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dc.contributor.authorAdams, Thomas P.
dc.contributor.authorProud, Roland
dc.contributor.authorBlack, Kenneth D.
dc.date.accessioned2015-08-07T11:10:04Z
dc.date.available2015-08-07T11:10:04Z
dc.date.issued2015-05
dc.identifier207998466
dc.identifieraef418ed-4d7a-4c35-94c7-c63c63a68690
dc.identifier000355884400006
dc.identifier84929868825
dc.identifier000355884400006
dc.identifier.citationAdams , T P , Proud , R & Black , K D 2015 , ' Connected networks of sea lice populations : dynamics and implications for control ' , Aquaculture Environment Interactions , vol. 6 , no. 3 , pp. 273-284 . https://doi.org/10.3354/aei00133en
dc.identifier.issn1869-215X
dc.identifier.otherORCID: /0000-0002-8647-5562/work/35710931
dc.identifier.urihttps://hdl.handle.net/10023/7148
dc.descriptionThis work was supported by a grant from the European Fisheries Fund (European Union). Date of Acceptance: 14/04/2015en
dc.description.abstractIn studies of the population dynamics of parasitic sea lice and the implications of outbreaks for salmon farms, several types of mathematical models have been implemented. Delay differential equation models describe the temporal dynamics of average adult lice densities over many farm sites. In contrast, larval transport models consider the relative densities of lice at farm sites by modelling larval movements between them but do not account for temporal dynamics or feedbacks created by reproduction. Finally, several recent studies have investigated spatiotemporal variation in site lice abundances using statistical models and distance-based proxies for connectivity. We developed a model which integrates connectivity estimates from larval transport models into the delay differential equation framework. This allows representation of sea lice developmental stages, dispersal between sites, and the impact of management actions. Even with identical external infection rates, lice abundances differ dramatically between farms over a production cycle (dependent on oceanographic conditions and resulting between-farm connectivity). Once infected, lice dynamics are dominated by site reproduction and subsequent dispersal. Lice control decreases actual lice abundances and also reduces variation in abundance between sites (within each simulation) and between simulation runs. Control at sites with the highest magnitude of incoming connections, computed directly from connectivity modelling, had the greatest impact on lice abundances across all sites. Connectivity metrics may therefore be a reasonable approximation of the effectiveness of management practices at particular sites. However, the model also provides new opportunities for investigation and prediction of lice abundances in interconnected systems with spatially varying infection and management.
dc.format.extent12
dc.format.extent641622
dc.language.isoeng
dc.relation.ispartofAquaculture Environment Interactionsen
dc.subjectMetapopulationen
dc.subjectSpatial dynamicsen
dc.subjectDispersalen
dc.subjectPopulation connectivityen
dc.subjectSea lice managementen
dc.subjectSalmon salmo-salaren
dc.subjectFarmed Atlantic salmonen
dc.subjectLouse lepeophtheirus-salmonisen
dc.subjectMathematical-modelen
dc.subjectScotlanden
dc.subjectHardangerfjorden
dc.subjectDispersionen
dc.subjectInfectionen
dc.subjectGrowthen
dc.subjectTrouten
dc.subjectQH301 Biologyen
dc.subjectQL Zoologyen
dc.subject.lccQH301en
dc.subject.lccQLen
dc.titleConnected networks of sea lice populations : dynamics and implications for controlen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Pelagic Ecology Research Groupen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.identifier.doi10.3354/aei00133
dc.description.statusPeer revieweden


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