220 A. Honda et al.


We placed cameras within a pre-mapped grid and spaced at least 500 m apart in large forests (.100 km2) and 100–500 m apart in smaller forest patches (e.g. Pulau Ubin, Singapore). We standardized methods between all deployments, attaching camera traps to trees 0.2–0.3 m above the ground along hiking or wildlife trails. We deployed cameras for c. 60–90 days in each landscape during December 2013–March 2019. To ensure that model outputs were comparable spatially across multiple land- scapes and to prevent spatial pseudo-replication, we resampled the capture data into hexagonal grid cells with an apothem of 1 km (Rayan & Linkie, 2020). In most cases, each sampling unit contained only one camera associated with a unique value for each habitat covariate, but we averaged covariate values when multiple cameras fell within the same grid cell. We considered captures to be notionally independent if they occurred at least 30 min apart. We produced detection history matrices based on a sampling occasion of 5 days and containing presence/ absence data (0 = species not detected; 1 = species detected; NA= inactive sampling unit or occasion). See Supplemen- tary Table 2 for the complete deployment details for all new camera-trapping sessions.


Regional habitat associations


We projected the habitat suitability for binturongs using an ensemble species distribution model considering eight algo- rithms (Generalized Linear Model, Generalized Boosted Regression, Multivariate Adaptive Regression Splines, Classification Tree Analysis, Random Forest, Maximum Entropy, Artificial Neural Networks and Support Vector Machine; Liu et al., 2019). We employed the SSDM package in R 4.0.4 (Schmitt et al., 2017; R Core Team, 2020) using presence-only data and spatial environmental variables with 1-km resolution. We selected a suite of anthropogenic and biogeographical variables that have been shown to in- fluence the detection, occupancy and distribution of other civet species in the region (Dehaudt et al., 2022; Dunn et al., 2022). We included only presences recorded after the year 2000 from the four data sources described previ- ously to reduce the effect of historical records in areas where the species or habitat could have been subsequently lost (Fig. 1a). To address sampling bias, we spatially thinned presences to reach a nearest-neighbour distance of at least 10 km amongst all points as binturongs have a home range of 1.54–6.90 km2 (Grassman et al., 2005; Chutipong et al., 2015; Nakabayashi & Ahmad, 2018). We removed covari- ates when Pearson’s correlation r.0.7, to reduce multi- collinearity (Supplementary Fig. 2). We generated pseudo- absences following Barbet-Massin et al. (2012), setting the number, repetition and geographical sampling space ac- cording to parameters proposed to improve the perform- ance of each algorithm. We combined the top models


from each algorithm after 10 repetitions (inclusion thresh- old of area under the curve .0.75) using the area under the curve-weighted mean. We assessed variable importance using the Pearson’s correlation between a full model and those reduced by each variable (calculated as a score of


1−r × 100;denoted as 1−r). Finally, we calculated the area of remaining forest and the percentage of protected area within the species’ extant range, based on the IUCN Red List range map (Wilcox et al., 2016)using theIUCNWorld Database on Protected Areas (Protected Planet, 2020).


Landscape-scale habitat associations


We explored variation in binturong detections in camera- trap studies using GLMMs with zero-inflated Poisson error distribution. Our response variable was counts and we included fixed effects to control for study effort (mea- sured in trap-nights) and random effects for landscape. We note that this approach does not account for variation in detection probability and there is unexplained variation because of differences in equipment and deploymentmeth- odology amongst studies. Both sources of measurement error could reduce our modelling power and our ability to detect true relationships (Sollmann et al., 2013). We selected a suite of biophysical and disturbance vari-


ables (Supplementary Table 1) that have been shown to in- fluence the detection, occupancy and distribution of other civet species in the region (Dehaudt et al., 2022;Dunn etal., 2022). The covariate values describe the area within a 20-km radius around the centroid of each landscape. We used this vast study area (1,256 km2) to account for some large camera-trapping grids and the possible low precision of cen- troid coordinates provided or inferred from the landscape descriptions in some studies. We tested each variable inde- pendently and with a non-linear term. High collinearity amongst covariates inhibited multivariate analyses and test- ing for interactions. We used the Akaike information criter- ion corrected for small sample size (AICc) to identify the most parsimonious model. All GLMMs were implemented in package GLMMadaptive in R (Rizopoulos, 2019).


Local-scale (within-landscape) habitat associations


We used single-season occupancy modelling to explore binturong occupancy within landscapes sampled using new camera-trapping sessions (MacKenzie et al., 2002). Detection probability is expected to be low when studying semi-arboreal binturongs using ground-based camera traps; we counteracted this with our immense trapping effort. Furthermore, ground-based camera traps have previously been shown to record the majority of arboreal mammals at the area of deployment (Rovero & Ahumada, 2017; Moore et al., 2020). To reduce the effects of spatial dependence between nearby camera traps, we resampled


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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