Integrated Watershed Management

Integrated Watershed Management

Modeling the distribution of Persian oak (Quercus brantii Lindl) in Holilan, Iran, using MaxEnt method

Document Type : Original Article

Author
Behroz Naseri
Department of Forest Sciences, Islamic Azad University, Ilam, Iran
10.22034/iwm.2025.2041786.1174
Abstract
Extended abstract
Introduction: The Zagros forests, one of the largest and most significant vegetation zones in Iran, play a vital role in sustaining natural resources and environmental stability. These forests provide critical ecosystem services, including groundwater recharge, soil erosion reduction, climate regulation, biodiversity conservation, and socio-economic benefits. Among the dominant species, Quercus brantii (Persian oak) holds a crucial position, widely distributed across the Zagros forests. However, this species has become highly vulnerable and is at risk of extinction due to threats such as overexploitation, habitat destruction, and climate change. This study aims to model the distribution of Quercus brantii using the maximum entropy (MaxEnt) method, evaluate the influence of various environmental factors on its distribution, and produce an optimal distribution map for the species in Holilan County, Ilam Province. The findings will support targeted conservation strategies and sustainable management of Zagros forests.
 Materials and Methods: To model the distribution of Quercus brantii in the forests of Holilan County, Ilam Province, the MaxEnt method was employed. For model development, 75%  of the data (89 pints) were randomly selected as training data, and the remaining 25% (30 points) were used as test data for independent model evaluation. The maximum number of background points was set to 10,000 with 15 repetitions. Nineteen climatic variables, three physiographic variables (elevation, slope, aspect), and snow cover data were utilized. Initially, the desired environmental layers were prepared using ArcGIS software, and then the MaxEnt model was used to assess the species’ current and future (2050-2070) distribution. Model performance was evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) metric.
 Results and Discussion: The evaluation of the modeling accuracy based on the ROC curve showed that the model's accuracy was at an excellent level (AUC = 0.947). The model identified annual temperature, mean monthly temperature, isothermality, annual precipitation, and elevation as the most influential variables, collectively explaining 58% of the distribution variance. Suitable habitats for Quercus brantii covered 7,067 hectares (excellent potential) and 10,779 hectares (good potential), while 54,750 hectares showed low-to-moderate suitability. The species primarily occurred at elevations between 1,000–2,339 meters, with higher prevalence on southern, eastern, and southeastern slopes. Presence peaked at slopes up to 25%, beyond which habitat suitability declined.
 Conclusion: The overall findings of this study highlight the significant role of variables such as annual mean temperature, mean monthly temperature, isothermality, annual precipitation, and elevation in modeling the distribution of Quercus brantii. The species is predominantly distributed in southern aspects and at elevations ranging from 1,000 to 2,339 meters above sea level. This research provides valuable insights into the ecological tolerance range of Quercus brantii in relation to environmental variables, which can serve as a scientific basis for management decisions. The information obtained is not only effective for prioritizing protected areas and implementing conservation and restoration measures but also enhances the success rate of plantation and rehabilitation projects, aiding in the preservation and development of this species in vulnerable regions.
Keywords
Quercus brantii
Maximum entropy
Modeling
Holilan
GIS

Subjects

Ahmed, M., Husain, T., Sheikh, A.H., Hussain, S.S., & Siddiqui, M.F. (2006). Phytosociology and structure of Himalayan forests from Himalayan climatic zones of Pakistan. Journal of Botany, 38(2), 361-383.
Amini, A., & Shahbazi, Z. (2015). Estimating the recreational value of oak forest in Sirvan and Chardavol Townships using contingent valuation method (CVM). Spatial Planning, 5(1), 27-48. (In Persian)
Baldwin, R.A. (2009). Use of maximum entropy modeling in wildlife research. Kearney agricultural center. University of California, Entropy , 11, 854-866. https://doi.org/10.3390/e11040854
Bordbar, K., Sagheb-Talebi, Kh., Hamzehpour, M., Joukar, L., Pakparvar, M., & Abbasi, A. R. (2010). Impact of environmental factors on distribution and some quantitative characteristics of Manna Oak (Quercus brantii Lindl.) in Fars Province. Iranian Journal of Forest and Poplar Research, 18(3), 390-404. (In Persian)
Bradley, A.P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition, 30(7), 1145-1159. https://doi.org/10.1016/S0031-3203(96)00142-2
Elith, J. Graham, C.H., & Anderson, R.P. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Echography, 29, 129-151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
Fatahi, M. (1995). The history of Zagros vegetation, especially Pistacia atlantica forests, an article presented in the first national seminar of Pistacia atlantica (Green Pearl). Ilam. 26-62. (In Persian)
Gonçalves, L., Subtil, A., Oliveira, M. R., & de Zea Bermudez, P. (2014). ROC curve estimation: An overview. REVSTAT-Statistical Journal, 12(1), 1-20. https://doi.org/10.57805/revstat.v12i1.141 
Hashemnia., M. (2014). Ecological Survey of Crataegus pontica species and rehabitation of suitable regions for plantation in Abdanan. M.Sc. Thesis Faculty of Agriculture. Ilam University (In Persian)
Khanum, R., Mumtaz, A.S., & Kumar, S. (2013). Predicting impacts of climate change on medicinal asclepiads of Pakistan using Maxent modeling. Acta Oecologica, 49, 23-31. https://doi.org/10.1016/j.actao.2013.02.007
Marvi Mohajer, M. (2006). Forestry and Forestry.Tehran University Press, Iran: Tehran. (In Persian)
Miller, J. (2010). Species Distribution Modeling. Geography Compass. 4(6), 490-509. https://doi.org/101111/j.1749-8198.2010.00351.x
Parisaei, B. (2006). Investigation of important climatic factors on distribution of vegetation types in Charmahal provience using statistical multivariate methods and GIS. Master's thesis, Faculty of Natural Resources, Isfahan University of Technology. (In Persian)
Peterson, A.T., & Shaw, J. (2003). Lutzomyia vectors for cutaneous leishmaniasis in southern Brazil: ecological niche models. predicted geographic distribution, and climate change effects. International Journal of Parasitology, 33, 919-931. https://doi.org/10.1016/s0020-7519(03)00094-8
Phillips, S.J. Anderson, R.P., & Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modeling, 190, 231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Querejeta, J. I., Egerton-Warburton, L. M., & Allen, M. F. (2009). Topographic position modulates the mycorrhizal response of oak trees to interannual rainfall variability. Ecology, 90(3), 649–662. https://doi.org/10.1890/07-1696.1
Remya K., Ramachandran A., & Jayakumar, S. (2015). Predicting the current and future suitable habitat distribution of Myristica dactyloides Gaertn using MaxEnt model in the Eastern Ghats, India. Ecological Engineering, 82, 184-188. https://doi.org/10.1016/j.ecoleng.2015.04.053
Sarangzai, A. M., Ahmed, M., Ahmed, A., Tareen, L., & Jan, S. U. (2012). The ecology and dynamics of Juniperus excelsa forestin Balochistan - Pakistan. Pakistan Journal of Botany, 44(5), 1617-1625.
Sass-Klaassen, U., Leuschner, H.H., Buerkert, A., & Helle, G. (2008). Tree- ring analysis of Juniperus excelsa from the northern Oman Mountains. TRACE Den- drosymposium. 3-6 may, Riga, Latvia.
Titshall, L.W. O’Connor, T.G., & Morris, C.D. (2009). Effect of long-term exclusion of fire and herbivory on the soils and vegetation of sour grassland. African Journal of Range and Forage Science, 17, 70–80. https://doi.org/10.2989/10220110009485742
Yaghmaei, L., Khodagholi, M., Soltani, S., & Saboohi, R. (2009). Effect of climatic factors on distribution of forest types using multivariate statistical methods. Iranian Journal of Forest, 1(3), 239-251. (In Persian)
Yang, X.Q. Kushwaha, S.P.S. Saran. S. Xu, J., & Roy, P.S. (2013). Maxent modeling for predicting the potential distribution of medicinal plant, Justiciaadhatoda L. in Lesser Himalayan foothills. Ecological Engineering, 51, 83–87. http://doi.org/10.1016/j.ecoleng.2012.12.004
Integrated Watershed Management
Volume 5, Issue 3 - Serial Number 17
Autumn 2025
Pages 105-117

  • Receive Date 23 September 2024
  • Revise Date 04 April 2025
  • Accept Date 20 April 2025