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.

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.

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.

Background The páramo is a high-elevation biogeographical province in the northern Andes, known for its great biodiversity and ecosystem services. Because there have been very few biogeographic studies encompassing the entire province to date, this study aimed at conducting a phytogeographical regionalisation of the páramo. Specifically, (1) clustering analyses were conducted to identify the main phytogeographical units in the three altitudinal belts: sub-páramo, mid-páramo and super-páramo, and examine their diagnostic flora, (2) an ordination complemented the geo-climatic characterization of the obtained units and (3) a hierarchical classification transformation was obtained to evaluate the relationships between units. Methods The study area included the entire Andean páramo range in northern Peru, Ecuador, Colombia and Venezuela. The analyses were based on 1,647 phytosociological plots from the VegPáramo database. The K-means non-hierarchical clustering technique was used to obtain clusters identifiable as phytogeographical units, and the Ochiai fidelity index was calculated to identify their diagnostic species. A principal component analysis was conducted to obtain the geo-climatic characterization of each unit. Finally, the relationships between clusters were traced using a hierarchical plot-based classification. Results Fifteen clusters were obtained, 13 natural and two artificial, of which two represented the sub-páramo, nine the mid-páramo and four the super-páramo. Even though data representativeness was a potential limitation to segregate certain sub-páramo and super-páramo units, the overall bioregionalisation was robust and represented important latitudinal, altitudinal and climatic gradients. Discussion This study is the first to bioregionalise the páramo province based on a substantial widely distributed biological dataset, and therefore provides important novel scientific insight on its biogeography. The obtained phytogeographical units can be used to support further research on the páramo at smaller scale and on the humid Neotropical high-elevation ecosystems at broader-scale. Finally, several units were highlighted in our results as particularly worthy of further scientific and conservation focus.

The Andean páramo is a biodiverse and vulnerable tropical high-mountain region, whose spatio-ecological patterns remain understudied. The lack of general characterization of its overall extent, land-cover classes, and treeline spatial features hinders our capacity to understand its responses to human impacts and predict future land-system changes. To address this knowledge gap, we classified the land-cover of the páramo in the Northern Andes. Moreover, we estimated (1) the páramo’s total extent and its distribution among the Andean countries, (2) the relative extent of 12 of its main land-cover classes, categorized into natural vegetation, natural abiotic and anthropogenic groups, and (3) the preliminary position and anthropogenic influence of its bordering treeline. Relying on Landsat 8 imagery, we performed hybrid manual-automated classifications using the Maximum Likelihood and Random Forest algorithms. The two resulting final classifications were manually checked for errors compared to Google Earth and VegPáramo data, and used to produce the expert classification. Finally, we delimited the treeline based on regional forest connectivity, and applied it to the expert classification to evaluate páramo elevations, surface areas and land-cover classes above the Andean treeline. The páramo extent was estimated at 24,301 km², distributed between Ecuador (47%), Colombia (43%), Venezuela (8%) and Peru (2%). Natural vegetation, especially shrublands, rosette plant communities and grasslands were dominant (altogether, 65%), whereas classes reflecting intense land-use covered 12% overall. The average treeline reached 3546 m and was bordered uphill at 16% with anthropogenic land-cover classes. The páramo’s extent is smaller than previously suggested. It remains a (semi-) natural region, yet the expansion of crops and pastures towards high elevations is a critical concern for long-term sustainability. Future research can build on our findings to predict land-system changes and assess priority areas for conservation. We recommend for future research to focus on remnant forest patches and treeline connectivity in priority.