Time-series clustering of cage-level sea lice data

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.

Effect of handling and crowding on the susceptibility of Atlantic salmon (Salmo salar L.) to Lepeophtheirus salmonis (Krøyer) copepodids

“…Sea lice count is mandatory in Norway, and a fish farmer must report the amount of sea lice on the fish on a weekly basis to monitor the infestation level and to maintain it below legal thresholds (Abolofia et al., 2017; Marques et al., 2018; Norwegian Government, 2019). This scenario means that fish must be sampled in their cages by netting and maintained out of the water, air‐exposed, before anaesthesia and manual inspection.…”

Using time-series similarity measures to compare animal movement trajectories in ecology

“…In addition, the similarity measures described here could be applied to other timeseries (e.g. Marques et al 2018). For example, temperature-depth recorders (TDR) are often used to record the behaviour of diving animals (seabirds, pinnipeds, etc.).…”

Coupling sparse Cox models with clustering of longitudinal transcriptomics data for trauma prognosis

“…It considers the cost over all possible alignment distances that map time series onto each other as the positive kernel. The GAK distance is considered one of the most coherent measures and has been shown to provide the most stable results compared with other distance measures, as for instance DTW distance [ 38 ]. To reduce global alignment kernel’s complexity, it is used a triangular local kernel for integers where is possible to specify the kernel’s order.…”