A systematic appraisal of ecological niche modelling in the context of phytodiversity conservation
Author:
Funder
CSIR-UGC NET Fellowship
UGC-DAE Consortium for Scientific Research, University Grants Commission
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10668-024-04994-8.pdf
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2. Abolmaali, S.M.-R., Tarkesh, M., & Bashari, H. (2018). MaxEnt modeling for predicting suitable habitats and identifying the effects of climate change on a threatened species, Daphne mucronata, in central Iran. Ecological Informatics, 43, 116–123. https://doi.org/10.1016/j.ecoinf.2017.10.002
3. Al-Qaddi, N., Vessella, F., Stephan, J., Al-Eisawi, D., & Schirone, B. (2017). Current and future suitability areas of kermes oak (Quercus coccifera L.) in the Levant under climate change. Regional Environmental Change, 17(1), 143–156. https://doi.org/10.1007/s10113-016-0987-2
4. Ali, S., & Smith, K. A. (2006). On learning algorithm selection for classification. Applied Soft Computing, 6(2), 119–138. https://doi.org/10.1016/j.asoc.2004.12.002
5. Amatulli, G., Domisch, S., Tuanmu, M.-N., Parmentier, B., Ranipeta, A., Malczyk, J., & Jetz, W. (2018). A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific Data, 5(1), 1–15. https://doi.org/10.1038/sdata.2018.40
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