Fraser River sockeye salmon: data synthesis and cumulative impacts

databases (e.g., corrections to past estimates of smolt emigration) are automatically corrected inthe integrated database. This avoids the problem of having a centralized database with duplicatebut out-of-date or incorrect versions of historical data. This approach is gradually beingimplemented in the Trinity River Restoration Program, and has also been recommended for otherrivers in the Western U.S., including the Klamath, Sacramento and San Joaquin. 15 There areconsiderable technical and institutional challenges in setting up such an approach, but thebenefits include fewer errors in data assembly, ease of inter-disciplinary integration, and muchmore timely application of quantitative analyses.In addition to improving the data available for understanding both the stressors affecting sockeyeand their life-stage specific survival rates, there needs to be improved application of quantitativemethods to these data. The goals of these analyses should be to reduce uncertainties critical tofisheries management decisions, and to improve our retrospective understanding of the factorsthat have affected sockeye survival rates and productivity. The quantitative methods that weapplied in this report were the simplest approaches that could be feasibly completed within thetime available. A small working group could consider other methods that should be applied tothe database that we’ve assembled (as well as future improvements to it), including: simulationand statistical approaches incorporating non-linear and non-additive interactions; functionalregression analyses for continuously measured variables like temperature, salinity and discharge;control chart approaches to examine changes in the variability of both response measures andenvironmental variables; and Bayesian approaches which assign probability distributions to eachfactor and their interactions. Potential methods are more fully described in Appendix 3. Weemphasize the importance of extending these kinds of analyses to all 64 stocks assessed byPeterman and Dorner (2011), and others not included in their data set (e.g., Okanagan sockeye).The greater contrasts in both stock productivity and stressors provided by larger data sets willyield stronger insights on driving factors.Due to ecosystem complexity and year to year variability in environment-recruitmentcorrelations (see section 3.1 and English et al. 2011), we think that it will be very difficult todevelop reliable pre-season models to accurately predict sockeye returns. A more reasonableexpectation is that quantitative analyses will be primarily retrospective, and can yield only verygeneral forecasts (e.g., whether marine survival rates over the next two years are likely to bebelow average, about average, or above average). As discussed in English et al. 2011, in-seasondata and models will likely continue to be the primary tools used to manage harvest.15 See http://trrp.net/science/IIMS.htm for more information.106