علی اکبر صفدری

In recent years, advancements in computer technologies, remote sensing systems, software, and various models have enabled the prediction of ecological niches for diverse plant and animal species. Over the past decades, alterations in human lifestyles, industrialization, and production processes have resulted in increased atmospheric pollutants, leading to severe climate change. Global climate change has induced shifts in plant growth ranges, with an expansion of warm-weather-adapted plants and a decline in cold-weather-adapted ones. These changes consequently modify the structure and ecosystems of the entire planet, directly and indirectly impacting ecosystem services crucial for human well-being and economic prosperity. Consequently, predicting the effects of climate change on plant distribution has emerged as a pivotal research area to inform conservation strategies and programs. Species distribution models primarily predict the impact of climate change on plant growth ranges. Accurate predictions of species distribution are essential for effective conservation planning and sustaining forest ecosystem services in the face of climate change. Given the significance of this issue, this research aimed to identify the most critical climatic and environmental factors influencing the distribution of Rhume ribes L. species and ascertain its current geographical range within Razavi Khorasan Province, located in northeastern Iran.

For this purpose, 68 bioclimatic variables including soil characteristics (45 cases), topographical factors (four cases), and climatic factors (19 cases) were first subjected to correlation analysis as predictive variables and variables with high correlation (above 80%) were removed. Due to the large size of the studied area, sampling of presence points was done with field visits during the period of 1400-1401 of the introduced areas, and a total of 232 presence points from eight regions were registered as presence points using the global positioning system (GPS). Then all the environmental data and presence points in R software using Biomed 2 package models which include GLM, GBM, GAM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt models in determining the relationship between vegetation and environmental factors in pastures Razavi Khorasan Province was predicted in the present time. The accuracy of the models was evaluated using the values of KAPPA, TSS, and ROC indices, which are prominent and widely used indices for determining and identifying the potential areas.

The results of this research showed that according to the accuracy evaluation index, the best modeling for the current time is the random forest model with an accuracy of 95.5%, which indicates the accuracy of the modeling at an excellent level. Also, the relative importance of the selected models and the variables that have had the greatest impact at present include digital elevation model (DEM), Average monthly (BIO2), This is the sum of all total monthly precipitation values (BIO12), The average temperatures experienced during the wettest quarter (BIO 8) and the amount of sand at a depth of 15-30 cm from the soil surface (Sand 15-30), which indicates the great influence of climatic factors on the distribution of this species, and in the next stage, the height above sea level and finally the soil factors have the greatest influence. The most distribution of Rhume ribes L. species at present is in the east of Razavi Khorasan Province including the cities of Bakharz, Torbat Jam, Taibad, Zaveh, Khaf, and Rashtkhwar in the form of a strip on their border and in the west of the Province on the border of Koh Sorkh and Neishabur cities and the north of the Province on the border Binaloud, Zabarkhan and Mashhad cities and the south of the Province in Gonabad city has spread in a strip and limited way.

The results of this research can be used to improve, protect, and economically exploit and expand the habitat of the Rhume ribes L. species. Destructive human activities, such as livestock grazing and the corrupt exploitation of rhubarb, combined with climate change, have endangered the current habitats of this species in Razavi Khorasan Province. These unprincipled exploitations, disregarding environmental capacities in natural resource management, are a significant problem in Razavi Khorasan Province and the country, gradually leading to water, soil, and plant loss in the region. While this study sufficiently examined current climatic and soil factors to identify areas suitable for rhubarb species, a deeper understanding is required to effectively restore damaged areas, preserve those at risk, and enhance the predictive capabilities of ecological models. In addition to climatic and soil factors, the potential habitats of plant species are influenced by various factors, including human activities, exploitation methods, livestock grazing, wildlife, economic and social conditions, and other direct and indirect impacts on distribution. Numerous studies have been conducted on different plant species. This research evaluated various machine learning-based species distribution models, selecting random forests as the most suitable. Species distribution models are valuable, cost-effective tools for natural resource managers, increasing their awareness and decision-making abilities regarding the effects of climate change on species.

Global climate change has led to change in the ecological amplitude of plant growth, expand plant adaptation to hot climates, and decrease plant adaptation to cold climates. Climate change resulting from human activities occurs at such a speed that many species will not be able to adapt to it. These changes have led to a change in the range of plants growth. Such high-speed changes have caused subsequent changes in the structure and entire ecosystems of the earth, therefore predicting the effect of climate change on the distribution of plant species has become a major field of research for its conservation measures and programs. Changes in the range of distribution of plants are mostly predicted by species distribution models. In this sense, every environmental factor affecting the distribution of plant species has a minimum, maximum and optimal value, which, in combination with other factors, separates the territory of the species and forms an ecological niche. These models are used to investigate species distribution and are based on ecological niche theory. This research was conducted with the aim of determining the potential habitats of Pistacia vera L. species and the factors affecting it in the present and future in Razavi Khorasan province.

For this purpose, 28 bioclimatic variables including topographic (4 cases), climatic (19 cases), soil (4 cases), and geological (1 case) factors as prediction variables have been analyzed for the correlation coefficient. The variables with high correlation (more than 80 %) have been removed. Environmental variables in ASCII format along with presence points were added for modeling in R software of the desired species. According to the size of the study area, sampling of data points was done based on the field visit during the period 2021-2022 from the introduced areas. through using the Global Positioning System (GPS) of 129 points from 8 regions (as points of presence) were recorded. Then, in order to prevent spatial autocorrelation and reduce the sampling error, the useful areas were converted into 1000×1000 meters grids in ArcGIS 10.5 software, and one presence point was obtained from each cell. In the modeling process, 70 % of the presence points (Pistacia vera L.) were used to generate models and 30 % of the presence points were used to evaluate the performance of the models. Also, to increase the modeling accuracy, the number of repetitions was considered 10. Then all data and points through R software and using Biomed 2 package models including GLM, GBM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt Phillips models, in determining the relationship between vegetation and environmental factors in rangelands of Khorasan Razavi province at current and future distribution of this species in 2080-2100 were predicted under climate scenarios ssp1-2.6 and ssp5-8.5 model. The accuracy of the models was evaluated using the values of KAPPA, TSS and ROC indices, which are prominent and widely used indices for determining and identifying the areas of equal potential.

The variables of climatic factors were removed from the modeling due to the high correlation of 80 %, and the analysis was done using four topographic factors, eight climatic factors, four soil factors and one geological factor. The results of this research showed that according to the accuracy evaluation index, the best modeling for the present time is done by the random forest (RF) model with the ROC, KAPPA, and TSS equal to 100. In the future, the 2.6 and 8.5 scenarios of the random forest model for the ROC, KAPPA, and TSS indicators, with the accuracy of 0.999, 0..982, and 0.989 respectively, have the highest level of accuracy; Also, in the random forest model, the factors that had the greatest impact included: Bio12 (annual precipitation) and Bio15 (seasonal precipitation changes) and land unit at the present time, in the future time under the scenario 2.6 Bio12 (annual precipitation) and Bio15 (seasonal precipitation changes) and DEM and in the scenario 8.5 Bio15 (seasonal precipitation changes) and Bio12 (annual precipitation) and aspect. The results of the relative importance show the great influence of climatic factors on the distribution of this species. It is most present in the habitat with an annual rainfall of 200-285 mm, and more than this amount of rainfall was associated with a decrease in suitability for the establishment of the species. Besides, the height of 800-1300 meters above sea level and rainfall changes up to 7.8 mm in seasonal rainfall also had a positive effect on the suitability of the habitat for the presence of wild pistachio. Also, the most desirable habitat is in low to relatively high hills with a rounded and sometimes flat top consisting of limestone, metamorphic, conglomerate, and shale sandstones and a slope of 40 to 50 % and with shallow to relatively deep gravelly soils. The highest distribution of Pistacia vera L. species is in the northeastern region to the east of Khorasan province. In general, by examining the outputs of the random forest model and comparing the areas prone to the growth of Pistacia vera L. species in the present and future climate scenarios, it can be stated that the trend of stable habitat in the province can be expected.

The results of this research can be used to identify areas prone to growth, improvement, development, protection, economic exploitation, and expansion of the habitat of Pistacia vera L. species. From the ecological point of view, the wild pistachio species is considered as one of the most important factors preventing and destroying land in the high mountains of arid and semi-arid regions in many geographical and ecological regions. On the other hand, the economic importance and the income-generating aspect of wild pistachios are also important for local operators. In general, it can be stated that vector machine models provide very good performance for identifying such prone areas. In this research, an attempt was made to evaluate different species distribution vector machine models, and then the most suitable model, which was random forest, was selected.