Quantitative analysis of the collective movement and migratory behaviour of Atlantic salmon

Kundegorski, Mikolaj Edmund (2025) Quantitative analysis of the collective movement and migratory behaviour of Atlantic salmon. PhD thesis, University of Glasgow.

Abstract

The migration of Atlantic salmon (Salmo salar ) is a complex ecological process of great importance to conservation and ecosystem management. Despite a rapid decline in the Atlantic salmon population, little is known about the fine-scale behaviour of juvenile migration to the sea, which occurs in an environment under strong anthropogenic pressure and results in high mortality.

Juvenile salmon migration involves large quantities of small animals travelling underwater through a complex riverine landscape. It is difficult to study, but recent advances in tracking technology and mathematical methods now allow the behaviour and ecology of downstream migrating salmon to be explored in depth.

This thesis reviews recent advances in the study of Atlantic salmon, and develops further the mathematical and computational methods of movement ecology. These help in forming hypotheses about how the fish behave, leading to laboratory experiments that focus on their responses to flow conditions, social influences during obstacle navigation, and the behavioural differences between wild and hatchery-reared individuals

Impounded waters pose a challenge for migrating smolts due to the lack of strong flow to provide directional cues. In laboratory experiments, I established the baseline flow values that prompt a behavioural response in salmon smolts, necessary for the design of river structures. In another experiment, I showed evidence of collective decision-making in navigating obstacles during movement in an experimental flume. This finding emphasises the density-dependent factors in migration success and necessitates further study of the collective behaviour of this species that have thus far been mostly under-explored. I also show clear differences in behaviour of hatchery animals compared to wild ones, providing guidance on the usability of hatchery smolts in further studies and design of river infrastructure.

Modern machine learning methods allow improved analysis of data in many contexts. Improvements to visual tracking of fish based on deep-learning models are presented, allowing for detailed analysis of movement in laboratory and field experiments where video cameras are becoming ever more prevalent. In this thesis, I present a new visual tracking method that is tailored to correctly predict the movement of animals and tested on simulated data inspired by common movement models.

The Bayesian modelling framework of Approximate Bayesian Computation is leveraged to analyse movement patterns from acoustic telemetry data. This simulation-based method combines the hypotheses about the fine-scale movement with computational methods that allow efficient parallelisation on GPU-accelerated hardware.

This thesis provides insights into the migratory behaviour of Atlantic salmon smolts, with significant implications for conservation efforts, ecological engineering applications, and the design of effective river infrastructure. The findings emphasise the necessity of considering social behaviours and the differences between wild and hatchery fish in both modelling and practical implementations to aid in the preservation of this important species.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: Supported by funding from the James S. McDonnell Foundation Complex Systems Scholar Award 220020441.
• Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences; Q Science > QA Mathematics
• Colleges/Schools: College of Science and Engineering > School of Mathematics and Statistics
• Supervisor's Name: Torney, Professor Colin, Killen, Professor Shaun and Adams, Professor Colin
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-84996
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 07 Apr 2025 14:25
• Last Modified: 07 Apr 2025 14:30
• Thesis DOI: 10.5525/gla.thesis.84996
• URI: https://theses.gla.ac.uk/id/eprint/84996