جستجوی مقالات مرتبط با کلیدواژه « مدل سازی پراکنش گونه ای » در نشریات گروه « زیست شناسی »

Lorestan province, as a predominantly mountainous region, covers important part of critical habitats of Persian leopard (Panthera pardus) in west of Iran. However, there is little information on geographical distribution of the leopard in this province. This study aims to identify suitable habitats of the Persian leopard in Lorestan province, using an ensemble model consisting of seven species distribution models. The ensemble model identified 6,185 km2 (21.85%) of the study area as suitable habitat for the Persian leopard. Distance to conservation areas (30%), distance to village (29%), distance to farmlands (11%), annual precipitation (8%) and distance to forest (7%) made the highest contribution to the model performance. About 49% of the identified suitable habitat covered by conservation areas. Establishing more conservation areas in suitable habitats and re-evaluation of the network of conservation areas are crucial strategies to conserve endangered populations of the leopard. Many large carnivores, due to their large home ranges, depend on habitat patches outside protected areas and corridors between them. Thus, conserving the habitat patches and connectivity between them are crucial. Monitoring and surveillance of habitats, using effective conservation strategies, and promoting local community involvement are other important issues.

One of the major impacts of climate change on plants is the change in their geographical range. Predicting the potential distribution of endangered plants in response to climate change is essential for their conservation and policy management. This study aimed at predicting the effect of climate change on the geographical distribution of Fritillaria imperialis in Chaharmahal & Bakhtiari province in the central Zagros region. We used RCP4.5 and RCP8.5 (two climate change scenario) and general circulation model HadGEM2-CC in order to predict the future geographic distribution of the species. In this study, we used 19 bioclimatic variables related to rainfall and temperature and three physiographical variables to create the models. In this regard, we used 5 modelling approaches, Generalized Linear Model (GLM), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Generalized Boosting Method (GBM) and Random Forest (RF) under the ensemble using biomod2 package in the free statistical software environment R 3.3.2. The results of the model showed that annual precipitation and temperature seasonality had the most important to provide habitat suitability of this species. All the models were accurate enough to predict the species distribution, among them, RF presented the most reliable model for the prediction. Under RCP4.5 and RCP8.5 climate scenario, the habitat of the species will shrink to 19.7% and 61, respectively in 2050.