In this post, Rhys Green describes a Practitioner’s Perspective article about a practical demonstration of how science can be more effective in informing policy: “On Formally Integrating Science and Policy: Walking the Walk” by Jim Nichols and colleagues.
In the rare instances where applied ecology informs conservation and wildlife management at all, it usually happens by two steps that are only tenuously connected. Typically, scientists study an ecological problem, describe its causes and assess the likely effects of various possible remedial interventions. They present their results in reports, scientific papers and talks. Policymakers, decision-makers and resource managers read and listen and then interpret the scientific findings, decide what to do about the problem and sometimes implement policies intended to solve it. Although the scientists might come back later to study the effectiveness of the interventions and suggest modifications, this is not usually the case. The result of this incomplete connection between science and action is that the scientists often do not get practice in specifying the type and amount of action required, and the level of uncertainty attached to their prescription and the model underlying it, with sufficient clarity and precision. Equally, decision-makers often misinterpret the scientific findings and act inappropriately or insufficiently in response. The process frequently fails to help in solving the problem and participants from each of the two camps tend to blame the other. In effect, there is a disconnect between the science and the operational implementation of its findings. A potential solution is to formally integrate scientific research into the decision-making process within an adaptive management framework. But how should that be done? And does it work?
In their Practitioner’s Perspective, Nichols and colleagues report on a rare example of adaptive management in the regulation of the hunting of North American mallard ducks Anas platyrhynchos. The term “adaptive management” has been used by others to describe a wide range of activities, many of which are not really adaptive. In the duck-hunting case, there was a formal and iterative decision process in which choices about the limits imposed on the number of ducks shot by hunters in each year were made with the objectives of both conserving the resource, and also testing a set of competing models about how the ducks’ demographic rates respond to changes in environmental variables, population size and the hunters’ take. This was done by comparing how changes in demography conformed or disagreed with differences in the models’ predictions. Over a period of 20 years, this adaptive learning process has refined the level of statistical support for different models within the competing set that were initially considered equally plausible. For example, the evidential support for models with density-dependent annual survival rates and breeding productivity has dwindled, weakening the optimistic case that shooting a lot of ducks will be compensated for by a resulting density-dependent enhancement of the survival or productivity of those that remain. For this reason, the best substantiated optimal levels of permitted hunting take are now much less liberal (i.e. a smaller proportion of ducks permitted to be shot) than was the case 20 years ago, when substantial density-dependent compensation seemed quite plausible.
Although this story of the regulation of the North American mallard hunt shows that the adaptive management process can be informative and effective, it remains rare. Governments and statutory resource management agencies appear to be reluctant to support long-term, rigorous decision-support procedures like this. One reason may be that such a process locks them into a strict evidence-based regime and therefore restricts the scope for vested interests to influence decision-making. A currently topical illustration of this is the regulation of offshore wind power by governments in the United Kingdom. These governments and their agencies currently rely upon science of poor quality in assessing the effects of wind farms on seabird populations. Critically, they have also failed to require the renewable energy industry to improve the evidence base for decision-making by implementing a rigorous adaptive learning process in which high-quality evidence is progressively gathered about the impacts of those offshore wind farms that are already built and used to assess the predictions of competing models of how they affect seabird population processes. They should ask Dr Nichols for some advice.
Nichols, J.D., Johnson, F.A., Williams, B.K. & Boomer, G.S. (2014) On Formally Integrating Science and Policy: Walking the Walk. Journal of Applied Ecology DOI: 10.1111/1365-2664.12406.