WILDLIFE HEALTH 2.0: BRIDGING THE KNOWLEDGE-TO-ACTION GAP

In the context of the Internet, the term Web 2.0 refers to changes in the way Web pages are made and used rather than to specific technical updates. It is the combination of technologies that maximize the potential to create a greater diversity of information through a dialogue between information producers and users. In many ways, it mirrors the concept of knowledge mobilization in which connections are made between research expertise and policy or practice to improve outcomes. The growing frustration in many fields that research is inadequately being used to inform decisions to improve outcomes (Pullin et al. 2004; Watson 2005; Orton et al. 2011) suggests a need to acquire, apply, and practice the skills needed to bridge the knowledge-to-action gap. In my opinion, wildlife health research aims to improve the health status of wildlife. Just as Web 2.0 relies on Web 1.0, wildlife health decisions depend on sound scientific data, but such data are only a single facet of decision making. Here, I argue that Wildlife Health 2.0, a wildlife health research agenda for the 21st century, needs to spend as much time investigating the complexity of translating different types of knowledge and the constraints that might limit its application as it does in understanding the mechanisms and impacts of health outcomes.

If one accepts that health and disease are products of complex systems (Stephen 2014), one must conclude that scientific certainty is not only elusive in wildlife health research but also that multiple points of view are needed for evidence to be helpful (Colucci-Gray et al. 2006). Wildlife health is the outcome of nonlinear, dynamic interactions between individual animals and their social, biotic, and abiotic environments (Stephen 2014). Those environments are highly influenced by decisions we make about environmental and resource management, which, in turn, are affected by our values, economics, and politics. As we move from individual animals to the population and ecosystem level, the decision-making context becomes more variable, uncertain, and complex. Historically, health science has approached management decisions by generating data to estimate the probability and magnitude of effects of a risk factor, often through mathematical models. Over time, it has been found that failure to appreciate the unpredictability of complex systems or to incorporate ethical and cultural dimensions of decisions has limited the contribution of this approach to decision making (Colucci-Gray et al. 2006). Wildlife health is the cumulative effect of a complex system of interacting threats, hazards, and determinants of health that vary in time, space and populations, making research results subject to effect modification in different populations at different times. Being a product of a complex system, surprises, unanticipated outcomes, and emerging phenomena are a normal part of health (Stephen et al. 2015). A postnormal science perspective could lead one to conclude that wildlife health information might only be valid at a local level for a specific time, for a portion of the causal chain, or for a particular purpose. Evidence aiming to establish scientific truths on how something works, in general, is different from evidence that determines what might work and how it might work under specific circumstances. Wildlife health science will continue to be challenged in translating research into policy and practice if it remains limited to the current modes of research (Colborn 1995).

Wildlife health research has been adept at discovering threats, describing their consequences, and proposing options for responses, but it has been less able to directly inspire action on its own. Health research has historically emphasized questions related to “what is this” or “how does this work,” rather than “how do we turn knowledge into action now?” Another style of research and policy making is needed for the 21st century: one that recognizes that context matters and that there is not a single truth out there that can be seen, understood, and controlled in a rational manner (Hodgkin 1996). Lavis et al. (2003) proposed the capacity of research to influence decision makers at the right scale as an emphasis for determining if research is good.

Dobrow et al. (2004) concluded that good research generates evidence that is right for the local decision-making context. In contrast to the customary scientific emphasis on validity and conformity with the scientific method, the knowledge-to-action perspective also emphasizes the relevance, accessibility, and applicability of evidence to a specific decision. There is growing recognition that evidence-based decision makers need to understand the interactions between context, relevance, and research quality (Dobrow et al. 2004). The four stages of decision making—problem delineation, option development, implementation strategies, and evaluation—all benefit from research that is informed by the local decision-making context, especially when researchers recognize that the type of evidence needed at each stage can be very different (Orton et al. 2011).

“Evidence-based decision-making is centered on the justification of decisions” (Dobrow et al. 2004, p. 207). Decision makers view evidence as anything that gives reason to believe in something, which is a less restrictive view than the scientific perspective of evidence as information generated through a prescribed set of processes and procedures recognized as being scientific (Lomas et al. 2005). Evidence for decision making can be context free (such as disease pathogenesis) or context specific (e.g., human dimensions of a wildlife disease problem). The evidence can be quantitative (such as economic effects of disease or effect modifications due to local ecologic variation) or qualitative (e.g., community priorities). The method to generate one type of evidence is not the same as another, but this does not imply one type of evidence is better than another. All are needed to inform decisions. Wildlife Health 2.0 research teams will need to be as adept at providing the scientific justification for options to respond to wildlife health threats as they will need to be in producing the social evidence to identify implementation strategies that are feasible, sustainable, understandable, and acceptable to decision makers.

The transfer of research into action is not straightforward. Research and management that seek to integrate various forms of biophysical and social evidence to affect change demand approaches to problem identification, option development, and problem solving that cross traditional boundaries (Parkes and Panelli 2001). Impediments for researchers, policy makers, and practitioners to bridge boundaries at the knowledge-to-action gap include 1) an imbalance between scientific quality and novelty and problem relevance when selecting research questions, 2) failure to negotiate and integrate competing influences on decisions, including science, politics, values, finances, and public pressures, 3) incompatible time frames between time needed to make a decision and time needed to complete a scientific inquiry, and 4) obstacles to communicating evidence needs and outcomes between researchers and decision makers (Frenk 1992; Orton et al. 2011). These impediments can be traced to the ongoing separation of science and decision making.

Having healthy sustainable wildlife is a widely shared goal that increasingly needs attention to the links between science, systems, and society. Managing wildlife health based on social consensus of what is good evidence requires an expanded set of tools and perspectives that reflects the complexity and multidimensional character of decisions about wildlife health. Wildlife Health 2.0 must also focus on evidence that affects moving research into action. There are a number of perspectives on how to improve mobilization of science for action, none of which have been validated, but it is well-known that behavior change in humans is not a rational process that follows linearly and logically from the acquisition of new information (Hargreaves 2011). Efforts to more effectively integrate science into decisions are more likely to be effective when they cross boundaries in ways that simultaneously enhance relevance, credibility, and legitimacy of the information they produce for people who will make or be affected by management decisions (Cash et al. 2003). A Wildlife Health 2.0 agenda is not a rejection of the current research paradigm. Indeed, basic and applied sciences to understand the pathology, epidemiology, and ecology of health outcomes are critical to sound decision making. Rather, Wildlife Health 2.0 advocates for an expansion of our areas of inquiry to ensure the exchange of information between research generators and users to provide additional contextual understanding to help the latter make good choices. Although there are many external factors that drive policy agendas, fulfilling the vision of healthy, sustainable wildlife requires increased attention to the links between various social, biomedical, and ecologic information. This will require new approaches to wildlife health research that are participatory and can address both the need for relevant evidence for decision making and the excellence of scientific inquiry.