222 A. Honda et al.
(GBIF, 2021), and 101 from the Borneo Carnivore Database (Kramer-Schadt et al., 2016; Fig. 1). We retained 149 records after thinning for spatial independence with a minimum nearest-neighbour distance of 10 km. Individual algorithm projections are shown in Supplementary Fig. 3, details on model performance are provided in Supplementary Table 3 and the top ensemble species distribution model results are shown in Fig. 1(e). The habitats with the high- est suitability for binturongs were in central Borneo, cen- tral and western Sumatra, much of Peninsular Malaysia, northern Myanmar, central Viet Nam, south-eastern Lao People’s Democratic Republic and western Cambodia. Habitat suitability was lowest in western Thailand, central Myanmar, central Cambodia and the northern and south- ern regions of Viet Nam. The variables containing the high- est amount of information when used in isolation in the top
ensemble model were annual rainfall (1−r = 51.77), fol- lowed by elevation (1−r = 9.73), forest cover (1−r = 9.11) and human density (1−r = 7.72). The top ensemble models include projections with moderate pairwise correlations to
single model projections (r = 0.492–0.792). The extant range of binturongs (as defined by the IUCN
Red List) within our study region was 2.651 million km2. Only 38% of this range was forested as of 2015 and 11% was protected (Supplementary Table 4).
Landscape-scale habitat associations
We used GLMMs with zero-inflated Poisson error distribu- tion to assess the variation in 181 independent binturong cap- tures from 91 studies in 41 landscapes. The three best predictors (based on AICc) were night lights (negative effect;
β =−10.330 ± SE 7.252;P = 0.155), forest cover (positive, non- linear effect; β = 1.296 ± SE 0.410;P = 0.002) and human foot-
print (negative effect; β =−0.853 ± SE 0.316,P= 0.007; Table 1). After removing surveys from Singapore where the
species was never detected, the best predictors were oil palm
(negative, non-linear effect; β =−0.700 ± SE 0.303,P = 0.020) and forest intactness (negative effect; β =−0.290 ± SE 0.172, P=0.097). There were no studies detecting binturongs in
landscapes that retained ,40% forest cover within the 1,256 km2 covering the sampling areas (Supplementary Fig. 4).
Local-scale habitat associations
The new camera-trapping effort included 10 landscapes, 20 sessions, 1,218 cameras and 58,608 trap-nights (Supplemen- tary Table 2). We obtained 54 independent captures of bin- turongs from nine landscapes (e.g. Plate 1); we did not detect the species in Singapore. Encounter rate (0.315 detections per 100 trap-nights) and naïve occupancy (detected by 10.2% of all cameras) were highest in Ulu Muda in Peninsular Malaysia. Details on the compiled published and new camera-trapping data by landscape are in Supplementary Tables 5 & 6. We used AICc model selection and determined that the
top variable in our hierarchical occupancy modelling was elevation, which showed a positive effect (β = 2.350 ± SE 1.117;P = 0.035; Fig. 2). Distance to rivers (β = 0.798 ± SE 0.502;P = 0.112) and concentration of oil palm plantations
(β =−0.796 ± SE 0.677;P = 0.240) also performed better than the null model (Table 2). Disturbance variables in- cluding degraded forest, human population, human foot- print, forest integrity, forest loss and distance to forest
TABLE 1 Model selection explaining the variation in camera-trap detections of binturongs Arctictis binturong amongst the landscapes assessed in this study (Fig. 2). The table shows univariate model selection criteria from the zero-inflated Poisson generalized linear mixed modelling assessing variation in independent detections of the binturong, including study effort and landscape as random effects. All covariates were averaged for the 20-km radius areas surrounding the study area, then centred and standardized so that effect sizes can be interpreted relative to each other. The sample sizes were 181 detections from 72 studies in 38 landscapes excluding Singapore, and 181 detections from 91 studies in 41 landscapes including Singapore.
Covariate Forest intactness Reduced (effort only) Estimate
Model selection excluding data from Singapore Oil palm
−0.70 −0.29
0.83
Model selection including data from Singapore Night lights Forest cover Forest cover
−10.10 −0.88
1.20
Forest integrity Human footprint Null
−1.36 −1.15
1.30 AICc1
276.9 279.9 280.0
280.2 285.3 287.3 287.6 288.7 296.4
LogLik2
−131.2 −134.0 −135.4
−134.2 −135.5 −137.8 −136.6 −137.2 −143.7
ΔAICc3
0.0 3.0 3.1
0.0 5.1 7.1 7.4 8.5
16.2
1AICc, Akaike information criterion corrected for small sample size (lower values indicate better model performance). 2LogLik, log-likelihood (higher values indicate better model fit). 3ΔAICc, difference of AICc to the best-performing model.
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 Akaike weight
0.69 0.16 0.15
0.87 0.07 0.03 0.02 0.01 0.00
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