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Species distribution modelling typically relies completely or partially on climatic variables as predictors. These variables themselves are predictors, with associated uncertainties. The authors provide a new set of ecologically-relevant satellite-based climatic predictor data, critical for their work in the tropical forests of Africa, where there is sparse meteorological station data. This improved their ability to model species distribution for up to 40% more species than models calibrated on WorldClim. In this context, remote sensing data should be the preferred choice, particularly when model transferability to novel climates or inferences on causality are invoked.
 
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An article by the RAINBIO group
 
Citation
Deblauwe V., Droissart V., Bose R., Sonké B., Blach-Overgaard A., Svenning J.-C., Wieringa J.J., Ramesh B.R., Stévart T., Couvreur T.L.P. (2016) Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics. Global Ecology and Biogeography 25(4): 443-454. <doi: 10.1111/geb.12426>