K Évin Cilleros

Luc Allard

R Égis Vigouroux

S Ébastien Brosse

Ga Ël Grenouillet

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GTR+I+G, GTR+G Gtr+i+g

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+ Coi

GTR+I+G Gtr+g -78017 Gtr+i+g

GTR+I+G Coi 3+12s Gtr+i+g

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Alice Valentini

Tony Dejean

Roselyne Etienne

Amaia Iribar

Pierre Taberlet

Disentangling spatial and

I would like to first thank the courageous readers that will take the time to read at least one part of this manuscript. I hope you will learn something or find what you were looking for within it.

My gratitude also goes to N úria and Catherine who accepted to review my work, and all the jury members. As the time of writing these words, I don't know how things will happen but in every cases, I know that all your comments and your external view will be helpful to improve these works and get new research avenues.

La suite de mes remerciements (en franc ¸ais) va au Centre d'Etude de la Biodiversit é Amazonienne qui a financ é cette th èse et à ces membres qui m'ont fait confiance pour mener ce projet à son terme.

Pour rester sur la th ématique tropicale, je souhaite aussi remercier Damien Monchaux, Simon Clavier, Laurent Guillemet et Roland, membres d'HYDRECO, avec qui j'ai partag é une partie du travail de terrain en 2015 (et ma premi ère pose de filet, mon premier voyage en pirogue, mon premier passage de saut). Merci aussi à R égis Vigouroux pour son accueil et sa visite guid ée de Cayenne et Kourou.

Merci aussi Tony Dejean, Alice Valentini et Rapha ël Civade pour leur accueil au Bourget-du-Lac et les conseils concernant le metabarcoding.

Je remercie aussi Sarah Delavigne et Coline Gaboriaud, deux stagiaires qui ont particip é au d éveloppement des bases de donn ées morphologiques et mol éculaires.

Huit ann ées sur le campus et quatre ans pass ées au laboratoire EDB, c'est aussi des ann ées pass és avec des personnes grandioses, des amis et une seconde famille. Je ne vais pas faire un inventaire exhaustif de ces personnes (il y en a bien assez dans la suite du texte) mais rapidement, je retiendrai la communaut é des r ésidences Jean Mermoz et adjacentes (Cl ément, Marc, Romain, Julie, Marie, Adeline, Hugo), la communaut é M2 (Seb, Eva, Meli, Caroline, Lucie, Jan, Andrea et en particulier David et Chaton qui m'ont accueilli chez eux) et la communaut é doctorants et post-doctorants (Jade, C éline, Eline, Aurore, Carine, MC Solaire, F élix le chat, Fabian, Jess, Sandra, les petits nouveaux, les anciens d'EDB). Merci aux secondes mamans ou grandes soeurs Amaia, Catherine et Lucie Z., et aux grands rigolos, Pierrick et Guillaume, qui m'ont souvent fait rire.

Un gros bravo Jade qui a su trouver l' énergie de me supporter durant ces derni ères ann ées, entre mes blagues qui ne faisaient rire que moi ou mes perp étuelles discussions avec moi m ême. Bravo aussi à Seb pour avoir tenu deux ans et demi.

Je tiens aussi à remercier Linda, Catherine, Dominique, Nicole, aupr ès desquelles les questions administratives, financi ères ou d'organisation ont su trouver des r éponses, et Pierre pour l'excellente assistance informatique (surtout quand l'ordinateur ne s'allume plus).

De l'autre c ôt é du miroir du laboratoire, il y a une famille, sur laquelle j'ai toujours pu compter et dans laquelle j'ai pu me ressourcer lorsqu'il le fallait. Merci à mes parents Chapter I: Introduction I.1 Biodiversity: from pattern to process I.1.1 The historical roots of biological diversity

, then stared its popularity among scientists, politics and people.

I.1.2 The current definition of biodiversity

].

Finally, we can look at the total diversity across all the communities or γ-diversity. It reflects information given by both α-and β-diversity measures. For example, if communities share a lot of species, the total diversity tends to be close to the maximal value of α-diversity. In contrast, γ-diversity will be high if each community has a unique set of species.

 Anderson et al., 2011) but all converge towards the idea that within-, between-and total diversity

I.3.2 Taxonomic data: assemblage composition in streams and rivers

Stream fish assemblage composition was obtained from sampling during the dry seasons, with a single sampling occasion by site (except for a few sites). Fish were collected using rotenone, a non selective piscicide. Rotenone is a chemical compound from the ketone family, naturally produced by tropical plants of the Leguminosae II.

of life from genes to ecosystems. From this,"life" will have a different meaning at each of these levels. For example, the diversity of species at the community or assemblage level is the most known descriptor of biodiversity. Biodiveristy can also be described at the gene level using the diversity of alleles or genes (genetic polymorphism), or at the population level with the diversity in individual genotypes and phenotypes. To determine those of biodiversity descriptors, Colwell's definition lies on evolutionary and ecological characteristics. These characteristics have been commonly used to delineate species, but they can also apply to other levels of organization (genetic lineage, individuals, functional groups or landscapes).

I.1.3 Biodiversity at the community level

Using Colwell's definition, species can be used as the unit of diversity at the community level. If we consider a set of communities, a first and simple diversity descriptor will be the list or number of species, also called species richness, in each community (Figure I.1). We can also look if all species have the same abundance in each community or if some species dominate the community. In this case, the equitability or evenness of the community is used as a descriptor of diversity. Together, species richness and equitability reflect community composition and represent community diversity.

These measures of community diversity apply to each community and represent within-community diversity, or α-diversity. Summarizing communities using their species richness and evenness allows comparisons across communities but does not detect potential differences in the species compositions between communities. For instance, in Figure I.1, the two communities have each 4 species (α-diversity = 4) but they do not share all their species (only two are shared). One way to deal with this is to compare communities and look at between-community diversity, or β-diversity. A classical measure of β-diversity is the percentage of species that are shared (or not shared) between communities (e.g. [START_REF] Jaccard | The distribution of the flora in the Alpine zone[END_REF]or Sørensen, 1948 indices). In the Figure I.1, the two communities have 2 species in common and 4 species are only found in one of the two communities. So, the diversity between the two communities is represent different facets of biodiversity, that provide complementary information to describe patterns of diversity.

Figure I.1:

Biodiversity can be spatially decomposed in 3 levels: the diversity of a local assemblage or habitat (α-diversity), the difference in diversity between two or more assemblages or habitats (βdiversity), and the total diversity of assemblages or habitats. In the example above, each community is composed of 4 different species (thus α-diversity = 4 for each). If we compare the 2 communities, we see that they share two species and each community has 2 unique species. The 2 communities thus share 2 species in common over 6 species, that is 2 6 ≈ 33%, and the diference in diversity between the two communities (β-divesity) is 1 -2 6 = 4 6 ≈ 67%. In total, the diversity in these communities is equal to 6 species (γ-diversity)

I.1.4 The patterns of biodiversity

Diversity is not evenly distributed across the globe and thus exhibits spatial variability, from large to local spatial scales. At the global scale, the best studied biodiversity pattern is the latitudinal diversity gradient. Several authors have reported that a high number of species occur around the equator, and that their number decrease toward the poles, causing a hump shaped relationships between species richness and latitude (Figure I.2). This pattern is shared by most organisms (woody plants, birds, freshwater fish) despite some variability in the position of the peak of richness and the rate of richness decrease with increasing or decreasing latitude [START_REF] Hillebrand | On the Generality of the Latitudinal Diversity Gradient[END_REF]. Diversity also differs between continents and regions for a same latitudinal range. For example, the number of freshwater fish species in South America is 1.3 times higher to that of Africa (L év êque et al., 2007). But the most striking difference between these regions is the difference in the identity of species. Such difference in species composition between regions is among the most striking biogeographical patterns, and was used to define the biogeographic realms in early biogeographic studies from the XIX th century [START_REF] Wallace | The Geographical Distribution of Animals: With a Study of the Relations of Living and Extinct Faunas as Elucidating the Past Changes of Earth's Surface[END_REF][START_REF] Sclater | On the general Geographical Distribution of the Members of the Class Aves[END_REF][START_REF] Engler | Versuch einer Entwicklungsgeschichte der Pflanzenwelt, insbesondere der Florengebiete seit der Terti ärperiode: Die extratropischen Gebiete der n ördlichen Hemisph äre[END_REF]. Within a region, local diversity is also unequally distributed between the different ecosystems and habitat features, with patterns and gradients highly variable. Forest ecosystems do not have the same communities (group of directly or indirectly interacting species, co-occurring [START_REF] Gaston | Pattern and Process in Macroecology[END_REF], [START_REF] Rolland | Faster speciation and reduced extinction in the tropics contribute to the mammalian latitudinal diversity gradient[END_REF], [START_REF] Pyron | Phylogenetic niche conservatism and the evolutionary basis of ecological speciation[END_REF] and [START_REF] Oberdorff | Global and regional patterns in riverine fish species richness: A review[END_REF] respectively in space and time) and the same species richness as grassland ecosystems. In freshwater ecosystems, within a drainage basin, species richness increases from small upstream sites to large rivers and freshwater communities change along this gradient, with species associated to headwater streams and others endemic of large and deep channels (Huet's zonation (1959) and River Continuum Concept from [START_REF] Vannote | The River Continuum Concept[END_REF]. Understanding what shape these patterns remains an outstanding key question in ecology and requires combining several research fields (phylobiogeography, niche and spatial modelling, evolutionary biology). The improvement of modeling techniques and the development of new methods and tools of have accompanied the rapid and vast gathering of biodiversity data (via automatic recording devices, high-throughput DNA sequencing, citizen science; [START_REF] Bush | Connecting Earth observation to high-throughput biodiversity data[END_REF] and their availability through online databases [GBIF (http://gbif.org), GenBank [START_REF] Benson | [END_REF], The Ocean Biogeographic Information System [START_REF] Grassle | The Ocean Biogeographic Information System (OBIS): An online, worldwide atlas for accessing, modeling and mapping marine biological data in a multidimensional geographic context[END_REF]] allow to tackle this question.

I.1.5 A diversity of processes

The processes that shape the spatial patterns of biodiversity are scale dependent, although often not exclusive to a given spatial (or temporal) scale. A process that explains the global variation of species richness will therefore have a low influence on local diversity variation. For instance, the latitude is highly influential at the global scale, but it hardly affects local biodiversity variation. The reverse for more local processes also holds (e.g. interactions between species occur at a local scale and hardly affect diversity gradients measured at higher spatial scales). We can thus sort these processes according to the spatial scale considered. This led to formalize these processes as hierarchical filters [START_REF] Tonn | Climate change and fish communities: A conceptual framework[END_REF][START_REF] Poff | Landscape filters and species traits: Towards mechanistic understanding and prediction in stream ecology[END_REF] acting at global, regional or local scales on a set of candidate species (or pool of species). Those filters therefore select the species occurring at a given scale from a set of potential species co-occurring at higher scales [START_REF] Srivastava | Using local-regional richness plots to test for species saturation: Pitfalls and potentials[END_REF].

Global scale processes

At the global scale, four main non-exclusive groups of hypotheses have been proposed to explain the latitudinal diversity gradient (LDG).

First, the observed pattern might result from random distribution of species range along a latitudinal axis bounded at the two poles, without the need of any environmental effect. This geometric rule predicts that majority of species midpoint range will fall near the centre of the axis, corresponding to tropical regions (the mid-domain effect; Figure I.4, Colwell & Hurtt, 1994).

Aside from this neutral hypothesis, geographical differences between tropical and temperate regions can also contribute to the latitudinal diversity gradient. Tropical regions form a continuous entity, whereas temperate and polar regions are distributed part aside of it and form smaller entities. Larger areas are associated with higher species richness, through a greater speciation rate, a lower extinction rate and higher diversity of habitat (species-area relationship, [START_REF] Preston | The canonical distribution of commonness and rarity: Part I[END_REF].

Tropical regions also receive higher solar radiation that can translate in higher primary productivity and in higher climatic stability and lower seasonality variability [START_REF] Hutchinson | Homage to Santa Rosalia or Why Are There So Many Kinds of Animals[END_REF][START_REF] Hawkins | Relative influences of current and historical factors on mammal and bird diversity patterns in deglaciated North America[END_REF][START_REF] Currie | Predictions and tests of climate-based hypotheses of broad-scale variation in taxonomic richness[END_REF]. The species-energy relationship has been criticized because experimental studies showed that the species richness indeed increase with energy, but stops increasing or decreases after a threshold, and it thus might only be applicable under particular conditions [START_REF] Hurlbert | When should species richness be energy limited, and how would we know[END_REF].

A third group of hypotheses deals with the biogeographic historic differences between tropical and temperate regions, and associated evolutionary consequences. These hypotheses stipulate that diversity patterns are caused by differential speciation and extinction rates between tropical and temperate regions [START_REF] Rolland | Faster speciation and reduced extinction in the tropics contribute to the mammalian latitudinal diversity gradient[END_REF]. Two mechanisms have been proposed to explain those differences: either the tropical regions are older and more species originated from those areas [START_REF] Wiens | Phylogenetic origins of local-scale diversity patterns and the causes of Amazonian megadiversity[END_REF], or diversification rates are higher in the tropical regions compared to those observed in the temperate regions [START_REF] Mittelbach | Evolution and the latitudinal diversity gradient: Speciation, extinction and biogeography[END_REF][START_REF] Pyron | Large-scale phylogenetic analyses reveal the causes of high tropical amphibian diversity[END_REF]. Some studies also suggested the existence of phylogenetic tropical niche conservatism, the tendency of tropical species to retain ecological traits related to tropical environment [START_REF] Wiens | Historical biogeography, ecology and species richness[END_REF]. This would limit their dispersal capacity to regions outside of the tropics (dispersal limitation). Recently, Morini ère et al. (2016) proposed a similar mechanism for taxa that present inverse latitudinal gradients, based on temperate niche conservatism for lineages originating from temperate regions.

More recently, biotic interactions, although acting mainly at local scales, has regained importance as a mechanism that can affect global scale diversity patterns [START_REF] Schemske | Is there a latitudinal gradient in the importance of biotic interactions? Annual Review of Ecology[END_REF][START_REF] Pianka | Latitudinal Gradients in Species Diversity: A Review of Concepts[END_REF]. The relative stability of the climate over geological times for tropical areas allows the establishment of numerous interactions between species. In addition, this stability makes the environment more predictable and therefore relaxes the strength of selection by abiotic factors. In contrast, abiotic environment is the main constraint to species evolution in temperate regions and its variability selects for more generalist species [START_REF] Stevens | The Latitudinal Gradient in Geographical Range: How so Many Species Coexist in the Tropics[END_REF]. The shift from the dominance of abiotic constraints to the dominance of biotic constraints in tropical areas makes the opportunity for co-evolution to be a strong driver of speciation, where optimum phenotypes always change, facilitating adaptation and speciation [START_REF] Schemske | Is there a latitudinal gradient in the importance of biotic interactions? Annual Review of Ecology[END_REF][START_REF] Pianka | Latitudinal Gradients in Species Diversity: A Review of Concepts[END_REF] Biotic interactions include the effect of competition that promotes speciation through niche specialization. It also includes predation, which strength in tropical ecosystems (due to the high number of predator species) can reduce the competitive exclusion of prey species and thus enhance diversity [START_REF] Janzen | Herbivores and the number of tree species in tropical forests[END_REF][START_REF] Connell | On the role of natural enemies in preventing competitive exclusion in some marine animals and in rain forest trees[END_REF].

Despite the apparent separation of geometrical, geographical, historical and biotic hypotheses, they are not exclusive and together contribute in shaping diversity at the global scale.

Regional scale processes

At the regional scale, the pool of species is constrained by biogeographic, climatic and geographic variations within the considered region. Similarly to the global processes, differences in diversification rates between regions caused by geologic events can Introduction cause difference in diversity between regions located under same latitudes. For example, ancient refuge areas during glaciation or marine incursion periods have higher species richness and endemism than other areas [START_REF] Svenning | Ice age legacies in the geographical distribution of tree species richness in Europe[END_REF][START_REF] Lawes | Resilient forest faunal communities in South Africa: A legacy of palaeoclimatic change and extinction filtering[END_REF][START_REF] Reyjol | Patterns in species richness and endemism of European freshwater fish[END_REF]. Climatic and landscape features also restrict the identity of species that occur in a region. This combination of environmental characteristics and biotic component (the species present) have been used to define ecoregions (area with relative homogeneity of ecosystems), even if recent definitions put the emphasis more on the biotic component [START_REF] Olson | Terrestrial Ecoregions of the World: A New Map of Life on Earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity[END_REF][START_REF] Abell | Freshwater ecoregions of the world: A new map of biogeographic units for freshwater biodiversity conservation[END_REF].

Local scale processes

At the local scale, two kinds of processes, or assembly rules, have been proposed to determine diversity patterns, deterministic processes and neutral processes, which represent the two extremes of a spectrum.

Deterministic or niche-based processes put the emphasis on the role of abiotic and biotic environment on community composition that successively act on the species pool to define occurring species. First, species that can establish and persist under given abiotic conditions (e.g. climate, temperature, soil characteristic...) will constitute a potential set of species. This process is referred as environmental filtering. Then, biotic interactions between species will restrain this set of species to the observed occurring species. Traditionally, competition is considered as the main biotic constraint that will prevent species with similar ecology to co-occur (a process also called limiting similarity), but predation and facilitation also affect community composition. A first approach to make the distinction between abiotic and biotic constrains on species assemblages was through the use of the ratio between the number of genera found in a community and the number of species [START_REF] Elton | Competition and the Structure of Ecological Communities[END_REF]. A low species-to-genus ratio indicates that the community is composed of relatively distantly related species and is expected is limiting similarity is the dominant process. On the contrary, a higher ratio is expected under environmental filtering. Its use was however greatly criticized as it highly depends of the sample size, with the species-to-genus ratio increasing with an increasing number of species [START_REF] Gotelli | Quantifying biodiversity: Procedures and pitfalls in the measurement and comparison of species richness[END_REF][START_REF] Jarvinen | Species-To-Genus Ratios in Biogeography: A Historical Note[END_REF].

At the other end of the spectrum, the neutral processes root in Hubbells neutral theory [START_REF] Hubbell | The Unified Neutral Theory of Biodiversity and Biogeography[END_REF] where all individuals and species are functionally equal, so the environment impacts them equally, and only dispersal capacity and survival affect community composition. It is a dynamic equilibrium between migration from the species pool to local communities and local ecological drift that shape the community composition. Proposed as an alternative to more deterministic models, neutral hypotheses are currently used as a null model to establish random expectations against which the observed patterns are tested. Differences between these expectations and the observed patterns are often interpreted as the sign of the effect of deterministic processes in community assembly.

I.1.6 Unlocking the "Same pattern from several processes" problem

The process-from-pattern drawbacks

As explained above, the interpretation of clustering patterns as the result of local environmental filtering and overdispersed patterns as the result of local competition served as a basis to measure the strength of environmental and biotic processes in community assembly, but combinations of several processes can lead to the same pattern. For instance, if distant related species have converged toward the same ecology (niche convergence) and a strong environmental filtering act locally, the species-to-genius ratio will erroneously indicate that competition is the main process structuring the community. Similarly, competition between species can result in both clustering and overdispersed pattern [START_REF] Mayfield | Opposing effects of competitive exclusion on the phylogenetic structure of communities[END_REF]. For example, if soil type is the main driver of competition between plant species and if soil preference is phylogenetically conserved, close related species will mostly compete between themselves, leading to an overdispersed pattern. However, if light is the main driver of competition, taller species will outcompete the smaller ones and if height is conserved, competition will drive clustering.

The same problem can arise for dispersal limitation between communities. It is indeed difficult to differentiate the effect of historical dispersal limitation and recent dispersal limitation without knowledge of the studied system (Figure I.5).

Figure I.5:

Recent and historical dispersal limitation can cause the same observed pattern of taxonomic turnover between two communities (ellipses) made of two to four species (symbols). a: No dispersal limitation between the two communities. The assemblages can exchange species and it results in a low species turnover between the two assemblages. b: Recent dispersal limitation. The two assemblages have been connected but have been recently separated by a geographic barrier (e.g. a river). Species can evolve in each assemblage and it results in a strong turnover between the two assemblages. c: Historic dispersal limitation. The two assemblages have historically been separated and species have evolved within each assemblage. It causes a strong turnover of species between assemblages

Incorporating functional and phylogenetic information

In the last decades, an increasing consensus emerged highlighting that biodiversity is not only the identity of species, but also encompasses their role in the ecosystem and their evolutionary history. Biodiversity can thus be described via the identity of the species (taxonomic diversity), their ecological function (functional diversity) or the evolutionary history they represent (phylogenetic diversity). This view roots in the field of biological conservation, where a great attention has been given to maintaining ecosystem functions in addition to species.

Phylogenetic diversity approaches root in the study of [START_REF] Vane-Wright | What to protect? Systematics and the agony of choice[END_REF] that proposed to use cladistic (or phylogenetic) relationships between species as an additional component of biodiversity for conservation assessment. Indeed, species are not equal and this distinctiveness can be approximated via phylogenetic diversity, that is related to both past history of species (extinction, speciation, colonization) but also to ecosystem functioning. In addition, the capacity of species to evolve, which is related with the past history of the species and their phylogenetic relationships, is linked to the ability of the species to adapt to changing environment in the context of global changes. Its use have expended through the development of statistical and modeling tools allowing to handle complex phylogenetic data and to buildup robust phylogenetic trees for large number of species [START_REF] Smith | Mega-phylogeny approach for comparative biology: An alternative to supertree and supermatrix approaches[END_REF] based on genetic material from single genetic markers to complete genome [START_REF] Kappas | A Time-Calibrated Mitogenome Phylogeny of Catfish (Teleostei: Siluriformes)[END_REF]Jarvis et al., 2014).

Species can also be considered by their role in ecosystem functioning, giving rise to functional diversity. Functional diversity can be assessed by measuring directly species impact on ecosystem processes (decomposition rate, carbon and nitrogen fluxes), or by measuring species role in ecosystems through functional traits. Functional traits are any traits that impacts fitness indirectly via its effects on growth, reproduction and survival [START_REF] Violle | Let the concept of trait be functional![END_REF]. They are thus linked to ecosystem processes. These functional traits have been frequently to characterize plant and aquatic insects communities for decades. Their extensions to others animals have only recently expended through the development of life-history traits and/or morphological attributes databases for various taxa. Similarly to phylogenetic diversity, functional diversity necessitates handling simultaneously several functional traits to functionally describe species and has benefited from methodological advances that facilitate the comparisons between taxonomic, phylogenetic and functional diversities. For instance, β-diversities metrics developed to measure species replacement (Jaccard or Sorensen) have now their equivalents for functional β-diversity (Vill éger et al., 2011b) and phylogenetic β-diversity [START_REF] Leprieur | Quantifying phylogenetic beta diversity: Distinguishing between 'true' turnover of lineages and phylogenetic diversity gradients[END_REF][START_REF] Lozupone | UniFrac: An effective distance metric for microbial community comparison[END_REF]. This allows having similar frameworks for the three diversity facets and therefore to analyze the relationships between those facets to better understand assembly rules.

Comparing diversity facets to understand the processes

Community composition and spatial structure are affected by both evolutionary and ecological processes. To disentangle them, the simultaneous use of the different facets of biodiversity has been proposed to overcome the "Same pattern from several processes" problem [START_REF] Pavoine | Measuring biodiversity to explain community assembly: A unified approach[END_REF][START_REF] Baraloto | Using functional traits and phylogenetic trees to examine the assembly of tropical tree communities[END_REF][START_REF] Kraft | Functional trait and phylogenetic tests of community assembly across spatial scales in an Amazonian forest[END_REF][START_REF] Swenson | Opposing assembly mechanisms in a neotropical dry forest: Implications for phylogenetic and functional community ecology[END_REF]. Previous attempts focused on only on one facet (the taxonomic) or two (taxonomic and phylogenetic or taxonomic or functional). The latter cases lie on the hypothesis that functional and phylogenetic diversities are highly correlated, because functional traits are shaped through evolution. This hypothesis, that set the basis for community phylogenetic field [START_REF] Webb | Exploring the phylogenetic structure of ecological communities: An example for rain forest trees[END_REF][START_REF] Webb | Phylogenies and community ecology[END_REF], is now debated, as not all traits are phylogenetically constrained, and phylogenetic convergence can lead to the same pattern as phylogenetic conservatism [START_REF] Cavender-Bares | Phylogenetic overdispersion in Floridian oak communities[END_REF]. This fact was already noticed by [START_REF] Webb | Phylogenies and community ecology[END_REF], but trait conservatism is often assumed (and not tested), which might lead to major drawbacks.

α-diversity relationships

If functional traits are conserved through the phylogeny, close related species have similar ecological attributes, and increasing taxonomic diversity of assemblages will increase (or at least not decrease) functional and phylogenetic diversities. However the different assembly processes will affect the rate at which functional or phylogenetic diversities increase compare to the increase of taxonomic diversity. In other terms, the increase of functional and phylogenetic diversities can be higher or lower than expected knowing the increase in taxonomic diversity depending on the process that mainly structure local assemblages. The expected increase can be look via the use of null models that create random communities under specific rules. There is a variety of null model, and they can be applied to taxonomic, functional and phylogenetic diversity. For example, null models applying to taxonomic diversity are based on the randomization of the abundance or the occurrence of species in communities, by redistributing individuals or species from the species pool to communities, to test if some constrains prevent species to occur in some communities [START_REF] Gotelli | Null Models in Ecology[END_REF]. For functional and phylogenetic diversity, null models often permute species traits or species placement in the phylogeny to keep the observed taxonomic diversity fixed and avoid entangling the processes that affect only one diversity facet.

For example, if we look at the α-diversity, increasing species richness will increase to lead to higher functional and phylogenetic richness. However, adding new species into the community with similar ecological attributes to those already present will lead to a low functional increase. Such low increase will be lower than expected under a random species selection. This can occur if environmental filtering drives local community composition [START_REF] Mouillot | Limiting similarity, niche filtering and functional diversity in coastal lagoon fish communities[END_REF]. The same situation occurs for phylogenetic diversity if the species derive from recent speciation: a new species that is more closely related to the species already present will not increase greatly Combining information on taxonomic, functional and phylogenetic diversity can help to disentangle the main processes that structure communities, both within-and between-communities. On each plot, dominant processes are indicated. For local processes (α-diversity), the main expectation is that higher taxonomic diversity causes higher functional and phylogenetic diversity. Deviation from this expectation (blue and red lines) indicates that one process structure local community. Phylogenetic and functional diversity must be correlated (phylogenetic constrain or trait conservatism), but communities can deviate from it. For between-communities diversity (β-diversity), the more dissimilar the communities are in their species identity, the more functionally and phylogenetically dissimilar communities will be. Departure from this expectation indicates that one process is stronger than the other. Although each plot links only two facets, conclusions must be taken from the comparison of the three facets simultaneously phylogenetic richness [START_REF] Xiang | Regional differences in rates of plant speciation and molecular evolution: A comparison between eastern asia and eastern north america[END_REF].

On the other hand, if limiting similarity rules community assembly, each species will be ecologically different to the others (as a result of competitive exclusion), and hence will greatly contribute to increasing functional richness, more than under a random species selection [START_REF] Mouillot | Limiting similarity, niche filtering and functional diversity in coastal lagoon fish communities[END_REF]. If species present in the community originate from different regions (following recolonization or after introduction) or if evolutionary or speciation rates are faster due to particular environmental conditions (barriers to gene flows, habitat heterogeneity) or biological attributes of species (dispersal rates, population structure), phylogenetic richness will greatly increase [START_REF] Xiang | Regional differences in rates of plant speciation and molecular evolution: A comparison between eastern asia and eastern north america[END_REF].

Lastly, the relationship between functional and phylogenetic α-diversity, if deviating from the expectation that more distant species are more functionally different, can either reveals limiting similarity (if close related species occurrence causes higher functional richness than expected) or indicates dominant and strong environmental filtering that causes strong niche conservatism or trait convergence, with distant species having similar ecological traits [START_REF] Safi | Understanding global patterns of mammalian functional and phylogenetic diversity[END_REF].

β-diversity relationships

For β-diversities relationships, the main expectation is that the more pairs of communities will have different species (higher β-diversity), the more they will be different both functionally and phylogenetically, due to increasing environmental differences and isolation by distance. Deviation from this null expectation, with one diversity facet being higher or lower than the other, can inform which processes dominate community structure.

If communities are composed of different species (high taxonomic β-diversity) but these species are ecologically similar (low functional β-diversity), this could provide hints for strong dispersal limitation between communities, preventing species to occur in both communities [START_REF] Fukami | Species divergence and trait convergence in experimental plant community assembly[END_REF]. This dispersal limitation can be caused by the presence of a barrier to the dispersion or can result from different colonization history and competitive exclusion [START_REF] Fukami | Historical contingency in community assembly: Integrating niches, species pools, and priority effects[END_REF]. On the opposite, communities with similar species composition but with some species that differ greatly in their ecological attributes can result from differences in particular environmental conditions between the two sites that select for species with specialized ecology.

Comparing taxonomic and phylogenetic β-diversities can help to differentiate between dispersal limitation caused by recent or ancient isolation [START_REF] Weinstein | Taxonomic, phylogenetic, and trait beta diversity in South American hummingbirds[END_REF]. Ancient isolations of communities result in independent evolution of communities that causes high values of dissimilarities in both diversities. If communities were recently isolated, they had the possibility to exchange species before isolation and species of the communities will be phylogenetically related. After the isolation, communities evolved independently, but not during a sufficient time to blur their past common history, thus resulting in lower phylogenetic dissimilarities than expected. On the opposite, recent connection between previously isolated assemblages can allow the exchange of some phylogenetically distant species between communities (the one that can colonize new communities), thus reducing taxonomic dissimilarity between communities. However phylogenetic dissimilarity will stay relatively high compared to taxonomic dissimilarity.

Lastly deviations from the expected relationship between functional and phylogenetic β-diversities can inform on traits convergence between isolated communities in similar environmental conditions or high trait lability in recently isolated communities [START_REF] Swenson | Phylogenetic and functional alpha and beta diversity in temperate and tropical tree communities[END_REF][START_REF] Yang | Local-scale partitioning of functional and phylogenetic beta diversity in a tropical tree assemblage[END_REF]. This last relationship is difficult to test, as phylogenetic diversity and functional diversity are independantly computed and thus cannot be decoupled.

Here I described relationships between diversity facets two by two. However, they must all be considered altogether to have the full picture of the processes. For example, observing low dissimilarities in the 3 diversity facets will reflect communities in similar environment with no dispersal limitation, whereas high dissimilarities in taxonomic and functional diversity but lower phylogenetic dissimilarity reflects recent isolations of communities that evolve in different environments.

Freshwater fish as a model to study assembly rules

This framework thus needs exhaustive information on the communities and on the species that compose them. Rich and variable communities might be promising cases to disentangle assembly processes. Within Neotropical regions, plants communities and their assembly processes have been heavenly studied [START_REF] Kraft | Functional traits and niche-based tree community assembly in an Amazonian forest[END_REF]Swenson et al., 2012a;[START_REF] Kraft | Functional trait and phylogenetic tests of community assembly across spatial scales in an Amazonian forest[END_REF][START_REF] Fortunel | Environmental factors predict community functional composition in Amazonian forests[END_REF]. In contrast, freshwater fish assemblages have long been restrained to species inventories, species-habitat relationships and anthropogenic impacts on the species. Building on those previous knowledge helps analysing assembly rules. Among freshwater organisms, fish are the best known, they benefit from a quite exhaustive taxonomic knowledge and from information (at least partial for tropical species) on species distribution. They therefore constitute an interesting biological model for studying assembly rules. Moreover, freshwatrer fises are ectothermic organisms and their distribution is thus highly constrained by their environment. Moreover, their movement depends on their own capacity, but also on the configuration of the hydrological network [START_REF] Landeiro | Spatial eigenfunction analyses in stream networks: Do watercourse and overland distances produce different results?[END_REF]. This network also represents a closed environment for freshwater fishes, as the marine water represents barrier to their dispersal between the different drainage basins. The history of drainage basins will thus be reflected in the fish communities [START_REF] Hugueny | West African rivers as biogeographic islands: Species richness of fish communities[END_REF]. All these characteristics thus affect how environment, space and past history structure fish assemblages and will modulate the strength of the assembly processes.

Within freshwater tropical fauna, fish from French Guiana benefited from extensive inventories in the seventies that led to the publication of the atlas of Freshwater fish from French Guiana [START_REF] Planquette | Atlas des poissons d'eau douce de Guyane (tome 1)[END_REF][START_REF] Keith | Atlas des Poissons d'eau douce de Guyane. Tome 2, fascicule I. Batrachoidiformes, Mugiliformes, Beloniformes, Cyprinodontiformes, Synbranchiformes, Perciformes, Pleuronectiformes, Tetraodontiformes[END_REF][START_REF] Bail | Dimensions of biodiversity in Chesapeake Bay demersal fishes: Patterns and drivers through space and time[END_REF] and to gather solid information about the taxonomy of this speciose fauna. This knowledge on fish taxonomy and distribution constitute the first, but essential, step to unravel the processes that structure fish assemblages.

I.2 Application to French Guiana freshwater fish assemblages

I.2.1 The Guianese territory

French Guiana is a French overseas department and region located on the Northwest coast of South America between Brazil and Suriname. It covers about 83,500 km 2 . Its coastal location close to the equator allows a relative homogeneity of the climate, with constant temperature around 25 • C. Only the rainfall highly varies between months and determines two main seasons in French Guiana: the main dry season with low precipitation between September and December and two wet seasons (January to February and April to August).

The hydrological network is structured in 8 major drainage basins and small coastal creeks, that flow from South to North. The two largest drainages are the Maroni (65,830 km 2 ) that form the Western boundary with Suriname and the Oyapock (26820 km 2 ) at the boundary with Brazil. The network represents 112,000 km of watercourse, with 80% being small streams of less than 1 m depth and 10 m wide. The downstream part of the network is under marine influence, with tide influence reaching up to 50 km long watercourse due to the low slope of the downstream part of the watersheds (Tito de [START_REF] De Morais | Zonation longitudinale des peuplements ichtyques avant mise en eau de la retenue de Petit-Saut (Guyane franc ¸aise)[END_REF].

I.2.2 Guianese demography and anthropogenic activities

Population in French Guiana have greatly increased since the late 1980 (Figure I.7), and is actually 4 times higher than in 1970. Until 1990, this increase was essentially caused by a high immigration rate that has reduced through the years. Even if the population growth rate slows down (+3.6% per year between 1999and 2009, and +2.4% per year between 2009and 2014;INSEE 2017), demographic models predict that population will exceed 300,000 inhabitants in 2030. The population is mainly concentrated on the coast, with 80% of the population located on a 20 km East-West coastal band.

This coastal band thus concentrates the majority of anthropogenic infrastructures (roads and urban area) and gathers the majority of industrial and agricultural activities that will mainly impact the downstream part of the hydrological network. On the contrary, the interior of the department is composed mainly of forest. Two main activities are conducted in the forested part of the territory: forestry and gold-mining. The forestry activity is managed by the National Forests Office (ONF) that sets rules to reduce environmental impacts of this exploitation on freshwater ecosystems. In addition, gold-mining activities are increasing [START_REF] Hammond | Causes and Consequences of a Tropical Forest Gold Rush in the Guiana Shield, South America[END_REF], especially localized non-legal mining that strongly impact freshwater ecosystems.

I.2.3 History and biogeography

French Guiana is part of the Guiana Shield, an upland region of South America, which formed during Precambrian around 1.7 Ma [START_REF] Lujan | The Guiana Shield[END_REF]. The recent ichtyofauna of French Guiana however results from more recent events of sea level oscillations caused by the alternation of hot and cold periods, that occurred during Late Pleistocene [START_REF] Boujard | Biog éographie des poissons d'eau douce de Guyane franc ¸aise[END_REF]. During hot and humid periods, sea level raised and the huge quantity of freshwater discharged by the Amazon River created continuous littoral swamps that connected all the river mouth of French Guiana and homogenized the ichtyofauna between drainage basins. These swamps also contributed to the colonization of fish species from the Amazon River to the East and from the Essequibo River and Rio Branco Riverto to the North. During subsequent cold period (-18,000 years), sea level went down and the littoral swamp reduced. The connection between the different drainage basins stopped, except between a few neighboring basins (Maroni and Mana on the West and Approuague and Oyapock on the East). The isolation of some fish led to speciation cases, probably explaining the current differentiation between Western and Eastern basins (Le [START_REF] Bail | Updated checklist of the freshwater and estuarine fishes of French Guiana[END_REF][START_REF] De M Érona | Fish-habitat relationships in French Guiana rivers : A review[END_REF]. In addition, river captures events in the upper portion of rivers had also favored species colonization of the headwaters from the South between Guianese and Amazonian tributaries [START_REF] Cardoso | Unexpected diversity in the catfish Pseudancistrus brevispinis reveals dispersal routes in a Neotropical center of endemism: The Guyanas Region[END_REF]. All these events thus contributed to the large diversity and organization of freshwater fish in French Guiana.

I.2.4 Aquatic ecology

Guianese freshwater fish fauna counts of 405 species, with 386 strictly freshwater fish species, from 12 orders (Siluriformes and Characiformes are the most represented). 91 of these species (26%) are endemic of drainage basins located in French Guiana. Within French Guiana, wider drainage basins have more fish species (de M érona et al., 2012), and species identity differs between Eastern and Western drainage due to past history of the region (Le [START_REF] Bail | Updated checklist of the freshwater and estuarine fishes of French Guiana[END_REF].

Ecological studies conducted on freshwater fish assemblages in French Guiana have been mainly conducted on the main rivers, especially on the Sinnamary and the Approuague Rivers leading to distinguish fish fauna from estuaries, middle and upper main streams and headwaters (Tito de [START_REF] De Morais | Zonation longitudinale des peuplements ichtyques avant mise en eau de la retenue de Petit-Saut (Guyane franc ¸aise)[END_REF][START_REF] Boujard | Zonation longitudinale du peuplement ichtyque du fleuve Sinnamary (Guyane Franc ¸aise)[END_REF]. At a lower scale, five types of habitats have been described [START_REF] Boujard | Micro-r épartition spatio-temporelle du peuplement ichthyologique d'un haut bassion fluvial de Guyane, l'Arataye[END_REF]: creeks or small streams (width 10 m and depth 1 m), rapids (high flow and important presence of rocks) and pools with different bottom substrates (eroded concave part, sedimentary convex part and intermediary).

Freshwater stream assemblages and anthropogenic disturbances

Studies of stream assemblages started in the 90ies with extensive studies on the streams of the Sinnamary basin before and after the filing of the Petit-Saut's dam, an hydroelectric barrage on the middle course of the Sinnamary River, focused the attention of freshwater ecological studies toward this river and its tributaries. Those studies led to analyses fish densities according to habitat structural complexity, with higher density in habitat with higher complexity, water level and distance of the site to the river [START_REF] Ponton | Spatio-temporal distribution of young fish in tributaries of natural and flow-regulated sections of a neotropical river in French Guiana[END_REF]. Spatial variation in assemblages was linked with water quality (oxygen and turbidity) that affect the identity of species according to their life-history traits between streams.

More recently, the development of illegal mining activities led to extensive works on small forest streams with the aim to quantify the strength of the mining disturbance on aquatic assemblages (fish and benthic macroinvertebrates). This work conducted during [START_REF] Allard | Elaboration d'un Indice de Qualit é Des Eaux Bas é Sur La Structure Taxonomique et Fonctionnelle Des Assemblages de Poissons Des Petits Cours d'eau de Guyane[END_REF] and [START_REF] Dedieu | Mise au point d'un outil d' évaluation de la qualit é biologique des petites rivi ères de Guyane sur la base des invert ébr és benthiques[END_REF] PhD theses, allowed to sample over 150 sites throughout Guiana, in both undisturbed and disturbed (gold mining and logging) environments. Physical and chemical characteristics of undisturbed and logged sites were similar, but logged sites had finer bottom particles [START_REF] Dedieu | Physical habitat and water chemistry changes induced by logging and gold mining in French Guiana streams[END_REF]. However, gold mined sites greatly differed from undisturbed sites, with higher water turbidity and coarser bottom particles, especially gravel. This modification of the local habitat translated in a modification of assemblage structure [START_REF] Dedieu | Assessing the impact of gold mining in headwater streams of Eastern Amazonia using Ephemeroptera assemblages and biological traits[END_REF][START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF]. Both taxonomic and functional structure of assemblage differed between undisturbed and gold mined sites, with a shift from small stream specialist species to larger ubiquitous species inhabiting rivers for fish and from herbivorous and swimmers species to endobenthic burrowers and collector filterers for macroinvertebrates (Figure I.8).

Although these works contributed to understand how tropical streams and freshwater assemblages are affected by anthropogenic disturbances, the processes that structure local assemblages remained poorly known.

I.3 Thesis

I.3.1 Aim

Previous work on freshwater fish of French Guiana highlighted the impact of anthropogenic disturbances on these assemblages, both in large rivers and small streams. Understanding the processes that govern these assemblages will help evaluating future impacts of anthropogenic disturbances. The main goal of my thesis was thus to elucidate the assembly rules that govern freshwater fish assemblages of French Guiana. To do, I combined information on 4 types of data: taxonomic, functional and molecular data, and site descriptors. family found in Australia, Southeast Asia and South America, like jewel vines Derris spp. or lacepods Lonchocarpus spp. It acts by inhibiting cellular use of oxygen by gillbreathing organisms [START_REF] Lindahl | The effect of rotenone on respiration and its point of attack[END_REF]. This aspect implies that it also affect not only fish, but also freshwater insects, and have thus a strong disturbing effect on local fauna. Nevertheless, it is the only method available to get a good picture of local fish assemblages [START_REF] Allard | Electrofishing efficiency in low conductivity Neotropical streams: Towards a non-destructive fish sampling method[END_REF]. To limit its impact, sampled sites were located closed to a confluence to dilute Rotenone and weaken its effect downstream. Moreover, the lowest possible quantity of rotenone was used, and downstream fish mortality was never observed. All the samples were authorized by the French ministry of environment (DEAL), and the Guianese National Park (PAG) when samples were taken in streams belonging to the PAG.

The sampling protocol takes place as follows: i) a portion of the stream is isolated with fine mesh (4 mm mesh size) stop nets to avoid fish escape out of the station (Figure I.9a); ii) the rotenone is introduced upstream and homogenized before and in the portion; iii) fish are collected; iv) when all fish have been collected, a last passage is made to collect fish lying at the bottom of the stream; v) nets are removed and fish stopped by them collected; vi) fish are identified directly or stored to be identified later.

The portion of the stream isolated was defined to represent one hydromorphological unit (pool, riffle, rapid, fall or run), but was sometimes constrained by the presence of obstacles in the stream, (e.g. trees and branches fallen in the stream making areas where collecting fish is not possible). Several subsequent portions were sampled on the same stream in some sites to represent the hydromorphological diversity of the stream.

Most of the samples were collected during [START_REF] Allard | Elaboration d'un Indice de Qualit é Des Eaux Bas é Sur La Structure Taxonomique et Fonctionnelle Des Assemblages de Poissons Des Petits Cours d'eau de Guyane[END_REF] thesis, as part of a Guiana National Park-DEAL-HYDRECO project (2011)(2012)(2013)(2014), but some sites have been sampled under other projects: CNRS-Nouragues projects (2008; 2010), PAG Itoupe project (2010), DIADEMA project (LabEx CEBA, 2013-2015), and Our Planet Reviewed Mitaraka project (2015).

For rivers, fish are collected using 50 meters long gill-nets with different mesh sizes (from 15 to 35 mm). In each site an overnight sampling with a standard set of 20 gillnets was achieved. Gill-nets are placed on riverbanks (Figure I.9b). The nets are taken out of the water the following morning and identified after all nets removal. Contrary to streams, each site was sampled several times among the years. These samples are conducted to comply with the European Water Framework Directive (DCE). Fish surveys are part of DEAL and OEG projects, and technically operated by Hydreco lab, with the contribution of the EDB lab members (S. Brosse and myself) in Maroni and Approuague sites.

For my thesis, I used sampling data ranging from 2007 to 2016. Although abundance data can help to disentangle some of the assembly processes (e.g. competition between species), the differences in sampling protocol and in sensibility of species to each sampling methods can bias conclusions on assemblages determinants. We thus chose to convert all abundance data into occurrence data.

I.3.3 Functional data: morphological measures and functional traits

To compute functional diversity of assemblages, we used 15 functional traits that account for two main functions: locomotion and nutrition. These functional traits have been widely used to asses functional diversity of fish assemblages [START_REF] Vill Éger | Functional ecology of fish: Current approaches and future challenges[END_REF][START_REF] Leit Ão | Disentangling the pathways of land use impacts on the functional structure of fish assemblages in Amazon streams[END_REF]. The lack of ecological knowledge on Guianese fishes prevented from estimating others key functions (life history strategy, feeding composition). For each species, I extracted 14 morphological measures from [START_REF] Toussaint | Global functional diversity of freshwater fish is concentrated in the Neotropics while functional vulnerability is widespread[END_REF] database that contain morphological measures. In addition, we took pictures of fish during 2015 and 2016 sampling sessions to complement our functional database (32 species) and to maximize the number of individuals per species, to reduce potential bias due to intraspecific variability. The morphological measures are taken from photographs on a lateral view of the fish (Figure I.10 and Table I.1a) and characterize body shape and fin size and surface, eye and mouth size and position. In addition to these measures, the maximum length of each species was recovered from [START_REF] Froese | FishBase. World Wide Web electronic publication[END_REF].

Figure I.10:

The 14 morphological measurements taken on each fish species to calculate the functional traits Introduction These measures were then combined into ratios that described body shape, fins eye and mouth relative position on the body or their relative size. These ratios reflect species capacity to feed (prey capture and detection, location in the water column) and to swim (Table I.1b, Vill éger et al., 2017;[START_REF] Toussaint | Global functional diversity of freshwater fish is concentrated in the Neotropics while functional vulnerability is widespread[END_REF].

I.3.4 Molecular data: molecular markers and phylogenetic relationship

During field sampling campaigns and fish collection, a small piece of fin was collected on at least 3 individuals per species belonging to different drainage when possible. In total, 6896 individuals were sampled, belonging to 259 species. DNA from these tissue samples were extracted at EDB laboratory using salt-extraction protocol [START_REF] Aljanabi | Universal and rapid salt-extraction of high quality genomic DNA for PCR-based techniques[END_REF]. Three mitochondrial markers were first amplified: the cytochrome c oxidase I gene (COI), the cytochrome b gene (cytb) and the mitochondrial 12S ribosomal RNA (12S rRNA, or 12S). The cytb marker was removed from the analyses as amplification failed for a substantial part of the samples, and the remaining data did not provided additional information to COI and 12S data. I also included all the sequences available on GenBank, although Guianese freshwater fish are poorly informed in Genebank (142 species have at least one of the two makers). COI and 12S were used to reconstruct phylogenetic relationships between species.

The 12S was also used to build the reference database for barcoding studies. After extraction, amplification and sequences cleaning, the molecular data contains 943 sequences. COI and 12S were informed for 422 individuals, COI only was informed for 386 individual, and 135 individuals were informed only for 12S.

I.3.5 Sites descriptors: environment and space

In addition to species related information, environmental characteristics of sites were obtained from field measurements and geographic information system (GIS). These descriptors can be categorized in 3 spatial scales: drainage basin, stream or river reach and local environmental features (Table I.2).

At the drainage basin scale, the identity of the drainage basin in which the site is located was used to distinguish between basins.

At the reach scale, distance from the source, slope and altitude were obtained from GIS and represent the position of the site in the upstream-downstream gradient of the hydrological network. For streams, the pH and the conductivity of the water were also considered as reach descriptors. They were measured on the field with a pH meter (WTW pH 3110 with WTW pH-SenTix 41 electrod) and a conductometer (WTW Cond 3310 with tetraCon 325 captor).

At the local scale, each stream portion was described by its hydromorphological unit (pool, riffle, run, according to the typology of [START_REF] Malavoi | Description standardis ée des principaux faci ès d' écoulement observables en rivi ère: Cl é de d étermination qualitative et mesures physiques[END_REF], , the forest cover, its bottom grain size (silt, sand, gravel, pebble, boulder and bedrock), the presence of shelters for fish (wood, macrophytes, litter, under-bank and tree roots) and its width and depth. For width, at least 3 measures were taken perpendicular to stream flow, and along each line, the stream depth was measured every metre.

In rivers, local variables were not measured, because fish sampling is achieved using a net sampling with 20 50-metres long gillnets (15 to 35 mm mesh sizes). Sampling therefore occurs over the entire reach, encompassing heterogeneous local Introduction features.

I.3.6 Thesis conduct and structure

The above described data was used to investigate assembly rules of Guianese freshwater fish following these three main points: 1) Description of the diversity of streams fish assemblages: I first described spatial patterns of fish taxonomic diversity in streams, and investigated the environmental determinants of species richness and composition in these streams. I also investigated how space and environment shape the change in species composition between assemblages (manuscript A).

2) Assembly rules of freshwater assemblages: In this chapter, I elucidated the processes that structure freshwater fish assemblages, both in streams and in rivers, using all the information available on assemblages (taxonomic, phylogenetic and functional data). In a first part, I compared temperate and tropical stream assemblages (manuscript B), that are known to differ in their α-diversity and β-diversity. Using taxonomic and functional diversities, I tested if these differences could be explained by different strength in the processes that structure assemblages. Then I focused on Guianese assemblages and used the multi-faceted approach, with the taxonomic, functional and phylogenetic data to elucidate these processes.

3) A new sampling protocol: Stream assemblage data were obtained using rotenone, a toxicant that kills local fauna, which use is now banned by the law. River assemblage data, collected using nets placed on riverbanks, only represent a small fraction of the fauna and cause substantial fish mortality. Developing a new sampling protocol that can be used in both streams and rivers and that provides an exhaustive image of assemblages is a crucial step to pursue ecological studies in French Guiana. I thus tested the efficiency of eDNA metabarcoding, a new and promising molecular sampling method, to detect fish species, recover assemblage composition and to describe ecological patterns (manuscript C).

For each part, a summary of the corresponding manuscript is given to introduce the chapter.

Chapter II: Diversity of freshwater fish assemblages in undisturbed small streams

II.1 Summary

Compared to the temperate freshwater ecosystems that set the basis for freshwater ecological concepts, tropical streams have received less attention, and studies and management of these streams are highly driven by the temperate concepts. This situation is even more pronounced for small tropical streams and the fish assemblages they host. Indeed tropical lowland streams have easier access for sampling and they host more species with economical interest that the small upstream watercourses. In French Guiana, the first ecological studies of freshwater fish assemblages have thus been devoted to large rivers (especially the Sinnamary and the Approuague Rivers). In contrast, fish assemblages of the small streams have been mainly studied through the effect of anthropogenic disturbances on fish assemblage composition [START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF]. Without information on the structure of freshwater fish assemblages and its determinants, predictions of fish assemblage and evaluating the impact of anthropogenic disturbances on them remain limited. We used fish assemblage structure and environmental descriptors for 152 undisturbed sampling sites to describe the patterns of diversity in these streams (species richness and assemblage variation). We then evaluated the effect of environment and space (distance between sites) on these patterns. Each site was described at three spatial scales: the drainage basin scale (corresponding to the identity of basin), the reach scale (position in the upstream-downstream gradient and physic-chemical characteristic) and the site scale (forest cover, shelters presence, bottom grain size, width and depth). For the site scale, we used two different approaches to analyze species richness and assemblage variation: for species richness analyses, we calculated a habitat diversity index using the different categories of shelters and grain size, and classes of width and depth, whereas for assemblage variation, we kept separate these variables. This choice was guided by the niche theory that stipulates that the number of species in a site is related to the habitat diversity, whereas the identity of species is related to the habitat characteristics. In addition to these site-specific descriptors, we also took into account spatial relationships between the sites, based on the distance along the stream between sites located within a same drainage basin.

Species richness in the sites increased from upstream to downstream and in sites with higher habitat diversity. These patterns also hold for in the main fish orders (Characiformes and Siluriformes), testifying that the global pattern did not result from the replacement of different groups of fish. This upstream-downstream gradient was however marked by a succession of five group of species that characterize five main types of stream habitats (Figure II.1): altitude and torrential streams (1), small (2) and large (3) non-torrential streams , muddy streams (4) and confluence areas with larger 5). This zonation of stream fish species can thus be used as a reference point for future studies on anthropogenic disturbances.

For assemblage composition analyses, space and environment explained similar part of variation (11% and 8% respectively). Contrary to our predictions, the drainage basin did not greatly affect assemblage composition variation. Spatial relationships within watershed were the main responsible for assemblage composition variation, suggesting that dispersion of small stream species is limited by larger streams or rivers. For the environment component, the assemblage composition variation was not explained by few highly influential variables, but all the environmental descriptors slightly affected assemblage variation. This contrasts with some other studies [START_REF] Mesquita | Spatial variation in fish assemblages across small Mediterranean drainages: Effects of habitat and landscape context[END_REF][START_REF] Nakagawa | Contribution of environmental and spatial factors to the structure of stream fish assemblages at different spatial scales[END_REF] that found few variables explaining assemblage variation. The difference may lie in the fact that species distributions are affected by different components of the environment. This contribution of all environmental components to fish assemblage diversity highlights that anthropogenic disturbances, often modifying several environmental characteristics at the same time, can have great impacts on fish assemblage.

Our study also underlined that a large amount of information remains unknown since the variation explained by our models does not exceed 25%. This can be explained by the high number of species with low occurrence (about 3/4 of the species occurred in less than 10 sites, Figure II.2), that can limit the explanatory power of multivariate analyses. Removing the rarest species (species occurring in less than 5 sites) did not impact greatly the variation explained (from 25% to 30%). Removing a higher proportion of species by increasing the minimum occurrence threshold might increase the explanatory power of species, but in turn, information on those species are lost. Rare species can occupy important function in the ecosystems [START_REF] Mouillot | Rare Species Support Vulnerable Functions in High-Diversity Ecosystems[END_REF] but are however the species that are the most prone to go extinct. The equilibrium between the explanatory power and the loss of information is thus an important thing to consider and depending on the desired goal, one might be favored over the other.

INTRODUCTION

The spatial patterns of biological diversity result from factors acting at different scales. According to the hierarchical filter model proposed by [START_REF] Tonn | Climate change and fish communities: A conceptual framework[END_REF], large or regional scale processes, related with climatic or biogeographic differences between regions, will define a species pool, i.e. a set of candidate species that could occur at smaller scale [START_REF] Srivastava | Using local-regional richness plots to test for species saturation: Pitfalls and potentials[END_REF]. From this regional species pool, species that actually occur in local assemblage are selected through local processes. Those processes encompass biotic interactions between species (predation, competition and facilitation) and the effect of the local environment on local assemblages [START_REF] Jackson | What controls who is where in freshwater fish communities: The roles of biotic, abiotic, and spatial factors[END_REF]. The determinant role of the local environment on species persistence relies on the Hutchinsons niche concept [START_REF] Hutchinson | Concluding remarks[END_REF], which still has strong support in the current development of niche modelling approaches in both terrestrial and aquatic ecosystems (e.g. [START_REF] Elith | Species distribution models: Ecological explanation and prediction across space and time[END_REF]. For instance, in freshwaters, species distribution within drainage basins can be explained by the variability in the environment along the stream, especially temperature or hydromorphology, creating distinct fish assemblages across this upstream-downstream gradient. These assemblages differ either by the replacement of species due to changing environments and isolation by the distance, or by the gradual addition of species caused by a gradual increase in the size or/and in the diversity of local habitat [START_REF] Ibarra | Nested patterns of spatial diversity revealed for fish assemblages in a west European river[END_REF][START_REF] Rahel | Fish assemblages and habitat gradients in a rocky mountain-great plains stream: Biotic zonation and additive patterns of community change[END_REF]. Such upstream-downstream environment-driven changes in species assemblages are consistent with some of the predictions of the River Continuum Concept [START_REF] Vannote | The River Continuum Concept[END_REF] that set the basis for further models (e.g. the Riverine Ecosystem Synthesis, [START_REF] Thorp | The riverine ecosystem synthesis: Biocomplexity in river networks across space and time[END_REF]the Stream Biome Gradient Concept, Dodds et al., 2015) that have informed the currently accepted integrated view of river functioning. In addition to this deterministic effect of the spatial variability on assemblages, stream fish assemblages are also influenced by the hydrological stochasticity (succession of droughts and floods or high-and low-flows between years or seasons), which can override deterministic processes [START_REF] Grossman | Stochasticity in structural and functional characteristics of an indiana stream fish assemblage: A test of community theory[END_REF][START_REF] Grossman | Community Ecology of Stream Fishes: Concepts, Approaches, and Techniques[END_REF].

Such templates, developed in temperate rivers, have recently been extended to the tropics where the overall patterns of species richness and assemblage structure across the upstream-downstream gradient are broadly comparable to those known in temperate rivers. For instance, [START_REF] Ara Újo | Longitudinal patterns of fish assemblages in a large tropical river in southeastern Brazil: Evaluating environmental influences and some concepts in river ecology[END_REF] found an increase in species richness and a change in the species composition in fish assemblages along the upstream-downstream gradient in a large Brazilian river. Similarly, [START_REF] Petry | Longitudinal changes and indicator species of the fish fauna in the subtropical Sinos River, Brazil[END_REF] reported an upstream-downstream fish zonation in the Sinos River (Southeast Brazil). However, despite an increase in research undertaken on the ecology of tropical rivers, most of information has been obtained from the lowland sections of major river systems, whereas the fish assemblages of headwater streams have been much less studied [START_REF] Anderson | A regional perspective on the diversity and conservation of tropical Andean fishes[END_REF]. This bias is probably due to the easier accessibility to lowland streams for sampling and to the limited commercial interest of the, often small-bodied, fishes inhabiting low-order streams compared to the large and heavily exploited downstream species [START_REF] Allan | Overfishing of inland waters[END_REF]. However, large rivers represent only a small part of the river network. For instance, in French Guiana, more than 70% of the permanent river network is represented by streams < 10 m wide [START_REF] Dedieu | Physical habitat and water chemistry changes induced by logging and gold mining in French Guiana streams[END_REF]. Moreover, such small rivers host about a half of the species inhabiting each river drainage, as shown by [START_REF] Junk | Freshwater fishes of the Amazon River basin: Their biodiversity, fisheries, and habitats[END_REF] for the Amazon, or by [START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF] for the five main Guianese river basins.

To date, most studies have been devoted to measuring the impact of human disturbance on the fish assemblages in small tropical streams [START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF][START_REF] Dias | Effects of reduced-impact logging on fish assemblages in Central Amazonia[END_REF][START_REF] Mol | Downstream effects of erosion from small-scale gold mining on the instream habitat and fish community of a small neotropical rainforest stream[END_REF]. The lack of conceptual understanding of the determinants of assemblage structure in these small streams nevertheless limits our ability to predict the fish assemblages they host and hence the impact of ongoing anthropogenic disturbances (e.g. mining, deforestation and urbanisation) on those assemblages. Here, we quantified the determinants of species richness and composition of freshwater fish assemblages in non-impacted headwater streams across the major Guianese drainages. We considered environmental determinants encompassing different hierarchical scales, from drainage basins, to reaches, to microhabitats, while considering river network connectivity that can affect fish distributions [START_REF] Elith | Species distribution models: Ecological explanation and prediction across space and time[END_REF]. We therefore predicted that local species richness (1) increases with stream size (from upstream to downstream) and ( 2) is higher in sites with high environmental diversity. We also tested if these predictions about determinants of diversity held for the two most speciose fish orders (Characiformes and Siluriformes) or if the global pattern observed resulted from the overlap of different responses across fish clades. Moreover, as the fish community composition differs among river drainages according to the biogeographic history of the region (Le [START_REF] Bail | Updated checklist of the freshwater and estuarine fishes of French Guiana[END_REF][START_REF] Lujan | The Guiana Shield[END_REF], the pool of species inhabiting the upper reaches should be constrained by the regional context. We hence predict that species assemblage composition (3) differs primarily between drainage basins with different biogeographic histories; (4) is secondly influenced by the sites connectivity within each drainage and; (5) is finally affected by the reach position in the upstream-downstream continuum and the local scale habitat characteristics that both determine the environmental niche of the species. We achieved complete fish inventories in 152 stream sites in French Guiana (Figure II.3). All the streams considered are small (first to third order) perennial streams flowing in a primary forest environment. Their width varied between 1 and 10 m and depth from 0.1 to 1 m on average. None of the streams were disturbed by human activities as the entire drainage upstream of the sampling sites was free of settlements. The fish surveys were conducted under several different research projects conducted between 2010 and 2015, and each site was sampled once. All sites were sampled during the dry seasons to ensure similar hydrological conditions and optimal detection rate of the species [START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF]. We did not consider inter-annual or inter-seasonal variability that would require repeated samples on the same sites at different seasons and during several years. Most of the sites are remote, and multiple sampling was not possible due to the heavy logistic needed to access to the sites. Moreover, sampling during the rainy season was not possible because of river overflows. The sampling protocol was standardised for all sites. Each site was a homogeneous hydrological unit (pool, run, riffle, rapid, waterfall), and its length was on average 27.49 ± 17.73 m (mean ± SD). It was proportional to stream width and was 6.48 ± 8.84 times longer than stream width. Since little is known on the home range of Neotropical tropical fishes, the length of our stream sites was the longest possible given that the streams are obstructed by fallen trees and/or dense vegetation where distinguishing fish is difficult. We nevertheless considered the length of sampled stream sites sufficient, as it was similar to that used to analyse fish assemblages in first to fifth order North American streams [START_REF] Grossman | Assemblage organization in stream fishes: Effects of environmental variation and interspecific interactions[END_REF][START_REF] Hoeinghaus | Local and regional determinants of stream fish assemblage structure: Inferences based on taxonomic vs. functional groups[END_REF]. Fish were collected using rotenone (PREDATOX R : a 6.6% emulsifiable solution of rotenone extracted from Derris elliptica by Saphyr, Antibes, France) a non selective piscicide, which is traditionally used to catch fishes by Amazonian tribes. Such a method is currently the only way to collect exhaustive information on local fish assemblages, because electrofishing is not efficient in those low conductivity streams [START_REF] Allard | Electrofishing efficiency in low conductivity Neotropical streams: Towards a non-destructive fish sampling method[END_REF]. Nets such as seine and cast nets were not efficient in streams cluttered with fallen trees and branches and visual observation strongly underestimated nocturnal and cryptic species [START_REF] Allard | Electrofishing efficiency in low conductivity Neotropical streams: Towards a non-destructive fish sampling method[END_REF]. We were therefore allowed by the authorities (French ministry of environment and National Amazonian Park) to use rotenone pending the development of an efficient alternative to this destructive method, such as environmental metabarcoding [START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF]. We nevertheless reduced the impact of our rotenone samples by paying a particular attention to releasing as little toxicant as possible to avoid fish mortality downstream from the section studied. This dose was sufficient to detect the entire fish community [START_REF] Allard | Elaboration d'un Indice de Qualit é Des Eaux Bas é Sur La Structure Taxonomique et Fonctionnelle Des Assemblages de Poissons Des Petits Cours d'eau de Guyane[END_REF]. As there is no published estimation of the efficiency of the method to measure fish abundance according to species behaviour or to environmental characteristics of the sites, we transformed species abundance into species occurrence to control for potential differences in fish capture efficiency according to sites or species as recommended by [START_REF] Oberdorff | A probabilistic model characterizing fish assemblages of French rivers: A framework for environmental assessment[END_REF].

METHODS

Fish data collection

In each stream, one to three subsequent sites with homogeneous hydromorphological units were selected. Subsequent hydrological units (i.e. sites) were separated using two fine mesh (4 mm) stop nets. A particular attention was devoted to set stop nets simultaneously to avoid fish movement between sites. Rotenone was released a few metres upstream of the stop net located upstream from the upstream site. When two or three subsequent sites were sampled, one or two operators were in charge of collecting fish in each site allowing collecting fish simultaneously from all the subsequent sites with a single rotenone release. At the end of each sampling session we searched for fishes lying on the bottom or hidden in the leaves and debris. In almost all sites, cryptic fish were collected, including highly cryptic Siluriformes such as Farlowella, Harttiella, Lithoxus, or Ancistrus, bottom-dwelling fishes such as Ituglanis or Loricaria, and litter-bank fishes such as small killifishes. Several species of Gymnotiformes inhabiting small Guianese streams, although known to be resistant to rotenone, were also caught.

Description of sites

We characterised each site by three groups of variables defined according to the scale at which they were measured (Table II.1). At the regional scale, we distinguished sites by drainage (from West to East: Maroni, Mana, Sinnamary, Approuague and Oyapock; Figure II.3). At the reach scale, we measured chemical characteristics of the water (pH and conductivity) using pH meter (WTW pH 3110 with WTW pH-SenTix 41 electrod) and conductometer (WTW Cond 3310 with tetraCon 325 captor), and we extracted topographic metrics (distance from the source, slope and altitude) from a GIS (QGIS Development Team, 2016). At the site scale, we measured the percentage of forest canopy cover visually as in [START_REF] Dedieu | Physical habitat and water chemistry changes induced by logging and gold mining in French Guiana streams[END_REF] and classified each site to an hydromorphological unit (pool, run, riffle, rapid and waterfall) according to [START_REF] Delacoste | Classification et cl é de d étermination des faci ès d' écoulements en rivi ères de montagne[END_REF]. We then measured the local stream habitat variables, including substratum granulometry, shelter availability and channel morphology. The substratum [2, 4m[ [4, 6m[ [6, 8m[ [8, 10m[ ≥ 10m granulometry was described by estimating visually the percentage of streambed particle grain size cover (silt, sand, pebble, boulder and bedrock were defined according to [START_REF] Cailleux | Limites dimensionnelles et noms des fractions granulometriques[END_REF] methodology; see Table II.1 for size classes). Such visual assessment of stream bed particle size clustering has often been used to analyse the relationships between freshwater fish and their physical habitat (e.g. [START_REF] Brosse | Modelling roach (Rutilus rutilus) microhabitat using linear and nonlinear techniques[END_REF][START_REF] Grossman | Population dynamics of mottled sculpin (pisces) in a variable environment: Information theoretic approaches[END_REF]. Shelter availability (presence of wood debris, macrophytes, litter, under-banks, tree roots) was measured as a percent coverage of each shelter type as in [START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF] and the percent coverage without shelter was categorised as open water. Channel morphology was recorded using stream width and depth. We measured stream width on at least three transects perpendicular to the stream flow, and every 5 m if the sampled site was longer than 10 m. Stream depth was recorded every metre across transects. We then calculated the mean and the coefficient of variation (CV) of the depth and width for each site. Those local habitat variables, known to be the main predictors of fish habitat [START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF][START_REF] Brosse | Modelling roach (Rutilus rutilus) microhabitat using linear and nonlinear techniques[END_REF][START_REF] Gorman | Habitat structure and stream fish communities[END_REF][START_REF] Grossman | Population dynamics of mottled sculpin (pisces) in a variable environment: Information theoretic approaches[END_REF] were used to analyse the species composition of the sites. In contrast, the niche theory predicts that the species diversity in a site is more related to the overall environment and its structural diversity than to its different components [START_REF] Hutchinson | Concluding remarks[END_REF][START_REF] Kovalenko | Habitat complexity: Approaches and future directions[END_REF][START_REF] Stein | Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales[END_REF] . The structural diversity of the site was estimated using the ShannonWiener equitability index (-S i=1 p i log(p i )/log(S); [START_REF] Shannon | A mathematical theory of communication[END_REF]. We calculated this index separately for grain size classes and percentages of shelter types. We did the same for depth and width after sorting them into six classes. A regular increment of 2 m for width and 0.2 m for depth was chosen as it provided a balanced representation of all width and depth classes (see Table II.1 for classes). Those four diversity metrics were summed to obtain a single habitat structural diversity measure for each site, with a maximum value of four reflecting maximum diversity. The habitat structural diversity index and overall descriptors of the environment (distance from the source, slope, altitude, pH, conductivity, forest canopy cover and hydromorphology) were used to explain species richness patterns.

Spatial relationships among sites within drainage basins

We described the spatial structure of the sites within individual drainages with distance-based Morans eigenvector maps (dbMEM, [START_REF] Borcard | All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices[END_REF][START_REF] Dray | Spatial modelling: A comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM)[END_REF]. We used the spatial coordinates of the sites to calculate an in-stream distance matrix using the shortest river path between sites. We then derived the dbMEM variables from the distance matrix taking into account the drainage membership of the sites [START_REF] Declerck | Scale dependency of processes structuring metacommunities of cladocerans in temporary pools of High-Andes wetlands[END_REF]. For each drainage, a set of dbMEM variables was computed to describe the spatial structure of the sites within the drainage. Sites from others drainages are given a zero value. We did not use Euclidean distances as they do not represent the dendritic structure of streams and thus provide irrelevant distances to explain assemblage variation for strictly aquatic species which dispersal capacities are strongly constrained by water availability [START_REF] Landeiro | Spatial eigenfunction analyses in stream networks: Do watercourse and overland distances produce different results?[END_REF][START_REF] Yunoki | Fish metacommunity dynamics in the patchy heterogeneous habitats of varzea lakes, turbid river channels and transparent clear and black water bodies in the Amazonian Lowlands of Bolivia[END_REF].

Statistical analyses

To explain variation in species richness between sites, we used generalised linear-mixed effect models (GLMM) with a Poisson distribution of error terms and with a log link function to explore the relationship between species richness and environmental variables (distance from the source, slope, altitude, pH, conductivity, vegetation cover, hydromorphology and habitat structural diversity). A GLMM was preferred to a classical GLM to account for the spatially nested pattern of our data (site within reach within drainage) and to remove the dependency between sites within a reach [START_REF] Rhodes | GLMM applied on the spatial distribution of koalas in a fragmented landscape[END_REF]. Reach membership was treated as a random effect nested within the drainage membership random effect. Between-drainage and between-reach within drainage variations were modelled by random intercepts only. All variables were centred and scaled to SD. We used a model-selection approach [START_REF] Burnham | Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach[END_REF] to determine the most important factors in explaining variation in species richness. No prior knowledge about the factors explaining the variation in species richness was used. We thus tested all possible combinations of the eight variables, resulting in 257 candidate models (all combinations + a null model). We used Akaike's information criterion (AICc; [START_REF] Burnham | Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach[END_REF] corrected for small samples to rank the models, with the lowest AICc indicating the best model, and we computed for each model its Akaike weights (w i ). As the Akaike weight of the best model was low (w i < 0.5), we retained models with a ∆AICc to the best model ≤ 4. We then used model averaging to estimate parameters, standard errors (SE), variable relative importance and 95% confidence intervals [START_REF] Burnham | Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach[END_REF]. For each model, we computed marginal R 2 (R 2 m , variance explained by fixed factors) and conditional R 2 (R 2 c , variance explained by the entire model, i.e. by fixed and random factors), following [START_REF] Nakagawa | A general and simple method for obtaining R2 from generalized linear mixed-effects models[END_REF]. To test for congruence across clades, we repeated this analysis on the two most speciose fish orders (Characiformes and Siluriformes, that account for 45% and 30% of the species in our database respectively). Perciformes were not considered here since recent phylogenies have revealed that the previous definition of Perciformes is not valid because it included several phylogenetically distinct valid fish orders [START_REF] Betancur-R | The tree of life and a new classification of bony fishes[END_REF][START_REF] Sanciangco | Phylogenetic placement of enigmatic percomorph families (Teleostei: Percomorphaceae)[END_REF]. Those valid orders did not contain sufficient species in our data to be analysed independently (see Table II.S1 in Supporting Information).

For composition variation, we used a detrended correspondence analysis (DCA) to select the best ordination method [START_REF] Ter Braak | CANOCO Reference Manual and CanoDraw for Windows User's Guide: Software for Canonical Community Ordination[END_REF] and choose between a linear (redundancy analysis) and a unimodal (canonical correspondence analysis, CCA) response. DCA revealed that a unimodal model was the most suitable (gradient length > 4 standard deviation, indicating a complete species turnover along the axis) and we thus used CCA to explore the relationships between assemblage composition variation and spatial and environmental variables [START_REF] Lepš | Multivariate Analysis of Ecological Data Using CANOCO[END_REF].

We used variation partitioning to separate the effect of overall spatial configuration of sites (drainage-scale and dbMEM spatial variables) and the environment (reachand site-scale) on the overall species assemblage composition [START_REF] Borcard | Partialling out the spatial component of ecological variation[END_REF]. We first selected variables using forward selection as recommended by [START_REF] Blanchet | Forward selection of explanatory variables[END_REF]: variables were included in the CCA model as long as they did not exceed a p > .05 (p-values were assessed using 999 permutations) and if the R 2 adj of the tested model did not exceed the R 2 adj of the CCA model including all the tested variables. We then calculated the percentage of variation explained by each retained variable while taking into account all the other variables for a given scale (conditional R 2 adj ). We then ran a final CCA with selected variables and calculated R 2 adj for the four components (drainage, dbMEM, reach and site) by permutations of constraining matrix using varcanv software [START_REF] Peres-Neto | Variation partitioning of species data matrices: Estimation and comparison of fractions[END_REF]. We also tested if the unique parts explained by the spatial and environment components differed. We assessed significance of fractions and difference between components with 999 permutations. We performed the analysis on the entire assemblages (all the 147 species were considered) and on a reduced dataset, where rare species (occurring in less than five sites, i.e. occurrence < 3% of the sites, see Table II.S1) were removed. In this reduced dataset 58 rare species, which might affect the quality of the CCA, were removed.

Then, to evaluate how species composition differs between sites and reaches, we classified species into groups based on their scores extracted from the two-first axes of the CCA including only reachand local-scale variables, using K-means partitioning. We tested two to fifteen clusters. The final number of groups was selected based on the simple structure index (ssi), with the highest value indicating the best partition [START_REF] Dolnicar | A tale of three cities: Perceptual charting for analysing destination images[END_REF]. Although maximal resolution was achieved for a clustering of nine groups, five-group clustering provided similar ecological information (Figures II.S1 and II.S2). We hence considered five groups as the best compromise between species group clustering and interpretability of the results.

We then compared fish species identity between the different drainages to confront the results provided by the CCA and variation partitioning to the known differentiation of fish community composition between river drainages in French Guiana. We computed the turnover component of Jaccards dissimilarity index between the different drainages to remove the effect of richness difference between them [START_REF] Baselga | The relationship between species replacement, dissimilarity derived from nestedness, and nestedness[END_REF]. We then used the dissimilarity values in hierarchical clustering analysis with the complete linkage method, based on maximising the correlation between the original distances and the cophenetic distances (Pearsons correlation: r = 0.86; [START_REF] Farris | On the cophenetic correlation coefficient[END_REF]. As for the previous analysis, we repeated these steps on Characiformes and Siluriformes, with single linkage (r = 0.79) and average linkage (r = 0.81) methods respectively.

All analyses were performed using R software (R Core Team, 2015) with the packages "adespatial" version 0.0-8 [START_REF] Dray | Adespatial: Multivariate Multiscale Spatial Analysis[END_REF], "lme4" version 1.1-12 [START_REF] Bates | Fitting linear mixed-effects models using lme4[END_REF], "MuMIn" version 1.15.6 [START_REF] Barton | MuMIn: Multi-Model Inference[END_REF] and "vegan" version 2.4-2 [START_REF] Oksanen | Vegan: Community Ecology Package. R package version 2.4-1c[END_REF].

RESULTS

We collected 147 species belonging to 10 orders of fish (Table II.S1). Characiformes and Siluriformes were the most represented orders, with 66 and 45 species respectively, and together represent more than 75% of the species. At the drainage scale, the Maroni River drainage was the richest in stream species (108 species recorded), followed by the Approuague (79 species), the Sinnamary (65 species), the Oyapock (52 species) and the Mana River drainage had the lowest number of stream species (43 species). Sixty-one species were only found in a single drainage. On average, 12 (± 8) species were collected in each site, and species richness ranged from to 1 to 43 per site. Among the 257 candidate GLMM predicting species richness, eight were retained as the best models (∆AICc < 4, Table II.2). The distance from the source, the altitude and the habitat structural diversity were retained in the eight models, whereas the hydromorphological unit was never retained.

The best model retained two reach scale variables, distance from the source and altitude, and one local scale variable, the habitat structural diversity. In this model, the variance of the intercept for the drainage random effect was null and the variance for the reach random effect was estimated at 0.15. The 95% confidence intervals of these three variables did not include zero indicating a significant effect of these variables on species richness (Table II.2). In contrast, the effect of forest canopy cover, the conductivity and the pH were not significant as the 95% confidence intervals included zero for those variables. Species richness increased with the distance from the source (0.27 ± 0.057 (estimate ± SE); Figure II.4a) and to a lower extent with habitat structural diversity (0.11 ± 0.042; Figure II.4b), and decreased with the altitude (-0.43 ± 0.066; Figure II.4c). Considering the two main fish orders (Characiformes and Siluriformes) showed that their species richness followed the same increasing pattern according to the distance from the source as the whole fish fauna (Figure II.S3). The species richness in Characiformes also decreased with altitude, while the species richness in Siluriformes increased with habitat structural diversity.

Table II.2: The eight best models retained to explain variation in species richness. The models are ranked by their Akaike's information criterion corrected for small samples (AICc). For each model, the variables retained are indicated by a '+'. The Akaike weigth (wi) the R 2 m and R 2 c are indicated. For each variable, the model-averaged coefficient (estimate ± SE), its relative importance (the sum of model wi in which the variable is included) and its 95% confidence interval (CI) are given. R 2 m refers to the marginal R 2 (variance explained by fixed factors) and R 2 c refers to the conditional R 2 (variance explained by the entire model, i.e. by fixed and random factors) After independent forward selections of variables, the five reach scale variables and 14 out of the 18 local scale variables were retained (Table II.3). When we partitioned variation in species composition between the three different scales and the spatial structure derived from dbMEM for the full set of species, drainage membership, reach position and local habitat characteristics explained 5.2%, 7.7% and 10.1% of the variation respectively, and the spatial component explained 10.8% of the variation (Table II.4). When accounting for the other variables, the spatial structure of sites explained the highest amount of species composition variation (7.6%), local habitat characteristics and the drainage membership explained similar variation (4.1% and 4.0% respectively) and the reach-scale variables explained the lowest variation (1.9%). Overall, the spatial structure (drainage and dbMEM variables) explained a similar percentage of variation than the environment component (R 2 adj spatial = 10.9%; R 2 adj environment = 7.9%; p = .37). When removing the rare species (species occurring in less than five sites), the percentage of explained variation increased slightly (5% of gain) and the ranking between the different components did not differ, with the spatial component explaining the highest variation in species composition (Tables II.S3 and II.S4).

The forward selection step on all environmental variables (reachand local-scales) excluded silt, sand, under-bank shelters, tree roots and CV of width from CCA analysis. The CCA on all assemblages constrained by all of the retained variables was significant (F = 2.22, p = .001) and the full set of constraining variables explained 13.9% of the variation in assemblage composition (raw R 2 = 27.5%; Figure 3). We clustered species in five groups with the K-means analysis according to the simple structure index criterion (Table II.S1 and Figure II.S1). The first group of species (group 1 in Figure II.5) was composed of only two species (Hartiella n. sp. and Lithoxus boujardi) that are characteristic of torrential mountainous upstream sites, with high altitude, marked slopes and waterfalls (Figure 4). Group 2 (19 species) represented species inhabiting upstream sites with less torrential and mountainous characteristics (Figure II.6). Those species are replaced downstream by more ubiquitous ones (group 3, 63 species). Species in group 4 (40 species) preferentially Species turnover between drainages was moderate, with dissimilarities ranging from 0.13 to 0.59 (mean ± SD: 0.41 ± 0.14). Hierarchical classification based on species turnover separated western (Maroni and Mana) and eastern (Approuague and Oyapock) river drainages (Figure II.7). The Sinnamary River drainage remained distinct from these two groups. Characiformes species turnover was on average slightly lower than that of the entire fauna (0.36 ± 0.13) but remained of the same

∩ Spatial < 0 < 0 Drainage ∩ Reach 1.1 1.1 Drainage ∩ Local 3.6 < 0 Spatial ∩ Reach 1.7 1.5 Spatial ∩ Local 2.5 1.3 Reach ∩ Local 2.1 1.9 Drainage ∩ Spatial ∩ Reach < 0 < 0 Drainage ∩ Spatial ∩ Local < 0 3.8 Drainage ∩ Reach ∩ Local < 0 4.0 Spatial ∩ Reach ∩ Local 1.8 1.4 Drainage ∩ Spatial ∩ Reach ∩ Local 2.2
< 0 Unexplained variation 56.0 75.1 magnitude (range: 0.15 -0.59) and no clear distinction between the river drainages was found. In contrast, Siluriformes experienced strong turnover between river drainages (0.52 ± 0.12, range: 0.36 -0.73). The distinction between the groups Mana-Maroni and Approuague-Oyapock was marked and the Siluriformes fauna of the Sinnamary River drainage remained intermediate between the two groups.

DISCUSSION

Despite the marked differences in species richness and composition between tropical and temperate fish faunas [START_REF] Oberdorff | Global diversity of fish (Pisces) in freshwater[END_REF][START_REF] Oberdorff | Global and regional patterns in riverine fish species richness: A review[END_REF], Neotropical rainforest stream fish assemblages were found to be shaped similarly to those of temperate streams. Indeed, local species richness exhibited the expected increase along the upstream-downstream gradient, which was also associated with an increase in local habitat structural diversity. Such a gradient in species richness has also been reported elsewhere in the Neotropical streams and rivers [START_REF] De M Érona | Fish-habitat relationships in French Guiana rivers : A review[END_REF][START_REF] Mol | Fishes of Lely and Nassau Mountains, Suriname. In: RAP Bulletin of Biological Assessment: A Rapid Biological Assessment of the Lely and Nassau Plateaus, Suriname (with Additional Information on the Brownsberg Plateau)[END_REF][START_REF] Ponton | Early dry-season community structure and habitat use of young fish in tributaries of the River Sinnamary (French Guiana, South America) before and after hydrodam operation[END_REF][START_REF] Terra | Fish assemblages in Atlantic Forest streams: The relative influence of local and catchment environments on taxonomic and functional species[END_REF], but in addition to this upstream-downstream pattern, we here show that habitat structural diversity of local S1 for complete information about species membership for each cluster; bottom right panel (d) position of sites in the CCA ordination habitat had a positive, although slight, effect on local species richness. Habitat structural diversity includes a wide range of components (from flow velocity to substrate granulometry) and it thus can be described by several measurements [START_REF] Gorman | Habitat structure and stream fish communities[END_REF][START_REF] Ponton | Spatio-temporal distribution of young fish in tributaries of natural and flow-regulated sections of a neotropical river in French Guiana[END_REF][START_REF] Willis | Habitat structural complexity and morphological diversity of fish assemblages in a Neotropical floodplain river[END_REF]. Here, we quantified habitat structural diversity as the variability in channel morphology (width and depth), granulometry and shelter availability. Indeed, hydromorphological heterogeneity caused by local variations of the slope or by the presence of woody debris creates areas of reduced current velocity that may be used as refuges for species [START_REF] Fausch | Experimental analysis of microhabitat selection by juvenile steelhead (Oncorhynchus mykiss) and coho salmon (O. kisutch) in a British Columbia stream[END_REF]. Habitat heterogeneity may also act via a trophic pathway where debris and crevices in substrate favour higher diversities of stream invertebrates and periphyton that can be consumed by a variety of fish species with different feeding modes and habits [START_REF] Downes | Habitat structure and regulation of local species diversity in a stony, upland stream[END_REF][START_REF] Robson | Spatial patterns of invertebrate species richness in a river: The relationship between riffles and microhabitats[END_REF]. Unlike the effect of the distance from the source, of the altitude and of the habitat structure diversity, which significantly influenced the local species richness, the role of river drainage identity was negligible. Hence, historical and macroevolutionary processes that shape the size of the species pool occurring in a river drainage [START_REF] Jackson | What controls who is where in freshwater fish communities: The roles of biotic, abiotic, and spatial factors[END_REF], hardly affected the local species richness.

Our results also highlighted that the overall species richness pattern was congruent within the two major fish orders (Characiformes and Siluriformes; Figure II.S3). Species richness in both orders increased from upstream to downstream. The number of Siluriformes species also increased with habitat structural diversity, but we did not detect such a relationship for Characiformes. The increase in diversity in streambed materials (grain size, shelters) primarily favoured the diversity of benthic species. Most of the Siluriformes species are benthic whereas Characiformes species often occupy open waters [START_REF] Winemiller | Fish ecology in tropical streams[END_REF]. This overall difference of fish position in the water column between the two orders might explain the lack of relationship between habitat structural diversity and Characiformes species richness. We also detected a lower richness of Characiformes in the rapids, probably due to the morphology of most Characiformes that is more suited to swimming in open waters, but less to the turbulent water of rapids [START_REF] Winemiller | Fish ecology in tropical streams[END_REF]. Apart from these differences, the overall fish richness pattern also holds for the two main fish orders, meaning that the overall species richness gradient resulted from a gradual increase in diversity rather than a species replacement between different fish orders.

Turning from the determinants of species richness to the determinants of assemblage composition revealed that the environment and spatial effects explained no more than 25% of the composition of species assemblages. Although this value is less than previous studies that reported a much higher explained variation (e.g. [START_REF] Brosse | Determinants of fish assemblage structure in Mount Itoup é mountain streams (French Guiana)[END_REF][START_REF] Terra | Fish assemblages in Atlantic Forest streams: The relative influence of local and catchment environments on taxonomic and functional species[END_REF], it should be noted that the effect we report is corrected for sample size and for the number of variables, which was not the case in previous studies. Without such correction, our explained variation reached 44% (see Table II.4), a value comparable to of earlier studies. Although correcting for sample size and for the number of variables lowered the amount of variance explained, it permitted an unbiased measurement of the effect and of the importance of the environmental variables on fish assemblage composition [START_REF] Peres-Neto | Variation partitioning of species data matrices: Estimation and comparison of fractions[END_REF], and the patterns of assemblage variation we detected were nonetheless significant. The large amount of unexplained variance recorded in the present study, but also in the literature on the determinants of fish assemblage structure in Neotropical streams (e.g. [START_REF] Brosse | Determinants of fish assemblage structure in Mount Itoup é mountain streams (French Guiana)[END_REF][START_REF] Terra | Fish assemblages in Atlantic Forest streams: The relative influence of local and catchment environments on taxonomic and functional species[END_REF], has often been afforded to the low occurrence of most species (c.a. 75% of species occurred in < 10% of the sites), a common problem in tropical community ecology [START_REF] Hercos | Local and regional rarity in a diverse tropical fish assemblage[END_REF]ter Steege et al., 2013). Nevertheless, removing rare species (occurring in less than five sites) only slightly increased the explained percentage of variation in species composition, meaning that unexplained variance was only slightly affected by rare species. An alternative explanation is that temporal variability in the environment promotes some stochasticity in the species composition of assemblages [START_REF] De M Érona | Fish-habitat relationships in French Guiana rivers : A review[END_REF][START_REF] Grossman | Stochasticity in structural and functional characteristics of an indiana stream fish assemblage: A test of community theory[END_REF]. Testing for environmental stochasticity would need repeated samples on the same sites, which is problematic given the destructive nature of rotenone sampling. The development of a non-destructive alternative to rotenone samples is therefore an urgent priority.

Among the influential determinants of assemblage composition, spatial and environmental factors explained similar amounts of variation, which contradicted our expectation that spatial factors should override environmental variables. Although almost half of the species were found in a single drainage, thereby giving rise to a strong spatial effect, the remaining half of the fauna was made up of widespread species, thus constituting a common core to all the river drainages and offsetting the role of the spatial component. This contrasts with the findings of [START_REF] Brosse | Determinants of fish assemblage structure in Mount Itoup é mountain streams (French Guiana)[END_REF] based on a few sites from the same mountainous area. In that particular case, the regional effect (drainage membership) was of primary importance to explain variation in fish assemblages. Extending our analysis the entire Guianese region therefore provided a more comprehensive view of the determinants of assemblage structure, paralleling therefore the work of [START_REF] Garzon-Lopez | Effects of sampling scale on patterns of habitat association in tropical trees[END_REF] on the scale dependence effect on tropical trees assemblages. Although the regional determinants were not the major force driving fish assemblages, they nonetheless had a marked effect on assemblage variation. The species turnover between drainages was responsible for the clustering of the river drainages along an East-West gradient, reflecting the biogeographic history of the region [START_REF] Boujard | Biog éographie des poissons d'eau douce de Guyane franc ¸aise[END_REF][START_REF] Bail | Updated checklist of the freshwater and estuarine fishes of French Guiana[END_REF]. This clustering of river drainages was mirrored in their stream fish assemblages, and was particularly marked in the Siluriformes. Indeed, a substantial part of the siluriform fauna inhabiting streams is made of strictly rheophilic taxa restricted to the upstream parts of rivers [START_REF] Cardoso | Unexpected diversity in the catfish Pseudancistrus brevispinis reveals dispersal routes in a Neotropical center of endemism: The Guyanas Region[END_REF][START_REF] Lujan | The Guiana Shield[END_REF]. Their low dispersal ability probably accentuates the regional effect observed in the Siluriformes. This regional effect was, however, weaker compared to the spatial structure of the assemblages within each drainage, which explained the highest part of variation in assemblage composition. The low regional effect compared to the strong spatial structure within drainages probably reflects dispersal limitations between different streams belonging to the same drainage [START_REF] Cilleros | Taxonomic and functional diversity patterns reveal different processes shaping European and Amazonian stream fish assemblages[END_REF][START_REF] Vitorino J Únior | Riverine networks constrain β-diversity patterns among fish assemblages in a large Neotropical river[END_REF]. Indeed, the main channels of the rivers and the presence of rapids can act as dispersal barriers for some small-bodied stream fishes either by way of a distance effect [START_REF] Datry | Metacommunity patterns across three Neotropical catchments with varying environmental harshness[END_REF] or due to predation byor competition withspecies inhabiting large rivers [START_REF] Wisz | The role of biotic interactions in shaping distributions and realised assemblages of species: Implications for species distribution modelling[END_REF].

The environmental characteristics also affected species composition of fish assemblages. This effect did not result from a few environmental characteristics having a dominant effect, but rather from the combination of several environmental characteristics having slight, but nonetheless significant, effects. We therefore hypothesise that there is no consistent response of all species to environmental variables, but more probably species-specific responses to particular variables, with the result that almost all variables have some significantalbeit low explanatory power. This multifactorial contribution of the environment to the fish community composition contrasts with the situation found in temperate streams, where the fish composition is determined by a few dominant environmental features related to stream morphology and streambed substratum size [START_REF] Mesquita | Spatial variation in fish assemblages across small Mediterranean drainages: Effects of habitat and landscape context[END_REF][START_REF] Nakagawa | Contribution of environmental and spatial factors to the structure of stream fish assemblages at different spatial scales[END_REF]; but see [START_REF] Johnson | Ecological relationships between stream communities and spatial scale: Implications for designing catchment-level monitoring programmes[END_REF]. Although we did not identify a strong environmental gradient shaping fish assemblages, we distinguished five successive groups of species along the upstream-downstream gradient (Figure II.6), revealing an upstream-downstream species succession equivalent to that reported in temperate streams (e.g. [START_REF] Allan | Stream Ecology: Structure and Function of Running Waters[END_REF]. In addition, a particular fauna was found close to the confluence with larger rivers. This last zone might be composed of a specific fish fauna [START_REF] Albanese | Ecological correlates of fish movement in a network of Virginia streams[END_REF], or be a transition between streams and rivers and hence host a mixed fauna [START_REF] Fernandes | Amazonian ecology: Tributaries enhance the diversity of electric fishes[END_REF]. This last situation holds for Guianese streams where we did not detect species strictly inhabiting these zones, but a mix between stream and river fishes, with the occurrence of species usually found in large rivers such as Hemiodus quadrimaculatus or Hypostomus gymnorhynchus (Le [START_REF] Bail | Updated checklist of the freshwater and estuarine fishes of French Guiana[END_REF].

The pattern of stream fish zonation we describe could constitute a benchmark for future studies measuring the impact of anthropogenic disturbances on Neotropical forest streams. Moreover, the contribution of almost all the environmental descriptors of the local environment to both species richness and assemblage composition suggests that modifications to only a single component of the environment might alter fish assemblage composition. This is of particular importance since damming, mining and logging are already known to affect the characteristics of Neotropical streams [START_REF] De M Érona | Alteration of fish diversity downstream from Petit-Saut Dam in French Guiana. Implication of ecological strategies of fish species[END_REF][START_REF] Dedieu | Physical habitat and water chemistry changes induced by logging and gold mining in French Guiana streams[END_REF][START_REF] Dias | Effects of reduced-impact logging on fish assemblages in Central Amazonia[END_REF], and are thus very likely to influence of both species richness and composition of stream fish assemblages.

∩ Spatial < 0 < 0 Drainage ∩ Reach 1.1 1.0 Drainage ∩ Local 5.2 < 0 Spatial ∩ Reach 1.7 1.5 Spatial ∩ Local 2.4 1.4 Reach ∩ Local 3.0 2.7 Drainage ∩ Spatial ∩ Reach < 0 < 0 Drainage ∩ Spatial ∩ Local < 0 2.4 Drainage ∩ Reach ∩ Local < 0 2.9 Spatial ∩ Reach ∩ Local 2.6 2.3 Drainage ∩ Spatial ∩ Reach ∩ Local 2.9
< 0 Unexplained variation 54.2 70.2 Tropical regions are generally characterized by a higher species richness than temperate ones (Latitudinal Diversity Gradient, see I, but also by a higher turnover of species between assemblages [START_REF] Soininen | A multivariate analysis of beta diversity across organisms and environments[END_REF]. Differences in the strength of dispersal limitation and environmental filtering have been proposed to explain differences in beta-diversity patterns between regions. However, their relative roles remain unclear and have been shown to differ between tropical and temperate environments [START_REF] Kraft | Disentangling the drivers of β-diversity along latitudinal and elevational gradients[END_REF][START_REF] Myers | Beta-diversity in temperate and tropical forests reflects dissimilar mechanisms of community assembly[END_REF]. Using the same framework of multi-faceted diversity can help disentangle the strength of dispersal limitation and environmental filtering in these assemblages.

Using the data available for French Guiana (used in this work) and for continental France (French National Agency for Water and Aquatic Environments data for species occurrence and Toussaint et al. global functional database), I compare the relative strength of dispersal limitation and environmental filtering in structuring fish assemblages of small streams between the two regions. Assembly rules being sensitive to the spatial scale, I first constrained the two datasets to have about the same spatial structure and environmental characteristics. Once the sites have been selected, I compared the taxonomic and functional structures of fish assemblages to determine whether dispersal limitation and environmental filtering act in the same way in the two regions. We predict that, if taxonomic and functional turnovers between assemblages are higher in the tropical region than in the temperate region, dispersal limitation and environmental filtering have the same relative strength in the two regions. On the contrary, if functional and taxonomic turnovers are decoupled, the relative strengths of the two processes are differing between the two regions.

As expected and reported for other taxa [START_REF] Ricklefs | Aspect diversity in moths: A temperate-tropical comparison[END_REF][START_REF] Novotny | Why are there so many species of herbivorous insects in tropical rainforests?[END_REF], species richness was higher in the Guianese assemblages, at both local and regional scales, and this caused a higher functional richness for Guianese fauna compared to the French one. The higher functional richness thus represented an addition of functional attributes between the two regions caused by higher number of available niches and not by the presence of functionally distinct species.

Turning to β-diversity revealed that taxonomic turnover between Guianese assemblages was higher than taxonomic turnover between French assemblages. On the contrary, functional turnover was higher between French assemblages. In addition, comparisons with simulations showed that, on average, both taxonomic and functional turnovers were higher than null expectations, except for taxonomic turnover for French In tropical regions large rivers can act as dispersal barriers for stream fish species, promoting therefore dispersal limitation processes. In temperate streams the thermic gradient between cold upstream water and cooler downstream water that act as an environmental filter for fish assemblages. (Photo: Ji-Elle, K. Cilleros) assemblages. Based on the above stated hypotheses, we concluded that dispersal limitation and environmental filtering did not have the same relative strength in the two regions: dispersal limitation is the main process that structures Guianese fish assemblages, whereas environmental filtering mainly constrains French assemblages.

This difference might be rooted in the difference in the habitat heterogeneity between the two regions and their past history. Guianese streams might be more locally diverse in their environment but more environmentally homogeneous between them than their temperate counterparts that exhibit a strong variability between lowland and piedmont zones, explaining the higher functional local richness but a lower functional turnover. French fish fauna also results from recent post-glacial recolonization by species with strong dispersal capacities, which can reduce the strength of dispersal limitation. In contrast, Guianese fauna is composed of species that originate from highly dynamic modification of the hydrological network [START_REF] Lujan | The Guiana Shield[END_REF] and from recent dispersal events from different biogeographical regions (Amazon basin and Orinoco basin) that could have favored a high taxonomic turnover.

This first work on assembly rules and could benefit of two main improvements for further studies. Firstly, the incorporation of the phylogenetic information might help resolve underlying processes structuring local assemblages. Indeed, here we did not look if local assemblages were assembled under limiting similarity, environmental filtering or neutral process. We might expect a difference in the main process structuring local assemblages due to difference in environmental conditions between regions (especially in seasonality and variability) and past history. Secondly, to confirm these results, it will be important to look at the relationships between beta-diversity and environmental dissimilarities and/or geographical distance between sites. If environmental filtering is dominant across assemblages, sites must have different environmental conditions even at close distance, whereas, under dispersal limitation, sites with similar environment should be separated by large distance.

The difficulty is how to define the environment in this case: does the environment must be described independently in each region or are the same measures needed? The former might be easier to implement when using databases, but such standardized environmental data is not available and this heterogeneity might blur the effect of assembly processes. The later will focus mainly on GIS derivable measures (distance from the source or to sea, slope, land use) and instream characteristics (size, bottom grain size) common to all streams. It will be easier to implement and comparisons will be based on same set of variables but this approach can miss environmental variables that affect the most assemblage structure. Depending of the organisms, one of the two solution can be favored (e.g. for tree, landscape features and soil content can be standardized across regions and can thus be used for environmental data), but for fish, the second might be the less difficult and stream characteristic are often correlated with the upstream-downstream gradient that can be described easily with GIS data.

INTRODUCTION

One of the most striking characteristics of tropical regions is their species richness compared to temperate areas. The causes of these differences have been debated for more than a century, and three nonexclusive groups of hypotheses have been proposed to explain the patterns observed, i.e. historical, evolutionary and ecological hypotheses. Historical and evolutionary causes are closely linked and have been handled through a biogeographic context. Ecological hypotheses refer to mechanisms and factors that promote species presence and coexistence through their ecology, including higher available energy [START_REF] Wright | Species-energy theory: An extension of species-area theory[END_REF], more diverse habitats [START_REF] Preston | The canonical distribution of commonness and rarity: Part I[END_REF][START_REF] Macarthur | The Theory of Island Biogeography[END_REF] and/or decreased abiotic harshness in tropical regions [START_REF] Fischer | Latitudinal variations in organic diversity[END_REF].

In addition to the higher local species richness in tropical than in temperate assemblages, a higher turnover in species between localities has also been reported [START_REF] Soininen | A multivariate analysis of beta diversity across organisms and environments[END_REF]. For instance, [START_REF] Kraft | Disentangling the drivers of β-diversity along latitudinal and elevational gradients[END_REF] reported a global decrease in β-diversity with increasing latitude, and a stronger species turnover occurs in the tropics compared to the temperate areas for both frog and fungus assemblages [START_REF] Dahl | Beta diversity of frogs in the forests of New Guinea, Amazonia and Europe: Contrasting tropical and temperate communities[END_REF][START_REF] Bahram | The distance decay of similarity in communities of ectomycorrhizal fungi in different ecosystems and scales[END_REF]. Dispersal limitation and environmental filtering are the two main processes proposed to explain these patterns [START_REF] Nekola | The distance decay of similarity in biogeography and ecology[END_REF], but their relative roles remain unclear and have been shown to differ between tropical and temperate environments [START_REF] Kraft | Disentangling the drivers of β-diversity along latitudinal and elevational gradients[END_REF][START_REF] Myers | Beta-diversity in temperate and tropical forests reflects dissimilar mechanisms of community assembly[END_REF]. For instance, [START_REF] Myers | Beta-diversity in temperate and tropical forests reflects dissimilar mechanisms of community assembly[END_REF] reported that tropical tree assemblages are mainly structured by dispersal limitation, whereas temperate assemblages are more influenced by environmental filtering.

Functional diversity approaches have been proposed as a way to disentangle these processes, by examining the correlation between taxonomic and functional facets of diversity [START_REF] Devictor | Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: The need for integrative conservation strategies in a changing world[END_REF]. Indeed, the assemblages filtered by the environmental characteristics should differ functionally according to the local environmental characteristics, therefore promoting functional turnover between assemblages. In contrast, assemblages dominated by dispersal limitation evolve independently in a homogeneous environment and should therefore differentiate distinct species with similar strategies. Based on this, we predict that (1) if functional turnover and taxonomic turnover cover a similar range, environmental filtering and dispersal limitation have a similar strength (Figure III.2a), (2) if functional turnover is higher than taxonomic turnover, environmental filtering is stronger than dispersal limitation, and (3) if taxonomic turnover is higher than functional turnover, dispersal limitation is stronger than environmental filtering. Using this theoretical framework, we also predict that if both the taxonomic and the functional turnover in the tropics are higher than in the temperate zones, environmental filtering and dispersal limitation act in the same way in both environments (Figure III.2b). In contrast, if despite an increase in taxonomic turnover between temperate and tropical zones, we show an opposite trend for functional turnover, this would indicate a prominent role of dispersal limitation in tropical assemblages and of environmental filtering in temperate assemblages (Figure III.2c). Such a framework deserves to be built using phylogenetic diversity approaches [START_REF] Graham | Phylogenetic beta diversity: Linking ecological and evolutionary processes across space in time[END_REF], but no robust global phylogeny is currently available for South American fishes, especially Guianese ones. (a) Assemblages with similar taxonomic and functional turnover (area 1) are structured with similar strengths of environmental filtering and dispersal limitation. Outside this area, pairs of assemblages with higher functional turnover are structured by different environmental filters and promote species with different ecological strategies (area 2), whereas pairs with higher taxonomic turnover are dominated by dispersal limitation, with distinct species sharing similar strategies (area 3). We hence expect that a higher functional turnover in tropical assemblages (blue dashed line) than in temperate ones (orange dotted line) will reveal that both processes act in the same way in the two regions (b). In contrast, a higher functional turnover in temperate assemblages will reveal that the relative strength of dispersal limitation and environmental filtering differs between the two regions (c) Here, we compared taxonomic and functional diversity of French and Guianese stream fish assemblages. We selected a set of samples having similar topographic characteristics (altitude, stream width) and similar spatial structure (extent and grain size) between the two regions to ensure their comparability [START_REF] Steinbauer | Current measures for distance decay in similarity of species composition are influenced by study extent and grain size[END_REF]. We first quantified local and regional taxonomic and functionalα-diversity in the two regions. Then, we compared the spatial patterns in taxonomic and functional turnover between France and French Guiana, and inferred the processes shaping assemblage structure according to the relationships between taxonomic and functional turnover in temperate and tropical streams.

METHODS

Fish data

Analyses were based on complete fish inventories in small streams in both French Guiana and continental France. In both cases, fish abundances were converted into species occurrences to avoid potential bias due to differences in sampling techniques and potential differences in sampling efficiency between sites and between climate zones [START_REF] Oberdorff | A probabilistic model characterizing fish assemblages of French rivers: A framework for environmental assessment[END_REF][START_REF] Macnaughton | A comparison of electrofishing and visual surveying methods for estimating fish community structure in temperate rivers[END_REF]. Species lists obtained at the same location from 2008 to 2013 were merged to get the most complete image of fish assemblages. The Guianese database contained the occurrence of freshwater fish species in 84 stream reaches dispersed throughout French Guiana (Figure III.3a). The fish surveys that were used to build this database were conducted under several different research projects (CNRS-Nouragues, PAG-DEAL-HYDRECO project, CEBA-DIADEMA project). The sampling protocol was standardized for all sites [START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF]. At each site, a river section located upstream of a confluence was isolated using two fine mesh (4 mm) stop nets. Fish were collected after releasing a small quantity of rotenone a few metres upstream from the first net. At the end of each sampling session we searched for fishes lying on the bottom or hidden in the leaves and debris. In almost all sites, cryptic fish were collected, including highly cryptic silurids such as Farlowella or Harttiella, bottom-dwelling fishes such as Ituglanis or Loricaria, and litter bank fishes such as small killifishes (see Table III.S1 for lists of species). Gymnotids, although known as resistant to rotenone, were also captured in almost all the sites. For continental France, we used a database compiled by the French National Agency for Water and Aquatic Environments (Onema). Extensive surveys were conducted on stream sections with a standardized two-pass removal electrofishing protocol [see [START_REF] Poulet | Time trends in fish populations in metropolitan France: Insights from national monitoring data[END_REF]]. All the fish captures were in accordance with French laws and guidelines concerning live animals, and all the experiments were approved by the Direction of Environment of the French ministry of Environment (DEAL), the French Guiana National Park (Parc Amazonien de Guyane), and the Onema.

Site selection

These two databases were built in order to characterize local fish assemblages in the two regions. French and Guianese sampling sites were thus selected to balance environmental, spatial and topographic discrepancies between the two regions that can bias comparisons between distant areas. Among the French sites, we selected those having the same physical characteristics (slope, altitude and distance from the source) and sampled during the same time period (2008 -2013) as Guianese sites. All sites were upstream sites located between 0.5 and 16 km from the source. They belonged to lowland and piedmont rivers and were located from 27 to 633 m above sea level, with a slope between 0.57 and 18.77 . Then, to ensure a similar spatial structure between Guianese and French sites, we translated Guianese site coordinates to the part of mainland France where the spatial congruence was the best, corresponding here to the north-east of the country. We then selected French sites with more than four species (because the functional space was calculated in four dimensions, see below) that best matched Guianese site location (Figure III.3b). The resulting selection of 84 French sites showed similar spatial structure to the Guianese sites (see Table III.S2). This selection procedure provided two comparable databases of freshwater fish species occurrence in 84 assemblages for each region.

Functional data

We characterized fish for two main functions, locomotion and food acquisition, combining 14 morpho-anatomic measures taken on a picture (lateral view) of one adult individual per species using ImageJ software1 and fish size was described using the log-transformed maximum body length (values taken from Fishbase [START_REF] Froese | FishBase. World Wide Web electronic publication[END_REF]; see Figure III.S1, Table ?? and Toussaint et al. (2016) for details). Juveniles were not considered as morphological changes can occur during ontogeny. The effect of intraspecific variability on our results was assessed with all the species with several individuals measured (i.e. all French species and 17 Guianese species). Intraspecific variation in each trait was lower than interspecific variation for all the species, with interspecific variability accounting for 75.10 ± 18.83% (mean ± SD) of the total variability. Moreover, a simulation analysis randomly selecting individuals within species (999 replicates) showed that the French functional richness reported here was never underestimated, and overestimated in 30% of the local sites. The Guianese fish functional richness never differed. This means that intraspecific variability did not affect our conclusions, however, in 30% of the sites it did accentuate the magnitude of the difference between French and Guianese functional richness.

Morpho-anatomic measures were combined as 14 unitless ratios (see Table III.S1b). Their use has first been recommended by [START_REF] Webb | Body Form, Locomotion and Foraging in Aquatic Vertebrates[END_REF] and [START_REF] Winemiller | Ecomorphological diversification in lowland freshwater fish assemblages from five biotic regions[END_REF], and these unitless ratios still remain the most commonly used to describe fish locomotion and nutrition (e.g. [START_REF] Bellwood | Fifty million years of herbivory on coral reefs: Fossils, fish and functional innovations[END_REF][START_REF] Brandl | Exploring the nature of ecological specialization in a coral reef fish community: Morphology, diet and foraging microhabitat use[END_REF][START_REF] Leit Ão | Rare species contribute disproportionately to the functional structure of species assemblages[END_REF][START_REF] Toussaint | Global functional diversity of freshwater fish is concentrated in the Neotropics while functional vulnerability is widespread[END_REF]. We then computed functional distance matrix between species with Gowers distance based on the morphological ratios and the fish size to consider missing data. We used a principal coordinate analysis (PCoA) on the functional distance matrix encompassing 192 species (155 species from French Guiana and 37 species from continental France) to obtain coordinates of the species in a multidimensional space [START_REF] Vill Éger | New multidimensional functional diversity indices for a multifaceted framework in functional ecology[END_REF][START_REF] Leit Ão | Rare species contribute disproportionately to the functional structure of species assemblages[END_REF]. The method of [START_REF] Maire | How many dimensions are needed to accurately assess functional diversity? A pragmatic approach for assessing the quality of functional spaces[END_REF] was used to evaluate the best compromise to represent the functional information and keep the number of dimensions as low as possible (and hence keep considering assemblages with a few species). It revealed that a 4-D approach was the most appropriate. We, therefore, kept the first four principal components to describe the functional space, representing 80.45% of the inertia (32.18%, 22.51%, 16.19% and 9.58% for the first four axes of the PCoA respectively). Allometric or isometric relationships existing between morpho-anatomic measures can bias species ordination in a multivariate space toward a first component representing a size effect rather than a shape or functional difference between species [START_REF] Winemiller | Ecomorphological diversification in lowland freshwater fish assemblages from five biotic regions[END_REF][START_REF] Baur | Analysis of ratios in multivariate morphometry[END_REF]. We thus checked that species ordination was not driven by a few variables having a preponderant effect by inspecting correlations between functional traits and the four PCoA components (see Table III.S4). For instance, fish body length has been considered as an integrative functional variable [START_REF] Winemiller | Patterns of life-history diversification in north american fishes: Implications for population regulation[END_REF], but here its effect did not blur the effect of the others variables.

Taxonomic and functional diversity measures

We defined regional richness as the total richness found in each of the two regions considered, with the number of species found in all the French and Guianese sites, respectively, being the regional taxonomic richness and the percentage of convex hull volume occupied by all the species from one region being the functional richness [START_REF] Cornwell | A trait-based test for habitat filtering: Convex hull volume[END_REF][START_REF] Vill Éger | New multidimensional functional diversity indices for a multifaceted framework in functional ecology[END_REF]. Local richness (α-diversity) was defined as the taxonomic richness (the number of species) and functional richness (the volume occupied in the functional space by the species present in one local assemblage) in one stream reach assemblage.

Pairwise taxonomic β-diversity (hereafter βT j ) between two assemblages was computed using Jaccards dissimilarity index. This index is widely used in ecology [START_REF] Koleff | Measuring beta diversity for presence-absence data[END_REF][START_REF] Myers | Beta-diversity in temperate and tropical forests reflects dissimilar mechanisms of community assembly[END_REF] and its meaning is easily understandable (the percentage of unshared species between two assemblages). It is based on a broad-sense measure of dissimilarity [START_REF] Koleff | Measuring beta diversity for presence-absence data[END_REF], representing the sum of the species replacement between two assemblages (hereafter called turnover) and the difference in species richness between them (nestedness). The turnover was calculated according to [START_REF] Baselga | The relationship between species replacement, dissimilarity derived from nestedness, and nestedness[END_REF]:

taxonomic turnover = 2 × min(b, c) a + 2 × min(b, c) .
Taxonomic turnover is null when one assemblage contains all the species from the other and equals 1 (i.e. βTj) when assemblages have no species in common.

We then calculated the relative contribution of the turnover component to overall dissimilarity (p turn ) to get an easily interpretable measure of the importance of species turnover on β-diversity [START_REF] Toussaint | Historical assemblage distinctiveness and the introduction of widespread non-native species explain worldwide changes in freshwater fish taxonomic dissimilarity[END_REF], with:

p turn = taxonomic turnover βT j .
The p turn metric is defined only if dissimilarity is not null, meaning that a pair of assemblages differs by at least one species. It is null when assemblages are nested and it equals 1 when assemblages have the same species richness (taxonomic nestedness component is null) or when they have no species in common.

We computed pairwise functional β-diversity (βF j ) within the same framework as for taxonomy, but using convex hull volumes of assemblage instead of species richness (Vill éger et al., 2011a). So βF j can be computed with the a, b and c quantities with a being the intersection of volumes of two assemblages, and b and c the volumes unique to each assemblage. Functional dissimilarity will be equal to 1 when volumes do not overlap and 0 when volumes are identical. The same decomposition in turnover and nestedness of functional dissimilarity were also computed as for taxonomy [START_REF] Vill Éger | Decomposing functional β-diversity reveals that low functional β-diversity is driven by low functional turnover in European fish assemblages[END_REF]. Functional turnover here represents the overlap within functional spaces between assemblages. The relative contribution of the functional turnover component is computed in the same way as its taxonomic counterpart.

Statistical analysis

We compared functional richness at the regional scale while accounting for differences in species richness. We randomly selected as many Guianese species as French species and then calculated the functional richness that they represented. We repeated this 9,999 times and compared the regional functional richness of continental France to this distribution. For the local functional richness, we used the same approach by replacing the French species by the selected Guianese species, thus conserving the occurrence matrix. We then calculated local functional richness for each site and compared, for each site, the French local functional richness to the simulated values for the corresponding sites.

To check for the potential effect of the difference in the number of drainage basins between the two regions on taxonomic and functional diversity measures, taxonomic pairwise comparisons were classified in either intra-or inter-basin comparison. We tested whether intra-basin and inter-basin comparisons significantly differed for each region with a permutation procedure as implemented in 'diffmean' function in R package 'simba' [START_REF] Jurasinski | Simba: A Collection of Functions for Similarity Analysis of Vegetation[END_REF] with 9,999 permutations.

We tested the difference in βT j , βF j and their turnover component between continental France and French Guiana using analysis of homogeneity of multivariate dispersion based on distance-to-centroids (Anderson et al., 2011). In this context, β-diversity was defined as the average distance of sites from the centroid formed by all the sites of a group. Distance-to-centroid and average dissimilarity were highly correlated (Pearson's correlation > .92, p < .001 for each metric). We thus used Jaccard's framework to represent graphically β-diversity.

Taxonomic and functional dissimilarity and turnover were regressed against the logarithm of the pairwise geodesic distance between sites to test the difference in spatial pattern between continental France and French Guiana. We used the Mantel test with 9,999 permutations and the Spearman correlation coefficient to test the significance of the relationship between dissimilarity and distance. Intercept and slope were estimated with a 95% confidence interval by bootstrap with 9,999 replicates [START_REF] Davidar | The effect of climatic gradients, topographic variation and species traits on the beta diversity of rain forest trees[END_REF]. Differences in slope and intercept between continental France and French Guiana were tested using 9,999 permutations, following [START_REF] Nekola | The distance decay of similarity in biogeography and ecology[END_REF] with 'diffslope' and 'diffic' functions. For each run, couples of value dissimilarity/turnover -distance were randomly attributed to one of the two regions and the difference in slope or intercept was calculated. The differences calculated were used as a null distribution to compare with observed difference.

We tested whether observed turnover differed from random expectations using two null models to disentangle which processes had a predominant influence on community assembly between environmental filtering, dispersal limitation and stochastic processes. The first model tested if the observed taxonomic turnover differed from random sampling of species from the regional pool and evaluated the strength of both dispersal limitation and environmental filtering compared to stochasticity. For this, we constructed random assemblages by maintaining the species richness observed in the sites and the species occurrence in the database using a trial swap algorithm [START_REF] Mikl Ós | Randomization of presence-absence matrices: Comments and new algorithms[END_REF]). The second model tested if observed functional turnover differed from expectation where species traits are randomly distributed among assemblages and thus discriminate environmental filtering from stochastic processes. We permuted species identity in the species-trait matrix while maintaining the sites-species matrix and thus the local species richness and taxonomic turnover. For each null model, we calculated a standardized effect size (SES) as the difference between observed values and the mean of 999 simulated values, divided by the standard deviation of the simulated values. Pairs with an SES value between -1.96 and 1.96 did not differ significantly from random expectations. The medians of SES values were tested for departure from zero to assess if environmental filtering or dispersal limitation govern community assembly [START_REF] Stegen | Stochastic and deterministic assembly processes in subsurface microbial communities[END_REF]. In contrast, no departure from zero would indicate that stochastic processes govern community assembly.

RESULTS

Regional species richness was higher in French Guiana than in continental France, with 155 and 37 species respectively (Figure III.4a). Regional functional richness showed a similar pattern. Guianese species occupied 87.29% of the total functional volume, whereas French species accounted for 14.77% of this volume (Figure III.4b). It should be noted that the ratio between Guianese and French diversity increased when turning from taxonomic to functional diversity (4.19 vs. 5.91). Moreover, the functional space occupied by French species was largely included in the functional space of Guianese species. Indeed, only 4.88% of the French functional space was not shared with Guianese functional space (Figure III.5, see Figure III.S2). However, controlling for species richness differences between the two regions revealed that the regional functional richness of French Guiana was not different from the functional richness of continental France (p = .21), and hence the functional differences observed between the two regions were due to a species richness effect.

At the local scale, French assemblages contained fewer species (7.88 ± 3.05) than Guianese assemblages (19.92 ± 9.32) (Figure III.4c; Wilcoxon rank-sum test: z = 9.46, p < .001) which were less functionally diverse (.74 ± 1.02% vs. 7.26 ± 6.28%) (Figure III.4d; Wilcoxon rank-sum test: z = 9.64, p < .001). Again, the distinction between Guianese and French streams was more marked from a functional than from a taxonomic point of view. Accounting for species richness differences by randomly replacing French species by Guianese species showed that only four French sites had a lower local functional diversity (p < .05). The functional diversity of the 80 remaining French sites did not significantly differ from a random replacement of French species by Guianese species. Hence, the functional differences reported between Guianese and French sites were mainly due to a species richness effect.

Testing the effect of the difference in the number of drainage basins revealed that both βT j and taxonomic turnover showed higher mean inter-basin dissimilarity values than intra-basin values for continental France (permutation tests: βT j , F = 52.4, p < .001; taxonomic turnover, F = 43.6, p < .001) and French Guiana (βT j , F = 14.2, p < .001; taxonomic turnover, F = 44.6, p < .001).

βT j and taxonomic turnover were higher for Guianese assemblages (βT j = .84 ± .096; taxonomic turnover = .76 ± .14) than for French assemblages (βT j = .65 ± .16; taxonomic turnover = .52 ± .23) (Figure III.6a; homogeneity of multivariate dispersion test on average distance-to-centroids: F = 117.17, p < .001 for βT j and F = 86.77, p < .001 for taxonomic turnover). Contribution of turnover to taxonomic dissimilarity was high for both regions, but its contribution was higher for French Guiana (.91 ± For both French Guiana and continental France, βF j and βT j were significantly correlated (French Guiana: r = .60, p < .001; continental France: r = .56, p < .001) as well as taxonomic and functional turnover components (French Guiana: r = .49, p < .001; continental France: r = .72, p < .001). However, the magnitude of the taxonomic and of the functional β-diversity and turnover differed between the two regions. For French Guiana, taxonomic β-diversity and turnover were higher than functional β-diversity and turnover (βF j -βT j : Wilcoxon signed-rank test: z = -21.62, p < .001; turnover: Figure III.7a; z = 48.05, P < .001). In contrast, in continental France, functional β-diversity and turnover were higher than taxonomic β-diversity and We found significant distance decay relationships for the Guianese bTj and taxonomic turnover for both regions (Table III.1). Slopes of the regressions of βT j and taxonomic turnover did not differ between continental France and French Guiana (βT j : p = .074; taxonomic turnover: p = .12), but the intercept differed (βT j : p < .001; taxonomic turnover: p < .001), being higher for French Guiana. Both slopes and intercepts differed for βF j (slope: p = .0033; intercept: p = .023), with higher intercept and a more pronounced distance decay for French assemblages. The slope for functional turnover in French assemblages was higher than the slope for French Guiana (p < .001), but the intercept did not differ between the two regions (p = .13).

For French Guiana, null models showed that 23 comparisons of 3486 for taxonomic turnover were significantly different from random expectations. The median of distribution differed significantly from zero (median = .10, z = 5.99, p < .001). A greater number of pairs of communities were different from random expectation when looking at functional diversity turnover while conserving the observed taxonomic turnover (131 out of 3486). The median of simulated functional turnover distribution was significantly different from zero (median = .19, z = 7.79, p < .001). In continental France, 197 taxonomic turnover measures were not consistent with random assembly. Taxonomic turnover distribution showed a median of .052 that did not differ from zero (z = .47, p = .64). A lower number of pairs of communities were different from random expectation when looking at the functional diversity facet while conserving the observed taxonomic β-diversity (113 pairs). However, the median of the simulated distribution was significantly different from zero (functional turnover: median = .50, z = 17.88, p < .001).

DISCUSSION

A major limitation in studies comparing biodiversity patterns from distant areas is the availability of databases collected at similar grain and extents. The total species pool and the distance between samples are affected by the geographical extent of the .040 .20 studies [START_REF] Steinbauer | Current measures for distance decay in similarity of species composition are influenced by study extent and grain size[END_REF], and grain size affects α-diversity through a species-area effect, with an increase in habitat heterogeneity [START_REF] Devictor | Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: The need for integrative conservation strategies in a changing world[END_REF].

β-diversity is also sensitive to grain size as smaller grains are less species-rich than larger ones and thus more likely to share fewer species between each other than larger grains [START_REF] Garzon-Lopez | Effects of sampling scale on patterns of habitat association in tropical trees[END_REF]. In addition, to compare community assembly processes across regions, data should also encompass similar environmental or topographic gradients between regions (e.g. altitude range or stream order), as these gradients have a profound effect on assemblages structure [START_REF] Melo | Environmental drivers of beta-diversity patterns in New-World birds and mammals[END_REF][START_REF] Jaramillo-Villa | Altitudinal variation in fish assemblage diversity in streams of the central Andes of Colombia[END_REF]. Here, these potential biases were controlled by collecting European and South American data at the same grain size and by constraining European data to fit the spatial extent and the topographic gradient encompassed by South American sites, therefore allowing us to explore differences between temperate and tropical faunas independently of scale and environmental gradient effects.

The higher taxonomic diversity we report in Guiana compared to France at both regional and local grains is known for most taxa [START_REF] Ricklefs | Aspect diversity in moths: A temperate-tropical comparison[END_REF][START_REF] Novotny | Why are there so many species of herbivorous insects in tropical rainforests?[END_REF]. A similar trend is also forecast for functional richness, which may be positively related to the number of species [START_REF] Safi | Understanding global patterns of mammalian functional and phylogenetic diversity[END_REF][START_REF] Lamanna | Functional trait space and the latitudinal diversity gradient[END_REF]. The differences we report between temperate and tropical environments increased when turning from taxonomic to functional diversity, at both regional and local scales. However, when controlling for the number of species, the Guianese fish fauna is not more functionally diverse than the French fauna. This was also the case at the local scale and suggests that the higher functional diversity in Guiana mainly results from an addition of functional attributes across the region. The greater functional richness found in the tropical region was hence not driven by a triggered functional diversity of the tropical species, but more probably by a higher diversity of habitats which allows a greater diversity of ecological niches and thus a greater number of species [START_REF] Koeck | Functional differences between fish communities on artificial and natural reefs: A case study along the French Catalan coast[END_REF].

Turning from α-to β-diversity patterns sheds light on the assembly processes that structure the fish faunas. Species replacement between sites (turnover) was higher in Guianese assemblages, which are thus composed of a higher number of species whose identity differed in a large part between sites. In contrast, functional turnover was higher in French assemblages than in Guianese ones. According to our predictions, Guianese fish assemblages result from a prominent effect of dispersal limitation that promotes a stronger taxonomic turnover than functional turnover [START_REF] Swenson | Phylogenetic and functional alpha and beta diversity in temperate and tropical tree communities[END_REF][START_REF] Myers | Beta-diversity in temperate and tropical forests reflects dissimilar mechanisms of community assembly[END_REF]. However, French assemblages appear more strongly influenced by environmental filtering that promotes lower taxonomic turnover than functional turnover (Swenson et al., 2012a;[START_REF] Myers | Beta-diversity in temperate and tropical forests reflects dissimilar mechanisms of community assembly[END_REF]. Null models confirmed this with species more aggregated within assemblages than expected in French Guiana and a greater functional dissimilarity than expected between assemblages in continental France.

The differences in diversity patterns and processes governing assemblage structure between the two regions might root in their biogeographical history and evolutionary causes. First, tropical climates have been hypothesized to have been more stable than temperate climates and thus led a longer time for species diversification than temperate regions [START_REF] Gaston | The tropics as a museum of biological diversity: An analysis of the New World avifauna[END_REF]. Moreover, faster diversification in tropical regions has been found in equal time periods, linked with less drastic extinction events during Quaternary climatic fluctuations [START_REF] Rolland | Faster speciation and reduced extinction in the tropics contribute to the mammalian latitudinal diversity gradient[END_REF]. Secondly, the Guianese ichthyofauna results from the ancient complex biogeographical history of the Neotropical region, involving events of marine incursions, uplift of the Andes and the raising of palaeoarches that modified South Americas hydrological network [START_REF] Lujan | The Guiana Shield[END_REF], and from more recent dispersal events via costal routes from the Amazon in the south and from the Orinoco in the Northwest that occurred during the Miocene [START_REF] Hubert | Historical biogeography of South American freshwater fishes[END_REF][START_REF] Cardoso | Unexpected diversity in the catfish Pseudancistrus brevispinis reveals dispersal routes in a Neotropical center of endemism: The Guyanas Region[END_REF]. The current fauna thus presents different species pools that contribute to the high regional taxonomic α-and β-diversity in French Guiana. The studied French fauna on the other hand results mainly from a more recent recolonization of Western Europe by a single Danubian species pool after the Last Glacial Maximum (26,500 -19,000 years ago) [START_REF] Taberlet | Comparative phylogeography and postglacial colonization routes in Europe[END_REF][START_REF] Kotlík | Phylogeography of the barbel (Barbus barbus) assessed by mitochondrial DNA variation[END_REF]. The recolonization from a single pool of species hence reduced both the richness in species and the taxonomic turnover across Western Europe.

Although differences in biogeographical history explain taxonomic patterns, they do not provide information on assembly rule processes. Here, we show that in Guianese streams, despite the density of the river network that could favour faunistic exchanges between basins, the local fish assemblages were effected of by both inter-and intra-basin dispersal limitation. Despite this high turnover in species composition, we report a moderate functional turnover in Guianese streams. Functional diversity represents the range of ecological strategies of the species [START_REF] Violle | Let the concept of trait be functional![END_REF][START_REF] Cadotte | Beyond species: Functional diversity and the maintenance of ecological processes and services[END_REF][START_REF] Mouillot | Functional structure of biological communities predicts ecosystem multifunctionality[END_REF] and thus niche availability in local habitats. The high local species richness in Guianese streams hence testifies for a high local diversity of niches, but the same niches are probably shared by most sites explaining therefore the low functional β-diversity [START_REF] Weinstein | Taxonomic, phylogenetic, and trait beta diversity in South American hummingbirds[END_REF]. The limited effect of environmental filtering may be due to the relative homogeneity in available habitats between sites. Guianese fish fauna, even historically resulting from different biogeographical origins, is thus composed of species that converged in the use of similar niches, and the co-occurrence of those species being allowed by the high complexity in local habitats.

In the French fish assemblages, functional turnover was higher than taxonomic turnover meaning that the fauna exhibited local adaptations to different environments. Such environmental filtering might be due to the low similarity of available niches between sites [START_REF] Weinstein | Taxonomic, phylogenetic, and trait beta diversity in South American hummingbirds[END_REF]. Indeed, under temperate climates, the thermal regime of streams can be markedly different between lowland and piedmont areas, therefore constituting a strong environmental filter for the fauna. Cool-water (lowland) and cold-water (piedmont and mountain) streams are indeed inhabited by phylogenetically and functionally distinct fish faunas [START_REF] Blanchet | Regional vs local drivers of phylogenetic and species diversity in stream fish communities[END_REF]. Moreover, the reduced strength of dispersal limitation in France also has historical roots, as the French fauna results from a recent post-glacial recolonization by high-dispersal species [START_REF] Griffiths | Pattern and process in the ecological biogeography of European freshwater fish[END_REF][START_REF] Hof | Latitudinal variation of diversity in European freshwater animals is not concordant across habitat types[END_REF], that are hence likely to cross longer distances than Guianese stream fishes.

The use of phylogenetic diversity in combination with taxonomic and functional diversity may allow a more complete view of community assembly rules to be assessed [START_REF] Weinstein | Taxonomic, phylogenetic, and trait beta diversity in South American hummingbirds[END_REF]. An assumption behind this framework is that closely related species tend to have similar functional traits and ecological niches [START_REF] Webb | Exploring the phylogenetic structure of ecological communities: An example for rain forest trees[END_REF]. If true, then environmental filtering should result in communities with more closely related species than randomly assembled communities. Conversely, communities with more distant-related species than random communities should be driven by competitive interactions. So, developing and using a robust phylogeny of freshwater fish present in both regions (or for all fish species) will be a major future step in comparative analyses of assembly processes.

Beyond their conceptual interest, understanding the processes affecting species assembly are also informative in a biodiversity conservation context [START_REF] Mouillot | Niche-assembly vs. dispersal-assembly rules in coastal fish metacommunities: Implications for management of biodiversity in brackish lagoons[END_REF][START_REF] Rossetto | Dispersal limitations, rather than bottlenecks or habitat specificity, can restrict the distribution of rare and endemic rainforest trees[END_REF]. Indeed, faunas assembled through dispersal limitation, such as Guianese stream fishes, are sensitive to local extinctions, because the recolonization of disturbed sites from distant assemblages is almost impossible. Therefore, the restoration of sites after human disturbance might not result in the re-establishment of the initial fauna. This might explain why local studies report limited post-disturbance recolonization, even after a complete recovery of the stream physical and chemical characteristics [START_REF] Brosse | Smallscale gold mining erodes fish assemblage structure in small neotropical streams[END_REF][START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF]. Particular attention should therefore be devoted to the conservation of pristine sites to avoid local species extirpations. In contrast, in Europe, restoring and managing habitat diversity may be a priority, as assemblages being less constrained by dispersal limitations could have more chance to recolonize restored streams.

Size and strength of jaw Position in the water column

Eye position

Eh Hd

Position of fish and/or of its prey in the water column [START_REF] Winemiller | Ecomorphological diversification in lowland freshwater fish assemblages from five biotic regions[END_REF] Body elongation

Bl Bd

Position of fish and/or of its prey in the water column [START_REF] Winemiller | Ecomorphological diversification in lowland freshwater fish assemblages from five biotic regions[END_REF] Swimming Body lateral shape

Hd Bd

Relative depth of the head compared to the body Pectoral fin position P F i Bd Pectoral fin use for maneuverability [START_REF] Dumay | Functional groups of lagoon fish species in Languedoc Roussillon, southern France[END_REF] Pectoral fin shape P F l 2 P F s Pectoral fin use for propulsion [START_REF] Fulton | The relationship between swimming ability and habitat use in wrasses (Labridae)[END_REF] Pectoral fin size

P F l Bl

Pectoral fin use for propulsion [START_REF] Fulton | The relationship between swimming ability and habitat use in wrasses (Labridae)[END_REF] Caudal peduncle throttling

CF d CP d

Caudal propulsion efficiency through reduction of drag [START_REF] Webb | Body Form, Locomotion and Foraging in Aquatic Vertebrates[END_REF] Caudal fin aspect ratio

CF d 2 CF s
Caudal fin use for propulsion and/or direction [START_REF] Gatz | Ecological morphology of freshwater stream fishes[END_REF][START_REF] Webb | Body Form, Locomotion and Foraging in Aquatic Vertebrates[END_REF] Fin surface ratio

P F s CF s

Fin use for swimming Relative fin surface

(P F s+CF s) (Bl×Bd)
Fin total surface compared to body lateral surface

III.2 Inclusion of phylogenetic diversity for Guianese assemblages

After showing that the processes structuring fish assemblages differed between different regions, I focused on the Guianese freshwater fish assemblages and I included the phylogenetic diversity facet to the analyses to go further into the processes that structure these assemblages, based on the framework developped in the Introduction (

Molecular material

We sampled tissues from 703 specimens belonging to 230 species (1 to 3 specimens per species), and stored them in 96% ethanol. We extracted DNA from 0.05-0.25 cm 2 of tissue using salt-extraction protocol [START_REF] Aljanabi | Universal and rapid salt-extraction of high quality genomic DNA for PCR-based techniques[END_REF]. DNA template. For 12S, the PCR mixture was denatured at 95 • C for 10 min, followed by 35 cycles of 30 s at 95 • C, 30 s at 55 • C and 1 min at 72 • C, and followed by a final elongation at 72 • C for 7 min, in a room dedicated to amplified DNA, with negative air pressure and physically separated from the DNA extraction rooms. For COI, the PCR mixture was denatured at 94 • C for 2 min, followed by 35 cycles of 30 s at 94 • C, 40 s at 52 • C and 1 min at 72 • C, and followed by a final elongation at 72 • C for 10 min. A 600 bp product of the 12S rRNA gene and a 655 bp product of the COI gene was obtained and sequenced using Sanger sequencing method by Genoscreen biotechnology company. Sequences were analysed using GENEIOUS version 9.02 , [START_REF] Kearse | Geneious basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data[END_REF].

We also added to our data the sequences available on GenBank, corresponding to 472 sequences (128 for the 12S and 344 for the COI) for 142 species. In total, we had 811 sequences for the COI and 621 sequences for the 12S. For each species, individuals with the two markers were kept. The remaining individuals with a single marker were combined to produce pseudo-individuals combining the two markers. This procedure permitted to consider 460 individuals (290 real individuals and 170 pseudo-individuals). We also kept species for which no real or pseudo-individuals were individuals (only one marker was available for them, representing 160 individuals with one of the two markers). We used three outgroup species to root our tree: the pouched lamprey (Geotria australis), the smalltooth sawfish (Pristis pectinata) and the bull shark (Carcharhinus leucas) that all occur in nearby tropical marine waters [START_REF] Froese | FishBase. World Wide Web electronic publication[END_REF] (Table III.1).

Phylogenetic inferences

COI sequences were then aligned using TranslatorX [START_REF] Abascal | TranslatorX: Multiple alignment of nucleotide sequences guided by amino acid translations[END_REF], with the multiple alignment build with MAFFT version 7.222 [START_REF] Katoh | MAFFT multiple sequence alignment software version 7: Improvements in performance and usability[END_REF] and automatic detection of reading frame with vertebrate mitochondrial genetic code. 12S rRNA sequences were aligned with MAFFT v7.22 using structural alignement methods (Q-INS-i iterative refinement method), with sequences firstly aligned by Orders (outgroup species sequences were also aligned together). The Order multiple sequence alignements were then merge into one alignement using the "merge" option of MAFFT. The 12S and COI alignemnts were trimmed using Gblocks version 0.91b [START_REF] Castresana | Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis[END_REF], with half of allowed gap positions and default values for other parameters. The two alignments were concatenated, and partition scheme (with first, second and third codon for COI and 12S) and corresponding substitution models were obtained using PartitionFinder version 2.1.1 [START_REF] Lanfear | PartitionFinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses[END_REF]. The best partition scheme was to keep all markers as one, with the GTR+I+G model of substitution.

Phylogenetic inferences were conducted using maximum likelihood with RAxML version 8.2.4 [START_REF] Stamatakis | RAxML version 8: A tool for phylogenetic analysis and postanalysis of large phylogenies[END_REF] with the "autoMRE" option for automatic determination of bootstrap replicates. The analysis was constrained using a taxonomic tree determined at the Order level. From the resulting tree, all individuals Table III.1: Accession number of the supplementary sequences for the COI and 12S markers retrieved from GenBank used in the analyses.

Table III.1 continued

Pristis pectinata (outgroup) KP400584 KP400584

from the same species were collapsed to form only one tip. For some species, individuals did not form monophyletic groups and were closely related to individuals from other species. For these, the most recent common ancestor to all the individuals was used to transform all descendending branches into polytomy and only one tip per species was kept. The polytomies were then randomly converted into dichotomies with branch length of 0 for further analyses. The resulting tree was composed of 303 species and was then dated using a semi-parametric method based on penalized likelihood [START_REF] Sanderson | Estimating absolute rates of molecular evolution and divergence times: A penalized likelihood approach[END_REF]. The resulting phylogenetic tree is presented in Figure III.2.

We then matched assemblage data, functional traits data and the phylogenetic tree to only keep the species common to the three datasets for subsequent analyses (195 species in total). We did this separatly for streams and rivers, as our goal was not to compare streams and rivers and the difference in sampling protocol can affect the results.

Functional space

We computed functional distance matrix between species with Gowers distance based on the morphological ratios and the fish size as previously. We used the mean ratio values for species with replicates, but we did not take into account intraspecific varibility because 40% of the species did not have replicate data. We used a PCoA on the functional distance matrix to obtain the coordinates of the species in the functional space [START_REF] Vill Éger | New multidimensional functional diversity indices for a multifaceted framework in functional ecology[END_REF][START_REF] Leit Ão | Rare species contribute disproportionately to the functional structure of species assemblages[END_REF]. We kept the first four principal components to describe the functional space according to [START_REF] Maire | How many dimensions are needed to accurately assess functional diversity? A pragmatic approach for assessing the quality of functional spaces[END_REF], representing 80.6% of the inertia (33.5%, 22.6%, 14.5% and 10.0% for the first four axes of the PCoA respectively).

Phylogenetic signal in functional traits

We measured the degree of phylogenetic signal at two levels. First we compared overall functional distance between species (obtained in the Functional traits measurment section) to the phylogenetic distance between species using Mantel test [START_REF] Legendre | Numerical Ecology. Developments in Environmental Modelling[END_REF]. We then test the degree of phylogenetic signal in each trait with the K statistic and [START_REF] Blomberg | Testing for phylogenetic signal in comparative data: Behavioral traits are more labile[END_REF]. K values of 1 indicate that the trait distribution on the phylogeny perfectly matches a Brownian motion expectation of trait evolution on the phylogeny. K values less than 1 indicate greater convergence in values than expected under a Brownian trait evolution model, and K values greater than 1 indicates a higher degree of phylogenetic signal than Brownian motion. K values significances were assessed by comparing their values to the distribution of values obtained by shuffling the species across the tips of the phylogeny 1,000 times.

Overall, functional distances between species were correlated with phylogenetic distance (Mantel r = .49, p = .001). Looking trait by trait, K values were all less than 1, meaning that the traits are less conserve than expeted under a Brownian motion model. However, all K values are more conserved than predicted by random association between phylogeny and traits. These results thus indicated a moderate trait conservatism.

Within-assemblage scale: α-diversity measures

At the within-assemblage scale, we used species richness as a measure of taxonomic α-diversity. For functional diversity, we calculated functional richness as the relative volume occupied in the functional space by the species present in one local assemblage [START_REF] Cornwell | A trait-based test for habitat filtering: Convex hull volume[END_REF][START_REF] Vill Éger | New multidimensional functional diversity indices for a multifaceted framework in functional ecology[END_REF]. We used Faith's PD as a measure of phylogenetic richness [START_REF] Faith | Conservation evaluation and phylogenetic diversity[END_REF]. These two metrics are correlated with species richness, but as our goal was not to compare them between sites, we did not use other metrics that are less correlated with species richness [e.g. dispersion measure [START_REF] Webb | Exploring the phylogenetic structure of ecological communities: An example for rain forest trees[END_REF][START_REF] Vill Éger | New multidimensional functional diversity indices for a multifaceted framework in functional ecology[END_REF]].

To assess if the observed functional and phylogenetic richness differed from random values given the taxonomic richness, we used two null models (one for each diversity facets) that shuffle species identity in the functional trait matrix or in the phylogenetic tree. We simulated 999 random matrices/trees and we calculated functional and phylogenetic richness. We used these null distributions to compute a standardized effect size (SES) for each assemblage as follows: SES = obs-null sd(null) . Negative SES values mean that the observed functional or phylogenetic diversity value is lower than expected with the observed taxonomic richness, whereas positive SES value designate higher observed diversity than expected. In addition, we calculated a p-value for each assemblage by ranking the observed value in the simulated values and dividing it rank by 1,000 (1 observed value + 999 simualted values).

For stream, taxonomic richness and functional richness were highly correlated (Spearman's correlation: ρ= 0.90, p < .001; Figure III.3a) as well as taxonomic richness and phylogenetic richness (ρ= 0.95, p < .001; Figure III.3b). Phylogenetic and functional richness were also correlated (ρ= 0.94, p < .001). However, when controlling for taxomic richness, very few assemblages showed lower functional or phylogenetic richness than expected (Figure III.3a and b). Only one site (crique Apa) showed significant lower functional and phylogenetic richness than expected.

For river, taxonomic richness and functional richness were not significatively correlated (ρ= 0.50, p = .10; Figure III.3c). Taxonomic richness and phylogenetic richness were highly correlated (ρ= 0.82, p < .001; Figure III.3d). Phylogenetic and functional richness were not correlated (ρ= 0.22, p = .47). When controlling for taxonomic richness, only 3 assemblages showed lower functional richness than expected.

These results showed that functional and phylogenetic diversity of local assemblages do not differ from random expection, suggesting that neutral processes shaped local diversity of fish assemblages in French Guiana, both in rivers and in streams. For streams, we did not observed the presence of strong environmental filtering or limiting similarity. Limiting similarity is a process that first affects species c,d). Red (blue) dots indicates that the observed functional or phylogenetic diversity is higher (lower) than expected under null models. Filled dots indicates that the observed diversity is significantly different from values obtained from null model abundance, that can lead to competitive exclusion, thus affecting species occurence. Abundances thus can better detect this type of process that affects abundances [START_REF] Freilich | Phylogenetic community structure when competition and environmental filtering determine abundances[END_REF]. Environmental filtering was not a dominant process for local assembly. Environmental filtering occurs when some environmental characteristics select species. However if local environment is heterogeneous and do not constrain species to have particular trait, local assemblages will not be different from randomly assembled assemblages [START_REF] Hubbell | The Unified Neutral Theory of Biodiversity and Biogeography[END_REF]. We already reported that local richness was mainly determined by the reach position, and thus by the size of the stream in the network and to a lesser extent by the local environment (see Chapter II). This might be reinforce the hypothesis of neutral assembly processes for stream assemblages. For river, we could have expected that environmental filtering will mainly strucure assemblages as we sampled only river bank and thus the species associated to this habitat. Two explanations can be hypothezised. First, as for streams, neutral processes structured local assemblages, meaning that the environment has a low effect. Secondly, a sampling and subsequent analysis effect can explian these patterns. The species caught in the rivers are species that are only inhabiting river banks (or that go to river banks for a time during the day). This pool of river species is the one used to construct random assemblages for assesing difference between observed and simulated diversity measures. The random assemblages are thus composed of species only catchable in river banks. Environmental filters might thus act on the river species pool and not on the species within this pool, and will not be detectable without information about the other species present in the river. This would be a major improvment to understand local assembly processes in rivers.

Between-assemblage scale: β-diversity measures

For β-diversity, we used Jaccard's dissimilarity index to compute pairwise taxonomic β-diversity [START_REF] Jaccard | The distribution of the flora in the Alpine zone[END_REF]. We also computed its turnover component (species replacement between two assemblages) according to [START_REF] Baselga | The relationship between species replacement, dissimilarity derived from nestedness, and nestedness[END_REF].

We used the same framework for functional and phylogenetic β-diversities. For functional β-diversity, we used convex hull volumes of assemblage and their intersection (Vill éger et al., 2011a). Functional dissimilarity will be equal to 1 when volumes do not overlap and 0 when volumes are identical. For phylogenetic β-diversity, we used the fraction of branch length shared (or not shared) between two assemblages (UniFrac index, [START_REF] Lozupone | UniFrac -An online tool for comparing microbial community diversity in a phylogenetic context[END_REF]. Phylogenetic dissimilarity will be equal to 1 when no branch are shared between the assemblages and 0 all branches are shared. We also extracted the turnover component of functional and phylogenetic β-diversities [START_REF] Vill Éger | Decomposing functional β-diversity reveals that low functional β-diversity is driven by low functional turnover in European fish assemblages[END_REF][START_REF] Leprieur | Quantifying phylogenetic beta diversity: Distinguishing between 'true' turnover of lineages and phylogenetic diversity gradients[END_REF].

We then compared the observed values of β-diversities to three different null models. First we compared the observed taxonomic β-diversity to values obtained from 999 randomly assembled assemblages by constraining both species richness within assemblages and species occurrence ('trial-swap' algorithm, Mikl ós & Podani, 2004). A second set of null models shuffled species identity in the trait matrix or in the phylogenetic tree without altering assemblage matrices (with 999 simulations), thus maintening the observed taxonomic β-diversity. For each null models, we calculated SES and p values by assemblages as before and looked if overall, SES values departed from 0. Only results for taxonomic and phylogenetic will be presented here, as the functional null model presented computation errors that need to be examined in details.

For stream, the three β-diversity metrics were highly correlated (Pearson's correlation: taxonomic-phylogenetic: r = .78, p < .001, Figure III.4a; taxonomic-functional: r = .68, p < .001, Figure III.4b; phylogenetic-functional: r = .70, p < .001, Figure III.4c). Taxonomic β-diversity was higher than phylogenetic β-diversity (Wilcoxon signed-rank test: z = 61.27, p < .001) and higher than functional β-diversity (z = 10.69, p < .001). Phylogenetic β-diversity was lower than functional βdiversity (z = -54.23, p < .001).

Turnover metrics were also correlated (taxonomic-phylogenetic: r = .64, p < .001, Figure III.4d; taxonomic-functional: r = .45, p < .001, Figure III.4e; phylogenetic-functional: r = .60, p < .001, Figure III.4f). Taxonomic turnover was higher than phylogenetic turnover (z = 58.58, p < .001) and higher than functional turnover (z = 46.86, p < .001). Phylogenetic turnover was lower than functional Null models revealed that a low number of pairwise comparisons significantly differed from null values for taxonomic and phylogenetic diversities [taxonomic β-diversity: 33/2740/2 (p < 0.025/not different/p > 0.975); taxonomic turnover: 33/2740/2; phylogenetic β-diversity: 36/2538/201; phylogenetic turnover: 41/2511/223], but overall SES values were different and higher than 0 (taxonomic β-diversity: median = 0.12, z = 5.14, p < .001; taxonomic turnover: median = 0.14, z = 6.13, p < .001; phylogenetic β-diversity: median = 0.29, z = 14.48, p < .001; phylogenetic turnover: median = 0.37, z = 16.94, p < .001).

For rivers, the three β-diversity metrics were correlated (r = .70, p < .001, Figure III.4a; taxonomic-functional: r = .57, p < .001, III.4b; phylogenetic-functional: r = .70, p < .001, III.4c). Taxonomic β-diversity was higher than phylogenetic β-diversity (z = 8.91, p < .001) but not different from functional β-diversity (z = 1.28, p = .20). Phylogenetic β-divrsity was lower than functional β-diversity (z = -8.94, p < .001).

Turnover metrics were also correlated (taxonomic-phylogenetic: r = .64, p < .001, Figure III.4d; taxonomic-functional: r = .45, p < .001, Figure III.4e; phylogenetic-functional: r = .60, p < .001, Figure III.4f). Taxonomic turnover was higher than phylogenetic turnover (z = 9.13, p < .001) and higher than functional turnover (z = 7.19, p < .001). Phylogenetic turnover was lower than functional turnover (z = -2.51, p = .01). Null models revealed that less than half of pairwise comparisons differed from null values for taxonomic diversity (taxonomic β-diversity: 17/47/14; taxonomic turnover: 17/47/14) and overall, SES values did not differ from 0 (taxonomic β-diversity: median = 0.52, z = 0.71, p = .47; taxonomic turnover: median = 0.52, z = 0.70, p = .49). All observed phylogenetic diversity values were lower than expected. Overall SES values were different and lower than 0 only for phylogenetic turnover (phylogenetic β-diversity: median = 0.06, z = -0.06, p = .95; phylogenetic turnover: median = -0.61, z = -4.60, p < .001).

These results showed that dispersal limitation globally structured fish assemblages in French Guiana, based on the relationship between functional and taxonomice turnover (higher taxonomic turnover than functional turnover). This still needs to be confirmed with the appropriate null models. However, relationships between taxonomic and phylogenetic turnover highlighted that streams and rivers differed in how dispersal limitation strucures fish assemblages. For streams, taxonomic and phylogenetic β-diversity were on average higher than expected, suggesting that stream fish assemblage have been isolated and have evolved more independently than rivers assemblages. For these rivers assemblages, the phylogenetic turnover was lower than expected, suggesting that their isolation was more recent than for stream assemblages. These differences might roots in the past history of Guianese fish assemblage and the evolution of drainage basins. The connections between drainage basins between hot periods permited the exchange of fauna between basins [START_REF] Boujard | Biog éographie des poissons d'eau douce de Guyane franc ¸aise[END_REF][START_REF] Dias | Global imprint of historical connectivity on freshwater fish biodiversity[END_REF], which can be facilitated for species inhabiting rivers than species from the upstream part of the hydrological network [START_REF] Cardoso | Unexpected diversity in the catfish Pseudancistrus brevispinis reveals dispersal routes in a Neotropical center of endemism: The Guyanas Region[END_REF], both in the travel distance and in the physiological requirment (dispersal capacity, salinity and turbidity tolerances). For stream species, reaching other stream sections from the same drainage basin or from other basins needs high dispersal capacity and the ability to tolerate different conditions.

These hypotheses of strong dispersal limitation and low environmental filtering need however to be tested using spatial and environmental information. Indeed, looking at how β-diversities change with distance can help to confirm the processes that structure fish assemblages. Taxonomic β-diversity should increase with geographical distance and environmental distance. If the main process structuring assemblages is dispersal limitation, environmental distance between these assemblages should have a lower effect than spatial distance (environmental distance will be low between the assemblages). On the opposite, stong effect of environmental distance is expected if environmental filtering occurs.

In conclusion, freshwaters fish assemblages of French Guiana are mainly structured by dispersal limitation between drainage basins or between tributaries within each drainage basin. The environment seems to have a slight impact on fish assemblages strucure in non impacted stream or rivers. We could expect that, as anthopogenic disturbances greatly change local environmental conditions (high turbidity and modification of stream characteristics, [START_REF] Dedieu | Physical habitat and water chemistry changes induced by logging and gold mining in French Guiana streams[END_REF], the processes structuring both local assemblages and their strucures will also change (e.g. a increase of environmental filtering). Testing this however necessitates to increase our knowledge of local fish assemblages (both in space and the species present), which can only be made currently via the development of more exhaustive sampling methods.

Chapter IV: Toward a new sampling protocol: a molecular approach with eDNA metabarcoding

IV.1 Summary

Extending the previous templates to others part of the hydrographic network of French Guiana or testing the impact of anthropogenic disturbances on the processes structuring fish assemblages depend on our capacity to exhaustively describe local assemblages. However, there is currently no method available to do so in French Guiana. Indeed, the rotenone sampling used to build the fish assemblage database of the previous chapters has been regulated by European authorities since 2008 (European laws 2008/296/CE and 2008/317/CE) and is now banned. Moreover, its strong deleterious impact on the fauna makes it a relatively bad tool for biodiversity conservation projects. Nets sampling for large rivers suffers from the same shortcoming: gill-nets cause a substantial fish mortality. Nets sampling is also selective toward species and only efficient for the species living near the bank and having a specific size (defined by the mesh size of the nets) are sampled. Alternatives to these destructive sampling protocols are needed to pursue ecological and evolutionary studies that comply with laws on animal welfare.

Among the sampling methods for freshwater fish fauna, electrofishing, which is widely used and has a low impact on fish, has been proposed as an alternative to rotenone sampling in small Guianese streams. However, the conditions for its application (high conductivity and low water depth) were limited to a small number of streams [START_REF] Allard | Elaboration d'un Indice de Qualit é Des Eaux Bas é Sur La Structure Taxonomique et Fonctionnelle Des Assemblages de Poissons Des Petits Cours d'eau de Guyane[END_REF]. Direct underwater observations of fish assemblages, although not impacting freshwater fauna and being used both in streams and rivers can require high sampling effort depending on the water turbidity and the presence of diurnal and nocturnal or cryptic species. The development of a sampling method with a relatively low sampling effort, applicable to both streams and rivers and that provide a reliable image of fish assemblages is still pending.

Such an alternative might rise through the development of DNA metabarcoding (Figure IV.1). DNA metabarcoding designates the DNA-based identification of multiple species from a mix of specimens or from the DNA present in an environmental sample (soil, water, air). In the latter case, the method is qualified as environmental DNA metabarcoding (eDNA metabarcoding). The eDNA metabarcoding approach has demonstrated its efficiency in temperate streams, where it often gave more complete image of local assemblages than traditional methods. However, its applications to tropical freshwaters are scarce [START_REF] Simpfendorfer | Environmental DNA detects Critically Endangered largetooth sawfish in the wild[END_REF][START_REF] Bellemain | Trails of river monsters: Detecting critically endangered Mekong giant catfish Pangasianodon gigas using environmental DNA[END_REF][START_REF] Lopes | eDNA metabarcoding: A promising method for anuran surveys in highly diverse tropical forests[END_REF] and tests on highly diverse assemblages such as the Guianese freshwater fish are needed.

A first lock to apply eDNA metabarcoding to Guianese water is the lack of genetic Several protocols have been proposed (Blast, Altschul et al., 1990;[START_REF] Edgar | Search and clustering orders of magnitude faster than BLAST[END_REF]OBItools, Boyer et al., 2016;phylogenetic placement, Munch et al., 2008). Here we used ecotag to identify the species present in the sample. 5: The site species list obtained by eDNA metabarcoding. 6: The list of species produced by metabarcoding was compared to the list of species obtained with other sampling methods (nets and rotenone) or from external databases. (Photo: K. Cilleros)

resources available in public repositories (e.g. GenBank). We thus first built a genetic reference database for the 12S eDNA metabarcoding fragment on 181 freshwater fish species of French Guiana using our tissue collection. We than sampled 39 sites for eDNA, in both streams and rivers in 2014 and 2015 and compared the identity of species detected using traditional sampling (rotenone for streams and nets for rivers) and those detected using eDNA metabarcoding. We also compared the diversity patterns (α-and β-diversity) derived from traditional and eDNA sampling. First, we compared the eDNA to the Genbank public reference database to search for false detection of species belonging from others biogeographic realms and confirmed that only Amazonian species were detected. We then used our custom reference database to compare fish assemblages derived from eDNA and traditional samples using gillnets (for rivers) and rotenone (for streams). Among the 216 species detected by at least one method, 119 species were detected by both methods, 13 were exclusively detected by eDNA metabarcoding and 84 only by traditional methods. This last number was mainly due to species uninformed in the reference database ( 54), and to a lower extent by species that cannot be discriminated using the 12S eDNA fragment ( 23). The remaining 8 species account for those informed but not detected. At the drainage basin scale, only five species out of the 132 detected by the eDNA metabarcoding were detected outside their known distribution. In river sites, we were able to detect species inhabiting streams and rivers, and in stream sites, we only detected stream species. This can be interpreted as a transport of eDNA with the water flow. Locally, the congruence between the two sampling methods was relatively low (about 27% of species in common) and highly variable between streams and rivers. We however found that richness estimates were correlated as well as species relative occurrence. Moreover, the distinction of assemblages by drainage basin was more marked when using eDNA metabarcoding, whereas the sampling method mostly affected assemblage composition variation.

This work is currently under review in Molecular Ecology Resources.

INTRODUCTION

Evaluating the distribution or the occurrence of organisms is a crucial step in biodiversity science. Achieving those tasks can be difficult when assemblages are species rich and/or when the organisms cannot be directly observed [START_REF] Murphy | Fisheries Techniques, 2nd Edition[END_REF]. This is particularly true for fish in tropical freshwater ecosystems, where local assemblages count dozens of species and their observation is limited by water turbidity, depth, and current velocity. Hence, fish are often sampled using nets, electricity and even toxicants [START_REF] Allard | Elaboration d'un Indice de Qualit é Des Eaux Bas é Sur La Structure Taxonomique et Fonctionnelle Des Assemblages de Poissons Des Petits Cours d'eau de Guyane[END_REF][START_REF] Murphy | Fisheries Techniques, 2nd Edition[END_REF][START_REF] Portt | A Review of Fish Sampling Methods Commonly Used in Canadian Freshwater Habitats[END_REF]. These traditional methods are selective towards species [START_REF] Gunzburger | Evaluation of seven aquatic sampling methods for amphibians and other aquatic fauna[END_REF], and some of these methods, such as gill nets and toxicants, are destructive for the fauna [START_REF] Dalu | An assessment of the effect of rotenone on selected non-target aquatic fauna[END_REF][START_REF] Snyder | Invited overview: Conclusions from a review of electrofishing and its harmful effects on fish[END_REF]. Their use for scientific purposes is highly debated and the development of alternative non-destructive methods is urgently needed to comply with ethics and laws on animal welfare and biodiversity conservation [START_REF] Ellender | Underwater video analysis as a non-destructive alternative to electrofishing for sampling imperilled headwater stream fishes[END_REF][START_REF] Hickey | Evaluating the potential of night spotlighting as a method for assessing species composition and brown trout abundance: A comparison with electrofishing in small streams[END_REF][START_REF] Thomsen | Environmental DNA -An emerging tool in conservation for monitoring past and present biodiversity[END_REF]. With advances in sequencing technologies, the use of environmental DNA (eDNA), total DNA present in environment samples, like faeces, soil, air or water, has gained huge consideration to study biodiversity in the last years [START_REF] Taberlet | Environmental DNA[END_REF][START_REF] Valentini | DNA barcoding for ecologists[END_REF]. To date, eDNA usefulness and efficiency has been assessed under controlled and natural conditions in temperate freshwaters where eDNA provided a realistic picture of fish species assemblages [START_REF] Jerde | Sight-unseen" detection of rare aquatic species using environmental DNA[END_REF][START_REF] Thomsen | Detection of a diverse marine fish fauna using environmental DNA from seawater samples[END_REF][START_REF] Civade | Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system[END_REF][START_REF] Lawson Handley | Environmental DNA metabarcoding of lake fish communities reflects long-term data from established survey methods[END_REF][START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF]. Environmental DNA hence offers the opportunity to reduce sampling effort [START_REF] Jerde | Sight-unseen" detection of rare aquatic species using environmental DNA[END_REF][START_REF] Olds | Estimating species richness using environmental DNA[END_REF], and to save time and money compared to traditional fish sampling methods under temperate environments [START_REF] Fukumoto | A basin-scale application of environmental DNA assessment for rare endemic species and closely related exotic species in rivers: A case study of giant salamanders in Japan[END_REF][START_REF] Jerde | Sight-unseen" detection of rare aquatic species using environmental DNA[END_REF].

The situation markedly differs in the tropics that host higher species richness than temperate areas. For instance, French Guiana counts as much fish species as Western Europe (380 species) while its surface accounts for less than 1% of Western Europe [START_REF] Bail | Updated checklist of the freshwater and estuarine fishes of French Guiana[END_REF][START_REF] Melki | Poissons d'eau douce de Guyane: plong ée dans les eaux de l'Amazonie franc ¸aise[END_REF]. Moreover our ability to make fish inventories in tropical rivers is limited because the frequent low conductivity of rainforest streams makes electrofishing ineffective, and the turbidity of large rivers limits direct underwater observations [START_REF] Allard | Elaboration d'un Indice de Qualit é Des Eaux Bas é Sur La Structure Taxonomique et Fonctionnelle Des Assemblages de Poissons Des Petits Cours d'eau de Guyane[END_REF]. These limitations led to use a combination of netting methods (gillnets, cast nets, hand nets), sometimes combined with toxicants (e.g. rotenone), to get a picture of the fauna in both rivers and streams [START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF][START_REF] Ara Újo | Longitudinal patterns of fish assemblages in a large tropical river in southeastern Brazil: Evaluating environmental influences and some concepts in river ecology[END_REF]. Despite the use of multiple sampling methods, such inventories often remain incomplete, especially in large rivers, because most species have a low density and hence are difficult to detect [START_REF] Hercos | Local and regional rarity in a diverse tropical fish assemblage[END_REF]. As a consequence, gathering data on entire fish assemblages in the tropics are almost impossible without sacrifying a substantial part of the fauna, and/or strongly disturbing the environment. This obviously acts as a barrier to scientific advances on ecosystem structure and functioning, and on associated biodiversity conservation and management issues. Developing fish eDNA in tropical freshwaters is therefore more than a way to reduce sampling effort, as it would open avenues for tropical biodiversity research and conservation.

Nevertheless, there is no guarantee that eDNA will be as efficient in tropical rivers as in temperate ones. The higher temperature of tropical waters do not affects eDNA degradation [START_REF] Robson | Fine-tuning for the tropics: Application of eDNA technology for invasive fish detection in tropical freshwater ecosystems[END_REF], but the stronger solar radiation and the water acidity and turbidity might speed up its degradation or impact its detection rate and thus restrict its efficiency [START_REF] Barnes | Environmental conditions influence eDNA persistence in aquatic systems[END_REF][START_REF] Matheson | Assessing PCR inhibition from humic substances[END_REF][START_REF] Pilliod | Factors influencing detection of eDNA from a stream-dwelling amphibian[END_REF]. Field studies using eDNA in tropical environment are growing [START_REF] Bellemain | Trails of river monsters: Detecting critically endangered Mekong giant catfish Pangasianodon gigas using environmental DNA[END_REF][START_REF] Lopes | eDNA metabarcoding: A promising method for anuran surveys in highly diverse tropical forests[END_REF][START_REF] Simpfendorfer | Environmental DNA detects Critically Endangered largetooth sawfish in the wild[END_REF] but most of them focused on a single species (but see [START_REF] Lopes | eDNA metabarcoding: A promising method for anuran surveys in highly diverse tropical forests[END_REF]. Those studies detected the targeted species in fewer locations than expected. This bias was more pronounced in large rivers, leaving uncertainties on the method efficiency in large tropical rivers and/or on the current spatial distribution of the species determined by traditional sampling methods [START_REF] Bellemain | Trails of river monsters: Detecting critically endangered Mekong giant catfish Pangasianodon gigas using environmental DNA[END_REF][START_REF] Simpfendorfer | Environmental DNA detects Critically Endangered largetooth sawfish in the wild[END_REF]. To our knowledge, [START_REF] Lopes | eDNA metabarcoding: A promising method for anuran surveys in highly diverse tropical forests[END_REF] was the only study using group-specific primers to identify simultaneously several taxa in a tropical freshwater environmental sample (hereafter called eDNA metabarcoding; [START_REF] Taberlet | Environmental DNA[END_REF]. This study focussed on a small fraction of South American anuran species (11 frogs from Brazilian streams) and succeeded to detect those species, even at low densities. Such metabarcoding approach therefore deserves to be tested on entire and more diverse assemblages, but this implies to develop reference molecular databases made of known species sequences to be able to assign species to eDNA sequences [START_REF] Ardura | Applications of DNA barcoding to fish landings: Authentication and diversity assessment[END_REF][START_REF] Pochon | Early detection of eukaryotic communities from marine biofilm using high-throughput sequencing: An assessment of different sampling devices[END_REF]. Such reference databases are currently lacking for most tropical freshwater species.

Here, we tested the efficiency of using eDNA with metabarcoding approach to freshwater fish diversity. We aimed to get a picture of freshwater fish assemblages in 39 rivers and streams of French Guiana. We first developed a reference database IV.1 gathering 191 species (out of the 380 known freshwater fish species; [START_REF] De M Érona | Fish-habitat relationships in French Guiana rivers : A review[END_REF][START_REF] Bail | Updated checklist of the freshwater and estuarine fishes of French Guiana[END_REF], to assign eDNA sequences to species. Then, we compared the fish species assemblage detected by metabarcoding to the local fish fauna known from these sites. Here, we used a hierarchical framework and tested if metabarcoding results were consistent with the fauna known from the river drainage basin, the hydrologic unit (stream vs river) and the local site. Finally, we measured the congruence between the diversity patterns (richness, occurrence, β-diversity) estimated using metabarcoding and those derived from traditional sampling methods. The Guianese fish assemblages partly differ between river drainages due to biogeographic reasons [START_REF] Bail | Updated checklist of the freshwater and estuarine fishes of French Guiana[END_REF]. They also partly differ between upstream (streams, with low species richness) and downstream areas (rivers, higher species richness), as some species only inhabit streams whereas others live only in rivers (de M érona et al., 2012). We thus tested how the biogeographical and habitat patterns derived from eDNA metabarcoding and traditional methods fit with the theoretical knowledge on Guianese fish assemblages.

METHODS

Study area and sample location

We sampled 39 watercourse sections of French Guiana for fish using both traditional methods (fish nets and toxicants) and eDNA metabarcoding (see Table IV These sites were located within all the 8 major river drainages of French Guiana, upstream from the main human settlements and away from major human disturbances (mining or deforestation). This insured that fish assemblages were not modified by human activities during the sampling period (2010 to 2015). Traditional and metabarcoding sampling occurred on the same year for rivers (2015). Streams were sampled from 2010 to 2013 using traditional methods, and metabarcoding samples were done in 2014 (Table IV.1). For streams, traditional sampling was not possible in 2014 because we were not allowed to use rotenone since late 2013. Nevertheless, local fish assemblages should not change across years because stream fish have low dispersal abilities [START_REF] Cilleros | Taxonomic and functional diversity patterns reveal different processes shaping European and Amazonian stream fish assemblages[END_REF] and are strongly dependent on the physical structure of their habitat [START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF][START_REF] Brosse | Determinants of fish assemblage structure in Mount Itoup é mountain streams (French Guiana)[END_REF]. Moreover, all the samples were collected during the dry season (September-November) to ensure fish assemblage differences between methods are not due to seasonal changes in habitat use or migrations. In each site, we converted fish species abundance data into occurrence data to avoid potential bias due to differences in sampling techniques and potential differences in sampling efficiency between sites.

Traditional methods

We adapted the traditional method protocol to the type of watercourse. For small streams, we conducted fish surveys under several different research projects between 2010 and 2013 (CNRS-Nouragues, PAG-DEAL-HYDRECO, CEBA-DIADEMA projects). We standardized the sampling protocol for all sites [START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF]. At each site, we isolated a river section located upstream of a confluence using two fine mesh (4 mm) stop nets. The length of each section was proportional to stream width and was on average 33.67 ± 12.13 m. We collected fish after releasing a small quantity of rotenone (PREDATOX R : a 6.6% emulsifiable solution of rotenone extracted from Derris elliptica by Saphyr, Antibes, France) a few meters upstream of the first net.

The rotenone is a nonselective piscicide traditionally used by Amazonian tribes, but also often used by scientists to sample fish in tropical streams [START_REF] Allard | Elaboration d'un Indice de Qualit é Des Eaux Bas é Sur La Structure Taxonomique et Fonctionnelle Des Assemblages de Poissons Des Petits Cours d'eau de Guyane[END_REF][START_REF] Murphy | Fisheries Techniques, 2nd Edition[END_REF] and control fish populations [START_REF] Finlayson | Rotenone Use in Fisheries Management: Administrative and Technical Guidelines Manual[END_REF]. All stunned fish were collected and at the end of each sampling session, we searched for fishes lying on the bottom or hidden in the leaves and debris. No rotenone sample was collected after 2013, since the use of rotenone was banned, according to European laws 2008/296/EC and 2008/317/EC, making therefore impossible to collect entire fish assemblages using this method since this date. There is still no alternative to rotenone, since electrofishing is not efficient due to the low conductivity of Guianese streams and attempts to make inventories using others methods (snorkelling, hand nets, cast nets, traps) yielded incomplete fish inventories [START_REF] Allard | Elaboration d'un Indice de Qualit é Des Eaux Bas é Sur La Structure Taxonomique et Fonctionnelle Des Assemblages de Poissons Des Petits Cours d'eau de Guyane[END_REF]. For rivers, we conducted fish surveys in November 2015. At each site, we placed 20 50-meters long gill-nets with different mesh sizes (15, 20, 25, 30 and 35 mm) on river banks and removed them after an overnight sampling to collect fishes. In addition, we achieved cast net and fine meshed hand net samples close to the banks to complement inventories for small species not captured using gill nets. For both methods, we identified each individual to the species level and we collected tissues for each species to build up the reference DNA database.

Reference DNA database

We sampled tissues from 503 specimens belonging to 231 species (1 to 3 specimens per species), and stored them in 96% ethanol. We extracted DNA from 0.05-0.25 cm 2 of tissue using salt-extraction protocol [START_REF] Aljanabi | Universal and rapid salt-extraction of high quality genomic DNA for PCR-based techniques[END_REF]. We performed DNA amplification in a final volume of 25 µL including 1U of GoTaq R (Promega), 5X buffer (Promega), 10 µM of dNTP, 20 µM of each primer (teleo R 5'-CTTCCGGTACACTTACCATG-3' and V05F 898 5'-AAACTCGTGCCAGCCACC-3', [START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF] and 1µL of DNA template. The PCR mixture was denatured at 95 • C for 10 min, followed by 35 cycles of 30 s at 95 • C, 30 s at 55 • C and 1 min at 72 • C, and followed by a final elongation at 72 • C for 7 min, in a room dedicated to amplified DNA, with negative air pressure and physically separated from the DNA extraction rooms. A 600 bp product of the 12S rRNA gene were obtained and sequenced using Sanger sequencing method by Genoscreen biotechnology company, except for 27 species for which no DNA fragment was amplified. Sequences were analysed using GENEIOUS version 9.01 , [START_REF] Kearse | Geneious basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data[END_REF].

eDNA metabarcoding sampling and analysis

We collected eDNA samples in November 2014 for streams and in October 2015 for rivers, based on the [START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF] protocol for running waters. For each sample, we used a filtration kit made of a sterile filtration capsule (Enviroheck HV 1 µm; Pall Corporation, Ann Arbor, MI, USA), a peristaltic pump (Vampir Sampler; B ürkle GmbH, Bad Bellingen, Germany) and sterile single use tubing. All the materials were handled with sterile gloves. The peristaltic pump, although not in contact with the water, was sterilized using sodium hypochlorite between each site to avoid contamination. At each site, we placed the input part of tubing in a high flow part of the watercourse located in the middle of the stream or river channel. Sampling was achieved in rapids hydromorphologic units to ensure an optimal homogenisation of the water thought the water column. The operator always remained downstream from the filtration area, and stayed on the bank (for small streams) or on emerging rocks (for larger streams and rivers). Water was pumped about 20 cm below the surface and each filtration lasted 30 minutes at 1.67 L.min -1 . Each sample hence results from the filtration of c.a. 50 litres of water. For sites located along the same river course, we did sampling from downstream to upstream to avoid contamination by eDNA transport by the boat (for rivers) or on our clothes. For the same reason, we took eDNA samples just upstream from the nets. At the end of the filtration, we emptied the filtration capsule of water, filled it with 150 mL of preservation buffer (Tris-HCl 0.1 M, EDTA 0.1 M, NaCl 0.01 M, and N-lauroyl sarcosine 1%, pH 7.58) and stored it in the dark in sterile individual plastic bags. Samples were then stored at room temperature before DNA extraction. Extraction was achieved at the end of the field session, 1 to 3 weeks after sampling. For DNA extraction, filtration capsules were left at 56 • C for 2 h, agitated manually for 5 minutes and then emptied into three 50 mL tubes. In total, approximately 120 mL were retrieved in three tubes that were centrifuged for 15 minutes at 15,000 g. Supernatant was removed with a sterile pipette, leaving 15 mL of liquid at the bottom of the tube. Subsequently, 33 mL of ethanol and 1.5 mL of 3M sodium acetate were added to each 50 mL tube. The three tubes were centrifuged at 15,000 g for 15 min at 6 • C and the supernatant was discarded. After this step, 360 µL of ATL Buffer of the DNeasy Blood & Tissue Extraction Kit (Qiagen) were added to the first tube, the tube was vortexed and the supernatant was transferred to the second tube [START_REF] Tr Éguier | Environmental DNA surveillance for invertebrate species: Advantages and technical limitations to detect invasive crayfish Procambarus clarkii in freshwater ponds[END_REF]. This operation was repeated for all tubes. The supernatant of the third tube was finally transferred to a 2 mL tube and the DNA extraction was performed following the manufacturer's instructions. Two negative extraction controls were also performed. They were amplified and sequenced in the same way and in parallel to the samples to monitor possible contaminations. We performed DNA amplifications were performed in a final volume of 25 µL including 1 U of AmpliTaq Gold DNA Polymerase (Applied Biosystems, Foster City, CA), 10 mM of Tris-HCl, 50 mM of KCl, 2.5 mM of MgCl2, 0.2 mM of each dNTP, 0.2 µM of teleo primers (teleo R 5'-CTTCCGGTACACTTACCATG-3' and teleo F 5'-ACACCGCCCGTCACTCT-3', [START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF] and 3 µL of DNA template. We added also to the mixture 4 µM of human blocking primer for teleo primers and 0.2 µg/µL of bovine serum albumin (BSA, Roche Diagnostic, Basel, Switzerland). The teleo primers were 5'-labeled with a seven-nucleotide tag unique to each sample (with at least three differences between any pair of tags) allowing the assignment of each sequence to the corresponding sample during sequence analysis. Tags for forward and reverse primers were identical for each sample. The teleo primer was preferred to Mifish primer [START_REF] Miya | MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species[END_REF], because [START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF] demonstrated that Mifish provide slightly higher taxonomic resolution that teleo while being twice longer.

Teleo primers were therefore preferred to optimize the robustness of the amplification. PCR was conducted for 12 replicates with the same protocol described in the 'Reference DNA database' section increasing the number of PCR cycles to 50. We also amplified two extraction negative controls and three PCR controls and sequenced them in parallel with the 39 samples. We pooled the purified PCR products in equal volumes, to achieve an expected sequencing depth of 400 000 reads per sample. Library preparation and sequencing were performed at Fasteris facilities (Geneva, Switzerland). Four libraries were prepared using the Metafast protocol2 . For streams samples, we carried out the paired-end sequencing (2x125 bp) in an Illumina MiSeq sequencer (Illumina, San Diego, CA, USA) using the Paired-end MiSeq Reagent Kit V2 (Illumina, San Diego, CA, USA) following the manufacturer's instructions. For rivers samples, we ran the libraries on an Illumina HiSeq 2500 (2x125 bp) (Illumina, San Diego, CA, USA) using the HiSeq SBS Kit v4 (Illumina, San Diego, CA, USA) following the manufacturer's instructions. We analyzed sequence reads using the programs implemented in the OBITools package3 [START_REF] Boyer | Obitools: A unix-inspired software package for DNA metabarcoding[END_REF] following the protocol described in [START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF]. We performed the taxonomic assignment of Molecular Operational Taxonomic Units (MOTUs) using the program ecotag with the local reference database built for this study and with GenBank nucleotide database. The program works in two steps: first, it looks for the reference sequence that has the highest similarity with the query sequence. This value of similarity is then used as a threshold to look for sequences in the reference database for which the similarity with the first match reference sequence is equal or lower than the threshold. The query sequence is then assigned to the most recent common ancestor of the first matched sequence and the second. MOTUs with similarity lower than 98% with the reference database (local or GenBank) were discarded. To take into account tag jumping [START_REF] Schnell | Tag jumps illuminated -reducing sequence-to-sample misidentifications in metabarcoding studies[END_REF], we discarded all sequences with a frequency of occurrence below 0.0003 per taxon per sample and per sequencing run. These thresholds were empirically determined to clear all reads from the extraction and PCR negative controls included in our global data production procedure [START_REF] De Barba | DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: Application to omnivorous diet[END_REF].

Statistical analyses

First, we compared the species detected by eDNA metabarcoding to the list of species known at three different spatial scales: the river drainage scale, the watercourse type and the site scale. At the drainage scale, we used an updated version of [START_REF] Bail | Updated checklist of the freshwater and estuarine fishes of French Guiana[END_REF] and [START_REF] Melki | Poissons d'eau douce de Guyane: plong ée dans les eaux de l'Amazonie franc ¸aise[END_REF] checklists to verify if the species detected using eDNA metabarcoding belong to the species assemblage known to inhabit the considered river drainage. At the watercourse type scale, we merged all available ecological information on the species using [START_REF] Keith | Atlas des Poissons d'eau douce de Guyane. Tome 2, fascicule I. Batrachoidiformes, Mugiliformes, Beloniformes, Cyprinodontiformes, Synbranchiformes, Perciformes, Pleuronectiformes, Tetraodontiformes[END_REF]; Le [START_REF] Bail | Dimensions of biodiversity in Chesapeake Bay demersal fishes: Patterns and drivers through space and time[END_REF]; [START_REF] Planquette | Atlas des poissons d'eau douce de Guyane (tome 1)[END_REF] to determine if each species inhabits only streams, only rivers or both streams and rivers. At the site scale, we compared the list of species, genera and families obtained by metabarcoding to the list of species, genera and families collected by traditional sampling. Then, we calculated the percentage of species detected in each site by each method as the number of species detected by one method divided by the total number of species detected at this site. We also calculated the percentage of species detected only by metabarcoding or only by traditional methods by separating species detected only by one method from species detected by both methods.

Afterwards, we tested the congruence of diversity patterns given by the two methods. We calculated the species richness at each site for both methods and tested the correlation between them using Pearson's r correlation coefficient. We calculated species relative occurrence as the percentage of sites where a species was detected and we tested if the occurrence patterns were correlated using Kendall's τrank correlation coefficient.

Finally, we used β-diversity between sites to characterize spatial patterns of fish assemblage diversity. We calculated β-diversity using the turnover component of the Jaccard's dissimilarity index [START_REF] Baselga | The relationship between species replacement, dissimilarity derived from nestedness, and nestedness[END_REF] and used non-metric multidimensional scaling (NMDS) to visualize β-diversity patterns in two dimension plots. To test the congruence of produced ordinations between the two methods, we used Procrustes analyses with 999 permutations. We measured the goodness-of-fit between ordinations using m statistic, which varies from 0 (perfect congruence) to 1 (no congruence). We ran this procedure after assigning metabarcoding reads to species. We also tested the congruence between methods using the MOTUs without species assignation matrices to determine if raw metabarcoding data can be used to describe spatial patterns in fish assemblages. We used nonparametric multivariate analysis to test if fish assemblages differed in their composition and their variability i) between streams and rivers, and ii) between river drainages. In this last analysis, in addition to the complete dataset, we also used a reduced dataset comprising only the streams. The difference of species composition between river drainages based on river fish fauna (excluding streams) was not tested because the number of river sites (n = 8) was not sufficient to provide a meaningful representation of each river drainage. We tested differences in composition between groups with a permutational multivariate analysis of variance (PERMANOVA, [START_REF] Anderson | A new method for non-parametric multivariate analysis of variance[END_REF] and differences in assemblage variation within a group with an analysis of homogeneity of multivariate dispersion (PERMADISP, [START_REF] Anderson | Distance-based tests for homogeneity of multivariate dispersions[END_REF]. Significance was assessed with 999 permutations.

All statistical analyses were performed using R.3.3.2 (R Core Team, 2015) and the package 'vegan' version 2.4-1 [START_REF] Oksanen | Vegan: Community Ecology Package. R package version 2.4-1c[END_REF]), 'betapart' version 1.3 (Baselga et al., 2013) and 'dunn.test' version 1.3.4 (Dinno, 2017). Significance threshold was fixed at p < .05.

RESULTS

With traditional methods, we collected 7,029 individuals that represented 203 species (Table IV.1) from 12 orders. In almost all sites, cryptic fish were collected, including highly cryptic Siluriformes and litter bank fishes such as small killifishes. Several species of Gymnotiformes inhabiting small Guianese streams, although known to be resistant to rotenone, were also caught.

Among the 503 specimens sequenced to build-up the 12S mitochondrial gene reference database, 191 unique sequences were obtained. They were assigned to species using the 12S metabarcoding fragment as in [START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF] (see Materials and Methods). When a sequence matched several reference taxa, they were assigned to the lowest taxonomic level (genera, family or order) grouping all the matched reference taxa (Table IV.S1). 181 unique sequences were identified to the species level, six to the genus (Bryconops, Gymnotus, Hemigrammus, Leporinus, Moenkhausia, Pimelodella), three to the family (Characidae, Hypopomidae, Prochilodontidae) and one to the order (Characiformes; Table IV.S1).

For the eDNA metabarcoding analysis, a total of 17,775,665 reads were obtained, among which 2,015,596 were assigned when using GenBank database as reference (11%, see Table IV.S2). The assigned taxa corresponded to fish species present in French Guiana or in neighbouring Amazonian regions (1,050,515 reads), but reads were also assigned to other diverse taxa (Anura, Mammalia, Aves, Insecta and Reptilia; Table IV.S3). Among the reads assigned to fish taxa, 904,782 (86%) were assigned to 28 taxonomic units occurring in French Guiana (22 species and 4 genera; Table IV.S3). The 14% remaining reads were assigned to species closely related to Guianese ones (all those genera are present in French Guiana). At the site scale, the number of reads assigned to GenBank data ranged from 0 to 296,917 (median: 19,715 reads).

Using our custom reference database, 8,109,492 reads were assigned (46%), with a number of reads assigned at each site ranging from 18,895 to 870,235 (median: 112,997 reads, Table IV.S2). The reads assigned to the reference database were distributed among 148 taxonomic units: 132 species (7,521,532 reads), nine genera (550,264 reads), two sub-families (638 reads), four families (29,503 reads) and one order (495 reads). When comparing the complete lists of species detected with the two methods (i.e. removing from the analysis all individuals and assigned reads Species caught only with traditional methods are in black (species informed in the reference database) and white (absent from the reference database), detected only with metabarcoding in dark grey and by both metabarcoding and traditional methods in light grey determined at the genus level or higher), we found that 119 species were detected by the two methods, 13 only by metabarcoding and 84 only by traditional methods (Table IV.1). Within these 84 species, 53 had no reference sequence. Among the 31 species detected by traditional methods and referenced in our reference sequence database, 23 were not assigned to a species but to higher taxonomic level (from genus to order), and only 8 were not detected by eDNA while referenced in our reference sequence database.

At the river drainage scale, five species (i.e. 3.79% of the 132 species detected by metabarcoding) were detected outside their known Guianese river drainages distribution. These detections represented 12 out of a total of 680 fish occurrences (i.e. 1.76% of fish occurrences). Moreover, three out of the five species were detected no more than twice in river drainages out of their known distribution (Corydoras aeneus detected at one site in the Oyapock River drainage, Hemiodus quadrimaculatus once in the Maroni, Ancistrus aff. temminckii once in the Kourou and once in the Sinnamary and Krobia itanyi in two sites in the Approuague). The remaining species, Krobia aff. guianensis sp1, was detected six times in the Approuague River drainage.

When focusing on the watercourse type, 27 species (20.45% of the 132 species) detected in rivers have not been caught in rivers and are only known from streams. For streams, only three detected species (2.27% of the 132 species) have not been caught in streams (Crenicichla multispinosa, Mastiglanis cf. asopos and Plagioscion squamosissimus).

At the site scale, the number of species detected was lower with metabarcoding than with traditional methods in 29 out of the 39 sampled sites and the number of species common to both methods was low (mean ± SD: 20.74 ± 10.90 percent of species in common). When we excluded the species not informed in the reference 3). For three sites the richness estimation was equal between methods and a higher species richness was detected by metabarcoding in 16 sites.

The two methods showed low congruence in detected species identity, with an average of 27.18% (± 13.92) of detected species in common, and each method detected an equal amount of species (Kruskal-Wallis rank sum test: χ 2 = 5.56, p = .062; Figure IV.4). Distinguishing between streams and rivers showed that the latter held true for streams (χ 2 = 2.89, p = .24) but not for rivers (χ 2 = 9.77, p = .0076), where traditional methods detected most of the species.

The total percentage of species detected by each method (shared between the two methods and only metabarcoding vs. shared and only traditional) did not differ between the two methods (Wilcoxon rank-sum test: z = -1.03, p = .30, Figure IV.5). Such a trend was verified for streams (z = 0.61, p = .61, Figure IV.5), but in the rivers the traditional methods detected a greater percentage of species than metabarcoding (z = -2.68, p = .0073, Figure IV.5).

At higher taxonomic resolution scales, the results were similar using the genus taxonomic level (Figure IV.S1a,Fig. IV.S2a and IV.S3a). The congruence in detected genus identity was low between the two methods (29.90 ± 14.94 percent of genera in common) and a higher percentage of genera was detected by traditional methods when considering all the sites together or only rivers (Wilcoxon test: global: z = -3.07, p = .0022; streams: z = -1.84, p = .066; rivers: z = -2.70, p = .0035). The percentage of genera detected by both methods (shared genera) was similar to that obtained at the Considering the assemblage descriptors, the species richness estimated by the two methods were significantly correlated (r = .64, p = .001, Figure IV.7a and b). Species occurrences were also correlated, with the most widespread species detected in a greater number of sites with metabarcoding ( = .39, p < .001, Figure IV.7c). Ordinations of sites based on β-diversity showed a significant but weak concordance between the two methods (Procrustes analysis: m 2 = .57, p = .001). When analyzing rivers and streams separately, concordances between the two methods were also significant, but weak (rivers: m 2 = .42, p = .029; streams: m 2 = .70, p = .001). When using complete MOTUs data, the concordance between traditional and metabarcoding methods was still significant but lower (Procrustes analysis: m 2 = .78, p = .001).

NMDS ordinations of all sites showed a distinction between the river and the stream sites for both methods (Figure IV.8a and b). Assemblage variability within group (i.e. stream or river) did not differ for both methods (PERMADISP: eDNA metabarcoding: F 1,37 = 2.08, p = .16; traditional: F 1,37 = 2.29, p = .14). Species composition however differed between rivers and streams, with stronger differences when using traditional methods (PERMANOVA: eDNA metabarcoding: F 1,37 = 7.07, p = .001; traditional: F 1,37 = 12.12, p = .001).

When testing the effect of river drainage membership, assemblages assessed from eDNA differed between river drainages (PERMANOVA: F 1,37 = 3.06, p = .001) but their variability within river drainage did not differ (PERMADISP: F 1,37 = 1.75, p = .13). When using traditional methods either assemblage variability (PERMADISP: F 1,37 = 8.95, p = .001) or species composition differed between river drainages. But those differences were weaker compared to the results obtained with metabarcoding (PERMANOVA: F 1,37 = 1.56, p = .011). Nevertheless, metabarcoding provided a better discrimination of the stream fauna between river drainages than traditional methods (Figure IV.8c and d).

For streams, assemblages detected using metabarcoding did not vary significantly within river drainage (Figure IV.8e, PERMADISP: F 7,23 = 1.64, p = .20), but their composition differed between river drainages (PERMANOVA: F 7,23 = 3.85, p = .001). The variability within river drainage of fish assemblages and their composition based on traditional methods differed significantly between river drainages (Figure IV.8f, PERMADISP: F 7,23 = 7.11, p = .001; PERMANOVA: F 7,23 = 1.64, p = .004).

DISCUSSION

This first study of freshwater fish diversity using metabarcoding approach in tropical freshwater showed contrasting results compared to temperate areas, where it provided an exhaustive picture of the fish assemblages [START_REF] Civade | Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system[END_REF][START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF]. The fish inventories we made using eDNA metabarcoding over the Guianese territory revealed that the local assemblage inventories were not consistent between methods. Fifty percent of the fish fauna was detected by both traditional methods and eDNA metabarcoding in all but one of the considered sites but this discrepancy might be explained by several reasons explained below.

Firstly, the incompleteness of fish assemblages inventories in tropical streams and rivers using traditional methods do not provide an exhaustive picture of the fauna [START_REF] Mojica | Quantifying fish species richness and abundance in Amazonian streams: Assessment of a multiple gear method suitable for Terra firme stream fish assemblages[END_REF]. Indeed, most traditional methods are known as species selective [START_REF] Murphy | Fisheries Techniques, 2nd Edition[END_REF] and investigate the fauna from selected habitats and/or for a restricted reach of the river or stream that does not encompass all available habitats [START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF], and thus some species might remain undetected locally. In contrast, eDNA metabarcoding provide a way to detect fish independently from the species local habitat [START_REF] Olds | Estimating species richness using environmental DNA[END_REF], and integrates fish data over larger scales (from a few hundred meters to several kilometres) than the local sampling habitat [START_REF] Civade | Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system[END_REF][START_REF] Deiner | Transport distance of invertebrate environmental DNA in a natural river[END_REF][START_REF] Deiner | Environmental DNA reveals that rivers are conveyer belts of biodiversity information[END_REF][START_REF] Fukumoto | A basin-scale application of environmental DNA assessment for rare endemic species and closely related exotic species in rivers: A case study of giant salamanders in Japan[END_REF]. Our inventories are nevertheless incomplete as a substantial part of the fauna known to occur in each river, previously detected using traditional methods, was not detected using metabarcoding.

Secondly, imperfect detection [START_REF] Mojica | Quantifying fish species richness and abundance in Amazonian streams: Assessment of a multiple gear method suitable for Terra firme stream fish assemblages[END_REF][START_REF] Willoughby | The importance of including imperfect detection models in eDNA experimental design[END_REF] or erroneous assignations of reads to species probably explain incompleteness in eDNA-based inventories. The incompleteness of the reference database (ca. 25% of the species caught are not informed in the reference database, representing 24.31 ± 7.23% of species at each site) might for instance explain the grouping of some reads in higher taxonomic units (genera or family). In other words, slight differences between reference sequences of the same rank, especially genus, can result in the assignment of reads to one unique unit [START_REF] Ardura | Applications of DNA barcoding to fish landings: Authentication and diversity assessment[END_REF][START_REF] Pochon | Early detection of eukaryotic communities from marine biofilm using high-throughput sequencing: An assessment of different sampling devices[END_REF]. That was probably the case for some genera with closely related species from a morphological point of view (and probably also from a molecular point of view, [START_REF] Brown | Divergence thresholds and divergent biodiversity estimates: Can metabarcoding reliably describe zooplankton communities?[END_REF][START_REF] Flynn | Toward accurate molecular identification of species in complex environmental samples: Testing the performance of sequence filtering and clustering methods[END_REF] as Bryconops, Leporinus or Pimelodella genera. Those genera were indeed represented by a high number of reads in our results, but species discrimination was not possible and those genera were excluded from our analyses.

Gathering more molecular data on species is therefore a crucial step to develop a precise method based on eDNA to inventory species rich ecosystems [START_REF] Valentini | DNA barcoding for ecologists[END_REF]. Public repositories are for the moment lacking information on the species occurring in these ecosystems. Here, using GenBank to assign our eDNA sequences yielded few Guianese fish taxa assignments, and some of the eDNA sequences also matched to non-fish taxa. Although the teleo primers were designed to amplify Teleostei DNA, they may also amplify non-target taxa without the need of mismatches on the primers [START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF]. We here extended reference databases of Neotropical fauna using 12S rRNA molecular marker for 114 new species. Although these species account for only 5% of the 4035 Neotropical freshwater fish species reported in L év êque et al. (2007), they nevertheless account for a wide range of genera (18.6% of the 705 Neotropical freshwater fish genera) and families (60.8% of the 74 Neotropical freshwater fish families). As the species considered represent most of the major fish orders of the Neotropics, such reference database can thus be used in future metabarcoding fish inventories using family taxonomic resolution throughout the Neotropics. Indeed, using family taxonomic resolution, both metabarcoding and traditional methods detected a high proportion of families in common. In contrast, using genera besides of species did not improve the congruence between traditional and metabarcoding results because more than half of the genera considered are monospecific in French Guiana (see Table IV.S1). Metabarcoding fish inventories at finer taxonomic resolution (genus or species) over larger spatial areas (Guiana shield, Amazon River drainage or entire Neotropics) will nevertheless need complements on the reference database. We hence appeal forthcoming studies to complement our reference data on more species.

Despite the imperfect local species detection, the fish assemblages detected using metabarcoding were consistent with the fauna known to occur at higher spatial scales since only 5 out of the 132 species detected using metabarcoding were detected outside of their spatial distribution range. Three of these species probably account for the actual presence of the species in the considered river drainage, while they were never detected using traditional methods. Those species are indeed known either from adjacent river drainages (Krobia aff. guianensis sp1 or Satanoperca rhynchitis; Le [START_REF] Bail | Dimensions of biodiversity in Chesapeake Bay demersal fishes: Patterns and drivers through space and time[END_REF][START_REF] Bail | Updated checklist of the freshwater and estuarine fishes of French Guiana[END_REF] or for having a large distribution in the Neotropics that encapsulates French Guiana. This is the case for Corydoras aeneus [START_REF] Froese | FishBase. World Wide Web electronic publication[END_REF] which presence in the Oyapock River drainage is therefore probable. Those last three species were probably erroneously assigned to closely related species due to the incompleteness of our reference database. For instance, Ancistrus aff. temminckii was detected out of its known range, in areas colonised by the closely related Ancistrus aff. hoplogenys. A. aff. hoplogenys was not informed in our reference database, so sequences of A. aff. hoplogenys were hence probably wrongly assigned to A. aff. temminckii, the most similar species in the reference database. Identically, Hemiodus quadrimaculatus was detected in the Maroni River drainage, instead of Hemiodus huraulti, a closely related species, that was not informed in the reference database.

Within river drainages, we adequately differentiated small stream fauna from large rivers using metabarcoding. Only three out of the 86 species detected by metabarcoding in small streams were only detected in rivers using traditional methods, but two (Crenicichla multispinosa and Mastiglanis cf. asopos) are known to occur in small streams [START_REF] Keith | Atlas des Poissons d'eau douce de Guyane. Tome 2, fascicule I. Batrachoidiformes, Mugiliformes, Beloniformes, Cyprinodontiformes, Synbranchiformes, Perciformes, Pleuronectiformes, Tetraodontiformes[END_REF][START_REF] Planquette | Atlas des poissons d'eau douce de Guyane (tome 1)[END_REF], although not found using traditional methods in our study. Only one occurrence of Plagoscion squamossissimus in a small stream is unexpected. In rivers, metabarcoding allowed to detected both stream and river fish fauna, due to the upstream-downstream drift of eDNA throughout the watercourse, as shown by [START_REF] Civade | Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system[END_REF]. Our river samples therefore encapsulate the fish fauna from the river together with the fish belonging to the nearby streams, making eDNA using metabarcoding approach less efficient than traditional methods to discriminate the fauna from large rivers to that of the nearby streams.

In contrast, the small stream fauna from distinct river drainages was better discriminated using metabarcoding than using traditional methods. This might be due to the integrative characteristic of eDNA using metabarcoding approach [START_REF] Civade | Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system[END_REF] that, although incomplete, provides a more representative species list of the river drainage biodiversity than using traditional methods which provide a spatially localized inventory (dozens of metres in small rivers and hundreds of metres in large rivers). Traditional methods make sampling efficient only in a part of available habitats. For instance, large rivers are traditionally sampled using gill nets that are inefficient to capture small sized species known to occur both in streams and in the rapids of large rivers [START_REF] Keith | Atlas des Poissons d'eau douce de Guyane. Tome 2, fascicule I. Batrachoidiformes, Mugiliformes, Beloniformes, Cyprinodontiformes, Synbranchiformes, Perciformes, Pleuronectiformes, Tetraodontiformes[END_REF][START_REF] Bail | Dimensions of biodiversity in Chesapeake Bay demersal fishes: Patterns and drivers through space and time[END_REF][START_REF] Melki | Poissons d'eau douce de Guyane: plong ée dans les eaux de l'Amazonie franc ¸aise[END_REF][START_REF] Planquette | Atlas des poissons d'eau douce de Guyane (tome 1)[END_REF]. Moreover, nets are hardly usable and their efficiency is limited in burden areas or rapids and hence they are unable to detect the species living only in those habitats. The same habitat types are sampled in all the sites [START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF], making inter-drainage discrepancies less marked and hence under-estimating the faunistic distinctiveness between river drainages. Those two distinct perceptions of the fauna might explain why despite a significant correlation in fish richness and occurrence between metabarcoding and traditional methods, the species detected by the two methods markedly differ.

CONCLUSION

Given the limitations of both traditional methods and metabarcoding, those two approaches provided distinct perceptions of fish assemblages. Therefore, each method captured a distinct fraction of the real fish assemblage. While traditional methods provided an almost complete, but spatially limited picture of the fish species diversity [START_REF] Allard | Elaboration d'un Indice de Qualit é Des Eaux Bas é Sur La Structure Taxonomique et Fonctionnelle Des Assemblages de Poissons Des Petits Cours d'eau de Guyane[END_REF], the metabarcoding provided a partial, but more spatially extended inventory. This makes the two approaches complementary to get a realistic picture of the biodiversity at the reach scale.

The results obtained using metabarcoding differed from those obtained in temperate environments, where metabarcoding provided an exhaustive picture and even an overrepresentation of the fish diversity [START_REF] Lawson Handley | Environmental DNA metabarcoding of lake fish communities reflects long-term data from established survey methods[END_REF][START_REF] Olds | Estimating species richness using environmental DNA[END_REF][START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF]. Protocols improvement in sampling, like increasing the sampling effort per site (sampling time length of sampling or number of filtration) might increase species detection (H änfling et al., 2016). On the other hand, the lack of reference sequences and bias in detection for abundant species or in amplification for abundant sequences [START_REF] Adams | A unique signal distorts the perception of species richness and composition in high-throughput sequencing surveys of microbial communities: A case study of fungi in indoor dust[END_REF]Kelly et al., 2014) might also decreased the number of species detected using eDNA, and those methodological limitations should be quantified in the future.

Despite those pitfalls and limitations, the eDNA metabarcoding is a promising approach for fish biodiversity assessment in tropical areas. Given the rare erroneous species detection, the significant correlations between fish diversity and occurrences for both traditional methods and eDNA metabarcoding, as well as the higher capacity of metabarcoding than traditional methods to discriminate between river drainages, it appears that metabarcoding can be used as a rough, but rapid biodiversity assessment method in the Neotropics. Turning eDNA metabarcoding into a more exhaustive inventory tool will need to expand the reference database and optimize field and laboratory protocols [START_REF] Rees | Applications and limitations of measuring environmental DNA as indicators of the presence of aquatic animals[END_REF][START_REF] Roussel | The downside of eDNA as a survey tool in water bodies[END_REF]. The eDNA metabarcoding method nevertheless deserves to be used since now as a complementary tool to traditional methods pending future developments of this methodology to make it more exhaustive.

The development of metabarcoding is challenging because it would overcome the current destructive inventory tools (e.g. rotenone, gillnets) that are currently banned from most countries for both ethical and legal reasons. For instance, in Europe, the use of rotenone has been regulated since 2008 (European laws 2008/296/CE and 2008/317/CE). Although few exceptional authorizations have been obtained to conduct scientific studies, its use has now completely been banned. Developing a new non-destructive sampling method would unlock the current situation where scientists and environmental managers can no longer achieve complete species inventories. The implementation of eDNA-based methods would therefore permit to continue collecting information on fish assemblages in tropical freshwaters, which is of particular importance given the current rise of anthropogenic disturbances, and associated decline of aquatic biodiversity on Neotropical ecosystems [START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF][START_REF] Hammond | Causes and Consequences of a Tropical Forest Gold Rush in the Guiana Shield, South America[END_REF][START_REF] Winemiller | Balancing hydropower and biodiversity in the Amazon, Congo, and Mekong[END_REF].

Table IV.S1: List of species found using eDNA metabarcooding and traditional methods. Occurrences are expressed as the percentage of sites where the species was found. For each species, we indicated the taxonomic unit at which the 12S rRNA metabarcoding fragment was identified. "-" indicates species absent from the reference database

Species

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

Leporinus

Leporinus affinis

Chilodus

Chilodus punctatus

Hoplias

Hoplias malabaricus

Prochilodus Prochilodus

Pterengraulis

Pterengraulis atherinoides

Electrophorus

Electrophorus electricus

Gymnotus

Gymnotus carapo

Eigenmannia

Eigenmannia sp. CBM-ZF-10620

Potamotrygon

Potamotrygon motoro

Auchenipterus

Auchenipterus demerarae

Tatia

Tatia intermedia

Doras

Doras micropoeus

Ancistrus

Ancistrus sp. 2a Parente, 2015

Cteniloricaria

Cteniloricaria platystoma

Farlowella

Farlowella reticulata

Harttia

Harttia guianensis

Hartiella

Harttiella lucifer

Hemiancistrus

Hemiancistrus medians

Hypostomus

Hypostomus gymnorhynchus

Lithoxus

Lithoxus planquettei

Metaloricaria

Metaloricaria paucidens

Panaqolus

Panaqolus koko

Pseudancistrus

Pseudancistrus barbatus

Rineloricaria Rineloricaria

Pimelabditus

Pimelabditus moli

Pimelodus

Pimelodus ornatus

Taxon †

Ancistrus

IV.3 Additional analyses

Analyses of the distance of detection on one stream

In addition to this analysis but not included in the submitted/accepted version, I used a subset of the stream sites that were sampled along the same stream (Nouvelle France stream, Figure IV.9) to roughly estimate the distance of detection for eDNA. This study remains a preliminary approach of the distance of detection (no replicate), but it can nevertheless provide some hints for future researches. We compared each site to all the downstream sites and counted the number of unique species to this site, i.e. species that are found only in the upstream site, for both traditional sampling and eDNA metabarcoding. We have seen in the Chapter II that species assemblages change along the upstream-downstream gradient. We thus expect that the number of unique species should increase with the distance between sites. If eDNA is transported along the stream, the species occurring only in upstream sites should still be detected downstream, and the number of unique species in the upstream site should be lower when using eDNA metabarcoding than for traditional sampling. We observed that, with traditional sampling, the number of species unique to the upstream site was always higher than five and did not increase with distance (Figure IV.10a). In comparison, the number of unique species was lower with eDNA metabarcoding (Figure IV.10b) and increase with the distance when comparing to NFS2. For the most upstream sites (NFS4 to NFS6), the species were almost all detected from one site to the others. The detection distance of eDNA thus correspond to the distance between the upstream and the downstream sites (about 8 km). In temperate streams, this distance was evaluated between hundreds of meters [START_REF] Jane | Distance, flow and PCR inhibition: eDNA dynamics in two headwater streams[END_REF] to kilometers (about 2km in Civade et al., 2016, 10km Altermatt, 2014). The environmental characteristics of Guianese streams that could have affected DNA degradation (acidity, turbidity or UV), and reduce eDNA metabarcoding efficiency, seem to have little effect on this detection distance.

Detection and environmental range

In another exploratory analysis, I checked if species detection fell into the known environmental range of species distribution based on traditional sampling. Their distributions are highly influenced by the upstream-downstream gradient and I thus used the distance of the site from the source to compare the detection and the known range. A large number of species have distributed across a wide range of sites IV.11, but for species with smaller range, little detection fell outside the known range (10% of wrong detections). These wrong detections were often at higher distance from the source, that can partly be explained by the detection distance of the eDNA. However, wrong assignments or the lack of reference for some species, as suggested in the submitted work, might also caused this wrong detection. We should also be aware that traditional sampling methods do not provide an exhaustive image of the fish fauna and some inconsistencies of species distributions can either be due to traditional or eDNA biases. Nevertheless, the majority of species are detected within their known distribution range and focusing our attention on species for which detections are not congruent would be beneficial to understand the specificity of eDNA in tropical freshwaters. Once this achieved, eDNA metabarcoding will help to refine species distribution range.

Comparison of the cost and specificity of traditional and eDNA metabarcoding methods

If we compare both methods from technical and financial point of view (Table IV.2), eDNA metabarcoding is much easier to achieve compared to traditional sampling, since a single operator with a peristaltic pump is needed, compared to a 3 to 5 persons team with heavy equipment for traditional samples (gill-nets, block-nets, hand nets). For the sampling cost, the complete eDNA analysis (sampling tools, sampling and subsequent analyses) are estimated to 530eper sample, once the reference database is implemented. The cost for metabarcoding is lower compared to traditional sampling (around 700eper sample) and it will probably decreases in the future, with the decrease of molecular analysis cost.

In conclusion, eDNA metabarcoding is applicable to assess freshwater fish assemblage in French Guiana and given these first results, it is for the moment more a complementary sampling methods than an alternative to the traditional samples. It nevertheless represents the only efficient sampling method that can still be used in streams since rotenone samples are no longer allowed. Environmental DNA developments are still pending and determining an optimal sampling effort (filtration volume, number of replicate) and distance of detection, but also to complement the genetic reference database appear as the most urgent developments needed to enhances eDNA metabarcoding relevance in Guyanese freshwaters.

Chapter V: Conclusion V.1 Assembly processes of Guianese freshwater assemblages

These works is in continuation of previous works that have allowed increasing the knowledge about Guianese fish ecology. This thesis allowed to extend the previous database about fish species assemblages with the integration of new sampling sites (especially the rivers), the development of a genetic database that will be used for two different goals (phylogeny and metabarcoding) and the inclusion of morphological measurements to measure functional traits. The database updating is still an ongoing project, with new sampling planned to increase the tissue and photographic collections, and local assemblage information. This will allow extending the approach I used in this thesis to other part of the hydrological network (other river sites or estuary areas).

All these data allowed me to characterize the taxonomic, phylogenetic and functional diversities of Guianese freshwater fish assemblages, and these three facets of diversity revealed the processes that shaped fish assemblages. The results of these works highlight that high diversity of fish observed in the tropical waters in French Guiana is structured along an upstream to downstream gradient, with higher local diversity downstream (Chapter II). This pattern is coupled with the effect of the spatial structure of the hydrological network, that limits dispersion of fish species between assemblages (Chapters II and III). Rivers act as a barrier to dispersal for stream associated species, thus creating a strong turnover of species between stream assemblages. We also observed dispersal limitation between river assemblages, but the biogeographical history of Guianese region had differently impacted streams and rivers assemblages. Streams assemblages are isolated for a longer time compared to rivers assemblages, which had exchange species during low sea level period.

V.2 Phylogenetic characteristics of assemblages

Working with different types of data requires using the intersection of these data and thus some species are excluded from the analysis. Here, in my thesis, 239 species were identified in the assemblage data (both rivers and streams) but only 195 species were informed both in the assemblage dataset, in the morphological traits database and in the phylogenetic tree. We thus excluded 44 species from the analyses, either due to their low occurrence or their absence in the phylogenetic tree. For the species that are missing from this phylogenetic tree, some are missing due to the fact that no tissue sample was available for a minority of them (and will thus need supplementary fieldworks), or that sequence amplification did not work (especially species from the Loricariidae family). For those last species, supplementary collections of tissue and changing the body part collected (muscle instead of fin tissue, Raphael Covain, pers. comm.) can solve amplification problem. An alternative could be to incorporate them as polytomies within their respective genera [START_REF] Qian | Phylogenetic beta diversity of angiosperms in North America[END_REF][START_REF] Webb | Phylomatic: Tree assembly for applied phylogenetics[END_REF]. However for species that are the unique representative of their genera (e.g. Metynnis lippincottianus), their placement in the phylogenetic tree will create polytomies at higher taxonomic level (subfamily or family) and thus will impact phylogenetic diversity estimates [START_REF] Davies | Incompletely resolved phylogenetic trees inflate estimates of phylogenetic conservatism[END_REF]. The best estimates of phylogenetic diversity would be given by a phylogeny including all the species found in Guianese assemblages, and even more if those species are replaced in a larger context like the evolutive history of Neotropical ichthyofauna.

V.3 Functional characteristics of assemblages

Recently, the intraspecific variability in functional traits has been highlighted as an important component to estimate functional diversity and can help to assess the processes structuring assemblages that can be missed when using mean values [START_REF] Albert | On the importance of intraspecific variability for the quantification of functional diversity[END_REF][START_REF] Schleuter | A user's guide to functional diversity indices[END_REF][START_REF] Spasojevic | When does intraspecific trait variation contribute to functional beta-diversity[END_REF]. Measuring this intraspecific variability at the individual level would however be impossible for Guianese freshwater assemblages. Indeed, nets sampling select individuals based on their size and their habitat and this will lead to underestimate intraspecific variability in assemblages. [START_REF] Carmona | Traits without borders: Integrating functional diversity across scales[END_REF] proposed a framework based trait probability density to overcome this problem. The distribution of trait values can be estimated from the measures and an estimated variability. This probabilistic framework however needs to be tested, but is a first step to incorporate individual variability for tropical fish assemblages. This inclusion of intraspecific variability should also be accompanied with a reflexion on the spatial scale at which it will be taken into account. We could expect that for species inhabiting both streams and rivers, individuals of these species will differ between these two habitats. Differentiating intraspecific variability estimations for these species between the different habitats will thus be needed to correctly estimate functional diversity of assemblages. This needs to be tested, and maybe thought at other spatial scales (difference between upstream and downstream, between drainage basins) to draw robust conclusions on assembly rules.

V.4 Assembly processes and anthropogenic disturbances

Although some processes needs to be confirmed by further analyses (Chapter III.2), this work can be used as a reference to understand how anthropogenic disturbances affect fish assemblage. Data for fish assemblages in disturbed streams were already available (Allard, 2014 PhD thesis) and we now have the information for river site under anthropogenic disturbances. With these data at hand, doing the same complete analysis of assembly rules (Chapter III) in these disturbed sites will allow to identify the dominant processes under disturbed conditions. We can then verify if local neutral processes and dispersal limitation are still the dominant processes explaining the diversity and the structure of fish assemblages or if disturbances modified the relative strength of the different processes that shifts assemblage composition and structure.

The observed changes of assemblage compositions between non-impacted and disturbed sites [START_REF] Allard | Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages[END_REF][START_REF] Dedieu | Assessing the impact of gold mining in headwater streams of Eastern Amazonia using Ephemeroptera assemblages and biological traits[END_REF] might be explained by a shift in local processes from neutral to deterministic processes, with a strong environmental filtering for taller and ubiquitous species.

However, the future studies of assembly rules rely on the development of a sampling method that give exhaustive picture of assemblages. We see that eDNA metabarcoding is at the moment in a development phase and several tests are need before it can be routinely used to asses fish assemblages. The first essential step will be to evaluate the sampling effort of the eDNA metabarcoding (the number of replicates or the volume of filtered water). Metabarcoding samples from the same location can greatly differ in their assemblage composition [START_REF] Ficetola | Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data[END_REF]. The number of replicates to obtain an almost full picture of the local assemblages will create a financial constrain and a choice between the number of sampling sites (and thus the generalization of the results) and the representativeness of local assemblages (and thus the confidence in the results). In addition, if assemblages detected with metabarcoding are biased toward particular species irrespective of the environmental conditions (the rarest species are never detected for example), looking at assembly rules might give the same results as for the river assemblages studied in the Chapter III.2, i.e. the difference between the scale at which processes act and the scale at which we observe assemblages might blur the effect of deterministic processes. Once this calibration phase of the metabarcoding method for tropical stream completed, its use will be a major step for inventories or ecological and conservation studies in these areas.

V.5 Leaving the "process from pattern" way of thinking?

Finally, I think that the greatest improvement that can be done in the assessment of assembly rules will be to shift the way we assess them. For the moment, a large part of studies assembly rules rely on the "process from pattern" template, which have been highly criticized (see Chapter I) and led to the development of strong frameworks and hypotheses about the patterns expected under each process or their combinations (as developed in these thesis). This warning about the inductive way of thinking has been also reported as biogeography [START_REF] Crisp | Hypothesis testing in biogeography[END_REF] or in spatial ecology [START_REF] Mcintire | Beyond description: The active and effective way to infer processes from spatial patterns[END_REF]. A process from experimental test template maybe will strengthen our view on assembly rules. This type of experimental approach is mainly used in plants communities [START_REF] Baer | Environmental heterogeneity has a weak effect on diversity during community assembly in tallgrass prairie[END_REF][START_REF] Loranger | Recasting the dynamic equilibrium model through a functional lens: The interplay of trait-based community assembly and climate[END_REF][START_REF] Hillerislambers | Rethinking community assembly through the lens of coexistence theory[END_REF], because abiotic and biotic conditions are easily controlled and the strength of each processes can be modified. An alternative is to associate observational and experimental studies, as [START_REF] Fayle | Experimentally testing and assessing the predictive power of species assembly rules for tropical canopy ants[END_REF] who confirmed their field observations by experimentally induced interactions between ant species. These two ways would be more difficult (if not impossible) to implement for larger species from speciose area, and will require a lot of work, but tending to them (for example looking at the ecological and life history traits of species in addition to their morphological traits) would be the greatest help in understanding the processes that structure assemblages.

Community ecology is a field that successfully conciliated and integrated a variety of theoretical work (e.g. metacommunity and neutral theories, eco-phylogenetic, functional trait). However, this has made community ecology complex (numerous available indices, spatial and taxonomic scales importance, phylogenetic and functional relationships) and it raised warnings by several authors about its new fuzziness [START_REF] Kraft | Community assembly, coexistence and the environmental filtering metaphor[END_REF][START_REF] Cadotte | Should Environmental Filtering be Abandoned?[END_REF][START_REF] Gerhold | Phylogenetic patterns are not proxies of community assembly mechanisms (they are far better)[END_REF]. This might be the call for a rethinking of community ecology concepts to reach a new synthesis of community ecology.

Glossary

α-diversity the diversity found within a spatial unit.

β-diversity the diversity between two spatial units.

γ-diversity the diversity over a group of spatial units. It combines the information of αand β-diversity.

assemblage the set of taxonomically related species co-occurring in space and time, i.e. a subset of the community [START_REF] Stroud | Is a community still a community? Reviewing definitions of key terms in community ecology[END_REF]. Here, an assemblage is the co-occurring fish species in a locality. All the considered fish belong to the Teleosts, to the exception of the freshwater stingray which belongs to the Chondrichtyans.

assembly rule deterministic and neutral processes (competition, environmental filtering, historical contingency), that specify which subset of species in the total pool would form a community [START_REF] Keddy | Assembly and response rules: Two goals for predictive community ecology[END_REF].

biodiversity the variety of life, at all levels of organization, classified bth by evolutionary (phylogenetic) and ecological (functional) criteria [START_REF] Colwell | Biodiversity: Concepts, patterns and measurement[END_REF]. community a group of interacting species (directly or indirectly), co-occurring in space and time [START_REF] Stroud | Is a community still a community? Reviewing definitions of key terms in community ecology[END_REF].

dispersal limitation rare dispersal events prevents species establishment in suitable habitat [START_REF] Shurin | Local and regional zooplankton species richness: A scale-independent test for saturation[END_REF]. Dispersion can be limited due to species intrinsic capacities or due to the presence of barriers to the dispersion (mountain, river, sea).

environmental filtering environmental filtering occurs when a species arrives at a site but failed to establish or persist due to the local abiotic environment [START_REF] Kraft | Functional trait differences and the outcome of community assembly: An experimental test with vernal pool annual plants[END_REF]. The common hypothesis related is that assemblages are composed of species with similar trait values. Extended to multiple sites, environmental filtering occurs if sites have different environments that promote species with different ecological strategies.

functional diversity the diversity in the traits of species that influence their performance and thus ecosystem functioning [START_REF] Dı Áz | Vive la diff érence: Plant functional diversity matters to ecosystem processes[END_REF].

functional trait any trait which impact fitness indirectly via its effects on growth, reproduction and survival [START_REF] Violle | Let the concept of trait be functional![END_REF]. limiting similarity there is a limit to the similarity of competing species and to their number [START_REF] Macarthur | The limiting similarity, convergence, and divergence of coexisting species[END_REF]. The co-occurrence between species with similar functions must thus be limited. The common hypothesis related is that assemblages are composed of species with divergent trait values.

nestedness one of the component of β-diversity. It represents the difference in species richness between two assemblages, where one assembblage is a subset of the other. Nestedness reflects selective extinction or colonization or nestedness of habitat [START_REF] Baselga | Disentangling distance decay of similarity from richness gradients: Response to Soininen et al[END_REF].

phylogenetic diversity the diversity in evolutionary history of species [START_REF] Faith | Conservation evaluation and phylogenetic diversity[END_REF].

phylogenetic signal the tendency of related species to have similar ecology and phenotype [START_REF] Losos | Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species[END_REF].

phylogenetic trait or niche conservatism the extent to which species retain ancestral ecological traits and environmental distributions [START_REF] Crisp | Phylogenetic biome conservatism on a global scale[END_REF]. Generally, closely related species have more similar ecology or phenotype than expected based on their phylogenetic relationships [START_REF] Losos | Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species[END_REF].

species pool a set of candidate species that could occur at smaller scale [START_REF] Srivastava | Using local-regional richness plots to test for species saturation: Pitfalls and potentials[END_REF].

taxonomic diversity the diversity in species, with each species treated equally. One of the traditional measures of taxonomic diversity is the species richness (the number of species found in one spatial unit).

trait convergence species occurring in a particular set of environmental conditions tend to share similar functional and phenotypic traits [START_REF] Pillar | A framework for metacommunity analysis of phylogenetic structure[END_REF].

turnover one of the component of β-diversity. It represents the change in species composition due to environmental sorting, dispersal limitation or speciation [START_REF] Baselga | Disentangling distance decay of similarity from richness gradients: Response to Soininen et al[END_REF].

Diversity and assembly processes of Guianese freshwater fish assemblages

Tropical ecosystems, especially Amazonian ecosystems, host a great diversity of terrestrial and aquatic organisms. However, the causes and the processes behind this high diversity for freshwater fish assemblages are little known, but their identification will be an asset in the assessment of anthropogenic impacts that are increasing in these regions. We studied the processes that shape the diversity and the structure of freshwater fish assemblages of non-impacted streams and rivers located in French Guiana. Within-assemblage diversity increased along an upstream/downstream gradient and was higher in sites where the habitat was diversified. Species identity changed along this gradient, which created zones along the stream. Spatial relationships between assemblages and their isolation also greatly impacted species assemblages. Using information about species traits (functional diversity) and their phylogenetic relationships (phylogenetic diversity), we showed that within-assemblage diversity was not influenced by the environment or by species interactions. We also confirmed that dispersal limitation, linked with the past history of drainage basins, had a strong effect on assemblage structure in both streams and in rivers. Future investigations on the processes structuring fish assemblages will need to acquire more exhaustive biological data, and therefore to develop an efficient, and non-destructive sampling method. To this aim, we evaluated the efficiency of environmental metabarcoding applied to aquatic assemblages (the molecular identification of species present from a water sample) and compared it to traditional sampling methods. Currently, metabarcoding gives complementary information to traditional sampling. It thus needs developments and further tests to increase its efficiency and allow its use for assembly processes studies. Pursuing the formalization of a conceptual framework to investigate assembly rules together with the development of an efficient fish sampling protocol are now needed to better understand the structure of tropical fish assemblages. Those theoretical and practical developments will contribute to better evaluate anthropogenic disturbances on aquatic ecosystems.

Figure I. 2 :

 2 Figure I.2: The latitudinal diversity gradient is described in several taxa, from New World birds (a), mammals (b), amphibians (c) and freshwater fishes (d). Figures from Gaston & Blackburn (2008), Rolland et al. (2014), Pyron et al. (2015) and Oberdorff et al. (2011) respectively

Figure I. 3 :

 3 Figure I.3:Processes structuring local assemblages. The processes can be considered as a succession of filters that act at different spatial scales (continental, regional, local scales). Symbols represent species

Figure

  Figure I.6:Combining information on taxonomic, functional and phylogenetic diversity can help to disentangle the main processes that structure communities, both within-and between-communities. On each plot, dominant processes are indicated. For local processes (α-diversity), the main expectation is that higher taxonomic diversity causes higher functional and phylogenetic diversity. Deviation from this expectation (blue and red lines) indicates that one process structure local community. Phylogenetic and functional diversity must be correlated (phylogenetic constrain or trait conservatism), but communities can deviate from it. For between-communities diversity (β-diversity), the more dissimilar the communities are in their species identity, the more functionally and phylogenetically dissimilar communities will be. Departure from this expectation indicates that one process is stronger than the other. Although each plot links only two facets, conclusions must be taken from the comparison of the three facets simultaneously

Figure I. 7 :

 7 Figure I.7: Evolution of Guianese population from 1954 to 2014 (-) and projection to 2030 from the Omphale model (---). Data from INSEE (03/09/2017)

Figure I. 8 :

 8 Figure I.8: Modification of Guianese freshwater assemblages by anthropogenic disturbances. Taxonomic structure of Ephemeroptera assemblages (a, distribution of genera; b, distribution of sites clustered by site condition based on taxonomic data) and functional structure of fish assemblages (c, correlation circle of fish functional traits; d, positioning of each site condition centroids based on functional data) differed between undisturbed and logged sites, and gold-mined sites. Abbreviations used in c: Phyto: phytophagous species; SL max: maximum standard length; Ubiquit: ubiquitous species; Omni: omnivorous species; Pred: predatory species. Figures from Dedieu et al. (2015) and Allard et al. (2016); Ephemeroptera image by George Starr (CC BY-NC-SA 3.0)

Figure I. 9 :

 9 Figure I.9: Examples of stream (a) and river (b) sampling sites. (Photo: L. Allard and I. Cantera)

Figure II. 1 :

 1 Figure II.1: Small streams of French Guiana can be divided into five main types areas along the upstream to downstream gradient. Main characteristics of the area are indicated on the left and some characteristic species of the area on the right. (Photo: L. Allard, S. Brosse, I. Cantera, K. Cilleros, F. Melki, Guyane Wild Fish, M.N.H.N.)

Figure II. 3 :

 3 Figure II.3: Map of French Guiana showing the location of the study sites. Point size is proportional to the number of sites sampled in each area

Figure II. 4 :

 4 Figure II.4: Relationships between species richness and the distance of the site from the source (a), the habitat diversity (b) and the altitude (c). Fitted values (solid lines) and 95% confidence interval (dashed lines) are derived from the averaged estimates of the Poisson generalised linear-mixed effect models

Figure II. 5 :

 5 Figure II.5: Canonical correspondence analysis (CCA) ordination illustrating fish species assemblage constrained by all environmental variables along the two-first axis (axis 1: 17% of the explained variation, p = .001; axis 2: 12% of the explained variation, p = .001). Upper left panel (a) quantitative environmental variables are represented by arrows with abbreviations as in TableII.1; upper right panel (b) hydromorphological units; bottom left panel (c) species position in the CCA ordination. Species were grouped according to K-means analysis with K = 5. Apistogramma gossei is abbreviated A. gossei. See TableS1for complete information about species membership for each cluster; bottom right panel (d) position of sites in the CCA ordination

Figure II. 6 :

 6 Figure II.6: Stream sites position along the distance from the source and the altitudinal gradients. For each site, the percentage of species belonging to each cluster is represented (see Figure II.5 and Table II.S1 for details on species clustering). Sites belonging to the same stream reach were grouped together to simplify the figure Figure II.7: Hierarchical clustering of the species turnover between the five drainages (Jaccards turnover component) with complete linkage method

Figure

  Figure II.S1: Species clusters (left panel) and simple structure index (ssi) values (right pannel) for K ranging from two to fifteen. The first local maxima (•) and the maximal value of ssi (•) are indicated by dots and the corresponding clustering are highlighted on the left panel by black rectangles

Figure III. 1 :

 1 Figure III.1:Temperate and tropical streams show different relationship between taxonomic and functional turnover, revealing different processes shaping fish assemblages. In tropical regions large rivers can act as dispersal barriers for stream fish species, promoting therefore dispersal limitation processes. In temperate streams the thermic gradient between cold upstream water and cooler downstream water that act as an environmental filter for fish assemblages. (Photo: Ji-Elle, K.Cilleros)

Figure III. 2 :

 2 Figure III.2: Theorical framework (a) and expectations (b, c) about the relative strength of environmental filtering and dispersal limitation based on the relationship between taxonomic and functional turnover.(a) Assemblages with similar taxonomic and functional turnover (area 1) are structured with similar strengths of environmental filtering and dispersal limitation. Outside this area, pairs of assemblages with higher functional turnover are structured by different environmental filters and promote species with different ecological strategies (area 2), whereas pairs with higher taxonomic turnover are dominated by dispersal limitation, with distinct species sharing similar strategies (area 3). We hence expect that a higher functional turnover in tropical assemblages (blue dashed line) than in temperate ones (orange dotted line) will reveal that both processes act in the same way in the two regions (b). In contrast, a higher functional turnover in temperate assemblages will reveal that the relative strength of dispersal limitation and environmental filtering differs between the two regions (c)

Figure III. 3 :

 3 Figure III.3: The location of the 84 studied fish assemblages within French Guiana (a) and continental France (b)

Figure III. 4 :

 4 Figure III.4: Taxonomic and functional adiversity of fish at regional scale (a, b) and at local scale (c, d). The functional richness represents the percentage of the convex hull volume occupied by all the species of one region (b) or of one site (d) out of the total convex hull volume of all the species. Significant differences between the two regions are indicated above the boxes (*)

Figure III. 5 :

 5 Figure III.5: Ordination of fish species along the axes of the first two principal components (representing 54.7% of the total inertia) based on the functional distance matrix computed with Gower's distance on the 15 morpho-anatomic measures. Convex hulls for continental France (•) and French Guiana ( ) are represented

Figure III. S2 :

 S2 Figure III.S2: Ordination of fish species along the first and the third principal components (representing 48.4% of the total inertia) (a) and along the first and the fourth principal components (representing 41.8% of the total inertia) (b) based on the functional distance matrix computed with Gowers distance on the 15 morpho-anatomic measures. Convex hulls for continental France (•) and French Guiana ( ) are represented

  Figure I.6). These analyses are based on the fish inventories on 75 stream sites and 13 river sites in non-impacted or lowly distrubed areas (Figure III.1).The first step was thus to build the phylogenetic relationships between Guianese species.

Figure III. 1 :

 1 Figure III.1: Map of French Guiana showing the location of stream (open circle) and river (open square) study sites

  We performed DNA amplification in a final volume of 25 µL including 1U of GoTaq R (Promega), 5X buffer (Promega), 10 µM of dNTP, 20 µM of each primer (12S: teleo R 5'-CTTCCGGTACACTTACCATG-3' and V05F 898 5'-AAACTCGTGCCAGCCACC-3', Valentini et al., 2016; COI: FishF1 5'-TCAACCAACCACAAAGACATTGGCAC-3' and FishR1 5'-TAGACTTCTGGGTGGCCAAAGAATCA-3', Ward et al., 2005) and 1µL of

Figure III. 2 :

 2 Figure III.2: Maximum likelihood phylogenetic tree of Guianese freshwater fish species based on 12S and COI. Only bootstrap values greater than 70 are indicated

Figure III. 3 :

 3 Figure III.3: Taxonomic, functional and phylogenetic α-diversities of assemblages located in streams (a,b) and in rivers (c,d). Red (blue) dots indicates that the observed functional or phylogenetic diversity is higher (lower) than expected under null models. Filled dots indicates that the observed diversity is significantly different from values obtained from null model

Figure III. 4 :

 4 Figure III.4: Taxonomic, functional and phylogenetic β-diversities of assemblages located in streams. Green dots and bars represent medians and SD respectively. The line 1:1 is indicated

Figure III. 5 :

 5 Figure III.5: Taxonomic, functional and phylogenetic β-diversities of assemblages located in rivers. Green dots and bars represent medians and SD respectively. The line 1:1 is indicated

Figure IV. 1 :

 1 Figure IV.1: Summary of the eDNA metabarcoding protocol used to sample Guianese freshwaters streams and asses fish assemblages. 1: For each site, we filtered about 60 liters of water (30 minutes filtering time) in a rapid flow area. 2: DNA extraction, amplification and curation of the samples and reads analyses were handled by SPYGEN (a private company specialized on the use of eDNA for species detection). 3: We built a genetic reference database to assign eDNA. Sequences were mainly obtained by sequencing individuals caught during fieldwork sessions and complemented by samples provided by HYDRECO lab (a). Reference database was complemented with a few additional sequences available from GenBank public repository (b). 4: Assignment of eDNA reads to the genetic reference database.Several protocols have been proposed(Blast, Altschul et al., 1990;[START_REF] Edgar | Search and clustering orders of magnitude faster than BLAST[END_REF] OBItools, Boyer et al., 2016; phylogenetic placement, Munch et al., 2008). Here we used ecotag to identify the species present in the sample. 5: The site species list obtained by eDNA metabarcoding. 6: The list of species produced by metabarcoding was compared to the list of species obtained with other sampling methods (nets and rotenone) or from external databases. (Photo: K. Cilleros)

Figure IV. 2 :

 2 Figure IV.2: Location of the 39 studied sites (a). Stream (e.g. Apa, site 1; b) and river sites (Machicou, site 32; c) are marked with open and full circles, respectively. Numbers are as in TableIV.1

  .1 and Fig. IV.2a). The sites included both small streams (less than 10 m wide and 1 m deep, 31 sites; Fig. IV.2b) and rivers (more than 20 m wide and 1 m deep, 8 sites; Fig. IV.2c).

Figure IV. 3 :

 3 Figure IV.3: Species richness per site detected with traditional and eDNA metabarcoding methods. Species caught only with traditional methods are in black (species informed in the reference database) and white (absent from the reference database), detected only with metabarcoding in dark grey and by both metabarcoding and traditional methods in light grey

Figure IV. 4 :

 4 Figure IV.4: Percentage of species detected with traditional and eDNA metabarcoding methods. Only species informed in the reference database were considered. Species caught only with traditional methods are in black, those detected only with metabarcoding in dark grey and those detected by both metabarcoding and traditional methods (shared) in light grey

Figure IV. 5 :

 5 Figure IV.5: Percentage of species detected at each site with metabarcoding (dark grey) or traditional methods (black). Difference between eDNA and traditional methods were tested using Wilcoxon ranksum test, ns: p > .05; ** p < .01

Figure IV. 7 :

 7 Figure IV.7: Relationship between species richness (a, b) and species occurrences (c) obtained with traditional methods and metabarcoding when using (a) the entire data and (b) after removing the species not informed in the reference database. Species occurrences are expressed as the percentage of sites where a species was detected. The line 1:1 is represented as the dashed line on all plots. For a and b, sites are classified according to the watercourse type ( for streams and • for rivers)

Figure IV. 8 :

 8 Figure IV.8: Non metric multidimensional scaling (NMDS) ordinations of (a, c) entire dataset for metabarcoding, (b, d) entire dataset for traditional methods, (e) only stream assemblages detected with metabarcoding and (f) only stream assemblage detected with traditional methods. For a and b, sites are classified according to the watercourse type ( for streams and • for rivers) and for c, d, e and f, sites are classified according to the river drainage (see legend for river drainage membership). Ellipses represent standard deviation for each group

  taxa are fish present in French Guiana; italized taxa are fish not known from French Guiana Homo Chapter IV: Tropical freshwater biodiversity and eDNA

  taxa are fish present in French Guiana; italized taxa are fish not known from French

  taxa are fish present in French Guiana; italized taxa are fish not known from French

  taxa are fish present in French Guiana; italized taxa are fish not known from French

  taxa are fish present in French Guiana; italized taxa are fish not known from French

  taxa are fish present in French Guiana; italized taxa are fish not known from French

  taxa are fish present in French Guiana; italized taxa are fish not known from French

  taxa are fish present in French Guiana; italized taxa are fish not known from French

  taxa are fish present in French Guiana; italized taxa are fish not known from French

  taxa are fish present in French Guiana; italized taxa are fish not known from French

  taxa are fish present in French Guiana; italized taxa are fish not known from French

  taxa are fish present in French Guiana; italized taxa are fish not known from French

  taxa are fish present in French Guiana; italized taxa are fish not known from French

  taxa are fish present in French Guiana; italized taxa are fish not known from French

  taxa are fish present in French Guiana; italized taxa are fish not known from French

  Figure IV.S1: Number of genera (a) and families (b) detected at each site with traditional and eDNA metabarcoding methods. Taxa caught only with traditional methods are in black (taxa informed in the reference database) and white (absent from the reference database), detected only with eDNA metabarcoding in dark grey and by both eDNA and traditional methods in light grey. Sites are indicated by numbers (seeTable 1 in the main text for number signification)

  Figure IV.9: Location of the 6 sampling sites (from upstream NFS6 site to downstream NFS1 site) on the Nouvelle France stream. The location of the stream is indicated in red on the map of French Guiana

Figure IV. 10 :

 10 Figure IV.10: For each site (colored line), the number of species only found in this site compared to the downstream sites is indicated when using rotenone (a) or eDNA metabarcoding (b). Distance from the first sites (NSF6) is indicated.

Figure IV. 11 :

 11 Figure IV.11: Known distribution of species along the distance from the source gradient based on traditional sampling (violin, when more than 2 occurences with height proportional to the number of occurrences, and when less than 3 occurrences). Dots represent sites where the species was detected using eDNA metabarcoding, with • representing detection within the known range and • for detection outside the known range

  AUTEUR : K évin CILLEROS TITRE: Diversit é et r ègles d'assemblage des communaut é de poissons d'eau douce de Guyane DIRECTEUR DE TH ÈSE : S ébastien BROSSE LIEU ET DATE DE SOUTENANCE : Universit é Paul Sabatier, le 4 d écembre 2017Les écosyst èmes tropicaux, en particulier les écosyst èmes amazoniens, sont connus pour abriter une importante diversit é d'organismes, terrestres ou aquatiques. Cependant, les causes et les processus responsables de cette grande diversit é dans les assemblages de poissons d'eau douce restent encore peu connus, et leur identification est un fort enjeu dans l' évaluation future des impacts des perturbations humaines qui sont grandissantes dans ces milieux. Nous avons étudi é les processus qui fac ¸onnent la diversit é et la structure des assemblages de poissons d'eau douce de Guyane dans les petits cours d'eau et les fleuves non impact és par les activit és humaines. La diversit é au sein des assemblages de petits cours d'eau augmente le long du gradient amont/aval et dans les milieux o ù l'habitat est plus diversifi é. L'identit é des esp èces change le long de ce gradient, d éfinissant des zones le long du cours d'eau. Les relations spatiales entre les assemblages et leur isolement ont aussi un fort effet sur les assemblages. En incorporant les informations sur les traits des esp èces (diversit é fonctionnelle) et leur relation de parent é (diversit é phylog én étique), nous avons montr é que la diversit é au sein des assemblages n' était pas influenc ée par l'environnement ou les interactions entre les esp èces. Nous avons aussi confirm é le fort effet de la limite à la dispersion entre les assemblages, en lien avec l'histoire pass ée des bassins versants, dans les petits cours d'eau et dans les fleuves. De telles études sur les processus structurant les assemblages n écessitent l'acquisition de donn ées biologiques plus compl ètes, et donc le d éveloppement d'une nouvelle m éthode d' échantillonnage qui soit exhaustive et non invasive. Pour cela, nous avons test é le metabarcoding environnemental (l'identification mol éculaire des esp èces pr ésentes à partir d'un échantillon d'eau). Cette m éthode donne des r ésultats compl émentaires aux p êches traditionnelles et n écessite encore un travail de d éveloppement et des tests suppl émentaires pour am éliorer son efficacit é et permettre son utilisation pour identifier les processus structurant les assemblages. Ces travaux, aussi bien pratiques que th éoriques, sont n écessaires au d éveloppement d'un meilleur cadre conceptuel sur la structure des assemblages de poissons tropicaux, et dans la construction d'indicateurs d'impacts d'activit és humaines sur les écosyst èmes. MOTS-CL ÉS : Ecologie des communaut és, r ègles d'asemblages, écomorphologie, écophylog én étique, poisson, metabarcoding environnemental, Guyane DISCIPLINE ADMINISTRATIVE : ÉCOLOGIE INTITUL É ET ADRESSE DE L'U.F.R. OU DU LABORATOIRE : Laboratoire Évolution et Diversit é Biologique (EDB) UMR5174 (CNRS/ENSFEA/IRD/UPS) Universit é Paul Sabatier, B âtiment 4R1 118 route de Narbonne, 31062 TOULOUSE CEDEX9 -FRANCE

Table I .

 I

	1: The 14 morphological measurements used (a) and the functional traits calculated (b) on each
	fish species
	(a) Morphological measurements
	Symbole Name	Definition
	Bl	Body length	Standard Length
	Hd	Head depth	Head depth along the vertical axis of the eye
	CPd	Caudal	Minimal caudal peduncle depth
		peduncle
		depth
	CFd	Caudal fin	Maximum depth of caudal fin
		depth
	Ed	Eye diameter	Vertical diameter of the eye
	Jl	Maxillary Jaw	Length from snout to the corner of the mouth
		Length
	Bbl	Barbel	Length of the longest barbel
		maximum
		Length
	PFl	Pectoral fin	Length of the longest ray of the pectoral fin
		length
	PFi	Pectoral fin	Vertical distance between the upper insertion of the
		position	pectoral fin to the bottom of the body
	Bd	Body depth	Maximum body depth
	Eh	Eye position	Vertical distance between the centre of the eye to the
			bottom of the body
	Mo	Oral gape	Vertical distance from the top of the mouth to the
		position	bottom of the body
	CFs	Caudal fin	Total fin surface
		surface
	PFs	Pectoral fin	Total fin surface
		surface
	MaxLength Maximum	Maximum adult length (obtained from FishBase
		length	(Froese & Pauly, 2015))

Table I . 2 :

 I2 Variables used to describe streams and rivers can be classified into 3 classes of spatial scale: drainage basin, reach and local scales.

			Scale
	Watercourse	Drainage	Reach	Local
	type	basin
	Stream	Identity	Slope, distance from	Width, depth, bottom grain
			the source, altitude, ph,	size, shelters, forest cover,
			conductivity	hydromorphological unit
	River	Identity	Distance from the source,
			altitude

Table II .1: Drainage

 II basin, reach and local scale variables measured at each site. Codes used for each variable are in bold. Distance from the source, slope and altitude were derived from GIS. Conductivity and pH were measured with conductometer and pH meter in the field. Percent of forest coverage, bottom particles (Sand, Gravel, Pebble, Boulder, Bedrock) and of each shelter type (wood, aquatic macrophytes, litter, under-banks, tree roots, open water) were visually estimated. Stream width was measured on transects perpendicular to stream flow and depth was measured every meter on these transects.

	Scale	Variables (mean ± SD [min -max])
	Drainage	Identity	Maroni, Mana, Sinnamary,
	basin		Approuague, Oyapock
	Reach	Distance from the source (km)	3.23 ± 3.71 [0.4 -16]
		(Dis)
		Slope ( ) (Slp)	5.24 ± 3.86 [0.49 -18.8]
		Altitude (m) (Alt)	182.84 ± 149.45 [28.21 -632.57]
		pH	5.88 ± 0.94 [3.75 -7.65]
		Conductivity (S.cm -1 ) (Cnd)	30.13 ± 15.84 [8.4 -108]
	Local	Forest cover (%) (Foc)	68.78 ± 29.86 [0 -100]
		Hydromorphological unit	Run, riffle, rapid, fall, pool
		Bottom particle grain size (%)	Silt (< 0.05mm)
			Sand (0.05 -2mm)
			Gravel (

Grv) (2 -10mm) Pebble (Pbb) (1 -3cm) Boulder (Bld) (3 -50cm) Bedrock (Bdk) (> 50cm)

	Available shelters (%)	Wood
		Macrophyte (Mac)
		Litter (Lit)
		Under-banks (Ubk)
		Tree roots (Trt)
		Open water (Owa)
	Depth (m)	Mean: 0.24 ± 0.16 [0.01 -1.13]
		(mDp)
		CV: 0.43 ± 0.16 [0 -0.93] (cvDp)
		< 0.2m
		[0.2, 0.4m[
		[0.4, 0.6m[
		[0.6, 0.8m[
		[0.8, 1m[
		≥ 1m
	Width (m)	Mean: 3.45 ± 2.03 [0.95 -10] (mWd)
		CV: 0.19 ± 0.12 [0 -0.57] (cvWd)
		< 2m

Table II . 3 :

 II3 Spatial, reach-and local-scales variables retained after the forward selection step and used in the variation partitioning analysis of overall fish assemblage. The cumulative R 2 adj value given for each variable corresponds to the R 2 adj of the CCA model containing the variable and all the previous ones. The conditional R 2 adj represents the percentage of variation explained by theselected variable while taking into account all the other variables for a given scale. Excluded variables are given in italics streams covered by dense canopy, with a high percentage of litter and macrophytes. The last group of species contained those that mainly occur in downstream sites with wide and deep morphology and located close to the confluence with a larger river (group 5, 23 species; Figure II.6). The same zonation was found when removing the rare species from the analysis (Figure II.S4).

	Scale Variable	Cumulative R 2 (%) Conditional R 2 adj (%)
	Reach Altitude	2.8	2.7
		Distance from the source 5.4	2.1
		pH	6.8	0.4
		Conductivity	7.3	0.5
		Slope	7.7	0.3
	Local	Mean width	2.2	1.2
		Open water	3.6	0.3
		Boulder	4.5	0.4
		Forest cover	5.2	0.6
		Mean depth	5.8	0.4
		Hydromorphological unit 7.2	1.7
		Macrophytes	7.7	0.6
		Depth CV	8.0	0.3
		Litter	8.4	0.7
		Pebble	8.9	0.5
		Wood	9.2	0.4
		Gravel	9.5	0.3
		Width CV	9.8	0.3
		Bedrock	10.1	0.4
		Under-bank	10.3	-
		Roots	10.3	-
		Sand	10.2	-
		Silt	10.2	-
	Spatial dbMEM 9	2.3	2.4
		dbMEM 1	4.1	1.8
		dbMEM 4	5.5	1.8
		dbMEM 5	6.6	1.2
		dbMEM 6	7.6	1.1
		dbMEM 7	8.4	1.3
		dbMEM 8	9.6	1.2
		dbMEM 3	10.2	0.6
		dbMEM 2	10.8	0.7
	inhabit muddy lowland

Table II . 4 :

 II4 Variation partitioning of the fish species occurrence matrix, between the four components (drainage, spatial, reach and local). This partition was achieved on the entire assemblage data after forward selection of the variables. R 2 and R 2 adj values are expressed in percentage

		R 2	R 2 adj	p
	Total explained variation	44.0 25.0
	Global effects
	Drainage	8.0	5.2	.001
	Spatial	16.6 10.8 .001
	Reach	11.1 7.7	.001
	Local	21.8 10.1 .001
	Pure-effects
	Drainage	5.2	4.0	.001
	Spatial	10.8 7.6	.001
	Reach	4.1	1.9	.001
	Local	13.0 4.1	.001
	Shared effects
	Drainage

Table II .

 II S1: List of the species studied, cluster membership (cluster numbers are as in Figure3) and species occurrence in the 152 sampling sites)

		Table II.S1 continued Table II.S1 continued
	Characiformes Characiformes	Hemigrammus ocellifer (Steindachner Moenkhausia chrysargyrea (G ünther	3	9.2 15.1
		1882) 1864)
	Order Characiformes Characiformes	Genus species Hemigrammus rodwayi (Durbin 1909) Moenkhausia collettii (Steindachner	Cluster Occurence 3.9 3 16.4
	Characiformes	Hemigrammus unilineatus (Gill 1858) 1882)	(%) 16.4
	Beloniformes Characiformes Characiformes	Potamorrhaphis guianensis (Jardine Hemiodus quadrimaculatus (Pellegrin Moenkhausia georgiae (G éry 1965)	5	0.7 1.3 5.9
	Characiformes	1843) 1909) Moenkhausia hemigrammoides (G éry	4	7.2
	Characiformes Characiformes	Acestrorhynchus falcatus (Bloch 1794) Hoplerythrinus unitaeniatus (Spix & 1965)	9.2 2.0
	Characiformes Characiformes	Anostomus brevior (G éry 1963) Agassiz 1829) Moenkhausia moisae	(G éry,	3	2.0 10.5
	Characiformes Characiformes	Aphyocharacidium Hoplias aimara (Valenciennes 1847) melandetum Planquette & Le Bail 1995)	0.7 19.7
	Characiformes Characiformes	(Eigenmann 1912) Hoplias malabaricus (Bloch 1794) Moenkhausia oligolepis (G ünther	3	11.2 31.6
	Characiformes Characiformes	Astyanax bimaculatus (Linnaeus 1758) Hyphessobrycon borealis (Zarske, Le 1864)	7.2 12.5
	Characiformes Characiformes	Astyanax validus (G éry, Planquette & Bail & G éry 2006) Moenkhausia surinamensis (G éry	3	5.9 15.1
	Characiformes	Le Bail 1991) Hyphessobrycon copelandi (Durbin 1965)	2.0
	Characiformes Characiformes	Bryconamericus aff. hyphesson 1908) Myloplus ternetzi (Norman 1929)	5	3.9 3.3
	Characiformes Characiformes Characiformes	Bryconamericus guyanensis (Zarske, Hyphessobrycon roseus (G éry 1960) Nannostomus bifasciatus (Hoedeman	3	34.9 5.9 11.2
	Characiformes	Le Bail & G éry 2010) Hyphessobrycon simulatus (G éry 1954)	2.0
	Characiformes Characiformes	Bryconops affinis (G ünther 1864) 1960) Parodon guyanensis (G éry 1959)	3	32.9 2.0
	Characiformes Characiformes Characiformes	Bryconops caudomaculatus (G ünther Hypomasticus despaxi (Puyo 1943) Phenacogaster wayana (Le Bail and	3	2.6 4.6 10.5
	Characiformes	1864) Jupiaba abramoides Lucena 2010)	(Eigenmann	20.4
	Characiformes Characiformes	Bryconops melanurus (Bloch 1794) 1909) Poptella brevispina (Reis 1989)	3	8.6 15.8
	Characiformes Characiformes Characiformes	Characidium zebra (Eigenmann 1912) Jupiaba keithi (G éry, Planquette & Le Pristella maxillaris (Ulrey 1894)	5	28.9 8.6 3.3
	Characiformes Characiformes	Charax gibbosus (Linnaeus 1758) Bail 1996) Pyrrhulina filamentosa (Valenciennes	4	0.7 40.8
	Characiformes Characiformes	Copella carsevennensis (Regan 1912) Jupiaba meunieri (G éry, Planquette & 1847)	35.5 0.7
	Characiformes Characiformes	Creagrutus melanzonus (Eigenmann Le Bail 1996) Roeboexodon geryi (Myers 1960)	5	0.7 2.0
	Characiformes Characiformes	1909) Leporinus friderici (Bloch 1794) Tetragonopterus rarus (Zarske, G éry &	5	1.3 1.3
	Characiformes Characiformes	Creagrutus planquettei (G éry & Renno Leporinus gossei (G éry, Planquette & Isbr ücker 2004)	1.3 5.3
	Characiformes	1989) Le Bail 1991) Thayeria ifati (G éry 1959)			3	4.6
	Characiformes Characiformes Cichliformes	Cynopotamus Leporinus granti (Eigenmann 1912) essequibensis Aequidens tetramerus (Heckel 1840)	4	2.6 8.6 4.6
	Characiformes Cichliformes	(Eigenmann 1912) Leporinus lebaili (G éry & Planquette Apistogramma gossei (Kullander	4	1.3 1.3
	Characiformes	Cyphocharax helleri (Steindachner 1983) 1982)	4.6
	Characiformes Cichliformes	1876) Leporinus nijsseni (Garavello 1990) Cleithracara maronii (Steindachner	4	0.7 3.3
	Characiformes Characiformes	Cyphocharax spilurus (G ünther 1864) Leporinus pellegrini (Steindachner 1881)	1.3 0.7
	Characiformes Cichliformes	Erythrinus 1910) Crenicichla albopunctata (Pellegrin erythrinus (Bloch &	4	17.1 19.7
	Characiformes	Schneider 1801) Melanocharacidium cf. blennioides 1904)	2.0
	Characiformes Characiformes Cichliformes	Gasteropelecus sternicla (Linnaeus Melanocharacidium dispilomma Crenicichla johanna (Heckel 1840)	3	7.2 3.9 2.6
	Cichliformes	1758) (Buckup 1993) Crenicichla saxatilis (Linnaeus 1758)	4	6.6
	Characiformes Characiformes Cichliformes	Hemibrycon surinamensis (G éry 1962) Microcharacidium eleotrioides (G éry Geophagus camopiensis (Pellegrin	3	17.8 10.5 0.7
	Characiformes	Hemigrammus 1960) 1903)	boesemani	(G éry	1.3
	Characiformes Cichliformes	1959) Moenkhausia aff. grandisquamis Guianacara geayi (Pellegrin 1902)	3	3.3 7.2
	Characiformes Characiformes Cichliformes	Hemigrammus Moenkhausia aff. intermedia guyanensis Guianacara owroewefi (Kullander & (G éry	3	2.0 2.6 2.0
		1959) Nijssen 1989)

Table II .S1 continued

 II

	Cichliformes	Krobia aff. guianensis sp1		3.9
	Cichliformes	Krobia aff. guianensis sp2		21.1
	Cichliformes	Krobia itanyi (Puyo 1943)		11.2
	Cichliformes	Nannacara aureocephalus (Allgayer	15.1
		1983)
	Cichliformes	Satanoperca rhynchitis (Kullander	2.6
		2012)
	Cyprinodontiformes Anablepsoides	gaucheri	(Keith,	1.3
		Nandrin & Le Bail 2006)
	Cyprinodontiformes Anablepsoides holmiae (Eigenmann	2.0
		1909)
	Cyprinodontiformes Anablepsoides igneus (Huber 1991)	21.7
	Cyprinodontiformes Anablepsoides lungi (Berkenkamp	7.9
		1984)
	Cyprinodontiformes Laimosemion agilae (Hoedeman 1954)	11.8
	Cyprinodontiformes Laimosemion cf. geayi		0.7
	Cyprinodontiformes Laimosemion geayi (Vaillant 1899)	22.4
	Cyprinodontiformes Laimosemion xiphidius (Huber 1979)	9.9
	Gobiiformes	Eleotris pisonis (Gmelin 1789)		1.3
	Gymnotiformes	Brachyhypopomus beebei (Schultz	3.3
		1944)
	Gymnotiformes	Eigenmannia virescens (Valenciennes	6.6
		1836)
	Gymnotiformes	Electrophorus electricus (Linnaeus	2.0
		1766)
	Gymnotiformes	Gymnotus carapo (Linnaeus 1758)	46.7
	Gymnotiformes	Gymnotus coropinae (Hoedeman	29.6
		1962)
	Gymnotiformes	Hypopomus artedi (Kaup 1856)		9.9
	Gymnotiformes	Hypopygus lepturus (Hoedeman 1962)	2.6
	Gymnotiformes	Japigny kirschbaum (Meunier, J égu &	3.3
		Keith 2011)
	Gymnotiformes	Sternopygus macrurus (Bloch &	24.3
		Schneider 1801)
	Incertae sedis in	Polycentrus schomburgkii (M üller &	0.7
	Ovalentaria	Troschel 1849)
	Myliobatiformes	Potamotrygon orbignyi (M üller & Henl	é	0.7
		1841)
	Siluriformes	Ancistrus aff. hoplogenys (G ünther	1.3
		1864)

Table II .S1 continued

 II

		Table II.S1 continued
	Siluriformes Siluriformes	Ancistrus Ituglanis nebulosus (de Pinna & Keith aff. temminckii	4	1.3 23.7
		(Valenciennes 1840) 2003)
	Siluriformes Siluriformes	Ancistrus cf. leucostictus (G ünther Lithoxus boujardi (Muller & Isbr ücker	2	17.1 4.6
		1864) 1993)
	Siluriformes Siluriformes	Batrochoglanis raninus (Valenciennes Lithoxus planquettei (Boeseman 1982)	3	18.4 9.9
	Siluriformes	1840) Lithoxus stocki (Nijssen & Isbr ücker	4.6
	Siluriformes	Callichthys callichthys (Linnaeus 1758) 4 1990)	5.9
	Siluriformes Siluriformes	Cetopsidium orientale (Vari, Ferraris & Megalechis thoracata (Valenciennes	3	4.6 0.7
		Keith 2003) 1840)
	Siluriformes Siluriformes	Chasmocranus brevior (Eigenmann Ochmacanthus cf. alternus (Myers	2	4.6 0.7
		1912) 1927)
	Siluriformes Siluriformes	Chasmocranus longior (Eigenmann Ochmacanthus reinhardti	3	5.3 0.7
		1912) (Steindachner 1882)
	Siluriformes Siluriformes	Corydoras aeneus (Gill 1858) Otocinclus mariae (Fowler 1940)	4	2.0 1.3
	Siluriformes Siluriformes	Corydoras amapaensis (Nijssen 1972) 4 Phenacorhamdia tenuis (Mees 1986)	1.3 5.3
	Siluriformes Siluriformes	Corydoras geoffroy (Lacep ède 1803) Pimelodella cristata (M üller & Troschel	3	12.5 13.2
	Siluriformes	Corydoras guianensis (Nijssen 1970) 1849)	5	2.0
	Siluriformes Siluriformes	Corydoras spilurus (Norman 1926) Pimelodella geryi (Hoedeman 1961)	5	1.3 5.3
	Siluriformes Siluriformes	Cteniloricaria platystoma (G ünther Pimelodella procera (Mees 1983)	5	0.7 9.9
	Siluriformes	1868) Pseudancistrus brevispinis (Heitmans,	4.6
	Siluriformes	Farlowella reticulata (Boeseman 1971) 3 Nijssen & Isbr ücker 1983)	5.9
	Siluriformes Siluriformes	Farlowella rugosa (Boeseman 1971) Rhamdia quelen (Quoy & Gaimard	3	1.3 8.6
	Siluriformes	Glanidium leopardum (Hoedeman 1824)	3	3.3
	Siluriformes	1961) Rineloricaria stewarti (Eigenmann	5.9
	Siluriformes	Harttia fowleri (Pellegrin 1908) 1910)		2	0.7
	Siluriformes Siluriformes	Harttia guianensis (Rapp Py-Daniel & Tatia brunnea (Mees 1974)	3	2.6 1.3
	Siluriformes	Oliveira 2001) Tatia intermedia (Steindachner 1877)	3.9
	Siluriformes Siluriformes	Harttiella longicauda (Covain & Fisch-Trachelyopterus galeatus (Linnaeus	3	1.3 1.3
		Muller 2012) 1766)
	Siluriformes Siluriformes	Harttiella lucifer (Covain & Fisch-Zungaro zungaro (Humboldt 1821)	2	3.9 0.7
	Muller 2012) Synbranchiformes Synbranchus	marmoratus	(Bloch	2.0
	Siluriformes	Harttiella nsp 1795)				1	3.3
	Siluriformes	Helogenes marmoratus	(G ünther	4	36.2
		1863)
	Siluriformes	Heptapterus bleekeri	(Boeseman	2	3.9
		1953)
	Siluriformes	Hypostomus gymnorhynchus (Norman	5	1.3
		1926)
	Siluriformes	Ituglanis amazonicus (Steindachner	4	4.6
		1882)

Table II .

 II S2: Models retained to explain variation in the number of Characiformes (a) and Siluriformes (b). The models are ranked by their Akaike's Information Criterion corrected for small samples (AICc). All the models with an AICc within 4 units from the best model are indicated. For each model, the Akaike weigth (w i ) is indicated, the variables included are noted with + and the R 2 m and R 2 c are indicated. For each variable, the model-averaged coefficient (estimate ± SE), its relative importance (the sum of model w i in which the variable is included) and its 95% confidence interval (CI) are indicated. For hydromorpholical unit, detailed results for each type are given below the table. Abbreviations used: Dis, distance from the source; Alt, altitude; Cpx, complexity; Slp, slope; Foc, forest cover; Cnd, conductivity; Hdu, hydromorphological unit.

							Table II.S2 continued
	(b) Siluriformes
			Variable
		Model	Dis	Alt	Slp	Foc	Cnd	pH	Cpx	Hdu AICc	w i R 2 m R 2 c
		1	+							+	535.58 .19 .22 .28
		2	+			+				+	536.68 .11 .24 .27
	(a) Characiformes 3 +						+	+	537.12 .09 .23 .28
		4	Variable +		+					+	537.25 .08 .23 .27
		Model 5	Dis +	Alt		Slp	Foc +	Cnd	pH	Cpx +	Hdu AICc 537.62 .07 .22 .28 w i R 2 m R 2 c
		1 6	+ +	+				+		+	+ 699.47 .23 .97 .98 537.72 .07 .23 .28
		2 7	+ +	+		+			+	+ +	+ 699.66 .21 .97 .98 538.62 .04 .24 .27
		3 8	+ +	+	+	+			+	+	+ 701.57 .08 .97 .98 538.67 .04 .24 .27
		4 9	+ +	+		+	+ +			+	+ 701.71 .07 .97 .98 538.76 .04 .24 .27
		5 10	+ +	+			+		+ +	+ +	+ 701.75 .07 .97 .98 538.86 .04 .24 .28
		6 11	+ +	+		+		+ +		+	+ 701.78 .07 .97 .98 538.87 .04 .24 .27
		7 12	+ +	+		+				+	+ 701.77 .07 .97 .98 + 538.98 .04 .30 .32
		8 13	+ +	+	+		+		+	+ +	+ 702.01 .06 .97 .98 539.00 .03 .24 .28
		9 14	+ +	+				+ +	+	+ +	+ 702.02 .06 .97 .98 539.06 .03 .23 .27
		10 15	+ +	+		+				+	+ 702.02 .06 .97 .98 539.29 .03 .18 .27
	Estimate 16	0.43 ± +	-0.66 ± +	-4.17x10-4 ±	0.0021 ±	-0.0012 ± +	-0.0063 ±	0.043 ± +	†	539.39 .03 .23 .28
		± SE 17	0.074 +	0.11 +	0.033	0.027 +	0.035	0.036	0.062 +	539.44 .03 .23 .27
	Importance Estimate	1 0.27 ±	1 0.0091 ±	.14 0.017 ±	.14 0.0041 ±	.14 -2x10-4 ±	.15 0.012 ±	.47 0.15 ±	‡	1
		95% CI ± SE	0.28 -0.051	-0.88 -0.036	-0.18 -0.18 0.044	-0.12 -0.027	-0.20 -0.029	-0.20 -0.040	-0.058 -0.06	†
	Importance	0.57 1	0.44 .22	.27	0.15 .17	0.18 .17	0.12 .24	0.40 .97	.04
	† Estimate ± SE (95% CI) for each unit. Pool (intercept): 1.40 ± 0.17 (1.07 -1.73); Plate: -0.21 ± 0.17 (-0.56 -0.13); Run: -0.56 ± 0.32 (-1.19 -0.08); Riffle: 95% CI 0.17 --0.091 --0.063 --0.010 --0.14 -0.14 -0.084 -0.032 - ‡
		0.21 ± 0.31 (-0.41 -0.83); Riffle/run: -0.0072 ± 0.16 (-0.32 -0.30); Waterfall (No species of Characiformes was found in this hydromorphological unit): -19.37 0.37 0.18 0.19 0.15 0.19 0.28
		± 3295 (-6533 -6494).

‡ Estimate ± SE (95% CI) for each unit. Pool (intercept): 1.40 ± 0.17 (1.07 -1.73); Plate: -0.21 ± 0.17 (-0.56 -0.13); Run: -0.56 ± 0.32 (-1.19 -0.08); Riffle: 0.21 ± 0.31 (-0.41 -0.83); Riffle/run: -0.0072 ± 0.16 (-0.32 -0.30); Waterfall (No species of Characiformes was found in this hydromorphological unit): -19.37 ± 3295

(-6533 -6494)

.

Table II .

 II S3: Spatial, reach-and local-scales variables retained after the forward selection step and used in the variation partitioning analysis of fish assemblage while removing the rare species (found in less than 5 sites). The cumulative R 2 adj value given for each variable corresponds to the R 2 adj of the CCA model containing the variable and all the previous ones. The conditional R 2 adj represents the percentage of variation explained by the selected variable while taking into account all the other variables for a given scale. Excluded variables are given in italics

	Scale Variable	Cumulative R 2 (%) Conditional R 2 adj (%)
	Reach Altitude	4.2	3.9
		Distance from the source 7.1	2.5
		pH	9.0	0.8
		Conductivity	9.9	0.9
		Slope	10.4	0.5
	Local	Mean width	2.6	1.2
		Boulder	4.6	0.6
		Open water	6.0	0.5
		Forest cover	7.0	0.8
		Macrophytes	7.9	0.8
		Depth CV	8.6	0.4
		Litter	9.1	0.7
		Pebble	9.8	0.6
		Wood	10.3	0.5
		Gravel	10.7	0.4
		Mean depth	11.2	0.4
		Hydromorphological unit 13.1	0.3
		Width CV	13.5	0.4
		Under-bank	13.8	0.4
		Bedrock	14.0	-
		Roots	14.1	-
		Sand	14.1	-
		Silt	14.2	-
	Spatial dbMEM 4	1.9	2.4
		dbMEM 1	3.8	2.0
		dbMEM 8	5.0	1.9
		dbMEM 7	6.8	1.8
		dbMEM 5	7.9	1.2
		dbMEM 3	8.9	1.0
		dbMEM 9	9.7	0.8
		dbMEM 6	10.3	0.6
		dbMEM 2	10.7	-

Table II .

 II S4: Variation partitioning of the fish species occurrence matrix after removing species occurring in less than 5 sites, between the four components (drainage, spatial, reach and local). This partition was achieved on the entire assemblage data after forward selection of the variables. R 2 and R 2

				adj values are
	expressed in percentage
		R 2	R 2 adj	p
	Total explained variation	44.8 29.8
	Global effects
	Drainage	8.7	6.2	.001
	Spatial	14.9 10.3 .001
	Reach	13.3 10.4 .001
	Local	24.0 13.9 .001
	Pure-effects
	Drainage	6.2	5.4	.001
	Spatial	9.8	7.4	.001
	Reach	4.6	2.9	.001
	Local	13.0 5.4	.001
	Shared effects
	Drainage

Table III . 1 :

 III1 Regression parameters of the relationship between diversity metrics (βT j , βF j and turnover) of the fish faunaus and the pairwise geodesic distance (logged value) between sites for continental France and French Guiana estimated by bootstrap based on 9,999 permutations. Spearman correlation coefficient r and p were obtained from Mantel tests with 9,999 permutations

	Metric	Region	Intercept (95%	Slope (95% CI)	r	p
			CI)
	βT j	Continental	.54 (.51 -.57)	.026 (.019 -	.069 .069
		France		.032)
		French Guiana	.75 (.73 -.76)	.021 (.017 -	.13 .0043
				.024)
	Taxonomic	Continental	.37 (.33 -.41)	.034 (.025 -	.087 .034
	turnover	France		.043)
		French Guiana	.64 (.61 -.66)	.029 (.024 -	.14 .001
				.034)
	βF j	Continental	.80 (.77 -.84)	.018 (.010 -	.015 .39
		France		.026)
		French Guiana	.76 (.74 -.79)	.0064 (.0016 -	.026 .31
				.011)
	Functional	Continental	.48 (.42 -.54)	.034 (.021 -	.034 .23
	turnover	France		.049)
		French Guiana	.44 (.41 -.48)	.010 (.0024 -
				.018)

Table III .S1 continued

 III

	(b) French Guiana
	Order	Genus species
	Beloniformes	Potamorrhaphis guianensis (Jardine, 1843)
	Characiformes	Acestrorhynchus falcatus (Bloch, 1794)
	Characiformes	Anostomus brevior (Gry, 1963)
	Characiformes	Astyanax bimaculatus (Linnaeus, 1758)
	Characiformes	Astyanax leopoldi (Gry, Planquette & Le Bail, 1991)
	Characiformes	Astyanax validus (Gry, Planquette & Le Bail, 1991)
	Characiformes	Bryconamericus aff. hyphesson
	Characiformes	Bryconamericus guyanensis (Zarske, Le Bail & Gry,
		2010)
	Characiformes	Bryconops aff. caudomaculatus (Gnther, 1864)
	Characiformes	Bryconops affinis (Gnther, 1864)
	Characiformes	Bryconops caudomaculatus (Gnther, 1864)
	Characiformes	Bryconops melanurus (Bloch, 1794)
	Characiformes	Characidium zebra (Eigenmann, 1909)
	Characiformes	Creagrutus melanzonus (Eigenmann, 1909)
	Characiformes	Creagrutus planquettei (Gry & Renno, 1989)
	Characiformes	Curimatopsis crypticus (Vari, 1982)
	Characiformes	Cynopotamus essequibensis (Eigenmann, 1912)
	Characiformes	Cyphocharax aff. spilurus
	Characiformes	Cyphocharax gouldingi (Vari, 1992)
	Characiformes	Cyphocharax helleri (Eigenmann & Eigenmann,
		1889)
	Characiformes	Cyphocharax spilurus (Gnther, 1864)
	Characiformes	Erythrinus erythrinus (Bloch & Schneider, 1801)
	Characiformes	Gasteropelecus sternicla (Linnaeus, 1758)
	Characiformes	Hemibrycon surinamensis (Gry, 1962)
	Characiformes	Hemigrammus boesemani (Gry, 1959)
	Characiformes	Hemigrammus guyanensis (Gry, 1959)
	Characiformes	Hemigrammus ocellifer (Steindachner, 1882)
	Characiformes	Hemigrammus ora (Zarske, Le Bail & Gry, 2006)
	Characiformes	Hemigrammus rodwayi (Durbin, 1909)
	Characiformes	Hemigrammus unilineatus (Gill, 1858)
	Characiformes	Hemiodus quadrimaculatus (Pellegrin, 1909)
	Characiformes	Hoplerythrinus unitaeniatus (Spix & Agassiz, 1829)
	Characiformes	Hoplias aimara (Valenciennes, 1847)
	Characiformes	Hoplias malabaricus (Bloch, 1794)
	Characiformes	Hyphessobrycon borealis (Zarske, Le Bail & Gry,
		2006)
	Characiformes	Hyphessobrycon copelandi (Durbin, 1908)

Table III .S1 continued

 III

	Characiformes	Hyphessobrycon roseus (Gry, 1960)
	Characiformes	Hyphessobrycon simulatus (Gry, 1960)
	Characiformes	Hypomasticus despaxi (Puyo, 1943)
	Characiformes	Jupiaba abramoides (Eigenmann, 1909)
	Characiformes	Jupiaba keithi (Gry, Planquette & Le Bail, 1996)
	Characiformes	Jupiaba meunieri (Gry, Planquette & Le Bail, 1996)
	Characiformes	Leporinus acutidens (Valenciennes, 1837)
	Characiformes	Leporinus friderici (Bloch, 1794)
	Characiformes	Leporinus gossei (Gry, Planquette & Le Bail, 1991)
	Characiformes	Leporinus granti (Eigenmann, 1912)
	Characiformes	Leporinus lebaili (Gry & Planquette, 1983)
	Characiformes	Leporinus nijsseni (Garavello, 1990)
	Characiformes	Leporinus pellegrini (Steindachner, 1910)
	Characiformes	Melanocharacidium cf. blennioides
	Characiformes	Melanocharacidium dispilomma (Buckup, 1993)
	Characiformes	Microcharacidium eleotrioides (Gry, 1960)
	Characiformes	Moenkhausia aff. grandisquamis
	Characiformes	Moenkhausia aff. intermedia
	Characiformes	Moenkhausia chrysargyrea (Gnther, 1864)
	Characiformes	Moenkhausia collettii (Steindachner, 1882)
	Characiformes	Moenkhausia georgiae (Gry, 1965)
	Characiformes	Moenkhausia hemigrammoides (Gry, 1965)
	Characiformes	Moenkhausia moisae (Gry, Planquette & Le Bail,
		1995)
	Characiformes	Moenkhausia oligolepis (Gnther, 1864)
	Characiformes	Moenkhausia surinamensis (Gry, 1965)
	Characiformes	Myloplus ternetzi (Norman, 1929)
	Characiformes	Nannostomus beckfordi (Gnther, 1872)
	Characiformes	Nannostomus bifasciatus (Hoedeman, 1954)
	Characiformes	Parodon guyanensis (Gry, 1959)
	Characiformes	Phenacogaster wayampi (Le Bail & Lucena, 2010)
	Characiformes	Phenacogaster wayana (Le Bail & Lucena, 2010)
	Characiformes	Poptella brevispina (Reis, 1989)
	Characiformes	Pristella maxillaris (Ulrey, 1894)
	Characiformes	Pyrrhulina filamentosa (Valenciennes, 1847)
	Characiformes	Roeboexodon guyanensis (Puyo, 1948)
	Characiformes	Serrapinnus gracilis (Gry, 1960)
	Characiformes	Steindachnerina varii (Gry, Planquette & Le Bail,
		1991)
	Characiformes	Thayeria ifati (Gry, 1959)
	Cichliformes	Aequidens tetramerus (Heckel, 1840)

Table III .S1 continued

 III

	Siluriformes	Chasmocranus longior (Eigenmann, 1912)
	Siluriformes	Corydoras aeneus (Gill, 1858)
	Siluriformes	Corydoras amapaensis (Nijssen, 1972)
	Siluriformes	Corydoras geoffroy (Lacepde, 1803)
	Siluriformes	Corydoras guianensis (Nijssen, 1970)
	Siluriformes	Corydoras solox (Nijssen & Isbrcker, 1983)
	Siluriformes	Corydoras spilurus (Norman, 1926)
	Siluriformes	Cteniloricaria platystoma (Gnther, 1868)
	Siluriformes	Farlowella reticulata (Boeseman, 1971)
	Siluriformes	Farlowella rugosa (Boeseman, 1971)
	Siluriformes	Glanidium leopardum (Hoedeman, 1961)
	Siluriformes	Harttia fowleri (Pellegrin, 1908)
	Siluriformes	Harttia guianensis (Rapp Py-Daniel & Oliveira,
		2001)
	Siluriformes	Harttiella longicauda (Covain & Fish-Muller, 2012)
	Siluriformes	Helogenes marmoratus (Gnther, 1863)
	Siluriformes	Heptapterus bleekeri (Boeseman, 1953)
	Siluriformes	Heptapterus tenuis (Mees, 1986)
	Siluriformes	Hypostomus gymnorhynchus (Norman, 1926)
	Siluriformes	Imparfinis pijpersi (Hoedeman, 1961)
	Siluriformes	Ituglanis amazonicus (Steindachner, 1882)
	Siluriformes	Ituglanis nebulosus (de Pinna & Keith, 2003)
	Siluriformes	Lithoxus boujardi (Muller & Isbrcker, 1990)
	Siluriformes	Lithoxus planquettei (Boeseman, 1982)
	Siluriformes	Lithoxus stocki (Nijssen & Isbrcker, 1990)
	Siluriformes	Loricaria aff. parnahybae
	Siluriformes	Loricaria cataphracta (Linnaeus, 1758)
	Siluriformes	Megalechis thoracata (Valenciennes, 1840)
	Siluriformes	Ochmacanthus reinhardtii (Steindachner, 1882)
	Siluriformes	Pimelodella cristata (Mller & Troschel, 1849)
	Siluriformes	Pimelodella geryi (Hoedeman, 1961)
	Siluriformes	Pimelodella macturki (Eigenmann, 1912)
	Siluriformes	Pimelodella procera (Mees, 1983)
	Siluriformes	Pseudancistrus brevispinis (Heitmans, Nijssen &
		Isbrcker, 1983)
	Siluriformes	Rhamdia quelen (Quoy & Gaimard, 1824)
	Siluriformes	Rineloricaria platyura (Mller & Troschel, 1849)
	Siluriformes	Tatia brunnea (Mees, 1974)
	Siluriformes	Tatia intermedia (Steindachner, 1877)
	Siluriformes	Trachelyopterus galeatus (Linnaeus, 1766)
	Synbranchiformes	Synbranchus marmoratus (Bloch, 1795)

Table III .

 III S2: Physical characteristics and pairwise geodesic distance among the selected sites for French Guiana and continental France (mean ± SD)

	Table III.S3: Morphological measurements (a) and functional traits (b) measured on each fish species
	(a) Morphological measurements
	Characteristics Symbole Name Bl Body length	French Guiana Definition Standard Length	Continental France
	Distance from the source (km) Hd Head depth Head depth along the vertical axis of the eye 4.02 ± 4.22 7.96 ± 3.73
	Altitude (m) CPd Caudal	176.53 ± 171.01 Minimal caudal peduncle depth	234.64 ± 27.81
	Slope ( )	peduncle	5.05 ± 2.90	4.29 ± 3.08
	Pairwise distance between sites (km) 117.90 ± 73.26 depth CFd Caudal fin Maximum depth of caudal fin	98.97 ± 57.22
		depth
	Ed	Eye diameter	Vertical diameter of the eye
	Jl	Maxillary Jaw	Length from snout to the corner of the mouth
		Length
	Bbl	Barbel	Length of the longest barbel
		maximum
		Length
	PFl	Pectoral fin	Length of the longest ray of the pectoral fin
		length
	PFi	Pectoral fin	Vertical distance between the upper insertion of the
		position	pectoral fin to the bottom of the body
	Bd	Body depth	Maximum body depth
	Eh	Eye position	Vertical distance between the centre of the eye to the
			bottom of the body
	Mo	Oral gape	Vertical distance from the top of the mouth to the
		position	bottom of the body
	CFs	Caudal fin	Total fin surface
		surface
	PFs	Pectoral fin	Total fin surface
		surface
	MaxLength Maximum	Maximum adult length (obtained from FishBase
		length	(Froese & Pauly, 2015))

Figure III.S1: Morphological measurements measured on each fish species

Table III .

 III S4: Spearman's correlations between the functional traits and the four PCoA components

	Functional traits Axis 1 Axis 2 Axis 3 Axis 4
	Log(M axLength)	0.23	-0.49	-0.45 -0.066
	Ed Hd	-0.59	0.38 0.090	0.26
	Bbl Bl	0.49 0.057 -0.011	0.20
	M o Hd	-0.70	-0.17 -0.031	-0.11
	Jl Hd	-0.15	-0.43	0.00	0.10
	Eh Hd	0.52	-0.14	-0.26	0.22
	Bl Bd	0.40	-0.21	-0.10	0.29
	Hd Bd	0.55	0.20	0.43	-0.05
	P F i Bd	0.072	-0.79	0.18	0.33
	P F l 2 P F s	-0.34	0.60	-0.28	-0.31
	P F l Bl	0.059	0.35	0.34 -0.042
	CF d CP d	-0.41	0.59	-0.27	-0.21
	CF d 2 CF s	-0.60	0.44	-0.50	-0.31
	P F s CF s	0.65 -0.038	0.36	0.27
	(P F s+CF s) (Bl×Bd)	0.33 0.027	0.63	0.50

Table IV . 1 :

 IV1 List of studied sites with their river drainage membership and the type of watercourse (stream or river). See Figure1for site locations. Coordinates are given in the WGS84 coordinate reference system

	Site	Site name	Drainage	Watercourse	Latitude Longitude Date	of	Date	of
	number		basin	type			traditional	eDNA
							sampling		sampling
	1	Apa	Maroni	Stream	5.3432	-54.0869	2012
	2	Crique des Cascades	Maroni	Stream	5.3476	-54.1053	2012
	3	Crique Bastien	Maroni	Stream	5.27003 -54.23433 2012
	4	Crique Penta	Maroni	Stream	5.21758 -54.28227 2012
	5	Papaichton	Maroni	River	3.80456 -54.16561 2015
	6	Saut Sonnelle	Maroni	River	3.66042 -53.95992 2015
	7	Twenke	Maroni	River	3.35922 -54.05492 2015
	8	Pikin Tabiki	Maroni	River	3.23267 -54.08319 2015
	9	Apsik Icholi	Maroni	River	2.93669 -54.174	2015
	10	Crique Nouvelle France 6 Maroni	Stream	3.62707 -53.16569 2011
	11	Crique Nouvelle France 5 Maroni	Stream	3.62707 -53.16692 2011
	12	Crique Nouvelle France 4 Maroni	Stream	3.61284 -53.16884 2011
	13	Crique Nouvelle France 3 Maroni	Stream	3.59726 -53.17847 2013
	14	Crique Nouvelle France 2 Maroni	Stream	3.57697 -53.19268 2011
	15	Crique Nouvelle France 1 Maroni	Stream	3.56573 -53.1975	2011
	16	Crique Voltalia 4	Mana	Stream	5.37639 -53.663	2011
	17	Crique Voltalia 3	Mana	Stream	5.35789 -53.66336 2011
	18	Crique Voltalia 2	Mana	Stream	5.3427	-53.66158 2011
	19	Crique Petit laussat Aval Mana	Stream	5.40887 -53.58121 2012
	20	Crique l'Est	Mana	Stream	3.66264 -53.22197 2012
	21	Crique Organabo	Organobo	Stream	5.46971 -53.534	2012

Table IV .1 continued

 IV

	22	Crique Toussaint	Sinnamary	Stream	5.30908 -53.05873 2010
	23	Crique Paracou	Sinnamary	Stream	5.28692 -52.90708 2012
	24	Crique Eau Claire	Kourou	Stream	5.14711 -52.87149 2010
	25	Crique Humus	Kourou	Stream	4.91867 -52.54702 2012
	26	Saut Bief	Comte	River	4.55343 -52.49948 2015
	27	Crique Petit Approuague Comte	Stream	4.36481 -52.32803 2011
	28	Crique Kapiri 6	Approuague Stream	4.15347 -52.16586 2011
	29	Crique Kapiri 1	Approuague Stream	4.13208 -52.17138 2012
	30	Crique Kapiri 2	Approuague Stream	4.10373 -52.08655 2012
	31	Athanase	Approuague River	4.17772 -52.35567 2015
	32	Machicou	Approuague River	3.89741 -52.58253 2015
	33	Crique parare 2	Approuague Stream	4.03861 -52.67697 2010
	34	Crique parare 5	Approuague Stream	4.04399 -52.68746 2010
	35	Crique parare 8	Approuague Stream	4.04848 -52.69214 2010
	36	Crique SM	Oyapock	Stream	3.8587	-51.87103 2012
	37	Crique Marie	Oyapock	Stream	3.87064 -51.85653 2012
	38	Crique Pied Saut	Oyapock	Stream	3.80605 -51.89595 2012
	39	Crique Minette	Oyapock	Stream	3.82033 -51.875	2012

Table IV .S1 continued

 IV

	Table IV.S1 continued
	Auchenipterus nuchalis (Spix & Agassiz, 1829) Copella carsevennensis (Regan, 1912)	Species -	0 -	7.69 48.72
	Batrochoglanis raninus (Valenciennes, 1840) Corydoras aeneus (Gill, 1858)	Species Species	35.90 7.69	43.59 0
	Bivibranchia bimaculata (Vari, 1985) Corydoras aff. guianensis	Species Species	2.56 5.13	10.26 5.13
	Brachyhypopomus beebei (Schultz, 1944) Corydoras amapaensis (Nijssen, 1972)	Species Species	2.56 0	5.13 7.69
	Brachyplatystoma vaillantii (Valenciennes, 1840) Corydoras geoffroy (Lacep ède, 1803)	Species Species	0 12.82	2.56 10.26
	Brycon falcatus (M üller & Troschel, 1844) Corydoras solox (Nijssen & Isbr ücker, 1983)	Species Species	2.56 5.13	7.69 0
	Brycon pesu (M üller & Troschel, 1845) Corydoras spilurus (Norman, 1926)	Species Species	2.56 0	7.69 2.56
	Bryconamericus aff. hyphesson Creagrutus melanzonus (Eigenmann, 1909)	Species Species	0 5.13	2.56 0
	Bryconamericus guyanensis (Zarske, Le Bail & G éry, 2010) Creagrutus planquettei (G éry & Renno, 1989)	Species Species	43.59 2.56	41.03 2.56
	Bryconops aff. caudomaculatus (G ünther, 1864) Crenicichla albopunctata (Pellegrin, 1904)	Genus Species	Bryconops 12.82	10.26 15.38
	Bryconops affinis (G ünther, 1864) Crenicichla johanna (Heckel, 1840)	Genus Species	Bryconops 0	46.15 5.13
	Bryconops caudomaculatus (G ünther, 1864) Crenicichla multispinosa (Pellegrin, 1903)	Genus Species	Bryconops 5.13	17.95 2.56
	Bryconops melanurus (Bloch, 1794) Crenicichla saxatilis (Linnaeus, 1758)	Genus Species	Bryconops 15.38	20.51 28.21
	Caenotropus maculosus (Eigenmann, 1912) Cteniloricaria platystoma (G ünther, 1868)	Species Species	2.56 2.56	7.69 0
	Callichthys callichthys (Linnaeus, 1758) Curculionichthys sp	Species Species	25.64 7.69	2.56 2.56
	Cetopsidium orientale (Vari, Ferraris & Keith, 2003) Curimata cyprinoides (Linnaeus, 1766)	Species Species	15.38 2.56	12.82 7.69
	Chalceus macrolepidotus (Cuvier, 1818) Curimatopsis crypticus (Vari, 1982)	Species Species	2.56 2.56	7.69 2.56
	Characidium zebra (Eigenmann, 1909) Cynodon meionactis (G éry, Le Bail & Keith, 1999)	Species Species	28.21 2.56	48.72 7.69
	Charax gibbosus (Linnaeus, 1758) Cynopotamus essequibensis (Eigenmann, 1912)	Species -	2.56 -	10.26 10.26
	Chasmocranus brevior (Eigenmann, 1912) Cyphocharax aff. spilurus	Species -	10.26 -	7.69 2.56
	Chasmocranus longior (Eigenmann, 1912) Cyphocharax helleri (Steindachner, 1910)	Species Species	41.03 20.51	17.95 12.82
	Chilodus zunevei (Puyo, 1946) Cyphocharax spilurus (G ünther, 1864)	Species Species	5.13 10.26	5.13 15.38
	Cichla ocellaris (Bloch & Schneider, 1801) Doras carinatus (Linnaeus, 1776)	Species Species	5.13 5.13	5.13 7.69
	Cichlasoma bimaculatum (Linnaeus, 1758) Doras micropoeus (Eigenmann, 1912)	Species Species	5.13 2.56	0 7.69
	Cleithracara maronii (Steindachner, 1881) Eigenmannia virescens (Valenciennes, 1836)	Species Species	23.08 41.03	12.82 35.90
	Copella arnoldi (Regan, 1912) Electrophorus electricus (Linnaeus, 1766)	Species Species	2.56 23.08	0 5.13

Table IV .S1 continued

 IV

	Metynnis lippincottianus (Cope, 1870)	-	-	2.56
	Microcharacidium eleotrioides (G éry, 1960)	Species	35.90	15.38
	Moenkhausia aff. grandisquamis	Genus	Moenkhausia	7.69
	Moenkhausia aff. intermedia	Species	5.13	2.56
	Moenkhausia chrysargyrea (G ünther, 1864)	Genus	Moenkhausia	38.46
	Moenkhausia collettii (Steindachner, 1882)	Species	5.13	28.21
	Moenkhausia georgiae (G éry, 1965)	Species	12.82	7.69
	Moenkhausia grandisquamis (M üller & Troschel, 1845)	Genus	Moenkhausia	12.82
	Moenkhausia hemigrammoides (G éry, 1965)	Species	0	10.26
	Moenkhausia moisae (G éry, Planquette & Le Bail, 1995)	Genus	Moenkhausia	23.08
	Moenkhausia oligolepis (G ünther, 1864)	Species	25.64	41.03
	Moenkhausia surinamensis (G éry, 1965)	Genus	Moenkhausia	15.38
	Myloplus rhomboidalis (Cuvier, 1818)	Species	2.56	7.69
	Myloplus rubripinnis (M üller & Troschel, 1844)	-	-	12.82
	Myloplus ternetzi (Norman, 1929)	Species	30.77	20.51
	Nannacara aureocephalus (Allgayer, 1983)	Species	35.90	38.46
	Nannostomus beckfordi (G ünther, 1872)	Species	2.56	5.13
	Nannostomus bifasciatus (Hoedeman, 1954)	Species	12.82	15.38
	Ochmacanthus cf. alternus (Myers, 1927)	-	-	2.56
	Ochmacanthus reinhardtii (Steindachner, 1882)	Species	0	2.56
	Otocinclus mariae (Fowler, 1940)	Species	5.13	2.56
	Pachypops fourcroi (Lacep ède, 1802)	Species	2.56	7.69
	Parodon guyanensis (G éry, 1959)	Species	2.56	5.13
	Phenacogaster wayana (Le Bail & Lucena, 2010)	Species	0	10.26
	Phenacorhamdia tenuis (Mees, 1986)	Species	7.69	7.69
	Piabucus dentatus (Koelreuter, 1763)	Species	0	5.13

Table IV .S1 continued

 IV

	Semaprochilodus varii (Castro, 1988)			Family	Prochilodontidae 5.13
	Serrapinnus gracilis (G éry, 1690)			Family	Characidae	5.13
	Serrasalmus eigenmanni (Norman, 1929 )			Species	2.56	12.82
	Serrasalmus rhombeus (Linnaeus, 1766)			-	-	12.82
	Steindachnerina varii (G éry, Planquette & Le Bail, 1991)		Species	12.82	0
	Sternopygus macrurus (Bloch & Schneider, 1801)		Species	48.72	46.15
	Synbranchus marmoratus (Bloch, 1795)			Species	61.54	7.69
	Tatia brunnea (Mees, 1974)			7,870	Species	15.38	2.56
	Tatia intermedia (Steindachner, 1877)		357,436 2,039	Species	10.26	7.69
	Tetragonopterus chalceus (Spix & Agassiz, 1829)	23,943	Species	2.56	12.82
	Tetragonopterus rarus (Zarske, G éry & Isbr ücker, 2004) Thayeria ifati (G éry, 1959) Crique parare 5 Approuague Stream Crique parare 8 Approuague Stream	1,033,352 209,312 461,007	Species Species	10.26 2.56	2.56 10.26
	Trachelyopterus galeatus (Linnaeus, 1766) Crique parare 2 Approuague	Stream	44,967	Species	2.56	5.13
	Triportheus brachipomus (Valenciennes, 1850) Pikin Tabiki Maroni River Saut Sonnelle Maroni River	1,565,695 852,552	-	-	12.82
	Papaichton	Maroni	River	1,235,661
	Saut Bief	Comte	River	677,714
	Machicou	Approuague	River	1,954,347
	Athanase	Approuague	River	2,031,480
	Twenke	Maroni	River	1,346,169
	Apsik Icholi	Maroni	River	442,803
	Crique Marie	Oyapock	Stream	803,477
	Crique SM	Oyapock	Stream	441,709
	Crique Minette	Oyapock	Stream	470,174
	Crique Pied Saut	Oyapock	Stream	451,058
	Crique Kapiri 2	Approuague	Stream	98,842
	Crique Kapiri 1	Approuague	Stream	96,132
	Crique Kapiri 6	Approuague	Stream	145,703
	Crique Petit Approuague	Comte	Stream	145,206
	Crique Humus	Kourou	Stream	134,347
	Crique Eau Claire	Kourou	Stream	124,136
	Crique Paracou	Sinnamary	Stream	116,440
	Crique Toussaint	Sinnamary	Stream	107,175
	Crique Organabo	Organobo	Stream	261,145
	Crique Petit laussat Aval	Mana	Stream	304,807
	Crique Voltalia 2	Mana	Stream	259,417
	Crique Voltalia 3	Mana	Stream	382,708
	Crique Voltalia 4	Mana	Stream	250,053
	Crique des Cascades	Maroni	Stream	878,385
	Apa	Maroni	Stream	126,689
	Crique Bastien	Maroni	Stream	106,933
	Crique Penta	Maroni	Stream	91,667
	Crique Nouvelle France 1	Maroni	Stream	144,262
	Crique Nouvelle France 2	Maroni	Stream	123,139
	Crique Nouvelle France 6	Maroni	Stream	175,609
	Crique Nouvelle France 5	Maroni	Stream	161,476
	Crique Nouvelle France 4	Maroni	Stream	125,194
	Crique Nouvelle France 3	Maroni	Stream	303,219
	Crique à l'Est	Mana	Stream	124,856
				17,775,665
				100

Table III .

 III S2 Number of reads obtained from the NGS runs per sample before and after bioinformatic filtering Percentage of the initial number of reads

	Site name	Number of reads per sample with an occurrence > 10 and a length > 20 bp Site name 0.98 with the reference database per sample (obiclean) Number of non internal reads Number of reads with an identity ≥ Number of reads
						GenBank GenBank	Guyana fish Guyana fish
						6,867 6,775 6,775	0	6,775 0
						161,434 142,281 142,281	0	142,345 0
	Control Control	Control			0		0	0	0	0	0
						23,095 21,516 21,516	0	21,764 0
						Watercourse 224,975 224,975 255,980	Number of reads 226,523 30 0
	Site name Crique parare 5 Crique parare 5 Crique parare 5	Watershed 193,738 18,980 18,980 type	per sample 191,662 129,546 129,546
	Crique parare 8 Crique parare 8 Crique parare 8		444,353	0	0	433,617 288,050 288,050
	Crique parare 2 Crique parare 2 Crique parare 2		43,272 1,406 1,406	42,587	43,046 42,587
	Pikin Tabiki Pikin Tabiki	Pikin Tabiki	Control 1,297,118 235,833 235,833	1,163,918 870,235 870,235
	Saut Sonnelle Saut Sonnelle Saut Sonnelle		427,764 135,301 135,301	383,321 245,896 245,896
	Papaichton Papaichton	Papaichton			744,182 221,837 221,837	645,537 252,025 252,025
	Saut Bief Saut Bief	Saut Bief			595,816 95,681 95,681	572,171 356,512 356,512
	Machicou Machicou	Machicou		1,584,607 32,689 32,703	1,392,239 599,182 599,182
	Athanase Athanase	Athanase		1,246,918 296,928 296,917	1,118,966 281,453 281,453
	Twenke Twenke	Twenke			924,817 136,037 136,037	833,361 488,319 488,319
	Apsik Icholi Apsik Icholi	Apsik Icholi			382,995 64,877 64,888	377,733 278,497 278,497
	Crique Marie Crique Marie Crique Marie		781,847 85,177 85,177	763,115 601,068 601,195
	Crique SM Crique SM	Crique SM			430,751 11,685 11,685	422,454 349,260 349,292
	Crique Minette Crique Minette Crique Minette		458,874 16	16	446,353 396,781 396,781
	Crique Pied Saut Crique Pied Saut Crique Pied Saut		441,135 10,413 10,413	430,122 296,396 296,396
	Crique Kapiri 2 Crique Kapiri 2 Crique Kapiri 2		88,579 15,149 15,149	56,315	88,187 56,315
	Crique Kapiri 1 Crique Kapiri 1 Crique Kapiri 1		87,573 13,123 13,123	52,373	86,935 52,373
	Crique Kapiri 6 Crique Kapiri 6 Crique Kapiri 6		132,328 11,318 11,318	83,442	130,847 83,442
	Crique Petit Approuague Crique Petit Approuague 130,677 Crique Petit Approuague 23,125 23,125	76,364	130,004 76,330
	Crique Humus Crique Humus Crique Humus		122,502 20,259 20,259	75,113	121,979 75,113
	Crique Eau Claire Crique Eau Claire Crique Eau Claire		113,437 18,110 18,110	61,752	112,633 61,752
	Crique Paracou Crique Paracou Crique Paracou		107,621 20,609 20,609	53,752	106,219 53,752
	Crique Toussaint Crique Toussaint Crique Toussaint		97,072 14,083 14,083	56,092	96,506 56,092
	Crique Organabo Crique Organabo Crique Organabo		245,412 108,878 108,878	38,537	238,741 38,537
	Crique Petit laussat Aval Crique Petit laussat Aval 283,906 Crique Petit laussat Aval 22,765 22,765	276,842 135,767 135,767
	Crique Voltalia 2 Crique Voltalia 2 Crique Voltalia 2		244,628 7,634 7,634	233,249 136,534 136,534
	Crique Voltalia 3 Crique Voltalia 3 Crique Voltalia 3		368,328 43,364 43,364	355,762 151,289 151,289
	Crique Voltalia 4 Crique Voltalia 4 Crique Voltalia 4		230,714 39,632 39,632	224,726 142,318 142,318
	Crique des Cascades Crique des Cascades Crique des Cascades	843,037 88,671 88,671	819,217 607,259 607,259
	Apa	Apa	Apa			113,730 13,796 13,796	75,131	113,217 75,131
	Crique Bastien Crique Bastien Crique Bastien		96,177 22,293 22,312	47,675	95,055 47,675
	Crique Penta Crique Penta Crique Penta		82,231 10,616 10,616	51,866	81,858 51,866
	Crique Nouvelle France Crique Nouvelle France 1 130,206 Crique Nouvelle France 1 15,890 15,907	129,229 112,348 112,348
	Crique Nouvelle France Crique Nouvelle France 2 109,505 Crique Nouvelle France 2 17,583 17,583	92,319	109,139 92,305
	Crique Nouvelle France Crique Nouvelle France 6 159,009 Crique Nouvelle France 6 31,661 31,661	71,505	157,235 71,505
	Crique Nouvelle France Crique Nouvelle France 5 144,998 Crique Nouvelle France 5 22,421 22,421	96,060	143,798 96,060
	Crique Nouvelle France Crique Nouvelle France 4 112,408 Crique Nouvelle France 4 17,991 17,991	75,643	111,985 75,643
	Crique Nouvelle France Crique Nouvelle France 3 273,455 Crique Nouvelle France 3 36,508 36,508	272,158 213,130 213,130
	Crique à l'Est Crique à l'Est Crique à l'Est		112,323 33,268 33,268	70,149	110,986 70,149
	Total	Total	Total		14,428,043 2,015,668 2,015,596	13,564,122 8,108,699 8,108,492
	Percentage of the initial number of reads Percentage of the initial number of reads Percentage of the initial number of reads	81.2	11.3 11.3	45.6	76.3 45.6
				Total

with an with a frequency of occurrence > 0.0003 per taxon per sample and per sequencing run Site name

  Chapter IV: Tropical freshwater biodiversity and eDNA

					Atelopus
					Atelopus flavescens
					Rhinella
					Rhinella dapsilis
			Dendrobatidae	Anomaloglossus	Anomaloglossus baeobatrachus
				Hyla	Hyla helenae
					Hypsiboas boans
					Hypsiboas geographicus
					Hylinae
					Osteocephalus cannatellai
					Osteocephalus oophagus
					Osteocephalus taurinus
			Leptodactylidae	Lithodytes	Lithodytes lineatus
			Ranidae	Rana	Rana palmipes
		Galliformes	Cracidae	Crax	Crax rubra
		Passeriformes	Tyrannidae	Mionectes	Mionectes oleagineus
	Bacteria	Thermales	Thermaceae	Meiothermus	Meiothermus silvanus DSM 9946
			Anostomidae	Leporinus	Leporinus affinis
			Chilodontidae	Chilodus	Chilodus punctatus
			Erythrinidae	Hoplias	Hoplias malabaricus
			Prochilodontidae	Prochilodus	Prochilodus
		Clupeiformes	Engraulidae	Pterengraulis	Pterengraulis atherinoides
				Electrophorus	Electrophorus electricus
				Gymnotus	Gymnotus carapo
			Sternopygidae	Eigenmannia	Eigenmannia sp. CBM-ZF-10620
		Myliobatiformes Potamotrygonidae	Potamotrygon	Potamotrygon motoro
				Auchenipterus	Auchenipterus demerarae
				Tatia	Tatia intermedia
			Doradidae	Doras	Doras micropoeus
					Ancistrus
					Ancistrus sp. 2a Parente, 2015
				Cteniloricaria	Cteniloricaria platystoma

Table III .

 III S3 Results from assignment using GenBank nucleotide database

	Chapter IV: Tropical freshwater biodiversity and eDNA
	Taxonomic group	Order	Family	Genus	Taxon †
	Taxonomic group "Fish"	Order Siluriformes Perciformes	Family Pimelodidae Polycentridae	Taxon † Pseudoplatystoma Pseudoplatystoma fasciatum Genus Polycentrus Polycentrus schomburgkii
	Insecta Mammalia	Odonata Didelphimorphia Primates	Libellulidae Didelphidae Bufonidae Cebidae Hominidae	Atelopus Caluromys Cebus Rhinella Homo	Libellulidae Caluromys philander Cebus albifrons Homo Homo sapiens
					Homininae
	Amphibia Sauria	Anura Squamata Testudines	Iguanidae Geoemydidae	Hypsiboas Polychrus Rhinoclemmys	Polychrus marmoratus Rhinoclemmys
			Hylidae	Total
				†: Bold taxa are fish present in French Guiana; italized
				Osteocephalus taxa are fish not known from French Guiana
	Aves
		Characiformes
		Gymnotiformes	Gymnotidae
			Auchenipteridae
				Ancistrus
	"Fish"
		Siluriformes	Loricariidae
				Pseudancistrus
			Pimelodidae

https://imagej.nih.gov/ij/index.html

https://www.geneious.com/

https://www.geneious.com/

http://www.fasteris.com/dna/?q=content/metafast-protocol-amplicon-metagenomic-analysis

https://git.metabarcoding.org/obitools/obitools/wikis/home

Acknowledgements

Acknowledgements pour le soutien qui m'a permis d' étudier à l'universit é.

Que serait des remerciements en bonne et due forme sans un mot aux personnes qui m'ont fait confiance tout au long de ces quatre ann ées: Ga ël et S ébastien. Cette aventure a commenc é par le stage de M2: mes r éels premiers pas dans le monde de la recherche. Puis la th èse, une autre marque de confiance pour moi. Gr âce à vous, j'ai pu aller dans des lieux o ù je n'aurais jamais pu aller. Ces ann ées de formation m'ont beaucoup apport é tant sur le c ôt é professionnel (c'est un peu le but d'une th èse non ?) que sur le c ôt é personnel et surtout la confiance en soi.

Une petite pens ée pour les compositeurs et artistes qui ont berc é mes oreilles durant cette p ériode et particuli èrement sur cette fin de th èse, en particulier Yoko Shimomura et Shoji Meguro. Ces écrits ne les atteindront s ûrement jamais, mais je suis arriv é jusque l à gr âce à eux aussi.

ACKNOWLEDGMENTS

This work has benefited from "Investissement dAvenir" grants managed by the French Agence Nationale de la Recherche (CEBA, ref. . We are indebted to the DEAL Guyane, the Guiana National Park (PAG), the CNRS Guyane and the Our Planet Reviewed 'Mitaraka' project for financial and technical support. We are grateful to Peter Winterton for correcting the English, to C éline J éz équel for hydrographic distance computing and to four anonymous reviewers for their helpful comments.

ACKNOWLEDGMENTS

We are indebted to the DEAL Guyane, the Guiana Amazonian Park and the Investissement dAvenir grants managed by the French Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01) for financial support. Onema provided continental France fish data. Computations were performed on EDB-Calc Cluster which uses software developed by the Rocks(r) Cluster Group (San Diego Supercomputer Center, University of California). We thank Pierre Solb ès for support. We are grateful to Peter Winterton for editing the article for language.

ACKNOWLEDGMENTS

This work has benefited from the Laboratoire d'Excellence (LabEx) entitled CEBA (ANR-10-LABX-25-01). We are indebted to the DEAL Guyane, the Office de l'eau Guyane, the Guiana National Park (PAG), the CNRS Guyane and the Our Planet Reviewed 'Mitaraka' project for financial and technical support. We are grateful to Sebastien Lereun, Damien Monchaux, Simon Clavier, Roland, Tom Pouce, Alaou and Helicoptere for field help. We are also grateful to Sarah Delavigne and Coline Gaboriaud for laboratory help and Lucie Zinger for molecular analysis advices.

GenBank accession number

Genus species COI 12S Acanthodoras cataphractus EU490847 AY264120 Acaronia nassa AY263862 Ageneiosus ucayalensis EU490849 AY264122 Anchovia clupeoides EU552651 Anchoviella lepidentostole JQ365218 EU552653 Anostomus ternetzi KF415311 Apteronotus albifrons JQ667494 AB054132 Astronotus ocellatus AY263859 AP009127 Astyanax bimaculatus FJ439410 Bagre bagre GU702396 DQ990572 Batrochoglanis raninus EU179809 JX899730 EU179829 Brachyhypopomus brevirostris KF533324 KF533325 Brachyhypopomus pinnicaudatus GU701847 GU701848 GU701851 Brachyplatystoma filamentosum FJ418760 FJ978041 JF898675 JF898676 Brachyplatystoma rousseauxii FJ418759 JF898672 Brachyplatystoma vaillantii HM453213 JF898666 Bunocephalus coracoideus DQ508071 AP012006 Bunocephalus verrucosus EU490846 Callichthys callichthys EU359408 GU702211 JN988766 JN988767 HM114376 HM114377 HM114378 HM114379 Caranx hippos FJ347905 FJ347906 JQ841097 JQ842407 KF461147 Caranx latus JQ365253 JQ365254 JQ635255 JQ840438 JQ841100

The 14 morphological measurements used to calculate functional traits. .