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Predictive modeling and mapping of Malayan sun bear (Helarctos malayanus) distribution using maximum entropy


Nazeri, Mona and Jusoff, Kamaruzaman and Madani, Nima and Mahmud, Ahmad Rodzi and Bahman, Abdul Rani and Kumar, Lalit (2012) Predictive modeling and mapping of Malayan sun bear (Helarctos malayanus) distribution using maximum entropy. PLOS ONE, 7 (10). art. no. e48104. pp. 1-9. ISSN 1932-6203


One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum Entropy (MaxEnt) is a recently developed modeling method that can be successfully calibrated using a relatively small number of records. In this research, the MaxEnt model was applied to describe the distribution and identify the key factors shaping the potential distribution of the vulnerable Malayan Sun Bear (Helarctos malayanus) in one of the main remaining habitats in Peninsular Malaysia. MaxEnt results showed that even though Malaysian sun bear habitat is tied with tropical evergreen forests, it lives in a marginal threshold of bio-climatic variables. On the other hand, current protected area networks within Peninsular Malaysia do not cover most of the sun bears potential suitable habitats. Assuming that the predicted suitability map covers sun bears actual distribution, future climate change, forest degradation and illegal hunting could potentially severely affect the sun bear’s population.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
Faculty of Forestry
Faculty of Veterinary Medicine
DOI Number: https://doi.org/10.1371/journal.pone.0048104
Publisher: Public Library of Science
Keywords: Malaysian sun bear; MaxEnt model; Mapping; Maximum entropy
Depositing User: Nabilah Mustapa
Date Deposited: 05 May 2017 02:59
Last Modified: 05 May 2017 02:59
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi10.1371/journal.pone.0048104
URI: http://psasir.upm.edu.my/id/eprint/52124
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