950 S. Tudge et al.
larger species in our study area are typically .1 km2,we interpreted occupancy as the proportion of sites used by a species (MacKenzie et al., 2006; Da Silva et al., 2018). We used the geoprocessing tools in QGIS 3.0.2 (QGIS Development Team, 2018) to derive indicators of human activity and habitat quality hypothesized to influence oc- cupancy (ψ) and/or detection probability (p, the probability that a species is detected given that it occupies a site). For each camera location, we calculated proximity to the Dja Biosphere Reserve, proximity to the road, village, river and nearest production forest, altitude, human population den- sity (Center for International Earth Science Information Network, 2018), slope and per cent of tree cover within a 200 m buffer (where land that was not forest was cropland) based on a land-cover map of Africa (ESA CCI Land Cover Team, 2017). Pearson correlation coefficients (r) were used to test for correlation between pairs of covariates; if r was .0.7 we excluded one of the pair and kept the covariate that most closely aligned with the aims of our study and our knowledge of the study area (Murphy et al., 2017). The final covariates included in our occupancy models were proximity to the road, Reserve and river, tree cover and slope (Supplementary Table 1). Slope was only used to model detection probability and the river to model oc- cupancy, and the other covariates were used for both. Human population density and distance to the Reserve were highly correlated (r = 0.9). Covariates were standard- ized before modelling (Reilly et al., 2017). We followed a systematic model selection approach, in
which all combinations of covariates weremodelled, follow- ing Murphy et al. (2017). Model selection was based on the second-order Akaike information criterion (AICc), includ- ing adjustment for overdispersion if the dispersion pa- rameter ĉ was .1 (QAICc; Burnham & Anderson, 2002; MacKenzie & Bailey, 2004). We excluded models that did not converge or produced estimates of p,0.15 and ψ = 1 (MacKenzie et al., 2002). The top-ranked models were those with Δ(Q)AICc,2 (MacKenzie et al., 2006). Where there was more than one top-ranked model, we conduct- ed model-averaging with the AICcmodavg package in R (Burnham & Anderson, 2002;Mazerolle, 2017). The weight of evidence for each covariate was calculated by summing theAIC weightsacrosscandidate models containing that covariate (Burnham & Anderson, 2002;Santos etal., 2016).
Results
Species richness and composition Three memory cards malfunctioned in the field and did not provide any data, and another camera only worked for 1 day and therefore was excluded. The remaining 26 cameras took
FIG. 2 Species accumulation curve for mammals identified by camera trapping during August–November 2017 in the community forest and surrounding habitat close to the Dja Biosphere Reserve, Cameroon (Fig. 1). The shaded ribbon indicates the 95% confidence interval.
16,050 photographs over 1,730 trap-days. The mammal species accumulation curve nearly reached a plateau, indi- cating that we detected most of the mammals within our survey area, except perhaps the most elusive or rare species, such as the leopard Panthera pardus (Bruce et al., 2018c; Fig. 2). Thirty-one species were detected: six birds, one monitor lizard and 24 mammals, including six mammals of conservation concern (Near Threatened bay duiker Cephalophus dorsalis, yellow-backed duiker Cephalophus silvicultor and putty-nosed monkey Cercopithecus nictitans, Endangered tree pangolin Phataginus tricuspis and chim- panzee, and Critically Endangered western lowland gorilla) and one galago that could not be identified to species. We identified four large mammals (.25 kg), 16 medium-sized mammals (1–25 kg) and three small mammals (,1 kg) to species. We did not include birds or reptiles in our occu- pancy analyses as they were not the targets of our survey. Gorillaswere recorded rarely (eight photographs), result-
ing in insufficient data for occupancy analysis. Putty-nosed monkey and moustached guenon Cercopithecus cephus were also recorded rarely, but as these primates are arboreal, occupancy analysis based on detections of these species from ground-level camera traps would not have been appro- priate. The species recorded the most were blue duiker Philantombamonticola, Peter’sduiker Cephalophus callipygus, agile mangabey Cercocebus agilis and brush-tailed porcupine Atherurus africanus (Table 1). We did not detect Critically Endangered forest elephants, large felids, Endangered giant ground pangolin Smutsia gigantea or the largest ungulates predicted to occur in the area (Supplementary Table 2). Thirty per cent of the medium-sized and large species we detected were listed in the IUCN Red List as Near Threatened or in one of the threatened categories, as were 50% (eight species) of those that we did not detect.
Oryx, 2022, 56(6), 947–955 © The Author(s), 2022. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605321000806
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