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پیشبینی اثر تغییر اقلیم بر پراکنش پلت (.Acer velutinum Boiss) با استفاده از مدلهای پراکنش گونهای در جنگلهای هیرکانی | ||
| تحقیقات جنگل و صنوبر ایران | ||
| مقاله 10، دوره 34، شماره 1 - شماره پیاپی 103، فروردین 1405، صفحه 57-81 اصل مقاله (2.17 M) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2025.370079.2204 | ||
| نویسندگان | ||
| خدیجه اصغری1؛ حامد اسدی* 2؛ حمید جلیلوند3؛ سید محمد حجتی4؛ محیا تفضلی5 | ||
| 1دانشجوی دکتری، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران | ||
| 2نویسنده مسئول، استادیار، گروه علوم و مهندسی جنگل، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران | ||
| 3استاد، گروه علوم و مهندسی جنگل، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران | ||
| 4دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری | ||
| 5استادیار، گروه علوم و مهندسی جنگل، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران | ||
| چکیده | ||
| سابقه و هدف: بهمنظور کاهش جنگلزدایی و تخریب جنگلهای طبیعی، احیا و توسعه جنگلها از طریق جنگلکاری در جهان بهسرعت در حال افزایش است. بهنظر میرسد که یکی از راهحلهای احیای جنگلهای مخروبه هیرکانی در ایران استفاده از گونههای بومی باشد. پلت (Acer velutinum Boiss.) بهعنوان یکی از گونههای بومی در این جنگلها، از فراوانترین و بلندترین افراهای ایران محسوب میشود. این گونه ازنظر کمی و کیفی، نقش مهمی در جنگلهای هیرکانی ایفا میکند و در برنامههای جنگلکاری بهطور خالص یا آمیخته با گونههای بومی دیگر در این منطقه، مورد توجه قرار گرفته است. کسب اطلاعات در خصوص رویشگاههای مناسب فعلی و آینده آن بهمنظور اطمینان از موفقیت جنگلکاری بهویژه تحت تأثیر تغییر اقلیم، لازم و ضروری است. ازاینرو، پژوهش پیشرو بهمنظور مدلسازی و شناسایی مناسب رویشگاه پلت در جنگلهای هیرکانی انجام شد. مواد و روشها: این پژوهش در کل سطح جنگلهای هیرکانی در سه استان گیلان، مازندران و گلستان و با هدف تعیین رویشگاه مطلوبِ پلت برای احیای جنگل با استفاده از سه روش مختلف مدلسازی شامل: جنگل تصادفی (RF)، ماشین بردار پشتیبان (SVM) و بیشینه آنتروپی (MaxEnt) انجام گرفت. برای بررسی تأثیر تغییر اقلیم بر پراکنش پلت، متغیرهای زیستاقلیمی با تفکیک فضایی یک کیلومتر موجود در پایگاه دادههای Worldclim تا سال 2100 استفاده شد. متغیرهای زیستاقلیمی آینده با استفاده از مدل گردش عمومی جو (MPI-ESM1-2-HR) تحت چهار سناریوی SSP 126، SSP 245، SSP 370 و SSP 585 تا سال 2100 میلادی استخراج شد. الگوریتمهای مدلسازی و اهمیت نسبی متغیرها در بسته caret در نرمافزار R پیادهسازی و محاسبه شدند. همچنین برای پیشبینی حضور/غیاب بهمنظور ارزیابی دقت در هریک از روشهای مدلسازی مورد استفاده در برآورد حضور گونه از آمارههای کاپا، سطح زیر منحنی و صحت استفاده شد. نتایج: مدل RF بهعلت مقادیر زیادِ شاخصهای ارزیابی در پیشبینی رویشگاه مطلوب پلت، بهترین عملکرد را نسبت به مدلهای دیگر داشت (مقدار سطح زیر منحنی 9/0). نتایج ارزیابی اهمیت نسبی متغیرها با استفاده از مدل RF و MaxEnt نشان داد که متغیرهای ایزوترمالیتی و تغییرات فصلی دما بهترتیب بیشترین تأثیر را بر پراکندگی پلت داشتند. براساس مدل SVM نیز بیشترین تأثیر متعلق به متغیرهای بارندگی در خشکترین ماه و تغییرات فصلی دما بود. پیشبینی تغییرات رویشگاه مطلوب پلت با استفاده از مدل RF در جنگلهای هیرکانی تحت سناریوهای تغییر اقلیم آینده تا سال 2100 نشان داد که در خوشبینانهترین حالت (SSP 126)، رویشگاه مناسب پلت در بخش مرکزی جنگلهای هیرکانی، تغییر معنیداری نخواهد کرد، اما این گونه 43 درصد از رویشگاه مطلوب خود را در قسمتهای شرق و غرب جنگلهای مذکور از دست خواهد داد. همچنین، در بدبینانهترین حالت (SSP 585) فقط بخش اندکی از استان مازندران در مرکز این جنگلها و قسمتهایی در غرب این استان، رویشگاه مناسب این گونه باقی خواهد ماند و 33 درصد از رویشگاه مطلوب این گونه تا سال 2100 میلادی از بین خواهد رفت. ازآنجاییکه سناریو SSP 126 خوشبینانهترین حالت است، شرایط آینده منطقه را با کاهش کمترِ سطح رویشگاه مناسب گونه نسبت به حال حاضر پیشبینی کرده است. نتایج مدل MaxEnt نشان داد که در سناریوهای SSP 370 و SSP 585 هیچ منطقهای برای حضور پلت تا سال ۲۱۰۰ مناسب نخواهد بود. نتیجهگیری کلی: طبق نتایج این پژوهش، مدل RF پیشبینی بهتری از حضور پلت در جنگلهای هیرکانی در آینده ارائه داد. بهطورکلی براساس همه مدلها و سناریوهای مورد مطالعه، سطح وسیعی از رویشگاه مطلوب پلت از بین خواهد رفت. بیشترین آنها در مناطق شرقی و غربی جنگلهای هیرکانی متمرکز است. بهعلاوه، نقشههای ترسیمیِ مطلوبیت رویشگاه میتواند بهعنوان مبنایی برای طرحهای احیایی جنگلها بهویژه در مناطق با تخریب بیشتر استفاده شود. مدل RF نقش مهمی در پیشنهاد این گونه برای عملیات احیا و توسعه پایدار بومسازگانهای جنگلی دارد و میتواند منبعی برای مدیران در امر برنامهریزی حفاظت و احیای جنگلهای تخریبشده در منطقه مورد بررسی باشد. | ||
| کلیدواژهها | ||
| جنگلهای هیرکانی؛ رویشگاه مطلوب؛ سناریوهای تغییر اقلیم؛ مدل بیشینه آنتروپی؛ مدل جنگل تصادفی | ||
| عنوان مقاله [English] | ||
| Predicting the impact of climate change on the distribution of .Acer velutinum Boiss using species distribution models in the Hyrcanian forests, Iran | ||
| نویسندگان [English] | ||
| Khadijeh Asghari1؛ Hamed Asadi2؛ Hamid Jalilvand3؛ Seyed Mohammad Hojjati4؛ Mahya Tafazoli5 | ||
| 1Ph.D. Student, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran | ||
| 2Corresponding author, Assistant Prof., Department of Forest Science and Engineering, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran | ||
| 3Prof., Department of Forest Science and Engineering, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran | ||
| 4Sari Agricultural Sciences and Natural Resources University | ||
| 5Assistant Prof., Department of Forest Science and Engineering, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran | ||
| چکیده [English] | ||
| Background and Objectives: To reduce deforestation and the degradation of natural forests, reforestation and afforestation projects are rapidly expanding worldwide. In northern Iran, the use of native species has been suggested as an effective approach for restoring degraded forests. Acer velutinum Boiss., one of the tallest and most abundant maple species in the Hyrcanian forests, plays an important ecological role in terms of both quantity and quality. It has been widely applied in afforestation programs, either as pure stands or in mixture with other native species. Understanding its current and future suitable habitats is therefore essential for the success of restoration projects, especially under the increasing pressure of climate change. The present study aimed to model and identify suitable habitats for A. velutinum in the Hyrcanian forests. Methodology: The study was conducted across the entire Hyrcanian forest region in Iran, covering Gilan, Mazandaran, and Golestan provinces. Three species distribution modeling approaches were employed: Random Forest (RF), Support Vector Machine (SVM), and Maximum Entropy (MaxEnt). Bioclimatic variables at 1 km spatial resolution were obtained from the WorldClim database. Future climate variables were derived from the MPI-ESM1-2-HR general circulation model under four scenarios (SSP 126, SSP 245, SSP 370 and SSP 585) up to the year 2100. Modeling algorithms and variable importance were implemented in the caret package in R. Model performance was evaluated using Kappa statistics, area under the curve (AUC), and accuracy. Results: The RF model outperformed the others, achieving the highest accuracy in predicting suitable habitats for A. velutinum (AUC = 0.9). Variable importance analysis indicated that isothermality and temperature seasonality were the most influential predictors in the RF and MaxEnt models, while precipitation of the driest month and temperature seasonality were the most important variables in the SVM model. According to RF projections, under the most optimistic scenario (SSP 126), suitable habitats in the central parts of the Hyrcanian forests will remain largely stable, but the species is expected to lose about 43% of its suitable habitat in the eastern and western regions. Under the most pessimistic scenario (SSP 585), only limited areas in central and western Mazandaran province will remain suitable, with 33% habitat loss by 2100. Furthermore, the MaxEnt model predicted that under SSP 370 and SSP 585 scenarios, no suitable habitat will remain for the species by 2100. Conclusion: The RF model provided the most reliable predictions of A. velutinum distribution under future climate change. Overall, all models and scenarios indicated substantial habitat loss, especially in the eastern and western regions of the Hyrcanian forests. The habitat suitability maps generated in this study provide a valuable basis for forest restoration planning, particularly in highly degraded areas. The RF model highlights the importance of this species in restoration efforts and supports its use in sustainable forest ecosystem management, offering a reliable reference for managers in conservation and reforestation planning. | ||
| کلیدواژهها [English] | ||
| Climate change scenarios, Hyrcanian forests, maximum entropy model, random forest model, suitable habitat | ||
| مراجع | ||
|
- Adjuik, T.A. and Davis, S.C., 2022. Machine learning approach to simulate soil CO2 fluxes under cropping systems. Agronomy, 12(1): 197.
- Ahmadi, K., Alavi, S.J., Amiri, G.Z., Hosseini, S.M., Serra-Diaz, J.M. and Svenning, J.C., 2020. The potential impact of future climate on the distribution of European yew (Taxus baccata L.) in the Hyrcanian Forest region (Iran). International Journal of Biometeorology, 64(9): 1451-1462.
- Akhani, H., Djamali, M., Ghorbanalizadeh, A. and Ramezani, E., 2010. Plant biodiversity of Hyrcanian relict forests, N Iran: an overview of the flora, vegetation, palaeoecology and conservation. Pakistan Journal of Botany 42: 231-258.
- Alavi, S.J., Ahmadi, K., Hosseini, S.M., Tabari, M. and Nouri, Z., 2019. The response of English yew (Taxus baccata L.) to climate change in the Caspian Hyrcanian Mixed Forest ecoregion. Regional Environmental Change, 19(1): 1495-1506.
- Alipour, Sh., Badehian, Z., Yousefzadeh, H., Asadi, F., Espahbodi, K. and Walas, Ł., 2023. Predicting past, current and future suitable habitat for endemic Hyrcanian species Populus caspica Bornm. New Forests, 54(2): 325-342.
- Araújo, M.B. and New, M., 2007. Ensemble forecasting of species distributions. Trends in Ecology and Evolution, 22(1): 42-47.
- Asadi, H., Jalilvand, H., Tafazoli, M. and Hosseini, S.F., 2025. Modeling suitable habitats of Parrotia persica (DC.) C.A.Mey. in the Hyrcanian Forests, Iran using environmental factors. Iranian Journal of Forest and Poplar Research, 33(1): 50-68 (In Persian with English summary).
- Bakhshi Khaniki, Gh. and Mohammadi, B., 2012. Ecological study of some species of the genus Salsola (Chenopodiaceae) in Golestan Province. New Cellular and Molecular Biotechnology Journal, 2(6): 45-52 (In Persian with English summary).
- Catani, F., Lagomarsino, D., Segoni, S. and Tofani, V., 2013. Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Natural Hazards and Earth System Sciences, 13(11): 2815-2831.
- Chauvier, Y., Thuiller, W., Brun, P., Lavergne, S., Descombes, P., Karger, D.N., … and Zimmermann, N.E., 2021. Influence of climate, soil, and land cover on plant species distribution in the European Alps. Ecological Monographs, 91(2): e01433.
- Chen, X., Liang, Y. and Feng, X., 2024. Influence of model complexity, training collinearity, collinearity shift, predictor novelty and their interactions on ecological forecasting. Global Ecology and Biogeography, 33(3): 371-384.
- Damaneh, J.M., Ahmadi, J., Rahmanian, S., Sadeghi, S.M.M., Nasiri, V. and Borz, S.A., 2022. Prediction of wild pistachio ecological niche using machine learning models. Ecological Informatics, 72: 101907.
- Du, Q., Rossi, S., Lu, X., Wang, Y., Zhu, H., Liang, E. and Camarero, J.J., 2020. Negative growth responses to temperature of sympatric species converge under warming conditions on the southeastern Tibetan Plateau. Trees, 34(2): 395-404.
- Duan, R.Y., Kong, X.Q., Huang, M.Y., Fan, W.Y. and Wang, Z.G., 2014. The predictive performance and stability of six species distribution models. PLoS ONE, 9(11): e112764.
- Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E. and Yates, C.J., 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17: 43-57.
- Falk, W. and Mellert, K.H., 2011. Species distribution models as a tool for forest management planning under climate change: risk evaluation of Abies alba in Bavaria. Journal of Vegetation Science, 22: 621-634.
- Fathollahzadeh, A., 2018. Response curve and species distribution model of beech, basswood, ironwood, maple and alder in Educational and Research Forest of Tarbiat Modares University. M.Sc. thesis, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, Iran, 85p (In Persian with English summary).
- Ghareghan, F., Ghanbarian, G., Pourghasemi, H.R. and Safaeian, R., 2020. Prediction of habitat suitability of Morina persica L. species using artificial intelligence techniques. Ecological Indicators, 112: 106096.
- Gould, S.F., Beeton, N.J., Harris, R.M.B., Hutchinson, M.F., Lechner, A.M., Porfirio, L.L. and Mackey, B.G., 2014. A tool for simulating and communicating uncertainty when modelling species distributions under future climates. Ecology and Evolution, 4(24): 4798-4811.
- Graham, J. and Kimble, M., 2019. Visualizing uncertainty in habitat suitability models with the hyper-envelope modeling interface, version 2. Ecology and Evolution, 9(1): 251-264.
- Greener, J.G., Kandathil, S.M., Moffat, L. and Jones, D.T., 2022. A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology, 23(1): 40-55.
- Guisan, A., Thuiller, W. and Zimmermann, N.E., 2017. Habitat Suitability and Distribution Models: With Applications in R. Cambridge University Press, Cambridge, United Kingdom, 462p.
- Gunaratne, M.D.N., De Silva, S.H.N.P. and Amarasinghe, R.K., 2022. Can NASA power climatic data fill the gap of climatic data required for agriculture and forest ecosystems modeling? Proceedings of the 26th International Forestry and Environment Symposium. Jayewardenepura, Sri Lanka, 20-21 Jan. 2022: 141-141.
- Guo, Y., Lu, C., Gu, W., Zhao, Z. and Yang, D., 2023. The potential impacts of climate change on the distribution of tree species. Frontiers in Forests and Global Change, 6: 1301579.
- Habibikilak, S., Alavi, S.J. and Esmailzadeh, O., 2025. Investigating the influence of different environmental variables in modeling the distribution of yew (Taxus baccata L.) using the MaxEnt model in Hyrcanian forests. Forest Research and Development, 11(1): 25-39 (In Persian with English summary).
- Haghdoust, N., Akbarinia, M., Hoseini, S.M. and Varamesh, S., 2012. Effects of substitution of degraded natural forests with plantations on soil carbon sequestration and fertility in north of Iran. Journal of Environmental Studies, 38(3): 135-146 (In Persian with English summary).
- Hedayati Kaliji, S., Hosseini, S.M., Alavi, S.J. and Amiri, M., 2025. Current and future distribution modeling of oriental beech (Fagus orientalis Lipsky) in Hyrcanian forests. Forest Research and Development, 10(4): 527-543 (In Persian with English summary).
- Homami Totmaj, L., Ramezani, E., Alizadeh, K. and Behling, H., 2021. Four millennia of vegetation and environmental history above the Hyrcanian forest, northern Iran. Vegetation History and Archaeobotany, 30: 611-621.
- Intergovernmental Panel on Climate Change (IPCC), 2023. Summary for Policymakers: 1-34. In: Lee, H. and Romero, J. (Eds.). Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Geneva, Switzerland.
- Iturrate-Garcia, M., O'Brien, M.J., Khitun, O., Abiven, S., Niklaus, P.A. and Schaepman‐Strub, G., 2016. Interactive effects between plant functional types and soil factors on tundra species diversity and community composition. Ecology and Evolution, 6(22): 8126-8137.
- Jaafari, A., Pazhouhan, I. and Bettinger, P., 2021. Machine learning modeling of forest road construction costs. Forests, 12(9): 1169.
- Jafarian, Z. and Kargar, M., 2017. Distribution modeling of protective and valuable plant species in the tourist area of Polour using generalized linear model (GLM) and generalized additive model(GAM). Geography and Development, 46: 117-132 (In Persian with English summary).
- Kamer Aksoy, Ö., 2022. Predicting the potential distribution area of the Platanus orientalis L. in Turkey today and in the future. Sustainability, 14(18): 11706.
- Kangas, A.S. and Kangas, J., 1999. Optimization bias in forest management planning solutions due to errors in forest variables. Silva Fennica, 33(4): 303-315.
- Kangas, A.S., 1999. Methods for assessing uncertainty of growth and yield predictions. Canadian Journal of Forest Research, 29(9): 1357-1364.
- Khalatbari Limaki, M., Eshagh Nimvari, M., Alavi, S.J., Mataji, A. and Kazemnezhad, F., 2023. Elevational shift of Carpinus betulus L. under the future climate change in northern Iran. Journal of Renewable Natural Resources Research, 13(2): 71-85.
- Li, X., Zhang, G., Xie, C., Qiu, J. and Liu, X., 2024. Prediction of the potential distribution area of Jacaranda mimosifolia in China under climate change using the MaxEnt model. Frontiers in Forests and Global Change, 7: 1377689.
- Li, Z., Bi, S., Hao, S. and Cui, Y., 2022. Aboveground biomass estimation in forests with random forest and Monte Carlo-based uncertainty analysis. Ecological Indicators, 142: 109246.
- Ly, H.B., Asteris, P.G. and Pham, T.B., 2021. Accuracy assessment of extreme learning machine in predicting soil compression soefficient. Vietnam Journal of Earth Sciences, 42(3): 228-336.
- Mahmoud, A.R., Farahat, E.A., Hassan, L.M. and Halmy, M.W.A., 2025. Predicting the future impact of climate change on the distribution of species in Egypt’s Mediterranean ecosystems. BMC Plant Biology, 25(1): 644.
- Marvie Mohadjer, M.R., 2011. Silviculture. University of Tehran Press, Tehran, Iran, 418p (In Persian).
- Mi, C., Huettmann, F., Guo, Y., Han, X. and Wen, L., 2017. Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence. PeerJ, 5: e2849.
- Monfaredi, H., Jahdi, R. and Taheri Abkenar, K., 2022. Qualitative and quantitative traits and regeneration status in maple plantation and natural forests. Journal of Plant Research, 35(4): 806-817 (In Persian with English summary).
- Monni, S., Peltoniemi, M., Palosuo, T., Lehtonen, A., Mäkipää, R. and Savolainen, I., 2007. Uncertainty of forest carbon stock changes – implications to the total uncertainty of GHG inventory of Finland. Climatic Change, 81(3): 391-413.
- Naimi, B., Skidmore, A.K., Groen, T.A. and Hamm, N.A., 2011. Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling. Journal of biogeography, 38(8): 1497-1509.
- Niamir, A., Skidmore, A.K., Munoz, A.R., Toxopeus, A.G. and Real, R., 2019. Incorporating knowledge uncertainty into species distribution modelling. Biodiversity and Conservation, 28(3): 571-588.
- Pecchi, M., Marchi, M., Burton, V., Giannetti, F., Moriondo, M., Bernetti, I. and Chirici, G., 2019. Species distribution modelling to support forest management. A literature review. Ecological Modelling, 411: 108817.
- Peltoniemi, M., Palosuo, T., Monni, S. and Mäkipää, R., 2006. Factors affecting the uncertainty of sinks and stocks of carbon in Finnish forests soils and vegetation. Forest Ecology and Management, 232(1-3): 75-85.
- Petrenko, T.Y., Korznikov, K.A., Kislov, D.E., Belyaeva, N.G. and Krestov, P.V., 2022. Modeling of cold-temperate tree Pinus koraiensis (Pinaceae) distribution in the Asia-Pacific region: Climate change impact. Forest Ecosystems, 9: 100015.
- Probst, P., Boulesteix, A.L. and Bischl, B., 2019. Tunability: Importance of hyperparameters of machine learning algorithms. Journal of Machine Learning Research, 20(53): 1-32.
- Qin, A., Liu, B., Guo, Q., Bussmann, R.W., Ma, F., Jian, Z., … and 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.
- Qin, Z., Zhang, J.E., DiTommaso, A., Wang, R.L. and Wu, R.S., 2015. Predicting invasions of Wedelia trilobata (L.) Hitchc. with Maxent and GARP models. Journal of Plant Research, 128: 763-775.
- Ravanbakhsh, M., Babakhani, B., Ghasemnezhad, M., Serpooshan, F. and Biglouie, M.H., 2022. Morpho-physiological responses in Alnus subcordata and Acer velutinum seedlings to drought stress. Plant Process and Function, 11(51): 241-259 (In Persian with English summary).
- Sabeti, H., 2008. Forests, Trees and Shrubs of Iran. Published by Yazd University, Yazd, Iran, 886p (In Persian).
- Sagheb Talebi, Kh., Sajedi, T. and Pourhashemi, M., 2014. Forests of Iran: A Treasure from the Past, a Hope for the Future. Springer, Dordrecht, Netherlands, 152p.
- Sękiewicz, K., Salvà-Catarineu, M., Walas, Ł., Romo, A., Gholizadeh, H., Naqinezhad, A., ... and Boratyński, A., 2024. Consequence of habitat specificity: a rising risk of habitat loss for endemic and sub-endemic woody species under climate change in the Hyrcanian ecoregion. Regional Environmental Change, 24(2): 68.
- Shrestha, N., 2020. Detecting multicollinearity in regression analysis. American Journal of Applied Mathematics and Statistics, 8(2): 39-42.
- Sparks, A.H., 2018. nasapower: A NASA POWER global meteorology, surface solar energy and climatology data client for R. Journal of Open Source Software, 3(30): 1035.
- Taleshi, H., Jalali, S.G., Alavi, S.J., Hosseini, S.M., Naimi, B. and Zimmermann, N.E., 2019. Climate change impacts on the distribution and diversity of major tree species in the temperate forests of Northern Iran. Regional Environmental Change, 19(8): 2711-2728.
- Taleshi, H., Jalali, S.Gh., Alavi, S.J., Hosseini, S.M. and Naimi, B., 2018. Impacts of climate change on the distribution of oriental beech (Fagus orientalis Lipsky) in the Hyrcanian Forests, Iran. Iranian Journal of Forest, 10(2): 251-266 (In Persian with English summary).
- Taleshi, H., Jalali, S.Gh., Alavi, S.J., Hosseini, S.M. and 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. Ecology of Iranian Forests, 8(15): 10-21 (In Persian with English summary).
- Thampan, J., Srivastava, J., Saraf, P.N. and Samal, P., 2025. Habitat distribution modelling to identify areas of high conservation value under climate change for an endangered arid land tree Tecomella undulata. Journal of Arid Environments, 227: 105317.
- Thuiller, W., Lafourcade, B., Engler, R. and Araújo, M.B., 2009. BIOMOD – a platform for ensemble forecasting of species distributions. Ecography, 32(3): 369-373.
- Vorpahl, P., Elsenbeer, H., Märker, M. and Schröder, B., 2012. How can statistical models help to determine driving factors of landslides? Ecological Modelling, 239: 27-39.
- Wang, W.J., He, H.S., Thompson, F.R.III, Spetich, M.A. and Fraser, J.S., 2018. Effects of species biological traits and environmental heterogeneity on simulated tree species distribution shifts under climate change. Science of The Total Environment, 634: 1214-1221.
- Wisz, M.S., Pottier, J., Kissling, W.D., Pellissier, L., Lenoir, J., Damgaard, C.F., ... and Svenning, J.C., 2013. The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biological Reviews, 88(1): 15-30.
- Xie, C., Chen, L., Li, M., Jim, C.Y. and Liu, D., 2023. Bioclim modeling for predicting suitable habitat for endangered tree Tapiscia sinensis (Tapisciaceae) in China. Forests, 14(11): 2275.
- Zhang, L., Huettmann, F., Liu, S., Sun, P., Yu, Z., Zhang, X. and Mi, C., 2019. Classification and regression with random forests as a standard method for presence-only data SDMs: a future conservation example using China tree species. Ecological Informatics, 52: 46-56.
- Zhong, Y., Xue, Z., Jiang, M., Liu, B. and Wang, G., 2021. The application of species distribution modeling in wetland restoration: A case study in the Songnen Plain, Northeast China. Ecological Indicators, 121: 107137.
- Zurell, D., Franklin, J., König, C., Bouchet, P.J., Dormann, C.F., Elith, J., ... and Merow, C., 2020. A standard protocol for reporting species distribution models. Ecography, 43(9): 1261-1277.
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