detection and attribution of long-term vegetaton i changes ... · stats: abiotic factors over time...
TRANSCRIPT
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Detection and Attribution of Long-Term Vegetation Changes
in Northern Alaska
Thesis Defense – Biology Master’s Candidate Rob Barrett Grand Valley State University, Biology Department
Committee members:Bob Hollister (Chair), Jim Dunn, Gary Greer
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Presentation Outline• Ch I: Introduction• Ch II: Responses to long-term warming• Ch III: Ambient change over time• CH IV: Conclusions
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Ch I: Introduction
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NCADAC v. 11 Jan 2013
Sea Ice (end of the summer)
Observed change
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Greenness (NDVI) increasing (Woods Hole, MA)
The Arctic is becoming “greener”Observed change
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Global average air temp. is increasing Variability is also higher
Observed change
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Main reason for polar amplification
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Reduced albedo (reflectiveness) will amplify warming
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Trophic mis match
Post & Forchhammer (2008)
Observed change
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Vegetation changes will have local and world-wide effects on herbivores
Post & Forchhammer (2008)
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Vegetation changes will have local and world-wide effects on herbivores
Post & Forchhammer (2008)
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Understanding how Arctic plants respond to climate change is critical
Cooler Warmer
CarbonRelease
Microbial Decomposition
CarbonRelease
Carbon BudgetTrophic
InteractionsEnergy Balance
Why Plants are important
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Arctic plants play critical roles in regulating global processes
Earlier growth & flowering shift in herbivory altered community
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Arctic plants play critical roles in regulating global processes
Less snow more energy trapped
greater warming & faster melting
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The International Tundra EXperiment (ITEX) has played a key role in understanding plant responses to warming by using…
• Standardized protocols• Simple & effective exp.
design• Collaborative data analysis• Variety of backgrounds &
experience• Long-term datasets• Variety of variables• Variety of plants• Variety of locations
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The study
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ARCTIC & ALPINE TUNDRA
PLANTS ANIMALSMICROBES FUNGI
GRASSES, HERBS & SHRUBSMOSSES & LIVERWORTSLICHENS
ABIOTIC FACTORS
GROWTH & REPRODUCTIONABUNDANCE
BIOTIC FACTORS
Short-term (3-5 yrs)
The GVSU AEP & other ITEX members have played key roles in studying short-term tundra
plant responses to experimental warming
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NORTHERNBOREAL FOREST
CLIMATE CHANGE & TERRESTRIAL BIOMES
ARCTIC & ALPINE TUNDRA
PLANTS ANIMALSMICROBES FUNGI
GRASSES, HERBS & SHRUBSMOSSES & LIVERWORTSLICHENS
ABIOTIC FACTORS
GROWTH & REPRODUCTIONABUNDANCE
BIOTIC FACTORS
Short-term (3-5 yrs)
Long-term (15-20 yrs)
Problem: we don’t know how plants will respond to long-term warming nor how to best predict their responses
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Study SitesBarrow
71°18’N, 156°40’WAtqasuk70°29’N, 157°25’W
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Hollister MS Thesis
Open Top Chambers (OTC’s) effectively warm by ~2°C
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Experimental Design
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Abiotic Factors
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Abiotic Factors
Temps.2 m
0 cm (ground)10 cm
-10 cm
Collected & averaged every hour
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1
2
Tem
p (°
C)
0
Day1 2 3 4 5 6 7
1 2 2 2 1 00 + + + + ++ = 8 TDD
1
2
Tem
p (°
C)
0
Day1 2 3 4 5 6 7
0 1 1 1 0 00 + + + + ++ = 3 GDD1°C
Better predictor if plant’s minimal growing temp is 0 °C
Better if it’s minimum is 1 °C
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Abiotic Factors
Temps.2 m
0 cm (ground)10 cm
-10 cm
Collected & averaged every hour
Collected & averaged every day
Soil Moisture.
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Thaw depth
• Measured once at end of season
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Snow-free date
• Recorded date for each plot
• Used correlation with ground temp. when not directly observed
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Plant Traits
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Inflorescence Height
• 3-6 individuals per plot
• Tallest height by end of season
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Leaf Length
• 3-6 individuals per plot
• Max length by end of season
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Flower number
• Max. per plotper season
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Flowering phenology
• Earliest inf. or flower burst per plot
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Atqasuk Dry Site Atqasuk Wet Site Barrow Dry Site Barrow Wet Site
Forb Polygonum bistorta Pedicularis sudetica Papaver hultenii Cardamine pratensis
Potentilla hyparctica Draba lactaea
Senecio atropurpureus Saxifraga cernua
Stellaria laeta Saxifraga foliolosa
Saxifraga punctata Saxifraga hieracifolia
Saxifraga hirculis
Stellaria laeta
Gra
min
oid Carex bigelowii Carex aquatilis Arctagrostis latifolia Carex stans
Hierachloe alpina Eriophorum angustifolium Luzula arctica Dupontia fisheri
Luzula arctica Eriophorum russeolum Luzula confusa Eriophorum triste
Luzula confusa Poa arctica Heirachloe pauciflora
Trisetum spicatum Juncus biglumis
Luzula arctica
Luzula confusa
Poa arctica
E. s
hrub Cassiope tetragona Cassiope tetragona
Diapensia lapponica
Ledum palustre
Vaccinium vitis-idaea
D. S
hrub Salix rotundifolia ♀
Salix rotundifolia ♂
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Chapter II:
1. How do arctic plants respond to long-term warming?
2. How consistent are these responses over time?
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Statistical Methods• Warming effect on plants
– Used meta-analysis to calculate effect size of warming for each species (Hedges’ d)
– Examined trends in effect sizes using weighted linear regressions (MetaWin)
• Temperature trends at sites– Used simple linear regressions to look for
temperature trends over time (Program R)
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Temperature shows NS trends toward warming at all 4 sites
Year
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
TDD
100
200
300
400
500
600
700
800
900
Atqasuk Dry
Barrow Dry
Atqasuk Dry
Barrow Dry
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Year
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
TDD
100
200
300
400
500
600
700
800
900
Used data subset: years with all plant traits AND all AF’s of interest
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Statistical Methods• Warming effect on plants
– Used meta-analysis to calculate effect size of warming for each species (Hedges’ d)
– Examined trends in effect sizes using weighted linear regressions (MetaWin)
• Temperature trends at sites– Used simple linear regressions to look for
temperature trends over time (Program R)
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Flow
er b
urst
dat
e
230
220
210
200
190
180
170
Control (Mean & SE)Warmed (Mean & SE)
Meta-analysis: Calculating effect sizes
All species
Effe
ct s
ize
of w
arm
ing
on fl
ower
bur
st d
ate
2
0
-2
-4
-6
-8
Barrow Dry site 1994
d = ((XE -XC)/S)J
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1. How do arctic plants respond to long-term warming?
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1994 1997 2000 2003 2006 2009 2012-2
-1
0
1
2
1994 1997 2000 2003 2006 2009 2012-2
-1
0
1
2
1994 1997 2000 2003 2006 2009 2012-15
-10
-5
0
5
10
1994 1997 2000 2003 2006 2009 2012-2
-1
0
1
2
3
4(a) (c)
(d)(b)
Inflorescence height Flowering date
Reproductive effortLeaf length
y = 0.10x - 197
= significant weighted linear regression = non-significant weighted linear regression
Warming responses over time (17-19 years warming)
Effe
ct s
ize
(Hed
ges’
d)
Effe
ct s
ize
(Hed
ges’
d)
Effe
ct s
ize
(Hed
ges’
d)
Effe
ct s
ize
(Hed
ges’
d)
NS trend: reduced ES over time
Increased inflorescence heights (24/37 species)
ES = 0.89
NS trend: reduced ES over time
Increased leaf lengths (19/36 species)
ES = 0.39
Earlier flowering dates (13/35 species)
ES = 0.39
Sig Trend: reduced ES over time
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Warming ES decrease in warmer conditions (all traits)Ef
fect
siz
e (H
edge
s’ d)
Effe
ct s
ize
(Hed
ges’
d)
Effe
ct s
ize
(Hed
ges’
d)
Effe
ct s
ize
(Hed
ges’
d)
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Ch III - Study Questions:
1. How have abiotic factors and plant traits changed over time at these sites?
2. Is there evidence that shifts in abiotic factors could be driving changes in plant traits?
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Stats: Abiotic factors over time
• Simple linear regressions• Program R
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Abiotic factors consistently showed non-significant patterns across sitesOnly significant trend was toward deeper thaw at AD Site
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Abiotic factors consistently showed non-significant patterns across sitesOnly significant trend was toward deeper thaw at AD Site
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Few significant trends in plant traits over time
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Few significant trends in plant traits over time
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Few significant trends in plant traits over time
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• Stats • Transition of one graph poa arctica to next
figure
Is there evidence that shifts in abiotic factors could be driving changes in plant traits?
Specifically we ask what abiotic factor is most correlated with a given plant trait
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Stats: Plant traits over time
• Linear Mixed Models (LMM’s)– Fixed effects: year– Random effects: year, plot
• Significance of results– Chi-squared likelihood ratio test with & without
fixed effect
• Program R– lme4 package
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Stats: Relationship between abiotic factors and plant traits
• Linear Mixed Models (LMM’s)– Fixed effects: year– Random effects: year, plot
• Significance of results– Chi-squared likelihood ratio test with & without fixed effect– Benjamini-Hochberg procedure (false discovery rate at 5%)
• Program R– lme4 package
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Air and Soil temps highly predictive
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Air and Soil temps highly predictive
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Conditions during year prior to plant trait measure improve reproductive effort predictions
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• Conditions during year prior to plant trait measure improve reproductive effort predictions
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Conditions during year prior to plant trait measure improve reproductive effort predictions
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Air and Soil temps highly predictive
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Because of the interest in changing summer air temperatures
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Trait & Site SpeciesEffect size
(Hedges' d)LMM
Marginal R2
Inflorescence heightBarrow Dry
Luzula confusa 1.00 0.15Poa arctica 1.33 0.29Potentilla hyparctica 1.65 0.30
Reproductive effortBarrow Dry
Cassiope tetragona 0.84 0.53*Poa arctica 0.37 0.25*
Reproductive phenologyBarrow Dry
Cassiope tetragona -6.30 0.55Luzula confusa -2.39 0.46Papaver hultenii -5.62 0.58Poa arctica -2.32 0.60Potentilla hyparctica -7.55 0.44
Barrow WetLuzula arctica -2.17 0.45
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CH IV: Overall ConclusionsIn Summary:
– As Arctic continues to warm, tundra plants will likely• Grow taller inflorescences• Grow longer leaves• Flower earlier in the years
– Long-term records of air temps and other AF’s will be highly useful in predicting plant responses
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How will species’ responses to climate change affect the systems they influence?
Cooler Warmer
CarbonRelease
Microbial Decomposition
CarbonRelease
Carbon BudgetTrophic
InteractionsEnergy Balance
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Acknowledgements• Special thanks to Bob Hollister for all his
guidance, training, support, and patience• Thank you to the GVSU AEP, especially…
– Bob Hollister, Tim Botting, Kelsey Wright, Jeremy May, Jenny Liebig, and Sarah Elmendorf!
• Thank you to WMAES!
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Phenology (timing) of life events are shifting
Otso Ovaskainen et al. PNAS 2013;110:13434-13439©2013 by National Academy of Sciences
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Permafrost thaw has many negative implications
Thaw slumping habitat disturbance
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The Arctic is particularly vulnerable to climate change
1960-2011, NASA
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The Arctic growing season is getting longer
Chen et al (2015)
Snow
Ons
etSn
ow E
ndSn
ow D
urat
ion
Farther North Later Snow Arrival
Farther North Earlier Snow End
Farther North Shorter Snow Duration
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Shrubs and treesmoving northward
Sturm et al (2001)
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Shrubs and trees moving northward
I. H. Myers-Smith et al., AMBIO40 (2011)
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Permafrost is thawing
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Permafrost thaw has many negative implications
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Permafrost thaw has many negative implications
(V. Romanovsky)
Property damage from erosion
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UTEP, 2015
Permafrost thaw has many negative implications
Thaw lakes habitat disturbance
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Permafrost thaw has many negative implications
Methane (Greenhouse gas)
release
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Changes in the Arctic will have global repercussions
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Changes in the Arctic will have global repercussions
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Permafrost thaw will create massive amounts of further warming
Image: National Science Foundation
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Arctic plants play critical roles in regulating global processes
Image: Zina Deretsky, NSF
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Vegetation changes will have local and world-wide effects on herbivores
Arctic National Wildlife RefugeUS Fish & Wildlife Service
Golden plover 11
Andy Johnson, 2011
Tundra swan 12
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Permafrost thaw will have many negative implications
“Drunken forests” habitat disturbance
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1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
BD Site X X X X XBW Site X X X X X
AD Site X X X X XAW Site X X X X X
Study Sites & Data Collection
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NOAA
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Arctic plant responses to warming are highly variable
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Generally: warmed tundra plants grow longer leaves, taller flowers, flower earlier, and make more flowers
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Other problems: Earlier studies were shorter in length & examined relatively
warmer regions
Brete-Harte et al (2002)
USGS (2013)
Toolik Lake Field StationChapin & Shaver (1985)
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Barrow temps
Source: Alaska Climate Research Center
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Snowmelt date
NOAA ESRL GMD Barrow Observatory (BRW) RS STone
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Snowmelt date
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Thaw depth
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Fall temps