Assessment of the potential impact of future climate change on human health and well-being (the latter via effects on animal health) is hindered by the sheer number of pathogens, their diversity, varied linkages to climate and ecosystems and, often, lack of data. Here we propose to exploit a unique database developed at the University of Liverpool which will soon contain a set of records for all known pathogens of humans and domestic animals. We will use expertise present within the University of Liverpool, the international co-investigators and our project partners to generate a subset of the list, namely all those pathogens that occur in proximity to the UK, France and the Netherlands or threaten these countries; are of major impact in terms of the magnitude and likelihood of impact on human health or well-being; and have epidemiological linkages to temperature or moisture levels in air or the environment and, hence, may be expected to be susceptible to the effects of climate change. This subset of diseases will be subjected to qualitative or quantitative risk assessment to estimate how they will change (in terms of distribution, incidence and severity) under scenarios of future climate change within the next half-century. Our underlying principle is that the data and pathways on which our conclusions are based should be fully recorded, referenced and transparent; as better data become available, it will be possible to update the model outputs. A benefit of our approach is that it is 'bottom-up', at the start giving equal weight to all possible pathogens that could be affected by climate change, and then reducing the list according to agreed criteria. This approach is balanced, allowing the conclusion, for example, that the highest-impact pathogens are largely insensitive to climate change. By contrast, most previous assessments of the impacts of climate change are top-down, starting (and often ending) with the premise that a few key vector-borne pathogens of (usually) humans (malaria, dengue, yellow fever) need urgent consideration. We will listen closely to stakeholders and end-users while designing our risk assessment pathways, and wish to communicate our scientific approaches and findings to them effectively. To the end, we plan to adopt participatory methods throughout the project.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

It is widely recognised that ecosystems provide numerous services that are of benefit to humans but, in decisions regarding land and resource use, these tend to be overlooked. Within towns and cities this is particularly the case as nature is often considered to be absent in urban areas. However, as nearly 80% of the UK population live in urban areas there is considerable potential for improvements in ecosystem services to have a large impact on quality of life. As a result the Defra funded Ecosystem Services in the Urban Water Environment (ESUWE) project has begun to apply an ecosystem services approach to demonstrate the benefits that improvements in the urban water environment can have. It has also been recognised that a collaborative approach to decision making assists with the integrated planning that is required for sustainable catchment management. Therefore, the work of ESUWE also aims to provide tools to communicate and engage stakeholders in order to facilitate a participatory approach to catchment management. The ESUWE project has identified numerous ecosystem services provided in urban environments and developed metrics to quantify the costs and benefits associated with these. It is now working in four demonstration areas of varying sizes to map and evaluate ecosystem services and to pilot use in local catchment planning. It is hoped that by communicating information about benefits of environmental improvements, decisions can be better informed and that by mapping ecosystem services, areas where interventions will result in multiple-benefits can be identified and prioritised. Throughout the ESUWE project, Green Infrastructure (GI) has been highlighted as being important for delivering benefits to urban societies along with providing environmental and hydrological improvements. Therefore, the potential to expand the scope of the work beyond those directly involved with catchment planning has been identified. The Innovation Project will enable the application of the research conducted under the ESUWE project to meet the needs of a wider range of end users such as local nature partnership, local planning authorities and construction companies to be investigated so that the impact of the work can be increased. The Innovation Project will facilitate co-development of an ecosystem services mapping approach to the planning of GI with those responsible for land use decisions at local and national levels. This will ensure that the needs of end users are incorporated into the development of decision support tools that facilitate GI planning and help create standardised metrics that can express the benefits of GI for use in differing sectors. Work in four demonstration areas will explore the practical application of the ecosystem services approach, demonstrating the benefits provided by GI and identifying opportunities for these to be increased. This will improve strategic understanding so that the effects of potential land use decisions on levels of services provided in urban area can be explored. This will help to provide an evidence base that can inform decisions regarding trade-offs and promote interventions that provide increased and multiple benefits. The Innovation Project will also result in case studies quantifying the value of GI which can be used to promote the need for increased considerations of its provision in land use decision at both local and national levels. A partnership approach will also identify how mapping can aid integrated local decision making to support other place based initiatives. Finally, by considering how GI can be implemented in a way that delivers multiple benefits, best practice will be identified and promoted.

The ways in which humans transmit infections depend on the nature of the pathogen and how individuals contact one another. Some individuals, who are particularly well connected, might expect to receive and spread infection frequently, while others that are socially isolated might be expected to avoid general epidemics. In practice, human populations are usually divided into social cliques, around families, schools and workplaces, where interaction is more intense, set against a background of looser connections. Such variable patterns of interaction can be mapped into a social network, where the characteristics of individuals might relate to their risks of being infected and of being a source of infection. Human social networks and their association with infection are quite well understood, particularly for childhood infections, such as measles, and sexually transmitted diseases, such as HIV. The same is not true of wild animals, where we still tend to think in population terms, of individuals being uniform and having simple social lives. Social networks of infection have been studied in a few wildlife species, including Tasmanian devils, meerkats and giraffes. In one or two cases, including our pilot studies of badgers, the network positions of individuals have been shown to relate to the presence of infection. However, it is hard to know which came first, the infection or the position? Did animals catch infection because they were leading risky lives, were stressed or had reduced immunity? Or were they putting their resources into reproduction instead of fighting infections and so enhancing their lifetime fitness? Did the infection itself affect their behaviour? Or did they occupy this position because they were infected, perhaps because they were ostracised by other members of their social group? Given that many important infections of humans and livestock, such as influenza viruses, originate from wild animals, it would be useful to understand how to manage such emerging infections in wildlife. Badgers are a good example. Because they are a reservoir of bovine tuberculosis (TB), which can affect a range of mammals including cattle and humans, they are often culled in an attempt to control the disease. They are also hosts for rabies and U.K. rabies contingency plans currently provide for badgers to be culled to protect human health. The current pilot badger culls being conducted in England are a test of whether effective culling can be implemented by the farming industry, in the hope that this might control TB infection in cattle. Unexpectedly, it has been shown in earlier trials that culling badgers can also bring about increases in new cases of TB in badgers, and in cattle. This has been hypothesised to stem from a "perturbation effect", whereby culling upsets the otherwise stable social behaviour of badgers, causing them to roam, and transmit infection, more widely. Despite the prominence of the badger TB problem and widespread awareness of this "perturbation effect", we know surprisingly little about how infection is transmitted between badgers and how this is affected by culling. This project will look in detail at infection in wild animal networks, using badgers as an example, paying particular attention to social behaviour and individual condition, and working out what these mean for disease control. We are proposing to use intensively monitored populations of wild badgers to study a range of "indicator" infections and how they relate to behaviour, stress, immune function, reproductive activity and success. Our work will give us an understanding of how infections spread, or do not spread, across real networks and will help improve understanding of the "perturbation effect". Then we will build a computer model of infections in our observed networks and use the model to test strategies to determine which is best for disease control, for badgers and TB, and for animal diseases more generally.