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)

patches of resource depletion showing above- and below-ground

A community of self-assembling virtual plants

A single propagule …

… about to grow

Abundant growth above- and below-ground …

… with zones of resource depletion

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 !

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

The virtual plants interact with their environment and neighbours

They possess most properties of real individuals and populations

For example …

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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3000

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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10000

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

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 !

Some working definitions …

Species within one functional group share a single important trait

Species within one functional type share a similar set of traits.

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’

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 ”

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

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

So we don’t build with these … we build with these !

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 …

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

Competition between two different types of plant …

Small size, rapid growth and fast reproduction

Medium size, moderately fast in growth and reproduction

(Red enters its 2nd generation)

White has won !

This one-on-one competition is very realistic …

… but most communities involve more than two types of plant

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

For example, we grow an equal mixture of all seven types together …

… in an environment with high levels of resource above- and below-ground

Blue has eliminated almost everything except White and Green …

… and the simulation has almost run out of space

Environmental gradients

= stepwise increases in resource level

Whittaker-type niches emerge for each PFT

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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

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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1

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3

4

5

0 5 10 15 20 25 30 35

Resources (= 1/stress)

Num

ber o

f pla

nt ty

pes

surv

ivin

g (m

ax 7

)

Greatest biodiversity at intermediate stress

0

1

2

0 0.2 0.4 0.6 0.8 1

Probability of disturbance

Num

ber

of p

lant

type

s su

rviv

ing

(max

7)

Greatest biodiversity at intermediate disturbance

Stresses and disturbances can be applied together …

… in many forms and combinations, generating a big range of productivity classes

R 2 = 0.534

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5

0 2000 4000 6000 8000 10000 12000

Total biomass (productivity)

Num

ber o

f pla

nt ty

pes

surv

ivin

g (m

ax 7

)

Greatest biodiversity at intermediate productivity

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

In our closed system, the biodiversity-productivity hump eventually collapses with time …

Community biomassModel iterations

Number of plant types

Community biomassModel iterations

Number of plant types

But additional treatments can prevent this collapse …

• Environmental heterogeneity

(spatial and / or temporal)

• ‘ Seed rain ’

(propagules introduced from external sources)

Community biomassModel iterations

Number of plant types

6

0

6

5

4

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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)

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.

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

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/