Modeling Salmon Louse Resistance to Pest Controls

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By Milthon Lujan

Sea lice of salmon. Source: Nofima
Sea lice of salmon. Source: Nofima

Persistent challenges from pests in agriculture and aquaculture, developing resistance to control measures, demand a nuanced understanding of evolutionary dynamics to craft effective and sustainable pest management strategies.

To comprehend how different factors influence evolution at the metapopulation level, scientists from Deakin University, Nofima, Institute of Marine Research, and Curtin University published the results of an innovative study delving into the evolutionary complexities of the salmon louse (Lepeophtheirus salmonis), a notorious parasite affecting salmon aquaculture in Norway.

Evolutionary Models and Pest Management

The study underscores the opportunity evolutionary models offer to explore how pressures experienced on farms shape the epidemiology and evolution of parasites on large spatial and temporal scales.

Factors Influencing Pest Evolution

The study reveals that the evolution of pest resistance is closely linked to factors such as the type of control methods employed, the selective pressure imposed by these methods, and gene flow between farms. Understanding these factors at the metapopulation level, where pest populations interact through a network of salmon farms, is crucial for designing pest management strategies that curb resistance evolution.

The Salmon Louse Model

Researchers developed a sophisticated model to simulate the metapopulation and evolutionary dynamics of the salmon louse in half of Norway’s salmon farms. Various management scenarios were explored, comparing strategies that differed in implementation methods, impact on the louse life cycle, and overall efficacy.

Continuous vs. Discrete Strategies

The study found that continuous action strategies, such as the use of louse-resistant salmon, were generally more effective than discrete strategies in controlling lice. Strategies increasing louse mortality during early developmental stages demonstrated higher efficacy. However, a cautionary note emphasized the risk of continuous strategies imposing frequent and strong selection on lice, potentially driving rapid adaptation.

Genetic Drift and Resistance Loss

The destiny of resistant alleles was found to be influenced by factors like their recessiveness, fitness advantage, and origin in areas of low agricultural density. Resistant alleles were more likely to be lost due to genetic drift under certain conditions, providing crucial insights for anticipating and managing resistance.

Spatial Dynamics

The north-flowing current along the Norwegian coastline played a fundamental role in dispersing resistant genes from south to north. Understanding these spatial dynamics and limiting gene flow in the opposite direction became key considerations for designing effective pest control strategies.

Practical Implications

The study demonstrates how evolutionary models can provide quantitative predictions on large spatial and temporal scales, offering valuable insights for practical pest management decisions at a regional level. This knowledge helps minimize the risk of resistance development, ensuring the sustainability of salmon aquaculture.

However, it is important to note that the model has limitations. “In our simulations, all fish farms in the study area imposed the same selection pressure. This greatly increased the overall selection strength at the metapopulation level for resistance. In reality, there are multiple technologies available for farms that are implemented heterogeneously, slowing the rate of evolution towards any treatment,” the researchers reported.


The study on salmon lice provides a model for developing specific and sustainable pest management strategies, offering hope for the future of aquaculture.

“We have demonstrated how evolutionary models can not only replicate general evolutionary trends but also make quantitative predictions on how salmon lice can adapt under specific management strategies. These quantitative results can be leveraged during the complex process of practical management decision-making to combat resistance. Our simulations predicted that continuous action strategies increasing chalimus mortality were more successful in reducing lice infestations,” the researchers conclude.

Additionally, the researchers identified aspects of resistance that were more conducive to rapid evolution of sea lice when:

  1. The discrete strategy imposing selection had overall low efficacy,
  2. A continuous strategy imposed selection on chalimus weekly survival,
  3. There was a pronounced selection gradient among genotypes,
  4. Resistance was dominant, or
  5. Resistance initially emerged in southern Norway.

As we continue to refine our understanding of pest evolution, we pave the way for resilient and effective solutions in the ongoing quest for food security.

The study is part of the “CrispResist: Harnessing cross-species variation in sea lice resistance” project, funded by the Norwegian Seafood Research Fund.

Andrew Coates
Sustainable Aquaculture Laboratory – Temperate and Tropical (SALTT), Queenscliff Marine Science Centre, Deakin University
Burwood, Victoria 3225, Australia
Email: andrew.coates@deakin.edu.au

Reference (open access)
Coates, A., Robinson, N. A., Dempster, T., Johnsen, I., & Phillips, B. L. (2023). Evolutionary predictions for a parasite metapopulation: Modelling salmon louse resistance to pest controls in aquaculture. Evolutionary Applications, 00, 1–17. https://doi.org/10.1111/eva.13618

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