A gap analysis on modelling of sea lice infection pressure from salmonid farms. I. A structured knowledge review

Moriarty, Meadhbh and Murphy, Joanne M. and Brooker, Adam J. and Waites, William and Revie, Crawford W. and Adams, Thomas P. and Lewis, Matt and Reinardy, Helena C. and Phelan, John P. and Coyle, Johnny P. and Rabe, Berit and Ives, Stephen C. and Armstrong, John D. and Sandvik, Anne D. and Asplin, Lars and Karlsen, Ørjan and Garnier, Soizic and á Norði, Gunnvør and Gillibrand, Philip A. and Last, Kim S. and Murray, Alexander G. (2024) A gap analysis on modelling of sea lice infection pressure from salmonid farms. I. A structured knowledge review. Aquaculture Environment Interactions, 16. pp. 1-25. ISSN 1869-7534 (https://doi.org/10.3354/aei00469)

[thumbnail of Moriarty-etal-AEI-2024-A-gap-analysis-on-modelling-of-sea-lice-infection-pressure-from-salmonid-farms-I]
Preview
 Text. Filename: Moriarty-etal-AEI-2024-A-gap-analysis-on-modelling-of-sea-lice-infection-pressure-from-salmonid-farms-I.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (2MB)| Preview

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 5 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.

CORE (COnnecting REpositories)