Forests, savannas, and grasslands : bridging the knowledge gap between ecology and dynamic global vegetation models

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 
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.
show moreshow less

Metadaten
Author:Mara Baudena, Stefan C. Dekker, Peter M. van Bodegom, Barbara Cuesta, Steven Ian Higgins, Veiko Lehsten, Christian H. Reick, Max Rietkerk, Simon Scheiter, Zun Yin, Miguel Ángel de Zavala, Victor Brovkin
URN:urn:nbn:de:hebis:30:3-372204
DOI:http://dx.doi.org/10.5194/bg-12-1833-2015
ISSN:1726-4189
ISSN:1726-4170
Parent Title (German):Biogeosciences
Publisher:Copernicus
Place of publication:Katlenburg-Lindau [u. a.]
Document Type:Article
Language:English
Date of Publication (online):2015/03/20
Date of first Publication:2015/03/20
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2015/04/18
Volume:12
Issue:6
Pagenumber:16
First Page:1833
Last Page:1848
Note:
© Author(s) 2015. This work is distributed under the Creative Commons Attribution 3.0 License. 
HeBIS PPN:368983994
Institutes:Biowissenschaften
Senckenbergische Naturforschende Gesellschaft
Biodiversität und Klima Forschungszentrum (BiK-F)
Dewey Decimal Classification:550 Geowissenschaften
Sammlungen:Universitätspublikationen
Sondersammelgebiets-Volltexte
Licence (German):License LogoCreative Commons - Namensnennung 3.0

$Rev: 11761 $