788 C. L. Johnson et al.
TABLE 2 Model selection results for covariate effects in determining occupancy probability of M. nigra across its native range in North Sulawesi. The top ranked models are shown as those with ΔAIC c,3 followed by the null and average model. All models depicted (ΔAICc ,2) were included in the averaged model.
Model1
ψ(Forest, PA) p(PA, Forest, HFI) ψ(Forest) p(PA, Forest, HFI)
NPar2 7
ψ(Forest + NDVI) p(PA + Forest + HFI) 7 ψ(Forest + Edge) p(PA + Forest + HFI)
6 7
ψ(Forest, PA, NDVI) p(PA, Forest, HFI) 8 Null model p(.)ψ(.)
Average model
1PA, protected area; HFI, Human Footprint Index. 2Number of parameters. 3Akaike information criterion corrected for small sample size. 4Relative difference in AICc values compared to top-ranked model. 5AICc model weight. 6Estimate of occupancy ± SE. 7Cumulative AICc model weight. 8Estimate of detection probability ± SE.
(at least until a camera needs to be serviced, in this case after c. 3 months, or K = 18) we defined the optimum monitoring effort for M. nigra as one that achieves the target standard error of 0.05 in our estimates whilst minimizing
s.Wedid this by resolving the Equation for s and assuming a duration of 3 months for the surveys. As smaller absolute declines in occupancy become more
detectable as the number of seasons of monitoring increases (Beaudrot et al., 2018), we calculated the number of seasons needed to detect a 10% annual occupancy decline if the op- timal monitoring effort (calculated by Equation 1) is chosen as the long-term monitoring protocol (survey effort = s × K). To do this, we simulated data for an annual 10% decline across a time series. We used the estimates from our best model to determine the initial input parameters for occu- pancy and detection and set colonization to zero, so as to simulate a decline. We then took the generated data and fitted it to a dynamic occupancy model without covariates (Mackenzie et al., 2003). These were then assessed to deter- mine the number of seasons required to detect an annual decline of 10% with 80% confidence. Our methods follow those detailed by Beaudrot et al. (2018) and all simulations and analyseswere done in R, using code available on Github (Ahumada, 2017).
Results
Detection and occupancy From 9,749 camera-trap days, M. nigra was detected on 473 separate days in 71 of 111 sites, yielding a naïve occupancy estimate of 0.64. These 71 camera locations confirmed the presence of the species in 12 spatially distinct forest
TABLE 3 Summary of conditional model averaged parameters based on the best-supported models identified in Table 2. Estimates of the β coefficient are reported for standardized covariates (scaled to mean = 0 and unit variance of 2). See Table 2 for the models with covariates for both ψ and p.
Parameter1 p(PA: inside)
p(Forest: inside) p(HFI)
ψ(Forest: inside) ψ(PA: inside) ψ(NDVI) ψ(Edge)
Estimate ± SE 0.518 ± 0.130
0.357 ± 0.188 −0.177 ± 0.068
1.149 ± 0.528 0.677 ± 0.452 0.200 ± 0.232 0.192 ± 0.295
z value 3.99
1.90 2.60 2.18 1.50 0.86 0.65
1PA, protected area; HFI, Human Footprint Index. *Significant.
fragments, distributed across North Sulawesi, indicating a greater distribution than previously known. Investigating covariate influence on detection probabil-
ity, whilst holding occupancy constant, revealed p as a posi- tive function of being inside both a protected area and for- est, and a negative function of the Human Footprint Index (Supplementary Table 2, Supplementary Fig. 1). This model gave a detection probability of 0.28 ± SE 0.025 and was sub- sequently used to explore which combination of covariates best explained M. nigra occupancy. From the different combinations of covariates, the most parsimonious occupancy model also included protected area and forest (Table 2). M. nigra occupancy was higher in forests (β coefficient 1.14 ± SE 0.5) and inside protected areas (0.69 ± SE 0.13). However, five models were ranked ,2 ΔAICc units and thus considered equally supported (Table 3, Fig. 2). We therefore estimated detectability and occupancy by averaging across these models, resulting in
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 P(.|z|)
,0.001* 0.057
0.009* 0.030* 0.134 0.390 0.516
AICc3 ΔAICc4 Wi
1666.5 0.00 1666.6 0.09 1668.0 1.54 1668.2 1.70 1668.3 1.75
1695.8 30.4
0.28 0.27 0.13 0.12 0.12
5/ψ6 ± SE CumulativeWi 0.28
0.54 0.67 0.79 0.91
0.655 ± 0.046 0.662 ± 0.081
7
ψ ± SE/p8 ± SE 0.663 ± 0.076
0.662 ± 0.062 0.661 ± 0.073 0.662 ± 0.072 0.661 ± 0.085
0.296 ± 0.013 0.280 ± 0.025
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