DOI: 10.5061/dryad.zw3r228cn
title: Data from: Microenvironmental variability differently predicts microorganism- and fauna-driven litter decomposition

description: Plant litter decomposition is a key process for ecosystem carbon and
 nutrient cycling. Growing evidence suggests that substantial variation in
 litter decomposition occurs at a fine scale, but the contributions of
 microorganism- and fauna-driven decomposition to this variation, and the
 relative control of biotic and abiotic drivers over this variation, remain
 virtually unexplored. To address this knowledge gap, we used a spatially
 explicit network of 113 evenly spread plots in a 9-ha subtropical
 coniferous forest to evaluate the variation and controls of microorganism-
 and fauna-driven decomposition at a scale where macroclimate, dominant
 vegetation, and litter quality are kept constant. Despite keeping
 dominant decomposition drivers constant, the variation in decomposition
 was larger than that commonly reported in regional studies and amounted to
 ca. a third of the variation previously reported at the global
 scale. Furthermore, while abiotic factors, including topographic
 conditions and soil fertility, could explain 31% of the variation in
 microorganism-driven decomposition, they could not explain any variation
 in the fauna-driven decomposition, suggesting contrasting sensitivities of
 microorganism- and fauna-driven decomposition activity to
 microenvironmental factors. Synthesis: Our study highlights the need to
 consider the local-scale variation in litter decomposition rate using a
 spatially explicit approach, in order to better identify the factors
 driving the microbial and faunal pathways of organic matter turnover.
description: <strong>Study area</strong><br>We conducted
 this experiment in a subtropical forest located in the Yingzuijie National
 Nature Reserve (26°46' N - 26°59' N, 109°49' E -
 109°58' E, 270 - 938 m a.s.l), within Huitong County, Hunan province,
 China. All fieldwork was conducted with the authorization of the Hunan
 Huitong National Research Station of Forest Ecosystem. The site has a
 humid mid-subtropical monsoon climate characterised by a mean annual
 temperature of 16.5 °C and a mean annual precipitation of 1,200 mm. The
 soil is classified as Anthrosols and Alisols (IUSS Working Group WRB,
 2022). The stand structure corresponds to a forest plantation with Chinese
 fir as the dominant tree species (30-year-old stems and 29 m2 ha-1 basal
 area) with an initial planting density of 2 m × 2.3 m after clear-cut and
 burning. During the stand development, native woody species regenerated,
 including Camellia oleifera, Itea omeiensis, and Loropetalum chinense. We
 focused our study on a 9-ha (300 m × 300 m) area, which is divided into
 225 plots of 20 m × 20 m each. To evaluate the effect of local-scale
 variation in environmental conditions and litter decomposition, we
 selected 113 non-adjacent plots from the 225 plots for our study (Fig.
 1). <strong>Litter decomposition
 experiment</strong><br>To compare the response of litter
 decomposition, driven by microorganisms and soil fauna, to environmental
 variation at local scale, we quantified the decomposition rates of Chinese
 fir leaf litter by using litterbags with fine (0.1 mm) and large (2.0 mm)
 mesh sizes in all 113 plots and 10 analytical replicates, leading to 2260
 litterbags (113 plots × 2 mesh sizes × 10 analytical replicates). To
 obtain the needle litter, we collected freshly fallen litter from trees in
 the immediate surroundings of the study site. The collected litter was
 air-dried at room temperature (25 ℃), and only complete needles were used.
 Variation in chemical properties of the Chinese fir litter used in this
 study was minimal (see detailed initial chemical determination and
 properties described in Yin et al. (2023). We prepared 15 cm × 15 cm
 litterbags with nylon mesh to allow the access of microorganisms only, we
 used 0.1 mm mesh for both sides; to allow the access of microorganisms and
 micro- and mesofauna, we used a 2 mm mesh for the upper side to allow soil
 animal access, and a 0.1 mm mesh for the side facing the soil surface to
 prevent losses of litter fragments. We then filled the litterbags with 5 g
 (± 1 mg) of air-dried litter. To convert air-dry mass into oven-dry mass
 for initial litter, we weighed initial air-dry litter subsamples, dried
 them at 60°C for 48 h, and reweighed them to obtain a conversion factor.
 In September 2019, ten pairs of litter bags, each pair consisting of two
 litter bags with different mesh, were randomly placed in each 20 m × 20 m
 plot on top of the forest floor and incubated for 383 days in situ. To
 obtain a robust estimate of microorganism- and fauna-driven decomposition
 for each 20 × 20 m plot, the ten pairs were placed in ten subplots, which
 were randomly selected out of the 16 possible 5 × 5 m subplots. Upon
 sampling, the remaining litter was taken out of the litter bags and gently
 rinsed with tap water to remove soil particles. <br>The samples were
 dried at 60 °C to constant mass and weighed. We used percentage mass loss
 (ML) as a measure of decomposition rate, which we calculated with the
 following equation:<br>ML=(M_0-M_t)/M_0×100    (1)<br>where Mt
 is the remaining mass, M0 is the initial mass of the litter after
 correction for moisture (i.e., equivalent mass at 60 °C). <br>We
 calculated the ML for the 0.1- and 2-mm bags to represent the
 decomposition rate driven by microorganisms (MLM) and the decomposition
 rate driven by both fauna and microorganisms (MLF&M),
 respectively. The difference (MLF&M - MLM) between them was
 calculated as the fauna-driven decomposition (MLF) (Coq et al., 2010). To
 overcome the non-independence of within-plot replicates and to avoid
 pseudo-replication, we used the mean values of the ML from the 10
 analytical replicates in each plot for statistical analyses.<br>To
 compare our range of variability in decomposition rates with that observed
 in other studies, we also calculated the litter decomposition rate
 constant (k) with the following equation: <br>k = -ln⁡〖(M_t/M_0 )/
 t〗      (2)<br>where Mt is the remaining mass, M0 is the initial
 mass of the litter after correction for moisture (i.e., equivalent mass at
 60 °C), and t is the number of days. Similar to ML, we calculated the k
 for the 0.1- and 2-mm bags to represent the decomposition rate driven by
 microorganisms (kM) and the decomposition rate driven by both fauna and
 microorganisms (kF&M), respectively. The difference (kF&M
 – kM) between them was calculated as the fauna-driven decomposition
 (kF).  <strong>Topographic condition measurements
 and calculations</strong><br>To characterise the topographic
 conditions of each plot, we generated an elevation raster with a pixel
 size of 20 × 20 m corresponding to each plot. This was obtained by
 conducting kriging interpolation to the elevation data collected at the
 four corners of each plot (Burrough, 1986; McBratney & Webster,
 1986). Slope, aspect, and topographic wetness index (TWI) were calculated
 in ArcGIS 10.8 (ESRI, 2018) based on the elevation raster data. To obtain
 a meaningful measure of aspect, we transformed the direction of downhill
 slope faces (aspect0) in compass degrees (0-360°) into a variable ranging
 from 0 to 1 (the higher the value, the more southward the aspect) with the
 following equation:<br>Aspect=sin(π×[aspect]_0/360)  
  (3)<br>TWI represents the spatial distribution of surface
 saturation and potential water accumulation in plots and has been widely
 used to estimate spatial variability of local soil hydrological and
 physical properties (Seibert et al., 2007; Xin et al., 2016). TWI was
 formulated as follows:<br>TWI = ln(a/tanβ)    (4)<br>where a
 is the upslope contributing area per unit contour length (or Specific
 Catchment Area, SCA) and tanβ is the local slope gradient for estimating a
 hydraulic gradient. The estimates of a and β were calculated following the
 method proposed by Qin et al. (2011), which has been shown to reflect
 local terrain and drainage conditions well. 
 <strong>Soil sampling and
 analysis</strong><br>To determine how well soil chemical and
 microbial characteristics predicted decomposition, we collected one soil
 core (0-10 cm in the topsoil layer) using an auger (2 cm in diameter) in
 May 2019 in each of the 16 possible 5 × 5 m subplots within each 20 × 20 m
 plot. This led to 16 cores, which we then pooled and homogenised to form a
 composite sample. We removed visible debris and stones from the soil
 samples, sieved these soil samples through a 2-mm mesh, and divided them
 into three subsamples. One subsample was stored at 4 °C to determine the
 contents of available nutrients, another was air-dried at room temperature
 to determine the total contents of nutrients, and the remaining one was
 freeze-dried and stored at -80 °C for microbial
 determination. <br>To determine soil fertility, we followed standard
 methods described by Liu et al. (1996). Briefly, we measured soil organic
 carbon (SOC) and soil total nitrogen (TN) using an element analyser (Vario
 Macro Elementar, GmbH, Germany). Total phosphorus content (TP) was
 determined by inductively coupled plasma mass spectrometry (ICP-Q, Thermo
 Scientific, America) after digestion with a mixed solution of HNO3: HF
 (with a volume ratio of 5:1). Soil carbon-to-nitrogen (C:N) and
 carbon-to-phosphorus (C:P) ratios were calculated for each plot. Soil
 available nitrogen (AN) was extracted using a 2 mol L-1 KCl solution, and
 soil available phosphorus (AP) was extracted by a 0.03 mol L-1 NH4F-0.025
 mol L-1 HCl solution, then determined by a continuous-flow analyser (AA3,
 SEAL Analytical, Australia). <br>To determine soil microbial
 communities, soil microbial DNA was extracted from 0.3 g of sample using
 the MoBio PowerSoil DNA isolation kit (MoBio Laboratories, Carlsbad, CA,
 United States). The bacterial 16S rRNA gene and fungal ITS2 region were
 amplified using the primers 515F/909R (Tamaki et al., 2011) and
 ITS4/gITS7F (Ihrmark et al., 2012), respectively. Sequencing libraries
 were generated using TruSeq® DNA PCR-Free Sample Preparation Kits and
 sequenced on an Illumina MiSeq platform with the 2 × 250 bp V2 Reagent
 Kits. We then used the Quantitative Insights Into Microbial Ecology
 (QIIME, V1.9.0) pipeline to process DNA sequence data. Bacterial and
 fungal sequences were independently clustered into operational taxonomic
 units (OTUs) at a 97% identity threshold using UPARSE software (Edgar,
 2013). The results of OTU abundance tables were rarefied to an even number
 of sequences per sample (7108 and 24325 sequences per sample for bacteria
 and fungi, respectively), corresponding to the minimum number of sequences
 for a single soil sample (details in Zhai et al., 2023). All the
 sequencing values were deposited in the National Center for Biotechnology
 Information (NCBI database) with the BioProject accession no. PRJNA752698
 (https://www.ncbi.nlm.nih.gov/sra/PRJNA752698). 
 <strong>Vegetation survey and plant attribute
 calculation</strong><br>To evaluate the effect of vegetation
 attributes on decomposition, we estimated, for each plot the basal area
 (BA) using diameter at breast height (DBH) of all woody plants as a proxy
 of vegetation biomass, species richness (SR) as a proxy of diversity, and
 the coefficient of variation of DBH (CVD) as a proxy of forest structural
 diversity (Ali, 2019). Since Chinese fir (CF) and regenerated woody plants
 (RWP) species may affect decomposition differently due to their different
 biomass, diversity, and position in the forest (Rago et al., 2021), we
 calculated the basal area and CVD for Chinese fir and RWP separately, and
 considered the RWP species richness, leading to five indicators (BACF,
 CVDCF, BARWP, CVDRWP, and SRRWP). <br>To do so, we measured the
 diameter at breast height (DBH) ≥ 1 cm of all woody plants in each plot
 following a standard field protocol (Condit, 1998), between September 2019
 and February 2020. We calculated the basal area using the following
 equation:<br>BA=∑_0^m∑_0^n[(π×[(DBH]_n]_ /2)]^2 )/(A)  ]   
 (4)<br>where BA is the relative basal area (m2/ha), m is the species
 number of RWPs (or 1 when calculating the basal area of Chinese fir) in
 the specific plot, n is the number of trees of the species m in each plot,
 〖DBH〗_n is the DBH (m) of the tree n, and A is the area of each plot (0.04
 ha).  <strong>Data
 analysis</strong><br>We conducted a linear mixed effects model
 using the ‘lmer’ function from the ‘lme4’ (Bates et al., 2015) package to
 compare the contribution of different groups of decomposers (i.e., fauna
 plus microorganisms, microorganisms, and fauna) on decomposition rate (ML
 and k), with ‘plot’ as a random effect. The ‘anova’ and ‘ranova’ functions
 from the ‘lmerTest’ package (Kuznetsova et al., 2017) were used to test
 the significance of fixed and random effects. A Tukey HSD test was used to
 determine significant differences between MLF&M, MLM, and MLF, and
 between kF&M, kM, and kF across all plots. In addition, we used
 Pearson’s correlation analysis to evaluate relationships between
 MLF&M, MLM, and MLF, and between kF&M, kM, and kF. We used
 the ‘PCA’ function from the ‘FactoMineR’ package (Lê et al., 2008) to
 visualise how different groups of environmental predictors were related
 (including 4 topographic conditions indicators, 4 soil fertility
 indicators, and 5 plant attributes). For each principal component analysis
 (PCA), component variables were centred and standardised before
 ordination. In addition, we used a PCA to visualise soil bacterial and
 fungal community composition based on the OTUs composition matrix in each
 plot, using Aitchison distance after CLR (centred logratio
 transform)-matrix transformation with the “vegan” and “compositions”
 packages (Boogaart et al., 2024; Oksanen et al., 2022). <br>To
 determine the relative importance of topographic conditions, soil
 fertility, plant attributes, fungal community, and bacterial community
 composition on MLF&M, MLM, and MLF, separately, we used individual
 and multiple regression models to determine how much variation of each
 response variable could be explained by these explanatory variables.
 Before multiple regression model analyses, each PC1 of topographic
 condition (PC1TC), soil fertility (PC1SF), plant attribute (PC1PA), fungal
 community (PC1FC), and bacterial community (PC1BC) were standardised by
 the ‘scale’ function to eliminate unit dimension effects. Full regression
 models were executed using the 'lm' function with the R syntax:
 ML ~ PC1TC + PC1SF + PC1PA + PC1FC + PC1BC. Model selection was performed
 with the 'dredge' function of the 'MuMIn' package
 (Bartoń, 2022), which ranks all candidate models (i.e., all combinations
 of explanatory variables included in the full model) based on the lowest
 corrected Akaike information criterion (AICc). We then estimated the
 relative importance (Σwi) of each predictor by summing AIC weights in
 models that included the explanatory variable, following Burnham and
 Collins (2015). Last, we reported the R2 of the most parsimonious models.
 We ran these analyses similarly on kF&M, kM, and kF. All data
 analyses and visualization were performed using R, v.4.1.2 (R Core Team,
 2021).
description: # Data from: Microenvironmental variability differently predicts
 microorganism- and fauna-driven litter decomposition Dataset DOI:
 [10.5061/dryad.zw3r228cn](10.5061/dryad.zw3r228cn) ## Description of the
 data and file structure We compiled a dataset comprising the decomposition
 rate and abiotic and biotic microenvironmental factors in order to predict
 litter decomposition. This was based on a spatially explicit network of
 113 evenly distributed plots within a 9-hectare Chinese fir plantation
 located in the Yingzuijie National Nature Reserve (26°46' N -
 26°59' N, 109°49' E - 109°58' E, 270 - 938 m a.s.l) in
 Huitong County, Hunan Province, China. ### Files and variables #### File:
 Original_values_of_different_litterbags.csv **Description:**  The file
 named ‘Original values of different litterbags.csv“ contains the mass loss
 and k for each litterbag. ##### Variables * **no_plot**: the count of the
 113 plots * **replicate**: the count of replicate litterbags in each plot.
 * **Plot**: the code symbol of each plot in the 9-ha large-scale permanent
 network * **mesh_size**: the mesh size of each fine (0.1 mm) and large
 (2.0 mm) mesh size litterbag. * **primary_weight**: the primary/initial
 weight (g) of the litter mass after correction for moisture (i.e.,
 equivalent mass at 60 °C) in a given litterbag. * **final_weight**: the
 remaining weight (g) of the litter mass after 383 days *in situ* incubated
 and dried at 60 °C to constant mass in a given litterbag. *
 **relative_massloss**: the percentage mass loss (ML) as a measure of
 decomposition rate (%). * **k**: the litter decomposition rate constant
 (k) as a measure of decomposition rate (yr-1) #### File:
 Microenvironmental_drivers.csv **Description:** The file named
 'Microenvironmental drivers.csv' contains the microenvironmental
 factors which were used as predictors of litter decomposition in this
 study. ##### Variables * **Plot**: the code symbol of each plot in the
 9-ha large-scale permanent network * **Elevation**: the elevation at a
 given 20 × 20 m plot (m). * **Slope**: the slope degree at a given 20 × 20
 m plot * **Aspect**: the slope aspect at a given 20 × 20 m plot *
 **TWI**: the topographic wetness index (TWI) at a given 20 × 20 m plot *
 **AN**: the content of soil available nitrogen at a given plot (mg/kg).  *
 **AP**: the content of soil available phosphorus at a given plot (mg/kg). 
 * **CN**: the carbon to nitrogen ratio means the ratio of the content of
 soil organic carbon (SOC) and soil total nitrogen (TN) at a given plot. *
 **CP**: the carbon to phosphorus ratio means the ratio of the content of
 soil organic carbon (SOC) and soil total phosphorus (TP) at a given plot *
 **BACF**: the relative basal area of Chinese fir at a given plot (m2/ha).
 * **BARWP**: the relative basal area of regenerated woody plants at a
 given plot (m2/ha). * **SRRWP**: the species richness of regenerated woody
 plants at a given plot. * **CVDCF**: the coefficient of variation of DBH
 (CVD) of Chinese fir at a given plot (%). * **CVDRWP**: the coefficient of
 variation of DBH (CVD) of regenerated woody plants at a given plot (%).
 #### File: otutabF.csv **Description:** This file contains the OTU tables
 of soil fungi in each plot. #### File: otutabB.csv **Description:**  This
 file contains the OTU tables of soil bacteria in each plot. #### File:
 code_dynamic.html **Description:** This file contains the code to
 reproduce the analyses. ## Code/software All data analyses and
 visualization were performed using R, v.4.1.2.