Northwest Fisheries Science Center

Forecast of Adult Returns for coho salmon and Chinook Salmon

The anomalous warm ocean conditions that have persisted since September of 2014 might be dissipating. While ocean ecosystem indicators in 2015 and 2016 suggested some of the poorest outmigration years for juvenile salmon survival in the 20 year time series, some of the indicators in 2017 were fair, indicating that the ecosystem might be returning to normal. The PDO was strongly positive (warm) throughout the first half of 2017, however the index declined to more neutral levels from July through November 2017. Strong La Niña conditions at the equator persisted from August through December of 2016, and then became neutral throughout most of 2017. Prior to the onset of upwelling in 2017, ocean temperatures off Newport Oregon remained warm and fresh. However, after the onset of upwelling, sea surface temperatures were cooler than average and the near bottom water on the shelf was salty. In 2015 and 2016, the seasonal shift from a warm winter copepod community to a cold summer community did not occur because of the extended period of warm ocean conditions. However, in June 2017, the copepod community transitioned to a cold water community, signaling that the marine ecosystem is transitioning back to normal.

Our annual summary of ecosystem indicators during 2017 is here, and our "stoplight" rankings and predictions are shown below in Table SF-01Table SF-02, and Figure SF-01. Despite some signs that the ecosystem might be transitioning back to normal, most of the indicators suggest that 2017 was another year of poor ocean 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 2017a) 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 2018 (solid line) and 2019 (dashed line). The mean rank of the ocean ecosystem indicators in 2016 was 16.4 forecasting a return of 87,600 and 235,200 adult spring and fall Chinook salmon to the Bonneville Dam respectively in 2018 (top two panels). The mean rank of the ocean ecosystem indicators in 2017 was 13.9 forecasting slightly increased adult returns in 2019 of 106,350 and 292,260 spring and fall Chinook salmon respectively (top two panels- dashed lines). Using the rank of the ecosystem indicators of 13.9 from 2017, the forecast of the smolt to adult survival of coho salmon to Oregon coastal streams is 1.8 percent in 2018. However, the relationship between the ocean ecosystem indicators and coho salmon survival was not very strong (R2 = 0.28), so this forecast should be used with caution.

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.

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. Forecasts were created from a DLM (Dynamic Linear Models) with log of sibling counts (for the Chinook models only) and PC1 as predictor variables.

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).