Context Identifying risk zones for wildlife-vehicle incidents is essential for creating effective mitigation efforts on major road networks. Wildlife-vehicle collision data are often used to identify hotspot areas without consideration of species spatial distributions. Objectives Evaluating both can reveal spatiotemporal patterns that can improve mitigation success. Methods We summarized elk-vehicle incident (EVI) data on State Route 20 (SR 20) in Washington State between 2012 and 2019. We also collared 23 elk residing in the vicinity of SR 20 and used GPS location data to identify home ranges and road crossings. We compared EVI and elk road crossing data to identify hotspot locations on SR 20 to help inform mitigation. Results Our EVI and elk crossing data had a non-random distribution along a 38 km section of SR 20 associated with the 95% home ranges of 8 female elk sub-herds. We found EVI data alone were an effective indicator of elk spatial distribution and movement in relation to collision hotspots along SR 20. Our results also indicated a strong association between elk crossings and EVIs by milepost. While the spatial distribution of elk sub-herds was a good predictor of EVI risk zones, EVI frequency was not associated with an increase in elk population. Conclusions Classifying EVI and road crossing distributions as high risk zones is the first step preceding mitigation and protection measures to prevent elk-vehicle collisions. Specific identification of hotspots will result in more effective and successful installations of high cost mitigation efforts such as wildlife crossing structures.

Background: The spatial distribution and seasonal movement patterns of isolated populations of mountain goats (Oreamnos americanus) in the North Cascade range of Washington State is not fully understood. Determining harvest potential in these populations is challenging without a clear understanding of spatiotemporal movement, space use, and spatial overlap. Mountain goat populations in the North Cascades are fragmented and many have declined considerably from historic estimates. Identification of harvestable populations requires a clear understanding of population size, distribution, and movement. We investigated the population trends and spatial distribution of mountain goats in the Boulder River North Harvest Area in Boulder River Wilderness of Washington State. Methods: We reviewed recent mountain goat population estimates and used Global Positioning System collar data to determine year-round and seasonal home range distributions, spatial overlap within these ranges, and proximity of mountain goats to roads and trails.Results: We found 2 populations of mountain goats inhabiting the Whitehorse and Three Fingers Mountains in the Boulder River North Harvest Area. These 2 populations were spatially distinct and did not intermix during our study period. We also found mountain goats using exclusive areas seasonally on Whitehorse Peak that may represent separate or isolated groups. The Whitehorse population appeared to be more vulnerable to harvest than Three Fingers based on its proximity to roads and trails. Conclusions and Management Recommendations: This study provides space use and movement information on mountain goats in the North Cascades Range that can be used to improve harvest management of fragmented and isolated populations. Our results indicate that a re-evaluation of harvest level, harvest unit boundary, and monitoring strategy may be warranted for the Boulder River North mountain goat harvest area.

No abstract