Centre for Veterinary and Epidemiological Research, Department of Health Management, Atlantic Veterinary College, University Prince Edward Island, Charlottetown, Prince Edward Island, Canada.
Måsøval Fiskeoppdrett AS, Sistranda, Norway.
PLoS One. 2018 Sep 25;13(9):e0204319. doi: 10.1371/journal.pone.0204319. eCollection 2018.
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.
海虱 Lepeophtheirus salmonis (Krøyer) 是一种主要的外寄生虫，影响大多数主要的鲑鱼养殖地区的养殖大西洋鲑。为了控制海虱，投入了大量资源，并且为此目的开发了新技术。确定和了解鲑鱼养殖场中每个笼子里的海虱种群模式如何变化，对于任何海虱控制策略的设计和分析都是重要的一步。挪威密集的监测工作为鲑鱼养殖者和研究人员提供了大量的海虱感染数据。经常记录的参数是每个笼子里的成年雌性海虱数量。这些时间序列数据可以进行描述性分析，使用能够处理此类动态数据的聚类算法来量化时间序列之间的相似性，以便能够识别出笼子之间的群体和模式。我们应用这些算法来研究挪威三个大西洋鲑鱼养殖场中雌性海虱计数的笼子间模式。我们探索了涉及距离测量和原型组合的一系列策略，并使用聚类有效性指数进行了聚类评估。不同距离和质心组合的聚类成员的重复一致性被认为是聚类的一个强有力指标，而这些结果的稳定性则加强了这种可能性。尽管这里没有深入调查聚类的驱动因素，但似乎在放养时的鱼体重和其他管理措施与聚类成员密切相关。除了这些内部驱动因素外，外部感染源也可能导致一组笼子里的海虱感染模式；例如，那些最接近外部来源的笼子。这种探索性方法被证明是鲑鱼养殖场中笼子的一种有用的模式发现工具。
