Framework 3: methods for gaining a predictive understanding
Here, we use foresight tools, including scenario analysis, participatory modelling, and theories of change to better understand the likely responses of consumers and markets to potential interventions, and the efficacy of wildlife trade policies more broadly, in order to inform plans for effective future actions.
New large-scale policy interventions, technological advances and social change take us into unknown territory in terms of how demand will respond. A predictive approach is required to understand how these external drivers will impact on the outcomes of interventions. Currently there is very little emphasis on predictive conservation science pertaining to people’s responses to interventions and external events; this is something our team has highlighted (Milner-Gulland, 2012, Nicholson et al., 2009), and has been touched on by other researchers (e.g. Polasky, 2005, Oliver et al., 2015). Within economics, predictive modelling, both theoretical and empirically-based, is much more standard (e.g. Giglio et al., 2016 on predicting macroeconomic shocks). Similarly, much climate science is about prediction (e.g. Allen & Ingram, 2002). Conservation can learn a lot from these subject areas about how best to predict changes in conditions and their effects on market and consumer behaviour, and develop interventions that are robust to these changes.
Framework 3 draws together our research in the case studies to consider the potential outcomes of policy interventions. It demonstrates also the potential of participatory model- building, with the development of a theoretical model for particularly contentious policy issues. Finally, through development of a theory of change it provides a tool for evaluating the likely impact of international wildlife trade policies in CITES.
This will enable us to explore how external change and interventions may interact to drive wildlife markets, and hence the impact of the illegal wildlife trade on biodiversity. Importantly, it will also highlight the remaining critical uncertainties in determining outcomes and emerging issues, which will shape our future research agenda.
The four main strands within this framework are to:
- Draw empirical evidence from the programme’s case studies to develop predictive understanding of the likely potential impacts of a range of interventions, in the light of external conditions, using a scenario planning approach.
- Adapt theoretical modelling through participatory approaches, to highlight uncertainties and clarify the basis for conflicting viewpoints on policy options; specifically to understand the potential effects of three additional case studies, on particularly contentious policy issues: 1) Variable quota setting for the lion bone trade, 2) Synthetic and legal horn trades on illegal rhino horn markets, and 3) The use and trade of pangolin scales.
- Utilise horizon scanning as a technique for systematically identifying emerging challenges and opportunities that are not yet widely recognised as important, but which have a high probability of societal impact (Amanatidou et al., 2012, Konnola et al., 2012). Their early warning function reduces the unpredictability of these impacts, and gives time for technological progress or policy change to take place to address the issue. Sutherland et al. (2015) have carried out horizon scans for a range of conservation and environmental issues – this expertise will be drawn upon to structure and implement our horizon scan specific to the illegal wildlife trade.
- Evaluate the use of evidence in CITES, and through development of a theory of change, identify the conditions under which trade regulations in the convention would likely contribute to improving the status of species. For further details, see the project brief here.
Researchers: Prof E.J. Milner-Gulland, Dr Dan Challender, Michael ’t Sas-Rolfes, Nafeesa Esmail
Collaborating organisations: South African National Biodiversity Institute (SANBI), University of the Witwatersrand