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. 2018 Sep 25;13(9):e0204319.
doi: 10.1371/journal.pone.0204319. eCollection 2018.

Time-series clustering of cage-level sea lice data

Ana Rita Marques  1 , Henny Forde  2 , Crawford W Revie  1
Affiliations

Time-series clustering of cage-level sea lice data

Ana Rita Marques et al. PLoS One. .

Abstract

Sea lice Lepeophtheirus salmonis (Krøyer) are a major ectoparasite affecting farmed Atlantic salmon in most major salmon producing regions. Substantial resources are applied to sea lice control and the development of new technologies towards this end. Identifying and understanding how sea lice population patterns vary among cages on a salmon farm can be an important step in the design and analysis of any sea lice control strategy. Norway's intense monitoring efforts have provided salmon farmers and researchers with a wealth of sea lice infestation data. A frequently registered parameter is the number of adult female sea lice per cage. These time-series data can be analysed descriptively, the similarity between time-series quantified, so that groups and patterns can be identified among cages, using clustering algorithms capable of handling such dynamic data. We apply such algorithms to investigate the pattern of female sea lice counts among cages for three Atlantic salmon farms in Norway. A series of strategies involving a combination of distance measures and prototypes were explored and cluster evaluation was performed using cluster validity indices. Repeated agreement on cluster membership for different combinations of distance and centroids was taken to be a strong indicator of clustering while the stability of these results reinforced this likelihood. Though drivers behind clustering are not thoroughly investigated here, it appeared that fish weight at time of stocking and other management practices were strongly related to cluster membership. In addition to these internally driven factors it is also possible that external sources of infestation may drive patterns of sea lice infestation in groups of cages; for example, those most proximal to an external source. This exploratory method proved useful as a pattern discovery tool for cages in salmon farms.

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Conflict of interest statement

We have the following interests: Henny Forde is employed by Måsøval Fiskeoppdrett AS, who provided the data for this study. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.

Figures

Fig 1
Fig 1. Farm I CVI plots.
Cluster validity index plots for the most stable algorithms applied to the time-series data for Farm I in 2012-2013. (A) individual CVI plots; (B) average values for each group of CVIs.
Fig 2
Fig 2. Farm I clustering for 2012-2013.
Time-series plots for the most stable clustering algorithms for Farm I, production cycle of 2012-2013.
Fig 3
Fig 3. Farm I clustering for 2014-2015.
Time-series plots for the most stable clustering algorithms for Farm I, production cycle of 2014-2015.
Fig 4
Fig 4. Farm I clustering for 2016-2017.
Time-series plots for the most stable clustering algorithm for Farm I, production cycle of 2016-2017.
Fig 5
Fig 5. Farm II clustering per production cycle.
Time series plots for the most stable clustering algorithm for Farm II, one from each production cycle.
Fig 6
Fig 6. Farm III clustering per production cycle.
Time series plots for the most stable clustering algorithm for Farm III, one from each production cycle.
See this image and copyright information in PMC

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