Titles
description
Transcript of Titles
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with acknowledgements to
Self-assembling plants and integration across ecological scales
Philip Grime (Sheffield, UK)Andrew Askew (Sheffield, UK)
Presentation ready
Roderick Hunt (Exeter, UK)Ric Colasanti (Corvallis, OR)
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patches of resource depletion showing above- and below-ground
A community of self-assembling virtual plants
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A single propagule …
… about to grow
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Abundant growth above- and below-ground …
… with zones of resource depletion
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Above-ground binary tree base module
Below-ground binary tree base module
Above-ground array
Below-ground array
Above-ground binary tree ( = shoot system)
Below-ground binary tree ( = root system)
A branching module
An end module
Each plant is built like this …
… only a diagram, not a painting !
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Water and nutrients from below-ground
Parent or offspring modules can pass resources to any adjoining modules
End-modules capture resources:
Light and carbon dioxide from above-ground
… so whole plants can grow
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The virtual plants interact with their environment and neighbours
They possess most properties of real individuals and populations
For example …
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S-shaped growth curves Partitioning between root and shoot
Functional equilibria
Foraging towards resources
Self-thinning in crowded populations
0
500
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0 20 40 60 80 100 120 140Time (iterations)
Bio
mas
s (m
odul
es p
er p
lant
)
Light 1 Nutrient 6 Light 2 Nutrient 6
Light 1 Nutrient 8 Light 2 Nutrient 8
0.9
1
1.1
1.2
1.3
0 5 10 15 20Units of nutrient per cell
1 Light unit
2 Light units
Root/shoot allometric coefficient
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10
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1 10 100
Planting density
Bio
mass (
mo
du
les)
per
pla
nt
Slope -2/1
Size
Time
Allometric coefficient
Below-ground resource
Individual sizeSelf-thinning line
Population density
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The foregoing plants all have the same functional specification (modular rulebase)
But specifications can be changed if we want some plants to behave differently …
… and we can simulate plant functional types
… not yet comparative plant ecology !
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Some working definitions …
Species within one functional group share a single important trait
Species within one functional type share a similar set of traits.
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Functional types are multi-species levels of organization, lying above the population but below the community
… and some implications
A single species can simultaneously be a member of several functional groups, e.g. both ‘legume’ and ’woody’ …
… but a member of only one functional type, e.g. ‘K-strategist’
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Why use functional types?
They reduce the high dimensionality of real plant life
“ There are many more actors on the stage than roles that can be played ”
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PFTs give a continuous view of vegetation even when relative abundances and identities of species are in flux
Tools exist to allocate types to species (and type-mixes to whole communities)
Large-scale (or cross-scale) studies of effects of environment or management on (e.g.) biodiversity, vulnerability and stability become possible
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How do we recreate basic PFTs within the self-assembling model ?
… we change the modular rulebases controlling morphology, physiology and reproduction …
… but we must begin to model at a high enough level to get “ airborne ” ... we need access to the emergent properties
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So we don’t build with these … we build with these !
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Type Morphology Physiology Reproductionmodule size, tissue longevity, flowering speed,
1 Large Fast Slow 2 Small Slow Slow 3 Small Fast Fast 4 Medium Medium Slow 5 Small Medium Medium 6 Medium Fast Medium 7 Medium Medium Medium
resource demand RGR, SLA, allocation,decomposability propagule size
Building a set of PFTs …
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Three levels in each of our three ‘ super-traits ’
= 27 possible PFTs …
… but we model only 7 types
the other 20 would include Darwinian Demons that do not respect evolutionary tradeoffs
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Competition between two different types of plant …
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Small size, rapid growth and fast reproduction
Medium size, moderately fast in growth and reproduction
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(Red enters its 2nd generation)
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White has won !
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This one-on-one competition is very realistic …
… but most communities involve more than two types of plant
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Seven PFTs can cover the entire range of variation shown by herbaceous plant life …
… and to a first approximation, these seven can simulate complex community processes very realistically
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For example, we grow an equal mixture of all seven types together …
… in an environment with high levels of resource above- and below-ground
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Blue has eliminated almost everything except White and Green …
… and the simulation has almost run out of space
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Environmental gradients
= stepwise increases in resource level
Whittaker-type niches emerge for each PFT
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0
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Resource (= 1/stress)
% B
iom
ass
in m
ixtu
re
C
S
SC
(types)
Three PFTs in an initially equal mixture
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The equal mixture of all seven types again …
… but under environmental gradients of increasing mineral nutrient resource or increasing trampling disturbance
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0
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Resources (= 1/stress)
Num
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f pla
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ax 7
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Greatest biodiversity at intermediate stress
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0
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Probability of disturbance
Num
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lant
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s su
rviv
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7)
Greatest biodiversity at intermediate disturbance
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Stresses and disturbances can be applied together …
… in many forms and combinations, generating a big range of productivity classes
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R 2 = 0.534
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Total biomass (productivity)
Num
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pes
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ax 7
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Greatest biodiversity at intermediate productivity
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The biomass-driven ‘humpbacked’ relationship … one of the highest-level properties of real plant communities …
… emerges solely from the resource-capturing activity of modules in the self-assembling plants
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In our closed system, the biodiversity-productivity hump eventually collapses with time …
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Community biomassModel iterations
Number of plant types
Community biomassModel iterations
Number of plant types
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But additional treatments can prevent this collapse …
• Environmental heterogeneity
(spatial and / or temporal)
• ‘ Seed rain ’
(propagules introduced from external sources)
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Community biomassModel iterations
Number of plant types
6
0
6
5
4
3
2
1
0
1 0 0
1 0 0 0 2 0 0 0
3 0 0 0 4 0 0 0 2 0 0
3 0 0 4 0 0
5 0 0
P = 0 .0 1
P = 0 .0 0 1
P = 0 .0 0 0 1
P = 0
No external seed rain
Probability of one external seeding event per cell per iteration (random plant types)
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Real experiments with virtual plants
… individual, population and community processes emerge from one modular rulebase
We can ‘plant’ communities in any PFT mix and grow them under any environmental or management regime …
… to look at topics like biodiversity, vulnerability, resistance, resilience, stability, habitat / community heterogeneity, etc, etc.
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The modular rulebase is simply a string of numbers
2 3 1 4 2 3 2 1 2 2 1 3 3 1 2 3
controlling how big, how much, how long, how often …
2 3 1 4 2 3 2 1 2 2 1 3 3 1 2 3
2 3 1 4 2 3 2 1 2 2 1 2 3 1 2 3
2 3 1 4 2 3 2 1 2 2 3 2 1 1 2 3
… so we can modify this virtual genome when / wherever we like
either accurately
or inaccurately
and follow the downstream community consequences of GM
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In real experiments with virtual plants …
One overnight run on one PC
Approx. 100 person-years of growth experiments
Want to get airborne with us ?
http://www.ex.ac.uk/~rh203/
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