Terrestrial Ecology

(1) Ecohydrology and Optimality: Plant

adaptation to the environment (2) Climate-vegetation feedbacks

Stefan C. Dekker

Environmental Sciences, Utrecht University,

The Netherlands

Background:

• MSc Physical Geography, Soil physics, University of Amsterdam

• PhD: University of Amsterdam, ‘Modelling and Monitoring Forest Evapotranspiration from different perspectives’

• Senior researcher and lecturer at Utrecht University, Environmental Sciences, Faculty Geosciences

Research:

Global change and ecosystems

Teaching:Teaching:

Bachelor Environmental Sciences

Master Sustainable Development

• Vegetation adapts itself to environmental conditions;

Ecohydrology and Optimality

• Vegetation adapts itself to environmental conditions; • Different hypothesis for optimal vegetation/fitness of vegetation:

Ecohydrology

• Different hypothesis for optimal vegetation/fitness of vegetation: Examples– Maximization of soil water: Eagleson(1978):– Carbon gain-water los: Farquahar (1982) + Ball/Berry-Leuning– Marginal Carbon gain: Bloom et al. 1985 – Individuals act together to minimize water stress: Rodrigues Iturbe (1999)– Self-organisation of vegetation: Rietkerk et al. (2004)

Coupled to the atmosphere: Dekker et al. (2007), Konings et al. (2011): – Maximum Entropy production: Kleidon (2004)– Stomatal adaptation, carbon-gain/water loss: Franks and Beerling (2009)/Konrad (2008)/de Boer

et al. 2011: – Optimality of Net Carbon Profit: Schymanski (2007), Dekker et al. (2011)

Ecohydrology: Plant adaptation to the environment

a) Self-organisation of vegetation

b) Stomatal adaptation to rising CO2

c) Optimality of vegetation by using Net Carbon Profit

a. Self organization of vegetation

Positive feedback in arid ecosystems

+

Plants

+

Soil water

Increased

plant

growth

Enhanced

infiltration

A simple model

PlantsRainfall

Losses

Soil water

InfiltrationUptake/

growth+

Model predictions

Alternative stable states

No plants,

little infiltration

Lot of plants

high infiltration

Catastrophic shifts…E

qu

ilib

riu

m p

lan

t sta

nd

ing

cro

p

Rainfall levels

Eq

uili

bri

um

pla

nt

sta

nd

ing

cro

p

Including Spatial processes

Surface water

PlantsRainfall

Losses (e.g. grazing)

Surface water

Soil water

InfiltrationUptake +

Rietkerk et al AmNat 2002Rietkerk,Dekker et al. Science 2004, Dekker et al. GCB 2007

Spatial model

[ ] [ ] [ ]flowoverlandoninfiltratirainfallt

txO±−=

∂

∂ ),(r

[ ] [ ] [ ] [ ]txW∂ ),(v

[ ] [ ] [ ] [ ]movementwaternevaporatiouptakeoninfiltratit

txW±−−=

∂

∂ ),(v

[ ] [ ] [ ]dispersallossgrowtht

,txP±−=

∂

∂ )(r

Rietkerk et al AmNat 2002Rietkerk,Dekker et al. Science 2004, Dekker et al. GCB 2007

Grid approach

x-directionx-direction

y-direction

Increased grazing or decreased rainfall

Model simulations

gaps labyrints spots

400 m

grid size = 2 x 2 mRietkerk et al AmNat 2002

Rietkerk,Dekker et al Science 2004

Decreased grazing or increased rainfall

Deblauwe et al, Glob. Ecol. Biogeog., 2008

Rietkerk,Dekker et al Science 2004Solé Nature 2007

Increased productivity in arid systems

Ave

rage

sta

ndin

g cr

op (

g/m

2)

20

10

Homogeneous system

Patterned system

Precipitation (mm/day)

Ave

rage

sta

ndin

g cr

op (

g/m

0.5 1.0 1.50

Patterned system

More efficient water use in patterned vegetation

More efficient water use in patterned vegetation

Conclusions self-organisation

• Organized pattern more productive (optimality)

• Bistability due to hysteresis

• Spots can be seen as early warning signals for desertification

• ? How influenced by climate-vegetation feedbacks? Next lecture

� How realistic? Model describes the observed patterns

but is the mechanism correct..

• Constant rainfall

• No radiation balance

• Other mechanisms:

Influence of root systems?Influence of root systems?

Soil water repellency, influenced by organic matter (induced by the plants or by algae/bacteria?

Models are only hypotheses, search to alternative mechanismss

b. Stomatal adaptation

Stomata: exchange of CO2 and water vapor

Guard cells regulate diffusive conductance of the leaf (gs)

between 0 (stomata closed) and maximal (gsmax) (fully open)

Guard cells open/close stomata in seconds

Taxodium distichum (Bald Cypress)

Calculation of maximal conductance (gsmax )

We can measure gsmax on fossil leaves:

• stomatal density (D)• pore size (amax)• pore depth (l):

1

• pore depth (l):

ππmax2

maxmax

al

aDdg w

s+

⋅⋅=

2

Fossil leaves reveal that CO2 forces evolution of gsmax

• Plants have increased gsmax with lowering CO2 at geological timescales.

• This has decreased Water-Use Efficiency (WUE)

Franks and Beerling, 2009, PNAS. Franks and Beerling, 2009, Geo Biol.

Stomatal size (µm

-2)

Palaeo-data shows that CO2 forces evolution of gsmax

• High g is only

• Low CO2 � High

gsmax (and vice versa)

Stomatal size (

• High gsmax is only reached with high stom. density (D)

Franks and Beerling, 2009, PNAS

Explain stomatal adaptation from optimization theory:

• Adaptation of gsmax by optimization of photosynthesis A (good) with transpiration E (bad) under the constraint of a cost of water loss (λ)

low CO2

high CO2

Carbon gain A= gs(Ca-Ci)

Water loss E=a.gs.D

carbon gain (A)

transpiration (E)

Note, carbon and water pass through same stomata!

max

max

s

s

gE

gA

∂∂

∂∂=λ

Stomatal adaptation at 3 timescales:

• Dynamic adaptation (seconds ~ hours) � plants open/close stomata

• Structural adaptation (> years ~ centuries) � plants grow leaves

different number and size stomata within phenotypic plasticity

Relevant at the timescale of anthropogenic climate change!

different number and size stomata within phenotypic plasticity

• Genetic adaptation (> ~centuries) � natural selection alters ranges of

phenotypic plasticity

Can we measure stomatal adaptations at decadal to centennial timescales? Yes!

• We obtained fossil leaves from sediment cores ( ) and herbaria ( )

• Moisture and temperature have been constant in Florida over last century

Temperature

Rainfall

CO2

Leaves are preserved in sediments and stored in herbaria

D, amax and l are measured under microscope...

Measurements of D and amax on Florida common forest species

• Data from 1880 to present (+100ppm) on 8 canopy species from Florida (n=837)

Pine forest (-51%)

4 species, n=278

Change in gsmax since 280ppmv

All species show decrease in gsmax!

.s-1)

Hammock forest (-32%)

6 species, n=403

Cypress swamp (-36%)

3 species, n=156

gsm

ax(m

ol.m

-2.s

CO2 (ppm) �

Questions for future climate change:

1: Will gsmax continue to decrease with CO2 rising further?

2: Will this alter transpiration? And how much?

We propose a modelling approach based on optimization of gsmax.

Simulate gsmax under rising CO2 with optimization model:

• Species adapt gsmax structurally to optimize photosynthesis with drought resistance

model fitModel predicts continuing decrease of gsmax from optimization.

Is this reasonable?Is this reasonable?

Maybe not....

Plants may reach their limits of phenotypic plasticity at current rate of CO2 increase

Simulated stomatal gas exchangeDoubling CO2 for three models:

(1) No optimization (GfixMod)

(2) Just optimization (GoptMod)

(3) Optimization with limits to phenotypic plasticity (GlimMod)

Result: 30% decrease in conductance and transpiration with optimizationoptimization

-30% -30%

Canopy transpiration decrease at double CO2

0

15

30

45

LE

[W

. m-2

]

0

0.5

1

1.5

1]

-75

-50

-25

∆ L

E [W

Ar Ic Mc Ql Qn Pe Pt Td Mean

-3

-2.5

-2

-1.5

-1

-0.5

species

∆ E

[m

m.d

-1

GfixMod

GlimMod

GoptMod

Canopy transpiration decrease at double CO2

0

15

30

45

LE

[W

. m-2

]

0

0.5

1

1.5

1]

Already 1mm/d less transpiration since 1880! 2mm/d to go!

-75

-50

-25

∆ L

E [W

Ar Ic Mc Ql Qn Pe Pt Td Mean

-3

-2.5

-2

-1.5

-1

-0.5

species

∆ E

[m

m.d

-1

GfixMod

GlimMod

GoptMod

Conclusions :

• Structural adaptation of gsmax reduces transpiration at rising CO2

• Potentially large changes in hydrological cycle due to ongoing structural adaptation (runoff shows this indeed!*).

• Ranges of phenotypic plasticity constrain structural adaptation at the current rate of CO increase. (Faster than evolution?)the current rate of CO2 increase. (Faster than evolution?)

• Species with highest response limits have competitive advantage in high CO2 world.

• Can we expect changes in species composition in natural environments if CO2 rises beyond response limits?

* Gedney et al. 2006; Betts et al. 2007 , Nature

DISCUSSION

Comment : Miglietta F, Peressotti A, Viola R, Körner C, Amthor JS

(2011) Stomatal numbers, leaf and canopy conductance, and the control of transpiration. Proc Natl Acad Sci USA, 10.1073/pnas.1105831108.

Main argument:Main argument:• It is the atmosphere that calculates the transpiration

demand

• But suppose if transpiration will decrease, then it will be compensated by other sources.

Do you agree?

• Our argument: plant itself regulates NPP (so not the atmospheric demand)

•Interesting debate how positive and negative feedbacks to atmosphere will work out

c. Idea of vegetation optimality• Many monitoring data done in forested ecosystems, Euroflux,

Ameriflux

• SVAT Models are calibrated against these data sets (>20 fit • SVAT Models are calibrated against these data sets (>20 fit parameters)

• Limited use for prediction

Optimality Models

• No site and species specific data needed;

• Climate + soil = Optimal vegetation distributions

Idea of vegetation optimality• No site and species specific data needed;

• Climate + soil = Optimal vegetation distributions Carbon gain f = g (C -C )Carbon gain fc = gs(Ca-Ci)

Water loss fe=a.gs.D

Objective function: f(gs)=fc(gs)-λfe(gs)

Idea of vegetation optimality• No site and species specific data needed;

• Climate + soil = Optimal vegetation distributions Carbon gain f = g (C -C )Carbon gain fc = gs(Ca-Ci)

Water loss fe=a.gs.D

Objective function: f(gs)=fc(gs)-λfe(gs)

Optimality:

λ : relation between Carbon gain and water loss

( )( )

0)()(

=∂

−∂

s

sesc

g

gfgf λ

Vegetation Optimality Model

VOM: Vegetation tries to maximize its own Net Carbon Profit

NCP= assimilation – costs

• foliage turnover cost= f(LAI), parameterized by GlopNet

• maintenance root costs = f(root density)

• maintenance vascular system = f (cover, rooting depth)

1.Vegetation Optimality Model

With maximum LAI/Cover/Rooting depth/Root dens

� maximum Assimilation

� maximum water loss costs

� maximum turnover and root costs

SO every vegetation has its optimal LAI, cover, rooting depth and root density

Pb

Tree water storage Md, Mq

Light Absorption CO2 Uptake

Ag Transpiration Et

Electron Transport

Capacity Jmax,i

i = 2

i = 3

i = m

i = 1

Leaf Layer

s

Maintenance Costs

Rr, Rf, Rv

Stomatal Conductance

Gs

VOM_mc

• Coupled photosynthesis and transpiration model

• Above- and belowground processes

• Multi-layer canopy and multi-layer soil horizons

dyui

Saturated zone

i = 2

i = 3

i = N

i = 1

yr

Root Water Uptake

Qr,i

Soil Layers

r f v

Unsaturated zone

Yu

-> 6 parameters to optimize

-> search for maximum NCP

(= assimilation-costs)

3 structural parameters: Gapfraction, LAI (number of canopy layers), rootdepth

+3 specific parameters: describing Vcmax and intrinsic water use efficiency (λ)

All forest types can be described

� Nearby optimal solutions of NCP: to take into account structural model and measurement errors

Optimization Differential Evolution Adaptive Metropolis (DREAM) algorithm (by Vrugt et al. 2008,2009)

• Search for posterior probability density function of NCP containing the best solution and its underlying uncertainty

θ1

the best solution and its underlying uncertainty

• Stochastic optimization• Based on MCMC Markov Chains Monte Carlo• Log(likelihood) measure to accept ensemble� In fact efficient random sampling

20 chains, each 250 parameter sets � ensemble of 5000 ‘different species’ nearby optimal NCP

Run the model for Veluwe Climate

Criteria to interpret the results• Posterior distribution parameters• Variability of simulated variables: ET, CO2, albedo (cover)• Variability of simulated variables: ET, CO2, albedo (cover)• Comparing mean NCP, GPP with literature values• Comparing mean ensemble with measured variables of ‘natural

vegetation’;

0.97 0.98 0.99 1.000.0

0.1

0.2

0.3

0.4 a *

MA (-)

0.51 0.55 0.59 0.63 0.67 0.710.0

0.1

0.2

0.3

0.4 b*

yr (m)

0.2

0.3

0.4 d

0.2

0.3

0.4 e

*

8 12 160.0

0.1

0.2

0.3

0.4 c

*

m (-)

0.2

0.3

0.4 f

*

Marg

inal

Den

sit

y

1. Posterior distribution for all six parameters

And distribution for NCP

* indicates best fit with SCE-UA

2.7 3.1 3.5 3.8 4.2 4.60.0

0.1 *

ce (-)

-1.2 -0.9 -0.7 -0.4 -0.2 0.10.0

0.1

*

me (-)

102 168 234 299 365 4310.0

0.1

Jmax,t=0 (µµµµmol m-2 s-1)

118 120 122 125 127 1290.0

0.1

0.2

0.3

0.4 g

*

NCP (mol m-2)

Marg

inal

Den

sit

y

Dekker et al. (2011), Ecohydrology

0.97 0.98 0.99 1.000.0

0.1

0.2

0.3

0.4 a *

MA (-)

0.51 0.55 0.59 0.63 0.67 0.710.0

0.1

0.2

0.3

0.4 b*

yr (m)

0.2

0.3

0.4 d

0.2

0.3

0.4 e

*

8 12 160.0

0.1

0.2

0.3

0.4 c

*

m (-)

0.2

0.3

0.4 f

*

Marg

inal

Den

sit

y

2.7 3.1 3.5 3.8 4.2 4.60.0

0.1 *

ce (-)

-1.2 -0.9 -0.7 -0.4 -0.2 0.10.0

0.1

*

me (-)

102 168 234 299 365 4310.0

0.1

Jmax,t=0 (µµµµmol m-2 s-1)

118 120 122 125 127 1290.0

0.1

0.2

0.3

0.4 g

*

NCP (mol m-2)

Marg

inal

Den

sit

y

What do we observe:

a Cover (MA) and rooting depth (yr) not normally distributed;

b Nr layers (m): LAI between 1.6 and 3.2

* indicates best fit with SCE-UADekker et al. (2011), Ecohydrology

0.97 0.98 0.99 1.000.0

0.1

0.2

0.3

0.4 a *

MA (-)

0.51 0.55 0.59 0.63 0.67 0.710.0

0.1

0.2

0.3

0.4 b*

yr (m)

0.2

0.3

0.4 d

0.2

0.3

0.4 e

*

8 12 160.0

0.1

0.2

0.3

0.4 c

*

m (-)

0.2

0.3

0.4 f

*

Marg

inal

Den

sit

y

R=-0.95

2.7 3.1 3.5 3.8 4.2 4.60.0

0.1 *

ce (-)

-1.2 -0.9 -0.7 -0.4 -0.2 0.10.0

0.1

*

me (-)

102 168 234 299 365 4310.0

0.1

Jmax,t=0 (µµµµmol m-2 s-1)

118 120 122 125 127 1290.0

0.1

0.2

0.3

0.4 g

*

NCP (mol m-2)

Marg

inal

Den

sit

y

c High correlation between ce and me, both describing intrinsic water use (λ)

* indicates best fit with SCE-UA

Dekker et al. (2011), Ecohydrology

98 101 104 107 109 1120.0

0.1

0.2

0.3

ΣΣΣΣCO2 (mol m-2)

Ma

rgin

al

De

ns

ity

0.2

0.3

Ma

rgin

al

De

ns

ity

2. Variability (climate models)

Cumulative ET:

• Large differences for transpiration (50%)

Grass

Forest

Cumulative Assimilation:

• Only 15% differences

0.30 0.34 0.39 0.43 0.47 0.520.0

0.1

ΣΣΣΣET (m)

Ma

rgin

al

De

ns

ity

1.6 2.4 3.20.0

0.1

0.2

0.3

LAI (m2 m-2)

Ma

rgin

al

De

ns

ity

(50%)

• Difference between grass and forest only 20%

LAI:

• 50% difference

Dekker et al. (2011), Ecohydrology

3 . Mean ensemble of VOMmlsc compared to data

NCP GPP

• Mean VOM 110 280 mol*m-2

• Literature1 61.5-67 90-150 mol*m-2• Literature 61.5-67 90-150 mol*m

1 based on meta-analysis of Luyssaert et al. 2007, Waring et al. 2008, Ryan et al. 1997

4 . Mean ensemble of VOMmlsc and data

r=0.81

• Mean ensemble overestimation(38%)

• Low uncertainty

• Mean ensemble overestimation (5%)

• High uncertainty (300-500 mm)

r=0.88

r=0.72 � Limited information, large

4 Comparisons with water content

information, large drainage to deep groundwater

Dekker et al. (2011), Ecohydrology

Conclusions

• Vegetation Optimality Model: – one single criterion to describe biological fitness is difficult;

– Especially the λ is difficult to optimize.

– Costs and benefits can be species dependent?

• Next steps: – Is optimality a way to proceed for ecological modelling?

– How will this work out if coupled to climate models? So, can vegetation change the climate to an optimal climate?