مصطفی ترکش

Astragalus is the vegetation of many mountains of Iran's plateau and plays a major role in providing ecosystem services due to its pillow shape and deep rooting system, they facilitate the control and penetration of precipitation into the soil. The correlation of Astragalus ecosystems with arid and semi-arid climates has made them vulnerable to climate change. In this study, a runoff yield map based on the Budyco curve under current and future conditions of climate change (2050) was prepared using climate and temperature data from the Chelsea site (CanESM2 GCM) in TerrSet software and by using maps of sub-watersheds, annual precipitation, annual potential evapotranspiration, soil depth, plant accessible water and the current and future "Land Cover - Land Use" map, with a combination of field methods and species distribution models at the local scale of the Shur River watershed of Dehaghan (Central Zagros). Finally, the excess runoff damage produced due to climate change was estimated using the replacement cost method. The results indicated an increase in the annual runoff volume of the watershed from 70 million cubic meters to 105 million cubic meters under climate change conditions for the RCP26 scenario in 2050. Taking into account the cost of 10 million Rials for controlling 530 cubic meters of runoff through various watershed management projects, preventing the damages of excess runoff produced requires a credit amounting to 660 billion Rials based on the present value. This study proved the ability of TerrSet software to predict and produce an ecosystem service map of runoff yield under climate changes or land use changes and with the purpose of valuation on a local scale. Also, the above valuation can be the basis for planning and providing credit for the study and implementation of watershed management projects to deal with the threats of climate change.

Temperature is the most important parameter for studying spatiotemporal phenological changes in plants. Thus, the current study was aimed to investigate the potential of MODIS land surface temperature (LST) data for mapping growing degree days (GDD) and different phenological stages of Bromus tomentllus and Astragalus effusus in Chaharmahal and Bakhtiari Province. MODIS extracted maps of maximum, minimum and mean temperature, GDD index and phenological stages from 2018 to 2019 during growing season were assessed against weather station data and also field-based phenilogical data using Pearson analysis in three regions with different altitudes. Results showed that MODIS LST and GDD maps had more than 91 and 99% correlations with field-based air temperature and GDD data, respectively (p<0.001). In early growing season, GDD values were less than 16 degree-days and they were more than 5200 degree-days in the late growing season which explained one and all the phenological stages of the studied species in the study area, respectively. The study findings indicated that MODIS data have high capability in spatiotemporal stratification of phenological stages of the Bromus tomentllus and Astragalus effuses plant species. The knowledge of different phenological stages is essential in species conservation and rangeland sustainable utilization, therefore, species phenology map can be used as an effective tool in rangeland management in the related organizations.

Invasive species are currently the concern of ecologists, conservationists and natural resource managers, and they may decrease biodiversity due to their rapid spread. These species cause changes in ecological processes, function and structure of communities in natural ecosystems. The most obvious change in the invaded areas is the reduction of biodiversity and the creation of a pure community of invasive plants. One of the invasive species in our country is Prosopis juliflora, which is important in the field of combat desertification, biological control and stabilization of quicksand dunes in the southern regions of Iran due to its resistance to adverse environmental conditions.

In the present study, the efficiency of five discrimination group models (GLM, GBM, ANN, SRE, RF) and a profile model (MAXENT) and their ensemble with the weighted average approach in the spatial distribution of this species in Makuran region and determining the most important environmental factors affecting the invasion distribution were investigated. By recording 63 occurrence points and 100 absence points, using climatic, physiographic and human variables as environmental variables, and evaluating the performance of models by Area under Curve (AUC), True Skill Statistics (TSS), Sensitivity and Specificity, the species invasion potential was determined.

Among the single algorithms, according to the threshold-independent and threshold-dependent evaluation criteria, two machine learning techniques, i.e. RF and GBM, predicted the climatic habitat of this invasive species with higher accuracy. Also, the evaluation criteria in the ensemble prediction were higher than the average of all single modeling algorithms. According to the ensemble model, P. juliflora habitats occupy about 15% of the total study area. After generalization of the models to the geographical space, it was found that the invaded areas have spread in a uniform strip on the shores of the Oman Sea and the Persian Gulf. Evaluation of variable importance indicated that altitude was the most important independent variable justifying about half of the changes in the ensemble model and has the greatest effect on species distribution. The variable of distance from the road was the next important variable. However, the aspect was mentioned as the least important environmental variable affecting the scattering of the invasion. Based on response curves, the maximum probability of the species' presence was observed at the altitude of 50 m above sea level and a distance less than 50 m from the road. Also, the species is most likely to be present, if the temperature in the warmest month and the coldest season of the year is more than 34 and 14 °C, respectively, and the precipitation seasonality is 100-150. 

It was found that the integrated algorithms in the framework of ensemble modeling showed higher accuracy and the maps derived from the potential distribution of species invasion make it possible to restrict and manage the distribution range of invasive species by providing management solutions and conserving plans to protect native species. In fact, the results of this study can be used as a basis for subsequent monitoring to prevent further spread of invasive species and to create a balance between the native vegetation protection programs of the region and desertification management measures.

In the present study, the relationship between the distribution of Astragalus verus, climate, soil and topography in Isfahan province was investigated using the CART (Classification and Regression Trees) non-parametric regression. According to the vegetation types dominated by Astragalus verus, 287 sites were selected using stratified-random sampling and 106 presence points were recorded. Non-normally distributed soil variables were normalized according to the skewness type by data transformation. For variables that were not normalized by the data transformation, the inverse distance weighted and for normally distributed variables, kriging methods were used for mapping. To investigate the spatial continuity of these variables, the best variogram was selected. Using the principle component analysis (PCA) and correlation matrix, the most effective factors on distribution were clay percentage, mean temperature of the coldest (Bio11), and driest (Bio9) quarter, minimum temperature of the coldest month (Bio6), annual mean temperature (Bio1), saturated moisture and organic carbon, respectively. Model evaluation using replacement method and using independent data indicated high accuracy of the model. According to the optimum threshold of 0.41 and fuzzy-based habitat suitability map, the suitable habitat for the species was 34.5 % of the area. The results are used in sustainable rangeland management, conservation and their restoration.

Iran is one of the principal centers of Astragalus genus. Astragalus adscendens is a valuable endemic plant. There is less information about the effect of climate change on Astragalus genus especially A. adscendens. In this study, we used the ensemble modeling based on seven species distribution models to predict the spatial distribution of A. adscendens. The presence of A. adscendens points was recorded from our field surveys in Chaharmahal-va-Bakhtiari province as a semi-arid part of Central Zagros, Iran between 2015 and 2016. The future projections were made for the year 2050 and 2070 with two Representative Concentration Pathways (RCPs) scenarios (4.5 and 8.5). Also, in this approach, species occurrence data (140 points), 19 bioclimatic predictors from HadGEM2-CC (Hadley Centre Global Environmental Model, version two - Carbon Cycle), MRI-CGCM3 (Meteorological Research Institute Coupled Global Climate Model Version three) and three physiographic variables were used. According to the ensemble model, 33.58% of the study area (548678 ha) was suitable for the A. adscendens. This research showed annual precipitation, isothermality, temperature annual range and slope have played the most important role in habitat suitability of this species. The response curves showed that occurrence probability of A. adscendens mostly exists in habitats with annual precipitation from 380 mm to 630 mm, isothermality from 35.7 to 36.8 (dimensionless), temperature annual range from 40.5 to 43°C and slope of 0.1 to 30 degree. The decline of suitable habitats will be 59.3% to 89.7% by 2050 and 2070. In contrast, 18.1% to 56.2% of currently unsuitable habitats can become suitable with climate changes. Evaluations showed that Random Forest was found to be the most reliable model for species prediction. Predicting the potential future changes in suitable habitat for A. adscendens will allow more reliable planning and management of this valuable species for experts.

One of the main scientific topics on the effects of global climate change is to assess changes in the carbon cycle in rangelands. Net Primary Production (NPP) is an important component of this cycle, in terms of carbon storage, and a key indicator for assessing the ecosystem function. This research aimed to investigate the correlation between NPP and ocean-atmospheric oscillations, monthly and seasonally, from 2000 to 2016 in the north of Iran’s Alborz Mountains. Net Primary Production of terrestrial vegetation was extracted from MODIS data and used in a model along with ocean-atmospheric oscillations. Multivariate regression analysis was used to investigate the simultaneous and lagged status in different timescales. Mann-Kendal test was used for trend analysis in different seasons over the studied period. Results showed that the highest NPP values were 2.06 and 1.30 g C m-2 d-1 in spring and summer and the lowest were 0.68 and 0.55 g C m-2 d-1 in autumn and winter, respectively. The trend of NPP variations was significantly different in autumn and winter. Overall, it was showed that NPP was affected by climatic variables, especially precipitation, and variables  related to ENSO indicator are the main factors affecting precipitation, thereby affecting NPP in the north of Iran.

Species distribution modeling is a common method for understanding the relationships between a species and its environment and is used to predict the changes in distribution due to environmental changes. A lot of research has been done around the world based on these models. In this study, the optimal habitat of Juniperus excelsa, an indicator species, and the most important tree species in Irano-Turanian forests in the Alborz Mountains have been investigated using 38 environmental parameters and domain and logistic regression models.

The study area consists of parts of the Alborz Mountains located in the north of Iran with an area of 14656 km2. In the present study, a stratified random sampling method was used to determine the sampling sites. Finally, 390 occurrence sites (240 presence sites and 150 absentee sites) of J. excelsa at a 30-second scale (approximately 1 km×1 km) as the input model was studied. Also, 38 environmental parameters were considered as predictive variables for implementing two models of the domain and logistic regression.

The results showed that the domain model had a high performance for predicting the habitat of J. excelsa with AUC =0.97, Kappa =0.730, and TSS = 0.91. Based on the results, the areas with the lowest potential for the presence of J. excelsa were 5665.95 km2, the moderate potential was 2033.1 km2, the good potential was 3076.38 km2, the very good potential was 3063.42 km2, and the high potential was 817.29 km2. The results obtained from the implementation of the logistic regression model indicated that 5084.37 km2 of the studied area was in the class of least potential, 2539.35 km2 had moderate potential, 1410.21 km2 had good potential, 1104. 84 km2 had very good potential, and 4517.37 km2 had high potential. Also, the results showed that the suitable habitats for J. excelsa were regions with a mean diurnal range of 13 °C and 15.5 °C, annual precipitation of 120-220 mm, precipitation of 64-90 mm, precipitation of coldest quarter of 35-60 mm, the altitude from the sea level of 3100-1800 m, and a slope of 30-10% in the southern, southeast, and east directions. Also, the probability of species occurrence was more in areas near the snow and water line as well as on calcic rocks. Also, in the habitat of J. excelsa the NDVI index varied between 0.38 and 0.20. The results of the performance evaluation of the models showed that the domain model had higher performance in predicting the suitable habitat of J. excelsa than the logistic regression model in the study area.

Species distribution models can provide a suitable management plan for different parts of the habitat. Areas with low potential of suitable habitat are usually partial habitats and populations in these areas are more vulnerable than the others, whereas areas with very good potential are excellent for a protected area. Areas with good to the excellent potential that do not have significant tree cover are suitable for forestry and native ecosystem restoration. The present study showed that the domain model, despite using only presence data, can be a useful method for predicting the suitable habitat of J. excelsa in the Alborz Mountains. Therefore, it can be stated that species distribution models, with acceptable accuracy, have enough performance in the evaluation of species distribution and can be used to execute conservation strategies.

Plants are one of the most important components of the ecosystem which are affected by natural and human factors. Therefore, the study of net primary production (NPP) is one of the main subjects in ecology. The main purpose of this research was to model spatial and temporal distributions of NPP  and also to determine the degradation of vegetation types using Carnegie Ames Stanford Approach (CASA), Rain Use Efficiency  (RUE)  and  Light Use Efficiency (LUE) models in semi-steppe rangelands of Isfahan Province. For this purpose, the 16- day MODIS NDVI  images, metrological data, land cover maps and field study  were applied in the study area. The  results showed that the NPP rate increased from March (11.44gC/m2/mo) to May (41.07gC/m2/mo) while demonstrating a decreasing trend from the onset of June  (2.2 g C/m2/mo) due to soil dryness. Climate , vegetation type and rangeland conditions had important roles  in  annual plant NPP and therefore the highest and lowest NPP were observed in Astragalus- Daphnae (38.85 gC/m2 y-1) and Artemisia sieberi - Scariola  (4 g C/m2 y-1) vegetation types  with maximum (0.13 g C (MJ)-1) and minimum (0.005 g C (MJ)-1) LUE, respectively. The amount of RUE decreased in degraded rangelands. Moreover, the correlation between field measurements and the CASA model decreased in semiarid warm climate and degraded rangelands. Therefore, rangeland conditions, vegetation type and climate condition must be taken into consideration in NPP monitoring and rangelands management.

Soil moisture is one of the most important factors that can affect productivity in ecosystems in arid and semiarid regions. The aim of this study was to investigate soil moisture and vegetation changes in the Isfahan province at the seasonal scale. For this purpose, MODIS Land Surface Temperature (LST) and NDVI data were used to calculate the TVDI index, and the rate of soil moisture content was also measured at several soil depths including 5, 10, 20, 30 cm. in the growing season. Seasonal changes of LST and NDVI indices were also studied in different climate regions ranging from humid to hyperarid. The results showed that the changes in NDVI and LST in this region were different, depending on the climate type and soil conditions; the LST and its changes mostly depended on the amount of vegetation cover NDVI changes based on the plant phenology in humid regions, which was were greater than that in arid and semi-arid climates. Soil moisture monitoring indicated that the relationships between TDVI and different soil depths varied based on the seasonal conditions. In the early growing season, the soil moisture at the depth of 0-5 cm had a higher correlation with TVDI, but in the middle of growing season, the deeper soil moisture (10-30 cm) showed the highest correlation. Therefore, the findings of this research indicated the importance of the growing season, soil conditions and vegetation percentage and types in the soil moisture studies by using satellite data.

Changes in the geographical distribution of plants are one of the major impacts of the climate change. This study was aimed to predict the potential changes in the distribution of Artemisia aucheri Boiss in Isfahan rangelands. Therefore, six bioclimatic variables and two physiographic variables were used under the Generalized Linear Model (GLM), Flexible Denotative Analysis (FDA), Surface Range Envelope (SRE), Random Forest (RF), and Multivariate Adaptive Regression Spline (MARS), as well as in an ensemble model framework. Among the environmental factors, the mean temperature of the coldest quarter and the mean annual precipitation played the most important role or exhibited relative importance in model fitting. Based on the model evaluation, the ensemble model and then MARS presented the most accurate prediction in the determination of the climatic habitat. There was also a significant difference between area under curve (AUC) of the SRE model with a good performance and other models with a high performance. The maximum presence of the species occurred at the 2000-3000 m elevation and the 100-250 mm annual precipitation. In both CCSM4 and MPI-ESM-LR climatic models, the highest amount of habitat displacement is likely to happen under the RCP8.5 scenario in 2070, while the lowest amount may happen under the RCP2.6 scenario in 2050. Also, the habitat of species will be displaced more in 2070 than in 2050. The results of this study can be used to prioritize conservation measures or species introduction into areas with similar ecological conditions.

Currently, conservation planning based on the future distribution of species is among the most important adaptive conservation approaches to reduce the  negative impacts of climate change on species. In this study, by adapting an ensemble modelling approach, scenarios of RCP 2.6 and RCP 8.5 and five global circulation models,  the distribution of Goitered gazelle (Gazellea subgutturosa) under climate change was predicted in the central Iran (Isfahan, Semnan, Markazi, Fars and Yazd provinces). In addition, by comparing the current and future distributions, suitable habitats that would remain so in the future with the potential to function as climatic refugia were identified. The results revealed that by 2070, Goitered gazelle will have lost 46% and 70% of its habitats based on RCP 2.6 and RCP 8.5, respectively. Across central Iran, only proportions of habitats in Isfahan and Semnan at higher latitudes were predicted to function as the climatic refugia. Based on RCP 8.5, however, all refugia in Isfahan, except for those in Mooteh Wildlife Refuge, were projected to disappear. According to the findings of this study, expanding the protected network and increasing connectivity in the areas where refugia are distributed, and enhancing the protection status of key no-hunting areas and wildlife refugees are recommended as the management approaches for the effective conservation of this species against climate change.

Understanding the effect of climate change on distribution of plant species is essential for sustainable management and conservation of rangelands, especially in arid and semi-arid ecosystems which are vulnerable to desertification, because of their sensitivity and frangibility to climate change. Due to the complexity of natural systems and phenomenon of climate change, species distribution models are used to understand the impact of climate change on potential niche of species. In this study, five modeling algorithms including artificial neural network, generalized boosting method, classification tree analysis, multivariate adaptive regression splines and maximum entropy were used to predict current and future distribution of Artemisia sieberi Besser in Central Iran. First, the ensemble model as the average predicted probability of single models’ occurrence was created. Next, pessimistic (RCP 8.5) and optimistic (RCP 2.6) scenarios of CCSM4 and NorESM1-ME climate models for the years of 2050 and 2070 were evaluated to assess the trend in spatio-temporal variations of distribution. For this purpose, layers of environmental factors including six bioclimatic and two physiographic variables were used as inputs of species distribution models. Among the environmental variables, altitude, annual precipitation, isothermality, and slope had the most impact on the habitat suitability. Modeling evaluation indicated that the generalized boosting model had better predictions of climatic habitats than other models, and ensemble model than single models. Maximum probability of species presence was determined in plains and low slope areas at altitude of 1000-2000 m and annual precipitation of 100-200 mm. Analysis of the climate change scenarios showed that, the species habitat would be decreased in 2070 more than 2050, leading to the expansion of desert areas. The results can be used for planning to combat desertification in the habitat of Artemisia sieberi, as well as its restoration and rehabilitation in the vast regions of Iran.

Identifying the migration routes of species between different habitats is essential for development of conservation strategies at landscape scale. Maintaining connectivity in the short to medium-term provides possibility of dispersal, access to suitable habitats, seasonal migration and dynamics of the meta populations, and in the long-term, ensure connectivity between appropriate habitat patches to support movements in response to climate change. In this study, we identified and evaluated migration corridors for vulnerable ungulate species, wild sheep (Ovis orientalis isphahanica) between Haftad Gholleh protected area in Markazi Province, and Mooteh Wildlife Refuge in Isfahan Province in central Iran. To identify migration corridors, two models were used, Maximum Entropy (MaxEnt), and Circuit Theory. Using MaxEnt, species distribution modeling was done in two steps with and without presence data in the unprotected study area. Then, Outputs of MaxEnt model was used to identify connectivity with electrical circuit model. The results shows that maximum connectivity (lowest resistance) is most pronounced only in one rout, however near Haftad ghole protected area, there are several high current paths with the highest habitat suitability. We conclude that protecting and incorporating the remaining suitable migration corridors into the existing protected areas network of Iran seems crucial.

This study was aimed to predict the potential distribution of Ferula ovina (Boiss) in Feridoonshar, in the western part of Isfahan province, and to produce species response curves in relation to the environmental variables using the Generalized Linear Model (GLM). The presence and absence of the species in 278 sites (including 138 presence sites & 140 absence sites) were collected using random stratified sampling. Digital elevation model was used to produce elevation classes, aspect and slope maps. Seventy soil profiles and 10 climate stations data were used to produce 31 environmental maps including climate and soil maps using kriging methods. According to the results, the presence of Ferula ovina was correlated with silt percent, average annual temperature, elevation, organic matter content, soil saturation percentage and CaCO3 content. The produced species distribution model had high accuracy, as indicated by calculated Kappa coefficient (0.79) and ROC area under curve plots (0.83). The result of this study can be, therefore, used in the rehabilitation and restoration of this valuable species in the rangeland ecosystems.

Net primary production (NPP) is one of the most important factors in ecosystem's function, which is affected by biological and climatic factors. The main objective of the current research was to investigate the potential of the CASA (carnegie-ames-stanford approach) model in determining the spatial and temporal distribution of NPP in rangland types of arid regions of Isfahan province using MODIS and Landsat. For this purpose, satellite and climatic data such as rainfall, temperature, monthly and sunny hours, were used. The NPP was modeled on two Landsat and MODIS scales and correlations between field measurements and CASA model were investigated based on rangeland conditions and vegetation types. Results showed that the highest plant production in the region was during the March-May months. Crrelation between CASA model with field data occurred in MODIS was higher than Landsat data. Based on the relations, the values of NPP and LUE (light use efficiency) were investigated in the region. The NPP spatial distribution and LUE values indicated that the amount of annual production and photosynthesis efficiency were decreased in degraded rangelands (poor and very poor conditions) compared to fair rangeland condition. The highest and lowest amount of NPP observed in Artemisia sieberi – Zygophyllum Spp (9.35 g C/m2 y-1) and Annual grasses - Annual forbs (0.2 g C/m2 y-1), respectively. The amount of NPP and LUE in shrublands and annual plant types were higher than bushlands.Results of this research probes the importance of biological classification, climate and scale of study in NPP modeling. Moreover, the stratification of rangelands based on life forms and range conditions has principal role in accuracy of the NPP estimation.

Understanding where threatened species prefer to live and distinguishing their basic habitat requirements are the first priority for any decision making and action plans. Habitat suitability models is one of the available tools for improving this knowledge. Wild goat (Capra aegagrus) is a vulnerable species which is widely distributed in Iran, but the ecology of this species is not sufficiently studied. The purpose of this study was modelling seasonal habitat suitability and habitat associations of wild goat in Kolah-Qazi National Park, one of its typic habitats in cental Asia .Logistic Regression approach was used to model wild goat habitat suitability using both the presence and absence points. Elevation, distance to water trough, distance to settlement, distance to public road, and distance to guard patrol road were recognised as the most important variables determining habitat suitability of wild goat. According to the produced habitat suitability maps, in winter wild goat herds aggregated in mountain borders but in autumn their distribution is more evenness and the extent of suitable habitats in winter was more than the summer. The accuracy of both winter and revealed good efficiency The obtained results have implications for conservation of wild goat across its distribution range.

Stachys byzantina is one the major increaser species in rangelands of north face of Alborz Mountain in Mazandaran province. The purpose of this study was to assess the effects of its invasion on vegetation under some disturbances in Vaz Chamestan summer rangelands in Mazandaran province. Vegetation sampling was carried out in exclosure, and invaded rangelands including abandoned dryland, moderate grazing and heavy grazing sites. The vegetation and some characteristics of Stachys were sampled in 1 m2 quadrates along transects. The results showed that Stachys byzantina had greater quantities in abandoned dryland and rangelands under moderate grazing. The cover percentage of total perennials, perennial grasses and forbs significantly reduced from exclosure to heavy grazing and abandoned dryland. Total annuals plants, annual grasses and forbs significantly increased in abandoned dryland. A significant negative correlation was found between cover percentage of perennials and the cover and density of Stachys byzantina. Moreover, the forage production of forbs was negatively correlated withthe cover and density of Stachys byzantina. Multivariate analysis showed that species composition significantly changed under invasion, showing a reduction for palatable species.

Identifying forest areas susceptible to decline in order to take preventive measures can play a significant role in inhibiting this phenomenon. Previous studies suggest the high performance of modeling to identify such areas. Hence, we used 15 models to identify forest areas prone to decline in Lorestan province. For modeling, forest areas with over 50% tree mortality were used as dependent variable, and environmental factors including annual mean rainfall, annual mean temperature, relative humidity, aridity index, evapotranspiration, dust storm index, drought index, distance to surface waters and agricultural lands, slope, aspect and NDVI as independent variables were introduced into the models. AUC of each model was multiplied by its output and the mean of these models was considered as the combined model. The forest decline risk map resulted from the combined model indicated a decline trend from central parts of the Lorestan’s forests to the south and south-western parts. The Random forest and Support vector machine were recognized as the best models with AUC value of 1 and the Bioclim as the weakest model with AUC of 0.75. According to the combined model, approximately 23.7%, 7.5%, and 19.5% of the studied forests had low, medium, and high risk of decline respectively. The climatic factors including aridity index, rainfall, temperature, and evapotranspiration were the most influencing environmental factors, respectively. The present research, in addition to emphasizing the modeling efficiency in identification of forest areas susceptible to decline, indicated that the combination of models yields better result rather than their separate use.

Climatic conditions largely determine the status and characteristics of plants and plant communities in different regions. Therefore, in order to study range plant species, the tolerance of each species to these factors should be determined. In this study, climatic features and requirements of two important grass species with conservation and forage importance (Agropyron trichophorum Richt. & Bromus tomentellus Boiss.) were studied in the Central Zagros. For this purpose, 19 bioclimatic variables used as inputs to Random Forest (as a machine learning approach) in R statistacial environment. The results showed that total annual precipitation (BIO12), the average temperature of the wettest season (BIO8) and the mean diurnal range (BIO2) for both species had the greatest importance and the sum of these three variables described more than 68 and 64 percent of variance in the B. tomentellus and A. trichophorum, respectively. The estimated accuracy by TSS index was calculated for B. tomentellus as 0.95 and Sensitivity and Specificity values were estimated as 0.93 and 0.96, respectively. For A. trichophorum, these values (TSS, Sensitivity& Specificity) calculated as 0.92, 0.89 and 0.93; respectively. The results of this study explain the cliamtic niche of the studied species. Top priority places, where protective actions has to be taken, can be determined using the results of this study. Potentially climatic suitable habitats can be proposed for reintroduction of these valuable species as well.

The climate is determinative of the soil and flora and fauna of a region and in wide scale causes variation in ecosystem and the species combination. In this study, distribution of Juniperus excelsa were studied on the current and future climate condition(2050, 2070) under two climate change scenarios of Rcp2.6 and RCP8.5 with the GCM dataof HadGEM2-ES by using of MaxEnt model. 22 variables of climate and topography in 160 sites were employed . The resultsshowed that in addition to height above sea level, precipitation in coldest season and annual mean temperature are the next important variables on Juniperus excelsa distribution. Also the results show that if the current trend of climate change continues, habitat area of species under the scenario of RCP2.6 till 2070, increases in middle habitat class (82.4%), in good and very good habitat classes, 31.3, 67.3 percent, respectively, and will be reduced and under the scenario of RCP8.5, faces with too much decreasing the area (in middle, good and very good habitat classes, 76.5, 98 and 100 percent, respectively). According to the results , the models like MaxEnt, with acceptable accuracy have enough performance in evaluation of species distribution and can be used for strategies of conservation by the experts.

The effect of climatic changes including: temperature, CO2, and moisture content on photosynthetic characteristics and dry matter yield of rangeland species Bromus tomentellus Boiss – an important forage plant in the West of Isfahan province pastures – was explored in a factorial experiment with a completely randomized design. After collecting a mother plant of this species and asexual reproduction to required number, the climatic variables on each time step in the future (based on A2 climate change scenario) were applied as a treatment on pots except for one group which was taken as control. The photosynthetic pigments, including chlorophyll a and b, total chlorophyll, carotenoids, and aerial dry matter (as the criterion to determine the amount of plant yield in each pot) were measured in these groups. By recording these parameters and relating them to the climatic variables, the impact of climatic changes on them were explored. The results showed that the physiological response of species to the changes in the climatic parameters of temperature, CO2, and moisture content was different. A change in these three parameters (temperature, CO2, & moisture content) significantly decreased chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids in the species under the study. Dry matter yield of single pot also decreased significantly (p

A field experiment was conducted to investigate the effects of mono and mixed culture on crude protein contents of Medicago sativa L. and Bromus tomentellus Boiss, total nitrogen, available phosphorus and potassium of soil using factorial experiment based on completely randomized design with 5 replications. Treatments consisted of three crop compositions (M. sativa, B. tomentellus or mixture of the two), three density levels (low (4 rows, row spacing 70 cm), Medium (8 rows, row spacing 30 cm) and high (16 rows, row spacing 14 cm). Results demonstrated that the forage crude protein in mixed culture was significantly higher than in monoculture of B. tomentellus (p 0.05). Percentage of plant crude protein decreased with increasing plant density both in monoculture and mixed culture. Medicago sativa monoculture and mixed culture increased soil fertility that confirms the importance of forage legumes in forage quality maintenance and preservation of soil fertility. According to the results, mixed culture of M. sativa and B. tomentellus with 8 rows density (30 cm row spacing) is recommended in dryland fields of Fereydoon-Shahr and similar areas.

Knowledge of species’ habitat needs is considered as one of the requirements of wildlife management. We studied seasonal habitat suitability and habitat associations of wild goat (Capra aegagrus) in Kolah-Qazi National Park, one of its typical habitats in central Asia, using Maximum Entropy approach. The study area was confined to mountainous areas as the potential habitat of the wild goat. Elevation, distance to water sources, distance to human settlements, and distance to guard patrol roads were recognised as the most important variables determining habitat suitability of the species. The extent of suitable habitats was maximum in spring (3882.25 ha) and the least in summer (1362.5 ha). The AUC values of MaxEnt revealed acceptable to good efficiency (AUC ≥0.7). The obtained results may have implications for conservation of the wild goat in similar habitats across its distribution range.

Due to the insufficient information about the distribution of plant and animal species, the use of modeling to predict the distribution of species has increased in recent years. For this purpose, a wide range of modeling techniques has been developed. In this study, the potential distribution map of Ferula ovina Boiss.was prepared using the environmental factors and occurrences data and genetic algorithms model (GARP). Species occurrences (137 sites) were collected using stratified random sampling method and all environmental layers including physiographic layers (slope, aspect and elevation derived from digital elevation model), climate and soil layers were produced using interpolation methods in ARCGIS (with a pixel size of 70 × 70 m). All data were analyzed by Desktop Garp and the distribution map of Ferula ovina was prepared. The model evaluation was performed by AUC of ROC plot, indicating a good performance of this model (AUC=0.766). According to the results of sensitive analysis, silt and elevation were the most important factors affecting the distribution of Ferula ovina. This model could be used in the interpretation of relationships between species and environment and assessing areas with high protection potential as well as determining suitable areas for the revival of Ferula ovina and climate change and management scenarios.