brody sandel 1, leah j. goldstein 2, nathan kraft 1, jordan okie 3, michal shuldman 1, david d....
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Brody Sandel1 , Leah J. Goldstein2, Nathan Kraft1, Jordan Okie3, Michal Shuldman1, David D. Ackerly1, Elsa Cleland4 and Katharine N. Suding2
(1)Department of Integrative Biology, UC Berkeley, Berkeley, CA (2)Ecology and Evolutionary Biology, UC Irvine, Irvine, CA(3)Department of Biology, University of New Mexico, Albuquerque, NM(4)Ecology, Behavior & Evolution Section, UC San Diego, La Jolla, CA
Contrasting predictions of
experimental and observational
studies of the response of plant communities to changing precipitation
How will the composition of plant assemblages respond to climate change?
Precipitation change Weltzin et al. 2003, Bioscience.
Plant functional traits Suding et al. 2008, Glob. Change Biol.
Experimental/observational Rustad 2006, Plant Ecol.
Introduction
Plant responses to climate change
Wright et al. 2005, Glob. Ecol. Biogeogr.
Introduction
Traits and climate change
N:P
Reich and Oleksyn 2004, PNAS
Traits vary with climate Can they predict response to changing
climate?
Advantages of trait-based predictionsMechanistic interpretationsAllows synthesesPredictions are generalizable
Introduction
Traits and climate change
Similar predictions?Direction and magnitude of effect
Shifts in functional trait composition are the bases for comparison
Introduction
Experimental and observational
Introduction
Similar predictions?
Control + Precip
Mea
n tr
ait
valu
e
Precipitation
Mea
n tr
ait
valu
e
Direction
Control + Precip
Mea
n tr
ait
valu
e
Precipitation
Mea
n tr
ait
valu
e
Introduction
Similar predictions?
Direction Magnitude
∆TE =? ∆TO
Control + Precip
Mea
n tr
ait
valu
e
Precipitation
Mea
n tr
ait
valu
e
Equivalent toexperiment
∆TE ∆TO
Introduction
Similar predictions?
Combining resultsSame direction, different magnitude(My a priori expectation)
Precipitation
Mea
n tr
ait v
alue
Experimentalstudies
Observationalgradient
CT
C
CT
T
Introduction
Similar predictions?
Experimental water additions Natural precipitation gradient Match species lists to trait databases Calculate plot mean trait values
Test for effects of increased water
Compare experimental and observational outcomes Direction Magnitude
Methods
Methods overview
Four water addition experiments Konza LTER (1991-2005) Knapp et al. 2001, Ecosystems.
Shortgrass Steppe LTER (2000) Sevilleta LTER (2004-2006) Baez et al. In prep.
Jasper Ridge Global Change Experiment (1999-2002) Zavaleta et al. 2003, Ecol. Monogr.
Between 10% and 190% (mean 50%) precip. increases
Plant community composition data Grasslands or mixed grass-shrublands 219 species total
Methods
Experimental data
VegBank (vegbank.org) 21,566 plots from across the country Plant assemblage of all plots 7813 species total
Used PRISM climate data to obtain 30-year mean precipitation values
Methods
Observational data
TraitsMethods
Matched species lists to trait databasesUSDA PlantsKew Gardens Seed Information DatabaseGlopnet leaf traits Wright et al. 2004. Nature.
More leaf traits Tjoelker et al. 2005, New Phyt.; Reich and Oleksyn 2004, PNAS.
Height Cleland et al. 2008. Ecology.
Exp. Nat. Grad.
Trait Coverage Coverage
LL 30% 21%
SLA 41% 34%
Nmass 42% 43%
Narea 40% 32%
Amass 38% 23%
Aarea 40% 23%
Seed 94% 80%
Form 100% 89%
Lifespan 98% 90%
Height 100%
TraitsMethods
Abundance-weighted trait means for each plot Percentage cover by a group All analyses performed on these plot-level values
Experimental ANOVA using last year of each study
Observational Aggregated cells at 1 x 1 degree resolution Linear regression
Methods
Analyses
log(Precip (mm))
log(
See
d m
ass
(mg)
)
Results
Seed size example
log(
See
d m
ass
(mg)
)
log(Precip (mm))
Results
Seed size example
log(Precip (mm))
log(
See
d m
ass
(mg)
)
Results
Seed size example
log(Precip (mm))
log(
See
d m
ass
(mg)
)
Results
Seed size example
log(
See
d m
ass
(mg)
)
log(Precip (mm))
Results
Seed size example
Results
Seed size example
Tre
atm
ent e
ffec
tlo
g(S
eed
mas
s (m
g))
per
log(
Pre
cip
(mm
))
Year
Slopes of line segments through time
Results
Summary of all traits Experimental Natural Gradient
Trait Effect P Effect r2
LL - 0.0129 + 0.154
SLA + 0.0297 NS 0.006
Nmass NS † 0.1601 - 0.158
Narea + 0.0003 - 0.309
Amass Mixed † 0.0189 - 0.047
Aarea NS † 0.3116 - 0.101
Seed - 0.0071 + 0.362
Grass NS † 0.0717 - 0.373
Forb + 0.0091 - 0.066
Annual - <.00001 - 0.122
Short - † <.00001
† indicates a significant site by treatment interaction
Results
Summary of all traits Experimental Natural Gradient
Trait Effect P Effect r2
LL - 0.0129 + 0.154
SLA + 0.0297 NS 0.006
Nmass NS † 0.1601 - 0.158
Narea + 0.0003 - 0.309
Amass Mixed † 0.0189 - 0.047
Aarea NS † 0.3116 - 0.101
Seed - 0.0071 + 0.362
Grass NS † 0.0717 - 0.373
Forb + 0.0091 - 0.066
Annual - <.00001 - 0.122
Short - † <.00001
† indicates a significant site by treatment interaction
Results
Summary of all traits Experimental Natural Gradient
Trait Effect P Effect r2
LL - 0.0129 + 0.154
SLA + 0.0297 NS 0.006
Nmass NS † 0.1601 - 0.158
Narea + 0.0003 - 0.309
Amass Mixed † 0.0189 - 0.047
Aarea NS † 0.3116 - 0.101
Seed - 0.0071 + 0.362
Grass NS † 0.0717 - 0.373
Forb + 0.0091 - 0.066
Annual - <.00001 - 0.122
Short - † <.00001
† indicates a significant site by treatment interaction
Experimental studiesTall, long-lived forbs with short leaf lifespans,
high leaf N concentrations, high specific leaf area, and small seeds
Observational analysisLong-lived woody species with long leaf
lifespans, low leaf N concentrations and photosynthetic capacity, and large seeds
Results
How will communities change?
One is right, the other wrongExperimental artifactsUnmeasured covariates
The different responses may reflect a real, two-phased response to climate change
Discussion
Why the mismatch?
Response to climate change may occur over distinct phasesWhy two phases?Why might the responses in each phase differ?What determines the time scale of the phases?
Discussion
A two-phase model
Discussion
Two phases
Premise – Abundance changes happen more quickly than species gain and lossPhase 1 – Changes in local species abundancePhase 2 – Changes in species pool
Calculating plot trait values not weighted by abundance revealed fewer treatment effectsAbundance shifts were critical in experiments
Discussion
Two phases
Phase 1 – Abundance changes
Phase 2 – Species pool changes
Increased water
Time
Discussion
Phase differences
Why might the trait responses differ in the two phases? Changing interactions among
species Shifts in the limiting resource
The traits of local species that increase are not the same as those of immigrating species
Discussion
Phase differences
Increased water
Time
Increasing species are able to take advantage of increased
resource availability (tall, high leaf N, short-lived
leaves, small seeds)
Taller stature - light limitation
Species must cope with low light environment
(woody, low leaf N, long-lived leaves, large
seeds)
Discussion
Time scales Little evidence for phase 2 in the experiments
No convergence through time towards observational results No treatment affect on species-time relationships
JRG KNZ
SEV
What determines the length of phase 1?Spatial extent of climate changeLife histories of local species (annual/perennial)
At least decades in this caseLengthened by experimental limitations
Discussion
Time scales
Traits useful predictors Mismatch between experimental and observational
results Could be due to different time scales captured by
these two types of study
Use the appropriate data to predict for a given time scale
Discussion
Main messages
NCEAS, and the coordinators and participants in the distributed graduate seminar
William Lauenroth Alan Knapp William Pockman Erika Zavaleta Funding –
NSF grant to NCEAS (EF-0553768) UC Santa Barbara LTER network office for cross-site research NSF LTER program (DEB0218210, BSR 88-11906, DEB9411976, DEB0080529,
DEB0217774, DEB0217631) David and Lucile Packard Foundation Morgan Family Foundation Jasper Ridge Biological Preserve
The many VegBank contributors Ian Wright and Peter Reich (Glopnet)
Acknowledgments
Discussion
A two-phase model
