General abstract

The abyssal seafloor covers more than half of planet Earth. It can host a large number of, mostly small and still undescribed, organisms (~50,000-5 million individuals/m 2 ), contributing to key ecosystem functions such as nutrient cycling, sediment stabilisation and transport, or secondary production. Technological developments in the past 30 years have allowed remarkable advances, yet due to the vastness and remoteness of deep-sea habitats, ecological studies have been limited to local or regional scales. Indeed, we have so far explored less than 1% of the deep seafloor, although the latter is under increased threat from a variety of anthropogenic pressures.

This PhD aimed at bringing new perspectives for the study of biodiversity and biogeography in the deep-sea, to bridge this large knowledge gap, and advance toward the development of efficient biomonitoring protocols to preserve this vast and elusive backyard.

We investigated the potential of multi-marker environmental DNA (eDNA) metabarcoding to assess the extent and distribution patterns of biodiversity in this remote ecosystem. Using mitochondrial and nuclear marker genes, this PhD aimed at producing and testing an optimized eDNA metabarcoding workflow for deep-sea sediments, on a bioinformatic, molecular, and sample processing level, applicable to multiple life compartments including bacteria, protists, and metazoans.

Biodiversity assessment with eDNA is confronted with the difficulty in defining accurate "species-level" molecular Operational Taxonomic Units (OTUs), as numerous sources of error induce frequent overestimations. The first part of this thesis describes how newly developed bioinformatic tools can be combined in order to get access different levels of biotic diversity, and underline the advantages of clustering and LULU-curation for producing metazoan biodiversity inventories at the level of the morphospecies.

Moreover, the accuracy of protocols based on eDNA in deep sea sediments still needs to be assessed, as results may be biased by ancient DNA, resulting in biodiversity assessments not targeting live organisms.This thesis assessed the potential bias of ancient DNA by 1) evaluating the effect of removing short DNA fragments, and 2) comparing communities revealed by coextracted DNA and RNA in five deep-sea sites. Results indicated that short DNA fragments do not affect alpha and beta diversity, but that DNA obtained from 10g of sediment should be favoured over RNA for logistically realistic, repeatable, and reliable surveys. Results also eDNA METABARCODING IN THE DEEP-SEA iii confirm that increasing the number of biological rather than technical replicates is important to infer robust ecological patterns.

Sieving sediment to separate benthic size classes increased the number of detected meiofauna OTUs, but was not essential for achieving robust biodiversity estimates, and should be avoided if unicellular organisms are also of interest. More importantly, aboveground water and superficial sediment detected significantly different communities in all taxonomic compartments, even when large volumes of water were sampled, emphasising that eDNA metabarcoding of aboveground water samples is not suitable for benthic biodiversity surveys.

Finally, this thesis applied the optimized eDNA metabarcoding protocols to investigate the influence of biotic and abiotic factors on the extent and distribution of deep-sea metazoan biodiversity on an East-West transect ranging from the Central Mediterranean to the Mid-Atlantic Ridge. Results, consistent to morphology-based studies, confirm that small-scale biotic and abiotic factors lead to significant vertical changes in metazoan richness and community structure within the sediment, and highlight that regional beta-diversity patterns result from a combined influence of past biogeography and present day processes.

This thesis opens the way to large-scale eDNA-based studies in the deep-sea realm, thus contributing to a better understanding of biodiversity, biogeography, and ecosystem function in this vast and still poorly known biome.

eDNA METABARCODING IN THE DEEP-SEA iv

Résumé général

Les fonds abyssaux couvrent plus de la moitié de la planète Terre. Ils peuvent héberger un grand nombre d'organismes (~ 50000 à 5 millions d'individus/m 2 ), pour la plupart petits et encore non décrits, contribuant à des fonctions écosystémiques clés telles que le recyclage du carbone ou la productivité secondaire.

Les développements technologiques ont permis des avancées remarquables, mais l'immensité et l'éloignement des habitats profonds ont restreints les études aux niveaux local et régional. Nous avons exploré moins de 1% des fonds marins, alors que ces-derniers forment l'un des plus grands biomes sur Terre et sont de plus en plus sous pression anthropique.

Cette thèse vise à apporter de nouvelles perspectives pour l'étude de la biodiversité et de la biogéographie en environnements profonds, pour combler ce déficit de connaissances et permettre le développement de protocoles de biosurveillance efficaces.

Nous avons étudié le potentiel du métabarcoding d'ADN environnemental (ADNe) pour évaluer l'étendue et la distribution de la biodiversité en environnements profonds. À l'aide de gènes marqueurs mitochondriaux et nucléaires, cette thèse vise à produire un protocole de métabarcoding d'ADNe pour les sédiments des grands fonds, optimisé à un niveau de traitement bioinformatique, moléculaire et d'échantillonnage, et applicable à plusieurs compartiments du vivant.

L'évaluation de la biodiversité avec l'ADNe est confrontée à la difficulté de définir des Unités Taxonomiques Opérationnelles (OTU) au niveau de «l'espèce», car de nombreuses sources d'erreur induisent de fréquentes surestimations. Le premier chapitre de cette thèse décrit comment des outils bioinformatiques nouvellement développés peuvent être combinés afin d'accéder à divers niveaux de diversité biotique, et soulignent les avantages du clustering et de l'outil LULU pour produire des inventaires de biodiversité métazoaire plus proches du niveau de l'espèce morphologique.

De plus, la précision des protocoles basés sur l'ADNe dans les sédiments profonds devait être évaluée, car les résultats peuvent être biaisés par de l'ADN ancien archivé dans le sédiment, ce qui conduirait à des estimations de biodiversité passée plutôt que présente. Dans un second temps, nous avons donc estimé le biais de l'ADN ancien en 1) évaluant l'effet de l'élimination de courts fragments d'ADN, et 2) en comparant les communautés révélées par l'ADN et l'ARN co-extraits. Les résultats indiquent que les fragments d'ADN courts n'affectent pas la diversité eDNA METABARCODING IN THE DEEP-SEA v alpha et bêta, que l'ADN obtenu à partir de 10 g de sédiments est plus approprié que l'ARN pour des études exhaustives logistiquement réalistes, et que les réplicas biologiques plutôt que techniques sont importants pour inférer des patrons écologiques fiables.

Le tamisage des sédiments séparant les organismes benthiques par classe de taille a augmenté le nombre d'OTU métazoaires détectées, mais n'était pas essentiel pour obtenir des patrons écologiques robustes, et devrait être évité si des taxons unicellulaires sont également ciblés. De même la comparaison de communautés détectées par des échantillons d'eau affleurante et de sédiments a montré que l' eaux affleurante ne représente pas une alternative aux sédiments pour effectuer des inventaires de diversité benthique.

Enfin, les protocoles optimisés de métabarcoding d'ADNe ont été appliqué pour étudier l'influence de facteurs biotiques et abiotiques sur la biodiversité métazoaire des grands fonds, allant de la Méditerranée centrale à la dorsale médio-atlantique. Les résultats confirment que des facteurs agissant à très petite échelle (cm) conduisent à des changements verticaux significatifs de la richesse et de la structure des communautés dans les sédiments, et soulignent que les tendances régionales de diversité bêta résultent d'une influence combinée de la biogéographie passée et de phénomènes actuels.

Cette thèse ouvre la voie à des études de biodiversité globale dans les environnements profonds, contribuant ainsi à une meilleure compréhension de la biogéographie et des fonctionnements écosystémiques dans ce vaste biome encore mal connu.

eDNA METABARCODING IN THE DEEP-SEA vi Declaration I declare that this doctoral thesis, entitled "Metabarcoding in the abyss: uncovering deepsea biodiversity through environmental DNA", has been composed solely by myself and that it has not been submitted, in whole or in part, in any previous application for a degree. Except where states otherwise by reference or acknowledgment, the work presented is entirely my own.

Chapter I. Introduction and literature review I.I.

Why study biodiversity?

How many species are there on Earth? What influences their distribution? These two simple questions about Life remain enigmatic. These questions are important, as biodiversitythe variety of Life on Earth -is a major component of Earth's life-support system, the product of over 3 billion years of evolution, but is increasingly under threat from human activities.

Natural ecosystems provide great benefits to human societies, and there is global consensus that human well-being depends on healthy ecosystems [START_REF] Stokstad | Taking the Pulse of Earth's Life-Support Systems[END_REF]. Global efforts to understand and preserve our natural world have increased since the 1970s, with a milestone set during the Rio Earth Summit, the first United Nations Conference of the Parties (COP). This conference was the starting point of the United Nations Framework Convention on Climate Change (UNFCCC) and the Convention on Biological Diversity (CBD), which are the world's first commitments to sustainable development and the conservation of biological diversity.

Since then, a lot of research has highlighted the relationship between biodiversity and ecosystem health. Biodiversity encompasses variation among genes, species, and functional traits [START_REF] Cardinale | Biodiversity Loss and Its Impact on Humanity[END_REF] .The value of biodiversity lies in the myriad of roles all these life forms perform, and which make complex biotic systems possible. The functioning of these ecosystems, i.e. the way they store resources, produce biomass, decompose and recycle nutrients, is tightly linked to the biodiversity they harbour. Research has also shown that higher levels of diversity are associated with higher ecosystem stability through time [START_REF] Cardinale | Biodiversity Loss and Its Impact on Humanity[END_REF]. Finally, natural ecosystems, and thus the biodiversity they harbour, also provide a series of so-called "ecosystem services". These range from valuable goods in industry or agriculture, to clean drinking water, or the regulation and stability of fundamental Life equilibria such as disease outbreak mitigation, pollination, or climate stability [START_REF] Palmer | Ecology for a Crowded Planet[END_REF][START_REF] Cardinale | Biodiversity Loss and Its Impact on Humanity[END_REF][START_REF] Rohr | Towards Common Ground in the Biodiversity-Disease Debate[END_REF].

Despite the Rio agreements, a 2010 review of the state of biodiversity showed ongoing declines and increasing levels of anthropogenic pressure [START_REF] Butchart | Global Biodiversity: Indicators of Recent Declines[END_REF]. A primary CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 2 consequence of these stressors is an accelerated loss of populations and species in the past century [START_REF] Cardinale | Biodiversity Loss and Its Impact on Humanity[END_REF]. This is associated with increased rates of resource collapse, decreased ecosystem productivity, decreased resistance and resilience capacities, and decreased water quality [START_REF] Worm | Impacts of Biodiversity Loss on Ocean Ecosystem Services[END_REF]. Overall, the impacts of diversity loss on ecosystem functioning might be among the major drivers of global environmental change [START_REF] Cardinale | Biodiversity Loss and Its Impact on Humanity[END_REF]. In order to maintain ecosystem services, and preserve human well-being, it is therefore essential that we acquire a better understanding of the natural patterns and processes that sustain ecosystem functioning, among which biological diversity.

Estimating biodiversity

Walter Rosen first introduced the term biodiversity, contraction of biological diversity, in 1986, during the National Forum on Biological Diversity in Washington DC. The United Nations Environment Program (UNEP) now defines biological diversity as "the variability among living organisms from all sources […] and the ecological complexes of which they are part; this includes diversity within species, between species, and of ecosystems". Hubbell (2001) proposed a simpler and more precise definition, describing biodiversity as "species richness and their relative abundance in space and time".

The biodiversity concept, although intuitively easy to grasp, is hard to define mathematically due to the various definitions it has been given in the past, and the confusion in terminology that still exist nowadays (e.g species richness vs. species diversity). Diversity is usually defined at different spatial scales. [START_REF] Whittaker | Vegetation of the Siskiyou Mountains, Oregon and California[END_REF] first introduced this concept as he recognized that total species diversity in a landscape could be considered to consist of conceptually different components. He used alpha, beta, and gamma to refer to these components. In this way, Gamma diversity is the total species diversity in a landscape or ecosystem. It is composed of the local species diversity (measured in spatially limited and homogenous samples at the habitat-scale), called Alpha diversity, and the compositional differences among these local systems, called Beta diversity.

Consequently, when measuring biological diversity in natural samples, one has to be aware of three central elements [START_REF] Purvis | Getting the Measure of Biodiversity[END_REF]. First, species richness, or the number of species in a sample, is the simplest and most intuitive component of biodiversity. It however assumes that species definition and classification is well known (which is a matter of debate, see below!) and that all species are equivalent (each species weighs equally in the richness value, regardless its abundance). Simple counts of species in samples underestimate true species CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 3 richness and strongly depend on sampling effort, as it is hardly possible to sample all species in an ecosystem. Thus, there are potentially numerous undiscovered species in any species inventory. Generally, two approaches can be used to estimate species richness in incomplete samples: an asymptotic approach via species richness estimators (Fisher's alpha, Chao estimators, Jackknife estimators, Coverage-based estimators) or a non-asymptotic approach via rarefaction and extrapolation [START_REF] Chao | Species Richness: Estimation and Comparison[END_REF].

Second, sample evenness or equitability estimates the relative abundance of species in a sample, i.e. the extent to which individuals are distributed evenly among species. Indeed, individuals from a very abundant species contribute less to biodiversity than individuals from a rare species. Sample diversity is thus higher when individuals are distributed evenly among species. Equitability estimators therefore evaluate the deviation of the observed species distribution from a uniform distribution, and the Pielou index, J, is the most widely used equitability index [START_REF] Purvis | Getting the Measure of Biodiversity[END_REF].

Ideally, alpha (local) diversity should be a measure of both species richness and species relative abundances. Commonly used alpha diversity indices such as the Shannon or Simpson indices, evaluate both richness and equitability. It is important to note that alpha diversity indices mainly differ in the weight they give to abundant vs. rare species, for e.g. the Simpson index is more sensitive to dominant species. As they quantify diversity in different ways, they are thus only a proxy of the variable they try to quantify. Modern diversity estimators have unified classical indices, using measures of entropy that can be expressed as an "effective number of species", i.e. Hill numbers of order q [START_REF] Grabchak | The Generalized Simpson's Entropy Is a Measure of Biodiversity[END_REF][START_REF] Chao | Rarefaction and Extrapolation with Hill Numbers: A Framework for Sampling and Estimation in Species Diversity Studies[END_REF]. These measures only depend on q, the exponent of the species frequencies in the index. This q, is what determines the sensitivity of the index to the species frequencies (typically q=0-2). In this framework, when q=0, diversity is species richness and the same importance to all species, thus greatest possible weight is given to rare species. When q=1, diversity is the Shannon index, all individuals weigh the same, thus species weigh differently depending on their relative abundance. When q=2, diversity becomes the Simpson index, which gives less weight to rare species, explaining why it is usually called "the number of very abundant species" [START_REF] Tuomisto | A Consistent Terminology for Quantifying Species Diversity? Yes, It Does Exist[END_REF]).

These classical diversity measures are so-called species-neutral diversity measures, as they do not consider interspecific distances. However, two species of a same genus are obviously more related than two species of different families, and some species assemblages gather species with similar or very distinct functions in the community. Phylogenetic and functional

CHAPTER I INTRODUCTION AND LITERATURE REVIEW 4

diversity measures take into account the phylogenetic and/or phenotypic differences by evaluating disparity, the mean divergence between species. The last component of biodiversity thus measures the extent to which species are different, giving insights into evolutionary history of the community or allowing evaluating ecosystem productivity, functioning, and resilience.

Disparity can also be measured among samples to describe the degree of compositional differentiation of communities according to changes in the environment, i.e. beta diversity.

Ecologists usually look at community differentiation in two possible ways: turnover by reference to a specific gradient, or variation in community structure [START_REF] Anderson | Navigating the Multiple Meanings of Beta Diversity: A Roadmap for the Practicing Ecologist[END_REF].

When looking at turnover, the idea is to measure the change in community structure from one sampling unit to another, along a spatial, temporal, or environmental predefined gradient. The change can be measured via species identities, relative abundances, biomass, or percentage cover of individuals. Overall, turnover can always be expressed as a rate, as in a distance-decay plot. When looking at variation, the idea is to evaluate community structure among a set of sample units within a given category (space, time, habitat type, experimental treatment…).

Variation is measured among all possible pairs of units, without a reference to a gradient, and it quantifies the proportion of unshared species among all sampling units.

Pairwise dissimilarities between sampling units form the basis of the analysis of beta diversity. The different approaches mentioned above (turnover-based vs. variation-based studies) differ essentially by the type of distance metric calculated. For turnover-based studies, pairwise, non-Euclidean distances are usually computed, and analysed with respect to the predefined gradient. Linear or non-linear models (regressions or distance-decay models) consequently allow evaluating the turnover or rate of turnover along the chosen gradient. In variation-based studies, the gradient is unknown and the variation, i.e. pairwise dissimilarities, visualized in ordinations (i.e. 2-D representations of dissimilarity). Ordinations can be unconstrained or constrained. In unconstrained ordinations, one does not impose the nature of the ordination axes and only associates environmental variables by a posteriori superimposing environmental labels or vectors. This is called indirect gradient analysis. In constrained ordinations, also called direct gradient analysis, one tries to partition variation according to some factors or continuous environmental variable, resulting in the fact that the ordination axes are defined (constrained). This approach however requires good knowledge of the studied samples, or a predefined working hypothesis. There are numerous ordination techniques that differ essentially in the type of distance (Euclidean or non-Euclidean distances) they use to calculate pairwise differences [START_REF] Legendre | Beta Diversity as the Variance of Community Data: Dissimilarity Coefficients and Partitioning[END_REF]. [START_REF] Anderson | Navigating the Multiple Meanings of Beta Diversity: A Roadmap for the Practicing Ecologist[END_REF] CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 5 emphasize the importance of comparing ordinations based on different dissimilarity measures, as they correspond to different underlying ecological hypotheses. More importantly, it is crucial to distinguish between incidence-based and abundance-based measures, as dramatically different results can be obtained when relative abundance data are included. Second, inclusion vs. exclusion of joint-absence information (number of species absent in both compared units) is also to be considered, as this information can be relevant when studying the disappearance of species following an environmental impact, predation, or biological invasion.

Overall, making an informed choice of the indices to use for a particular study necessitates an understanding of which aspects of diversity the indices quantify, and what is needed to answer a specific ecological question. There is no universal measure, and it should always be kept in mind that most indices are only a proxy of diversity itself. Moreover, in specific applications such as environmental impact or conservation studies, other measures than the "generalist" summarized here are likely more appropriate. For example, biotic indices based on indicator organisms are commonly used in impact assessments [START_REF] Washington | Diversity, Biotic and Similarity Indicies. A Review with Specieal Relevence to Aquatic Systems[END_REF][START_REF] Aylagas | Environmental Status Assessment Using DNA Metabarcoding: Towards a Genetics Based Marine Biotic Index (GAMBI)[END_REF]2018), while a measure of endemicity may be more useful for conservation management efforts (Costello and Chaudhary 2017).

The species concept

The Linnaean project, initiated some 265 years ago, is arguably the longest-running, most successful, and most impactful biology megaproject of all (Blaxter 2016). Carl Linnaeus proposed a binomial system to "name" groups of organisms that are recognizable as distinct natural types (i.e. species), therefore allowing to communicate complex concepts across the globe. The species constitutes the core of biodiversity inventories for biological and ecological studies, and helps organizing agriculture, trade, and industry (e.g. species used for the production of biomaterial) as well as measuring the impact of human activity on the Earth's ecosystems (e.g. biomarker taxa and pathogenic or invasive species).

While biotic diversity can be valued and assessed at various levels, including that of the individual organism and the genetic locus, the key level remains the species, and some authors conclude that species richness, while not perfect, is the best metric [START_REF] Freudenstein | Biodiversity and the Species Concept -Lineages Are Not Enough[END_REF].

A main issue in answering the question of how many species is that it requires a consensus on the definition of "species". Before Darwin, the delimitation of species focused largely on phenotypic uniqueness, i.e. common morphology. Darwin (1859) added history into the species definition, by depicting connections through time between species and their "offspring". Within CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 6

an evolutionary framework, the possibility arose of delimiting species, at least conceptually, by their unique history, and the word lineage became common to speak of populations and species through time.

In the mid-20 th century, evolutionary synthesists such as Mayr and Simpson recognised populations (groups of similar individuals) as fundamental units in nature, and outlined that it is the relationship among populations, i.e. interbreeding, that is critical in the concept of species [START_REF] Freudenstein | Biodiversity and the Species Concept -Lineages Are Not Enough[END_REF]. [START_REF] Mayr | Systematics and the Origin of Species, from the Viewpoint of a Zoologist[END_REF] developed his Biological Species Concept (BSC), and defined species as "groups of actually or potentially interbreeding natural populations, which are reproductively isolated from other such groups". [START_REF] Simpson | The Species Conept[END_REF] formalized his

Evolutionary Species Concept (ESC) as "a phyletic lineage (ancestral-descendent sequence of interbreeding populations) evolving independently of others, with its own separate and unitary evolutionary role and tendencies".

Recently, specialists from the eukaryotic [START_REF] Freudenstein | Biodiversity and the Species Concept -Lineages Are Not Enough[END_REF]Costello and Chaudhary 2017), micro-eukaryotic [START_REF] Fenchel | Where Are All the Species?[END_REF], and prokaryotic (Rosselló- [START_REF] Rosselló-Mora | The Species Concept for Prokaryotes[END_REF] worlds have stressed the importance of role in the second part of Simpson's sentence. Indeed, while there has been an increasing trend toward viewing species only as historical lineages [START_REF] Freudenstein | Biodiversity and the Species Concept -Lineages Are Not Enough[END_REF]), these authors argue that this contradicts the original species concept and misaligns with the key position of species as units of biodiversity. They re-define the ESC as "A species is a lineage or group of connected lineages with a distinct role.", or call it the "pheno-phyletic" or "phylo-phenetic" species concept.

Other species definitions gave similar emphasis on role, like [START_REF] Valen | Ecological Species, Multispecies, and Oaks[END_REF] Ecological Species Concept ("a lineage, or a closely related set of lineages, which occupies an adaptive zone minimally different from that of any other lineage in its range and which evolves separately from all lineages outside its range"), or [START_REF] Levin | +ecogenetic+concept&ots=nEZzYsN7Cs&sig=Ho-ZiFIuFuCZgpXJclMSatRw31Y#v=onepage&q=Levin[END_REF] ecogenetic concept that considered ecological function as part of species definition. Cohan's bacterial "ecotype" is also similar to this view, as ecotypes are necessarily ecologically distinct [START_REF] Cohan | Bacterial Species and Speciation[END_REF]2002). Later in his career, even Mayr (1982) came to view role as critical with his revised definition of species as "a reproductive community of populations (reproductively isolated from others) that occupies a specific niche in nature."

Thus, it seems that there is increasing recognition that ecological function should be part of species definition. Simpson described role as "definable by their equivalence to niches" [START_REF] Freudenstein | Biodiversity and the Species Concept -Lineages Are Not Enough[END_REF], although this has encountered criticism as ecological niches are difficult to precisely delimitate [START_REF] Hengeveld | Mayr's Ecological Species Criterion[END_REF]. [START_REF] Freudenstein | Biodiversity and the Species Concept -Lineages Are Not Enough[END_REF] view role broadly CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 7

as "the ways in which individuals interact with their environment and the total complement of expressed properties (beyond genotype) that they exhibit", and call it an "extended phenotype".

What does this extended phenotype encompass? Most intuitively, morphological and physiological features, as those are related to ecological role (Simpson, 1961). Phenotypic change is related to genotypic change, but the latter are not strictly linked, as epigenetic manipulation of the genome and extra-genomic determinants (ecological, cultural, parental inheritance) of phenotypical characteristics have been described [START_REF] Danchin | Beyond DNA: Integrating Inclusive Inheritance into an Extended Theory of Evolution[END_REF]. It is thus apparent that phenotypic as well as genotypic features have to be considered to determine role during species description and detection. Thus, although based on the ESC, methods describing biodiversity solely through genetic proxies are inherently limited as they do not encompass the complete "extended phenotype".

Global biodiversity estimates, and their limitations

How many species?

As there is yet no consensus on the definition of "species" for prokaryotes, the single-celled but extremely diverse Archaea and Bacteria [START_REF] Konstantinidis | The Bacterial Species Definition in the Genomic Era[END_REF], the question of how many has mostly been asked for eukaryotes, i.e. protists, plants, fungi, and animals [START_REF] Pimm | Biodiversity: Not Just Lots of Fish in the Sea[END_REF].

So far, expert opinions and extrapolations from macro-ecological patterns or from species description rates have been the main approaches used to estimate total species richness both in the terrestrial and marine realms (Table 1). At the beginning of the 1990s, global species richness estimates were hardly more than "educated guesses", ranging from around three to over fifty million with no associated estimates of uncertainty [START_REF] Mora | How Many Species Are There on Earth and in the Ocean?[END_REF]. As of today, species richness estimates have not converged (Table 1), and range from ~2.0 million to 76.5 million eukaryotic species globally [START_REF] Caley | Global Species Richness Estimates Have Not Converged[END_REF][START_REF] Costello | Predicting Total Global Species Richness Using Rates of Species Description and Estimates of Taxonomic Effort[END_REF][START_REF] Vargas | Eukaryotic Plankton Diversity in the Sunlit Ocean[END_REF].

Given that ~1.2 million eukaryotes have been catalogued so far [START_REF] Mora | How Many Species Are There on Earth and in the Ocean?[END_REF]) and that some authors predicted up to 1 trillion (10 12 ) species of prokaryotes on Earth (Locey and

Lennon 2016), it is clear that biodiversity research has still a long way to go, and many uncertainties to clarify.

Cataloguing biodiversity requires extraordinary knowledge of individual taxa, and thus extraordinary amounts of taxonomic experts [START_REF] Pimm | Biodiversity: Not Just Lots of Fish in the Sea[END_REF]. Although there is growing concern of declining taxonomic expertise, evidence shows that the numbers of scientists describing new Table 1. An overview of biodiversity estimates and utilized methods in the past three decades. Adapted from [START_REF] Mora | How Many Species Are There on Earth and in the Ocean?[END_REF] and [START_REF] Appeltans | The Magnitude of Global Marine Species Diversity[END_REF].

Number of species estimated Method Reference (year)

5 ->50 million globally Extrapolation from the frequency of large to small species [START_REF] May | How Many Species Are There on Earth?[END_REF] 3-5 million Ratio of numbers of tropical to temperate and boreal species May (1990) 5-15 million globally Analysis of available global estimates Stork N (1993) 12.5 million Compilation and extrapolation from regional estimates Hammond (1992) > 10 million marine species Extrapolation of deep-sea benthos samples

Grassle & Maciolek (1992) species, taxonomic publications, and species discovery rates have been increasing in the past decades (Costello and Chaudhary 2017;[START_REF] Costello | Predicting Total Global Species Richness Using Rates of Species Description and Estimates of Taxonomic Effort[END_REF][START_REF] Appeltans | The Magnitude of Global Marine Species Diversity[END_REF]. Current catalogues of biodiversity are available for terrestrial and marine realms (e.g. Catalogue of Life, and WoRMS, the World Register of Marine Species). Major sources of uncertainty in these catalogues are fourfold: 1) frequent occurrence of synonyms may inflate diversity estimates; 2) significant amounts of cryptic diversity may have been undetected by morphology-based approaches; 3) the potential hyper-diversity of small organisms may have been overlooked; and 4) under-sampled habitats such as the deep-sea may harbour large amounts of unknown biodiversity.

The synonyms

A potential large amount of extant named species may be synonyms, i.e. duplicate names for the same biological entity [START_REF] Alroy | How Many Named Species Are Valid?[END_REF]. Disconcertingly high proportions of synonyms have been reported for marine species (40%), but also for terrestrial animals (e.g. 31% of insect species), plants (78%), or freshwater fish (81%). Overall, synonyms may inflate current catalogues by about 20%, thus future species discoveries will be balanced by recognition of synonyms (Costello and Chaudhary 2017).

The cryptic species

Cryptic species refer to species that can only be differentiated by genetic but not by morphological features (Costello & Chaudhary 2017). Advances in DNA analysis have revealed high levels of cryptic diversity across the tree of life, likely driven by habitat heterogeneity and fragmentation [START_REF] Poulin | Global Analysis Reveals That Cryptic Diversity Is Linked with Habitat but Not Mode of Life[END_REF]. However, conflating cryptic diversity and cryptic species is misleading, as much of the detected cryptic genetic diversity does not result in formally described species, as this requires the characterisation of biological and ecological traits (Costello and Chaudhary 2017;[START_REF] Pante | Species Are Hypotheses: Avoid Connectivity Assessments Based on Pillars of Sand[END_REF].

Additionally, genetic markers also have downfalls for species discrimination: levels of mutation rates in mitochondrial DNA (mtDNA) sequences differ across animal species [START_REF] Galtier | Mitochondrial DNA as a Marker of Molecular Diversity: A Reappraisal[END_REF][START_REF] Nabholz | Determination of Mitochondrial Genetic Diversity in Mammals[END_REF], and some basal metazoan lineages exhibit such low rates of evolution that species cannot be distinguished on the basis of mtDNA sequences [START_REF] Shearer | Slow Mitochondrial DNA Sequence Evolution in the Anthozoa (Cnidaria)[END_REF][START_REF] Shearer | Barcoding Corals: Limited by Interspecific Divergence, Not Intraspecific Variation[END_REF][START_REF] Huang | Slow Mitochondrial COI Sequence Evolution at the Base of the Metazoan Tree and Its Implications for DNA Barcoding[END_REF]). MtDNA diversity is thus highly variable among taxonomic groups, not consistently correlated with population size [START_REF] Bazin | Population Size Does Not Influence Mitochondrial Genetic Diversity in Animals[END_REF][START_REF] Mulligan | Comment on 'Population Size Does Not Influence Mitochondrial Genetic Diversity in Animals[END_REF], depends on life-span [START_REF] Nabholz | The Erratic Mitochondrial Clock: Variations of Mutation Rate, Not Population Size, Affect MtDNA Diversity across Birds and Mammals[END_REF], and is affected by bacterial CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 10 symbionts [START_REF] Hurst | Problems with Mitochondrial DNA as a Marker in Population, Phylogeographic and Phylogenetic Studies: The Effects of Inherited Symbionts[END_REF]. Thus, the detection of cryptic species cannot solely rely on molecular proxies, as these need to be combined with morphological and ecological analyses to establish accurate species boundaries. Nevertheless, true cryptic species, i.e. only discernible through genetic data, do exist, and may balance the decrease in the number of catalogued species resulting from the discovery of synonyms (Costello and Chaudhary 2017).

The small

A major source of uncertainty in the current catalogue of diversity is related to the fact that it was built on three centuries of morphological information and thus exhibits a strong bias towards large organisms. The record for vertebrates may be close to complete, but this is likely not the case for taxa with smaller body size [START_REF] Blaxter | Molecular Systematics: Counting Angels with DNA[END_REF][START_REF] Cristescu | From Barcoding Single Individuals to Metabarcoding Biological Communities: Towards an Integrative Approach to the Study of Global Biodiversity[END_REF]). Indeed, it has now become clear that the overwhelming majority of organisms are microscopic, i.e. smaller than 1 mm (Bacteria, Archaea, protists, but also most Metazoa). Lack of easily recognized morphological characters, incompleteness of early descriptions, phenotypic plasticity, and the high numbers of organisms compared to the relatively few numbers of taxonomists are all factors suggesting that current inventories may underestimate microscopic biodiversity (Blaxter 2016). Recent estimates suggest the presence of 10 million insect species globally (Hebert et al. 2016b), and possibly over 1 million species of nematodes (Blaxter 2016). Similarly, DNAbased studies in the marine realm showed an unprecedented eukaryotic genetic diversity in planktonic and benthic environments, emphasizing the protistan knowledge gap, and suggesting that eukaryotic diversity may increase with decreasing body size. In the Tara Oceans expedition (https://oceans.taraexpeditions.org/en/m/about-tara/), protists accounted for over 85% of the diversity, raising previous biodiversity estimates to 16.5 million terrestrial and 60 million marine eukaryotic species [START_REF] Vargas | Eukaryotic Plankton Diversity in the Sunlit Ocean[END_REF]. Similarly, the BioMarks project on benthic diversity in European coastal waters concluded that 30%-70% of protists remain to be discovered [START_REF] Forster | Benthic Protists: The under-Charted Majority[END_REF]. Finally, the latest estimate of bacterial diversity based on highthroughput molecular data predicted up to 1 trillion (10 12 ) species of prokaryotes on Earth [START_REF] Locey | Scaling Laws Predict Global Microbial Diversity[END_REF].

These DNA-based approaches thus suggest an enormous amount of undescribed microbial species. However, high levels of alpha diversity do not imply high global (gamma) diversity.

Studies have shown that small organisms, while exhibiting high local species richness, display decreasing diversity at larger spatial scales [START_REF] Azovsky | Size-Dependent Species-Area Relationships in Benthos: Is the World More Diverse for Microbes?[END_REF]. Biodiversity patterns at the microscopic scale differ markedly from those at the macroscopic scale: small species are often CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 11 found to be cosmopolitan, i.e. thriving wherever local habitat conditions are suitable [START_REF] Finlay | Protist Diversity Is Different?[END_REF], to exhibit high and random dispersal, asexual reproduction and increased horizontal gene transfer, as well as short generation times combined with large population sizes [START_REF] Finlay | Global Dispersal of Free-Living Microbial Eukaryote Species[END_REF]. Together, these characteristics support high genetic diversity, enabling rapid adaptation to changing environmental conditions, but also lower speciation rates due to higher gene flow (Costello and Chaudhary 2017). Indeed, speciation rates have actually been found to be higher in multicellular eukaryotes compared to prokaryotes [START_REF] Lynch | The Origins of Genome Complexity[END_REF]. Thus, while high levels of genetic diversity may be detected in small organisms, these do not necessarily point toward high species diversity [START_REF] Rossberg | Are There Species Smaller than 1 Mm?[END_REF]. Indeed, studies in vertebrates and plants have found no correlation between genetic and species richness (Costello and Chaudhary 2017). Moreover, it has been found that body size does not predict species richness in the Metazoa [START_REF] Orme | Body Size Does Not Predict Species Richness among the Metazoan Phyla[END_REF], highlighting that small does not necessarily mean species rich.

Nevertheless, morphology-based investigations showed that 37% of meiofauna species (42 µm -1 mm size ranges) sampled in a well-known ecosystem (Western Mediterranean shallow water) were new to science, indicating that much of the small diversity remains to be described [START_REF] Curini-Galletti | Patterns of Diversity in Soft-Bodied Meiofauna: Dispersal Ability and Body Size Matter[END_REF].

The under-sampled

Another bias in the catalogue of diversity is related to the fact that humans are terrestrial animals and have therefore more extensively explored terrestrial habitats compared to aquatic ones. Consequently, biodiversity research has mostly been focused on terrestrial, usually temperate fauna, and targeted mostly mammals, birds, and arthropods (Table 1 ;[START_REF] Stork | How Many Species Are There?[END_REF][START_REF] May | How Many Species Are There on Earth?[END_REF][START_REF] Hendriks | Allocation of Effort and Imbalances in Biodiversity Research[END_REF][START_REF] Hendriks | Biodiversity Research Still Grounded[END_REF]. However, of the 36 animal phyla described today, all but one are found in the marine environment, and 40% are exclusively marine [START_REF] Pimm | Biodiversity: Not Just Lots of Fish in the Sea[END_REF]. This highlights the extreme breadth of oceanic biodiversity, but also the fact that terrestrial species have evolved from marine ones (Costello and Chaudhary 2017). The last decades were marked by the will to elucidate marine diversity and initiatives like the Census of Marine Life and the World Register of Marine Species (WoRMS) greatly improved our knowledge of the marine realm (Table 1). More marine biodiversity research was performed in the last 60 years than never before, yet, only 16% of described species are marine. This low proportion of marine species may be due to under-sampling and still disproportionally small research efforts (particularly in the deep-sea; [START_REF] Hendriks | Biodiversity Research Still Grounded[END_REF], or to the biological reality CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 12

that diversity is higher on land than in the sea, due to higher productivity, higher habitat complexity, and thus more ecological niches (Costello and Chaudhary 2017). microbiologists used these advances to survey the diversity of bacteria and archaea using the 16S small subunit (SSU) ribosomal RNA (16S rRNA) gene, showing that prokaryote diversity was at least 100 times higher than previously expected [START_REF] Blaxter | Molecular Systematics: Counting Angels with DNA[END_REF]. Microbiologists soon used sequence data for "species" descriptions, the bacterial taxa being defined as phylotypes or "Molecular Operational Taxonomic Units" (MOTUs or OTUs). These DNA-based diversity estimation methods were built on the observation that there generally is a gap between the distributions of intraspecific and interspecific divergence in gene sequences, termed the barcode gap by Meyer and Paulay (2005, Fig. 1). The term "barcode" is a figurative analogy to commercial barcodes found on price tags, where the width and spacing among parallel lines identify products. Similarly, the sequences of nucleotides (Adenine (A), Thymine (T), Guanine (G), and Cytosine (C)) in barcode genes is taxon specific. Consequently, DNA sequences of barcode genes enable species identification and recognition, while complementing formal species description by providing molecular diagnostic characters (Hebert et al. 2003a;[START_REF] Bucklin | DNA Barcoding of Marine Metazoa[END_REF].

I.II. Estimating biodiversity in

CHAPTER I INTRODUCTION AND LITERATURE REVIEW 13

In Finally it should display a barcode gap (Fig. 1), i.e. marked divergence and no overlap between intra-and interspecific genetic distances [START_REF] Bucklin | DNA Barcoding of Marine Metazoa[END_REF].

For animals (metazoans), the mitochondrial genome has several advantages over the nuclear genome, such as lack of introns, mostly uniparental (maternal) inheritance and thus INTRODUCTION AND LITERATURE REVIEW 14 little recombination, and predominantly neutral evolution, allowing it to serve as a "molecular clock". These features combine with the presence of high copy numbers in every cell, making amplification more successful , and with the presence of conserved regions allowing the design of "universal primers" that amplify a broad range of taxa [START_REF] Folmer | DNA Primers for Amplification of Mitochondrial Cytochrome c Oxidase Subunit I from Diverse Metazoan Invertebrates[END_REF][START_REF] Geller | Redesign of PCR Primers for Mitochondrial Cytochrome c Oxidase Subunit I for Marine Invertebrates and Application in All-Taxa Biotic Surveys[END_REF].

Moreover, due to elevated mutation rates, the mitochondrial COI gene offers the best specieslevel resolution in most taxa except for ctenophores, sponges, nematodes, and some benthic cnidarians (corals and anemones), for which COI is either difficult to amplify or not resolutive enough [START_REF] Bucklin | DNA Barcoding of Marine Metazoa[END_REF]Blaxter 2016). However, these advantages are not universally valid [START_REF] Galtier | Mitochondrial DNA as a Marker of Molecular Diversity: A Reappraisal[END_REF], and studies have reported a lack of conserved regions leading to considerable taxonomic bias during PCR [START_REF] Deagle | DNA Metabarcoding and the Cytochrome c Oxidase Subunit I Marker: Not a Perfect Match[END_REF], or the presence of nuclear mitochondrial pseudogenes (NUMTS) leading to considerable overestimation of biodiversity [START_REF] Song | Many Species in One: DNA Barcoding Overestimates the Number of Species When Nuclear Mitochondrial Pseudogenes Are Coamplified[END_REF]. Consequently, various variable regions of the 18S SSU ribosomal RNA (rRNA) gene have been increasingly used as barcodes (18S V1-3, V4-5, V7, or V9), particularly in taxa for which COI is difficult to amplify (including unicellular eukaryotes). However, as rRNA genes evolve more slowly than protein-coding genes, they tend to provide less taxonomic resolution, leading to the potential underestimation of diversity [START_REF] Tang | The Widely Used Small Subunit 18S RDNA Molecule Greatly Underestimates True Diversity in Biodiversity Surveys of the Meiofauna[END_REF]). Among the variable regions used in 18S barcoding, the V1-3 region was found to show greatest sequence variability and thus highest taxonomic identification power, although currently underrepresented in taxonomic databases [START_REF] Tanabe | Comparative Study of the Validity of Three Regions of the 18S-RRNA Gene for Massively Parallel Sequencing-Based Monitoring of the Planktonic Eukaryote Community[END_REF].

For plants, two plastid genes (matK and rbcL) were selected as core barcodes, supplemented by more variable barcodes from non-coding regions (plastid inter-genic spacer, or nuclear ribosomal internal transcribed spacer) to allow more precise differentiation at lower taxonomic levels. Similarly, the nuclear ribosomal internal transcribed spacer (ITS) is the standard DNA barcode for fungi, but secondary markers are being looked for, as this region is sometimes too variable for robust species-level identification (Hebert et al. 2016a;[START_REF] Bellemain | ITS as an Environmental DNA Barcode for Fungi: An in Silico Approach Reveals Potential PCR Biases[END_REF].

Overall, as there is no unique, ideal, and universal barcode gene, it is thus widely recommended to use sequences from multiple loci [START_REF] Bucklin | DNA Barcoding of Marine Metazoa[END_REF][START_REF] Cowart | Metabarcoding Is Powerful yet Still Blind: A Comparative Analysis of Morphological and Molecular Surveys of Seagrass Communities[END_REF].

The great taxonomic coverage but low species-level resolution of slowly evolving genes, such as rRNA genes, are well complemented by mtDNA or plastid loci that allow deeper taxonomic identification (Hebert et al. 2016a).

As research in barcode genes intensified, so did the effort in developing well curated sequence databases, which are essential for the taxonomic identification of sequence data.

CHAPTER I INTRODUCTION AND LITERATURE REVIEW 15

Consequently, efforts to establish large public databases have been considerable in the past ten and (3) the level of degradation of the DNA extract, determining the length of the barcode region to be used (Taberlet et al. 2012a).

Figure 2 illustrates a typical workflow of high-throughput metabarcoding studies based on eDNA, allowing the estimation of alpha and beta diversity, taxonomic community profiling, but also connectivity studies (through OTU networks), or coalescence analyses (via phylogenetic reconstructions of the marker genes).

CHAPTER I INTRODUCTION AND LITERATURE REVIEW 16

Challenges and uncertainties of metabarcoding approaches

After PCR amplification of barcode fragments, DNA amplicon libraries can be prepared in numerous ways for HTS, all generally involving the ligation of sequencing platform-specific adapters, sample-specific indexes, DNA purification, and pooling of libraries at equal concentration for multiplexed sequencing. Following HTS, typically conducted on Illumina platforms, the user is confronted with tens to hundreds of millions of raw sequences that need to be bioinformatically processed to produce a list of putative taxa. The bioinformatic analysis of metabarcoding data has evolved a great deal in recent years, with a plethora of algorithms developed for each processing step. Bik et al. (2012a) provide an overview of bioinformatic processing steps and the tools and suites available, but many other algorithms and pipelines were made available in the last years such as USEARCH [START_REF] Edgar | Search and Clustering Orders of Magnitude Faster than BLAST[END_REF] , VSEARCH (Rognes

CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 17 et al. 2016), OBITOOLS [START_REF] Boyer | OBITOOLS: A UNIX-Inspired Software Package for DNA Metabarcoding[END_REF], DADA2 [START_REF] Callahan | DADA2: High-Resolution Sample Inference from Illumina Amplicon Data[END_REF], FROGS [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF], or the web-application SLIM [START_REF] Dufresne | SLIM: A Flexible Web Application for the Reproducible Processing of Environmental DNA Metabarcoding Data[END_REF].

First, bioinformatic processing usually includes various quality-filtering steps, where primers, sample tags, and sequencing adaptors are removed from raw sequences (Fig 3). These are then trimmed to remove low-quality ends and/or quality-filtered (based on nucleotide Quality-scores or error rates). Next, a key step is to decide on the molecular entity that will serve as a proxy for taxa in the dataset. This can result from either grouping (clustering)

processed sequences within a user-defined similarity threshold, resulting in Operational Taxonomic Units (OTUs), or denoising sequences, resulting in Amplicon Sequence Variants (ASVs in [START_REF] Callahan | Exact Sequence Variants Should Replace Operational Taxonomic Units in Marker-Gene Data Analysis[END_REF], also called ZOTUs in (Edgar 2018d). Illumina sequence correction algorithms such as Deblur [START_REF] Amir | Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns[END_REF], UNOISE2 (Edgar 2016c), or DADA2 [START_REF] Callahan | DADA2: High-Resolution Sample Inference from Illumina Amplicon Data[END_REF]) are relatively new, but are increasingly popular as they effectively remove sequencing errors by applying a data-based and quality-aware correction algorithm.

Biological biases in eDNA metabarcoding: dead or live biodiversity?

Numerous biological biases affect the number and abundance of molecular clusters retrieved by a metabarcoding analysis (Fig. 4). First and most intuitively, the size, biomass, and spatial distributions of organisms will affect their detection rate. Genetically, the characteristics Figure 3. Typical bioinformatic processing steps for analysis of metabarcoding data. ASV: Amplicons Sequence Variants, OTU: Operational Taxonomic Unit.

INTRODUCTION AND LITERATURE REVIEW 18

of the gene region chosen as barcode will influence amplification success and resulting cluster abundances. In addition, eDNA is a complex mixture of genomic DNA present in living or inactive cells, extra-organismal (e.g., organelle) DNA, and extracellular DNA originating from the degradation of organic material and biological secretions [START_REF] Torti | Origin, Dynamics, and Implications of Extracellular DNA Pools in Marine Sediments[END_REF]. Extracellular DNA is very abundant in the environment, for e.g. it has been shown to represent 50-90% of the total DNA pool in marine sediments [START_REF] Corinaldesi | Extracellular DNA as a Genetic Recorder of Microbial Diversity in Benthic Deep-Sea Ecosystems[END_REF][START_REF] Dell'anno | Ecology: Extracellular DNA Plays a Key Role in Deep-Sea Ecosystem Functioning[END_REF]. However, extra-organismal and extracellular DNA may not only comprise DNA from contemporary communities, as DNA can persist in the environment due to adsorption onto clay particles, low temperatures, high salt concentrations, or the absence of UV light [START_REF] Torti | Origin, Dynamics, and Implications of Extracellular DNA Pools in Marine Sediments[END_REF][START_REF] Nagler | Extracellular DNA in Natural Environments: Features, Relevance and Applications[END_REF]. Up to 125,000-year-old ancient DNA (aDNA) has been reported in oxic and anoxic marine sediments at various depths [START_REF] Boere | Preservation Potential of Ancient Plankton DNA in Pleistocene Marine Sediments[END_REF]Lejzerowicz et al. 2013a;[START_REF] Coolen | Evolution of the Plankton Paleome in the Black Sea from the Deglacial to Anthropocene[END_REF]. Ancient DNA may thus bias eDNA metabarcoding biodiversity inventories towards describing past, rather than present communities, particularly in environments known to favour DNA persistence such as marine sediments. In contrast, aDNA bias will likely not be an issue in studies targeting aquatic environments, as it has been shown that DNA molecules released in the water column degrade rapidly [START_REF] Dejean | Persistence of Environmental DNA in Freshwater Ecosystems[END_REF][START_REF] Collins | Persistence of Environmental DNA in Marine Systems[END_REF].

Technical biases in the number of molecular entities

Numerous technical biases affect the number of molecular clusters retrieved by a metabarcoding analysis (Fig. 4). First, the taxonomic composition retrieved from metabarcoding data can be biased by the specificity of PCR primers, as primer mismatch can hinder PCR amplification and thus species detection. Taxon detection can also suffer from strong sampling effects due to insufficient sequencing depth, or because DNA extractions, typically performed on small amounts of material, make large organisms not necessarily well represented in eDNA extracts [START_REF] Creer | The Ecologist's Field Guide to Sequence-Based Identification of Biodiversity[END_REF]Cordier et al. 2019b).

Several studies have shown that spurious clusters are a serious issue in molecular biodiversity inventories, and highlighted the need for stringent quality filtering steps and/or clustering programmes in order to avoid overestimation of the number of OTUs/ASVs, and approach a 1:1 correspondence with species sampled in situ [START_REF] Clare | The Effects of Parameter Choice on Defining Molecular Operational Taxonomic Units and Resulting Ecological Analyses of Metabarcoding Data[END_REF]Edgar 2013;[START_REF] Bokulich | Quality-Filtering Vastly Improves Diversity Estimates from Illumina Amplicon Sequencing[END_REF] problem leading to the over-inflation of cluster richness, and many tools have been developed

to detect and remove them during bioinformatic processing [START_REF] Edgar | UCHIME Improves Sensitivity and Speed of Chimera Detection[END_REF]Bik et al. 2012a).

In addition, HTS sequencing is performed on pooled equimolar sample libraries. Tag-switching (also called cross-talk), i.e. the assignment of sequences to the wrong sample, is a common phenomenon in these multiplexed sequencing libraries, and can cause a substantial amount of false positives [START_REF] Schnell | Tag Jumps Illuminated -Reducing Sequence-to-Sample Misidentifications in Metabarcoding Studies[END_REF]. The problem is particularly severe if samples from different origins but similar ecosystems are multiplexed in the same sequencing run. It is thus essential to implement a "tag-switching filter" during bioinformatic processing (Fig. 3). Although not often used in practice, such filters have been developed and are usually based on OTU filtering based on cumulative frequency (Edgar 2016b;2018a;[START_REF] Wangensteen | Metabarcoding Techniques for Assessing Biodiversity of Marine Animal Forests[END_REF].

Even after all these filtering steps, many OTU/ASV table entries are singletons (i.e., have total abundance of 1), or comprise clusters with low sequence ("read") counts. Small counts are more likely to be spurious, especially singletons, either because the OTU/ASV itself is spurious (e.g., an undetected chimera), or because of tag switching. It is thus current practice to remove singletons and filter molecular clusters based on their relative abundance per sample or in the total dataset [START_REF] Wangensteen | Metabarcoding Techniques for Assessing Biodiversity of Marine Animal Forests[END_REF]. These minimal abundance filters (Fig. 3) have to be chosen with caution as they significantly affect qualitative detection measures. To avoid the arbitrary filtering based on relative abundance, [START_REF] Frøslev | Algorithm for Post-Clustering Curation of DNA Amplicon Data Yields Reliable Biodiversity Estimates[END_REF] have developed an alternative curation algorithm that filters OTUs/ASVs based on their identity and co-occurrence rates to more abundant OTUs/ASVs. As this tool was developed on plant ITS2 data, it still needs to be adjusted to other taxonomic compartments, as minimum identity thresholds vary among marker genes and taxa. The applicability of LULU to metazoans is one of the goals of chapter 2.

Figure 4. Sources of biological and technical variation in a metabarcoding workflow that can affect the number and the abundance of molecular entities.

CHAPTER I INTRODUCTION AND LITERATURE REVIEW 21

Technical biases in cluster abundances

The abundance of sequences in ASVs or OTUs is not only influenced by species abundance, but also the number of copies of the marker gene in the genomes, and by the number of cells for multicellular organisms (Fig. 4). The latter is known to vary widely among eukaryotes [START_REF] Bik | Intra-Genomic Variation in the Ribosomal Repeats of Nematodes[END_REF][START_REF] Weber | Can Abundance of Protists Be Inferred from Sequence Data: A Case Study of Foraminifera[END_REF] and to a lesser extent for prokaryotes [START_REF] Klappenbach | Rrndb: The Ribosomal RNA Operon Copy Number Database[END_REF]. Other PCR-related biases also affect the number of sequences produced from each template DNA molecule (Fig. 4). Primer mismatches (decreasing PCR efficiency), unevenness in the oligonucleotide mixture of degenerate primers, template sequence lengths (shorter sequences amplify more efficiently), GC content of the template DNA, type of polymerase used (Fonseca, V. G. 2018;[START_REF] Nichols | Minimizing Polymerase Biases in Metabarcoding[END_REF][START_REF] Lamb | How Quantitative Is Metabarcoding: A Meta-Analytical Approach[END_REF][START_REF] Pinol | Universal and Blocking Primer Mismatches Limit the Use of High-Throughput DNA Sequencing for the Quantitative Metabarcoding of Arthropods[END_REF]) are all factors leading to uneven amplification of template DNA, and as PCR amplification is exponential, this can lead to large biases in read counts.

Studies targeting particular taxonomic groups, such as insects [START_REF] Piñol | The Choice of Universal Primers and the Characteristics of the Species Mixture Determine When DNA Metabarcoding Can Be Quantitative[END_REF][START_REF] Krehenwinkel | Estimating and Mitigating Amplification Bias in Qualitative and Quantitative Arthropod Metabarcoding[END_REF] or fishes and amphibians [START_REF] Pont | Environmental DNA Reveals Quantitative Patterns of Fish Biodiversity in Large Rivers despite Its Downstream Transportation[END_REF][START_REF] Jo | Rapid Degradation of Longer DNA Fragments Enables the Improved Estimation of Distribution and Biomass Using Environmental DNA[END_REF][START_REF] Evans | Quantification of Mesocosm Fish and Amphibian Species Diversity via Environmental DNA Metabarcoding[END_REF] found correlations between biomass and read abundance using taxon-specific primer pairs. This seems however unlikely to achieve for studies using "universal" (let alone degenerate) primers to encompass the broadest possible range of diversity. Authors have therefore generally concluded that metabarcoding assessments should rely on presence-absence metrics, particularly for metazoans [START_REF] Lamb | How Quantitative Is Metabarcoding: A Meta-Analytical Approach[END_REF][START_REF] Elbrecht | Can DNA-Based Ecosystem Assessments Quantify Species Abundance? Testing Primer Bias and Biomass-Sequence Relationships with an Innovative Metabarcoding Protocol[END_REF]Edgar 2017b).

Because of the issues described above, many diversity metrics are invalid, meaningless, or hard to interpret, as neither cluster abundance nor incidence can truly accurately be determined from HTS data. For example, some alpha diversity metrics, like Chao1/Chao2 estimators, explicitly use singleton counts or frequencies in their formulas. When singletons or low abundance clusters are discarded, these calculations are invalid. As singletons are suspect for reasons detailed above, metrics including them are misleading or meaningless.

The above considerations show that it is impossible to measure meaningful and accurate values for any diversity metric using HTS data. Diversity metrics can nevertheless be compared among samples analysed through standard sampling, molecular, and bioinformatic pipelines because the errors and biases are mostly systematic, i.e. occur in the same way and at the same magnitude in all samples. To ensure that this is truly the case, it is thus crucial to standardize sampling and molecular protocols and to normalise sequencing depth among samples before calculation of biodiversity metrics. This can be done via rarefaction to the lowest sequencing depth or other normalisation methods based on relative abundance. Some authors have

CHAPTER I

proposed different approaches in visualizing alpha diversity patterns between samples, for example, by extrapolating rarefaction curves [START_REF] Hsieh | INEXT: An R Package for Rarefaction and Extrapolation of Species Diversity (Hill Numbers)[END_REF], or by visualizing octave plots [START_REF] Edgar | Octave Plots for Visualizing Diversity of Microbial OTUs[END_REF]. For beta diversity, shared ASV/OTU presence can be effectively compared with the Jaccard index, a dissimilarity that measures the commonness between samples once double-absences have been removed. In the cases where cluster abundance is considered meaningful, then Bray-Curtis or weighed Jaccard dissimilarities can be computed on relative abundance (or other normalized) data.

Biological interpretation of molecular entities

The relevance of clustering sequences into OTUs is now being discussed as the reproducibility and comparability of ASVs across studies challenge the need for clustering sequences [START_REF] Callahan | Exact Sequence Variants Should Replace Operational Taxonomic Units in Marker-Gene Data Analysis[END_REF]Edgar 2018d). This may be true for prokaryotes, for which optimal clustering thresholds for species definition were found to be >99% (Edgar 2018d).

However, it has to be kept in mind that the construction of OTUs, apart from reducing noise due to sequencing or PCR errors, also allows to reduce noise due to intraspecific variation. For metazoans, this is critical, as intraspecific polymorphism is known to be higher than in prokaryotes and varies strongly across taxa and gene regions due to both evolutionary and biological specificity [START_REF] Bucklin | DNA Barcoding of Marine Metazoa[END_REF][START_REF] Phillips | Incomplete Estimates of Genetic Diversity within Species: Implications for DNA Barcoding[END_REF]. This likely results in very different numbers of ASVs produced among individuals and/or species. Metabarcoding inventories based on ASVs, while accurately resolving fine-scale genetic variation, may thus be biased in favour of taxa with high levels of intraspecific diversity, even though the latter are not necessarily the most abundant ones [START_REF] Bazin | Population Size Does Not Influence Mitochondrial Genetic Diversity in Animals[END_REF]. The biological applicability of ASVs vs. OTUs for metazoans is further investigated in chapter 2.

While ASVs may "achieve the best possible phenotype resolution", this will occur "at the expense of an increased tendency to split species and strains into multiple [ASVs]" (Edgar 2018d) due to cryptic diversity and/or intraspecific diversity. Lumping and/or splitting of species will also occur in OTU datasets, at any clustering threshold. Indeed, OTU clustering thresholds are usually determined based on the barcode gap observed in the marker gene used, i.e. its level of intra vs interspecific divergence. However, there is no consensus on OTU delimitation thresholds, as there is no uniform interspecific divergence threshold across taxonomic groups in barcode genes [START_REF] Meyer | DNA Barcoding: Error Rates Based on Comprehensive Sampling[END_REF][START_REF] Brown | Divergence Thresholds and Divergent Biodiversity Estimates: Can Metabarcoding Reliably Describe Zooplankton Communities?[END_REF][START_REF] Candek | DNA Barcoding Gap: Reliable Species Identification over Morphological and Geographical Scales[END_REF]. Even within a single animal order, there can be large differences in this threshold value between families [START_REF] Tempestini | Species Identification and Connectivity of Marine Amphipods in Canada's Three Oceans[END_REF], highlighting that OTU delimitation CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 23 thresholds are data-dependent. Imposing a "universal" clustering threshold on metabarcoding datasets is thus also introducing bias, penalizing groups with lower interspecific divergence, and overestimating species diversity in groups with higher interspecific divergence. However, this can be alleviated with tools such as swarm v2, a single-linkage clustering algorithm [START_REF] Mahé | Swarm v2: Highly-Scalable and High-Resolution Amplicon Clustering[END_REF]. Based on network theory, swarm v2 aggregates sequences iteratively and locally around seed sequences and determines coherent groups of sequences, independent of amplicon input order, allowing highly scalable and fine-scale clustering.

Inaccuracy in taxonomic assignments

Although ecological patterns can be investigated without taxonomic identities, species names are useful for inferring biological traits or ecosystem function, as behind each name, there is a phenotype (with all its variability and life forms), an ecological role, and a geographic distribution. Numerous approaches therefore exist to link the detected genetic entities to a Linnaean taxonomy by comparing query sequences to sequences present in reference databases.

They include sequence alignment-based (identity-based) methods such as BLAST [START_REF] Altschul | Basic Local Alignment Search Tool[END_REF], probabilistic classifiers such as the RDP Bayesian classifier or SINTAX, or phylogenetic (tree-based) assignment methods (Bik et al. 2012a;Edgar 2016a). A study comparing taxonomy prediction algorithms on 16S rRNA and ITS sequences found that alignment-based methods provided similar accuracy than probabilistic methods, although the latter have the advantage of providing a confidence level for each taxonomic rank (Edgar 2018b).

The limitations in taxonomic assignment quality are therefore mostly due to the limited amount of data available (both query and reference) rather than algorithms. Indeed, the assignment accuracy of all these methods is dependent on the quality of the reference database, the database coverage of target groups, the length of the query sequences, and the nature of the marker gene used as barcode [START_REF] Macheriotou | Metabarcoding Free-Living Marine Nematodes Using Curated 18S and CO1 Reference Sequence Databases for Species-Level Taxonomic Assignments[END_REF]. Although considerable efforts have been undertaken to produce large, public, and curated databases, annotation errors may still be widespread, for e.g., one in five taxonomy annotations in SILVA and Greengenes were found to be wrong (Edgar 2018c). Moreover, many taxa are drastically under-represented in public databases, leading to poor accuracy in taxonomic assignments, especially in studies targeting poorly-known ecosystems (Bik et al. 2012a). Edgar (2018b) highlighted that the length of query sequences and the nature of genetic markers also strongly affect taxonomic accuracy. Longer query sequences provided higher taxonomic accuracy (genus accuracy was ≤50% on 16S V4

CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 24 sequences, it increased to ~60-70% when using full-length 16S sequences), and more variable loci always provided higher accuracy at lower taxonomic ranks (genus accuracy was close to 90% with ITS sequences). The taxonomic resolution of a study should therefore be adjusted according to the marker gene and study objectives (i.e. which taxonomic ranks are actually needed).

Advantages and applications of eDNA metabarcoding for biodiversity assessments

Despite these limitations, metabarcoding techniques provide several key benefits for achieving comprehensive biodiversity assessments. First, as metabarcoding does not require specimen isolation, it represents a practical and efficient tool in large and hard-to-access ecosystems. For example, it has been successfully applied to study pro-and eukaryote biodiversity in the marine realm, both in the water column (Pernice et al. 2015b;[START_REF] Salazar | Global Diversity and Biogeography of Deep-Sea Pelagic Prokaryotes[END_REF][START_REF] Sunagawa | Structure and Function of the Global Ocean Microbiome[END_REF][START_REF] Vargas | Eukaryotic Plankton Diversity in the Sunlit Ocean[END_REF][START_REF] Bakker | Environmental DNA Reveals Tropical Shark Diversity and Abundance in Contrasting Levels of Anthropogenic Impact[END_REF], and on the seafloor, from coastal (Fonseca, V. G. et al. 2010;[START_REF] Cowart | Metabarcoding Is Powerful yet Still Blind: A Comparative Analysis of Morphological and Molecular Surveys of Seagrass Communities[END_REF][START_REF] Chariton | Metabarcoding of Benthic Eukaryote Communities Predicts the Ecological Condition of Estuaries[END_REF][START_REF] Forster | Benthic Protists: The under-Charted Majority[END_REF] to deep-sea environments (Bik et al. 2012b;[START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF][START_REF] Pawlowski | Eukaryotic Richness in the Abyss: Insights from Pyrotag Sequencing[END_REF]Cordier et al. 2019a).

Another main advantage is the possibility to study the diversity of various biological compartments simultaneously from a single sample by targeting the appropriate barcode genes.

Multigene approaches therefore allow the assessment of entire biotic compartments (e.g. zooplankton, benthos), including organisms of various size ranges, providing more comprehensive ecological surveys [START_REF] Cowart | Metabarcoding Is Powerful yet Still Blind: A Comparative Analysis of Morphological and Molecular Surveys of Seagrass Communities[END_REF][START_REF] Drummond | Evaluating a Multigene Environmental DNA Approach for Biodiversity Assessment[END_REF][START_REF] Stefanni | Multi-Marker Metabarcoding Approach to Study Mesozooplankton at Basin Scale[END_REF][START_REF] Tedersoo | Tree Diversity and Species Identity Effects on Soil Fungi, Protists and Animals Are Context Dependent[END_REF]. Because it enables faster community description, metabarcoding has also gained adoption in diverse applied contexts. It is for example increasingly used to identify or detect agricultural pests and pathogens, to detect invasive species, or in the context of wildlife forensics (Hebert et al. 2016a). Moreover, studies have validated its use for assessing environmental impacts (Cordier et al. 2019a;[START_REF] Laroche | A Cross-Taxa Study Using Environmental DNA/RNA Metabarcoding to Measure Biological Impacts of Offshore Oil and Gas Drilling and Production Operations[END_REF], and biomonitoring using biotic indices [START_REF] Aylagas | Environmental Status Assessment Using DNA Metabarcoding: Towards a Genetics Based Marine Biotic Index (GAMBI)[END_REF]2018;Cordier and Pawlowski 2018;[START_REF] Pawlowski | The Future of Biotic Indices in the Ecogenomic Era: Integrating (e)DNA Metabarcoding in Biological Assessment of Aquatic Ecosystems[END_REF] or using bioindicator taxa [START_REF] Pawlowski | Environmental Monitoring through Protist Next-Generation Sequencing Metabarcoding: Assessing the Impact of Fish Farming on Benthic Foraminifera Communities[END_REF][START_REF] Laroche | First Evaluation of Foraminiferal Metabarcoding for Monitoring Environmental Impact from an Offshore Oil Drilling Site[END_REF]Pawlowski et al. 2016b;a).

Finally, evaluating the diversity of life is challenging as the majority of organisms are small (< 1 mm), cryptic, rare, and belong to poorly known groups. Traditional visual inventories remain limited by the difficulty of sampling certain organisms (for e.g. due to behavioural CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 25

avoidance), the difficulty of morphologically identifying smaller taxa, and the lack of taxonomic experts (Blaxter 2016;[START_REF] Carugati | Metagenetic Tools for the Census of Marine Meiofaunal Biodiversity: An Overview[END_REF][START_REF] Leray | DNA Barcoding and Metabarcoding of Standardized Samples Reveal Patterns of Marine Benthic Diversity[END_REF].

Metabarcoding is thus a very effective approach for detecting diversity of small organisms (bacteria, unicellular eukaryotes, meiofauna), otherwise largely disregarded in visual biodiversity inventories.

I.III. The deep sea, the last frontier on earth

While diversity patterns, their predictors and effects have been relatively well-studied in land-based systems [START_REF] Loreau | Biodiversity and Ecosystem Functioning: Current Knowledge and Future Challenges[END_REF], our understanding of global marine diversity and its influence on ecosystem functioning has been limited, although studies have shown strong differences to widely-held terrestrial paradigms [START_REF] Tittensor | Global Patterns and Predictors of Marine Biodiversity across Taxa[END_REF][START_REF] Emmerson | Consistent Patterns and the Idiosyncratic Effects of Biodiversity in Marine Ecosystems[END_REF][START_REF] Chaudhary | Bimodality of Latitudinal Gradients in Marine Species Richness[END_REF]). This contrasts with the fact that most of the world's population is increasingly living in urban areas near the coast [START_REF] Palmer | Ecology for a Crowded Planet[END_REF]), bringing marine environments under increased pressure of human activity.

Global studies have shown that virtually no part of the oceans are unaffected by human activity, not even open oceans or deep sea environments, and that up to 41% of ocean areas are heavily impacted by anthropogenic stressors [START_REF] Halpern | A Global Map of Human Impact on Marine Ecosystems[END_REF][START_REF] Peng | The Ocean's Ultimate Trashcan: Hadal Trenches as Major Depositories for Plastic Pollution[END_REF]). These stressors range from overfishing, to pollution from land-based or aquatic activities, habitat alteration, or disease spread and biological invasions (Costello et al. 2010b;[START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF][START_REF] Halpern | A Global Map of Human Impact on Marine Ecosystems[END_REF]. Species extinctions in the marine realm have not been as documented as in terrestrial environments. Yet, it has been shown that at regional scales, ecosystems like estuaries, coral reefs, or coastal and oceanic fish communities are rapidly losing populations, species, or entire functional groups [START_REF] Worm | Impacts of Biodiversity Loss on Ocean Ecosystem Services[END_REF]). While marine defaunation seems to be less severe than on land, the current low extinction rates may just be the beginning of a major marine extinction pulse, as the impact of human (3) massive polymetallic sulphide deposits on hydrothermal vents. Both types of extraction industries are associated with potentially high-levels of habitat destruction and chemical pollution and therefore high impacts on deep-sea biodiversity [START_REF] Fisher | How Did the Deepwater Horizon Oil Spill Impact Deep-Sea Ecosystems?[END_REF][START_REF] Ramirez-Llodra | Man and the Last Great Wilderness: Human Impact on the Deep Sea." Edited by Roopnarine[END_REF]) and ecosystem functioning (Zeppilli et al. 2016). Finally, although different anthropogenic impacts have different and potentially localized effects on deep-sea habitats and fauna, synergies between two or more impacts are largely unknown but likely to magnify individual effects (Fig. 5).

CHAPTER I INTRODUCTION AND LITERATURE REVIEW 27

Given the magnitude of these impacts and their potentially global consequences, there is crucial need to increase our knowledge and understanding of the patterns and drivers of biodiversity in the marine biome. This is especially urgent for environments that are hard to access but may host a large variety of life forms and perform key roles in global nutrient cycles.

The oceans cover 71% of the planet and are on average ~3,700 m deep. Half of all marine waters are below 3,000 m and approximately 90% of the oceans are considered deep sea [START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF]. Marine regions deeper than 2,000 m cover ~60% of the Earth's surface and have been postulated to be both a great reservoir of biodiversity and a source of important ecosystem services (Smith, C. R. et al. 2008;[START_REF] Pawlowski | Eukaryotic Richness in the Abyss: Insights from Pyrotag Sequencing[END_REF][START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF]Smith, K. L. et al. 2009). Yet, human exploration has described more about the surface of the moon and Mars than it has about this enigmatic backyard.

CHAPTER I INTRODUCTION AND LITERATURE REVIEW 28

The beginning of deep-sea exploration took place in the Mediterranean in the midnineteenth century during the cruise of the H.M.S Beacon (1841Beacon ( -1842)), where Edward Forbes and his colleagues were dredging in the Aegean Sea down to approximately 500 m. They noticed that biodiversity in sediments decreased with increasing sampling depth, and suggested that no life could be present below 600 m, a hypothesis known today as the "Azoic Theory" [START_REF] Forbes | Report on the Mollusca and Radiata of the Aegean Sea, and on Their Distribution, Considered as Bearing on Geology[END_REF]. This theory was highly debated, especially because evidence of life well below 600 m already existed [START_REF] Risso | Improved Taxonomic Assignment of Human Intestinal 16S RRNA Sequences by a Dedicated Reference Database[END_REF][START_REF] Mcintyre | Abyssal Environment and Ecology of the World Oceans[END_REF]

I.IV. Oceanic regions and their associated deep-sea ecosystems

There are two broad realms in the oceans: the pelagic and the benthic. Pelagic refers to the open water in which swimming (the nekton) and floating (the plankton) organisms live. Benthic zones are defined as the bottom sediments or surfaces, and organisms living in or on it are called the benthos. Biologists have traditionally divided oceanic regions depending on depth (Fig. 6), although according to a recent meta-analysis of the largest worldwide databases it remains unclear whether depth zonation is ecologically meaningful in deeper waters [START_REF] Costello | Ocean Depths: The Mesopelagic and Implications for Global Warming[END_REF].

The epipelagic, or photic zone, comprises the first 200 m of the water column where photosynthesis can take place, leading to high oxygen and low nutrient concentrations. Shallow benthic habitats close to the shore are additionally distinguished by tidal influence: the intertidal (interface between land and sea) hosts distinct communities adapted to air, wave action, and particular kinds of grazing and predation, while the subtidal comprises all the seafloor on continental shelves, to around 200 m depth. Deeper, light is too faint for photosynthesis to take place, but animals use this zone for feeding or avoiding predators. This so-called mesopelagic Figure 6. Oceanic divisions in the pelagic and benthic environments. Adapted from commons.wikipedia.org

or "twilight" zone (200-~1,000 m) is therefore characterised by lower oxygen concentrations.

Deeper still, the bathyal (~1,000 to ~2,000), the abyssal (~2,000-6,000 m), and the hadal (deep trenches below 6,000 m, see Fig. 6) are "true" deep-sea zones, characterised by low environmental variation, low temperatures, no sunlight, high oxygen concentrations, and higher nutrient levels [START_REF] Costello | Ocean Depths: The Mesopelagic and Implications for Global Warming[END_REF] The three-dimensionality of the water column and the fact that ~90% of the oceans are "DHABs"-Deep Hypersaline Anoxic Basins [START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF][START_REF] Merlino | Microbial Ecology of Deep-Sea Hypersaline Anoxic Basins[END_REF].

Abyssal plains

Lying between continental margins and mid-ocean ridges, abyssal plains are the greatest and least explored expanses on Earth (Fig. 8). They cover over 50% of the planet, are possibly the largest reservoirs of biodiversity and play a major role in important ecosystem services such as carbon cycling or calcium carbonate dissolution (Smith, K. L. et al. 2009;Smith, C. R. et al. 2008). The abyssal seafloor is mostly covered by very fine sediments (clays), termed abyssal mud or "ooze" (mud with a high percentage of organic remains). These sediments originate from the accumulation of pelagic organisms that sink after they die or from terrigenous particles derived from rock weathering on land. Hard substrates, such as manganese nodules, rock outcrops, or fault scarps also occur in many parts of the abyss, and these habitats host faunal assemblages that are different from those found in the surrounding soft sediments (Smith, C. R. et al. 2006). Abyssal plains are also characterized by an absence of in situ primary production (except at the spatially rare vents and seeps), well-oxygenated waters (except in OMZs) and low but constant temperatures of -0.5-3.0 °C. Regional differences can exist, such as in the Mediterranean and Red Sea where the average temperatures are higher, i.e. 14°C and 21°C

respectively [START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF]Smith, C. R. et al. 2006). Abyssal seafloor communities are food-limited as their productivity depends on the input of organic material falling down from the surface waters, termed marine snow (Smith, C. R. et al. 2008). Moreover, the abyssal seafloor is a dynamic environment, with regular (tidal currents, bottom currents, seasonal sedimentation) and episodic (benthic storms) disturbances that can affect benthic fauna [START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF]. While this fauna is not as conspicuous as in other deep-sea habitats, the abyssal seafloor is colonized by a great variety of mega-(macrourid fish, holothurians, echinoids…), macro-(crustaceans, polychaete worms, nematodes, gastropods…), and meiofauna (nematodes, harpacticoid copepods, foraminiferans, rotifers and other protists)

with potentially high population densities [START_REF] Smith | Climate, Carbon Cycling, and Deep-Ocean Ecosystems[END_REF][START_REF] Smith | Abyssal Food Limitation, Ecosystem Structure and Climate Change[END_REF]De Broyer et al. 2004).

CHAPTER I INTRODUCTION AND LITERATURE REVIEW 32

Mid-ocean ridges

Mid-ocean ridges are a type of divergent plate boundary; they are deep-sea volcanic chains and the longest mountain ranges on Earth. They extend through all major ocean basins, with a total length over 60,000 km (Fig. 9). They usually occur in the middle part of the oceans (with the exception of the east Pacific rise) and their crests rise around 1,000-3,000 m above the adjacent seafloor [START_REF] Wilson | Igneous Petrogenesis: A Global Tectonic Approach[END_REF].

They are so-called ocean spreading centres, as magma constantly emerges onto the seafloor to form new ocean crust. Mid-ocean ridges generate about three km 2 of new seafloor every year, Figure 8. Present-day Earth topography, with abyssal regions (-4,000 to -6,000 m) in dark-blue and purple. From NOAA's NCEI.

INTRODUCTION AND LITERATURE REVIEW 33

Mid-ocean ridges offer a high diversity of habitats, from hills and seamounts to axial valleys and fracture zones dropping to more than 4,000 m. The presence of these huge mountain ranges affects the distribution of both pelagic and benthic organisms, as they represent dispersal barriers for species distributed in neighbouring abyssal plains. The substratum present along the ridges is primarily rocky because these areas are too geologically new to have accumulated much sediment. Thus, ridges provide habitat to a variety of sessile fauna, from filter feeders to chemosynthetic organisms, which take advantage of the specific hydrographic conditions produced along the ridge.

In particular, hydrothermal vent ecosystems arise when cold seawater seeps down into the ocean crust and reacts with magma to generate hot (up to 407 °C), chemically-laden fluids [START_REF] Haase | Fluid Compositions and Mineralogy of Precipitates from Mid Atlantic Ridge Hydrothermal Vents at 4°48[END_REF]. Chemosynthetic bacteria and Archaea use these reduced minerals exported by the vent fluids as sources of energy to fix inorganic carbon. These primary producers can be found both free living, forming microbial mats, and in symbiosis with many mega-, macro, and meiofauna [START_REF] Dubilier | Symbiotic Diversity in Marine Animals: The Art of Harnessing Chemosynthesis[END_REF][START_REF] Bellec | A Nematode of the Mid-Atlantic Ridge Hydrothermal Vents Harbors a Possible Symbiotic Relationship[END_REF]. The latter comprise invertebrates that filter or graze on the microorganisms (e.g. barnacles, limpets), and numerous invertebrate taxa that host the microorganisms as epi-or endosymbionts, such as worms (e.g., siboglinid polychaete tubeworms, flatworm, nemerteans, nematodes), bivalves (e.g., mytilids, vesicomyids, lucinids, and thyasirids), gastropods (e.g., abyssochrysoideans), and many families of decapod crustaceans (e.g., alvinocaridid shrimp or galatheid squat lobsters) [START_REF] Dover | Evolution and Biogeography of Deep-Sea Vent and Seep Invertebrates[END_REF][START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF][START_REF] Martin | Decapod Crustaceans from Hydrothermal Vents and Cold Seeps: A Review through 2005[END_REF][START_REF] Desbruyeres | HANDBOOK OF DEEP-SEA HYDROTHERMAL VENT FAUNA[END_REF]Dover, Van et Figure 9. World distribution of mid-ocean ridges (from US Geological Survey).

CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 34 al. 2001;[START_REF] Bellec | A Nematode of the Mid-Atlantic Ridge Hydrothermal Vents Harbors a Possible Symbiotic Relationship[END_REF]. Even though these food-rich oases are often space and time limited, they support high-biomass communities that are different from those in/on the surrounding seafloor. The establishment of symbiosis between chemoautotrophic microorganisms and fauna allows the latter to harness abundant chemical energy and explains the success of vent, seep, and food fall communities as well as the high biomass observed, especially in the megafauna.

Over 600 vent and seep species have been described since 1977, and more than 50 new species have been recorded from whale falls in the North Pacific alone. Many of these taxa have diversified within these reducing habitats at high taxonomic levels [START_REF] Dover | Evolution and Biogeography of Deep-Sea Vent and Seep Invertebrates[END_REF][START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF]). However, although exhibiting high densities, these ecosystems support a low diversity compared to the surrounding benthos, with communities usually dominated by a few species. It has been suggested that this is due to the extreme conditions (high temperatures, H2S…) encountered in these environments, which select for a small number of taxa [START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF].

Active and passive continental margins

Continental margins are the zones of the ocean floor that separate oceanic crust from continental crust. They have very high habitat heterogeneity and are the most geologically diverse components of the seafloor [START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF]. Geologists differentiate geologically active from passive continental margins (Fig. 10).

Active margins are convergent plate boundaries and occur mostly in the Pacific and Indian oceans. They are so-called subduction zones, regions in which the denser oceanic plate sinks under the terrestrial plate, back into the Earth's interior. This creates ocean trenches plunging >10,000 km deep and active magmatism resulting in a great diversity of deep-sea geological formations, from volcanic islands or mountain chains, to submerged volcanoes known as seamounts (island arcs), and back-arc basins that produce similar habitats than mid-ocean ridges [START_REF] Wilson | Igneous Petrogenesis: A Global Tectonic Approach[END_REF][START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF].

Passive margins occur when an ocean rift has split a continent in two (Fig. 10).

Sedimentation is the primary driving force of passive margins and all processes affecting sediment input (types of continental rocks, topography of adjacent land masses, and productivity of surface waters…) greatly influence the margin geomorphology. This results in the formation of distinct habitats including sedimentary slopes, submarine canyons or coldwater coral reefs and gardens. Among those, canyons have been shown to be essential habitats for the local fauna, i.e. habitats used by fauna for critical aspects of their life cycle. Also, canyons modify local current regimes and are important conduits for the transport of particles between the continental shelf and the abyss. Along continental margins, sub-seafloor geological processes, like groundwater discharge or organic matter decomposition, also influence the environment and give rise to cold seeps, where hydrocarbon-rich fluids leek out of the ocean floor (oil or gas seeps, brine pools, and mud volcanoes). These geological features produce very specific types of substrata and thus sustain different geochemical and, mainly chemosynthetic, microbial processes [START_REF] Olu | Structure and Distribution of Cold Seep Communities along the Peruvian Active Margin: Relationship to Geological and Fluid Patterns[END_REF][START_REF] Sibuet | Biogeography, Biodiversity and Fluid Dependence of Deep-Sea Cold-Seep Communities at Active and Passive Margins[END_REF][START_REF] Bernardino | Comparative Composition, Diversity and Trophic Ecology of Sediment Macrofauna at Vents, Seeps and Organic Falls[END_REF]Vanreusel et al. 2010b). Cold seeps therefore harbour fauna similar to those found on hydrothermal vents, especially at higher taxonomic levels. Like vents, cold seeps are chemosynthetic systems supporting dense communities of faunal groups such as bivalves (mytilids, vesicomyids, lucinids, thyasirids), siboglinid tubeworms, decapod crustaceans (shrimp and crabs), gastropods, and cladorhizid sponges [START_REF] Sibuet | Biogeography, Biodiversity and Fluid Dependence of Deep-Sea Cold-Seep Communities at Active and Passive Margins[END_REF][START_REF] Roy | Cold Seep Communities in the Deep Eastern Mediterranean Sea: Composition, Symbiosis and Spatial Distribution on Mud Volcanoes[END_REF][START_REF] Olu | Biogeography and Potential Exchanges among the Atlantic Equatorial Belt Cold-Seep Faunas[END_REF].

Seamounts

Seamounts are topographically isolated and submerged peaks of volcanic origin, rising more than 1,000 m above the surrounding seabed, although recent definitions include prominences of 100-1,000 m in height [START_REF] Etnoyer | How Large Is the Seamount Biome?[END_REF]). More than 100,000 seamounts have been revealed by satellite gravimetry data worldwide [START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF], and estimates range from hundreds of thousands to over 1 million [START_REF] Staudigel | The Geological History of Deep-Sea Volcanoes: Biosphere, Hydrosphere, and Lithosphere Interactions[END_REF]Costello et al. 2010a). Long chains of seamounts can also occur, marking the presence of "magmatic hotspots" (like the Hawaiian Islands). Their overall abundance makes them one of the most common but least understood marine biomes on Earth, covering an area at least the size of Europe and Russia combined [START_REF] Etnoyer | How Large Is the Seamount Biome?[END_REF]). Most seamounts have a complex topography, which modifies surrounding ocean currents, resulting in increased productivity over and around these seascapes. Due to this concentration of organic matter, seamounts can harbour large communities with complex trophic networks, making them hot spots of diversity and nurseries for commercial species. Seamounts also provide a rocky substratum due to their steepness, and therefore offer distinct benthic habitats compared to the surrounding sedimentary ocean floor. Seamounts are thus colonized by a range of mainly epifaunal suspension feeders, dominated by cnidarians (gorgonians, zoanthids, antipatharians, actinians, pennatulids, and CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 37 hydroids), while sponges, cirripeds, molluscs, crinoids, ascidians, ophiurids, asteroids, and holothurians can also be found [START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF].

Hadal trenches

The hadal zone extends from ~6,000 m down to the deepest trenches at almost 11,000 m depth, accounting for 45% of the total ocean depth range. Hadal research has been revived in the past ten years thanks to technological developments, and studies have described 46 distinct and often extremely isolated hadal habitats. They comprise 33 trenches, occurring in tectonic convergence zones and resulting from subduction or faulting, and 13 troughs, hadal basins within abyssal plains, not formed at convergent plate boundaries (Jamieson 2015;[START_REF] Jamieson | Ecology of Deep Oceans: Hadal Trenches[END_REF].

Similarly to abyssal and bathyal ecosystems, hadal environments display low temperatures (1-4 °C) with limited within-trench variability, and low food supply, although the latter can be greater than in neighbouring abyssal habitats, suggesting that trenches may accumulate organic matter due to their steep topography [START_REF] Glud | High Rates of Microbial Carbon Turnover in Sediments in the Deepest Oceanic Trench on Earth[END_REF][START_REF] Leduc | Comparison between Infaunal Communities of the Deep Floor and Edge of the Tonga Trench: Possible Effects of Differences in Organic Matter Supply[END_REF]). However, the combination of low temperature, high-pressure (650-1,100 atm), and low food supply makes the hadal zone a unique environment, requiring particular physiological adaptations [START_REF] Bellec | A Nematode of the Mid-Atlantic Ridge Hydrothermal Vents Harbors a Possible Symbiotic Relationship[END_REF]. Combined with geographical isolation, this explains the high levels of species endemism reported in hadal habitats (Jamieson 2015;[START_REF] Blankenship-Williams | Living Deep: A Synopsis of Hadal Trench Ecology[END_REF].

Characteristic members of the macrofauna of hadal zones include scavenging amphipods and snail fishes [START_REF] Jamieson | Hadal Trenches: The Ecology of the Deepest Places on Earth[END_REF][START_REF] Linley | Fishes of the Hadal Zone Including New Species, in Situ Observations and Depth Records of Liparidae[END_REF]. Although lower diversity was reported, smaller taxa (<1 cm) are the most abundant members of benthic communities, with densities (~100-~1,000 individuals per 10 cm -2 ) similar to values in abyssal environments [START_REF] Bellec | A Nematode of the Mid-Atlantic Ridge Hydrothermal Vents Harbors a Possible Symbiotic Relationship[END_REF].

Other known benthic ecosystems: OMZs and organic falls

Oxygen depletion is widespread in the world oceans, and zones of permanent hypoxia are defined as oxygen minimum zones (OMZs), in which oxygen concentrations are below 0.5 ml.l - influence benthic assemblages (Levin, L. A. 2003;[START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF]. OMZs allow the establishment of extensive mats of large sulphide-oxidizing bacteria and high-density, lowdiversity protozoan and metazoan communities that have specific adaptations to hypoxia.

Adaptations include small, thin bodies, enhanced respiratory surface areas, blood pigments such as haemoglobin, increased numbers of pyruvate oxidoreductases, formation of biogenic support structures for stability in soupy sediment, and the prevalent association to chemosynthetic symbionts similar to those of hydrothermal vents and cold seeps (Levin, L. A. 2003). Dense aggregations of protists and metazoan meiofauna including calcareous foraminifera thrive in OMZs. In contrast, low-diversity macro-and megafauna assemblages are common on the edges of OMZs [START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF].

As most of the deep seafloor is typically food-limited and highly oligotrophic, sunken wood, cetacean carcasses, or other food falls represent local and temporally fluctuating resources. Providing food, shelter, and substrate, whale and wood falls produce new habitat that is distinct from the surrounding ocean floor. Indeed, cold temperatures, high hydrostatic pressures, and slow decomposition rates allow these organic falls to remain intact, permitting the establishment of complex but localized ecosystems that can last for decades (Smith, C. R. and Baco 2003). Whale fall communities undergo at least three successional stages that are characterized by different faunal assemblages. First a mobile scavenger stage, characterized by large animals such as sleeper sharks, hagfish and other invertebrate scavengers, followed by an opportunistic stage during which the organically-enriched sediment gets colonized by opportunistic heterotrophic invertebrates (mainly polychaetes and small crustaceans). Finally a sulfophilic stage in which the whale fall gets colonized by highly specialized and dense communities of chemosynthesis-driven fauna, including mytilid mussels, vesicomyid clams, polychaete worms, diverse crustaceans (giant isopods, shrimps, lobsters), gastropods, ctenophores, or lancelets [START_REF] Fujiwara | Three-year Investigations into Sperm Whale-fall Ecosystems in Japan[END_REF][START_REF] Goffredi | Unusual Benthic Fauna Associated with a Whale Fall in Monterey Canyon, California[END_REF]). Although strong differences can exist between the organisms inhabiting vents, seeps, and food falls, the communities of these highly sulphidic environments share many dominant taxa at the family and genus level, suggesting widespread dispersal mechanisms between chemosynthetic habitats [START_REF] Bernardino | Comparative Composition, Diversity and Trophic Ecology of Sediment Macrofauna at Vents, Seeps and Organic Falls[END_REF][START_REF] Teixeira | High Connectivity across the Fragmented Chemosynthetic Ecosystems of the Deep Atlantic Equatorial Belt: Efficient Dispersal Mechanisms or Questionable Endemism?[END_REF][START_REF] Pimm | Biodiversity: Not Just Lots of Fish in the Sea[END_REF]. Moreover, some generalist species even seem to inhabit multiple types of reducing ecosystems, although this may be undermined by cryptic speciation [START_REF] Dover | Evolution and Biogeography of Deep-Sea Vent and Seep Invertebrates[END_REF]. It has thus been suggested that large organic falls serve as stepping-stones for the evolution and dispersal of highly specialized chemosynthetic taxa inhabiting hot vents and cold seeps [START_REF] Bienhold | How Deep-Sea Wood Falls Sustain Chemosynthetic Life[END_REF].

CHAPTER I INTRODUCTION AND LITERATURE REVIEW 39

I.V. Deep-sea benthic biodiversity patterns

The major part of research in the deep-sea has been directed towards mid-ocean ridges, and their associated chemosynthetic hydrothermal vent ecosystems. Although scientifically interesting, these ecosystems only represent a small area of the ocean floor. Ridges cover 9.2% of the seafloor, and < 1% of the latter are hydrothermal vents. In comparison, abyssal plains represent 75% of deep-sea habitats and < 1% have been investigated (Smith, K. L. et al. 2009;[START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF].

The bathyal and abyssal heterotrophic sedimentary seafloor was until recently believed to be a monotonous and poor ecosystem, interspersed by oases of extremely high productivity and high biomass, where organic material falling down the water column accumulates (e.g., seamounts, canyons, food falls) or where nutrient-rich fluids allow the establishment of chemosynthesis-driven ecosystems. In contrast, deep-sea benthic sedimentary communities were found to harbour high species diversity and high levels of evenness, some authors suggesting that they may be comparable to tropical rainforests (R. [START_REF] Hessler | Faunal Diversity in the Deep-Sea[END_REF][START_REF] Grassle | Species Diversity in Deep-Sea Communities[END_REF]Smith, C. R. and Snelgrove 2002).

Investigations of deep-sea sedimentary habitats during the 1970s to 1990s were centred on macrofauna of continental shelves and bathyal depths, predominantly along the North American and European margins (Levin, L. A. et al. 2001;Smith, C. R. and Snelgrove 2002).

Research on smaller benthic size compartments was geographically restricted [START_REF] Thiel | Meiobenthos and Nanobenthos of the Deep-Sea[END_REF][START_REF] Snider | The Composition and Distribution of Meiofauna and Nanobiota in a Central North Pacific Deep-Sea Area[END_REF][START_REF] Tietjen | Abundance and Biomass of Metazoan Meiobenthos in the Deep Sea[END_REF][START_REF] Danovaro | Vertical Distribution of Meiobenthos in Bathyal Sediments of the Eastern Mediterranean Sea: Relationship with Labile Organic Matter and Bacterial Biomasses[END_REF][START_REF] Soltwedel | Metazoan Meiobenthos along Continental Margins: A Review[END_REF]. However, these studies revealed the extreme patchiness of species distributions in the deep-sea, and highlighted that research on species diversity in this biome must include this variability at small (centimetres), local (meters), and large (kilometres) spatial scales [START_REF] Rex | Community Structure in the Deep-Sea Benthos[END_REF]. Patchiness is mostly a result from variations in food availability, and the great diversity and evenness observed in deep sea sediments are in part a response for optimizing the exploitation of the limited food sources, and have positive consequences on the stability and resilience of deep-sea benthic communities [START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF]). Nevertheless, significant regional variations in the relationship between species diversity and abundance with food availability do exist, thought to result from the influence of environmental variation (pressure, temperature, oxygen concentrations, sediment granulometry) and biotic interactions (Levin, L. A. et al. 2001;[START_REF] Rosli | Review of Recent Trends in Ecological Studies of Deep-Sea Meiofauna, with Focus on Patterns and Processes at Small to Regional Spatial Scales[END_REF]).

Research at multiple spatial scales [START_REF] Fonseca | Variation in Nematode Assemblages over Multiple Spatial Scales and Environmental Conditions in Arctic Deep Seas[END_REF][START_REF] Danovaro | Multiple Spatial Scale Analyses Provide New Clues on Patterns and Drivers of Deep-Sea Nematode Diversity[END_REF][START_REF] Gambi | A Multiple-Scale Analysis of Metazoan Meiofaunal Distribution in the Deep Mediterranean Sea[END_REF][START_REF] Gaever | Spatial Scale and Habitat-Dependent Diversity Patterns in Nematode Communities in Three Seepage Related Sites along the Norwegian Sea Margin[END_REF][START_REF] Bianchelli | Nematode Diversity Patterns at Different Spatial Scales in Bathyal Sediments of the Mediterranean Sea[END_REF], and targeting a diversity CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 40 of ecosystems (Danovaro et al. 2009a;[START_REF] Bianchelli | Metazoan Meiofauna in Deep-Sea Canyons and Adjacent Open Slopes: A Large-Scale Comparison with Focus on the Rare Taxa[END_REF][START_REF] Roy | Cold Seep Communities in the Deep Eastern Mediterranean Sea: Composition, Symbiosis and Spatial Distribution on Mud Volcanoes[END_REF][START_REF] Zeppilli | Meiofauna Assemblages of the Condor Seamount (North-East Atlantic Ocean) and Adjacent Deep-Sea Sediments[END_REF][START_REF] Clark | The Ecology of Seamounts: Structure, Function, and Human Impacts[END_REF][START_REF] Smet | The Community Structure of Deep-Sea Macrofauna Associated with Polymetallic Nodules in the Eastern Part of the Clarion-Clipperton Fracture Zone[END_REF] has strongly increased in the past twenty years, with a notable effort on taxa with smaller body sizes. Large international long-term collaborations allowed shedding light on ecosystems or ocean basins at large spatial scales [START_REF] Danovaro | Deep-Sea Biodiversity in the Mediterranean Sea: The Known, the Unknown, and the Unknowable[END_REF][START_REF] Vanreusel | Biodiversity of Cold Seep Ecosystems along the European Margins[END_REF]Danovaro et al. 2009b), or on very remote ocean regions, such as the southern ocean (Brandt, A. et al. 2007b;a;[START_REF] Brandt | Southern Ocean Deep-Sea Biodiversity-From Patterns to Processes[END_REF] and the arctic [START_REF] Hasemann | Small-Scale Heterogeneity in Deep-Sea Nematode Communities around Biogenic Structures[END_REF][START_REF] Górska | Bathymetric Variations in Vertical Distribution Patterns of Meiofauna in the Surface Sediments of the Deep Arctic Ocean (HAUSGARTEN, Fram Strait)[END_REF][START_REF] Bodil | Diversity of the Arctic Deep-Sea Benthos[END_REF][START_REF] Renaud | Nematode and Macrofaunal Diversity in Central Arctic Ocean Benthos[END_REF]. Research in the Pacific has also expanded, with a particular focus on the New-Zealand margin (Leduc et al. 2012a) and pacific hadal trenches [START_REF] Itoh | Bathymetric Patterns of Meiofaunal Abundance and Biomass Associated with the Kuril and Ryukyu Trenches, Western North Pacific Ocean[END_REF][START_REF] Kitahashi | Assemblages Gradually Change from Bathyal to Hadal Depth: A Case Study on Harpacticoid Copepods around the Kuril Trench (North-West Pacific Ocean)[END_REF][START_REF] Leduc | Comparison between Infaunal Communities of the Deep Floor and Edge of the Tonga Trench: Possible Effects of Differences in Organic Matter Supply[END_REF]). More recently, eDNA metabarcoding tools were successfully applied to deep-sea sediments, for e.g., in the Mediterranean [START_REF] Guardiola | Spatio-Temporal Monitoring of Deep-Sea Communities Using Metabarcoding of Sediment DNA and RNA[END_REF]Cordier et al. 2019a) and the Atlantic [START_REF] Pawlowski | Eukaryotic Richness in the Abyss: Insights from Pyrotag Sequencing[END_REF]Bik et al. 2012b;[START_REF] Lejzerowicz | Patchiness of Deep-Sea Benthic Foraminifera across the Southern Ocean: Insights from High-Throughput DNA Sequencing[END_REF]. Overall, these studies highlight that biogeographic and species distribution patterns in the deep-sea show considerable variability with body size, life history, and taxonomic identity. To achieve a global synthesis of these patterns, deep-sea research must thus include both spatial and biological variability at various scales (Smith, C. R. et al. 2006).

Benthic size classes and their main differences

Deep-sea benthic fauna is divided into four major somewhat overlapping categories, primarily based on their body size, but also habitat, ecological features (e.g. feeding mode), and taxonomy [START_REF] Rex | Community Structure in the Deep-Sea Benthos[END_REF][START_REF] Thiel | Meiobenthos and Nanobenthos of the Deep-Sea[END_REF]. Assemblages and species ranges have mostly been investigated for larger taxa (Levin, L. A. et al. 2001) that of the macrofauna, and megafauna accounts for lower abundance and biomass throughout depth ranges from 0-6,000 m [START_REF] Rex | Community Structure in the Deep-Sea Benthos[END_REF][START_REF] Rex | Global Bathymetric Patterns of Standing Stock and Body Size in the Deep-Sea Benthos[END_REF].

Meiofauna includes both metazoans as well as some small single-celled protists. The boundaries between meiofauna and macrofauna were defined by mesh sizes of the sieves used for extracting these organisms from the sediments, and as different studies used different mesh sizes, these boundaries could vary widely among researchers. Today a size range of 32 µm to 1 mm seems generally accepted [START_REF] Soltwedel | Metazoan Meiobenthos along Continental Margins: A Review[END_REF][START_REF] Thiel | Meiobenthos and Nanobenthos of the Deep-Sea[END_REF][START_REF] Snider | The Composition and Distribution of Meiofauna and Nanobiota in a Central North Pacific Deep-Sea Area[END_REF]. As the separation between meio-and macrofauna is biologically speaking artificial, some groups are found in both size fractions. This means that the meiofauna size class may include juveniles or larvae of macrobenthos (e.g., cnidarian polyps, annelids, copepods, or tunicates), also called temporary meiofauna [START_REF] Giere | Meiobenthology: The Microscopic Motile Fauna of Aquatic Sediments[END_REF]. Similarly, large nematodes or copepods will be part of the macrofauna. Better-known metazoan taxa that are predominantly in the meiofauna size class comprise nematodes, copepod and ostracod crustaceans, certain malacostracan crustaceans (e.g., members of the Isopoda, Amphipoda, Tanaidacea), but also tardigrades, kinorhynchs, loriciferans or halacaroid mites [START_REF] Thiel | Meiobenthos and Nanobenthos of the Deep-Sea[END_REF][START_REF] Giere | Meiobenthology: The Microscopic Motile Fauna of Aquatic Sediments[END_REF]. However, there are also many smaller and/or soft-bodied taxa, largely disregarded in morphological inventories, probably because their bodies get broken during the sieving process. These include interstitial cnidarians (hydrozoans, scyphozoans, and anthozoans), free-living platyhelminths, the Gnathifera (Gnathostomulida, rotifers, micrognathozoans), the Gastrotricha, the Sipuncula, some chaetognaths, but also brachiopods and bryozoans. Unicellular heterotrophic meiofauna, often neglected by zoologists, are also surprisingly diverse in the meiofauna and comprise members of the Foraminifera, the Heliozoa, the Amoebozoa, or the Ciliophora [START_REF] Giere | Meiobenthology: The Microscopic Motile Fauna of Aquatic Sediments[END_REF]. Finally, the nanofauna comprises all organisms smaller than 42 µm, and includes some metazoans, but mainly consists of flagellates, ciliates and yeasts [START_REF] Thiel | Meiobenthos and Nanobenthos of the Deep-Sea[END_REF][START_REF] Tietjen | Abundance and Biomass of Metazoan Meiobenthos in the Deep Sea[END_REF].

A global-scale analysis of abundance and biomass of major benthic size classes found that all animal size classes (metazoan meio-, macro-, and megafauna) significantly decrease in abundance and biomass with depth, while the values showed no decline with depth for bacteria [START_REF] Rex | Global Bathymetric Patterns of Standing Stock and Body Size in the Deep-Sea Benthos[END_REF]). The decrease observed in metazoans was less steep for the smaller meiofauna, than for the macro-and megafauna, indicating that animal sizes in deep-sea communities as a whole decrease with depth. This leads to an increase in the relative abundance of small organisms (meiofauna, bacteria) with increasing water depth, their smaller size allowing them to cope better with low food availability [START_REF] Thiel | Meiobenthos and Nanobenthos of the Deep-Sea[END_REF][START_REF] Rosli | Review of Recent Trends in Ecological Studies of Deep-Sea Meiofauna, with Focus on Patterns and Processes at Small to Regional Spatial Scales[END_REF]. Meiofauna are thus an CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 42

important component of deep-sea benthic communities due to their high relative abundance and diversity, their close connection to other size compartments of the benthos, and their important role in benthic food webs [START_REF] Bellec | A Nematode of the Mid-Atlantic Ridge Hydrothermal Vents Harbors a Possible Symbiotic Relationship[END_REF][START_REF] Schratzberger | Meiofauna Matters: The Roles of Meiofauna in Benthic Ecosystems[END_REF]. Meiofauna abundances can range from 100 to 1,000 individuals per m 2 (Tietjen 1992), communities being dominated by foraminiferans, nematodes, and copepods [START_REF] Thiel | Meiobenthos and Nanobenthos of the Deep-Sea[END_REF][START_REF] Snider | The Composition and Distribution of Meiofauna and Nanobiota in a Central North Pacific Deep-Sea Area[END_REF][START_REF] Tietjen | Abundance and Biomass of Metazoan Meiobenthos in the Deep Sea[END_REF]. Nematodes generally comprise ~ 90% of metazoan individuals, compared to 3% to 10% for copepods. However, nematodes do not dominate meiobenthic biomass to the same extent that they do abundance, as individual body weights can be larger in other organisms. Nematodes thus constitute 13% to 65 % of meiofaunal biomass in most deep-sea sediments, compared to for e.g. 15% to 75% for copepods [START_REF] Tietjen | Abundance and Biomass of Metazoan Meiobenthos in the Deep Sea[END_REF]. [START_REF] Snider | The Composition and Distribution of Meiofauna and Nanobiota in a Central North Pacific Deep-Sea Area[END_REF] first highlighted the importance of Foraminifera in meiofaunal communities. The authors showed that Foraminifera comprised ~50% of meiofauna individuals in sediments of the North Pacific, and made up 87% of biomass. The extraordinary numbers and diversity of Foraminifera in deep-sea sediments has been subsequently confirmed by numerous investigations worldwide (Brandt, A. et al. 2007a;[START_REF] Gooday | Biodiversity of Foraminifera and Other Protists in the Deep Sea: Scales and Patterns[END_REF][START_REF] Gooday | Soft-Walled, Monothalamous Benthic Foraminiferans in the Pacific, Indian and Atlantic Oceans: Aspects of Biodiversity and Biogeography[END_REF]. eDNA-based studies have confirmed the great diversity of nematodes, which are usually found to be the most diverse metazoan group [START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF][START_REF] Guardiola | Spatio-Temporal Monitoring of Deep-Sea Communities Using Metabarcoding of Sediment DNA and RNA[END_REF]. They also highlighted the diversity of less-studied metazoan phyla like the Platyhelminthes, the Nemertea, and the Xenacoelomorpha [START_REF] Pawlowski | Eukaryotic Richness in the Abyss: Insights from Pyrotag Sequencing[END_REF][START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF][START_REF] Guardiola | Spatio-Temporal Monitoring of Deep-Sea Communities Using Metabarcoding of Sediment DNA and RNA[END_REF], and confirmed the unprecedented abundance of other, mostly unicellular, eukaryotic groups, like the SAR and the Fungi [START_REF] Pawlowski | Eukaryotic Richness in the Abyss: Insights from Pyrotag Sequencing[END_REF][START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF][START_REF] Guardiola | Spatio-Temporal Monitoring of Deep-Sea Communities Using Metabarcoding of Sediment DNA and RNA[END_REF].

In terms of biomass, smaller size classes replace larger size classes with increasing depth.

While mega-and macrofauna dominate biomass at upper bathyal depths (above ~2,000 m), this is reversed in the abyss. It was thus suggested that the bathyal zone (i.e. upper continental slopes), providing higher levels of energy supply, offers more ecological and evolutionary opportunities for adaptive radiation, at least for larger organisms [START_REF] Rex | Global Bathymetric Patterns of Standing Stock and Body Size in the Deep-Sea Benthos[END_REF].

Examinations of depth ranges of deep-sea gastropods and bivalves lead to the proposal of the slope-abyss source-sink (SASS) hypothesis for abyssal diversity [START_REF] Rex | A Source-Sink Hypothesis for Abyssal Biodiversity[END_REF]. The authors suggested that the abyssal seafloor might constitute a vast sink of larval refugees from upper continental slopes, whose populations are not reproductively self-sustaining. It has however been found that abyssal macrofauna populations are unlikely sustained by bathyal standing CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 43

stocks alone, and that local abyssal reproduction has to be considered, especially in highproductivity areas [START_REF] Hardy | Can the Source-Sink Hypothesis Explain Macrofaunal Abundance Patterns in the Abyss? A Modelling Test[END_REF].

In terms of distribution ranges, strong differences exist depending on size class, life history, and taxonomic identity. Interestingly, abundant genera seem to be abundant all over the world, so-called "cosmopolitan deep-sea genera". Some taxa of the mega-(e.g. rattail fishes, elasipod holothurians), macro-(e.g., isopods, amphipods, neogastropods), and even meiofauna (e.g., Foraminifera, harpacticoid copepods) exhibit very wide (> 1,000 km) distribution ranges (Smith, C. R. et al. 2006;[START_REF] Menzel | Submarine Ridges Do Not Prevent Large-Scale Dispersal of Abyssal Fauna: A Case Study of Mesocletodes (Crustacea, Copepoda, Harpacticoida)[END_REF][START_REF] Easton | Do Some Deep-Sea, Sediment-Dwelling Species of Harpacticoid Copepods Have 1000-Km-Scale Range Sizes?[END_REF][START_REF] Gooday | Soft-Walled, Monothalamous Benthic Foraminiferans in the Pacific, Indian and Atlantic Oceans: Aspects of Biodiversity and Biogeography[END_REF]). For larger fauna, this is explained by their benthopelagic lifestyle, and/or their good dispersal capacities, planktotrophic larvae being able to survive in the water column for months to over a year (Smith, C. R. et al. 2006;Costello and Chaudhary 2017). For the metazoan meiofauna, lacking a planktonic life stage, this is surprising and has been coined the meiofauna paradox [START_REF] Giere | Meiobenthology: The Microscopic Motile Fauna of Aquatic Sediments[END_REF][START_REF] Carugati | Metagenetic Tools for the Census of Marine Meiofaunal Biodiversity: An Overview[END_REF]. Some authors suggest that passive transport by bottom currents after resuspension may enhance dispersal in these small taxa [START_REF] Menzel | Submarine Ridges Do Not Prevent Large-Scale Dispersal of Abyssal Fauna: A Case Study of Mesocletodes (Crustacea, Copepoda, Harpacticoida)[END_REF]), but it is still unclear whether these are enough to explain the observed wide distribution ranges. In addition, molecular studies have revealed that cosmopolitan megafauna and macrofauna species are often complexes of cryptic species that each have much smaller distribution ranges [START_REF] Teixeira | High Connectivity across the Fragmented Chemosynthetic Ecosystems of the Deep Atlantic Equatorial Belt: Efficient Dispersal Mechanisms or Questionable Endemism?[END_REF][START_REF] Havermans | Genetic and Morphological Divergences in the Cosmopolitan Deep-Sea Amphipod Eurythenes Gryllus Reveal a Diverse Abyss and a Bipolar Species[END_REF]). Thus, the generality of wide distribution ranges remains to be confirmed, especially for meiofauna.

Similarly, the strong species turnover observed between sites or regions may simply reflect global under sampling of deep-sea environments. Populations described as different morphospecies due to discrete and distant distribution ranges may be the result of sampling artefacts and in fact be the same species genetically. Consequently, it is still extremely difficult to differentiate between rarity and endemicity, and the high degrees of endemicity as well as the high percentages of new species found may decrease as more information is gathered (Smith, C. R. et al. 2006;Brandt, A. et al. 2007a;[START_REF] Teixeira | High Connectivity across the Fragmented Chemosynthetic Ecosystems of the Deep Atlantic Equatorial Belt: Efficient Dispersal Mechanisms or Questionable Endemism?[END_REF].

Ecological patterns at regional (~100-10,000 km) spatial scales

Understanding species distribution patterns has been a primary interest of biologists since the beginning of large-scale voyages of scientific exploration in the late 17 th century. Indeed, biodiversity is distributed heterogeneously across planet Earth: while some regions appear to CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 44 be extremely diverse (rainforest, coral reefs), others seem devoid of life (deserts, polar regions)

and most are somewhere in between [START_REF] Gaston | Global Patterns in Biodiversity[END_REF]. Since the 1970s, a considerable amount of work has tried to explain broad-scale geographical patterns in marine biodiversity. Indeed, large-scale distribution boundaries reveal the importance of global factors that can influence species distribution, such as continental drift, salinity and temperature, sea-level rise, or glaciation (Costello et al. 2017) Overall, large-scale biogeographic regions do exist on the deep-seafloor both for chemosynthetic and heterotrophic ecosystems and correlate well with the major ocean basins [START_REF] Dover | Evolution and Biogeography of Deep-Sea Vent and Seep Invertebrates[END_REF]Bik et al. 2012b;[START_REF] Moalic | Biogeography Revisited with Network Theory: Retracing the History of Hydrothermal Vent Communities[END_REF][START_REF] Watling | A Proposed Biogeography of the Deep Ocean Floor[END_REF]Costello et al. 2017). However, most studies attempting to delimitate these boundaries focused on megafauna or nanofauna, both considered to have good dispersal abilities, as such these boundaries remain to be confirmed for the (metazoan) meiofauna. Menzies et al. (1973) summarized the distributions of many megafauna as well as isopod crustaceans to delineate five large biogeographic regions in depths > 4,000 m, one in each ocean basin (Pacific, Arctic, Atlantic, Indian, and Antarctic). This work was recently extended by the Global Open Ocean and Deep Seabed (GOODS) classification using high-resolution water mass characteristics (temperature and salinity) and particulate organic-matter flux data to the seafloor. This resulted in the delineation of 14 lower bathyal, 14 abyssal, and 10 hadal geographic provinces within the five biogeographic regions [START_REF] Watling | A Proposed Biogeography of the Deep Ocean Floor[END_REF]. The classification into geographic areas may not truly represent biogeographic realms, as it lacks species information. The latest global study and first holistic analysis of the Ocean Biogeographic Information System (OBIS) revealed 18-continental-shelf and 12 offshore deep-sea realms, reflecting the wider distribution ranges currently recorded for many deep-sea species (Costello et al. 2017).

Overall, these studies show that regional geological history can affect diversity as events such as glaciation or isolation can induce higher extinction or speciation rates. Historical events, particularly during the Cenozoic, have resulted in both geological and oceanographic changes (e.g. isthmus closures, opening of ocean basins, sea level rise and fall, periods of deep-sea anoxia). These have been important in defining contemporary biogeography of many deep-sea taxa by controlling larval dispersal and survival [START_REF] Herrera | Evolutionary and Biogeographical Patterns of Barnacles from Deep-Sea Hydrothermal Vents[END_REF][START_REF] Dover | Evolution and Biogeography of Deep-Sea Vent and Seep Invertebrates[END_REF]Smith, C. R. et al. 2006;Costello et al. 2017).

Furthermore, large-scale spatial distributions in the marine biome are primarily driven by temperature, salinity, habitat complexity, and food (and oxygen) availability [START_REF] Tittensor | Global Patterns and Predictors of Marine Biodiversity across Taxa[END_REF]Smith, C. R. et al. 2008;Fonseca, V. G. et al. 2014;Costello and Chaudhary 2017;

CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 45 Costello et al. 2017). Latitude is a proxy for temperature and solar radiation (including day length and seasonality), which are known to influence primary and secondary productivity.

Similar to what has been observed on land, marine species richness thus varies with latitude.

Unimodal marine latitudinal gradients in species richness, i.e. diversity increasing from high to low latitudes, was reported in the Atlantic for some deep-sea macrofauna [START_REF] Rex | Global-Scale Latitudinal Patterns of Species Diversity in the Deep-Sea Benthos[END_REF]).

However, these patterns are not confirmed globally. Indeed, studies have found increasing nematode diversity from the tropics northwards and very similar meiofaunal taxa richness at all latitudes (Ramirez-Llodra et al. 2010 and references therein). Moreover, the most comprehensive study in the Southern Ocean challenges ideas that deep-sea diversity is lower at higher latitudes, given the extraordinary diversity found in both meio-and macrofauna in the southern ocean (Brandt, A. et al. 2007b). Recent global studies reported bimodal gradients with latitude, with highest species numbers in the subtropics and a dip near the equator [START_REF] Chaudhary | Bimodality of Latitudinal Gradients in Marine Species Richness[END_REF]2017). They suggested that temperature is the main driver explaining species richness patterns, a statement congruent with what has been observed in euphotic plankton [START_REF] Sunagawa | Structure and Function of the Global Ocean Microbiome[END_REF][START_REF] Ibarbalz | Global Trends in Marine Plankton Diversity across Kingdoms of Life[END_REF]. In contrast, in the deep-sea, where temperatures are uniformly low, biodiversity patterns are primarily driven by food supply. This has been

shown by numerous studies at the regional to global scales, for taxa from all size compartments [START_REF] Woolley | Deep-Sea Diversity Patterns Are Shaped by Energy Availability[END_REF]Levin, L. A. et al. 2001). Large-scale studies of meiobenthic diversity even suggested that it is primarily niche-driven, i.e. dependent on contemporary ecology and food supply rather than historical events [START_REF] Lambshead | Latitudinal Diversity Patterns of Deep-Sea Marine Nematodes and Organic Fluxes: A Test from the Central Equatorial Pacific[END_REF]Fonseca, V. G. et al. 2014).

Food availability, i.e. nutrient supply to the deep sea, varies depending on 1) distance from the coast, 2) depth, and 3) large-scale ocean currents. Thus, any variation in these three parameters will directly influence species abundance and diversity.

Effect of food and oxygen availability

Particulate organic flux towards the abyss varies as a function of primary production in the surface waters. It has been calculated that only 0.5-2.0 % of the net primary production reaches the deep seafloor below 2000 m (Ramirez-Llodra et al. 2010). Deep-sea benthic communities are thus amongst the most food-limited on the globe (Smith, C. R. et al. 2008). The primary productivity of ocean surface waters varies both regionally and seasonally, thus seasonal patterns of diversity occur in the deep-sea benthos (Smith, K. L. et al. 2009;[START_REF] Massana | Marine Protist Diversity in European Coastal Waters and Sediments as Revealed by High-Throughput Sequencing[END_REF][START_REF] Guardiola | Spatio-Temporal Monitoring of Deep-Sea Communities Using Metabarcoding of Sediment DNA and RNA[END_REF], as well as regional differences, for e.g., between upwelling zones and oligotrophic central gyres (Smith, C. R. et al. 2006). Studies on meiofauna clearly CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 46 demonstrate regional differences on a global scale: richer communities are generally found in areas with increased productivity and enhanced organic matter flux to the seafloor [START_REF] Soltwedel | Metazoan Meiobenthos along Continental Margins: A Review[END_REF]. Ocean thermohaline circulation patterns greatly influence carbon export flux to the deepsea and this, combined with higher productivity in subtropical surface waters and higher proximity to continental margins, explains why deep-sea species show maximum richness at higher (30-50°) latitudes [START_REF] Woolley | Deep-Sea Diversity Patterns Are Shaped by Energy Availability[END_REF]. Diversity can be increased in high productivity regimes, but this is not always the case, as high organism abundance induces high competition levels and low oxygen concentrations (Levin, L. A. et al. 2001;[START_REF] Rosli | Review of Recent Trends in Ecological Studies of Deep-Sea Meiofauna, with Focus on Patterns and Processes at Small to Regional Spatial Scales[END_REF].

Effect of depth

Studies have indicated depth to be a main factor influencing the distribution of deep-sea organisms, mainly due to the depth-related decrease in productivity (Levin, L. A. et al. 2001;[START_REF] Olu | Biogeography and Potential Exchanges among the Atlantic Equatorial Belt Cold-Seep Faunas[END_REF]Bik et al. 2012b).

Qualitative [START_REF] Rex | Community Structure in the Deep-Sea Benthos[END_REF]) and quantitative [START_REF] Etter | Patterns of Species Diversity in the Deep Sea as a Function of Sediment Particle Size Diversity[END_REF]) studies in the North Atlantic indicated that diversity-depth patterns in the deep-sea benthos are unimodal, with a peak in diversity at intermediate depths (300 -4,700 m). The depth of the peak was found to decrease with size class, megafauna showing a diversity peak at ~1,900-2,300 m, while metazoan meiofauna diversity peaked at 3,000 m and foraminiferans showed highest diversity at > 4,000 m [START_REF] Rex | Community Structure in the Deep-Sea Benthos[END_REF]). However, unimodal patterns are not universal, vary regionally with environmental gradients or oceanographic conditions, and between taxa (Levin, L. A. et al. 2001;[START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF]Costello and Chaudhary 2017). The latest global study, including data from 243,000 species catalogued in WoRMS confirmed a peak in species richness at 400-500 m depth (Costello and Chaudhary 2017), a figure in accordance with the patterns found for megafauna in the Atlantic, and reflecting the bias of the database towards large size classes.

Ecological patterns at habitat to small spatial scales

Habitat-scale (100 m-100 km) influences

Generally, areas with greater variation in environmental and topographical conditions support more species and thus exhibit higher regional diversity, explaining why macrohabitat heterogeneity contributes significantly to diversity on a global scale (Vanreusel et al. 2010a;[START_REF] Rosli | Review of Recent Trends in Ecological Studies of Deep-Sea Meiofauna, with Focus on Patterns and Processes at Small to Regional Spatial Scales[END_REF]. Current regimes, although generally low in the abyss, can rise locally due to seafloor topography and influence benthic assemblages [START_REF] Stefanoudis | Abyssal Hills: Influence of Topography on Benthic Foraminiferal Assemblages[END_REF] (Zeppilli et al. 2016;[START_REF] Rosli | Review of Recent Trends in Ecological Studies of Deep-Sea Meiofauna, with Focus on Patterns and Processes at Small to Regional Spatial Scales[END_REF]. This partly explains higher species abundance and biomass in canyons and around seamounts. Similarly, hadal trenches also concentrate food particles, and are thus associated with surprisingly high abundance of meio-and nanofauna [START_REF] Schmidt | Unexpectedly Higher Metazoan Meiofauna Abundances in the Kuril-Kamchatka Trench Compared to the Adjacent Abyssal Plains[END_REF][START_REF] Bellec | A Nematode of the Mid-Atlantic Ridge Hydrothermal Vents Harbors a Possible Symbiotic Relationship[END_REF]).

These habitat-specific environmental conditions are unique and distinct from adjacent regions of the ocean floor, leading to the presence of specific taxa adapted to these particular environments [START_REF] Zeppilli | Pockmarks Enhance Deep-Sea Benthic Biodiversity: A Case Study in the Western Mediterranean Sea[END_REF]2013;[START_REF] Jamieson | Ecology of Deep Oceans: Hadal Trenches[END_REF][START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF]. Subsurface deposit-feeders (e.g., polychaetes, echiurans) dominate on organic-rich margin sediments;

surface deposit-feeders prevail on the oligotrophic abyssal seafloor (e.g., holothurians, other polychaetes, asteroids), suspension feeders (e.g., corals, sponges, crinoids, ascidians) dominate in habitats where currents are stronger like on rocky slopes of seamounts, canyons, ridges, and banks. Taxa living exclusively in canyons (tanaids, echinoid larvae) as well as specific morphological adaptations to cope with increased current regimes in foraminiferans (agglutinated vs. organic-welled) or in nematodes [START_REF] Bellec | A Nematode of the Mid-Atlantic Ridge Hydrothermal Vents Harbors a Possible Symbiotic Relationship[END_REF][START_REF] Rosli | Review of Recent Trends in Ecological Studies of Deep-Sea Meiofauna, with Focus on Patterns and Processes at Small to Regional Spatial Scales[END_REF]) have also been reported. Similarly, chemoautotrophy is the main feeding mode in reducing ecosystems (vents, seeps, and food falls), which are thus associated to symbiotic taxa.

Kinorhynchs are particularly abundant at cold seeps and other habitats that undergo drastic changes in salinity. Hypoxic or anoxic environments typical of OMZs or DHABs are associated to a decrease of species abundance and biomass, with the exception of nematodes and loriciferans that are particularly well-adapted to low oxygen conditions [START_REF] Bellec | A Nematode of the Mid-Atlantic Ridge Hydrothermal Vents Harbors a Possible Symbiotic Relationship[END_REF].

Finally, sediment granulometry is known to affect deep-sea benthic diversity, and particle size heterogeneity has been shown to be positively correlated to species diversity [START_REF] Etter | Patterns of Species Diversity in the Deep Sea as a Function of Sediment Particle Size Diversity[END_REF]Leduc et al. 2012b). Significant differences in meiofauna abundance have been reported between hemipelagic vs. turbidite sediments, and these were related to median grain size and percent content of silt-clay particles [START_REF] Woods | Horizontal and Vertical Distribution of Meiofauna on Sandy Beaches[END_REF].

Influence of local (10 cm-100 m) to small (1-10 cm) scale factors

Any habitat-scale factors mentioned above that can vary also at a local scale are likely to influence benthic community composition. Primarily, substratum type greatly affects benthic assemblages as it defines species composition and influences spatial variability in species CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 48 distribution [START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF]). In addition, high community dissimilarities can occur at local-scale due to the occurrence of different sub-habitats within a sampling site [START_REF] Gaever | Spatial Scale and Habitat-Dependent Diversity Patterns in Nematode Communities in Three Seepage Related Sites along the Norwegian Sea Margin[END_REF]. This can also affect local-scale species abundance due to strong food patchiness, particularly in seep and vent habitats [START_REF] Rosli | Review of Recent Trends in Ecological Studies of Deep-Sea Meiofauna, with Focus on Patterns and Processes at Small to Regional Spatial Scales[END_REF]. In contrast, little local variation has been reported in terms of species abundance and diversity for heterotrophic sediments, particularly in taxa with locomotory abilities [START_REF] Woods | Horizontal and Vertical Distribution of Meiofauna on Sandy Beaches[END_REF][START_REF] Rosli | Review of Recent Trends in Ecological Studies of Deep-Sea Meiofauna, with Focus on Patterns and Processes at Small to Regional Spatial Scales[END_REF].

Energy availability within sediments is positively correlated with sediment-community respiration, rate of organic carbon burial within the sediment, and benthic biomass and abundance (Levin, L. A. et al. 2001). Organic matter input and oxygen levels, sediment particle size and heterogeneity, as well as bioturbation by larger organisms are all factors influencing the amount of nutrients available within the sediment and can therefore affect both community diversity and composition [START_REF] Lambshead | Comparison of the Vertical Distribution of Nematodes from Two Contrasting Abyssal Sites in the Northeast Atlantic Subject to Different Seasonal Inputs of Phytodetritus[END_REF][START_REF] Rosli | Review of Recent Trends in Ecological Studies of Deep-Sea Meiofauna, with Focus on Patterns and Processes at Small to Regional Spatial Scales[END_REF]. Substantial small-scale horizontal and vertical variation in benthic assemblages have thus long been reported in both macro-and meiofauna size compartments [START_REF] Rex | Community Structure in the Deep-Sea Benthos[END_REF]. Considerable vertical zonation has been reported in meiofauna communities of deep-sea sediments worldwide [START_REF] Thiel | Meiobenthos and Nanobenthos of the Deep-Sea[END_REF][START_REF] Danovaro | Vertical Distribution of Meiobenthos in Bathyal Sediments of the Eastern Mediterranean Sea: Relationship with Labile Organic Matter and Bacterial Biomasses[END_REF][START_REF] Gallucci | Small-Scale Spatial Patterns of Meiobenthos in the Arctic Deep Sea[END_REF][START_REF] Rosli | Differences in Meiofauna Communities with Sediment Depth Are Greater than Habitat Effects on the New Zealand Continental Margin: Implications for Vulnerability to Anthropogenic Disturbance[END_REF]. In all studies, most meiofauna organisms were located in the upper 3 cm of sediment, but some found organisms up to 10 cm or 30 cm within the sediment [START_REF] Snider | The Composition and Distribution of Meiofauna and Nanobiota in a Central North Pacific Deep-Sea Area[END_REF][START_REF] Danovaro | Vertical Distribution of Meiobenthos in Bathyal Sediments of the Eastern Mediterranean Sea: Relationship with Labile Organic Matter and Bacterial Biomasses[END_REF][START_REF] Shirayama | Vertical Distribution of Meiobenthos in the Sediment Profile in Bathyal, Abyssal and Hadal Deep Sea Systems of the Western Pacific[END_REF]. Assemblages were found to vary between sediment layers, and this generally reflects the taxa's ability to cope with lower oxygen concentrations. For example, crustaceans, mainly harpacticoid copepods and ostracods, are most abundant in upper sediment layers due to their increased oxygen consumption, while nematodes cope well with low oxygen concentrations and can thus penetrate deeper into the sediment [START_REF] Thiel | Meiobenthos and Nanobenthos of the Deep-Sea[END_REF][START_REF] Tietjen | Abundance and Biomass of Metazoan Meiobenthos in the Deep Sea[END_REF]. Species-interactions such as avoidance of predators, or competitive exclusion are also thought to play a role in the vertical distribution patterns observed [START_REF] Lambshead | Comparison of the Vertical Distribution of Nematodes from Two Contrasting Abyssal Sites in the Northeast Atlantic Subject to Different Seasonal Inputs of Phytodetritus[END_REF][START_REF] Steyaert | The Importance of Fine-Scale, Vertical Profiles in Characterising Nematode Community Structure[END_REF][START_REF] Gallucci | Effects of Megafauna Exclusion on Nematode Assemblages at a Deep-Sea Site[END_REF]. Some authors even reported that sediment depth had a greater influence on meiofauna communities than horizontal factors such as sampling stations or habitats [START_REF] Rosli | Differences in Meiofauna Communities with Sediment Depth Are Greater than Habitat Effects on the New Zealand Continental Margin: Implications for Vulnerability to Anthropogenic Disturbance[END_REF][START_REF] Górska | Bathymetric Variations in Vertical Distribution Patterns of Meiofauna in the Surface Sediments of the Deep Arctic Ocean (HAUSGARTEN, Fram Strait)[END_REF].

CHAPTER I INTRODUCTION AND LITERATURE REVIEW 49

I.VI. Aims and objectives

While ocean exploration is relatively recent, studies in the last decades have started shedding light on biodiversity and biogeography in the deep-sea realm. However, these studies were confronted with the extraordinary vastness of deep-sea ecosystems, the difficulty of sampling in these remote and high-pressure locations, as well as the high costs and time involved in collecting and analysing samples.

Analytical methods based on extrapolation from known samples clearly indicated that deep-sea life is undeniably diverse, although estimates remain highly uncertain, primarily due to under-sampling and to the difficulty of identifying specimens. The large marine databases assembled in recent years include too little information about deep-sea species in order to make extrapolation approaches a useful tool for the estimation of deep-sea biodiversity [START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF]. Experimental approaches also highlight the strong link between surface and deep-ocean regions, showing that benthic deep-sea communities are affected by climate-driven variations in carbon cycles and can therefore directly influence carbon remineralisation and sequestration processes (Smith, K. L. et al. 2009;2013). However, monitoring these surfacedriven changes in deep-sea benthic communities is costly and difficult to sustain over long-term periods.

Deep-sea sedimentary habitats cover more than 50% of the Earth's surface, can host high numbers of organisms (50,000-5 million individuals per square meter), which perform key ecosystem roles such as nutrient cycling, sediment stabilisation and transport, or secondary production (Bik et al. 2012b;Fonseca, V. G. et al. 2010) Environmental DNA metabarcoding approaches have revolutionized biodiversity research in the past decade and have already been successfully applied in marine sedimentary habitats [START_REF] Pawlowski | Eukaryotic Richness in the Abyss: Insights from Pyrotag Sequencing[END_REF]Bik et al. 2012b;Fonseca, V. G. et al. 2010;[START_REF] Cristescu | From Barcoding Single Individuals to Metabarcoding Biological Communities: Towards an Integrative Approach to the Study of Global Biodiversity[END_REF][START_REF] Cowart | Metabarcoding Is Powerful yet Still Blind: A Comparative Analysis of Morphological and Molecular Surveys of Seagrass Communities[END_REF][START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF][START_REF] Forster | Benthic Protists: The under-Charted Majority[END_REF]Cordier et al. 2019a). They represent useful tools for CHAPTER I

INTRODUCTION AND LITERATURE REVIEW 50

increasing the spatial scale of deep-sea studies, while allowing to target biodiversity of various biological compartments in parallel, including the commonly overlooked meio-and nanofauna.

However, while this tool greatly facilitates the study of remote ecosystems, many challenges remain to be resolved in order to apply eDNA methods on a broad scale [START_REF] Cristescu | Uses and Misuses of Environmental DNA in Biodiversity Science and Conservation[END_REF]. In particular, the use of eDNA to assess metazoan biodiversity remains complex due to the difficulty in defining accurate species-level molecular Operational Taxonomic Units (OTUs) and improvements in the bioinformatic data processing are necessary to achieve more accurate and reliable biodiversity inventories. Moreover, the accuracy of protocols based on eDNA in deep sea sediments still needs to be assessed, as analysis outcomes may be biased by ancient (archived) DNA (aDNA), resulting in biodiversity assessments not targeting live organisms.

Objectives

The first, primarily technical aims of this thesis are thus to help developing accurate eDNA metabarcoding protocols for the study of deep-sea biodiversity across multiple life compartments, i.e. prokaryotes, unicellular eukaryotes, and metazoans. Using mitochondrial and nuclear marker genes, the eDNA workflow for deep sea sediments was evaluated and optimized on a bioinformatic and molecular processing level:

1. In order to limit the pitfalls regarding the number of molecular entities, the second chapter of this thesis thus describes how newly developed bioinformatic tools were assessed and combined in order to get more reliable biodiversity inventories, approaching a 1:1 species-OTU correspondence.

2. The third chapter details the assessment of the potential bias of aDNA through 1) the evaluation of the effect of removing short DNA fragments via size-selection or ethanol reconcentration, and 2) the comparison of communities revealed by co-extracted DNA and RNA in five deep-sea sites.

3. The fourth chapter assesses sampling techniques for deep-sea sediment and water in order to define optimal ways to achieve most comprehensive biodiversity inventories, and evaluates whether aboveground water and sediment samples yield comparable communities.

CHAPTER I INTRODUCTION AND LITERATURE REVIEW 51

Finally, the fifth chapter of this thesis shows the application of these optimized eDNA metabarcoding protocols on deep seafloor of the Atlantic-Mediterranean transition zone. The influence of local abiotic factors on deep-sea benthic metazoan OTU richness and community structure are evaluated at the local, habitat, and regional scales, along this west east transect ranging from the Western North Atlantic to the Ionian Sea.

Chapter II. Bioinformatic pipelines combining correction and clustering tools allow for flexible and comprehensive prokaryotic and eukaryotic metabarcoding

Published as:

Brandt

In revision at Molecular Ecology Resources:

Brandt MI, Trouche B, Günther B, Quintric L, Wincker P, Poulain J, and Arnaud-Haond S.

Bioinformatic pipelines combining correction and clustering tools allow more flexible and comprehensive prokaryotic and eukaryotic metabarcoding.

CHAPTER II BIOINFORMATIC PIPELINE COMPARISONS 53

A significant source of error in molecular biodiversity inventories of metazoans is due to the fact that metazoans are multicellular organisms, and the marker genes targeted for metabarcoding are present in multiple copies per cell [START_REF] Krehenwinkel | Estimating and Mitigating Amplification Bias in Qualitative and Quantitative Arthropod Metabarcoding[END_REF]). Thus, sequencing errors, amplification errors, and mutations of marker genes within organisms lead to the fact that single species and even single individuals produce several Operational Taxonomic Units (OTUs). As OTUs are used as a proxy for species (as defined by morphological criteria), it is essential that this proxy remains valid to maintain the reliability of metabarcoding inventories.

Metabarcoding bioinformatic pipelines have been in constant refinement, and recent advances have produced new Illumina sequence correction [START_REF] Callahan | DADA2: High-Resolution Sample Inference from Illumina Amplicon Data[END_REF]) and cluster filtering [START_REF] Frøslev | Algorithm for Post-Clustering Curation of DNA Amplicon Data Yields Reliable Biodiversity Estimates[END_REF]) tools.

Clustering sequences also alleviates the noise originating from errors and intraspecific variation, as it pools similar but not identical sequences. New clustering methods now allow highly scalable and fine-scale clustering [START_REF] Mahé | Swarm v2: Highly-Scalable and High-Resolution Amplicon Clustering[END_REF], avoiding imposing a "universal"

clustering threshold on metabarcoding datasets.

In this chapter, we implement these new tools in a bioinformatic pipeline and assess the level of diversity they allow describing by evaluating their performance on mock communities and deep-sea sediment samples.

Question addressed:

Do new bioinformatic tools such as DADA2, LULU, and swarm v2 allow achieving biodiversity inventories at the level of the morphospecies ?

Résumé en français

Le metabarcoding par ADN environnemental (ADNe) est un outil puissant pour étudier la biodiversité. Cependant, les approches bioinformatiques doivent s'adapter à la diversité des compartiments taxonomiques ciblés ainsi qu'aux spécificités de chaque gène marqueur. Nous CHAPTER II

BIOINFORMATIC PIPELINE COMPARISONS 55

Abstract

Environmental metabarcoding is a powerful tool for studying biodiversity. However, bioinformatic approaches need to adjust to the diversity of taxonomic compartments targeted as well as to each barcode gene specificities. We built and tested a pipeline based on read correction with DADA2 allowing analysing metabarcoding data from prokaryotic (16S) and eukaryotic (18S, COI) life compartments. We implemented the option to cluster Amplicon Sequence Variants (ASVs) into Operational Taxonomic Units (OTUs) with swarm, a networkbased clustering algorithm, and the option to curate ASVs/OTUs using LULU. Finally, taxonomic assignment was implemented via the Ribosomal Database Project Bayesian classifier (RDP) and BLAST. We validate this pipeline with ribosomal and mitochondrial markers using metazoan mock communities and 42 deep-sea sediment samples. The results

show that ASVs and OTUs describe different levels of biotic diversity, the choice of which depends on the research questions. They underline the advantages and complementarity of clustering and LULU-curation for producing metazoan biodiversity inventories at a level approaching the one obtained using morphological criteria. While clustering removes intraspecific variation, LULU effectively removes spurious clusters, originating from errors or intragenomic variability. Swarm clustering affected alpha and beta diversity differently depending on genetic marker. Specifically, d-values > 1 appeared to be less appropriate with 18S for metazoans. Similarly, increasing LULU's minimum ratio level proved essential to avoid losing species for sample-poor datasets. Comparing BLAST and RDP underlined that accurate assignments of deep-sea species can be obtained with RDP, but highlighted the need for a concerted effort to build comprehensive, ecosystem-specific databases.

Introduction

High-throughput sequencing (HTS) technologies are revolutionizing the way we assess biodiversity. By producing millions of DNA sequences per sample, HTS allows broad taxonomic biodiversity surveys through metabarcoding of bulk DNA from complex communities or from environmental DNA (eDNA) directly extracted from soil, water, and air samples. First developed to unravel cryptic and uncultured prokaryotic diversity, metabarcoding methods have been extended to eukaryotes as powerful, non-invasive tools, allowing detection of a wide range of taxa in a rapid, cost-effective way using a variety of sample types [START_REF] Valentini | DNA Barcoding for Ecologists[END_REF]Taberlet et al. 2012a;[START_REF] Creer | The Ecologist's Field Guide to Sequence-Based Identification of Biodiversity[END_REF][START_REF] Stat | Ecosystem Biomonitoring with EDNA: Metabarcoding across the Tree of Life in a Tropical Marine Environment[END_REF]). In the last decade, these tools have been used to describe past and present biodiversity in terrestrial [START_REF] Ji | Reliable, Verifiable and Efficient Monitoring of Biodiversity via Metabarcoding[END_REF][START_REF] Yoccoz | DNA from Soil Mirrors Plant Taxonomic and Growth Form Diversity[END_REF][START_REF] Yu | Biodiversity Soup: Metabarcoding of Arthropods for Rapid Biodiversity Assessment and Biomonitoring[END_REF][START_REF] Slon | Neandertal and Denisovan DNA from Pleistocene Sediments[END_REF][START_REF] Pansu | Environmental DNA Metabarcoding to Investigate Historic Changes in Biodiversity[END_REF], freshwater [START_REF] Valentini | Next-Generation Monitoring of Aquatic Biodiversity Using Environmental DNA Metabarcoding[END_REF] [START_REF] Frøslev | Algorithm for Post-Clustering Curation of DNA Amplicon Data Yields Reliable Biodiversity Estimates[END_REF].

The authors validated their approach on metabarcoding of plants using ITS2 (nuclear ribosomal internal transcribed spacer region 2) and evaluated it on several pipelines. Their results show that ASV definition with DADA2, subsequent clustering to address intraspecific variation, and final curation with LULU is the safest pathway for producing reliable and accurate metabarcoding data. The authors concluded that their validation on plants is relevant to other organism groups and other markers, while recommending future validation of LULU on mock communities as LULU's minimum match parameter may need to be adjusted to less variable marker genes.

The impact of errors being strongly decreased by correction algorithms such as DADA2

and LULU, the relevance of clustering sequences into OTUs is now being debated. Indeed, after presenting their new algorithm on prokaryotic communities, the authors of DADA2 proposed that the reproducibility and comparability of ASVs across studies challenge the need for clustering sequences, as OTUs have the disadvantage of being study-specific and defined using arbitrary thresholds [START_REF] Callahan | Exact Sequence Variants Should Replace Operational Taxonomic Units in Marker-Gene Data Analysis[END_REF]). Yet, clustering sequences may still be necessary in metazoan datasets, where very distinct levels of intraspecific polymorphism can exist in the same gene region among taxa, due to both evolutionary and biological specificity [START_REF] Bucklin | DNA Barcoding of Marine Metazoa[END_REF][START_REF] Phillips | Incomplete Estimates of Genetic Diversity within Species: Implications for DNA Barcoding[END_REF]. ASV-based inventories will thus be biased in favour of taxa with high levels of intraspecific diversity, even though these are not necessarily the most abundant ones [START_REF] Bazin | Population Size Does Not Influence Mitochondrial Genetic Diversity in Animals[END_REF]). Such bias is magnified with presence-absence data, commonly used for metazoan metabarcoding [START_REF] Ji | Reliable, Verifiable and Efficient Monitoring of Biodiversity via Metabarcoding[END_REF]). However, as intraspecific polymorphism and interspecific divergence are phylum-specific, imposing a universal clustering threshold on metabarcoding datasets is also introducing bias, penalizing groups with lower polymorphism or divergence levels, while overestimating species diversity in groups with higher interspecific divergence. Universal clustering thresholds can be avoided with tools such as swarm v2, a single-linkage clustering algorithm [START_REF] Mahé | Swarm v2: Highly-Scalable and High-Resolution Amplicon Clustering[END_REF], implemented in recent bioinformatic pipelines, such as FROGS [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF] or SLIM [START_REF] Dufresne | SLIM: A Flexible Web Application for the Reproducible Processing of Environmental DNA Metabarcoding Data[END_REF]. Based on network theory, swarm v2 aggregates sequences iteratively and locally around seed sequences, based on d, the number of nucleotide differences, to determine coherent groups of sequences, independent of amplicon input order, allowing highly scalable and fine-scale clustering.

Finally, it is widely recognized that homogeneous entities sharing a set of evolutionary and ecological properties, i.e. species [START_REF] Mayr | Systematics and the Origin of Species, from the Viewpoint of a Zoologist[END_REF][START_REF] Queiroz | Ernst Mayr and the Modern Concept of Species[END_REF], sometimes referred to "ecotypes" for prokaryotes [START_REF] Cohan | Bacterial Species and Speciation[END_REF][START_REF] Gevers | Re-Evaluating Prokaryotic Species[END_REF], represent a fundamental category of biological organization that is the cornerstone of most ecological and evolutionary theories and empirical studies. Maintaining ASV information for feeding databases and crosscomparing studies is not incompatible with their clustering into OTUs, and this choice depends on the purpose of the study, i.e. providing a census of the extent and distribution of genetic polymorphism for a given gene, or a census of biodiversity to be used and manipulated in ecological or evolutionary studies.

Here we evaluate DADA2 and LULU, using them alone and in combination with swarm v2, to assess the performance of these new tools for metabarcoding of metazoan communities.

Using both mitochondrial COI [START_REF] Leray | A New Versatile Primer Set Targeting a Short Fragment of the Mitochondrial COI Region for Metabarcoding Metazoan Diversity: Application for Characterizing Coral Reef Fish Gut Contents[END_REF]) and the V1-V2 region of 18S ribosomal RNA (rRNA) [START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF], we evaluated the need for clustering and the effectiveness of LULU curation to select pipeline parameters delivering the most accurate resolution of two deep-sea mock communities. We then test the different bioinformatic tools on a deep-sea sediment dataset in order to select an optimal trade-off between inflating biodiversity estimates and loosing rare biodiversity. As a baseline for comparison, and in the perspective of the joint study of metazoan and microbial taxa, we also analysed the 16S V4-V5 rRNA barcode [START_REF] Parada | Every Base Matters: Assessing Small Subunit RRNA Primers for Marine Microbiomes with Mock Communities, Time Series and Global Field Samples[END_REF]) on these environmental samples.

CHAPTER II BIOINFORMATIC PIPELINE COMPARISONS 59

Our objectives were to (1) discuss the use of ASV vs OTU-centred datasets depending on taxonomic compartment and study objectives, and (2) determine the most adequate swarmclustering and LULU curation thresholds that avoid inflating biodiversity estimates while retaining rare biodiversity.

Materials and methods

Preparation of samples

Mock communities

Two genomic-DNA mass-balanced metazoan mock communities (5 ng/µL) were prepared using standardized 10 ng/µL DNA extracts of ten deep-sea specimens belonging to five taxonomic groups (Polychaeta, Crustacea, Anthozoa, Bivalvia, Gastropoda; Table S1).

Specimen DNA was extracted using a CTAB extraction protocol, from muscle tissue or from whole polyps in the case of cnidarians. The mock communities differed in terms of ratios of total genomic DNA from each species, with increased dominance of three species and secondary species DNA input decreasing from 3% to 0.7%. We individually barcoded the species present in the mock communities: PCRs of both target genes were performed using the same primers as the ones used in metabarcoding (see below

Environmental DNA

Sediment cores were collected from fourteen deep-sea sites ranging from the Arctic to the Mediterranean during various cruises (Table S2). Sampling was carried out with a multicorer or with a remotely operated vehicle. Three tube cores were taken at each sampling station (GPS coordinates in Table S2). The latter were sliced into depth layers that were transferred into ziplock bags, homogenised, and frozen at -80°C on board before being shipped on dry ice to the CHAPTER II BIOINFORMATIC PIPELINE COMPARISONS 60 laboratory. The first layer (0-1 cm) was used in the present study. DNA extractions were performed using approximately 10 g of sediment with the PowerMax Soil DNA Isolation Kit (Qiagen, Hilden, Germany). To increase the DNA yield, the elution buffer was left on the spin filter membrane for 10 min at room temperature before centrifugation. The ~5 mL extract was then split into three parts, one of which was kept in screw-cap tubes for archiving purposes and stored at -80°C. For the four field controls, the first solution of the kit was poured into the control zip-lock bag, before following the usual extraction steps. For the two negative extraction controls, a blank extraction (adding nothing to the bead tube) was performed alongside sample extractions.

Amplicon library construction and high-throughput sequencing

Two primer pairs were used to amplify the mitochondrial COI and the 18S V1-V2 rRNA barcode genes specifically targeting metazoans, and one pair of primer was used to amplify the prokaryote 16S V4-V5 region. PCR amplifications, library preparation, and sequencing were carried out at Genoscope (Evry, France) as part of the eDNAbyss project. Four (16S), eight (18S), and ten (COI) control PCRs were performed alongside sample PCRs, depending on the amount of trials needed to achieve successful amplification.

Eukaryotic 18S V1-V2 rRNA gene amplicon generation

Amplifications were performed with the Phusion High Fidelity PCR Master Mix with GC buffer (Thermo Fisher Scientific, Waltham, MA, USA) and the SSUF04 (5'-GCTTGTCTCAAAGATTAAGCC-3') and SSUR22mod (5'-CCTGCTGCCTTCCTTRGA-3') primers [START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF], preferentially targeting metazoans, the primary focus of this study. The PCR reactions (25 μL final volume) contained 2.5 ng or less of DNA template with 0.4 μM concentration of each primer, 3% of DMSO, and 1X Phusion Master Mix. Three PCR replicates (98 °C for 30 s; 25 cycles of 10 s at 98 °C, 30 s at 45 °C, 30 s at 72 °C; and 72 °C for 10 min) were performed in order to smooth the intra-sample variance while obtaining sufficient amounts of amplicons for Illumina sequencing.

Eukaryotic COI gene amplicon generation

Metazoan COI barcodes were generated using the mlCOIintF (5'-GGWACWGGWTGAACWGTWTAYCCYCC-3') and jgHCO2198

(5'-TAIACYTCIGGRTGICCRAARAAYCA-3') primers [START_REF] Leray | A New Versatile Primer Set Targeting a Short Fragment of the Mitochondrial COI Region for Metabarcoding Metazoan Diversity: Application for Characterizing Coral Reef Fish Gut Contents[END_REF]. Triplicate PCR reactions (20 μl final volume) contained 2.5 ng or less of total DNA template with 0.5 μM final concentration of each primer, 3% of DMSO, 0.175 mM final concentration of dNTPs, and 1X

Advantage 2 Polymerase Mix (Takara Bio, Kusatsu, Japan). Cycling conditions included a 10 min denaturation step followed by 16 cycles of 95 °C for 10 s, 30s at 62°C (-1°C per cycle), 68 °C for 60 s, followed by 15 cycles of 95 °C for 10 s, 30s at 46°C, 68 °C for 60 s and a final extension of 68 °C for 7 min.

Prokaryotic 16S rRNA gene amplicon generation

Prokaryotic barcodes were generated using 515F-Y (5'-GTGYCAGCMGCCGCGGTAA-3') and 926R (5'-CCGYCAATTYMTTTRAGTTT-3') 16S-V4V5 primers [START_REF] Parada | Every Base Matters: Assessing Small Subunit RRNA Primers for Marine Microbiomes with Mock Communities, Time Series and Global Field Samples[END_REF]. Triplicate PCR reactions were prepared as described above for 18S-V1V2, but cycling conditions included a 30 s denaturation step followed by 25 cycles of 98 °C for 10 s, 53 °C for 30 s, 72 °C for 30 s, and a final extension of 72 °C for 10 min.

Amplicon library preparation

PCR triplicates were pooled and PCR products purified using 1X AMPure XP beads (Beckman Coulter, Brea, CA, USA) clean up. Aliquots of purified amplicons were run on an Agilent Bioanalyzer using the DNA High Sensitivity LabChip kit (Agilent Technologies, Santa Clara, CA, USA) to check their lengths and quantified with a Qubit fluorimeter (Invitrogen, Carlsbad, CA, USA). One hundred nanograms of pooled amplicon triplicates were directly endrepaired, A-tailed and ligated to Illumina adapters on a Biomek FX Laboratory Automation Workstation (Beckman Coulter, Brea, CA, USA). Library amplification was performed using a Kapa Hifi HotStart NGS library Amplification kit (Kapa Biosystems, Wilmington, MA, USA)

with the same cycling conditions applied for all metagenomic libraries and purified using 1X

AMPure XP beads.

Sequencing library quality control

Amplicon libraries were quantified by Quant-iT dsDNA HS assay kits using a Fluoroskan Ascent microplate fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) and then by qPCR with the KAPA Library Quantification Kit for Illumina Libraries (Kapa Biosystems, Wilmington, MA, USA) on an MxPro instrument (Agilent Technologies, Santa Clara, CA, CHAPTER II BIOINFORMATIC PIPELINE COMPARISONS 62 USA). Library profiles were assessed using a high-throughput microfluidic capillary electrophoresis system (LabChip GX, Perkin Elmer, Waltham, MA, USA).

Sequencing procedures

Library concentrations were normalized to 10 nM by addition of 10 mM Tris-Cl (pH 8.5) and applied to cluster generation according to the Illumina Cbot User Guide (Part # 15006165).

Amplicon libraries are characterized by low diversity sequences at the beginning of the reads due to the presence of the primer sequence. Low-diversity libraries can interfere in correct cluster identification, resulting in a drastic loss of data output. Therefore, loading concentrations of libraries were decreased (8-9 pM instead of 12-14 pM for standard libraries) and PhiX DNA spike-in was increased (20% instead of 1%) in order to minimize the impacts on the run quality.

Libraries were sequenced on HiSeq2500 (System User Guide Part # 15035786) instruments (Illumina, San Diego, CA, USA) in a 250 bp paired-end mode.

Bioinformatic analyses

All bioinformatic analyses were performed using a Unix shell script run on a home-based For all analyses, the mock communities were analysed alongside all environmental samples, and used to validate the metabarcoding pipeline in terms of detection of correct species and presence of false-positives. The details of the pipeline, along with specific parameters used for all three metabarcoding markers are listed in Table S3.

Reads preprocessing

Our multiplexing strategy relies on ligation of adapters to amplicon pools, meaning that contrary to libraries produced by double PCR, the reads in each paired sequencing run can be forward or reverse. DADA2 correction is based on error distribution differing between R1 and R2 reads. We thus developed a custom script (abyss-preprocessing in abyss-pipeline) allowing separating forward and reverse reads in each paired run and reformatting the outputs to be compatible with DADA2. Briefly, the script uses cutadapt v1.18 to detect and remove primers, while separating forward and reverse reads in each paired sequence file to produce two pairs of sequence files per sample named R1F/R2R and R2F/R1R. Cutadapt parameters (Table S3) were set to require an overlap over the full length of the primer (default: 3 nt), with 2-4 nt mismatches allowed for ribosomal loci, and 7 nt mismatches allowed for COI (default: 10%). Each identified forward and reverse read is then renamed which the correct extension (/1 and /2 respectively), which is a requirement for DADA2 to recognize the pairs of reads. Each pair of renamed sequence files is then re-paired with BBMAP Repair v38.22 in order to remove singleton reads (non-paired reads). Optionally, sequence file names can also be renamed if necessary using a CSV correspondence file.

Read correction, amplicon cluster generation and taxonomic assignment

Pairs of Illumina reads were corrected with DADA2 following the online tutorial for paired-end HiSeq data (https://benjjneb.github.io/dada2/bigdata_paired.html). Reads containing ambiguous bases removed and trimming lengths were adjusted based on sequence quality profiles, so that Q-scores remained above 30 (truncLen at 220 for 18S and 16S, 200 for COI, maxEE at 2, truncQ at 11, maxN at 0). Error model calculation (for R1F/R2R read pairs and then R2F/R1R read pairs), read correction, and read merging was performed at default settings. Amplicons were filtered by size, with size ranges set to 330-390 bp for the 18S SSU rRNA marker gene, 300-326 bp for the COI marker gene, and 350-390 bp for the 16S rRNA marker gene, based on raw size distributions observed. Chimera removal and taxonomic assignment were performed with default methods implemented in DADA2.

A second taxonomic assignment method was optionally implemented in the pipeline, allowing assigning ASVs using BLAST+ (Basic Local Alignment Search Tool v2.6.0) based on minimum similarity and minimum coverage (-perc_identity 70 and -qcov_hsp 80). An initial test implementing BLAST+ to assign taxonomy only to the COI dataset using a 96% percent identity threshold led to the exclusion of the majority of the clusters. Given observed interspecific mitochondrial DNA divergence levels of up to 30% within a same polychaete genus [START_REF] Zanol | Phylogeny of the Bristle Worm Family Eunicidae (Eunicida, Annelida) and the Phylogenetic Utility of Noncongruent 16S, COI and 18S in Combined Analyses[END_REF] or among some closely related deep-sea shrimp species [START_REF] Shank | Miocene Radiation of Deep-Sea Hydrothermal Vent Shrimp (Caridea: Bresiliidae): Evidence from Mitochondrial Cytochrome Oxidase Subunit I[END_REF], and considering our interest in the identities of multiple, largely unknown taxa in poorly characterized communities, more stringent BLAST thresholds were not implemented at this stage. However, additional filters were performed during downstream processing described below, and only clusters with assignments reliable at phylum-level were retained in the analysis.

CHAPTER II BIOINFORMATIC PIPELINE COMPARISONS 64

The Silva132 reference database was used for 16S and 18S SSU rRNA marker genes [START_REF] Quast | The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools[END_REF] The vast majority of prokaryotes usually show low levels (< 1%) of intra genomic variability for the 16S SSU rRNA gene [START_REF] Acinas | Divergence and Redundancy of 16S RRNA Sequences in Genomes with Multiple Rrn Operons[END_REF][START_REF] Pei | Diversity of 16S RRNA Genes within Individual Prokaryotic Genomes[END_REF]. These low intragenomic divergence levels can be efficiently removed with swarm clustering at low dvalues. Although LULU curation may still be useful to merge redundant phylotypes in specific cases such as haplotype network analyses, this was not tested in this study. Indeed, parallelization not being currently available for LULU curation, the richness of prokaryote communities implied an unrealistic calculation time, even on a powerful cluster (e.g. LULU curation was at 20 -40% after 4 days of calculation on our cluster).

In order to have reliable BLAST phylum assignments for pipeline comparison, final datasets were taxonomically filtered by retaining only clusters having a minimum hit identity of 86% for rRNA loci and 80% for COI. These values were chosen as they represent approximate minimum identities for reliable phylum assignment [START_REF] Stefanni | Multi-Marker Metabarcoding Approach to Study Mesozooplankton at Basin Scale[END_REF].

Statistical analyses

Data was analysed using R with the packages phyloseq v1. To evaluate the functionality of the bioinformatic tools with the mock communities, taxonomically assigned metazoan clusters were considered as derived from one of the ten species used for the mock communities when the assignment delivered the corresponding species, genus, family, or class. Clusters not fitting the expected taxa were labelled as 'Others'.

These non-target clusters may originate from contamination by external DNA or from DNA of associated microfauna, or gut content in the case of whole polyps used for cnidarians.

Alpha diversity detected using each pipeline in the environmental samples was evaluated with the number of observed clusters in the rarefied datasets via analyses of variance (ANOVA) on generalized linear models based on quasipoisson distribution models. Homogeneity of multivariate dispersions were verified with the betadisper function of the betapart package v.1.5.1 [START_REF] Baselga | Betapart : An R Package for the Study of Beta Diversity[END_REF]. The effect of site and LULU curation on community composition was tested by PERMANOVA, using the function adonis2 (vegan), with Jaccard incidence dissimilarities for metazoans and Bray-Curtis dissimilarities for prokaryotes, and significance was evaluated by permuting 999 times. Beta-diversity patterns were visualised via non-metric multidimensional scaling (NMDS) using the same dissimilarities stated above.

Finally, BLAST and RDP taxonomic assignments were compared at the most adequate pipeline settings for each locus. BLAST and RDP datasets were compared on ASV-level for prokaryotes, and OTU-level for metazoans (swarm d=1, LULU with minimum match at 84%

and minimum ratio at 1 for COI, and 90% and 100 respectively for 18S). As trials on MIDORI-UNIQUE resulted in very poor performance of RDP for COI (assignments belonging mostly to Insecta), the comparison was performed with MIDORI-UNIQUE subsampled to marine taxa only. For the global dataset, full ranges of BLAST hit identities and phylum-level bootstraps were plotted and numbers of clusters left after phylum-level and genus-level quality filtering were calculated, while for evaluation on the mock samples, rarefied data was subsampled to reliable phylum-level assignments (i.e. ≥ 80% / 86% similarity, ≥ 80% phylum-level bootstraps).

Results

Alpha diversity in mock communities

A total of 1.5 million (COI) and 2 million (18S) raw reads were obtained from the two mock communities (Table S4). After refining (decontamination, renormalisation, removal of non-target taxa, and clusters unassigned at phylum-level or with unreliable phylum-level assignments), these numbers were decreased to 0.7 million for COI and 1.3 million for 18S.

All ten mock species were detected in the COI dataset ( Curating ASVs/OTUs with LULU allowed reducing the number of clusters produced per species for both loci, and optimal results were obtained in datasets clustered at d ≥ 1 for COI and d = 1 for 18S. The number of unexpected clusters ("Others") was hardly affected by LULU curation (Table 1). In the COI dataset, curating with LULU at 84% or 90% minimum match resulted in similar OTU numbers, although 84% performed slightly better in Mock 3 (Table 1).

Increasing the minimum ratio parameter to 100 or 1000 resulted in the retention of more error OTUs and thus higher OTU numbers in each mock species (data not shown). For 18S, both LULU minimum match and minimum ratio affected species recovery. LULU curation with minimum ratio = 1 led to the loss of the shrimp Chorocaris sp. at both minimum match levels and the gastropod Paralepetopsis sp. at 84% minimum match (Table S6). With minimum ratio at 100, Chorocaris sp. was retained in the dataset at both minimum match levels and Paralepetopsis sp. with minimum match at 90% (Table 1). With minimum ratio at 1000, both species were retained at both minimum match levels but more OTUs were retained for another species (Munidopsis sp., Table S6). As LULU curation with higher minimum ratio levels resulted in more accurate species compositions in the mock samples with 18S, we only present LULU curation with minimum ratio = 100 for the environmental samples.

Alpha-diversity patterns in environmental samples

High-throughput sequencing results

A total of 44 million (18S), 33 million (COI) and 16 million (16S) reads were obtained from 42 sediment samples, 4 field controls, 2 extraction blanks, and 4 (16S), 8 (18S), and 10

(COI) PCR blanks (Table S4). The final datasets contained ~5 million (COI) to ~8 million (18S) marine metazoan target reads and ~7 million prokaryotic 16S reads (Table S4). COI reads produced 13,397 ASVs, 3,518 -5,563 OTUs after swarm clustering (d = 1-13), and 1,758 -10,028 OTUs after LULU curation (Table S7). Final 18S reads comprised 8,280 ASVs,

Number of clusters among pipelines

The number of metazoan clusters detected in the deep-sea sediment samples varied significantly with bioinformatic pipeline and site (Table 2). The pipeline effect was consistent across sites (Table 2), although mean cluster numbers detected per sample spanned a wide range in all loci (50 -500 for 18S, 100 -1,000 for COI, and 1,500 -4,000 for 16S, Fig. 1).

As expected, clustering significantly reduced the number of detected molecular clusters per sample for all loci. Consistent to results observed in mock communities, clustering at d = 1-13 resulted in comparable OTU numbers for COI, while significantly higher OTU numbers were obtained at d = 1 than with d >1 for ribosomal loci (Fig. 1, Table 2). LULU curation of ASVs or OTUs decreased the number of COI and 18S clusters detected (Fig. 1). This decrease was significant for both ASVs and OTUs with COI, but less marked for 18S as LULU's minimum ratio was set to 100 (Table 2). For COI, where LULU curation was performed with minimum ratio = 1, the minimum match parameter had a strong influence on alpha diversity. Indeed, LULU curation of ASVs or OTUS with minimum match at 90% resulted in significantly more clusters than at 84% (Table 2). In contrast, the magnitude of the minimum match parameter did not significantly affect the number of clusters for 18S, where LULU curation was performed with minimum ratio = 100. LULU curation of ASVs resulted in more OTUs than swarm clustering for both loci, with both minimum match levels tested (Fig. 1, Table 2). Similarly, LULU curation of ASVs resulted in significantly more clusters than LULU curation of OTUs produced with any d-value (Fig. 1, Table 2).

Looking at mean ASV and OTU numbers detected per phylum with each pipeline showed consistent effects of swarm clustering and LULU curation, but highlighted strong differences in the amount of intragenomic variation between taxonomic groups. For all loci investigated, some taxa displayed high ASV to OTU ratios, while others were hardly affected by clustering or LULU curation in terms of numbers of clusters detected (Fig S1 ).

Patterns of beta-diversity between pipelines

PERMANOVAs confirmed that sites differed significantly in terms of community structure, accounting from 46% to 89% of variation in data. Evaluating the effect of LULU curation for metazoans showed that LULU-curated data resolved similar community compositions than non-curated data, accounting for < 1% of variation in data (Fig. 2).

Although ASV and OTU datasets detected similar amounts of variation due to sites in

Taxonomic assignment quality

Assigning with BLAST resulted in mock community assignments comparable to described above. With COI, eight of the ten species produced one single OTU, with six correctly assigned at genus-level, and two species were taxonomically correctly assigned only to class-level and produced 2-3 OTUs (Fig S3). With 18S, seven species were recovered (4 correctly assigned at genus-level), with two producing more than one OTU, and the three vesicomyid bivalve species were taxonomically unresolved and assigned up to family-level while generating 2 OTUs.

Assigning the COI dataset with RDP using the MIDORI-UNIQUE database resulted in assignments of the mock samples that did not match the expected taxa and were mostly belonging to arthropods, a problem not observed with BLAST (data not shown). When the database was reduced to marine-only taxa, RDP results were comparable to BLAST, with seven species correctly assigned at genus-level. Assigning the 18S dataset with RDP produced results comparable to BLAST, although taxonomic assignments were less accurate for two species.

BLAST and RDP assigned similar amounts of OTUs in the prokaryote dataset, but BLAST assigned 20% (18S) and 70% (COI) less OTUs at phylum-level than RDP in the metazoan datasets, even at minimum hit identity of 70% (Table S8). BLAST hit identities of the overall datasets varied strongly depending on phyla and marker gene (Fig. 3). For 18S, 90% of metazoan OTUs had assignment identities ≥ 86%, corresponding roughly to accurate phylumlevel [START_REF] Stefanni | Multi-Marker Metabarcoding Approach to Study Mesozooplankton at Basin Scale[END_REF]Edgar 2017a). Only 34% had reliable genus-level assignments, i.e.

with > 95% similarity (Table S8). For COI, only 30% of metazoan assignments were reliable at phylum-level (≥ 80%), and only 1% at genus-level (> 93%). BLAST hit identity was much higher for prokaryotes, with 98% of ASVs assigned with ≥ 86% similarity to sequences in databases, and 65% had reliable genus-level assignments (> 95% similarity). With RDP, 77%

of metazoan 18S OTUs and 96% of prokaryote 16S ASVs had phylum-level bootstraps ≥ 80%, and 59% and 76% also had genus-level bootstraps ≥ 80%, respectively. For COI, applying a minimum phylum-level bootstrap of 80% resulted in an unviable decrease in the number of target OTUs, as only 242 metazoan OTUs (~1%) remained after filtering, and only 112 (0.3%)

with acceptable genus-level bootstraps (Table S8). Indeed, most OTUs, primarily assigned to arthropods, cnidarians, molluscs, vertebrates, and poriferans still had phylum-level bootstraps < 60% (Fig. 3).

Figure 3. Taxonomic assignment quality of BLAST and RDP methods on metazoan (COI, 18S) and prokaryote (16S) metabarcoding datasets of 14 deep-sea sites. Metazoan data was clustered with swarm at d=1 and curated with LULU at 90% (minimum ratio = 100) for 18S and 84% (minimum ratio = 1) for COI. Taxonomic assignments were performed on the Silva132 database for 18S and 16S, and on the MIDORI-UNIQUE database subsampled to marine taxa for COI.

Discussion

ASVs vs OTUs: a choice depending on taxon of interest and research question

ASVs have recently been advocated to replace OTUs "as the standard unit of marker-gene analysis and reporting" [START_REF] Callahan | Exact Sequence Variants Should Replace Operational Taxonomic Units in Marker-Gene Data Analysis[END_REF]): an advice for microbiologists that may not apply when studying metazoans. Life histories of organisms, together with intrinsic properties of marker genes, determine the level of intragenomic and intraspecific diversity. Metazoans are well known to exhibit variable and sometimes very high intraspecific polymorphism. This intraspecific variation is a recognised problem in metabarcoding, known to generate spurious clusters [START_REF] Brown | Divergence Thresholds and Divergent Biodiversity Estimates: Can Metabarcoding Reliably Describe Zooplankton Communities?[END_REF], especially in the COI barcode marker. Indeed, this gene region has increased intragenomic variation due to its high evolutionary rate (Machida and Knowlton 2012;Machida et al. 2012), but also due to heteroplasmy and the abundance of pseudogenes, such as NUMTs, playing an important part of the supernumerary OTU richness in COImetabarcoding [START_REF] Bensasson | Mitochondrial Pseudogenes: Evolution's Misplaced Witnesses[END_REF][START_REF] Song | Many Species in One: DNA Barcoding Overestimates the Number of Species When Nuclear Mitochondrial Pseudogenes Are Coamplified[END_REF]. Concerted evolution, a common feature of SSU rRNA markers such as 16S [START_REF] Hashimoto | Rates and Consequences of Recombination between RRNA Operons[END_REF][START_REF] Klappenbach | Rrndb: The Ribosomal RNA Operon Copy Number Database[END_REF] and 18S [START_REF] Carranza | Evidence That Two Types of 18S RDNA Coexist in the Genome of Dugesia (Schmidtea) Mediterranea (Platyhelminthes, Turbellaria, Tricladida)[END_REF], limits the amount of intragenomic polymorphism. In metazoans, a lower level of diversity is thus expected for 18S than for COI. This is reflected in the lower ASV (DADA2) to OTU (DADA2+swarm) ratios observed here for 18S (1.4 -2.5) compared to COI (2.3 -3.2), at clustering d-values comprised between one and seven (Table S7),

underlining the different influence -and importance -of clustering on these loci, and the need for a versatile, marker by marker choice for clustering parameters.

The results on the mock samples showed that even single individuals produced very different numbers of ASVs, suggesting that ASV-centred datasets do not accurately reflect species composition in metazoans. Intragenomic and intraspecific polymorphism are highly variable across taxa [START_REF] Plouviez | Comparative Phylogeography among Hydrothermal Vent Species along the East Pacific Rise Reveals Vicariant Processes and Population Expansion in the South[END_REF][START_REF] Teixeira | High Connectivity across the Fragmented Chemosynthetic Ecosystems of the Deep Atlantic Equatorial Belt: Efficient Dispersal Mechanisms or Questionable Endemism?[END_REF], as confirmed by the very variable decrease in cluster numbers observed with clustering in this study for different phyla (Fig. S1). The taxonomic compositions of samples based on ASVs may thus reflect genetic rather than species diversity. This distinction is important to keep in mind, as the species, i.e.

"a lineage or group of connected lineages with a distinct role" [START_REF] Freudenstein | Biodiversity and the Species Concept -Lineages Are Not Enough[END_REF], constitutes the core of biodiversity inventories for biological and ecological studies. The species is a core concept in ecology and evolution that helps organizing agriculture, trade, and industry (e.g. species used for the production of biomaterial), as well as measuring the impact of human activity on Earth's ecosystems (e.g. biomarker taxa and pathogenic or invasive species). While biotic diversity can be valued and assessed at various levels, including that of the individual organism and the genetic locus, many theoretical and applied developments in ecology are deeply rooted in the species concept, and species richness, while not perfect, remains an essential metric [START_REF] Freudenstein | Biodiversity and the Species Concept -Lineages Are Not Enough[END_REF].

Clustering ASVs into OTUs alleviated the numerical inflation in the mock samples, but some species still produced more than one OTU, even at d-values as high as d = 11-13. While clustering improved numerical results in the mock communities, it led to poorer taxonomic assignments, for e.g., the vesicomyid bivalves only being identified up to class-level in clustered datasets with both loci. With 18S, clustering at d-values > 1 even led to the loss of the shrimp species Chorocaris sp., which was merged to the closely related A. muricola (Table 1).

Similarly, a d-value at 11 led to significantly lower OTU numbers than any other tested d-value

for both ribosomal loci (Table 2), explaining the much higher ASV to OTU ratios observed (4.1 -4.4, Table S7). When studying natural habitats, very likely to harbour closely related cooccurring species, clustering at d-values higher than 1 is thus likely to lead to the loss of true species diversity, particularly in taxa known to be poorly resolved (e.g. cnidarians with COI, Hebert et al. 2003), and in general with markers having lower taxonomic resolution such as 18S.

The reproductive mode and pace of selection in microbial populations may lead to locally lower levels of intraspecific variation than those expected for metazoans. Prokaryotic alpha diversity was however also affected by the clustering of ASVs (Fig. 1), supporting the estimation of a 2.5-fold greater number of 16S rRNA variants than the actual number of bacterial "species" [START_REF] Acinas | Divergence and Redundancy of 16S RRNA Sequences in Genomes with Multiple Rrn Operons[END_REF]). The significant decrease in the number of OTUs after clustering at d = 1 (Table 2, Fig. 1, decrease of ~30%) suggests the occurrence of very closely related 16S rRNA sequences, possibly belonging to the same ecotype/species. Such entities may still be important to define in studies aiming for example at identifying species associations (i.e. symbiotic relationships) across large distances and ecosystems, where drift or selection can lead to slightly different ASVs in space and time, with their function and association remaining stable.

Finally, apart from alpha diversity estimates, clustering also affected the resolution of ecological patterns in ribosomal loci when d-values were higher than 1 (Fig. 2). This can be explained by the fact that clustering gives more weight to large distinct OTUs compared to many small (i.e. with low read numbers) ASVs. OTUs and thus suggesting an ecological signal in fine-scale sequence variants. This is in accordance with other studies reporting differences in beta diversity patterns in ASV vs OTU datasets for ribosomal loci, when large divergence thresholds were used for clustering [START_REF] Xiong | Testing Clustering Strategies for Metabarcoding-Based Investigation of Community-Environment Interactions[END_REF][START_REF] Bokulich | Quality-Filtering Vastly Improves Diversity Estimates from Illumina Amplicon Sequencing[END_REF]. This also reveals the interdependence of alpha and beta diversity components, so that clustering ASVs into OTUs and thereby reducing alpha diversity, leaves more space for beta diversity to be expressed, as observed in both population genetics (Jost 2008;[START_REF] Beaumont | Evaluating Loci for Use in the Genetic Analysis of Population Structure[END_REF] and community analysis [START_REF] Jost | Partitioning Diversity into Independent Alpha and Beta Components[END_REF]. Overall, these results confirm the advantage of combining error-correction tools with clustering and postclustering curation tools, as this allows access to both interspecies and intraspecies information [START_REF] Turon | From Metabarcoding to Metaphylogeography: Separating the Wheat from the Chaff[END_REF].

Importance of parameter adjustment for LULU curation

LULU curation proved effective in limiting the number of multiple clusters produced by single individuals in the mock samples, confirming its efficiency to correct for intragenomic diversity (Table 1). Moreover, the fact that the number of unexpected clusters ("Others", Table 1) was not affected by LULU curation also shows that LULU specifically removes spurious OTUs and not true species diversity. However, careful adjustment of LULU parameters was needed, particularly for the minimum ratio, as at default level (1) it led to the loss of up to two mock species with 18S. This need for relaxed minimum ratio values can be explained by the non-ideal design of the mock samples. Indeed, LULU should be applied on datasets containing as many samples as possible, which should have compositional similarities (i.e. overlapping species lists). If this is not the case, LULU will work as a pure clustering algorithm, at defined minimum match levels. Here, all species were co-occurring in the mock samples at consistent abundance ratios and some mock species were not occurring (or rarely) in environmental samples. For those, random amplification biases leading to consistently low read numbers in both mock samples resulted in LULU merging them to closely related mock species. Increasing the minimum ratio, i.e. the expected minimal abundance ratio between a true OTU and an associated spurious sequence, allowed detecting all mock species with 18S. With minimum ratio at 100, one mock species (the gastropod Paralepetopsis sp) was still lost when minimum match was at 84%, which could indicate that minimum match at 90% is more appropriate for 18S. However, as all mock species were retained at both minimum match levels with minimum CHAPTER II BIOINFORMATIC PIPELINE COMPARISONS 79 ratio at 1000, the loss of that species at 84% may also just reflect the non-ideal mock design (Paralepetopsis sp. being very poorly amplified by 18S, it got merged to a bivalve OTU as their similarity was greater than 84%). Given the fact that 18S is evolving much slower than COI, this marker is taxonomically much less resolutive and phylum-level similarity is at ~86% [START_REF] Stefanni | Multi-Marker Metabarcoding Approach to Study Mesozooplankton at Basin Scale[END_REF]. As error OTUs are produced within each individual, it is reasonable to think that their similarity to their parent OTUs will be greater than phylum-level similarity, justifying the use of 90% minimum match. This increased minimum match also has the added benefit to decrease calculation time on large datasets. For COI, although results in the mock samples showed the best performance at minimum ratio of 1 and little effect of the minimum match parameter (90% vs 84%), both minimum match levels resulted in significantly different OTU numbers in the environmental samples (Table 2, Fig. 1). This was not the case for 18S, where both 84% and 90% minimum match resulted in similar numbers of OTUs in the environmental samples (minimum ratio at 100). Thus, increasing the minimum ratio parameter is essential for not losing species in sample-poor datasets, and will be more correct than adjusting the minimum match.

The mock communities used in this study, apart from being taxonomically limited to just 10 species, did unfortunately not contain several haplotypes of the same species (intraspecific variation). This could explain the comparable results obtained with LULU curation of ASVs and LULU curation of OTUs in the mock samples, and lead to the hasty conclusion of a limited effect of clustering. Communities detected in environmental samples are much more complex, likely comprising many different haplotypes of the same species. However, LULU curation of ASVs cannot substitute clustering algorithms to account for natural haplotype diversity. Indeed, not all haplotypes co-occur and when they do so, they may vary in proportion and dominance relationships, making clustering the best tool to account for natural haplotypic diversity. This is in line with LULU developers [START_REF] Frøslev | Algorithm for Post-Clustering Curation of DNA Amplicon Data Yields Reliable Biodiversity Estimates[END_REF], who recommend clustering ASVs for addressing the average intraspecific variation of the target group, and subsequent curation with LULU. In the environmental samples, LULU curation of the ASV datasets led to significantly more OTUs than LULU curation of swarm-clustered OTUs with both metazoan loci (Table 2).

This indicates that LULU curation merges less ASVs than the amount grouped through clustering, and highlights the different purposes of both tools, LULU effectively removing spurious OTUs, while clustering allows removing haplotype diversity.

Taxonomic resolution and assignment quality

The COI locus allowed the detection of all ten species present in the mock samples, compared to seven in the 18S dataset (Table 1). This locus also provided much more accurate assignments, most of them correct at the genus (and species) level, confirming that COI uncovers more metazoan species and offers a better taxonomic resolution than 18S [START_REF] Tang | The Widely Used Small Subunit 18S RDNA Molecule Greatly Underestimates True Diversity in Biodiversity Surveys of the Meiofauna[END_REF][START_REF] Clarke | Effect of Marker Choice and Thermal Cycling Protocol on Zooplankton DNA Metabarcoding Studies[END_REF]Andújar et al. 2018b). Our results also support approaches combining nuclear and mitochondrial markers to achieve more comprehensive biodiversity inventories [START_REF] Cowart | Metabarcoding Is Powerful yet Still Blind: A Comparative Analysis of Morphological and Molecular Surveys of Seagrass Communities[END_REF][START_REF] Drummond | Evaluating a Multigene Environmental DNA Approach for Biodiversity Assessment[END_REF]Zhan et al. 2014). Indeed, strong differences exist in amplification success among taxa [START_REF] Bhadury | Molecular Detection of Marine Nematodes from Environmental Samples: Overcoming Eukaryotic Interference[END_REF][START_REF] Carugati | Metagenetic Tools for the Census of Marine Meiofaunal Biodiversity: An Overview[END_REF], exemplified by nematodes, which are well detected with 18S but not with COI [START_REF] Bucklin | DNA Barcoding of Marine Metazoa[END_REF]). The 18S barcode marker performed better in the detection of nematodes, annelids, platyhelminths, and xenacoelomorphs while COI mostly detected cnidarians, molluscs, and poriferans (Fig. 3, Fig. S1), highlighting the complementarity of these two loci. This high complementarity of COI and 18S in terms of targeted taxa also supports the approach taken by [START_REF] Stefanni | Multi-Marker Metabarcoding Approach to Study Mesozooplankton at Basin Scale[END_REF], indeed subsampling each gene dataset for its "best targeted phyla" and subsequently combining both seems to be a very efficient way to produce comprehensive and non-redundant biodiversity inventories.

Finally, compared to BLAST assignments, similar taxonomic assignments were observed using the RDP Bayesian Classifier on the mock samples for 18S and for COI when using the MIDORI-UNIQUE marine-only database (Fig. S3). Poor performance of RDP using the full MIDORI database is likely due to the size of the database, and to its low coverage of deep-sea species. Indeed, small databases, taxonomically similar to the targeted communities, and with sequences of the same length as the DNA fragment of interest, are known to maximise accurate identification [START_REF] Macheriotou | Metabarcoding Free-Living Marine Nematodes Using Curated 18S and CO1 Reference Sequence Databases for Species-Level Taxonomic Assignments[END_REF][START_REF] Risso | Improved Taxonomic Assignment of Human Intestinal 16S RRNA Sequences by a Dedicated Reference Database[END_REF]. The problem of underrepresentation of deep-sea taxa is especially apparent with the BLAST assignments, which generally displayed low identities to sequences in databases, especially for COI (Fig. 3). Minimum similarities of 80% for COI and 86% for 18S as cut-off values for metazoans have been used to improve the taxonomic quality of metazoan metabarcoding datasets [START_REF] Stefanni | Multi-Marker Metabarcoding Approach to Study Mesozooplankton at Basin Scale[END_REF]). However, phylogenies of marine invertebrates are characterised by high levels of species divergence (up to ~30%), even within genera [START_REF] Zanol | Phylogeny of the Bristle Worm Family Eunicidae (Eunicida, Annelida) and the Phylogenetic Utility of Noncongruent 16S, COI and 18S in Combined Analyses[END_REF]). Studies on deep-sea taxa have found that some invertebrate species had COI sequences diverging more than 20% from any other species present in molecular databases [START_REF] Shank | Miocene Radiation of Deep-Sea Hydrothermal Vent Shrimp (Caridea: Bresiliidae): Evidence from Mitochondrial Cytochrome Oxidase Subunit I[END_REF][START_REF] Herrera | Evolutionary and Biogeographical Patterns of Barnacles from Deep-Sea Hydrothermal Vents[END_REF]. At present, it thus seems difficult to work at taxonomic levels beyond phylum-level with deep-sea metabarcoding data when using large public databases. When using the reduced marine-only COI database, RDP provided the most accurate assignments in the mock samples (Fig. S3). However, filtering to accurate phylum-level bootstraps (≥ 80) drastically reduced the number of OTUs in the overall dataset (1% of OTUs left, Table S8). The development of custom-built marine RDP training sets, without overrepresentation of terrestrial species, is therefore needed for this Bayesian assignment method to be effective on deep-sea datasets. With reduced and more specific databases, removing clusters with phylum-level bootstraps < 80 should be an efficient way to increase taxonomic quality of deep-sea metabarcoding datasets. At present, if accurate taxonomic assignments are sought while using universal primers, we advocate assigning taxonomy in two steps: first, using BLAST and a large database including all phyla amplifiable by the primer set as BLAST performs better than RDP in terms of speed. The clusters belonging to the groups of interest can then be extracted and re-assigned using RDP and a smaller, taxonspecific database.

Conclusions and perspectives

Using mock communities and environmental samples, we evaluate several recent algorithms and assess their capacity to improve the quality of molecular biodiversity inventories of metazoans and prokaryotes. Our results support the fact that ASV data should be produced and communicated for reusability and reproducibility following the recommendations of [START_REF] Callahan | Exact Sequence Variants Should Replace Operational Taxonomic Units in Marker-Gene Data Analysis[END_REF]. This is especially useful in large projects spanning wide geographic zones and time scales, as different ASV datasets can easily be merged a posteriori, and clustered if necessary afterwards. However, our results confirm that both ASVs and OTUs describe relevant, yet different levels of biotic diversity. ASVs comprehensively describe genetic diversity (incl. intraspecies) while OTUs more accurately reflect interspecies diversity.

Considering 16S polymorphism observed in prokaryotic species [START_REF] Acinas | Divergence and Redundancy of 16S RRNA Sequences in Genomes with Multiple Rrn Operons[END_REF]) and the possible geographic segregation of their populations, using OTUs may also be suitable in prokaryotic datasets, for example in studies screening for species associations, as symbionts may be prone to differential fixation through enhanced drift [START_REF] Shapiro | What Is Speciation?" Edited by Matic, I[END_REF]).

This study emphasized that swarm clustering needs to be adapted to each genetic marker and taxonomic compartment, in order to identify an optimal balance between the correction for spurious clusters and the loss of species. Specifically, d-values > 1 appeared to be less appropriate with 18S for metazoans. Our results also demonstrated that LULU effectively curates metazoan biodiversity inventories obtained through metabarcoding. They underline the CHAPTER II BIOINFORMATIC PIPELINE COMPARISONS 82 need to adapt parameters for LULU curation, in particular the minimum ratio level in the case of sample-poor datasets, where co-occurrence and abundance patterns may be distorted.

Finally, this study also showed that accurate taxonomic assignments of deep-sea species can be obtained with the RDP Bayesian Classifier, but only with reduced databases containing ecosystem-specific sequences.

Chapter III. An assessment of environmental metabarcoding protocols aiming at favouring contemporary biodiversity in inventories of deep-sea communities

Published as: The ability to capture all taxa representative of a given community with a minimal set of barcoding primers is a particular challenge in marine sediments due to the high abundance of taxa that are difficult to amplify by PCR (especially the meiofaunal nematodes). To attempt minimizing the influence of primer bias, a nuclear and a mitochondrial primer set were selected to capture the broadest range of metazoan taxa possible (mitochondrial COI and the 18S V1-V2 rRNA marker genes), and combined with two additional nuclear primers targeting unicellular eukaryotes (18S V4 rRNA marker gene) and prokaryotes (16S V4-V5 rRNA marker gene).

However, extracellular DNA is abundant in marine sediments and has been estimated to account for up to 50% of the total DNA pool. This DNA (benthic and potentially non-benthic) may be archived in deep-sea sediments due to lower degradation rates and thus significantly bias biodiversity inventories towards describing past, rather than present communities [START_REF] Torti | Origin, Dynamics, and Implications of Extracellular DNA Pools in Marine Sediments[END_REF][START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF][START_REF] Corinaldesi | Extracellular DNA as a Genetic Recorder of Microbial Diversity in Benthic Deep-Sea Ecosystems[END_REF]. To evaluate the bias produced by this ancient DNA (aDNA), biodiversity inventories produced by five molecular methods were compared at five sites from various habitats (seamount, mud volcano, hydrothermal vent).

Using commercially available extraction kits, we investigated the impacts of two methods aiming at removing small extracellular DNA fragments on biodiversity estimates of pro-and eukaryotes from deep-sea sediments, and compared inventories obtained from co-extracted DNA and RNA in order to evaluate whether RNA may be more suitable for describing the live community compartment.

Question addressed:

Does extracellular DNA archived in deep-sea sediments lead to significant bias in molecular biodiversity inventories of prokaryotes, unicellular eukaryotes, and metazoans?

Résumé en français

Les fonds abyssaux couvrent plus de 50%

Abstract

The abyssal seafloor covers more than 50% of planet Earth and is a large reservoir of still mostly undescribed biodiversity. It is increasingly targeted by resource-extraction industries and yet is drastically understudied. In such remote and hard-to-access ecosystems, environmental DNA (eDNA) metabarcoding is a useful and efficient tool for studying biodiversity and implementing environmental impact assessments. Yet, eDNA analysis outcomes may be biased towards describing past rather than present communities as sediments contain both contemporary and ancient DNA.

Using commercially available kits, we investigated the impacts of five molecular processing methods on eDNA metabarcoding biodiversity inventories targeting prokaryotes (16S), unicellular eukaryotes (18S-V4), and metazoans (18S-V1, COI). As the size distribution of ancient DNA is skewed towards small fragments, we evaluated the effect of removing short DNA fragments via size-selection and ethanol reconcentration using eDNA extracted from ~10

g of sediment at five deep-sea sites. We also compare communities revealed by eDNA and environmental RNA (eRNA) co-extracted from ~2 g of sediment at the same sites.

Results show that removing short DNA fragments does not affect alpha and beta diversity estimates in any of the biological compartments investigated. Results also confirm doubts regarding the possibility to better describe live communities using eRNA. With ribosomal loci, eRNA, while resolving similar spatial patterns than co-extracted eDNA, resulted in significantly higher richness estimates, supporting hypotheses of increased persistence of ribosomal RNA (rRNA) in the environment and unmeasured bias due to over-abundance of rRNA and RNA release. With the mitochondrial locus, eRNA detected lower metazoan richness and resolved fewer spatial patterns than co-extracted eDNA, reflecting high messenger RNA lability. Results also highlight the importance of using large amounts of sediment (≥10 g) for accurately surveying eukaryotic diversity.

We conclude that eDNA should be favoured over eRNA for logistically realistic, repeatable, and reliable surveys, and confirm that large sediment samples (≥10 g) deliver more complete and accurate assessments of benthic eukaryotic biodiversity and that increasing the number of biological rather than technical replicates is important to infer robust ecological patterns.

Introduction

Environmental DNA (eDNA) metabarcoding is an increasingly used tool for biodiversity inventories and ecological surveys. Using high-throughput sequencing (HTS) and bioinformatic processing, it allows the detection or the inventory of target organisms using their DNA directly extracted from soil, water, or air samples (Taberlet et al. 2012a). As it does not require specimen isolation, it represents a practical and efficient tool in large and hard-to-access ecosystems, such as the marine realm. Besides allowing studying various biological compartments simultaneously, metabarcoding is also very effective for detecting diversity of small organisms (micro-organisms, meiofauna) largely disregarded in visual biodiversity inventories due to the difficulty of their identification based on morphological features [START_REF] Carugati | Metagenetic Tools for the Census of Marine Meiofaunal Biodiversity: An Overview[END_REF] .

The deep sea, covering more than 50% of Planet Earth, remains critically understudied, despite being increasingly impacted by anthropogenic activities and targeted by resourceextraction industries [START_REF] Ramirez-Llodra | Man and the Last Great Wilderness: Human Impact on the Deep Sea." Edited by Roopnarine[END_REF]. The abyssal seafloor is mostly composed of sedimentary habitats containing high numbers of small (< 1 mm) organisms, and characterized by high local and regional diversity [START_REF] Grassle | Deep-Sea Species Richness: Regional and Local Diversity Estimates from Quantitative Bottom Samples[END_REF][START_REF] Smith | A Riot of Species in An Environmental Calm[END_REF].

Given the increased time-efficiency offered by eDNA metabarcoding and its wide taxonomic applicability, this tool is a good candidate for large-scale biodiversity surveys and Environmental Impact Assessments (EIAs) in the deep-sea biome.

eDNA is a complex mixture of genomic DNA present in living cells, extra-organismal DNA, and extracellular DNA originating from the degradation of organic material and biological secretions [START_REF] Torti | Origin, Dynamics, and Implications of Extracellular DNA Pools in Marine Sediments[END_REF]. Extracellular DNA has been shown to be very abundant in marine sediments, representing 50-90% of the total DNA pool [START_REF] Corinaldesi | Extracellular DNA as a Genetic Recorder of Microbial Diversity in Benthic Deep-Sea Ecosystems[END_REF][START_REF] Dell'anno | Ecology: Extracellular DNA Plays a Key Role in Deep-Sea Ecosystem Functioning[END_REF]. However, this extracellular DNA compartment may not only contain DNA from contemporary communities. Indeed, nucleic acids can persist in marine sediments as their degradation rate decreases due to adsorption onto the sediment matrix (Corinaldesi, Beolchini, & Dell'Anno, 2008;[START_REF] Torti | Origin, Dynamics, and Implications of Extracellular DNA Pools in Marine Sediments[END_REF]. Low temperatures, high salt concentrations, and the absence of UV light are additional factors enhancing long-term archiving of DNA in deep-sea sediments [START_REF] Torti | Origin, Dynamics, and Implications of Extracellular DNA Pools in Marine Sediments[END_REF][START_REF] Nagler | Extracellular DNA in Natural Environments: Features, Relevance and Applications[END_REF]. Decreased rates of abiotic DNA decay can thus allow DNA persistence over millennial timescales. Indeed, up to 125,000-year-old ancient DNA (aDNA) has been reported in oxic and anoxic marine sediments at various depths [START_REF] Boere | Preservation Potential of Ancient Plankton DNA in Pleistocene Marine Sediments[END_REF] 2016) detected marked differences between RNA and DNA inventories for most eukaryotic groups, but found that both biomolecules detected similar patterns of ecological differentiation, concluding that "dead" DNA did not blur patterns of community structure. Laroche andcoworkers (2018, 2017) found stronger responses to environmental impact in alpha diversity measured with eRNA, while eDNA was better at detecting effects on community composition. Finally, long-term archived and even fossil RNA were also reported in sediment and soil [START_REF] Orsi | Deep Sequencing of Subseafloor Eukaryotic RRNA Reveals Active Fungi across Marine Subsurface Provinces[END_REF]Cristescu 2019), casting doubts as to its advantage over DNA to inventory contemporary biodiversity.

The design of a sound environmental metabarcoding protocol to inventory biodiversity on the deep seafloor relies on a better understanding of the potential influence of aDNA on the different taxonomic compartments targeted. Using commercially available kits based on 2 g and 10 g of sediment, we studied samples from five deep-sea sites encompassing three different habitats and spanning wide geographic ranges, in order to select an optimal protocol to survey contemporary benthic deep-sea communities spanning the tree of life. We analyse eDNA and eRNA extracts via metabarcoding, targeting the V4-V5 regions of the 16S ribosomal RNA 2) Compare eDNA and eRNA inventories resulting from the same samples via a 2 g joint extraction kit,

3) Assess the aforementioned kits in terms of repeatability and suitability for different taxonomic compartments.

CHAPTER III MOLECULAR METHODS COMPARISONS 92 2 Materials and methods

Collection of samples

Sediment cores were collected from five deep-sea sites from various habitats (mud volcano, seamounts, and an area close to hydrothermal vents, Table S1). Triplicate tube cores were collected with a multicorer or with a remotely operated vehicle at each sampling site The sediment cores were sliced into layers, which were transferred into zip-lock bags, homogenised, and frozen at -80°C on board before being shipped on dry ice to the laboratory. The first layer (0-1 cm) was used for the present analysis. In each sampling series, an empty bag was kept as a field control processed through DNA extraction and sequencing.

Nucleic acid extractions and molecular treatments

eDNA with the 10 g-PowerMax kit

DNA extractions were performed using ~10 g of sediment with the PowerMax Soil DNA Isolation Kit (MO BIO Laboratories, Inc.; Qiagen, Hilden, Germany). To increase the DNA yield, the elution buffer was left on the spin filter membrane for 10 min at room temperature before centrifugation. For field controls, the first solution of the kit was poured into the control zip lock, before following the usual extraction steps. DNA extracts were stored at -80°C.

Size-selection of eDNA extracts

Size-selection of total eDNA extracted as detailed above from ~10 g of sediment was carried out to remove small DNA fragments. NucleoMag NGS Clean-up and Size Select beads (Macherey-Nagel, Düren, Germany) were used at a ratio of 0.5X for removing DNA fragments < 1,000 bp from 500 µL of extracted eDNA. The target fragments were eluted from the beads with 100 µL elution buffer, and successful size-selection verified by electrophoresis on an Agilent TapeStation using the Genomic DNA High ScreenTape kit (Agilent Technologies, Santa Clara, CA, USA).

Ethanol reconcentration of eDNA extracts

A 3.5 mL aliquot of eDNA extracted from ~10 g of sediment was reconcentrated with 7 mL of 96% ethanol (EtOH) and 200 µl of 5 M sodium chloride (NaCl), according to the guidelines in the Hints and Troubleshooting Guide of the PowerMax Soil DNA Isolation Kit.

As this protocol does not include any incubation time, it favours large DNA fragments. The

DNA pellet was washed with 1 mL 70% EtOH, centrifuged again for 15 min at 2500 x g, and air-dried before being resuspended in 450 µL elution buffer.

Joint environmental DNA/RNA with the 2 g-RNeasy PowerSoil kit

Joint RNA/DNA extractions were performed with the RNA PowerSoil Total RNA Isolation Kit combined with the RNeasy PowerSoil DNA elution kit (MO BIO Laboratories, Inc.; Qiagen, Hilden, Germany). Between 3 and 5 g of wet and frozen sediment were used, following the manufacturer's suggestions for marine sediments (Table S2). Extraction controls were performed alongside sample extractions. The RNA pellet was resuspended in 60 µL of RNase/DNase-free water. Extracted RNA was then transcribed to first-strand complementary DNA (cDNA) using the iScript Select cDNA synthesis kit (Bio-Rad laboratories, CA, USA)

with its proprietary random primer mix. Quality control 16S-V4V5, 18S-V1, and COI PCRs were performed on the RNA extracts to test for potential DNA contamination.

PCR amplification and sequencing

Nucleic acid extracts were normalised to 0.25 ng/µL and 10 µL of standardized samples were used in PCR. Four primer pairs were used to amplify one mitochondrial and three rRNA barcode loci targeting metazoans (COI, 18S-V1), micro-eukaryotes (18S-V4) and prokaryotes (16S-V4V5, Table S3). [START_REF] Mahé | Swarm v2: Highly-Scalable and High-Resolution Amplicon Clustering[END_REF]) that aggregates sequences iteratively and locally around seed sequences based on d, the number of nucleotide differences, to determine coherent groups of sequences, independent of amplicon input order, allowing highly scalable and fine-scale clustering. ASVs were swarm clustered at d values of 4 for 18S-V1 and 6 for COI, using the FROGS pipeline [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF].

We chose to evaluate micro-eukaryote and prokaryote diversity at the ASV level due to its increasing use in the literature [START_REF] Callahan | Exact Sequence Variants Should Replace Operational Taxonomic Units in Marker-Gene Data Analysis[END_REF]. Although the use of OTUs may also be justified for microbial diversity depending on study objectives (Brandt, M. I. et al. 2020), we did not expect an alteration of alpha and beta diversity patterns between ASV and OTU levels for the different molecular treatments investigated. ASVs and OTUs were taxonomically assigned via BLAST+ (v2.6.0) based on minimum similarity and minimum coverage (-perc_identity 70 and -qcov_hsp 80). For ASVs, sequences obtained with DADA2 were subsequently assigned with blastn. For OTUs, BLAST assignment in FROGS was performed using the affiliation_OTU.py command. It is not uncommon for deep-sea taxa to have closest relatives in databases (even congenerics) exhibiting nucleotide divergence exceeding 20% [START_REF] Shank | Miocene Radiation of Deep-Sea Hydrothermal Vent Shrimp (Caridea: Bresiliidae): Evidence from Mitochondrial Cytochrome Oxidase Subunit I[END_REF][START_REF] Herrera | Evolutionary and Biogeographical Patterns of Barnacles from Deep-Sea Hydrothermal Vents[END_REF]. Considering our interest in diverse and poorly characterized communities, more stringent BLAST thresholds were thus not implemented at this stage. However, additional filters were performed during downstream bioinformatic processing described below, and taxonomic information was used at phylum-level, only when the assignment was deemed reliable at this taxonomic level. The Silva132 reference database was used for taxonomic assignment of rRNA marker genes [START_REF] Quast | The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools[END_REF], and MIDORI-UNIQUE [START_REF] Machida | Data Descriptor: Metazoan Mitochondrial Gene Sequence Reference Datasets for Taxonomic Assignment of Environmental Samples[END_REF]) was used for COI.

Molecular inventories were refined in R v.3.5.1 (R Core Team 2018). A blank correction was made using the decontam package v.1.2.1 [START_REF] Davis | Simple Statistical Identification and Removal of Contaminant Sequences in Marker-Gene and Metagenomics Data[END_REF], removing all clusters that were more prevalent in negative control samples than in true or mock samples. Unassigned and non-target clusters were removed. Additionally, for metazoan loci, all clusters with a terrestrial assignment (groups known to be terrestrial-only) were removed. Samples with fewer than 10,000 target reads were discarded. We performed an abundance renormalization to remove spurious ASVs/OTUs due to random tag switching [START_REF] Wangensteen | Metabarcoding Techniques for Assessing Biodiversity of Marine Animal Forests[END_REF]. The COI OTU table was further curated with LULU v.0.1 [START_REF] Frøslev | Algorithm for Post-Clustering Curation of DNA Amplicon Data Yields Reliable Biodiversity Estimates[END_REF] to limit the bias due to

pseudogenes, using a minimum co-occurrence of 0.93 and a minimum similarity threshold of 84%.

Statistical analyses

Sequence tables were analysed using R with the packages phyloseq v1. Homogeneity of multivariate dispersions were evaluated with the betapart package v.1.5.1 [START_REF] Baselga | Betapart : An R Package for the Study of Beta Diversity[END_REF], and statistical tests performed on balanced datasets for COI as dispersions were different between 2 g and 10g datasets (Table S5). Data were rarefied for metazoans and Hellinger-normalised for microbial data.

Differences in community compositions resulting from molecular processing were evaluated with Mantel tests (Jaccard and Bray-Curtis dissimilarities for metazoan and microbial taxa respectively; Pearson's product-moment correlation; 1000 permutations). Permutational multivariate analysis of variance (PERMANOVA) was performed on normalised datasets to evaluate the effect of molecular processing and site on community compositions, using the function adonis2 (vegan) with Jaccard dissimilarities (presence/absence) for metazoan and Bray-Curtis dissimilarities for prokaryotes and micro-eukaryotes. The rationale behind this choice is that metazoans are multicellular organisms of extremely varying numbers of cells, organelles, or ribosomal repeats in their genomes, and can also be detected through a diversity of remains. The number of reads can thus not be expected to reflect the abundance of detected OTUs. Significance was evaluated via marginal effects of terms, using 10,000 permutations with site as a blocking factor. Pairwise post-hoc comparisons were performed via the pairwiseAdonis package, with site as a blocking factor. Differences between samples were visualized via Principal Coordinates Analyses (PCoA) based on abovementioned dissimilarities.

Finally, taxonomic compositions in terms of cluster and read abundance were compared between molecular processing methods. In order to compare accurately phylum-level CHAPTER III MOLECULAR METHODS COMPARISONS 96 taxonomic compositions, datasets were subsampled to clusters having a minimum hit identity of 86% for rRNA loci and 80% for COI. These values were chosen as they represent approximate minimum identity for reliable phylum assignment [START_REF] Stefanni | Multi-Marker Metabarcoding Approach to Study Mesozooplankton at Basin Scale[END_REF].

Results

High-throughput sequencing results

A total of 70 million 18S-V1 reads, 61 million COI reads, 30 million 18S-V4 reads, and 45 million 16S-V4V5 reads were obtained from four Illumina HiSeq runs of pooled amplicon libraries built from triplicate PCR replicates of 75 sediment samples, 2 mock communities (for 18S-V1 and COI), 3 extraction blanks, and 2-4 PCR negative controls(Table S6). One to seven sediment samples failed amplification in each dataset. These were always coming from the same sampling sites (MDW-ST117 and MDW-ST38), and predominantly comprised RNA samples (Table S6). After bioinformatic processing, read numbers were reduced to 44 million for 18S-V1, 45 million for COI, 16 million for 18S-V4, and 24 million for 16S-V4V5 (Table S6). For eukaryote markers, fewer reads were retained in negative controls (2-64%) than in true or mock samples (49-83%), while the opposite was observed for prokaryotes with 16S-V4V5 (62% of reads retained in control samples against 49-57% in true samples). Negative control samples (extraction and PCR blanks) contained 0.001-0.6% of total processed reads, compared to 1.3-1.5% in a true samples.

DNA extracts obtained from the joint DNA/RNA protocol based on the 2-g kit produced fewer eukaryotic reads than DNA extracts from the 10-g kit, while similar yields were obtained for prokaryotes. RNA extracts produced more reads than DNA extracts with the ribosomal loci, while they produced fewer reads with the mitochondrial COI locus (Table S6).

After data refining, abundance renormalisation (Wangensteen and S6).

CHAPTER III MOLECULAR METHODS COMPARISONS 97

Alpha diversity between processing methods

Rarefaction curves showed a plateau was reached for all samples, suggesting an overall sequencing depth adequate to capture the diversity present (Fig. S1). Processing methods significantly affected the number of recovered eukaryote and prokaryote clusters, and significant variability among sites was detected for 18S-V1 and 18S-V4 (Table 1, Fig. S2).

Molecular processing designed to remove small DNA fragments (i.e. size-selection of DNA to remove fragments < 1,000 bp and ethanol reconcentration) did not significantly affect recovered cluster numbers obtained from eDNA extracted from 10 g of sediment, for any of the loci investigated (Fig. 1, Table 1, Tukey's HSD multiple comparisons tests, p>0.9).

Extracts based on the 2-g kit resulted in more variability, reflected by greater standard errors in mean recovered cluster numbers (15-26% of the mean for eukaryotes, 7-9% for prokaryotes) than in DNA extracts based on 10 g of sediment (8-11% for eukaryotes, 3-6% for prokaryotes).

DNA extracted using the 2-g kit recovered significantly fewer eukaryotic clusters than extracts based on ~10 g of sediment (Fig. 1, Table 1), a trend consistent across most taxa (Fig.

CHAPTER III MOLECULAR METHODS COMPARISONS 98

The joint RNA/DNA extracts shared 15% (COI) to 25% (18S-V1) of metazoan OTUs, 14% of protistan 18S-V4 ASVs, and 25% of prokaryotic 16S ASVs (Fig. S3). With COI, most unique OTUs were present in DNA extracts (74%), and RNA detected significantly fewer metazoan OTUs than co-extracted DNA (Fig. 1, mean of 44±12 vs. 113±27 respectively), a trend observed in most detected metazoan phyla (Fig. 2). Contrastingly, with ribosomal loci, most clusters were unique to RNA (56% for 18S-V1, 63% for 18S-V4, 45% for 16S, Fig. S3), which recovered significantly more clusters than co-extracted DNA (Fig. 1, Table 1). For prokaryotes, RNA extracts even detected significantly more ASVs than DNA extracts based on 10 g of sediment (Table 1, Fig. 1), a pattern observed in most prokaryotic clades, except for the Actinobacteria, Nanoarchaeaeota, Omnitrophicaeota, and the Thaumarchaeota (Fig. 2). For 18S-V4 and 18S-V1, RNA detected a cluster richness comparable to DNA-10 g extracts (Tukey's HSD multiple comparisons tests, p>0.16), yet, average cluster numbers per sample were higher in RNA than in DNA-10g extracts in numerous groups (Fig. 2).

Table 1. Changes in cluster richness and community structures with molecular processing method (DNA 10g: DNA extracts from ~10 g of sediment with the PowerMax Soil kit; DNA/RNA 2g: DNA/RNA extracts from ~2g of sediment with the RNeasy PowerSoil kit) and site, for the four studied genes. ANODEVs were performed on mixed models with negative binomial distributions using rarefied datasets. PERMANOVAs were calculated on normalised datasets by permuting 10,000 times with Site as a blocking factor, using Jaccard dissimilarities for 18S-V1 and COI, and Bray-Curtis dissimilarities for 18S-V4 and 16S. Significant p values are in bold, and significance codes are p<0.001: '***'; p<0.01: '**'; p<0.05: '*'.

Effect of molecular processing methods on beta-diversity patterns

PERMANOVA showed that, although site was the main source of variation among samples (accounting for 20 to 57% of variability), significant differences existed among molecular methods in terms of community structure for all loci investigated over and above any variation due to site (Table 1). Pairwise comparisons indicated no significant effect of small DNA fragment removal on revealed community composition (Table 1), and high and significant correlations in Mantel tests (r: 0.92-1.0, p=0.001) confirmed the minor effect of size-selection and ethanol reconcentration. Based on these results, the size-selected and ethanolreconcentrated DNA data were removed from further analyses, and community structures of the DNA-10g extracts were compared with those derived from co-extracted DNA/RNA using the 2g kit.

Pairwise comparisons showed significant differences in community structures between RNA and DNA for all markers analysed (Table 1). Ordinations, confirmed the predominant effect of site as the first two PCoA axes mostly resolved spatial effects (Fig. S4), but also revealed that communities detected by RNA differed from those detected by DNA (co-extracted DNA and DNA-10g), the level of differentiation varying among sites (Fig. 3). Pairwise comparisons also indicated significant differences in community structure between DNA extracts from the 2 g and 10 g kits (Table 1), possibly due to higher variability among replicate cores in the DNA-2g method as seen in ordinations (Fig. 3).

Extraction kit vs nature of nucleic acid

PERMANOVA of the dataset containing DNA-10g, DNA-2 g, and RNA-2g extracts confirmed that site was the predominant effect, explaining ~20% of variation for metazoans, 33% of variation for micro-eukaryotes, and 54% of variation for prokaryotes. The analysis also indicated that the differences observed between processing methods were predominantly due to the type of nucleic acid rather than the kit used for extraction. Nucleic acid nature (DNA vs RNA) led to significant differences among assemblages for all loci, while DNA extraction kit resulted in significant differences only for 18S-V1 and 18S-V4 (Table S7).

Figure 3. PCoA ordinations showing community differences between RNA and DNA molecular processing methods, using either RNA/DNA extracted jointly from ~2 g of sediment (RNA 2g/DNA 2g) or DNA extracted from ~10g of sediment (DNA 10g) in five deep-sea sites using four barcode markers targeting metazoans (COI, 18S-V1), micro-eukaryotes (18S-V4), and prokaryotes (16S). PCoAs were calculated using Jaccard dissimilarities for metazoans and Bray-Curtis dissimilarities for unicellular organisms. Inserts show pairwise PCoAs.

CHAPTER III MOLECULAR METHODS COMPARISONS 102

This supported observations in relative taxonomic compositions, which were more similar between samples based on DNA (Fig. 4), a pattern consistent across cores within each site (Fig. S5). Expectedly, when looking at read numbers, resolved taxonomic structures were also more similar among DNA-based methods (Fig. S6). Comparing read and cluster abundances revealed that relative taxonomic compositions based on read numbers (Fig. S6) were comparable to those based on cluster numbers (Fig. 4) for micro-eukaryotes and prokaryotes, and confirmed that this was not the case for metazoans.

Discussion

The aim of this study was to evaluate different molecular methods in order to select the most appropriate eDNA metabarcoding protocol to inventory contemporary deep-sea communities, with the lowest possible bias due to aDNA.

Using RNA rather than DNA to inventory contemporaneous communities has been suggested as a means of avoiding the bias due to long-term persistence of DNA in marine sediments. Indeed, RNA is only produced by living organisms and is thought to quickly degrade when released in the environment, due to spontaneous hydrolysis and the abundance of RNases [START_REF] Torti | Origin, Dynamics, and Implications of Extracellular DNA Pools in Marine Sediments[END_REF]. Expectedly, in our COI dataset, RNA resulted in fewer OTUs (Fig. 1) and detected fewer phyla (Fig. 2) than co-extracted DNA. Contrastingly, for ribosomal loci, RNA detected higher cluster numbers than co-extracted DNA (Fig. 1), resulting in more clusters per sample for most of the taxonomic groups detected (Fig. 2). In these joint datasets, 45-63% of clusters were unique to RNA (Fig. S2). These unique clusters were not singleton clusters as only up to 2.2% of them had fewer than three reads, even if 5-28% had fewer than ten reads (data not shown). Although proportions vary strongly among investigations, other studies using ribosomal loci have also reported increased recovery of OTUs in RNA datasets as well as considerable amounts of unshared OTUs between joint RNA and DNA data [START_REF] Guardiola | Spatio-Temporal Monitoring of Deep-Sea Communities Using Metabarcoding of Sediment DNA and RNA[END_REF]Laroche et al., 2017 and references therein).

This difference observed here between COI and ribosomal loci is likely related to the nature of the targeted RNA molecule. The rapid hydrolysis of RNA mostly applies to random coils (like messenger RNA), while helical conformations (including most types of RNA, such as ribosomal RNA, transfer RNA, viral genomic RNA, or ribozymes) are less prone to hydrolysis by water molecules [START_REF] Torti | Origin, Dynamics, and Implications of Extracellular DNA Pools in Marine Sediments[END_REF]. The degradation of rRNA is thus likely to be much slower than that of messenger RNA, which, combined with decreased digestion by RNases due to adsorption onto sediment particles [START_REF] Torti | Origin, Dynamics, and Implications of Extracellular DNA Pools in Marine Sediments[END_REF], makes long-term persistence of rRNA possible, and observed in sediments and even in fossils [START_REF] Orsi | Deep Sequencing of Subseafloor Eukaryotic RRNA Reveals Active Fungi across Marine Subsurface Provinces[END_REF]Cristescu 2019)..

Finally, the great abundance of RNA over DNA in living organisms (e.g. 20.5% vs 3.1% in E.

coli) may also favour its persistence in the environment. This is especially true for rRNA, which is represented in a cell's RNA pool as many times as there are ribosomes, while only being present in a few copies (10-150) in the genome [START_REF] Torti | Origin, Dynamics, and Implications of Extracellular DNA Pools in Marine Sediments[END_REF].

While RNA has been reported as an effective way to depict the active community compartment [START_REF] Baldrian | Active and Total Microbial Communities in Forest Soil Are Largely Different and Highly Stratified during Decomposition[END_REF]Lejzerowicz et al. 2013b;[START_REF] Pawlowski | Environmental Monitoring through Protist Next-Generation Sequencing Metabarcoding: Assessing the Impact of Fish Farming on Benthic Foraminifera Communities[END_REF], variation in activity levels between taxonomic groups as well as differences in life histories, life CHAPTER III MOLECULAR METHODS COMPARISONS 104 strategies, and non-growth activities may confound this interpretation and generate taxonomic bias [START_REF] Blazewicz | Evaluating RRNA as an Indicator of Microbial Activity in Environmental Communities: Limitations and Uses[END_REF]. Instead, DNA/RNA ratios might reflect different genomic architectures (variation in rDNA copy number) among taxonomic groups, rather than different relative activities [START_REF] Massana | Marine Protist Diversity in European Coastal Waters and Sediments as Revealed by High-Throughput Sequencing[END_REF]. Thus, environmental RNA data need to be interpreted with caution, as some molecular clusters could be overrepresented due to increased cellular activities [START_REF] Pochon | Wanted Dead or Alive? Using Metabarcoding of Environmental DNA and RNA to Distinguish Living Assemblages for Biosecurity Applications[END_REF]). This could explain the higher cluster numbers detected here for ribosomal loci with eRNA compared to eDNA for several taxa (Fig. 2).

Moreover, many of the unique RNA ASVs/OTUs may be artefacts from the reverse transcription of RNA to cDNA, a process known to generate errors that are difficult to measure and detect in bioinformatic analyses [START_REF] Laroche | Metabarcoding Monitoring Analysis: The Pros and Cons of Using Co-Extracted Environmental DNA and RNA Data to Assess Offshore Oil Production Impacts on Benthic Communities[END_REF]), but highlighted by the greater amounts of chimeras detected in RNA extracts with ribosomal loci (Table S6). This overestimation of RNA-based data will affect non-clustered data more than clustered datasets, in line with the results observed here for microbial ASVs and metazoan OTUs.

In terms of beta diversity patterns, although RNA and DNA detected significantly different communities (Table 1), DNA and RNA samples resolved similar spatial configurations, with samples clustering by site (Fig. 3). This is consistent with [START_REF] Guardiola | Spatio-Temporal Monitoring of Deep-Sea Communities Using Metabarcoding of Sediment DNA and RNA[END_REF], who also reported similar patterns of ecological differentiation between DNA and RNA in deep-sea sites although both datasets resolved different communities. Although the comparative study performed here targeted only the first 1 cm layer of sediment, the comparable results obtained by [START_REF] Guardiola | Spatio-Temporal Monitoring of Deep-Sea Communities Using Metabarcoding of Sediment DNA and RNA[END_REF] on 5 cm suggest these findings may be expanded to deeper layers of sediments. However, spatial variation was more pronounced with DNA samples for eukaryotes, which is congruent with [START_REF] Laroche | Metabarcoding Monitoring Analysis: The Pros and Cons of Using Co-Extracted Environmental DNA and RNA Data to Assess Offshore Oil Production Impacts on Benthic Communities[END_REF], who suggested that eDNA may be more reliable for assessing differences in community composition.

Thus, due to its suspected persistence in the environment, and the unknown but potentially additional sources of bias suspected here, using eRNA for metabarcoding of deep-sea sediments does not seem to effectively address the problem of aDNA, and even less so for ribosomal loci.

Other studies suggested that a more efficient way to deal with aDNA may be to use joint RNA and DNA datasets, and trim for shared OTUs [START_REF] Laroche | Metabarcoding Monitoring Analysis: The Pros and Cons of Using Co-Extracted Environmental DNA and RNA Data to Assess Offshore Oil Production Impacts on Benthic Communities[END_REF][START_REF] Pochon | Wanted Dead or Alive? Using Metabarcoding of Environmental DNA and RNA to Distinguish Living Assemblages for Biosecurity Applications[END_REF]. This is however particularly stringent (given the low shared OTU proportions observed in this and other studies), and may result in a substantial number of false negatives. With COI, while mRNA may be more effectively targeting living organisms, the approach remains confronted with the taxonomic bias mentioned above, combined with higher in vitro lability of mRNA CHAPTER III

MOLECULAR METHODS COMPARISONS 105

making it more challenging to work with (highlighted by the increased failure of RNA extracts in this study, Table S6).

Removing small DNA fragments via size-selection (removing fragments < 1,000 bp) or ethanol reconcentration did not affect recovered cluster numbers in any of the biological compartments investigated (Fig. 1). The methods also did not result in any significant difference in community structures (Table 1), suggesting that small, likely ancient, DNA fragments have a negligible impact on biodiversity inventories produced through eDNA metabarcoding. This finding is in line with results from the deep-sea [START_REF] Guardiola | Spatio-Temporal Monitoring of Deep-Sea Communities Using Metabarcoding of Sediment DNA and RNA[END_REF][START_REF] Ramírez | Minimal Influence of Extracellular DNA on Molecular Surveys of Marine Sedimentary Communities[END_REF]) and various other habitats [START_REF] Lennon | How, When, and Where Relic DNA Affects Microbial Diversity[END_REF], which showed no evidence that spatial patterns were blurred by ''dead'' DNA persistence, and suggested a minimal effect of extracellular DNA on estimates of taxonomic and phylogenetic diversity.

None of the methods evaluated in the present study remove DNA not enclosed in living cells (e.g. DNA in organelles, DNA from dead cells…). It is still unclear how long DNA can remain intracellular after cell death or within organelles. Future research quantifying the rate at which "dead" intracellular DNA becomes extracellular and degraded, and investigation of deeper layers of sediment, will be valuable to estimate the potential bias of archived intracellular DNA in eDNA metabarcoding inventories of extant communities. However, there is increasing evidence that DNA from non-living cells is mostly contemporary [START_REF] Lennon | How, When, and Where Relic DNA Affects Microbial Diversity[END_REF]. This ability to detect extant taxa that were not present in the sample at the time of collection highlights the capacity of eDNA metabarcoding to detect local presence of organisms even from their remains or excretions, and even with a small amount of environmental material.

It remains to be elucidated whether more cost and time effective extraction protocols specifically targeting extracellular DNA offer similar ecological resolution as total DNA kits.

This is suggested to be the case for terrestrials soils [START_REF] Bienhold | Diversity and Biogeography of Bathyal and Abyssal Seafloor Bacteria[END_REF]Taberlet et al. 2012b), although authors have highlighted that conclusions from these studies should be interpreted with caution as results might be influenced by actively released and ancient DNA [START_REF] Nagler | Extracellular DNA in Natural Environments: Features, Relevance and Applications[END_REF]. The only available study testing this in the deep-sea showed that richness patterns were strikingly different in several metazoan phyla between extracellular DNA and total DNA. The authors suggested this to be the result of activity bias: sponges and cnidarians were overrepresented in the extracellular DNA pool because they continuously expel DNA, while nematodes were underrepresented as their cuticles shield DNA [START_REF] Guardiola | Spatio-Temporal Monitoring of Deep-Sea Communities Using Metabarcoding of Sediment DNA and RNA[END_REF]. As this comparison was performed on samples collected in two consecutive years, differences observed CHAPTER III MOLECULAR METHODS COMPARISONS 106 may partly result from temporal variation. However, another study of shallow and mesobenthic macroinvertebrates showed that targeting solely the extracellular eDNA compartment of marine sediments led to the detection of more than 100 taxa fewer than bulk metabarcoding or morphology, suggesting that extracellular DNA may not be adequate for marine sediments [START_REF] Aylagas | Benchmarking DNA Metabarcoding for Biodiversity-Based Monitoring and Assessment[END_REF].

Larger amounts of sediment (≥10g) allowed detecting significantly more eukaryotic clusters. This was not true for prokaryotes, for which both ~2 and ~10 g of sediment detected similar numbers of ASVs (Table 1, Fig. 1). It may be suggested that in the joint RNA/DNA kit, DNA elution occurring after RNA elution induces partial DNA loss. However, such effect would be expected to equally affect eu-and prokaryotes, which was not the case here, supporting the fact that quantity of starting material significantly affects results for eukaryotes.

The importance of adjusting the amount of starting material to the biological compartment investigated has already been documented [START_REF] Creer | The Ecologist's Field Guide to Sequence-Based Identification of Biodiversity[END_REF][START_REF] Dopheide | Impacts of DNA Extraction and PCR on DNA Metabarcoding Estimates of Soil Biodiversity[END_REF], and this study confirms that while 2-5 g of deep-sea sediment may be enough to capture prokaryote diversity, microbial eukaryotes and metazoans are more effectively surveyed with larger sediment volumes.

Finally, the ~2 g protocols were generally associated to higher variability among replicate cores for all loci investigated (Fig. 1, Fig. 3). This variability increases confidence intervals, reduces statistical power, and increases the risk of not identifying differences among communities, and thus impacts in EIA studies (Type II errors). Small-scale (cm to metres) patchiness has often been reported in the deep-sea [START_REF] Grassle | Deep-Sea Species Richness: Regional and Local Diversity Estimates from Quantitative Bottom Samples[END_REF]Smith, C. R. and Snelgrove 2002;[START_REF] Lejzerowicz | Patchiness of Deep-Sea Benthic Foraminifera across the Southern Ocean: Insights from High-Throughput DNA Sequencing[END_REF]. While technical (PCR) replicates allow increasing taxon detection probability (decrease false positives), this within-site variability can only be mitigated by collecting more biological replicates per sampling station, and using a sufficiently high amount of starting material to extract nucleic acids.

Chapter

CHAPTER IV SAMPLING METHODS COMPARISONS 109

Size-class sorting such as sieving or elutriation is usually performed on sediment samples in order to split the organisms by size and facilitate morphological characterization of meiofauna and macrofauna. For metabarcoding approaches, it also has the advantage of limiting the over dominance of large organisms in DNA extracts. However, sieving requires larger volumes of sediment and is very time-consuming, and studies have found that the use of nonsieved material does not significantly alter metazoan diversity patterns [START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF],

suggesting DNA dominance of large bodied taxa does not result in very important biases.

Besides, for logistic reasons, the analysis of non-sieved samples is preferable to (1) minimize on-board processing for the team involved, (2) minimize risks of contaminations, and (3) keep the extracellular DNA for potential future studies.

Application of eDNA metabarcoding on deep-sea aboveground water could be useful to evaluate dispersal capacities of benthic organsims as well as benthopelagic diversity. However, sampled water volume, a crucial aspect for efficient species detection, has been variable among studies and it remains unclear whether small volumes (1-2 L) are sufficient for species detection in the deep-sea.

Introduction

Environmental DNA (eDNA) metabarcoding is an increasingly used tool for non-invasive and rapid biodiversity surveys and impact assessments. Using high-throughput sequencing (HTS) and bioinformatic processing, target organisms are detected using their DNA directly extracted from soil, water, or air samples (Taberlet et al. 2012a). Covering more than 50% of Planet Earth, the deep seafloor is mostly comprised of sedimentary habitats, characterised by a predominance of small organisms [START_REF] Rex | Global Bathymetric Patterns of Standing Stock and Body Size in the Deep-Sea Benthos[END_REF][START_REF] Snelgrove | Getting to the Bottom of Marine Biodiversity: Sedimentary Habitats[END_REF]) difficult to identify based on morphological features [START_REF] Carugati | Metagenetic Tools for the Census of Marine Meiofaunal Biodiversity: An Overview[END_REF], and by high local and regional diversity [START_REF] Grassle | Deep-Sea Species Richness: Regional and Local Diversity Estimates from Quantitative Bottom Samples[END_REF]Smith, C. R. and Snelgrove 2002;[START_REF] Hauquier | Distribution of Free-Living Marine Nematodes in the Clarion-Clipperton Zone: Implications for Future Deep-Sea Mining Scenarios[END_REF]. Given its increased time-efficiency and its wide taxonomic applicability, eDNA metabarcoding is thus a good candidate for large-scale biodiversity surveys and Environmental Impact Assessments in the deep-sea biome.

Size-class sorting such as sieving or elutriation is usually performed on sediment samples in order to split the organisms by size and facilitate morphological characterization of meiofauna and macrofauna. For metabarcoding approaches, it also has the advantage of limiting the over dominance of large organisms, which may produce higher amounts of DNA template, resulting in an incomplete detection of small and abundant taxa. However, sieving requires large volumes of sediment, is very time-consuming, and previous studies have found that the use of non-sieved material does not significantly alter metazoan diversity patterns [START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF], suggesting that dominance of large (and often rare) taxa in the DNA extract does not result in important biases. Besides, for logistic reasons, the use of non-sieved sediment samples is preferable to (1) minimize on-board processing time, (2) minimize risks of contamination, and (3) allow other future applications (e.g., characterization of microbial communities, RNA sequencing, and investigation of extracellular DNA).

Finally, studies from various marine habitats have reported that benthic taxa could be found in aboveground water (overlying water layer to 6.5 m above seafloor), possibly due to sediment resuspension and transport, but also to active dispersal [START_REF] Boeckner | Revisiting the Meiofauna Paradox: Dispersal and Colonization of Nematodes and Other Meiofaunal Organisms in Low-and High-Energy Environments[END_REF][START_REF] Klunder | A Molecular Approach to Explore the Background Benthic Fauna Around a Hydrothermal Vent and Their Larvae: Implications for Future Mining of Deep-Sea SMS Deposits[END_REF]Zhao et al. 2020). Application of eDNA metabarcoding on deep-sea aboveground water could thus be a convenient alternative to surface sediment collection, as it involves simplified sample processing and shipping, while additionally allowing investigating benthopelagic diversity and dispersal capacities of benthic organisms. However, distance above seafloor has been variable (0.5 m -6.5 m) among studies [START_REF] Boeckner | Revisiting the Meiofauna Paradox: Dispersal and Colonization of Nematodes and Other Meiofaunal Organisms in Low-and High-Energy Environments[END_REF][START_REF] Klunder | A Molecular Approach to Explore the Background Benthic Fauna Around a Hydrothermal Vent and Their Larvae: Implications for Future Mining of Deep-Sea SMS Deposits[END_REF]Zhao et al. 2020), and so has the water volume sampled (12 L -1,000 L). As the latter is a crucial CHAPTER IV SAMPLING METHODS COMPARISONS 113 aspect for efficient species detection [START_REF] Cantera | Optimizing Environmental DNA Sampling Effort for Fish Inventories in Tropical Streams and Rivers[END_REF], it remains unclear whether small volumes (< 10 L) are sufficient to obtain comprehensive species inventories in the deep-sea.

To evaluate the effect of sampling strategy on eDNA metabarcoding inventories targeting prokaryotes (16S V4-V5), unicellular eukaryotes (18S V4), and metazoans (18S V1-V2, COI) from deep-sea sediment and aboveground water, we compared biodiversity inventories resulting from 1) sieved vs. unsieved sediment and 2) on-board filtration of ~7.5 L of water collected with a sterile sampling box vs. in situ filtration of large volumes (~6,000 L) using a newly-developed pump.

Materials and methods

Sample collection

Sediment cores and water samples were collected from a continental slope site during the EssNaut16 cruise in the Mediterranean in April 2016 (Table S1). Sampling was carried out with a human operated vehicle (Nautile, Ifremer). Two sediment sampling methods were compared on triplicate tube cores, using the upper first centimetre sediment layer S2). PCR amplifications for each locus were carried out in triplicate in order to smooth intra-sample variance while obtaining sufficient amounts of amplicons for Illumina sequencing. PCR triplicates were pooled and amplicon libraries were prepared for sequencing by ligation of Illumina adapters on 100 ng of amplicons, following the Kapa Hifi HotStart NGS library Amplification kit (Kapa Biosystems, Wilmington, MA, USA). After quantification and quality control, library concentrations were normalized to 10 nM, and 8-9 pM of each library containing a 20% PhiX spike-in were sequenced on a HiSeq2500 (System User Guide Part # 15035786) instruments in a 250 bp paired-end mode. For sediment samples, this procedure was carried out on two DNA aliquots, leading to two triplicate amplicon libraries per sample. For water samples collected with the sampling box, the three size fractions were processed separately but, expectedly due to the differential size of micro-and macroorganisms, not all could be successfully amplified or sequenced for each locus. For metazoans, the data thus CHAPTER IV SAMPLING METHODS COMPARISONS 116 comprise the 2-20 µm and > 20 µm size fractions, while for microbial loci the data comprise the 0.2-2 µm and 2-20 µm size fractions.

Bioinformatic analyses

All bioinformatic analyses were performed using a Unix shell script, available on Gitlab along with specific parameters used for all metabarcoding markers, are given in Table S3.

Pairs of Illumina reads were corrected with DADA2 v. Variants (ASVs). Data from COI and 18SV1-V2, preferentially targeting metazoans, were further clustered into Operational Taxonomic Units (OTUs) with swarm v2 [START_REF] Mahé | Swarm v2: Highly-Scalable and High-Resolution Amplicon Clustering[END_REF] using the FROGS pipeline [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF]. Swarm v2 is a single-linkage clustering algorithm that aggregates sequences iteratively and locally around seed sequences based on d, the number of nucleotide differences, to determine coherent groups of sequences. This avoids a universal clustering threshold, which is particularly useful in highly biodiverse samples such as those analysed in this study. Metazoan ASVs were swarm-clustered at d=3 for 18S V1-V2 and d=6 for COI, which has been shown to be appropriate for evaluating species diversity in samples (Brandt, M. I. et al. 2020). We chose to evaluate unicellular eukaryote and prokaryote diversity at the ASV level due to their reproducibility and increasing use in the literature [START_REF] Callahan | Exact Sequence Variants Should Replace Operational Taxonomic Units in Marker-Gene Data Analysis[END_REF]. Although the use of OTUs may be justified for microbial diversity depending on study objectives (Brandt, M. I. et al. 2020), we did not expect a significant alteration of alpha and beta diversity patterns between ASV and OTU levels for the different sampling methods investigated.

Clusters were taxonomically assigned with BLAST+ (v2.6.0) based on minimum similarity (70%) and minimum coverage (80%). The Silva132 reference database was used for taxonomic assignment of the 16S V4-V5 and 18S V1-V2 rRNA marker genes [START_REF] Quast | The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools[END_REF]), PR2 v4.11 (Guillou et al. 2013) was used for 18S V4, and MIDORI-UNIQUE [START_REF] Machida | Data Descriptor: Metazoan Mitochondrial Gene Sequence Reference Datasets for Taxonomic Assignment of Environmental Samples[END_REF] reduced to marine taxa only was used for COI. Considering our interest in diverse and poorly characterized communities, more stringent BLAST thresholds were not implemented at this CHAPTER IV SAMPLING METHODS COMPARISONS 117

stage. Indeed, it is not uncommon for deep-sea taxa to have closest relatives in databases (even congenerics) exhibiting nucleotide divergence exceeding 20% [START_REF] Shank | Miocene Radiation of Deep-Sea Hydrothermal Vent Shrimp (Caridea: Bresiliidae): Evidence from Mitochondrial Cytochrome Oxidase Subunit I[END_REF][START_REF] Herrera | Evolutionary and Biogeographical Patterns of Barnacles from Deep-Sea Hydrothermal Vents[END_REF]. However, additional filters were performed during downstream bioinformatic processing described below, and only clusters with assignments reliable at phylum-level were retained in the analysis.

Molecular inventories were refined in R v.3.5.1 (R Core Team 2018). A blank correction was made using the decontam package v. 1.2.1 (Davis et al. 2018), removing all clusters that were more prevalent in negative control samples (PCR and extraction controls) than in true samples. After comparison, results from the technical duplicates produced for sediment samples were merged and read counts were summed for identical OTUs. Clusters unassigned at phylumlevel and non-target clusters were removed. Additionally, for metazoan loci, all clusters with a terrestrial assignment (groups known to be terrestrial-only) were removed. Samples were checked to ensure they had more than 10,000 target reads. Metazoan OTU tables were further curated with LULU v.0.1 [START_REF] Frøslev | Algorithm for Post-Clustering Curation of DNA Amplicon Data Yields Reliable Biodiversity Estimates[END_REF] to limit bias due to intraspecific variation and pseudogenes, using a minimum co-occurrence of 0.90 and a minimum similarity threshold of 84% for COI and 90% for 18S V1-V2.

Finally, refined datasets were taxonomically filtered by retaining only clusters having a minimum hit identity of 86% for rRNA loci and 80% for COI. These values were chosen as they represent approximate minimum identity for reliable phylum assignment [START_REF] Stefanni | Multi-Marker Metabarcoding Approach to Study Mesozooplankton at Basin Scale[END_REF].

Statistical analyses

Data were analysed using R with the packages phyloseq v1. adonis2 function (vegan) and significance was evaluated using 1,000 permutations. Incidencebased Jaccard dissimilarities were used for metazoans, while Bray-Curtis dissimilarities were used for prokaryotes and unicellular eukaryotes. The rationale behind this choice is that metazoans are multicellular organisms of extremely varying numbers of cells, organelles, or ribosomal repeats in their genomes, and can also be detected through a diversity of remains.

The number of reads can thus not be expected to reliably reflect the abundance of detected PCR negative controls (Table S4). The in situ pump yielded less raw reads for COI and 16S

(Fig. S1, F = 4.02-14.4, p = 0.0003-0.03), while more raw reads were recovered from both water sampling methods with 18S V4 (F = 6.4, p = 0.007). Water samples generally yielded fewer raw clusters (F = 5.2-35.1, p = 3.2x10 -6 -0.02), except for 18S V4 where numbers were comparable across sample types (Fig. S1).

Bioinformatic unassigned or non-target clusters), rarefaction curves showed a plateau was reached for all samples except sediment samples with 18S V4, suggesting that not all sediment protist diversity was captured at this sequencing depth (Fig. S1). LULU curation (only for metazoan data) and taxonomic refinement (removal of clusters with assignments not reliable at phylum-level, i.e. < 86% BLAST identity for rRNA loci, and < 80% for COI), resulted in final datasets that comprised between 4.6 and 5.8 million target reads for eukaryotes and 7 million 16S V4-V5

for prokaryotes. Target reads delivered 405 (18S V1-V2) and 507 (COI) metazoan OTUs, 7,081

protist ASVs (18S V4), and 38,816 prokaryote 16S ASVs (Table S4).

Alpha diversity between sampling methods

Significantly fewer molecular clusters were detected in water samples than in sediment samples for all loci except 18S V4 where both sample types recovered similar levels of diversity (Table 1, Fig. 1). However, this trend was not consistent across taxonomic groups, as recovered diversity in each sample type strongly differed depending on phylum (Fig. 2).

For metazoans, water samples led to the detection of a significantly higher number of OTUs for Arthropoda (COI and 18S V1-V2), Rotifera (COI), and Ctenophora (18S V1-V2, t-tests, p = 0.002-0.04) than sediment samples, and some phyla like Chordata, Echinodermata, Gastrotricha, or Brachiopoda were only detected in water samples (Fig. 2). In contrast, phyla such as Cnidaria, Mollusca, Platyhelminthes, Porifera, Nematoda, or Xenacoelomorpha produced significantly more OTUs in sediment than water samples (t-tests, p = 4x10 -5 -0.02), and kinorhynchs or tardigrades were only recovered with sediment samples (Fig. 2).

Similarly, some protistan groups, such as the Acantharea, Chlorophyta, Dinophyceae, Haptophyta, and Syndiniales (Fig. S2), were predominant in water samples (t-tests, p = 0.002 -0.04), while others were significantly more diverse in sediment (e.g., Filosa groups, Ciliophora, Labyrinthulea, t-tests, p = 0.02-0.04).

For prokaryotes, most lineages were predominant in sediment (t-tests, p = 4.4x10 -7 -0.02 e.g., Archaea, Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Dadabacteria, Delta-, Gammaproteobacteria, Gemmatimonadetes, Lentisphaerae Nitrospirae, Patescibacteria, Planctomycetes), and only Cyanobacteria were significantly more diverse in water samples (ttest, p = 0.0009).

CHAPTER IV SAMPLING METHODS COMPARISONS 120

Sieved and unsieved sediment resulted in comparable total cluster numbers in all loci investigated (Table 1, Fig. 1). However, recovered levels of alpha diversity varied by phyla and organism size class (Fig. 2). For metazoans, more OTUs were detected from sieved then from unsieved sediment in meiofauna phyla (Kinorhyncha, Nematoda, Platyhelminthes, Rotifera, Tardigrada, Xenacoelomorpha), although this difference was only significant for Platyhelminthes with 18S V1-V2 (paired t-tests, p = 0.02). Sieved and unsieved sediment The water sampling box and the in situ pump recovered similar total OTU/ASV numbers for metazoans (COI), unicellular eukaryotes (18S V4), and prokaryotes (16S V4-V5, Table 1).

However, considerable variation in detected cluster numbers was observed between size fractions of the sampling box, as underlined by the amplification failure of the smallest size fraction (0.2-2 µm) with primers targeting metazoans and the largest size fraction (>20 µm)

with primers targeting microbial communities. Thus, and as expected, larger size fractions better detected metazoan taxa, while smaller size fractions better detected microbial taxa (Fig. 1). For metazoans resolved with 18S V1-V2, for which only the 2-20 µm size fraction from the sampling box samples was successfully sequenced, significantly fewer OTUs were detected with the sampling box compared to the in situ pump (Fig. 1, Table 1).

Water sampling methods strongly differed in terms of recovered alpha diversity depending on taxonomic compartment. The in situ pump generally detected more metazoan diversity than the sampling box (Fig. 2), and this difference was significant for Arthropoda, Rotifera (COI and 18S V1-V2), Annelida, Ctenophora, Mollusca, Nematoda, and Vertebrata (18S V1-V2, ttests, p = 0.0001-0.04). For protists and prokaryotes, the in situ pump detected significantly more ASVs compared to the sampling box only in some taxonomic groups (i.e., Bacillariophyta, Phaeodarea, Acidobacteria, Bacteroidetes, Delta-, Gammaproteobacteria, Lentisphaerae, Omnitrophicaeota, Planctomycetes, and Patescibacteria, Fig. S2, t-tests, p = 4.3x10 -5 -0.03). Other clades were significantly more diverse in the sampling box (e.g., Haptophyta, Telonemia, and Cyanobacteria, t-tests, p = 0.001-0.02). With the sampling box, the smallest size fraction (0.2-2 µm) allowed recovering more alpha diversity in all microbial groups than the larger size fraction (2-20 µm). This difference was significant only for Labyrinthulea and Chloroflexi (paired t-tests, p = 0.02-0.03), although non-significant comparisons may result from the limited sample sizes available in this comparison. The two size fractions available with the sampling box for COI (2-20 µm, > 20 µm) did not reveal differences in diversity recovery with size class, as most phyla were detected equally well in both (Fig. 2).

Table 1. Effect of sampling method on cluster richness and community structure for the 4 studied genes.

ANOVAs were performed on models with quasipoisson distributions using on rarefied datasets. PERMANOVAs were calculated on rarefied datasets by permuting 1,000 times, using Jaccard distances for metazoans and Bray-Curtis distances for 18S V4 OTU numbers were calculated on rarefied datasets.

Effect of sampling method on community structures

Sediment and aboveground water samples detected significantly different communities for all investigated loci (Table 1), and pairwise PERMANOVAs showed that sample type (water or sediment) accounted for 45-54% (COI), 52-60% (18S V1-V2), 37-51% (18S V4), and 58-78% (16S) of variation in data. Relative taxonomic compositions revealed by aboveground water samples differed from sediment samples, with high proportions of arthropods, chordates, annelids, tunicates in the water samples, while nematodes, poriferans, platyhelminths, and xenacoelomorphs were predominant in the sediment samples (Fig. S3). Similarly, Dinophyceae, Haptophyta, Phaeodarea, Syndiniales, Alphaproteobacteria, and Cyanobacteria represented higher proportions of community structures in water than in sediment samples, while Ciliophora, Labyrinthulea, RAD-B, Acidobacteria, Chloroflexi, and Archeae were more abundant in sediment samples (Fig. S3).

Only 6% (COI), 10% (18S V1-V2), 9% (18S V4), and 5% (16S) of clusters were shared between sediment and water samples, and this resulted in strong segregation in PCoA ordinations (Fig. 3). For metazoans, these shared taxa were mostly hydrozoans (COI, 46%, 18S, 12%), calanoid and harpacticoid copepods (COI, 7%, 18S, 22%), gastropods (COI, 14%), demosponges (COI, 11%), or polychaetes (18S, 17%), and chromadorean nematodes (18S, 17%). For protists, ASVs shared among sediment and water samples primarily belonged to the Syndiniales (39%), but other taxa included dinophyceans (10%), filosans (9%), labyrinthuleans (5%), and bacillariophytes (6%). For prokaryotes, shared ASVs were predominantly belonging to the Proteobacteria (Gamma, 19%, Alpha, 10%, Delta, 8%), Bacteroidetes (15%), or Planctomycetes (16%).

Sediment processing did not significantly affect detected community structures as sieved and unsieved sediment resolved comparable communities (Table 1). However, sieving showed a higher impact on the characterization of microbial (eukaryotic or prokaryotic) communities, as indicated by the stronger segregation of sieved and unsieved sediment samples in PCoA ordinations (Fig. 3). Processing method accounted for 23% (COI and 18S V1-V2), 26% (18S V4), and 42% (16S) of variation among sediment samples, and 25% (COI), 28% (18S V1-V2), 9% (18S V4), and 22% (16S) of OTUs/ASVs were shared among sieved and unsieved sediment samples. Shared metazoan OTUs primarily belonged to Hydrozoa (18S, 2.7%, COI, 51%, Siphonophorae, Anthoathecata, Leptothecata), Demospongiae (COI, 14%), Gastropoda (COI, 20%), Nematoda (18S, 63% Chromadorea, 16% Enoplea), Polychaeta (18S, 4.5%), or

Copepoda (18S, 5.4%). Microbial ASVs shared among sieved and unsieved sediment mostly CHAPTER IV SAMPLING METHODS COMPARISONS 125 belonged to Syndiniales (18%), Filosa (16%), Ciliophora (11%), Dinophyceae (9%), Planctomycetes (22%), Acidobacteria (10%), or Proteobacteria (Gamma, 9%, Alpha, 8%, Delta, 11%).

In contrast, sampling method significantly affected detected community structure for water, as samples collected with the sampling box resulted in significantly different communities than those from the in situ pump (Table 1). Sampling method accounted for 26% (COI), 36% (18S V1-V2), 47% (18S V4), and 46% (16S) of variation among water samples.

Only 9% (COI), 5% (18S V1-V2), 7% (18S V4), and 3% (16S) of ASVs/OTUs were shared between the in situ pump and the sampling box (Fig. 3). Taxonomic structures resolved by both sampling methods clearly changed due to targeted size fraction (Fig. S3). For metazoans with COI, the > 20 µm size fraction targeted by both the sampling box and the in situ pump displayed similar relative taxonomic compositions, while the sampling box's 2-20 µm size fraction resolved different community structures than the in situ pump for both metazoan markers.

Similarly, both water-sampling methods never targeted the same size fraction for microbial data, resulting in different community structures. The in situ pump, targeting the > 20 µm size class, detected higher relative abundances of Bacillariophyta, Ciliophora, and Phaeodarea for protists, and higher relative abundances of Delta-, Gammaproteobacteria, Lentisphaerae, and Planctomycetes for prokaryotes. Both size fractions of the sampling box were characterised by increased relative abundances of cryptophytes, haptophytes, and telonemians (18S V4), as well as Alphaproteobacteria, Marinimicrobia, and Thaumarchaeota (16S).

Discussion

Importance of substrate nature

Sediment samples, whether sieved or unsieved, led to the detection of higher numbers of metazoan OTUs and prokaryote ASVs than water samples (Fig. 1), indicating that more diversity could be found in the benthos compared to the pelagos at this Mediterranean site for those groups. For unicellular eukaryotes, the difference in diversity between sediment and aboveground water was not significant. However, this may primarily be due to the fact that some benthic taxa (filosans, labyrinthuleans, ciliates) were also well detected by water samples (Fig. S2). Indeed, 22% of protist sediment ASVs were also detected in the water samples, while for other loci this percentage was closer to 10%. These findings are congruent with other studies in the marine realm that reported notably higher diversity in sediments compared to seawater [START_REF] Forster | Benthic Protists: The under-Charted Majority[END_REF][START_REF] Probandt | Permeability Shapes Bacterial Communities in Sublittoral Surface Sediments[END_REF]Zinger et al. 2011) for microbial communities, and show that higher diversity can also be expected for metazoans.

Community compositions differed markedly between sediment and aboveground water samples for all life compartments investigated (Fig. 3), and only 5 to 10% of total molecular clusters were shared between substrate types, a range congruent with previous findings (Zinger et al. 2011;Zhao et al. 2020;[START_REF] Antich | Marine Biomonitoring with EDNA: Can Metabarcoding of Water Samples Cut It as a Tool for Surveying Benthic Communities?[END_REF]. Metazoan infauna taxa (e.g., nematodes, platyhelminths, kinorhynchs, tardigrades, and xenacoelomorphs) were specifically detected by sediment samples, while other epibenthic, benthopelagic, and pelagic metazoans were more prevalent in water samples (e.g., echinoderms, chordates, ctenophores). Similarly, with protists and prokaryotes, sediment samples detected lineages typically reported in the deep seafloor, with prokaryotic communities mostly comprised of Proteobacteria, Acidobacteria, Planctomycetes, Thaumarchaeota, Bacteroidetes, and Chloroflexi [START_REF] Liao | Microbial Diversity in Deep-Sea Sediment from the Cobalt-Rich Crust Deposit Region in the Pacific Ocean[END_REF][START_REF] Bienhold | Diversity and Biogeography of Bathyal and Abyssal Seafloor Bacteria[END_REF]Zhang, J. et al. 2015;[START_REF] Liu | Mitochondrial Capture Enriches Mito-DNA 100 Fold, Enabling PCR-Free Mitogenomics Biodiversity Analysis[END_REF], and protist communities characterized by benthic heterotrophic groups such as ciliates, labyrinthuleans, and filosans (Zhao et al. 2017;[START_REF] Rodríguez-Martínez | Controlled Sampling of Ribosomally Active Protistan Diversity in Sediment-Surface Layers Identifies Putative Players in the Marine Carbon Sink[END_REF]. Water samples instead recovered taxa commonly reported in pelagic studies, with microbial eukaryotes such as dinoflagellates (Dinophyceae, Syndiniales), radiolarians (Acantharea, Phaeodarea, Spumellarida), or MAST (e.g. diatoms, Chlorophyta, Chrysophyceae) (Pernice et al. 2015a;[START_REF] Massana | Marine Protist Diversity in European Coastal Waters and Sediments as Revealed by High-Throughput Sequencing[END_REF]Zhao et al. 2020), and bacterial groups such as Proteobacteria, Bacteroidetes and Cyanobacteria [START_REF] Salazar | Global Diversity and Biogeography of Deep-Sea Pelagic Prokaryotes[END_REF][START_REF] Díez-Vives | Delineation of Ecologically Distinct Units of Marine Bacteroidetes in the Northwestern Mediterranean Sea[END_REF][START_REF] Lochte | Bacteria and Cyanobacteria Associated with Phytodetritus in the Deep Sea[END_REF].

CHAPTER IV SAMPLING METHODS COMPARISONS 128

Most of the metazoans shared among sediment and water samples displayed benthopelagic life cycles with a benthic adult and a pelagic larvae (hydrozoans, gastropods, demosponges, polychaetes), confirming that the detection of benthic taxa in water samples may predominantly reflect the occurrence of dispersal phases of those organisms. Similarly, Bacteroidetes and Planctomycetes, bacteria that were predominant in this shared fraction are known to occur at the sediment-water interface [START_REF] Stokke | Functional Interactions among Filamentous Epsilonproteobacteria and Bacteroidetes in a Deep-Sea Hydrothermal Vent Biofilm[END_REF][START_REF] Probandt | Permeability Shapes Bacterial Communities in Sublittoral Surface Sediments[END_REF]. Finally, the fact that diatoms (Bacillariophytes), and dinoflagellates (Dinophyceae and parasitic Syndiniales) were abundant both in sediment and water samples supports the fact that some planktonic protists can sink to deep seafloor [START_REF] Agusti | Ubiquitous Healthy Diatoms in the Deep Sea Confirm Deep Carbon Injection by the Biological Pump[END_REF].

Overall, our results confirm previous findings showing that sample nature strongly affects the type organisms targeted by eDNA metabarcoding [START_REF] Koziol | Environmental DNA Metabarcoding Studies Are Critically Affected by Substrate Selection[END_REF][START_REF] Roussel | Comparison of Microbial Communities Associated with Three Atlantic Ultramafic Hydrothermal Systems[END_REF], and underlines that eDNA from water samples cannot be used to comprehensively survey benthic communities [START_REF] Hajibabaei | Watered-down Biodiversity? A Comparison of Metabarcoding Results from DNA Extracted from Matched Water and Bulk Tissue Biomonitoring Samples[END_REF][START_REF] Antich | Marine Biomonitoring with EDNA: Can Metabarcoding of Water Samples Cut It as a Tool for Surveying Benthic Communities?[END_REF][START_REF] Gleason | Assessment of Stream Macroinvertebrate Communities with EDNA Is Not Congruent with Tissue-Based Metabarcoding[END_REF], even when large volumes of aboveground water are collected.

Sieving sediment is not essential for comprehensive benthic biodiversity surveys

Studies investigating the effect of size-sorting in macroinvertebrates showed that sorting organisms by size and pooling them proportionately according to their abundance led to a more equal amplification of taxa, the sorted samples recovering 30% more taxa than the unsorted samples at the same sequencing depth [START_REF] Elbrecht | Sorting Things out: Assessing Effects of Unequal Specimen Biomass on DNA Metabarcoding[END_REF]. The size fractions used in this study were specifically aiming to separate the macrofauna (> 1 mm) from the meiofauna (32 µm -1mm) compartment, which is known to be important in deep-sea sediments, both in terms of abundance and biomass [START_REF] Thiel | Meiobenthos and Nanobenthos of the Deep-Sea[END_REF][START_REF] Rex | Global Bathymetric Patterns of Standing Stock and Body Size in the Deep-Sea Benthos[END_REF][START_REF] Bellec | A Nematode of the Mid-Atlantic Ridge Hydrothermal Vents Harbors a Possible Symbiotic Relationship[END_REF]. Meiofauna taxa, best captured by 18S V1-V2, were more numerous in sieved than unsieved sediment samples, although this difference was only significant for Platyhelminthes (Fig. 2). It could be that the equimolar pooling performed with DNA extracts from each different size fraction maintained biases in abundance, as larger organisms contributed more DNA molecules within each size fraction. This would explain the non-significant differences observed between sieved and unsieved sediment for most metazoan phyla and total OTU numbers. Proportional pooling may be a better approach, but is feasible only if relative abundance of organisms in each size class can be calculated (e.g., using dry sample and specimen weights). A more accurate approach would be to sequence each size fraction separately; this however also increases five-fold CHAPTER IV SAMPLING METHODS COMPARISONS 129 sequencing costs. However, the fact that more diversity was detected when sieving than when not sieving at the same sequencing depth for the 18S marker (Fig. S4), indicates sieving effectively reduces biomass biases, thus allowing detecting more diversity with the same sampling depth. Alternatively, new technologies affording much higher sequencing depths [START_REF] Singer | Comprehensive Biodiversity Analysis via Ultra-Deep Patterned Flow Cell Technology: A Case Study of EDNA Metabarcoding Seawater[END_REF]) might allow circumventing the need for size-class sorting in the future.

The advantage provided by sieving observed in this study for meiofauna may also result from the fact that five DNA extractions were performed for the sieved treatment (one for each size fraction), when only one was performed for non-sieved sediment. As number and type of DNA extraction are known to affect pro-and eukaryote taxon recovery [START_REF] Webster | Assessment of Bacterial Community Structure in the Deep Sub-Seafloor Biosphere by 16S RDNA-Based Techniques: A Cautionary Tale[END_REF][START_REF] Cruaud | Influence of DNA Extraction Method, 16S RRNA Targeted Hypervariable Regions, and Sample Origin on Microbial Diversity Detected by 454 Pyrosequencing in Marine Chemosynthetic Ecosystems[END_REF][START_REF] Nascimento | Sample Size Effects on the Assessment of Eukaryotic Diversity and Community Structure in Aquatic Sediments Using High-Throughput Sequencing[END_REF], it remains to be tested whether several unsieved extractions would allow achieving similar detection levels.

Elutriation (i.e. resuspension of organisms and pouring of supernatant on a 32-µm sieve)

or density extraction techniques are other methods traditionally used to study meiofauna [START_REF] Brannock | Meiofaunal Community Analysis by High-Throughput Sequencing: Comparison of Extraction, Quality Filtering, and Clustering Methods[END_REF][START_REF] Burgess | An Improved Protocol for Separating Meiofauna from Sediments Using Colloidal Silica Sols[END_REF]). These allow to process whole sediment layers more rapidly than sieving, and effectively concentrate metazoan organisms [START_REF] Brannock | Meiofaunal Community Analysis by High-Throughput Sequencing: Comparison of Extraction, Quality Filtering, and Clustering Methods[END_REF]. However, if the retention of organisms is achieved using only a single mesh size marking the lower size boundary of meiofauna, this also maintains size-abundance biases.

Thus, whether sieving, elutriating, or density extracting, mesh sizes for size-class sorting have to be carefully chosen in order to reach the best compromise between processing time and biomass biases. As underlined by [START_REF] Elbrecht | Sorting Things out: Assessing Effects of Unequal Specimen Biomass on DNA Metabarcoding[END_REF], sorting is most useful when samples contain specimens with biomasses spanning several orders of magnitude. Given that deep-sea sediments contain large numbers of small organisms, and given the high detection capacity of metabarcoding, implementing five mesh sizes for sorting metazoans may be excessive. Instead, separating organisms into small, medium, and large size categories, as performed by [START_REF] Elbrecht | Sorting Things out: Assessing Effects of Unequal Specimen Biomass on DNA Metabarcoding[END_REF] for freshwater macroinvertebrates and by [START_REF] Leray | DNA Barcoding and Metabarcoding of Standardized Samples Reveal Patterns of Marine Benthic Diversity[END_REF] for coastal benthic communities may be sufficient to maximize metazoan species detection.

However, the rationale behind size sorting should be carefully considered when implementing an eDNA metabarcoding study on the deep seafloor. Indeed, for most integrative ecological studies, the proportion of abundant taxa is most relevant to reach accurate conclusions, and it may not be necessary to detect all small and rare taxa in such studies, at least not for metazoans. Moreover, effects of size sorting on other taxonomic compartments have to be taken into consideration. For microbial organisms, sieving down to a 20-µm mesh size is very likely to result in the loss of most small and/or free-living taxa. This idea is supported by CHAPTER IV SAMPLING METHODS COMPARISONS 130 the fact that metazoan OTUs shared between sieved and unsieved sediment were mainly assigned to large taxonomic groups, indicating that small taxa predominantly explain the differences obtained between both methods. For protists and prokaryotes, although sieved and unsieved sediment uncovered comparable alpha diversity levels (Fig. 1), and resolved similar taxonomic compositions at phylum level (Fig. S3), ordinations indicated that communities segregated considerably with processing method (Fig. 3). Many sediment microorganisms are living within biofilms (e.g., Bacteroidetes, Archeae), attached to sediment particles (e.g, Planctomycetes) or as symbionts of larger taxa (e.g., Syndiniales, some Dinophyceae and Proteobacteria), making their retention on a 20-µm sieve possible. Our results support this idea, as microbial ASVs shared among sieved and unsieved sediment were mostly belonging to those groups or to taxa larger than 20 µm (e.g. ciliates), possibly explaining the non-significant difference we obtained in PERMANOVA (Table 1).

Finally, sieving is associated to higher contamination risks, as sieves need to be carefully washed between samples and water used for sieving (or elutriation) needs to be ultra-filtered (which can be problematic for the large volumes needed). Considering the limited improvement gained by sieving on metazoan communities, the logistic inconvenience, and the risk of bias for other taxonomic compartments, DNA extractions performed directly on 10 g of sediment appear as a satisfactory approach for large-scale biodiversity surveys targeting multiple life compartments.

Adjusting water sample volume and filter mesh size to target organisms

Numerous aquatic metabarcoding studies have highlighted that sampled water volume is a key factor affecting species detection rates with eDNA, and has to be adapted to the target ecosystem [START_REF] Goldberg | Critical Considerations for the Application of Environmental DNA Methods to Detect Aquatic Species[END_REF]. Positive relationships between increased water volume and increased detection rate have been reported for macroinvertebrates and amphibians (Mächler et al. 2016;[START_REF] Lopes | EDNA Metabarcoding: A Promising Method for Anuran Surveys in Highly Diverse Tropical Forests[END_REF], and studies in freshwater ecosystems have shown that 20 litres to 30-68 litres of water are necessary to detect entire metazoan communities [START_REF] Hänfling | Environmental DNA Metabarcoding of Lake Fish Communities Reflects Long-Term Data from Established Survey Methods[END_REF][START_REF] Cantera | Optimizing Environmental DNA Sampling Effort for Fish Inventories in Tropical Streams and Rivers[END_REF][START_REF] Evans | Fish Community Assessment with EDNA Metabarcoding: Effects of Sampling Design and Bioinformatic Filtering[END_REF]. While 1 L may be appropriate for macroinvertebrate detection in rivers [START_REF] Mächler | Fishing in the Water: Effect of Sampled Water Volume on Environmental DNA-Based Detection of Macroinvertebrates[END_REF] or marine surface waters [START_REF] Grey | Effects of Sampling Effort on Biodiversity Patterns Estimated from Environmental DNA Metabarcoding Surveys[END_REF], the results presented here clearly show that 7.5 L of deep-sea water are not sufficient to accurately detect metazoan fauna. The sampling boxes detected less metazoan diversity than the in situ pump (Fig. 1), and failed to detect many phyla with 18S V1-V2 (Fig. 2). This reflects CHAPTER IV SAMPLING METHODS COMPARISONS 131 the low abundance and biomass of large organisms in deep waters, combined with the very limited lifetime of extracellular DNA in seawater [START_REF] Andruszkiewicz | Persistence of Marine Fish Environmental DNA and the Influence of Sunlight[END_REF][START_REF] Dejean | Persistence of Environmental DNA in Freshwater Ecosystems[END_REF][START_REF] Collins | Persistence of Environmental DNA in Marine Systems[END_REF][START_REF] Sassoubre | Quantification of Environmental DNA (EDNA) Shedding and Decay Rates for Three Marine Fish[END_REF].

Water sampling methods for eDNA metabarcoding relying on on-board filtration or precipitation are intrinsically limited by the amount of water that can be processed. Although purpose-built sampling equipment has been developed for increased efficiency and standardization, filtration flow rates rarely exceed 1 L/min [START_REF] Thomas | ANDe TM : A Fully Integrated Environmental DNA Sampling System[END_REF]). New developments allowing processing thousands of litres of water, such as the SALSA in situ pump presented here, or tow net methods developed for lentic ecosystems [START_REF] Schabacker | Increased EDNA Detection Sensitivity Using a Novel High-volume Water Sampling Method[END_REF], improve the detection sensitivity for metazoan taxa in low biomass environments and will allow for more comprehensive and reliable surveys.

With protists and bacteria, taxonomic structures recovered by each sampling method clearly changed with targeted size class (Fig. S2, Fig. S3). Most protistan micro-to mesoplankton were better detected by the in situ pump (e.g., diatoms, phaeodareans, Acantharea, Ciliophora), while pico-to nanoplankton were preferentially targeted by the sampling box (e.g., Haptophyta, Telonemia), with many groups mostly by the smallest size fraction (0.2-2 µm, Chlorophyta, Labyrinthulea, Chrysophyceae, MAST). For bacteria, groups known to occur in aggregates, on larger particles, or in association with larger organisms were better recovered by the in situ pump (e.g., Actinobacteria, Bacteroidetes, Delta-, Gammaproteobacteria, Lentisphaerae), while other, likely free-living, bacterioplankton were predominant in the sampling box samples (Cyanobacteria, Marinimicrobia). This differential taxon recovery of water collection methods has already been reported in shallower studies [START_REF] Massana | Marine Protist Diversity in European Coastal Waters and Sediments as Revealed by High-Throughput Sequencing[END_REF], and highlights the importance of targeting the 0.2-2 µm for accurately surveying microbial diversity.

Although the SALSA prototype presented here has since been improved to pump through a 5-µm nylon mesh, in situ filtration techniques are inherently limited by mesh size in order to filter large volumes of water. Thus, although targeting large volumes such as the ones allowed by SALSA represents the most suitable strategy for assessing metazoan diversity in deep-sea waters, its limitation in terms of mesh size leads to the detection of only a fraction of microbial diversity, i.e. mostly larger planktonic groups or taxa fixed on larger faunal specimens or mineral particles. On board filtration of smaller volumes of water remains necessary to access the pico-and nanoplankton, highlighting that both sampling methods are complementary and should be deployed in parallel for integrative biodiversity surveys across the tree of life.

CHAPTER IV SAMPLING METHODS COMPARISONS 132

Overall, this comparative study helps advancing towards more comprehensive and more reliable assessments of metazoan and microbial deep-sea communities based on eDNA metabarcoding. First, only sediment samples can allow the characterization of benthic taxa and aboveground water samples do not provide a good alternative. Second, sieving sediment leads to an improvement of taxa detection for metazoans, but as expected, also modifies the retrieved community composition for protists and prokaryotes. Thus, for studies targeting only metazoans, it is advisable to first separate the organisms from the sediment particles using sieving, elutriation, or density extraction techniques as recommended by [START_REF] Brannock | Meiofaunal Community Analysis by High-Throughput Sequencing: Comparison of Extraction, Quality Filtering, and Clustering Methods[END_REF]. If both metazoan and microbial communities are targeted, and provided sample volume is large enough, an ideal sampling design would be to use multiple sub-samples for microbial taxa and size-sort the remaining sediment for detecting metazoans, as suggested by Nascimento et al. [START_REF] Nascimento | Sample Size Effects on the Assessment of Eukaryotic Diversity and Community Structure in Aquatic Sediments Using High-Throughput Sequencing[END_REF]. Alternatively, as shown here, using sufficient volumes of unsorted sediment seems to be satisfactory for integrative studies across taxonomic compartments. Finally, water sample volume and mesh size need to be carefully chosen depending on taxa of interest, and while volumes collected by sampling boxes (or Niskin bottles) allow surveying microbial diversity, much larger volumes are needed to detect deepsea metazoans.

Résumé en français

Le habitats sédimentaires abyssaux couvrent plus de 50% de la planète Terre et sont un grand réservoir de biodiversité encore largement non décrite, bien que de plus en plus sous

Abstract

The abyssal sedimentary seafloor covers more than 50% of planet Earth and is a large reservoir of still mostly undescribed biodiversity, although being increasingly under target of resource-extraction industries. In such remote and vast ecosystems, the high detection power of environmental DNA (eDNA) metabarcoding on samples easier to gather than specimen collections, offers new perspectives for the standardized investigation of large-scale biodiversity and biogeography patterns.

Using both mitochondrial COI and the V1-V2 region of 18S ribosomal RNA (rRNA), we investigated small-scale and large-scale metazoan biodiversity patterns in the Atlantic-Mediterranean transition zone, using eDNA extracted from deep-sea sediments of 13 sites spanning from the Central Mediterranean to the Mid Atlantic Ridge. We evaluated the influence of sediment layer, sediment grain size, organic matter content, as well as microbial communities (18S V9 for protists, 16S V4-V5 for prokaryotes), on the extent and structure of metazoan biodiversity in this region.

Our results highlight that small-scale (centimetres) factors strongly affect deep-sea metazoan richness and community composition. A significant decrease in OTU richness was observed with sediment layer, from 1 cm down to 15 cm within the sediment, and significant vertical segregation in community structure was revealed in all regions for both meiofauna and macrofauna. The upper five centimetres harboured most metazoan OTUs (94% for 18S, 98%

for COI), with numbers ranging from 2-168 per sample for 18S and 81-1,259 for COI.

Expectedly, large-scale factors (>100 km) affected beta-diversity more than alpha diversity. Organic matter composition and sediment grain size were found to vary strongly at regional scales, with higher organic matter content in Mediterranean sediments and larger particle sizes in the Atlantic. Both significantly contributed to explain differences in community composition among sites. A strong correlation was observed between the meio-and the macrofauna (RV = 0.87), confirming strong trophic interactions between these taxonomic compartments. A similar level of correlation (RV = 0.84) was also observed between protists and prokaryotes, suggesting that trophic interctions are strongest among organisms of similar size classes. Finally, the Gibraltar Strait was an additional factor explaining the very strong regional differences in community compositions, supporting a combined influence of past biogeography and present day movements of water masses on the distribution of benthic diversity.

Introduction

While ocean exploration is relatively recent, studies have started shedding light on biodiversity and biogeography patterns in the deep-sea realm during the last decades [START_REF] Rex | Community Structure in the Deep-Sea Benthos[END_REF][START_REF] Grassle | Species Diversity in Deep-Sea Communities[END_REF][START_REF] Grassle | Deep-Sea Species Richness: Regional and Local Diversity Estimates from Quantitative Bottom Samples[END_REF]Vanreusel et al. 2010a;[START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF][START_REF] Danovaro | Deep-Sea Biodiversity in the Mediterranean Sea: The Known, the Unknown, and the Unknowable[END_REF][START_REF] Woolley | Deep-Sea Diversity Patterns Are Shaped by Energy Availability[END_REF]. However, these studies were confronted with the extraordinary vastness of deep-sea ecosystems, the difficulty of sampling in these remote and high-pressure locations, as well as the high costs and time involved in collecting and analysing samples [START_REF] Danovaro | Challenging the Paradigms of Deep-Sea Ecology[END_REF]. Analytical methods based on extrapolation from known samples have clearly indicated that deep-sea life is much more diverse than prefiously thought, although estimates remain highly uncertain, primarily due to under-sampling and to the difficulty of identifying specimens. The large marine databases assembled in recent years include too little information about deep-sea species to allow reasonable extrapolation for the estimation of deep-sea biodiversity [START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF]. Studies also highlighted the strong link between surface and deep-ocean regions, showing that benthic deep-sea communities are affected by climate-driven variations in carbon cycles and can therefore directly influence carbon remineralisation and sequestration processes (Smith, K. L. et al. 2009;2013). However, monitoring these surface-driven changes in deep-sea benthic communities is costly and difficult to sustain over long-term periods.

Deep-sea sedimentary habitats cover more than 50% of the Earth's surface, can host high numbers of organisms (50,000-5 million individuals per square meter), which perform key ecosystem roles such as nutrient cycling, sediment stabilisation and transport, or secondary production (Bik et al. 2012b;Fonseca, V. G. et al. 2010;[START_REF] Snelgrove | Getting to the Bottom of Marine Biodiversity: Sedimentary Habitats[END_REF]. The deep seafloor is also characterised by high local and regional diversity [START_REF] Grassle | Deep-Sea Species Richness: Regional and Local Diversity Estimates from Quantitative Bottom Samples[END_REF]Smith, C. R. and Snelgrove 2002;[START_REF] Rosli | Review of Recent Trends in Ecological Studies of Deep-Sea Meiofauna, with Focus on Patterns and Processes at Small to Regional Spatial Scales[END_REF]). Yet, whether this holds true on a global scale is still under debate (Costello and Chaudhary 2017), partly due to the difficulty to integrate local or regional studies made by different taxonomic experts and teams based on distinct protocols (Vanreusel et al. 2010a). Despite this, they are under increased threat from a variety of ongoing habitats [START_REF] Pawlowski | Eukaryotic Richness in the Abyss: Insights from Pyrotag Sequencing[END_REF]Bik et al. 2012b;Fonseca, V. G. et al. 2010;[START_REF] Cristescu | From Barcoding Single Individuals to Metabarcoding Biological Communities: Towards an Integrative Approach to the Study of Global Biodiversity[END_REF][START_REF] Cowart | Metabarcoding Is Powerful yet Still Blind: A Comparative Analysis of Morphological and Molecular Surveys of Seagrass Communities[END_REF][START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF][START_REF] Forster | Benthic Protists: The under-Charted Majority[END_REF]Cordier et al. 2019a). Using high-throughput sequencing (HTS) and bioinformatics, these methods allow the detection or the inventory of target organisms using their DNA directly extracted from soil, water, or air samples (Taberlet et al. 2012a). As they do not require specimen isolation, they are practical and efficient tools in large and hard-to-access ecosystems, such as the deep-sea realm. Besides allowing the study of various biological compartments simultaneously, eDNA metabarcoding is also very effective for detecting the diversity of small organisms (micro-organisms, meiofauna), very abundant in deep-sea sediments, but largely disregarded in visual biodiversity inventories due to the difficulty of their identification based on morphological features [START_REF] Carugati | Metagenetic Tools for the Census of Marine Meiofaunal Biodiversity: An Overview[END_REF]. Finally, given the increased time-efficiency and above all standardization offered by this technique, eDNA metabarcoding also allows increasing the spatial scale of deep-sea studies.

Here, we apply eDNA metabarcoding on deep-sea sediments to investigate small-scale and large-scale metazoan biodiversity patterns in the Atlantic-Mediterranean transition zone. Using both mitochondrial COI and the V1-V2 region of 18S ribosomal RNA (rRNA), our aims were to 1) assess the extent of metazoan biodiversity and its distribution in the Atlantic-Mediterranean transition region, 2) evaluate the influence of current environmental conditions vs spatial, i.e. historical effects on metazoan community structure, and 3) evaluate the level of correlation between metazoan and microbial communities, resulting from direct or indirect biotic interactions.

Materials and methods

Preparation of samples

Environmental DNA Sediment cores were collected from thirteen deep-sea sites located along a west-east gradient in the Mediterranean-Atlantic transition zone (Fig. 1, Table S1). Triplicate tube cores were collected with a multicorer or with a remotely operated vehicle at each sampling site, except for ESN-300m where only a blade corer was available Each sediment core was sliced into five depth layers down to 15 cm (0-1 cm, 1-3 cm, 3-5 cm, 5-10 cm, 10-15 cm). The latter were transferred into zip-lock bags, homogenised, and frozen at -80°C on board before being shipped on dry ice to the laboratory. In each sampling series, an empty bag was kept as a field control processed through DNA extraction and sequencing.

Processing areas were cleaned with bleach, rinsed with MilliQ water, and dried with 70%

ethanol. During all procedures, filter pipet tips and clean gloves were used, by wearing two pairs of gloves, allowing to easily and regularly changing the upper pair. DNA extractions were performed using ~10 g of sediment with the PowerMax Soil DNA Isolation Kit (Qiagen, Hilden, Germany). To increase the DNA yield, the elution buffer was left on the spin filter membrane for 5-10 min at room temperature before centrifugation. For field controls, the first solution of the kit was poured into the control zip lock, before following the usual extraction steps. DNA extracts were stored at -80°C.

Mock samples

Two metazoan mock communities (5 ng/µL) were used as positive controls throughout the PCR and sequencing processes. They were prepared using standardized 10 ng/µL DNA extracts of ten deep-sea specimens belonging to five taxonomic groups (Polychaeta, Crustacea, Anthozoa, Bivalvia, Gastropoda; Table S2). Specimen DNA was extracted using a CTAB extraction protocol, from muscle tissue or from whole polyps for cnidarians. The mock communities differed in terms of ratios of total genomic DNA from each species, with increased dominance of three species and secondary species DNA input decreasing from 3% to 0.7% (Table S2). We individually barcoded the species present in the mock communities: PCRs of the COI and 18S V1-V2 target genes were performed using the same primers as the ones used in metabarcoding (see below). The PCR reactions (25 μL final volume) contained 2 µL DNA template with 0.5 μM concentration of each primer, 1X Phusion Master Mix, and an additional 1 mM MgCl2 for COI. PCR products (98°C for 30 s; 40 cycles of 10 s at 98°C, 45 s at 48°C

(COI) or 57°C (18S), 30 s at 72°C; and 72°C for 5 min) were cleaned up with ExoSAP (Thermo Fisher Scientific, Waltham, MA, USA) and sent to Eurofins (Eurofins Scientific, Luxembourg)

for Sanger sequencing. The barcode sequences obtained for all mock specimens were added to the databases used for taxonomic assignments of metazoan datasets, and were submitted on Genbank under accession numbers MN826120-MN826130 and MN844176-MN844185.

Organic matter content and sediment grain size

Organic matter (OM) content and the distribution of particle size distributions were measured for each sample (FILAB, Dijon, France). For OM content, ~2 g of sediment were dried by heating them at 100°C overnight. Their percent content of OM was determined by their loss on ignition, the dried samples being decarbonised by heating at 550°C for four hours.

Liquid dispersion laser diffraction was performed on each sample for particle size analysis, taking a minimum of four measures per sample.

PCR amplification and sequencing

Four primer pairs were used to amplify one mitochondrial and three rRNA barcode loci targeting metazoans (COI, 18S V1-V2), micro-eukaryotes (18S V9) and prokaryotes (16S V4-V5). PCR amplifications, library preparation, and sequencing were carried out at Genoscope (Evry, France) as part of the eDNAbyss project.

Eukaryotic 18S V1-V2 rRNA gene amplicon generation

Amplifications were performed with the Phusion High Fidelity PCR Master Mix with GC buffer (Thermo Fisher Scientific, Waltham, MA, USA) and the SSUF04 (5'-GCTTGTCTCAAAGATTAAGCC-3') and SSUR22mod (5'-CCTGCTGCCTTCCTTRGA-3') primers [START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF], preferentially targeting metazoans, the primary focus of this study. The PCR reactions (25 μL final volume) contained 2.5 ng or less of DNA template with 0.4 μM concentration of each primer, 3% of DMSO, and 1X Phusion Master Mix. Triplicate PCR amplifications (98 °C for 30 s; 25 cycles of 10 s at 98 °C, 30 s at 45 °C, 30 s at 72 °C; and 72 °C for 10 min) were carried out in order to smooth the intra-sample variance while obtaining sufficient amounts of amplicons for Illumina sequencing.

amplification was performed using a Kapa Hifi HotStart NGS Library Amplification kit (Kapa Biosystems, Wilmington, MA, USA) with the same cycling conditions applied for all metagenomic libraries and purified using 1X AMPure XP beads.

Sequencing library quality control

Amplicon libraries were quantified by Quant-iT dsDNA HS assay kits using a Fluoroskan Ascent microplate fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) and then by qPCR with the KAPA Library Quantification Kit for Illumina Libraries (Kapa Biosystems, Wilmington, MA, USA) on an MxPro instrument (Agilent Technologies, Santa Clara, CA, USA). Library profiles were assessed using a high-throughput microfluidic capillary electrophoresis system (LabChip GX, Perkin Elmer, Waltham, MA, USA).

Sequencing procedure

Library concentrations were normalized to 10 nM by addition of 10 mM Tris-Cl (pH 8.5) and applied to cluster generation according to the Illumina Cbot User Guide (Part # 15006165).

Amplicon libraries are characterized by low diversity sequences at the beginning of the reads due to the presence of the primer sequence. Low-diversity libraries can interfere in correct cluster identification, resulting in a drastic loss of data output. Therefore, loading concentrations of libraries were decreased (8-9 pM instead of 12-14 pM for standard libraries) and PhiX DNA spike-in was increased (20% instead of 1%) in order to minimize the impacts on the run quality.

Libraries were sequenced on HiSeq4000 (System User Guide Part #15011190) instruments in a 150 bp paired-end mode for 18S V9, and on HiSeq2500 (System User Guide Part #15035786) instruments (Illumina, San Diego, CA, USA) in a 250 bp paired-end mode for all other amplicons.

Bioinformatic analyses

All bioinformatic analyses were performed using a Unix shell script available on Gitlab (https://gitlab.ifremer.fr/abyss-project/), on a home-based cluster (DATARMOR, Ifremer). The details of the pipeline, along with specific parameters used for all markers, are given in Table S3 and in [START_REF] Brandt | A Flexible Pipeline Combining Bioinformatic Correction Tools for Prokaryotic and Eukaryotic Metabarcoding[END_REF]. Pairs of Illumina reads were corrected with DADA2 v. 1.10 (Callahan et al. 2016) following the online tutorial for paired-end HiSeq data (https://benjjneb.github.io/dada2/bigdata_paired.html).

Prokaryote and unicellular eukaryote diversity was evaluated with Amplicon Sequence Variants (ASVs), while metazoan data was further clustered into OTUs with FROGS [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF] using swarm v2 at d=1 for 18S V1-V2 and d=6 for COI [START_REF] Mahé | Swarm v2: Highly-Scalable and High-Resolution Amplicon Clustering[END_REF]. ASVs and OTUs were taxonomically assigned via BLAST+ (v2.6.0) based on minimum similarity and minimum coverage (-perc_identity 70 and -qcov_hsp 80). The Silva132 reference database was used for taxonomic assignment of the 18S V1-V2 and 16S rRNA marker genes [START_REF] Quast | The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools[END_REF], MIDORI-UNIQUE [START_REF] Machida | Data Descriptor: Metazoan Mitochondrial Gene Sequence Reference Datasets for Taxonomic Assignment of Environmental Samples[END_REF]) subsampled to marine taxa only was used for COI, while Silva132 and PR2 [START_REF] Guillou | The Protist Ribosomal Reference Database (PR2): A Catalog of Unicellular Eukaryote Small Sub-Unit RRNA Sequences with Curated Taxonomy[END_REF] were used for 18S V9.

Molecular inventories were refined in R v.3.5.1 (R Core Team 2018). A blank correction was made using the decontam package v.1.2.1 [START_REF] Davis | Simple Statistical Identification and Removal of Contaminant Sequences in Marker-Gene and Metagenomics Data[END_REF], removing all clusters that were more prevalent in negative control samples than in true or mock samples. Clusters unassigned at phylum-level and with non-target assignments were removed. For 18S V9, clusters assigned to prokaryotes with Silva132 were removed. For metazoan loci, all clusters with a terrestrial assignment (groups known to be terrestrial-only) were removed. Samples with less than 10,000 target reads were discarded. We then performed an abundance renormalization to remove spurious ASVs/OTUs due to random tag switching [START_REF] Wangensteen | Metabarcoding Techniques for Assessing Biodiversity of Marine Animal Forests[END_REF].

The metazoan OTU tables were further curated with LULU v.0.1 [START_REF] Frøslev | Algorithm for Post-Clustering Curation of DNA Amplicon Data Yields Reliable Biodiversity Estimates[END_REF] to filter out spurious OTUs originating from intraspecific variation and/or pseudogenes, using a minimum co-occurrence of 0.90, and a minimum match threshold of 84% for COI and 90% for 18S. Finally, we taxonomically filtered the data to ensure taxonomic reliability at phylum-level:

only clusters with minimum hit identity of 86% for rDNA loci and 80% for COI were retained.

These values were chosen as they represent approximate minimum identity for reliable phylum assignment [START_REF] Stefanni | Multi-Marker Metabarcoding Approach to Study Mesozooplankton at Basin Scale[END_REF][START_REF] Yarza | Uniting the Classification of Cultured and Uncultured Bacteria and Archaea Using 16S RRNA Gene Sequences[END_REF].

Statistical analyses

Metazoan OTU tables were analysed using R with the packages phyloseq v1. evaluated with Wald Chi-square and likelihood ratio tests. Pairwise post-hoc comparisons were performed via Tukey HSD tests using the emmeans package. Numbers of shared OTUs among sediment horizons and regions in rarefied datasets were visualised with upset plots using the UpSetR package [START_REF] Conway | UpSetR: An R Package for the Visualization of Intersecting Sets and Their Properties[END_REF]. Correlation between environmental variables (organic matter content and sediment grain size) and depth in the sediment or OTU richness was measured with the cor.test function (Pearson's product-moment correlation).

Homogeneity of multivariate dispersions were evaluated with the betadisper function of the betapart package v.1.5.1 [START_REF] Baselga | Betapart : An R Package for the Study of Beta Diversity[END_REF], and region and site effects were evaluated on balanced datasets, as dispersions were not homogenous among regions.

Permutational multivariate analysis of variance (PERMANOVA) was performed on incidence data of rarefied datasets to evaluate the effect of sediment horizon, region, and site on community compositions, using the function adonis2 (vegan) with Jaccard dissimilarities. The rationale behind this choice is that metazoans are multicellular organisms of extremely varying numbers of cells, organelles, or ribosomal repeats in their genomes, and can also be detected through a diversity of remains. The number of reads can thus not be expected to reflect the abundance of detected OTUs. Significance was evaluated using 1,000 permutations with region as a blocking factor, and site as a plot factor (for evaluating region effect). Pairwise post-hoc comparisons were performed via the pairwiseAdonis package, with region as a blocking factor.

Differences among samples for meio-and macrofauna phyla as well as for all metazoan phyla combined were visualized via Principal Coordinates Analyses (PcoA) and Canonical

Analysis of Principal Coordinates (CAP) based on Jaccard dissimilarities [START_REF] Anderson | Canonical Analysis of Principal Coordinates: A Useful Method of Constrained Ordination for Ecology[END_REF]. Finally, combined analysis of macro-, meiofauna, unicellular eukaryotes (18S V9), and prokaryotes (16S V4-V5) was performed via STATIS analysis [START_REF] Lavit | The ACT (STATIS Method)[END_REF]) in ade4 [START_REF] Dray | The Ade4 Package: Implementing the Duality Diagram for Ecologists[END_REF]. Correlation among taxonomic compartments was evaluated through RV coefficients obtained. For these combined analyses, data were reduced to contain only molecular clusters occurring at 0.05% in at least one sample.

Results

High-throughput sequencing results

A S4). Numbers of sediment samples were lower in the COI dataset as more amplification failures occurred, especially in the deeper horizon samples (5-10 cm and 10-15 cm). Numbers of raw reads varied significantly with sediment horizon, with a decrease in read yield in the first three layers for COI (Chisq = 168.5, p < 0.001). For 18S, raw read abundance also varied with sediment horizon, but this not consistently among regions (Chisq = 37.2, p < 0.001), and significant differences among sites were also observed (Chisq = 9.2, p = 0.002). Quality-filtering and chimera removal reduced read numbers to 83 million for 18S V1-V2 and 61 million for COI (Table S4). Individual sediment samples contained between 0.6% and 0.8% of total processed reads, compared to 0.02% -0.03% for field and extraction blanks and 0.004% -0.007% for PCR blanks.

After taxonomic refining, decontamination [START_REF] Davis | Simple Statistical Identification and Removal of Contaminant Sequences in Marker-Gene and Metagenomics Data[END_REF], abundance renormalisation [START_REF] Wangensteen | Metabarcoding Techniques for Assessing Biodiversity of Marine Animal Forests[END_REF], and LULU curation [START_REF] Frøslev | Algorithm for Post-Clustering Curation of DNA Amplicon Data Yields Reliable Biodiversity Estimates[END_REF], metazoan datasets comprised 29 (18S) and 21.4 (COI) million reads. Rarefaction curves were comparable among sites, but a plateau was not fully reached in some 0-1 cm horizon samples for 18S, suggesting that some diversity was not captured in these samples with this marker (Fig. S1). With 18S, 9 out of 10 species were detected in both metazoan mock samples, although assignment accuracy ranged from genus to class-level, and three bivalve species were not correctly resolved and together only produced 1-2 OTUs. In contrast, COI detected all species in the mock samples, with assignments accurate down to genus-level for six species. The remaining species were correctly assigned to the class level, but two of them (scleractinian and gastropod) produced more than one OTU. Loci were subsampled to target phyla based on detection rate (Fig. 2), and final datasets comprised 6.7 (18S) and 21.3 (COI) million target reads, delivering 1,780 and 11,808 metazoan OTUs for 18S and COI respectively (Table S4).

OTU richness decreases with depth in the sediment

Metazoan OTU richness significantly decreased with increasing depth in the sediment, although the magnitude of this decrease varied significantly among regions (18S: Chisq = 219.8, p < 0.001; COI: Chisq = 1662.5, p < 0.001), and although there was significant site variability (18S: Chisq = 238.4, p < 0.001; COI: Chisq = 1608.2, p < 0.001). This pattern was observed in both studied marker genes (Fig. 3), and in major target phyla, except Tunicates, whose OTU numbers increased below 5 cm with 18S in three regions (Fig. S2).

The upper 5 cm of sediment comprised 94% (18S) to 98% (COI) of all OTUs. The first horizon (0-1 cm) was the richest and contained the highest amount of unique OTUs, i.e. 46%

for COI, 49% for 18S (Fig. S3). Following sediment layers shared more OTUs with their adjacent upper layer, then with their adjacent lower layer. However, communities in deeper sediment layers were not only a subsample of upper layers, as horizon contained from 14% to 31% of unique OTUs. Few OTUs were shared across sediment horizons (Fig. S3). Indeed, only 0.6% (COI) to 1.4% (18S) of OTUs were shared among all horizons (i.e. from 0-15 cm). OTUs shared across the first 10 cm accounted for only ~3% (COI) to 3.5% (18S) of all clusters, and these numbers were at 15% (COI) and 9% (18S) for the first 5 cm. The top two sediment layers shared more OTUs, as 34% (COI) and 30% (18S) of all clusters co-occurred in these horizons (0-1 cm and 1-3 cm).

Organic matter content and particle grain size were negatively correlated with depth in the sediment (Pearson's product-moment ρ = -0.15, p-value = 0.07 and ρ = -0.2, p-value = 0.02 respectively). However, OTU richness was neither correlated to OM content (ρ18S = 0.04, ρCOI was observed in samples with highly distinct values for these environmental variables (Fig. S4).

Numbers and nature of OTUs change across the Gibraltar Strait

As regional and habitat (i.e. site) scales significantly affected metazoan OTU richness (Fig.

3

), we investigated unique and shared OTUs in each region. Only 12.5% (18S) and 13.3% (COI) of OTUs were found on both sides of the Gibraltar Strait (Fig. 4). The richest sites were located in the regions around the strait , i.e. the Gulf of Cadiz and the Alboran Sea. Both had mean

OTU richness values at ~75 (18S) and 500 (COI) per site, compared to ≤ 50 (18S) and ~375

(COI) for other regions (Fig. 4). Both the Gulf of Cadiz (west of the strait) and the Alboran Sea (east of the strait) contained 59-67% of unique OTUs, and shared only 8% (18S, 111 OTUs) and 14% (COI, 947 OTUs) of their OTUs (Fig. 4). The North Atlantic sites harboured the highest levels of unique OTUs (68% for 18S and 83% for COI), compared to 27% (COI) to 40% (18S) of unique OTUs within the Western Mediterranean region. This region shared most of its OTUs with the Alboran Sea, but shared more OTUs with the North Atlantic sites than with the more closely located Gulf of Cadiz (Fig. 4).

Influence of small and large-scale factors on community structures

PERMANOVA showed that community structures varied significantly among regions and sites within regions (Table 1). Community structure was also significantly affected by sediment horizon, although the way assemblages segregated with sediment layers varied in magnitude across sites and regions. Large-scale geographic patterns accounted for most variation in data (16-24% for Region, 14-15% for Site), and sediment horizon accounted for approximately 5% (18S) to 8% (COI) of variation among communities.

Pairwise comparisons indicated that, for 18S, strongest community segregation among sediment horizons occurred within the first 5 cm, while communities located between 3 cm and 15 cm were similar. In contrast, for COI, the two uppermost sediment layers were similar in terms of community structure, and community segregation was strongest from 3 cm to 10 cm in the sediment.

Combined analysis of macro-, meiofauna, protists, and prokaryotes confirmed that three bioregions exist across the transition zone, with deep sites hosting similar communities across regions, while mesopelagic sites harboured different communities across the Gibraltar Strait, with the Gulf of Cadiz harbouring different communities than sites in the Mediterranean. mesopelagic Mediterranean sites. Finally, and in contrast to mesopelagic sites, deep (> 1,000 m) meiofauna communities were similar in Mediterranean and Atlantic sites. For macrofauna, communities showed a strong segregation by ocean depth for both Mediterranean and Atlantic sites, with mesopelagic sites more similar across regions. They also differed among sediment horizons, with deeper horizons hosting more similar communities across regions. Finally, Canonical Analysis of Principal Coordinates (CAP) showed that sediment grain size, ocean depth, depth in the sediment, and longitude were significanlty explaining community structures, while organic matter (OM) was non-significant as it was redundant with longitude (Fig. 6).

Local environmental variables mostly explained community differences among sites Indeed, community structures at bathyal and mesopelagic Mediterranean sites were characterized by higher OM content and small particle sizes, while assemblages in the North Atlantic (bathyal) and the Gulf of Cadiz (mesopelagic) were associated to larger particle sizes and lower OM content. However, CAPs also revealed a strong influence of depth on community composition, as mesopelagic sites (200-1,000 m) segregated from deep sites (>1,000 m), regardless the region.

Figure 6. Principarl Coordinates Analysis (PCoA) and Canonical Analysis of Principal Coordinates (CAP) ordinations showing metazoan community differences between 13 deep-sea sites located in four regions covering the Mediterranean-Atlantic transition zone. The sites are coloured according to the region they belong to: green-scale for Western Mediterranean sites, red-scale for Alboran Sea, yellow-scale for Gulf of Cadiz, and blue-scale for North Atlantic. CAPs were calculated on rarefied datasets, using Jaccard dissimilarities.

Discussion

The aim of this study was to investigate the extent and distribution of metazoan biodiversity at nested spatial scales across the Atlantic-Mediterranean transition zone, based on metabarcoding of environmental DNA using the 18S and COI barcode markers.

The taxonomic resolution of datasets differed markedly between both barcode genes used. This motivated our choice to subsample each marker to its best-detected phyla, thus avoiding taxonomic redundancy in the ecological analysis. Such approach has already been adopted for studying zooplankton patterns at ocean-basin scale [START_REF] Stefanni | Multi-Marker Metabarcoding Approach to Study Mesozooplankton at Basin Scale[END_REF] and is an effective way to take advantage of the complementarity of the 18S rRNA and COI barcode regions. Fig. 2 highlights this complementarity, showing that the greatest coverage of metazoan phyla can be achieved by combining both markers, and underlining that there is no clade (e.g., Metazoa, Protostomia, Lophotrochozoa) for which the use of either COI or 18S allows the detection of all phyla. Combining 18S and COI in a taxonomically non-redundant way therefore seems to be the most effective way to achieve more comprehensive biodiversity inventories. Finally, even though we subsampled each dataset based on numbers of OTUs detected in each phylum, it is noteworthy to highlight that 18S seemed to be better at detecting meiofauna (< 1 mm), while COI mostly detected macro-and megafauna (> 1 mm). The metazoan community structures were highly correlated (RV at 0.8), illustrating the strong interactions between metazoan size classes, confirming patterns reported in numerous studies of the deep-sea benthos based on morphological inventories [START_REF] Gallucci | Effects of Megafauna Exclusion on Nematode Assemblages at a Deep-Sea Site[END_REF][START_REF] Buhl-Mortensen | Biological Structures as a Source of Habitat Heterogeneity and Biodiversity on the Deep Ocean Margins[END_REF][START_REF] Hasemann | Small-Scale Heterogeneity in Deep-Sea Nematode Communities around Biogenic Structures[END_REF]. Meiofauna communities, primarily detected by 18S, were found to be less correlated to prokaryote communities (RV=0.5) than the macro-and megafauna detected by COI (RV=0.65). This is in line with morphology-based studies that did not find an influence of prokaryote abundance, biomass, or activity on meiofauna organisms in the deepsea [START_REF] Danovaro | Vertical Distribution of Meiobenthos in Bathyal Sediments of the Eastern Mediterranean Sea: Relationship with Labile Organic Matter and Bacterial Biomasses[END_REF][START_REF] Górska | Bathymetric Variations in Vertical Distribution Patterns of Meiofauna in the Surface Sediments of the Deep Arctic Ocean (HAUSGARTEN, Fram Strait)[END_REF]).

This study confirmed that deep-sea metazoan richness and community structure can vary at very small scales, i.e. centimetres [START_REF] Rosli | Review of Recent Trends in Ecological Studies of Deep-Sea Meiofauna, with Focus on Patterns and Processes at Small to Regional Spatial Scales[END_REF][START_REF] Leduc | Effect of Core Surface Area and Sediment Depth on Estimates of Deep-Sea Nematode Genus Richness and Community Structure[END_REF][START_REF] Rosli | Differences in Meiofauna Communities with Sediment Depth Are Greater than Habitat Effects on the New Zealand Continental Margin: Implications for Vulnerability to Anthropogenic Disturbance[END_REF]Leduc et al. 2012b;[START_REF] Ingels | Structural and Functional Diversity of Nematoda in Relation with Environmental Variables in the Setúbal and Cascais Canyons, Western Iberian Margin[END_REF][START_REF] Gallucci | Small-Scale Spatial Patterns of Meiobenthos in the Arctic Deep Sea[END_REF][START_REF] Danovaro | Vertical Distribution of Meiobenthos in Bathyal Sediments of the Eastern Mediterranean Sea: Relationship with Labile Organic Matter and Bacterial Biomasses[END_REF]. Indeed, significant vertical segregation in community structure was revealed by the multivariate analyses, with sediment layer accounting for 5-8% of variation among communities, regardless the sampling region or barcode marker. Diversity as measured by OTU richness also significantly decreased with increasing depth in the sediment, although the magnitude of this decrease varied among regions and sites. Although most morphology-based studies only CHAPTER V DEEP-SEA BIODIVERSITY PATTERNS THROUGH eDNA 158 investigated the upper 5 cm of sediment, decreases in species abundance and diversity with sediment layer have repeatedly been reported in deep-sea sediment assemblages, for e.g. in the Arctic [START_REF] Fonseca | Variation in Nematode Assemblages over Multiple Spatial Scales and Environmental Conditions in Arctic Deep Seas[END_REF][START_REF] Górska | Bathymetric Variations in Vertical Distribution Patterns of Meiofauna in the Surface Sediments of the Deep Arctic Ocean (HAUSGARTEN, Fram Strait)[END_REF][START_REF] Pfannkuche | Meiobenthic Stocks and Benthic Activity on the NE-Svalbard Shelf and in the Nansen Basin[END_REF] or Pacific oceans [START_REF] Rosli | Differences in Meiofauna Communities with Sediment Depth Are Greater than Habitat Effects on the New Zealand Continental Margin: Implications for Vulnerability to Anthropogenic Disturbance[END_REF][START_REF] Leduc | Influence of Mesh Size and Core Penetration on Estimates of Deep-Sea Nematode Abundance, Biomass, and Diversity[END_REF][START_REF] Snider | The Composition and Distribution of Meiofauna and Nanobiota in a Central North Pacific Deep-Sea Area[END_REF]. First investigations using eDNA metabarcoding in the deep-sea also reported these patterns [START_REF] Guardiola | Spatio-Temporal Monitoring of Deep-Sea Communities Using Metabarcoding of Sediment DNA and RNA[END_REF]. For taxa revealed by 18S, the decrease was most apparent below 3 cm, while for taxa revealed by COI there was a marked drop in richness below the uppermost centimetre of sediment in three out of four sampling regions (Fig. 3). This reflects different segregation patterns between the two types of taxa detected by both markers. Indeed, benthic megafauna, mostly revealed by COI, are epifaunal organisms living and feeding on the sediment surface, while taxa revealed by 18S are predominantly interstitial meiofauna, which can penetrate deeper into the sediment [START_REF] Rex | Community Structure in the Deep-Sea Benthos[END_REF]. Consistently, the top sediment layer (0-1 cm) was dominated by mega and macrofauna OTUs well detected by COI, such as arthropods, cnidarians, molluscs, poriferans, or echinoderms, whose OTU numbers decreased strongly below 1 cm (Fig. S2). In contrast, 18S detected considerable OTUs numbers for meiofauna taxa such as nematodes, gastrotrichs, kinorhynchs, or tardigrades, and revealed a surprising diversity of platyhelminths and xenacoelomorphs in all sampling regions. This was also highlighted by other studies having applied eDNA metabarcoding in deep-sea sediments [START_REF] Guardiola | Spatio-Temporal Monitoring of Deep-Sea Communities Using Metabarcoding of Sediment DNA and RNA[END_REF]. Meiofauna have been shown to be primarily located in the upper 3 cm [START_REF] Snider | The Composition and Distribution of Meiofauna and Nanobiota in a Central North Pacific Deep-Sea Area[END_REF][START_REF] Thiel | Meiobenthos and Nanobenthos of the Deep-Sea[END_REF] to 5 cm [START_REF] Giere | Meiobenthology: The Microscopic Motile Fauna of Aquatic Sediments[END_REF] of the sediment, although capable of penetrating as deep as 30 cm [START_REF] Shirayama | Vertical Distribution of Meiobenthos in the Sediment Profile in Bathyal, Abyssal and Hadal Deep Sea Systems of the Western Pacific[END_REF]. A constant peak in abundance is generally observed in the first centimetre, with the exception of some peculiar ecosystems or where strong currents are present [START_REF] Zeppilli | Changes in Nematode Communities in Different Physiographic Sites of the Condor Seamount (North-East Atlantic Ocean) and Adjacent Sediments[END_REF][START_REF] Pimm | Biodiversity: Not Just Lots of Fish in the Sea[END_REF]. These patterns are in congruence with the results observed here for 18S.

It is important to add that detected OTU numbers were substantially lower for 18S, but this is mostly due to the different taxonomic levels reached by each marker: 18S is less resolutive, revealing family to genus diversity, while COI more accurately reveals species diversity.

Although OTU richness of most taxa decreased with sediment depth, with some phyla visibly more diverse in specific regions (e.g. Tardigrada in the Gulf of Cadiz), a notable exception was observed for the tunicates revealed by 18S, whose diversity increased below 5 cm in three out of the four studied regions (Fig. S2), highlighting and confirming that some taxa thrive deeper within the sediment [START_REF] Steyaert | The Importance of Fine-Scale, Vertical Profiles in Characterising Nematode Community Structure[END_REF].

Grain size and organic matter slightly decreased with increasing depth in the sediment, yet, OTU richness was not correlated to these variables, and regions with higher OM content CHAPTER V DEEP-SEA BIODIVERSITY PATTERNS THROUGH eDNA 159 (Alboran Sea and Western Mediterranean, Fig. S3) did not show smoother gradients in OTU decrease with sediment depth, indicating that vertical richness patterns in the sediment cannot be explained by food availability alone. Similarly, while sediment granulometry is important in controlling faunal horizontal patterns (Zeppilli et al. 2016), it has been shown not to be the dominant factor explaining vertical diversity patterns, especially in deeper layers [START_REF] Steyaert | The Importance of Fine-Scale, Vertical Profiles in Characterising Nematode Community Structure[END_REF]). Rather, it has been suggested that vertical patterns in deep-sea sediments arise from interrelating abiotic and biotic factors, such as oxygen and nitrogen content [START_REF] Soetaert | Nematode Distribution in Ocean Margin Sediments of the Goban Spur (Northeast Atlantic) in Relation to Sediment Geochemistry[END_REF], organic matter composition and availability [START_REF] Pfannkuche | Meiobenthic Stocks and Benthic Activity on the NE-Svalbard Shelf and in the Nansen Basin[END_REF][START_REF] Danovaro | Vertical Distribution of Meiobenthos in Bathyal Sediments of the Eastern Mediterranean Sea: Relationship with Labile Organic Matter and Bacterial Biomasses[END_REF][START_REF] Pusceddu | Organic Matter Composition, Metazoan Meiofauna and Nematode Biodiversity in Mediterranean Deep-Sea Sediments[END_REF], as well as interactions with larger fauna, for e.g. predator avoidance or facilitation due to bioturbation resulting in increased sediment oxygenation [START_REF] Lambshead | Comparison of the Vertical Distribution of Nematodes from Two Contrasting Abyssal Sites in the Northeast Atlantic Subject to Different Seasonal Inputs of Phytodetritus[END_REF][START_REF] Hasemann | Small-Scale Heterogeneity in Deep-Sea Nematode Communities around Biogenic Structures[END_REF][START_REF] Gallucci | Effects of Megafauna Exclusion on Nematode Assemblages at a Deep-Sea Site[END_REF]. The measurement of in situ nutrient levels combined with better proxies for food availability such as protein and lipid concentrations [START_REF] Danovaro | Vertical Distribution of Meiobenthos in Bathyal Sediments of the Eastern Mediterranean Sea: Relationship with Labile Organic Matter and Bacterial Biomasses[END_REF] was unfortunately not possible in this study, highlighting the need for integrative research programs combining biological and geochemical measurements to better elucidate deep-sea benthic biodiversity drivers at small scales. However, our results also highlight the importance of standardized sediment sampling schemes to ensure comparability among samples and studies, as different sediment layers do not reveal the same communities (Figs. 56).

Large-scale effects were predominant in explaining beta-diversity patterns at habitat (>100 m) and regional (>100 km) scales, accounting for 30-40% of variation among communities detected. Similarly, significant differences in OTU richness were observed among sites within regions and among regions, with lowest richness in the Western Mediterranean Sea compared to regions located westward (Alboran Sea to North Atlantic), a result comparable to observations made by [START_REF] Bianchelli | Metazoan Meiofauna in Deep-Sea Canyons and Adjacent Open Slopes: A Large-Scale Comparison with Focus on the Rare Taxa[END_REF] on the basis of morphological data. Although some studies have found small-scale effects to be more strongly affecting alpha and beta diversity than habitat or region effects, these studies usually focused on a single geographical location and/or habitat type [START_REF] Górska | Bathymetric Variations in Vertical Distribution Patterns of Meiofauna in the Surface Sediments of the Deep Arctic Ocean (HAUSGARTEN, Fram Strait)[END_REF][START_REF] Ingels | Structural and Functional Diversity of Nematoda in Relation with Environmental Variables in the Setúbal and Cascais Canyons, Western Iberian Margin[END_REF] or a single genus [START_REF] Fonseca | Species Richness of the Genus Molgolaimus (Nematoda) from Local to Ocean Scale along Continental Slopes[END_REF]. Consistently to our study, investigations including local to regional scales have found that variability in abundance of organisms, richness, or community structures is higher at larger scales [START_REF] Pusceddu | Organic Matter Composition, Metazoan Meiofauna and Nematode Biodiversity in Mediterranean Deep-Sea Sediments[END_REF]. This could be explained by the very distinct ecosystems occurring over large scales, highlighted here by the diversity of seascapes sampled (Table S1).

Large-scale factors appeared to affect beta-diversity more than alpha diversity, as communities differed much more in terms of structure (Fig. 5) than in terms of richness (Fig 4) CHAPTER V DEEP-SEA BIODIVERSITY PATTERNS THROUGH eDNA 160 across regions and sites. This in line with a previous study based on morphological inventories and comparing 3 deep-sea regions in the Mediterranean and North East Atlantic found high regional differences in beta diversity, but similar values of alpha diversity (Danovaro et al. 2009a). Similarly, habitat-scale effects have been shown to strongly affect community structure but showed little effect, if any, on taxonomic or functional diversity [START_REF] Danovaro | Multiple Spatial Scale Analyses Provide New Clues on Patterns and Drivers of Deep-Sea Nematode Diversity[END_REF]. This is congruent with our results showing that differences in ommunity composition were significantly linked to changes in organic matter content and sediment particle size (Fig 6), while these variables were not correlated to OTU richness (Fig. S3). Different availability, composition, and size spectra of food particles in sediments at habitat (> 100 m) to local (~1-100 m) scales are known to strongly affect the composition of deep-sea assemblages [START_REF] Danovaro | Multiple Spatial Scale Analyses Provide New Clues on Patterns and Drivers of Deep-Sea Nematode Diversity[END_REF]. Consequently, differences in biochemical composition of sediment organic matter were found to explain high beta-diversity between regions, as they increase diversification of benthic food webs [START_REF] Pusceddu | Organic Matter Composition, Metazoan Meiofauna and Nematode Biodiversity in Mediterranean Deep-Sea Sediments[END_REF]. Similarly, differences in sediment characteristics are known to affect community diversity and composition [START_REF] Etter | Patterns of Species Diversity in the Deep Sea as a Function of Sediment Particle Size Diversity[END_REF][START_REF] Pape | Unravelling the Environmental Drivers of Deep-Sea Nematode Biodiversity and Its Relation with Carbon Mineralisation along a Longitudinal Primary Productivity Gradient[END_REF]. Coarser sediments are associated with higher diversity on a broad horizontal scale, likely due to an increased range of microhabitats [START_REF] Snider | The Composition and Distribution of Meiofauna and Nanobiota in a Central North Pacific Deep-Sea Area[END_REF][START_REF] Steyaert | The Importance of Fine-Scale, Vertical Profiles in Characterising Nematode Community Structure[END_REF], and sediment particle size affects organism size, feeding mode, and locomotion mode (Vanreusel et al. 2010a;Leduc et al. 2012b). Our results highlight that organic matter composition and sediment grain size vary more strongly at the habitat (> 100 m) and regional (>100 km) scales, and thus mostly contribute explaining beta diversity patterns at larg spatial scales, rather than local alpha diversity.

Finally, depth also was shown to have a much stronger effect on community structure than on richness, as deep (> 1,000 m) and mesopelagic sites sites harboured significantly different communities , although displaying comparable richness levels (Fig. 4), even though the depth effect was stronger in the Western Mediterranean for the meiofauna. This is in line with previous morphological studies investigating benthic diversity at intra-basin scales (Danovaro et al. 2009a;[START_REF] Bianchelli | Metazoan Meiofauna in Deep-Sea Canyons and Adjacent Open Slopes: A Large-Scale Comparison with Focus on the Rare Taxa[END_REF], highlighting that benthic assemblages strongly differ between mesopelagic and deep-sea environments (> 1,000 m), and supporting the hypothesis that depth is an important factor to consider for defining marine biogeographic realms (Levin, L. A. et al. 2001;[START_REF] Watling | A Proposed Biogeography of the Deep Ocean Floor[END_REF]. Our results overall support the depth zonation proposed by [START_REF] Costello | Ocean Depths: The Mesopelagic and Implications for Global Warming[END_REF], who, based on a global marine diversity dataset identified 200 m and 1000 m to be critical depths shaping marine diversity. CHAPTER V DEEP-SEA BIODIVERSITY PATTERNS THROUGH eDNA 161 Howeeever, this work did not provide strong evidence that deep water are less species rich.

Instead, it highlighted that deep waters offer very distinct habitat conditions leading to specific niches and thus different species.

The deep sea was long described as homogeneous, lacking the obvious barriers to dispersal that characterize shallow waters, such as emerged lands or strong wind-induced water movements [START_REF] Mcclain | The Dynamics of Biogeographic Ranges in the Deep Sea[END_REF]. Few studies have tackled the distribution of deep-sea diversity across distinct ocean basins thus far (Vanreusel et al. 2010a). In the present work, only approx. 13% of total OTUs were shared across the Gibraltar Strait, indicating a very limited exchange between Atlantic and Mediterranean basins. This highlights that the transition between Mediterranean and Atlantic basins is both a biogeographic barrier involved in vicariant events during environmental changes over geological timescales [START_REF] Patarnello | Pillars of Hercules: Is the Atlantic-Mediterranean Transition a Phylogeographical Break?[END_REF][START_REF] Duranton | The Origin and Remolding of Genomic Islands of Differentiation in the European Sea Bass[END_REF]) and a present day barrier to connectivity between the Mediterranean and Atlantic ocean basins [START_REF] Catarino | The Role of the Strait of Gibraltar in Shaping the Genetic Structure of the Mediterranean Grenadier, Coryphaenoides Mediterraneus, between the Atlantic and Mediterranean Sea[END_REF][START_REF] Duranton | The Spatial Scale of Dispersal Revealed by Admixture Tracts[END_REF]. It remains unclear whether this barrier is a barrier to dispersal or to recruitement. K-tables analyses showed that community structures were more similar among deep regions across the strait than among shallow regions within the strait (Fig. 5). The Gulf of Cadiz being an inactive mud volcano habitat, is known to harbour exclusive species (Zeppilli et al. 2011), underlining that habitat-specific conditions are predominant in determining community structures. Our results thus support the hypothesis that the Gibraltar Strait is a barrier to recruitment rather than dispersal, however, our sampling effort was very fragmented, so this remains to be confirmed.

The deep seafloor (>200 m depth) covers >60% of planet Earth [START_REF] Snelgrove | Getting to the Bottom of Marine Biodiversity: Sedimentary Habitats[END_REF]Smith, C. R. et al. 2008;Costello et al. 2010a). It can host high numbers of mostly small organisms (50,000-5 million individuals per square meter) that perform key ecosystem roles such as nutrient cycling, sediment stabilisation and transport, or secondary production [START_REF] Rex | Global Bathymetric Patterns of Standing Stock and Body Size in the Deep-Sea Benthos[END_REF][START_REF] Bellec | A Nematode of the Mid-Atlantic Ridge Hydrothermal Vents Harbors a Possible Symbiotic Relationship[END_REF]Smith, K. L. et al. 2009). Although technological developments in the past 30 years have allowed remarkable advances, most deep-sea studies have been limited to local and regional scales due to the sheer vastness and remoteness of this biome, together with the long time required for morphological inventories and the lack of objective standards needed to merge together works performed by distinct experts. Consequently, we have so far explored less than 1% of the deep seafloor, and this contrasts with the fact that deep-sea ecosystems are under increased threat from a variety of direct and indirect anthropogenic pressures [START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF].

Reducing deep-sea biodiversity gaps is therefore essential to better understand and predict how biodiversity will change in the context of global climate modifications, and how this will affect Earth's life-support systems. A recent review of scientific advances needed to reduce biodiversity gaps identified seven priorities [START_REF] Saeedi | Global Marine Biodiversity in the Context of Achieving the Aichi Targets: Ways Forward and Addressing Data Gaps[END_REF], two of which are the core elements of this thesis, namely: 1) the "Improvement and standardization of genetic, genomic, and other "omics" tools to aid in discovery, assessment, description, and cataloguing of biodiversity" and 2) the need for "Identifying biodiversity and biogeographic knowledge gaps and promoting efforts to reduce such gaps". Indeed, while eDNA metabarcoding was identified as one of the most promising tools for achieving faster, cost-effective, and more comprehensive marine biodiversity assessments [START_REF] Danovaro | Implementing and Innovating Marine Monitoring Approaches for Assessing Marine Environmental Status[END_REF], many challenges remain to be resolved in order to apply eDNA methods on a broad scale [START_REF] Cristescu | Uses and Misuses of Environmental DNA in Biodiversity Science and Conservation[END_REF].

This thesis addresses and evaluates several crucial methodological aspects for applying eDNA metabarcoding in deep-sea ecosystems, and provides an example of how this new tool can accelerate deep-sea exploration, supporting the idea that eDNA metabarcoding offers new perspectives to increase our understanding of deep sea biodiversity and biogeography [START_REF] Danovaro | The Deep-Sea under Global Change[END_REF][START_REF] Cristescu | From Barcoding Single Individuals to Metabarcoding Biological Communities: Towards an Integrative Approach to the Study of Global Biodiversity[END_REF]. This work presented here explored paths allowing optimizing the eDNA metabarcoding workflow at bioinformatic (chapter 2), molecular (chapter 3), and sampling (chapter 4) levels. Major points for the successful and large application of eDNA metabarcoding in the deep-sea are discussed below.

CHAPTER VI

GENERAL DISCUSSION 165

Bioinformatic pipelines need to combine new tools in a flexible and user-friendly way

Abyss-pipeline (https://gitlab.ifremer.fr/abyss-project), the bioinformatic pipeline developed during this thesis (chapter 2) incorporates the newest advances for processing Illumina-sequenced metabarcoding data (Fig. 1). It addresses major sources of error by implementing the following tools. First, raw reads are corrected with DADA2 for effectively removing sequencing errors, the process also producing a read track table for obtaining a valuable and informative overview of read numbers throughout the process. Second, chimeras are removed after ASV inference, and again after swarm-clustering if this process is activated.

Third, an abundance-renormalization filter is available to remove spurious clusters due to tag switching, and LULU-curation is available to remove additional spurious clusters. ASVs can optionally be clustered into OTUs using swarm v2, an iterative single-linkage algorithm that allows more fine-scale and data-dependent clustering than previous algorithm based on arbitrary thresholds. Finally, taxonomic assignment can be performed via BLAST and the RDP Bayesian Classifier for both ASVs and OTUs. All these processes are implemented independently to allow maximum user-control, and application on metazoan mock samples showed that the combination of these tools allowed achieving a near 1:1 species-OTU relationship.

Chapter 2 highlighted that the choice of the molecular entity used as a proxy for taxa is crucial to obtain reliable inventories, and this choice depends on the taxonomic compartment of interest. While ASVs accurately describe high-resolution genetic diversity and may be appropriate for the study of unicellular organisms exhibiting lower intraspecific variation rates, or to infer the distribution of genetic rather than species diversity, they lead to an overestimation of the number of clusters for metazoans (Brandt, M. I. et al. 2020). For these taxa, sequence error correction thus needs to be combined with clustering and LULU-curation in order to obtain more realistic species inventories.

While Graphical User Interfaces in web applications such as SLIM [START_REF] Dufresne | SLIM: A Flexible Web Application for the Reproducible Processing of Environmental DNA Metabarcoding Data[END_REF] may facilitate bioinformatic analyses, especially for less-experienced users, web applications remain limited in the quantity of data they can process, and the limited ability for parameter adjustment, especially during initial data preparation, quality filtering, or error correction. The analysis of large or multi-marker datasets therefore still requires customizable scripts, such as the shell scripts provided by abyss-pipeline.

As E.F. Schumacher highlights in his book "Small Is Beautiful", technology's primary purpose is to lighten the burden of work, and we therefore need methods and equipment which are "cheap enough so that they are accessible to everyone, suitable for small-scale applications [i.e. low-cost], compatible with man's need for creativity". Most metabarcoding-related bioinformatic tools are freely available online, however implementing and learning to use them remains complex and difficult for most members of the research community, as most are new to informatics. A significant way forward towards the simplification of bioinformatic processing would be to adapt the tools to the users (and not the other way around!) by making them directly usable in R, a software familiar to most biologists. This idea is supported by the fact that recent algorithmic advances used in this thesis such as DADA2 and LULU are already coded in R. Furthermore, these tools could then be combined into an R-based pipeline, as exemplified by the "Just Another Metabarcoding Pipeline"

(https://github.com/VascoElbrecht/JAMP) R package [START_REF] Elbrecht | Estimating Intraspecific Genetic Diversity from Community DNA Metabarcoding Data[END_REF].

eRNA is associated to more bias than eDNA potentially containing traces of ancient DNA Chapter 3 largely confirmed doubts about the capacity of eRNA to better describe live communities (Cristescu 2019). This study was the first to compare co-extracted eRNA and eDNA biodiversity inventories using ribosomal and mitochondrial markers targeting prokaryote, protistan, and metazoan life compartments.

With ribosomal loci, RNA, while resolving similar spatial patterns than co-extracted DNA, resulted in significantly higher richness estimates. This supports hypotheses of increased persistence of rRNA in the environment, and of increased amounts of spurious clusters with eRNA due to high but unmeasured artefacts produced during reverse transcription of RNA to cDNA (Cristescu 2019;[START_REF] Laroche | Metabarcoding Monitoring Analysis: The Pros and Cons of Using Co-Extracted Environmental DNA and RNA Data to Assess Offshore Oil Production Impacts on Benthic Communities[END_REF], highlighted here by the greater amounts of chimeras observed with RNA in ribosomal loci. Contrastingly, with the mitochondrial COI marker, RNA detected significantly lower metazoan richness, resolved less spatial patterns than co-extracted DNA, and was associated to increased sample failures. This reflects high messenger RNA lability, making it unsuitable for large-scale ecological surveys.

Moreover, eRNA may lead to potentially significant taxonomic bias using any marker gene, due to abundant but taxon-specific RNA release, either passively after death (exacerbated by the fact that RNA is way more abundant than DNA in living organisms), or actively and this varying according to metabolic levels and/or life stages [START_REF] Torti | Origin, Dynamics, and Implications of Extracellular DNA Pools in Marine Sediments[END_REF][START_REF] Blazewicz | Evaluating RRNA as an Indicator of Microbial Activity in Environmental Communities: Limitations and Uses[END_REF].

In contrast, our approach aiming to remove ancient DNA by removing DNA fragments in the aDNA size range (< 1,000 bp, [START_REF] Lennon | How, When, and Where Relic DNA Affects Microbial Diversity[END_REF][START_REF] Boere | Preservation Potential of Ancient Plankton DNA in Pleistocene Marine Sediments[END_REF]Lejzerowicz et al. 2013a;[START_REF] Coolen | Evolution of the Plankton Paleome in the Black Sea from the Deglacial to Anthropocene[END_REF], did not show any effect on alpha or beta diversity patterns. Of course, aDNA may also be archived in vesicles or other organelles, although there is increasing evidence that DNA from non-living cells is mostly contemporary [START_REF] Lennon | How, When, and Where Relic DNA Affects Microbial Diversity[END_REF]). This suggests that, even if aDNA may be present in deep-sea sediments, the eDNA metabarcoding workflow will primarily target contemporary DNA most likely due to 1) its overabundance in the environment and 2) DNA extraction protocols unsuited for aDNA preservation.

Standardized and replicated sampling is needed to ensure comprehensive, reproducible, and comparable results

It is generally known that ~10% of PCRs fail [START_REF] Andreson | Predicting Failure Rate of PCR in Large Genomes[END_REF], and this for a number of technical reasons (see Chapter 1, Fig. 4). Consequently, strong research focus has gone into evaluating the effect of technical PCR replicates, and numerous studies have stressed that replication of PCR reactions, as well as their independent sequencing is essential to increase detection probability and reliability of results [START_REF] Ficetola | Replication Levels, False Presences and the Estimation of the Presence/Absence from EDNA Metabarcoding Data[END_REF][START_REF] Alberdi | Scrutinizing Key Steps for Reliable Metabarcoding of Environmental Samples[END_REF] unfortunately been associated with a decrease in attention on biological replication. This is highlighted by the general absence of stated rationales explaining the number and types of replicates, and the frequent absence or inadequacy of replication in many metabarcoding studies [START_REF] Dickie | Towards Robust and Repeatable Sampling Methods in EDNA-Based Studies[END_REF].

Of course, replication should ideally be performed on both technical and biological levels.

However, given the cost of sampling, DNA extraction, PCR, and sequencing, it is essential to determine an appropriate trade-off between logistic feasibility and adequate replication, and this depends on the study objectives. For example, it appears important to process PCR replicates independently throughout the metabarcoding workflow if the detection of rare or ancient species is the primary research interest [START_REF] Ficetola | Replication Levels, False Presences and the Estimation of the Presence/Absence from EDNA Metabarcoding Data[END_REF]. However, PCR replicates can be pooled for sequencing in order to smooth intra-sample variance and address PCR bias, effectively reducing sequencing costs while still allowing characterizing large-scale patterns [START_REF] Dickie | Towards Robust and Repeatable Sampling Methods in EDNA-Based Studies[END_REF].

Small-scale (centimetres to metres) patchiness is common in the deep-sea, with patterns of patchiness varying with taxonomic compartment [START_REF] Grassle | Deep-Sea Species Richness: Regional and Local Diversity Estimates from Quantitative Bottom Samples[END_REF][START_REF] Rex | Community Structure in the Deep-Sea Benthos[END_REF][START_REF] Lejzerowicz | Patchiness of Deep-Sea Benthic Foraminifera across the Southern Ocean: Insights from High-Throughput DNA Sequencing[END_REF]. This leads to considerable within-site variability, which can only be mitigated by collecting adequate numbers of biological replicates per sampling site, but also been addressed by adapting the sampling gear to each benthic size compartment. Indeed, multicorers are generally used for nano-and meiofauna as they are the only tools preserving vertical sediment stratification, box corers are often used for macrofauna as they cover a larger sampling area, and epibenthic sledges are commonly used for megafauna [START_REF] Montagna | Comparison of Sampling Methods for Deep-Sea Infauna[END_REF]. Given that a full deployment series of these gears takes approx. 40 hours, ecological research in the deep-sea is inherently confronted with the trade-off between number of gear deployments per site and number of sites to sample (Daniela Zeppilli, pers. comm.). It can be argued that, eDNA metabarcoding effectively detecting small traces of organisms (mucus, shed cells), multicorers may also be adequate for the study of larger fauna, as those will be detected even if not present in the sediment. Moreover, box corers and sledges are known to create strong bow waves that wash off the upper layer of sediment, thus leading to the loss of many organisms. This explains why a recent long-term study found that box corers underestimated total macrofauna density by a factor of 2.9 times compared to multicorers, and reported that they underestimate richness relative to area sampled [START_REF] Montagna | Comparison of Sampling Methods for Deep-Sea Infauna[END_REF].

Given the extent of the unknown in the deep sea, there is unsurprisingly a lack of consensus on the type and number of replicates appropriate to collect for a typical spatial study on the deep-sea benthos: some authors argue that individual cores from the same multicorer deployment are statistically not ideal as these cores are not collected independently, and are thus pseudoreplicates rather than replicates [START_REF] Colegrave | Using Biological Insight and Pragmatism When Thinking about Pseudoreplication[END_REF]. However, [START_REF] Montagna | Comparison of Sampling Methods for Deep-Sea Infauna[END_REF] found more variability between cores of the same multicorer deployments (i.e., pseudoreplicates) than between deployments (true replicates), consistent with the strong smallscale patchiness in benthic fauna reported in the deep-sea and the limited overall variability reported at local scales (see chapter 1). Moreover, the difficulty of controlling the exact location of multicorer deployments and the significant patchiness in species distribution, primarily resulting from local scale seafloor heterogeneity (Zeppilli et al. 2016) makes it unclear to what extent multicorer deployments are representative replicates of single sampling location.

Results obtained in chapter 5 highlight this issue, as the multicorer deployments (sites) sampled in each region showed significant differences in richness and community structure, even when they were targeting the same location (e.g., Gulf of Cadiz, Alboran Sea).

It therefore appears that spatial studies should consider sampling more stations/locations, each with few multicore deployments. Whether cores within deployments can be considered true replicates remains to be confirmed on a global scale as results from [START_REF] Montagna | Comparison of Sampling Methods for Deep-Sea Infauna[END_REF] were geographically restricted although covering a 14 year time period. If their findings can e generalised, subsamples of cores within a deployment could allow increased precision per replicate. Measuring this within-plot variability could help better understanding the spatial heterogeneity of deep-sea benthic organisms [START_REF] Dickie | Towards Robust and Repeatable Sampling Methods in EDNA-Based Studies[END_REF]).

Finally, chapter 3 highlighted that sediment quantities ≥10 g should be used to accurately detect eukaryote (incl. non-metazoan) diversity and that 2 g of sediment were insufficient to account for small-scale spatial heterogeneity, a finding already reported in numerous other studies in terrestrial soils and marine sediments [START_REF] Creer | The Ecologist's Field Guide to Sequence-Based Identification of Biodiversity[END_REF][START_REF] Nascimento | Sample Size Effects on the Assessment of Eukaryotic Diversity and Community Structure in Aquatic Sediments Using High-Throughput Sequencing[END_REF].

While organism size sorting through sediment sieving allowed, as expected [START_REF] Elbrecht | Sorting Things out: Assessing Effects of Unequal Specimen Biomass on DNA Metabarcoding[END_REF], detecting higher meiofauna diversity (chapter 4), similar spatial patterns and taxon compositions were obtained in sieved and non-sieved samples, indicating that the considerable time-costs associated with sieving are not essential for inferring robust ecological patterns. This is supported by the fact that sample washing and sieving may lead to substantial loss of organisms [START_REF] Montagna | Comparison of Sampling Methods for Deep-Sea Infauna[END_REF], added to the increased risk of contamination by foreign DNA, particularly if the protist and prokaryote size compartments are of interest. Moreover, chapter 4 highlighted that aboveground water cannot be used for assessing benthic diversity with eDNA, even when large water volumes are sampled, supporting recent studies performed at shallower depths [START_REF] Antich | Marine Biomonitoring with EDNA: Can Metabarcoding of Water Samples Cut It as a Tool for Surveying Benthic Communities?[END_REF]). Finally, chapter 5 highlighted that reporting sediment depth layers used is crucial to allow comparability among studies, and that sampling design

should include the 0-1 cm, 1-3 cm, and 3-5 cm sediment horizons as these were associated with low processing failures and consistent differences across all sites for both the COI and 18S markers. Moreover, sampling should combine measurements of physical and chemical parameters with biological species detection to allow better estimation of community structure and function [START_REF] Costello | Stratifying Ocean Sampling Globally and with Depth to Account for Environmental Variability[END_REF]. While deep water samples specifically targeted pelagic organisms (chapter 4), sampling tools significantly affected the type of taxa detected. In situ pumps were shown to have great potential in low biomass deep-sea waters, but being limited by mesh size, they are therefore more appropriate for assessing metazoan diversity, but only capture a fraction of microorganisms. Sampling tools allowing the recovery of small size classes remain necessary to comprehensively detect microbial diversity.

Taxonomic assignments of deep-sea metabarcoding datasets are unreliable beyond phylum-level when using public reference databases

Obtaining species names is useful for inferring ecological traits, as behind each name, there is a phenotype (with all its variability and life forms), an ecological role, and a geographic distribution. However, taxonomic assignments of sequences are only as good as the databases they are based upon. Deep-sea benthic metabarcoding datasets face the double challenge of focusing on taxonomic groups that are both highly diverse and poorly represented in public sequence reference databases. Chapter 2 thus highlighted that it is difficult to obtain accurate taxonomic assignments even for megafauna, as we failed to obtain high-resolution taxonomic assignments for several species in the mock samples, for both the COI and 18S markers. With 18S, a high number of OTUs were left unassigned at the phylum-level, and percentage identities to reference sequences of OTUs in the sediment samples ranged from 80% to 100%. They were ≤ 86% for most OTUs with COI.

It has been suggested that critical cut-off values to ensure correct phylum-level assignment are 80% identity for COI reads, and 86% identity for 18S reads [START_REF] Stefanni | Multi-Marker Metabarcoding Approach to Study Mesozooplankton at Basin Scale[END_REF]). This suggests that, while most of the 18S OTUs analysed in this thesis were most likely correctly identified at phylum-level, this is not the case for COI OTUs. Similarly, accurate taxonomic assignments down to genus or species level are unlikely using currently available databases, even for rRNA, where confidence in taxonomic assignments at the genus-level can only be ensured above 95% identity (Edgar 2018b). This explains why only ~2% of COI and ~39% of 18S OTUs were found to have acceptable genus RDP bootstraps in chapter 2. As taxonomic assignments of OTUs in our deep-sea metabarcoding datasets were not satisfactory using publicly available databases, especially not for COI, we thus chose to focus chapter 5 solely on numerical ecology.

Sequence-based techniques require the availability of comprehensive but also high-quality reference databases. Concerns about misannotation errors in large public repositories (i.e.

GenBank, ENA, and DNA Data Bank of Japan) have been emitted based on analyses of particular groups [START_REF] Leray | GenBank Is a Reliable Resource for 21st Century Biodiversity Research[END_REF], and these have resulted in a general mistrust in these repositories [START_REF] Bidartondo | Preserving Accuracy in GenBank[END_REF]). This has prompted the development of curated sequence databases for particular taxonomic groups and genes, including BOLD, PR2, SILVA, GREENGENES, and MIDORI. Surprisingly, latest studies evaluate at ~17% the annotation errors in some of these curated databases (Edgar 2018c) and found remarkably accurate metazoan identifications in GenBank, even at low taxonomic levels (likely < 1% error rate at the genus level, [START_REF] Leray | GenBank Is a Reliable Resource for 21st Century Biodiversity Research[END_REF]. This suggests that the limiting factor towards accurate taxonomic assignments is not the quality of database submissions, but rather their quantity.

Accurate taxonomic assignments in large-scale deep-sea biodiversity studies will thus only be obtained if a concerted effort is made to fill database gaps, which requires the integration of barcoding (and associated morphological identification) and metabarcoding approaches.

Moreover, arbitrarily large databases containing a great diversity of taxa, and sequences that have not been truncated to the target sequence length can decrease the number of accurate taxonomic assignments [START_REF] Macheriotou | Metabarcoding Free-Living Marine Nematodes Using Curated 18S and CO1 Reference Sequence Databases for Species-Level Taxonomic Assignments[END_REF]. Thus, concerted effort should also be directed towards developing user-friendly methods to build taxon-specific databases from large repositories. These methods could easily be part of R-based bioinformatic pipelines, as R scripts that automatically retrieve target sequences from databases are already available at https://github.com/metabarpark/R_scripts_querying_databases.

Improving and filling database gaps is going to take time. However, in the context of global change, there is urgent need to develop and apply biomonitoring programmes in the marine biome. This is highlighted by the significant number of studies evaluating the performance of metabarcoding-based environmental impact assessment (Pawlowski et al. 2016b;a;Cordier et al. 2019a;[START_REF] Chariton | Metabarcoding of Benthic Eukaryote Communities Predicts the Ecological Condition of Estuaries[END_REF]Gibson, J. F. et al. 2015;[START_REF] Aylagas | Adapting Metabarcoding-Based Benthic Biomonitoring into Routine Marine Ecological Status Assessment Networks[END_REF][START_REF] Stat | Ecosystem Biomonitoring with EDNA: Metabarcoding across the Tree of Life in a Tropical Marine Environment[END_REF][START_REF] Vivien | Molecular Barcoding of Aquatic Oligochaetes: Implications for Biomonitoring[END_REF], and developing genetic biotic indices [START_REF] Pawlowski | Inferring Biotic Indices from Metabarcoding Data: Promises and Challenges[END_REF][START_REF] Visco | Environmental Monitoring: Inferring the Diatom Index from Next-Generation Sequencing Data[END_REF][START_REF] Aylagas | Environmental Status Assessment Using DNA Metabarcoding: Towards a Genetics Based Marine Biotic Index (GAMBI)[END_REF]Cordier and Pawlowski 2018;[START_REF] Keeley | Development and Preliminary Validation of a Multi-Trophic Metabarcoding Biotic Index for Monitoring Benthic Organic Enrichment[END_REF][START_REF] Pawlowski | The Future of Biotic Indices in the Ecogenomic Era: Integrating (e)DNA Metabarcoding in Biological Assessment of Aquatic Ecosystems[END_REF]. As the calculation of most indices relies on taxonomic identities, applications of eDNA metabarcoding in biomonitoring and impact assessment are especially dependent on good taxonomy. To circumvent this, and allow efficient application in a time of database gaps, new approaches using supervised machine learning (SML) have demonstrated that SML can be used to predict accurate biotic indices from metabarcoding data [START_REF] Cordier | Predicting the Ecological Quality Status of Marine Environments from EDNA Metabarcoding Data Using Supervised Machine Learning[END_REF]. SML approaches even outperform assessments relying solely on taxonomically assigned sequences, and this for a variety of eukaryotic and prokaryotic markers (Cordier et al. 2018), highlighting that accurate eDNA bioassessment is possible even in poorly referenced ecosystems such as the deep-sea.

Large-scale ecological studies are now possible in the deep-sea, even within a typical research-project timeframe.

Morphology-based studies, although essential for species descriptions, are limited in terms of large-scale ecological applications. They usually focus on a few, well-described taxa due to the limited amount of specialised taxonomists. The time-consuming (~ 1 month for one sediment core with five sediment horizons) identification of organisms explains why these studies rarely go beyond local to regional scales. Moreover, taxon identification is highly dependent on investigators, making it difficult to combine inventories from several studies.

Chapter 5, investigating benthic biodiversity patterns in the deep Atlantic-Mediterranean transition zone, found concordant results with morphological studies from the last decades, indicating that eDNA metabarcoding is an appropriate tool for ecological research on the deep seafloor. It provides major advantages including allowing studying various biological compartments simultaneously, effectively detecting diversity of small organisms (microorganisms, meiofauna) and even traces of organisms, as well as being cost and time efficient.

As significantly different assemblages were found at mesopelagic (<1,000 m) vs bathyal and abyssal depths (> 1,000 m), Chapter 5 also highlighted the need to consider depth for defining biogeographical realms, something often disregarded in previous studies, even those covering "all accessible data for all taxa in all oceans" (Costello et al. 2017). Recent work has shown that the global latitudinal marine species richness gradient follows a bi-modal pattern related to temperature and habitat availability, temperature being primarily an indicator of food availability [START_REF] Chaudhary | Bimodality of Latitudinal Gradients in Marine Species Richness[END_REF]2017). However, temperature is not a good proxy for food availability in the deep-sea, where organic matter input, as measured by, for e.g., POC flux, is considered more important in defining species distribution patterns [START_REF] Woolley | Deep-Sea Diversity Patterns Are Shaped by Energy Availability[END_REF].

Conclusions and future directions

In 2010, the Conference of the Parties of the Convention on Biological Diversity agreed on the Strategic Plan for Biodiversity 2011-2020, and established five "Strategic Goals" that were divided into 20 targets. Each so-called 'Aichi Target' was designed to better understand and predict biodiversity, in particular, how biological diversity underpins ecosystem function, and how ecosystem services are essential for human well-being. Meeting these Aichi Targets ultimately secures livelihoods and economic development, and is essential for biodiversity maintenance and poverty reduction [START_REF] Tittensor | Global Patterns and Predictors of Marine Biodiversity across Taxa[END_REF][START_REF] Shepherd | Status and Trends in Global Ecosystem Services and Natural Capital: Assessing Progress Toward Aichi Biodiversity Target 14[END_REF].

The Strategic Plan for Biodiversity 2011-2020 made bridging biodiversity knowledge gaps, as well as improving marine environmental status assessment and biodiversity monitoring a requirement in many countries worldwide, and eDNA metabarcoding was been identified as a tool that will allow achieving these requirements [START_REF] Danovaro | Implementing and Innovating Marine Monitoring Approaches for Assessing Marine Environmental Status[END_REF].

Biodiversity knowledge gaps are especially severe in the deep-sea, where >90% of expanses remain unexplored. This thesis evaluated and optimized the eDNA metabarcoding workflow for deep-sea sediments on a bioinformatic, molecular, and sample processing level, across multiple life compartments, opening the door to large-scale biodiversity surveys in the deep-sea realm, thus contributing to a better understanding of biodiversity, biogeography, and ecosystem functioning in this vast and elusive backyard. This also paves the way for establishing efficient biomonitoring protocols that are increasingly needed in areas of resource extraction.

Elucidating biodiversity-ecosystem functioning relationships with eDNA metabarcoding still remains a challenge, especially in poorly referenced ecosystems such as the deep-sea where taxonomic assignments are of poor accuracy. Methods for detecting functional traits from sequence data are only starting to be investigated, and various approaches, including attribute prediction software (Edgar 2017a), ancestral state reconstruction via reference phylogenetic trees [START_REF] Keck | Boosting DNA Metabarcoding for Biomonitoring with Phylogenetic Estimation of Operational Taxonomic Units' Ecological Profiles[END_REF]) and machine-learning approaches [START_REF] Vacher | Learning Ecological Networks from Next-Generation Sequencing Data[END_REF]Cordier et al. 2018) have been evaluated. Currently, supervised machine learning represents the most promising way towards ascribing ecological roles to OTUs. However, these SML approaches still require reference datasets upon which they can be trained to make accurate predictions of unknown samples, and this will require controlled mesocosm experiments. Ecological networks are useful for representing and analysing all the interactions between species, thereby offering

an understanding of ecosystem functioning, and machine-learning approaches can be used for reconstructing such networks based on HTS co-occurrence data [START_REF] Vacher | Learning Ecological Networks from Next-Generation Sequencing Data[END_REF]. Future developments of these methods may allow thus us to discover and monitor species interactions, even in unknown environments. PCR-based approaches ultimately limit the ability of metabarcoding to accurately depict species abundance in complex communities, especially for metazoans with strongly varying numbers of marker gene copies and cells. Shotgun sequencing of mitochondrial genomes, i.e.

genome skimming, has been shown to more reliably describe read-biomass relationships, while also allowing more accurate species identification as taxonomic assignment is based on whole mitogenomes [START_REF] Bista | Performance of Amplicon and Shotgun Sequencing for Accurate Biomass Estimation in Invertebrate Community Samples[END_REF][START_REF] Gómez-Rodríguez | Validating the Power of Mitochondrial Metagenomics for Community Ecology and Phylogenetics of Complex Assemblages[END_REF][START_REF] Fernández | Phylogenetic Community Ecology of Soil Biodiversity Using Mitochondrial Metagenomics[END_REF]

COI 18S V1-V2 16S V4-V5

Table S7. Number of raw, refined, and LULU-curated molecular clusters obtained for each pipeline in the three datasets. Data refining was performed in R (decontamination, renormalisation, removal of non-target taxa, and clusters unassigned at phylum-level or with unreliable phylum-level assignments), based on BLAST assignments obtained using the Silva v132 database for 18S and 16S, and on the MIDORI database for COI. LULU curation was performed at minimum ratio = 100 for 18S, and minimum ratio = 1 for COI (default). and18S) were clustered (swarm with d=1) and curated with LULU at 90% for 18S (min-ratio=100) and 84% for COI (min-ratio=1), while ASVs were used in the prokaryote dataset. Taxonomic affiliations were obtained using the Silva v132 database for 18S and 16S, and the MIDORI-UNIQUE database subsampled for marine taxa for COI. BLAST assignments were performed with minimal hit identity of 70%. Phylum-level taxonomy filter was performed by keeping only clusters with BLAST identities ≥ 86% for ribosomal loci and ≥ 80% for COI, or with phylum bootstrap ≥ 80%. Genus-level taxonomy filter was performed by keeping only clusters with BLAST identities > 95% for ribosomal loci and > 93% for COI, or with genus bootstrap ≥80%. Table S1. Sampling sites, their GPS locations, and associated habitats. Sieved sediment was sieved through five mesh sizes (1,000; 500; 250; 40; 20 µm), and DNA was extracted from each size fraction separately. An equimolar pool of the five DNA extracts of each size fraction was then made for PCR and sequencing of the sieved samples. Volume for PCR was always 10 µl, for template stock standardized at ≤0.25 ng/µl. Table S4. Number of reads and clusters (ASVs for 18S V4 and 16S, OTUs for 18S V1-V2 and COI) obtained at different analysis steps, depending on sample processing category. Data refining was performed in R, based on BLAST assignments obtained using the Silva v132 database for 18S V1-V2 and 16S loci, on the PR2 database for 18S V4, and on the MIDORI marine-only database for COI. Final number of target reads represent the number of target-taxa reads after data refining (decontamination, removal of unassigned and unknown clusters), additional LULU curation for metazoans, and removal of all clusters with less than 86% BLAST hit identity for rDNA loci and 80% for COI. Final number of target clusters are the corresponding ASVs for 18S V4 and 16S, and the corresponding OTUs for 18S V1-V2 and COI.

LOCUS

The abyssal seafloor covers more than half of planet Earth. It can host a large number of, mostly small and still undescribed, organisms (~50,000-5 million individuals per square meter), contributing to key ecosystem functions such as nutrient cycling, sediment stabilisation and transport, or secondary production. Technological developments in the past 30 years have allowed remarkable advances, yet due to the vastness and remoteness of deep-sea habitats, ecological studies have been limited to local and regional scales. Indeed, we have so far explored less than 1% of the deep seafloor, and this contrasts with the fact that deep-sea ecosystems form one of the largest biomes on Earth, and are under increased threat from a variety of direct and indirect anthropogenic pressures. This PhD aims at bringing new perspectives for the study of biodiversity and biogeography in the deep-sea, to bridge this large knowledge gap, and advance toward the development of efficient biomonitoring protocols to preserve this vast and elusive backyard. We investigated the potential of multimarker environmental DNA (eDNA) metabarcoding to assess the extent and distribution patterns of biodiversity in this remote ecosystem. Using mitochondrial and nuclear marker genes, this PhD aimed at producing and testing an optimized eDNA metabarcoding workflow for deep-sea sediments, on a bioinformatic, molecular, and sample processing level, applicable to multiple life compartments including microbiota and metazoans.

Biodiversity assessment with eDNA is confronted with the difficulty in defining accurate "species-level" Operational Taxonomic Units (OTUs), as numerous sources of error induce frequent overestimations. The first part of this thesis describes how newly developed bioinformatic tools can be combined in order to get more conservative and reliable biodiversity inventories, approaching a 1:1 species-OTU correspondence, and underline the advantages of clustering and LULU-curation for producing more reliable metazoan biodiversity inventories. Moreover, the accuracy of protocols based on eDNA in deep sea sediments still needs to be assessed, as results may be biased by ancient DNA, resulting in biodiversity assessments not targeting live organisms.This thesis assessed the potential bias of ancient DNA by 1) evaluating of the effect of removing short DNA fragments, and 2) comparing communities revealed by co-extracted DNA and RNA in five deep-sea sites. Results indicated that short extracellular DNA fragments do not affect alpha and beta diversity, but that DNA obtained from 10g of sediment should be favoured over RNA for logistically realistic, repeatable, and reliable surveys. Results also confirm show that increasing the number of biological rather than technical replicates is important to infer robust ecological patterns. Sieving sediment to separate benthic size classes increased the number of detected metazoan OTUs, but was not essential for achieving comprehensive and accurate biodiversity estimates, and should be avoided if unicellular taxonomic compartments are also of interest. Finally, this thesis applied the optimized eDNA metabarcoding protocols to investigate the influence of biotic and abiotic factors on the extent and distribution of deep-sea metazoan biodiversity on an East-West transect ranging from the Central Mediterranean to the Mid-Atlantic Ridge. Results, consistent to morphology-based studies, confirm that small-scale biotic and abiotic factors lead to significant vertical changes in metazoan richness and community structure within the sediment, and highlight that regional beta-diversity patterns result from a combined influence of past biogeography and present day processes. This thesis opens the way to large-scale eDNAbased studies in the deep-sea realm, thus contributing to a better understanding of biodiversity, biogeography, and ecosystem function in this vast and still poorly known biome.

  Since the description of Deoxyribonucleic Acid (DNA) in 1953, significant advances in molecular biology have allowed researchers to develop techniques to exponentially amplify DNA molecules, so-called Polymerase Chain Reaction (PCR), and determine the order of their nucleotides (their building blocks), a process known as DNA sequencing. During the late 1990s,

  2003, Hebert et al. proposed to extend this approach to eukaryotes and suggested that the mitochondrially encoded Cytochrome Oxidase I gene (COI) could serve as a DNA barcode for all animal taxa(Hebert et al. 2003b). Since then, much work has been undertaken for determining standardized DNA barcodes for all domains of life. An ideal barcode gene should have three main characteristics. First, as PCR amplification depends on primers, short DNA fragments that bind to the DNA to be amplified, a barcode gene should possess conserved flanking sites to allow successful primer binding across broad taxonomic levels, thus avoiding the non-detection of taxa due to unsuccessful primer binding (primer bias). Second, it should possess a strong enough phylogenetic signal, i.e. have mutation rates (and thus intraspecific variation) that allow discrimination of closely related taxonomic groups (ideally species).

Figure 1 .

 1 Figure 1.Schematic representation of the barcoding gap. Frequency distributions of genetic distances within (red) and between (yellow) species. (a) Ideal world for barcoding, with discrete distributions of intraspecific and interspecific variation and no overlap. (b) A common situation with significant overlap between intra-and interspecific variation and no barcode gap. From Bucklin et al. (2011).

Figure 2 .

 2 Figure2. Typical workflow for high-throughput eDNA metabarcoding studies. Environmental samples such as sediments are usually frozen upon collection (-80°C to preserve RNA) and brought back to the lab for extraction of eDNA. Marker genes (e.g. 16S, 18S rRNA, COI) are amplified from genomic extracts using primer pairs targeting a wide variety of taxa. Following high-throughput sequencing (typically conducted on Illumina platforms), sequences are bioinformatically processed, and molecular entities defined. The latter are then taxonomically assigned, and used to conduct α-and β-diversity analyses, summarize community taxonomy, and interpret assemblages in a phylogenetic context. FromBik et al. 2012a.

Figure 5 .

 5 Figure 5. Anthropogenic impacts on deep-sea ecosystems and potential synergies. The lines link impacts that have synergistic effects on habitat or faunal communities. The lines are coloured to indicate the direction of the synergy. LLRW: low-level radioactive waste; CFCs: chlorofluorocarbons; PAHs: polycyclic aromatic hydrocarbons. From Ramirez-Llodra et al. 2011.

Fig. 7 ,

 7 Figure 7. The Northeast Atlantic seafloor showing some distinct deep-sea ecosystems such as continental margins, which can include canyons, cold seeps, and cold-water coral reefs; the abyssal plain, seamounts, and the mid-ocean ridge where hydrothermal vents are found. From Ramirez-Llodra et al. 2010.

  driving continental drift and seafloor spreading at a rate of 1-10 cm/year. The current spreading episode began around 200 million years ago, with the opening of the Atlantic and Indian ocean basins, which are still growing, while the Pacific is decreasing[START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF][START_REF] Wilson | Igneous Petrogenesis: A Global Tectonic Approach[END_REF]).

  . Despite this, they are under increased threat from a variety of ongoing or forecasted human activities, ranging from climate changeinduced indirect threats due to modifications in ocean biogeochemistry to direct threats from activities such as waste disposal, pollution, or resource exploitation (Ramirez-Llodra et al. 2010; Smith, C. R. et al. 2008; Ramirez-Llodra et al. 2011). Better knowledge of deep-sea biodiversity patterns and the development of deep-sea biomonitoring protocols are therefore becoming necessary in order to preserve this vast and elusive backyard. This PhD thus aims at bringing new perspectives to the study of biodiversity in deep-sea sediments to bridge this knowledge gap.

  MI, Trouche B, Quintric L, Wincker P, Poulain J, and Arnaud-Haond S. A flexible pipeline combining clustering and correction tools for prokaryotic and eukaryotic metabarcoding. BioRxiv, 717355, ver. 3 peer-reviewed and recommended by PCI Ecology. DOI: 10.1101/717355.

  avons construit et testé un pipeline basé sur la correction de séquences avec DADA2 permettant d'analyser des données de métabarcoding de compartiments de vie procaryotes (16S) et eucaryotes (18S, COI). Nous avons implémenté l'option de regrouper les variants de séquence d'amplicon (ASV) en unités taxonomiques opérationnelles (OTU) avec swarm, un algorithme de clustering basé sur l'analyse des réseaux, et la possibilité de filtrer les ASV / OTU à l'aide de LULU. Enfin, l'assignation taxinomique a été mise en place via le classificateur bayésien du Ribosomal Database Project (RDP) et BLAST. Nous évaluaons ce pipeline avec des marqueurs ribosomaux et mitochondriaux à l'aide de communautés métazoaires connues et de 42 échantillons de sédiments abyssaux. Les résultats montrent que les ASV et les OTU décrivent différents niveaux de diversité biotique, dont le choix dépend des questions de recherche. Ils soulignent les avantages et la complémentarité du clustering et de la filtration avec LULU pour produire des inventaires de la biodiversité métazoaire à un niveau proche de celui obtenu à partir de critères morphologiques. Alors que le clustering supprime la variation intraspécifique, LULU supprime efficacement les unités génétiques erronées, provenant d'erreurs techniques ou de variabilité intragénomique. Le clustering a affecté la diversité alpha et bêta différemment selon le marqueur génétique. Plus précisément, les valeurs de swarm à d > 1 se sont avérées moins appropriées avec 18S pour les métazoaires. De même, augmenter le niveau du minimum ratio de LULU s'est avéré essentiel pour éviter de perdre des espèces dans des jeux de données pauvres en échantillons. La comparaison de BLAST et de RDP a souligné que des assignations taxonomiques précises peuvent être obtenues pour les espèces d'eau profonde avec RDP, mais a souligné la nécessité d'un effort concerté pour créer des bases de données complètes et spécifiques à l'écosystème.

  //gitlab.ifremer.fr/abyss-project/) and is based on DADA2 v.1.10 (Callahan et al. 2016) and FROGS[START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF] as core processing tools. It allows the use of sequence data obtained from libraries produced by double PCR or adaptor ligation methods, as well as having built-in options for using six commonly used metabarcoding primers.

  22.3 (McMurdie and Holmes 2013) following guidelines on online tutorials (http://joey711.github.io/phyloseq/tutorialsindex.html), and vegan v2.5.2 (Oksanen et al. 2018). The datasets were normalized by rarefaction to their common minimum sequencing depth (COI: 15,575; 18S: 33,916; 16S: 70,474), before analysis of mock communities and environmental samples.

Figure 1 .

 1 Figure 1. Number of metazoan (COI, 18S) and prokaryote (16S) clusters detected in sediment of 14 deep-sea sites with ASV vs OTU-centred datasets. ASVs were obtained with the DADA2 metabarcoding pipeline, and clustered with swarm at different d values. Metazoan ASVs and OTUs were curated with LULU at 84% and 90% minimum match. LULU curation was performed with minimum ratio = 100 for 18S and minimum ratio = 1 for COI. Cluster abundances were obtained from datasets rarefied to same sequencing depth. Boxplots represent medians with first and third quartiles. Red dots indicate means.

  PERMANOVAs, clustering levels affected the ecological patterns resolved by ordinations in rRNA loci(Fig 2). Metazoan 18S ASVs showed strong segregation by ocean basin, with samples grouped by depth within each basin, and prokaryote ASVs showed both strong segregation by ocean basin and depth (Fig.2). Clustering at d-values > 1 decreased differences among deep sites (> 1,000 m) across ocean basins, emphasizing the depth effect over the basin effect. This change in ecological pattern occurred consistently with d-values from 3 to 11 (Fig.2, Fig.S2).

Figure 2 .

 2 Figure 2. Metazoan (COI, 18S) and prokaryote (16S) beta-diversity patterns in ASV and OTUcentred datasets. Nonmetric multidimensional scaling (NMDS) ordinations showing community differentiation observed between sites with different clustering scenarios. ASVs were obtained with the DADA2 metabarcoding pipeline, and clustered with swarm at d = 1, 5, and 13 (COI) and d = 1, 3, 11 (18S, 16S). Metazoan ASVs and OTUs were curated with LULU at 84% and 90% minimum match. LULU curation was performed with minimum ratio = 100 for 18S and minimum ratio = 1 for COI. R 2 values and associated p-values obtained in PERMANOVAs are shown under the ordination plots. Significance codes: ***: p<0.001; **: p<0.01; *: p<0.05. Site colour codes: Green: Mediterranean > 1,000 m; Red: Mediterranean Gibraltar Strait 300-1,000 m; Yellow: Atlantic Gibraltar Strait 300-1,000 m; Blue: North Atlantic > 1,000 m; Purple: Arctic > 1,000 m.

  Brandt MI, Trouche B, Henry N, Liautard-Haag C, Maignien L, de Vargas C, Wincker P, Poulain J, Zeppilli D, and Arnaud-Haond S. An assessment of environmental metabarcoding protocols aiming at favouring contemporary biodiversity in inventories of deep-sea communities. 2020. Frontiers in Marine Science. DOI: 10.1101/836080 CHAPTER III MOLECULAR METHODS COMPARISONS 85

  de la planète Terre et représentent un grand réservoir de biodiversité encore largement méconnu . Malgré cette méconnaissance, ils sont de plus en plus sous la menace d'activités anthropiques. Dans ces écosystèmes vastes et difficiles d'accès, le métabarcoding par ADN environnemental (ADNe) est un outil utile et efficace pour étudier la biodiversité et mettre en oeuvre des programmes d'évaluation d'impact. Pourtant, son application sur des sédiments profonds est potentiellement biaisée par la présence d'ADN archivé provenant d'organismes morts. Or, l'inclusion de cet ADN ancien (ADNa) aboutirait à des inventaires de biodiversité passée plutôt que présente. À l'aide de kits d'extractions d'ADN disponibles dans le commerce, nous avons étudié les impacts de cinq méthodes de traitement moléculaire sur les inventaires de la biodiversité produits par métabarcoding, ciblant les procaryotes (16S-V4V5), les eucaryotes unicellulaires (18S-V4) et les métazoaires (18S-V1, COI). Dans un premier temps, des inventaires basés sur l'ADN furent comparés à ceux révélés par l'ARN. En effet, ce-dernier, étant produit uniquement par des organismes vivants, a été présenté comme une approche plus pratique pour cibler la partie active des communautés. Parallèlement, l'ADN ancien étant principalement constitué de petits fragments, nous avons aussi évalué l'effet de l'élimination de fragments d'ADN courts par sélection de taille et reconcentration par éthanol. Les résultats montrent que l'élimination de fragments d'ADN courts n'affecte pas les estimations de la diversité alpha et bêta dans aucun des compartiments biologiques étudiés. Les résultats confirment également les doutes quant à la possibilité de mieux décrire les communautés vivantes en utilisant l'ARN environnemental (ARNe). Sur les marqueurs ribosomaux, l'ARN, tout en résolvant des schémas spatiaux similaires à l'ADN co-extrait, a entraîné des estimations de richesse significativement plus élevées, soutenant les hypothèses de persistance accrue de l'ARN ribosomal (ARNr) dans l'environnement, et l'existence d'un biais additionnel et non mesuré en raison de la surabondance d'ARNr dans l'environnement et d'ARN sécrété à taux variables en fonction de l'activité métabolique des organismes. Sur le locus mitochondrial, l'ARN a détecté une richesse métazoaire inférieure tout en résolvant moins de différences écologiques que l'ADN co-extrait, reflétant la grande labilité de l'ARN messager.Les résultats soulignent également l'importance d'utiliser de grandes quantités de sédiments (≥ 10 g) pour étudier avec précision la diversité eucaryote. Nous concluons donc que l'ADN est plus pertinent que l'ARN pour des études logistiquement réalistes, reproductibles, et fiables.Nous confirmons aussi que des quantités de sédiments plus grandes (≥ 10 g) fournissent des évaluations plus complètes et précises de la biodiversité eucaryote benthique et qu'il faut favoriser l'augmentation du nombre de réplicas biologiques plutôt que techniques pour déduire des patrons écologiques fiables.

(

  rRNA) barcode[START_REF] Parada | Every Base Matters: Assessing Small Subunit RRNA Primers for Marine Microbiomes with Mock Communities, Time Series and Global Field Samples[END_REF] for prokaryotes, the 18S-V4 rRNA barcode region for micro-eukaryotes[START_REF] Stoeck | Multiple Marker Parallel Tag Environmental DNA Sequencing Reveals a Highly Complex Eukaryotic Community in Marine Anoxic Water[END_REF], and the 18S-V1V2 rRNA (thereafter 18S-V1) and Cytochrome c Oxidase I (COI) barcode markers for metazoans[START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF]the effect of removing short DNA fragments from DNA extracts obtained using a 10 g extraction kit;

  Two metazoan mock communities (detailed in[START_REF] Brandt | A Flexible Pipeline Combining Bioinformatic Correction Tools for Prokaryotic and Eukaryotic Metabarcoding[END_REF] were included for 18S-V1 and COI. For each sample and marker, triplicate amplicon libraries (see Supporting Information for amplification details) were prepared by ligation of Illumina adapters on 100 ng of amplicons following the Kapa Hifi HotStart NGS library Amplification kit (Kapa Biosystems, Wilmington, MA, USA). After quantification and quality control, library concentrations were normalized to 10 nM, and 8-9 pM of each library containing a 20% PhiX spike-in were sequenced on a HiSeq2500 (System User Guide Part # 15035786) instruments in a 250 bp paired-end mode.Bioinformatic analysesAll bioinformatic analyses were performed using a Unix shell script(Brandt, M. I. et al. 2020), available on Gitlab (https://gitlab.ifremer.fr/abyss-project/), on a home-based cluster (DATARMOR, Ifremer). The details of the pipeline, along with specific parameters used for all metabarcoding markers, are given in

  //joey711.github.io/phyloseq/tutorials-index.html), and vegan v2.5.2[START_REF] Oksanen | Vegan: Community Ecology Package[END_REF]. Alpha diversity between molecular processing methods was estimated with the number of observed target clusters in rarefied datasets. Cluster abundances were compared via analyses of deviances (ANODEV) on generalized linear mixed models using negative binomial distributions, as the data were overdispersed. Pairwise post-hoc comparisons were performed via Tukey HSD tests using the emmeans package.

  2). DNA-2g extracts recovered an average of 110±16 18S-V1 and 113±27 COI metazoan OTUs per sample, compared to 264±26 (18S-V1) and 222±23 (COI) in the DNA-10g extracts.Similarly, DNA-10g extracts recovered on average 1,117±100 protistan 18S-V4 ASVs per sample, compared to 595±109 detected in DNA from the 2-g kit. Contrastingly to eukaryotes, all DNA methods, whether based on ~2 g or ~10 g of sediment, resulted in comparable prokaryote ASV numbers detected (Figs.1-2, Table1, p>0.8), ranging from 5,330 ±199 to 5,810 ±170 per sample on average.

Figure 1 .

 1 Figure 1. Violin plot showing detected numbers of metazoan OTUs (COI, 18S-V1), microeukaryote (18S-V4) ASVs, and prokaryote (16S) ASVs recovered by the five molecular processing methods evaluated in this study (DNA 10g: crude DNA extracts from ~10 g of sediment with the PowerMax Soil kit; DNA 10g EtOH rec.: ethanol reconcentrated 10g DNA extracts; DNA 10g S-S: size-selected 10g DNA extracts; DNA/RNA 2g: crude DNA/RNA extracts from ~2g of sediment with the RNeasy PowerSoil kit). Cluster abundances were calculated on rarefied datasets. Boxplots show medians with interquartile ranges. Red dots indicate mean values.

Figure 2 .

 2 Figure 2. Mean number of metazoan OTUs (COI, 18S-V1), protist ASVs (18S-V4), and prokaryote ASVs (16S) detected per sample for each of the five processing methods (DNA 10g: crude DNA extracts from ~10 g of sediment with the PowerMax Soil kit; DNA 10g EtOH rec.: ethanol reconcentrated 10g DNA extracts; DNA 10g S-S: size-selected 10g DNA extracts; DNA/RNA 2g: crude DNA/RNA extracts from ~2g of sediment with the RNeasy PowerSoil kit). Cluster numbers were calculated on rarefied datasets. Error bars represent standard errors.

Figure 4 .

 4 Figure 4. Patterns of relative cluster abundance resolved by metabarcoding of sediment RNA and DNA from five deep-sea sites, using either RNA/DNA extracted jointly from ~2 g of sediment (RNA 2g/DNA 2g) or DNA extracted from ~10g of sediment (DNA 10g), and using four barcode markers targeting metazoans (A: COI, 18S-V1), micro-eukaryotes (B: 18S-V4), and prokaryotes (B: 16S). Values were calculated on balanced datasets.

  minor revisions at Scientific Reports: Brandt MI, Pradillon F, Trouche B, Henry N, Liautard-Haag C, Cambon-Bonavita MA, Wincker P, Poulain J, Arnaud-Haond S, and Zeppilli D. 2021. Evaluating sediment and water sampling methods for the estimation of deep-sea biodiversity using environmental DNA.

  Does sieving sediment allow achieving more reliable biodiversity inventories in the deepsea? How does the SALSA in situ pump compare with traditional water sampling devices for biodiversity detection of aboveground water samples? Bien qu'elle représente l'un des plus grands biomes du monde, la biodiversité des grands fonds marins est encore mal connue. Le métabarcoding sur ADN environnemental offre des perspectives inédites pour des inventaires et des études d'impact rapides, mais nécessite des méthodes d'échantillonnage standardisées et un choix judicieux de substrat environnemental. Ici, nous avons cherché à optimiser l'évaluation génétique des communautés procaryotes (16S), protistes (18S V4) et métazoaires (18S V1-V2, COI), en évaluant des stratégies d'échantillonnage pour les sédiments et les eaux profondes affleurant le sédiment, déployées simultanément à un site abyssal. Pour les sédiments, alors que le tri des classes de taille par tamisage n'a eu aucun effet sur la diversité alpha totale détectée et a résolu des compositions taxonomiques similaires au niveau du phylum pour tous les marqueurs étudiés, il a effectivement augmenté la détection des phylums de la méiofaune. Pour l'eau, de grands volumes obtenus à partir d'une pompe in situ (~ 6000 L) ont détecté beaucoup plus de diversité métazoaire que 7,5 L collectés dans des boîtes d'échantillonnage. Cependant, la pompe étant limitée par des mailles plus grandes (> 20 µm), ne capturait qu'une fraction de la diversité microbienne, tandis que des boîtes d'échantillonnage permettaient d'accéder au pico-et au nanoplancton. Plus important encore, les communautés caractérisées par les échantillons d'eau affleurante différaient significativement de celles caractérisées par des sédiments, quel que soit le volume utilisé, et les deux types d'échantillons ne partageaient qu'entre 5% et 10% des unités moléculaires. Ensemble, ces résultats soulignent que le tamisage peut être recommandé pour cibler la méiofaune, et que les eaux affleurantes ne représentent pas une alternative à l'échantillonnage des sédiments pour les inventaires de la diversité benthique.

  . The sediment samples were either 1) transferred into zip-lock bags and frozen at -80°C on board or 2) sieved through five different mesh sizes(1,000 µm, 500 µm, 250 µm, 40 µm, and 20 µm) in order to concentrate organisms and separate them by size-class. Sieving was performed with cold surface water filtered at 0.2 µm. Each mesh concentrate was subsequently stored in a separate zip-lock bag and frozen at -80°C. All samples were shipped on dry ice to the laboratory.Two different aboveground water-sampling methods were evaluated during EssNaut16 to target microbial and metazoan taxa. All water samples were collected at most 1 m above the seafloor. Water was collected with a newly developed in situ pump, the Serial Autonomous Larval Sampler (SALSA), i.e. a McLane WTS-LV sampler adapted by Ifremer, Brest, France to allow replicated sampling. SALSA pumps up to 30,000 L of seawater through a 20-µm nylon mesh, concentrating this water into five 2.8 L sampling bowls that can be used as biological replicates, each representing ~6,000 L of concentrated seawater targeting the > 20 µm size fraction. Two deployments were performed at the study site (PL07, PL11) and one deployment within the same habitat but at shallower depth due to technical reasons impeding deployment at the original site (PL09). Analyses were performed with and without PL09, and as no significant difference was observed between deployments, results from PL09 were included in the study. Two replicates per SALSA deployment were used in this work. Each replicate was filtered on board through polycarbonate membrane filters with 2-µm mesh size (Millipore, Burlington, MA, USA, ref. TTTP04700) to concentrate all retained particles on the filter membrane. Water was also collected using two ~7.5 L Nautile-deployed sterile and watertight sampling boxes[START_REF] Roussel | Comparison of Microbial Communities Associated with Three Atlantic Ultramafic Hydrothermal Systems[END_REF]. These samples were filtered on board successively through membrane filters with 20 µm, 2 µm, and 0.2 µm mesh size (Millipore, Burlington, MA, USA, refs. NY2004700, TTTP04700, GTTP04700), generating three size fractions (>20 µm, 2-20 µm, and 0.2-2 µm). Each water filter was stored in an individual Petri dish, frozen atNucleic acid extractionsFor sediment, DNA extractions were performed using 2-10 g of sediment with the PowerMax Soil DNA Isolation Kit (MOBIO Laboratories Inc.; Qiagen, Hilden, Germany). All DNA extracts were stored at -80°C. For sieved sediment, DNA was extracted from each size fraction separately, and an equimolar pool of the DNA extracts of each size fraction was prepared for PCR and sequencing. Water DNA extractions were carried out by Genoscope (Évry, France) using the same protocol as described by[START_REF] Alberti | Viral to Metazoan Marine Plankton Nucleotide Sequences from the Tara Oceans Expedition[END_REF] for Tara Oceans water samples. The protocol is based on cryogenic grinding of membrane filters, followed by nucleic acid extraction with NucleoSpin RNA kits combined with the NucleoSpin DNA buffer set (Macherey-Nagel, Düren, Germany). A negative extraction control was performed alongside sample extractions for both water and sediment samples (adding nothing instead of sample).PCR amplification and sequencingDNA extracts were normalised to 0.25 ng/µL and 10 µL of standardized sample were used for PCR (see Supporting Information for amplification details). Four primer pairs were used to amplify one mitochondrial and three ribosomal RNA (rRNA) barcode loci preferentially targeting metazoans (COI, 18S V1-V2), unicellular eukaryotes (18S V4), and prokaryotes (16S V4-V5, Table

(

  https://gitlab.ifremer.fr/abyss-project/), on a home-based cluster (DATARMOR, Ifremer), and the samples of the present study were analysed in parallel with 12 to 28 other deep-sea water samples for more accurate error correction and LULU filtering. The details of the pipeline,

  22.3 (McMurdie and Holmes 2013), following guidelines in online tutorials (http://joey711.github.io/phyloseq/tutorialsindex.html), and vegan v2.5.2[START_REF] Oksanen | Vegan: Community Ecology Package[END_REF]). Read and cluster abundances were evaluated via analyses of variance (ANOVA) on generalised linear models using quasipoisson distributions. Pairwise post-hoc comparisons were performed via Tukey HSD tests using the emmeans package. Alpha and beta diversity were compared among sampling methods using datasets rarefied to the minimum sequencing depth(COI: 60,242; 18S V1: 118,401; 18S V4: 33,037; 16S: 100,205). Differences in community composition were assessed with Venn diagrams (computed using the venn function in the venn package) and with permutational multivariate analysis of variance (PERMANOVA). The latter were performed using the CHAPTER IV SAMPLING METHODS COMPARISONS 118

  OTUs. Pairwise post-hoc comparisons among sampling methods were performed with the pairwiseAdonis package. Differences among samples were visualized via Principal Coordinates Analyses (PCoA) based on abovementioned dissimilarities. Finally, taxonomic compositions in terms of cluster abundance were compared among processing methods. 19 million raw 18S V1-V2 reads, 26 million COI reads, 14 million 18S V4 reads, and 17 million 16S V4-V5 reads were obtained from Illumina HiSeq runs of amplicon libraries built from pooled triplicate PCRs of 22 environmental samples, 2 extraction blanks, and 4-6

Figure 1 .

 1 Figure 1. Numbers of metazoan OTUs (COI, 18S V1-V2,), unicellular eukaryote (18S V4) and prokaryote (16S V4-V5) ASVs recovered by deep-sea sediment (brown) and aboveground water (blue), with two sampling methods for each sample type. Sediment was either sieved through 5 mesh sizes to size-sort organisms prior DNA extraction, or DNA was extracted directly from crude sediment samples. Water was collected with a 7.5 L sampling box, allowing recovery of up to two size classes per taxonomic compartment, or sampled in large volumes with an in situ pump. Cluster abundances were calculated on rarefied datasets. Red dots indicate mean values. Bars represent standard errors.

Figure 2 .

 2 Figure 2. Mean numbers (±SE) of metazoan COI and 18S V1-V2 OTUs detected in target phyla for sediment (brown) and water (blue), using two sampling methods for both sample types. Sediment was either sieved to size-sort organisms prior DNA extraction, or DNA was extracted directly from crude sediment samples. Water was collected with a 7.5 L sampling box, allowing recovery of two size classes, or sampled in large volumes with an in situ pump. OTU numbers were calculated on rarefied datasets.

Figure 3 .

 3 Figure 3. Venn diagrams (left) and Principal Coordinates Analyses (PCoA) ordinations showing differences in community compositions detected by deep-sea sediment (brown) and aboveground water (blue) for metazoans (COI and 18S V1-V2), micro-eukaryotes (18S V4), and prokaryotes (16S V4-V5). Community segregation is strongest between sample types, but also among target size class in the water samples. Sediment was either sieved to size-sort organisms prior DNA extraction, or DNA was extracted directly from crude sediment samples. Water was collected with a 7.5 L sampling box, allowing recovery of two size classes in each taxonomic compartment, or sampled in large volumes with an in situ pump.

  pression d'activités anthropiques. Dans de tels écosystèmes vastes et difficiles d'accès, le pouvoir de détection élevé du métabarcoding d' ADN environnemental (ADNe), sur des échantillons plus faciles à recueillir que les collections de specimens morphologiques, offre de nouvelles perspectives pour l'investigation standardisée de biodiversité et biogéographie à grande échelle. En combinant le marqueur génetique mitochondrial COI et la région V1-V2 de l'ARN ribosomal 18S, nous avons étudié la biodiversité métazoaire à petite et à grande échelle dans la zone de transition Atlantique-Méditerranée, à l'aide d'ADNe extrait de sédiments profonds provenant de 13 sites allant de la Méditerranée centrale à la dorsale médio-atlantique. Nous avons évalué l'influence de la couche de sédiments, de la taille des grains de sédiments, de la teneur en matière organique ainsi que des communautés microbiennes (18S V9, 16S V4-V5), sur l'étendue et la structure de la biodiversité métazoaire dans cette région. Nos résultats soulignent que les facteurs à petite échelle (centimètres) affectent fortement la richesse métazoaire des grands fonds marins et la composition des communautés. Une diminution significative de la richesse en unités taxonomiques moléculaires (OTU) fut observée avec chaque couche de sédiments, de 1 cm à 15 cm de profondeur, et une ségrégation verticale importante dans la structure des communautés a été révélée dans toutes les régions pour la méiofaune et la macrofaune. Les premiers cinq centimètres de sédiment abritaient la plupart des métazoaires (94% pour 18S, 98% pour COI), avec des nombres d'OTU allant de 2 à 168 par échantillon pour 18S et de 81 à 1259 pour COI. Les facteurs à grande échelle (> 100 km) ont davantage affecté la diversité bêta que la diversité alpha. Le contenu de matière organique et la taille des grains de sédiments ont montré une forte variation à l'échelle régionale, avec une teneur en matière organique plus élevée dans les sédiments méditerranéens et des particules de plus grande taille dans l'Atlantique. Ces deux variables environnementales contribuèrent de manière significative à expliquer les différences de composition des communautés entre sites. La méio et la macrofaune ont révélés un fort niveau de correlation (RV = 0.87), confirmant des interactions trophiques fortes entre ces deux compartiments taxonomiques. De même, les compartiments protistes et procaryotes étaient corrélés à un niveau similaire (RV = 0.84), suggérant que les interactions trophiques sont plus marquées entre organismes de classes de taille comparables. Enfin, le détroit de Gibraltar fut CHAPTER V DEEP-SEA BIODIVERSITY PATTERNS THROUGH eDNA 137 un facteur supplémentaire expliquant les très fortes différences régionales dans la composition des communautés, soutenant une influence combinée de facteurs historiques et de mouvements actuels des masses d'eau sur la distribution de la diversité benthique. CHAPTER V DEEP-SEA BIODIVERSITY PATTERNS THROUGH eDNA 138

  or forecasted human activities, ranging from climate change-induced indirect threats due to modifications in ocean biogeochemistry to direct threats from activities such as waste disposal, pollution, or resource exploitation[START_REF] Ramirez-Llodra | Deep, Diverse and Definitely Different: Unique Attributes of the World's Largest Ecosystem[END_REF] Smith, C. R. et al. 2008;[START_REF] Ramirez-Llodra | Man and the Last Great Wilderness: Human Impact on the Deep Sea." Edited by Roopnarine[END_REF]. Better knowledge of deep-sea biodiversity patterns and the development of large-scale deep-sea biomonitoring protocols are therefore becoming necessary in order to preserve this vast and elusive backyard. CHAPTER V DEEP-SEA BIODIVERSITY PATTERNS THROUGH eDNA 140 Environmental DNA metabarcoding approaches represent a new perspective for obtaining large-scale inventories of biodiversity and infer ecological and biogeographic drivers of life in deep-sea sediments, to bridge this knowledge gap. They have revolutionized biodiversity research in the past decade and have already been successfully applied in marine sedimentary

Figure 1 .

 1 Figure 1. Map of sampling locations. Thirteen sites were sampled in four regions (North Atlantic, Gulf of Cadiz, Alboran Sea, and Western Mediterranean) along a west-east transect covering the Atlantic-Mediterranean transition zone.

22. 3 (

 3 Figure 2. Numbers of Operational Taxonomic Units (OTUs) detected per metazoan phylum by each marker used in this study, after all filtering steps.

  number of 163 million 18S V1-V2 reads and 89 million COI reads were obtained from triplicate PCR replicates of 133 (18S) and 82 (COI) sediment samples, 2 mock communities, CHAPTER V DEEP-SEA BIODIVERSITY PATTERNS THROUGH eDNA 149 12 extraction blanks, and 18-19 PCR negative controls (Table

Figure 3 .

 3 Figure 3. Metazoan Operational Taxonomic Unit (OTU) richness among sediment horizons and regions. Boxplots show detected numbers of metazoan OTUs (18S V1-V2, COI) recovered in each sediment layer, for the four regions evaluated in this study. Cluster abundances were calculated on rarefied datasets. Boxplots show medians with interquartile ranges. Red dots indicate mean values.

Figure 4 .

 4 Figure 4. Richness and connectivity among regions of the Mediterranean-Atlantic transition zone. Venn diagrams showing numbers of shared OTUs among regions.

Figure 5 .

 5 Figure 5. Correspondence Analysis (CA) ordinations of combined macro-, meiofauna, protist, and prokaryote datasets as performed by STATIS in ade4. Sample differences displayed by region (left) and by sediment horizon (right) show that deep (> 1,000 m) sites cluster across the transition zone, while mesopelagic community composition differs on either side of the Gibraltar Strait. Communities also differ among sediment layers, but the segregation differs n magnitude depening on size class.

Figure 1 .

 1 Figure 1. Schematic of the bioinformatic pipeline developed during this thesis for the improved analysis of metabarcoding data.

  résultats confirment également les doutes quant à la possibilité de mieux décrire les communautés vivantes en utilisant l'ARN environnemental (ARNe). Sur les marqueurs ribosomaux, l'ARN, tout en résolvant des schémas spatiaux similaires à l'ADN co-extrait, a entraîné des estimations de richesse significativement plus élevées, soutenant les hypothèses de persistance accrue de l'ARN ribosomal (ARNr) dans l'environnement, et l'existence d'un biais additionnel et non mesuré en raison de la surabondance d'ARNr dans l'environnement et d'ARN sécrété à taux variables en fonction de l'activité métabolique des organismes. Sur le locus mitochondrial, l'ARN a détecté une richesse métazoaire inférieure tout en résolvant moins de différences écologiques que l'ADN co-extrait, reflétant la grande labilité de l'ARN messager.Les résultats soulignent également l'importance d'utiliser de grandes quantités de sédiments (≥ 10 g) pour étudier avec précision la diversité eucaryote. Dans le troisième chapitre, nous montrons donc que l'ADN est plus pertinent que l'ARN pour des études logistiquement réalistes, reproductibles, et fiables. Nous confirmons aussi que des quantités de sédiments plus grandes (≥ 10 g) fournissent des évaluations plus complètes et précises de la biodiversité eucaryote benthique et qu'il faut favoriser l'augmentation du nombre de réplicas biologiques plutôt que techniques pour déduire des patrons écologiques fiables.Bien que le tri par taille des organismes moyennant tamisage des sédiments ait permis, comme attendu(Elbrecht et al.2017), de détecter une diversité métazoaire plus élevée (chapitre APPENDIX RÉSUMÉ SUBSTANTIEL EN FRANÇAIS 215 4), des ségrégrations spatiales et des compositions taxonomiques similaires ont été obtenus dans des échantillons tamisés et non tamisés, indiquant que le temps considérable associé au tamisage n'est pas essentiel pour faire des évaluations écologiques robustes. Cela est confirmé par le fait que le lavage et le tamisage des échantillons peuvent entraîner une perte substantielle d'organismes (Montagna et al.2017), ajouté au risque accru de contamination par de l'ADN allochtone, en particulier si les compartiments de taille protiste et procaryote présentent un intérêt d'étude. Les protocoles de métabarcoding optimisés dans les chapitres précédents ont été utilisés dans le cinquième chapitre pour réévaluer la biodiversité profonde dans la zone de transition Atlantique-Méditerranée, offrant une preuve de concept pour l'étude de la biodiversité des grands fonds marins à large échelle à travers l'ADNe. En effet, alors que les habitats sédimentaires représentent la grande majorité des habitats dans les abysses, et qu'ils sont un grand réservoir de biodiversité encore largement non décrite, largement moins de 1% des grands fonds a été étudié à ce jour. Dans de tels écosystèmes vastes et difficiles d'accès, le pouvoir de détection élevé du métabarcoding d'ADN environnemental(ADNe), sur des échantillons plus faciles à recueillir que les collections de spécimens morphologiques, offre de nouvelles perspectives pour l'investigation standardisée de biodiversité et biogéographie à grande échelle. En combinant le marqueur génétique mitochondrial COI et la région V1-V2 de l'ARN ribosomal 18S (ARNr), nous avons étudié la biodiversité métazoaire à petite et à grande échelle dans la zone de transition Atlantique-Méditerranée, à l'aide d'ADNe extrait de sédiments profonds provenant de 13 sites allant de la Méditerranée centrale à la dorsale médio-atlantique. Nous avons évalué l'influence de la couche de sédiments, de la taille des grains de sédiments, de la teneur en matière organique ainsi que des communautés procaryotes (16S V4-V5), sur l'étendue et la structure de la biodiversité métazoaire dans cette région. Nos résultats soulignent que les facteurs à petite échelle (centimètres) affectent fortement la richesse des métazoaires des grands fonds marins et la composition des communautés. Une diminution significative de la richesse en OTU a été observée avec chaque couche de sédiments, allant de 1 cm à 15 cm de profondeur, et une ségrégation verticale importante dans la structure des communautés a été révélée dans toutes les régions pour la méiofaune et la macrofaune. Les premiers cinq centimètres de sédiment abritaient la plupart des OTU métazoaires (94% pour APPENDIX RÉSUMÉ SUBSTANTIEL EN FRANÇAIS 216 18S, 98% pour COI), avec des nombres d'OTU allant de 2 à 168 par échantillon pour 18S et de 81 à 1259 pour COI. Les facteurs à grande échelle (> 100 km) ont davantage affecté la diversité bêta que la diversité alpha. Le pourcentage de matière organique et la taille des grains de sédiments montrèrent une forte variation à l'échelle régionale, avec une teneur en matière organique plus élevée dans les sédiments méditerranéens et des particules de plus grande taille dans l'Atlantique. Ces deux variables contribuèrent de manière significative à expliquer les différences de composition des communautés entre sites. La méio et la macrofaune ont également montré une forte relation avec le compartiment procaryote (RV = 0,5-0,65), ceci peut être dû à une dépendance similaire aux paramètres abiotiques, ainsi qu'à des relations biotiques directes ou indirectes. Enfin, le détroit de Gibraltar était un facteur supplémentaire expliquant les très fortes différences régionales dans la composition des communautés, soutenant une influence combinée de facteurs historiques et de mouvements actuels des masses d'eau sur la distribution de la diversité benthique. En 2010, la Conférence des Parties à la Convention sur la Diversité Biologique a établi le Plan Stratégique pour la Biodiversité 2011-2020, et a délimité cinq « objectifs stratégiques » qui ont été divisés en 20 objectifs (Aichi Targets). Chaque « Objectif d'Aichi » a été conçu pour mieux comprendre et prévoir la biodiversité, en particulier, comment la diversité biologique sous-tend le fonctionnement des écosystèmes et comment les services écosystémiques sont essentiels pour le bien-être humain. L'accomplissement de ces objectifs d'Aichi garantit en fin de compte nos moyens de subsistance ainsi que le développement économique, et est essentielle pour le maintien de la biodiversité et la réduction de la pauvreté. Les lacunes dans les connaissances sur la biodiversité sont particulièrement sévères dans les abysses, où > 90% des étendues restent inexplorées. Cette thèse a évalué et optimisé les protocoles de métabarcoding par ADNe pour les sédiments des grands fonds à un niveau de traitement bioinformatique, moléculaire et d'échantillonnage, et en ciblant de multiples compartiments du vivant, ouvrant la porte à des études à grande échelle sur la biodiversité dans les grands fonds marins, contribuant ainsi à une meilleure compréhension de la biodiversité, de la biogéographie et du fonctionnement des écosystèmes dans cet univers vaste et insaisissable.Ce travail ouvre également la voie à l'établissement de protocoles de biomonitoring efficaces qui sont de plus en plus nécessaires dans les zones ciblées par les industries minières et pétrolières.

From barcoding of single species to metabarcoding of whole communities

  years, and have led to the development of BOLD (Barcode Of Life Database), compiling standard barcode sequences for animal COI, fungal, and plant sequence data. Moreover, curated

	and taxon-specific mitochondrial and ribosomal references databases for prokaryotes and
	eukaryotes have emerged, with most notable examples being MIDORI for COI, SILVA,
	GreenGenes, and PR2 (Protist Ribosomal Reference Database) for ribosomal DNA (rDNA),
	and ITS2 (Internal Transcribed Spacer 2 Ribosomal DNA database) for internal transcribed
	spacer sequences.

Since 2005, the development of high-throughput sequencing (HTS) technologies, has allowed producing millions of DNA sequences from individual samples. This high throughput allows reliable, rapid, and inexpensive analysis of community samples, representing a new generation of sequencing technologies that are becoming increasingly available for the investigation of biodiversity at inter-and intraspecific scales. HTS can be applied to marker gene analysis (i.e. metabarcoding), allowing the description of biodiversity at the species-level, while total DNA approaches (e.g. shotgun sequencing, RNA sequencing, restriction siteassociated DNA sequencing, coined RAD seq), are effective tools for resolving individual and population-level genetic diversity. Molecular biodiversity assessment can be performed from bulk DNA extracted from a collection of organisms, approach termed DNA metabarcoding, or from environmental samples where DNA is extracted directly from air, water, or soil samples, termed environmental DNA (eDNA) metabarcoding. Metabarcoding studies mainly differ in

(1)

the type of barcode gene (genetic marker) used, (2) the precision of the taxonomic identification they allow considering the reference databases available and genetic marker used,

  There were historically two main reasons for clustering sequences into Operational Taxonomic Units (OTUs). The first was to limit the bias due to PCR, sequencing errors, and intragenomic variability (e.g. pseudogenes) by clustering erroneous sequences with error-free target sequences. The second was to delineate OTUs as clusters of homologous sequences (by grouping the alleles/haplotypes at the same locus) that would best fit a "species level", i.e. the

	CHAPTER II	BIOINFORMATIC PIPELINE COMPARISONS	57
	Operational Taxonomic Units defined using a classical phenetic proxy (Sokal and Crovello
	1970). Recent bioinformatic algorithms alleviate the influence of errors and intraspecific
	variability in metabarcoding datasets. First, amplicon-specific error correction methods,
	commonly used to correct sequences produced by pyrosequencing (Coissac et al. 2012), have
	now become available for Illumina-sequenced data. Introduced in 2016, DADA2 effectively

;[START_REF] Deiner | Environmental DNA Reveals That Rivers Are Conveyer Belts of Biodiversity Information[END_REF][START_REF] Bista | Monitoring Lake Ecosystem Health Using Metabarcoding of Environmental DNA: Temporal Persistence and Ecological Relevance[END_REF][START_REF] Dejean | Persistence of Environmental DNA in Freshwater Ecosystems[END_REF][START_REF] Evans | Quantification of Mesocosm Fish and Amphibian Species Diversity via Environmental DNA Metabarcoding[END_REF]

, and marine

(Fonseca, V. G. et al. 2010;[START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF][START_REF] Pawlowski | Eukaryotic Richness in the Abyss: Insights from Pyrotag Sequencing[END_REF][START_REF] Massana | Marine Protist Diversity in European Coastal Waters and Sediments as Revealed by High-Throughput Sequencing[END_REF][START_REF] Vargas | Eukaryotic Plankton Diversity in the Sunlit Ocean[END_REF][START_REF] Salazar | Global Diversity and Biogeography of Deep-Sea Pelagic Prokaryotes[END_REF][START_REF] Boussarie | Environmental DNA Illuminates the Dark Diversity of Sharks[END_REF] Bik et al. 2012b

) environments.

As every new technique brings on new challenges, a number of studies have put considerable effort into delineating critical aspects of metabarcoding protocols to ensure robust and reproducible results (see Fig.

1

in

[START_REF] Fonseca | Pitfalls in Relative Abundance Estimation Using Edna Metabarcoding[END_REF]

. Recent studies have addressed many issues regarding sampling methods

[START_REF] Dickie | Towards Robust and Repeatable Sampling Methods in EDNA-Based Studies[END_REF]

, contamination risks

[START_REF] Goldberg | Critical Considerations for the Application of Environmental DNA Methods to Detect Aquatic Species[END_REF]

, DNA extraction protocols

[START_REF] Brannock | Meiofaunal Community Analysis by High-Throughput Sequencing: Comparison of Extraction, Quality Filtering, and Clustering Methods[END_REF][START_REF] Deiner | Choice of Capture and Extraction Methods Affect Detection of Freshwater Biodiversity from Environmental DNA[END_REF][START_REF] Bienhold | Diversity and Biogeography of Bathyal and Abyssal Seafloor Bacteria[END_REF]

, amplification biases and required PCR replication levels for improved detection probability

[START_REF] Nichols | Minimizing Polymerase Biases in Metabarcoding[END_REF][START_REF] Alberdi | Scrutinizing Key Steps for Reliable Metabarcoding of Environmental Samples[END_REF][START_REF] Ficetola | Replication Levels, False Presences and the Estimation of the Presence/Absence from EDNA Metabarcoding Data[END_REF]

. Similarly, computational pipelines, through which molecular data are transformed into ecological inventories of putative taxa, have also been in constant improvement. PCR-generated errors and sequencing errors are major bioinformatic challenges for metabarcoding pipelines, as they can strongly bias biodiversity estimates

[START_REF] Coissac | Bioinformatic Challenges for DNA Metabarcoding of Plants and Animals[END_REF][START_REF] Bokulich | Quality-Filtering Vastly Improves Diversity Estimates from Illumina Amplicon Sequencing[END_REF]

. A variety of tools have thus been developed for quality-filtering amplicon data to remove erroneous reads and improve the reliability of Illumina-sequenced metabarcoding inventories

[START_REF] Bokulich | Quality-Filtering Vastly Improves Diversity Estimates from Illumina Amplicon Sequencing[END_REF][START_REF] Eren | A Filtering Method to Generate High Quality Short Reads Using Illumina Paired-End Technology[END_REF][START_REF] Minoche | Evaluation of Genomic High-Throughput Sequencing Data Generated on Illumina HiSeq and Genome Analyzer Systems[END_REF]

. Studies that evaluated bioinformatic processing steps have generally found that sequence quality-filtering parameters and clustering thresholds most strongly affect molecular biodiversity inventories, resulting in considerable variation during data analysis

[START_REF] Brannock | Meiofaunal Community Analysis by High-Throughput Sequencing: Comparison of Extraction, Quality Filtering, and Clustering Methods[END_REF][START_REF] Clare | The Effects of Parameter Choice on Defining Molecular Operational Taxonomic Units and Resulting Ecological Analyses of Metabarcoding Data[END_REF][START_REF] Brown | Divergence Thresholds and Divergent Biodiversity Estimates: Can Metabarcoding Reliably Describe Zooplankton Communities?[END_REF][START_REF] Xiong | Testing Clustering Strategies for Metabarcoding-Based Investigation of Community-Environment Interactions[END_REF]

.

corrects Illumina sequencing errors and has quickly become a widely used tool, particularly in the microbial world, producing more accurate biodiversity inventories and resolving fine-scale genetic variation by defining Amplicon Sequence Variants (ASVs)

[START_REF] Callahan | DADA2: High-Resolution Sample Inference from Illumina Amplicon Data[END_REF][START_REF] Nearing | Denoising the Denoisers: An Independent Evaluation of Microbiome Sequence Error-Correction Approaches[END_REF]

. Second, LULU is a recently developed curation algorithm designed to filter out spurious clusters, originating from PCR and sequencing errors, or intra-individual variability (pseudogenes, heteroplasmy), based on their similarity (minimum match) and cooccurrence rate (minimum relative cooccurence) with more abundant clusters, allowing the acquisition of curated datasets while avoiding arbitrary abundance filters

  , and MIDORI-UNIQUE[START_REF] Machida | Data Descriptor: Metazoan Mitochondrial Gene Sequence Reference Datasets for Taxonomic Assignment of Environmental Samples[END_REF] was used for COI. The databases were downloaded from the DADA2 website (https://benjjneb.github.io/dada2/training.html) and from the FROGS website (http://genoweb.toulouse.inra.fr/frogs_databanks/assignation). at 84% (default) and slightly higher at 90%, following recommendations of the authors for less variable loci than ITS. The design of the mock samples was not ideal to test LULU, as some mock species were not occurring (or rarely occurring) in environmental samples, but all species were always co-occurring in the mock samples and this at consistent abundance ratios. With the minimum ratio parameter at the default value of 1, this led to the loss of closely related but true mock species for 18S, due to random amplification biases leading to consistent read abundance patterns. In order to remove only errors and avoid losing true mock species, we thus tested minimum ratio at 100 and 1000,

	Finally, to evaluate the effect of swarm clustering, ASV tables were clustered with swarm v2
	(Mahé et al. 2015) in FROGS (http://frogs.toulouse.inra.fr/) at d-values (i.e. nucleotide
	differences) ranging from 1 to 13 (d = 1, 3, 4, 5, 11 for 18S/16S, and d = 1, 5, 6, 7, 13 for COI),
	based on settings previously used in the literature (Clare et al. 2016; Atienza et al. 2020; Turon
	et al. 2020; Djurhuus et al. 2017; Cordier et al. 2019a; Sawaya et al. 2019; Wood et al. 2019; which allows removing only clusters that are 100/1000 times less abundant than a potential
	Laroche et al. 2018; Andújar et al. 2018a). Resulting OTUs were chimera-filtered and parent OTU.
	taxonomically assigned via RDP and BLAST+ with the databases stated above, using standard
	FROGS procedures.
	Turon	2016),	the	original	R	script	being	available	at
	https://github.com/metabarpark/R_scripts_metabarpark.
	To test LULU curation (Frøslev et al. 2017), refined 18S and COI ASVs/OTUs were
	curated with LULU v.0.1 following the online tutorial (https://github.com/tobiasgf/lulu). The
	LULU algorithm detects erroneous clusters by comparing their sequence similarity and co-

Molecular clusters were refined in R v.3.5.1 (R Core Team 2018). A blank correction was made using the decontam package v.

1.2.1 (Davis et al. 2018)

, removing all clusters that were prevalent (more frequent) in negative control samples. ASV/OTU tables were refined based on their BLAST or RDP taxonomy. For both assignment methods, clusters unassigned at phylumlevel were removed. With BLAST, assigned clusters represented 33% of COI data, 76% of 18S data, and 97% of 16S data. With RDP, assigned clusters represented 95-99% of data. Non-target clusters (i.e. either non-metazoan or non-bacterial) were removed. Additionally, for metazoans, clusters with terrestrial assignments (taxonomic groups known to be terrestrial-only, such as Insecta, Arachnida, Diplopoda, Amphibia, terrestrial mammals, Stylommatophora, Aves, Onychophora, Succineidae, Cyclophoridae, Diplommatinidae, Megalomastomatidae, Pupinidae, Veronicellidae) were removed. Samples were checked to ensure that a minimum of 10,000 reads were left after refining. Finally, as tag-switching is to be expected in multiplexed metabarcoding analyses

[START_REF] Schnell | Tag Jumps Illuminated -Reducing Sequence-to-Sample Misidentifications in Metabarcoding Studies[END_REF]

, an abundance renormalization was performed to remove spurious positive results due to reads assigned to the wrong sample (Wangensteen and occurrence rate with more abundant ("parent") clusters. LULU was applied on the full dataset (mock and environmental samples) with a minimum relative co-occurrence of 0.95 (default), using a minimum similarity threshold (minimum match)

Table 1

 1 three bivalve species remained unresolved. Taxonomic assignments were correct at the genus-level for six species with COI and three species for 18S, but all mock species produced ASVs/OTUs correctly assigned up to family or class level. Dominant species generally produced more reads in both the clustered and non-clustered datasets, with the notable exception of the gastropod Paralepetopsis sp, which was poorly detected with 18S (TableS5).When ASVs were clustered with swarm v2, this generally led to a reduction in taxonomic recovery: the two bivalves P. kilmeri and C. regab were taxonomically misidentified with COI at d ≥ 1 and Chorocaris sp. was not detected with 18S at d > 1. Clustering ASVs with swarm v2 reduced the number of molecular clusters produced per species, but some species still produced multiple OTUs even at d values as high as d = 13 for COI(D. dianthus, A. muricola,

	CHAPTER II	BIOINFORMATIC PIPELINE COMPARISONS	67
	recovered and the

), even with minimum relative DNA abundance levels as low as 0.7% (Mock 5). With 18S, seven species were Chorocaris sp., and Paralepetopsis sp.) and d = 11 for 18S

(A. arbuscula, A. muricola, Munidopsis sp., and E. norvegica)

.

Table 1 .

 1 Number of ASVs/OTUs detected per species in the mock communities using different bioinformatic pipelines. White cells indicate an exact match with the number of OTUs expected (i.e., 1 OTU for each mock species), light grey cells indicate a number of OTUs differing by ±3 from the number expected, dark grey cells indicate a number of OTUs > 3 times the one expected, and black cells a number ≥ 10 times the one expected. Ø indicates absence of expected OTU. Taxonomy is given up to the lowest common rank assigned to OTUs from mock species. "Others" represents unexpected OTUs, i.e. with assignments not related to any species in the mocks. These may represent contamination or symbionts of the mock species. LULU was run at minimum ratio = 100 for 18S and minimum ratio = 1 for COI.

		COI	DADA2 + Ø DNA in %	90% 84% d1 d5 d6 d7 d13 d1 d5 d6 d7 d13 d1 d5 d6 d7 d13 LULU swarm swarm+LULU 90% swarm+LULU 84%
		Acanella arbuscula	20
		Hexacorallia; D.dianthus	3
		Alvinocaris ; A. muricola	3
		Chorocaris sp.	3
	Mock5 Mock3 Mock5 Mock3	Munidopsis sp. Gastropoda; Paralepetopsis sp. Bivalvia; C. regab°3 3 20 Phreagena kilmeri°3 Vesicomya gigas 3 Polychaeta; E.norvegica 40 Others 0 Acanella arbuscula 10 Hexacorallia; D.dianthus 0.7 Alvinocaris ; A. muricola 0.7 Chorocaris sp. 0.7 Munidopsis sp. 0.7 Gastropoda; Paralepetopsis sp. 5 Bivalvia; C. regab°0.7 Phreagena kilmeri°0.7 Vesicomya gigas 0.7 Polychaeta; E.norvegica 80 Others 0 18S Alcyonacea; A.arbuscula 20 Caryophylliidae; D.dianthus 3 Alvinocaris muricola 3 Chorocaris sp. 3 Munidopsis sp. 3 Gastropoda; Paralepetopsis sp. 20 Vesicomyidae; P. kilmeri/C. regab/V. gigas* 9 Polychaeta; E.norvegica 40 Others 0 Alcyonacea; A.arbuscula 10 Caryophylliidae; D.dianthus 0.7 Alvinocaris muricola 0.7 Chorocaris sp. 0.7 Munidopsis sp. 0.7 Gastropoda; Paralepetopsis sp. 5	90% 84% d1 d3 d4 d5 d11 d1 d3 d4 d5 d11 d1 d3 d4 d5 d11 Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø Ø
		Vesicomyidae; P. kilmeri/C. regab/V. gigas*	2.1
		Polychaeta; E.norvegica	80
		Others	0

°Bivalvia was common rank for OTUs of P. kilmeri and C. regab for all pipelines with swarm clustering *Bivalvia was common rank for all pipelines with d > 1

Chi-square p-value significant parwise comparisons R^2 p-value significant parwise comparisons 18S-V1V2

	Molecular processing	50.3	< 0.001	Molecular processing	0.06	< 0.001
		16.2	< 0.001		0.23	< 0.001
	Site (random effect)			Site
				Molecular processing x Site	0.19	0.16
	COI
	Molecular processing	57.3	< 0.001	Molecular processing	0.09	< 0.001
	Site (random effect)	2.2	0.14	Site	0.20	< 0.001
				Molecular processing x Site	0.17	0.0013
	18S-V4
	Molecular processing	38.3	< 0.001	Molecular processing	0.08	< 0.001
	Site (random effect)	15.9	< 0.001	Site	0.35	< 0.001
				Molecular processing x Site	0.20	< 0.001
	16S-V4V5
	Molecular processing	55.0	< 0.001	Molecular processing	0.06	< 0.001
	Site (random effect)	3.4	0.07	Site	0.57	< 0.001
				Molecular processing x Site	0.14	< 0.001

Table 2 .

 2 Changes in community structure with region, site, and sediment horizon.

					pairwise
	LOCUS	F-value	R^2	p-value	comparisons
					among horizons
	18S V1-V2
	Region	3.6	0.16	0.001
	Site(Region)	1.8	0.14	0.001
	Sediment horizon	1.6	0.05	0.001
	Region:Sediment horizon	1.4	0.11	0.001
	Site(Region):Sediment horizon	1.2	0.17	0.008
	COI
	Region	3.9	0.24	0.01
	Site(Region)	1.7	0.15	0.01
	Sediment horizon	2.1	0.08	0.001
	Region:Sediment horizon	1.5	0.09	0.001
	Site(Region):Sediment horizon

PERMANOVAs were calculated on normalised datasets by permuting 1,000 times with Region as a blocking factor and site as a plot factor, using Jaccard distances. Significant p values are in bold. For pairwise comparisons, significance codes are p < 0.001: '***'; p < 0.01: '**'; p < 0.05: '*'.

  . This approach has been successfully applied on bulk samples, where DNA concentrations are typically high compared to extracts based on environmental samples. The application of these methods on deep-sea sediments will thus require mtDNA enrichment method, such as sequence-capture by hybridization techniques[START_REF] Liu | Mitochondrial Capture Enriches Mito-DNA 100 Fold, Enabling PCR-Free Mitogenomics Biodiversity Analysis[END_REF][START_REF] Jones | Targeted Capture in Evolutionary and Ecological Genomics[END_REF][START_REF] Gasc | Sequence Capture by Hybridization to Explore Modern and Ancient Genomic Diversity in Model and Nonmodel Organisms[END_REF][START_REF] Wilcox | Capture Enrichment of Aquatic Environmental DNA: A First Proof of Concept[END_REF][START_REF] Cruz-Dávalos | Experimental Conditions Improving In-Solution Target Enrichment for Ancient DNA[END_REF].En particulier, l'application de ces méthodes chez les métazoaires reste compliquée car il est difficile de définir des unités taxonomiques opérationnelles moléculaires (OTUs) décrivant correctement la diversité au niveau spécifique. En effet, une source importante d'erreur dans les inventaires moléculaires de métazoaires provient du fait que ces-derniers sont des organismes multicellulaires, et que les marqueurs génétiques utilisés par le métabarcoding sont présents en copies multiples dans leurs génomes, et ce à des taux différents selon les taxons (Fig. 4, chapitre 1). Les erreurs de séquençage, de PCR, et les importantes variations génétiques intraspécifiques résultent dans le fait qu'une espèce, voire un individu, produit plusieurs unités taxonomiques opérationnelles (OTUs). Or, comme ces OTUs sont le proxy moléculaire pour la description d'espèces, il est important que la correspondance OTU-espèce soit maintenue afin de conserver la fiabilité de l'inventaire moléculaire de biodiversité. particulier chez les métazoaires. Pour finir, les ASVs / OTUs finales peuvent être filtrées en fonction de leurs taux d'identité et de cooccurrence en utilisant LULU. Les résultats montrent que des inventaires de diversité fiables peuvent être obtenus en utilisant les algorithmes de correction DADA2 et LULU, mais soulignent que le clustering des ASVs en OTUs, combiné à la filtration additionnelle de LULU, est nécessaire pour produire des inventaires de biodiversité métazoaire fiables. Aussi, les seuils d'identités de LULU sont à choisir soigneusement selon la variabilité du marqueur utilisé. Pour les marqueurs mitochondriaux, le seuil défaut de 84% était approprié mais trop bas pour les marqueurs ribosomaux tel que 18S où il conduisait à la perte d'espèces dans les communautés artificielles et a donc dû être augmenté à 90%. Enfin, deux méthodes d'assignation taxonomique des ASVs/OTUs ont été implémentées dans le pipeline : le classificateur bayésien (RDP) et BLAST, un algorithme basé sur l'identité des séquences. La comparaison de BLAST et du classificateur RDP a souligné le potentiel de ce dernier à fournir de très bonnes assignations, mais a mis en évidence la nécessité d'un effort concerté par la communauté scientifique pour développer des bases de données exhaustives et spécifiques aux communautés étudiées. Les résultats montrent que l'élimination de fragments d'ADN courts n'affecte pas les estimations de la diversité alpha et bêta dans aucun des compartiments biologiques étudiés. Les

	APPENDIX APPENDIX	RÉSUMÉ SUBSTANTIEL EN FRANÇAIS RÉSUMÉ SUBSTANTIEL EN FRANÇAIS	213 214
	nombreux obstacles restent à surmonter pour appliquer les méthodes basées sur l'ADNe de manière reproductible et fiable. Le pipeline développé est basé sur la correction des séquences Illumina avec DADA2, et permet d'analyser des données de métabarcoding produites à partir de marqueurs ribosomaux et mitochondriaux, de compartiments taxonomiques procaryotes et eucaryotes. Nous avons implémenté l'option de regrouper les variantes génétiques identiques (« amplicon sequence protocoles de métabarcoding d'ADNe afin de sélectionner l'approche permettant de décrire au mieux les communautés vivantes. Dans le troisième chapitre, nous avons pour cela utilisé cinq sites profonds couvrant des habitats allant de monts sous-marins à des sources hydrothermales et des volcans de boue, et nous avons ciblé en parallèle les procaryotes (16S-V4V5), les protistes (18S-V4), et les métazoaires (18S-V1, COI). Dans un premier temps, des inventaires basés sur l'ADN furent comparés à ceux révélés par l'ARN. En effet, ce-dernier, étant produit uniquement par des organismes vivants, a été présenté comme une approche plus pratique pour cibler la partie active des communautés. Parallèlement, l'ADN ancien étant principalement constitué de petits fragments, nous avons aussi évalué l'effet de l'élimination de fragments d'ADN courts par sélection de taille et métabarcoding, en minières, et pétrolières (Ramirez-Llodra et al. 2010; 2011; Levin, L. A. et al. 2016). reconcentration par éthanol.
	variant », ASV) produites par DADA2 en unités taxonomiques opérationnelles (OTU) avec Une meilleure connaissance de la biodiversité marine profonde et des facteurs biotiques et
	swarm v2, un algorithme de regroupement (« clustering ») basé sur la théorie des réseaux, plus abiotiques influençant sa distribution est donc nécessaire, afin de mettre en place des stratégies
	sensible aux données. En effet, les algorithmes de clustering, regroupant des séquences proches de protection et de gestion de ces écosystèmes. Même si le metabarcoding d'ADNe est une
	mais non-identiques, ont été développés pour diminuer le biais engendré par les erreurs méthode efficace dans ces écosystèmes vastes et difficiles d'accès, son application sur des
	produites durant le séquençage et la PCR, mais aussi pour diminuer le biais de la sédiments profonds est potentiellement biaisée par la présence d'ADN archivé provenant
	démultiplication des OTUs dû aux variations intraspécifiques. Le clustering reste donc
	potentiellement une étape importante dans le processus d'analyse des données de

Finally, while new sequencing technologies are increasingly reliable, cost effective, and accessible, they remain inadequate for low-resource field-based applications. New portable, low-cost HTS devices (e.g. MinION sequencer from Oxford Nanopore Technologies) combined with portable lab systems such as miniPCR

[START_REF] Marx | PCR Heads into the Field[END_REF]

, are opening the door to real-time biodiversity assessment, and successful applications have already been reported in space

[START_REF] Castro-Wallace | Nanopore DNA Sequencing and Genome Assembly on the International Space Station[END_REF]

, in the arctic

[START_REF] Goordial | In Situ Field Sequencing and Life Detection in Remote (79°26′N) Canadian High Arctic Permafrost Ice Wedge Microbial Communities[END_REF]

, or in the rainforest

[START_REF] Pomerantz | Real-Time DNA Barcoding in a Rainforest Using Nanopore Sequencing: Opportunities for Rapid Biodiversity Assessments and Local Capacity Building[END_REF]

.

Les pipelines bioinformatiques pour l'analyse de données de métabarcoding par ADNe sont en développement constant, et des avancées récentes permettent maintenant de corriger les séquences ADN (DADA2), de regrouper les séquences plus précisément en construisant des réseaux haplotypiques (swarm v2), et de filtrer les OTU en comparant leur taux d'identité et de cooccurrence (LULU), ce qui permet d'éviter les filtres d'abondance relative arbitraires.

En utilisant des communautés artificielles et 42 échantillons de sédiments profonds, le deuxième chapitre de la thèse cherche à évaluer ces nouveaux outils d'analyse de séquences ADN et de mettre en place un pipeline bioinformatique pour améliorer la qualité et la fiabilité des inventaires moléculaires de biodiversité.

L'utilisation des nouvelles générations de séquençage (NGS) combiné à l'ADNe constitue une avancée considérable pour la recherche en environnement profond car les fonds abyssaux restent très peu étudiés du fait de leur difficulté d'accès, et constituent potentiellement un grand réservoir de biodiversité. En effet, ils recouvrent plus de 50% de la planète Terre, et ils ont d'importants rôles écosystémiques, comme le recyclage du carbone, ou la production secondaire de matière organique

(Smith, K. L. et al. 2009; Bik et al. 2012b)

. Mais ces habitats dits 'profonds' subissent l'impact grandissant des activités humaines, allant de menaces indirectes dû au changement climatique affectant l'équilibre physico-chimiques des océans, aux menaces directes tel que le stockage de déchets, la pollution, ou l'exploitation de ressources naturelles, d'organismes morts. Or, l'inclusion de cet ADN ancien (ADNa) aboutirait à des inventaires de biodiversité passée plutôt que présente. Ainsi, le second objectif de cette thèse est d'évaluer des

Table S4 .

 S4 DADA2 read-track table. Number of reads obtained in samples after each processing step. Data refining was performed in R (decontamination, renormalisation, removal of non-target taxa, and clusters unassigned at phylum-level or with unreliable phylum-level assignments), based on BLAST assignments obtained using the Silva training set available on the DADA2 website for 18S and 16S, and on the MIDORI database for COI. .03 0.01 0.01 0.02 0.01 0.03 0.01 0.01 0.01 0 0.03 0.01 0.01 0.01 0.01 TableS5. Relative read abundance (%) detected per species in the mock communities using different bioinformatic pipelines. Taxonomy is given up to the lowest common rank assigned to OTUs from mock species. "Others" represents unexpected OTUs, i.e. with assignments not related to any species in the mocks. These may represent contamination or symbionts of the mock species. °Bivalvia was common rank for P. kilmeri and C. regab for all pipelines with swarm clustering. *Bivalvia was common rank for pipelines with d > 1.

	Locus	DADA2 + DNA in % 20 3 3 3 3 20 Bivalvia; C. regab°3 Acanella arbuscula Hexacorallia; D.dianthus Alvinocaris ; A. muricola Chorocaris sp. Munidopsis sp. Gastropoda; Paralepetopsis sp. 0.3 Chorocaris sp. Ø 90% 84% 11 11 11 4 4 4 5 5 5 1 1 1 15 15 15 28 27 27 0.2 0.2 3 1 1 1 Munidopsis sp. 3 8 8 9 Gastropoda; Paralepetopsis sp. 20 0.01 0.01 0 Vesicomyidae; P. kilmeri/C. regab/V. gigas* 9 19 19 19 Polychaeta; E.norvegica 40 14 14 14 Others 0 0.05 0.04 0.04 0.06 0.04 0.05 d1 d5 d6 11 11 11 4 4 4 5 5 5 1 1 1 15 15 15 28 28 28 1 0 0 8 8 8 0.01 0.03 0.03 0.03 0.01 0.02 0.02 0.02 0.02 0.02 d7 d13 d1 d5 d6 d7 d13 11 11 11 11 11 11 11 4 4 4 4 4 4 4 5 5 5 5 5 5 5 1 1 1 1 1 1 1 15 15 15 16 16 16 16 28 28 27 27 27 27 27 0 0 1 0 0 0 0 8 8 8 8 8 8 8 19 19 19 19 19 19 19 19 19 19 14 14 14 14 14 14 14 14 14 14 0 0.03 0.04 0.06 0.06 0.06 0.06 0.05 0.04 0.06 0.06 0.06 d1 d5 d6 d7 d13 11 11 11 11 11 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 15 15 15 15 15 27 27 27 27 27 1 0 0 0 0 9 8 8 8 8 0 0 0 0 0 19 19 19 19 19 14 14 14 14 14 Alcyonacea; A.arbuscula Polychaeta; E.norvegica 80 32 33 32 33 32 32 32 32 33 32 32 32 32 32 32 32 32 32 Others 0 0.03 0.02 swarm swarm+LULU 90% swarm+LULU 84% COI minimum-ratio = 1 0.02 0LULU 8 8 8 8 8 8 8 8 8 8 8 7 7 7 7 8 8 8 8 6 6 6 6 6 7 6 6 6 6 6 Pipeline Mock3 Mock5 Number of raw ASVs/OTUs Number of Number of Number of target ASVs/OTUs after all refining steps target OTUs after LULU 90% target OTUs after LULU 84% ASV to OTU ratio ASV to OTU ratio (LULU 90%) ASV to OTU ratio (LULU 84%) Dada2 78,785 13,397 10,028 5,539 ---Dada2+d=1 64,669 5,563 4,422 2,263 2.4 2.3 2.4 Dada2+d=5 53,749 4,503 3,761 2,016 3.0 2.7 2.7 Dada2+d=6 52,216 4,324 3,656 1,971 3.1 2.7 2.8 Dada2+d=7 50,919 4,190 3,568 1,922 3.2 2.8 2.9 Dada2+d=13 44,684 3,518 3,158 1,758 3.8 3.2 3.2 Dada2 57,661 8,280 6,909 6,518 ---Dada2+d=1 44,948 6,015 5,063 4,677 1.4 1.4 1.4 Dada2+d=3 34,569 4,194 3,676 3,322 2.0 1.9 2.0 Dada2+d=4 31,509 3,716 3,278 2,945 2.2 2.1 2.2 Dada2+d=5 28,764 3,313 2,949 2,636 2.5 2.3 2.5 Dada2+d=11 19,504 1,869 1,676 1,469 4.4 4.1 4.4 Dada2 56,577 53,815 -----Dada2+d=1 41,746 38,972 --1.4 --Dada2+d=3 29,023 26,676 --2.0 --Dada2+d=4 25,406 23,165 --2.3 --Dada2+d=5 22,841 20,697 --2.6 --Mock5 Mock3 Dada2+d=11 14,631 12,800 --4.2 --

Table S8 .

 S8 Read and cluster abundance with data refining based on BLAST and RDP taxonomy. Number of ASVs/OTUs obtained in datasets when refining was performed based on BLAST or RDP assignments (blast / rdp). Metazoan datasets (COI

	Number of raw ASVs/OTUs	% raw ASVs/OTUs assigned at phylum-level (BLAST / RDP)	Number of target clusters before taxonomy quality filter (BLAST / RDP)	Number of target clusters after phylum-level quality-filter (BLAST / RDP)	Number of target clusters after genus-level quality-filter (BLAST / RDP)
			10,113 / 39,269		105 / 112
	44,948	76% / 97%		5,063 / 5,410	1,916 / 4,187
					35,614 / 40,827

Table S2 .

 S2 Nucleic acid concentrations of samples from each of the five moleccular processing methods evaluated in this study. Original extracts were normalised to 0.25 ng/µL and 10 µL of standardized samples were used in PCR.

	Sample

name Extraction kit Sample type DNA preparation method Sediment amount for extraction Concentration of original extract (ng/µL)

		Mock community 3	CTAB extraction	mock DNA sample mass-balanced mix of 10 species	na	5.0
		Mock community 5 MDW_ST179_CT1_0_1 MDW_ST179_CT2_0_1 MDW_ST179_CT3_0_1 MDW_ST117_CT1_0_1	CTAB extraction PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit	mock DNA sample mass-balanced mix of 10 species DNA crude extract DNA crude extract DNA crude extract DNA crude extract	na 10g 10g 10g 10g	5.0 8.1 11.0 9.7 0.9			clusters before refining	Number of samples after refining	Final number of target reads	Final number of target clusters
	LOCUS	MDW_ST117_CT2_0_1 MDW_ST117_CT3_0_1	PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit	DNA DNA	crude extract crude extract	10g 10g	2.2 0.9
	18S-V1	MDW_ST38_CT1_0_1	PowerMax Soil DNA Isolation Kit	DNA	crude extract	9.5g	0.2
	Control DNA 10g	MDW_ST38_CT2_0_1 MDW_ST38_CT3_0_1 MDW_ST23_CT1_0_1	PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit 5 2,921,651 PowerMax Soil DNA Isolation Kit 15 12,969,202	DNA DNA 1,654,366 DNA 9,978,581	crude extract crude extract 1,601,427 crude extract 9,141,929	10g 10g 1,380,613 10g 8,577,414	4.7 1,379,141 4.3 1.4 8,463,482	0.11 1.33	47 65		0 15
	MDW_ST23_CT2_0_1 DNA 10g EtOH rec.	PowerMax Soil DNA Isolation Kit 15 13,646,370	DNA 10,577,221	crude extract 9,757,954	10g 9,271,161	9.2 9,129,915	1.52	67		15
	MDW_ST23_CT3_0_1 MRM_ST48_PC09_0_1 MRM_ST48_PC15_0_1 DNA 10g S-S DNA 2g	PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit 15 11,735,871 14 8,476,073	DNA DNA DNA 8,938,926 6,402,605	crude extract crude extract crude extract 7,990,574 5,840,025	9g 10g 10g 7,403,206 5,215,391	0.7 6.5 8.2 7,328,224 5,168,422	1.01 0.9	62 61	42,876	15 14	16,157,973	6,031
		MRM_ST48_PC16_0_1	PowerMax Soil DNA Isolation Kit	DNA	crude extract	8.7g	4.5
	MDW_ST179_CT1_0_1 Positive Control (Metazoa only)	PowerMax Soil DNA Isolation Kit 2 2,096,631	DNA 1,607,219	EtOH-reconcentrated 1,438,424	10g 1,432,399	30.3 1,293,985	9.66	62		2
		MDW_ST179_CT2_0_1	PowerMax Soil DNA Isolation Kit	DNA	EtOH-reconcentrated	10g	6.4
	RNA 2g	MDW_ST179_CT3_0_1	PowerMax Soil DNA Isolation Kit 14 18,130,054	DNA 14,322,872	EtOH-reconcentrated 13,311,099	10g 12,180,021	16.3 11,591,521	4.83	64		13
	COI Control	MDW_ST117_CT1_0_1 MDW_ST117_CT2_0_1 MDW_ST117_CT3_0_1	PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit 7 642,571	DNA DNA DNA 414,329	EtOH-reconcentrated EtOH-reconcentrated EtOH-reconcentrated 410,866	10g 10g 10g 410,260	8.9 15.5 7.0 410,189	0.02	64		0
	DNA 10g	MDW_ST38_CT1_0_1	PowerMax Soil DNA Isolation Kit 15 13,804,664	DNA 11,871,147	EtOH-reconcentrated 11,645,233	9.5g 10,515,311	1.0 10,437,446	0.74	76		15
	MDW_ST38_CT2_0_1 MDW_ST38_CT3_0_1 DNA 10g EtOH rec.	PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit 15 12,735,345	DNA DNA 10,966,940	EtOH-reconcentrated EtOH-reconcentrated 10,758,928	10g 10g 9,634,212	23.7 23.6 9,560,863	0.76	75		15
	MDW_ST23_CT1_0_1 MDW_ST23_CT2_0_1 MDW_ST23_CT3_0_1 DNA 10g S-S DNA 2g	PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit 15 13,172,416 14 10,992,972	DNA DNA DNA 11,357,075 8,962,857	EtOH-reconcentrated EtOH-reconcentrated EtOH-reconcentrated 11,141,979 8,748,635	10g 10g 9g 10,019,802 7,478,953	7.3 38.7 3.1 9,948,428 7,439,814	0.71 0.52	76 68	45,508	15 14	10,977,614	4,333
	MRM_ST48_PC09_0_1 MRM_ST48_PC15_0_1 Positive Control (Metazoa only)	PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit 2 1,482,785	DNA DNA 1,261,045	EtOH-reconcentrated EtOH-reconcentrated 1,253,408	10g 10g 1,252,485	31.4 32.7 1,226,728	2.06	83		2
	RNA 2g 18S-V4	MRM_ST48_PC16_0_1 MDW_ST179_CT1_0_1 MDW_ST179_CT2_0_1	PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit 9 8,085,884 PowerMax Soil DNA Isolation Kit	DNA DNA 6,548,055 DNA	EtOH-reconcentrated size selected 6,405,869 size selected	8.7g 10g 5,780,511 10g	26.4 5,749,188 32.6 35.3	0.54	71		9
	Control DNA 10g	MDW_ST179_CT3_0_1 MDW_ST117_CT1_0_1 MDW_ST117_CT2_0_1	PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit 5 38,028 PowerMax Soil DNA Isolation Kit 15 5,108,793	DNA DNA DNA 3,852,156 1,088	size selected size selected 1,005 size selected 3,244,507	10g 10g 10g 3,081,831 786	33.0 786 3.2 9.0 3,073,436	0 0.27	2 60		0 15
	MDW_ST117_CT3_0_1 MDW_ST38_CT1_0_1 MDW_ST38_CT2_0_1 MDW_ST38_CT3_0_1 DNA 10g EtOH rec. DNA 2g DNA 10g S-S	PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit 13 2,569,170 13 4,812,187 15 3,675,283	DNA DNA DNA DNA 1,853,940 3,622,684 2,779,263	size selected size selected size selected size selected 1,539,838 3,014,540 2,334,265	10g 9.5g 10g 10g 1,422,958 2,884,756 2,222,758	2.4 0.2 14.4 1,419,415 19.3 2,876,930 2,216,696	0.25 0.27 0.27	55 60 60	65,832	13 13 15	8,654,710	40,868
	RNA 2g	MDW_ST23_CT1_0_1	PowerMax Soil DNA Isolation Kit 13 14,024,345	DNA 10,695,784	size selected 8,260,707	10g 6,978,244	2.7 6,876,335	1.46	49		13
	MDW_ST23_CT2_0_1 MDW_ST23_CT3_0_1 16S-V4V5	PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit	DNA DNA	size selected size selected	10g 9g	14.0 1.2
	Control	MRM_ST48_PC09_0_1	PowerMax Soil DNA Isolation Kit 5 1,100,024	DNA 815,505	size selected 692,998	10g 687,737	18.9 686,244	0.22	62		0
	MRM_ST48_PC15_0_1 MRM_ST48_PC16_0_1 MDW_ST179_CT1_0_1 MDW_ST179_CT2_0_1 DNA 10g EtOH rec. DNA 10g DNA 10g S-S	PowerMax Soil DNA Isolation Kit PowerMax Soil DNA Isolation Kit 15 6,228,145 RNeasy PowerSoil DNA elution kit RNeasy PowerSoil DNA elution kit 15 7,400,388 15 7,163,763	DNA DNA 4,351,718 DNA DNA 5,167,853 5,039,022	size selected size selected 3,445,745 crude extract crude extract 4,033,571 4,045,902	10g 8.7g 3,436,831 4g 3.9g 4,024,943 4,037,814	23.1 3,311,742 16.5 33.2 33.3 3,861,002 3,891,760	3.64 4.07 3.62	53 52 54	148,797	15 15 15	21,740,351	138,478
	DNA 2g RNA 2g	MDW_ST179_CT3_0_1 MDW_ST117_CT1_0_1 MDW_ST117_CT2_0_1	RNeasy PowerSoil DNA elution kit RNeasy PowerSoil DNA elution kit 15 7,788,742 RNeasy PowerSoil DNA elution kit 14 15,248,792	DNA DNA 5,409,390 DNA 10,719,709	crude extract crude extract 4,582,930 crude extract 7,858,978	4g 3.2g 4,573,875 4.7g 7,854,365	33.3 4,428,719 0.9 0.4 7,438,875	3.17 5.29	57 49		15 14
		MDW_ST117_CT3_0_1	RNeasy PowerSoil DNA elution kit	DNA	crude extract	4g	0.1
		MDW_ST38_CT1_0_1	RNeasy PowerSoil DNA elution kit	DNA	crude extract	4g	1.9
		MDW_ST38_CT2_0_1	RNeasy PowerSoil DNA elution kit	DNA	crude extract	3.7g	3.0
		MDW_ST38_CT3_0_1	RNeasy PowerSoil DNA elution kit	DNA	crude extract	4.5g	5.4
		MDW_ST23_CT1_0_1	RNeasy PowerSoil DNA elution kit	DNA	crude extract	4g	36.7
		MDW_ST23_CT2_0_1	RNeasy PowerSoil DNA elution kit	DNA	crude extract	4g	31.9
		MDW_ST23_CT3_0_1	RNeasy PowerSoil DNA elution kit	DNA	crude extract	5g	34.7
		MRM_ST48_PC09_0_1	RNeasy PowerSoil DNA elution kit	DNA	crude extract	4g	36.7
		MRM_ST48_PC15_0_1	RNeasy PowerSoil DNA elution kit	DNA	crude extract	4g	35.3
		MRM_ST48_PC16_0_1	RNeasy PowerSoil DNA elution kit	DNA	crude extract	4.5g	34.7
		MDW_ST179_CT1_0_1	RNeasy PowerSoil	cDNA	crude extract	4g	6.07
		MDW_ST179_CT2_0_1	RNeasy PowerSoil	cDNA	crude extract	3.9g	10.60
		MDW_ST179_CT3_0_1	RNeasy PowerSoil	cDNA	crude extract	4g	5.39
		MDW_ST117_CT1_0_1	RNeasy PowerSoil	cDNA	crude extract	3.2g	0.927
		MDW_ST117_CT2_0_1	RNeasy PowerSoil	cDNA	crude extract	4.7g	0.7
		MDW_ST117_CT3_0_1	RNeasy PowerSoil	cDNA	crude extract	4g	0.56
		MDW_ST38_CT1_0_1	RNeasy PowerSoil	cDNA	crude extract	4g	2.94
		MDW_ST38_CT2_0_1	RNeasy PowerSoil	cDNA	crude extract	3.7g	3.35
		MDW_ST38_CT3_0_1	RNeasy PowerSoil	cDNA	crude extract	4.5g	5.97
		MDW_ST23_CT1_0_1	RNeasy PowerSoil	cDNA	crude extract	4g	8.2
		MDW_ST23_CT2_0_1	RNeasy PowerSoil	cDNA	crude extract	4g	4.44
		MDW_ST23_CT3_0_1	RNeasy PowerSoil	cDNA	crude extract	5g	6.8
		MRM_ST48_PC09_0_1	RNeasy PowerSoil	cDNA	crude extract	4g	24.1
		MRM_ST48_PC15_0_1	RNeasy PowerSoil	cDNA	crude extract	4g	15.1
		MRM_ST48_PC16_0_1	RNeasy PowerSoil	cDNA	crude extract	4.5g	10.4
		MOBIO_10g_DNA_extraction_blank PowerMax Soil DNA Isolation Kit	extraction and field blank	na	na	0.0
		MOBIO_2g_DNA_extraction_blank RNeasy PowerSoil DNA elution kit extraction blank	na	na	0.0
		MOBIO_2g_RNA_extraction_blank RNeasy PowerSoil	extraction blank	na	na	0.2

Sample name ENA sample alias Extraction kit Sample type Size fraction (µm) Sample volume for DNA extraction Concentration of original extract (ng/µL)

								Depth (m)	Latitude	Longitude	Habitat	Region
	ESSNAUT_PL06_CT2_0_1_rep1	eDNAB0000081	PowerMax Soil DNA Isolation Kit	DNA	NA	2 g	1.1	2,417	42.9422	6.7422	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL06_CT2_0_1_rep2	eDNAB0000081	PowerMax Soil DNA Isolation Kit	DNA	NA	2 g	1	2,417	42.9422	6.7422	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL07_CT2_0_1_rep1	eDNAB0000122	PowerMax Soil DNA Isolation Kit	DNA	NA	5.1 g	3.2	2,415	42.9423	6.7426	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL07_CT2_0_1_rep2	eDNAB0000122	PowerMax Soil DNA Isolation Kit	DNA	NA	5.1 g	3	2,415	42.9423	6.7426	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL11_CT2_0_1_rep1	eDNAB0000189	PowerMax Soil DNA Isolation Kit	DNA	NA	4 g	2.7	2,418	42.9423	6.7423	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL11_CT2_0_1_rep2	eDNAB0000189	PowerMax Soil DNA Isolation Kit	DNA	NA	4 g	2.6	2,418	42.9423	6.7423	Continental slope	Gulf of Lyon, Western Mediterranean
					>1,000; 500-1,000;
	ESSNAUT_PL06_CT4_0_1_pool_rep1	eDNAB0003158	PowerMax Soil DNA Isolation Kit	DNA	250-500; 40-250; 20-	2.2+0.7+1.5+2+2.2=7.6 g	0.4	2,417	42.9422	6.7422	Continental slope	Gulf of Lyon, Western Mediterranean
					40
					>1,000; 500-1,000;
	ESSNAUT_PL06_CT4_0_1_pool_rep2	eDNAB0003158	PowerMax Soil DNA Isolation Kit	DNA	250-500; 40-250; 20-	2.2+0.7+1.5+2+2.2=7.6 g	0.6	2,417	42.9422	6.7422	Continental slope	Gulf of Lyon, Western Mediterranean
					40
					>1,000; 500-1,000;
	ESSNAUT_PL07_CT4_0_1_pool_rep1	eDNAB0003159	PowerMax Soil DNA Isolation Kit	DNA	250-500; 40-250; 20-	1.4+1.2+1.5+10+10=24.1 g	0.9	2,415	42.9423	6.7426	Continental slope	Gulf of Lyon, Western Mediterranean
					40
					>1,000; 500-1,000;
	ESSNAUT_PL07_CT4_0_1_pool_rep2	eDNAB0003159	PowerMax Soil DNA Isolation Kit	DNA	250-500; 40-250; 20-	1.4+1.2+1.5+10+10=24.1 g	1.2	2,415	42.9423	6.7426	Continental slope	Gulf of Lyon, Western Mediterranean
					40
					>1,000; 500-1,000;
	ESSNAUT_PL11_CT4_0_1_pool_rep1	eDNAB0003160	PowerMax Soil DNA Isolation Kit	DNA	250-500; 40-250; 20-	0.7+0.4+5.4+6.9+10=23.4 g	0.3	2,418	42.9423	6.7423	Continental slope	Gulf of Lyon, Western Mediterranean
					40
					>1,000; 500-1,000;
	ESSNAUT_PL11_CT4_0_1_pool_rep2	eDNAB0003160	PowerMax Soil DNA Isolation Kit	DNA	250-500; 40-250; 20-	0.7+0.4+5.4+6.9+10=23.4 g	0.3	2,418	42.9423	6.7423	Continental slope	Gulf of Lyon, Western Mediterranean
					40
	EssNaut_DNA_extraction blank	eDNAB0003157	PowerMax Soil DNA Isolation Kit	DNA	NA	NA	0.2	NA	NA	NA	Continental slope	Gulf of Lyon, Western Mediterranean
	Water_extraction_blank	eDNAB0003316	Tara Oceans extraction protocol	DNA	NA	NA	0.2	NA	NA	NA	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL11_Salsa3Bol2_20	eDNAB0000222	Tara Oceans extraction protocol	DNA	> 20	6,300 L	3.02	2,417	42.9425	6.7440	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL11_Salsa3Bol4_20	eDNAB0000224	Tara Oceans extraction protocol	DNA	> 20	6,300 L	3.22	2,417	42.9425	6.7440	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL7_Salsa1Bol3_20	eDNAB0000149	Tara Oceans extraction protocol	DNA	> 20	4,740 L	6.96	2,417	42.9423	6.7426	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL7_Salsa1Bol5_20	eDNAB0000150	Tara Oceans extraction protocol	DNA	> 20	4,740 L	5	2,417	42.9423	6.7426	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL9_Salsa2Bol2_20	eDNAB0000155	Tara Oceans extraction protocol	DNA	> 20	5,400 L	16.3	1,152	43.2237	6.8876	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL9_Salsa2Bol4_20	eDNAB0000157	Tara Oceans extraction protocol	DNA	> 20	5,400 L	6.46	1,152	43.2237	6.8876	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL10_PBT2_0.2	eDNAB0002506	Tara Oceans extraction protocol	DNA	0.2-2.0	7.5 L	3.6	2,420	42.9425	6.7444	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL10_PBT2_2	eDNAB0002507	Tara Oceans extraction protocol	DNA	2.0-20	7.5 L	0.01	2,420	42.9425	6.7444	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL10_PBT2_20	eDNAB0002508	Tara Oceans extraction protocol	DNA	> 20	7.5 L	0.01	2,420	42.9425	6.7444	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL7_PBT2_20	eDNAB0002505	Tara Oceans extraction protocol	DNA	> 20	7.5 L	0.01	2,417	42.9423	6.7426	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL7_PBT2_2	eDNAB0002504	Tara Oceans extraction protocol	DNA	2.0-20	7.5 L	0.01	2,417	42.9423	6.7426	Continental slope	Gulf of Lyon, Western Mediterranean
	ESSNAUT_PL7_PBT2_0.2	eDNAB0002503	Tara Oceans extraction protocol	DNA	0.2-2.0	7.5 L	1	2,417	42.9423	6.7426	Continental slope	Gulf of Lyon, Western Mediterranean

Organic matter input varies among regions but also with depth, highlighting that it is unlikely that depth does not play a role in marine species distributions.

Acknowledgements ..................................................................................................... Appendix ..................................................................................................................... Acknowledgements This work is part of the "Pourquoi Pas les Abysses?" project, funded by Ifremer and the eDNAbyss (AP2016 -228) project, funded by France Génomique (ANR-10-INBS-09) and Genoscope-CEA. We wish to thank Caroline Dussart for her contribution to the early developments of bioinformatic scripts,Patrick Durand for bioinformatic support, and Cathy Liautard-Haag for her help with lab management. We also wish to express our gratitude to the participants and mission chiefs of the Essnaut (Marie-Anne Cambon Bonavita), MarMine (Eva Ramirez Llodra, project 247626/O30 (NRC-BI), PEACETIME (Cécile Guieu and Christian Tamburini), CanHROV (Marie Claire Fabri) and MEDWAVES (Covadonga Orejas) cruises. The MEDWAVES cruise was organised in the framework of the ATLAS Project, supported by the European Union 2020 Research and Innovation Programme under grant agreement number: 678760. We thank Stefaniya Kamenova, Tiago Pereira, and four anonymous reviewers for their comments on previous versions of this manuscript. An earlier version of this manuscript has been peer-reviewed and recommended by Peer Community In Ecology (https://dx.doi.org/10.24072/pci.ecology.100043). Acknowledgements This work is part of the "Pourquoi Pas les Abysses?" project funded by Ifremer, and the project eDNAbyss (AP2016 -228) funded by France Génomique (ANR-10-INBS-09) and Genoscope-CEA. This work also received funding from the European Union's Horizon 2020 research and innovation programme under grant the agreement No 678760 (ATLAS), and the 'Investissements d'Avenir' program OCEANOMICS (ANR-11-BTBR-0008; NH and CV). CHAPTER III MOLECULAR METHODS COMPARISONS 107 This output reflects only the authors' view and the European Union cannot be held responsible for any use that may be made of the information contained therein. We wish to thank Laure Quintric, Patrick Durand, Caroline Belser, and Stéphane Pesant for bioinformatic and archiving support, as well as Jan Pawlowski and Eva Ramirez Llodra for useful advice on this work. We also wish to express our gratitude to the crew, participants, and mission chiefs of the MarMine cruise (Eva Ramirez Llodra, project 247626/O30, funded by the Research Council of Norway and associated industrial partners) and the MEDWAVES cruise supported by the ATLAS project and the Spanish Ministry of Economy, Industry and Competitivity (Covadonga Orejas and all the crew from the Sarmiento de Gamboa), as well as to all the people who helped collecting samples (Perregrino Cambeiro, Juancho Movilla, Maria Rakka, Joana Boavida, Anna Addamo). This manuscript has been released as a Pre-Print at https://doi.org/10.1101/836080. Acknowledgements This work is part of the "Pourquoi Pas les Abysses?" project funded by Ifremer, and the project eDNAbyss (AP2016 -228) funded by France Génomique (ANR-10-INBS-09) and Genoscope-CEA. This work also received funding from the 'Investissements d'Avenir' program OCEANOMICS (ANR-11-BTBR-0008; NH). We wish to thank Laure Quintric and Patrick Durand for bioinformatic support and Stéphane Pesant for help in data management.

We also wish to express our gratitude to the crew of the R/V Atalante, to the Nautile crew and operators, and to the participants of the EssNaut16 cruise.

Acknowledgements

This work is part of the "Pourquoi Pas les Abysses?" project funded by Ifremer, and the project eDNAbyss (AP2016 -228) funded by France Génomique (ANR-10-INBS-09) and Genoscope-CEA. This work also received funding from the European Union's Horizon 2020 research and innovation programme under grant the agreement No 678760 (ATLAS). This output reflects only the author's view and the European Union cannot be held responsible for any use that may be made of the information contained therein. We wish to thank Bastien Mérigot for help on statistical analyses as well as Jan Pawlowski and Eva Ramirez Llodra for useful advice on this work. We also wish to express our gratitude to the crew of the Sarmiento de Gamboa and the participants of the MEDWAVES cruise (supported by the ATLAS project and the Spanish Ministry of Economy, Industry, and Competitivity), especially to all the people who helped collecting samples (Perregrino Cambeiro, Juancho Movilla, Maria Rakka, Joana CHAPTER V DEEP-SEA BIODIVERSITY PATTERNS THROUGH eDNA 162 Boavida, Anna Addamo). We wish to thank the mission chiefs, crew, and participants of the ESSNAUT, PEACETIME and CANHROV cruises.

Acknowledgements

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Data accessibility

The data for this work can be accessed in the European Nucleotide Archive (ENA) database (project: PRJEB33873), please refer to the metadat excel sheet for ENA file names. The data set, including raw sequences, databases, and ASV/OTU tables, has been deposited on https://doi.org/10.12770/0b5d250b-8418-4dda-b39c-960c4481df93. Bioinformatic scripts, config files, and R scripts are available on Gitlab (https://gitlab.ifremer.fr/abyss-project/).

Data accessibility:

The raw data for this work can be accessed in the European Nucleotide Archive database (Study accession numbers: PRJEB37673 for water, PRJEB33873 for sediment). Please refer to the metadata excel file for ENA file names. The dataset, including raw sequences, databases, as well as raw and refined ASV/OTU tables are available on https://doi.org/10.12770/2deb785a-74c5-4b9d-84d6-82a81e0dda6d. Bioinformatic scripts can be accessed following the Gitlab link.

Data accessibility

The data for this work can be accessed in the European Nucleotide Archive (ENA) database (PRJEB38767 and PRJEB33873, see metadata excel sheet for ENA sample names). The dataset, including sequences, databases, as well as raw and refined ASV/OTU tables are

Conflict of interest disclosure

The authors declare that they have no financial conflict of interest with the content of this article. Sophie Arnaud-Haond is one of the PCI Ecology recommenders.

Author contributions

MIB and SAH designed the study, MIB and JP carried out the laboratory and molecular work; MIB and BT performed the bioinformatic and statistical analyses. LQ led the bioinformatic development and participated in the study design. MIB, BG, and SAH wrote the manuscript. All authors contributed to the final manuscript.

Author contributions

MB, CL-H, and SA-H designed the study, MB, JP, and CL-H carried out the laboratory work. MB and BT performed the bioinformatic analyses. MB, BT, and NH performed the statistical analyses. MB and SA-H wrote the manuscript. All authors contributed to the final manuscript.

Data accessibility

The data for this work has been submitted to the European Nucleotide Archive (ENA) under the following project: PRJEB33873. . Please refer to the sample metadata in Supplementary Material to download samples from ENA. Additionally, the full dataset, including raw sequences, databases, ASV/OTU tables, and scripts (Gitlab link) are available through https://doi.org/10.12770/cf00aa7b-67e7-49c4-8939-038c4a9d887f.

Conflicts of Interest

The authors declare no competing interests.

Abstract

Despite representing one of the largest biomes on earth, biodiversity of the deep seafloor is still poorly known. Environmental DNA metabarcoding offers prospects for fast inventories and surveys, yet requires standardized sampling approaches and careful choice of environmental substrate. Here, we aimed to optimize the genetic assessment of prokaryote (16S), protistan (18S V4), and metazoan (18S V1-V2, COI) communities, by evaluating sampling strategies for sediment and aboveground water, deployed simultaneously at one deepsea site.

For sediment, while size-class sorting through sieving had no effect on total detected alpha diversity and resolved similar taxonomic compositions at the phylum level for all markers studied, it effectively increased the detection of meiofauna phyla. For water, large volumes obtained from an in situ pump (~6000 L) detected significantly more metazoan diversity than 7.5 L collected in sampling boxes. However, the pump being limited by larger mesh sizes (> 20 µm), only captured a fraction of microbial diversity, while sampling boxes allowed access to the pico-and nanoplankton. More importantly, communities characterized by aboveground water samples significantly differed from those characterized by sediment, whatever volume used, and both sample types only shared between 5% and 10% of molecular units.

Together, these results underline that sieving may be recommended when targeting meiofauna, and aboveground water does not represent an alternative to sediment sampling for inventories of benthic diversity. Questions addressed:

a. How do abiotic factors such as sediment layer, sediment grain size, and organic matter content affect metazoan biodiversity patterns across regional scales?

b. How do regional and habitat differences explain deep-sea benthic biodiversity patterns along the Mediterranean-Atlantic transition zone?

c. Does the Gibraltar straight constitute a connectivity barrier between the two ocean basins?

Eukaryotic COI gene amplicon generation

Metazoan COI barcodes were generated using the mlCOIintF (5'-GGWACWGGWTGAACWGTWTAYCCYCC-3') and jgHCO2198

(5'-TAIACYTCIGGRTGICCRAARAAYCA-3') primers [START_REF] Leray | A New Versatile Primer Set Targeting a Short Fragment of the Mitochondrial COI Region for Metabarcoding Metazoan Diversity: Application for Characterizing Coral Reef Fish Gut Contents[END_REF] amplification, but cycling conditions included a 30 s denaturation step followed by 25 cycles of 98 °C for 10 s, 57 °C for 30 s, 72 °C for 30 s, and a final extension of 72 °C for 10 min.

Prokaryotic 16S V4-V5 rRNA gene amplicon generation

Prokaryotic barcodes were generated using 515F-Y (5'-GTGYCAGCMGCCGCGGTAA-3') and 926R (5'-CCGYCAATTYMTTTRAGTTT-3') 16S-V4V5 primers [START_REF] Parada | Every Base Matters: Assessing Small Subunit RRNA Primers for Marine Microbiomes with Mock Communities, Time Series and Global Field Samples[END_REF].

Triplicate PCR reactions were prepared as described above for 18S V1-V2, but cycling conditions included a 30 s denaturation step followed by 25 cycles of 98 °C for 10 s, 53 °C for 30 s, 72 °C for 30 s, and a final extension of 72 °C for 10 min.

Amplicon library preparation

PCR triplicates were pooled and PCR products purified using 1X ( (Fonseca, V. G. et al. 2010;[START_REF] Forster | Benthic Protists: The under-Charted Majority[END_REF][START_REF] Pawlowski | Eukaryotic Richness in the Abyss: Insights from Pyrotag Sequencing[END_REF][START_REF] Cowart | Metabarcoding Is Powerful yet Still Blind: A Comparative Analysis of Morphological and Molecular Surveys of Seagrass Communities[END_REF][START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF]Cordier et al. 2019a) ou les milieux aquatiques [START_REF] Deiner | Environmental DNA Reveals That Rivers Are Conveyer Belts of Biodiversity Information[END_REF][START_REF] Sunagawa | Structure and Function of the Global Ocean Microbiome[END_REF][START_REF] Vargas | Eukaryotic Plankton Diversity in the Sunlit Ocean[END_REF][START_REF] Ibarbalz | Global Trends in Marine Plankton Diversity across Kingdoms of Life[END_REF][START_REF] Boussarie | Environmental DNA Illuminates the Dark Diversity of Sharks[END_REF].

Le Table S3. ABYSS metabarcoding pipeline.

APPENDIX SUPPLEMENTARY MATERIAL CHAPTER II. 220

Two sediment samples failed amplification for the COI marker gene (PCT_FA_CT2_0_1 and CHR_CT1_0_1). For metazoans, less reads were retained after bioinformatic processing in negative controls (36% for 18S, 47% for COI) compared to true samples (~60% for 18S, ~70% for COI), while the opposite was observed for 16S (74% of reads retained in control samples against 53% in true samples). Negative control samples (field, extraction, and PCR controls) contained 2,186,230 (~8%) 18S reads, 1,015,700 (~4%) COI reads, and 2,618,729 (28%) 16S reads. These reads were mostly originating from the field controls for metazoans (48% for 18S, 55% for COI) and extractions controls for 16S (50%). After blank correction, data refining, and abundance renormalization, rarefaction curves showed that a plateau was achieved for all samples in both clustered and non-clustered datasets, suggesting an overall sequencing depth adequate to capture the diversity presen

Gastropoda; Paralepetopsis sp. 20

Gastropoda; Paralepetopsis sp.

Gastropoda; Paralepetopsis sp. 20

Table S6. The effect of LULU minimum match and minimum ratio parameters. Number of 18S ASVs/OTUs detected per species in the mock communities using DADA2 with or without swarm clustering, and LULU curation at two different minimum match (84% and 90%) and minimum ratio (1 and 1000) parameters. Taxonomy is given up to the lowest common rank assigned to OTUs from mock species. "Others" represents unexpected OTUs, i.e. with assignments not related to any species in the mocks. These may represent contamination or symbionts of the mock species. *Bivalvia was common rank for pipelines with d > 1. (minimum ratio = 1) for COI. Only taxonomic assignments reliable at phylum-level were retained during refining (BLAST hit identity ≥ 86% for 18S and ≥ 80% for COI, RDP assignments phylumlevel bootstraps ≥ 80%). Silva132 was used as a reference database for 18S and MIDORI-UNIQUE, subsampled to marine-only taxa was used for COI. Cluster abundances were calculated on rarefied datasets.

SUPPLEMENTARY MATERIAL CHAPTER III. 227

Supplementary material Chapter III.

Supplemental materials and methods

Eukaryotic 18S-V1 rRNA gene amplicon generation

Eukaryotic 18S-V1V2 barcodes were generated using the SSUF04 (5'-GCTTGTCTCAAAGATTAAGCC-3') and SSUR22mod (5'-CCTGCTGCCTTCCTTRGA-3') primers [START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF] and 72 °C for 10 min) of all samples were carried out in triplicate in order to smooth the intrasample variance while obtaining sufficient amounts of amplicons for Illumina sequencing.

Amplicon triplicates were pooled and PCR products were purified using 1X AMPure XP beads (Beckman Coulter, Brea, CA, USA) cleanup. Aliquots of purified amplicons were run on an Agilent Bioanalyzer using the DNA High Sensitivity LabChip kit (Agilent Technologies, Santa Clara, CA, USA) to check their lengths, and quantified with a Qubit fluorometer (Invitrogen, Carlsbad, CA, USA).

Eukaryotic 18S-V4 rRNA gene amplicon generation

Eukaryotic 18S-V4 barcodes were generated using the TAReukF1 (5'-CCAGCASCYGCGGTAATTCC-3') and TAReukR (5'-ACTTTCGTTCTTGATYRA-3')

primers [START_REF] Stoeck | Multiple Marker Parallel Tag Environmental DNA Sequencing Reveals a Highly Complex Eukaryotic Community in Marine Anoxic Water[END_REF]. Triplicate PCR reactions were prepared as described above, but amplification was performed by a nested PCR with the first annealing temperature being 53°C for 10 cycles, followed by 48°C for 15 cycles. After PCR product cleanup using 1X AMPure XP beads, amplicon lengths and amounts were checked as described above.

Prokaryotic 16S-V4V5 rRNA gene amplicon generation

Prokaryotic barcodes were generated using the 515F-Y (5′-GTGYCAGCMGCCGCGGTAA-3′) and 926R (5′-CCGYCAATTYMTTTRAGTTT-3′)

primers [START_REF] Parada | Every Base Matters: Assessing Small Subunit RRNA Primers for Marine Microbiomes with Mock Communities, Time Series and Global Field Samples[END_REF]. Triplicate PCR reactions were prepared as described above for 18S-V1V2, but annealing temperature was at 53 °C. After PCR product cleanup using 1X

AMPure XP beads, amplicon lengths and amounts were checked as described above.

APPENDIX SUPPLEMENTARY MATERIAL CHAPTER III. 228

Eukaryotic COI gene amplicon generation

Metazoan COI barcodes were generated using the mlCOIintF 5'-GGWACWGGWTGAACWGTWTAYCCYCC-3'and jgHCO2198 5'-TAIACYTCIGGRTGICCRAARAAYCA-3' primers [START_REF] Leray | A New Versatile Primer Set Targeting a Short Fragment of the Mitochondrial COI Region for Metabarcoding Metazoan Diversity: Application for Characterizing Coral Reef Fish Gut Contents[END_REF]. The PCR reactions (20 μL final volume) contained 2.5 ng or less of total DNA template with 0.5 μM final concentration of each primer, 3% of DMSO, 0.175 mM final concentration of dNTPs, and 1X

Advantage 2 Polymerase Mix (Takara Bio, Kusatsu, Japan). Nested PCR amplifications were carried out in triplicates and consisted of an initial denaturation at 95 °C for 10 min, and 16 cycles of 10 s at 95°C, 30 s at 62 °C (-1°C per cycle), 60 s at 68 °C followed by 15 cycles of 95 °C for 10 s, 30 s at 46°C, 68 °C for 60 s, and a final extension of 68 °C for 7 min.

Amplicon library preparation

One hundred ng of amplicons were directly end-repaired, A-tailed and ligated to Illumina adapters on a Biomek FX Laboratory Automation Workstation (Beckman Coulter, Brea, CA, USA). Library amplification was performed using a Kapa Hifi HotStart NGS library Amplification kit (Kapa Biosystems, Wilmington, MA, USA) with the same cycling conditions applied for all metagenomic libraries and purified using 1X AMPure XP beads.

Sequencing library quality control

Libraries were quantified by Quant-iT dsDNA HS assay kits using a Fluoroskan Ascent microplate fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) and then by qPCR with the KAPA Library Quantification Kit for Illumina Libraries (Kapa Biosystems, Wilmington, MA, USA) on a MxPro instrument (Agilent Technologies, Santa Clara, CA, USA). Library profiles were assessed using a high-throughput microfluidic capillary electrophoresis system (LabChip GX, Perkin Elmer, Waltham, MA, USA).

Sequencing procedure

Library concentrations were normalized to 10 nM by addition of 10 mM Tris-Cl (pH 8.5) and applied to cluster generation according to the Illumina Cbot User Guide (Part # 15006165).

Amplicon libraries are characterized by low diversity sequences at the beginning of the reads due to the presence of the primer sequence. Low-diversity libraries can interfere in correct cluster identification, resulting in a drastic loss of data output. Therefore, loading concentrations SUPPLEMENTARY MATERIAL CHAPTER III. 229 of libraries were decreased (8-9 pM instead of 12-14 pM for standard libraries) and PhiX DNA spike-in was increased (20% instead of 1%) in order to minimize the impacts on the run quality.

Libraries were sequenced on HiSeq2500 (System User Guide Part # 15035786) instruments (Illumina, San Diego, CA, USA) in 250 base pairs paired-end mode. Table S7 Community differentiation between RNA and DNA molecular processing methods, using either RNA/DNA extracted jointly from ~2 g of sediment (RNA 2g/DNA 2g) or DNA extracted from ~10g of sediment (DNA 10g) in five deep-sea sites using four barcode markers targeting metazoans (COI, 18S-V1), micro-eukaryotes (18S-V4), and prokaryotes (16S-V4V5). PERMANOVAs were calculated on normalised datasets by permuting 10,000 times with Site as a blocking factor, using Jaccard distances for 18S-V1 and COI, and Bray-Curtis distances for 18S-V4 and 16S. Significant p values are in bold. Supplementary material Chapter IV.

Supplemental tables

Site

Supplemental materials and methods

Eukaryotic 18S-V1V2 rRNA gene amplicon generation Eukaryotic 18S-V1V2 barcodes were generated using the SSUF04 (5'-GCTTGTCTCAAAGATTAAGCC-3') and SSUR22mod (5'-CCTGCTGCCTTCCTTRGA-3') primers [START_REF] Sinniger | Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos[END_REF]) and the Phusion High Fidelity PCR Master Mix with GC buffer (ThermoFisher Scientific, Waltham, MA, USA). The PCR reactions (25 μL final volume) contained 2.5 ng or less of DNA template with 0.4 μM concentration of each primer, 3% of DMSO, and 1X Phusion Master Mix.

PCR amplifications (98 °C for 30 s; 25 cycles of 10 s at 98 °C, 30 s at 45 °C, 30 s at 72 °C;

and 72 °C for 10 min) of all samples were carried out in triplicate in order to smooth the intrasample variance while obtaining sufficient amounts of amplicons for Illumina sequencing.

Amplicon triplicates were pooled and PCR products were purified using 1X AMPure XP beads (Beckman Coulter, Brea, CA, USA) cleanup. Aliquots of purified amplicons were run on an Agilent Bioanalyzer using the DNA High Sensitivity LabChip kit (Agilent Technologies, Santa Clara, CA, USA) to check their lengths, and quantified with a Qubit fluorometer (Invitrogen, Carlsbad, CA, USA).

Eukaryotic 18S-V4 rRNA gene amplicon generation Eukaryotic 18S-V4 barcodes were generated using the TAReukF1 (5'-CCAGCASCYGCGGTAATTCC-3') and TAReukR (5'-ACTTTCGTTCTTGATYRA-3')

primers [START_REF] Stoeck | Multiple Marker Parallel Tag Environmental DNA Sequencing Reveals a Highly Complex Eukaryotic Community in Marine Anoxic Water[END_REF]. Triplicate PCR reactions were prepared as described above, but amplification was performed by a nested PCR with the first annealing temperature being 53°C for 10 cycles, followed by 48°C for 15 cycles. After PCR product cleanup using 1X AMPure XP beads, amplicon lengths and amounts were checked as described above.

Prokaryotic 16S-V4V5 rRNA gene amplicon generation Prokaryotic barcodes were generated using the 515F-Y (5′-GTGYCAGCMGCCGCGGTAA-3′) and 926R (5′-CCGYCAATTYMTTTRAGTTT-3′) primers [START_REF] Parada | Every Base Matters: Assessing Small Subunit RRNA Primers for Marine Microbiomes with Mock Communities, Time Series and Global Field Samples[END_REF]. Triplicate PCR reactions were prepared as described above for APPENDIX SUPPLEMENTARY MATERIAL CHAPTER IV. 243 18S-V1V2, but annealing temperature was at 53 °C. After PCR product cleanup using 1X

AMPure XP beads, amplicon lengths and amounts were checked as described above.

Eukaryotic COI gene amplicon generation

Metazoan COI barcodes were generated using the mlCOIintF 5'-GGWACWGGWTGAACWGTWTAYCCYCC-3'and jgHCO2198 5'-TAIACYTCIGGRTGICCRAARAAYCA-3' primers [START_REF] Leray | A New Versatile Primer Set Targeting a Short Fragment of the Mitochondrial COI Region for Metabarcoding Metazoan Diversity: Application for Characterizing Coral Reef Fish Gut Contents[END_REF] SUPPLEMENTARY MATERIAL CHAPTER V. 252

Supplementary material Chapter V.

Supplemental tables