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\begin{document}

\title{What controls forest litter decomposition? A coordinated, distributed
teabag experiment across 10 mountains}



\author[1]{Shiyu Ma}%
\author[2]{Shengbin Chen}%
\author[3]{Yi Ding}%
\author[4]{Zhongsheng He}%
\author[5]{Gang Hu}%
\author[6]{Jie Liu}%
\author[7]{Ya-huang Luo}%
\author[1]{Kun Song}%
\author[8]{Yongchuan Yang}%
\author[4]{Xiaolei Huang}%
\author[9]{Meixiang Gao}%
\author[10]{Lan Liu}%
\author[4]{Bo Chen}%
\author[8]{Xianjin He}%
\author[1]{Xiaorong Lu}%
\author[1]{Bingwei lv}%
\author[7]{Liang-Liang Ma}%
\author[1]{Yani Meng}%
\author[1]{Zhongping Tian}%
\author[1]{Hong-Wei Zhang}%
\author[1]{Xijin  Zhang}%
\author[1]{Yansong Zhang}%
\author[1]{Zhaochen Zhang}%
\author[11]{Shaopeng Li}%
\author[12]{Jian Zhang}%
\affil[1]{East China Normal University}%
\affil[2]{Chengdu University of Technology}%
\affil[3]{Chinese Academy of Forestry}%
\affil[4]{Fujian Agriculture and Forestry University}%
\affil[5]{Nanning Normal University}%
\affil[6]{Kunming Institute of Botany, Chinese Academy of Sciences}%
\affil[7]{Kunming Institute of Botany Chinese Academy of Sciences}%
\affil[8]{Chongqing University}%
\affil[9]{Ningbo University}%
\affil[10]{Hainan University}%
\affil[11]{East China Normal University,}%
\affil[12]{School of Ecological and Environmental Sciences, East China Normal University}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\selectlanguage{english}
\begin{abstract}
Forest litter decomposition is considered as an essential ecosystem
process affecting carbon and nutrient cycling in mountains. However,
there exists high uncertainty in accurately estimating the contribution
of litter decomposition to terrestrial ecosystems, largely due to the
incomparability of different studies and the data limitation in
microclimate and non-climatic factors at spatially matched scales. Here
we used the tea bag index (TBI) as a standardized protocol to evaluate
spatial variations in forest litter decomposition rate (k) and
stabilization factor (S) across 10 mountains spanning a wide range of
subtropical and tropical forests. Based on the coordinated experiment of
6,864 teabags in 568 sampling sites along elevations, we evaluated the
importance of 10 environmental factors covering soil microclimate,
edaphic properties, plant diversity, and topography on k and S by using
model averaging and linear-mixed effects models. Of the 10 mountains, we
found a consistently decreasing pattern of k and an increasing pattern
for S along elevations. And the significant effect of k with elevation
was mainly found in the western and northmost mountains, while the
effect of S occurred in the western and southernmost mountains. For
microclimate, there was a general importance of soil temperature (coef.
= 0.48) and temperature variation in the growing season (coef. = 0.36)
in k, and soil temperature (coef. = -0.46) and moisture variation on S
(coef. = -0.36). The dominant role of soil microclimate was mainly found
in western mountains with relatively cold environments. For non-climatic
drivers, a significant effect of tree diversity on k and a negative
correlation of edaphic and topography with S in the western and southern
mountains were detected. These findings provide a general understanding
of spatial variations of driving factors in forest litter decomposition
and highlight a dominant control of soil microclimate in cold forests in
high elevations and latitudes.%
\end{abstract}%



\sloppy


\textbf{What controls forest litter decomposition? A coordinated,
distributed teabag experiment across 10 mountains}

\section*{Abstract}

{\label{abstract}}

Forest litter decomposition is considered as an essential ecosystem
process affecting carbon and nutrient cycling in mountains. However,
there exists high uncertainty in accurately estimating the contribution
of litter decomposition to terrestrial ecosystems, largely due to the
incomparability of different studies and the data limitation in
microclimate and non-climatic factors at spatially matched scales. Here
we used the tea bag index (TBI) as a standardized protocol to evaluate
spatial variations in forest litter decomposition rate (\emph{k} ) and
stabilization factor (\emph{S} ) across 10 mountains spanning a wide
range of subtropical and tropical forests. Based on the coordinated
experiment of 6,864 teabags in 568 sampling sites along elevations, we
evaluated the importance of 10 environmental factors covering soil
microclimate, edaphic properties, plant diversity, and topography
on\emph{k} and \emph{S} by using model averaging and linear-mixed
effects models. Of the 10 mountains, we found a consistently decreasing
pattern of \emph{k} and an increasing pattern for \emph{S} along
elevations. And the significant effect of \emph{k} with elevation was
mainly found in the western and northmost mountains, while the effect of
\emph{S}occurred in the western and southernmost mountains. For
microclimate, there was a general importance of soil temperature
(\emph{coef.} = 0.48) and temperature variation in the growing season
(\emph{coef.} = 0.36) in\emph{k} , and soil temperature (\emph{coef.} =
-0.46) and moisture variation on \emph{S} (\emph{coef.} = -0.36). The
dominant role of soil microclimate was mainly found in western mountains
with relatively cold environments. For non-climatic drivers, a
significant effect of tree diversity on \emph{k} and a negative
correlation of edaphic and topography with \emph{S} in the western and
southern mountains were detected. These findings provide a general
understanding of spatial variations of driving factors in forest litter
decomposition and highlight a dominant control of soil microclimate in
cold forests in high elevations and latitudes.

Keywords: altitudinal gradient; climate change; experimental
macroecology; litter decay; microclimate

\section*{Introduction}

{\label{introduction}}

Mountainous forest ecosystems account for only 1.8\% of the Earth's
surface (Price et al. 2011), but they hold extremely high carbon storage
globally (Cuni-Sanchez et al. 2021), and more than half of them are
returned to soil through litter decomposition (Wardle et al. 2004).
Forest litter decomposition is also considered as an essential ecosystem
process affecting nutrient cycling and species diversity in an ecosystem
(Mayer et al. 2021). However, there exists high uncertainty in
accurately estimating the contribution of litter decomposition to
terrestrial carbon cycle and its feedbacks to ecosystems, which largely
hampers the development and prediction abilities of earth system models
(Jones et al. 2005) and dynamic global vegetation models (Sitch et al.
2003). To enhance the estimates of the role of forest litter
decomposition in global element cycling and multi-taxon biodiversity,
the fundamental understanding of the dominant controls on decomposition
across different mountains and forests is critically needed (Bradford et
al. 2016).

The mass loss of litter components is mainly regulated by temperature,
moisture availability, edaphic factors, and the quality of the plant
litter (Tenney and Waksman 1929, Bradford et al. 2016). Air temperature
and moisture at broad spatial scales are currently used as the
predominant factor controlling decomposition rates due to their direct
and indirect controls on decomposers and litter quality (Wall et al.
2008, Pablo Garc\selectlanguage{ngerman}ía-Palacios et al. 2013, Steidinger et al. 2019, Ma et
al. 2022). However, this climate-centred evidence relies heavily on air
temperature and moisture at coarse spatiotemporal scales or in open
areas that are not matched with the closely experienced microclimate
within forest litter layers (Steidinger et al. 2019, Joly et al. 2023).
For instance, soil temperature can differ from air temperature up to 10
\selectlanguage{english} owing to buffering effects by habitat terrain (Lembrechts et al.
2022). Despite a few recent studies assessing the role of microclimate
on decomposition, its contrasting (little vs. significantly positive)
effects on decomposition rates were reported, with a substantial bias
towards tundra ecosystems and temperate forests (Chen et al. 2018, 2023,
von Oppen et al. 2024). This mismatch and data bias lead to knowledge
gaps in the regulating effects of microclimate variation on litter
decomposition, particularly the underrepresented subtropical and
tropical forests where microclimate is perennially buffered by dominated
evergreen tree species when compared to other terrestrial ecosystems.

Non-climatic biotic and abiotic factors (e.g., soil physiochemistry,
microtopography and vegetation composition) can impact substantially
litter degradation by adjusting directly and indirectly climate and the
activity of soil microbes (Cornwell et al. 2008, Kaspari et al. 2008,
Mori et al. 2020). But the extent and strength of these variables vary
dramatically in different forests and mountains, causing discrepancies
in the general importance of the same factor in decomposition processes.
For instance, variations in soil pH change its association with
decomposition processes via different extracellular enzyme activities
under acidic or alkaline environments (Romani et al. 2006). Tree
richness ranging from 1 to 4 had no effect on decomposition rates in a
temperate forest (Fujii et al. 2017), while a gradient up to 24 in a
subtropical forest explained 54.3\% variation in decomposition
(Seidelmann et al. 2016). Such discrepancy of inconsistent roles of
these variables on decomposition among findings might associate with
insufficient gradients due to sampling bias and the limitation in
studied spatial scales (Keuskamp et al. 2013). Therefore, from a
perspective of experimental macroecology, a distributed and coordinated
study with wide environmental gradients across different mountains could
shed insights into a general understanding of the underlying mechanisms
that regulate forest litter decomposition.

Another advantage of experimental macroecology in forest litter
decomposition is to avoid the incomparability among a large number of
studies in different ecosystems with diverse plant composition, which is
a common issue in ecology (Spake et al. 2022). This top incomparability
can be attributed to distinctive litter quality. The composition and
quality of forest foliar litter are highly location-specific at both
local and regional scales due to diverse species composition. The high
plant diversity determines the diversity of litter, yielding an
overriding influence of litter quality on decomposition processes across
any type of ecosystem (Mori et al. 2020). Therefore, to tease
environmental effects on decomposition apart from litter quality per se,
standard litters, such as a full mix of all plant litters from a study
site or uniform litter type that does not belong to any study sites
(e.g., tea bags and wood stick), are a premium method to control the
intrinsic effects by litter quality (Fanin et al. 2020, Joly et al.
2023). Except for the huge difference in litter quality, the
incomparability also reflects on the absence or presence of the same
environmental factors in different studies (Bradford et al. 2016).
Hence, the use of standardized litter substrate and the inclusion of
multiple environmental factors across multiple mountains are certainly
helpful to reduce the biases induced by incomparability.

In this study, we conducted a macroecological litter decomposition
experiment across 10 mountains spanning a wide range of Chinese
subtropical and tropical forests. These mountains provide high
divergence in abiotic and biotic environmental gradients. We buried
6,864 standardized teabags across 568 elevational sites in 10 mountains
and quantitatively evaluated the influence of soil microclimate, tree
diversity, soil physiochemistry, and topography on decomposition rate
and litter stabilization factor within and among mountains.
Specifically, we addressed three questions: 1) Are there general
patterns of forest litter decomposition rates and stabilization along
elevational gradients in these mountains? We predict that decomposition
rates decrease along elevations and stabilization increase because of
harsh environmental conditions and increased inhibition at high
elevations. 2) Do soil microclimate (temperature, moisture, and their
variations) play the predominant role in controlling decomposition rates
and stabilization across mountains? We predict that temperature and its
variation increase decomposition rates due to their direct and indirect
controls on decomposers (Glassman et al. 2018), and the moisture and its
variations affect stabilization because soil humidity conditions can
modify the absorption and adhering of litter organic carbon on soil
surface or aggregate (Morffi-Mestre et al. 2023, Feyissa et al. 2023).
3) To what degree do non-climatic factors (edaphic factors, tree
diversity, and topography) contribute to explaining the variations in
litter decomposition within and among mountains? We predict negative
effects of high soil pH and slope on stabilization due to increased
cation exchange capacity and water erosion (Zhu et al. 2019), and strong
constraints of low tree diversity on decomposition rates because of
potentially lower microbial diversity (Joly et al. 2017).

\section*{Material and Methods}

{\label{material-and-methods}}

\subsection*{Study sites along elevations in 10
mountains}

{\label{study-sites-along-elevations-in-10-mountains}}

This study was conducted in 10 mountains of the BEST (Biodiversity along
Elevational gradients: Shifts and Transitions research network
(https://BEST-mountains.org). This network was designed to use standard
field methods (e.g., plot design, plant survey, and sample collection)
to demonstrate biogeographic patterns and ecological processes across
regions. The 10 mountains belong to subtropical and tropical regions
(Fig. 1a) where the climate is heavily buffered by dense canopies
compared to temperate and boreal forests (De Frenne et al. 2019). The
geographical distribution of the studied region spanned from 98\selectlanguage{english}@42' --
119@\selectlanguage{english}26' in longitude and 19@\selectlanguage{english}4' -- 31@\selectlanguage{english}9' in latitude. Elevation ranges
from 252 to 3835 m a.s.l.. The mean soil temperature of the growth
season (June to August) ranges from \emph{ca} . 13  -- 22 , with the
coolest climate in Jade Dragon Snow Mountain (YMT) and the warmest
climate in the tropical mountain Bawangling (BWL). Along elevational
gradients, species composition often shifts sequentially, i.e.,
evergreen-broadleaved species, mixed evergreen-broadleaved and deciduous
species, deciduous and conifer species, and high mountain shrubs. In
each mountain, 20 m \selectlanguage{ngerman}× 20 m permanent sample plots were established from
the mountain bottom to the top at a distance of\emph{ca} . 100 m
difference. In total, 142 plots were included in our study (Table S1).
Within each plot, all tree individuals with stems \textgreater{} 1 cm
were identified to species level. Tree species richness (using ``Tree''
when referred as environmental factor) was calculated and used as the
biotic factor in decomposition processes.

\subsection*{Standardized teabag
experiment}

{\label{standardized-teabag-experiment}}

We used the tea bag index (TBI) to investigate litter decomposition
processes along the elevation of each mountain by following the protocol
from Keuskamp et al. (2013). In this approach, two types of teabags
(green tea: EAN8722700055525 and rooibos tea: EAN8722700188438) were
used as standard leaf litter bags, which can be used globally and across
biomes to generate comparable results (Keuskamp et al. 2013, Djukic et
al. 2018). The material of the teabag is made of polypropylene and has a
mesh size of 0.25 mm allowing the access of microfauna, microbes, and
very fine roots (Fig 1c, d).

Within each plot, we selected four sampling sites according to the
criteria below: 1) understory species composition at each site is
representative and similar to neighbor sites; 2) homogeneous in
microhabitat between sites, e.g., slope and canopy cover; 3) no clear
rocks, big tree roots, anthropogenic disturbances; 4) four sites were as
evenly distributed as possible within a plot. Each site was labeled with
a PVC tube (Fig. 1b). Before the start of the field experiment, all
teabags were oven-dried at 70 °C for 24 h. Each teabag was identified
and buried in the upper 8 cm of the top soil layer for three months. At
each site, we buried 12 teabags (6 red vs. 6 green). In total, 6,864
teabags were buried (6 replications × 2 tea types × 4 sites × 142
plots), and 87\% (5,996) of the bags were retrieved. With intensive
cooperation from each region, this field experiment was conducted nearly
simultaneously across all mountains, buried between late May and early
June 2021 and retrieved between late August and early September 2021.
This decomposing period was designed to capture energy flow in peak
growing season in subtropical and tropical forests.

At the end of the decomposition period, we retrieved all tea bags and
transported them immediately in ice-isolated boxes to the Lab at East
China Normal University, Shanghai. During the process of retrieval and
transportation, sterilized gloves and sampling bags were used to avoid
any contamination. Soil particles adhered~on the surface of the litter
bag were removed once received and dried at 70 \selectlanguage{english} for 48 h. Dried litter
bags were again cleaned by hand carefully to avoid ash falls. Then we
recorded the extent of damage to each tea bag according to the number
and size of holes. Undamaged bags with one or several minor holes
(\textless{} 1 mm) were used for weighing the remaining materials.
According to the decomposition protocol, we calculated TBI, which
includes the decomposition rate constant (\(k\)) and
stabilization factor (\emph{S} ). In short, mass loss
(\(W\)) at time \emph{t} is a double-exponential function
of decomposed fraction (\(a\)) with the constant k (eqn.
1). The \emph{k} represents how fast the labile carbon is about to be
decomposed under a certain environment, reflecting the velocity of
carbon loss. S indicates the fraction of the recalcitrant which
stabilized from a theoretically hydrolysable fraction
(\(H\)) due to environmental constraints (eqn. 2):

\(W\left(t\right)=\text{ae}^{-kt}+\left(1-a\right)\ \) eqn. 1

\(S=1-\frac{a}{H}\) eqn. 2

Meanwhile, we measured three microhabitat factors (Cover: canopy cover,
Thick: ground litter thickness, and slope) which can potentially affect
decomposition directly and indirectly. Canopy cover was measured at 50
cm above the soil surface during the sunny day (avoid solar radiation at
noon) at each site three times by using a fish-lance (238\selectlanguage{english}@, wide-angle
view). Canopy pictures were analyzed using the software Gap Light
Analyzer (Frazer et al. 1999). Ground litter thickness was the layer of
dead leaves and debris that covered the soil surface. The slope of each
site was measured with consistent orientation, i.e., standing the
downhill side and facing the mountain slope.

\subsection*{Microclimatic and edaphic
variables}

{\label{microclimatic-and-edaphic-variables}}

Soil microclimate (temperature and humidity) was recorded at the centre
of each plot at a depth of 8 cm relative to the soil surface at
15-minute intervals using temperature and moisture loggers (TMS4, TOMST
Ltd.). Due to the high risk of monkey disturbance in EMS, the
microclimate in this mountain was recorded using iButton (Maxim
Integrated DS1925), which can fully be buried under the soil. Soil
microclimate indices of the three months included 8 temperature indices
and 7 moisture indices referring to the bioclimatic variables in
WorldClim or CHELSA. Eventually, four microclimatic indices were
retained after controlling the collinearity. These four indices reflect
both the mean and variation of soil microclimate matching with
decomposing period (growing season), including the monthly mean soil
temperature (Temp; ), standard deviation of soil temperature (TempV),
the monthly mean soil moisture (Mois), and standard deviation of soil
moisture (MoisV).

Soil samples were collected at 0 -- 10 cm using 5-cm augers. In each
plot, five soil cores were taken randomly and mixed homogeneously, after
the removal of visible roots, debris, and stones. Soil samples were
air-dried and sieved through a 1-mm mesh. We measured soil pH and total
phosphorus (P) following the standard protocol of Ma et al. (2019). We
focused on the two soil factors because: 1) soil pH plays a major role
in the structure of microbial community composition globally locally
(Hendershot et al. 2017, Ma et al. 2022); 2) the large variation of soil
total P across the studied regions implies its potential constraints on
ecosystem processes (He et al. 2016).

\subsection*{Statistical analyses}

{\label{statistical-analyses}}

We first tested the elevational pattern of decomposition rate (\emph{k}
) and stabilization (\emph{S} ) within each mountain using least square
regression followed by model diagnostics of residuals normality and
homoscedasticity using the function \emph{autoplot} from the R
package\emph{ggfortify} (Tang et al. 2016). Before the test, we scaled
the elevation of each mountain to 0 -- 1 to avoid scale differences in
elevation ranges between mountains. The regression was also used to
examine the correlation between \emph{k} and \emph{S} within each
mountain. The same correlation method was used for testing the
correlation significance of environmental drivers (Fig. S1). Spearman
correlation coefficients (\textless{} \textbar{}0.7\textbar{}) were used
as a threshold to identify multicollinearity (Dormann et al. 2013) when
multiple regression analyses were applied.

To quantify the dominant role (\emph{w} ) of soil microclimate in
decomposition processes, we applied all possible combinations of the ten
environmental variables and fitted these component models with the least
square regression for \emph{k} and \emph{S} in each mountain separately,
and calculated the relative importance value (\emph{w} ) as the weight
of Akaike's Information Criterion (AIC) based on model averaging. For
each mountain and the response variable, we extracted the best model
which contained the lowest AIC and was used to indicate the significant
drivers in each mountain. To avoid multicollinearity, we calculated VIF
values and refit the best model by excluding variables that had a VIF
value \textgreater{} 4. This operation only occurred in the three best
models (Temp in BWL when predicting \emph{k} , Temp and TempV in TMS
when predicting \emph{S} , and TempV and Cover in EMS when
predicting\emph{S} ). As the extent of the significance matched well
with the value of the weight (Table S2), we used the latter to represent
the relative importance because full model averaging optimizes the
uncertainties and biases from threshold-selected models (Burnham and
Anderson 2002). Since the data of Tree, edaphic factors, Mois, and MoisV
were unavailable in EMS, we removed EMS and focused on 9 mountains when
comparing the relative importance of each factor in decomposition.

Finally, we assessed the specific effect of the four soil microclimatic
factors and three non-climatic factors on \emph{k} and \emph{S} within
each mountain and across all mountains. The three factors were tree
diversity, soil pH, and slope, which were representative in terms of its
profound direct and indirect influences on decomposition via litter
quality, decomposers, and microhabitat, respectively. We used least
square regression models and a linear mixed-effects models (mountain as
a random effect) to test each factor's influence on k and S within each
mountain and across all mountains. The mixed-effects model was estimated
using the \emph{lme4} package (Bates et al. 2015), and the values of
explained variance (\emph{R} \textsuperscript{2}) of the models were
calculated using the function \emph{r.squaredGLMM} in R
package\emph{MuMin} (Barto\selectlanguage{polish}ń \selectlanguage{english}2023).

All statistical analyses were conducted in R 4.3.1 (R Core Team 2014).

\section*{Results}

{\label{results}}

\subsection*{Decomposition rates and stabilization in 10
mountains}

{\label{decomposition-rates-and-stabilization-in-10-mountains}}

Across the 10 mountains, the decomposition rate (\emph{k} ) ranged from
0.002 to 0.05 (0.02 on average), and the stabilization factor (\emph{S}
) ranged from 0.006 to 0.35 (0.13 on average). JFS experienced the
fastest mass loss (\emph{k} = 0.02), which was significantly higher than
that in six mountains on its east side (Fig. S2). The highest mean
\emph{S}occurred in EMS and YMT (0.18), which was significantly higher
than\emph{S} in GMT and the five eastern mountains (Fig. S3). There were
no significant correlations between \emph{k} and \emph{S} (except DBS)
(Fig. S4).

Although both significant and non-significant correlation existed, there
was a consistent pattern in each mountain that \emph{k} decreased along
elevations (but see a slight hump-shape in YMT), and an opposite trend
for \emph{S} (Fig. 2). For \emph{k} , the significant elevational
pattern was mainly found for four western mountains (GMT, YMT, EMS, and
JFS) and the northmost mountain (DBS). For \emph{S} , the statistically
significant trend occurred in three western mountains (GMT, YMT, and
JFS), and the southernmost mountain (BWL).

\subsection*{The importance of microclimate in
decomposition}

{\label{the-importance-of-microclimate-in-decomposition}}

Among 10 climatic and non-climatic variables, soil microclimate related
variables, mainly Temp, TempV, and MoisV, emerged as the most important
factors in controlling litter decomposition in the western mountains
(Figs 3-4, Table S2). Across all mountains, \emph{k} was mainly impacted
by soil temperature (\emph{coef.} = 0.48, \emph{P} \textless{} 0.001)
and its variation (\emph{coef.} = 0.36, \emph{P} \textless{} 0.001),
while \emph{S} was subjected to significant effects from all
microclimatic factors, with particularly strong relation with soil
temperature (\emph{coef.} = -0.46, \emph{P} \textless{} 0.001) and the
variation of soil moisture (\emph{coef} . = -0.36, \emph{P} \textless{}
0.001) (Fig. 5).

\subsection*{The effects of non-climatic drivers in
decomposition}

{\label{the-effects-of-non-climatic-drivers-in-decomposition}}

Non-climatic factors, i.e., tree diversity, soil (pH and P), and
microhabitat (litter thickness, canopy cover, and slope) emerged as
important as soil microclimate in western mountains and the northmost
mountain (DBS) (Figs 3-4, Table S2). Among mountains, three
representative factors (tree diversity, soil pH, and slope) showed
substantially spatial variations in its relations with \emph{k}
and\emph{S} (Fig. 6). Across all mountains, tree diversity ranged from 4
to 93 and increased \emph{k} significantly (\emph{coef.} = 0.25,
\emph{P}\textless{} 0.001). Soil pH (ranged from 3.06 -- 7.16) and slope
(0.25 -- 42.5) had no significant effect in this respect. For \emph{S} ,
we found no significant effects of tree diversity but a significantly
negative association with soil pH (\emph{coef} . = -0.17,
\emph{P}\textless{} 0.001) and slope (\emph{coef} . = -0.14, \emph{P}
\textless{} 0.001).

\section*{Discussion}

{\label{discussion}}

Lacking a general understanding of dominant factors driving forest
little decomposition leads to high uncertainties in the estimation of
carbon dynamics at both regional and global scales and its feedback to
climate changes (Davidson and Janssens 2006, Krishna and Mohan 2017).
From the perspective of experimental macroecology, this study took
advantage of highly diversified environmental gradients along elevations
of multiple mountain ecosystems, and analysed the effects and relative
importance of 10 abiotic and biotic factors in decomposition processes
both within and among mountains by using standardized litter materials.
The results demonstrate a high variation of decomposition rates (0.002
-- 0.05) and stabilization (0.006 -- 0.35) across study sites. Within
mountains, soil microclimate played a dominant role in decomposition
processes mainly in the western high-altitude mountains with colder
environments, a similar magnitude of importance for non-climatic factors
emerged in the western and the northernmost mountain, indicating the
spatial variation of driving factors in decomposition processes. Across
mountains, although tree richness impacted decomposition rates, and
moisture, soil pH, and slope affected stabilization significantly, soil
temperature possessed the strongest association with both decomposition
rates (0.48) and stabilization (-0.46), supporting the importance of
microclimate and environmental range or gradient in controlling
decomposition processes (Bradford et al. 2014, Gallois et al. 2023). Our
study provides a general understanding of microclimate and non-climatic
factors' effects on decomposition across mountains, shedding light on
the underlying mechanisms for forest carbon dynamics under global
change.

\subsection*{Decomposition rates and stabilization along
elevations}

{\label{decomposition-rates-and-stabilization-along-elevations}}

Among mountains, the velocity of litter mass loss is generally higher in
the east than in the west due to the premium hydrothermal conditions.
The highest decomposition rate reached 0.24, with no significant
difference between the southeastern mountains. This result is comparable
to a study in Kilimanjaro Mountain which used the same standardized
litter. Their maximum mean decomposition rate is nearly the same as our
observations. The difference is that the stabilization in Kilimanjaro is
generally higher possibly caused by the obvious transition between
warm-dry and warm-wet weather during the decomposition period (Becker
and Kuzyakov 2018). For both studies, high elevations are projected to
promote carbon stocks due to slow decomposition rates and/or higher
capacity in stabilization possibly contributed by large macroaggregates
(Feyissa et al. 2023).

\subsection*{The dominant role of microclimate in
decomposition}

{\label{the-dominant-role-of-microclimate-in-decomposition}}

Of the 10 mountains, the significant changes in decomposition rates and
stabilization with elevation are nearly in line with mountains that
experienced significant correlations between decomposition
(stabilization) and microclimatic factors. Although non-linear and
linear correlations emerged for the same factor among mountains, there
is a potential convergence of significant effects of microclimate in
mountains located in the west. The western mountains generally possess
higher elevations and cooler environments because of the expanded
monsoon~that was caused by the fast uplift of the Tibetan Plateau at
\textasciitilde{} 41 million years ago (Wu et al. 2022). Colder
environments are often associated with a higher thermal sensitivity of
microbes or soil animals, resulting in increased decomposition rates
with temperatures (Koven et al. 2017). In contrast, the expected
positive effects from the evident temperature gradient on decomposition
rates in warmer tropical environments can be neutralized by
thermal-adapted microbes (Koven et al. 2017).

Across mountains, we found a significant change in the slope of the
decomposition rate as a function of soil mean temperature across all
mountains (the best breakpoint at 19.79 , \emph{N} = 998,
\emph{P}\textless{} 0.001). Below the breakpoint, decomposition rates
increased with soil temperatures significantly, and the mean soil
temperatures of these western mountains fell into this range. Beyond the
breakpoint, the increase in soil temperature no longer accelerates
decomposition rates. Of the 10 mountains, this ecotone mountain is the
only one with an east-west orientation and acts as a barrier to the
southward flow of the cold air (Wang et al. 2016), which also
experienced a mean soil temperature lower than the breakpoint. Taken
together, these observations stress the key role of microclimate in
decomposition processes and imply that there might be a threshold of
temperature effects on decomposition rates.

\subsection*{Inconsistent effects of non-climatic drivers on
decomposition}

{\label{inconsistent-effects-of-non-climatic-drivers-on-decomposition}}

Non-climatic environmental drivers (e.g., tree species richness, soil
biochemistry, and topography) emerge as significant factors constraining
litter decay across mountains. For instance, litter decomposition rates
increased with tree species diversity, echoing the facilitation of high
plant species diversity on decomposition rates via complementary effects
caused by multiple functional traits (Handa et al. 2014). Given
temperature and species diversity co-vary at the global scale, i.e.,
tropical forests often possess higher species diversity than boreal
forests, it is essential to consider their interaction to increase the
predicting accuracy of carbon stocks in forest ecosystems (Spohn et al.
2023). In complex terrain, topography emerges as a significant driver
regulating decomposition through the effect on stabilization of
litter-derived organic carbon (Fig. 6). Across the globe, complex
terrain accounts for more than 50\% of the land surface (Rotach et al.
2014), and the explanatory power of slope on the variance of soil
CO\textsubscript{2} flux exceeds 50\% in such complexed ecosystems
(Reyes et al. 2017). Our results thus advocate that the prediction of
ecosystem functions at large spatial scales should take the assumption
of microhabitat (e.g., topography and canopy cover) into account, which
has been currently underrepresented in multiple earth system models
(Bonan et al. 2013, Phillips et al. 2019, Ren et al. 2024).

\subsection*{Forest litter decomposition towards experimental
macroecology}

{\label{forest-litter-decomposition-towards-experimental-macroecology}}

With massively accumulated evidence of different factors controlling
decomposition (Cornwell et al. 2008, Becker and Kuzyakov 2018, Forrester
et al. 2023), the incomparability between findings impeded our general
understanding of drivers in this critical process because of
differentiations in approaches, designs, and methodologies (Spake et al.
2022, Catford et al. 2022). This extent of incomparability can be
partially addressed by statistical methods in meta-analyses, while a
straightforward and efficient solution is conducting a standardized
experimental design at multiple locations at once (``coordinated
distributed experiments'') (Vellend 2016). For instance, a meta-analysis
focusing on surface litterbags at a global scale (a wide vegetation
type) uncovers that 70.2\% variation in decomposition rates has been
attributed to litter quality but not climate (Zhang et al. 2008). A
macroecological experiment spanning from Mediterranean forests to boreal
forests in Europe verifies the dominant control of macroclimate in
decomposition (Joly et al. 2023). These contrasting results expand our
view of decomposition but also indicate that there are intrinsic
differences in terms of system characteristics that cause divergent
results, such as surface vs. buried decomposition, and forest vs.
non-forested ecosystem. Our coordinated distributed experiment controls
this difference one step forward to the microenvironment in mountainous
forests where the microclimate has been intensively buffered by dense
canopies and tree diversity is generally high. As tea bags have been
used as standardized litter in many ecosystems (e.g., grassland, alpine
and boreal forests) (Althuizen et al. 2018, Chen et al. 2018; von Oppen
et al. 2024), future macroecological experiments may consider the
transferability of these standardized results to realistic mass loss of
local litters, contributing profoundly to current and future dynamics of
terrestrial carbon and nutrient cycling.

In conclusion, our macroecological experiment across 10 mountains
provides empirical evidence that there is a large spatial variation of
soil microclimate and non-climatic factors controlling decomposition
processes, with a dominant role of microclimate in the western mountains
with colder environments. Across all mountains, drivers differ between
decomposition rates and stabilization, with a positive effect of
temperature-related variables and tree diversity on decomposition rates
and mainly negative effects of microclimate, pH, and slope on
stabilization. This coordinated distributed experiment generates a
general understanding of abiotic and biotic influences in decomposition,
laying underlying mechanisms for carbon dynamics in complex terrain.

\section*{References}

{\label{references}}

Althuizen, I. H. J. et al. 2018. Long-term climate regime modulates the
impact of short-term climate variability on decomposition in alpine
grassland soils. - Ecosystems 21: 1580--1592.

Barto\selectlanguage{polish}ń, \selectlanguage{english}K. 2023. MuMIn: Multi-Model Inference.

Bates, D. et al. 2015. Fitting Linear Mixed-Effects Models Using lme4. -
J. Stat. Softw. 67: 1--48.

Becker, J. N. and Kuzyakov, Y. 2018. Teatime on Mount Kilimanjaro:
Assessing climate and land-use effects on litter decomposition and
stabilization using the Tea Bag Index. - L. Degrad. Dev. 29: 2321--2329.

Bonan, G. B. et al. 2013. Evaluating litter decomposition in earth
system models with long-term litterbag experiments: an example using the
Community Land Model version 4 (CLM4). - Glob. Chang. Biol. 19:
957--974.

Bradford, M. A. et al. 2014. Climate fails to predict wood decomposition
at regional scales. - Nat. Clim. Chang. 4: 625--630.

Bradford, M. A. et al. 2016. Understanding the dominant controls on
litter decomposition. - J. Ecol. 104: 229--238.

Burnham, K. P. and Anderson, D. R. 2002. Model selection and inference:
a practical information-theoretic approach. - In: 2nd Editio.
Springer-Verlag, New York.

Catford, J. A. et al. 2022. Addressing context dependence in ecology. -
Trends Ecol. Evol. 37: 158--170.

Chen, Y. et al. 2018. Microclimate exerts greater control over litter
decomposition and enzyme activity than litter quality in an alpine
forest-tundra ecotone. - Sci. Rep. 8: 1--13.

Chen, B. et al. 2023. Microclimate along an elevational gradient
controls foliar litter cellulose and lignin degradation in a subtropical
forest. - Front. For. Glob. Chang. 6: 1--13.

Cornwell, W. K. et al. 2008. Plant species traits are the predominant
control on litter decomposition rates within biomes worldwide. - Ecol.
Lett. 11: 1065--1071.

Cuni-Sanchez, A. et al. 2021. High aboveground carbon stock of African
tropical montane forests. - Nature 596: 536--542.

Davidson, E. A. and Janssens, I. A. 2006. Temperature sensitivity of
soil carbon decomposition and feedbacks to climate change. - Nature 440:
165--173.

De Frenne, P. et al. 2019. Global buffering of temperatures under forest
canopies. - Nat. Ecol. Evol. 3: 744--749.

Djukic, I. et al. 2018. Early stage litter decomposition across biomes.
- Sci. Total Environ. 628--629: 1369--1394.

Dormann, C. F. et al. 2013. Collinearity: a review of methods to deal
with it and a simulation study evaluating their performance. - Ecography
36: 27--46.

Fanin, N. et al. 2020. Relative importance of climate, soil and plant
functional traits during the early decomposition stage of standardized
litter. - Ecosystems 23: 1004--1018.

Feyissa, A. et al. 2023. Soil carbon stabilization and potential
stabilizing mechanisms along elevational gradients in alpine forest and
grassland ecosystems of Southwest China. - Catena 229: 107210.

Forrester, J. A. et al. 2023. Experimental evidence that forest
structure controls detrital decomposition. - Ecosystems 26: 1396--1410.

Frazer, G. W. et al. 1999. Gap light analyzer (GLA): Imaging software to
extract canopy structure and gap light transmission indices from
true-colour sheye photographs, users manual and program documentation.

Fujii, S. et al. 2017. Disentangling relationships between plant
diversity and decomposition processes under forest restoration. - J.
Appl. Ecol. 54: 80--90.

Gallois, E. C. et al. 2023. Summer litter decomposition is moderated by
scale-dependent microenvironmental variation in tundra ecosystems. -
Oikos 2023: e10261.

Glassman, S. I. et al. 2018. Decomposition responses to climate depend
on microbial community composition. - Proc. Natl. Acad. Sci. U. S. A.
115: 11994--11999.

Handa, I. T. et al. 2014. Consequences of biodiversity loss for litter
decomposition across biomes. - Nature 509: 218--221.

He, X. et al. 2016. Altitudinal patterns and controls of plant and soil
nutrient concentrations and stoichiometry in subtropical China. - Sci.
Reports 2016 61 6: 1--9.

Hendershot, J. N. et al. 2017. Consistently inconsistent drivers of
microbial diversity and abundance at macroecological scales. - Ecology
98: 1757--1763.

Joly, F.-X. et al. 2017. Tree species diversity affects decomposition
through modified micro-environmental conditions across European forests.
- New Phytol. 214: 1281--1293.

Joly, F. X. et al. 2023. Resolving the intricate role of climate in
litter decomposition. - Nat. Ecol. Evol. 7: 214--223.

Jones, C. et al. 2005. Global climate change and soil carbon stocks;
predictions from two contrasting models for the turnover of organic
carbon in soil. - Glob. Chang. Biol. 11: 154--166.

Kaspari, M. et al. 2008. Multiple nutrients limit litterfall and
decomposition in a tropical forest. - Ecol. Lett. 11: 35--43.

Keuskamp, J. A. et al. 2013. Tea Bag Index: A novel approach to collect
uniform decomposition data across ecosystems. - Methods Ecol. Evol. 4:
1070--1075.

Koven, C. D. et al. 2017. Higher climatological temperature sensitivity
of soil carbon in cold than warm climates. - Nat. Clim. Chang. 7:
817--822.

Krishna, M. P. and Mohan, M. 2017. Litter decomposition in forest
ecosystems: a review. - Energy, Ecol. Environ. 2: 236--249.

Lembrechts, J. J. et al. 2022. Global maps of soil temperature. - Glob.
Chang. Biol. 28: 3110--3144.

Ma, S. et al. 2019. Plant species identity and soil characteristics
determine rhizosphere soil bacteria community composition in European
temperate forests. - FEMS Microbiol. Ecol. 95: 1--11.

Ma, L. et al. 2022. When microclimates meet soil microbes: Temperature
controls soil microbial diversity along an elevational gradient in
subtropical forests. - Soil Biol. Biochem. 166: 108566.

Mayer, M. et al. 2021. Soil fertility relates to fungal-mediated
decomposition and organic matter turnover in a temperate mountain
forest. - New Phytol. 231: 777--790.

Morffi-Mestre, H. et al. 2023. Leaf litter decomposition rates:
influence of successional age, topography and microenvironment on six
dominant tree species in a tropical dry forest. - Front. For. Glob.
Chang. 6: 1--13.

Mori, A. S. et al. 2020. A meta-analysis on decomposition quantifies
afterlife effects of plant diversity as a global change driver. - Nat.
Commun. 11: 1--9.

Pablo Garc\selectlanguage{ngerman}ía-Palacios et al. 2013. Climate and litter quality
differently modulate the effects of soil fauna on litter decomposition
across biomes. - Ecol. Lett. 16: 1045--1053.

Phillips, J. et al. 2019. Differences in carbon stocks along an
elevational gradient in tropical mountain forests of Colombia. -
Biotropica 51: 490--499.

Price, M. F. et al. 2011. Mountain forests in a changing world:
realizing values, addressing challenges.

R Core Team 2014. R: A language and environment for statistical
computing. R Foundation for Statistical Computing.

Ren, S. et al. 2024. Projected soil carbon loss with warming in
constrained Earth system models. 15: 1--10.

Reyes, W. M. et al. 2017. Complex terrain influences ecosystem carbon
responses to temperature and precipitation. - Global Biogeochem. Cycles
31: 1306--1317.

Romaní, A. M. et al. 2006. Interactions of bacteria and fungi on
decomposing litter: Differential extracellular enzyme activities. -
Ecology 87: 2559--2569.

Rotach, M. W. et al. 2014. The world is not flat: Implications for the
global carbon balance. - Bull. Am. Meteorol. Soc. 95: 1021--1028.

Seidelmann, K. N. et al. 2016. Direct vs. Microclimate-driven effects of
tree species diversity on litter decomposition in young subtropical
forest stands. - PLoS One 11: 1--16.

Sitch, S. et al. 2003. Evaluation of ecosystem dynamics, plant geography
and terrestrial carbon cycling in the LPJ dynamic global vegetation
model. - Glob. Chang. Biol. 9: 161--185.

Spake, R. et al. 2022. Improving quantitative synthesis to achieve
generality in ecology. - Nat. Ecol. Evol. 6: 1818--1828.

Spohn, M. et al. 2023. The positive effect of plant diversity on soil
carbon depends on climate. - Nat. Commun. 14: 1--10.

Steidinger, B. S. et al. 2019. Climatic controls of decomposition drive
the global biogeography of forest-tree symbioses. - Nature 569:
404--408.

Tang, Y. et al. 2016. ggfortify: unified interface to visualize
statistical result of popular R packages. - R J. 8.2 2: 474--485.

Tenney, F. G. and Waksman, S. A. 1929. Composition of natural organic
materials and their decomposition in the soil: iv. The nature and
rapidity of decomposition of the various organic complexes in different
plant materials, under aerobic conditions. - Soil Sci. 28: 55--84.

Vellend, M. 2016. The Theory of Ecological Communities.pdf. - Princeton
University.

von Oppen, J. et al. 2024. Microclimate explains little variation in
year-round decomposition across an Arctic tundra landscape. - Nord. J.
Bot. 1: e04062.

Wall, D. H. et al. 2008. Global decomposition experiment shows soil
animal impacts on decomposition are climate-dependent. - Glob. Chang.
Biol. 14: 2661--2677.

Wardle, D. A. et al. 2004. Ecological linkages between aboveground and
belowground biota. - Science 304: 1629--1633.

Wu, F. et al. 2022. Reorganization of Asian climate in relation to
Tibetan Plateau uplift. - Nat. Rev. Earth Environ. 3: 684--700.

Zhang, D. et al. 2008. Rates of litter decomposition in terrestrial
ecosystems: global patterns and controlling factors. - J. Plant Ecol. 1:
85--93.

Zhu, M. et al. 2019. The role of topography in shaping the spatial
patterns of soil organic carbon. - Catena 176: 296--305.



\section*{Figure Legends}

{\label{figure-legends}}

\textbf{Fig. 1} The geographic location of the 10 mountains and
experimental setup. a: the distribution of the studied mountains
spanning subtropical and tropical regions in China. From the West to
East: GMT: Gaoligong Mountain; YMT: Jade Dragon Snow Mountain; EMS:
E'mei Mountain; JFS: Jinfo Mountain; DMS: Daming Mountain; BWL:
Bawangling; GS: Guan Mountain; DBS: Dabie Mountain; DYS: Daiyun
mountain; TMS: Tianmu mountain. Different elevations are shown in
colours. b: An example of decomposing design. c: standard tea bags
before burying. d: retrieved tea bags after 90-day decomposition.

\textbf{Fig. 2} Elevational patterns of decomposition rate (a) and
stabilization (b) in each mountain. Colours represent different
mountains. Dashed and solid least squares~regression~lines represent
non-significant and significant relationships between elevation and
decomposition rates or stabilization, respectively. The elevation of
each mountain has been scaled between 0 to 1.

\textbf{Fig. 3} The relative importance of environmental drivers for
explaining the elevational variations of decomposition rate (\emph{k} )
in each mountain. The importance value of each driver is extracted from
AIC weight based on model averaging. Colours indicate the four groups
(navy blue-Microclimate; gray-plant; yellow-Soil physiochemistry;
red-microhabitat) of environmental drivers. Temp: mean temperature of
the growth season; TempV: temperature variation of the growing season;
Mois: mean moisture of the growth season; MoisV: moisture variation of
the growth season, Tree: tree species richness; Soil P: soil total
phosphorus, Cover: Canopy cover; Thick: ground litter thickness.

\textbf{Fig. 4} The relative importance of environmental drivers for
explaining the elevational variations of stabilization (\emph{S} ) in
each mountain. Other explanations can be found in Figure 3.

\textbf{Fig. 5} Scatter plots showing the effect of four microclimatic
drivers on decomposition rates (a, b, c, d) and stabilization (e, f, g,
h) in each mountain. The black regression line in each panel represents
the relationship across all mountains. The shaded area shows the
confidence interval at the 95\% level. Solid and dashed lines indicate
significant and non-significant effects, respectively.

\textbf{Fig. 6} Scatter plots showing the effect of three selected
non-climatic drivers on decomposition rates (a, b, c) and stabilization
(d, e, f) in each mountain. Other explanations can be found in Figure 5.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

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\section*{Figure 1}

{\label{figure-1}}

\section*{Figure 2}

{\label{figure-2}}\selectlanguage{english}
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\section*{Figure 3}

{\label{figure-3}}\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
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\section*{Figure 4}

{\label{figure-4}}\selectlanguage{english}
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\end{center}
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\section*{Figure 5}

{\label{figure-5}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
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\section*{Figure 6}

{\label{figure-6}}\selectlanguage{english}
\begin{figure}[H]
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\end{center}
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