Reviewing the performance of adaptive forest management strategies with robustness analysis
J. Hörl, K. Keller, and R. Yousefpour
For. Policy Econ. (1 October 2020)
DOI: 10.1016/j.forpol.2020.102289
Forests are prone to direct and indirect effects of climate change. Adaptation strategies have been developed to increase the resistance of forests towards climate change and to reduce the associated risks. However, the direction and degree of climate change remain deeply uncertain. This deep uncertainty is often neglected in forest management. Thus, alternative approaches such as robust decision-making are needed to deal with this deep uncertainty. The aim of this paper is to review current studies on adaptive forest management and improve the understanding of how robust decision-making approaches can help to evaluate and enhance adaptive forest management strategies. An extensive literature review explores the concepts of deep uncertainty and robust decision-making and adapts both to the context of adaptive forest management. We conduct a comprehensive meta-analysis of current studies (42 papers) that provide quantitative outputs for alternative forest management scenarios across various climate scenarios. In addition to the general characteristics of included studies and characterizations of adaptive forest management measures, we focus on the quality and type of stated recommended strategies within studies. We demonstrate the application of two robustness criteria - ‘maximin’ and ‘safety-first’ - to identify robust strategies that, respectively, maximize outcome at the worst case or safeguard a minimum outcome regardless of scenario. Based on this assessment, we compared the overall robustness of proposed adaptive forest management scenarios within studies with the identified robust strategy. We found that the vast majority of studies (40 out of 42) provided no unique recommended strategy for adaptive forest management. 68
keywords: Adaptive management; Robust; Uncertainty; Silviculture; Forest economy; Climate change