DOI: 10.5061/dryad.2ngf1vj03
title: Data from: Fungal energy channeling sustains soil animal communities across forest types and regions

description: Emerging evidence suggests that microbivory prevails in soil animal
 communities, yet the relative importance of bacteria, fungi, and plants as
 basal resource energy channels across taxa and forest types remains
 unstudied. We developed a novel framework combining stable isotope
 analysis of essential amino acids (eAAs) and energy fluxes to quantify
 basal resource contributions and trophic positions of meso- and macrofauna
 detritivores (Collembola, Oribatida, Diplopoda, Isopoda, Lumbricidae) and
 predators (Mesostigmata, Chilopoda) in 48 forest sites of different
 management intensity across Germany. Fungal energy channeling dominated,
 with the highest energy fluxes and 73 % fungal eAAs across forests and
 regions. Chilopoda, however, acquired more energy from bacteria and
 plants. Energy fluxes to Lumbricidae were highest, but decreased,
 alongside those to other macrofauna, in acidic forests. Trophic positions
 varied between regions, reflecting changes in community structure linked
 to regional factors. Our findings highlight the stability and pivotal role
 of fungal energy channeling for forest soil animal communities.
description: # Data from: Fungal energy channeling sustains soil animal communities
 across forest types and regions [https://doi.org/10.5061/dryad.2ngf1vj03
 ](https://doi.org/10.5061/dryad.2ngf1vj03) ## Description of the data and
 file structure Metadata, biomass, and essential amino acid data for soil
 animal communities (Lumbricidae, Oribatida, Collembola, Diplopoda,
 Chilopoda, Mesostigmata) sampled from 48 forest sites in northern,
 central, and southern Germany. Descriptions indicate the files needed to
 reproduce specific figures and results. Please note that the file
 "Assimilation efficiency resources.xlsx" is not required for the
 analysis and given as xlsx to show formulas for interested readers. All
 statistical analyses have been conducted in R version 4.4.3 (R Core Team
 2025). ### Files and variables Please note that some special characters,
 such as σ, may show up as ? in the data files. #### File:
 C\_standardized\_Final.csv **Description:** *δ*13C values of amino acids
 measured using a Thermo Trace GC 1310 gas chromatograph linked via a GP
 interface to a delta plus mass spectrometer (Thermo, Bremen, Germany)
 equipped with an Agilent J&W VF-35 ms GC column (30 m ⨯ 0.32 mm ⨯ 1.00
 µm). Carbon isotope ratios were corrected for added carbon during
 derivatization following O'Brien et al. (2002) and expressed relative
 to Vienna Pee Dee Belemnite. Samples and standards were measured in
 triplicate, and means are reported. *δ*13C values in this file reproduce
 Figure 1. After normalization via the central mean of eAAs in R, they are
 further used in mixing models to reconstruct the proportion of basal
 resources to consumer diets (Fig.2). ##### Variables * Plot: Unique
 identifier for the sampling site. * Region: The three sampling regions in
 northern, central, and southern Germany. * forest_type_detail: Forest
 types of unmanaged beech forests (Beech150), old managed beech forests
 (Beech70), young managed beech forests (Beech30), and coniferous forests
 (Coniferous) * forest_type: Classification of sampling sites in deciduous
 forests (Beech) and coniferous forests (Coniferous). * Consumer: Latin
 order names of soil animal groups. * ID: Running sample number used during
 the extraction of amino acids. * type: Clarifying whether the sample
 contains consumer or basal resources. Only consumers are present in this
 file. * Ile: *δ*13C values of Isoleucine. * Leu: *δ*13C values of
 Leucine.  * Met: *δ*13C values of Methionine. * Phe: *δ*13C values of
 Phenylalanine. * Thr: *δ*13C values of Threonine. * Val: *δ*13C values of
 Valine.  #### File: Training\_data.csv **Description:** *δ*13C values of
 amino acids from basal resources of fungi, plants, and bacteria used as
 training data for the stable isotope fingerprinting (see Methods). The
 output of the training data LDA is shown in Fig. S2.  ##### Variables *
 Identifier 1: Unique identifier for each sample, note that leaf litter
 samples measured in this study are labeled as BE (beech) and CO
 (coniferous). All other training data stem from Larsen et al. (2013, 2016)
 and Pollierer et al. (2020) * Ile: *δ*13C values of Isoleucine. *
 Leu: *δ*13C values of Leucine. * Met: *δ*13C values of Methionine. *
 Phe: *δ*13C values of Phenylalanine. * Thr: *δ*13C values of Threonine. *
 Val: *δ*13C values of Valine. * Plot: Unique identifier for the sampling
 site. * Month: The type of organisms amino acids were extracted from. *
 Species: Organisms from which amino acids were extracted from classified
 into algae, plants, fungi, and bacteria. Note that algae were excluded
 from the analysis.  * type: Clarifying whether the sample contains
 consumer or basal resources. Only resource data (training) in this file.
 #### File: Biomasses\_complete.csv **Description:** Fresh weight estimates
 derived using group-specific length and width mass regressions for each
 soil animal group (see Table S3 in the supporting information). Fresh
 weights were derived for each species, multiplied by their density per
 site, and finally multiplied by a conversion factor to obtain total
 biomass per m². ##### Variables * PlotID: Unique identifier for the
 sampling site. * forest_type: Forest types of unmanaged beech forests
 (Beech150), old managed beech forests (Beech70), young managed beech
 forests (Beech30), and coniferous forests (Coniferous). * Region: The
 three sampling regions in Germany [Swabian Alb (Alb), Hainich Dün (Hai),
 Schorfheide Chorin (Sch)]. * Region2: The three sampling regions according
 to their location in northern, central, and southern Germany. *
 Fresh_*mg_m2*_*adult*_oribatida: Fresh weight estimates in mg per square
 meter for all adult oribatid mites. *
 Fresh_*mg_m2*_*juvenile*_oribatida: Fresh weight estimates in mg per
 square meter for all juvenile oribatid mites. *
 Fresh_*mg_m2*_*Diplopoda: *Fresh weight estimates in mg per square meter
 for all Diplopoda. * Fresh_*mg_m2*_*Isopoda:* Fresh weight estimates in mg
 per square meter for all Isopoda. * Fresh_*mg_m2*_Lumbricidae: *Fresh
 weight estimates in mg per square meter for all Lumbricidae. *
 Fresh_*mg_m2*_*Collembola: *Fresh weight estimates in mg per square meter
 for all Collembola. * Fresh_*mg_m2*_*Mesostig: *Fresh weight estimates in
 mg per square meter for all Mesostigmata. * Fresh_*mg_m2*_*Chilopoda:
 *Fresh weight estimates in mg per square meter for all Chilopoda. ####
 File: Mixsiar\_forest\_detail.csv **Description:** Proportional estimates
 of essential amino acids from basal resources of fungi, plants, and
 bacteria to soil animal communities in different forest types quantified
 by Bayesian mixing models implemented in the MixSIAR package (see methods
 for details). The model diagnostics are uploaded as
 Diagnostics*Mixsiarforest*detail.txt. Proportional estimates are
 visualized in Fig.S4c. ##### Variables * treat: Latin names for soil
 animal orders * resource: Forest types of unmanaged beech forests
 (Beech150), old managed beech forests (Beech70), young managed beech
 forests (Beech30), and coniferous forests (Coniferous). * source: Basal
 resources of fungi, plants, and bacteria. * Mean: Mean of the relative
 proportion of essential amino acids acquired from fungi, plants, and
 bacteria.  * SD: Standard deviation of the relative proportion of
 essential amino acids acquired from fungi, plants, and bacteria.  * 2.50%:
 2.50 % credible intervals. * 5%: 5 % credible intervals. * 25%: 25 %
 credible intervals * 50%: 50 % credible intervals * 75%: 75 % credible
 intervals * 95%: 95 % credible intervals * 97.50%: 97.50 % credible
 intervals #### File: Diagnostics\_Mixsiar\_forest\_detail.txt
 **Description:** The Gelman-Rubin diagnostics of the Bayesian mixing model
 with forest type and soil animal group as factors. The Gelman-Rubin
 diagnostic was used to assess the convergence of Markov Chain Monte Carlo
 (MCMC) simulations in Bayesian mixing models. Gelman-Rubin diagnostics of
 all variables were below 1.05, indicating no model convergence issues (see
 Methods for details).  #### File: Diagnostics\_Mixsiar\_Region.txt
 **Description:** The Gelman-Rubin diagnostics of the Bayesian mixing model
 with region and soil animal group as factors. The Gelman-Rubin diagnostic
 was used to assess the convergence of Markov Chain Monte Carlo (MCMC)
 simulations in Bayesian mixing models. Gelman-Rubin diagnostics of all
 variables were below 1.05, indicating no model convergence issues (see
 Methods for details).  #### File: Diagnostics\_Mixsiar\_group.txt
 **Description:** The Gelman-Rubin diagnostics of the Bayesian mixing model
 with soil animal group as a factor. The Gelman-Rubin diagnostic was used
 to assess the convergence of Markov Chain Monte Carlo (MCMC) simulations
 in Bayesian mixing models. Gelman-Rubin diagnostics of all variables were
 below 1.05, indicating no model convergence issues (see Methods for
 details). #### File: Mixsiar\_group.csv **Description:** Proportional
 estimates of essential amino acids from basal resources of fungi, plants,
 and bacteria to soil animal communities across forest types and regions
 quantified by Bayesian mixing models implemented in the MixSIAR package
 (see methods for details). The model diagnostics are uploaded as
 Diagnostics*Mixsiargroup*.txt. Proportional estimates are visualized in
 Fig.2 and Fig. S4a. ##### Variables * treat: Latin names for soil animal
 orders * source: Basal resources of fungi, plants, and bacteria. * Mean:
 Mean of the relative proportion of essential amino acids acquired from
 fungi, plants, and bacteria.  * SD: Standard deviation of the relative
 proportion of essential amino acids acquired from fungi, plants, and
 bacteria.  * 2.50%: 2.50 % credible intervals. * 5%: 5 % credible
 intervals. * 25%: 25 % credible intervals * 50%: 50 % credible intervals *
 75%: 75 % credible intervals * 95%: 95 % credible intervals * 97.50%:
 97.50 % credible intervals #### File: Mixsiar\_Region.csv
 **Description:** Proportional estimates of essential amino acids from
 basal resources of fungi, plants, and bacteria to soil animal communities
 in three different regions of Germany were quantified by Bayesian mixing
 models implemented in the MixSIAR package (see methods for details). The
 model diagnostics are uploaded as Diagnostics*MixsiarRegion*.txt.
 Proportional estimates are visualized in Fig. S4b. ##### Variables *
 treat: Latin names for soil animal orders * resource: The three sampling
 regions in northern, central, and southern Germany. * source: Basal
 resources of fungi, plants, and bacteria. * Mean: Mean of the relative
 proportion of essential amino acids acquired from fungi, plants, and
 bacteria.  * SD: Standard deviation of the relative proportion of
 essential amino acids acquired from fungi, plants, and bacteria.  * 2.50%:
 2.50 % credible intervals. * 5%: 5 % credible intervals. * 25%: 25 %
 credible intervals * 50%: 50 % credible intervals * 75%: 75 % credible
 intervals * 95%: 95 % credible intervals * 97.50%: 97.50 % credible
 intervals #### File: AA\_discrimination\_noMet.csv **Description:** We
 assume direct routing, so all discrimination factors were set to 0 (See
 Manlick & Newsome, 2022). This file is needed to run the mixing
 models. ##### Variables * source: Basal resources of fungi, plants, and
 bacteria. * MeanIle: Mean discrimination factor for Isoleucine. * SDIle:
 Standard deviation of the discrimination for Isoleucine. * MeanLeu: Mean
 discrimination factor for Leucine.  * SDLeu: Standard deviation of the
 discrimination for Leucine. * MeanPhe: Mean discrimination factor for
 Phenylalanine.  * SDPhe: Standard deviation of the discrimination factor
 for Phenylalanine. * MeanThr: Mean discrimination factor for Threonine. *
 SDThr: Standard deviation of the discrimination factor for Threonine. *
 MeanVal: Mean discrimination for Valine.  * SDVal: Standard deviation of
 the discrimination factor for Valine. #### File: bipartite\_group.csv
 **Description:** This file contains identical means and standard
 deviations of proportional estimates of essential amino acids from basal
 resources of fungi, plants, and bacteria as the file
 "Mixsiar_group.xlsx". However row and column structure was
 changed to allow plotting with the bipartite package. Data in this file
 was used to produce Figure 2.  ##### Variables * :Basal resources of
 fungi, plants, and bacteria (The column needs to be empty). *
 Mesostigmata: Mean proportional estimates of essential amino acids from
 basal resources of fungi, plants, and bacteria to Mesostigmata. *
 Mesostigmata_SD: Standard deviations of proportional estimates of
 essential amino acids from basal resources of fungi, plants, and bacteria
 to Mesostigmata. * Collembola: Mean proportional estimates of essential
 amino acids from basal resources of fungi, plants, and bacteria to
 Collembola. * Collembola_SD: Standard deviation of proportional estimates
 of essential amino acids from basal resources of fungi, plants, and
 bacteria to Collembola. * Isopoda: Mean proportional estimates of
 essential amino acids from basal resources of fungi, plants, and bacteria
 to Isopoda. * Isopoda_SD: Standard deviations of proportional estimates of
 essential amino acids from basal resources of fungi, plants, and bacteria
 to Isopoda. * Chilopoda: Mean proportional estimates of essential amino
 acids from basal resources of fungi, plants, and bacteria to Chilopoda. *
 Chilopoda_SD: Standard deviations of proportional estimates of essential
 amino acids from basal resources of fungi, plants, and bacteria to
 Chilopoda. * Diplopoda: Mean proportional estimates of essential amino
 acids from basal resources of fungi, plants, and bacteria to Diplopoda. *
 Diplpoda_SD: Standard deviations of proportional estimates of essential
 amino acids from basal resources of fungi, plants, and bacteria to
 Diplopoda. * Lumbricidae: Mean proportional estimates of essential amino
 acids from basal resources of fungi, plants, and bacteria to Lumbricidae.
 * Lumbricidae_SD: Standard deviations of proportional estimates of
 essential amino acids from basal resources of fungi, plants, and bacteria
 to Lumbricidae #### File: TP\_measurements\_R.csv **Description:** *δ*15N
 values of amino acids measured using a Thermo Trace GC 1310 gas
 chromatograph linked via a GP interface to a delta plus mass spectrometer
 (Thermo, Bremen, Germany) equipped with an Agilent J&W VF-35 ms GC
 column (30 m ⨯ 0.32 mm ⨯ 1.00 µm). Nitrogen isotope ratio of AAs was
 expressed relative to atmospheric N by normalizing measured values (vs.
 reference gas) using scales derived from known *δ*15N values (versus
 atmospheric N) in AAs of derivatized standard mixtures. Samples and
 standards were measured in triplicate, and means are reported. Data in
 this file was used to produce Fig.5. ##### Variables * PlotID: Unique
 identifier for the sampling site. - forest_*type_*detail Forest types of
 unmanaged beech forests (B150), old managed beech forests (B70), young
 managed beech forests (B30), and coniferous forests (Coniferous). *
 forest_type: Classification of sampling sites in deciduous forests (Beech)
 and coniferous forests (Coniferous). * Consumer: Latin order names of soil
 animal groups. * ID: Running number used during the extraction of amino
 acids. * Mean 15N/14N Glx: Mean stable nitrogen isotope ratio of Glutamic
 acid. * Mean 15N/14N Phe: Mean stable nitrogen isotope ratio of
 Phenylalanine. * Std.Dev 15N/14N Glx: Standard deviation of isotope ratio
 of Glutamic acid. * Std.Dev 15N/14N Phe: Standard deviation of isotope
 ratio of Phenylalanine. * TP: Trophic position, see methods for the
 formula. * ß: Mean ß-values measured from leaf litter in our study sites
 for beech forests and coniferous forests. * σ² glx: Squared Mean 15N/14N
 Glx. * σ² phe: Squared Mean 15N/14N Phe. * σ ß: Standard deviation of ß. *
 σ² ß: Sqaured standard deviation of ß. * {}: (Mean 15N/14N Glx*(Mean
 15N/14N Glx-Mean 15N/14N Phe+ß))², used to calculate the uncertainty of
 TP.  * σ TP: Uncertainty of TP using the trophic discrimination factor
 (TDF = 7.56) as follows: (1/7.56)² ~ 0.0175: SQRT(0.0175*σ² glx+0.0175σ²
 phe+0.0175σ² ß{}**(σ²TDF)). Formulas can be traced back in the CSV.  * σ²
 TP: Squared uncertainty of TP.  #### File:
 consumer\_MixSIAR\_noMet\_norm.csv **Description:** *δ*13C values
 normalized via central mean of eAAs of essential amino acids from soil
 animal communities to run the mixing models.  ##### Variables * : Row
 number * Plot: Unique identifier for the sampling site. * Region: The
 three sampling regions in northern, central, and southern Germany. *
 forest_type_detail: Forest types of unmanaged beech forests (Beech150),
 old managed beech forests (Beech70), young managed beech forests
 (Beech30), and coniferous forests (Coniferous) *
 forest_type: Classification of sampling sites in deciduous forests (Beech)
 and coniferous forests (Coniferous). * Consumer: Latin order names of soil
 animal groups. * ID: Running sample number used during the extraction of
 amino acids. * type: Clarifying whether the sample contains consumer or
 basal resources. Only consumers are present in this file. * Ile:
 Mean-standardized *δ*13C values of Isoleucine. * Leu:
 Mean-standardized *δ*13C values of Leucine. *
 Phe: Mean-standardized *δ*13C values of Phenylalanine. * Thr:
 Mean-standardized *δ*13C values of Threonine. * Val:
 Mean-standardized *δ*13C values of Valine. #### File:
 Training\_MixSIAR\_noMet.csv **Description:** *δ*13C values of training
 data normalized via central mean of eAAs for basal resources of fungi,
 plants, and bacteria.  ##### Variables * source: Organism origin of *δ*13C
 values. * MeanIle: Mean *δ*13C values of Isoleucine measured in fungi,
 plants, and bacteria. * SDIle: Standard deviations of *δ*13C values of
 Isoleucine measured in fungi, plants, and bacteria. * MeanLeu: Mean *δ*13C
 values of Leucine measured in fungi, plants, and bacteria. *
 SDLeu: Standard deviations of *δ*13C values of Leucine measured in fungi,
 plants, and bacteria. * MeanPhe: Mean *δ*13C values of Phenylalanine
 measured in fungi, plants, and bacteria. * SDPhe: Standard deviations
 of *δ*13C values of Phenylalanine measured in fungi, plants, and bacteria.
 * MeanThr: Mean *δ*13C values of Threonine measured in fungi, plants, and
 bacteria. * SDThr: Standard deviations *δ*13C values of Threonine measured
 in fungi, plants, and bacteria. * MeanVal: Mean *δ*13C values of Valine
 measured in fungi, plants, and bacteria. * SDVal: Standard
 deviations *δ*13C values of Valine measured in fungi, plants, and
 bacteria. n: number of fungi, plants, and bacteria measured  #### File:
 SilviculturalManagementIndex.csv **Description:** Indicator of
 Silvicultural Management Intensity (SMI) taken from (Schall & Ammer
 2013,2023; see methods). Results shown in Fig. S1. ##### Variables * SMI:
 Silvicultural Management Intensity calculated from stand age, proportion
 of non-native tree species, and the deviation of the actual stocking from
 a fully stocked mature forest * year: The year which SMI was conducted  *
 Plot: Identifier of the investigated forest site * Forest: Forest types of
 different management intensity ranging from unmanaged beech forests
 (Beech150), old managed beech forests (Beech70), young managed beech
 forests (Beech30), and coniferous forests (Coniferous), see methods for
 details.  #### File: Assimilation efficiency resources.xlsx
 **Description:** Excel file with nitrogen contents (%) of Plants (leaf
 litter collected from our study sites; Sheet 1), Fungi (Sheet 2), and
 Bacteria (Sheet 3) used to calculate resource-specific assimilation
 efficiencies for consumers. Nitrogen contents of Fungi and Bacteria were
 taken from the literature (see methods). This file is not directly needed
 to reproduce the results. The results of these calculations are given in
 the R-Script. Resource assimilation efficiencies have been calculated
 based on the nitrogen content (%) of bacteria, fungi, and plants following
 the formula proposed by Jochum et al. (2017; see methods). The resource
 assimilation efficiencies, the proportional estimates of basal resource
 contributions from the mixing models, and the biomasses of soil animals
 are needed to reproduce Fig.3 and Fig.4.  #### File:
 Junggebauer\_et\_al\_2025\_ELE\_Rev2.R **Description:** Fully annotated
 R-code to reproduce all results presented in the manuscript. The code is
 identical to the one uploaded to Zenodo.  ## Code/software All data were
 analyzed in R v 4.4.3 (R Core Team, 2025) using the provided R-Script.
 Please note that running Bayesian mixing models with MixSIAR requires JAGS
 on your machine (please see
 here: [https://mcmc-jags.sourceforge.io/](https://mcmc-jags.sourceforge.io/)). ## Access information Data was derived from the following sources: * SMI: Schall, P. & Ammer, C. (2023). SMI annual dynamics - Silvicultural Management Intensity Dynamics on all forest EPs, 2008 - 2020. Version 9. Biodiversity Exploratories Information System. Dataset ID= 31217 * Chapin, F.S., Matson, P.A. & Vitousek, P.M. (2011). *Principles of terrestrial ecosystem ecology. *2nd edn. Springer, New York. * Chen, G., Yuan, J., Wang, S., Liang, Y., Wang, D. & Zhu, Y.* et al. *(2023). Soil and microbial C:N:P stoichiometry play a vital role in regulating P transformation in agricultural ecosystems. *Pedosphere*, 34, 44-41. * Larsen, T., Ventura, M., Andersen, N., O’Brien, D.M., Piatkowski, U. & McCarthy, M.D. (2013). Tracing carbon sources through aquatic and terrestrial food webs using amino acid stable isotope fingerprinting. *PLoS One*, 8, e73441. * Larsen, T., Pollierer, M.M., Holmstrup, M., D’Annibale, A., Maraldo, K. & Andersen, N.* et al. *(2016). Substantial nutritional contribution of bacterial amino acids to earthworms and enchytraeids: A case study from organic grasslands. *Soil. Biol. Biochem.*, 99, 21–27. * Pollierer, M.M., Scheu, S. & Tiunov, A.V. (2020). Isotope analyses of amino acids in fungi and fungal-feeding Diptera larvae allow differentiating ectomycorrhizal and saprotrophic fungi‐based food chains. *Funct. Ecol.*, 34, 2375–2388. * Reiners, W.A. (1986). Complementary Models for Ecosystems. *Am. Nat.*, 127, 59–73. * Richter, A., Schöning, I., Kahl, T., Bauhus, J. & Ruess, L. (2018). Regional environmental conditions shape microbial community structure strongly than local forest management intensity. *For. Ecol. Manag.*, 409, 250–259. * Zhang, J. & Elser, J.J. (2017). Carbon:Nitrogen:Phosphorus Stoichiometry in Fungi: A Meta-Analysis. *Front. Microbiol.*, 8, 1281. ## Version changes: 11-apr-2025: In the previous version, reading the "consumer_MixSIAR_noMet_norm.csv" file in R to run the mixing models resulted in an error because the columns were separated by semicolons rather than commas. We therefore replaced all the semicolons with commas and reuploaded the file to ensure full reproducibility.