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Islands have been used as model systems to study ecological and evolutionary processes, and they provide an ideal set‐up for validating new biodiversity monitoring methods. The application of environmental DNA metabarcoding for monitoring marine biodiversity requires an understanding of the spatial scale of the eDNA signal, which is best tested in island systems. Here, we investigated the variation in Actinopterygii and Elasmobranchii species composition recovered from eDNA metabarcoding along a gradient of distance‐to‐reef in four of the five French Scattered Islands in the Western Indian Ocean. We collected surface water samples at an increasing distance from reefs (0 m, 250 m, 500 m, 750 m). We used a metabarcoding protocol based on the 'teleo' primers to target marine reef fishes and classified taxa according to their habitat types (benthic or pelagic). We investigated the effect of distance‐to‐reef on β diversity variation using generalised linear mixed models and estimated species‐specific distance‐to‐reef effects using a model‐based approach for community data. Environmental DNA metabarcoding analyses recovered distinct fish species compositions across the four inventoried islands and variations along the distance‐to‐reef gradient. The analysis of β‐diversity variation showed significant taxa turnover between the eDNA samples on and away from the reefs. In agreement with a spatially localised signal from eDNA, benthic species were distributed closer to the reef than pelagic ones. Our findings demonstrate that the combination of eDNA inventories and spatial modelling can provide insights into species habitat preferences related to distance‐to‐reef gradients at a small scale. As such, eDNA can not only recover large compositional differences among islands but also help understand habitat selection and distribution of marine species at a finer spatial scale. [ABSTRACT FROM AUTHOR]

Environmental DNA (eDNA) metabarcoding provides an efficient approach for documenting biodiversity patterns in marine and terrestrial ecosystems. The complexity of these data prevents current methods from extracting and analyzing all the relevant ecological information they contain, and new methods may provide better dimensionality reduction and clustering. Here we present two new deep learning‐based methods that combine different types of neural networks (NNs) to ordinate eDNA samples and visualize ecosystem properties in a two‐dimensional space: the first is based on variational autoencoders and the second on deep metric learning. The strength of our new methods lies in the combination of two inputs: the number of sequences found for each molecular operational taxonomic unit (MOTU) detected and their corresponding nucleotide sequence. Using three different datasets, we show that our methods accurately represent several biodiversity indicators in a two‐dimensional latent space: MOTU richness per sample, sequence α‐diversity per sample, Jaccard's and sequence β‐diversity between samples. We show that our nonlinear methods are better at extracting features from eDNA datasets while avoiding the major biases associated with eDNA. Our methods outperform traditional dimension reduction methods such as Principal Component Analysis, t‐distributed Stochastic Neighbour Embedding, Nonmetric Multidimensional Scaling and Uniform Manifold Approximation and Projection for dimension reduction. Our results suggest that NNs provide a more efficient way of extracting structure from eDNA metabarcoding data, thereby improving their ecological interpretation and thus biodiversity monitoring. [ABSTRACT FROM AUTHOR]

The bathymetric and geographical distribution of marine species represent a key information in biodiversity conservation. Yet, deep‐sea ecosystems are among the least explored on Earth and are increasingly impacted by human activities. Environmental DNA (eDNA) metabarcoding has emerged as a promising method to study fish biodiversity but applications to the deep‐sea are still scarce. A major limitation in the application of eDNA metabarcoding is the incompleteness of species sequences available in public genetic databases which reduces the extent of detected species. This incompleteness by depth is still unknown. Here, we built the global bathymetric and geographical distribution of 10,826 actinopterygian and 960 chondrichthyan fish species. We assessed their genetic coverage by depth and by ocean for three main metabarcoding markers used in the literature: teleo and MiFish‐U/E. We also estimated the number of primer mismatches per species amplified by in silico polymerase chain reaction which influence the probability of species detection. Actinopterygians show a stronger decrease in species richness with depth than Chondrichthyans. These richness gradients are accompanied by a continuous species turnover between depths. Fish species coverage with the MiFish‐U/E markers is higher than with teleo while threatened species are more sequenced than the others. "Deep‐endemic" species, those not ascending to the shallow depth layer, are less sequenced than not threatened species. The number of primer mismatches is not higher for deep‐sea species than for shallower ones. eDNA metabarcoding is promising for species detection in the deep‐sea to better account for the 3‐dimensional structure of the ocean in marine biodiversity monitoring and conservation. However, we argue that sequencing efforts on "deep‐endemic" species are needed. [ABSTRACT FROM AUTHOR]

Human activities can degrade the quality of coral reefs and cause a decline in fish species richness and functional diversity and an erosion of the ecosystem services provided. Environmental DNA metabarcoding (eDNA) has been proposed as an alternative to Underwater Visual Census (UVC) to offer more rapid assessment of marine biodiversity to meet management demands for ecosystem health indices. Taxonomic information derived from sequenced eDNA can be combined with functional traits and phylogenetic positions to generate a variety of ecological indices describing ecosystem functioning. Here, we inventoried reef fish assemblages of two contrasting coastal areas of Curaçao, (i) near the island's capital city and (ii) in a remote area under more limited anthropogenic pressure. We sampled eDNA by filtering large volumes of seawater (2 × 30 L) along 2 km boat transects, which we coupled with species ecological properties related to habitat use, trophic level, and body size to investigate the difference in fish taxonomic composition, functional and phylogenetic indices recovered from eDNA metabarcoding between these two distinct coastal areas. Despite no marked difference in species richness, we found a higher phylogenetic diversity in proximity to the city, but a higher functional diversity on the more isolated reef. Composition differences between coastal areas were associated with different frequencies of reef fish families. Because of a partial reference database, eDNA only partly matched those detected with UVC, but eDNA surveys nevertheless provided rapid and robust species occurrence responses to contrasting environments. eDNA metabarcoding coupled with functional and phylogenetic diversity assessment can serve the management of coastal habitats under increasing threat from global changes. [ABSTRACT FROM AUTHOR]

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Aim: Environmental DNA metabarcoding has recently emerged as a non‐invasive tool for aquatic biodiversity inventories, frequently surpassing traditional methods for detecting a wide range of taxa in most habitats. The major limitation currently impairing the large‐scale application of eDNA‐based inventories is the lack of species sequences available in public genetic databases. Unfortunately, these gaps are still unknown spatially and taxonomically, hindering targeted future sequencing efforts. Innovation: We propose GAPeDNA, a user‐friendly web interface that provides a global overview of genetic database completeness for a given taxon across space and conservation status. As an application, we synthetized data from regional checklists for marine and freshwater fishes along with their IUCN conservation status to provide global maps of species coverage using the European Nucleotide Archive public reference database for 19 metabarcoding primers. This tool automatizes the scanning of gaps in these databases to guide future sequencing efforts and support the deployment of eDNA inventories at larger scale. This tool is flexible and can be expanded to other taxa and primers upon data availability. Main conclusions: Using our global fish case study, we show that gaps increase towards the tropics where species diversity and the number of threatened species are the highest. It highlights priority areas for fish sequencing like the Congo, the Mekong and the Mississippi freshwater basins which host more than 60 non‐sequenced threatened fish species. For marine fishes, the Caribbean and East Africa host up to 42 non‐sequenced threatened species. By presenting the global genetic database completeness for several primers on any taxa and building an open‐access, updatable and flexible tool, GAPeDNA appears as a valuable contribution to support any kind of eDNA metabarcoding study. [ABSTRACT FROM AUTHOR]