Bawean warty pig and Bawean deer 893


November and a wet season during December–May (Hamada et al., 2002). Central to the island is an extinct vol- cano, which reaches 655maltitude. All remaining forests on Bawean are encompassed within the 46.6 km2 Bawean Island Nature Reserve and Wildlife Sanctuary and are under varying levels of protection (Semiadi & Meijaard, 2013; Fig. 1a).


Methods


Data collection Camera trapping took place during November 2014– December 2015. We divided the forested area into 3,636 grid cells of 100 × 100 m, and selected cells randomly as locations for 23 camera traps (Long Range IR/E2, Cuddeback, De Pere, USA), which weremoved periodically between locations. During August–October, because of in- creasing concern about the low numbers of deer recorded up to then, we set five camera traps preferentially for deer, based on local advice. We assumed these locations were still random for warty pigs. To prevent spatial autocorrelation, camera traps were spaced at least 150mapart. In small forest fragments, because of difficult terrain, we selected the first random point and then placed subsequent camera traps every 300 m in a randomly generated direction. We set camera traps to record 30-s videos, with a 1-minute interval until the next trigger. At each camera trap location we recorded the latitude


and longitude (with a global positioning system), altitude and habitat variables. These included the major habitat type (rice cultivation: n = 18;gardencultivation:n = 7; shrubland and degraded forest: n = 7;teakplantation: n= 10;tall forest: n = 76; community forest: n = 8;for defi- nitions see Rademaker et al., 2016), mean tree diameter at breast height, mean tree height in a 10 × 10 mplot around the camera-trap location, and tree density using the T-square method (Rode et al., 2013)withtwo sample points (for details see Rademaker et al., 2016). We calcu- lated the mean litter depth in four 1 × 1 msubplotsin the corners of the plot. Distances to the nearest village and protected area border (approximately coinciding with the forest border) were calculated in ArcGIS v. 10.0 (ESRI, Redlands, USA). Minimum and maximum daily temperatures and precipitation data were obtained from Sangkapura meteorological station. Lunar illumination was retrieved from MOONDV v. 1 (Thomas, 1998). For the random encounter model, we also recorded the angle of detection, radial distance at detection and distance trav- elled for the first three video records of Bawean warty pigs on each camera (Rowcliffe et al., 2011).We assumed these parameter values to be valid throughout the trapping period.


Data analysis


We report relative abundance for all species encountered, with the relative abundance index (RAI) defined as all inde- pendent detections of a given species summed for all camera traps over all days, multiplied by 100, and divided by the total number of camera-trap nights (O’Brien et al., 2003). Weemployed a 1-hour interval to define independent events (Rovero et al., 2013; Rademaker et al., 2016). As datawere not normally distributed and could not be transformed, we used non-parametric Kruskal–Wallis tests and Mann–Whitney U post-hoc tests to check for differences in relative abun- dance index between seasons and locations. For the influ- ence of habitat variables on encounter rates, we report single-season occupancy modelling outcomes using the Rv. 3.4.3 (R Core Team, 2013) package unmarked (Fiske & Chandler, 2011). We removed records that were incomplete because of missing covariate values. Models assessed the effects of all previously described site-level covariates on probability of occupancy, and the effects of the observation- level covariates moonlight, temperature and rainfall on probability of detection. Sample sizes for Bawean deer were too small to run these analyses. To estimate population density using random encounter


modelling we defined the camera-trap rate as the total num- ber of independent captures divided by the total number of camera-trap days. We calculated absolute population num- bers for four trapping periods of 44–83 days. The random encounter model assumes that the population is closed; i.e. there is a fixed number of individuals in the area throughout the estimation period (Rowcliffe et al., 2008). As there is no immigration or emigration on Bawean, poten- tial bias comes only from births and deaths, the effects of which we assume to be negligible for the chosen trapping periods. We could not standardize period length because of logistical challenges. We also report the combination of all four periods because single periods violate the assump- tion of at least 50 camera-trap locations (Rovero et al., 2013). The random encounter model used here is described in detail in Rademaker et al. (2016) and follows Rowcliffe et al. (2008, 2011, 2014). Day range was defined as the mean speed of movement of the animals in front of the camera in m/s, extrapolated to km/day and multiplied by the proportion of time spent active. The result of the ran- dom encounter modelling was multiplied by the mean group size of the relevant species, as a group is the entity of detection. However, the result must be considered to be a minimum estimate, as a video maymiss some individuals in a social group. We extrapolated the estimated densities per km2 to the total protected area on Bawean to provide absolute population estimates. To account for the effects of uncertainty of parameter variables on uncertainty of the density function, we used a propagation of error ap- proach, calculated in Rademaker et al. (2016). As only adult


Oryx, 2020, 54(6), 892–900 © 2019 Fauna & Flora International doi:10.1017/S0030605318000996


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