Flood is one of the most destructive natural disasters with social, economic and environmental consequences. Therefore, comprehensive flood management is necessary to reduce the effects of floods on human life and livelihood. The main goal of this study is to investigate the application of the maximum entropy model (Entropy MaxEnt) in R software for flood susceptibility mapping in Fars province (Jahrom urban basin). First, by using the information of natural resources of Fars province and field visits, flood-prone points (50 points) were determined. In the next step, environmental variables such as altitude, slope, and distance from the river, drainage density, average annual rainfall, land use, soil type, and geology were selected by performing the multiple collinearity test, and vegetation cover and topographic humidity index were removed. Among the selected environmental variables, the three factors of height distance from the waterway and land use have had the greatest impact in the modeling process. After that, the receiver operating characteristic curve (ROC) was drawn for the flood sensitivity map, and the value of training data (0.943) and test data (0.932) was obtained. In the following, the model was optimized and upgraded using the genetic algorithm. As a result, this flood susceptibility map can be useful for researchers and planners in flood mitigation strategies.

Recognizing the habitat requirements of wildlife species, especially endangered species, is of vital part of wildlife management. The purpose of this study was to habitat suitability modeling for vulnerable species of marbled teal (Marmaronetta angustirostris) in the northern part of Shadegan International Wetland. The presence of species was achieved by conducting field studies and reports from local communities and DOE experts. Environmental factors used as effective variables on presence include water depth, water temperature, electrical conductivity, acidity, soluble oxygen content, vegetation type, vegetation density index (NDVI) and distance from the village. Then, the variables needed for modeling were prepared using the GIS and finally, a Maximum Entropy Model (MaxEnt) was used to prepare the habitat suitability map. The results showed that the MaxEnt based on AUC had a good operation in this study (AUC = 0. 981). Also EC, DO, Distance to village, vegetation type and depth was as important environmental variables respectively.

Wildcat Felis silvestris, is one of the 8 members of felids in Iran which is distributed widely across the country and is observed all over the country except forests located in north slopes of Alborz Mountains. As there is little information about the status of the wildcat and its habitat, distribution modelling of the species across the country was performed using maximum entropy algorithm (MaxEnt), which is a modelling method, based on only-presence data. Potential distribution map of wildcat and factors affecting distribution of the species were obtained using 144 occurrence records along with 10 eco-geographical variables. Performance of the model was tested by the area under the curve (ROC). The value of AUC=0.816 for training data revealed that the model has a good possibility for producing potential habitats of Wild Cat in the country. In addition, results of Jack Knife's analysis, land- use, slope and dem out of 10 variables were identified as important factors affecting wildcat distribution in Iranian plateau. Response curve of wildcat to land-use variation showed farms, arid places and desert limit Wild Cat presence in different landscape in present study furthermore Model indicated northern slopes of Alborz Mountains are potentially suitable for wildcat distribution. Based on results of present study, systematic habitat study in order to finding environmental requirements for Wild Cat, additionally conservation planning for wildcat populations in designated regions by the model particularly protected areas away from human settlements is highly recommended.

The global burden of leptospirosis as a fatal zoonotic disease is increasing all over the world [1]. As there is not any significant decrease in yearly reported cases trend in Iran and potential spatial distribution of leptospirosis remain unknown in national level, we tried to figure out the geographic distribution pattern of leptospirosis in all parts of Iran. The aim of this study is producing leptospirosis risk map by analyzing relations between disease data reported by the Ministry of Health and nine environmental factors, for a period of 2009 to 2018, using Geospatial Information System (GIS) and Remote Sensing (RS) capabilities and Maximum Entropy (MaxEnt) model. Altitude, precipitation, average temperature, maximum temperature, Normalized Difference Vegetation Index (NDVI), land cover, displacement (roads, railways and border entrance points), slope and water areas with 1km * 1km resolution were entered to the model as contributing factors, and patients home locations were used as disease incidence points. ArcGIS 10. 6. 1 and ENVI 5. 3 were used to prepare the nine factors for analysis and interpretation of the results. To create the potential distribution, MaxEnt as an ecological niche model was used which is a method that its performance in disease distribution modelling has been proved [2, 3]. An advantage of this model is that variables can be either continuous or categorical and can be run for even less than 100 points as incidence data [2]. In this study, 60 percent of disease data was selected randomly for training and other 40 percent was applied as test data. Jackknife manipulation technique was performed to investigate the contribution of each variable in model. Our findings on spatial pattern of leptospirosis at least hint that except north parts of Iran that obviously are most vulnerable areas to the leptospirosis outbreaks, west parts of Iran specially Kermanshah are not safe from the spread of the disease, so health policy makers should consider these areas for monitor and control programs specially after severe rainfall or flood in spring and summer. Jackknife results showed that precipitation and altitude by 43. 5 and 37 percent contribution, are the two major factors for risk prediction of leptospirosis. On other hand, maximum temperature, water areas and slope have not meaningful impact on incidence of leptospirosis. Land cover with 11. 9%, NDVI with 4%, average temperature with 1. 3% and displacement with 1. 1% were participated in the model. Also, yearly models have been created for years between 2009 to 2018 to investigate that how parameters contributions change over years. Results showed that the incidence rate was related to altitude around 40% for all these ten years, but precipitation contribution percentage is fluctuating over years. Response curves showed a direct relation between incidence rate of disease and precipitation which means more rainfall causes more incidence. It also showed that altitudes around zero are the most suitable height condition on current distribution of leptospirosis. Also, the landcover output curve showed that Post-flooding or irrigated croplands, artificial surfaces and associated areas, mosaic forests or shrublands and grasslands are the most suitable landcovers for incidence of leptospirosis. To assess the model efficiency, Receiver Operating Characteristic (ROC) was employed. The Area under the Receiver Operating Characteristic Curve (AUC) for training data and test data was 0. 956 and 0. 955, respectively.

Introduction and Objective: Determining the distribution status of species and habitats under their occupation is very important in species conservation and management programs, but the available time and budget make large-scale species studies difficult and in many cases impossible. Therefore, in this field, species distribution modeling methods are used. Modeling the distribution of plant species with the aim of identifying areas prone to conservation conservation and afforestation is important. The aim of this study was to model the distribution of pistachio species using methods and feasibility study of the development of this species in Ilam province. Material and Methods: For this purpose, the presence points of the species throughout the province were collected using stratified random sampling method and their geographical coordinates were entered into the software MaxEnt. Map of environmental factors including 19 climatic variables, 3 topographic variables and snow cover variables were prepared. These variables were used as model input and the relationship between attendance data and environmental variable maps was defined mathematically using software. Then species distribution prediction maps were prepared using Maximum Entropy (MaxEnt) modeling method. Results: The results of model evaluation showed that the model with the area under the plot curve AUC: 0. 947 has an excellent prediction versus 0. 5 which means that the prediction is random. Jack Knife test also showed that the model was successful in predicting the distribution of pistachio species and the factors of annual temperature range, mean temperature, isotherm index, annual precipitation and altitude, respectively, had the greatest impact on the presence of this species in Ilam province. Conclusion: The results of this study have provided key and important information about the tolerance range of Pistica species to environmental variables. This information is effective in making management decisions to prioritize conservation areas and to take remedial and conservation measures, especially in areas where vegetation is being degraded, and increases the chances of success in planting and rehabilitation projects.

Background and Objectives: Studying the distribution of marine mammals and identifying the factors influencing their habitat selection are among the crucial aspects for understanding the ecological conditions of these animals and the various threats they face, in order to develop conservation plans for coastal ecosystems. The Persian Gulf, as one of the most important marine areas globally, possesses diverse and highly complex ecology, where many species are endangered due to various human threats. The Indian Ocean humpback dolphin (Sousa plumbea) is one of the key marine seen in coastal areas and shallow waters of Persian Gulf, and its population is declining according to the International :union: for Conservation of Nature s Red List. Mousa Bay in the northwestern Persian Gulf is an important, but highly industrialised habitat for this species. This study aims to model the habitat suitability of the humpback dolphin, identifying not only potential marine habitats but also the most influential physical factors affecting their habitat preferences Methods: Field studies were conducted in the fall and winter of two consecutive years 1399-1400 Also local communities' reports were utilized to identify the species' presence areas, then marked on the regional map and entered into ArcGIS software. A total of 30 presence points of this species were recorded and then entered into the model along with environmental variables. Environmental factors applied as variables affecting species presence included chlorophyll concentration, sea surface temperature, salinity, water depth, seabed slope, distance from the coast, and distance from the inlet channels of the Mahshahr estuarine area (northwest Persian Gulf). Required variables for modeling were prepared using geographic information system (GIS), and finally, the MaxEnt model was used to generate a habitat suitability map. The MaxEnt method is a widely used tool for modeling habitat suitability and predicting species distribution patterns using presence data and environmental variables. Finding: The results showed that the MaxEnt model performed well in this study, with an area under the curve (AUC) of 0.91, indicating high accuracy and efficiency of the model in identifying the most suitable distribution areas. Furthermore, water depth, slope and distance to coast were identified as the most important parameters in identifying suitable areas, and the probability of humpback dolphin presence in the vicinity of the main inlet channel of the Mahshahr estuarine area was higher compared to other areas. According to the findings of this study, only 10% of the area had high suitability for the species habitat, 15.98% of the area (32.30 square kilometers) had moderate suitability, and ultimately, the highest percentage of the area, accounting for 75.95% (140.25 square kilometers), either lacked suitability for species presence or had low suitability. Conclusion: Since the Monitoring and assessing marine biodiversity relies upon adequate and accurate knowledge of population and distribution patterns of ecologically important species and because of dense maritime activities and accelerated industrialisation together with placement of the coastal cities of Mahshahr and Sarbandar have resulted in extreme disturbances to natural habitats of the area.,This study provides a basis for monitoring humpback dolphin and assessing ecosystem health of northern Persian Gulf

Predictive distribution models appropriate response in relation to ecology, biogeography and conservation of species. In this study biogeographic factors affecting the potential distribution of the species was assessed using the maximum entropy in the Bamoo National Park. For this purpose, 15 biogeographic uncorrelated variables were analyzed with spatial resolution of 100 meters. Determine the most influential variables were analyzed using the curve Jackknife. Evaluation of the survey show a good performance MaxEnt model in deer distribution (AUC = 0.992).Logistic map to predict the potential distribution of habitat types that are more favorable habitat for this species in the Tange-chah mahaki, Mehrab Haji and the well is deep-fried and wind pumps. Jack Knife assessment results and calculated the relative contribution of the variables showed that the distance from the source, the type of vegetation, away from the presence of geological leopard and deer are the most important variables affecting distribution.

Knowledge of effective factors on species habitat suitability and range of suitable habitats have essential importance in planning in order to wildlife protecting and managing. Dena protected area is 93820 ha located that is located in 36 km far from Yasouj County in Kohgilouia and boyerahmad province. Wild goat is indicator species of Dena protected area. Propose of this study is analyzing of wild goat's dispersion in Dena region and eastern Dena. Species presence points' data were collected in spring and divided into two clusters randomly of Training data and Test data. Then other habitat variables ran in MaxEnt model. The result of Jackknife sensitivity indicated that distance from rocky regions, distance from forest, and roughness are most important effective variables in habitat suitability. The result of study demonstrated that suitable habitat for this species is located in rocky zones far from man-made regions and incline to sparse plant covered region with high elevation.