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\begin{document}

\title{Microclimate explains little variation in year-round decomposition
across an Arctic tundra landscape}



\author[1]{Jonathan  von Oppen}%
\author[1]{Jakob Assmann}%
\author[2]{Anne Bjorkmann}%
\author[1]{Urs Treier}%
\author[3]{Bo Elberling}%
\author[1]{Signe Normand}%
\affil[1]{Aarhus Universitet}%
\affil[2]{University of Gothenburg}%
\affil[3]{University of Copenhagen}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\selectlanguage{english}
\begin{abstract}
Litter decomposition represents a major path for atmospheric carbon
influx into Arctic soils, thereby controlling below-ground carbon
accumulation. Yet, little is known about how tundra litter decomposition
varies with microenvironmental conditions, hindering accurate
projections of tundra soil carbon dynamics with future climate change.
Over 14 months, we measured landscape-scale decomposition of two
contrasting standard litter types (Green Tea and Rooibos Tea) in 90
plots covering gradients of micro-climate and -topography, vegetation
cover and traits, and soil characteristics in Western Greenland. We used
the Tea Bag Index protocol to estimate relative variation in litter mass
loss, decomposition rate (k) and stabilisation factor (S) across space,
and Structural Equation Modelling to identify relationships among
environmental factors and decomposition. Contrasting our expectations,
microenvironmental factors explained little of the observed variation in
both litter mass loss, as well as k and S, suggesting that the variables
included in our study were not the major controls of decomposer activity
in the soil across the studied tundra landscape. We use these
shortcomings of our study combined with findings from the current
literature to discuss future avenues for improving our understanding of
the drivers of tundra decomposition and, ultimately, carbon cycling
across the warming Arctic.%
\end{abstract}%



\sloppy


\section*{Abstract}

{\label{abstract}}

Litter decomposition represents a major path for atmospheric carbon
influx into Arctic soils, thereby controlling below-ground carbon
accumulation. Yet, little is known about how tundra litter decomposition
varies with microenvironmental conditions, hindering accurate
projections of tundra soil carbon dynamics with future climate change.
Over 14 months, we measured landscape-scale decomposition of two
contrasting standard litter types (Green Tea and Rooibos Tea) in 90
plots covering gradients of micro-climate and -topography, vegetation
cover and traits, and soil characteristics in Western Greenland. We used
the Tea Bag Index protocol to estimate relative variation in litter mass
loss, decomposition rate (\emph{k} ) and stabilisation factor (\emph{S}
) across space, and Structural Equation Modelling to identify
relationships among environmental factors and decomposition. Contrasting
our expectations, microenvironmental factors explained little of the
observed variation in both litter mass loss, as well as \emph{k}
and\emph{S} , suggesting that the variables included in our study were
not the major controls of decomposer activity in the soil across the
studied tundra landscape. We use these shortcomings of our study
combined with findings from the current literature to discuss future
avenues for improving our understanding of the drivers of tundra
decomposition and, ultimately, carbon cycling across the warming Arctic.

\section*{Keywords}

{\label{keywords}}

Arctic tundra, carbon cycling, litter decomposition, microclimate, soil
moisture, soil temperature, stratified random sampling, structural
equation modelling, Tea Bag Index\emph{}

\section*{Introduction}

{\label{introduction}}

Feedbacks from high-latitude carbon cycling are pivotal for global
climate change (Schuur et al. 2015, 2022). Not only is the Arctic
experiencing rates of warming at three to four times the global average
(AMAP 2021, Rantanen et al. 2022), tundra soils also store around half
of terrestrial organic carbon (Tarnocai et al. 2009). Decomposition of
leaf litter represents a key ecosystem process in this context, as it
mediates the transition from living plant biomass to soil organic
matter, thereby representing the primary carbon input to Arctic soils.
Generally, it is assumed that decomposition rates in the Arctic will
increase with warming (Aerts 2006, Xue et al. 2016), depending on
moisture availability (Hicks Pries et al. 2013). However, as tundra
ecosystems are characterised by high spatial heterogeneity in abiotic
and biotic conditions, precise predictions of tundra soil carbon
dynamics require a detailed understanding of how this variation in
fine-scale soil temperature and moisture, hereafter termed `soil
microclimate', translates into litter decomposition dynamics (Bradford
et al. 2016).

Local energy fluxes and hence soil abiotic conditions controlling
decomposition rates are determined by tundra terrain features,
vegetation and soil (Aalto et al. 2018, von Oppen et al. 2022; Figure
1). During the growing-season, topographical features, such as higher
elevation, reduced sunlight exposure, or terrain depressions (Opedal et
al. 2015, Aalto et al. 2018), and dense shrub cover (Blok et al. 2010,
Myers-Smith and Hik 2013, Kemppinen et al. 2021, von Oppen et al. 2022)
can cause lower soil temperatures that slow down litter decomposition
rates by reducing decomposer activity. Similarly, terrain depressions or
very fine soil texture can lead to extremely moist soil conditions that
retard decay processes locally (Bryant et al. 1998, Robinson 2002,
Christiansen et al. 2017, Sarneel et al. 2020, Venn and Thomas 2021). In
contrast, tall shrubs might enhance decomposition rates (Blok et al.
2016) as they trap additional snow and create warmer soils through snow
insulation during winter (Kropp et al. 2021). Generally, microclimatic
variation has been shown to drive soil enzyme and microbial activity, as
well as composition (Zak and Kling 2006, Wallenstein et al. 2009, Feng
et al. 2020, Rijkers et al. 2023), thus causing variation in soil
organic carbon stocks across tundra landscapes (Kemppinen et al. 2021).
However, how the combined effects of terrain, vegetation and soil
characteristics on soil microclimate affect \emph{in situ} tundra
decomposition rates remains uncertain.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\subsubsection*{Figure 1:}

{\label{figure-1}}

\textbf{We hypothesise that direct and indirect effects of topography,
vegetation and soil characteristics control litter decomposition in the
Arctic tundra.} Solid boxes indicate variables with field data. We did
not collect data on soil organisms or microbial communities, but instead
rely on strong established links between vegetation, soil, and
microclimate with soil organisms and, ultimately, decomposition.

Plant functional traits as well as soil characteristics directly
influence soil organisms, particularly microbes, and hence affect local
decomposition rates and carbon cycling (Cornwell et al. 2008, Eskelinen
et al. 2009, Sundqvist et al. 2011, Freschet et al. 2012).
Decomposability of leaf litter, associated with traits such as leaf dry
matter content (LDMC) or leaf carbon:nitrogen ratio (leaf C/N; e.g.,
Freschet et al. 2012), can differ widely between plant functional types
(Cornelissen et al. 2007). Thus, traits of the local plant community can
directly influence soil microbial communities and decomposition rates
(Strickland et al. 2009, Keiser et al. 2011, DeMarco et al. 2014, Veen
et al. 2015). Soil pH and nutrient concentrations also structure
microbial communities in the ground and can therefore be expected to
affect decomposition (Figure 1; Fierer and Jackson 2006, Eskelinen et
al. 2009). However, we do not yet understand to what extent these
vegetation and soil characteristics influence decomposition rates
directly, relative to the indirect influences of terrain and vegetation
via their impact on microclimate. Disentangling these relationships is
required for predicting future decomposition rates in changing tundra
environments.

Here, we address this knowledge gap by adapting the Tea Bag Index (TBI;
(Keuskamp et al. 2013) protocol for a network of stratified randomly
placed microclimate monitoring plots. We buried pairs of standard tea
bags with litter of contrasting quality along gradients of topography,
vegetation productivity and water availability across an Arctic tundra
landscape at Qeqertarsuaq (Disko Island), Western Greenland (Figure 2),
with the following research questions in mind: (1) How important is
micro-environmental variation in determining variation in decomposition
rates across a tundra landscape?; (2) Does the relative importance of
environmental drivers of mass loss differ between labile (Green Tea) and
recalcitrant (Rooibos Tea) litter?; and (3) Within the hypothesised
system of topography, vegetation, soil and decomposition (Figure 1), are
direct (traits) or indirect (microclimate) effects of variation in
vegetation more influential for determining decomposition?

Our study improves our understanding of the factors driving variation in
litter decomposition across a tundra landscape and will help to inform
predictions of future decomposition and carbon cycling dynamics in the
changing Arctic.

\section*{Material and methods}

{\label{material-and-methods}}

\subsection*{Study area and plot
layout}

{\label{study-area-and-plot-layout}}

The study was performed across the eastern and western slopes of the
Bl\selectlanguage{ngerman}æsedalen valley outside the town of Qeqertarsuaq, Disko Island,
Western Greenland. The landscape features a heterogeneous topography,
basaltic soils, and dwarf shrub-dominated tundra vegetation of variable
density. We selected a total of 90 locations stratified randomly, aiming
to cover gradients of elevation, vegetation productivity and water
availability, across an area of 15 km\textsuperscript{2} (von Oppen et
al. 2022; Figure 2a). At plot establishment, we buried a soil
temperature logger (HOBO MX-2201, Onset Corp., Bourne, MA, USA) in 6 cm
depth at 10 cm into true South direction from the plot centre.
Additionally, we placed a TMS-4 microclimate logger (TOMST, Prague,
Czech Republic; Wild et al. 2019) at 10 cm into true North direction
from the plot centre, which recorded topsoil moisture content (Figure
2b). We established the plots between 26 June and 01 July 2019 and
retrieved tea bags and soil temperature loggers between 19 and 27 August
2020, with the length of incubation period varying by a maximum of 12
days across plots.

\par\null

\subsubsection*{Figure 2:}

{\label{figure-2}}

\textbf{Study design and environmental variation. a)} Study area on
Disko Island, Western Greenland. White circles indicate plot locations
and numbers show elevation strata used for stratified random plot
placement (separated by black contours). \textbf{b)} Positioning of
loggers (TMS-T4: black dot; HOBO: black diamond) and teabags (triangles)
within sample plots. Arrangement of tea types was randomised between
plots. \textbf{c)} Variation of topographic (brown), vegetation/soil
(green) and microclimate (blue) variables across sample plots.
Horizontal jittering of points indicates the density of the value
distribution. Grey points in the background for the gridded topographic
variables show values for 1000 randomly sampled points within the study
area (representing the full environmental variation in the region).

\subsection*{Topography, vegetation and soil
variables}

{\label{topography-vegetation-and-soil-variables}}

We recorded position and elevation at the centre of each plot location
using a Trimble R8s dGNSS system (Trimble Inc., Sunnyvale, California,
U.S.). We also measured local slope direction and inclination across a 4
m-diameter circle using a handheld inclinometer, from which we
calculated local solar radiation as mean Solar Radiation Index (SRI;
Keating et al. 2007) across the study period. In addition, we used the
ArcticDEM (digital elevation model) at 2 m resolution (version 3.0;
Porter et al. 2018) to extract (i) Topographic Position Index (TPI;
Weiss 2001) within a 30 m radius, and (ii) Topographic Wetness Index
(TWI; Beven and Kirkby 1979), following recommendations by Kopecký et
al. (2021), through the SAGA GIS (Conrad et al. 2015) TPI and TWI
modules, respectively, for our plot locations.

During the 2019 growing season, we visually quantified the proportional
cover of shrubs, graminoids, forbs, bryophytes and lichens within a
circle of 50 cm radius around the plot centre (Figure 2b) and
subsequently summed their cover values into a single vegetation cover
variable. Values for vegetation cover could surpass 100~\% in case of
multiple vegetation layers. In addition, we sampled functional traits
for a subset of the most dominant species in the 60 plots on the eastern
slope. As dominant species we considered those with \textgreater{}5 \%
cover in one or more of the first 18 plots surveyed across low, mid and
high elevation levels, plus additional locally abundant species with
\textgreater{}5 \% cover in any given plot. For each species within each
plot, we selected up to three (if available) healthy individuals closest
to the plot centre and within the plot radius. For these individuals, we
measured both vegetative and maximum plant height as the vertical
distance from the ground to the tallest vegetative plant part or tallest
plant part irrespective of tissue type. Furthermore, we sampled up to
three leaves, or a small branch for small-leaved dwarf shrubs, from each
individual for leaf trait assessment (\emph{n} = 522 samples). We then
determined the leaves' fresh weight and scanned them for leaf area
calculation through ImageJ (Schindelin et al. 2012), before drying
leaves at 70 °C for 48~hours. Using the leaves' dry weight, we
calculated Specific Leaf Area (SLA) as dry weight per area, and Leaf Dry
Matter Content as leaf dry weight divided by fresh weight. Due to
limited resources, we could only determine chemical traits for a subset
of 300 samples, with one random sample per species, elevation band and
approximate water availability category (see sampling design in von
Oppen et al. 2022). Dry soil and leaf samples were milled. We then used
5 to 20 mg per sample, depending upon organic matter content, to measure
total C and N as well as isotopic ratios of C (13C/12C) and N (15N/14N).
These analyses were carried out using an elemental analyzer by Dumas
combustion at 1020 °C (Flash 2000, Thermo Scientific, Bremen, Germany)
coupled to a Thermo Delta V Advantage IRMS in continuous flow.

We imputed community-level trait moments for the remaining 30 plots
using hierarchical nonparametric bootstrapping through
the\emph{traitstrap} package (Maitner et al. 2021) incorporating the
plots' moisture and productivity classes as determined during stratified
plot placement. We subsequently performed a principal component analysis
(PCA) from which we extracted plot scores along the first and second
main axis of trait variation, which were related to leaf economics (PC1)
and plant size (PC2), respectively (Figure S1). These axes aligned well
with previous findings for global (Díaz et al. 2016) and tundra plant
functional trait space (Thomas et al. 2020).

We took soil samples 10 cm north (``central'') as well as at 50 and 150
cm distance into all intercardinal directions from the plot centre using
a metal corer with 6~cm diameter and 3 cm depth. Samples were only taken
if there was enough soil to fill at least 50 \% of corer volume. Samples
were oven-dried at 45 °C to constant mass and sieved to obtain gravel
(\textgreater{} 2 mm diameter) and soil (\textless{} 2 mm) fractions. If
less than 40 g of dry soil were available for a central sample, we
selected one of the outer samples with sufficient material (\emph{n}
\textsubscript{50 cm} = 30, \emph{n} \textsubscript{150 cm} = 16). If
that was not the case for any of the nine samples, we pooled soil across
samples, recording the contribution from each location (\emph{n} = 22).
Subsamples of 10 g were used to determine contents of several macro-
(carbon, nitrogen, phosphorus) and micronutrients (Ca, K, Mg, Na), as
well as soil pH and cation exchange capacity. Particle size fractions
were determined from the remaining 30 g of soil (see Table S1 for a list
of instruments used for soil analyses). We then performed a principal
component analysis including all soil variables and, for each plot,
extracted the scores for the first and second axis of variation. The two
axes mainly corresponded to nutrient content, and soil pH and texture
(higher silt, lower sand content), respectively (Figure S2).

\subsection*{Soil temperature and moisture
data}

{\label{soil-temperature-and-moisture-data}}

We set soil temperature loggers to record soil temperature at 15-minute
intervals throughout the tea bag incubation period. After visual
assessment of temperature and moisture curves, we removed measurements
from known periods of disturbance (field observations) during the 2019
growing season (± 1 day). These occurred for example if animals had
removed loggers from the ground (\emph{n} = 5). We also excluded records
entirely if temperature loggers had been removed from the ground between
2019 and 2020 (\emph{n} = 5). For each plot, we eventually calculated
temperature sums (growing-degree days above 0 °C, GDD\textsubscript{0})
across the incubation period.

Soil moisture loggers recorded at 10-minute intervals over the 2019
growing season (27 June -- 07 August). We removed values from known
periods of logger disturbance (\emph{n} = 2) and where extremely low
moisture signals indicated insufficient contact of the probe with the
soil matrix during the measurement period (\emph{n} = 2). We calibrated
soil moisture using soil type-specific calibration curves provided by
the manufacturer, assigning soil types to plots based on minimum
Euclidean distances for particle size fractions. As we obtained a few
negative moisture values for some plots due to imperfect calibration, we
scaled soil moisture values to the range of individual measurements. We
then calculated mean soil moisture across the 2019 growing season for
each plot.

\subsection*{Burial and processing of tea bags, calculation of
decomposition
metrics}

{\label{burial-and-processing-of-tea-bags-calculation-of-decomposition-metrics}}

To measure decomposition dynamics, we used commercially available Green
Tea and Rooibos Tea (EAN 8714100770542 and 8711327514348; Lipton,
Unilever Inc.), on the basis of the TBI protocol (Keuskamp et al. 2013).
The TBI has been used previously in numerous locations across the Arctic
tundra (Sarneel et al. 2020, Gallois et al. 2022, Björnsdóttir et al.
2022, Thomas et al. 2023). The method should therefore be well suited to
investigate the effects of multiple landscape-scale environmental
gradients on litter decomposition.

We labelled and dried tea bags at 70 °C for 48 hours before recording
the initial dry weight of each individual bag. During plot
establishment, we buried tea bags at 2 cm depth and 10 cm distance into
intercardinal directions from loggers (i.e., NW and NE of microclimate
loggers and SW and SE of temperature loggers; Figure 2b), in order NW -
NE - SW - SE irrespective of tea type to randomise positioning,
corresponding to two replicates per type per plot and a total of 360 tea
bags. Exact position and/or depth could vary slightly in response to
local soil conditions. We retrieved tea bags after approx. 14 months
(416 to 428 days). Due to occasional damage or disturbance by animals,
we collected 285 intact bags (81 \%). The number of plots with at least
one intact sample was 83 for Green Tea, 79 for Rooibos Tea, and 79 for
both Green Tea and Rooibos Tea. We dried the bags at 70 °C for 72 hours
and placed them in ziplock bags with silica gel for transport and
storage. Starting in August 2021, we dried tea bags again at 60 °C for
24 hours to remove any moisture potentially drawn during the storage
period. We then determined post-burial dry weight and subtracted the
average weight of 10 empty bags from all weights to obtain initial and
remaining weight as well as mass loss of tea for each sample. Finally,
we calculated relative mass loss for Green Tea and Rooibos Tea for each
sample, and mean values per plot.

The TBI (Keuskamp et al. 2013; see therein for all parameters and
equations in this subsection) describes litter decomposition dynamics
according to a two-phase model - fast decomposition during the initial
phase and slow decomposition during the second phase. Green Tea as a
labile substrate is characterised by a high hydrolysable fraction
(\(H_{g}\) = 0.842). Green Tea therefore has a relatively short
first phase of decomposition, which enables estimation of how quickly a
labile substrate stabilises in a given environment. The stabilisation
factor\emph{S} quantifies this amount of undecomposed labile litter,
with high values of \emph{S} indicating less favourable conditions for
decomposition. It is calculated as:

\begin{equation}
S=1-a_{g}\cdot H_{g}^{-1}\nonumber \\
 \end{equation}

with \(a_{g}\) as relative mass loss of Green Tea. Second,
Rooibos Tea as a more recalcitrant material has a lower hydrolysable
fraction (\(H_{r}\) = 0.552) and its first phase of
decomposition therefore stretches over a longer time period. However,
assuming that \emph{S} is constant for both substrate types, the
theoretically decomposable fraction of Rooibos Tea (\(a_{r}\))
can be calculated as:

\begin{equation}
a_{r}=H_{r}\cdot(1-S)\nonumber \\
 \end{equation}

This can then be used to calculate the decomposition rate
constant\emph{k} , which describes how fast this gradual decay process
occurred for the decomposable litter fraction:

\begin{equation}
k=ln(\frac{a_{r}}{M_{r}-(1-a_{r})})\cdot\frac{1}{t}\nonumber \\
 \end{equation}

Here, \(M_{r}\) is the relative remaining mass of Rooibos Tea,
and \(t\)the length of the incubation period in days. High
values of \emph{k}therefore indicate rapid turnover of litter.

\subsection*{Tundra-wide decomposition
data}

{\label{tundra-wide-decomposition-data}}

To put decomposition rates from our study area into context, we obtained
data on mass loss, \emph{k} and \emph{S} from the TundraTea initiative,
which encompasses coordinated studies applying the TBI protocol across
Arctic and alpine tundra regions (Thomas et al. 2023). We only included
data from year-round incubations in untreated plots comparable to our
study, and from regions with at least three unique plots to ensure
variation in environmental conditions. This resulted in a total of 191
plots from 11 regions (1318 individual tea bags; Table S2).

\subsection*{Statistical analyses}

{\label{statistical-analyses}}

From the 79 plots with complete decomposition response values, we
removed: one extreme outlier with 2.4 times the next largest value
for\emph{k} , one plot which had no trait values due to sparse
vegetation cover, and six plots with erroneous or missing microclimate
measurements (resulting \emph{n} = 71).

We used Structural Equation Modelling (SEM) to analyse the effects of
variation in topography, vegetation and soil, and microclimate on
decomposition dynamics. We chose SEM as the method is well suited to
test our hypothesised system of direct and indirect processes
influencing tundra soil decomposition, while also allowing us to account
for any potential latent relationships not specified by us (Grace et al.
2015). According to our hypothesis (Figure 1), we fitted eleven
sub-models (Table S3) describing the local impacts (i) of topography
(elevation, solar radiation, topographic position and wetness) on
vegetation (vascular plant cover and community-level leaf economic and
size trait composition) as well as soil (nutrients and texture); (ii) of
topography, vegetation and soil on microclimate (year-round
GDD\textsubscript{0} and growing-season soil water content); and (iii)
of plant community traits, soil and microclimate on each of the
decomposition response variables, i.e. mass loss of either tea type, as
well as on decomposition rate \emph{k} and stabilisation factor\emph{S}
. We then combined the sub-models into a single multiple linear
regression model within the SEM, using the \emph{piecewiseSEM} package
(Lefcheck 2016) in R. We inspected residual distributions for all
sub-models to ensure assumptions about homoscedasticity were met. We
also calculated pairwise linear relationships between decomposition
response and explanatory variables within sub-models for comparison
(i.e., not incorporating any covariates, residual variation from other
models, or effect size standardisation; Table S4). These individual
relationships were consistent with SEM results in terms of magnitude and
direction of relationships as well as significance level (Tables S3,
S4), hence we describe and discuss only SEM results in the following, as
they better represent our initial hypothesis.

All data management and analyses were performed in R v4.1.1 (R Core Team
2021).



\section*{Results}

{\label{results}}

Our study plots covered a range of topographic, vegetation and
microclimatic conditions (Figure 2c). Plots were distributed between 53
m and 773 m a.s.l. and experienced solar radiation values between 0.81
SRI and 0.96 SRI. Relative positioning of plots in the landscape varied
as well, with TPI ranging from -3.9 to 1.9 and TWI ranging from 4.3 to
10.6. Vegetation contexts varied widely, from almost barren plots
(minimum 5 \% cover) to densely vegetated plots with overlapping
vegetation layers (maximum 168 \% cover), also reflected in plant
communities' variable leaf economic and size traits (Figure S1).
Similarly, plots covered a range of soil conditions regarding nutrient
content and texture (Figure S2). Finally, microclimate differed largely
across plots, with accumulated soil temperatures over the incubation
period ranging from 593 to 2200 GDD\textsubscript{0}, and scaled mean
growing-season volumetric soil moisture ranging between 2.8 and 68.9 \%
(Figure 2c).

Median mass loss in Green Tea and Rooibos Tea across sample plots was
60.3 \% (95 \% confidence interval: 51.1 -- 71.9 \%) and 24.8 \% (18.6
-- 31.4 \%), respectively, over the approx. 14 months of incubation
(Figure 3a,S3). Median decomposition rate \emph{k} was 0.002 (0.0015 --
0.0040) and median stabilisation factor \emph{S} was 0.284 (0.141 --
0.393; Figure 3b). These values were generally comparable to other
studies from across the tundra biome (Figure 3), with relatively low
values for \emph{k} reflecting a slightly longer incubation time than
those observed by other studies (Table S2).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\subsubsection*{Figure 3:}

{\label{figure-3}}

\textbf{Tea mass loss and decomposition metrics were comparable to other
sites across the tundra.} Points and whiskers represent site-specific
medians and 95 \% quantiles, respectively, of \textbf{a)} relative mass
loss in the two litter types and \textbf{b)} decomposition rate
constant\emph{k} and stabilisation factor \emph{S} . White diamonds and
coloured whiskers show values for this study, while grey dots and
whiskers show values from 11 other sites across the tundra (Table S2;
Thomas et al. 2023). Note that axes are truncated at the lower end.

We found that the micro-environmental predictors explained little
variation in tea mass loss and decomposition metrics (Figure 4). While
we observed some differences in the importance of environmental
predictors for decomposition between the two tea types, the overall
predictive power was low (Figure 4a). Specifically, Green Tea mass loss
was lower in warmer soils (\emph{p} \textless{} 0.01) and was typically
greater with higher soil moisture, though the relationship was not
significant (\emph{p} = 0.15). Green Tea mass loss did not vary
significantly with community leaf traits or soil characteristics (Figure
4a). None of the environmental factors predicted variation in Rooibos
Tea mass loss (Figure 4a) and we observed no particular spatial patterns
in mass loss for either litter type (Figure S4).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\subsubsection*{Figure 4:}

{\label{figure-4}}

\textbf{Tea mass loss and decomposition metrics showed little variation
in response to environmental factors.} Points represent plot-level
averages of \textbf{a)} relative mass loss in the two litter
types,\textbf{b)} ~decomposition rate constant \emph{k} , and
\textbf{c)}stabilisation factor \emph{S} . Trend lines and ribbons show
mean and 95\% confidence intervals of predicted linear relationships,
respectively. Slope, intercept and \emph{p-} values were extracted from
individual sub-models within the Structural Equation Model (see Figure
5).

Environmental variation explained only a small amount of variation in
our observed decomposition rate constant (\emph{k} ) and stabilisation
rate (\emph{S} ). However, we observed a slightly higher explanatory
power for microclimate (indirect vegetation/soil influence) compared to
plant trait or soil-related explanatory variables (direct
vegetation/soil influence; Figures 4b,c). Warmer plots had
higher\emph{S} (\emph{p} \textless{} 0.01) and slightly
(non-significant) higher \emph{k} (\emph{p} = 0.08), and \emph{S} was
slightly lower in moist soils (\emph{p} = 0.15). However, neither leaf
traits nor soil characteristics explained variation in decomposition
metrics (Figures 4b,c). Also, no spatial patterns were visible for
either decomposition variable (Figure S4).

Overall, our hypothesised system of topography, vegetation and soils,
and microclimate explained little variation in decomposition, reflected
in a low overall model fit of the SEM (Fisher's \emph{C} =
769.5,\emph{df} = 106, \emph{p} \textless{} 0.001; Table S3).
Explanatory power was highest for leaf economic traits
(\emph{R\textsuperscript{2}}= 0.45) and soil parameters (pH / texture:
\emph{R\textsuperscript{2}} = 0.28; nutrients:
\emph{R\textsuperscript{2}} = 0.27; GDD\textsubscript{0}:
\emph{R\textsuperscript{2}} = 0.38) but low for decomposition variables
(Green Tea mass loss:\emph{R\textsuperscript{2}} = 0.16; Rooibos Tea
mass loss:\emph{R\textsuperscript{2}} = 0.05; \emph{k}
:\emph{R\textsuperscript{2}} = 0.08; \emph{S}
:\emph{R\textsuperscript{2}} = 0.16; Figure 5). Moreover, SEM
relationships between variables besides decomposition only partly
reflected our expectations (Figures 1,5). Elevation and solar radiation
were the most influential topographic predictors. Higher elevation
significantly predicted lower vegetation cover, more acquisitive leaf
traits, lower soil nutrient content, and lower soil temperatures
(all\emph{p} \textless{} 0.001), as well as higher silt and lower sand
content in the soil (\emph{p} \textless{} 0.05). Higher solar radiation
was related to higher soil moisture (\emph{p} \textless{} 0.01, with
solar radiation being highly negatively correlated with slope (Pearson's
r = -0.93)), as well as to cooler soils, smaller plants, and more
acquisitive leaf traits (all \emph{p} \textless{} 0.05). In addition,
higher TWI predicted higher soil pH and higher silt rather than sand
content (\emph{p} \textless{} 0.01) and higher soil nutrient
concentration (\emph{p} \textless{} 0.05). TPI did not predict any
variation in vegetation, soil or microclimate variables (Figure 5). Only
one of the tested relationships between vegetation and microclimate was
significant, as higher vegetation cover predicted lower soil
temperatures (\emph{p} \textless{} 0.001). Soil texture did not predict
any variation in soil moisture (Figure 5).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\subsubsection*{Figure 5:}

{\label{figure-5}}

\textbf{Results of the structural equation model analysing links between
topography, vegetation, soil, and microclimate on
decomposition.}Individual relationships were tested using multiple
linear regression. Purple arrows indicate negative, orange arrows
positive, and grey dotted arrows non-significant links. Thicker lines
correspond to stronger identified links (larger effect sizes).
\emph{R\textsuperscript{2}}values represent model fit for individual
sub-models. Relationships between vegetation, soil and microclimate
predictors and decomposition responses were tested for each
decomposition response variable individually, indicated by the dashed
boxes, but GDD\textsubscript{0}only significantly predicted variation in
stabilisation factors (see also Figure 4). See Table S3 for full model
output and Table S4 for comparison with individual pairwise
relationships of decomposition responses with their explanatory
variables.

\section*{Discussion}

{\label{discussion}}

Our structural equation modelling of the variation in decomposition
rates across the tundra landscape in Western Greenland indicated that:
(1) micro-environmental factors play a minor role in explaining
variation in decomposition at the landscape scale; (2) environmental
predictors differ little in their importance for mass loss of Green Tea
(soil temperature and moisture) and Rooibos Tea (no significant
predictors); and (3) only some indirect effects of vegetation on
decomposition through microclimate were significant, while there was no
evidence for direct (trait) related effects. However, the overall
explanatory power of our SEM was low and the overall fit was
non-significant. This suggests that other factors than the
topographical, vegetation, soil and microclimate variables included here
control the mechanisms behind decomposition across our studied tundra
landscape, such as decomposer communities or annual and seasonal
variation in micro-environmental conditions (see detailed discussion
below). Overall, our results highlight the need for further research to
advance our understanding of decomposition processes and carbon cycling
in the changing Arctic. Below, we discuss the findings in relation to
our research questions and derive some general recommendations for
future decomposition studies in the tundra.

\emph{Weak links between microclimate, plant traits, and decomposition
at the landscape scale could be explained by absence of other known
controls not accounted for in our analysis}

Despite considerable variation in both environmental conditions (Figure
2c) and decomposition (Figure 3) across our study plots, the observed
relationships between environmental conditions and decomposition were
weak (Figure 4). This suggests that the topographical, vegetation, soil,
and microclimate predictors we measured did not capture all ecologically
relevant conditions and processes that control decomposition dynamics
within this tundra landscape. For instance, we did not explicitly
measure differences in decomposer communities and/or temporal activity.
Comparable SEM studies from both tundra (Robinson et al. 2022) and other
environments (e.g., Tresch et al. 2019, Xiao et al. 2019) identified
decomposer community composition and activity as a key control on
decomposition (but see Joly et al. 2023), and vegetation patterns along
elevational gradients might not always reflect differences in microbial
communities (Bryant et al. 2008, Fierer et al. 2011). Furthermore, soil
organic matter content has been shown to drive abundances of soil
microbial groups as well as decomposition rates in subarctic Iceland
(Robinson et al. 2022). Also, longer-term soil moisture measurements
might more accurately capture the variability in soil moisture
conditions experienced by decomposers than our short (one growing
season) time-series and hence could hold more power for predicting
decomposition rates (Blok et al. 2010, Christiansen et al. 2017).
Similarly, as environmental conditions in the Arctic tundra vary
considerably between growing season and winter, complementing burial
periods (e.g., growing season vs. winter vs. year-round) would help to
disentangle potential seasonal differences in the controlling factors of
Arctic decomposition dynamics, as well as differences in early vs.
late-stage decomposition (Keuskamp et al. 2013, Garc\selectlanguage{ngerman}ía-Palacios et al.
2016, Canessa et al. 2021). We suggest that future studies account for
these additional factors to model tundra plant litter decomposition more
accurately.

\emph{Little difference in importance of environmental variables among
contrasting tea types}

Although relative mass loss was higher in Green Tea than in Rooibos Tea
(Figure S3), relationships of mass loss with plant traits, soil
characteristics, and microclimate differed little between tea types
(Figure 4a). This indicates that, overall, variation in environmental
conditions did not selectively stimulate primary decomposer groups that
prefer either labile or recalcitrant litter. Higher mass loss in Green
Tea compared to Rooibos Tea reflected differences in substrate quality,
as for instance nitrogen content and water-soluble fraction in Green Tea
are two to three times higher than in Rooibos Tea, respectively
(Keuskamp et al. 2013). This result corroborates the importance of
litter identity in the light of tundra vegetation changes and
increasingly recalcitrant litter (Cornelissen et al. 2007, Myers-Smith
et al. 2011, Thomas et al. 2023). Yet, largely constant mass loss across
environmental gradients suggests that both bacterial (main decomposers
of labile litter) and fungal communities (main decomposers of
recalcitrant litter) varied little among plots (Yang et al. 2021). The
only relationships that differed slightly between tea types were those
associated with microclimatic variables. For instance, we observed lower
mass loss at warmer and drier conditions for Green Tea, but not for
Rooibos Tea (Figure 4a). These differences could reflect the effect of
warming-induced evaporation, which can reduce decomposer activity at
suboptimal moisture conditions (Christiansen et al. 2017). Overall, our
findings suggest that vegetation changes and reduced litter quality
might be more important for future tundra decomposition than immediate
microclimatic changes related to warming (Cornelissen et al. 2007,
Cornwell et al. 2008, Thomas et al. 2023). However, more targeted
sampling of decomposer communities could help to disentangle these
relationships.

\emph{Direct vs. indirect controls of decomposition}

How future warming and associated vegetation changes will impact
decomposition rates in the tundra will also depend on the balance of
microbial community priming by plant traits (Veen et al. 2015) and
increases in decomposer activity with warming (Rijkers et al. 2023). The
weak relationships between environmental controls and decomposition
responses observed in our study suggest that neither the direct (traits)
nor indirect (microclimatic) vegetation effects that we measured were
particularly important for decomposition in our study system.
Microclimate was associated with weak trends in Green Tea mass loss and
stabilisation rates, while we did not find any link between plant
community or soil traits and decomposition. These findings could point
to a higher importance of abiotic conditions, for instance induced by
shrub shading (Blok et al. 2010, Myers-Smith and Hik 2013), snow
accumulation (Sturm et al. 2001) or bryophyte cooling (Blok et al.
2011), compared to substrate quality effects for controlling
decomposers. This is also in line with earlier predictions that climate
represents the dominant control on litter decomposition in harsh
environments (Coûteaux et al. 1995). While our study was limited to
certain predictors and gradients, follow-up studies explicitly
stratifying across these microenvironmental factors might help elucidate
this further.

\emph{Relationships between topography, vegetation, soil and
microclimate}

Our study sites covered a range of abiotic and biotic environmental
conditions (Figure 2c), indicating that the stratified random sampling
approach overall captured environmental gradients in the area. Yet,
relationships between topographic, vegetation and soil variables and
microclimate only partly reflected our expectations (Figure 5). As
expected, soil temperature decreased with higher elevation and
underneath higher vegetation cover, corroborating trends previously
found in Fennoscandian tundra (Kemppinen et al. 2021). In contrast,
solar radiation predicted trends opposite to expected relationships,
particularly lower rather than higher soil temperature but higher rather
than lower soil moisture, possibly due to its close association with
slope values. Local topography as quantified by TPI did not explain
variation in any vegetation or soil parameter, perhaps because variation
in TPI was more constrained than in other variables (Figure 2c), thus
not well representing differences in snow accumulation or wind exposure
across the landscape. Also, neither TWI nor soil texture explained
variation in growing-season soil moisture among plots. Here, our
gradient most likely did not cover the entire spectrum of conditions
present in the area, as very wet locations were not covered (Figure 2c),
probably as a result of the dry summer in 2019 and of standing water
areas being masked out during the stratification process. The example
highlights the difficult issue of obtaining reliable soil moisture
predictions in tundra ecosystems (Kemppinen et al. 2018). In addition,
implicit uncertainty in gridded topographic variables might have added
further noise to our dataset.

\emph{Towards improved stratified random studies of tundra
decomposition}

Although our findings were overall inconclusive with regard to
quantifying controls of tundra decomposition dynamics, they point to
potential avenues for improving Arctic decomposition studies.

Incubating locally sampled litter alongside standardised substrate, in
addition to including community traits as explanatory variables, would
be an essential step to contextualise decomposition patterns in standard
litter and enable more precise conclusions on feedbacks of vegetation
change on decomposition (Joly et al. 2023). In a laboratory experiment,
Duddigan et al. (2020) found that both tea types generally follow
patterns of natural leaf litter decomposition, and studies in specific
alpine (Didion et al. 2016, von Oppen 2017) and Arctic (Thomas et al.
2023) sites have shown that the two tea types well represent mass loss
in local litter. However, a comparative study is still missing for the
Arctic tundra biome at large, resulting in uncertainty about the
representativeness of mass loss rates inferred from standard substrate.
More generally though, the informative value of standard-litter
experiments can be limited if nutritional differences between local
litter and standard substrate are not accounted for explicitly (Joly et
al. 2023). Given the importance of tundra carbon cycling, a systematic
assessment of the representativeness of standard decomposition protocols
(e.g., TBI) will be essential for adequate quantification of litter
decomposition in Arctic environments.

Many regions across the Arctic tundra biome are experiencing notable
vegetation changes in response to warming or degrading permafrost
(Heijmans et al. 2022). However, a lack of coordinated systematic
investigations of tundra decomposition dynamics across tundra zones and
habitat types, is impeding biome-wide predictions of tundra carbon
dynamics (Bonan et al. 2013); but see Thomas et al. 2023). Large-scale
replication of stratified-random sampling could fill this gap, enabling
representative sampling of environmental gradients across and within
sites, as well as evaluation of the relative importance of macro- vs.
microclimate for tundra litter decomposition (Joly et al. 2023). Our
findings underline the importance of considering variables representing
the complete decomposition process, particularly decomposer organisms as
well as both local and standard litter, and both fine- and more
broad-scale environmental data. Ultimately, such coordinated studies
could be of high value to inform predictions of the future carbon
balance of tundra soils with projected vegetation and environmental
changes (van Gestel et al. 2018).

\subsection*{Conclusion}

{\label{conclusion}}

We incubated two types of standard litter of contrasting quality (Green
Tea and Rooibos Tea) over 14 months across an Arctic tundra landscape,
covering several environmental gradients of topography, vegetation, soil
and microclimate. Although we found significant links between
topography, vegetation, soils and microclimate, the micro-environmental
variation itself explained little variation in decomposition responses.
This suggests that other factors not accounted for in our study, like
long-term moisture availability, snow patterns, or other seasonal
effects might control activity and community composition of decomposers.
Nonetheless, our systematic approach could serve as guidance for
coordinated studies of tundra decomposition dynamics, particularly
across large extents. Further research should explicitly include local
litter material, decomposer communities, and temporal variation to
improve model fit and explanatory power, and to support better
predictions of Arctic tundra litter decomposition and carbon cycling
dynamics.

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