Fraser River sockeye salmon: data synthesis and cumulative impacts

This project is unusual in its scope. While the response variable, ln(R/S), is available for 19stocks across B.C. and approximately 50 years of data are available for each stock, the number ofpotential covariates is very large. A total of 126 quantitative and 5 qualitative data sets wereprovided to us from the other technical reports (Table A3.4-9). We then calculated an additional32 data sets (i.e., derived variables based on the data provided) that were more appropriate forour analyses.It is possible for a single data set to be linked to (i.e., hypothesized to impact) multiple life stagesof Fraser River sockeye. In addition, there are up to 4 different age types (i.e., 4sub2, 5sub2,4sub1, and 3sub1). These links result in a total of 1058 possible covariates to include in theanalysis. However, not all covariates are available for all years and stocks. Models can only becompared when the models are fit using the same data. The implication of this is that we cannotcompare all models of interest on the full data set but instead must identify time periods withcomplete data for different subsets of the covariates.For example, there is a small subset of the covariates (e.g., sea surface salinity for the Strait ofGeorgia) that have data extending back to 1950, but there are other covariates that only have datastarting in 1996 (e.g., chlorophyll a). If we wish to compare models with these two covariates(i.e. salinity and chlorophyll), we would have to either reduce the data set to those years withdata for both covariates (i.e., limit the model to 1996-present and sacrifice the earlier data forsalinity), or exclude covariates with limited years of data (i.e., limit the model to only salinityand ignore chlorophyll a, but extend the analysis back to 1950). Choosing any particular set ofcovariates forces you to truncate longer time series to the length of the shortest data set.Choosing any particular time period forces you to limit your analyses to those covariates with aperiod of record that is sufficiently long.We chose to evaluate different time-periods independently because each time period presents adifferent trade-off between the length of the data and the number of covariates that can beincluded. Within each time-period we generated different model sets. A model set represents aset of covariates that have complete data over the chosen time-period. Within each model set,different models (i.e. combinations of variables) can be tested to determine their ability toexplain the observed variability in the dependent variable, sockeye productivity in this case.Expressed another way, a ‘model set’ is simply a suite of candidate models within a given timeperiodthat are organized to address a particular question. For example, one question of interest iswhether the set of factors affecting a particular life stage are more important than others.26