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挪威海水养殖鲑鱼场之间和场内鲑虱传播的时空建模。

Space-time modelling of the spread of salmon lice between and within Norwegian marine salmon farms.

机构信息

Norwegian Computing Center, Oslo, Norway.

出版信息

PLoS One. 2013 May 20;8(5):e64039. doi: 10.1371/journal.pone.0064039. Print 2013.

DOI:10.1371/journal.pone.0064039
PMID:23700455
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3659056/
Abstract

Parasitic salmon lice are potentially harmful to salmonid hosts and farm produced lice pose a threat to wild salmonids. To control salmon lice infections in Norwegian salmonid farming, numbers of lice are regularly counted and lice abundance is reported from all salmonid farms every month. We have developed a stochastic space-time model where monthly lice abundance is modelled simultaneously for all farms. The set of farms is regarded as a network where the degree of contact between farms depends on their seaway distance. The expected lice abundance at each farm is modelled as a function of i) lice abundance in previous months at the same farm, ii) at neighbourhood farms, and iii) other, unspecified sources. In addition, the model includes explanatory variables such as seawater temperature and farm-numbers of fish. The model gives insight into factors that affect salmon lice abundance and contributing sources of infection. New findings in this study were that 66% of the expected salmon lice abundance was attributed to infection within farms, 28% was attributed to infection from neighbourhood farms and 6% to non-specified sources of infection. Furthermore, we present the relative risk of infection between neighbourhood farms as a function of seaway distance, which can be viewed as a between farm transmission kernel for salmon lice. The present modelling framework lays the foundation for development of future scenario simulation tools for examining the spread and abundance of salmon lice on farmed salmonids under different control regimes.

摘要

寄生性鲑鱼虱对鲑鱼宿主具有潜在危害,养殖的虱类对野生鲑鱼也构成威胁。为了控制挪威鲑鱼养殖中的鲑鱼虱感染,定期对虱类进行计数,并每月报告所有鲑鱼养殖场的虱类丰度。我们开发了一个随机时空模型,同时对所有养殖场的每月虱类丰度进行建模。该养殖场集合被视为一个网络,其中养殖场之间的接触程度取决于它们的海路距离。每个养殖场的预期虱类丰度被建模为以下三个因素的函数:i)同一养殖场前几个月的虱类丰度,ii)邻近养殖场的虱类丰度,iii)其他未指定的来源。此外,该模型还包括海水温度和养殖场鱼类数量等解释变量。该模型深入了解了影响鲑鱼虱丰度的因素和感染的来源。本研究的新发现是,66%的预期鲑鱼虱丰度归因于养殖场内的感染,28%归因于邻近养殖场的感染,6%归因于未指定来源的感染。此外,我们还展示了邻近养殖场之间感染的相对风险作为海路距离的函数,这可以看作是鲑鱼虱的养殖场间传播核。目前的建模框架为开发未来的情景模拟工具奠定了基础,可用于在不同控制机制下检查养殖鲑鱼中鲑鱼虱的传播和丰度。

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