MotivationThe 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 includedThe 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 grainBioTIME 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 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).Time period and grainBioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.Major taxa and level of measurementBioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.Software format.csv and .SQL.
The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.
AimWe addressed the roles of environmental filtering, historical biogeography and evolutionary niche conservatism on the phylogenetic structure of tropical tree communities with the following questions. (1) What is the impact of mesoclimatic gradients and dispersal limitation on phylogenetic turnover and species turnover?(2) How does phylogenetic turnover between continents compare in intensity with the turnover driven by climatic gradients at a regional scale? (3) Are independent phylogenetic reconstructions of the mesoclimatic niche of clades congruent between continents?Location Panama Canal Watershed and Western Ghats (India), two anciently divergent biogeographic contexts but with comparable rainfall gradients.Methods Using floristic data for 50 1-ha plots in each region, independent measures of phylogenetic turnover (PST) and species turnover (Jaccard) between plots were regressed on geographic and ecological distances. Mesoclimatic niches were reconstructed for each node of the phylogeny and compared between the two continents. Results(1) The phylogenetic turnover within each region is best explained by mesoclimatic differences (environmental filtering), while species turnover depends both on mesoclimatic differences and geographic distances (dispersal limitation).(2) The phylogenetic turnover between continents (PST = 0.009) is comparable to that caused by mesoclimatic gradients within regions (PST = 0.010) and both effects seem cumulative. (3) Independent phylogenetic reconstructions of the mesoclimatic niches were strongly correlated between the two continents (r = 0.61), despite the absence of shared species. Main conclusionsOur results demonstrate a world-wide deep phylogenetic signal for mesoclimatic niche within a biome, indicating that positive phylogenetic turnover at a regional scale reflects environmental filtering in plant communities.
Aim Species distribution modelling typically relies completely or partially on climatic variables as predictors, overlooking the fact that these are themselves predictions with associated uncertainties. This is particularly critical when such predictors are interpolated between sparse station data, such as in the tropics. The goal of this study is to provide a new set of satellite‐based climatic predictor data and to evaluate its potential to improve modelled species–climate associations and transferability to novel geographical regions. Location Rain forests areas of Central Africa, the Western Ghats of India and South America. Methods We compared models calibrated on the widely used WorldClim station‐interpolated climatic data with models where either temperature or precipitation data from WorldClim were replaced by data from CRU, MODIS, TRMM and CHIRPS. Each predictor set was used to model 451 plant species distributions. To test for chance associations, we devised a null model with which to compare the accuracy metric obtained for every species. Results Fewer than half of the studied rain forest species distributions matched the climatic pattern better than did random distributions. The inclusion of MODIS temperature and CHIRPS precipitation estimates derived from remote sensing each allowed for a better than random fit for respectively 40% and 22% more species than models calibrated on WorldClim. Furthermore, their inclusion was positively related to a better transferability of models to novel regions. Main conclusions We provide a newly assembled dataset of ecologically meaningful variables derived from MODIS and CHIRPS for download, and provide a basis for choosing among the plethora of available climate datasets. We emphasize the need to consider the method used in the production of climate data when working on a region with sparse meteorological station data. In this context, remote sensing data should be the preferred choice, particularly when model transferability to novel climates or inferences on causality are invoked.
The spatially implicit neutral model (SINM) of S. P. Hubbell predicts species' abundance distributions at two levels: local communities where extinction balances immigration, characterized by the immigration number I, and the metacommunity, a source pool of migrants where speciation balances extinction. Previously, a plot's I was estimated from its species abundance distribution. Here, we relate neutral theory to the additive partitioning of species diversity and calculate the immigration rate into different plots from the variation in species composition among them. We revisit the G(ST) statistic of population genetics to introduce a new version, G(ST)(k), conditional on each community sample k. We derive an analytical expectation of G(ST)(k) as a function of the local immigration number, I(k), under a generalized version of the SINM, which allows the pool of migrants to deviate from the large-scale speciation-extinction balance. Simulations and field data suggest that G(ST)(k) provides reasonable estimates of immigration numbers, which were compared with the results from alternative likelihood-based estimations.
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