Masoumeh Sharifi; Bagher Nezami Balouchi; Javad Ramezani; Behzad Rayegani; Ali Jahani
Abstract
Introduction: The brown bear (Ursus arctos) is the largest carnivore which has a wide range of distribution but like other large carnivores, there are low frequency in the country. Habitats of this species in the country, especially at the local level are very fragmented. Therefore, investigation and ...
Read More
Introduction: The brown bear (Ursus arctos) is the largest carnivore which has a wide range of distribution but like other large carnivores, there are low frequency in the country. Habitats of this species in the country, especially at the local level are very fragmented. Therefore, investigation and recognition of the potentially suitable habitat regions are of great importance in the conservation of this species. Also, identifying the factors affecting the selection of habitat plays an important role in describing species behavior and allows to compare different habitats in terms of their quality and to use it in the effective management. Evaluating the response of models and impacts of the biogeoclimatic factors, on habitat suitability, are the other goals of this study.Material and methods: This study was performed in the northwestern part of Iran in the Arasbaran ecoregion, which is known as the Caucasian geographical climate. The Caucasus ecoregion part of Iran has two separate plant communities, Hyrcanian and Arasbarani. The definite presence points of this species were identified by conducting field visits and collecting confirmed observations and reports of game wardens and experts and 64 points were employed in modeling after validating the obtained points and applying the buffer. Six modeling techniques were used in the BIOMOD package of software to create an efficient species distribution model and identify potentially suitable regions. Finally, the habitat suitability map was acquired from the resulting outputs using the combined Ensemble method.Results and discussion: The results from the models’ outputs and overlapping the map of the protected areas indicated that more than two-thirds of these areas are suitable for the brown bears. based on results, the variables of slop percentage and vegetation index involve the largest share in determining habitat desirability, so moderate slop and higher vegetation density will lead to more suitability for the species. In determining the desirability of the habitat of the brown bear, the variable of distance from rivers has less effect, and distance from the village did not play an important role. The flatness of areas, as it increase human access, reduced the level of security and thus reduced the suitability of the habitat of this species. There is more probability of the presence of species in the slope range of 10 to 40% and the suitability of habitat was raised in the moderate slope and increased the vegetation density. Regions with moderate rainfall and higher altitudes also play an important role in attracting bears and increasing habitat suitability. On the other hand, rising temperature, congested roads, and increasingly concentrated human activities reduce habitat desirability with an almost steep trend. 67% of the identified suitable and preferred habitats of the species have been located within the four protected areas category and 33% of them are outside of these areas, which is an acceptable level for conservation. Although a wide range of the protected areas in the study area is suitable for the species, the density of the brown bears in this part of the country is much lower than in the north and west of the country. This is maybe due to the high number of human casualties and damages in these areas and that most areas are not broad and wide enough to cover the entire home range of this species in the northwest. The most common reason for habitat fragmentation is the local road network, dispersion of rural areas and dispersed villages, and scattered individual livestock farming.Conclusion: One of the most significant conservation strategies for the large carnivorous species, such as the brown bear, with high mobility behavior, low population density, high conflict, and extensive conservation requirements is connectivity between the species habitats by defining new protected areas according to the suitable habitats maps. Also, the most important actions to reduce conflict and retaliatory measures are organizing the scattered livestock farming, implementing the educational programs, and more importantly, compensating the damages caused by bears to rural communities.
Shadman Darvishi; Karim Solaimani
Abstract
Introduction: Monitoring and evaluation of land surface condition is one of the basic needs in investigating the changes occurring at different levels, including global, regional and local, which include environmental changes. Today, the rapid growth of remote sensing technology, GIS and computer science ...
Read More
Introduction: Monitoring and evaluation of land surface condition is one of the basic needs in investigating the changes occurring at different levels, including global, regional and local, which include environmental changes. Today, the rapid growth of remote sensing technology, GIS and computer science has led to the emergence of many models to present current and future patterns of land use change. In order to high population growth in large cities and the population's need for land resources, this provides for the destruction of land use, especially vegetation. Kermanshah city as one of the growing areas in recent years has experienced a large population growth and due to the role of population in land use changes and vegetation cover, this issue requires awareness of the vegetation status of this area for proper management of natural resources. The purpose of this study is to monitor and predict vegetation changes in Kermanshah city using NDVI index and CA-Markov model. Material and methods: In this study, vegetation density of Kermanshah city using NDVI index in four classes of low, medium, dense and highest dense vegetation was extracted from Landsat images in 1987, 2002 and 2017 and then the results were validated using ground control points. Also, in order to predict vegetation density for 2032, vegetation map of 2017 was first simulated by applying CA-Markov model and then results were validated using actual vegetation map of the same year using validate module in IDRISI Terrset software followed by validation results and by applying the mentioned model, vegetation density map was predicted in 2032. Results and discussion: The results of vegetation maps with over 85% accuracy show that the area with low, medium and highest dense vegetation classes had a decreased and dense vegetation class had an increased trend during the period of 1987 to 2017. Changes in vegetation classes in elevation classes over the 30 year period show low vegetation in classes of 1042 to 1587 and 2133 to 2678 meters, medium vegetation in classes of 1042 to 1587, 1587 to 2133 and 2678 to 3224 meters, dense vegetation in classes of 2133 to 2678 meters and highest dense vegetation in classes of 1042 to 1587 and 1587 to 2133 meter had a decreased trend. Also, vegetation density in slope classes showed that slope of 0-25% had the highest and slopes of 50-75% and more than 75% had the lowest vegetation density. Also, CA-Markov model results with more than 80% accuracy show that vegetation density in 2032 will be similar to previous periods and medium vegetation cover will have the highest vegetation area in Kermanshah city. The increasing and decreasing trend of vegetation classes indicates that the medium vegetation class will decrease compared to 2017 and the classes with low, dense and high dense vegetation will increase and assessment of vegetation classes in elevation and slope classes shows that at altitudes of 1042 to 1587, 1587 to 2133 and 2133 to 2678 meters and slopes of 0 to 25 percent, the highest vegetation density was related to medium and dense vegetation classes but at altitudes of 2678 to 3224 meters and the slope of 50 to 75 and more than 75 percent the highest vegetation density will be the low vegetation class. Conclusion: In general, the results of this study showed that using NDVI and CA-Markov models with respect to the validation results of these methods can provide acceptable results from the vegetation status of an area.
