The Use of Generalized Additive Models for Forecasting the Abundance of Queets River Coho Salmon

Wang, Shizhen; Morishima, Gary; Sharma, Rishi; Gilbertson, Larry

doi:10.1577/m07-055.1

Cited by 8 publications

(6 citation statements)

References 33 publications

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“…These include moving averages [14], generalized additive models [15], [16], spawner-recruit relationships [17], time series analysis [18], [19], and neural networks [20]. One of the simplest and most common methods involves a sibling regression model, which uses the abundance of returning precocious males (i.e., for spring Chinook, these are fish that spend only one winter in the ocean, often referred to as jacks) as an indicator of adult returns.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Models of Adult Pacific Salmon Returns

et al. 2013

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Most modeling and statistical approaches encourage simplicity, yet ecological processes are often complex, as they are influenced by numerous dynamic environmental and biological factors. Pacific salmon abundance has been highly variable over the last few decades and most forecasting models have proven inadequate, primarily because of a lack of understanding of the processes affecting variability in survival. Better methods and data for predicting the abundance of returning adults are therefore required to effectively manage the species. We combined 31 distinct indicators of the marine environment collected over an 11-year period into a multivariate analysis to summarize and predict adult spring Chinook salmon returns to the Columbia River in 2012. In addition to forecasts, this tool quantifies the strength of the relationship between various ecological indicators and salmon returns, allowing interpretation of ecosystem processes. The relative importance of indicators varied, but a few trends emerged. Adult returns of spring Chinook salmon were best described using indicators of bottom-up ecological processes such as composition and abundance of zooplankton and fish prey as well as measures of individual fish, such as growth and condition. Local indicators of temperature or coastal upwelling did not contribute as much as large-scale indicators of temperature variability, matching the spatial scale over which salmon spend the majority of their ocean residence. Results suggest that effective management of Pacific salmon requires multiple types of data and that no single indicator can represent the complex early-ocean ecology of salmon.

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Section: Introductionmentioning

confidence: 99%

Multivariate Models of Adult Pacific Salmon Returns

et al. 2013

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“…4 is intended to account for these effects implicitly, it would be more desirable to include environmental effects explicitly. Several nonparametric methods have been developed to include environmental covariates in a SR relationship (Chen and Ware 1999, Chen et al 2000, Chen and Irvine 2001, Schirripa et al 2009, Wang et al 2009). Extending the nonparametric Bayesian method to account for environmental effects, either by taking a fully nonparametric approach or by using a parametric approach to describe the environmental effects (e.g., Mantzouni et al 2010), is an important direction for future work.…”

Section: Extensionsmentioning

confidence: 99%

A semiparametric Bayesian approach to estimating maximum reproductive rates at low population sizes

Sugeno

Munch

2013

Ecological Applications

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The maximum annual reproductive rate (i.e., the slope at the origin in a stock-recruitment relationship) is one of the most important biological reference points in fisheries; it sets the upper limit to sustainable fishing mortality. Estimating the maximum reproductive rate by fitting parametric models to stock-recruitment data may not be a robust approach because two statistically indistinguishable models can generate radically different estimates. To mitigate this issue, we developed a flexible, semiparametric Bayesian approach based on a conditional Gaussian process prior specifically designed to estimate the maximum annual reproductive rate, and applied it to analyze simulated stock-recruitment data sets. Compared with results based on other Gaussian process priors, we found that the conditional Gaussian process prior provided superior results: the accuracy and precision of estimates were enhanced without increasing model complexity. Moreover, compared with parametric alternatives, performance of the conditional Gaussian process prior was comparable to that of the data-generating model and always better than the wrong model.

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“…Yet variation in sibling relationships can be substantial (Noakes et al 1990), and a variety of factors could contribute to that variation. 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).…”

Section: Introductionmentioning

confidence: 99%

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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The Use of Generalized Additive Models for Forecasting the Abundance of Queets River Coho Salmon

Cited by 8 publications

References 33 publications

Multivariate Models of Adult Pacific Salmon Returns

Multivariate Models of Adult Pacific Salmon Returns

A semiparametric Bayesian approach to estimating maximum reproductive rates at low population sizes

Expected future performance of salmon abundance forecast models with varying complexity

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