Data from: Estimating population size in the presence of temporary migration using a joint analysis of telemetry and capture recapture data

1.Temporary migration – where individuals can leave and re-enter a sampled population – is a feature of many capture–mark–recapture (CMR) studies of mobile populations which, if unaccounted for, can lead to biased estimates of population capture probabilities and consequently biased estimates of population abundance. 2. We present a method for incorporating radiotelemetry data within a CMR study to eliminate bias due to temporary migration using a Bayesian state-space model. 3. Our results indicate that using a relatively small number of telemetry tags, it is possible to greatly reduce bias in estimates of capture probabilities using telemetry data to model transition probabilities in and out of the sampling area. In a capture–recapture data set for trout Cod in the Murray river, Australia, accounting for temporary migration led to overall higher estimates of capture probabilities than models assuming permanent or zero migration. Also, individual heterogeneity in detectability can be managed through explicit modelling. We show how accounting for temporary migration when estimating capture probabilities can be used to estimate the abundance and size distribution of a population as though it were closed. 4. Our model provides a basis for more complex models that might integrate telemetry data into other CMR scenarios, thus allowing for greater precision in estimates of vital rates that might otherwise be biased by temporary migration. Our results highlight the importance of accounting for migration in survey design and parameter estimation, and the potential scope for supplementing large-scale CMR data sets with a subset of auxiliary data that provide information on processes that are hidden to primary sampling processes.

1. 临时迁移——即个体能够离开并重新进入抽样种群——是诸多针对移动种群的标记重捕（capture–mark–recapture, CMR）研究的固有特征；若未对该过程加以考量，则会导致种群捕获概率的估计出现偏差，进而使种群丰度的估计结果失真。
2. 本研究提出一种将无线电遥测（radiotelemetry）数据纳入标记重捕研究的方法，借助贝叶斯状态空间模型（Bayesian state-space model）消除临时迁移带来的估计偏差。
3. 研究结果表明，仅需使用数量相对较少的遥测标签，即可通过无线电遥测数据对进出采样区域的转移概率进行建模，从而大幅降低捕获概率估计中的偏差。针对澳大利亚墨累河鳟鳕（trout Cod）的标记重捕数据集开展的分析显示，相较于假设永久滞留或零迁移的模型，考虑临时迁移的模型得到的捕获概率整体估计值更高。此外，可通过显式建模对检测过程中的个体异质性进行处理。本研究证实，在估计捕获概率时纳入临时迁移因素，能够像对待封闭种群一样实现种群丰度与体型分布的估计。
4. 本模型可为将无线电遥测数据整合至其他标记重捕场景的更复杂模型提供理论支撑，从而提升原本会因临时迁移而出现偏差的生命率估计精度。研究结果凸显了在调查设计与参数估计过程中纳入迁移因素的重要性，同时也展现了通过辅助数据子集补充大规模标记重捕数据集的潜在应用空间——这类辅助数据可提供初级采样过程无法观测到的种群过程信息。