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Kokulu Ardıç’ın (Juniperus foetidissima Willd.) Günümüz ve Gelecekteki Potansiyel Yayılışının Makine Öğrenmesi ile Modellenmesi

Yıl 2021, Sayı: 22, 1 - 12, 31.01.2021
https://doi.org/10.31590/ejosat.848961

Öz

Küresel ölçekte incelendiğinde, biyolojik çeşitlilik sağlama ve karbon tutma bakımından önemli ekosistemler arasındaki ormanların, biyotik, abiyotik ve antropojenik etkenler nedeniyle önemli değişimler içerisinde olduğu görülmektedir. Bu süreçte, iklimin belirleyici bir rolü olduğu kabul edilmiştir. Buna ek olarak, değişen iklim koşullarının, ormanların dağılımını, bileşimini, işlevini önemli oranda değiştireceği ve sonuç olarak biyolojik çeşitliliği tehdit edeceği öngörülmüştür. Bu çalışmanın amacı; Türkiye’de doğal yayılış gösteren ve orman ekosistemine ekolojik katkısı ile ön planda olan türler arasındaki kokulu ardıçın (Juniperus foetidissima Willd.), günümüz ve gelecekteki potansiyel coğrafi dağılımının tür varlığı verisi ve çevresel değişkenler (biyoiklimsel değişkenler ve yükseklik) ile modellenmesidir. Ayrıca, J. foetidissima Willd.’nin potansiyel yayılış alanlarının, günümüzde ve gelecekte alansal ve konumsal olarak nasıl değişiklik gösterdiği de değişim analizleri ile ortaya konmuştur. Bu kapsamda, tür dağılımı SSP2 4.5 ve SSP5 8.5 senaryolarına göre 2041-2060 ve 2081-2100 periyotlarını kapsayacak şekilde belirlenmiştir. Maksimum entropi modelinin kullanıldığı bu çalışmada, her bir tahmin değişkeninin göreceli katkısı Jackknife (çek-çıkar) testi ile belirlenmiş, çoklu doğrusallığı önlemek amacıyla Pearson korelasyonundan yararlanılmıştır. Bulgular, J. foetidissima Willd.’nin yayılışında en önemli değişkenlerin sırasıyla yükseklik, soğuk ayın en az sıcaklığı (BIO6) ve en nemli mevsimin ortalama sıcaklığı (BIO8) olduğunu göstermiştir. Tahmin doğruluğunu ifade eden ROC eğrisi, kullanılan modelin simülasyon gücünün çok yüksek olduğunu ifade etmektedir. Model sonuçlarına göre, J. foetidissima Willd.’nin potansiyel yayılışında önemli miktarda değişiklikler olacağı ve türün iklim değişikliğinden ciddi oranda olumsuz etkileneceği tahmin edilmiştir. Değişim analizi sonuçları, habitat uygunluğundaki alansal kaybın, kazanç miktarından daha fazla olduğunu göstermektedir. Sonuç olarak, bu çalışmada J. foetidissima Willd.’nin iklim değişikliklerine adaptasyon direncinin düşük olacağı ve dolayısıyla tür koruma çalışmalarına dâhil edilmesi gerektiği vurgulanmıştır. Bu çalışmanın bulguları, türün gelecekte sürdürülebilmesi ve korunmasına yönelik güçlü stratejilerin belirlenebilmesi amacıyla kullanılabilir.

Kaynakça

  • Abdelaal, M., Fois, M., Fenu, G., ve Bacchetta, G. (2019). Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép. In Egypt. Ecological Informatics, 50, 68-75.
  • Akkemik, Ü. (2018). Türkiye’nin Doğal-Egzotik Ağaç ve Çalıları. Ankara: T.C. Orman ve Su İşleri Bakanlığı Orman Genel Müdürlüğü.
  • Arslan, E. S., Akyol, A., Örücü, Ö. K., ve Sarıkaya, A. G. (2020). Distribution of rose hip (Rosa canina L.) under current and future climate conditions. Regional Environmental Change, 20(3), 107.
  • Barve, N., Barve, V., Jiménez-Valverde, A., Lira-Noriega, A., Maher, S. P., Peterson, A. T., … Villalobos, F. (2011). The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling, 222(11), 1810-1819.
  • Bouahmed, A., Vessella, F., Schirone, B., Krouchi, F., ve Derridj, A. (2019). Modeling Cedrus atlantica potential distribution in North Africa across time: New putative glacial refugia and future range shifts under climate change. Regional Environmental Change, 19(6), 1667-1682.
  • Chakraborty, A., Joshi, P. K., ve Sachdeva, K. (2016). Predicting distribution of major forest tree species to potential impacts of climate change in the central Himalayan region. Ecological Engineering, 97, 593-609.
  • Chase, J. M., ve Leibold, M. A. (2003). Ecological Niches: Linking Classical and Contemporary Approaches. University of Chicago Press.
  • Choudhary, J. S., Mali, S. S., Fand, B. B., ve Das, B. (2019). Predicting the invasion potential of indigenous restricted mango fruit borer, Citripestis eutraphera (Lepidoptera: Pyralidae) in India based on MaxEnt modelling. Current Science, 116(4), 636-642.
  • Damschen, E. I., Harrison, S., ve Grace, J. B. (2010). Climate change effects on an endemic-rich edaphic flora: Resurveying Robert H. Whittaker’s Siskiyou sites (Oregon, USA). Ecology, 91(12), 3609-3619.
  • Du, Z., He, Y., Wang, H., Wang, C., ve Duan, Y. (2021). Potential geographical distribution and habitat shift of the genus Ammopiptanthus in China under current and future climate change based on the MaxEnt model. Journal of Arid Environments, 184, 104328.
  • Dyderski, M. K., Paź, S., Frelich, L. E., ve Jagodziński, A. M. (2018). How much does climate change threaten European forest tree species distributions? Global Change Biology, 24(3), 1150-1163.
  • Eler, Ü., ve Çeti̇n, A. (2006). Ardıç tohumunun çimlendirilme olanakları. Süleyman Demirel Üniversitesi Orman Fakültesi Dergisi Seri: A, 0(1), 33-45.
  • Elith, J., Graham, C. H., A, R. P., Dudík, M., Ferrier, S., Guisan, A., … Zimmermann, N. E. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), 129-151.
  • Elith, J., Phillips, S. J., Hastie, T., Dudik, M., Chee, Y. E., ve Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43-57.
  • Feng, X., Park, D. S., Walker, C., Peterson, A. T., Merow, C., ve Papeş, M. (2019). A checklist for maximizing reproducibility of ecological niche models. Nature Ecology & Evolution, 3(10), 1382-1395.
  • Ferrarini, A., Alsafran, M. H. S. A., Dai, J., ve Alatalo, J. M. (2019). Improving niche projections of plant species under climate change: Silene acaulis on the British Isles as a case study. Climate Dynamics, 52(3), 1413-1423.
  • Friend, A. D., Lucht, W., Rademacher, T. T., Keribin, R., Betts, R., Cadule, P., … Woodward, F. I. (2014). Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proceedings of the National Academy of Sciences, 111(9), 3280-3285.
  • 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 (s. 151). Geneva, Switzerland.
  • Kaky, E., ve Gilbert, F. (2019). Assessment of the extinction risks of medicinal plants in Egypt under climate change by integrating species distribution models and IUCN Red List criteria. Journal of Arid Environments, 170, 103988.
  • Kariyawasam, C. S., Kumar, L., ve Ratnayake, S. S. (2019). Invasive Plants Distribution Modeling: A Tool for Tropical Biodiversity Conservation With Special Reference to Sri Lanka. Tropical Conservation Science, 12, 1940082919864269.
  • Köse, H. (2000). Doğal Bitki Örtüsünde Bulunan Bazı Odunsu Peyzaj Bitkilerinin Tohum Çimlendirme Yöntemleri Üzerinde Araştırmalar III. Juniperus oxycedrus L. (Katran ardıcı). 10(2), 88-100.
  • Köse, N., Okan, T., ve Akkemik, Ü. (2018). Understanding the Impacts of Illegal Logging in Turkey: A Case Study on Junipers in Eskişehir. Baltic Forestry, 24(1), 109-116.
  • Le Breton, T. D., Zimmer, H. C., Gallagher, R. V., Cox, M., Allen, S., ve Auld, T. D. (2019). Using IUCN criteria to perform rapid assessments of at-risk taxa. Biodiversity and Conservation, 28(4), 863-883.
  • Li, G., Huang, J., Guo, H., ve Du, S. (2020). Projecting species loss and turnover under climate change for 111 Chinese tree species. Forest Ecology and Management, 477, 118488.
  • Li, J., Fan, G., ve He, Y. (2020). Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. Science of the Total Environment, 698, 134141.
  • Mamıkoğlu, N. G., ve Ergüven, E. (2012). Türkiye’nin ağaçları ve çalıları. NTV yayınları.
  • Manish, K., Telwala, Y., Nautiyal, D. C., ve Pandit, M. K. (2016). Modelling the impacts of future climate change on plant communities in the Himalaya: A case study from Eastern Himalaya, India. Modeling Earth Systems and Environment, 2(2), 92.
  • Miller, J. (2010). Species Distribution Modeling. Geography Compass, 4(6), 490-509. https://doi.org/10.1111/j.1749-8198.2010.00351.x
  • Mod, H. K., Scherrer, D., Luoto, M., ve Guisan, A. (2016). What we use is not what we know: Environmental predictors in plant distribution models. Journal of Vegetation Science, 27(6), 1308-1322. https://doi.org/10.1111/jvs.12444
  • Moukrim, S., Lahssini, S., Rhazi, M., Alaoui, H. M., Benabou, A., Wahby, I., … Rhazi, L. (2019). Climate change impacts on potential distribution of multipurpose agro-forestry species: Argania spinosa (L.) Skeels as case study. Agroforestry Systems, 93(4), 1209-1219. https://doi.org/10.1007/s10457-018-0232-8
  • Muller, J. J., Nagel, L. M., ve Palik, B. J. (2019). Forest adaptation strategies aimed at climate change: Assessing the performance of future climate-adapted tree species in a northern Minnesota pine ecosystem. Forest Ecology and Management, 451, 117539. https://doi.org/10.1016/j.foreco.2019.117539
  • Naudiyal, N., Wang, J., Ning, W., Gaire, N. P., Peili, S., Yanqiang, W., … Ning, S. (2021). Potential distribution of Abies, Picea, and Juniperus species in the sub-alpine forest of Minjiang headwater region under current and future climate scenarios and its implications on ecosystem services supply. Ecological Indicators, 121, 107131. https://doi.org/10.1016/j.ecolind.2020.107131
  • Ncube, B., Shekede, M. D., Gwitira, I., ve Dube, T. (2020). Spatial modelling the effects of climate change on the distribution of Lantana camara in Southern Zimbabwe. Applied Geography, 117, 102172. https://doi.org/10.1016/j.apgeog.2020.102172
  • OGM, O. G. M. (2009). Ormanlarımızda Yayılış Gösteren Asli Ağaç Türleri.
  • Pearson, R. G., Raxworthy, C. J., Nakamura, M., ve Townsend Peterson, A. %J J. of biogeography. (2007). Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. 34(1), 102-117.
  • Peterson, A. T., Papes, M., ve Eaton, M. (2007). Transferability and model evaluation in ecological niche modeling: A comparison of GARP and Maxent. Ecography, 30(4), 550-560. https://doi.org/10.1111/j.2007.0906-7590.05102.x
  • Peterson, A. Townsend, Soberón, J., Pearson, R. G., Anderson, R. P., Martínez-Meyer, E., Nakamura, M., ve Araújo, M. B. (2011). Ecological Niches and Geographic Distributions (MPB-49). Princeton University Press.
  • Phillips, S. J., Anderson, R. P., ve Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3), 231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
  • Pinna, M. S., Cañadas, E. M., Fenu, G., ve Bacchetta, G. (2015). The European Juniperus habitat in the Sardinian coastal dunes: Implication for conservation. Estuarine, Coastal and Shelf Science, 164, 214-220. https://doi.org/10.1016/j.ecss.2015.07.032
  • Prevéy, J. S., Parker, L. E., ve Harrington, C. A. (2020). Projected impacts of climate change on the range and phenology of three culturally-important shrub species. PLOS ONE, 15(5), e0232537. https://doi.org/10.1371/journal.pone.0232537
  • Rana, S. K., Rana, H. K., Luo, D., ve Sun, H. (2020). Estimating climate-induced ‘Nowhere to go’ range shifts of the Himalayan Incarvillea Juss. Using multi-model median ensemble species distribution models. Ecological Indicators, 107127. https://doi.org/10.1016/j.ecolind.2020.107127
  • Regos, A., Gagne, L., Alcaraz-Segura, D., Honrado, J. P., ve Domínguez, J. (2019). Effects of species traits and environmental predictors on performance and transferability of ecological niche models. Scientific Reports, 9(1), 4221. https://doi.org/10.1038/s41598-019-40766-5
  • Remya, K., Ramachandran, A., ve Jayakumar, S. (2015). Predicting the current and future suitable habitat distribution of Myristica dactyloides Gaertn. Using MaxEnt model in the Eastern Ghats, India. Ecological Engineering, 82, 184-188. https://doi.org/10.1016/j.ecoleng.2015.04.053
  • Rupprecht, F., Oldeland, J., ve Finckh, M. (2011). Modelling potential distribution of the threatened tree species Juniperus oxycedrus: How to evaluate the predictions of different modelling approaches? Journal of Vegetation Science, 22(4), 647-659. https://doi.org/10.1111/j.1654-1103.2011.01269.x

Modeling of the current and future potential distribution of Stinking juniper (Juniperus foetidissima Willd.) with machine learning techniques

Yıl 2021, Sayı: 22, 1 - 12, 31.01.2021
https://doi.org/10.31590/ejosat.848961

Öz

Forest ecosystems, which are seen relevant in terms of providing biodiversity and capturing carbon, have been affected by significant changes due to biotic, abiotic, and anthropogenic factors over time. There is no doubt that climate has a decisive role in this process. In addition, previous studies claim that changing climatic conditions will significantly alter the distribution, composition, and function of forests and, consequently, threaten biodiversity. This study aims to model the present and future potential geographic distribution of the stinking juniper (Juniperus foetidissima Willd.), which has a natural and potential distribution in Turkey as an ecologically important species, with presence data and diverse environmental variables (bioclimatic predictors and altitude). In this context, species distributions were modelled to cover the 2041-2060 and 2081-2100 periods under the SSP2 4.5 and SSP5 8.5 scenarios. Using maximum entropy model, this study analyzed the relative contribution of each environmental predictor by Jackknife test. In order prevent high correlation and multicollinearity, the correlated factors were determined by Pearson correlation coefficient. The findings showed that the most significant variables in the distribution of J. foetidissima were elevation, minimum temperature of coldest month (BIO6), and mean temperature of wettest quarter (BIO8), respectively. Representing the prediction accuracy, ROC curve indicates that the predictive power of the model used in this study was great. According to the model results, it was predicted that there would be significant changes in the potential distribution of J. foetidissima Willd. and this species would be seriously adversely affected by climate change. The results of change analysis pointed out that the spatial loss in habitat suitability was greater than the amount of gain. As a result, this study suggested that J. foetidissima Willd.'s adaptation resistance to climate changes would be low, and therefore should be incorporated into species conservation. The findings of this study can be used to identify robust strategies for the future survival and conservation of the species.

Kaynakça

  • Abdelaal, M., Fois, M., Fenu, G., ve Bacchetta, G. (2019). Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép. In Egypt. Ecological Informatics, 50, 68-75.
  • Akkemik, Ü. (2018). Türkiye’nin Doğal-Egzotik Ağaç ve Çalıları. Ankara: T.C. Orman ve Su İşleri Bakanlığı Orman Genel Müdürlüğü.
  • Arslan, E. S., Akyol, A., Örücü, Ö. K., ve Sarıkaya, A. G. (2020). Distribution of rose hip (Rosa canina L.) under current and future climate conditions. Regional Environmental Change, 20(3), 107.
  • Barve, N., Barve, V., Jiménez-Valverde, A., Lira-Noriega, A., Maher, S. P., Peterson, A. T., … Villalobos, F. (2011). The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling, 222(11), 1810-1819.
  • Bouahmed, A., Vessella, F., Schirone, B., Krouchi, F., ve Derridj, A. (2019). Modeling Cedrus atlantica potential distribution in North Africa across time: New putative glacial refugia and future range shifts under climate change. Regional Environmental Change, 19(6), 1667-1682.
  • Chakraborty, A., Joshi, P. K., ve Sachdeva, K. (2016). Predicting distribution of major forest tree species to potential impacts of climate change in the central Himalayan region. Ecological Engineering, 97, 593-609.
  • Chase, J. M., ve Leibold, M. A. (2003). Ecological Niches: Linking Classical and Contemporary Approaches. University of Chicago Press.
  • Choudhary, J. S., Mali, S. S., Fand, B. B., ve Das, B. (2019). Predicting the invasion potential of indigenous restricted mango fruit borer, Citripestis eutraphera (Lepidoptera: Pyralidae) in India based on MaxEnt modelling. Current Science, 116(4), 636-642.
  • Damschen, E. I., Harrison, S., ve Grace, J. B. (2010). Climate change effects on an endemic-rich edaphic flora: Resurveying Robert H. Whittaker’s Siskiyou sites (Oregon, USA). Ecology, 91(12), 3609-3619.
  • Du, Z., He, Y., Wang, H., Wang, C., ve Duan, Y. (2021). Potential geographical distribution and habitat shift of the genus Ammopiptanthus in China under current and future climate change based on the MaxEnt model. Journal of Arid Environments, 184, 104328.
  • Dyderski, M. K., Paź, S., Frelich, L. E., ve Jagodziński, A. M. (2018). How much does climate change threaten European forest tree species distributions? Global Change Biology, 24(3), 1150-1163.
  • Eler, Ü., ve Çeti̇n, A. (2006). Ardıç tohumunun çimlendirilme olanakları. Süleyman Demirel Üniversitesi Orman Fakültesi Dergisi Seri: A, 0(1), 33-45.
  • Elith, J., Graham, C. H., A, R. P., Dudík, M., Ferrier, S., Guisan, A., … Zimmermann, N. E. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), 129-151.
  • Elith, J., Phillips, S. J., Hastie, T., Dudik, M., Chee, Y. E., ve Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43-57.
  • Feng, X., Park, D. S., Walker, C., Peterson, A. T., Merow, C., ve Papeş, M. (2019). A checklist for maximizing reproducibility of ecological niche models. Nature Ecology & Evolution, 3(10), 1382-1395.
  • Ferrarini, A., Alsafran, M. H. S. A., Dai, J., ve Alatalo, J. M. (2019). Improving niche projections of plant species under climate change: Silene acaulis on the British Isles as a case study. Climate Dynamics, 52(3), 1413-1423.
  • Friend, A. D., Lucht, W., Rademacher, T. T., Keribin, R., Betts, R., Cadule, P., … Woodward, F. I. (2014). Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proceedings of the National Academy of Sciences, 111(9), 3280-3285.
  • 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 (s. 151). Geneva, Switzerland.
  • Kaky, E., ve Gilbert, F. (2019). Assessment of the extinction risks of medicinal plants in Egypt under climate change by integrating species distribution models and IUCN Red List criteria. Journal of Arid Environments, 170, 103988.
  • Kariyawasam, C. S., Kumar, L., ve Ratnayake, S. S. (2019). Invasive Plants Distribution Modeling: A Tool for Tropical Biodiversity Conservation With Special Reference to Sri Lanka. Tropical Conservation Science, 12, 1940082919864269.
  • Köse, H. (2000). Doğal Bitki Örtüsünde Bulunan Bazı Odunsu Peyzaj Bitkilerinin Tohum Çimlendirme Yöntemleri Üzerinde Araştırmalar III. Juniperus oxycedrus L. (Katran ardıcı). 10(2), 88-100.
  • Köse, N., Okan, T., ve Akkemik, Ü. (2018). Understanding the Impacts of Illegal Logging in Turkey: A Case Study on Junipers in Eskişehir. Baltic Forestry, 24(1), 109-116.
  • Le Breton, T. D., Zimmer, H. C., Gallagher, R. V., Cox, M., Allen, S., ve Auld, T. D. (2019). Using IUCN criteria to perform rapid assessments of at-risk taxa. Biodiversity and Conservation, 28(4), 863-883.
  • Li, G., Huang, J., Guo, H., ve Du, S. (2020). Projecting species loss and turnover under climate change for 111 Chinese tree species. Forest Ecology and Management, 477, 118488.
  • Li, J., Fan, G., ve He, Y. (2020). Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. Science of the Total Environment, 698, 134141.
  • Mamıkoğlu, N. G., ve Ergüven, E. (2012). Türkiye’nin ağaçları ve çalıları. NTV yayınları.
  • Manish, K., Telwala, Y., Nautiyal, D. C., ve Pandit, M. K. (2016). Modelling the impacts of future climate change on plant communities in the Himalaya: A case study from Eastern Himalaya, India. Modeling Earth Systems and Environment, 2(2), 92.
  • Miller, J. (2010). Species Distribution Modeling. Geography Compass, 4(6), 490-509. https://doi.org/10.1111/j.1749-8198.2010.00351.x
  • Mod, H. K., Scherrer, D., Luoto, M., ve Guisan, A. (2016). What we use is not what we know: Environmental predictors in plant distribution models. Journal of Vegetation Science, 27(6), 1308-1322. https://doi.org/10.1111/jvs.12444
  • Moukrim, S., Lahssini, S., Rhazi, M., Alaoui, H. M., Benabou, A., Wahby, I., … Rhazi, L. (2019). Climate change impacts on potential distribution of multipurpose agro-forestry species: Argania spinosa (L.) Skeels as case study. Agroforestry Systems, 93(4), 1209-1219. https://doi.org/10.1007/s10457-018-0232-8
  • Muller, J. J., Nagel, L. M., ve Palik, B. J. (2019). Forest adaptation strategies aimed at climate change: Assessing the performance of future climate-adapted tree species in a northern Minnesota pine ecosystem. Forest Ecology and Management, 451, 117539. https://doi.org/10.1016/j.foreco.2019.117539
  • Naudiyal, N., Wang, J., Ning, W., Gaire, N. P., Peili, S., Yanqiang, W., … Ning, S. (2021). Potential distribution of Abies, Picea, and Juniperus species in the sub-alpine forest of Minjiang headwater region under current and future climate scenarios and its implications on ecosystem services supply. Ecological Indicators, 121, 107131. https://doi.org/10.1016/j.ecolind.2020.107131
  • Ncube, B., Shekede, M. D., Gwitira, I., ve Dube, T. (2020). Spatial modelling the effects of climate change on the distribution of Lantana camara in Southern Zimbabwe. Applied Geography, 117, 102172. https://doi.org/10.1016/j.apgeog.2020.102172
  • OGM, O. G. M. (2009). Ormanlarımızda Yayılış Gösteren Asli Ağaç Türleri.
  • Pearson, R. G., Raxworthy, C. J., Nakamura, M., ve Townsend Peterson, A. %J J. of biogeography. (2007). Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. 34(1), 102-117.
  • Peterson, A. T., Papes, M., ve Eaton, M. (2007). Transferability and model evaluation in ecological niche modeling: A comparison of GARP and Maxent. Ecography, 30(4), 550-560. https://doi.org/10.1111/j.2007.0906-7590.05102.x
  • Peterson, A. Townsend, Soberón, J., Pearson, R. G., Anderson, R. P., Martínez-Meyer, E., Nakamura, M., ve Araújo, M. B. (2011). Ecological Niches and Geographic Distributions (MPB-49). Princeton University Press.
  • Phillips, S. J., Anderson, R. P., ve Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3), 231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
  • Pinna, M. S., Cañadas, E. M., Fenu, G., ve Bacchetta, G. (2015). The European Juniperus habitat in the Sardinian coastal dunes: Implication for conservation. Estuarine, Coastal and Shelf Science, 164, 214-220. https://doi.org/10.1016/j.ecss.2015.07.032
  • Prevéy, J. S., Parker, L. E., ve Harrington, C. A. (2020). Projected impacts of climate change on the range and phenology of three culturally-important shrub species. PLOS ONE, 15(5), e0232537. https://doi.org/10.1371/journal.pone.0232537
  • Rana, S. K., Rana, H. K., Luo, D., ve Sun, H. (2020). Estimating climate-induced ‘Nowhere to go’ range shifts of the Himalayan Incarvillea Juss. Using multi-model median ensemble species distribution models. Ecological Indicators, 107127. https://doi.org/10.1016/j.ecolind.2020.107127
  • Regos, A., Gagne, L., Alcaraz-Segura, D., Honrado, J. P., ve Domínguez, J. (2019). Effects of species traits and environmental predictors on performance and transferability of ecological niche models. Scientific Reports, 9(1), 4221. https://doi.org/10.1038/s41598-019-40766-5
  • Remya, K., Ramachandran, A., ve Jayakumar, S. (2015). Predicting the current and future suitable habitat distribution of Myristica dactyloides Gaertn. Using MaxEnt model in the Eastern Ghats, India. Ecological Engineering, 82, 184-188. https://doi.org/10.1016/j.ecoleng.2015.04.053
  • Rupprecht, F., Oldeland, J., ve Finckh, M. (2011). Modelling potential distribution of the threatened tree species Juniperus oxycedrus: How to evaluate the predictions of different modelling approaches? Journal of Vegetation Science, 22(4), 647-659. https://doi.org/10.1111/j.1654-1103.2011.01269.x
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

E. Seda Arslan 0000-0003-1592-5180

Derya Gülçin 0000-0001-7118-0174

Ayşe Gül Sarıkaya 0000-0002-0641-4445

Zafer Ölmez 0000-0001-6199-6284

Süleyman Gülcü 0000-0002-1995-8580

İsmail Şen 0000-0002-9905-3537

Ömer K. Örücü 0000-0002-2162-7553

Yayımlanma Tarihi 31 Ocak 2021
Yayımlandığı Sayı Yıl 2021 Sayı: 22

Kaynak Göster

APA Arslan, E. S., Gülçin, D., Sarıkaya, A. G., Ölmez, Z., vd. (2021). Kokulu Ardıç’ın (Juniperus foetidissima Willd.) Günümüz ve Gelecekteki Potansiyel Yayılışının Makine Öğrenmesi ile Modellenmesi. Avrupa Bilim Ve Teknoloji Dergisi(22), 1-12. https://doi.org/10.31590/ejosat.848961