Escapement goals for sockeye salmon with informative prior probabilities based on habitat considerations

Geiger, Harold J.; Koenings, Jeffery P.

doi:10.1016/0165-7836(91)90004-y

Cited by 20 publications

(8 citation statements)

References 2 publications

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“…This use of Bayesian methods is now pervasive in all fields of statistics and natural resources. Geiger and Koenings [1991] and Walters and Ludwig [1994] were two of the first papers to utilize Bayesian methods in fisheries; excellent overviews of the approach are by Punt and Hilborn [1997] and Francis and Shotten [1997]. Hilborn [1992] predicted correctly that Bayesian methods would be the future of stock assessment.…”

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confidence: 99%

Ruminations on the Development and Future of Population Dynamics Models in Fisheries

Quinn

2003

Natural Resource Modeling

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ABSTRACT. I trace the development of fisheries models (i.e., fish population dynamics models of species subject to fisheries) to the 21st century. The first real efforts occurred in the period 1900 1920 with the work of Baranov (the “Grandfather” of fisheries population dynamics) and the formation of the International Council for the Exploration of the Sea (ICES). The establishment of the science occurred between 1920 1960 with multi‐species modeling, age‐ and size‐structure dynamics, and production models. Fundamental work during this time was done by Ricker (the “Father” of fisheries population dynamics), Beverton and Holt (the “Prophets” of fisheries population dynamics), Chapman, Dickie, DeLury, Graham, Gulland, Leslie, Lotka and Volterra, Russell, Schaefer, and Thompson. During this time, most of the workwas deterministic and mathematical. Between 1960 and 1980, statistical methodology evolved greatly but was separate from mathematical advances for the most part. The development of statistical principles for the estimation of animal abundance was further enhanced by Arnason, Buckland, Burnham and Anderson and White, Cormack, Eberhardt, Jolly, Manly, Pollock, Ricker, Robson, and Seber, among others. Fisheries models evolved in a deterministic setting, with advances in age‐structured models (Gulland, Pope, Doubleday), surplus production models (Pella, Tomlin‐son, Schnute, Fletcher, Hilborn), growth models, bioeconomic models (C. Clark) and management control models (Hilborn, Walters). The period 1980 2000 was the Golden Age. The integration between mathematics and statistics occurred when likelihood and least squares techniques were formally combined with mathematical models of population change. The number of fisheries modelers grew exponentially during this time, resulting in a concomitant increase in publications. A major advance in the 1990s has been the development of Bayesian and time series methods, which have allowed explicit specification of uncertainty. Currently, theory allows realistic modeling of age‐ and size‐structured populations, migratory populations and harvesting strategies. These models routinely incorporate measurement error, process error (stochasticity) and time variation. But data needs often overwhelm the performance of models, and greater demands are being placed on models to answer complex questions. There has been poor communication between fisheries and ecological modelers, between fisheries researchers and statisticians, and among fisheries researchers in different geographic locales. Future models will need to deal better with habitat and spatial concerns, genetics, multispecies interactions, environmental factors, effects of harvesting on the ecosystem, model misspecification and so‐cioeconomic concerns. Meta‐analysis, retrospective analysis and operating models are some modern approaches for dealing with uncertainty and providing for sustainable fisheries. However, I fear that current attacks on single‐species models and management may result in rejection of these advances an...

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confidence: 99%

Ruminations on the Development and Future of Population Dynamics Models in Fisheries

Quinn

2003

Natural Resource Modeling

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“…Walters and Ludwig (1994) and Punt and Hilborn (1997) caution against using data from other populations to construct informative priors for parameters that represent abundance scaling and habitat size such as the carrying capacity parameter, K. However, informative priors for such parameters could be constructed using habitat considerations in addition to data from similar populations (e.g. Geiger and Koenings, 1991;Hoenig et al, 1994). For example, K could be reparameterized as the product of the spatial area occupied by the species, and carrying capacity per unit area, K=kA.…”

Section: Informative and Non-informative Priorsmentioning

confidence: 98%

Bayesian stock assessment: a review and example application using the logistic model

McAllister

Kirkwood

1998

ICES Journal of Marine Science

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. 1998. Bayesian stock assessment: a review and example application using the logistic model. -ICES Journal of Marine Science, 55: 1031-1060.Bayesian statistical methods have recently been combined with conventional methods for fisheries stock assessment (e.g. catch-age analysis) to provide a conceptually elegant approach for providing fishery management advice under uncertainty. Uncertainties in the advice provided can be conveyed using posterior probability distributions (or ''posteriors'') for the potential outcomes of each policy option. Posteriors can be estimated using data (e.g. catch-age data and relative abundance indices) for the fish population of interest and prior probability distributions for population model parameters (e.g. stock-recruit function parameters) based on data from similar fish populations. Despite growing interest, Bayesian methods remain accessible to relatively few. To increase the accessibility of these methods, the conceptual basis for Bayesian statistical estimation is reviewed and set in the context of fisheries stock assessment. The use of Bayesian methods is illustrated by fitting a logistic model to relative abundance indices for Namibian hake (Merlucius capensis and M. paradoxus) and presenting a decision analysis of alternative harvest policy options. Some alternative approaches are outlined for constructing prior and posterior probability distributions and some recent applications in fisheries. Some of the problems that can be encountered while implementing Bayesian methods are also discussed.1998 International Council for the Exploration of the Sea

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“…Deterministic Ricker model in terms of natural parameters (a,b) or management-related parameters (C*,S*) and (C*,h*). confidence in management decisions arising from the analysis (as in Geiger and Koenings 1991). We then believe that two points should be carefully addressed.…”

Section: Parameters Transformations and Choice Of Priormentioning

confidence: 98%

“…As advised by Geiger and Koenings (1991) and Walters and Ludwig (1994), we limit the number of parameters to estimate by treating the standard deviation of process errors σ as a nuisance parameter. Therefore, the posterior P(θ,σ|{S,R}) in eq.…”

Section: Problem Formulation: Bayesian Conditional Reasoning Allows Smentioning

confidence: 99%

How robust are Bayesian posterior inferences based on a Ricker model with regards to measurement errors and prior assumptions about parameters?

Rivot¹,

Prévost²,

Parent³

2001

Can. J. Fish. Aquat. Sci.

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We present a Bayesian approach of a Ricker stock-recruitment (S/R) analysis accounting for measurement errors on S/R data. We assess the sensitivity of posterior inferences to (i) the choice of Ricker model parameterizations, with special regards to management-related ones, and (ii) prior parameter distributions. Closed forms for Ricker parameter posterior distributions exist given S/R data known without error. We use this property to develop a procedure based on the Rao-Blackwell formula. This procedure achieves integration of measurement errors by averaging these closed forms over possible S/R data sets sampled from distributions derived from a stochastic model relating field data to the S and R variables. High-quality Bayesian estimates are obtained. The analysis of the influence of different parameterizations and of the priors is made easier. We illustrate our methodological approach by a case study of Atlantic salmon (Salmo salar). Posterior distributions for S and R are computed from a mark-recapture stochastic model. Ignoring measurement errors underestimates parameter uncertainty and overestimates both stock productivity and density dependence. We warn against using management-related parameterizations because it makes the strong prior assumption of long-term sustainability of stocks. Posterior inferences are sensitive to the choice of prior. The use of informative priors as a remedy is discussed.Résumé : Nous présentons une analyse Bayesienne d'un modèle stock-recrutement (S/R) de Ricker qui intègre les erreurs de mesure sur les données S/R. Nous étudions la sensibilité des inférences a posteriori (i) à différentes paramétrisations du modèle de Ricker, notamment à celles reliées à la gestion, et (ii) aux distributions a priori sur les paramètres. Conditionnellement à une série S/R connue sans erreur, les distributions a posteriori des paramètres peuvent s'exprimer analytiquement. Nous développons une procédure de Rao-Blackwell qui s'appuie sur cette propriété. Les erreurs de mesure sont intégrées en moyennant ces formes analytiques sur un échantillon de séries S/R tirées dans leur distribution a posteriori issue d'un modèle stochastique reliant les données de terrain aux variables S et R. Les estimateurs bayesiens obtenus sont de grande qualité et l'étude de sensibilité aux choix des différentes paramétrisations et des priors est facilitée. Nous illustrons notre approche méthodologique par un cas d'étude sur le Saumon atlantique (Salmo salar). Les distributions a posteriori de S et R sont issues d'un modèle probabiliste de capture-recapture. Ignorer les erreurs de mesure sous-estime l'incertitude et surestime la productivité du stock et la densité dépendance. Nous ne recommandons pas l'utilisation systématique des paramètres reliés à la gestion car cela nécessite l'hypothèse a priori que le stock peut se renouveler seul. Les inférences a posteriori sont sensibles au choix des priors. L'utilisation de priors informatifs pourrait permettre d'y remédier.

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Escapement goals for sockeye salmon with informative prior probabilities based on habitat considerations

Cited by 20 publications

References 2 publications

Ruminations on the Development and Future of Population Dynamics Models in Fisheries

Ruminations on the Development and Future of Population Dynamics Models in Fisheries

Bayesian stock assessment: a review and example application using the logistic model

How robust are Bayesian posterior inferences based on a Ricker model with regards to measurement errors and prior assumptions about parameters?

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