DOI: 10.5061/dryad.pk0p2ngth
title: Tree diversity effects on litter decomposition are mediated by litterfall and microbial processes

description: Forest ecosystems are critical for their carbon sequestration potential.
 Increasing tree diversity was shown to enhance both forest productivity
 and litter decomposition. Litter diversity increases litter
 decomposability by increasing the diversity of substrates offered to
 decomposers. However, the relative importance of litter decomposability
 and decomposer community in mediating tree diversity effects on
 decomposition remains unknown. Moreover, tree diversity modulation of
 litterfall spatial distribution, consequently, litter decomposition has
 rarely been tested. We studied tree diversity effects on leaf litter
 decomposition and its mediation by the amount of litterfall, litter
 species richness and decomposability, and soil microorganisms in a
 large-scale tree diversity experiment in subtropical China. Furthermore,
 we examined how litter functional identity and diversity affect leaf
 litter decomposability. Finally, we tested how leaf functional traits,
 tree biomass, and forest spatial structure drive the litterfall spatial
 distribution. We found evidence that tree species richness increased
 litter decomposition by increasing litter species richness and the amount
 of litterfall. We showed that soil microorganisms in this subtropical
 forest perform 84–87% of litter decomposition. Moreover, changes in the
 amount of litterfall and microbial decomposition explained 19–37% of the
 decomposition variance. Additionally, up to 20% of the microbial
 decomposition variance was explained by litter decomposability, while
 litter decomposability itself was determined by litter functional
 identity, diversity, and species richness. Tree species richness increased
 litter species richness and the amount of litterfall (+200% from
 monoculture to 8-species neighborhood). We further demonstrated that the
 amount of species-specific litterfall increased with increasing tree
 proximity and biomass and was modulated by leaf functional traits. These
 litterfall drivers increased the spatial heterogeneity of litter
 distribution, thus, litter decomposition. We highlighted multiple biomass-
 and diversity-mediated effects of tree diversity on ecosystem properties
 driving forest nutrient cycling. We conclude that considering spatial
 variability in biotic properties will improve our mechanistic
 understanding of ecosystem functioning.
description: Cf. Joined paper Study site The
 study site is located in south-east China near the town of Xingangshan
 (Jiangxi province, 29.08-29.11° N, 117.90-117.93° E). Our experimental
 site is part of the BEF-China experiment (Site A, Bruelheide et al.,
 2014), which was planted in 2009 after a clear-cut of the previous
 commercial plantations. The region is characterized by a subtropical
 climate with warm, rainy summers and cool, dry winters with a mean annual
 temperature of 16.7 °C and a mean annual rainfall of 1.8 mm (Yang et al.,
 2013). Soils in the region are Cambisols and Cambisol derivatives, with
 Regosol on ridges and crests (Geißler et al., 2012; Scholten et al.,
 2017). The natural vegetation consists of species-rich broad-leaved
 forests dominated by <em>Cyclobalanopsis glauca</em>,
 <em>Castanopsis eyrei</em>, <em>Daphniphyllum
 oldhamii</em>, and <em>Lithocarpus glaber
 </em>(Bruelheide et al., 2011, 2014).
 Study
 design To identify the effect of tree spatial
 organization on litterfall distribution and decomposition, we measured
 litterfall and decomposition between tree species pairs (hereafter, TSP)
 across various neighborhoods. Each TSP consisted of two trees next to each
 other (~1.28 m), and we defined its neighborhood as the ten trees directly
 adjacent in the planting grid. Each TSP was replicated three times in five
 tree species richness levels (1, 2, 4, 8, and ≥ 16 species), when available according to the
 experimental design (see “broken stick design”, Bruelheide et al., 2014).
 In total, we surveyed 24 combinations of tree species resulting in a total
 of 180 TSPs in 52 plots (Suppl. S1).
 Litterfall
 sampling In
 September 2018, a litter trap of 1 m<sup>2</sup> was set up at
 a height of 1 m above the soil surface between each TSP (Suppl. S1).
 Litter was collected in December 2018 to cover the main litterfall season
 in the region (Huang et al., 2017). To measure litterfall composition
 (i.e., species-specific litter biomass), each leaf of the litter trap was
 sorted and identified to species level. Each species' litter was
 dried at 40⁰C for two days and weighed (± 0.1 g). Litter species richness
 was assessed as the number of species identified in the trap, and the
 total amount of litterfall was calculated as the sum of the dried biomass
 of all species in 1 m<sup>2</sup>.
 Litter decomposition
 experiments We
 performed two complementary decomposition experiments: one in the TSPs to
 measure microbial and total decomposition (H1-2), and one in a Common
 Garden experimental field site to assess decomposability (H3-4; Suppl.
 S1). For both experiments, litterbags (10 cm x 10
 cm), with different mesh sizes (see details below) were filled with 2 g (±
 0.01 g) of dried litter according to litter trap species composition
 (i.e., amount of species-specific litter) of the different TSPs.
 Therefore, the litter composition of the litterbags matched the litterfall
 composition collected in the corresponding TSP. The litterbags for both
 experiments were installed in December 2018 and collected in September
 2019 before litterfall, i.e., after nine months of decomposition when
 about 30 to 50% of mass loss is expected in this area (Lin et al. 2021)
 while avoiding interaction with freshly fallen litter. The litterbags were
 water-cleaned by dissolution and gentle removal of soil particles as well
 as dried at 40⁰C for two days. The residual litter was weighed (± 0.01 g)
 and milled for further chemical content analyses. The water-cleaning
 treatment of the bag was considered neglectable in comparison to heavy
 rain and sediment runoff in the region (e.g., up to 250 mm of
 precipitation on average in May between 2009 and 2012, with up to 130 mm
 in 24 h in 2010 and significant runoff, see Seitz et al.,
 2015). <em>Decomposition experiment in between the
 TSPs</em> To
 assess total litter decomposition (total C and N loss, including
 fauna-mediated decomposition) and microbial decomposition (microbial C and
 N loss, excluding fauna-mediated decomposition), two large-mesh (5 mm
 mesh, total litter decomposition) and two small-mesh (0.054-mm-mesh,
 microbial decomposition) litterbags were set up between the TSPs,
 respectively, with plot-specific litter. Small-mesh litter bags excluded
 meso- and macro-detritivores by using a fine mesh size (0.054-mm-mesh) to
 assess microbial decomposition, while large-mesh litter bags were built
 using a 5-mm-mesh in the upper half of the bag to provide access to
 macro-decomposers, and a 0.054-mm-mesh only at the bottom to prevent loss
 of fine leaf litter particles to assess total litter decomposition (see
 Bradford et al., 2002). All litterbags were covered by a 50 cm x 50 cm
 grid to prevent heavy rainfalls from dislocating the litterbags (1 cm mesh
 size, see Suppl. S1). <em>Decomposition experiment in the Common
 Garden</em> The
 Common Garden decomposition experiment was performed in a monoculture from
 the BEF China site to ensure decomposition experiments were performed
 under comparable environmental conditions (e.g. seasonal macroclimatic
 fluctuations). The setting consisted of a monoculture stand of
 <em>Schima superba</em>, a species that was not included in
 the TSP experiment; thereby, we were able to exclude any home-field
 advantages (Fanin et al., 2021). <em>Schima superba</em> was
 chosen to maximize the phylogenetic distance with our target species and
 minimize environmental heterogeneity within the plot (i.e., productive
 species with closed canopy). <em>Schima superba</em>'s
 litter was removed from the ground before deploying the litterbags at a
 distance of 10 cm from each other in two blocks (one TSP replicate per
 block, Suppl. S1). To measure litter decomposability, two small-mesh
 litterbags (0.054-mm-mesh) representing the litter composition of each TSP
 were incubated in the Common Garden experiment. Only small-mesh litterbags
 were considered to avoid interaction between litterbags from different TSP
 (e.g., composition over attracting the
 macro-decomposers). Leaf and litter trait
 measurements Leaf
 functional traits were assessed at the species- and plot-level in
 September 2018, following Davrinche and Haider (2021). For each TSP
 species, several fresh leaves were collected, and the reflectance spectra
 were measured using ASD FieldSpec® 4 Wide-Resolution Spectroradiometer
 (Malvern Panalytical Ltd., Malvern, United Kingdom). Leaf functional
 traits were predicted from the reflectance spectra of a calibration
 dataset of the same species, where both reflectance spectra and leaf
 functional traits were measured. For leaf morphological traits – specific
 leaf area (SLA, leaf area divided by dry weight) and leaf dry matter
 content (LDMC, ratio of leaf dry weight to fresh weight) were measured
 before and after drying for 72 h at 80°C. Leaf areas were measured from
 scans with a resolution of 300 dpi of the fresh leaves using the WinFOLIA
 software (Regent Instruments, Quebec, Canada). Leaf chemical contents,
 i.e., carbon (C), nitrogen (N), phosphorus (P), magnesium (Mg), calcium
 (Ca), and potassium (K) contents, were measured from dried leaves ground
 into a fine powder (Mixer Mill 400, Retsch, Haan, Germany). About 5 mg of
 leaf powder was used to determine C and N contents with an elemental
 analyzer (Vario EL Cube, Elementar, Langenselbold, Germany); a 200-mg
 subsample was used to measure P content via nitric acid digestion and
 spectrophotometry using the acid molybdate technique. The filtrate
 resulting from nitric acid digestion was analyzed with atomic absorption
 spectrometry (ContrAA 300 AAS, Analytik Jena, Jena, Germany) for Mg, Ca,
 and K contents. Of these calibration samples, the relation between the
 leaf spectra and the measured leaf traits was analyzed with the software
 Unscrambler X (version 10.1, CAMO Analytics, Oslo, Norway) to predict leaf
 functional traits of each leaf, then averaged at species- and
 plot-specific. For each litterbag, we calculated the total amount of
 nutrients (i.e., C, N, P, Mg, Ca, K) as the sum of all species’
 contributions, and leaf morphological traits community weighted mean
 (i.e., CWM SLA and LDMC) using species-specific litter dry weight and
 species- and plot-specific leaf functional traits. In addition, we
 calculated the variance of each litter functional trait (i.e., C, N, P,
 Mg, Ca, K, SLA, LDMC) within the litterbags.
 Litter C and N content after
 decomposition were measured from the residual litter with an elemental
 analyzer (Vario EL Cube, Elementar, Langenselbold, Germany). To
 estimate soil
 contamination, the ash content of the sample was measured as it represents
 the amount of soil using the loss on ignition method. Refer to methods in
 the paper for formulae.  Decomposition measures
 C
 and N loss (%) in the litterbags between December 2018 and September 2019
 were used as a measure of the total decomposition (i.e., measured via the
 large mesh-size in the TSP experiment), microbial decomposition (i.e.,
 using small mesh size in the TSP experiment), and litter decomposability
 (i.e., using small mesh size in the Common Garden
 experiment). Statistical methods
 A
 description of all the variables used in this study can be found in Suppl
 S1. All data handling and statistical calculations were performed using
 the R statistical software version 4.1.0 (R Core Team 2021). All R scripts
 and statistical models used for this project can be found in a GitHub
 repository. All following linear multiple-predictors models were tested in
 R using the 'lm' function (R Core Team, 2021), and statistical
 hypotheses (i.e., residuals normality, homoscedasticity, homogeneity of
 variance) of the following linear models were tested using the
 'model_check' function from the 'performance' package
 (Lüdecke et al., 2020, Suppl. S3).
 Tree diversity effect on
 carbon and nitrogen loss (H1) We used
 linear models and normal distribution assumptions to test the effects of
 neighborhood tree species richness on total decomposition ("C
 loss" and "N loss" measured between the TSPs) and microbial
 decomposition ("C loss" and "N loss" measured between
 the TSPs, when soil meso- and macro-fauna were excluded). In addition, we
 used linear models and normal distribution assumptions to test the effects
 of litter species richness on litter decomposability ("C loss"
 and "N loss" measured in the Common Garden
 experiment). Tree diversity effect on the amount of
 litterfall and litter species richness We used
 linear models and normal distribution assumptions to test the effect of
 neighborhood tree species richness on the amount of litterfall, and litter
 species richness. Mediation of tree species richness effects on
 litter decomposition To test
 the effects of litter species richness on litter decomposability ("C
 loss" and "N loss" in the Common Garden experiment), we
 used linear models and normal distribution assumptions. To test the
 effects of litter species richness, amount of litterfall, and
 decomposability ("C loss" and "N loss" in the Common
 Garden experiment) on litter microbial decomposition ("C loss"
 and "N loss" between the TSPs, when soil meso- and macro-fauna
 were excluded), we used linear multiple predictor models and normal
 distribution assumptions, where all predictor values were rescaled using
 the R function 'scale' (R Core Team 2021). To test the effects
 of litter species richness, amount of litterfall, and litter microbial
 decomposition ("C loss" and "N loss" between the TSP,
 when soil meso- and macro-fauna were excluded) on litter decomposition
 ("C loss" and "N loss" between the TSP, when soil
 meso- and macro-fauna were included), we used linear multiple predictor
 models and normal distribution assumptions, where all predictor values
 were rescales using the R function 'scale' (R Core Team, 2021 -
 H2). All previously cited model outputs can be found in Suppl.
 S3. To test the mediation of tree species richness
 effects on litter decomposition by the amount of litterfall and litter
 species richness effects on decomposability, we implemented the previous
 relationships in a Structural Equation Model (SEM) framework (see Suppl.
 S3 for model structure); this comparison being possible as (1) each TSP
 litter composition was replicated in all experiments, (2) all the three
 on-site experiments were temporally synchronous, and (3) all variables
 were centered and reduced to compare effect sizes. Our SEM was fitted
 using the 'sem' function from the 'lavaan' package
 (Rosseel, 2012). The quality of our model fit on the data was estimated
 using three complementary indices: (i) the root-mean-squared error of
 approximation (RMSEA), (ii) the comparative fit index (CFI), and (iii) the
 standardized root mean squared residuals (SRMR); a model fit was
 considered acceptable when RMSEA &lt; 0.10, CFI &gt; 0.9 and SRMR
 &lt; 0.08. Litterfall composition effect on litter
 decomposability (H4) To test
 the effects on litter functional identity and diversity on litter
 decomposability, we first summarized changes in litter functional identity
 (i.e., total amount of C, N, P, Mg, Na, K, and the CWM of the litter SLA
 and LDMC in the litterbag) using a principal component analysis (PCA).
 Second, we summarized changes in litter functional diversity (i.e.,
 variance of C, N, P, Mg, Na, K, SLA, and LDMC in the litterbag) using a
 PCA (R function 'prcomp'), and third, we tested the effects of
 litter species richness and litter functional identity and diversity on
 litter decomposability. The
 first two axes of the litter functional identity PCA covered 76% of the
 litter functional identity variance between the litterbags (Suppl. S3).
 The first axis (i.e., "Litter nutrient content" axis) was
 correlated with the chemical content (total amount of C, N, P, Mg, Na, K)
 of the material in the litterbag, while the second axis (i.e.,
 "Litter morphology" axis) was correlated with the litter
 morphological traits (i.e., CWM of SLA and LDMC within the litterbag). We
 extracted the first two axes of the PCA ("Litter nutrient
 content" and "Litter morphology") for the following
 analyses. The first two axes of the litter functional diversity PCA
 explained 91% of the variance in litter functional diversity between the
 litterbags (Suppl. S3). We extracted the first two axes of the PCA
 ("Litter fun. diversity 1" and "Litter fun. diversity
 2") for the following analysis. To test the effects of litter species
 richness, litter nutrient content, morphology, and functional diversity on
 litter decomposability (i.e., "C loss" and "N loss" in
 the Common Garden experiment), we used linear multiple predictor models
 and normal distribution assumptions, where all explanatory variables were
 rescaled using the R function 'scale'. Explanatory variables
 were selected using forward and backward step selection based on AIC, R
 'step' function from 'stats' package (R Core Team,
 2021). Tree biomass, functional traits and planting
 pattern effects on litterfall composition (H5)
 To
 test the effects of tree biomass, the tree proximity to the traps
 ("1/dist") and tree ecological strategies (Pierce et al., 2017)
 on the amount of species-specific litterfall in our traps, we first
 summarized changes in leaf functional traits (i.e., C, N, P, Mg, Na, K,
 SLA and LDMC) using a principal component analysis (PCA, Pierce et al.
 2017). The first two axes of the PCA covered 77% of the leaf functional
 identity variance. Second, we extracted the first two axes of the PCA
 (i.e., Leaf Economics Spectrum axes: "LES 1" and "LES
 2") for the following analysis. Third, we fitted linear effect
 multiple predictor models with normal distribution assumptions using the R
 'lm' function, where the explanatory variables were rescaled
 using the R function 'scale' (R Core Team 2021) and selected
 using forward and backward step selection based on AIC (R 'step'
 function).
description: # Tree diversity effects on litter decomposition are mediated by
 litterfall and microbial processes - DATA --- Three main dataset used for
 the analyses in the article "Tree diversity effects on litter
 decomposition are mediated by litterfall and microbial processes"
 published in Oikos Related article DOI: 10.1111/oik.09751 Analyses scripts
 release: 10.5281/zenodo.8039140 ## Description of the data and file
 structure Three datasets are present in this release related to the three
 main analyses of the manuscript. The columns description can be found in
 the analyses scripts. In short: * df.csv: experimental data measured to
 test the effect of tree species richness on litter production and
 decomposition $ TSP : (chr) Sampling point ID $ C.loss_Mi1 : (num) Carbon
 total decomposition (% of Closs) measured at the sampling point $
 C.loss_Ma1 : (num) Nitrogen total decomposition (% of Nloss) measured at
 the sampling point $ N.loss_Mi1 : (num) Carbon microbial decomposition (%
 of Closs) measured at the sampling point $ N.loss_Ma1 : (num) Nitrogen
 microbial decomposition (% of Nloss) measured at the sampling point $
 C.loss_CG : (num) Carbon decomposability (% of Closs) measured in the
 common garden experiment $ N.loss_CG : (num) Nitrogen decomposability (%
 of Nloss) measured in the common garden experiment $ lit.rich : (num)
 number of species in the littertrap $ neigh.sp.rich: (num) number of
 species in the 12 trees surrounding the sampling point $ fall : (num)
 total litterfall (g) $ plot : (chr) plot ID $ comp : (chr) plot species
 composition * df-2.csv: experimental data measured to identify
 species-specific litterfall drivers $ TSP : (chr) Sampling point ID $ plot
 : (chr) Plot ID $ spe.rich : (num) Plot species richness $ species : (chr)
 Species name $ litter.biomass : (num) Species specific litter biomass (g)
 $ biomass : (num) Species specific tree biomass (sum of neighboring trees
 biomass) $ dist : (num) Sum of inverse distances from the trees $ n.rich :
 (num) Species richness of the neighboring trees $ area : (num) Litter trap
 area (m2) $ litter.biomass.area : (num) Species specific litter biomass
 corrected for area (g/m2) $ SLA : (num) Species SLA $ LDMC : (num) Species
 leaf dry matter content $ C : (num) Species leaf carbon content $ N :
 (num) Species leaf nitrogen content $ Mg : (num) Species leaf magnesium
 content $ Ca : (num) Species leaf calcium content $ K : (num) Species leaf
 potassium content $ P : (num) Species leaf phosphorus content $ pca.1 :
 (num) Species leaf trait PCA axis 1 $ pca.2 : (num) Species leaf trait PCA
 axis 2 $ log.litter.biomass.area: (num) Log of litter per area biomass $
 log.biomass : (num) Log of tree biomass * df-3.csv: experimental data
 measured to identify decomposability drivers $ ID : (num) Sample ID $
 CG_ID : (chr) Common garden sample ID $ TSP : (chr) Sampling point ID $
 weight.bf : (num) Initial leaf mass content $ weight_af : (num) Final mass
 content $ ini.SLA : (num) CWM of SLA from initial leaf $ ini.LDMC : (num)
 CWM of SLA from initial leaf $ ini.C : (num) Initial carbon content (mg) $
 ini.N : (num) Initial nitrogen content (mg) $ ini.Mg : (num) Initial
 magnesium content (mg) $ ini.Ca : (num) Initial calcium content (mg) $
 ini.K : (num) Initial potassium content (mg) $ ini.P : (num) Initial
 phosphorus content (mg) $ var.SLA : (num) Variance between leaves SLA $
 var.LDMC : (num) Variance between leaves LDMC $ var.C : (num) Variance
 between leaves carbon content $ var.N : (num) Variance between leaves
 nitrogen content $ var.Mg : (num) Variance between leaves magnesium
 content $ var.Ca : (num) Variance between leaves calcium content $ var.K :
 (num) Variance between leaves potassium content $ var.P : (num) Variance
 between leaves phosphorus content $ CN : (num) Initial CN ratio $ NP :
 (num) Initial NP ratio $ compo.pca.1 : (num) first pca axis of initial
 nutrient contents $ compo.pca.2 : (num) second pca axis of initial
 nutrient contents $ div.pca.1 : (num) first pca axis of initial nutrient
 contents variance $ div.pca.2 : (num) first pca axis of initial nutrient
 contents variance $ var.x : (num) variance between species all chemical
 properties $ var.CNP : (num) variance between species CNP $ var.t : (num)
 variance between species all traits $ var.tough : (num) variance between
 species toughness traits (SLA LDMC) $ ash : (num) decomposed sample ash
 content $ dry.content : (num) sample dry content $ sample.C.mg.g : (num)
 decomposed sample carbon content (mg/g) $ sample.N.mg.g : (num) decomposed
 sample nitrogen content (mg/g) $ lit.rich : (num) litter species richness
 $ sample.C.mg : (num) decomposed sample nitrogen content (mg) $
 sample.N.mg : (num) decomposed sample nitrogen content (mg) $ sample.soil
 : (num) calculated soil content (g) $ sample.soil.C.mg: (num) calculated
 soil C content (mg) $ sample.soil.N.mg: (num) calculated soil C content
 (mg) $ sample.Cc.mg : (num) corrected litter C content (mg) $ sample.Nc.mg
 : (num) corrected litter N content (mg) $ sample.weigh : (num) corrected
 litter final mass (mg) $ C.loss : (num) carbon loss during decomposition
 (%) $ N.loss : (num) Nitrogen loss during decomposition (%) $ plot : (chr)
 Plot ID ## Sharing/Access information NA ## Code/Software R software, see
 Zenodo release for the related scripts.