The authors evaluated the ecological realism of BioScore, a European scale species distribution model (SDM) of 1476 terrestrial vascular plant species. It is shown that the species niche optima of temperature are ecologically most realistic, which reflects that temperature is one of the most important factors driving the distribution of species at large scales.
Species distribution models (SDMs) are routinely applied to assess current as well as future species distributions, for example to assess impacts of future environmental change on biodiversity or to underpin conservation planning. It has been repeatedly emphasized that SDMs should be evaluated based not only on their goodness of fit to the data, but also on the realism of the modeled ecological responses. However, possibilities for the latter are hampered by limited knowledge on the true responses as well as a lack of quantitative evaluation methods.
The authors compared modeled niche optima, obtained from European-scale SDMs of 1476 terrestrial vascular plant species, with empirical ecological indicator values indicating the preferences of plant species for key environmental conditions. For each plant species they first fitted an ensemble SDM including three modeling techniques (GLM, GAM and BRT) and extracted niche optima for climate, soil, land use and nitrogen deposition variables with a large explanatory power for the occurrence of that species. Then they compared these SDM-derived niche optima with the ecological indicator values by means of bivariate correlation analysis.
Weak to moderate correlations were found in the expected direction between the SDM-derived niche optima and ecological indicator values. The strongest correlation occurred between the modeled optima for growing degree days and the ecological indicator values for temperature. Correlations were weaker for SDM-derived niche optima with a more distal relationship to ecological indicator values (notably precipitation and soil moisture). Further, correlations were consistently highest for BRT, followed by GLM and GAM.
The method gives insight into the ecological realism of modeled niche optima and projected core habitats and can be used to improve SDMs by making a more informed selection of environmental variables and modeling techniques.