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Increasing human pressures and global environmental change may severely affect the diversity of species assemblages and associated ecosystem services. Despite the recent interest in phylogenetic and functional diversity, our knowledge on large spatio-temporal patterns of demersal fish diversity sampled by trawling remains still incomplete, notably in the Mediterranean Sea, one of the most threatened marine regions of the world. We investigated large spatio-temporal diversity patterns by analysing a dataset of 19,886 hauls from 10 to 800 m depth performed annually during the last two decades by standardised scientific bottom trawl field surveys across the Mediterranean Sea, within the MEDITS program. A multicomponent (eight diversity indices) and multi-scale (local assemblages, biogeographic regions to basins) approach indicates that only the two most traditional components (species richness and evenness) were sufficient to reflect patterns in taxonomic, phylogenetic or functional richness and divergence. We also put into question the use of widely computed indices that allow comparing directly taxonomic, phylogenetic and functional diversity within a unique mathematical framework. In addition, demersal fish assemblages sampled by trawl do not follow a continuous decreasing longitudinal/latitudinal diversity gradients (spatial effects explained up to 70.6% of deviance in regression tree and generalised linear models), for any of the indices and spatial scales analysed. Indeed, at both local and regional scales species richness was relatively high in the Iberian region, Malta, the Eastern Ionian and Aegean seas, meanwhile the Adriatic Sea and Cyprus showed a relatively low level. In contrast, evenness as well as taxonomic, phylogenetic and functional divergences did not show regional hotspots. All studied diversity components remained stable over the last two decades. Overall, our results highlight the need to use complementary diversity indices through different spatial scales when developing conservation strategies and defining delimitations for protected areas. SI

Abstract Motivation Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co‐occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ‘sPlot’, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open‐access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local‐to‐regional datasets to openly release data. We thus present sPlotOpen, the largest open‐access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained Vegetation plots (n = 95,104) recording cover or abundance of naturally co‐occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot‐level data also include community‐weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain Global, 0.01–40,000 m². Time period and grain 1888–2015, recording dates. Major taxa and level of measurement 42,677 vascular plant taxa, plot‐level records. Software format Three main matrices (.csv), relationally linked. Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung http://dx.doi.org/10.13039/501100001711 National Research, Development and Innovation Office, Hungar http://dx.doi.org/10.13039/501100018818 German Centre for Integrative Biodiversity Research http://dx.doi.org/10.13039/501100020056 Russian Foundation for Basic Research http://dx.doi.org/10.13039/501100002261 Grantová Agentura České Republiky http://dx.doi.org/10.13039/501100001824 Agence Nationale de la Recherche http://dx.doi.org/10.13039/501100001665 Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659 Brazil’s National Council of Scientific and Technological Development Horizon 2020 Framework Programme http://dx.doi.org/10.13039/100010661 U.S. National Science Foundation http://dx.doi.org/10.13039/100000001 Akademie Věd České Republiky http://dx.doi.org/10.13039/501100004240 H2020 European Research Council http://dx.doi.org/10.13039/100010663 Spanish Research Agency http://dx.doi.org/10.13039/501100011033 Narodowe Centrum Nauki http://dx.doi.org/10.13039/501100004281 Volkswagen Foundation http://dx.doi.org/10.13039/501100001663 Basque Government http://dx.doi.org/10.13039/501100003086 Fundación BBVA http://dx.doi.org/10.13039/100007406 Villum Fonden http://dx.doi.org/10.13039/100008398 NSF http://dx.doi.org/10.13039/100003187 Latvia grant