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مرتع 2026, 20(1): 1-15 |
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Using Maxent modeling in predicting potential habitat for Agropyron tauri species (case study: rangeland of Taleghan miany). مرتع 2026; 20 (1) :1-15
URL: http://rangelandsrm.ir/article-1-1366-en.html
URL: http://rangelandsrm.ir/article-1-1366-en.html
Abstract: (8 Views)
Background and Objectives: In recent decades, assessing plant species distributions and evaluating their ecological habitats have become increasingly important for sustainable land management and biodiversity conservation. Rangeland ecosystems in semi-arid regions such as Iran are under mounting pressure from climatic variability and anthropogenic disturbances. Identifying the ecological niches of valuable native species—such as Agropyron tauri, a perennial grass with high forage quality and significant ecological value—can support effective restoration and conservation strategies. Species distribution models (SDMs), particularly those based on presence-only data, are essential tools in this context. Among these, the Maxent model has demonstrated strong performance by applying the principle of maximum entropy, making it well suited for data-limited ecological studies. This study aimed to evaluate the efficiency of the Maxent model in predicting the potential distribution of A. tauri, identifying the key environmental variables influencing its distribution, and producing a habitat suitability map for the Middle Taleghan rangelands in northwestern Iran.
Methodology: The study area is located in the Middle Taleghan watershed in Alborz Province, encompassing elevations from 1,800 to 4,000 m above sea level. Vegetation data were collected using four 150-m transects per sampling unit, each consisting of ten 1 m² quadrats, following the minimal area method and a randomized systematic sampling design. Simultaneously, soil samples were collected along each transect to determine physical properties (sand, silt, and clay percentages) and chemical characteristics (pH, lime content, organic matter, and electrical conductivity). Environmental layers, including topographic variables (elevation, slope, and aspect), climatic data, and geological maps, were generated using satellite imagery and digital elevation models (DEMs). Geostatistical interpolation through kriging and variogram analysis was performed in ArcGIS to produce continuous spatial layers. The Maxent model was applied using presence records of A. tauri and the compiled environmental variables. Model performance was evaluated using the Kappa coefficient and the area under the receiver operating characteristic curve (AUC).
Results: Model outputs indicated that topographic variables—particularly elevation and aspect—along with soil texture components such as sand and clay content, were the most influential factors governing the presence of A. tauri. Jackknife analysis confirmed the significant contribution of these variables, as model accuracy declined when they were excluded. Response curves showed that the probability of A. tauri occurrence increased with moderate levels of soil organic matter and silt and was highest in soils with 0–2% lime content and low electrical conductivity (<0.4 dS m⁻¹). In contrast, the probability of occurrence decreased with increasing lime content and salinity. The habitat suitability map revealed that the most favorable areas for A. tauri were concentrated in the mid-elevation western parts of the study area. Model performance was validated by a high Kappa coefficient (0.85) and an AUC value of 0.936, indicating excellent predictive accuracy.
Conclusion: The results clearly demonstrate that Agropyron tauri exhibits a non-random distribution pattern that is strongly influenced by topographic conditions and soil properties. The Maxent model proved to be a robust and efficient tool for predicting the species’ potential habitat using presence-only data. Given its high predictive accuracy, the model is highly applicable to ecological planning, rangeland restoration, and habitat management for forage species. The study suggests that areas characterized by moderate elevations, loamy soils, moderate organic matter content, and low lime and salinity levels should be prioritized for A. tauri development and restoration initiatives. Future research should integrate dynamic climate models, remote sensing data, and seasonal variables to enhance temporal predictions under changing environmental conditions. Overall, this study highlights the value of Maxent as a decision-support tool in biodiversity management and its important role in guiding conservation strategies for vulnerable rangeland ecosystems in arid and semi-arid regions.
Methodology: The study area is located in the Middle Taleghan watershed in Alborz Province, encompassing elevations from 1,800 to 4,000 m above sea level. Vegetation data were collected using four 150-m transects per sampling unit, each consisting of ten 1 m² quadrats, following the minimal area method and a randomized systematic sampling design. Simultaneously, soil samples were collected along each transect to determine physical properties (sand, silt, and clay percentages) and chemical characteristics (pH, lime content, organic matter, and electrical conductivity). Environmental layers, including topographic variables (elevation, slope, and aspect), climatic data, and geological maps, were generated using satellite imagery and digital elevation models (DEMs). Geostatistical interpolation through kriging and variogram analysis was performed in ArcGIS to produce continuous spatial layers. The Maxent model was applied using presence records of A. tauri and the compiled environmental variables. Model performance was evaluated using the Kappa coefficient and the area under the receiver operating characteristic curve (AUC).
Results: Model outputs indicated that topographic variables—particularly elevation and aspect—along with soil texture components such as sand and clay content, were the most influential factors governing the presence of A. tauri. Jackknife analysis confirmed the significant contribution of these variables, as model accuracy declined when they were excluded. Response curves showed that the probability of A. tauri occurrence increased with moderate levels of soil organic matter and silt and was highest in soils with 0–2% lime content and low electrical conductivity (<0.4 dS m⁻¹). In contrast, the probability of occurrence decreased with increasing lime content and salinity. The habitat suitability map revealed that the most favorable areas for A. tauri were concentrated in the mid-elevation western parts of the study area. Model performance was validated by a high Kappa coefficient (0.85) and an AUC value of 0.936, indicating excellent predictive accuracy.
Conclusion: The results clearly demonstrate that Agropyron tauri exhibits a non-random distribution pattern that is strongly influenced by topographic conditions and soil properties. The Maxent model proved to be a robust and efficient tool for predicting the species’ potential habitat using presence-only data. Given its high predictive accuracy, the model is highly applicable to ecological planning, rangeland restoration, and habitat management for forage species. The study suggests that areas characterized by moderate elevations, loamy soils, moderate organic matter content, and low lime and salinity levels should be prioritized for A. tauri development and restoration initiatives. Future research should integrate dynamic climate models, remote sensing data, and seasonal variables to enhance temporal predictions under changing environmental conditions. Overall, this study highlights the value of Maxent as a decision-support tool in biodiversity management and its important role in guiding conservation strategies for vulnerable rangeland ecosystems in arid and semi-arid regions.
Keywords: Maxent model, Species distribution, Potential habitat, Agropyron tauri, Rangeland management, Environmental factors.
Type of Study: Research |
Subject:
Special
Received: 2026/01/25 | Accepted: 2026/04/4 | Published: 2026/04/4
Received: 2026/01/25 | Accepted: 2026/04/4 | Published: 2026/04/4
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