2018 Bristol Bay Sockeye Salmon Forecast

University of Washington

Alaska Salmon Program

November 16, 2017

The 2018 Bristol Bay sockeye salmon forecast is 47.6 million. This forecast is 11% higher than the recent 10-year average (42.8 million) and 25% higher than the recent 20-year average (38.1 million). This estimate is the sum of individual predictions for each of the predominant age classes (1.2, 1.3, 2.2, 2.3) for all nine major river systems – Kvichak, Egegik, Ugashik, Naknek, Alagnak, Wood, Nushagak- Mulchatna, Igushik, and Togiak, and the contribution of Nushagak 0.3 and 1.4 age classes (Table 1, Figure 1). The predicted total harvest based on this forecast is 33.5 million sockeye with an estimated weight of 194.9 million pounds (Table 2). To generate the forecast for total harvest we subtracted 1) our estimate of escapement for each river; in most cases we used the number for the upper bound of the lower bin of the ADF&G escapement goal range, except in the cases of the Kvichak and Alagnak rivers where we assumed a harvest rate of 50%, and 2) an estimate of the 2018 South Peninsula harvest from the predicted total run. South Peninsula catch for 2018 is estimated as the average of the catch (South Unimak and Shumagin Islands) from 1990 to 2017. Harvest values for 2018 given in Tables 1 and 2 are “forecasted” inshore harvest, differing from what we have referred to as “potential” harvest in prior preseason forecasts only by the removal of the estimated 2018 South Peninsula catch. This harvest estimate depends on observed escapement in 2018 equaling the assumed values in Table 1, and industry’s ability to harvest all surplus fish. To determine the harvest in pounds for each age group we multiplied the forecasted catch by the long-term average weight of 2 or 3 ocean fish for Bristol Bay sockeye runs totaling 40 million sockeye or greater (4.8 lbs and 6.6 lbs, respectively). Historical catch and escapement data collected by the Alaska Department of Fish and Game from 1963 to present were used to generate the 2018 forecast. Pre-season forecasts generated between 2004 and 2011 used a shorter time series of these data (1978-2011) to make predictions because 1978 is commonly recognized as a point when long-term trends in productivity of the North Pacific and Bristol Bay sockeye stocks showed a dramatic increase. However, large-scale climate patterns have become more variable since 2000. We now use the 1963 to present and the 1980 to present sockeye return data sets selectively for each individual prediction, based upon how well forecast models using each data set have performed over the last 10 years. The majority of 2018 stock-age forecasts (38 individual predictions) were generated from models based on prior returns of “siblings” or younger ocean age-classes from the same stock and brood year, but returning in previous years. Returns of siblings are informative because they experienced the environmental conditions as juveniles in freshwater and at ocean entry, and should exhibit similar patterns in survival. For all forecasts generated based on sibling abundance data, rather than simply choosing the best sibling relationship for each age and river, we use a technique that weights the forecasts for all potential predictor sibling models according to how well they have performed in the past. While the best sibling relationship carries the most weight in our forecasts for each stock-age group, retrospective analysis indicates that there is useful information conveyed by other models (i.e. sibling models that include alternative age classes and different combinations thereof), and that this information increases forecast accuracy.

In addition to sibling-based forecasts we have included other forecast model types due to their superior performance in recent years. In four instances (Egegik 2.2, Igushik – 1.2 and 2.2, and Nushagak 2.2) we used auto-regressive integrated moving average models (ARIMA). ARIMA models generate forecasts based on how well patterns in a time series of data predict future values. These models consider the level of autocorrelation in the time series of returns for stock-age groups in addition to the moving average of the forecast errors in prior years, and are independent of the information provided by the abundance of sibling age classes. In addition to ARIMA models, in recent years we have increased reliance on forecasts generated by ensemble models. Ensemble models simply average the range of forecasts generated by all model types, under the assumption that both sibling regression and ARIMA models provide predictive information. In four instances (Kvichak 2.3, Naknek 2.2, Egegik 1.2, and Ugashik 1.2) an Ensemble model was selected as the best estimate for 2018.

The 2018 forecast of 47.6 million sockeye is 20% and 9% lower than the observed sockeye runs in 2017 and 2016 respectively. However, this forecast is similar to both of those observed runs and the 2017 and 2016 forecast in that the forecast total results from relatively strong predictions across most rivers and age classes (Figure 1). In most years with a forecast exceeding 40 million sockeye the overall forecast is dominated by a single river and often a single age group (e.g. Kvichak or Egegik 2.2s), however the 2018 forecast is based on a relatively even mix of production from a range of stocks and age classes. The 2018 forecast of 47.6 million is significantly above the long-term (1960-2017) average Bristol Bay run size of 33.3 million sockeye, and of this total we expect 47% 2-ocean sockeye and 53% 3-ocean sockeye. Historically, the average range for weight of 2-ocean sockeye is 4.6-5.4 lbs and 6.4-7.5 lbs for 3-ocean sockeye.

t1 18 

t2 18 

t3 18 

f1 18f3 18