An estimate or index of target species density is important in determining oral rabies vaccination (ORV) bait densities to control and eliminate specific rabies variants. From 1997–2011, we indexed raccoon (Procyon lotor) densities 253 times based on cumulative captures on 163 sites from Maine to Alabama, USA, near ORV zones created to prevent raccoon rabies from spreading to new areas. We conducted indexing under a common cage trapping protocol near the time of annual ORV to aid in bait density decisions. Unique raccoons (n = 8,415) accounted for 68.0% of captures (n = 12,367). We recaptured raccoons 2,669 times. We applied Schnabel and Huggins mark‐recapture models on sites with ≥3 years of capture data and ≥25% recaptures as context for raccoon density indexes (RDIs). Simple linear relationships between RDIs and mark‐recapture estimates supported application of our index. Raccoon density indexes ranged from 0.0–56.9 raccoons/km2. For bait density decisions, we evaluated RDIs in the following 4 raccoon density groups, which were statistically different: (0.0–5.0 [n = 70], 5.1–15.0 [n = 129], 15.1–25.0 [n = 31], and >25.0 raccoons/km2 [n = 23]). Mean RDI was positively associated with a higher percentage of developed land cover and a lower percentage of evergreen forest. Non‐target species composition (excluding recaptured raccoons) accounted for 32.0% of captures. Potential bait competitors accounted for 76.5% of non‐targets. The opossum (Didelphis virginiana) was the primary potential bait competitor from 27°N to 44°N latitude, north of which it was numerically replaced by the striped skunk (Mephitis mephitis). We selected the RDI approach over mark‐recapture methods because of costs, geographic scope, staff availability, and the need for supplemental serologic samples. The 4 density groups provided adequate sensitivity to support bait density decisions for the current 2 bait density options. Future improvements to the method include providing random trapping locations to field personnel to prevent trap clustering and marking non‐targets to better characterize bait competitors. © 2020 The Authors. The Journal of Wildlife Management published by Wiley Periodicals LLC on behalf of The Wildlife Society.

Roads are not the only determining factor for wildlife movement across the landscape, but due to the extensive distribution of the road network their impact can be dramatic. Although it has been well documented that roads decrease habitat connectivity for wildlife due to animal-vehicle collisions, habitat fragmentation, and avoidance behavior, approaches for identifying connectivity across the landscape often do not fully examine the barrier effect of roads. Here, we explored the extent of the impact of roadways on wildlife connectivity by using Omniscape to model connectivity including and without the barrier effect of roads, then evaluating the difference between these two models. We created these connectivity models for three organisms that represent different taxa, movement types, and habitat requirements: northern red-legged frog, Pacific-slope flycatcher, and Columbian black-tailed deer. We found that roads had a strong impact on connectivity for all three species. Change in flow was most pronounced on the roads, especially where they ran through permeable habitat for a species. Roads also influenced connectivity well beyond the footprint of the roadway, affecting flows intersecting the roads and diffusely around them. The extent and nature of this impact depended on the species, road density, and surrounding habitat. The different effects across species highlight the importance of considering different taxa simultaneously while planning. Moreover, the ability to assess modeled wildlife habitat connectivity in the absence of existing widespread linear infrastructure allows for critical evaluation of where mitigation activities, such as wildlife crossing structures and fencing, may be most beneficial. Hence, this novel approach has practical application for increasing connectivity for wildlife across roads.