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