Can Video Traps Reliably Detect Animals? Implications for the Density Estimation of Animals without Individual Recognition

Abstract

Abstract. Several statistical models have recently been developed to estimate animal density using camera trappings without individual animal recognition. However, most models assume that detection by camera traps of animals passing a specific area of the camera view is perfect. A recently developed REST model (Nakashima et al. 2018; Journal of Applied Ecology 55: 735–744) also depends on the trapping rates and staying times within a specific area. We tested whether commercial camera traps provided unbiased estimates of these parameters by conducting an experimental trial using a domestic dog in a city park in Japan. Additionally, we tested the effects of camera angle on the estimation of these parameters using the Bushnell camera. The Bushnell camera captured the dog 96% of the time, while the Ltl-Acorn camera missed about half of his passes. The staying time was underestimated by 4% using the Bushnell and overestimated by 25% using the Ltl-Acorn camera. The bias in density estimation was < 10% using the Bushnell camera. Camera angle did not affect detection probability, while the downward-angled cameras underestimated staying time due to the delayed trigger. We hope to share the results with manufacturers to make camera traps more suitable for animal density estimation.