Retrospective Evaluation of Preseason Forecasting Models for Pink Salmon

Haeseker, Steven L.; Peterman, Randall M.; Su, Zhenming; Wood, Chris

doi:10.1577/m04-085.1

Cited by 24 publications

(43 citation statements)

References 26 publications

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“…Our results suggest that the performance of individual models varied across forecast evaluation methods, complicating the selection of a single best model. While these results are derived from the specific case study of the SI, the findings are similar to those of other salmon forecast analyses (e.g., Haeseker et al 2005) and are germane to other forecast scenarios.…”

Section: Discussionsupporting

confidence: 84%

“…Out-of-sample predictions from models of log-transformed data (Models 6-13) were adjusted before they were back-transformed to the arithmetic scale so that the predictions represented the expected SI (Beauchamp and Olson 1973;Sprugel 1983;Haeseker et al 2005):…”

Section: Forecast Performancementioning

confidence: 99%

“…Six summary performance metrics were considered (see online supplementary material, Table S1.1 1 ) that reflected different aspects of forecast error (Zhou 2003;Haeseker et al 2005;Francis 2006). We focused on two of the summary metrics, mean error (ME) and root mean square error (RMSE), on the arithmetic scale.…”

Section: Figmentioning

confidence: 99%

“…In an attempt to reduce forecast errors, several models have been developed that incorporate environmental and biotic variables along with salmon freshwater return data (e.g., Roth et al 2007;Wang et al 2009;Rupp et al 2012b;Burke et al 2013). However, it is not yet clear whether incorporation of environmental or biotic variables into salmon forecast models results in substantial improvements in future forecast performance relative to simpler models (Haeseker et al 2005). Methods and metrics used to assess future performance of abundance forecast models differ among studies; hence, expected future forecast skill can be difficult to assess.…”

Section: Introductionmentioning

confidence: 99%

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Expected future performance of salmon abundance forecast models with varying complexity

Winship

O’Farrell

Satterthwaite

et al. 2015

Can. J. Fish. Aquat. Sci.

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We evaluated the scope for improving abundance forecasts for fishery management using Sacramento River fall Chinook salmon (Oncorhynchus tshawytscha) as a case study. A range of forecast models that related the Sacramento Index (SI; an index of adult ocean abundance) to jack (estimated age 2) spawning escapement the previous year were considered. Alternative models incorporated effects of density dependence, local environmental conditions, the abundance of the previous cohort, and trends or autocorrelation in the jack-to-SI relationship. Forecast performance was assessed in terms of bias, accuracy, ability to track trends in the SI, and management objectives. Several models achieved higher accuracy than the model used for management, but no single model performed best across all criteria, and substantial forecast error remained across all approaches considered. Environmental models generally performed better than the management model, but there were differences in the relative importance of individual environmental variables over time and among model formulations. Accounting for model selection uncertainty in environmental models decreased their forecast performance. Simpler models often had similar or better performance than environmental models. In particular, the model incorporating temporally autocorrelated errors demonstrated potential for modest forecast improvement with relatively little additional model complexity.Résumé : Nous avons évalué le potentiel d'amélioration des prévisions d'abondance pour la gestion des pêches en utilisant le saumon quinnat (Oncorhynchus tshawytscha) d'automne du fleuve Sacramento comme étude de cas. Divers modèles de prévision reliant l'indice Sacramento (SI; un indice de l'abondance océanique des adultes) à l'échappée de géniteurs unibermarins (âge estimé de 2 ans) l'année précédente ont été examinés. Les modèles incorporaient, selon le cas, les effets de la dépendance sur la densité, des conditions ambiantes locales, de l'abondance de la cohorte précédente et des tendances ou de l'autocorrélation du lien unibermarins-SI. La performance de prévision a été évaluée en termes des biais, de l'exactitude, de la capacité de faire ressortir des tendances du SI et des objectifs de gestion. Plusieurs modèles ont produit une plus grande exactitude que le modèle utilisé pour la gestion, mais aucun modèle n'a surclassé les autres pour tous les critères, et des erreurs de prévision importantes subsistaient pour toutes les approches examinées. Les modèles environnementaux donnaient généralement de meilleurs résul-tats que le modèle de gestion, mais il y avait des différences en ce qui concerne l'importance relative de variables environnementales précises dans le temps et entre les formulations de modèle. La prise en compte de l'incertitude associée au choix du modèle dans les modèles environnementaux réduisait leur performance de prévision. Dans bien des cas, les modèles plus simples performaient aussi bien ou mieux que les modèles environnementaux. Plus particulièrement, le modèle incorpo...

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Section: Discussionsupporting

confidence: 84%

Section: Forecast Performancementioning

confidence: 99%

Section: Figmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Expected future performance of salmon abundance forecast models with varying complexity

Winship

O’Farrell

Satterthwaite

et al. 2015

Can. J. Fish. Aquat. Sci.

View full text Add to dashboard Cite

show abstract

“…Historically, pink salmon returns have been notoriously diffi cult to predict in many regions due to limited pre-season fi shery information, complicated stock dynamics with odd-and even-year cycles, and highly variable rates of annual adult returns (Adkison and Peterman 1999;Adkison 2002;Haeseker et al 2005;Shevlyakov and Koval 2012). Pink salmon, which are the smallest, most abundant Pacifi c salmon species, also have the simplest overall life history, spending only one winter at sea before returning to spawn (Heard 1991).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Pink Salmon Production in Southeast Alaska Using Ecosystem Indicators in Times of Climate Change

Orsi¹,

Fergusson²,

Wertheimer³

et al. 2016

NPAFC Bull.

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Researchers in Alaska have provided accurate pre-season annual forecasts of pink salmon harvest to resource stakeholders of Southeast Alaska (SEAK) in times of climate change. Since 1997, the Southeast Alaska Coastal Monitoring project has collected biophysical data associated with seaward migrating juvenile salmon from May to August, and it has used these data along with larger basin-scale indexes to forecast SEAK pink salmon returns via regression and ecosystem metric models. In nine of the past eleven years (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014), predictions from linear regression models ranged 0-17% of actual harvests, an average absolute forecast deviation of 10%. The primary explanatory variable was juvenile pink salmon peak catch. In some years, models included secondary variables to improve fi t. A supplemental modeling approach was tested recently to better inform stakeholders. This approach incorporated both an annual rank score forecast outlook based on ecosystem metrics, and a visual stoplight color-code graphic. Accurate pre-season salmon forecasts and descriptive outlooks from this applied research has increased economic effi ciency of the fi sh processing industry, enabled managers and resource stakeholders to anticipate harvest with more certainty, helped promote resource sustainability, and provided insight into ecosystem mechanisms related to pink salmon production in a changing climate.

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Potential for ecological nonlinearities and thresholds to inform Pacific salmon management

et al. 2020

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Ecology is often governed by nonlinear dynamics. Nonlinear ecological relationships can include thresholds—incremental changes in drivers that provoke disproportionately large ecological responses. Among the species that experience nonlinear and threshold dynamics are Pacific salmon (Oncorhynchus spp.). These culturally, ecologically, and economically significant fishes are in many places declining and management focal points. Often, managers can influence or react to ecological conditions that salmon experience, suggesting that nonlinearities, especially thresholds, may provide opportunities to inform decisions. However, nonlinear dynamics are not always invoked in management decisions involving salmon. Here, we review reported nonlinearities and thresholds in salmon ecology, describe potential applications that scientists and managers could develop to leverage nonlinear dynamics, and offer a path toward decisions that account for ecological nonlinearities and thresholds to improve salmon outcomes. It appears that nonlinear dynamics are not uncommon in salmon ecology and that many management arenas may potentially leverage them to enable more effective or efficient decisions. Indeed, decisions guided by nonlinearities and thresholds may be particularly desirable considering salmon management arenas are often characterized by limited resources and mounting ecological stressors, practical constraints, and conservation challenges. More broadly, many salmon systems are data‐rich and there are an extensive range of ecological contexts in which salmon are sensitive to anthropogenic decisions. Approaches developed to leverage nonlinearities in salmon ecology may serve as examples that may inform analogous approaches in other systems and taxa.

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Retrospective Evaluation of Preseason Forecasting Models for Pink Salmon

Cited by 24 publications

References 26 publications

Expected future performance of salmon abundance forecast models with varying complexity

Expected future performance of salmon abundance forecast models with varying complexity

Forecasting Pink Salmon Production in Southeast Alaska Using Ecosystem Indicators in Times of Climate Change

Potential for ecological nonlinearities and thresholds to inform Pacific salmon management

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