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.

The development and use of a spatially explicit model for river otters to evaluate environmental hazards: a case study on the Department of Energy's Savannah River Site.

The Department of Energy's (DOE) Savannah River Site (SRS) faces a legacy of radionuclide and metal contamination from industrial processes that occurred throughout the site. Northern river otters (Lontra canadensis) are appropriate receptors for studying the effects of long-term, low-level contamination because they are long-lived, higher trophic level organisms susceptible to accumulating high levels of pollutants. The purpose of this study was to use latrine surveys to examine patterns of wetland latrine usage; explicitly model northern river otter resource selection on the landscape level; and utilize the model results within an ecological risk assessment (ERA) framework to assess potential effects of metals and radiocesium (137Cs) on the population for the SRS as a case study. River drainages and associated wetlands were surveyed for latrine sites and scats were collected and analyzed for 137Cs activity to validate model results. The spatially explicit resource model predicted otter drainage reach use and was used in an ERA to develop exposure models for nine heavy metals as well as 137Cs on the SRS population of river otters. The evaluation predicted that the only contaminant occurring at high enough levels to cause population effects was mercury and that the observed concentrations were probably not high enough to cause significant impairment. However, multiple metals were above action level thresholds. The field validation process showed an unexpected preference for one man-made treatment wetland that was heavily contaminated, showing that the ERA process is complex and must be approached using multiple scales.

The spatial distribution of the Japanese beetle, Popillia japonica, in soybean fields.

The Japanese beetle, Popillia japonica Newman (Coleoptera: Scarabaeidae), is a serious pest of many agricultural and horticultural plants. Relatively little research has investigated the distributions of Japanese beetles in agricultural fields, and this lack of information makes pest management more difficult. In the present study, the spatial distribution of Japanese beetles in soybean fields was examined. Specifically, how the distribution and abundance of beetles was affected by distance from an edge, edge direction, and edge type was examined. An edge effect for density was discovered; beetle numbers decreased significantly with increasing distance from the field edge. The east and south sides averaged higher numbers of beetles than the north and west. Downwind edges, in particular downwind edges adjacent to hedgerows, also had significantly higher beetle densities. In addition, females relatively far from the edge had larger egg loads than those closer to the edge. Differences in aggregation seeking behavior, in combination with movement in relation to wind and obstructions such as hedgerows, are possible explanations for these spatial patterns.