Impacts of including trait variation on predictions of global carbon fluxes and vegetation distribution

Peter van BodegomDepartment of Systems Ecology

VU University AmsterdamThe Netherlands

Traits vary considerably within and between communities

Kattge et al. 2011 GCB

Traits also tend to respond to climate manipulations

Cornelissen et al. 2007 EcolLett; Aerts, van Bodegom and Cornelissen 2012 New Phytol

incr

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ing

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ion

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-0.04

-0.02

0

0.02

0.04

0.06

low altitude incubation

High altitude incubation

global change treatment

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ease

in d

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tion

rate

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This trait variation is not well-captured by classification in biomes/PFTs

Van Bodegom et al. 2012 GEB

Keeping trait values PFT-constant within DGVMs has various disadvantages

Van Bodegom et al. 2012 GEB

Constant trait values in modelling hampers:1. including acclimation and adaptation processes2. Accounting for non-random species turnover3. Quantifying vegetation-environment feedbacks

For these reasons, trait variation/responses are increasingly incorporated into the DGVMs

A partial solution: incorporation of observation-driven trait/process estimates

Brovkin et al. 2012 BiogeosciencesGlobal litter stocks

Cornwell et al. 2008 EcolLett

Potential solutions for further incorporating trait responses/ranges into DGVMs

1. more PFTS2. Incorporating variation within PFTs:

a. Based on habitat filtering principlesb. Based on evolutionary principles

3. Fully traits-based approach

1. More PFTs may not be a fruitful approach given functional redundancy

observedpr

edic

ted

2a. Incorporation of trait variation within PFTs: habitat filtering principles

Based on assembly theory: environment acts a ‘filter’

Filtering by environment

Potential range of trait values

Trait range in habitat 1

Trait range in habitat 2

Ordonez et al. 2009 GEB

2a. Incorporation of habitat filtering principles into JSBACH

For PFT 1: trait X = a * temperature + b * radiation + CO2-acclimation

1 2 3 4 534

38

42

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YearSL

A (m

2 / k

g C)

1 2 3 4 534

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Year

SLA

(m2

/ kg

C)

C3-grasses

Default: fixed traits variable traits responses

C3-grasses

Verheijen et al. 2012 Biogeosci.Disc.

2a. JSBACH-simulated trait variation based on habitat filtering

Red dots: fixed values from default setting

Verheijen et al. 2012 Biogeosci.Disc.

2a. Impacts of JSBACH-simulated trait variation on productivity

Verheijen et al. 2012 Biogeosci.Disc.

2a. Impacts of JSBACH-simulated trait variation on vegetation distribution

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variable traits

2a. Impacts of JSBACH-simulated trait variation on future carbon sink

Verheijen et al. 2012 in prep.

2b. Incorporation of trait variation based on evolutionary principles

Van Bodegom & Franklin in prep.

GPP

(gC

m-2

year

-1)

Latitude

N:C & allocation (no root. comp)N:C & allocation (with root. comp.)Allocation (with root. comp.)N:C

Effects included

- Forest stand model- No water limitation- Maximizing net growth & reproduction

2b. Incorporation of trait variation based on evolutionary principles: on-site evaluation of

variable allocation

Van Bodegom & Franklin in prep.

Prod

uctiv

ity (k

g C ha

-1ye

ar-1 )

0.06 0.08 0.1 0.12 0.140

100

200

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500

All xx C0 5 1 5All2 xx C0 1.09 5 1 5AffR2 xx C0 1.03 1 5

xx

Soil N availability (max gN g root C -1year-1)

0.06 0.08 0.1 0.12 0.140

50

100

150

200

All xx C0 5 1 0All xx C0 5 1 1All xx C0 5 1 2AffR2 xx C0 1.03 1 0AffR2 xx C0 1 1 1AffR2 xx C0 1 1 2

xx

stem

fine-roots

foliage

a b

Fixed allocationOptimal allocation

3. A fully traits-based approach: separating trait predictions from vegetation distribution

predictions

Douma et al 2012 EcographyVan Bodegom et al. in revision

3. Trait predictions based on trait-environment relationships

Van Bodegom et al. in revision

3. Predicting vegetation probabilities from traits: kernel density fitting

Douma et al 2012 Ecography Van Bodegom et al. in revision

SSD

LMASeed mass

Biome A

Biome B

Biome C

Seed massLMA

SSD

For each position in trait space, multiple plant functional types may in principle be possible. The probability of each is described by Gaussian kernels

3. Predicting vegetation probabilities from traits: global vegetation distribution

Van Bodegom et al. in revision

Conclusions

Trait responses to climate (manipulations) are important and strong impact (predictions of ) vegetation distribution and functioning.

There are multiple ways to continue refining DGVMs.

Exchange of ideas between modellers and experimentalists will remain essential for more reliable predictions of our future climate.

Douma et al 2012b Ecography