Managing aquatic parasites for reduced drug resistance: lessons from the land

McEwan, Gregor; Groner, Maya L.; Burnett, Danielle L.; Fast, Mark D.; Revie, Crawford W.

doi:10.1098/rsif.2016.0830

Cited by 13 publications

(18 citation statements)

References 40 publications

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“…Therefore, bio-physical models can potentially produce far better estimates for sea lice larval modeling (assuming model assumptions are realistic), in order to adequately characterize sea lice networks. Given the complexity of problems surrounding sea lice control globally, such as the development of populations resistant to treatments and the high cost of administering treatments ( 26 , 52 , 74 – 76 ), spatial planning considering sea lice epidemiology and larval dispersal capabilities is crucial for the sustainability of the industry.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2018

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Connectivity in an aquatic setting is determined by a combination of hydrodynamic circulation and the biology of the organisms driving linkages. These complex processes can be simulated in coupled biological-physical models. The physical model refers to an underlying circulation model defined by spatially-explicit nodes, often incorporating a particle-tracking model. The particles can then be given biological parameters or behaviors (such as maturity and/or survivability rates, diel vertical migrations, avoidance, or seeking behaviors). The output of the bio-physical models can then be used to quantify connectivity among the nodes emitting and/or receiving the particles. Here we propose a method that makes use of kernel density estimation (KDE) on the output of a particle-tracking model, to quantify the infection or infestation pressure (IP) that each node causes on the surrounding area. Because IP is the product of both exposure time and the concentration of infectious agent particles, using KDE (which also combine elements of time and space), more accurately captures IP. This method is especially useful for those interested in infectious agent networks, a situation where IP is a superior measure of connectivity than the probability of particles from each node reaching other nodes. Here we illustrate the method by modeling the connectivity of salmon farms via sea lice larvae in the Broughton Archipelago, British Columbia, Canada. Analysis revealed evidence of two sub-networks of farms connected via a single farm, and evidence that the highest IP from a given emitting farm was often tens of kilometers or more away from that farm. We also classified farms as net emitters, receivers, or balanced, based on their structural role within the network. By better understanding how these salmon farms are connected to each other via their sea lice larvae, we can effectively focus management efforts to minimize the spread of sea lice between farms, advise on future site locations and coordinated treatment efforts, and minimize any impact of farms on juvenile wild salmon. The method has wide applicability for any system where capturing infectious agent networks can provide useful guidance for management or preventative planning decisions.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2018

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“…In general, the application of fewer treatments will reduce selection for tolerance. Data from terrestrial systems and mathematical models of aquaculture systems suggest that combination treatments, which kill pests using multiple mechanisms, have more effective control and result in a slower rate of resistance (or tolerance) evolution than applying a single treatment ( REX Consortium 2013, McEwan et al 2016. This likely occurs during wellboat treatments as a result of physical disruption of the sea lice followed by filtration of the water (Reynolds 2013, Powell et al 2015.…”

Section: Strategies To Avoid Tolerance Evolutionmentioning

confidence: 99%

Evaluating the potential for sea lice to evolve freshwater tolerance as a consequence of freshwater treatments in salmon aquaculture

Groner¹,

Laurin²,

Stormoen³

et al. 2019

Aquacult. Environ. Interact.

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Increasing usage of non-medicinal methods (NMMs) to control sea louse infestations on salmon farms has raised questions about whether sea lice may be able to evolve tolerance of NMMs. Of particular concern is the potential for sea lice to evolve freshwater tolerance as a result of freshwater treatments. Wild trout and some juvenile salmonids swim into freshwater to control infestations and regain ionic balance after disruption by sea lice; freshwater tolerance would compromise this potentially adaptive behavior. Here we evaluated the potential for freshwater tolerance to evolve in the sea louse Lepeophtheirus salmonis. When exposed to low-salinity water, parasitic stages of sea lice are able to osmoregulate through the host, while larval planktonic stages are not. Transcriptomic work suggests that sea lice mount a costly polygenic stress response when exposed to brackish water. The population structure of sea lice is panmictic in both the Pacific and Atlantic, making it conducive to rapid evolutionary responses. It is unknown how much heritable genetic variation these panmictic populations have for freshwater treatments. While usage of freshwater treatments on wellboats is increasing, it is unclear whether the freshwater itself is a strong selective force; during the freshwater exposure, sea lice can die from physical disruption during pumping and filtration on the wellboat. Future studies are advised to quantify the heritable variation in freshwater tolerance in sea louse populations, characterize mechanisms for freshwater tolerance in planktonic and attached sea lice, and assess the risk of freshwater tolerance evolution under different management strategies.

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“…For this study, we modified an ABM that we have used for previous studies to explore sea louse infestations on Atlantic salmon farms (McEwan et al 2015(McEwan et al , 2016. In this section, we describe how this model has been adapted for this study.…”

Section: Modelmentioning

confidence: 99%

“…Myhre Jensen et al 2017). Such a level of efficacy would not last for long as the sea lice would develop resistance (explored by McEwan et al 2016), but in this study, each simulation was only run for a single production cycle.…”

Section: 32mentioning

confidence: 99%

Modelling sea lice control by lumpfish on Atlantic salmon farms: interactions with mate limitation, temperature and treatment rules

et al. 2019

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Atlantic salmon farming is one of the largest aquaculture sectors in the world. A major impact on farm economics, fish welfare and, potentially, nearby wild salmonid populations, is the sea louse ectoparasite Lepeophtheirus salmonis. Sea louse infestations are most often controlled through application of chemicals, but in most farming regions, sea lice have evolved resistance to the small set of available chemicals. Therefore, alternative treatment methodologies are becoming more widely used. One increasingly common alternative treatment involves the co-culture of farmed salmon with cleaner fish, which prey on sea lice. However, despite their wide use, little is understood about the situations in which cleaner fish are most effective. For example, previous work suggests that a low parasite density results in sea lice finding it difficult to acquire mates, reducing fecundity and population growth. Other work suggests that environmental conditions such as temperature and external sea louse pressure have substantial impact on this mate limitation threshold and may even remove the effect entirely. We used an Agent-Based Model (ABM) to simulate cleaner fish on a salmon farm to explore interactions between sea louse mating behaviour, cleaner fish feeding rate, temperature and external sea louse pressure. We found that sea louse mating has a substantial effect on sea louse infestations under a variety of environmental conditions. Our results suggest that cleaner fish can control sea louse infestations most effectively by maintaining the population below critical density thresholds.

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Managing aquatic parasites for reduced drug resistance: lessons from the land

Cited by 13 publications

References 40 publications

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

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

Evaluating the potential for sea lice to evolve freshwater tolerance as a consequence of freshwater treatments in salmon aquaculture

Modelling sea lice control by lumpfish on Atlantic salmon farms: interactions with mate limitation, temperature and treatment rules

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