Evolutionary predictions for a parasite metapopulation: Modelling salmon louse resistance to pest controls in aquaculture

Publisert 2023

Sammendrag

Pests often evolve resistance to pest controls used in agriculture and aquaculture.
The rate of pest adaptation is influenced by the type of control, the selective pressure
it imposes, and the gene flow between farms. By understanding how these factors
influence evolution at the metapopulation level, pest management strategies that
prevent resistance from evolving can be developed. We developed a model for the
metapopulation and evolutionary dynamics of the salmon louse (Lepeophtheirus salmonis), which is a major parasite affecting salmon aquaculture. Different management
scenarios were simulated across a network of salmon farms covering half of Norway,
and their effects on louse epidemiology and evolution were investigated. We compared louse controls that differed in how they were deployed through time (discrete
vs. continuous), how they impacted the louse life cycle, and in their overall efficacy.
We adjusted the strength of selection imposed by treatments, the dominance effect
of the resistant allele, and the geographic location at which resistance originated.
Continuously acting strategies (e.g., louse-resistant salmon) were generally more effective than discrete strategies at controlling lice, especially when they increased
louse mortality during early developmental stages. However, effective strategies also
risked imposing frequent and/or strong selection on lice, thus driving rapid adaptation. Resistant alleles were more likely to be lost through genetic drift when they
were recessive, had a low-fitness advantage, or originated in low-farm-density areas.
The north-flowing current along the Norwegian coastline dispersed resistant genes
from south to north, and limited gene flow in the opposite direction. We demonstrate
how evolutionary models can produce quantitative predictions over large spatial and
temporal scales and for a range of pest control scenarios. Quantitative outputs can be
translated into practical management decisions applied at a regional level to minimise
the risk of resistance developing.