Assessing the camera trap methodologies used to estimate density of unmarked populations

Population density estimations are essential for wildlife management and conservation. Camera traps have become a promising cost-effective tool, for which several methods have been described to estimate population density when individuals are unrecognizable (i.e. unmarked populations). However, comparative tests of their applicability and performance are scarce. Here, we have compared three methods based on camera traps to estimate population density without individual recognition: Random Encounter Model (REM), Random Encounter and Staying Time (REST) and Distance Sampling with camera traps (CT-DS). Comparisons were carried out in terms of consistency with one another, precision and cost-effectiveness. We considered six natural populations with a wide range of densities, and three species with different behavioural traits (red deer Cervus elaphus, wild boar Sus scrofa and red fox Vulpes vulpes). In three of these populations, we obtained independent density estimates as a reference. The densities estimated ranged from 0.23 individuals/km(2) (fox) to 34.87 individuals/km(2) (red deer). We did not find significant differences in terms of density values estimated by the three methods in five out of six populations, but REM has a tendency to generate higher average density values than REST and CT-DS. Regarding the independents' densities, REM results were not significantly different in any population, and REST and CT-DS were significantly different in one population. The precision obtained was not significantly different between methods, with average coefficients of variation of 0.28 (REST), 0.36 (REM) and 0.42 (CT-DS). The REST method required the lowest human effort. Synthesis and applications. Our results show that all of the methods examined can work well, with each having particular strengths and weaknesses. Broadly, Random Encounter and Staying Time (REST) could be recommended in scenarios of high abundance, Distance Sampling with camera traps (CT-DS) in those of low abundance while Random Encounter Model (REM) can be recommended when camera trap performance is not optimal, as it can be applied with less risk of bias. This broadens the applicability of camera trapping for estimating densities of unmarked populations using information exclusively obtained from camera traps. This strengthens the case for scientifically based camera trapping as a cost-effective method to provide reference estimates for wildlife managers, including within multi-species monitoring programmes.