Dynamic Spatial Forecasting of Sea Lice Abundances

Sea lice, Lepeophtheirus salmonis, are a major challenge to both aquaculture and wild salmon populations. L. salmonis infect salmonids, feeding on mucous, skin and flesh of infected fish, reducing fitness and increasing morbidity. The management of sea lice is a key consideration for farm operators and governments. This necessitates a comprehensive understanding of their biology, dispersal, and population dynamics to inform management strategies for sustainable production of salmonids. This thesis explores the current modelling approaches to the planktonic and attached stages of L. salmonis and explores a method of combining these modelling approaches. A combined modelling approach will increase understanding of the dynamics of sea lice populations on farms, improve management strategies with greater understanding and ultimately reduce infestation pressure on farmed and wild salmonids.<br/>The first chapter reviews existing modelling approaches for sea lice population<br/>dynamics, highlighting limitations and justifying the need for integrated methods<br/>that account for both environmental and biological factors. This sets the foundation for the subsequent investigations.<br/>The second chapter investigates larval dispersal and connectivity between<br/>salmon farms in Loch Linnhe and Loch Sunart on Scotland’s west coast. Using<br/>a hydrodynamic model coupled with a particle tracking tool, the chapter simulates larval transport based on oceanographic factors such as currents, tides, and salinity, as well as biological parameters including mortality and development rates. It reveals distinct spatio-temporal dispersal patterns, with exposed locations like Bloody Bay experiencing greater larval movement compared to more sheltered sites. Connectivity analyses demonstrate seasonal and daily variability in infestation pressure between farms, underscoring the importance of coordinated management practices to minimize larval transmission.<br/>The third chapter employs delay differential equations and matrix population<br/>models to explore the attached life-stage dynamics of L. salmonis. These models are driven by observed chalimus data to estimate the impact of factors such as mortality, development times, and attachment rates on population growth. The chapter highlights challenges in accurately modelling the later attached stages of L. salmonis due to variability in observed data and uncertainties in chalimus counts. Adjustments to chalimus proportions reveal the sensitivity of model outputs to these parameters, emphasizing the need for improved data collection and model refinement.<br/>The fourth chapter describes a method of integrating the output of particle<br/>tracking tools with population models to describe the whole life cycle of L. salmonis. By combining hydrodynamic simulations with population dynamics, the chapter evaluates the feedback loop between larval source populations and farm infestations. The models account for environmental influences and management interventions, such as treatment applications, to assess their impact on population dynamics. Challenges such as over-prediction of larval attachment and the exclusion of vertical larval movement are discussed, alongside recommendations for model improvement.<br/>The final chapter synthesizes the findings, discusses their implications for L.<br/>salmonis management, and outlines future research directions. By addressing key limitations in current approaches, this thesis contributes to the development of more effective, data-driven strategies for controlling sea lice populations and ensuring sustainable aquaculture practices.

Name dc:type.qualificationname

Doctor of Philosophy (awarded by UHI)

Level dc:type.qualificationlevel

Doctoral Thesis

Grantor dc:publisher.institution

University of the Highlands and Islands

Year dc:date.issued

2025

Author dc:creator

• Phelan, John

Advisors dc:contributor.advisor

• Burrows, Michael
• Davidson, Keith

Language dc:language

eng

Identifier

oai:pure.atira.dk:studenttheses/7f54043a-ca2e-4c0d-b2f8-cc40d919f67c

OAI identifier oai:identifier

oai:pure.atira.dk:studenttheses/7f54043a-ca2e-4c0d-b2f8-cc40d919f67c