Peter van Bodegom Department of Systems Ecology VU University Amsterdam The Netherlands
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Transcript of Peter van Bodegom Department of Systems Ecology VU University Amsterdam The Netherlands
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Impacts of including trait variation on predictions of global carbon fluxes and vegetation distribution
Peter van BodegomDepartment of Systems Ecology
VU University AmsterdamThe Netherlands
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Traits vary considerably within and between communities
Kattge et al. 2011 GCB
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Traits also tend to respond to climate manipulations
Cornelissen et al. 2007 EcolLett; Aerts, van Bodegom and Cornelissen 2012 New Phytol
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This trait variation is not well-captured by classification in biomes/PFTs
Van Bodegom et al. 2012 GEB
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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
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A partial solution: incorporation of observation-driven trait/process estimates
Brovkin et al. 2012 BiogeosciencesGlobal litter stocks
Cornwell et al. 2008 EcolLett
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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
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1. More PFTs may not be a fruitful approach given functional redundancy
observedpr
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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
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2a. Incorporation of habitat filtering principles into JSBACH
For PFT 1: trait X = a * temperature + b * radiation + CO2-acclimation
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C3-grasses
Default: fixed traits variable traits responses
C3-grasses
Verheijen et al. 2012 Biogeosci.Disc.
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2a. JSBACH-simulated trait variation based on habitat filtering
Red dots: fixed values from default setting
Verheijen et al. 2012 Biogeosci.Disc.
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2a. Impacts of JSBACH-simulated trait variation on productivity
Verheijen et al. 2012 Biogeosci.Disc.
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2a. Impacts of JSBACH-simulated trait variation on vegetation distribution
default
variable traits
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2a. Impacts of JSBACH-simulated trait variation on future carbon sink
Verheijen et al. 2012 in prep.
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2b. Incorporation of trait variation based on evolutionary principles
Van Bodegom & Franklin in prep.
GPP
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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
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2b. Incorporation of trait variation based on evolutionary principles: on-site evaluation of
variable allocation
Van Bodegom & Franklin in prep.
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Fixed allocationOptimal allocation
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3. A fully traits-based approach: separating trait predictions from vegetation distribution
predictions
Douma et al 2012 EcographyVan Bodegom et al. in revision
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3. Trait predictions based on trait-environment relationships
Van Bodegom et al. in revision
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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
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3. Predicting vegetation probabilities from traits: global vegetation distribution
Van Bodegom et al. in revision
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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.
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Douma et al 2012b Ecography
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