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  • Su, Zhenming  (6)
  • 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
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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
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  • 3
    Online Resource
    Online Resource
    Canadian Science Publishing ; 2004
    In:  Canadian Journal of Fisheries and Aquatic Sciences Vol. 61, No. 12 ( 2004-12-01), p. 2471-2486
    In: Canadian Journal of Fisheries and Aquatic Sciences, Canadian Science Publishing, Vol. 61, No. 12 ( 2004-12-01), p. 2471-2486
    Abstract: To improve the understanding of effects of environmental factors on spawner-to-recruit survival rates of pink salmon (Oncorhynchus gorbuscha), we developed several spatial hierarchical Bayesian models (HBMs). We applied these models to 43 pink salmon stocks in the Northeast Pacific. By using a distance-based, spatially correlated prior distribution for stock-specific parameters, these multistock models explicitly allowed for positive correlation among nearby salmon stocks in their productivities and coefficients of early summer coastal sea surface temperature (SST). To our knowledge, this is the first time that such distance-based, spatial prior probability distributions for parameters have been applied to fisheries problems. We found that the spatial HBMs produce more consistent and precise estimates of effects of SST on productivity than a single-stock approach that estimated parameters for each stock separately. Similar to earlier results using mixed-effects models for the same stocks, we found significant positive effects of SST on survival rates of northern pink salmon stocks, but weaker negative effects of SST on survival rates of southern pink salmon stocks. However, we show a smoother transition in magnitude of effects between these regions.
    Type of Medium: Online Resource
    ISSN: 0706-652X , 1205-7533
    Language: English
    Publisher: Canadian Science Publishing
    Publication Date: 2004
    detail.hit.zdb_id: 7966-2
    detail.hit.zdb_id: 1473089-3
    SSG: 21,3
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    Canadian Science Publishing ; 2009
    In:  Canadian Journal of Fisheries and Aquatic Sciences Vol. 66, No. 12 ( 2009-12), p. 2199-2221
    In: Canadian Journal of Fisheries and Aquatic Sciences, Canadian Science Publishing, Vol. 66, No. 12 ( 2009-12), p. 2199-2221
    Abstract: An important management challenge is to maintain productive populations of Pacific salmon ( Oncorhynchus spp.), despite highly variable environments and our weak understanding of future climatic conditions and mechanisms that link them to salmon. This understanding could be improved by including environmental covariates in salmon population models and applying advanced “meta-analyses” to large data sets to better estimate underlying functional relationships. However, the performance of such models needs to be determined in the context of an overall system. We therefore simulated a 15-population salmon fishery system and compared the performance (in terms of catch and an index of conservation concern) of 10 forecasting and stock assessment models, ranging from simple to complex, by stochastically simulating components of a salmon fishery using a “closed-loop simulation” (or “management strategy evaluation”) under a variety of plausible future climatic scenarios. We found that complex models perform better in some situations. However, their incremental benefits are small and are swamped by the large variability in outcomes of management actions caused by “outcome uncertainty”, which reflects noncompliance of fishing vessels with regulations as well as variation in catchability. Reduction of this outcome uncertainty should therefore be a top priority, as should evaluations of more complex stock assessment models before adopting them.
    Type of Medium: Online Resource
    ISSN: 0706-652X , 1205-7533
    Language: English
    Publisher: Canadian Science Publishing
    Publication Date: 2009
    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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  • 5
    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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  • 6
    Online Resource
    Online Resource
    Elsevier BV ; 2012
    In:  Ecological Modelling Vol. 224, No. 1 ( 2012-1), p. 76-89
    In: Ecological Modelling, Elsevier BV, Vol. 224, No. 1 ( 2012-1), p. 76-89
    Type of Medium: Online Resource
    ISSN: 0304-3800
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2012
    detail.hit.zdb_id: 191971-4
    detail.hit.zdb_id: 2000879-X
    SSG: 12
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