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Evidence of large‐scale biodiversity degradation in marine ecosystems has been reported worldwide, yet most research has focused on few species of interest or on limited spatiotemporal scales. Here we assessed the spatial and temporal changes in the taxonomic and functional composition of fish communities in European seas over the last 25 years (1994–2019). We then explored how these community changes were linked to environmental gradients and fishing pressure. We show that the spatial variation in fish species composition is more than two times higher than the temporal variation, with a marked spatial continuum in taxonomic composition and a more homogenous pattern in functional composition. The regions warming the fastest are experiencing an increasing dominance and total abundance of r‐strategy fish species (lower age of maturity). Conversely, regions warming more slowly show an increasing dominance and total abundance of K‐strategy species (high trophic level and late reproduction). Among the considered environmental variables, sea surface temperature, surface salinity and chlorophyll‐a most consistently influenced communities' spatial patterns, while bottom temperature and oxygen had the most consistent influence on temporal patterns. Changes in communities' functional composition were more closely related to environmental conditions than taxonomic changes. Our study demonstrates the importance of integrating community‐level species traits across multi‐decadal scales and across a large region to better capture and understand ecosystem‐wide responses and provides a different lens on community dynamics that could be used to support sustainable fisheries management. [ABSTRACT FROM AUTHOR]

While extinction risk categorization is fundamental for building robust conservation planning for marine fishes, empirical data on occurrence and vulnerability to disturbances are still lacking for most marine teleost fish species, preventing the assessment of their International Union for the Conservation of Nature (IUCN) status. In this article, we predicted the IUCN status of marine fishes based on two machine learning algorithms, trained with available species occurrences, biological traits, taxonomy, and human uses. We found that extinction risk for marine fish species is higher than initially estimated by the IUCN, increasing from 2.5% to 12.7%. Species predicted as Threatened were mainly characterized by a small geographic range, a relatively large body size, and a low growth rate. Hotspots of predicted Threatened species peaked mainly in the South China Sea, the Philippine Sea, the Celebes Sea, the west coast Australia and North America. We also explored the consequences of including these predicted species' IUCN status in the prioritization of marine protected areas through conservation planning. We found a marked increase in prioritization ranks for subpolar and polar regions despite their low species richness. We suggest to integrate multifactorial ensemble learning to assess species extinction risk and offer a more complete view of endangered taxonomic groups to ultimately reach global conservation targets like the extending coverage of protected areas where species are the most vulnerable. Empirical data on occurrences and vulnerability are still lacking for most marine teleost fish species, preventing assessment of their IUCN extinction risk status. This study uses machine learning with occurrence data, species biological traits, taxonomy and human usage to infer a 12.8% extinction risk for marine fish species, surpassing existing estimates. [ABSTRACT FROM AUTHOR]

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]

Biodiversity loss in river ecosystems is much faster and more severe than in terrestrial systems, and spatial conservation and restoration plans are needed to halt this erosion. Reliable and highly resolved data on the state of and change in biodiversity and species distributions are critical for effective measures. However, high‐resolution maps of fish distribution remain limited for large riverine systems. Coupling data from global satellite sensors with broad‐scale environmental DNA (eDNA) and machine learning could enable rapid and precise mapping of the distribution of river organisms. Here, we investigated the potential for combining these methods using a fish eDNA dataset from 110 sites sampled along the full length of the Rhone River in Switzerland and France. Using Sentinel 2 and Landsat 8 images, we generated a set of ecological variables describing both the aquatic and the terrestrial habitats surrounding the river corridor. We combined these variables with eDNA‐based presence and absence data on 29 fish species and used three machine‐learning models to assess environmental suitability for these species. Most models showed good performance, indicating that ecological variables derived from remote sensing can approximate the ecological determinants of fish species distributions, but water‐derived variables had stronger associations than the terrestrial variables surrounding the river. The species range mapping indicated a significant transition in the species occupancy along the Rhone, from its source in the Swiss Alps to outlet into the Mediterranean Sea in southern France. Our study demonstrates the feasibility of combining remote sensing and eDNA to map species distributions in a large river. This method can be expanded to any large river to support conservation schemes. [ABSTRACT FROM AUTHOR]

Understanding the ecological rules structuring the organization of species interactions is a prerequisite to predicting how ecosystems respond to environmental changes. While the ecological determinants of single networks have been documented, it remains unclear whether network ecological rules are conserved along spatial and environmental gradients. To address this gap, we reconstructed 48 plant–herbivore interaction networks along six elevation gradients in the Central European Alps in Switzerland, using DNA metabarcoding on orthoptera faeces. We developed hypotheses on the ecological mechanisms expected to structure interaction networks, based on plant phylogeny, plant abundance, leaf toughness, leaf nitrogen content and plant metabolomics. We show that plant phylogenetic relationships and species abundance have the greatest explanatory power regarding the structure of the ecological networks. Moreover, we found that leaf nitrogen content is a key determinant of interactions in warmer environments, while phenolic compounds and tannins are more important in colder environments, suggesting that determinants of species interactions can shift along environmental gradients. With this work, we propose an approach to study the mechanisms that structure the way species interact with each other between bioregions and ecosystems. [ABSTRACT FROM AUTHOR]

Summary: The geographic distribution of plant diversity matches the gradient of habitat heterogeneity from lowlands to mountain regions. However, little is known about how much this relationship is conserved across scales.Using the World Checklist of Vascular Plants and high‐resolution biodiversity maps developed by species distribution models, we investigated the associations between species richness and habitat heterogeneity at the scales of Eurasia and the Hengduan Mountains (HDM) in China.Habitat heterogeneity explains seed plant species richness across Eurasia, but the plant species richness of 41/97 HDM families is even higher than expected from fitted statistical relationships. A habitat heterogeneity index combining growing degree days, site water balance, and bedrock type performs better than heterogeneity based on single variables in explaining species richness. In the HDM, the association between heterogeneity and species richness is stronger at larger scales.Our findings suggest that high environmental heterogeneity provides suitable conditions for the diversification of lineages in the HDM. Nevertheless, habitat heterogeneity alone cannot fully explain the distribution of species richness in the HDM, especially in the western HDM, and complementary mechanisms, such as the complex geological history of the region, may have contributed to shaping this exceptional biodiversity hotspot. This article is part of the Special Collection 'Global plant diversity and distribution'. See https://www.newphytologist.org/global-plant-diversity for more details. [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]

Environmental DNA (eDNA) metabarcoding is a method to detect taxa from environmental samples. It is increasingly used for marine biodiversity surveys. As it only requires water collection, eDNA metabarcoding is less invasive than scientific trawling and might be more cost effective. Here, we analysed data from both sampling methods applied in the same scientific survey targeting Northeast Atlantic fish in the Bay of Biscay. We compared the methods regarding the distribution of taxonomic, phylogenetic, and functional diversity. We found that eDNA captured more taxonomic and phylogenetic richness than bottom trawling and more functional richness at the local scale. eDNA was less selective than trawling and detected species in local communities spanning larger phylogenetic and functional breadths, especially as it detected large pelagic species that escaped the trawl, even though trawling detected more flat fish. eDNA indicated differences in fish community composition that were comparable to those based on trawling. However, consistency between abundance estimates provided by eDNA metabarcoding and trawl catches was low, even after accounting for allometric scaling in eDNA production. We conclude that eDNA metabarcoding is a promising method that can complement scientific trawling for multi-component biodiversity monitoring based on presence/absence, but not yet for abundance. [ABSTRACT FROM AUTHOR]

Aim: Evaluating the similarity of diversity patterns across micro‐ to macroevolutionary scales in natural communities, such as species–genetic diversity correlations (SGDCs), may inform on processes shaping community assembly. However, whether SGDCs not only hold across communities but also across lineages has never been explored so far. Here we investigated SGDCs across co‐distributed taxa for different spatial components (α, β, γ), and formally tested the influence of dispersal traits on β‐SGDCs. Location: Western Indian Ocean. Time period: 2016–2017. Major taxa studied: Tropical reef fish species with contrasting dispersal traits. Methods: Using double‐digest restriction‐site associated DNA sequencing (ddRADseq) Single Nucleotide Polymorphism data for 20 tropical reef fishes and distribution data of 2,446 species belonging to 12 families, we analysed the correlations between within‐species genetic diversity and within‐family species diversity (i.e., lineage diversity) for the three spatial components (α, β, γ‐SGDCs). We then related the strength of β‐SGDCs per species to proxies of larval dispersal abilities. Results: We detected positive and significant lineage‐based SGDC only for the β component, that is, the families showing the greatest level of species turnover among sites contain the species with the greatest levels of genetic differentiation. We showed that the Monsoon Drift mainly explained the β‐diversity patterns at both intraspecific and interspecific levels. Higher β‐SGDCs were found for species with short pelagic larval duration and weak larval swimming capacity. Main conclusions: Our study reveals a strong correlation between genetic and species β‐diversity, a result explained by the presence of a 'soft' barrier and mediated by larval dispersal processes. This suggests that vicariance and dispersal limitation are major processes shaping β‐diversity patterns from microevolutionary to macroevolutionary scales in tropical reef fishes. [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]

Summary: The documentation of biodiversity distribution through species range identification is crucial for macroecology, biogeography, conservation, and restoration. However, for plants, species range maps remain scarce and often inaccurate.We present a novel approach to map species ranges at a global scale, integrating polygon mapping and species distribution modelling (SDM). We develop a polygon mapping algorithm by considering distances and nestedness of occurrences. We further apply an SDM approach considering multiple modelling algorithms, complexity levels, and pseudo‐absence selections to map the species at a high spatial resolution and intersect it with the generated polygons.We use this approach to construct range maps for all 1957 species of Fagales and Pinales with data compilated from multiple sources. We construct high‐resolution global species richness maps of these important plant clades, and document diversity hotspots for both clades in southern and south‐western China, Central America, and Borneo. We validate the approach with two representative genera, Quercus and Pinus, using previously published coarser range maps, and find good agreement.By efficiently producing high‐resolution range maps, our mapping approach offers a new tool in the field of macroecology for studying global species distribution patterns and supporting ongoing conservation efforts. [ABSTRACT FROM AUTHOR]

Increasing speed and magnitude of global change threaten the world's biodiversity and particularly coral reef fishes. A better understanding of large-scale patterns and processes on coral reefs is essential to prevent fish biodiversity decline but it requires new monitoring approaches. Here, we use environmental DNA metabarcoding to reconstruct well-known patterns of fish biodiversity on coral reefs and uncover hidden patterns on these highly diverse and threatened ecosystems. We analysed 226 environmental DNA (eDNA) seawater samples from 100 stations in five tropical regions (Caribbean, Central and Southwest Pacific, Coral Triangle and Western Indian Ocean) and compared those to 2047 underwater visual censuses from the Reef Life Survey in 1224 stations. Environmental DNA reveals a higher (16%) fish biodiversity, with 2650 taxa, and 25% more families than underwater visual surveys. By identifying more pelagic, reef-associated and crypto-benthic species, eDNA offers a fresh view on assembly rules across spatial scales. Nevertheless, the reef life survey identified more species than eDNA in 47 shared families, which can be due to incomplete sequence assignment, possibly combined with incomplete detection in the environment, for some species. Combining eDNA metabarcoding and extensive visual census offers novel insights on the spatial organization of the richest marine ecosystems. [ABSTRACT FROM AUTHOR]

Functional diversity (FD), the diversity of organism attributes that relates to their interactions with the abiotic and biotic environment, has been increasingly used for the last two decades in ecology, biogeography and conservation. Yet, FD has many facets and their estimations are not standardized nor embedded in a single tool. mFD (multifaceted functional diversity) is an R package that uses matrices of species assemblages and species trait values as building blocks to compute most FD indices. mFD is firstly based on two functions allowing the user to summarize trait and assemblage data. Then it calculates trait‐based distances between species pairs, informs the user whether species have to be clustered into functional entities and finally computes multidimensional functional space. To let the user choose the most appropriate functional space for computing multidimensional functional diversity indices, two mFD functions allow assessing and illustrating the quality of each functional space. Next, mFD provides 6 core functions to calculate 16 existing FD indices based on trait‐based distances, functional entities or species position in a functional space. The mFD package also provides graphical functions based on the ggplot library to illustrate FD values through customizable and high‐resolution plots of species distribution among functional entities or in a multidimensional space. All functions include internal validation processes to check for errors in data formatting which return detailed error messages. To facilitate the use of mFD framework, we built an associated website hosting five tutorials illustrating the use of all the functions step by step. [ABSTRACT FROM AUTHOR]

The contributions of species to ecosystem functions or services depend not only on their presence but also on their local abundance. Progress in predictive spatial modelling has largely focused on species occurrence rather than abundance. As such, limited guidance exists on the most reliable methods to explain and predict spatial variation in abundance. We analysed the performance of 68 abundance‐based species distribution models fitted to 800 000 standardised abundance records for more than 800 terrestrial bird and reef fish species. We found a large amount of variation in the performance of abundance‐based models. While many models performed poorly, a subset of models consistently reconstructed range‐wide abundance patterns. The best predictions were obtained using random forests for frequently encountered and abundant species and for predictions within the same environmental domain as model calibration. Extending predictions of species abundance outside of the environmental conditions used in model training generated poor predictions. Thus, interpolation of abundances between observations can help improve understanding of spatial abundance patterns, but our results indicate extrapolated predictions of abundance under changing climate have a much greater uncertainty. Our synthesis provides a road map for modelling abundance patterns, a key property of species distributions that underpins theoretical and applied questions in ecology and conservation. [ABSTRACT FROM AUTHOR]

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