Prediction of Climatic Ecological Nest of Artemisia aucheri Boiss, in Tehran Province, Iran Based on Modeling

Optimal habitat has a beneficial effect on species survival. In this research, a logistic regression statistical method was used to prepare the potential distribution map of Artemisia aucheri Boiss, in Tehran province, Iran in 2020-2021. The maps of 19 bioclimatic and three physiographic variables with a resolution of 4.9 km under current conditions were prepared. Then, the sampling information in relation to the presence and absence of species and environmental variables was recorded and the relationship between species distribution and environmental factors was determined using logistic regression. Finally, the map of potential distribution of Tehran province was produced. The results of logistic regression model showed that in the current situation, 427057 ha (31% of the province) had a probability of more than 75% presence of this species. The kappa coefficient for evaluating the regression model was 0.86 that is one of the models with good accuracy according to Koch and Smith classification. In order to study the effect of climate change on the geographical distribution of the species, climate bio maps were prepared based on the MRI-ESM2-0 circulation model under two scenarios of 4.5 and 8.5 for 2050. The species under study is currently in the range of 1600 to 2800 m. The results showed that by changing the conditions from a balanced to a pessimistic situation, the area with the presence of more than 75% of the species decreases and the area with the presence of 25-50% increases. Also, results showed that the minimum and maximum altitudes in scenario 4.5 will be changed to 1500 and 2500 m, respectively. While according to scenario 8.5, the presence of this species will be only at above 2500 m. In fact, according to the 8.5 scenario, the species will be migrated to higher altitudes. The results identify climate sensitive areas and possible future shelters of A. aucheri to be used in conservation and rangeland planning.