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| Titel |
Forests, savannas, and grasslands: bridging the knowledge gap between ecology and Dynamic Global Vegetation Models |
| VerfasserIn |
M. Baudena, S. C. Dekker, P. M. van Bodegom, B. Cuesta, S. I. Higgins, V. Lehsten, C. H. Reick, M. Rietkerk, S. Scheiter, Z. Yin, M. A. Zavala, V. Brovkin |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 12, no. 6 ; Nr. 12, no. 6 (2015-03-20), S.1833-1848 |
| Datensatznummer |
250117871
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| Publikation (Nr.) |
 copernicus.org/bg-12-1833-2015.pdf |
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| Zusammenfassung |
| 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. |
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