Material and
Methods
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˚42′ – 119˚26′ in longitude and 19˚4′ – 31˚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 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 × 20 m permanent sample plots were
established from the mountain bottom to the top at a distance ofca . 100 m difference. In total, 142 plots were included in our
study (Table S1). Within each plot, all tree individuals with stems
> 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.
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 ℃ 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
(< 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 (S ). In short, mass loss (\(W\)) at time t is a
double-exponential function of decomposed fraction (\(a\)) with the
constant k (eqn. 1). The 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˚, 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.
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).
Statistical analyses
We first tested the elevational pattern of decomposition rate (k )
and stabilization (S ) within each mountain using least square
regression followed by model diagnostics of residuals normality and
homoscedasticity using the function autoplot from the R packageggfortify (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 k and S within each
mountain. The same correlation method was used for testing the
correlation significance of environmental drivers (Fig. S1). Spearman
correlation coefficients (< |0.7|) were used
as a threshold to identify multicollinearity (Dormann et al. 2013) when
multiple regression analyses were applied.
To quantify the dominant role (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 k and S in each mountain separately,
and calculated the relative importance value (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 > 4. This operation only occurred in the three best
models (Temp in BWL when predicting k , Temp and TempV in TMS when
predicting S , and TempV and Cover in EMS when predictingS ). 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 k and 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 lme4 package (Bates et al. 2015), and the values of
explained variance (R 2) of the models were
calculated using the function r.squaredGLMM in R packageMuMin (Bartoń 2023).
All statistical analyses were conducted in R 4.3.1 (R Core Team 2014).