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The global network of protected areas has rapidly expanded in the past decade and is expected to cover at least 30% of land and sea by 2030 to halt biodiversity erosion. Yet, the distribution of protected areas is highly heterogeneous on Earth and the social-environmental preconditions enabling or hindering protected area establishment remain poorly understood. Here, using fourteen socioeconomic and environmental factors, we characterize the multidimensional niche of terrestrial and marine protected areas, which we use to accurately establish, at the global scale, whether a particular location has preconditions favourable for paestablishment. We reveal that protected areas, particularly the most restrictive ones, over-aggregate where human development and the number of non-governmental organizations are high. Based on the spatial distribution of vertebrates and the likelihood to convert non-protected areas into strictly protected areas, we identify 'potential' versus 'unrealistic' conservation gains on land and sea, which we define as areas of high vertebrate diversity that are, respectively, favourable and unfavourable to protected area establishment. Where protected areas are unrealistic, alternative strategies such as other effective area-based conservation measures or privately protected areas, could deliver conservation outcomes. In this study, the authors show that the placement of protected areas is globally highly heterogenous but can be accurately predicted from a reduced set of socioeconomic and environmental factors. These predictions highlight that most unprotected areas critical for the conservation of vertebrates are located in unfavourable conditions to establish future protected areas. [ABSTRACT FROM AUTHOR]

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]