786 C. L. Johnson et al.


FIG. 1 Camera-trap placements across North Sulawesi province in relation to protected and unprotected areas and the main habitat types. The line represents the boundary between Macaca nigra and Macaca nigrescens (Johnson et al., 2019).


spatial dataset of covariates that could potentially explain any heterogeneity in M. nigra occupancy. These were pres- ence of forest cover (Yes/No), elevation, slope, normalized difference vegetation index (NDVI), distance to roads and settlements, protected area (Yes/No), level of anthropogenic disturbance in the landscape (measured by the Human Footprint Index; Venter et al., 2016), and distance from the edge of continuous forest (Table 1). All covariates were extracted for the exact camera location, except for NDVI, slope and elevation. As a measure of landscape rough- ness, and therefore accessibility, we calculated the mean of slope and elevation across each site. We calculated NDVI using red and near infrared spectral bands in Landsat 8 imagery (Hansen et al., 2013), averaged across each site. All spatial datasets for covariates were calculated and de- rived in ArcGIS 10.2 (Esri, Redlands, USA).


Data analyses: occupancy modelling


We discretized our sampling data, following Rovero & Spitale (2016), into 24 consecutive 5-day sampling occasions (of 1, 5 and 10 days, a 5-day duration best facilitated model convergence). For each site and each occasion, a 1 indicated detection (a photograph) and 0 indicated non-detection (no photograph) of M. nigra. To assess the influence of ecological and anthropogenic


factors on occupancy, we fitted single-season occupancy models to the data using the package unmarked (Fiske &


Chandler, 2011)in R 3.5.1 (R Development Core Team, 2010). Single-season occupancy models estimate both the probability that a site is occupied (ψ) and the probability that the species is detected if it is present (p) using a max- imum likelihood approach (Mackenzie et al., 2002). All nu- merical covariates were first standardized into z-scores and assessed for collinearity using Pearson’s rank correlation. As no covariates were found to exhibit collinearity .0.6, no covariates were excluded in any of the candidate models (Supplementary Table 1). We next examined the effect of covariates (Table 1)on


our parameter of interest, ψ. However, we expected that a number of covariates that influence occupancy of M. nigra may also affect the abundance of M. nigra and therefore detectability (Royle & Nichols, 2003). Additionally, there are other factors that could also influence the detectabil- ity. We therefore began our model selection process by ex- plicitly accounting for p. Following recommendations of MacKenzie et al. (2006), we identified a suitable covariate structure for p whilst holding the covariate structure for ψ constant. Covariate structure for p was assessed using the Akaike information criterion corrected for small sample sizes (AICc).Wethen fixed the covariate structure for p with the covariate structure that had the lowest AICc (Burnham & Anderson, 2002) and proceeded to assess the role of our covariates on ψ, ranking the competing models according to their AICc values. If multiple models were within 2 ΔAICc points, they were considered equally supported (Burnham & Anderson, 2002) and estimates were instead derived by


Oryx, 2020, 54(6), 784–793 © The Author(s), 2020. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605319000851


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