Remote sensing data can improve predictions of species richness by stacked species distribution models: A case study for Mexican pines

Abstract

Aim: Remote sensing data have been used in a growing number of studies to directly predict species richness or to improve the performance of species distribution models (SDMs), but their suitability for stacked species distribution models (S-SDMs) remains unclear. In this case study, we evaluated the potential and limitations of remotely sensed data in S-SDMs and addressed the commonly observed overestimation of species richness by S-SDMs. Location: Mexico. Methods: Phenological and statistical metrics were derived from remotely sensed time series data (2001-2009) of the Terra-MODIS enhanced vegetation index and land surface temperature products. In a series of climatic and remote sensing-based SDMs, the distribution ranges of 40 species of the genus Pinus (Pinaceae) were modelled based on presence-only herbarium and field data using the maximum entropy algorithm and summed to estimate species richness. Three different species-specific thresholds were applied to convert continuous model predictions into binary maps. Modelled species richness was compared to independent data from the Mexican National Forest Inventory. Results: The inclusion of remote sensing data led to significantly better predictions of species richness in comparison to the climate-based models for the summed suitabilities and all thresholds considered. Both climatic and remote sensing-based models allowed us to identify the areas with the highest pine species richness based on presence-only data. Remote sensing-based models compare closely with climate-derived patterns, but provide better spatial resolution and more detailed information on local habitat availability. Main conclusions: The results of this case study provide general guidance for the potential and limitations of using remote sensing data in S-SDMs. Our results confirmed that remote sensing data may not only have the capability for improving individual SDMs, but also can be a potential tool for reducing the overestimation of species richness by S-SDMs. This approach opens up new possibilities for species richness predictions in areas where biological survey data are scarce and where no species richness inventory data exist. © 2013 John Wiley & Sons Ltd.