Connected networks of sea lice populations : dynamics and implications for control
This work was supported by a grant from the European Fisheries Fund (European Union). Date of Acceptance: 14/04/2015 In 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 diffe...
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ftstandrewserep:oai:research-repository.st-andrews.ac.uk:10023/7148 2023-07-02T03:31:43+02:00 Connected networks of sea lice populations : dynamics and implications for control Adams, Thomas P. Proud, Roland Black, Kenneth D. University of St Andrews. School of Biology University of St Andrews. Pelagic Ecology Research Group University of St Andrews. Scottish Oceans Institute 2015-08-07T11:10:04Z 12 application/pdf http://hdl.handle.net/10023/7148 https://doi.org/10.3354/aei00133 eng eng Aquaculture Environment Interactions Adams , 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/aei00133 1869-215X PURE: 207998466 PURE UUID: aef418ed-4d7a-4c35-94c7-c63c63a68690 WOS: 000355884400006 Scopus: 84929868825 ORCID: /0000-0002-8647-5562/work/35710931 http://hdl.handle.net/10023/7148 https://doi.org/10.3354/aei00133 © The authors 2015. Open Access under Creative Commons by Attribution Licence. Use, distribution and reproduction are unrestricted. Authors and original publication must be credited. Metapopulation Spatial dynamics Dispersal Population connectivity Sea lice management Salmon salmo-salar Farmed Atlantic salmon Louse lepeophtheirus-salmonis Mathematical-model Scotland Hardangerfjord Dispersion Infection Growth Trout QH301 Biology QL Zoology QH301 QL Journal article 2015 ftstandrewserep https://doi.org/10.3354/aei00133 2023-06-13T18:27:35Z This work was supported by a grant from the European Fisheries Fund (European Union). Date of Acceptance: 14/04/2015 In 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. Publisher PDF Peer reviewed Article in Journal/Newspaper Atlantic salmon Salmo salar University of St Andrews: Digital Research Repository Aquaculture Environment Interactions 6 3 273 284 |
institution |
Open Polar |
collection |
University of St Andrews: Digital Research Repository |
op_collection_id |
ftstandrewserep |
language |
English |
topic |
Metapopulation Spatial dynamics Dispersal Population connectivity Sea lice management Salmon salmo-salar Farmed Atlantic salmon Louse lepeophtheirus-salmonis Mathematical-model Scotland Hardangerfjord Dispersion Infection Growth Trout QH301 Biology QL Zoology QH301 QL |
spellingShingle |
Metapopulation Spatial dynamics Dispersal Population connectivity Sea lice management Salmon salmo-salar Farmed Atlantic salmon Louse lepeophtheirus-salmonis Mathematical-model Scotland Hardangerfjord Dispersion Infection Growth Trout QH301 Biology QL Zoology QH301 QL Adams, Thomas P. Proud, Roland Black, Kenneth D. Connected networks of sea lice populations : dynamics and implications for control |
topic_facet |
Metapopulation Spatial dynamics Dispersal Population connectivity Sea lice management Salmon salmo-salar Farmed Atlantic salmon Louse lepeophtheirus-salmonis Mathematical-model Scotland Hardangerfjord Dispersion Infection Growth Trout QH301 Biology QL Zoology QH301 QL |
description |
This work was supported by a grant from the European Fisheries Fund (European Union). Date of Acceptance: 14/04/2015 In 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. Publisher PDF Peer reviewed |
author2 |
University of St Andrews. School of Biology University of St Andrews. Pelagic Ecology Research Group University of St Andrews. Scottish Oceans Institute |
format |
Article in Journal/Newspaper |
author |
Adams, Thomas P. Proud, Roland Black, Kenneth D. |
author_facet |
Adams, Thomas P. Proud, Roland Black, Kenneth D. |
author_sort |
Adams, Thomas P. |
title |
Connected networks of sea lice populations : dynamics and implications for control |
title_short |
Connected networks of sea lice populations : dynamics and implications for control |
title_full |
Connected networks of sea lice populations : dynamics and implications for control |
title_fullStr |
Connected networks of sea lice populations : dynamics and implications for control |
title_full_unstemmed |
Connected networks of sea lice populations : dynamics and implications for control |
title_sort |
connected networks of sea lice populations : dynamics and implications for control |
publishDate |
2015 |
url |
http://hdl.handle.net/10023/7148 https://doi.org/10.3354/aei00133 |
genre |
Atlantic salmon Salmo salar |
genre_facet |
Atlantic salmon Salmo salar |
op_relation |
Aquaculture Environment Interactions Adams , 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/aei00133 1869-215X PURE: 207998466 PURE UUID: aef418ed-4d7a-4c35-94c7-c63c63a68690 WOS: 000355884400006 Scopus: 84929868825 ORCID: /0000-0002-8647-5562/work/35710931 http://hdl.handle.net/10023/7148 https://doi.org/10.3354/aei00133 |
op_rights |
© The authors 2015. Open Access under Creative Commons by Attribution Licence. Use, distribution and reproduction are unrestricted. Authors and original publication must be credited. |
op_doi |
https://doi.org/10.3354/aei00133 |
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Aquaculture Environment Interactions |
container_volume |
6 |
container_issue |
3 |
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273 |
op_container_end_page |
284 |
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