ارزیابی زیستگاه گرگ خاکستری در قلمروهای کوهستانی (مطالعه موردی: منطقه حفاظت‌شده بیجار، استان کردستان)

Assessing habitat suitability for wildlife species provides a vital foundation for their effective conservation and management. This study investigates the habitat preferences of the gray wolf (Canis lupus) and identifies key environmental factors influencing its habitat selection in the Bijar Protected Area, located in Kurdistan Province, Iran, during 2022–2023. Although the gray wolf is not currently endangered in Iran, increasing human-induced habitat disturbances pose a significant threat to its long-term population viability. As the largest carnivore in the region with a notable population, the gray wolf was selected as the focal species. A total of 148 presence points were used—50 obtained from the Department of Environment and local rangers, and 98 recorded during systematic field surveys using GPS. Thirty environmental variables were initially considered, including 19 bioclimatic indicators, digital elevation model (DEM), slope, aspect, vegetation density, land use, and distances from prey sources, villages, springs, rivers, roads, and ranger stations. After correlation analysis, 15 key variables were retained for modeling. Habitat suitability was modeled using the Maximum Entropy (MaxEnt) approach. The results revealed that the central part of the study area holds the highest concentration of wolf presence, while the northern, southern, and southeastern areas show lower densities. Topographic variables and proximity to prey were identified as the most influential factors shaping habitat suitability Of the region's 31,769 hectares, 11,955 hectares (equivalent to 37.6%) were identified as favorable habitat for the gray wolf. Given the gray wolf's role as a large-bodied apex predator and umbrella species, conserving these suitable areas is expected to benefit the broader carnivore community coexisting within the region.
 
Extended Abstract
 
Introduction
Habitat loss and overhunting are among the principal drivers of species extinction and ecosystem degradation. The urgent need to preserve natural habitats, critical for sustaining wildlife populations, has become more pressing than ever. As a keystone carnivore, the gray wolf plays a vital role in maintaining ecological balance in mountainous ecosystems. However, habitat fragmentation, climate change, and escalating human-wildlife conflicts pose significant threats to the survival of this species. Evaluating habitat suitability in mountainous areas is essential for understanding the ecological requirements of the gray wolf and developing effective conservation strategies. One of the most widely used presence-only modeling methods for predicting species distribution is the Maximum Entropy (MaxEnt) algorithm. Despite the gray wolf's current classification as "Least Concern" on the IUCN Red List, its populations in Iran are experiencing decline, largely due to human-induced threats. This necessitates the formulation of targeted conservation programs aimed at ensuring the long-term viability of wolf populations across various Iranian landscapes.
 
Methodology
The Bijar Protected Area, located in Kurdistan Province, Iran, supports a variety of wildlife species and features diverse vegetation. To assess gray wolf habitat suitability and identify key environmental variables influencing habitat selection and distribution, a comprehensive set of 30 ecological variables was initially considered. Data sources included field surveys, expert consultations, and literature reviews. To mitigate multicollinearity, Pearson correlation analysis was performed using Idrisi software, and variables with correlation coefficients exceeding 0.7 were excluded. This refinement process reduced the number of independent variables to 15. The final set included: slope, aspect, vegetation density, land use, distance to roads, springs, prey sources, rivers, and villages; annual temperature range; precipitation seasonality; and rainfall during the driest and coldest seasons.
Presence data were divided into training (75%) and test (25%) datasets to evaluate model accuracy. Habitat suitability was then modeled using the MaxEnt algorithm, and the relative contribution of each variable to the model was calculated.
 
Results and Discussion
In the MaxEnt output, three types of Jackknife curves are plotted for test data, training data, and AUC (Area Under the Curve). However, it is sufficient to rely on the Jackknife curve and AUC for interpretation. The environmental variables with the highest individual contribution are the distance to prey and distance to water springs, which appear to be the most informative on their own.
If the AUC value ranges from 0.7 to 0.8, the model is considered good; 0.8 to 0.9, excellent; and above 0.9 indicates outstanding predictive performance (Mobaraki et al., 2018). The AUC for the training data is 0.943, indicating excellent model performance in correctly distinguishing positive and negative instances within the training set. The test data AUC is 0.901, which is also very good but slightly lower than that of the training data, possibly indicating slight overfitting.
A random prediction would result in an AUC of 0.5, representing a baseline model with no discriminatory power. Overall, the AUC of the model suggests strong performance. The response curves for individual environmental variables can be interpreted as follows:
Elevation: Wolf presence is highest at mid-elevations due to a balance between prey availability and suitable climatic conditions.
Slope: Wolf occurrence decreases on very steep slopes (above 5 degrees); they primarily use areas with gentle slopes for movement and hunting. Aspect: Northern and eastern aspects, which tend to have higher moisture and better vegetation cover, are more favorable for wolves. Vegetation Index: Wolf presence increases with vegetation density up to an optimal level, after which it plateaus or decreases. Wolves prefer moderate vegetation cover for hunting and concealment, while overly dense forests may hinder movement. Land Use: Wolves are more frequently found in specific land use types such as rangelands or sparsely wooded forests. Urban or intensive agricultural areas are less favorable. Thus, habitat destruction, such as converting rangelands to farmland, can limit wolf distribution. Distance to Prey: The closer to prey (e.g., Armenian mouflon or hare), the higher the likelihood of gray wolf presence. Access to prey is a key factor in habitat selection. Distance to Villages: Wolves tend to avoid human settlements, seeking refuge in remote areas due to fear of humans and potential conflict. Distance to Water Sources: Wolves are frequently present near springs and rivers, indicating that water sources are also critical for their survival. Distance to Roads: Wolves tend to avoid roads, as they can fragment habitats and increase the risk of vehicle collisions.
Meteorological Parameters: In general, areas with moderately cool and temperate climates, stable precipitation patterns, and adequate winter rainfall—supporting prey and water availability—are more suitable for wolves.
 
Conclusion
Findings indicate that 11,554 hectares (approximately 36.4%) of the 31,769-hectare Bijar Protected Area constitute suitable habitat for the gray wolf. Habitat suitability is highest in the central region, with lower densities observed in the northern, southern, and southeastern zones. Topographic features and prey availability emerged as the most influential factors affecting habitat selection. Variables such as distance from roads and human settlements, as well as proximity to water and food sources, play pivotal roles in shaping the spatial ecology of wolves. The spatial separation of wolf habitats from population centers, particularly through informed land-use planning, is essential for effective species management. Additionally, the availability of water resources significantly enhances habitat quality for wolves in the region. Given the ecological role of the gray wolf and the pressures it faces in Iran, conservation measures tailored to regional environmental conditions are vital. The study underscores the necessity of integrating ecological, geographical, and anthropogenic parameters into habitat conservation frameworks for the sustainable management of apex predators.