The effect of terrain and female density on survival of neonatal white‐tailed deer and mule deer fawns

Abstract

Juvenile survival is a highly variable life‐history trait that is critical to population growth. Antipredator tactics, including an animal's use of its physical and social environment, are critical to juvenile survival. Here, we tested the hypothesis that habitat and social characteristics influence coyote (Canis latrans) predation on white‐tailed deer (Odocoileus virginianus) and mule deer (O. hemionus) fawns in similar ways during the neonatal period. This would contrast to winter when the habitat and social characteristics that provide the most safety for each species differ. We monitored seven cohorts of white‐tailed deer and mule deer fawns at a grassland study site in Alberta, Canada. We used logistic regression and a model selection procedure to determine how habitat characteristics, climatic conditions, and female density influenced fawn survival during the first 8 weeks of life. Fawn survival improved after springs with productive vegetation (high integrated Normalized Difference Vegetation Index values). Fawns that used steeper terrain were more likely to survive. Fawns of both species had improved survival in years with higher densities of mule deer females, but not with higher densities of white‐tailed deer females, as predicted if they benefit from protection by mule deer. Our results suggest that topographical variation is a critical resource for neonates of many ungulate species, even species like white‐tailed deer that use more gentle terrain when older. Further, our results raise the possibility that neonatal white‐tailed fawns may benefit from associating with mule deer females, which may contribute to the expansion of white‐tailed deer into areas occupied by mule deer.

Estimating Coyote Densities with Local, Discrete Bayesian Capture‐Recapture Models

“…This prompted efforts to estimate coyote population size and evaluate the potential for direct effects (i.e., predation) on Sonoran pronghorn (Bright and Hervert 2005). For ungulates in many systems, predation is a significant source of juvenile mortality (Linnell et al 1995), and coyotes have been implicated as the primary predators of neonatal white‐tailed deer ( Odocoileus virginianus ; Whittaker and Lindzey 1999, Lingle et al 2008, Kilgo et al 2012, Nelson et al 2015, Bonar et al 2016), mule deer ( Odocoileus hemionus ; Hamlin et al 1984, Whittaker and Lindzey 1999, Lingle et al 2008, Bonar et al 2016), and pronghorn ( Antilocapra americana ; Barrett 1984, Gese et al 1988 b , Berger et al 2008). In a 1995–2002 study, researchers reported 50% of predation‐caused Sonoran pronghorn mortalities ( n = 6) were attributable to coyotes (Bright and Hervert 2005).…”

Mule deer (Odocoileus hemionus) resource selection: trade-offs between forage and predation risk

“…Our landscape-scale results are consistent with mule deer exploiting terrain features as a predator avoidance mechanism by selecting steeper or more rugged slopes when stalking cover was lower (i.e., visibility was higher). Selection of areas with steeper slopes has been related to increased fawn survival for mule deer (Bonar et al, 2016). However, mule deer use of more rugged areas would be a better predator avoidance strategy for coursing rather than stalking predators (Bonar et al, 2016;Dellinger et al, 2019).…”

“…Selection of areas with steeper slopes has been related to increased fawn survival for mule deer (Bonar et al, 2016). However, mule deer use of more rugged areas would be a better predator avoidance strategy for coursing rather than stalking predators (Bonar et al, 2016;Dellinger et al, 2019). Mountain lions commonly select for areas with topographic complexity that also provide stalking cover (Robinson et al, 2015;Blake and Gese, 2016;Peterson et al, 2021), which may enhance kill success (Elbroch et al, 2013).…”

A model-based estimate of winter distribution and abundance of white-tailed deer in the Adirondack Park

“…We then intersected each survey segment with the GPS locations for each white-tailed deer group detected. Segment-level spatial covariates included a bivariate smooth of geographic coordinates (Universal Transverse Mercator [UTM]) to account for spatially autocorrelated whited-tailed deer detections and land cover types known to influence deer abundance such as distance from agriculture cover (m) [40][41][42][43], distance from developed cover (m) [42][43][44][45], distance from forest cover (m) [42,46,47], distance from shrub cover (m) [43,48,49], distance from timber cuts (m) [48,50], distance from wetland cover (m) [43,51,52], distance from water (m) [51,53], elevation (m) [12,54,55], and days with snow cover [56,57]. Land cover such as agriculture, developed, forest, shrub, timber cuts, wetland, and water were identified using an updated land cover map of the Park for 2016 that combined Landsat 8 satellite imagery (30 m resolution) acquired from the USGS Global Visualization Viewer for imagery, Normalized Difference Vegetation Index, National Land Cover Dataset, the Adirondack Park Agency's wetlands data, and timber treatment data provided by regional timber companies [24].…”