Towards a best‐practices guide for camera trapping: assessing differences among camera trap models and settings under field conditions

Abstract

Camera trapping is a widely used tool in wildlife research and conservation, and a plethora of makes and models of camera traps have emerged. However, insufficient attention has been paid to testing their performance, particularly under field conditions. In this study, we have comparatively tested five of the most frequently used makes of camera trap (Bushnell, KeepGuard, Ltl Acorn, Reconyx and Scoutguard) to identify the key factors behind their probability of detection (i.e. the probability that the camera successfully capturing a usable photograph of an animal passing through the field of view) and trigger speed (i.e. the time delay between the instant at which a motion is detected, and the time at which the picture is taken). We used 45 cameras (nine devices of each make) with infrared flash in a field experiment in which a continuous remote video was used in parallel (as a gold-standard) to discover the animals that entered the camera trap detection zone. The period (day/ night), distance between animals and cameras, model, species, deployment height and activation sensitivity were significantly related to the probability of detection. This probability was lower during the night than during the day. There was a greater probability of detecting a given species when the cameras were set at its shoulder height. The interaction between species and the distance between the animals and the cameras significantly affected the trigger speed, meaning that the closer the animals that entered the detection zone, the higher the trigger speed, with substantial differences among species. This was probably related by movement speed. In conclusion, this study shows differences in the performance of camera trap models and settings, signifying that caution is required when making direct comparisons among results obtained in different experiments, or when designing new ones. These results provide empirical guidelines for best practices in camera trapping and highlight the relevance of field experiments for testing the performance of the camera traps.

Random encounter model is a reliable method for estimating population density of multiple species using camera traps

“…Cusack et al., 2015; Garrote et al., 2021; Loonam et al., 2021; Massei et al., 2018; Rowcliffe et al., 2008). In this respect, some studies have evidenced that detection zone is determined by different factors such as environmental conditions and camera trap settings (Palencia, Vicente, et al., 2021; Rowcliffe et al., 2011). More relevant, other studies have described a positive relationship between species body mass and detection zone dimensions (Hofmeester et al., 2017).…”

“…On the other hand, underestimation of detection zone could lead to overestimation of density. Theoretically, detection zone size is estimated accurately using detection distances and applying distance sampling if detection probability is certain for at least some distance from the camera (Rowcliffe et al., 2011), which can be achieved by setting the cameras at shoulder height of the target species (Palencia, Vicente, et al., 2021). Thus, future studies in which appropriate procedures are applied to estimate all the parameters are necessary to confirm that, under these scenarios, accurate REM densities are estimated.…”

Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning

“…The suitability of CTs for various conservation studies is widely acknowledged [ 50 , 51 , 52 , 53 ]. However, we focus on CT features that are most important for optimum target detection and classification within CT images.…”

“…Additionally, new CT advancements, including Trailguard AI [ 48 ], have enhanced rechargeable batteries and can now reach up to 1.5 years on a single battery. More advanced CTs using network power tend to consume more battery, due to the WiFi SD cards drawing power, but advancements in rechargeable batteries and solar energy usage should see improvements with future models [ 50 ]. A wide FOV is essential for optimal target detection and classification [ 54 ].…”

Laying low: Rugged lowland rainforest preferred by feral cats in the Australian Wet Tropics

“…Due to equipment failure, four of the forest camera-traps at Mount Zero-Taravale Wildlife Sanctuary were replaced with Reconyx Hyperfire PC800 infrared cameras. While different camera-trap brands have different detection probabilities, the fact that feral cats were rarely detected off-roads in our study makes it unlikely this difference affected our results (Palencia et al, 2022). The infrared flashes on the road camera-traps were set to high to ensure as much of the road as possible would be visible in the pictures, while forest infrared flashes were set to low to avoid overexposing the image.…”