Treegrass: a 3D, process-based model for simulating plant interactions in tree–grass ecosystems
Introduction
Savannas are defined as ecosystems where a continuous grass layer and a discontinuous tree layer coexist (Scholes and Archer, 1997). Savanna ecosystems cover about 20% of continental surfaces (Scholes and Hall, 1996) and 40% of tropical land surfaces (Solbrig et al., 1990). In addition to their highly heterogeneous vegetation structure, these ecosystems are characterised by complex interactions between tree and grass individuals that compete for light, water and nutrient resources. Being able to predict grass and tree functioning separately does not enable to predict the functioning of the coupled tree–grass system. This restricts our ability to predict tree–grass stability and dynamics in savannas (Scholes and Archer, 1997).
Assessment of tree–grass interactions has mainly been addressed by field studies. Most of them have focused on the effects of trees on the biomass and primary production of the grass layer (e.g. Knoop and Walker, 1985, Stuart-Hill and Tainton, 1989, Weltzin and Coughenour, 1990, Belsky, 1994, Mordelet and Menaut, 1995), on the soil water balance (e.g. Knoop and Walker, 1985, Joffre and Rambal, 1988, Mordelet, 1993a, Le Roux and Bariac, 1998) or on soil nutrient availability (e.g. Isichei and Muoghalu, 1992, Mordelet et al., 1993, Cruz, 1997, Rhoades, 1997). Though necessary, these studies do not point out the different processes that determine the integrated effect of one vegetation component on the other, but rather appear as a list of particular case studies.
Thus, for some authors, the only way to gain a comprehensive understanding of tree–grass coexistence and to account for the effect of vegetation structure on ecosystem physiology is to build specific models (Jeltsch et al., 1996, Scholes and Archer, 1997). During the last two decades, several modelling approaches have been proposed to simulate the functioning of tree–grass systems (Scholes and Archer, 1997). Some authors have developed models of tree–grass equilibrium that focused on the competition for soil water (Walker et al., 1981, Eagleson and Segarra, 1985). These models were generally based on a spatial segregation between grass roots exploiting mainly surface soil layers, and tree roots exploiting mainly deeper layers. More recently, simulation models predicting the effects of tree–grass interactions on grass and tree production have been developed. Among them, the GRASP model (Littleboy and McKeon, 1997) represents competition for water and nutrients, and the CENTURY-Savanna model (Parton and Scholes, unpublished), a tree–grass version of CENTURY (Parton, 1996), is based on competition for nutrients. These two models were designed to compute the bulk functioning of the tree and grass components of tree–grass systems, and use bulk information on vegetation structure (i.e. tree leaf and root biomasses or tree basal area computed at site scale) to drive tree–grass competition. The SAVANNA model (Coughenour, 1994) is a process-based model that is spatially explicit at the landscape scale (i.e. it is not individual based but each pixel is an association of one tree–grass zone and one pure grass zone). However, the choice of a relevant variable to define the respective size and dynamics of these two areas is still unclear (Coughenour, pers. comm.). To our knowledge, the only savanna model that accounts for tree individual spatial structure is the automaton model of Jeltsch et al. (1996). This model is suitable for predicting the effects of natural or man induced disturbances on tree dynamics and tree–grass equilibrium, but was not designed to study the effect of vegetation structure on water or carbon fluxes in savannas. Other modelling studies have emphasized the importance of spatial patterns (Korzukhin and Ter-Mikaelian, 1996, Pacala and Deutschman, 1995, Weishampel and Urban, 1996).
In this paper, we present a simulation model, named TREEGRASS, designed to test the effects of the fine scale vegetation structure (i.e. tree density, tree spatial distribution, crown shape and crown size distribution) on tree–grass interactions (i.e. water and carbon budgets at the site level). TREEGRASS takes into account competition for light and water in a mechanistic and spatially explicit way, and uses a biologically based approach to compute net primary production. The model is derived from three existing models: (1) the 3D RATP model (Radiation Absorption, Transpiration and Photosynthesis) (Sinoquet et al., 2000) that computes radiation and energy budgets within vegetation canopies; (2) the PEPSEE model (Production Efficiency and Phenology in Savanna EcosystEms) (Le Roux et al., 1996) that simulates primary production and soil water balance; (3) the MUSE simulation framework (MUltistrata Spatially Explicit model) (Gignoux et al., 1996) designed to represent an ecosystem as a set of individuals and their geometric features by a spatially explicit approach. In the next section, the TREEGRASS model is presented and is parameterised for a humid savanna ecosystem (Lamto, Ivory Coast). The ability of the model to simulate radiation absorption, primary production and soil water balance in pure grass and tree–grass areas is tested against field data. Limitations and possible applications of TREEGRASS are discussed.
Section snippets
The TREEGRASS model
The main original features of the 3D TREEGRASS model are that (1) trees are represented individually, (2) radial extensions of tree foliage and roots are taken into account, (3) the foliage and the root system are distributed into a grid of 3D cells, and (4) competition for light and water are treated mechanistically (i.e. most relationships used are biophysical).
This model runs with a hourly/daily time step over one or a few vegetation cycles. The model has been developed in Borland Pascal 7.
Parameterisation of the model
The model has been parameterised for the humid savanna of Lamto, Ivory Coast (Menaut and César, 1979) (Table 1).
Pure grass site
Although the model slightly overestimated primary production at the beginning of each cycle, the seasonal dynamics of biomass and necromass were adequately simulated (Fig. 4). The water stress effect in the middle of the 1992 vegetation cycle was satisfactorily simulated. Over the two years, measured and simulated biomasses and necromasses were well correlated (R2=0.83, F1,26=130.6, P=0.0001 for biomass; and R2=0.88, F1,26=193.2, P=0.0001 for necromass). The seasonal courses of soil water
Discussion
The RATP model and its ability to simulate the distribution of light regime, carbon acquisition and transpiration within plant foliage had already been tested by its authors (Sinoquet et al., 2000). The radiation absorption submodel ability to reproduce grass radiation absorption had also been tested for a savanna grassland at Lamto (Le Roux et al., 1997). The production/water balance module of PEPSEE had been tested for savanna grasslands as well (Le Roux et al., 1996).
Our results showed that
Conclusion
Tests described in this paper and using a 1×1 m resolution were conclusive:
- 1.
Seasonal variations in biomass, necromass and soil water contents in layers 1 and 2 were satisfactorily simulated by the model in the case of a pure grass site.
- 2.
Primary production values computed by the model were consistent with values reported in the literature.
- 3.
The model correctly simulated the seasonal course of the soil water content in layer 1 under tree clump.
- 4.
Tree PAR absorption efficiency was also correctly
Acknowledgements
This work was supported by the European Terrestrial Ecosystem Modelling Activity (ETEMA) and by the Programme National de Recherche en Hydrologie (grant 99-PNRH 14, publication #189).
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