Endangered Phoebe bootanica tree populations may face severe habitat contraction and potential distributional shifts under projected climate change in Northeast India.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Endangered Phoebe bootanica tree populations may face severe habitat contraction and potential distributional shifts under projected climate change in Northeast India.
Συγγραφείς:	Devajit Y; Laboratory for Conservation and Genetic Improvement of Forest Trees (Lab-C&GIFT), Department of Forestry, School of Agricultural Sciences, Manipur University, Canchipur, Imphal, 795003, Manipur, India., Thangjam N; Laboratory for Conservation and Genetic Improvement of Forest Trees (Lab-C&GIFT), Department of Forestry, School of Agricultural Sciences, Manipur University, Canchipur, Imphal, 795003, Manipur, India., Vaishnav V; Laboratory for Conservation and Genetic Improvement of Forest Trees (Lab-C&GIFT), Department of Forestry, School of Agricultural Sciences, Manipur University, Canchipur, Imphal, 795003, Manipur, India. vivekvaishnaw@live.in.
Πηγή:	Environmental monitoring and assessment [Environ Monit Assess] 2026 Oct 10; Vol. 198 (11). Date of Electronic Publication: 2026 Oct 10.
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2959 (Electronic) Linking ISSN: 01676369 NLM ISO Abbreviation: Environ Monit Assess Subsets: MEDLINE
Imprint Name(s):	Publication: 1998- : Dordrecht : Springer Original Publication: Dordrecht, Holland ; Boston : D. Reidel Pub. Co., c1981-
Ιατρικοί όροι (MeSH):	Climate Change* , Ecosystem* , Endangered Species* , Trees* , Environmental Monitoring*, India ; Biodiversity
Περίληψη:	The present study applies an ensemble of small models (ESM) to assess current and future (2021-2040 and 2081-2100) habitat suitability for the populations of Phoebe bootanica (Lauraceae), an over-exploited timber species endemic to Northeast India's Indo-Myanmar Biodiversity Hotspot. A total of 122 field occurrence records were compiled, of which 22 spatially thinned records were retained for the final modelling, along with 12 environmental variables (including bioclimatic, topographic, edaphic, land cover). Six algorithms (artificial neural network (ANN), classification tree analysis (CTA), generalized additive model (GAM), generalized boosting model (GBM), multivariate adaptive regression splines (MARS), and random forests (RF)) generated bivariate models, which were ensembled via weighted averaging and evaluated using area under the curve (AUC) (0.905), true skill statistic (TSS) (0.745), and the Boyce index (0.795), indicating excellent performance. Elevation (1.56 relative contribution) emerged as the primary driver, favoring altitudes of 1500-2000 m, followed by the precipitation of the driest month (1.47), the warmest quarter (1.35), isothermality (1.33), and soil total nitrogen (1.19). Currently, 1168.58 km² is highly suitable (> 0.6 habitat suitability index (HSI)), mainly in mid-elevation forests. Under SSP1-2.6, highly suitable area contracts 99.54% by 2040 (to 5.41 km²), recovering slightly to 539.82 km² by 2100, with the modelled suitable habitat projected to shift from Manipur/Nagaland toward Meghalaya's West Khasi Hills. SSP5-8.5 projects a 92.92% loss by 2040 (82.75 km²), rebounding to 1440.03 km² by 2100, with a concentration in stable refugia, such as the West Khasi Hills. Moderately suitable habitats decline by 3-51% across the projected scenarios, while not-suitable areas expand by up to 34% in the near future (2021-2040) before contracting by up to 30% by 2081-2100 as habitat conditions partially recover. The ESM approach helped address the constraints of limited occurrence data for this rare species, underscoring Phoebe bootanica's vulnerability to warming/drying trends. Prioritizing mid-elevation nature reserves in regions of niche suitability and persistent refugia is recommended to enhance the species' resilience and sustainable management. (© 2026. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
Competing Interests:	Declarations. Ethical approval: The manuscript does not involve experimentation with humans or animals. All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Consent for publication: Not applicable. Employment: Not applicable. Competing interests: The authors declare no competing interests.
References:	Abedin, I., Mukherjee, T., Kim, A. R., Kim, H.-W., Kang, H.-E., & Kundu, S. (2024). Distribution model reveals rapid decline in habitat extent for endangered Hispid hare: Implications for wildlife management and conservation planning in future climate change scenarios. Biology, 13(3), 198. https://doi.org/10.3390/biology13030198. (PMID: 10.3390/biology13030198) Ahmadi, M., Farhadinia, M. S., Cushman, S. A., Hemami, M.-R., Nezami Balouchi, B., Jowkar, H., & Macdonald, D. W. (2020). Species and space: A combined gap analysis to guide management planning of conservation areas. Landscape Ecology, 35(7), 1505–1517. https://doi.org/10.1007/s10980-020-01033-5. (PMID: 10.1007/s10980-020-01033-5) Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B., & Anderson, R. P. (2015). spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography, 38(5), 541–545. https://doi.org/10.1111/ecog.01132. (PMID: 10.1111/ecog.01132) Allouche, O., Tsoar, A., & Kadmon, R. (2006). Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43(6), 1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x. (PMID: 10.1111/j.1365-2664.2006.01214.x) Araújo, M. B., & New, M. (2007). Ensemble forecasting of species distributions. Trends in Ecology & Evolution, 22(1), 42–47. https://doi.org/10.1016/j.tree.2006.09.010. (PMID: 10.1016/j.tree.2006.09.010) Barbet-Massin, M., Jiguet, F., Albert, C. H., & Thuiller, W. (2012). Selecting pseudo-absences for species distribution models: How, where and how many? Methods in Ecology and Evolution, 3(2), 327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x. (PMID: 10.1111/j.2041-210X.2011.00172.x) Bhuyan, A., Bawri, A., Saikia, B. P., Baidya, S., Hazarika, S., Thakur, B., Chetry, V., Deka, B. S., Bharali, P., Prakash, A., Sarma, K., & Devi, A. (2025a). Predicting habitat suitability of Illicium griffithii under climate change scenarios using an ensemble modeling approach. Scientific Reports, 15(1), Article 9691. https://doi.org/10.1038/s41598-025-92815-x. (PMID: 10.1038/s41598-025-92815-x) Bhuyan, A., Hazarika, S., Baidya, S., Sarma, K., Thakur, B., Prakash, A., & Devi, A. (2025b). Assessing current and future potential habitat of Vatica lanceifolia (Roxb.) Blume, a critically endangered tree species of Northeastern India. Theoretical and Applied Climatology, 156(2), Article 144. https://doi.org/10.1007/s00704-025-05369-3. (PMID: 10.1007/s00704-025-05369-3) Booth, T. H. (2022). Checking bioclimatic variables that combine temperature and precipitation data before their use in species distribution models. Austral Ecology, 47(7), 1506–1514. https://doi.org/10.1111/aec.13234. (PMID: 10.1111/aec.13234) Boyce, M. S., Vernier, P. R., Nielsen, S. E., & Schmiegelow, F. K. A. (2002). Evaluating resource selection functions. Ecological Modelling, 157(2), 281–300. https://doi.org/10.1016/S0304-3800(02)00200-4. (PMID: 10.1016/S0304-3800(02)00200-4) Bracho-Estevanez, C. A., Arenas-Castro, S., Gonzalez-Varo, J. P., & Gonzalez-Moreno, P. (2024). Spatially explicit metrics improve the evaluation of species distribution models facing sampling biases. Ecological Informatics, 84, Article 102916. https://doi.org/10.1016/j.ecoinf.2024.102916. (PMID: 10.1016/j.ecoinf.2024.102916) Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324. (PMID: 10.1023/A:1010933404324) Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. Chapman and Hall/CRC. https://doi.org/10.1201/9781315139470. (PMID: 10.1201/9781315139470) Breiner, F. T., Guisan, A., Bergamini, A., & Nobis, M. P. (2015). Overcoming limitations of modelling rare species by using ensembles of small models. Methods in Ecology and Evolution, 6(10), 1210–1218. https://doi.org/10.1111/2041-210X.12403. (PMID: 10.1111/2041-210X.12403) Breiner, F. T., Nobis, M. P., Bergamini, A., & Guisan, A. (2018). Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods in Ecology and Evolution, 9(4), 802–808. https://doi.org/10.1111/2041-210X.12957. (PMID: 10.1111/2041-210X.12957) Broennimann, O., Di Cola, V., & Guisan, A. (2025). Ecospat: Spatial ecology miscellaneous methods. R package version 4.1.2. https://github.com/ecospat/ecospat. Burnham, K. P., & Anderson, D. R. (Eds.). (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd edn). Springer. https://doi.org/10.1007/978-0-387-22456-5_6. (PMID: 10.1007/978-0-387-22456-5_6) Cerasoli, F., D’Alessandro, P., & Biondi, M. (2022). Worldclim 2.1 versus Worldclim 1.4: Climatic niche and grid resolution affect between‐version mismatches in habitat suitability models predictions across Europe. Ecol. Evol., 12(2), e8430. https://doi.org/10.1002/ece3.8430. Charbonnel, A., Lambert, P., Lassalle, G., Quinton, E., Guisan, A., Mas, L., Paquignon, G., Lecomte, M., & Acolas, M.-L. (2023). Developing species distribution models for critically endangered species using participatory data: The European sturgeon marine habitat suitability. Estuarine, Coastal and Shelf Science, 280, Article 108136. https://doi.org/10.1016/j.ecss.2022.108136. (PMID: 10.1016/j.ecss.2022.108136) Chefaoui, R. M., & Lobo, J. M. (2008). Assessing the effects of pseudo-absences on predictive distribution model performance. Ecol. Modell., 210(4), 478–486. https://doi.org/10.1016/j.ecolmodel.2007.08.010. (PMID: 10.1016/j.ecolmodel.2007.08.010) Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B., & Thomas, C. D. (2011). Rapid range shifts of species associated with high levels of climate warming. Science, 333(6045), 1024–1026. https://doi.org/10.1126/science.1206432. (PMID: 10.1126/science.1206432) Çorbacıoğlu, ŞK., & Aksel, G. (2023). Receiver operating characteristic curve analysis in diagnostic accuracy studies: A guide to interpreting the area under the curve value. Turkish Journal of Emergency Medicine, 23(4), 195–198. https://doi.org/10.4103/tjem.tjem_182_23. (PMID: 10.4103/tjem.tjem_182_23) Cutler, D. R., Edwards, T. C., Jr., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783–2792. https://doi.org/10.1890/07-0539.1. (PMID: 10.1890/07-0539.1) D’heygere, T., Goethals, P. L. M., & De Pauw, N. (2006). Genetic algorithms for optimisation of predictive ecosystems models based on decision trees and neural networks. Ecological Modelling, 195(1), 20–29. https://doi.org/10.1016/j.ecolmodel.2005.11.005. (PMID: 10.1016/j.ecolmodel.2005.11.005) de Kok, R. (2023). Phoebe bootanica . The IUCN Red List of Threatened Species 2023: e.T177756483A62020480. https://doi.org/10.2305/IUCN.UK.2023-1.RLTS.T177756483A62020480.en . De’ath, G. (2007). Boosted trees for ecological modeling and prediction. Ecology, 88(1), 243–251. https://doi.org/10.1890/0012-9658(2007)88%5B243:BTFEMA%5D2.0.CO;2. Devajit, Y., Aashish, L., Wairokpam, B., Vaishnav, V., & Puni, L. (2024). Ecology and silviculture of Phoebe hainesiana Brandis: A data deficient but commercially significant timber species of Indo-Myanmar biodiversity hotspot in India. Proceedings of the National Academy of Sciences, India - Section B: Biological Sciences, 94(1), 1–7. https://doi.org/10.1007/s40011-023-01454-7. (PMID: 10.1007/s40011-023-01454-7) Di Cola, V., Broennimann, O., Petitpierre, B., Breiner, F. T., D’Amen, M., Randin, C., Engler, R., Pottier, J., Pio, D., Dubuis, A., Pellissier, L., Mateo, R. G., Hordijk, W., Salamin, N., & Guisan, A. (2017). ecospat: An R package to support spatial analyses and modeling of species niches and distributions. Ecography, 40(6), 774–787. https://doi.org/10.1111/ecog.02671. (PMID: 10.1111/ecog.02671) Dikshit, K. R., & Dikshit, J. K. (2013). Weather and climate of North-East India. North-east India: Land, people and economy (pp. 149–173). Springer. https://doi.org/10.1007/978-94-007-7055-3_9. (PMID: 10.1007/978-94-007-7055-3_9) Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., Münkemüller, T., McClean, C., Osborne, P. E., Reineking, B., Schröder, B., Skidmore, A. K., Zurell, D., & Lautenbach, S. (2013). Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x. (PMID: 10.1111/j.1600-0587.2012.07348.x) Dowerah, E. (1995). Studies on impact of micro fungal contamination on Phoebe goalparensis hut bonsum seed viability during germination seedling establishment and growth [Guwahati]. https://shodhganga.inflibnet.ac.in/handle/10603/66236. Elith, J., Ferrier, S., Huettmann, F., & Leathwick, J. (2005). The evaluation strip: A new and robust method for plotting predicted responses from species distribution models. Ecological Modelling, 186(3), 280–289. https://doi.org/10.1016/j.ecolmodel.2004.12.007. (PMID: 10.1016/j.ecolmodel.2004.12.007) Elith, J., H. Graham*, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., Li, J., G. Lohmann, L., A. Loiselle, B., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., McC. M. Overton, J., Townsend Peterson, A., … E. Zimmermann, N. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x. Feeley, K. J., & Silman, M. R. (2011). Keep collecting: Accurate species distribution modelling requires more collections than previously thought. Diversity and Distributions, 17(6), 1132–1140. https://doi.org/10.1111/j.1472-4642.2011.00813.x. (PMID: 10.1111/j.1472-4642.2011.00813.x) Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086. (PMID: 10.1002/joc.5086) Friedman, J. H. (1991). Multivariate adaptive regression splines. Annals of Statistics, 19(1), 1–67. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451. (PMID: 10.1214/aos/1013203451) Fukuda, S., De Baets, B., Waegeman, W., Verwaeren, J., & Mouton, A. M. (2013). Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models. Environmental Modelling & Software, 47, 1–6. https://doi.org/10.1016/j.envsoft.2013.04.005. (PMID: 10.1016/j.envsoft.2013.04.005) Grimmett, L., Whitsed, R., & Horta, A. (2020). Presence-only species distribution models are sensitive to sample prevalence: Evaluating models using spatial prediction stability and accuracy metrics. Ecological Modelling, 431, Article 109194. https://doi.org/10.1016/j.ecolmodel.2020.109194. (PMID: 10.1016/j.ecolmodel.2020.109194) Guisan, A., Chevalier, M., Adde, A., Zarzo-Arias, A., Goicolea, T., Broennimann, O., Petitpierre, B., Scherrer, D., Rey, P.-L., Collart, F., Riva, F., Steen, B., & Mateo, R. G. (2025). Spatially nested species distribution models (N-SDM): An effective tool to overcome niche truncation for more robust inference and projections. Journal of Ecology, 113(7), 1588–1605. https://doi.org/10.1111/1365-2745.70063. (PMID: 10.1111/1365-2745.70063) Hernandez, P. A., Graham, C. H., Master, L. L., & Albert, D. L. (2006). The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29(5), 773–785. https://doi.org/10.1111/j.0906-7590.2006.04700.x. (PMID: 10.1111/j.0906-7590.2006.04700.x) Herrera, C., Jurado-Rivera, J. A., & Leza, M. (2023). Ensemble of small models as a tool for alien invasive species management planning: Evaluation of Vespa velutina (Hymenoptera: Vespidae) under Mediterranean island conditions. J. Pest Sci. (2004), 96(1), 359–371. https://doi.org/10.1007/s10340-022-01491-7. (PMID: 10.1007/s10340-022-01491-7) Hijmans, R. J. (2025). Terra: Spatial data analysis. R package version 1.8–60. https://CRAN.R-project.org/package=terra. Hijmans, R. J., Barbosa, M., Ghosh, A., & Mandel, A. (2024). Geodata: Access geographic data. R package version 0.6–2. https://rspatial.r-universe.dev/geodata/citation. Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C., & Guisan, A. (2006). Evaluating the ability of habitat suitability models to predict species presences. Ecol. Modell., Predicting Species Distributions, 199(2), 142–152. https://doi.org/10.1016/j.ecolmodel.2006.05.017. (PMID: 10.1016/j.ecolmodel.2006.05.017) Hysen, L., Nayeri, D., Cushman, S., & Wan, H. Y. (2022). Background sampling for multi-scale ensemble habitat selection modeling: Does the number of points matter? Ecological Informatics, 72, Article 101914. https://doi.org/10.1016/j.ecoinf.2022.101914. (PMID: 10.1016/j.ecoinf.2022.101914) IPCC. (2023). Summary for policymakers. In H. Lee & J. Romero (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 (pp. 1–34). IPCC. https://doi.org/10.59327/IPCC/AR6-9789291691647.001. (PMID: 10.59327/IPCC/AR6-9789291691647.001) Jamali, F., Amininasab, S. M., Taleshi, H., & Madadi, H. (2024). Using an ensemble modeling to predict the potential distribution and habitat suitability of caracal (Caracal caracal) in southwestern Iran. Global Ecology and Conservation, 52, Article e02968. https://doi.org/10.1016/j.gecco.2024.e02968. (PMID: 10.1016/j.gecco.2024.e02968) Jiménez-Valverde, A., & Lobo, J. M. (2006). The ghost of unbalanced species distribution data in geographical model predictions. Diversity and Distributions, 12(5), 521–524. https://doi.org/10.1111/j.1366-9516.2006.00267.x. (PMID: 10.1111/j.1366-9516.2006.00267.x) Lomba, A., Pellissier, L., Randin, C., Vicente, J., Moreira, F., Honrado, J., & Guisan, A. (2010). Overcoming the rare species modelling paradox: A novel hierarchical framework applied to an Iberian endemic plant. Biological Conservation, 143(11), 2647–2657. https://doi.org/10.1016/j.biocon.2010.07.007. (PMID: 10.1016/j.biocon.2010.07.007) Lütolf, M., Kienast, F., & Guisan, A. (2006). The ghost of past species occurrence: Improving species distribution models for presence-only data. Journal of Applied Ecology, 43(4), 802–815. https://doi.org/10.1111/j.1365-2664.2006.01191.x. (PMID: 10.1111/j.1365-2664.2006.01191.x) Manzoor, S. A., Griffiths, G., & Lukac, M. (2018). Species distribution model transferability and model grain size – Finer may not always be better. Scientific Reports, 8(1), Article 7168. https://doi.org/10.1038/s41598-018-25437-1. (PMID: 10.1038/s41598-018-25437-1) Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K., & Thuiller, W. (2009). Evaluation of consensus methods in predictive species distribution modelling. Diversity and Distributions, 15(1), 59–69. https://doi.org/10.1111/j.1472-4642.2008.00491.x. (PMID: 10.1111/j.1472-4642.2008.00491.x) Morato, T., González-Irusta, J.-M., Dominguez-Carrió, C., Wei, C.-L., Davies, A., Sweetman, A. K., Taranto, G. H., Beazley, L., García-Alegre, A., Grehan, A., Laffargue, P., Murillo, F. J., Sacau, M., Vaz, S., Kenchington, E., Arnaud-Haond, S., Callery, O., Chimienti, G., Cordes, E., … Carreiro-Silva, M. (2020). Climate-induced changes in the suitable habitat of cold-water corals and commercially important deep-sea fishes in the North Atlantic. Global Change Biology, 26(4), 2181–2202. https://doi.org/10.1111/gcb.14996. (PMID: 10.1111/gcb.14996) Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K., & Toxopeus, A. G. (2014). Where is positional uncertainty a problem for species distribution modelling? Ecography, 37(2), 191–203. https://doi.org/10.1111/j.1600-0587.2013.00205.x. (PMID: 10.1111/j.1600-0587.2013.00205.x) Noce, S., Caporaso, L., & Santini, M. (2020). A new global dataset of bioclimatic indicators. Scientific Data, 7(1), 398. https://doi.org/10.1038/s41597-020-00726-5. (PMID: 10.1038/s41597-020-00726-5) Paul, A. R., & Maity, R. (2023). Future projection of climate extremes across contiguous northeast India and Bangladesh. Scientific Reports, 13(1), Article 15616. https://doi.org/10.1038/s41598-023-42360-2. (PMID: 10.1038/s41598-023-42360-2) Peterson, A. T., Soberón, J., Pearson, R. G., Anderson, R. P., Martínez-Meyer, E., Nakamura, M., & Araújo, M. B. (2011). Ecological niches and geographic distributions. Princeton University Press. https://doi.org/10.1515/9781400840670. (PMID: 10.1515/9781400840670) Phillips, N. D., Reid, N., Thys, T., Harrod, C., Payne, N. L., Morgan, C. A., White, H. J., Porter, S., & Houghton, J. D. R. (2017). Applying species distribution modelling to a data poor, pelagic fish complex: The ocean sunfishes. Journal of Biogeography, 44(10), 2176–2187. https://doi.org/10.1111/jbi.13033. (PMID: 10.1111/jbi.13033) Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. The Soil, 7(1), 217–240. https://doi.org/10.5194/soil-7-217-2021. (PMID: 10.5194/soil-7-217-2021) Poggio, L., Simonetti, E., & Gimona, A. (2018). Enhancing the WorldClim data set for national and regional applications. Science of the Total Environment, 625, 1628–1643. https://doi.org/10.1016/j.scitotenv.2017.12.258. (PMID: 10.1016/j.scitotenv.2017.12.258) Porfirio, L. L., Harris, R. M. B., Lefroy, E. C., Hugh, S., Gould, S. F., Lee, G., Bindoff, N. L., & Mackey, B. (2014). Improving the use of species distribution models in conservation planning and management under climate change. PLoS ONE, 9(11), Article e113749. https://doi.org/10.1371/journal.pone.0113749. (PMID: 10.1371/journal.pone.0113749) Principe, S. C., Acosta, A. L., Andrade, J. E., & Lotufo, T. M. C. (2021). Predicted shifts in the distributions of Atlantic reef-building corals in the face of climate change. Front. Mar. Sci., 8. https://doi.org/10.3389/fmars.2021.673086. QGIS Development Team. (2025). QGIS 3.42.0 Geographic Information System API Documentation. QGIS Association. https://www.qgis.org. R Core Team. (2025). R version 4.4.3: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/. Rajula, H. S. R., Verlato, G., Manchia, M., Antonucci, N., & Fanos, V. (2020). Comparison of conventional statistical methods with machine learning in medicine: Diagnosis, drug development, and treatment. Medicina (Kaunas, Lithuania), 56(9), 455. https://doi.org/10.3390/medicina56090455. (PMID: 10.3390/medicina56090455) Ravindranath, N. H., Rao, S., Sharma, N., Nair, M., Gopalakrishnan, R., Rao, A. S., Malaviya, S., Tiwari, R., Sagadevan, A., Munsi, M., Krishna, N., & Bala, G. (2011). Climate change vulnerability profiles for North East India. Current Science, 101(3), 384–394. Rew, J., Cho, Y., & Hwang, E. (2021). A robust prediction model for species distribution using bagging ensembles with deep neural networks. Remote Sensing, 13(8), Article 1495. https://doi.org/10.3390/rs13081495. (PMID: 10.3390/rs13081495) Ripley, B. D. (2007). Pattern recognition and neural networks. Cambridge University Press. Rotich, B., Szegi, T., Gelsleichter, Y. A., Fuchs, M., Ocansey, C. M., Phenson, J. N., Abdulkadir, M., Kipkulei, H., Wawire, A., Mutuma, E., Mesele, S. A., Michéli, E., & Csorba, Á. (2025). Variation in soil organic carbon and total nitrogen stocks across elevation gradients and soil depths in the Mount Kenya East Forest. Land, 14(6), 1217. https://doi.org/10.3390/land14061217. (PMID: 10.3390/land14061217) Rubenstein, M. A., Weiskopf, S. R., Bertrand, R., Carter, S. L., Comte, L., Eaton, M. J., Johnson, C. G., Lenoir, J., Lynch, A. J., Miller, B. W., Morelli, T. L., Rodriguez, M. A., Terando, A., & Thompson, L. M. (2023). Climate change and the global redistribution of biodiversity: Substantial variation in empirical support for expected range shifts. Environ. Evid., 12(1), 7. https://doi.org/10.1186/s13750-023-00296-0. (PMID: 10.1186/s13750-023-00296-0) Sarma, B., & Tanti, B. (2022). Ecological niche modeling for reintroduction and conservation of Aristolochia cathcartii Hook.f. & Thomson (Aristolochiaceae), a threatened endemic plant in Assam, India. J. Threat. Taxa., 14(2), 20597–20605. https://doi.org/10.11609/jott.6999.14.2.20597-20605. Sarma, K., Roy, S. J., Kalita, B., Regon, P., Bawri, A., Sahariah, D., Saikia, A., & Tanti, B. (2024). Impact of climate change on potential distribution and altitudinal shift of critically endangered Amentotaxus assamica D.K. Ferguson in Arunachal Pradesh Himalaya, India. Theoretical and Applied Climatology, 155(1), 261–271. https://doi.org/10.1007/s00704-023-04628-5. (PMID: 10.1007/s00704-023-04628-5) Scherrer, D., Christe, P., & Guisan, A. (2019). Modelling bat distributions and diversity in a mountain landscape using focal predictors in ensemble of small models. Diversity and Distributions, 25(5), 770–782. https://doi.org/10.1111/ddi.12893. (PMID: 10.1111/ddi.12893) Seema, Arya, V., & Kumar, B. (2026). Climate-driven early flowering in Rhododendron arboreum: Field observations across altitudes in Bageshwar, Uttarakhand. Research Square. https://doi.org/10.21203/rs.3.rs-6657153/v2. Segurado, P., & Araújo, M. B. (2004). An evaluation of methods for modelling species distributions. Journal of Biogeography, 31(10), 1555–1568. https://doi.org/10.1111/j.1365-2699.2004.01076.x. (PMID: 10.1111/j.1365-2699.2004.01076.x) Shi, N., Naudiyal, N., Wang, J., Gaire, N. P., Wu, Y., Wei, Y., He, J., & Wang, C. (2022). Assessing the impact of climate change on potential distribution of Meconopsis punicea and its influence on ecosystem services supply in the southeastern margin of Qinghai-Tibet Plateau. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2021.830119. (PMID: 10.3389/fpls.2021.830119) Silva, L. D., de Azevedo, E. B., Reis, F. V., Elias, R. B., & Silva, L. (2019). Limitations of species distribution models based on available climate change data: A case study in the Azorean Forest. Forests, 10(7), Article 575. https://doi.org/10.3390/f10070575. (PMID: 10.3390/f10070575) Tang, C. Q., Matsui, T., Ohashi, H., Dong, Y.-F., Momohara, A., Herrando-Moraira, S., Qian, S., Yang, Y., Ohsawa, M., Luu, H. T., Grote, P. J., Krestov, P. V., Ben LePage, Werger, M., Robertson, K., Hobohm, C., Wang, C.-Y., Peng, M.-C., Chen, X., … López-Pujol, J. (2018). Identifying long-term stable refugia for relict plant species in East Asia. Nat. Commun., 9(1), 4488. https://doi.org/10.1038/s41467-018-06837-3. Tao, L.-D., Liu, Y.-H., Dao, Z.-L., Liu, D.-T., Yang, J., & Sun, W.-B. (2024). Guiding conservation strategies for China’s Corybas species through species distribution modeling. Glob. Ecol. Conserv., 53, Article e03008. https://doi.org/10.1016/j.gecco.2024.e03008. (PMID: 10.1016/j.gecco.2024.e03008) Tashi, S., Singh, B., Keitel, C., & Adams, M. (2016). Soil carbon and nitrogen stocks in forests along an altitudinal gradient in the eastern Himalayas and a meta-analysis of global data. Global Change Biology, 22(6), 2255–2268. https://doi.org/10.1111/gcb.13234. (PMID: 10.1111/gcb.13234) Thuiller, W. (2024). Ecological niche modelling. Current Biology, 34(6), R225–R229. https://doi.org/10.1016/j.cub.2024.02.018. (PMID: 10.1016/j.cub.2024.02.018) Thuiller, W., Georges, D., Gueguen, M., Engler, R., Breiner, F., Lafourcade, B., Patin, R., & Blancheteau, H. (2025). Biomod2: Ensemble platform for species distribution modeling. R package version 4.2–6–2. https://CRAN.R-project.org/package=biomod2. Wairokpam, B., Wagh, V. V., Singh, H. C., Maurya, A., Singh, L. A., & Rana, T. S. (2025). Integrating ecological niche modeling and natural regeneration assessment to identify conservation priorities for Garcinia pedunculata in India. Environmental Monitoring and Assessment, 197(4), 476. https://doi.org/10.1007/s10661-025-13925-7. (PMID: 10.1007/s10661-025-13925-7) Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert, D., Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P., Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J. G. L., Roberts, M. J., … Xavier, P. K. (2018). The Met Office global coupled model 3.0 and 3.1 (GC3.0 and GC3.1) configurations. Journal of Advances in Modeling Earth Systems, 10(2), 357–380. https://doi.org/10.1002/2017MS001115. (PMID: 10.1002/2017MS001115) Wisz, M. S., & Guisan, A. (2009). Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data. BMC Ecology, 9(1), Article 8. https://doi.org/10.1186/1472-6785-9-8. (PMID: 10.1186/1472-6785-9-8) Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, L., Tsendbazar, N.-E., … Arino, O. (2021). ESA WorldCover 10 m 2020 v100 . Zenodo. https://zenodo.org/records/5571936. Zhang, H.-T., & Wang, W.-T. (2023). Prediction of the potential distribution of the endangered species Meconopsis punicea Maxim under future climate change based on four species distribution models. Plants, 12(6), Article 1376. https://doi.org/10.3390/plants12061376. (PMID: 10.3390/plants12061376) Zhao, Q., Mi, Z.-Y., Lu, C., Zhang, X.-F., Chen, L.-J., Wang, S.-Q., Niu, J.-F., & Wang, Z.-Z. (2022). Predicting potential distribution of Ziziphus spinosa (Bunge) H.H. Hu ex F.H. Chen in China under climate change scenarios. Ecology and Evolution, 12(2), Article e8629. https://doi.org/10.1002/ece3.8629. (PMID: 10.1002/ece3.8629) Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010). A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution, 1(1), 3–14. https://doi.org/10.1111/j.2041-210X.2009.00001.x. (PMID: 10.1111/j.2041-210X.2009.00001.x)
Contributed Indexing:	Keywords: ecospat; Climate change; Conservation; SDM; Threatened timber species
Entry Date(s):	Date Created: 20261010 Date Completed: 20261011 Latest Revision: 20261010
Update Code:	20261011
DOI:	10.1007/s10661-026-16009-2
PMID:	42859287
Βάση Δεδομένων:	MEDLINE

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