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 0 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.

Chilgoza pine is an economically and ecologically important evergreen coniferous tree species of the dry and rocky temperate zone, and a native of south Asia. This species is rated as near threatened (NT) by the International Union for Conservation of Nature (IUCN). This study hypothesized that climatic, soil and topographic variations strongly influence the distribution pattern and potential habitat suitability prediction of Chilgoza pine. Accordingly, this study was aimed to document the potential habitat suitability variations of Chilgoza pine under varying environmental scenarios by using 37 different environmental variables. The maximum entropy (MaxEnt) algorithm in MaxEnt software was used to forecast the potential habitat suitability under current and future (i.e., 2050s and 2070s) climate change scenarios (i.e., Shared Socio-economic Pathways (SSPs): 245 and 585). A total of 238 species occurrence records were collected from Afghanistan, Pakistan and India, and employed to build the predictive distribution model. The results showed that normalized difference vegetation index, mean temperature of coldest quarter, isothermality, precipitation of driest month and volumetric fraction of the coarse soil fragments (>2 mm) were the leading predictors of species presence prediction. High accuracy values (>0.9) of predicted distribution models were recorded, and remarkable shrinkage of potentially suitable habitat of Chilgoza pine was followed by Afghanistan, India and China. The estimated extent of occurrence (EOO) of the species was about 84,938 km2, and the area of occupancy (AOO) was about 888 km2, with 54 major sub-populations. This study concluded that, as the total predicted suitable habitat under current climate scenario (138,782 km2) is reasonably higher than the existing EOO, this might represent a case of continuous range contraction. Hence, the outcomes of this research can be used to build the future conservation and management plans accordingly for this economically valuable species in the region.

Abstract:The potential distribution of Olea ferruginea was predicted by Maxent model for present and the upcoming hypothetical (2050) climatic scenario. O. ferruginea is an economically beneficial plant species. For predicting the potential distribution of O. ferruginea in Pakistan, Worldclim variables for current and future climatic change scenarios, digital elevation model (DEM) slope, and aspects with the occurrence point were used. Pearson correlation was used to reject highly correlated variables. A total of 219 sighting points were used in the Maxent modeling. The area under curve (AUC) value was higher than 0.98. The approach used in this study is considered useful in predicting the potential distribution of O. ferruginea species, and can be an effective tool in the conservation and restoration planning for human welfare. The results show that there is a significant impact under future bioclimatic scenarios on the potential distribution of O. ferruginea in Pakistan. There is a significant decrease in the overall distribution of O. ferruginea due to loss of habitats under current distribution range, but this will be compensated by gain of habitat at higher altitudes in the future climate change scenario (habitat shift). It is recommended that the areas predicted suitable for the O. ferruginea may be used for plantation of this species while the deforested land should be restored for human welfare.

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