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Climatic Change

, Volume 118, Issue 3–4, pp 579–593 | Cite as

Key factors affecting the future provision of tree-based forest ecosystem goods and services

  • Livia RascheEmail author
  • Lorenz Fahse
  • Harald Bugmann
Article

Abstract

The continuous provisioning of forest ecosystem goods and services (EGS) is of considerable interest to society. To provide insights on how much EGS provision will change with a changing climate and which factors will influence this change the most, we simulated forest stands on six climatically different sites in Central Europe under several scenarios of species diversity, management, and climate change. We evaluated the influence of these factors on the provision of a range of tree-based EGS, represented by harvested basal area, total biomass, stand diversity, and productivity. The most influential factor was species diversity, with diverse forest stands showing a lower sensitivity to climate change than monocultures. Management mainly influenced biomass, with the most intensively managed stands retaining more of their original biomass than others. All three climate-change scenarios yielded very similar results. We showed that (1) only few factor combinations perform worse under climate-change conditions than others, (2) diversity aspects are important for adaptive management measures, but for some indicators, management may be more important than diversity, and (3) at locations subject to increasing drought, the future provision of EGS may decrease regardless of the factor combination. This quantitative evaluation of the influence of different factors on changes in the provision of forest EGS with climate change represents an important step towards the design of more focused adaptation strategies and highlights key factors that should be considered in simulation studies under climate change.

Keywords

Electronic Supplementary Material Basal Area Adaptive Management Drought Index Potential Natural Vegetation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank Niklaus Zimmermann and Dirk Schmatz (WSL) for providing the climate change scenario data, Christof Bigler for suggestions on statistical analyses, and Xavier Morin and three anonymous reviewers for comments on the manuscript. This research was funded by the Swiss State Secretariat for Education and Research under COST Action FP0603.

Supplementary material

10584_2012_664_MOESM1_ESM.pdf (516 kb)
ESM 1 (PDF 515 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Forest Ecology, Institute of Terrestrial Ecosystems, Department of Environmental SciencesSwiss Federal Institute of Technology ETHZürichSwitzerland
  2. 2.Research Unit Sustainability and Global ChangeUniversity HamburgHamburgGermany
  3. 3.Institute of MathematicsUniversity Koblenz-LandauLandauGermany

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