A Unifying Model for Capture–Recapture and Distance Sampling Surveys of Wildlife Populations
统一野生动物种群捕获—再捕获与距离抽样调查的模型AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要讨论野生动物种群抽样与位置测量误差，未提供所列经济计量领域的直接依据。
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统一野生动物种群捕获—再捕获与距离抽样调查的模型
- 研究问题
- 如何在统一模型中利用不同完整程度的动物位置资料估计种群数量或密度，并处理距离观测及其他位置信息的测量误差？
- 方法与思路
- 作者构建涵盖非空间捕获—再捕获、空间显式捕获—再捕获、距离抽样及标记再捕获距离抽样的模型类。
- 材料陈述的贡献
- 摘要称，该模型类既统一相关方法，也允许加入辅助位置信息改善推断并纳入测量误差；以长臂猿、青蛙和鲸类调查展示用途。
- 阅读关注点
- 应核对各类调查模型如何作为特例出现、探测与位置误差机制的设定，以及增加位置信息后的可识别性和不确定性变化。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；探测机制、参数识别条件及改进幅度未说明，不能推断模型适用于所有生态调查。
原文摘要
A fundamental problem in wildlife ecology and management is estimation of population size or density. The two dominant methods in this area are capture-recapture (CR) and distance sampling (DS), each with its own largely separate literature. We develop a class of models that synthesizes them. It accommodates a spectrum of models ranging from nonspatial CR models (with no information on animal locations) through to DS and mark-recapture distance sampling (MRDS) models, in which animal locations are observed without error. Between these lie spatially explicit capture-recapture (SECR) models that include only capture locations, and a variety of models with less location data than are typical of DS surveys but more than are normally used on SECR surveys. In addition to unifying CR and DS models, the class provides a means of improving inference from SECR models by adding supplementary location data, and a means of incorporating measurement error into DS and MRDS models. We illustrate their utility by comparing inference on acoustic surveys of gibbons and frogs using only capture locations, using estimated angles (gibbons) and combinations of received signal strength and time-of-arrival data (frogs), and on a visual MRDS survey of whales, comparing estimates with exact and estimated distances. Supplementary materials for this article are available online.