ArticleJournal of the Royal Society, Interface2016
Managing aquatic parasites for reduced drug resistance: lessons from the land.
Article in Journal of the Royal Society, Interface, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 19 citations in OpenAlex.
- Modelling the Effectiveness of Gene-Edited Salmon at Sea Lice Control and the Use of Refugia to Mitigate Counter-Adaptation.Evolutionary applications · 2025Article
- Evidence for the Evolution of Resistance to Non-Chemical Parasite Controls: Salmon Lice From Submerged Cages Produce Larvae That Swim Deeper.Evolutionary applications · 2025Article
- Evolutionary predictions for a parasite metapopulation: Modelling salmon louse resistance to pest controls in aquaculture.Evolutionary applications · 2023Article
- Parasite management in aquaculture exerts selection on salmon louse behaviour.Evolutionary applications · 2021Article
- Evaluation of the Infectious Potential ofMicroorganisms · 2021Article
- The Application of a Mathematical Model to Evaluate the Effectiveness of Control Strategies AgainstFrontiers in veterinary science · 2019Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atlantic salmon farming is one of the largest aquaculture industries in the world. A major problem in salmon farms is the sea louse ectoparasite Lepeophtheirus salmonis, which can cause stress, secondary infection and sometimes mortality in the salmon host. Sea lice have substantial impacts on farm economics and potentially nearby wild salmonid populations. The most common method of controlling sea louse infestations is application of chemicals. However, most farming regions worldwide have observed resistance to the small set of treatment chemicals that are available. Despite this, there has been little investigation of treatment strategies for managing resistance in aquaculture. In this article, we compare four archetypical treatment strategies inspired by agriculture, where the topic has a rich history of study, and add a fifth strategy common in aquaculture. We use an agent-based model (ABM) to simulate these strategies and their varying applications of chemicals over time and space. We analyse the ABM output to compare how the strategies perform in controlling louse abundance, number of treatments required and levels of resistance in the sea louse population. Our results indicated that among the approaches considered applying chemicals in combination was the most effective over the long term.
Indexed as
Identifiers
What OpenQuestion holds
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.