DOI: 10.5061/dryad.0rxwdbsd1
title: The use of plant, bacterial and fungal resources in soil food webs of ecto- and arbuscular mycorrhiza-dominated deciduous forests

description: Soil food webs, driven by complex interactions among plants, microbes, and
 invertebrates, are crucial for carbon and nutrient cycling in forest
 ecosystems. Over the last decade, it has become evident that forests
 dominated by arbuscular mycorrhizal fungi (AMF) or ectomycorrhizal fungi
 (EMF) differ in their litter chemistry and microbial community
 composition, leading to different carbon and nutrient cycling. Still, the
 role of soil invertebrates in soil food webs of AMF- and EMF-dominated
 forests remains undescribed. Here, we tested whether trophic positions
 (TP) and basal resources of nine soil invertebrate groups (Araneae,
 Chilopoda, Collembola, Diplopoda, Diptera, Isopoda, Lumbricina,
 Mesostigmata, and Oribatida) are different in AMF- (maple and/or ash) and
 EMF-dominated (beech and/or oak) deciduous forests. We addressed this
 question using compound-specific stable isotope analysis of carbon and
 nitrogen in amino acids (CSIA-AA) – a novel method that allows precise
 estimation of the TP and basal resource use (bacteria, fungi, plants) of
 consumers in field conditions. Plant- and microbial-derived resources of
 soil invertebrates generally varied little between AMF and EMF-dominated
 forests. We only found a slight tendency of soil invertebrates in
 AMF-dominated forests relying more on fungal-derived resources (ca. 2%
 difference) and more on bacteria-derived resources in EMF-dominated
 systems (ca. 2% difference). Most of the variation in resource use was
 explained by the taxonomic identity of invertebrates, independently of the
 forest type and location. Mesofauna, particularly Collembola and
 Oribatida, were key consumers of fungi (relative contribution 49-72.1%),
 while macrofauna (except earthworms) relied more on plant resources
 (relative contribution 42.1-58.9%), suggesting that different size
 compartments of soil food webs predominantly use different basal
 resources. Earthworms and Myriapoda (Chilopoda and Diplopoda) had the
 highest proportions of bacteria-derived essential amino acids (relative
 contribution 23.2-35.2%). TPs of taxa remained largely consistent across
 mycorrhizal types (except for an elevated TP of Isopoda in EMF-dominated
 forests), suggesting similar vertical organisation of soil food webs
 across the studied forests. Overall, our study shows that TPs and basal
 resource use of soil fauna vary little across temperate deciduous forests,
 being mainly explained by the faunal taxonomic identity.
description: # The use of plant, bacterial and fungal resources in soil food webs of
 ecto- and arbuscular mycorrhiza-dominated deciduous forests Dataset DOI:
 [10.5061/dryad.0rxwdbsd1](10.5061/dryad.0rxwdbsd1) ## Description of the
 data and file structure Data was collected to analyze the use of different
 basal resources and trophic position of nine soil invertebrate species
 using compound-specific isotope analysis of amino acids Datasets included:
 (1) AA_discrimination_4EAA (2) consumer_MixSIAR-4EAA_norm (3)
 Training_4EAA_MixSIAR (4) training_data (5) TP_measurements ### Files and
 variables #### (1) AA_discrimination_4EAA.xlsx **Description**: We assume
 direct routing, so all discrimination factors were set to 0 (see Manlick
 & Newsome, 2022). A file is needed for mixing models. **Variables**:
 **source** - Basal resources of bacteria, fungi, and plants **MeanLeu** -
 Mean discrimination factor for leucine **SDLeu** - Standard deviations of
 the discrimination for leucine **MeanMet** - Mean discrimination factor
 for methionine **SDMet** - Standard deviations of the discrimination for
 methionine **MeanPhe** - Mean discrimination factor for phenylalanine
 **SDPhe** - Standard deviations of the discrimination for phenylalanine
 **MeanThr** - Mean discrimination factor for threonine **SDThr** -
 Standard deviations of the discrimination for threonine #### (2)
 consumer_MixSIAR_4EAA_norm.xlsx **Description**: δ13C values normalized
 via the central mean of eAAs of essential amino acids from soil animal
 communities to run the mixing models. **Variables**: **SampleID** -
 Running number used during the extraction of amino acids **AnimalGroup** -
 Names of animal groups **Site** - Site ID **MycType** - Associated
 mycorrhizal type (AMF: arbuscular mycorrhiza; EMF: ectomycorrhiza) **Leu**
 - Mean-standardized δ13C values of leucine (Leu) **Met** -
 Mean-standardized δ13C values of methionine (Met) **Phe** -
 Mean-standardized δ13C values of phenylalanine (Phe) **Thr** -
 Mean-standardized δ13C values of threonine (Thr) #### (3)
 Training_4EAA_MixSIAR.xlsx **Description**: δ13C values of training data
 normalized via the central mean of eAAs for basal resources of bacteria,
 fungi, and plants. **Variables**: **source** - Organism origin of δ13C
 values **MeanLeu** - Mean δ13C values of leucine measured in bacteria,
 fungi, and plants **SDLeu** - Standard deviations of δ13C values of
 leucine measured in bacteria, fungi and plants **MeanMet** - Mean δ13C
 values of methionine measured in bacteria, fungi, and plants **SDMet** -
 Standard deviations of δ13C values of methionine measured in bacteria,
 fungi and plants **MeanPhe** - Mean δ13C values of phenylalanine measured
 in bacteria, fungi, and plants **SDPhe** - Standard deviations of δ13C
 values of phenylalanine measured in bacteria, fungi and plants **MeanThr**
 - Mean δ13C values of threonine measured in bacteria, fungi, and plants
 **SDThr** - Standard deviations of δ13C values of threonine measured in
 bacteria, fungi and plants **n** - Number of bacteria, fungi, and plants
 measured **(4) training_data.xlsx** **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). **Variables**:
 **Identifier.1** - Unique identifier for each sample. Data labelled with
 OBS are site-specific litter data derived for this study. All other
 training data stem from Larsen et al. (2013, 2016), Pollierer et al.
 (2020), and Junggebauer et al. (2025) **Ile** - δ13C values of isoleucine
 (Ile) **Leu** - δ13C values of leucine (Leu) **Met** - δ13C values of
 leucine (Leu) **Phe** - δ13C values of leucine (Leu) **Thr** - δ13C values
 of leucine (Leu) **Val** - δ13C values of valine (Val) **Plot** -
 Identifier for the sampling site **Month** - The type of organism amino
 acids were extracted from **Species** - Organisms from which amino acids
 were extracted. Note that algae, coniferous trees, herbaceous plants, and
 mycorrhiza were excluded from the analysis. **(5) TP_measurements.xlsx**
 **Description**: δ15N values of amino acids measured using a Thermo Trace
 gas-chromatograph connected through a GP interface to a DeltaPlus mass
 spectrometer (Thermo, Bremen, Germany), which was equipped with an Agilent
 J&W VF-35ms GC column (30m x 0.32mm, film thickness 1 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. The data in this file was used to produce Figure 3.
 **Variables**: **SampleID** - Running number used during the extraction of
 amino acids **AnimalGroup** - Names of animal groups **Site** - Site ID
 **MycType** - Associated mycorrhizal type (AMF: arbuscular mycorrhiza;
 EMF: ectomycorrhiza) **glx** - Mean stable nitrogen isotope ratio of
 glutamic acid (glx) **SDglx** - Standard deviation of isotope ratio of glx
 **phe** - Mean stable nitrogen isotope ratio of phenylalanine (phe)
 **SDphe** - Standard deviation of isotope ratio of phe **σ² glx** -
 Squared mean 15N/14N glx **σ² phe** - Squared mean 15N/14N phe **β** -
 Mean β-values measured from site-specific leaf litter samples **{}** -
 (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 = 7.6) as follows: (1/7.6)² ~ 0.0173:
 SQRT(0.0173*σ² glx + 0.0173*σ² phe + 0.0173*σ² ß + {}*(σ²TDF)). **σ² TP**
 - Squared uncertainty of TP **TPNew** - Trophic position, see method
 section for formula ## Code/software (copy from "Data analyses"
 part in the manuscript) All statistical analyses (Table 1) were performed
 in R v.4.3.0 (R Core Team, 2023). To analyse the effect of soil
 invertebrate group and mycorrhizal type on the use of different basal
 resources (bacteria, fungi, plants), we ran a multivariate analysis of
 variance (MANOVA) on the linear discriminant analysis (LDA)
 classification, which was based on the δ13C values of the eAAs including
 *Leu*, *Phe*, *Met*, and *Thr* (Figure S1). To estimate the relative
 contribution of bacteria, fungi, and plants to the diet of consumers,
 Bayesian mixing models were used based on eAAs δ¹³C values centred to the
 mean value of all eAAs in a sample (“MixSIAR” package; Stock et al.,
 2018). The normalized δ¹³C values of *Ile* and *Val* in consumers fell
 outside the range observed in resources and thus beyond the bounds of the
 mixing space, leading to their exclusion from the mixing model (Figure
 S2). These deviations may result from analytical challenges, such as
 co-elution with nearby peaks (*Ile*) and instability in early
 chromatographic regions (*Val*) - or from metabolic processes, as both
 amino acids share similar pathways (Vane et al. 2025). Instead, we focused
 on the four eAAs: *Leu*, *Thr*, *Phe*, and *Met*. For the classification
 of basal resource contribution, we used training data from Larsen et al.,
 (2009, 2016) and Pollierer et al., (2020). We further supplemented the
 training data for plants with site-specific litter values from our
 experimental sites and freshly fallen beech leaves from different regions
 across Germany (Junggebauer et al., 2025). While bacterial training data
 included only lab-derived, and plant training data only field-derived
 endmembers, fungal training data included both lab- and field-derived
 fungal samples to cover the full variation of fungal signatures (see
 sensitivity analysis, Figure S4). Statistical inferences regarding the
 estimated relative contributions of resources between AMF- and
 EMF-dominated forests were based on the Bayesian approach followed by
 visual interpretation of the mixing model output and its credible
 intervals (Figure S3). Therefore, the observed patterns should be regarded
 as tentative descriptive trends rather than statistically significant
 effects. TP of soil invertebrates was derived from δ¹⁵N values of *Glu*
 and *Phe *averaged across the triplicate measurements for each sample,
 with β representing the specific and averaged differences in δ¹⁵N of *Glu*
 and *Phe* in primary producers. For each TP calculation, we used a
 site-specific β value to better address the issue of potential background
 variability (Steffan et al., 2013). For the trophic discrimination factor
 (TDF), we used the value of 7.6 ± 1.2‰, following the recommendations by
 Chikaraishi et al. (2014). The TP of each animal group was calculated
 separately per location and mycorrhizal type using the following equation:
 The relationships between TP, animal group and mycorrhizal type were
 analysed using linear mixed-effect models (*lmer *function of the “lme4”
 package, Bates et al., 2015) with animal identity and mycorrhizal type as
 predictors, and site nested within location as random effect. To assess
 the impact of mycorrhizal type on the TP of soil invertebrate groups, we
 conducted contrasts to examine differences in their TP between the two
 mycorrhizal types using the “emmeans” package (Lenth, 2024). The contrasts
 represented the differences in estimated marginal means of TP for the
 different animal groups within AMF- and EMF- dominated systems. Mean
 values and standard deviations are reported in the main text, with a
 p-value of 0.05 set as the threshold for statistical significance.
 (Information added to Dryad) More detailed information about the Bayesian
 model can be found here:
 [https://github.com/brianstock/MixSIAR](https://github.com/brianstock/MixSIAR) ## Access information Training data were derived from the following sources (see manuscript for details): * Larsen, T., Lee Taylor, D., Leigh, M. B., & O’Brien, D. M. (2009). Stable isotope fingerprinting: A novel method for identifying plant, fungal, or bacterial origins of amino acids. *Ecology*, *90*(12), 3526–3535. [https://doi.org/10.1890/08-1695.1](https://doi.org/10.1890/08-1695.1) - Larsen, T., Pollierer, M. M., Holmstrup, M., D’Annibale, A., Maraldo, K., Andersen, N., & Eriksen, J. (2016). Substantial nutritional contribution of bacterial amino acids to earthworms and enchytraeids: A case study from organic grasslands. *Soil Biology and Biochemistry*, *99*, 21–27. [https://doi.org/10.1016/j.soilbio.2016.03.018](https://doi.org/10.1016/j.soilbio.2016.03.018) - 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. *Functional Ecology*, *34*(11), 2375–2388. [https://doi.org/10.1111/1365-2435.13654](https://doi.org/10.1111/1365-2435.13654) - Junggebauer, A., Jüds, M., Klarner, B., Dyckmans, J., Pollierer, M.M., Scheu, S. (2025). Fungal energy channeling sustains soil animal communities across forest types and regions. *Ecology Letter*s, 28(5), e70122. [https://doi.org/10.1111/ele.70122](https://doi.org/10.1111/ele.70122)