Communicating complexity and uncertainty in decision making contexts: Bayesian approaches to forest research

Abstract

SUMMARY Ineffective communication of scientific research to decision makers and the public has often proved a barrier to uptake of knowledge by relevant stakeholders. One difficulty in communicating scientific information lies with the non-intuitive analytical language commonly used by scientists comprised of Frequentist statistical procedures. The more intuitive alternative, Bayesian inference, is not widely known among forest scientists. In contrast to the Frequentist approach, Bayesian results are given in terms of the probability of a hypothesis being true, and are therefore considerably more accessible to non-scientists. Additionally, and of particular benefit to scientists working in socially and ecologically complex forest environments, Bayesian inference allows the simultaneous consideration of multiple hypotheses and the integration of different types of information from many sources, reflecting scientific judgement as well as existing empirical data. Furthermore, the analysis proceeds by building on existing knowledge, and as such Bayesian inference is very well suited to adaptive management and decision making under uncertainty.