Northwest Fisheries Science Center

Outlook of adult returns for coho and Chinook Salmon

Our annual summary of ecosystem indicators during 2018 is here, and our "stoplight" rankings and predictions are shown below in Table SF-01, Table SF-02, and Figure SF-01. While ocean ecosystem indicators in 2015 and 2016 suggested some of the poorest outmigration years for juvenile salmon survival in the 21 year time series, some of the indicators in 2017 were fair, and the indicators in 2018 pointed towards much more neutral conditions, for juvenile salmon survival.




 

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. 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 2018a) 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 outlook for adult returns of Chinook salmon in 2019 (solid line) and 2020 (dashed line). With a mean rank of the ocean ecosystem indicators in 2017 of 14.5, 101,500 spring Chinook and 277,400 fall Chinook salmon are expected to return to the Bonneville Dam in 2019 (top two panels-solid lines). Chinook salmon that went to sea last spring (in 2018), when ocean conditions had a mean rank of 11.6, are expected to return at about average levels in 2020; 127,1000 spring Chinook and 356,800 fall Chinook salmon adults returning in 2020 (top two panels- dashed lines). Using the rank of the ecosystem indicators of 11.6 from 2018, the outlook for the smolt to adult survival of coho salmon to Oregon coastal streams is 2.2 percent in 2019 (lower panel).

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.

*outliers were excluded using Cook's Distance

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).