Fraser River sockeye salmon: data synthesis and cumulative impacts

periods showed that 4 to 6 stocks (of the 19 that have data) consistently made up about 80% ofthe run for each time period.Cross project analysis: The project also involves correlation analyses to assess correlationamong variables across projects and also comparisons among groups (run timing, year types,Fraser vs. non-Fraser, climate periods, migration routes or timing.Multivariate analyses: These include discriminant analysis or logistic regression to determinewhich stressors are best able to “discriminate” among groups of stocks with similar productivitypatterns (don’t have covariates beyond the Fraser to follow that through). Principal componentanalysis (data reduction and interpretability among predictor variables) may provide (?a betteridea of the uncertainty around relationships?).Multiple regression can assess the relative importance of each stressor as well as hypothesizedinteractions and is probably the best tool to address the question of relative and cumulativeeffects. The intent is to use an information theoretic approach: to develop candidate models, to fitmodels and compute AICs, and to assess relative weight for each model.Caveats are that while this quantitative analysis is important, it also has important limitations(e.g. stocks with no data don’t even enter into the comparisons). There is a tendency to think thatthe numbers trump everything else in terms of validity, but these should be seen as just one pieceto add to the overall weight of evidence and every effort will be made to communicate that veryclearly.Questions: These include how to deal with missing data: Is interpolation possible or will it benecessary to drop incomplete rows? Is it better to use a reduced set of years with data from morestressors or to look at a reduced set of stressors (or both)? Should the project try to come up witha status-only dataset across all stressors? For the productivity metric, which is the right responsevariable: current slope, CU status or trends? Knowing that there will be many gaps, it is proposedto use expert opinion to develop a complete data set: Is that a viable approach?Feedback was sought on redundancies and gaps (vs. what other projects were doing), priorityanalyses and additional suggestions.DiscussionJohannes: In looking at habitat loss and degradation, is 1990 to 2010 an appropriate time period?I have an accumulation of individual data sets and I don’t know how to address them, but theconceptual model starts to address the question of how to look at them.Pickard: Where is the overlap between stressors in space and time? In a more complex model,we use that with the life history model to (…?). If we have data – how to use the conceptualmodels to drive (…?) to be able to look at the relative importance of different models toexplain the productivity data. I’m not confident that it’s going to work, so we’ll take theunivariate analysis and the trends where we do have data. It won’t be conclusive but we willhave all the evidence summarized as best we can.Christensen: I like the idea of expert opinion data sets. Without complete time series, if yourestrict it to years with data it would put more weight on those that have the long time series.Hall: We have limited data on stressors for that time period, so that is future work.67