Oral Presentation- Symposium 12th International Mammalogical Congress

Estimating abundance by combining camera trap and GPS tracking data: A case study using the Tasmanian devil (#103)

Joanne Potts 1 , Chris Johnson 2 , Menna Jones 2 , Georgina Andersen 2
  1. The Analytical Edge Statistical Consulting, Blackmans Bay, Tasmania, Australia
  2. School of Biological Sciences, The University of Tasmania, Sandy Bay, Tasmania, Australia

Estimating animal abundance, N, or density, D, is difficult, especially when the target species is rare, cryptic or sparsely distributed. Historically, capture-mark-recapture (CMR) and distance-sampling (DS) methods have been used, but despite substantial research, shortfalls in theory and practicality still exist. Recent developments have focused on combining aspects of both CMR and DS theory (e.g., spatially explicit capture-recapture and trapping point transects). Here, we present a new abundance estimation technique that relies on camera trapping to obtain encounter rate information (i.e. individuals do not need to be uniquely identifiable) whilst concurrently tracking a subset of the population via GPS collars to obtain detectability information. Unlike CMR where the survey area isn't easily quantified due to edge effects, in this approach {N} can be easily converted into a readily interpretable estimate of D. A simulation study to assess bias in {N} under various home range shapes, and for differing sample size and distribution of camera traps in the survey region, was encouraging; relative bias was low (< 1%) with small population sizes (N = 20) and decreased to essentially zero with increasing N. We applied this method to a population of Tasmanian devils (Sarcophilus harrisii), in Tasmania, Australia. In 2013, 42 cameras were deployed (with 67 detection events) and 7 devils were collared. In 2014, 31 cameras were deployed (with 106 detection events) and 12 devils were collared. Surveys whereby GPS tracking and camera traps are deployed concurrently are increasingly common – so this method potentially has wide applicability in situations when individuals cannot be uniquely identified from camera traps.