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’.

Indications of a climate effect on Mediterranean fisheries

Using the Food and Agriculture Organization’s (FAO) Mediterranean capture fisheries production dataset in conjunction with global and Mediterranean sea surface temperatures, we investigated trends in fisheries landings and landings per unit of effort of commercially important marine organisms, in relation to temperature oscillations. In addition to the overall warming trend, a temperature shift was detected in the Mediterranean Sea in the late 1990s. Fisheries landings fluctuations were examined for the most abundant commercial species (59 species) and showed significant year-to-year correlations with temperature for nearly 60 % of the cases. From these, the majority (~70 %) were negatively related and showed a reduction of 44 % on average. Increasing trends were found, mainly in the landings of species with short life spans, which seem to have benefited from the increase in water temperature. Τhe effect of oceanic warming is apparent in most species or groups of species sharing ecological (e.g. small and medium pelagic, demersal fish) or taxonomic (e.g. cephalopods, crustaceans) traits. A landings-per-unit-of-effort (LPUE) proxy, using data from the seven Mediterranean European Union member states, also showed significant correlation with temperature fluctuations for six out of the eight species examined, indicating the persistence of temperature influence on landings when the fishing effect is accounted for. The speed of response of marine landings to the warming of the Mediterranean Sea possibly shows both the sensitivity and the vulnerable state of the fish stocks and indicates that climate should be examined together with fisheries as a factor shaping stock fluctuations.

Social impacts of a temperate fisheries closure: understanding stakeholders' views

The social dimensions of marine protected areas (MPAs) play an important role in MPA success, yet these social dimensions are little understood. We explore the social impacts arising from the establishment of an MPA using Lyme Bay (south west England) as a case study. Through a series of small group semi-structured interviews the social impacts experienced by fishermen (mobile and static gear), recreational users (divers and sea anglers) and recreation service providers (charter boat and dive businesses) were explored. The social impacts expressed varied according to activity in which the stakeholder group engaged. Negative themes included lengthening fishing trips, tension and conflict, fishermen identity, equity and uncertainty in the long-term. Positive themes included improved experiences for both commercial fishermen and recreational users, and expectations for long-term benefits. These impacts need to be understood because they influence stakeholder behaviour. Failure to interpret stakeholder responses may lead to poor decision-making and worsening stakeholder relations. These findings have implications for the success of the MPA in Lyme Bay, but also for the future network of marine conservation zones around the UK. Any assessment of MPA impacts must therefore identify social as well as economic and environmental change.

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.