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  • Haeseker, Steven L.  (4)
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  • 1
    Online Resource
    Online Resource
    Wiley ; 2005
    In:  North American Journal of Fisheries Management Vol. 25, No. 3 ( 2005-08), p. 897-918
    In: North American Journal of Fisheries Management, Wiley, Vol. 25, No. 3 ( 2005-08), p. 897-918
    Abstract: Models for making preseason forecasts of adult abundance are an important component of the management of many stocks of Pacific salmon Oncorhynchus spp. Reliable forecasts could increase both the profits from fisheries and the probability of achieving conservation and other management targets. However, the predictive performance of salmon forecasting models is generally poor, in part because of the high variability in salmon survival rates. To improve the accuracy of forecasts, we retrospectively evaluated the performance of eight preseason forecasting models for 43 stocks of pink salmon O. gorbuscha over a total of 783 stock‐years. The results indicate that no single forecasting model was consistently the most accurate. Nevertheless, across the 43 stocks we found that two naïve time series models (i.e., those without explicitly modeled mechanisms) most frequently performed best based on mean raw error, mean absolute error, mean percent error, and root mean square error for forecasts of total adult recruits. In many cases, though, the best‐performing model depended on the stock and performance measure used for ranking. In 21% of the stocks, a new multistock, mixed‐effects stock–recruitment model that included early‐summer sea surface temperature as an independent variable along with spawner abundance demonstrated the best performance based on root mean square error. The best‐performing model for each pink salmon stock explained on average only 20% of the observed variation in recruitment. Owing to the uncertainty in forecasts, a strong precautionary approach should be taken to achieve conservation and management targets for pink salmon on the West Coast of North America.
    Type of Medium: Online Resource
    ISSN: 0275-5947 , 1548-8675
    Language: English
    Publisher: Wiley
    Publication Date: 2005
    detail.hit.zdb_id: 2192453-3
    SSG: 21,3
    SSG: 12
    Location Call Number Limitation Availability
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  • 2
    Online Resource
    Online Resource
    Wiley ; 2008
    In:  North American Journal of Fisheries Management Vol. 28, No. 1 ( 2008-02), p. 12-29
    In: North American Journal of Fisheries Management, Wiley, Vol. 28, No. 1 ( 2008-02), p. 12-29
    Abstract: Using comprehensive data sets for chum salmon Oncorhynchus keta (40 stocks) and sockeye salmon O. nerka (37 stocks) throughout their North American ranges, we compared the retrospective performance of 11 models in preseason forecasting of adult abundance. Chum and sockeye salmon have more complicated age structures than pink salmon O. gorbuscha, which we investigated previously (Haeseker et al. 2005), and this complexity presents new challenges as well as opportunities for forecasting. We extended our previous work to include two new forecasting models that make use of leading indicators: either the survival rate of earlier‐maturing pink salmon from the same brood year (Ricker pink salmon index model) or the abundance of earlier‐maturing siblings (hybrid sibling model, a new version of the standard sibling model). No single forecasting model was consistently the best for either chum or sockeye salmon, but the hybrid sibling model frequently performed best based on mean absolute error, mean percent error, and root mean square error. As was observed for pink salmon, several naïve models (i.e., simple time series models without explicitly modeled mechanisms) also performed well, as did forecast averaging models composed of two models with the least‐correlated forecasting errors. In general, model ranking depended on the particular stock and performance measure used. However, even the top‐ranked model for each stock explained on average only 21% of the observed interannual variation in chum salmon recruitment and only 36% of the variation in sockeye salmon recruitment. Although improvements may be possible for some stocks in specific circumstances, a major breakthrough in general forecasting ability seems unlikely given the breadth of stocks and models examined to date. Therefore, better in‐season updates and adjustments to fishing regulations and a cautious approach to opening and closing fisheries should remain high priorities.
    Type of Medium: Online Resource
    ISSN: 0275-5947 , 1548-8675
    Language: English
    Publisher: Wiley
    Publication Date: 2008
    detail.hit.zdb_id: 2192453-3
    SSG: 21,3
    SSG: 12
    Location Call Number Limitation Availability
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  • 3
    Online Resource
    Online Resource
    Wiley ; 2007
    In:  North American Journal of Fisheries Management Vol. 27, No. 2 ( 2007-05), p. 634-642
    In: North American Journal of Fisheries Management, Wiley, Vol. 27, No. 2 ( 2007-05), p. 634-642
    Abstract: The sibling model is often one of the best methods for calculating preseason forecasts of adult return abundance (recruits) for populations of Pacific salmon Oncorhynchus spp. This model forecasts abundance of a given age‐class for a given year based on the abundance of the previous age‐class in the previous year. When sibling relations fit historical data well, the sibling model generally performs better than other forecasting methods, such as stock–recruitment models. However, when sibling relations are weak, better forecasts are obtained by other models, such as naïve models that simply use an historical average. We evaluated the performance of a hybrid model that used quantitative criteria for switching between a sibling model and a naïve model when generating forecasts for 21 stocks of chum salmon O. keta and 37 stocks of sockeye salmon O. nerka in the northeastern Pacific Ocean. Compared with the standard sibling model, the hybrid model reduced the root mean square error (RMSE) of forecasts by an average of 27% for chum salmon stocks and 28% for sockeye salmon stocks. Compared with a naïve model, the hybrid model reduced the RMSE of forecasts by an average of 16% for chum salmon stocks and 15% for sockeye salmon stocks. Our results suggest that hybrid models can improve preseason forecasts and management of these two species.
    Type of Medium: Online Resource
    ISSN: 0275-5947 , 1548-8675
    Language: English
    Publisher: Wiley
    Publication Date: 2007
    detail.hit.zdb_id: 2192453-3
    SSG: 21,3
    SSG: 12
    Location Call Number Limitation Availability
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  • 4
    Online Resource
    Online Resource
    Canadian Science Publishing ; 2008
    In:  Canadian Journal of Fisheries and Aquatic Sciences Vol. 65, No. 9 ( 2008-09), p. 1842-1866
    In: Canadian Journal of Fisheries and Aquatic Sciences, Canadian Science Publishing, Vol. 65, No. 9 ( 2008-09), p. 1842-1866
    Abstract: Temporal trends in productivity of Pacific salmon ( Oncorhynchus spp.) stocks are important to detect in a timely and reliable manner to permit appropriate management responses. However, detecting such trends is difficult because observation error and natural variability in survival rates tend to obscure underlying trends. A Kalman filter estimation procedure has previously been shown to be effective in such situations. We used it on a Ricker spawner–recruit model to reconstruct indices of annual productivity (recruits per spawner (R/S) at low spawner abundance) based on historical data for 120 stocks of pink ( Oncorhynchus gorbuscha ), chum ( Oncorhynchus keta ), and sockeye ( Oncorhynchus nerka ) salmon. These stocks were from Washington, British Columbia, and Alaska. The resulting estimated temporal trends in productivity show large changes (on average 60%–70% differences in R/S and average ratios of highest to lowest R/S between 5.4 and 7.9 for the three species). Such changes suggest that salmon stock assessment methods should take into account possible nonstationarity. This step will help provide scientific advice to help managers to meet conservation and management objectives. The Kalman filter results also identified some stocks that did not share temporal trends with other stocks; these exceptions may require special monitoring and management efforts.
    Type of Medium: Online Resource
    ISSN: 0706-652X , 1205-7533
    Language: English
    Publisher: Canadian Science Publishing
    Publication Date: 2008
    detail.hit.zdb_id: 7966-2
    detail.hit.zdb_id: 1473089-3
    SSG: 21,3
    SSG: 12
    Location Call Number Limitation Availability
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