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Reduction of conflicts arising from human-wildlife interactions is necessary for coexistence. Collisions between animals and automobiles cost the world's economy billions of dollars, and wildlife management agencies often are responsible for reducing wildlife-vehicle collisions. But wildlife agencies have few proven options for reducing wildlife-vehicle collisions that are effective and financially feasible at large spatiotemporal scales germane to management. Recreational hunting by humans is a primary population management tool available for use with abundant wild ungulates that often collide with automobiles. Therefore, we tested how well policies designed to increase human hunting of deer (longer hunting seasons and increased harvest limits) reduced collisions between white-tailed deer and automobiles along 618 km of high-risk roadways in Indiana, USA. We used a 20-y dataset that compiled >300,000 deer-vehicle collisions. Targeted recreational hunting decreased deer-vehicle collisions by 21.12 % and saved society up to $653,756 (95 % CIs = $286,063-$1,154,118) in economic damages from 2018 to 2022. Potential savings was up to $1,265,694 (95 % CIs = $579,108-$2,402,813) during the same 5-y span if relaxed hunting regulations occurred along all high-risk roadways. Moreover, license sales from targeted hunting generated $206,268 in revenue for wildlife management. Targeted hunting is likely effective in other systems where ungulate-vehicle collisions are prevalent, as behavioral changes in response to human hunting has been documented in many ungulate species across several continents. Our methods are attractive for management agencies with limited funds, as relaxed hunting regulations are relatively inexpensive to implement and may generate substantial additional revenue., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

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-km 2 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., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Spatially explicit densities of wildlife are important for understanding environmental drivers of populations, and density surfaces of intraspecific classes allow exploration of links between demographic ratios and environmental conditions. Although spatially explicit densities and class densities are valuable, conventional design‐based estimators remain prevalent when using camera‐trapping methods for unmarked populations.We developed a density surface model that utilized camera trap distance sampling data within a hierarchical generalized additive modelling framework. We estimated density surfaces of intraspecific classes of a common ungulate, white‐tailed deer Odocoileus virginianus, across three large management regions in Indiana, United States. We then extended simple statistical theory to test for differences in two ratios of density.Deer density was influenced by landscape fragmentation, wetlands and anthropogenic development. We documented class‐specific responses of density to availability of concealment cover, and found strong evidence that increased recruitment of young was tied to increased resource availability from anthropogenic agricultural land use. The coefficients of variation of the total density estimates within the three regions we surveyed were 0.11, 0.10 and 0.06.Synthesis and applications. Our strategy extends camera trap distance sampling and enables managers to use camera traps to better understand spatial predictors of density. Our density estimates were more precise than previous estimates from camera trap distance sampling. Population managers can use our methods to detect finer spatiotemporal changes in density or ratios of intraspecific‐class densities. Such changes in density can be linked to land use, or to management regimes on habitat and harvest limits of game species. [ABSTRACT FROM AUTHOR]

Fecal-pellet surveys provide density estimates of pellet groups, which offer a quick and reliable index of population densities for white-tailed deer Odocoileus virginianus and other wildlife species. Comparisons of differing fecal-pellet survey methodologies have previously shown that they vary in the precision. However, researchers made these comparisons outside of the United States in regions with lower white-tailed deer densities. In this study, we compared pellet-group density estimates and precision from line transects, quadrat sampling, and strip transects. At each site, three observers surveyed simultaneously with each being responsible for one method. Like other studies, we found that each method produced similar estimates of pellet-group density, but quadrat sampling produced the most precise estimates. Furthermore, all three methods suggested that the central region of Indiana had both the highest pellet-group density and the highest precision. These results suggest a more homogenous distribution of pellet groups in areas with higher white-tailed deer densities, which may increase the precision of all methods. Our results suggest that quadrat sampling may be the most effective method for estimating pellet-group densities within woodlots of the U.S. Midwest, and that precision may increase in areas with higher white-tailed deer densities. This study not only improves deer management within the Midwest but provides guidelines for other studies to potentially advance the conservation and management of other species. [ABSTRACT FROM AUTHOR]