predictive habitat distribution modeling of amygdalus scoparia spach in moshakieh rangelands of qom province

this study aims at comparing the performance of logistic regression, maximum entropy, and multilayer perceptron techniques in preparing the predictive habitat distribution map of amygdalus scoparia in rangelands of qom province. for this purpose, vegetation sampling was done using random systematic methods after identifying pure habitats of this species. for soil sampling, eight profiles were excavated and two samples were taken from 0-30 and 30-80 cm depth. after analyzing the soil characteristics in the laboratory and generating the layers of physiographic characteristics (slope, aspect, and elevation), geology and physical and chemical characteristic of soil, predictive habitat distribution modeling was performed. then the accuracy of generated map was evaluated by kappa. based on the calculated kappa coefficient, logistic regression model was able to predict the habitat distribution of studied species at the excellent level (kappa= 0.91). meanwhile, predictive maps derived from maximum entropy and multi-layer perceptron had very good aggrement with actual map (kappa 0.85 and 0.8, respectively). these results indicate that the logistic regression model is more accurate than other methods for predicting the distribution of this species due to specific circumstances. based on logistic regression model, geological formation (igneous formations) and soil gravel amount are the most influential factors effecting the the presence of this species in this habitat. these results indicate that in order to select the optimal modeling approach, it should be given special attention to the ecological niche of the species studied in addition to the capabilities of each method.