Fraser River sockeye salmon: data synthesis and cumulative impacts

Model: C1a1996-2004For 1996-2004, it was not possible to test a model set with both QCS and SoG because the timeperiod was too short for the number of variables to be included for the two regions. Thealternative approach was to develop two model sets, one for each region, to test the importanceof chlorophyll against the other variables independently within each region (Tables A4.3-19 andA4.3-22). Even though this is an extremely short time period, it reflects the earliest data availablefor chlorophyll, which is known to be an important factor (McKinnell et al., 2011)and thus itseemed important to test this model set.Table A4.3-19. Model specifications for the 1996-2004 (brood years) model set for Queen Charolotte Sound. Thistable shows the variables included in each of the 9 models tested (i.e. M1 to M9) within this model set.Table 4.4-1 explains which specific data sets were used for each of these variables. “Rank of model”reflects the AIC c score showing level of support (#1 ranked model had the highest level of support andlowest AIC c score).Region Variable M1 M2 M3 M4 M5 M6 M7 M8 M9QCS Chlorophyll X X X X X XQCS Temperature X X X X X XQCS Salinity X X X X X XQCS Discharge X XQCS Wind XRank of model 9 6 4 2 3 8 5 7 1Within the QCS model set, the four models with the lowest AICc scores are M9 (chlorophyll),M4 (chlorophyll and SST), M5 (chlorophyll and SSS), and M3 (chlorophyll, SST and SSS),respectively (Table A4.3-20 and Table A4.3-21). The values reported suggest there is not asubstantial difference among these four models in terms of their level of support (∆AICc=2.33),and because each of these model only vary by one parameter, they would not be considered to belegitimately competing models (Arnold 2010). This result suggests that chlorophyll may be animportant metric in explaining the variation in sockeye salmon productivity over the period of1996-2004.258