Estimating the economic loss of recent North Atlantic fisheries management

It is accepted that world’s fisheries are not generally exploited at their biological or their economic optimum. Most fisheries assessments focus on the biological capacity of fish stocks to respond to harvesting and few have attempted to estimate the economic efficiency at which ecosystems are exploited. The latter is important as fisheries contribute considerably to the economic development of many coastal communities. Here we estimate the overall potential economic rent for the fishing industry in the North Atlantic to be B€ 12.85, compared to current estimated profits of B€ 0.63. The difference between the potential and the net profits obtained from North Atlantic fisheries is therefore B€ 12.22. In order to increase the profits of North Atlantic fisheries to a maximum, total fish biomass would have to be rebuilt to 108 Mt (2.4 times more than present) by reducing current total fishing effort by 53%. Stochastic simulations were undertaken to estimate the uncertainty associated with the aggregate bioeconomic model that we use and we estimate the economic loss NA fisheries in a range of 2.5 and 32 billion of euro. We provide economic justification for maintaining or restoring fish stocks to above their MSY biomass levels. Our conclusions are consistent with similar global scale studies.

Review of climate change impacts on marine fisheries in the UK and Ireland

1.Commercial fishing is an important socio-economic activity in coastal regions of the UK and Ireland. Ocean–atmospheric changes caused by greenhouse gas emissions are likely to affect future fish and shellfish production, and lead to increasing challenges in ensuring long-term sustainable fisheries management. 2.The paper reviews existing knowledge and understanding of the exposure of marine ecosystems to ocean-atmospheric changes, the consequences of these changes for marine fisheries in the UK and Ireland, and the adaptability of the UK and Irish fisheries sector. 3.Ocean warming is resulting in shifts in the distribution of exploited species and is affecting the productivity of fish stocks and underlying marine ecosystems. In addition, some studies suggest that ocean acidification may have large potential impacts on fisheries resources, in particular shell-forming invertebrates. 4.These changes may lead to loss of productivity, but also the opening of new fishing opportunities, depending on the interactions between climate impacts, fishing grounds and fleet types. They will also affect fishing regulations, the price of fish products and operating costs, which in turn will affect the economic performance of the UK and Irish fleets. 5.Key knowledge gaps exist in our understanding of the implications of climate and ocean chemistry changes for marine fisheries in the UK and Ireland, particularly on the social and economic responses of the fishing sectors to climate change. However, these gaps should not delay climate change mitigation and adaptation policy actions, particularly those measures that clearly have other ‘co-benefits’.

Projecting marine fish production and catch potential in Bangladesh in the 21st century under long-term environmental change and management scenarios

The fisheries sector is crucial to the Bangladeshi economy and wellbeing, accounting for 4.4% of national Gross Domestic Product (GDP) 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 (EEZ) by less than 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 to 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 less than 20% compared to current catches. The results demonstrate that management can mitigate or exacerbate the effects of climate change on ecosystem productivity.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.