Abstract Monitoring wildlife populations is essential for conservation and management. Many statistical methods are now available for estimating population density based on camera trapping when animals are unmarked and not individually recognisable. Several of these methods assume that the detection probability is known, or rely on detection functions derived from distance sampling theory, which assume that animals are perfectly detectable when in the immediate vicinity of the camera trap. We test this last assumption on the wild boar ( Sus scrofa ) in the Mediterranean forest of Castelporziano (Roma, Italy), carrying out three experiments. (1) We evaluate how detection probability varies with distance (0–6 m) from camera traps using one experimental and three control cameras across 12 sites. (2) We assess the impact of imperfect detectability near camera traps on population estimates by conducting a survey with 63 camera traps deployed in a systematic-random design, in the same habitats and season as in experiment 1; we compared survey results when we substituted the detection function derived from experiment 1 with the standard detection functions used in the Random Encounter Model. (3) We compare the Bolyguard SG2060-K and Uovision UV595-HD (used in experiments 1 and 2) with the high-performance Browning Spec Ops HP5 to assess if the camera trap model may have affected our findings. We found that (1) the detection probability is less than 1 close to the camera trap; (2) the Random Encounter Model underestimated population density by 17%; and (3) there are no significant differences in performance among the three camera trap models when monitoring wild boars. These findings indicate that camera trap-based population models may yield biased estimates unless they account for detection probabilities below 1.
Focardi et al. (Fri,) studied this question.