@article{fdi:010066731,
  title = {{R}emotely sensed temperature and precipitation data improve species distribution modelling in the tropics},
  author = {{D}eblauwe, {V}incent and {D}roissart, {V}incent and {B}ose, {R}. and {S}onke, {B}. and {B}lach-{O}vergaard, {A}. and {S}venning, {J}. {C}. and {W}ieringa, {J}. {J}. and {R}amesh, {B}. {R}. and {S}tevart, {T}. and {C}ouvreur, {T}homas},
  editor = {},
  language = {{ENG}},
  abstract = {{A}im{S}pecies distribution modelling typically relies completely or partially on climatic variables as predictors, overlooking the fact that these are themselves predictions with associated uncertainties. {T}his is particularly critical when such predictors are interpolated between sparse station data, such as in the tropics. {T}he 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. {L}ocation{R}ain forests areas of {C}entral {A}frica, the {W}estern {G}hats of {I}ndia and {S}outh {A}merica. {M}ethods{W}e compared models calibrated on the widely used {W}orld{C}lim station-interpolated climatic data with models where either temperature or precipitation data from {W}orld{C}lim were replaced by data from {CRU}, {MODIS}, {TRMM} and {CHIRPS}. {E}ach predictor set was used to model 451 plant species distributions. {T}o test for chance associations, we devised a null model with which to compare the accuracy metric obtained for every species. {R}esults{F}ewer than half of the studied rain forest species distributions matched the climatic pattern better than did random distributions. {T}he 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 {W}orld{C}lim. {F}urthermore, their inclusion was positively related to a better transferability of models to novel regions. {M}ain conclusions{W}e 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. {W}e emphasize the need to consider the method used in the production of climate data when working on a region with sparse meteorological station data. {I}n this context, remote sensing data should be the preferred choice, particularly when model transferability to novel climates or inferences on causality are invoked.},
  keywords = {{A}ssociation test ; {CHIRPS} ; ecological niche model ; {GLM} ; habitat ; suitability ; {M}ax{E}nt ; {MODIS} ; null model ; {TRMM} ; {W}orld{C}lim ; {AFRIQUE} ; {INDE} ; {AMERIQUE} {DU} {SUD} ; {ZONE} {TROPICALE}},
  booktitle = {},
  journal = {{G}lobal {E}cology and {B}iogeography},
  volume = {25},
  numero = {4},
  pages = {443--454},
  ISSN = {1466-822{X}},
  year = {2016},
  DOI = {10.1111/geb.12426},
  URL = {https://www.documentation.ird.fr/hor/fdi:010066731},
}