Enhancing the Global Ocean Observing System to meet evidence based needs for the ecosystem-based management of coastal ecosystem services

Ecosystem-based approaches (EBAs) to managing anthropogenic pressures on ecosystems, adapting to changes in ecosystem states (indicators of ecosystem health), and mitigating the impacts of state changes on ecosystem services are needed for sustainable development. EBAs are informed by integrated ecosystem assessments (IEAs) that must be compiled and updated frequently for EBAs to be effective. Frequently updated IEAs depend on the sustained provision of data and information on pressures, state changes, and impacts of state changes on services. Nowhere is this truer than in the coastal zone, where people and ecosystem services are concentrated and where anthropogenic pressures converge. This study identifies the essential indicator variables required for the sustained provision of frequently updated IEAs, and offers an approach to establishing a global network of coastal observations within the framework of the Global Ocean Observing System. The need for and challenges of capacity-building are highlighted, and examples are given of current programmes that could contribute to the implementation of a coastal ocean observing system of systems on a global scale. This illustrates the need for new approaches to ocean governance that can achieve coordinated integration of existing programmes and technologies as a first step towards this goal.

Channel Catchments Cluster. Recent developments in tools and techniques for water quality management in the France (Channel) England region

The Channel Catchments Cluster (3C) aims to capitalise on outputs from some of the recent projects funded through the INTERREG IVa France (Channel) England programme. The river catchment basins draining into the Channel region drain an area of 137,000km2 and support a human population of over 19M. Throughout history, these catchments, rivers and estuaries have been centres of habitation, developed through commerce and industry, providing transport links to hinterland areas. These catchments also provide drinking water and food through provision of agriculture, fisheries and aquaculture. In addition, many parts of the region are also economically important now for the tourism and leisure industries. Consequently, there is a need to manage the balance of these many and varied human activities within the catchments, rivers, estuaries and marine areas to ensure that they are maintained or restored to good environmental condition . This document highlights some of the recent work carried out by projects within the INTERREG IVa programme that provide tools and techniques to assist in the achievement of these goals.

The importance of independent marine evidence provision in a multi-use environment: MBA information resources and The Marine Life Information Network.

One of the most pressing challenges today is the need to manage our oceans on a sustainable basis, balancing opportunities for exploitation with the need for conservation and protection. A vital tool for informing sustainable management is access to accurate, up-to-date marine environmental data and information, which is also seen as ‘independent’ by industry, conservationists, policy-makers and other Stakeholders. The Marine Biological Association has specialised in providing independent evidence for over a century and hosts a number of programmes dedicated to independent evidence provision. For example, the Marine Life Information Network (MarLIN) is the most comprehensive information resource for the marine environment of the British Isles and also the largest review of the effects of human activities and natural events on marine species and habitats ever undertaken. MarLIN, along with the Data Archive for Seabed Species and Habitats (DASSH and other MBA information resources, is currently being used to support a wide range of UK and European legislation as well as providing vital underpinning information for industry (e.g. through informing EIAs). We provide an overview of MarLIN in particular whilst examining the importance of ‘independent’ scientific information in a multi-use environment.

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.