ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2016
Lessons from sea louse and salmon epidemiology.
Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed, 54 citations in OpenAlex.
- Effects of temporal, geographical and environmental factors on salmon lice (Lepeophtheirus salmonis) levels of Atlantic salmon (Salmo salar) in Ireland.Scientific reports · 2025Article
- Threshold-based disease treatment approach modulates economic, conservation and evolutionary trade-offs in sea louse-salmon aquaculture system.Theoretical ecology · 2025Article
- Temperature affects settlement success of ectoparasitic salmon lice (Lepeophtheirus salmonis) and impacts the immune and stress response of Atlantic salmon (Salmo salar).Journal of fish diseases · 2022Article
- Parasite management in aquaculture exerts selection on salmon louse behaviour.Evolutionary applications · 2021Article
- Discovery and surveillance of viruses from salmon in British Columbia using viral immune-response biomarkers, metatranscriptomics, and high-throughput RT-PCR.Virus evolution · 2021Article
- Parasite induced mortality is context dependent in Atlantic salmon: insights from an individual-based model.Scientific reports · 2019Article
- Parasitic sea louse infestations on wild sea trout: separating the roles of fish farms and temperature.Parasites & vectors · 2018Article
- Association between sea lice (Lepeophtheirus salmonis) infestation on Atlantic salmon farms and wild Pacific salmon in Muchalat Inlet, Canada.Scientific reports · 2018Article
- The Use of Kernel Density Estimation With a Bio-Physical Model Provides a Method to Quantify Connectivity Among Salmon Farms: Spatial Planning and Management With Epidemiological Relevance.Frontiers in veterinary science · 2018Article
- New Paradigms to Help Solve the Global Aquaculture Disease Crisis.PLoS pathogens · 2017Article
- Marine disease impacts, diagnosis, forecasting, management and policy.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2016Article
- Infectious diseases in oyster aquaculture require a new integrated approach.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2016Review
Corrections and comments
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Authors and funding
12 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Effective disease management can benefit from mathematical models that identify drivers of epidemiological change and guide decision-making. This is well illustrated in the host-parasite system of sea lice and salmon, which has been modelled extensively due to the economic costs associated with sea louse infections on salmon farms and the conservation concerns associated with sea louse infections on wild salmon. Consequently, a rich modelling literature devoted to sea louse and salmon epidemiology has been developed. We provide a synthesis of the mathematical and statistical models that have been used to study the epidemiology of sea lice and salmon. These studies span both conceptual and tactical models to quantify the effects of infections on host populations and communities, describe and predict patterns of transmission and dispersal, and guide evidence-based management of wild and farmed salmon. As aquaculture production continues to increase, advances made in modelling sea louse and salmon epidemiology should inform the sustainable management of marine resources.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.