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Development of a Sea Lice Surveillance Ontology for Data Integration from Wild Salmon Monitoring Programs on the West Coast of Canada

Thitiwan Patanasatienkul1* and Crawford W. Revie1
  • 1 University of Prince Edward Island, Health Management, Canada

Sea lice are parasitic copepods of the family Caligidae, which infest both wild and farmed salmonids. They feed on the skin, mucus, and blood of hosts, causing wounds and, sometimes, infections (Mustafa 1997). Some lice species (e.g. Lepeophtheirus salmonis) can cause host morbidity and mortality (Pike and Wadsworth 1999; Johnson and Fast 2004). The issue of sea lice infestation on wild salmon in British Columbia has drawn public attention over the past two decades (Groner et al. 2016). A number of surveys and monitoring programs have investigated sea lice infestations on wild salmon along the British Columbia (BC) coast, Canada (Jones and Nemec 2004; Krkošek et al. 2005; Saksida et al. 2011; Patanasatienkul et al. 2013; Elmoslemany et al. 2015). High levels of spatial and inter-annual variation of infestation have been reported in the Broughton Archipelago region (Patanasatienkul et al. 2015; Rees et al. 2015). Additionally, a shift in the dominant sea lice species over time was reported in this area (Patanasatienkul et al. 2013). L. salmonis was found to dominate from 2003 to 2008, while Caligus clemensi appeared to be the more common species from 2009 to 2012. In contrast C. clemensi were rarely observed in the Muchalat Inlet region of BC over this same period (Elmoslemany et al. 2015). These spatio-temporal variations are illustrative of the questions that drive an interest in comparing sea lice infestation levels on wild salmon across different areas of the BC coast. A new research collaboration, which involves salmon producers, first nation communities, the government sector, and university scientists, has been developed to study the epidemiology of sea lice on wild salmon from various sea lice surveillance programs on the BC coast since 2001. It is expected that this will provide a clearer overview of sea lice infestation patterns on wild salmon populations along the BC coast. Different sampling techniques and study designs have been used in these monitoring programs, with variations in the types of information collected on sea lice, including lice counts, species, gender, and developmental stages being enumerated. A range of terms have been used to describe the developmental stages of sea lice, some of which have definitions that vary from one study to another. The diverse terms and storage formats used across the multiple sea lice surveillance programs are a challenge to any data integration process. Data integration has become increasingly important to research within the life sciences (Gomez-Cabrero et al. 2014). There are a variety of approaches to combine data from diverse sources into a system that provides the ability to transparently manage data across multiple datasets (Cruz and Xiao 2005). Biological data integration can be challenging due to their differences in terms of data formats, data dimensions, and levels of disagreement between datasets (Gligorijević and Pržulj 2015). A unifying system to store these data is required for data integration, data analysis, and to ensure their reusability. One increasingly common approach in complex life science setting is the application of ontologies (McArthur et al. 2013); these provide a way of representing knowledge using a set of agreed properties, characteristics, or terms, and the relationships between them (Bard and Rhee 2004). Developing a sea lice surveillance ontology as a standard way of describing sea lice surveillance data would facilitate the integration of sea lice data from multiple sources, as well as potentially improve the study design plans of future sea lice research. This ontology-based data integration approach has been applied to other animal species (Lozano-Fuentes et al. 2013), and may become the accepted method to standardize biological representation of diseases and organisms in veterinary epidemiological research (Dórea et al. 2015). The objectives of this study are to explore the application of an ontology to standardize data structures for surveillance of sea lice on wild salmon based on data collected across multiple monitoring programs on the west coast of Canada. This will support the creation of pragmatic data-driven solutions with optimal data structures to accommodate sea lice data from historical and on-going research. We are in the process of integrating a range of data from differing sea lice surveillance programs. These data involve approximately one million fish sampled at over 300 locations from around a dozen regions along the BC coast over a 16 year period since 2001. Each dataset contains at least two types of information: sampling events and details associated with the fish caught in each sampling, including sea lice counts. A hybrid ontology development approach, involving a combination of local and global ontologies, will be used to develop a sea lice surveillance ontology. First, a local ontology will be created for each dataset, and compared with others to identify properties/fields that are similar or different. These will then be merged into a shared global ontology. Finally, the sea lice data will be integrated into a unified system based on the newly developed sea lice surveillance ontology. The process to develop a sea lice surveillance ontology is illustrated in Figure 1. This research could also provide a template for similar data integration and epidemiological analyses in other salmon farming regions, and facilitate comparative studies of sea lice epidemiology on a global level.

Figure 1
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Acknowledgements

The authors would like to thank BC Salmon Farmer Association (BCSFA) and the Canada Excellence Research Chairs Program in Aquatic Epidemiology (CERC) at the University of Prince Edward Island for project funding. We thank all research collaborators for data sharing, and Mainstream Biological Consulting Inc. for help with the initial data transfers.

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Keywords: sea lice, Wild salmon, surveillance, British Columbia, data integration, ontology

Conference: AquaEpi I - 2016, Oslo, Norway, 20 Sep - 22 Sep, 2016.

Presentation Type: Oral

Topic: Aquatic Animal Epidemiology

Citation: Patanasatienkul T and Revie CW (2016). Development of a Sea Lice Surveillance Ontology for Data Integration from Wild Salmon Monitoring Programs on the West Coast of Canada. Front. Vet. Sci. Conference Abstract: AquaEpi I - 2016. doi: 10.3389/conf.FVETS.2016.02.00045

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Received: 13 May 2016; Published Online: 14 Sep 2016.

* Correspondence: DVM, PhD. Thitiwan Patanasatienkul, University of Prince Edward Island, Health Management, Charlottetown, Prince Edward Island, Canada, thitiwan.patanasatienkul@gmail.com