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。这项研究是由生态学家遇到的实际问题推动的。大部分研究都涉及捕获-再捕获实验。在这些实验中，动物被捕获、标记、释放，然后被重新捕获或重视。捕获和重新捕获/再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。 这项研究的目的是开发新的统计方法来帮助回答这样的问题：“是否有证据表明，哥伦比亚河流域的黑鼬捕食造成了补偿性死亡。”在这种情况下，数以千计的黑猩猩在许多水坝上被贴上带有探测器的PIT(无源集成应答器)标签，这些黑猩猩在下游迁徙和成年后返回时会遇到这些标签。在过去的20年里，随着燕鸥和鹈鹕群体在盆地的扩张，鸟类的捕食量一直在稳步增加。捕食压力在一年内也有所不同。可以在鸟类群体上检测到来自一些被捕食的黑燕的标签，作为鸟类捕食行为的一种衡量标准(在调整了群体上的沉积和检测效率后)。这是一项自然的实验，旨在调查鸟类捕食者造成的死亡与所有其他死亡来源之间的关系。如果没有关系，那么捕食者控制可能对成鱼随后的回归影响不大，可能需要其他方法来帮助恢复濒危种群。 应用于这类数据的标准统计方法不足以区分补偿性死亡率和亚组间生存的异质性、抽样相关性等看起来像补偿性死亡率的伪像以及竞争风险引入的偏差。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20年中标记了数百万个SMOLT)在使用贝叶斯模型对数据进行拟合方面带来了计算挑战。还存在着修改探测器阵列安装位置的机会，这可能导致检测补偿效应的效率的显著提高--预期的改进幅度有多大，与简单地增加标记努力相比，它是否具有成本效益？ 所开发的统计方法方法广泛适用于许多野生动物管理情况，在这些情况下，有不同的死亡来源和在生命周期的不同部分选择增加存活率的管理行动。是否有证据表明管理层的行动会产生影响？作为生命周期中的哪个阶段，最适合执行此类操作？

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?