Fraser River sockeye salmon: data synthesis and cumulative impacts

exclude covariates with limited years of data (i.e. limit the model to only salinity and sacrificethe extra covariate, but extend it back to 1950). Choosing any particular set of covariates forcesyou to truncate longer time series to the length of the shortest data set. Choosing any particulartime period forces you to limit your analyses to those covariates with period of record that issufficiently long. We chose to evaluate different time-periods independently because each timeperiod presents a different trade-off between the length of the data and the number of covariatesthat can be included. Within each time-period we generated different model sets. A model setrepresents a set of covariates that have complete data over a specified period of time. Withineach model set, different models (i.e. combinations of variables) can be tested to determine theirability to explain the observed variability in the dependent variable, sockeye productivity in thiscase. Expressed another way, a ‘model set’ is simply a suite of candidate models within a giventime-period that are organized to address a particular question. For example, one question ofinterest is whether factors affecting a particular life stage are more important than others. Mostprojects or papers only consider a single ‘model set’ by this definition. However, the large butincomplete data set combined with the range of questions this project attempts to addressrequired this additional layer.Key points:• Models may differ in the number and type of covariates, linear vs. non-linear terms, andthe presence of interaction terms.• Many models are possible, but we should only test models that have biologically justifiedhypotheses.• In order to compare the relative performance of different models using Akaike’sInformation Criterion (AIC c ), models should be fit using the same data.• Comparison of AIC c scores does not tell us the best model possible, but rather helps us tounderstand the relative support for models we have estimated.• You need more data (n) than parameters (k) in order to be able to estimate theparameters. In addition, if the ratio (n/k) < 40, small sample size corrections should beemployed in the assessment of model fit (Burnham and Anderson (p76), 1998).Data reductionWe had hoped there would not be substantial correlation among metrics from a single contractor– one of the criteria specified was that the data metrics submitted should be independent.However, we found that in many cases we received several correlated data sets (e.g., sea surfacesalinities for 5 months). We first dropped any variable by life stage combinations where therewas not a reasonable hypothesis of a potential impact (e.g., we know that smolts are not in the210