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\begin{document}

\title{Declining temperature and increasing moisture sensitivity of shrub
growth in the Low-Arctic erect dwarf-shrub tundra of western Greenland}



\author[1]{Stef Weijers}%
\affil[1]{Rheinische Friedrich-Wilhelms-Universitat Bonn}%


\vspace{-1em}



  
  \date{May 21, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
1. Evergreen dwarf shrubs respond swiftly to warming in the cool and dry
High Arctic, but their response in the warmer Low Arctic, where they are
expected to be outcompeted by taller species under future warming,
remains to be clarified. 2. Here, 12,528 annual growth increments,
covering 122 years (1893-2014), were measured of 764 branches from 25
individuals of the evergreen dwarf shrub \emph{Cassiope tetragona} from
a Low-Arctic erect dwarf-shrub tundra site in western Greenland. In
addition, branch initiation and mortality frequency time-series were
developed. The influence of seasonal climate and correspondence with
fluctuations in regional normalized difference vegetation index (NDVI),
a satellite-proxy for vegetation productivity, were studied. 3. Winter
temperatures were an important co-driver, summer temperatures the main
driver of growth. During past and recent warm episodes, shrub growth
diverged from summer temperatures. In recent decades, early summer
precipitation has become the main growth-limiting factor for some
individuals, likely through micro-topography-determined soil moisture
availability, and more than half of the shrubs studied became
irresponsive to summer temperatures. There was correspondence between
climatic drivers, \emph{C. tetragona} growth and branch initiation
frequency, and satellite-observed vegetation productivity, suggesting
the area's shrub-dominated tundra vegetation is limited by similar
climatic factors. Winter warming events were likely the predominant
cause of branch mortality, while branching increased after years with
poor growth and cooler-than-average summers. 4. \emph{Synthesis} These
findings show that the erect dwarf-shrub tundra in the Low Arctic has
and will likely become less temperature- and increasingly
moisture-limited and that winter warming supports shrub growth, but
increased extreme winter warming event-frequency may increase branch
mortality and vegetation damage. Such counter-acting mechanisms could
offer an explanation for the vegetation stability observed over large
parts of the Arctic.%
\end{abstract}%



\sloppy


\textbf{Declining temperature and increasing moisture sensitivity of
shrub growth in the Low-Arctic erect dwarf-shrub tundra of western
Greenland}

Stef Weijers

Department of Geography, University of Bonn, Meckenheimer Allee 166,
D-53115 Bonn, Germany

Phone: +49 228 73 2093

E-mail: weijers@uni-bonn.de

OrcID: 0000-0003-3386-5417

\textbf{Abstract}

\begin{enumerate}
\tightlist
\item
  Evergreen dwarf shrubs respond swiftly to warming in the cool and dry
  High Arctic, but their response in the warmer Low Arctic, where they
  are expected to be outcompeted by taller species under future warming,
  remains to be clarified.
\item
  Here, 12,528 annual growth increments, covering 122 years (1893-2014),
  were measured of 764 branches from 25 individuals of the evergreen
  dwarf shrub \emph{Cassiope tetragona} from a Low-Arctic erect
  dwarf-shrub tundra site in western Greenland. In addition, branch
  initiation and mortality frequency time-series were developed. The
  influence of seasonal climate and correspondence with fluctuations in
  regional normalized difference vegetation index (NDVI), a
  satellite-proxy for vegetation productivity, were studied.
\item
  Winter temperatures were an important co-driver, summer temperatures
  the main driver of growth. During past and recent warm episodes, shrub
  growth diverged from summer temperatures. In recent decades, early
  summer precipitation has become the main growth-limiting factor for
  some individuals, likely through micro-topography-determined soil
  moisture availability, and more than half of the shrubs studied became
  irresponsive to summer temperatures. There was correspondence between
  climatic drivers, \emph{C. tetragona} growth and branch initiation
  frequency, and satellite-observed vegetation productivity, suggesting
  the area's shrub-dominated tundra vegetation is limited by similar
  climatic factors. Winter warming events were likely the predominant
  cause of branch mortality, while branching increased after years with
  poor growth and cooler-than-average summers.
\item
  \emph{Synthesis} These findings show that the erect dwarf-shrub tundra
  in the Low Arctic has and will likely become less temperature- and
  increasingly moisture-limited and that winter warming supports shrub
  growth, but increased extreme winter warming event-frequency may
  increase branch mortality and vegetation damage. Such counter-acting
  mechanisms could offer an explanation for the vegetation stability
  observed over large parts of the Arctic.
\end{enumerate}

\textbf{Keywords}

branching, branch mortality, browning, greening, NDVI, shrub expansion,
soil moisture, winter warming events

\textbf{Introduction}

Arctic and alpine areas are warming at faster rates than other areas,
partly because of decreased albedo after advanced snowmelt , and
feedback mechanisms related to the decline in Arctic sea-ice extent .
The Arctic tundra biome is divided into five bioclimatic subzones, which
are defined by summer temperatures and dominant plant growth forms in
each zone . The vertical tundra vegetation structure varies from a
discontinuous layer of very short plants in the coldest zone to
multi-layered, complex canopies in the warmest zone. As a consequence of
the rapid warming, these vegetation zones are expected to shift
northward and into higher elevations. Plant height has already increased
over the past thirty years throughout the tundra biome, largely caused
by species turnover due to immigration of taller species from warmer
areas . This is at least partially related to the increase in
productivity and abundance of deciduous shrub species . Such an increase
in canopy height, because of increased abundance of tall shrub species
may lead to an increase in competition for light and a decrease in dwarf
shrub abundance, particularly in the Low- and Sub-Arctic tundra .
However, the potential of deciduous shrubs to increase with summer
warming, likely depends on soil moisture availability . Deciduous shrub
growth has increased rapidly with recent warming in warmer wetter areas,
while it has declined in dry tundra areas in recent decades, possibly in
relation to diminishing sea-ice extent . In the cooler and drier High
Arctic, evergreen dwarf shrub growth has increased rapidly with recent
warming and leaf size and plant height of evergreen dwarf shrubs
increases to long-term experimental warming . However, it remains
unknown whether evergreen dwarf shrubs, which are generally better
adapted to drier conditions and are found on well-drained, relatively
dry soils , can respond with increased growth to the amplified warming
in the already warm Low Arctic.

The satellite-based normalized difference vegetation index (NDVI) is
generally regarded as a proxy for biomass and productivity of
terrestrial vegetation. NDVI analyses have revealed long-term greening
trends over large parts of the tundra biome in the northern hemisphere
in recent decades . Nonetheless, a significant percentage of Arctic
ecosystems appears to be stable and the mechanisms behind this stability
are poorly understood . Interannual variation in NDVI in the Siberian
Arctic and Arctic-alpine northwest North America have been related to
interannual variation in shrub growth and summer temperatures. This
suggests a possible link between summer warming-induced Arctic greening
and shrub growth. Although many tundra areas have experienced greening
over the past decades, other regions have experienced browning events in
recent times . Such browning events have been linked to the occurrence
of extreme warm spells in winter, which result in snowmelt, often
accompanied by rain-on-snow events . These may result in the encasement
of plants in ground-ice or leave plants exposed to subsequent periods of
severe frost without insulation by a sufficiently deep snow layer .
Extreme winter warming and rain-on-snow events, the frequency of which
is likely to increase in the future , can damage vegetation and lower
its productivity in the subsequent summer . Single events have been
linked to branch mortality of several shrub species,
including\emph{Cassiope tetragona} . Still, shrub branch-mortality and
branch-initiation frequency time-series and possible links with climate
and NDVI have not been studied thus far.

Here, the climate-growth relationships in the evergreen dwarf
shrub\emph{Cassiope tetragona} are studied at a Low-Arctic site in
western Greenland. The species is found throughout the Arctic, but is
most prevalent in the High Arctic . The following research questions
were addressed:

\begin{enumerate}
\tightlist
\item
  What are the main seasonal climate drivers of \emph{C. tetragona}shrub
  growth at the site and individual shrub level and are these stable
  over time?
\item
  Are there differences in individual shrub growth responses to climate
  and are these related to micro-topographic positions?
\item
  Is annual variation in branch initiation and mortality frequency
  related to variation in seasonal climate variables?
\item
  Are area-wide, satellite-observed vegetation productivity, \emph{C.
  tetragona} growth, branch initiation and mortality frequencies, and
  seasonal climate factors inter-related?
\end{enumerate}

\textbf{Materials and Methods}

\emph{Site description}

On 6 August 2014, 25 \emph{Cassiope tetragona} shrubs were sampled at a
mesic tundra site (69.26 \selectlanguage{ngerman}°N, 53.48 °W, 45 m above sea level {[}m
a.s.l.{]}; Fig. 1a-c) located in the Blæsedalen valley about 2 km
northeast of Arctic Station near Qeqertarsuaq on the southern tip of
Disko Island, western Greenland. The area's soils consist of basaltic
rock fragments, covered by a 5--10 cm organic horizon, while active
layer depths can reach 1.5 m . The geology of the area is characterized
by a low bench of gneiss bedrock, Tertiary breccia and plateau lava .
Shrub samples were collected in an area of approx. 60 m × 100 m. A
minimum of 1-2 m distance was kept between sampling spots to avoid
repeated sampling of the same shrub individual. It was attempted to
sample complete (excl. roots) shrub individuals by following shrub
branches until reaching the root-shoot boundary.

The vegetation at the site can be described as erect dwarf-shrub tundra,
which covers 9.7 (13.7) percent, i.e. 689 ×
10\textsuperscript{3}km\textsuperscript{2}, of the (non-glaciated)
Arctic . It is dominated by the deciduous shrub species \emph{Betula
nana} , \emph{Salix glauca} , and \emph{Vaccinium uliginosum} and the
evergreen shrub species\emph{Cassiope tetragona} , \emph{Empetrum
nigrum} ssp.\emph{hermaphroditum} , and less frequently,
\emph{Rhododendron tomentosum} . The climate at the site is Low-Arctic
with a mean annual air temperature of -2.3 °C measured over the period
1994-2013 at ClimateBasis Disko , 3 km southwest of the site, and an
interpolated annual precipitation sum of 330 mm over the same period
(Fig. 1d). February was the month with the lowest mean air temperature
(-13.0 °C) and March the month with the lowest mean minimum air
temperature (-16.4 °C). July is the warmest month with a mean air
temperature of 8.5 °C and a mean maximum of 11.4 °C. Mean monthly air
temperatures remain below zero from November through April, hence most
precipitation falls as snow. Frosts are likely to occur as late as June
and as early as September (Fig. 1d). September is the wettest month with
a mean precipitation sum of 39.6 mm and February the driest (16.1 mm).

The site is located on a gentle south-southeast-facing slope (Fig. 1e)
at which \emph{C. tetragona} is the dominant species (Fig. 1f). Further
west near Ilulissat on mainland Greenland, the species is only found on
north-facing slopes, while further south in the Disko Bay area, near
Aasiaat, the species is absent at low elevations.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1 Location of the research site near Qeqertarsuaq on Disko Island
(green square); weather station locations are indicated with red
triangles (a). The inset map shows the location of the Disko Bay area
(red square) within Greenland. Location of the research site (green
square) northeast of Arctic Station (b). Local weather is recorded at
ClimateBasis Disko, approx. 3 km southwest of the site. Individual shrub
sampling spots (c). Climate diagram showing monthly mean air
temperatures (red) and precipitation sums (blue) at Qeqertarsuaq for the
period 1994-2013 (d). Values to the left of the temperature axis
indicate the mean minimum temperature of the coldest month (February)
and the mean maximum temperature of the warmest month (July). Photo of
the research site taken from sample 4 in southwestern direction (e).
Vegetation at sample spot 24 in north-western direction dominated by
Cassiope tetragona shrubs (f).

\emph{Species}

\emph{Cassiope tetragona} (L.) D. Don., common name Arctic Bell Heather,
is an evergreen dwarf shrub of the Ericaceae family. The species has a
hemi-prostrate growth form and reaches heights of up to 10-15 cm at the
research site, but can grow up to 30 cm tall when supported by, for
example, rocks. It is the dominant species in prostrate/ hemi-prostrate
dwarf-shrub tundra, but less so in the erect dwarf-shrub tundra as found
at the research site, and has a circumarctic distribution . It occurs in
the northernmost polar desert at 83 \selectlanguage{ngerman}°N in northern Greenland , where it
is the most common heath-forming species . In western Greenland it is
found as far south as 63.35 °N, but towards the south only at alpine
sites or on north-facing slopes . The species' leaves are attached to
its stems in opposite pairs, which alternate in 90° angles resulting in
four rows of leaves (hence \emph{tetragona} ). Leaf lengths rapidly
increase at the beginning of the growing season and decrease thereafter,
resulting in a wave-like pattern with each wave representing a single
growing season . The discovery of so-called wintermarksepta , i.e. dark
bands coinciding with lows in leaf lengths visible in lateral
cross-sections of \emph{C. tetragona} branches, has enabled the
construction of \textgreater{}century-long growth chronologies . WMS
consist of green and with increasing age, brown lignified meristem cells
(see Fig. S1).

\emph{Shrub growth chronologies}

Annual shoot length growth was measured as distances between WMS for
multiple branches of each of the 25 sampled shrubs. This was done for a
total 764 branches, 10-65 per shrub. Shoot length growth series of
individual branches were cross-dated using the sampling year of shoots
with complete and green tips, by plotting and visually comparing
similarities in growth patterns between branch series of shrub
individuals, and the dates of branching. Older branch series, without
any overlap with other series from the same shrub were cross-dated with
dated branch series from other shrubs. This mainly concerned some series
dating prior to the 1960s (cf. Fig. S2; ).

Individual branch series of \emph{C. tetragona} generally exhibit growth
trends with increasing growth rates during the initial years of growth,
approx. during the first ten years in this study. Shoot length growth
rates stabilize thereafter, before they start to decline when reaching
ages of approx. 50 years . This may result in an overall positive trend
in the mean site chronology (Fig. 2) and mean shrub chronologies (Fig.
S2). Three different standardization or detrending methods were compared
for this study (Table S1, Fig. S3) to remove such growth trends: 1)
standardization with a horizontal line through the mean , 2) Regional
Curve Standardization , and 3) signal-free multiple RCS-standardization
. With the first method, each individual branch series was divided by
the related branch mean. In RCS-detrending age-related growth trends are
in principle removed, while lower-frequency climatic trends are retained
, with an age-related growth curve. In SF-MRCS detrending, multiple RCS
curves are calculated for different groups to prevent potential bias
resulting from differing growth rates of slow and fast growing
individuals . Here, RCS-curves were calculated for 11 groups (each with
n\textgreater{}50) of branch series ranging from the fastest growing
shortest series, to the slowest growing longest series. SF-MRCS
detrending was executed in CRUST . RCS detrending and detrending with a
horizontal line was performed in R version 4.0.3 with the `dplR' package
.

All methods resulted in a lowered first-order autocorrelation (Table
S1). RCS-detrending retained both short-term trends and the overall
positive trend in site-level mean shoot length growth. In contrast,
SF-MRCS detrending resulted in the removal of most trends. Detrending
with a horizontal line through the mean had an intermediate effect (Figs
2 and S3). Both RCS- and SF-MRCS detrending reduced the mean correlation
between individual branch series (rbar), signal-to-noise ratio, and
expressed population signal (EPS; Table S1), and these methods may thus
have removed some of the common climatic signal present within the
series. Therefore, the series standardized with a horizontal line were
used in subsequent analyses.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2 Raw (a) and standardized (b) shoot length growth series (grey
lines) together with the site mean (black lines). The red lines through
the site means are 30-year cubic smoothing splines with a 50 percent
frequency cut-off, which indicate the longer-term trends in the mean
site chronologies. The individual branch series in (b) were standardized
with a horizontal line through the mean. Sample depth (blue area) and
Subsample Signal Strength (SSS; ; red lines) for the raw (c) and
standardized (d) site chronology.

\emph{Climate data}

Hourly air temperatures measured at 2 m above ground approx. 3 km
southwest of the research site at ClimateBasis Disko (69.24 \selectlanguage{ngerman}°N, 53.53
°W, 1 m a.s.l.; Fig. 1b) covering the period 23.09.1993 -- 31.12.2019
were downloaded from the GEM database (https://data.g-e-m.dk accessed on
9.3.2021; ). Daily mean temperatures were calculated based on these
hourly measurements. Days with fewer than 16 hourly measurements were
not further processed. Mean monthly temperatures were calculated based
on the daily means for months with at least 20 daily means. This
resulted in a mean monthly temperature series from October 1993 to
December 2019, with gaps in 1993 (December), 1995 (May-July), 1997
(January, September -- December) and 1998 (January). These gaps were
filled and the monthly temperature record was extended back to 1880 with
temperatures from the homogenized monthly temperature dataset from
Ilulissat (; https://crudata.uea.ac.uk/cru/data/greenland/ accessed on
19.03.2021). Monthly mean temperatures from this dataset were
transferred using linear regression between this dataset and the
ClimateBasis monthly means to match the level of the latter. These
records are strongly correlated for each month (0.79\selectlanguage{english}[?]r\selectlanguage{english}[?]0.99,
p\textless{}0.0001), with on average slightly lower temperatures
recorded in Ilulissat.

Monthly precipitation sums (1901-2019) were extracted from the monthly
gridded dataset CRU TS4.04 for the grid 69-69.5 degN, 53.5-53 degW
(https://crudata.uea.ac.uk accessed on 14.5.2021; ). This gridded
dataset was extended through regression with monthly precipitation sums
calculated from daily data measured at Ilulissat (1880-1991; 69.22 degN,
51.1 degW, 39 m a.s.l.) as obtained from the Global Historical Climatology
Network (GHCN accessed on 10.3.2021; ). Precipitation sums from these
records are strongly correlated (0.80\selectlanguage{english}[?]r\selectlanguage{english}[?]0.97, p\textless{}0.0001) with
slightly higher values recorded in Ilulissat than suggested for the grid
matching the research site. The resulting monthly temperature and
precipitation records span the entire period of shrub growth (1892-2013)
studied here.

\emph{Climate-growth model comparisons}

To test the influence of seasonal climate on growth, linear
mixed-effects model analyses were performed. In these, climate--growth
models were compared with a null model. Before the analyses, both the
climate and standardized individual shoot length series were normalized.
This was done by subtraction of the mean of the series, followed by a
division with the standard deviation. Annual shoot lengths of the
individual branches were included in the models as the response
variable. A total of 20 climate-growth models were compared with the
null model. Mean temperatures or precipitation sums of the previous
summer (June-August of the previous year), previous autumn
(September-October of the previous year), early winter
(November-December of the previous year), mid-winter (January-February),
late winter (March-April), winter (previous November-April), spring
(May), early summer (June-July), late summer (July-August), or summer
(June-August) sums were included as fixed effect. Furthermore, an
autocorrelation structure (AR1, autoregressive process of order one) was
included in each model to account for possible autocorrelation in the
shoot length series. Random intercepts were included in the models
for\emph{year} to account for spatially correlated environmental
conditions not captured by the fixed effects. The null model was
identical to the climate models with a mean of one instead of a climate
variable as fixed effect. The linear mixed-effect model analyses were
performed with the R-package nlme v.3.1-152 . Maximum likelihood
estimation was used for model comparison and restricted maximum
likelihood estimation was used to calculate slope estimates .
Coefficients of determination values for each model were calculated
using the r.squaredGLMM function of the MuMIn package v.1.43.17 . The
year 2014 was excluded from the analyses, as growth may not have been
completed at the time of sampling in early August. The model comparison
was done both at the site and shrub level. To test the stability of the
climate-growth relationships over time, the model comparison was done
for three different periods: the complete period (1893-2013), the most
recent 50 years (1964-2013) as there was a clear increase in sample
depth after 1964 (cf. Fig. 2) and the last complete climate period
(1984-2013).

\emph{Branch initiation and mortality frequencies}

Branch initiation and mortality frequencies were measured to test for
links with climate. After cross-dating, in case the oldest part of a
branch was intact with visible WMS, the year its initiation was recorded
as the first calendar year of growth. This was possible for 717 of the
764 branches measured. In addition, the last year of growth was
determined for 96 branches which had an intact dead tip, with dark-brown
senesced primordial leaves. The first branch initiated in 1893. However,
as for many years during the period 1893-1928 no branch initiations were
recorded, branch initiation frequency analysis was restricted to the
period 1929-2013. The oldest dead branch dated, died in 1966 (last year
of growth) or before the growing season of 1967. Only four dead branches
could be dated that had died before 1987/1988. Hence, branch mortality
frequency analysis covered the period 1987-2013. Both frequency series
contained trends caused by either low number of old branches and a bias
towards analysis of longer branch series, i.e. relatively disregarding
the shortest branches (cf. Fig. 3a), or by senescence and organic
breakdown of dead branch tips, which easily break off, and a consequent
quick decline of intact dead branches over time (cf. Fig. 3b).
Therefore, both series were detrended with a cubic smoothing spline 2/3
of the series length and a 50 percent frequency cut-off (Fig. 3ab).

Both the branch initiation and mortality series as well as the climate
data were normalized by subtraction of the mean, followed by division
with the standard deviation, before testing for possible links. For the
latter Pearson's correlation coefficients were calculated. This was done
between standardized branch initiation frequency and seasonal climate
(mean temperatures and precipitation sums) from early summer prior to
branch initiation to late summer of the year of initiation. In addition,
correlation coefficients were calculated between standardized branch
mortality frequency and seasonal climate from spring of the last year of
growth to late winter after the last year of growth. The same
definitions of seasons were used as for the climate-growth models (see
above).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3 Branch initiation (a) and mortality (b) frequencies for the
periods 1929-2013 and 1987-2013 respectively. The black lines represent
the cubic smoothing splines (2/3 of the series length -- 50 percent
frequency cut-off) with which the series were detrended. Standardized
branch initiation frequency and previous summer (June-August)
temperature (r=-0.23, p\textless{}0.05) (c). Standardized branch
mortality frequency and following early winter (November-December)
temperature (r=0.59, p\textless{}0.002) (d).

\emph{NDVI}

Bi-weekly normalized difference vegetation index (NDVI) data for the
approx. 8 \selectlanguage{ngerman}× 8 km\textsuperscript{2} pixel incorporating the research
area was extracted from the third generation of the Global Inventory
Modeling and Mapping Studies (GIMMS\textsubscript{3g}) dataset for the
period July 1981 - 2015. The GIMMS\textsubscript{3g}-NDVI dataset is
assembled from different sensors of the Advanced Very High-Resolution
Radiometers (AVHRR) satellites. It was tested whether there were
positive linear trends in maximum NDVI (NDVI\textsubscript{max}) and
mean late summer (July-August) NDVI over time with one-sided Pearson's
correlation tests. Furthermore, it was tested whether there were
positive relationships between the mean standardized shoot length
chronology / branch initiation on the one hand and
NDVI\textsubscript{max} / late summer NDVI on the other. In addition,
negative relationships between standardized branch mortality frequencies
in the previous year and NDVI\textsubscript{max} / late summer NDVI were
hypothesized and tested. Lastly, positive relationships between seasonal
climate variables (mean temperatures and precipitation sums; the same as
in the climate-growth models) were hypothesized and tested for
significance with one-sided Pearson's correlation tests.

\textbf{Results}

\emph{Shoot length series}

A total of 12,528 annual growth increments were measured of 764 branches
and 25 shrubs, covering 122 years over the period 1893-2014 (Fig. 2,
Table S1). Mean annual shoot length was 5.43 ± 1.90 mm on site level and
ranged between 4.26 ± 2.10 mm and 10.20 ± 3.86 mm between shrubs (Table
S2). Individual shrub series spanned between 59 (1956-2014) and 122
(1893-2014) years (Fig. S2, Table S2). There were clear similarities in
growth variations over time between branch series, especially within,
but also between shrubs, as shown by the mean interseries correlation
coefficients of 0.53 and 0.42, respectively. The Gini coefficient was
0.19 across all specimen, suggesting intermediate variability in growth,
comparable to that found for the same species in the polar desert in
northern Greenland . Gini coefficients at the shrub level varied between
0.16 and 0.28, suggesting intermediate to high variability of growth of
individual shrubs in comparison to variation in tree-ring widths from
mesic to xeric sites .

\emph{Climate-growth: site level}

The climate-growth model comparison calculated at the site level over
the entire period (1893-2013) indicated that summer temperatures were
the main limiting climate factor for annual shoot length growth
of\emph{Cassiope tetragona} at the research site on Disko Island,
Greenland (Table 1, Fig. 4). Higher summer temperatures led to longer
shoot length increments (slope: 0.25 ± 0.03). The winter temperature
model (slope: 0.12 ± 0.04) was the best non-summer model at the site
level over this period, suggesting a positive influence of winter
temperatures on growth. The site-level climate-growth model comparison
computed over 1964-2013 gave similar results (Table S3). The mean summer
temperatures plotted together with mean site level shoot length (Fig.
4a) show that summer temperature and shoot length generally fluctuated
in the same direction from year-to-year. However, growth did not keep up
with extreme summer temperatures, especially during the warm summers of
the late 1940s, early 1960s and in recent decades. The site level model
comparison over 1984-2013 showed that summer temperatures were still the
most limiting climate factor for growth during this period, although
mean late summer (July-August) temperatures became more important than
mean summer (June-August) temperatures. Notably, the slopes of the
summer temperature models calculated over the recent period (1984-2013)
are lower than those calculated over the complete period (1893-2013).
The early summer precipitation model was the only non-summer temperature
model with \selectlanguage{english}[?]AIC\textgreater{}2, suggesting that June-July precipitation
sums have been a limiting factor for shoot length growth at the site
level during 1984-2013.

Table 1 Statistics of the selected climate-growth models
(\selectlanguage{english}[?]AIC\textgreater{}2) at the site level calculated over the periods
1893-2013 (a) and 1984-2013 (b). AIC, Akaike Information Criterion; SE,
standard error; mR\textsuperscript{2}, marginal coefficient of
determination (proportion of variance explained by fixed effect);
cR\textsuperscript{2}, conditional coefficient of determination
(proportion of variance explained by fixed and random effects); T, mean
temperature; P, precipitation sum; prev., previous.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
(a) \emph{1893-2013} & & & &\tabularnewline
\midrule
\endhead
model & \textbf{[?]AIC} & \selectlanguage{ngerman}\textbf{Slope ± SE} &
\textbf{m\emph{R}\textsuperscript{2}} &
\textbf{c\emph{R}\textsuperscript{2}}\tabularnewline
summer T & 49.63 & 0.25 \selectlanguage{ngerman}± 0.03 & 0.07 & 0.17\tabularnewline
early summer T & 44.64 & 0.24 \selectlanguage{ngerman}± 0.03 & 0.06 & 0.17\tabularnewline
late summer T & 36.76 & 0.22 \selectlanguage{ngerman}± 0.03 & 0.06 & 0.17\tabularnewline
winter T & 6.23 & 0.12 \selectlanguage{ngerman}± 0.04 & 0.01 & 0.16\tabularnewline
prev. fall P & 5.18 & 0.10 \selectlanguage{ngerman}± 0.04 & 0.01 & 0.17\tabularnewline
early winter T & 5.15 & 0.10 \selectlanguage{ngerman}± 0.04 & 0.01 & 0.16\tabularnewline
mid-winter T & 3.99 & 0.10 \selectlanguage{ngerman}± 0.04 & 0.01 & 0.16\tabularnewline
prev. summer T & 3.36 & 0.09 \selectlanguage{ngerman}± 0.04 & 0.01 & 0.16\tabularnewline
(b) \emph{1984-2013} & & & &\tabularnewline
model & \textbf{[?]AIC} & \selectlanguage{ngerman}\textbf{Slope ± SE} &
\textbf{m\emph{R}\textsuperscript{2}} &
\textbf{c\emph{R}\textsuperscript{2}}\tabularnewline
late summer T & 5.78 & 0.18 \selectlanguage{ngerman}± 0.06 & 0.04 & 0.18\tabularnewline
summer T & 4.42 & 0.17 \selectlanguage{ngerman}± 0.07 & 0.03 & 0.18\tabularnewline
early summer T & 2.76 & 0.15 \selectlanguage{ngerman}± 0.07 & 0.03 & 0.18\tabularnewline
early summer P & 2.32 & 0.20 \selectlanguage{ngerman}± 0.09 & 0.02 & 0.18\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4 Mean shoot length with the fixed factor of the best climate
model at the site level calculated over the periods 1893-2013 (a) and
1984-2013 (b), and with the best non-summer temperature model over the
periods 1893-2013 (c) and 1984-2013 (d). All parameters shown are
standardized (z-scores).

\emph{Climate-growth: shrub level}

A similar picture emerged from the climate-growth model analyses on the
individual shrub level (Table S4, Figs S4 and 5). Computed over the
complete period (1893-2013), all shrubs had a summer climate model as
best model. The summer temperature model was the best model for 12/25,
the late summer temperature model for 8/25, and the early summer
temperature model for 4/25 shrubs. For one shrub, the previous summer
precipitation model was the best model. All best models had a positive
slope, which ranged between 0.15 and 0.37 (Table S4a). For 14/25 shrubs
sampled, non-summer climate models had a [?]AIC\textgreater{}2, 11 of
which were winter temperature models: growth of five shrubs responded to
winter (November - April), three to early winter (November - December),
and three to late winter (March - April) temperatures. All winter
temperature models had a positive slope (between 0.18 and 0.33; Table
S4a), suggesting a beneficial effect of higher winter temperatures
preceding the growing season on shoot length development at the study
site for 11 shrubs. Climate in spring (May) was found to be the best
non-summer climate model for two of the shrubs: May temperature for one
and May precipitation for the other. Both these models had a positive
slope. Winter precipitation had a negative impact on shoot length of one
of the shrubs (DI25), which otherwise pre-dominantly responded to late
summer temperature. The model comparison calculated over 1964-2013 at
the individual shrub level produced comparable results (Table S5). As
seen on the site level, annual shoot length growth of many individual
shrubs tracked year-to-year variability in summer temperatures during
most years with divergence during warm extremes in recent decades, as
well as earlier periods (Figures S4a and S5). There was a spatial
pattern visible in the distribution of shrub response to summer climate,
with shrubs responding to early summer temperatures confined to up-slope
positions, a late summer temperature response found mostly downslope,
and a summer temperature response in between and at overlapping
positions with either (Fig. 5a).

Summer temperature models best explained growth for 12/25, i.e. less
than half of the shrubs, when the analysis was performed over the period
1984-2013 (Table S4b, Fig. S4b). Shoot length growth of seven shrubs
responded to late summer (July-August) temperature and that of five to
summer (June-August) temperature. These summer temperature models had a
positive slope, which ranged between 0.17 and 0.34 (Table S4b). For 5/25
shrubs, the early summer precipitation model was the best model (slope
between 0.25 and 0.38). For 4/25 shrubs, a non-summer climate model was
the best model. For two shrubs, early or late winter temperature models
best explained shoot length growth. Both models had a positive slope.
Two other shrubs showed a negative response to previous fall
temperatures (Table S4b). Four of the 25 shrubs did not respond to
climate over this period. Over 1984-2013, non-summer climate models had
a [?]AIC\textgreater{}2 for 11/25 shrubs, 7 of which were winter
temperature models: one early (prev. November- prev. December), three
late (March-April) and three winter (prev. November-April) temperature
models, all with a positive slope (Table S4b). Three shrubs showed a
negative response to previous fall temperatures. In contrast to the
calculation over 1893-2013, in the recent period (1984-2013) late winter
precipitation had a positive impact on shoot length of one of the shrubs
(DI24), which pre-dominantly responded to late summer temperature.
Shrubs with a non-summer model as best model and non-responsive shrubs
were generally positioned further upslope (Fig. 5c), as were shrubs with
previous fall temperature as best non-summer model (Fig. 5d).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\emph{Branch initiation and mortality}

Over the period studied (1929-2013) branch initiation frequency was
negatively correlated with mean summer temperatures in the year prior to
branch initiation (r=-0.23, p\textless{}0.05; Fig. 3c), i.e. warm
summers were linked with a lower amount of new branches formed in the
year after. Over the period 1987-2013, branch mortality frequency was
positively correlated with mean early winter temperatures following the
growing season in the year of death (r=0.59, p\textless{}0.002; Fig. 3d)
and with mean temperatures in spring prior to the growing season (0.50,
p\textless{}0.01). Higher temperatures before and after the growing
season were thus related with higher branch mortality.

\emph{NDVI}

Standardized mean site-level shoot length growth was positively
correlated with late summer (July-August) NDVI (r=0.44,
p\textless{}0.01, Fig. 6a) as well as NDVI\textsubscript{max} (r=0.40,
p\textless{}0.05). There was a weak link between standardized branch
initiation and NDVI\textsubscript{max} (r=0.26, p\textless{}0.1; Fig.
6b). There were no negative correlations between standardized branch
mortality frequency and NDVI in the research area. There was a slight
positive trend in late summer NDVI over the period 1981-2013 (r=0.24,
p\textless{}0.1; Fig. 6a), but not in NDVI\textsubscript{max}.

Late summer NDVI and NDVI\textsubscript{max} were positively related to
summer and, notably, winter temperatures (Table 3). Furthermore, there
were positive relationships between late summer NDVI and spring
precipitation sums, and weakly, late winter precipitation sums (Table
3).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Figure 6 Standardized mean shoot length site-chronology and mean late
summer (July-August) NDVI over 1981-2013 (r=0.44, p\textless{}0.01) (a).
There was a positive trend in late summer NDVI over this period (r=0.24,
p\textless{}0.1). Standardized branch initiation frequency and maximum
NDVI (r=0.26, p\textless{}0.1) (b).

Table 3 Pearson correlation coefficients between climate variables and
late summer (July-August) NDVI/ maximum NDVI over the research area.
Only coefficients with p\textless{}0.1 are
shown.\textsuperscript{.}p\textless{}0.1,
\textsuperscript{*}p\textless{}0.5,\textsuperscript{**}p\textless{}0.01.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\emph{variable} & late summer NDVI &
NDVI\textsubscript{max}\tabularnewline
\midrule
\endhead
summer T & 0.47\textsuperscript{**} &
0.48\textsuperscript{**}\tabularnewline
early summer T & 0.45\textsuperscript{**} &
0.44\textsuperscript{**}\tabularnewline
late summer T & 0.40\textsuperscript{*} &
0.35\textsuperscript{*}\tabularnewline
late winter T & 0.32\textsuperscript{*} &
0.25\textsuperscript{.}\tabularnewline
spring P & 0.30\textsuperscript{*} & -\tabularnewline
winter T & 0.30\textsuperscript{*} &
0.26\textsuperscript{.}\tabularnewline
late winter P & 0.26\textsuperscript{.} & -\tabularnewline
early winter T & 0.23\textsuperscript{.} & -\tabularnewline
\bottomrule
\end{longtable}

\textbf{Discussion}

\emph{Temperature-growth decoupling}

The shoot length growth response to summer temperatures of\emph{Cassiope
tetragona} at the site level over the entire period studied (1893-2013)
can be classified as highly sensitive ([?]AIC = 49.63; cf. ). However, in
recent decades (1984-2013) this sensitivity can be categorized as low
([?]AIC = 5.78) and it was completely absent for over half of the
individuals studied, as shown by the analyses on individual shrub level.
While late summer (July-August) temperatures were the most limiting
factor for growth at the site level over the period 1984-2013, early
summer precipitation was identified as an important co-driver of growth
at the site level and as the most limiting factor for 20 percent of the
individuals studied here. A similar divergence between growing season
temperatures and evergreen dwarf shrub growth has recently been
demonstrated in the French Alps for the species \emph{Rhododendron
ferrugineum} .

The observed lower sensitivity of shrub growth to summer temperature in
recent decades was probably caused by the on average warmer summers
during this period (7.13 \selectlanguage{ngerman}°C in 1984-2013 vs. 6.40 °C in 1893-1983,
p\textless{}0.001; Fig. S6). This, in combination with the observed
non-linear growth response to summer temperatures, with a smaller, lack
of, or even negative response to mean summer temperatures exceeding a
threshold of approx. 7 °C (Fig. S5). Although the shrubs were capable of
maintaining high levels of shoot length growth in summers with mean
temperatures exceeding this threshold, other factors than summer
temperature became more limiting for the majority of the shrubs studied.
Interestingly, reported a weakening in temperature-growth relationships
in \emph{Betula nana} and \emph{Salix glauca} shrubs in more continental
Greenland, 272 km southeast of Disko Island, when summer (there:
May-August) temperatures passed a threshold of approx. 7 °C. A similar
non-linear response of \emph{C. tetragona} shoot length growth was
observed over a gradient of growing degree-day sums
(GDD\textsubscript{5}; cumulative daily mean temperatures above 5 °C)
across three sites, two in High Arctic Svalbard, one in Sub-Arctic
Sweden . It is, therefore, likely that this evergreen dwarf shrub
species will not be able to respond with increased growth rates to
warmer summers in the Low and Sub-Arctic under future global warming.
This may lower its competitiveness, especially for light, in relation to
taller deciduous shrub species, which are migrating into tundra
ecosystems . However, growth of such species has been shown to be less
sensitive to warming at drier sites, than at moister sites . The dwarf
shrub \emph{C. tetragona} , on the other hand, is restricted to
relatively dry soils and adapted to dry conditions with its xeromorphic
leaves . Therefore, it may remain competitive in drier habitats.

In this study, a positive influence of summer precipitation on growth
of\emph{C. tetragona} was detected for some of the shrub individuals
studied here over the period 1984-2013. Summer precipitation was
observed to be a co-driver of \emph{C. tetragona} growth for one of four
sites on Ellesmere Island in High Arctic Canada . However, a similar
influence had not yet been observed for this species at other sites. Not
at relatively warm tundra sites in the Sub-Arctic (; ), nor in the High
Arctic , including the northernmost polar desert in northern Greenland .
In addition, \emph{C. tetragona} growth did not respond to
experimentally doubled summer precipitation in High Arctic Svalbard .
The reason for a lack of detection of a summer precipitation signal in
other Arctic sites could be (micro)site-specific. Soil moisture
availability may be largely decoupled from summer precipitation and
instead determined by downslope meltwater run-off, active layer thaw,
and low evapotranspiration rates in the High Arctic . At Arctic-alpine
sites with late snow cover, where the species is found , snowmelt likely
provides sufficiently high soil moisture levels throughout the growing
season. In addition, at other sites, climate-growth relationships have
not been assessed at the individual level and sensitivity to
precipitation may not only be site-specific, but also micro-site
specific, due to, for example greater soil moisture availability in
small depressions or snow accumulation behind rocks. Remarkably, two of
the shrubs studied here (DI15 and DI16) grew near rocks and their growth
remained sensitive to summer temperatures in recent decades (Fig. S4).
Furthermore, climate-growth links have mostly solely been studied over
longer periods, and did not focus on the recent warm period. However,
growth of the deciduous dwarf shrub \emph{Betula nana} in the
northeastern Siberian tundra was shown to be sensitive to summer
precipitation in warm, but not in cool summers . In addition, shoot
length growth of \emph{C. fastigiata} was detected to be sensitive to
moisture availability in the tree-line ecotone and growth
of\emph{Juniperus indica} shrubs was found to be driven by early growing
season precipitation , both in the central Himalayas of Nepal. In
addition, low amounts of summer precipitation as a co-determinant of
drought conditions has been shown to indirectly drive declining
deciduous shrub growth at drier sites, while summer temperature drives
increasing deciduous shrub growth at wetter sites in the Arctic .

\emph{Influence of winter temperatures}

Winter temperatures preceding the growing season were observed to be the
main co-driver of \emph{C. tetragona} growth at the site level and for
11/25 shrubs studied over the period 1893-2013. In recent decades, it
was found to be a co-driver for five shrubs and the main climatic driver
for two. Notably, a similar positive effect of warm winters on dwarf
shrub growth was observed for the deciduous dwarf shrub \emph{Betula
nana} at an adjacent site on Disko Island . In their study, the positive
effect of winter temperatures on growth was interpreted as a result of
warmer winters and related spring air temperatures causing snow to melt
out earlier, allowing soils to drain and warm faster. This then results
in longer growing seasons, leading to increased growth. For \emph{C.
tetragona} at the study site this mechanism is less plausible, as early
summer temperatures were the main climatic driver of only four shrubs
(1893-2013) and of none in recent decades. This suggests that this
species may not benefit from earlier snow melt-out, which is in line
with the observation that \emph{C. tetragona} growth benefits from
warmer Julys, but not from longer growing seasons near its European
southern distribution limit in Abisko, Sweden . An alternative
explanation could be grounded on the fact that winter air temperatures
are positively related to winter precipitation sums in Disko Island
(Figs S7 and S8). In warmer winters, an increase in winter snow
precipitation may result in a deeper snowpack and higher winter soil
temperatures due to insulation. Moreover, in recent decades winter soil
temperatures have been directly coupled with winter air temperatures in
the area . Warmer winter soil temperatures may be linked to higher
microbial activity in winter and result in greater nutrient availability
in the subsequent growing season and hence support increased shrub
growth. In addition, warm winters are negatively linked with sea ice
extent in the Disko Bay area and earlier sea ice retreat leads to warmer
summers . This may explain the positive correlation between (late)
winter and summer temperatures at the site (Figs S7 and S8) and partly
or alternatively explain the observed association between winter
temperatures and \emph{C. tetragona} growth. A similar possible link
between sea-ice conditions prior to the growing season, summer
temperatures, and \emph{C. tetragona} growth has previously been
suggested in northern Greenland .

\emph{Micro-topography}

Shrubs responsive to early summer temperatures were restricted to
upslope positions, while those responsive to late summer temperatures
were found further downslope. This pattern may be related to the
redistribution of snow by wind with accumulation of snow according to
topography . In the study area, winter winds blow mainly from northern
direction , and the downslope positions may thus be more wind-sheltered
and be covered in a deeper snow layer. Upslope positions would then be
snow-free earlier, which may explain the responsiveness to early summer
temperatures found there. Upslope soils will drain and warm faster and
are thus drier, which could be the reason for the response to previous
summer precipitation of the northernmost shrub in this study. This could
also explain the negative response to previous autumn temperatures over
the period 1984-2013 of some of the upslope shrubs, with warm dry
autumns resulting in lower soil moisture availability in the following
growing season. A negative influence of autumn temperatures was recently
also observed for shoot length growth of the related boreal
shrub\emph{Empetrum nigrum} , growing near its southern lowland
distribution limit in northern Germany . Over the period 1984-2013,
non-responsive shrubs were mostly found at upslope positions and shrubs
responsive to early summer precipitation sums mostly mid-slope. Shrubs
at downslope positions were mostly still responsive to late summer
temperatures. This is in line with the expected gradient in snow depth.

\emph{Branching and branch mortality}

Warm early winters (November-December) after the growing season may have
led to higher branch mortality rates. In years with high branch
mortality, hourly temperatures were in general more frequently above
freezing point in November-December than in years with low branch
mortality (Fig. S9). Such winter warming events can cause reduced snow
cover leaving the plants vulnerable to subsequent freezing damage and
dieback , as observed for \emph{C. tetragona} in Svalbard . Moreover,
winter warming often coincides with rain-on-snow events , the frequency
of which has increased in Greenland in recent decades . Such events can
lead to encasement of plants in ground-ice, which increases branch
mortality in \emph{C. tetragona} , as has been shown experimentally in
Svalbard . In the same icing-experiment, it was found that shoot length
of surviving branches increases after icing treatment. This may offer an
additional or alternative explanation for the positive influence of
winter temperatures on \emph{C. tetragona} observed here.

Warm springs (Mays) prior to the growing season may have resulted in
increased branch mortality, following a similar mechanism. Spring
warming may lead to an advanced start of the growing season. This can
leave plants exposed to late frost, which can result in damage to soft
tissues and reduced growth, as was observed the evergreen
shrubs\emph{Empetrum nigrum} ssp. \emph{hermaphroditum} , at a near
tree-line site in the Central Norwegian Scandes and \emph{Rhododendron
ferrugineum} at a low-alpine site in the French Alps .

Branching occurred more often after years with cool summers than after
years with warm summers. There was also a negative correlation between
shoot length growth in the previous year and branch initiation frequency
(r=-0.21, p\textless{}0.05). Hence, when shoot length growth in the
previous growing season is below average, perhaps due to a cool
summer,\emph{C. tetragona} shrubs tend to invest more in new branches to
increase or maintain its photosynthetic capacity. observed an increase
in the number of branches in \emph{C. tetragona} shrubs in response to
experimental shading. Similarly, the shrubs seem to form more
side-branches after recent stress, such as damage to branch tips (own
observation). Dead or damaged \emph{C. tetragona} shoot tips with a
cluster of new branches are also a remarkable feature of shrubs exposed
to experimental icing . Likewise, browsing by ptarmigan and moose leads
to increased production of vegetative shoots in the deciduous tall
shrub\emph{Salix alaxensis} in northern Alaska . The capacity of making
new branches, sometimes, though rarely, on decades-old \emph{C.
tetragona}stem segments, may be related to their presence at the basal
part of annual shoot increments , i.e. near WMS, which consist of green
meristem tissue. \emph{C. tetragona} may thus be well-adapted to and
capable of recovery from branch dieback caused by winter warming events,
as its primordia are often protected under old leaves at lower stem
positions.

\emph{Correspondence between growth, branching and NDVI}

Annual variation in \emph{C. tetragona} shoot-length growth and
branching frequency positively related to fluctuations in
satellite-observed vegetation greenness over the research area. This
suggests that there is, at least to some degree, correspondence in
annual variation in community-level vascular plant biomass. This is in
line with , who found that annual growth of this species represents
annual above-ground tundra vegetation productivity at the local scale.
Moreover, the same climate variables, i.e. summer and winter
temperatures, as observed for the evergreen dwarf shrub \emph{C.
tetragona} drove year-to-year satellite-observed variation in greenness.
The same climate parameters determine growth rates of the co-dominant
deciduous dwarf shrub \emph{B. nana} in the research area in the
Blæsedalen valley on Disko Island . Such similarity in climatic drivers
of growth patterns between shrub species with contrasting life
strategies and NDVI was earlier observed over Arctic-alpine areas in
northwest North America . The lack of a link between branch mortality
and NDVI may be due to increased branching after winter warming events .
Satellite-observed vegetation productivity in the research area did not
correlate with early summer precipitation, but there were positive
relations with late winter and spring precipitation. Growth and
productivity of the shrub-dominated vegetation in the Blæsedalen valley
may thus be increasingly moisture-limited. It is, however, not in
decline, as observed for \emph{B. nana} and \emph{S. glauca} in a warmer
and drier area on the mainland of Greenland , approx. 270 km
south-southeast of Disko Island. While there was a slight increasing
trend in late summer NDVI, there was no trend in
NDVI\textsubscript{max}, which is in line with the widespread vegetation
stability observed over large parts of the Arctic .

\textbf{Conclusions}

Since at least the late 19\textsuperscript{th} century, summer
temperatures have been the main climatic driver of growth of the
evergreen dwarf shrub \emph{Cassiope tetragona} at the Low Arctic
research site near Qeqertarsuaq on the southern tip of Disko Island,
western Greenland. Winter temperatures have been a co-driver of shrub
growth. In recent and past warm decades, however, shrub growth has
diverged from summer temperatures. For some individual shrubs,
apparently mostly those growing at positions with lower soil moisture
availability as determined by micro-topography, early summer
precipitation has become the main driver during the recent climatic
period (1984-2013), while over half of the shrubs studied became
insensitive to summer temperatures. The correspondence between climatic
drivers, \emph{C. tetragona} growth and branch initiation frequency, and
satellite-observed vegetation productivity (NDVI), suggests that the
climate-growth observations for \emph{C. tetragona} at least partly
apply to the entire shrub-dominated vegetation in the research area.
Winter warming events are likely the most prevalent cause of branch
mortality in \emph{C. tetragona} . Increased branching after years with
poor growth and cooler than average summers, seems related to stress and
may enable the shrub to overcome damage, for example, caused by winter
warming events.

These findings suggest that erect dwarf-shrub tundra in the Low Arctic
will likely become decreasingly temperature- and increasingly
moisture-limited under future warming and that, although winter warming
supports shrub growth, increased frequency of extreme winter warming
events will likely lead to increased branch mortality and damage the
dwarf shrub tundra. The observed divergence between summer temperatures
and shrub growth during warm phases and the contrasting, potentially
balancing effects of warmer winter temperatures may offer a mechanistic
explanation for the still widespread stability of Arctic tundra
vegetation.

\textbf{Acknowledgements}

Fieldwork for this study, including travel to and stay at Arctic Station
was funded through the transnational access program INTERACT --
International Network for Terrestrial Research and Monitoring in the
Arctic. I am grateful to the Government of Greenland for sampling
permission. I express my thanks to Anders Michelsen and Daan Blok for
support with fieldwork preparation and sample transport. I thank Lennard
van Rij for field assistance and Till Groh for lab assistance.

\textbf{Authors' contributions}

SW conceived and designed the study, performed the analyses and wrote
the manuscript.

\textbf{Conflict of Interest}

There is no conflict of interest.

\textbf{Data Accessibility}

\begin{itemize}
\tightlist
\item
  Shoot length data will be made available at DataDryad.org upon
  acceptance of the manuscript for publication.
\item
  Hourly air temperatures from ClimateBasis Disko: GEM database:
  https://data.g-e-m.dk
\item
  Ilulissat homogenized monthly temperatures:
  https://crudata.uea.ac.uk/cru/data/greenland/
\item
  CRU TS4.04: https://crudata.uea.ac.uk
\item
  Ilulissat monthly precipitation sums: Global Historical Climatology
  Network (GHCN): https://www.ncei.noaa.gov
\item
  Bi-weekly normalized difference vegetation index (NDVI) data from the
  Global Inventory Modeling and Mapping Studies (GIMMS3g) dataset:
  http://poles.tpdc.ac.cn/en/data/9775f2b4-7370-4e5e-a537-3482c9a83d88/
\end{itemize}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-5/Figure-5}

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