Zaini, S. | bioRxiv Science

bioRxiv2025

Occupancy estimation of wild species in a palm oil plantation using unstructured data

In 2020-2024, plantation workers at a large oil palm plantation in West Kalimantan recorded sightings of wild species of several species groups. They did not use a standardized field method and produced no comprehensive reports but recorded only one or a few species per visit. Such unstructured data are generally viewed as challenging for statistical analysis. Here, we used occupancy models to estimate what proportion of spatial units (both non-natural and forest) a given species occupied annually in the plantation. We tested models with different covariates for species for which most data were available: 13 birds, 3 reptiles and 7 mammals, among which the iconic Orangutan (Pongo pygmaeus). For each species, the model which best fitted the data was selected. Two shortcomings in our data complicated the analyses. First, occupancy models require detection and non-detection records, but because no fixed species lists were used, there is no unique way to generate non-detections. We generated non-detection records in several ways and ran the models again to evaluate the consequences for occupancy estimates. Second, imbalances in sampling may occur because of a lack of sampling design to select study sites. Most concerning are sites surveyed only once in 2020-2024. We ran the models without those sites to examine whether results were different. Although the shortcomings mentioned turned out not to distort our results, the occupancy estimates were imprecise for many study species because of low detection rates, and extra efforts are needed to improve that.

bioinformatics↗

bioRxiv2025

A novel citizen science-based wildlife monitoring and management tool for oil palm plantations

Agricultural expansion is one of the greatest threats to global biodiversity. At the same time, many wildlife species survive or even thrive in agricultural landscapes that retain patches of natural ecosystems. This is especially true for tropical oil palm (Elaeis guineensis, Jacq.) plantations that have both replaced tropical forest and other species-rich ecosystems, but as perennial crops 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 fragmented oil palm landscapes. Still, the lack of large, quantitative datasets on species abundance impedes learning. We piloted a novel citizen science-based biodiversity monitoring system in Austindo Nusantara Jayas seven Indonesian oil palm estates, across different biogeographical regions, over a 5-year period. The company-wide monitoring system called PENDAKI, the Indonesian acronym for Care for Biodiversity, is the first of its kind in the palm oil industry. Here we demonstrate that such unstructured and opportunistic data collected mainly by lay people can result in valuable information on temporal and spatial changes in species occupancy. Between September 2019 and June 2024, PENDAKI has resulted in 148,286 wildlife observations, that included 699 reliably identified faunal and 186 floral species, with contributions from 3,950 employee contributors. We estimate species-specific occupancy rates using Bayesian occupancy modeling, ideally suited for opportunistic data where survey effort is unknown. We show that these occupancy data can reliably show temporal and spatial changes in the distribution of iconic wildlife such as orangutans (Pongo pygmaeus). We combined data from reliably identified species with at least 50 records to create the "Living Plantation Index", an estate-specific annual index of wildlife diversity based on occupancy estimates. We conclude that citizen science-based biodiversity monitoring works remarkably well in oil palm plantations because of the large number of people typically working there. In the process we discovered the emergence of co-benefits such as increasing environmental stewardship awareness across the workforce, raising the profile of the conservation department within the company. We also noted the benefits in terms of suitable data to meet regulatory and voluntary disclosure requirements.

scientific communication and education↗