In the context of fisheries stock assessment, tag-recapture data have the potential to improve identification of stock structure and estimate key parameters, including migration/movement rates, natural mortality, selectivity, and growth. Tagging data can be processed on their own, to provide estimates that are independent of the stock assessment process, or they can be incorporated directly into the stock assessment estimation framework using integrated stock assessment models.
The basic principle of tag-recapture is straightforward. Animals are marked in some way and released into the wild again. Prior to release, the animals are typically measured, sexed (if possible) and weighed. When these animals are subsequently recaptured, a wealth of useful scientific information about their population dynamics and the fishery dynamics becomes available.
Objectives
- How to set up a large-scale tag-recapture project and what pitfalls to avoid
- Reviewing the information content and quality of tagging data – simple calculations and visualizations in R (e.g., time at liberty, distance travelled) using real tag recapture data
- Estimating growth, selectivity and mortality from tag-recapture data outside of the stock assessment model
- Insights into stock structure, behaviour, movement, and migration provided by all types of tag-recapture data (i.e., both conventional and electronic tags)
- Integrating tag-recapture data into stock assessment models (the case of the Northeast Atlantic Mackerel, and other examples in Stock Synthesis)
All worked examples and homework will be based on R scripts and real tag-recapture data.
Practicalities
The course is organized as online teaching on 4-8 October 2021, over Microsoft Teams. Participants will follow an online session 4 hours each day from 12 – 16 CET. Participants prepare for the online session by doing exercises intersessionally.
Instructions on how to install the required software (free and open source) and background reading materials will be distributed prior to the course.
Requirements for attending
Participants should have basic knowledge of R and population dynamics/stock assessment theory. Prior experience with stock assessment models is helpful but not required.
Registration
