Oceans and Human Health (OHH): a European Perspective from the Marine Board of the European Science Foundation (Marine Board-ESF)

The oceans and coastal seas provide mankind with many benefits including food for around a third of the global population, the air that we breathe and our climate system which enables habitation of much of the planet. However, the converse is that generation of natural events (such as hurricanes, severe storms and tsunamis) can have devastating impacts on coastal populations, while pollution of the seas by pathogens and toxic waste can cause illness and death in humans and animals. Harmful effects from biogenic toxins produced by algal blooms (HABs) and from the pathogens associated with microbial pollution are also a health hazard in seafood and from direct contact with water. The overall global burden of human disease caused by sewage pollution of coastal waters has been estimated at 4 million lost person-years annually. Finally, the impacts of all of these issues will be exacerbated by climate change. A holistic systems approach is needed. It must consider whole ecosystems, and their sustainability, such as integrated coastal zone management, is necessary to address the highly interconnected scientific challenges of increased human population pressure, pollution and over-exploitation of food (and other) resources as drivers of adverse ecological, social and economic impacts. There is also an urgent and critical requirement for effective and integrated public health solutions to be developed through the formulation of politically and environmentally meaningful policies. The research community required to address "Oceans & Human Health" in Europe is currently very fragmented, and recognition by policy makers of some of the problems, outlined in the list of challenges above, is limited. Nevertheless, relevant key policy issues for governments worldwide include the reduction of the burden of disease (including the early detection of emerging pathogens and other threats) and improving the quality of the global environment. Failure to effectively address these issues will impact adversely on efforts to alleviate poverty, sustain the availability of environmental goods and services and improve health and social and economic stability; and thus, will impinge on many policy decisions, both nationally and internationally. Knowledge exchange (KE) will be a key element of any ensuing research. KE will facilitate the integration of biological, medical, epidemiological, social and economic disciplines, as well as the emergence of synergies between seemingly unconnected areas of science and socio-economic issues, and will help to leverage knowledge transfer across the European Union (EU) and beyond. An integrated interdisciplinary systems approach is an effective way to bring together the appropriate groups of scientists, social scientists, economists, industry and other stakeholders with the policy formulators in order to address the complexities of interfacial problems in the area of environment and human health. The Marine Board of the European Science Foundation Working Group on "Oceans and Human Health" has been charged with developing a position paper on this topic with a view to identifying the scientific, social and economic challenges and making recommendations to the EU on policy-relevant research and development activities in this arena. This paper includes the background to health-related issues linked to the coastal environment and highlights the main arguments for an ecosystem-based whole systems approach.

Tropical Marginal Seas: Priority Regions for Managing Marine Biodiversity and Ecosystem Function

Tropical marginal seas (TMSs) are natural subregions of tropical oceans containing biodiverse ecosystems with conspicuous, valued, and vulnerable biodiversity assets. They are focal points for global marine conservation because they occur in regions where human populations are rapidly expanding. Our review of 11 TMSs focuses on three key ecosystems—coral reefs and emergent atolls, deep benthic systems, and pelagic biomes—and synthesizes, illustrates, and contrasts knowledge of biodiversity, ecosystem function, interaction between adjacent habitats, and anthropogenic pressures. TMSs vary in the extent that they have been subject to human influence—from the nearly pristine Coral Sea to the heavily exploited South China and Caribbean Seas—but we predict that they will all be similarly complex to manage because most span multiple national jurisdictions. We conclude that developing a structured process to identify ecologically and biologically significant areas that uses a set of globally agreed criteria is a tractable first step toward effective multinational and transboundary ecosystem management of TMSs.

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.