Fraser River Sockeye Fisheries and Fisheries Management - Cohen ...

Pre-season forecastsAs noted above, these set the stage for the season but provide little influence onmanagement decisions once the run has begun to arrive in the Bay. A variety of methodshave been tried/used for system-specific pre-season forecasting in Bristol Bay, but can begeneralized into four types: (1) means models, (2) spawner-recruit models combined withassumed age composition, (3) sibling models, and (4) smolt models combined withassumed age composition. Means models consist of simply assuming that a goodprediction of future returns will equal the average of returns from the previous three, five,ten years and so on. Spawner-recruit models use a Ricker curve (Ricker 1954) to predicthow many returns can be expected from a given year’s spawning escapement andapportions these predicted returns across future years based on previously observed agecompositions. Sibling models use simple regression to predict how many fish will returnat older ages based on how many fish of the same brood year returned at younger ages.Finally, smolt models use the number of out-migrating smolts estimated for a given yearand multiplied by previously observed age composition and marine survival estimates toproject the number of returns in coming years. Detailed descriptions and comparisons ofthese methods are found in Fried and Yuen (1987), Henderson et al. (1987), Bocking andPeterman (1988), and Adkison and Peterman (1999). Up until 2000, ADF&G has usedall four methods (although, smolt data was available for only some of the systems andyears) and averaged their outputs giving each equal weight (T. Baker, pers. comm.;Eggers 2003; Fried and Yuen 1987; Bocking and Peterman 1988). Beginning in 2001, allmodels were tried, but only the top performing model over the previous three years hasbeen used for the upcoming forecast (T. Baker, pers. comm.). In addition to the fourmodels mentioned above, nonlinear forms of the sibling model as per Bocking andPeterman (1988) and time series processes (e.g., autoregressive 1, 2, etc.) in all models asper Adkison and Peterman (1999) are tried.Given the number of published articles comparing forecasting methods, we have notincluded such comparisons in this report. Rather, we will describe the observed error foreach system and year based on the forecasts reported by ADF&G. The type of modelused for each system and year was not provided by ADF&G—only the forecasted returnwas available. For each system and year we estimated the median percent error (MPE)and the median absolute percent error (MAPE) between the forecasted and observedreturns, as well as, R 2 , and tests of intercept= 0 and slope= 0 when log 10 (observed run)was plotted against log 10 (forecasted run) (Figure 33, MPE not shown graphically; seeAppendix F for detailed descriptions of these metrics). The log 10 transformation wasnecessary to stabilize the variance and to achieve normality.149