Rabies Management Implications Based on Raccoon Population Density Indexes

Abstract

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.

Accounting for animal movement improves vaccination strategies against wildlife disease in heterogeneous landscapes

“…In rural and agricultural areas, the standard approach to bait delivery is by fixed‐wing aircraft using GPS technologies and automated machinery to standardize and accurately deliver baits along preprogrammed and precisely spaced flight lines (Slate et al, 2009 ). In densely populated urban–suburban landscapes, however, fixed‐wing aircraft delivery is not feasible, and NRMP baiting is accomplished predominantly by hand baiting from vehicles along roadways (Slate et al, 2009 ) Our work indicates that alternative delivery methods such as helicopters or bait stations, which extend bait application beyond major roads in developed areas could improve baiting effectiveness and vaccination coverage (Berentsen et al, 2018 ; Slate et al, 2020 ). In addition, spatial bait coverage along roads could be further improved by incorporating minor roads into delivery planning in addition to major roads.…”

“…The area is characterized by suburban and urban land‐use types embedded within a matrix composed of undeveloped land and open space, deciduous forests, and farmland. The meso‐carnivore species composition in this area includes several species that can act as bait competitors such as the domestic cat ( Felis catus ) and Virginia opossum ( Didelphis virginianus ) (Slate et al, 2020 ).…”

“…Comparing results across the continuum of habitat specialization and variability allows for inference about how baiting designs can be optimized based on knowledge of local animal movement behavior and the influence of individual‐level movement heterogeneity (e.g., McClure et al, 2020 ). Each habitat selection scenario was applied at three raccoon densities that represent a realistic range of raccoon densities in suburban–urban landscapes (5, 15, 30 raccoons/km 2 ; Urban, 1970 , Moore & Kennedy, 1985 , Slate et al, 2020 ). Each raccoon movement scenario (habitat selection scenario × raccoon density) was repeated 100 times.…”

“…We assumed each raccoon co‐located with a bait in a grid cell had a bait consumption probability of 0.5 based on the expert opinion of NRMP (Figure 2 ). Spatial overlap between baits and target animals on the landscape are necessary for bait uptake by target animals, but other factors such as bait competition both from conspecifics and non‐target species also influence bait uptake probability (Elmore et al, 2017 ; Slate et al, 2020 ). We accounted for bait competition from conspecifics by randomly selecting one raccoon to encounter the bait when multiple raccoons were co‐located in a cell, leading to a bait consumption probability of less than 0.5 when raccoon densities were high within grid cells.…”

“…We accounted for bait competition from conspecifics by randomly selecting one raccoon to encounter the bait when multiple raccoons were co‐located in a cell, leading to a bait consumption probability of less than 0.5 when raccoon densities were high within grid cells. To account for competition from non‐target carnivores such as opossums (Slate et al, 2020 ) and decreasing bait palatability over time, we included a discrete decay function for bait availability in which 30% of baits within a grid cell were randomly removed every 12 h (discrete equation: n h= n init × .70 h/12 , where h = hour, n = number of baits, init = initial time step; similar to bait disappearance rates reported by Smyser et al, 2013 ), with a maximum bait availability time of 10 days. Last, we assumed that seroconversion probability, given consumption, was 0.9 to reflect immunogenicity following oral contact with the vaccine (Brown et al, 2012 ; Gilbert, Johnson, Walker, et al, 2018 ).…”