Fraser River sockeye salmon: data synthesis and cumulative impacts

suggest any underlying mechanism, but it does provide an excellent opportunity to emphasizesome of the potential limitations of this analysis and considerations that need to be kept in mind.First, it is possible due to strong regional differences, that the mechanisms that might connectSSS with sockeye salmon productivity are in fact different in the two regions. An exploration ofsome of the oceanographic and climatic variables over time shows that there are many ways inwhich the two regions appear quite distinct from each other. Second, correlational analyses findrelationships among data, but correlation does not imply causation – just because there is anegative correlation over time between SSS in SoG and productivity of sockeye salmon does notmean that there is a direct mechanism relating the two. The fact that the direction of therelationship with SSS is opposite as for QCS could imply that there is another factor that isunique to SoG, for which we do not have data in the model, that confounds the expectedrelationship between SSS and sockeye productivity. Third, it is important to consider the scale ofthe underlying measurements. In this case, the SSS data come from point measurements at twoparticular lighthouses. These data sources were chosen by Cohen Commission contractors asbeing the most representative of the two regions but it is possible that there is fine scale variationthat is lost when using a point measure as a regional index. For example, SSS measured at onelighthouse in SoG may not always reflect conditions experienced along the migration paths ofsockeye salmon or the depth at which they travel.Table A4.3-17. C3a candidate models ordered by AICc from best (lowest) to worst (biggest). M.ID=modelidentification, M.AIC=the estimated AIC for the model, num.obs=the total number of observations (i.e.,complete rows in the data set), num.par=the total number of fixed effects + random effects, Correction= thedifference between the AICc (corrected for small sample size compared to number of parameters) and theAIC, M.AICC= the AICc for the model, min.AICC = the smallest AICc observed within the model set,delta= the difference between the min.AICC and each M.AICC, and AICC_wts= the Akaike weight (i.e.,support) for each model.M.ID M.AIC num.obs num.par Correction M.AICC min.AICC delta AICC_wtsM4 1136.05 423 24 3.02 1139.06 1139.06 0 45.62M5 1136.89 423 23 2.77 1139.66 1139.06 0.60 33.87M2 1137.88 423 25 3.27 1141.15 1139.06 2.09 16.04M7 1142.86 423 22 2.53 1145.39 1139.06 6.33 1.93M9 1143.81 423 22 2.53 1146.34 1139.06 7.28 1.20M1 1142.84 423 29 4.43 1147.27 1139.06 8.21 0.75M10 1146.07 423 22 2.53 1148.60 1139.06 9.54 0.39M6 1148.06 423 23 2.77 1150.83 1139.06 11.77 0.13M8 1149.92 423 22 2.53 1152.45 1139.06 13.39 0.056M3 1151.84 423 25 3.27 1155.11 1139.06 16.05 0.015256