[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
:: Volume 9, Issue 18 (10-2021) ::
PEC 2021, 9(18): 363-388 Back to browse issues page
Habitat modeling and determination of environmental factors affecting on distribution of Persian oak (Quercus brantii Lindl.) in forest habitats of Lorestan Province
Sorour Mahmoudvand, Hamed Khodayari *, Farajollah Tarnian
, khodayari.h@lu.ac.ir
Abstract:   (1628 Views)
Species distribution models (SDMs) are the most important ecological models used to model species distribution, identify new habitats, and protect endangered species. In this study, habitat potential modeling and determine the most important factors affecting on distribution of Persian oak (Quercus brantii Lindl.) was investigated using MaxEnt model in Lorestan province. To map the potential distribution of Persian oak, 22 environmental layers were prepared in ArcGIS 10.4.1 and converted to ASCII format. Then 5064 species presence points were extracted using 1.5 × 1.5 km grid from the map of geographical distribution of the Persian oak species (1:250000) and converted to CSV format. After preparing the layers, MaxEnt 3.3.3 software was used to model distribution of the species. The results showed that 7 variables were more important among of the 22 environmental variables affecting on distribution of Persian oak. Annual mean temperature, Precipitation of wettest quarter and Min temperature of coldest month had the highest impact on distribution of the species with 38, 24.2 and 13.3 %, respectively. The area under the curve (AUC) of the model was 0.669 and the threshold value of 0.33 was used to convert the probability map to the presence and absence maps. The potential vegetation map of Persian oak shows that approximately 62.5% of the area of Lorestan province has the potential for growing Persian oak. The main new habitat is located in Khorramabad, Aligudarz, Kuhdasht and Poldokhtar, which recommended for restoration and rehabilitation.
Keywords: Persian oak, Bioclimatic variables, Ecological models, MaxEnt.
Full-Text [PDF 1341 kb]   (512 Downloads)    
Type of Study: Applicable | Subject: Special
Received: 2020/04/3 | Accepted: 2021/01/30 | Published: 2021/09/28
1. Abdelaa, M., Fois, M., Fenu, G., Bacchetta, G. 2019. Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép. in Egypt. Ecological Informatics, 50: 68-75.
2. Adhikari, D., Barik, S.K., Upadhaya, K. 2012. Habitat distribution modelling for reintroduction of Ilex khasiana Purk., a critically endangered tree species of northeastern India. Ecological Engineering, 40: 37-43.
3. Amissah, L., Mohren, G.M.J., Bongers, F., Hawthorne, W.D., Poorter, L. 2014. Rainfall and temperature affect tree species distribution in Ghana. Journal of Tropical Ecology, 30: 435-446.
4. Araujo, M. B., Guisan, A. 2006. Five (or so) challenges for species distribution modelling. Journal of Biogeography (J. Biogeogr.), 33:1677–1688.
5. Coban, H.O. Örücü, O.K., Arslan, E.S. 2020. MaxEnt Modeling for Predicting the Current and Future Potential Geographical Distribution of Quercus libani Olivier. Sustainability, 12: 1-19.
6. Elith J., Graham, C.H., Anderson, R.P., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.M., Peterson, A.T., Phillips, S.J., Richardson, K., Scachetti Pereira, R., Schapire, R.E., Soberon, J., Williams, S., Wisz, M.S., Zimmermann, N.E. 2006. Novel methods improve prediction of species’ distribution from occurrence data. Ecography, 29: 129-151.
7. Hidalgo, P.J., Marı´n, J.M., Quijada, J., Moreira, J.M. 2008. A spatial distribution model of cork oak (Quercus suber) in southwestern Spain: A suitable tool for reforestation. Forest Ecology and Management, 255: 25-34.
8. Jiménez-Valverde, A. 2012. Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Global Ecology and Biogeography, (Global Ecol. Biogeogr.), 21: 498–507.
9. Kumar, S., Neven, L.G., Yee, W.L. 2014. Evaluating correlative and mechanistic niche models for assessing the risk of pest establishment. Ecosphere, 5(7): 1-23.
10. Liu, C., Newell, G., White, M. 2016. On the selection of thresholds for predicting species occurrence with presence-only data. Ecology and Evolution, 2016, 6(1): 337–348.
11. Liu, C., White, M., Newell, G. 2013. Selecting thresholds for the prediction of species occurrence with presence-only data. Journal of Biogeography (J. Biogeogr.), 40: 778–789.
12. Lobo, J.M., Jiménez-Valverde, A., Real, R. 2008. AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17: 145–151.
13. Mclaughlin, B.C., Zavaleta, E.S. 2012. Predicting species responses to climate change: demography and climate microrefugia in California valley oak (Quercus lobata). Global change biology, 18(7): 2301-2312.
14. Morales, N., Fernández, I.C., Baca-González, V. 2017. MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review. PeerJ, 1-16.
15. Norris, D. 2014. Model thresholds are more important than presence location type: Understanding the distribution of lowland tapir (Tapirus terrestris) in a continuous Atlantic forest of southeast Brazil. Tropical Conservation Science, 7(3):529-547.
16. O’Donnell, M.S., Ignizio, D.A. 2012. Bioclimatic predictors for supporting ecological applications in the conterminous United States: U.S. Geological Survey Data Series, 691, 10 p.
17. Pausas, J.G., Austin, M. 2001. Patterns of plant species richness in relation to different environments: an appraisal. Journal of Vegetation Science, 12: 153–166.
18. Pearson, R.G., Dawson, T.P., Liu, C. 2004. Modelling species distributions in Britain: a hierachical integration of climate and landcover data. Ecography, 27: 285-298.
19. Peterson, A.T., Papes, M., Soberon, J. 2008. Rethinking receiver operating characteristic analysis applications in ecological niche modelling. Ecological Modelling, 213, 63–72.
20. Phillips, S.J. 2017. A Brief Tutorial on Maxent. Available from url: htt://biodiversityinformatics.amnh.org/ open_source/ maxent/. Accessed on XXXX-XX-XX.
21. Phillips, S.J., Dudik, M., Schapire, R.E. 2004. A maximum entropy approach to species distribution modelling, In: Proceeding of the 21st International Conference on Machine Learning. ACMPress, New York. pp. 655-662.
22. Sagheb Talebi, K.H., Sajedi, T., Pourhashemi, M. 2014. Forests of Iran: A Treasure from the Past, A Hope for the Future. Springer, New York, 152.
23. Shi, Z., Gao, J., Yang, X., Jia, Z., Shang, J., Feng, C., Lü, S. 2012. Response of Mongolian pine radial growth to clima in Hulunbuir Sand Land, Inner Mongolia, China. Journal of Food, Agriculture & Environment. 10(2): 884-890.
24. Swaine, M. 1996. Rainfall and soil fertility as factors limiting forest species distributions in Ghana. Journal of Ecology, 84:419–428.
25. Swets, J. A. 1988. Measuring the accuracy of diagnostic systems. Science, 240(4857):1285-1293.
26. Taleshi, H., Maasoumi Babarabi, M. 2013. Leaf morphologycal variation of Quercus brantii Lindl. Along an altitudinal gradient in Zagros forests of Fars Province, Iran. European Journal of Experimental Biology, 3(5): 463-468.
27. Terribile, L.C., Olalla Tárraga, M.Á., Diniz Filho, J.A.F., Rodríguez, M.Á. 2009. Ecological and evolutionary components of body size: geographic variation of venomous snakes at the global scale. Biological Journal of the Linnean Society, 98(1): 94-109.
28. Toledo, M., Peña-Claros, M., Bongers, F., Alarcón, A., Balcázar, J., Chuviña, J., Leaño, C., Licona, J.C., Poorter, L. 2012. Distribution patterns of tropical woody species in response to climatic and edaphic gradients. Journal of Ecology, 100: 253–263.
29. van Proosdij, A.S.J., Sosef, M.S.M., Wieringa, J.J., Raes, N. 2016. Minimum required number of specimen records to develop accurate species distribution models. Ecography, 39: 542–552.
30. Veloz, S.D. 2009. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. Journal of Biogeography (J. Biogeogr.), 36:2290–2299.
31. Vessella, F., Schirone, B. 2013. Predicting potential distribution of Quercus suber in Italy based on ecological niche models: Conservation insights and reforestation involvements. Forest Ecology and Management, 304: 150-161.
32. Yi, Y.J., Cheng, X., Yang, Z.F., Zhang, S.H. 2016. Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan. China. Ecological. Engineering, 92: 260–269.
33. Zhang, K., Yao, L., Meng, J., Tao, J. 2018. Maxent modeling for predicting the potential geographical distribution of two peony species under climatic change. Science of the Total Enviroment, 634: 1326-1334.
Send email to the article author

Add your comments about this article
Your username or Email:


XML   Persian Abstract   Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Mahmoudvand S, Khodayari H, Tarnian F. Habitat modeling and determination of environmental factors affecting on distribution of Persian oak (Quercus brantii Lindl.) in forest habitats of Lorestan Province. PEC 2021; 9 (18) :363-388
URL: http://pec.gonbad.ac.ir/article-1-681-en.html

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 9, Issue 18 (10-2021) Back to browse issues page
مجله حفاظت زیست بوم گیاهان Journal of Plant Ecosystem Conservation
Persian site map - English site map - Created in 0.05 seconds with 30 queries by YEKTAWEB 4533