Evaluation of chum salmon fishery performance using Ricker and Beverton-Holt stock recruitment approaches in a Bayesian framework (original) (raw)
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Canadian Journal of Fisheries and Aquatic Sciences, 2004
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.
Canadian Journal of Fisheries and Aquatic Sciences, 2004
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. Résumé : Afin de mieux comprendre les effets des facteurs du milieu sur les taux de survie reproducteurs-recrues du saumon rose (Oncorhynchus gorbuscha), nous avons mis au point plusieurs modèles spatiaux hiérarchiques bayésiens (HBMs). Nous avons appliqués ces modèles à 43 stocks de saumons roses du nord-ouest pacifique. En utilisant des distributions a priori basées sur la distance et corrélées spatialement pour les paramètres spécifiques aux stocks, ces modèles multi-stock permettent explicitement une corrélation positive entre les stocks voisins de saumons, en ce qui a trait à leurs productivités et les coefficients de température de surface de la mer (SST) sur la côte en début d'été. C'est à notre connaissance la première fois qu'une telle distribution de probabilité spatiale a priori basée sur les distances des paramètres est utilisée dans une étude sur les poissons. Les HBMs spatiaux produisent des estimations plus concordantes et plus précises des effets de SST sur la productivité qu'une méthodologie qui estime les paramètres séparément pour chaque stock. Comme dans nos résultats obtenus à l'aide de modèles d'effets mixtes sur les mêmes stocks, il y a des effets positifs significatifs de SST sur la survie des stocks plus nordiques de saumons roses et des effets négatifs plus faibles de SST sur la survie des stocks de saumons roses plus au sud. Cependant, nous montrons une transition plus graduelle de l'importance de ces effets ente les deux régions.
Experiences in Bayesian Inference in Baltic Salmon Management
Statistical Science, 2014
We review a success story regarding Bayesian inference in fisheries management in the Baltic Sea. The management of salmon fisheries is currently based on the results of a complex Bayesian population dynamic model, and managers and stakeholders use the probabilities in their discussions. We also discuss the technical and human challenges in using Bayesian modeling to give practical advice to the public and to government officials and suggest future areas in which it can be applied. In particular, large databases in fisheries science offer flexible ways to use hierarchical models to learn the population dynamics parameters for those by-catch species that do not have similar large stock-specific data sets like those that exist for many target species. This information is required if we are to understand the future ecosystem risks of fisheries.
Statistical models of Pacific salmon that include environmental variables
Past attempts to improve population models of Pacific salmon Oncorhynchus spp. by adding indices of freshwater or marine conditions have shown mixed success. To increase chances that such models will remain reliable over the long term, we suggest adding only environmental covariates that have a spatial scale of positive correlation among monitoring locations similar to, or greater than, that of the salmon variables that scientists are trying to explain. To illustrate this approach, we analyzed spawner and recruit data for 120 populations (stocks) of pink O. gorbuscha, chum O. keta, and sockeye O. nerka salmon from Washington, British Columbia, and Alaska. Salmon productivity of a given species was positively correlated across stocks at a spatial scale of about 500-800 km. Compared to upwelling and sea-surface salinity, summer sea-surface temperature (SST) showed a more appropriate spatial scale of positive covariation for explaining variation in salmon productivity, and was a significant explanatory variable when added to both single-stock and multi-stock spawner-recruit models. This result suggests that future models of these salmon populations should possibly include stock-specific, summer SST. To further explore our understanding of salmon population dynamics, we developed 24 alternative stock-recruitment models. We compared these models in three ways: (1) their fit to all past data, (2) their ability to forecast recruitment, and (3) their performance inside an "operating model," which included components for dynamics of the natural ecological system, stock assessments based on simulated sampling of data, regulationsetting based on those assessments, and variation in implementing those regulations (reflecting noncompliance or other sources of outcome uncertainty). We also compared single-stock models with multi-stock models (meta-analyses). The latter led to more precise estimates of the effects of SST on log e (recruits/spawner) and greater accuracy of preseason forecasts for some stocks. Analyses with the operating model show that reducing outcome uncertainty should be a top management priority.
Background/Question/Methods Understanding the density dependence of fish reproduction is critically important for management and conservation of commercial fisheries. Stock-recruitment relationships describe how reproductive output changes relative to the number of fish in a population. Common models used to fit fisheries data include the Beverton-Holt model, which describes a system where total recruitment levels off at higher spawner densities (called compensation), and the Ricker model, where total recruitment declines at high densities (overcompensation). While large amounts of data allow an appropriate model to be chosen for a species or population, no clear method has been developed to objectively determine the appropriate stock-recruitment relationship for commercial species where limited or no stock-recruitment data exist. To address this gap, we developed a hierarchical model that uses Bayesian inference to link stock-recruitment parameters among species, taxonomic orders (...
Finland's goal in the Salmon Action Plan-program of IBSFC is to restore salmon stocks of Bothnian Bay rivers. As part of a research program of the Academy of Finland, the multidisciplinary Bireme-SAP-project is working for this goal. Sociological viewpoint is involved to open the black box of fishers' role in the restoring process. Our task has been to find out fishers' perceptions of salmon stocks, of rebuilding the stocks and of the management issues, and to model these with Bayesian networks. The method was chosen to model the restoration process interdisciplinary, by linking social aspects with biological factors. Understanding fishers' point of view makes it easier to cope with uncertainty in fisheries management. From the beginning "commitment" has been emphasized as the crucial point leading to consensus between different actors around salmon. Commitment means finding the best ways to increase the probability of successful restoration, so that the pr...
ICES Journal of Marine Science, 2014
ABSTRACT We developed a hierarchical Bayesian integrated life cycle model for Atlantic salmon that improves on the stock assessment approach currently used by ICES and provides some interesting insights about the population dynamics of a stock assemblage. The model is applied to the salmon stocks in eastern Scotland. It assimilates a 40-year (1971–2010) time-series of data compiled by ICES, including the catches in the distant water fisheries at Faroes and West Greenland and estimates of returning fish abundance. Our model offers major improvements in terms of statistical methodology for A. salmon stock assessment. Uncertainty about inferences is readily quantified in the form of Bayesian posterior distributions for parameters and abundance at all life stages, and the model could be adapted to provide projections based on the uncertainty derived from the estimation phase. The approach offers flexibility to improve the ecological realism of the model. It allows the introduction of density dependence in the egg-to-smolt transition, which is not considered in the current ICES assessment method. The results show that this modifies the inferences on the temporal dynamics of the post-smolt marine survival. In particular, the overall decrease in the marine survival between 1971 and 2010 and the sharp decline around 1988–1990 are dampened when density dependence is considered. The return rates of smolts as two-sea-winter (2SW) fish has declined in a higher proportion than return rates as one-sea-winter (1SW) fish. Our results indicate that this can be explained either by an increase in the proportion maturing as 1SW fish or by an increase in the mortality rate at sea of 2SW fish, but the data used in our analyses do not allow the likelihood of these two hypotheses to be gauged.
Global Change Biology, 2018
Understanding how species might respond to climate change involves disentangling the influence of co-occurring environmental factors on population dynamics, and is especially problematic for migratory species like Pacific salmon that move between ecosystems. To date, debate surrounding the causes of recent declines in Yukon River Chinook salmon (Oncorhynchus tshawytscha) abundance has centered on whether factors in freshwater or marine environments control variation in survival, and how these populations at the northern extremity of the species range will respond to climate change. To estimate the effect of factors in marine and freshwater environments on Chinook salmon survival, we constructed a stagestructured assessment model that incorporates the best available data, estimates incidental marine bycatch mortality in trawl fisheries, and uses Bayesian model selection methods to quantify support for alternative hypotheses. Models fitted to two index populations of Yukon River Chinook salmon indicate that processes in the nearshore and marine environments are the most important determinants of survival. Specifically, survival declines when ice leaves the Yukon River later in the spring, increases with wintertime temperature in the Bering Sea, and declines with the abundance of globally enhanced salmon species consistent with competition at sea. In addition, we found support for density-dependent survival limitations in freshwater Accepted Article This article is protected by copyright. All rights reserved. but not marine portions of the life cycle, increasing average survival with ocean age, and agespecific selectivity of bycatch mortality in the Bering Sea. This study underscores the utility of flexible estimation models capable of fitting multiple data types and evaluating mortality from both natural and anthropogenic sources in multiple habitats. Overall, these analyses suggest that mortality at sea is the primary driver of population dynamics, yet under a warming climate Chinook salmon populations at the northern extent of the species' range may be expected to fare better than southern populations, but are influenced by foreign salmon production.