Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmon
Sea lice are marine parasites affecting salmon farms, and are considered one of the most costly pests of the salmon aquaculture industry. Infestations of sea lice on farms significantly increase opportunities for the parasite to spread in the surrounding ecosystem, making control of this pest a chal...
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ftdoajarticles:oai:doaj.org/article:3a97fb912064419e83648a6b78d45ec2 2023-05-15T15:31:04+02:00 Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmon Adel Elghafghuf Raphael Vanderstichel Sophie St-Hilaire Henrik Stryhn 2018-09-01T00:00:00Z https://doi.org/10.1016/j.epidem.2018.04.002 https://doaj.org/article/3a97fb912064419e83648a6b78d45ec2 EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S1755436517301731 https://doaj.org/toc/1755-4365 1755-4365 doi:10.1016/j.epidem.2018.04.002 https://doaj.org/article/3a97fb912064419e83648a6b78d45ec2 Epidemics, Vol 24, Iss , Pp 76-87 (2018) Infectious and parasitic diseases RC109-216 article 2018 ftdoajarticles https://doi.org/10.1016/j.epidem.2018.04.002 2022-12-31T10:42:04Z Sea lice are marine parasites affecting salmon farms, and are considered one of the most costly pests of the salmon aquaculture industry. Infestations of sea lice on farms significantly increase opportunities for the parasite to spread in the surrounding ecosystem, making control of this pest a challenging issue for salmon producers. The complexity of controlling sea lice on salmon farms requires frequent monitoring of the abundance of different sea lice stages over time. Industry-based data sets of counts of lice are amenable to multivariate time-series data analyses.In this study, two sets of multivariate autoregressive state-space models were applied to Chilean sea lice data from six Atlantic salmon production cycles on five isolated farms (at least 20 km seaway distance away from other known active farms), to evaluate the utility of these models for predicting sea lice abundance over time on farms. The models were constructed with different parameter configurations, and the analysis demonstrated large heterogeneity between production cycles for the autoregressive parameter, the effects of chemotherapeutant bath treatments, and the process-error variance. A model allowing for different parameters across production cycles had the best fit and the smallest overall prediction errors. However, pooling information across cycles for the drift and observation error parameters did not substantially affect model performance, thus reducing the number of necessary parameters in the model. Bath treatments had strong but variable effects for reducing sea lice burdens, and these effects were stronger for adult lice than juvenile lice. Our multivariate state-space models were able to handle different sea lice stages and provide predictions for sea lice abundance with reasonable accuracy up to five weeks out. Keywords: State-space models, State process, Prediction horizon, Sea lice abundance, Atlantic salmon Article in Journal/Newspaper Atlantic salmon Directory of Open Access Journals: DOAJ Articles Epidemics 24 76 87 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Infectious and parasitic diseases RC109-216 |
spellingShingle |
Infectious and parasitic diseases RC109-216 Adel Elghafghuf Raphael Vanderstichel Sophie St-Hilaire Henrik Stryhn Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmon |
topic_facet |
Infectious and parasitic diseases RC109-216 |
description |
Sea lice are marine parasites affecting salmon farms, and are considered one of the most costly pests of the salmon aquaculture industry. Infestations of sea lice on farms significantly increase opportunities for the parasite to spread in the surrounding ecosystem, making control of this pest a challenging issue for salmon producers. The complexity of controlling sea lice on salmon farms requires frequent monitoring of the abundance of different sea lice stages over time. Industry-based data sets of counts of lice are amenable to multivariate time-series data analyses.In this study, two sets of multivariate autoregressive state-space models were applied to Chilean sea lice data from six Atlantic salmon production cycles on five isolated farms (at least 20 km seaway distance away from other known active farms), to evaluate the utility of these models for predicting sea lice abundance over time on farms. The models were constructed with different parameter configurations, and the analysis demonstrated large heterogeneity between production cycles for the autoregressive parameter, the effects of chemotherapeutant bath treatments, and the process-error variance. A model allowing for different parameters across production cycles had the best fit and the smallest overall prediction errors. However, pooling information across cycles for the drift and observation error parameters did not substantially affect model performance, thus reducing the number of necessary parameters in the model. Bath treatments had strong but variable effects for reducing sea lice burdens, and these effects were stronger for adult lice than juvenile lice. Our multivariate state-space models were able to handle different sea lice stages and provide predictions for sea lice abundance with reasonable accuracy up to five weeks out. Keywords: State-space models, State process, Prediction horizon, Sea lice abundance, Atlantic salmon |
format |
Article in Journal/Newspaper |
author |
Adel Elghafghuf Raphael Vanderstichel Sophie St-Hilaire Henrik Stryhn |
author_facet |
Adel Elghafghuf Raphael Vanderstichel Sophie St-Hilaire Henrik Stryhn |
author_sort |
Adel Elghafghuf |
title |
Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmon |
title_short |
Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmon |
title_full |
Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmon |
title_fullStr |
Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmon |
title_full_unstemmed |
Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmon |
title_sort |
using state-space models to predict the abundance of juvenile and adult sea lice on atlantic salmon |
publisher |
Elsevier |
publishDate |
2018 |
url |
https://doi.org/10.1016/j.epidem.2018.04.002 https://doaj.org/article/3a97fb912064419e83648a6b78d45ec2 |
genre |
Atlantic salmon |
genre_facet |
Atlantic salmon |
op_source |
Epidemics, Vol 24, Iss , Pp 76-87 (2018) |
op_relation |
http://www.sciencedirect.com/science/article/pii/S1755436517301731 https://doaj.org/toc/1755-4365 1755-4365 doi:10.1016/j.epidem.2018.04.002 https://doaj.org/article/3a97fb912064419e83648a6b78d45ec2 |
op_doi |
https://doi.org/10.1016/j.epidem.2018.04.002 |
container_title |
Epidemics |
container_volume |
24 |
container_start_page |
76 |
op_container_end_page |
87 |
_version_ |
1766361568024461312 |