Capture–recapture studies are widely used in ecology to estimate population sizes and demographic rates. In some capture–recapture studies, individuals may be visually encountered but not identified. For example, if individual identification is only possible upon capture and individuals escape capture, visual encounters can result in failed captures where individual identities are unknown. In such cases, the data consist of capture histories with known individual identities, and counts of failed captures for individuals with unknown identities. These failed captures are ignored in traditional capture–recapture analyses that require known individual identities. Here, we show that if animals can be encountered at most once per sampling occasion, failed captures provide lower bounds on population size that can increase the precision of abundance estimates. Analytical results and simulations indicate that visual encounter data improve abundance estimates when capture probabilities are low, and when there are few repeat surveys. We present a hierarchical Bayesian approach for integrating failed captures and auxiliary encounter data in statistical capture–recapture models. This approach can be integrated with existing capture–recapture models and may prove particularly useful for hard to capture species in data‐limited settings.

中文翻译：

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使用视觉相遇数据来改善捕获-捕获的丰度估计

捕获-捕获研究在生态学中被广泛使用，以估计种群数量和人口统计率。在一些捕获-捕获研究中，可能会在视觉上遇到个体，但无法识别。例如，如果仅在捕获时才有可能进行个体识别，而个体则无法捕获，则视觉相遇会导致捕获失败，而个体身份未知。在这种情况下，数据包括具有已知个人身份的捕获历史记录，以及具有未知身份的个体的失败捕获计数。这些失败的捕获在需要已知个人身份的传统捕获-捕获分析中被忽略。在这里，我们表明，如果每个采样机会最多只能遇到一次动物，失败的捕获提供了种群数量的下限，可以提高丰度估计的精度。分析结果和模拟表明，当捕获概率较低且重复调查很少时，视觉相遇数据会提高丰度估计。我们提出了一种分级贝叶斯方法，用于将失败的捕获和辅助遭遇数据集成到统计捕获-捕获模型中。这种方法可以与现有的捕获-捕获模型集成，并且对于难以在数据受限的环境中捕获物种特别有用。我们提出了一种分级贝叶斯方法，用于将失败的捕获和辅助遭遇数据集成到统计捕获-捕获模型中。这种方法可以与现有的捕获-捕获模型集成，并且对于难以在数据受限的环境中捕获物种特别有用。我们提出了一种分级贝叶斯方法，用于将失败的捕获和辅助遭遇数据集成到统计捕获-捕获模型中。这种方法可以与现有的捕获-捕获模型集成，并且对于难以在数据受限的环境中捕获物种特别有用。