We develop a novel method of estimating population size from imperfectly detected counts of individuals and a separate estimate of detection probability. Observed counts are separated into classes within which detection probability is assumed constant. Within a detection class, counts are modeled as a single binomial observation X with success probability p where the goal is to estimate index N. We use a Horvitz–Thompson‐like estimator for N and account for uncertainty in both sample data and estimated success probability via a parametric bootstrap. Unlike capture–recapture methods, our model does not require repeated sampling of the population. Our method is able to achieve good results, even with small X. We show in a factorial simulation study that the median of the bootstrapped sample has small bias relative to N and that coverage probabilities of confidence intervals for N are near nominal under a wide array of scenarios. Our methodology begins to break down when P(X=0)>0.1 but is still capable of obtaining reasonable confidence coverage. We illustrate the proposed technique by estimating (1) the size of a moose population in Alaska and (2) the number of bat fatalities at a wind power facility, both from samples with imperfect detection probabilities, estimated independently.

中文翻译：

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使用参数引导程序通过不完善的检测来估计人口规模

我们开发了一种从不完全检测的个体计数和检测概率的单独估计中估算种群规模的新方法。观察到的计数被分为几类，其中检测概率被假定为常数。内的检测类，计数被建模为一个单一的二项式观察X成功概率p，其中目标是估计索引Ñ。我们对N使用与Horvitz-Thompson相似的估计量，并通过参数自举法解决了样本数据的不确定性和估计的成功概率。与捕获-捕获方法不同，我们的模型不需要对总体进行重复采样。即使使用较小的X，我们的方法也能取得良好的结果。我们在析因仿真研究中显示，自举样本的中位数相对于N具有较小的偏差，并且在多种情况下N的置信区间的覆盖概率接近标称值。当P（X = 0）> 0.1时，我们的方法开始崩溃，但仍然能够获得合理的置信度。我们通过估计（1）阿拉斯加驼鹿种群的大小和（2）风力发电设施中蝙蝠死亡人数的数量来说明拟议的技术，这两个样本均来自具有不完善检测概率的样本，并分别进行了估计。