Abstract
Spatial structure and movement have important implications for stock assessment of highly migratory species and management of fisheries that target them. Telemetry data from electronic tags (E-tags) are valuable for determining habitat utilization and behavior and offer a unique path to providing fishery-independent information on movement rates. The bridge between individual E-tag deployments and population level inference can be summarized through Markovian movement matrices, stratified in space and time. To properly apply E-tag information to populations, a thorough understanding of the limitations of the technologies and the methods by which information is derived is necessary. Chapter 1 provides a review of E-tag types and geolocation techniques for estimated location and location error. A practical integration ofgeolocation models and population level inference is presented in Chapter 2 as a package for the R statistical software, SatTagSim. The methods draw from an advection-diffusion framework to produce simulations based on E-tag geolocation estimates and error structures. The products and methods in Chapter 2 are applied to a large E-tag dataset for Atlantic bluefin tuna (Thunnus thynnus) in Chapter 3. Tagging data from both the eastern and western Atlantic are used to generate seasonal movement matrices. These matrices are designed to be used in a variety of spatially explicit operational stock assessment models and management strategy evaluations. Results suggest that estimates of movement rates were more reliable for simpler movement patterns (e.g., movement among fewer areas). Deriving movement estimates from E-tag data can benefit spatially explicit stock assessments and operating models for simulation testing by providing movement estimates that are independent of fishing patterns. They can be beneficial in the estimation process of spatially explicit stock assessments and operating models for management strategy evaluations. This dissertation provides tools, readily available results for future assessments, and general guidelines on the trade-off between data availability and spatial inference.