Using Agent-Based Modelling to Predict the Role of Wild Refugia in the Evolution of Resistance of Sea Lice to Chemotherapeutants

A major challenge for Atlantic salmon farming in the northern hemisphere is infestation by the sea louse parasite Lepeophtheirus salmonis. The most frequent method of controlling these sea louse infestations is through the use of chemical treatments. However, most major salmon farming areas have obs...

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Published inPloS one Vol. 10; no. 10; p. e0139128
Main Authors McEwan, Gregor F, Groner, Maya L, Fast, Mark D, Gettinby, George, Revie, Crawford W
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 20.10.2015
Public Library of Science (PLoS)
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Summary:A major challenge for Atlantic salmon farming in the northern hemisphere is infestation by the sea louse parasite Lepeophtheirus salmonis. The most frequent method of controlling these sea louse infestations is through the use of chemical treatments. However, most major salmon farming areas have observed resistance to common chemotherapeutants. In terrestrial environments, many strategies employed to manage the evolution of resistance involve the use of refugia, where a portion of the population is left untreated to maintain susceptibility. While refugia have not been deliberately used in Atlantic salmon farming, wild salmon populations that migrate close to salmon farms may act as natural refugia. In this paper we describe an agent-based model that explores the influence of different sizes of wild salmon populations on resistance evolution in sea lice on a salmon farm. Using the model, we demonstrate that wild salmon populations can act as refugia that limit the evolution of resistance in the sea louse populations. Additionally, we demonstrate that an increase in the size of the population of wild salmon results in an increased effect in slowing the evolution of resistance. We explore the effect of a population fitness cost associated with resistance, finding that in some cases it substantially reduces the speed of evolution to chemical treatments.
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Conceived and designed the experiments: GFM MLG CWR GG. Performed the experiments: GFM. Analyzed the data: GFM MLG CWR MDF GG. Wrote the paper: GFM MLG CWR MDF. Contributed to the design of the model: GFM MLG GG MDF CWR. Model Implementation: GFM MLG. Provided expert experience for parameterisation of the model: MLG MDF CWR. Design of analysis techniques: GFM CWR GG.
Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0139128