224 A. Honda et al.


TABLE 2 Model performance for assessing local (within-site) variation in binturong occupancy amongst the landscapes assessed in this study. Nomultivariatemodels improved performance by.2 AICc points fromthe null/reducedmodel,which contained the sampling unit effort as a covariate in the detection formula and the trapping session as covariate affecting occupancy, which were included in all models.


Variable included Elevation


Distance to river Oil palm


Null/reduced model


Estimate ± SE 2.350 ± 1.120


0.798 ± 0.502 −0.796 ± 0.677


NA


P-value 0.035


0.112 0.240 NA


K


20 20 20 19


ΔAICc 0.0


4.9 5.7 6.2


Akaike weight 0.839


0.074 0.049 0.038


can retain their keystone role of dispersing fig seeds in degraded forests and edges, especially in mid-elevation habitats. Our study has three important caveats. Firstly, hunting


FIG. 3 Binturong diel activity patterns. (a) Variation amongst three different landscapes (Ulu Muda, Leuser and Danum) and amongst three surveys at Ulu Muda in Peninsular Malaysia (A and B refer to different locations within the Ulu Muda land- scape). Activity patterns differed amongst forests with (b) high vs low Human Footprint Index values and (c) at cameras that were within 1 km of a forest edge vs cameras at forest interior sites. We considered sites with a Human Footprint Index.3 to have a high human footprint. (Readers of the printed journal are referred to the online article for a colour version of this figure.)


pressure was estimated indirectly through variables such as human access and infrastructure (i.e. the Human Footprint Index, and distance to nearest river and nearest human settle- ment), so these results need to be interpreted with caution. Secondly,many assumptions are necessary in assessing detec- tion rates amongst a variety of studies within a single frame- work. The resulting noise in the data limited our ability to detect trends amongst landscapes, including in terms of how the detectability of semi-arboreal binturongs by terres- trial cameras could be influenced by forest degradation (Chutipong et al., 2014;Gregory et al., 2014;Haysom et al., 2021). For example, a previous camera-trapping study of orangutans in Borneo suggests that although these primates naturally sometimes travel on the ground, increased use of terrestrial substrates was associated with higher levels of logging (Ancrenaz et al., 2014). Other studies have noted hunting-induced behavioural changes amongst arboreal ani- mals(Whitworth et al., 2019), and future studies with access to more data should include this in the detection function of their occupancy models. Finally, although this is the largest synthesis of binturong data to date, we still had small sample sizes for some analyses and were unable to include additional covariates in the detection function of our local-scale occu- pancy model (we recorded only 54 independent detections over 58,608 trap-nights). Our findings are thus preliminary and we encourage researchers working in underrepresented areas (e.g. Cambodia, Lao People’s Democratic Republic and Viet Nam) to publish the summary data from their camera-trap studies to fill in these gaps. Because of the low number of detections, we were unable to assess regional trends, which have been shown to vary between Sundaic and non-Sundaic areas (Ke& Luskin, 2019).Most other sam- pling methods (e.g. line transects) present challenges in de- tecting nocturnal and/or cryptic species such as binturongs (Whitworth et al., 2016; Bowler et al., 2017). Binturong detect- ability may be improved by using arboreal camera traps, as suggested by 41 independent binturong detections recorded previously over 2,973 trap-nights (Debruille et al., 2020), which is a capture rate 10 timeshigherthan thatreported here, using terrestrial cameras.


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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