Binturong ecology and conservation 221


FIG. 1 Binturong Arctictis binturong range and habitat suitability. (a) The species’ extant range according to IUCN and the locations of occurrence records, by data source: Global Biodiversity Information Facility (GBIF; GBIF, 2021), Small Carnivores Database (DB; Kramer- Schadt et al., 2016) and camera-trap records. (b) Forest cover within the species’ range as of 2015, with non- forested areas assumed to be unoccupied. (c) Projection from the top ensemble model visualizing the habitat suitability for binturongs, including areas outside forests and the IUCN-estimated range of the species. (d) The top ensemble model projection of the habitat suitability for binturongs within the remaining forest.


the data into hexagonal grid cells with an apothem of 1 km and defined these 2.6-km2 cells as sampling units. In most cases, each sampling unit contained only one camera asso- ciated with a unique value for each habitat covariate, but we averaged covariate values when multiple cameras fell within the same grid cell. We included a study as a fixed effect to maintain the spatial and temporal independence of our sam- pling units and to satisfy the assumption of population closure in the models. We extracted covariate values in the 1-km radius around camera traps (Supplementary Table 1)aswell as the distance to the nearest forest edge and the nearest river.


Diel activity patterns


We used time-stamped detections from our new camera- trapping sessions to investigate variability in the diel activity of binturongs across sampling units and between landscapes and to compare these activity patterns to those of humans. Wecomputed vonMises kernel density estimates in R using the densityPlot() function from the overlap package (Meredith & Ridout, 2020), with default smoothing parameters. We


Results


Regional habitat associations We gathered 215 geo-referenced occurrence records of binturongs for our species distribution models, including 80 from new and published camera-trapping surveys, 34 from the Global Biodiversity Information Facility database


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


excluded camera sessions with insufficient detections (n,4). To compare the distributions of capture times between landscapes, we computed coefficients of overlap using the R package overlap (Ridout & Linkie, 2009) and performed an Anderson–Darling k-sample test using the R package kSamples (Scholz & Zhu, 2019). We tested for significant differences in diel activity patterns between various habitat types (e.g. degraded vs non-degraded, using the median values of the disturbance variables) using a bootstrap proced- ure to simulate 1,000 distributions of activity pattern data to conduct a Wald test using the function compareAct() in the R package activity (Rowcliffe et al., 2014).


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