پیش‌بینی اثر تغییر اقلیم بر پراکنش گونۀ بلوط ایرانی (.Quercus brantii Lindl) در جنگل‌های زاگرس، استان فارس

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری جنگل‌شناسی و اکولوژی جنگل، دانشکدۀ منابع طبیعی، دانشگاه تهران، کرج، ایران.

2 دانشیار، گروه جنگلداری و اقتصاد جنگل، دانشکدۀ منابع طبیعی، دانشگاه تهران، کرج، ایران.

3 دانشیار، گروه مهندسی آبیاری و آبادانی، دانشکدۀ مهندسی و فناوری کشاورزی، دانشگاه تهران، کرج، ایران.

چکیده

مقدمه: تغییر اقلیم می‌تواند پراکنش طبیعی گونه‌ها را تغییر دهد و سبب از بین رفتن تنوع زیستی در اکوسیستم‌های جنگلی شود. این تحقیق با هدف بررسی پیامدهای تغییرات اقلیمی بر پراکنش جغرافیایی گونۀ بلوط ایرانی به‌منظور مدیریت حفاظت از این گونه و انتخاب منطقۀ مطلوب برای معرفی گونه صورت گرفت.
مواد و روش ها: در این پژوهش پراکنش گونۀ بلوط ایرانی در سه دورۀ حال حاضر، سال‌های 2050 و 2070 و تحت دو سناریوی RCP2.6 و RCP8.5 مدل گردش عمومی HadGEM2-ES با استفاده از مدل مکسنت (Maxent) و متغیرهای اقلیمی و توپوگرافی استان فارس بررسی شد.
یافته ها: نتایج نشان داد که مساحت کل مناطق بالقوه مطلوب برای بلوط ایرانی در حال حاضر 8/7 درصد (5/9794 کیلومتر مربع) از کل مساحت استان است که تحت سناریوی RCP2.6 در 2050 به 6/6 درصد کاهش و در 2070 به 3/13 افزایش می‌یابد. همچنین مقدار کل منطقۀ مطلوب تحت سناریو RCP8.5 به 1/8 درصد در سال 2050 و 2/10 درصد در سال 2070 افزایش می‌یابد. مناطق با مطلوبیت متوسط، زیاد و بسیار زیاد به‌ترتیب از 8/1، 5/1 و 7/1 درصد در حال حاضر طبق سناریو RCP2.6 در 2050 به 1/1، 6/0 و 3/0 درصد کاهش و مجدداً در 2070 به 9/2، 6/1 و 2/1 درصد افزایش پیدا می‌کند و براساس سناریو RCP8.5 در 2050 به 6/1، 6/0 و 3/0 درصد کاهش و در 2070 به مقادیر 5/2، 9/0 و 2/0 درصد تغییر می‌یابد.
نتیجه گیری: این تحقیق آشکار می‌کند که پتانسیل پراکنش بلوط ایرانی براساس هر دو سناریو اقلیمی تحت تأثیر تغییر اقلیم قرار می‌گیرد. همچنین مدل Maxent توانایی زیادی در پیش‌بینی پراکنش گونه‌ها نشان داد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Predicting climate change impacts on distribution of Brant's oak trees (Quercus brantii Lindl.) in the Zagros forests, Fars Province

نویسندگان [English]

  • N Khajei 1
  • V Etemad 2
  • J Bazrafshan 3
1 Ph.D. Student of Silviculture and Forest Ecology, Faculty of Natural Resources, University of Tehran, Karaj, I. R. Iran.
2 Associate Prof., Dept. of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, I. R. Iran.
3 Associate Prof., Dept. of Irrigation and Reclamation Engineering, Faculty of Agriculture Engineering and Technology, University of Tehran, Karaj, I. R. Iran.
چکیده [English]

Introduction: Climate change has the potential to alter the natural distribution of species and reduce biodiversity in forest ecosystems. This study aimed to investigate the effects of climate change on the geographical diversity of Brant's oak (Quercus brantii L.) to identify the suitable area for this species' occurrence and manage its conservation.
Material and Methods: The distribution of Quercus brantii was studied under current and future climate conditions (2050, 2070) using two climate change scenarios, RCP2.6 and RCP8.5, HadGEM2-ES General Circulation Model, the Maxent model, and topography data of Fars province.
Findings: The results showed that the current potentially suitable areas for Q. brantii constitute 7.8% (9794.5 Km²) of the total area of the province. Under the RCP2.6 scenario, this will decrease to 6.6% in 2050 and increase to 13.3% in 2070. Also, under the RCP8.5 scenario, the suitable area will increase to 8.1% in 2050 and 10.2% in 2070. Areas with moderate, high, and very high suitability, currently at 1.8%, 1.5%, and 1.7% respectively, will decrease to 1.1%, 0.6%, and 0.3% in 2050 according to the RCP2.6 scenario, and then increase to 2.9%, 1.6%, and 1.2% in 2070. According to the RCP8.5 scenario, these values will decrease to 1.6%, 0.6%, and 0.3% in 2050, and finally change to 2.5%, 0.9%, and 0.2% in 2070.
Conclusion: The study demonstrated that the distribution of Quercus brantii is affected by climate change based on both scenarios. Also, the Maximum Entropy model has proven highly effective in predicting the distribution of this species.

کلیدواژه‌ها [English]

  • Climate change
  • Distribution
  • Fars province
  • Maxent
  • Quercus brantii
 
Ab Lah, N.Z., Yusop, Z., Hashim, M., Mohd Salim, J., & Numata, S. (2021). Predicting the Habitat Suitability of Melaleuca cajuputi Based on the MaxEnt Species Distribution Model. Forests12(11), 1449. https://doi.org/10.3390/f12111449.
Aertsen, W., Kint, V., Van Orshoven, J., Ozkan, K., & Muys, B. (2010). Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecological Modelling, 221, 1119–1130. https://doi.org/10.1016/j.ecolmodel.2010.01.007.
Anderson, R.P., & Raza, A. (2010). The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. Journal of Biogeography37(7), 1378–1393. https://doi.org/10.1111/j.1365-2699.2010.02290.x.
Arvin, A.A., & Shojaeezadeh, K. (2014). Assessment of climate tourism in Shiraz city using physiologic equivalence temperature and predicted mean vote indexes. Physical Geography Quarterly,7(26), 87–98.  (In Persian).
Attarod, P., Kheirkhah, F., Khalighi Sigaroodi, S., Sadeghi, S.M.M., Dolatshahi, A., & Bayramzadeh, V. (2017). Trend analysis of meteorological parameters and reference evapotranspiration in the Caspian region. Iranian Journal of Forest, 9(2), 171–185. (In Persian).
Attarod, P., Beiranvand, S., Asgari, M., Fanaei, N., & Hashemzadeh, M. (2021). The effects of rainfall fluctuations on declining Zagros Forests in Ilam and Lorestan provinces. Iranian Journal of Forest, 13(2), 141–154. https://doi.org/10.22034/IJF.2021.136938. (In Persian).
Cao, Y., Feng, J., Hwarari, D., Ahmad, B., Wu, H., Chen, J., & Yang, L. (2022). Alterations in Population Distribution of Liriodendron chinense (Hemsl.) Sarg. and Liriodendron tulipifera Linn. Caused by Climate Change. Forests13(3), 488. https://doi.org/10.3390/f13030488.
Çoban, 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(7), 1-11. https://doi.org/10.3390/su12072671.
Dai, G., Yang, J., Huang, C., Sun, C., Jia, L., & Ma, L. (2017). The Effects of Climate Change on the Development of Tree Plantations for Biodiesel Production in China. Forests8(6), 207. https://doi.org/10.3390/f8060207.
Deb, J.C., Phinn, S., Butt, N., & McAlpine, C.V. (2017). The impact of climate change on the distribution of two threatened Dipterocarp trees. Ecology and Evolution, 7(7), 2238-2248. https://doi.org/10.1002/ece3.2846.
Erfanian, M.B., Sagharyan, M., Memariani, F., & Ejtehadi, H. (2021). Predicting range shifts of three endangered endemic plants of the Khorassan‑Kopet Dagh foristic province under global change. Scientifc Reports, 11,9159. https://doi.org/10.1038/s41598-021-88577-x.
Guo, Y., Li, X., Zhao, Z., & Nawaz, Z. (2019). Predicting the impacts of climate change, soils and vegetation types on the geographic distribution of Polyporus umbellatus in China. Science of the Total Environment648, 1–11.  https://doi.org/10.1016/j.scitotenv.2018.07.465.
Huang, J., Li, G., Li, J., Zhang, X., Yan, M., & Du, S. (2017). Projecting the Range Shifts in Climatically Suitable Habitat for Chinese Sea Buckthorn under Climate Change Scenarios. Forests9(1), 9.  https://doi.org/10.3390/f9010009.
Jazirehi, M.H., & Ebrahimi Rostaghi, M. (2003). Silviculture in Zagros: University of Tehran Press. P560. (In Persian).
Mahatara, D., Acharya, A.K., Dhakal, B.P., Sharma, D.K., Ulak , S., & Paudel, P. (2021). Maxent modelling for habitat suitability of vulnerable tree Dalbergia latifolia in Nepal. Silva Fennica55(4), 2242-4075. https://doi.org/10.14214/sf.10441.
Mataji, A., Abdi, F., Etemad, V., & Kiadaliri H. (2016). Effects of seed origin on survival morphology and growth of Iranian oak (Quercus brantii Lindl.). Iranian Journal of Forest, 8(1), 11–22. (In Persian).
Pramanik, M., Paudel, U., Mondal, B., Chakraborti, S., & De, P. (2018). Predicting climate change impacts on the distribution of the threatened Garcinia indica in the Western Ghats, India. Climate Risk Management19, 94-105. https://doi.org/10.1016/j.crm.2017.11.002.
Qin, A., Liu, B., Guo, Q., Bussmann, R.W., Ma, F., Jian, Z., Xu, G., & Pei, S. (2017). Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China. Global Ecology and Conservation, 10, 139-146. https://doi.org/10.1016/j.gecco.2017.02.004.
Shcheglovitova, M., & Anderson, R.P. (2013). Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecological Modelling269, 9-17. https://doi.org/10.1016/j.ecolmodel.2013.08.011.
Shi, X., Yin, Q., Sang, Z., Zhu, Z., Jia, Z., & Ma, L. (2021). Prediction of potentially suitable areas for the introduction of Magnolia wufengensis under climate change. Ecological Indicators127, 107762. https://doi.org/10.1016/j.ecolind.2021.107762.
Shiraz municipality (2018).  Shiraz Munisipality annual report. Shiraz: Shiraz municipality. (In Persian).
Ye, X., Yu, X., Yu, C., Tayibazhaer, A., Xu, F., Skidmore, A.K., & Wang, T. (2018). Impacts of future climate and land cover changes on threatened mammals in the semi-arid Chinese Altai Mountains. Science of the Total Environment612, 775–787. https://doi.org/10.1016/j.scitotenv.2017.08.191.
Zhao, Q., Zhang, Y., Li, W.N., Hu, B.W., Zou, J.B., Wang, S.Q., Niu, J.F., & Wang, Z.Z. (2021). Predicting the Potential Distribution of Perennial Plant Coptis chinensis Franch. in China under Multiple Climate Change Scenarios. Forests12(11), 1464. https://doi.org/10.3390/f12111464.
Zuza, E.J., Maseyk, K., Bhagwat, S.A., Sousa, K., Emmott, A., Rawes, W., & Araya, Y.N. (2021). Climate suitability predictions for the cultivation of macadamia (Macadamia integrifolia) in Malawi using climate change scenarios. PLoS ONE, 16(9), e0257007. https://doi.org/10.1371/journal.pone.0257007.