Binturong ecology and conservation 219


malayanus and Asian elephants Elephas maximus (Scotson et al., 2017; Ong et al., 2022). Binturongs are threatened by habitat loss and degrada-


tion, their use in traditional medicine and the pet trade, indiscriminate snaring and some limited direct exploita- tion for bushmeat (Lau et al., 2010;D’Cruze et al., 2014; Bourgeois et al., 2020). Consequently, the species is categor- ized as Vulnerable on the IUCN Red List, with an inferred population decline of 30% over three generations (c. 18 years; Wilcox et al., 2016). However, binturongs are cryptic and their habitat associations remain poorly understood, limiting inferences on their conservation status and their role in supporting seed dispersal in degraded habitats (Debruille et al., 2020). Binturongs have been observed in both primary and secondary forests including logging con- cessions, areas near agricultural plantations and at eleva- tions from sea level to .1,500 m (Semiadi et al., 2016; Wilcox et al., 2016). Prior studies have suggested they are tolerant to moderate logging but not open plantations or large clearings; however, most such work has suggested that the species prefers large expanses of intact lowland and hill forests (Grassman et al., 2005; Wilcox et al., 2016; Nakabayashi et al., 2017; Debruille et al., 2020). We compiled information collected through previously


published and new camera-trapping data to investigate bin- turong persistence and behaviour in degraded forests. We employed ensemble species distribution modelling, general- ized linear mixed modelling (GLMM) and occupancy mod- elling using camera-trapping datasets to assess their habitat associations. We predicted that binturongs would avoid de- graded habitats and edges at all spatial scales but that they would be positively associated with oil palm plantations be- cause of the availability of fruit and high tree cover in these areas (Luskin & Potts, 2011; Luskin et al., 2014; Dehaudt et al., 2022). We also sought to elucidate contrasting findings on binturong activity patterns, with reports ranging from them being strictly nocturnal to primarily diurnal, crepus- cular or cathemeral (Grassman et al., 2005; Wilcox et al., 2016; Debruille et al., 2020). We tested whether these varia- tions in diel activity are associated with different habitats or are driven by temporal avoidance of people, which has been noted for hunted species in other tropical forest regions (Frey et al., 2017; Cremonesi et al., 2021; Pardo et al., 2021; Negret et al., 2023).


Methods Data collection


We compiled occurrence data from four sources: (1)the Global Biodiversity Information Facility database (GBIF, 2021), a presence-only repository that includes museum records and citizen science reports (we removed in- complete or erroneous records), (2) the Borneo Small


Carnivores Database (Kramer-Schadt et al., 2016), (3)published reports of camera-trapping studies, and (4) new camera- trapping sessions (Supplementary Fig. 1a). For regional species distribution modelling we used presences from all data sources. For all camera trapping we defined a single ‘study’ as a continuous sampling effort using more than five cameras within a landscape (10–1,000 km2) and we refer to the area sampled as a ‘landscape’, which was usually a national park, production forest or collection of forest patches within a 100-km2 area (Supplementary Fig. 1). We use ‘regional scale’ when reporting results from presence-only species distribution modelling across the species’ range, ‘landscape scale’ when analysing variation amongst published camera-trapping studies and ‘local scale’ for hierarchical occupancy modelling of new camera-trapping sites.


Collating detections from published camera-trapping studies


We located previous camera-trapping studies using aWeb of Science (Clarivate, Philadelphia, USA) search with the following key terms: camera trap* AND (Asia* OR Thai* OR Malay* OR Indonesia* OR Singapore* OR Borneo* OR Cambodia* OR Vietnam* OR Lao* OR Myanmar* OR Burm* OR Sumatra*). We examined the references in these matches for additional studies.We included stud- ies written in English, conducted after the year 2000 and that reported sampling effort (number of cameras and de- ployment duration or number of total trap-nights) and number of independent detections (generally defined based on a 30–60 min interval between detections of the same species). To control for detectability, we only in- cluded studies that used unbaited cameras placed at ,0.4m height in forests, usually facing trails or other areas fre- quently used by wildlife. This is the standard deployment approach used widely in the region and is suitable for the majority of semi-terrestrial species .1 kg (Rovero & Ahumada, 2017). We recorded the data on locations (land- scape name and coordinates), detections, sampling effort and a variety of other covariates (Supplementary Table 1). We grouped multiple studies from the same landscape per year by summing detections and effort and averaging the covariate values.


New camera-trapping sessions


We conducted 20 new camera-trapping sessions in 10 land- scapes across Thailand, Peninsular Malaysia, Sumatra, Borneo and Singapore (Supplementary Table 2), with vary- ing levels of human disturbance and forest degradation. We deployed 18–112 passive infrared camera traps (various models from Bushnell, Overland Park, USA, and Reconyx, Holmen, USA) across areas of 10–813km2 in each landscape.


Oryx, 2024, 58(2), 218–227 © The Author(s), 2023. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605322001491


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