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: δ13C 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. δ13C 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: δ13C values of Isoleucine.
• Leu: δ13C values of Leucine. 
• Met: δ13C values of Methionine.
• Phe: δ13C values of Phenylalanine.
• Thr: δ13C values of Threonine.
• Val: δ13C values of Valine. 

File: Training_data.csv

Description: δ13C 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: δ13C values of Isoleucine.
• Leu: δ13C values of Leucine.
• Met: δ13C values of Methionine.
• Phe: δ13C values of Phenylalanine.
• Thr: δ13C values of Threonine.
• Val: δ13C 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 DiagnosticsMixsiarforestdetail.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 DiagnosticsMixsiargroup.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 DiagnosticsMixsiarRegion.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: δ15N 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 δ15N 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: δ13C 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 δ13C values of Isoleucine.
• Leu: Mean-standardized δ13C values of Leucine.
• Phe: Mean-standardized δ13C values of Phenylalanine.
• Thr: Mean-standardized δ13C values of Threonine.
• Val: Mean-standardized δ13C values of Valine.

File: Training_MixSIAR_noMet.csv

Description: δ13C values of training data normalized via central mean of eAAs for basal resources of fungi, plants, and bacteria. 

Variables

• source: Organism origin of δ13C values.
• MeanIle: Mean δ13C values of Isoleucine measured in fungi, plants, and bacteria.
• SDIle: Standard deviations of δ13C values of Isoleucine measured in fungi, plants, and bacteria.
• MeanLeu: Mean δ13C values of Leucine measured in fungi, plants, and bacteria.
• SDLeu: Standard deviations of δ13C values of Leucine measured in fungi, plants, and bacteria.
• MeanPhe: Mean δ13C values of Phenylalanine measured in fungi, plants, and bacteria.
• SDPhe: Standard deviations of δ13C values of Phenylalanine measured in fungi, plants, and bacteria.
• MeanThr: Mean δ13C values of Threonine measured in fungi, plants, and bacteria.
• SDThr: Standard deviations δ13C values of Threonine measured in fungi, plants, and bacteria.
• MeanVal: Mean δ13C values of Valine measured in fungi, plants, and bacteria.
• SDVal: Standard deviations δ13C 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/).

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.