Capture-recapture modelling of population dynamics

这项研究涉及使用统计学来研究动物种群的动态和其他生态问题。这项研究的动机是生态学家遇到的实际问题。很多研究都涉及捕捉-再捕捉实验。在这些实验中，动物被捕获、贴上标签、释放，然后再被捕获或重新观察。捕获和重新捕获/重新观察的模式使研究人员能够估计存活率，种群中存在的动物数量和其他种群参数。这项研究旨在开发新的统计方法，以帮助回答诸如“哥伦比亚河流域幼崽被捕食是否有补偿性死亡的证据”等问题。在这种情况下，成千上万的幼崽在下游迁徙和成年后返回时遇到的许多水坝上都贴上了带有探测器的PIT（被动集成应答器）标签。在过去的20年里，随着燕鸥和鸬鹚栖息地在盆地的扩张，鸟类的捕食一直在稳步增加。捕食压力在一年内也会发生变化。一些被捕食的幼崽的标记可以在鸟类群体上被检测到，作为鸟类捕食的衡量标准（在调整了种群上的沉积和检测效率后）。这就为研究鸟类捕食者造成的死亡与所有其他死亡来源之间的关系提供了一个自然的实验。如果没有关系，那么捕食者控制可能对成年鱼随后的回归影响不大，可能需要其他方法来帮助恢复濒临灭绝的种群。应用于这类数据的标准统计方法不足以区分代偿性死亡率与亚组间生存的异质性、看起来像代偿性死亡率的人为因素（如抽样相关性）以及来自竞争风险的偏差。需要复杂的贝叶斯模型来理清这些影响。此外，庞大的数据集（在过去的20年里已经标记了数百万的小鲑鱼）在使用贝叶斯模型来拟合数据时引入了计算挑战。也有机会修改探测器阵列的安装位置，这可能会大大提高检测补偿效应的效率——可以预期的改进有多大，与简单地增加标记工作相比，它是否具有成本效益？所开发的统计方法广泛适用于许多野生动物管理情况，在这些情况下，在生命周期的不同阶段存在不同的死亡来源和选择管理行动以提高存活率。是否有证据表明管理层的行动会产生任何影响？生命周期中的哪个阶段是实现这些操作的最佳位置？

This research involves the use of statistics in studying the dynamics of animal populations and other ecological problems. The research is motivated by real problems encountered by ecologists. Much of the research involves capture-recapture experiments. In these experiments, animals are captured, tagged, released and then subsequently recaptured or resighted. The pattern of captures and recaptures/resightings enables the researcher to estimate survival rates, the number of animals present in the population and other population parameters.The research aims to develop new statistical methodology to help answer questions such as "Is there evidence of compensatory mortality from predation of smolts in the Columbia River Basin." In such cases, many thousands of smolts are tagged with PIT (passive integrated transponder) tags with detectors on many dams that the smolts encounter during their downstream migration and their return as adults. Avian predation has been steadily increasing over the last 20 years with the expansion of tern and cormorant colonies in the basin. Predation pressure also varies within a year. The tags from some predated smolts can be detected on the avian colonies as a measure of avian predation (after adjusting for deposition and detection efficiency on the colonies). This makes a natural experiment to investigate the relationship between mortality due to avian predators and all other sources of mortality. If there is no relationship, then predator controls may have little impact on subsequent returns of adult fish and other methods may be needed to help restore endangered stocks. Standard statistical methods applied to this type of data are inadequate to to distinguish compensatory mortality from heterogeneity in survival among subgroups, artefacts that look like compensatory mortality such as sampling correlation, and biases introduced from competing risks. Complex Bayesian models are needed to disentangle these effects. Furthermore, the large dataset (millions of smolts have been marked over the last 20 years) introduces computational challenges in using fitting a Bayesian model to the data. Opportunities are also present to modify where detector arrays are installed which may lead to dramatic improvements in efficiency to detect compensatory effects -- just how big of an improvement can be expected and is it cost effective compared to simply increasing tagging efforts?The statistical methods methods developed are broadly applicable in many wildlife management situations where there are different sources of mortality and a choice of management actions to increase survival at different parts of the life cycle. Is there evidence that the management action would have any impact? As what stage in the lifecycle is the best place to implement such actions?