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

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.