Grass-tree interactions and the ecology of African savannas under current and future climates [Elektronische Ressource] / Simon Scheiter

Informations

• Publié par: technische_universitat_munchen
• Publié le: 01 janvier 2009
• Nombre de lectures: 10
• Langue: English
• Poids de l'ouvrage: 10 Mo

Extrait

¨ ¨TECHNISCHE UNIVERSITAT MUNCHEN
Lehrstuhl fu¨r Vegetations¨okologie
Grass-tree interactions and the
ecology of African savannas under
current and future climates
Simon Scheiter
Vollst¨andiger Abdruck der von der Fakult¨at Wissenschaftszentrum Weihenstephan fu¨r
Ern¨ahrung, Landnutzung und Umwelt der Technischen Universita¨t Mu¨nchen zur Erlangung
des akademischen Grades eines
Doktors der Naturwissenschaften
genehmigten Dissertation.
Vorsitzender: Univ.-Prof. Dr. H. Pretzsch
Pru¨fer der Dissertation: 1. Univ.-Prof. Dr. St. I. Higgins, Ph.D.
(Goethe-Universit¨at Frankfurt am Main)
2. Univ.-Prof. Dr. J. Pfadenhauer
3. Univ.-Prof. Dr. J. Schnyder
Die Dissertation wurde am 1.12.2008 bei der Technischen Universit¨at Mu¨nchen eingereicht
und durch die Fakult¨at Wissenschaftszentrum Weihenstephan fu¨r Ern¨ahrung, Landnutzung
und Umwelt am 14.05.2009 angenommen.Abstract. Tropical savannas are generally defined by the co-dominance of a homogeneous
understorey of C -grasses and a discontinuous tree layer. Savannas are primarily determined4
bycompetitionforresources,byseasonaldroughtandbydisturbancessuchasherbivoryand
fire. The nature of grass-tree interactions, and thereby the grass-tree ratio and fire regimes
strongly vary over environmental gradients. Despite intense savanna research during the last
decades, important questions have not been answered conclusively. Two specific questions
are (1) how do grasses and trees manage to coexist in savannas while excluding each other
in grasslands or rainforests and (2) how do savannas respond to anticipated climate change.
This thesis presents two different savanna models to explore these questions. The first
modelgivesaheuristicrepresentationofsavannasanditisbasedonthepartitioningbetween
aboveground and belowground biomass of grasses and trees. This partitioning allows us to
simulate that fire and herbivory only consume aboveground biomass while belowground
biomass provides a buffer from which vegetation can recover from fire and herbivory. The
model predicts that when competition is balanced and low that grass-tree coexistence is
stableandfireisnotnecessaryfor coexistence. Fireonlychangesthedynamicsfrom astable
equilibrium to stable limit-cycles. When light competition is intense and trees potentially
out-competegrasses,thenfiremightreducecompetitionsufficientlytoallowcoexistence.An
indirectparametrizationofthemodelwithempiricaldatashowsthatfireisnotnecessaryfor
coexistence at a rainfall gradient between 200 mm and 1200 mm mean annual precipitation.
The second model, the adaptive dynamic vegetation model (aDGVM) is a process and
individual-based simulation model that imitates biophysical, physiological and ecological
processes. The model combines generally accepted model components with novel and flex-
ible sub-models for phenology, carbon allocation and fire. The model allows us to simulate
the response of vegetation to fire and climate change at the plant level. The sensitivity anal-
ysis shows high responses of the simulation results to the parameters describing vegetation
characteristicsandweconcludethatvegetation modelsshouldbemoreflexibleandadaptive
in the sense that plant characteristics can change in response to the environmental condi-
tions instead of being constant as it is assumed in most existing vegetation models. We used
the model to simulate current and future vegetation in Africa in presence and absence of
fire. The model correctly predicts the current distribution of major biomes. Fire suppression
experiments indicate high fire impacts on regional scale and a 13% increase of biomass for
Africa. Simulations under IPCC climate change scenarios predict strong increases in tree
biomass and a significant shift towards tree dominated biomes, indicating a huge poten-
tial of savannas to store carbon. The carbon storage potential is not saturated at ambient
conditions and will further increase in response to future climate change.
This thesis contributes to the current savanna and climate change research as it presents
the first deterministic grass-tree coexistence model that can simulate coexistence on a broad
environmental gradient and as it presents a dynamic savanna vegetation model that allows
one to explore how grass-tree systems respond to climate change. We conclude that future
research should focus on including adaptive mechanisms into vegetation models, coupling
climate and vegetation models and investigating impacts of landuse in savannas.Contents
1 Introduction 7
I Heuristic grass-tree coexistence models 15
2 Partitioning of root and shoot competition and the stability of savannas 17
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2 Model description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3 General model behavior . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.4 Analysis of the simplified model . . . . . . . . . . . . . . . . . . . . . . 29
2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.6 Appendix: Isoclines of the grass shoot-woody shoot system . . . . . . . 45
2.7 Appendix: Fixed points . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3 The stability of African savannas 49
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.2 Model description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.3 Model fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.6 Appendix: Isoclines and fixed-points. . . . . . . . . . . . . . . . . . . . 66
II Dynamic vegetation modelling and the future vegetation of
savannas 69
4 aDGVM: An adaptive dynamic global vegetation model 71
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.2 Modelling concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.3 Input data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.4 Leaf photosynthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.5 Individual plant model . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.6 Stand scale dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.7 Synthesis of sub-models . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.8 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.9 Perspectives – adaptive vegetation modelling . . . . . . . . . . . . . . . 112
54.10 Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
4.11 Model parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
5 Climate change in Africa: a modelling study 131
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
5.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
6 Discussion and conclusions 165
6.1 Summary of the main results. . . . . . . . . . . . . . . . . . . . . . . . 165
6.2 Fire effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
6.3 Model selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
6.4 Model parametrization and validation . . . . . . . . . . . . . . . . . . . 170
6.5 Model uniqueness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
6.6 Land use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
6.7 Coupling of vegetation and climate models . . . . . . . . . . . . . . . . 175
6.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
Zusammenfassung 179
Acknowledgements 183
Bibliography 185
Electronic Appendix: Source code of the aDGVM 205
61 Introduction
Tropical savannas are generally characterized as ecosystems with a continuous under-
storey of C grasses and a more or less discontinuous tree layer, that is by the co-4
dominance of grasses and trees (Huntley and Walker 1982; Scholes and Walker 1993).
The savanna ecosystem covers about 12% of the earth’s surface and it is distributed
over large areas of Africa, South America, Australia and Asia (Huntley and Walker
1982, Figure 1.1). In Africa, savannas cover about 65% of the Sub-Saharan land surface
and are thus the dominant ecosystem (Huntley and Walker 1982). As a consequence,
savannas significantly contribute to the world’s carbon cycle. Savannas are responsible
for about 30% of the world’s net primary production (Grace et al. 2006) and African
Figure 1.1: The distribution of major biomes of the world. Source:
http://soils.usda.gov/use/worldsoils/mapindex/biomes.html.
71 Introduction
savannas are responsible for about 6% (Williams et al. 2007) of the world’s net pri-
mary production. Apart from their significance for the carbon cycle, savannas are also
socio-economically important, particularly in Africa where large areas face increasing
pressureofhumanlandusesuchaslivestockproduction,deforestationandcropproduc-
tion (Scholes and Ar