Knowledge update in adaptive management of forest resources under climate change: a Bayesian simulation approach

We develop a modelling concept that updates knowledge and beliefs about future climate changes, to model a decision-maker’s choice of forest management alternatives, the outcomes of which depend on the climate condition. & Aims Applying Bayes’ updating, we show that while the true climate trajectory is initially unknown, it will eventually be revealed as novel information become available. How fast the decision-maker will form firm beliefs about future climate depends on the divergence among climate trajectories, the long-term speed of change, and the short-term climate variability. & Methods We simplify climate change outcomes to three possible trajectories of low, medium and high changes. We solve a hypothetical decision-making problem of tree species choice aiming at maximising the land expectation value (LEV) and based on the updated beliefs at each time step. & Results The economic value of an adaptive approach would be positive and higher than a non-adaptive approach if a large change in climate state occurs and may influence forest decisions. & Conclusion Updating knowledge to handle climate change uncertainty is a valuable addition to the study of adaptive forest management in general and the analysis of forest decisionmaking, in particular for irreversible or costly decisions of long-term impact.