seymour | Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships[END_REF]. Mutualism interactions benefit to both organisms.

For instance, they can consist in the exchange of metabolisms by both organisms.

Antagonist interactions Antagonist interactions imply that one of the organisms is negatively affected by the interaction. Amensalism interactions result in a negative effect on one organism with no benefit for the other. This type of interactions can occur when one organism modifies its environment or releases metabolites that happen to be toxic or inhibiting for other organisms. Competition interactions are negative for both protagonists. They can be passive or active [START_REF] Cavaliere | Cooperation in microbial communities and their biotechnological applications[END_REF]. Passive competition occurs when two organisms are interested by the same limiting resource or by the need of space and light. This interaction becomes active when one of the organisms intentionally releases toxic compounds in order to inhibit the other competitors. Organisms that release biochemical substances influencing others are called allelopathic organisms [START_REF] Fuhrman | Marine microbial community dynamics and their ecological interpretation[END_REF]. Predatory and parasitic interactions are called win-lose interactions. Predation happens when one organism, either heterotrophic or mixotrophic, feeds on another organism. For instance, protists are known to be major predators of bacterial communities [START_REF] Jardillier | Effects of viruses and predators on prokaryotic community composition[END_REF]. Predation was thought to be restricted to protists but it has also been detected recently in a planctomycete bacteria [START_REF] Shiratori | Phagocytosis-like cell engulfment by a planctomycete bacterium[END_REF]. Parasitism consists in an interaction where an organism is entirely dependant to another, and physically associated to it. Organisms that parasite can be fed, hosted, and protected by their host, which results in a beneficial interaction for the parasite and a harmful one for the host. Some parasites can exploit several hosts during their life cycle.

A good example of a hub of interaction is the phycosphere surrounding the phytoplankton [START_REF] Seymour | Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships[END_REF]. This area is a theatre of diverse types of interactions generated by the release of metabolites and dissolved organic matter by the phytoplankton.

Visualisation of the interactions

As previously mentioned, interactions between microorganisms can be studied using various methods. Laboratory approaches such as fluorescence in situ hybridization (FISH), EpicPCR, single cell genomics, or NanoSIMS allow to investigate the associations between microorganisms [START_REF] Yoon | Single-cell genomics reveals organismal interactions in uncultivated marine protists[END_REF][START_REF] Musat | Tracking microbial interactions with nanosims[END_REF][START_REF] Spencer | Massively parallel sequencing of single cells by epicpcr links functional genes with phylogenetic markers[END_REF][START_REF] Grujcic | Cryptophyta as major bacterivores in freshwater summer plankton[END_REF]. For instance, the FISH technic allows to target microorganisms using specific probes. Combined with single-cell techniques, it can allow to detect associated organisms in a predation or parasitic interaction. However, these techniques are time consuming and they need to design specific probes for the targeted microorganisms since the diversity of probes is restricted to a few taxa. Inferring interactions using co-occurrence analyses allows targeting interactions of potential interest among the large diversity of possible of interactions. After the identification of interactions, laboratory approaches are complementary to be able to validate or not the predicted relationships.

Microbial network inference is based on statistical co-occurrences of sequence reads generated using high-throughput sequencing technologies [START_REF] Faust | Microbial interactions: from networks to models[END_REF]. Predictions of microbial associations are based on community matrix, i.e. a table containing the number of reads obtained for each OTU and site. The networks are based on the significant and nonrandom co-occurrences between taxa. Briefly, if two taxa present the same trends, such as rise or decrease of their respective abundances at the same time, then they can be considered as interacting positively [START_REF] Faust | Microbial interactions: from networks to models[END_REF]. A large variety of methods has been elaborated

to infer association networks between microorganisms, several of them being reviewed in [START_REF] Röttjers | From hairballs to hypotheses-biological insights from microbial networks[END_REF]. They differ in several ways and try to overcome the issues inherent to NGS datasets. They can be based on various measurements of correlations (e.g. Pearson correlations, matrix of variance-covariance). They can take into account the sparseness of the dataset (i.e. the number of 0 in the matrix, representing either an absence of the specie or a non-detection). The methods have also different ways to treat the compositionality of the datasets [START_REF] Gloor | Microbiome datasets are compositional: and this is not optional[END_REF]. Data are compositional because they are composed of samples with unequal sequencing depth and are not true abundance counts. Finally, the methods try to avoid spurious correlations, for instance due to the effect of environmental parameters or because two species are positively correlated to a third one and therefore are correlated together [START_REF] Kurtz | Sparse and compositionally robust inference of microbial ecological networks[END_REF].

Microbial communities are structured in space and time

In all types of ecosystems, microbial communities are not static and they change in space and time. In order to get a good overview of the microbial communities in an ecosystem and to study their dynamics, it is not suitable to focus on a single sampling point. Large spatial and/or temporal surveys are needed to capture the complexity of the microbial communities.

The spatial scale can be for instance the survey over several localisations on the surface of a pond [START_REF] Lear | Fine-scale spatial patterns in bacterial community composition and function within freshwater ponds[END_REF]. The temporal scale can be the sampling of one ecosystem every month over six years [START_REF] Gilbert | Defining seasonal marine microbial community dynamics[END_REF]. Generally, the larger the number of samples is, the more organisms are detected. Indeed, variation over space and time generates habitat heterogeneity and, therefore, potential variations in the microbial community composition.

Moreover, considering more samples increases the probability to detect rare taxa and allows to study the rare biosphere [START_REF] Hugoni | Structure of the rare archaeal biosphere and seasonal dynamics of active ecotypes in surface coastal waters[END_REF][START_REF] Shade | Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity[END_REF]. The taxonomic scale represents the scope at which the microbial community is considered. For instance, we can focus on the three domains of life, or only on a specific phylum within one domain. Finally, the surveys can be realized at different levels [START_REF] Ladau | Spatial, temporal, and phylogenetic scales of microbial ecology[END_REF]. The grain of the analysis corresponds to the breadth of each sample. For instance, it can be the volume of water collected for the analysis (e.g. 100 ml or 3 l) or the frequency of the temporal survey (e.g. daily or monthly). The extent corresponds to the breadth of the all survey (e.g. entire surface of one large ecosystem or only littoral zone) To study the microbial communities in an ecosystem, it is possible to focus on three scales (spatial, temporal and taxonomic) and at two levels (grain and extent) (see Figure 1.7, Ladau and Eloe-Fadrosh, 2019). Incorporating different spatio-temporal dimensions in ecology can help to answer the hypothesis of Baas-Becking, "everything is everywhere, but the environment selects" [START_REF] Baas-Becking | Review and re-analysis of domain-specific 16s primers[END_REF]. Indeed, the variations in space and time of environmental conditions and microbial community composition can help to decipher community structuration and the role of abiotic and biotic factors on the communities. Large dataset of microbial communities can be constituted thanks to the development of new sequencing techniques that are faster and cheaper. However, they still require a high amount of time and money to be conducted, which makes them rather scarce [START_REF] Faust | Metagenomics meets time series analysis: unraveling microbial community dynamics[END_REF]. Moreover, most of the spatial and/or temporal survey focus on bacterial community composition while microbial eukaryote communities are often overlooked.

Besides, literature also lacks of studies considering simultaneously the three domains of life [START_REF] Caron | Protists are microbes too: a perspective[END_REF][START_REF] Debroas | Overview of freshwater microbial eukaryotes diversity: a first analysis of publicly available metabarcoding data[END_REF].

Spatial variability of microbial communities

The properties of aquatic ecosystems are highly dependent of their spatial characteristics. Depending on the size of the ecosystem, on its mixing, and on its proximity to the littoral area or to an input water zone for instance, the environmental conditions and the habitats will change.

With spatial surveys, the biogeography of microbial communities can be better understood, while either hotspots or in the contrary deserts of biodiversity can be discovered [START_REF] Gonzalez | Characterizing microbial communities through space and time[END_REF]. Spatial variation of community composition can happen at different scales, ranging from the microscale to kilometres. The microscale corresponds to the cell-to-cell distances, which are close interactions between microoganisms [START_REF] Cordero | Microbial interactions and community assembly at microscales[END_REF]. In this thesis, I will not consider the microscale changes of microorganisms, because I aim at focusing on community composition on larger scale. Spatial variations of communities can be considered following a vertical gradient, that is the distribution of microorganism along the water column. According to lake and marine systems functioning, environmental conditions along the water column vary strongly (see section 1.1 and Figure 1.8). These variations correspond for instance to gradients of salinity, pressure, temperature, or dissolved oxygen [START_REF] Shade | The influence of habitat heterogeneity on freshwater bacterial community composition and dynamics[END_REF][START_REF] Sp | Depth and location influence prokaryotic and eukaryotic microbial community structure in new zealand fjords[END_REF]. Concomitantly, depth investigation of microbial communities in marine systems reveal a vertical zonation of taxa [START_REF] Delong | Community genomics among stratified microbial assemblages in the ocean's interior[END_REF]. In lakes, there are few studies that explore depth distribution of microbial communities [START_REF] Kurt | Depth distribution of microbial diversity in lakes[END_REF]. However, they allowed to detect structuring factors of communities (e.g. in the distribution of microbial eukaryotes in the southern basin of Lake Baikal or in temperate lakes in France; [START_REF] Yi | High-throughput sequencing of microbial eukaryotes in lake baikal reveals ecologically differentiated communities and novel evolutionary radiations[END_REF][START_REF] Keshri | Taxonomical resolution and distribution of bacterioplankton along the vertical gradient reveals pronounced spatiotemporal patterns in contrasted temperate freshwater lakes[END_REF]. Vertical surveys give the opportunity to make assumptions on the functional role of taxa. For instance, [START_REF] Lepere | Vertical structure of small eukaryotes in three lakes that differ by their trophic status: a quantitative approach[END_REF] investigated the structure of microbial eukaryotes in three lakes with three different trophic statuses. On the basis of the distribution of taxa and knowing the mixing regime of the different lakes it is possible to discuss the potential importance of mixotrophy in the functioning of the lakes.

The horizontal gradient corresponds to the distribution of communities at different locations (latitude/longitude). The sampling can be conducted within one ecosystem at several points of the surface, or in several distinct ecosystems distributed over a given study area. The horizontal gradient has also its importance in lotic water-bodies, from upstream to downstream. Indeed, changes in the composition of microbial communities were detected along the water course, indicating changes of water chemistry and possible immigration from the bank and sediments [START_REF] Crump | Biogeography of bacterioplankton in lakes and streams of an arctic tundra catchment[END_REF]. In freshwater ecosystems, several studies have compared different ecosystems over the same geographical area in order to investigate the biogeographic patterns. For instance, [START_REF] Eiler | Composition of freshwater bacterial communities associated with cyanobacterial blooms in four swedish lakes[END_REF] sampled bacterial communities in four lakes and showed that despite geographic proximity and under similar cyanobacterial bloom events, communities were significantly different.

Combination of horizontal and vertical gradients can help resolve global distribution of taxa. This approach is more common in marine systems than in freshwater systems. For instance, [START_REF] Kirkham | Basinscale distribution patterns of photosynthetic picoeukaryotes along an atlantic meridional transect[END_REF] studied the distribution of two marine picophytoplanktonic groups, the Prymnesiophyceae and the Chrysophyceae, using a spatial survey conducted in the Atlantic. The two classes revealed strongly complementary distributions along the transects, with differences in the N:P requirements and on the latitudinal repartition (Figure 1.9). In conclusion, spatial surveys over the vertical gradient can help understand better the distribution of organisms along the water column. Over the horizontal gradient, they help unravel the variations resulting from the biogeography and the influence of ecosystem structure (i.e. coastal versus pelagic areas). However, it is important to be careful when considering time in spatial studies. The observed biogeographic patterns can be biased if time is not took into account during the sampling (e.g. several weeks between two samples). Moreover, spatial patterns are conducted using discrete time point, which does not allow to depict the entire communities inhabiting the systems.

Temporal evolution of microbial communities

Temporal evolution of community composition is of major importance to understand the functioning of an ecosystem, its ecology and the structuration of its communities [START_REF] Moreira | Time series are critical to understand microbial plankton diversity and ecology[END_REF]. Temporal surveys are a way to determine if microbial communities remain stable over time, to detect periodic and non-periodic patterns, and to evaluate the effect of perturbations of the communities, as for instance drought events [START_REF] Simon | Resilience of freshwater communities of small microbial eukaryotes undergoing severe drought events[END_REF]. They can be conducted at different time scales (i.e. from hours to years) as microbial communities are dynamic over multiple time scales [START_REF] Fuhrman | Marine microbial community dynamics and their ecological interpretation[END_REF]. First, community changes can be investigated at an hour scale. This time scale corresponds for instance to day and night variations [START_REF] Trench-Fiol | Metatranscriptomics from a small aquatic system: Microeukaryotic community functions through the diurnal cycle[END_REF]. It can also be useful to investigate the immediate effect of a perturbation, for instance of nutrients amendment (Newton and McMahon, 2011). It can help to capture variations in the physiological response of communities. Indeed, the generation time of organisms can be of one day for the fastest, like copiotrophic organisms [START_REF] Mangot | Shortterm dynamics of diversity patterns: evidence of continual reassembly within lacustrine small eukaryotes[END_REF][START_REF] Fuhrman | Marine microbial community dynamics and their ecological interpretation[END_REF]. For instance, a day versus night study allowed to show that coral reef microbial communities collected at the same period are more similar than the ones sampled at day and night within one site [START_REF] Kelly | Diel population and functional synchrony of microbial communities on coral reefs[END_REF]. Then, the communities can change over daily to weekly time ranges. This frequency of sampling allows to study the effect of environmental perturbations, such as the effect of algal blooms. It is also a good scale to infer interactions between microorganisms with co-occurrence networks. In the case of lakes, this time range allowed to study the impact of the mixing regime of lakes on microbial communities [START_REF] Shade | The influence of habitat heterogeneity on freshwater bacterial community composition and dynamics[END_REF]. Finally, time surveys can be conducted at monthly to seasonal range. Monthly surveys are the most common in literature. These surveys can help identifying global effect of climatic variation, regional changes, or changes in the tropic status of the ecosystems. For instance, using community dissimilarity estimations or autocorrelation of time-series, long surveys allowed to identify strong seasonal patterns in marine and freshwater environments (see Figure 1.10, [START_REF] Gilbert | Defining seasonal marine microbial community dynamics[END_REF][START_REF] Fuhrman | Marine microbial community dynamics and their ecological interpretation[END_REF][START_REF] Yan | Nearly a decade-long repeatable seasonal diversity patterns of bacterioplankton communities in the eutrophic lake donghu (wuhan, china)[END_REF][START_REF] Giner | Comparative 16s rrna analysis of lake bacterioplankton reveals globally distributed phylogenetic clusters including an abundant group of actinobacteria[END_REF]. Therefore, temporal surveys at different frequencies are needed to investigate the various dynamics and the ecology of microorganisms inhabiting freshwater ecosystems. Every year, there are some regular variations of the environmental conditions in ecosystems (e.g. succession of seasons).

In addition, there are unusual perturbations (e.g a warmer winter than usually) that disturb the regular variations. Therefore, if the duration of the survey is too short, these perturbations will not be detected like such. Hence, to obtain reliable conclusions on the patterns detected, long surveys are needed. Several long time-series are being conducted in marine and freshwater systems and follow the same environments over several years. Long term marine surveys are reviewed in [START_REF] Buttigieg | Marine microbes in 4d-using time series observation to assess the dynamics of the ocean microbiome and its links to ocean health[END_REF]. Long term freshwater surveys are conducted in various ecosystems, such as peri-alpine lakes with the "Observatoire des Lacs"1 or north-temperate lakes with the US Long-Term Ecological Research Network2 . These long time surveys investigate for instance the link between microbial communities and abiotic parameters, the temporal changes in community composition, the interactions between microbial communities or even the impact of climate change [START_REF] Anneville | Long-term study (1974-1998) of seasonal changes in the phytoplankton in lake geneva: a multi-table approach[END_REF]; [START_REF] Karl | Microbial oceanography and the hawaii ocean time-series programme[END_REF]; [START_REF] Herren | Keystone taxa predict compositional change in microbial communities[END_REF]; [START_REF] Obertegger | Multifaceted aspects of synchrony between freshwater prokaryotes and protists[END_REF]. The grain of these studies is often based on a monthly sampling, but rarely on smaller frequencies. For instance, the Linnaeus Microbial Observatory3 , consists in a weekly sampling since 2011, and focuses on the three domains of life (bacteria, phytoplankton, zooplankton) as well as on viruses. Among other results, the data collected with the LMO allow to decipher at a finer scale the variability of microbial communities and their influence on biogeochemical cycles [START_REF] Bunse | High frequency multi-year variability in baltic sea microbial plankton stocks and activities[END_REF].

Microbial ecology studies benefit from long time surveys on the understanding of microbial communities' structuration processes. However, these surveys remain scarce in freshwater systems. In those ecosystems, surveys focus mostly on bacterioplankton composition, with time scales ranging from five to ten years and they covered different environments such as freshwater bog lakes and Lake Mendota (Winsconsin, USA), a shallow lake in Estonia, Lake Donghu (China), Lake Tiefwaren (Germany) [START_REF] Shade | Interannual dynamics and phenology of bacterial communities in a eutrophic lake[END_REF][START_REF] Rösel | Long-term characterization of free-living and particleassociated bacterial communities in lake tiefwaren reveals distinct seasonal patterns[END_REF][START_REF] Kara | A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic lake mendota, wi, usa[END_REF][START_REF] Tammert | Contrasting seasonal and interannual environmental drivers in bacterial communities within a large shallow lake: evidence from a seven year survey[END_REF][START_REF] Linz | Bacterial community composition and dynamics spanning five years in freshwater bog lakes[END_REF][START_REF] Yan | Nearly a decade-long repeatable seasonal diversity patterns of bacterioplankton communities in the eutrophic lake donghu (wuhan, china)[END_REF]. Studies on phytoplankton are also conducted, using microscopy most of the time. For instance, a study in Lake Mondsee (Austria) of 6 years compared the pattern detected between seasonal and a monthly sampling of the phytoplanktonic communities [START_REF] Bergkemper | Do current european lake monitoring programmes reliably estimate phytoplankton community changes?[END_REF]. The longest temporal survey focusing on both eukaryotic and prokaryotic communities that we found in literature was conducted in Lake Tovel (Italy), with a monthly survey over four years [START_REF] Obertegger | Multifaceted aspects of synchrony between freshwater prokaryotes and protists[END_REF].

We also found a study covering 20 years of survey in Lake Gjersjøen (Norway) [START_REF] Xiao | Use of high throughput sequencing and light microscopy show contrasting results in a study of phytoplankton occurrence in a freshwater environment[END_REF]. The sampling was conducted weekly, but on discontinuous periods. However, even if they used high throughput sequencing of both eukaryotic and prokaryotic communities, they did not investigate the temporal variations of community composition. In the case of small freshwater ecosystems,we were not able to find long term surveys on microbial. However, a six-year survey of the water quality and the macrophytes communities revealed that increasing the time of sampling improves the understanding of the ecosystems because it allows to capture more hydrological variability [START_REF] Pätzig | How much information do we gain from multiple-year sampling in natural pond research?[END_REF]. This underlines the importance to conduct long time surveys to study the functioning of small freshwater ecosystems.

Aims of the thesis

As explained in section 1.1, freshwater ecosystems are diverse and can be divided between lentic and lotic ecosystems, as well as between large and small ecosystems. This results into a wide variability in their functioning. Large freshwater ecosystems represent a large volume of water, but are rather rare in the landscape [START_REF] Downing | Emerging global role of small lakes and ponds: little things mean a lot[END_REF]. These ecosystems are expected to be stabler in time, because their large volume of water induces a strong buffering effect.

At the same time, they are more subject to spatial variations within the ecosystem, through horizontal and vertical gradients. On the contrary, small freshwater ecosystems are common and numerous. Therefore, to have a good overview of the latter, it is better to sample several of them in order to capture variability in terms of abiotic composition, shape, or size for instance.

Moreover, their small size makes them highly sensitive to global environmental variations and to punctual perturbations. Hence, it is important essential to focus on their variability over time at different time scales.

In this thesis, I aim at investigating the functioning of freshwater ecosystems, through the lens of microorganisms communities. The study of any kind of ecosystems requires multiple steps: explore the diversity and the autoecology of the communities inhabiting the ecosystems;

determine the influence of top-down and bottom-up factors on the communities; conduct analyses with consistent spatiotemporal scales to have a broad picture of the different mechanisms.

To do so, I focused on the biggest lake in the world, that is Lake Baikal, and five common and small ecosystems, that are four ponds and one brook from the Ile-de-France region (France). I used two approaches, consisting in a spatial survey in Lake Baikal, and a temporal survey of the five distinct small freshwater ecosystems. These two dimensions are important to capture the maximum of the community complexity. Finally, I focused on the microbial diversity and on the impact of abiotic and biotic factors on the communities using various tools, that is multivariate statistics and co-occurrence networks inference. I summarized all the methodological aspects in a chapter dedicated to the material and methods (Chapter 2). A more detailed version is presented in a specific section of each chapter.

Describe the eukaryotic and prokaryotic diversity in two types of freshwater ecosystems

Before trying to understand the mechanisms taking place in an ecosystem, it is mandatory to explore and to determine the communities that inhabit it.

Lake Baikal is a highly studied ecosystem because of its importance on Earth, e.g. it is the largest freshwater volume. However, despite several studies on its metazoan communities, its microbial communities are still missing descriptions. Especially, there is a small number of studies focusing on the global scale, that is taking into account the entire lake surface and depth range. Therefore, I conducted a description of Lake Baikal microbial eukaryotic communities over the horizontal and vertical dimensions in summer (Chapter 3).

Small freshwater ecosystems are poorly studied ecosystems. The current literature indicates that they host highly diverse microbial communities, but we lack information on both their microbial eukaryotes and prokaryotes communities. In complement to a previous work on the microbial eukaryotic communities over a monthly survey of two years (from April 2011 to April 2013), I investigated the prokaryotic communities over the same period (Chapter 4). In order to get a more integrative view of the communities, we conducted in the same ecosystems a seasonal survey of eight years (October 2013 to January 2019) using a more recent sequencing method (Chapter 5). For the two-year monthly survey, we focus only on the small microbial communities (size in the range between 0.22 and 5 µm) because they are less described and known. For the seasonal eight-year survey, we considered a larger part of the communities, in order to capture potential predators and attached organisms (size in the range between 0.22 and 30 µm).

Describe the spatio-temporal dynamics of microbial communities from the three domains of life

The spatio-temporal dimension is crucial for an integrative view of the functioning of aquatic ecosystems. The dimensions of Lake Baikal allowed us to conduct a large spatial survey, in the horizontal gradient, that is at different points at the surface of the lake, and in the vertical gradient, that is at different depths. This large survey allowed us to identify the factors that drive community composition in summer in the lake (Chapter 3). However, the temporal dimension is also very important. Some temporal variability exists in Lake Baikal, but the Lake's response to any environmental change is slow because of its large size. On the contrary, small freshwater ecosystems are very sensitive to environmental variations because of their small volume of water that induces a weak buffering capacity. Moreover, small ecosystems are easier to sample on a regular basis due to their small size and proximity. Hence, I aimed at describing the temporal dynamics of the global microbial communities over time in five small freshwater ecosystems. A monthly survey offered a detailed view of the dynamics (Chapter 4). A seasonal survey allowed us to check if the observed temporal patterns can be confirmed on a longer time scale, or only result from a particular event that happened during the short survey. Also, as the seasonal survey was longer, it gave us the opportunity to investigate temporal patterns at the OTU scale (Chapter 5).

Explore the microbial interactions through co-occurrence networks Microbial communities are structured by abiotic and biotic factors. In order to explore the interactions between the microorganisms detected in our ecosystems, I conducted co-occurrence analysis. The inference of interaction networks at the various depth levels in Lake Baikal allowed us to determine if the depth induced different structures of communities (Chapter 3).

In the small freshwater ecosystems, I explored the global community structure by building an interaction network combining the data of the five ecosystems (Chapter 5). Then, I focused on temporal patterns, in order to determine whether the structure of the communities was different between seasons, and/or if it was specific of each season (Chapter 6).

Chapter 2

Materials and methods water transparency, and dissolved oxygen level, but also low salt content and dissolved organic level [START_REF] Pavlichenko | Identification of a putatively multixenobiotic resistance related abcb1 transporter in amphipod species endemic to the highly pristine lake baikal[END_REF][START_REF] Kurilkina | Bacterial community composition in the water column of the deepest freshwater lake baikal as determined by next-generation sequencing[END_REF][START_REF] Khodzher | Current chemical composition of lake baikal water[END_REF][START_REF] Cabello-Yeves | Marine-freshwater prokaryotic transitions require extensive changes in the predicted proteome[END_REF]. Lake extends over a large region, from latitude 55°51'40" N to latitude 51°28'15" N and has a length of 636 km and a width of 79 km. It is commonly divided Because of the permeability of the upper substrate and the impermeability of the clays, the region favourites the formation of wetlands [START_REF] Lefebvre | Diagnostic et quantification des flux nappe-rivière: modélisations hydrodynamique et géochimique du bassin versant de l'yvette amont (france)[END_REF]. Around 1300 standing small fresh ecosystems have been counted in the PNR-HVC. In 2011, five small freshwater ecosystems of the PNR-HVC were selected with the help of the PNR-HVC managers (see Figure 2.2). All ecosystems are located less than 10 km apart but present specific environmental characteristics, despite their geographic proximity. Furthermore, they are located at about 30 min drive from our laboratory which facilitates their regular sampling during the year [START_REF] De Meester | Ponds and pools as model systems in conservation biology, ecology and evolutionary biology[END_REF]. We applied the same protocol to all the samples. Each time, the sampling of the five ecosystems was conducted within a restricted time frame (in max. 3 days). This allowed to have similar meteorological conditions between ecosystems, which is crucial to compare their communities. We collected the water at the same spot (deepest point) at surface in the five ecosystems, using sterile 500 ml containers. For each ecosystem, 15 litres of water were collected in sterilised plastic carboys. Several physico-chemical parameters (water temperature, conductivity, dissolved oxygen concentration and pH) were measured in-situ, using a multiparameter probe WTW-Multi 350i: water temperature, conductivity, dissolved oxygen and pH.

Back to the laboratory, we immediately performed serial filtrations of the water samples through 100 and 30 µm nylon mesh (Millipore), then through 5 and 0.22 µm Nucleopore membranes (Whatman). The total volume of water filtered varied between 40 and 2490 ml. Membranes were then stored at -80 ∘ C in 2 ml Eppendorf tubes, until DNA extraction.

Measure of physico-chemical parameters in the laboratory

The same day of sampling, we measured the concentrations of dissolved nutrients using colorimetric tests (Hach-Lange, Düsseldorf, Germany) once back to the laboratory. For all the samples, we measured the concentration of nitrites (NO - 2 ), nitrates (NO - 3 ), ammonia (NH 3 ) and orthophosphates (PO 3- 4 ) from the 30 µm filtration. For future analyses of organic carbon concentration (DOC), water filtered through 0.22 µm membrane was frozen at -20 ∘ C. Finally, we collected plankton biomass smaller than 100 µm on two GF/F filters that were conserved at -20 ∘ C until chlorophyll a extraction.

We measured DOC concentration using a TOC VCSH analyzer (Shimadzu, Tokyo, Japan) following the protocol of [START_REF] Rochelle-Newall | Cdom fluorescence as a proxy of doc concentration in natural waters: a comparison of four contrasting tropical systems[END_REF]. We measured chlorophyll a concentrations using a protocol of ethanol pigment extraction, adapted by Claude Yéprémian from [START_REF] Ritchie | Consistent sets of spectrophotometric chlorophyll equations for acetone, methanol and ethanol solvents[END_REF]. Briefly, frozen filters were dried for one hour at 37 ∘ C, then grounded in 7 ml absolute ethanol and heated at 70 ∘ C for 20 min. Samples were centrifuged at 4000 rpm and 1 ml of the supernatant was collected in order to measure optical densities at 665 and 750 nm with a spectrophotometer (DR5000, Hach-Lange). We estimated the chlorophyll a concentration with the formula:

[Chla] = 11.9 × OD 665 -OD 750 𝑤 × V 𝑒 V 𝑓 (2.1)
where [Chla] is the chlorophyll a concentration (µg.l -1 ), "OD 𝑥 " the optical density where x is the wave length (nm) of optical density measurement, "w" the width of the spectroscopic cuvette, "V 𝑒 " the volume of ethanol (ml) used for the pigment extraction and "V 𝑓 " the volume of water initially filtered (in litres) through the membranes. The value was arbitrarily validated only when differences between the two replicates were lower than 10 percent.

Samples preparation and processing

The acquisition of the sequence followed the same steps for the small freshwater ecosystems and for Lake Baikal. In the small freshwater ecosystems, we targeted both eukaryotic and prokaryotic communities, whereas for Lake Baikal, we only focused on the eukaryotic communities.

Extraction and amplification of DNA

I extracted DNA using the "PowerSoil DNA extraction" kit (Quiagen), following the manufacturer instructions. I first cut the filters using sterile scalpel or razor blades. For the monthly survey in the ponds, we only used the filters of 0.22 pore-size, covering the 0.22-5 µm sizefraction. For the seasonal survey in the ponds and the Baikal spatial survey, we considered the size fraction ranging from 0.22 to 30 µm, using the filters of 0.22 and 5 µm pore-size. This size fractionation allows to filter higher volumes but especially to obtain samples enriched in diverse functional groups, i.e. picoplankton (0.2-2 µm), nanoplankton (2-20 µm) and microplankton (20-30 µm).

Portions of the targeted genes were amplified using a polymerase chain reaction (PCR). For the eukaryotic community, the targeted region of the 18S ARNr gene was located on the V4 region and was of about 550 pb long. To do so, I used 565F/1134R-UnonMet primers, the latter being biased against metazoa [START_REF] Bower | Preferential pcr amplification of parasitic protistan small subunit rdna from metazoan tissues[END_REF]. For the study of the prokaryotes, I also targeted a portion of the hyper-variable region V4 of the 16S ARNr gene. To do so, I used the universal U151F/U806R primers. The targeted region had a length of about 290 pb. All primers were tagged using molecular identifiers (MIDs) of 10 pb, in order to track the sample of origin for each sequence, i.e. MID49 for January 2017 in Etang des Vallées. As I was sequencing together several samples, MIDs determine from which sample each sequence came from during the cleaning step of the sequences (see 2.3.3). I conducted the amplifications in a 25 µlmixcontaining1.5mMofMgCl 2 , 0.2 mM of dNTPs, 0.3 mM of each primer, 0.5 U of Taq Polymerase (Taq Platinium, Invitrogen, Carlsbad, CA, USA) and between 0.3 and 3 µl of the sample DNA. PCR cycles were composed of three steps:

(i) a denaturation step (2 min at 94 ∘ C) (ii) followed by 35 amplification cycles (15 sec at 94 ∘ C, then 30 sec at 55 ∘ C for the eukaryotes or 30 sec at 58 ∘ C for the prokaryotes, and finally 90 sec at 72 ∘ C), (iii) and a final extension step (10 min at 72 ∘ C). I pooled five PCR products for each sample. Finally, I purified each pool using a purification QIAquick kit (Quiagen, Hilden, Germany). All the sampling and the bench work was performed by Paola Bertolino, Marianne Simon and myself.

Sequencing

I measured the DNA quantity in each purified pool and I sent a tube with a mix containing an equivalent amount of amplified DNA of all pools to sequencing.

Sequences of the monthly survey were produced by pyrosequencing 454 GS FLX Titanium technology (Roche) by Beckman Coulter Genomics (Danvers, USA). Their length was of about 550 bp. The sequences of the seasonal and Baikal samples were obtained with Illumina MiSeq (Eurofins Genomics, Ebersberg, Germany). Their length was of about 300 bp.

Processing of the sequences

The sequences obtained through both technologies had to be cleaned before any analysis, to eliminate artefacts and sequencing mistakes.

Monthly survey -Pyrosequencing 454 FLX Titanium

The treatment of the pyrosequencing sequences was conducted through a local pipeline, by Drs. Marianne Simon and Philippe Deschamps (DEEM Team, ESE), following several steps: 1. Cleaning of the sequences: First, reads were cleaned using the AmpliconNoise software to eliminate (i) errors in the primers, (ii) too short reads and (iii) errors that were produced during PCR amplification or pyrosequencing [START_REF] Quince | Removing noise from pyrosequenced amplicons[END_REF].

2. OTUs building: Clean reads were grouped into Operational taxonomic units (OTU).

Eukaryotic and prokaryotic sequences were respectively gathered at 98% and 97% of identity. The grouping was conducted with the average linkage clustering method, using AmpliconNoise [START_REF] Quince | Removing noise from pyrosequenced amplicons[END_REF]. The most abundant sequence in each OTU was selected as the reference sequence. All OTUs represented by only one sequence (i.e. singleton) were deleted.

3. OTUs affiliation: The representative sequences were blasted against two databases, SILVA and PR2, in order to determine their taxonomic affiliation. 4. Final modifications: All sequences were attributed to their sample of origin using the MIDs. To limit the presence of potential chimeras, OTUs detected in only one ecosystem were checked using KEYDNATOOL (http://KeyDNATools.com) and then manually blasted. OTUs affiliated to mitochondria and chloroplasts were deleted.

Seasonal and spatial survey -Illumina MiSeq

I conducted the treatment of the Illumina MiSeq sequences in collaboration with Guillaume Reboul and Philippe Deschamps (DEEM Team, ESE). The treatment steps are detailed below.

Step 1 to 3 were performed using a local pipeline.

1. Cleaning of the sequences: Paired-end sequences were merged using the "flash" tool, generating Clean Merged Reads (CMRs) [START_REF] Madsen | Flash: fast length adjustment of short reads to improve genome assemblies[END_REF]. CMRs containing errors either in the primers or in the MIDs were removed. The remaining correct primers and MIDs were removed from the CMRs using the cutadapt tool [START_REF] Martin | Cutadapt removes adapter sequences from high-throughput sequencing reads[END_REF]. Thereafter, the CMRs were dereplicated into unique sequences using the "vsearch" tool [START_REF] Rognes | Vsearch: a versatile open source tool for metagenomics[END_REF]. Furthermore, chimeras were removed using a de novo process. For this, the sequences dataset and an algorithm are used. The sequences are pairwised compared in order to detect sequences (i.e. the chimeras) that are composed of portions of other sequences.

2. OTUs construction: Sequences were gathered into OTUs at 95% of identity, for both eukaryotes and prokaryotes, using the cd-hit tool [START_REF] Li | Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences[END_REF][START_REF] Fu | Cd-hit: accelerated for clustering the next-generation sequencing data[END_REF]. This taxonomic threshold grouped microorganisms at the genus level [START_REF] Yarza | Uniting the classification of cultured and uncultured bacteria and archaea using 16s rrna gene sequences[END_REF]. The cd-hist tool performs a classic de novo clustering of the OTUs (i.e. an iterative process group amplicons together one by one depending on their pairwise sequence similarity).

Each OTU was represented by its most abundant sequence (i.e. reference sequence).

Singletons were eliminated from the dataset.

OTUs affiliation:

The reference sequence of each OTU was compared to a local database using the "vsearch" tool [START_REF] Rognes | Vsearch: a versatile open source tool for metagenomics[END_REF]. The local database was based on cultivated and environmental sequences obtained from SILVAv132 and PR2v4.5 [START_REF] Quast | The silva ribosomal rna gene database project: improved data processing and web-based tools[END_REF][START_REF] Guillou | The protist ribosomal reference database (pr2): a catalog of unicellular eukaryote small sub-unit rrna sequences with curated taxonomy[END_REF]. OTUs affiliated to mitochondria and chloroplast were removed. 4. Taxonomic verification: Sequences with a percentage of identity with an environmental best-it lower than 80% were manually checked. Their affiliations were compared to reference sequences with a phylogenetic placement. When the position in the phylogenetic tree was incorrect, I conducted a blast in NCBI [START_REF] Sayers | Database resources of the national center for biotechnology information[END_REF]. The correct affiliation was then modified by hand in the dataset.

Data analysis

Multivariate statistical analysis

Subsequent to the data acquisition and preparation steps, data-frames were created, which are two dimensional arrays, with samples in row and OTUs in column (see example in Table 2.2).

In complement to the data-frames, the taxonomic affiliations of the OTUs were summarized in another table . To answer the ecological questions on the studied ecosystems, I computed several multivariate statistical analysis and indicators estimations on the data. Statistical analyses were performed using R software (R Core Team, 2019). Prior to any analysis, I conducted a normalization of the data. Metabarcoding data are compositional, which means that they are not equivalent to real counting of OTUs abundance [START_REF] Tsilimigras | Compositional data analysis of the microbiome: fundamentals, tools, and challenges[END_REF][START_REF] Gloor | Microbiome datasets are compositional: and this is not optional[END_REF]. It is mainly due because the total number of reads obtained depends on data collection, on the PCR amplification and on the sequencing technology. Therefore, it can be irrelevant to compare two samples without a preliminary normalization. For the pyrosequencing data, I applied two types of normalization:

-For simple community comparison: a total-sum normalization, where the number of reads for each OTU was transformed into a frequency, depending on the total number of reads per sample.

-For statistical analysis based on Bray-Curtis dissimilarities (see 2.4.1.1): a Wisconsin double standardization, where the reads of each OTU were first divided by their column maximum and then divided by the total number of reads in each sample. This method reduces the impact of abundant OTUs versus rare OTUs, which can be amplified with kind of dissimilarity calculation.

I conducted a rarefaction of the data for the Illumina MiSeq datasets. To do so, I selected the sample with the smallest number of reads. Then, for each sample, I selected a subsample of OTUs and reads to obtain a total number of reads equal the smallest number of reads selected previously.

Community composition in and within the ecosystems

The first step for analysing the microbial communities in a specific ecosystem consisted in the description of the community composition. In this work, I applied several indexes and analyses:

1. Dissimilarities between samples Several of the following analyses are based on the calculation of differences in the composition of pairwise samples. I calculated these dissimilarities between a sample X and a sample Y using the Bray-Curtis dissimilarities function 𝑑 (Equation 2.2, [START_REF] Bray | An ordination of the upland forest communities of southern wisconsin[END_REF].

𝑑 = ∑ 𝑖=1 (|x 𝑖 -y 𝑖 |) ∑ 𝑖=1 (x 𝑖 + y 𝑖 ) (2.2)
where x 𝑖 and y 𝑖 are the frequencies of the i OTU in the X and Y samples. If d is equal to 0, then the two samples are identical. If d is equal to 1, then the two samples share no common OTUs.

Diversity indexes

Three indexes were used in this study to describe community composition in samples.

They were all computed using the vegan R package [START_REF] Oksanen | The vegan package[END_REF].

Richness

The richness represents the total number of OTUs detected in one sample.

Evenness

The evenness indicates whether the sample is composed of OTUs with relatively similar abundances, or composed of a few very abundant OTUs belonging to a community mostly composed of rare OTUs. The evenness 𝑒 was calculated following Pielou (1966) (Equation 2.3).

𝑒 = ∑ 𝑖 f 𝑖 × ln(f 𝑖 ) ln(S) (2.3)
where f 𝑖 is the OTU 𝑖 frequency and S the number of OTUs observed in the sample.

Simpson index

The Simpson index 𝐷 represents the probability that two random sequences belong to two different OTUs. It indicates the level of diversity found in the ecosystem. If 𝐷 is equal to 1, the sample presents no diversity. 𝐷 was calculated following Simpson (1949) (Equation 2.4)

𝐷 = 1 - ∑ 𝑖 f 2 𝑖 (2.4)
where f 𝑖 is the OTU 𝑖 frequency.

Community composition comparison

As I was handling samples collected at various locations, depth and time periods, using the differences in terms of community composition was the best way to compare the data.

Venn diagrams

In order to compare the OTU richness, Venn diagrams were generated using the gplots R package. They determine the amount of shared or exclusive OTUs within the sampling sites.

Non-metric multidimensional scaling (nMDS)

This analysis presents in two dimensions the similarities between samples, based on Bray-Curtis dissimilarities. Several nMDS analyses were computed using the vegan R package. If the samples clustered into groups, a complementary analysis of similarities (ANOSIM) between groups was performed. It was useful to quantify and to statistically test for the differences between observed groups.

Hierarchical clustering analysis

This clustering method combines similar samples into clusters in a global dendrogram. Bray-Curtis dissimilarities were used, combined to the Ward's method [START_REF] Murtagh | Ward's hierarchical agglomerative clustering method: which algorithms implement ward's criterion?[END_REF]. The analysis was conducted using the stats R package.

Impact of the abiotic factors on community variation

The variations of the community composition can be explained by various factors, including abiotic factors such as physico-chemical parameters sampling period, or sampling depth.

Therefore, I conducted two analyses to evaluate the impact of abiotic factors on community composition. They were conducted using the vegan R package.

PERMANOVA

Permutational Analyses of Variance assesses globally the impact of various factors on community composition [START_REF] Anderson | Permutational multivariate analysis of variance (permanova)[END_REF]. For instance, in the temporal analysis, I used the role of the habitat (pond or stream), the ecosystem, the season, and the year of sampling as explanatory parameters.

Canonical Correspondence Analysis

I used Canonical Correspondence Analysis (CCA) to estimate the contribution of each physico-chemical parameter to the community variance. In this analysis, the community tables were constrained by the physico-chemical table. At the same time, the putative multicollinearity between physico-chemical parameters was checked with the variance inflation factor (VIF). Physico-chemical parameters with a strong multicollinearity were removed to avoid bias in the analysis (under the condition that VIF > 10). Finally, a forward selection was conducted to identify the most significant physico-chemical parameters in the model (by means of Monte Carlo permutation tests, on the basis of 999 permutations). I also used this analysis to estimate the influence of eukaryotic phyla on prokaryotic composition and inversely.

Time-lag analysis

The composition of communities and abiotic factors are changing over time. Time-lag analysis (TLA) is a way to quantify the rate of change in the ecosystems [START_REF] Collins | A method to determine rates and patterns of variability in ecological communities[END_REF]. This approach is commonly conducted to decipher multiple temporal patterns [START_REF] Fuhrman | Marine microbial community dynamics and their ecological interpretation[END_REF]. For the communities, I compared the Bray-Curtis dissimilarities between all the possible sampling dates over increasing time lags. I used Euclidean distances for physico-chemical parameters. Linear regressions were computed to estimate the global evolution of community dissimilarities (or of environmental parameters) over time.

The slope and the significance of the regression line indicated the degree and direction of the changes, i.e. convergence, divergence (directional change) or stability (Figure 2.3).

To test for the accuracy of the observation, a randomization procedure was conducted on the data, with 5000 permutations, using the lmPerm R package [START_REF] Wheeler | Package 'lmperm'. R package version[END_REF].

Polynomial regressions were computed to identify cycling patterns (e.g. seasonal). The best fitted polynomial model was selected using the Akaike Information Criterion (AIC) [START_REF] Akaike | Maximum likelihood identification of gaussian autoregressive moving average models[END_REF]. Co-inertia analysis I investigated the synchrony of community composition using co-inertia analysis (CIA) [START_REF] Bock | Synchrony of eukaryotic and prokaryotic planktonic communities in three seasonally sampled austrian lakes[END_REF]. CIA were computed using the ade4 R package [START_REF] Dray | The ade4 package: implementing the duality diagram for ecologists[END_REF].

With this analysis, I identified similar structure between two datasets within the same samples (i.e. the eukaryotic and the prokaryotic datasets). The correlation of the multivariate vectors, summarized as the RV index, assesses the strength of the synchrony between the two communities. It was tested using 999 permutations.

Turnover of OTUs

In complement to the evolution of community comparison, I calculated the turnovers which indicates the type of changes occurring over time. For instance, if the turnover was high, the dissimilarities between samples could be explained by drastic changes in community composition. Turnovers were calculated between pairwise seasons (e.g. summer versus winter) or between samples collected at the same season but for different years.

Turnovers were computed using the codyn R package, following the formula:

Turnover = S 𝑔𝑎𝑖𝑛𝑒𝑑 + S 𝑙𝑜𝑠𝑡 S 𝑡𝑜𝑡𝑎𝑙 (2.5)
where S 𝑔𝑎𝑖𝑛𝑒𝑑 and S 𝑙𝑜𝑠𝑡 are the numbers of species gained and lost between the first and the second time points respectively and S 𝑡𝑜𝑡𝑎𝑙 is the total number of species observed in the time points.

Identification of recurrent OTUs

Recurrent OTUs were detected using a method developed by [START_REF] Giner | Comparative 16s rrna analysis of lake bacterioplankton reveals globally distributed phylogenetic clusters including an abundant group of actinobacteria[END_REF]. The method is based on the calculation of a recurrence index (RI). I used the RI to identify the OTUs presenting a temporal pattern, i.e. occurring at the same period several times during the survey. The index calculation is based on the comparison between an autocorrelation function of the OTU (that-is a correlation of the time series with itself and a null distribution generated with a randomization of OTU abundances (1000 occurrences). RI was calculated for all OTUs in each ecosystem using the ecolutils R package [START_REF] Salazar | Ecolutils: Utilities for community ecology analysis[END_REF]. However, OTUs occurring less than 4 times during the survey were excluded from the analysis. Indeed, these OTUs were not detected enough times to present a recurrence pattern.

Co-occurrence networks 2.4.2.1 PLN models

A large amount of models has been developed to generate microbial co-occurrence networks.

Here, I applied a recent method based on Poisson Log Normal models (PLN), called PLNmodel [START_REF] Chiquet | Variational inference for sparse network reconstruction from count data[END_REF]. With this method, co-occurrence networks are inferred using inverted matrix of variance-covariance, called precision matrix. Using this matrix avoid the detection of associations resulting from indirect covariations (between two OTUs due to their direct association to a third OTU for instance) [START_REF] Kurtz | Sparse and compositionally robust inference of microbial ecological networks[END_REF]. PLNmodel is based on the counts of the OTUs reads that are considered as Poisson distributions. The model selection is conducted by choosing one optimal penalty parameter. This parameter controls the number of edges in order to obtain the most stable network. The optimal penality was selected using the Bayesian Information Criterion (BIC), among a 50-size grid of penalties [START_REF] Chen | Extended bayesian information criteria for model selection with large model spaces[END_REF].

Finally, the model takes into account several explicative parameters, such as the sequencing depth (i.e. total number of reads per sample) and various covariables, either quantitative (e.g. abiotic factors) or qualitative (e.g. season). These covariables can improve the model by taking into account environmental variability, but more importantly, by avoiding spurious interactions [START_REF] Chiquet | Variational inference for sparse network reconstruction from count data[END_REF][START_REF] Röttjers | From hairballs to hypotheses-biological insights from microbial networks[END_REF].

During this thesis, I constructed seasonal networks for the small freshwater ecosystem dataset. I built a network for each season and each ecosystem, using OTUs detected in at least 6 seasons over the 8 years of survey (e.g. 6 over the 8 autumn periods sampled) and having a mean abundance higher than 0.05%. I used three abiotic parameters (conductivity, dissolved oxygen, pH) as covariables in the model. I also constructed networks with Lake Baikal dataset. I have built one network for each depth class, on the OTUs present in 20% of the samples, and with a mean abundance higher than 0.01%. I considered the sampling basin (north, central or south) as covariable in order to avoid spurious associations

Visualization of the networks

Based on the precision matrix, I built the networks using the ggnet2 R package. The OTUs are represented by nodes, and the direct covariations between them by the edges. I used graphical option to colour nodes according to their taxonomy or lifestyle, and to differentiate positive from negative nodes.

Topology of the networks

For each network, I calculated classical networks properties, using the igraph R package [START_REF] Csardi | The igraph software package for complex network research[END_REF]. The first characteristics are the total number of nodes (i.e. the number of OTUs implemented in the model) and the number of connected nodes (i.e. the number of nodes involved in the final network). Then, I counted the number of edges, with a distinction between the edges representing a positive or a negative relationship. For each node, I estimated the node degree, i.e. the average number of edges that connected the nodes. I also calculated the mean degree of all the nodes for each network. Finally, I calculated three indicators called average path length, clustering coefficient and connectance. The average path length represents the mean of all the shortest path between two nodes in the network [START_REF] Berry | Deciphering microbial interactions and detecting keystone species with co-occurrence networks[END_REF].

The clustering coefficient was used to estimate the tendency of nodes to cluster together. It represents the probability that two nodes having a similar neighbour are connected to each other [START_REF] Delmas | Analysing ecological networks of species interactions[END_REF]. The connectance quantifies the proportion of links that are actually established in the network compared to the number of possible potential links. It is equal to one when all possible links are established. I calculated the connectance 𝑐 using a formula adapted to undirected networks (Equation 2.6, [START_REF] Barrat | Dynamical processes on complex networks[END_REF].

𝑐 = 2E N × (N -1) (2.6)
where E is the number of edges and N the number of nodes in the network.

Globally, co-occurrence networks presented complex structures. To simplify them, I broke down networks into groups. For each network, I applied a stochastic block model approach based on the precision matrix using the blockmodels R package [START_REF] Leger | Blockmodels: A r-package for estimating in latent block model and stochastic block model, with various probability functions, with or without covariates[END_REF]. This method synthetizes the overall network structure by gathering nodes into a blockmodel group (BMG) and characterized by similar modes of interactions. For instance, the nodes can be highly connected to the rest of the network or poorly connected to the network but featuring high interconnections between the BMG members.

To summarize the information provided by the different properties of the networks, I conducted principal component analysis (PCA). The dataset was constituted of the following variables, centred and scaled: percentage of connected nodes, percentage of positive links, clustering coefficient, connectance, mean degree, average path length.

I used generalized linear models to estimate the variation of properties depending on different effects, e.g. ecosystem, season, or lifestyle. They were based on a quasi-binomial distribution.

The first model, the model "degree", was used to estimate the impact of different effects on the node degree. The lifestyle of the OTU and the interaction with the variables season and ecosystem were used as co-variables. An ANOVA conducted on the results of the model allowed us to estimate the importance and the significance of the chosen co-variables. The second model, the model "parasite", was used to estimate the effect of season on the number of edges established with putative parasitic OTUs. A supplementary covariable was added to the model, in order to take into account the initial proportion of putative parasites in the networks. I tested the different covariable combination of both models using the emmeans R package [START_REF] Lenth | Emmeans: Estimated marginal means, aka least-squares means[END_REF].

Chapter 3

Environmental drivers of plankton protist communities along latitudinal and vertical gradients in the oldest and deepest freshwater lake

Context and objective

Lake Baikal is located in Siberia (Russia) and is a "one-of-a-kind" lake. Especially, it is the oldest lake on Earth, it contains the largest freshwater volume and it is the deepest lake.

However, even so it has been intensively investigated, it remains poorly investigated in terms of microbial communities. A survey was conducted by our team in July 2017 to the structure of the microbial communities inhabiting the lake over horizontal and vertical profiles. Water and sediment samples were collected, in order to have a detailed insight into the microbial composition of the lake over the summer period. As Lake Baikal reaches a maximal depth of ∼1640m, it is the unique freshwater ecosystem where it is possible to explore the variation of community composition along so deep vertical profiles. Moreover, Lake Baikal can be viewed as "freshwater sea", which differentiate it from other freshwater lakes and can lead to specific communities and functioning. Metabarcoding data obtained using Illumina MiSeq on 18S rRNA genes, allowed me to:

1. Describe and compare microbial eukaryotic communities in Lake Baikal over horizontal (latitude) and vertical (depth) gradients.

2. Investigate for the potential presence of typical marine lineage in the freshwater sea that is Lake Baikal.

Manuscript

Gwendoline M. David 1 , David Moreira 1 , Guillaume Reboul 1 , Nataliia V. Annenkova 2 , Luis J.

Galindo 1 , Paola Bertolino 1 , Ana I. López-Archilla 3 , Ludwig Jardillier 1 and Purificación López García

Abstract

Identifying which abiotic and biotic factors determine microbial community assembly is crucial to understand ecological processes and predict how communities will respond to environmental change. While global surveys aim at addressing this question in the world's oceans, equivalent studies in large freshwater systems are virtually lacking. Being the oldest, deepest and most voluminous freshwater lake on Earth, Lake Baikal offers a unique opportunity to test the effect of horizontal versus vertical gradients in community structure. Here, we characterized the structure of planktonic microbial eukaryotic communities (0.2-30 µm cell size) along a North-South latitudinal gradient (∼600 km) from samples collected in coastal and pelagic waters and from surface to the deepest zones (5-1400 m) using an 18S rRNA gene metabarcoding approach.

Our results show complex and diverse protist communities dominated by alveolates (ciliates and dinoflagellates), ochrophytes and holomycotan lineages, with cryptophytes, haptophytes, katablepharids and telonemids in moderate abundance and many low frequency lineages, including several typical marine members, such as diplonemids, syndinians and radiolarians. Depth had a strong significant effect on protist community stratification. By contrast, the effect of the latitudinal gradient was marginal and no significant difference was observed between coastal and surface open water communities. Co-occurrence network analyses showed that epipelagic communities are much more interconnected than meso-and bathypelagic communities and suggest specific biotic interactions between autotrophic, heterotrophic and parasitic lineages that influence protist community structure. Since climate change is rapidly affecting Siberia and Lake Baikal, our comprehensive protist survey constitutes a useful reference to monitor ongoing community shifts.

Introduction

Of all ecosystems, freshwater reservoirs are the most dynamic and concentrate a high biodiversity [START_REF] Rolls | Scaling biodiversity responses to hydrological regimes[END_REF]. Freshwater ecosystems are particularly vulnerable to climate change owing to a higher exposure and sensitivity to increasing temperature and other altered conditions, limited dispersal across these fragmented habitats and little-known, but likely modest, resilience potential [START_REF] Woodward | Climate change and freshwater ecosystems: impacts across multiple levels of organization[END_REF][START_REF] Markovic | Vulnerability of european freshwater catchments to climate change[END_REF]. Since microorganisms are crucial in biogeochemical cycles, the impact of climate change will strongly depend on how they will respond to environmental challenge [START_REF] Cavicchioli | Scientists' warning to humanity: microorganisms and climate change[END_REF]. Permafrost-covered areas in the Arctic region [START_REF] Schuur | Climate change and the permafrost carbon feedback[END_REF] and forest-steppe ecotones in Siberia are among the most heavily impacted regions by global warming [START_REF] Mackay | Holocene carbon dynamics at the forest-steppe ecotone of southern siberia[END_REF]. This includes Lake Baikal, in southern Siberia, which is the oldest (ca. 30 Myr), deepest, and most capacious freshwater lake on Earth [START_REF] Müller | Late pliocene sedimentation in lake baikal: implications for climatic and tectonic change in se siberia[END_REF]. Lake Baikal is rapidly changing, as can be told from trends in hydrological and hydrochemical processes [START_REF] Moore | Climate change and the world's "sacred sea"-lake baikal, siberia[END_REF][START_REF] Shimaraev | Trends in hydrological and hydrochemical processes in lake baikal under conditions of modern climate change[END_REF]. The lake sediments represent a continuous record of past climate for over 12 million years [START_REF] Kashiwaya | Orbit-related long-term climate cycles revealed in a 12-myr continental record from lake baikal[END_REF][START_REF] Prokopenko | Age of long sediment cores from lake baikal[END_REF] such that Lake Baikal is a unique model to understand and predict microbial community change and how this is linked to carbon cycling and hydrological processes. A mandatory prerequisite for such a task is to have comprehensive information about the existing microbial community structure. However, if the broad biodiversity of Lake Baikal metazoans, including many endemisms (1455 out of 2595 species described), has been amply documented in the past two centuries, that of microbial life is highly fragmentary. One of the reasons relates to the large dimensions of the lake, which is around 640 km long, attains a depth of ca. 1650 m and contains around 20% of the Earth's unfrozen freshwater [START_REF] Sherstyankin | Computer-based bathymetric map of lake baikal[END_REF]UNDP-GEF, 2015). This, together with its geographical location and its association to a rifting zone make Lake Baikal unique and listed as UNESCO World Heritage Site (UNDP-GEF, 2015). The lake is divided in three major basins (Northern, Central, Southern) by, respectively, the Academician Ridge and the Selenga river delta [START_REF] Mats | A new perspective on evolution of the baikal rift[END_REF]. Its surface freezes in winter for several months, favoring coastal downwelling and deep-water oxygenation [START_REF] Schmid | Lake baikal deepwater renewal mystery solved[END_REF][START_REF] Moore | Climate change and the world's "sacred sea"-lake baikal, siberia[END_REF]. As a result, Lake Baikal ultraoligotrophic waters are globally cold (∼4°C) and oxygen-rich down to the bottom [START_REF] Schmid | Lake baikal deepwater renewal mystery solved[END_REF][START_REF] Moore | Climate change and the world's "sacred sea"-lake baikal, siberia[END_REF][START_REF] Shimaraev | Trends in hydrological and hydrochemical processes in lake baikal under conditions of modern climate change[END_REF][START_REF] Troitskaya | Cyclonic circulation and upwelling in lake baikal[END_REF]. Baikal also uniquely hosts methane hydrates, which are stabilized by the low temperatures and high pressures [START_REF] Batist | Active hydrate destabilization in lake baikal, siberia?[END_REF][START_REF] Granin | Methane hydrate emergence from lake baikal: direct observations, modelling, and hydrate footprints in seasonal ice cover[END_REF]. All these features make Lake Baikal akin a freshwater sea. Microbial diversity in Lake Baikal plankton was first studied by classical observation and cultural approaches [START_REF] Maksimova | Vertical distribution of microbial plankton in the southern part of lake baikal in 1969[END_REF][START_REF] Maksimov | The classification and the monitoring of the state of mouth riverine and lacustrine ecosystems in lake baikal based on the composition of local microbiocenoses and their activity[END_REF][START_REF] Belkova | Microbial biodiversity in the lake baikal water[END_REF] before molecular tools started to be applied at the beginning of the century [START_REF] Giner | Comparative 16s rrna analysis of lake bacterioplankton reveals globally distributed phylogenetic clusters including an abundant group of actinobacteria[END_REF] and expanded more recently with the generalization of high-throughput sequencing. Several 16S rRNA gene-based metabarcoding studies have targeted pelagic bacteria diversity [START_REF] Kurilkina | Bacterial community composition in the water column of the deepest freshwater lake baikal as determined by next-generation sequencing[END_REF][START_REF] Zakharenko | Bacterial communities in areas of oil and methane seeps in pelagic of lake baikal[END_REF][START_REF] Belikov | Diversity and shifts of the bacterial community associated with baikal sponge mass mortalities[END_REF][START_REF] Wilburn | Environmental drivers define contrasting microbial habitats, diversity and functional redundancy in lake baikal, siberia[END_REF] and, more recently, metagenomic analyses have been used to characterize planktonic prokaryotic communities from sub-ice [START_REF] Cabello-Yeves | Genomes of novel microbial lineages assembled from the sub-ice waters of lake baikal[END_REF] and deep waters [START_REF] Cabello-Yeves | Microbiome of the deep lake baikal, a unique oxic bathypelagic habitat[END_REF], virus-bacteria assemblages in coastal waters [START_REF] Butina | Estimate of the diversity of viral and bacterial assemblage in the coastal water of lake baikal[END_REF] or viruses from the pelagic zone [START_REF] Potapov | Metagenomic analysis of virioplankton from the pelagic zone of lake baikal[END_REF]. Microbial eukaryotes have only been partially studied by 18S rRNA gene metabarcoding. Several of these studies focused on phytoplankton, either on specific groups, such as diatoms [START_REF] Zakharova | The structure of microbial community and degradation of diatoms in the deep near-bottom layer of lake baikal[END_REF] or dinoflagellates [START_REF] Annenkova | Dinoflagellates associated with freshwater sponges from the ancient lake baikal[END_REF], or on whole communities, from winter sub-ice waters [START_REF] Bashenkhaeva | Sub-ice microalgal and bacterial communities in freshwater lake baikal, russia[END_REF] to spring blooms [START_REF] Mikhailov | Similarity of structure of taxonomic bacterial communities in the photic layer of lake baikal's three basins differing in spring phytoplankton composition and abundance[END_REF](Mikhailov et al., , 2019b,a),a). Remarkably few studies have aimed at charactering the diversity of all microbial eukaryotes, especially in a comparative manner. [START_REF] Yi | High-throughput sequencing of microbial eukaryotes in lake baikal reveals ecologically differentiated communities and novel evolutionary radiations[END_REF] analyzed protist diversity by 454 sequencing of 18S rRNA gene V9-region amplicons along the Southern basin water column (52-1450m). More recently, Annenkova et al. (2020a) determined the community structure of small protists (0.45-8 µm cell-size fraction) from surface waters (1-15-50 m) across the lake via 18S rRNA gene V4-region metabarcoding and suggested that some clades within known protist groups might be endemic. Nonetheless, we still lack a comprehensive view about how microbial eukaryotes distribute in the lake plankton, across basins and throughout the complete water column and, crucially, which are the most influential parameters determining community structure. In this work, we carry out a wide-ranging comparative study of Lake Baikal planktonic protist communities in the 0.2-30 µm cell-size range using a 18S rRNA gene metabarcoding approach to study distribution patterns and to test whether depth, latitude or the coastal versus pelagic location determine community structure. With this aim, we analyze 65 samples from 17 sites across a ∼600 km latitudinal North-South transect along the three lake basins and from littoral shallow areas to deep water columns covering the epi-, meso-and bathypelagic region. Our results show complex and diverse protist communities that are mostly structured by depth and that include several typical marine lineages in low abundance. Network analyses show that epipelagic communities are much more interconnected than meso-and bathypelagic communities, suggesting potential specific biotic interactions between autotrophs, heterotrophs and parasites.

Material and methods

Lake Baikal water samples were collected at different depths from seventeen sites distributed along a North-South transect during a French-Russian research cruise in the summer of 2017.

Sites were chosen to cover littoral [START_REF] Moore | Climate change and the world's "sacred sea"-lake baikal, siberia[END_REF] and open water [START_REF] Shimaraev | Trends in hydrological and hydrochemical processes in lake baikal under conditions of modern climate change[END_REF] samples, including the deepest zones in the three major basins of the lake. In total, 65 water samples were collected from depths ranging from 5 to 1400 m; deep samples were collected far from the bottom to avoid sediment disturbance (Supplementary Table 3.A.1). Samples were collected with Niskin bottles (5 l for epipelagic waters, 10 l for mesoand bathypelagic waters). The physicochemical parameters of lake waters were measured with a multiparameter probe Multi 350i (WTW, Weilheim, Germany). The water was sequentially filtered onboard immediately after collection through 30 µm and 0.22 µm pore-size Nucleopore filters (Whatman, Maidstone, UK) and 0.2 µm pore-size Cell-Trap units (MEM-TEQ Ventures Ltd, Wigan, UK). Volumes of water samples filtered through Cell-Traps were smaller (samples indicated with an asterisk in Figure 3.1). The recovered biomass and biomass-containing filters were fixed in absolute ethanol and stored at -20 ∘ C until processed. DNA purification, 18S rRNA gene-fragment amplification and sequencing DNA was purified using the Power Soil 𝑇 𝑀 DNA purification kit (Qiagen, Hilden, Germany). 18S rRNA gene fragments (∼530 bp) encompassing the V4 region were PCR-amplified using EK-565F-NGS (5'-GCAGTTAAAAAGCTCGTAGT-3') and UNonMet (5'-TTTAAGTTTCAGCCTTGCG-3'), the latter biased against metazoans [START_REF] Bower | Preferential pcr amplification of parasitic protistan small subunit rdna from metazoan tissues[END_REF]. Primers were tagged with specific 10 bp molecular identifiers (MIDs)

for multiplexed sequencing. To minimize PCR-associated biases, five PCR reaction products per sample were pooled. PCR reactions were conducted in 25 µl reaction mixtures containing 0.5-3 µl of eluted DNA, 1.5 mM MgCl2, 0.2 mM dNTPs, 0.3 µM primers and 0.5 U Platinum

Taq DNA Polymerase (Invitrogen, Carlsbad, CA) for 35 cycles (94 ∘ C for 30 s, 55-58 ∘ C for 30-45 s, 72 ∘ C for 90 s) preceded by 2 min denaturation at 94 ∘ C and followed by 5 min extension at 72 ∘ C. Pooled amplicons were purified using QIAquick PCR purification kit (Qiagen, Hilden, Germany). Amplicons were sequenced using paired-end (2x300é bp) Illumina MiSeq (Eurofins Genomics, Ebersberg, Germany). Sequences have been deposited in GenBank under the BioProject number PRJNA657482 (BioSamples SAMN15830589 to SAMN15830657).

Sequence and phylogenetic analyses

We used an in-house bioinformatic pipeline to process raw sequences. Paired-end reads were merged with FLASH (Magoč and Salzberg, 2011) under strict criteria and assigned to specific samples based on their MIDs. MID and primer sequences were trimmed using CU-TADAPT [START_REF] Martin | Cutadapt removes adapter sequences from high-throughput sequencing reads[END_REF]. Cleaned merged reads were next dereplicated to unique sequences using VSEARCH [START_REF] Rognes | Vsearch: a versatile open source tool for metagenomics[END_REF], which was also used to detect and eliminate potential chimeras. Non-chimeric sequences from all samples were pooled together to define operational taxonomic units (OTUs) at a conservative threshold of 95% identity for 18S rRNA genes using CD-HITEST [START_REF] Fu | Cd-hit: accelerated for clustering the next-generation sequencing data[END_REF] and SWARM [START_REF] Mahé | Swarm v2: highly-scalable and high-resolution amplicon clustering[END_REF]. Singletons were excluded from subsequent analyses. OTUs were assigned to taxa based on their similarity with a local 18S rRNA database build from SILVA v128 [START_REF] Quast | The silva ribosomal rna gene database project: improved data processing and web-based tools[END_REF] and PR2v4.5 [START_REF] Guillou | The protist ribosomal reference database (pr2): a catalog of unicellular eukaryote small sub-unit rrna sequences with curated taxonomy[END_REF]. OTUs less than 80% identical to their best environmental hit were blasted against the GenBank nr database (https://ncbi.nlm.nih.gov/) and assigned manually by phylogenetic placement analyses. Briefly, the closest hits to our OTUs in SILVA and PR2 were aligned with full 18S rDNA reference sequences covering the eukaryotic diversity using MAFFT [START_REF] Katoh | Mafft multiple sequence alignment software version 7: improvements in performance and usability[END_REF]. After removal of uninformative sites with trimAl [START_REF] Capella-Gutiérrez | trimal: a tool for automated alignment trimming in large-scale phylogenetic analyses[END_REF], we built a tree with full reference sequences with IQ-tree [START_REF] Nguyen | Iq-tree: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies[END_REF] under a GTR-+G+I sequence evolution model. OTU sequences were aligned to the reference alignment and then placed in the reference phylogenetic tree 150 using EPA-ng [START_REF] Barbera | Global phylogenetic community structure and 𝛽-diversity patterns in surface bacterioplankton metacommunities[END_REF].

OTUs with no reliable affiliation were maintained as "Unclassified". Maximum likelihood phylogenetic trees of diplonemid and radiolarian OTUs were reconstructed from specific MAFFT alignments including their closest blast hits and reference sequences with PhyML [START_REF] Guindon | A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood[END_REF] applying a GTR+G+I (4 categories) model of sequence evolution. Bootstrap values were obtained from 100 replicates.

Statistical analyses

We generated a table of eukaryotic OTU read abundance in the different samples of Lake Baikal for diversity and statistical analyses (Supplementary Tables 3.A.1 and 3.A.2). To avoid biases due to differences in absolute numbers of reads per sample, we rarefied our sequences to the second smaller number of reads (9771 in BK16.500m). BK28.100m was excluded from this process due to its lower number of reads. Statistical analyses were conducted on these data with R (R Core Team, 2019). Richness and diversity indices were calculated using the 'vegan' package [START_REF] Oksanen | The vegan package[END_REF]. Evenness was calculated according to Pielou [START_REF] Pielou | The measurement of diversity in different types of biological collections[END_REF]. To see if these indices were significantly different between sampling depths and basins, we performed Wilcoxon tests between the groups distributions using R. Likewise, to test the effect of sampling point, basin and depth class on protist community composition across samples, we conducted permutational multivariate analyses of variance (PERMANOVA) based on Wisconsin-standardized Bray-Curtis dissimilarities, using the adonis function of the vegan package. Across-sample community composition differences were visualized using non-metric multidimensional scaling (NMDS) analysis, also on Wisconsin-standardized Bray-Curtis dissimilarities. To connect communities according to specific origin we drew ellipses with the 'ade4' package [START_REF] Dray | The ade4 package: implementing the duality diagram for ecologists[END_REF]. To test the significance of groups revealed by NMDS, we applied analysis of similarity (ANOSIM) tests with 999 permutations. Principal component analysis (PCA) of abiotic parameters based on centered and scaled data was performed with FactoMineR [START_REF] Lê | Factominer: an r package for multivariate analysis[END_REF].

Network analysis

We built co-occurrence networks for each depth category (epipelagic, mesopelagic, bathypelagic using a multivariation Poisson lognormal model with the 'PLNmodels' R-package [START_REF] Chiquet | Variational inference for sparse network reconstruction from count data[END_REF] in order to account for depth-class differences between samples and potential additional covariables (specifically the sampling basin). We retained for the analysis OTUs present in more than 20% of samples and abundances higher than 0.01%. For model selection,

we used Bayesian information criteria with a 50 size grid of penalties. Networks were visualized with the 'ggnet' R-package. To further analyze network structure, we carried out a block model analysis using a stochastic block model approach on the binary co-occurrence network using the 'blockmodel' R-package [START_REF] Leger | Blockmodels: A r-package for estimating in latent block model and stochastic block model, with various probability functions, with or without covariates[END_REF], which synthetizes the overall network structure by gathering nodes in groups with similar modes of interactions. Network properties were calculated using the igraph R-package [START_REF] Csardi | The igraph software package for complex network research[END_REF]. Properties included the number of positive and negative edges, the total number of nodes and number of connected nodes. , where E is the number of edges and N the number of nodes [START_REF] Barrat | Dynamical processes on complex networks[END_REF]. Connectance is 1 when all possible links are established. The clustering coefficient is the probability that two nodes having a similar neighbor are connected to each other [START_REF] Delmas | Analysing ecological networks of species interactions[END_REF]. It varies between 0 and 1; low values indicated poor connectivity.

Results and discussion

Abiotic variables across sampling sites

We collected Lake Baikal water samples along the Northern, Central and Southern basins from the same established depths in the water column (except for the deepest sample, which was collected close to the bottom but at sufficient distance -minimum 45 m-to avoid sediment influence) (Figure 3.1A; Supplementary Table 3.A.1). Samples from coastal areas were always collected at 5 m depth in the water column. The measured physicochemical parameters were remarkably stable across sites and depths. Temperature ranged from 3.6 to 15.3 ∘ C, but was globally low (average 5.7 ∘ C; only five surface samples exceeded 9.5 ∘ C), and significantly higher in epipelagic samples (Supplementary Figure 3.B.1). pH ranged from 7.45 to 8.47. Salinity was extremely low (always 0.0 PSU) as, accordingly, conductivity and total dissolved solids (TDS).

Dissolved oxygen was high (mean 79.5%). Like temperature, pH, conductivity and dissolved oxygen in mesopelagic waters were significantly lower than in epipelagic samples. Bathypelagic parameters were similar to those of the mesopelagic zone but more variable. In terms of basins, temperature, pH, conductivity and dissolved oxygen were higher in the Southern basin, which is also more impacted by human activities and pollution, notably aromatic hydrocarbons and mercury brought by the Selenga river [START_REF] Adams | Lake sediment records of persistent organic pollutants and polycyclic aromatic hydrocarbons in southern siberia mirror the changing fortunes of the russian economy over the past 70 years[END_REF][START_REF] Roberts | Mercury loading within the selenga river basin and lake baikal, siberia[END_REF], although only oxygen and, marginally, conductivity were significantly different (Supplementary Figure 3.B.1). The two main axes of a PCA considering these abiotic parameters explained 58% of the variance (Figure 3.1B). Surface samples correlated with higher temperature, conductivity and, to a lower extent, pH and dissolved oxygen. These observations suggest that depth, as a proxy for light accessibility but also temperature and other abiotic parameters, might be a strong environmental driver for community structure.

Composition of planktonic protist communities

To study the diversity and relative abundance of microbial eukaryotes in Lake Baikal plankton, we concentrated cells in the 0.2-30 µm diameter fraction by successive filtration steps. This fraction thus integrated pico-(0.2-2 µm), nano-(2-20 µm) and small microplankton (20-30 µm), covering a wider protistan spectrum than some previous comparative studies (Annenkova et al., 2020a). We purified DNA and massively sequenced (MiSeq Illumina, 2x300 bp) multiplexed 18S rRNA gene V4 region amplicons. After discarding low-quality reads, we generated 6 405 343 high-quality merged paired-end sequences that we clustered in operational taxonomic units (OTUs) at different thresholds. We determined 27 504 OTUs and 9 700 OTUs at, respectively, 98% and 95% sequence identity (CD-HIT). SWARM yielded 11 590 OTUs (Supplementary Table 3.A.1), only slightly higher than the number of OTUs defined at the latter cut-off. For our subsequent comparative analyses, we deliberately retained OTUs defined at 95% sequence identity threshold. Many diversity studies focus on exact sequence variants after sequence error correction [START_REF] Callahan | Dada2: high-resolution sample inference from illumina amplicon data[END_REF] that can inform about individual strain variation. However, for the purpose of this comparative study, we chose to use conservatively defined OTUs that, on average (this varies across phylogenetic groups), correspond to the genus or species-genus level [START_REF] Caron | Protists are microbes too: a perspective[END_REF]. This taxonomy cutoff level is relevant for broad comparative ecological studies (members of the same genus are likely to have similar general functions, despite inter-strain or species-specific niche differences), while operationally diminishing the number of handled OTUs. Based on sequence MIDs, the abundance of the different OTUs was determined for each sample (Supplementary Table 3.A.1). To avoid potential biases in diversity and relative abundance estimates linked to differences in the total number of reads, we rarefied Color codes for sample basin and depth origin as well as for the different taxa are indicated. sequences to the same number across samples, which resulted in a global number of 4 570 genus-level OTUs. Nonetheless, accumulation curves showed that the diversity of planktonic protists was far from reaching saturation, even at the conservative genus level (Supplementary Figure 3.B.2). Richness significantly decreased in deep as compared to surface waters; so did evenness (Supplementary Figure 3.B.3). A lower evenness may be partly explained by the lower cell abundance in deeper waters, as the counts for each OTU become more aleatory.

We did not observe richness differences across lake basins, but evenness appeared significantly higher in the Northern basin. From a phylogenetic perspective, our defined OTUs affiliated to at least 27 eukaryotic phyla belonging to several major eukaryotic supergroups (Figure 3.1C; Supplementary Table 3.A.2): the SAR clade (Stramenopiles, Alveolata, Rhizaria), Amebozoa, Archaeplastida, Excavata, Opisthokonta and Hacrobia. Although we considered Hacrobia as originally described [START_REF] Okamoto | Molecular phylogeny and description of the novel katablepharid roombia truncata gen. et sp. nov., and establishment of the hacrobia taxon nov[END_REF], they should be possibly split in two or more groups as the eukaryotic phylogeny progressively resolves [START_REF] Burki | The new tree of eukaryotes[END_REF]. Ciliates and dinoflagellates (Alveolata), Ochrophyta (Stramenopiles) and Holomycota (Fungi and related lineages within the Opisthokonta) dominated plankton samples representing, respectively 48.4%, 21.5%, 12.6% and 8% relative sequence abundance. Cryptophyta, Haptophyta, Kathablepharida and Tenomemida displayed moderate abundances (0.5 to 5% reads) and were followed by a long tail of lower-frequency taxa in rank:abundance curves (Supplementary Figure 3.B.4). The major dominant groups were similar in all depths, with small variations in the deepest waters. Ciliates were by far the most abundant in terms of sequence reads. However, this observation is to be pondered by the fact that, in ciliate somatic macronuclei, rRNA genes are amplified several thousand times (e.g. ∼9000 copies in Tetrahymena thermophila [START_REF] Ward | The intranuclear organization of normal, hemizygous and excision-deficient rrna genes during developmental amplification in tetrahymena thermophila[END_REF]), such that their relative abundance in term of cells is certainly much lower. Although diatoms (Bacillariophyta, Ochrophyta), several of them considered endemic, are well known in Lake Baikal plankton [START_REF] Moore | Climate change and the world's "sacred sea"-lake baikal, siberia[END_REF][START_REF] Zakharova | The structure of microbial community and degradation of diatoms in the deep near-bottom layer of lake baikal[END_REF][START_REF] Bashenkhaeva | Sub-ice microalgal and bacterial communities in freshwater lake baikal, russia[END_REF]Mikhailov et al., 2019b;[START_REF] Roberts | Diatom evidence of 20th century ecosystem change in lake baikal, siberia[END_REF], they represented only 6.1% ochrophyte reads distributed in 64 OTUs. Optical microscopy on board showed that diatoms were numerous, but their long frustules prevented most of them from being retained in the analyzed plankton fraction. Members of the Holomycota were very diverse. Classical fungi represented ca. 60% holomycotan sequences, most of them corresponding to chytrids, although the Dicarya (Ascomycota, Basidiomycota) were relatively abundant too (Supplementary Figure 3.B.5). Most Dicarya belonged to typical terrestrial fungi entering the lake waters with river in-flow or from the surrounding land. However, chytrids (flagellated fungi) are more likely to be truly planktonic organisms.

Interestingly, members of Rozellida (Cryptomycota) and Aphelida, were also relatively abundant, making up to almost 40% of the holomycotan sequences. Rozellids and aphelids, together with their microsporidian relatives are parasites [START_REF] Karpov | Morphology, phylogeny, and ecology of the aphelids (aphelidea, opisthokonta) and proposal for the new superphylum opisthosporidia[END_REF][START_REF] Bass | Clarifying the relationships between microsporidia and cryptomycota[END_REF]. Although rozellids (cryptomycotes) are often included within fungi, they are phagotrophic organ-isms, unlike fungi (which are osmotrophs), and they branch more deeply than aphelids in the Holomycota tree [START_REF] Torruella | Global transcriptome analysis of the aphelid paraphelidium tribonemae supports the phagotrophic origin of fungi[END_REF]. Our data suggest that the majority of actual fungallike planktoners in Lake Baikal are parasites. Overall, despite methodological differences, our identified plankton protist communities were consistent with previous studies in surface waters or in a water column previously sampled in the Southern basin, with ciliates, dinoflagellates and ochrophytes being highly represented [START_REF] Yi | High-throughput sequencing of microbial eukaryotes in lake baikal reveals ecologically differentiated communities and novel evolutionary radiations[END_REF]Annenkova et al., 2020a).

Marine signature taxa

Although marine-freshwater transitions are thought to be rare (Mukherjee et al., 2019) and salinity, a major driver of microbial community composition [START_REF] Lozupone | Global patterns in bacterial diversity[END_REF], high-throughput environmental studies are revealing an increasing number of typically marine eukaryotic lineages in freshwater systems. Among those are members of the parasitic perkinsids [START_REF] Bråte | Freshwater perkinsea and marine-freshwater colonizations revealed by pyrosequencing and phylogeny of environmental rdna[END_REF], haptophytes [START_REF] Simon | New haptophyte lineages and multiple independent colonizations of freshwater ecosystems[END_REF], Bolidophyceae [START_REF] Richards | Molecular screening of free-living microbial eukaryotes: diversity and distribution using a meta-analysis[END_REF]Annenkova et al., 2020a) and several Marine Stramenopiles (MAST) clades [START_REF] Massana | Phylogenetic and ecological analysis of novel marine stramenopiles[END_REF][START_REF] Massana | Distribution and abundance of uncultured heterotrophic flagellates in the world oceans[END_REF], such as MAST-2, MAST-12, MAST-3 and possibly MAST-6 [START_REF] Simon | Complex communities of small protists and unexpected occurrence of typical marine lineages in shallow freshwater systems[END_REF].

Recently, diplonemids, a cosmopolitan group of oceanic excavates particularly abundant and diverse in the deep ocean [START_REF] Lara | Pan-oceanic distribution of new highly diverse clades of deep-sea diplonemids[END_REF][START_REF] Vargas | Eukaryotic plankton diversity in the sunlit ocean[END_REF] were identified in deep freshwater lakes [START_REF] Yi | High-throughput sequencing of microbial eukaryotes in lake baikal reveals ecologically differentiated communities and novel evolutionary radiations[END_REF][START_REF] Moss | Widespread dominance of kinetoplastids and unexpected presence of diplonemids in deep freshwater lakes[END_REF]. Likewise, Syndiniales, a clade of parasitic alveolates (often parasitizing their dinoflagellate relatives) widely distributed in oceans [START_REF] López-García | Unexpected diversity of small eukaryotes in deep-sea antarctic plankton[END_REF][START_REF] Guillou | Widespread occurrence and genetic diversity of marine parasitoids belonging to syndiniales (alveolata)[END_REF], were recently identified in Baikal surface plankton (Annenkova et al., 2020a). We identified members of all these lineages in our large Lake Baikal plankton dataset, albeit mostly in low proportions (Figure 3.2A; Supplementary Table 3

.A.3).
Bolidophytes and, collectively, MAST clades were nonetheless relatively abundant in the lake.

However, MAST clades are not monophyletic and they exhibited different abundance patterns.

Clades previously detected in freshwater systems, MAST-2, MAST-6, MAST-12 and to a lesser extent MAST-3, were relatively abundant. But MAST clades not previously observed in other freshwater systems, including MAST-1, MAST-4, MAST-8 and MAST-20 occurred in very low proportions in a few samples. In addition to the rare diplonemids, which were widely but sporadically present across Lake Baikal samples (Figure 3.2A-B), we identified OTUs belonging to the emblematic Radiolaria, to our knowledge never before identified in freshwater plankton.

These OTUs were members of the Polycystinea (Figure 3.2C) and exhibited extremely low frequencies. The low abundance of some of these typically marine lineages partly explains the fact that they failed to be detected in previous studies of freshwater systems, suggesting that these ecological transitions have been so far underestimated [START_REF] Paver | Reevaluating the salty divide: phylogenetic specificity of transitions between marine and freshwater systems[END_REF]. However, an additional explanation might be found in the particular features of the Lake Baikal, includ- ing its considerable depth, marked oligotrophy and even the presence of deep-venting [START_REF] Müller | Late pliocene sedimentation in lake baikal: implications for climatic and tectonic change in se siberia[END_REF][START_REF] Sherstyankin | Computer-based bathymetric map of lake baikal[END_REF]UNDP-GEF, 2015), which make it qualify in all points but salinity as a freshwater sea. To test whether planktonic protist communities were influenced by abiotic factors (clearly correlated to sample spatial origin; Figure 3.1), we carried out permutational multivariate analysis of variance (PERMANOVA) of Wisconsin-standardized Bray-Curtis distances between communities as a function of sample spatial origin. PERMANOVA tests revealed significant differences in microbial eukaryotic communities as a function of basin (latitudinal region), sampling site (coordinates) and depth within sampling sites (Table 3.1). However, the most influential effects were those of the water column location, 23.7%, which combine latitudinal and vertical determinants, and depth within each single water column, i.e. vertical variation alone (16.3%). The effect of the sampling basin was significant but small (5.3%). To better visualize differences between communities, we carried out an NMDS analysis on the global Bray-Curtis distance matrix. Points from most water columns did not show a marked differentiation, as most water columns overlapped to some extent (Supplementary Figure 3 ). These observations were statistically supported by ANOSIM tests, which showed significant and marked differences among communities according to depth, significant but weak differences according to basin origin, and no correlation at all between coastal and pelagic samples (Supplementary Table 3.A.3). These results suggest that depth is the major environmental factor structuring Lake Baikal protist communities. Depth is in turn a proxy for a variety of abiotic parameters, notably light, but also, despite their limited variation, temperature, dissolved oxygen, conductivity and pH (Figure 3.1). These environmental variables and others, such as the nature of dissolved organic matter (TDS amount does not vary significantly; Figure 3.1B), are likely to influence prokaryotic communities as well [START_REF] Kurilkina | Bacterial community composition in the water column of the deepest freshwater lake baikal as determined by next-generation sequencing[END_REF]. Consequently, the nature of prey available for bacterivorous protists is possibly different. This may, in turn, select for protists with particular preying affinities, such that biotic interactions with other planktonic members may be also important determinant factors of community structure and function.

Functional groups and biotic interactions

To look for potential ecological interactions between members of protist communities, we first explored the distribution of major functional classes with depth. We attributed protists to three major categories based on knowledge about the lifestyle and ecological function of the corresponding phylogenetic lineages: autotrophs, free-living heterotrophs and parasites (Supplementary Table 3.A.5). We acknowledge that these are very broad categories and that many photosynthetic organisms can be mixotrophs [START_REF] Massana | Eukaryotic picoplankton in surface oceans[END_REF][START_REF] Mitra | Defining planktonic protist functional groups on mechanisms for energy and nutrient acquisition: incorporation of diverse mixotrophic strategies[END_REF]. However, information about mixotrophy is still scarce and it is difficult to predict this ability from sequence data only. Therefore, our category "autotrophs" included also photosynthetic organisms that can additionally use heterotrophic feeding modes. Free-living heterotrophs include predatory protists but also osmotrophic organisms feeding on organic matter, such as fungi or some

Stramenopiles. The relative abundance of the three functional categories in Lake Baikal significantly followed the same trend in the three water column zones, with autotrophs being less abundant than heterotrophs and parasites being in much lower proportion (Figure 3.4). Low proportions of parasitic protists are consistent with affordable parasite loads for an ecosystem, as was previously observed [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF]. Nonetheless, the relative amount of parasitic lineages diminished with depth, potentially suggesting that a relatively important proportion of protists identified in deep waters might be inactive. This is indeed likely the case for most photosynthetic organisms that were identified below the epipelagic region. Although the proportion of autotrophs diminished with depth, they still made up to 30% of the total in bathypelagic waters. As mentioned, some of these protists may be mixotrophic and prey on bacteria or other protists in the dark water column. However, the majority of photosynthetic lineages may simply be inactive, dormant or on their way to decay, serving as food for the heterotrophic component of microbial communities. The presence of relatively abundant photosynthetic protists in the Baikal dark water column and sediments is well documented [START_REF] Zakharova | The structure of microbial community and degradation of diatoms in the deep near-bottom layer of lake baikal[END_REF][START_REF] Yi | High-throughput sequencing of microbial eukaryotes in lake baikal reveals ecologically differentiated communities and novel evolutionary radiations[END_REF], low temperatures possibly helping their preservation during sedimentation. Finally, free-living heterotrophs were the most abundant functional category throughout the water column. This might seem at odds with a pyramidal food-web structure whereby primary producers should be more abundant than consumers. However, several factors might explain this. First, the presence of ciliates likely introduces a positive bias in this functional category. Second, many autotrophs might be, on average, larger than heterotrophic protists and their biomass exceed that of consumers. Finally, many heterotrophic protists might depend on bacteria or on larger organisms (e.g. fungi degrading decaying plant material). 3.A.5.

To further explore biotic interactions, and given that protist community differences were essentially seen throughout the water column, we reconstructed co-occurrence networks of OTUs found in epipelagic, mesopelagic and bathypelagic zones. To build the networks, we retained OTUs present in more than 20% of samples at relative abundances higher than 0.01% (Supplementary Table 3.A.6). The structure of the three networks was markedly different (Figure 3.5). The epipelagic network was denser, having more interconnected OTUs, more positive interactions and several hub-type OTUs that interact with many OTUs. Mean node degrees were also higher in the epipelagic network (Supplementary Table 3.A.7). Meso-and bathypelagic networks had less connected nodes and most correlations were negative. Only one positive interaction was observed in mesopelagic waters (ciliate-fungus) and in bathypelagic waters (rozellid-ochrophyte). The latter might suggest a specific parasitic interaction. Although bathypelagic waters exhibited the least connected nodes, both the connectance and the clustering coefficient of the network were the highest. A block-model representation of the three networks indicated the occurrence of pairs of OTU sets sharing similar properties that were highly interconnected with each other and only loosely to other sets (Supplementary Figure 3.B.8). Collectively, our network data suggest that epipelagic communities are more active and have more positive and negative interactions, whereas in very deep waters, communities are more stable with a restricted but strongly connected core.

Concluding remarks

Lake Baikal in Southern Siberia is a unique freshwater system by its volume, maximum depth

(1 642 m) and topographical features that include rifting associated with hydrothermalism.

With its highly oligotrophic waters, it amounts to an inner freshwater sea in all points but an extremely low salinity. Freshwater ecosystems are particularly threatened by climate change and, being located in Southern Siberia, one of the most rapidly changing zones, Lake Baikal is being severely impacted [START_REF] Mackay | Holocene carbon dynamics at the forest-steppe ecotone of southern siberia[END_REF]). Yet, despite the importance of the lake and its uniqueness, its microbial planktonic communities have been only partially studied and we lack reference comprehensive comparative community data to assess ongoing and future change and infer how it may affect microbial functions and the ecology of the lake. In this study, we have analyzed the composition of microbial eukaryotic communities in plankton collected from different water columns along a transect of ∼600 km across the three lake basins, from surface (5 m) to high depth (1 400 m) and from littoral to open waters. Our study shows widely diverse communities covering all eukaryotic supergroups, with ciliates, dinoflagellates, chrysophytes and flagellated fungi plus related lineages (rozellids, aphelids) being the most relatively abundant, together with cryptophytes, haptophytes, katablepharids, telonemids and several MAST lineages. Interestingly, confirming previous observations in Lake Baikal, we observed members of typically marine lineages, including bolidophytes, syndineans, diplonemids and, for the first time, radiolarians. These observations suggest that the salinity barrier is relatively easy to cross and that the "marine" determinants might be more related with the oligotrophic nature of the system and the occurrence of a deep water column than with salinity itself. Despite the relatively homogeneous values of several physicochemical parameters, planktonic protist communities were highly and significantly stratified in Lake Baikal, suggesting that depth, as a proxy for light but also temperature, pH, oxygen and conductivity, is a major determinant of community structure. By contrast, the effect of latitude (basins) was minor, if not negligible. Consistent with vertical stratification, the relative proportion of autotrophs, free-living heterotrophs and parasites is altered with depth, where photosynthetic lineages are still present but, like parasites, in lower proportions. Biotic factors are also important in structuring Lake Baikal communities. Co-occurrence network analyses showed highly interconnected communities in surface waters, with positive and negative interactions. By contrast, deep, bathypelagic communities exhibit much less connected OTUs, although they are strongly, and mostly negatively, correlated. This might be suggestive of much more diluted and potentially inactive populations, but with a conserved core of highly interconnected OTUs.

Our results pave the way for future comparative analyses of protist communities through time, notably in the context of rapid climate change that is affecting Siberia and Lake Baikal.

Supplementary Table 3.A.1 Sampling sites in Lake Baikal water column and associated physico-chemical, sequence and diversity data.

Operational taxonomic units (OTUs) were defined at 95% sequence identity, except when indicated (SWARM

0 0 0 0 Katablepharidophyta 1 0 0 1 1 Labyrinthulea 0 0 0 0 0 Lobosa 0 0 0 0 0 MAST 3 2 8 1 4 Mesomycetozoa 0 0 1 0 1 Ochrophyta 31 32 65 17 28 18 Oomycota 1 0 3 0 2 Opalinata 0 0 0 0 0 Perkinsea 0 1 3 0 1 Picozoa 0 0 0 0 0 Pirsonia 0 1 0 0 0 Radiolaria 0 0 0 0 0 Syndiniales 0 0 0 0 0 Telonemia 0 2 1 2 2 Uncertain_Alveolata 0 0 0 0 0 Uncertain_Opisthokonta 0 0 0 0 0 Uncertain_Stramenopiles 0 0 0 0 0 Supplementary

Context and objective

In the previous chapter, I investigated the microbial eukaryote communities in Lake Baikal, the biggest freshwater ecosystem on Earth. In this chapter, I focused on fairly common but also mostly unexplored ecosystems. Indeed, small freshwater ecosystems are overlooked in the literature, even if they present a clear ecological importance. Therefore, the knowledge of the diversity and community composition of microbial communities inhabiting these ecosystems is still scarce. Furthermore, their temporal evolution is also poorly understood. A previous study conducted in the laboratory by M. Simon, a former PhD student, described the composition of small microbial eukaryotes in five small freshwater ecosystems located in the south-west of Paris (France) [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF]. The sampling was conducted monthly during two years and the data were obtained by metabarcoding, using pyrosequencing 454 FLX of 18S rRNA genes.

This study revealed that these small freshwater ecosystems harboured very diverse microbial eukaryotic communities. Moreover, the microbial eukaryotic communities were found to be highly dynamic, and presented strong seasonal patterns. Here, I completed the dataset by analyzing the prokaryotic communities. The prokaryotes dataset was obtained similarly to the eukaryote dataset. Therefore, I used the same samples but I enlarged the study to the whole microbial community (bacteria, archaea and microbial eukaryotes) to understand its structuring over time. My objectives were:

1. To decipher the bacterial and archaeal community composition in these small freshwater ecosystems. Indeed, the diversity of these communities is mostly unknown in small freshwater ecosystems. To do so, we used amplicon sequences obtained by pyrosequencing 454 FLX of the 16S rRNA genes.

2. To identify a potential seasonality of prokaryotic communities. As the microbial eukaryotes revealed strong seasonal patterns, I determined whether prokaryotes shared the same temporal dynamics, or if their temporal patterns were different.

3. To describe the temporal variations of whole microbial communities in the size fraction 0.22 to 5 µm. With an approach based on time-lag analysis and turnover, I described the global dynamics of community composition.

4. To identify the abiotic parameters involved in microbial community composition and to determine their relative importance.

Manuscript

Gwendoline M. David 1 , Purificación López-García 1 , David

Abstract

Despite small freshwater ecosystems are biodiversity reservoirs and significantly contribute to greenhouse fluxes, their microbial communities remain largely understudied. Yet, microorganisms intervene in biogeochemical cycling and impact water quality. Because of their small size, these ecosystems are in principle more sensitive to disturbances, seasonal variation and pluri-annual climate change. However, how microbial community composition varies over space and time, and whether prokaryotic and eukaryotic communities behave similarly, remain fully open questions. Here, we aim at unravelling the composition and intra/inter-annual temporal dynamic patterns for both prokaryotes and microbial eukaryotes in a set of small freshwater ecosystems. We monitored prokaryote composition during 24 consecutive months in four ponds and one brook from North-Western France by 16S rDNA amplicon sequencing (microbial eukaryotes were previously investigated for the same systems). Unexpectedly for oxic environments, bacterial Candidate Phyla Radiation (CPR) were highly diverse and locally abundant.

Our results suggest that microbial community structure is mainly driven by environmental conditions acting over space (ecosystems) and time (seasons). A low proportion of OTUs (<1%) was shared by the five ecosystems despite their geographical proximity, making microbial communities almost unique in each ecosystem and highlighting the strong selective influence of local environmental conditions. Marked and similar seasonality patterns were observed for prokaryotes and microbial eukaryotes in all ecosystems despite strong turnovers of rare OTUs.

Over the two-year survey, microbial community composition varied despite relatively stable environmental parameters. This suggests that biotic associations play an important role in inter-annual community assembly.

Introduction

Small freshwater ecosystems such as ponds or shallow lakes remain largely unexplored, despite being widespread and numerically very important (3.2x10 9 ) and, covering altogether a large area (0.8 billion km 2 ) [START_REF] Downing | Emerging global role of small lakes and ponds: little things mean a lot[END_REF]. Collectively, they contribute up to 15% of CO 2 and, especially, 40% of CH 4 emissions [START_REF] Holgerson | Large contribution to inland water co 2 and ch 4 emissions from very small ponds[END_REF][START_REF] Yvon-Durocher | Long-term warming amplifies shifts in the carbon cycle of experimental ponds[END_REF].

They exhibit highly diverse physicochemical conditions and are reservoirs of an important biodiversity in terms of small invertebrates and microorganisms as identified by classical observation [START_REF] Dudgeon | Freshwater biodiversity: importance, threats, status and conservation challenges[END_REF]) and a few molecular diversity studies (e.g. [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF][START_REF] Simon | Complex communities of small protists and unexpected occurrence of typical marine lineages in shallow freshwater systems[END_REF][START_REF] Qin | Bacterial abundance and diversity in pond water supplied with different feeds[END_REF][START_REF] Liu | Comparative analysis of microbial community structure in the ponds with different aquaculture model and fish by high-throughput sequencing[END_REF]. Because of their small volume, small aquatic ecosystems have lower buffering capacity toward environmental variation at short i.e. seasons and longer, multiple-year time scales. Consequently, their communities and functioning are in principle more reactive to environmental changes than those of larger ecosystems [START_REF] Mckee | Response of freshwater microcosm communities to nutrients, fish, and elevated temperature during winter and summer[END_REF][START_REF] Downing | The global abundance and size distribution of lakes, ponds, and impoundments[END_REF][START_REF] Downing | Emerging global role of small lakes and ponds: little things mean a lot[END_REF]. Understanding the temporal dynamics of organisms inhabiting these overlooked ecosystems is one of a first step to understand and predict their functioning in the long run. Indeed, because their fundamental roles in ecosystem functioning [START_REF] Azam | The ecological role of water-column microbes in the sea[END_REF][START_REF] Bar-On | The biomass distribution on earth[END_REF][START_REF] Cho | Major role of bacteria in biogeochemical fluxes in the ocean's interior[END_REF], changes in microbial community composition can have profound impacts on ecosystem processes due to changes in their functional capacities [START_REF] Bell | The contribution of species richness and composition to bacterial services[END_REF]. However, most studies of microbial communities in freshwater systems have focused on their composition and spatial distribution [START_REF] Debroas | Overview of freshwater microbial eukaryotes diversity: a first analysis of publicly available metabarcoding data[END_REF][START_REF] Grossmann | Protistan community analysis: key findings of a large-scale molecular sampling[END_REF][START_REF] Richards | Molecular screening of free-living microbial eukaryotes: diversity and distribution using a meta-analysis[END_REF][START_REF] Richards | Molecular diversity and distribution of marine fungi across 130 european environmental samples[END_REF][START_REF] Shade | A meta-analysis of changes in bacterial and archaeal communities with time[END_REF][START_REF] Zhong | Prokaryotic community structure driven by salinity and ionic concentrations in plateau lakes of the tibetan plateau[END_REF][START_REF] Zwirglmaier | Global phylogeography of marine synechococcus and prochlorococcus reveals a distinct partitioning of lineages among oceanic biomes[END_REF]. Temporal surveys in aquatic systems have been only occasionally carried out, mostly at relatively short scales (days to months) [START_REF] Bunse | Marine bacterioplankton seasonal succession dynamics[END_REF][START_REF] Fuhrman | Marine microbial community dynamics and their ecological interpretation[END_REF][START_REF] Scherer | Temporal dynamics of the microbial community composition with a focus on toxic cyanobacteria and toxin presence during harmful algal blooms in two south german lakes[END_REF]. However, longer-term studies eventually reflecting seasonal patterns are still very rare [START_REF] Gilbert | Defining seasonal marine microbial community dynamics[END_REF][START_REF] Karl | Microbial oceanography and the hawaii ocean time-series programme[END_REF][START_REF] Linz | Bacterial community composition and dynamics spanning five years in freshwater bog lakes[END_REF]. Furthermore, most microbial diversity studies, including temporal surveys, focus on either prokaryotic or eukaryotic communities, but an integrated view of how the two change with time, eventually revealing underlying biotic interactions, are extremely scarce and restricted to large environments such as oceans and lakes [START_REF] Biller | Marine microbial metagenomes sampled across space and time[END_REF][START_REF] Obertegger | Multifaceted aspects of synchrony between freshwater prokaryotes and protists[END_REF][START_REF] Steele | Marine bacterial, archaeal and protistan association networks reveal ecological linkages[END_REF]). Yet, differences in overall metabolic capabilities, physiology, trophic features, generation time and dispersal might reflect in distinct temporal patterns of prokaryotes (bacteria, archaea) versus microbial eukaryotes. For instance, microbial eukaryotes have much more restricted metabolic capabilities than prokaryotes widely diverse energy and carbon metabolisms. In contrast, many microbial eukaryotes are predators, including some microalgae [START_REF] Bjorbaekmo | The planktonic protist interactome: where do we stand after a century of research?[END_REF][START_REF] Sherr | Bacterivory and herbivory: key roles of phagotrophic protists in pelagic food webs[END_REF][START_REF] Zubkov | High bacterivory by the smallest phytoplankton in the north atlantic ocean[END_REF], while predation is exceptional in prokaryotes [START_REF] Shiratori | Phagocytosis-like cell engulfment by a planctomycete bacterium[END_REF]. Here, we investigate the bacterial and archaeal community composition during 24 consecutive months in four shallow ponds and one brook located in the natural regional park of the Haute Vallée de Chevreuse (France) using 16S rDNA amplicon sequencing. We subsequently compare the temporal patterns of prokaryotic communities to those of microbial eukaryotes previously analyzed for the same ecosystems [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF], analyzing them as a function of several environmental parameters using diverse metrics and multivariate statistical analyses. Although the five ecosystems harbour specific microbial communities, their temporal patterns were similar. Interestingly, both the prokaryotic and eukaryotic communities also show similar inter-annual temporal dynamics.

Material and methods

Sampling and measurement of physicochemical parameters

We sampled monthly at the deepest site surface waters from five representative small and shallow freshwater ecosystems (the four ponds Etang des Vallées (EV), La Claye (LC), Mare Gabard (MG), Saint Robert (SR), and the brook Ru Sainte Anne (RSA)), previously described [START_REF] Simon | Complex communities of small protists and unexpected occurrence of typical marine lineages in shallow freshwater systems[END_REF], between April 2011 and April 2013 (24 sampling dates) using sterilized plastic carboys. A total of 106 samples were collected (LC and RSA dried out occasionally).

Single samples were analysed at each time point of this study since our protocol ensured very similar replicates [START_REF] Simon | Complex communities of small protists and unexpected occurrence of typical marine lineages in shallow freshwater systems[END_REF]. The five ecosystems are located in the Natural Regional Park of the Chevreuse Valley (Ile-de-France, France), less than 10 km apart. During our twoyear survey, the Ru Sainte Anne and La Claye experienced several dry periods, from August to September 2012 for Ru Sainte Anne, and from July to December 2011 and in September 2012 for La Claye. These five ecosystems differ in their environmental conditions [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF].

Water temperature (T), dissolved oxygen concentration (O 2 ), pH and conductivity (Cond)

were measured in situ using a multiparameter probe Multi 350i (WTW, Weilheim, Germany). PowerSoil DNA extraction kit (Qiagen, Hilden, Germany) as previously described [START_REF] Simon | Complex communities of small protists and unexpected occurrence of typical marine lineages in shallow freshwater systems[END_REF]. 16S rDNA fragments of ca. 290 bp, encompassing the V4 hypervariable region, were PCR-amplified using the universal primers U515F (5'-GTGCCAGCMGCCGCGGTAA-3') and 806R (5'-GGACTACVSGGGTATCTAAT-3') targeting both bacteria and archaea [START_REF] Baas-Becking | Review and re-analysis of domain-specific 16s primers[END_REF][START_REF] Takai | Rapid detection and quantification of members of the archaeal community by quantitative pcr using fluorogenic probes[END_REF]. These primers were tagged with different 10 bp Molecular Noise [START_REF] Quince | Removing noise from pyrosequenced amplicons[END_REF]. Filtered reads were then clustered into Operational Taxonomic Units (OTUs) with a 97% similarity cut-off [START_REF] Stackebrandt | Taxonomic note: a place for dna-dna reassociation and 16s rrna sequence analysis in the present species definition in bacteriology[END_REF] using CD-HIT [START_REF] Fu | Cd-hit: accelerated for clustering the next-generation sequencing data[END_REF]. Singletons (OTUs composed of only one single sequence) were excluded.

Each OTU was represented by its most abundant sequence, used as reference. Reference sequences were assigned to taxonomic groups based on similarity with sequences of the SILVA 128 database (http://www.arb-silva.de). Reads affiliated to mitochondria and chloroplast were excluded from the dataset. At the end, 1 131 900 high-quality reads were retained and assigned to 16 573 OTUs. Sequences from all OTUs were then attributed to samples according to their MIDs. OTU sequences of 0.2-5 µm microbial eukaryotes were obtained following a similar procedure to that followed for 18S rDNA amplicons [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF]. Sequencing data for the last four months of the temporal series in Mare Gabard could unfortunately not be generated. Prokaryotic sequences have been deposited at GenBank under Bioproject number PRJNA539931.

Statistical analyses

All statistical analyses were conducted with the R software (http://cran.r-project.org) (R Core Wisconsin-standardized OTU frequencies were pooled into a single table without changing the initial relative OTU frequencies. In each ecosystem, richness, evenness and diversity index were calculated. Richness was evaluated by rarefaction analyses as the estimated number of OTUs in a random subsample of each sequence library, which is of the same size as the smallest one (Hurlbert, 1971). Evenness, e, was calculated according to [START_REF] Pielou | The measurement of diversity in different types of biological collections[END_REF]:

𝑒 = ∑ 𝑖 f 𝑖 ×ln(f 𝑖 ) ln(S)
with f 𝑖 the frequency of OTU 𝑖 and S the observed number of OTUs in the sample. Diversity was estimated according to the Gini-Simpson index [START_REF] Simpson | Measurement of diversity[END_REF][START_REF] Lee | Both species sorting and neutral processes drive assembly of bacterial communities in aquatic microcosms[END_REF]) calculated as follows using the diversity function of the 'vegan' R-package [START_REF] Oksanen | The vegan package[END_REF]:

𝐷 = 1 - ∑ 𝑖 f 2
𝑖 where f 𝑖 is the OTU 𝑖 frequency. Microbial community structuring was investigated in the different ecosystems. We drawn Venn diagrams to show shared OTUs between ecosystems, using the 'gplots' R-package [START_REF] Warnes | Comparing the effects of resource enrichment and grazing on viral production in a meso-eutrophic reservoir[END_REF]. Hierarchical cluster analysis was build using Bray-Curtis dissimilarities and the Ward2 algorithm [START_REF] Murtagh | Ward's hierarchical agglomerative clustering method: which algorithms implement ward's criterion?[END_REF] in the hclust function of the 'stats' R-package to determine clusters of microbial communities based on the distances between every samples of the five ecosystems. Patterns in the temporal dynamics of prokaryotes and eukaryotes were explored at intra-(seasons) and inter-annual (between years) scales. Pairwise Bray-Curtis dissimilarities were computed between samples collected the first year and samples from the same system the second year over increasing time-lags (from onemonth to 12-months) to characterize the temporal dynamics of microbial communities at the year-scale. Time-lag analyses (TLA, [START_REF] Collins | A method to determine rates and patterns of variability in ecological communities[END_REF] between sampling dates over increasing time-lags (from one-month to 23-months) were computed using Bray-Curtis dissimilarities for microbial communities and Euclidean distances for environmental parameters to determine their variability over consecutive years. We computed polynomial regressions (fitting better than sinusoidal regressions) of Bray-Curtis dissimilarities over time to estimate the frequency of any putative temporal cyclic pattern and linear regressions over time to estimate global shifts in the microbial community compositions and environmental parameters over the twoyear survey using the lm function of the 'stats' R-package. The best fitted polynomial model was determined based on the Akaike Information Criterion (AIC, [START_REF] Akaike | Maximum likelihood identification of gaussian autoregressive moving average models[END_REF]. In TLA, the slope of the linear regression indicates the rate and direction of temporal changes. To test for the significance of slopes for each ecosystem, a randomization procedure was applied with 5 000 permutations using the lmperm function of the 'lmPerm' R-package [START_REF] Wheeler | Package 'lmperm'. R package version[END_REF]. A null distribution was estimated from each slope that was calculated on randomized data. The observed slopes were then compared to the distribution of F-values obtained under the null hypothesis. OTU turnovers were calculated between seasons or between years of the two-year survey, using the 'codyn' R-package based on the following formula (Diamond, 1969;[START_REF] Hallett | codyn: An r package of community dynamics metrics[END_REF][START_REF] Macarthur | An equilibrium theory of insular zoogeography[END_REF] Correlation of multivariate vectors (RV index) between prokaryotes and microbial eukaryotes was assessed based on co-inertia analyses in order to assess synchrony of their dynamics and was tested by permutations (999 permutations) provides the variability explained between the two communities [START_REF] Dolédec | Co-inertia analysis: an alternative method for studying speciesenvironment relationships[END_REF]. CCA combined to forward selection were conducted to determine how prokaryotes explain the variance of microbial eukaryotes and inversely.

Results

We generated prokaryotic 16S rDNA amplicon data (Supplementary Table 4.A.1) for plankton samples (106 samples; 0.2-5 µm size-fraction) collected monthly for two years from five shallow freshwater ecosystems for which protist communities had been previously studied [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF].These spatially close ecosystems varied in their physico-chemical conditions. The main differences were lower pH (6.2 on average) and nitrite concentrations (0.0086 mg.l -1 on average) in Mare Gabard, lower ammonia concentrations (0.07 mg.l -1 on average) in La Claye, variable ammonia inputs (<0.1 to 2.65 mg.l -1 ) in Saint Robert, higher oxygen (79.2% on average) and lower orthophosphate (0.05 mg.l -1 ) concentrations in Etang des Vallées, and higher conductivity (657.3 µS.cm -1 ) in the stream Ru Sainte Anne. All the ecosystems were characterized by strong temporal variations in water temperature (thermal amplitude of 19.4 ∘ C ± 2.3 on average, for the five ecosystems) whereas the other environmental parameters had no apparent temporal variability [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF].

Prokaryotic community composition

From the total number of retained high-quality 16S rRNA gene reads (1 131 900), we determined a total of 16 573 prokaryotic OTUs (14 701 bacterial and 1 872 archaeal). The total number of OTUs in the Ru Sainte Anne brook was higher than in the four ponds, where similar amounts of OTUs were detected (Figure 4.2.1A), an observation consistent with that of eukaryotic diversity [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF]. Consequently, richness, evenness and diversity ( 71against 64 on average for the other ecosystems) was found in Ru Sainte Anne. Bacteria clearly dominated (98.0% of all reads) the prokaryotic community in the five ecosystems. Archaea were globally rare at the exception of Ru Sainte Anne, where they reached their higher abundances (8.1% of all reads). Woesearchaeota prevailed within Archaea (1.7% of all reads), whereas Bacteria were dominated in the five ecosystems by four phyla: Proteobacteria (on average, 34.1% of reads; including, by decreasing frequency order, Beta-, Gamma-, Alpha-, Delta-and Epsilonproteobacteria), Actinobacteria (20.0%), Bacteroidetes (19.3%), and Verrucomicrobia (9.0%).

Although Proteobacteria was the most abundant phylum in all ecosystems, the other phyla contributed differently to the structure of the prokaryotic communities in the five ecosystems (Supplementary Figure 4.B.1C). The bacterial Candidate Phyla Radiation (CPR) was represented in our samples by 27 phyla that accounted for 9.9% of all reads. They corresponded mainly to the super-phyla Parcubacteria (67.2% of CPR-affiliated reads), Microgenomates (8.5%) and to the phyla Gracilibacteria (8.1%). In contrast with its relatively small frequency in the ponds, Parcubacteria was the most abundant group in Ru Sainte Anne (28.4% of all reads).

The five ecosystems harboured very diverse prokaryotic communities at the OTU level. ecosystems all along our survey, even in summer, representing on average 0.7% of all reads.

Comparison of eukaryotic and prokaryotic community structures

After analyzing prokaryotic diversity alone, we incorporated data of the small (0.2-5 µm) eukaryotes present in the same samples [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF]. NMDS analysis of pooled prokaryotic and eukaryotic OTU frequencies revealed marked differences in microbial community composi- In Ru Sainte Anne, microbial communities appeared to be only linked to conductivity.

tion
There was no apparent correlation between the measured environmental parameters and the abundant prokaryotes and microbial eukaryotes in the other ecosystems.

To assess putative biotic interactions between prokaryotes and microbial eukaryotes, we computed the RV index. Considering all communities in the five ecosystems together, coinertia analysis shown that the dynamics of prokaryote and protist communities were highly 4.A.3). By contrast, those for prokaryotes, microbial eukaryotes and all microbes considered together were characterized by significant and positive linear regressions. All the slopes were stronger for the microbial communities than for the abiotic parameters (from 0.091 for La Claye to 0.192 for Mare Gabard).

These results suggest no or weak temporal evolution of abiotic parameters when microbial communities diverged increasingly over time. These effects were visible and significant even when considering similar numbers of pairwise Bray-Curtis dissimilarity comparisons for all time-lag periods (data not shown). The model that best fitted the distribution of the time-lag analyses for prokaryotes, eukaryotes and physico-chemical parameters was a fifth order polynomial regression that revealed marked changes in dissimilarities at a 6-month frequency. This analysis revealed significant seasonal patterns for microbial communities and the environmental conditions in the five ecosystems. The dispersion of the data was better explained for the biotic than for the abiotic factors. Pairwise Bray-Curtis dissimilarities calculated on rare and abundant OTU frequencies between the two years of sampling revealed seasonality for prokaryotes, microbial eukaryotes and both considered together. Globally, the temporal dynamics of the abundant OTUs was characterized by seasonality, with higher dissimilarities for samples separated by 5 to 6 months (i.e. opposite seasons, 0.79 average Bray-Curtis dissimilarity) than for temporally contiguous samples (consecutive months, 0.62 average Bray-Curtis dissimilarity) (Figure 4.2.5).

Rare OTUs showed, to a lower extent, similar patterns (average Bray-Curtis dissimilarity of 0.94 and 0.86 for opposite seasons and consecutive months, respectively). The seasonality of abundant OTUs was most pronounced in La Claye whereas there was no difference in the seasonality of rare OTUs among the five ecosystems. NMDS analysis based on OTU frequencies highlighted that the strongest shifts in community composition occurred between winter and summer (Supplementary Figure 4.B.7), in agreement with previous similar observation for microbial eukaryotes [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF]. Turnover values for the abundant prokaryotic and eukaryotic OTUs were more marked between years (19.6 and 31.4% on average, respectively)

than between seasons (10.9 and 22.7% on average, respectively). Turnover indices calculated for each couple of seasons were higher between summer and winter, at the exception of La Claye.

Rare OTUs, both prokaryotic and eukaryotic ones, were characterized by higher OTU turnover between seasons and between years than abundant OTUs (Supplementary Figure 4.B.8).

Discussion

Local environmental selection drives different community compositions in small freshwater ecosystems

In order to study the diversity and temporal dynamics of planktonic microorganisms (0.2-5 µm size-fraction) of the three domains of life in small freshwater systems, we generated 16S rDNA amplicon sequences for samples collected over 24 months from 4 ponds and one brook for which we previously generated 18S rDNA datasets from the same original samples [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF].

These systems were characterized by differences in their physico-chemical conditions [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF]. Ru Sainte Anne and La Claye experienced drought events that lasted up to five months. We retrieved a total number of 16 573 OTUs affiliated to prokaryotes (14 701 bacterial

OTUs and 1 872 archaeal OTUs) for a total of 3 583 OTUs affiliated to microbial eukaryotes.

These numbers are higher than those usually retrieved in other freshwater ecosystems [START_REF] Bock | Synchrony of eukaryotic and prokaryotic planktonic communities in three seasonally sampled austrian lakes[END_REF][START_REF] Llirós | Bacterial community composition in three freshwater reservoirs of different alkalinity and trophic status[END_REF] even after our very stringent analysis of the sequence data (see Methods; Supplementary Tables 4.A.1 and 4.A.2). The higher diversity actually reflects the cumulative effect of observed OTUs along time series and highlights the importance of integrating temporal scales to better depict global microbial biodiversity [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF].

Not surprisingly, microbial communities were mainly composed of rare OTUs (Supplementary Table 2), for both prokaryotes and microbial eukaryotes. Rare OTUs were affiliated to all the 78 detected prokaryotic phyla whereas abundant OTUs (≥0.1%) were restricted to 32 phyla.

Most prokaryotic phyla were widespread in the five ecosystems; only a few low-abundant ones were not detected in all of them. On average, the prokaryotic OTU richness was about five times higher than that of eukaryotes at each sampling date (Figure 4.2.1B), which is in agreement with the global higher diversity of prokaryotes (e.g. [START_REF] Hug | A new view of the tree of life[END_REF]. Proteobacteria, Bacteroidetes, Actinobacteria and Verrucomicrobia dominated (Supplementary Figure 4.B.1), as often observed in freshwater lentic ecosystems (Newton et al., 2011;[START_REF] Riemann | The native bacterioplankton community in the central baltic sea is influenced by freshwater bacterial species[END_REF][START_REF] Zwart | Typical freshwater bacteria: an analysis of available 16s rrna gene sequences from plankton of lakes and rivers[END_REF]. Cyanobacteria were detected at low abundances, likely because we focused on the small size-fraction (0. ronments, including lakes [START_REF] Bock | Synchrony of eukaryotic and prokaryotic planktonic communities in three seasonally sampled austrian lakes[END_REF][START_REF] Linz | Bacterial community composition and dynamics spanning five years in freshwater bog lakes[END_REF]. Each of the five ecosystems appears to represent a specific habitat for diverse prokaryotic communities because of particular physico-chemical conditions, nutrient composition, availability as previously described for these ecosystems [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF], and for other freshwater environments [START_REF] Fisher | Effects of resources and trophic interactions on freshwater bacterioplankton diversity[END_REF][START_REF] Peura | Bacterial and phytoplankton responses to nutrient amendments in a boreal lake differ according to season and to taxonomic resolution[END_REF]. In clear contrast with the four ponds analyzed, the prokaryotic community in the Ru Sainte Anne brook was very different. This lotic ecosystem harboured a prokaryotic community that was characterized by the highest richness and evenness as well as by the highest abundances of Archaea and CPR bacteria (Figure 4.2.1A-C and 4.2.2B, Supplementary Figure 4.B.5). This higher richness might be related to a higher input on allochthonous microbes (e.g. from soil or upstream waters) into the system (e.g. [START_REF] Crump | Microbial diversity in arctic freshwaters is structured by inoculation of microbes from soils[END_REF][START_REF] Teachey | Rapid and stable microbial community assembly in the headwaters of a third-order stream[END_REF]. In addition to this major difference in community composition between the brook and the four ponds, we also observed differences among all the five small freshwater ecosystems Many microbes exhibit a high dispersion capacity promoted by natural vectors such as wind, rain, and animals (birds and mammals). However, the capacity to colonize a new environment faces diverse biotic and abiotic filters (e.g. competition, chemistry of the ecosystem, hydrology).

(
Among these factors, competition between autochthonous and dispersing microbes is known to be crucial to limit successful immigration [START_REF] Comte | Contribution of different bacterial dispersal sources to lakes: population and community effects in different seasons[END_REF][START_REF] Logue | Species sorting affects bacterioplankton community composition as determined by 16s rdna and 16s rrna fingerprints[END_REF].

In this regard, we detected 62.7 and 39.6% of prokaryotic and eukaryotic OTUs, respectively, that occurred only once in all ecosystems over the two-year survey. In addition to their very sporadic occurrence, the abundance of these OTUs was very low, with less than 15 reads on average. These OTUs likely represent dispersing cells that did not succeed to colonize our ecosystems and were no longer detected at subsequent dates. However, this was not the case for all rare OTUs, since some of them reached high abundances punctually. For instance, a rare OTU affiliated to Firmicutes accounted for 2 403 (30.2%) reads in La Claye in July 2012.

We can hypothesize that these rare OTUs come from a local seed bank and may bloom when very particular environmental conditions occur. The fact that two of our ecosystems (La Claye and Ru Sainte Anne) experienced drought events that lasted 1 to 5 months and that, each time, microbial communities recovered their original structure within a month after water refill (Supplementary Figure 4.B.2) also strongly advocates for the existence of seed banks in these ecosystems for both the abundant and part of the rare OTUs [START_REF] Shade | Fundamentals of microbial community resistance and resilience[END_REF][START_REF] Simon | Resilience of freshwater communities of small microbial eukaryotes undergoing severe drought events[END_REF].

Prokaryotes and microbial eukaryotes share similar temporal patterns

Our multivariate statistical analyses showed that the microbial community composition in the five freshwater ecosystems was not only driven by the particular environmental conditions of each system but also by their temporal variations at different time scales (Table 4.2.1).

Microbial communities can exhibit non-seasonal dynamics due to marked and fast shifts in environmental conditions. These shifts can easily take place in small freshwater ecosystems because their shallow depths and small volumes make them sensitive to local and short-time scale climate variations [START_REF] Bock | Synchrony of eukaryotic and prokaryotic planktonic communities in three seasonally sampled austrian lakes[END_REF][START_REF] Downing | Emerging global role of small lakes and ponds: little things mean a lot[END_REF][START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF]. In fact, even if rapid changes can occur in larger ecosystems such as lakes [START_REF] Mangot | Shortterm dynamics of diversity patterns: evidence of continual reassembly within lacustrine small eukaryotes[END_REF], climate has usually a lower impact at short-time scales on lakes and oceans, where microbial seasonal successions are well documented [START_REF] Fuhrman | Marine microbial community dynamics and their ecological interpretation[END_REF][START_REF] Tammert | Contrasting seasonal and interannual environmental drivers in bacterial communities within a large shallow lake: evidence from a seven year survey[END_REF]. Nevertheless, despite punctual marked shifts in environmental conditions, we observed global seasonal patterns of both the environmental parameters (Figure 4.2.4), and the prokaryotic and eukaryotic microbial communities ( this study (e.g. light quantity and quality, wind, rainfall, specific hydrochemistry), biotic interactions were most likely also involved in determining the microbial community assembly as is assumed for other ecosystems [START_REF] Lindström | Local and regional factors influencing bacterial community assembly[END_REF][START_REF] Yannarell | Geographic and environmental sources of variation in lake bacterial community composition[END_REF]. Many microbial eukaryotes prey on prokaryotes (Boenigk and Arndt, 2002;[START_REF] Pernthaler | Predation on prokaryotes in the water column and its ecological implications[END_REF]. Likewise, diverse symbioses (from mutualistic to parasitic ones) are established between microorganisms of the three domains of life [START_REF] González-Olalla | Climate-driven shifts in algal-bacterial interaction of high-mountain lakes in two years spanning a decade[END_REF][START_REF] Zehr | Unusual marine unicellular symbiosis with the nitrogen-fixing cyanobacterium ucyn-a[END_REF] and influencing community composition. In return, the composition of prey and host communities may affect that of their respective predators and symbionts (Šimek et al., 2013). Time series with adapted time scale and sampling frequency are crucial to resolve these biotic interactions between microorganisms [START_REF] Kelly | Diel population and functional synchrony of microbial communities on coral reefs[END_REF][START_REF] Moreira | Time series are critical to understand microbial plankton diversity and ecology[END_REF]. Although our monthly sampling was likely too sparse to accurately assess potential synchrony between prokaryotes and microbial eukaryotes (that can often experience rapid changes on daily or weekly basis), we were able to identify an overall marked correlation between the changes in prokaryote community composition and those of microbial eukaryotes (Supplementary Table 4.A.4). This observation supports, beyond physicochemical change, the occurrence of top-down processes between heterotrophic eukaryotes and prokaryotes [START_REF] Massana | Eukaryotic versus prokaryotic marine picoplankton ecology[END_REF].

Symbiosis may be a major process in small freshwater microbial ecosystems

If predation is one of the main recognized biotic interactions at the base of trophic webs, symbioses (including interactions going from parasitism to mutualism) are widespread among microbial communities and can also play key roles [START_REF] Harding | Symbiotic unicellular cyanobacteria fix nitrogen in the arctic ocean[END_REF][START_REF] Hudson | Is a healthy ecosystem one that is rich in parasites?[END_REF].

We observed a large proportion of probable symbiotic taxa among the microbial eukaryotes and prokaryotes identified in our ecosystems. For example, based on the very limited biosynthetic capacities encoded in their genomes, CPR bacteria are inferred to live in symbiosis with other prokaryotes (Castelle and Banfield, 2018). Overall, in the five ecosystems, the most abun-dant CPR phyla were Azambacteria (3.14%), Nomurabacteria (0.92%), Gracilibacteria (0.80%), Saccharibacteria (0.52%) and Peregrinibacteria (0.43%). Interestingly, CPR bacteria have been found mostly in oxygen-depleted, sulfide-rich environments (Castelle et al., 2018). Our sampled ecosystems are somewhat oxygen-depleted but display oxic conditions on the surface. We recorded the highest proportions (44.9% of reads, 46.3% of OTUs) in Ru Sainte Anne (47.2%

[O 2 ] on average) and low proportions (2.3% of reads, 24.3% of OTUS) in Mare Saint Robert (38.6% [O 2 ] on average). We cannot rule out the hypothesis that CPR bacteria were more abundant in the sediments or in the suboxic deeper waters of our ecosystems, and that those that we detected were cells suspended again from the bottom to the surface since these ecosystems are characterized by frequent water column mixing. Nevertheless, we did retrieve in our samples very low numbers of sequences from anaerobic prokaryotes typical of sediments, such as methanogenic archaea (0.14% of all reads in all ecosystems) or sulfate-reducing Desulfobacteraceae (0.068%). This suggests a moderate input of cells from the sediment, so that the much larger amounts of CPR bacteria that we detected most likely correspond to bona fide members of the planktonic community and not cells suspended again from the sediment. Nonetheless, methanogenesis and sulfate-reduction are active local processes associated to the degradation of abundant organic matter (bubbling and strong H 2 S smell during sampling are observed in some systems, e.g. Mare Gabard, especially in warmer seasons). Therefore, CPR and their hosts might be more linked to sulfide availability than to the absence of dissolved oxygen per se. In addition, CPR bacteria were consistently retrieved from the five ecosystems even if their presumed size [START_REF] Luef | Diverse uncultivated ultra-small bacterial cells in groundwater[END_REF] is below the 0.2 µm pore-size of the membranes that we used to collect cells. Therefore, their relative proportion might have been underestimated in our study, since many cells unattached to their hosts might have escaped detection. At any rate, the relatively high abundance of CPR bacteria in these small water bodies opens intriguing questions about their ecological role. Despite their likely dependence on other organisms, CPR bacteria exhibit a wide range of metabolic activities. For example, among the CPR lineages detected in this study, Microgenomates have the potential to degrade a large range of carbon substrates [START_REF] Danczak | Members of the candidate phyla radiation are functionally differentiated by carbon-and nitrogen-cycling capabilities[END_REF], Parcubacteria can be involved in the nitrogen cycle through denitrification and Saccharibacteria (TM7) and Dojkabacteria (WS6) have been reported as parasites (Castelle and Banfield, 2018). The same situation might apply to the potentially parasitic Woesearchaeota [START_REF] Liu | Insights into the ecology, evolution, and metabolism of the widespread woesearchaeotal lineages[END_REF], which reach relative high abundance in some of our shallow freshwater ecosystems. The importance of parasitism in aquatic plankton communities is well known [START_REF] Hudson | Is a healthy ecosystem one that is rich in parasites?[END_REF][START_REF] Jephcott | Ecological impacts of parasitic chytrids, syndiniales and perkinsids on populations of marine photosynthetic dinoflagellates[END_REF][START_REF] Lafferty | Parasites dominate food web links[END_REF]. In particular, since parasites have complex life cycles and are known to infect organisms at various trophic levels, they increase the complexity of planktonic trophic networks by extending the food chains [START_REF] Grami | Functional effects of parasites on food web properties during the spring diatom bloom in lake pavin: a linear inverse modeling analysis[END_REF] and enhancing the level of biodiversity [START_REF] Hudson | Is a healthy ecosystem one that is rich in parasites?[END_REF]. This, in turn, may result in the increase of both the connectivity and efficiency of carbon fluxes, although these potential consequences are still subject to debate [START_REF] Grami | Functional effects of parasites on food web properties during the spring diatom bloom in lake pavin: a linear inverse modeling analysis[END_REF].

In summary, our study revealed a high diversity of prokaryotes in addition to that previously observed for microbial eukaryotes in small freshwater ecosystems. Despite a microbial community structure characterized by a predominance of rare taxa and a marked OTU turnover rate, we observed marked seasonal patterns and community drift over the two-year survey for both prokaryotes and microbial eukaryotes of five different microbial communities.

These observations suggest that diverse prokaryote and eukaryote communities react similarly to environmental changes over time (seasonal to multiple-year scale). These changes were only partially driven by measured environmental conditions. By contrast, biotic interactions, such as symbiosis, are suspected to play more significant roles. The abundance of CPR bacteria in some of these ecosystems suggests that they may be important ecological players also in oxic environments.

Supplementary Table 4

Context and objective

Following the monthly survey conducted during two years, a seasonal survey was conducted in the same five small freshwater ecosystems. Long term surveys are essential to describe community composition and temporal dynamics. Indeed, microbial communities and ecosystems are changing in time, and are subject to perturbations. Therefore, long surveys allow to catch more variability than studies lacking of temporal dimension. As the detection of a consistent seasonal pattern requires more than two years of observations, it was necessary to continue the sampling [START_REF] Fuhrman | Marine microbial community dynamics and their ecological interpretation[END_REF]. It is even more important in our small freshwater ecosystems, because they respond rapidly to environmental perturbation. Indeed, their small volume of water reduces their buffering capacity. Therefore, a long survey is needed to cover a wide range of potential environmental conditions. We performed the seasonal sampling for eight years, which represents about 30 samples for each ecosystem. The data were obtained using Illumina MiSeq sequencing of 16S and 18S rDNA amplicons. In this new approach, I targeted a larger fraction of the microbial communities (i.e. 0.22 to 30 µm instead of 0.22 to 5 µm), to consider pico-to micro-planktonic microorganisms. In this study, my aims were to:

1. Extend our description of the microbial composition of the microorganisms of the three domains of life, as we did not reach a saturation plateau of diversity in the previous studies.

2. Confirm or infirm the temporal patterns observed at the two-year scale with the data of the eight-year survey.

3. Investigate specific temporal dynamics to detect OTUs with clear recurrent patterns.

4. Infer a global interaction network to study the community structure shared between the ecosystems.

Manuscript

Abstract

Small and shallow freshwater ecosystems are widely distributed and known to be responsible for significant greenhouse gases emission and adsorption (CO 2 and CH 4 ). They are sensitive to any environmental change because of their small volume that induces a low buffering capacity. However, the knowledge of the diversity, ecology, and roles of microbial communities inhabiting these systems are still scarce. Hence, the functioning of small freshwater ecosystems is poorly understood. In this study, we used Illumina MiSeq sequencing data of 16S and 18S rDNA amplicons from five small freshwater ecosystems located in northwestern France sampled during eight years (2011-2019) at a seasonal frequency. As observed in a previous study, small freshwater ecosystems harbored very diverse microbial communities (respectively 22 764 and 25 536 operational taxonomic units -OTUs-at 95% identity). Moreover, they were characterised by specific microbial communities despite their geographic proximity. Microbial eukaryotes and prokaryotes communities exhibited seasonal patterns featuring a marked seasonality (intra-annual) and an increase of dissimilarities between communities over the years (inter-annual). A seasonal recurrence was detected at the OTU level for less than 2% of the microbial community. In each ecosystem, the measured physico-chemical parameters had only a low influence on microbial community variance. This suggests that biotic factors, such as predation, mutualisms, or parasitism play a major role in community composition. The global structure over the five ecosystems indicated that the most abundant OTUs were grouped in the network following their taxonomic affiliation, suggesting that they share similar ecological niches.

Introduction

Aquatic ecosystems have a major importance in climate regulation and in carbon cycle, participating actively to its sequestration [START_REF] Williamson | Lakes and streams as sentinels of environmental change in terrestrial and atmospheric processes[END_REF][START_REF] Riebesell | Mesocosm co 2 perturbation studies: from organism to community level[END_REF][START_REF] Cavicchioli | Scientists' warning to humanity: microorganisms and climate change[END_REF]. Microorganisms (microbial eukaryotes, bacteria and archaea) are known to play essential roles in all aquatic ecosystems, as key players in the microbial loop [START_REF] Pinet | Invitation to oceanography[END_REF]. Microbial eukaryotes and prokaryotes act at different levels: phytoplankton performs photosynthesis, bacteria and archaea are highly involved in the degradation of dissolved and particulate organic matter, heterotrophic and mixotrophic protists are active predators of prokaryotes [START_REF] Sherr | Significance of predation by protists in aquatic microbial food webs[END_REF][START_REF] Kritzberg | Bacterial growth on allochthonous carbon in humic and nutrient-enriched lakes: results from whole-lake 13 c addition experiments[END_REF][START_REF] Zubkov | High bacterivory by the smallest phytoplankton in the north atlantic ocean[END_REF][START_REF] Falkowski | The microbial engines that drive earth's biogeochemical cycles[END_REF]. Prokaryotes are also highly involved in all biogeochemical cycles [START_REF] Falkowski | The microbial engines that drive earth's biogeochemical cycles[END_REF][START_REF] Jetten | The microbial nitrogen cycle[END_REF][START_REF] Worden | Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes[END_REF]. Prokaryotes and microbial eukaryotes feature diverse biological traits [START_REF] Massana | Eukaryotic versus prokaryotic marine picoplankton ecology[END_REF]. For example, microbial eukaryotes are able to phagocyte other cells, an almost exclusive characteristic to this domain of life [START_REF] Shiratori | Phagocytosis-like cell engulfment by a planctomycete bacterium[END_REF]. On the other side, prokaryotes present higher growth rates, phylogenetic and metabolic diversities.

Microbes are at the base of trophic webs and are thus major actors in ecosystem functioning, in both marine and freshwater systems. However, the vast majority of microbial ecology studies have mainly been conducted in large ecosystems, while small ecosystems were barely studied because of their supposed little importance at the global scale [START_REF] Mullins | Big things come in small packages: why limnologists should care about small ponds[END_REF].

Freshwater ponds(surface area <0.001km 2 ), are shallow, widespread on continents, and be numerically the most abundant lentic freshwater bodies [START_REF] Downing | The global abundance and size distribution of lakes, ponds, and impoundments[END_REF][START_REF] Downing | Emerging global role of small lakes and ponds: little things mean a lot[END_REF][START_REF] Bartout | A new approach to inventorying bodies of water, from local to global scale[END_REF][START_REF] Biggs | The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers[END_REF]. These ecosystems are very sensitive and are often facing drought periods [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF][START_REF] Biggs | The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers[END_REF]. Because of their small volumes, they present low buffering capacities towards disturbances. They are also actively involved in biogeochemical cycles, such as the carbon cycle through CO 2 fixation and CH 4 emissions (with contributions up to 15% and 40% respectively) [START_REF] Céréghino | The ecological role of ponds in a changing world[END_REF][START_REF] Holgerson | Large contribution to inland water co 2 and ch 4 emissions from very small ponds[END_REF][START_REF] Yvon-Durocher | Long-term warming amplifies shifts in the carbon cycle of experimental ponds[END_REF]. However, despite their ecological importance, there are still major gaps in our knowledge on the functioning of these ecosystems.

Understanding the functioning of an aquatic ecosystem of any kind, marine or freshwater, can be addressed through spatial or temporal surveys [START_REF] Gonzalez | Characterizing microbial communities through space and time[END_REF][START_REF] Widder | Challenges in microbial ecology: building predictive understanding of community function and dynamics[END_REF]. Several spatial surveys have been conducted in marine systems and lakes [START_REF] Kirkham | Basinscale distribution patterns of photosynthetic picoeukaryotes along an atlantic meridional transect[END_REF][START_REF] Lepère | Geographic distance and ecosystem size determine the distribution of smallest protists in lacustrine ecosystems[END_REF][START_REF] Sunagawa | Structure and function of the global ocean microbiome[END_REF], but long temporal surveys are scarce and often restricted to coastal marine systems. The majority of the temporal surveys are less than one year long [START_REF] Yannarell | Geographic and environmental sources of variation in lake bacterial community composition[END_REF][START_REF] Gilbert | The seasonal structure of microbial communities in the western english channel[END_REF] but few studies are going from several years to decade-long surveys [START_REF] Fuhrman | Annually reoccurring bacterial communities are predictable from ocean conditions[END_REF][START_REF] Chow | Temporal variability and coherence of euphotic zone bacterial communities over a decade in the southern california bight[END_REF][START_REF] Linz | Bacterial community composition and dynamics spanning five years in freshwater bog lakes[END_REF]. Because of the highly variable environmental conditions among small freshwater ecosystems and the little characterisation of these systems are there is a need to conduct spatiotemporal surveys to better appreciate their functioning [START_REF] Pätzig | How much information do we gain from multiple-year sampling in natural pond research?[END_REF]. Long term temporal studies focusing on both microbial eukaryotes and prokaryotes are rarely conducted in marine systems [START_REF] Chénard | Temporal and spatial dynamics of bacteria, archaea and protists in equatorial coastal waters[END_REF][START_REF] Lambert | Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations[END_REF] and freshwaters [START_REF] Su | Temporal patterns of phyto-and bacterioplankton and their relationships with environmental factors in lake taihu, china[END_REF][START_REF] Bock | Synchrony of eukaryotic and prokaryotic planktonic communities in three seasonally sampled austrian lakes[END_REF][START_REF] Obertegger | Multifaceted aspects of synchrony between freshwater prokaryotes and protists[END_REF]. Temporal studies are crucial to decipher structuring patterns of microbial communities. Indeed, surveys conducted at diverse time scales revealed that microbial communities are changing over short and long periods, going from hours to years [START_REF] Mangot | Shortterm dynamics of diversity patterns: evidence of continual reassembly within lacustrine small eukaryotes[END_REF][START_REF] Fuhrman | Marine microbial community dynamics and their ecological interpretation[END_REF]. Communities are sometime presenting seasonal temporal patterns [START_REF] Gilbert | Defining seasonal marine microbial community dynamics[END_REF][START_REF] Linz | Bacterial community composition and dynamics spanning five years in freshwater bog lakes[END_REF][START_REF] Giner | Comparative 16s rrna analysis of lake bacterioplankton reveals globally distributed phylogenetic clusters including an abundant group of actinobacteria[END_REF] and when studied together, microbial eukaryotes and prokaryotes can be synchrones [START_REF] Bock | Synchrony of eukaryotic and prokaryotic planktonic communities in three seasonally sampled austrian lakes[END_REF][START_REF] Obertegger | Multifaceted aspects of synchrony between freshwater prokaryotes and protists[END_REF].

The main objective of this study was to decipher the composition of microbial communities (microbial eukaryotes, bacteria and archaea) of a large size-range (0.22-30 µm) over multiple years, in the overlooked small freshwater ecosystems. Therefore, we conducted an eight-years survey at a seasonal frequency in five small freshwater ecosystems (four ponds and one brook) located in North-Western France, using Illumina MiSeq sequencing of 18S/16S rDNA amplicons.

In parallel, the measurements of environmental conditions was used to estimate the impact of abiotic parameters on the community structuration. Previous works realised in the same ecosystems for two-year revealed a high diversity of small microbial eukaryotes and prokaryotes (<5 µm) [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF]David et al. 2020, submitted). Despite their marked differences in composition in the five ecosystems, microbial eukaryotes and prokaryotes had similar temporal patterns. Here, we investigated the composition and its variations over time in the same ecosystems microbial but at a longer time scale, using the latest sequencing technology, and considered a larger size fraction of microorganisms. We aimed at confirming the temporal patterns observed previously at and at deciphering the temporal pattern of recurrences in specific operational taxonomic units (OTUs). Even if the global community presented a recurrent seasonal pattern, we found that only a small proportion of the detected OTUs displayed a marked recurrence over the eight-year survey. from cells collected in two size-fractions 0.22-5 µm and 5-30 µm. DNA from both size fraction were mixed proportionally to the volume of water filtered. Eukaryotic 18S rRNA gene fragments including the V4 hypervariable region were amplified using primers EK-565F (5'-35 GCAGTTAAAAAGCTCGTAGT) and EK-1134-R-UNonMet (5'-TTTAAGTTTCAGC-CTTGCG), biased against metazoa [START_REF] Bower | Preferential pcr amplification of parasitic protistan small subunit rdna from metazoan tissues[END_REF]. To target both bacterial and archaeal, 16S rRNA gene fragments encompassing the V4 hypervariable region were PCR-amplified using U515F (5'-GTGCCAGCMGCCGCGGTAA) and U806R (5'-GGACTACVSGGGTATCTAAT)

Material and Methods

Sampling and measurement of physico-chemical parameters

primers. These primers were tagged with 10 bp Molecular IDentifiers (MIDs) to allow differentiation of PCR products from 89 distinct samples pooled together for multiplex sequencing.

PCR were conducted in a total volume of 25 µl using 1.5 mM MgCl 2 , 0.2 mM dNTPs, 0.3 µM primers, 0.3-3 µl of DNA sample and 0.5 U HotStart Taq polymerase (Taq Platinum, Invitrogen, Carlsbad, CA, USA). The amplifications were performed in a thermocycler. After a 2 min denaturation step at 94°C, 35 cycles of amplification were performed (94°C for 15s, 55°C

or 58°C for 30s (for eukaryotes and prokaryotes respectively), 72°C for 90s). The process ended with a final extension set at 72°C for 10 min. For each sample, five PCR products were pooled together. Pools were then purified using the QIAquick PCR purification kit (Qiagen, Hilden, Germany). Equivalent amounts of purified amplicons of 89 different samples were multiplexed and sequenced using paired-end (2x300 bp) MiSeq Illumina technology (Eurofins Genomics, Ebersberg, Germany).

MiSeq Illumina analysis

A total of 7 007 571 raw merged reads was obtained for the eukaryotes, and 5 737 732 for the prokaryotes. Reads were filtered through a modified local pipeline to remove spurious sequences and to regroup reads in operational taxonomic units (OTUs). Briefly, reads are merged using FLASH (Magoč and Salzberg, 2011), primers and MIDs are trimmed with 'cutadapt' [START_REF] Martin | Cutadapt removes adapter sequences from high-throughput sequencing reads[END_REF], and dereplication of the clean merged reads was realised using 'vsearch' [START_REF] Rognes | Vsearch: a versatile open source tool for metagenomics[END_REF] with a 'uchime_denovo' option to eliminate potential chimeras.

Cleaned reads were gathered at 95% identity cut-off for both microbial eukaryotes and prokaryotes using CD-HIT-TEST [START_REF] Fu | Cd-hit: accelerated for clustering the next-generation sequencing data[END_REF]. Singletons, which are OTUs composed of only one single sequence, were excluded. OTUs reference sequences were assigned to taxonomic groups based on similarity with sequences of a local database including sequences from cultured organisms and environmental datasets retrieved from SILVAv128 and PR2v4. To avoid wrong affiliations, OTU representative sequences with a similarity best-it lower than 80% were verified by phylogenetic placement in reference phylogenetic trees. Wrong affiliations were modified manually. Reads affiliated to mitochondria and chloroplast were excluded from the dataset.

Finally, filtered sequences from all OTUs were attributed to samples according to their MIDs.

At the end, 5 630 664 correct reads were retained for microbial eukaryotes, and 4 576 603 for prokaryotes.

Statistical analyses

All statistical analyses were conducted with the R-software (http://cran.r-project.org) (R Core Team, 2019). All analyses with no further indications were performed with the 'vegan' Rsoftware package [START_REF] Oksanen | The vegan package[END_REF]. Bray-Curtis dissimilarities were conducted to determine distances between and within microbial communities. Wisconsin standardization of the reads was performed prior to dissimilarities calculations to limit putative biases due to the influence of very abundant OTUs. The abiotic parameters were scaled and centred in all multivariate analysis. To see if the ecosystems were different in terms of abiotic parameters, we performed a principal correspondence analysis (PCA) on abiotic parameters. Rarefaction, i.e. a randomly selected subset of sequences in each sample based on the lowest number of sequence, was applied to the global sequencing dataset to provide a more accurate comparison of the five ecosystems. Microbial eukaryotic and prokaryotic communities were rarefied to their smallest level of reads (respectively 6 355 and 5 574 reads per sample). The potential influence of rarefaction depths (i.e. from 5000 to 20000 reads) was tested on the statistical analyses performed afterwards. All analyses were conducted using a rarefied table based on the smallest level of reads, referred as the "global OTU dataset". The different statistical analyses based on microbial eukaryotes and prokaryotes were conducted either using the global OTU dataset, or using the two datasets representing the abundant and rare OTUs. Abundant OTUs had a mean relative abundance superior to 0.1% and detected in at least 20% of the samples within one ecosystem while rare OTUs were represented by the remaining OTUs. Richness was determined as the number of OTUs detected per sample, and evenness was calculated following the formula described in [START_REF] Pielou | The measurement of diversity in different types of biological collections[END_REF] with the 'vegan' r package. The number of OTUs shared by the five ecosystem was determined using the 'gplots' of the R-software package [START_REF] Warnes | Comparing the effects of resource enrichment and grazing on viral production in a meso-eutrophic reservoir[END_REF] and represented in Venn diagrams.

The three following analyses were based on Bray-Curtis dissimilarities. Non-metric multidimensional scaling (NMDS) were performed on the global OTU dataset or on each ecosystem to visualise the differences in community composition between sites or seasons. An analysis of similarities (ANOSIM) was performed between pairs of groups, with 9 999 permutations, to statistically test and quantify the differences between the NMDS groups [START_REF] Clarke | Statistical design and analysis for a'biological effects' study[END_REF]. We performed permutational analyses of variance (PERMANOVA), to assess whether the habitat (pond or stream), the ecosystem (localization), the season or the year explained significantly community variance.

Turnovers between two seasons over the 8-year survey (e.g. summer versus winter) were calculated considering one season as the sum of all samples collected in this particular season.

The turnover between samples collected at the same season but during different years was also estimated. Turnover was determined through the comparison of the number of OTUs gained and lost between two samples, using the 'codyn' R-software package [START_REF] Hallett | codyn: An r package of community dynamics metrics[END_REF]. Time-lag analysis (TLA) was performed to identify patterns in temporal dynamics of microbial communities and environmental variables [START_REF] Collins | A method to determine rates and patterns of variability in ecological communities[END_REF]. TLA were performed on (i) Wisconsin standardized Bray-Curtis dissimilarities for microbial communities and on (ii)

Euclidean distances for environmental data. Linear regression was fitted on the dissimilarity distributions using a linear model using the 'stats' package of R-software. Permutations were performed using the 'lmPerm' package to test for the robustness of regressions [START_REF] Wheeler | Package 'lmperm'. R package version[END_REF].

The Recurrence Index (RI) developed by [START_REF] Giner | Comparative 16s rrna analysis of lake bacterioplankton reveals globally distributed phylogenetic clusters including an abundant group of actinobacteria[END_REF] was applied for the detection of OTUs or group of OTUs showing a seasonal pattern. This index is based on auto-correlation functions that are calculated over 1 000 randomized occurrences. The recurrence index was applied on all OTUs with a mean frequency > 0.01. All the OTUs occurring less than 3 times were considered as "Uncertain". An OTU or a group of OTUs were considered seasonal if RI was significant. To estimate the contribution of each environmental parameter in community variance, we performed canonical correspondence analyses (CCA). Putative multicollinearity between environmental parameters was checked by computing the variance inflation factors The global community network was built based on co-occurrence analysis. The co-occurrence relationships between OTUs were inferred using a multivariate Poisson lognormal model with the 'PLNmodels' package of R-software [START_REF] Chiquet | Variational inference for sparse network reconstruction from count data[END_REF]. This model infers direct statistical covariations by focusing on the inverse of the covariance matrix between OTU abundances, also called precision matrix. The precision matrix allows to avoid false connections between nodes, e.g. a covariation between two nodes induced by their direct association to a third node [START_REF] Kurtz | Sparse and compositionally robust inference of microbial ecological networks[END_REF]. Moreover, the model allows to take into account the differences in sampling depth between samples, as well as various covariables such as environmental parameters. We conducted the analysis on the OTUs present in 35% of the samples, and with a mean abundance higher than 0.01%. The sequencing depth was added as an offset of the model. The sampling site and the temperature were considered as covariables, to avoid spurious associations. The selection of the optimal model was conducted on a 50-size grid of penalties, using the BIC criteria. The visualization of the network was conducted using the 'ggnet' R-software package [START_REF] Tyner | Network visualization with ggplot2 UNDP-GEF (2015) The ecological atlas of the baikal basin[END_REF]. We obtained an undirected network, with the OTUs represented by the nodes, and the relationship between them by the edges. Network topology was investigated with the calculation of classical network properties as well as a block model (BM) analysis.

The network properties were calculated using the 'igraph' R-software package [START_REF] Csardi | The igraph software package for complex network research[END_REF]. We estimated the number of positive and negative edges, the total number of nodes and the number of connected nodes for the network. The mean degree of the network was also , where E is the number of edges and N the number of nodes [START_REF] Barrat | Dynamical processes on complex networks[END_REF]. The clustering coefficient was calculated to estimate the probability that two nodes sharing the same neighbour are connected with another [START_REF] Delmas | Analysing ecological networks of species interactions[END_REF]. The block model analysis was conducted using a stochastic block model approach, developed in the 'blockmodel' R-software package, and based on the binary network reporting the presence-absence of significant direct associations [START_REF] Leger | Blockmodels: A r-package for estimating in latent block model and stochastic block model, with various probability functions, with or without covariates[END_REF]. With this method, we were able to gather nodes according to similar modes of interactions. The detection of modules in the networks, i.e. groups of OTUs that constitute a sub-network, was conducted using the 'igraph' R-software package.

Results

Environmental ecosystem characterisation

Five ecosystems were sampled seasonally (autumn in October, winter in January, Spring in April and Summer in July) from October 2011 to January 2019. They were selected because they were representative of small freshwater ecosystems in the "Parc Naturel Régional de la Haute Vallée de Chevreuse". Etang des Vallées (EV) is a large pond, Ru Sainte Anne (RSA) is a stream, Saint Robert (SR) is a village pond, La Claye (LC) is a peat bog, Mare Gabard (MG) is a forest pond (see [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF] for more details). Over the 8-year survey, they experienced marked variations in their abiotic environment (Supplementary Table 5.A.1 and The PCA (Supplementary Figure 5.B.1) revealed that RSA was characterised by higher conductivity, EV by higher DO concentrations and MG by lower pH and conductivity values.

Etang des Vallées had the highest mean dissolved oxygen (78.1%; DO), nitrate (3.94 mg/l) and nitrite (0.05 mg/l) concentrations and pH (7.3) but lower ammonia (0.17 mg/l), orthophosphate (0.05 mg/l) and dissolved organic carbon concentration (3410 µM C; DOC). Mare Gabard was characterised by the highest average DOC concentration (6239 µM C) and the lowest pH (6.55), conductivity (67.4 µS/cm), DO (37.2%) and nitrite concentrations (0.01 mg/l). Saint Robert featured the highest mean chlorophyll a (72.9 mg/l), orthophosphate (0.23 mg/l) and ammonia (0.62 mg/l) concentrations and the lowest nitrate (1.51 mg/l) concentration. Ru Sainte Anne had the highest average conductivity (654.6 µS/cm) and lowest chlorophyll a concentration (1.34 mg/l) (Supplementary Table 5.A.1). Time Lag Analyses (TLA) revealed that environmental conditions were globally stable at the 8-year scale in Etang des Vallées, Mare Gabard and Ru Sainte Anne but changed significantly over time in La Claye and Saint Robert (Figure 1). However, there was significant changes of particular parameters, such as conductivity that decreased in Etang des Vallées, La Claye and Ru Sainte Anne when it increased in Mare Gabard over the 8-year survey (Table S1). Also, >0.1% and present in 20% of the ecosystem samples) were composed of less than 2.0% of the total number of OTUs, but they represented between 31.5% (Ru Sainte Anne) and 84.7%

(Etang des Vallées) of the reads. Only 71 OTUs were detected in all samples in one ecosystem.

Comparison of microbial communities between ecosystems The differences between communities were analysed based on the presence and absence of OTUs (Figure 5.2.3). A low percentage of OTUs were shared between the five ecosystems (1.2% for eukaryotes, 4.2% for prokaryotes). However, they represented up to 50.3% of the reads for the eukaryotes, and 69.6% for the prokaryotes. The differences between samples and ecosystems were investigated based on community dissimilarities. NMDS revealed that microbial communities were rather distinct from each other. According to the overlapping of the ellipse grouping the samples of each ecosystem, prokaryotic communities seemed more dissimilar than their microbial eukaryote counterparts (Figure 5.2.3). These observations were confirmed by significant and high pairwise ANOSIM analyses between each ecosystem (Supplementary table 5.A.4). For both communities, the highest values of dissimilarities were found between Ru Sainte Anne and the other ecosystems, and also between Etang des Vallées and Mare Gabard.

Using PERMANOVA analysis, we tested factors to explain Bray-Curtis variances among samples (Supplementary Table 5.A.5). Only a small proportion of the variance of microbial eukaryote communities was explained, mainly by differences between ecosystems (2.6% of the variance explained by the type of habitat (e.g. lentic or lotic) and 4.4% explained by the ecosystem, p-value = 0.0002). If the season played also a role, the year was not a significant parameter (Supplementary Table 5.A.5). For the prokaryote communities, all the variable played a significant role. In particular, the ecosystem and the year explained the most prokaryotic community variance (respectively 6.0 and 5.9%). Turnover analysis on abundant and rare OTUs between seasons, independently from the year, revealed lower turnover rates within prokaryotes than within microbial eukaryotes. OTUs and prokaryotic abundant OTUs of Mare Gabard did not present a significant variation of their dissimilarities over time.

Global description of the temporal dynamics

Factors explaining community composition changes

To link microbial communities and measured environmental parameters, the global dataset was constrained with abiotic parameters. The measured abiotic parameters explained a small proportion of the community variance (3.8% for the eukaryotes and 6.8% for the prokaryotes) (Supplementary figure 5.B.7). Abiotic parameters that explained this variance were the same for eukaryotes and prokaryotes (i.e. conductivity, dissolved oxygen and temperature), at the exception of pH which played a significant role only for prokaryotes.

The same analysis was conducted considering the ecosystems separately (Supplementary table 5.A.7). For the eukaryotes, temperature explained about 6.0% of the variance in Etang des Vallées, La Claye and Mare Gabard. The explained variance was much higher in Ru Sainte Anne (15.6%) and the community composition was driven by dissolved oxygen and nitrate concentrations as well as conductivity. In Saint Robert, variance of the community composition was not explained by the abiotic parameters. For the prokaryotes, the explained variance was higher than for eukaryotes, with a mean value for the five ecosystems of 14.2%. Dissolved oxygen concentration was a significant parameter in all ecosystems. We did not find pH as a parameter explaining significantly community variance in any ecosystem.

Seasonal changes at a finer scale

Dissimilarities between communities sampled at different seasons A NMDS analysis was conducted on each ecosystem and each community to confirm the seasonal patterns observed with the time-lag analysis. The samples were then grouped according to their season (Supplementary figure 5.B.8). Ellipses defining seasons only overlapped slightly for the prokaryotes of Saint Robert. An ANOSIM analysis determined the significance and the strength of the dissimilarities between pairs of seasons. For each ecosystem except Saint Robert and Ru Sainte Anne for the eukaryotes and Mare Gabard for the prokaryotes, the highest dissimilarity was observed for the comparison of winter and summer.

Identification of recurrent OTUs over time The Recurrence Index [START_REF] Giner | Comparative 16s rrna analysis of lake bacterioplankton reveals globally distributed phylogenetic clusters including an abundant group of actinobacteria[END_REF] determines the proportion of recurrent OTUs in the different ecosystems. We applied this analysis for each ecosystem separately. OTUs present less than 3 times in the dataset were excluded because they could not be considered as recurrent. The results of Saint Robert are not presented since the OTUs detected as recurrent in this ecosystem did not present clear seasonal dynamics.

Overall, recurrent OTUs only represented a small proportion of the communities. They corresponded to a mean value of 2.23% of the OTUs affiliated to microbial eukaryotes and 1.11%

for the prokaryotes (Table 5.2.1). They represented 13.2% and 10.3% of the total number of reads (respectively for microbial eukaryotes and prokaryotes, mean value for the four ecosystems). Not all these OTUs featured a clear seasonal pattern, that is an occurrence each year at one specific season, and an absence the rest of the year. They represented respectively a mean value of 1.15 and 0.48% of the total number of OTUs for microbial eukaryotes and prokaryotes, and a mean value of 11.3 and 5.0% of the total number of reads, respectively. OTUs with a clear recurrent pattern were mostly detected in summer and in winter .

Table 5.2.1 Repartition of all the rarefied OTUs in 4 categories, with the percentage of OTUs and their total relative abundance within the categories for microbial eukaryotes and prokaryotes. First, OTUs with a mean abundance lower than 0.01%. Then, within the remaining OTUs, the OTUs present less than 3 times in the ecosystem. Finally, according to the recurrence index, the recurrent or non-recurrent OTUs. For microbial eukaryotes, Ru Sainte Anne presented the highest proportion of seasonal recurrent OTUs, with 1.9% of the OTUs corresponding to 13.4% of the reads. The proportion of OTUs had similar rates in Etang des Vallées (1.7%). In Mare Gabard, recurrent OTUs were not abundant (0.5%) but their proportion in terms of reads was the highest (14.9%). The lowest proportion was detected in La Claye, with only 0.5% of the OTUs that were characterised by a recurrent pattern, corresponding to 6.5% of the reads. For the prokaryotes, the highest

Global structure of the microbial communities

The global network presented only 0.4% of positive links (3 over 673 edges) (Figure 5.2.4). Over the 874 selected OTUs, only 40% where connected in the network. The connectance of the network was 0.003, with a clustering coefficient of 0.20. The global mean degree of the network was 1.54. Over the 351 nodes, 50.1% were affiliated to bacteria, 46.7% to eukaryota and 3.13% to archaea. There was a majority of interactions between the same domains of life (40.9% of interactions within bacteria, 47.8% with eukaryote). Microbial eukaryotes had a higher mean degree than the two other domains (mean degree of 4.28 for the eukaryotes). The majority of interactions was conducted within heterotrophs (44.9% of the interactions). There were also many interactions happening between autotrophs and heterotrophs (18.6% of the interactions).

Putative parasites were hardly connected to autotrophs and heterotrophs (2.1% of the interactions between putative parasites and autotrophs, and 3.7% between putative parasites and heterotrophs). They were more connected between each other (13.7% of interactions between parasites). Globally, autotrophs and heterotrophs had a similar degree (mean degree of 3.98 for the autotrophs and 4.15 for the heterotrophs). Putative parasites were less connected, with a mean degree of only 2.90. The 22 nodes with a degree higher than 10 were mostly heterotrophs (63.6%) and were not dominated by a specific phylum.

The block model analysis differentiated eight groups in the networks (Supplementary Figure

Discussion

Overview of microbial communities in the five ecosystems

The analyses of the 16S and 18S rDNA amplicon sequences revealed a high diversity of microbial eukaryotes and prokaryotes in the five small freshwater ecosystems. Microbial communities were dominated by a low number of very abundant taxa. They belonged to typical freshwater phyla, i.e. Proteobacteria, Actinobacteria, Bacteroidetes, Verrucomicrobia for prokaryotes, and Ciliophora, Ochrophyta and Cryptophyta for microbial eukaryotes [START_REF] Zwart | Typical freshwater bacteria: an analysis of available 16s rrna gene sequences from plankton of lakes and rivers[END_REF][START_REF] Pernthaler | A 'rare biosphere' microorganism contributes to sulfate reduction in a peatland[END_REF][START_REF] Salcher | Same same but different: ecological niche partitioning of planktonic freshwater prokaryotes[END_REF][START_REF] Debroas | Overview of freshwater microbial eukaryotes diversity: a first analysis of publicly available metabarcoding data[END_REF]. Compared to our previous monthly survey, we detected new phyla of microbial eukaryotes and prokaryotes, e.g. Hilomonadea, Picobiliphyta for microbial eukaryotes, Kuenenbacteria, Kazanbacteria for prokaryotes (David et al.,submitted,see Chapter 4). The microbial diversity in the present study differs from the one described in David et al. 2020 because of the longer survey duration (8 vs 2 years), large size fraction targeted (0.22-30 µm vs 0.22-5 µm) and the sequencing technology (Illumina MiSeq vs 454-pyrosequencing). For example, a higher number of OTUs affiliated to ciliates was detected in this study (mean relative abundance of 29.0% in this study in contrast with 18.2% in David et al.,submitted,see Chapter 4) though this could also be due to the higher number of rDNA genes copy within alveolates [START_REF] Prescott | The dna of ciliated protozoa[END_REF] were also detected (mean abundance between 0.5% in Saint Robert and 7.0% in Ru Sainte Anne). These bacteria and archaea are suspected to be potential parasites or symbionts because of their restricted genomes (Castelle and Banfield, 2018). They could also be important players in small freshwater ecosystem functioning because of their potential role in CO2 fixation [START_REF] Lannes | Carbon fixation by marine ultrasmall prokaryotes[END_REF]. Cyanobacteria were not abundant (mean abundance between 0.14% in La Claye and 3.4% in Etang des Vallées) and not recurrent in summer , which was surprising knowing the eutrophic status of these small freshwater ecosystems.

Microbial communities were specific to each ecosystem despite their close geographical localisation, suggesting a strong filtering effect of the environment. The global community composition differed significantly between ecosystems (Figure 5.2.3B and 5.2.3D, Supplementary Table 5.B.5) and few OTUs were common to the five ecosystems (respectively 1.2 and 4.2% of the microbial eukaryotes and prokaryotes communities, Figure 5.2.3A and 5.2.3C). Time series analysis revealed that a large proportion of the diversity was composed of OTUs occurring only one time in the ecosystems (54.8% of the microbial eukaryotic OTUs appearing a single time during the eight-year, and 39.3% for the prokaryotes). They could be always present in background at too low abundances to be detected or they could be result from immigration processes (e.g. shore, banks, animals, rain, wind). However, dispersing taxa may not have profound impacts on composition of communities likely because they do not succeed to settle in the ecosystem because of mass effect for example [START_REF] Jones | Species-sorting may explain an apparent minimal effect of immigration on freshwater bacterial community dynamics[END_REF][START_REF] Comte | Contribution of different bacterial dispersal sources to lakes: population and community effects in different seasons[END_REF].

Inter-annual patterns of community composition

Despite specific communities in each ecosystem, a similar inter-annual temporal pattern of microbial community composition was observed in the five ecosystems, for both prokaryotes and microbial eukaryotes, at the exception of microbial eukaryotes in Etang des Vallées. This temporal dynamic, which has been previously observed in the two-year survey, is thus validated on a longer time scale. However, we observed different rates of change in microbial eukaryotes and prokaryotes. Linear regressions applied to the microbial eukaryotes showed lower slopes than those obtained for prokaryotes. The global evolution of the prokaryotes is therefore stronger, and resulted in more changes in the community composition over time. This evolution pattern was inversed in the two-year survey. [START_REF] Logares | Contrasting prevalence of selection and drift in the community structuring of bacteria and microbial eukaryotes[END_REF] discussed that both prokaryotes and microbial eukaryotes had different structuring processes, with bacteria mainly structured by selection and microbial eukaryotes by ecological drift. However, longer time surveys, even if they were only focusing on prokaryotic communities, indicated that prokaryotes were characterized by strong inter-annual changes of community composition [START_REF] Shade | Interannual dynamics and phenology of bacterial communities in a eutrophic lake[END_REF][START_REF] Tammert | Contrasting seasonal and interannual environmental drivers in bacterial communities within a large shallow lake: evidence from a seven year survey[END_REF][START_REF] Linz | Bacterial community composition and dynamics spanning five years in freshwater bog lakes[END_REF].

In Etang des Vallées, no inter-annual evolution of microbial eukaryotes was observed, and prokaryotes harbored the lowest rate of change. Therefore, microbial communities appeared to be more stable in this ecosystem compared to the other four. The Etang des Vallées is the largest ecosystem of this study, in terms of surface, depth and water volume, resulting in a higher buffering capacity toward environmental disturbances. One hypothesis is that in larger ecosystems, the abundant microbial communities in place are well established, with a high number of organisms. In this situation, it could be more difficult for immigration process to happen, because of a stronger competition [START_REF] Comte | Contribution of different bacterial dispersal sources to lakes: population and community effects in different seasons[END_REF].

Intra-annual patterns of community composition

Strong intra-annual patterns were detected in the five ecosystems. The seasonal survey revealed strong similarities between microbial communities of the same season over the eightyear, whereas communities at opposite seasons (e.g. summer versus winter) were the most dissimilar (Figure 5.2.1). Intra-annual temporal patterns (i.e. seasonality) in aquatic microbial communities have been observed multiple times at different time scale at surface [START_REF] Kim | Monthly to interannual variability of microbial eukaryote assemblages at four depths in the eastern north pacific[END_REF][START_REF] Yan | Nearly a decade-long repeatable seasonal diversity patterns of bacterioplankton communities in the eutrophic lake donghu (wuhan, china)[END_REF]. Seasonal patterns were detected when taking into account the whole microbial communities (microbial eukaryotes or prokaryotes) but also at the scale of abundant and rare taxa. However, the temporal patterns were less marked for rare OTUs. The rhythmic-ity of rare microbial taxa has been already evidenced in marine bacterioplankton [START_REF] Alonso-Sáez | The hidden seasonality of the rare biosphere in coastal marine bacterioplankton[END_REF]. Not surprisingly, environmental conditions were also experiencing marked seasonal patterns, which lead to variations in microbial habitat over time that can, in return, impact microbial communities. When conditions are not optimal, some microorganisms are able to enter in dormant stages (e.g. cysts, spores, reduced metabolism) [START_REF] Finlay | Global dispersal of free-living microbial eukaryote species[END_REF][START_REF] Lennon | Microbial seed banks: the ecological and evolutionary implications of dormancy[END_REF][START_REF] Locey | Dormancy dampens the microbial distance-decay relationship[END_REF]. Habitat variations may also favor constant immigration.

Even if environmental conditions harbored similar intra-annual patterns than community composition, a low proportion of the community variance was explained by environmental parameters in the five ecosystems, as previously observed in marine or freshwater systems [START_REF] Kara | A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic lake mendota, wi, usa[END_REF][START_REF] Ward | Annual community patterns are driven by seasonal switching between closely related marine bacteria[END_REF] (Supplementary figure 5.B.8). Microbial communities are often more related together than to the environmental factors [START_REF] Steele | Marine bacterial, archaeal and protistan association networks reveal ecological linkages[END_REF][START_REF] Lidicker | Determinants of community structure in the global plankton interactome[END_REF]. The variance was slightly higher when considering ecosystems separately (Supplementary table 5.A.7). However, variance explained by abiotic parameters has already reached up to 30% for microbial eukaryotic communities in marine systems [START_REF] Genitsaris | Small-scale variability of protistan planktonic communities relative to environmental pressures and biotic interactions at two adjacent coastal stations[END_REF][START_REF] Giner | Comparative 16s rrna analysis of lake bacterioplankton reveals globally distributed phylogenetic clusters including an abundant group of actinobacteria[END_REF]. Interestingly, the significant abiotic parameters explaining microbial community variance in our five ecosystems were those related to seasonal variations (i.e. conductivity, temperature and pH). Dissolved oxygen and pH were positively correlated which could underline the role played by photosynthetic activity in the strong modification of its environment in these ecosystems.

Within the five ecosystems, Etang des Vallées and Ru Sainte Anne presented more clear seasonal patterns, with regular variations of the dissimilarities over the increasing time-lags and high differences between the mean minimum and maximum dissimilarities (Figure 5.2.1).

Microbial community were stable at the intra-and inter-annual scales in Etang des Vallées, probably because of its higher buffering capacity. Ru Sainte Anne was the only lotic system of this study. The first hypothesis was that it would be the most variable ecosystem: it was characterised by the highest number of OTUs and its diversity was constantly rising over time (Supplementary Figure 5.B.2). Moreover, running water can induce a mixing of the banks and the streambed communities [START_REF] Crump | Microbial diversity in arctic freshwaters is structured by inoculation of microbes from soils[END_REF]. Ru Sainte Anne was also more subject to perturbations of the banks by wild animals (as frequently observed on site), and the ecosystem experienced several drought periods. However, we observed marked seasonality patterns and low turnover rates in this ecosystem. Also, the lowest proportion of OTUs present in less than 10 samples during the eight-year survey was recorded in this stream (minimum 95.7% of the OTUs for RSA and maximum of 99.6% for SR for microbial eukaryotes, and 93.2% of the OTUs for RSA and maximum of 97.1% for SR for the prokaryotes).

Patterns of recurrence in OTUs

The identification of recurrent OTUs revealed that even if the global communities featured an intra-annual pattern, only a low number of OTUs were characterized by clear seasonal recurrence patterns. Recurrence mostly happened in summer and winter. This could be explained by the environmental conditions of these seasons, when the minimum and maximal values of temperature were recorded for example. They were detected in all ecosystems at the exception of Saint Robert. La Claye was the ecosystem with the lowest percentage of recurrent OTUs.

Over the five ecosystems, only Saint Robert and La Claye experienced an inter-annual variation of the abiotic parameters. The abundance of recurrent OTUs could therefore be linked to environmental parameter stability. The percentage of recurrent OTUs detected in these freshwater ecosystems was lower compared to a previous study on planktonic micro-algae in marine environments [START_REF] Giner | Comparative 16s rrna analysis of lake bacterioplankton reveals globally distributed phylogenetic clusters including an abundant group of actinobacteria[END_REF]. Only a few phyla harboured OTUs with recurrent patterns (Supplementary figure 5.B.9). Even if the ecosystems harboured all intra-annual patterns, they did not present the same recurrent phyla. In Ru Sainte Anne, we found mostly OTUs characterized by heterotrophic trophic modes, whereas in Mare Gabard, the recurrent OTUs where dominated by autotrophic trophic modes (Supplementary figure 5.B.9).

As in marine systems [START_REF] Giner | Comparative 16s rrna analysis of lake bacterioplankton reveals globally distributed phylogenetic clusters including an abundant group of actinobacteria[END_REF], we found recurrent OTUs in non-recurrent phylogenetic groups. The measure of the recurrent index on phylogenetic classes revealed that oly a low proportion were recurrent (data not showed). For example, Berkelbacteria were recurrent in Etang des Vallées, but no single OTU belonging to this class was recurrent. However, in all ecosystems, phyla belonging to the super-phyla Parcubateria were recurrent, as well as single OTUs. Therefore, several members of the CPR presented a seasonality, with peaks of abundances during winter.

Finally, recurrence analysis revealed OTUs of potential interest for further investigations the functioning in the five freshwater ecosystems. In Etang des Vallées and La Claye, one of the phyla harboring the highest abundance of recurrent OTUs was Cryptophyta. This phylum is known to be characterized by bacterivorous members, implying that it can have major implication in the control of microbial abundance and potentially composition [START_REF] Grujcic | Cryptophyta as major bacterivores in freshwater summer plankton[END_REF]. Moreover, recurrence analysis confirmed the observation made during the 2-year survey that a toxic algae was occurring almost every summer in Mare Gabard. This OTU is a flagellated algae, affiliated to Gonyostomum semen. G. semen is known to be invasive and to release toxic trichocytes in its environment [START_REF] Hagman | The occurrence and spread of gonyostomum semen (ehr.) diesing (raphidophyceae) in norwegian lakes[END_REF]. It was only found in Mare Gabard, which can be explained by the lower pH of its water [START_REF] Cronberg | Mass development of the flagellate gonyostomum semen (raphidophyta) in swedish forest lakes-an effect of acidification?[END_REF]. The OTU X789795 and X827462 were also recurrent, but with lower abundances (respectively 1.6 and 0.05%). They have been detected in summer 2013, 2014, 2016 and 2017. Members of the same class had been detected over the two-year survey, but not as toxic recurrent organisms. These dinophytes have been affiliated to Azadinium, a group releasing toxic compounds in the environment [START_REF] Tillmann | Azadinium spinosum gen. et sp. nov.(dinophyceae) identified as a primary producer of azaspiracid toxins[END_REF]. These toxic algae may control not only other microorganism but also macroorganisms occurrences.

Shared co-occurrence interactions between small freshwater ecosystems

The study of the modules and the block model analysis revealed that OTUs of the same phyla or the same trophic groups were clustered together (highly connected to each other and less with the rest of the network), or presented the same type of connection (i.e. in the same block model groups) (Supplementary Figures 5.B.10 and 5.B.11). The more frequent co-occurrence of OTUs belonging to the same taxonomic groups had already been observed in other networks [START_REF] Hou | Co-occurrence patterns and assembly processes of microeukaryotic communities in an early-spring diatom bloom[END_REF] . These clusters could be linked to turnovers of OTUs with the same trophic mode, but adapted to different conditions over time.

In summary, our study in five small freshwater ecosystems during an 8-year survey confirmed that microbial communities living in geographic close environments were specific to their ecosystem. Microbial eukaryotes and prokaryotes followed the same global community structure over time, with a majority of the diversity composed of rare OTUs. Microbial communities presented a strong intra-annual pattern (seasonal variations) but were also characterized by an ecological drift at the inter-annual scale. Environmental parameters explained only a restricted proportion of community variance over time. At the OTUs scale, strong turnovers were observed for abundant and rare communities, and a low percentage of OTUs harboured recurrent seasonal dynamics. Moreover, the abundance, the taxonomic affiliation and the trophic mode of recurrent microorganisms varied depending on the ecosystem, suggesting that the ecosystems have functioning particularities. Supplementary Table 5.A.1 Summary of the physico-chemical composition of the five ecosystems. Mean, minimal and maximal value of the different measured abiotic parameters over the eight-year survey are presented. In complement, the results of linear regressions performed for each parameters over the eight years are indicated.

Context and objective

The study of microbial communities in five small freshwater ecosystems with different temporal frequency and duration allowed me to discover a large diversity of microorganisms. These inhabitants of these ecosystems presented recurrent seasonal patterns as well as constant directional changes of community composition. To explain these dynamics, I previously focused on the abiotic parameters that I measured in these ecosystems. However, these parameters were not enough to explain most of the variation of microbial community composition. Therefore, I decided to focus on biotic parameters, that is the interactions between microorganisms. The idea was to investigate the structure of communities at each season, in each ecosystem. To do so, I inferred co-occurrence networks based on the most abundant microorganisms, using the PLNmodels approach [START_REF] Chiquet | Variational inference for sparse network reconstruction from count data[END_REF]. The objectives of these exploratory studies were to:

1. Determine the structure of co-occurrence networks in each ecosystem during the four seasons.

2. Establish if the seasonal structuration of communities is similar between seasons despite dissimilar communities.

3. Detect the topological properties that are specific to each seasonal network.

Manuscript

Gwendoline M. David 1 , Purificación López García 1 , Ludwig Jardillier

Abstract

Microbial communities in small freshwater ecosystems present strong seasonal variations in their composition. At the same time, microorganisms are known to be in constant interactions with their environment and other organisms, through beneficial or antagonistic relationships. In the present study, we used an eight-years survey to investigate the seasonal structure of microbial communities in five small freshwater ecosystems. We previously observed that abiotic factors did not fully explain variations in community composition. Therefore, we decided to focus on the interactions among microorganisms based on metabarcoding of 16S and 18S rRNA genes.

We inferred interaction networks for each season in each ecosystem, using the most abundant microbial operational taxonomic units (OTUs, occurring at an abundance > 0.05% in more than 70% of the samples). We also compared the topological properties of each network to find structuring patterns of microbial communities. Summer networks were more complex, whereas their winter counterparts suggested a preponderant role of parasitic microorganisms.

This study provides a first insight into the seasonal structuring of microbial communities and underlines the potential importance of particular groups in small freshwater ecosystems.

Introduction

Microorganisms are highly diverse, widely distributed and play key roles in all biogeochemical cycles [START_REF] Salcher | Same same but different: ecological niche partitioning of planktonic freshwater prokaryotes[END_REF][START_REF] Worden | Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes[END_REF]. Similar to macroorganisms, the composition of microbial communities is controlled by bottom-up and top-down factors as well as environmental parameters [START_REF] Pernthaler | Predation on prokaryotes in the water column and its ecological implications[END_REF][START_REF] Worden | Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes[END_REF]. Microbial communities succeed to each other over seasons leading to permanent reassembling [START_REF] Gilbert | Defining seasonal marine microbial community dynamics[END_REF] even at time scales as short as 2-3 days [START_REF] Mangot | Shortterm dynamics of diversity patterns: evidence of continual reassembly within lacustrine small eukaryotes[END_REF], with communities becoming dominant thanks to optimal conditions of growth [START_REF] Treusch | Seasonality and vertical structure of microbial communities in an ocean gyre[END_REF]. Because of their important functional roles, any change in microbial community composition can have in return profound impacts on the functioning of their ecosystem. Identifying structuring patterns at different time and spatial scales is thus of primary importance. It is now well acknowledged that resources, mortality factors (predators and parasites, including viruses) and environmental parameters control significantly both the biomass and the composition of microbial communities [START_REF] Jones | Species-sorting may explain an apparent minimal effect of immigration on freshwater bacterial community dynamics[END_REF]. However, the variation in microbial composition that remains unexplained is surprisingly large even when taking into account several of these factors [START_REF] Muylaert | Relationship between bacterial community composition and bottom-up versus top-down variables in four eutrophic shallow lakes[END_REF][START_REF] Steele | Marine bacterial, archaeal and protistan association networks reveal ecological linkages[END_REF][START_REF] Lidicker | Determinants of community structure in the global plankton interactome[END_REF]. This may result from the difficulties to identify the multiple and complex interactions between microorganisms that may drive microbial taxa temporal dynamics [START_REF] Steele | Marine bacterial, archaeal and protistan association networks reveal ecological linkages[END_REF]. It is therefore crucial to investigate further biotic interactions between microorganisms to better decipher the community structuring over space and time. These interactions are diverse and include for example competition for resources, predation, parasitism and cooperation. For instance, algae are grazed by zooplankton and release dissolved organic carbon used by prokaryotes [START_REF] Azam | Microbial structuring of marine ecosystems[END_REF], heterotrophic protists are known to be major predators of prokaryotes [START_REF] Sherr | Bacterivory and herbivory: key roles of phagotrophic protists in pelagic food webs[END_REF] and bacteria

of the Candidate Phyla Radiation (CPR) are suspected to have a symbiosis lifestyle because of their reduced genome (Castelle and Banfield, 2018). These specific ecological relationships between microbial taxa remain mostly unknown because it is much more difficult to identify them in vivo for microorganisms than macroorganisms. Natural microbial communities are very complex, composed of cells small in size that make their identification difficult and belong to a tremendous number of taxa that can interact between each other in multiple ways. A culture based approach could allow identifying interactions between microorganisms and revealing the precise mechanisms involved in these interactions. Such an approach is highly limited by the low diversity of cultivated microorganisms (<1%) and results would not necessarily reflect what happens in the natural environment. Recently developed single cell genomic based approaches, based on the sequencing of genes or transcripts, considerably expended our understanding of the processes taking place in the interactions between microorganisms. However, the advances brought by these two approaches are restricted to decipher mechanisms of a limited number of microbial interactions. A first step screening at the community scale for particular interactions of potential evolutionary or ecological interest was missing. Co-occurrence networks, originally developed for macroorganisms to identify interactions based on direct observations, gained in interest in microbial ecology over the last years thanks to the rapid development of massive sequencing technologies. Applied to metabarcoding dataset, it is now possible to infer all type of microbial interactions at multiple scales (e.g. community versus populations, intra versus interannual). In combination with the analysis of the topology of co-occurrence networks, it is now possible to explore structuring of complex microbial communities over time and space. Over the last decade, diverse methods based on co-occurrence analyses allowed better apprehending community structuring in various types of environments, from soil to marine systems [START_REF] Barberán | Using network analysis to explore co-occurrence patterns in soil microbial communities[END_REF][START_REF] Genitsaris | Small-scale variability of protistan planktonic communities relative to environmental pressures and biotic interactions at two adjacent coastal stations[END_REF][START_REF] Röttjers | From hairballs to hypotheses-biological insights from microbial networks[END_REF]. In lakes for instance, the impact of phytoplankton blooms on community composition is now better understood [START_REF] Li | Annual periodicity in planktonic bacterial and archaeal community composition of eutrophic lake taihu[END_REF][START_REF] Escalas | Drivers and ecological consequences of dominance in periurban phytoplankton communities using networks approaches[END_REF]. Usually, particular groups of microbes attracted higher interest, such as Actinobacteria (e.g. [START_REF] Eiler | Coherent dynamics and association networks among lake bacterioplankton taxa[END_REF], to the detriment of a global understanding of microbial community structuring (e.g. [START_REF] Bock | Factors shaping community patterns of protists and bacteria on a european scale. Environmental Microbiology Boenigk J, Arndt H[END_REF]. Also, the study of the structure of microbial communities is essentially conducted in large ecosystems, such as Oceans and lakes. Ecosystems smaller in size are neglected although they may contribute significantly to the cycles of CO 2 and CH 4 for example [START_REF] Holgerson | Large contribution to inland water co 2 and ch 4 emissions from very small ponds[END_REF][START_REF] Yvon-Durocher | Long-term warming amplifies shifts in the carbon cycle of experimental ponds[END_REF]. These ecosystems have the particularity to be more sensitive to environmental changes due to their small volumes. They can also experience algae blooms, which can compromise their water quality and human safety at the same time pressure of human activities on surface freshwaters is increasing. There is therefore an urgent need to unravel microbial community structuring in these ecosystems to, in return, improve our understanding of their functioning.

The microbial community composition in five small freshwater ecosystems located in the "Parc Naturel Regional de la Haute Vallée de Chevreuse", near Paris (France), was described thanks to pluri-annual surveys (David et al. in prep., see Chapter 5). Microbial eukaryote and prokaryote communities were highly diverse, and specific to each ecosystem despite their close localization.

These microbial communities experienced similar temporal patterns, such as marked seasonal dynamics and a global evolution of community composition over years. The variations in community composition over time and among ecosystems were hardly explained by a set of environmental parameters. Biotic interactions were thus suspected to play important roles in the structuring of these microbial communities [START_REF] Steele | Marine bacterial, archaeal and protistan association networks reveal ecological linkages[END_REF][START_REF] Karimi | Microbial diversity and ecological networks as indicators of environmental quality[END_REF].

In this study, we constructed co-occurence networks for each season combining the data of a 8-years survey. We aimed at unravelling particular temporal structuring due to microbial interactions. To do so, we investigated seasonal patterns in the topology of microbial networks.

We hypothesized that the seasonal structuring observed in the community composition was the result of intense biotic interactions. For instance, we expected that communities of the warmer seasons, i.e. spring and summer, featured more active communities compared to winter [START_REF] Wilhelm | Seasonal changes in microbial community structure and activity imply winter production is linked to summer hypoxia in a large lake[END_REF]. This would result in return to more complex networks in summer compared to winter [START_REF] Zhu | Seasonal succession and spatial distribution of bacterial community structure in a eutrophic freshwater lake, lake taihu[END_REF]. To test this hypothesis, we analysed the topology of seasonal co-occurrence networks in four small freshwater ecosystems, constructed using the Poisson Log-Normal model. With this study we aimed at identifying (i) structural patterns within the seasonal co-occurrence networks, (ii) topological characteristics that distinguished the different seasons, and (iii) key interactions among microbial groups.

6.2.3 Material and methods

Study sites and sampling design

In this study, five small freshwater ecosystems located in the "Parc Naturel Regional de la Haute Vallée de Chevreuse" (PNR-HVC), and located less than 10 km apart from each other, were sampled at surface. They were composed of four ponds (Etang des Vallées (EV), La Claye (LC), Mare Gabard (MG) and Saint Robert (SR)) and one brook (Ru Sainte Anne (RSA)).

Sampling took place every 3 months between October 2011 and January 2019. Water was collected using sterile plastic carboys. Once back to the laboratory, the water was immediately serially filtered using 100, 30, 5 and 0.22 µm pore-size Nucleopore membranes (Whatman) and then stored frozen at -80°C until DNA extraction. Four physico-chemical parameters (water temperature, dissolved oxygen concentration, pH and conductivity) were directly measured in situ using a multiparameter probe Multi 350i (WTW, Weilheim, Germany).

DNA extraction and sequencing

DNA extraction was performed using the PowerSoil DNA extraction kit (MoBio, Carlsvad, CA, USA) on cells of 0.22-30 µm in size (see [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF] for more details). To analyze the composition of eukaryote and prokaryote communities, we amplified the V4 hypervariable region of the 18S and the 16S rRNA gene, respectively. For eukaryotes, we used the primer sets EK-565F (5'-GCAGTTAAAAAGCTCGTAGT) and EK-1134-R-UNonMet (5'-TTTAAGTTTCAGCCTTGCG), the latter being biased against metazoa [START_REF] Bower | Preferential pcr amplification of parasitic protistan small subunit rdna from metazoan tissues[END_REF].

For the prokaryotes, we used the primers U515F (5'-GTGCCAGCMGCCGCGGTAA) and U806R (5'-GGACTACVSGGGTATCTAAT). In order to affiliate the reads to their sample in the sequencing runs, primers were tagged using 10 bp Molecular Identifiers (MIDs). PCR reactions were performed in a mix containing 1.5 mM MgCl 2 , 0.2 mM dNTPs, 0.3 µM primers, 0.3-3 µl of DNA sample and 0.5 U HotStart Taq polymerase (Taq Platinum, Invitrogen, Carlsbad, CA, USA), for a total volume of 25 µl. In the thermocycler, samples were exposed to a 2 min denaturation step at 94°C, then to 35 cycles of amplification (at 94°C for 15s for both eukaryotes and prokaryotes, then at 55°C and 58°C for 30s for eukaryotes and prokaryotes, respectively, and at 72°C for 90s for both eukaryotes and prokaryotes), and finally to a 10 min extension step at 72°C. Five PCR products were pooled for each sample, and then purified using the QIAquick PCR purification kit (Qiagen, Hilden, Germany). Purified pools were then multiplexed, by adding equivalent amounts of DNA. Amplicons were sequenced by paired-end MiSeq Illumina technology (2x300 bp, Eurofins Genomics, Ebersberg, Germany).

Network construction

All statistical analyses were performed under the R-software (R Core Team, 2019). Cooccurrence analyses were conducted using the package 'PLNmodels' in R-software [START_REF] Chiquet | Variational inference for sparse network reconstruction from count data[END_REF]. This method uses a multivariate Poisson LogNormal model to infer associations between OTUs while taking into account effects of environmental covariables that might explain the covariations in OTUs counts between samples. As SPiEC-Easi, PLNmodels infer associations as direct statistical covariations by focusing on the precision matrix, which is the inverse of the covariance matrix between OTU abundances [START_REF] Kurtz | Sparse and compositionally robust inference of microbial ecological networks[END_REF]. It avoids the detection of associations resulting from indirect covariations (e.g. covariations between two OTUs due to their direct association with a third OTU). This method uses raw data reads and accounts for differences in sampling depth between samples through an offset in the model. It thus avoids building co-occurrence networks from compositional data, which may lead to spurious associations [START_REF] Faust | Microbial interactions: from networks to models[END_REF][START_REF] Gloor | Microbiome datasets are compositional: and this is not optional[END_REF]. Wrong associations can also result from OTUs covarying concomitantly with the environmental parameters [START_REF] Deutschmann | Disentangling environmental effects in microbial association networks. BMC Microbiome Diamond JM[END_REF]. Therefore, three abiotic variables were included in the model. More precisely, we added the effect of conductivity, dissolved oxygen, and pH because of their influence on community structure in the five ecosystems (David et al,in prep,see Chapter 5). Model selection was performed using a 50-size grid of penalties, following the same methodology as Chiquet et al.

(2019).

To investigate seasonal community structuring in each ecosystem, samples collected at the same season were gathered. For each ecosystem, the dataset was split into four groups (one per season). Each group was composed of 8 time points in autumn and winter and 7 time points in spring and summer (sampling period from October 2011 to January 2019), with three exceptions (7 time points in autumn in La Claye, 6 times points in spring in Mare Gabard and 5 times points in summer in Ru Sainte; due to droughts or missing data). Because of the sparsity of the dataset (large proportion of zeros), for each season, only OTUs present in at least 6 times over the 8 years of survey and having a mean abundance higher than 0.05% were taken into account.

Network visualization

Networks were plotted using the 'ggnet' R-software package [START_REF] Tyner | Network visualization with ggplot2 UNDP-GEF (2015) The ecological atlas of the baikal basin[END_REF]. Nodes in the networks represent OTUs, while edges represent relationships between OTUs. The networks were undirected. Each OTU was categorized by its taxonomic affiliation (Eukaryota, Bacteria, Archaea) and by its lifestyle, i.e. its functional group (Autotroph, Heterotroph, Putative parasite).

Network topology analysis

For each network, several properties were calculated using the 'igraph' R-software package [START_REF] Csardi | The igraph software package for complex network research[END_REF]. Networks were characterized by their numbers of positive and negative edges as well as nodes and connected nodes. The network mean degree corresponds to the average number of edges established by each node. The average path length is an indicator of the mean number of edges that are necessary to link a given node to another one. Network complexity was estimated using two indicators: connectance and clustering coefficient. The connectance was calculated as 𝑐 = 2E N×(N-1) , where E is the number of edges and N the number of nodes [START_REF] Barrat | Dynamical processes on complex networks[END_REF]. The connectance quantifies the proportion of links established in the network and compared to the number of possible potential links. The connectance is equal to one when all possible links are realised. The clustering coefficient is the probability that two nodes having similar neighbor are connected to each other [START_REF] Delmas | Analysing ecological networks of species interactions[END_REF]. The clustering coefficient varies between 0 and 1, low values indicating globally poorly connected neighborhood.

A block model analysis was performed to analyze further the topology of the networks. To do so, a stochastic block model approach was used, implemented in the 'blockmodel' R-software package [START_REF] Leger | Blockmodels: A r-package for estimating in latent block model and stochastic block model, with various probability functions, with or without covariates[END_REF], on the binary network reporting the presence-absence of significant direct associations. This approach allows to synthetize the overall network structure by gathering the nodes into groups with similar modes of interactions (e.g. group of strongly connected OTUs, group of OTUs associated with the same set of OTUs, groups of OTUs with few significant associations).

Principal Component Analysis were conducted on centered and scaled network properties using the 'FactomineR' R-software package [START_REF] Husson | Package 'factominer[END_REF] to identify any clustering within the sampling seasons or ecosystems.

Linear regressions were performed to detect relationships between network properties and the OTU diversity, without taking into account ecosystems or seasons. The diversity was estimated using either the richness, the evenness or the Simpson index. These three indicators were computed with the 'vegan' R-software package on the basis of the dataset selected for the network construction [START_REF] Oksanen | The vegan package[END_REF].

Two generalized linear models were used to assess the effects of several variables on network properties. The first model, called the "degree" model, quantifies the effect of both the node's lifestyle (i.e. the trophic mode of the OTU) and the season on the normalized degree of the nodes in the networks. The effect of the ecosystem and of its statistical interaction with the season were also considered in the model. The second model, called the "parasite" model, was designed to quantify the effect of the season on the percentage of edges established with putative parasitic OTUs in networks. It also takes into account the initial proportion of putative parasitic OTUs in the dataset selected for the network construction. Both models were ran using the 'glm' function of the R-software. The variables were fitted with quasi-binomial distributions to account for overdispersion. An ANOVA analysis was used to estimate the significance of the different variables in the models. The estimated marginal means of each qualitative level was estimated using the 'emmeans' R-software package to conduct multiple pairwise comparisons between seasons and between lifestyles [START_REF] Lenth | Emmeans: Estimated marginal means, aka least-squares means[END_REF].

Results

General network properties

We inferred networks and calculated properties for each ecosystem at each season (Figure 6.2.1, Table 6.2.1). For Saint Robert, the PLN model did not converge in summer and autumn. This ecosystem was thus excluded from this study.

In Etang des Vallées, the number of selected OTUs for network construction was the highest, varying between 378 and 1118. The final percentage of connected nodes was low, ranging between 7.8 and 24.3%. The percentage of positive links varied strongly between seasons, from less than 1% to 39.1%. Connectance revealed that only about 5% of the possible connections between nodes were inferred, at the exception of spring (17.4%). The mean degree varied between 3.8 and 7.1. La Claye was characterized by the lowest number of OTUs (46 to 97) implemented in the networks. OTUs were highly interacting in the networks of this ecosystem, as revealed by the marked proportions of connected nodes (44.3-82.6%) and the highest connectances (11.7% of the possible connections were realized on average). In total, 48.9% of the connections were positive. Overall, the mean degree in La Claye varied between 3.6 and 5.6. Mare Gabard networks were constructed using between 118 and 534 OTUs. About 21% of these OTUs were connected, except at summer when 74.6% of the OTUs were connected. The proportion of potential links established in the network was low, of about 4.6% except in spring (12.4%). Globally, a large proportion (45.7%) of the links were positive. The mean degree varied between 3.4 and 4.2.

In Ru Sainte Anne, networks were constructed using between 282 and 636 OTUs. The percentage of connected nodes was low (mean value of 15.6% in winter, spring and autumn), except in summer were it reached 68.8%. The percentage of positive links was low, of 23.6% in total over the four seasons. The connectance was low (about 4.0%), except in winter where it reached 17.1%. The mean degree of the nodes varied between 3.6 and 4.9.

Overall, seasonal specificities were observed in each ecosystem. In Etang des Vallées and Ru Sainte Anne, the highest percentage of positive links and connected nodes were found in summer. The lowest proportion of positive nodes was detected in winter in all ecosystems. In Table 6.2.1 Summary of the seasonal co-occurrence networks properties in the four ecosystems. The networks were built on the OTUs present in at least 6 seasons over the 8 years and presenting a relative abundance higher than 0.05%. La Claye and Mare Gabard, the lowest percentage of connected nodes also occured in winter.

The clustering coefficient was higher in spring for all ecosystems, except for Ru Sainte Anne where it was higher in winter. It reached up to 0.45 in Etang des Vallées. Along with the high connectance, it suggests a higher node interconnection in spring. These seasonal features were underlined in the PCA analysis based on the network properties (Figure 6.

Complexity level in the networks

The nodes with similar patterns of connections in the networks (i.e. highly connected to the rest of the network) were gathered into Block Model Groups (BMG). Overall, this analysis revealed that the networks were always structured by a restricted number (2-5) of BMG. The more BMG are present in networks, the more complex the network is. They often comprised one or two small groups of OTUs that were characterized by very high numbers of connections with other OTUs in the network and a few larger groups of weakly connected OTUs.

In Etang des Vallées, all seasons were characterized by 4 BMG, except in summer (5 BMG) (Supplementary figure 6.B.1 and 6.B.2). In winter, 10 OTUs were highly connected together and to the other OTUs in the network. They were dominated by Ciliophora (50% of the nodes in the BMG). The majority (61.5%) of the OTUs affiliated to autotrophic taxa were linked to each other, in a specific BMG. In summer, a BMG was composed of 6 OTUs that were highly connected to each other but had low connections with the other OTUs of the network.

One BMG gathered 8 OTUs that were highly and positively connected to the other OTUs of the network. They comprised five autotrophs which belonged to Chlorophyta, Cryptophyta and Ochrophyta. The three others were affiliated to Betaproteobacteria, Verrucomicrobia and Fungi. In autumn, one BMG was represented by only 2 OTUs, one autotrotroph (Ochrophyta) and one putative parasite (Apicomplexa), which were highly connected to other BMGs of the model.

In La Claye, all networks featured 3 BMG (Supplementary figure 6.B.1 and 6.B.3). In winter, the network was dominated by one BMG constituted of 3 OTUs, including two autotrophic Cryptophyta and one Gammaproteobacteria. They were highly connected to the two other BMGs. In summer, the BMG containing the most connected nodes was composed of 10 OTUs, 6 of them being affiliated to autotrophic taxa. The four others being heterotrophic taxa. As for the network obtained in winter, the autumn network was composed of one BMG composed of only 2 OTUs including one autotroph (Cryptophyta) and one heterotroph (Bacteroidetes).

They were highly connected to the two other BMGs.

In Mare Gabard, the season that showed the most complex structure was summer, with 5 BMG (Supplementary figure 6.B.1 and 6.B.4). In winter, one of the BMGs was composed of only 3 OTUs, belonging to the three lifestyle classes (autotroph, heterotroph, putative parasite). It was connected to the other BMGs, mostly through negative edges. However, the winter network was also characterized by a BMG composed of 8 OTUs, including five autotrophs (Ochrophyta) and three heterotrophs (two Actinobacteria and one Ciliophora), most of them being only linked with positive edges to the other BMGs. The spring network was only composed of two BMGs, including one constituted of two heterotrophic bacteria (Verrucomicrobia and Epsilonproteobacteria). In summer, one BMG was composed of two highly connected autotrophs, one dinophyta and one ochrophyta (Gonyostomum), already described in the ecosystem as potential toxic algae [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF]David et al, in prep see Chapter 5). This latter OTU was characterized by the highest degree of positive edges.

In Ru Sainte Anne, spring and autumn networks were the most complex, featuring 5 BMG (Supplementary figure 6.B.1 and 6.B.5). In winter, only 3 BMG were found, including one with only two OTUs. In spring, one of the BMGs was only represented by OTUs affiliated to Parcubacteria, presenting 65 edges, mostly negative. Two BMGs were composed of a majority of nodes affiliated to bacteria of the CPR, negatively and highly interconnected. In summer, the network was also constituted of one BMG comprising only 2 OTUs, affiliated to Ciliophora and Parcubacteria. These OTUs were highly interconnected to the rest of the BMG. A large number of OTUs (157) were poorly connected (with a degree of 1 or 2). Similar to spring, the network in autumn was characterised by the presence of a BMG that was constituted of only one node. In this case, it was an Ochrophyta (Chrysophyceae). Also, in autumn, BMG5 was poorly connected to the rest of the network, and was composed of half autotrophic nodes and half heterotrophic nodes.

Relationships between ecosystem diversity and network topology

Among diverse network properties measured, only the percentage of connected nodes, the percentage of positive edges and the percentage of edges established with putative parasites were significantly linked to one or several diversity indicators (Supplementary figure 6.B.6, Supplementary table 6.A.1). The percentage of positive edges was negatively linked to the richness calculated for each ecosystem. The percentage of connected nodes significantly decreased with diversity as found for the richness, the evenness and the Simpson index. The percentage of edges including putative parasites was positively linked to evenness.

Global taxonomic composition of the nodes in the networks

The initial number of OTU used to infer the co-occurrence networks varied among ecosystems (Table 6.2.1, Supplementary figure 6.B.7 and 6.B.8). Indeed, OTUs were arbitrarily selected based on marked occurrence and abundance to construct the networks (detected with a mean abundance >0.05% in 70% of the samples per ecosystem and season). For both eukaryotes and prokaryotes, the lowest numbers of OTUs were detected in La Claye in autumn (3 and 43 OTUs, respectively) and the highest is in Etang des Vallées in winter (278 and 840 OTUs, respectively) (Supplementary table 6.A.2).

The composition of the connected nodes varied among the ecosystems, but also within seasons (Supplementary table 7 and8). Bacteria dominated among the connected nodes in all networks. They represented a minimum of 49.2% in autumn and a maximum of 97.6% in spring in Etang des Vallées (Supplementary table 6.A.2).

In Etang des Vallées, microbial eukaryotes were highly interacting in autumn. They represented 46.2% of the total number of connected nodes. Globally, the number of nodes affiliated to putative parasites in the networks was low, except in winter when they represented up to 43.8% of the connected nodes. Nodes affiliated to autotrophic taxa occurred in high proportions, with a mean value of 19.9% of the connected nodes over the four seasons. In La Claye, archaea were never found in the networks, except in autumn when two DPANN nodes were retrieved. Nodes affiliated to autotrophic taxa were mostly present in summer and represented globally 29.7% of the connected nodes. Prokaryotic OTUs affiliated to parasitic taxa (only prokaryotes) reached their highest proportions in autumn (34.2% of the connected nodes). Autumn was also characterized by a low proportion of nodes affiliated to microbial eukaryotes (7.9% of the connected nodes). In Mare Gabard, microbial eukaryote nodes represented a mean value of 16.7% of the connected nodes, except in spring when they reached 35.7%. Nodes affiliated to autotrophic taxa were mostly abundant in summer (10.8% of the connected nodes). The proportions of nodes affiliated to putative parasites were relatively stable over the seasons (average percentage of 15.3% of the connected nodes). In Ru Sainte Anne, nodes of putative parasites dominated the networks, ranging from 35.7% in autumn to 55.6% in winter. Contrary to the other ecosystems, nodes affiliated to autotrophic taxa reached their highest abundances in autumn (14.3% of the connected nodes).

Globally, nodes affiliated to putative parasites were more abundant in the networks in winter in all ecosystems, except in La Claye. In addition, La Claye and Mare Gabard featured the highest proportions of nodes affiliated to autotrophic taxa.

Among the different ecosystems and seasons, the composition of unconnected nodes varied greatly (Supplementary figure 6.B.7 and 6.B.8), although they were often affiliated to Proteobacteria and CPR members. Proteobacteria constituted 16.8% to 36.7% of the unconnected nodes, respectively in winter in Etang des Vallées and in summer in Mare Gabard., while CPR represented 5.2 to 39.8% of these nodes, respectively in summer in Etang des Vallées and spring in Ru Sainte Anne.

Focus on the central nodes in the networks

Among the five most connected nodes inferred at each season, those affiliated to putative parasites were only observed in winter, at the exception of Ru Sainte Anne where parasitic OTUs were observed in each season (Supplementary table 6.A.3). The mean degree of these five most connected nodes was higher in summer (30.2 in Etang des Vallées, 16 in La Claye, and 59.6 in Ru Sainte Anne), except in Mare Gabard where the winter nodes were the most connected (mean degree of 39.6 in winter and 29 in summer). As observed in the BM analyses, the most connected nodes in summer were dominated by OTUs affiliated to autrotrophs. In particular, this was the case of an OTU affiliated to Gonyostomum semen in Mare Gabard, that was central in the network. This OTU was characterised by the highest degree (52 interactions, Supplementary table 6.A.3). Among these highly connected nodes, 28 were positive and connected G. semen to 12 different phyla, while 24 were negative and connecting 8 different phyla. Parcubacteria were well represented in the most connected nodes despite global low degree, especially in Ru Sainte Anne (Supplementary table 6.A.3).

Variations of the node degree among seasons and lifestyles

All the parameters (i.e. lifestyles, seasons, ecosystems and statistical interactions between seasons and ecosystems) had a significant effect on the node degree (Supplementary Table 6.A.4).

There was no significant effect of statistical interactions between lifestyles and ecosystems or functional groups and season, suggesting that the differences in the proportions of lifestyles do not depend on the ecosystem or the season.. The degree of the nodes affiliated to autotrophic taxa was, overall, significantly higher than that of nodes of heterotrophs and parasites (Supplementary Table 6.A.4, Tukey test, estimate = 0.491, z.ratio = 3.418, p = 0.0018 and estimate = 0.428, z.ratio = 2.512, p = 0.0321 respectively). Moreover, nodes in summer and spring were found to be globally significantly more connected than nodes in winter (Supplementary Table 6.A.4, Tukey test, estimate = 0.742, z.ratio = 4.130, p = 0.0002 and estimate = 0.564, z.ratio = 2.708, p = 0.0342 respectively). However, the statistical interaction between ecosystems and seasons had a significant effect. Taken individually, a clear distinction between summer/spring and winter was only observed in Etang des Vallées and Mare Gabard (Supplementary Table 6.A.5), while node degrees did not significantly depend on the season in La Claye and Ru Sainte Anne.

Inferred biotic interactions depend on the functional group of the node

In order to better describe the network topology, we summarized the nature of the edges between the three lifestyles (autotrophs, heterotrophs or putative parasites) (Supplementary Table 6.A.6). We observed that the proportion of edges established with nodes affiliated to putative parasites depended on seasons ("parasite" model, glm, F = 4.07, p = 0.036). It was significantly higher in winter than in summer or spring for all ecosystems (Tukey test, estimate = 1.556, z.ratio = 2.848, p = 0.023 and estimate = 1.518 , z.ratio = 2.865 and p = 0.022 respectively), whatever was the proportion of parasites initially present in the network (Supplementary table 6.A.7). The number of OTUs affiliated to putative parasites in the network significantly increased the proportion of edges established with parasites ("parasite" model, glm, F = 35.3, p < 0.001). Autumn and winter, as well as spring and summer, did not differ significantly in their proportions of edges established with nodes affiliated to parasites (Supplementary Table 6.A.7). A majority of positive edges happened between nodes affiliated to autotrophic and heterotrophic taxa (Supplementary table 6.A.6). The only exceptions were found in autumn in Etang des Vallées, La Claye and Mare Gabard, as well as in Ru Sainte Anne in winter and summer.

Discussion

Important efforts have been invested in the understanding of microbial community structuring over time and space since the beginning of microbial ecology. Because of their small size and supposed low importance at the global scale, small freshwater ecosystems have been overlooked

for the profit of their large counterparts. Their importance in greenhouse gases fluxes such as CO 2 and CH 4 has been revealed over the last years, increasing the interest in their functioning [START_REF] Holgerson | Large contribution to inland water co 2 and ch 4 emissions from very small ponds[END_REF][START_REF] Yvon-Durocher | Long-term warming amplifies shifts in the carbon cycle of experimental ponds[END_REF]. Previous investigations showed microbial communities differing among the five small freshwater ecosystems investigated that all were characterised by seasonal patterns [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF]David et al., submitted, David et al., in prep., see Chapters 4 and 5). The differences in communities between ecosystems and their temporal structuring were little explained by environmental parameters, suggesting that biotic interactions could strongly shape microbial communities over space and time. This spatiotemporal structuration could result in contrasting co-occurrence network topologies, which was the case between seasons and ecoystems (Figure 6.2.1). However, we also detected features common to the different seasons in these ecosystems, as already observed in lakes [START_REF] Kara | A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic lake mendota, wi, usa[END_REF][START_REF] Zhao | Network analysis reveals seasonal variation of co-occurrence correlations between cyanobacteria and other bacterioplankton[END_REF]. This is in line with the hypothesis that ecosystems have the same global functioning processes, but conducted by different organisms [START_REF] Louca | Function and functional redundancy in microbial systems[END_REF].

A few common seasonal network properties across ecosystems

The global structure of the networks varied between and within ecosystem, without obvious clear seasonal pattern. Networks topology revealed common features between seasons, with a distinction between warm (summer, spring) and cold (winter) seasons.

Summer networks were characterized by a higher percentage of positive edges and of connected nodes (Figure 6.2.2). Globally, the degree of nodes was significantly higher in spring and summer compared to autumn and winter (Supplementary Table 6.A.4). Therefore, warmer season networks tended to be larger in size, and to present more positive interactions. More complex networks in summer were also observed in bacterial networks in eutrophic urban lakes [START_REF] Zhao | Network analysis reveals seasonal variation of co-occurrence correlations between cyanobacteria and other bacterioplankton[END_REF]. Similarly, in Lake Mendota, a large freshwater ecosystem, spring networks presented more co-occurrences than at other seasons [START_REF] Kara | A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic lake mendota, wi, usa[END_REF]. Moreover, we obtained higher connectance values in spring and summer in all ecosystems at the exception of Ru Sainte Anne where it was observed in winter. Connectance is often linked to network stability, but it has contrasted effects on stability depending on the types of interactions involved (e.g. mutualistic or trophic) or on the measures of stability considered [START_REF] Thébault | Stability of ecological communities and the architecture of mutualistic and trophic networks[END_REF][START_REF] Landi | Complexity and stability of ecological networks: a review of the theory[END_REF]. While the relationship between the connectance of co-occurrence networks and community stability remains to be solved, our results suggest that network stability might differ between seasons. In other aquatic ecosystems studies, the connectance is often low (around 0.02 for lakes and marine systems, [START_REF] Peura | Resistant microbial cooccurrence patterns inferred by network topology[END_REF][START_REF] Hernández-Ruiz | Seasonal succession of small planktonic eukaryotes inhabiting surface waters of a coastal upwelling system[END_REF]. These connectance values were lower than those obtained in the seasonal network of our small ecosystems (Table 6.2.1). It can indicate either different stability of the small ecosystems at the seasonal level, or more likely a strong variation of connectance when comparing a specific season or the entire time-serie. Indeed, the connectance of a global network gathering all ecosystems and sampling point was lower than those obtained by ecosystems and seasons (David et al., in prep, see Chapter 5). However, these differences could also be the result of the network construction method used. In our case, we based our networks on direct associations through the use of the inverse covariance matrix instead of correlations [START_REF] Kurtz | Sparse and compositionally robust inference of microbial ecological networks[END_REF]. We also added the effect of environmental parameters, which remove spurious correlations, likely decreasing the connectance value.

On the opposite, winter periods exhibited a significantly higher percentage of edges involving putative parasitic nodes (Supplementary Table 6.A.7). In colder season, the parasite hosts can be under stress, either due to the lower nutrient concentration or the lower light level. Therefore, parasites can be more efficient in infecting their host, which could result in a higher proportion of interactions with parasites [START_REF] Cleary | Unexpected prevalence of parasite 18s rdna sequences in winter among antarctic marine protists[END_REF].

The pattern of higher node degree in warm compared to cold seasons was observed only for the Etang des Vallées and Mare Gabard (Supplementary Table 6.A.5). This is coherent with the complexity of the BMG, which were more numerous in Etang des Vallées and Mare Gabard in Summer (Supplementary Figure 6.B.2). Therefore, the pool of OTUs selected in La Claye and Ru Sainte Anne did not reflect clear seasonal patterns in terms of mean degree and network complexity. For instance, the level of complexity revealed by the number of BMG in La Claye was stable over time (Supplementary Figure 6.B.2). Even if we observed previously common patterns between ecosystems in the global community composition, this is not fully reflected by the network structures (David et al., in prep, see Chapter 5). 6.2.5.2 Autotrophic OTUs are central in the co-occurrence networks Autotrophic primary producers (i.e. phytoplankton) are at the basis of the trophic webs, producing organic carbon through photosynthesis that is intensively used by heterotrophic prokaryotes and being grazed by microbial or metazoan predators. The released organic matter generates micro-areas with higher concentration of organic matter compared to the rest of the ecosystem [START_REF] Worden | Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes[END_REF]. Therefore, this region surrounding phytoplankton, called the phycosphere, attracts heterotrophic organisms such as free-living prokaryotes [START_REF] Bell | Chemotactic and growth responses of marine bacteria to algal extracellular products[END_REF][START_REF] Seymour | Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships[END_REF]. In our networks, we observed that primary producers were more connected that heterotrophs or putative parasites, independently from the ecosystem or the season (Supplementary Table 6. A.3 and 6.A.4). Moreover, we detected that over all the ecosystem and season, the higher amount of positive interactions happened between autotrophic and heterotrophic nodes (Supplementary Table 6.A.6). The higher number of positive edges underlines the importance of the interactions between phytoplankton and various heterotrophic microorganisms [START_REF] Paver | Interactions between specific phytoplankton and bacteria affect lake bacterial community succession[END_REF][START_REF] Linz | Time-series metatranscriptomes reveal conserved patterns between phototrophic and heterotrophic microbes in diverse freshwater systems[END_REF]. The diversity of the heterotrophs attracted by the phycosphere is high, probably because of the variety of organic compounds released by the phytoplankton.

Harmful algae were also detected in the ecosystems [START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF]David et al. in prep, see Chapter 5). In Mare Gabard, the Raphidophyceae Gonyostomum semen (Stramenopiles) was detected at high abundance in summer. Temperature is known to be one of the drivers of the development of G. semen, which can explain their presence in summer [START_REF] Rengefors | Temperature as a driver for the expansion of the microalga gonyostomum semen in swedish lakes[END_REF]. No phyla interacted specifically with G. semen, but both types of interactions (positive and negative) were mainly conducted with heterotrophic bacteria, underlying the potential importance of the phycosphere around the algae.

Importance of predator-prey interactions

Ciliates are major grazers in aquatic ecosystems, feeding on phytoplankton and bacteria [START_REF] Seong | Bacterivory by co-occurring red-tide algae, heterotrophic nanoflagellates, and ciliates[END_REF][START_REF] Pernthaler | Predation on prokaryotes in the water column and its ecological implications[END_REF][START_REF] Simek | Microbial food webs in hypertrophic fishponds: Omnivorous ciliate taxa are major protistan bacterivores[END_REF]. In our networks, members of the ciliophora phylum were not present in high abundances in terms of nodes in the networks (Supplementary Figure 6.B.7). They represented at maximum 12.3% of the nodes (autumn in Etang des Vallées). However, when they were present, they had a high degree (mean degree of ciliates over all ecosystems and seasons of 7.7). They were also well represented in the most connected nodes, and found in the BMG presenting the highest connectivity with the rest of the network (Supplementary Table 6. A.3,Supplementary Figures 6.B.2 to 6.B.5). Despite a weak representativeness, this reveals the importance of the ciliates in the construction of the networks.

Predation by ciliates impacts bacterial community composition, either by directly reducing specific taxa, or indirectly through the excretion of nutrients useful for other microbes [START_REF] Sherr | Significance of predation by protists in aquatic microbial food webs[END_REF][START_REF] Jardillier | Effects of viruses and predators on prokaryotic community composition[END_REF].

Parcubacteria as the dominant putative parasites

The majority of the parasitic nodes involved in the networks were affiliated to bacteria of the Candidate Phyla Radiation, and more precisely to the Parcubacteria. Most of the Parcubacteria were connected to a low number of phyla, as underlined by their presence in the poorly connected BMG. However, among the most connected Parcubacteria, the majority of the links were established with those poorly connected Parcubacteria. Intra-connections are often observed in co-occurrence networks, and can indicate a competition for the same resources [START_REF] Zelezniak | Metabolic dependencies drive species co-occurrence in diverse microbial communities[END_REF][START_REF] Bock | Factors shaping community patterns of protists and bacteria on a european scale. Environmental Microbiology Boenigk J, Arndt H[END_REF]. For the case of Parcubacteria, it could be a competition for the same hosts [START_REF] Nelson | The reduced genomes of parcubacteria (od1) contain signatures of a symbiotic lifestyle[END_REF].

Conclusion

In this study, we used four small freshwater ecosystems to decipher the seasonal structure of microbial communities. Despite the apparent dissimilarities between the seasonal networks between the ecosystems, several network properties showed common seasonal patterns. Summer and spring networks were often characterized by larger networks, with more nodes, higher degree and a higher proportion of positive interactions. On the opposite, winter networks had the particularity to favor interactions with parasitic nodes. Finally, some expected freshwater interactions were observed in the networks. The position of primary producers and ciliates in the networks validates the ecological coherence of our method. Further highlights on key interactions in the seasons will help to improve our understanding of the functioning of small freshwater ecosystems over time. Supplementary Table 6 The main aim of this thesis was to understand microbial community structure in the more poorly studied freshwater ecosystems. Because of their important roles in biogeochemical cycles and trophic webs, any change in microbial communities may affect the functioning of freshwater ecosystems. To reach this objective, I considered (i) two types of freshwater ecosystems (the largest lake in the world and five representatives of small ecosystems), (ii) two spatial surveys (horizontal and vertical), and (iii) two temporal surveys (at various frequencies and duration). Moreover, I focused on different techniques, with (iv) two size-fractionation (0.22 to 5 or 0.22 to 30 µm), (v) two massive sequencing technologies (Pyrosequencing 454 and Illumina MiSeq), and (vi) two types of analyses (multivariate analyses and network inferences).

The first study focused on a spatial survey of the community of microbial eukaryotes in Lake Baikal, along both latitudinal and depth gradients (Chapter 3). This exploration allowed me to investigate the spatial variability of microbial community composition punctually in a large aquatic environment. Then, I explored another component of spatial variability by studying five contrasted small ecosystems. First, I explored the small microbial communities (with sizes in the range 0.2 -5 µm) using a 2-years monthly survey, with a particular focus on the small prokaryotic communities (Chapter 4). Then I explored the temporal variability of microbial communities composition at both fine and large scales with respect to different organisms size (Chapter 5). Finally to better understand the functioning of these communities I characterized the interactions between microorganisms in Lake Baikal and in the small freshwater ecosystems by inferring co-occurrence networks (Chapters 3 and 6).

Microbial community composition in freshwater ecosystems

The description of the organisms inhabiting an ecosystem is one of the prerequisite steps to take before addressing its functioning. Microbial communities in Lake Baikal are sporadically depicted in the literature, where only particular areas of the lake were investigated and rarely along the entire water column. For instance, [START_REF] Kurilkina | Bacterial community composition in the water column of the deepest freshwater lake baikal as determined by next-generation sequencing[END_REF] explored the prokaryotic communities in water columns of the central basin whereas Annenkova et al. (2020a) studied the microbial eukaryotic communities in the entire latitudinal gradient of the lake, but the sampling was not deeper than 50 m. Therefore, integrative studies of the microbial communities in both vertical and horizontal dimensions are missing. In small freshwater ecosystems, despite their clear ecological interest and practical advantages, microbial communities remain poorly described today (see Chapter 1). Therefore, for both our ecosystem models, I first started by describing the composition of their microbial communities.

Global composition of microbial communities

The eukaryotic microbial communities of the five small freshwater ecosystems and of Lake Baikal present similarities in terms of phyla composition. The most important phyla are Ciliophora, Ochrophyta, Fungi and Cryptophyta for both types of ecosystems, at the exception of Dinophyta which were found to be abundant in Lake Baikal only. Such a community structure is consistent with what was found in other freshwater ecosystems, such as lakes [START_REF] Debroas | Overview of freshwater microbial eukaryotes diversity: a first analysis of publicly available metabarcoding data[END_REF]. Likewise, [START_REF] Humbert | Comparison of the structure and composition of bacterial communities from temperate and tropical freshwater ecosystems[END_REF] compared the bacterial composition of temperate and tropical lakes, and found that they presented similar communities in terms of relative abundance of bacterial phyla. Therefore, it appears that freshwater aquatic ecosystems feature a similar global microbial community composition. However, there are large differences in the community composition at the OTU scale. Comparative analysis of the small freshwater ecosystems revealed that a large proportion of the OTUs where not shared between ecosystems, despite their geographical proximity (Simon et al., 2015, Chapter 4 and 5). This observation is consistent with other studies on freshwater ecosystems, such as the comparison of three Austrian lakes or of eight bog lakes in Wisconsin, USA [START_REF] Linz | Bacterial community composition and dynamics spanning five years in freshwater bog lakes[END_REF][START_REF] Bock | Synchrony of eukaryotic and prokaryotic planktonic communities in three seasonally sampled austrian lakes[END_REF]. Moreover, compared to Lake Baikal and to other studies on microbial communities using Illumina MiSeq sequencing, we detected a higher richness in the small freshwater ecosystems [START_REF] Bock | Synchrony of eukaryotic and prokaryotic planktonic communities in three seasonally sampled austrian lakes[END_REF][START_REF] Obertegger | Multifaceted aspects of synchrony between freshwater prokaryotes and protists[END_REF]. Therefore, small freshwater ecosystems seem to harbour more diverse communities than larger ones. However, this observation can be the result of different biases induced during library construction (see the paragraph below). Moreover, we conducted the survey over eight-years, which allow us to capture the largest part of the rare biosphere [START_REF] Massana | Distribution and abundance of uncultured heterotrophic flagellates in the world oceans[END_REF]. Another hypothesis is that the pelagic area of small ecosystems is more subject to disturbances and to variations of environmental conditions, which generates higher turnovers of communities at the intra-and inter-annual scales.

In both Lake Baikal and small ecosystems surveys, the representation of the communities that we obtained has to be handled carefully. Indeed, the metabarcoding approach based on 16S and 18S rDNA can generate a number of biases. Among them, we can underline the fact that the data are compositional [START_REF] Gloor | Microbiome datasets are compositional: and this is not optional[END_REF]. This means that we do not have a precise quantitative image of the communities. Some microorganisms such as ciliates present a higher abundance of gene copies for example [START_REF] Prescott | The dna of ciliated protozoa[END_REF][START_REF] Ward | The intranuclear organization of normal, hemizygous and excision-deficient rrna genes during developmental amplification in tetrahymena thermophila[END_REF]. This can result in an amplification of their detection rate and can make them appear more important in the community than they really are. Also, as we are targeting rDNA, we do not differentiate the metabolic states of the microorganisms. For instance, we can easily hypothesise that the photosynthetic organisms detected at the bottom of Lake Baikal, i.e. in the euphotic zone, are inactive cells. The proportion of inactive cells can vary between ecosystems [START_REF] Lennon | Microbial seed banks: the ecological and evolutionary implications of dormancy[END_REF]. Therefore, it is important to take into account the presence of both active and inactive cells in consideration in the interpretations. Finally, the comparison of communities described in previous studies has to be handled carefully. Indeed, various methodological aspects can generate differences between the metabarcoding libraries. Among others, the primer sets used can generate biases in the amplification, which therefore affects the richness estimation [START_REF] Debroas | Overview of freshwater microbial eukaryotes diversity: a first analysis of publicly available metabarcoding data[END_REF].

Frontier between freshwater and marine systems

Transitions of microorganisms between marine and freshwater systems have long been considered to be rare [START_REF] Logares | Infrequent marine-freshwater transitions in the microbial world[END_REF]. The main difference between the two types of ecosystems is their salinity, which constitutes a strong frontier for microorganisms. Indeed, freshwater microorganisms are adapted to a low salinity level: they have to maintain a constant osmotic pressure, the salinity inside the cell being higher than the one in its environment [START_REF] Logares | Infrequent marine-freshwater transitions in the microbial world[END_REF]. Salinity being one of the major drivers of microbial communities, freshwater and marine systems harbour different inhabitants [START_REF] Lozupone | Global patterns in bacterial diversity[END_REF]. For instance, haptophytes are very abundant in marine systems, but are found in low proportion in freshwaters.

Moreover, some marine groups (e.g. diplonemides, radiolarians) have never (or rarely) been observed in freshwater systems. Nowadays, some studies are revaluating the frequency of these marine/freshwater transitions [START_REF] Simon | Complex communities of small protists and unexpected occurrence of typical marine lineages in shallow freshwater systems[END_REF][START_REF] Paver | Reevaluating the salty divide: phylogenetic specificity of transitions between marine and freshwater systems[END_REF]. In the case of our small freshwater ecosystems and Lake Baikal, several taxa known as typically marine lineages have been detected. Indeed, several lineages of Marine Stramenopiles have been identified in our freshwater ponds, whereas some of them had only been detected in marine systems previously [START_REF] Simon | Complex communities of small protists and unexpected occurrence of typical marine lineages in shallow freshwater systems[END_REF]. It may be less surprising for Lake Baikal, which has the properties of a freshwater sea: it is the deepest lake and the largest volume of freshwater in the world, and it is highly oligotrophic. However, it presents a very low salinity level (0.0 in Lake Baikal versus an average value of 35 in marine systems; [START_REF] Madsen | Flash: fast length adjustment of short reads to improve genome assemblies[END_REF]. Several marine lineages have been detected in Lake Baikal, in the water column and in sediment samples (Annenkova et al., 2020a;[START_REF] Yi | High-throughput sequencing of microbial eukaryotes in lake baikal reveals ecologically differentiated communities and novel evolutionary radiations[END_REF]Reboul et al., submitted). Furthermore, members of the microbial eukaryotic communities of Lake Baikal are phylogenetically close to Arctic lineages, indicating that they might share similar ancestors (Annenkova et al., 2020a,b).

7.2 Spatial changes in microbial community composition 7.2.1 Effect of depth on microbial communities

The strongest environmental effect driving microbial eukaryotic communities in Lake Baikal appears to be the sampling depth, that can be considered as a proxy for light variation, nutrient concentration or pressure for instance. However, we did not detect clear changes in the stratification with depth of the measured environmental parameters, which remain rather stable whatever the depth is. As we did not measure a large panel of abiotic parameters, we did not find which ones affected the most the communities. Microbial communities in the different depth classes (i.e. epi-, meso-and bathypelagic) present dissimilarities in terms of community composition and structure. This depth-related fractionation has been also observed in Lake Baikal for prokaryotic communities (e.g. Mikhailov et al., 2019b, and is common in freshwater ecosystems [START_REF] Kurt | Depth distribution of microbial diversity in lakes[END_REF]. Lake stratification and light variation lead to different environmental conditions along the water column, than can explain variations in community composition. The community composition of bathypelagic sample may be also influenced by the proximity with the sediments at the bottom of the lake, even if the sampling has been conducted carefully in order to avoid any possible disturbance. Sediment communities in freshwater ecosystems are different from water column communities [START_REF] Jiang | Microbial diversity in water and sediment of lake chaka, an athalassohaline lake in northwestern china[END_REF]. The mixing of the sediment with the planktonic communities can explain part of the variation of the microbial communities along the water column. In fact, the detection of photosynthetic organisms below the epipelagic area, likely inactive, could be an indicator of the exchanges at the water-sediment interface [START_REF] Zakharova | The structure of microbial community and degradation of diatoms in the deep near-bottom layer of lake baikal[END_REF]. In small freshwater ecosystems, the water column is to shallow to conduct correctly a sampling at different depth. However it is likely that there is also a variation of community composition with depth, for instance between the communities close to the sediment and the surface communities. However, the frequent mixing of the water column may attenuate the differences, with sediment being able to reach surface more often [START_REF] Blottière | Effects of mixing on the pelagic food web in shallow lakes[END_REF]. This implies a strong temporal variability in the ecosystems.

Variation of microbial communities depending of the sampling station

The horizontal variations of community composition can be estimated with two complementary approaches. We can focus either on different ecosystems and calculate the dissimilarities between their respective communities, or on one specific ecosystem, often large, and estimate the differences of community composition at different sampling points within it.

The comparison of several ecosystems often reveals strong differences in community composition . The segregation of samples according to the ecosystem of origin is observed in several studies, for both eukaryotes and prokaryotes (e.g. [START_REF] Chase | Drought mediates the importance of stochastic community assembly[END_REF]; [START_REF] Lear | Fine-scale spatial patterns in bacterial community composition and function within freshwater ponds[END_REF][START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF]; [START_REF] Linz | Bacterial community composition and dynamics spanning five years in freshwater bog lakes[END_REF]; [START_REF] Bock | Synchrony of eukaryotic and prokaryotic planktonic communities in three seasonally sampled austrian lakes[END_REF]. It underlines the fact that communities are influenced by a large number of drivers. One the contrary, we found that the choice of the sampling basin has a small effect on the microbial communities inventoried in Lake Baikal. At a given depth from North to South, microbial eukaryotic communities remained similar (Chapter 3). The same result was obtained for prokaryotic communities by Mikhailov et al. (2019b) and for eukaryotic communities in the southern basin by [START_REF] Yi | High-throughput sequencing of microbial eukaryotes in lake baikal reveals ecologically differentiated communities and novel evolutionary radiations[END_REF]. These differences can be explained by possible changes in environmental conditions. Indeed, the southern basin is more impacted by human activities than the northern basin which is a preserved area. However, it has been shown that the dissimilarities in the composition of microbial communities are in general lower within ecosystems than between them [START_REF] Yannarell | Within-and between-lake variability in the composition of bacterioplankton communities: investigations using multiple spatial scales[END_REF]. In small freshwater ecosystems, for practical reasons and due to their limited sizes, we chose to focus on only one sampling point, at the middle of the ecosystems in the pelagic area. A preliminary check for the homogeneity in community composition within ecosystems was conducted previously by Simon et al. (pers. comm.). The comparison of the microbial communities sampled at three different locations in the Etang des Vallées revealed a high similarity within the ecosystem. However, it was a punctual comparison, and surface communities might not be homogeneous. For instance, [START_REF] Lear | Fine-scale spatial patterns in bacterial community composition and function within freshwater ponds[END_REF] established in alpine ponds that there were significant dissimilarities of bacterial communities within aquatic ecosystems, especially for samples separated by more than 20 meters. Moreover, even if we did not detect a coastal effect in Lake Baikal, we can hypothesis that in small freshwater ecosystems the littoral zone offers more different environmental conditions than the pelagic area does. In particular, the littoral zone hosts denser populations of macroorganisms, is adjacent to the potential terrestrial perturbation, and receives all the ecosystem inflow [START_REF] Lewis | ) Encyclopedia of Inland Waters[END_REF][START_REF] Obertegger | Temporal variability of bacterioplankton is habitat driven[END_REF][START_REF] Søndergaard | Pond or lake: does it make any difference?[END_REF].

Temporal evolution of microbial communities

Our temporal surveys of the small freshwater ecosystems have two major advantages: (i) we focused on the three domains of life and (ii) during a long term temporal survey. Indeed, the temporal surveys often treat only eukaryotic, bacterial, or prokaryotic communities, and the average number of samples collected is only 15 time points per time series [START_REF] Buckley | Multivariate methods for testing hypotheses of temporal community dynamics[END_REF][START_REF] Lambert | Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations[END_REF].

Using two temporal surveys differing in frequency and duration, I detected two types of temporal patterns in the five small freshwater ecosystems, for both eukaryotic and prokaryotic communities. The first pattern is a seasonal pattern. Intra-annual seasonal patterns have been widely observed at the surface of marine and freshwater aquatic ecosystems (e.g. et al. (2006) in their study of small microbial eukaryotes in Lake Pavin. Given the multiple environmental perturbations that face small freshwater ecosystems during the year and their small buffering effect, the seasonal patterns of microbial communities I found in small freshwater ecosystems were rather unexpected. Moreover, even if I obtained clear seasonal patterns, dissimilarities between samples remained high. This underlines that, globally, there is a significant change in the whole microbial community composition at the monthly and the seasonal scale, for both eukaryotes and prokaryotes. The second pattern is an inter-annual rise of the dissimilarities between microbial community composition in the five small freshwater ecosystems.

Dissimilarities between communities are rising with time lag between samples. Such a temporal evolution was depicted in other ecosystems, such as subtropical reservoirs or marine systems [START_REF] Chow | Temporal variability and coherence of euphotic zone bacterial communities over a decade in the southern california bight[END_REF][START_REF] Cram | Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years[END_REF][START_REF] Liu | Phytoplankton communities exhibit a stronger response to environmental changes than bacterioplankton in three subtropical reservoirs[END_REF][START_REF] Bock | Synchrony of eukaryotic and prokaryotic planktonic communities in three seasonally sampled austrian lakes[END_REF]. Both patterns were observed for eukaryotic and prokaryotic communities, underlining concurrent temporal variations between prokaryote and microbial eukaryote communities [START_REF] Liu | Phytoplankton communities exhibit a stronger response to environmental changes than bacterioplankton in three subtropical reservoirs[END_REF][START_REF] Bock | Synchrony of eukaryotic and prokaryotic planktonic communities in three seasonally sampled austrian lakes[END_REF][START_REF] Obertegger | Multifaceted aspects of synchrony between freshwater prokaryotes and protists[END_REF]. This temporal synchrony between prokaryotes and eukaryotes indicates that they have similar responses over time despite their ecological differences (Massana and Logares, 2013). It can be driven indirectly by the influence of abiotic factors, or directly because of biotic interactions such as predation or parasitism [START_REF] Kent | Synchrony in aquatic microbial community dynamics[END_REF][START_REF] Bock | Synchrony of eukaryotic and prokaryotic planktonic communities in three seasonally sampled austrian lakes[END_REF]. Moreover, these patterns have also been detected for both abundant and rare microbial communities. For the abundant community, it was surprising to observe a directional change. Indeed, abundant taxa can be considered as a core community, well adapted to the ecosystems [START_REF] Massana | Distribution and abundance of uncultured heterotrophic flagellates in the world oceans[END_REF]. As the measured physico-chemical parameters remained stable in time, the abundant community could have followed the same pattern. It was also surprising to observe a seasonality of the rare community, because it is composed of low abundancy and low frequency taxa and it presented high turnover rate at the intra-and inter-annual scale.

However, seasonality in the occurrence of rare communities has already been described in marine bacterioplankton [START_REF] Alonso-Sáez | The hidden seasonality of the rare biosphere in coastal marine bacterioplankton[END_REF]. This underlines the fact that rare communities are not only dormant cells, but play an active role in microbial communities [START_REF] Logares | Rarity in aquatic microbes: placing protists on the map[END_REF][START_REF] Debroas | Evidence for an active rare biosphere within freshwater protists community[END_REF].

One hypothesis to explain these gradual changes over time in community composition could be that physico-chemical conditions in ecosystems changed over time. Therefore, I investigated how abiotic parameters could explain the temporal dynamics of microbial communities.

However, we did not detect strong directional changes over the eight-year survey in the measured environmental parameters. On the other hand, the seasonal variation of the parameters is clearly marked in all ecosystems, similarly to what we observed for microbial communities.

Over the entire survey, the measured abiotic parameters explain less than 10% of the community composition variation. Even if several abiotic parameters such as temperature or pH are known to significantly influence microbial community composition (see Chapter 1, section 1.3.1), the total proportion of the community variance explained by measured abiotic parameters remains low [START_REF] Kent | Synchrony in aquatic microbial community dynamics[END_REF][START_REF] Lidicker | Determinants of community structure in the global plankton interactome[END_REF][START_REF] Linz | Bacterial community composition and dynamics spanning five years in freshwater bog lakes[END_REF][START_REF] Berdjeb | Short-term dynamics and interactions of marine protist communities during the spring-summer transition[END_REF]. According to the models predator-prey based on phytoplanktonic and zooplanktonic communities of [START_REF] Dakos | Interannual variability in species composition explained as seasonally entrained chaos[END_REF], inter-annual changes in communities can arise without an inter-annual variability of the abiotic parameters. Dakos et al. emit the hypothesis that seasonal dynamics generates a form of chaos, that favors an inter-annual variability.

At the taxa scale, several patterns could be distinguished. OTUs had either hectic, recurrent, or sporadic dynamics. These taxa dynamics are globally poorly known, because they are overlooked [START_REF] Lambert | Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations[END_REF]. Indeed, microbial communities are complex and diverse, and it is difficult to investigate the dynamic of each taxon. Despite the clear seasonal patterns we observed at the whole community scale, only a small proportion of OTUs presented a clear recurrence at a specific season. Several OTUs have been frequently detected at various abundances along the seasonal surveys times, encompassing several seasons, without distinct pattern. Recurrent OTUs were composed of abundant and rare taxa. Their proportions (in terms of both number of reads and OTUs) were similar to what has been observed in marine communities [START_REF] Giner | Comparative 16s rrna analysis of lake bacterioplankton reveals globally distributed phylogenetic clusters including an abundant group of actinobacteria[END_REF][START_REF] Lambert | Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations[END_REF]. Recurrent OTUs occurred either at a seasonally or without any identified occurrence periods. The seasonality of a taxa was characterised by a peak of abundance during one specific season, and an absence or a very low abundance the rest of the year. Non-seasonal recurrence corresponded to OTUs that peaked for instance during two consecutive seasons several times. Finally, in our study, a large number of OTUs have been detected only once over the whole survey (representing respectively 54.7% and 39.3% of the eukaryotic and prokaryotic OTUs). It is consistent with other surveys, with for instance the study of [START_REF] Shi | Temperature mainly determines the temporal succession of the photosynthetic picoeukaryote community in lake chaohu, a highly eutrophic shallow lake[END_REF] where 50% of the richness was represented by OTUs detected in less than three samples over 12. Similarly, [START_REF] Giner | Comparative 16s rrna analysis of lake bacterioplankton reveals globally distributed phylogenetic clusters including an abundant group of actinobacteria[END_REF] observed that almost 90% of the OTUs were detected less than 10 times over 120 sampling points. These infrequent OTUs are probably detected during an immigration process, but they do not succeed in colonizing the ecosystem and are not detected later during the survey [START_REF] Jia | Community assembly processes of the microbial rare biosphere[END_REF].

7.4 Exploration of the interactions in the communities 7.4.1 Spatio-temporal structure of co-occurrence networks

In aquatic ecosystems, organisms are in constant interaction, which results in either beneficial or negative impacts on the protagonists. Studying these interactions is a major computational and experimental challenge. In this thesis, I used co-occurrence networks to have a first insight into the organisation of the microbial communities inhabiting different ecosystems.

The study of the co-occurrence networks in the three depth classes (i.e. epi-, meso-and bathy-pelagic) of Lake Baikal revealed different microbial community interaction patterns.

Planktonic communities at surface appeared to be more complex, showing highly interconnected OTUs. As I did not found large differences in microbial communities between the three basins (they only differ with depth) with multivariate analyses, I analysed samples from the same depth zone simultaneously. It allowed me to have available a network based on a larger number of samples. In order to consider the slight effect of the sampling basin, I added it as a covariable in the networks. However, in 2019, Mikhailov et al. showed a variation of network topology as a function of geographical location in Lake Baikal during spring blooms in the pelagic zone. Contrarily to our study, they focused on both microbial eukaryote and prokaryote communities, which provided them a punctual view of planktonic communities at surface of Lake Baikal. In terms of depth variation of community structure, the differences in network complexity (e.g. more connected nodes, edges or higher connectance) between surface and bottom samples have already been observed in other ecosystems [START_REF] Jiao | Disentangling the seasonal co-occurrence patterns and ecological stochasticity of planktonic and benthic bacterial communities within multiple lakes[END_REF]. Communities at surface may be more complex compared to deepest communities. Indeed, the constant effect of external disturbances such as rain, wind, and the mixing of the water column induces more perturbations, as well as more dispersal events [START_REF] Zeng | Patterns and assembly processes of planktonic and sedimentary bacterial community differ along a trophic gradient in freshwater lakes[END_REF][START_REF] Jiao | Disentangling the seasonal co-occurrence patterns and ecological stochasticity of planktonic and benthic bacterial communities within multiple lakes[END_REF].

To study the interactions between microbial communities over time, the datasets are often summarized in one network taking into account all the samples (e.g. [START_REF] Steele | Marine bacterial, archaeal and protistan association networks reveal ecological linkages[END_REF][START_REF] Chow | Temporal variability and coherence of euphotic zone bacterial communities over a decade in the southern california bight[END_REF]Parada and Fuhrman, 2017). The comparison of the structure of the community over several years with one network per season remains scarce (e.g. [START_REF] Jones | Ecological patterns among bacteria and microbial eukaryotes derived from network analyses in a low-salinity lake[END_REF][START_REF] Zhu | Seasonal succession and spatial distribution of bacterial community structure in a eutrophic freshwater lake, lake taihu[END_REF][START_REF] Jiao | Disentangling the seasonal co-occurrence patterns and ecological stochasticity of planktonic and benthic bacterial communities within multiple lakes[END_REF]. Moreover, co-occurrence patterns are rarely compared between different ecosystems [START_REF] Williams | Demonstrating microbial co-occurrence pattern analyses within and between ecosystems[END_REF]. Here, we described microbial communities inhabiting ecosystems differing in their abiotic conditions and sizes, but also compared them one to each other by building one network per season per ecosystem. Because of the seasonality of the sampling design, I obtained a snapshot of the community structure at the seasonal scale but I was not able to infer specific interactions between two microorganisms into details. I highlighted different network structures depending on the ecosystem considered. Differences in community structure between ecosystems could be driven by their nutrient status. Indeed, a study on lake communities has highlighted that surface networks are more complex in oligotrophic systems than in eutrophic ones [START_REF] Cao | Heterogeneity of interactions of microbial communities in regions of taihu lake with different nutrient loadings: A network analysis[END_REF][START_REF] Zeng | Patterns and assembly processes of planktonic and sedimentary bacterial community differ along a trophic gradient in freshwater lakes[END_REF]. The five small freshwater ecosystems that I considered were all eutrophic, but I did not rank them following an eutrophication level in this work. However, even if the topology of the networks were globally different, I detected some common seasonal features between ecosystems. For instance, we found that networks in spring and summer (warm period) networks were globally more complex in term of number of connections than those in autumn and winter (cold period). The positive link between warmer conditions and the increase of interaction had already been observed in bacterioplankton communities in lakes [START_REF] Kara | A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic lake mendota, wi, usa[END_REF][START_REF] Zhao | Network analysis reveals seasonal variation of co-occurrence correlations between cyanobacteria and other bacterioplankton[END_REF]. Warm seasons are characterized by a higher primary productivity, which could explain that there are more interactions within the ecosystems, especially direct or indirect interactions with the autotrophic microorganisms [START_REF] Cotner | Small players, large role: microbial influence on biogeochemical processes in pelagic aquatic ecosystems[END_REF].

Finally, the interactions that I inferred in the networks were biologically meaningful. More specifically, typical features of microbial community structuring, such as the tight link between autotrophs and heterotrophs, are expected to be retrieved in co-occurrence networks.

Autotrophic organisms appear to be central in the networks, often corresponding to the most connected nodes. These highly connected microorganisms can be considered as keystone species [START_REF] Berry | Deciphering microbial interactions and detecting keystone species with co-occurrence networks[END_REF]. Further investigations on the interactions in the five small freshwater ecosystems could be based on these highly connected taxa. Indeed, they have a key role in the functioning of the ecosystems and can help to predict changes in community composition [START_REF] Herren | Keystone taxa predict compositional change in microbial communities[END_REF].

Large variety of methods to infer co-occurrence networks

The construction of co-occurrence networks faces different methodological issues when using metabarcoding data. It is important to be aware of pros and cons of the many construction methods that are available [START_REF] Röttjers | From hairballs to hypotheses-biological insights from microbial networks[END_REF]. The most standard way to investigate interactions between organisms is done using pairwise correlations, such as the Pearson's correlation coefficient [START_REF] Woodhouse | Microbial communities reflect temporal changes in cyanobacterial composition in a shallow ephemeral freshwater lake[END_REF]. When using this method, correlations are calculated between each pair of organisms and only the most significant and important interactions are kept for the network constructions. However, this method can lead to spurious correlations resulting from compositional effects for instance, and should not be used alone [START_REF] Weiss | Correlation detection strategies in microbial data sets vary widely in sensitivity and precision[END_REF]. Alternatively, several approaches such as SparCC are taking into account compositional effects to produce more robust variance-covariance matrix of the taxa [START_REF] Friedman | Inferring correlation networks from genomic survey data[END_REF]. However, they often do not allow to include environmental covariates and they do not take into account the existence of indirect interactions. In this thesis, I decided to build co-occurrence networks based on a method recently developed by [START_REF] Chiquet | Variational inference for sparse network reconstruction from count data[END_REF].

For now, only few studies are based on this method, but it presents several advantages (e.g. [START_REF] Sassenhagen | Protist interactions and community structure during early autumn in the kerguelen region (southern ocean)[END_REF]. The PLNmodel method is based on the precision matrix, that is the inverted variance-covariance matrix. This transformation allows to detect and delete associations resulting from indirect covariations, such as covariations between two OTUs due to their direct association to a third OTU [START_REF] Kurtz | Sparse and compositionally robust inference of microbial ecological networks[END_REF]. A comparison of three constructions methods revealed that networks based on simple Pearson's correlation calculations and variance-covariance matrices were more complex than those based on the precision matrix produced by PLNmodels (see Figure 7.4.1). The PLNmodel approach is more stringent and avoids spurious correlations. This method has many assets, that were important for me. Indeed, it is robust regarding compositionality, it uses raw count data, it allows to take into account both eukaryotic and prokaryotic dataset in the same network, and to take into account both quantitative and qualitative covariables (e.g. physico-chemical parameters or sampling season).

Finally, once the method to analyse the data is chosen, the interpretation must be conducted carefully. Indeed, the association networks do not represent true associations [START_REF] Derocles | Biomonitoring for the 21st century: integrating next-generation sequencing into ecological network analysis[END_REF]. Positive and negative edges can be interpreted by various types of interactions, and sometimes the same in the case of a parasitism for instance, because of the co-presence of both host and parasite.

Distribution of the functional groups in the ecosystems

After the description of the composition of microbial communities over space and time, of their link to environmental parameters, and the investigation of the structure of the communities, I explored the temporal dynamics and interactions of the main microbial functional groups to address the functioning of an ecosystem.

A classical way to address this question using metabarcoding data is to attribute to each phyla or genus a trophic mode or lifestyle (i.e. autotroph, heterotroph, putative parasite) based on the literature. These attributions are not highly precise because the known microorganisms groups can harbour different types of trophic strategies [START_REF] Genitsaris | Small-scale variability of protistan planktonic communities relative to environmental pressures and biotic interactions at two adjacent coastal stations[END_REF]. For instance, gammaproteobacteria encompass bacteria that are heterotrophic and others that are autotrophic. Microorganisms can also harbour mixotrophic lifestyle and are therefore capable of both autotrophy or heterotrophy. I established a corresponding list between taxonomic groups and lifestyle. For the taxonomic groups, I used different levels (e.g. phyla, class) in order to be as accurate as possible.

In both small freshwater ecosystems and Lake Baikal, I detected a majority of heterotrophic organisms (respectively about 53.9% and 60.3% of the total number of reads of the microbial community in Baikal and in the freshwater ponds). With the seasonal networks analysis in the small freshwater ecosystems, I observed that the majority of the positive links were established between autotrophs and heterotrophs. These relationships can be representative of predatorprey interactions, such as for instance the grazing of phytoplankton by heterotrophic predators [START_REF] Pernthaler | Predation on prokaryotes in the water column and its ecological implications[END_REF]. The putative parasitic organisms were more present in the networks in winter in small freshwater ecosystems, whereas in Lake Baikal, the global putative parasitic load tended to be lower in deep samples. In winter, putative parasitic organisms could take advantage of a certain stress of other organisms due to lower nutrient and light level [START_REF] Cleary | Unexpected prevalence of parasite 18s rdna sequences in winter among antarctic marine protists[END_REF]. Deeper, the low abundance of putative parasites could be the effect of deep microbial communities constituted mostly of inactive cells that sink to the bottom of the ecosystem [START_REF] Jones | Dormancy contributes to the maintenance of microbial diversity[END_REF].

Chapter 8

Perspectives and Conclusions In this thesis, I took into account several physico-chemical parameters to explore the drivers of microbial community composition. However, there is a very large variety of physico-chemical parameters that can be measured in aquatic ecosystems, and I only considered few of them. As for Lake Baikal, even if it is a highly oligotrophic lake, it could have been interesting to have more data on nutrient concentrations, for instance. Indeed, the chemical composition of the lake is complex, and varies as a function of horizontal and vertical stratifications [START_REF] Khodzher | Current chemical composition of lake baikal water[END_REF]. Therefore, adding data on phosphorus or nitrogen loads in the lake could give better insights into microbial community spatial structure. Moreover, the impact of nutrient concentrations on bacterial communities is already documented in Lake Baikal [START_REF] Kurilkina | Bacterial community composition in the water column of the deepest freshwater lake baikal as determined by next-generation sequencing[END_REF]. In the case of small freshwater ecosystems, it would be interesting to have a better view on the meteorological context (e.g. rain events, wind speed), and on the short temporal variations of abiotic parameters (e.g. daily scale). For instance, a meteorological station placed in the middle of an ecosystem and recording daily the temperature, the precipitations, the light intensity, and the wind speed could help to have a better view of the meteorological events that the ecosystems are facing and therefore help results interpretation. Indeed, a strong rain event the day before the sampling may affect temporary the communities through the inflow of water, with for instance changes a dilution effect in the relative proportion of the taxa and of the abiotic parameters [START_REF] Jones | Seasonality and disturbance: annual pattern and response of the bacterial and microbial eukaryotic assemblages in a freshwater ecosystem[END_REF][START_REF] Adams | Metacommunity dynamics of bacteria in an arctic lake: the impact of species sorting and mass effects on bacterial production and biogeography[END_REF][START_REF] Staley | Species sorting and seasonal dynamics primarily shape bacterial communities in the upper mississippi river[END_REF]. Similarly, the wind speed can have an impact on the microbial communities, by inducing the mixing of the water, as it has been showed on phytoplankton biomass [START_REF] Blottière | Effects of mixing on the pelagic food web in shallow lakes[END_REF][START_REF] Rusak | Wind and trophic status explain within and among-lake variability of algal biomass[END_REF]. Moreover, wind can generate waves in the ecosystems, and therefore passively transport microorganisms [START_REF] Garneau | Short-term displacement of planktothrix rubescens (cyanobacteria) in a pre-alpine lake observed using an autonomous sampling platform[END_REF]. Combined to the survey of wind speed to identified wind-induced mixing, we could monitor the water temperature over a vertical profile in the ecosystems in order to define precisely the stratification in the water column over time [START_REF] Martinsen | Water temperature dynamics and the prevalence of daytime stratification in small temperate shallow lakes[END_REF].

Finally, a regular survey of the water level would allow to have a precise idea of the drought events that ecosystems are facing, and to investigate their resilience over time [START_REF] Simon | Resilience of freshwater communities of small microbial eukaryotes undergoing severe drought events[END_REF]. Moreover, low water levels can lead to more interactions with the sediments and also more turbidity in the ecosystems, resulting in lower light availability for photosynthesis. Sediment resuspension has a strong impact on microbial communities through the seed bank and their role as vehicle for microbial dispersion in the ecosystems (e.g. bacteria associated to sediment particles) (see section 8.1.1.3 and [START_REF] Amalfitano | Tracing particulate matter and associated microorganisms in freshwaters[END_REF].

Incorporating other actors of the trophic web

The integration of the three domains of life in our study and the analyses of a large size fraction allowed me to construct networks including many actors of the trophic web, from primary producers to predators. However, considering the web structure (see Figure 1.5), my dataset missed other major actors, such as zooplankton and viruses which are known to be mortality factors of microbial communities. These missing protagonists could explain a part of the currently unexplained variance. On the one hand, viruses are known to have a direct impact on community composition, for instance by regulating prokaryotic communities [START_REF] Jardillier | Effects of viruses and predators on prokaryotic community composition[END_REF][START_REF] Warnes | Comparing the effects of resource enrichment and grazing on viral production in a meso-eutrophic reservoir[END_REF]. They can control the most abundant communities, a theory known as the "kill the winner rule" [START_REF] Thingstad | Elements of a theory for the mechanisms controlling abundance, diversity, and biogeochemical role of lytic bacterial viruses in aquatic systems[END_REF]. On the other hand, zooplankton plays a major predating role. Moreover, they release dissolved organic matter in the ecosystem, and therefore provide food for heterotrophic organisms [START_REF] De Corte | Metagenomic insights into zooplankton-associated bacterial communities[END_REF]. They also move microbial communities through the water column, in particular in the vertical dimension [START_REF] Grossart | Bacteria dispersal by hitchhiking on zooplankton[END_REF]. Therefore, to better tackle the interactions which take place within ecosystems, I could determine the composition and abundance of the zooplanktonic communities, and incorporate them in the co-occurrence networks. For both viruses and zooplankton, samples are available and should be considered for future analyses.

There can also be an effect of indirect players on microbial communities. Fishes, amphibians such as tadpoles, and macroinvertebrates generate bioturbation by moving the sediments (e.g.

while they are searching for food, [START_REF] Meysman | Bioturbation: a fresh look at darwin's last idea[END_REF]. We never investigated the fish communities, but they are at least present in the Etang des Vallées, where fishing activity is supervised by the "Office National des Forêts". The bioturbation of the sediments can play a role in the resuspension of currently inactive cells that can therefore be remobilized, and it can also affect nutrient concentration [START_REF] Rugenski | Tadpoles enhance microbial activity and leaf decomposition in a neotropical headwater stream[END_REF][START_REF] Adámek | Bioturbation of sediments by benthic macroinvertebrates and fish and its implication for pond ecosystems: a review[END_REF]. Moreover, studying fishes and amphibians may also be the opportunity to investigate their microbiome.

Indeed, these microorganisms have not been not investigated during our study, but they host part of the microbial communities of the ecosystems. Fishes and other macroorganisms can be a habitat for microorganisms, especially for rare taxa, and they maintain the microbial diversity of ecosystems by "sustaining the rare" [START_REF] Troussellier | Sustaining rare marine microorganisms: macroorganisms as repositories and dispersal agents of microbial diversity[END_REF]. For instance, amphibians are well represented in our ecosystems, and they can also be considered as a putative immigration source.

A comparative study between the microbial communities of our small freshwater ecosystems and of the microbiome of different amphibians revealed that they share about 40% of OTUs (Dupuis and Pollet, unpublished).

Finally, we are missing a large part of the microbial communities in the interaction networks.

For methodological reasons, we had to build networks only based on the highly abundant members of the communities [START_REF] Röttjers | From hairballs to hypotheses-biological insights from microbial networks[END_REF]. However, rare OTUs are known to have a major importance in the functioning of ecosystems (see Chapter 1, [START_REF] Jousset | Where less may be more: how the rare biosphere pulls ecosystems strings[END_REF].

Incorporating infrequent taxa would allow us to investigate their important potential role in the community structure. This modelling issue is a whole current field of research. For instance, [START_REF] Cougoul | Rarity of microbial species: In search of reliable associations[END_REF] offer a model integrating the associations between abundant and rare organisms, but not yet between pairs of rare ones.

Identifying and quantifying external imports of microorganisms

Freshwater ecosystems are not closed systems and are thus in constant interaction with the surrounding environment. In particular, there is a wide range of dispersal pathways for microorganisms, from the surroundings. To better understand community structuring over time, a sampling of the communities inhabiting the potential immigration sources (e.g. rain, atmospheric deposition, and ecosystem banks) would allow determining their contribution in community changes [START_REF] Jones | Species-sorting may explain an apparent minimal effect of immigration on freshwater bacterial community dynamics[END_REF][START_REF] Comte | Contribution of different bacterial dispersal sources to lakes: population and community effects in different seasons[END_REF]. For example, a survey could be conducted in Mare Gabard because it is easily accessible and there is only one water inlet that could be sampled. However, even if they have already been identified as potential vector of immigration, animal vectors (e.g. birds, wild boars) would be more difficult to investigate [START_REF] Incagnone | How do freshwater organisms cross the "dry ocean"? a review on passive dispersal and colonization processes with a special focus on temporary ponds[END_REF]. At the same time, it would be of great interest to sample water in the littoral areas of the freshwater ecosystems. Even if there were no differences observed in Lake Baikal between littoral and pelagic areas, it is known that littoral areas are hydrologically unstable [START_REF] Obertegger | Temporal variability of bacterioplankton is habitat driven[END_REF]. Indeed, they receive the water inflow from the precipitations, and they are an area of interactions with external perturbations, such as animals coming to the shore and mixing the sediments [START_REF] Hairston | Temporal dispersal: ecological and evolutionary aspects of zooplankton egg banks and the role of sediment mixing[END_REF]. This area is therefore more subject to allochtonous inputs, and may harbor more diverse communities.

More generally, it would be interesting to explore sediment communities in the ecosystems. The benthic communities can contribute significantly to pelagic community composition, through the regular mixing events and the bioturbation by macroorganisms. Moreover, sediments are characterised by a high microbial diversity [START_REF] Lozupone | Global patterns in bacterial diversity[END_REF]. Even if sediment taxa are mostly anaerobic and not able to survive at the surface, a part of the sediment community is composed of taxa representing a seed bank of microorganisms. Indeed, dormant and inactive cells sink to the bottom of the ecosystems, and can develop in the case of changes in the environmental conditions [START_REF] Jones | Dormancy contributes to the maintenance of microbial diversity[END_REF][START_REF] Lennon | Microbial seed banks: the ecological and evolutionary implications of dormancy[END_REF]. They constitute a "reservoir of genetic and phenotypic diversity" that can contribute to ecosystem functioning [START_REF] Shoemaker | Evolution with a seed bank: the population genetic consequences of microbial dormancy[END_REF]. Therefore, we could explore sediment communities using sediment traps to have a better view of the communities that can be remobilized in the water column. In Lake Baikal, the effect of mixing on water column communities is not as important as the one in the small freshwater systems. Previous analyses in Lake Baikal have revealed that the lake harboured different microbial eukaryotic communities between the sediments and the water column in the south basin in July [START_REF] Yi | High-throughput sequencing of microbial eukaryotes in lake baikal reveals ecologically differentiated communities and novel evolutionary radiations[END_REF]. In our case, sediments were collected and analysed at several stations along the lake (Reboul et al., submitted).

Therefore, we could conduct a comparative study of sediment and the pelagic samples to check if these differences are observed in the entire lake. This will also allow to identify taxa that are shared between the sediment and the water-column sample, underlying potential interactions.

Determine the proper sampling frequency

One of the main issues that we are facing when handling a long time survey sampled with a large frequency is: do the observed variations correspond to biological variations? I worked at two frequencies, namely monthly and seasonally. Monthly surveys are the most often used, as seen for instance with the long-term microbial observatories in marine systems (reviewed in [START_REF] Buttigieg | Marine microbes in 4d-using time series observation to assess the dynamics of the ocean microbiome and its links to ocean health[END_REF]. Indeed, it is considered as a suitable frequency for the study of microbial ecology [START_REF] Schauer | Seasonal changes in the taxonomic composition of bacterioplankton in a coastal oligotrophic system[END_REF]. Seasonal survey was a good compromise to obtain reliable data on the ecosystem over a long time period without being too much time consuming and costly.

It allowed me to explore in depth the dynamics of the whole microbial community (<30µm in size) and specific OTUs at a medium time scale of eight-years. Also, it confirmed the temporal trends observed at the monthly scale. However, the seasonal survey missed some periods, such as drought events. For instance, according to the monthly survey, Ru Sainte Anne had no water in August and September 2012, but it is not revealed by the seasonal survey. Similarly, La Claye was dry during 5 months in 2011 and it only impacted one time point at the beginning of the seasonal survey. Therefore, important events (e.g. drought) are not taken into account in the interpretation of microbial community composition.

Large frequency surveys allow to investigate the global patterns of microbial community composition, but they are not enough to understand the fine-scale of variability of microorganisms [START_REF] Bunse | High frequency multi-year variability in baltic sea microbial plankton stocks and activities[END_REF]). To answer this latter question, we could conduct surveys at higher frequencies which is something that has never been done in small freshwater ecosystems.

Microbial communities are known to change over very short periods of time [START_REF] Mangot | Shortterm dynamics of diversity patterns: evidence of continual reassembly within lacustrine small eukaryotes[END_REF][START_REF] Fuhrman | Marine microbial community dynamics and their ecological interpretation[END_REF]. With a more frequent sampling (e.g. weekly), I could for instance catch more precisely the variations of abundance of the conditionally rare taxa (i.e. rare taxa that are become occasionally very abundant) and understand the mechanisms leading to these dynamics [START_REF] Lindh | Disentangling seasonal bacterioplankton population dynamics by highfrequency sampling[END_REF]. A survey of day and night variations could also be conducted.

Indeed, small freshwater ecosystems present daytime stratification, which could induce changes in composition along the water column within the day [START_REF] Martinsen | Water temperature dynamics and the prevalence of daytime stratification in small temperate shallow lakes[END_REF]. This time lag is principally used to detect day/night cycles and day-to-day changes in metabolic processes and gene expression of microbes, using metatranscriptomic [START_REF] Needham | Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling[END_REF][START_REF] Linz | Time-series metatranscriptomes reveal conserved patterns between phototrophic and heterotrophic microbes in diverse freshwater systems[END_REF][START_REF] Trench-Fiol | Metatranscriptomics from a small aquatic system: Microeukaryotic community functions through the diurnal cycle[END_REF]. Moreover, when the objective of a study is to infer precise interactions between microorganisms using co-occurrence networks, low frequency surveys are not efficient because they do not reflect the lifespan of individuals. Therefore, we would need a biologically adapted time-lag to resolve specific microbial biotic interactions.

In conclusion, it is important to study ecosystems at various sampling frequencies and related durations that are complementary because they address biological questions occurring at diverse time scales [START_REF] Ladau | Spatial, temporal, and phylogenetic scales of microbial ecology[END_REF]. The combination of the different results

will provide an integrative view of the structure of microbial communities in time, and of the processes that take place in the ecosystems.

Focus on Candidate Phyla Radiation

During this thesis, I conducted a global analysis of the ecosystems. I described the communities and investigated the spatio-temporal modifications of their structure. This approach allowed me to identify some organisms playing potential important roles in ecosystem functioning, such as bacteria of the Candidate Phyla Radiation (CPR). These microorganisms are well represented in the co-occurrence networks, often being at the central position. As presented in the introduction (Chapter 1), CPR members have no cultured representatives, they present a cryptic life cycle, and their functions in the ecosystems remain mostly unknown [START_REF] Solden | The bright side of microbial dark matter: lessons learned from the uncultivated majority[END_REF]. They are suspected to be in interactions with other microorganisms, through parasitism for instance. However, they are also suspected to be producers of molecules (e.g. volatile fatty acids, hydrogen) for methanogens and methanotrophs [START_REF] Vigneron | Ultra-small and abundant: Candidate phyla radiation bacteria are potential catalysts of carbon transformation in a thermokarst lake ecosystem[END_REF]. CPR members were detected in all our ecosystems sometimes in high abundances, in particular in Ru Sainte

Anne. Therefore, it could be interesting to have a specific focus on their dynamics and their role as potential symbionts, in order to identify their role in this ecosystems.

Conclusions

My thesis project was integrated in the global ecological question of the functioning of freshwater ecosystems. A first step to answer this question is to better understand the composition and the structure of all the microbial communities (bacteria, archaea and microbial eukaryotes) inhabiting freshwater ecosystems. To do so, I used spatio-temporal surveys and I focused on two types of freshwater ecosystems: large lakes with the study of Lake Baikal, and small freshwater ecosystems, with four ponds and one brook. The dataset for Lake Baikal consisted in a spatial survey of the microbial communities over horizontal and vertical gradients. The dataset of the small freshwater systems consisted in a temporal survey of the three domains of life, with two durations and frequencies. I can draw the following conclusions:

Freshwater ecosystems present a wide diversity of microorganisms. In both Lake Baikal and small freshwater ecosystems, we detected all the eukaryotic supergroups.

In addition, we detected all the prokaryotic supergroups in small freshwater ecosystems.

The most abundant phyla in both type of ecosystems were emblematic of freshwater microbial communities. Communities in the ecosystems were divided into abundant taxa and diverse rare taxa.

Salinity barrier seems to be less selective than what was previously thought.

Several typical marine lineages were discovered over the entire Lake Baikal, concordantly with other studies. Marine affiliated groups were also observed, although to a lesser extent, in small freshwater systems. The salinity barrier crossing may not be a rare event.

The composition of microbial communities is highly influenced by depth. Microbial composition is stratified over the water column, with surface communities being different from their benthic counterparts. Depth can be considered as a proxy for various environmental parameters (e.g. light), and is a strong factor governing community composition.

Microbial communities are dissimilar among different ecosystems, and similar within a given one. The microbial communities seem rather homogeneous inside a single ecosystem, even a large one. However, microbial communities are specific to one ecosystem, and present strong differences from one ecosystem to another, even if they are close to each other.

Seasonality is a marked pattern in microbial communities observed in small freshwater ecosystems. In terms of community composition and structuration, each season presents specificities. The strongest differences are observed between winter and summer.

Microbial community composition is in constant evolution over time. Along the eight-year survey, I observed a global divergence of the community composition over time.

Therefore, microbial communities are not stable over time in the five small freshwater ecosystems studied in this thesis.

Eukaryotic and prokaryotic communities share similar features. Despite dissimilarities in cell organisation, metabolisms and ecological behaviour, microbial eukaryotes and prokaryotes share common temporal dynamics.

Abiotic parameters have a low explanation power. The physico-chemical parameters that we measured in Lake Baikal and small freshwater ecosystems did not explain most of the variations observed in microbial community composition. Therefore, they are not the only factors driving community composition.

Co-occurrence networks in our studies reflect spatio-temporal variations. The inference of interactions at the different depth classes in Lake Baikal revealed different types of structuration with, in particular, a more important connection of communities living in surface water. The seasonal networks built in the small freshwater ecosystems revealed common features between the same season despite different global structuration.

The analysis conducted in Lake Baikal provided first insights into the diversity and composition of microbial eukaryotic communities over the entire lake, which had never been investigated before. However, it provided an overview of the microorganisms inhabiting the lake during the summer period only. Comparative analysis over time (e.g. at different season over several years) in the same stations should be conducted later, especially in the global context of climate change and its implication for the communities. The analyses in small freshwater ecosystems confirmed the ecological importance of these overlooked systems. They are good models to explore the temporal dynamics of microbial communities. Among others, they are easy to access and to sample, and they are largely distributed which generates a wide range of environmental characteristics. Microorganism are key players in aquatic ecosystems and this work allowed to better understand their composition and structuration over time. Therefore, it can help to improve the management of these common ecosystems in order to preserve their biodiversity.

To conclude, I would say that at first sight, studying in the same project the biggest lake in the world and small freshwater systems could have appeared contradictory. However, even with ecosystems of completely different scales and characteristics, I highlighted similarities in the composition and structuration of pelagic communities. At a broader scale, similarities can also be observed between marine and freshwater systems [START_REF] Stockner | Phototrophic picoplankton: an overview from marine and freshwater ecosystems[END_REF]. For instance, the microbial loop is a model initially developed in marine systems, but which also fits in freshwater ones [START_REF] Fenchel | The microbial loop-25 years later[END_REF]. At the opposite, the Plankton Ecology Group model was established in freshwater systems, and it also find applications in marine ones [START_REF] Sommer | Beyond the plankton ecology group (peg) model: mechanisms driving plankton succession[END_REF]. Therefore, aquatic ecosystems seem to share a similar functioning in terms of microbial community composition and dynamics. Despite strong variations in the spatiotemporal scales and the speed of biological process inherent to the ecosystem size, the use of small freshwater ecosystems as model of aquatic ecosystems to understand microbial communities functioning can help future researches in aquatic ecology.

Résumé en français

Les écosystèmes d'eau douce représentent moins de 3% de l'eau terrestre, mais ils sont très divers, aussi bien en terme de fonctionnement que de taille. Les microorganismes sont omniprésents dans une grande partie des écosystèmes terrestres. Ils sont des acteurs majeurs du fonctionnement de ces écosystèmes, en particulier de celui des écosystèmes aquatiques. Les microorganismes représentent la majeure partie de la biodiversité terrestre et peuvent être répartis dans trois grands domaines : les bactéries, les archées et les eucaryotes. Indépendamment des domaines, les microorganismes présentent des caractéristiques communes, par exemple la petite taille, la diversité dans les populations et l'abondance. Cependant, selon les groupes phylogénétiques, les microorganismes peuvent présenter des caractères très divers, notamment en ce qui concerne leur physiologie, leur capacité métabolique ou encore leur mode trophique. Les microorganismes des trois domaines du vivant sont en constante interaction. De ce fait, il est important de s'intéresser aux trois domaines du vivants lorsque l'on veut étudier un écosystème et les communautés qu'il héberge.

L'un des grands objectifs de l'écologie microbienne est de mieux comprendre le fonctionnement des écosystèmes. Pour chercher à apporter des éléments de réponse à cette question globale dans un écosystème, plusieurs étapes peuvent être réalisées. Tout d'abord, il est nécessaire de décrire la composition et la structuration des communautés dans l'écosystème. Ensuite, il faut identifier les facteurs abiotiques qui peuvent influencer la structuration de la communauté. En effet, les communautés microbiennes peuvent être influencées par une large gamme de conditions physico-chimiques. Il est donc important de comprendre comment l'environnement influence les communautés. Enfin, il est important de s'intéresser aux interactions biotiques entre microorganismes, car elles ont également un grand rôle dans la structuration des communautés. L'ensemble de ces études doit être intégré dans un cadre spatio-temporel. L'étude d'un écosystème à un moment ou dans un endroit donné ne donne pas une vision représentative de la diversité et de la complexité des communautés. L'objectif de cette thèse de doctorat a été d'étudier le fonctionnement des écosystèmes d'eau douce en se focalisant sur leurs communautés microbiennes. Pour cela, deux types d'écosystèmes ont été explorés : un grand lac, le lac Baïkal (Russie), qui est le plus ancien et le plus profond et le plus profond de la planète ainsi que cinq petits écosystèmes d'eau douce localisés dans la région Ile-de-France (France) : quatre étangs présentant différentes caractéristiques environnementales (Etang des Vallées, La Claye, Mare Gabard et Saint Robert) et un ruisseau (Ru toniques (taille des cellules de 0,2 à 30 µm) dans le lac. Mes résultats montrent que les communautés de protistes dans le lac sont complexes et diverses. Elles sont dominées par des alvéolés (ciliés et dinoflagellés), des ochrophytes et des lignées d'holomycètes, avec des cryptophytes, des haptophytes ou encore des katablepharides en abondance modérée. J'ai également détecté de nombreuses lignées présentes en faible abondance, dont notamment plusieurs lignées typiquement marines, telles que les diplonemides, les syndiniales et les radiolaires. Ces observations suggèrent que la barrière de salinité est relativement facile à franchir et que les déterminants "marins" pourraient être davantage liés à la nature oligotrophe du système et la présence d'une colonne d'eau profonde plutôt qu'à la salinité elle-même.

Ensuite, l'étude des communautés selon le gradient latitudinal a révélé que celui-ci jouait un faible rôle sur la structuration des communautés. En effet, il n'y a pas de différence significative dans la composition des communautés selon le bassin de prélèvement, malgré la grande échelle de distance. De même, les communautés sont similaires entre les échantillons prélevés en zones côtières et en zones pélagiques. En revanche, j'ai pu observer que la profondeur a un effet significatif fort sur la stratification des communautés protistes. En effet, les communautés de protistes planctoniques sont fortement et significativement stratifiées dans le lac Baïkal. Cela suggère que la profondeur, qui peut être considérée comme un proxy de la lumière, de la température, du pH, de l'oxygène ou encore de la conductivité, est un déterminant majeur de la structuration des communautés microbiennes. Enfin, j'ai étudié les interactions entre les microorganismes au sein des différentes classes de profondeur. Pour cela, j'ai construit un réseau de co-occurrence par classe de profondeur, en prenant en compte uniquement les OTUs (Unité Taxonomique Opérationnelle) les plus abondantes. L'analyse des réseaux de cooccurrence a montré que les communautés microbiennes de surface (épipélagiques) sont beaucoup plus interconnectées que les communautés des zones méso-et bathypélagiques. Les réseaux de co-occurrence obtenus suggèrent également que les interactions biotiques entre les microorganismes autotrophes, hétérotrophes et parasitaires influencent la structure des communautés protistes.

Similarité des patrons temporels au cours d'un suivi mensuel de deux ans dans de petits écosystèmes d'eau douce malgré des communautés microbiennes différentes Malgré leur importance écologique, les petits écosystèmes d'eau douce sont négligés dans les études scientifiques. De ce fait, peu de connaissances ont été acquises sur la diversité et la composition des communautés microbiennes qui les habitent. Un précédent travail, conduit par M. Simon, a décrit la composition de petits eucaryotes microbiens dans cinq petits écosystèmes d'eau douce situés au sudouest de Paris (France). L'échantillonnage a été effectué mensuellement pendant deux ans, sur la fraction de taille de 0,2 à 5 µm. Les données ont été obtenues par metabarcoding, grâce au pyroséquençage 454 FLX de gènes ARNr 18S. Cette étude a révélé que ces petits écosystèmes d'eau douce abritaient une grande variété d'eucaryotes microbiens. En outre, les communautés se sont révélées être très dynamiques, avec des patrons saisonniers très marqués. Dans ce travail, j'ai analysé les communautés procaryotes du même échantillonnage, en m'intéressant aux gènes d'ARNr 16S. J'ai également considéré l'ensemble des données collectées sur les deux années pour étudier simultanément l'ensemble de la communauté microbienne (bactéries, archées et eucaryotes microbiens) afin de mieux comprendre la dynamique des communautés au cours du temps. Tout d'abord, j'ai décrit la diversité et la composition des communautés bactériennes et archéennes dans ces petits écosystèmes. J'ai pu observer une très grande diversité de procaryotes, avec l'ensemble des grands groupes connus représentés. De façon inattendue pour les environnements oxiques, j'ai détecté de nombreux membres de la Candidate Phyla Radiation (CPR), qui étaient localement très abondants. La comparaison des communautés microbiennes entre les différents écosystèmes a montré qu'ils présentaient des communautés très différentes. Par exemple, une faible proportion d'OTUs (<1%) était commune aux cinq écosystèmes malgré leur proximité géographique. Ces communautés microbiennes presque uniques à chaque écosystème mettent en évidence la forte influence de sélection des conditions environnementales locales. Ensuite, j'ai exploré les variations temporelles de l'ensemble de la communauté microbienne. J'ai pu observer des patrons de saisonnalité dans les cinq écosystèmes, pour les procaryotes et les eucaryotes microbiens. Cette tendance est marquée, malgré la présence de fort changements au cours du temps dans la composition des communautés. En parallèle de ces dynamiques saisonnières, j'ai pu remarquer un changement global de communautés au cours des deux années. Enfin, j'ai cherché à identifier les paramètres abiotiques impliqués dans la composition de la communauté microbienne et à déterminer leur importance relative. L'analyse des variations temporelles des paramètres physico-chimiques mesurés à révélé qu'ils présentaient également une saisonnalité marquée. En revanche, contrairement aux communautés microbiennes, ces paramètres sont caractérisés par une relative stabilité au cours des deux années. L'analyse de l'effet global des paramètres abiotiques mesurés a montré qu'ils n'expliquaient qu'une faible partie de la variation des communautés microbiennes. Cette observation souligne l'importance probable des interactions biotiques dans la composition et la structure des communautés microbiennes Suivi saisonnier des communautés microbienne eucaryotes et procaryotes pendant 8 ans dans de petits écosystèmes d'eau douce : patrons intra-et inter-annuels L'étude des communautés microbiennes sur un suivi mensuel de deux ans a permis de mettre en évidence l'importance des petits écosystèmes d'eau douce en terme de diversité microbienne (intra et inter écosystèmes) et en terme de complexité dans leurs dynamiques temporelles. Cependant, une durée d'étude de deux ans n'est pas suffisante pour valider les dynamiques temporelles observées. En effet, des études sur le plus long terme sont essentielles pour décrire et prédire les dynamiques temporelles des communautés microbiennes. Elles sont d'autant plus importantes pour des petits écosystèmes d'eau douce, qui réagissent rapidement aux perturbations de l'environnement. Un long suivi est donc nécessaire pour couvrir un large éventail des conditions environnementales potentielles. Nous avons donc effectué un échantillonnage saisonnier pendant huit ans, ce qui représente environ 30 échantillons pour chaque écosystème. Les données ont été obtenues grâce à Illumina Séquençage MiSeq des amplicons d'ADNr 16S et 18S. Dans cette nouvelle approche, j'ai ciblé une plus grande fraction de taille de communautés microbiennes, à savoir de 0,22 à 30 µm au lieu de 0,22 à 5 µm. Tout d'abord, cette étude m'a permis de conduire une description plus poussée de la composition microbienne dans les cinq petits écosystèmes d'eau douce, car aucun plateau de saturation de la diversité n'avait été atteint dans les précédentes études portant sur les deux années de suivi. Même si le plateau de saturation n'a toujours pas été atteint, ce suivi saisonnier de huit années a permis d'identifier respectivement 22 764 et 25 536 OTUs à 95% d'identité pour les procaryotes et les eucaryotes. Les OTUs appartienne à tous les grands groupes microbiens actuellement connus. En outre, comme l'a montré le suivi mensuel sur deux années, les communautés microbiennes sont spécifiques à chaque écosystème malgré leur proximité géographique. Ces observations confirment la grande diversité microbi-enne abritée par les petits écosystèmes d'eau douce, et donc l'importance de les étudier.

Ensuite, j'ai pu confirmer les tendances temporelles observées à l'échelle des deux ans grâce aux données du suivi sur huit ans. Les communautés microbiennes des petits écosystèmes d'eau douce présentent une forte saisonnalité, c'est-à-dire un patron intra-annuel, les plus grandes différences étant observées entre les communautés d'hiver et d'été. Le patron inter-annuel d'augmentation des dissimilarités au cours du temps entre les communautés a également été observé dans tous les écosystèmes, à l'exception des eucaryotes microbiens de l'écosystème Etang des Vallées. Cette absence de changements globaux au cours du temps dans cet écosystème peut s'expliquer par sa taille. C'est en effet le plus grand écosystème échantillonné dans cette étude, et on peut raisonnablement faire l'hypothèse qu'il présente un effet tampon plus fort que les autres, donc des communautés microbiennes plus stables. A l'échelle des OTUs, j'ai pu observer des patrons de récurrence saisonnière pour moins de 2% des OTUs. Les OTUs, ainsi que les groupes auxquels elles sont affiliées et leur abondance étaient variables selon les écosystèmes.

Enfin, j'ai pu observer que les paramètres physico-chimiques mesurés n'avaient de nouveau qu'une faible influence sur la variance de la communauté microbienne. Cela suggère que les facteurs biotiques, tels que la prédation, les mutualismes ou le parasitisme jouent un rôle majeur dans la composition des communautés. J'ai donc construit un réseau de co-occurrence basé sur les OTUs les plus abondantes détectées dans les cinq écosystèmes. Les interactions inférées montrent que les OTUs sont regroupées par même groupes taxonomiques, ce qui suggère qu'il existe un partage des OTUs par niche dans les écosystèmes.

Etude de la topologie des réseaux de co-occurrence saisonniers dans des petits écosystèmes d'eau douce L'étude des communautés microbiennes dans cinq petits écosystèmes d'eau douce à différentes fréquences et durées m'ont permis de découvrir une grande diversité de microorganismes présentant des patrons intra-annuels marqués. Sachant que les paramètres physico-chimiques n'étaient pas suffisants pour expliquer la variabilité des communautés microbiennes, j'ai décidé de me concentrer sur les paramètres biotiques, c'est-à-dire sur les interactions entre les microorganismes. J'ai donc inféré des réseaux de co-occurrence pour chaque écosystème à toutes les saisons, basés sur les microorganismes les plus abondants (c'est-à-dire ceux dont l'abondance relative est supérieure à 0,05% et qui sont détectés dans plus de 70% des échantillons). J'ai suivi l'approche développée par [START_REF] Chiquet | Variational inference for sparse network reconstruction from count data[END_REF], PLNmodels. Afin de déterminer la structure des réseaux de co-occurrence dans chaque écosystème pendant les quatre saisons, je me suis intéressée aux indices topologiques caractérisant les réseaux (nombre de noeuds, nombre de liens, connectance, etc.). La comparaison des indices entre les différentes saisons et les différents écosystèmes a révélé des similitudes dans la structuration des communautés microbiennes. Notamment, les réseaux obtenus pour l'été et le printemps sont majoritairement caractérisés par une plus grande taille, avec plus de noeuds, et avec une proportion plus élevée d'interactions positives. A l'inverse, comparés aux autres saisons, les réseaux d'hiver présentent plus d'interactions impliquant des parasites potentiels. Ces différences ont pu être testés grâce à des modèles statistiques. J'ai également pu observer de nombreuses interactions entre producteurs primaires et ciliés (prédateurs) dans les réseaux, ce qui valide écologiquement notre méthode. Il existe dans dans les écosystèmes des similitudes dans la structuration des communautés au cours de l'année, et ce malgré d'importantes différences en terme de composition des communautés microbiennes. Cette étude exploratoire donne par conséquent un premier aperçu de la structuration saisonnière des communautés microbiennes dans de petits écosystèmes d'eau douce. Elle introduit également une méthodologie de comparaison de réseaux d'interactions et souligne l'importance de certains groupes taxonomiques dans le fonctionnement des écosystèmes étudiés.

Conclusions

L'analyse menée dans le lac Baïkal a permis d'établir une image de la diversité et de la composition des communautés microbiennes eucaryotes sur l'ensemble du lac, ce qui n'avait jamais été fait auparavant. Cependant, cette image est uniquement celle des micro-organismes de la période estivale. Il faudrait pouvoir réaliser une analyse comparative dans le temps, aux différentes saisons et sur plusieurs années pour avoir une représentation complète des microorganismes eucaryotes dans le lac Baïkal. La caractérisation réalisée ici constitue un référent unique pour le suivi temporel de cet écosystème dans le contexte du changement climatique qui affecte particulièrement la Sibérie et offre des pistes pour savoir quels facteurs environnementaux vont impacter de manière plus sévère les microorganismes planctoniques qui s'y développent. Les analyses menées sur les petits écosystèmes d'eau douce ont permis de confirmer l'importance écologique de ces systèmes le plus souvent négligés dans les études. Ils hébergent des communautés microbiennes très diverses et très dynamiques dans le temps. Les microorganismes sont des acteurs clés de ces écosystèmes, et il est essentiel de mieux comprendre leur composition et leur structuration dans le temps. Par conséquent, ce travail peut contribuer à améliorer la gestion de ces écosystèmes communs afin de mieux préserver la biodiversité.

Sampling

October 2016 to April 2019 Seasonal sampling of five small freshwater ecosystems of the "Parc Naturel Régional de la Haute Vallée de Chevreuse". The studied ecosystems and the sampling procedure are described in Chapter 2).

August 2019, 3 weeks Sampling of ponds and lakes around Syrdakh (Yakoutia, Russia). The Central Yakutia region is an area where a strong thawing of the permafrost is observed, resulting from climatic and anthropogenic drivers. A group of researchers, conducted by Dr. A. Séjourné, monitor this area since several years, in order to investigate the regional changes in terms of hydrological, thermal, and geomorphological characteristics. In particular, they investigate the release of the dissolved carbon from ponds and lakes that are created by the thawing. Theses freshwater ecosystems present various ages of formations, that can be determined with the help of satellite images. As microorganisms are key players in the functioning of aquatic ecosystems and in particular in the carbon cycle, knowing the microbial community composition of these particular freshwater ecosystems is important. The aim of this sampling campaign was to collect for the first time samples to decipher the microbial community composition in several freshwater ecosystems following different ages of formation. Abstract: Microorganisms are the most abundant and diverse forms of life on Earth and are characterized by high phylogenetic and metabolic diversities. They are thus involved in biogeochemical cycles and trophic webs, which make them key players in ecosystem functioning. To decipher the ecology of microorganisms, it is crucial to include spatial, temporal and taxonomic scales. Although several abiotic and biotic parameters have been identified as drivers of microbial community composition in aquatic ecosystems (e.g. temperature, orthophosphate concentration, predation, symbiosis), more investigations are needed to better understand how microbial community structure is shaped. However, investigating biotic interactions involving microbes is challenging because of microbial features (e.g. small size, high diversity, low cultivation efficiency). This PhD thesis aims at describing the microbial diversity inside two overlooked types of freshwater ecosystems and at identifying the factors driving microbial community composition. The first section of this thesis aims at comprehensively describing the spatial distribution (horizontal and vertical) of planktonic microbial eukaryotes in Lake Baikal (Siberia, Russia). We focus on samples collected in summer 2017 along a transect of ∼600 km across the three basins of the lake, from the surface to the deepest areas (∼1500 m) and from littoral to open waters. The three other sections present an eight-year investigation of the composition and temporal dynamics of microbial communities belonging to the three domains of life at the surface of five small freshwater ecosystems (located in the South West of Paris, France). Samples were collected at two different frequencies, monthly (2011-2013) and seasonally (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019). The composition of planktonic communities was assessed by the sequencing of the phylogenetic marker genes 16S and 18S rRNA. In all the ecosystems studied, the microbial communi-ties were diverse, covering all eukaryotic and prokaryotic supergroups. Moreover, they included typically marine lineages, especially in Lake Baikal, (e.g. diplonemid, MAST) which suggested that the frontiers between marine and freshwater systems may be thinner than previously thought. They also included taxa that remain enigmatic, such as bacteria of the Candidate Phyla Radiation. Multivariate analysis showed that only a low fraction of the variance can be explained by the measured physico-chemical parameters. In terms of spatial variations, there was a weak variability of communities in Lake Baikal in summer across sampling basins, but a strong stratification along the water column. Depth, which is a proxy and a summary of the variations of the environmental conditions (e.g. light) along the water column, appeared to be a major driver of community composition. The small freshwater ecosystems harbored different microbial communities despite their geographic proximity. In terms of temporal variations, two types of patterns were detected. At the intra-annual scale, global communities were characterized by a strong seasonality. However, at the Operational Taxonomic Unit level, less than 2% of the community were characterized by recurrent seasonal patterns. This suggests that ecosystems have a yearly seasonal functioning, despite the presence of some unpredictable microbial dynamics. At the inter-annual scale, microbial communities experienced an increase of dissimilarities over the eight years, indicating turnovers in community composition. Finally, the structure of the communities studied through co-occurrence network inference reflected the spatiotemporal variations previously observed. Indeed, communities were more connected at the surface of Lake Baikal compared to the bottom. Moreover, ecosystems shared similar structural properties at each season. This underlines the importance of ecological interactions in the composition of microbial community over space and time.

Others

Université Paris-Saclay Espace Technologique / Immeuble Discovery Route de l'Orme aux Merisiers RD 128 / 91190 Saint-Aubin, France

  les membres du laboratoire Ecologie, Systématique et Evolution. Ce fut enrichissant et agréable de passer ces quelques années avec vous. Et merci à Emmanuelle pour ta bonne humeur, ta patience et ton aide inestimable face aux démarches administratives ! Merci à Judith Legrand, Nicolas Delpierre et Stéphane Bazot de m'avoir fait confiance pour prendre en charge leurs TPs et TDs. Cette expérience de monitorat aura été très formatrice pour moi. Et un grand merci à Marianne pour sa bonne humeur et sa gentillesse, et pour avoir fait en sorte que tous mes enseignements se passent toujours au mieux. Merci à Antoschka, Frrred et Lefe et à toute l'équipe Kickass

3. 1

 1 Sampling sites and overall planktonic protist community composition in Lake Baikal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Marine signature taxa detected in Lake Baikal plankton . . . . . . . . . . . . . . 3.3 Non-metric multidimensional scaling (NMDS) analysis of Lake Baikal plankton samples as a function of protist community similarities . . . . . . . . . . . . . . 3.4 Box plots showing the distribution of relative abundances of major functional categories of protists in Lake Baikal plankton . . . . . . . . . . . . . . . . . . . 3.5 Co-occurrence networks of planktonic protists 700 in the Lake Baikal water column 3.B.1Box plots of physico-chemical parameters measured in Lake Baikal water samples 3.B.2Accumulation curves for OTUs identified in Lake Baikal plankton samples before and after rarefaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v 3.B.3Box plots showing diversity (richness) and evenness values in Lake Baikal water samples as a function of basin and depth . . . . . . . . . . . . . . . . . . . . . . 3.B.4Rank:abundance curves of protist OTUs in Lake Baikal plankton samples . . . . 3.B.5KRONA representation of the global diversity of Holomycota in Lake Baikal plankton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.B.6Non-metric multidimensional scaling (NMDS) analysis of Lake Baikal plankton samples based on differences in protist community composition . . . . . . . . . . 3.B.7Non-metric multidimensional scaling (NMDS) analysis of Lake Baikal plankton samples as a function of protist community similarities based on SWARMdetermined OTUs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.B.8Block-model representation of the networks of the planktonic protists in Lake Baikal for the three major depth categories . . . . . . . . . . . . . . . . . . . . . 4.2.1 (a) Cumulative curves showing the total number of prokaryotic OTUs detected in each ecosystem from April 2011 until April 2013 during the temporal survey in the five small freshwater ecosystems; (b) Richness and diversity estimators computed for all the samples; (c) Histograms showing the relative abundance of 16S rDNA amplicon reads assigned to high-rank taxa averaged over the two-year survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Venn diagram showing the number of OTUs shared by several ecosystems or specific to an ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Canonical correspondence analyses conducted on all OTUs affiliated either to microbial eukaryotes or prokaryotes retrieved in the five ecosystems over the two-year survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.4 Time-lag analyses (TLA) were computed for each ecosystem between sampling dates over increasing time-lags over the two-year survey (from one-month to 23-months) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.5 Wisconsin standardized pairwise Bray-Curtis dissimilarities were calculated between samples separated from 1 to 12 months for abundant and rare OTUs belonging to microbial eukaryotes, prokaryotes and whole microbial community detected in the five ecosystems over the two-year survey . . . . . . . . . . . . . . 4.B.1Rank-abundance histograms of all OTUs detected in the five ecosystems (A), OTUs with a mean relative abundance of at least 0.5% (B) and rank-abundance histograms of taxonomic groups in each small freshwater ecosystem . . . . . . . vi 4.B.2Histograms of monthly relative proportions of 16S rDNA amplicon reads assigned to high-rank taxa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.B.3Histograms of average relative proportions of 16S rDNA amplicon reads assigned to archaeal high-rank taxa, from the five ecosystems over the 2-years survey . . 4.B.4Non-metric multidimensional scaling (NMDS) plot, built on Wisconsin standardized Bray-Curtis dissimilarities between combined microbial eukaryote and prokaryote OTUs of all samples collected in the five ecosystems over the 2-years survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.B.5Hierarchical cluster analysis build using Wisconsin standardized Bray-Curtis dissimilarities between every samples collected over the 2-years survey for the five small freshwater ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.B.6Canonical correspondance analysis (CCA) conducted on abundant microbial eukaryote (A) and prokaryote (B) Wisconsin-standardized OTUs retrieved in the five systems over the 2-years survey . . . . . . . . . . . . . . . . . . . . . . . . . 4.B.7Non-metric MultiDimensional Scaling (NMDS) plots, based on Bray-Curtis distances computed after Wisconsin standardization of all OTU frequencies in samples from the five ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.B.8Box plots of turnover rates computed for rare and abundant OTUs affiliated to microbial eukaryotes and prokaryotes in the five small freshwater ecosystems over the 2-years survey. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Time-lag analyses (TLA) were computed for each ecosystem between sampling dates over increasing time lags over the 8-year survey (from one-month until 29-month) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Histograms showing the relative abundance of 16S and 18S rDNA amplicon reads assigned to high-rank taxa average over the 8-year survey in the five ecosystems. 5.2.3 Venn diagram showing the number of rarefied OTUs over the 8-year survey; (B) Non-metric multidimensional scaling plot constructed with Wisconsin-standardized Bray-Curtis dissimilarities applied on rarefied data between all samples . . . . . 5.2.4 Co-occurrence network inferred by the PLN approach and representing the global structure of microorganisms over the five ecosystems . . . . . . . . . . . . . . . 5.B.1Temporal dynamics of the physico-chemical parameters and the chlorophyll a concentration measured over the 8-year survey . . . . . . . . . . . . . . . . . . . 5.B.2Principal Component Analysis (PCA) on all physico-chemical parameters measured in the five ecosystems during the 8-year survey . . . . . . . . . . . . . . . vii 5.B.3(A) Richness and evenness were computed on the rarefied microbial eukaryotic and prokaryotic communities in the samples collected during the 8-year survey in the five ecosystems; (B) Cumulative curves showing the total number of eukaryotic and prokaryotic rarefied new OTUs detected in each ecosystems . . . . 5.B.4Rank-abundance histograms of all rarefied OTUs in the entire dataset for microbial eukaryotes and prokaryotes . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.B.5Box plots of turnover rates computed for rare and abundant rarefied OTUs, affiliated to microbial eukaryotes and prokaryotes in the five ecosystems between samples regrouped by season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.B.6(A) Successive turnover rates computed for rare and abundant rarefied OTUs, affiliated to microbial eukaryotes and prokaryotes in the five ecosystems between samples regrouped by sampling years; (B) Mean value of the turnover rate for each ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.B.7Pie-chart summarizing the results of Canonical correspondence analyses (CCA) conducted on all rarefied OTUs affiliated either to microbial eukaryotes or prokaryotes in all samples collected during the 8-year survey . . . . . . . . . . . . . . . 5.B.8Non-metric Multi-Dimensional Scaling (NMDS) plots, based on pair-wise Bray-Curtis dissimilarities computed after Wisconsin standardization of rarefied OTU in samples for each of the five ecosystems . . . . . . . . . . . . . . . . . . . . . . 5.B.9Histograms of the recurrent OTUs per ecosystem in terms of diversity and abundance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.B.10 Block-model representation of the co-occurrence network inferred by the PLN approach and representing the global structure of microorganisms over the five ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.B.11 Module representation of the co-occurrence network inferred by the PLN approach and representing the global structure of microorganisms over the five ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Seasonal co-occurrence networks over the eight-year survey in the four studied ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Principal Component Analysis (PCA) of the topological properties of the seasonal network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.B.1Block-model representation of the seasonal networks over the eight-year survey in the four ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii 6.B.2Barplots presenting the OTU composition of the block model groups (BMG) for the season networks of the Etang des Vallées . . . . . . . . . . . . . . . . . . . . 6.B.3Barplots presenting the OTU composition of the block model groups (BMG) for the season networks of La Claye . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.B.4Barplots presenting the OTU composition of the block model groups (BMG) for the season networks of Mare Gabard . . . . . . . . . . . . . . . . . . . . . . . . 6.B.5Barplots presenting the OTU composition of the block model groups (BMG) for the season networks of Ru Sainte Anne . . . . . . . . . . . . . . . . . . . . . . . 6.B.6Representation of the links between network topology and diversity indexes . . . 6.B.7Histogram showing the composition of the eukaryotic nodes in the networks . . . 6.B.8Histogram showing the composition of the prokaryotic nodes in the networks . . 7.4.1 Co-occurrence network of the microbial communities in Mare Gabard in spring using three different methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix List of Tables 2.1 General characteristics of the studied ecosystem . . . . . . . . . . . . . . . . . . 2.2 Example of a reduced data-frame used for the analyses, exposing the read numbers for each samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Permutational multivariate analyse of variance (PERMANOVA) of Lake Baikal plankton samples across basin, sampling site and depth . . . . . . . . . . . . . . 3.A.1Sampling sites in Lake Baikal water column and associated physico-chemical, sequence and diversity data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.A.2Identification, phylogenetic affinity and abundance of eukaryotic OTUs identified in Lake Baikal plankton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.A.3Diversity, abundance and distribution of eukaryotic lineages previously thought to be exclusively marine identified in Lake Baikal plankton . . . . . . . . . . . . 3.A.4ANOSIM analyses between pairs of Lake Baikal plankton sample groups defined as a function of depth, basin of origin and coastal vs. pelagic location . . . . . . 3.A.5Tentative classification of phylogenetic lineages within broad functional categories 3.A.6Functional and phylogenetic classification of OTUs used for network construction 3.A.7Properties of networks built upon Lake Baikal plankton OTUs for the three depth categories of the water column . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Permutational multivariate analyses of variance (PERMANOVA) partitioning of the whole microbial community, prokaryotes and microbial eukaryotes, based on Bray-Curtis dissimilarities calculated using Wisconsin standardized OTU relative abundances among hierarchical-defined clusters 1 and 2 . . . . . . . . . . . . . . 4.A.1Names, numbers of reads, OTUs and diversity estimates obtained for the different samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.A.2Number of abundant and all OTUs affiliated to prokaryotes and microbial eukaryotes in the five ecosystems and in the five ecosystems considered together . . xi 4.A.3Linear and polynomial models were computed based on time-lag analyses (TLA) between sampling dates over increasing time-lags over the 2-years survey (from one-month to 23-months) for the five ecosystems . . . . . . . . . . . . . . . . . . 4.A.4RV index were calculated based on co-inertia analyses to assess co-dynamic between prokaryotes and microbial eukaryotes in the five ecosystems and in all ecosystems considered together . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Repartition of all the rarefied OTUs in 4 categories, with the percentage of OTUs and their total relative abundance within the categories for microbial eukaryotes and prokaryotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.A.1Summary of the physico-chemical composition of the five ecosystems . . . . . . . 5.A.2Description of the samples used in the analyses. Names, number of rarefied microbial eukaryotic and prokaryotic OTUs, evenness for microbial eukaryotes and prokaryote and number of reads before rarefaction for microbial eukaryotes and prokaryotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.A.3Total number of OTUs, mean number of OTUs and number of abundant OTUs per ecosystem for microbial eukaryotes and prokaryotes . . . . . . . . . . . . . . 5.A.4ANOSIM analysis between each pair of ecosystem based on Bray-Curtis dissimilarities computed after Wisconsin standardization of rarefied OTUs (9999 permutations) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.A.5Permutational multivariate analyses of variance (PERMANOVA) calculated using Wisconsin standardization on rarefied OTUs for microbial eukaryotes and prokaryotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.A.6Linear models were computed based on the time-lag (TLA) calculated between sampling dates over increasing time-lags over the 8-year survey (from one-month to 30-month) for the five ecosystems . . . . . . . . . . . . . . . . . . . . . . . . 5.A.7Canonical correspondence analyses (CCA) conducted on all rarefied OTUs affiliated either to microbial eukaryotes or prokaryotes and on physico-chemical parameters in the samples collected during the 8-year survey in each ecosystem . 5.A.8ANOSIM analysis conducted for each ecosystems between each pair of season, based on Bray-Curtis dissimilarities computed after Wisconsin standardization of rarefied OTUs (9999 permutations) . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Summary of the seasonal co-occurrence networks properties in the four ecosystems194 6.A.1Linear regressions conducted between network properties and diversity indexes . xii 6.A.2Summary of the seasonal co-occurrence networks properties in the four ecosystems207 6.A.3Summary of the five most connected nodes in the seasonal co-occurrence networks for each ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 6.A.4Output of the "degree" model, quantify the effect of both the node's lifestyle and the season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 6.A.5Details of the pairwise comparison of the "degree" model between the season within the different ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 6.A.6Summary of percentage of the type of interactions developed in the seasonal co-occurrence networks for each ecosystem . . . . . . . . . . . . . . . . . . . . . 211 6.A.7Output of the "parasite" model, quantifying the effect of the season on the percentage of edges established with putative parasitic OTUs in networks . . . . . being regarded as the cinderellas of aquatic science, temporary waters represent significant components of the global landscape." D. Williams 1

Figure 1 . 1

 11 Figure 1.1 Left panel : Front page of the book written by L. Joblot and published in 1754 (posthumously), with the mention of "animalcules"; Middle Panel : Illustrations of L. Joblot, describing his microscopic observations of an infusion of oak bark. Right panel : Technical drawing of one of the Joblot's microscopes (Source of the images: BNF, Bibliothèque Nationale de France, https://gallica.bnf.fr)

Figure 1 . 2

 12 Figure 1.2 Comparison of the zonation of marine systems (left panel) and freshwater lakes (right panel). Inspired from[START_REF] Madsen | Flash: fast length adjustment of short reads to improve genome assemblies[END_REF] and[START_REF] Brönmark | The biology of lakes and ponds[END_REF].

Figure 1 . 3

 13 Figure 1.3 Upper panel (A): View of the phylogenetic tree of life from Hug et al. (2016) build on sequenced genomes. The red dots indicate lineages lacking an isolated representative. Lower panel (B): Details on the phylogenetic tree of eukaryotes based on a consensus of recent phylogenomic studies.

Figure

  Figure taken from Burki et al. (2020).

Figure 1 .

 1 Figure 1.4 Rank-abundance curve representing the abundance of individuals (e.g. OTUs) ranked according to their abundance. The red area corresponds to the abundant community. The blue area corresponds to the rare community. Figure adapted from Pedrós-Alió (2006).

Figure 1 . 5

 15 Figure 1.5 Representation of the marine food webs showing the role of microbial communities. The microbial loop is highlighted with the purple arrow. POM corresponds to the Particulate Organic Matter and DOM to the Dissolved Organic Matter. Figure taken from Worden et al. (2015).

Figure 1 . 7

 17 Figure 1.7 Illustration of the scales used to investigate microbial community structuration. Ecological scale has three axes: space, time, and phylogeny. Each of these has two components: grain, that is the scope of the unit of observations, and extent, that is the scope of the entire set of observations (adapted from Ladau and Eloe-Fadrosh, 2019).

Figure 1 . 8

 18 Figure 1.8 Variation of the environmental parameters in the water column in a subtropical ocean (taken from Sigman and Hain, 2012 and generated with data collected at the Bermuda Atlantic Timeseries Station (BATS) in July 2008). DCM corresponds to the Deep Chlorophyll Maximum which is a water layer enriched in chlorophyll.

Figure 1 . 9

 19 Figure 1.9 Example of a spatial survey along both vertical and horizontal gradients (Kirkham et al., 2011). (A) Location of the sampling stations of the AMT15 cruise in the Atlantic Ocean. The stations are indicated by black dots. Each station was sampled at six depths, defined by a percentage of surface light intensity penetrating the water column. (B) Distribution patterns of two groups of photosynthetic picoplankton based on dot blot hybridization technique. Black lines indicate the depth in meters. Black dots mark the sampling points.

Figure 1 .

 1 Figure 1.10 Example of analysis on long temporal surveys. Upper panel (A): Bray-Curtis similarity plot of samples collected on a monthly basis at the San Pedro Ocean Time-series over ten years. Each data point is the average of pairwise measurements of samples collected times the number of months apart. The sinusoidal curve indicates that community composition follows a seasonal pattern (Figure taken from Fuhrman et al., 2015). Lower panel (B): Seasonal changes over a nine-year survey in Lake Donghu (China). Temporal patterns were revealed using discriminant function (to resume the dynamics of a pool of species) combined to an autocorrelation analysis (Figure taken from Yan et al., 2017).

  into three basins, distinguished by their bathymetry (see Figure2.1, panel B). Contrary to the South basin, the North basin is well preserved from anthropic disturbances. For this study, the sampling was conducted over the entire Lake area, from North to South and at multiple depths and from the surface to the deepest locations. This work was conducted in collaboration with the Limnological Institute (Irkutsk, Russia).

Figure 2 . 1

 21 Figure 2.1 Location of Lake Baikal, in Siberia (Russia). Panel A represent Lake Baikal with the position of the 17 sampling sites. The red dot line corresponds to the longitudinal profile of the lake, which is represented in the panel B. The three basins are delimited by depth variations. Longitudinal bathymetric profile was taken from Sherstyankin et al. (2006). Maps were generated using the software Ocean Data View.

2. 4

 4 .1.3 Temporal evolution of the communities I studied the temporal evolution of communities by carrying out four different analyses. The aim was to identify patterns in the dynamic of the global community or of individual OTUs. It was thus possible to evaluate the rate of change of the community composition over time and to compare temporal changes in eukaryote and prokaryote community composition.

Figure 2 . 3

 23 Figure 2.3 Possible interpretations of the linear model applied on the time-lag analysis.Adapted from[START_REF] Collins | A method to determine rates and patterns of variability in ecological communities[END_REF]

  Network mean degrees correspond to the average number of established edges. The average path length indicates the mean number of edges necessary to link a given node randomly to another. Network complexity was estimated using two indicators: connectance and clustering coefficient. The connectance was calculated as 𝑐 = 2E N×(N-1)

Figure 3 . 1

 31 Figure 3.1 Sampling sites and overall planktonic protist community composition in Lake Baikal. A, map of Lake Baikal showing the different sampling sites across the three major lake basins (indicated by colors). B, Principal component analysis (PCA) of samples according to their associated physicochemical parameters. The number near the points correspond to the sampling site, and the color of the points indicates their sampling depth. TDS, total dissolved solids; DO, dissolved oxygen; ORP, oxidation-reduction potential. Blue tones indicate the sampling depth in the water column, as indicated. C, Relative abundance of different high-rank eukaryotic taxa in Baikal plankton based on read counts for the defined OTUs. The asterisk indicates samples retrieved from Cell-Traps (Methods).

Figure 3 . 2

 32 Figure 3.2 Marine signature taxa detected in Lake Baikal plankton. A, heat map showing the relative abundance of different typically marine taxa across Baikal plankton samples. The frequency of the different phylogenetic groups is indicated by different shades of blue. B, Maximum Likelihood (ML) phylogenetic tree of OTUs belonging to diplonemids and a related group of euglenozoan excavates (594 unambiguously aligned positions). C, ML tree of radiolarian OTUs (534 unambiguously aligned positions).

  .B.6). Likewise, samples from different basins did not show a clear differentiation, although samples from the Southern basin tended to segregate from the two other basins (Figure 3.3A). Samples from coastal versus open waters did not segregate at all (Figure 3.3B). However, planktonic communities clearly segregated as a function of the water column zonation, with epipelagic, mesopelagic and bathypelagic communities well separated in the NMDS plot (Figure 3.3C). NMDS analyses based on SWARM-defined OTUs yielded very similar results (Supplementary Figure 3.B.7

Figure 3 .

 3 Figure 3.3 Non-metric multidimensional scaling (NMDS) analysis of Lake Baikal plankton samples as a function of protist community similarities. The NMDS plot was constructed with Wisconsin standardized Bray-Curtis dissimilarities between all samples. A, plankton samples highlighted by basin origin. B, plankton samples from coastal, shallow sites versus open water sites. C, samples grouped according to their depth origin in the water column; epipelagic (<200 m), mesopelagic (200-500 m), bathypelagic (>500 m).

Figure 3 . 4

 34 Figure 3.4 Box plots showing the distribution of relative abundances of major functional categories of protists in Lake Baikal plankton. The three plots show the relative abundance of sequences affiliated to autotrophic, heterotrophic and parasitic protists for each sampling depth class. The thickest line inside each box represents the median on the distribution, bottom and top borders of boxes correspond to the first and the third quartiles and whiskers extend to minimal and maximal distances. Significant differences between distributions are indicated with stars (p-values <0.05, <0.005 and <0.0005 are respectively indicated by one, two and three stars). For the assignation of taxa to functional categories, see Supplementary Table3.A.5.

Figure 3

 3 Figure 3.5 Co-occurrence networks of planktonic protists 700 in the Lake Baikal water column.A, network obtained from epipelagic (<200 m) samples across the lake. B, network obtained from mesopelagic (200-500 m) samples. C, network obtained from bathypelagic (>500 m samples). Networks were built on OTUs present in more than 20% samples and having a relative abundance higher than 0.01%. OTUs are represented by nodes and direct covariations between them, by edges. Nodes and taxa labeled with an asterisk correspond to putative parasites.

  Phylogenetic group Number of OTUs Number of reads No. samples where they occur

  Concentrations of dissolved nutrients (total phosphorus (P 𝑡𝑜𝑡 ), orthophosphate (PO 4 3-), total nitrogen (N 𝑡𝑜𝑡 ), nitrite (NO - 2 ), nitrate (NO 3 -) and ammonia (NH 3 )) were measured on the sampling day using colorimetric tests following manufacturer instructions (Hach-Lange, Düsseldorf, Germany). Samples filtered onto 0.2µm pore-size Nuclepore membranes (Whatman)were immediately frozen at -20 ∘ C until measurement (Rochelle-Newall et al., 2014) of dissolved organic carbon (DOC) using a TOC VCSH analyzer (Shimadzu, Tokyo, Japan). Water samples were transported back to the laboratory and immediately serially filtered through 100, 30, 5 and 0.2 µm pore-size Nuclepore filters (Whatman). Filters were immediately stored frozen at -80 ∘ C until DNA extraction. 4.2.3.2 DNA extraction, amplification and sequencing of 16S and 18S rDNA fragments DNA was extracted from cells collected in the smallest size-fraction (0.2-5 µm) using the

  IDentifiers (MIDs) to allow differentiation of PCR products from 20 distinct samples pooled together for multiplex sequencing. PCR amplification was conducted in a total volume of 25 µl using 1.5 mM MgCl 2 , 0.2 mM dNTPs, 0.3 µM primers, 0.3-3 µl of DNA sample and 0.5 U HotStart Taq polymerase (Taq Platinum, Invitrogen, Carlsbad, CA, USA). Twenty-five amplification cycles (94 ∘ C for 30 s, 58 ∘ C for 45 s and 72 ∘ C for 90 s) were preceded by a 3-min denaturation step at 94 ∘ C and ended by a 10 min for final extension at 72 ∘ C. The products of five PCR reactions were pooled together for each sample. These pools were then purified with the QIAquick PCR purification kit (Qiagen, Hilden, Germany). Equivalent amounts of purified amplicons of 20 different samples were pooled and pyrosequenced using the 454 GS-FLX Titanium technology from Roche (Beckman Coulter Genomics, Danvers, MA, USA). In the case of eukaryotes, 18S rDNA fragments of ca. 550 bp, encompassing the V4 hypervariable region, were PCR amplified[START_REF] Simon | Complex communities of small protists and unexpected occurrence of typical marine lineages in shallow freshwater systems[END_REF] with primers EK-565F and 18S-EUK-1134-R-UnonMet biased against Metazoa[START_REF] Bower | Preferential pcr amplification of parasitic protistan small subunit rdna from metazoan tissues[END_REF].4.2.3.3 Analysis of 16S and 18S rDNA 454-pyrosequencesA total of 1 705 835 16S rDNA amplicon reads was obtained. Reads were filtered through an inhouse pipeline to remove spurious sequences. Briefly, pyrosequences without the two flanking primer sequences or containing errors in primer/MIDs sequences or positions with undetermined bases were discarded. To further eliminate PCR and pyrosequencing errors, pairwise flowgram alignments were clustered and treated with the PyroNoise program of Amplicon-

  Team, 2019). Missing physico-chemical values (13% of the physico-chemical dataset) were replaced by the overall average value of the parameter and nutrient concentrations below kit detection limits (19% of the physico-chemical dataset) were set as the limit value minus a small random number (normal distribution µ = 0; 𝜎 = 10 -6 ) for all statistical analyses. To take into account the measured environmental factors altogether in the diverse statistical analyses, their values were normalized (µ = 0; 𝜎 = 1). Relative OTU abundances were obtained by dividing the number of reads for each OTU by the total number of reads retrieved in the sample. OTUs were divided into two groups based on their relative abundance and temporal frequencies as in[START_REF] Simon | Marked seasonality and high spatial variability of protist communities in shallow freshwater systems[END_REF] : abundant OTUs, with a mean relative abundance superior to 0.1% and detected in at least 25% of the samples within one ecosystem, and rare OTUs, represented by all the remaining OTUs. All subsequent statistical analyses based on Bray-Curtis dissimilarities[START_REF] Bray | An ordination of the upland forest communities of southern wisconsin[END_REF] were conducted on Wisconsin-standardized OTU relative frequencies to balance the weight between abundant OTUs and rare OTUs. For global microbial community analyses, i.e. considering eukaryotic and prokaryotic datasets together, tables of

  conducted to determine the contribution of each environmental factor to the variation of microbial community composition. Microbial community assembly was investigated considering the potential interplay between habitats (i.e. ecosystems) and time. To assess how close are microbial community compositions between sampling sites or seasons, non-metric multidimensional scaling (NMDS) analyses were performed based on Bray-Curtis dissimilarities using the 'vegan' R-package. To quantify the role played by the ecosystem clusters (i.e. groups of ecosystems defined in the hierarchical cluster analysis), the habitat (i.e. ecosystems), the time (i.e. year of sampling or the sampling season) in the structuring of the microbial communities, we conducted permutational multivariate analyses of variance (PERMANOVA) using Bray-Curtis dissimilarities calculated on OTU relative frequencies using the adonis function of the 'vegan' R-package. We explored biotic interactions by inferring how prokaryotes and microbial eukaryotes may influence each other assembly over time in each ecosystem. To do so, putative multicollinearity between environmental parameters was checked by computing the variance inflation factors (VIF) with the function 'vif.cca' of the 'vegan' R-package. A VIF value above 10 indicated a strong multicollinearity. Environmental factors were excluded from the analysis if they were characterized by multicollinearity. Forward selection was then used to sort environmental parameters explaining significantly community composition variation for CCA model (Monte Carlo permutation tests, 999 permutations).

Figure 4

 4 Figure 4.2.1 (a) Cumulative curves showing the total number of prokaryotic OTUs detected in each ecosystem from April 2011 until April 2013 during the temporal survey in the five ecosystems (Etang des Vallées, La Claye, Mare Gabard, Saint Robert and Ru Sainte Anne). Dashed lines indicate missing data due to the drought: Ru Sainte Anne was dried in August and September 2012, and La Claye from the end of July to December 2011 and in September 2012. (b) Richness and diversity estimators computed for all the samples. Richness corresponds to the expected number of OTUs in random subsamples of the size of the smallest sequence library (1309 reads in Mare Gabard in May 2011). The thickest line inside each box represents the median on the distribution, bottom and top borders of boxes correspond to the first and third quartiles and whiskers extend to minimal and maximal distances. (c) Histograms showing the relative abundance of 16S rDNA amplicon reads assigned to high-rank taxa averaged over the two-year survey, from the five ecosystems (CPR: Candidate Phyla Radiation, FCB: Fibrobacteres-Chlorobi-Bacteroidetes, PVC: Planctomycetes-Verrucomicrobia-Chlamydiae). One star (*) indicates Microgenomates and two stars (**) shows Parcubacteria.

  Figure 4.2.2 Venn diagram showing the number of OTUs shared by several ecosystems or specific to an ecosystem. All samples from the two-year survey are pooled for each ecosystem. (b) Non-metric multidimensional scaling plot, built on Wisconsin-standardized Bray-Curtis dissimilarities between all samples. Stress value is displayed on the right border of the plot.

  among the five ecosystems(Supplementary Figure 4.B.4). Hierarchical clustering analysis based on Bray-Curtis dissimilarities of OTU frequencies supported these differences between the ecosystems. As in the case of the prokaryotic community, the Ru Sainte Anne global microbial community was set apart of the cluster composed of the four ponds (Supplementary Figure4.B.5). Interestingly, the cluster of Ru Sainte Anne samples also contained a sample collected in La Claye just after a drought event (LC37, October 2012). To determine the effect of the environment on the microbial community composition, we conducted CCA on either prokaryotic or eukaryotic OTUs and the significant physico-chemical parameters sorted out by a forward analysis. Conductivity, water temperature, concentrations of nitrite and dissolved oxygen were the only parameters that had a significant effect on the microbial communities (10.0 and 6.9% of variance for prokaryotes and eukaryotes, respectively) (Figure4.2.3). In addition, the influence of nitrate concentration appeared to be significant only for prokaryotes. Conductivity, a proxy of the total ion concentration, explained a third of the variance of prokaryotes and microbial eukaryotes on average. The remaining four significant abiotic factors contributed similarly to explain the community structure. To further explore how the environment controls the most abundant microbial taxa, we carried out CCA between various physicochemical parameters and the most abundant OTUs (Supplementary Figure4.B.6). Prokaryotes and microbial eukaryotes in Etang des Vallées were highly related to pH, concentrations of dissolved oxygen, nitrite and nitrate.

Figure 4

 4 Figure 4.2.3 Canonical correspondence analyses conducted on all OTUs affiliated either to microbial eukaryotes or prokaryotes retrieved in the five ecosystems over the two-year survey. OTU proportion were Wisconsin standardized. Among all measured physico-chemical parameters, only conductivity, temperature, concentrations of dissolved oxygen nitrate and nitrite were explained significantly the variance of the OTUs (P<0.05). Considered together, these abiotic parameters explained 6.95 and 9.98% of the global variance of the OTUs belonging to microbial eukaryotes and prokaryotes, respectively. Values within each section of the pie charts indicate the proportion of variance explained by each parameter of the global variance of the OTUs.

  4.3 Intra-and inter-annual dynamics of abiotic parameters and microbial community composition We conducted time-lag analyses based on linear and polynomial regressions of Bray-Curtis distances over time on environmental factors and microbial community composition to assess the temporal patterns at intra-and inter-annual scales. The slopes of the linear regressions of abiotic parameters were either not significant (Etang des Vallées, La Claye and Saint Robert) or only slightly marked (Mare Gabard and Ru Sainte Anne, linear regression slopes of 0.025 and 0.036, respectively) (Figure 4.2.4 and Supplementary Table

Figure 4

 4 Figure 4.2.4 Time-lag analyses (TLA) were computed for each ecosystem between sampling dates over increasing time-lags over the two-year survey (from one-month to 23-months). Wisconsin standardized Bray-Curtis dissimilarities were used with all OTUs of prokaryotes, microbial eukaryotes and whole microbial communities while Euclidean distances were applied with environmental parameters.Linear regressions, in red, and polynomial regressions of the 5th order, in green were calculated for each TLA dataset. Significant regressions (P<0.05) are indicated with a green star (linear regressions) or a red star (polynomial regressions).

Figure 4

 4 Figure 4.2.5 Wisconsin standardized pairwise Bray-Curtis dissimilarities were calculated between samples separated from 1 to 12 months for abundant (detected in each ecosystem in at least 25% of samples, and with >0.1% mean relative abundance per sample ) and rare OTUs (detected in each ecosystem in less than 25% of samples, and with ≤ 0.1% mean relative abundance per sample; curves highlighted with a grey shade) belonging to microbial eukaryotes (triangles), prokaryotes (circles) and whole microbial community (squares) detected in the five ecosystems over the two-year survey. Each point represents mean value of pairwise Bray-Curtis dissimilarities and is plotted with standard errors.

  2-5 µm) whereas freshwater Cyanobacteria are usually of larger size or filamentous. CPR bacteria and the Diapherotrites-Parvarchaeota-Aenigmarchaeota-Nanoarchaeota-Nanohaloarchaeota (DPANN) supergroups, which have few cultured representatives and are likely composed of symbionts (Castelle et al., 2018), were observed in the five ecosystems. In particular, Woesearchaeota was the most abundant archaeal phylum (Liu et al., 2018) (Figure 4.2.1C, Supplementary Figure 4.B.3). Despite these overall similarities and despite high OTU turnover rates at intra-and inter-annual scales (Supplementary Figure 4.B.8), each ecosystem harboured specific prokaryotic communities mainly composed of non-shared OTUs (Figure 4.2.2 and Supplementary Figure 4.B.5) as observed in other freshwater envi-

  Figure 4.2.2, Supplementary Figures 4.B.4 and 4.B.5) despite their spatial proximity. These differences may be the result of a combination of diverse ecological processes[START_REF] Goldford | Emergent simplicity in microbial community assembly[END_REF] that can explain the high OTU turnover rates observed (Supplementary Figure4.B.8).

(

  VIF). A VIF value above 10 indicated a strong multicollinearity and resulted in the exclusion of the environmental factors. Then, forward selection was used to identify the most important environmental parameters in the CCA model (Monte Carlo permutation tests, 999 permutations).

  calculated, corresponding to the average number of edges established by each node. The average path length, representing the mean number of edges that are needed to link two random nodes was determined. The complexity of the network was estimated with two indicators. The connectance, which quantifies the proportion of edges established in the network compared to all the potential edges, was calculated as 𝑐 = 2E N×(N-1)

Figure

  Figure 5.B.1).

  ammonia concentration reached higher values in Etang des Vallées and Mare Gabard over time. Chlorophyll a concentrations decreased in Mare Gabard and Saint Robert during the study. Nitrate concentrations were lower in La Claye over the survey. DOC concentration increased

Figure 5

 5 Figure 5.2.1 Time-lag analyses (TLA) were computed for each ecosystem between sampling dates over increasing time lags over the 8-year survey (from one-month until 29-month). Wisconsin standardized Bray-Curtis dissimilarities were calculated on the rarefied microbial eukaryotic and prokaryotic datasets. Euclidean distances were calculated on measured physico-chemical parameters. Each point represents the mean value of pairwise Bray-Curtis dissimilarities or Euclidean distances for a given time-lag and is plotted with associated standard errors. Triangles indicate the prokaryotic data, circles indicate the eukaryotic data and square indicate the physico-chemical data. Linear regression was calculated on the raw dissimilarities and distances, previously to the mean transformation. Significant regressions (p<0.05) are indicated with a star.

Figure 5

 5 Figure 5.2.2 Histograms showing the relative abundance of 16S and 18S rDNA amplicon reads assigned to high-rank taxa average over the 8-year survey in the five ecosystems.

  Along the time survey, community structure can change drastically between two season; e.g. for the eukaryotes in Mare Gabard, community diversity changed from a community dominated by Ciliophora (Abundance of 93.0% in MG77, Autumn 2017) to a community dominated by Ochrophyta (Abundance of 83.9% in MG79, Winter 2017). Prokaryotes were less represented by phyla dominating the community in one ecosystem, at the exception of Proteobacteria which reached 79.1% in Mare Gabard (MG59, Summer 2014). This observation is coherent with the evenness values (0.52 for eukaryotic and 0.69 for prokaryotic communities).

Figure 5

 5 Figure 5.2.3 Venn diagram showing the number of rarefied OTUs over the 8-year survey shared by the different ecosystems or specific to one ecosystem for microbial eukaryotes and prokaryotes (EV Etang des Vallées, LC La Claye, MG Mare Gabard, SR Saint Robert, RSA Ru Sainte Anne). (B) Non-metric multidimensional scaling plot constructed with Wisconsin-standardized Bray-Curtis dissimilarities applied on rarefied data between all samples, for eukaryotic and prokaryotic communities. Stress value is displayed on the right border of the plot.

  5.B.10). Several block model groups (BMG) were mainly composed of microbial eukaryote nodes (BMG1, 4, 5 and 8). The block model groups were often dominated by one specific taxonomic group (e.g. BMG3 and BMG7 by Parcubacteria, or the BMG4 by Ochrophytes). BMG4 was dominated by autotrophic microbial eukaryotes. Overall, the different BMG seemed to gather nodes with the same taxonomy or the same trophic mode.Seven modules were detected in the network (Supplementary Figure5.B.11). As for the block model analysis, nodes were gathered in the module according to the taxonomic affiliation.For example, module 3 was dominated by Ochrophyta and module 4 by Fungi. Modules 6 and 7 were clearly isolated from the rest of the network. They were mainly composed of OTUs affiliated to parasites. A majority of Woesearchaeota and Parcubacteria composed module 6 while module 7 was dominated by Parcubacteria. OTUs (31) that did not belong to a specific module were separated from the global network and interacted by pair or triplets.

Figure 5

 5 Figure 5.2.4 Co-occurrence network inferred by the PLN approach and representing the global structure of microorganisms over the five ecosystems. The network is inferred based on the OTUs present in 35% of the samples, and with a mean abundance higher than 0.01%. The sequencing depth was added as an offset of the model, and the sampling site and the temperature were considered as covariables.

  A.7 Canonical correspondence analyses (CCA) conducted on all rarefied OTUs affiliated either to microbial eukaryotes or prokaryotes and on physico-chemical parameters in the samples collected during the 8-year survey in each ecosystem. A forward selection process was applied to only select the parameters explaining significantly community variance

1

  Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique Evolution, 91405, Orsay, France 2 Sorbonne Université, UPEC, CNRS, IRD, INRAE, Institute d'Ecologie de des Sciences de l'Environnement de Paris, iEES-Paris, F-75005 Paris, France Article in preparation.

Figure 6

 6 Figure 6.2.1 Seasonal co-occurrence networks over the eight-year survey in the four studied ecosystems. The networks were built on OTUs present in at least 6 seasons over the 8 years and presenting a relative abundance higher than 0.05%. For each network, the number of connected nodes n is indicated. The shape and the color of the nodes indicate respectively the kingdom and the lifestyle of the OTU. Red and grey edges indicate respectively positive and negative relationships.

  0

  2.2). The two first axes of the PCA explained up to 71.9% of the variations of network properties. According to the point distribution, summer and winter appeared to be different, with summer networks characterized by a higher percentage of positive links and connected OTUs. Spring showed the largest variabilities in terms of network properties.

Figure 6

 6 Figure 6.2.2 Principal Component Analysis (PCA) of the topological properties of the seasonal network. The shape of the point indicates the ecosystem and the color the season of sampling. Ellipses highlight the seasonal clusters.

  .B.3 Barplots presenting the OTU composition of the block model groups (BMG) for the season networks of La Claye. Near to the BMG number is indicated the total number of OTU involved in the BMG. Taxonomic affiliation is indicated for each bar. The color of the bar indicates the kingdom and the lifestyle of the taxa. Shade of green correspond to autotrophic OTUs, shade of blue correspond to heterotrophic OTUs and shade of orange correspond to putative parasite OTUs.

Figure 7

 7 Figure 7.4.1 Co-occurrence network of the microbial communities in Mare Gabard in spring using three different methods. Left panel: network based on Pearson's correlation, with a selection of the correlation ≥0.7 and with a p-value ≤0.01. Middle panel: network based on PLNmodel but without an inversion of the variance-covariance matrix. Right panel: network based on PLNmodel. For each network, the number of nodes (n) and edges (e) is indicated.

  January 2017 to March 2018 Non-permanent representative of the laboratory ESE (Ecologie, Systématique et Evolution) Titre: Structuration spatio-temporelle des communautés microbiennes dans les écosystèmes d'eau douce Mots clés: Microorganismes, Ecologie, Diversité, Co-occurrence, Fonctionnement écosystèmes, Statistiques Résumé: Les microorganismes constituent la forme de vie la plus abondante et diverse sur Terre et ils présentent une grande diversité phylogénétique et métabolique. Ils sont donc impliqués dans les cycles biogéochimiques et les réseaux trophiques, ce qui en fait des acteurs clés du fonctionnement des écosystèmes. Pour décrypter l'écologie des microorganismes, il est essentiel de prendre en compte les échelles spatiales, temporelles et taxonomiques. Bien que des paramètres abiotiques et biotiques aient été identifiés comme influençant la composition des communautés microbiennes dans les écosystèmes aquatiques (e.g. la température, la prédation), des études complémentaires sont nécessaires pour mieux comprendre la structure des communautés microbiennes. Cependant, l'étude des interactions biotiques entre microorganismes est difficile en raison de leur petite taille, de la grande diversité et du peu d'individus cultivés. Cette thèse de doctorat vise à décrire la diversité microbienne au sein de deux types d'écosystèmes d'eau douce encore peu étudiés, et à identifier les facteurs qui déterminent la composition de leurs communautés microbiennes. La première partie de cette thèse vise à décrire la distribution spatiale (horizontale et verticale) des protistes planctoniques du lac Baïkal (Sibérie, Russie). Nous nous sommes intéressés à des échantillons collectés en été 2017 le long d'un transect de ∼600 km couvrant les trois bassins du lac, de la surface aux profondeurs (∼1500 m) et du littoral au pélagique. Les trois autres parties présentent une étude de huit ans de la composition et de la dynamique temporelle des communautés microbiennes des trois domaines du vivant, à la surface de cinq petits écosystèmes d'eau douce (sud-ouest de Paris, France). Les échantillons ont été collectés à deux fréquences, mensuelle (2011-2013) et saisonnière (2011-2019). Les communautés planctoniques ont été caractérisées par le séquençage des gènes ARNr 16S et 18S. Dans tous les écosystèmes, les communautés microbiennes sont très diverses, couvrant tous les super-groupes eucaryotes et procaryotes connus. Elles incluent des lignées typiquement marines (e.g. diplonémide, MAST), ce qui suggère que la frontière entre le marin et l'eau douce pourrait être plus fine que prévu. Des taxons encore peu connus ont aussi été détectés, tels que des bactéries du Candidate Phyla Radiation. Des analyses multivariées ont montré que seule une faible fraction de la variance des communautés peut être expliquée par les paramètres abiotiques étudiés. Pour les variations spatiales, nous avons constaté une faible variabilité des communautés du lac Baïkal dans les différents bassins, mais avec une forte stratification le long de la colonne d'eau. La profondeur, qui traduit les variations environnementales (e.g. la lumière) dans la colonne d'eau, semble influencer significativement les communautés. Les petits écosystèmes abritent différentes communautés microbiennes malgré leur proximité géographique. Pour les variations temporelles, deux dynamiques ont été identifiées. À l'échelle intra-annuelle, les communautés sont caractérisées par une forte saisonnalité. Cependant, moins de 2% des unités taxonomiques opérationnelles présentent une récurrence saisonnière. Cela suggère que les écosystèmes ont un fonctionnement saisonnier, malgré des dynamiques individuelles imprévisibles. À l'échelle interannuelle, les communautés microbiennes sont de plus en plus différentes au cours des huit années, indiquant des changements continus dans leur composition. Enfin, l'inférence des interactions microbiennes grâce aux réseaux de cooccurrence reflète les variations spatiotemporelles précédemment observées. En effet, les communautés sont plus complexes à la surface du lac Baïkal qu'en profondeur. De plus, les petits écosystèmes partagent des topologies similaires pour chaque saison. Cela souligne l'importance des interactions écologiques chez les communautés microbiennes, dans l'espace et le temps. Title: Spatio-temporal structuring of microbial communities in freshwater ecosystems Keywords: Microorganisms, Ecology, Diversity, Co-occurrence, Ecosystem functioning, Statistics

Table 2

 2

	.1 General characteristics of the studied ecosystems
		Lake Baikal	Etang des Vallées	La Claye	Mare Gabard	Saint Robert	Ru Sainte Anne
	Approximate perimeter (m)	2125.10 3	430	100	55	70
	Approximate width (cm)						30-100
	Approximate surface (m 2 )	31722.10 6	11000	480	200	350
	Approximate maximal depth (cm)	1640.10 2	150	60	50	70	25
	2.1.2.2 Description of the five ecosystems
	The five studied ecosystems are called Etang des Vallées (EV), La Claye (LC), Mare Gabard
	(MG), Saint Robert (SR) and Ru Sainte Anne (RSA) (Figure 2.2). Among them, four are lentic
	ecosystems, i.e. still water, and one is a lotic ecosystem, i.e. running water. The ecosystems
	are representative of the diversity of small freshwater ecosystems in the Ile-de-France region.

Lotic ecosystem: Ru Sainte Anne is a brook with highly variable flow and water level (GPS coordinates: 48°36'45.91" N; 1°58'16.61" E). The sampling point for the brook is located in a forest, close to a house, and to a national road. Many animals (e.g. wild boars) come there to drink.

Table 2 .

 2 2 Example of a reduced data-frame used for the analyses, exposing the read numbers for each samples. The samples are in row, and the OTUs are in column.

		X118553 X118622 X118871 X119360
	EV21	35	1	420	0
	EV27	16	0	107	3
	EV33	6	0	63	1

1 1

 1 Université Paris-Saclay, CNRS, AgroParisTech, ESE, 91405, Orsay, France 2 Limnological Institute, Siberian Branch of the Russian Academy of Sciences, Irkutsk, Russia

3 Departamento de Ecología, Universidad Autónoma de Madrid, Madrid, Spain Submitted in Environmental Microbiology, currently under review. Available in preprint on bioRxiv (https://www.biorxiv.org/content/10.1101/2020.09.26.308536v1).

Table 3 .

 3 1 Permutational multivariate analyse of variance (PERMANOVA) of Lake Baikal plankton samples across basin, sampling site and depth. PERMANOVA was calculated using Wisconsin standardization on rarefied OTUs belonging to the 65 studied plankton samples. Df, degrees of freedom

	Effect	Df F.Model	R2	P value
	Basin	2	1.8369	5.3%	***
	Sampling site	15	1.1857 23.7%	***
	Depth Sampling site 9	1.2861 16.3%	***
	3.2.4.4 Environmental drivers of protist community structure

  Supplementary Table3.A.3 Diversity, abundance and distribution of eukaryotic lineages previously thought to be exclusively marine identified in Lake Baikal plankton.

		Raw reads Clean merged reads ♯ otus Raw reads Clean merged reads ♯ otus 170009 135771 1050 87940 85032 879 143354 133777 825 102066 176014 132046 954 97459 649 62438 129314 103211 1156 55549 726 131337 109882 91554 854 114477 743 120296 70538 68416 197 107489 544 132170 150492 137629 980 109849 915 106227 10085 9771 41 97251 798 105172 56767 51846 261 96013 1065 59072 136920 95373 1178 57656 344 72297 130943 102135 518 69907 363 144322 149603 102765 698 140470 436 68506 161996 105383 806 59443 812 124684 102618 89102 752 114488 1198 89182 101431 77741 727 76475 1043 59454 340083 275886 1261 44623 685 77234 92074 86915 621 60742 647 129657 174174 171428 663 103961 727 142012 94831 85391 851 128621 567 188707 146124 126201 1079 150315 1204 144537 88294 78071 1217 114062 1102 228022 49540 46572 664 188147 1301 72429 233612 207530 1919 55468 693 36746 146124 102945 1000 31456 324 114918 50396 35832 567 109195 378 197087 115229 107228 811 166833 830 321169 48005 44526 543 235066 2973 70786 103557 88299 1067 53923 965 111378 96709 80346 1222 84474 1042 81308 129517 74958 236 64784 993 133522 160563 155621 456 118083 943 3234 39227 36443 362 3022 9 111545 66984 62868 373 107430 220 ). Singletons are excluded from ♯ swarm otus ♯ otus after raref°S impson Eveness 1216 248 0.87 0.51 1128 357 0.94 0.6 1052 276 0.94 0.6 167 104 0.85 0.55 1309 286 0.91 0.56 26 41 0.89 0.67 54 135 0.82 0.52 1039 322 0.91 0.54 712 147 0.7 0.4 927 203 0.73 0.42 1168 229 0.91 0.53 868 237 0.9 0.55 727 229 0.83 0.48 1515 357 0.87 0.5 674 366 0.87 0.56 274 268 0.93 0.67 1237 307 0.95 0.62 1264 323 0.92 0.58 991 378 0.9 0.53 659 282 0.92 0.57 1477 414 0.94 0.6 1276 277 0.92 0.57 585 332 0.93 0.58 1033 236 0.85 0.47 478 228 0.73 0.41 1001 309 0.84 0.46 1240 389 0.89 0.5 103 114 0.94 0.7 305 151 0.82 0.51 306 229 0.81 0.46 250 185 0.82 0.49 ♯ swarm otus ♯ otus after raref°S impson 1159 281 0.82 813 337 0.94 701 264 0.93 634 271 0.85 838 311 0.89 685 248 0.85 511 149 0.57 879 279 0.87 791 250 0.9 831 320 0.89 107 172 0.91 383 188 0.73 301 156 0.7 655 336 0.8 565 347 0.8 743 340 0.87 640 306 0.91 533 296 0.84 864 218 0.77 514 157 0.47 954 312 0.88 977 343 0.91 828 326 0.91 691 306 0.86 299 204 0.87 459 157 0.82 1102 177 0.79 1993 698 0.94 755 361 0.89 866 349 0.84 1075 353 0.89 1257 297 0.92 10 9 0.82 101 94 0.7 Eveness 0.49 0.63 0.62 0.53 0.53 0.48 0.31 0.5 0.52 0.55 0.63 0.39 0.38 0.51 0.43 0.5 0.57 0.48 0.39 0.28 0.53 0.54 0.56 0.51 0.5 0.45 0.43 0.59 0.53 0.46 0.51 0.54 0.8 0.38	Continued on next page
		do (%)	83.8	do (%) 70.8 68.3 114 76 80.6 69.5 98 80.5 83 71.8 82.6 73 80.3 68 77.6 71.8 73.3 94 71.3 75.8 68 78 94.5 78.6 83.5 80.6 26 77 87.7 80.3 83.3 68 87.1 84 86.1 65 80.2 77 81.4 80 82.6 80 78.5 82 82.8 75 97.2 83.1 88 80 104 77 70 76.6 85.4 77 83.7 70.5 85 71.5 77 63.5 112 77
	Supplementary Table 3.A.1 -Continued from previous page	Longitude Bottom depth (m) Temperature (°C) pH Cond. (𝜇S/cm) tds (mg/L) orp (mV) Longitude Bottom depth (m) 107°53'094" E 592 Temperature (°C) pH Cond. (𝜇S/cm) tds (mg/L) 4.9 7.86 74.7 76.5 5.2 7.85 75.8 74.5 7.8 7.77 75.2 72.2 4.6 4.7 7.88 75.7 73.5 106.7 108°24'583" E 4.6 7.67 75.7 69.6 79.5 NA 106°53'659" E 4.4 7.82 74.3 73 106.1 35 7.76 77 76.7 9.5 8.47 87.3 72.8 7.8 8.11 81.3 76.7 8.6 109°16'104" E 846 4.3 8.16 74.7 70.9 106.1 7.74 84 78.7 4.8 4.2 7.81 74.4 74.1 105.5 7.67 75.5 77.4 4.7 4.2 7.68 74.2 77.4 101.8 106°44'208" E 4.2 7.68 73.8 76.1 67.6 1083 7.63 75.2 78.7 4.3 7.6 74.7 76.1 4.3 108°54'14" E 36 5.6 8.07 77.5 76.1 100.3 7.6 74.6 74.8 4 5.6 7.92 73.9 74.8 136.1 7.45 74.5 76.7 4.8 4.9 7.9 73.6 76.1 134.8 7.48 79.1 79.3 12.2 4.8 7.87 73.8 76.1 132.6 7.98 95.2 80.6 8.1 108°30'613" E 896 4.5 7.86 73.3 77.4 130.4 106°02'90" E 105 7.56 86.8 83.2 8 4 7.74 72.9 74.8 131.8 7.54 78.9 74.5 6.5 4.8 7.62 73.3 76.7 117.6 106°02'90" E 6.5 7.71 75.9 74.1 88.5 10 7.88 83.5 83.2 13.7 7.62 97.5 80.6 8.7 109.31.465 E 129 5.5 7.63 78.1 68.3 118.9 8.18 83.6 78.7 6.5 109°1'25.98"E 13 6.2 7.9 84 66 80.9 8.06 76.1 76.7 4.7 108°42'911" E 13 5.9 7.92 80.4 74.8 124.1 7.87 74.4 78 4.6 4.8 7.92 76.1 78 113.9 105°15'294" E 1408 7.84 73.9 78 4.1 4.4 108°40'195" E 156 7.91 76.3 75.4 118 5.1 7.82 78.9 778.7 115 7.8 73.3 79.3 4 7.71 73.2 78.7 3.9 4.6 7.85 77.6 73.1 109.1 7.64 73 78.7 3.9 109°5'9.56'E 10 15.3 8.33 82.2 76.7 136.3 8.13 79 85.2 11.7 5.5 7.9 77.1 78.7 134.3 8.28 89 76.7 5.6 4.1 7.81 74.1 77.4 134.8 8.14 77.1 71.5 7 4.2 7.81 73.8 78 134.6 7.9 78 70 4.6 4.2 108°24'583" E 1512 7.81 74.3 78 134.7 4 7.84 74.2 76.7 133.3 104°46'449" E 1381 7.87 75.2 77.4 4.6 7.8 75.4 77.3 4.9 3.7 7.7 74.4 75.4 127.3 7.69 76.1 78 6.4 3.7 7.55 73.6 73.5 122.7 7.77 78.7 76.7 4.8 3.6 7.7 74.5 80 114 8.02 80.7 81.3 orp (mV) 86 80.6 72.8 29.6 44.4 31.8 176.4 179.9 181.5 183.2 184 189.6 195 204.4 224 234.3 205.6 202 134 138.2 131 130.1 124.8 119.9 109.5 80.2 90.7 95.3 98.3 94.2 107.8 96.9 87.3 45.6
		Latitude	Latitude 53°23'524" N	53°14'596" N	53°00'525" N	55°06'259" N	52°41'401" N	54°51'372" N	54°08'361" N 52°15'70" N	52°15'70" N	54.09.926 N	53°53'53.10"N	53°51'346" N	51°52'638" N	53°50'658" N	53°46'12.03"N	53°31'096" N 51°47'817" N
		Sampling Date Sampling Date 04.07.2017	29.06.2017	05.07.2017	02.07.2017	28.06.2017	03.07.2017	03.07.2017 28.06.2017	28.06.2017	01.07.2017	01.07.2017	30.06.2017	05.07.2017	30.06.2017	30.06.2017	29.06.2017 06.07.2017
		Sampl. depth (m) Sampl. depth (m) 5 50 200 550 5 5 25 5 50 5 100 25 200 50 500 100 800 200 5 500 5 1000 25 5 50 25 100 50 200 85 500 5 850 5 5 25 5 50 5 100 5 200 25 500 50 1000 100 1350 5 5 5 25 25 50 50 100 100 200 200 500 500 1000 1000 1300 1400
		Basin Sampling site Basin Sampling site BK22 the OTU counts. Sample name Sample name BK22.5m BK22.50m BK22.200m BK22.550m BK16.5m BK04 BK16.25m BK24 BK16.50m BK04.5m BK24.5m BK03.5m BK16 Central BK16.100m BK16.200m BK16.500m BK03 BK16.800m.CT BK03.25m BK03.50m BK03.100m BK03.200m BK18 BK18.5m BK03.500m BK20.5m BK03.1000m BK20.25m BK01.5m BK20.50m North BK20 BK01 BK20.100m BK20.200m BK01.25m BK01.50m BK01.85m BK20.500m BK02 BK20.850m BK02.5m BK26.5m BK14 BK14.5m BK26.25m BK13 BK13.5m BK26.50m BK09 BK09.5m BK26.100m BK26 BK06.5m BK26.200m South BK06 BK06.25m BK06.50m BK26.500m BK26.1000m BK06.100m BK26.1350m BK11 BK11.5m BK28.5m BK05.5m BK28.25m BK05.25m BK28.50m BK05.50m BK28.100m BK28 BK05 BK05.100m BK05.200m Central BK05.500m BK05.1000m BK28.200m BK28.500m BK28.1000m.CT BK28.1300m.CT BK05.1400m

Table 3

 3

		R	p-value
	Epi-Meso	0.3821 0.0001
	Epi-Bathy	0.7262 0.0001
	Meso-Bathy	0.7262 0.0001
	Southern-Central 0.09237 0.0001
	Southern-North	0.2201 0.0001
	Central-North	0.1442 0.0001
	Coastal-Pelagic 0.05955 0.2339

.A.4

ANOSIM analyses between pairs of Lake Baikal plankton sample groups defined as a function of depth, basin of origin and coastal vs. pelagic location. ANOSIM were calculated upon 999 permutations between pairs of sample groups.

  Table3.A.7 Properties of networks built upon Lake Baikal plankton OTUs for the three depth categories of the water column. Only OTUs that were present in more than 20% of samples with a relative abundance higher than 0.01% were considered.

		Epipelagic	Mesopelagic	Bathypelagic
			%		%		%
	Covariable	Basin		Basin		Basin
	Positive edges	10	4.80	1	1.60	1	1.30
	Negative edges	198	95.2	61	98.4	78	98.7
	Total number of edges	208		62		79
	Total number of nodes	206		227		173
	Connected nodes	97	47.1	55	24.2	33	19.1
	Connectance	0.04		0.041		0.14
	Clustering coefficient	0.31		0.25		0.43
	Most connected node X406443 (27)		X403371 (9)		X401791, X403173 (18)
	Average path length	3.51		2.5		2.11
	Mean node degree	2.02		0.55		0.91

  Moreira 1 , Benjamin Alric 2 , Philippe Deschamps 1 , Paola Bertolino 1 , Gwendal Restoux 3 , Emma Rochelle-Newall 4 , Elisa Thébault 4 ,

	Marianne Simon 1 and Ludwig Jardillier 1
	1 Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique Evolution, 91405, Or-
	say, France
	2 Irstea, UR RiverLy, Laboratoire d'écotoxicologie, centre de Lyon-Villeurbanne, 5 rue de la
	Doua CS 20244, F-69625, Villeurbanne, France

3 GABI, INRA, AgroParisTech, Universiteé Paris-Saclay, 78350 Jouy-en-Josas, France 4 Sorbonne Université, UPEC, CNRS, IRD, INRAE, Institute d'Ecologie de des Sciences de l'Environnement de Paris, iEES-Paris, F-75005 Paris, France Submitted in Molecular Ecology, currently under review.

  Simpson index) were similar in the four ponds and reached higher values (mean richness of 546, Simpson index of 0.99 and evenness of 0.85) in the Ru Sainte Anne (Figure 4.2.1B). The spread of the quartiles of the box plots suggested that these indices varied strongly over time and differently among ecosystems (Figure 4.2.1B). Overall, only 271 OTUs (considering only one occurence for OTUs detected in several ecosystems; 1.6% of all OTUs) were abundant (i.e. detected in each ecosystem in at least 25% of samples and with ≥0.1% relative abundance per sample) but represented 84.0% of the total number of reads (Supplementary Table 4.A.2). The vast majority of the prokaryotic diversity was thus composed of rare OTUs (Supplementary Figure 4.B.1A and 4.B.1B).

Prokaryotic OTUs affiliated to 78 different phyla, which we classified into 8 super-groups of different taxonomic levels: Archaea (not further subdivided in taxa owing to their lower relative diversity and abundance in our samples), Fibrobacteres-Chlorobi-Bacteroidetes (FCB), Microgenomates, Parcubacteria, Proteobacteria, Planctomycetes-Verrucomicrobia-Chlamydiae (PVC), Terrabacteria and Other Bacteria (Figure

4

.2.1C). The highest number of phyla (

  January 2012 in Saint Robert and La Claye (54.4% and 46.6%, respectively). Bacteria belonging to the CPR varied also greatly over time and between ecosystems. They dominated in June 2011 and June 2012 (62.9% and 63.5%, respectively) in Ru Sainte Anne and represented up to 25.2% of reads in October 2012 in La Claye. Parcubacteria and Microgenomates exhibited similar temporal patterns. By contrast, Cyanobacteria were low abundant in the five

Proteobacteria reached their higher abundances in June 2012 in Mare Gabard (81.8% of all reads) and Saint Robert (60.4%) and in October 2011 in Ru Sainte Anne (49.8%). Actinobacteria peaked in Etang des Vallées in March 2013 (50.3%) and at similar levels in June and November 2012 in La Claye (46.7% and 45.0%, respectively). The highest abundances of Bacteroidetes were recorded in Saint Robert in March 2013 (63.7%) and in

  Table 4.A.4). CCA analysis based on OTUs grouped by phyla showed that eukaryotes explained as much as 38.6% of the variance of the prokaryotic community, mainly by four eukaryotic phyla (Cryptomycota, Oomyceta,

	Marine Stramenopiles, Chrysophyta) for which the co-inertia was significant. Inversely, five
	prokaryotic phyla explained 21.2% of the eukaryotic variance (Proteobacteria, Actinobacte-
	ria, Bacteroidetes, Amesbacteria and Woesebacteria). A PERMANOVA analysis was used to
	quantify the role played by habitat (i.e. ecosystem) and time (season and year) in the micro-

bial community structuring at the OTU level. Habitat and time had similar influences on the composition of the global microbial, prokaryotic and eukaryotic communities. Differences in the global microbial communities between the Ru Sainte Anne and the other ecosystems were greater (F.model value of 4.739) than between these four remaining ecosystems (F.model value of 2.9607) (Table

4

.2.1). To a lower extent, microbial communities shifted over time at seasonal and year scales (F.model values of 1.4079 and 1.2424, respectively).

Table 4

 4

			.2.1 Permutational multivariate analyses of variance (PERMANOVA) partitioning of the
	whole microbial community, prokaryotes and microbial eukaryotes, based on Bray-Curtis dissimilarities
	calculated using Wisconsin standardized OTU relative abundances among hierarchical-defined clusters
	1 and 2 (see Supplementary Figure 4.B.5), location (ecosystem), season and year.
			Cluster		Location | Cluster	Season	Year
		Df F.model	r 2	Pvalue Df F.model	r 2	Pvalue Df F.model	r 2	Pvalue Df F.model	r 2	Pvalue
	Whole
	microbial	1	4.74	4.1•10 -2 5.0•10 -4 3	2.96	7.7•10 -

Table 4

 4

	.2.1 and Supplementary Table 4.A.3, Figures 4.2.4-4.2.5 and
	Supplementary Figure 4.B.7). These communities were more differentiated at summer and
	winter, possibly because of higher OTU turnover rates occurring at these two opposed seasons.
	Concomitantly, communities also experienced composition changes at a monthly scale, more

pronounced for rare taxa than for abundant taxa

(Supplementary Figure 4.B.8)

. Interestingly, at a much longer time scale, we also observed constant directional shift (increasing dissimilarity over time) of both the prokaryotic and eukaryotic communities, and the whole microbial community structures over the two-year survey

(Figure 4.2.4, Supplementary Table 4.A.3

), a phenomenon that has already been observed in larger lakes

[START_REF] Liu | Phytoplankton communities exhibit a stronger response to environmental changes than bacterioplankton in three subtropical reservoirs[END_REF]

. As mentioned

  .A.1 Names, numbers of reads, OTUs and diversity estimates ob-

	4.A. Supplementary Tables
			Supplementary Table 4.A.1 -Continued from previous page Supplementary Table 4.A.1 -Continued from previous page
	tained for the different samples Ecosystem Sample Sampling Ecosystem Sample Sampling name date Ecosystem Sample Sampling name date Supplementary Table 4.A.3 Linear and polynomial models were computed based on time-Number of removed Number of Observed Number of removed Number of Observed Richness Evenness singletons reads number of OTUs (Standard error) Evenness Number of removed Number of Observed Richness Evenness singletons reads number of OTUs (Standard error) Evenness Richness Evenness name date singletons reads La Claye LC41 December 2012 1884 15124 537 295.71 (10.17) 0.39 Saint Robert SR33 August 2012 1385 11398 339 196.84 (7.45) 0.47 lag analyses (TLA) between sampling dates over increasing time-lags over the 2-years survey number of OTUs (Standard error) La Claye LC43 January 2013 1545 9379 366 262.08 (7.84) 0.44 Saint Robert SR35 September 2012 1127 10454 281 165.08 (6.94) 0.42 (from one-month to 23-months) for the five ecosystems. Wisconsin standardized Bray-Curtis
	Etang des Vallées La Claye Saint Robert dissimilarities were used for all OTUs (separated into prokaryotes, microbial eukaryotes and EV11 April 2011 1089 19801 296 251.77 (5.37) LC45 February 2013 975 7887 200 154.73 (5.51) SR37 October 2012 845 9394 199 125.80 (5.76)	0.49 0.45 0.45
	Etang des Vallées Etang des Vallées La Claye La Claye Saint Robert whole microbial communities) while Euclidean distances were applied with environmental pa-EV13 May 2011 765 9156 160 157.85 (1.43) EV15 June 2011 765 9414 193 189.28 (1.87) LC47 March 2013 740 9056 178 137.30 (5.06) LC49 April 2013 963 19006 164 92.92 (5.19) SR41 December 2012 620 4439 129 109.75 (3.79) Saint Robert SR43 January 2013 890 10916 129 83.06 (4.41) rameters. Non-significant P-values are highlighted in red.	0.48 0.49 0.45 0.43 0.49 0.43
	Etang des Vallées Etang des Vallées Etang des Vallées Etang des Vallées Etang des Vallées Etang des Vallées Etang des Vallées Etang des Vallées Etang des Vallées Mare Gabard Mare Gabard Mare Gabard Mare Gabard Mare Gabard Mare Gabard Mare Gabard Mare Gabard Mare Gabard Saint Robert Saint Robert Saint Robert Ru Sainte Anne Ru Sainte Anne Ru Sainte Anne Ru Sainte Anne Ru Sainte Anne Ru Sainte Anne Linear model EV17 EV19 EV21 EV23 EV25 EV27 EV29 EV31 EV33 MG3 MG5 MG7 MG9 MG11 MG13 MG15 MG17 MG19 SR45 SR47 SR49 RSA3 RSA5 RSA7 RSA9 RSA11 RSA13 Etang des Vallées Polynomial model	July 2011 September 2011 October 2011 November 2011 December 2011 January 2012 February 2012 March 2012 April 2012 April 2011 May 2011 June 2011 July 2011 September 2011 October 2011 November 2011 December 2011 January 2012 February 2013 March 2013 Prokaryotes Microbial Whole microbial Physico-chemical 1320 11258 325 309.01 (3.74) 0.49 1178 10701 280 270.90 (2.85) 0.50 659 11774 182 173.42 (2.72) 0.45 1078 11326 248 234.77 (3.39) 0.47 1550 14347 347 305.75 (5.63) 0.45 1231 11606 234 220.74 (3.37) 0.48 788 8043 234 232.58 (1.18) 0.48 2286 25281 650 440.67 (10.59) 0.42 1420 22989 260 201.78 (5.85) 0.43 3475 4190 63 49.03 (2.92) 0.42 3496 1204 90 91.00 (0.00) 0.56 774 4952 148 104.21 (4.76) 0.49 956 7409 151 91.26 (4.83) 0.47 943 4692 236 165.47 (6.13) 0.49 1060 10752 198 102.56 (5.47) 0.45 453 10451 77 33.30 (3.62) 0.36 1298 9055 350 160.49 (7.95) 0.42 1640 11962 313 109.41 (7.29) 0.38 827 6663 123 87.02 (4.51) 0.45 330 5054 69 59.76 (2.60) 0.48 April 2013 576 6264 92 68.55 (3.71) eukaryotes communities parameters 0.47 April 2011 3779 5033 938 885.99 (6.75) 0.54 May 2011 4011 6273 1058 938.34 (9.61) 0.54 June 2011 3703 10396 1328 992.68 (13.99) 0.53 July 2011 2595 6723 721 637.14 (8.08) 0.53 September 2011 3214 6606 898 789.84 (9.12) 0.53 October 2011 2524 11242 663 485.17 (10.24) 0.46 Slope 0.10 0.22 0.16 0.02 R 2 0.02 0.05 0.05 0.00 Pvalue 2.6 x 10 -4 2.2 x 10 -16 1.1 x 10 -7 0.13 R 2 0.23 0.41 0.41 0.14 Pvalue 2.2 x 10 -16 2.2 x 10 -16 2.2 x 10 -16 2.2 x 10 -16
	Etang des Vallées Mare Gabard Ru Sainte Anne	EV35 MG21 RSA15	May 2012 February 2012 November 2011 Slope	0.09	1043 1163 1841	21075 8045 3662 0.18	195 426 627 0.15		157.27 (4.88) 194.05 (8.95) 627.01 (0.99) -0.01	0.41 0.40 0.51
	Etang des Vallées Etang des Vallées Etang des Vallées Etang des Vallées Etang des Vallées Mare Gabard Mare Gabard Mare Gabard Mare Gabard Mare Gabard Ru Sainte Anne Ru Sainte Anne Ru Sainte Anne Ru Sainte Anne Ru Sainte Anne Linear model EV37 EV39 EV41 EV43 EV45 MG23 MG25 MG27 MG29 MG31 RSA17 RSA19 RSA21 RSA23 RSA25 La Claye Polynomial model	June 2012 July 2012 August 2012 September 2012 October 2012 March 2012 April 2012 May 2012 June 2012 July 2012 December 2011 January 2012 February 2012 March 2012 April 2012 R 2 Pvalue 4.6 x 10 -4 686 780 1181 1122 476 823 1446 1637 1012 624 3726 3925 2917 3187 3796 0.02 R 2 0.32 Etang des Vallées Pvalue 2.2 x 10 -16 2.2 x 10 -16 13426 13351 22879 12838 7106 12218 18702 17200 11175 5201 5612 8822 7219 12007 8953 0.05 2.2 x 10 -5 RV index Pvalue 0.25 0.896 0.015 2.2 x 10 -16 186 244 280 270 137 127 469 279 157 94 975 1182 1105 1412 1169 0.05 2.7 x 10 -5 0.41 La Claye 0.726 0.487	171.72 (3.38) 225.67 (3.86) 217.23 (6.09) 254.32 (3.62) 137.00 (0.00) 50.15 (4.45) 154.83 (8.18) 90.37 (6.30) 65.47 (5.07) 61.96 (4.02) 898.04 (7.99) 920.39 (12.89) 940.35 (10.89) 981.92 (15.11) 908.65 (12.84) 0.00 0.45 0.09 1.7 x 10 -6	0.48 0.47 0.44 0.48 0.50 0.38 0.40 0.37 0.34 0.46 0.54 0.52 0.53 0.51 0.51
	Etang des Vallées Etang des Vallées Etang des Vallées Etang des Vallées Etang des Vallées Etang des Vallées Mare Gabard Mare Gabard Mare Gabard Mare Gabard Saint Robert Saint Robert Ru Sainte Anne Ru Sainte Anne Ru Sainte Anne Ru Sainte Anne Ru Sainte Anne Ru Sainte Anne Linear model EV47 EV49 EV51 EV53 EV55 EV57 MG33 MG35 MG37 MG41 SR3 SR5 RSA27 RSA29 RSA31 RSA37 RSA39 RSA41 Mare Gabard Polynomial model	November 2012 December 2012 January 2013 February 2013 March 2013 April 2013 August 2012 September 2012 October 2012 December 2012 April 2011 May 2011 May 2012 June 2012 July 2012 October 2012 November 2012 December 2012 Slope R 2 Pvalue 2.2 x 10 -7 626 1069 1444 1435 879 735 943 1114 668 414 3849 1308 4630 4314 1864 2870 3286 5472 0.19 0.07 Mare Gabard Saint Robert Ru Sainte Anne Pvalue 2.2 x 10 -16 2.2 x 10 -16 10432 11220 16834 8385 7882 8256 9711 9944 7931 5493 7439 15499 14919 12101 3464 7429 5746 14362 0.21 0.07 1.6 x 10 -7 0.730 0.194 0.880 0.004 0.855 0.240 R 2 0.23 0.34 All ecosystems 0.745 0.001	160 259 311 326 166 157 195 198 172 90 256 248 1597 1779 585 1065 979 2187 0.18 0.08 3.8 x 10 -7 2.2 x 10 -16 0.38	156.23 (1.85) 245.62 (3.42) 258.81 (6.04) 321.55 (2.08) 165.41 (0.76) 155.97 (1.01) 92.13 (5.93) 87.69 (5.81) 91.33 (5.47) 64.63 (3.59) 166.83 (6.67) 115.62 (6.40) 998.99 (16.59) 1 195.60 (17.18) 586.00 (0.00) 905.79 (10.65) 894.28 (8.32) 1 352.19 (19.29) 0.03 0.01 0.01 7.8 x 10 -6 0.05	0.49 0.47 0.40 0.47 0.47 0.46 0.35 0.39 0.41 0.50 0.45 0.42 0.51 0.53 0.54 0.55 0.54 0.53
	La Claye La Claye La Claye La Claye La Claye La Claye Saint Robert Saint Robert Saint Robert Saint Robert Saint Robert Saint Robert Ru Sainte Anne Ru Sainte Anne Ru Sainte Anne Ru Sainte Anne Linear model LC3 LC5 LC7 LC19 LC21 LC23 SR7 SR9 SR11 SR13 SR15 SR17 RSA43 RSA45 RSA47 RSA49 Saint Robert Polynomial model	April 2011 May 2011 June 2011 January 2012 February 2012 March 2012 June 2011 July 2011 September 2011 October 2011 November 2011 December 2011 January 2013 February 2013 March 2013 April 2013 Slope R 2 Pvalue 2.2 x 10 -12 2.2 x 10 -16 679 9537 1101 10347 729 8756 1721 5891 897 3492 1988 11862 1361 13413 1298 10148 1400 11442 2033 1882 706 6649 1133 4246 4908 11201 5247 11468 3360 6195 4987 14204 0.15 0.23 0.09 0.13 R 2 0.23 0.33 Pvalue 2.2 x 10 -16 2.2 x 10 -16	220 256 141 490 248 516 274 235 232 91 147 256 1742 1694 1018 1906 0.19 0.15 2.2 x 10 -16 0.44 2.2 x 10 -16	160.84 (5.97) 177.39 (6.65) 110.21 (4.48) 421.89 (7.23) 246.49 (1.56) 326.85 (9.72) 131.13 (6.90) 133.98 (6.51) 137.55 (6.01) 92.00 (0.00) 106.98 (4.78) 209.09 (5.78) 1 205.56 (16.83) 1 159.55 (16.69) 903.55 (9.44) 1 188.11 (18.14) 0 0 0.77 0.04 1.0 x 10 -4	0.44 0.42 0.43 0.46 0.48 0.41 0.42 0.42 0.47 0.52 0.36 0.48 0.53 0.52 0.53 0.52
	La Claye La Claye La Claye La Claye La Claye La Claye Saint Robert Saint Robert Saint Robert Saint Robert Saint Robert Saint Robert Linear model LC25 LC27 LC29 LC31 LC33 LC37 SR19 SR21 SR23 SR25 SR27 SR29 Ru Sainte Anne Polynomial model	April 2012 May 2012 June 2012 July 2012 August 2012 October 2012 January 2012 February 2012 March 2012 April 2012 May 2012 June 2012 Slope R 2 Pvalue 1.8 x 10 -9 1352 1085 660 791 764 1633 756 953 823 1500 1312 1030 0.17 0.07 R 2 0.17 Pvalue 2.2 x 10 -16 2.2 x 10 -16 13723 13897 8701 11284 11253 2832 6801 11088 12081 11675 15174 17177 0.19 0.06 7.2 x 10 -8 0.34	272 203 162 154 133 555 140 295 241 316 316 242 0.18 0.09 4.7 x 10 -10 0.32 2.2 x 10 -16	163.45 (7.06) 121.26 (6.16) 127.74 (4.78) 106.85 (5.11) 90.54 (4.80) 556.00 (0.00) 98.38 (4.80) 163.03 (7.34) 132.97 (6.36) 165.85 (7.44) 156.22 (7.29) 111.95 (6.25) 0.04 0.03 1.5 x 10 -4 0.14 9.9 x 10 -16	0.41 0.39 0.41 0.42 0.42 0.56 0.42 0.41 0.43 0.45 0.40 0.40
	La Claye Saint Robert	LC39 SR31	November 2012 July 2012		1530 580	12505 6355	435 127	259.39 (9.35) 90.55 (4.58) Continued on next page Continued on next page	0.36 0.45

123 Supplementary Table

4

.A.4 RV index were calculated based on co-inertia analyses to assess co-dynamic between prokaryotes and microbial eukaryotes in the five ecosystems and in all ecosystems considered together. Values in red indicate P-values higher than 0.05, corresponding to non-significant RV index.

  markedly in Etang des Vallées over time. 5.2.4.2 Overall structure of microbial communities in and within the five ecosystems Diversity of microbial eukaryotes The 5 630 664 clean merged reads obtained for the eukaryotes were rarefied at a minimum of 6 355 reads per sample. They were clustered into 22 764 OTUs (Figure 5.2.2 ). The mean richness per sample was 469.6, and the most diverse community was harboured by Ru Sainte Anne with a mean richness of 941 (Supplementary figure 5.B.3, Supplementary table 5.A.2 and 5.A.3). Evenness varied strongly in the ecosystems over the survey, with a maximal difference of 0.71 in Mare Gabard (Supplementary figure 5.B.3).

	Diversity of prokaryotes For the prokaryotes, 4 576 603 clean merged reads were rarefied
	to 5 574 reads per sample. Reads were clustered into 25 536 OTUs (Figure 5.2.2). The mean
	richness per sample was 471.8. As for eukaryotic communities, the mean richness in Ru Sainte
	Anne was higher compared to other ecosystems, with a mean number of 2 184 OTUs per
	sample (Supplementary figure 5.B.3, Supplementary table 5.A.2 and 5.A.3). Eveness indices
	were higher for prokaryotes than for microbial eukaryotes, but Ru Sainte Anne still presented
	the highest mean value over the five ecosystems (0.88) (Supplementary figure 5.B.3). OTUs
	were affiliated to 38 phyla, belonging to all currently recognized prokaryotic super-groups.
	Considering all ecosystems, the diversity was dominated by Proteobacteria, Actinobacteria, and
	Bacteroidetes (respective abundance of 34.8, 16.2 and 15.9%). These three phyla dominated in

The highest mean value of eveness, 0.73, was founded in Ru Sainte Anne. The detected OTUs belonged to 37 different phyla. The most abundant phyla were Ciliophora, Ochrophyta and Cryptophyta with a total abundance of 29, 22.3 and 18%, respectively. The more diversified phyla were Ciliophora, Cryptophyta, Fungi and Ochrophyta, each composed of 144 different OTUs in the dataset. A low proportion of OTUs (less than 2.0%) were detected as abundant in each ecosystem (i.e. mean abundance > 0.1% and present in 20% of the ecosystem samples), but they represented between 51.9% (Ru Sainte Anne) and 75.4% (Etang des Vallées and Mare Gabard) of the total number of reads. Only 13 OTUs were present at every time point in one of the ecosystems. all ecosystem except Ru Sainte Anne, where Proteobacteria, Parcubacteria and Woesearchaota dominated the communities over the eight-year survey (Supplementary figure 5.B.4). Archaea represented a small proportion of the reads (13 801 OTUs representing 0.49% of the reads), including DPANN members. A large diversity of bacteria of the Candidate Phyla Radiation (32 phyla, 48 076 OTUs) was also detected. The most abundant OTUs (i.e. mean abundance

  Temporal evolution of communities at the OTU level was examined using Bray-Curtis dissimilarities. Microbial communities were characterised by seasonal patterns, as showed by lowest dissimilarities when comparing the same season of different years and highest when comparing "opposite" seasons, e.g. summer versus winter (Figure5.2.1). This pattern was strongly marked for the microbial eukaryotes of Etang des Vallées and Ru Sainte Anne, and less in Saint Robert. Dissimilarities over time were however strong (minimal dissimilarity obtained for the prokaryotes in Ru Sainte Anne, with a value of 79.3). As for the turnover, we observed that microbial communities in Ru Sainte Anne were less dissimilar than in the other ecosystems. In addition to the intra-annual seasonal pattern, the microbial community composition also featured a global inter-annual pattern. Dissimilarities raised over increasing time lags in the time lag analyses in all ecosystems and for prokaryotes and microbial eukaryotes.

	Turnovers
	of abundant prokaryotes were globally lower (mean value of 4.6%) than for microbial eukary-
	otes (15.0%). In most of the ecosystems, we found the highest turnover values when comparing
	microbial communities in summer and winter (Supplementary figure 5.B.5). The same ob-
	servation was realized for the turnover analysis on abundant and rare OTUs collected at the
	same season between following years. For both communities, SR presented the highest mean
	turnover value (43% for abundant, 89.6% for rare) in all ecosystems and RSA the lowest (14.1%
	for abundant, 83.6% for rare) (Supplementary figure 5.B.6). This can be linked to the number

of OTUs present only between 1 and 10 time in the ecosystem (minimum of 95.7% for eukaryotes and 93.2% for prokaryotes, both in Ru Sainte Anne and maximum of 99.6% for eukaryotes in Saint Robert and 97.1% for prokaryotes in La Claye, 96.8% in Saint Robert). Prokaryote communities in winter and summer were characterized by lower turnovers, according to the expansion of the boxplot. This points to continuous changes of the microbial communities during the 8-year survey (Figure 5.2.1). Eukaryotes in the Etang des Vallées were the only exception to this general pattern (non-significant linear regression). Eukaryotes experienced lower changing rates than prokaryotes (average slope of 5.4% and 14.4%, respectively) (Supplementary table 5.A.6). The same analysis was performed taking into account separately abundant and rare OTUs. If the same seasonal pattern was observed, there were differences in the inter-annual pattern (Supplementary table 5.A.6). The eukaryotic abundant OTUs of La Claye, as well as the eukaryotic rare

  proportion of seasonal recurrent OTUs was detected in Etang des Vallées (0.6% of the OTUs, 10.0% of the reads). Similarly to the microbial eukaryotes, La Claye harbored the lowest amount of recurrent OTUs (0.2% of the OTUs, 1.1% of the reads). Ochrophytes represented a large proportion of the recurrent OTUs in all the ecosystems, in particular in Mare Gabard (13.2% of the total number of reads, corresponding to 13 OTUs) (Supplementary figure 5.B.9).One OTU was responsible for this domination of the community, OTU X788345. This OTU was affiliated to Gonyostomum semen (Raphidophyceae) and represented 12.8% of the reads in Mare Gabard. In Etang des Vallées and Ru Sainte Anne, Ciliophora were among the most abundant recurrent OTUs (respectively 5.90% and 5.89% of the OTUs reads). In La Claye,

recurrent OTUs were dominated by the Ochrophytes (5.86% of the recurrent OTUs reads). The highest proportion of seasonal recurrent prokaryote OTUs was detected for Proteobacteria in La Claye, Mare Gabard and Ru Sainte Anne (1.44, 16.8, 3.94% of the total number of reads respectively). Members of the Candidate Phyla Radiation were recurrent in all ecosystems, but at higher proportion for the super-group Parcubacteria (respectively 0.13, 0.21, 0.97 and 1.29% of the reads in Etang des Vallées, La Claye, Mare Gabard and Ru Sainte Anne. (Supplementary figure 5.B.9).

  . Similar results were obtained for the global structure of microbial communitiesThe same phyla dominated the communities, namely Cryptophyta, Ochrophyta and Ciliophora for the microbial eukaryotes, and Proteobacteria, Actinobacteria and Verrucomicrobia for the prokaryotes (Supplementary figure 5.B.4). The wide majority of the microbial diversity was composed of low-abundant OTUs, i.e. rare OTUs, as in David et al., submitted (see Chapter 4).

A wide diversity of members of the Candidate Phyla Radiation was observed (mean abundance between 2.4% in Etang des Vallées and 35.2% in Ru Sainte Anne). Members of the DPANN (Diapherotrites-Parvarchaeota-Aenigmarchaeota-Nanoarchaeota-Nanohaloarchaeota) group

  Supplementary Table5.A.2 Description of the samples used in the analyses. Names, number of rarefied microbial eukaryotic and prokaryotic OTUs, evenness for microbial eukaryotes and prokaryote and number of reads before rarefaction for microbial eukaryotes and prokaryotes. The final number of reads was the same in all sample because of the rarefaction of the data (6355 for the microbial eukaryotes, 5574 for the prokaryotes). Supplementary Table5.A.6 Linear models were computed based on the time-lag (TLA) calculated between sampling dates over increasing time-lags over the 8-year survey (from one-month to 30-month) for the five ecosystems. TLA calculation was based for the microbial communities on Wisconsin Standardized Bray-Curtis dissimilarities of rarefied OTUs, and based on Euclidean distances for environmental parameters. Linear models were applied for physico-chemical parameters, prokaryotes (all community, abundant OTUs only, rare OTUs only), and microbial eukaryotes (all community, abundant OTUs only, rare OTUs only). Abundant OTUs were detected in each ecosystem in at least 20% of samples and with >0.1%of mean relative abundance per sample. Non-significant P values are highlighted in red.

	pvalue Supplementary Table 5.A.2 -Continued from previous page 0.42 0.17 0.60 0.06 0.80 0.28 0.02 0.70 0.11 0.01 Sampling Microbial Prokaryotes Microbial Prokaryotes date eukaryotes eukaryotes Supplementary Table 5.A.2 -Continued from previous page Sampling Microbial Prokaryotes Microbial Prokaryotes date eukaryotes eukaryotes Supplementary Table 5.A.2 -Continued from previous page Sampling Microbial Prokaryotes Microbial Prokaryotes date eukaryotes eukaryotes Slope -0.02 0.05 -0.01 0.01 0.00 0.00 0.03 0.00 0.04 Summer 2012 Linear regression 505 515 0.62 0.59 Winter 2015 698 1686 0.62 0.83 Autumn2017 427 477 0.48 0.55 Supplementary Table 5.A.3 Total number of OTUs, mean number of OTUs and number of Ecosystem Sample Microbial Prokaryotes name eukaryotes Ecosystem Sample Microbial Prokaryotes name eukaryotes Ecosystem Sample Microbial Prokaryotes name eukaryotes La Claye LC31 15866 Mare Gabard MG63 31487 Saint Robert SR85 42133 -0.05 La Claye LC37 Autumn2012 796 1327 0.67 0.79 37733 Mare Gabard MG65 Spring 2015 178 706 0.35 0.58 9694 Saint Robert SR87 Winter 2018 707 925 0.62 0.66 34798 abundant OTUs per ecosystem for microbial eukaryotes and prokaryotes. Mare Gabard Min Max 1.10 20.2 5.40 8.36 6.10 78.9 43.5 209 1.00 3.75 0.00 0.02 0.00 1.68 0.02 0.38 823.2 17543 0.22 149.2 Observed number Eveness Number of reads of rarefied OTUs La Claye LC43 Winter 2013 143 1066 0.32 0.67 37400 La Claye LC49 Spring 2013 366 605 0.42 0.56 40415 Mare Gabard MG67 Summer 2015 115 433 0.34 0.62 5574 Mare Gabard MG69 Autumn2015 396 889 0.53 0.73 16743 Saint Robert SR89 Spring 2018 191 481 0.47 0.65 29093 Saint Robert SR91 Summer 2018 314 807 0.37 0.64 Total number of OTUs Mean number of OTUs Number of abundant OTUs 135749 before rarefaction Ecosystem Sample Sampling Microbial Prokaryotes Microbial Prokaryotes Microbial Prokaryotes name date eukaryotes eukaryotes eukaryotes La Claye LC51 Summer 2013 319 266 0.34 0.58 14093 La Claye LC53 Autumn2013 246 554 0.32 0.61 13236 Mare Gabard MG71 Winter 2016 252 578 0.47 0.62 38507 Mare Gabard MG73 Spring 2016 298 1571 0.39 0.84 39392 Saint Robert SR93 Autumn2018 257 502 0.47 0.63 94514 Saint Robert SR95 Winter 2019 357 777 0.45 0.66 35114 Microbial Prokaryotes Microbial Prokaryotes Microbial Prokaryotes eukaryotes eukaryotes eukaryotes
	La Claye MG75 EV21 LC55 RSA13 5611 Etang des Vallées La Claye Mare Gabard Ru Sainte Anne EV Etang des Vallées EV65 La Claye LC75 Mare Gabard MG95 Ru Sainte Anne RSA57 Etang des Vallées EV59 Etang des Vallées EV61 Etang des Vallées EV63 La Claye LC69 La Claye LC71 La Claye LC73 Mare Gabard MG89 Mare Gabard MG91 Mare Gabard MG93 Ru Sainte Anne RSA51 Ru Sainte Anne RSA53 Ru Sainte Anne RSA55 Etang des Vallées EV27 Etang des Vallées EV33 Etang des Vallées EV39 Etang des Vallées EV45 La Claye LC57 La Claye LC59 La Claye LC61 La Claye LC63 Mare Gabard MG77 Mare Gabard MG79 Mare Gabard MG81 Mare Gabard MG83 Ru Sainte Anne RSA19 Ru Sainte Anne RSA25 LC 5197 Ru Sainte Anne RSA31 Ru Sainte Anne RSA37 MG 5301 SR 5532 Etang des Vallées EV57 La Claye LC67 Mare Gabard MG87 Ru Sainte Anne RSA49 Etang des Vallées EV51 La Claye LC65 Mare Gabard MG85 Ru Sainte Anne RSA43 RSA 9558	Linear regression Autumn 2011 Min Max Slope pvalue Mean 1.7 22.0 -0.01 0.67 11.3 6.65 7.7 -0.01 0.61 6.55 4.20 91.2 -0.01 0.59 37.2 Winter 2014 Summer 2016 Autumn 2011 5651 Spring 2014 Summer 2016 Winter 2019 Spring 2014 Summer 2013 Autumn2013 Winter 2014 Autumn2015 Winter 2016 Spring 2016 Spring 2018 Summer 2018 Autumn2018 Summer 2013 Autumn2013 Winter 2014 Winter 2012 Spring 2012 Summer 2012 Autumn2012 Spring 2014 Summer 2014 Autumn2014 Winter 2015 Autumn2016 Winter 2017 Spring 2017 Summer 2017 Winter 2012 Spring 2012 7492 Summer 2012 Autumn2012 8110 5840 Spring 2013 Summer 2015 Winter 2018 Spring 2013 Winter 2013 Spring 2015 Autumn2017 Winter 2013 18055	131 520 -0.02 0.01 67.4	0.49 9.49 -0.04 0.03 1.69 314 0.00 0.11 -0.03 0.28 0.01 227 299 844 462 0.01 3.40 0.04 0.12 0.20 426 516 266 1037 683 425 548 169 203 512 542 369 454 914 838 893 504 447 616 431 436 301 138 215 234 110 209 313 772 685 305 989 989 345 306 394 583 1175 1183 551 212 505 861 941	0.02 0.49 -0.01 0.77 0.11	1018 19246 0.04 0.11 6239 387 1.87 239 0.04 0.088 401 567 1051 41.6 391 733 901 2583 248 379 627 763 544 772 1317 714 1628 1970 2196 2392 455 256 422 549 379 363 461 458 768 549 362 463 1549 2047 1794 1743 398 439 1789 1963 860 239 546 1895	520 Ru Sainte Anne 598 800 510 Linear regression 2184	0.54 0.35 0.51 0.68 Min Max 1.50 16 0.57 0.42 0.57 0.89 0.66 0.77 0.63 0.31 0.37 0.47 0.62 0.55 0.51 0.67 0.67 0.74 pvalue 0.17 0.56 0.66 0.61 0.6 0.41 0.31 0.18 0.38 0.36 0.21 0.44 0.48 0.74 0.64 0.74 0.74 Slope -0.02 0.57 0.61 0.81 0.8 0.62 0.27 0.62 0.79	6.55 8.03 0.91 0.00	0.00 85.6 0.64 0.01	0.69 0.65 0.59 0.7 101 396 1247 0.51 10.7 0.00 0.08 0.65 0.69 0.6 0.92 0.63 0.58 0.67 0.66 0.61 0.7 0.81 0.69 0.87 0.84 0.89 0.91 0.03 0.16 0.26 0.66 0.59 0.68 0.74 0.65 0.65 0.6 0.62 0.66 0.65 0.58 0.61 0.89 0.9 40 0.85 0.87 65 44 -0.04 -0.04 -0.02 0.58 0.66 0.9 0.93 0.68 0.66 0.66 0.93 129	0.03 1.36 0.39 0.01	0.01 0.32 0.07 0.00	18145 40896 115 957.3 10185 14846 135979 36751 0.29 2.60 19744 36234 27144 30327 38809 35840 0.33 0.44 59548 17245 111831 26567 30543 74 97799 71297 106188 95 103 0.01 29608 37867 0.00 37214 31039 101	30281 7339 15802 66358 15614 25057 109886 102453 76322 8646 10264 12946 7966 27294 42646 8232 9974 29181 17838 40565 24414 10725 7701 10435 8690 26367 8107 24603 25315 33870 19194 33327 21019 17091 43143
	Etang des Vallées RSA59 EV67 LC77 SR13 EV69 EV71 LC79 LC81 SR19 SR31 RSA61 RSA63 Supplementary Table 5.A.4 ANOSIM analysis between each pair of ecosystem based on Linear regression Min Max Slope pvalue Mean 3.30 24.0 -0.01 0.39 11.1 6.70 8.30 0.01 0.17 7.25 31.0 150 0.00 0.91 38.9 234 346 0.00 0.90 297 1.00 8.11 0.00 0.97 2.73 0.00 0.21 -0.01 0.67 0.01 0.00 0.80 0.01 0.04 0.33 0.00 0.20 0.00 0.80 0.12 499 12136 0.04 0.01 5597 7.70 28.9 0.00 0.94 23.7 Saint Robert pvalue Mean 0.39 9.52 0.17 7.26 0.08 47.5 0.66 655 0.08 2.89 0.45 0.03 0.16 0.23 0.94 0.11 0.21 3851 0.07 Etang des Vallées Summer 2014 541 444 0.69 0.75 23324 La Claye Autumn2016 132 658 0.25 0.61 5827 Saint Robert Autumn 2011 285 445 0.53 0.63 16425 Ru Sainte Anne Summer 2014 890 2607 0.62 0.92 28706 88235 1.34 Etang des Vallées Autumn2014 637 444 0.62 0.65 26184 32412 Etang des Vallées Winter 2015 353 1063 0.52 0.75 32571 La Claye Winter 2017 133 643 0.24 0.59 11439 La Claye Spring 2017 329 547 0.4 0.6 8362 Saint Robert Winter 2012 85 355 0.31 0.59 20340 Saint Robert Summer 2012 185 507 0.17 0.58 21676 Ru Sainte Anne Autumn2014 840 2173 0.69 0.86 45434 9673 Ru Sainte Anne Winter 2015 1097 2566 0.88 0.92 52190 72473 Etang des Vallées EV73 Spring 2015 289 336 0.51 0.71 27944 110056 Etang des Vallées EV75 Summer 2015 271 279 0.47 0.7 23712 112734 La Claye LC83 Summer 2017 383 602 0.43 0.72 16490 La Claye LC85 Autumn2017 342 430 0.5 0.68 17426 Saint Robert SR37 Autumn2012 553 445 0.62 0.59 19642 Saint Robert SR43 Winter 2013 58 709 0.26 0.58 17410 Ru Sainte Anne RSA65 Spring 2015 1076 2408 0.8 0.89 79086 Ru Sainte Anne RSA69 Autumn2015 948 2062 0.66 0.87 22604 Bray-Curtis dissimilarities computed after Wisconsin standardization of rarefied OTUs (9999 permutations). *** pvalue < 0.001 Linear regression Min Max Slope 3.20 21.5 -0.02 6.50 8.15 -0.02 0.60 130 0.04 278 615 0.00 0.02 6.54 0.02 0.00 0.16 -0.02 0.00 4.25 -0.05 0.02 1.26 0.07 0.03 418 -0.07 Etang des Vallées EV77 Autumn2015 414 560 0.66 0.73 23494 75869 Etang des Vallées EV79 Winter 2016 369 596 0.58 0.7 30739 116208 Etang des Vallées EV81 Spring 2016 404 798 0.67 0.74 31314 84705 Etang des Vallées EV83 Summer 2016 397 491 0.57 0.7 26524 69645 La Claye LC87 Winter 2018 304 482 0.56 0.65 14587 La Claye LC89 Spring 2018 252 387 0.43 0.64 13325 La Claye LC91 Summer 2018 462 495 0.57 0.68 36894 La Claye LC93 Autumn2018 105 979 0.24 0.61 25943 Saint Robert SR49 Spring 2013 309 360 0.43 0.52 7167 Saint Robert SR51 Summer 2013 100 555 0.26 0.54 16250 Saint Robert SR53 Autumn2013 297 494 0.33 0.65 94042 Saint Robert SR55 Winter 2014 184 425 0.33 0.61 83285 Ru Sainte Anne RSA71 Winter 2016 1014 2592 0.81 0.93 23844 Ru Sainte Anne RSA73 Spring 2016 1013 2437 0.84 0.91 Microbial eukaryotes Prokaryotes 27393 Ru Sainte Anne RSA75 Summer 2016 1007 2662 0.78 0.92 R pvalue R pvalue 34334 Ru Sainte Anne RSA77 Autumn2016 805 2478 0.67 0.9 60866 EV-LC 0.65 *** 0.61 *** 982.8 19537 Etang des Vallées EV85 Autumn2016 216 537 0.33 0.67 39584 La Claye LC95 Winter 2019 93 180 0.36 0.65 25804 Saint Robert SR57 Spring 2014 380 506 0.57 0.72 108027 Ru Sainte Anne RSA79 Winter 2017 1039 2372 0.74 0.9 35978 EV-MG 0.76 *** 0.83 *** 75731 1.80 Etang des Vallées EV87 Winter 2017 559 852 0.67 0.73 22961 63152 Mare Gabard MG13 Autumn 2011 273 300 0.56 0.64 33251 Saint Robert SR59 Summer 2014 234 392 0.3 0.7 165410 Ru Sainte Anne RSA81 Spring 2017 720 2209 0.62 0.87 6355 EV-SR 0.62 *** 0.69 ***
	Etang des Vallées Etang des Vallées Mare Gabard Mare Gabard Saint Robert Saint Robert Ru Sainte Anne Ru Sainte Anne Etang des Vallées Mare Gabard Saint Robert Ru Sainte Anne Etang des Vallées Mare Gabard Mare Gabard Saint Robert Saint Robert Ru Sainte Anne Etang des Vallées Etang des Vallées La Claye Mare Gabard Mare Gabard Mare Gabard Saint Robert Saint Robert Saint Robert Ru Sainte Anne Ru Sainte Anne Etang des Vallées Etang des Vallées Mare Gabard Mare Gabard Saint Robert Saint Robert La Claye Mare Gabard Saint Robert	EV89 EV91 MG19 MG31 SR61 SR63 RSA85 RSA87 EV93 MG37 SR65 RSA89 EV95 MG43 MG61 SR67 SR83 RSA91 EV97 EV99 LC25 MG49 MG51 MG59 SR69 SR71 SR81 RSA93 RSA95 EV101 EV103 MG53 MG55 SR73 SR75 LC19 MG57 SR79	Mean Spring 2017 Water temperature (°C) 13.2 pH 7.30 Summer 2017 Dissolved oxygen (%) 78.1 Winter 2012 Summer 2012 Autumn2014 Winter 2015 Autumn2017 Winter 2018 EV-RSA 0.99 Conductivity (µS/cm) 286 Nitrate (mg/l) 3.94 543 Nitrite (mg/l) 0.05 416 353 89 180 115 1031 1282 Autumn2017 581 Autumn2012 249 Spring 2015 297 Spring 2018 1284 LC-MG 0.45 Winter 2018 545 Winter 2013 615 Autumn2014 168 Summer 2015 237 Summer 2017 63 Summer 2018 535 LC-SR 0.33 Spring 2018 539 Summer 2018 641 Spring 2012 190 Spring 2013 803 Summer 2013 157 Summer 2014 242 Autumn2015 634 Winter 2016 559 Spring 2017 440 Autumn2018 710 Winter 2019 1049 LC-RSA 0.75 MG-SR 0.49 Autumn2018 406 Winter 2019 390 Autumn2013 194 Winter 2014 126 Spring 2016 688 Summer 2016 278 MG-RSA 0.71 Winter 2012 231 Spring 2014 326 Winter 2017 161 SR-RSA 0.56	Ammonia (mg/l) 0.17	Orthophosphate (mg/l) 0.05 *** Dissolved Organic Carbon (µM/C) 3410 696 Chlorophyll a (mg/l) 16.0 415 801 306 576 496 2488 2717 487 376 501 2245 *** 1019 1331 528 488 285 1987 *** 661 331 677 1707 642 287 553 508 519 2133 1842 *** *** 284 345 451 593 402 251 *** 1389 405 542 ***		Mean 0.67 Water temperature (°C) 12.2 0.63 0.46 0.12 0.29 0.41 0.7 0.86 0.95 0.65 0.44 0.61 0.81 0.59 0.67 0.74 0.41 0.43 0.19 0.59 0.44 0.66 0.71 0.33 0.83 0.32 0.41 0.69 0.58 0.42 0.73 0.79 0.74 0.73 0.57 0.56 0.4 0.43 0.75 0.37 0.82 0.4 0.61 0.18 0.83	pH 7.25	Dissolved oxygen (%) 46.3 *** Conductivity (µS/cm) 445 *** *** *** *** *** ***	Nitrate (mg/l) 1.51 0.71 Nitrite (mg/l) 0.03 0.69 0.65 0.6 0.68 0.59 0.91 0.93 0.67 0.61 0.61 0.86 0.73 0.77 0.68 0.64 0.43 0.83 0.72 0.63 0.66 0.89 0.63 0.42 0.71 0.49 0.62 0.89 0.88 0.64 0.69 0.64 0.59 0.57 0.45 0.72 0.51 0.62	Ammonia (mg/l) 0.62	Orthophosphate (mg/l) 0.23	4516 24952 72.9 29082 40405 32834 17942 14129 34825 Continued on next page 36879 27841 13458 9514 15635 85530 14304 17776 8952 49119 79286 Continued on next page Continued on next page 28110 10611 13340 19945 114424 Dissolved Organic Carbon (µM C) 15457 50173 57537 23059 29423 11325 29403 11059 11850 22615 28902 30775 69341 69053 Chlorophyll a (mg/l) 103792 38041 66247 14541 15257 28785 12356 24524 28769 14309 11837 21703

  Table 5.A.8 ANOSIM analysis conducted for each ecosystems between each pair of season, based on Bray-Curtis dissimilarities computed after Wisconsin standardization of rarefied OTUs (9999 permutations). *** p-value < 0.001, ** p-value < 0.01, * p-value <

	0.05
			EV		LC		MG		SR	RSA
	Microbial eukaryotes	R	pvalue	R	pvalue	R	pvalue	R	pvalue	R	pvalue
	Winter-Spring	0.58	***	0.17		0.12		-0.11		0.55	***
	Winter-Summer	0.88	***	0.44	***	0.62	***	0.39	**	0.76	***
	Winter-Autumn	0.64	***	0.15		0.39	**	0.23	*	0.53	***
	Spring-Summer	0.68	**	0.40	***	0.46	**	0.41	***	0.85	***
	Spring-Autumn	0.40	***	0.23	**	0.40	**	0.24	*	0.71	***
	Summer-Autumn	0.33	***	0.17	*	0.17		0.30	*	0.21	*
	Prokaryotes	R	pvalue	R	pvalue	R	pvalue	R	pvalue	R	pvalue
	Winter-Spring	0.27	*	0.12		0.13		0.07		-0.04
	Winter-Summer	0.83	***	0.37	**	0.4	**	0.44	***	0.3	*
	Winter-Autumn	0.64	***	0.32	**	0.3	**	0.32	*	0.11
	Spring-Summer	0.48	**	0.27	*	0.34	*	0.1		0.24	*
	Spring-Autumn	0.32	***	0.3	**	0.58	***	0.28	*	0.13
	Summer-Autumn	0.15		0.07		0.05		0.14		-0.1

  .A.1 Linear regressions conducted between network properties and diversity indexes (richness, evenness and Simpson index).

	7.89
	3
	37.8			Richness	Evenness	Simpson
	14		lag	-704.0	-0.2692	-0.2243
	Percentage of positive edges	r²	0.167	0.1034	0.0522
	22.0		pvalue 0.06511	0.1207	0.198
	9		lag	1.781	0.0004154 0.0005878
	Number of connected nodes	r²	0.02907	-0.04401	-0.01551
	18.6		pvalue	0.2486	0.5539	0.3948
	8		lag	-6.569	-0.0029616 -0.0036425
	Percentage of connected nodes 46.2	r² pvalue 0.004582 0.4084	0.418 0.004052	0.6825 4.884e-05
	30		lag	-530.8	-0.008382	-0.10935
		Clust coef	r²	-0.001035	-0.07134	-0.05616
	25		pvalue	0.3379	0.9734	0.6597
	23		lag	233.2	-0.08394	0.00474
	2.38	Average path length	r² pvalue	0.02734 0.253	-0.007231 0.3609	-0.07122 0.9591
	1		lag	3.213	0.002171	0.001491
	Percentage of links involving a parasitic node 25.3	r² pvalue	0.0571 0.1886	0.2233 0.037	0.0701 0.1664
			lag	-157.6	0.78081	0.47885
		Connectance	r²	-0.07037	0.05897	-0.02148
			pvalue	0.908	0.1854	0.4219
			lag	45.18	0.02697	0.02523
		Mean degree	r²	-0.03991	-0.01505	-0.02119
			pvalue	0.5253	0.3928	0.4205

  Supplementary Table6.A.6 Summary of percentage of the type of interactions developed in the seasonal co-occurrence networks for each ecosystem. All the edges in the network were categorize following the lifestyle of the nodes interacting, and the type of the interaction (i.e. positive or negative).

	Put. Parasites							0	18.1
	Autumn Autumn			Autotrophs Autotrophs	Heterotrophs Heterotrophs	Put. Parasites Put. Parasites
	Autotrophs Autotrophs		0 0		1.33 9	6.67 4.8	21.3 15.6	0 0	12 25.7
	Heterotrophs Heterotrophs					21.3 0.6	24 4.2	1.33 0.6	12 8.4
	Etang des Vallées Pos. edges Neg. edges Pos. edges Neg. edges Pos. edges Neg. edges Winter Autotrophs Heterotrophs Put. Parasites Put. Parasites 0 0 Pos. edges Neg. edges Pos. edges Neg. edges Pos. edges Neg. edges Mare Gabard Put. Parasites 0 31.1
	Autotrophs Winter		0.6 Autotrophs 5.5	0 Heterotrophs 20	0 Put. Parasites 1.82
	Heterotrophs Autotrophs		2.42	4.37	0 17	34.5 20.9	0 1.94	23 12.1
	Put. Parasites Heterotrophs					6.31	10.7	0 0	14.5 22.3
	Spring Put. Parasites			Autotrophs	Heterotrophs	Put. Parasites 0 1.94
	Autotrophs Spring		0	7.3 Autotrophs	0.67 Heterotrophs 35.3	0 Put. Parasites 0
	Heterotrophs Autotrophs		0		2.13	0 36.2	56.7 0	0 2.13	0 0
	Lifestyle Put. Parasites Heterotrophs Summer Put. Parasites Autotrophs Summer		Autotroph-Heterotroph 12.8 Autotroph-Parasite Autotrophs Heterotrophs 0.491 38.3 0.428 Heterotroph-Parasite -0.063 5.31 6.28 29 21.3 Autotrophs Heterotrophs	3.418 2.512 -0.391	0 0 Put. Parasites 0 8.51 0.0018 0 0 0.0321 0.9194 0 1.45 Put. Parasites
	Heterotrophs Autotrophs		4.3	Summer-Spring 6.45	2.9 15.1	0.179 30 39.2	0.940 1.93 3.22	0.7835 1.93 9.68
	Put. Parasites Heterotrophs				Summer-Autumn	4.3	0.349 14.5	1.978 0.54 0	0.1964 2.69 0
	Autumn Put. Parasites Season Autotrophs Autumn		Summer-Winter Autotrophs Spring-Autumn 4.88 7.31 Autotrophs	0.742 Heterotrophs 0.170 17.1 16.3 Heterotrophs	4.130 0.830 4.88 Put. Parasites 0 0 0.0002 0.8402 4.88 Put. Parasites
	Heterotrophs Autotrophs		1.76	Spring-Winter 1.18	4.88 11.8	0.564 26 13.5	2.708 1.63 0.59	0.0342 10.6 1.18
	Put. Parasites Heterotrophs				Autumn-Winter	15.9	0.393 41.8	2.019 1.63 4.12	0.1810 1.62 8.23
	La Claye Put. Parasites	Results anova Pos. edges Neg. edges Pos. edges Neg. edges Pos. edges Neg. edges 0 0
	Variable Winter Ru Sainte Anne Pos. edges Neg. edges Pos. edges Neg. edges Pos. edges Neg. edges Deviance F pvalue Autotrophs Heterotrophs Put. Parasites
	NULL Autotrophs Winter		0	8600.2 10.4 Autotrophs	9.1 Heterotrophs 28.6	0 Put. Parasites 2.6
	Lifestyle Heterotrophs Autotrophs		1.7	245.69 3.33	1.3 0	8.366 20.8 13.3	2.6 0	2.5x10-4 13 1.67
	Season Put. Parasites Heterotrophs				460.54	0	10.45 50	0 0	9.0x10-7 11.7 30
	Ecosystem Spring Put. Parasites			1438.09 Autotrophs	32.65 Heterotrophs	2.2x10-16 Put. Parasites 0 0
	Season:Ecosystem Autotrophs Spring	404.2 2.97 Autotrophs 3.96	3.058 14.9 Heterotrophs 18.8	1.3x10-3 8.9 Put. Parasites 2.97
	Heterotrophs Autotrophs		0.46	0.91	14.8 7.76	16.8 2.28	8.91 8.22	6.93 3.2
	Put. Parasites Heterotrophs					3.65	11.9	0 3.2	0 28.31
	Summer Put. Parasites			Autotrophs	Heterotrophs	Put. Parasites 0 30.1
	Autotrophs Heterotrophs Summer Autotrophs Supplementary Figure 61.94 Autotrophs 16.5 0.29 1.15	15.5 15.5 Heterotrophs 29.1 16.5 3.15 7.16	0.97 0 Put. Parasites 3.88 0 6.88 9.17
	Put. Parasites Heterotrophs					10	10.3	0 3.44	0 30.4

  Explore the missing pieces in ecosystem functioningThis thesis gave me a comprehensive view of the microbial ecology in diverse freshwater ecosystems, at different scales: spatio-temporal dynamics, three domains of life, and two types of ecosystems (i.e. the largest on Earth, and common small freshwater ecosystems). However, precisions on physico-chemical conditions, or on the actors in the trophic networks are still needed to go further in the understanding the functioning of aquatic ecosystems.

	8.1 Perspectives
	8.1.1

8.1.1.1 Ameliorate the survey of physico-chemical variations

  En effet, ils peuvent être divisés entre écosystèmes lentiques et lotiques, ainsi qu'entre grands et petits écosystèmes. Les grands écosystèmes d'eau douce contiennent un volume important d'eau et sont considérés comme plus stables dans le temps. En effet, leur grand volume d'eau induit un fort effet tampon ce qui permet de réguler les changements environnementaux. Cependant, ces grands écosystèmes sont plus sujets à des variations spatiales au sein de l'écosystème, via des gradients horizontaux et verticaux. Les petits écosystèmes d'eau douce sont eux très largement distribués et sont très nombreux. Leur superficie cumulée en zone tempérée est quasiment équivalent à celle des grands écosystèmes. Les petits écosystèmes d'eau douce sont caractérisés par une profondeur et un volume restreints, ce qui correspond à un faible effet tampon. Ces écosystèmes ont donc un fonctionnement particulier, et sont en principe plus sensibles aux perturbations, aux variations saisonnières et aux changements climatiques globaux. De plus, ils contribuent significativement au cycle du carbone, notamment dans les émissions de méthane et de dioxyde de carbone des eaux continentales.

https://www6.inrae.fr/soere-ola/Sites-d-etude

https://lter.limnology.wisc.edu

https://lnu.se/en/research/searchresearch/linnaeus-microbial-observatory-lmo/

3.B Supplementary Figures

4.B Supplementary Figures

5.B Supplementary Figures

6.B Supplementary Figures

Remerciements

Sainte Anne). L'étude des différents écosystèmes d'eau douce s'est faite via (i) l'exploration de la diversité et de l'autoécologie des communautés qui habitent les écosystèmes, (ii) l'identification de l'influence des facteurs top-down et bottom-up sur les communautés, (iii) la conduite d'analyses multivariées selon des échelles spatio-temporelles cohérentes, de façon à avoir une vue d'ensemble des différents mécanismes en jeu dans les écosystèmes.

Structuration latitudinale et verticale des communautés de protistes dans le Lac Baïkal

Le lac Baïkal est situé en Sibérie (Russie). Parmi ses différentes caractéristiques, ce lac est le plus vieux lac terrestre, il contient le plus grand volume d'eau douce et c'est également le lac le plus profond. Ses différentes caractéristiques font que le lac Baïkal peut être visualisé comme une "mer d'eau douce", ce qui le différencie des autres lacs et peut conduire à des variations dans son fonctionnement et dans les communautés microbiennes qui y résident. De ce fait, le lac Baïkal fait l'objet de recherches intensives. Cependant, ses communautés microbiennes restent peu étudiées, notamment selon les gradients horizontaux et verticaux. Dans cette étude, nous avons cherché à déterminer l'impact de ces gradients sur les eucaryotes planctoniques du lac. Pour cela, nous avons étudié des échantillons collectés pendant l'été 2017, le long d'un gradient latitudinal Nord-Sud (sur environ 600 km) recouvrant les trois bassins du lac (Nord, Central et Sud). Les prélèvements ont été conduits dans les eaux côtières et pélagiques, de la surface aux zones les plus profondes (5 à 1400 m, avec une répartition des échantillons dans trois zones de profondeur, épi-, méso-et bathypélagique). Les données sur les communautés microbiennes ont été obtenues par une approche de metabarcoding, avec un séquençage Illumina MiSeq de l'ARNr 18S. Tout d'abord, j'ai caractérisé les communautés microbiennes eucaryotes planc-

Supplementary Material

3.A Supplementary Tables Supplementary Table 3.A.2 Identification, phylogenetic affinity and abundance of eukaryotic OTUs identified in Lake Baikal plankton.

(too large to be displayed in this document, available in excel format) Supplementary Figure 3 Chapter 6 Topology of seasonal co-occurrence networks reveals similar community structuration in different small freshwater ecosystems Supplementary Material 6.A Supplementary Tables Supplementary Table 6.A.2 Summary of the seasonal co-occurrence networks properties in the four ecosystems. The networks were built on the OTUs present in at least 6 seasons over the 8 years and presenting a relative abundance higher than 0.05%. Chapter 7

General Discussion