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Many of the ocean ecosystem indicators in 2015 suggest this being a relatively poor year for juvenile salmon survival. The PDO was strongly positive (warm) throughout 2015, coinciding with anomalously warm ocean conditions in the NE Pacific called “The Blob” that began in the fall of 2013 and have persisted through 2015. El Niño conditions also turned positive in April 2015 and have remained strongly positive, signaling a strong El Niño at the equator. Despite the strongest upwelling observed since 1998, sea surface and deep water temperatures off Newport Oregon remained warmer than usual (+2°C) throughout most of 2015. During the strongest upwelling period in June, shelf waters did cool and were salty, but returned to positive temperature anomalies quickly from July onward. The zooplankton community remained in a lipid-deplete state throughout 2015, and was dominated by small tropical and sub-tropical copepods and gelatinous zooplankton that generally indicate poor feeding conditions for small fishes upon which juvenile salmon feed. Krill biomass was also among the lowest in 20 years. On the other hand, the biomass of larval fish species that are common in salmon diets in spring was above average this year, however, there were also high concentrations of larval rockfish and Northern anchovy which are generally indicators of poor feeding conditions for salmon. We also observed many new copepod species that have never been seen off Newport since sampling began in 1969. Our annual summary of ecosystem indicators during 2015 is here, and our "stoplight" rankings and predictions are shown below in Table SF-01, Table SF-02, and Figure SF-01.
Table SF-01 Ocean ecosystem indicators of the Northern California Current. Colored squares indicate positive (green), neutral (yellow), or negative (red) conditions for salmon entering the ocean each year. In the two columns to the far right, colored dots indicate the forecast of adult returns based on ocean conditions in 2015 (coho salmon) and 2014 (Chinook salmon).
|Juvenile Migration Year||Adult Return Outlook|
|Large– scale ocean and atmospheric indicators|
|PDO (May — Sept)||■||■||■||■||●||●|
|Local and regional physical indicators|
|Sea surface temperature anomalies||■||■||■||■||●||●|
|Deep water temperature||■||■||■||■||●||●|
|Deep water salinity||■||■||■||■||●||●|
|Local biological indicators|
|Northern copepod anomalies||■||■||■||■||●||●|
|Biological spring transition||■||■||■||■||●||●|
|Key||■||good conditions for salmon||●||good returns expected|
|■||intermediate conditions for salmon||—||no data|
|■||poor conditions for salmon||●||poor returns expected|
Table SF-02 shows rank scores for the color-coding in Table SF-01. Scores were assigned based on their effect on juvenile salmonids. We show variables that are correlated with returns of coho salmon after 1 year and of Chinook salmon after 2 years. For example, positive PDO values (and red colors) indicate poor ocean conditions in coastal waters off the northern California Current. Similarly, higher sea surface temperatures in summer are a negative indicator for salmon, but particularly so for resident coho salmon. Table SF-03 shows the values of each variable shown by rank in Table SF-02.
Table SF-03. Data for rank scores of ocean ecosystem indicators.
Data for rank scores of ocean ecosystem indicators. Click HERE to download the data as a *.csv.
Figure SF-01 shows correlations between adult Chinook salmon counts at the Bonneville Dam and coho salmon smolt to adult survival (%) (PFMC 2014a) versus a simple composite integrative indicator – the mean rank of all the ecosystem indicators (the second line from the bottom) in Table SF-02. This index explains about 50% of the variance in adult returns. A weakness of this simple non-parametric approach is that each indicator is given equal weight, an assumption that may not be true. Therefore, we are exploring a more quantitative analysis of the ocean indicators shown in Table 3, using principal component analysis (PCA).
Principal component analysis (PCA) was run on the indicator data. This procedure reduces the number of variables in the dataset as much as possible, while retaining the bulk of information contained in the data (a sort of weighted averaging of the indicators). Another important feature of PCA is that the principal components (PCs) are uncorrelated. This eliminates one of the original problems with the indicator data set (i.e., multi co-linearity). The first principal component (PC1) explains 55% of the ecosystem variability among years while the second principal component explains only 12%. Therefore, PC1 is used as a new predictor variable in a linear regression analysis of adult salmon returns (this process is termed principal component regression, or PCR) and those results are shown below in Figure SF-02.
In addition to correlating PC1 with salmon returns, we incorporated this metric into a more formal modeling structure. Specifically, we used sibling regression and dynamic linear modeling (DLM; Scheuerell & Williams 2005) to relate PC1 to returns. DLMs are similar to linear regression, but allow the regression coefficient(s) to vary over time, effectively allowing for a shift in the magnitude of response to ocean conditions. We also ran models with and without siblings, as sibling data have been shown to be highly correlated with older-age adults. In all models, we allowed the intercept and the coefficients for siblings and PC1 to vary. However, the best model often showed no support for much of these dynamics and we therefore simplified the models to allow for only one parameter to vary over time. For coho salmon, there was no support for any parameter to vary, resulting in a simple linear regression model (of logit-transformed SAR). For Chinook, the models with siblings showed support for a dynamic effect of jack counts, whereas models without siblings showed support for a dynamic effect of PC1.
Figure SF-03.compares the actual adult returns of adult spring and fall Chinook salmon and coho salmon to the forecasted returns derived from a maximum covariance analysis (MCA) of the ecosystem indicators. This technique is similar to the principal component regression illustrated in Figure SF-02. This is work being conducted by Brian Burke (NWFSC/FE).
Although the PCA and DLM analyses represent a general description of ocean conditions, we must acknowledge that the importance of any particular indicator will vary among salmon species/runs. We are working towards stock-specific salmon forecasts by using methods that can optimally weight the indicators for each response variable in which we are interested (Burke et al. 2013).