Plant traits and associated data from a warming experiment, a seabird colony, and along elevation in Svalbard

Abstract

The Arctic is warming at a rate four times the global average, while also being exposed to other global environmental changes, resulting in widespread vegetation and ecosystem change. Integrating functional trait-based approaches with multi-level vegetation, ecosystem, and landscape data enables a holistic understanding of the drivers and consequences of these changes. In two High Arctic study systems near Longyearbyen, Svalbard, a 20-year ITEX warming experiment and elevational gradients with and without nutrient input from nesting seabirds, we collected data on vegetation composition and structure, plant functional traits, ecosystem fluxes, multispectral remote sensing, and microclimate. The dataset contains 1,962 plant records and 16,160 trait measurements from 34 vascular plant taxa, for 9 of which these are the first published trait data. By integrating these comprehensive data, we bridge knowledge gaps and expand trait data coverage, including on intraspecific trait variation. These data can offer insights into ecosystem functioning and provide baselines to assess climate and environmental change impacts. Such knowledge is crucial for effective conservation and management in these vulnerable regions.

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Plant traits and associated data
from a warming experiment,
a seabird colony, and along
elevation in Svalbard
Vigdis Vandvik
1,2 ✉ , Aud H. Halbritter
1,2, Inge H. J. Althuizen
2,3, Casper T. Christiansen4,
Jonathan J. Henn
5, Ingibjörg Svala Jónsdóttir
6, Kari Klanderud
7, Marc Macias-
Fauria8, Yadvinder Malhi
8, Brian Salvin Maitner
9, Sean Michaletz
10, Ruben E. Roos7,
Richard J. Telford
1, Polly Bass11, Katrín Björnsdóttir6, Lucely Lucero Vilca Bustamante12,
Adam Chmurzynski9, Shuli Chen9, Siri Vatsø Haugum1,2, Julia Kemppinen
13, Kai Lepley
14,
Yaoqi Li15, Mary Linabury16, Ilaíne Silveira Matos
17, Barbara M. Neto-Bradley
18,
Molly Ng
19, Pekka Niittynen13, Silje Östman1, Karolína Pánková20, Nina Roth
21,
Matiss Castorena9, Marcus Spiegel
8, Eleanor Thomson8, Alexander Sæle Vågenes1 &
Brian J. Enquist
9 ✉
The Arctic is warming at a rate four times the global average, while also being exposed to other global
environmental changes, resulting in widespread vegetation and ecosystem change. Integrating
functional trait-based approaches with multi-level vegetation, ecosystem, and landscape data
enables a holistic understanding of the drivers and consequences of these changes. In two High Arctic
study systems near Longyearbyen, Svalbard, a 20-year ITEX warming experiment and elevational
gradients with and without nutrient input from nesting seabirds, we collected data on vegetation
composition and structure, plant functional traits, ecosystem uxes, multispectral remote sensing, and
microclimate. The dataset contains 1,962 plant records and 16,160 trait measurements from 34 vascular
plant taxa, for 9 of which these are the rst published trait data. By integrating these comprehensive
data, we bridge knowledge gaps and expand trait data coverage, including on intraspecic trait
variation. These data can oer insights into ecosystem functioning and provide baselines to assess
climate and environmental change impacts. Such knowledge is crucial for eective conservation and
management in these vulnerable regions.
1Department of Biological Sciences, University of Bergen, Bergen, Norway. 2Bjerknes Centre for Climate Research,
University of Bergen, Bergen, Norway. 3NORCE, Norwegian Research Centre AS, Bjerknes Centre for Climate
Research, Bergen, Norway. 4Department of Biology, University of Copenhagen, Copenhagen, Denmark. 5Institute
of Arctic and Alpine Research, University of Colorado Boulder, Boulder, USA. 6Life- and Environmental Sciences,
University of Iceland, Reykjavík, Iceland. 7Faculty of Environmental Sciences and Natural Resource Management,
Norwegian University of Life Sciences, Ås, Norway. 8School of Geography and the Environment, University of Oxford,
Oxford, UK. 9Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, USA. 10Department
of Botany, University of British Columbia, Vancouver, Canada. 11Department of Ethnobotany, University of Alaska,
Fairbanks, Canada. 12Universidad Nacional de San Antonio Abad del Cusco, Cusco, Perú. 13Geography Research Unit,
University of Oulu, Oulu, Finland. 14School of Geography, Development and Environment, University of Arizona,
Tucson, USA. 15Department of Health and Environmental Sciences, Xi’an Jiaotong-Liverpool University, Suzhou,
China. 16Department of Biology, Colorado State University, Fort Collins, USA. 17Department of Environmental Science
Policy and Management, University of California, Berkeley, Berkeley, USA. 18Department of Plant Sciences, University
of Cambridge, Cambridge, United Kingdom. 19Section of Botany, Carnegie Museum of Natural History, Pittsburgh,
USA. 20Department of Botany, Charles University, Prague, Czech Republic. 21Department of Physical Geography,
Stockholm University, Stockholm, Sweden. ✉e-mail: vigdis.vandvik@uib.no; benquist@email.arizona.edu
DATA DESCRIPTOR
OPEN
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Background & Summary
Arctic regions are currently warming at rates four times the global average1,2 while also being aected by other
global environmental changes, such as the ongoing loss of seabird populations, which have declined by more
than 70% since the 1950s3,4. Despite the substantial magnitude and impact of global changes in polar regions,
biodiversity and ecosystems do not always follow suit. For example, High Arctic sites are oen reported to be
relatively resistant to both climate and environmental change5–7. Variable ecosystem responses to rapid envi-
ronmental changes across the Arctic biome call for integrated assessments to understand variations in the mag-
nitude of global environmental change drivers, processes underlying the responses of Arctic vegetation, the
consequences of these environmental and vegetation changes for ecosystem functioning, and potential feed-
backs to the climate system8.
Because the primary productivity of Arctic vegetation is generally temperature-limited, climatic warming has
the potential to substantially impact the biodiversity, structure, and functioning of this unique and characteristic
biome. Accordingly, widespread vegetation changes are being reported across the Arctic, including advancing
phenologies, species range shis, shrubication, shis in plant community composition and productivity, and
associated changes in ecosystem carbon, nutrient, and water uxes9–12. ese widespread vegetation changes
emphasize the urgent need to understand and characterize the intricate responses of Arctic ecosystems to ongo-
ing climate change. Seabirds play important roles as ‘ecosystem engineers’ of terrestrial ecosystems on islands
worldwide by interconnecting distant land areas and by transferring signicant amounts of nutrients from the
sea to land, where they deposit large amounts of nutrient near seabird colonies13–17. Seabird colony eects on
terrestrial ecosystems are especially important in polar regions, where vegetation is generally dispersal- and
nutrient-limited, and areas below seabird colonies thus support unique Arctic habitats and biodiversity18,19.
Seabird colonies in the High Arctic include nests within scree slopes (dominated by little auk) and nests on
steep clis (dominated by kittywakes and Brünich gillemots). A better understanding of the role and impact
of sea-to-land transport of nutrients on terrestrial biodiversity and functioning represents an important rst
step toward better understanding of the consequences of seabird declines on terrestrial Arctic biodiversity and
ecosystems.
High Arctic land areas are typically relatively isolated, oen species-poor, and support unsaturated oras
and faunas. Trait-based approaches present valuable opportunities for generalization and enhanced insights
in these systems although questions still remain about the relevance of traits for vegetation changes in the
Arctic20. Focusing on functional traits rather than taxonomic composition enables comparisons among plant
communities with dierent taxonomic compositions within and across sites and regions21,22. At ne spatial
and temporal scales, and of particular relevance in relatively species-poor High Arctic vegetation, intraspecic
trait variation can inform on individual and population-level responses to global change, including response to
shiing selection pressures5,23,24. Trait-based approaches further allow insight into the processes governing both
community assembly (via “response traits”) and consequences of vegetation changes ecosystem functioning
(“eect traits”)25–27. At landscape scales, vegetation and trait data can be combined with multispectral imagery
to upscale information on plant functioning, chemistry, and water relations28,29. Integrating traits with data
from various biological levels, including plant physiology, vegetation functioning, ecosystem dynamics, and
remote sensing, can thus facilitate comprehensive assessments and enhance our understanding of how arctic
biodiversity and ecosystems respond to global change at various scales levels of organization, from intraspecic
to ecosystem and from plot-scale to landscapes30,31.
In this study, we report on an integrated dataset combining plant functional traits with plot-scale vegetation,
ecosystem, and climate data and landscape-scale multispectral imagery to assess the role of climate warming
and nutrient inputs from marine sources vis seabirds on biodiversity, functional traits, ecosystem processes,
and landscape patterns in High Arctic vegetation near Longyearbyen, Svalbard(Fig. 1). First, we sampled an
International Tundra Experiment (ITEX, https://www.gvsu.edu/itex/) warming experiment established in 2001
spanning three dierent habitats along a snowmelt gradient, from dry and early melt-out Dryas heath via mesic
Cassiope to moist and late melt-out snowbeds, to assess eects of climate warming on the biodiversity and func-
tioning of High Arctic vegetation. Second, in 2018, we established two elevational gradients (from sea level to
approximately 200 m a.s.l.); comparing a gradient below a seabird colony (dominated by little auks), where birds
deposit nutrients from the sea, to a reference gradient with no such impact. In the ITEX experiment, vegetation
community composition and climate data have been recorded three times since 2003. In a 2018 eld campaign,
we measured a range of functional trait-related data in both study systems, including vegetation structure, vas-
cular plant and bryophyte functional traits, ecosystem CO2 and water uxes, remote sensing, spectral reec-
tance, and associated microclimate data. In 2018, we also recorded species composition at the seabird colony
nutrient input gradient and the reference gradient. While some of these data have been used in previous publi-
cations5,28, here we present and integrate all the available data from these campaigns to safeguard the data for the
future, expand trait data coverage, make data available to others, and allow future exploration into biodiversity
assembly, ecosystem functioning, and global change impacts in the High Arctic. Such knowledge is crucial for
eective conservation and management in vulnerable Arctic and Alpine biomes.
e dataset consists of 16,160 unique trait measurements across 34 vascular plant taxa covering 52.7% of
the species in the local plant communities, along with 1,048 bryophyte trait measurements from 10 abundant
bryophytes (Table1). is extends existing vascular plant trait data from the regional ora by nine taxa for which
no previous trait data exists in databases or in the published literature and increases the number of unique trait
measurements from this regional ora by 33%, relative to the public TRY database32. ese data allow explora-
tion of intraspecic trait variation in response to experimental treatments and environmental gradients, see for
example5, and oer vegetation, ecosystem ux, reectance, remote sensing, and microclimate data from the same
sites and plots (Table1), thereby oering opportunities for a comprehensive exploration of linkages to environ-
mental drivers and feedback to climate. Our data were collected as part of the Plant Functional Traits Courses
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(PFTC4), a program for international students specializing in trait-based theory and methods (https://plant-
functionaltraitscourses.w.uib.no/), see also33,34. e data aligns with information from similar courses and eld
campaigns conducted in China35, Peru36, and Western Norway, paving the way for future comparative studies.
Methods
Data management and workflows. We adopt best-practice approaches for open and reproducible
research planning, execution, reporting, and management throughout the project (e.g.37–40) Specically, we use
community-approved standards for experimental design and data collection. We clean and manage the data using
a fully scripted and reproducible data workow, with data and code deposited at open repositories (see Fig.2 in41
for a schematic representation of our approach to data management). e paper reports on data available in 10
main data tables, linked by keys related to time, sampling locations, and species (Fig.2).
Research site selection and basic site information. Our study took place in High Arctic vegetation
near Longyearbyen, Svalbard (Fig.1). We sampled a warming experiment using Open Top Chambers (OTC) in
three distinct habitats along a snowmelt gradient, see5 and two elevational gradients, one located below a bird-cli
with nutrient input from nesting seabirds and one without the inuence of sea birds. e study area is character-
ized by a dry Arctic climate with a mean annual temperature of −2.6 °C and annual precipitation of 190 mm28.
e prevailing wind direction in the area is from the east, and the soils are typical cryosols with a thin organic
layer on top of inorganic sediments42.
ITEX warming experiment. The ITEX warming experiment is situated on the south–southeast facing
hillside of Endalen (78.18°N, 15.75°E), four kilometers east of Longyearbyen, Svalbard, at 80 m a.s.l. (Fig.1)5.
is experiment is part of the International Tundra Experiment (ITEX), a research network established in 1990
to study the long–term responses of tundra plants and vegetation to climate warming43,44. e experiment was
established in 2001 in three characteristic High Arctic habitats diering in the timing of snowmelt and hence the
duration of the growing season (see5 for further description). e relatively dry Dryas heath (DH) is found in areas
with thin snow cover (ca. 10 cm) and early snowmelt. It is dominated by Dryas octopetala with abundant Carex rup-
estris, B. vivipara, and Salix polaris as common vascular plant species. e mesic Cassiope heath (CH) is found in
areas with medium snow depth and snowmelt dates. It is dominated by Cassiope tetragona with abundant S. polaris
and B. vivipara as other common vascular plants. e moist snowbed (SB) habitat is found in areas with deep snow
(over 100 cm) and late snowmelt. It supports more herbaceous vegetation, co-dominated by Salix polaris, Bistorta
vivipara, Poa arctica, and Festucarichardsonii. Ten 75 × 75 cm plots were established within each habitat, half of
which were randomly assigned to a warming treatment using Open Top Chambers (OTC) in 2002. e other half
served as controls. e OTCs have a base diameter of 1.5 meters and a height of 40 cm. See5 for further information.
Elevation gradients with and without marine-derived nutrient input by seabirds. In 2018, we
established two elevational gradients to study the eects of marine-derived nutrient input from seabirds on High
Dataset Response variable Number of data points in ITEXa,
gradientsbNumber of taxa in ITEXa,
gradientsbTemporal range in ITEXa,
gradientsbCitation information for raw
data, clean data, and code
iPlant community
composition 1,273a
689b
26 vascular plants, 1 fungus,
8 lichens, 22 bryophytesa
50 vascular plantsb
2003, 2009, 2015a
2018bRaw data 66, clean data66,
code67
ii Vegetation structure and
height 61a
756b2003; 2009, 2015a
2018bRaw data 66, clean data66,
code67
iii Vascular plant and
bryophyte traits
5,339a
11,345b (10,297 vascular plants; 1048
bryophytes)
19 vascular plantsa
31 vascular plants, 19
bryophytesb2018a,b Raw data66, clean data66,
code67
vi Soil carbon and nitrogen 70b2022bR aw data66, clean data66,
code67
vEcosystem CO2 uxes raw ux measurements
129a
59b2018a,b Raw data 66, clean data66 code5
vi Remote sensing
7 sites, 28,500 (x5) individual
multispectral images;
340 leaf spectroscopy readings,
117 ground-truthing pointsa,b
18 species of moss,
graminoid, and dwarf
shruba,b 2018a,b Clean data66
vii Climate data station: 815,339a
loggers: 937,388a
162b
station: 2015–2018a
loggers: 2004–2005; and
2015–2018a
2018b
Raw data 66, clean data66,
code67
Tab le 1. Description and location of the datasets plant functional traits and associated data from an ITEX
warming experiment and two elevation gradients, with and without marine nutrient input from nesting
seabirds, near Longyearbyen, Svalbard. is table summarizes information on dataset number, response
variable(s), number of observations, taxa, the data’s temporal range, location of the primary data, the nal
published data, and the code for extracting and cleaning data from the primary data. e superscript letters
refer to aITEX warming experiment, bGradients, Note: e ITEX climate data consists of two data tables; one for
the climate station, one for climate logger data.
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Arctic vegetation and ecosystem functioning (Fig.1). One gradient is near Bjørndalen (78.24°N, 15.35°E), home
to a seabird nesting colony predominantly occupied by the little auk (Alle alle). Nestled among rocky outcrops and
talus slopes beneath a steepcli, these birds deposit nutrients as guano on the slope below. Nutrient deposition
should generally be highest near the nesting seabirds and decrease with increasing distance downslope. However,
small-scale topography strongly inuences distribution of nutrients, which are higher in concave areas and small
depressions in the slope28. We hereaer refer to this as the ‘nutrient input gradient’. e second gradient, referred
to as the ‘reference gradient’, Lindholmhøgda (78.20°N, 15.72°E), is free from seabird inuence. Both elevational
gradients are situated on mountains of comparable elevation and slope, share similar bedrock45, and are primary
grazing grounds for the Svalbard reindeer (Rangifer tarandus platyrhynchus). e slope under the seabird colony
at Bjørndalen faces northwest, beginning at the seashore. e reference gradient at Lindholmhøgda, located about
one kilometer inland, faces northeast (Fig.1).
At the nutrient input gradient, we established ve study sites between 12 and 170 m a.s.l. Because the sea-
birds nest at the top of the slope, just below the clis, this is both a gradient in elevation and in marine-derived
nutrient from birds, as the nutrient input from the birds increases with elevation. e highest-elevation site at
this nutrient input gradient was chosen as close to the bird nests as possible while avoiding disturbing the birds,
and the sites along the gradient were chosen to be equally spaced in elevation while avoiding dangerously steep
terrain and convex depressions in the slope where water and nutrients accumulate28. At the reference gradient
we established seven sites at roughly equally spaced elevations from 10 to 238 m a.s.l., avoiding exposed ridges
and snowbeds. Given the absence of nesting seabirds nearby, we assumed the import of marine-derived nutri-
ents to be low and consistent across the entire elevational gradient. ese sites thusform a gradient based solely
on elevation.
At each site per gradient, we set up seven vegetation plots measuring 75 by 75 cm, except for the highest
site at the reference gradient, where only four plots were established due to limited vegetation coverage (n = 35
plots at the nutrient input gradient, n = 46 at the reference gradient). Plots were placed 5 meters apart on mesic
HighArctic heath/dwarf-shrub dominated plant communities, again avoiding placing plots in obvious depres-
sions, snowbeds or exposed ridges, as described for the site selection above.
Fig. 1 Experimental site and gradients for the traits and associated data sampling in High Arctic Svalbard. (a) Inset
map and aerial photo showing the location of the study area on Svalbard and the location of the seabird nutrient
input gradient (bird icon), reference gradient (R), and ITEX warming experiment (I) in relation to Longyearbyen.
(b) Schematic illustration of the elevational distribution of sites (marked by their elevation) and nutrient inuence
(lighter area below the little auk colony) within the reference (R) and nutrient input (bird icon) gradient. (c) Schematic
illustration of the relative topographic positions of the Dryas heath (DH), Cassiope heath (CH), and snowbed (SB)
habitats, each represented by one Open Top Chamber, along a snowmelt gradient within the ITEX site (I), Note that
the full ITEX site design includes ve OTCs and ve control plots (not shown) within each habitat.
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ese site and plot selection criteria helped minimize heterogeneity within and between gradients due to
factors other than elevation and bird inuence, which was especially important given the dierent topography
at the two gradients, and the importance of topography for environmental variation andvegetation patterns
within gradients28. We marked the plots using dierential GPS and determined their aspect and slope using a
digital model of the site28.
Other study systems. Together with the three focal sites described above, the remote sensing team obtained
Unmanned Aerial Vehicle (UAV, drone) imagery (see the descriptions of dataset iv and28 for details on these
methods and data) from four other study systems. ese are:
Flux tower. is site consists of a continuous permafrost polygonal tundra lowland on a river terrace on the
at part of a large alluvial fan in Adventdalen around an eddy covariance (EC) tower established in 2011 to
conduct ux measurements of CO2 and CH4. Vegetation includes Salix polaris in drier areas and Eriophorum
scheuchzeri and Carex subspathacea in wetter locations. Shrubs dominate the drier polygons and moss domi-
nates the depressions46.
Snow fences. This site consists of an area underlain by permafrost in Adventdalen (origin 78.174387 N,
16.05769E), dominated by the dwarf shrubs Dryas octopetala on ridges and Cassiope tetragona in concavities,
with Salix polaris throughout the site. At this site, snow regime was manipulated using snow fences47.
Valley opposite the ITEX site. We ew over the valley opposite the ITEX site. It consists of a north–northeast–
facing hillside in Endalen (origin 78.18 °N, 15.76 °E), of similar topography to the ITEX site. e ight was
conducted to provide a comparable site to theITEX sitebut with a dierent aspect.
Alluvial fan. We conducted a drone flight over a well-defined alluvial fan with patterned ground in
Adventdalen (origin 78.17 °N, 16.04 °E), not far from the Snow Fences site. Vegetation over the site was very
sparse and the site was chosen to capture the geomorphological features of the fan.
Species identication, taxonomy, and ora. All vegetation and functional trait data were based on
plants sampled in the eld. We collected plants or vouchers for verication, identied them using the oras
available at the time of collection, and checked the nal data against the Svalbardora (https://www.svalbard-
ora.no/). Specimens with identication challenges, such as non-owering Draba and grasses, some Poa spp.
individuals, were assigned a descriptive name and stored as vouchers. Taxon names were standardized using the
TNRS R package48 based on the Taxonomic Name Resolution Service49, Tropicos50, e Plant List51 and USDA52
databases. We identied bryophyte species following Swedish Nationalnyckeln for mosses53. All sampled species
were identied as native to Svalbard.
Dataset (i): Species community composition sampling. ITEX warming experiment. All vascular
plant species, bryophytes, and lichens were recorded in peak growing season in each plot in 2003, 2009, and
2015 using the point intercept method as outlined in the ITEX manual54. We used a 75 × 75 cm frame with 100
evenly distributed points and recorded all hits within the canopy until the pin reached the cryptogam layer (com-
posed of bryophytes and lichens), bare ground, or litter. e amount of dead plant tissue on the ground (litter),
un-vegetated soil surface (bare ground), and rock was also recorded. e dataset also contains information on
plant functional groups to which each species belongs (woody, graminoid, forb, bryophyte, lichen). In the winter
of 2008–2009, ice formation at ground level resulted in the death of many Cassiope shrubs in two control plots and
two OTC plots in the Cassiope heath, which we noted in the dataset.
Elevational gradients. We recorded vascular plant species composition in all plots along both elevational gradi-
ents in July 2018. In each plot, the percentage cover of all vascular plant species was visually estimated to the near-
est 1%, where total coverage could exceed 100 due to vegetation layering. In addition, we recorded if species were
fertile (i.e., presence of buds, owers, and seeds). e team veried the taxonomy with available literature and
databases and consulted experts as needed (see above). We also surveyed the bryophytes, using a simplied meth-
odology due to time limitations and logistic constraints. Specically, at three of the plots at each elevation, the
three most abundant bryophyte taxa were identied. Note that these are presence-only data with no information
on bryophyte abundance, and that the bryophyte community data are available via the trait dataset (see below).
Dataset (ii): Vegetation height and structure. ITEX warming experiment. Vegetation height was
measured in 2009 and 2015 using two dierent methods. In 2009 the height of the highest individuals in the
center of each of four subplots in a 75 × 75 cm plot was measured. In 2015, the height was measured at 100 regu-
larly spaced points in each plot and then averaged per plot.
Elevation gradients. Average and maximum vegetation height was calculated based on measurements at four
randomly selected points within each plot. We also visually estimated each plot’s total cover of vascular plants,
bryophytes, biocrust, lichens, litter, rocks, and bare ground.
Dataset (iii): Vascular plant and bryophyte functional trait sampling and lab analyses. Plot-level
vascular plant sampling for leaf trait analyses. We collected whole plants for leaf trait analysis in the ITEX warm-
ing experiment and the elevational gradients in July 2018. ree whole individuals or ramets of plants, including
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roots, were collected per species and plot for all species recorded with more than 1% cover in that plot. e plants
were collected outside the plot but within the close surroundings (inside the OTC, for warmed plots) to not
destructively sample from within the plots. To avoid repeated sampling from a single clone, we selected individu-
als that were visibly separated from other ramets of that species.
Plot-level bryophyte sampling for trait analysis. We sampled bryophyte tussocks for functional trait analysis
at both elevational gradients in July 2018. ree tussocks of the three most dominant bryophyte species were
sampled at three plots at each elevation site for a maximum of nine samples per site. e bryophytes were
collected near but not in the plots to avoid destructive sampling within plots. e following taxa were col-
lected: Aulacomnium turgidum, Dicranum sp., Hylocomium splendens, Racomitrium canescens, Racomitrium sp.,
Polytrichum piliferum, Polytrichum sp., Sanionia sp., Syntrichia ruralis, and Tomentypnum nitens.
Processing and storage. e samples were typically processed within one day aer eld collection, although
some specimens were stored for up to 4 days. Collected plants were stored under cool (ca.6 °C) moist condi-
tions. Prior to processing, we conducted plant identication checks (see above). In the case of vascular plants,
we sampled up to three healthy, fully expanded leaves from each individual. If the leaves were very small, we col-
lected several leaves to reach a combined area of ca.3 cm2. e leaves were cut o as close to the stem as possible,
including the blade, petiole, and stipules, as present. For Equisetum, where stems are the main photosynthetic
structure, we sampled an 8 cm long section of the stem on which measurements were made (i.e., all photosyn-
thetic tissue, including stem, branches, and microphylls).
For bryophytes, we collected at least ve living (i.e., green) shoots (considering approximate biomass needed
for chemical analysis), including any non-green lower parts of those shoots, from each tussock. We carefully
cleaned these shoots from soil and debris using tweezers under a stereo microscope. Subsequent processing was
conducted within 24 hours (see below).
Functional trait measurements. For vascular plants, we measured 14 leaf functional traits reecting the (i)size,
(ii)leaf economic trade-o in the acquisition and utilization of resources (e.g., carbon, water) that govern the
potential physiological growth rates and environmental tolerance of plants, and (iii) plant nutrient status,fol-
lowing the standardized protocols described by Pérez-Harguindeguy et al.55: plant height (cm), leaf wet mass (g),
dry mass (g), leaf area (cm2), leaf thickness (mm), leaf dry matter content (LDMC, g/g), specic leaf area (SLA,
cm2/g), carbon (C, %), nitrogen (N, %), phosphorus (P, %), carbon to nitrogen ratio (C:N), nitrogen phosphorus
ratio (N:P), carbonisotope ratio (δ13C, ‰), and nitrgen isotope ratio (δ15N, ‰).
For bryophytes, we selected easy-to-measure so traits similar to those selected for vascular plants, related to
size, trait trade-os related to leaf economics, and nutrient status, following protocols described in56. Specically,
we measured wet mass (g), dry mass (g), shoot length (length of total and green living part; cm), shoot ratio, specic
shoot length (SSL cm g−1, as described in57, water holding capacity (WHC, g g−1), carbon (C, %), nitrogen (N, %),
phosphorus (P, %), carbon to nitrogen ratio (C:N), nitrogen to phosphorus ratio (N:P), carbonisotope ratio (δ13C,
‰), and nitrogenisotope ratio (δ15N, ‰). Note that because of the large morphological dierences between bryo-
phytes and vascular plants, the selected traits of the two primary producer groups may not be directly comparable.
Initial leaf processing and size and leaf economic traitswere measuredat the University Centre inSvalbard,
Longyearbyen, Svalbard, and nutrient traits were measured at the University of Arizona, Tucson, Arizona,in the
following steps:
• Vascular plant height. Before sampling the plants in the eld, we measuredstanding height (measured in cm)
for each individual as the distance from the ground surface to the highest tip of a photosynthetic leaf, exclud-
ing orescences but including stem leaveswhen relevant.
• Vascular plant leaf wet (fresh)mass. For vascular plants, each leaf (including blade, petiole, and stipules when
present) was gently blottedwith paper towels to remove excess water and any debris before it wasweighted
to the nearest 0.001 g using a Mettler AE200, Mettler TOLEDO, or AG204 DeltaRange (0.1 mg precision).
• Vascular plant leaf area. We attened leaves to maximize their area, and scanned them using a Canon LiDE
220 atbed scanner at 300dpi. Leaves that naturally grow folded, such asthose of Festuca species, were scanned
as such, and the area was then doubled during data processing. Any dark edges on the scans were automat-
ically removed during data processing. Leaf area was calculated using ImageJ58 and the LeafArea package59.
• Vascular plant leaf thickness. Leaf thickness was measured at three locations on each leaf blade with a digital
caliper (Mitutoyo 293–348), and the average was calculated for further analysis. When possible, the three
measurements were taken on the middle vein of the leaf and the lamina with and without veins. e petiole
or stipule thickness was not measured.
• Vascular plant leaf dry mass. Leaves (including blade, petiole, and stipules when present) were then dried for
at least 72 hours at 60 °C before dry mass was measured to the nearest 0.0001 g.
• We calculated vascular plant specic leaf area (SLA) by dividing leaf area by dry mass and Leaf dry matter
content (LDMC) as the ratio of dry to wet mass.
• Bryophyte shoot length. e stretched length of three shoots (both the length of the total shoot and the length
of the green living part) of each bryophyte sample was measured. In cases where the shoots had multiple tips,
only the longest (main) shoot was measured.
• Bryophyte wet mass. First, the bryophyte shoots were soaked in demineralized water for 30 minutes. Subse-
quently, shoots were kept in sealed Petri-dishes lined with moist tissue paper overnight to ensure full water
saturation. en, the shoots were blotted dry with tissue paper and weighed for wet mass (AG204 DeltaRange,
Mettler Toledo).
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• Bryophyte dry mass. e samples were dried to a constant mass at 60 °C for 72 h and weighed.
• Bryophyte water holding capacity (WHC). WHC was expressed as (wet mass – dry mass)/dry mass (g g−1).
• Bryophyte specic shoot length (SSL). SSL was calculated by dividing the total shoot length by its dry mass (cm g−1).
• Vascular plant leaf and bryophyte shoot stoichiometry and isotopes. A subset of leaves (ITEX: n = 2,405; refer-
ence: n = 1,596; nutrient input = 1,384) and bryophyte shoots (n = 304) were sent to the University of Arizona
for leaf stoichiometry and isotope assays (P, N, C, δ15N, and δ13C). e samples were stored in a drying oven
at 65 °C before shipping and processing. Each leaf (including blade, petiole, and stipules when present) or
bryophyte shoot sample was ground into a ne homogenous powderfor measurements.
• We determined the total phosphorus concentration using persulfate oxidation and the acid molybdate tech-
nique (APHA 1992), followed by colorimetric measurement of the phosphorus concentration with a spectro-
photometer (TermoScientifc Genesys20, USA).
• Nitrogen, carbon, stable nitrogen (δ15N), and carbon (δ13C) isotopes were measured in the Department of
Geosciences Environmental Isotope Laboratory at the University of Arizona using ash combustion analysis
of organic matter via a continuous-ow gas-ratio mass spectrometer (Finnigan Delta PlusXL) coupled to
an elemental analyzer (Costech). e process involved combusting samples of 1.0 ± 0.2 mg in the elemental
analyzer. Standardization relied on acetanilide for elemental concentration, NBS-22 and USGS-24 for δ13C,
and IAEA-N-1 and IAEA-N-2 for δ15N. Precision is at least ± 0.2 for δ15N (1 s), based on repeated internal
standards.
• Finally, we calculated ratios between C:N and N:P.
Dataset (iv): Soil carbon and nitrogen sampling. Samples of the top 5 cm of soil, including both the
organic and mineral soil layer, were taken at the end of July and August 2022 in both elevational gradients. At each
elevational site, we took three random soil samples, except for the middle site at the nutrient input gradient, where
we only took 2 samples due to a sampling error (nutrient input: n = 14; reference: n = 21). For each sample, we
used a soil corer with a diameter of 5.7 cm. Litter and above-ground vegetation, including vascular plants and live
parts of cryptogams (bryophytes, lichens, soil crust, if present), were removed from the top of each sample before
the soil cores were cut at 5 cm below the soil surface. e samples were pre-dried in the lab at 30 °C for at least one
week, then properly dried for two days at 60 °C. Stones and roots were removed using a 2 mm sieve. e resulting
soil samples thus did not contain live above- or below-ground vascular or cryptogam plant material but included
any dead parts of the cryptogamic community along with litter embedded in the soil prole. Carbon and nitrogen
content were analyzed from well-mixed subsamples using dry combustion60.
Dataset (v): Ecosystem CO2 uxes. Plot-level uxmeasurements. In July 2018, ecosystem CO2 uxes
were measured in all plots at the ITEX warming experiment. We also measured ecosystem CO2 uxes at the high-
est elevation (site 5, 170 m a.s.l.) at the nutrient input gradient and all sites except the lowest along the reference
gradient (site 2–7). Due to bad weather conditions, we could not measure uxes in all the plots and sites along the
two elevational gradients.
To estimate Net Ecosystem Exchange (NEE), ecosystem respiration (Reco), Gross Primary Production (GPP),
and soil respiration (Rs), following61, we employed a static chamber method to measure CO2 uxes. e cham-
ber, constructed from plexiglass with dimensions of 25 × 25 × 40 cm, featured two fans for air circulation and
was connected to an infrared gas analyzer (Li-840, LI-COR Biosciences, Lincoln, NE, USA) to measure CO2
uxes.
For each plot, CO2 uxes were measured twice: once under light conditions, and once under dark conditions.
Light measurements, taken during cloud-free conditions, captured photosynthetic CO2 uptake and respiratory
CO2 release from the ecosystem. For dark measurements, an opaque hood was employed to block out sunlight62,
thereby ceasing photosynthesis and enabling the measurement of respiratory CO2 release from the ecosystem,
encompassing bothplant and soil respiration.
For each measurement, continuous measurements of ambient CO2 and H2O were taken for 30 seconds before
placing the chamber over the plot and measurements then continued for approximately 90 seconds within the
chamber. e fans ensured ecient mixing of ambient air and air mixing inside the chamber. Previous studies
have demonstrated that aer 90 seconds, changing concentrations of CO2 and H2O in the closed system begin
to impact stomatal conductance63,64. is duration also mitigates the inuence of increasing temperature on the
plants within the chamber. e chamber’s closure was achieved by sealing it with a long canvas skirt along the
base, weighed down with a heavy chain. To equilibrate air conditions inside the chamber with the ambient air,
the chamber was aired for 1 minute between each measurement.
Soil respiration measurements. Soil respiration was measured in all transect sites except for the lowest elevation
at both gradients. In each plot, we inserted a PVC collar with a diameter of 10 cm to function as the chamber
space for soil respiration measurements. We measured the height of each collar at four points to calculate the
volume of the collar for ux calculations.
Environmental measurements. We measured environmental data in each plot during the ecosystem CO2 uxes
measurements or right before/aer. Photosynthetic active radiation (PAR; µmol photons m-2 s-1) was recorded
approximately every 30 seconds during the 90–120 second measuring interval using a quantum sensor (Li-190,
LI-COR Biosciences, Lincoln, NE, USA). Soil moisture (% volume) was measured using a ML3 etaProbe Soil
Moisture Sensor from Delta-T Devices at four points evenly distributed within each plot aer each measurement
in dark conditions of the chamber and twice for the soil respiration collars. Soil temperature (°C) was meas-
ured using a digital thermometer with an accuracy of ±0.1 °C at two locations within each plot and each soil
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respiration collar during all ux measurements. Finally, canopy temperature (°C) with an accuracy of ±0.1 °C at
vegetation level, was measured with an IR-thermometer with a laser pointer. For each plot, three measurements
were evenly distributed across the plot aer each ux measurement.
Calculations. NEE was calculated from the light measurements, Recofrom the dark measurements, and GPP
from both measurementsas follows (Note that calculations weredone forthe ITEX warming experimentdata
only). All raw ux data were visually evaluated for quality, and only measurements that showed a consistent
linear relationship between CO2 and time for at least 60 s were used for calculations. NEE was calculated from
the temporal change of CO2 concentration within the closed chamber during light measurementsaccording to
the following formula:
δ
δ
=×
×
×× +.
NEE
CO
t
PV
RA T(273 15)
2
where δCO2/δt is the slope of the CO2 concentration against time (µmol mol-1 s-1), P is the atmospheric pressure
(kPa), R is the gas constant (8.314 kPa m3 K-1 mol-1), T is the air temperature inside the chamber (°C), V is
the chamber volume (m3), A is the surface area (m2), and 273.15 converts temperature from degrees Celsius to
Kelvin.Reco were calculated in the same way from dark measurements.
We dene NEE such that negative values reect CO2 uptake in the ecosystem, and positive values reect CO2
release from the ecosystem to the atmosphere. GPP was calculated from light and dark measurements using this
formula: GPP = NEE + Reco.
Dataset (vi): Remote sensing. UAV multispectral imagery, leaf spectroscopy, and ground-truthing data
were collected in the ITEX warming experiment and the two elevation gradients (nutrient input and reference)
in July 2018. Further, we conducted ights and collected drone multispectral imagery in four other study systems
(see above), named “Alluvial Fan”, “Flux tower”, “Snow Fences”, and “ITEX_ValleyOpposite” (see above and28 for
description of these sites and systems).
UAV imagery acquisition. UAV imaging data were acquired from all sites using a 3DR Solo drone equipped
with a 5-band multispectral camera and light sensor (MicaSense RedEdge-MX multispectral camera - which
measures surface reectance at ve narrow bands: blue (475 nm), green (560 nm), red (668 nm), Red Edge
(717 nm) and NIR (840 nm) - and the MicaSense RedEdge Downwelling Light Sensor). e drone was own
at 40–60 m above the ground (depending on the elevational gradient present at each site), resulting in imagery
with a pixel resolution ner than 7 cm in all cases. Multiple overlapping ights were done to cover the seven
study areas. Ground control points (GCPs) for georeferencing were taken using the Emlid Reach + dierential
GNSS system. e drone imagery was processed in Pix4Dmapper (v.4.3.31, Pix4D, Lausanne, Switzerland) using
a workow whereby images from all ights were processed in the same project to form a single orthomosaic
per site. Each orthomosaic was georeferenced with GCPs and radiometrically calibrated using a MicaSense
reectance panel as a calibration target and the readings from the Downwelling Light Sensor onboard the drone.
Vegetation sampling for leaf spectroscopy and ground-truthing (turfs). At the ITEX warming experiment and
the nutrient input gradient, we collected 68 20 × 20 cm single-species turfs, representing the most common
plant functional types identied across all sites (bryophytes, graminoids, and dwarf shrubs). We extracted
high-accuracy GNSS coordinates from the locations of the extracted turfs. e turfs were cut to a substrate
depth of approximately 5 cm, sealed inside plastic bags, and transported back to the lab for species identication
(as described above)and further analysis. Turfs representing a range of tissue degradation, probably as a result of
drought or frost damage65, were also collected from the dry and mesic heaths at the ITEX warming experiment.
e extent of tissue degradation was assessed on all samples and labeled as ‘Healthy’, ‘Medium’, ‘Severe’ or ‘Dead’.
Field spectroscopy measurements. From each turf eld, we did spectroscopy measurements in the lab (ASD
Fieldspec Pro; Analytical Spectral Devices, Boulder, CO, USA), taking the reectance measurements within
24 h of turf cutting. If multiple plant species were present across the turf, we only took measurements from areas
where the main species dominated. e contact probe was pushed rmly down onto the turf, so all extraneous
light was excluded from the measurement. We undertook ve measurements at dierent locations across each
turf.
Plant trait sampling. From each healthy turf, we cut three 5 × 5 cm vegetation samples. For each of these sam-
ples, we harvested all vegetation and substrate. Fresh and dry mass was weighed, and leaf traits measured as
described above (see dataset (iii), Functional trait measurements).
Ground-truthing vegetation points. We further obtained additional high-accuracy GNSS coordinates for vege-
tation points from all three core study areas (ITEX warming experiment and the two elevation gradients).
For more details on how these data were collected and processed, see28.
Dataset (vii): Climate data. ITEX warming experiment. We recorded air temperature, precipitation, and
humidity two meters above the ground from 2015 to 2018 using a 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
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(HOBO S-SMC-M005) at 5 cm depth. e data were collected at 10-minute intervals. For soil temperature meas-
urements, we employed iButtons (iButtonLink Technologies, USA) at a depth of −5 cm and surface level in 3–4
plots per habitat and treatment (n = 20) during 2014, 2015, 2017, and 2018. ese measurements were recorded
at 3-hour intervals. Additionally, soil temperature and surface temperature at the same depth as the iButtons were
measured in three plots per habitat and treatment (n = 18) using TinyTags from 2004 to 2005. e data for these
measurements were recorded at 1-hour intervals.
Elevational gradients. We installed temperature loggers (ermoChron iButtons, San Jose, CA, USA) at c.
7.5 cm below the soil surface in all plots along the elevational reference (n = 46) and nutrient input gradients
(n = 35). Soil temperature was measured at 4-hour intervals throughout 19.7.2018 - 10–8.2018. Further, we
recorded plot-level snapshot data on soil temperature and soil moisture in all plots along the elevation ref-
erence (n = 46) and nutrient input gradients (n = 35) on the 19. July 2018. For these measurements, we used
hand-held time-domain reectometry sensors to measure volumetric water content (VWC %) at three points up
to a depth of 7.5 cm (FieldScout TDR 300; Spectrum Technologies, Plaineld, IL, USA). We used high-accuracy
digital thermometers measuring soil temperature (°C) in the center of each plot up to a depth of 7.5 cm (TD 11
ermometer; VWR International bcba; Leuven, Belgium).
Additional data. We also measured photosynthesis-temperature response curves for Alopecurus boreale,
Bistorta vivipara, Cerastium arctica, Oxyria digyna, Ranunculus sulphureus, and an unidentied rosette. ese
data will be integrated with data from other sites and published in a companion manuscript (Michaletz et al. in
prep).
Data Records
is paper reports on data from an ITEX warming experiment and two elevation gradients, a gradient aected
by marine-derived nutrients from birds nesting at the top of the slope and a reference gradient without such
inuence, in High Arctic vegetation near Longyearbyen, Svalbard(Fig.1). It contains data on plant community
composition, vegetation structure, plant functional traits, soil C and N, ecosystem CO2 uxes, remote sensing
and environmental data collected between 2003 and 2022. Data outputs consist of eight datasets, the (i) species
composition, (ii) vegetation height and structure, (iii) plant functional traits, (iv) soil carbon and nitrogen, (v)
ecosystem CO2 uxes, (vi) remote sensing data and (vii) climate data sampled from the ITEX warming experi-
ment and along the gradients (Table1). Remote sensing data exists from some additional sites (see description
for “dataset vii” under Methods) and other data also exists from these sites (see additional data). e data pre-
sented here were checked and cleaned according to the procedures described under Methods and Technical
validation before nal data les and associated metadata were produced.
e nal data les (see Table1 for an overview) and all raw data, including leaf scans, are available at Open
Science Framework (OSF)66. For each data type, we provide separate les for the ITEX warming experiment and
the gradients (Table1). e code necessary to access the raw data and produce these cleaned datasets, as well as
the calculations and statistical tests in the Data Records section, is available in an open GitHub repository, with
a versioned copy archived in Zenodo67. e reader is referred to the code and the detailed coding, data cleaning,
and data accuracy comments and the associated raw and cleaned data and metadata tables for detailed informa-
tion about the data cleaning process. e Usage Notes section in this paper summarizes the data accuracy and
data cleaning procedures, including caveats regarding data quality and our advice on ‘best practice’ data usage.
Dataset (i): Plant community composition. e plot-level plant community dataset from the ITEX
warming experiment has a total of 57 taxa and 1,273 observations (taxa × plots × years) (Tables1, 2). e overall
mean species richness per plot, treatment, and year (mean ± SE) is 14.14 ± 0.33 species, including vascular plants,
bryophytes and lichens. e species richness ranges from 11.4 ± 0.57 in the snowbed (SB) via 14.33 ± 0.46 in the
Cassiope heath to 17.0 ± 0.53 in the Dryas heath (DH). Shannon diversity and evenness show the same pattern.
For more details on diversity and community responses, see5.
The plot-level plant community dataset from the gradients has a total of 50 taxa and 698 observations
(taxa × plots × years) (Tables1, 3). Mean species richness (including graminoids, forbs and bryophytes) per plot
is 10 ± 0.58 species for the reference gradient and 6.8 ± 0.69 for the nutrient input gradient. Shannon diversity
and evenness were also slightly higher at the reference gradient.
A Non-metric Multidimensional Scaling (NMDS) ordination diagram of all vegetation plots shows grad-
ual variation in species composition within and across our two study systems (Fig.2a). e ITEX warming
experiment is found on the le-hand side of the diagram, characterized by Dryas octopetala, Equisetum spp.,
Bistorta vivipara, and a number of bryophytes (Fig.2b). e Dryas and Cassiope heath overlap in community
composition and are located towards the lower parts of the diagram whereas the snowbed is the most distinct
among the ITEX habitat types, located further up and to the right in the diagram and is thus more similar to the
vegetation at the lower part of the reference gradient. Within all ITEX habitats, the warmed plots (open symbols)
are generally located further to the upper le in the diagram than the respective controls (lled plots). e two
elevation gradients are found at the right-hand side of the NMDS space. Within each gradient, lower-elevation
plots are found near the center of the diagram and higher-elevation plots are found further to the right, so that
NMDS axis 1 partly reects a temperature gradient form warmer sites and treatments at the le-hand side to
colder sites at the right. Accordingly, several species characteristic of colder habitats are found at the center to
right-hand side of the plot, including Draba species, Luzula spp., and Salix polaris. e nutrient input gradient
is generally found further to the right in the diagram relative to the reference gradient, reecting nutrients as an
additional factor towards the far-right parts of NMDS axis 1. In particular, the highest-elevation plots from the
nutrient input gradient, which are most aected by deposition of marine-derived nutrients from the seabirds,
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are relatively distinct and form a cluster at the upper far right-hand side of the diagram, characterized by several
nutrient-demanding species such as Cochleria groenlandica, Oxyria digyna, Cerastium arcticum, Draba spp., and
Saxifraga spp. In contrast, towards the lower right-hand end of the ordination diagram are species characteristic
of relatively nutrient-poor habitats, including Salix polaris, Luzula confusa and L. nivalis. Note that small species
pool and high intraspecic trait variation of High Arctic plants in Svalbard5 implies that many species have wide
habitat and environmental ranges, and are thus found across our study systems, treatments, and gradients.
Fig. 2 Non-metric Multidimensional Scaling (NMDS) ordination depicting variation in taxonomic composition of
vascular and nonvascular plants in the 96 vegetation plots from the Endalen ITEX site (three habitats; Dryas heath,
Cassiope heath, snowbed) and two elevation gradients (seabird colony nutrient input, reference). (a) Plot scores
on NMDS axes 1 and 2, based on 74 taxa (see67 for details). Shapes and colors indicate habitats and experimental
treatment within the two study systems (Dryas heath (red), Cassiope heath (pink), snowbed (blue)) from the ITEX
warming experiment (lled: control, open: warmed), and the seabird nutrient input (green) and reference (gray)
gradients. Opacity indicates elevation of the sites, with darker color corresponding to higher elevation. (b) Species
scores of selected taxa on NMDS axes 1 and 2 (see67 for code to generate the NMDS scores for the full list of taxa).
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For an overview of the clean datasets and links to the code to clean and extract these data from the raw data,
see Table1. e nal clean data are provided in the “Community” folder, a species list over species and experi-
ments is provided in the same folder, and the raw data are provided in the “RawData” folder on OSF66. e code
to download and clean the data can be found in the GitHub repository67 in the le R/community_plan.R.
Dataset (ii): Vegetation height and structure. Vegetation height and structure data from the ITEX
warming experiment has a total of 60 observations (site × treatm ent × plot; Tables1, 4). Vegetation height did not
dier between the control and warming treatment or among habitats, except in 2009, where height was lower in
the Dryas heath (E = −2.18 ± 0.651, t5, 24 = −3.35, P = 0.003).
Vegetation height and structure data from the gradient has a total of 756 observations (gradi-
ent × site × plot × variable; Tables1, 5). Vegetation height increased with increasing elevation at the nutrient
input gradient (E = 0.01 ± 0.004, t1,71 = 2.56, P = 0.013), but not at the reference gradient. e vascular plant
cover decreased with elevation (E = −0.06 ± 0.026, t1,71 = −2.14, P = 0.035), but did not dier between the two
gradients. Bryophyte cover increased with elevation, but more so at the nutrient input gradient (E = 0.24 ± 0.08,
t1,71 = 3.02, P = 0.010). Litter cover did not vary with elevation or between the gradients.
For an overview of the clean datasets and links to the code to clean and extract these data from the raw data,
see Table1. e nal clean data are provided in the “Community” folder, and the raw data are provided in the
“RawData” folder on OSF66. e code to download and clean the data can be found in the GitHub repository67
in the le R/community_plan.R.
Dataset (iii): Plant functional traits. In the ITEX warming experiment, we measured size, leafeconomic,
and nutrient traits (plant height, wet mass, dry mass, leaf area, leaf thickness, specic leaf area [SLA], and leaf
dry matter content [LDMC], Carbon [C], Nitrogen [N], Phosphorus, C:N and NP ratios, and isotoperatios
[δ13C, δ15N]) for 436 leaf samples from 19 taxa across all sites and treatments, for a total of 5,339 trait obser-
vations (site × treatment × plot; Tables1, 6). We also happened to sample three leaves of a lonely individual of
Betula nana we encountered growing close to the site. ere are similar numbers of leaves per site (DH = 1,894;
CH = 1,737; SB = 1,666) and treatment (CTL = 2,691; OTC = 2,606).
Visual inspection of the unweighted trait distributions show that “size-related traits” such as height, mass,
and area tend to increase towards habitats with more snow cover (Fig.3a). Further,leaves from snowbeds tend
to have a higher carbon content and δ15N and lower nitrogen compared to leaves from the drier Dryas heath,
whereas leaves from the Cassiope heath have intermediate values. None of the other unweighted trait distribu-
tions show clear trends. For more detailed analyses and interpretation of the trait responses, see5.
Along the gradients, we measured size, leaf economic, and nutrient traits (plant height, wet mass, dry mass,
leaf area, leaf thickness, shoot length, shoot ratio, specic leaf area [SLA], and leaf dry matter content [LDMC],
water holding capacity [WHC], specic shoot length [SSL], carbon [C], nitrogen [N], phosphorus, C:N and N:P
ratios, and isotoperatios [δ13C, δ15N]) for 1,181 leaf samples from 41 taxa across all plots and both gradients, for
a total of 11,345 trait observations (site × treatme nt × plot; Tables1, 7). Of those 1,048 observations and 10 taxa
were from bryophytes. e number of samples diered between the two gradients (reference = 7,061; nutrient
input = 4,284).
Visual inspection of the unweighted trait distributions indicate that plants inuenced by nutrients from a
seabird colony are taller and have larger leaves, higher SLA and leaves with lower carbon and higher N and dN15
content compared to the reference gradient (Fig.3b).
Variable name Description Variable type Variable range or levels Units How measured
Year Year of sampling numeric 2003–2015 yyyy recorded
Site Site as the habitat; DH = Dryas heath, CH = Cassiope heath,
SB = snowbed categorical CH - SB dened
Treatment Warming treatment; CTL = ambient conditions,
OTC = warmed by Open top chamber (OTC) categorical CTL - OTC dened
PlotID Unique ID as the combination of Site and number; SB-1 categorical CH-1 - SB-9 dened
Tax on Species name including genus and species categorical alectoria nigricans -
unidentied pleurocarp
moss sp identied
Abundance Estimated species abundance numeric 1–191 recorded
FunctionalGroup Plant functional group; graminoid, forb, dshrub (deciduous
shrub), eshrub (evergreen shrub), moss, liverwort, lichen and
fungi categorical dshrub - moss recorded
Elevation_m Elevation of site numeric 80–80 m a.s.l. recorded
Latitude_N Latitude of site numeric 78.183 - 78.183 Degree north recorded
Longitude_E Longitude of site numeric 15.75 - 15.75 Degree east recorded
Flag Flagging problems in the data categorical Iced - Iced recorded
Tab le 2. Data dictionary for vascular plant community composition (dataset i-a) from an ITEX warming
experiment in Endalen, Svalbard. e dataset contains 1,273 observations of the covers of 57 taxa in 30
vegetation plots sampled across three dierent habitats, over a period of 12 years. Variable names, description
variable type, range or levels, units and short description is given for all variables.
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e trait datasets from both the ITEX warming experiment and the gradients are suitable for exploring
community weighted trait distributions since we have measurements for species making up at least 80% of the
cumulative cover for all traits in all plots (calculations based on datasets i). In the warming experiment 96.1%
and at the gradient 73.4% of the plots meet this criterion for local (plot-level) trait measurements which makes
the data well-suited to study community-level consequences of intraspecic trait variation. Note that due to
limitedleaf biomass available for chemical analyses,data coverage is lower for traitsbased on these analyses,
for which we have plot-level measurements forspecies making up 77% of the cumulative coverin the warming
experiment and 26% in the gradients.
For an overview of the clean datasets and links to the code to clean and extract these data from the raw
data, see Table1. e nal clean data are provided in the “Traits” folder, and the raw data are provided in the
“RawData” folder on OSF66. e code to download and clean the data can be found in the GitHub repository67
in the le R/trait_plan.R.
Dataset (iv) Soil carbon and nitrogen. e soil carbon and nitrogen dataset from the gradients has
70 observations (gradient × site × plot × variable; Tables1, 8). ere are 21 observations for C and N at the ref-
erence gradient and 14 at the nutrient input gradient. Soilcarbon ornitrogen content did not vary between the
gradients or with elevation.
The average soil carbon content at the nutrient input gradient was 11.6 ± 2.17% and nitrogen con-
tent 0.45 ± 0.07%. At the reference gradient, soil carbon content was 6.24 ± 0.77% and nitrogen content
0.243 ± 0.02%, both lower than under the inuence of seabirds.
For an overview of the clean datasets and links to the code to clean and extract these data from the raw data,
see Table1. e nal clean data are provided in the “Soil” folder, and the raw data are provided in the “RawData”
folder on OSF66. e code to download and clean the data can be found in the GitHub repository67 in the le R/
soil_plan.R.
Variable name Description Variable type Variable range or levels Units How measured
Site Site as the habitat; DH = Dryas heath,
CH = Cassiope heath and SB = snowbed categorical CH – SB dened
Treatment Warming treatment; CTL = ambient conditions,
OTC = warmed by Open top chamber (OTC) categorical CTL – OTC dened
PlotID Unique ID as the combination of Site and number;
SB-1 categorical CH-1 – SB-9 dened
Year Year of sampling numeric 2009 – 2015 yyyy recorded
Height Vegetation height recording height at 100 pinpoint
(2015) or highest individuals (2009) numeric 0.423 – 7.35 cm recorded
Method Methods used to measure vegetation height categorical highest_ind – pinpoint recorded
Flag Flagging problems in the data categorical Iced – Iced recorded
Tab le 4. Data dictionary for community height (dataset ii-a) from an ITEX warming experiment in Endalen,
Svalbard. e dataset contains 60 observations of 30 vegetation plots in 2009 and 2015. Variable names,
description variable type, range or levels, units and short description is given for all variables.
Variable name Description Variable type Variable range or levels Units How measured
Year Year of sampling numeric 2018 - 2018 yyyy recorded
Date Date of sampling date 2018-07-17 - 2018-07-21 yyyy-mm-dd recorded
Gradient Elevational gradient; C = reference, B = nutrient
input gradient input (seabird colony) categorical B - C dened
Site Site as a number 1–7 numeric 1–7 dened
PlotID Plot ID from A to G categorical A - G dened
Tax on Species name including genus and species categorical alopecurus ovatus - unknown sp identied
Cover Estimated species cover numeric 0–70 recorded
Fertile Numeric value indicating if an indiviual is fertile
(1; i.e. presence of buds, owers, and seeds) or
not (0). numeric 0–10 recorded
Weather Weather during sampling categorical cloudy - windy recorded
Elevation_m Elevation of site numeric 9.759 - 238.159 m a.s.l. recorded
Longitude_E Longitude of site numeric 15.34 - 15.712 Degree east recorded
Latitude_N Latitude of site numeric 78.207 - 78.239 Degree north recorded
Tab le 3. Data dictionary for vascular plant community composition (dataset i-b) from elevational gradients
with and without nutrient input from a seabird colony, in Bjørndalen and on Lindholmhøgda, respectively, near
Longyearbyen, Svalbard. e dataset contains 698 observations of the covers of 50 taxa in 63 vegetation plots
sampled at the two elevational gradients in 2018. Variable names, description variable type, range or levels,
units, and short description is given for all variables.
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Dataset (v): Ecosystem CO2 ux. e ecosystem CO2 ux dataset (NEE, Reco and GPP) from the ITEX
warming experiment has 135 individual ux measurements from peak growing season in 2018, paired with their
environmental metadata (site × plot × variable; Table1). Fluxes are generally larger in the dry Dryas heath than
the wet snowbed community, with the Cassiope heath being intermediate. Across the three sites, experimental
warming increasesboth Reco and GPP uxes, yielding similar NEE across treatments5.
For an overview of the datasets and links to the code to clean and extract clean data from the raw data,
see Table1. e raw CO2 ux data from both the ITEX warming and gradients is provided as zip les in the
“RawData/RawData_C-Flux” folder onOSF66. e CO2 ux data from the ITEX warming experiment is pro-
vided on OSF66 as non-standardized raw data “C-Flux/Cux_SV_ITEX_2018.csv” and as standardized data
“C-Flux/Endalen_paper/ITEX_all.Rdata”. For the code to clean and standardize the ITEX ux data, see5. Note
that we do not provide a clean version of the ux data for the reference and nutrient input gradient.
Dataset (vi): Remote sensing. In total, we created 5-band orthomosaics, radiometrically calibrated and
georeferenced with GCPs, from seven areas, covering 118 ha, built using 28,500 overlapping geolocated images,
and with pixel resolutions that range from 2.90 cm to 6.72 cm. We further collected 68 turfs from two sites (ITEX
warming experiment and nutrient input gradient) that we used for ground-truthing, and from which we obtained
leaf spectroscopy readings and functional traits. ese turfs represented 18 species of moss, graminoids, and
dwarf shrubs, and generated a total of 340 leaf spectroscopy measurements (spectra). Finally, an additional 117
ground-truthing points were geolocated in the three core sites identied as dwarf shrub, graminoid, or moss
(Table9).
e data are organized in six main categories, namely: (a) Handheld spectra, which contains all the hyper-
spectral data from the turfs; (b) UAV imagery, which contains the multispectral orthomosaics for each of the
sites that were own; (c) turf species; (d) turf traits; (e) UAV spectra, which contains the multi-spectral infor-
mation extracted from the orthomosaics for the points where the turfs and ground-truthed species coordinates
were taken - read28 for further information. A sixth category (f )corresponds to Sentinel imagery used to upscale
the maps produced in28. A readme text le has been produced for each of these data categories, explaining the
metadata in detail.
e remote sensing data can be found on the OSF66 repository.
Dataset (vii): Climate. Climate weather stationdata from the ITEX warming experiment has a total of
815,339 observations, including airtemperature, PAR, relative humidity, water content, and solar radiation data
throughout 2015–2018 (date × variable; Tables1, 10). Average values over the whole period were 153.71 ± 0.64
μmol m−2 s−1 PAR, 79.11 ± 0.02% relative humidity, 67.14 ± 0.28 W/m² solar radiation, −1.51 ± 0.02 °C and
0.18 ± 0.00 m3/m3 soilwater content. For more details, see5.
Temperature loggerdata from the ITEX warming experiment has a total of 937,388 observations of soiland
surfacetemperaturesfrom 2004–2005 and 2015–2018 (date × site × treatment × logger; Tables1, 11).
e mean summer groundsurface temperature (June–September) in the periods between 2004–2005 and
2015–2018 was 7.7 ± 0.01 °C and the soil temperature was 5.37 ± 0.01 (dataset vi-a-1, Table11). e OTCs
increase the summer groundsurface temperature bybetween 0.62–1.67 °C and thesoil temperature by 0.49–
1.03 °C, except for inthe Cassiope heath where the temperature was −0.70 °C colder in the OTC compared to
the control plots in this period.
Climate data from the gradient has a total of 162 observations (n = 81 for soil moisture and temperature each;
gradient × site × plot × variable; Tables1, 12). Soil temperature was higher at the reference gradient (E = 2.68,
t71 = 6.11, P < 0.001). Soil moisture decreased with elevation but more strongly at the nutrient input gradient
(E = −0.10, t71 = −3.42, P = 0.001).
For an overview of the clean datasets and links to the code to clean and extract these data from the raw
data, see Table1. e nal clean data are provided in the “Climate” folder, and the raw data are provided in the
Variable name Description Variable type Variable range or levels Units How measured
Year Year of sampling numeric 2018 - 2018 yyyy recorded
Date Date of sampling date 2018-07-17 - 2018-07-21 yyyy-mm-dd recorded
Gradient Elevational gradient; C = reference,
B = nutrient input gradient input
(seabird colony) categorical B – C dened
Site Site as a number 1–7 numeric 1–7 dened
PlotID Plot ID from A to G categorical A – G dened
Val u e Height or cover value numeric 0 – 98 cm, percentage recorded
Elevation_m Elevation of site numeric 9.759 – 238.159 m a.s.l. recorded
Longitude_E Longitude of site numeric 15.34 – 15.712 Degree east recorded
Latitude_N Latitude of site numeric 78.207 – 78.239 Degree north recorded
Tab le 5. Data dictionary for community height and structure (dataset ii-b) from elevational gradients with
and without nutrient input from a seabird colony, in Bjørndalen and on Lindholmhøgda, respectively, near
Longyearbyen, Svalbard. e dataset contains 756 observations in 81 vegetation plots sampled across the two
gradients in 2018. Variable names, description variable type, range or levels, units and short description is given
for all variables.
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“RawData” folder on OSF66. e code to download and clean the data can be found in the GitHub repository67
in the le R/climate_plan.R.
Technical Validation
Taxonomic validation. We took vouchers from all taxa, and Pernille Bronken Eidessen (UNIS)and other
local experts checked taxonomic identication. We identied the species to the lowest taxonomic level possible,
but in some cases, the taxonomy changed during the course of the 17-year study, and we were not always able
to distinguish closely related taxa, such as for example within Poa pratensis (seediscussion inhttps://www.sval-
bardora.no/).Specimens that were unidentiable to species in the eld were given a descriptive name, and the
voucher was stored. e community data thus has 19 unidentied specimens where only the genus is known,
and one completely unknown specimen. Fieen of those are lichen and bryophytes from the ITEX experiment,
and the other four are forbs and graminoids from the gradients (dataset i). ere are no unidentied taxa in
the trait data (dataset iii). e nal community taxonomy and trait data were checked and corrected against
the Taxonomic Nomenclature Resolution Service (TNRS)48,49 (see above). Note that for some common taxa on
Svalbard, such as Festuca richardsonii, there is a discrepancy between the TNRS accepted name and the name
used in thecurrent Svalbardora (https://www.svalbardora.no/). For clarity, we refer to these taxa by their TNRS
names in the text. A full species list of all identied species, including their authority across datasets, is also avail-
able in the OSF repository66 in the ‘Community’ folder (PFTC4_Svalbard_2018_Species_list.csv).
Community data validation. We checked and corrected missing or unrealistic cover values against the eld
notes for typing errors. e data-checking and outcomes of correctionprocedures is documented in the code67.
Trait data validation. Trait data were thoroughly checked and validated as follows. First, we checked and
corrected missing or erroneous sample identications in one or more measurements against eld notes and notes
on the leaf envelopes. Second, unrealistically high or low values of one or more trait values were checked and
corrected against the lab and eld notes for typing errors, and/or leaf scans were checked for problems during the
scanning process (e.g., empty scans, double scans, blank areas within the leaf perimeter, dirt, or other non-leaf
objects on scans). Issues that could be resolved were corrected (e.g., recalculating the leaf area manually to include
missing leaf parts on the scan, the wrong match between scan and leaf ID, etc.). Any remaining samples with
apparent measurement errors that resulted in unrealistic trait values were removed, as follows: Leaves with spe-
cic leaf area values greater than 500 cm2 g−1 were removed (n = 76). Because it was dicult to nd out why the
SLA values were so high, thedry mass and leaf area values of those leaves were also removed. Leaves with carbon
values higher than 6.4% (n = 8) and phosphorus values higher than 5% (n = 25) were deemed unrealistic andalso
removed. See the code67 for details. We further plotted the data (e.g., wet mass vs. dry mass) and checked for
Variable name Description Variable type Variable range or levels Units How measured
Project Project from where data were collected; ITEX = ITEX
warming experiment, Gradient = elevational gradients categorical ITEX – ITEX recorded
Year Year of sampling numeric 2018 – 2018 yyyy recorded
Date Date of sampling date 2018-07-19 – 2018-07-25 yyyy-mm-dd recorded
Site Site as the habitat; DH = Dryas heath, CH = Cassiope
heath and SB = snowbe d categorical CH – SB dened
Treatment Warming treatment; CTL = ambient conditions,
OTC = warmed by Open top chamber (OTC) categorical CTL – OTC dened
PlotID Unique ID as the combination of Site and number; SB-1 categorical 8-OTC – SB-9 dened
Individual_nr Individual number numeric 1–5 dened
ID Unique leaf ID consisting of 3 letters and 4 numbers categorical AEC8296 – CMO6669 dened
Tax on Species name including genus and species categorical lopecurus ovatus – trisetum
spicatum identied
Trait
Plant functional leaf trait including plant height, wet/
dry mass, leaf area, leaf thickness, specic leaf area, leaf
dry matter content, carbon, nitrogen and phosphorus
content, CN and NP ratio, d13C and d15N isotope ratio
categorical C_percent – Wet_Mass_g dened
Val u e Leaf trait value numeric −34.173 – 401.5
cm, g, cm2,
mm, cm2/g,
percentage,
permil
recorded
Elevation_m Elevation of site numeric Inf–Inf m a.s.l. recorded
Latitude_N Latitude of site numeric Inf–Inf Degree north recorded
Longitude_E Longitude of site numeric Inf–Inf Degree east recorded
Functional_group Plant functional group; vascular categorical vascular – vascular identied
Tab le 6. Data dictionary for plant functional traits (dataset iii-a) from an ITEX warming experiment in
Endalen, Svalbard. e dataset contains 5,339 observations of the covers of 19 taxa in 30 vegetation plots
sampled across three habitats in 2018. Variable names, description variable type, range or levels, units and short
description is given for all variables.
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Fig. 3 Unweightedtrait distributions from (a) the ITEX warming experiment in Endalen and (b) thetwo
elevational gradients with and without seabird colony nutrient input, in Bjørndalen and on Lindholmhøgda,
respectively, near Longyearbyen, Svalbard. Distributions are given for three habitat types (Dryas heath, Cassiope
heath, snowbed) within the ITEX experiment and for the two elevational gradients. e plots are based on all
sampled leaves, using local trait values for each plot when available. e size traits (height, mass, length, area
and thickness) are log-transformed. Note that 4 values at the elevational gradients where N:P ratio was >100
were removed before plotting the gure.
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outliers. e data checking and outcomes for these various correctionprocedures are available and documented
in the code67 and associated readme le.
Ecosystem CO2 flux validation. Each fluxmeasurement curvewas assessed visually for quality.
We checked for inconsistencies within the data of each measurement by plotting CO2 air concentration vs. time.
e linearity of CO2 increase/decrease was assessed by r2 values of linear regression models. Time-intervals used
for calculationswere adjusted manuallyif inconsistencies occurred (e.g., due tooutliers or signs of leakage).
Remote sensing data validation. Remote sensing data were collected using best-practice and thor-
oughly checked and validated. e UAV was own with a MicaSense RedEdge Downwelling Light Sensor (DLS).
e imagery was radiometrically calibrated using a MicaSense reectance panel as the calibration target. Ground
Variable name Description Variable type Variable range or levels Units How measured
Project Project from where data were collected; ITEX = ITEX
warming experiment, Gradient = elevational
gradients categorical Bryophytes – Gradient recorded
Year Year of sampling numeric 2018 – 2018 yyyy recorded
Date Date of sampling date 2018-07-17 – 2018-07-24 yyyy-mm-dd recorded
Gradient Elevational gradient; C = reference, B = nutrient
input gradient (seabird colony) categorical B - C dened
Site Site as a number 1–7 categorical 1–7 dened
PlotID Plot ID from A to G categorical A–G dened
Individual_nr Individual number numeric 1–5 dened
ID Unique leaf ID consisting of 3 letters and 4 numbers categorical AAZ7235 - CWR4667 dened
Tax on Species name including genus and species categorical alopecurus ovatus - trisetum
spicatum identied
Trait
Plant functional leaf trait including plant height,
wet/dry mass, leaf area, leaf thickness, shoot length,
shoot ratio, specic leaf area, leaf dry matter content,
water holding capacity, specic root length, carbon,
nitrogen and phosphorus content, CN and NP ratio,
d13C and d15N isotope ratio
categorical C_percent - WHC_g_g dened
Val u e Leaf trait value numeric −32.999 - 419
cm, g, cm2,
mm, cm2/g,
percentage,
permil
recorded
Elevation_m Elevation of site numeric 9.759 - 238.159 m a.s.l. recorded
Latitude_N Latitude of site numeric 78.207 - 78.239 Degree north recorded
Longitude_E Longitude of site numeric 15.34 - 15.712 Degree east recorded
Functional_group Plant functional group; vascular and bryophyte categorical bryophyte - vascular identied
Tab le 7. Data dictionary for plant functional traits (dataset iii-b) from an ITEX experiment in Endalen
and two elevational gradients with and without nutrient input from a seabird colony, in Bjørndalen and on
Lindholmhøgda, respectively, near Longyearbyen, Svalbard. e dataset contains 11,345 observations of the
covers of 41 taxa in 63 vegetation plots sampled across six sites, three re histories, and three years. Variable
names, description variable type, range or levels, units and short description is given for all variables.
Variable name Description Variable type Variable range or levels Units How measured
Gradient Elevational gradient; C = reference,
B = nutrient input gradient (seabird
colony) categorical B – C dened
Site Site as a number 1–7 numeric 1–7 dened
PlotID Plot ID from A to G categorical A–G dened
Var i a ble Soil nutrient variable; C = carbon,
N = nitrogen categorical C–N dened
Val u e Soil nutrient content numeric 0.096 – 28.388 percentage recorded
Weight_mg Weight of soil sample numeric 3.522 – 8.812 mg recorded
Elevation_m Elevation of site numeric 9.759 – 238.159 m a.s.l. recorded
Longitude_E Longitude of site numeric 15.34 – 15.712 Degree east recorded
Latitude_N Latitude of site numeric 78.207 – 78.239 Degree north recorded
Tab le 8. Data dictionary for soil carbon and nitrogen (dataset iv) from elevational gradients with and without
nutrient input from a seabird colony, in Bjørndalen and on Lindholmhøgda, respectively, near Longyearbyen,
Svalbard. e dataset contains 70 observations sampled across the two elevational gradients. Variable names,
description variable type, range or levels, units and short description is given for all variables.
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control points for georeferencing were taken using the Emlid Reach + dierential GNSS system (Emlid, Hong
Kong). RMS errors for all orthomosaics were <0.2 m. Pix4D quality reports ensured the images were stitched to
the highest possible quality and accuracy.
Aer every turf measurement, the eld spectroradiometer was optimized and calibrated for dark current
and white light a. Each measurement consisted of 40 internally averaged reectance readings to increase the
signal-to-noise ratio. e spectra were visually assessed to ensure there were no bad measurements. Turf traits
values were checked for unrealistically high or low valuesas described above.
Climate data validation. e climate data of each plot was visually inspected, and unrealistic high or low
values were removed.
Usage Notes
To properly use these data, be aware that: (a) e community data contains a few unidentied specimens, and
for some taxonomic groups, especially within Poa, identications may be uncertain (see above, and comments
or ags in the raw data66). (b) e ITEX experiment community data (dataset i) contains two control plots and
two OTC plots in the Cassiope heath with icing damage, agged with “iced” in the “Flag” column, that should be
removed from the dataset to avoid bias in the species richness and abundance of the Cassiopehabitat due tothese
plots. (c) In the height data from the ITEX warming experiment, vegetation height was measured dierently in
2009 and 2015 and while both methods are valid the measurements cannot be directly compared. (d) In the traits
Site name
UAV ights Spectroscopy/Trait collection Georeferenced extra ground
points
Area (ha)
Pixel
Resolution
(cm) Latitude (°N) Longitude (°E)
Num of
geolocated
images
Single-species
turfs for
spectroscopy &
traits (spectra
read)
Num Species
measured
through eld
spectroscopy
GNSS
vegetation
ground
truthing
points
Num of plant
functional
groups in
ground truthing
points
ITEX warming
experiment 17,6334 4,58 78,1887 15,7429 4535 23 (115) 3*37 3
Reference gradient 46,4251 6,08 78,2164 15,6876 10520 — — 18 3
Nutrient input gradient 20,0827 6,72 78,2410 15,3355 2255 45 (225) 13 62 3
ITEX_ValleyOpposite 7,5274 4,54 78,1844 15,7623 1595 — — — —
Snow Fences 10,0716 3,54 78,1744 16,0577 3750 — — — —
Alluvial Fan 8,1944 2,90 78,1726 16,0354 2910 — — — —
Flux Tower 8,0564 3,50 78,1871 15,9138 2935 — — — —
TOTAL/AVERAGE 117,9910 4,55 — — 28500 68 (340) 18 117 —
Tab le 9. Summary of the remote sensing data (dataset vi) generated for this paper. “UAV ights”: main
characteristics of the orthomosaics built for each study site.For each ight, radiometrically calibrated
reectance values exist for 5 bands (Red, Green, Blue, Red-Edge, and NIR); Latitude and Longitude correspond
to the origin of the raster (north-west corner); Num of geolocated images shows the number of individual
overlapping images used to build each orthomosaic. “Spectroscopy/Trait collection”: information on the
single-species turfs collected for ground-truthing, leaf spectroscopy, and trait measurements. “Georeferenced
extra ground points”: number and functional group of identied vegetation types in the eld which were geo-
referenced with a dierential GNSS system. *plus 1 moss & 1 graminoid - unidentied
Variable name Description Variable type Variable range or levels Units How measured
DateTime Date and time of sampling date_time 2015-08-13 12:00:00 - 2018-
09-18 23:50:00 yyyy-mm-dd recorded
LoggerLocation Location of logger; air categorical air - air dened
LoggerType Logger type; weather station categorical WeatherStation -
WeatherStation dened
Var i a ble Climate variable; PAR, water content,
air temperature, relative humidity, and
solar radiation categorical PAR - WaterContent dened
Val u e Climate variable value numeric −23.835 - 2048.7
umol m-2
s-1, m3 m-3,
degree celsius,
percentage, W
m-2
recorded
Table 10. Data dictionary for the weather stationclimate data (dataset vii-a-1) from a climate station at an
ITEX warming experiment in Endalen, Svalbard. e dataset contains three years of data for temperature, PAR,
relative humidity, water content, and solar radiation. Variable names, description variable type, range or levels,
units and short description is given for all variables.
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data we have followed what we consider best practice for data quality and ltered out what we consider unrelia-
ble data, e.g., dry mass for very small leaves approaching the limits for balance accuracy, leaf area in the case of
clearly erroneous scans, and someclearly unrealistic measurementsor calculations (see above). (e) e trait data
from the ITEX warming experiment contains leaves from one Betula nana individual. is plant is not part of
the ITEX experiment and was found near the study site. is is the only Betula nana individual we encountered
during the eld work, and VV could not be restrained from collecting a few leaves. During data cleaning, AHH
could not bear to remove these precious leaves from the dataset, and all authors agreed they deserved to live on
as an electronic legacy of these events. erefore, we rely on the user’s responsibility to read this usage note and
remove this species from their analysis as necessary. (f) e slopes in the ecosystem CO2 ux data from the ITEX
experiment were calculated using a linear model5, and we provide the raw data on OSF66 if users want to use a
dierent method. e ux data should be standardized by temperature, PAR and/or biomass estimates before
being used. For an example of how to do this, see5. We only provide raw ux data from the gradients.
Note that the ITEX site is part of the larger ITEX community (https://www.gvsu.edu/itex/) and some of the
data reported here along with additional data from the site are part of a community database within this net-
work, and alsoof scientic publications within the ITEX community.
e two elevational gradients are designed to be comparable (e.g. similar bedrock, grazing regime, andmicro-
habitat) and comparative studies are encouraged. However, there are topographical dierences (e.g. slope, micro-
topography,microclimate), which should be taken into consideration when comparing the two gradients, see28.
Data and terminology. Note that the nutrient input gradient is coded as ‘B’ in all datasets, referring to the
general impact from seabird colonies or bird-clis (although note that in Bjørndalen, the little auk nest in the
ground on the upper part of the talus slope below the cli, so this is a colony, not a bird-cli). In this paper, we use
the terminology ‘nutrient input gradient’ as this focus on nutrients, not birdsper se, isthe most relevant from the
plant’s point of view. We did not change the terminology in the data, however, as these have already been used in
publications5,28. Also, note that species names are lower case variables in the data, and will need to be corrected
(capitalize genus names, use italics) for usage in text and gures.
Variable name Description Variable type Variable range or levels Units How measured
Year Year of sampling numeric 2018 - 2018 yyyy recorded
LoggerType Type of logger; iButton categorical iButton - iButton dened
LoggerLocation Location of logger; soil categorical soil - s oil dened
Gradient Elevational gradient; reference or nutrient input gradient
(seabird colony) categorical B - C dened
Site Site as a number 1–7 categorical 1–7 dened
PlotID Plot ID from A to G categorical A - G dened
Var i a ble Climate variable; soil temperature and soil moisture categorical SoilMoisture -
SoilTemperature dened
Val u e Climate value numeric 3.6–55.1 Degree celsius,
(m3 water × m-3
soil) × 100 recorded
Table 12. Data dictionary for the climate data (dataset vii-b) from elevational gradients with and without
marine nutrient input from a seabird colony, in Bjørndalen and on Lindholmhøgda, respectively, near
Longyearbyen, Svalbard. e dataset contains 70 observations sampled across two elevational gradients.
Variable names, description variable type, range or levels, units and short description is given for all variables.
Variable name Description Variable type Variable range or levels Units How measured
DateTime Date and time of sampling date_time 2004-09-03 16:00:29 - 2018-
07-10 16:02:01 yyyy-mm-dd recorded
Site Site as the habitat; DH = Dryas
heath, CH = Cassiope heath and
SB = snowbed categorical CH - SB dened
Treatment Warming treatment; CTL = ambient
conditions, OTC = warmed by Open
top chamber (OTC) categorical CTL - OTC dened
PlotID Unique ID as the combination of Site
and number; SB-1 categorical CH-1 - SB-9 dened
LoggerType Logger type; iButton and Tiny Tag categorical iButton - TinyTag dened
LoggerLocation Location of logger; surface and soil categorical soil - surface dened
Var i a ble Climate variable; surface and soil
temperature categorical Temperature - Temperature dened
Val u e Temperature value numeric −27.35 - 54.062 Degree celsius recorded
Table 11. Data dictionary for the climate loggerdata (dataset vii-a-2) from an ITEX warming experiment in
Endalen, Svalbard. e dataset contains climate loggerobservations sampled across three habitats between 2004 and
2018. Variable names, description variable type, range or levels, units and short description is given for all variables.
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For all datasets, see the code67 for our suggested data cleaning and checking procedures that result in pro-
ducing what we consider the clean and ‘best practice’ nal datasets and the various ‘Flag’ and ‘Comment’ col-
umns in the dierent dataset tables that indicate additional specic data points or individual observations(e.g.,
leaves,data rows) that could be removed to create even more robust datasets.
Code availability
e code used for checking, cleaning, and analyzing the data is available in the open GitHub repository “https://
github.com/Plant-Functional-Trait-Course/PFTC_4_Svalbard”, of which a versioned copy is available at Zenodo67.
Received: 7 July 2023; Accepted: 11 August 2023;
Published: xx xx xxxx
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Acknowledgements
is research was conducted at the University Centre inSvalbard(UNIS), which provided background knowledge
of the study sites and systems, accommodation, lab space, and logistical support for lab and eld work during the
PFTC4 course. Funding provided by the Norwegian Center for International Cooperation in Education (SIU)
and the Research Council of Norway (grants 2013/10074, HNP2015/10037, INTPART 274831) made it possible
to conduct this eld course at Svalbard with 21 students from 12 nationalities and 4 continents as participants
and co-authors to this data paper. e ITEX experiment and eldsite was funded by UNIS and the University
of Iceland Research Funds (grants to ISJ) and the Research Council of Norway (grant246080/E10). We thank
Pernille Bronken Eidesen for introducing us to the local study systems and invaluable assistance with taxonomic
identications, Geir Wing Gabrielsen for background information on the seabird nutrient input gradient below
the little auk colony in Bjørndalen, and Christine Schirmer and her team of internship students at the University
of Arizona for assistance with stoichiometric and isotope analysis.
Author contributions
B.J.E., V.V., A.H.H., B.S.M. and R.J.T. designed, secured funding for, and led the plant functional traits course
PFTC4; I.A., J.H., M.M.F., S.M., R.R. and Y.M. led and taught student groups during the course; all authors and
especially the PFTC4 students P.B., K.B., L.L.V.B., A.C., S.C., S.V.H., J.K., K.L., Y.L., M.L., I.S.M., B.M.N.-B., M.N.,
P.N., S.Ö., K.P., N.R., M.C., M.S., E.T. and A.S.V. collected and/or measured data; I.S.J., K.B. and L.V. organized
practicalities for the eld work. L.V. veried the species identications; A.H.H. managed and curated the data;
A.H.H. and V.V. draed the paper, gures and tables, whereaer all authors contributed critically important
recollections, insights, and information to the nal paper. Following the CreDiT taxonomy68, we thus recognize
the following author contributions, Conceptualization (co), Data curation (da), Formal analysis (fo), Funding
acquisition (fu), Investigation (in), Methodology (me), Project administration (pr), Resources (re), Soware (so),
Supervision (su), Validation (va), Visualization (vi), Writing – original dra (wo), and Writing – review & editing
(wr), as follows: V.V. (co, fu, in, me, pr, re, su, va, vi, wo), A.H.H. (co, da, fo, fu, in, me, pr, so, su, va, vi, wo), I.A.
(co, da, in, me, re, va, vi, wr), C.T.C., J.J.H., I.S.J., K.K. (co, da, in, me, re, va, vi, wr), M.M.-F., Y.M., B.S.T.M.
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(co, da, in, me, re,so, va, vi, wr), S.T.M., R.E.R. (co, da, in, me, re, va, vi, wr), P.B., K.B., L.L.V.B., A.C., S.C., S.V.H.,
J.K., K.L., Y.L., M.L., I.S.M., B.M.N.-B., M.N., P.N., S.Ö., K.P., N.R., M.C., M.S., E.T., A.S.V. (da, in, me, va, wr), and
B.J.E. (co, fu, in, me, pr, re, su, wr).
Funding
Open access funding provided by University of Bergen.
Competing interests
e authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to V.V. or B.J.E.
Reprints and permissions information is available at www.nature.com/reprints.
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