Genomics approaches to the management of mixed stock fisheries in Canada: developing tools for promoting fishery stability and the protection of biodiversity

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Species usually consist of multiple populations, each associated with a particular environment, and often harbouring adaptations to the local environment or a particular ecological context. Fisheries that target multiple populations can benefit from this adaptive diversity by being more productive, stable, resilient to environmental change, and subject to fewer closures, a set of benefits known as the portfolio effect. However, to benefit from the portfolio effect, mixed stock fisheries must target individual populations in a controlled fashion; this in turn requires tools that enable identification of those populations. Genetic methods have long been used for this purpose, but traditional approaches often fail to resolve stocks and critical units of adaptive diversity. Recently, ('next generation', NGS) DNA sequencing technologies that examine millions of DNA bases and thousands of genes have proven capable of fine-scale resolution of local populations and their genetic traits. The novelty of these methods and the complexity of the data they produce have so far limited their application in fisheries management. We will develop DNA and software tools to identify important populations, enable management of mixed-stock fisheries and conservation of biodiversity within economically and culturally important fish species. We will use NGS to address these objectives in Atlantic salmon in Lake Melville and coastal Labrador, and Atlantic cod along Newfoundland and Labrador's coast. These species support mixed stock harvests, have suffered major declines, and risk losing important diversity. Salmon are important for aboriginal food fisheries, and may be affected by a planned hydroelectric dam. We will also develop a software package to ease analysis of NGS data and testing for associations between DNA variations and environmental or phenotypic features, thus enabling scientists without bioinformatic training to more easily access the power of NGS data. Using extensive genomic analyses, we will identify strains or populations that may be especially well suited to support stable fisheries, and produce tools that that can be used to conserve diverse adaptive portfolios, including software tools for other scientists to pursue similar objectives.