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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2018-09-01
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| Series: | Epidemics |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1755436517301731 |
| _version_ | 1818942563373023232 |
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| 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 |
| collection | DOAJ |
| 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 |
| first_indexed | 2024-12-20T07:13:25Z |
| format | Article |
| id | doaj.art-3a97fb912064419e83648a6b78d45ec2 |
| institution | Directory Open Access Journal |
| issn | 1755-4365 |
| language | English |
| last_indexed | 2024-12-20T07:13:25Z |
| publishDate | 2018-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Epidemics |
| spelling | doaj.art-3a97fb912064419e83648a6b78d45ec22022-12-21T19:48:50ZengElsevierEpidemics1755-43652018-09-01247687Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmonAdel Elghafghuf0Raphael Vanderstichel1Sophie St-Hilaire2Henrik Stryhn3Corresponding author.; Centre for Veterinary Epidemiological Research, Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, C1A 4P3, CanadaCentre for Veterinary Epidemiological Research, Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, C1A 4P3, CanadaCentre for Veterinary Epidemiological Research, Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, C1A 4P3, CanadaCentre for Veterinary Epidemiological Research, Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, C1A 4P3, CanadaSea 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 salmonhttp://www.sciencedirect.com/science/article/pii/S1755436517301731 |
| spellingShingle | 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 Epidemics |
| title | 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_short | 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 |
| url | http://www.sciencedirect.com/science/article/pii/S1755436517301731 |
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