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Arctic Plant Migration by 2100: Comparing Predictions with Observations. M.S. in Ecology Thesis Defense Dahl Winters Advisor: Aaron Moody November 16, 2008. Introduction – importance of Arctic plant migration, the Arctic Climate Impact Assessment, and the goals of this research

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Arctic plant migration by 2100 comparing predictions with observations

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

Presentation Overview


  • 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)



  • 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.



  • 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?



  • 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.



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.

Research Objective

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

The Model Validation Process

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

Study data1

Study Data

Study data2

  • 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

Study data3

  • 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

Study data4

  • 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

Study data5

Tree Growth Increases

Stand Density Increases





(alt or lat gradient)

Open Forest Limit

Closed Forest Limit


  • The Process of Plant Migration

Study Data

Model data

Present-day vegetation Predicted 2100 vegetation

  • BIOME4 Model Data for Validation

    • Present-day and 2100 prediction maps from Kaplan et al. (2003)

    • Both maps at 0.5° resolution

Model Data


  • GIS Framework for Precise Data-Model Comparison vegetation

    • 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 representedin GIS.

    • however, yielded 94 total study sitessuitable for model validation.



  • Scoring Study Sites for Change Trends vegetation

    • 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.



  • Measurement Uncertainties of Study Sites vegetation

    • 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.



  • Comparing Study Trends with Model Predictions vegetation

    • 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.



  • Study locations vegetationon present-day vegetationmap

  • yellow = points

  • red boundaries = polygons



  • Study locations vegetationon modelprediction mapfor 2100

  • yellow = points

  • red boundaries = polygons



  • Altitudinal Migration vegetation

  • green = treeline recession

  • yellow = none observed

  • red = treeline advance





  • Tree Growth vegetation

    • form change

    • height growth

    • radial growth

    • canopy cover

  • green = growth reduction or dieback

  • yellow = none observed

  • red = increased growth or increased stem initiation



  • Stand Density and Abundance vegetation

  • green = dieback or loss of individuals

  • yellow = none observed

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



  • Overall Change vegetation

  • green = negative trend; transition to a cooler biome expected

  • yellow = zero trend

  • red = positive trend; transition to a warmer biome expected



  • Model Predictions vegetation

  • green = transition to a cooler biome predicted

  • yellow = no transition

  • red = transition to a warmer biome expected



  • Validation Results by Study Site vegetation

  • 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



  • Validation Results over All Sites vegetation

    • 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 BIOME4has performed for all 30 studies.



  • Mixed Effects Model Results vegetation

  • m1<-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).



94 study sites vegetation

68/94 (72.3%) validated model

26/94 (27.7%) invalidated model

43/68 (63.2%) >1 biome type within 0.5°)

11/68 (16.2%) polygons (0.5° error)

23/26 (88.5%) >1 biome type within 0.5°)

4/26 (15.4%) polygons (0.5° error)

  • Summary of Validation Results

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

Mean Number of Biome Types within 0.5°

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



  • Resolution – vegetationA 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





  • Research Objective Met vegetation

    • 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.



Tree Growth Increases vegetation

Stand Density Increases




Space(alt or lat gradient)

Open Forest Limit

Closed Forest Limit


  • 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



  • Directions for Further Research vegetation

  • 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 - migrationcould be faster without these.

  • Understanding the broader impacts of plant migration on

    • Arctic carbon storage

    • global climate change



  • Questions? vegetation

  • Dahl Winters

  • UNC Ecology