Staff of Section for Environment and Natural Resources – University of Copenhagen

English > Staff > Environment Natural Re...

A framework for modeling adaptive forest management and decision making under climate change

Research output: Contribution to journalJournal articleResearchpeer-review

Rasoul Yousefpour, Christian Temperli, Jette Bredahl Jacobsen, Bo Jellesmark Thorsen, Henrik Meilby, Manfred J. Lexer, Marcus Lindner, Harald Bugmann, Jose G. Borges, João H.N. Palma, Duncan Ray, Niklaus E. Zimmermann, Sylvain Delzon, Antoine Kremer, Koen Kramer, Christopher P. O. Reyer, Petra Lasch-Born, Jordi Garcia-Gonzalo , Marc Hanewinkel

Adapting the management of forest resources to climate change involves addressing several crucial aspects to provide a valid basis for decision making. These include the knowledge and belief of decision makers, the mapping of management options for the current as well as anticipated future bioclimatic and socioeconomic conditions, and the ways decisions are evaluated and made. We investigate the adaptive management process and develop a framework including these three aspects, thus providing a structured way to analyze the challenges and opportunities of managing forests in the face of climate change. We apply the framework for a range of case studies that differ in the way climate and its impacts are projected to change, the available management options, and how decision makers develop, update, and use their beliefs about climate change scenarios to select among adaptation options, each being optimal for a certain climate change scenario. We describe four stylized types of decision-making processes that differ in how they (1) take into account uncertainty and new information on the state and development of the climate and (2) evaluate alternative management decisions: the “no-change,” the “reactive,” the “trend-adaptive,” and the “forward-looking adaptive” decision-making types. Accordingly, we evaluate the experiences with alternative management strategies and recent publications on using Bayesian optimization methods that account for different simulated learning schemes based on varying knowledge, belief, and information. Finally, our proposed framework for identifying adaptation strategies provides solutions for enhancing forest structure and diversity, biomass and timber production, and reducing climate change-induced damages. They are spatially heterogeneous, reflecting the diversity in growing conditions and socioeconomic settings within Europe.
Original languageEnglish
Article number40
JournalEcology and Society
Volume22
Issue number4
Number of pages25
ISSN1708-3087
DOIs
StatePublished - 2017

Number of downloads are based on statistics from Google Scholar and www.ku.dk


No data available

ID: 188265382