Comparing the suitability of classified land cover data and remote sensing variables for modeling distribution patterns of plants

Given the rapid loss of biodiversity worldwide and the resulting impacts on ecosystem functions and services, we more than ever rely on current and spatially continuous assessments of species distributions for biodiversity conservation and sustainable land management. Over the last decade, the usefulness of categorical land cover data to account for the human-induced degradation, transformation and loss of natural habitat in species distribution models (SDMs) has been questioned and the number of studies directly analyzing remotely sensed variables has lately multiplied. While several assumptions support the advantages of remote sensing data, an empirical comparison is still lacking. The objective of this study was to bridge this gap and compare the suitability of an existing categorical land cover classification and of continuous remote sensing variables for modeling the distribution patterns of 30 Mexican tree species. We applied the Maximum Entropy algorithm to predict species distributions based on both data types independently, quantified model performance and analyzed species-land cover relationships in detail. As part of this comparison, we focused on two particular aspects, namely the effects of (1) thematic detail and (2) spatial resolution of the land cover data on model performance. Our analysis revealed that remote sensing data were significantly better model predictors and that the main obstacle of the land cover-based SDMs were their bolder predictions, together with their overall overestimation of suitability. Among the land cover-based models, we found that thematic detail was more important than spatial resolution for SDM performance. However, our results also suggest that the suitability of land cover data differs largely among species and is dependent on their habitat distinctiveness. Our findings have relevant implications for future species distribution modeling studies which aim at complementing their set of topo-climatic predictors by data on land surface characteristics. (C) 2013 Elsevier B.V. All rights reserved.