The influence of the delay-period setting on camera-trap data storage, wildlife detections and occupancy models

Abstract

Context The use of camera traps in ecological research has grown exponentially over the past decade, but questions remain about the effect of camera-trap settings on ecological inference. The delay-period setting controls the amount of time that a camera trap is idle between motion-activated triggers. Longer delay periods may potentially extend battery life, reduce data-storage requirements, and shorten data-analysis time. However, they might result in lost data (i.e. missed wildlife detections), which could bias ecological inference and compromise research objectives. Aims We aimed to examine the effect of the delay period on (1) the number of camera-trap triggers, (2) detection and site-occupancy probabilities for eight mammalian species that varied in size, movement rate and commonness and (3) parameter estimates of habitat-based covariates from the occupancy models for these species. Methods We deployed 104 camera traps for 4 months throughout an extensive urban park system in Cleveland, Ohio, USA, using a spatially random design. Using the resultant data, we simulated delay periods ranging from 10s to 60min. For each of these delay periods and for each of our eight focal species, we calculated the number of camera-trap triggers and the parameter estimates of hierarchical Bayesian occupancy models. Key results A simulated increase in the delay period from 10s to 10min decreased the number of triggers by 79.6%, and decreased detection probability and occupancy probability across all species by 1.6% and 4.4% respectively. Further increases in the delay period (i.e. from 10 to 60min) resulted in modest additional reductions in the number of triggers and detection and occupancy probabilities. Variation in the delay period had negligible effects on the qualitative interpretations of habitat-based occupancy models for all eight species. Conclusions Our results suggest that delay-period settings ranging from 5 to 10min can drastically reduce data-storage needs and analysis time without compromising inference resulting from occupancy modelling for a diversity of mammalian species. Implications Broadly, we provide guidance on designing camera-trap studies that optimally trade-off research effort and potential bias, thereby increasing the utility of camera traps as ecological research tools.

A review of factors to consider when using camera traps to study animal behavior to inform wildlife ecology and conservation

“…For example, CTs vary in the sensitivity of their passive infrared detection band, leading to marked differences in how animals are detected given their speed, position, time of day, and ambient temperature (Rovero et al, 2013;Rovero & Zimmermann, 2016). Variation between and within models when using different settings can result in differences in animal detectability due to variation in trigger speed, sensitivity, detection zone, and field of view (Apps & McNutt, 2018;Glen, Warburton, Cruz, & Coleman, 2014;Meek, Ballard, & Fleming, 2015), leading to missed detections (Wellington et al, 2014;Lepard et al, 2019). More importantly, for behavioral studies, such variation can affect detectability of behavioral states within a species, such as oversampling resting and undersampling fleeing, that could, subsequently lead to spurious conclusions about behavior.…”

Hunter-gatherers in context: Mammal community composition in a northern Tanzania landscape used by Hadza foragers and Datoga pastoralists

“…We set the cameras so that they would record a maximum of one picture per minute. This set-up ensures relatively long battery life, and reduces data storage requirements without compromising detection probability [56]. At each camera trap site, on the day of deployment, we set distance markers at 2, 5, and 10 meters away from the camera trap to allow for distance estimation of animal detections.…”

Hunter-Gatherers in context: Mammal community composition in a northern Tanzania landscape used by Hadza foragers and Datoga pastoralists

“…We set the cameras so that they would record a maximum of one picture per minute. This set-up ensures relatively long battery life, and reduces data storage requirements without compromising detection probability [ 62 ]. Photos were entered and stored in the open source software program Camelot [ 63 ].…”