دوره 8، شماره 15 - ( بهار و تابستان 1399 )
جلد 8 شماره 15 صفحات 21-10 |
برگشت به فهرست نسخه ها
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Taleshi H, Jalali S G, Alavi S J, Hosseini S M, Naimi B. (2020). Projection of Climate Change Impacts on Potential Distribution of Chestnut-leaved oak (Quercus castaneifolia C.A.M.) Using Ensemble Modeling in the Hyrcanian Forests of Iran. ifej. 8(15), 10-21. doi:10.52547/ifej.8.15.10
URL: http://ifej.sanru.ac.ir/article-1-272-fa.html
URL: http://ifej.sanru.ac.ir/article-1-272-fa.html
طالشی حمید، جلالی سید غلامعلی، علوی سید جلیل، حسینی سید محسن، نعیمی بابک. پیش‌بینی اثر تغییر اقلیم بر پراکنش بالقوه گونه بلوط بلند مازو (Quercus castaneifolia C.A.M.) با استفاده از مدل‌سازی ترکیبی
در جنگل‌های هیرکانی ایران بوم شناسی جنگل های ایران (علمی- پژوهشی) 1399; 8 (15) :21-10 10.52547/ifej.8.15.10
دانشگاه تربیت مدرس
چکیده: (3542 مشاهده)
بررسی‌ها نشان می‌دهد که بیست سال اخیر، میانگین دمای ناحیه رویشی خزری، 74/0 درجه سانتی‌گراد افزایش یافته است. در این پژوهش برای کاهش عدم قطعیّت، رویشگاه‌های مطلوب فعلی و آینده گونه بلوط بلندمازو تحت تأثیر تغییر اقلیم با استفاده از یک چارچوب تلفیقی شامل پنج روش مختلف مدل‌سازی، پراکنش گونه بلوط بلندمازو مدل‌سازی شد. برای پیش‌بینی اثر تغییر اقلیم در پراکنش گونه بلوط بلندمازو از پنج مدل گردش عمومی جو تحت دو سناریوی خط سیر غلظت (RCP) استفاده شد. همچنین با استفاده از تحلیل تغییرات اندازه محدوده گونه بلوط بلندمازو، رویشگاه‌های افزایش‌یافته، کاهش‌یافته و حفظ‌شده تحت تأثیر اقلیم برای هریک از RCP ها در سال 2070 میلادی پیش بینی شد. نتایج نشان داد که در سال 2070 میلادی مساحت رویشگاه‌های مطلوب با قطعیّت بالا تحت سناریوی RCP 4.5 معادل 80/16 درصد افزایش و تحت سناریوی RCP 8.5 معادل 80/29 درصد کاهش خواهد یافت. همچنین 12/54 درصد از رویشگاه‌های مطلوب فعلی تحت سناریوی RCP 4.5 پایدار خواهند ماند؛ درحالی‌که این مناطق تحت سناریوی RCP 8.5 به 85/23 درصد کاهش خواهد یافت. همچنین در سال 2070 میلادی مساحت رویشگاه‌های مطلوب اضافه‌شده تحت سناریوی RCP 4.5 برابر با 68/62 درصد و مساحت این مناطق تحت سناریوی RCP 8.5 برابر با 36/42 درصد خواهد بود. پیشنهاد می‌شود تا تصمیم‌گیری‌های مدیریتی و حفاظتی در رابطه با این گونه با مدنظر قرار دادن اثر تغییر اقلیم و سازگار با این تغییرات در جنگل‌های هیرکانی انجام شود. همچنین در طرح‌های جنگل‌کاری و احیا با استفاده از این گونه اثرات تغییر اقلیم بر مطلوبیّت رویشگاه‌های انتخاب‌شده مدنظر قرار گیرد.
نوع مطالعه: پژوهشي |
موضوع مقاله:
اکولوژی جنگل
دریافت: 1397/7/4 | پذیرش: 1397/10/17 | انتشار: 1399/4/2
دریافت: 1397/7/4 | پذیرش: 1397/10/17 | انتشار: 1399/4/2
فهرست منابع
1. IPCC. 2013. Climate Change 2013: The physical science basis: Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change; Cambridge University Press, 1535 pp.
2. Babaeian, I., Z. Najafinik, F. Zabol Abbasi, M. Habibi Nokhandan, H. Adab and S. Malbousi. 2010. Climate change assessment over Iran during 2010-2039 by using statistical downscaling of ECHO-G Model. Geography and Development, 7: 135-152 (In Persian).
3. Jafari, M. 2008. Investigation and analysis of climate change factors in Caspian Zone forests for last fifty years. Iranian Journal of Forest and Poplar Research, 16, 314-326 (In Persian).
4. Attarod, P., F. Kheirkhah, S. Khalighi Sigaroodi, S.M.M. Sadeghi, A. Dolatshahi and V. Bayramzadeh. 2017. Trend analysis of meteorological parameters and reference evapotranspiration in the Caspian region. Iranian Journal of Forest, 9: 171-185 (In Persian).
5. Marvi Mohadjer, M. R. 2012. Silviculture, University of Tehran, 417 pp (In Persian).
6. Sagheb-Talebi, K., T. Sajedi and M. Pourhashemi. 2014. Forests of Iran: A Treasure from the Past, a Hope for the Future, Springer Berlin, 152 pp. [DOI:10.1007/978-94-007-7371-4]
7. Gorgi Bahri, Y., R. Faraji Poul and S. Kiadaliri. 2013. Study on growth and silvicultural analysis of young stand of Quercus castaneifolia CAM in Neyrang Forest, Nowshahr. Iranian Journal of Forest and Poplar Research, 21: 387-395 (In Persian).
8. Wang, H., X. Shao, Y. Jiang, X. Fang and S. Wu. 2013. The impacts of climate change on the radial growth of Pinus koraiensis along elevations of Changbai Mountain in northeastern China. Forest Ecology and Management, 289: 333-340. [DOI:10.1016/j.foreco.2012.10.023]
9. Koralewski, T. E., H.H. Wang, W.E. Grant and T.D. Byram. 2015. Plants on the move: Assisted migration of forest trees in the face of climate change. Forest Ecology and Management, 344: 30-37. [DOI:10.1016/j.foreco.2015.02.014]
10. Zomer, R.J., J. Xu, M. Wang, A. Trabucco and Z. Li. 2015. Projected impact of climate change on the effectiveness of the existing protected area network for biodiversity conservation within Yunnan Province, China. Biological Conservation, 184: 335-345. [DOI:10.1016/j.biocon.2015.01.031]
11. Remya, K., A. Ramachandran and S. Jayakumar. 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. [DOI:10.1016/j.ecoleng.2015.04.053]
12. Guillera-Arroita, G., J.J. Lahoz-Monfort, J. Elith, A. Gordon, H. Kujala, P.E. Lentini, M.A. McCarthy, R. Tingley and B.A. Wintle. 2015. Is my species distribution model fit for purpose? Matching data and models to applications. Global Ecology and Biogeography, 24: 276-292. [DOI:10.1111/geb.12268]
13. Guisan, A., W. Thuiller and N.E. Zimmermann. 2017. Habitat Suitability and Distribution Models: with Applications in R. Cambridge University Press. [DOI:10.1017/9781139028271]
14. Hof, A.R., R. Jansson and C. Nilsson. 2012. The usefulness of elevation as a predictor variable in species distribution modelling. Ecological Modelling, 246: 86-90. [DOI:10.1016/j.ecolmodel.2012.07.028]
15. Lindner, M., J.B. Fitzgerald, N.E. Zimmermann, C. Reyer, S. Delzon, E. van der Maaten, M.J. Schelhaas, P. Lasch, J. Eggers and M. van der Maaten-Theunissen. 2014. Climate change and European forests: What do we know, what are the uncertainties, and what are the implications for forest management? Journal of environmental management, 146: 69-83. [DOI:10.1016/j.jenvman.2014.07.030]
16. Pacifici, M., W.B. Foden, P. Visconti, J.E. Watson, S.H. Butchart, K.M. Kovacs, B.R. Scheffers, D. G. Hole, T. G. Martin and H. R. Akcakaya. 2015. Assessing species vulnerability to climate change. Nature Climate Change, 5: 215. [DOI:10.1038/nclimate2448]
17. Araújo, M.B. and, M. New. 2007. Ensemble forecasting of species distributions. Trends in ecology & evolution, 22: 42-47. [DOI:10.1016/j.tree.2006.09.010]
18. Vieilledent, G., C. Cornu, A.C. Sanchez, J.M.L. Pock-Tsy and P. Danthu. 2013. Vulnerability of baobab species to climate change and effectiveness of the protected area network in Madagascar: Towards new conservation priorities. Biological conservation, 166: 11-22. [DOI:10.1016/j.biocon.2013.06.007]
19. Iverson, L.R. and A.M. Prasad. 2002. Potential redistribution of tree species habitat under five climate change scenarios in the eastern US. Forest Ecology and Management, 155: 205-222. [DOI:10.1016/S0378-1127(01)00559-X]
20. Trisurat, Y., R.P. Shrestha and R. Kjelgren. 2011. Plant species vulnerability to climate change in Peninsular Thailand. Applied Geography, 31: 1106-1114. [DOI:10.1016/j.apgeog.2011.02.007]
21. Benito Garzón, M., R. Sánchez de Dios and H. Sainz Ollero. 2008. Effects of climate change on the distribution of Iberian tree species. Applied Vegetation Science, 11: 169-178. [DOI:10.3170/2008-7-18348]
22. Leng, W., H.S. He, R. Bu, L. Dai, Y. Hu and X. Wang. 2008. Predicting the distributions of suitable habitat for three larch species under climate warming in Northeastern China. Forest Ecology and Management, 254: 420-428. [DOI:10.1016/j.foreco.2007.08.031]
23. Xu, Z., C. Zhao and Z. Feng. 2009. A study of the impact of climate change on the potential distribution of Qinghai spruce (Picea crassifolia) in Qilian Mountains. Acta Ecologica Sinica, 29: 278-285. [DOI:10.1016/j.chnaes.2009.09.004]
24. Chala, D., C. Brochmann, A. Psomas, D. Ehrich, A. Gizaw, C.A. Masao, V. Bakkestuen and N.E. Zimmermann. 2016. Good-bye to tropical alpine plant giants under warmer climates? Loss of range and genetic diversity in Lobelia rhynchopetalum. Ecology and evolution, 6: 8931-8941. [DOI:10.1002/ece3.2603]
25. Wang, T., G. Wang, J. Innes, C. Nitschke and H. Kang. 2016. Climatic niche models and their consensus projections for future climates for four major forest tree species in the Asia-Pacific region. Forest Ecology and Management, 360: 357-366. [DOI:10.1016/j.foreco.2015.08.004]
26. López-Tirado, J., F. Vessella, B. Schirone and P.J. Hidalgo. 2018. Trends in evergreen oak suitability from assembled species distribution models: assessing climate change in south-western Europe. New Forest, 49(4): 471-487. [DOI:10.1007/s11056-018-9629-5]
27. Dyderski, M.K., S. Paź, L.E. Frelich and A.M. Jagodziński. 2018. How much does climate change threaten European forest tree species distributions?. Global change biology, 24(3): 1150-1163. [DOI:10.1111/gcb.13925]
28. Sadat Fatemi Azarkhavarani, S., M. Rahimi, M. Tarkesh and H. Ravanbakhsh. 2017. Prediction of Juniperus excelsa M. Bieb. geographical distribution using by climate data under the conditions of current and future in Semnan Province. Iranian Journal of Forest, 9: 233-248 (In Persian).
29. Piri Sahragard, H., M. Zare Chahouki, M. Ajorlo and M. Nohtani. 2017. Predictive habitat distribution modeling of Amygdalus scoparia Spach in Moshakieh rangelands of Qom Province. Journal of Forests and Wood Products, 69: 725-734 (In Persian).
30. Haidarian Aghakhani, M., R. Tamartash, Z. Jafarian, M. Tarkesh Esfahani and M. Tatian. 2017. Forecasts of climate change effects on Amygdalus scoparia potential distribution by using ensemble modeling in Central Zagros. Journal of RS and GIS for Natural Resources, 8: 1-14 (In Persian).
31. Haidarian Aghakhani, M., R. Tamartash, Z. Jafarian, M. Tarkesh Esfahani and M. Tatian. 2017. Predicting the impacts of climate change on Persian oak (Quercus brantii) using Species Distribution Modelling in Central Zagros for conservation planning. Journal of Environmental Sciences, 43: 497-511 (In Persian).
32. Valavi, R., H. Shafizadeh-Moghadam, A. Matkan, A. Shakiba, B. Mirbagheri and S.H. Kia. 2018. Modelling climate change effects on Zagros forests in Iran using individual and ensemble forecasting approaches. Theoretical and Applied Climatology, 1-11. [DOI:10.1007/s00704-018-2625-z]
33. Zhang, M.G., Z.K. Zhou, W.Y. Chen, J.F. Slik, C.H. Cannon and N. Raes. 2012. Using species distribution modeling to improve conservation and land use planning of Yunnan, China. Biological Conservation, 153: 257-264. [DOI:10.1016/j.biocon.2012.04.023]
34. Arino, O., J.J. Ramos Perez, V. Kalogirou, S. Bontemps, P. Defourny and E. Van Bogaert. 2012. Global land cover map for 2009 (GlobCover 2009). ESA & UCL.
35. Di Cola, V., O. Broennimann, B. Petitpierre, F.T. Breiner, M. D'Amen, C. Randin, R. Engler, J. Pottier, D. Pio and A. Dubuis. 2017. Ecospat: An R package to support spatial analyses and modeling of species niches and distributions. Ecography, 40: 774-787. [DOI:10.1111/ecog.02671]
36. R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
37. Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International journal of climatology, 25: 1965-1978. [DOI:10.1002/joc.1276]
38. Hijmans, R., J. van Etten, J. Cheng, M. Mattiuzzi, M. Sumner and J. Greenberg. 2017. Raster: Geographic Data Analysis and Modeling. R package version 2.3-33, 2016..
39. Naimi, B., N.A. Hamm, T.A. Groen, A.K. Skidmore and A. G Toxopeus. 2014. Where is positional uncertainty a problem for species distribution modelling? Ecography, 37:191-203. [DOI:10.1111/j.1600-0587.2013.00205.x]
40. Barbet-Massin, M., F. Jiguet, C.H. Albert and W Thuiller. 2012. Selecting pseudo-absences for species distribution models: how, where and how many? Methods in Ecology and Evolution, 3: 327-338. [DOI:10.1111/j.2041-210X.2011.00172.x]
41. Thuiller, W., D. Georges and R. Engler. 2014. biomod2: Ensemble platform for species distribution modeling. R package version 3.1-64. Availablt at: http://CRAN. R-project.org/package= biomod2 (accessed February 2015).
42. Franklin, J. Mapping species distributions: spatial inference and prediction, Cambridge University Press, 2010, 320 pp. [DOI:10.1017/CBO9780511810602]
43. Shirley, S., Z. Yang, R. Hutchinson, J. Alexander, K. McGarigal and M. Betts. 2013. Species distribution modelling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions. Diversity and Distributions, 19: 855-866. [DOI:10.1111/ddi.12093]
44. Lin, W.C., Y.P. Lin, W.Y. Lien, Y.C. Wang, C.T. Lin, C.R. Chiou, J. Anthony and N.D. Crossman. 2014. Expansion of Protected Areas under Climate Change: An Example of Mountainous Tree Species in Taiwan. Forests, 5: 2882-2904. [DOI:10.3390/f5112882]
45. Beckage, B., B. Osborne, D.G. Gavin, C. Pucko, T. Siccama and T. Perkins. 2008. A rapid upward shift of a forest ecotone during 40 years of warming in the Green Mountains of Vermont. Proceedings of the National Academy of Sciences, 105: 4197-4202. [DOI:10.1073/pnas.0708921105]
46. Cheaib, A., V. Badeau, J. Boe, I. Chuine, C. Delire, E. Dufrêne, C. François, E.S. Gritti, M. Legay and C. Pagé. 2012. Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty. Ecology letters, 15: 533-544. [DOI:10.1111/j.1461-0248.2012.01764.x]
47. Lenoir, J., J.C. Gégout, P. Marquet, P. De Ruffray and H.A. Brisse. 2008. Significant upward shift in plant species optimum elevation during the 20th century. Science, 320: 1768-1771. [DOI:10.1126/science.1156831]
48. Nogués-Bravo, D., M.B. Araújo, M. Errea and J. Martinez-Rica. 2007. Exposure of global mountain systems to climate warming during the 21st Century. Global Environmental Change, 17: 420-428. [DOI:10.1016/j.gloenvcha.2006.11.007]
49. Iverson, L.R., A.M. Prasad, S.N. Matthews and M. Peters. 2008. Estimating potential habitat for 134 eastern US tree species under six climate scenarios. Forest Ecology and Management, 254: 390-406. [DOI:10.1016/j.foreco.2007.07.023]
50. Dale, V.H., M.L. Tharp, K.O. Lannom and D.G. Hodges. 2010. Modeling transient response of forests to climate change. Science of the Total Environment, 408: 1888-1901. [DOI:10.1016/j.scitotenv.2009.11.050]
51. Zolkos, S.G., P. Jantz, T. Cormier, L.R. Iverson, D.W. McKenney and S.J. Goetz. 2015. Projected Tree Species Redistribution Under Climate Change: Implications for Ecosystem Vulnerability Across Protected Areas in the Eastern United States. Ecosystems, 18: 202-220. [DOI:10.1007/s10021-014-9822-0]
52. Yousefpour, R., C. Temperli, J. B. Jacobsen, B. J. Thorsen, H. Meilby, M. Lexer, M. Lindner, H. Bugmann, J. Borges and J. Palma. 2017. A framework for modeling adaptive forest management and decision making under climate change. Ecology and Society, 22(4): 40. [DOI:10.5751/ES-09614-220440]
53. Brang, P., P. Spathelf, J.B. Larsen, J. Bauhus, A. Bončìna, C. Chauvin, L. Drӧssler, C. García-Güemes, C. Heiri and G. Kerr. 2014. Suitability of close-to-nature silviculture for adapting temperate European forests to climate change. Forestry: An International Journal of Forest Research, 87: 492-503. [DOI:10.1093/forestry/cpu018]
54. Maroufi Aghdam, B., J. Torkaman, M. Ghodskhah, S. Karamzadeh and M. Ahmadi. 2015. Comparison between the Effects of Temperature and Solar Radiation on Growth of Quercus castaneifolia C. A. Mey. in Astara Forests using the DendrochronologyMethod, Ecology of Iranian Forests, 3(5): 1-10.
55. Ostakh, E., J. Soosani, B. Pilehvar, L. Poursartip and S. Musavi. 2014. Investigation on Climate Variables (Temperature and Precipitation) Effects onAnnual Width Rings of Pinus brutia in Lorestan Province Esmat, Javad, Babak, Ladan and Samaneh Musavi5, Ecology of Iranian Forest, 2(4): 19-27.
ارسال پیام به نویسنده مسئول
| بازنشر اطلاعات | |
 |
این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است. |
