Volume 19, Issue 1 (3-2025)
مرتع 2025, 19(1): 88-107 |
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Azizi Kalesar M, Moameri M, Ghorbani A, Alavi S J. Habitat Prediction Modeling for the Medicinal Species Vaccinium arctostaphylos L. Using Maximum Entropy Globally. مرتع 2025; 19 (1) :88-107
URL: http://rangelandsrm.ir/article-1-1330-en.html
URL: http://rangelandsrm.ir/article-1-1330-en.html
Department of Range and Watershed Management, Faculty of Natural Resources, University of Mohaghegh Ardabili, Ardabil
Abstract: (261 Views)
Background and objectives: Understanding the environmental factors affecting the establishment and survival of native and medicinal plants is crucial for habitat conservation, especially for species at risk of extinction. This study aims to model the habitat suitability of the rare medicinal plant Vaccinium arctostaphylos L. using the Maximum Entropy (MaxEnt) method.
Methodology: We identified the habitat of V. arctostaphylos in Iran, considering the region’s diverse topography, and conducted field studies. Occurrence records were extracted from the GBIF database at a global scale, while climate data—including 11 temperature variables and 8 precipitation variables—were sourced from WorldClim with a spatial resolution of 30 seconds (~1 km²). A total of 19 bioclimatic variables were incorporated into the model along with digital elevation models (DEM), slope maps, and geographic orientation maps. Using ArcGIS 10.8, we overlaid these factors with species occurrence points to generate a habitat suitability map.
Results: The MaxEnt model achieved an area under the curve (AUC) value of 0.97, indicating strong predictive power. Jackknife analysis identified Bio17 (precipitation of the driest quarter) and Bio18 (precipitation of the warmest quarter) as the most influential factors in V. arctostaphylos distribution. Other significant predictors included Bio4 (temperature seasonality), Bio12 (annual precipitation), and Bio14 (precipitation of the driest month). Among topographic variables, slope percentage was most critical. Response curves demonstrated that species occurrence positively correlated with precipitation variables (Bio12, Bio14, Bio17, Bio18) and slope, while showing an inverse relationship with temperature seasonality (Bio4). The highest probability of presence was recorded in areas with annual precipitation between 1200–1700 mm, temperatures ranging from 5–10°C, and slopes between 15–30%.
Conclusion: The model’s accuracy, assessed using the Kappa index (0.67), confirmed the reliability of MaxEnt in predicting suitable habitats for V. arctostaphylos. These findings highlight critical environmental parameters that shape the species' global distribution and provide valuable insights for conservation and habitat restoration efforts.
Methodology: We identified the habitat of V. arctostaphylos in Iran, considering the region’s diverse topography, and conducted field studies. Occurrence records were extracted from the GBIF database at a global scale, while climate data—including 11 temperature variables and 8 precipitation variables—were sourced from WorldClim with a spatial resolution of 30 seconds (~1 km²). A total of 19 bioclimatic variables were incorporated into the model along with digital elevation models (DEM), slope maps, and geographic orientation maps. Using ArcGIS 10.8, we overlaid these factors with species occurrence points to generate a habitat suitability map.
Results: The MaxEnt model achieved an area under the curve (AUC) value of 0.97, indicating strong predictive power. Jackknife analysis identified Bio17 (precipitation of the driest quarter) and Bio18 (precipitation of the warmest quarter) as the most influential factors in V. arctostaphylos distribution. Other significant predictors included Bio4 (temperature seasonality), Bio12 (annual precipitation), and Bio14 (precipitation of the driest month). Among topographic variables, slope percentage was most critical. Response curves demonstrated that species occurrence positively correlated with precipitation variables (Bio12, Bio14, Bio17, Bio18) and slope, while showing an inverse relationship with temperature seasonality (Bio4). The highest probability of presence was recorded in areas with annual precipitation between 1200–1700 mm, temperatures ranging from 5–10°C, and slopes between 15–30%.
Conclusion: The model’s accuracy, assessed using the Kappa index (0.67), confirmed the reliability of MaxEnt in predicting suitable habitats for V. arctostaphylos. These findings highlight critical environmental parameters that shape the species' global distribution and provide valuable insights for conservation and habitat restoration efforts.
Type of Study: Research |
Subject:
Special
Received: 2025/05/30 | Accepted: 2025/03/30 | Published: 2025/03/30
Received: 2025/05/30 | Accepted: 2025/03/30 | Published: 2025/03/30
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