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A salmon lice prediction model.

Leif Christian Stige¹, Lars Qviller¹, Hildegunn Viljugrein¹

¹Norwegian Veterinary Institute, Elizabeth Stephansens vei 1, Ås N-1433, Norway.

Preventive Veterinary Medicine

|December 20, 2024

Summary

This study presents a predictive model to help salmon farmers manage salmon lice (Lepeophtheirus salmonis) infestations. The model forecasts parasite levels, aiding in compliance and sustainable aquaculture practices.

Keywords:

Bayesian model Parasite control Salmon lice Lepeophtheirus salmonis

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Area of Science:

Aquaculture
Parasitology
Mathematical Modeling

Background:

Salmon lice (Lepeophtheirus salmonis) pose a significant challenge to sustainable salmonid farming in open net pens.
Current management requires weekly manual counts and adherence to thresholds for adult female lice.

Purpose of the Study:

To develop a predictive model for forecasting salmon lice populations in fish farm cages.
To provide decision support for fish farmers to manage lice infestations and prevent exceeding regulatory limits.

Main Methods:

A statistical model was developed to jointly analyze count data for adult female, other motile, and sessile salmon lice stages.
Input variables included current lice counts, infestation pressure, sea temperature, fish weight, and cleaner fish presence.

Main Results:

The model successfully predicted a substantial portion of the variance in salmon lice counts, with 50% of farm-level variance in adult females predicted two weeks ahead.
While effective for adult females, simpler "next week is the same as this week" predictions were comparable for other stages at the farm level.
The model quantifies prediction uncertainty, providing a likely range of outcomes.

Conclusions:

The developed model can serve as valuable decision support for fish farmers in managing salmon lice.
Improved risk assessment regarding lice limits can be achieved through this predictive tool.
This aids in more effective and sustainable salmon farming operations.