Ghahremanian Amir, Ahmadi Abbas, Toranjzar Hamid, Varvani Javad, Abdi Nourollah
Department of Natural Resources and Environment, Ar.C., Islamic Azad University, Arak, Iran.
Food Security Research Centre, Ar.C., Islamic Azad University, Arak, Iran.
Scientifica (Cairo). 2025 Mar 31;2025:4003408. doi: 10.1155/sci5/4003408. eCollection 2025.
This study aims to evaluate the potential habitat of sp. using three different species distribution modeling methods: the maximum entropy (MaxEnt) model, the Genetic Algorithm for Rule-Set Production (GARP), and logistic regression. The primary objective was to identify key environmental factors that influence the spatial distribution of sp. in the Savar-Abad basin's rangelands. Vegetation sampling was carried out across diverse vegetation types within the study area, using 2-10 square meter plots to capture a representative sample of plant species distribution. Soil sampling was conducted at varying depths to capture essential soil properties, including physical (clay, gravel, silt, and sand) and chemical factors (organic matter, electrical conductivity, pH, and lime). Soil maps were generated using interpolation techniques to visualize soil variation across the area. The sampling strategy was designed to ensure comprehensive data collection, allowing for robust model training and validation. MaxEnt, which is a presence-only model, outperformed both the GARP and logistic regression models in predicting suitable habitats for sp. Results revealed that soil salinity, elevation, and soil acidity significantly influenced species distribution. The findings also suggest that elevation and salinity have the most substantial effects on habitat suitability, while soil texture (clay, silt, and sand) plays a secondary role. These results are valuable for rangeland management, offering insights into areas where sp. could thrive or where interventions might be necessary to improve habitat conditions. In terms of management, this study highlights the importance of considering both ecological and environmental factors when planning conservation and restoration activities for rangelands. The ability to predict species distribution can help optimize resource allocation for habitat restoration and enhance biodiversity conservation efforts.
最大熵（MaxEnt）模型、规则集生成遗传算法（GARP）和逻辑回归。主要目标是确定影响萨瓦尔 - 阿巴德盆地牧场中某物种空间分布的关键环境因素。在研究区域内的不同植被类型中进行了植被采样，使用2 - 10平方米的样地来获取植物物种分布的代表性样本。在不同深度进行了土壤采样，以获取基本的土壤特性，包括物理特性（粘土、砾石、淤泥和沙子）和化学因素（有机质、电导率、pH值和石灰）。使用插值技术生成土壤图，以可视化该区域的土壤变化。采样策略旨在确保全面的数据收集，以便进行强大的模型训练和验证。MaxEnt作为一种仅存在模型，在预测某物种的适宜栖息地方面优于GARP和逻辑回归模型。结果表明，土壤盐分、海拔和土壤酸度显著影响物种分布。研究结果还表明，海拔和盐分对栖息地适宜性影响最大，而土壤质地（粘土、淤泥和沙子）起次要作用。这些结果对于牧场管理具有重要价值，为某物种可能茁壮成长的区域或可能需要进行干预以改善栖息地条件的区域提供了见解。在管理方面，本研究强调了在规划牧场保护和恢复活动时考虑生态和环境因素的重要性。预测物种分布的能力有助于优化栖息地恢复的资源分配，并加强生物多样性保护工作。
