Quantifying demography of wildlife is vital to population monitoring; however, studies using physical capture methods can prove challenging. Camera traps have gained popularity as a density estimator tool in recent decades due to noninvasive data collection, reduced labor, cost efficiency, and large-scale monitoring capabilities. Many wildlife populations are comprised of individuals with no unique natural markers for individual identification, resulting in the need for unmarked abundance models. The recently developed Space-to-Event (STE) model offers a method for density estimation of unmarked populations using timelapse photography. STE relates detections of animals to camera sampling area (i.e., viewshed), resulting in density estimates that can be extrapolated to abundance over large areas. Consequently, this makes STE sensitive to estimates of viewshed area as small changes in viewshed could significantly affect density estimation. Using STE, we estimated density and recruitment of white-tailed deer (Odocoileus virginianus) in a densely forested landscape using measurements of viewshed per camera. We compared estimates of abundance derived from uniquely measured viewshed to estimates of abundance derived from an assumed viewshed area held constant across all cameras. When using a constant viewshed across all cameras, our point estimates of abundance shifted away from uniquely measured viewshed estimates in predictable ways, depending upon how much area was sampled. Additionally, we demonstrated the need for further exploration of animal availability at fine temporal scales by comparing estimates of density derived from sampling the full diel period to estimates derived from periods of peak activity (i.e., crepuscular periods). Finally, we extended the usefulness of the STE model by using densities of fawns and adult females to derive estimates of fawn recruitment.

We collected photo data over two years (2021 and 2022) from two study sites (the James Collins Wildlife Management Area and the Sans Bois Wildlife Management Area) in southeast Oklahoma. We deployed 100 cameras (50 per site) in June and retrieved them in December of each sampling year. We outfitted each camera with a 32-gigabyte SD card programmed to take timelapse images at 10-minute intervals as well as motion-triggered images in bursts of three with no time delay between triggers. Once deployed, cameras synchronously took timelapse images to create instantaneous sampling occasions at each 10-minute timestep (i.e., 09:00, 09:10, 09:20, etc.). We used random sampling in the form of generalized random tessellation stratified sampling (GRTS) to generate 50 camera deployments sites per study site. We calculated viewshed area per camera using the camera lens angle and measurements of maximum distance of detection. We used a viewshed board to divide the camera lens into 6 sectors and took a maximum distance measurement per sector. Area was calculated per sector and the summation of the 6 sectors resulted in a unique sampling area per camera.