دکتر علی اصغر نقی پور

The conservation of species depends on the protection of their natural habitats, and this is possible with a more detailed analysis of habitats (15, 31). In this regard, habitat suitability modeling is a scientific and practical solution to estimate the distribution range of species (12). Modeling allows ecologists and wildlife experts to gain knowledge about the ecological needs of species, barriers and factors limiting their distribution, predicting the effects of climate change, habitat destruction, and species invasion. One of the most widely used and best methods for modeling the habitat suitability of species that only uses presence data is the maximum entropy (MaxEnt) method (24). Studying the habitats of all species on a large scale is challenging due to budget and time limitations. It is imperative to direct the limited budgets and efforts towards conservation of greater ecological significance or at greater risk. The Persian Gazelle, Gazella bennettii shikarii, is an important hereditary mammal species that is part of the Bovidae family and the antlope subfamily. It can be found in India, Pakistan, southern Afghanistan, and Iran. In Iran, Persian gazelle prefers dry and very dry plains with an average annual rainfall of 50-250 mm and usually hills (2). Several studies conducted on the habitat suitability of this species in Iran protected areas including Kavir national park (18), Naybandan wildlife refuge (3) and Copper Ore Protected Area (22). To conduct a more comprehensive analysis of meta populations, we need to address the gaps in the distribution and habitat requirements of this species in peripheral areas, including the study area. The present study aims to evaluate the suitability of existing habitats and to identify the potential habitats of Persian gazelle (Gazella bennettii) in Hormod protected area using the MaxEnt method, so that its results can be used for better conservation and management of this species meta populations.

Study area: Hormod protected area with an area of ​​208,731 hectares located in southeast of Fars province, Iran (Fig.1). Most important threat factors of this area, are the droughts, illegal hunting, illegal grazing, crossing the area by Shiraz-Bandar Abbas Road, agriculture and harvesting edible mushrooms by local peoples.Recording species presence points: during the extensive field visits in different seasons of 2022-23, along the specific routes of the plain and hilly areas as the actual habitats of the species, using a Global Positioning System (GPS) 45 presence points was recorded (Fig. 1).Environmental data: based on the literature review, four groups of environmental variables including climatic factors (Tables 1 and 2), vegetation cover, topographical factors (slope, aspect and elevation) and human factors (distance from the village, water sources, road, agricultural lands and environmental checkpoints) which are effective in determining the habitat suitability of this species were used in the modeling.Modeling: Species presence points were converted into digital layers in ArcGIS 10.3 software, then these presence points were considered as dependent variables and selected environmental data (layers) as independent variables. To run the model, 10,000 background points and a maximum of 15 repetitions were considered. Also, 70% of presence points were used to build the model and the remaining 30% were used to evaluate the modeling results.Model evaluation: In this study, the area under the curve (AUC) of the receiver operating characteristic (ROC) was used to check the validity of the results obtained from the model, which is one of the most common methods for evaluating predictive models (8). Jackknife test was also used to determine the importance of each environmental variable in predicting the suitability of the habitat of the species.

Habitat desirability: According to the results, about 36746 hectares equivalent to 17.6% of Hormod protected area is considered as a suitable habitat and about 171985 hectares equivalent to 82.4% is an unsuitable habitat for the Persian gazelle (Fig. 2).Model evaluation: In this study, the value of the area under the curve (AUC) calculated equal to 0.87, that indicates the “very good” predictive performance of the model (20) (Fig. 3).The participation of variables in model: in Hormod protected area, the variables of distance from the villages, vegetation cover, slope and average daily temperature with 41.1, 16.6, 13.7 and 10.3 percent of influence are the most important variables affecting the suitability of Persian gazelle habitat, respectively (Fig. 4). However, according to the jackknife test, the average daily temperature variable has the highest power to predict the suitability of the Persian gazelle habitat in the Hormod protected areas (Fig. 5), in the other words by removing this variable, the predictive power of the model decreases more than removing any other variables.Species response curves: Finally, the response curves of the species to the four main environmental variables that affect its habitat suitability were examined (Fig. 6).

In this study, the habitat suitability of Persian gazelle was mapped within the protected area of ​​Hormod using MaxEnt software. The suitable habitats of species are located in the northern and northeastern parts of the study area, which have the greatest distance from human factors, especially villages. The distance from the village has the most importance on the desirability of Persian gazelle habitat in the study area. Based on the results, with increasing the distance from the village, the habitat desirability for Persian gazelle increases. According to the response curves, with an increase in the distance of 10 km from the village, the habitat suitability for the species increases. It seems that moving away from the village in order to find a shelter to prevent hunting is very important as one of the parameters of habitat selection in terms of the species of Persian gazelle in the study area. In a study conducted in the southern regions of Tunisia on Gazella dorcas (5), it was found that human variables and especially the distance from agricultural lands were the most important factors shaping the distribution pattern of this species. Also, the study conducted on the condition of the gazelle (Gazella cuvieri) habitat in North Africa (14) also showed that the presence and distribution of this species is affected by the increasing distance from human settlements and areas with low human population density. The results of the present study showed that vegetation plays a role as the second most important factor in the desirability of the habitat of this species, and the highest presence of the species is in the type of salt desert rangeland (Salsola tomentosa). In similar studies, the importance of vegetation in the desirability of the habitats of Persian Gazelle and other wildlife species has also been confirmed (13, 18, 19, 29). Considering the drastic decrease in the population size and the range of distribution of the Persian gazelle in Iran (3) and that this type of prey is used by important carnivores such as leopards and cheetahs, we suggest to preventing the habitat destruction and proper protection of the suitable habitat of this species in the study area.

Carbon storage in terrestrial ecosystems is an important part of global carbon storage and plays a vital role in mitigating climate change. The purpose of this research is to investigate the effects of different types of land uses/land covers (dense rangelands, semi-dense rangelands, low density rangelands, barren lands and agricultural lands) on the amount of soil carbon and plant biomass sequestration as an important ecosystem service in Sefiddasht watershed in in Chaharmahal va Bakhtiari province. Sampling of soil and vegetation was done randomly and systematically. For this purpose, 60 plots of 4 square meters were used for collecting soil, plant material and litter. Plant biomass was sampled by direct measurement method. Soil samples were also collected from the depth of 0-30 cm and 20 samples in each area. The results showed that there is a significant difference between the investigated regions in terms of total carbon storage. The total carbon storage from the highest to the lowest in order includes dense rangelands (46.42 tons/ha), semi-dense rangelands (38.49 tons/ha), agricultural lands (31.62 tons/ha), low-density rangelands (26.12 tons) per hectare) and barren lands (17.21 tons per hectare). The economic value of the total carbon storage per hectare of the examined uses, including dense rangelands, semi-dense rangelands, agricultural lands, low-dense rangelands, and barren lands, was determined to be $5446, $4516, $3710, $3065, and $2019, respectively. Therefore, it can be concluded that the implementation of appropriate policies to prevent or minimize the conversion of land uses with a higher carbon storage capacity to land uses with a lower carbon storage capacity is of particular importance.

Understanding the patterns of spatial presence of species and their environmental dependence is one of the basic goals in ecology and evolution. Currently, there are several software packages for ecological niche modeling of species. The Wallace software package is designed as an accessible tool for researchers and conservation professionals and it has been introduced as an ideal resource for education. In the current study, a dataset comprising 262 records on the occurrence of Caspian snowcock, along with 12 environmental and human predictors, was used. Within the context of a comparative analysis using three models—Bioclim, MaxNet, and MaxEnt—under the Wallace software package, we conducted a modeling exercise to depict the geographical distribution of the Caspian snowcock in Iran. In all three models, the high AUC values (>0.96) suggesting excellent model fit with data. Based on the results of three models, about 4.6 to 5.5 % of the study area can be considered as the suitable habitat of the Caspian snowcock. Accordingly, Alborz, Zagros Koppeh Dagh, Azerbaijan and Caucasus Mountains are of great importance as suitable habitats for the Caspian snowcock. The variables of topographical roughness (36.8 %), elevation (20.9 %), isothermality (18.6 %) and annual precipitation (9.1 %) showed the most participation in modeling. The present study emphasizes the necessity of using different algorithms in species distribution modeling and the importance of using different variables including topography, vegetation, food resources, climate, coexist species, competition and human factors in modeling the distribution of the Caspian snowcock.

Rangeland condition assessment is crucial for effective management. While qualitative methods like Landscape Function Analysis (LFA) provide valuable information on ecosystem functions, their application can be time-consuming and require expertise. This study aimed to identify relationships between quantitative plant diversity and functional diversity indices with the qualitative functional characteristics assessed by LFA in Tang e Sayad protected area, Chaharmahal va Bakhtiari Province, Iran.

Three rangeland sites experiencing different long-term grazing pressures were selected along a grazing intensity gradient. Within each site, two representative areas were chosen, and three 30 x 30 m macroplots were established. Plant canopy cover and functional characteristics were sampled in May 2021, corresponding to peak plant growth. Functional diversity was assessed using plant performance characteristics like specific leaf area index, leaf length, leaf nitrogen, leaf phosphorus, and growth form. Species diversity indices (species richness, Shannon-Evenness, and Shannon diversity) and functional diversity indices (Quadratic Entropy Rao, Functional Richness, Functional Evenness, and Functional Dispersion) were calculated using vegan and FD packages in R software (version 4.3.0). LFA software was used to analyze data from 162 patches and the space between patches (interpatch) to obtain three functional characteristics: stability, infiltration, and nutrient cycling. Correlations and univariate regressions were performed using ggcorrplot, ggpubr, and ggpmisc packages in R to establish relationships between diversity indices, functional diversity indices, and LFA functionalities.

  A positive and significant relationship was found between the nutrients index, Functional Evenness, and community-weighted mean leaf length (R² = 0.3). Landscape infiltration showed a stronger positive and significant association (P < 0.05) with Functional Evenness, Functional Dispersion, Quadratic Entropy Rao, and community-weighted mean leaf nitrogen (R² = 0.4) compared to Shannon diversity, Functional Richness, and community-weighted mean leaf length (R² = 0.3). The landscape stability index exhibited a positive and significant relationship (P < 0.05) with biodiversity indices (species richness and Shannon diversity), functional diversity indices based on multiple characteristics (Functional Richness, Functional Evenness, Functional Dispersion, and Quadratic Entropy Rao), and functional diversity indices based on single characteristics (community-weighted mean of leaf phosphorus, specific leaf area, and perennial grass), while displaying a negative and significant relationship (R² = 0.5) with the community-weighted mean of perennial forbs.

The positive correlations observed between plant diversity and functional diversity metrics with LFA functional characteristics suggest their potential application in rangeland condition assessment. The complex interactions between these indices highlight the need for further research on integrating them into a robust assessment protocol applicable across different climatic regions. This study opens doors for developing a practical method for evaluating rangeland health using quantitative biodiversity and functional diversity indicators.

Specialized species living in high mountain areas face more threats compared to species living in low altitudes, primarily due to habitat destruction or fragmentation and climate change.The Caspian snowcock is a  species with a specialized ecological niche and serves as an indicator of mountain ecosystems. Despite the high sensitivity of this species to habitat changes and its role as an umbrella or flagship species in mountainous areas, there is a scarcity of data on the ecology of this species. In the current study, we predicted the geographical range of this bird using 262 presence localities and 10 environmental and anthropogenic variables within an ensemble framework, resulting from five modeling algorithms. Based on the ensemble model, about 96527.4 km2 (5.8%) of the country was identified as a suitable habitat for the Caspian snowcock. Potential habitats of the species appeared to be strongly influenced by topographical roughness (30.02), average annual temperature (29.61), altitude (18.57), and isothermality (14.14). About 16.04% and 23.13% of the predicted suitable range overlapped with no-hunting  and protected areas. It is suggested that effective conservation measures should be taken by focusing on the possible threats caused by human activities, including the development of tourism infrastructure, road networks, overgrazing, and poaching.

Climate change is one of the main determinants of plant species redistribution and biodiversity loss. This study aims to predict the impacts of climate change on the geographic distribution of Agropyron intermedium in Chaharmahal-va-Bakhtiari province as a part of Central Zagros, Iran. The presence points of studied species were recorded from our field surveys in the studied area. In this study, we used the ensemble predictions based on five species distribution models. The future projections were made for the year 2070 with three scenarios SSP126, SSP370, and SSP585, and two general circulation models GFDL-ESM4 and MRI-ESM2-0. According to the results, Random forest model and the generalized boosted model were recognized as the most reliable models for predicting species distribution. The most effective variables in the suitability of the A. intermedium species habitat were, respectively, elevation, Precipitation of wettest month, and slope. According to the finding, about 21.26% of the study area for A. intermedium species have had suitable habitats. The decline of suitable habitats of A. intermedium will be 36.06% to 63.20% under the GFDL-ESM4 general circulation model and 36.69% to 65.17% under MRI- ESM2 general circulation model due to climate change. The results also indicated that climate change will alter the range size of studied species and will probably shift to higher elevations in the future. The results of this study can be used to protect the habitat of the range plant species, as well as its rehabilitation and restoration.

Lorestan province, as a predominantly mountainous region, covers important part of critical habitats of Persian leopard (Panthera pardus) in west of Iran. However, there is little information on geographical distribution of the leopard in this province. This study aims to identify suitable habitats of the Persian leopard in Lorestan province, using an ensemble model consisting of seven species distribution models. The ensemble model identified 6,185 km2 (21.85%) of the study area as suitable habitat for the Persian leopard. Distance to conservation areas (30%), distance to village (29%), distance to farmlands (11%), annual precipitation (8%) and distance to forest (7%) made the highest contribution to the model performance. About 49% of the identified suitable habitat covered by conservation areas. Establishing more conservation areas in suitable habitats and re-evaluation of the network of conservation areas are crucial strategies to conserve endangered populations of the leopard. Many large carnivores, due to their large home ranges, depend on habitat patches outside protected areas and corridors between them. Thus, conserving the habitat patches and connectivity between them are crucial. Monitoring and surveillance of habitats, using effective conservation strategies, and promoting local community involvement are other important issues.

Prangos ferulacea (L.) Lindl is a perennial plant that besides fodder and medicinal values the plant can help to control soil erosion. There is little knowledge about the effect of climate change on the Prangos genus especially P. ferulacea. In this study, we used ensemble modeling based on five species distribution models to predict the potential effects of climate change on the future geography distribution of Prangos ferulace. The presence of P. ferulacea points was recorded from our field surveys (during the years 2019 and 2020) in Chaharmahal-va-Bakhtiari province as a part of Central Zagros, Iran. The future projections were made for the year 2070 with three SSP126, SSP370, and SSP585 scenarios and general circulation model GFDL-ESM4. To do this, species occurrence data (48 points), along with 11 environmental variables including bioclimatic, physiographic and land cover/land use variables were used. Our findings show that the estimated suitable habitats of the species covers about 30.88% of the study area for the P. ferulacea. The Precipitation of the wettest month, Mean diurnal range and Precipitation seasonality had the greatest effects on the species’ distribution in the study area. The decline of suitable habitats will be 23.98% to 32.4% by 2070. In contrast, 9.3% to 20.3% of currently unsuitable habitats can become suitable with climate changes. Also, the results showed excellent performance of the Ensemble model (AUC= 0.97, TSS= 0.83). The results of the present study can provide valuable information for policymakers and planners to adapt and conserve this species against future climate change.

Google Earth images are a valuable resource for understanding and studying the natural area's ecology due to their high spatial resolution. Considering that these images have been available for many years, in our country, these valuable data have not been used enough, especially in order to study vegetation and optimal natural areas management. So, the requirement to standardize the utilization of these images seems necessary to study plant ecological units as a basis for natural resource management. Therefore, in this study, a protocol for google earth images visual interpretation has been developed to extract plant ecological units using six key indicators of shape, texture, color, tone, pattern, and shadow.

In this study, google earth system images related to 2018 were used. For this purpose, first, using the Offline Map Maker software, images related to the study area from google earth with the required maximum magnification (magnification -20) were used. Six key visual interpretation indicators of images were used to prepare a protocol for the plant ecological unit’s separation. In order to have sufficient repetition in defining and applying these indicators, three experts were used. These three experts, having the study area google earth images and based on this protocol, performed plant ecological units visual interpretation and separation in GIS10.5 Arc software. Finally, three digital maps were prepared. In order to evaluate the developed protocol, a ground control map was prepared by field visit and taking 200 observation points of the mentioned units. After preparing the plant ecological units maps by each expert, error matrix analysis was used to accuracy analysis and maps validation. So that using the ground reality map obtained from the ground control points for each map, the amount of producer accuracy, user accuracy, kappa index of agreement, and overall accuracy were calculated separately.

According to the key indicators of visual interpretation defined in this protocol, each expert prepared plant ecological unit’s maps. The error matrix analysis results show that the producer accuracy varied between 69 to 96%, but in most units, they had a producer accuracy of more than 80%. In addition, most plant ecological units with one dominant species had higher producer accuracy compared to units with two species predominance. Regarding user accuracy, the accuracy varies between 63 and 96%. The second and third ecological units had 96% user accuracy. Kappa index of agreement also represents an agreement between 68 and 95% between the ground reality map and the maps obtained from the classification (visual interpretation) of experts. While the seventh plant ecological unit agrees with 68%, the first and fourth units 95% agree with the ground reality map. For plant ecological units with two dominant species, in addition to producer accuracy, user accuracy and kappa index of agreement have also decreased. The overall accuracy of the maps produced by these three experts includes 93%, 80%, and 90%, respectively, which have a good overall accuracy, which shows the very high accuracy and spatial resolution of google earth images.

The results showed that using the developed protocol, there is a significant agreement between experts in the visual interpretation of google earth images. Therefore, plant ecological units can be separated and map prepared with relatively high accuracy. By developing such protocol using key elements introduced, beginner and inexperienced experts can develop maps with acceptable accuracy for better management and different ecosystems organization. However, according to this study's findings, it is recommended that this be used more for units that are structurally different to provide more acceptable accuracy maps. Therefore, it is recommended in areas where the vegetation composition is mostly dominated by individual plant species, and the use of this protocol can lead to a more acceptable accurate visual interpretation of google earth images.

Although turtles are the most threatened taxonomic group within the reptile class, we have a very limited understanding of how turtles respond to climate change. Here, we evaluated the effects of climate changes on the geographical distribution of Caspian pond turtle (Mauremys caspica). We used an ensemble approach by combining six species distribution models including artificial neural network, generalized boosted model, generalized linear model, flexible discriminant analysis, random forest and multivariate adaptive regression splines. To predict the future distribution, modelling projection of MRI-CGCM3 was used for the year 2070 under four scenarios of representative concentration pathways (RCP). Based on the findings, the suitable habitat of Caspian pond turtle was estimated to be about 835941 km2 (about 8.73%) of the study area. Our model projections showed that about 26 to 33% of the current suitable habitats will be unsuitable by 2070 due to climate change. The annual precipitation (24.56%), precipitation of wettest quarter (24.28%), precipitation seasonality (16.93%) and temperature seasonality (14.64%) had the highest contribution to model performance of Caspian pond turtles. Overall, our findings emphasize the need for a comprehensive understanding of the complex effects of climate change on the species, specially turtles.

Climate change affects many plants and animals by changing the growing season or temperature patterns that stimulate life cycle changes. Hordeum bulbosum is widely distributed in the Zagros region, especially under the storey of western oak forests in the vegetation zone of the Irano-Turanian region, and is considered as an important species for rangeland restoration and improvement. In this study, the effect of climate change on the geographical distribution of this species was assessed in Chaharmahal and Bakhtiari Province, located in central Zagros region. For this purpose, 73 points of presence were recorded using the global positioning system (GPS). In addition,11 environmental variables including bioclimatic, physiographic and landuse variables were used in the modeling process. Ensemble modeling include artificial neural network model, generalized boosting method, generalized linear model, flexible discriminant analysis, random forest and multivariate adaptive regression. Finally, the most reliable model was determined as the random forest model. The final forecast for 2070 is based on three scenarios of increasing greenhouse gases (SSP126, SSP370, and SSP585) as well as two general circulation models 9GFDL-ESM4 and MRI-ESM2-0). Findings showed that 25% of the study area is currently identified as suitable habitat for Hordeum bulbosum, and the most effective variables in species distribution are the Annual precipitation, mean temperature of the wettest quarter, and Total rainfall is the hottest season of the year. It is also predicted that based on GFDL-ESM4 and MRI-ESM2-0 models, 27.32% (SSP370) - 31.02% (SSP585) and 26.06% (SSP370)-31.68% (SSP585) of the suitable habitats will be reduced, respectively, by 2070 due to climate change. The present and future habitat maps prepared in this study can be used for the development and implementation of the given species’ management and conservation plans.

Fire is one of the important ecological factors that affect the dynamics of rangeland vegetation. In recent decades, the incidence of wildfires in semi-steppe rangelands has increased significantly, challenging the adaptive capacity of plants for post-fire regeneration. In the present study, the germination response of eight annual and perennial herbaceous species of semi-steppe rangelands of Chaharmahal va Bakhtiari province to fire products treatments including heat (60, 80 and 100° C), smoke, ash, and the combined effect of heat and smoke treatments were investigated. Also, percentage cover change of studied species, one year after the fire were measured in the field. The results showed that the germination of seeds of eight studied plant species showed a significant positive or negative reaction to at least one of the fire product treatments. Combined treatment of smoke and 60° C caused a significant increase in the mean germination percentage of Heteranthelium piliferum. This treatment also significantly reduced the mean germination time of Cousinia cylindracea. Ash treatment also increased the mean germination percentage of Alyssum linifolium and Stachys lavandulifolia. Vegetation sampling results also showed that among the eight species studied, H. piliferum and Taeniatherum crinitum were fire adapted and six species were tolerant. The results of this study can be used as a potential factor to understand the dynamics of vegetation and restoration of degraded rangelands.

 The degradation of resources in many parts of the world is a serious threat to humanity due to its growing trend. Desertification, which is one of the manifestations of this degradation, has affected most countries and has been introduced as the third challenge of the 21st century after the two challenges of climate change and freshwater scarcity. Desertification is the degradation of land in arid, semi-arid, and semi-humid areas. This situation is caused by a series of important processes, the most important of which are the two factors of human activity and climate change. Several methods have been developed to determine the process of desertification, one of which is widely used, the Medalus method. Assessing the status of desertification processes (land degradation) in a village, region or country is important because it provides the opportunity to make informed decisions about the financial dimension and the amount of investment needed to control it. Considering the development of the desertification phenomenon in the Sefiddasht-Borujen region and the need to pay attention to the importance of the results of this destructive phenomenon in the future. The purpose of this study is to evaluate desertification using the Madalus model in the Sefiddasht-Borujen watershed with an area of 92565 hectares, located in Chaharmahal and Bakhtiari province.

 Land use changes were investigated and detected using the distance measurement model. For this purpose, the images of 1998, 2009, and 2018 were used. The amount of changes during this period was determined, and the points where the most changes occurred were selected. Then using these points, in the Medalus model, the factors affecting desertification and its current situation were considered. Then, the effective parameters in desertification were studied in these points separately and the Medalus model was implemented in them. According to the Medalus method, effective factors in the desertification of the region were identified and each factor including climate, vegetation, soil, groundwater, and management and policy was considered as a criterion. Then, the characteristics of the mentioned criteria that were effective in the desertification of this region were considered as indicators. After each indicator received weight in relation to its impact on desertification and by evaluating them, their impact of them on the desertification process was determined. Finally, using the indicators of these criteria, the criteria map and finally, the desertification map were obtained from their geometric mean n order to study the climate criteria, three indices of rainfall, direction, and drought index were considered. The study of the climate was evaluated from 3 sample points in the meteorological and water weight stations of the province, which are harvested as points. To evaluate the soil condition, some physical and chemical properties such as soil texture, acidity, electrical conductivity, and the amount of organic matter were selected. To determine the soil properties, the first 170 sampling points were identified in the study area, and from 0 to 20 cm soil level, sampling was performed and transferred to the laboratory.

The results showed a score of climatic criteria calculated at 1.80 was determined in two classes and had the most role in desertification in the region. The score of management and policy, vegetation, and soil criteria respectively were calculated at 1.76, 1.71, and 1.55 and was determined into two classes severe and very severe. Also, water criteria were calculated at 1.33 and were determined in the middle class. Based on the Medalus model, the current desertification score was estimated to be 1.63. According to this map, the desertification situation of the region was divided into two classes, severe and very severe. Finally, it was calculated that 56% of this area is faced with severe and 44% of it very severe desertification. The result showed that the northern part of the study area is highly vulnerable, while the southern part of the region is less vulnerable to desertification. However, this region has high desertification intensity. The foretold sensitivity of this region to the phenomenon of desertification was consistent. In this model, climate, soil, vegetation, groundwater, management, and policy criteria were selected. According to the results, climate, management, policy, vegetation, soil, and groundwater criteria, respectively, had the greatest impact on desertification of this region due to the low precipitation, drought in recent years, illegal excavation of wells, and uncontrolled extractions more than the capacity of groundwater aquifers in Sefiddasht has caused the drying of most wells in this area. Also, the drying of Dehno Wetland is another reason for the intensity of desertification in the study area.

 According to the obtained results, the phenomenon of desertification in this region is accelerating and would cause a destructive consequence. The study area, according to the proposed definition of desertification, has both natural and human desertification conditions. Natural factors such as unfavorable climatic conditions such as lack of rainfall, successive droughts, limited water resources, on the one hand, and destructive human factors such as traditional agricultural system, overgrazing, overexploitation of groundwater, conversion of pastures to land Agriculture, industries, mines and facilities, destruction of vegetation and shrubs, on the other hand, have led to the destruction of pastures and natural resources and accelerated desertification in the region. Evidence shows that in this region water table is lower than in other areas and as a consequence desertification is accelerated. Among the ways to deal with desertification in the region, it is possible to use the pastures and pastures in principle, according to their capacity at the right time, to permanently enclose the region under biological and mechanical desertification activities, proper exploitation of groundwater and prevents land use change.

Nowadays, using satellite imagery is one of the fastest and lowest-cost methods in rangeland assessment. Also, remote sensing-based vegetation indices are among the most widely used tools to assess and monitor vegetation changes, especially in the post-fire period, and to map the burned regions in rangelands. The present study was conducted considering the importance and extent of rangelands and the recently increased prevalence of fires in the semi-steppe rangelands of Iran, especially in Chaharmahal and Bakhtiari Province. The main objective of this study was to distinguish and identify the burned areas during 1-3 year and 3-5 year periods to adopt an appropriate post-fire management program in these areas using spectral indices. After calculating the spectral indices, the M statistical parameter was determined to designate the separation capability of the burned areas from the adjacent ones. According to the findings, using NBRT, NBR, and CSI indices is recommended to identify and distinguish the burned areas 1-3 years after the fire from the adjacent areas in semi-steppe rangeland regions of Iran. Overall, these indices are of high efficiency in separating these ranges. Moreover, T.C. Brightness and NBRT indices can efficiently identify and separate the burned areas 3-5 years after the fire. Among the studied indices for both periods of fire in the studied semi-steppe rangelands, the NBRT index showed a high potential for identifying the burned area from the adjacent areas.

The climate changes have an important role in distribution of plant species. Statistical species distribution models (SDMs) are widely used to predict the changes in species distribution under climate change scenarios. In the peresent study, the distribution of Alopecurus textilis in the current and future climate condition (2050) under the influence of climate change and two scenarios of RCP 4.5 and RCP 8.5 with the GCM data series of general circulation models BCC-CSM1-1، CCSM4 and MRI-CGCM3 by using of Five species distribution models such as Generalized Linear Model, Generalized Additive Model, Classification Tree Analyses, Generalized Boosting Model and Random Forest method in Mazandaran province were investigated. For this purpose, the number of 92 species presence data was recorded using Global Positioning System and along with layers of environmental factors including six bioclimatic variables and two physiographic variables were used in modeling. Among the environmental variables, mean temperature of driest quarter, precipitation seasonality, precipitation of warmest quarter and precipitation of coldest quarter had the most impact on the habitat suitability. Modeling evaluation indicated that the random forest and generalized boosting model had better predictions of climatic habitat than other models. Results showed that in comparison with current conditions under the emission scenarios of RCP 4.5 and RCP 8.5 respectively 22.25 and 36.22 percent of the climatic suitable area for this species will be reduced by 2050. Finally, the results of the present study can be an efficient tool for biodiversity protection, ecosystem management, and species re-habitation planning under future climate change scenarios.

This study aimed at predicting the effect of climate change on the geographic distribution of Thymus kotschyanus Boiss and Hohen in Mazandaran province. Species presence data were recorded via the Global Positioning System (GPS). The distribution of T. kotschyanus Boiss and Hohen under current and future climatic conditions 2050 and 2070 (1428- 1448 Solar) applying  two scenarios of RCP 4.5 and RCP 8.5 climate change by using the GCM data series of general circulation models BCC-CSM1-1، CCSM4 and MRI-CGCM3  and the five species distribution models such as Generalized Linear Model, Generalized Additive Model, Classification Tree Analyses, Generalized Boosting Model and Random Forest methods was investigated. For this purpose, layers of environmental factors, including six bioclimatic and two physiographic variables, were used as inputs to species distribution models. Among the environmental variables, precipitation seasonality, precipitation in the coldest quarter, and isotherm had the greatest impact on habitat suitability. The assessment of the modelling indicated that the Generalized Additive Model and Generalized Boosting Model had better predictions of climate habitat than the other models. The results also indicate that climate change will change the range size of the T. kotschyanus Boiss and Hohen, and will move toward higher elevations in the future. The results of the present study may be used to plan habitat protection for the medicinal species of T. kotschyanus Boiss and Hohen, as well as its restoration and rehabilitation in the vast regions of the country.

Introduction:

 A deep understanding of the process of managerial change from traditional to the current conventional management in the field of rangeland is effective in identifying most of their problems and challenges and, thus, help to develop appropriate strategies for sustainable rangeland management.

After a brief introduction, in line with rangeland management developments, five main aims are examined. First, a history of traditional rangeland management is presented (1). Following this, the historical trend of weakening the traditional rangeland management is described (2). Then, rangeland management in the period after the Islamic Revolution of Iran (from 1978 onwards) is explained (3). In the following, laws and regulations in the context of rangeland management are mentioned and the emergence of new challenges is examined (4). Finally, while reviewing rangeland statistics in different historical periods, based on the latest statistics, a report on rangeland survey is presented (5).

Based on a review study and use of sources and documents, and by reviewing historical sources in the field of rangeland management, the paper evaluates the data and information of historical periods.Geographical area of research The geographical scope of the present study is the historical evaluation of rangeland management developments, based on a review study and the use of sources and documents on an Iranian scale.

The results of summarizing and reviewing the sources show that during the developments of historical periods, rangeland management has undergone major changes due to various factors.

In the direction of an  integrated management, it is necessary to pay attention to a comprehensive perspective that can use all the potentials of a region for sustainable rangeland management. In the second part, in an other paper, demographic changes and livelihood developments of rangeland users in the context of rangeland management  Iran are examined.

Climate change may make challenges to the conservation of plant species such as the Fritillaria imperialis that have narrow geographical distribution. In this study, the modeling suitable habitats of F. imperialis in Iran was done in the current condition and under climate change (2050). In so doing, 78 species presence data along with 12 environmental variables including bioclimatic, physiographic and land cover/land use variables were used. The ensemble modelling consisting of seven Species Distribution Models (SDMs) was coupled with three general circulation models and four Representative Concentration Pathways (RCPs) scenario. Our findings showed that estimated potential habitats of the species cover about 7.69% of the study area while approximately 9.08% of these suitable areas were covered by conservation areas. Due to climate change, the least and most decline of suitable habitats 46.1% to 77.37% might occur in 2050 respectively. Minimum temperature of the coldest month, elevation, and annual precipitation, had the greatest effects on the species’ distribution in the study area. In keeping with the results, F. imperialis was predicted to shift toward higher elevation under climate change. The accuracy of the maps was assessed and functioning of all models was acceptable (AUC> 0.87 and TSS> 0.75). The suitable habitat identified could be considered for the adoption of management and conservation approaches, including re-introduction and the establishment of new protected areas in order to protect F. imperialis against the expected climate change impacts.

Stipa hohenackeriana in terms of forage production and soil protection is especially important. In this study, was predicted the potential effects of climate change on the future geography distribution of this species in Chaharmahal va Bakhtiari province located in Central Zagros region. To do this, 122 species presence point of this species is collected by GPS, along with 9 environmental variables including bioclimatic, physiographic and land cover/land use variables were used. Ensemble methods were used to predict S. hohenackeriana distribution including the Generalized Linear Model (GLM), Artificial Neural Network (ANN), Generalized Boosting Method (GBM), Flexible Discriminant Analysis (FDA), Multivariate Adaptive Regression Splines (MARS), Maximum Entropy (MaxEnt) and Random Forest (RF). The future projections were made for both year 2050 and 2070 with four Representative Concentration Pathways (RCPs) scenario (2.6, 4.5, 6 and 8.5) and general circulation model MRI-CGCM3. Our findings show that estimated suitable habitats of the species covers about 22.37% of the study area for the S. hohenackeriana. The mean temperature of the wettest quarter, Precipitation Seasonality and Isothermality had the greatest effects on the species’ distribution in the study area. Due to climate change, the least and most decline of suitable habitats of 18.72% (RCP2.6) to 42.87% (RCP8.5) may occur by 2050 and 2070, respectively. Based on the assessments, Random Forest was determined to be the most reliable model among the seven models studied. The region mapped in the study as suitable habitats’ for the species could be effective for the re-establishment and re-introduction of S. hohenackeriana species.

Habitat loss is the main threat to the endangered populations of wildlife and anthropogenic climate change is expected to exacerbate this. Here, we identify suitable habitats of wild sheep in Lorestan Province and how these habitats are affected by the climate change scenarios, in order to address conservation and management efforts. The ensemble modeling based on six species distribution models (SDMs) was used to predict current and future distributions, in response to the changing climate. Our models predicted that 7.4% of the 28,294 km2 study area is currently suitable habitat for the species. Land cover, temperature seasonality (bio4), distance to spring, human footprint and distance to escape terrain made the highest contribution (80%) to the distribution model performance. Findings show that about 62.14 (RCP4.5) to 76.97% (RCP8.5) of present suitable habitat would be lost by 2050 due to climate change under four future representative concentration pathways within BCC-CSM1-1 general circulation model. In contrast, habitat gains observed for models were 5.89 (RCP8.5) to 17.01% (RCP4.5). Our findings could provide useful information for conservation planning to protect and restore wild sheep populations.

Climate, soil characteristics, topography, land use, and biological relationships at various scales are the most important influencing factors on distribution and ecological niches of species. The climate is one of the most important determinants of plant distribution. Therefore throughout the past ecological history, climate change has had profound consequences on the current conditions of the world's ecosystems, including the existing distribution of species. Changes in the distribution of one species in a given geographical area due to the climate change can lead to shifting the presence regions of that species toward higher elevation that leads up to vegetative restriction or even extinction of the species. Shifting, or changing the geographical distribution of species is a strategy to be resistant to the climate change. Therefore, in order to protect the key ecological and valuable plant species, it is necessary to determine suitable habitats via identifying the most important environmental and human factors affecting the species presence in the current and future conditions. Astragalus L. (Fabaceae) is a genus widely distributed throughout the temperate regions. The Astragalus verus Olivier is a small, valuable shrub with many branches. In addition to its protective role from the point of view of the soil, this species has medicinal and industrial values. In recent decades, the geographical range of the A. verus variety has been significantly declined due to factors such as land degradation and over utilization. Despite the national importance of the Astragalus genus, so far little research has been done on the consequences of the climate change on the distribution of species of this genus. The present study was conducted to accomplish the following objectives; 1) To identify suitable habitats and determin the geographical distribution of A. verus in Central Zagros in the current situation; 2) to predict of the consequences of climate change by 2050 and 2070 under different scenarios on geographical distribution of A. verus; 3) to determin the most important factors affecting the distribution of this species. distribution of A. verus; 3) to determin the most important factors affecting the distribution of this species.

 This study was carried out in Chaharmahal and Bakhtiari province in an area about 1.65 million hectare thai is totally located in Central Zagros region. Extensive field studies were integrated to collect geographical coordinates of the presence point (112 points) of this species by using Global Positioning System (GPS) throughout Chaharmahal and Bakhtiari province. Bioclimatic (bio1–bio19), Physiographic variables (elevation, aspect, and slope) and land cover/land variables were used for modeling. Before modeling, two methods of Pearson correlation analysis and Variance Inflation Factor (VIF) were used to check out the correlation between the various environmental variables. In order to model, 19 environmental variables including bioclimatic variables, physiography and land cover / land use were applied to model the distribution based on correlation analysis. Variables with Pearson’s correlation coefficient, r2<±0.8, VIF<3) were selected. Finally and after the omission of the layers having high correlation, nine variables were used for modeling. In order to predict the distribution of the suitable habitats of the Astragalus verus Olivier, Biomad2 software package in R environment (3.1.2 version) was used. In this study, ensemble methods including Maximum Entropy (MaxEnt), Artificial Neural Network (ANN), Generalized Boosting Method (GBM), the Generalized Linear Model (GLM), Flexible Discriminant Analysis (FDA), Random Forest (RF) and Multivariate Adaptive Regression Splines (MARS) were used to estimate the suitable habitats. We used 80% of the occurrence points as training data for model calibration and 20% of the rest of the data set to evaluate the predition of the models. Prediction of the geographical distribution of the Astragalus verus Olivier in the future (years 2050 and 2070) was made based on four scenarios of the increase in the greenhouse gases (Representative Concentration Pathways; RCPs) RCP2.6, RCP4.5, RCP6, RCP8.5 in general circulation model MRI-CGCM3. Model performance was assessed by using the area under the receiver operating curve (AUC) and the true skill statistic (TSS).

 Our results revealed that the most effective variables in desirability of the study species habitat were the isothermality, mean temperature of the wettest season of the year and seasonal precipitation variables respectively. In keeping with the findings, the Astragalus verus Olivier mostly exists in habitats with Isothermality (bio3) from + 36.8 to + 39.7 °C, Mean Temperature of the Wettest season of the year (bio8) from - 2 to + 3.5 °C, and seasonal precipitation variables (bio15) from 100 to 112 mm and the Annual Precipitation of 280 mm to 490 mm. Based on the results of modeling of current conditions, in comparison to the other regions, northeast and east of the province had the most habitat importance for the Astragalus verus Olivier. Our findings show that about 27.43% of the study area was identified as suitable habitats for the Astragalus verus Olivier. Prediction of the geographical distribution of the Astragalus verus Olivier in the future (years 2050 and 2070) was made based on four scenarios of the increase in the greenhouse gases (Representative Concentration Pathways; RCPs) RCP2.6, RCP4.5, RCP6, RCP8.5 in general circulation model MRI-CGCM3. Based on the future projections were made for the year 2050 and 2070 with four Representative Concentration Pathways (RCPs) scenario (2.6, 4.5, 6 and 8.5) and general circulation model MRI-CGCM3. In keeping with our findings, climate change can have significant consequences for the Astragalus verus Olivier suitable habitats in the study area. Based on various senarios, about 45.70 percent (year 2050, RCP2.6) to 89.88 percent (year 2070, RCP8.5) of the current habitats for the Astragalus verus Olivier will be unsuitable due to the climate change by 2050 and 2070. While in the same period of time, about 1.58 (RCP8.5, 2050) to 13.19 percent (RCP2.6, 2070) may be added to the suitable habitats of this species in areas with higher elevation. According to all scenarios, the suitable habitats of this species will decrease in all habitats, especially in areas with lower elevation. The climate change consequences especially the probability of decling and shifting the geographical range of the plant species in various habitats of Iran especially in the central Zagros and also in Central Iran range are predicted. Assessments showed that the models had acceptable accuracy and Random Forest model was determined as the most reliable model to predict the distribution of this species.

 Generally, this study indicated that ensemble model might predict the potential distribution of the Astragalus verus Olivier with a relatively high accuracy (AUC= 0.92 and TSS= 0.79). The scenarios used in this study predict the probability of the shift of the geographical range of the studied species under climate change scenarios of 2050 and 2070. According to the results, it seems that the suitable habitat extent of the Astragalus verus Olivier in the study area has been decreased and will shit toward the higher elevation. Although land degradation and over utilization may be considered as two important factors in habitat degradation of this species but this study highlights the importance the effects of climate change on the distribution of the Astragalus verus Olivier. As a result of the severe and inappropriate harvest of the Astragalus verus Olivier, the range of its distribution and density has decreased in some areas, which has increased the intensity of phenomena such as soil erosion. This issue requires a double attention of the managers and experts of natural resources to the Astragalus verus Olivier and the other species with similar performance in ecosystems having importance from the view point of economic productivity as well as their ability to conserve the soil.

Understanding of how plants respond to a fire is essential to predict the characteristics and distribution of many ecosystems. This research is aimed at identifying, classifying and analyzing plant functional traits that can be used as a means for determining changes in plant communities through the fire at various time intervals.

The present study was conducted in the Karsanak region in Chaharmahal Va Bakhtiari province. Six sites with one and five years after the last fire were selected. A stratified random sampling was used. In each plot, after identifying the existing species, the percentage of the cover of each species was estimated and the plant characteristics were measured.

The results showed that vegetative traits such as plant height, production, SLA leaf area index, leaf area and leaf dry weight, composition of Gramineae species, herbaceous plants and shrub, the percentage of plants with class of II and III palatability, percentage of species composition with form of Hemicryptophyte and chamaephyte had a significant difference in fire and control areas. According to Principal Component Analysis, the most effective of trait were SLA leaf area index, palatability of class II, the leaf length, Hemicryptophytes, forbs and long of the leaf which had a direct relationship with five-year fire and one year fire.

Therefore, it can be concluded that plant traits play an important role in determining the response of plant species to environmental disturbances and hence can influence the process of the post-fire rangeland secondary sequence.

The Egyptian vulture, Neophron percnopterus, as a globally endangered species, is significantly affected by conflicts with human, leading to the decline of this species population. Availability of food resources is known as one of the most important factors affecting the distribution of the Egyptian vulture. In this study, we employed an ensemble modelling approach to predict the  suitability of the  foraging habitat for the Egyptian vulture based on seven distribution modelling algorithms. According to our results, suitable foraging habitats for this bird accounted for about 4.19% (1021.40 km2) of the study area, of which only approximately 31.92% (326 km2) was covered by the conservation areas. Livestock density (31.82%), the presence of wild ungulates (15.36%), distance to dump sites (14.77%), landcover (12.46%) and elevation (11.85%) made the highest contribution to the ensemble model of this species. Our results indicated that Egyptian vultures mainly rely on dump sites, nomadic areas and livestock farming sites, thereby reflecting the vulture's tendency to livestock carcasses. Thus, it is important to develop effective strategies for proper monitoring and managing human activities in these areas.