Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/117367
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Type: Journal article
Title: Population estimates of Bornean orang-utans using Bayesian analysis at the greater Batang Ai-Lanjak-Entimau landscape in Sarawak, Malaysia
Author: Pandong, J.
Gumal, M.
Alen, L.
Sidu, A.
Ng, S.
Koh, L.P.
Citation: Scientific Reports, 2018; 8(1):1-11
Publisher: Nature Publishing Group
Issue Date: 2018
ISSN: 2045-2322
2045-2322
Statement of
Responsibility: 
Joshua Pandong, Melvin Gumal, Lukmann Alen, Ailyn Sidu, Sylvia Ng and Lian Pin Koh
Abstract: The integration of Bayesian analysis into existing great ape survey methods could be used to generate precise and reliable population estimates of Bornean orang-utans. We used the Marked Nest Count (MNC) method to count new orang-utan nests at seven previously undocumented study sites in Sarawak, Malaysia. Our survey teams marked new nests on the first survey and revisited the plots on two more occasions; after about 21 and 42 days respectively. We used the N-mixture models to integrate suitability, abundance and detection models which account for zero inflation and imperfect detection for the analysis. The result was a combined estimate of 355 orang-utans with the 95% highest density interval (HDI) of 135 to 602 individuals. We visually inspected the posterior distributions of our parameters and compared precisions between study sites. We subsequently assess the strength or reliability of the generated estimates using identifiability tests. Only three out of the seven estimates had <35% overlap to indicate strong reliability. We discussed the limitations and advantages of our study design, and made recommendations to improve the sampling scheme. Over the course of this research, two of the study sites were gazetted as extensions to the Lanjak-Entimau Wildlife Sanctuary for orang-utan conservation.
Keywords: Animals
Animals, Wild
Pongo pygmaeus
Bayes Theorem
Ecosystem
Population Density
Malaysia
Rights: © The Author(s) 2018. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
DOI: 10.1038/s41598-018-33872-3
Grant ID: ARC
Published version: https://www.nature.com/articles/s41598-018-33872-3
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