Reconciling Economic and Biological Modeling of Migratory Fish Stocks:Optimal Management of the Atlantic Salmon Fishery in the Baltic Sea

The paper puts forward a model of the Atlantic salmon fishery in the Baltic Sea that integrates the salient biological and economic characteristics of migratory fish stocks. Designed to be compatible with the framework used for actual stock assessments, the model accounts for agestructured population dynamics, the seasonal harvest and competing harvesting by commercial and recreational fishermen. It is calibrated using data and parameter estimates for the Simojoki River stock. The socially optimal policy for maximizing discounted net benefits from the fishery within an uncertain environment is determined using a dynamic programming approach and numerical solution method. Our results indicate that substantial economic benefits could be realized under optimal management without compromising stock sustainability.Resource /Energy Economics and Policy,

Estimating life history parameters of European hake using Bayesian models

Sustainability and Maximum Sustainable yield (MSY) are nowadays the main fishery management goals, which are mainly addressed through single stock assessment models. However, there is a need of increasing the number of stocks assessed and improve the quality of existing assessments. The stock assessment is based on models that link the fishing activity to population dynamics based on biological processes. A common problem when starting to develop a stock assessment model for a specific species is that the biological knowledge for some processes is poor or even absent (frequently for M but also growth). In these cases, the lack of prior knowledge can be replaced with empirical estimates. The theory of life history invariants states (in general) that for a similar taxa, k/M and Lm/Linf tend to be relatively constant. This theory highlights two helpful considerations: one that whether you know one parameter you can estimate the other and two that those parameters cannot vary since they are correlated. Within this frame, life history invariants theory and hierarchical Bayesian models can be combined to better understand biological processes needed in most stock assessment models (maturity, growth and natural mortality) providing the required parameters together with their statistical structure (posterior distributions). In order to perform this meta-analysis, bibliographic life history parameters for different hake species all over the world have been collected and analysed. The results of these parameters could be useful to help in the configuration of biological processes of hake stock assessment models

Bayesian decision theory for fisheries management of migratory species with multiple life histories

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Reconciling economic and biological modeling of migratory fish stocks: optimal management of the Atlantic salmon fishery in the Baltic Sea

vo

Reconciling economic and biological modeling of migratory fish stocks : optimal management of the Atlantic salmon fishery in the Baltic Sea

v2008o

In-season assessment and management of salmon stocks using a Bayesian time-density model

We document a time-density model for in-season assessment of salmon stocks that integrates both relative and absolute indicators of abundance and incorporates preseason information on run-size and migration timing using a Bayesian framework. We evaluate different data collection programs for Fraser River sockeye salmon (Oncorhynchus nerka) by examining the precision, bias and timeliness of resulting run-size estimates with a retrospective analysis. We quantify the run-size bias if migration was early versus late and evaluate the impact of run-size uncertainty on the ability to reach management objectives. In-season assessments greatly improve the accuracy and precision of run-size estimates compared to preseason forecasts. For the in-season assessment of Fraser River sockeye, CPUE data from seaward marine test fisheries, although less precise, were more informative at the peak marine migration than more precise terminal, in-river hydroacoustic data obtained on the same date, but conveying information of an earlier stage in the migration. Throughout the season, the best fisheries management results were obtained by relying on in-season assessments using both marine CPUE data as well as marine reconstructed abundance estimates derived from in-river hydroacoustic estimates, thereby taking advantage of the benefits of both sources of information.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Reconciling Economic and Biological Modeling of Migratory Fish Stocks:Optimal Management of the Atlantic Salmon Fishery in the Baltic Sea

The paper puts forward a model of the Atlantic salmon fishery in the Baltic Sea that integrates the salient biological and economic characteristics of migratory fish stocks. Designed to be compatible with the framework used for actual stock assessments, the model accounts for agestructured population dynamics, the seasonal harvest and competing harvesting by commercial and recreational fishermen. It is calibrated using data and parameter estimates for the Simojoki River stock. The socially optimal policy for maximizing discounted net benefits from the fishery within an uncertain environment is determined using a dynamic programming approach and numerical solution method. Our results indicate that substantial economic benefits could be realized under optimal management without compromising stock sustainability

Impact of time-varying productivity on estimated stock–recruitment parameters and biological reference points

Models with time-varying parameters are increasingly being considered in the assessment of fish stocks, but their reliability when used to derive biological reference points or benchmarks has not been thoroughly evaluated. Here, we evaluated stock–recruitment models with and without time-varying productivity in a simulation framework for sockeye salmon (Oncorhynchus nerka) under different scenarios of productivity and exploitation. Ignoring trends in productivity led to overestimates of productivity and underestimates of capacity when both exploitation rates and productivity declined over time, resulting in an underestimation on average of benchmarks of biological status. Despite being less biased, time-varying models had relatively poor fit based on AICc and BIC model selection criteria. Our simulation results were compared with empirical analyses of 12 Fraser River sockeye salmon stocks in British Columbia, Canada. Although benchmarks were less biased when based on time-varying models, underlying true benchmarks based on spawner abundances at maximum sustainable yield, SMSY, trend downwards when productivity declines, which may not be aligned with conservation objectives. We conclude with best practices when adapting biological benchmarks to time-varying productivity.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author