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Habitat prediction modeling of the medicinal species of Vaccinium arctostaphylos L. with the maximum entropy in the world.. مرتع 2025; 19 (1)
URL: http://rangelandsrm.ir/article-1-1330-en.html
URL: http://rangelandsrm.ir/article-1-1330-en.html
Abstract: (158 Views)
Background and objectives: It is essential to gain scientific knowledge about the relationship between environmental factors that influence the establishment and survival of native and medicinal plants, as well as the habitat distribution of species at risk of extinction. This understanding is crucial for effectively managing and utilizing rangelands, as well as for assessing how species respond to environmental changes and ensuring their overall sustainability. This study aims to develop a habitat prediction model for the rare and medicinal species Vaccinium arctostaphylos L. using the Maximum Entropy method.
Methodology: To achieve this, we first identified the habitat of Vaccinium arctostaphylos in Iran, taking into account the region's diverse topography and the objectives of our research through field studies. We then gathered existing biological data and recorded occurrences of V. arctostaphylos from the GBIF database at a global level. Climate data were obtained from the WorldClim database, which provided 11 temperature variables and 8 precipitation variables, all with a spatial resolution of 30 seconds (~1 km²). This included a total of 19 bioclimatic variables derived from temperature and precipitation data. Additionally, we incorporated digital elevation models (DEM), slope maps, and geographic orientation maps as topographic variables for our model input. Finally, by overlaying the maps of recorded occurrence points with those of the topographical and climatic factors, we generated a predictive habitat map for V. arctostaphylos using ArcGIS version 10.8 software.
Results: The results of the AUC curve from the maximum entropy model for Vaccinium arctostaphylos yielded a value of 0.97, indicating strong predictive power. We examined the impact of environmental factors on species distribution using the jackknife method and response curves. The jackknife analysis revealed that the variables Bio17 (precipitation of the driest quarter) and Bio18 (precipitation of the warmest quarter) had the most significant influence on the distribution of V. arctostaphylos. Other important variables included Bio4 (temperature seasonality), Bio12 (annual precipitation), and Bio14 (precipitation of the driest month). Among the topographical factors, slope percentage emerged as the most critical variable in the studied habitats. The response curves indicated a direct relationship between the presence of V. arctostaphylos and annual precipitation (Bio12), precipitation of the driest month (Bio14), precipitation of the driest quarter (Bio17), precipitation of the warmest quarter (Bio18), and slope. Conversely, there was an inverse relationship with temperature seasonality (Bio4). The highest probability of presence for this species occurred within an annual precipitation range of 1200 to 1700 mm and an annual temperature range of 5-10 °C. Additionally, the species was most frequently observed on slopes ranging from 15% to 30%.
Conclusion: The Kappa index was employed to assess the alignment between the predicted map and the actual distribution map. The results indicated that the accuracy of the maximum entropy modeling (Kappa: 0.67) was acceptable, demonstrating that this model effectively predicted the presence of V. arctostaphylos in relation to its actual habitat. Overall, the maximum entropy method proved to be a robust predictive tool for identifying suitable habitats for V. arctostaphylos, which thrives under specific environmental conditions. Generally, plant species exhibit significant relationships with climatic and topographical factors based on their growing area characteristics, ecological requirements, and tolerance ranges.
Methodology: To achieve this, we first identified the habitat of Vaccinium arctostaphylos in Iran, taking into account the region's diverse topography and the objectives of our research through field studies. We then gathered existing biological data and recorded occurrences of V. arctostaphylos from the GBIF database at a global level. Climate data were obtained from the WorldClim database, which provided 11 temperature variables and 8 precipitation variables, all with a spatial resolution of 30 seconds (~1 km²). This included a total of 19 bioclimatic variables derived from temperature and precipitation data. Additionally, we incorporated digital elevation models (DEM), slope maps, and geographic orientation maps as topographic variables for our model input. Finally, by overlaying the maps of recorded occurrence points with those of the topographical and climatic factors, we generated a predictive habitat map for V. arctostaphylos using ArcGIS version 10.8 software.
Results: The results of the AUC curve from the maximum entropy model for Vaccinium arctostaphylos yielded a value of 0.97, indicating strong predictive power. We examined the impact of environmental factors on species distribution using the jackknife method and response curves. The jackknife analysis revealed that the variables Bio17 (precipitation of the driest quarter) and Bio18 (precipitation of the warmest quarter) had the most significant influence on the distribution of V. arctostaphylos. Other important variables included Bio4 (temperature seasonality), Bio12 (annual precipitation), and Bio14 (precipitation of the driest month). Among the topographical factors, slope percentage emerged as the most critical variable in the studied habitats. The response curves indicated a direct relationship between the presence of V. arctostaphylos and annual precipitation (Bio12), precipitation of the driest month (Bio14), precipitation of the driest quarter (Bio17), precipitation of the warmest quarter (Bio18), and slope. Conversely, there was an inverse relationship with temperature seasonality (Bio4). The highest probability of presence for this species occurred within an annual precipitation range of 1200 to 1700 mm and an annual temperature range of 5-10 °C. Additionally, the species was most frequently observed on slopes ranging from 15% to 30%.
Conclusion: The Kappa index was employed to assess the alignment between the predicted map and the actual distribution map. The results indicated that the accuracy of the maximum entropy modeling (Kappa: 0.67) was acceptable, demonstrating that this model effectively predicted the presence of V. arctostaphylos in relation to its actual habitat. Overall, the maximum entropy method proved to be a robust predictive tool for identifying suitable habitats for V. arctostaphylos, which thrives under specific environmental conditions. Generally, plant species exhibit significant relationships with climatic and topographical factors based on their growing area characteristics, ecological requirements, and tolerance ranges.
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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