Using remote sensing data in ecological niche models to forecast species' extinction risks
Traditionally, Ecological Niche Models (ENMs) are calculated with variables from long temporal data series, representing averaged weather conditions, and probably hiding current trends originated from the current environment. Satellite imagery can provide an instantaneous picture of the environment, identifying natural and human-induced changes, which in turn will be reflected in ENMs calculated only with remote sensing data (RS-ENMs). Human-induced changes (e.g. habitat degradation and fragmentation) will be associated with low values of habitat suitability, and thus to a low species' presence probability. Species living in places with larger environmental changes (e.g. urban areas) may be prone to higher extinction risks. Other variables like surface temperature will be associated to species' vulnerability to extreme weather periods. Considering climate change events, species adapted to cold habitats will present higher extinction risks with temperature increments, and species of hotter habitats, lower risks. Therefore, comparison by Gap Analysis of RS-ENMs of successive dates will identify potential expansions and contractions in species' ranges. Species with accumulated ranges' contractions will be more threatened. RS-ENMs will map quantitatively the spatial distribution of potential extinction risks to monitor how species respond to human-mediated environmental changes and to develop dynamic forecasts for future changes.