Dead zones, obstructions, and missed captures: A novel approach for determining the Effective Capture Area for remote camera traps

Abstract

1. Camera traps (CTs) have become cemented as an important tool of wildlife research, yet, their utility is now extending beyond academics, as CTs can contribute to more inclusive place-based wildlife management. From advances in analytics and technology, CT-based density estimates of wildlife is an emerging field of research. Most CT-based density methods require an estimate of the size of the viewshed monitored by each CT, a parameter that may be highly variable and difficult to quantify. 2. Here, we developed and tested a standardized field and analytical method allowing us to predict the probability of photographic capture as it varies within CT viewshed. We investigated how capture probability changes due to environmental influences, i.e., vegetation structure, ambient temperature, speed of subject, time of day, in addition to internal factors from CTs themselves, i.e., sensitivity settings, number of photos taken, and CT brand. We then summarize these spatial capture probability kernels into a Realised Viewshed Size (RVS)--the capture-probability corrected size of a CTs viewshed 3. We found that RVS values are heavily influenced by location-specific environmental factors, i.e., vegetation structure, technological delays associated with CTs themselves, i.e., refractory period, and internal CT settings, i.e., sensitivity, number of photographs taken. We also found that the RVS values computed using our methodology are substantially smaller than reported values in the literature. 4. Imprecision surrounding CT viewshed areas can create propagating bias when implementing CT-based density estimators. Our method can change how practitioners consider photographs for use in CT density estimators thus increasing the reliability of CT-based density estimation, and contribute to more accessible wildlife management practices.