Empirical evaluation of the spatial scale and detection process of camera trap surveys.

BACKGROUND: Camera traps present a valuable tool for monitoring animals but detect species imperfectly. Occupancy models are frequently used to address this, but it is unclear what spatial scale the data represent. Although individual cameras monitor animal activity within a small target window in front of the device, many practitioners use these data to infer animal presence over larger, vaguely-defined areas. Animal movement is generally presumed to link these scales, but fine-scale heterogeneity in animal space use could disrupt this relationship. METHODS: We deployed cameras at 10 m intervals across a 0.6 ha forest plot to create an unprecedentedly dense sensor array that allows us to compare animal detections at these two scales. Using time-stamped camera detections we reconstructed fine-scale movement paths of four mammal species and characterized (a) how well animal use of a single camera represented use of the surrounding plot, (b) how well cameras detected animals, and (c) how these processes affected overall detection probability, p. We used these observations to parameterize simulations that test the performance of occupancy models in realistic scenarios. RESULTS: We document two important aspects of animal movement and how it affects sampling with passive detectors. First, animal space use is heterogeneous at the camera-trap scale, and data from a single camera may poorly represent activity in its surroundings. Second, cameras frequently (14-71%) fail to record passing animals. Our simulations show how this heterogeneity can introduce unmodeled variation into detection probability, biasing occupancy estimates for species with low p. CONCLUSIONS: Occupancy or population estimates with camera traps could be improved by increasing camera reliability to reduce missed detections, adding covariates to model heterogeneity in p, or increasing the area sampled by each camera through different sampling designs or technologies.