Abstract

Abstract: We used Species Distribution Modeling to predict the probability of Iberian pine (Pinus nigra subsp. salzmannii [Dunal] Franco) occurrences in southern Spain in response to environmental variables and to forecast the effects of climate change on their predicted geographical distribution. The ensemble modeling approach “biomod2” was used, together with present Iberian pine data, to predict the current potential distribution based on bioclimatic explanatory variables (200 m resolution) and to forecast future suitability by studying three periods (2040, 2070, and 2100), considering the Global Circulation Models BCM2, CNCM3, and ECHAM5, and the regional model EGMAM, for different scenarios (SRAB1, SRA2, SRB1). Model evaluation was performed using Kappa, True Skills Statistic (TSS), and Area Under the Curve (AUC) values. The biomod2 approach highlighted the average number of days with a minimum temperature equal to or below 0°C, annual precipitation, and aridity index as the most important variables to describe the P. nigra occurrence probability. Model performances were generally satisfactory and the highest AUC values and high stability of the results were given by GAM and GLM, but MaxEnt and the SRE model were scarcely accurate according to all our statistics. The ensemble Species Distribution Modeling of P. nigra in Andalusia predicted the highest probability of species occurrence in the eastern areas, Sierra de Cazorla being the area with the highest occurrence of P. nigra in Andalusia. In the future habitat, the general probability of P. nigra occurrence in Andalusia will decrease widely; the species is expected to lose habitat suitability at moderate altitudes and its occurrence probability will have decreased by nearly 70% on average by 2100, affected by the selected scenario. Populations in Sierra de Cazorla are those most suitable for P. nigra growth, even under the most pessimistic scenarios. It is likely that the natural southern populations of P. nigra will be very sensitive to changes in climate.

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