Updating beliefs and combining evidence in adaptive forest management under climate change: A case study of Norway spruce (Picea abies L. Karst) in the Black Forest, Germany

Rasoul Yousefpour, Christian Temperli, Harald Bugmann, Che Elkin, Marc Hanewinkel, Henrik Meilby, Jette Bredahl Jacobsen, Bo Jellesmark Thorsen

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

We study climate uncertainty and how managers' beliefs about climate change develop and influence their decisions. We develop an approach for updating knowledge and beliefs based on the observation of forest and climate variables and illustrate its application for the adaptive management of an even-aged Norway spruce (Picea abies L. Karst) forest in the Black Forest, Germany. We simulated forest development under a range of climate change scenarios and forest management alternatives. Our analysis used Bayesian updating and Dempster's rule of combination to simulate how observations of climate and forest variables may influence a decision maker's beliefs about climate development and thereby management decisions. While forest managers may be inclined to rely on observed forest variables to infer climate change and impacts, we found that observation of climate state, e.g. temperature or precipitation is superior for updating beliefs and supporting decision-making. However, with little conflict among information sources, the strongest evidence would be offered by a combination of at least two informative variables, e.g., temperature and precipitation. The success of adaptive forest management depends on when managers switch to forward-looking management schemes. Thus, robust climate adaptation policies may depend crucially on a better understanding of what factors influence managers' belief in climate change.

Original languageEnglish
Pages (from-to)56-64
Number of pages9
JournalJournal of Environmental Management
Volume122
DOIs
StatePublished - 5 Jun 2013
Externally publishedYes

Keywords

  • Adaptive decision-making
  • Bayesian updating
  • Biomass production
  • Dempster's rule
  • LandClim

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