Fraser River sockeye salmon: data synthesis and cumulative impacts

subject less fit to deal with major stresses late in the life cycle; a rapid accumulation in the earlystages that causes an extended sub-lethal level (i.e. leaving the subject vulnerable to any smalladditional impact); or a steady, even accumulation of stressors over the entire life span (steppedpattern – death by a thousand cuts). If the situation with Fraser sockeye resembles the lastpattern, it would be very hard to prove that.Weight of evidence approach: A key challenge is how to use imperfect evidence – complexevidence, with confounding factors and data gaps – to gain as much insight as possible to guiderational decisions. This requires looking for plausible mechanisms, evidence of exposure andadverse effects, including the specificity and timing of effects (i.e. ensure there is a match inspace and time). Additional questions to be asked include whether the exposure level exceedsthresholds, whether the effect can be experimentally confirmed and whether removal of thestressor reduces the effect.Qualitative approaches: Key elements of the proposed approach for this project include aconceptual model of stressors over the life history, showing pathways and interactions, a spatiallife history diagram that gives a better idea of the scale and spatial overlap of stressors and anexpert evaluation of relative likelihood that integrates multiple sources of evidence.Quantitative approaches: The presentation also reviewed data availability and limitations,noting that data was expected from all of the projects except the diseases and parasites project(lack of data), the aquaculture contract (uncertainty re timing), and the fisheries and science/management projects (not applicable). The extent of data available and the ability to correlatepotential stressors to Fraser sockeye both temporally and spatially over their life history variesdramatically between projects. Limitations include missing data for some stocks and some years,having nothing more than snapshot data in some cases, the large number of parameters, data thatis sometimes only available at a different spatial scales for some stocks and gaps in terms ofbeing able to verify data quality, which raises the question of how to weight it (e.g. data collectedfor other species and purposes that is mined after that fact).Potential analyses: The data can be organized by life history stage or habitat type, by location orby project. The proposed approach is to look for evidence through any of these lenses (e.g.evidence from several projects that relates to stressors occurring a particular life-history stage orgeographic location).Univariate analyses: Peterman’s productivity data were used to do trend analyses and stockprofile summaries (e.g. are fewer stocks making the system less resilient?). Also beingconsidered are control charts to see if there is any evidence of variability that is abnormal for thesystem. A lot can also be done also to assess patterns for stressors in time and space. Theindividual projects are covering this so this project will mainly focus on presenting those resultssimultaneously (e.g. likelihood table).An example was presented of the trend analysis for one stock, Early Stuart, showing 1965 as thebreakpoint in an up/down productivity trend since 1950. The intent is to get an idea of the recentproductivity trend so as to focus in on that and also to see if it’s possible to determine whether allstocks started to decline at once or if certain groups of stocks showed similar patterns. TheKalman filter is another way of doing this. For Early Stuart, the best-fit model was that a changein productivity happened in 1965. Other stocks show a straight line pattern, which suggests thatit may be more complicated than something that affected all stocks at the same time. Acomparison of stock composition for the total Fraser sockeye run over different (12-year) time66