Tree-Grass Modelling Meeting

NCEAS, Santa Barbara

10th - 13th May 1998

 

This meeting was a continuation of the SCOPE tree-grass modelling group which bought together data holders with data from a wide variety of tree-grass (savanna) systems, and ecosystem modellers with models capable of handling both tree and grass components and the interactions between them and their environment. It was the first of two workshops to be held at NCEAS, Santa Barbara, USA. The goal of this workshop was to identify what kind of product(s) the group wished to produce, the format of the data, the nature of the data analysis and modelling experiments and how this will be carried out.

NCEAS has provided a web site for general, public access, information about the project and a password protected area onto which the group can place and access the common data sets. Once the group has finished their analyses, the data sets and results will also become public access. NCEAS normally maintains these sites for two years, after this the data could be stored on the Oak Ridge DAAC site.

This exercise should help to focus on what kind of science we need to improve our understanding of tree-grass interactions. We need to:

 

List of Participants

Steve Archer, Texas A & M University, USA

David Breshears, Los Alamos National Laboratory, USA

Mike Cougenhour, NREL, Colorado State University, USA

Mike Dodd, Ruakura Research Centre, New Zealand

David Hall, King’s College London, UK

Niall Hanan, University of California, Santa Barbara, USA

Jo House, King’s College London, UK

Richard Joffre, CEFE-CNRS, France

John Ludwig, CSIRO Wildlife and Ecology, Australia

Ruben Montes, Universidad Simon Bolivar, Venezuela

Charles Moyo, Matopos Research Station, Zimbabwe

William Parton, NREL, Colorado State University, USA

Jose San Jose, CIET, IVIC, Venezuela

Joe Scanlan, Robert Wicks Research Centre, Australia

Robert Scholes, Forestek, CSIR, South Africa

Jonathan Scurlock, Oak Rdige National Laboratory, USA

Bruce Thurrold, Ruakura Research Centre, New Zealand

Apologies:

Jaques Gignoux, ENS, France

Xavier Le Roux, INRA, France

Jean-Clude Menaut, Orstom, France

 

The Data Sets

During the first day of the meeting we had presentations from all of the participants who have tree-grass savanna data sets. The presentations ranged from 15-30 minutes and described the type of data collected at the sites and theoretical tree-grass competition issues that could be addressed using the data sets. A detailed description of the presentations for each data set will be attached to this report later, however, Table 1 summarizes information about the sites where tree-grass competition data sets are available.

Table 1. The models available for the synthesis exercise

Characteristics

Inputs

Model name

Space/

time

Process

Features

Climate

Soil

Vegetation parameters

Distur-bance

Century-savanna

Bill Parton

NREL

Patch

Day-month

C, N&P biogeochemistry

water budget

Nutrient limitation

Tree-grass competition through N allocation.

Can be run globally and for long times

Widely used

Monthly (or daily) precip, Tmin, Tmax

Sand, clay, OC, Tot N,

(Tot P, OP, Ext P)

Lignin, N, (P)

Fertilise

Graze

Burn

Plow

Harvest

Savanna

Mike Cougenhour

NREL

Patch-

Landscape

(100km)

week

Surface energy balance, demography,

light, water, nutrients,

herbivory,

fire

Semi-spatially explicit at the tree scale, explicit at landscape scale

Daily rain, VPD, radiation

Topography, sand, clay,

OC, TotN

Gs, Amax, WUE, Leaf N

 

GRASP

Joe Scanlan QDPI

Patch

Daily

Water balance

Grass prodn

Water (and N) competition

Cattle and sheep prodn

Soil loss

Daily rain

Annual N uptake

WHC

Depth

Basal area

Clearing

Grazing

MUSE*

Jacques Gignoux ENS

Ian Noble ANU

Patch (1ha, 1000 trees)

Day-year

Structure, demography, matter and energy fluxes.

Light, water, (nutrients).

Fire.

Competition

3D spatially explicit, object-oriented,

framework for user Pascal code

Depends on the particular user code incorporated. Typically it would include site data such as climate, spatially variable soil data, initial tree location and geometry, plant biology. The output data are logged from the model.

*MUlti-stratum Spatially Explicit model

The Models

The models included in this exercise represent a gradation of complexity and scope. GRASP is a pasture growth model driven by transpiration at a daily time step where the trees are regarded only in terms of their competition with grasses for resources and allocation is weighted according to demand. CENTURY-SAVANNA is a biochemically based model operating at a monthly time step (daily or annual also possible) where available nutrients (N) are the primary control on production and trees take what they need first according to tree basal area. SAVANNA models woody population dynamics and is driven by carbon assimilation at the plant scale with trees and grasses competing for water and nutrients based on demand, and for light, at a weekly time step. It is quasi spatial with different patches of vegetation tracked across heterogenic landscape patches. Each was designed for a different purpose and is likely to be appropriate for different purposes in this exercise. The MUSE model was unfortunately not represented in this meeting as none of the French participants was able to attend, but it will hopefully be included in future exercises, workshops and meetings. It is not currently intended to include more models in this exercise unless they can add a significant new perspective or dimension. Agroforestry models were discussed such as ICRAF’s tree grass model.

GRASP (Scanlan)

Joe wrote a page - Appendix E, so below is my shortened version:

GRASP is a pasture production model where tree production, population dynamics (except Moore version) and spatial distribution are not simulated, but the tree does compete with grasses for resources. Growth and competition are driven by the water needs of the plants to meet their respective transpiration demands which are based on basal area x pan evaporation for trees and green cover x pan evaporation for grasses. Soil water availability is divided into layers, and trees and grasses are supplied with the same proportion of their demand according to water availability and plant demand in each layer which is based on root profiles. Nitrogen is taken up in the transpiration stream at a constant concentration (although a switch allows tree uptake to be limited). Moisture stress, frost or lack of nitrogen reduce grass transpiration demand, however, there are no environmental effects on trees. Measurements of the effects of trees on microclimate at a community level are being progressively implemented. Grazing and fire effects the grass layer, but not the trees (except Moore version).

CENTURY-SAVANNA (Parton)

The CENTURY-SAVANNA model represents the competition between trees and grasses by allowing for competition for soil water and nutrients. Grass plant growth can also be reduced when tree leaf area gets large enough to shade grass plants. The major mechanism for competition is the equation that controls nitrogen uptake by trees and grasses as a function of the available nitrogen content of soil and the tree basil area. The model grows trees and grasses using separate tree and grass submodels and then allocates nutrients depending on the tree basil area. The Century model also simulates the dynamics of soil organic matter and soil nutrients, and soil temperature and water using a monthly time step.

SAVANNA (Cougenhour)

Mike to write a paragraph, Jo’s notes below:

SAVANNA looks at the role of landscape heterogeneity on ecosystem function and how water in the landscape effects this. It is quasi spatial in that it keeps track of patches in the landscape and the proportion of trees/herbs/shrubs, allowing for a clumping parameter. Water and nutrients are allocated according to demand in the different patches, and there is also competition for light. The model tracks woody population and individuals in different size classes. It calculates NPP according to carbon assimilation at an intermediate level of physiological detail. Population dynamics of grazers and browsers can be simulated, dietary selection and palatability can be included. Fire frequency and intensity scheduled, but could be stochastic if wish.

David Breshears also presented a four compartment water model based on Walter’s two-layer hypothesis. The model has two layers by depth and tree roots are split into deep-rooted or shallow rooted. There are also two horizontal zones with tree roots spreading into the inter-canopy as well as the below canopy zone. Although this is still simplistic it makes the predictions more realistic.

 

Tree-Grass Theories

Resource Allocation Questions

There are two core questions relating to how trees and grasses coexist in the same environment:

  1. How are the resources apportioned between trees and grasses? [the NPP problem]
  2. What controls the trends in time in the proportions of trees and grasses? [the coexistence problem]

These questions are related, but they operate at different time scales, the first being the static allocation of resources (water, nutrients and light) at a point in time, and the second being dynamic in relation to various control factors including disturbance (fire, grazing, flooding, etc.) as well as competition for resources. If savannas were an equilibrium system, (1) would lead to (2), but it seems more likely that savannas are in disequilibrium.

 

Resource Apportionment Rules

In answer to the first question, many conceptual models have been proposed to account for resource apportionment between trees and grasses. Some experiments done on individual sites give evidence for or against the above theories, but no synthesis of the situation in a large-enough number of sites has been carried out to prove of disprove the validity of these theories globally.

- inter-tree competition

- tree-grass facilitation between canopies [Scanlan, 1992]

 

Some Apportionment Scenarios

Different rules lead to different scenarios of how tree and grass NPP vary in relation to each other, and what the total NPP might be.

See Scholes Diagrams plotting tree NPP and grass NPP against % tree

 

Trends in Time (Equilibrium theories)

Theories regarding the controls of competition on trends in time lead to different equilibrium theories:

See Scholes Diagrams

In many savanna sites some degree of tree clumping takes place where there can be isolated tree clumps or larger patches of trees interspersed by open grassy areas. This adds further complexity to competition and coexistence theories. Conditions are often very different between the under-canopy and inter-canopy areas. In areas with very pure, distinct clumps there may be virtually no tree-grass competition taking place, with a more dispersed pattern, there can be all kinds of relationship.

 

Further questions

These theories to explain resource apportionment and trends in time lead to a number of more detailed questions:

 

Addressing These Questions With the Available Data

 

These kind of savanna theories and issues have been highlighted before, but there has never been a large enough synthesis of a wide variety of data in order to answer them conclusively. This will be the first large-scale savanna data synthesis, and also the first savanna model synthesis. It should be possible to answer many basic questions using statistical analyses, these statistical analyses will show if any underlying patterns emerge. The models can be used as tools to answer more complex questions. It is interesting to look at many of these questions statically and dynamically. The former is possible with statistical analyses or models, the latter is only possible with models.

This group has identified a large amount of data sites available across a variety of topo-edaphic and management regimes and at various levels of detail. All of the sites have data on either tree biomass or grass biomass at least above ground, fewer sites have both tree and grass biomass recorded above and belowground. Some sites have data on the effects of different levels of trees on grasses, or grasses on trees due to different treatments or environmental gradients. Other sites have a range of basal areas for similar climate and soils. Some sites just have measurements at one point in time, but others have measurements for a number of years with climatic and seasonal differences. Several sites have patches of different vegetation structure due to climate, soils or disturbance, and where trees are highly clumped, these can be considered as unique patches compared to the surrounding vegetation. This range of data sets available can be viewed in a hierarchical structure:

Level 1 = Structural data

1a) at one point in time

1b) over a time period

Basic data such as site information (lat., long., elevation, slope, run-on/off, disturbance history), climate (monthly ppt, tmax, tmin, S.D. for annual ppt), soil (type, texture, depth, field capacity, total N, pH [CEC]), tree (dominant species, BA/cover*, spatial pattern, % evergreen, % N-fixers), herbaceous biomass (dominant species, %annual/perennial, % grass/non-grass, % C3/C4), root distribution, etc. (See Appendix A)

Questions addressable:

 

Level 2 = Annual NPP, community/patch scale

Includes the above plus NPP data* (above and belowground and litter) for trees and grasses preferably over a range of soils/climate, over time, or under different treatments (including no tree) which can be considered separate patches of the same site. Data on tree clumping, under/outside canopy data.

Questions addressable:

Bill P to fill this in with results of his group discussion if any more needs to be addeed

 

Level 3 = Monthly NPP, detailed community or plant scale

Data at a finer temporal scale (grass growth and turnover leaf/stem/root, live/dead), more detailed spatial data (landscape and plant patches), plant allometry (leaf/stem/root), litterfall and turnover, root live/dead by depth, plant chemistry (leaf N, lignin), physiological data, population data, water balance (transpiration rates, stomatal conductance, leaf water potential, sap flow, daily ppt, pan ET, VPD, max-min), PAR, soil moisture/nutrients by depth, fire frequency/temperature/severity, grazing/removals details. (See Appendix B)

Questions addressable:

 

*Data notes:

- Leaf Area Index (LAI) is often a better predictor of resource allocation and NPP than other measures of treeness as it is usually also related to root area, and it is used by many models. Tree basal area (BA) can act as a functional proxy for LAI as it is more commonly measured.

- Both tree and grass NPP measurements are flawed if they do not take account of fine root turnover. For trees this is never the case. Some grass NPP measurements do take account of fine root turnover, but most are based on standing crop.

- Shrubs vs. Trees: In some sites shrubs play an important role, and it may be necessary to separate these out from tree woody biomass. For the purposes of this exercise, shrubs can be defined as an intermediate woody component that would never reach the height of the tree canopy (maximum potential height 1.8 m at maturity). They are usually also multi-stemmed, but multi-stemmed, short coppiced woody vegetation should be counted as trees if they were to grow as trees had they not been coppiced.

 

Proposed Data Structure

Not all sites will have data available at all levels, and not all statistical/modelling tests will wish to use data at all levels. Therefore it is proposed that the data sets be organised into distinct file types in a hierarchical structure. All suitable sites should provide as much of the data needed as possible in the first two file-types.

SITE file:

Site name, data provider, references published, long term climate (monthly ppt, tmin, tmax), history, pointer to patches (for many sites may be only 1)

PATCH file: (topo-edaphically defined, unique abiotic)

Patch type, history, % total area covered. Level 1 soil, topography, tree and grass data.

Pointers to TIME file and/or TREAT file if included

TIME file:

Any data that has some time element over any time scale (daily/weekly/monthly/annual).

TREAT file:

Describes the treatment given to a particular patch described in the PATCH file (eg. number of trees, initial tree BA). If the data has a time element, this should state the initial conditions and point to the TIME file.

SPECIES file:

If more detail is available on a specific species such as stomatal conductance, leaf N, lignin content, etc. it can be filled in here.

These data hierarchies lead to a hierarchical set of questions that can be addressed both statically and dynamically with this:

Level 1 Questions:

Data needs: SITE, PATCH

Analysis approaches: Statistics, CENTURY, SAVANNA

Level 2 Questions:

Data needs: SITE, PATCH, TREAT, TIME

Analysis approaches: GRASP, CENTURY, SAVANNA

Level 3 Questions:

Data needs: Level 3 - SITE, PATCH, TREAT, TIME, SPECIES

Analysis approaches: SAVANNA

An estimate of the data sets available at each level from within this group are outlined in Appendix C.

 

 

 

 

 

Analysis Approaches

Level 1:

These questions can be studied statically using statistical analyses, and dynamically using the models with more basic data requirements (GRASP, CENTURY). The statistical approach would be to fit surfaces for tree BA based for several variables using multivariate, least-square optimisations (analysis of (co-)varience). This has been carried out on a few small data sets, but never a large global data set. Once the statistical surfaces have been plotted, the outliers can be identified and the reasons for each outlier examined - this should lead to questions of mechanisms that can be answered at Level 3.

This approach can be carried out with very basic data as this optimises the number of sites available. As a minimum, the sites should have annual precipitation and clay content as previous studies have shown these to be strong control factors in savanna systems. The disturbance history of the site must be known. The statistical analyses can start with relatively undisturbed sites in order to develop the surfaces. Extra variables can be added where data is available to see how much effect these factors have (eg. soil chemistry, clumping, shrubs). Finally sites with large disturbances or unusual circumstances (high fire, heavy grazing, flooding, introduced species, etc.) can be added and the outliers plotted. This could provide important information for modellers on how to prescribe the effects of disturbances such as fire in their models. It is also a test of the models if they can predict this surface and account for the outliers given enough information, and whether they can represent the dynamic effects (rate and outcome) of changes.

 

Level 2:

This will be a modelling exercise that can incorporate all the models, several model experiments are initially envisaged:

  1. Transects across gradients: The models can be run at points along transects with varying climate and woody cover. The initial focus would be to get a good handle on the water budget and it’s role in tree-grass interaction: flows of water between shallow and deep layers, plant uptake from different layers, bare soil evaporation, interception and run-off. It would then be possible to look at the effects of changes in soil texture along these gradients.
  2. Effect of trees (individuals) on soils and vegetation, time trend if possible.
  3. Ecosystem-scale tree effect on herbaceous NPP: This would be examined using data sets with herbaceous biomass/NPP at different tree densities (where herbaceous biomass was measured randomly across the site).
  4. Landscape level effect of trees on pasture production: Seek generalities, deal with exceptions.
  5. Patch-scale tree effect: Where data is available for herbaceous biomass/NPP under trees and in inter-canopy zones, the models can be run to simulate what happens in these different zones. SAVANNA is already spatial, but GRASP and CENTURY would have to be forced by modelling each zone as separate patches and scaling up to ecosystem level by using % cover of each patch. If models can represent the observed results, this would show they were robust and further clumping issues could be examined.
  6. Disturbance experiment: Simulate known grazing and fire regimes and try to produce the observed patterns of tree/grass biomass. This experiment can also consider the grazing effect on the grass fuel loading and consequences for fire. It may be necessary to acquire further data sets with more detail on fire/grazing regimes, but even without this the exercise could still be used to examine how disturbances effect biomass along environmental gradients using current data and model capabilities.
  7. Develop a spatial representation of trees and their effects in GRASP.

 

Level 3:

This will be a modelling test to look at what mechanisms are needed to explain the observed relationships from level 1 and 2 analyses. Only SAVANNA has sufficient detail to explore these questions, and MUSE if it is incorporated. It will look at the processes behind the way the tree grass balance is affected by resource partitioning and disturbance. The resources can be allocated with or without depth, with or without spread (under and outside the tree canopy), with or without phenological differences and at all combinations of the above (8 possibilities) to see which make a significant effect under what circumstances. The same can be done with the disturbances varying with/without fire and with/without grazing, making a further four possibilities (for example, under higher moisture and nutrient regimes, resource partitioning could be expected to become less important, and grazing to increase). There are at least eight sites for which this should be possible incorporating a range of precipitation, clay content and nutrient availability, and under different treatments. Parameterising these sites is a large, but manageable, especially if it is possible to identify as few site specific parameters as possible. The importance of different site specific factors in the model can be examined by parameterising the model with all available information, then removing some (replacing them with general parameters for all sites) to see how much difference this makes.

 

Outputs

  1. Structured database, in spreadsheet format, with full variable definitions and space for remarks. This will be prepared as soon as possible, a draft version has already been created. This will be sent to data holders and will also be available in the public area of the NCEAS web-site for downloading by any interested parties.
  2. Emerging Issues in Savanna Ecology paper to highlight the work being carried out by this group, describe the models and the type of data being used. This could potentially lead to other data holders becoming included in this exercise if they have the type of data needed, and they wish to put it in the context of a larger model synthesis. It could also mean that those with current or planned field studies could have an indication of the type of data it may be useful to collect to make their data set comparable with others. It is also important to highlight the models as much of the research community is not aware of the potential of these models in a savanna setting, what they are capable of and what type of data they need. This could be envisaged as a full refereed paper, but a summary of the information should also be circulated in as many appropriate newsletters, etc. as possible.
  3. Level 1 data synthesis to be completed by December 1998. Co-ordinators appointed for each area (North America, South America, Africa, Australia). All data will ultimately be public access after this group has finished with it.
  4. Level 1 data analyses. Some initial statistical analyses will probably be carried out by a sub-group including the level 1 co-ordinators prior to the next NCEAS meeting in May 1998, and further analyses will be carried out at that meeting. This would lead to at least one main paper: "Can tree quantity in tree/grass systems be predicted from climate soil and disturbance? - a global analysis" (authors = main contributors only). It would be good to publish this in a high-profile journal that allows for the data to be presented in an appendix, or separately available in diskette. Other papers may also result from these analyses.
  5. Level 2 data synthesis. Co-ordinator appointed. Data to be synthesised by September 1998.
  6. Level 2 data analysis (modelling experiment). This will be carried out at a sub-meeting of the modellers in South Africa around September 1988 and will lead to one or several papers.
  7. Level 3 data synthesis will be carried out by the data holder at each site and provided for SAVANNA parameterisation as and when possible, but should be ready by December 1998.
  8. Level 3 data analysis (SAVANNA modelling experiment). This will take place as and when the data sets and sufficient time becomes available. This will lead to one or several papers.
  9. CENTURY-SAVANNA description paper as this is a new module to CENTURY that has never been described in the literature. Update papers could also be written to describe current working versions of GRASP and SAVANNA.
  10. Synthesis book. It would be good to publish all the papers in a synthesis volume such as a SCOPE volume.

 

Appendix A: Level 1, Structural data requirements

Contact person

Papers

SITE:

Site name, lat, long, elevation, aspect, slope, soil type,

topographic position - does it receive supplemental H2O (run-on, underground, etc)

runoff (qualitative) -can ignore <5%

-some happens 5-20%

-big >20% (may render site unusable if too large)

Each site can be split into topo-edaphically defined PATCHES which are homogenous abiotically eg. treat run-on/run-off patches separately. List % landscpe this patch occupies, other facets (patches) linked to this site.

SOILS:

Where site clearly clumped, these factors should be given separately for under and between canopies where possible texture (%sand, clay, gravel) topsoil/subsoil, depth topsoil/subsoil, field capacity (measured or estimated) topsoil/subsoil, below subsoil - impervious layer, water table, more soil.

Would like: Total N topsoil, CEC topsoil, pH topsoil, bulk density topsoil

CLIMATE:

monthly max/min temp, monthly precipittion (ppt), S.D. for annual ppt.

Would like long-term precip

VEGETATION:

TREE: basal area of woody, canopy cover of woody (projected),

% woody leaf mass which is evergreen, % woody cover potentially N-fixing

fraction trees > 2m, Mean max height for trees > 2m,

spatial pattern at tree scale regular

random

aggregated

clumped (50% of woody cover contiguous/overlapping crowns)

if clumped - % cover by clumps, mean clump size,

HERBACEOUS: % annual/perennial, C3/C4, grass/non-grass,

peak herbaceous AG biomass.

Would like: herbaceous cumulative x% roots at y depth

% bare ground (>0.5m patches)

DISTURBANCE/MANAGEMENT:

fire -none (<1 in 50 yr)

-10-50 yr

-3-10 yr

-1 or 2 yr

grazing - % ANPP consumed

browsing - % ANPP consumed

wood harvesting - kg/ha removed

frequency of flooding

DERIVED VARIABLES

residual water

runoff estimate (for under/outside canopies searately if clumped)

monthly PET

VPD/humidity

 

 

 

Appendix B: Level 3, Predicting resource allocation and NPP at less than annual time steps

To answer questions

  1. identification of the factors controlling tree and grass growth
  2. effects of plant spatial distribution and demography
  3. allocation of water and nutrients between plant types
  4. spatial and temporal separation of resource acquisition and production

For applications such as

  1. deriving an approximation of daily effects at a larger sale for other models
  2. derive frequency distribution of critical events (+/- climate change)

Data required

Who has this data?

     

Plant

 

2 to 4 weekly grass growth and turnover

Joe, Bruce, Niall

leaf/stem, live/dead

 

total root biomass

 

live/dead

Joe (some), Niall

by depth

 

turnover rates

 

species

 

Tree/shrub growth rates

 

leaf, wood

Bruce (Annual only)

Joe (A), Niall

Litter fall

 

Bruce (M), Joe (A)

Leaf area

Niall, Bruce

Nutrient content of growth

Joe (grass), Bruce

leaf/stem

 

N,P?

 

Transpiration data

 

trees, grass

 

sap flow, stomatal conductance, leaf water potential

Niall, HortResearch NZ

CO2 fluxes

Niall

   

Climate and light

 

PAR levels

Bruce, Joe

Daily precip, pan ET,VPD, max-min

Niall, Joe, Bruce

   

Spatial plant distribution and demography

Bruce

   

Fire/Grazing

 

frequency

 

temperature

 

severity

 

Soils

 

soil moisture by depth (frequently)

Joe, Niall, Bruce (some)

nutrients by depth

Joe, Niall, Bruce

Appendix C: Data available at each level

Level 1: to be prepared by December 1998

South Africa x 20 - Scholes

Zimbabwe x 5 - Moya

Kalahari x 5 - Scholes

West Africa x 4 - Moyo

NATT (Australia) x 32 - Ludwig

(Other Australian Burroughs/Anderson x 40, John Carter x 4 - Ludwig)

USA x 12 - Breshears

Venezuela x 26 - San Jose

Level 2 (at least three treatments of basal area): to be prepared by September 1998

E. populina - Scanlan

A. aneura - Scanlan

P. radiata - Thurrold

Eucalypt - Dodd

Acacia - Dodd

Mopane - Scholes

A. mellifera - Scholes

Pinyon-Juniper - Breshears

Pordrosa - Breshears

Acacia - Archer

Level 3: to be prepared as and when possible

Nylsvley x 2 - Scholes

Tikitere - Thurrold

Boatman - Scanlan

Sahel - Hannan

Los Alamos - Breshears

Venezuela x 2 - Montes

Te Kulti x 2 - Dodd

 

Appendix E: GRASP model description, Joe Scanlan

The heart of the way in which GRASP simulates tree-grass interactions is the competition for water to meet their respective transpiration demands.

Potential tree transpiration is calculated from tree basal area and pan evaporation. Potential transpiration increases linearly with tree basal until tree basal area reaches 25 m2 ha-1 at which potential transpiration is equal to pan evaporation. Potential grass transpiration is the product of proportional green cover and pan evaporation. Potential soil evaporation is calculated from the proportion of ground not covered by litter and pan evaporation. If tree basal area and green cover are both high, potential evapotranspiration can reach a maximum of 1.3 times pan evaporation.

The soil cannot supply these demands except when soil moisture is high. The ability of each layer of the soil to supply the transpiration demand is calculated from supply functions from which is calculated a soil water index (0 to 1). The root profiles of trees are also used to estimate the amount of water use from each soil layer. Grass root profiles are embedded in the calculation of the supply functions. The demand is multiplied by the soil water index and trees and grasses are supplied with the same proportion of their transpiration demand.

Nitrogen is taken up in the transpiration stream at a constant concentration (expressed as kg/ha/100mm transpiration). GRASP has the ability to allocate proportionally less of the nitrogen to the trees than to the grasses. This is based on the idea that grass root systems are located in the surface soil layers in which most of the organic matter and therefore where nitrogen is located. This feature has not been widely tested as data sets upon which to parameterise this behaviour are very limited.

Green cover of grasses can be reduced by moisture stress, frost or lack of nitrogen. This decreases transpiration. However, there are no environmental effects on tree transpiration demand.

Apart from the model developed by Jocelyn Moore and others for mulga, tree production and tree populations are not simulated.

Also, tree are not spatially located, rather tree effects are evenly distributed across the landscape.

Measurements of the effect of trees on microclimate (especially vapour pressure deficit) at a community level have been made. These effects are being progressively implemented in GRASP.

Grazing by livestock leads to changes in grass basal area and this influences initial growth of grass at the start of the growing season. There are no simulated effects of grazing on trees. Similarly, there are no effects of fire on trees (apart from the Moore version).

Appendix D: Participant’s Tasks

Steve Archer La Copita site @ level 3

David Breshears USA level 1 coordinator, Pinyon site @ level 2

Mike Cougenhour Level 2 & 3 model experiment

Mike Dodd Two Te Kulti sites @ level 3

David Hall Writing meeting coordinator; funding

Niall Hanan HAPEX-Sahel site @ level 3

Jo House Emerging Issues in Savanna Ecology paper

Richard Joffre Dehessa site @ level 3

John Ludwig Australia level 1 coordinator

Ruben Montes Two Venezuela sites @ level 3

Charles Moyo Africa level 1 coordinator

William Parton Level 2 modelling coordinator; level 2 modelling

Jose San Jose South America level 1 coordinator

Joe Scanlan Mulga site @ level 2 & 3; Poplar Box @ level 2; level 2 modelling

Robert Scholes Database variables description, Nylsvley site @ level 3

Jonathan Scurlock Database structure

Bruce Thurrold Level 2 data coordinator; Tikitere site @ level 2 & 3