MOVEMENTS OF LIVE SALMONIDS IN SCOTLAND: CONSERVATION OF NETWORK STRUCTURE
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1
University of Stirling, Institute of Aquaculture, United Kingdom
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2
Marine Scotland Science, United Kingdom
Introduction
Infectious diseases are major concerns for aquaculture industries. Horizontal disease transmission due to movements of live fish is considered to be a prominent hazard, and has been associated with previous spread of pathogens in salmon farming in Scotland (Murray et al 2002; 2011). In order to better prevent and control diseases, a risk-based surveillance system, in line with EU directive 2006/88/EC, has been implemented by the Marine Scotland. However, compared to other agriculture sectors, aquaculture is a dynamic and fast-changing industry, and it is challenging for the surveillance system to remain robust and effective. Application of network analysis of live fish movements is a powerful tool for investigating connections between production sites and overall industry structure, in order to identify epidemiological risks. To date, Green et al (2011; 2012) demonstrated that the Scottish live salmonid movement network conserved its properties and structure over two-year periods between 2002 and 2004. Here, we investigate the network structures for a longer period between 2002 and 2011 and implications for the surveillance effort.
Material and Methods
The live fish movement records of salmon and trout farming sites in Scotland were collected by the Fish Health Inspectorate (FHI) during their regular inspections (that can be between 1- and 3-years interval based on their risk categorizations). The movement records contain; date of movement, source and destination sites, species, stages, number of fish moved, and mode of transport. These historical records of 2002-2004 and 2009-2011 were quality controlled, digitized, with repeated fish movements within a same week combined. R Studio and R packages (i.e. igraph, linkcomm and clues) were used to assess the agreement between network partitions across different year. Farming sites were classified into cluster of communities using best-fit partitioning algorithms for respective partitions (i.e. edge betweeness, random walktrap, label propagation, and infomap community). A pair-based measure, the Adjusted Rand Index (Hubert & Arabie 1985) was used to assess the similarity across different clusters.
Results
The total of 559 salmon sites reported 4883 live salmon movements, and 115 trout sites reported 1581 movements during the 2002-2004 and 2009-2011 periods in Scotland. For salmon movement networks, the agreements between different partitions showed a clear negative correlation over time, and maintaining high level of congruency (Adjusted Rand Index > 0.75) up to two-year differences (Fig.1). The agreement between three-year consolidated networks (i.e. 2002-2004 network against 2009-2011 network) was trivial (Adjusted Rand Index = 0.31). There was no correlation of agreements for trout networks between different time points.
[INSERT FIGURE HERE]
Fig.1. The adjusted Rand index as a measure of agreement for network communities based on the live salmonid movements in Scotland (2002-2004, and 2009-2011). “Zero” indicating no departure from random agreements on any pair of points, and “one” indicating two identical clusters. All possible combination of yearly networks are shown.
Discussion and conclusion
Salmon movement networks only conserve their structures up to two-year differences, indicating significant changes in the industry during the study period. The change is indicative of the outcomes of various measures implemented by the industry (e.g. the code of good practice in 2006) and the authority (e.g. aquatic animal health regulations in 2009) reflecting the lessons learnt from past outbreaks of infectious diseases in Scotland. The results also highlight that Scottish salmon industry is dynamic and more frequent updates of models are desirable to assess the epidemiological risks. Further research on simulating disease spread on these networks to evaluate the correlation between a level of network conservation and epidemiological properties, as well as analysis on financial implications would further optimise the targeted surveillance system in Scotland.
Acknowledgements
We thank the Marine Scotland Science and Marine Alliance for Science and Technology for Scotland (MASTS) for funding the research.
References
Green, D.M. et al., (2011) Tools to study trends in community structure: application to fish and livestock trading networks. Preventive veterinary medicine 99; 225–228.
Green D.M., Werkman M. & Munro L.A. (2012) The potential for targeted surveillance of live fish movements in Scotland. Journal of Fish Diseases 35; 29–37.
Hubert, L. and Arabie, P. (1985) Comparing partitions. Journal of Classification 2; 193–218.
Murray A.G., Smith, R.J. & Stagg, R.M. (2002) Shipping and the spread of infectious salmon anemia in Scottish Aquaculture. Emerging Infectious Diseases 8; 1–5.
Murray A.G., Munro L.A., Wallace I.S., Peeler E.J. & Thrush M.A. (2011) Bacterial Kidney Disease: Assessment of risk to Atlantic salmon farms from infection in trout farms and other sources. Scottish Marine and Freshwater Science. Marine Scotland Science. 80p.
Keywords:
Aquaculture,
Scotland,
Epidemiology,
Network analysis,
salmonids
Conference:
AquaEpi I - 2016, Oslo, Norway, 20 Sep - 22 Sep, 2016.
Presentation Type:
Oral
Topic:
Aquatic Animal Epidemiology
Citation:
Yamamoto
K,
Turnbull
J,
Murray
A and
Green
D
(2016). MOVEMENTS OF LIVE SALMONIDS IN SCOTLAND: CONSERVATION OF NETWORK STRUCTURE.
Front. Vet. Sci.
Conference Abstract:
AquaEpi I - 2016.
doi: 10.3389/conf.FVETS.2016.02.00030
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Received:
13 Apr 2016;
Published Online:
14 Sep 2016.
*
Correspondence:
Mr. Koji Yamamoto, University of Stirling, Institute of Aquaculture, Stirling, FK9 4LA, United Kingdom, koji.yamamoto@stir.ac.uk