پیمان کرمی

Snakebite as a conflict with reptiles is effective on public health.Identifying the range of distribution of poisonous snakes can help to plan to reduce the possibility of snakebite and also to plan to deal with it.This study was carried out with the aim of evaluating the distribution of Iranian horned vipers in Yazd province and also the possibility of conflicts and snake bites in the province.In this regard, the presence points of the species were collected from all regions of the province, then the habitat of the species was modeled using Domian and Bioclim presence only methods.The output from these models was used to prepare pseudo-absence points.Finally, by combining the findings of 8 models, the species distribution map in the province was obtained.The effect of habitat variables on the distribution was calculated using the random forest model.To check the vulnerability and area of the habitat in each city, the true skill statistic(TSS)threshold was used on the suitability map, and finally, the number of local communities involved in the conflict was identified.The findings showed that the single models used in modeling were successful.The sensitivity and AUC for the Ensembel model were calculated as0.98 and0.99, respectively.Land use, altitude and Hillshade have the greatest effect on the presence of the species.75.86%of Taft county and 413 human settlements in it are at risk of snakebite.The settlements of Bahabad and Ardakan are also in the next ranks.The prediction map of this study can be considered for the implementation of health and safety programs against snakebite.

Knowledge of wildfire-prone areas is useful for identifying future challenges and formulating management strategies, therefore, monitoring and studying the behavior of this phenomenon can be effective in this regard. In this study, the areas affected by wildfires in Kermanshah province were obtained using the information related to the active fire of MODIS satellite during the years 2001 to 2022. Then, using the kernel density function, the density of fire occurrences for each year was calculated and the wildfire data was prepared in the form of density per square kilometer. Different density maps were entered into the non-parametric Mann-Kendall analysis in the form of a time series to calculate the trend of changes at a significance level of 95% or more (99%) with this method. According to the results, during the past 21 years, an area equal to 3613.31 square kilometers of the province's land has experienced an increase in the density of fires, and this amount is equal to 644.39 square kilometers for the decreasing trend. None of the protected areas of Kermanshah Province have a decreasing trend of wildfire density; however, areas from the north and northwest, west and east of the study area have an increasing trend of wildfire density. Most of the areas with an increasing trend of wildfires are located in high areas with high rainfall and are covered by Zagros forests. Among the protected areas, Shahu and Kohsalan as well as Buzin and Markhil have the most fluctuations and changes in wildfires density. The fact that a large part of these areas is covered by oak forests, drought, temperature increase, and the spread of intentional fires may be among the effective and important factors in this phenomenon. The fact that a large part of these areas is covered by oak forests may be among the effective and important factors in this phenomenon

Extracting honey from beehives is one of the economic activities for local communities, which is effective in the direct and indirect employment of villagers and as a result, the sustainable development of these areas. Among the pollinating insects, bees play a much more prominent role, and usually the location of the hives can determine the extent of benefit from this ecosystem service. Among bees of Iran, Carnica hybrid (Apis mellifera meda) has a special place in honey production. Despite various studies conducted on this hybrid, so far none has investigated the suitable conditions for the placement of beehives of this species, so this study seeks to identify suitable areas for the establishment of beehives. Awareness of the areas that are prone to beehive placement can be one of the priorities of the planners in the field of agriculture and animal husbandry in Markazi Province.

In this study, in order to model the areas that are susceptible to pollination, variables such as spring density, height, topographic humidity, light shade, average spot size, land use/cover, landscape diversity edge density, distance from agricultural land, average wind speed up to a height of 10 meters, roughness of vegetation and vegetation density were used. Since it is difficult to access all the areas that are not suitable for the establishment of hives in the modeling process, alternative methods such as pseudo-absence methods were used. However, identifying suitable areas for recording pseudo-absence points can also result in errors. Therefore, first, using the output of presence-only models, pollination desirability was calculated. Then, by subtracting the desirable areas from the entire surface of the land, pseudo-absence points were randomly created in the remaining areas. After preparation of this group of points, the presence/pseudo-absence models were ready to be implemented. In order to evaluate distribution models, TPR variables and the Kappa index were used. TPR, which is also referred to as sensitivity, is a numerical value that identifies the percentage of presence points that are detected again after applying the presence point threshold. Also, a random forest model was used to calculate the impact of this data set on environmental changes.

The presence-only models in this study were implemented with adequate power. The value of AUC was calculated as 0.89, 0.90 and 0.76, respectively, for Bioclim, Domian and single-class support vector machine models. The results of the evaluation of the used models showed that all models have well predicted the presence of beehives in the areas of pseudo-absence of beehives. The Kappa index for this category of models was at least equal to 0.83. On the other hand, based on the TPR criterion, many of the hive points have been detected again after applying the hive threshold, which can indicate a good level of prediction of the used models. Also, the findings showed that the diversity of the landscape had a greater impact on the quality of pollination than the proximity to agricultural lands. The height of up to 1813 meters above sea level, as well as the wind speed of 3.47 meters per second, were the best conditions for the presence of beehives. Among the different cities, Arak, Farahan, Khandab, Shazand and Khomein had the highest value for pollination.

Planning for the protection of natural areas as well as areas prone to the establishment of beehives can be done with emphasize on the location of Arak, Farahan, Khandab, Shazand and Khomein cities. The findings of this study show that the use of species distribution models can be effective in identifying suitable areas for beehive establishment and pollination activity. On the other hand, combining the findings of this group of studies with other spatial data that determine the patterns of the landscape can provide a clear view of the influence of the landscape.

Forecasting changes in the environment has now become one of the important branches of environmental studies. One of these predictions is to investigate the possible effects of temperature changes on plant and animal species. In this research, the aim is used the land surface temperature (LST) of the MODIS satellite, separately for spring, summer, autumn and winter seasons, to investigate the changes in the average LST in the protected areas and the habitat of focal species. Focal species of the province include Panthra pardus, Acinonyx jubatus, Capra aegagrus, Ovis orientalis, Gazella subgutturosa and Gazella bennettii. In a field study, the presence points were collected between 1399 and 1402 from all over the province and protected areas, and the seasonal average of LST from 2003 to 2033 was prepared using MODIS satellite products (MYD11A1) in the Google Earth Engine system. Species habitat was modeled by using SDMs and combining regression models and machine learning. Also, the TSS threshold was used to convert the habitat desirability probability map into a binary map. Man-Kendall (MK) trend analysis test was used to analyze the trend of increasing and decreasing temperature changes at a significance level of 95%. The findings show that the decreasing changes in the earth's surface temperature have occurred in a scattered and non-continuous manner on the surface, but the increasing temperature changes are associated with more spatial continuity. The highest decrease in temperature occurred in the spring season with 15,212.69 hectares in the Iranian deer habitat, while there was no decrease in temperature in the winter season. Protected areas have experienced the least increasing and decreasing LST trends in spring.

Considering the harms of air pollution on human health and the environment, it seems necessary to reduce and solve this problem based on accurate knowledge of pollutants and criteria affecting it and identifying polluted areas. Therefore, using mathematical models in the form of machine learning is an optimal and cost-efficient approach to air pollution modeling. This research is applied in terms of purpose and its method is descriptive-analytical. The novelty of this research is presenting a new combination approach to determine the effective criteria for predicting the amount of air pollution. Therefore, the purpose of this study was to evaluate and compare the capabilities of two machine learning models, namely Support Vector Machine (SVM) and Random Forest (RF) in combination with Genetic Algorithm (GA) to predict air pollution in Tehran. The data used in this research include particulate matter and gaseous pollutants in Tehran in 2020, which was obtained from Tehran Traffic Control Company. MATLAB and ArcMap software were used to analyze the data. The value of coefficient of determination (R2) obtained from the combined RF-GA method was 0.997, which indicates the high compatibility of this model with the data of this study. Moreover, the Root Mean Square Error (RMSE) value from the combined RF-GA method was 0.153, which indicates high accuracy of this model. Based on the data obtained from Tehran Traffic Control Company, the results of the RF method indicate the appropriateness of selecting the model to estimate the amount of air pollution in Tehran.

Due to the increase in the risk of floods, especially in the cities, and the emergence of human, financial, and environmental risks due to its increase, the flood zoning areas are of great importance. Therefore, in this study, flood susceptible areas in Birjand plain were tried to be zoned with the help of effective criteria. In this regard, the data-driven methods of support vector machine (SVM) and random forest (RF) were used in combination with genetic algorithm to zoning flood susceptible areas. Therefore, in order to implement and validate the mentioned models, 42 flood prone locations in the study area were extracted. In addition, 19 hydrogeological, topographical, geological and environmental criteria affecting flood susceptibility in the study area were extracted to be used to predict flood susceptibility map. Area under the curve (AUC) and a variety of other statistical indicators including coefficient of determination (R2) and Root mean square error (RMSE) were used to evaluate the performances of the models. The values of R2, RMSE and AUC obtained from the SVM-GA method were 0.9032, 0.2751 and 0.931, respectively, and the RF-GA method were 0.9823, 0.2321 and 0.914, respectively, which indicate the compatibility and The RF model is more accurate than the SVM model. The results also showed that the susceptibility of flooding in the central areas of the study area, due to lower altitude and slope angle, is higher than other areas.

A variety of organisms are present in Freshwater ecosystems that the knowledge of species distribution is essential to understand the ecological and evolutionary factors that determine patterns of biodiversity. The construction of a species distribution model is an alternative approach for the study of its geographic range. Therefore, in this study, the distribution of freshwater amphipods of the genus Gammarus in Iran was investigated using SDMs distribution models. The ensemble modeling approach (eSDM), GAM, GLM, MARS and RF models were used. AUC and Kappa criteria were used to evaluate the mentioned models. Also, climatic variables, topography, vegetation and soil quality were used for modeling purposes. The results showed that single models predicted the suitable habitat for taxon (AUC=0.90). The increasing level in soil organic carbon (SOC) and Bio18 variables lead to an increase in habitat suitability. Also, they are mostly recorded at an altitude between 1000 to 2500 meters, but, the increase or decrease in height does not have a linear effect on the habitat desirability in this genus. Therefore, the predicted distribution for this genus indicates the combined role of soil biomass, vegetation and precipitation parameters that can determine the distribution range of the genus Gammarus in Iran.

Roe deer and Persian Squirrel are among indicator species of Zagros forests in the Bozin and Marakhil protected area. The aim of this study is to use vegetation indexes for investigate the trend of changes in vegetation indexes in the areas that species are present and also to compare their dependence on vegetation cover. 11 series of Landsat 8 satellite images during the period from 2013 to 2018 were used. Required pre-processing was carried out and then the NDVI and LAI were calculated for all of the mentioned images. Analysis was conducted in ENVI 5.3. Values of these indexes for the presence of each spices are provided and used along with the non-parametric Mann-Kendall method to determine the trend. K-mean clustering method was used to study the similarities of 2 species presence regions based on vegetation indexes. In order to clustering R3.5.2 was used.Results showed that there was a significant decrease in NDVI index in presence areas of Roe deer (P> 0.05) while based on the results, there was no significant change for Persian Squirrel. Changes in LAI in Roe deer habitat did not have a significant trend, but changes of this index for Persian Squirrel has an incremental and meaningful trend (P> 0.05). The results of clustering showed that the mentioned species have the possibility of coexistence in the central and northeastern parts of the region. The results demonstrated that vegetation indices can be used to monitor habitat changes.

Due to delay in receiving emergency medical services, a high number of injured people and patients annually lose their lives. Determining the medical service area and correct routing of rescuing operation is influential on the reduction of rescuers’ response time. Changing the traffic flow leads to change of medical service area. Therefore, it is expected that by observing changing traffic, the service area of any hospital would be updated. Using the required hardware and software equipment, which observe traffic flow, would be pricey. In order to utilize such equipment, it is required to have information about the degree of effect of traffic dynamic changes on reducing the service time to the demand points.In this research, the allocation of medical center services using static traffic data was evaluated against the allocation with dynamic traffic data on different days and hours. Then, the influence of different regional areas from traffic changes was investigated. Genetic and particle swarm optimization meta-heuristic algorithms were used in the allocation of services, then the average access time (or response time) to all demand points and border points were investigated separately in the 5th district of Tehran. According to the results, the average response time to all demand points when using static traffic data was 13.4% more than the response time to the same points when using dynamic traffic data. Also, while allocation with static traffic data, the average response time to border points (checkpoints) was 34.2% equivalent to 69 seconds more than allocation with dynamic traffic data. These results reveal an increase in response time delay to border points when services allocation with static traffic data. In addition, in the afternoon hours of the day, which coincide with the increase in requests for assistance, the difference in response time to the demand points during service allocation with static and dynamic traffic data increased. The research results show the important role of dynamic allocation in service improvement.

Bats with a wide range of distribution are considered as one of the most important indicators of environmental changes. In this study, the distribution range and suitable habitat patches of the large-tailed bat (Rhinopoma microphyllum) in Iran were investigated. First, the presence points of the species in the country were collected and then modeling was done using MaxEnt. The habitat variables used in this study are a combination of climate, human, vegetation and topographic variables. In order to implement the model, the bias file was used to select the pseudo-absence points of the background using the kernel density function within a radius of 50 km of the presence points. The model was carried out with 10 replications and the average prediction map was analyzed as the result map. The TSS threshold was used to identify habitat patches. Based on the results, the model was successful in the average performance (AUC=0.90) and differed from the random model in all thresholds (P-value <0.001). According to Jack Knife analysis, vegetation, climatic and topographic variables had the greatest effect on species distribution respectively. Thirty habitat patches were identified, the largest one started in the south and covered the western part of the country. Other distribution patches are scattered in the southeast and central parts of the country. The area of the patches varies from 221.04 Km2 as the smallest patches to 219416.78 Km2 as the largest patches and in total, they cover 15.9 percent of the country.

Habitat analysis using landscape metrics can be efficient in better management of habitat. As a critically endangered subspecies, the Baluchistan black bear is scattered in the Bahr Asman and Zaryab areas in Kerman province. The purpose of this study was to model the distribution of the sub-species and evaluate the quality of its habitat patches, using landscape metrics. Distribution modeling was conducted using an ensemble approach, resulting from the combination of presence-only and presence/pseudo-absence data in ModEco software. True Skill Statistic (TSS) threshold was applied to the ensemble model and the habitat patches were analyzed, using landscape metrics. The results showed that the distribution rage is beyond the border of the studied protected areas and the variables related to humidity had the greatest effect on the presence of the species. Based on the metrics analysis, habitat patches in the Zaryab wildlife refugee have higher connectivity and less margin than Bahr Asman protected area. In Bahr Asman protected area, the number of habitat patches is greater than the Zaryab wildlife refugee and the lack of continuity among the patches has led to the edge formation. The results of this study can be used for the habitat management of this critically endangered subspecies

The present study aimed at preparing the potential distribution map and identifiying the environmental requirement of Prosopis farcta L. using tree-based and regression methods in the marginal rangeland of Niatak river in Sistan region. For this purpose, species presence data was recorded randomly. Environmental variables were prepared using field sampling and digital elevation model. In order to achieve the pseudo-absence points, first habitat modeling was performed using the domain model, then pseudo-absence points were prepared using the prediction map obtained from this method. Species distribution modelling was conducted using random forest (RF), classification and regression trees (CART) and generalized additive model (GAM). The accuracy of the models used was evaluated using the area under curve criterion. Result showed that the RF with area under curve 0.98 has the highest accuracy. Generalized additive models and classification and regression trees were ranked after RF. The highest and lowest values of kappa index were assigned to the RF with 0.75, and GAM with 0.43 Kappa value. Accordingly, the RF model is the most accurate model in predicting the potential habitat distribution. Analysis of the variable’s importance showed that in the studied scale, edafic factors and distance from the river have greater effect on species distribution than other factors. So that, in all models used, acidity and electrical conductivity were identified as the most important variables. In general, it is suggested that habitat development plans for Prosopis farcta should be planned in the central and marginal parts of the Niatek river due to better suitability of these regions for species distribution.

Brown bear (Ursus arctos) is a species with high distribution range in Kermanshah province and this has created precondition of conflict. The aim of this study is to identify key bear habitats, ecological Niche fluctuations, and their desirable habitat cover in protected areas and counties of the province. In this regard, after field surveys, the presence of species was identified for 3 years (from 2016 to 2018). Nine habitat variables including ecological, topographic and anthropogenic were used to investigate distribution status. Distribution modeling was performed using Maximum Entropy algorithm. The significance of variables in modeling was calculated by jack knife method and model validity was calculated by ROC function. Width quantification of ecological nests was also calculated using related metrics in ENMTOOLS software. TSS threshold was used to quantify habitat area. Based on the results, appropriate habitat coverage of the species by the protected areas is not suitable and this weakness is evident in the southern parts of the province so that area of 265842.84 hectares of species habitat is outside the protected areas boundary and is prone to conflict with other Land use/land cover. Among the protected areas of the province, Ghaleje and Buzin and Marakhil had the most cover of this an umbrella spices. About half of Paveh county (% 46.68) is suitable for species and the least suitable was located in Qasrshirin county. The results of this study necessitate more attention to the continuity and habitat coverage of this species in the southern parts of the province.

The information about the geographical distribution and habitat selection is the core of the conservation and management of threatened species. Habitat destruction is one of the major threats to biodiversity. Therefore, knowledge of the need for biological and identify favorable habitats for wild boar have very important role in the way of habitat species management. This study is going to model the habitat of wild boar in Jassib wildlife sanctuary and determine the most important environmental factors affecting the choice of species-habitat. The provided survey has conducted in one-year period in four different seasons of the year. So Ecological Niche Factor Analysis (ENFA) and software Bayvmpr was used. Information layers used as effective variables on the species consisted of, slope, aspect, elevation, distance from the source, the distance from the village, away from canals, roads, vegetation index, vegetation type, land use and distance to human development, respectively. The results of the analysis showed that ecological niche factor, Boyce index values for average algorithm median, mean geometric distance, the harmonic mean and minimum distance during four seasons (spring, summer, autumn and winter) and a one-year period is not really enough. Boyce can be concluded based on index values; the accuracy is not very good production. The reason for this variance is high, indicating low precision and low predictive power of the model. Based on index values of Boyce it can be concluded that the accuracy production is not very good. The reason is for the higher variance which indicating lower precision predictive power of the model. Thus, ecological niche factor analysis model is less functional due to low quality and small-scale habitats. The ecological niche factor analysis is not a good way to study the habitat of Jassib hogs Wildlife. Therefore, identification of species distribution and dispersion of wild boar in Jassib wildlife sanctuary have paly important role as a facilitator for executives and environmental experts and farmers in order to achieve an efficient management to reduce conflict as well as identify areas that are at high risk damage and their protection must be in a priority as well.

The amphipods of genus Niphargus Schiödte, 1847 inhabit in subterranean waters and constitute a substantial part of the groundwater biodiversity. In the last decades, there have been many developments for using of modelling algorithms, although not exclusive to the specific case of subterranean habitats. In this study, ecological variables were used to investigate the effect of environmental factors on Niphargus genus distribution. The occurrence points of the genus in north, west and northwest provinces were obtained from previous studies. Then, climatic variables, topography (slope, elevation, and moisture) were used, as well as temperature and moisture data at depths of 0 to 10 cm and 10 to 40 cm. In order to evaluate the similarity between habitat areas, k-mean clustering were used. Then, species distribution modeling was performed using Maximum Entropy Method at the country scale. The results showed that among all the variables used, only Bio14 had no normal distribution (P-value>0/05). Clustering results also showed that, the occurrence points of genus members is placed in two clusters, cluster 1 in the north of Iran and cluster 2 along of Zagros mountain range. There is similarities between two clusters. Two variables include the annual rainfall and soil temperature at depths of 10 to 40 cm have the most effect on the distribution of genus Niphargus. The results also showed that due to distance from the ground surface and the environmental variables, the same conditions exist in most occurrence points of this taxon.

In Iran, no hunting areas are managed for maintaining the biodiversity for 3 to 5 years to join the four conservation areas managed by the Department of environment in the event of improved habitat and wildlife. Habitat suitability models can provide a rapid assessment of the status of index species as a tool. So with strong support, protection level of no hunting areas is enhanced. One of these areas with high-diversity is no hunting area of Eshkevarat. The purpose of this study was to evaluate the status of four species in the region including wild goat (Capra aegagrus), Roe deer (Capreolus Capreolus), brown bear (Ursus arctos) and leopard (Panthera pardus). For this purpose, random forest (RF) model was used and the validity of the model was evaluated by using area under curve (AUC), root mean square (RMSE) and crossed mean entropy (MXE) statistics. Model evaluation results showed that RF method has been successful in implementation. Elevation was evaluated as a factor in the suitability of bear, goat, and roe deer habitats while proximity to prey had the greatest impact on leopard distribution. The optimal habitat area for bears, leopards, roe deer and goats was 34.79%, 2.12%, 3.48% and 11.34% of the total area, respectively. Spatial distribution of habitat patches of the mentioned species indicates high habitat diversity in the area, which reveals the strong role of this area in biodiversity conservation of the mentioned species and upgrading its level of protection can eliminate many of the challenges facing the area.

The striped hyena (Hyaena hyaena) is a global scale endangered species and has a high risk of local extinction in its population, therefore, investigation and evaluation of its habitat for covered areas seems necessary. This study was done to investigate the distribution status of this species in the Shaho Mountain domain in Kermanshah province. In this study, after collecting species presence points, habitat variables including slope direction, elevation, distance from rangelands, distance from agricultural land, distance from main road, residential density, ecotone, slope percentage and viewshed was identified and used in the analysis. In this regard, firstly, using single-class support vector machine models the habitat of the species was modeled. By confirming the validity of the model output through AUC criteria was used from the binary output of the model in order to provide quasi-absence sites 10 times the presense points within almost 5 km distance. Then maximum entropy models (MaxEnt), Back-Propagation (BP) Neural Network model (BP-ANN), and two-class support vector machine (SVM) were then used for modeling. The validity of which were calculated as 0.97, 0.97, 0.89 respectively. Then, all models were used in an aggregation scenario according to the weight obtained from model validation (AUC). Model sensitization was performed using random forest method. The results showed that the variables; viewshed, road distance and distance from the fields were the most important habitat variables affecting the striped hyena habitat in the region.

Dams are one of the largest man-made structures that severely affect the hydrology cycle. In general, their effects can be divided into positive and negative categories. Detecting the effects of these structures on wildlife habitat is one of the important aspects of development. Tang Hammam Reservoir dam located in west of Kermanshah province and Sarpol-e-Zahab country has effects on the Persian gazelle (Gazella S.Subgutturosa) habitat. This study aimed to quantify these effects on the habitat of the mentioned species and positive and negative effects were evaluated by affecting vegetation density and habitat suitability. Habitat suitability was determined using maximum entropy method. The TSS threshold was used to create binary maps. Quantification of the effect on vegetation was also performed using the normalized different vegetation index (NDVI) for the years 2014 to 2019 at different intervals between 0 and 500 m from the water body. Totally, from 2014 to 2019, the area of underwater habitat has increased in the study area. If water zones increase in the coming years, central patches of the region and communication with the Iraq country will be threatened. However, the effects on vegetation density indicate an increase in mean density index at distances up to 500 m relative to dam. Results showed that in the current conditions the dam has more positive effects but this increase of water behind the dam in future will not have any positive effects for the region.

Destruction and habitat fragmentation are considered as the most challenging issues of biodiversity and establishment of protected areas is one of preventive methods to reduce biodiversity. Therefore, there are different criteria to choose it. Kermanshah province has covered different habitats due to its diverse ecologic condition. The current study aims to evaluate and compare the distribution of effective indices on habitat diversity in protected areas of Kermanshah province. In this regard, Surrogates such as altitude, slope, aspect, roughness  of altitude, Shortwave Infrared Water Stress Index (SIWSI), Compound Topographic Index (CTI), normalized different vegetation index (NDVI), vegetation type and Roughness of NDVI have been used as Biodiversity surrogates. Since the protected areas should be indicative samples of their surroundings, all the mentioned variables were prepared for Kermanshah province area and then all maps for slope and cover type were divided into 10 classes based on Jenks method. Shannon-Wiener (H), Simpson (D) and Macintosh (D) indices were used for diversity analysis, and Pielou (J') and Macintosh (E) indices were used for richness analysis. The results show that in terms of diversity indices for slope, CTI, altitude, NDVI, roughness  of altitude, Roughness of NDVI, SIWSI and slope ,Hashilan no hunting area, Ghalajeh protected area, Nava Koh no hunting area, Dalakhani and Amroleh no hunting area, Bistoon protected area and Bistoon Wildlife Refuge have the highest diversity indices, respectively. The slope and CTI variables share most similarity in terms of distribution of classes among the protected areas, and the vegetation type variable has had the least similarity among the protected areas. Among the hunting-banned and protected areas, the highest diversity exists in the Amroleh and Dalakhani and Bistoon protected area, respectively.The results of this study can be useful in choosing the correct complementary protected logic.

Species distribution models (SDMs) are the basis of informed decisions in vegetation management by quantifying the relationship between species distribution and influential environmental variables. The present study aimed to evaluate the GAM and CART models' performance in estimating the potential habitat distribution as well as recognizing the ecological needs of plant species in the Khezri rangelands of Bayaz plain of southern Khorasan. According to the regional condition and field observation, in an area of about 14500 hectares, vegetation sampling was done using the randomized-systematic method. Eighteen environmental variables including land characteristics, Normalized Difference Vegetation Index (NDVI), and salinity index were used as an estimator to generate maps of predictor variables. After modeling the habitat distribution prediction using CART and GAM methods in R 3.5.2 software, the accuracy of the models was assessed using the subsurface area (AUC) statistics. After determining the threshold by the TSS method, the continuous utility map was converted to the presence/absence map and the degree of conformity of the maps with the kappa index was calculated. Based on the results of the used models, the variables of the base level of the channels network, the vertical distance to the channels network, the depth of the valley, the wetness index, and the relative position of the slope are effective in habitat suitability for species establishment. In general, the GAM method has high accuracy in estimating the habitat distribution range of all species studied (Kappa≥ 0.9). According to the maps obtained from the GAM model, the highest and lowest potential habitats belong to S. rigida and T. serotina species. Therefore, the GAM method can be useful in accurately identifying the ecological needs of plant species and therefore their distribution useful at the local scale. As a result, it is suggested that this method be used as part of a management support system in the protection and restoration of vegetation in the rangelands of the Bayaz plain.

To investigate habitat suitability of Wild Sheep in Markazi province, presence/absent methods including Random Forest (RF), and Classification and Regression Tree (CART) were used. For this purpose, 215 presence points of the species from Rasvand, Chal-khtoon,, Alvand, Polangab, Haftad Gholeh, Jasb, Kolahe, Bazarjan and Kharaghan protected areas along with 10000 pseudo-absences points from the entire province were included in the modeling. 30% of the presence/absence points were randomly assigned to the test. In performing of the model, 12 effective variables regarding distribution of Wild Sheepwere used. By using Youden test appropriate threshold was calculated. Validation of continuous and binary maps was performed by using related statistics. According to the results, the RF model was more efficient than the CART. The findings of the two models showed that the distance from the high quality rangelands with the type of Astragalus-Agropyrum, Artemisia sieberi-Stipa, has the most effect on the distribution of species. In the tree CART method, the altitude and distance from poor rangelands variables and in the RF method, altitude and slope are in the later stages from affecting the habitat suitability point of view. Alvand, Chal-khtoon, Rasvand, Kolahe, Kharqan and Jasb are areas that more than half of their extent is suitable for Wild Sheep as a potential habitat. This find reveal the need for more protection of Wild Sheep habitat in these areas. The results of this study show the efficiency of the RF method in habitat modeling as a nonparametric, flexible and interpretable method.

Mountains are unique ecosystems which are described by variety and complexity. Extreme changes in topography and climatic and biological gradients with seasonal and fragile contrasts are characteristic of these areas. With increasing pressure on wildlife species, highlands play the role as the last wildlife sanctuaries. Altitude plays a key role in the formation of these areas which influences other environmental factors either direct or indirect. The altitude constraint in the use of land has been an obstacle to the severe development and consequently manipulation of these areas. In order to investigate the displacement of species of protected areas in the province, the observation of 17 species of birds and mammals in the province was identified then, the resistance map was prepared using weighting to different heights. The least cost path and circuit cape methods as well as the metrics related to each of them, were used to model and analyze the wildlife movement pathway. According to theoretical results, 15 habitat corridors with a length of 1363 km were identified. Bisotoun wildlife refuge and Bozin and Merkhil no hunting areas play the most important role in establishing the territorial communications of protected areas. Based on the results of the least cost analysis, 26 corridors with a length of 4317 km were modeled. Utility and density metrics of using the corridors in the least cost method indicated that the most desirable corridor for the displacement of wildlife species along the paths of the Bisotoun, Paraw and Khorin mountains is located on the Shaho Mountain.

Ecological niche is an area of one or more of the environmental factors in which the growth, reproduction and survival of a living organism is possible. In this study, Maximum entropy method was used to model the distribution and quantification of bear ecological niche in Ilam province. To select the background points, the network bias was used using the kernel density method. 70 percent of the points were used for training and 30 percent were used for the test. The results showed that the model was successful in its running (AUC = 0.97) and had a significant difference with randomized design in all thresholds (P<0.05). In order to determine the variation of the ecological niche of the species in the distribution range, a 50% threshold (0.5) was applied to the habitat suitability map and the output of this map was used to determine the grid range. Grid selection was made according to the home range. For each network, the suitability map was cut and entered the ENMtools software to determine the ecological niches breadth. Non-parametric Mann-Kendall test was used to determine the process of ecological nest width changes. The results showed that altitude, NDVI and distance from semi-dense forests had the most effect on species distribution and bears in Manesht and Qalarang areas have the highest ecological niche breadth. The trend of ecological niche showed that the breadth of ecological niche of species from the northwest of the province to the south east has a significant decreasing trend.

Communication is an important feature of the landscape, which in the event of destruction of biodiversity gets in serious denger. Persian Gazelle (Gazella subgutturosa subgutturosa) is one of the salient species of Iranian plain which is distributed in the no hunting area of Qaraviz in the west of Kermanshah province. Due to the vulnerability of habitats of this species, numerous threats have potential to affect this species. As a result, preservation of suitable habitats and communication pathways between these habitats is necessary. The aim of this study is identifying the communication pathways of the species in the Qaraviz no hunting area with western parts of the distribution range in Iraq. Initially, suitability of the species habitat was obtained using the Maxent model. Then, the results of the model were extended to the boundary that includes the connectivity range between Iran and Iraq. In order to modelling, presence points of species and 9 habitat variables were used. By applying the threshold on the habitat suitability map, the habitat blocks were identified. The reversal of the suitability map was used as transfer cost map. Paradigm modeling was conducted using the least cost analysis method between the habitat blocks. The results showed that the variables of distance from the road, height and slope had the most effect on the species. Based on the density and suitability of landscape metrics, the highest protection value for the displacement between habitat spots, belongs to the western corridors of the Qaraviz area in the vicinity of Iraq.

In environmental models, Vegetations are considered as an important part in controlling environmental changes. To determine the importance of vegetation on land surface temperature (LST), preliminary preprocessing was performed on Landsat 8 image and a split window procedure was used to determine surface temperature. Temperature difference with the surrounding synoptic stations was estimated to be about 2.5 °C, which confirmed the accuracy of the computational temperature. In addition to Normalized difference vegetation index (NDVI), other variables such as Normalized difference water index (NDWI), elevation, Aspect, Landuse/Landcover (LU/LC) and heat loading index were used to show the effect of vegetation on temperature. LU/LC map of city area was obtained by using a 7 categories training samples by Neural Network Classification (MLP). Total Kappa index was calculated 0.82. Then, the points were randomly extracted by stratified sampling method separated for 7 LU/LC categories and divided into two groups of testing and training. Values of the mentioned variables were extracted for these points and random forest regression was used to investigate their effect on surface temperature. Performance of the model was evaluated and validated using mean absolute error (MAE), mean square error (MSE) and root mean squared error (RMSE). Based on sensitivity analysis by Mean decrease accuracy, NDVI, Aspect and NDWI had the most effect on surface temperature distribution. Highest surface temperature with 29 °C was for uncoated rangelands and its lowest with 25 °C was for human-made areas, including Javanrud and peripheral villages. The results of this study showed that the NDVI plays a very important role in land surface temperature changes.