Citizen science-based biodiversity monitoring in oil palm plantations.

Authors

Nunik Maharani, N Nardiyono, Claudia Retina Munthe, Priya Swayanuar, Safwanah Ni'Matullah, Syafiie Sueif, Syahmi Zaini, Jatna Supriatna, Mirza Kusrini, Rona Dennis, Bas van Balen, Arco van Strien, Erik Meijaard

Published in

PloS one. Volume 21. Issue 8. Pages e0318046. Epub Aug 25, 2026.

Abstract

Agricultural expansion is one of the main threats to global biodiversity. Yet, many wildlife species survive, and sometimes even thrive, in agricultural landscapes that retain patches of natural ecosystems. This paradox is evident in tropical oil palm (Elaeis guineensis, Jacq.) plantations that have both replaced tropical forest and other species-rich ecosystems, but can also function as wildlife habitat, especially if fragments of natural ecosystems are retained. There is an urgent need to understand how to manage and monitor wildlife in these landscapes. The lack of large, quantitative datasets on species occurrence, however, impedes learning and adaptive management. To address this gap, we piloted a citizen science-based biodiversity monitoring system in seven Indonesian oil palm estates, across different biogeographical regions, over a 5-year period. Between September 2019 and June 2024, contributions from 3,950 company employees resulted in 148,286 wildlife observations of 699 reliably identified faunal and 186 floral species. This is, to our knowledge, the first biodiversity monitoring system of its kind in the oil palm sector. Management support at all levels of the company, together with an employee reward system, proved vital for its implementation. We demonstrate how these observations can be used to estimate species occupancy and quantify wildlife use across heterogeneous plantation landscapes. Monitoring costs ranged from USD 0.14/ha/y to USD 0.32/ha/y, substantially lower than those of many conventional wildlife survey approaches. We conclude that citizen science can provide a robust, scalable and cost-effective approach in oil palm plantations, generating the quantitative data needed to support adaptive management.

PMID:
42640909
Bibliographic data and abstract were imported from PubMed on 26 Aug 2026.

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