Wild felids in Sarawak, Malaysian Borneo 253
and the smaller leopard cat Prionailurus bengalensis (2–2.5 kg) and flat-headed cat Prionailurus planiceps (1.5–2.5 kg; Wilting et al., 2007; Phillipps & Phillipps, 2016). The leopard cat is listed as Protected and the other species as Totally Protected under the Sarawak Wild Life Protection Ordinance of 1998. Offences involving a Pro- tected or Totally Protected species can result in impris- onment for 1 or 2 years and fines of MYR 10,000–25,000, respectively. The bay and flat-headed cats are categorized as Endangered on the IUCN Red List, the clouded leopard as Vulnerable, the marbled cat as Near Threatened and the leopard cat as Least Concern (IUCN, 2021). Lack of infor- mation on these cryptic species is hampering conservation planning in the changing landscapes of Sarawak. Bornean felids are distributed across a broad range
of forest types: lowland mixed dipterocarp, mangrove, peat swamp and montane forest (Wilting & Fickel, 2012; Jennings et al., 2013; Mohamed et al., 2013). Some also occur in human-modified habitats such as plantations, logged forests, orchards or urban areas (Gehring & Swihart, 2003; Meijaard & Sheil, 2008; Mohamed et al., 2013; Jennings et al., 2015). Bornean felids differ in their spa- tial and temporal habitat use (Hearn et al., 2018). Here we report extensive data on Bornean felids from camera-trap surveys over 17 years in 31 study areas across Sarawak to (1) describe spatio-temporal occurrence patterns, (2) update data on the distribution of felids in the state, and (3) deter- mine the occurrence probabilities of felids based on pro- tected area status, habitat type, and other anthropogenic and ecological variables.
Study area
Sarawak has a total area of c. 124,450 km2, with c. 79% covered by various types of forests (intact, degraded, mangrove and peat swamp forests); c. 21% of the state is agricultural land or plantation (Bryan et al., 2013; Gaveau et al., 2014). We conducted camera-trap surveys intermit- tently during May 2003–February 2020 in 31 study areas (Supplementary Table 1) across Sarawak. Study sites in- cluded 24 protected areas (21 national parks, two wildlife sanctuaries and one nature reserve) and seven unprotected areas (two highland areas, four production forests and one oil palm plantation–forest matrix; Fig. 1). Many of the pro- tected areas in the state (including some of those included in this study) are small, fragmented and surrounded by human settlements, agricultural plantations or logging concessions (Mathai et al., 2010; Hon & Shibata, 2013). Most of the pro- tected areas weremore easily accessible than some of the un- protected sites, which were remote and/or accessible only by private, guarded logging
roads.Themain ethnic groups in the study area in Sarawak are the Iban, Bidayuh, Kenyah, Kayan and Penan peoples,many of whomdepend on wildlife hunt- ing for subsistence. Study areas were dominated by mixed
dipterocarp forest, followed by peat swamp, heath (called ker- angas locally), mangrove,montane, beach and riverine forests (Hazebroek&bin AbangMorshidi, 2000;Gaveauetal., 2014).
Methods
Data collection We deployed a total of 861 camera stations across the 31 study areas, with usable data obtained from 845 units. Cameras were deployed singly and were operational con- tinuously. We standardized camera-trap deployment across all study sites, following Mohd-Azlan & Engkamat (2013) and Brodie et al. (2015b). We secured camera traps (RM45,Reconyx, Holmen,USA,and TrophyCam, Bushnell, Overland Park, USA) to trees, at a height of 30–70 cm above the ground for optimal detection of mammals across a range of body sizes. Cameras were faced away from direct sunlight to prevent false triggers. Camera traps were usually positioned on animal trails or pathways, or near natural salt licks, and spaced at least 1 km apart. They were set to take three burst shots when triggered, followed by a quiet period of 1–2 minutes before the next trigger, to reduce the likelihood of capturing the same individual repeatedly. To ensure inde- pendence of captures, we only retained records of the same species at a given station that were at least 1 hour apart.
Analysis of activity patterns
We structured, analysed and visualized species-specific activity patterns using the packages camtrapR (Niedballa et al., 2016) and overlap (Ridout & Linkie, 2009)in R 4.0.3 (R Core Team, 2020). We assumed the relative frequency of captures at different times of day to be related to the animals’ activity patterns (Kawanishi, 2002; Mohd-Azlan, 2006), and considered species diurnal when recorded dur- ing 6.00–18.00 and nocturnal when recorded during 18.00–6.00. We used a kernel density estimator to assess the area under the distribution curves of two sympatric species, where Δ is the actual coefficient of overlap:
D(f , g) = min { f (x), g(x)}dx 1
where f(x) and g(x) are the density distributions of detection times for each species. Subsequently, we used the ^ to compare activity overlap (Ridout & Linkie, 2009).
Δ1 estimator
Analysis of species occurrence We determined how the occurrence of species was related to four ecological and anthropogenic factors: elevation (m) and the distances (m) to the nearest road, river or longhouse as measured with a GPS and/or a GIS with satellite imagery.
Oryx, 2023, 57(2), 252–261 © The Author(s), 2022. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605321001484
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