Oscar Puebla

Sophie Von Der Heyden

Frédérique Viard

Merci À Laetitia

Nadia Sandra

Loïc Suzie Thomas

Claire Naima

Alexandre Hugo

Emilie Boulanger
email: emilie.boulanger@hotmail.com

Nicolas Loiseau

Alice Valentini

Véronique Arnal

Pierre Boissery

Tony Dejean

Julie Deter

Nacim Guellati

Florian Holon

Jean-Baptiste Juhel

Philippe Lenfant

Stéphanie Manel

David Mouillot

Environmental DNA metabarcoding reveals and unpacks a biodiversity conservation paradox in Mediterranean marine reserves

Keywords: marine biodiversity, marine protected areas, connectivity, eDNA, seascape genomics, Mediterranean Sea Global change and conservation environmental DNA metabarcoding, marine reserves, alpha and beta diversity

Human activities and their resulting pressures on natural ecosystems are altering biodiversity and induce a loss of species, population and genetic diversity. Across the marine realm, networks of marine protected areas (MPAs) are increasingly established to alleviate pressures and restore biodiversity and ecosystem processes. Yet, the mechanisms by which MPAs can preserve biodiversity and sustain fisheries are still unclear, though they are essential to ensure the success and implementation of MPAS in order to reach the 30% protection target.

My PhD uses Next-Generation Sequencing (NGS) tools to study biodiversity patterns of Mediterranean fishes at three levels -communities, populations and genes -and to better guide their conservation under increasing climate warming and fishing pressure.

Understanding the effect of MPAs on fish communities requires the detection of a wide range of species. Yet, visual censuses only detect a fraction of biodiversity. I apply environmental DNA (eDNA) metabarcoding to study the diversity of fish communities in six Mediterranean MPAs. This approach allows the detection and identification of species from the thousands of sequences found in a single water sample. The results highlight a new conservation paradox: MPAs are less species-rich than their surroundings. However, they host different species assemblages compared to fished areas thus increasing regional diversity. This difference is mainly driven by species often overlooked by conventional visual methods but detected with eDNA: cryptobenthic, pelagic, and rare fishes.

Biodiversity also depends on the connectivity between populations through the successful dispersal of propagules and individuals, leading to gene flow. I investigate the Mediterranean-wide connectivity of two fishes, the striped red mullet (Mullus surmuletus) and white seabream (Diplodus sargus). I develop a spatial graph approach that extends propagule dispersal networks over multiple generations to better match the spatio-temporal scale of genetic connectivity. Comparing these new estimates with the analysis of 1,153 neutral SNPs from 47 putative populations reveals how mullet populations achieve high levels of gene flow through multi-generational stepping-stone dispersal. Kinship estimations also reveal that seabream is capable of exceptional long-distance dispersal (about 2,000km) in only two or three generations. Such high levels of gene flow however can diminish population structure and impede local adaptation. To better understand these mechanisms, I analyse 823 individuals genotyped II for 704 highly differentiated (i.e. outlier) SNPs potentially under selection. I can thus disentangle the relative influence of spatial distance, larval dispersal and environmental conditions -which are expected to change under climate change -on the genomic structure of both species. These analyses reveal that temperature plays a significant role in the genomic structure of mullet and seabream, allowing the detection of genes potentially involved in local adaptation. By combining powerful genomic and statistical tools, this thesis deciphers new patterns of biodiversity with novel implications for conservation. The results highlight how MPAs can preserve fish biodiversity and community structure in the face of fishing pressure. They also suggest how networks of well-connected MPAs could ensure the persistence and adaptive potential of populations facing climate change.

Résumé

Les écosystèmes marins présentent une grande diversité d'espèces, de populations et de gènes, ce qui se traduit par une variété de fonctions et services écosystémiques. Cette biodiversité est confrontée à des menaces anthropiques croissantes et, par conséquent, diminue plus rapidement que jamais. Des aires marines protégées (AMP) permettent de préserver la biodiversité à ses différents niveaux ainsi que les mécanismes qui la génèrent. Pourtant, les mécanismes par lesquels les AMP peuvent préserver cette biodiversité sont insuffisamment connus. Cette thèse s'appuie sur le séquençage nouvelle génération (NGS) pour étudier la biodiversité des poissons méditerranéens à trois niveaux -communautés, populations et gènes -et améliorer leur conservation dans un contexte de réchauffement climatique et pression de pêche croissante.

Comprendre l'effet des AMP sur les communautés de poissons nécessite la détection d'un large éventail d'espèces. Pourtant, les méthodes visuelles ne détectent qu'une fraction de la biodiversité. J'ai appliqué le metabarcoding de l'ADN environnemental (ADNe) pour étudier la diversité des communautés de poissons dans six AMP méditerranéennes. Cette approche permet de recenser les espèces présentes à partir des milliers de séquences obtenus d'un échantillon d'eau seulement. Les résultats mettent en évidence un nouveau paradoxe de la conservation : les AMP sont moins riches en espèces que les zones hors AMP. Cependant, elles abritent des assemblages d'espèces différents comparés aux zones extérieures, augmentant ainsi la diversité régionale. Cette différence est principalement due à des espèces souvent non détectées par les méthodes classiques mais détectées par l'ADNe : les poissons cryptobenthiques, pélagiques et rares.

La biodiversité dépend également de la connectivité entre les populations grâce à une dispersion de larves ou d'individus entraînant un flux de gènes. J'ai donc étudié la connectivité de deux poissons, le rouget de roche (Mullus surmuletus) et le sar commun (Diplodus sargus), à l'échelle de la mer Méditerranée. J'ai d'abord développé une approche basée sur les graphes spatiaux qui étend un réseau de dispersion larvaire à plusieurs générations, correspondant aux échelles spatio-temporelles de la connectivité génétique. Ensuite, j'ai comparé ces nouvelles estimations de dispersion avec l'analyse de 1,153 SNP neutres provenant de 47 populations potentielles. Cette approche permet d'expliquer en partie comment les populations de rougets atteignent des flux de gènes élevés par le biais d'une dispersion en stepping-stone sur plusieurs générations. Des calculs de parenté révèlent ensuite que le sar est capable d'une dispersion IV exceptionnelle sur de longues distances (environ 2,000 km) en seulement deux ou trois générations.

De tels niveaux de flux de gènes pourraient cependant diminuer la structure des populations et freiner l'adaptation locale. Pour mieux comprendre ces mécanismes, j'ai analysé 823 individus génotypés pour 704 SNP hautement différentiés. Ceci m'a permis de démêler les influences relatives de la distance spatiale, la dispersion larvaire et les conditions environnementales -qui devraient changer sous l'effet du changement climatique -sur la structure génomique des deux espèces. Les analyses révèlent que la température joue un rôle important dans la structure génomique du rouget et du sar, permettant la détection de gènes potentiellement impliqués dans l'adaptation locale.

En combinant des outils génomiques et statistiques performants, cette thèse révèle de nouveaux patrons de biodiversité avec des implications pour la conservation. Les résultats soulignent comment les AMP peuvent préserver la biodiversité des poissons et la structure des communautés face à la pression de pêche. Ils suggèrent également comment des réseaux d'AMP connectés pourraient assurer la persistance et le potentiel adaptatif des populations confrontées au changement climatique.

Biodiversity at different organisational levels

Biological diversity, or biodiversity, describes the variability of life at different levels ranging from ecosystems and ecological communities down to species, populations and genes (United [START_REF] Nations | Convention on Biological Diversity[END_REF]. Through variations among species, traits, populations and individuals, biodiversity forms an integral part of ecosystems and plays a key role in their functioning [START_REF] Hooper | Effects of biodiversity on ecosystem functioning: a consensus of current knowledge[END_REF][START_REF] Cadotte | Beyond species: Functional diversity and the maintenance of ecological processes and services[END_REF][START_REF] Stange | The importance of genomic variation for biodiversity, ecosystems and people[END_REF]). Higher biodiversity enhances ecosystem functions (i.e. biomass production, see glossary) and stability as a result of the complementarity or independent dynamics among species [START_REF] Tilman | Biodiversity: population versus ecosystem stability[END_REF][START_REF] Loreau | Biodiversity and Ecosystem Functioning: Current Knowledge and Future Challenges[END_REF][START_REF] Hooper | Effects of biodiversity on ecosystem functioning: a consensus of current knowledge[END_REF][START_REF] Wang | Biodiversity and ecosystem stability across scales in metacommunities[END_REF]. For marine fishes, increased species richness increases biomass production of coral reefs (Mora et al. 2011a[START_REF] Duffy | Biodiversity enhances reef fish biomass and resistance to climate change[END_REF]), but also promotes resilience against (over)exploitation at the ecosystem level by providing a portfolio of fisheries resources (Figure 1.1) [START_REF] Worm | Impacts of Biodiversity Loss on Ocean Ecosystem Services[END_REF].

Within a species, different populations can vary significantly from each other in phenotypic traits and underlying genotypes. This variation influences community structure and ecosystem functions, sometimes even as much as variation among species (Des Roches et al.

2018, [START_REF] Raffard | The community and ecosystem consequences of intraspecific diversity: a meta-analysis[END_REF]. For example a portfolio of sockeye salmon subpopulations with staggered age structures provides resilience against spawning and recruitment fluctuations thus contributing to the stable productivity of the total population [START_REF] Schindler | Population diversity and the portfolio effect in an exploited species[END_REF].

Genetic diversity consists of the different alleles of genes that occur in the gene pool of a species or a population [START_REF] Ellegren | Determinants of genetic diversity[END_REF]. Genetically diverse populations have higher resistance to disturbance and increased rates of ecosystem functions as a result of the complementarity among genotypes in e.g. their resistance to various stresses such as grazing 1.1 Biodiversity of marine ecosystems in the Anthropocene

Fishing effects on biodiversity

Currently the greatest and direct threat to marine biodiversity is the exploitation of resources, as highlighted by the recent IPBES (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services) report [START_REF] Díaz | Summary for policymakers of the global assessment report on biodiversity and ecosystem services -unedited advance version[END_REF]. Humans have been harvesting

ocean wildlife for thousands of years, but our limited access to the oceans has historically hampered drastic alterations like we can observe on land [START_REF] Mccauley | Marine defaunation: Animal loss in the global ocean[END_REF]. The recent industrialisation of fishing practices has increased the reach and efficiency of marine resource exploitation [START_REF] Lotze | Depletion, Degradation, and Recovery Potential of Estuaries and Coastal Seas[END_REF][START_REF] Jouffray | The Blue Acceleration: The Trajectory of Human Expansion into the Ocean[END_REF]. Fisheries are increasingly unsustainable and currently 34.2% of fish stocks are overfished (FAO 2020a). Other studies are less optimistic, and estimate that nearly 40 to 50% of tropical and temperate stocks are overfished [START_REF] Link | Global ecosystem overfishing: Clear delineation within real limits to production[END_REF]. As a consequence, regional ecosystems are rapidly losing populations, species or entire functional groups [START_REF] Trindade-Santos | Global change in the functional diversity of marine fisheries exploitation over the past 65 years: Fisheries and Functional Diversity[END_REF][START_REF] Pacoureau | Half a century of global decline in oceanic sharks and rays[END_REF].

Such population and species losses reverberate through entire ecosystems and their functioning as all species are connected to others through ecological interactions [START_REF] Johnson | Biodiversity losses and conservation responses in the Anthropocene[END_REF][START_REF] Rocha | Cascading regime shifts within and across scales[END_REF]. For example, humans harvest differently across the trophic levels in marine food webs [START_REF] Smith | Impacts of Fishing Low-Trophic Level Species on Marine Ecosystems[END_REF]). Many fisheries focus on large-bodied and high trophic level species effectively "fishing down marine food webs" [START_REF] Pauly | Fishing down marine food webs[END_REF][START_REF] Pauly | Fishing Down Marine Food Web: It is Far More Pervasive Than We Thought[END_REF]. In other fisheries, large-bodied herbivores and detrivores such as parrotfish and surgeonfish are targeted [START_REF] Graham | Human Disruption of Coral Reef Trophic Structure[END_REF], while many pelagic fisheries target planktonfeeding fish such as anchovies and sardines [START_REF] Smith | Impacts of Fishing Low-Trophic Level Species on Marine Ecosystems[END_REF]. This way humans effectively reshape trophic pyramids in the seas with cascading consequences both up and down the food web. Consequences can be drastic, such as the loss of kelp forest habitat as a result of herbivore predation release [START_REF] Smith | Behavioral responses across a mosaic of ecosystem states restructure a sea otter-urchin trophic cascade[END_REF].

Climate change

The Anthropocene is also defined by the increasing threat due to climate change. Rising cumulative emissions of (human) atmospheric greenhouse gasses have already increased global temperatures by ~1 °C, and temperature, wind, and precipitation regimes have become more variable and extreme [START_REF] Scheffers | The broad footprint of climate change from genes to biomes to people[END_REF]. The oceans absorb the excess heat and capture large parts of the anthropogenic CO2, moderating climate change at the expense of its fundamental chemistry and physics [START_REF] Gattuso | Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios[END_REF]. The predominant consequences are ocean warming, acidification, deoxygenation, reductions in near-surface nutrients, and changes in primary production [START_REF] Kwiatkowski | Twenty-first century ocean warming, acidification, deoxygenation, and upper ocean nutrient decline from CMIP6 model projections[END_REF]. Temperature is the leading environmental factor determining species distributions and biodiversity patterns in the oceans, including

Loss of genetic diversity

Declines in abundance and loss of local populations also erodes the genetic diversity in wild populations [START_REF] Leigh | Estimated six per cent loss of genetic variation in wild populations since the industrial revolution[END_REF]. This loss of genetic diversity imperils the viability of populations because small, isolated and genetically depauperate populations are more likely to become extinct due to demographic stochasticity, genetic drift and inbreeding depression than larger and more genetically diverse ones [START_REF] Kardos | The crucial role of genome-wide genetic variation in conservation[END_REF]. The loss of genetic diversity also reduces the adaptive and evolutionary rescue potential of populations imperilling their persistence in the face of climate change [START_REF] Bell | Evolutionary Rescue[END_REF]). In addition, fishing could alter genetic diversity within populations by targeting and removing large individuals and driving selection for slow-growing and early maturing fish [START_REF] Heino | Fisheries-Induced Evolution[END_REF], although empirical evidence is debated [START_REF] Pinsky | Genomic stability through time despite decades of exploitation in cod on both sides of the Atlantic[END_REF]).

Marine Protected Areas to safeguard and restore biodiversity

Marine protected areas (hereafter MPAs) have become one of the principal tools to mitigate anthropogenic impacts and preserve marine biodiversity [START_REF] Woodcock | Your evidence or mine? Systematic evaluation of reviews of marine protected area effectiveness[END_REF].

MPAs are defined by the IUCN as "a clearly defined geographical space, recognised, dedicated, and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values" [START_REF] Dudley | Guidelines for applying protected area management categories including IUCN WCPA best practice guidance on Recognising Protected Areas and Assigning Management Categories and Governance Types[END_REF]). Their establishment is globally increasing at a rapid pace from 2.5% coverage in 2010 to 7.7% in 2021 (Figure 1.4) (Maxwell et al. 2020, UNEP-WCMC andIUCN 2021). Global targets are continuously being set to incite governments to extend protection measures and hold them accountable of their commitment. The latest IUCN World Conservation Congress (2020/2021) has reiterated the importance of fully protecting 30% of the oceans by 2030, rebuking the Convention on Biological Diversity's (CBD) Aichi Target 11 to protect at least 10% of coastal and marine areas by 2020.

However, the definition of MPAs is rather broad and MPAs vary in their size, objectives and protection level, leaving room for ambiguity. Only a fraction of the announced MPAs are truly implemented, and an even smaller fraction (2.7% as of 2021) is implemented as strongly or fully protected areas [START_REF] Sala | No-take marine reserves are the most effective protected areas in the ocean[END_REF](Sala et al. , mpaatlas.org 2021)). These numbers are reflected in the Mediterranean Sea, where 6% of the basin is covered by MPAs but most lack sufficient regulations and only 0.06% of the basin is fully protected [START_REF] Claudet | Underprotected Marine Protected Areas in a Global Biodiversity Hotspot[END_REF]. Fully protected, no-take MPAs, hereafter marine reserves, offer the strictest form of protection where all extractive practices and many visitation practices are prohibited [START_REF] Costello | Long live Marine Reserves: A review of experiences and benefits[END_REF]. They are or even benefit from intermediate levels of protection [START_REF] Micheli | Trajectories and correlates of community change in no-take marine reserves[END_REF][START_REF] Claudet | Marine reserves: Fish life history and ecological traits matter[END_REF][START_REF] Loiseau | Maximizing regional biodiversity requires a mosaic of protection levels[END_REF]. Such differential responses to protection could drive changes in local, regional and between-site diversity depending on species interactions and cascading effects.

For example, sea urchin (Paracentrotus lividus and Arbacia lixula) densities are negatively impacted by protection in Mediterranean marine reserves due to an increase in abundance of one of their predators, the sea bream Diplodus sargus [START_REF] Giakoumi | Ecological effects of full and partial protection in the crowded Mediterranean Sea: A regional meta-analysis[END_REF]. Similar interactions could occur within fish communities driving local species losses instead of gains [START_REF] Takashina | Paradox of marine protected areas: Suppression of fishing may cause species loss[END_REF]. Protection could then result in adverse effects on biodiversity, a pattern that remains poorly understood. Reviews or meta-analyses evaluating the effectiveness of MPAs on fishes often do not report species richness [START_REF] Woodcock | Your evidence or mine? Systematic evaluation of reviews of marine protected area effectiveness[END_REF][START_REF] Sala | No-take marine reserves are the most effective protected areas in the ocean[END_REF][START_REF] Goetze | Increased connectivity and depth improve the effectiveness of marine reserves[END_REF]. When they do, effects of protection on richness are mixed. [START_REF] Lester | Biological effects within no-take marine reserves: A global synthesis[END_REF] and [START_REF] Giakoumi | Ecological effects of full and partial protection in the crowded Mediterranean Sea: A regional meta-analysis[END_REF] found that the effect on species richness is positive but small compared to other effects whereas [START_REF] Claudet | Marine reserves: Size and age do matter[END_REF] and [START_REF] Loiseau | Maximizing regional biodiversity requires a mosaic of protection levels[END_REF] found that the effect is generally insignificant or totally neutral for reef fishes, respectively. In addition, community composition is often overlooked but essential to decipher the underlying effects on species richness. A better understanding of how protection affects species richness and community structure would require a holistic and uniform sampling of fish communities across multiple reserves.

If fishing pressure depletes genetic diversity of populations, marine reserves could potentially maintain or restore this diversity. [START_REF] Pérez-Ruzafa | Effects of fishing protection on the genetic structure of fish populations[END_REF] found a higher allelic richness in white seabream individuals inside MPAs than outside. However, more recent work did not find any effect of protection on the genetic diversity of white seabream [START_REF] Pujolar | Understanding the effectiveness of marine protected areas using genetic connectivity patterns and Lagrangian simulations[END_REF], two-banded seabream [START_REF] Sahyoun | Patterns of fish connectivity between a marine protected area and surrounding fished areas[END_REF] or across multiple species (Benestan et al. under review, Wennerström et al. 2017). Since marine reserves are generally small (on average 500 ha in the Mediterranean Sea; [START_REF] Claudet | Marine reserves: Size and age do matter[END_REF]) while marine populations are generally large, highly connected and thus covering large spatial extents with dispersal often > 40 km [START_REF] Manel | Long-Distance Benefits of Marine Reserves: Myth or Reality?[END_REF], single, small reserves might not suffice to influence neutral genetic diversity. Instead, benefits to genetic diversity will occur beyond MPA boundaries and across networks of multiple MPAs.

Benefits beyond boundaries and the importance of MPA networks

MPAs and reserves yield benefits beyond their boundaries. Density-dependent increases of biomass just outside reserves, called spillover, increase populations in neighbouring areas and adjacent fishing grounds [START_REF] Goñi | Spillover from six western Mediterranean marine protected areas: Evidence from artisanal fisheries[END_REF][START_REF] Lorenzo | Spillover from marine protected areas to adjacent fisheries has an ecological and a fishery component[END_REF], 2020) making reserves a suitable tool for fishery management [START_REF] Cabral | Designing MPAs for food security in open-access fisheries[END_REF]. Dispersal of larvae spawned within reserves provides valuable larval subsidies to neighbouring as well as long-distance (> 40 km) habitats, sustaining distant populations and effectively seeding fishing grounds outside [START_REF] Christie | Larval Connectivity in an Effective Network of Marine Protected Areas (SJ Goldstien[END_REF][START_REF] Manel | Long-Distance Benefits of Marine Reserves: Myth or Reality?[END_REF]. Dispersal leads to demographic connectivity if individuals contribute to the population growth and resulting dynamics in the new population (e.g. competition for resources). It further leads to genetic connectivity if individuals successfully reproduce in the new population leading to gene flow (Lowe andAllendorf 2010, Cayuela et al. 2018).

Accounting for connectivity in marine spatial planning allows to preserve the processes that maintain biodiversity while benefiting from within-species diversity. Protecting source populations to favour connectivity can provide crucial larval subsidies to populations with little or insufficient self-recruitment [START_REF] Nanninga | Not finding Nemo: limited reef-scale retention in a coral reef fish[END_REF][START_REF] Dedrick | Persistence of a reef fish metapopulation via network connectivity: theory and data[END_REF] or overharvested populations (Xuereb et al. 2019). Reserve networks also stabilize the effect of recruitment fluctuations by allowing the import of recruits from other reserves through what is called a connectivity portfolio effect [START_REF] Harrison | A connectivity portfolio effect stabilizes marine reserve performance[END_REF]. Quantifying connectivity patterns thus allows to plan and implement reserve networks that ensure the persistence of populations and contribute to the replenishment of exploited fish stocks under fishing and global change impacts [START_REF] Andrello | Global mismatch between fishing dependency and larval supply from marine reserves[END_REF], Magris et al. 2018).

Networks of reserves also promote genetic diversity by maintaining connectivity and gene flow among populations. Source populations with a high level of genetic diversity or unique genetic variations should in turn be protected to ensure the spread and maintenance of genetic variation (Xuereb et al. 2019). Reserve networks that optimize genetic connectivity can this way contribute to maintain the adaptive or evolutionary potential of a given species.

Populations that are locally adapted to extreme conditions, such as high temperatures (e.g. [START_REF] Selmoni | Seascape genomics reveals candidate molecular targets of heat stress adaptation in three coral species[END_REF], could serve as sources to spread adaptive variations towards populations that are expected to face the same conditions under climate change (Xuereb et al. 2019).

Estimating the amount of (genetic) connectivity can then help evaluate whether natural gene flow will be sufficient to spread beneficial variations (Xuereb et al. 2019[START_REF] Selmoni | Seascape genomics as a new tool to empower coral reef conservation strategies: An example on north-western Pacific Acropora digitifera[END_REF]).

Measuring MPA effects and potential

Biodiversity surveys

Species richness is potentially underestimated in previous MPA evaluation studies because of the limitations encountered by conventional sampling methods. Fish assemblages in MPAs are usually assessed with visual-based methods such as underwater visual census (UVC) [START_REF] Bosch | How" and "what" matters: Sampling method affects biodiversity estimates of reef fishes[END_REF] or baited remote underwater videos (BRUV) [START_REF] Langlois | A field and video annotation guide for baited remote underwater stereovideo surveys of demersal fish assemblages[END_REF], and just like any method they have associated biases. Taxonomic identification of cryptic species or early life stages is often difficult. As a result, visual census methods are subject to observer bias [START_REF] Macneil | Detection heterogeneity in underwater visual-census data[END_REF][START_REF] Bernard | Observer bias and detection probability in underwater visual census of fish assemblages measured with independent double-observers[END_REF]. Potential avoidance behaviour of fishes to diver presence (in the case of UVC) [START_REF] Dickens | Quantifying relative diver effects in underwater visual censuses[END_REF][START_REF] Gray | Comparison of reef fish survey data gathered by open and closed circuit SCUBA divers reveals differences in areas with higher fishing pressure[END_REF] or differential sensitivity to bait (in the case of BRUVs) [START_REF] Langlois | A field and video annotation guide for baited remote underwater stereovideo surveys of demersal fish assemblages[END_REF]) can also result in the underrepresentation of certain species. Rare and elusive species can pass outside the visual field or exact time frame and thus go undetected [START_REF] Boussarie | Environmental DNA illuminates the dark diversity of sharks[END_REF][START_REF] Polanco Fernández | Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes[END_REF]. Cryptobenthic species on the other hand live in close association with the benthos and require careful and time consuming observation or lethal sampling methods [START_REF] Brandl | The hidden half: ecology and evolution of cryptobenthic fishes on coral reefs[END_REF]). As a result, studies evaluating MPA effectiveness nearly always focus on conspicuous and/or economically important species, either implicitly or explicitly (e.g. [START_REF] Rojo | Detection of protection benefits for predatory fishes depends on census methodology[END_REF][START_REF] Goetze | Increased connectivity and depth improve the effectiveness of marine reserves[END_REF]). We largely ignore the effect of protection on entire communities of fishes that are overlooked by conventional methods. New methods targeting the DNA traces left by species in their environment, i.e. environmental DNA, could allow the detection of species irrespective of their cryptic or elusive nature and difficulty for taxonomic identification (Box 2).

Box 2. Environmental DNA and DNA barcoding

Environmental DNA (eDNA) refers to the mixture of cellular and extracellular DNA left by living organisms in their environment (Taberlet et al. 2012a). eDNA can be extracted from environmental samples such as water, sediment and soil, but also faeces and even honey [START_REF] Taberlet | Environmental DNA for Biodiversity Research and Monitoring[END_REF]). This eDNA is excreted by organisms as e.g. sloughed cells, mucus or gametes. Methods to extract species information from eDNA were initially developed by microbiologists and paleontologists [START_REF] Handelsman | Molecular biological access to the chemistry of unknown soil microbes: A new frontier for natural products[END_REF][START_REF] Willerslev | Diverse plant and animal genetic records from holocene and pleistocene sediments[END_REF], and their potential to study contemporary biodiversity has not escaped ecologists (Taberlet et al. 2012b).

The amplification and/or sequencing of eDNA has the potential to detect both microscopic pelagic gametes or larvae as well as adults of different size classes from species with different habitat uses, as long as they shed DNA in their environment and this DNA is more widespread than the individual itself (Lacoursière-Roussel and Deiner 2019). The first vertebrate eDNA study showed that sampling even small amounts of water (15 mL) can allow the detection and identification of species in rivers [START_REF] Ficetola | Species detection using environmental DNA from water samples[END_REF]. Two distinct molecular approaches have emerged: § Species-specific approaches, which focus on identifying and sometimes quantifying the abundance of single species § Community approaches, which focus on identifying the multiple species comprising local ecological communities

DNA barcoding

DNA barcodes are short gene sequences taken from a standardized portion of the genome that can be used to genetically identify species [START_REF] Kress | DNA barcodes for ecology, evolution, and conservation[END_REF]. From eDNA, highly specific barcodes can be amplified to infer the presence of a species [START_REF] Rooyen | Development of an environmental DNA assay for detecting multiple shark species involved in human-shark conflicts in Australia[END_REF] or even quantify its abundance with real-time quantitative PCR (qPCR) after careful calibration [START_REF] Spear | Application of eDNA as a tool for assessing fish population abundance[END_REF]. eDNA barcodes can also be sequenced and compared to a reference database of sequences to discern the species' identity among close congeners [START_REF] Ardura | Rapid assessment of non-indigenous species in the era of the eDNA barcoding: A Mediterranean case study[END_REF].

Beyond single-species detection, environmental DNA comprises a mixture of the DNA of all species present and, with the appropriate sequencing technology and barcodes, can reveal the multi-species diversity of entire assemblages (see 1.3.1 NGS at the community level: eDNA metabarcoding).

Estimating connectivity

Many marine species, including fishes, have a bipartite life cycle and their dispersal occurs mainly during the pelagic larval stage. The large clutch size, tiny body size and high mortality of larvae make them virtually impossible to track [START_REF] Swearer | A review of biophysical models of marine larval dispersal[END_REF]. Instead, dispersal and marine connectivity can be studied using biophysical models or natural tags, or both. Biophysical models of larval dispersal combine biophysical processes (e.g. currents, eddies, river runoff) and larvae biological parameters (e.g. pelagic larval duration, spawning season, swimming depth) to estimate the potential dispersal of larvae between habitats or populations (e.g. [START_REF] Cowen | Larval dispersal and marine population connectivity[END_REF], Andrello et al. 2013[START_REF] Rogers | Using a biophysical model to investigate connectivity between spawning grounds and nursery areas of King George whiting (Sillaginodes punctatus: Perciformes) in South Australia's gulfs. -Fish[END_REF]). The use of molecular markers in combination with a population genetics approach (Box 3) can serve as natural tags to trace realized and effective dispersal. The realized dispersal, upon survival and settlement into the new habitat, can be estimated with genetic methods such as parentage analyses and assignment tests that assign individuals back to their parents or population of origin respectively [START_REF] Manel | Assignment methods: Matching biological questions with appropriate techniques[END_REF][START_REF] Nielsen | Gene-associated markers provide tools for tackling illegal fishing and false eco-certification[END_REF][START_REF] Benestan | RAD genotyping reveals fine-scale genetic structuring and provides powerful population assignment in a widely distributed marine species, the American lobster (Homarus americanus)[END_REF][START_REF] Almany | Larval fish dispersal in a coral-reef seascape[END_REF], Bode et al. 2019[START_REF] Rogers | Spatial connectivity during the early life history of a temperate marine fish inferred from otolith microstructure and geochemistry[END_REF]). The effective dispersal, finally, depends on the successful reproduction of individuals in their new population and can be estimated with indirect measures of gene flow by quantifying population differentiation and structure (Lowe andAllendorf 2010, Cayuela et al. 2018). Such indirect measures of gene flow integrate dispersal over multiple generations, whereas direct estimates infer gene flow at the scale of one or a few generations.

Approaches using a small number of markers, e.g. microsatellites (Box 3), often lack in power to resolve population structure in marine species, whose populations are characterized by large effective population sizes and high levels of gene flow [START_REF] Allendorf | Genomics and the future of conservation genetics[END_REF][START_REF] Benestan | RAD genotyping reveals fine-scale genetic structuring and provides powerful population assignment in a widely distributed marine species, the American lobster (Homarus americanus)[END_REF]. Additionally, most microsatellites are presumed to be neutral and estimating populations' adaptive potential with microsatellites requires prior hypotheses about target genes and selective pressures [START_REF] Allendorf | Genomics and the future of conservation genetics[END_REF]. Here having a high number of anonymous (i.e.

without targeting specific loci) markers distributed across the genome is essential to increase the resolution and statistical power to uncover neutral processes as well as increase our chances to have markers involved in adaptive processes [START_REF] Allendorf | Genomics and the future of conservation genetics[END_REF][START_REF] Hohenlohe | Population genomics for wildlife conservation and management[END_REF].

Box 3. Population genetics and molecular markers

Population genetics

Population genetics focus on the study of the spatio-temporal variation of allele frequencies to explain the origin and maintenance of genetic diversity in populations [START_REF] Hamilton | Population Genetics[END_REF]. For natural populations this consists of estimating parameters such as genetic diversity [START_REF] Selkoe | Taking the chaos out of genetic patchiness: Seascape genetics reveals ecological and oceanographic drivers of genetic patterns in three temperate reef species[END_REF]), evaluating population structure -which can be defined as the amount of genetic differentiation between groups of individuals or populations [START_REF] Waples | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF]) -, testing for isolation by distance [START_REF] Wright | Isolation by Distance[END_REF][START_REF] Benestan | Restricted dispersal in a sea of gene flow[END_REF]) and isolation by resistance patterns [START_REF] Thomas | Isolation by resistance across a complex coral reef seascape[END_REF] as well as estimating dispersal distances and migration rates [START_REF] Broquet | Molecular Estimation of Dispersal for Ecology and Population Genetics[END_REF]Petit 2009, Cayuela et al. 2018).

Molecular markers

The genetic variation among populations can be examined looking at the variation of molecular markers. A molecular marker is a polymorphic region on the genome, i.e. a DNA sequence with more than one allele, present in sufficiently high frequency to be used as a representative marker of a population or species. The first molecular markers developed, allozymes, were based on proteins migrating at different distances through an electric field. The development of first generation sequencing technologies then allowed to quantify variations in DNA sequences [START_REF] Freeland | Molecular Ecology[END_REF]). Among the sequenced markers, microsatellites, which are highly repetitive sequences of a few bases, have long been the central tool for population genetic studies [START_REF] Selkoe | Microsatellites for ecologists: A practical guide to using and evaluating microsatellite markers[END_REF]. In the last two decades and since the advent of next generation sequencing technologies, the use of Single Nucleotide Polymorphisms (SNP) has increased exponentially and overtaken microsatellites, marking the turn from population genetics to population genomics (see 1.3.2 NGS at the population level: genome-wide markers and seascape genomics) (Allendorf 2017).

Next generation sequencing: new tools for conservation science

Biodiversity conservation can benefit from the technological advances in the fields of DNA sequencing and molecular ecology to efficiently monitor the ecological status of communities and populations; tracking the rapidly advancing biodiversity crisis [START_REF] Goodwin | DNA Sequencing as a Tool to Monitor Marine Ecological Status[END_REF], Díaz et al. 2019a). Genetic methods have been used since the early 1970s from a conservation perspective to study and inform conservation actions by taking some tissue samples from the target species [START_REF] Allendorf | Conservation and the Genetics of Populations[END_REF]. Nowadays, new insights on biodiversity at the species level can be gained from water or soil samples by metabarcoding environmental DNA [START_REF] Gilbey | Life in a drop: Sampling environmental DNA for marine fishery management and ecosystem monitoring[END_REF]. Revolutionary advances were also made at the population level as we can now trace back the parents of dispersing larvae up to 120 km apart to study connectivity [START_REF] Almany | Larval fish dispersal in a coral-reef seascape[END_REF], Bode et al. 2019). We can also determine the resilience of a species against climate change in their natural populations by conducting genome scans and environmental associations [START_REF] Selmoni | Seascape genomics as a new tool to empower coral reef conservation strategies: An example on north-western Pacific Acropora digitifera[END_REF][START_REF] Layton | Genomic evidence of past and future climate-linked loss in a migratory Arctic fish[END_REF]. The emergence of new research questions stems from the significant advances in both sequencing and analytical methods.

Generations of sequencing advances

The first generation of sequencing technologies allowed to sequence one sample at a time, and saw the first breakthrough with the development of Sanger's chain-termination technique [START_REF] Sanger | DNA sequencing with chain-terminating inhibitors[END_REF] which set the stage for automated, high-resolution sequencing using fluorescent "terminator" dNTPs/nucleotides and capillary electrophoresis. The development of polymerase chain reaction (PCR) [START_REF] Saiki | Primer-directed enzymatic amplification of DNA with a thermostable DNA polymerase[END_REF]) soon thereafter made it possible to amplify high concentrations of pure DNA necessary for sequencing, and target specific genomic regions or markers. The robustness and accuracy of Sanger sequencing make it still useful to this day, especially for small-scale sequencing applications. DNA barcoding (Box 2) consists of identifying species based on their gene sequences and still relies on Sanger sequencing to e.g. identify mislabelled seafood [START_REF] Pardo | DNA barcoding revealing mislabeling of seafood in European mass caterings[END_REF] or delineate cryptic species [START_REF] Carugati | Combined COI barcode-based methods to avoid mislabelling of threatened species of deep-sea skates[END_REF].

promising unrestricted sequencing length (Table 1. 1). But due to its relatively high error rate (up to 10%), it is not ready yet for high-precision applications [START_REF] Taberlet | Environmental DNA for Biodiversity Research and Monitoring[END_REF]. [START_REF] Andrews | Harnessing the power of RADseq for ecological and evolutionary genomics[END_REF]. At the community level, it is now also possible to sequence one fragment from up to > 150 species in a single sample and characterize the species diversity of communities [START_REF] Stauffer | How many replicates to accurately estimate fish biodiversity using environmental DNA on coral reefs ? -bioRxiv Prepr[END_REF].

or fishes (e.g. [START_REF] Thomsen | Detection of a Diverse Marine Fish Fauna Using Environmental DNA from Seawater Samples[END_REF][START_REF] Juhel | Accumulation curves of environmental DNA sequences predict coastal fish diversity in the coral triangle[END_REF], very wide taxonomic groups by using universal primers amplifying e.g. eukaryotes [START_REF] Stat | Ecosystem biomonitoring with eDNA: Metabarcoding across the tree of life in a tropical marine environment[END_REF][START_REF] Dibattista | Environmental DNA can act as a biodiversity barometer of anthropogenic pressures in coastal ecosystems[END_REF] or metazoans [START_REF] Grey | Effects of sampling effort on biodiversity patterns estimated from environmental DNA metabarcoding surveys[END_REF], or different groups across kingdoms or phyla by combining multiple primers and thus barcodes [START_REF] Günther | Metabarcoding of marine environmental DNA based on mitochondrial and nuclear genes[END_REF][START_REF] Berry | Marine environmental DNA biomonitoring reveals seasonal patterns in biodiversity and identifies ecosystem responses to anomalous climatic events[END_REF][START_REF] Jeunen | Environmental DNA (eDNA) metabarcoding reveals strong discrimination among diverse marine habitats connected by water movement[END_REF][START_REF] West | eDNA metabarcoding survey reveals fine-scale coral reef community variation across a remote, tropical island ecosystem[END_REF][START_REF] Holman | Animals, protists and bacteria share marine biogeographic patterns[END_REF]. For marine fishes, the most widely used primer pairs are the ribosomal 12S teleo [START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF]), 12S MiFish [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] and the 16S Fish primers [START_REF] Deagle | Studying seabird diet through genetic analysis of faeces: A case study on macaroni penguins (Eudyptes chrysolophus[END_REF][START_REF] Berry | DNA metabarcoding for diet analysis and biodiversity: A case study using the endangered Australian sea lion (Neophoca cinerea)[END_REF].

After amplification and sequencing, several steps which constitute a bio-informatic pipeline are needed to clean the data, assign each sequence to its sample of origin, filter for quality and remove variants and/or PCR errors [START_REF] Boyer | Obitools: A unix-inspired software package for DNA metabarcoding[END_REF][START_REF] Mathon | Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification[END_REF]. At a final step, the resulting sequences can be assigned to known and referenced species, genera or families to obtain species lists, their associated traits and infer ecological patterns [START_REF] Aglieri | Environmental DNA effectively captures functional diversity of coastal fish communities[END_REF][START_REF] Djurhuus | Environmental DNA reveals seasonal shifts and potential interactions in a marine community[END_REF], Marques et al. 2021b) (Figure 1. [START_REF] Dornelas | Assemblage time series reveal biodiversity change but not systematic loss[END_REF]. Species assignment is reliant on the comprehensiveness of reliable reference databases [START_REF] Juhel | Accumulation curves of environmental DNA sequences predict coastal fish diversity in the coral triangle[END_REF]). If such a reference database is unavailable, (nearly) identical sequences can be clustered into Molecular Operational Taxonomic Units (MOTUs) to obtain taxonomy-free diversity estimates (Marques et al. 2020).

Advantages of eDNA metabarcoding for marine biodiversity assessment

The first use of eDNA metabarcoding to detect macroorganisms served to identify marine fishes [START_REF] Thomsen | Detection of a Diverse Marine Fish Fauna Using Environmental DNA from Seawater Samples[END_REF]. Since then, eDNA metabarcoding has been successfully applied to survey marine fishes in various ecosystems and habitats such as tropical coral reefs [START_REF] West | eDNA metabarcoding survey reveals fine-scale coral reef community variation across a remote, tropical island ecosystem[END_REF][START_REF] Polanco Fernández | Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes[END_REF][START_REF] Oka | Environmental DNA metabarcoding for biodiversity monitoring of a highly diverse tropical fish community in a coral reef lagoon: Estimation of species richness and detection of habitat segregation[END_REF], temperate kelp forests [START_REF] Port | Assessing vertebrate biodiversity in a kelp forest ecosystem using environmental DNA[END_REF], Gold et al. 2021a), the open ocean [START_REF] Andruszkiewicz | Biomonitoring of marine vertebrates in Monterey Bay using eDNA metabarcoding[END_REF] and urbanized areas such as harbours [START_REF] Sigsgaard | Seawater environmental DNA reflects seasonality of a coastal fish community[END_REF]. Despite the fluid and highly connected nature of marine ecosystems, eDNA is sufficiently heterogeneous across a seascape to represent and differentiate distinct communities even between nearby habitats [START_REF] Port | Assessing vertebrate biodiversity in a kelp forest ecosystem using environmental DNA[END_REF][START_REF] Jeunen | Environmental DNA (eDNA) metabarcoding reveals strong discrimination among diverse marine habitats connected by water movement[END_REF][START_REF] Oka | Environmental DNA metabarcoding for biodiversity monitoring of a highly diverse tropical fish community in a coral reef lagoon: Estimation of species richness and detection of habitat segregation[END_REF][START_REF] Lafferty | At Palmyra Atoll , the fish-community environmental DNA signal changes across habitats but not with tides[END_REF]. Marine eDNA metabarcoding then presents unique advantages and the opportunity to develop reliable, non-invasive as well as effort-and costefficient sampling protocols to holistically monitor fish communities in MPAs. eDNA metabarcoding is capable of simultaneously detecting conspicuous but also small, cryptic and elusive fish taxa which are difficult to observe and thus often overlooked with visual surveys [START_REF] Dibattista | Assessing the utility of eDNA as a tool to survey reef-fish communities in the Red Sea[END_REF][START_REF] Stat | Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity[END_REF][START_REF] Dressler | Looking where it's hard to see: a case study documenting rare Eucyclogobius newberryi presence in a California lagoon[END_REF]. By detecting all species that inhabit or passed through the study area, it can give a holistic view of the sampled ecosystem. The sampling, sequencing and bio-informatic treatment can be standardized and applied to census different [START_REF] Sigsgaard | Using vertebrate environmental DNA from seawater in biomonitoring of marine habitats[END_REF]) and inaccessible [START_REF] Lacoursière-Roussel | eDNA metabarcoding as a new surveillance approach for coastal Arctic biodiversity[END_REF][START_REF] Mcclenaghan | Harnessing the power of eDNA metabarcoding for the detection of deep-sea fishes[END_REF]) habitats, as well as areas covering large spatial scales [START_REF] West | Large-scale eDNA metabarcoding survey reveals marine biogeographic break and transitions over tropical north-western Australia[END_REF]. Finally, species identification does not rely on expert taxonomic knowledge and is thus not subject to the same sampler bias as visual methods, if a reliable and high-quality taxonomic reference database is available [START_REF] Ji | Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding[END_REF].

In this thesis, I utilize a broad sense population genomics approach to study neutral and adaptive processes.

Neutral (broad sense) population genomics

Selectively neutral processes such as genetic drift and gene flow affect the genome as a whole. The ability to sequence many markers across the genome increases the resolution to answer population genetics questions of neutral processes and estimate parameters such as genetic diversity, population differentiation and connectivity [START_REF] Luikart | Population Genomics: Advancing Understanding of Nature[END_REF]). In the marine environment, this is especially beneficial because many species show little apparent genetic differentiation due to their large effective population sizes and high levels of gene flow in an inherently dynamic environment. For example, D'Aloia et al. (2020) could resolve finer population structure with SNP data than with microsatellites in a dispersive coral reef fish (Elacatinus lori). A higher resolution can also reveal kin relationships beyond parent-offspring pairs increasing our capacity to find closely related pairs, such as full siblings or half siblings, and detect realized dispersal while reducing sampling demands (Feutry et al. 2017, Flanagan and[START_REF] Flanagan | The future of parentage analysis: From microsatellites to SNPs and beyond[END_REF].

Understanding neutral population structure and genetic connectivity patterns is crucial to establish appropriate conservation measures. For example in fisheries, the definition of distinct populations, or inversely large panmictic populations, allows to redefine fishing stocks that align with the real population dynamics and adjust management plans accordingly [START_REF] Mckinney | Managing mixed-stock fisheries: Genotyping multi-SNP haplotypes increases power for genetic stock identification[END_REF][START_REF] Benestan | Population Genomics Applied to Fishery Management and Conservation[END_REF]. Connectivity patterns are also taken into consideration for planning the placement of new MPAs into a network [START_REF] Lagabrielle | Connecting MPAs -eight challenges for science and management[END_REF][START_REF] Magris | Integrating multiple species connectivity and habitat quality into conservation planning for coral reefs[END_REF] or evaluating existent networks (Truelove et al. 2015, Jenkins and[START_REF] Jenkins | Assessing connectivity between MPAs: Selecting taxa and translating genetic data to inform policy[END_REF].

Seascape genomics, following landscape genetics and genomics [START_REF] Manel | Ten years of landscape genetics[END_REF], provides the framework to identify landscape, or in this case seascape, features associated with dispersal patterns obtained with genetic data [START_REF] Riginos | Navigating the currents of seascape genomics: How spatial analyses can augment population genomic studies[END_REF][START_REF] Selkoe | A decade of seascape genetics: Contributions to basic and applied marine connectivity[END_REF]). Those dispersal estimates can then be combined with biophysical models of larval dispersal to identify barriers and corridors to connectivity (Bode et al. 2019).

Local adaptation

The high number of genome-wide markers also allow us to detect genomic signatures or "footprints" of divergent selection and consequent local adaptation in natural populations.

Local adaptation refers to the patterns and mechanisms of adaptation to local environmental conditions of one or several populations that are connected, at least potentially, by gene flow (Williams 1966, Kawecki and[START_REF] Kawecki | Conceptual issues in local adaptation[END_REF]. Positive selection (also called adaptive or directional selection) acting differently among local populations will result in a stronger differentiation of the genomic regions under selection [START_REF] Lewontin | Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms[END_REF]Krakauer 1973, Luikart et al. 2003). It is thus possible to detect potential signals of local adaptation by scanning the genome for highly differentiated regions, which is possible with genome-wide SNPs [START_REF] Hohenlohe | Using Population Genomics to Detect Selection in Natural Populations: Key Concepts and Methodological Considerations[END_REF]. Deciphering the link between these highly differentiated regions and the local environmental conditions, under a seascape genomics approach, aims to determine the recent selective forces driving population differentiation [START_REF] Rellstab | A practical guide to environmental association analysis in landscape genomics[END_REF][START_REF] Balkenhol | Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations[END_REF]. Identifying spatial patterns of local adaptation to climate conditions can then inform which populations are potentially vulnerable or resilient to climate change [START_REF] Selmoni | Seascape genomics as a new tool to empower coral reef conservation strategies: An example on north-western Pacific Acropora digitifera[END_REF][START_REF] Nielsen | Distinct interspecific and intraspecific vulnerability of coastal species to global change[END_REF]. Populations showing potential for local adaptation can be considered more resilient as they already possess beneficial alleles. These populations can then be targeted for the placement of new MPAs in a framework to integrate evolutionary forces in conservation planning (Hanson et al. 2020a).

Aims of the thesis

The objective of my PhD is to apply molecular (NGS) tools to investigate the multifaceted biodiversity of fishes across the Mediterranean Sea and evaluate how protection measures such as MPAs could safeguard species and populations from direct (i.e. fishing or habitat destruction) and indirect (i.e. climate change) anthropogenic stressors. More precisely I study biodiversity from the community level using eDNA metabarcoding, to the population level with neutral SNPs, to finally the gene level with outlier SNPs (Figure 1.10).

MPA biodiversity monitoring

eDNA metabarcoding is increasingly used for the biomonitoring of various taxa across different marine habitats and under multiple anthropogenic impacts, targeting e.g. microbial [START_REF] Laroche | A cross-taxa study using environmental DNA/RNA metabarcoding to measure biological impacts of offshore oil and gas drilling and production operations[END_REF][START_REF] Stoeck | Environmental DNA metabarcoding of benthic bacterial communities indicates the benthic footprint of salmon aquaculture[END_REF][START_REF] Garlapati | Assessing the in situ bacterial diversity and composition at anthropogenically active sites using the environmental DNA ( eDNA )[END_REF], plankton [START_REF] Berry | Marine environmental DNA biomonitoring reveals seasonal patterns in biodiversity and identifies ecosystem responses to anomalous climatic events[END_REF]) and macroinvertebrate [START_REF] Aylagas | Benchmarking DNA Metabarcoding for Biodiversity-Based Monitoring and Assessment[END_REF] assemblages. Although many studies have used eDNA to survey fish diversity, only a few studies have taken advantage of its potential for direct marine conservation applications [START_REF] Stat | Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity[END_REF], Gold et al. 2021a). In the Mediterranean sea in particular, only one eDNA metabarcoding study focusing on fish communities has been published so far comparing eDNA to visual-and capture-based census methods [START_REF] Aglieri | Environmental DNA effectively captures functional diversity of coastal fish communities[END_REF]. Their work, along with that of others, shows that eDNA can reliably characterize fish diversity and is complementary [START_REF] Stat | Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity[END_REF][START_REF] Aglieri | Environmental DNA effectively captures functional diversity of coastal fish communities[END_REF][START_REF] Valdivia-Carrillo | Integrating eDNA metabarcoding and simultaneous underwater visual surveys to describe complex fish communities in a marine biodiversity hotspot[END_REF] or even superior [START_REF] Polanco Fernández | Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes[END_REF]) to other methods. The main limiting factor identified across studies comparing methods or primers is the general incompleteness of fish genetic reference databases for non-COI primers, greatly limiting the detection abilities of eDNA metabarcoding [START_REF] Mcelroy | Calibrating Environmental DNA Metabarcoding to Conventional Surveys for Measuring Fish Species Richness[END_REF][START_REF] Zhang | Comprendre l'effet des AMP sur les communautés de poissons nécessite la détection d'un large éventail d'espèces allant des plus grands prédateurs aux plus petites proies. Le metabarcoding d'ADNe permet de détecter simultanément les poissons communs ainsi que les poissons cachés ou en passage transitoires, qui sont difficiles à observer et donc souvent négligés par les méthodes classiques[END_REF]. If this limitation can be reduced, eDNA metabarcoding could more holistically study the effect of human disturbances and management on fish communities. It could thus offer new perspectives on key questions in conservation that have previously been addressed with "conventional" visual-or capture-based census methods: how does protection influence or re-establish fish diversity and community composition?

Population connectivity

Understanding connectivity patterns and distances is essential to ensure correct implementation of reserve networks. Direct genetic methods tracing the realized dispersal of individuals, such as parentage analysis and assignment tests, mostly find evidence of dispersal up to 30 km [START_REF] Planes | Larval dispersal connects fish populations in a network of marine protected areas[END_REF][START_REF] Schunter | Kinship analyses identify fish dispersal events on a temperate coastline[END_REF][START_REF] D'aloia | Patterns , causes , and consequences of marine larval dispersal[END_REF], Almany et al. 2017, 1.4 Aims of the thesis Bode et al. 2019). Larval dispersal models, on the other hand, often predict dispersal distances over 10s to 1 000s of km [START_REF] Manel | Long-Distance Benefits of Marine Reserves: Myth or Reality?[END_REF][START_REF] Álvarez-Noriega | Global biogeography of marine dispersal potential[END_REF]. Indirect genetic connectivity estimates, finally, often find high gene flow and little differentiation between populations > 1 000 km apart (Dalongeville et al. 2018b). While the direct genetic methods and larval dispersal models estimate dispersal in a single generation, indirect genetic methods estimate dispersal over multiple generations representing evolutionary time scales. Integrating (larval) dispersal modelling over multiple generations could narrow the gap between larval and genetic connectivity estimates while providing insights on the temporal scale at which marine population connectivity operates.

Direct genetic dispersal estimates, on the other hand, potentially underestimate dispersal distance due to the high sampling demand of parentage analyses and subsequent small spatial scale of studies [START_REF] Manel | Long-Distance Benefits of Marine Reserves: Myth or Reality?[END_REF]). However, the few studies with a large scale and exhaustive sampling design found that dispersal distances can be up to 250 km [START_REF] Christie | Larval Connectivity in an Effective Network of Marine Protected Areas (SJ Goldstien[END_REF], Williamson et al. 2016[START_REF] Almany | Larval fish dispersal in a coral-reef seascape[END_REF], Bode et al. 2019[START_REF] Catalano | Quantifying dispersal variability among nearshore marine populations[END_REF]). Thus, combining kinship analyses beyond parent-offspring pairs with a widespread individual-based sampling could increase our capacity to identify closely related long-distance pairs and characterize the amplitude of dispersal even over extensive areas [START_REF] Iacchei | Combined analyses of kinship and FST suggest potential drivers of chaotic genetic patchiness in high gene-flow populations[END_REF][START_REF] Venables | Genome-wide SNPs detect no evidence of genetic population structure for reef manta rays (Mobula alfredi) in southern Mozambique[END_REF][START_REF] Benestan | Restricted dispersal in a sea of gene flow[END_REF].

Population adaptive potential

The placement of MPAs and marine reserves in the Mediterranean Sea is highly biased towards the north-west [START_REF] Claudet | Underprotected Marine Protected Areas in a Global Biodiversity Hotspot[END_REF] which coincides with the lower end of the temperature and salinity gradients [START_REF] Kyprioti | Is the current Mediterranean network of marine protected areas resilient to climate change?[END_REF]. The south-eastern populations on the other hand experience the highest temperature and salinity conditions but are also projected to suffer the highest impact of climate change [START_REF] Coll | The Mediterranean Sea under siege: Spatial overlap between marine biodiversity, cumulative threats and marine reserves[END_REF]). These populations could represent important sources of locally adapted genes warranting their protection. Alternatively, a lack of local adaptation could indicate a lower vulnerability to climate drivers or higher levels of gene flow and a higher potential for dispersal, foreboding climate-change induced range shifts [START_REF] Pinsky | Marine Taxa Track Local Climate Velocities[END_REF][START_REF] Lenoir | Species better track climate warming in the oceans than on land[END_REF]. Previous studies assessing local adaptation patterns at a Mediterranean-wide scale found significant influences of temperature and/or salinity on genomic patterns but focused solely on one species [START_REF] Milano | Outlier SNP markers reveal fine-scale genetic structuring across European hake populations (Merluccius merluccius)[END_REF], Dalongeville et al. 2018a). A recent study on two species of wrasses found distinct patterns of genetic differentiation and local adaptation but focused solely on the Western Mediterranean [START_REF] Torrado | Individual-based population genomics reveal different drivers of adaptation in sympatric fish[END_REF]. Species with different ecological and life history traits can demonstrate different levels of connectivity and local adaptation despite sharing the same geographic range [START_REF] Gagnaire | Comparative genomics approach to evolutionary process connectivity[END_REF][START_REF] Nielsen | Multi-model seascape genomics identifies distinct environmental drivers of selection among sympatric marine species[END_REF]. They can consequently present different vulnerabilities to climate change which would require species-specific conservation measures [START_REF] Nielsen | Distinct interspecific and intraspecific vulnerability of coastal species to global change[END_REF].

Comparing genetic differentiation and local adaptation patterns of species with different dispersal abilities across the Mediterranean Sea, spanning both ends of the environmental gradients, would allow to untangle the different influences of gene flow versus local adaptation and elucidate potential species-specific responses to climate change.

Thesis structure

Following the introduced key issues in marine fish conservation and the limitations of previous studies, I present the field work I conducted in the Mediterranean Sea to collect eDNA from different MPAs and their proximate outside, as well as build a genetic reference database for the 12S marker of local fishes. I also present the Mediterranean-wide SNP dataset used to assess the seascape genomics of two species. The presentation of these materials and methods constitutes Chapter 2.

In Chapter 3, I focus on biodiversity at the community level by comparing species richness and composition obtained from eDNA metabarcoding inside and outside six strict notake reserves. I aim to uncover how fishing pressure and protection thereof affect whole fish communities, including cryptic and elusive species overlooked by conventional methods.

Next, I'll investigate population (within-species) genetic variation in Chapter 4 using spatial graphs as a tool to study the different components of connectivity between populations, extend dispersal estimations across generations to match genetic connectivity, and to better estimate the prevalence of long-distance dispersal. Chapter 5 will in turn assess how gene flow and selection interplay to shape genomic variation within species, and how heterogeneous environmental factors can lead to local adaptation which should be protected to ensure the species viability under climate change.

Finally, the general discussion presents in Chapter 6 a synthetic analysis of the main results but also some limitations of the thesis, as well as perspectives and future applications.

increasing evaporation and mixing increase its salinity towards the Levantine sea where the now dense water sinks to form the Levantine Intermediate Waters (LIW). The LIW circulates back westward to finally exit the Mediterranean sea through the Gibraltar Strait [START_REF] Zavatarelli | A Numerical Study of the Mediterranean Sea Circulation[END_REF]. In the Gulf of Lions (French Mediterranean) in the Western Basin and the Adriatic Sea in the Eastern basin, cold winter temperatures and strong winds dramatically increase water density leading to downwelling which creates the Mediterranean deep water. In the centre, the Siculo-Tunisian strait connects the Western and Eastern basin while also deviating both surface and intermediate waters contributing to the within-basin circulation patterns. In the east, the opening of the Suez Canal has provided a connection with the Red Sea which introduced a large amount of alien species. Overall, the Mediterranean Sea is characterized by a temperature gradient increasing west to east and north to south, a strong salinity gradient with increasing distance from the Atlantic, and localized peaks of productivity (notably in the Gulf of Lion and Northern Adriatic Sea) amongst a generally oligotrophic sea [START_REF] Bosc | Seasonal and interannual variability in algal biomass and primary production in the Mediterranean Sea, as derived from 4 years of SeaWiFS observations[END_REF][START_REF] Coll | The biodiversity of the Mediterranean Sea: Estimates, patterns, and threats[END_REF]).

A hotspot for biodiversity under pressure

Hotspot for biodiversity

Historic drastic changes in geology, climate, sea level and salinity have made the Mediterranean Sea a hotspot for biodiversity [START_REF] Coll | The biodiversity of the Mediterranean Sea: Estimates, patterns, and threats[END_REF]). The Mediterranean functioned as a `biodiversity pump` from the Atlantic as most species colonized the Mediterranean through the Strait of Gibraltar [START_REF] Bianchi | Biodiversity issues for the forthcoming tropical Mediterranean Sea[END_REF]. Strong environmental gradients and reduced opportunities for North-South migration subjected these species to high evolutionary pressures, making the Mediterranean a `factory to produce endemics` [START_REF] Lejeusne | Climate change effects on a miniature ocean: the highly diverse, highly impacted Mediterranean Sea[END_REF]). The Suez Canal, marine traffic in general and aquaculture have also brought in scores of alien species.

As a result, the Mediterranean sea is the home to a current estimate of over 20 000 species, of which 20% are endemics [START_REF] Coll | The biodiversity of the Mediterranean Sea: Estimates, patterns, and threats[END_REF], Pascual et al. 2017). These species represent 4-18% of the world's known marine species, depending on the taxonomic group taken into consideration, despite covering only 0.82% of the world's ocean area and 0,32% of its volume [START_REF] Bianchi | Marine biodiversity of the Mediterranean Sea: Situation, problems and prospects for future research[END_REF]. The Mediterranean is home to several unique and threatened habitats such as Posidonia oceanica seagrass meadows and coralligenous reefs. The sea also hosts different levels of genetic diversity and differentiation, depending on the species' evolutionary history, ecological traits and their susceptibility to barriers or corridors [START_REF] Dalongeville | Ecological traits shape genetic diversity patterns across the Mediterranean Sea: A quantitative review on fishes[END_REF], Pascual et al. 2017).

A sea under pressure

The Mediterranean region is also characterised by a high cultural diversity and has witnessed the flourishing and decline of many civilizations [START_REF] Coll | The biodiversity of the Mediterranean Sea: Estimates, patterns, and threats[END_REF]. Nowadays, the Mediterranean Sea is also a hotspot of anthropogenic stress [START_REF] Stock | Uncertainty analysis and robust areas of high and low modeled human impact on the global oceans[END_REF].

Overexploitation and habitat loss have grown exponentially since the Industrial Revolution and cumulate with increasing (land-based) pollution, extraction, eutrophication and climate change to threaten Mediterranean biodiversity [START_REF] Coll | The Mediterranean Sea under siege: Spatial overlap between marine biodiversity, cumulative threats and marine reserves[END_REF][START_REF] Micheli | Cumulative human impacts on Mediterranean and Black Sea marine ecosystems: Assessing current pressures and opportunities[END_REF][START_REF] Ramírez | Spatial congruence between multiple stressors in the Mediterranean Sea may reduce its resilience to climate impacts[END_REF]. Overall, the Mediterranean Sea is the most overfished sea in the world with 75% of its fish stocks exploited at unsustainable levels (FAO 2020b). At the same time, temperatures are increasing faster than the global average [START_REF] Marbà | Footprints of climate change on Mediterranean Sea biota[END_REF]. Increasing temperatures are driving species northwards [START_REF] Parravicini | Climate change and warm-water species at the north-western boundary of the Mediterranean Sea[END_REF], but continental masses and oceanographic barriers effectively trap them leading to the collapse of native biodiversity [START_REF] Rais Lasram | The Mediterranean Sea as a "cul-de-sac" for endemic fishes facing climate change[END_REF][START_REF] Givan | Thermal affinity as the dominant factor changing Mediterranean fish abundances[END_REF][START_REF] Albano | Native biodiversity collapse in the eastern Mediterranean[END_REF].

In an effort to mitigate these threats, > 1 200 marine protected areas (MPAs) have already been established in the Mediterranean Sea (MedPAN and SPA/RAC 2019). No-take reserves in the Mediterranean Sea have since long served as examples of efficient MPAs with demonstrated benefits in terms of fish biomass, abundance and diversity (e.g. Harmelin, Bachet and Garcia, 1995;[START_REF] Claudet | Assessing the effects of marine protected area (MPA) on a reef fish assemblage in a northwestern Mediterranean marine reserve: Identifying communitybased indicators[END_REF][START_REF] Mesa | Gradients of abundance of sea breams across the boundaries of a Mediterranean marine protected area[END_REF][START_REF] Guidetti | Large-scale assessment of mediterranean marine protected areas effects on fish assemblages[END_REF]. But efficient reserves remain too scarce as most Mediterranean MPAs are so-called "paper parks": despite the 6% of the Mediterranean covered by MPAs, only 0.23% represents true no-take reserves (Figure 2.3) [START_REF] Claudet | Underprotected Marine Protected Areas in a Global Biodiversity Hotspot[END_REF]). In addition, the coverage is highly unbalanced and the representativeness across ecoregions is highly variable. Most reserves are located in the northwestern Mediterranean, whereas the south-east is largely neglected (Figure 2.3 b) (MedPAN and SPA/RAC 2019). However this area of the Mediterranean is projected to suffer the highest impact from climate change [START_REF] Coll | The Mediterranean Sea under siege: Spatial overlap between marine biodiversity, cumulative threats and marine reserves[END_REF]).

eDNA sampling

General field protocol

Water samples were collected and immediately filtered on site following a protocol initially developed by SPYGEN to detect freshwater fish [START_REF] Pont | Environmental DNA reveals quantitative patterns of fish biodiversity in large rivers despite its downstream transportation[END_REF]) and that has since also successfully been applied for marine fish [START_REF] Juhel | Accumulation curves of environmental DNA sequences predict coastal fish diversity in the coral triangle[END_REF][START_REF] Polanco Fernández | Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes[END_REF].

Filtering the particles and DNA from seawater on site instead of transporting it to the lab allows to filter large volumes which increases detection probability [START_REF] Cantera | Optimizing environmental DNA sampling effort for fish inventories in tropical streams and rivers[END_REF][START_REF] Bessey | Maximizing fish detection with eDNA metabarcoding[END_REF]. We used a peristaltic pump and sterile tubing to collect 30 L of seawater by continuously pumping water for 30 minutes at a flow of 1 L/min. The water then immediately passes across a 0.20-µM cross flow filtration capsule (VigiDNA®) (Figure 2.5a). Water filtering is preferably done from a dinghy on which two peristaltic pumps are fixed, allowing the parallel sampling of two field replicates (Figure 2.5b). These are attached on each side and a weight is attached to one end of the tubing and fixed to sample at 1m below the surface. The tubing and gloves

Reference database completion

The taxonomic identification of species from eDNA metabarcoding samples is done by comparing the obtained sequences to sequences of known species in a reference database. Such a reference database should be as comprehensive as possible for the study region and contain multiple sequences per species to account for within-species variability. Global reference databases can be extracted from public databases such as NCBI's GenBank. However such sequence databases have a lot of missing species (Marques et al. 2021a), can contain missannotated sequences (Heller et al. 2018, Leray et al. 2019, Nobre et al. 2016, Wakeling et al. 2019) and non-local individuals with distinct haplotypes from the local population. Regionally augmented databases address these shortcomings to provide the most reliable taxonomic assignment [START_REF] Stoeckle | Improved Environmental DNA Reference Library Detects Overlooked Marine Fishes in New Jersey, United States[END_REF], Gold et al. 2021b).

At the onset of this PhD, only 240 out of 771 Mediterranean species were publicly available for the 12S genetic marker we targeted (teleo). Remarkably, many of the species most common to the North-Western Mediterranean, e.g. the striped red mullet Mullus surmuletus, the white seabream Diplodus sargus, or the salema porgy Sarpa salpa, were missing from this public reference database. Our objective was to sample and sequence as many missing species as possible in the North-Western Mediterranean Sea to start the completion of its 12S reference barcode database, as well as resample previously referenced species to create a database that is robust to within-species variation.

Sampling fish tissue to complete the reference database

A massive sampling campaign with the help of university students was set up to collect fin clips of the missing species. Fin clips were mostly collected from fishermen stands or at fish auctions. Non-commercial species were further completed in collaboration with local aquaria and research institutes, or by fishing ourselves. Tissue samples were immediately preserved in 2 ml tubes filled with 96% alcohol. This alcohol was replaced after 24 hrs to ensure the tissue is correctly dehydrated in order to preserve the DNA. The species was identified in the field but pictures were also taken for archiving and to be able to verify the taxonomic identification of each sample afterwards (Figure 2 2.11. Representativeness of the genetic reference database for the regional species pool. I assessed the coverage by trophic level (a,b), common length (c,d), vulnerability to fishing (e,f), vertical distribution (g,h), rarity (i,j; Occurrence category) and commercial importance (k,l). I compared the number of species (left) as well as the proportion of species (right) between sequenced (dark grey) versus notsequenced (light grey) for our targeted 12S rRNA fragment (teleo). Fisher's exact tests showed that there was no bias between categories of each trait for the 25% missing species (p-value = 0.318, 0.0718, 0.117, 0.166, 0.385 and 0.160 respectively).

quantity and quality: the higher the number of fragments, the lower the sequencing depth per fragment [START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF]). In the case of mullet and seabream, the use of this enzyme led to an extremely large number of loci being sequenced but at a very shallow depth [START_REF] Dalongeville | Variation génétique et persistance des populations en milieu marin : implications pour la conservation[END_REF].

Pooled dataset

Reference genomes were not available at the time for either species to align reads to and call SNPs. Instead, a de novo pipeline was used to align reads to each other without reference genome (the Tassel 3.0 Universal Network-Enabled Analysis Kit or UNEAK; [START_REF] Lu | Switchgrass Genomic Diversity, Ploidy, and Evolution: Novel Insights from a Network-Based SNP Discovery Protocol[END_REF].

To overcome the low coverage and high rate of missing data, the mullet individuals were pooled by sampling location and SNP calling was performed by considering the pools as individuals.

This strategy produced allele frequencies of 1 153 SNPs at 47 "pools" of nine to eighteen individuals (Dalongeville et al. 2018b). The allele frequencies were robust to variations in the pools' sample size [START_REF] Dalongeville | Variation génétique et persistance des populations en milieu marin : implications pour la conservation[END_REF]. Outlier SNPs were removed (Dalongeville et al. 2018a) and the resulting dataset of 1 123 neutral SNPs was used in Chapter 4. Due to problems arising during sequencing of seabream by the sequencing institution, the samples were not available for the pooled analysis. Sequencing was repeated with the Pst1/Bfa1 enzyme pair and were later analysed in the individual dataset.

Individual dataset

A follow-up project of SEACONNECT, the RESERVEBENEFIT project (2017-2019), focused on 4 exploited species (among which the mullet and seabream) sampled inside and outside eight Mediterranean MPAs to evaluate the benefits of MPAs in providing withinspecies benefits (maintenance of genetic diversity, population connectivity and spillover to fishing grounds). As part of the project, the genomes of the mullet and seabream were sequenced and annotated [START_REF] Fietz | New genomic resources for three exploited Mediterranean fishes[END_REF]. The availability of reference genomes provided the opportunity to re-analyse the individual mullet and seabream data with a higher robustness to missing data. Quality filtering and SNP calling was performed by Pierre-Edouard Guerin, a bioinformatics engineer at CEFE, with the FreeBayes software [START_REF] Garrison | Haplotype-based variant detection from short-read sequencing[END_REF] as implemented in the dDocent pipeline [START_REF] Puritz | dDocent : a RADseq, variant-calling pipeline designed for population genomics of non-model organisms[END_REF]. The FreeBayes software requires a reference genome and allows reads of variable length. This allowed us to keep many reads otherwise discarded for UNEAK or Stacks [START_REF] Catchen | Stacks : Building and Genotyping Loci De Novo From Short-Read Sequences. -G3 Genes[END_REF]

Preface

Marine protected areas (MPAs), and especially no-take reserves can influence the local presence, abundance and density of species. However they are not expected to have the same effect on all species since fishing does not target all species similarly [START_REF] Claudet | Marine reserves: Fish life history and ecological traits matter[END_REF][START_REF] Mouillot | A functional approach reveals community responses to disturbances[END_REF]. Beyond the direct removal of certain species, fishing pressure can also change the interactions between species. By affecting different species differently, protection can then result in diversity patterns that deviate from the typically expected increase in species richness.

Elucidating the effect of marine reserves on fish species richness requires the simultaneous detection of all or almost all fish species constituting local communities, from large predators to small prey, from conspicuous species to cryptic, furtive and transient species.

Metabarcoding of eDNA using NGS technologies allows to sequence thousands of reads from multi-species samples, and identify their corresponding species using a robust reference database. In this chapter, I analyse the eDNA metabarcoding data that was obtained from six no-take reserves. In total, 97 species were identified from 72 eDNA samples (Figure 3.1), averaging 30 (±9) per species for 639 364 (±293 380) reads per sample. Beyond obtaining new species inventories, I test how the diversity of these communities changes across MPAs and how species with different traits react differently to anthropogenic pressure or protection thereof. To consider the confounding effects of habitat and environmental conditions on the composition and diversity of fish communities, I also quantified various habitat and environmental variables and include them analyses.

Introduction

Marine ecosystems and their resources are severely threatened by multiple pressures including climate change [1], over-exploitation [START_REF] Fernandes | Coherent assessments of Europe's marine fishes show regional divergence and megafauna loss[END_REF], and habitat degradation [START_REF] Rogers | Fisheries productivity under progressive coral reef degradation[END_REF]. However, despite the prevailing trend of biodiversity loss at the global scale [START_REF] Díaz | Pervasive human-driven decline of life on Earth points to the need for transformative change[END_REF][START_REF] Yan | Overfishing and habitat loss drive range contraction of iconic marine fishes to near extinction[END_REF], the number of species does not necessarily decline at the local scale [6,[START_REF] Dornelas | Assemblage time series reveal biodiversity change but not systematic loss[END_REF]. Long-term time series show that only 3% of coastal marine ecosystems are experiencing a local decline in species richness while a positive trend was reported in 16% of the studied cases [START_REF] Elahi | Recent trends in local-scale marine biodiversity reflect community structure and human impacts[END_REF]. This biodiversity conservation paradox, i.e. species richness can increase under disturbance, can be explained by a balance between extinction and colonization rates for a given location and a high species turnover [START_REF] Dornelas | Assemblage time series reveal biodiversity change but not systematic loss[END_REF]9]. Such turnover can occur when endemic species are replaced by exotic species [10] or by the range expansion of species from adjacent regions under climate change [START_REF] Elahi | Recent trends in local-scale marine biodiversity reflect community structure and human impacts[END_REF]. Biodiversity can also increase in disturbed areas if the removal of certain vulnerable species allows the establishment and coexistence of more resistant species under the intermediate disturbance hypothesis [11]. This conservation paradox has received little attention in the spatial context of protected areas. Protected areas are expanding worldwide following the new commitment to protect at least 30% of the global ocean and land by 2030, to achieve both biodiversity and climate goals [12][13][14]. Since some conservation-dependent species can be rapidly extirpated by human activities outside protected areas [15,16], we can expect more species within protected areas than their non-protected counterparts. Yet, this assumption is supported by scarce evidence [17,18], while other studies fail to show any marked difference in species richness as a result of protection [19][20][21][22][23] or even report higher local species richness in humanmodified natural habitats [24]. Here, we suggest that this lack of consensus may come from incomplete species detection and uncontrolled habitat or environmental covariates, at least in coastal marine ecosystems.

Marine protected areas (MPAs), and in particular marine reserves which are strictly no-take MPAs [25], offer a unique opportunity to test this conservation paradox and some underlying hypotheses. Marine reserves are widely recognized as effective conservation tools supporting greater density and biomass of exploited species within their boundaries than nearby fished areas [21,[26][27][28]. Comparatively, the extent to which marine reserves and nearby fished areas support different levels of species richness or different species compositions remains unclear. On one side, large-bodied and predator species are often overexploited by fisheries and extirpated outside marine reserves increasing species richness within reserves [15]. On the other side, marine reserves can restore predator populations and thus strengthen 'top-down' trophic cascades thereby affecting biodiversity at lower trophic levels [29]. Elucidating a potential marine conservation paradox would thus require the detection of a broad range of fish species constituting assemblages from large predators to small prey. Yet, many fish species are missed by most capture-or visual-based surveys because they are cryptobenthic, rare, or elusive [30][31][32][33][34]. Moreover, mobile species may not be recorded as they only occur for short amounts of time in a given location [30].

As an alternative, the environmental DNA (eDNA) metabarcoding approach overcomes some shortcomings of classical surveys to characterize marine fish assemblages [35,36] including small, cryptic, and elusive species [33,37]. eDNA is made of small fragments of intra-and extracellular DNA generated by organisms in their proximate environment, and can be sampled to infer the presence of species, monitor coastal ecosystems, and unveil ecological processes [33,[38][39][40]]. Yet, the gaps in public genetic databases can limit the breadth of species inventories based on eDNA metabarcoding [41]. In this study, we take advantage of eDNA detectability potential, using a regionally augmented genetic reference database, to uncover a previously hidden conservation paradox in coastal fish assemblages: marine reserves host less species than nearby fished areas after controlling for environmental conditions. We also show that changes in fish species composition along a gradient of human impact are mainly driven by species groups typically overlooked in most MPA studies, namely cryptobenthic, pelagic, and rare fishes.

Results

A stratified sampling design was carried out to survey six Mediterranean no-take reserves and their surrounding sites, hereafter referred to as regions (see Methods, figure 1a; electronic supplementary material, table S1). Three sites were considered for each of the six regions: one within the reserve boundaries, one outside at 5 km from the reserve boundaries, and one outside at 10 km, hereafter referred to as protection levels (figure 1b). eDNA was filtered along 2 km transects using a protocol optimized for monitoring coastal species with four replicates per site. We assembled a new reference genetic database (115 species sequenced to reach 75% coverage) for North-Western Mediterranean coastal fishes to assign more eDNA sequences to known species using a stringent bioinformatic pipeline.

(a) Metabarcoding and taxonomic assignment

The 72 eDNA samples (6 regions × 3 sites × 4 replicates per site) yielded a total of 51 506 234 reads, with on average 715 364 reads per sample (±s.d. = 293 934). Assigning reads to the reference database detected 122 unique fish taxa with on average 35 taxa per sample (±10), among which 104 were identified at species level whereas 16 could only be assigned to the genus level and two to the family level. After removal of foreign species, uncertain assignments, and freshwater fishes (electronic supplementary material, Methods), 46 034 170 reads were assigned to a known marine fish species. The mean number of reads per sample dropped to 639 364 (±293 380), and read abundances were transformed to presence/absence for all subsequent analyses.

A total of 97 fish species were identified across all samples covering 74 genera and 43 families, with on average 30 fish species (±9) per sample (figure 2). Almost half of the species detected belong to four families, i.e. sparids (Sparidae, n = 16), gobies (Gobiidae, n = 12), wrasses (Labridae, n = 7), and combtooth blennies (Blenniidae, n = 7), whereas 30 families were represented by only one species (electronic supplementary material, figure S2). Detections included common Mediterranean taxa such as the damselfish (Chromis chromis), the salema porgy (Sarpa salpa), and the white seabream (Diplodus sargus), as well as rare species like the grey triggerfish (Balistes capriscus) and the blue shark (Prionace glauca).

(b) Species richness paradox

Overall, fish species richness seemingly increased outside the reserve with, on average, 28 (±6) fish species within the reserve, 30 (±10) species at 5 km outside, and 32 (±11) species at 10 km outside, albeit no significant difference was detected (Kruskal-Wallis χ 2 = 5.1, d.f. = 2, p = 0.078) (figure 2a).

To consider the confounding effects of environmental conditions (habitat and climate) on the protection level effect, we created 500 m buffer zones around each transect and extracted the coverage of each substrate type (electronic supplementary material, Methods), mean bottom depth, and the mean benthic and surface chlorophyll a. We recorded the sea surface temperature (SST) during sampling and calculated the mean distance of each transect to the closest point on land. To avoid collinearity between all these covariates, we performed a principal component analysis (PCA). The first four orthogonal PCA axes explained 74.2% of the total variance among sites and were retained as explanatory variables in the next analyses to control for environmental confounding factors (electronic supplementary material, figure S3).

We used generalized linear models (GLMs) to investigate the effect of protection on species richness while accounting for environmental differences represented by the PCA axes. The influence of protection on fish richness was highly significant (GLM, R 2 = 0.40, p < 0.01; figure 2b; electronic supplementary material, table S2). We detected no effect of royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20210112 the region on model residuals (Kruskal-Wallis χ 2 = 5.76, d.f. = 5, p = 0.33). The model revealed a significant 45% increase in overall species richness at 10 km outside the reserve compared to inside ( p < 0.01; electronic supplementary material, table S3).

(c) Species dissimilarity between assemblages

We estimated species dissimilarity or β-diversity between fish assemblages using the Jaccard distance. Two independent patterns may occur when measuring β-diversity: turnover and nestedness [42,43]. Turnover occurs when species present at one site are absent at another site but are replaced by other species absent from the first. Nestedness occurs when species present at one site are absent at another but are not replaced by new species.

Species dissimilarity between protection levels was high with an average β-diversity of 58.8% between the reserve and 5 km outside (figure 2c), 57.6% between the reserve and 10 km outside (figure 2d), and 57.5% between 5 km and 10 km outside (figure 2e). On average, 42.3% (±15.5%) of fish species were replaced between sites under different protection levels. This turnover represented 74% (±22.4%) of the pairwise dissimilarities, whereas nestedness represented the remaining 26% (±22.4%). Eight species were only recorded inside a reserve while 18 species were only detected outside a reserve across the six regions (electronic supplementary material, figure S4).

Distance-based redundancy analysis (dbRDA) on Jaccard distances showed that both protection and environmental variables significantly explained the dissimilarity in species composition (F-test = 2.53, p < 0.001, R 2 = 0.20). The turnover component was also significantly explained by both the protection and the environment (F-test = 2.66, p < 0.001, R 2 = 0.32). The nestedness, however, is not significantly explained by any of the variables ( p > 0.05) (electronic supplementary material, tables S4-S5).

Partial dbRDA revealed that the protection level, after accounting for environmental conditions, significantly explained 4.8% of fish assemblages (F-test = 1.80, p < 0.001, R 2 = 0.05) (figure 3a). Fish assemblages inside the reserves were mostly characterized by pelagic fishes, whereas assemblages outside reserves were predominantly characterized by cryptobenthic fish species as shown by the strongest contribution of species scores on the partial dbRDA axis 1 (CAP1, figure 3b; electronic supplementary material, figure S5).

(d) Unpacking the paradox by species traits

We then analysed all species scores along the first axis of the partial dbRDA to determine which traits characterize the species' associations to the reserves or to the fished areas (left versus right side on figure 3a). Species scores were significantly correlated with their trophic level (Kendall tau = -0.20, p < 0.01), common length (Kendall tau = -0.18, p = 0.01), and vulnerability to fishing (Kendall tau = -0.21, p < 0.01) (figure 4). We also found significant differences in species scores according to their vertical position in the water column (ANOVA F-value = 7.64, p < 0.001), with pelagic species significantly differing from cryptobenthic species (Tukey HSD = -0.12, p < 0.001) as well as, to a lesser extent, benthic (Tukey HSD = -0.07, p = 0.01) and demersal species (Tukey HSD = -0.08, p < 0.01) (figure 4d). Species richness of the different fish categories (cryptobenthic, pelagic, and rare) were significantly explained by protection and environment (GLM, R 2 = 0.40, 0.48, and 0.45, respectively, all p < 0.01) except for highly vulnerable fishes ( p = 0.09). Each model accounted for unmeasured variations among regions with model residuals being not significant (Kruskal-Wallis test = 9.37, 0.70, 7.54, and 0.55, respectively, all p > 0.05). Cryptobenthic species richness increased by 66% ( p < 0.01) at 5 km outside compared to inside the reserves and by 136% at 10 km outside ( p < 0.001) (figure 5a). Although pelagic species richness decreased with increasing distance to the reserve, this was mainly driven by environmental differences with no significant marginal effect of protection ( p > 0.05, figure 5b). Rare fish species richness significantly increased by 53% at 5 km outside ( p < 0.01) and 69% at 10 km outside the reserve ( p = 0.01) (figure 5c). Vulnerable fish species richness was homogeneous across protection levels with, on average, one species per site (figure 5d).

Discussion (a) Less but more vulnerable species in marine reserves

This study, showing less but different species inside reserves compared to fishing grounds nearby, does not negate the key role of reserves in protecting biodiversity but sheds new light on how under-represented species in classical visual surveys-cryptobenthic, pelagic, and rare-can react counterintuitively to fishing pressure. We reveal, through the sampling of six no-take reserves using a standardized eDNA protocol while accounting for environmental differences, that fish species richness decreases with protection. This paradox could only emerge with a reliable eDNA metabarcoding approach and an extensive genetic reference database. Besides, we highlight a marked species turnover along the protection gradient, indicating a strong difference of fish assemblages within and outside reserves. Reserves offer protection to fishes that are characterized by a high trophic level and large body size, so vulnerable to fishing, as previously shown [44,45]. By contrast, fished areas host less vulnerable species with a lower trophic level and smaller body size (figure 4).

The disparity in trophic level could explain the paradoxical changes in species richness, as fishing pressure and protection can change trophic interactions. Predation is one of the key processes influencing the richness and composition of ecological assemblages [46][47][48]. Since fishing pressure often targets large predatory fishes [15,49], their removal in fished areas can induce a decrease of top-down control on prey species increasing their richness. Conversely, marine reserves increase predator populations and thus can restore trophic cascades within their boundaries [29], thereby affecting biodiversity at lower trophic levels and potentially causing the local extinction of prey species inside MPAs [50].

This hypothesis is supported by the increasing diversity of cryptobenthic fishes with increasing distance from the reserve (figure 5a). Cryptobenthic fishes represent a large but overlooked dimension of fish biomass and diversity on reef ecosystems [31]. Combined with their rapid growth, high productivity, and high mortality due to predation [51,52], cryptobenthic fishes represent almost 60% of consumed reef fish biomass [53]. As they are not targeted by fisheries but predated by almost every other fish, cryptobenthic fishes could find refuge from predators outside marine reserves and form highly diverse assemblages coexisting in human-dominated areas. However, because of their small body size and cryptic lifestyle, they are easily overlooked by conventional survey methods that do not target them specifically [54].

Our results also show more occurrences of rare species outside the reserves. This pattern can be explained by the 'oddity effect' where predators focus on conspicuous prey, in this case rare species, to optimize foraging success [48]. This strategy is especially beneficial when prey tend to form aggregations making it harder for predators to single out individual prey, called the 'confusion effect' [48]. So, predators could preferentially target low-abundance prey species inside reserves and remove them. Similar disproportionate effects of predation have previously been demonstrated in field and laboratory experiments. Stier et al. [55] found that predation by the peacock grouper Cephalopholis argus removed 64% of rare species from experimental reef patches but only 36% of common species. In the same vein, Almany et al. [48] observed that the brown dottyback Pseudochromis fuscus, a small generalist predator, targets rare prey in mixed assemblages irrespective of colouration or visual marks. They hypothesize that the odd behaviour of rare species compared to the common ones sets them apart and makes them an easier target for predators.

The strong turnover in fish assemblages under different protection levels suggests that different ecological processes and ecosystem services operate within and outside reserves [START_REF] Dornelas | Assemblage time series reveal biodiversity change but not systematic loss[END_REF]. Although less diverse, assemblages within reserves are characterized by larger and higher trophic-level species which typically have higher commercial and touristic values. Fish assemblages inside reserves are characterized by pelagic species, especially Sarda sarda, Engraulis encrasicolus, Chelon auratus, Xiphias gladius, and Trachurus mediterraneus. These species have a commercial interest but also contribute to reef productivity through water nutrient enrichment and could play important trophic roles inside reserves [56]. Outside reserves, we find higher biodiversity and assemblages dominated by smaller, (crypto)benthic and demersal species. The increased diversity of cryptobenthic species in impacted areas is hopeful for conservation as well. Cryptobenthic fishes fuel reef trophodynamics and provide crucial food resources for carnivorous fishes [53]. In doing so, they contribute to sustain fish populations in exploited areas. They also provide a reservoir of available biomass for exploited predator populations to recover if fishing pressure is alleviated or suspended in currently fished areas.

(b) Potential and limitations of eDNA metabarcoding

The development of eDNA as a reliable method to monitor biodiversity and evaluate anthropogenic impacts is crucial because important changes in biodiversity might currently occur under our radar [39,57]. Depending on the organisms of interest, eDNA can be used to sample whole eukaryote assemblages [58] or more specific taxonomic groups ranging from sponges and corals [59] to larger taxa such as sharks [30]. In our case, we potentially missed some native and common species in the Mediterranean Sea since the teleo marker cannot distinguish the wrasses Symphodus rostratus, S. cinnereus, S. mediterraneus, and S. roissali from each other, or Labrus merula from L. viridis, as well as the rarer pipefishes Syngnathus abaster and S. sp cfr taenionotus. Most regional and global reference databases for the teleo metabarcode also still need to be completed to avoid limited species assignments [41]. In our case, we enriched the online genetic database (European Nucleotide Archive) which covered only 31% of all Mediterranean fish species by sampling and sequencing additional 115 species to reach 75% coverage of the regional species pool (see Methods). This unprecedented effort allowed the detection of 97 species ranging from the very small Liechtenstein's goby Corcyrogobius liechtensteini (2.7 cm) to the large blue shark Prionace glauca (250 cm). Cryptobenthic, pelagic, and elusive species are often ignored in MPA assessments.

Without their detection, we would not be able to uncover the hidden biodiversity patterns between marine reserves and their proximate outsides. We also show that our genetic reference database was not biased towards some species groups (electronic supplementary material, figure S6), so we are confident that a more exhaustive database would provide the same patterns.

The detection of eDNA in seawater is partly due to its persistence in the environment, which depends on biotic and abiotic factors driving eDNA production, degradation, and transport [60][61][62]. Much is still unknown about the spatial and temporal resolution of eDNA in the marine coastal environment. Mesocosm experiments report variable decay rates of eDNA in seawater, with half-lives ranging from 1 up to 71 h [60,63]. However, decay and dilution happen faster in natural environments. A field experiment in coastal seawater finds that eDNA becomes undetectable only 1 h after introduction [64]. Coupling decay rates to dispersal distances, estimated by particle tracking models, suggests that suspended eDNA can on average be transported for only 1 km before 50% has decayed [36]. In our system, the average current velocity during sampling was 0.04 m s -1 or approximately 140 m h -1 . Combined with relatively short halflives, it is unlikely that sufficient detectable eDNA could be transported between our sites 5 km and 10 km apart confirming the independence of our sites and the local origin of our signal. These estimates are corroborated by the growing body of empirical studies finding strong spatial fidelity of eDNA signals, differentiating sites only hundreds of metres apart despite tidal and oceanic movements [65][66][67][68]. Together, these results demonstrate the applicability of eDNA for local monitoring studies [69]. Yet, accurate particle transport models which directly take into account eDNA concentration, advection, dilution, coastline morphology, and ground-truthed decay rates would allow a better understanding of eDNA transport and detectability patterns across the seascape.

(c) Diversifying managements for diversifying regional fish assemblages

The higher species richness found outside reserves does not imply that marine reserves fail to protect biodiversity. It rather tells us that species richness and site-level diversity metrics cannot be considered as reliable indicators of human pressure since they miss important species compositional changes and traits [6,58]. Our results shed light on how conservation, like fisheries management [70], can shape biodiversity patterns at a regional scale. Marine reserves do not necessarily increase species diversity. Rather, a mosaic of protection levels, that creates heterogeneous fishing pressures, can promote heterogeneous ecological processes at various intensities, thus increasing biotic dissimilarity between adjacent areas and the overall level of regional diversity (or γ-diversity). Since the sustainability of ecosystem functioning and the continuous delivery of ecosystem services at the regional scale is positively related to the number of species comprising the regional pool (γ-diversity) [71,72], which depends on both local or α-diversity and the dissimilarity in species composition between sites (β-diversity) [73], our study suggests that diversifying management options could better sustain ecosystem functioning and limit the ongoing biotic homogenization [74].

Methods (a) Study area and eDNA sampling

Six Mediterranean marine reserves with strict no-take policy and established at least six years prior to sampling were selected for our study (figure 1). The Mediterranean Sea is a hotspot of marine biodiversity [75] but all its ecoregions and territorial waters are under high human pressure [76], except for the very few fully protected marine reserves [77]. Four replicates of 30 l of seawater were collected along a 2 km transect inside each site within each reserve, 5 km, and 10 km outside, i.e. in impacted areas, and filtered using a 0.20-µM filtration capsule (electronic supplementary material, Methods).

(b) eDNA extraction and sequencing

eDNA was extracted and amplified by PCR with the fish-specific primer pair teleo targeting a 70 bp fragment at the end of the mitochondrial DNA 12S rRNA gene [78,79] (electronic supplementary material, Methods). PCR reactions were carried out in 12 replicates per sample and unique tags were given to each sample. Libraries were prepared using the MetaFast protocol.

A paired-end sequencing (2 × 125 bp) was carried out on a MiSeq (Illumina, San Diego, CA, USA) using the MiSeq Flow Cell Kit Version3 (Illumina, San Diego, CA, USA) following the manufacturer's instructions. Library preparation and sequencing were performed at Fasteris (Geneva, Switzerland).

(c) Reference database

At the onset of our study only 31% of all Mediterranean fish species [80] were referenced in the European Nucleotide Archive (ENA) [81] (release 138) for the 12S rRNA fragment targeted by the teleo primers. To supplement this reference database, fin clips of 115 fish species from the North-Western Mediterranean were collected from fisheries landings and added to the database. DNA was extracted from tissue samples and a 12S rRNA gene fragment of ca 675 bp encompassing the teleo metabarcoding fragment was targeted using the forward primer V05F_898 and the reverse teleo primer [82]. A 340 bp fragment was additionally targeted for 16 species using the newly designed forward primer MF12S_F (5 0 -CTAGAGGAGCCTGTYVT) and the reverse primer MF12S_R (5 0 -GRHAAGTCGTAACATGGTA) (electronic supplementary material, Methods).

The final reference database used in this study contained sequences of 320 species corresponding to 41% of all royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20210112

Mediterranean fish species but 75% of the regional North-Western species pool. The remaining gap did not bias the biodiversity assessment made in this study (electronic supplementary material, Methods, table S6, figure S6).

(d) Taxonomic assignment of reads

The sequence reads were analysed using the OBITools package [78,83]. Taxonomic assignment of reads was performed using the program ECOTAG, with both the new regional fish reference database and the public reference database of sequences extracted from ENA (release 140) using the ECOPCR program [84,85]. Reads showing less than 98% similarity were removed. Taxa were preferentially assigned based on the local reference database, except if the similarity was higher for the public reference database. The resulting dataset was manually checked to correct erroneous identifications and remove foreign species (electronic supplementary material, Methods).

(e) Diversity indices

We compared total fish species richness among protection levels as well as the richness of the cryptobenthic, pelagic, rare, and vulnerable species. Cryptobenthic species were selected based on their families [31]. Pelagic species were those defined by the 'Vertical Distribution' parameter in the FishMed database [86]. Rare species were those detected in two samples or less within each region. The vulnerability of species to fishing was obtained from FishBase [87]. The vulnerable species are those with a vulnerability higher than 70, which corresponds to 'high' or 'very high' vulnerability to fishing on a scale from 1 to 100 [87].

We estimated species dissimilarity or β-diversity between assemblages using the Jaccard distance. To determine the relative contribution of species turnover and nestedness to total β-diversity, we used the additive partitioning of the pairwise Jaccard dissimilarity [42]. This framework teases apart the variation in species composition from species turnover only, which is independent of richness, and from nested patterns [43]. We calculated the total dissimilarity, turnover, and nestedness between all samples using the R packages vegan and betapart [88,89].

(f ) Modelling reserve effect

We used GLMs to investigate the effect of protection on species richness while accounting for environmental differences represented by the PCA axes. After checking for their distribution, total, cryptobenthic, pelagic, and rare species richness were modelled using a Gaussian distribution, whereas vulnerable species richness was modelled using a Poisson distribution.

We determined model fit by calculating the R 2 for each model, and tested the conditional effect of coefficients by calculating the marginal effects with the R package margins [90]. We tested the effects of potentially missed important factors by comparing residuals of our models between regions using a Kruskal-Wallis test.

We used a dbRDA (function capscale, package vegan) to analyse changes in assemblage composition measured by Jaccard distance between samples in relation to the protection level and the four environmental PCA axes. We computed additional dbRDA on the species turnover and nestedness with each time the protection category and four environmental PCs as explanatory variables. Significance of the models as well as the significance of each axis and of the marginal effect of each variable were tested using ANOVA-like permutation tests with 9999 permutations as implemented in the vegan's anova.cca function [89,91].

Next, we computed a partial dbRDA using the Jaccard distance to isolate the effect of protection after accounting for environment [92]. From this partial dbRDA, we extracted the species scores along the axis that explains most of the variance to infer which species contribute most to the differences in assemblage composition between protection levels. We focused on species whose projected length on the first axis (CAP1) belongs to the top 25% of absolute species scores. We then used the Kendall rank correlation coefficient to test the correlation between the species scores and the species' trophic level, common length, and vulnerability to fishing. We used ANOVA and Tukey post hoc to test for the differences in species scores according to their vertical position (cryptobenthic, benthic, demersal, and pelagic). All analyses were carried out in R v. 3.6.1 [93].

Dornelas M, Gotelli

Preface

The findings of Chapter 3 revealed that marine protected areas shape local biodiversity by restoring species -typically large, predatory species -whose presence affects the presence of other species further down the food chain. Inversely, fishing pressure (outside MPAs) shapes local biodiversity by making room for species less vulnerable to human disturbance. The combination of protected and fished areas together increases regional diversity because of the strong turnover between local communities. Although not explicitly addressed, we also found that local habitat and environmental conditions play an important role in determining the diversity and composition of assemblages.

In addition to local conditions, biodiversity is largely driven by the degree of connectivity between communities and populations at a larger scale. Species' distribution shifts enabled by changing conditions and landscape connectivity create new communities at both the departure and arrival sites. Gene flow between populations increases genetic diversity while potentially spreading beneficial alleles from locally adapted populations. Genetic isolation on the other hand favours population or niche differentiation and ultimately speciation.

Understanding patterns of seascape -and population connectivity clarifies the mechanisms that sustain biodiversity and makes it possible to develop appropriate management and conservation strategies, such as designing networks of MPAs. The thousands of SNP markers that can be generated with NGS offer the necessary resolution to unravel genetic patterns of highly dispersing marine species.

In this chapter, I present a spatial graph approach that extends propagule dispersal networks over multiple generations to match the spatio-temporal scales of genetic connectivity.

I then apply this approach to unravel connectivity patterns of the striped red mullet Mullus surmuletus. I use the pooled dataset of 1 153 neutral SNPs of Dalongeville et al. (2018b) to estimate the genetic connectivity between 47 putative populations, as well as the larval dispersal probabilities between the same populations which were estimated with the biophysical model of Andrello et al. (2013). By comparing the single-generation larval dispersal network to that of gene flow, we show how genetic connectivity is realised at a much larger spatial scale than larval dispersal, which reflects the different temporal scales at which both act. The steppingstone approach to larval dispersal then allowed to estimate the multi-generational dispersal, which better explains the genetic connectivity patterns and highlights how intermediate stepping-stones for dispersal are crucial in maintaining genetic connectivity across large spatial scales. Comparing genetic connectivity and propagule dispersal requires matching propagule dispersal to the temporal scales of the processes shaping genetic connectivity. While propagule dispersal occurs within one generation, genetic structure results from gene flow and demographic processes over many generations which can, in the absence of strong barriers to dispersal, lead to connectivity over large spatial scales [START_REF] Hedgecock | Genetic approaches to measuring connectivity[END_REF]. Considering only single-generation dispersal events ignores the effect of stepping-stone dispersal through habitat patches leading to genetic connectivity over multiple generations [START_REF] Saura | Stepping stones are crucial for species' longdistance dispersal and range expansion through habitat networks[END_REF]. We thus need to integrate propagule dispersal across time to better understand genetic connectivity patterns and understand how multi-generational steppingstone dispersal shapes genetic population structure.

Manuscript

In the terrestrial environment, most landscape genetic studies analyse genetic connectivity using least-cost path estimates of habitat connectivity [START_REF] Epps | Optimizing dispersal and corridor models using landscape genetics[END_REF][START_REF] Castillo | Landscape effects on gene flow for a climate-sensitive montane species, the American pika[END_REF][START_REF] Row | Quantifying functional connectivity: the role of breeding habitat, abundance and landscape features on range-wide gene flow in sage-grouse[END_REF][START_REF] Schoville | Preserving genetic connectivity in the European Alps protected area network[END_REF]. Some recent advances have been made to combine spatial and temporal scales in landscape connectivity models. [START_REF] Saura | Stepping stones are crucial for species' longdistance dispersal and range expansion through habitat networks[END_REF] developed a generalized habitat network connectivity model which accounts for the potential role of stepping-stones enhancing species dispersal across generations. [START_REF] Martensen | Spatio-temporal connectivity: assessing the amount of reachable habitat in dynamic landscapes[END_REF] modelled spatio-temporal networks of connectivity in dynamic landscapes where patch availability changes through time. However, neither of these studies tested how such improved estimates of landscape connectivity correlate with genetic connectivity.

In the marine environment, important advances combined biophysical models and genetic analyses to describe population connectivity. Examples include mobile species such as reef fishes (e.g. Plectropomus maculatus in the southern Great Barrier Reef, Bode et al. 2019) and lobsters (e.g. Panulirus argus in the Caribbean sea, [START_REF] Truelove | Biophysical connectivity explains population genetic structure in a highly dispersive marine species[END_REF], and sedentary species such as sea cucumbers (e.g. Parastichopus californicus in the North-Eastern Pacific, [START_REF] Xuereb | Asymmetric oceanographic processes mediate connectivity and population genetic structure, as revealed by RADseq, in a highly dispersive marine invertebrate (Parastichopus californicus)[END_REF], molluscs (e.g. Kelletia kelletii in the Santa Barbara Channel, White et al. 2010) and seagrasses (e.g. Zostera marina in the North Sea, [START_REF] Jahnke | Seascape genetics and biophysical connectivity modelling support conservation of the seagrass Zostera marina in the Skagerrak-Kattegat region of the eastern North Sea[END_REF]. e two latter studies, White et al. (2010) and [START_REF] Jahnke | Seascape genetics and biophysical connectivity modelling support conservation of the seagrass Zostera marina in the Skagerrak-Kattegat region of the eastern North Sea[END_REF], integrated dispersal over multiple generations by applying Markov Chain matrix multiplication but missed herein a spatially explicit consideration.

Spatial graphs (Box 1) can help to better understand the dynamics of complex systems composed of many interacting units [START_REF] Rozenfeld | Network analysis identifies weak and strong links in a metapopulation system[END_REF], Dale and Fortin 2010[START_REF] Peterson | Spatially structured statistical network models for landscape genetics[END_REF]. ey offer a promising tool to study multi-generational dispersal by integrating stepping-stone dispersal through multiple sites and multiple pathways, and to study the spatial structure of gene flow between populations [START_REF] Murphy | Graph theory and network models in landscape genetics[END_REF]. Applications of spatial graphs to model multi-generational dispersal are scarce and restricted to the marine environment (e.g. modelling the dispersal of rafting brown algae at small spatial scale; Buonomo et al. 2017, and the dispersal of coral larvae at the scale of the Great Barrier Reef; Riginos et al. 2019). To our knowledge, only one recent study [START_REF] Jahnke | Seascape genetics and biophysical connectivity modelling support conservation of the seagrass Zostera marina in the Skagerrak-Kattegat region of the eastern North Sea[END_REF]) applied spatial graphs to explicitly compare patterns of demographic and genetic connectivity, but this study did not calculate the multigenerational connectivity.

Here, we present a spatial graph approach that extends propagule dispersal networks with a stepping-stone model to represent dispersal between suitable habitat patches over multiple generations. is approach allows to estimate the multi-generation long-distance connectivity that is potentially detected using genetic data, and to compare explicitly the spatial structure of multi-generational genetic connectivity, single-generation propagule dispersal and multi-generation dispersal networks. Our approach also accounts for unsampled stepping-stone sites that contribute to the connectivity patterns between the sampled sites (the so-called 'ghost population' effect; [START_REF] Beerli | Effect of unsampled populations on the estimation of population sizes and migration rates between sampled populations[END_REF][START_REF] Slatkin | Seeing ghosts: the effect of unsampled populations on migration rates estimated for sampled populations[END_REF]. We then illustrate our approach by analyzing connectivity of a high gene-flow species, the red mullet Mullus surmuletus, in the Mediterranean Sea. Finally, we discuss how to extend our approach to other systems.

Methods

General approach: multi-generational spatial graphs for genetic connectivity e first step of our approach to study multi-generational genetic connectivity is to model propagule dispersal in a given environment or habitat for a single generation. is first step produces dispersal probabilities between each pair of sites used to build a spatial graph (Box 1) of single-generation dispersal [START_REF] Treml | Modeling population connectivity by ocean currents, a graph-theoretic approach for marine conservation[END_REF][START_REF] Kininmonth | Graph theoretic topology of the Great but small Barrier Reef world[END_REF], Andrello et al. 2013).

1169 e second step examines nodes that are not directly connected in the single-generation dispersal graph and calculate the number of stepping-stones to connect the shortest paths between all pairs of nodes (Table 1). For example, if no direct connection exists between nodes a and c (Fig. 1, single-generation propagule dispersal), the shortest path algorithm identifies node b as the most suitable stepping-stone and indicates that one stepping-stone is needed to connect nodes a and c through b. In this way, the single-generation dispersal graph allows to calculate multi-generational connectivity through stepping-stone nodes (Fig. 1, multi-generational propagule dispersal). To calculate the shortest paths, dispersal probabilities between nodes are transformed into distance measures by taking the natural logarithm of their inverse; this transformation yields a distance matrix where pairs of nodes with the highest dispersal probabilities have the smallest distance, thereby ensuring that the shortest path between two nodes is effectively the most probable one [START_REF] Costa | On the calculation of betweenness centrality in marine connectivity studies using transfer probabilities[END_REF].

Table 1. Spatial graph metrics used in this study [START_REF] Urban | Landscape connectivity: a graphtheoretic perspective[END_REF][START_REF] Newman | e structure and function of complex networks[END_REF][START_REF] Urban | Graph models of habitat mosaics[END_REF].

Network assortativity Measure of how well nodes in a network show similar characteristics. Typically calculated as degree assortativity, which quantifies whether high-degree nodes tend to attach to other high-degree nodes (assortative mixing) or whether high-degree nodes rather attach to low-degree ones (disassortative mixing). Measured as the Pearson correlation coefficient r of the degrees at either ends of an edge, averaged over edges.

Network clustering

Probability that two nodes connected to a given third node are themselves connected, averaged for all the nodes in a network.

Node betweenness

Fraction of all shortest paths between nodes that passes through a given node. Quantifies the relative importance of a single node in passing information through the network. The betweenness centrality (C B ) of node i is calculated by taking the proportion of shortest paths connecting nodes s and t (σ st ) that passes through node i (σ st (i)), and summing them over all possible node pairs:

Shortest path

Minimum-weight path connecting two nodes. In an unweighted graph, the shortest path represents the path with the smallest number of edges. In a weighted graph, the shortest path represents the path in which the total weight of the sequence of edges is the smallest.

Box 1. How spatial graphs can illuminate connectivity

Spatial graphs stem from graph theory and help to better understand the dynamics of complex systems composed of many interacting units [START_REF] Rozenfeld | Network analysis identifies weak and strong links in a metapopulation system[END_REF], Dale and Fortin 2010[START_REF] Peterson | Spatially structured statistical network models for landscape genetics[END_REF]. ey explicitly consider the spatial context of data [START_REF] Urban | Graph models of habitat mosaics[END_REF] and can be used to study dispersal and connectivity between multiple sites simultaneously (Dale andFortin 2010, Eros et al. 2011). In graph theory, a graph G consists of a set of nodes or vertices that are pairwise linked by a set of edges E(G) [START_REF] Urban | Landscape connectivity: a graphtheoretic perspective[END_REF]. A graph can be built to study connectivity in a network by using populations or sampling localities as georeferenced nodes, rendering a spatial graph, and weighing the edges between them by their modelled dispersal to represent connection probability, or by their pairwise genetic distance to study gene flow (under the hypothesis that genetic differentiation is mainly the outcome of gene flow). In doing so, graph theory allows to visually represent the network of genetic connectivity and analyse topological features to test hypotheses of population structure [START_REF] Dyer | Population graphs: the graph theoretic shape of genetic structure[END_REF], Dale and Fortin 2010[START_REF] Xuereb | Asymmetric oceanographic processes mediate connectivity and population genetic structure, as revealed by RADseq, in a highly dispersive marine invertebrate (Parastichopus californicus)[END_REF]. At the same time, graph theory allows the quantitative estimation of network-and node-level properties [START_REF] Rozenfeld | Network analysis identifies weak and strong links in a metapopulation system[END_REF][START_REF] Albert | Assessing the robustness of networks of spatial genetic variation[END_REF].

Interpreting graph metrics

Node metrics allow the identification of nodes, e.g. populations, that are of increased importance to the whole network by simultaneously considering all nodes and edges of the graph. High node degrees indicate populations that exchange propagules with many others (in a dispersal graph) or that have already exchanged genes with many others (in a genetic connectivity graph). Such populations have potentially sent or received many migrants over evolutionary time scales, acting as sources or sinks in the network [START_REF] Rozenfeld | Network analysis identifies weak and strong links in a metapopulation system[END_REF]. Node betweenness centrality quantifies the importance of single nodes in passing information through the network by identifying nodes that represent important steppingstones for dispersers (in propagule dispersal graphs) or genes (in genetic connectivity graphs) [START_REF] Garroway | Applications of graph theory to landscape genetics[END_REF][START_REF] Rozenfeld | Network analysis identifies weak and strong links in a metapopulation system[END_REF], Andrello et al. 2013). Network-level metrics allow us to quantitatively characterize the topology of a graph. ey furthermore allow us to test landscape-genetic hypotheses by comparing the topology and associated characteristics of a genetic connectivity graph to graphs representing explanatory variables relating to structural or functional connectivity (Dale and Fortin 2010).

1170 e shortest paths between all possible node pairs can then be estimated using the Floyd-Warshall algorithm for directed graphs [START_REF] Floyd | Algorithms 97: shortest path[END_REF][START_REF] Warshall | A theorem on Boolean matrices[END_REF]. Unsampled sites potentially acting as stepping-stones are included in the multi-generational propagule dispersal graph. e third step of our approach estimates a spatial graph of genetic distances among sites, hereafter referred to as multi-generational genetic connectivity (Fig. 1) [START_REF] Rozenfeld | Network analysis identifies weak and strong links in a metapopulation system[END_REF][START_REF] Fortuna | Networks of spatial genetic variation across species[END_REF][START_REF] Albert | Assessing the robustness of networks of spatial genetic variation[END_REF].

e apparent discrepancies between genetic connectivity and propagule dispersal can in part be explained by the process of multi-generational dispersal connecting nodes over larger spatial scales than single-generation dispersal.

Figure 1. Spatial graphs to analyse single-generation propagule dispersal (top), multi-generational propagule dispersal (middle) and multigenerational genetic connectivity networks (bottom). Dots and arrows represent the nodes and the directional edges of the graph. Black dots are the sampled populations and grey dots are unsampled populations. Black and grey arrows are modelled propagule dispersal probabilities between sampled and unsampled populations, respectively. e width of the arrows is proportional to propagule dispersal probability (in the single-generation propagule dispersal graph) and inversely proportional to genetic distance (in the multi-generational genetic connectivity graph). In the multi-generational propagule dispersal graph, dispersal probabilities are used to estimate the number of stepping-stones needed to connect nodes that are not connected in the single-generation graph. Dashed lines represent multi-generational connectivity through stepping-stones and are highlighted for nodes a and c (brown) and nodes b and f (green). Adapted from [START_REF] Urban | Graph models of habitat mosaics[END_REF].

1171

Example: multi-generational genetic connectivity in the striped red mullet across the Mediterranean Sea

To illustrate our approach, we re-analysed a published genetic dataset of an economically important fish species with a pelagic larval stage, the striped red mullet Mullus surmuletus, in the Mediterranean Sea (Dalongeville et al. 2018b). We hypothesize that building a spatial graph of potential larval dispersal over multiple generations, can improve our understanding of the genetic structure across the Mediterranean Sea and of the temporal scale at which connectivity operates. We build the spatial graph of genetic connectivity to characterize the spatial pattern of genetic structure and to compare it to the graph of multi-generational larval dispersal.

Species description, study area, sampling and genetic data

e striped red mullet Mullus surmuletus is a demersal fish species that is commonly found in the Mediterranean Sea particularly in areas characterised by a narrow continental shelf with rough substrate and at depths ranging from 10 to 100 m [START_REF] Lombarte | Spatial segregation of two species of Mullidae in relation to habitat[END_REF]. Adults move to deeper sites for spawning (70-150 m, Machias et al. 1998), which occurs from April to May and produces larvae with a pelagic larval duration (PLD) of approximately 30 d (Reñones et al. 1995, Macpherson and Raventos 2006[START_REF] Arslan | Age, growth and reproduction of Mullus surmuletus (Linnaeus, 1758) in Saros Bay (northern Aegean Sea). -J. Black Sea[END_REF]. Mullus surmuletus is one of the most economically valuable species in commercial landings of coastal Mediterranean demersal fisheries (Reñones et al. 1995[START_REF] Félix-Hackradt | Discordant patterns of genetic connectivity between two sympatric species, Mullus barbatus (Linnaeus, 1758) and Mullus surmuletus (Linnaeus, 1758), in south-western Mediterranean Sea. -Mar[END_REF]).

e Mediterranean Sea harbours a complex oceanic circulation that shapes larval dispersal patterns and creates putative barriers for gene flow (Pascual et al. 2017). To maximise the detection of these various processes influencing population structure, M. surmuletus samples were collected along the entire Mediterranean coastal range including islands (47 sites, Fig. 2a). Fin clips were taken from specimens obtained at local artisanal fisheries landing sites. DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen) according to the manufacturer's protocol, and individuals were genotyped using a Genotyping by Sequencing method (Elshire et al. 2011). Individual genotypes were then pooled by their sampling locality to maximize sequencing coverage (see Dalongeville et al. 2018b for a complete description of sampling, molecular and bioinformatics procedure). is resulted in a dataset containing the allele frequencies of 1153 Single Nucleotide Polymorphism (SNP) loci for 47 pools of nine to 18 individuals, hereafter referred to as populations.

Genetic differentiation among these populations was calculated in the 'ade4' package in R v3.2.3 (Dray and Dufour 2007, R Core Team) using Cavalli-Sforza and Edwards' chord distance D c (Cavalli-Sforza and Edwards 1967), which is well-suited to distinguish genetically similar populations in high gene flow species [START_REF] Libiger | Comparison of genetic distance measures using human SNP genotype data[END_REF].

Step 1. Single-generation propagule dispersal

Larval dispersal was simulated using the biophysical model of Andrello et al. (2013), which was adapted to fit the life history parameters of M. surmuletus (Dalongeville et al. 2018b).

In brief, three-dimensional sea current velocities were obtained from the hydrodynamics model NEMOMED12 [START_REF] Beuvier | MED12, oceanic component for the modeling of the regional Mediterannean Earth system[END_REF]) and used to simulate passive larval dispersion using the software Ichthyop 3.1 [START_REF] Lett | A Lagrangian tool for modelling ichthyoplankton dynamics[END_REF]. Larvae were released in 1/10th degree cells covering the Mediterranean continental shelf (7703 cells; Andrello et al. 2015) every three days during the species' spawning season and allowed to passively disperse during 30 d. Pairwise probabilities of larval dispersal c(i,j) between cells i and j were calculated from the numbers of simulated larvae released in cell j that arrived in cell i after 30 d. Probabilities of dispersal were averaged over a grid of 100 cells covering the full coastal range of the Mediterranean Sea, resulting in a 100 × 100 asymmetric connectivity matrix C 100 . Not every grid cell could be sampled for the genetic analysis described above. To allow comparison between the larval dispersal probabilities and the genetic connectivity estimates between the 47 sampled sites, a 47 × 47 connectivity matrix C 47 was extracted from the C 100 matrix with the cell centroids corresponding to the 47 sites sampled for the genetic analysis (Supplementary material Appendix 1 Fig. A1).

We constructed directed spatial graphs of single-generation larval dispersal both for the 47 × 47 and 100 × 100 larval connectivity matrices. Edges of the spatial graphs were weighted by the modelled larval dispersal probabilities and nodes represented the cell-centroids of the 47 populations and the 100 modelled sites, respectively. e 47 populations are connected through single-generation larval dispersal by 108 edges.

Marine geographic distances (minimum distances constrained by water) were computed as least-cost path distances between all pairs of populations by assigning infinite resistance to land areas and constant zero resistance to water using the functions 'transition', 'geoCorrection' and 'costDistance' of the R package 'gdistance' v. 1.1-1 (van Etten 2017). e nearest geographical neighbour was additionally determined for each population.

Step 2. Multi-generational propagule dispersal

Both larval dispersal matrices (C 100 and C 47 ) reflect dispersal only from the current generation. We calculated larval connections for the matrix C 100 between non-connected nodes based on a stepping-stone approach. We hypothesize that larvae that dispersed from site j to i successfully settle in their arrival site and, upon reaching maturity, produce larvae that potentially disperse to other sites. As M. surmuletus populations occur across the full Mediterranean coastal range, we hypothesize that all the 100 grid cells represent suitable habitat and all sites contribute equally to larval production.

After transforming the single-generation larval dispersal probabilities of C 100 into distance measures, we calculated the shortest paths between all possible node pairs using the Floyd-Warshall algorithm embedded in the R package 'Rfast' [START_REF] Papadakis | Rfast: a collection of efficient and extremely fast R functions[END_REF]) (Supplementary material Appendix 1 Fig. A2). We then extracted the multi-generational dispersal measures between the 47 sampled populations from the C 100 multi-generational dispersal graph (Supplementary material Appendix 1 Fig. A3).

e final spatial graph of 1172 1173 multi-generational dispersal is constructed using the 47 populations as nodes and weighting the edges by the number of stepping stones connecting them.

Step 3. Spatial graph of multi-generational genetic connectivity

e network of genetic connectivity was constructed by designating the 47 sampled populations as nodes, and their pairwise genetic distance (D c ) as weighted edges. e construction of a spatial graph from all D c values results in a fully saturated network, where every node is connected to every other node.

e calculation of most graph metrics, such as node degree, network assortativity and network clustering, depends on the number of edges connected to the graph's nodes and cannot be calculated in a saturated network (Table 1). As the weights of the genetic graph have a small range (see further), the shortest-path algorithm always identified the direct path between two nodes as the most efficient one and impeded the calculation of informative node betweenness. We thus proceeded to prune the graph edges to get a more informative graph topology [START_REF] Garroway | Applications of graph theory to landscape genetics[END_REF]. Edges were pruned by retaining only the 108 edges with the smallest genetic distance (approximately 10% of all edges), thus obtaining a multi-generational genetic connectivity graph with the same number of edges as the single-generation larval dispersal graph. e retained connections are considered as the relevant genetic relationships for further network analyses.

As this pruning parameter is arbitrary, we also implemented an edge removal scenario where edges of decreasing genetic distances were discarded one by one. e resulting genetic connectivity spatial graphs were compared after each removal to test the influence of the pruning parameter choice on the graph topology. Additionally, we ranked each node according to their betweenness values for each node removal scenario and compared the variation in node ranks.

Analysis of the spatial graphs

Network topology was analysed by estimating several graph metrics (Table 1). For the multi-generational genetic connectivity graph, we calculated node degree and node betweenness centrality to identify important stepping-stone nodes in the network (Box 1) [START_REF] Rozenfeld | Network analysis identifies weak and strong links in a metapopulation system[END_REF]. Network assortativity and network clustering were calculated to evaluate the structure of the genetic network. To determine whether assortativity and clustering resulted from biological processes (such as habitat patch location and quality and between-habitat migration) versus random configuration, we generated 10 000 random networks with the same number of nodes and edges as our genetic connectivity network following the Erdös-Rényi model [START_REF] Erdös | On random graphs[END_REF]. e mean graph metrics (assortativity and clustering) and their standard deviations were calculated and compared to the values of the genetic graph. Network assortativity and clustering were also calculated for the single-generation propagule dispersal graph.

e pruned genetic graph was constructed with the 'gengraph' function in the R package 'adegenet', and all subsequent graph-theoretic analyses were conducted with the R package 'igraph' (functions 'degree', 'betweenness', 'assor-tativity_degree' and 'transitivity') [START_REF] Csardi | e igraph software package for complex network research[END_REF]Nepusz 2006, Jombart 2008). Random networks were generated using the 'sample_gnm' function.

Correlations between genetic distances and marine geographic distances, single-generation larval dispersal probabilities as well as multi-generational larval dispersal distances were estimated using the Mantel coefficient along with 9999 permutations (R package 'vegan', Oksanen et al. 2016). As the Mantel test is known to have an inflated type I error rate and low statistical power [START_REF] Legendre | Testing the significance of canonical axes in redundancy analysis[END_REF]Fortin 2010, Guillot and[START_REF] Guillot | Dismantling the Mantel tests[END_REF], we also computed maximum-likelihood population-effects (MLPE) mixed models [START_REF] Clarke | Confidence limits for regression relationships between distance matrices: estimating gene flow with distance[END_REF] which account for the non-independence of pairwise data and provide the greatest probability of identifying the true model from competing alternatives [START_REF] Shirk | A comparison of regression methods for model selection in individual-based landscape genetic analysis[END_REF]. We used the 'lmer' function of the R package 'lme4' to fit the MLPE mixed models with a random-effects term accounting for population-level influence [START_REF] Bates | Fitting linear mixed-effects models using lme4[END_REF][START_REF] Row | Developing approaches for linear mixed modeling in landscape genetics through landscape-directed dispersal simulations[END_REF]. Model fit was evaluated using the coefficient of determination R 2 calculated with the 'MuMIn' package [START_REF] Bartoń | MuMIn: multi-model inference[END_REF].

Results

Single-generation larval dispersal

e larval dispersal matrix C 47 is highly sparse, with only 108 realised connections out of 1081 (Supplementary material Appendix 1 Fig. A4). e constructed larval network connects all nodes to at least one other node with connections primarily made to the nearest neighbours. It is fragmented in three main clusters roughly representing the western Mediterranean, the Aegean sea and the Levantine sea (Fig. 3a, Supplementary material Appendix 1 Fig. A5). e graph has a high level of clustering and a positive degree assortativity (Supplementary material Appendix 1 Table A1).

e C 100 network of modelled larval dispersal however fully connects the Mediterranean Sea, i.e. the network consists of one large cluster connecting all nodes to at least three other nodes (Supplementary material Appendix 1 Fig. A6).

Multi-generational larval dispersal

e stepping-stone approach to larval connectivity converted our sparse connectivity matrix C 47 into a fully saturated connectivity matrix, although a substructuring separating the western and eastern basin was still visible (Fig. 3a). e spatial graph of the multi-generational larval dispersal network shows that the Aegean and Levantine Sea are connected when including one stepping-stone (Fig. 3b), and that the westeast barrier is breached when including two stepping-stones (Fig. 3c). Allowing for more stepping-stones increased graph connectivity (Fig. 3d-e), saturating the network with 24 stepping-stones (suggesting 25 generations).

1174

Multi-generational genetic connectivity

Pairwise genetic distance D c varied between 0.10 and 0.20.

e pruned spatial graph built with these genetic distances connected 29 out of 47 nodes (Fig. 2b). Topological analysis of the graph reveals no substructuring into clusters but instead more and stronger connections between the western and eastern Mediterranean basin than within each basin. Network metrics showed a high level of disassortative mixing with r = -0.57, which was considerably 1175 lower than that of the randomized networks (r 0 = -0.05) (Supplementary material Appendix 1 Table A1), which indicates a centralized network. is means that the genetic network has a central core of a few highly connected populations (i.e. with a high degree), that are in turn linked to lesser connected populations (i.e. with a low degree) in the periphery (Supplementary material Appendix 1 Fig. A7).

e level of clustering c = 0.58 was considerably higher than the mean of the randomized graphs (c 0 = 0.10). ese stronger levels of disassortativity and clustering demonstrate that the genetic connectivity network is more structured than would be expected from a random process alone. e centralised structure was robust under varying levels of pruning (Supplementary material Appendix 1 Fig. A8). e sequential removal of edges also shows that the unconnected nodes in the pruned graph are not completely isolated but rather increasingly differentiated from the central structure.

e values of betweenness centrality also suggest a centralized structure, which attribute an importance to only six out of the 29 connected nodes. In particular, populations 11_86 in Sicily (present in 50.0% of all shortest paths) and 55_56 in Turkey (23.3%) appear to be the main stepping stones for the gene flow through the network (Fig. 1b). e remaining populations with a nonzero betweenness are 19_27 in Corsica (9.9%), 13_15 in Sardinia (8.1%), 29_96 in Crete (7.8%) and 93_94 in Cyprus (0.9%). ese six nodes consistently ranked highest out of all nodes by their betweenness values under varying pruning criteria (Supplementary material Appendix 1 Fig. A9).

Comparison between single-generation larval dispersal, multi-generational larval dispersal and multi-generational genetic connectivity

e mean larval dispersal was estimated to 266 km (SD = 137) for the single-generation dispersal graph and to 1139 km (SD = 694) for the genetic connectivity graph, revealing the different spatial scales of both components of connectivity. Sixty-six of the 72 nearest neighbour pairs were connected in the single-generation larval dispersal graph, whereas only 16 of the nearest neighbour pairs were connected in the genetic connectivity graph.

e Mantel test and maximum-likelihood populationeffects (MLPE) mixed model between the genetic distances (pairwise D c ) and the geographic distances for the 47 populations were significant (Mantel r = 0.31, MLPE R 2 m = 0.17, both p < 0.001) (Table 2) indicating a pattern of isolation by distance (IBD). e Mantel test and MLPE model between genetic distances and single-generation larval dispersal were not significant (Table 2). Conversely, the Mantel test and MLPE model between genetic distances and multi-generational larval dispersal revealed a significant positive correlation (Mantel r = 0.18, MLPE R 2 m = 0.12, both p < 0.001) (Table 2), indicating an isolation by larval dispersal when considering multiple generations.

e multi-generational estimates of larval dispersal were also significantly correlated to the geographic distances (Mantel r = 0.70, p = 0.001).

Discussion

Here we demonstrate how stepping-stone dispersal graphs can be used as a proxies of multi-generational connectivity and increase our understanding of genetic connectivity. We present a spatial graph approach applied to red mullet in the Mediterranean Sea that calculates shortest paths between populations unconnected by direct modelled larval dispersal but showing strong genetic connectivity. e increased correlation between the resulting multi-generational connectivity and genetic connectivity, compared to single-generation dispersal, highlights the underlying discrepancy in spatiotemporal scales in our landscape genetic study.

e spatial graph of single-generation larval dispersal was mostly realized between nearest neighbours, resulting in a highly clustered structure. e spatial graph of genetic connectivity revealed an absence of subdivision between the western and eastern Mediterranean sub basins and a stronger between-basin than within-basin connectivity. No significant correlation could be found between the genetic distances and the direct larval dispersal probabilities at the scale of the Mediterranean Sea.

Visualisation of the larval dispersal and gene flow network showed that genetic connectivity is realised at a much larger spatial scale than larval dispersal, as already found by Dalongeville et al. (2018b), reflecting different temporal scales. Biophysical connectivity is usually modelled for a single generation of larvae (White et al. 2010), whereas genetic connectivity integrates gene flows over multi-generational time scales [START_REF] Hedgecock | Genetic approaches to measuring connectivity[END_REF]). Intermediate stepping-stones for dispersal are crucial in maintaining genetic connectivity across large spatial scales (Crandall et al. 2012). e shortest-path approach to larval dispersal explores the temporal Table 2. Mantel test and maximum-likelihood population-effects (MLPE) mixed model results comparing the multi-generational genetic connectivity (Cavalli-Sforza and Edwards' chord distance D c ) to the geographic distance, single-generation propagule dispersal (larval dispersal probability c ij ) and the multi-generational propagule dispersal (stepping-stone larval distance). Only the geographic distance and stepping-stone larval distance were significantly correlated to the genetic chord distance (p < 0.001). Connectivity can promote the spread of adaptive alleles from populations locally adapted to presently extreme environmental conditions that could become more common in the future. Such spreading of adaptive alleles can strengthen the adaptation of populations to future environmental conditions, and in some cases even result in genetic rescue of declining populations [START_REF] Whiteley | Genetic rescue to the rescue[END_REF], Xuereb et al. 2019). In the marine environment, populations can be locally adapted to climate-related variables such as salinity (Dalongeville et al. 2018a). Climate projections under a business-as-usual scenario (RCP8.5) predict that sea surface salinity (SSS) in the Mediterranean Sea will increase, with marked regional differences, by 0.13 practical salinity unit (± 0.13 PSU) for the 2021-2050 period and to come back to its current global climate value (± 0.01 PSU) for the 2071-2100 period [START_REF] Moullec | An end-to-end model reveals losers and winners in a warming Mediterranean Sea[END_REF]. In some areas, the increase in SSS might result in extreme environmental conditions that cannot be tolerated by populations of M. surmuletus, because the future conditions will be too different from the present conditions to which the populations are adapted [START_REF] Rellstab | Signatures of local adaptation in candidate genes of oaks (Quercus spp.) with respect to present and future climatic conditions[END_REF]. In order to persist, these populations will have to move to more suitable areas or adapt to the new local conditions from standing genetic variation and/or immigration of adaptive genotypes. Analysis of the association between SNP and salinity have suggested a signal of adaptation to local water salinity in Mediterranean populations of M. surmuletus (Dalongeville et al. 2018a). e results of our spatial graph analysis indicate that about five generations will be necessary to spread beneficial alleles from populations adapted to current high salinity in the east, to populations with similar projected future physiochemical conditions in the central Mediterranean (Fig. 3e). Given an average age at first reproduction of 1.5 yr for M. surmuletus (Reñones et al. 1995), this corresponds to one decade which has been judged as a small enough timescale to keep pace with the temporal scale of climatic changes [START_REF] Jönsson | e timescales of global surface-ocean connectivity[END_REF].

Predictor variable

e strong basin-wide genetic connectivity highlighted by the genetic spatial graph identified the Siculo-Tunisian Strait (STS) as an important corridor for gene flow. Beyond analysing the topology of a spatial network, graph theory allows to quantitatively analyse the properties of the nodes and edges in the spatial graph [START_REF] Rozenfeld | Network analysis identifies weak and strong links in a metapopulation system[END_REF]). e calculation of network-and node-metrics revealed a disassortative, centralised network. is means that the network consists of a central core with a few high-degree hubs, which are in turn linked to lesser connected nodes in the periphery [START_REF] Rozenfeld | Network analysis identifies weak and strong links in a metapopulation system[END_REF]. ese highly connected hubs could act as sources and/or sinks in the gene flow network [START_REF] Peery | Characterizing source-sink dynamics with genetic parentage assignments[END_REF]. Betweenness centrality measures the importance of a single node in relaying information through the network [START_REF] Rozenfeld | Network analysis identifies weak and strong links in a metapopulation system[END_REF]). e populations with high genetic betweenness could thus be interpreted as prominent stepping-stones for gene flow. ese are populations where individuals from multiple locations seem to have aggregated and reproduced, to subsequently further spread their genes to multiple other locations. Six populations, located in Sicily (11_86), Turkey (55_56), Corsica (19_27), Sardinia (13_15), Crete (29_96) and Cyprus (93_94) were identified as important stepping-stones in our network. ese populations lay along the longitudinal axis and seem to form a path connecting the western and eastern Mediterranean. Remarkably, five out of these six populations are located along islands and coincide with the location of the high degree nodes. ese results detect islands as important stepping-stones in the network-wide genetic connectivity of M. surmuletus, and suggest that islands can be important sites for gene flow between continental populations by providing intermediate suitable habitats for larval settlement in the deeper parts of the Mediterranean basin.

e construction of a genetic spatial graph requires defining a criterion to prune the initially saturated graph. e appropriate strategy depends largely on the questions asked. For example [START_REF] Dyer | Population graphs: the graph theoretic shape of genetic structure[END_REF] developed the population graph approach where graph edges are trimmed based on the genetic covariance among populations. is approach requires individual genotypes and is not applicable to our population-level data. Because one of our hypotheses was that genetic connectivity is in part the result of larval dispersal, edges were pruned by retaining only the 108 edges with the smallest genetic distance (approximately 10% of all edges), thus obtaining a multi-generational genetic connectivity graph with the same number of edges as the singlegeneration larval dispersal graph. e disassortative structure with a strong cross-basin connectivity in the gene flow graph remained robust under the edge removal scenario. e nodes with the higher betweenness values in the pruned gene flow graph also consistently ranked higher under varying pruning values. e consistency of these results validates that our pruned graph correctly represents the gene flow patterns of M. surmuletus in the Mediterranean Sea.

A large portion of the genetic differences still remains unexplained. Bottom-trawl surveys suggest that M. surmuletus migrates to deeper water during maturation but realises rather short-distance than long-distance movement (Machias et al. 1998). Together with the small home ranges of sympatric demersal species (reviewed by [START_REF] Calò | A review of methods to assess connectivity and dispersal between fish populations in the Mediterranean Sea[END_REF] this suggests that adult dispersal might be rather limited. Other processes that could explain the remaining genetic structure are larval behaviour and higher-resolution oceanographic circulation patterns not resolved by the hydrodynamic model used to simulate larval dispersal [START_REF] Briton | High-resolution modelling of ocean circulation can reveal retention spots important for biodiversity conservation[END_REF][START_REF] Faillettaz | Larval fish swimming behavior alters dispersal patterns from marine protected areas in the north-western Mediterranean Sea. -Front[END_REF]). Larvae at the settlement stage can use oriented swimming driven by e.g. odour or sound to detect favourable settlement habitats [START_REF] Simpson | Homeward Sound[END_REF][START_REF] Paris | Reef odor: a wake up call for navigation in reef fish larvae[END_REF]. Shoreward swimming at the end of the pelagic stage can strongly influence the rate of recruitment 1177 of larvae, resulting in longer dispersal distances than predicted with purely passive dispersal [START_REF] Faillettaz | Swimming speeds of Mediterranean settlement-stage fish larvae nuance Hjort's aberrant drift hypothesis[END_REF]. In contrast, vertical migration of larvae at night as well as homing behaviour of larvae returning to their natal reef result in greater retention than predicted by advection models and smaller dispersal distances [START_REF] Gerlach | Smelling home can prevent dispersal of reef fish larvae[END_REF], Andrello et al. 2013[START_REF] Bottesch | A magnetic compass that might help coral reef fish larvae return to their natal reef[END_REF]. is would decrease the amount of connectivity in the larval dispersal system and would highlight even more the importance of stepping-stone migration over multiple generations to realize the observed long-distance genetic connectivity. Predicting how larval behaviour would affect larval dispersal patterns thus requires including larval swimming and orientation mechanisms in the larval dispersal model, but the parametrization of these processes would be difficult because of the lack of biological knowledge on larval dispersal in M. surmuletus.

Although applied here to the larval dispersal of a demersal fish species, the stepping-stone approach to dispersal can be generalized to other environments or living systems. Connectivity estimates can be applied to any organisms with a dispersing propagule stage, by modelling wind dispersal [START_REF] Nathan | Mechanistic models of seed dispersal by wind[END_REF], animal-mediated seed dispersal [START_REF] García-Fernández | Herbivore corridors sustain genetic footprint in plant populations: a case for Spanish drove roads[END_REF] or riverine resistance [START_REF] Schick | Directed connectivity among fish populations in a riverine network[END_REF]Lindley 2007, Oliveira et al. 2019). e approach can be adapted by modelling dispersal between species-specific suitable habitats such as coral reefs (Williamson et al. 2016), forests [START_REF] Wang | Habitat suitability modelling to correlate gene flow with landscape connectivity[END_REF] or breeding ponds [START_REF] Fortuna | Spatial network structure and amphibian persistence in stochastic environments[END_REF][START_REF] Decout | Integrative approach for landscape-based graph connectivity analysis: a case study with the common frog (Rana temporaria) in human-dominated landscapes[END_REF]. Additionally, the model could be improved by taking into account species-specific settlement success rates, population-specific productivity rates and behavioural responses.

Long-distance dispersal

Preface

The approach presented in 0 compared the connectivity patterns of striped red mullet by building spatial graphs of both genetic distance and larval dispersal. In the resulting study, we observed wide-scale connectivity that is not reflected by the direct larval dispersal network.

The stepping-stone multi-generational extension of larval connectivity instead better matches the multi-generational time scale of genetic connectivity. The new multigene estimates of larval dispersal showed a higher correlation with genetic distances but a large part still remains unexplained. We hypothesized that this remaining gap could be explained by adult dispersal or larval behaviour unaccounted for in the biophysical model. According to the behaviour of both larvae and adults, dispersal could be more or less restricted than estimated from the biophysical model.

In this sub-chapter, I present preliminary results of a Mediterranean assessment of the relatedness for two species, the striped red mullet and white seabream. I analyse the "neutral" SNP based genotypes of 526 mullets and 297 seabreams that were collected in 64 and 59 locations respectively. By calculating the kinship coefficient k of Loiselle, Sork, Nason, & Graham (1995), I identified pairs of highly related individuals that were either full siblings, half siblings or first cousins. The distance at which these pairs were sampled contributes to our understanding of the potential for dispersal of both species.

Introduction

Connectivity may vary greatly across species, shaping population structure and driving evolutionary changes that are crucial for the long-term persistence under fluctuating environments and pressures [START_REF] Kool | Population connectivity: Recent advances and new perspectives[END_REF], Pascual et al. 2017). This consideration is especially important for exploited fish species, whose persistence might depend on the recruitment from neighbouring populations [START_REF] Waples | Separating the wheat from the chaff: Patterns of genetic differentiation in high gene flow species[END_REF][START_REF] Harrison | A connectivity portfolio effect stabilizes marine reserve performance[END_REF][START_REF] Dedrick | Persistence of a reef fish metapopulation via network connectivity: theory and data[END_REF]. Species-specific differences in connectivity and subsequently gene flow levels will influence the species' potential to adapt to new environmental conditions or rapidly shift geographic ranges in the face of climate change [START_REF] Pinsky | Marine Taxa Track Local Climate Velocities[END_REF][START_REF] Nielsen | Distinct interspecific and intraspecific vulnerability of coastal species to global change[END_REF].

Characterizing dispersal distances and realized connectivity patterns contribute to anticipate the response of species to ongoing global changes. Many marine species with a bipartite life cycle, whose mostly sedentary adult stage is preceded by a dispersing larval stage, are characterised by high genetic connectivity reported at thousands of genomics markers [START_REF] D'aloia | Patterns , causes , and consequences of marine larval dispersal[END_REF][START_REF] Hoey | Genomic signatures of environmental selection despite near-panmixia in summer flounder[END_REF][START_REF] Reguera-Rouzaud | Drivers for genetic structure at different geographic scales for Pacific red snapper (Lutjanus peru) and yellow snapper (Lutjanus argentiventris) in the tropical eastern Pacific[END_REF][START_REF] Vandamme | Reconciling seascape genetics and fisheries science in three codistributed flatfishes[END_REF][START_REF] Benestan | Restricted dispersal in a sea of gene flow[END_REF]). Long-distance dispersal, spanning distances greater than 40km, is likely to play a role in this pattern by increasing the amount of gene flow among populations over large spatial scales, thus counteracting population structuring (Pascual et al. 2017[START_REF] Manel | Long-Distance Benefits of Marine Reserves: Myth or Reality?[END_REF], Riginos et al. 2019). However direct genetic methods tracing the realized dispersal of individuals, such as parentage analysis and assignment tests, mostly find evidence of dispersal around 30 km [START_REF] Planes | Larval dispersal connects fish populations in a network of marine protected areas[END_REF][START_REF] Schunter | Kinship analyses identify fish dispersal events on a temperate coastline[END_REF][START_REF] D'aloia | Patterns , causes , and consequences of marine larval dispersal[END_REF][START_REF] Almany | Larval fish dispersal in a coral-reef seascape[END_REF], Bode et al. 2019). Larval dispersal models, on the other hand, often predict dispersal distances over 10s to 1 000s of km [START_REF] Manel | Long-Distance Benefits of Marine Reserves: Myth or Reality?[END_REF][START_REF] Álvarez-Noriega | Global biogeography of marine dispersal potential[END_REF].

Parentage studies are usually confined to small (1-5km) or medium (~10 km) scale inferences due to the high demands of almost exhaustively sampling both potential parent and offspring cohorts (e.g. [START_REF] Nanninga | Not finding Nemo: limited reef-scale retention in a coral reef fish[END_REF][START_REF] Sato | Marine protected area restricts demographic connectivity: Dissimilarity in a marine environment can function as a biological barrier[END_REF]). As a result, the maximal dispersal distance they find often equals their maximal sampling scale and the long-distance dispersal of fishes is often underestimated and limited by the spatial extent of sampling [START_REF] Manel | Long-Distance Benefits of Marine Reserves: Myth or Reality?[END_REF].

Blind kinship analyses, without pedigree information available, are a powerful avenue to highlight long-distance dispersal events and contribute to a deeper understanding of connectivity (e.g. Le [START_REF] Port | Temperate marine protected area provides recruitment subsidies to local fisheries[END_REF]. By focusing our sampling effort on collecting a few individuals from a large number of widespread locations rather than a large number of individuals from a few populations, we can increase our potential to identify closely related long-distance pairs and characterize the amplitude of dispersal over extensive areas [START_REF] Iacchei | Combined analyses of kinship and FST suggest potential drivers of chaotic genetic patchiness in high gene-flow populations[END_REF][START_REF] Venables | Genome-wide SNPs detect no evidence of genetic population structure for reef manta rays (Mobula alfredi) in southern Mozambique[END_REF]. Such an approach is especially suited to study mobile marine species that might exhibit long-distance dispersal because it does not rely on a sedentary life history and requires the sampling of only one generation, as opposed to parentage analyses [START_REF] Schunter | Kinship analyses identify fish dispersal events on a temperate coastline[END_REF][START_REF] Feutry | Inferring contemporary and historical genetic connectivity from juveniles[END_REF]).

Here, we investigate the prevalence of long-distance dispersal events among two fish species, the white seabream Diplodus sargus and the striped red mullet Mullus surmuletus. Both species have similar larval duration periods (PLD, Macpherson & Raventos, 2006) but different potential for adult dispersal. Seasonal migration has been reported for the seabream at up to 90 km [START_REF] Bibliography Abecasis | Home range, residency and movements of Diplodus sargus and Diplodus vulgaris in a coastal lagoon: Connectivity between nursery and adult habitats[END_REF], whereas very little is known about mullet adult movement which is assumed to be more restricted (Macpherson and Raventos 2006). By benefiting from an individual based sampling at the scale of the Mediterranean Sea combined with next-generation sequencing of 813 individuals, we estimate the kinship patterns at an unprecedented spatial scale spanning over 3 800 km.

Material & Methods

Genetic data

The sampling, sequencing and bio-informatic procedures are detailed in Chapter 2 & 5.

In short, 297 seabream and 526 mullet individuals were collected in 59 and 64 locations respectively from fisheries landing sites. Genomic DNA was extracted and sequenced using a Genotyping By Sequencing approach (Elshire et al. 2011). Raw sequences were filtered and mapped on the reference genomes of both species to yield Single Nucleotide Polymorphism (SNP) markers [START_REF] Puritz | dDocent : a RADseq, variant-calling pipeline designed for population genomics of non-model organisms[END_REF]

Kinship

We calculated the relatedness among individuals with the kinship coefficient k of Loiselle, Sork, Nason, & Graham (1995) available in GENODIVE [START_REF] Meirmans | GENODIVE version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids[END_REF]. For each pair of individuals, the kinship coefficient k represents the probability that homologous alleles chosen at random are identical by descent. It is calculated as the correlation of allele frequencies between pairs of individuals, relative to the allele frequencies of the total sample [START_REF] Loiselle | Spatial Genetic Structure of a Tropical Understory Shrub, Psychotria officinalis (Rubiaceae)[END_REF][START_REF] Wang | Estimating pairwise relatedness in a small sample of individuals[END_REF]. We describe the full range of kinship values as well as that of the 95 th quantile. We considered individual pairs with a kinship > 0.1 as closely related. More specifically, we considered individual pairs with values ranging from 0.57 -0.375 (median = 0.5) as nearly identical, 0.375 -0.1875 (median = 0.25) as full siblings and 0.1875 -0.09375 (median = 0.125) as half siblings [START_REF] Loiselle | Spatial Genetic Structure of a Tropical Understory Shrub, Psychotria officinalis (Rubiaceae)[END_REF][START_REF] Iacchei | Combined analyses of kinship and FST suggest potential drivers of chaotic genetic patchiness in high gene-flow populations[END_REF]).

Results

Kinship coefficients of white seabream, after excluding self-comparisons, ranged from -0.039 to 0.322 with an overall mean kinship of -0.00008 ± 0.00004 (mean ± se) (Figure 4.1a).

The 95 th quantile ranged from 0.012 to 0.322. Within this quantile, nine kinship values exceeded Kinship coefficients of M. surmuletus ranged from -0.053 to 0.437 with an overall mean kinship of 0.00004 ± 0.00004 (Figure 4.1c). The 95 th quantile of the kinship distribution ranged from 0.019 to 0.437. Only one M. surmuletus pair, out of the 108 811 unique pairs (0.0009%), had a kinship greater than 0.1 (Figure 4.1d). Both individuals (0.4% of all genotyped individuals) were sampled at the same site (Figure 4.2) and were nearly identical (kinship = 0.437), so no long-distance dispersal was detected for this species. 2020). Although the pelagic larval duration (PLD) of our two species is quite similar, 25-30 days, our results indicate that seabream has a much stronger genetic connectivity (i.e. realized dispersal) than mullet. These results corroborate the recent findings of [START_REF] Benestan | Restricted dispersal in a sea of gene flow[END_REF] who demonstrate restricted dispersal at small spatial scales (< 10 km) for mullet but none for seabream. Indeed, the detection of six pairs of closely related but geographically distant (on average 1 981 km) individuals in seabream demonstrate the species' unprecedented ability for long-distance dispersal. This is the first study finding closely related individuals of benthic or demersal species over ~2 000 km apart. The kinships values varying between 0.10 and 0.32 could represent full-siblings (kinship coefficients close to 0.25) and half-siblings (kinship coefficients close to 0.125) or, for the latter, alternative relationships such as aunt-niece, grandparent-grandchild relationships or 2 nd generation offspring (Le [START_REF] Port | Temperate marine protected area provides recruitment subsidies to local fisheries[END_REF][START_REF] Benestan | Restricted dispersal in a sea of gene flow[END_REF]. These pairs then represent migration patterns over one or two generations (Gagnaire andGaggiotti 2016, Le Port et al. 2017), and the total achieved distances correspond to dispersal scales of hundreds of kilometres per generation even when assuming dispersal paths follow opposite directions.

Previously, Simpson, Harrison, Claereboudt, and Planes (2014) leopardus), in turn, revealed dispersal distances of up to 250 km (Williamson et al. 2016, Bode et al. 2019). They attribute these patterns mainly to widespread larval dispersal. Biophysical modelling of larval dispersal across the Mediterranean Sea for a similar PLD and spawning season as seabream and mullet revealed that dispersal mainly occurs between 14 km and 522 km (Andrello et al. 2013). Such models might still underestimate the dispersal distances for seabream which reached 500 km to 1 000 km per generation in this study. Seabream larvae, as well as other temperate coastal fish, are able to swim faster than many coastal currents and endure for hours, even swimming up to 86 km in laboratory settings [START_REF] Baptista | Swimming abilities of temperate pelagic fish larvae prove that they may control their dispersion in coastal areas[END_REF]. This active larval behaviour, together with sensual acuity, allows them to find and swim towards nursery areas and could increase their dispersal capacity [START_REF] Teodósio | Biophysical processes leading to the ingress of temperate fish larvae into estuarine nursery areas: A review[END_REF]. This is not taken into account in most biophysical models (but see Bode et al. (2019) and might lead to the current underestimation. The long-distance dispersal could also be the result of extraordinary movements of D. sargus adults towards spawning grounds [START_REF] Aspillaga | Ordinary and extraordinary movement behaviour of small resident fish within a mediterranean marine protected area[END_REF] or from nursery grounds to adult habitats [START_REF] Bibliography Abecasis | Home range, residency and movements of Diplodus sargus and Diplodus vulgaris in a coastal lagoon: Connectivity between nursery and adult habitats[END_REF]. Striped red mullet have also been reported to move to deeper grounds for spawning (Machias et al. 1998) but they are further assumed to be sedentary (Galarza et al. 2009a), although information on adult movements from telemetric or tag-recapture studies is lacking to confirm low adult mobility

Implications for management

Genomic tools can benefit fisheries management by delimiting fish stocks and identifying management units that represent meaningful biological entities, such as populations [START_REF] Valenzuela-Quiñonez | How fisheries management can benefit from genomics? -Brief[END_REF][START_REF] Benestan | Population Genomics Applied to Fishery Management and Conservation[END_REF]. The absence of strong population structure within the Mediterranean basin (see Chapter 5) and prevalence of long-distance dispersal of seabream does not imply the absence of management but highlights the need for international cooperation when managing a common resource [START_REF] Kark | Cross-boundary collaboration: key to the conservation puzzle[END_REF][START_REF] Dunn | The importance of migratory connectivity for global ocean policy[END_REF]. The close kin seabream found up to 2 000 km apart connect sites governed by the administration of different countries crossing the borders of each country's exclusive economic zones (EEZs).

Our results contribute to the growing understanding that fish stocks, even at a global scale, are highly interconnected, either through adult dispersal of pelagic migratory species [START_REF] Block | Electronic tagging and population structure of Atlantic bluefin tuna[END_REF][START_REF] Harrison | The political biogeography of migratory marine predators[END_REF], larval dispersal of sedentary species [START_REF] Andrello | Global mismatch between fishing dependency and larval supply from marine reserves[END_REF], or both.

As a result, many countries' fish stocks depend on the productivity of the fish populations of their neighbouring EEZ and thus their effective management [START_REF] Ramesh | The small world of global marine fisheries: The cross-boundary consequences of larval dispersal[END_REF][START_REF] Palacios-Abrantes | The transboundary nature of the world's exploited marine species[END_REF].

Further analyses

These results are still preliminary and should be complemented with additional analyses. For the relatedness analyses, a maximum-likelihood approach such as implemented in COLONY [START_REF] Jones | COLONY: A program for parentage and sibship inference from multilocus genotype data[END_REF] or ML-Relate [START_REF] Kalinowski | ML-RELATE: A computer program for maximum likelihood estimation of relatedness and relationship[END_REF]) could be used to verify the results from the kinship coefficient by partitioning pairs of individuals as either fullsib, half-sib or nonrelated. Population assignment tests could allow to further infer the direction of gene flow if seabream individuals are assigned to a population of origin different from their sampling site [START_REF] Manel | Assignment methods: Matching biological questions with appropriate techniques[END_REF]. Fst values between the southern Alboran Sea and the rest of the Mediterranean Sea are sufficiently high (Fst > 0.02, see Chapter 5) to correctly assign individuals to either population [START_REF] Christie | Disentangling the relative merits and disadvantages of parentage analysis and assignment tests for inferring population connectivity[END_REF]. Such analysis could e.g. inform whether the individuals from Greek and Cyprian waters originated from the Algerian coast ( 2-We explored how space, dispersal and environment shape the genomic patterns of two marine fish species. We hypothesized that population structure and climate-associated genomic signatures of selection will be stronger in the less mobile species, as restricted gene flow tends to facilitate the fixation of locally adapted alleles.

3-To investigate our hypothesis, we genotyped two species that share a common environment but have contrasting dispersal abilities, the white seabream (Diplodus sargus) and striped red mullet (Mullus surmuletus). We collected 823 samples across the Mediterranean Sea, which ranks among the oceanic basins the most affected by climate change and human pressures. We used genotyping by sequencing (GBS) to detect 8 206 Single Nucleotides Polymorphisms (SNPs) for seabream and 2 794 for mullet. For each species, we identified highly differentiated genomic regions and disentangled the relative contribution of space, dispersal and environmental (climate, productivity) variables on genetic structure to test the prevalence of gene flow and local adaptation.

4-We observed contrasted patterns of gene flow and adaptive genetic variation between

the two species. The seabream showed a separate Alboran sea population but further panmictia across the Mediterranean Sea, while the mullet revealed additional differentiation. The within-Mediterranean differentiation of mullet was significantly correlated to summer -and winter temperatures as well as productivity. Functional annotation of climate-associated outlier SNPs then identified candidate genes involved in heat tolerance that could be examined to further predict species' responses to climate change. 5-Our results illustrate the key steps of a comparative seascape genomics study that aims to unravel the evolutionary processes at play in marine species, in order to better anticipate their response to climate change. Defining population adaptation capacities and climatic niches can then serve to include complementary evolutionary processes in species conservation planning.

Introduction

Species and populations are intricately influenced by their environmental conditions.

The spatio-temporal variation of climatic variables (e.g. temperature) can influence spatial genetic variation by reducing gene flow through the establishment of environmental barriers or by acting as a strong selective pressure inducing local adaptation [START_REF] Manel | Ten years of landscape genetics[END_REF]. Steep climatic gradients can drastically reduce gene flow between nearby localities, in a discrete way such as multispecies biogeographic breaks (e.g. [START_REF] Stanley | A climate-associated multispecies cryptic cline in the northwest Atlantic[END_REF] or in a continuous way as for clinal adaptation [START_REF] Dayan | Clinal Adaptation in the Marine Environment[END_REF]. Climate can also influence genetic diversity by promoting or impeding gene flow and influencing demographic events, e.g. through demographic fluctuations associated with environmental instability limiting diversity [START_REF] Mittelbach | Evolution and the latitudinal diversity gradient: Speciation, extinction and biogeography[END_REF][START_REF] Manel | Global determinants of freshwater and marine fish genetic diversity[END_REF]. Climate change is fundamentally altering environments and pushing species avoiding extinction to either adapt to new conditions within their current range, or track their preferred conditions in space or time through phenology or distribution shifts [START_REF] Lenoir | Species better track climate warming in the oceans than on land[END_REF]. Understanding how space, dispersal and environmental variables shape genetic variation is crucial to identify the climate-adaptive potential of populations and assess their vulnerability to climate change [START_REF] Pauls | The impact of global climate change on genetic diversity within populations and species[END_REF][START_REF] Capblancq | Genomic Prediction of (Mal)Adaptation across Current and Future Climatic Landscapes[END_REF].

Landscape and seascape genomic methods offer a relevant framework to decode gene flow and local adaptation patterns along with their drivers [START_REF] Balkenhol | Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations[END_REF][START_REF] Liggins | Seascape Genomics: Contextualizing Adaptive and Neutral Genomic Variation in the Ocean Environment[END_REF]. Landscape and seascape genomics involve a high number of loci to better understand processes behind genomic patterns such as local adaptation. Genome-wide markers offer a better resolution of population structure that is especially important for species with high gene flow, where statistical noise can be high [START_REF] D'aloia | Unraveling hierarchical genetic structure in a marine metapopulation: A comparison of three high-throughput genotyping approaches[END_REF]. For these species, highly differentiated regions of the genome (i.e. outlier loci) can improve detection of genetic structure and help identify signatures of selection [START_REF] Lewontin | Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms[END_REF] to delineate relevant ecological and evolutionary conservation units [START_REF] Gagnaire | Using neutral, selected, and hitchhiker loci to assess connectivity of marine populations in the genomic era[END_REF][START_REF] Benestan | Population Genomics Applied to Fishery Management and Conservation[END_REF].

Putative genomic signatures of adaptation to climate can be detected through the association of loci and environmental heterogeneity [START_REF] Schoville | Adaptive Genetic Variation on the Landscape: Methods and Cases[END_REF] and have been observed this way in a wide range of taxa [START_REF] Rellstab | A practical guide to environmental association analysis in landscape genomics[END_REF][START_REF] Benestan | Seascape genomics provides evidence for thermal adaptation and current-mediated population structure in American lobster (Homarus americanus)[END_REF][START_REF] Capblancq | Genomic Prediction of (Mal)Adaptation across Current and Future Climatic Landscapes[END_REF]. Because the environment is inherently spatially structured, genetic (e.g. gene flow) and demographic processes produce patterns such as isolation-by-distance and isolation-bycolonization that can coincide with environmental gradients [START_REF] Bierne | The coupling hypothesis: Why genome scans may fail to map local adaptation genes[END_REF][START_REF] Orsini | Drivers of population genetic differentiation in the wild: isolation by dispersal limitation, isolation by adaptation and isolation by colonization[END_REF]. Reduction of gene flow between dissimilar environments can also lead to isolation by environment, i.e. genetic differentiation increases with environmental differences, without local adaptation (Wang andBradburd 2014, Thomas et al. 2015). To remove confounding demographic effects, variation partitioning of spatial and environmental predictors can disentangle their relative contributions to genomic variation [START_REF] Peres-Neto | Variation partitioning of species data matrices: Estimation and comparison of fractions[END_REF][START_REF] Vandamme | Regional environmental pressure influences population differentiation in turbot (Scophthalmus maximus)[END_REF][START_REF] Dayan | Clinal Adaptation in the Marine Environment[END_REF]. The detected outlier loci associated to environmental conditions can then be mapped on (related) genomes when available, and used to detect candidate genes and interpret their function [START_REF] Manel | Genomic resources and their influence on the detection of the signal of positive selection in genome scans[END_REF]. Finally, when possible, introducing those candidate genes into species distribution models that predict the influence of climate change allows to take species-and population-specific eco-evolutionary responses into account [START_REF] Fitzpatrick | Ecological genomics meets community-level modelling of biodiversity: Mapping the genomic landscape of current and future environmental adaptation[END_REF], Razgour et al. 2019[START_REF] Waldvogel | Evolutionary genomics can improve prediction of species' responses to climate change[END_REF][START_REF] Nielsen | Distinct interspecific and intraspecific vulnerability of coastal species to global change[END_REF].

Marine organisms occupy an open ocean with few physical boundaries (Pascual et al. 2017). The strong dispersal potential of mobile species, either in their larval or adult stages (or both) [START_REF] Almany | Larval fish dispersal in a coral-reef seascape[END_REF][START_REF] Manel | Long-Distance Benefits of Marine Reserves: Myth or Reality?[END_REF]) often translates into high gene flow and low genetic structure [START_REF] Hoey | Genomic signatures of environmental selection despite near-panmixia in summer flounder[END_REF]Pinsky 2018, Vandamme et al. 2021). Despite this weak genetic structure, temperature and salinity emerge as important drivers of marine population structure [START_REF] Selkoe | A decade of seascape genetics: Contributions to basic and applied marine connectivity[END_REF]. A broad range of marine fishes show local adaptation to sea surface temperature [START_REF] Stanley | A climate-associated multispecies cryptic cline in the northwest Atlantic[END_REF][START_REF] Cayuela | Shared ancestral polymorphisms and chromosomal rearrangements as potential drivers of local adaptation in a marine fish[END_REF], salinity [START_REF] Tine | European sea bass genome and its variation provide insights into adaptation to euryhalinity and speciation[END_REF], Dalongeville et al. 2018a) or both [START_REF] Carreras | East is East and West is West: Population genomics and hierarchical analyses reveal genetic structure and adaptation footprints in the keystone species Paracentrotus lividus (Echinoidea). -Divers[END_REF]. Comparative genomic approaches further allow to decipher the influence of species' traits on their responses [START_REF] Nielsen | Multi-model seascape genomics identifies distinct environmental drivers of selection among sympatric marine species[END_REF], 2021[START_REF] Torrado | Individual-based population genomics reveal different drivers of adaptation in sympatric fish[END_REF]) but such datasets are rarely available for entire biogeographic regions or oceanic basins (Leigh et al. in revision;[START_REF] Gagnaire | Comparative genomics approach to evolutionary process connectivity[END_REF].

Here, we explored how climatic gradients in the Mediterranean Sea influence the genomic patterns of two fish species while accounting for spatial and larval dispersal patterns.

The Mediterranean Sea is a unique hotspot for biodiversity [START_REF] Coll | The biodiversity of the Mediterranean Sea: Estimates, patterns, and threats[END_REF]) but is projected to experience higher than average impacts of climate change including increasing temperatures, salinity and heat waves [START_REF] Marbà | Footprints of climate change on Mediterranean Sea biota[END_REF][START_REF] Darmaraki | Future evolution of Marine Heatwaves in the Mediterranean Sea[END_REF]. Linear increasing trends of temperature and salinity have been recorded in the Mediterranean Sea since 1900 [START_REF] Vargas-Yáñez | Climate change in the Western Mediterranean Sea 1900-2008[END_REF][START_REF] Borghini | The Mediterranean is becoming saltier[END_REF] with consequences on fish communities [START_REF] Givan | Thermal affinity as the dominant factor changing Mediterranean fish abundances[END_REF]. Local adaptation of fish populations in the Mediterranean Sea seems to be tightly linked to the thermal (from north to south) and saline (from west to east) gradients, as reported previously in the striped red mullet (Dalongeville et al. 2018a) and two wrasses [START_REF] Torrado | Individual-based population genomics reveal different drivers of adaptation in sympatric fish[END_REF]. Deciphering the effect of thermal and saline gradients on Mediterranean fish populations is the first step towards an understanding of the eco-evolutionary processes at play and anticipating the effects of climate change. More broadly, monitoring how species cope with climate change in this semi-enclosed sea could serve as an example of what could happen globally [START_REF] Lejeusne | Climate change effects on a miniature ocean: the highly diverse, highly impacted Mediterranean Sea[END_REF].

We focus on two socio-economically important species, the striped red mullet Mullus surmuletus, and the white seabream Diplodus sargus. Both species share similar distribution ranges across the Mediterranean Sea and Eastern Atlantic and are heavily targeted by smallscale fisheries [START_REF] Claudet | Marine reserves: Fish life history and ecological traits matter[END_REF]. Although sharing similar larval duration periods (PLD, Macpherson & Raventos, 2006), these species have contrasting movement patterns at the adult stage. The seabream shows seasonal adult migration, reported up to 90 km (Abecasis et al.

2009).

Little is known about the mullet adult movement, which is assumed to be more restricted (Macpherson and Raventos 2006). We take advantage of an individual-based sampling at an exceptionally large spatial scale and a genotyping by sequencing (GBS) approach providing a high number of genome-wide markers. By applying an original comparative seascape genomics approach, our objectives are to (i) describe the spatial distribution of genetic diversity, (ii) define outlier SNPs to improve the detection of population structure, (iii) disentangle the drivers influencing population structure and (iv) identify candidate genes potentially involved in local adaptation to environmental conditions affected by climate change. We hypothesize that population structure and environmental adaptive signatures will be lower in more mobile species, i.e. seabream, as gene flow tends to counteract natural selection.

Material & methods

Study species and sampling effort

The white seabream and striped red mullet are coastal fishes that spawn from March till June/July (Reñones et al. 1995[START_REF] Morato | Reproductive biology and recruitment of the white sea bream in the Azores[END_REF]. Both produce pelagic larvae that stay in the water column for approximately 20 to 35 days (Macpherson and Raventos 2006). The seabream occurs on rocky reefs and seagrass beds, whereas the mullet mostly occupies sandy and rocky habitats [START_REF] Froese | [END_REF].

We analysed 526 mullet and 297 seabream individuals collected in 64 and 59 locations in 2014 (Dalongeville et al. 2018b) (Fig. 1). Fin clips were taken from small-scale fisheries landings and conserved in 96% ethanol prior to storage at 4°C. Samples covered a large area,

Genetic data and SNP calling

The SNP datasets were produced using a Genotyping by Sequencing (GBS) approach, allowing to genotype a large number of individuals for a large number of SNPs covering the whole genome. Genomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen).

The equipment was cleaned with 100% alcohol between each sample to avoid contamination.

After quality and concentration assessment, GBS libraries of the mullet and seabream were constructed using restriction enzyme ApeKI (Elshire et al. 2011), and Pst1/Bfa1 respectively.

Libraries of 95 individuals per lane were prepared and sequenced at the Institute of Genomic Diversity at Cornell University using the Illumina HiSeq 2500 (100 bp, single-end reads). More details can be found in Dalongeville, Andrello, et al. (2018).

Raw GBS sequences were filtered and trimmed based on quality (Supplementary Methods). Demultiplexing was performed with the process_rad-tags program in STACKS v. 1.48 (Catchen et al. 2011). Sequences were mapped on the published reference genomes [START_REF] Fietz | New genomic resources for three exploited Mediterranean fishes[END_REF]: genome size = 785 and 613 Mbp, contig N50 = 1101 and 384 kbp for seabream and mullet respectively) with BWA2 [START_REF] Vasimuddin | Efficient architecture-aware acceleration of BWA-MEM for multicore systems[END_REF]. Variant calling was performed with the FreeBayes software [START_REF] Garrison | Haplotype-based variant detection from short-read sequencing[END_REF] as implemented in the dDocent pipeline [START_REF] Puritz | dDocent : a RADseq, variant-calling pipeline designed for population genomics of non-model organisms[END_REF]. Additional filters were further applied with VCFtools [START_REF] Danecek | The variant call format and VCFtools[END_REF]) and vcflib (https://github.com/vcflib/vcflib#vcflib) following O' Leary et al. (2018). In short, individuals with more than 50% missing data were removed, as well as Single Nucleotide Polymorphisms (SNPs) with a genotype call rate below 95%. SNPs were further filtered based on allele balance, mapping quality ratio and quality/depth ratio (Table S1).

Detecting outlier SNPs

We applied PCAdapt to detect outlier loci more differentiated than under a neutral model [START_REF] Luu | pcadapt : an R package to perform genome scans for selection based on principal component analysis[END_REF]. PCAdapt is currently the only individual-based method free of prior hypotheses on which environmental factors drive differentiation [START_REF] Liggins | Seascape Genomics: Contextualizing Adaptive and Neutral Genomic Variation in the Ocean Environment[END_REF]. PCAdapt conducts a Principal Component Analysis (PCA) on the individual genotypes and retains only those principal components that best explain genetic structure among individuals. All SNPs are then regressed against the retained ordination axes and outlier SNPs are selected based on their significant correlation with these axes. P-values for each SNP were obtained with the R function pcadapt(), and false discovery rates (FDR) were estimated with the function qvalue() [START_REF] Storey | qvalue: Q-value estimation for false discovery rate control[END_REF]. A conservative FDR cut-off (α = 0.005) was applied for outlier detection.

Genetic diversity was evaluated with the full dataset. Genetic structure was investigated separately for the outlier and neutral SNPs. For the neutral dataset, only SNPs in Hardy-Weinberg Equilibrium (HWE) were kept [START_REF] Beaumont | Evaluating loci for use in the genetic analysis of population structure[END_REF].

Genetic diversity

We estimated global genetic diversity as the expected heterozygosity (He) for all individuals with the function Hs() (R package adegenet, Jombart 2008). Because our sampling design is not stratified in populations, we further investigated the spatial variation of He using a grid-based approach modified from [START_REF] Larranaga | A Mesoamerican origin of cherimoya (Annona cherimola Mill.): Implications for the conservation of plant genetic resources[END_REF] and [START_REF] Thomas | Present Spatial Diversity Patterns of Theobroma cacao L. in the Neotropics Reflect Genetic Differentiation in Pleistocene Refugia Followed by Human-Influenced Dispersal[END_REF].

Individuals were replicated to nearby cells with a resolution of approximately 80km, and He was calculated for each cell (Supplementary Methods). The resulting grid contained on average 56 ± 33 individuals per cell for mullet, and 37 ± 25 individuals for seabream. He was calculated on bootstrapped subsamples of 20 individuals per cell to avoid sampling bias. We further calculated the observed individual heterozygosity (Ho) as the proportion of heterozygous loci per individual using VCFtools, and compare values between ecoregions and species.

Genetic structure

We inferred population structure using the Discriminant Analysis of Principal Components (DAPC) with adegenet. DAPC maximizes genetic differences between groups while minimizing variation within groups [START_REF] Jombart | Discriminant analysis of principal components: a new method for the analysis of genetically structured populations[END_REF]. We did not use the sampling sites as prior information, but selected the optimal number of clusters using the Bayesian Information Criterion (BIC) with the function find.clusters(). We calculated the pairwise FST between all DAPC clusters following [START_REF] Weir | Estimating F-statistics for the analysis of population structure[END_REF] with the function stamppFst from the package StAMPP [START_REF] Pembleton | StAMPP: an R package for calculation of genetic differentiation and structure of mixed-ploidy level populations[END_REF]. We also calculated the effective population size (Ne) for each of the DAPC clusters inferred with putatively neutral SNPs using NeEstimator v2.1 under a random mating model [START_REF] Do | NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data[END_REF]. Ne was calculated on neutral SNPs to avoid potential biases from loci under selection [START_REF] Waples | A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci[END_REF][START_REF] Candy | Population differentiation determined from putative neutral and divergent adaptive genetic markers in Eulachon (Thaleichthys pacificus, Osmeridae), an anadromous Pacific smelt[END_REF]) and a minimum allele frequency of 0.02 [START_REF] Waples | Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: A largely untapped resource for applied conservation and evolution[END_REF].

Characterizing the seascape features influencing population structure

Spatial variables

Marine geographic distances were calculated as the minimum distances constrained by water (Supplementary Methods). Pairwise geographic distances were then converted to distancebased Moran's eigenvector maps (dbMEM), which are uncorrelated (orthogonal) vectors that can be used as site-based variables in regression analyses (Dray et al. 2006, Legendre and[START_REF] Legendre | Numerical Ecology[END_REF]. dbMEM are computed from a principal coordinate analysis (PCoA) on the truncated distance matrix. The new eigenvectors with the highest eigenvalues describe broadscale spatial structures and were retained for subsequent analyses (Supplementary Methods, Fig. S1a-b). dbMEMs were calculated with the function dbmem() from the adespatial package with the default truncation threshold [START_REF] Dray | adespatial: Multivariate Multiscale Spatial Analysis[END_REF].

Larval dispersal ability

Larval dispersal was quantified using a biophysical model which estimates the probability of larval connection between each pair of sites (Andrello et al. 2015). We modified the model parameters with a pelagic larval duration of 30 days and release of larvae every 3 days from 1 May until 28 May, which corresponds to the spawning season of our studied species, and the same larval dispersal estimates were used for both species considering their respective sampled sites (Reñones et al. 1995[START_REF] Morato | Reproductive biology and recruitment of the white sea bream in the Azores[END_REF]. Pairwise dispersal probabilities were then converted to Asymmetric Eigenvector Maps (AEM). The AEM are orthogonal vectors similar to dbMEM but account for the directionality of larval dispersal [START_REF] Blanchet | Modelling directional spatial processes in ecological data[END_REF]. Probabilities were translated into a site-by-edge binary matrix where two sites were connected if the dispersal probability > 0 (Supplementary Methods). Orthogonal AEMs were then computed from the binary matrix with the function aem() from the adespatial package. Similar to dbMEM, the new AEM with the highest eigenvalues describe broad-scale dispersal structures and were retained for subsequent analyses (Fig. S1c).

Environmental variables

Monthly mean sea surface salinity, temperature and chlorophyll a (included as a proxy of productivity) values around each site were retrieved from the Bio-ORACLE database for the period 1987-2015 [START_REF] Tyberghein | Bio-ORACLE: A global environmental dataset for marine species distribution modelling[END_REF]. Salinity and chlorophyll values were averaged over the months and years, whereas temperature values were averaged over the years by season (i.e. spring, summer, autumn and winter) using the raster package (Hijmans 2020). To avoid problems of collinearity, we only kept the winter and summer mean temperature to represent extreme values (i.e. the coldest and warmest).

Disentangling drivers of genomic variation

We used a distance-based redundancy analysis (dbRDA) on Euclidean genetic distances estimated between individuals to characterize the relative contribution of spatial (dbMEM), dispersal (AEM vectors) and environmental (salinity, chlorophyll, winter and summer temperature) variables [START_REF] Legendre | Numerical Ecology[END_REF]. Starting with all selected variables, we first performed variable selection with vegan's ordiR2step() function (Oksanen et al. 2016) to retain the most informative explanatory variables and minimize variance-inflation. In a second step, variables with the larges variance inflation factor (VIF) were sequentially removed until all VIF values were < 3 and all variables were sufficiently uncorrelated (Supplementary Fig. S2). The selected set of explanatory variables was then tested in a final dbRDA model. We also conducted multiple partial dbRDA to test the effect of each environmental variable after accounting for the effect of spatial and dispersal variables. We then performed variance partitioning to quantify the separate and shared proportion of the genetic variation explained by the spatial (SPACE), dispersal (DISP) and environmental (ENV) variables with vegan's varpart() function. Significance of the models as well as the significance of each dbRDA axis were tested using ANOVA-like permutation tests with 9999 permutations as implemented in vegan's anova.cca() function [START_REF] Legendre | Testing the significance of canonical axes in redundancy analysis[END_REF].

Functional annotation & climatic variance

We retrieved the genomic position of the outlier SNPs onto the annotated genomes of seabream and mullet [START_REF] Fietz | New genomic resources for three exploited Mediterranean fishes[END_REF]. For SNPs that positioned in annotated coding regions, we extracted the flanking DNA sequences centered on the variant position (200 bp length) and aligned them against the Swiss-Prot protein sequence database using BLAST+ [START_REF] Camacho | BLAST+: architecture and applications[END_REF]). We only retained sequences ranking first or second, with a minimum identity of 70% and an E-value below 10 -6 . We then assessed whether SNP variations were synonymous or not using ANVAGE (https://github.com/Grelot/anvage). Non-synonymous variants were kept to increase the possibility that selected SNP have functional implications [START_REF] Rellstab | A practical guide to environmental association analysis in landscape genomics[END_REF].

Finally, we tested whether the alleles (heterozygous or homozygous for the reference or alternative allele) of these candidate SNPs were correlated to the environmental variables identified by the dbRDA.

Results

Successful SNP calling

We successfully genotyped a total of 8,206 filtered and informative SNPs from 297 white seabreams (2.74 % missing data) and 2,794 SNPs genotyped from 467 striped red mullets (2.83 % missing data) (Table S1). Out of these datasets, we detected 413 (5.0%) and 291 (10.5%) outlier SNPs for the seabream and mullet respectively. The remaining neutral SNPs, after filtering for HWE, contained 7,655 neutral SNPs for the seabream and 2,462 neutral SNPs for the mullet.

Genetic diversity patterns

The expected heterozygosity (He) calculated on all individuals was 0.26 and 0.24 for the seabream and mullet respectively. Spatial interpolation of He revealed the genetic diversity of seabream was mostly stable across the Mediterranean Sea with peaks in the Western Mediterranean and Aegean Sea (Fig. 2a). The genetic diversity of mullet was highest in the Alboran Sea and gradually decreased eastward (Fig. 2b). Individual observed heterozygosity (Ho) confirmed these patterns (Fig. 2c) and was significantly different between species, ecoregions and their interaction (two-factor ANOVA, all p-values < 0.001, Table S2).

Higher resolution for population structure with outlier SNPs

The DAPC on neutral SNPs highlighted almost complete panmixia for both species.

Nevertheless, we found a slight difference between them as the lowest BIC value corresponded to K = 2 for white seabream and K = 1 for surmullet. For white seabream, K = 2 indicated most individuals belonged to one cluster (Ne = 4556, Table S3) (Fig. S3a) while 27 individuals mostly from the south of the Alboran Sea formed the second genetic cluster (Ne = 255, Table S3) (pairwise FST = 0.02). For the mullet, we found one single cluster except for a few scattered individuals when forcing K = 2 (pairwise FST = 0.006) (Ne = 9489 and 607 respectively, Table S3) (Fig. S3b).

Climate-associated genetic variation

Based on their eigenvalues, the seven and five first dbMEM were selected for seabream and mullet respectively, while the six first AEM were selected for both species. Distance-based redundancy analysis on neutral SNPs produced significant (p-value = 0.0001) but low fit for both species (R 2 adj = 0.006 for the seabream and 0.004 for the mullet) and is not further discussed. The dbRDAs conducted on outlier SNPs were globally significant (p-value = 0.001) and explain 14.5% and 8% of genetic variation in the seabream and mullet respectively (Fig. 4). Only the first dbRDA axis was significant in both cases, accounting for 15% and 7.9% of the overall (unadjusted) outlier based genetic variation.

For the seabream, the salinity, summer -and winter temperature along with three geographic-distance vectors (dbMEM2, dbMEM3 and dbMEM6) and three larval-dispersal vectors (AEM1, AEM3 and AEM5) were retained (Fig. S5). The environmental partition uniquely explained 19% of the dbRDA variance (Fig. 5a), while salinity and summer temperature were significant predictors after accounting for the variance explained by spatial and dispersal variables in additional partial dbRDAs (p-value < 0.05; Table 1).

For the mullet, the chlorophyll, summer -and winter temperature along with one geographic-distance vector (dbMEM5) and five larval-dispersal vectors (AEM1, AEM3, AEM4, AEM5 and AEM6) were retained (Fig. S6). The environmental partition uniquely explained 18% of the dbRDA variance (Fig. 5b), while summer and winter temperatures and chlorophyll were significant predictors after accounting for the variance explained by spatial and dispersal variables in additional partial dbRDAs (p-value < 0.05; Table 1). that both species are influenced by the same biogeographic barrier to gene flow and their respective structure was driven by a combination of spatial, dispersal and environmental variables. Functional annotation of outlier SNPs identifies candidate genes associated with temperature suggesting local adaption to temperature for both species. The adaptive structure is stronger for mullet in the Western Mediterranean basin suggesting stronger local adaptation for the less mobile species. Overall, our results illustrate the important advances in seascape genomics and spatial ecology that aim to facilitate their integration into climate change vulnerability assessment.

Neutral processes structuring genetic variation and diversity

The spatial dbMEM and larval dispersal AEM together explained 35% and 60% of the modeled genomic variation of seabream and mullet respectively. This indicates that a combination of gene flow between connected populations and drift between disconnected ones, e.g. in the Alboran Sea, partially drives genetic structure. As population sizes are mostly large in our species and marine species in general, we expect a small effect of genetic drift and a stronger effect of gene flow [START_REF] Allendorf | Genomics and the future of conservation genetics[END_REF]).

In our study, the Almeria-Oran front (AOF) divides the Mediterranean Sea in two populations of seabream: the south of the Alboran Sea and the rest of the Mediterranean Sea (Fig. 3). The AOF is formed by the influx of the cold and fresher Atlantic water into the Mediterranean Sea through the Strait of Gibraltar, which influences oceanographic dynamics and creates a nearly permanent oceanic front [START_REF] Patarnello | Pillars of Hercules: Is the Atlantic-Mediterranean transition a phylogeographical break?[END_REF]). The influx of fresher water simultaneously creates an abrupt change in temperature and salinity, which further gradually increases eastward (Fig. 1c). The AOF acts as a barrier to gene flow and biogeographic break for numerous species [START_REF] Patarnello | Pillars of Hercules: Is the Atlantic-Mediterranean transition a phylogeographical break?[END_REF], Pascual et al. 2017) including the mullet (Galarza et al. 2009b). However it was not thought to differentiate seabream populations until now [START_REF] Bargelloni | The Atlantic-Mediterranean transition: Discordant genetic patterns in two seabream species, Diplodus puntazzo (Cetti) and Diplodus sargus (L.)[END_REF][START_REF] González-Wangüemert | Connectivity patterns inferred from the genetic structure of white seabream (Diplodus sargus L.)[END_REF]. This break was only detected for the mullet by using outlier SNPs. The lack of samples from the south of the Alboran Sea (Fig. 1), such as we have for the seabream, probably weakens the inference of a well-defined Alboran mullet population.

The AOF forms a semi-permeable barrier allowing for the mixture of two genetic pools at the contact zone, the Mediterranean pool and the Atlantic pool [START_REF] Carreras | East is East and West is West: Population genomics and hierarchical analyses reveal genetic structure and adaptation footprints in the keystone species Paracentrotus lividus (Echinoidea). -Divers[END_REF]. This is reflected in the spatial distribution of genetic diversity which slightly peaks in the Western Mediterranean for the seabream (Fig. 2a). In the absence of strong biogeographic barriers in the main Mediterranean basin, the seabream shows high levels of gene flow impeding further differentiation and homogenizing genetic diversity. This lack of spatial variation of genetic diversity in the Mediterranean sea is supported by a published study for 31 Mediterranean fishes, including our species, using microsatellite and mitochondrial markers [START_REF] Dalongeville | Ecological traits shape genetic diversity patterns across the Mediterranean Sea: A quantitative review on fishes[END_REF]. For the mullet, on the other hand, genetic diversity peaks in the Alboran Sea and gradually decreases eastward (Fig. 2). These patterns coincide with the genetic structure where the Western Mediterranean is differentiated in several clusters but the Eastern Mediterranean consists of one main cluster (Fig. 3). The dichotomy could be explained by the introgression of Atlantic haplotypes gradually eroding with increasing distance from the Atlantic-Mediterranean contact zone [START_REF] Duranton | The spatial scale of dispersal revealed by admixture tracts[END_REF].

Influence of climate on genetic variation and potential local adaptation

The influx of fresher Atlantic water creating the AOF simultaneously creates an abrupt change in temperature and salinity. In addition to neutral processes, local adaptation of the two seabream populations on either side of the front could maintain or even promote their differentiation as the environmental variables explain 19% of the modeled genomic variance of seabream (Figure 5a). These results contribute to the mounting evidence that the Atlantic-Mediterranean break recognized for many species is enabled by differential selection and local adaptation to contrasting temperature and salinity conditions [START_REF] Milano | Outlier SNP markers reveal fine-scale genetic structuring across European hake populations (Merluccius merluccius)[END_REF][START_REF] Pascual | Temporal and spatial genetic differentiation in the crab Liocarcinus depurator across the Atlantic-Mediterranean transition[END_REF][START_REF] Carreras | East is East and West is West: Population genomics and hierarchical analyses reveal genetic structure and adaptation footprints in the keystone species Paracentrotus lividus (Echinoidea). -Divers[END_REF]. The lack of further differentiation and local adaptation of seabream in the main Mediterranean basin concurs with our initial hypothesis as the seabream is capable of exceptional long-distance dispersal [START_REF] Bibliography Abecasis | Home range, residency and movements of Diplodus sargus and Diplodus vulgaris in a coastal lagoon: Connectivity between nursery and adult habitats[END_REF][START_REF] Aspillaga | Ordinary and extraordinary movement behaviour of small resident fish within a mediterranean marine protected area[END_REF]).

The mullet, on the other hand, is less mobile and shows finer genetic differentiation within the main Mediterranean basin. The spatial dbMEM, larval AEM, temperature, salinity and productivity patterns were sufficiently uncorrelated to be able to quantify their relative and shared importance (Fig. S1). Together, the environmental variables explained 18% of the modeled genomic variation of mullet not explained by spatial and dispersal structures (Fig. 5).

The combination of elevated productivity and cold winter temperatures correlated with the differentiation and clustering of geographically distant individuals in the Alboran Sea (DAPC cluster 3), in the Gulf of Lion (cluster 7) and the North Adriatic Sea (cluster 7) (Fig. 4b). Their differentiation breaks the gradual pattern expected under isolation-by-distance or isolation-by-environment and their genomic similarity can likely be attributed to a shared local adaptation. A very similar pattern of local adaptation was previously identified for ocellated wrasse, a reef-associated fish dispersing during a larval stage of 7 to 13 days [START_REF] Torrado | Individual-based population genomics reveal different drivers of adaptation in sympatric fish[END_REF]. Individuals sampled from the Alboran Sea and Gulf of Lion were grouped together by redundancy analysis and were identified as locally adapted to temperature and productivity [START_REF] Torrado | Individual-based population genomics reveal different drivers of adaptation in sympatric fish[END_REF]. Productivity, through food availability, as well as temperature affect the development, survival and settlement of larvae [START_REF] Robitzch | Productivity and sea surface temperature are correlated with the pelagic larval duration of damselfishes in the Red Sea[END_REF][START_REF] Raventos | Temperature reduces fish dispersal as larvae grow faster to their settlement size (C Eizaguirre[END_REF] acting as environmental selective pressures and prompting local adaptation in fishes [START_REF] Cayuela | Shared ancestral polymorphisms and chromosomal rearrangements as potential drivers of local adaptation in a marine fish[END_REF][START_REF] Torrado | Individual-based population genomics reveal different drivers of adaptation in sympatric fish[END_REF].

Identifying genomic signatures of selection associated with temperature from outliers can lead to the discovery of candidate genes that may be involved in species' adaptive response to climate change. Additionally, these genes confirm the adaptive nature of the associated outlier SNPs [START_REF] Manel | Perspectives on the use of landscape genetics to detect genetic adaptive variation in the field[END_REF]. The tubulin specific chaperone D gene, detected in mullet as having potentially locally adapted alleles, is associated to the stress-response to varying temperatures and salinities in fishes [START_REF] Whitehead | Functional genomics of physiological plasticity and local adaptation in killifish[END_REF][START_REF] Avarre | Plasticity of gene expression according to salinity in the testis of broodstock and F1 black-chinned tilapia, Sarotherodon melanotheron heudelotii[END_REF]) and mussels [START_REF] Tomanek | Proteomics of hyposaline stress in blue mussel congeners (genus mytilus): Implications for biogeographic range limits in response to Climate change[END_REF]. Homozygote genotypes A/A at the SNP located in chaperone D gene are found in individuals living in environments with the lowest winter temperatures, whereas homozygote G/G genotypes are more prevalent at higher temperatures (Figure 6b). Zinc finger proteins, for which we found two associated SNPs in seabream, are involved in heat and salinity stress response mechanisms in plants (e.g. [START_REF] Sakamoto | Arabidopsis Cys2 / His2-Type Zinc-Finger Proteins Function as Transcription Repressors under Drought[END_REF]Droll et al. 2013) and the thermal tolerance of fish (Yu et al. 2018). Homozygote G/G genotypes are most prevalent at higher summer temperatures, whereas T/T homozygotes mostly occur at lower temperatures (Figure 6a). Integrating genomic with phenotypic and fitness information would finally allow to functionally interpret potential genotype-environment associations and further unravel adaptive responses to a changing seascape (Gagnaire andGaggiotti 2016, Waldvogel et al. 2020).

From theory to the practice: conservation, climate and genomics

As climate change causes rapid environmental shifts, adaptive management has been proposed as the most efficient solution to efficiently preserve marine biodiversity on the long run [START_REF] Frazão Santos | Integrating climate change in ocean planning[END_REF][START_REF] Rilov | A fast-moving target: achieving marine conservation goals under shifting climate and policies[END_REF]. To help achieve these fast-moving targets, genomic studies provide crucial insights for the design of efficient MPA networks, as they inform on gene flow and (mal)adaptation of populations to their changing environment. Spatial conservation planning can use genomic metrics (e.g. adaptive genetic diversity) to encompass species' evolutionary potential into the design of MPA network, by prioritizing protection of populations adapted to specific climatic conditions [START_REF] Nielsen | Multispecies genetic objectives in spatial conservation planning[END_REF], Hanson et al. 2020a[START_REF] Xuereb | Incorporating putatively neutral and adaptive genomic data into marine conservation planning[END_REF]. Such locally adapted populations can act as sources of adaptive alleles for the meta-population [START_REF] Selmoni | Seascape genomics as a new tool to empower coral reef conservation strategies: An example on north-western Pacific Acropora digitifera[END_REF], hence promoting species' adaptation. For example, genotypes adapted to high temperatures can spread from the Levantine Sea to the western Mediterranean basin via the movement of individuals, allowing western populations to adapt to the temperature increase expected to occur. They can also be used for assisted migration through the transplantation of locally adapted individuals to increase the species' adaptive potential under climate change [START_REF] Bay | Genomic models predict successful coral adaptation if future ocean warming rates are reduced[END_REF]. Genomic data can also be incorporated into predictive models to forecast species' adaptive response to predicted environmental variations induced by climate change (Razgour et al. 2019).

General Discussion

CHAPTER 6

General Discussion

Synthesis of main results

Understanding the effect of protection on fish communities, characterizing connectivity patterns in high gene flow species and assessing populations' potential for local adaptation require methods that can holistically characterize fish communities and provide high-resolution genetic data to inform on evolutionary processes. The advent of next-generation sequencing (NGS) technologies has made it possible to sequence thousands of DNA fragments from multiple regions of a genome, or to sequence one fragment from multiple species found in a single sample, providing new tools to assess biodiversity across its different levels and better inform conservation decision-making [START_REF] Goodwin | DNA Sequencing as a Tool to Monitor Marine Ecological Status[END_REF][START_REF] Hohenlohe | Population genomics for wildlife conservation and management[END_REF].

My PhD combines NGS-based technological advances with robust statistical analyses

to study the biodiversity of Mediterranean fishes from communities to populations and genes with the aim to improve our capacity to protect them. In Chapter 3, I study the diversity and composition of fish communities and their response to protection by sampling and metabarcoding environmental DNA (eDNA) in six Mediterranean marine reserves and their surroundings. A nearly complete reference database of regional fish species is simultaneously developed, which is essential to accurately identify species from eDNA samples [START_REF] Mcelroy | Calibrating Environmental DNA Metabarcoding to Conventional Surveys for Measuring Fish Species Richness[END_REF][START_REF] Zhang | Comprendre l'effet des AMP sur les communautés de poissons nécessite la détection d'un large éventail d'espèces allant des plus grands prédateurs aux plus petites proies. Le metabarcoding d'ADNe permet de détecter simultanément les poissons communs ainsi que les poissons cachés ou en passage transitoires, qui sont difficiles à observer et donc souvent négligés par les méthodes classiques[END_REF]. In Chapter 4, I estimate the realized dispersal and large-scale connectivity between populations by analyzing genome-wide selectively neutral Single Nucleotide Polymorphism (SNP) markers. I combine such genomic data with a spatial graph approach that extends propagule dispersal networks with a stepping-stone model to estimate multigenerational larval dispersal. Highly differentiated or outlier SNPs are finally used in Chapter 5 to disentangle the spatial, dispersal and environmental factors that influence the genetic structure of two fish species. Functional annotation of these SNPs then identifies candidate genes of local environmental adaptation.

Combining genomic and statistical tools for a better understanding of diversity patterns

Across the different chapters of this PhD thesis, I benefit from the high resolution of NGS-tools to uncover new biodiversity patterns. At the community level and regional scale, protection from fishing re-establishes higher trophic level species inside reserves. As a result, the total species richness is lower inside reserves than outside, where lower-trophic level species are hypothesized to be relieved from top-down predator control. Cryptobenthic and rare fish in particular, which are both difficult to detect with visual methods but detected with eDNA metabarcoding, are twice as diverse at 10km from the reserves compared to inside. This result is paradoxical, as reserves aim to preserve or restore biodiversity, and somewhat unexpected, as several previous studies relying on visual methods to study Mediterranean MPAs find equal or higher species richness inside reserves (e.g. [START_REF] Guidetti | Large-scale assessment of mediterranean marine protected areas effects on fish assemblages[END_REF][START_REF] Giakoumi | Ecological effects of full and partial protection in the crowded Mediterranean Sea: A regional meta-analysis[END_REF].

However, our hypothesis to explain this new biodiversity pattern -namely that trophic structures are restored inside reserves -is not new and has previously been demonstrated in other MPAs as well (e.g. [START_REF] Guidetti | Marine reserves reestablish lost predatory interactions[END_REF][START_REF] Lamb | Trophic restructuring of coral reef fish communities in a large Marine reserve[END_REF][START_REF] Coleman | Functional traits reveal early responses in marine reserves following protection from fishing[END_REF][START_REF] Graham | Human Disruption of Coral Reef Trophic Structure[END_REF].

This study was the first to apply eDNA metabarcoding to assess the effect of protection on fish communities across multiple reserves. The sole other study using eDNA to monitor MPAs was conducted simultaneously to ours in temperate Californian kelp forests but only focused on a single MPA (Gold et al. 2021a). They boasted a complete local reference database and found higher species richness outside the protected area than inside, corroborating our results. However, they further dismiss these results as an inaccuracy of their eDNA methodology (Gold et al. 2021a). Simultaneously, [START_REF] Aglieri | Environmental DNA effectively captures functional diversity of coastal fish communities[END_REF] surveyed multiple Mediterranean MPAs and nearby fished areas but focused their study on comparing eDNA metabarcoding to other sampling methods instead of comparing the diversity of MPAs. A preliminary look at their open access data reveals that overall species richness does not vary according to MPA status. However, it is not clear whether they sampled inside the reserves or the partial protection zones surrounding them, and they had to discard 40% of their fish MOTUs making direct comparisons with our results difficult.

At the population level and Mediterranean-wide scale, the genetic connectivity obtained from genome-wide, neutral SNPs is higher than expected from larval dispersal modelling alone.

Constructing a network of genetic connectivity between 47 striped red mullet populations and comparing it to the network of larval dispersal over a single generation reveals how genetic connectivity occurs at a much larger spatial scale than larval dispersal, as previously found by Dalongeville et al. (2018b). The difference in spatial scale reflects the different temporal scales of the processes generating demographic and genetic connectivity. The stepping-stone approach then allows to estimate multigenerational larval dispersal, which better explains genetic connectivity patterns and highlights the importance of spatially intermediate populations in maintaining large-scale connectivity (White et al. 2010, Crandall et al. 2012, Buonomo et al. 2017). Individual-level data at the same extent could allow direct estimates of gene flow at an unprecedented spatial scale and provide insight into the realized dispersal at a time frame of one or a few generations. Kinship calculations of white seabream suggest this species is capable of exceptional long-distance dispersal (about 2,000km) in only two or three generations. Previous estimates of dispersal using kinship, parentage or assignment tests were often limited by the spatial extent of their sampling [START_REF] Schunter | Kinship analyses identify fish dispersal events on a temperate coastline[END_REF], 2019[START_REF] Manel | Long-Distance Benefits of Marine Reserves: Myth or Reality?[END_REF], and the few large-scale studies found that dispersal distances for a single generation can reach up to 250 km [START_REF] Christie | Larval Connectivity in an Effective Network of Marine Protected Areas (SJ Goldstien[END_REF], Williamson et al. 2016[START_REF] Almany | Larval fish dispersal in a coral-reef seascape[END_REF], Bode et al. 2019[START_REF] Catalano | Quantifying dispersal variability among nearshore marine populations[END_REF]. The new long-distance estimate suggests that MPA benefits can propagate at much wider scale than previously thought.

Yet, such high levels of gene flow could diminish population structure and prevent local adaptation. Outlier SNPs reveal stronger population structure than could be detected with neutral SNPs only, improving our capacity to discern the drivers of genetic structure. The mullet and seabream have contrasting patterns of genetic structure but their respective structure is driven by a combination of neutral processes (such as gene flow shaped by larval dispersal) and adaptive processes (i.e. local adaptation to environmental conditions), albeit to different degrees. Previous genome scans on the pooled mullet dataset revealed candidate genes for adaptation to salinity (Dalongeville et al. 2018a), but our individual dataset uncovers finer differentiation and local adaptation to temperature and productivity that had not yet been reported for the species. A previous analysis of mitochondrial and microsatellite markers in white seabream found potential local adaptation of lagoon populations to temperature and salinity extremes (González-Wangüemert and Pérez-Ruzafa 2012). However for marine populations, no such local adaptation had been reported yet and only weak to no genetic differentiation between the Atlantic and Mediterranean Sea localities had been found [START_REF] Bargelloni | The Atlantic-Mediterranean transition: Discordant genetic patterns in two seabream species, Diplodus puntazzo (Cetti) and Diplodus sargus (L.)[END_REF][START_REF] González-Wangüemert | Connectivity patterns inferred from the genetic structure of white seabream (Diplodus sargus L.)[END_REF], 2011). Outlier SNPs of seabream instead reveal a strong differentiation between individuals in the Alboran Sea and the rest of the Mediterranean Sea, driven at the same time by an isolation due to a recognized barrier to dispersal (i.e. the Almeria-Oran front) [START_REF] Patarnello | Pillars of Hercules: Is the Atlantic-Mediterranean transition a phylogeographical break?[END_REF], Pascual et al. 2017) and maintained by differential local adaptation to contrasting temperature and salinity levels [START_REF] Carreras | East is East and West is West: Population genomics and hierarchical analyses reveal genetic structure and adaptation footprints in the keystone species Paracentrotus lividus (Echinoidea). -Divers[END_REF]).

Implications for conservation

Overall, the conclusions of this thesis accentuate how the environment and anthropogenic pressures play an important role in shaping marine biodiversity patterns at its different organizational levels. Both directly and indirectly, the results of this thesis help redefine conservation outcomes, by assessing the effect of existing marine reserves on species diversity, and objectives, by providing information necessary to develop a network of MPAs protecting all levels of biodiversity.

Direct implications. The reserves that were studied in Chapter 3 do not, on their own, host more species that their non-protected counterparts. However, the composition of these communities was markedly different from the communities found outside protected areas. We hypothesize that reserves can restore some ecosystem functions (i.e. trophic interactions) within their boundaries that might be lost -to some degree -outside due to fishing. Inversely, fishing pressure outside and anthropogenic pressure in general can favour certain species at the expense of others creating a turnover of communities independent of local species richness (Hillebrand et al. 2018[START_REF] Chase | Species richness change across spatial scales[END_REF]. Because they host such different communities, the combination of reserves and fishing grounds can then increase the diversity at the regional scale. These eDNA results are corroborated by the recent findings of [START_REF] Loiseau | Maximizing regional biodiversity requires a mosaic of protection levels[END_REF], who analysed fish data obtained with underwater visual census (UVC) across 25 strictly protected areas, 19 restricted areas and respective non-protected areas. They found that adjacent temperate and coral reefs with different protection levels host very dissimilar species compositions, and a high proportion of the species were unique to each protection level at the regional scale (e.g. 22% of fish were found only in strictly protected areas, and 13% only in non-protected areas). Those results can reconcile conservation optimists, who want to protect as much biodiversity as possible, and realists, who want to include fisheries in conservation planning. The entirety of the oceans and seas cannot be put under integral, no-take protection. Millions of people worldwide depend on fisheries for their livelihoods [START_REF] Teh | Contribution of marine fisheries to worldwide employment[END_REF]Sumaila 2013, Lam et al. 2020) as well as for their consumption of protein and other essential nutrients [START_REF] Hicks | Harnessing global fisheries to tackle micronutrient deficiencies[END_REF][START_REF] Costello | The future of food from the sea[END_REF]. Instead, heterogeneous fishing regulations such as inside and outside reserves may increase the overall diversity through a high turnover among local communities.

Currently, only 0.23% of the Mediterranean Sea is highly or fully protected [START_REF] Claudet | Underprotected Marine Protected Areas in a Global Biodiversity Hotspot[END_REF] 130 vulnerability of mullet that are targeted by both small-scale and industrial fisheries. Since we cannot locally halt climate change, ensuring that potentially maladapted populations are covered by MPAs would give them a better chance to adapt to rising temperatures and prevent population collapse as a result of the double threat of climate change and overfishing [START_REF] Hsieh | Fishing elevates variability in the abundance of exploited species[END_REF][START_REF] Bris | Climate vulnerability and resilience in the most valuable North American fishery[END_REF][START_REF] Morrongiello | Fishing constrains phenotypic responses of marine fish to climate variability[END_REF]).

Populations at the higher end of the temperature gradient, e.g. in the Levantine Sea, are potentially locally adapted to higher temperatures. The placement of MPAs in the Mediterranean Sea is highly unbalanced with most conservation efforts concentrated in the north-west whereas the south-eastern basin severely lags behind (Figure 2.3, Claudet et al. 2020). Establishing no-take reserves here could help preserve beneficial genotypes and maintain the population's adaptive potential under climate change (Xuereb et al. 2019). The high levels of connectivity, as pointed out by the spatial graph approach, accentuate the prevalence of long-distance dispersal over multiple generations through stepping-stone dispersal. More MPAs should then be connected within a network to ensure that advantageous mutations for adaptation to rising temperatures could be spread naturally through multigenerational dispersal to keep pace with climate change (Razgour et al. 2019, Xuereb et al. 2019).

The seabream, on the other hand, is largely panmictic throughout the Mediterranean basin and kinship pairs span countries' EEZ (exclusive economic zone) borders. This is reassuring as the protection of one large population could contribute to the persistence of demographic populations further away, while highlighting the need for international cooperation when managing a common resource [START_REF] Kark | Cross-boundary collaboration: key to the conservation puzzle[END_REF]. Overall, these results illustrate how genomic tools can be used to inform conservation, and that species-specific differentiation and local adaptation patterns require appropriate management strategies [START_REF] Nielsen | Distinct interspecific and intraspecific vulnerability of coastal species to global change[END_REF]. In a general framework, the representativity of spatially defined populations subject to different local adaptation could be included in MPA coverage targets to preserve a portfolio of potential adaptations and maximize the preservation of genetic diversity [START_REF] Webster | Who Should Pick the Winners of Climate Change?[END_REF][START_REF] Xuereb | Incorporating putatively neutral and adaptive genomic data into marine conservation planning[END_REF]).

Limits

Monitoring MPAs with eDNA

One of the main advantages and sought-after outcomes of protection from fishing is the increase in density and biomass of exploited species (Giakoumi et al. 2017, Sala and[START_REF] Sala | No-take marine reserves are the most effective protected areas in the ocean[END_REF]. However, the data obtained through eDNA metabarcoding in this thesis does not allow to evaluate the relative gain or loss in species abundance between marine reserves and their proximate outside. This is due to inference problems at two levels. First, the abundance of DNA in the environment does not correlate to organism abundance in the same way for different species [START_REF] Ushio | Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing[END_REF]. The release of DNA in the environment depends on the ecology and physiology of each species and even varies within species, while the DNA's persistence further depends on biotic and abiotic factors of the given environment [START_REF] Barnes | The ecology of environmental DNA and implications for conservation genetics[END_REF][START_REF] Hansen | The sceptical optimist: challenges and perspectives for the application of environmental DNA in marine fisheries[END_REF][START_REF] Collins | Persistence of environmental DNA in marine systems[END_REF], Harrison et al. 2019, Rourke et al. 2021). Despite these difficulties, several studies increasingly report significant correlations between eDNA concentration and biomass of the targeted species (e.g. [START_REF] Thomsen | Environmental DNA from seawater samples correlate with trawl catches of subarctic, deepwater fishes[END_REF][START_REF] Yamamoto | Environmental DNA as a "snapshot" of fish distribution: A case study of Japanese jack mackerel in Maizuru Bay, Sea of Japan[END_REF][START_REF] Tillotson | Concentrations of environmental DNA (eDNA) reflect spawning salmon abundance at fine spatial and temporal scales[END_REF][START_REF] Stoeckle | Trawl and eDNA assessment of marine fish diversity, seasonality, and relative abundance in coastal New Jersey, USA[END_REF]. Although most advances are based on singlespecies studies, sample-and species-specific calibration might further overcome this issue for metabarcoding [START_REF] Ushio | Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing[END_REF][START_REF] Kirtane | Quantification of Environmental DNA (eDNA) shedding and decay rates for three commercially harvested fish species and comparison between eDNA detection and trawl catches[END_REF]. Second, the abundance of metabarcoding sequence reads in its current implementation cannot trustfully reflect the abundance of the initial eDNA in the sample due to several experimental problems [START_REF] Taberlet | Environmental DNA for Biodiversity Research and Monitoring[END_REF]. For example, the PCR process increases DNA copies exponentially so that stochasticity and small biases can lead to large differences in final DNA concentration [START_REF] Kelly | Understanding PCR Processes to Draw Meaningful Conclusions from Environmental DNA Studies[END_REF]. Despite similar initial species biomasses, metabarcoding read abundances can then vary greatly due to the variable amplification efficiency of PCR primers between species [START_REF] Elbrecht | Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass-sequence relationships with an innovative metabarcoding protocol[END_REF]Leese 2015, Kelly et al. 2019). In future work, such biases can be overcome by adding non-local 'standard' DNA sequences with known concentrations to samples. These initial concentrations could allow the calibration of final DNA concentrations and thus allow to estimate the relative eDNA abundances from multi-species samples [START_REF] Ushio | Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing[END_REF]. The further development of tools allowing to reliably quantify species abundances with eDNA metabarcoding will represent an invaluable advancement towards the application of eDNAbased methods in routine MPA surveillance and assessment studies.

The lack of abundance information could also make it harder to separate true and false positives or negatives. In their MPA evaluation using eDNA metabarcoding, Gold et al. (2021a) find a higher species richness outside their MPA but they attribute this to non-local or rare eDNA signatures. This low-abundant eDNA is detected outside the MPA but masked inside by the higher overall eDNA abundance reducing the probability of amplifying and detecting rare taxa. This "biomass masking problem" is unlikely to influence our results as we conducted 12 independent PCR replicates per sample, which in part overcomes the non-amplification of certain species by PCR stochasticity, and sufficiently large sequencing depth (theoretical sequencing depth of 1 000 000 reads per sample) to allow the saturation of species richness by sequencing depth per site (not shown). However, the biomass masking presents an interesting problem for interpreting species detection from eDNA metabarcoding data which merits formal testing. I could not compare the read abundance of current samples because they are not representative of the initial eDNA abundances. Instead, future experiments could quantify eDNA concentrations before amplification to test whether initial total DNA quantities vary between protection levels, which could influence species richness estimates. Further developments to quantify DNA abundance from metabarcoding data, such as [START_REF] Ushio | Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing[END_REF], would allow to directly assess the influence of overall and species-specific DNA abundance on species detection and richness estimates.

In the same vein, the eDNA data of this thesis does not inform on the developmental stage of the detected individuals. eDNA metabarcoding allows the identification of species at different life stages and size classes if sufficient DNA is released in the environment. Although this overcomes an identification difficulty and bias, it also means that the eDNA signal in its current form remains silent on important information that can help understand the ecological processes influencing the presence of a species. The size and trophic level of species play an important role in explaining the species richness patterns observed in marine reserves (Chapter 3). However fishes undergo dietary changes and thus change trophic guilds throughout ontogeny [START_REF] Sánchez-Hernández | Causes and consequences of ontogenetic dietary shifts: a global synthesis using fish models[END_REF]. They also do not inhabit the same habitats at each stage (Daly et al. 2021). As a result, marine reserves may play different roles for species at their different stages [START_REF] Grüss | Protecting juveniles, spawners or both: A practical statistical modelling approach for the design of marine protected areas[END_REF]. I could not test whether reserves could function as nurseries for juveniles, or inversely if juveniles undergo stronger predation pressure inside reserves and escape this pressure outside where only larger individuals are targeted by fishing. However, fish release different amounts of DNA at different life stages mostly depending on their size [START_REF] Maruyama | The release rate of environmental DNA from juvenile and adult fish[END_REF]) and activity such as spawning (Levi et al. 2019[START_REF] Takeuchi | Release of eDNA by different life history stages and during spawning activities of laboratory-reared Japanese eels for interpretation of oceanic survey data[END_REF].

Although distinguishing life stages from eDNA metabarcoding data is still difficult, species-specific approaches are being developed to inform on the timing of life history dynamics such as spawning aggregations (Bylemans et al. 2017[START_REF] Hansen | The sceptical optimist: challenges and perspectives for the application of environmental DNA in marine fisheries[END_REF].

The choice of metabarcoding primers allows to target specific taxonomic groups but also puts taxonomic constraints. Different assessments of primer and thus marker performance revealed that no single marker perfectly detects all targeted species [START_REF] Zhang | Comprendre l'effet des AMP sur les communautés de poissons nécessite la détection d'un large éventail d'espèces allant des plus grands prédateurs aux plus petites proies. Le metabarcoding d'ADNe permet de détecter simultanément les poissons communs ainsi que les poissons cachés ou en passage transitoires, qui sont difficiles à observer et donc souvent négligés par les méthodes classiques[END_REF][START_REF] Polanco | Comparing the performance of 12S mitochondrial primers for fish environmental DNA across ecosystems[END_REF][START_REF] Casey | DNA metabarcoding marker choice skews perception of marine eukaryotic biodiversity[END_REF]. This can be a problem for routine monitoring studies where important species are systematically missed. In our case, completing the reference database for the regional species pool reveals that the 12S teleo marker cannot distinguish certain closely related and recently diverged species from each other (mainly Symphodus spp & Syngnathus spp). Beyond having a reference database as complete as possible, it is then important to know the limitations of the chosen markers before drawing conclusions from a species' absence (Gold et al. 2021b). In a recent study, [START_REF] Polanco | Comparing the performance of 12S mitochondrial primers for fish environmental DNA across ecosystems[END_REF] compared the performance of two primer pairs commonly used in fish metabarcoding studies: the teleo primer pair [START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF], used in this thesis, and the MiFish-U primer pair [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]. They reanalysed a subset of four eDNA samples from this thesis (two from Carry-le-Rouet and two from Porquerolles) and found that while both primer pairs recovered the same number of taxa, the species detected by teleo and MiFish-U did not completely overlap. The MiFish-U primers notably identified Symphodus cinereus, S. mediterraneaus and S. melops which were missed by the teleo primers, but in turn missed e.g. Dentex dentex (Common dentex) and Mola mola (Ocean sunfish) [START_REF] Polanco | Comparing the performance of 12S mitochondrial primers for fish environmental DNA across ecosystems[END_REF]). These two latter species are targeted by fisheries (D. dentex) or caught as bycatch (M. mola) in the Mediterranean Sea and are classified as vulnerable on the IUCN Red List (IUCN 2021). Ideally, different primer pairs should then be combined to maximize the detection potential and taxonomic coverage of eDNA metabarcoding studies.

From adaptive genetic patterns to local adaptation

The correlation between the genetic structure and environmental variables does not in itself demonstrate the prevalence of local adaptation. The identification of non-synonymous variants on genes with a recognized function involved in environmental stress response strengthen the probability that the detected variants are involved in an adaptive response to environmental selective pressures. However, without a demonstrated fitness increase in this stress response, it is difficult to unambiguously attribute the results to local adaptation [START_REF] Manel | Genomic resources and their influence on the detection of the signal of positive selection in genome scans[END_REF]. Sequencing of the transcriptome -which is the momentary sum of all RNA transcripts -of individuals from differently adapted subpopulations of mullet and seabream would allow to look for variations in gene expression potentially related to environmental variables such as temperature (Meier et al. 2014[START_REF] Alvarez | Ten years of transcriptomics in wild populations: What have we learned about their ecology and evolution?[END_REF]. For example, comparing the transcriptome of individuals from the Alboran Sea in the west to individuals from the Levantine Sea in the east could give us more insights into the potential adaptive responses to temperature. Even further, individuals from each of the locally adapted subpopulations could be placed under common garden experiments to test their thermal tolerance and provide the phenotypic information necessary to further understand their vulnerability to climate change [START_REF] Capblancq | Genomic Prediction of (Mal)Adaptation across Current and Future Climatic Landscapes[END_REF].

In addition, the relatively low number of SNPs and even lower number of candidate SNPs mean that many ecologically relevant loci and genes potentially involved in adaptive responses are probably overlooked (Lowry et al. 2017). A combination of outlier selection or environmental association methods could increase the number of candidate SNPs detected (e.g. Dalongeville et al. 2018a[START_REF] Torrado | Individual-based population genomics reveal different drivers of adaptation in sympatric fish[END_REF]. However, the individual-focus sampling strategy precludes us from applying population differentiation methods (e.g. Bayescan, Foll and Gaggiotti 2008). Because our environmental variables are somewhat spatially structured and we aimed to remove the confounding effects of neutral processes resulting from spatial distance and larval dispersal, we could not conduct environmental association methods either for outlier detection. Instead, our three-step approach consisting of 1) detecting outliers related to individual genetic structure, 2) retrieving the functional annotation of selected SNPs and 3) assessing their correlation to previously identified environmental variables of interest provides the most conservative approach to look for potential signals of local adaptation among the available SNPs.

Future research directions

New references for long-term biodiversity monitoring

Documenting changes in biodiversity is crucial to understand and mitigate the impacts humans cause on ecosystems. Such ecological changes, especially in the marine realm, may occur "out of sight" because the baselines against which they are compared are progressively modified with each new generation, a problem known as the shifting baseline syndrome [START_REF] Pauly | Anecdotes and the shifitng baseline syndrome of fisheries[END_REF][START_REF] Dayton | Sliding baselines, ghosts, and reduced expectations in kelp forest communities[END_REF][START_REF] Soga | Shifting baseline syndrome: causes, consequences, and implications[END_REF]. Establishing baseline ecological assessments from historical data [START_REF] Jackson | Historical overfishing and the recent collapse of coastal ecosystems[END_REF] or conducting long-term monitoring programs, such as the Reef Life Survey [START_REF] Edgar | Establishing the ecological basis for conservation of shallow marine life using Reef Life Survey[END_REF], allow to keep track of marine biodiversity trends [START_REF] Stuart-Smith | Assessing national biodiversity trends for rocky and coral reefs through the integration of citizen science and scientific monitoring programs[END_REF]. Such programs require standardized sampling methods that can be difficult to implement at larger scales and repeated with identical protocols. The development

Improving our understanding of global change effects on biodiversity

The biodiversity patterns uncovered in this thesis raise some new questions and hypotheses that merit further investigation. Changes in the trophic interactions inside and outside reserves are likely driving the paradoxical changes in species richness and community structure observed in the Mediterranean reserves (Chapter 3). Building on the data obtained in this thesis, the eDNA metabarcoding results could be leveraged to construct co-occurrence networks that, combined with known consumer-resource interactions, represent the potential trophic webs at different sites and protection levels [START_REF] Djurhuus | Environmental DNA reveals seasonal shifts and potential interactions in a marine community[END_REF][START_REF] Dibattista | Environmental DNA can act as a biodiversity barometer of anthropogenic pressures in coastal ecosystems[END_REF][START_REF] D'alessandro | Sifting environmental DNA metabarcoding data sets for rapid reconstruction of marine food webs[END_REF]. These trophic webs could help elucidate the respective roles that cryptobenthic and larger fish play in Mediterranean communities. Changes in web structure could then reveal how fishing and protection thereof influence predator-prey dynamics (D'Alessandro and Mariani 2021).

For each analysis, only a subset of the total DNA is initially sequenced. eDNA extracts can be preserved and sequenced again several times later on (such as was done in [START_REF] Polanco | Comparing the performance of 12S mitochondrial primers for fish environmental DNA across ecosystems[END_REF], effectively functioning as time-capsules or "biobanks" holding available biodiversity archives (Jarman et al. 2018). To broaden our view of local biodiversity, the eDNA sampled in this thesis could be sequenced again with primer pairs targeting taxonomic groups complementary to fishes (e.g. crustaceans) [START_REF] Djurhuus | Environmental DNA reveals seasonal shifts and potential interactions in a marine community[END_REF] or universal primers targeting eukaryote taxa [START_REF] Bakker | Biodiversity assessment of tropical shelf eukaryotic communities via pelagic eDNA metabarcoding[END_REF][START_REF] Jeunen | Environmental DNA (eDNA) metabarcoding reveals strong discrimination among diverse marine habitats connected by water movement[END_REF]. Including these additional groups in diversity analyses and food webs would yield an even more complete view of Mediterranean Sea ecosystems across all trophic levels.

Different anthropogenic pressures at different intensities will not all have the same effect on local community composition and richness [START_REF] Bakker | Environmental DNA reveals tropical shark diversity in contrasting levels of anthropogenic impact[END_REF]. To assess the overall effect of protection on fish communities, I focused on comparing sites inside and outside marine reserves hypothesizing that the areas outside are subject to similar levels of fishing pressure.

However, the species richness levels are more variable at 5 and 10 km from the reserves (standard deviation = 10 -11) than inside (sd = 6), indicating that reserves may have a stabilizing effect on species richness compared to variable pressures outside. Although all coastal areas of the Mediterranean Sea are generally under high anthropogenic stress [START_REF] Micheli | Cumulative human impacts on Mediterranean and Black Sea marine ecosystems: Assessing current pressures and opportunities[END_REF], the nature and intensity of anthropogenic pressures outside, such as fishing but also e.g. coastal development and tourism, can vary locally [START_REF] Holon | Fine-scale cartography of human impacts along French Mediterranean coasts: A relevant map for the management of marine ecosystems[END_REF]. The Medtrix platform (https://plateforme.medtrix.fr) provides high-resolution maps of the French Mediterranean coast and different human impacts including, a.o., fishing, coastal population, 138 SNPs revealed significant isolation-by-distance at the smallest spatial scales, but none considering the total study area [START_REF] Benestan | Restricted dispersal in a sea of gene flow[END_REF]. Outlier SNPs could reveal further structuring, as was the case at the Mediterranean-wide scale, and could potentially reveal differently adapted populations. The inclusion of samples from multiple marine reserves can then help determine whether existing reserves, such as the Cerbère-Banyuls reserve (Figure 6.3), already offer protection to potentially cold-adapted populations or whether their coverage is insufficient and warrants additional protection.

Casting a wider net for species traits

At the different levels of biodiversity and scales of inference examined in this thesis, the influences of -and responses to pressures or environmental variables vary between fish species. At the regional scale, the species' ecological (e.g. size, position in the water column) and functional (i.e. trophic level) traits in part explain their response to protection (Chapter 3, but also e.g. [START_REF] Claudet | Marine reserves: Fish life history and ecological traits matter[END_REF], [START_REF] Coleman | Functional traits reveal early responses in marine reserves following protection from fishing[END_REF]). At the Mediterranean-wide scale, the species' dispersal potential likely explains their different levels of gene flow and prevalence of local adaptation (Chapter 5). In seascape genomics, comparative approaches are highly valuable to understand whether species with different life history and ecological traits are differently influenced by environmental gradients or whether influences are concordant across species (e.g. [START_REF] Stanley | A climate-associated multispecies cryptic cline in the northwest Atlantic[END_REF][START_REF] Nielsen | Multi-model seascape genomics identifies distinct environmental drivers of selection among sympatric marine species[END_REF], 2021[START_REF] Torrado | Individual-based population genomics reveal different drivers of adaptation in sympatric fish[END_REF]. Our two species only represent a small fraction of the different traits among marine species. Sampling and genotyping more species with more diverse life history and ecological traits at the same scale and extent would allow us to better understand which traits and to what extent they influence connectivity and local adaptation patterns [START_REF] Dalongeville | Ecological traits shape genetic diversity patterns across the Mediterranean Sea: A quantitative review on fishes[END_REF][START_REF] Barry | Life tables shape genetic diversity in marine fishes[END_REF][START_REF] Torrado | Impact of individual early life traits in larval dispersal: A multispecies approach using backtracking models[END_REF]. At a regional scale, the RESERVEBENEFIT data could again be used to compare adaptive patterns between the four marine species sampled (striped red mullet, white seabream, comber and European spiny lobster) which represent a variety of pelagic larval durations (PLD) and life-history traits [START_REF] Benestan | Restricted dispersal in a sea of gene flow[END_REF]. Our increased understanding of the interplay between traits and environment on genomic patterns could in turn be used to propose conservation measures tailored to different trait groups considering differences in connectivity, population structure and adaptive potential.

Appendices Supplementary material Manuscript A eDNA sample collection

Seawater samples were collected using a sampling protocol optimised for the monitoring of coastal marine communities using eDNA, based on four replicates per site.

Sampling was conducted from May to June 2018 and in July 2019. For each replicate. 30 L of seawater was collected by continuously pumping water for 30 minutes along a 2 km transect.

Transects were conducted by boat while navigating at 5 knots, and water was pumped from 1 m below the surface using sterile tubing and a peristaltic pump. For DNA extraction, each filtration capsule, containing the CL1 buffer, was agitated for 15 min on an S50 shaker (cat Ingenieurbüro™) at 800 rpm and then the buffer was emptied into two 50-mL tube before being centrifuged for 15 min at 15,000×g. The supernatant was removed with a sterile pipette, leaving 15 mL of liquid at the bottom of each tube. Subsequently, 33 mL of ethanol and 1.5 mL of 3M sodium acetate were added to each 50-mL tube and stored for at least one night at -20°C. The tubes were centrifuged at 15,000 × g for 15 min at 6°C, and the supernatants were discarded. After this step, 720 µL of ATL buffer from the DNeasy Blood & Tissue Extraction Kit (Qiagen) was added to each tube. Each tube was then vortexed, and the supernatant was transferred to a 2-mL tube containing 20 µL of Proteinase K. The tubes were finally incubated at 56°C for two hours. Subsequently, DNA extraction was performed using NucleoSpin® Soil (MACHEREY-NAGEL GmbH & Co., Düren Germany) starting from step 6 and following the manufacturer's instructions, and two DNA extraction were carried per filtration capsule. The elution was performed by adding 100 µL of SE buffer twice. The two DNA samples were pooled before the amplification step.

After the DNA extraction the samples were tested for inhibition by qPCR [START_REF] Biggs | Using eDNA to develop a national citizen science-based monitoring programme for the great crested newt (Triturus cristatus)[END_REF]. If the sample was considered inhibited it was diluted 5-fold before the amplification.

DNA amplifications were performed in a final volume of 25 µL, using 3 µL of DNA extract as the template. The amplification mixture contained 1 U of AmpliTaq Gold DNA Polymerase (Applied Biosystems, Foster City, CA), 10 mM Tris-HCl, 50 mM KCl, 2.5 mM MgCl2, 0.2 mM each dNTP, 0.2 µM "teleo" primers [START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF]), 4 µM human blocking primer for the "teleo" primers [START_REF] Valentini | Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding[END_REF]) and 0.2 µg/µL bovine serum albumin (BSA, Roche Diagnostic, Basel, Switzerland). The "teleo" primers were 5'-labeled with an eight-nucleotide tag unique to each PCR replicate (with at least three differences between any pair of tags), allowing the assignment of each sequence to the corresponding sample during sequence analysis. The tags for the forward and reverse primers were identical for each PCR replicate.

The PCR mixture was denatured at 95°C for 10 min, followed by 50 cycles of 30 s at 95°C, 30 s at 55°C and 1 min at 72 °C and a final elongation step at 72°C for 7 min in a room dedicated to amplified DNA with negative air pressure and physical separation from the DNA extraction rooms (with positive air pressure). Twelve replicate PCRs were run per sample.

After amplification, the samples were titrated using capillary electrophoresis (QIAxcel; Qiagen GmbH) and purified using the MinElute PCR purification kit (Qiagen GmbH). Before sequencing, purified DNA was titrated again using capillary electrophoresis. The purified PCR products were pooled in equal volumes to achieve a theoretical sequencing depth of 1 000,000 reads per sample.

Reference database

Tissue samples were preserved in 96% ethanol and DNA was extracted using Qiagen's DNeasy Blood & Tissue kit. following the manufacturer's instructions with few modifications. PCR parameters were adapted from [START_REF] Thomsen | Environmental DNA from seawater samples correlate with trawl catches of subarctic, deepwater fishes[END_REF] [START_REF] Thomsen | Environmental DNA from seawater samples correlate with trawl catches of subarctic, deepwater fishes[END_REF]. Reactions were performed with 1 -µl template DNA extract. 5-µl REDExtract-N-Amp™ PCR ReadyMix™ (Sigma Ref R4775). 2-µl ddH2O and 1-µl of each primer (at 2pM).

Thermocycling parameters were: 94°C for 30 seconds 45 cycles of 94°C for 30 s. 45°C for 30 s. 72°C for 1 minute, and a final elongation step of 72°C for 5 minutes. Purification and sequencing of PCR products were carried out by Eurofins Genomics (Ebersberg. Germany). The V05F_898 and teleo_R primer pair worked well for most of the species but produced too short or unsatisfactory sequences for 16 of them. We designed a new fish-specific primer pair targeting a 340 bp product at the end of the mitochondrial 12S rRNA gene region including the teleo eDNA metabarcode: the forward primer MF12S_F (5'-CTAGAGGAGCCTGTYVT) and the reverse primer MF12S_R (5'-GRHAAGTCGTAACATGGTA). PCR parameters were the same as above.

Electropherograms were checked using

A total of 115 species were added to our local reference database. 80 of these are completely new, whereas 35 species were already present in public databases but sampled and sequenced to insure sequences match the local population. The final reference database used in this study contained sequences of 320 species corresponding to 41% of all Mediterranean fish species(Froese and Pauly 2019), but 75% of the coastal North-Western Mediterranean species [START_REF] Charbonnel | Inventaire des espèces de poissons rencontrées sur la Côte Bleue[END_REF]) known to occur on our sampled habitat (table S6).

In order to assess the potential bias due to the 25% missing species in the reference database we investigated the number and proportion of species sequenced versus nonsequenced for the four traits considered in our study; vertical distribution, trophic level, vulnerability to fishing and common length, as well as for their regional rarity and commercial importance (Figure S6). The regional rarity was assessed based on the number of occurrences of each species within our study area referenced in GBIF (https://www.gbif.org). Species were categorized using on the quartile definition of rarity of [START_REF] Gaston | What is rarity? -In: Rarity[END_REF] [START_REF] Gaston | What is rarity? -In: Rarity[END_REF]): species in the lowest quartile of occurrences at the regional scale (i.e. the 25% of species with the least occurrences in our region) were categorized as rare. The remaining 75% of species were categorized as common. The commercial importance of each species was extracted from FishBase [START_REF] Froese | [END_REF].

We tested whether there was any significant bias by conducting Fisher's exact tests for each trait. Fisher's exact tests showed that there was no coverage bias between categories of each trait (p-value = 0.166, 0.318, 0.117, 0.0718, 0.385 and 0.160 for vertical distribution, trophic level, vulnerability, length, regional rarity and commercial importance respectively).

This absence of bias is also clear when looking at the proportion of species sequenced by category in Figure S6.

Removal of erroneous identifications and foreign species

The resulting dataset was manually inspected and corrected. One erroneous NCBI identification, Istiophorus albicans, was corrected as an Engraulis species. One taxa identified as Clupeocephola is a sequence intermediate to Istiophorus albicans and Engraulis, and was corrected as an Engraulis species. One freshwater species, Gambusia holbrooki, detected in a single sample was considered as a contamination and removed from the dataset. One brackish species, Salmo salar, is a commercially consumable fish and has previously been reported as a possible contaminant in marine eDNA studies [START_REF] Juhel | Accumulation curves of environmental DNA sequences predict coastal fish diversity in the coral triangle[END_REF]) and was removed from the dataset. Five foreign species, Cololabis saira, Encrasicholina punctifer, Schedophilus velaini, Dasyatis thetidis and Taeniurops meyeni, were detected in multiple samples. These species do not occur in the Mediterranean but all have local relatives that could be genetically identical for the metabarcode used here but are not referenced in the local or public databases. As we cannot know which of the potential local relatives was truly present on our sites, all foreign detections were omitted from the final dataset.

Habitat and environmental data

We extracted the substrate data around each realized transect using the seabed habitat data derived from the 2016 EUSeaMap broad-scale predictive model, produced by EMODnet Seabed Habitats (http://www.emodnet-seabedhabitats.eu). Bathymetric data was downloaded from the EMODnet-bathymetry portal (http://portal.emodnet-bathymetry.eu/) and chlorophyll levels were downloaded from bio-ORACLE (http://www.bio-oracle.org).

In total, 11 environmental variables (Table S8) were calculated to be included in our models and account for the environmental variability between samples, sites and regions.

Because our main focus is to test the effect of protection on species richness (GLM) and community dissimilarity (dbRDA), having too many covariables can lead to overfit models. In addition, many of the environmental variables covary amongst each other (figure S3). We thus conducted a Principal Component Analysis on the environmental variables (figure S3) which reduces the dimensionality of the dataset and yields a set of orthogonal axes. The first four axes explain most of the variance between our samples (74. Geographic distances were converted to distance-based Moran's eigenvector maps (dbMEM) (Dray et al. 2006, Legendre and[START_REF] Legendre | Numerical Ecology[END_REF]. dbMEMs are orthogonal vectors and were developed for detecting and quantifying spatial patterns from broad to fine scales.

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• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 0
• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 0
First, the geographic distance matrix is truncated by using the length of the longest edge of the minimum spanning tree as threshold. Second, a principal coordinate analysis (PCoA) is computed on the truncated distances producing new, uncorrelated eigenvectors and eigenvalues. The resulting principal coordinates are then site-based variables that together represent the multiscale distance relationships among sites. The vector's corresponding eigenvalue represent how much of the spatial information is captured by the new eigenvector.

In the case of dbMEM, the eigenvalues represent decreasing levels of spatial autocorrelation related to the Moran's index of autocorrelation [START_REF] Dray | Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM)[END_REF]Lapierre et al. 2018).

This approach produced too many dbMEM (~20) to be included in subsequent analyses.

Given the large spatial dependence expected between our environmental and spatial variables, we focused on large-scale MEM that account for the broad-scale processes such as environmental variation [START_REF] Jombart | Finding essential scales of spatial variation in ecological data: A multivariate approach[END_REF]. We selected the broad-scale dbMEM that contained most of the spatial information. The dbMEM eigenvalues, as implemented in adespatial package, have a norm equal to 1 [START_REF] Dray | adespatial: Multivariate Multiscale Spatial Analysis[END_REF]. Their cumulative value then represents the cumulative proportion of variance explained by each vector. We selected the first 7 dbMEM for seabream (Fig. S1a) and the first 5 dbMEM for mullet (Fig. S1b) that each cumulatively explain 74% of the variance.

Asymmetric eigenvector maps

The biophysical model estimated the probability of larval dispersal between 100 sites evenly distributed along the Mediterranean coast and containing our sampled sites (see [START_REF] Boulanger | Spatial graphs highlight how multi-generational dispersal shapes landscape genetic patterns[END_REF] for a detailed description of the model and 100 sites). To build asymmetric eigenvector maps (AEMs), larval dispersal probabilities between all 100 sites were translated into a site-by-edge binary matrix. The upper half of the larval dispersal matrix was kept to represent the potential dispersal in one direction. An edge (link between two sites) with a dispersal probability > 0 was considered connected and coded as 1, whereas the absence of a connection was coded as 0. A weight was attributed to each edge based on the dispersal probability derived from the biophysical model.

Résumé substantiel en français Introduction générale

La diversité biologique, ou biodiversité, décrit la variabilité de la vie à différents niveaux, depuis les écosystèmes et les communautés écologiques jusqu'aux espèces, aux populations et aux gènes (United Nations, 1992). Elle joue un rôle essentiel dans le fonctionnement et la stabilité des écosystèmes [START_REF] Hooper | Effects of biodiversity on ecosystem functioning: a consensus of current knowledge[END_REF][START_REF] Cadotte | Beyond species: Functional diversity and the maintenance of ecological processes and services[END_REF][START_REF] Stange | The importance of genomic variation for biodiversity, ecosystems and people[END_REF]. Les écosystèmes marins présentent une grande diversité d'espèces, de populations et de gènes, ce qui se traduit par une variété de fonctions écosystémiques. Cette biodiversité marine est aujourd'hui confrontée à des menaces anthropiques croissantes et, par conséquent, diminue plus rapidement que jamais [START_REF] Halpern | Recent pace of change in human impact on the world's ocean[END_REF][START_REF] Bowler | Mapping human pressures on biodiversity across the planet uncovers anthropogenic threat complexes (R Fish[END_REF][START_REF] O'hara | At-risk marine biodiversity faces extensive, expanding, and intensifying human impacts[END_REF]. La surexploitation des ressources et les changements climatiques entraînent la perte des populations locales, et à leur tour une perte de diversité génétique [START_REF] Cheung | Projecting global marine biodiversity impacts under climate change scenarios[END_REF][START_REF] Mccauley | Marine defaunation: Animal loss in the global ocean[END_REF] avec pour conséquences une modification structurelle des communautés [START_REF] Worm | Marine Biodiversity and Climate Change[END_REF].

Des aires marines protégées (ci-après AMP) ont été mises en place afin de protéger les différents niveaux de biodiversité, ainsi que les mécanismes qui soutiennent la diversité en créant des réseaux d'AMP connectés [START_REF] Reimer | Benefits and gaps in area-based management tools for the ocean Sustainable Development Goal[END_REF]. Grâce à la suppression ou l'atténuation des menaces directes telles que la pêche, les réserves permettent aux écosystèmes locaux de rétablir leur fonctionnement et d'augmenter l'abondance, la biomasse et la densité des espèces ciblées par la pêche [START_REF] Caselle | Recovery trajectories of kelp forest animals are rapid yet spatially variable across a network of temperate marine protected areas[END_REF][START_REF] Giakoumi | Ecological effects of full and partial protection in the crowded Mediterranean Sea: A regional meta-analysis[END_REF][START_REF] Sala | No-take marine reserves are the most effective protected areas in the ocean[END_REF][START_REF] Mcclanahan | Marine reserve more sustainable than gear restriction in maintaining long-term coral reef fisheries yields[END_REF]. Cependant, on ignore encore l'effet des réserves sur la richesse et la composition de l'ensemble des communautés, c'est à dire au-delà des espèces ciblées. Les AMP ont le potentiel de fournir des bénéfices au-delà de leurs limites par la dispersion des larves produites par les adultes dans les AMP, assurant ainsi aux populations en dehors des réserves non autosuffisantes ou surexploitées des bénéfices via un ensemencement et recrutement larvaires [START_REF] Harrison | A connectivity portfolio effect stabilizes marine reserve performance[END_REF][START_REF] Dedrick | Persistence of a reef fish metapopulation via network connectivity: theory and data[END_REF]. Assurer la connectivité entre réserves permet également de préserver les processus qui maintiennent la biodiversité (c'est-à-dire les flux de gènes). Les populations qui sont adaptées localement à des conditions extrêmes, telles que des températures élevées (p.ex. [START_REF] Selmoni | Seascape genomics reveals candidate molecular targets of heat stress adaptation in three coral species[END_REF], pourraient alors servir de sources de variations génétiques adaptatives pour les populations qui vont être confrontées aux mêmes conditions suite aux changements climatiques (Xuereb et al. 2019).

La richesse spécifique et la composition des communautés de poissons sont probablement sous-estimées dans les études précédentes car les méthodes conventionnelles négligent généralement les espèces cryptobenthiques, les espèces en passage transitoires et les espèces non ciblées par la pêche [START_REF] Boussarie | Environmental DNA illuminates the dark diversity of sharks[END_REF][START_REF] Brandl | The hidden half: ecology and evolution of cryptobenthic fishes on coral reefs[END_REF][START_REF] Goetze | Increased connectivity and depth improve the effectiveness of marine reserves[END_REF]. Ces marqueurs peuvent fournir la résolution nécessaire pour étudier les processus neutres tels que les flux gènes [START_REF] Luikart | Population Genomics: Advancing Understanding of Nature[END_REF]. Elles permettent également de détecter de potentiels signaux d'adaptation locale en scannant le génome à la recherche de régions hautement différenciées et potentiellement sous sélection [START_REF] Lewontin | Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms[END_REF]Krakauer 1973, Hohenlohe et al. 2010). La différence des niveaux trophiques pourrait expliquer la diminution paradoxale de diversité. La prédation est l'un des processus clés influençant la richesse et la composition des assemblages écologiques [START_REF] Hixon | Fish predation and local prey diversity[END_REF][START_REF] Almany | Predators target rare prey in coral reef fish assemblages[END_REF][START_REF] Johnston | Predator identity influences metacommunity assembly (P Amarasekare[END_REF]. Étant donné que la pression de pêche cible souvent les grands poissons prédateurs (Myers et Worm 2003[START_REF] Cinner | Gravity of human impacts mediates coral reef conservation gains[END_REF], les réserves marines augmentent les populations de prédateurs et peuvent donc rétablir les cascades trophiques à l'intérieur [START_REF] Bates | Climate resilience in marine protected areas and the 'Protection Paradox[END_REF]. Ceci affecte la biodiversité des niveaux trophiques plus bas et peut entraîner la disparition des proies à l'intérieur des AMP [START_REF] Takashina | Paradox of marine protected areas: Suppression of fishing may cause species loss[END_REF]. Les poissons cryptobenthiques ne sont pas ciblés par la pêche mais sont prédatés par quasiment tous les autres poissons (Brandl et al. 2019). Ils pourraient ainsi trouver refuge des prédateurs en dehors des réserves et former des assemblages diversifiés coexistant dans les zones dominées par l'homme.

La dispersion et

Le fort renouvellement des assemblages de poissons sous différents niveaux de protection suggère que différents processus écologiques et services écosystémiques fonctionnent à l'intérieur et à l'extérieur des réserves [START_REF] Dornelas | Assemblage time series reveal biodiversity change but not systematic loss[END_REF]. Nos résultats mettent en évidence la manière dont la conservation, tout comme la gestion des pêches évolutif des espèces (Hanson et al. 2020a[START_REF] Xuereb | Incorporating putatively neutral and adaptive genomic data into marine conservation planning[END_REF].

régionale en abritant différentes communautés. Ces résultats peuvent alors réconcilier les optimistes de la conservation, qui veulent protéger autant que possible, et les réalistes qui veulent inclure la pêche dans la planification de la conservation.

La structure populationnelle observée, liée à la salinité et à la chlorophylle ou la température, confirme les résultats d'études précédentes qui ont trouvé une adaptation locale chez les espèces marines malgré des niveaux de flux de gènes élevés (p.ex. [START_REF] Benestan | Seascape genomics provides evidence for thermal adaptation and current-mediated population structure in American lobster (Homarus americanus)[END_REF], Dalongeville et al. 2018a[START_REF] Stanley | A climate-associated multispecies cryptic cline in the northwest Atlantic[END_REF][START_REF] Nielsen | Multi-model seascape genomics identifies distinct environmental drivers of selection among sympatric marine species[END_REF][START_REF] Torrado | Individual-based population genomics reveal different drivers of adaptation in sympatric fish[END_REF]. Ici, la structure génétique du rouget a révélé de nouvelles niches environnementales qui devraient être couvertes par les aires protégées pour assurer la représentativité « évolutive » des AMP (Hanson et al. 2020b). Le sar, en revanche, a démontré une dispersion exceptionnelle sur de longues distances. Ceci confirme le potentiel des AMP à fournir des bénéfices à longue distance pour certaines espèces [START_REF] Manel | Long-Distance Benefits of Marine Reserves: Myth or Reality?[END_REF]. L'approche comparative a enfin montré comment l'environnement façonne la structure et la diversité génétique de façon différente selon l'espèce.

Ceci souligne l'importance d'adapter les stratégies de conservation en conséquence [START_REF] Nielsen | Distinct interspecific and intraspecific vulnerability of coastal species to global change[END_REF].

Les données collectées au cours de cette thèse et les résultats obtenus ouvrent la porte à

  Mots clés : biodiversité marine, aires marines protégées, connectivité, ADNe, génomique du paysage marin, mer Méditerranée

Figure

  Figure 2.11. Representativeness of the genetic reference database for the regional species pool. I assessed the coverage by trophic level(a,b), common length (c,d), vulnerability to fishing (e,f), vertical distribution (g,h), rarity (i,j; Occurrence category) and commercial importance(k,l). I compared the number of species (left) as well as the proportion of species (right) between sequenced (dark grey) versus notsequenced (light grey) for our targeted 12S rRNA fragment (teleo). Fisher's exact tests showed that there was no bias between categories of each trait for the 25% missing species(p-value = 0.318, 0.0718, 0.117, 0.166, 0.385 and 0.160 respectively).

Figure 1 .

 1 Figure 1. Map of the three sampling sites within each of the six studied regions containing a no-take marine reserve (a), and zoom on the transects conducted in each site near Carry-le-Rouet: inside the reserve, 5 km outside, and 10 km outside (b). The dashed line in (b) represents the boundary of the no-take reserve. (Online version in colour.)

Figure 2 .Figure 3 .Figure 4 .

 234 Figure 2. (a) Overall species richness by level of protection. (b) Predicted conditional species richness by level of protection after controlling for environmental variables. ** indicates significant difference at p < 0.01. (c-e) Partitioning of species dissimilarity (Jaccard distance; left in each plot) in its turnover (middle) and nestedness (right) components with comparisons between the levels of protection: (c) reserve versus 5 km outside, (d ) reserve versus 10 km outside, and (e) 5 km outside versus 10 km outside. (Online version in colour.)

Figure 5 .

 5 Figure 5. Results of the generalized linear models testing the effect of protection on species richness while accounting for environmental variables. The plots represent the conditional predicted species richness (+/-confidence intervals) under the different protection levels for each species category: (a) cryptobenthic, (b) pelagic, (c) rare, and (d ) vulnerable. Asterisks indicate significant changes in species richness at 5 km or 10 km outside in reference to the reserve (marginal effects test, p < 0.05 *, less than 0.01 **). (Online version in colour.) royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20210112

  connected to a given node. Quantifies the local importance of a node.

Figure 2 .

 2 Figure 2. Spatial graph of genetic connectivity. (a) Map of the Mediterranean Sea with the location of the 47 sampling sites, the Siculo-Tunisian Strait (STS), Mediterranean regions and ecoregions (ALB, Alboran Sea; ADR, Adriatic Sea; ION, Ionian Sea; AEG, Aegean Sea; LEV, Levantine Sea). (b) Spatial graph representing the genetic connectivity network of Mullus surmuletus in the Mediterranean Sea. Edges between nodes (populations) are weighted by their pairwise genetic distance (D c ), represented by a colour gradient (dark red for the smallest distances and thus the highest genetic similarity). e size of the nodes is proportional to their betweenness centrality.

Figure 3 .

 3 Figure 3. Temporal perspective of marine connectivity: spatial graphs representing the amount of generations needed to connect our 47 populations through stepping-stone larval dispersal. (a) Graph with all pairwise edges, the colours represent the number of edges in the shortest path (= number of generations) connecting each node pair. e darkest green colour (1 edge) represents the direct larval dispersal over one generation. e Mediterranean Sea is fully connected through larval dispersal when allowing up to 25 edges (or 24 stepping stones) in the shortest paths (a). (b-e) Graph built using edges representing two, three, four and five generations respectively.

  . Additional filters were further applied to retain only highly informative SNPs (O'Leary et al. 2018), and SNPs potentially under selection were detected as outliers with PCAdapt (Luu et al. 2017) and removed. The final dataset contained 7,655 neutral SNPs for seabream and 2,462 neutral SNPs for mullet.

  the 0.1 threshold indicating the corresponding pairs of individuals are close kin (Figure 4.1b). These represent 0.02% of all unique pairs (N = 43 956), and the 15 individuals with at least one close kin represent 5.1% of all genotyped individuals. Remarkably, six pairs consisted of individuals sampled geographically far apart, on average 1 981 km, highlighting long distance dispersal events (Figure 4.2). Two pairs were full siblings (k = 0.244 & 0.194) while four pairs were half siblings (k = 0.101, 0.118, 0.174 & 0.175). The three remaining pairs consisted of individuals that were found at the same location and were either half siblings (two pairs, k = 0.119 & 0.135) or full siblings (one pair, k = 0.322).

  similarly found evidence of long-distance dispersal for the Omani clownish (Amphiprion omanensis) between two regions 400 km apart, while Reguera-Rouzaud et al. (2020) assigned first generation migrants of Yellow snapper (Lutjanus argentiventris) to their source population up to 320 km away. Parentage analyses of bar-cheek (Plectropomus maculatus) and leopard coral trout (P.

Figure 4 1 -

 41 high gene flow A school of two-banded seabream (Diplodus vulgaris) in the distance over a typical Mediterranean seascape. Parc national des Calanques, Île Verte, La Ciotat 5.1 Preface In chapter 4, I assessed the Mediterranean-wide connectivity patterns of striped red mullet and white seabream. Both species demonstrated high levels of gene flow, either through exceptional long-distance dispersal in single generations (for seabream), or by relying on stepping-stone dispersal over multiple generations (for mullet). These high levels of gene flow could diminish population structure and prevent local adaptation, especially for seabream that is capable of more extensive dispersal than mullet. On the other hand, connectivity could promote the spread of beneficial alleles from locally adapted populations towards populations that are expected to face the same conditions under climate change. Understanding the extent to which geographic distance, larval dispersal and environmental conditions shape genetic diversity is then crucial to identify the climate-adaptive potential of populations and assess their vulnerability to climate change. Previous work on the pooled dataset of mullet already identified signals of adaptation to salinity (Dalongeville et al. 2018a). The new individual datasets of 2 794 mullet and 8 206 seabream SNPs present the opportunity to assess the influence of several environmental variables -which are expected to change under climate change -on the genetic structure of both species. Comparing the genetic structure and environmental influence between both species then allows to understand how their different levels of gene flow influence their potential for local adaptation. In this chapter, I first select statistically outlier SNPs to improve our resolution to detect genetic structure. I then disentangle the relative influences of spatial distance, larval dispersal, temperature, productivity and salinity on this structure. With the help of Laura Benestan and Pierre-Edouard Guerin, we further retrieved the functional annotation of the genes in which outlier SNPs were detected to identify potential candidate genes of adaptation to temperature and salinity. Journal of Animal Ecology Special Feature: understanding climate change response in the age of genomics. (in revision) Running title: Climate impact on fish genomics Authors: Emilie Boulanger 1,2 , Laura Benestan 1 , Pierre-Edouard Guerin 1 , Alicia Dalongeville 2 , David Mouillot 2,3 , Stéphanie Manel 1 Affiliations 1. CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France 2. MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Montpellier, France 3. Institut Universitaire de France, Paris, France Corresponding authors: Emilie Boulanger, emilie.boulanger@hotmail.com Stéphanie Manel, stephanie.manel@ephe.psl.eu UMR 5175-CEFE -EPHE-PSL Campus CNRS 1919 route de Mende 34293 Montpellier cedex 5 France Keywords: comparative seascape genomics, gene flow, local adaptation, Mediterranean Sea, Climate influences population genetic variation of marine species. Capturing those impacts remains challenging for marine fishes dispersing over a large geographic scale spanning steep environmental gradients. This requires an extensive spatial sampling of individuals or populations, representative of seascape heterogeneity, combined with a set of highly informative molecular markers able to reveal climatic-associated genomic variations.

  Transects were conducted as close to the coastline as possible to sample close to the substrate and ensure the sampling of coastal communities. Seawater samples were filtered on site through a VigiDNA® 0.20-µM cross flow filtration capsule (SPYGEN, le Bourget du Lac, France) while changing gloves between each sample and each step to avoid (cross-) contamination. At the end of each filtration, the water inside the capsule was emptied and the capsule was filled with 80 mL of CL1 Conservation buffer (SPYGEN, le Bourget du Lac, France) and stored at room temperature until extraction. eDNA extraction and sequencing eDNA extraction was performed in a dedicated room for water DNA sample extraction, equipped with positive air pressure, UV treatment and frequent air renewal. Before entering this extraction room, personnel changed into full protective clothing comprising disposable body suit with hood, mask, laboratory shoes, overshoes and gloves in a connecting zone. All benches were decontaminated with 10% commercial bleach before and after each manipulation.

  Codon code aligner version 4.2.7 for Windows (CodonCode Corporation. Dedham. MA. USA) and/or Geneious Prime 2019.0.4 (https://www.geneious.com).

Figure A8 .

 A8 Figure A8. Edge removal scenario showing 100%, 80%, 60%, 40, 20, 5% of all possible connections respectively. Edges were pruned sequentially by decreasing genetic distance.

Figure A9 .

 A9 Figure A9. Ranking of nodes in the genetic connectivity spatial graph based on their betweenness value for varying thresholds. The red point corresponds to the betweenness value of the corresponding node in the final genetic connectivity spatial graph.

  constant zero resistance to water using the functions transition(), geoCorrection() and costDistance() of the R package gdistance v.1.1-1 (van Etten 2017).

  la connectivité entre populations, à leur tour, sont généralement évaluées avec des marqueurs génétiques. Mais les marqueurs spécifiques à un locus, tels que les microsatellites, n'ont souvent pas la résolution nécessaire pour démêler la structure populationnelle des espèces marines à haut flux de gènes. De plus, les marqueurs microsatellites sont présumés neutres et ne renseignent donc pas sur une éventuelle adaptation locale. Les technologies de séquençage nouvelle génération (Next Generation Sequencing ou NGS) (Heather and Chain 2016) permettent de séquencer des milliers de fragments d'ADN provenant de plusieurs régions d'un génome, ou de séquencer un fragment provenant de plusieurs espèces trouvées dans un seul échantillon. Ces technologies offrent de nouveaux outils pour évaluer la biodiversité à travers ses différents niveaux et mieux informer la prise de décisions en conservation. Au niveau des espèces, le metabarcoding d'ADN environnemental (ADNe) avec les technologies NGS permet de caractériser l'ensemble des communautés présentes dans un environnement donné (Deiner et al. 2017). Au niveau des individus et des gènes, les technologies NGS sont utilisées pour séquencer des milliers de polymorphismes à un seul nucléotide (Single Nucleotide Polymorphism ou SNP) le long du génome d'une espèce.

L

  'objectif de ma thèse est d'utiliser les avancées technologiques basées sur les NGS pour étudier la biodiversité des poissons Méditerranéens à trois niveaux d'organisation -des communautés aux populations et aux gènes -afin d'améliorer nos connaissances et la prise de décision en conservation (Figure R.1). Plus précisément, cette thèse vise à : i) étudier la diversité des communautés de poissons à une échelle locale et régionale en appliquant le metabarcoding de l'ADN environnemental (ADNe) pour évaluer l'effet des AMP sur la richesse et la composition des espèces Les poissons cryptobenthiques en particulier étaient deux fois plus diversifiés à 10 km des réserves qu'à l'intérieur. La composition des communautés différait également entre les niveaux de protection (Figure R.2b). Les espèces associées aux réserves étaient principalement pélagiques, et se caractérisaient par un niveau trophique plus élevé, une plus grande taille et une plus grande vulnérabilité à la pêche.

(

  Ellingsen et al. 2020), peut façonner la biodiversité à l'échelle régionale. Bien que les réserves en elles-mêmes n'augmentent pas la richesse, en abritant des communautés différentes elles augmentent la diversité régionale tout en protégeant les espèces vulnérables et prédatrices qui ont généralement des valeurs commerciales et touristiques importantes. entre celles-ci soit par leur dispersion modélisée à partir des courants océanographiques, pour représenter la connectivité potentielle, soit par leur distance génétique, pour représenter le flux de gènes. Dans le chapitre 4, nous présentons une approche à base de graphes spatiaux qui étend les réseaux de dispersion de propagules avec un modèle stepping-stone pour intégrer la dispersion sur plusieurs générations (Figure R.3). Nous illustrons ensuite notre approche en analysant la connectivité d'une espèce à fort flux de gènes, le rouget de roche Mullus surmuletus, en mer Méditerranée. Des échantillons de rouget ont été collectés sur 47 sites le long de la quasi-totalité du littoral Méditerranéen, y compris les îles. Les individus ont été génotypés à l'aide d'une méthode de génotype par séquençage (GBS) reposant sur le NGS. Les génotypes individuels ont ensuite été regroupés par localités d'échantillonnage, ci-après appelées populations. Les SNP potentiellement sous sélection ont été écartés pour conserver uniquement les marqueurs neutres (Dalongeville et al. 2018a). Pour construire nos graphes spatiaux, nous avons d'abord utilisé le modèle biophysique d'Andrello et al. (2013) pour simuler la dispersion larvaire entre 100 sites répartis uniformément le long du littoral Méditerranéen. Nous avons ainsi obtenu des probabilités de dispersion larvaire entre chaque pair de sites. Ceci nous a permis de construire un graphe spatial représentant la dispersion sur une seule génération entre nos 47 populations échantillonnées. Ensuite, nous avons cherché le chemin le plus court entre tous les sites précédemment non connectés, et calculé les nouvelles distances de dispersion multigénérationnelles le long de ces chemins les plus courts. Nous avons à nouveau construit un graphe spatial représentant la dispersion multigénérationnelle entre les 47 populations de rouget. Troisièmement, nous avons construit un graphe spatial représentant la connectivité génétique en calculant la distance génétique entre nos 47 populations. En comparant le réseau de dispersion larvaire obtenu sur une seule génération au réseau génétique, nous montrons que la connectivité génétique se réalise à une échelle spatiale beaucoup plus grande que la dispersion larvaire, comme l'avaient déjà constaté Dalongeville et al. (2018b), reflétant des échelles temporelles différentes. Notre approche a ensuite permis d'estimer la dispersion larvaire multigénérationnelle, qui explique mieux les patrons de connectivité génétique et met en évidence l'importance des populations intermédiaires pour Dans le chapitre 5, nous avons sélectionné des marqueurs SNP hautement différentiés à partir du même jeu de données individuel que l'étude d'apparentement (chapitre 4.2). En utilisant ces marqueurs, nous avons d'abord examiné la structure génétique entre les individus et démêlé les influences relatives de la distance spatiale, de la dispersion des larves et des variables environnementales telles que la salinité et la température. Ensuite, nous avons cherché des gènes candidats parmi les SNP aberrants en récupérant leurs annotations fonctionnelles et en testant la relation entre les génotypes SNP et des variables sujets aux changements climatiques (température et salinité), ce qui indiquerait une adaptation locale potentielle. Nos résultats ont démontré une structure génétique et une adaptation locale potentielle contrastées entre les deux espèces, liée à leurs taux de dispersion différents (Figure R.5). Le sar a montré une population principalement panmictique, alors que le rouget a révélé une structuration plus prononcée. La structure génétique du rouget était associée à la salinité et au niveau de chlorophylle. Les niveaux élevés de chlorophylle sont fortement liés aux températures basses du Golfe du Lion et du Nord de la mer Adriatique en hiver, ce qui pourrait expliquer une adaptation locale de ces individus à la fois à la productivité et à la température. L'annotation fonctionnelle des SNP « aberrants » a permis d'identifier des gènes candidats à l'adaptation à la salinité et à la température. Nos résultats illustrent les étapes clés d'une étude de génomique comparative du paysage marin qui vise à démêler les processus éco-évolutifs chez les espèces marines, afin de mieux anticiper leur réponse aux changements climatiques. La description des capacités d'adaptation des populations et de leur niches climatiques peut ensuite servir à inclure les processus évolutifs (neutres et adaptatifs) dans l'élaboration de réseaux d'AMP afin de préserver le potentiel

  de futures études. Le protocole standardisé d'échantillonnage et metabarcoding d'ADNe que nous avons employé peut facilement être répliqué dans le temps et dans l'espace. Nos résultats caractérisant les communautés de poissons peuvent alors servir de nouvelle base de référence à laquelle de futures études peuvent être comparées pour suivre les changements temporels de la diversité des poissons. Notre étude pourrait également être reproduite dans des AMP tropicales afin de vérifier si le même paradoxe de conservation de la biodiversité s'applique à d'autres régions biogéographiques. De même, nos données SNP au niveau individuel peuvent être enrichies par de nouvelles espèces, échantillonnées sur la même étendue spatiale, avec des traits d'histoire plus diversifiés[START_REF] Barry | Life tables shape genetic diversity in marine fishes[END_REF]. Cela nous permettrait de mieux comprendre comment les différents traits influencent la connectivité et les patrons d'adaptation locale, et de proposer des mesures de conservation adaptées en fonction des traits des espèces à protéger.

Table 1 .

 1

	1. Overview of first, second and third generation sequencing methods' specificities.1
	bp = 1 base pair, 1 Mb = 1 mega base pairs = 1 000 000 bp, 1 Gb = 1 giga base pairs =
	1 000 000 000 bp.
	Technology	Output per run	Sequence length
	Sanger 1	96 kb	Up to 900 bp
	454/Roche (2008) 1	80-120 Mb	200 -300 bp
	Illumina/Solexa (2008) 1	1 Gb	30 -40 bp
	Illumina MiSeq (2019) 2	Up to 15 Gb	Up to 2x 300 bp
	Nanopore MinION (2020) 3	Up to 50 Gb	Up to 4 Mb

Source :

1

[START_REF] Morozova | Applications of next-generation sequencing technologies in functional genomics[END_REF]

, 2 https://www.illumina.com/systems/sequencingplatforms/miseq/specifications.html, 3 https://nanoporetech.com/products/minion Illumina is currently the most widely used NGS technique. Sequencing millions of fragments across a genome leads to the identification of thousands of informative single nucleotide polymorphisms (SNPs) at the population level

Table 2 .

 2 1. Overview of the eDNA sampling sessions that I led or participated in during my PhD.

	Location	Dates	Protocol # sites # samples Comments
	Menorca	11/03/2018 -	Offshore 7	14	MPA; Rough spring weather
	(Balearic)	17/03/2018
	Mallorca	17/03/2018 -	Offshore 4	8	MPA; Rough spring weather
	(Balearic)	21/03/2018
	Banyuls	04/04/2018 -	Offshore 5	10	MPA
		06/04/2018
	Calvi (Corsica) 18/05/2018 -	Coastal	4	18	MPA
		24/05/2018
	Carry-le-Rouet	05/06/2018	Coastal	3	12	MPA
	Porquerolles	15/06/2018	Coastal	3	12	MPA
	Cap Roux	21/06/2018	Coastal	3	12	MPA
	Villefranche	19/03/2019 -	Linear +	2	13	Gombessa V, no MPA
		21/03/2019	Deep
	Marseille	30/06/2019 -	Coastal,	4	22	Gombessa V + MPA
		03/07/2019	Linear +
			Deep
	Cap Lardier	05/07/2019 -	Linear +	2	14	Gombessa V, no MPA
		08/07/2019	Deep
	Banyuls	11/07/2019	Coastal	3	12	MPA
	Banyuls	20/05/2020	Coastal	3	12	Lockdown
	Carry-le-Rouet	27/05/2020	Coastal	3	12	Lockdown

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B Published as Boulanger E., Dalongeville A., Andrello M., Mouillot D., Manel, S. (2020) Spatial graphs highlight how multi-generational dispersal shapes landscape genetic patterns. -Ecography. 43: 1167-1179.

	When individual dispersal is followed by successful repro-
	duction, demographic connectivity results in genetic con-
	nectivity, a measure of gene flow and other evolutionary
	processes (Lowe and Allendorf 2010). Understanding how
	populations are connected across space and time is essential
	to assess the impact that habitat change and fragmentation
	can have on population persistence, and to develop sustain-
	able resource management and appropriate conservation
	strategies (Fischer and Lindenmayer 2007, Andrello et al.
	2015, Magris et al. 2018). It can also help to quantify the
	potential spreading of adaptive alleles in areas threatened
	by climate change (Razgour et al. 2019). Landscape genetic
	studies aim to characterize how habitat and environmental
	features promote or impede the movement of individu-
	als through landscapes, riverscapes and seascapes and thus
	influence microevolutionary processes, including gene
	flow and genetic drift (Manel et al. 2003, Anderson et al.
	1168

2010). For species whose connectivity is mainly realised by a dispersing propagule stage (e.g. eggs, larvae, seeds, spores), much interest has been given to understanding the drivers of population connectivity by estimating the dispersal of these propagules through water, wind or animal vectors and by assessing the extent to which propagule dispersal explains the observed genetic structure across space

[START_REF] Nathan | Spatial patterns of seed dispersal, their determinants and consequences for recruitment[END_REF][START_REF] Castorani | Fluctuations in population fecundity drive variation in demographic connectivity and metapopulation dynamics[END_REF][START_REF] Escalante | e interplay of riverscape features and exotic introgression on the genetic structure of the Mexican golden trout (Oncorhynchus chrysogaster), a simulation approach[END_REF]

).

Table S2 .

 S2 Estimated average marginal effect (AME) of the protection levels and habitat covariates for each richness model. The reserve level is taken as the intercept.

		Supplementary
		Total richness				-Cryptobenthic richness
					2.5%	97.5%			2.5%	97.5%
		AME	SE	p-value	CI	CI	AME SE	p-value	CI	CI
	PC1	-0.46	0.757 0.543	-1.943 1.023	0.233 0.258 0.367	-0.273	0.738
	PC2	-0.959 0.774 0.215	-2.476	0.557	-0.016 0.264 0.951	-0.533	0.5
	PC3	-3.701 1.563 0.018	-6.764	-0.637	-1.082 0.532 0.042	-2.125	-0.038
	PC4	-0.335 1.523 0.826	-3.321	2.65	-0.006 0.519 0.991	-1.023	1.011
	Outside10 12.699 4.12	0.002	4.623	20.775	6.178 1.404 0	3.427	8.929
	Outside5 5.193	2.779 0.062	-0.253 10.64	3.002 0.947 0.002	1.147	4.858
		Pelagic richness				Vulnerable richness
					2.5%	97.5%			2.5%	97.5%
		AME	SE	p-value	CI	CI	AME SE	p-value	CI	CI
	PC1	-0.15	0.119 0.209	-0.384 0.084	-0.15 0.119 0.209	-0.384	0.084
	PC2	0.021	0.105 0.84	-0.184 0.226	0.021 0.105 0.84	-0.184	0.226
	PC3	-0.057 0.221 0.797	-0.49	0.376	-0.057 0.221 0.797	-0.49	0.376
	PC4	0.203	0.228 0.373	-0.244 0.65	0.203 0.228 0.373	-0.244	0.65
	Outside10 0.214	1.039 0.837	-1.823	2.251	0.214 1.039 0.837	-1.823	2.251
	Outside5 0.836	1.491 0.575	-2.086	3.757	0.836 1.491 0.575	-2.086	3.757
		Rare richness				Common richness
					2.5%	97.5%			2.5%	97.5%
		AME	SE	p-value	CI	CI	AME SE	p-value	CI	CI
	PC1	-0.623 0.418 0.136	-1.443	0.197	0.164 0.414 0.693	-0.648	0.976
	PC2	-0.939 0.428 0.028	-1.777	-0.1	-0.021 0.424 0.961	-0.851	0.809
	PC3	-1.135 0.864 0.189	-2.829	0.559	-2.565 0.856 0.003	-4.243	-0.888
	PC4	0.446	0.842 0.596	-1.205 2.097	-0.782 0.834 0.349	-2.416	0.853
	Outside10 5.75	2.279 0.012	1.284	10.216	6.949 2.256 0.002	2.527	11.371
	Outside5 4.434	1.537 0.004	1.422	7.446	0.759 1.522 0.618	-2.223	3.741

2 %) and were used in the linear models of species richness and the multivariate redundancy analyses to test and account for environmental variability.

Supplementary

Table S7 .

 S7 Species corresponding to non-overlapping areas of the Venn diagram (Supplementary Fig.S4). Behaviour and Biology information come from FishBase.

	a) Unique to reserves
	Species	IUCN Vertical	Behaviour Biology / Diet	# sites
	Atherina boyeri	LC	Pelagic	Gregarious Feeding on […] fish larvae
	Synodus saurus	LC	Benthic	NA	Feeds mainly on other fishes
	Blennius ocellaris	LC	cryptobenthic Solitary	Feeds on small invertebrates
	Pseudaphya ferreri	LC	cryptobenthic Gregarious na
	Sphyraena sphyraena	LC	Pelagic	Gregarious Feeds mostly on fish
					Feeds mostly on [pelagic]
	Xiphias gladius	LC	Pelagic	Gregarious	fishes
	Chelidonichthys lucerna	LC	Benthic	NA	Feeds on fish
	Pteroplatytrygon violacea LC	Pelagic	Gregarious Feeds on fish
	b) Unique to fished areas (5km &10km outside )
	Species	IUCN Vertical	Behaviour Biology / Diet	# sites
						large individuals prey on
	Micromesistius poutassou	LC	Pelagic	Gregarious	[…] small fish
	Trisopterus capelanus	LC	NA	NA		Feeds on small fish
						Feeds on nekton. mainly
	Lophius budegassa	LC	Benthic	Solitary		fishes
	Corcyrogobius liechtensteini LC	cryptobenthic Solitary		na
	Gammogobius steinitzi	LC	cryptobenthic Solitary		na
	Millerigobius macrocephalus LC	cryptobenthic Solitary		na
	Odondebuenia balearica	LC	cryptobenthic Solitary		na
	Zosterisessor ophiocephalus	LC	cryptobenthic Solitary		na
						Feed on bryozoans.
	Ctenolabrus rupestris	LC	Benthic	Solitary		crustaceans and gastropods
	Euthynnus alletteratus	LC	Pelagic	Gregarious Opportunistic predator
	Spicara flexuosa	LC	Demersal	Gregarious Feed on zooplankton
						feeds on bottom-living
	Scophthalmus rhombus	LC	Benthic	Solitary		fishes
	Pegusa nasuta	DD	Benthic	Solitary		na
	Macroramphosus scolopax	LC	Demersal	Gregarious Feeds on invertebrates
						Feeds on benthic
	Balistes capriscus	VU	Benthic	Both		invertebrates
	Mustelus mustelus	VU	Demersal	Gregarious Feeds also on bony fishes
	Myliobatis aquila	VU	Demersal	Gregarious Feeds on (benthic) fish
						Feed on all kinds of benthic
	Raja asterias	NT	Benthic	NA		animals

  The weighted matrix is then subjected to main Mediterranean basin

			270	4556.2	4430.2
					4689.4
		Cluster 2:	27	255.3	249.8
		South Alboran Sea			261.1
	Striped red mullet	Cluster 1:	369	9489.1	9278.8
		main Mediterranean basin			9709.1
		Cluster 2:	24	606.7	589.0
		Alboran sea and scatterd ind.			625.4

Local adaptation

Acknowledgements

Acknowledgements. The authors thank all staff and students involved in the field and laboratory work, as well as the managers of the studied MPAs.

Acknowledgements -We thank Raquel Torres, Laure Velez, Jennifer Tournois, Eva Maire, Sebastien Villeger, Johanna Brun-Vitelli, Manuel Muntoni, Claire Seceh, Sofiène Ben Abdelhamid, Achraf Barbena, Itai Granot, Ori Frid and Shahar Malamud for collecting M. surmuletus samples, as well as all the fishermen that provided the fishes. We are grateful to the SMGE platform, and especially Veronique Arnal and Marie-Pierre Dubois for technical support during molecular analyses. We also thank Stéphane Lobreaux for the bioinformatic analyses. We thank the anonymous referees as well as the editor for their insightful comments.

Acknowledgements

We are grateful to all fishermen and collaborators involved in the sampling and molecular analyses. We thank Marco Andrello for contributing his codes for the spatial genetic diversity analyses. This research was funded by the 'Total foundation' through the SEACONNECT project, the 'ANR' through the RESERVEBENEFIT project, PSL Environment through the project RESMOD, and EB was partially funded by the Region Occitanie. Bioinformatic analyses benefitted from the Montpellier Bioinformatics Biodiversity (MBB) platform, which is supported by ANR program 'Investissements d'avenir' (ANR-10-LABX-04-01).

a permis de récolter des traces d'ADN et des bouts de nageoires tout le long de la côte franco-(& espagno-)

Funding -is study was funded by the 'ANR' through the RESERVEBENEFIT project, PSL Environment through the project RESMOD, and EB was partially funded by the Region Occitanie.

Data accessibility. The data and R codes to replicate analyses and figures are available at https://github.com/eboulanger/MEDeDNAreserves. The new teleo reference sequences and Illumnia raw sequences are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.18931zcx1 [94] and https://doi. org/10.5061/dryad.j9kd51cbr [95].

Data availability statement

Data for this study are available from the Dryad Digital Repository:

< https://doi.org/10.5061/dryad.31tk592 > [START_REF] Dalongeville | Data from: Geographic isolation and larval dispersal shape seascape genetic patterns differently according to spatial scale[END_REF]). e R script for the shortest-path analysis and to produce Fig. 3 is available at < https://github. com/eboulanger/seaConnect--spatialGraph >.

Data Availability Statement

All codes to reproduce bio-informatic processing and statistical analyses are available at https://gitlab.mbb.univ-montp2.fr/seaconnect. SNP data files were uploaded to Dryad (https://doi.org/10.5061/dryad.ffbg79cvs).

Authors' contributions. E.B., N.L., P.B., T.D., S.M., and D.M. designed the study. E.B., P.L., J.B.J., J.D., F.H., and N.G. conducted fieldwork. A.V. and T.D. supervised the biomolecular analysis and A.V. performed the bioinformatic analyses. E.B. and D.M. performed the statistical analyses and wrote the first draft. E.B., N.L., J.B.J., S.M., and D.M. interpreted data and wrote the paper. All authors offered revision suggestions and approved the final version of the manuscript.

Competing interests. We declare we have no competing interests. Funding. Funding was provided by the Region Occitanie, the Agence de l'Eau RMC and the RESERVEBENEFIT project through the 2015-2016 BiodivERsA COFUND call with the ANR (France). Author contributions -All authors generated hypotheses and designed research. AD conducted the sampling and molecular analyses; EB analysed the data and wrote the first draft. All authors helped to improve manuscript writing.

Author Contributions

EB, SM, AD, LB and DM conceived the ideas and designed methodology; AD collected the data; PEG performed bioinformatic analyses; EB performed statistical analyses; EB, LB and SM wrote the first draft. All authors contributed critically to the manuscript and gave final approval for publication.

Conflict of interest

The authors declare that they have no conflict of interest. vulnerability to fishing (e,f), vertical distribution (g,h), rarity (i,j; Occurrence category) and commercial importance (k,l). We compared the number of species (left) as well as the proportion of species (right) sequenced (dark grey) versus not-sequenced (light grey) for our targeted 12S rRNA fragment (teleo). Fisher's exact tests showed that there was no bias between categories of each trait for the 25% missing species (p-value = 0.318, 0.0718, 0.117, 0.166, 0.385 and 0.160 respectively). S5. Species scores of the partial dbRDA along axis CAP1. The barplot represents the species with the greatest absolute score on the partial dbRDA axis CAP1, thus representing the species mostly associated to the reserves (left) or the fished areas outside (right). These are the same species scores as MS Fig. 4b S3. Conditional predicted richness under three protection levels for each species category, predicted from their respective linear models in Supplementary Table 2. SE = standard error of fitted value. Data used for MS Fig. 6. S4. dbRDA results (species ~ protection + PC1 + PC2 + PC3 + PC4).

Supplementary Figures

Supplementary Figure S1

Supplementary Figure

Supplementary Table

Permutation test with 9999 permutations.

GBS data processing

PhiX174 sequences that were used for quality control and calibration of the sequencing run were filtered out using BBMap v38.06 [START_REF] Bushnell | BBMerge -Accurate paired shotgun read merging via overlap (PJ Biggs[END_REF], and Illumina adapter sequences were trimmed. Sequences with IUPAC ambiguous base calling were removed, as well as sequences with at least 40% low-quality bases (Phred Quality Score < 18). Sequences were trimmed to remove low-quality bases towards the end of the sequence (mean phred-score < 18) or presence of polyG repetitions. Trimmed sequences shorter than 76 bp were discarded.

After variant calling, SNPs with a quality score below 30, a minor allele count below 3, a genotype depth below 3 and a mean depth over all individuals below 20 were filtered.

Individuals with more than 50% missing data were removed, as well as SNPs with a genotype call rate below 95%. SNPs were further filtered based on allele balance, mapping quality ratio and quality/depth ratio. SNPs with a heterozygosity > 0.40 were also removed.

Grid-based genetic diversity

We investigated the spatial variation of expected heterozygosity (He) using a grid-based diversity analysis modified from [START_REF] Larranaga | A Mesoamerican origin of cherimoya (Annona cherimola Mill.): Implications for the conservation of plant genetic resources[END_REF] and [START_REF] Thomas | Present Spatial Diversity Patterns of Theobroma cacao L. in the Neotropics Reflect Genetic Differentiation in Pleistocene Refugia Followed by Human-Influenced Dispersal[END_REF]. We first constructed a grid with a cell size of 60 minutes (approximately 86 km in the study area). To obtain sufficient data points to construct the genetic diversity map, we replicated the individuals to their surrounding grid cells by applying circular neighbourhoods with a diameter of 10 degrees (approximately 852 km in the study area). This is done under the assumption that each individual is representative for its surrounding cells. The resulting grid contains 642 cells for M. surmuletus with on average 56 ± 33 individuals per cell, and 610 cells for D. sargus with on average 37 ± 25 individuals per cell. To correct for the uneven number of individuals per cell and ensure a sufficient sample size, cells with less than 20 individuals were discarded and He was calculated on bootstrapped subsamples of 20 individuals per cell, averaged over 200 iterations.

Seascape variables

Distance-based Moran's eigenvector maps

Marine geographic distances (i.e. the minimum distances constrained by water) were computed as least-cost path distances between all individuals by assigning infinite resistance to principal component analysis (PCA) yielding the orthogonal AEM eigenfunctions [START_REF] Legendre | Numerical Ecology[END_REF].

This approach yielded ~100 AEMs which was again too many to include in the redundancy analysis. Similar to the dbMEMs, the first AEMs with the largest eigenvalues represent large-scale structures of larval dispersal that account for most of the dispersal information in our system. Based on the scree plot of the eigenvalues, the 6 firs AEM were retained for subsequent analyses (Figure S1b). The values of each AEM were then extracted for the sampled sites of the two species respectively.

Supplementary Tables

Supplementary Table S1.