Statistical Methods and Computational Tools for Marine Animal Movement, Distribution and Population Size

在海洋环境中，越来越多的复杂时空数据正在被收集。这使得海洋科学家（包括生态学家、自然资源保护学家和渔业科学家）能够在比以前更精细的空间和/或时间尺度上提出问题，例如检查特定渔业管理区域内的丰度变化，而不是区域之间的丰度变化。这些新的数据来源，一些来自快速发展的数字技术（例如，用于海洋动物跟踪的全球定位系统），另一些来自迄今未得到充分利用的公民科学努力（例如，在新斯科舍省海滩上发现的夏季水母），以及随之而来的重要问题，需要在时空建模方面取得进展。管理和保护海洋动物具有挑战性，因为它们会迁徙，随着时间的推移会改变分布，而且很难观察到。

Increasingly large amounts of complex spatiotemporal data are being collected in the marine environment. This has enabled ocean scientists (including ecologists, conservationists, and fisheries scientists) to ask questions at finer spatial and/or temporal scales than previously possible, for example to examine variation in abundance within a particular fisheries management area as opposed to between areas. These new data sources, some derived from rapidly advancing digital technologies (e.g., global positioning systems for marine animal tracking) and others from hitherto under-utilized citizen science efforts (e.g., summer jellyfish sightings on Nova Scotia beaches), and the important questions that accompany them demand advancements in spatiotemporal modelling. Managing and conserving marine animals is challenging because they migrate, change their distributions through time, and are difficult to observe. My research program focuses on developing methodology and computational tools to answer important scientific questions related to marine conservation and management. The specific short-term objectives of my research are to: 1) develop methods for choosing the correct number of behavioural states and assessing goodness of fit suitable for both state-space model (SSM) and hidden Markov model (HMM) frameworks for animal movement; 2) incorporate spatial (and other) information into population dynamics models directly, for example to more accurately identify by-catch hotspots; and 3) develop Close Kin Mark Recapture (CKMR) methods for estimating population size for particular species of conservation concern. These objectives will be achieved by advancing standard approaches (e.g., SSMs for animal movement), critically assessing competing approaches (e.g., those for capturing and/or describing spatial dependence) through simulation, and confronting challenging statistical inference settings that are in their infancy (e.g., CKMR) by way of carefully chosen case studies. Every effort will be made to ensure robustness to outliers and other small departures from model assumptions. The methods to be developed will be broadly applicable, but I will focus on species of conservation and management concern in Atlantic Canada. The long-term objective of my research is to continually make available statistical methods and supporting computational tools that ensure Canada can steward its ocean resources with care.