Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning.

Continued spread of chronic wasting disease (CWD) through wild cervid herds negatively impacts populations, erodes wildlife conservation, drains resource dollars, and challenges wildlife management agencies. Risk factors for CWD have been investigated at state scales, but a regional model to predict locations of new infections can guide increasingly efficient surveillance efforts. We predicted CWD incidence by county using CWD surveillance data depicting white-tailed deer (Odocoileus virginianus) in 16 eastern and midwestern US states. We predicted the binary outcome of CWD-status using four machine learning models, utilized five-fold cross-validation and grid search to pinpoint the best model, then compared model predictions against the subsequent year of surveillance data. Cross validation revealed that the Light Boosting Gradient model was the most reliable predictor given the regional data. The predictive model could be helpful for surveillance planning. Predictions of false positives emphasize areas that warrant targeted CWD surveillance because of similar conditions with counties known to harbor CWD. However, disagreements in positives and negatives between the CWD Prediction Web App predictions and the on-the-ground surveillance data one year later underscore the need for state wildlife agency professionals to use a layered modeling approach to ensure robust surveillance planning. The CWD Prediction Web App is at https://cwd-predict.streamlit.app/ .

Targeted recreational hunting can reduce animal-vehicle collisions and generate substantial revenue for wildlife management agencies.

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.

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

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.

Expanding and Evaluating Public Satisfaction with Wildlife Governance: Insights from Deer Management in Indiana, USA.

Wildlife agencies in North America desire to incorporate broader public interests into decision-making so they can realize the principle of governing wildlife in the public trust. Public satisfaction is a key component of good governance but evaluating satisfaction with wildlife management focuses on traditional user experiences rather than perceptions of agency performance. We draw from political science, business, and conservation social science to develop a multidimensional concept of satisfaction with wildlife management that includes agency performance, service quality, trust in the managing agency, and informational trust. We use data collected from a 2021 survey of Indiana residents to analyze the social and cognitive determinants of satisfaction with white-tailed deer (Odocoileus virginianus) management. Quantile regression models revealed that respondents' acceptability of management methods and deer-related concerns most strongly affected performance and quality components, whereas respondent characteristics mostly affected trust components of the index. Future research should associate satisfaction with key variables we did not fully capture including perceived control, psychological distance, and norms of interaction between wildlife agencies and the public. Expanding agency conceptions of public satisfaction represents a critical step toward public trust thinking and the practice of good wildlife governance in North America.

Distribution of antibodies reactive to Borrelia lonestari and Borrelia burgdorferi in white-tailed deer (Odocoileus virginianus) populations in the eastern United States.

Southern tick-associated rash illness is a Lyme-like syndrome that occurs in the southern states. Borrelia lonestari, which has been suggested as a possible causative agent of southern tick-associated rash illness, naturally infects white-tailed deer (WTD; Odocoileus virginianus) and is transmitted by the lone star tick (Amblyomma americanum). To better understand the prevalence and distribution of Borrelia exposure among WTD, we tested WTD from 21 eastern states for antibodies reactive to B. lonestari using an indirect immunofluorescent antibody assay and Borrelia burgdorferi using the IDEXX SNAP 4Dx test. A total of 107/714 (15%) had antibodies reactive to B. lonestari, and prevalence of antibodies was higher in deer from southern states (17.5%) than in deer from northern states (9.2%). Using the SNAP 4DX test, we found that 73/723 (10%) were positive for B. burgdorferi, and significantly more northern deer (23.9%) were positive compared with southern deer (3.8%). Our data demonstrate that WTD are exposed to both Borrelia species, but antibody prevalence for exposure to the two species differs regionally and distributions correlate with the presence of Ixodes scapularis and A. americanum ticks.