Macaca nigra
TABLE 1 Definition and predicted effect of covariates used to model variation in detectability and occupancy of Macaca nigra across sites. Covariate Definition
Type Forest NDVI
Protected Area
Roads Village Edge
Whether camera was located within closed canopy forest or bush/scrub habitat (source: KLHK, 2015)
Normalized Difference Vegetation Index, averaged across grid cell (Hansen et al., 2013)
Whether camera was located within a protected area boundary (KLHK, 2015)
Euclidean distance (km) from camera location to nearest paved road
Euclidean distance (km) from camera location to nearest settlement/village
Euclidean distance (km) from camera location to the forest edge (negative for cameras in non- forested habitat)
Elevation Mean elevation (m) across entire site (calculated from digital elevation model; NASA/METI/AIST/ Japan Spacesystems, 2009)
Slope
Human Footprint Index
Camera Model
Mean slope angle (degrees) across entire site
Expressed as a standardized index for (1) built environments, (2) crop land, (3) pasture land, (4) population density, (5) night-time lights, (6) railways, (7) roads (Venter et al., 2016) Make of camera trap
Expected effect (rationale)
Categorical Positive (as M. nigra is predominantly forest- dwelling, occupancy/detectability will be higher in forest than in bush/scrub)
Continuous Positive (high NDVI values should be associated with greater food availability)
Categorical Positive (protected areas should have lower levels of disturbance, with less hunting&forests more intact)
Continuous Positive (as distance fromthe nearest road increases, a site typically becomes less accessible & therefore less likely to be subject to disturbance)
Continuous Positive (sites in close proximity to a village may be more frequently visited by people & hunting pressure may be higher)
Continuous Positive (as distance from the forest edge increases, the site becomes more difficult to access, & ex- ploitation & disturbance may therefore be lower)
Continuous Positive (increasing elevation & steeper slope angles contribute to landscape roughness; rougher land- scapes will be harder for people to access & disturbance will be lower)
Continuous Positive (see elevation)
Continuous Negative (higher values indicate higher intensity of human influence)
Categorical Unknown p (as different cameras could potentially vary in how well they detect animals, this is a potential source of heterogeneity in detectability)
averaging coefficients from across these multiple models using the MuMIn package (Barton, 2012)in R. We used a parametric bootstrap to check the adequacy of our model fit using the χ2 approach on the most saturated model (Burnham & Anderson, 2002). Once the best (or averaged) occupancy model was identi-
fied, we used the corresponding occupancy probability to es- timate the proportion of the 796 sites likely to be occupied by M. nigra.We thenmapped predicted occupancy probability of M. nigra across its native range using the approach suggested by Rovero&Spitale (2016). This approach involved fitting our best model to a data frame containing a grid of covariate values at the scale of 1 × 1 km across the range of M. nigra. Maps of estimated occupancy were created in ArcMap 10.2 (Esri, Redlands, USA). Finally, we used predicted occupancy to identify regions
that may be important to the species based on the presence of characteristics associated with a high occupancy probabil- ity.We classifiedthese as spatially distinct areaswith.50km2 of continuous forest cover and predicted occupancy .0.7. We defined spatially distinct as being separated by .100 m of habitat not capable of supporting the species. Although separation of forest patches as we define it here does not ne- cessarily precludemovement of M. nigra individuals between
populations, it enables easy demarcation of distinct forest blocks that may be exposed to different threats.
Data analyses: optimal study design and power analysis
We evaluated the optimal effort for an occupancy-based camera-trap monitoring protocol for M. nigra by first estimating the number of sites (s) and occasions (K, 5-day sampling periods) required to achieve a desired level of pre- cision for the ψ and p estimators. These were calculated using estimates from the best model and the corresponding asymptotic variance of the occupancy indicator (Equation 1; Mackenzie & Royle, 2005):
TS = s×K =
K ×c var( ˆ
c) (1−c)+ p∗−K ×p×(1−p)K−1
(1−p∗) (1)
where TS is total effort, s is the number of sites, K is the number of occasions, ψ the probability of occupancy, p the probability of detection provided the site is occupied, and p* the probability that the species is detected at least once after K occasions. As the number of occasions can be increased free of additional cost in a camera-trap survey
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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