Detection and Attribution of Long-Term Vegetaton i Changes ... · Stats: Abiotic factors over time...

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

Presentation Outline• Ch I: Introduction• Ch II: Responses to long-term warming• Ch III: Ambient change over time• CH IV: Conclusions

Ch I: Introduction

NCADAC v. 11 Jan 2013

Sea Ice (end of the summer)

Observed change

Greenness (NDVI) increasing (Woods Hole, MA)

The Arctic is becoming “greener”Observed change

Presenter

Presentation Notes

http://www.amnh.org/explore/science-bulletins/bio/visualizations/greening-of-the-arctic/dataset-information Summer greenness, 1982 – 2010 Satellite images of global vegetation density are available since 1982, allowing scientists to understand where and how the productivity of Earth’s vegetation has changed in the last 30 years. This pair of images shows the greenness of vegetation during the periods 1982 to 1986 and 2006 to 2010 compared to the full satellite record period from 1982 to 2010. Like the first dataset in the visualization, these images are based on the Normalized Difference Vegetation Index. At high northern latitudes, vegetation has predominantly become greener (more productive) over time, with productivity being relatively low in 1982 – 2006 (browner colors) and higher in recent years (2006 – 2010, greener colors). These images were produced for this data visualization by Scott Goetz, Pieter Beck, and Kevin Guay at the Woods Hole Research Center. How NDVI Works http://earthobservatory.nasa.gov/Features/MeasuringVegetation/measuring_vegetation_2.php NDVI is calculated from the visible and near-infrared light reflected by vegetation. Healthy vegetation (left) absorbs most of the visible light that hits it, and reflects a large portion of the near-infrared light. Unhealthy or sparse vegetation (right) reflects more visible light and less near-infrared light. The numbers on the figure above are representative of actual values, but real vegetation is much more varied. (Illustration by Robert Simmon).

Global average air temp. is increasing Variability is also higher

Observed change

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What’s the timescale here? Add that this is confirmed through multiple means (records, models, tree rings, sediments, ice cores, pollen samples, etc.)

Main reason for polar amplification

Reduced albedo (reflectiveness) will amplify warming

Trophic mis match

Post & Forchhammer (2008)

Observed change

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Post & Forchhammer 2008

Vegetation changes will have local and world-wide effects on herbivores


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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

Presenter

Presentation Notes

Growth Change size of plants -> Carbon & Energy Balance Change Duration & Timing of Growing Season Change Available Heat Energy Change size of plants -> Community Structure Alter Outcomes of Competition Reproduction Change in timing & quality -> Trophic impacts Change Duration & Timing of Flowering Change Available Heat Energy Change in reproduction -> Community Structure

Arctic plants play critical roles in regulating global processes

Earlier growth & flowering shift in herbivory altered community


Less snow more energy trapped

greater warming & faster melting

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

Presenter

Presentation Notes

Tower: C:\0 Pictures\2008 AK Pics\Barrow Dry Chamber: C:\0 Pictures\2008 AK Pics\Individual Plots ITEX Map http://rstb.royalsocietypublishing.org/content/368/1624/20120481

The study

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

Presenter

Presentation Notes

ITEX Map http://rstb.royalsocietypublishing.org/content/368/1624/20120481

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

Presenter

Presentation Notes

ITEX Map http://rstb.royalsocietypublishing.org/content/368/1624/20120481

Study SitesBarrow

71°18’N, 156°40’WAtqasuk70°29’N, 157°25’W

Hollister MS Thesis

Open Top Chambers (OTC’s) effectively warm by ~2°C

Experimental Design

Abiotic Factors

Abiotic Factors

Temps.2 m

0 cm (ground)10 cm

-10 cm

Collected & averaged every hour

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

Abiotic Factors

Temps.2 m

0 cm (ground)10 cm

-10 cm

Collected & averaged every hour

Collected & averaged every day

Soil Moisture.

Thaw depth

• Measured once at end of season

Snow-free date

• Recorded date for each plot

• Used correlation with ground temp. when not directly observed

Plant Traits

Inflorescence Height

• 3-6 individuals per plot

• Tallest height by end of season

Leaf Length

• 3-6 individuals per plot

• Max length by end of season

Flower number

• Max. per plotper season

Flowering phenology

• Earliest inf. or flower burst per plot

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 ♂

Presenter

Presentation Notes

C:\0 Pictures\2008 AK Pics\Plants

Chapter II:

1. How do arctic plants respond to long-term warming?

2. How consistent are these responses over time?

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)

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

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

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)

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

1. How do arctic plants respond to long-term warming?

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

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)

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?

Stats: Abiotic factors over time

• Simple linear regressions• Program R

Abiotic factors consistently showed non-significant patterns across sitesOnly significant trend was toward deeper thaw at AD Site

Abiotic factors consistently showed non-significant patterns across sitesOnly significant trend was toward deeper thaw at AD Site

Few significant trends in plant traits over time



• 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

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

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

Air and Soil temps highly predictive


Conditions during year prior to plant trait measure improve reproductive effort predictions

• Conditions during year prior to plant trait measure improve reproductive effort predictions

Conditions during year prior to plant trait measure improve reproductive effort predictions


Because of the interest in changing summer air temperatures

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

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

How will species’ responses to climate change affect the systems they influence?

Cooler Warmer

CarbonRelease

Microbial Decomposition

CarbonRelease

Carbon BudgetTrophic

InteractionsEnergy Balance

Presenter

Presentation Notes

Growth Change size of plants -> Carbon & Energy Balance Change Duration & Timing of Growing Season Change Available Heat Energy Change size of plants -> Community Structure Alter Outcomes of Competition Reproduction Change in timing & quality -> Trophic impacts Change Duration & Timing of Flowering Change Available Heat Energy Change in reproduction -> Community Structure

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!

Phenology (timing) of life events are shifting

Otso Ovaskainen et al. PNAS 2013;110:13434-13439©2013 by National Academy of Sciences

Presenter

Presentation Notes

http://www.pnas.org/content/110/33/13434.figures-only Patterns of climatic and phenological shift and variance. (A) Phenological shifts for the first occurrence of the common lizard (Zootoca vivipara; cyan), the start of the display flight of the Eurasian woodcock (Scolopax rusticola; blue), and the climatic event of daily average temperature moving above 0 °C (black). The lizard, bird, and temperature events have shifted at rates of −0.36, −0.39, and −0.19 (day/year), and their phenological variances are 7.32, 5.82, and 13.92 (day2), respectively. (B–E) Dots depict shifts (day/year) in plant phenology (B); bird phenology (C); insect, fungal, and herptile phenology (D); and climatic events (E). (F) Distributions of residual variances. The lines in B, C, and E show linear regression models through the part of the year with the most data (spring for bird and plant events and winter for weather events). The circles indicate shifts greater than 0.5 for which the location of the dot has been truncated in the figure. Significant shifts (P < 0.05; 61/213 phenological events and 15/77 climatic events) are indicated with a white center. The thick gray lines depict the pace of climate change from the point of view of average temperature (Fig. 1). (Left) Color key of the different taxonomical and climatic groups. Nature Reserve “Kivach” in Russian Karelia The study was performed in Nature Reserve “Kivach” in Russian Karelia (62° 17' N, 33° 55' E). The reserve was established in 1931 and has a total area of 10,900 ha, consisting of boreal forest, middle taiga subzone. During the period 1960–2010, the permanent research staff of the reserve conducted daily observations to record the dates at which a predefined list of weather-related and phenological events took place (most data types start around 1970; Dataset S6). The plant data are acquired along established routes, and the bird data are acquired at fixed observatories near bird settlement areas. The main deviation from constant sampling effort is that in 1960–1975, 1987–1988, and 1991–1993, only a single ornithologist worked in the reserve, whereas during the other periods there were two to three ornithologists. The data include 77 weather-related events, which we classified into five groups: temperature (30 events; e.g., temperature crossing a certain threshold value), snow (21 events; e.g., the first winter snowfall), ice (9 events; e.g., the first winter day with ice on ponds), frost (13 events; e.g., first day with frost after the summer), and atmospheric phenomena (4 events; e.g., the first fog of the year). The phenological events were classified into plants (97 events on 66 species), birds (78 events on 52 species, which we classified into three groups on the basis of their migration distance; Dataset S6), insects (including ticks, 19 events on 17 species), herptiles (amphibians and reptiles, 10 events on 3 species), and fungi (9 events on 6 species). The majority of the phenological events in these data are highly dependent on external conditions, as they relate, for example, to plant flowering, phenology of poikilothermic animals, and birds’ spring arrival dates (Dataset S6). After removing redundant event types (i.e., those essentially based on the same observation, see Dataset S6 for the list of included and excluded events and Dataset S7 for the raw data), the data consist of 10,425 dates representing 77 weather-related events and 213 phenological events.

Permafrost thaw has many negative implications

Thaw slumping habitat disturbance

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Presentation Notes

http://news.uaf.edu/lecture-to-highlight-the-effects-of-thawing-permafrost/

The Arctic is particularly vulnerable to climate change

1960-2011, NASA

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Presentation Notes

https://nsidc.org/cryosphere/arctic-meteorology/climate_change.html This image shows trends in mean surface air temperature over the period 1960 to 2011. Notice that the Arctic is red, indicating that the trend over this 50 year period is for an increase in air temperature of more that 2° C (3.6° F) across much of the Arctic, which is larger than for other parts of the globe. The inset shows linear trends over the period by latitude.�—Credit: NASA GISS

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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MODIS Satellite data in northern hemisphere http://www.nature.com/articles/srep16820/figures/1 Changes of snow onset date Do (a,d), snow end date De (b,e) and snow duration days Dd (c,f) over the NH snow covered landmass, over 14 years. Changes are derived from the linear slope multiplied by the time span. Black dots in (a–c) indicate that the changes are significant at 90% CL. The zonal distribution in (d–f) are mapped at a 0.5 degree resolution in latitude. The error bars in (d–f) are calculated using equation (1) in Methods. The figure was created using ArcGIS42.

Shrubs and treesmoving northward

Sturm et al (2001)

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http://www.nature.com/nature/journal/v411/n6837/fig_tab/411546a0_F1.html Sturm, Racine, Tape 2001 FIGURE 1. The Ayiyak River (N68° 53', W152° 31'), showing an increase in the density of shrub patches, the growth of individual shrubs and an expansion of shrubs into areas that were previously shrub-free.

Shrubs and trees moving northward

I. H. Myers-Smith et al., AMBIO40 (2011)

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Presentation Notes

http://www.sciencemag.org/site/multimedia/slideshows/341.6145.482/index.xhtml

Permafrost is thawing

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http:///



(V. Romanovsky)

Property damage from erosion

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http://www.lgt.lt/geoin/docs/frozen/frozen1.htm

UTEP, 2015


Thaw lakes habitat disturbance

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http://www.sciencedaily.com/releases/2015/03/150312123604.htm


Methane (Greenhouse gas)

release

Changes in the Arctic will have global repercussions

Changes in the Arctic will have global repercussions

Permafrost thaw will create massive amounts of further warming

Image: National Science Foundation

Presenter

Presentation Notes

http://wattsupwiththat.com/2010/10/30/might-arctic-warming-lead-to-catastrophic-methane-releases/


Image: Zina Deretsky, NSF

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Presentation Notes

http://suvratk.blogspot.com/2009/11/arctic-carbon-sink-or-source.html


Arctic National Wildlife RefugeUS Fish & Wildlife Service

Golden plover 11

Andy Johnson, 2011

Tundra swan 12

Presenter

Presentation Notes

http://www.fws.gov/refuge/arctic/birdworldmig.html

Permafrost thaw will have many negative implications

“Drunken forests” habitat disturbance

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Presentation Notes

arctic.blogs.panda.org/default/the-tipping-point

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

NOAA

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Presentation Notes

https://www.ncdc.noaa.gov/indicators/ Global average temperature is one of the most-cited indicators of global climate change, and shows an increase of approximately 1.4°F since the early 20th Century. The global surface temperature is based on air temperature data over land and sea-surface temperatures observed from ships, buoys and satellites. There is a clear long-term global warming trend, while each individual year does not always show a temperature increase relative to the previous year, and some years show greater changes than others. These year-to-year fluctuations in temperature are due to natural processes, such as the effects of El Ninos, La Ninas, and the eruption of large volcanoes. Notably, the 20 warmest years have all occurred since 1981, and the 10 warmest have all occurred in the past 12 years.

Arctic plant responses to warming are highly variable

Presenter

Presentation Notes

C:\Users\Rob Slider\Desktop\Arctic Photos\Wk 3 J200-206 “Diverse Plot ATK (10)”

Generally: warmed tundra plants grow longer leaves, taller flowers, flower earlier, and make more flowers

Presenter

Presentation Notes

C:\0 Pictures\2008 AK Pics\Individual Plots “rts (144)”

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)

Presenter

Presentation Notes

Primary and secondary stem growth in arctic shrubs: AK Map http://pubs.usgs.gov/fs/2013/3054/ Warming plots pic; : implications for community response to environmental Change (2002) M. SYNDONIA BRET-HARTE, GAIUS R. SHAVER† and F. STUART CHAPIN III

Barrow temps

Source: Alaska Climate Research Center

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Presentation Notes

https://toolkit.climate.gov/image/1151

http://climate.gi.alaska.edu/Trends/PABR.png

Snowmelt date

NOAA ESRL GMD Barrow Observatory (BRW) RS STone

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Presentation Notes

http://www.esrl.noaa.gov/gmd/grad/snomelt.html

Snowmelt date

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Presentation Notes

http://www.esrl.noaa.gov/gmd/grad/snomelt.html

Thaw depth

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http://globalcryospherewatch.org/assessments/permafrost/

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http://ak-wx.blogspot.com/2013_08_01_archive.html

Fall temps

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Presentation Notes

https://www.climate.gov/news-features/understanding-climate/barrow-alaska-climate-change-action

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Detection and Attribution of Long-Term Vegetaton i Changes ... · Stats: Abiotic factors over time...

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