Kaikki aineistot
Lisää
Abstract We investigated whether environmental filtering or dispersal-related factors mostly drive helophyte and hydrophyte species richness and community composition in 93 lakes situated in Baikal Siberia. Using partial linear regression and partial redundancy analysis, we studied (1) what are the relative roles of environmental variables, dispersal variables, spatial processes and region identity (i.e., river basins) in explaining variation in the species richness and species composition of helophytes and hydrophytes across 93 Siberian lakes, and (2) what are the differences in the most important explanatory variables driving community variation in helophytes versus hydrophytes? We found that, for both species richness and species composition, environmental variables clearly explained most variation for both plant groups, followed by region identity and dispersal-related variables. Spatial variables were significant only for the species composition of hydrophytes. Nutrient-salinity index, a proxy for habitat trophic-salinity status, was by far the most significant environmental determinant of helophytes and hydrophytes. Our results indicate that environmental factors explained the most variation in both species richness and species composition of helophytes and hydrophytes. Nevertheless, dispersal-related variables (i.e. spatial and dispersal) were also influential but less important than environmental factors. Furthermore, the dispersal-related variables were more important for hydrophytes than for helophytes. Most brackish permanent lakes were mostly located in the steppe biomes of southern Transbaikalia. This characteristic along with the oldest age, the largest distances to both river and settlements and the lowest temperatures in the study region distinguished them from freshwater, drained and more nutrient-rich floodplain lakes.
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.
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.