The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Default user image.

Veiko Lehsten

Researcher

Default user image.

Forests, savannas, and grasslands: bridging the knowledge gap between ecology and Dynamic Global Vegetation Models

Author

  • M. Baudena
  • S. C. Dekker
  • P. M. van Bodegom
  • B. Cuesta
  • S. I. Higgins
  • Veiko Lehsten
  • C. H. Reick
  • M. Rietkerk
  • S. Scheiter
  • Z. Yin
  • M. A. Zavala
  • V. Brovkin
Show all

Summary, in English

The forest, savanna, and grassland biomes, and the transitions between them, are expected to undergo major changes in the future due to global climate change. Dynamic global vegetation models (DGVMs) are very useful for understanding vegetation dynamics under the present climate, and for predicting its changes under future conditions. However, several DGVMs display high uncertainty in predicting vegetation in tropical areas. Here we perform a comparative analysis of three different DGVMs (JSBACH, LPJ-GUESS-SPITFIRE and aDGVM) with regard to their representation of the ecological mechanisms and feedbacks that determine the forest, savanna, and grassland biomes, in an attempt to bridge the knowledge gap between ecology and global modeling. The outcomes of the models, which include different mechanisms, are compared to observed tree cover along a mean annual precipitation gradient in Africa. By drawing on the large number of recent studies that have delivered new insights into the ecology of tropical ecosystems in general, and of savannas in particular, we identify two main mechanisms that need improved representation in the examined DGVMs. The first mechanism includes water limitation to tree growth, and tree grass competition for water, which are key factors in determining savanna presence in arid and semi-arid areas. The second is a grass fire feedback, which maintains both forest and savanna presence in mesic areas. Grasses constitute the majority of the fuel load, and at the same time benefit from the openness of the landscape after fires, since they recover faster than trees. Additionally, these two mechanisms are better represented when the models also include tree life stages (adults and seedlings), and distinguish between fire-prone and shade-tolerant forest trees, and fire-resistant and shade-intolerant savanna trees. Including these basic elements could improve the predictive ability of the DGVMs, not only under current climate conditions but also and especially under future scenarios.

Department/s

  • Dept of Physical Geography and Ecosystem Science
  • BECC: Biodiversity and Ecosystem services in a Changing Climate

Publishing year

2015

Language

English

Pages

1833-1848

Publication/Series

Biogeosciences

Volume

12

Issue

6

Document type

Journal article

Publisher

Copernicus GmbH

Topic

  • Physical Geography

Status

Published

ISBN/ISSN/Other

  • ISSN: 1726-4189