DOI: 10.5061/dryad.pvmcvdnqc
title: Abiotic and biotic drivers of tree trait effects on soil microbial biomass and soil carbon concentration

description: Forests are critical ecosystems to understand the global carbon budget,
 due to their carbon sequestration potential in both above- and belowground
 compartments, especially in species-rich forests. Soil carbon
 sequestration is strongly linked to soil microbial communities, and this
 link is mediated by the tree community, likely due to modifications of
 micro-environmental conditions (i.e., biotic conditions, soil properties,
 and microclimate). We studied soil carbon concentration and the soil
 microbial biomass of 180 local neighborhoods along a gradient of tree
 species richness ranging from 1 to 16 tree species per plot in a Chinese
 subtropical forest experiment (BEF-China). Tree productivity and different
 tree functional traits were measured at the neighborhood level. We tested
 the effects of tree productivity, functional trait identity and
 dissimilarity on soil carbon concentrations, and their mediation by the
 soil microbial biomass and micro-environmental conditions. Our analyses
 showed a strong positive correlation between soil microbial biomass and
 soil carbon concentrations. Besides, soil carbon concentration increased
 with tree productivity and tree root diameter while it decreased with
 litterfall C:N content. Moreover, tree productivity and tree functional
 traits (e.g. root fungal association and litterfall C:N ratio) modulated
 micro-environmental conditions with substantial consequences for soil
 microbial biomass. We also showed that soil history and topography should
 be considered in future experiments and tree plantations, as soil carbon
 concentrations were higher where historical (i.e., at the beginning of the
 experiment) carbon concentrations were high, themselves being strongly
 affected by the topography. Altogether, these results imply that the
 quantification of the different soil carbon pools is critical for
 understanding microbial community–soil carbon stock relationships and
 their dependence on tree diversity and micro-environmental conditions.
description: 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), and it was planted
 in 2009 after a clear-cut of the previous commercial plantation. The
 region is characterized by a subtropical climate with warm, rainy summers
 and cool, dry winters with a mean temperature of 16.7°C and a mean annual
 rainfall of 1.821 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, Castanopsis eyrei, Daphniphyllum oldhamii,
 </em>and<em> Lithocarpus glaber </em>(Bruelheide et al.
 2011; 2014). Study
 design We selected 180 small-scale sample locations
 across five species richness levels (1, 2, 4, 8, and 16 species) per plot,
 according to the BEF China planting design (Appendix S1).
 These small-scale locations
 are local
 neighborhoods of
 trees defined as the twelve trees directly adjacent in
 the planting grid with two central
 trees (Appendix S1: Figs. S1-S2, see Tree Species Pairs in
 Trogisch et al.
 2021). Each local
 neighborhood was replicated three times in each richness
 level when available (see “broken stick” design, Bruelheide et al.
 2014). Plot
 topography To control for the topography
 effect on soil carbon concentration, topography measures were calculated
 from a digital elevation model (DEM). The DEM was interpolated in 2015
 from elevation measurements with a differential global positioning system
 (DGPS) using the ordinary kriging algorithm and a cell size of 5 m x 5 m.
 Slope, altitude, plan curvature (Curv. PL), and profile curvature (Curv.
 PR) were calculated from the DEM at the plot level due to the low
 intra-plot variability (Scholten et al. 2017).
 Microclimate
 modeling The daily air temperature was
 recorded using 35 data loggers (HOBO® Pro v2, U23-001) installed at 1 m
 height in the center of 35 plots across the experiment, while a
 meteorological station was set up in the central part of the experimental
 site (see Appendix S2:
 Fig. S1 for more details; Bruelheide et al. 2014). To cover our full
 experimental area, the air temperature was modeled for all of our
 experimental plots using the available logger data. We modeled the
 temperature measurements of the 35 data loggers (i.e., daily minimum,
 mean, and maximum temperature) as a function of the meteorological station
 measurements (i.e., daily temperature, rainfall, and solar radiation),
 plot topography (i.e., latitude, longitude, altitude, orientation, slope,
 plot curvature, and mean annual solar radiation), forest vertical
 stratification (i.e. effective number of layers index, “ENL”, see below)
 and plot species richness (see Appendix S2 for more details).
 Spatio-temporal trends for the whole experiment were estimated using
 Gaussian radial basis functions (functions “auto-basis”, “eval_basis” from
 the R package FRK, see Appendix S2: Section S1 and Wikle,
 Zammit-Mangion, and Cressie 2019). Our model fits explained more than 90%
 of the loggers' temperature measurement variability. The fitted
 models were used to predict daily minimum, mean, and maximum temperature
 for all experimental plots with a standard error from 0°C to 2°C during
 our sampling period (see Appendix S2 for the complete procedure).
 Field
 sampling Our field measurements were
 performed from mid-August to the end of September 2018, before the main
 litterfall season (from September to December; Huang et al. 2017). To
 avoid spatio-temporal autocorrelation, each day another sampling area was
 randomly chosen. To test the effect of biotic conditions on soil microbial
 biomass and carbon concentrations, understory plant cover in each location was estimated on a
 five-level factorial scale from 'no understory plant' to
 'mainly covered by understory plants'. Although this is a
 relatively coarse measurement, while being comparable with the Londo scale
 (Londo 1976), it allows considering the influence of understory vegetation
 which was shown to be of importance (Vockenhuber et al., 2011). We
 encourage subsequent studies to assess the understory vegetation in a more
 detailed way. Starting from the center of the two central trees of the local
 neighborhood, we extracted two soil cores with 5 cm diameter
 and 10 cm depth, 5 cm away from the center (Appendix S1: Fig. S2). Two additional cores of
 the same dimensions were taken 20 cm away from the center in the direction
 of each tree. A composite soil sample was built for soil analyses from
 these four soil cores and sieved with a 2 mm mesh. As a first measure of
 the biotic environment, root fragments contained in the sieving residues
 were air-dried at 40°C for two days and weighed (± 0.01 g), while the
 composite soil samples were stored at -20°C. To
 estimate the effect of biotic conditions and especially nutrient
 availability effect on soil microbial biomass and soil carbon
 concentration, the litter cover on the ground between the two central trees of each location was estimated on a
 five-level factorial scale from 'no-litter' to 'litter
 layer thicker than five centimeters'. Leaf litter was collected
 between the central trees
 from the ground excluding green understory plant residuals, air-dried at
 40°C for two days, and milled to powder. Carbon and nitrogen
 concentrations were measured by micro-combustion from a subsample of 4 mg
 (Elementar Vario El III analyzer, Elementar, Hanau, Germany).
 Soil
 analyses Soil moisture was measured from
 a subset of 25 g soil by drying the soil at 40 °C for two days. A
 subsample was used to quantify soil pH in a 1:2.5 soil-water solution.
 Soil total nitrogen (TN) was determined on an auto-analyzer (SEAL
 Analytical GmbH, Norderstedt, Germany) using the Kjeldahl method
 (Bradstreet 1954). Soil total phosphorus (TP) was measured after wet
 digestion with H2SO4 and HClO4 (Parkinson and Allen 1975) using a UV-VIS
 spectrophotometer (UV2700, SHIMADZU, Japan). Soil total organic carbon
 (TOC) was measured by a TOC Analyzer (Liqui TOC II; Elementar
 Analysensysteme GmbH, Hanau, Germany). TOC in 2010 was quantified in a
 previous study (Scholten et al. 2017) at the plot level using the
 micro-combustion method (Elementar Vario El III analyzer, Elementar,
 Hanau, Germany). Soil microbial biomass
 Soil microbial biomass was measured using phospholipid fatty acid
 (PLFA) analysis. PLFAs were extracted from 5 g of frozen soil following
 Frostegård, Tunlid, and Bååth (1991). Biomarkers were assigned to
 microbial functional groups according to Ruess and Chamberlain (2010)
 using markers to assign bacteria (gram-positive bacteria: i15:0, a15:0,
 i16:0, i17:0; gram-negative bacteria: cy17:0, cy19:0; general bacteria
 markers: 16:1ω5; 16:1ω7), arbuscular mycorrhizal fungi (20:1ω9), and
 saprophytic and ectomycorrhizal fungi (18:1ω9 and 18:2ω6,9, see Appendix
 S3). Tree
 biomass Tree biomass was predicted for
 all neighbor trees using
 tree basal area (BA) and species-specific allometric relationships
 estimated on the two
 central trees. (1) Circumference at breast height (CBH) was
 measured in September 2018 for all trees in order to calculate the
 basal area of these trees. Tree height was measured for the two central trees using a laser
 meter (4.9 ± 2.1 m, PLR 50C, Robert Bosch GmbH, Gerlingen, Germany), and
 tree biomass was calculated following Huang et al. (2018). BA and biomass
 of the central trees were
 used to estimate species-specific allometric BA-biomass relationships (see
 Appendix S4). (3) These species-specific allometric relationships were
 used to calculate the neighborhood biomass (i.e., sum of the twelve surrounding trees’
 biomass). Leaf
 traits For each tree species of the
 experiment, ten samples
 consisting of 10 to 25 pooled fresh leaves were collected across all
 diversity levels from mid-August to October 2018 (Davrinche and Haider
 2021). Each sample was dried at 80°C for two days and milled for 5 min at 26 shakes per second.
 Carbon and nitrogen concentrations were measured by micro-combustion from
 a subsample of 5 mg (Elementar Vario El III analyzer, Elementar, Hanau,
 Germany). Root
 traits Root functional traits were
 measured from BEF-China Site A from September to October 2013 using two to
 three tree individuals per species per diversity level. First-order roots
 were collected, cleaned, scanned, and analyzed by WinRHIZO (Regent
 Software, Canada). After measurements, roots were air-dried at 60°C for
 two days and weighed. Average RD (in mm) and SRL (in m.g-1) were
 calculated from the measurements of each species at all species richness
 levels (Bu et al. 2017). The mycorrhizal status of the tree species was
 determined from the literature (Haug et al. 1994; Hawley and Dames 2004;
 Wang and Qiu 2006). The mycorrhizal status was assumed from the literature
 and confirmed by recent measurements in the same experiment (Singavarapu
 et al. 2021). However, intraspecific root functional trait variability can
 be high and may change over the course of an experiment depending on the
 biotic context (e.g., Zuppinger-Dingley et al. 2014), which could not be
 considered in the present study. Root functional trait
 variables We considered three functional
 root traits that are related to soil processes (Bardgett, Mommer, and de
 Vries 2014): root diameter (RD), specific root length (SRL), and
 mycorrhizal tree association (i.e., AM or EM). For each location, trait identity and
 diversity were calculated at the neighborhood level. At the neighborhood
 level, we calculated community-weighted means (CWM, Garnier et al. 2004)
 and functional dispersion (FDis) – defined as the weighted variance of the
 trait values within the neighborhood (Laliberté and Legendre 2010). All
 measures were weighted using tree BA. Calculations were made using the
 'dbFD' function from the 'FD' package in R (Laliberté
 et al. 2014). Forest vertical
 stratification To quantify the forest
 vertical stratification and estimate crown complementarity, we computed
 the Effective Number of Layers (ENL, Ehbrecht et al. 2016) using
 terrestrial laser scanning measurements. A high ENL value indicates more
 evenly distributed layers, which can be an indication of higher crown
 complementarity and, thus, increase of canopy packing (Ehbrecht et al.
 2016). A terrestrial laser scanning campaign took place in February-March
 of 2019 using a FARO Focus S120 and a FARO Focus X130 laser scanner (FARO
 Europe, Korntal-Münchingen, Germany; for more information see
 Perles–Garcia et al. 2021). The scanner was set up on a tripod at 1.3 m
 height in the center of each plot and a fully three-dimensional point
 cloud (360° x 305° field of view) with a spatial resolution of 6 mm at a
 distance of 10 m was acquired. For each plot, the Effective Number of Layers
 (ENL) was computed. First,
 the scans were filtered using a statistical outlier removal filter (SOR,
 N=10, SD=3) in CloudCompare 2.9.1 software. Taking into account the
 dimensions of each plot (~667 m²), each point cloud was clipped in a 20 m
 square around the scan center (~400 m²). The point clouds were voxelized
 into a voxel grid of 5 cm voxels using the R package VoxR (Lecigne,
 Delagrange, and Messier 2018). Then, they were grouped in vertical slices
 of 50 cm and, for each slice, we quantified the proportion of filled
 voxels. The ENL was the result of calculating the inverse Simpson-Index,
 where n refers to the number of slices, calculated as (heightmax –
 heightmin ) / 50cm; and pi is the proportion of filled voxels of the ith
 slice. Leaf
 litterfall measurement From September to
 December 2018, the freshly fallen leaf litter between the two central trees of each location was collected in a 1 m2
 litter trap (1 cm mesh). The collected litter was identified to species
 level, air-dried at 40°C for two days, and weighed (± 0.01 g). Annual
 amounts of litter carbon (i.e. "Clitterfall") and nitrogen (i.e.
 "Nlitterfall") deposited on the ground were calculated using
 species-specific leaf carbon and nitrogen contents and species-specific
 litter mass collected in the traps. We calculated the litterfall carbon to
 nitrogen ratio (CNlitterfall) from these measurements.
description: # Reference Information ## Provenance for this README * File name:
 README.md * Authors: Rémy Beugnon * Other contributors: Wensheng Bu, Helge
 Bruelheide, Andra Davrinche, Jianqing Du, Sylvia Haider, Matthias Kunz,
 Goddert von Oheimb, Maria D. Perles-Garcia, Mariem Saadani, Thomas
 Scholten, Steffen Seitz, Bala Singavarapu, Stefan Trogisch, Yanfen Wang,
 Tesfaye Wubet, Kai Xue, Bo Yang, Simone Cesarz & Nico Eisenhauer. *
 Date created: 2022-12-06 * Date modified: 2022-12-06 ## Dataset Version
 and Release History * Current Version: * Number: 1.0.0 * Date: 2022-12-05
 * Persistent identifier: DOI: 10.5061/dryad.pvmcvdnqc * Summary of
 changes: n/a * Embargo Provenance: n/a * Scope of embargo: n/a * Embargo
 period: n/a ## Dataset Attribution and Usage * Dataset Title: Data for the
 article "Abiotic and biotic drivers of tree trait effects on soil
 microbial biomass and soil carbon concentration" * Persistent
 Identifier: * Dataset
 Contributors: * Creators: Rémy Beugnon, Wensheng Bu, Helge Bruelheide,
 Andra Davrinche, Jianqing Du, Sylvia Haider, Matthias Kunz, Goddert von
 Oheimb, Maria D. Perles-Garcia, Mariem Saadani, Thomas Scholten, Steffen
 Seitz, Bala Singavarapu, Stefan Trogisch, Yanfen Wang, Tesfaye Wubet, Kai
 Xue, Bo Yang, Simone Cesarz & Nico Eisenhauer. * Date of Issue: 2022 *
 Publisher: Ecological Monographs * Suggested Citations: * Dataset
 citation: &gt; Beugnon R., Bu W., Bruelheide H., Davrinche A., Du J.,
 Haider S., Kunz M., von Oheimb G., Perles-Garcia M., Saadini M., Scholten
 T., Seitz S., Singavarapu B., Trogisch S., Wang Y., Wubet T., Xue K., Yang
 B., Cesarz S., Eisenhauer N. 2022. Data for the article "Abiotic and
 biotic drivers of tree trait effects on soil microbial biomass and soil
 carbon concentration", Dryad, Dataset,
 ## Contact Information *
 Name: Rémy Beugno * Affiliations: German Centre for Integrative
 Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, 04103
 Leipzig, Germany Leipzig Institute for Meteorology, Universitt Leipzig,
 Stephanstrae 3, 04103 Leipzig, Germany CEFE, Univ Montpellier, CNRS, EPHE,
 IRD, 1919, route de Mende, F-34293 Montpellier Cedex 5, France * ORCID ID:
 * Email:
 {.email} * Alternate Email:
 {.email} * Address: e-mail
 preferred --- # Additional Dataset Metadata ## Acknowledgements * We
 gratefully acknowledge funding by the Deutsche Forschungsgemeinschaft
 (DFG, German Research Foundation -- 319936945/GRK2324) and the University
 of Chinese Academy Sciences (UCAS). We acknowledge the support of the
 TreeD research group, especially many local helpers involved in collecting
 the samples. We also thank the lab members of the Experimental Interaction
 Ecology group for their support, especially Alfred Lochner, Anja Zeuner,
 Alla Kavtea, and Linnea Smith for their help during the lab measurements.
 The Experimental Interaction Ecology group is supported by the German
 Centre for Integrative Biodiversity Research (iDiv). We gratefully
 acknowledge the support of iDiv funded by the German Research Foundation
 (DFG-- FZT 118, 202548816). NE acknowledges funding by the DFG (Ei
 862/29-1). ## Dates and Locations * Dates of data collection: Field data
 collected in 2018 * Geographic locations of data collection: The study
 site is located in south-east China nearby the town of Xingangshan
 (Jiangxi province, 29.08-29.11 N, 117.90-117.93 E). --- # Methodological
 Information * Methods of data collection/generation: see manuscript for
 details --- # Data and File Overview ## Summary Metrics * File count: 1 *
 Total file size: 75 KB * File formats: .csv ## Table of Contents *
 data.csv ## Setup * Unpacking instructions: n/a * Relationships between
 files/folders: n/a * Recommended software/tools: n/a --- # File/Folder
 Details ## Details for: data.csv * Description: a comma-delimited file
 containing the data used for the manuscript statistical analyses. *
 Format(s): .csv * Size(s): 75.21 KB * Dimensions: 155 rows x 48 columns *
 Variables: * TSP: sample name * Plot: plot name * Sp.rich: plot species
 richness * AM.ECM: neighborhood fungal association * SRL: neighborhood
 specific root length CWM (m/g) * RD: neighborhood root diameter CWM (m) *
 FDis.RD: neighborhood root diameter functional dissimilarity (m) *
 FDis.SRL: neighborhood specific root length functional dissimilarity (m/g)
 * FDis.AM.ECM: neighborhood fungal association functional dissimilarity *
 FDis: neighborhood root functional dissimilarity * ENL: canopy effective
 number of layers * neigh.biomass: neighborhood biomass (kg) *
 C.litterfall: carbon release during litterfall (g/g) * N.litterfall:
 nitrogen release during litterfall (g/g) * CN.litterfall: litterfall
 carbon to nitrogen ratio * pH: soil pH in 2018 * Soil.C.2018: soil carbon
 concentration in 2018 (g/g) * Soil.N.2018: soil nitrogen concentration in
 2018 (g/g) * Soil.P.2018: soil phosphorus concentration in 2018 (g/g) *
 Soil.C.2010: soil carbon concentration in 2010 (g/g) * Soil.N.2010: soil
 nitrogen concentration in 2010 (g/g) * mic.bio: soil microbial biomass
 (mg/g) * min.T: daily minimum temperature (degrees Celcius) * mean.T:
 daily average temperature (degrees Celcius) * max.T: daily maximum
 temperature (degrees Celcius) * min.T.week: weekly minimum temperature
 (degrees Celcius) * mean.T.week: weekly average temperature (degrees
 Celcius) * max.T.week: weekly maximum temperature (degrees Celcius) *
 Soil.humidity: soil relative humidity * litter.ab: ground litter abundance
 * litter.C: ground litter carbon content (g/g) * litter.N: ground litter
 nitrogen content (g/g) * litter.CN: ground litter carbon:nitrogen ratio *
 Plant.ab: plant abundance * root.ab: soil core root abundance * SLOPE:
 plot slope (degrees) * CURV_PR: plot curvature (degrees) * CURV_PL"
 plot curvature (degrees) * ALTITUDE: plot altitude (m) * temperature:
 first PCA axis of temperature variables * Missing data codes: NA