Estimating white‐tailed deer density in West Virginia, USA, from camera traps and telemetry data

Population density is an important measure when monitoring wildlife populations. Various methods have been used to estimate density, including spatial capture‐recapture (SCR) models applied to camera trapping data. Although density estimates from SCR models fit to data from unmarked animals can be considerably imprecise, ancillary data can potentially improve precision of density estimates. We estimated densities of unmarked white‐tailed deer ( Odocoileus virginianus ) populations across 3 study areas in West Virginia, USA. We used SCR methods in a Bayesian framework and incorporated home range data from global positioning system (GPS) collared deer to inform model parameters. Mean population density estimates were 10 deer/km 2 in the central study area, 9 deer/km 2 in the eastern study area, and 8 deer/km 2 in the western study area. Although we do not know the true population density, our estimates are comparable to regional estimates from other studies. Density estimates from SCR models fit to data from unmarked animals are sensitive to model parameters, evidenced by the variation in estimated detection rates (0.28–0.41 detections/day) and home range size (73–98 ha) across our study areas. Although we demonstrate this technique with ancillary GPS data, other forms of ancillary data, including informative prior distributions, can be used to improve precision of density estimates obtained from completely unmarked animal populations. This method could be applied to a wide range of species, provided the appropriate ancillary data are available.