Predicting the Potential Distribution of Olea ferruginea in Pakistan incorporating Climate Change by Using Maxent Model

Abstract

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.

Ecological niche model comparison under different climate scenarios: a case study ofOleaspp. in Asia

“…In this study, we have explored diverse approaches to estimating ecological niches for O. europaea sensu lato under present-day conditions and two scenarios of future climate. Although six of seven approaches yielded model predictions that were better than random (significantly so), Maxent and SVM emerged as the two approaches that showed good ability also to discriminate between suitable and unsuitable sites within our Asian study area, particularly given the limited range of the species in the region (Ashraf et al 2016). These two approaches at least captured the limited distributional potential in the region, and thus were best able to Note: GARP, genetic algorithm for rule-set prediction; ANN, artificial neural networks; SVM, support-vector machines.…”

“…Although six of seven approaches yielded model predictions that were better than random (significantly so), Maxent and SVM emerged as the two approaches that showed good ability also to discriminate between suitable and unsuitable sites within our Asian study area, particularly given the limited range of the species in the region (Ashraf et al. ). These two approaches at least captured the limited distributional potential in the region, and thus were best able to discriminate between suitable and unsuitable areas, and avoid broad commission error.…”

“…, Ashraf et al. ) and from the Global Biodiversity Information Facility (http://www.gbif.org). A gap in the distributional data centered on Pakistan was filled by on‐ground surveys.…”

“…This species has been documented to be in the process of losing distributional area in some areas but gaining in other, higher‐elevation areas (Ashraf et al. ).…”

“…Primary occurrence data ( Fig. 1) with geographic coordinates were extracted from the literature (Ahmed et al 2009, Ashraf et al 2016 and from the Global Biodiversity Information Facility (www.gbif.org). A gap in the distributional data centered on Pakistan was filled by on-ground surveys.…”