Estimating abundance from multiple sampling capture-recapture data via a multi-state multi-period stopover model
Estimating abundance from multiple sampling capture-recapture data via a multi-state multi-period stopover model
复制标题
通过多状态多周期中途停留模型估计多次采样捕获-重捕获数据的丰度
DOI：
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复制
发表时间：
2018
影响因子：
1.8
通讯作者：
R. Griffiths
中科院分区：
文献类型：
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作者：
H. Worthington;R. McCrea;Ruth King;R. Griffiths
捕获-再捕获数据的收集通常涉及在相对短的时间段内在许多捕获场合收集数据。对于许多研究物种，这一过程是重复的，例如每年一次，从而产生跨越多个采样期的捕获信息。模型的稳健设计类提供了一个方便的框架，可以在单个似然表达式中分析所有可用的捕获数据。然而，这些模型通常依赖于假设在一个采样周期内封闭（封闭稳健设计）或条件在一个采样周期内捕获的个体数量（开放稳健设计）。我们在本文中开发的模型既不需要假设明确建模的人口内和之间的采样期间，这反过来又允许估计丰度的个人的运动。这些模型进一步扩展，使参数不仅取决于捕获时机，但也加入人口和多状态数据的情况下，有个别时变离散协变量信息的时间量。我们推导出一个有效的似然表达式的新的多状态多周期中途停留模型使用隐马尔可夫模型框架。我们证明了新的模型，通过模拟研究，然后考虑一个数据集上的大冠蝾螈，Triturus cristatus。
The collection of capture-recapture data often involves collecting data on numerous capture occasions over a relatively short period of time. For many study species this process is repeated, for example annually, resulting in capture information spanning multiple sampling periods. The robust design class of models provide a convenient framework in which to analyse all of the available capture data in a single likelihood expression. However, these models typically rely either upon the assumption of closure within a sampling period (the closed robust design) or condition on the number of individuals captured within a sampling period (the open robust design). The models we develop in this paper require neither assumption by explicitly modelling the movement of individuals into the population both within and between the sampling periods, which in turn permits the estimation of abundance. These models are further extended to allow parameters to depend not only on capture occasion but also the amount of time since joining the population and to the case of multi-state data where there is individual time-varying discrete covariate information. We derive an efficient likelihood expression for the new multi-state multi-period stopover model using the hidden Markov model framework. We demonstrate the new model through a simulation study before considering a dataset on great crested newts, Triturus cristatus.