Zoë Austin

Uncertainties in the governance of animal disease: an interdisciplinary framework for analysis

Uncertainty is an inherent feature of strategies to contain animal disease. In this paper, an interdisciplinary framework for representing strategies of containment, and analysing how uncertainties are embedded and propagated through them, is developed and illustrated. Analysis centres on persistent, periodic and emerging disease threats, with a particular focus on cryptosporidiosis, foot and mouth disease and avian influenza. Uncertainty is shown to be produced at strategic, tactical and operational levels of containment, and across the different arenas of disease prevention, anticipation and alleviation. The paper argues for more critically reflexive assessments of uncertainty in containment policy and practice. An interdisciplinary approach has an important contribution to make, but is absent from current real-world containment policy.

Identifying conflicts and opportunities for collaboration in the management of a wildlife resource: a mixed-methods approach

Context The sustainable management of many common-pool ecological resources can be strengthened through collaboration among stakeholder groups. However, the benefits of collaborative management are often not realised because of conflicts of interest among stakeholders. Effective strategies for enhancing collaborative management require an understanding of the trade-offs that managers make between different management outcomes and an understanding of the socioeconomic and location-specific differences that drive these preferences. Approaches based on quantitative or qualitative methods alone often fail to reveal some of the underlying factors inhibiting collaboration. Aims Our aim was to understand the relative importance that private-sector deer managers attach to changes in the following three outcomes of deer management: deer numbers, deer-related road-traffic accidents (RTAs) and deer impacts on conservation woodlands. Methods We used a mixed-methods approach, combining choice-experiment methodology with qualitative analysis of focus-group discussions from 10 study regions throughout Britain. Key results Our results showed that most of the private-sector stakeholders responsible for deer-management decisions at the local level would prefer to see a future with fewer deer-related RTAs but do not want to see a future with lower deer population levels. This is especially the case for those stakeholders managing for sporting purposes and those that rely on deer as a financial resource. Conclusions The preferences of many private-sector stakeholders responsible for deer management are at odds with those of private landowners currently experiencing economic and conservation damage from deer, and with the aims of government and non-government bodies seeking to reduce grazing and browsing damage through lower deer densities. Similar barriers to collaborative management are likely to exist in any situations where ecological resources deliver an unequal distribution of benefits and costs among stakeholders. Implications Overcoming barriers to collaboration requires enhanced understanding of how different collaborative mechanisms are viewed amongst the stakeholder community and how collaborative management can be promoted. More holistic approaches to deer management, which include greater public awareness, additional road-traffic speed restrictions and appropriate fencing, or perhaps include deer-population reduction as only one of a suite of mechanisms for delivering multiple benefits from the land, are likely to gain more support from private-sector stakeholders. Mixed-methods approaches can provide an important first step in terms of both quantifying preferences in relation to the management of ecological resources and enabling detailed insights into the motivations and behaviours underlying them.

Wrong, but useful:negotiating uncertainty in infectious disease modelling

For infectious disease dynamical models to inform policy for containment of infectious diseases the models must be able to predict; however, it is well recognised that such prediction will never be perfect. Nevertheless, the consensus is that although models are uncertain, some may yet inform effective action. This assumes that the quality of a model can be ascertained in order to evaluate sufficiently model uncertainties, and to decide whether or not, or in what ways or under what conditions, the model should be ‘used’. We examined uncertainty in modelling, utilising a range of data: interviews with scientists, policy-makers and advisors, and analysis of policy documents, scientific publications and reports of major inquiries into key livestock epidemics. We show that the discourse of uncertainty in infectious disease models is multi-layered, flexible, contingent, embedded in context and plays a critical role in negotiating model credibility. We argue that usability and stability of a model is an outcome of the negotiation that occurs within the networks and discourses surrounding it. This negotiation employs a range of discursive devices that renders uncertainty in infectious disease modelling a plastic quality that is amenable to ‘interpretive flexibility’. The utility of models in the face of uncertainty is a function of this flexibility, the negotiation this allows, and the contexts in which model outputs are framed and interpreted in the decision making process. We contend that rather than being based predominantly on beliefs about quality, the usefulness and authority of a model may at times be primarily based on its functional status within the broad social and political environment in which it acts.

Mapping wildlife: integrating stakeholder knowledge with modelled patterns of deer abundance by using participatory GIS

Context. Some species that are perceived by certain stakeholders as a valuable resource can also cause ecological or economic damage, leading to contrasting management objectives and subsequent conflict between stakeholder groups. There is increasing recognition that the integration of stakeholder knowledge with formal scientific data can enhance the information available for use in management. This is especially true where scientific understanding is incomplete, as is frequently the case for wide-ranging species, which can be difficult to monitor directly at the landscape scale. Aims. The aim of the research was to incorporate stakeholder knowledge with data derived from formal quantitative models to modify predictions of wildlife distribution and abundance, using wild deer in the UK as an example. Methods. We use selected predictor variables from a deer–vehicle collision model to estimate deer densities at the 10-km square level throughout the East of England. With these predictions as a baseline, we illustrate the use of participatory GIS as a methodological framework for enabling stakeholder participation in the refinement of landscape-scale deer abundance maps. Key results. Stakeholder participation resulted in modifications to modelled abundance patterns for all wild deer species present in the East of England, although the modifications were minor and there was a high degree of consistency among stakeholders in the adjustments made. For muntjac, roe and fallow deer, the majority of stakeholder changes represented an increase in density, suggesting that populations of these species are increasing in the region. Conclusions. Our results show that participatory GIS is a useful technique for enabling stakeholders to contribute to incomplete scientific knowledge, especially where up-to-date species distribution and abundance data are needed to inform wildlife research and management. Implications. The results of the present study will serve as a valuable information base for future research on deer management in the region. The flexibility of the approach makes it applicable to a range of species at different spatial scales and other wildlife conflict issues. These may include the management of invasive species or the conservation of threatened species, where accurate spatial data and enhanced community involvement are necessary in order to facilitate effective management.

Policy, practice and decision making for zoonotic disease management: Water and Cryptosporidium

Decision making for zoonotic disease management should be based on many forms of appropriate data and sources of evidence. However, the criteria and timing for policy response and the resulting management decisions are often altered when a disease outbreak occurs and captures full media attention. In the case of waterborne disease, such as the robust protozoa, Cryptosporidium spp, exposure can cause significant human health risks and preventing exposure by maintaining high standards of biological and chemical water quality remains a priority for water companies in the UK. Little has been documented on how knowledge and information is translated between the many stakeholders involved in the management of Cryptosporidium, which is surprising given the different drivers that have shaped management decisions. Such information, coupled with the uncertainties that surround these data is essential for improving future management strategies that minimise disease outbreaks. Here, we examine the interplay between scientific information, the media, and emergent government and company policies to examine these issues using qualitative and quantitative data relating to Cryptosporidium management decisions by a water company in the North West of England. Our results show that political and media influences are powerful drivers of management decisions if fuelled by high profile outbreaks. Furthermore, the strength of the scientific evidence is often constrained by uncertainties in the data, and in the way knowledge is translated between policy levels during established risk management procedures. In particular, under or over-estimating risk during risk assessment procedures together with uncertainty regarding risk factors within the wider environment, was found to restrict the knowledge-base for decision-making in Cryptosporidium management. Our findings highlight some key current and future challenges facing the management of such diseases that are widely applicable to other risk management situations.