Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity... Show moreMotivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km(2) (158 cm(2)) to 100 km(2) (1,000,000,000,000 cm(2)).Time period and grainBio: TIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates. Show less
AimDespite several recent efforts to map plant traits and to identify their climatic drivers, there are still major gaps. Global trait patterns for major functional groups, in particular, the... Show moreAimDespite several recent efforts to map plant traits and to identify their climatic drivers, there are still major gaps. Global trait patterns for major functional groups, in particular, the differences between woody and herbaceous plants, have yet to be identified. Here, we take advantage of big data efforts to compile plant species occurrence and trait data to analyse the spatial patterns of assemblage means and variances of key plant traits. We tested whether these patterns and their climatic drivers are similar for woody and herbaceous plants. LocationNew World (North and South America). MethodsUsing the largest currently available database of plant occurrences, we provide maps of 200 × 200 km grid‐cell trait means and variances for both woody and herbaceous species and identify environmental drivers related to these patterns. We focus on six plant traits: maximum plant height, specific leaf area, seed mass, wood density, leaf nitrogen concentration and leaf phosphorus concentration. ResultsFor woody assemblages, we found a strong climate signal for both means and variances of most of the studied traits, consistent with strong environmental filtering. In contrast, for herbaceous assemblages, spatial patterns of trait means and variances were more variable, the climate signal on trait means was often different and weaker. Main conclusionTrait variations for woody versus herbaceous assemblages appear to reflect alternative strategies and differing environmental constraints. Given that most large‐scale trait studies are based on woody species, the strikingly different biogeographic patterns of herbaceous traits suggest that a more synthetic framework is needed that addresses how suites of traits within and across broad functional groups respond to climate. Show less
