Inclusion of habitat availability in species distribution models through multi-temporal remote-sensing data?

In times of anthropogenic climate change and increasing rates of habitat loss in many areas of the world, spatially explicit predictions of species' ranges using species distribution models (SDMs) have become of central interest in conservation biology. Such predictions can be derived using species records from museum collections or field surveys combined with, for example, climate and/or land cover data stored in geographic information systems (GIS). Although much attention has been paid to the application of bioclimatic data for SDMs development, the inclusion of land cover information derived from remote sensing is still at the beginning. Herein, we tested for the first time whether SDMs can be improved by inclusion of seasonality information derived from multi-temporal remote-sensing data. We compared models computed for eight Mexican anurans using five different sets of predictors comprising either bioclimatic or remote-sensing data or combinations thereof. Our results suggested that the appropriate set of predictor variables is very much dependent on the species-specific habitat preferences and the patchiness/spatial fragmentation of the suitable habitat types. For species occupying spatially fragmented habitats, spatial predictions based on pure bioclimatic data were less detailed. On the contrary, especially for rather generalist species, SDMs using only remote-sensing data for model development tended to overpredict the species' ranges. For most species, the best strategy for SDM development was a combined model design using both climate and remote-sensing data, while the best methodology of combining the two data sets varied between species. This combined model design allowed us to incorporate the advantages of both approaches, i.e., inclusion of habitat availability using only remote-sensing data and high spatial definition by using bioclimatic variables, by avoidance of the drawbacks of each of them.