Evaluating camera trap methods for monitoring population trends in ungulates: insights from simulation

Camera traps have been widely used in the last decade to monitor abundance of unmarked animal populations. Most estimation methods rely either on the number of times animals pass through the detection zones, like random encounter models (REM) or on the number of capture occasions in a time-lapse program when animals were seen on the pictures, like the instantaneous sampling approach (IS). Yet, the ability of these two popular method classes to both reliably detect population trends and estimate population size has rarely been evaluated. We filled this gap by simulating a setup of either 100 or 25 camera traps randomly distributed on a 2600-ha area (respectively ≈ 4 and 1 trap/km ² ), along with the movements of a fictional population of 300 roe deer ( Capreolus capreolus ). Simulations were informed by field data on habitat, habitat selection and activity patterns of GPS-monitored roe deer. Assuming perfect knowledge of key parameters (e.g., day range), constant sensor sensitivity and full correction for imperfect visibility within the detection zone, both IS and REM provided unbiased estimates of population size, but uncertainty remained substantial (CV: 15% at high trap density, up to 30% at low trap density). Moreover, despite idealized conditions and large sampling efforts, a simulated 20% population decline over 5 years went undetected by both approaches in 65-75% of simulations at high trap density and 80% at low trap density. Testing other sampling strategies to improve sensitivity either led to an unchanged population size estimation precision (stratified sampling) or to biased estimated trends (sampling only in high-quality habitats). Simulating animals with a 10 times larger home-range, like red deer ( Cervus elaphus ), led to miss the decline less frequently (5% – 40% at high trap density, 33% – 67% at low trap density). These results suggest that the key metric for camera trap use is the average number of different traps visited per animal, which in turn depends on trap density, home-range size and space use heterogeneity. We provide a R package allowing the reader to reproduce these simulations, and carry out their own.