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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]

Projections of the impacts of climate change on marine ecosystems are a key prerequisite for the planning of adaptation strategies, yet they are inevitably associated with uncertainty. Identifying, quantifying, and communicating this uncertainty is key to both evaluating the risk associated with a projection and building confidence in its robustness. We review how uncertainties in such projections are handled in marine science. We employ an approach developed in climate modelling by breaking uncertainty down into (i) structural (model) uncertainty, (ii) initialization and internal variability uncertainty, (iii) parametric uncertainty, and (iv) scenario uncertainty. For each uncertainty type, we then examine the current state-of-the-art in assessing and quantifying its relative importance. We consider whether the marine scientific community has addressed these types of uncertainty sufficiently and highlight the opportunities and challenges associated with doing a better job. We find that even within a relatively small field such as marine science, there are substantial differences between subdisciplines in the degree of attention given to each type of uncertainty. We find that initialization uncertainty is rarely treated explicitly and reducing this type of uncertainty may deliver gains on the seasonal-to-decadal time-scale.Weconclude that all parts of marine science could benefit from a greater exchange of ideas, particularly concerning such a universal problem such as the treatment of uncertainty. Finally, marine science should strive to reach the point where scenario uncertainty is the dominant uncertainty in our projections. [ABSTRACT FROM AUTHOR]

Andonegi, E., Fernandes, J. A., Quincoces, I., Irigoien, X., Uriarte, A., Pérez, A., Howell, D., and Stefánsson, G. 2011. The potential use of a Gadget model to predict stock responses to climate change in combination with Bayesian networks: the case of Bay of Biscay anchovy. – ICES Journal of Marine Science, 68: 1257–1269.The European anchovy (Engraulis encrasicolus) is a short-lived pelagic species distributed in Atlantic European waters, with the Bay of Biscay being one of the main centres of abundance. Because it is a short-lived species, the state of the stock is determined largely by incoming recruitment. Recruitment is highly variable and depends on a variety of factors, such as the size of the spawning stock and environmental conditions in the area. The use of a coupled model that could serve to predict the evolution of the anchovy stock in the short, medium, and long term under several fishing-pressure scenarios and given climate scenarios is demonstrated. This coupled model consists of a Gadget (Globally Applicable Disaggregated General Ecosystem Toolbox) model that was used to analyse the status of the Bay of Biscay anchovy population and to simulate future scenarios based on the estimated recruitment levels, combined with a probabilistic Bayesian network model for recruitment estimation based on machine-learning methods and using climatic indices as potential forecasting factors. The results indicate that certain combinations of medium to high fishing pressure and adverse environmental conditions could force the stock outside its biological reference boundaries. [ABSTRACT FROM PUBLISHER]