Document Type : Research Paper

Authors

• Saeedeh Nateghi ¹
• Morteza Khodagholi ²
• Mahshid Souri ¹

¹ Assistant Professor, Rangeland Research Division, Research Institute of Forests and Rangelands, Tehran, Iran

² Professor, Rangeland Research Division, Research Institute of Forests and Rangelands, Tehran, Iran

Abstract

Abstract
 Introduction
   Rangeland ecosystems are very important and sensitive to changes in environmental factors. Small changes in temperature and rainfall regime or other climatic events can fundamentally reduce the composition, distribution and dispersion of plant species as well as their production. To better understand future climate change, it is essential to determine the current and future distribution of species. Species distribution modeling is currently the only tool that can be used to assess the number of changes in the distribution of multiple species in response to climate change. Therefore, considering the importance of climate in the distribution of plant species, this research examines the distribution of plant species in the future, taking into account the relationship between the important pasture species in Alborz province and the climate factor.
 
Materials and methods
   In this research, four plant species including Stipa arabica, Ferula ovina, Bromus tomentellus and Artemisia aucheri were investigated in Alborz province. In order to determine the amount of precipitation and temperature, the data of the synoptic stations, which were analyzed by the Man-Kendall method, were used. Using the maps of the Ecological Zones Recognition Plan of the Forestry and Rangeland Research Institute and ArcGIS, the vegetation cover map of the province and the current distribution of species, the presence and absence of species were drawn and recorded. In order to prepare the environmental information layer, 19 bio-climates for the present were calculated and downloaded from the WorldClim.org site for 2050 with an accuracy of 30 seconds. These data were obtained for two scenarios RCP4.5 and RCP8.5 for the future period. Then, the values of environmental variables were entered as independent variables and species presence and absence information as dependent variables in SPSS software, and the logistic regression statistical model was obtained from the presence of selected species. This statistical model was defined in the ArcGIS software environment and the potential species map was prepared. Kappa statistical coefficient was used to validate the resulting potential habitat maps model using 22 independent variables and with the help of error matrix.
 
 Results
   The results showed the rising temperature trends in all stations, and 65.76% of the trends were significant. In the northern and eastern heights of Alborz province, the increase in average temperature, especially in the early spring, late autumn and winter seasons, will cause early snow melting in the region, and due to the decrease in precipitation in the mountainous regions, it will cause many problems in water storage. According to the predictions of the logistic regression model, Stipa arabica species, 68626 hectares equivalent to 13% of the province, Bromus tomentellus, 298842 hectares equivalent to 58% of the province, Ferula ovina, 195465 hectares equivalent to 38% of the province, and Artemisia aucheri, 232539 hectares equivalent to 45% of the province have the probability of occurrence of class 75-100 The percentage is for the presence of these species. The evaluation of regression model using Kappa coefficient for Stipa arabica, Bromus tomentellus, Ferula ovina and Artemisia aucheri species was equal to 86, 85, 82 and 79 respectively, which are models with good accuracy according to Koch and Smith classification. 19 bio-climatic maps were prepared under two scenarios 4.5 and 8.5 for the year 2050. The current situation is similar to the 4.5 scenario, but in both models, we will see a decrease in the probability of the floor occurring by 75-100 percent in 2050. Also, the average of all temperature parameters in Karaj station will increase by 1.9 degrees Celsius by 2050 and the amount of precipitation will decrease by 1.33 mm.
 
Discussion and conclusion
   The researchers reported that although there have been no significant changes in rainfall over the past 50 years, the decrease in rainfall in April and the increase in rainfall in December and July can indicate possible climate change in these areas in the future, and the climate in Iran, especially in spring is warming, which confirms the results of the present study. The minimum and maximum height in which the Stipa Arabica species is present is 1400 and 2400 meters, respectively, and the modeling results in 2050 show this minimum and maximum height in the 4.5 scenario, 2400-2100 meters and in the 8.5 scenario, respectively. It shows 2900 meters. The minimum and maximum altitude where the Ferula ovina species is present is 1850 and 2600 meters, respectively. If the modeling results in 2050 show this minimum and maximum height unchanged in the 4.5 scenario and 2800-3000 meters in the 8.5 scenario, respectively. The Bromus tomentellus species is currently located at an altitude of 1600-2700 m, while in scenario 4.5 (equilibrium conditions) and scenario 8.5 (pessimistic scenario) it is at an altitude above 2500 m, which indicates that this species of There is not much difference in altitude opinion. The minimum and maximum altitudes where Artemisia aucheri is present are 1600 and 2800 meters, respectively. Slope and temperature are the two main parameters affecting the distribution of this species and so that the slope affects the depth of the soil and thus has an effect on the establishment of the roots. Due to heat compensation in pessimistic conditions, this species has moved to higher altitudes, i.e. around 3000 meters, which confirms the results of other researchers. With the increase in temperature due to climate change, the extent of habitat of the species under investigation will decrease and they will move to areas that are higher and therefore have lower temperature.

Keywords

• Climate change
• trend
• logistic regression
• climate scenario
• species distribution model