Ecological niche model comparison under different climate scenarios: a case study of Olea spp. in Asia

Abstract

Ecological niche modeling (and the related species distribution modeling) has been used as a tool with which to assess potential impacts of climate change processes on geographic distributions of species. However, the factors introducing variation into niche modeling outcomes are not well understood: To this end, we used seven algorithms to develop models (Maxent, GARP, BIOCLIM, artificial neural networks, support‐vector machines, climate envelope, and environmental distance) to estimate the potential geographic distribution of olives (Olea europaea sensu lato, including Olea ferruginea) under two climatic data sets (current 2000 and future 2050). Five general circulation models and two representative concentration pathway scenarios were used as predictor variables in future projections of the geographic potential of this species; models were fit at global extents (10′ spatial resolution) but transferred and interpreted for a region of particular interest in Central Asia, which largely avoids problems with truncation of niche estimates. We found marked differences among approaches in predicted distributions and model performance, as well as in the future distributional pattern reconstructed, from one algorithm to another. These general approaches, when model‐to‐model variation is managed appropriately, appear promising in predicting the potential geographic distribution of O. europaea sensu lato and thus can be an effective tool in restoration and conservation planning for wild populations, as well as possible commercial plantations of this species.