Henni Pulkkinen

Experiences in Bayesian Inference in Baltic Salmon Management

Abstract. We review a success story regarding Bayesian inference infisheries management in the Baltic Sea. The management of salmonfisheries is currently based on the results of a complex Bayesian pop-ulation dynamic model, and managers and stakeholders use the prob-abilities in their discussions. We also discuss the technical and humanchallenges in using Bayesian modeling to give practical advice to thepublic and to government officials and suggest future areas in which itcan be applied. In particular, large databases in fisheries science offerflexible ways to use hierarchical models to learn the population dynam-ics parameters for those by-catch species that do not have similar largestock-specific data sets like those that exist for many target species.This information is required if we are to understand the future ecosys-tem risks of fisheries. Key words and phrases: Bayesian inference, Baltic salmon, risk anal-ysis, fishery management, decision analysis.1. INTRODUCTIONWe introduce a case of fisheries managementwhere Bayesian inference has been extensively used.Fisheries management is a field of applied science,and one could easily argue that fisheries science is

Post-smolt survival of Baltic salmon in context to changing environmental conditions and predators

Post-smolt survival of Baltic salmon in context to changing environmental conditions and predators Kevin D. Friedland*, Johan Dannewitz, Atso Romakkaniemi, Stefan Palm, Henni Pulkkinen, Tapani Pakarinen, and Rainer Oeberst National Marine Fisheries Service, 28 Tarzwell Dr, Narragansett, RI 02882, USA 2 Department of Aquatic Resources, Institute of Freshwater Research, Swedish University of Agricultural Sciences, Drottningholm S-17893, Sweden Natural Resources Institute Finland, University Oulu FI-90014, Finland Natural Resources Institute Finland, Helsinki FI-00791, Finland Institute of Baltic Sea Fisheries, Rostock D-18069, Germany

Bayesian arrival model for Atlantic salmon smolt counts powered by environmental covariates and expert knowledge

Annual run size and timing of Atlantic salmon smolt migration was estimated using Bayesian model framework and data from six years of a video monitoring survey. The model has a modular structure. It separates sub-processes of departing, traveling and observing, of which the first two together define the arrival distribution. The sub-processes utilize biological background and expert knowledge about the migratory behavior of smolts and about the probability to observe them from the video footage under varying environmental conditions. Daily mean temperature and discharge were used as environmental covariates. The model framework does not require assuming a simple distributional shape for the arrival dynamics and thus also allows for multimodal arrival distributions. Results indicate that 20% - 43% of smolts passed the Utsjoki monitoring site unobserved during the years of study. Predictive studies were made to estimate daily run size in cases with missing counts either at the beginning or in the middle of the run, indicating good predictive performance.

International fisheries management and recreational benefits: The case of Baltic salmon

ABSTRACT This article studies how accounting for the benefits of recreational fisheries affects the formation and stability of an international fisheries agreement (IFA) on the management of Baltic salmon stocks. The interaction between four countries is modelled through a partition function game, under two scenarios. In the first scenario, countries take their participation decision for the IFA based only on the net present value of profits from commercial fisheries. In the second scenario, the net present value of the recreational benefits from angling is also considered. The results show that accounting for recreational benefits leads to the formation of the grand coalition, whereas only partial cooperation occurs when payoffs are confined to profits from commercial fisheries. JEL Codes: C70, F53, Q22.