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15 March 2021 Leveraging local knowledge to estimate wildlife densities in bornean tropical rainforests
Abrar Ahmad, Demi Gary, Rodiansyah ., Sinta ., Srifitria ., Wahyu Putra, Novia Sagita, Sadtata Noor Adirahmanta, Adam E. Miller
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Abstract

Wildlife managers and researchers need to understand the status of the wildlife populations they are trying to conserve. Though various methods have been developed to monitor wildlife in their natural habitats, the complexity and accessibility of most techniques often limit their usability. Such techniques often take significant resources and time to deliver results, and methodological noncompliance may lower the reliability of results. Given this need to study wildlife populations reliably, quickly and within financial and human resource constraints faced by wildlife managers, we assessed the reliability and effort required to carry the pooling local expert opinion (PLEO) method as articulated by Hoeven et al. (2004). We did this by comparing density estimates of several wildlife species derived by following the PLEO method with those estimated using a conventional method along with results from the literature on wildlife monitoring studies from Bornean rainforests. Our analysis shows that the PLEO methodology provides an effective and complementary tool to estimate wildlife densities in tropical rainforests. We suggest that by incorporating the PLEO methodology into regular monitoring activity, conservation NGOs can create a platform that allows for participatory wildlife monitoring and create the platform to involve local communities in biodiversity conservation.

© 2021 The Authors.
Abrar Ahmad, Demi Gary, Rodiansyah ., Sinta ., Srifitria ., Wahyu Putra, Novia Sagita, Sadtata Noor Adirahmanta, and Adam E. Miller "Leveraging local knowledge to estimate wildlife densities in bornean tropical rainforests," Wildlife Biology 2021(1), wlb.00771-, (15 March 2021). https://doi.org/10.2981/wlb.00771
Accepted: 8 February 2021; Published: 15 March 2021
KEYWORDS
density estimates
Indigenous knowledge
local communities
PLEO
wildlife monitoring
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