جستجوی مقالات مرتبط با کلیدواژه « روش بوستینگ تعمیم یافته » در نشریات گروه « منابع طبیعی »

Predicting the potential distribution of plants in response to climate change is essential for their conservation and management. Amygdalus scoparia is a wild almond species native to Iran Therefore, this study aimed at predicting the effect of climate change on the geographical distribution of A. scoparia in Chaharmahal and Bakhtiari province in the central Zagros region. In this regard, we used 5 modeling approaches, Generalized Linear Model (GLM), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Generalized Boosting Method (GBM) and Random Forest (RF) to determine relationships between the occurrence of species and environmental factors under the ensemble framework by using Biomod and R software. The results showed that AUC values greater than 0.9 and functioning of all models been excellent. The mean temperature of the driest quarter and Annual precipitation had the most important role for habitat suitability of this species and (85%) changes in A. scoparia distribution was justified. The results of the model showed that 9%, (148680 ha) of in Chaharmahal and Bakhtiari province for the A. scoparia have had high habitat suitability. Area of suitable habitat was calculated by ArcGIS software on current and future climate conditions. Under RCP4.5 and RCP8.5 climate scenario A. scoparia might lose (Respectively 43% and 59%) of its climatically suitable habitats due to climate change factors, by 2050, while in a number of areas (135% and 140%), the current unsuitable habitats may be converted to suitable. The results of this study can be used in planning, conservation and rehabilitation of A. scoparia.