Improving species distribution models using satellite data

Species distributions models (SDMs) rely on accurate climatic data to predict the potential ranges of species, however, climatic datasets are often based on interpolations between weather stations. In regions where stations are scarce, such as the tropics, modelling can therefore be problematic.

Data resources used via GBIF : 50,000 species occurrences
Mount Cameroon by jbdodane

Mount Cameroon by jbdodane licensed under CC BY-NC 2.0

Species distributions models (SDMs) rely on accurate climatic data to predict the potential ranges of species, however, climatic datasets are often based on interpolations between weather stations. In regions where stations are scarce, such as the tropics, modelling can therefore be problematic. In this study, authors evaluated three alternative sources of climatic data based on remote-sensing (e.g. satellite data) by building SDMs of angiosperm plants in three different tropical regions using data from GBIF and other sources. When comparing the performance of SDMs based on WorldClim data alone versus data from remote-sensing databases, the latter performed consistently better. One explanation offered by the authors is that remote-sensing methods are able to capture complex climatic features, such as the rain shadow effect of Mount Cameroon and flooded forest bands around the Amazon, Sangha and Congo rivers, which aren’t available in interpolated climatic datasets.

Citations

Deblauwe V, Droissart V, Bose R, Sonké B, Blach-Overgaard A, Svenning J-C, Wieringa JJ, Ramesh BR, Stévart T and Couvreur TLP (2016) Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics. Global Ecology and Biogeography. Wiley-Blackwell, 443–454. Available at doi:10.1111/geb.12426

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