Araştırma Makalesi
BibTex RIS Kaynak Göster

Castanea Sativa Mill.’in Bolu Orman Bölge Müdürlüğü Sınırları İçindeki Olası Yayılış Alanlarının İklim Değişkenlerine Bağlı Modellenmesi

Yıl 2025, Cilt: 21 Sayı: 1, 1 - 33, 30.06.2025
https://doi.org/10.58816/duzceod.1656326

Öz

Bu çalışma Bu çalışmada, Türkiye’nin Kuzeybatısında bulunan Bolu Orman Bölge Müdürlüğünde 2050, 2070 ve 2090 yılları için SSP2-4.5 ve SSP5-8.5 olmak üzere iki iklim senaryosu altında MaxEnt tür dağılım modelini kullanarak iklim değişikliğinin Castanea sativa (Anadolu Kestanesi) habitatları üzerindeki potansiyel etkileri değerlendirilmiştir. Biyoklimatik ve topografik faktörler dahil olmak üzere çevresel değişkenler, çoklu doğrusallığı en aza indirmek için ekolojik uygunluk ve korelasyon analizine dayalı olarak seçilmiştir. Model, eğitim verileri için 0,961 ve test verileri için 0,959 AUC değerleri ile yüksek tahmin doğruluğu göstererek sağlam bir performans sergilemiştir. Sonuçlar, yağış ve sıcaklık değişkenliğinin en etkili faktörler olduğunu, Bio17'nin (en kurak çeyreğin yağışı) modele %42,2 oranında katkıda bulunduğunu, bunu Bio15 (yağış mevsimselliği, %27,6) ve Bio4'ün (sıcaklık mevsimselliği, %15,7) izlediğini ortaya koymaktadır. SSP5-8.5 senaryosuna göre, C. sativa için uygun olmayan alanların 907.585,92 hektardan 2050 yılına kadar 910.104,16 hektara, 2070 yılına kadar 911.058,07 hektara ve 2090 yılına kadar 909.943,00 hektara yükseleceği öngörülmektedir. Yüksek uygunluktaki alanlar nispeten sabit kalarak 310,92 ha'dan 2090 yılına kadar 266,47 ha'a düşmüştür. Buna karşılık, SSP2-4.5 senaryosu, yüksek uygunluklu alanlarda daha önemli bir düşüş öngörmektedir; 310,92 ha'dan 2090 yılına kadar 133,26 ha'a düşecek ve uygun olmayan alanlar 912.802,41 ha'a genişleyecektir. Orta uygunluktaki alanlar SSP2-4.5 kapsamında 34.584,00 ha'dan 2090'a kadar 36.261,46 ha'a artış göstererek geçiş habitatlarına daha fazla destek verileceğini düşündürmektedir. Dikey yayılış bakımından değerlendirildiğinde, C. sativa'nın ağırlıklı olarak 1000 metrenin altındaki yüksekliklerde bulunduğunu ve uygun alanların %20,2'sinin 0-100 metre arasında yer aldığı görülmektedir. Gelecek senaryoları altında, türün daha düşük yüksekliklere doğru kayması ve SSP5-8.5 altında yüksek uygunluklu alanların 0-100 metrede yoğunlaşması öngörülmektedir. Bu bulgular, habitat kaybını ve parçalanmayı azaltmak için uyarlanabilir orman yönetimi ve koruma stratejilerinin kritik rolünü vurgulamaktadır. Çalışma bulguları özellikle iklim değişikliği karşısında C. sativa ve benzer türlerin korunması için iklim adaptasyonuna yönelik önlemlerinin alınması gerektiğini vurgulamaktadır.

Kaynakça

  • Akyol, A., & Örücü, Ö. (2019). İklim değişimi senaryoları ve tür dağılım modeline göre kızılcık türünün (cornus mas l.) odun dışı orman ürünleri kapsamında değerlendirilmesi. European Journal of Science and Technology, 224-233. https://doi.org/10.31590/ejosat.6150.
  • Aouinti, H., Moutahir, H., Touhami, I., Bellot, J., & Khaldi, A. (2022). Observed and predicted geographic distribution of acer monspessulanum l. using the maxent model in the context of climate change. Forests, 13 (12), 2049. https://doi.org/10.3390/f13122049.
  • Ashraf, U., Ali, H., Chaudry, M., Ashraf, I., Batool, A., & Saqib, Z. (2016). Predicting The Potential Distribution Of Olea Ferruginea In Pakistan Incorporating Climate Change By Using Maxent Model. Sustainability, 8, 722. https://doi.org/10.3390/su8080722
  • Atalay Dutucu, A. (2023). Anadolu Kestanesi’ nin (Castanea sativa) Anadolu’daki günümüz ve gelecek (2100) olası dağılışının modellenmesi. Journal of Human Sciences, 20 (4), 446-460. doi:10.14687/jhs.v20i4.6405
  • Aydın, M., & Sivri, N. (2023). Bi̇yoçeşi̇tli̇li̇k ve ekosi̇stemler. https://doi.org/10.53478/tuba.978-625-8352-58-0
  • Beale, C.M., & Lennon, J.J. (2012). Incorporating uncertainty in predictive species distribution modelling. Phil. Trans. R. Soc. B., 367, 247–258. doi:10.1098/rstb.2011.0178247
  • Beaumont, L.J., Hughes, L., & Pitman, A.J. (2008). Why is the choice of future climate scenarios for species distribution modelling important? Ecology Letters, 11(11), 1135-1146.
  • Beridze, B., Sękiewicz, K., Walas, Ł., Thomas, P.A., Danelia, I., Fazaliyev, V., & Dering, M. (2023). Biodiversity protection against anthropogenic climate change: conservation prioritization of Castanea sativa in the south caucasus based on genetic and ecological metrics. Ecology and Evolution, 13(5). https://doi.org/10.1002/ece3.10068
  • Bertrand, R., Lenoir, J., Piedall, C., Riofrío-Dillon, G., Ruffray. P., Vidal, C., Pierrat, J.C., & Gégout, J.C. (2011). Changes In Plant Community Composition Lag Behind Climate Warming In Lowland Forests. Nature, 479, 517. https://doi.org/10.1038/nature10548
  • Carbonbrief. (2018). CMIP6, the next generation of climate models explained. https://www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained, Erişim: 14.03.2025.
  • Cedano Giraldo, D., & Mumcu Küçüker, D. (2023). Assessing climate change impacts on the spatial distribution of Castanea sativa Mill. using ecological niche modeling. Anatolian Journal of Forest Research, 9(2), 170-177.
  • Chhogyel, N., Kumar, L., Bajgai, Y., & Jayasinghe, L. (2020). Prediction of bhutan's ecological distribution of rice (oryza satival.) under the impact of climate change through maximum entropy modelling. The Journal of Agricultural Science, 158(1-2), 25-37. https://doi.org/10.1017/s0021859620000350
  • Cobben, M., Van Treuren, R., Castañeda-Álvarez, N.P, Khoury, C.K., Kik, C., & Van Hintum, T.J. (2015). Robustness and Accuracy of Maxent Niche Modelling for Lactuca Species Distributions in Light of Collecting Expeditions. Plant Genetic Resources 13, 153-161. https://doi.org/10.1017/S1479262114000847
  • Conedera, M., Krebs, P., Gehring, E., Wunder, J., Hülsmann, L., Abegg, M., & Maringer, J., (2021). How future-proof is Sweet chestnut (Castanea sativa) in a global change context? Forest Ecology and Management, 494, 119320
  • Çoban, H.O., Örücü, Ö.K., & Arslan, E.S., (2020). MaxEnt modeling for predicting the current and future potential geographical distribution of Quercus libani olivier. Sustainability, 12, 2671.
  • Fick, S.E., & Hijmans, R.J. (2017). WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37 (12), 4302-4315. https://doi.org/10.1002/joc.5086
  • Franklin, J. (2009). Mapping species distributions: spatial inference and prediction. Cambridge University Press, Cambridge, UK
  • Gábor, L. Jetz, W., Lu, M., Rocchini, D., Cord, A., Malavasi, M., Zarzo-Arias, A., Barták, V., & Moudrý, V. (2022). Positional errors in species distribution modelling are not overcome by the coarser grains of analysis. Methods in Ecology and Evolution, 13(10), 2289-2302.
  • Gaston, K.J., & Blackburn, T.M. (1996). The Spatial Distribution Of Threatened Species: Macro-Scales And New World Birds. Proceedings of the Royal Society of London. Series B: Biological Sciences, 263, 235-240. https://doi.org/10.1098/rspb.1996.0037
  • Gustafson, E., Miranda, B., Dreaden, T., Pinchot, C., & Jacobs, D. (2022). Beyond blight: phytophthora root rot under climate change limits populations of reintroduced american chestnut. Ecosphere, 13(2). https://doi.org/10.1002/ecs2.3917
  • Hattab, T., Albouy, C., Lasram, F., Somot, S., Loc’h, F., & Leprieur, F. (2014). Towards a better understanding of potential impacts of climate change on marine species distribution: a multiscale modelling approach. Global Ecology and Biogeography, 23(12), 1417-1429. https://doi.org/10.1111/geb.12217
  • IPCC (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Core Writing Team, R.K. Pachauri, and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp
  • IPCC (2020) AR6 Synthesis Report, Climate Change 2022 IPCC. https://www.ipcc.ch/report/sixth-assessment-report-cycle/. Accessed 8 Aug 2022.
  • Koç, D. E., Dalfes, H. N., & Meral, A. (2022). Anadolu’da Konifer Ağaçların Yayılış Alanlarındaki Değişimler. Coğrafya Dergisi, (44), 81-95.
  • Lawler, J.J., Shafer, SL, White, D., Kareiva, P., Maurer, E.P., Blaustein, A.R., & Bartlein, P.J. (2009). Projected Climate‐İnduced Faunal Change In The Western Hemisphere. Ecology, 90, 588-597. https://doi.org/10.1890/08-0823.1
  • Li, S., Wang, Z., Zhu, Z., Tao, Y., & Xiang, J. (2023). Predicting the potential suitable distribution area of Emeia pseudosauteri in Zhejiang Province based on the MaxEnt model. Scientific Report, 13, 1806 (2023). https://doi.org/10.1038/s41598-023-29009-w
  • Martín, M., Mattioni, C., Cherubini, M., Taurchini, D., & Villani, F. (2010). Genetic diversity in european chestnut populations by means of genomic and genic microsatellite markers. Tree Genetics & Genomes, 6(5), 735-744. https://doi.org/10.1007/s11295-010-0287-9
  • Merow, C., Smith, M., & Silander, 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. https://doi.org/10.1111/j.1600-0587.2013.07872.x
  • Metreveli, V., Kreft, H., Akobia, I., Janiashvili, Z., Nonashvili, Z., Dzadzamia, L., & Gavashelishvili, A. (2023). Potential distribution and suitable habitat for chestnut (Castanea sativa). Forests, 14(10), 2076. https://doi.org/10.3390/f14102076
  • Mollah, T., Shishir, S., & Rashid, M. (2021). Climate change impact on the distribution of tossa jute using maximum entropy and educational global climate modelling. The Journal of Agricultural Science, 159(7-8), 500-510. https://doi.org/10.1017/s0021859621000897
  • Neldner, V. (2014). The contribution of vegetation survey and mapping to herbarium collections and botanical knowledge: a case study from queensland. Cunninghamia, 14, 77-87. https://doi.org/10.7751/cunninghamia.2014.14.005
  • O'Neill, B. C., Tebaldi, C., Van Vuuren, D., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.F., Lowe, J., Meehl, J., Moss, R., Riahi, K., & Sanderson, B.M. (2016). The scenario model intercomparison project (ScenarioMIP) for CMIP6
  • Öztürk, M., Çetinkaya, G., & Aydın, S. (2017). Köppen-geiger i̇klim sınıflandırmasına göre türkiye’nin i̇klim tipleri. Journal of Geography, 35, 17-27. https://doi.org/10.26650/jgeog295515
  • Pearson, R.G., Raxworthy, C.J., Nakamura, M., & Peterson, A.T., (2007). Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 1, 102–117.
  • Petford, M., & Alexander, G. (2021). Potential range shifts and climatic refugia of rupicolous reptiles in a biodiversity hotspot of south africa. Environmental Conservation, 48(4), 264-273. https://doi.org/10.1017/s0376892921000370
  • Phillips, S.J., & Elith, J. (2010). POC Plots: Calibrating species distribution models with presence‐only data. Ecology, 91, 2476-2484
  • Phillips, S.J., & Dudik, M. (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161–175.
  • Phillips, S.J., Anderson, R.P., Dudík, M., Schapire, R.E., & Blair, M.E., (2017). Opening the black box: An open‐source release of MaxEnt. Ecography, 40, 887-893.
  • Portela, E., Ferreira‐Cardoso, J., Louzada, J., & Gomes‐Laranjo, J. (2014). Assessment of boron application in chestnuts: nut yield and quality. Journal of Plant Nutrition, 38(7), 973-987. https://doi.org/10.1080/01904167.2014.963116
  • Preston, B. (2006). Risk-based reanalysis of the effects of climate change on u.s. cold-water habitat. Climatic Change, 76(1-2), 91-119. https://doi.org/10.1007/s10584-005-9014-1
  • Sargıncı, M., & Beyazyüz, F. (2022). Effects of climate change on forests: climate-smart forestry perspective. Anadolu Orman Araştırmaları Dergisi, 8(2), 142-149. https://doi.org/10.53516/ajfr.1139640
  • Sandercock, A., Westbrook, J., Zhang, Q., & Holliday, J. (2024). A genome-guided strategy for climate resilience in american chestnut restoration populations. Proceedings of the National Academy of Sciences, 121, (30). https://doi.org/10.1073/pnas.2403505121
  • Sohn, J., Saha, S., & Bauhus, J. (2016). Potential of forest thinning to mitigate drought stress: a meta-analysis. Forest Ecology and Management, 380, 261-273. https://doi.org/10.1016/j.foreco.2016.07.046
  • Stanton‐Jones, W., & Alexander, G. (2024). Gazing into the future: the potential impact of climate change on habitat suitability of the sungazer (smaug giganteus). Austral Ecology, 49(8). https://doi.org/10.1111/aec.13577
  • Tabor, K., Hewson, J., Tien, H., González‐Roglich, M., Hole, D., & Williams, J. (2018). Tropical protected areas under increasing threats from climate change and deforestation. Land, 7(3), 90. https://doi.org/10.3390/land7030090
  • Tam, L., Thinkhamrop, K., Suttiprapa, S., & Suwannatrai, A. (2024). Potential distribution of malaria vectors in central vietnam: a maxent modeling approach. Veterinary World, 1514-1522. https://doi.org/10.14202/vetworld.2024.1514-1522
  • Thuiller, W., Lavorel, S., Araújo, M.B., Sykes, M.T., & Prentice, I.C.(2005). Climate Change Threats to Plant Diversity in Europe. Proceedings of the National Academy of Sciences, 102, 8245-8250. https://doi.org/10.1073/pnas.0409902102
  • Travis, J. (2003). Climate change and habitat destruction: a deadly anthropogenic cocktail. Proceedings of the Royal Society B Biological Sciences, 270(1514), 467-473. https://doi.org/10.1098/rspb.2002.2246 Uzun, A., & Örücü, Ö. (2023). Spartium junceum l. (katırtırnağı)’un küresel iklim değişimi etkisi altındaki potansiyel yayılış alanlarının modellenmesi. Ağaç ve Orman, 4(2), 73-81. https://doi.org/10.59751/agacorman.1383004
  • Wang, Y.S., Xie, B.Y., Wan, F.H., Xiao, Q.M., & Dai, L.Y., (2007). The potential geographic distribution of Radopholus similis in China. Agricultural Sciences in China, 6, 1444-1449.
  • Williams, J.N., Seo, C., Thorne, J., Nelson, J.K., Erwin, S., O’Brien, J.M., & Mark W. S. (2009). Using species distribution models to predict new occurrences for rare plants. Diversity and Distribution, 15(4), 565-576.
  • Wilson, J., Bekessy, S., Parris, K., Gordon, A., Heard, G., & Wintle, B. (2012). Impacts of climate change and urban development on the spotted marsh frog (limnodynastes tasmaniensis). Austral Ecology, 38(1), 11-22. https://doi.org/10.1111/j.1442-9993.2012.02365.x
  • Wisz, M.S, Pottier, J., Kissling, W.D., Pellissier, L., Lenoir, J., Damgaard, C.F., Dormann, C.F., Forchhammer, M.D., Grytnes,J.A., Guisan,A., Heikkinen, R.K., Høye,T.T., Kühn,I., Luoto, M., Maiorano, L., Nilsson, M.C., Normand, S., Öckinger, E., Schmidt, N.M., Termansen, M., Timmermann, A., Wardle, D.A., Aastrup, P. & 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), 5-30
  • WorldClim, (2020). Global Climate Data, Url: https://worldclim.org/data/index.html. WWF, (2015). World Wide Fund for Nature, 122 key Turkish botanical sites: Important plant areas in Turkey, 464 p.
  • Yan, X., Wang, S., Duan, Y., Han, J., Huang, D., & Zhou, J. (2021). Current and future distribution of the deciduous shrub hydrangea macrophylla in china estimated by maxent. Ecology and Evolution, 11(22), 16099-16112. https://doi.org/10.1002/ece3.8288
  • Yi, Y.J, Cheng, X, Yang, Z.F., & Zhang, S.H. (2016). Maxent Modeling for Predicting The Potential Distribution of Endangered Medicinal Plant (H. riparia Lour) in Yunnan, China. Ecological Engineering, 92, 260-269. https://doi.org/10.1016/j.ecoleng.2016.04.010
  • Zhang, K., Yao, L., Meng, J., & Tao, J., (2018). Maxent Modeling for Predicting The Potential Geographical Distribution of Two Peony Species Under Climate Change. Science of the Total Environment, 634, 1326-1334. https://doi.org/10.1016/j.scitotenv.2018.04.112
  • Zhang, L., Zhu, L., Li, Y., Zhu, W., & Chen, Y. (2022). Maxent modelling predicts a shift in suitable habitats of a subtropical evergreen tree (Cyclobalanopsis glauca (Thunberg) Oersted) under climate change scenarios in China. Forests, 13(1), 126.

Modeling of Potential Distribution of Castanea sativa Mill. in Bolu Regional Directorate of Forestry Depending on Climate Variables

Yıl 2025, Cilt: 21 Sayı: 1, 1 - 33, 30.06.2025
https://doi.org/10.58816/duzceod.1656326

Öz

In this study, we assessed the potential impacts of climate change on Castanea sativa habitats using the MaxEnt model under SSP2-4.5 and SSP5-8.5 scenarios for 2050, 2070, and 2090 in the Bolu Regional Directorate of Forestry, northwestern Turkey. Bioclimatic and topographic variables were selected through ecological and correlation analyses. The model performed well, with AUC values of 0.961 (training) and 0.959 (test). Bio17 (precipitation in the driest quarter), Bio15 (precipitation seasonality), and Bio4 (temperature seasonality) were the most influential variables. Under SSP5-8.5, unsuitable areas slightly increase while high suitability areas decrease from 310.92 ha to 266.47 ha by 2090. SSP2-4.5 projects a greater reduction in high suitability (to 133.26 ha) and an increase in medium suitability areas, suggesting habitat transitions. C. sativa is currently found predominantly at elevations below 1000 meters, with 20.2% of suitable habitats located between 0–100 meters; in the future, the species is expected to shift to relatively lower elevations. These findings highlight the importance of adaptive forestry and conservation strategies to reduce habitat loss and fragmentation.

Kaynakça

  • Akyol, A., & Örücü, Ö. (2019). İklim değişimi senaryoları ve tür dağılım modeline göre kızılcık türünün (cornus mas l.) odun dışı orman ürünleri kapsamında değerlendirilmesi. European Journal of Science and Technology, 224-233. https://doi.org/10.31590/ejosat.6150.
  • Aouinti, H., Moutahir, H., Touhami, I., Bellot, J., & Khaldi, A. (2022). Observed and predicted geographic distribution of acer monspessulanum l. using the maxent model in the context of climate change. Forests, 13 (12), 2049. https://doi.org/10.3390/f13122049.
  • Ashraf, U., Ali, H., Chaudry, M., Ashraf, I., Batool, A., & Saqib, Z. (2016). Predicting The Potential Distribution Of Olea Ferruginea In Pakistan Incorporating Climate Change By Using Maxent Model. Sustainability, 8, 722. https://doi.org/10.3390/su8080722
  • Atalay Dutucu, A. (2023). Anadolu Kestanesi’ nin (Castanea sativa) Anadolu’daki günümüz ve gelecek (2100) olası dağılışının modellenmesi. Journal of Human Sciences, 20 (4), 446-460. doi:10.14687/jhs.v20i4.6405
  • Aydın, M., & Sivri, N. (2023). Bi̇yoçeşi̇tli̇li̇k ve ekosi̇stemler. https://doi.org/10.53478/tuba.978-625-8352-58-0
  • Beale, C.M., & Lennon, J.J. (2012). Incorporating uncertainty in predictive species distribution modelling. Phil. Trans. R. Soc. B., 367, 247–258. doi:10.1098/rstb.2011.0178247
  • Beaumont, L.J., Hughes, L., & Pitman, A.J. (2008). Why is the choice of future climate scenarios for species distribution modelling important? Ecology Letters, 11(11), 1135-1146.
  • Beridze, B., Sękiewicz, K., Walas, Ł., Thomas, P.A., Danelia, I., Fazaliyev, V., & Dering, M. (2023). Biodiversity protection against anthropogenic climate change: conservation prioritization of Castanea sativa in the south caucasus based on genetic and ecological metrics. Ecology and Evolution, 13(5). https://doi.org/10.1002/ece3.10068
  • Bertrand, R., Lenoir, J., Piedall, C., Riofrío-Dillon, G., Ruffray. P., Vidal, C., Pierrat, J.C., & Gégout, J.C. (2011). Changes In Plant Community Composition Lag Behind Climate Warming In Lowland Forests. Nature, 479, 517. https://doi.org/10.1038/nature10548
  • Carbonbrief. (2018). CMIP6, the next generation of climate models explained. https://www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained, Erişim: 14.03.2025.
  • Cedano Giraldo, D., & Mumcu Küçüker, D. (2023). Assessing climate change impacts on the spatial distribution of Castanea sativa Mill. using ecological niche modeling. Anatolian Journal of Forest Research, 9(2), 170-177.
  • Chhogyel, N., Kumar, L., Bajgai, Y., & Jayasinghe, L. (2020). Prediction of bhutan's ecological distribution of rice (oryza satival.) under the impact of climate change through maximum entropy modelling. The Journal of Agricultural Science, 158(1-2), 25-37. https://doi.org/10.1017/s0021859620000350
  • Cobben, M., Van Treuren, R., Castañeda-Álvarez, N.P, Khoury, C.K., Kik, C., & Van Hintum, T.J. (2015). Robustness and Accuracy of Maxent Niche Modelling for Lactuca Species Distributions in Light of Collecting Expeditions. Plant Genetic Resources 13, 153-161. https://doi.org/10.1017/S1479262114000847
  • Conedera, M., Krebs, P., Gehring, E., Wunder, J., Hülsmann, L., Abegg, M., & Maringer, J., (2021). How future-proof is Sweet chestnut (Castanea sativa) in a global change context? Forest Ecology and Management, 494, 119320
  • Çoban, H.O., Örücü, Ö.K., & Arslan, E.S., (2020). MaxEnt modeling for predicting the current and future potential geographical distribution of Quercus libani olivier. Sustainability, 12, 2671.
  • Fick, S.E., & Hijmans, R.J. (2017). WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37 (12), 4302-4315. https://doi.org/10.1002/joc.5086
  • Franklin, J. (2009). Mapping species distributions: spatial inference and prediction. Cambridge University Press, Cambridge, UK
  • Gábor, L. Jetz, W., Lu, M., Rocchini, D., Cord, A., Malavasi, M., Zarzo-Arias, A., Barták, V., & Moudrý, V. (2022). Positional errors in species distribution modelling are not overcome by the coarser grains of analysis. Methods in Ecology and Evolution, 13(10), 2289-2302.
  • Gaston, K.J., & Blackburn, T.M. (1996). The Spatial Distribution Of Threatened Species: Macro-Scales And New World Birds. Proceedings of the Royal Society of London. Series B: Biological Sciences, 263, 235-240. https://doi.org/10.1098/rspb.1996.0037
  • Gustafson, E., Miranda, B., Dreaden, T., Pinchot, C., & Jacobs, D. (2022). Beyond blight: phytophthora root rot under climate change limits populations of reintroduced american chestnut. Ecosphere, 13(2). https://doi.org/10.1002/ecs2.3917
  • Hattab, T., Albouy, C., Lasram, F., Somot, S., Loc’h, F., & Leprieur, F. (2014). Towards a better understanding of potential impacts of climate change on marine species distribution: a multiscale modelling approach. Global Ecology and Biogeography, 23(12), 1417-1429. https://doi.org/10.1111/geb.12217
  • IPCC (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Core Writing Team, R.K. Pachauri, and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp
  • IPCC (2020) AR6 Synthesis Report, Climate Change 2022 IPCC. https://www.ipcc.ch/report/sixth-assessment-report-cycle/. Accessed 8 Aug 2022.
  • Koç, D. E., Dalfes, H. N., & Meral, A. (2022). Anadolu’da Konifer Ağaçların Yayılış Alanlarındaki Değişimler. Coğrafya Dergisi, (44), 81-95.
  • Lawler, J.J., Shafer, SL, White, D., Kareiva, P., Maurer, E.P., Blaustein, A.R., & Bartlein, P.J. (2009). Projected Climate‐İnduced Faunal Change In The Western Hemisphere. Ecology, 90, 588-597. https://doi.org/10.1890/08-0823.1
  • Li, S., Wang, Z., Zhu, Z., Tao, Y., & Xiang, J. (2023). Predicting the potential suitable distribution area of Emeia pseudosauteri in Zhejiang Province based on the MaxEnt model. Scientific Report, 13, 1806 (2023). https://doi.org/10.1038/s41598-023-29009-w
  • Martín, M., Mattioni, C., Cherubini, M., Taurchini, D., & Villani, F. (2010). Genetic diversity in european chestnut populations by means of genomic and genic microsatellite markers. Tree Genetics & Genomes, 6(5), 735-744. https://doi.org/10.1007/s11295-010-0287-9
  • Merow, C., Smith, M., & Silander, 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. https://doi.org/10.1111/j.1600-0587.2013.07872.x
  • Metreveli, V., Kreft, H., Akobia, I., Janiashvili, Z., Nonashvili, Z., Dzadzamia, L., & Gavashelishvili, A. (2023). Potential distribution and suitable habitat for chestnut (Castanea sativa). Forests, 14(10), 2076. https://doi.org/10.3390/f14102076
  • Mollah, T., Shishir, S., & Rashid, M. (2021). Climate change impact on the distribution of tossa jute using maximum entropy and educational global climate modelling. The Journal of Agricultural Science, 159(7-8), 500-510. https://doi.org/10.1017/s0021859621000897
  • Neldner, V. (2014). The contribution of vegetation survey and mapping to herbarium collections and botanical knowledge: a case study from queensland. Cunninghamia, 14, 77-87. https://doi.org/10.7751/cunninghamia.2014.14.005
  • O'Neill, B. C., Tebaldi, C., Van Vuuren, D., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.F., Lowe, J., Meehl, J., Moss, R., Riahi, K., & Sanderson, B.M. (2016). The scenario model intercomparison project (ScenarioMIP) for CMIP6
  • Öztürk, M., Çetinkaya, G., & Aydın, S. (2017). Köppen-geiger i̇klim sınıflandırmasına göre türkiye’nin i̇klim tipleri. Journal of Geography, 35, 17-27. https://doi.org/10.26650/jgeog295515
  • Pearson, R.G., Raxworthy, C.J., Nakamura, M., & Peterson, A.T., (2007). Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 1, 102–117.
  • Petford, M., & Alexander, G. (2021). Potential range shifts and climatic refugia of rupicolous reptiles in a biodiversity hotspot of south africa. Environmental Conservation, 48(4), 264-273. https://doi.org/10.1017/s0376892921000370
  • Phillips, S.J., & Elith, J. (2010). POC Plots: Calibrating species distribution models with presence‐only data. Ecology, 91, 2476-2484
  • Phillips, S.J., & Dudik, M. (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161–175.
  • Phillips, S.J., Anderson, R.P., Dudík, M., Schapire, R.E., & Blair, M.E., (2017). Opening the black box: An open‐source release of MaxEnt. Ecography, 40, 887-893.
  • Portela, E., Ferreira‐Cardoso, J., Louzada, J., & Gomes‐Laranjo, J. (2014). Assessment of boron application in chestnuts: nut yield and quality. Journal of Plant Nutrition, 38(7), 973-987. https://doi.org/10.1080/01904167.2014.963116
  • Preston, B. (2006). Risk-based reanalysis of the effects of climate change on u.s. cold-water habitat. Climatic Change, 76(1-2), 91-119. https://doi.org/10.1007/s10584-005-9014-1
  • Sargıncı, M., & Beyazyüz, F. (2022). Effects of climate change on forests: climate-smart forestry perspective. Anadolu Orman Araştırmaları Dergisi, 8(2), 142-149. https://doi.org/10.53516/ajfr.1139640
  • Sandercock, A., Westbrook, J., Zhang, Q., & Holliday, J. (2024). A genome-guided strategy for climate resilience in american chestnut restoration populations. Proceedings of the National Academy of Sciences, 121, (30). https://doi.org/10.1073/pnas.2403505121
  • Sohn, J., Saha, S., & Bauhus, J. (2016). Potential of forest thinning to mitigate drought stress: a meta-analysis. Forest Ecology and Management, 380, 261-273. https://doi.org/10.1016/j.foreco.2016.07.046
  • Stanton‐Jones, W., & Alexander, G. (2024). Gazing into the future: the potential impact of climate change on habitat suitability of the sungazer (smaug giganteus). Austral Ecology, 49(8). https://doi.org/10.1111/aec.13577
  • Tabor, K., Hewson, J., Tien, H., González‐Roglich, M., Hole, D., & Williams, J. (2018). Tropical protected areas under increasing threats from climate change and deforestation. Land, 7(3), 90. https://doi.org/10.3390/land7030090
  • Tam, L., Thinkhamrop, K., Suttiprapa, S., & Suwannatrai, A. (2024). Potential distribution of malaria vectors in central vietnam: a maxent modeling approach. Veterinary World, 1514-1522. https://doi.org/10.14202/vetworld.2024.1514-1522
  • Thuiller, W., Lavorel, S., Araújo, M.B., Sykes, M.T., & Prentice, I.C.(2005). Climate Change Threats to Plant Diversity in Europe. Proceedings of the National Academy of Sciences, 102, 8245-8250. https://doi.org/10.1073/pnas.0409902102
  • Travis, J. (2003). Climate change and habitat destruction: a deadly anthropogenic cocktail. Proceedings of the Royal Society B Biological Sciences, 270(1514), 467-473. https://doi.org/10.1098/rspb.2002.2246 Uzun, A., & Örücü, Ö. (2023). Spartium junceum l. (katırtırnağı)’un küresel iklim değişimi etkisi altındaki potansiyel yayılış alanlarının modellenmesi. Ağaç ve Orman, 4(2), 73-81. https://doi.org/10.59751/agacorman.1383004
  • Wang, Y.S., Xie, B.Y., Wan, F.H., Xiao, Q.M., & Dai, L.Y., (2007). The potential geographic distribution of Radopholus similis in China. Agricultural Sciences in China, 6, 1444-1449.
  • Williams, J.N., Seo, C., Thorne, J., Nelson, J.K., Erwin, S., O’Brien, J.M., & Mark W. S. (2009). Using species distribution models to predict new occurrences for rare plants. Diversity and Distribution, 15(4), 565-576.
  • Wilson, J., Bekessy, S., Parris, K., Gordon, A., Heard, G., & Wintle, B. (2012). Impacts of climate change and urban development on the spotted marsh frog (limnodynastes tasmaniensis). Austral Ecology, 38(1), 11-22. https://doi.org/10.1111/j.1442-9993.2012.02365.x
  • Wisz, M.S, Pottier, J., Kissling, W.D., Pellissier, L., Lenoir, J., Damgaard, C.F., Dormann, C.F., Forchhammer, M.D., Grytnes,J.A., Guisan,A., Heikkinen, R.K., Høye,T.T., Kühn,I., Luoto, M., Maiorano, L., Nilsson, M.C., Normand, S., Öckinger, E., Schmidt, N.M., Termansen, M., Timmermann, A., Wardle, D.A., Aastrup, P. & 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), 5-30
  • WorldClim, (2020). Global Climate Data, Url: https://worldclim.org/data/index.html. WWF, (2015). World Wide Fund for Nature, 122 key Turkish botanical sites: Important plant areas in Turkey, 464 p.
  • Yan, X., Wang, S., Duan, Y., Han, J., Huang, D., & Zhou, J. (2021). Current and future distribution of the deciduous shrub hydrangea macrophylla in china estimated by maxent. Ecology and Evolution, 11(22), 16099-16112. https://doi.org/10.1002/ece3.8288
  • Yi, Y.J, Cheng, X, Yang, Z.F., & Zhang, S.H. (2016). Maxent Modeling for Predicting The Potential Distribution of Endangered Medicinal Plant (H. riparia Lour) in Yunnan, China. Ecological Engineering, 92, 260-269. https://doi.org/10.1016/j.ecoleng.2016.04.010
  • Zhang, K., Yao, L., Meng, J., & Tao, J., (2018). Maxent Modeling for Predicting The Potential Geographical Distribution of Two Peony Species Under Climate Change. Science of the Total Environment, 634, 1326-1334. https://doi.org/10.1016/j.scitotenv.2018.04.112
  • Zhang, L., Zhu, L., Li, Y., Zhu, W., & Chen, Y. (2022). Maxent modelling predicts a shift in suitable habitats of a subtropical evergreen tree (Cyclobalanopsis glauca (Thunberg) Oersted) under climate change scenarios in China. Forests, 13(1), 126.
Toplam 57 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ormancılık (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Selim Kalıpçıoğlu Bu kişi benim 0009-0004-7665-4921

Ahmet Salih Değermenci 0000-0002-3866-0878

Gönderilme Tarihi 12 Mart 2025
Kabul Tarihi 6 Mayıs 2025
Yayımlanma Tarihi 30 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 21 Sayı: 1

Kaynak Göster

APA Kalıpçıoğlu, S., & Değermenci, A. S. (2025). Modeling of Potential Distribution of Castanea sativa Mill. in Bolu Regional Directorate of Forestry Depending on Climate Variables. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi, 21(1), 1-33. https://doi.org/10.58816/duzceod.1656326

 DÜOD'da yayımlanan makaleler Creative Commons Atıf-GayriTicari 4.0 (CC BY-NC) kapsamında lisanslanmıştır.