Lysenko 91,92 | Armin Macanović 93 | Parastoo Mahdavi 94 | Peter Manning 35 | Corrado Marcenò 13 | Vassiliy Martynenko 95 | Maurizio Mencuccini 96 | Vanessa Minden 97 | Jesper Erenskjold Moeslund 54 | Marco Moretti 98 | Jonas V. Müller 99 | Abstract Aims: Vegetation-plot records provide information on the presence and cover or abundance of plants co-occurring in the same community. Vegetation-plot data are spread across research groups, environmental agencies and biodiversity research centers and, thus, are rarely accessible at continental or global scales. Here we present the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level.Results: sPlot version 2.1 contains records from 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected worldwide between 1885 and 2015. We complemented the information for each plot by retrieving climate and soil conditions and the biogeographic context (e.g., biomes) from external sources, and by calculating community-weighted means and variances of traits using gap-filled data from the global plant trait database TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. We present the first maps of global patterns of community richness and community-weighted means of key traits. Conclusions: The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale. K E Y W O R D S biodiversity, community ecology, ecoinformatics, functional diversity, global scale, macroecology, phylogenetic diversity, plot database, sPlot, taxonomic diversity, vascular plant, vegetation relevé 166 |

This dataset provides the Global Naturalized Alien Flora (GloNAF) database, version 1.2. GloNAF represents a data compendium on the occurrence and identity of naturalized alien vascular plant taxa across geographic regions (e.g. countries, states, provinces, districts, islands) around the globe. The dataset includes 13,939 taxa and covers 1,029 regions (including 381 islands). The dataset is based on 210 data sources. For each taxon‐by‐region combination, we provide information on whether the taxon is considered to be naturalized in the specific region (i.e. has established self‐sustaining populations in the wild). Non‐native taxa are marked as “alien”, when it is not clear whether they are naturalized. To facilitate alignment with other plant databases, we provide for each taxon the name as given in the original data source and the standardized taxon and family names used by The Plant List Version 1.1 (http://www.theplantlist.org/). We provide an ESRI shapefile including polygons for each region and information on whether it is an island or a mainland region, the country and the Taxonomic Databases Working Group (TDWG) regions it is part of (TDWG levels 1–4). We also provide several variables that can be used to filter the data according to quality and completeness of alien taxon lists, which vary among the combinations of regions and data sources. A previous version of the GloNAF dataset (version 1.1) has already been used in several studies on, for example, historical spatial flows of taxa between continents and geographical patterns and determinants of naturalization across different taxonomic groups. We intend the updated and expanded GloNAF version presented here to be a global resource useful for studying plant invasions and changes in biodiversity from regional to global scales. We release these data into the public domain under a Creative Commons Zero license waiver (https://creativecommons.org/share-your-work/public-domain/cc0/). When you use the data in your publication, we request that you cite this data paper. If GloNAF is a major part of the data analyzed in your study, you should consider inviting the GloNAF core team (see Metadata S1: Originators in the Overall project description) as collaborators. If you plan to use the GloNAF dataset, we encourage you to contact the GloNAF core team to check whether there have been recent updates of the dataset, and whether similar analyses are already ongoing.

Aim Species–area relationships (SARs) are fundamental scaling laws in ecology although their shape is still disputed. At larger areas, power laws best represent SARs. Yet, it remains unclear whether SARs follow other shapes at finer spatial grains in continuous vegetation. We asked which function describes SARs best at small grains and explored how sampling methodology or the environment influence SAR shape. Location Palaearctic grasslands and other non‐forested habitats. Taxa Vascular plants, bryophytes and lichens. Methods We used the GrassPlot database, containing standardized vegetation‐plot data from vascular plants, bryophytes and lichens spanning a wide range of grassland types throughout the Palaearctic and including 2,057 nested‐plot series with at least seven grain sizes ranging from 1 cm2 to 1,024 m2. Using nonlinear regression, we assessed the appropriateness of different SAR functions (power, power quadratic, power breakpoint, logarithmic, Michaelis–Menten). Based on AICc, we tested whether the ranking of functions differed among taxonomic groups, methodological settings, biomes or vegetation types. Results The power function was the most suitable function across the studied taxonomic groups. The superiority of this function increased from lichens to bryophytes to vascular plants to all three taxonomic groups together. The sampling method was highly influential as rooted presence sampling decreased the performance of the power function. By contrast, biome and vegetation type had practically no influence on the superiority of the power law. Main conclusions We conclude that SARs of sessile organisms at smaller spatial grains are best approximated by a power function. This coincides with several other comprehensive studies of SARs at different grain sizes and for different taxa, thus supporting the general appropriateness of the power function for modelling species diversity over a wide range of grain sizes. The poor performance of the Michaelis–Menten function demonstrates that richness within plant communities generally does not approach any saturation, thus calling into question the concept of minimal area.

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Aim Alpine ecosystems differ in area, macroenvironment and biogeographical history across the Earth, but the relationship between these factors and plant species richness is still unexplored. Here, we assess the global patterns of plant species richness in alpine ecosystems and their association with environmental, geographical and historical factors at regional and community scales. Location Global. Time period Data collected between 1923 and 2019. Major taxa studied Vascular plants. Methods We used a dataset representative of global alpine vegetation, consisting of 8,928 plots sampled within 26 ecoregions and six biogeographical realms, to estimate regional richness using sample‐based rarefaction and extrapolation. Then, we evaluated latitudinal patterns of regional and community richness with generalized additive models. Using environmental, geographical and historical predictors from global raster layers, we modelled regional and community richness in a mixed‐effect modelling framework. Results The latitudinal pattern of regional richness peaked around the equator and at mid‐latitudes, in response to current and past alpine area, isolation and the variation in soil pH among regions. At the community level, species richness peaked at mid‐latitudes of the Northern Hemisphere, despite a considerable within‐region variation. Community richness was related to macroclimate and historical predictors, with strong effects of other spatially structured factors. Main conclusions In contrast to the well‐known latitudinal diversity gradient, the alpine plant species richness of some temperate regions in Eurasia was comparable to that of hyperdiverse tropical ecosystems, such as the páramo. The species richness of these putative hotspot regions is explained mainly by the extent of alpine area and their glacial history, whereas community richness depends on local environmental factors. Our results highlight hotspots of species richness at mid‐latitudes, indicating that the diversity of alpine plants is linked to regional idiosyncrasies and to the historical prevalence of alpine ecosystems, rather than current macroclimatic gradients.

The genus Stipa L. comprises over 150 species, all native to the Old World, where they grow in warm temperate regions throughout Europe, Asia, and North Africa. It is one of the largest genera in the family Poaceae in Middle Asia, where one of its diversity hotspots is located. However, identification of Middle Asian Stipa species is difficult because of the lack of new, comprehensive taxonomic studies including all of the species recorded in the region. We present a critical review of the Mid-Asian representatives of Stipa, together with an identification key and taxonomic listing. We relied on both published and unpublished information for the taxa involved, many of which are poorly known. For each taxon, we present a taxonomic and nomenclatural overview, habitat preferences, distribution, altitudinal range, and additional notes as deemed appropriate. We describe four new nothospecies: S. ×balkanabatica M. Nobis & P. D. Gudkova, S. ×dzungarica M. Nobis, S. ×pseudomacroglossa M. Nobis, S. ×subdrobovii M. Nobis & A. Nowak, one subspecies S. caucasica Schmalh. subsp. nikolai M. Nobis, A. Nobis & A. Nowak, and eight varieties: S. araxensis Grossh. var. mikojanovica M. Nobis, S. caucasica var. fanica M. Nobis, P. D. Gudkova & A. Nowak, S. drobovii (Tzvelev) Czerep. var. jarmica M. Nobis, S. drobovii var. persicorum M. Nobis, S. glareosa P. A. Smirn. var. nemegetica M. Nobis, S. kirghisorum P. A. Smirn. var. balkhashensis M. Nobis & P. D. Gudkova, S. richteriana Kar. & Kir. var. hirtifolia M. Nobis & A. Nowak, and S. ×subdrobovii var. pubescens M. Nobis & A. Nowak. Additionally, 12 new combinations, Achnatherum haussknechtii (Boiss.) M. Nobis, A. mandavillei (Freitag) M. Nobis, A. parviflorum (Desf.) M. Nobis, Neotrinia chitralensis (Bor) M. Nobis, S. badachschanica Roshev. var. pamirica (Roshev.) M. Nobis, S. borysthenica Klokov ex Prokudin var. anomala (P. A. Smirn.) M. Nobis, S. holosericea Trin. var. transcaucasica (Grossh.) M. Nobis, S. kirghisorum P. A. Smirn. var. ikonnikovii (Tzvelev) M. Nobis, S. macroglossa P. A. Smirn. var. kazachstanica (Kotuchov) M. Nobis, S. macroglossa var. kungeica (Golosk.) M. Nobis, S. richteriana var. jagnobica (Ovcz. & Czukav.) M. Nobis & A. Nowak, and S. zalesskii Wilensky var. turcomanica (P. A. Smirn.) M. Nobis are proposed, and the lectotypes for 14 taxa (S. arabica Trin. & Rupr., S. bungeana Trin. ex Bunge, S. caspia K. Koch, S. ×consanguinea Trin. & Rupr., S. effusa Mez, S. ×heptapotamica Golosk., S. jacquemontii Jaub. & Spach., S. kungeica Golosk., S. margelanica P. A. Smirn., S. richteriana, S. rubentiformis P. A. Smirn., S. sareptana A. K. Becker, S. tibetica Mez, and Timouria saposhnikovii Roshev.) are designated. In Middle Asia the genus Stipa comprises 98 taxa, including 72 species, four subspecies, and 22 varieties. Of the 72 species of feather grasses, 23 are of hybrid origin (nothospecies). In Middle Asia, feather grasses can be found at elevations from (0 to)300 to 4500(to 5000) m, but most are montane species. The greatest species richness is observed at altitudes between 1000 and 2500 m. Nineteen species grow above 3000 m, but only nine above 4000 m. The number of taxa (species and subspecies) growing in each country also varies considerably, with the highest noted in Kazakhstan (42), Tajikistan (40), and Kyrgyzstan (35). Of the 76 taxa of Stipa (species and subspecies) recorded in Middle Asia, 41 are confined to the region, with some being known only from a single country or mountain range. Distribution maps of selected species are provided.

Despite the paramount role of plant diversity for ecosystem functioning, biogeochemical cycles, and human welfare, knowledge of its global distribution is still incomplete, hampering basic research and biodiversity conservation.Here, we used machine learning (random forests, extreme gradient boosting, and neural networks) and conventional statistical methods (generalized linear models and generalized additive models) to test environment-related hypotheses of broad-scale vascular plant diversity gradients and to model and predict species richness and phylogenetic richness worldwide. To this end, we used 830 regional plant inventories including c. 300 000 species and predictors of past and present environmental conditions.Machine learning showed a superior performance, explaining up to 80.9% of species richness and 83.3% of phylogenetic richness, illustrating the great potential of such techniques for disentangling complex and interacting associations between the environment and plant diversity. Current climate and environmental heterogeneity emerged as the primary drivers, while past environmental conditions left only small but detectable imprints on plant diversity.Finally, we combined predictions from multiple modeling techniques (ensemble predictions) to reveal global patterns and centers of plant diversity at multiple resolutions down to 7774 km 2 . Our predictive maps provide accurate estimates of global plant diversity available at grain sizes relevant for conservation and macroecology.

Aim Understanding the variation in community composition and species abundances (i.e., β‐diversity) is at the heart of community ecology. A common approach to examine β‐diversity is to evaluate directional variation in community composition by measuring the decay in the similarity among pairs of communities along spatial or environmental distance. We provide the first global synthesis of taxonomic and functional distance decay along spatial and environmental distance by analysing 148 datasets comprising different types of organisms and environments. Location Global. Time period 1990 to present. Major taxa studied From diatoms to mammals. Method We measured the strength of the decay using ranked Mantel tests (Mantel r) and the rate of distance decay as the slope of an exponential fit using generalized linear models. We used null models to test whether functional similarity decays faster or slower than expected given the taxonomic decay along the spatial and environmental distance. We also unveiled the factors driving the rate of decay across the datasets, including latitude, spatial extent, realm and organismal features. Results Taxonomic distance decay was stronger than functional distance decay along both spatial and environmental distance. Functional distance decay was random given the taxonomic distance decay. The rate of taxonomic and functional spatial distance decay was fastest in the datasets from mid‐latitudes. Overall, datasets covering larger spatial extents showed a lower rate of decay along spatial distance but a higher rate of decay along environmental distance. Marine ecosystems had the slowest rate of decay along environmental distances. Main conclusions In general, taxonomic distance decay is a useful tool for biogeographical research because it reflects dispersal‐related factors in addition to species responses to climatic and environmental variables. Moreover, functional distance decay might be a cost‐effective option for investigating community changes in heterogeneous environments.