Clustered and rotating designs as a strategy to obtain precise detection rates in camera trapping studies

Abstract

Abstract Camera traps have transformed the way we monitor wildlife and are now routinely used to address questions from a wide range of ecological and conservation aspects. Sampling design optimization and a better understanding of drivers determining the precision of detection rates (i.e. the number of detections per unit of effort) are important methodological issues. Little attention has been focused on the effect of placing more than one camera on each sampling point (hereafter, clustered design), and/or rotating (i.e. redeploying) the cameras to new placements during the sampling period. We explored the differences in the precision of detection rates between clustered vs. single camera designs when cameras remained in the same location during the study. Furthermore, the effect of keeping the placement of cameras fixed or rotating them (i.e. moving them to new locations during the sampling period), when a limited number of camera devices are available, was also evaluated. We used simulations and field data to test differences in detection rate precision for the different sampling designs. We simulated three different population distributions (random, trail‐based and aggregated) and three abundance scenarios. The simulations were validated with a field experiment focused on eight species with different behavioural traits, including artiodactyls, carnivores, lagomorphs, and birds. When a fixed number of sampling points were monitored simultaneously, clustered designs generally resulted in an increase in the precision of detection rates compared to single designs. The absolute reduction in the coefficient of variation by clustered designs was on average 0.07 units (min: 0.01, max: 0.15), which represents an average relative reduction in CV of 31% (min:6%, max:44%). An improvement in precision was also observed as a higher number of sampling points was used for all population distributions and sampling designs tested. When a fixed number of cameras were available, rotating the cameras to independent locations improved precision (an absolute reduction of 0.19 CV units) when monitoring aggregated populations, but not for random and trail‐based population distributions. Synthesis and applications: Our research provides a guideline for wildlife managers and researchers to improve the precision of camera trap detection rates and optimize resource allocation. In general, the study design should accommodate the behaviour of the target species (e.g. spatial aggregation and abundance), monitoring program logistic resources (both human and economic) and study area characteristics (e.g. accessibility and vandalism).