Just in case you didn’t catch my otherwise obscure editor’s note in a post earlier this week, federal scientists using a new, wider array of ocean indicators are predicting 221,000 above-Bonneville-bound spring Chinook back to the mouth of the Columbia this year.
That’s 50 percent, or 70,000 fish, more than their counterparts at the joint state-tribal-federal U.S. v. Oregon Technical Advisory Committee said would return when they put together their own prediction in early December.
The release of TAC’s forecast is among the first steps in shaping the scope of fishing seasons in the lower river. Meetings on that front begin later this month.
Meanwhile, yesterday, the wise guys at NOAA’s Northwest Fisheries Science Center posted the rationale for their 221,000-springer prediction. Though their data hasn’t been used to set fishing seasons in the past, the new model that scientist Brian Burke has come up with — our Terry Otto interviewed him for the December issue on this subject — really seems to fit well with recent year’s actual runs.
That being no guarantee that it’ll work this year.
There’s static in the signals both positive and negative, and this isn’t to take potshots at TAC who’ve done better than they’re given credit. Their 2013 forecast is based on more landward signals such as jack counts, which were down last year and also some ocean indicators — the Columbia Basin Bulletin has more on their methodology here.
But for what it’s worth, there’s some interesting stuff in the below material, blatantly ripped off the Science Center’s website, that Columbia anglers might take note of:
Forecast of Adult Returns for coho and Chinook Salmon
2012 was characterized by a steady move from La Niña conditions towards an ENSO-neutral state. Combined with persistently negative PDO values throughout the year, a high biomass of lipid-rich northern copepods supporting the base of the food-chain, and an above average abundance of winter-time ichthyoplankton (larval stages of fish-prey for salmon), 2012 had the potential to be a good year for supporting juvenile salmon entering the ocean. This positive bio-physical outlook was tempered a bit by a late start to upwelling, warm sea-surface temperatures through much of the summer, and a trend towards El Niño conditions, but overall the ocean conditions in 2012 appear to be greatly improved compared to the last several years.
|Juvenile Migration Year Outlook|
|Large– scale ocean and atmospheric indicators|
|PDO (May — Sept)||?||?||?||?||?||?|
|Local and regional physical indicators|
|Sea surface temperature anomalies||?||?||?||?||?||?|
|Physical spring transition||?||?||?||?||?||?|
|Deep water temperature and 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 2 shows rank scores for the color-coding in Table 1. 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. Table 3 shows the values of each variable shown by rank in Table 2.|
|Table 3.||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 A shows correlations between adult Chinook salmon counts at the Bonneville Dam and coho salmon smolt to adult survival (%) versus a simple composite integrative indicator — the mean rank of all the ecosystem indicators (the second line from the bottom) in Table 2. 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 52% of the ecosystem variability among years while the second principal component explains only 14%. The indices associated with PC2 were the three upwelling indicators- physical spring transition, upwelling anomaly and length of the upwelling season. Because these three indicators contribute little to our understanding of the ecosystem variability among years, they were removed from the overall ranking system in the stoplight chart.
We used PC1 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 B.
Although the PCA scores 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 therefore 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). Figure C compares the actual adult returns of adult yearling Chinook salmon, at three different locations along the Columbia River, 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 B. We chose these three locations because they roughly represent different salmon populations: Bonneville Dam counts represent all Columbia River spring Chinook salmon, Ice Harbor Dam counts represent Snake River spring/summer Chinook salmon, and Priest Rapids Dam counts represent Upper Columbia River spring Chinook salmon. This is work being conducted by Brian Burke (NWFSC/FE).
Similar to the past several years, individual indicators have sent a mixed message. Certain indicators suggest the potential for above average returns: i.e. persistence of strong La Niña conditions, a negative PDO, positive copepod indicators from May-September, and high catches of spring Chinook in the June survey. However, negative indicators include a late start to the upwelling season (first week of May), nearly a two month delay until upwelling became strong (not until early July), and very warm sea surface temperatures in June and July. The upwelling season was among the shorter ones, only 161 days (as compared to more than 200 days in 1999, 2002, and 2009). Our best guess is to expect average to above-average returns of coho in 2013 and Chinook in 2014, but similar to the statement we made last year, the mixed signals add greater uncertainty to our predictions.