Ash, E., Hallam, C., Chanteap, P., Kaszta, Ż., Macdonald, D. W., Rojanachinda, W., ... & Harihar, A. (2020). Estimating the density of a globally important tiger (Panthera tigris) population: Using simulations to evaluate survey design in Eastern Thailand. Biological Conservation, 241, 108349. https://doi.org/10.1016/j.biocon.2019.108349
Spatially explicit capture-recapture analysis is widely utilized for estimating densities of tigers (Panthera tigris). However, developing a robust study design capable of meeting assumptions and achieving study objectives may be difficult, particularly for low-density populations. Study design decisions for such fieldwork can be aided by simulations. Our goal was to (1) use simulations to investigate and evaluate study design and (2) generate a reliable estimate of density for a population of tigers in Thailand's Dong Phayayen-Khao Yai forest complex. Scenarios were parameterized with a range of potential density estimates ( and detection function parameters (g0 and σ). We designed a field-based trap configuration identified and compared it with simulated performance of a regular trapping array, over 45-day and 60-day sampling occasions. We compared simulation results (i.e. number of individuals [n], detections [ndet], relative standard error [RSE] and relative bias [RB]) and identified that the non-regular trapping array deployed for 60 sampling days would generate reliable density estimates. Our survey produced a density estimate of 0.63 ± SE0.22; (0.32–1.21) tigers per 100 km2, from a model incorporating variation in sex for g0 and σ, and a population estimate of 20 (14–33). Simulations closely reflected actual results under the null model. Our survey design performed reasonably well, generating a sufficient number of detections and individuals to estimate density of a globally important tiger population. Our results suggest simulations and use of non-regular trap arrays may be beneficial for areas with low species density in which generating sufficient detections is particularly challenging.