Methods for modeling population dynamics

动物种群的现代管理和保护需要对种群动态参数进行可靠、准确的估计。 对来自新标记设备的数据的分析（例如，来自相机陷阱的照片、用于较小动物的轻型标签、GPS标签、基因标签）以及对多个数据源的合并（例如，捕获-再捕获、公民科学计数数据、eDNA）对生态学家和管理者都提出了统计方法上的挑战。 这种知识差距的后果往往是初步的分析，不能利用数据中的所有信息，并可能提供误导性的结果。 我的研究项目侧重于开发新的统计方法和计算产品，以回答与保护和管理人口动态相关的重要科学问题。具体的短期目标是：1）使用利用多种数据类型的方法开发种群规模和生命率的可靠估计; 2）开发新方法，将标签丢失纳入捕获-再捕获模型，包括完全标签丢失; 3）开发扩展批次标记实验的方法，以纳入协变量，如年龄和大小信息，以及标签丢失;和4）探索通过时间和/或多个站点复制的计数数据模型的鲁棒性，并与捕获-再捕获估计值（包括确定参数冗余问题）进行比较，以协变量信息用于野生动物和流行病学应用。 这些目标将通过使用最先进的方法开发新方法来实现，例如隐马尔可夫模型（用于批次标记和计数数据模型），潜在多项式模型（用于模拟完整的标签丢失）和贝叶斯分层模型（用于综合人口模型），同时通过模拟对这些方法和竞争方法进行严格评估。 将努力制定评估拟合优度的方法，并探索偏离模型假设的情况。 这些方法将被广泛应用，通常通过在生态学（例如大沽河上的鲑鱼，石灰岩岛上的古代Muralts）和流行病学（温哥华岛上无家可归的人口）的合作现实世界的研究。 我的长期研究目标是使这些新的统计方法，以解决加拿大渔业科学家，野生动物管理者，生态学家和流行病学家面临的当前应用问题。

Modern management and conservation of animal populations requires robust, accurate estimates of population dynamics parameters. The analysis of data from new tagging devices (e.g. photographs from camera traps, light-weight tags for smaller animals, GPS tags, genetic tags) as well as the incorporation of multiple data sources (e.g. capture-recapture, citizen science count data, eDNA) has presented statistical methodology challenges for ecologists and managers alike. The consequence of this knowledge gap is often rudimentary analyses that do not harness all information in the data and may provide misleading results. My research program focusses on developing novel statistical methodology and computational products to answer important scientific questions related to population dynamics for conservation and management. Specific short-term goals are to: 1) develop robust estimates of population size and vital rates using approaches that take advantage of multiple data types; 2) develop new methods to incorporate tag loss in capture-recapture models including complete tag loss; 3) develop methods for extended batch marking experiments to incorporate covariates such as age and size information, and tag loss; and 4) explore the robustness of models for count data replicated through time and/or multiple sites with covariate information for wildlife and epidemiological applications in comparison to capture-recapture estimates including determination of parameter redundancy issues. These objectives will be achieved by developing new methods using state of the art methodology such as hidden Markov models (for batch marking and count data models), latent multinomial models (for modelling complete tag loss), and Bayesian hierarchical models (for integrated population models) while critically assessing these and competing approaches through simulation. Efforts will be made to develop methods to assess goodness-of-fit and explore deviations from model assumptions. These methods will be widely applicable, often through collaborative real-world studies in both ecology (for example salmon on the Taku river, Ancient Murrelets on Limestone Island) and epidemiology (homeless populations on Vancouver Island). My long-term research goal is to make these novel statistical methods available to tackle current applied problems faced by Canadian fisheries scientists, wildlife managers, ecologists, and epidemiologists.