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Changes in fish distribution are being observed across the globe. In Europe's Common Fisheries Policy, the share of the catch of each fish stock is split among management areas using a fixed allocation key known as 'Relative Stability': in each management area, member states get the same proportion of the total catch each year. That proportion is largely based on catches made by those member states in the 1970s. Changes in distribution can, therefore, result in a mismatch between quota shares and regional abundances within management areas, with potential repercussions for the status of fish stocks and the fisheries that depend on them. Assessing distribution changes is crucial to ensure adequate management and sustainable exploitation of our fish resources. We analysed scientific survey data using a three‐tiered analytical approach to provide, for the first time, an overview of changes in distribution for 19 northeast Atlantic fish species encompassing 73 commercial stocks over 30 yr. All species have experienced changes in distribution, five of which did so across management areas. A cross‐species analysis suggested that shifts in areas of suitable thermal habitat, and density‐dependent use of these areas, are at least partly responsible for the observed changes. These findings challenge the current use of relative stability to allocate quotas. [ABSTRACT FROM AUTHOR]

The fisheries sector is crucial to the Bangladeshi economy and wellbeing, accounting for 4.4% of national gross domestic product and 22.8% of agriculture sector production, and supplying ca. 60% of the national animal protein intake. Fish is vital to the 16 million Bangladeshis living near the coast, a number that has doubled since the 1980s. Here, we develop and apply tools to project the long-term productive capacity of Bangladesh marine fisheries under climate and fisheries management scenarios, based on downscaling a global climate model, using associated river flow and nutrient loading estimates, projecting high-resolution changes in physical and biochemical ocean properties, and eventually projecting fish production and catch potential under different fishing mortality targets. We place particular interest on Hilsa shad (Tenualosa ilisha), which accounts for ca. 11% of total catches, and Bombay duck (Harpadon nehereus), a low price fish that is the second highest catch in Bangladesh and is highly consumed by low-income communities. It is concluded that the impacts of climate change, under greenhouse emissions scenario A1B, are likely to reduce the potential fish production in the Bangladesh exclusive economic zone by ,10%. However, these impacts are larger for the two target species. Under sustainable management practices, we expect Hilsa shad catches to show a minor decline in potential catch by 2030 but a significant (25%) decline by 2060. However, if overexploitation is allowed, catches are projected to fall much further, by almost 95% by 2060, compared with the Business as Usual scenario for the start of the 21st century. For Bombay duck, potential catches by 2060 under sustainable scenarios will produce a decline of ,20% compared with current catches. The results demonstrate that management can mitigate or exacerbate the effects of climate change on ecosystem productivity. [ABSTRACT FROM AUTHOR]

The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts. [ABSTRACT FROM AUTHOR]

Climate change has already altered the distribution of marine fishes. Future predictions of fish distributions and catches based on bioclimate envelope models are available, but to date they have not considered interspecific interactions. We address this by combining the species-based Dynamic Bioclimate Envelope Model ( DBEM) with a size-based trophic model. The new approach provides spatially and temporally resolved predictions of changes in species' size, abundance and catch potential that account for the effects of ecological interactions. Predicted latitudinal shifts are, on average, reduced by 20% when species interactions are incorporated, compared to DBEM predictions, with pelagic species showing the greatest reductions. Goodness-of-fit of biomass data from fish stock assessments in the North Atlantic between 1991 and 2003 is improved slightly by including species interactions. The differences between predictions from the two models may be relatively modest because, at the North Atlantic basin scale, (i) predators and competitors may respond to climate change together; (ii) existing parameterization of the DBEM might implicitly incorporate trophic interactions; and/or (iii) trophic interactions might not be the main driver of responses to climate. Future analyses using ecologically explicit models and data will improve understanding of the effects of inter-specific interactions on responses to climate change, and better inform managers about plausible ecological and fishery consequences of a changing environment. [ABSTRACT FROM AUTHOR]