arctic plant migration by 2100: comparing predictions with observations

ConclusionResultsMethodsModel DataStudy DataIntroduction

Arctic Plant Migration by 2100:Comparing Predictions with Observations

M.S. in Ecology Thesis Defense

Dahl Winters

Advisor: Aaron Moody

November 16, 2008


Presentation Overview

Introduction – importance of Arctic plant migration, the Arctic Climate Impact Assessment, and the goals of this research

Study and Model Data – examples of recent Arctic vegetation changes, and predictions of future change

Methods – how the study data will be used to validate a key Arctic vegetation model

Results – maps of study findings, model validation, and assessment of errors

Conclusion – wrapping up


Introduction

Why the Arctic Region and its Vegetation Matter• Arctic defined as the region above 55°N latitude• Floristically simple, but 30-35% of terrestrial carbon is stored in

its boreal forests, primarily in the soil• Region warming the fastest due to climate change – average of

0.6-1.0°C this century • Warming there can accelerate warming everywhere due to

- albedo feedback of forest invasion (more heat absorption) and- increased soil microbial activity (more CO2 release)


Introduction

Plant Migration and Arctic Vegetation Change• Not unique to the present; extended periods of Eocene

volcanism allowed forests to grow across much of today’s Arctic tundra regions.

• Of concern is the present rate of CO2 input – faster than anyon record. Temperature expected to increase 1.5-6°C by 2100.

• Plants are expected to shift their ranges northward and upward in response to warmer temperatures.

• Potential for great impacts on both carbon storage and climate change.


Introduction

Important Questions

• How well do we understand Arctic plant migration?

• Can major Arctic research groups produce a model of future vegetation distributions that accurately reflects recent trends of change?

• How accurate might such a model be, and what might be some limitationson its accuracy?


Introduction

The Arctic Climate Impact Assessment (ACIA)• a 1024-page document produced in 2004 by two groups:• Arctic Council - a high-level intergovernmental forum (Canada,

Denmark, Finland, Iceland, Norway, the Russian Federation, Sweden, and the US)

• International Arctic Science Committee (IASC) - a non-governmental organization that facilitates cooperation in all research aspects throughout the Arctic.

• Project Goal - to evaluate and synthesize knowledge on climate variability, climate change, increased ultraviolet radiation, and their consequences.

http://www.acia.uaf.edu/pages/scientific.html


Research Objective

Research Objective • One component of ACIA was to predict vegetation distributions by

2100. They used 2 dynamic vegetation models, LPJ and BIOME4. • Objective is to validate one of the two ACIA models using

actual observations from the Arctic region. • Chose BIOME4 by Kaplan et al. (2003) over LPJ due to finer

resolution of biome types.


The Model Validation Process

Find study observations

Determine what change trends they indicate• + if they show vegetation change expected due to warming

temperatures, - if opposite, 0 if no change or not observed. • A necessary assumption is that the trend will continue into 2100.

Determine what the model predicts• Compare present-day with predicted vegetation to determine

what biome transitions are predicted.

Assess data-model matches• Map matches• Discuss sources of error


Study Data

Finding Observations for Model Validation

Restricted search to:• articles reporting plant migration or other climate-driven

vegetation changes in the Arctic region (> 55°N latitude) • studies where significant grazing was not noted, to focus only on

climate-induced vegetation changes.• Found 35 studies within the Arctic; an additional 15 studies were

found below 55°N latitude.


Study Data

Examples: Masek (2001) • little support for latitudinal migration, but coarse spatial resolution.


Study Data

Examples: Sturm et al. (2001)• this and other

aerial photograph studies provide greater spatial resolution over longer time scales.

Aerial photographs of the Ayiyak River in the Alaskan Arctic (N68°53’ W152°31’) showing an increase in shrub patch density, individual shrub growth, and shrub expansion into previously shrub-free areas. A and B denote the same locations in the two photographs. From Sturm et

al. (2001).


Study Data

Common Threads Among Study Observations• Dominant tree and shrub species are experiencing increases in

growth and new establishment throughout the Arctic. • Spatial and temporal resolution makes a difference• Migration <- water availability + connectivity + landscape• Periods of peak establishment <- water availability• Growth reductions <- drought stress or local cooling• Support for the plant migration process


Study Data

The Process of Plant Migration• Warming temperatures induce increased plant growth.• If site conditions are favorable, stand densities will increase over

time.• If conditions are favorable beyond a species’ current range, new

establishment will occur there = altitudinal or latitudinal migration.

• If conditions fail to remain favorable,growth and establishment will haltand dieback may occur.


Study Data

The Process of Plant Migration

Time

Space(alt or lat gradient)

Closed Forest Limit

Open Forest Limit

Treeline

Tree Growth

Increases

Tundra

Stand Density

IncreasesMigration


Model Data

BIOME4 Model Data for Validation• Present-day and 2100 prediction maps from Kaplan et al. (2003)

• Both maps at 0.5° resolution

Present-day vegetation Predicted 2100 vegetation


Methods

GIS Framework for Precise Data-Model Comparison• model prediction map georeferenced• study data imported when geocoordinates available• polygons digitized – introduces error• allows for easy comparison of model predictions and study

observations.

Only 30 of the 35 studies • had sufficient information to be represented

in GIS.• however, yielded 94 total study sites

suitable for model validation.


Methods

Scoring Study Sites for Change Trends• All study sites were scored in each of 4 categories: alt/lat

migration, tree growth, and stand density/abundance.• Each category was assigned a +1 for change expected due to

warming temperatures, -1 if change is opposite that of expected, and 0 if no change or not observed.

• Nested data structure: 12 of the 30 studies had multiple findings for multiple study sites; othershad single findings for one or more study sites.


Methods

Measurement Uncertainties of Study Sites• Of the 94 sites, all could be mapped as points except for 15

requiring polygons. • Points with geocoordinates provided had no introduced error;

polygons had up to 0.5 degree error due to vertex estimation.• Since the BIOME4 model output is limited by its climate model to

0.5-degree resolution, polygon error is expected to be a problem near biome boundaries and areas of hightopographic relief.


Methods

Comparing Study Trends with Model Predictions• Entered all study points and polygons into GIS• For each site, the sign of the reported change (+1 if expected

from warming temperatures) was recorded in the shapefile attribute table.

• Also recorded was the sign of the predicted change from BIOME4, determined by comparing the model output map to the present-day vegetation map.

• Agreement: determined by assessingwhether the data and model matched.

Data (Overall change) 1 1 1 0 0 0 -1 -1 -1

Model Prediction 1 0 -1 1 0 -1 1 0 -1

Match 1 0 0 0 1 0 0 0 1


Results

Study locationson present-day vegetationmap

yellow = points

red boundaries = polygons


Results

Study locationson modelprediction mapfor 2100

yellow = points

red boundaries = polygons


Results

Altitudinal Migration

green = treeline recession

yellow = none observed

red = treeline advance


Results

Latitudinal or Horizontal Migration

green = treeline recession


red = new establishment


Results

Tree Growth• form change

• height growth

• radial growth

• canopy cover

green = growth reduction or dieback


red = increased growth or increased stem initiation


Results

Stand Density and Abundance

green = dieback or loss of individuals


red = infilling of new individuals in existing stands (no extension of the range limit)


Results

Overall Change

green = negative trend; transition to a cooler biome expected

yellow = zero trend

red = positive trend; transition to a warmer biome expected


Results

Model Predictions

green = transition to a cooler biome predicted

yellow = no transition

red = transition to a warmer biome expected


Results

Validation Results by Study Site

BIOME4 was validated at 68 of the 94 total study sites (72.3%), and invalidated at 27 sites (28.7%).

yellow = no validation

red = validation


Results

Validation Results over All Sites• Binary match variable (1 = match, 0 = no match)• Study sites are nested within their respective studies

A generalized linear mixed effects model:• accounts for the nested data structure• will give a probability that all study sites will validate the

model (not all sites will validate it)• provides a measure of how well BIOME4

has performed for all 30 studies.


Results

Mixed Effects Model Resultsm1<-lmer(Match~1+(1|Study), dat, family="binomial")

Estimate Std. Error z value Pr(>|z|)

(Intercept) 1.0306 0.2875 3.585 0.000337 ***

• p = e^1.0306 / (1+e^1.0306)• Confidence intervals using +/- standard

error before solving for p:

p = 0.737(+0.0519 and -0.0593).


Results

Summary of Validation Results

Over All Sites: 73.7% (+5.19%/-5.93%)

Map Match No Match

Present Day 1.647059 1.629630

2100 1.352941 1.629630

Present+2100 3.000000 3.259259

94 study sites

26/94 (27.7%) invalidated model

68/94 (72.3%) validated model

4/26 (15.4%) polygons (0.5°

error)

11/68 (16.2%) polygons (0.5°

error)

23/26 (88.5%) >1 biome type

within 0.5°)

43/68 (63.2%) >1 biome type

within 0.5°)

Mean Number of Biome Types within 0.5°

Sites that invalidated the model had a higher mean number of biome types.


Results

Resolution – A Potential Cause of Mismatches

Numbers indicate # of biome types within 0.5° of each study site for both the present-day and 2100 prediction maps.

• Biome boundaries• Topographically complex areas


Results

Recap: Sites Where the Model Was Validated

yellow = no validation

red = validation


Conclusion

Research Objective Met• Objective was to validate a model used in ACIA, to test our

understanding of Arctic plant migration.• Was able to map where the model was validated, though more

than 30 points would have improved the validation.• Errors from geolocating study sites and scoring them for

changes – each study done differently, measuring different species.


Conclusion

Other Possible Reasons for Mismatches

Warming-induced drought stress not accounted for

Time lags - reasons for migration not being fast enough• Site constraints

• Topographic constraints

• Life history characteristics

• Dispersal constraints

Time

Space(alt or lat gradient)

Closed Forest Limit

Open Forest Limit

Treeline

Tree Growth

Increases

Tundra

Stand Density

IncreasesMigration


Conclusion

Directions for Further Research

Improving models of future Arctic vegetation• natural and anthropogenic disturbances (insects, forest

fragmentation – future Arctic resource development) • effects of acid rain, air pollution, and past grazing - migration

could be faster without these.

Understanding the broader impacts of plant migration on• Arctic carbon storage • global climate change


Conclusion

Questions?

Dahl Winters

UNC Ecology

[email protected]

arctic plant migration by 2100: comparing predictions with observations

Documents

arctic region

arctic council

arctic research groups

predicted vegetation

vegetation matterarctic

climate change average

todays arctic tundra

dynamic vegetation models