Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36555
Appears in Collections:Aquaculture Journal Articles
Peer Review Status: Refereed
Title: A gap analysis on modelling of sea lice infection pressure from salmonid farms: (I) A structured knowledge review
Author(s): Moriarty, Meadhbh
Murphy, Joanne M
Brooker, Adam J
Waites, William
Revie, Crawford W
Adams, Thomas P
Lewis, Matt
Reinardy, Helena C
Phelan, John P
Coyle, Johnny P
Rabe, Berit
Ives, Stephen C
Armstrong, John D
Sandvik, Anne D
Asplin, Lars
Contact Email: a.j.brooker@stir.ac.uk
Keywords: Aquaculture
Salmon louse
Environmental interactions
Dispersal modelling
Population modelling
Issue Date: 18-Jan-2024
Date Deposited: 27-Nov-2024
Citation: Moriarty M, Murphy JM, Brooker AJ, Waites W, Revie CW, Adams TP, Lewis M, Reinardy HC, Phelan JP, Coyle JP, Rabe B, Ives SC, Armstrong JD, Sandvik AD & Asplin L (2024) A gap analysis on modelling of sea lice infection pressure from salmonid farms: (I) A structured knowledge review. <i>Aquaculture Environment Interactions</i>, 16, pp. 1-25. https://doi.org/10.3354/aei00469
Abstract: Sustainability of aquaculture, an important component of the blue economy, relies in part on ensuring assessment of environmental impact and interactions relating to sea lice dispersing from open pen salmon and trout farms. We review research underpinning the key stages in the sea lice infection process to support modelling of lice on wild salmon in relation to those on farms. The review is split into five stages: larval production; larval transport and survival; exposure and infestation of new hosts; development and survival of the attached stages; and impact on host populations. This modular structure allows the existing published data to be reviewed and assessed to identify data gaps in modelling sea lice impacts in a systematic way. Model parameterisation and parameter variation is discussed for each stage, providing an overview of knowledge strength and gaps. We conclude that a combination of literature review, empirical data collection and modelling studies are required on an iterative basis to ensure best practice is applied for sustainable aquaculture. The knowledge gained can then be optimised and applied at regional scales, with the most suitable modelling frameworks applied for the system, given regional limitations.
DOI Link: 10.3354/aei00469
Rights: © A.J.B., W.W., C.W.R., T.P.A, M.L., H.C.R., J.P.P., J.P.C, A,D.S., L.A., Ø.K., G.à.N., P.A.G., K.S.L. and The Crown 2024. Open Access under Creative Commons by Attribution Licence. Use, distribution and reproduction are un restricted. Authors and original publication must be credited.
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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