This article is part of the special series "Applications of Bayesian Networks for Environmental Risk Assessment and Management" and was generated from a session on the use of Bayesian networks (BNs) in environmental modeling and assessment in 1 of 3 recent conferences: SETAC North America 2018 (Sacramento), SETAC Europe 2019 (Helsinki), and European Geosciences Union 2019 (Vienna). The 3 sessions aimed at showing the state-of-the art and new directions in the use of BN models in environmental assessment, focusing on ecotoxicology and water quality modeling. This series aims at reflecting the broad applicability of BN methodology in environmental assessment across a range of ecosystem types and scales, and discusses the relevance for environmental management.

Mäntyniemi, S., Kuikka, S., Rahikainen, M., Kell, L. T., and Kaitala, V. 2009. The value of information in fisheries management: North Sea herring as an example. – ICES Journal of Marine Science, 66: 2278–2283. We take a decision theoretical approach to fisheries management, using a Bayesian approach to integrate the uncertainty about stock dynamics and current stock status, and express management objectives in the form of a utility function. The value of new information, potentially resulting in new control measures, is high if the information is expected to help in differentiating between the expected consequences of alternative management actions. Conversely, the value of new information is low if there is already great certainty about the state and dynamics of the stock and/or if there is only a small difference between the utility attached to different potential outcomes of the alternative management action. The approach can, therefore, help when deciding on the allocation of resources between obtaining new information and improving management actions. In our example, we evaluate the value of obtaining hypothetically perfect knowledge of the type of stock–recruitment function of the North Sea herring (Clupea harengus) population.

A non-linear growth model was used to evaluate the effects of temperature and age on annual length increments of pikeperch, Sander lucioperca (L.), in seven lakes in Finland. Length increments were derived by back-calculation using the Fraser-Lee method. Annual length increments increased from age 1 to age 3 and then decreased, while at the same time length increments and air temperature had positive correlation until age 12. Ageand size-structured yield per recruit models were used in two lakes to evaluate the effects of temperature and gillnet mesh size on pikeperch yield. In these two lakes maximum yield could be obtained with 60 and 70 mm (bar length) gill nets. In the second lake, as typically in Finland, 45-50 mm gill nets are the most frequently used. The use of larger mesh size gill nets would increase pikeperch yield from 685 to 1000 g per recruit based on the present mean temperature. In both lakes increase in temperatures would increase yield if mesh size is simultaneously increased. Higher pikeperch yield can be expected in the future because of climate warming. K E Y W O R D S : growth, lakes, Sander lucioperca, temperature, yield per recruit models.

Mikkonen, S., Rahikainen, M., Virtanen, J., Lehtonen, R., Kuikka, S., and Ahvonen, A. 2008. A linear mixed model with temporal covariance structures in modelling catch per unit effort of Baltic herring. – ICES Journal of Marine Science, 65: 1645–1654. Changes in the structure and attributes of a fleet over time will break down the proportionality of catch per unit effort (cpue) and stock biomass. Moreover, logbook data from commercial fisheries are hierarchical and autocorrelated. Such features not only complicate the analysis of cpue data but also seriously limit the application of a generalized linear model approach, which nevertheless is applied commonly. We demonstrate a linear mixed model application for a large hierarchical dataset containing autocorrelated observations. In the analysis, the key idea is to explore the properties of the error term of the model. We modified the residual covariance matrix, allowing the introduction of assumed fisher behaviour, influencing the catch rate. Fisher behaviour consists of accumulated knowledge and learning processes from their earlier area- and time-specific catch rates. Also, we investigated the effects of vessel-specific parameters by introducing random intercepts and slopes in the model. A model with the autoregressive moving average residual covariance matrix structure was superior over the block-diagonal and autoregressive (AR1) structure for the data, having a time-dependent correlation among trawl hauls. The results address the benefits of statistically advanced methods in obtaining precise and unbiased estimates from cpue data, to be used further in stock assessment. Fisheries agencies are encouraged to monitor the relevant vessel and gear attributes, including engine power and gear size, and the deployment practices of the gear.

Catch per unit effort (CPUE) is widely used as an index of stock abundance. It is as widely acknowledged that CPUE can be a misleading index of abundance owing to a multitude of factors including fish behavior, fishing fleet interaction, and the increase in catchability over time caused by improvement in fishing technology. Based on information concerning the size of herring trawls manufactured in Finland since the early 1980s, an increase in fishing power of the fleet was postulated. Because we lacked direct information about the size of trawls aboard, we applied a model to estimate the changes over time. In the analysis, an analogy between fish and trawls was created by adopting the concepts and algorithms from fish stock assessment into assessment of the trawl "population", where both the total number of trawls and the size of individual trawls were being analyzed. The results indicate that the average gear size has nearly tripled in 20 years. Accepting the assumption that larger trawls are generally more effective than smaller ones, a substantial increase in fishing power has taken place. As a result, sequential population models calibrated with CPUE data will be severely biased as well.Résumé : Les captures par unité d'effort (CPUE) sont souvent utilisées comme indices de l'abondance des stocks, mais il est aussi généralement admis que les CPUE sont parfois des indices erronés à cause d'une multitude de facteurs, dont le comportement des poissons, l'interaction entre la flotte de pêche et les poissons et le raffinement de la technologie des pêches avec le temps. Compte tenu de la taille des chaluts à harengs construits en Finlande depuis le début des années 1980, nous avons postulé un accroissement du pouvoir de capture de la flotte. Bien que nous n'ayons aucun renseignement direct sur la taille des chaluts à bord, nous avons utilisé un modèle pour estimer les changements dans le temps. Dans cette analyse, nous avons fait une analogie entre les poissons et les chaluts en appliquant les concepts et les algorithmes utilisés pour l'évaluation des stocks de poissons à l'évaluation de la «population de chaluts» dans laquelle le nombre total de chaluts et leurs tailles respectives ont été analysés. Les résultats révèlent que la taille moyenne des engins de pêche a presque triplé en 20 ans. S'il est vrai que les chaluts plus grands sont généralement plus efficaces que les plus petits, un accroissement important du pouvoir de capture s'est donc produit. Il en résulte que les modèles démographiques séquentiels calibrés à partir des données de CPUE donnent lieu à des erreurs importantes.[Traduit par la Rédaction] Rahikainen and Kuikka 541