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PloS one · 2026

Citizen science-based biodiversity monitoring in oil palm plantations.

СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Maharani Nunik, Nardiyono N, Munthe Claudia Retina, Swayanuar Priya, Ni'Matullah Safwanah, Sueif Syafiie, Zaini Syahmi, Supriatna Jatna, Kusrini Mirza, Dennis Rona, van Balen Bas, van Strien Arco, Meijaard Erik

Аннотация

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.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты