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مقایسه تکنیک های رگرسیونی و یادگیری ماشینی در تعیین گستره جغرافیایی اسپرس کوهی (Onobrychis cornuta L.) تحت تأثیر ویژگی های محیطی و تغییر اقلیم با استفاده از مدل IPSL-CM6A-LR | ||
| محیط شناسی | ||
| مقاله 7، دوره 49، شماره 1، خرداد 1402، صفحه 107-120 اصل مقاله (1012.14 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jes.2023.354111.1008382 | ||
| نویسندگان | ||
| زینب جعفریان* 1؛ محدثه امیری2 | ||
| 1گروه مرتعداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران | ||
| 2-گروه مرتعداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران -گروه علوم کشاورزی، دانشگاه فنی و حرفه | ||
| چکیده | ||
| پیش بینی تأثیر تغییر اقلیم بر اکوسیستم های بومی یکی از اهداف دیرینه اکولوژیست هاست و امری ضروری جهت حفاظت و مدیریت آنهاست. مدل های پراکنش گونه ای (SDM) پرکاربردترین ابزار برای پیش بینی اثرات تغییر اقلیم بر محدوده جغرافیایی گیاهان هستند. در این مطالعه، تکنیک های رگرسیونی (GLM و MARS) و یادگیری ماشینی (ANN و RF) همراه با متغیرهای محیطی برای پیش بینی پراکنش Onobrychis cornuta L. به کار رفتند. پاسخ گونه به اقلیم آینده (2070-2050) تحت سناریوهای خوش بینانه (SSP1-2.6)، بدبینانه (SSP3-7.0) و خیلی بدبینانه (SSP5-8.5) مدل اقلیمی IPSL-CM6A-LR از مدل های CMIP6 بررسی شد. طبق نتایج، مدل اجماعی و سپس MARS دقیق ترین پیش بینی را داشتند. مدل ANN با اختلاف معنی دار با سایر مدل ها (0.05>p) کمترین صحت پیش بینی را داشت. آنالیز حساسیت، ارتفاع (%24.64)، حداکثر دمای گرمترین ماه (%20.31)، تغییرات فصلی دما (%16.57) و میانگین دامنه دمای روزانه (%16) را مؤثرترین متغیرها بر پراکنش گونه معرفی کرد. طبق مدل اجماعی، رویشگاه مناسب گونه، 27 درصد از منطقه را به خود اختصاص داده است، اما تحت اقلیم آینده، پراکنش آن کاهش خواهد یافت. سناریوی SSP5-8.5 بیشترین تأثیر را بر جابجایی محدوده پراکنش گونه خواهد داشت. نقشه های پیش بینی حاصل اطلاعات ارزشمندی را برای راهکارهای حفاظتی شامل شناسایی مکان های مناسب جهت معرفی مجدد و کشت آن در چارچوب طرح های مدیریت مراتع فراهم می سازند. | ||
| کلیدواژه‌ها | ||
| آشیان اکولوژیک؛ مطلوبیت رویشگاه؛ تغییر اقلیم؛ سناریوهای SSP؛ مدل‌های CMIP6 | ||
| عنوان مقاله [English] | ||
| Comparison of Regression and Machine Learning techniques in Determination of Geographical Range of Onobrychis cornuta L. under Environmental Characteristics and Climate Change using the IPSL-CM6A-LR Model | ||
| نویسندگان [English] | ||
| Zeinab Jafarian1؛ mohaddeseh amiri2 | ||
| 1Department of Range Management, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran | ||
| 2-Department of range management, Faculty of natural resources, Sari Agricultural Science and Natural Resources University, Sari, Iran -Department of Agricultural Science, Technical and Vocational University, Tehran, Iran | ||
| چکیده [English] | ||
| Predicting the effect of climate change on native ecosystems is one of the longstanding goals of ecologists and is essential for their conservation and management. Species distribution models (SDMs) are the most widely used tools to predict the effects of climate change on the geographical range of plants. In this study, two regression techniques (GLM and MARS) and two machine learning techniques (ANN and RF), along with environmental factors were used to predict the distribution of Onobrychis cornuta L. The species response to future climate (2050-2070) was investigated under optimistic (SSP1-2.6), pessimistic (SSP3-7.0) and very pessimistic (SSP5-8.5) emission scenarios of the IPSL-CM6A-LR climatic model from CMIP6 models. Based on results, the ensemble model and then MARS presented the most accurate prediction. ANN had the lowest prediction accuracy with a significant difference from other models (p<0.05). The sensitivity analysis revealed altitude (24.64%), maximum temperature of the warmest month (20.31%), temperature seasonality (16.57%) and diurnal range of mean temperature (16%) as the most effective variables on the distribution. According to the ensemble model, the suitable habitat occupies about 27% of the area, but its distribution will be decrease under the future climate. The SSP5-8.5 scenario will have the greatest impact on the displacement of the species distribution range. The resulting prediction maps provide valuable information for conservation strategies, including identifying suitable places for its reintroduction and cultivation in the framework of rangeland management plans. | ||
| کلیدواژه‌ها [English] | ||
| ecological niche, habitat suitability, climate change, SSP scenarios, CMIP6 models | ||
| مراجع | ||
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Abd El-Ghani, M., Soliman, A., & Abd El-Fattahr, A. (2014). Spatial distribution and soil characteristics of the vegetation associated with common succulent plants in Egypt. Turkish Journal of Botany, 38, 550-565. doi: 10.3906/bot-1309-49. Allouche, O., Tsoar, A., & Kadmon, R. (2006). Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43(6), 1223-1232. doi: 10.1111/j.1365-2664.2006.01214.x. Amiri, M., Tarkesh, M., & Jafari, R. (2019). Predicting the distribution of Artemisia sieberi Besser under climate change in steppe and semi-steppe of Iran-Touranian region. Desert Management, 13, 29-48. doi: 10.22034/JDMAL.2019.36534. (In Persian) Ansari, S., Dehban, H., Zareian, M. J., & Farokhnia, A. (2022). Investigation of temperature and precipitation changes in the Iran’s basins in the next 20 years based on the output of CMIP6 model. Iranian Water Research Journal, 16(1), 11-24. doi: 10.22034/IWRJ.2022.11204. (In Persian) Araújo, M. B., & New, M. (2007). Ensemble forecasting of species distributions. Trends in Ecology and Evolution, 22, 42-47. doi: 10.1016/j.tree.2006.09.010. Ardakani, M. R. (2014). Ecology. University of Tehran Press. (In Persian) Braunisch, V., Coppes, J., Arlettaz, R., Suchant, R., Schmid, H., & Bollmann, K. (2013). Selecting from correlated climate variables: A major source of uncertainty for predicting species distributions under climate change. Ecography, 36(9): 971-983. doi: 10.1111/j.1600-0587.2013.00138.x. Elith, J., Kearney, M., & Phillips, S. (2010). The art of modelling range‐shifting species. Methods in ecology and evolution, 1, 330-342. doi: 10.1111/j.2041-210X.2010.00036.x. Falkowski, P., Scholes, R. J., Boyle, E., Canadell, J., Canfield, D., Elser, J., Gruber, N., Hibbard, K., Hogberg, P., Linder, S., Mackenzie, F. T., Moore, B., Pedersen, T., Rosenthal, Y., Smetacek, V., & Stefen, W. (2000). The global carbon cycle: a test of our knowledge of earth as a system. Science, 290(5490), 291-296. doi: 10.1126/science.290.5490.291. Fallah Kalaki, M., Shokri Kuchak, V., & Ramezani Etedali, H. (2021). Simulating the effects of climate change on runoff using the CMIP5 and CMIP6 climate models by SWAT hydrological model (case study: Tashk-Bakhtegan basin). Iran-water Resource Research, 17(3), 345-359. (In Persian) Farzadmehr, J., & Sangoony, H. (2020). The effect of climate change on the geographical distribution of wild borage in Khorasan Razavi. Water and Soil Conservation, 27(3), 145-162. doi: 10.22069/JWSC.2020.16868.3221. (In Persian) Fatemi Azarkhavarani, S. S., Rahimi, M., Tarkesh, M., & Ravanbakhsh, H. (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(2), 233-248. (In Persian) Franklin, J. (2010). Mapping species distributions: spatial inference and prediction. Cambridge University Press. doi: 10.1017/CBO9780511810602. Gebrewahid, Y., Abrehe, S., Meresa, E., Eyasu, G., Abay, K., Gebreab, G., Kidanemariam, K., Adissu, G., Abreha, G. & Darcha, G. (2020). Current and future predicting potential areas of Oxytenanthera abyssinica (A. Richard) using MaxEnt model under climate change in Northern Ethiopia. Ecological Processes, 9, 1-15. doi: 10.1186/s13717-019-0210-8. Ghashangzadeh, A., & Fotouhi, F. (2021, November 24-25). Effect of precipitation fluctuations to climate change on crop production and water constraint management in Iran. 10th international conference on rainwater catchment systems. University of Kurdistan. https://civilica.com/doc/1411106 (In Persian) Hill, L., Hector, A., Hemery, G., Smart, S., Tanadini, M., & Brown, N. (2017). Abundance distributions for tree species in Great Britain: A two‐stage approach to modeling abundance using species distribution modeling and random forest. Ecology and Evolution, 7, 1043-1056. doi: 10.1002/ece3.2661. Hosseini, S. S., Tavili, A., Naghipoor Borj, A. A., & Khalighi Sigaroodi, S. (2022). Potential effects of climate change on the geographic distribution of the Hordeum bulbosum L. in the central Zagros region. Natural Environment, 74(4), 747-758. (In Persian) IPCC. (2021). Summary for policymakers Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press. Iverson, L. R., & McKenzie, D. (2013). Tree-species range shifts in a changing climate: detecting, modeling and assisting. Landscape Ecology, 28, 879-889. doi: 10.1007/s10980-013-9885-x. Jafarian, Z., Karimzadeh, A., Ghorbani, J., & Akbarzadeh, A. (2011). Determination of ecological species groups and effective environmental factors on them. Environmental Studies, 59, 77-88. (In Persian) Merow, C., Smith, M. J., & Silander, S. J. (2013). A practical guide to Maxent for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography, 36(10), 1058-1069. doi: 10.1111/j.1600-0587.2013.07872.x. Motamedi, J., Khodagholi, M., & Khalifezadeh, R. (2022). Prediction of current and future potential range of Artemisia aucheri under two climate warning models (Rcp4.5 and Rcp8.5) in southern Alborz rangeland habitats, Qazvin province. Environmental Sciences Studies, 7(2), 5015-5023. doi: 10.22034/JESS.2022.331331.1732. (In Persian) Nazari, S., Jafarian, Z., Alavi, S. J., & Naghipoor Borj, A. A. (2022). Predicting the geographical distribution of Alopecurus textilis Boiss rangeland species on basis consensus approach of climate change in Mazandaran province. Plant Ecosystem Conservation, 9(19), 137-155. (In Persian) ÓNeill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingtein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J. F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., & Sanderson, B. M. (2016). The scenario model intercomparison project (Scenario MIP) for CMIP6. Geoscientific Model Development, 9(9), 3461-3482. doi: 10.5194/gmd-9-3461-2016. Pearson, R. G., & Dawson, T. P. (2003). Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology and Biogeography, 12, 361-371. doi: 10.1046/j.1466-822X.2003.00042.x. Qahraman, A. (1988). Flora of Iran. Volume 11. Research Institute of Forests and Rangelands. (In Persian) Rana, S. K., Rana, H. K., Ghimire, S. K., Shrestha, K. K., & Ranjitkar, S. (2017). Predicting the impact of climate change on the distribution of two threatened Himalayan medicinal plants of Liliaceae in Nepal. Mountain Science, 14(3), 558-570. doi: 10.1007/s11629-015-3822-1. Santos, B. A., Barbosa, D. C. A., & Tabarelli, M. (2007). Directional changes in plant assemblages along an altitudinal gradient in northeast Brazil. Brazilian Journal of Biology, 67(4), 777-779. doi: 10.1590/S1519-69842007000400028. Sheikhzadeh Ghahnaviyeh, A., Tarkesh Esfahani, M., Bashari, H., & Soltani Koupaei, S. (2021). Investigating geographical shifts of Astragalus verus under climate change scenarios using random-forest modeling (Case study: Isfahan and Chaharmahal va Bakhtiari provinces). Journal of Rangeland, 15(4), 589-602. (In Persian) Stohlgren, T. J., Ma, P., Kumar, S., Rocca, M., Morisette, J. T., Jarnevich, C., & Benson, N. (2010). Ensemble habitat mapping of invasive plant species. Risk Analysis, 30(2), 224-235. doi: 10.1111/j.1539-6924.2009.01343.x. Taylor, M. A., Stephenson, T. S., Anthony Chen, A., & Stephenson, K. A. (2012). Climate change and the caribbean: Review and response. Caribbean Studies, 40(2), 169-200. Thuiller, W., Georges, D., Engler, R., Breiner, F., Georges, M. D., & Thuiller, C. W. (2016). Package ‘biomod2’. 114 p. Tsoar, A., Ahhouche, O., Steinitz, O., Rotem, D., & Kadmon, R. (2007). A comparative evaluation of presence-only methods for modeling species distribution. Diversity and Distributions, 13(4), 397-405. doi: 10.1111/j.1472-4642.2007.00346.x. Uğurlu, E., & Oldeland, J. (2012). Species response curves of oak species along climatic gradients in Turkey. International Journal of Biometeorology, 56(1), 85-93. doi: 10.1007/s00484-010-0399-9. Zarrin, A., & Dadashi-Roudbari, A. A. (2022). Assessing the Effect of Climate Change on Heavy Precipitation in Iran Based on a CMIP6 Ensemble Model. Water and Sustainable Development, 8(4), 119-124. (In Persian) Zarrin, A., Dadashi-Roudbari, A. A., & Salehabadi, N. (2021). Projected temperature anomalies and trends in different climate zones in Iran based on CMIP6. Journal of Geophysics, 15(1), 35-54. doi: 10.22059/ JPHGR.2021. 308361. 1007551. (In Persian) Zhang, J., Huang, S., & He, F. (2015). Half-century evidence from western Canada shows forest dynamics are primarily driven by competition followed by climate. Proceedings of the National Academy of Sciences, 112(13), 4009-4014. doi: 10.1073/pnas.1420844112. | ||
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