DOI: 10.5061/dryad.76hdr7t4g
title: Accounting for viewshed area and animal availability when estimating density and recruitment of unmarked white-tailed deer

description: 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.
description: 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.
description: # Accounting for viewshed area and animal availability when estimating
 density and recruitment of unmarked white-tailed deer Our dataset is
 comprised of timelapse and motion-detection photo data collected from two
 study sites across two sampling seasons (June-December of 2021 and 2022).
 Our dataset is divided into four R Data Serialization (RDS) files. Files
 are named by site (JC for James Collins Wildlife Management Area or SB for
 Sans Bois Wildlife Management Area) and year collected. ## Description of
 the data and file structure We designed our study to comply with the
 assumptions of the Space-to-Event (STE) unmarked abundance model. Each row
 corresponds to a single image. Columns necessary to run STE include
 "cam", "count", "area", and
 "datetime". The column corresponding to the taxa of interest
 should be renamed to "count". File structure is identical for
 the four RDS files. * datetime: Date and time of when the image was taken.
 * comments: Location for any necessary comments regarding an image. -
 opstate: Operating state of the camera (e.g., malfunctioning, burned,
 tilted...etc.). - cam: Camera ID. - area: Viewshed area per camera in
 square meters. - otherwhat: Categorical data consisting of a species
 common name. Corresponds to count data in the same row under the
 "other" column. - humanwhat: Categorical data consisting of
 types of humans (e.g., researcher, hunter, hiker...etc.). Corresponds to
 count data in the same row under the "human" column. **The
 following columns are comprised of count data (0-n) representing the
 number of individuals of a species detected per image.** * deer, human,
 turkey, armadillo, raccoon, coyote, feralhog, squirrel, opossum * other:
 Count data representing the number of individuals of a later specified
 species detected per image. Specified by "otherwhat" column.
 **The following columns are comprised of count data (0-n) representing the
 number of individuals of a sex or stage class detected per image.** *
 WTDantlerless: Adult female white-tailed deer. * WTDantlered: Adult male
 white-tailed deer. * WTDfawn: Juvenile white-tailed deer. * WTDunkn: Deer
 detections where sex or stage was not able to be determined. *
 Turkeyfemale, turkeymale, turkeypoult, turkeyunkn ## Code/Software We used
 the package 'spaceNtime' to estimate abundance of white-tailed
 deer using the STE model. We used the package 'SunCalc' to
 gather data on daily sunrise and sunset times within our sampling frame to
 constrain sampling efforts to crepuscular periods.