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.

中文翻译：

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基于浣熊人口密度指数的狂犬病管理意义

目标物种密度的估计或指数对于确定口服狂犬病疫苗接种（ORV）诱饵密度以控制和消除特定的狂犬病变异很重要。从1997年至2011年，我们根据从缅因州到美国阿拉巴马州的163个站点（ORV区附近）的累积捕获量，对浣熊（Procyon lotor）密度进行了253次索引，这是为了防止浣熊狂犬病传播到新地区而建立的。我们在年度ORV临近时根据通用笼式诱集协议进行了索引编制，以帮助确定诱饵密度。独特的浣熊（n  = 8,415）占捕获量的68.0％（n = 12,367）。我们夺回了浣熊2,669次。我们在捕获数据≥3年且捕获率≥25％的站点上应用了Schnabel和Huggins标记捕获模型作为浣熊密度指数（RDI）的背景。RDI与商标重新获得估算之间的简单线性关系支持了我们指数的应用。浣熊密度指数范围为0.0-56.9浣熊/ km ²。对于诱饵密度决定，我们评估了以下4个浣熊密度组中的RDI，它们在统计学上是不同的：（0.0–5.0 [ n  = 70]，5.1–15.0 [ n  = 129]，15.1–25.0 [ n  = 31]和> 25.0浣熊/公里² [ n = 23]）。平均RDI与较高的已开发土地覆盖率和较低的常绿森林百分比呈正相关。非目标物种组成（不包括捕获的浣熊）占捕获量的32.0％。潜在的诱饵竞争者占非目标诱饵的76.5％。负鼠（Didelphis virginiana）是北纬27°至44°N的主要潜在诱饵竞争者，在北侧被数字臭鼬（Mephitis mephitis）取代。）。由于成本，地理范围，人员可用性以及需要补充血清样本的原因，我们选择了RDI方法而不是标记回收方法。4个密度组提供了足够的灵敏度来支持当前2个诱饵密度选项的诱饵密度决定。该方法的未来改进包括向现场人员提供随机诱捕位置，以防止诱集器聚类并标记非目标，以更好地表征诱饵竞争对手。©2020作者。Wiley Periodicals LLC代表野生动物协会出版的《野生动物管理杂志》。