The impact of ecological dynamics and statistical properties in fisheries data on the sustainability of fish populations and harvest

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Since the early 20th century fisheries science has developed from the assumption that catch is proportional to both the underlying fish abundance and the amount of fishing activity (effort) applied to pursue them. This led to the widespread use of CPUE (catch-per-unit-effort) as an index abundance. By the end of 20th century the lack of proportionality between catch and abundance, due to fish aggregation, was broadly appreciated. However, the proportionality between catch and effort, the two known quantities, was not usually investigated. A proportional relationship was also implicit when converting effort into fishing mortality for more detailed population assessment models. Evidence based management requires the most accurate abundance information possible, but complex and disproportionate relationships between catch, effort, and abundance can undermine this accuracy. My lab's ongoing mission is to establish the impact of behavioural and statistical effects on the observed relationship between commercial catch and effort and their influence on estimates of fish abundance. Previous work in my lab has shown that local CPUE can be biased by the behaviour of fish harvesters who react to abundance in surrounding areas or who selectively pursue one (or a few species) in a multispecies fishery. Competitive interference among vessels can result in a decline in efficiency as total fleet effort increases. The impact of these influences may vary with the spatial distribution of fish and fish harvesters. Standard statistical methods that do not account for observation error in effort can result in apparent disproportionalities even where they do not exist. Generally, we expect catch and effort to appear to be disproportionate for statistical as well as behavioural reasons, from the level of single sets (trawls, traps, gillnets, etc.) to the aggregated annual summaries typically reported. To disentangle the relationship between catch and effort across scales, my group will employ a variety of quantitative models and methods (statistical, behavioural, economic, simulation) to commercial fisheries data in order to 1) determine the underlying proportionality of catch and effort, 2) adjust catch standardizations for disproportionalities, 3) identify causality and complexity in catch and effort data series, 4) predict the impact of spatially selective fishing on fish productivity, and 5) evaluate the potential effect of these phenomena on population assessments and fishery sustainability. This research will transform the use of fishing effort in conservation and management, improving our ability to detect threats to sustainability in commercial fisheries. The students (5 M.Sc., 1 Ph.D.) supporting this research will develop skills to immediately participate in fisheries research, harvest management, and conservation upon graduation.