ajfr Anadolu Orman Araştırmaları Dergisi 1309-856X 2564-7660 Cankiri Karatekin University 10.53516/ajfr.1732342 Forest Biodiversity Orman Biyoçeşitliliği Species distribution models and sustainable forest management Tür dağılım modelleri ve sürdürebilir orman yönetimi https://orcid.org/0009-0003-8375-3808 Öğüt Nazlı Isparta Uygulamalı Bilimler Üniversitesi Orman fakültesi https://orcid.org/0000-0002-1706-1542 Kurdoğlu Oğuz KARADENİZ TEKNİK ÜNİVERSİTESİ, ORMAN FAKÜLTESİ 12 31 2025 11 2 470 479 07 01 2025 09 10 2025 Copyright © 2015, Anatolian Journal of Forest Research 2015 Anatolian Journal of Forest Research

Background and Aims The aim of this study is to comprehensively address species distribution modelling methods in forest ecosystems and to reveal the role of these methods in sustainable forest management. Methods A literature review was conducted on six modeling methods, including Logistic Regression, Generalized Additive Models (GAM), Classification and Regression Trees (CART), Maximum Entropy (MaxEnt), Genetic Algorithms for Rule-Set Prediction (GARP), and Random Forest.Results It has been observed that species distribution models are used as an effective tool in creating potential distribution or habitat suitability maps for wild animals, reptiles, insects, birds and plant species. Furthermore these models play a important role in assessing the impacts of climate change and habitat loss, as well as in developing strategies for species conservation and management. It has been determined that topographic data, climate variables and remote sensing data come to the fore among the descriptive (independent) data sources used in the modeling processes.Conclusion Studies reveal that these methods have been successfully applied to understand the ecological requirements of species and to create habitat suitability maps. In this study, the role of species distribution modelling methods in the conservation and sustainable management of forest ecosystems is emphasised and the importance of these models for future research is explained.

Giriş ve Hedefler Bu çalışma, orman ekosistemlerinde tür dağılım modelleme yöntemlerini kapsamlı biçimde ele alarak bu yöntemlerin sürdürülebilir orman yönetimindeki rolünü ortaya koymak amacıyla hazırlanmıştır. Yöntemler Çalışmada, Lojistik Regresyon, Genelleştirilmiş Eklemeli Modeller (GAM), Sınıflandırma ve Regresyon Ağaçları (CART), Maksimum Entropi (MaxEnt), Genetik Algoritmalar ile Kural Seti Tahmini (GARP) ve Rastgele Orman olmak üzere altı modelleme yöntemleri ilgili literatür taraması yapılmıştır. Bulgular Tür dağılım modelleri, yaban hayvanları, sürüngenler, böcekler, kuşlar ve bitki türleri için potansiyel dağılım veya habitat uygunluk haritalarının oluşturulmasında etkin bir araç olarak kullanıldığı görülmüştür. Ayrıca, bu modeller, iklim değişikliğinin etkilerini değerlendirmek, türlerin korunması ve yönetimine yönelik stratejiler geliştirmek açısından önemli olduğu belirlenmiştir. Modelleme süreçlerinde kullanılan tanımlayıcı (bağımsız) veri kaynakları arasında topografik veriler, iklim değişkenleri ve uzaktan algılama verileri ön plana çıktığı tespit edilmiştir.Sonuçlar Çalışmalar, türlerin ekolojik gereksinimlerini anlamada ve habitat uygunluk haritalarının oluşturulmasında bu yöntemlerin başarıyla uygulandığını ortaya koymaktadır. Bu çalışmada tür dağılım modelleme yöntemlerinin orman ekosistemlerinin korunması ve sürdürülebilir yönetimindeki rolü vurgulanmış ve bu modellerin gelecekteki araştırmalar için taşıdığı önem hakkında açıklamalar yapılmıştır.

 Orman ekosistemleri iklim değişikliği ekolojik modelleme habitat uygunluğu Forest ecosystems climate change ecological modeling habitat suitability Abdelrahim, M., 2015. Contribution of non-wood forest products in support of livelihoods of rural people living in the area south of Blue Nile State, Sudan. International Journal of Agriculture, Forestry and Fisheries, 3, 189-194. Acarer, A., 2024. Habitat Suitability Modelling and Mapping of Anatolian Wild Sheep (Ovis gmelinii anatolica Valenciennes, 1856. International Journal of Advanced Multidisciplinary Research and Studies,4(5),945-952. Acarer, A., 2024. Role of climate change on Oriental spruce (Picea orientalis L.), Modeling and mapping. BioResources, 19(2), 3845. Akesen, A., Ekizoğlu, A., Kuvan, Y., Atmış, E. 2010. Ormancılık Politikası (Akesen A., Ekizoğlu, A., Ed.) Türkiye Ormancılar Derneği Eğitim Dizisi Yayın No, 6, Özdoğan Matbaa, Ankara, 37-63. Akhter, S., McDonald, M. A., van Breugel, P., Sohel, S., Kjaer, E. D., Mariott, R., 2017. Habitat distribution modelling to identify areas of high conservation value under climate change for Mangifera sylvatica Roxb of Bangladesh. Land Use Policy, 60, 223-232. Austin, M., 2007. Species distribution models and ecological theory, a critical assessment and some possible new approaches. Ecological Modelling, 200(1), 1-19. Baldwin, R., 2009. Use of maximum entropy modeling in wildlife research. Entropy, 11(4), 854-866. Beaumont, L. J., Graham, E., Duursma, D. E., Wilson, P. D., Cabrelli, A., Baumgartner, J. B., Laffan, S.W., 2016. Which species distribution models are more (or less) likely to project broad-scale, climate-induced shifts in species ranges?. Ecological Modelling, 342, 135-146. Behan, R., 1990. Multiresource forest management, A paradigmatic challenge to professional forestry. Journal of Forestry, 88, 12-18. Biau, G., Scornet, E., 2016. A random forest guided tour. Test, 25, 197-227. Booth, T.H., 2018. Species distribution modelling tools and databases to assist managing forests under climate change. Forest Ecology and Management, 430, 196–203. Braat, L.C., De Groot, R., 2012. The ecosystem services agenda, bridging the worlds of natural science and economics, conservation and development, and public and private policy, Ecosystem Services, 1, 4-15. Brooks, D. J., Grant, G. E., 1992. New approaches to forest management. Journal of Forestry, 90, 25-28. Cai, L., Kreft, H., Taylor, A., Denelle, P., Schrader, J., Essl, F., Weigelt, P., 2023. Global models and predictions of plant diversity based on advanced machine learning techniques. New Phytologist, 237(4), 1432-1445. Chu, C. M., Tsai, B. W., Chang, K. T., 2009. Integrating decision tree and spatial cluster analysis for landslide susceptibility zonation. International Journal of Geological and Environmental Engineering, 3(11), 389-393. Cook, D., Dixon, P., Duckworth, W. M., Kaiser, M. S., Koehler, K., Meeker, W. Q., Stephenson. W. R., 2001. Binary response and logistic regression analysis. Part of the Iowa State University NSF/ILI project Beyond Traditional Statistical Methods, pp.1-23. Çıvğa, A., Özdemir, S., Gülsoy, S. 2024. Prediction of potential geographic distribution of Capparis spinosa. Biological Diversity and Conservation, 17(3), 206-215. De'ath, G., Fabricius, K. E., 2000. Classification and regression trees, a powerful yet simple technique for ecological data analysis. Ecology, 81(11), 3178-3192. Duerr, W. A., Duerr, J. B., 1975. The Role of Faith in Forest Resource Management. In, F. Rumsey and W. A. Duerr (Eds.), Social Sciences in Forestry, A Book of Readings, Philadelphia, W. B. Saunders, pp. 30–41. Ehrlich, P. R., 1968. The population bomb, Rivercity press, New York, ISBN, 0-89190-867- 7. Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., Yates, C. J., 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43-57. Erdönmez, C., Atmış, E., Özden, S., 2010. Ormancılık Politikası. Türkiye’de Ormancılık Politikası. (ed, Akesen A., Ekizoğlu, A.,) Türkiye Ormancılar Derneği Eğitim Dizisi Yayın No, 6, Özdoğan Matbaa, Ankara, s.101-143. Erdönmez, C., Erol, S. Y., 2021. Türkiye’de ulusal ormancılık politikasının tarihsel gelişimi açısından bir dönüm noktası, 1255 sayılı yasa. Bartın Orman Fakültesi Dergisi, 23(1), 182-201. Evans, J. S., Murphy, M. A., Holden, Z. A., Cushman, S. A., 2010. Modeling species distribution and change using random forest. In Predictive species and habitat modeling in landscape ecology, Concepts and applications New York, NY, Springer, pp. 139-159. Falk, W., 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(4), 621-634. FAO, 2004. Non-wood forest products for rural income and sustainable development. Non-wood Forest Products (7). Rome, Italy. Ferrarini, A., Selvaggi, A., Abeli, T., Alatalo, J. M., Orsenigo, S., Gentili, R., Rossi, G., 2016. Planning for assisted colonization of plants in a warming world. Scientific Reports, 6(1), 28542. Fleischer, P., Pichler, V., Flaischer, P., Holko, L., Mális, F., Gömöryová, E., Cudlín, P., Holeksa, J., Michalová, Z., Homolová, Z., Skvarenina, J., Střelcová, K., Hlaváč, P., 2017. Forest ecosystem services affected by natural disturbances, climate and landuse changes in the Tatra Mountains. Climate Research, 73, 57–71. Gallego, D., Cánovas, F., Esteve, M. A., Galián, J., 2004. Descriptive biogeography of Tomicus (Coleoptera, Scolytidae) species in Spain. Journal of Biogeography, 31(12), 2011-2024. Gianola, D., Fernando, R. L., Stella, A., 2006. Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics, 173(3), 1761-1776. Gomez-Bagetthun, E., De Groot, R., Lomas, P.L., Montes, C., 2010. The history of ecosystem services in economic theory and practice, From early notions to markets and payment schemes, Ecological Economics, 69, 1209-1218. Guisan, A., Edwards Jr, T. C., Hastie, T., 2002. Generalized linear and generalized additive models in studies of species distributions, setting the scene. Ecological modelling, 157(2-3), 89-100. Guisan, A., Thuiller, W., 2005. Predicting species distribution, offering more than simple habitat models. Ecology letters, 8(9), 993-1009. Guisan, A., Tingley, R., Baumgartner, J.B., Naujokaitis-Lewis, I., Sutcliffe, P.R., Tulloch, A.I.T., Regan, T.J., Brotons, L., Mcdonald-Madden, E., Mantyka-Pringle, C., Martin, T.G., Rhodes, J.R., Maggini, R., Setterfield, S.A., Elith, J., Schwartz, M.W., Wintle, B.A., Broennimann, O., Austin, M., Ferrier, S., Kearney, M.R., Possingham, H.P., Buckley, Y.M., 2013. Predicting species distributions for conservation decisions. Ecology letters, 16, 1424–1435. Gümüş, C., 2004. Ormancılık Politikası. Karadeniz Teknik Üniversitesi Orman Fakültesi, Fakülte Yayın No, 34, Trabzon, s.444. Güney, C. O., Özkan, K., Şentürk, Ö., 2016. Modelling of spatial prediction of fire ignition risk in the Antalya-Manavgat district. Journal of the Faculty of Forestry Istanbul University, 66(2), 459-470. Gürcan, M., 1998. Lojistik regresyon analizi ve bir uygulama. Yüksek lisans tezi, Ondokuz Mayıs Üniversitesi, Fen Bilimleri Enstitüsü, Samsun. Haeckel, E., 1866. Generelle Morphologie der Organismen: Allgemeine Grundzüge der organischen Formen-Wissenschaft, mechanisch begründet durch die von Charles Darwin reformierte Descendenz-Theorie. Band 1: Allgemeine Anatomie. Band 2: Allgemeine Entwicklungsgeschichte. de Gruyter. Haimes, Y. Y., 1999. Risk modeling, assessment, and management. IEEE Transactions on Systems, Man, and Cybernetics, Part C, Applications and Reviews, 29(2), 315-315. Hanewinkel, M., Hummel, S. Cullmann, D.A., 2010. Modelling and economic evaluation of forest biome shifts under climate change in Southwest Germany. Forest Ecology and Management, 259, 710–719. Hanewinkel, M., Kuhn, T., Bugmann, H., Lanz, A., Brang, P., 2014. Vulnerability of uneven-aged forests to storm damage. Forestry, 87, 525–534. Hastie, T., Tibshirani, R., 1986. Generalized additive models. Statistical science, 1(3), 297-310. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, A Journal of the Royal Meteorological Society, 25(15), 1965-1978. Hussein, A., Workeneh, S., 2021. Modeling the impacts of climate changes on the distribution of aloe vera species in ethiopia. Research Square, 1-19. Imperatives, S. 1987. Report of the World Commission on Environment and Development, Our common future. Accessed Feb, 10(42,427), 1-223. Janowiak, M. K., Iverson, L. R., Fosgitt, J., Handler, S. D., Dallman, M., Thomasma, S., Hutnik, B., Swanston, C. W., 2017. Assessing stand-level climate change risk using forest inventory data and species distribution models. Journal of Forestry, 115(3), 222-229. Johnson, K. D., Birdsey, R., Finley, A. O., Swantaran, A., Dubayah, R., Wayson, C., Riemann, R., 2014. Integrating forest inventory and analysis data into a LIDAR-based carbon monitoring system. Carbon Balance and Management, 9, 1–11. Kalayci Kadak, M., Ozturk, S., & Mert, A. 2024. Predicting climate-based changes of landscape structure for Turkiye via global climate change scenarios: a case study in Bartin river basin with time series analysis for 2050. Natural Hazards, 120(14), 13289-13307. Karakaya, T., Yücel, E. 2021. Potential distribution modelling and mapping of dog rose (Rosa canina L.) in the nur mountains of gazİantep district, turkey. Applıed Ecology And Envıronmental Research, 19, 2741-2760. Karataş, R., Şentürk, Ö., Arslan, M., Güner, D., Negiz, M. G., Özkan, K. 2019. Türkmen Dağı’ndaki bazı odun dışı orman ürünlerinin potansiyel dağılımı. Ormancılık Araştırma Dergisi, 6(1), 15-28. Kıraç, A., 2021. Potential disribution of two Lynx species in europe under paleoclimatological scenarios and anthropogenic clima change scenarios, CERNE, 27, e102517. Liu, J. H., Xiong, X., Pan, Y., Xiong, Z., Deng, Z., Yang, L., 2011. Predicting potential distribution of oriental fruit fly, Bactrocera dorsalis in Jiangxi Province, South China based on maximum entropy model. Scientific Research and Essays, 6(14), 2888-2894. Marchi, M., Ducci, F., 2018. Some refinements on species distribution models using treelevel national forest inventories for supporting forest management and marginal forest population detection. Iforest-Biogeosciences and Forestry, 11, 291–299. Masek, J. G., Goward, S. N., Kennedy, R. E., Cohen, W. B., Moisen, G. G., Schleeweis, K., Huang, C., 2013. United States forest disturbance trends observed using landsat time series. Ecosystems, 16, 1087–1104. Mert, A., Gülsoy, S., Özdemir, S., Şenol, A., Çelik, N., Özkan, K., Öztürk, M. 2025. Model-Based Mapping Processes of Soil Characteristics in Forest Ecosystems. Forests and Carbon Sequestration (Ed, Suratman MN., Gandaseca, S., Latif, ZA), Cambridge Scholars Publishing, UK, pp. 261-290. Mert, A., Özkan, K., Şentürk, Ö., Negiz, M. G., 2016. Changing the potential distribution of Turkey Oak (Quercus cerris L.) under climate change in Turkey. Polish Journal of Environmental Studies, 25(4), 1633-1638. Mertler, C. A., Vannatta, R. A., LaVenia, K. N., 2021. Advanced and Multivariate Statistical Methods, Practical application and interpretation. Pyrczak Publishing, Los Angeles. Mezquida, E. T., Rubio, A., S´ anches-Palomares, O., 2010. Evaluation of the potential index model to predict habitat suitability of forest species, the potential distribution of mountain pine (Pinus uncinata) in the Iberian peninsula. European Journal of Forest Research 129, 133–140. Moisen, G. G., Freeman, E. A., Blackard, J. A., Frescino, T. S., Zimmermann, N. E., Edwards Jr, T. C. 2006. Predicting tree species presence and basal area in Utah, a comparison of stochastic gradient boosting, generalized additive models, and tree-based methods. Ecological modelling, 199(2), 176-187. Navarrate, E., Espinosa, M., 2011. Using the non-parametric classifies CART to model wood density. Journal of Data Science, 9(2), 261-270. Obiakara, M., Etaware, P., Chukwuka, K., 2020. Maximum entropy niche modelling to estimate the potential distribution of Phytophthora megakarya (Brasier & MJ Griffin) in tropical regions. European Journal of Ecology, 6(2), 23-40. Odum, E. P., & Barrett, G. W. 1971. Fundamentals of ecology. Saunders, Philadelphia. Özdemir, S. 2022. Batı Akdeniz'de iklim değişimine göre asli orman ağacı türlerinin dağılım modellemesi. Doktora tezi, Isparta Uygulamalı Bilimler Üniversitesi, Isparta. Özdemir, S. 2024a. Testing the Effect of Resolution on Species Distribution Models Using Two Invasive Species. Polish Journal of Environmental Studies, 33(2), 1325-1335. Özdemir, S. 2024b. Tür Dağılım Modellemesinin Kısa Tarihi: Web of Science üzerinden Bibliyometrik Çalışma. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi, 20(2), 334-351. Özdemir, S., 2018. Random Forest Yöntemi kullanılarak potansiyel dağılım modellemesi ve haritalaması, Yukarıgökdere Yöresi örneği. Turkish Journal of Forestry, 19(1), 51-56. Özdemir, S., Gülsoy, S., Mert, A., 2020. Predicting the effect of climate change on the potential distribution of Crimean Juniper. Kastamonu University Journal of Forestry Faculty, 20(2), 133-142. Özdönmez, M., İstanbullu, T., Akesen, A., Ekizoğlu, A. 1996. Ormancılık Politikası. İstanbul Üniversitesi, Üniversite Yayın No, 3968, Fakülte Yayın No, 435, İÜ Basımevi, İstanbul, s.416. Özer, M. A., 2001. Derin ekoloji. Çağdaş Yerel Yönetimler, 10(4), 61-79. Özkan, K. 2016. On the way of only one fundamental information layer for everything within new paradigm sense; ecosystem qualification mapping. Journal of the Faculty of Forestry Istanbul University, 66(2), 410-444. Özkan, K., 2013a. Yönetim ve geliştirme planlarının temel ekolojik altlıkları, İklim değişimine uyarlanabilir model tabanlı yetişme ortamı, biyoçeşitlilik, koruma alan değeri ve hedef tür habitat uygunluk haritaları. 2023'e doğru 2. Doğa ve Ormancılık Sempozyumu, 31 Ekim-03 Kasım 2013, Antalya, Türkiye, s. 129-148. Özkan, K., 2013b. Using the Non-Parametric Classifier CART to Model Lebanon Cedar (Cedrus libani A. Rich) Distribution in a Mountain Mediterranean Forest District. Polish Journal of Environmental Studies, 22(2), 495-501. Özkan, K., Makineci, E., Gülsoy, S., Özdemir, S., 2021. Orman Ekosistemleri ve İklim Değişimi. (Ed, Pakdemirli, B, Küçük, Ö, Bayraktar, Z, Takmaz, S), Ekoloji ve Ekonomi Ekseninde Türkiye’de Orman ve Ormancılık, Sonçağ Akademi, Ankara, s. 359–378. Özkan, K., Mert, A., 2010. Isparta Yukarı Gökdere yöresinde kasnak meşesinin senaryolarına göre 2050 ve 2080 yıllarında muhtemel potansiyel yayılış alanlarının coğrafi modellemesi, Çölleşme ile Mücadele Sempozyumu 17-18 Haziran, 2010, Çorum, Türkiye, s.17-18. Özkan, K., Sentürk, Ö., Mert, A., Negiz, M. G., 2015. Modeling and mapping potential distribution of Crimean juniper (Juniperus excelsa Bieb.) using correlative approaches. Journal of environmental biology, 36(1), 9-15. Özkan, K., Şentürk, Ö., 2012. The application of group discrimination techniques to predict the potential distribution of turbentine tree, Internatinal Scientific Conference People Buildings and Environment, 7-9 November, Lednice, Czech Republic. Pecchi, M., Marchi, M., Giannetti, F., Bernetti, I., Bindi, M., Moriondo, M., Maselli, F., Fibbi, L., Corona, P., Travaglini, D., Chirici, G., 2019. Reviewing climatic traits for the main forest tree species in Italy. iForest – Biogeosciences and Forestry, 12, 173–180. Peterson, A. T., Ball, L. G., Cohoon, K. P., 2002. Predicting distributions of Mexican birds using ecological niche modelling methods. Ibis, 144(1), E27-E32. Peterson, A. T., Bauer, J. T., Mills, J. N., 2004. Ecologic and geographic distribution of filovirus disease. Emerging infectious diseases, 10(1), 40-47. Pettorelli, N., Laurance, W. F., O'Brien, T. G., Wegmann, M., Nagendra, H., Turner, W., 2014. Satellite remote sensing for applied ecologists, opportunities and challenges. Journal of Applied Ecology, 51(4), 839-848. Phillips, S. J., Dudík, M., Elith, J., Graham, C. H., Lehmann, A., Leathwick, J., Ferrier, S., 2009. Sample selection bias and presence‐only distribution models, implications for background and pseudo‐absence data. Ecological applications, 19(1), 181-197. Phillips, S. J., Dudík, M., Schapire, R. E., 2004. A maximum entropy approach to species distribution modeling. In Proceedings of The Twenty-First İnternational Conference on Machine Learning, 4 July, New York, United States, pp. 83. Pompe, S., Badeck, F. W., Hanspach, J., Klotz, S., Thuiller, W., Kuhn, I., 2008. Projecting impact on plant distributionsunder climate change- a case study from Germany. BiologyLetters, 4(5), 564-567. Puglielli, G., Pärtel, M., 2023. Macroecology of plant diversity across spatial scales. New Phytologist, 237(4), 1432–1445. Rehfeldt, G. E., Worrall, J. J., Marchetti, S. B., Crookston, N. L., 2014. Adapting forest management to climate change using bioclimate models with topographic drivers. Forestry, 88,528–539. Rigatti, S. J., 2017. Random forest. Journal of Insurance Medicine, 47(1), 31-39. Ruppert, D., Wand, M. P., Carroll, R. J., 2009. Semiparametric regression during 2003–2007. Electronic journal of statistics, 3,1193. Schmithüsen, F. J., 2013. Three hundred years of applied sustainability in forestry. Working papers/Forest Policy and Forest Economics Department of Forest Sciences,1. Schmutzenhofer, H. 1992. IUFRO's birthday. IUFRO News, 21/1, 2-3. Süel, H. 2014. Isparta-Sütçüler yöresinde habitat uygunluk modellemesi. Doktora tezi (basılmamış), Süleyman Demirel Üniversitesi, Isparta. Tekeş, A., Özdemir, S., Aykurt, C., Gülsoy, S., Özkan, K., 2025. Species distribution modeling of red hawthorn (Crataegus monogyna Jacq.) in response to climate change. Šumarski list, 149,5-6. Tekin, S., Yalçınkaya, B., Acarer, A., Mert, A., (2018). Yaban hayatında uydu verilerinin kullanım olanakları üzerine bir çalışma, MaxEnt ile Karaca (Capreolus capreolus L.)'nın habitat uygunluk modellemesi. Bilge International Journal of Science and Technology Research, 2(2), 147-156. Thuiller, W., Lavorel, S., Arau´ jo, M. B., Sykes, M.T., Prentice, I. C., 2005. Climate change threats to plant diversity inEurope. Proceedings of the National Academy of Sciences, 102, 8245–8250. Thuiller, W., Pollock, L. J., Gueguen, M., Münkemüller, T., 2015. From species distributions to meta‐communities. Ecology letters, 18(12), 1321-1328. U.S. Forest Service, 2025a. Little-FIA Tree Atlas. https,//www.fs.usda.gov/nrs/atlas/littlefia/index.html#, Erişim, 24.06.225. U.S. Forest Service, 2025b. Climate Change Tree Atlas, https,//www.fs.usda.gov/nrs/atlas/tree/tree_atlas.html, Erişim, 24.06.225. United Nations, (1972). Report of the United Nations Conference on the Human Environment, Stockholm, 5-16 June 1972. UN. Walentowski, H., Falk, W., Mette, T., Kunz, J., Bräuning, A., Meinardus, C., Zang, C., Sutcliffe, L.M.E., Leuschner, C., 2017. Assessing future suitability of tree species under climate change by multiple methods, a case study in southern Germany. Annals of Forest Research, 60, 101–126. Wiersum, K. F., 1995. 200 years of sustainability in forestry, Lessons from history. Environmental management, 19, 321-329. Williams, M. I., Dumroese, R. K., 2013. Preparing for climate change, forestry and assisted migration. Journal of Forestry, 111(4), 287-297. Wisz, M. S., Hijmans, R. J., Li, J., Peterson, A. T., Graham, C. H., Guisan, A., NCEAS Predicting Species Distributions Working Group., 2008. Effects of sample size on the performance of species distribution models. Diversity and distributions, 14(5), 763-773. WRI (World Resources Institute)., 2002. World Resources 2000-2001, People and Ecosystems, Fraying web of life, 10 G St., NE, Washington, DC, ISBN, 1-56973-443- 7. Yee, T. W., Mitchell, N. D., 1991. Generalized additive models in plant ecology. Journal of vegetation science, 2(5), 587-602.