International audienceMarine recreational fishing (MRF) is a high-participation activity with large economic value and social benefits globally, and it impacts on some fish stocks. Although reporting MRF catches is a European Union legislative requirement, estimates are only available for some countries. Here, data on numbers of fishers, participation rates, days fished, expenditures, and catches of two widely targeted species were synthesized to provide European estimates of MRF and placed in the global context. Uncertainty assessment was not possible due to incomplete knowledge of error distributions; instead, a semi-quantitative bias assessment was made. There were an estimated 8.7 million European recreational sea fishers corresponding to a participation rate of 1.6%. An estimated 77.6 million days were fished, and expenditure was €5.9 billion annually. There were higher participation, numbers of fishers, days fished and expenditure in the Atlantic than the Mediterranean, but the Mediterranean estimates were generally less robust. Comparisons with other regions showed that European MRF participation rates and expenditure were in the mid-range, with higher participation in Oceania and the United States, higher expenditure in the United States, and lower participation and expenditure in South America and Africa. For both northern European sea bass (Dicentrarchus labrax, Moronidae) and western Baltic cod (Gadus morhua, Gadidae) stocks, MRF represented 27% of the total removals. This study highlights the importance of MRF and the need for bespoke, regular and statistically sound data collection to underpin European fisheries management. Solutions are proposed for future MRF data collection in Europe and other regions to support sustainable fisheries management

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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.

Smartphone applications (apps) that target recreational fishers are growing in abundance. These apps have the potential to provide data useful for management of recreational fisheries. We surveyed expert opinion in 20, mostly European, countries to assess the current and future status of app use in marine recreational fisheries. The survey revealed that a few countries already use app data to support existing data collection, and that this number is likely to increase within 5–10 years. The strongest barriers to use app data were a scarcity of useful apps and concern over data quality, especially biases due to the opt-in nature of app use. Experts generally agreed that apps were unlikely to be a “stand-alone” method, at least in the short term, but could be of immediate use as a novel approach to collect supporting data such as, fisheries-specific temporal and spatial distributions of fishing effort, and aspects of fisher behaviour. This survey highlighted the growing interest in app data among researchers and managers, but also the need for government agencies and other managers/researchers to coordinate their efforts with the support of survey statisticians to develop and assess apps in ways that will ensure standardisation, data quality, and utility.

The occurrence and density of ≥ 1+ brown trout, Salmo trutta L., and their relationship with prevailing instream and catchment characteristics were studied in 50 small forest streams, partially dredged for forest ditching. The occurrence of trout at a stream site was largely determined by the abundance of pools, size of upper catchment and water pH. Moreover, at sites where trout occurred, the abundance of pools was lower at dredged locations than at those in a natural state. In riffles in a natural state, there was a positive relationship between trout density and three instream variables: the abundance of stream pools, cascades and instream vegetation, while an inverse relationship was found with the abundance of substratum of 2–10 cm in diameter. Of the catchment variables, correspondingly, the proportion of forest in the upper catchment was positively related and the proportion of peatland negatively related to trout density. No significant regression model could be fitted for dredged riffles. The possibility of enhancing trout populations in dredged riffles is discussed.