DFO Doctoral Dissertation Defense by Benjamin Galuardi

Date:                 Monday, April 28, 2025

Time:                 1:00 p.m.

 

Topic:                Estimating Population-Level Movement Rates of Large Pelagic Species from Electronic Tag Information

Location:          SMAST East, Rooms 101-103

 

Zoom Link:     https://umassd.zoom.us/j/93461632396

Meeting ID: 934 6163 2396

Passcode: 351775

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 (e.g., seasons). 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 of geolocation 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 and 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 and can be beneficial in the estimation process of spatially explicit stock assessments and 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.

ADVISOR(S):                                Dr. Steven X. Cadrin, UMass Dartmouth

                                           (scadrin@umassd.edu )

COMMITTEE MEMBERS:          Geoffrey Cowles, UMass Dartmouth

                                                          Gavin Fay, UMass Dartmouth
Molly Lutcavage, UMass Boston

                                                          Timothy Miller (NEFSC)

NOTE:                All SMAST Students are ENCOURAGED to attend.