Northwest Fisheries Science Center

Outlook of adult returns for coho and Chinook Salmon

Our annual summary of ecosystem indicators is here (link to ‘2019 Indicator Summary page), and our "stoplight" rankings and predictions are shown below in Table SF-01, Table SF-02, and Figure SF-01.


Table SF-02 Rank scores derived from ocean ecosystem indicators data found in Table SF-03 and color-coded to reflect ocean conditions for salmon growth and survival (green = good; yellow = intermediate; red = poor). Lower numbers indicate better ocean ecosystem conditions, or "green lights" for salmon growth and survival. To arrive at these rank scores for each ocean ecosystem indicator, all years of sampling data from Table SF-03 were compared (within each row).

Table SF-03.   Data for rank scores of ocean ecosystem indicators.

Data for rank scores of ocean ecosystem indicators. Download data for rank scores of ocean ecosystem indicators 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 2019a) 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).

Scatter plot showing relationships between ocean indicators and counts of adult salmon at Bonneville Dam. Figure SF-01.  Salmon returns versus the mean rank of ecosystem indicators. Arrows show the forecasted returns for Chinook salmon in 2020 (solid line) and 2021 (dashed line). The mean rank of the ocean ecosystem indicators in 2018 was 11.8 forecasting a return of 131,000 and 379,000 adult spring and fall Chinook salmon to the Bonneville Dam respectively in 2020 (top two panels). The mean rank of the ocean ecosystem indicators in 2019 was 15.1 forecasting lower adult returns in 2021 of 104,000 and 294,000 spring and fall Chinook salmon respectively (top two panels- dashed lines). Using the rank of the ecosystem indicators of 15.1 from 2019, the forecast of the smolt to adult survival of coho salmon to Oregon coastal streams is 1.9 percent in 2020.

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 54% of the ecosystem variability among years while the second principal component explains only 14%. 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.

Salmon returns versus the axis 1 scoreFigure SF-02. Salmon returns versus the first principal axis scores (PC1) from a Principal Component Analysis on the environmental indicators from Table 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. In all models, we allowed the coefficients for siblings and PC1 to vary, but kept a constant model intercept.

The best model for both spring and fall Chinook salmon showed support for a dynamic effect of jack counts, but not of PC1. For coho salmon, there was no support for any parameter to vary, resulting in a simple linear regression model (of logit-transformed SAR).

Figure SF-03. Time series of observed spring Chinook salmon adult counts (top), fall Chinook salmon adult counts (middle), and coho salmon SAR (bottom) by out-migration year. In each plot, the dark line represents the model fit and lighter lines represent 95% confidence intervals. Outlooks were created from a DLM (Dynamic Linear Models) with log of sibling counts (for the Chinook models only) and PC1 as predictor variables.

We are working towards stock-specific salmon return outlooks by using methods that can optimally weight the indicators for each response variable in which we are interested (Burke et al. 2013).