TY  - JOUR
A1  - Baudena, Mara
A1  - Dekker, Stefan C.
A1  - Bodegom, Peter M. van
A1  - Cuesta, Barbara
A1  - Higgins, Steven Ian
A1  - Lehsten, Veiko
A1  - Reick, Christian H.
A1  - Rietkerk, Max
A1  - Scheiter, Simon
A1  - Yin, Zun
A1  - Zavala, Miguel Ángel de
A1  - Brovkin, Victor
T1  - Forests, savannas and grasslands : bridging the knowledge gap between ecology and dynamic global vegetation models
T2  - Biogeosciences discussions
N2  - 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 to understand vegetation dynamics under present climate, and to predict 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 modelling. Model outcomes, obtained including different mechanisms, are compared to observed tree cover along a mean annual precipitation gradient in Africa. Through these comparisons, and 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 an improved representation in the 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 occurrences 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 savanna trees, and fire-resistant and shade-intolerant forest 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.
Y1  - 2014
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/37221
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-372210
SN  - 1810-6277
N1  - © Author(s) 2014. This work is distributed under the Creative Commons Attribution 3.0 License.
VL  - 11
SP  - 9471
EP  - 9510
PB  - European Geosciences Union
CY  - Katlenburg-Lindau
ER  -