Growth response to climatic change over 120 years for Alnus viridis and Salix glauca in West Greenland

Journal of Vegetation Science 26 (2015) 155–165

Growth response to climatic change over 120 years forAlnus viridis and Salix glauca inWest Greenland

Rasmus H. Jørgensen, Martin Hallinger, Svenja Ahlgrimm, Juliane Friemel, JohannesKollmann & Henrik Meilby

Keywords

Annual ring width; Arctic tundra vegetation;

Betula pubescens; Dendrochronology; Global

warming; Growth modelling; Shrub expansion

Received 1 October 2013

Accepted 11 July 2014

Co-ordinating Editor: Richard Michalet

Jørgensen, R.H. (corresponding author,

[email protected]): Department of

Plant and Environmental Sciences, University

of Copenhagen, Rolighedsvej 21, 1958

Frederiksberg C, Denmark

Hallinger, M. ([email protected]):

Department of Ecology, Swedish University of

Agricultural Sciences, Box 7044, 75007,

Uppsala, Sweden

Ahlgrimm, S. ([email protected]) &

Friemel, J. ([email protected]):

Department of Botany and Landscape Ecology,

University of Greifswald, Soldmannstr. 15,

17487 Greifswald, Germany

Kollmann, J. ([email protected]):

Department of Ecology and Ecosystem

Management, Technische Universit€at

M€unchen, Emil-Ramann-Str. 6, 85350 Freising,

Germany

Meilby, H. ([email protected]): Department of

Food and Resource Economics, University of

Copenhagen, Rolighedsvej 23, 1958

Frederiksberg C, Denmark

Abstract

Questions: Which climatic variables are the main determinants of radial

growth and to what extent does their effect on growth vary among species?

What are the similarities between the temporal radial growth patterns of the

two common shrub species, Alnus viridis and Salix glauca? Do changing growth

conditions over the past 120 yr and their predicted impact on growth match

shrub expansion observed in the region?

Location: Arsuk Fjord and Disko Bay regions, WGreenland.

Methods: Alnus viridis and S. glauca specimens were sampled in the field and

radial growth was analysed using standard dendrochronological methods

(‘response functions’). The identified climatic variables were applied to model

radial growth using a linear mixedmodel and predict the growth for 1890–2010.

Results: The main determinants of radial growth were summer temperatures

and, although not significant in the final models, spring precipitation. The

empirical chronologies showed only somewhat similar growth patterns. They

responded to similar sets of climatic variables, but their similarity was weakened

because of the low number of replicates and local differences in growth condi-

tions. The similarity between predicted (modelled) chronologies was higher,

which was related to the response to similar sets of climatic variables and high

correlation between climatic variables across long distances.

Conclusion: Overall, estimated growth did not increase over the past 120 yr,

but considerable variations in growth are conspicuous andmatch known histor-

ical patterns of the Atlantic Multidecadal Oscillation. Perspectives regarding the

observed shrub expansion in W Greenland are discussed, and based on the esti-

mated growth patterns, we consider it unlikely that the recent 10–15-yr period

of favourable climate is the main responsible cause.

Introduction

Over the past five decades shrubs have expanded in

tundra areas in many parts of the Arctic. This is mostly

attributed to higher temperatures in these regions. In W

Greenland only a limited increase in temperatures

occurred during this time period, and the observed tem-

perature trends strongly depend on the observation per-

iod. Shrub expansion has been reported from the

region, but it is unclear whether this is a consequence

of improved climatic conditions.

In sub-arctic, arctic and alpine regions across the north-

ern hemisphere, a marked increase in shrub and tree cover

has been reported in studies based on historical photo-

graphs over the past 50 yr (Sturm et al. 2001b; Kullman

2006; Tape et al. 2006; Dial et al. 2007; Jørgensen et al.

2013). The expansion of shrubs in tundra ecosystems has

been related to increasing temperatures across most arctic

regions (Tape et al. 2006; Hallinger et al. 2010; Myers-

Smith et al. 2011a). It has been hypothesized that also

high precipitation and deep snow cover during winter lead

to increased shrub growth (Sturm et al. 2001a; Sturm

et al. 2005; Wahren et al. 2005). Several feedback mecha-

nisms have been proposed which could facilitate further

shrub expansion, including fire frequency, carbon and

water balance, soil mineralisation rates, albedo, snow

155Journal of Vegetation ScienceDoi: 10.1111/jvs.12224© 2014 International Association for Vegetation Science

distribution, permafrost thawing, species diversity and

regional warming (Myers-Smith et al. 2011a).

Alnus and Salix are among the most common arctic and

sub-arctic shrubs. In particular, Alnus viridis and Salix spp.

have been reported to expand in arctic, sub-arctic and

alpine ecosystems (Tape et al. 2006, 2012; Forbes et al.

2010; Myers-Smith et al. 2011a; Jørgensen et al. 2013).

Arctic shrubs from these genera have been shown to

respond positively to experimental warming and to pro-

duce wider growth rings in years with high summer tem-

peratures (Post & Pedersen 2008; Forbes et al. 2010;

Macias-Fauria et al. 2012; Tape et al. 2012). These find-

ings all point to a warming Arctic as the ultimate explana-

tion for the observed shrub expansion. Several studies

(Forbes et al. 2010; Hallinger et al. 2010; Myers-Smith

et al. 2011a,b and Macias-Fauria et al. 2012; Tape et al.

2012) used radial growth as a proxy for shrub expansion,

on the basis of significant links between climate, radial

growth and shrub biomass. Other studies have empha-

sized that also permafrost thawing (Lantz et al. 2009),

herbivory and human impact (Post & Pedersen 2008;

Kemper & Macdonald 2009; Jørgensen et al. 2013) can

influence growth and expansion of arctic shrubs, thus

obscuring warming effects.

In SW Greenland trends of increasing mean tempera-

tures are less pronounced compared with most other polar

regions (Hansen et al. 2010). Moreover, the climate has

fluctuated during the 20th century and the nature of

trends observed over the past decades depends highly on

the time period and climatic variable (Cappelen 2011),

making it difficult to say exactly how conditions for shrub

growth have developed in the region.

During the period 1961–2001, a significant cooling

trend with a reduction of the annual average temperature

by 1.3 °C was recorded for a composite of weather stations

(Hanna & Cappelen 2003). By contrast, the decade 2000–

2010 was characterized by a series of warm years resulting

in a modest increase in annual average temperatures

(+1.2 °C, 1961–2010; Boas & RiddersholmWang 2011). In

accordance with the moderate regional warming in a low-

Arctic region of SE Greenland, Dani€els et al. (2011) found

stable vegetation patterns over the past 40 yr. In a study of

repeat photographs, Jørgensen et al. (2013) observed

increased shrub cover at landscape level in SW Greenland.

These findings were explained with the land-use history of

reduced herbivory in SW Greenland. However, due to the

recent warming trend in SW Greenland, a climate-driven

expansion could not be excluded.

Results from a dendrochronological study of Betula

pubescens in S Greenland (Kuivinen & Lawson 1982) con-

firmed that high summer temperatures generally have

positive effects on the growth of woody plants. However,

precipitation and temperatures in other parts of the year

also had significant positive and, rarely, negative effects on

growth, which further complicates conclusions regarding

improvement of the growth conditions. Forbes et al.

(2010) reported that chronologies of Salix lantana (L.) cor-

related well with climate series across >1600 km in W

Siberia and E Europe. This indicates that shrubs can

respond synchronously across large regions in cases where

shrub expansion is caused by improved climatic condi-

tions. Despite the mountainous landscape of WGreenland,

causing a spatially heterogeneous local climate, we expect

that the most widespread erect shrub species, i.e.Alnus viri-

dis subsp. crispa (Aiton) Turrilland, Salix glauca (L.) subsp.

allicarpaea (Trautv.) B€ocher (B€ocher et al. 1978) are con-

strained by the same climatic variables, and thus exhibit

similar growth patterns over time.

In this study, we use stem discs from Alnus viridis and

Salix glauca collected in W Greenland. Based on the analy-

sis of growth-ring chronologies, we seek to answer the

following questions:

1. Which climatic variables are the main determinants of

radial growth and to what extent does their effect on

growth vary among species?

2. What are the similarities between the temporal radial

growth patterns of the two common shrub species, A. viri-

dis and S. glauca?

3. Do changing growth conditions over the past 120 yr

and their predicted impact on growth match the observed

shrub expansion in SWGreenland?

Methods

Collection sites

The samples of stem discs originate from the southern

and northern parts of the main range of erect shrubs in

W Greenland – a range covering a distance of ca.

1000 km from north to south. Collection of stem discs

of A. viridis was carried out in the Arsuk Fjord, SW

Greenland (Fig. 1, site A). Arsuk Fjord is located in the

sub-arctic vegetation zone, close (<90 km) to the south-

ernmost margin of the range of A. viridis. The annual

mean temperature is 1.2 � 1.2 °C (mean � SD) and

the mean temperature in June–August is 8.7 � 1.5 °C.Average annual precipitation is 1324 � 186 mm, and

306 � 118 mm during the main growth period, June–

August (Cappelen 2011). In the outer parts of the fjord,

shrub vegetation covers smaller proportions of the area

and does not grow as tall as further inland, and at the

mouth of the fjord erect shrub vegetation is absent. We

sampled stem discs from three sites in Arsuk Fjord:

One coastal site (‘Grønnedal’) as close to the mouth of

the fjord as possible, where A. viridis still met the

applied sampling criterion for stem height (>1 m), one

intermediate site (‘Bjørnebo’; ca. 10 km from Grønne-

Journal of Vegetation Science156 Doi: 10.1111/jvs.12224© 2014 International Association for Vegetation Science

Alnus and Salix in W Greenland R.H. Jørgensen et al.

dal) and one slightly more continental site close to the

head of the fjord (‘Bræhytten’; ca. 10 km from

Bjørnebo).

The collection of S. glauca stem discs was carried out at

two sites around Qeqertarsuup Tunua (Disko Bay): 14

individuals were sampled close to the shoreline (<80 m

a.s.l.) of the Kangerluk (Diskofjord; Figs 1, S1) and five

additional individuals along a small inlet close to the town

of Ilulissat (Figs 1, S2). The distance between the two sites

is 100–120 km. The area around Qeqertarsuup Tunua is

situated in the arctic vegetation zone, at the border to the

high-arctic zone north of the island of Qeqertarsuaq. At

the climate station in Aasiaat, which is located about

100 km S and SW of the collections sites, the annual mean

temperature is �4.5 � 1.6 °C, the mean for June–August

is 4.9 � 0.9 °C, annual precipitation is 445 � 129 mm,

and 127 � 53 mm during June–August (Boas & Ridders-

holmWang 2011).

Collection of stem discs

At each site, A. viridis stem discs were sampled along an

east–west transect on south-facing slopes in August 2009.

The transects at Bræhytten and Bjørnebo were located at

50 m a.s.l., while at Grønnedal no shrubs could be found

at this altitude and we therefore established the transect at

30 m a.s.l. Every 5 m along each transect, the tallest shrub

of A. viridis >1 m was chosen within 2.5 m distance from

the transect line. Stem discs were cut at the stem base, and

at heights of 50 cm and 100 cm along the stem; ten shrubs

were sampled both at Grønnedal and at Bjørnebo, and 11

were sampled at Bræhytten, amounting to a total of 31

stems and 93 stem discs.

The collection of 57 S. glauca stem discs was carried out

randomly on S-facing slopes in August 2010. Only shrubs

that were >5 m apart were used. From each shrub, the

thickest stem was cut, if possible below the root collar.

S. glauca stem discs were taken at regular intervals of

5–10 cm, depending on total stem length (rarely >1 m).

Preparation andmeasurement of stem discs

Preparation and measurement of stem discs followed den-

drochronological standard methods, described elsewhere

(Hallinger et al. 2010; Myers-Smith et al. 2011b), includ-

ing sanding, thin-sectioning in difficult specimens and

measuring under a stereomicroscope or partly on the com-

puter screen using the programs TSAPWin (Rinn 1996)

and Catras (Aniol 1983).

Cross-dating and the preparation of chronologies

Growth-ring series from different radii of each stem disc

were compared visually. The discs were examined for

dating disagreements caused by missing or false annual

rings, and corrected by removing or inserting rings in

the growth-ring series. We cross-dated and then aver-

aged the growth-rings series to create a series for each

stem disc, shrub individual, collection site and finally for

each species. Growth-ring records from a stem disc or a

whole individual that failed to fit the rest of the data

were discarded from further analysis. Among the 31

A. viridis shrubs (93 stem discs), 26 individuals could be

cross-dated and were used in the subsequent analysis,

whereas all 19 S. glauca individuals could be cross-dated

and used in the analysis.

We used the Dendrochronology Library in R (dplR;

Bunn 2008) for the detrending of growth-ring series and

the creation of standardized chronologies, based on the

cross-dated growth-ring series from the disc of each indi-

vidual extracted closest to the ground. For individuals with

a negative growth trend, a modified negative exponential

curve was fitted, and each annual growth was divided by

the fitted value. For individuals with non-negative trends,

the detrending was performed using the mean. For each

species, a mean chronology combining all sampling sites

Fig. 1. Location of the study areas in W Greenland: (A) collection site of

Alnus viridis; (B) collection site of Betula pubescens (Kuivinen & Lawson

1982), and (S1) and (S2) collection sites of Salix glauca. Climate series

originate from three meteorological stations indicated by filled circles: in

the north: Aasiaat (DMI station no. 04220); in the middle, Nuuk (DMI station

no. 04254); and in the south, Narsarsuaq (DMI Station no. 04270). The

dashed line represents the limit of the ice cap. In the inset, the dashed line

represents the polar circle.


R.H. Jørgensen et al. Alnus and Salix in W Greenland

was prepared as the robust mean of all cross-dated and

detrended individual series.

Data analysis

In this study, we extract climatic factors significantly influ-

encing – and thus potentially explaining – shrub growth

from analyses of the annual variation of A. viridis and

S. glaucamean chronologies. Based on the selected climatic

variables, and variables describing inherent age- or size-

dependent growth patterns, we develop radial growth

models, which are subsequently used for the construction

of chronologies independent of age trends. These chronol-

ogies based on statistical models will be termed ‘predicted’,

as opposed to the mean chronologies that were built using

standard methods of dendrochronology using growth-ring

measurements (‘empirical’ chronologies).

We used Pearson correlation (q), Pearson’s product-

moment correlation (r) test and the Gleichl€aufigkeit metric

(Glk) to compare the empirical chronologies, in addition to

the visual comparison. The Glk metric measures the pro-

portion of annual growth changes that have the same sign.

Pre-selecting significant climate variables

We applied the software package Dendroclim 2002 (Biondi

& Waikul 2004) to analyse standardized species chronolo-

gies for significant correlations caused by monthly mean

temperature and precipitation sum. For each species we

used the longest possible common interval covered both

by the climate series and the species chronology, but

excluded parts of the chronologies that were based on less

than two individuals (Fig. 2). For A. viridiswe used climate

data from the period 1963–2008 from the Narsarsuaq cli-

mate station (Boas & Riddersholm Wang 2011), ca.

150 km east of the collection sites (Fig. 1). For S. glaucawe

used climate data from the period 1959–2008 from the

Aasiaat climate station (Boas & Riddersholm Wang 2011),

ca. 100 km from the collection sites (Fig. 1).

Using response function analysis in Dendroclim, we

identified the most influential monthly climate variables

for each of the two species using a 12-month window

(September of previous year to August of current year).

Modelling the growth of A. viridis and S. glauca

The identified climate variables were used as independent

variables in a linear mixed model with autoregressive error

structure constructed in R v 2.15.1 (R Foundation for Sta-

tistical Computing, Vienna, AT) with the extension pack-

age ‘nlme’. Square-root transformation of the response

variable (ring width) and log-transformation of age in the

random part of the model were conducted to meet the

requirements of normality and homogeneous variance.

For model reduction we used ML (maximum likelihood)

estimation, and for parameter estimation REML (restricted

maximum likelihood). Missing rings were excluded from

the data set. The basic statistical model is given by:

ðRWitÞ1=2¼aþb1C1tþ ...þbmCmtþcAgeitþkilnðAgeitÞþtit

where the observation years are t = 1963. . .2008 for A. vir

idis and 1960. . .2008 for S. glauca; the individuals are

i = 1. . .26 for A. viridis and 1. . .19 for S. glauca; RWit is the

average ring width (mm 9 10�2) for individual i in year t;

Ageit is the age (years) of individual i in year t; a, b1. . .bmand c are fixed parameters to be estimated. ki ~ N(0, r2) isa random effect of ln(Age) for individual i, and C1t. . .Cmt

are pre-selected climate variables (monthly precipitation

sum in mm and average monthly temperatures in °C)that were found to be significant in the response function

analysis. Finally, υit are first-order autoregressive errors,

ti;t ¼ q ti;t�1 þ ei;t, where ei;t �Nð0; r2e Þ and q is the auto-

correlation coefficient.

In the final models for A. viridis (henceforth referred to

as Model Alocal) and S. glauca (Model Slocal) we removed

variables that were not significant at the 10% level. We

added a categorical variable to the models expressing the

collection sub-sites (three sites for A. viridis, two for S. glau-

ca), but this variable turned out to be non-significant

0

1

2

0

10

20

30

Sam

ple

dept

h

RW

I, A

lnus

RW

I, B

etul

aR

WI,

Sal

ix

0

1

2

0

10

20

30

Sam

ple

dept

h

1935 1945 1955 1965 1975 1985 1995 20050

1

2

0

10

20

30

Sam

ple

dept

h

Fig. 2. Empirical mean chronologies for Alnus viridis and Salix glauca.

Vertical axes show unit-less ring width indices (RWI); dashed lines show

the number of individuals included. For visual comparison, the Betula

pubescens chronology is included (Kuivinen & Lawson 1982, covering the

period 1876–1977), but only shown for the period common with the

A. viridis and S. glauca chronologies.



in both cases. Using the final calibrated Models Alocal and

Slocal and the associated climate variables, we predicted the

expected growth for A. viridis and S. glauca for the periods

1963–2008 and 1960–2008, respectively. When predicting

growth we kept the Age variable at a constant level = 1,

corresponding to predicting the growth of 1-yr-old stems

at any time throughout the prediction period.

We also fitted the growth models for both species using

a long climate series (1890–2010) from a weather station

in Nuuk, situated approximately midway between the col-

lection sites (Fig. 1). Despite the long distance between the

local climate stations and Nuuk, the correlations between

the local climate variables and the variables from Nuuk

were high (Table 1), suggesting that we could also use this

climate series for calibration of growth models and growth

estimation. We fitted growth-ring series for A. viridis

(Model ANuuk) and S. glauca (Model SNuuk) to the Nuuk

data for the periods 1963–2008 and 1960–2008, corre-

sponding to the time periods used in the pre-selection pro-

cedure and in fitting the models to local climate series. We

used the same set of monthly climate variables that was

also included in the reduced versions of the local models.

Again non-significant variables were removed. Finally, we

took the calibrated models (Models ANuuk and SNuuk) to

predict growth for both species for the full time span

covered by the Nuuk climate series (1890–2010).

Results

Response to climate

Summary statistics for the two empirical chronologies

Alnus viridis and Salix glauca, Table 2. The growth of A. viri-

dis and S. glauca responded significantly positively to

increasing summer temperatures, but precipitation vari-

ables also influenced growth (Table 3). For A. viridis, the

response function analysis identified June and July mean

temperatures and April precipitation sum as significant

and positive determinants of radial growth (Fig. 3). For

S. glauca, June and Julymean temperatures had significant

positive and previous year’s November precipitation sum

had significant negative effects on radial growth in the

response function analysis (Fig. 3).

In the model where growth of A. viridis was fitted to

local climate variables (Model Alocal), all three pre-selected

variables were significant (June and July temperatures

and April precipitation). The climate variables had positive

estimates (Table 3), corresponding to the results from the

response function analysis (Fig. 3). For the growth-ring

series of S. glauca fitted to local pre-selected climate vari-

ables (Model Slocal), June and July temperatures and age

variables remained in the model. For the growth-ring ser-

ies of A. viridis fitted to the long-term record of climate data

from Nuuk (Model ANuuk), April precipitation proved to be

non-significant and was therefore removed from the

model (June and July temperatures and age remained;

Table 3). For S. glauca growth-ring series fitted to climate

data from Nuuk (Model SNuuk) only June temperature and

age remained in the model. The systematic age variable

was significant (P < 0.1) and therefore kept in the model

during reduction using ML, but not when using REML (as

presented in Table 3).

The likelihood ratio returned R2 of 0.59 and 0.53 and

Glk values (comparing empirical and predicted chronolo-

gies) of 62% and 58% for A. viridis (Model ANuuk) and

S. glauca (Model SNuuk), respectively. These values were

almost as high as for the models based on local climate

variables, reflecting the high correlation between climatic

conditions observed at different stations along the coast of

WGreenland (Table 1).

Growth patterns over the past 120 yr

Empirical mean chronologies of A. viridis and S. glauca are

shown in Fig. 2. The somewhat similar year-to-year pat-

tern between the Alnus and Salix chronologies is also

reflected by a Glkmeasurement of 55%.

The predicted chronologies showed a high synchronicity

when compared with the empirical chronologies (annual

variation; Fig. 4), shown by the high likelihood ratio based

R2-values of 0.61 and 0.53 and Glk values (comparing

empirical and predicted chronologies) of 76% and 60% for

A. viridis and S. glauca, respectively. A clear tendency of

increasingly higher estimated growth can be seen in the

period 1963–2008 for A. viridis, and in 1960–2008 for

S. glauca based on the local models (Fig. 4). The highest

values of estimated radial growth occurred from the

Table 1. Pearson correlation between pre-selected variables from local

climate stations Narsarsuaq 1963–2008 and Aasiaat 1960–2008 (Boas &

Riddersholm Wang 2011) and the corresponding variables from the cli-

mate station in Nuuk (Cappelen 2011). Using a Pearson’s product-moment

correlation test, all comparisons were significant (P < 0.001). Only vari-

ables significant in the local models (Narsarsuaq and Aasiaat) are included.

Climate Variable Narsarsuaq and Nuuk Aasiaat and Nuuk

June Average Temperature 0.81 0.81

July Average Temperature 0.73 0.79

April Precipitation Sum 0.44 –

November Precipitation Sum – 0.47

Table 2. Summary statistics (SD, mean sensitivity and lag 1 autocorrela-

tion) for the two empirical chronologies Alnus viridis and Salix glauca.

SD Mean Sensitivity Autocorrelation (Lag = 1)

Alnus viridis 0.245 0.237 0.40

Salix glauca 0.257 0.244 0.33



late-1990s to 2008 (Fig. 4), indicating favourable growth

conditions during these years.

Estimating growth for A. viridis and S. glauca by extrapo-

lation based on the Nuuk data for the period 1890–2010

(Fig. 5) revealed similar increases in growth (1960–2010)

as found for the growth curves based on local climate series

(Fig. 4), especially during the most recent period of time

(1996–2010). Moreover, we observed only a slightly

increasing trend in growth during the whole period (1890–

2010). However, the medium-term fluctuations in growth

were pronounced and appeared more conspicuous than

the general long-term trend (Fig. 5): it appears that there

was an earlier growth maximum, approximately in the

period 1921–1960. During this period, the estimated radial

growth reached levels comparable to growth in the years

1996–2010. Growth in the most recent period of low

Res

pons

e co

effic

ient

s

–0.1

0.0

0.1

0.2

0.3PrecipitationTemperature

Res

pons

e co

effic

ient

s

–0.2

–0.1

0.0

0.1

0.2

s o n d J F M A M J J A s o n d J F M A M J J A

*

Alnus

Salix

**

*

*

*

Fig. 3. Top: Response coefficients for the Alnus viridis chronology (n = 26 individuals) for the period 1963–2008, and temperature and precipitation data

from previous year’s September (small letters) to current year’s August (capital letters). Bottom: Response curve for the Salix glauca chronology (n = 19

individuals) for the period 1960–2008, and temperature and precipitation data from previous year’s September (small letter s) to current year’s August

(capital letters). Significant values (P < 0.05) are marked with an asterisk.

Table 3. Summary statistics for mixed linear models describing growth ring widths (mm 9 10�2) for Alnus viridis (Alocal) and Salix glauca (Slocal) based on

local climate data (Narsarsuaq and Aasiaat respectively), and climate data from Nuuk (A. viridis, ANuuk; S. glauca, SNuuk). Likelihood ratio-based R2, autocor-

relation (q), within-group error SD (re) and SD of k (r): R2 = 0.61/q = 0.49/re = 1.74/r = 1.81 (Alocal); R2 = 0.53/q = 0.55/re = 4.02/r = 1.20 (Slocal);

R2 = 0.59/q = 0.51/re = 1.81/r = 1.82 (ANuuk); R2 = 0.53/q = 0.55/re = 4.05/r = 1.15 (SNuuk).

Species Variable Test of variable Classes

Model based on local climate data Model based on Nuuk climate data

Estimate (width^0.5) SE Pr(>|z|) Estimate (width^0.5) SE Pr(>|z|)

Alnus viridis (Models

Alocal and ANuuk)

Intercept 2.796 0.707 0.000 6.214 0.555 0.000

Age �0.058 0.015 0.000 �0.054 0.015 0.000

June average temperature 0.028 0.003 0.000 0.151 0.048 0.002

July average temperature 0.028 0.005 0.000 0.219 0.056 0.000

April precipitation sum 0.001 5.01*10�17 0.000 n.s.

Salix glauca (Models

Slocal and SNuuk)

Intercept 14.014 1.117 0.000 15.283 0.888 0.000

Age �0.069 0.038 0.071 �0.055 0.038 0.143

June average temperature 0.037 0.011 0.001 0.431 0.113 0.000

July average temperature 0.033 0.015 0.025 n.s.

Previous year’s November

precipitation sum

n.s. n.s.

n.s., not significant.



growth (1961–1995) appears to be of similar magnitude

(or only marginally higher for A. viridis) as in the first

period of low growth in 1890–1920 (Fig. 5).

Discussion

The current study applies a new method for extending the

length of chronologies by simulation using long climate

records for regions and species where old individuals suit-

able to construct long empirical chronologies are not pres-

ent. The method of modelling growth through pre-selected

climatic variables has the advantage of being void of effects

caused by shrub physiology (age trends) or other patterns

resulting from the statistical treatment of the data (detr-

ending/standardization). This includes the possible elimi-

nation of medium-term growth trends by detrending

procedures, when all (or a majority of) specimens have

been sampled in the same year.

The reliability of the method depends on the long-term

stability of the relationship between radial growth and the

selected set of climate variables included in the model.

Thus, the approach cannot be expected to produce accu-

rate results if major changes in climatic conditions change

the factors limiting growth of a species. However, if climate

in the simulated period remains within the multidimen-

sional envelope covered by the calibration data set, predic-

tions can be assumed to be accurate. The chronologies of

A. viridis and S. glauca were tested for temporal variation

in the growth response using the Dendroclim moving

interval (20-yr interval) response function analysis for the

climatic window April–August (Biondi & Waikul 2004).

The moving window analysis showed that the pre-selected

variables were not significant across the whole time inter-

val, but the signs of response estimates were stable

throughout the periods 1963–2008 (A. viridis) and1960–

2008 (S. glauca), indicating a steady response.

Which climatic variables are themain determinants of

radial growth?

Alnus viridis and S. glauca both responded positively to cur-

rent year summer temperatures (Fig. 3). This is in line

with findings from dendrochronological studies from

Greenland and other parts of the Arctic (Fritts 1976; Forbes

et al. 2010; Hallinger et al. 2010; Myers-Smith et al.

2011a; Macias-Fauria et al. 2012; Tape et al. 2012) and

from warming experiments (Walker et al. 2006; Elmen-

dorf et al. 2012).

We explain the significant positive effect of April precip-

itation for A. viridis by the importance of having sufficient

soil moisture before leafing and shoot elongation. The neg-

ative effect of previous year’s November precipitation for

S. glauca growth may be an indication that snow does not

Pre

dict

ed g

row

th [m

m]

Rin

g w

idth

inde

x

1960 1970 1980 1990 2000 2010

Pre

dict

ed g

row

th [m

m]

0.5

0.7

0.9

1.1

–1.0

–0.5

0.0

0.5

1.0

1.5

0.20

0.30

0.40

0.50

0.0

0.5

1.0

1.5

Rin

g w

idth

inde

x

Alnus

Salix

Fig. 4. Top: Solid dots show the empirical chronology of Alnus viridis

(Ring width index, unitless); open circles indicate the predicted growth of

A. viridis (mm), based on Narsarsuaq climate data (Model Alocal; Table 3;

fitted 1963–2008, estimated 1963–2008). Bottom: Solid dots show the

empirical chronology of Salix glauca (Ring width index, unitless); open

circles show the predicted growth of S. glauca (mm), based on Aasiaat

temperatures (Model Slocal; Table 3; fitted 1960–2008, estimated 1960–

2008).

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.2

0.22

0.24

0.26

0.28

0.3

0.32

0.34

Pre

dict

ed g

row

th [m

m]

1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Alnus

Salix

Fig. 5. Top: Estimated growth of Alnus viridis (predicted chronology,

Model ANuuk), fitted to Nuuk data (1963–2008). Bottom: Estimated growth

of Salix glauca (predicted chronology, Model SNuuk), fitted to Nuuk data

(1960–2008). Symbols indicate estimated growth 1890–2010 (black); 10-yr

running average (bold) and 95% confidence intervals (grey) of the running

average.



promote shrub growth in W Greenland, which would be

in line with findings from high-Arctic Greenland, showing

negative effects of snow cover on growth of S. arctica

(Schmidt et al. 2006, 2010), but contrasts with the snow–

shrub–microbe hypothesis (Sturm et al. 2001a; Sturm

et al. 2005), postulating positive effects of snow depth on

shrub growth. However, autocorrelated climate data may

also be an explanation for the significant negative effect of

previous year’s November precipitation. Hence, we found

a negative Pearson correlation between November precipi-

tation and June temperatures of the following year (Aasi-

at) (cor = �0.27, t = �1.96, P = 0.06). June and July

temperature also had positive effects on the growth of Betu-

la pubescens in S Greenland. In addition, Kuivinen & Law-

son (1982) detected a positive effect of April precipitation

on birch growth, similar to our finding for A. viridis.

What are the similarities betweenAlnus and Salix radial

growth?

Empirical mean chronologies of A. viridis and S. glauca are

shown together with an already published mean chronol-

ogy of B. pubescens (Ehrh.) (Fig. 2; Kuivinen & Lawson

1982). Comparison between the chronologies is hampered

by the limited overlap between the series. Comparison

between the series can only be made with regard to short-

term fluctuations, because longer-term patterns were

removed by detrending.

The similarity of the empirical chronologies – sampled

across a north–south range of 1000 km in West Greenland

– were reflected by Glk measures above 50% and caused

by the response of shrubs to similar climatic determinants

(see above) and by high correlation between the climate

variables inW Greenland that can be observed even across

large geographic distances (Table 1). At the same time, the

long geographic distance between sampling sites and

taxonomic differences may account for differences in local

growth conditions and thus reduce similarity. Correspond-

ing to the relative geographic closeness of the sampling

sites (Fig. 1), Glk was highest for the pair A. viridis – B. pu-

bescens (81%) and lower for S. glauca – A. viridis (55%),

whereas the S. glauca – B. pubescens chronologies had too

few samples in the overlapping period to make a sound Glk

measure.

Growth patterns and shrub expansion (1890–2010)

Since the early 1960s, there has been a considerable

increase in radial growth of A. viridis and S. glauca in W

Greenland, mainly caused by favourable climatic condi-

tions primarily prevailing from the late-1990s and onwards

(Fig. 4). Considering only the period since 1960 would

give the impression that growth conditions have improved

substantially, explaining the shrub expansion observed at

the landscape level by Jørgensen et al. (2013). Looking

only at the empirical chronologies, the improvement of

growth conditions seems less clear. This could be due to

the detrending procedure applied to remove age trends in

the empirical data prior to response function analysis, or

due to various local conditions potentially affecting shrub

growth, as for example herbivory and human impact (Post

& Pedersen 2008; Kemper & Macdonald 2009; Jørgensen

et al. 2013), or (local) climatic conditions not captured by

the weather stations. These differences that also result in

R2 values of only 0.61 and 0.53 highlight the strength of

using the predicted chronologies to overcome problems of

growth patterns being confounded with age trends, and to

emphasize the signal caused by climate. This applies in par-

ticular, when answering questions regarding long-term

effects of climatic trends.

The extrapolation of growth based on climate data

from Nuuk offers a longer-term perspective that can be

examined for growth patterns and may help understand

the shrub expansion in W Greenland. Across the whole

period analysed (1890–2010), there was only a weak ten-

dency of increasing growth (Fig. 5), but it is evident that

growing conditions improved during 1921–1930 at a

speed that resembled the development observed in 1996–

2010. This first period of favourable temperatures lasted

until about 1960, after which temperatures declined until

the most recent climate improvement started in the late-

1990s.

The mid-term pattern switching between periods of

warmer summer climate (1921–1960 and 1996–2010) and

comparatively colder summer climate (1900–1920 and

1961–1995) coincides with patterns of Atlantic sea temper-

atures caused by the North Atlantic Multidecadal Oscilla-

tion (AMO; Sutton et al. 2005). Due to the limited length

of the empirical chronologies and the applied detrending

procedure, there is only limited correspondence between

the empirical chronologies and the AMO index. The cyclic

variations of AMO are particularly strong in the NWAtlan-

tic and extend far back in time, according to palaeoclimatic

records (Delworth & Mann 2000; Gray et al. 2004; Sutton

et al. 2005).

The 1921–1960 period of warm climate in W Greenland

influenced several other biological systems in the region:

Atlantic cod (Gadus morhua) populations and fishery

increased in W Greenland from 1920 onwards, while seal

hunting decreased at approximately the same time as a

consequence of sea warming and possibly overhunting

(Hamilton et al. 2003). Slightly delayed, the caribou (Rang-

ifer tarandus) population in W Greenland started to

increase from the 1930s (Cuyler 2007). The cod fishery

remained high until a steep decline in the 1960s (similar to

the mid-term trend of our predicted chronologies) as a



consequence of colder water and overfishing (Hamilton

et al. 2003).

The marked mid-term fluctuations in estimated growth

of the two shrub species make interpretations complex

(Fig. 5). It is questionable if the development over the

whole period 1890–2010 would correctly express the long-

term trend of growth conditions, since it would not take

into account that our predicted growth record starts in

1890 in a cold period and ends in 2010 on top of a (likely)

cyclic mid-term fluctuation. We propose that valid periods

for comparison may be between the two periods of maxi-

mum growth, e.g. 1930–2010, or between two periods of

minimum growth, e.g. 1920–1990. In that perspective, the

long-term growth conditions for shrubs appear to have

been stable throughout the 20th century. Since the climate

variables that are included in both models based on Nuuk

data (Models ANuuk and SNuuk) are all summer tempera-

tures (Table 3), the strong increases in estimated growth

during the periods 1925–1965 and 1995–2010 are direct

consequences of warm summers during these periods.

Given the longer-term stability of 20th century growth

conditions, the increased shrub abundance observed by

Jørgensen et al. (2013) must have taken place during the

most recent period of favourable growth (1995–2010), if

the assumption of climate-driven expansion should be

upheld. Moreover, we must then expect that a shrub

expansion of the same magnitude took place during the

period 1925–1965. However, the slow establishment and

growth of arctic shrubs makes it unlikely that the shrub

expansion observed in SW Greenland is mainly caused by

the 10–15 yr of warming from the late-1990s to 2010. This

is supported by the repetition of historic vegetation surveys

and associated reports of stable or almost stable shrub

cover over the last 40 yr (Callaghan et al. 2011a; Dani€els

& deMolenaar 2011; Dani€els et al. 2011).

Due to the strong effect of AMO and the weak warming

trend in the region (Hansen et al. 2010), effects of a global

warming on shrubs in W Greenland can only be expected

on an even longer time scale than examined in this study.

The Little Ice Age (LIA) was strongest in the Atlantic part of

the Arctic, and most records show that warming started

from the late 19th century (Miller et al. 2010). Other stud-

ies on shrub expansion suggest that expansion started at

the end of the LIA (Tape et al. 2006; Hallinger et al. 2010).

Due to the shrub expansion in SW Greenland reported by

Jørgensen et al. (2013), contrasting studies from other parts

of Greenland (Callaghan et al. 2011a; Dani€els & de Mole-

naar 2011; Dani€els et al. 2011) and studies documenting

the strong effect of grazing (Post & Pedersen 2008; Calla-

ghan et al. 2011b), a delayed reaction to the improvement

of growth conditions from the late 19th century onward

seems less likely to be a main reason for the shrub expan-

sion observed in the study areas of Jørgensen et al. (2013).

Based on the results of the current study, summer

temperature is an important driver for shrub growth. How-

ever, due to the lack of a clear and general long-term trend

in growth conditions, a delayed response to cessation of

browsing by sheep in the 16th century and extinction of

caribou in S Greenland in the 19th century (possibly due

to hunting; Meldgaard 1986), in contrast to the positive

population development for caribou in W Greenland

(Cuyler 2007), seems to be the most likely explanation for

the shrub expansion observed by Jørgensen et al. (2013).

Similarly, the increasing altitude of the upper tree limit in

the Alps was found to be mainly related to reduced grazing

pressure (Gehrig-Fasel et al. 2007; Chauchard et al. 2010).

In S Norway, increasing altitude of the tree limit and

increased forest cover were rather consequences of land

abandonment than of climate change (Bryn 2008).

Acknowledgements

We thank Karl C. Kuivinen and Merlin P. Lawson for let-

ting us use their Betula pubescens data. Thanks are also due

to Jørgen Jørgensen for assistance during the fieldwork

and to Claudia Baittinger and Niels Bonde at the National

Museum of Denmark for guidance and for giving us access

to lab facilities. We are indebted to the Greenland Com-

mand (now Joint Arctic Command) for help with logistic

support and accommodation during our fieldwork in the

Arsuk Fjord area. Finally, we thank the Danish Meteoro-

logical Institute for providing climate data, and two anony-

mous reviewers for valuable comments on an earlier

version of the paper.

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Documents

Growth response to climatic change over 120 years for Alnus viridis and Salix glauca in West Greenland