Fraser River sockeye salmon: data synthesis and cumulative impacts

“likely” contributor to the overall pattern and a “very likely”, potentially major, contributor tothe poor 2009 returns.Using the data collected from the other Cohen Commission technical projects, we haveconducted quantitative analyses over several time periods. The analyses use multiple regressionto compare the ability of several different oceanographic and climatic variables (measured inQCS and SoG) to explain the observed variability in Fraser River sockeye salmon productivity(i.e., ln(recruits/spawner)). A brief overview of the approach used is provided in Section 3.3.6and the details of the methodology and results are described in Appendices 3 and 4, respectively.We tested three model sets with the data available for marine conditions in QCS and SoG (Table4.4-1). Each model set represents a set of covariates or independent variables that have completedata over a specified period of time. Within each model set, different models (i.e., combinationsof variables) can be tested to determine their ability to explain the observed variability in thedependent variable. In the present case, the dependent or response variable is sockeye salmonproductivity (ln (recruits/spawner)). Models can only be directly compared to other models in thesame model set (i.e., using the same set of data) but not to models in other model sets. The timeframes of the three model sets tested in this section are brood years 1969-2004, 1980-2004, and1996-2004. The key differences among the model sets examined are that sea surfacetemperatures were not available for QCS until 1980, and chlorophyll was not available until1996. The conclusions of these results are presented below, with details in Appendix 4.For 1969-2004 (Table 4.4-2), the results show that the SoG temperature model (M8) and theQCS salinity and discharge model (M4) were the two models with the most support, but neitherperformed substantially better than the “global” model, which is the model that contains all thevariables in the model set (i.e., M1 in Table 4.4-2). For SoG during this period, temperature (M8)is more valuable for explaining the observed variability in Fraser River sockeye salmonproductivity than salinity (M7). Overall, the analysis of this time period shows that there issupport for both QCS and SoG models – the top ranked model was for SoG, the second for QCS,and the third was the global model, including both regions. These results show that for theseparticular variables, over this particular time period, there is no clear evidence of any differencebetween the explanatory value of the two regions; however, the absence of temperature data forQCS is a substantial shortcoming of this model set, and chlorophyll is not included in any model.65