Abstract
MaxEnt is a widely used species distribution model (SDM) that works on the principle of maximizing entropy. Despite large body of species habitat research carried out using MaxEnt, till now there is no standardized accepted modeling procedure for obtaining reproducible research outcomes. There is a need to understand the nuances in the selection of model parameters and resulting uncertainties in the outcomes. We studied the global sensitivity and uncertainty in habitat projections of the species Quercus leucotrichophora (Banj oak) over Uttarakhand State of India in the Central Himalayas by varying the model parameters—regularization factor (RF), background points (BP), and k-fold cross-validations (CVs). The Sobol variance decomposition sensitivity analysis on the model outcomes indicates that high probable habitats and potential habitats are sensitive to RF and BP, while prediction of less probable habitats is relatively sensitive to the number of k-fold CVs. Accuracy of the model is also highly correlated with the RF (r = −0.75, p < 0.001), which has influenced the extent of potential and high probable habitat projections. We conclude that SDMs should be supplemented with the information on sensitive model parameters and the uncertainty associated with the model parameters for improved objectivity and reproducibility of research findings related to species conservation planning.
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The authors sincerely thank the support extended by the Knowledge Resource Center of CSIR-National Environmental Engineering Research Institute in processing the manuscript having the reference number CSIR-NEERI/KRC/2021/JAN/CTMD-WTMD/1.
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Kadaverugu, R., Dhyani, S., Kadaverugu, A., Biniwale, R. (2023). Global Sensitivity and Uncertainty Analysis of MaxEnt Model: Implications in Species Habitat Projections. In: Dhyani, S., Adhikari, D., Dasgupta, R., Kadaverugu, R. (eds) Ecosystem and Species Habitat Modeling for Conservation and Restoration. Springer, Singapore. https://doi.org/10.1007/978-981-99-0131-9_7
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