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

Rasoul Yousefpour, Forestry Economics and Forest Planning, University of Freiburg, Germany
Christian Temperli, Forest Ecology, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zürich, Switzerland; Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Jette Bredahl Jacobsen, Department of Food and Resource Economics and Centre for Macroecology, Evolution and Climate, University of Copenhagen, Denmark
Bo Jellesmark Thorsen, Department of Food and Resource Economics and Centre for Macroecology, Evolution and Climate, University of Copenhagen, Denmark
Henrik Meilby, Department of Food and Resource Economics, University of Copenhagen, Denmark
Manfred J. Lexer, Institute of Silviculture, University of Natural Resources and Life Sciences BOKU, Vienna, Austria
Marcus Lindner, Resillience Programme, European Forest Institute, Bonn, Germany
Harald Bugmann, Forest Ecology, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zürich, Switzerland
Jose G. Borges, Forest Research Centre, School of Agriculture, University of Lisbon, Portugal
João H. N. Palma, Forest Research Centre, School of Agriculture, University of Lisbon, Portugal
Duncan Ray, Forest Research, Roslin, Midlothian, UK
Niklaus E. Zimmermann, Forest Ecology, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zürich, Switzerland; Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Sylvain Delzon, BIOGECO, INRA University of Bordeaux, Cestas, France
Antoine Kremer, BIOGECO, INRA University of Bordeaux, Cestas, France
Koen Kramer, Wageningen Environmental Research; Wageningen University, The Netherlands
Christopher P. O. Reyer, Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
Petra Lasch-Born, Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
Jordi Garcia-Gonzalo, Forest Research Centre, School of Agriculture, University of Lisbon, Portugal; Forest Sciences Centre of Catalonia (CEMFOR-CTFC), Solsona, Spain
Marc Hanewinkel, Forestry Economics and Forest Planning, University of Freiburg, Germany

DOI: http://dx.doi.org/10.5751/ES-09614-220440

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Abstract

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.

Key words

behavioral adaptation; Europe; forest management; knowledge management; mathematical programming; process-based models; spatial planning

Copyright © 2017 by the author(s). Published here under license by The Resilience Alliance. This article is under a Creative Commons Attribution-NonCommercial 4.0 International License. You may share and adapt the work for noncommercial purposes provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license.