potential habitat evaluation and study of species geographical distribution is a key subject in most of ecological, environment protection and wildlife as well as variation trends studies in different scales. Species distribution prediction models have been becoming one of the important tools to prepare potential conservation map in recent years. Typically, such models define relationship between environmental variables and species events (presence and absence) to evaluate environmental conditions as mathematical models. Species distribution models can be used to assess climate change scenarios and their effect on species geographical distribution. The currentt study is aimed to predict potential distribution of Artemisia sieberi and its distribution under different climate scenarios in years 2030 and 2080. Artemisia sieberi is one of the most widely species distributed in Isfahan province and is important in respect to soil conservation and fodder production. Logistic regression was used to mapping Artemisia sieberi potential habitat. For this, 19 climate variables and 3 physiographic variables with resolution 1 km was prepared using IPCC. About 100 sites were recorded as presence-absence sites using stratified random sampling. For each sample site, data for species presence and absence and environmental variables were recorded and species distribution and environmental factors was specified using logistic regression and finally map for Artemisia sieberi potential distribution was produced in Isfahan province. Results obtained from logistic regression model showed that the main climatic factors affecting Artemisia sieberi distribution included minimum temperature for coldest month, the most humid season mean temperature, annual participation and participation in coldest season. Based on models prediction, about 2767110 h (25.83%) of province area had probability occurrence of 0.75-1 for Artemisia sieberi presence. Regression model was evaluated using statistical coefficient of Kappa and AUC of ROC plot about 0.66 and 0.86 which is fall into high precision models as per Lendice and Koch justify; LINE-HEIGHT: 90%; MARGIN: 0cm 0cm 8pt" Keywords: species distribution, Artemisia sieberi, logistic regression, geographical information system, climate change
