Intraspecific trait variability is a key feature underlying high Arctic plant community resistance to climate warming

Abstract

In the high Arctic, plant community species composition generally responds slowly to climate warming, whereas less is known about the community functional trait responses and consequences for ecosystem functioning. The slow species turnover and large distribution ranges of many Arctic plant species suggest a significant role of intraspecific trait variability in functional responses to climate change. Here we compare taxonomic and functional community compositional responses to a long‐term (17‐year) warming experiment in Svalbard, Norway, replicated across three major high Arctic habitats shaped by topography and contrasting snow regimes. We observed taxonomic compositional changes in all plant communities over time. Still, responses to experimental warming were minor and most pronounced in the drier habitats with relatively early snowmelt timing and long growing seasons (Cassiope and Dryas heaths). The habitats were clearly separated in functional trait space, defined by 12 size‐ and leaf economics‐related traits, primarily due to interspecific trait variation. Functional traits also responded to experimental warming, most prominently in the Dryas heath and mostly due to intraspecific trait variation. Leaf area and mass increased and leaf δ¹⁵N decreased in response to the warming treatment. Intraspecific trait variability ranged between 30% and 71% of the total trait variation, reflecting the functional resilience of those communities, dominated by long‐lived plants, due to either phenotypic plasticity or genotypic variation, which most likely underlies the observed resistance of high Arctic vegetation to climate warming. We further explored the consequences of trait variability for ecosystem functioning by measuring peak season CO2 fluxes. Together, environmental, taxonomic, and functional trait variables explained a large proportion of the variation in net ecosystem exchange (NEE), which increased when intraspecific trait variation was accounted for. In contrast, even though ecosystem respiration and gross ecosystem production both increased in response to warming across habitats, they were mainly driven by the direct kinetic impacts of temperature on plant physiology and biochemical processes. Our study shows that long‐term experimental warming has a modest but significant effect on plant community functional trait composition and suggests that intraspecific trait variability is a key feature underlying high Arctic ecosystem resistance to climate warming.

Full text

ARTICLE
Intraspecific trait variability is a key feature
underlying high Arctic plant community resistance
to climate warming
Ingibjörg S. J
onsd
ottir
1,2
| Aud H. Halbritter
3
| Casper T. Christiansen
4,5,6
|
Inge H. J. Althuizen
4
| Siri V. Haugum
3
| Jonathan J. Henn
7,8
|
Katrín Björnsd
ottir
9
| Brian Salvin Maitner
10
| Yadvinder Malhi
11
|
Sean T. Michaletz
12
| Ruben E. Roos
13
| Kari Klanderud
13
|
Hanna Lee
3,14
| Brian J. Enquist
15
| Vigdis Vandvik
3,4
1
Institute of Life and Environmental
Sciences, University of Iceland, Reykjavik,
Iceland
2
University Centre in Svalbard,
Longyearbyen, Norway
3
Department of Biological Sciences,
University of Bergen, Bergen, Norway
4
Bjerknes Centre for Climate Research,
University of Bergen and NORCE
Climate, Bergen, Norway
5
Center for Permafrost (CENPERM),
Department of Geoscience and Natural
Resource Management, University of
Copenhagen, Copenhagen, Denmark
6
Terrestrial Ecology Section, Department
of Biology, University of Copenhagen,
Copenhagen, Denmark
7
Department of Evolution, Ecology, and
Organismal Biology, University of
California Riverside, Riverside,
California, USA
8
Institute of Arctic and Alpine Research,
University of Colorado Boulder, Boulder,
Colorado, USA
9
Department of Biological and
Environmental Sciences, University of
Gothenburg, Gothenburg, Sweden
10
Department of Ecology and
Evolutionary Biology, University of
Connecticut, Storrs, Connecticut, USA
Abstract
In the high Arctic, plant community species composition generally responds
slowly to climate warming, whereas less is known about the community func-
tional trait responses and consequences for ecosystem functioning. The slow
species turnover and large distribution ranges of many Arctic plant species
suggest a significant role of intraspecific trait variability in functional
responses to climate change. Here we compare taxonomic and functional com-
munity compositional responses to a long-term (17-year) warming experiment
in Svalbard, Norway, replicated across three major high Arctic habitats shaped
by topography and contrasting snow regimes. We observed taxonomic compo-
sitional changes in all plant communities over time. Still, responses to experi-
mental warming were minor and most pronounced in the drier habitats with
relatively early snowmelt timing and long growing seasons (Cassiope and
Dryas heaths). The habitats were clearly separated in functional trait space,
defined by 12 size- and leaf economics-related traits, primarily due to interspe-
cific trait variation. Functional traits also responded to experimental warming,
most prominently in the Dryas heath and mostly due to intraspecific trait vari-
ation. Leaf area and mass increased and leaf δ
15
N decreased in response to the
warming treatment. Intraspecific trait variability ranged between 30% and 71%
of the total trait variation, reflecting the functional resilience of those commu-
nities, dominated by long-lived plants, due to either phenotypic plasticity or
genotypic variation, which most likely underlies the observed resistance of
high Arctic vegetation to climate warming. We further explored the conse-
quences of trait variability for ecosystem functioning by measuring peak sea-
son CO
2
fluxes. Together, environmental, taxonomic, and functional trait
Received: 2 March 2022 Revised: 3 August 2022 Accepted: 22 August 2022
DOI: 10.1002/ecm.1555
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2022 The Authors. Ecological Monographs published by Wiley Periodicals LLC on behalf of The Ecological Society of America.
Ecological Monographs. 2023;93:e1555. https://onlinelibrary.wiley.com/r/ecm 1of21
https://doi.org/10.1002/ecm.1555

11
Environmental Change Institute, School
of Geography and the Environment,
University of Oxford, Oxford, UK
12
Department of Botany and Biodiversity
Research Centre, The University of British
Columbia, Vancouver, British Columbia,
Canada
13
Faculty of Environmental Sciences and
Natural Resource Management, Ås,
Norway
14
Department of Biology, Norwegian
University of Science and Technology,
Trondheim, Norway
15
Department of Ecology and
Evolutionary Biology, University of
Arizona, Tucson, Arizona, USA
Correspondence
Vigdis Vandvik
Email: vigdis.vandvik@uib.no
Funding information
Norges Forskningsråd, Grant/Award
Numbers: 274831, 246080/e10, 294948;
Svalbardstiftelsen; The Norwegian Agency
for International Cooperation,
Grant/Award Number: HNP-2015/10037
“TraitTrain”; University of Iceland
Research Fund; University Centre in
Svalbard
Handling Editor: Daniel C. Laughlin
variables explained a large proportion of the variation in net ecosystem
exchange (NEE), which increased when intraspecific trait variation was
accounted for. In contrast, even though ecosystem respiration and gross eco-
system production both increased in response to warming across habitats, they
were mainly driven by the direct kinetic impacts of temperature on plant phys-
iology and biochemical processes. Our study shows that long-term experimen-
tal warming has a modest but significant effect on plant community functional
trait composition and suggests that intraspecific trait variability is a key feature
underlying high Arctic ecosystem resistance to climate warming.
KEYWORDS
climate change, CO
2
fluxes, community resilience, community resistance, experimental
warming, intraspecific trait variation, plant community change, plant functional traits,
Svalbard
INTRODUCTION
In the rapidly warming Arctic (IPCC, 2021), warming-
induced vegetation changes may mediate tundra
ecosystem functioning (Mekonnen et al., 2021), potentially
with positive climate feedback consequences. In general,
climate warming has stimulated Arctic plant growth in
recent decades, reflected by plant community changes,
increased plant abundance, and taller plants across the
Arctic in local plots (e.g., Bjorkman et al., 2018;
Elmendorf, Henry, Hollister, Björk, Boulanger-Lapointe,
et al., 2012a), as well as at larger scales (Bhatt et al., 2017;
Goetz et al., 2005). However, Arctic plant communities do
not respond evenly to climate warming across the tundra
biome or across habitats within sites (Bhatt et al., 2017;
Bjorkman et al., 2020; Elmendorf, Henry, Hollister,
Björk, Boulanger-Lapointe, et al., 2012a;Myers-Smith
et al., 2020). What drives heterogeneity in warming
responses in the tundra and the consequences of this
variability for ecosystem functioning and resilience to
climate change are not fully understood.
Overall, warming-induced changes in plant com-
munity structure and species composition have been
more prevalent at relatively warm low Arctic sites than
at colder high Arctic sites (e.g., Elmendorf, Henry,
Hollister, Björk, Bjorkman, et al., 2012b;Elmendorf,
Henry, Hollister, Björk, Boulanger-Lapointe,
et al., 2012a). This may seem surprising given the
higher rates of warming in the high Arctic
(IPCC, 2021), but it can be attributed to several
interacting biotic and abiotic factors. The small species
pools in the high Arctic relative to the low Arctic con-
sist of species under more severe life-history constraints
(Arft et al., 1999)thatareadaptedtomoresignificant
interannual climatic variability and shorter growing sea-
sons with longer photoperiods (Hudson & Henry, 2010;
J
onsd
ottir, 2005). At smaller spatial scales, greater plant
community responsiveness has been observed in wet rela-
tive to dry tundra habitats within both low Arctic
(Bjorkman et al., 2020; Elmendorf, Henry, Hollister, Björk,
Boulanger-Lapointe, et al., 2012a) and high Arctic sites
(Edwards & Henry, 2016; Hudson et al., 2011). It is
unknown, however, whether and how the observed resis-
tance of species composition to climate warming in the
high Arctic is associated with responses in plant commu-
nity functional composition.
2of21 JÓNSDÓTTIR ET AL.

Trade-offs limit the extent of variation in functional
traits that affect plant fitness, resulting in relatively few
essential trait combinations (Díaz et al., 2016). Globally,
most of the aboveground trait combinations (i.e., the
functional space) can be captured by two trait dimen-
sions, size (of whole plants as well as their parts) and leaf
economics spectrum, which balance leaf construction
costs against growth potential (Díaz et al., 2016). In turn,
plant functional traits may affect ecosystem carbon
sequestration, litter decomposition, and nutrient cycling
(e.g., Díaz et al., 2004; Sørensen et al., 2019). Although
tundra plant species only occupy a small part of the
global trait space along the size dimension, they demon-
strate almost the full trait variation along the leaf eco-
nomics spectrum. However, this variability also becomes
increasingly constrained toward the colder parts of the
tundra (Thomas et al., 2020).
Recent climate warming of the tundra biome has
been associated with a general shift in Arctic plant
communities toward taller plants. In contrast, the
strength and direction of warming responses of other size
traits and leaf economics are more variable, governed by
local conditions such as soil moisture (Bjorkman
et al., 2018). Less is known about whether and how other
aspects of the Arctic environment influence plant func-
tional trait responses to warming and to what extent
intraspecific trait variation (ITV) contributes to those
responses. For instance, snow regimes in Arctic land-
scapes are strongly shaped by topography, affecting both
growing season length and moisture (Rixen et al., 2022;
Walker, 2000) and may, in turn, affect tundra plant com-
munity diversity and functional responses to warming
(e.g., Niittynen et al., 2020).
To date, most studies of plant functional traits in
tundra systems have relied on species-level trait infor-
mation and so ignore ITV (e.g., Bjorkman et al., 2018;
Kemppinen et al., 2021). ITV can reflect both pheno-
typic plasticity within an individual and genetic differ-
entiation within a population (Albert et al., 2010;
Bolnick et al., 2011), offering two potential mechanisms
for functional responses to environmental change in sys-
tems with limited taxonomic change (i.e., plastic
responses and differential mortality, respectively).
Globally, ITV accounts for around 25% of plant commu-
nity trait variation (Siefert et al., 2015), with an even
more significant relative contribution locally (Messier
et al., 2017;Thomasetal.,2020). In general, ITV should
be relatively more important in communities with low
species richness (Siefert et al., 2015;Thomasetal.,2020)
and in communities dominated by species with broad
environmental niches (Bolnick et al., 2011;Sides
et al., 2014) or high phenotypic plasticity (Albert
et al., 2010). According to the climate variability
hypothesis (Spicer & Gaston, 1999), greater temperature
variability selects organisms with broad thermal toler-
ances. There is evidence that geographic range size is
related to niche breadth (Slatyer et al., 2013). In the high
Arctic, species richness is relatively low, and most plant
species have wide distribution ranges and are adapted to
high seasonal climate variability (e.g., J
onsd
ottir, 2005),
characteristics that predict relatively large ITV.
Furthermore, most Arctic plant species are long-lived
(J
onsd
ottir, 2011), which may slow down species turn-
over in response to climate warming, thereby enhancing
the resistance of plant community species composition
to warming and further increasing the relative impor-
tance of ITV in functional responses.
One of the most critical climate-related services that
permafrost-affected ecosystems provide relates to carbon
storage (IPCC, 2021). Abiotic drivers are usually consid-
ered the primary determinants of the ecosystem functions
associated with this climate feedback property (Schuur
et al., 2015). However, plant functional trait composition
also affects carbon balance. At the individual level, leaf
chemical and structural traits strongly influence
carbon-cycling processes as plants toward the fast-return
end of the leaf economics spectrum produce thin and
nutrient-rich leaves (e.g., leaves with high specific leaf
area [SLA] and N content; Wright et al., 2004) that pro-
mote higher photosynthesis rates (Reich et al., 1997) and
fast decomposition by soil microbes (Funk et al., 2017).
Leaf economics also scales to the community level, where
canopy N and leaf area index correspond well with pri-
mary production rates (Reich et al., 2014). In Arctic tun-
dra, habitats dominated by different plant functional
groups exhibit differences in carbon cycling processes.
Specifically, tall deciduous shrubs accelerate CO
2
exchange and decomposition rates either directly or indi-
rectly through internal ecosystem feedback
(e.g., Christiansen et al., 2018; Kropp et al., 2020;
Lafleur & Humphreys, 2018; Myers-Smith et al., 2011),
suggesting that functional traits should relate well to eco-
system function. Indeed, moving beyond functional
group classifications, recent studies showed that commu-
nity means of leaf economics and plant height affect tun-
dra ecosystems in their ability to fix CO
2
(Happonen
et al., 2022; Sørensen et al., 2018) owing to (1) the
trade-offs associated with being a “fast”or “slow”species
(Reich, 2014; Wright et al., 2004) and (2) plant height
scaling positively with size and, therefore, photosynthetic
tissue, i.e., leaf area (Lafleur & Humphreys, 2018).
Consequently, climate warming–induced plant commu-
nity changes, leading to tundra plant communities with
taller plants (Bjorkman et al., 2018), will likely cause sig-
nificant shifts in ecosystem properties related to carbon
cycling. These changes should be quantifiable and
ECOLOGICAL MONOGRAPHS 3of21

predictable using functional trait approaches—alone or
in concert with environmental drivers (Díaz et al., 2007).
Nevertheless, the utility of functional approaches in
predicting ecosystem responses has recently been
questioned (Funk et al., 2017), and few studies have
investigated the direct effects of functional trait change
and climate warming on tundra CO
2
exchange rates.
This study investigated the functional responses of
high Arctic vegetation to long-term experimental
warming. Specifically, we asked whether and how the
apparent resistance of high Arctic plant communities to
warming was mediated by ITV and to what extent such
trait variability within species has consequences for eco-
system functioning. To address these questions, we ana-
lyzed plant community composition data from a long-term
(17-year) warming experiment replicated across three
major habitats shaped by topography and contrasting
snow regimes in high Arctic Svalbard. In the final year, we
augmented these community data with detailed plot-scale
measurements of 12 size- and leaf economics–related plant
functional traits for all vascular plant species present. We
also measured peak-season carbon fluxes in the experi-
mental plots in the final study year to explore the potential
roles of the environment, plant community taxonomy, and
plant functional trait composition in mediating ecosystem
responses to warming.
We hypothesize that (1) in line with other high
Arctic sites, the taxonomic composition of the plant
community does not change, or only weakly changes, in
response to long-term experimental warming. Based on
the foregoing literature review, we further hypothesize
that (2) the plant functional trait composition is affected
by experimental warming and (3) if both 1 and 2 are
true, then we expect most community-level functional
trait responses to experimental warming to be driven by
ITV. Finally, we expect (4) rates of photosynthesis and
respiration to be governed by the realized trait variation
across habitats and climate warming treatments.
Incorporating ITV should thus improve model perfor-
mance and explanatory power for models of ecosystem
carbon fluxes.
METHODS
Study area
Annual temperature (1981–2000) at Svalbard Airport
(10 km northwest of the experimental site) averaged
4.6C, and annual precipitation averaged 191 mm
(Førland et al., 2011). The linear trend in mean annual
temperature during 1889–2018 was an increase of 0.32C
per decade and by 1.66C per decade from 1991 to 2018
(Nordli et al., 2020). Similarly, summer temperatures
(June–August) increased by 0.13C per decade during
1889–2018 and by 0.66 per decade during 1991–2018. The
experimental site (78110N, 15450E) is situated at the
south–southeast–facing slope of the Endalen valley at
approximately 80 m above sea level, where distinct habi-
tats are shaped by topography and snow accumulation in
winter. The experiment includes three common Arctic
habitats: a snowbed where deep snow accumulates
(>100 cm) causing late snow melt (on average in
mid-June), a Cassiope heath at intermediate exposure,
snow depth, and snowmelt time, and a more exposed
Dryas heath with only shallow snow (up to 10 cm) and
early snowmelt (on average in mid-May). Consequently,
the growing season duration varies between habitats and
is ~2.5, 3, and 3.5 months in the snowbed, Cassiope, and
Dryas heath, respectively. The soils are typical Cryosols
with a thin organic layer on top of inorganic sediments
(Jones et al., 2010). Soil moisture is related to topography
and increases with snow accumulation in winter.
In the snowbed, total vascular plant cover is low,
characterized by the forb Bistorta vivipara, the deciduous
prostrate dwarf shrub Salix polaris, and the grasses Poa
arctica and Festuca richardsonii. The moss cover is
around 70%, relatively deep (>5 cm), and dominated by
Tomentypnum nitens and Sanionia uncinata. In the
Cassiope heath, the erect evergreen dwarf shrub Cassiope
tetragona dominates. Other abundant vascular plant spe-
cies are S. polaris and B. vivipara, and the most abundant
moss species are T. nitens and S. uncinata. In the Dryas
heath, the evergreen dwarf shrub Dryas octopetala domi-
nates. Other abundant vascular species are Carex
rupestris,B. vivipara, and S. polaris. The moss layer is
well developed but shallower than in the other habitats,
with S. uncinata as the most abundant species.
Experimental design
In 2001, ten 75 75-cm (0.56 m
2
) permanent plots were
established in each of the three habitats. Five plots in
each habitat were randomly assigned to a warming treat-
ment in the following year. The other five served as con-
trols. Climate warming was simulated by the use of
hexagonal Plexiglas open-top chambers (OTCs) (Molau &
Mølgaard, 1996), with a basal diameter of 150 cm
(ca. 1.8 m
2
) and 47 cm in height, leaving ample space
inside the chamber for plant sampling for trait measure-
ments without disturbance to the permanent plot or
risking edge effects. Following rain-on-snow events in
the winter of 2008–2009, a large proportion of the
Cassiope shrubs were killed by basal ice formation in two
control plots and two OTC plots in the Cassiope heath.
4of21 JÓNSDÓTTIR ET AL.

Such extreme events are interesting aspects of climate
change. Still, since the paper primarily addresses the
effects of experimental warming on the plant communi-
ties, these plots were excluded from all subsequent
analyses.
Environmental variables
Climate data were obtained for the study area during the
last 3 years of the study by an automatic weather station
(HOBO H21-002, Bourne, MA, USA) placed in the
Dryas heath habitat, measuring air temperature (HOBO
S-THB-M008) and photosynthetic radiation (PAR)
(HOBO PAR S-LIA-M003) at 2 m height above ground
and soil water content (HOBO S-SMC-M005) at 5 cm
depth. Surface temperature and soil temperature were
monitored over the year in each plot during two different
time periods using TinyTag Plus data loggers (Gemini
Data Loggers, Chichester, UK) in 2004–2005 (soil temper-
ature at 10 cm) and iButton data loggers (DS1922L-F5
thermochrons, Maxim Integrated, San Jose, CA, USA)
in 2015–2018 (soil temperature at 5 cm). Volumetric
soil moisture was obtained from all 30 plots in 2018
with a handheld ML3 ThetaProbe (Delta-T Devices,
Cambridge, UK).
Plant community assessment in taxonomic
space
In all plots, a detailed vegetation analysis using the point
intercept method was performed in 2003, 2009, and 2015,
following standard protocols of the International Tundra
Experiment (Molau & Mølgaard, 1996). We used
100 points per plot and recorded all hits (intercepts)
through the canopy in each point, down to the bryophyte
and lichen layer. If no bryophytes or lichens were pre-
sent, the last hit was litter, biocrust, rock, or soil surface.
Canopy height, including reproductive structures, was
recorded in each point in 2015. Vascular plants were all
recorded to the species level and most of the bryophytes
and lichens (some only identified to genus level).
Plant community assessment in functional
space
We focused on 12 plant functional traits to investigate
plant community responses to warming along the two
main dimensions of the global plant functional trait space
(e.g., Díaz et al., 2016). Three traits reflected plant size
(plant height, leaf dry mass, leaf area), and nine reflected
the leaf economics spectrum (SLA, leaf thickness, leaf
dry matter content [LDMC], nitrogen content [%N], car-
bon content [%C], phosphorus content [%P], carbon:
nitrogen ratio [C:N], nitrogen isotope [δ
15
N], and carbon
isotope ratio [δ
13
C]). The isotope ratios were included
because they may indicate the water status of the plants
(C) and nutrient availability along environmental gradi-
ents (N) (Pérez-Harguindeguy et al., 2013), and both
ratios were therefore expected to respond to the warming
treatment. For each plot, up to three individuals of all
plant species covering more than 1% of the plot were
selected in mid-July 2018. To avoid destructive sampling
within the permanent long-term monitoring plots, the
plant material for trait measurements was collected out-
side these plots but within the much larger OTCs, and in
the surroundings close to each control plot. If more than
three individuals of a species were available, the individ-
uals to be sampled were selected using a randomization
procedure. The height of each sampled plant individual
was measured from the ground to the top of its highest
photosynthetic leaf (standing, unstretched height),
excluding inflorescences. The plant’s aboveground parts
were then sampled, placed in a ziplock plastic bag,
labeled by plot ID, date, height, taxon, and sample ID,
and transported to the lab. There was no precipitation
during sampling that could complicate subsequent fresh
weight measurements. The samples were stored outdoors
and in the shade (temperature around 6C) to ensure
they stayed water saturated until the measurements
started, within 24 h of collection. The procedure for trait
measurements followed standard trait measurement pro-
tocols (Pérez-Harguindeguy et al., 2013).
Leaves (including petioles and stipules for Dryas
octopetala, if present, but excluding sheaths for
graminoids) were separated from the plants using twee-
zers and scalpels and sorted into paper envelopes with a
standardized labeling system. Since leaf sizes were gener-
ally small and shape varied between plant species within
the study site, standardized rules were applied. For each
plant, up to three healthy and mature leaves were sam-
pled and stored in a separate envelope with a unique
sample ID. If the leaves of a species were tiny (e.g., Dryas
octopetala,Saxifraga oppositifolia,orSalix polaris), sev-
eral leaves equivalent to an area of ~3 cm
2
were collected
for each sample. Fresh, moist leaves were weighed
(in grams, rounded to four decimals; Mettler AE200,
Mettler TOLEDO, and AG204 DeltaRange [0.1 mg preci-
sion]) for wet mass. To estimate leaf area, leaves were
scanned (Canon Lide 220, resolution 300 dpi), and the leaf
area was calculated using ImageJ (Schneider et al., 2012)
and the LeafArea package (Katabuchi, 2015)inRversion
4.0.2. Three measurements of leaf thickness per sample
were taken, when possible, using a digital caliper (Mitutoyo
ECOLOGICAL MONOGRAPHS 5of21

293-348, Neuss, Germany). Measurements on the leaf veins
were avoided, although this was not possible for tiny leaves.
The leaf samples were then dried at 60C to a constant
mass (24–28 h) in a drying cupboard (Thermo Scientific
Heraeus, USA) and weighed for dry mass. We calculated
SLA as the leaf area divided by the dry mass (cm
2
/g) and
LDMC as the dry mass divided by the fresh mass (g/g).
Following the size and mass measurements, the
leaves were ground to a powder and analyzed for nutrients
(P, N, C) and isotope ratios (δ
15
N, δ
13
C) at The University
of Arizona. Total phosphorus concentration was deter-
mined using persulfate oxidation followed by the acid
molybdate method (APHA, 1992). Phosphorus concentra-
tion was then measured colorimetrically with a spectro-
photometer (Thermo Scientific Genesys20, Waltham, MA,
USA). Carbon (C), nitrogen (N), and their stable isotope
ratios were measured by the Department of Geosciences
Environmental Isotope Laboratory at the University of
Arizona on a continuous-flow gas-ratio mass spectrometer
(Finnigan Delta PlusXL, Waltham, MA, USA) along with
an elemental analyzer (Costech, Valencia, CA, USA).
Samples of 1.0 0.2 mg were combusted, and standardiza-
tion was based on acetanilide for N and C concentration,
NBS-22 and USGS-24 for δ
13
C, and IAEA-N-1 and
IAEA-N-2 for δ
15
N. The carbon-to-nitrogen ratio (C:N)
was also calculated and analyzed.
Ecosystem CO
2
flux measurements
In mid-July 2018, we measured peak growing season net eco-
system CO
2
exchange (NEE) and ecosystem respiration
(R
eco
) using an infrared gas analyzer (Li-840, LI-COR
Biosciences, Lincoln, NE, USA) connected to a custom-made
Plexiglass chamber (headspace volume =25 L). We used a
polyethylene skirt and a heavy chain to create an airtight seal
between the chamber and the atmosphere. Two fans fitted
within the chamber ensured sufficient air circulation during
measurements. Daytime fluxes were measured twice per
experimental plot, on separate days. For each plot and day,
we measured NEE during ambient light conditions, followed
by thorough aeration of the chamber headspace for 10–15 s
before measuring R
eco
by covering the chamber with an
opaque polyethylene cloth. Each flux measurement lasted
for at least 120 s, logging CO
2
concentration values every sec-
ond. During each flux measurement, we collected data on
microclimatic parameters, i.e., soil temperature at 5 cm
depth, canopy temperature, volumetric soil moisture inte-
grated across 0–10 cm depth, and PAR using handheld ther-
mometers (Fisher Scientific, Oslo, Norway), an infrared
thermometer (Biltema, Åsane, Norway), an ML3 ThetaProbe
(Delta-T Devices, Cambridge, UK), and a Li-190 quantum
sensor (LI-COR Biosciences, Lincoln, NE, USA), respectively.
All measurements were visually inspected before analyses,
and only measurements that showed a consistent linear rela-
tionship between CO
2
and time for at least 60 s were used.
PAR ranged from 493 to 1480 and 211 to 650 μmol photons
m
2
s
1
during the two measurements, respectively. Fluxes
werecalculatedastherateofchangein[CO
2
]overtime
using the following formula (Jasoni et al., 2005):
NEE ¼δCO2
δtPV
RATþ273:15ðÞ
where δCO2
δtis the slope of the CO
2
concentration against
time (μmol mol
1
s
1
), Pis the atmospheric pressure
(kPa), Ris the gas constant (0.008314 kPa m
3
K
1
mol
1
),
Tis the air temperature inside the chamber (C), Vis the
chamber volume (m
3
), and Ais the ground surface
area (m
2
).
Gross ecosystem productivity (GEP) was calculated as
NEE +R
eco
and standardized to PAR =700 μmol pho-
tons m
2
s
1
using a rectangular hyperbolic relationship
(Thornley & Johnson, 1990):
GEP ¼αGEPPAR GEPmax
αGEPPARðÞþGEPmax
where αGEP is the initial slope of the rectangular hyper-
bola, or apparent quantum yield of GEP, and GEP
max
is
the asymptotic maximum GEP at high light intensities.
We used the mean flux rate across measurement days for
each plot in the subsequent statistical analyses.
In addition to our static chamber measurements, we
calculated July (peak growing season) soil respiration
(R
soil
) rates based on data from passive, forced-diffusion
dynamic chambers (Eosense, Dartmouth, Canada) (Risk
et al., 2011), which recorded continuous soil respiration
rates at 4- to 6-h intervals in the Dryas heath during the
2015–2017 growing seasons (n=3 per treatment). For
each plot, we used the mean July R
soil
flux rate from the
three measurement years in the statistical analyses.
Data analyses
All analyses were done in R version 4.2.1 (R Core
Team, 2021).
Changes in taxonomic composition
To assess the effects of habitat, year, and the warming
treatment on plant taxonomic community composition,
we used principal component analysis (PCA), as
implemented in the rda function in the vegan package
(Oksanen et al., 2020). These analyses were based on
square-root-transformed abundance data, and we tested
6of21 JÓNSDÓTTIR ET AL.

whether community composition varied among treat-
ments, habitats, and years (treatment site year)
using the adonis function. For a more targeted test of the
effect of experimental warming over time, we conducted
PCAs for each habitat separately. This allowed us to
zoom in on the treatment–control contrast over time
(treatment time), which may otherwise be lost in the
much larger compositional differences between habitats
compared to the treatments.
We calculated the change in Bray–Curtis distances,
i.e., species compositional differences between plots, and
between the first to last survey from each plot, using the
vegdis function in the vegan package (Oksanen
et al., 2020). We also calculated change in the summed
abundances of all vascular plants; summed abundances
for the functional groups, forbs, graminoids, shrubs,
bryophytes, and lichens; and species richness (number of
species), diversity and species evenness (Shannon diver-
sity/log[richness]) from the first to last surveys. We tested
whether change in these community metrics varied by
treatment and habitat using ANOVA, where the response
was modeled as a function of habitat, treatment, and
their interaction. We also tested whether a change in
habitat–treatment combinations differed from zero using
t-tests.
Changes in functional trait composition
and the importance of ITV
To assess inter- and intraspecific trait variability, we cal-
culated the community-weighted mean trait values for
each plot using community species composition data
from 2015 and trait values from 2018. Since community
composition changes over time in this system are slow,
the time difference in collecting the last species commu-
nity data (2015) and trait (2018) data was assumed minor
for the trait analyses. We imputed trait data if any species
present in a plot did not have at least two measurements
for each trait from that plot to be able to generate trait
distributions (see following discussion). For this, we first
used measurements from that species in the same habitat
and treatment, and if that was not available, we used
measurements from any plot in the same habitat to fill in
the trait coverage for the whole community in each plot.
Thus, the majority of trait measurements used in
estimating community trait distributions for a plot came
from traits measured directly in the plot of interest. For
the traits plant height, dry mass, leaf area, thickness,
SLA, and LDMC, 90% of the plots had at least 90% cover-
age at the plot level. For the nutrient-related traits, 75%
of the plots had at least 76.2% coverage at the plot level.
The community trait distribution was generated using a
bootstrapping approach with the R package traitstrap
(Telford et al., 2021) by randomly sampling measured
trait values with replacement for each trait and each spe-
cies from each plot proportional to the abundance of that
species in that plot (Enquist et al., 2015; Wieczynski
et al., 2019). This procedure was repeated 100 times, and
for each repetition we calculated the mean of the trait
distribution for each trait. These 100 trait distribution
means were then averaged to determine each trait’s over-
all distribution mean in each plot. This approach has the
distinct advantage of allowing us to include intraspecific
variability and the hierarchical structure of the experi-
mental design in our estimates of community trait distri-
butions. For each trait we assessed the differences in trait
means between habitat types and treatments in all cases
using ANOVAs where the community mean trait values
response of interest were modeled as a function of habitat
type, warming treatment, and their interaction.
In parallel to the taxonomic compositional analyses,
we used PCA, as implemented in the rda function in the
vegan package (Oksanen et al., 2020) to assess the effects
of habitat and the warming treatment on plant trait com-
munity composition. We based these analyses on centered
and scaled data and tested whether trait community com-
position varied between habitats and treatments using the
adonis function. For a more targeted test of the effect of
experimental warming, PCA was also done for each habi-
tat separately to zoom in on the treatment–control con-
trast, which could otherwise be lost in the much more
significant differences between habitats compared to the
treatments. The effect of treatment was tested using the
same test as described earlier.
To quantify the relative importance of inter- versus
intraspecific variation in the functional community
response to warming, we compared the plot-level trait
distribution means using the bootstrapping method
described earlier, computed with and without the ITV.
First, measures including ITV, i.e., based on all trait mea-
surements for each species in each plot, were calculated
as described in the preceding section (referred to as the
specific mean). Second, measures excluding ITV were esti-
mated by calculating the average trait value for each spe-
cies across all control plots, and using these values to
calculate community-weighted mean trait values for each
plot, thereby ignoring plot-, habitat-, and treatment-specific
trait variability within species (referred to as the fixed
mean). Differences between the specific (including both
species turnover [i.e., interspecific variation] and ITV), and
fixed (only species turnover) trait means reflect the contri-
bution of ITV to the total observed trait variation across
treatments and habitats. To quantify the contribution of
ITV and species turnover to the variation in community
mean trait values, habitats, and treatments, separate
ECOLOGICAL MONOGRAPHS 7of21

ANOVAs with fixed, specific means and their difference as
response and the interaction of habitat and treatment as
predictor were performed. We then decomposed the sum
of squares from the ANOVAs as described by Lepˇ
setal.
(2011). To test whether the difference between specific and
fixed community-weighted traits in each treatment from
each habitat was different from zero, a t-test was used. We
alsodeterminedtherelativeimportanceofintraspecific
variation versus interspecific variation in traits by
partitioning trait variance into between- and within-species
components using a mixed model where the fixed effect
was only an intercept and a random effect for species inter-
cepts based on methods from Messier et al. (2010)using
the lme (Bates et al., 2015)andvarcomp(Qu,2017)
functions.
Ecosystem CO
2
fluxes
To examine whether taxonomic and functional trait
changes lead to shifts in ecosystem carbon fluxes, we
tested the effect of experimental warming on GEP
700
,
R
eco
, and NEE using general linear models. We specified
treatment and habitat as fixed main effects for all models,
including their interaction term and plot as random
effect. In addition, we used a general mixed-effects model
(lme4 package; Bates et al., 2015) to analyze treatment
effects on our multiyear R
soil
data, with treatment as fixed
main effect and plot as random effect to account for our
repeated measures design.
We investigated the effects of three groups of
variables: (1) environmental (plot-scale microclimate
measurements), (2) taxonomic (plant functional group
abundance [graminoid, forb, bryophyte, evergreen
shrub, deciduous shrub, lichen], evenness, richness,
diversity, canopy height), and (3) plant functional traits
(community-weighted traits including and not including
intraspecific variability: plant height, leaf area, leaf thick-
ness, LDMC, SLA, P, C, N, C:N) on ecosystem carbon
flux (i.e., GEP
700
,R
eco
, and NEE) across habitats and
treatments. We assessed the effect of individual predictor
variables on ecosystem carbon flux using linear models
on scaled predictor variables (Appendix S1: Figure S8).
Next, we constructed multiple linear models for all three
CO
2
flux components (GEP
700
,R
eco
, and NEE) across
habitats and treatments of the different predictor groups
(environment, taxonomic, and plant functional traits),
resulting in a total of nine models. Variables were
selected based on the stepwise backward selection using
the stepAIC function (MASS package; Ripley et al., 2020).
Variables were only excluded if they reduced the Akaike
information criterion (AIC) score by more than 2
(Burnham & Anderson, 2002). The resulting models
represented the overall effects of the three predictor
groups on different ecosystem carbon fluxes. To assess
the effect of ITV on ecosystem carbon fluxes, we com-
pared the three trait models selected by backward selec-
tion including ITV with models containing the same trait
predictors but with ITV excluded from the trait predic-
tors. Last, we combined the three models in one full com-
posite model, consisting of combined environmental,
taxonomic, and trait variables, for each CO
2
flux compo-
nent. This allowed us to assess the overall variation
explained by each group of variables and shared variation
explained across models.
RESULTS
Environmental characteristics
and community structure
There was substantial seasonal and annual
variation in the measured environmental parameters
(Appendix S1: Figure S1A). PAR reached a maximum
(>700 μmol m
2
s
1
) in late June in all measured years.
The monthly mean air temperature (2 m above ground)
was highest in July and ranged between 6.9 0.26C
(in 2017) and 8.7 0.30C (in 2016). Temperature fluctu-
ations were much more extensive during winter than
summer, with a few warm spells (temperature above
0C) each winter (Appendix S1: Figure S1A). At the plot
level, surface mean annual temperatures in control plots
ranged between 1.57 0.96C and 0.03 0.87C
and July temperatures between 9.1 0.54C and
12.0 0.45C across years and habitats, with larger varia-
tion during winter (Appendix S1: Figure S1B). Mean July
soil temperatures were ~3C lower than at the surface,
ranging between 5.8 0.41C and 8.7 0.04C across
years and habitats (Appendix S1: Figure S1B). Owing to
the sporadic data and considerable interannual variation,
it was not possible to detect trends in the timing of snow-
melt or temperature change in the control plots over the
17-year study period.
There was a trend toward increased annual surface
temperature in response to the warming treatment by
1.25C across all years and habitats (F
1,486
=3.5,
p=.062), but not for soil temperature (0.63C higher,
not significant). The warming treatment enhanced
July surface temperatures by, on average, 1.6C across all
years and habitats (F
1,45
=21.0, p< .001) and tended to
increase soil temperature (Appendix S1: Figure S1B). This
trend was also reflected in the July 2018 spot measure-
ments of soil temperature and a reduction in soil moisture
by the warming treatment (significant only in the Dryas
heath, reduction from 15% to 10% volumetric water
8of21 JÓNSDÓTTIR ET AL.

content; Appendix S1: Figure S2). The warming treatment
did not affect surface and soil temperatures in winter.
Canopy height was relatively low but varied among
the habitats. The low vascular plant cover in the snowbed
resulted in low average plot canopy height above the
moss layer (1.1 0.0 cm in 2015, Appendix S1: Figure S3).
The canopy height was highest in the Cassiope heath,
where the erect growing Cassiope tetragona dwarf shrub
dominated (average plot canopy height 1.3 0.1 cm;
Appendix S1: Figure S3), while the dominance of the
prostrate growing Dryas octopetala dwarf shrub resulted
in lower canopy height in the Dryas heath (average
canopy height 1.1 0.0 cm; Appendix S1: Figure S3). The
warming treatment significantly increased canopy height
in the Cassiope and Dryas heaths but not in the snowbed.
Taxonomic compositional change over
time and in response to warming
Plant community species composition differed over habi-
tats, treatments, and time, with significant differences in
treatment effects and temporal dynamics between the hab-
itats (Figure 1a). Most of the variation in community com-
position was found between habitats, which could account
for about two-thirds of the explained variation in the data
(Appendix S1: Table S1). In all habitats, there was a
change over time in plant community species composition
from the first plant community analysis in 2003 to the last
in 2015, both in the ambient controls and treatment plots
(Figure 1b–d). Also, there was a significant treatment
effect in the Cassiope and Dryas heaths, mainly reflecting
somewhat more pronounced community shifts over time
in the warmed plots than in the controls (Figure 1c,d).
A few other community metrics were affected by warming,
most strongly in the Dryas heath, where evenness
decreased and total vascular plant and evergreen shrub
abundance increased (Appendix S1: Figure S4).
Functional community compositional
variation and response to warming
The three communities were also well differentiated in
functional trait composition. The snowbed plots occupied
the resource-acquisitive part of the trait space, whereas
the other two communities were characterized by more
resource-conservative trait values and were, in turn, sepa-
rated by size-related traits, the Cassiope heath being char-
acterized by taller plants than the Dryas heath (Figure 2a;
Appendix S1: Table S2). Overall, experimental warming
significantly affected community trait composition
(Appendix S1: Table S2). Plants tended to be taller,
having larger leaves and higher LDMC and carbon con-
tent in the warmed plots, and this trend was most pro-
nounced (but not significant) in the Dryas heath
(Figure 2b–d).
In univariate analyses, all measured functional traits
except LDMC and %P differed between habitats
(Appendix S1: Figure S5; Appendix S1: Table S3) and over-
all, habitat type explained between 13% and 80% of the
total variation in individual traits (Appendix S1:
Figure S6). In most cases, this was because the snowbed
differed from the other two habitats, being characterized
by higher values for the traits SLA, dry mass, leaf area, %
N, and δ
15
N, along with lower values for leaf thickness, %
C, and C:N ratio (Figure 2; Appendix S1: Figure S5).
Overall, the warming treatment explained much less of
the trait variation than habitat (Appendix S1: Figure S6).
Warmed plots had consistently higher leaf dry mass and
leaf area and lower δ
15
N than control plots, with the stron-
gest responses in the Dryas heath. The warming treatment
did not significantly affect other traits (Appendix S1:
Figure S5).
Intraspecific trait variability
ITV accounted for between 30% (plant height) and 71%
(%P) of the total trait variation in the whole community
across all habitat types (Appendix S1: Table S4). The trait
variation attributable to the warming treatment or the
interaction between habitat and treatment tended to be
due to intraspecific variation (Appendix S1: Figure S6).
In both control and warmed plots, including the intra-
specific variation shifted the mean community-weighted
values of most traits and habitats (Figure 3). Within the
control plots, ITV significantly increased %N, decreased %
C, and marginally decreased C:N in snowbed controls, and
ITV significantly decreased dry mass, leaf area, and SLA
and tended to reduce %N and δ
15
NandincreasedC:N
(p< 0.1) in Dryas heath controls (Figure 3). Within the
warming plots, ITV contributed to increased dry mass and
leaf area in the snowbed and Dryas heath, and increased
SLA, decreased P%, and decreased δ
15
Nvaluesinwarming
plots in the Dryas heath (Figure 3). Overall, we found that
the importance of ITV (i.e., the effect of including the
intraspecific variation in the trait mean calculations) dif-
fered between warmed and control plots for four traits in
the Dryas heath (Appendix S1: Table S5). The trait mean
increased when ITV was included for dry mass, leaf area,
and SLA, whereas for δ
15
N it decreased under warming
relative to the controls. None of the other traits or habitat
types showed significant differences in the importance of
ITV for the trait mean between warming and control
treatments.
ECOLOGICAL MONOGRAPHS 9of21

FIGURE 1 (a) First two axes of principal component analysis (PCA) of plant community species composition in control plots (open
symbols) and experimentally warmed plots (closed symbols) in the three studied habitats (snowbed, Casiope heath, Dryas heath) over the
years 2003 (large symbols), 2009, and 2015, with the amount of variation explained by each PCA axis indicated. The species with the best fit
(length of vector) are visualized. (b–d) First two PCA axes for community species composition change of plots within each habitat.
10 of 21 JÓNSDÓTTIR ET AL.

FIGURE 2 Legend on next page.
ECOLOGICAL MONOGRAPHS 11 of 21

Habitat and warming effects on ecosystem
CO
2
fluxes
Peak growing season GEP
700
and R
eco
tended to differ
between the habitats (Appendix S1: Figure S7). Fluxes
measured from the Dryas heath were generally greater
than fluxes in the snowbed, whereas fluxes within the
Cassiope heath were intermediate between the snowbed
and the Dryas heath (Appendix S1: Table S6, Figure S7).
In contrast, NEE rates were similar between the Dryas
heath and the snowbed. Both habitats were net sources
of CO
2
to the atmosphere during our daytime measure-
ments, whereas the Cassiope heath was neither a sink
nor a source (i.e., NEE was not different from 0). Across
all habitats, experimental warming increased peak grow-
ing season R
eco
and, similarly, the warming treatment
enhanced July R
soil
within the Dryas heath (Appendix S1:
Table S6, Figure S7). There was also a significant effect of
the warming treatment on GEP
700
, but this effect was
driven primarily by a strong positive response within the
Cassiope heath (significant treatment habitat interac-
tion; Appendix S1: Table S6) and negligible change across
treatments within the other two habitats.
Effects of microclimate, taxonomic
composition, and functional trait
composition on ecosystem CO
2
fluxes
Ecosystem CO
2
fluxes were related, to different degrees,
to single variables representing the environmental, taxo-
nomic, and functional trait variable groups. Specifically,
whereas GEP
700
was affected by a multitude of different
taxonomic and functional trait variables, fewer single var-
iables significantly affected R
eco
and NEE (Appendix S1:
Figure S8). Surprisingly, the fixed and specific
(i.e., including ITV) versions of the functional trait vari-
ables often differed significantly both in the magnitude
and direction of their single trait effects on ecosystem
CO
2
fluxes (Appendix S1: Figure S8). Nevertheless, more
consistent trait patterns emerged when models were
constructed that included the impact of all environmen-
tal (microclimate), taxonomic, and plant functional trait
variables (Appendix S1: Table S7). For GEP
700
, environ-
mental and taxonomic variables accounted for more than
half of the explained variation, with canopy temperature,
canopy height, and leaf N variables retained in the final
model (Appendix S1: Table S7; Figure 4a,b). For R
eco
, the
variance that can be explained by environmental, taxo-
nomic, or functional trait variables (i.e., shared variance
due to the collinearity of these groups of predictors) dom-
inate (Figure 4a,b). Therefore, canopy temperature, plant
species diversity, and traits associated with both leaf eco-
nomics and plant size were retained in the final model
(Appendix S1: Table S7). For R
eco
, environmental vari-
ables alone explained more variance than taxonomic or
functional trait variables alone, although not by much
(Appendix S1: Table S7). In contrast, for NEE, the
resource acquisition-related traits, leaf area, and plant
height were clearly the most important group of explana-
tory variables (Appendix S1: Table S7; Figure 4a,b). All
three flux components retained some analog of tempera-
ture, plant size, and leaf economics variables in their
final models.
Including ITV enhanced the explanatory power of
traits for all ecosystem CO
2
fluxes, especially for GEP
700
and R
eco
(Figure 4c; Appendix S1: Table S7). For NEE,
including ITV also increased the total variance explained
by microclimate, taxonomic community, or functional
traits, whereas for GEP
700
and R
eco
it did not change sub-
stantially (Figure 4b vs. a). Negative covariance was rela-
tively minor for the three combined models.
DISCUSSION
Community species composition changed over time in all
three habitats. Although species compositional responses
to long-term experimental warming were found in two
habitats, the effects were minor relative to the overall
habitat and temporal variability. Plant community
functional trait composition was modestly affected by
the warming treatment, detected in three out of 12 traits
and mainly in one of the habitats. The trait responses to
warming were primarily driven by intraspecific trait
variability. Both peak season rates of photosynthesis
(GEP
700
) and respiration (R
eco
) increased in response to
the warming treatment across habitats. Together, envi-
ronmental, taxonomic, and functional trait variables
explained a large proportion of the total variation in peak
season CO
2
fluxes, and this proportion increased when
ITV was accounted for.
FIGURE 2 (a) First two axes of principal component analysis (PCA) of plot–scale plant community functional trait composition
(i.e., community-weighted trait means) of plots in different habitats (snowbed, Cassiope heath, Dryas heath) and treatments (control,
warming) with the amount of variation explained by each PCA axis indicated. Colored points indicate habitat type; point shape indicates
treatment type. Vectors indicate how traits are related to the first two axes and are labeled by the trait they represent. (b–d) First two PCA
axes for functional composition of plots within each habitat.
12 of 21 JÓNSDÓTTIR ET AL.

Plant community change over time
in taxonomic space and effects of warming
The change in taxonomic plant community composition in
the ambient control plots over the study period reflected
both directional response to recent climate warming in
Svalbard (Nordli et al., 2020) and dynamic plant community
responses to the much more significant climatic interannual
variability. Similar to other long-term studies from high
Arctic regions (Hollister et al., 2015;Hudson&Henry,2009;
Hudson & Henry, 2010), the directional changes were slow
in comparison with dynamic temporal changes (e.g., van der
Wal & Stien, 2014) and compared with community changes
found in warmer parts of the tundra (Elmendorf, Henry,
Hollister, Björk, Boulanger-Lapointe, et al., 2012a). Two
resurvey studies in central Svalbard after 70 and 85 years
also reported no directional changes or indications of
“greening”in response to ongoing climate change (Kapfer &
Grytnes, 2017;Prachetal.,2010).
The warming treatment only slightly modified species
compositional changes, and these effects were confined
to the Cassiope and Dryas heaths, our driest habitats,
where the abundance of evergreen shrubs increased.
This contrasts with earlier studies that found the
strongest responses in moist habitats (Edwards &
Henry, 2016; Elmendorf, Henry, Hollister, Björk,
Bjorkman, et al., 2012b). Furthermore, the commonly
detected decrease in soil moisture in response to the OTC
treatment (e.g., Bokhorst et al., 2013) was only found in
the Dryas heath, suggesting an environmental factor that
overrides soil moisture limitation. In Arctic landscapes,
topography determines snow accumulation and hydrol-
ogy (Walker, 2000) and, thus, the length of the growing
season, which is particularly critical in the high Arctic.
The early snowmelt in the Dryas heath resulted in a
1.4 times longer growing season, on average, compared
to the late-melting, moist snowbed, allowing a longer
cumulative response time to the warming treatment,
FIGURE 3 Contribution of intraspecific trait variation (ITV) to trait variation calculated as difference between mean community
functional trait distributions with (specific average) and without (fixed average) ITV in different habitats and treatments (control, warming).
The zero line indicates that ITV did not change the community trait distribution mean value, whereas positive values indicate that ITV
increased the community trait distribution mean, and negative values indicate that ITV decreased the community trait distribution mean.
Asterisks above each habitat and treatment combination indicate whether the mean difference is different from zero (t-test).
+
p< 0.1; *p< 0.05.
ECOLOGICAL MONOGRAPHS 13 of 21

which may explain why we saw the most significant
responses within this habitat.
Several factors might contribute to the resistance of
the plant community composition to both ambient and
long-term experimental warming in the high Arctic.
First, high Arctic regions are characterized by small spe-
cies pools, often substantial dispersal barriers, which
may delay the migration of more thermophilic and
FIGURE 4 Variance of ecosystem function, i.e., peak growing season GEP
700
,R
eco
, and net ecosystem exchange flux rates, across
habitats and treatments explained by three groups of variables: (1) environmental (plot-scale microclimate measurements), (2) taxonomic
(plant functional group abundance [graminoid, forb, bryophyte, evergreen shrub, deciduous shrub, lichen], evenness, richness, diversity,
canopy height), and (3) plant functional traits (community weighted traits with and without including intraspecific trait variability: plant
height, leaf area, leaf thickness, leaf dry matter content, specific leaf area, P, C, N, C:N). (a) Variance explained by unique and combined
group effects excluding intraspecific trait variation (ITV). (b) Variance explained by unique and combined group effects including ITV. (c)
Variance explained by plant functional traits with and without ITV (gray +black and gray bars, respectively). Note that negative values can
occur when combining two groups in a model that explains less of the variation than the individual groups.
14 of 21 JÓNSDÓTTIR ET AL.

responsive species, as illustrated by the substantial lagged
responses to climate in their subsequent establishment
into the plant communities observed throughout the
Arctic (Elmendorf et al., 2015). Second, most Arctic
plant species are long-lived, and many rely on various
forms of clonal growth for population maintenance
(e.g., J
onsd
ottir, 2011), enhancing their ability to persist
in the face of environmental change. Third, the plants
are adapted to sizeable interannual climate variability,
and it may take substantial, long-term warming to push
the system beyond that variability range (e.g., Hudson &
Henry, 2010;J

onsd
ottir, 2005). For example, in our study,
the 3C variability range for surface mean July tempera-
tures across study years exceeded the 1.6C experimental
temperature increase. However, the severe damage
caused by basal ice formation in some of the Cassiope
heath plots during our study indicates that increased fre-
quency of extreme events might accelerate community
changes in the high Arctic. Finally, as suggested by our
results, plant functional trait variation, particularly ITV,
may substantially enhance community functional resil-
ience by enabling plastic responses by individuals
(as demonstrated by dynamic community vegetation
responses to interannual climate variability) or adapta-
tion to long-term warming, thereby retaining fitness
within populations.
Community variation in functional space
and effects of warming
The first two axes of the plant community-weighted func-
tional trait space of our results primarily reflected the
two global dimensions of leaf economics spectrum and
plant size, respectively (Bruelheide et al., 2018; Díaz
et al., 2016), albeit with reduced trait ranges, particularly
for size traits, comparable to the “extreme”tundra plants
in a study by Thomas et al. (2020). Snowbeds were char-
acterized by resource-acquisitive traits, whereas the
Dryas and Cassiope heaths were characterized by
resource-conservative traits, and were again separated by
traits related to size and carbon dynamics.
Community functional trait composition responded
to experimental warming and involved increased values
of two size-related traits, leaf area and leaf dry mass,
and decreased δ
15
N, with the strongest responses in the
Dryas heath. This partly contradicts previous studies,
where leaf area decreased with warmer temperatures at
dry sites but increased with warming at moist sites
(Bjorkman et al., 2018). Furthermore, plant height did
not respond to warming, again contradicting other tun-
dra studies on individual and community levels (Baruah
et al., 2017;Bjorkmanetal.,2018;Hollisteretal.,2015;
Hudson et al., 2011). The inherently low stature of most
highArcticplantsmaybeanadaptationtotheharshcli-
mate. Such constraining adaptation in combination with
slow within-habitat species and genotype turnover may
explain this lack of community plant height response
and why size responses to experimental warming were
expressed by leaf traits (increased leaf area and dry
mass) rather than by increased plant height in this high
Arctic community.
The significant decrease in community foliar δ
15
Nin
response to warming contradicts shoot-level results for
five plant species at a high Arctic site in Canada, where
no warming responses were found (Hudson et al., 2011).
An increase in δ
15
N has been observed in relation to
N-availability gradients (Craine et al., 2015) and has also
been positively related to mean annual temperature and
precipitation (Amundson et al., 2003). It is possible that
the decrease in this trait along the snow–moisture gradi-
ent of our study, from relatively high in the snowbed to
lower levels in the Dryas heath, signals such an
N-availability gradient. Experimental warming generally
increases N-mineralization rates in cold regions (Salazar
et al., 2020), and we would therefore expect an increase
in leaf δ
15
N in response to warming. However, the oppo-
site happened in the Dryas heath. The decrease in soil
moisture may have played a role. Still, such general
trends may also be modulated by other functional traits
of the responding species at a local scale, in this case by
increased input of recalcitrant litter by the abundant
evergreen dwarf shrub Dryas octopetala. Indeed, Vowles
and Björk (2019) suggested that, in contrast to the accel-
erating impact of expanding deciduous shrubs on ecosys-
tem processes in response to climate warming, such as
rates of N mineralization (Mekonnen et al., 2021;
Myers-Smith et al., 2015), expanding evergreen shrubs
will decelerate ecosystem processes through their low
nutrient and recalcitrant litter, thereby counteracting
the direct effects of warming on those processes
(e.g., Cornelissen et al., 2007).
It has been suggested that the frequently observed
drop in leaf nutrient levels in response to short-term
experimental warming (2–5 years; e.g., Doiron
et al., 2014; Tolvanen & Henry, 2001) will level out in the
long term as increased soil N mineralization satisfies
plant nutrient demand (Michelsen et al., 2012). The lack
of leaf nutrient responses to warming in our study is con-
sistent with other long-term warming experiments in the
high Arctic (Hudson et al., 2011) and more likely reflects
either no direct warming impacts on soil N
mineralization or an indirect decelerating impact by
increased input of recalcitrant litter, or a combination
of these. This is supported by a recent study showing
no effects of long-term experimental warming on
ECOLOGICAL MONOGRAPHS 15 of 21

decomposition rates within our Svalbard communities
(Björnsd
ottir et al., 2022).
The role of ITV
The contribution of ITV to the total community-level trait
variation in our study, ranging between 30% and 71% for
different traits, was relatively large compared to both
global and tundra averages (Siefert et al., 2015; Thomas
et al., 2020). This supports the general expectation that
intraspecific variation should be relatively significant in
species-poor regions (Siefert et al., 2015) and harsh envi-
ronments (Niu et al., 2020).
It is usually assumed that a large ITV reflects a greater
ability of organisms to exist along broad environmental gra-
dients (large niche breadth) and to adjust to environmental
changes (Sides et al., 2014). In all the habitats of our
study, the most dominating plant species have relatively
large geographic ranges and are apparently adapted to
sizeable interannual climate variability (J
onsd
ottir, 2005).
Accordingly, high intraspecific variability in size-related
traits may contribute to dynamic annual variation in above-
ground plant biomass, as has been observed in Svalbard
plant communities in response to interannual variability in
summer temperatures (Petit Bon, 2020; van der Wal &
Stien, 2014), enhancing plant community resilience to cli-
mate variability.
Although the overall community functional trait
response to the long-term warming treatment of our
study was modest and confined to three of the 12 mea-
sured functional traits, it was primarily explained by ITV.
This supports our hypothesis that the plant community
responses to long-term warming in functional space are
mainly driven by intraspecific variation in traits. These
results indicate that studies that rely solely on changes in
taxonomic composition or community-weighted means
that do not incorporate intraspecific variation are likely
underestimating the effects of warming. Leaf size traits
measured at the individual level have been reported as
responsive to warming experiments for a range of Arctic
species (Baruah et al., 2017; Hudson et al., 2011), indicat-
ing plasticity. A transplant experiment in China revealed
a high relative plasticity in leaf δ
15
N among alpine plants
(Henn et al., 2018). That was most likely also the case for
this trait in the predominantly long-lived clonal plants in
response to warming at our Svalbard site.
Effects of warming and functional traits on
peak season ecosystem CO
2
fluxes
Apart from the Cassiope heath, the studied habitats
appeared to be net sources of atmospheric CO
2
at peak
growing season under ambient temperatures, as is often
observed in high Arctic regions (Christiansen et al., 2012;
Welker et al., 2004). Experimental warming increased
GEP
700
and R
eco
, resulting in negligible overall changes
to net ecosystem CO
2
balance, NEE. As there was rela-
tively little warming-induced change in the plant com-
munity in either taxonomic or functional trait space, we
attribute the largely offsetting warming responses in
GEP
700
and R
eco
to the direct kinetic impact of tempera-
ture on plant and soil microbial physiological activities.
Although our flux measurements were obtained over just
two measurement days, and the limited sample size
therefore warrants caution, the observed flux responses
are similar to reports from more flux-focused studies in
high Arctic Canada and Greenland (Lupascu et al., 2014;
Marchand et al., 2004; Welker et al., 2004). Therefore, we
believe our flux data adequately reflect the relative
importance of environmental, plant taxonomic, and
functional trait variables on CO
2
exchange rates,
i.e., ecosystem functionality, across habitats during this
critical time for annual plant-carbon uptake.
Overall, the largest proportion of the variation in
GEP
700
and R
eco
that was uniquely attributed to only one
group of variables, i.e., variation not shared between
groups, was accounted for by the environment (canopy
temperature). This likely reflects the short response time
by the main mechanisms responsible for these CO
2
fluxes. Nevertheless, variables belonging to the taxo-
nomic and functional trait groups also explained consid-
erable variation, either alone or as the shared variance
between two or more variable groups. For example, can-
opy height or the size-related traits of plant height and
leaf area, as well as the leaf economics trait leaf N con-
tent (Wright et al., 2004), consistently remained in the
final, reduced models for GEP
700
and R
eco
, reflecting the
importance of these traits for ecosystem CO
2
exchange
(Reich et al., 1997).
Overall, all three variable groups accounted for
broadly similar amounts of variation in GEP
700
and R
eco
rates (R
2
range across individual groups alone =0.2–0.37),
although with functional traits situated at the lower end
(functional trait group R
2
=0.2–0.25, when including
ITV). In contrast, plant functional traits uniquely
explained a large proportion of the overall variation in
NEE (>40%), primarily driven by productivity-related
traits, plant height, leaf area, and leaf N. The explanatory
power of our combined group models was moderate to
high (R
2
range across the three flux components, GEP
700
,
R
eco
, and NEE: 0.4–0.55), which is similar to or greater
than models from two recent subarctic tundra studies also
attempting to link traits to ecosystem CO
2
exchange rates
(Happonen et al., 2022; Sørensen et al., 2018). Although
studies that relate functional community composition
directly to measures of ecosystem function have generally
16 of 21 JÓNSDÓTTIR ET AL.

been lacking (Funk et al., 2017), an increasing number of
studies report strong effects of community-scaled traits on
ecosystem properties (Grigulis et al., 2013;Reich
et al., 2014). However, even if not all ecosystem properties
are explained well by functional community composition
(van der Plas et al., 2020), plant-related ecosystem func-
tions, such as carbon-cycling processes, consistently show
at least moderate and often good relationships (Funk
et al., 2017; Happonen et al., 2022;Reichetal.,2014;van
der Plas et al., 2020; this study). In addition, for all three
CO
2
flux components, we consistently found that a more
significant proportion of the variance explained by the
functional traits variable group alone was accounted for
when ITV was included. Thus, variability in size and leaf
economics traits—i.e., the functional traits that constrain
the two primary dimensions along which most individual
and community trait assembly occurs (Bruelheide
et al., 2018;Díazetal.,2016)—is an essential component
in determining community functionality at our high
Arctic site. Similarly, Happonen et al. (2022) found posi-
tive relationships between subarctic tundra CO
2
fluxes and
within-community variability in SLA and LDMC (but not
for plant height), suggesting that increasing functional
diversity is linked to ecosystem functionality (Cadotte
et al., 2011). Adding to this, we found that the total
explained variance in our final, reduced NEE model
increased when including ITV, indicating an effect of trait
plasticity on ecosystem carbon balance. Taken together,
these relatively straightforward effects of including trait
variation within species fit well with recent studies show-
ing that intraspecific variation in traits repeatedly impacts
ecosystem functionality at least as much as species turn-
over effects (e.g., Des Roches et al., 2018).
Although our analyses of the ecosystem CO
2
fluxes
shed new light on how plant functional traits and espe-
cially ITV can affect ecosystem functioning, we also note
that ecosystem CO
2
fluxes are highly variable in time and
space. Consequently, more extensive flux measurements
are required to capture the ecosystem characteristics.
Nevertheless, we show similar linkages, and explanatory
power, between tundra plant functional composition
and ecosystem CO
2
fluxes as other studies with more
flux measurements (Happonen et al., 2022; Sørensen
et al., 2019), and so our study adds to the evidence that
trait–functionality relationships, at least regarding CO
2
exchange rates, exist and are consistent across tundra
habitats and sites.
CONCLUSIONS
Our study provides new insights into the impacts of cli-
mate warming on plant communities across high Arctic
tundra landscapes and the role of plant functional trait
variation on community resilience and ecosystem func-
tioning. The studied plant communities were strongly dif-
ferentiated among habitats in taxonomic and trait
composition and showed a modest but significant func-
tional response to experimental warming. Consequently,
functional trait variation explained considerable variation
in ecosystem CO
2
exchange rates, broadly similar in mag-
nitude to environmental and taxonomic variables.
However, warming responses in CO
2
fluxes were mainly
driven by the direct kinetic impacts of temperature on
plant physiology and biochemical processes. The results
also provide evidence that ITV adds to community func-
tional resilience, which plays a vital role in vegetation
resistance to climate warming in high Arctic Svalbard.
Here we have focused on climate warming impacts in
relation to habitat differences in snow regimes. It is
likely, however, that, combined with other aspects of
predicted climate change, such as changes in precipita-
tion and snow accumulation and increased frequency of
extreme events, for instance, rain on snow, climate
change impacts will eventually exceed community resil-
ience in some habitats and cause substantial community
shifts in the future with consequences for ecosystem
functioning. Finally, as climate warms, the immigration
of more thermophilic and responsive plant species may
eventually facilitate faster community change.
AUTHOR CONTRIBUTIONS
Following the CreDiT taxonomy (https://casrai.org/credit/
[2019]) the author contributions are as follows:
Conceptualization: Ingibjörg S. J
onsd
ottir, Kari Klanderud,
Hanna Lee, Brian J. Enquist, and Vigdis Vandvik. Data
curation: Aud H. Halbritter, Inge H. J. Althuizen, Casper
T. Christiansen, Jonathan J. Henn, and Siri V. Haugum.
Formal analysis: Aud H. Halbritter, Jonathan J. Henn, Inge
H. J. Althuizen, Casper T. Christiansen, and Siri
V. Haugum. Funding acquisition: Ingibjörg S. J
onsd
ottir
and Vigdis Vandvik. Investigation: all authors.
Methodology: Ingibjörg S. J
onsd
ottir, Aud H. Halbritter,
Kari Klanderud, Hanna Lee, Brian Salvin Maitner, Brian
J. Enquist, and Vigdis Vandvik. Project administration:
Ingibjörg S. J
onsd
ottir, Aud H. Halbritter, and Vigdis
Vandvik. Resources: Ingibjörg S. J
onsd
ottir, Aud
H. Halbritter, Kari Klanderud, Hanna Lee, Brian Salvin
Maitner, Brian J. Enquist, and Vigdis Vandvik. Supervision:
Ingibjörg S. J
onsd
ottir, Inge H. J. Althuizen, Katrín
Björnsd
ottir, Casper T. Christiansen, Aud H. Halbritter,
Jonathan J. Henn, Siri V. Haugum, Brian Salvin Maitner,
Sean T. Michaletz, Ruben E. Roos, Kari Klanderud, Hanna
Lee, Yadvinder Malhi, and Vigdis Vandvik. Validation: Aud
H. Halbritter. Visualization: Aud H. Halbritter, Inge H. J.
Althuizen, Casper T. Christiansen, Jonathan J. Henn, and
ECOLOGICAL MONOGRAPHS 17 of 21

Siri V. Haugum. Writing—original draft preparation:
Ingibjörg S. J
onsd
ottir, Kari Klanderud, Hanna Lee, and
Vigdis Vandvik. Writing—review and editing: all authors.
Authorship order reflects author contributions, with alpha-
betical order within groups of equivalent authors, and with
Brian J. Enquist and Vigdis Vandvik as senior authors.
ACKNOWLEDGMENTS
Inger Moe, ´
Asta Eyth
orsd
ottir, Linda ´
Arsælsd
ottir (L ´
A),
Martin A. Mörsdorf, and ´
Agústa Helgad
ottir assisted with
plant community composition measurements. Students
in the 2018 course “19 Plant Functional Traits”in
Svalbard (PFTC4-Team)—Polly Bass, Lucely Lucero
Vilca Bustamante, Adam Chmurzynski, Shuli Chen, Julia
Kemppinen, Kai Lepley, Yaoqi Li, Mary Linabury, Ilaíne
Silveira Matos, Barbara M. Neto-Bradley, Molly Ng,
Pekka Niittynen, Silje Östman, Karolína P
ankov
a, Nina
Roth, Matiss Castorena Salaks, Marcus Spiegel, Eleanor
Thomson, and Alexander Sæle Vågenes—assisted in
collecting and processing samples for plant functional
trait values, and Chirstine Schirmir assisted with organiz-
ing and analyzing the plant nutrient data. The study was
funded by the University Centre in Svalbard (Ingibjörg
S. J
onsd
ottir), University of Iceland Research Fund (2015,
Ingibjörg S. J
onsd
ottir), Svalbardstiftelsen (2009,
Ingibjörg S. J
onsd
ottir), Research Council of Norway
No. 246080/E10 (L ´
A) and No. 294948 (Hanna Lee, Inge
H. J. Althuizen), HNP-2015/10037 “TraitTrain,”and
INTPART Project 274831 “RECITE,”from the
Norwegian Agency for International Cooperation (Vigdis
Vandvik) and Quality Enhancement in Higher Education
(DIKU) (Vigdis Vandvik). We are grateful to Haydn
Thomas, an anonymous reviewer, and the editor for con-
structive comments on the manuscript.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
Data (Halbritter et al., 2022) are available in the Open
Science Framework repository at https://doi.org/10.17605/
OSF.IO/SMBQH. Code (Halbritter, 2022)isavailablein
Zenodo at https://doi.org/10.5281/zenodo.7052598.
ORCID
Ingibjörg S. J
onsd
ottir https://orcid.org/0000-0003-
3804-7077
Aud H. Halbritter https://orcid.org/0000-0003-2597-
6328
Jonathan J. Henn https://orcid.org/0000-0003-1551-
9238
Sean T. Michaletz https://orcid.org/0000-0003-2158-
6525
Ruben E. Roos https://orcid.org/0000-0002-1580-6424
Kari Klanderud https://orcid.org/0000-0003-1049-7025
Vigdis Vandvik https://orcid.org/0000-0003-4651-4798
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SUPPORTING INFORMATION
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article.
How to cite this article: J
onsd
ottir, Ingibjörg S.,
Aud H. Halbritter, Casper T. Christiansen, Inge
H. J. Althuizen, Siri V. Haugum, Jonathan J. Henn,
Katrín Björnsd
ottir, et al. 2023. “Intraspecific Trait
Variability is a Key Feature Underlying High
Arctic Plant Community Resistance to Climate
Warming.”Ecological Monographs 93(1): e1555.
https://doi.org/10.1002/ecm.1555
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