Deer activity levels and patterns vary along gradients of food availability and anthropogenic development

Abstract

Animal activity reflects behavioral decisions that depend upon environmental context. Prior studies typically estimated activity distributions within few areas, which has limited quantitative assessment of activity changes across environmental gradients. We examined relationships between two response variables, activity level (fraction of each day spent active) and pattern (distribution of activity across a diel cycle) of white-tailed deer (Odocoileus virginianus), with four predictors-deer density, anthropogenic development, and food availability from woody twigs and agriculture. We estimated activity levels and patterns with cameras in 48 different 10.36-km² landscapes across three larger regions. Activity levels increased with greater building density, likely due to heightened anthropogenic disturbance, but did not vary with food availability. In contrast, activity patterns responded to an interaction between twigs and agriculture, consistent with a functional response in habitat use. When agricultural land was limited, greater woody twig density was associated with reduced activity during night and evening. When agricultural land was plentiful, greater woody twig density was associated with more pronounced activity during night and evening. The region with the highest activity level also experienced the most deer-vehicle collisions. We highlight how studies of spatial variation in activity expand ecological insights on context-dependent constraints that affect wildlife behavior.

Figure 1

Landcover types and sampled landscapes within Regional Management Units (RMU) 3 (west central), 4 (southern), and 9 (northeastern, two separate areas) within Indiana, USA. Within each RMU, we estimated the activity distributions of white-tailed deer using camera traps deployed within landscapes during the winters from 2019 to 2021.

Figure 2

The fraction of deer activity levels (± 95% confidence intervals) observed during morning, daytime, evening, and night within Regional Management Units (RMU) 3, 4, and 9 in Indiana, USA (A). Fractions of deer activity levels observed between RMUs and within times of day (B) or within RMUs and between times of day (C) are similar (P > 0.05) if sharing an identically colored line. We collected activity data using camera traps during the winters of 2019 to 2021.

Figure 3

Effects plot from a hierarchical Bayesian beta regression model predicting white-tailed deer activity levels (± standard error) as a function of the number of buildings within 3.2 × 3.2-km landscapes. Activity data were collected using camera traps during the winters of 2019 to 2021 in Indiana, USA.

Figure 4

Effects plot from a hierarchical Bayesian Dirichlet model regressing the fraction of white-tailed deer activity levels exerted during night (baseline), morning, daytime, and evening (± standard error, SE) across an interaction between the amount of land used for agriculture (0.13, 0.42, and 0.72 km² = − 1, mean, and 1 standard deviation, respectively) within the 3.2 × 3.2-km landscape and the density of woody twigs within the same landscape (twigs/m²). Activity data were collected using camera traps during the winters of 2019 to 2021 in Indiana, USA.