Predicting Road Encounter Hotspots for Infrequently Detected Species Using Opportunistic Data: A Case Study With Blanding's Turtle ( Emydoidea blandingii )

ABSTRACT For road mitigation measures to prevent roadkill and conserve landscape connectivity to be effective, the measures must be located where animals are most likely to encounter roads. However, accurate identification of road encounter hotspots is difficult when presence records are sparse and collected haphazardly, often the case with small, uncommon species. Blanding's Turtle Emydoidea blandingii is a threatened species for which road mortality contributes to population declines. Using opportunistic detections of Blanding's Turtle along roads, we investigated whether it is possible to predict road encounter hotspots throughout an extensive road network with such data. First, we used general linear modeling (GLM) to infer landscape features associated with Blanding's Turtle road encounter records. After locating spatial clusters of encounters, GLM was used to identify landscape features associated with these hotspots. Next, Blanding's Turtle least cost movement paths were delineated within the landscape, and sites where paths crossed roads were located. Blanding's Turtle locations were positively associated with proximity and extent of wetlands, and negatively associated with grasslands and developed land use. Hotspots were located along predicted Blanding's Turtle least cost movement paths, indicating that behavioral movement models are useful for predicting encounter locations. A significant fraction of road encounter records came from a small number of hotspot sites, located along the predicted movement paths. We conclude that it is possible to generate predictive models of road encounter hotspots even when data are sparse, collected opportunistically, and subject to spatial biases in reporting across a road network. These models can be applied throughout a road network to identify road segments that are good candidates for effective road mitigation.