# Does substrate diversity affect microbial heat dissipation in soil?

Louis J.P. Dufour, Naoise Nunan, Julia Schroeder, Tobias Bölscher, Katharina

H E Meurer, Erich Inselsbacher, Anke M Herrmann

► To cite this version:

Louis J.P. Dufour, Naoise Nunan, Julia Schroeder, Tobias Bölscher, Katharina H E Meurer, et al. Does substrate diversity affect microbial heat dissipation in soil?. Soil Science and Plant Nutrition, 2026, 72, pp.1-16. 10.1080/00380768.2026.2616657. hal-05466850

HAL Id: hal-05466850

https://hal.science/hal-05466850v1

Submitted on 20 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
<figure><img src="image_2.png" /></figure>

# Does substrate diversity affect microbial heat dissipation in soil?

Louis J.P. Dufour, Naoise Nunan, Julia Schroeder, Tobias Bölscher, Katharina H. E. Meurer, Erich Inselsbacher & Anke M. Herrmann

**To cite this article:** Louis J.P. Dufour, Naoise Nunan, Julia Schroeder, Tobias Bölscher, Katharina H. E. Meurer, Erich Inselsbacher & Anke M. Herrmann (19 Jan 2026): Does substrate diversity affect microbial heat dissipation in soil?, Soil Science and Plant Nutrition, DOI: 10.1080/00380768.2026.2616657

**To link to this article:** https://doi.org/10.1080/00380768.2026.2616657

<figure><img src="image_3.png" /></figure>

© 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

<figure><img src="image_4.png" /></figure>

View supplementary material

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

Published online: 19 Jan 2026.

<figure><img src="image_7.png" /></figure>

Submit your article to this journal

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

View related articles

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

View Crossmark data

<figure><img src="image_12.png" /></figure>

CrossMark
<figure><img src="image_13.png" /></figure>

RESEARCH ARTICLE

<figure><img src="image_14.png" /></figure>

OPEN ACCESS

<figure><img src="image_15.png" /></figure>

Check for updates

# Does substrate diversity affect microbial heat dissipation in soil?

Louis J.P. Dufourª, Naoise Nunan IDª,ᵇ, Julia Schroederᶜ,>d, Tobias Bölscherᵉ, Katharina H. E. Meurerª, Erich Inselsbacherf and Anke M. Herrmann IDª

ªDepartment of Soil and Environment, Swedish University of Agricultural Sciences, Uppsala, Sweden; ᵇInstitute of Ecology and Environmental Sciences, IEES Paris, Sorbonne Université, CNRS, IRD, INRA, UPEC, Univ Paris Diderot, Paris, France; ᶜThünen Institute of Biodiversity, Braunschweig, Germany; ᵈThünen Institute of Climate-Smart Agriculture, Braunschweig, Germany; ᵉUMR EcoSys, AgroParisTech, INRAE, Université Paris-Saclay, Palaiseau, France; ᶜDepartment of Ecosystem Management, Climate and Biodiversity, Institute of Soil Science, BOKU University, Vienna, Austria

## ABSTRACT

Most studies exploring the relation between the quality of organic substrates and microbial activity have been carried out with individual substrates. However, they have largely overlooked potential effects that might occur when microorganisms simultaneously process a heterogeneous mixture of organic substances. We investigated whether the degree of molecular richness of substrate mixtures had an impact on the overall activity of microbial communities, measured as heat dissipation over a day, in a laboratory incubation experiment. Topsoil samples were taken from a Swedish long-term field trial, established in 1983, in which a crop rotation dominated by spring cereals was either under-sown with perennial ryegrass as a cover crop or was without cover crops. No significant differences in the cumulative heat dissipation between soil with or without cover crops were observed. When all substrate mixtures were evaluated, the substrate richness was not significantly correlated with the overall microbial activity. However, we observed a bell-shaped curve between the total microbial activity and the nominal oxidation state of carbon (C) in the substrate mixtures. In most cases, the measured heat dissipated from soils that received substrate mixtures was significantly higher than the theoretically expected heat dissipation. The latter was based on the weighted sum of heat dissipated of individual substrates. This pattern was more visible in soil without cover crops than in soil with cover crops. The higher-than-expected heat dissipation from substrate mixtures suggests that the response of soil microbial communities to diverse substrates is not simply additive. There are several possible explanations for the observations made: (i) interactions among microbial metabolic pathways, (ii) added substrates may be preferentially used, and (iii) concentration of all the substrates in the mixtures was lower than when added individually, and therefore the metabolic pathways for each substrate was less likely to be saturated. Chemodiversity, the diversity of organic chemical compounds, is often not considered in models evaluating the dynamics of soil organic matter. Our results warrant further investigations into microbial metabolism and the processing of heterogeneous organic substances in order to constrain microbial- and substrate-specific models of soil organic matter.

## ARTICLE HISTORY

Received 15 September 2025
Accepted 7 January 2026

## KEYWORDS

Environment; microbiology
and biology; organic
chemistry; soil biochemistry

## 1. Introduction

Soil organic matter consists of heterogeneous organic materials ranging from small metabolites, through polymers, up to fragments of organisms at various stages of transformation (Lehmann et al. 2020). The activities of root, faunal, and microbial communities participate in the solubilization of soil organic matter and organic inputs (Dennis, Miller, and Hirsch 2010). The resulting dissolved organic matter contains a wide range of low and high molecular weight organic substances and colloids (Hawkes et al. 2019), many of which are likely to be readily available to microbial communities, albeit to

varying degrees (Ahamed et al. 2023; Marschner and Kalbitz 2003). The properties of dissolved organic matter can sometimes be used as a proxy for estimating the persistence of soil organic matter (Tian et al. 2025). However, despite the molecular diversity (i.e., chemodiversity – the diversity of organic chemical compounds) found in dissolved organic matter, it is not often considered in studies and models of soil organic matter with some notable exceptions (Hernández and Hobbie 2010; Steinauer, Chatzinotas, and Eisenhauer 2016; Weverka, Moeller, and Schimel 2023).

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>
<figure><img src="image_19.png" /></figure>

Many studies investigating microbial processing of carbon (C) in soil use single substrates as organic inputs (Bölscher et al. 2016; Gunina et al. 2017; Jones et al. 2018). Whilst the addition of single substrates may be useful for understanding the underlying mechanisms of soil processes, this simplification may not necessarily be scalable to situations where multiple substrates are bioavailable (Tang and Riley 2017). As a result, little is known about how microbial activities and functional traits are affected by the availability of multiple organic substrates (Loeppmann et al. 2016; Takriti et al. 2018).

There is some evidence that microbial activity can decrease with increased chemodiversity in soils (Orwin, Wardle, and Greenfield 2006) or lakes (Fonvielle et al. 2025). On the contrary, other studies suggest that increased chemodiversity of substrates enhances microbial activity in these environments (Okano, Hermsen, and Hwa 2021; Tanentzap et al. 2019). In soils, the specific growth rate of microbial communities was found to increase by approximately 7% when supplied with diverse root exudates compared to a single substrate alone (Blagodatskaya et al. 2009). Similarly, CO₂ emissions of soil supplied with amino acid mixtures were significantly higher than soil supplied with a single amino acid (Mondini et al. 2006). In marine systems, such nonadditive interactive effects are generally positive (synergistic) but can sometimes be negative (antagonistic) (Quigley et al. 2019). The different effects of chemodiversity may be due to interactions between substrate processing pathways, but little is known, and the underlying mechanisms remain to be elucidated.

Both the chemodiversity and the intrinsic properties of the organic substrates can affect microbial processing of substrates, but the relative importance of the two remains uncertain. Although Orwin, Wardle, and Greenfield (2006) suggest that the chemical nature of substrate mixtures may have a greater influence on microbial activity than the richness of organic compounds, the relative importance of chemical properties versus richness of substrates in soils has not yet been investigated. The nominal oxidation state of carbon (NOSC) and the specific energy content of organic matter may influence microbial processing, due to energetic constraints or microbial nutritional preferences (Nunan et al. 2015; Stumpf et al. 2025). Under aerobic conditions, microbial communities may favor the mineralization of organic matter with higher specific energy contents, leading to greater mineralization per unit of C added (Vaziourakis et al. 2025). Furthermore, Gunina et al. (2017) observed a negative linear relationship between the NOSC of individual substrates and the half-life of different low molecular weight organic substances in soil solution.

Besides possible effects of NOSC and the specific energy content of organic matter, different microbial communities are known to use substrate differently (Soares and Rousk 2019). This may be due to variations in the metabolic capabilities of the microbial communities (Gralka, Pollak, and Cordero 2023), as well as in different substrate preferences (Basan et al. 2020).

This study aims to improve our understanding of how diversified organic inputs to soil influence microbial activity and assess the extent to which microbial activity is affected by chemodiversity of substrate mixtures and/or the intrinsic energetic properties of the individual substrates contained in the mixtures. Specifically, we examined whether substrates are processed in a simple additive manner or whether there are deviations from the additive model when they are added to the soil as a mixture. We used arable soils from a long-term field trial with a crop rotation consisting mainly of spring cereals and under-sown with perennial ryegrass as cover crops or without cover crops. These soil treatments were selected as we expected that the microbial communities under cover crops would be better adapted to more diverse C substrates due to the greater diversity of plant cover and thus presumably also greater diversity of organic C inputs to the soil.

We hypothesized that the chemical diversity of substrates, i.e., chemodiversity, would not affect microbial activity and that microbial communities process organic substrates in an additive manner. In other words, microbial communities utilize substrates according to their proportion in the substrate mixture and independently of the composition or richness of substrates available to them.

## 2. Materials and methods

### 2.1. Soil

In November 2022, soil samples were taken from the Mellby cover crop long-term field trial (R0-8403, 56°29' 46.32"N, 13°2'22.56"E), situated on the south-east coast of Sweden. The field trial was established in 1983; perennial ryegrass (L. perenne L.) is used as a cover crop under-sown in a crop rotation dominated by spring cereals. Crop rotations without cover crops were included as a control treatment. There were three field replicates for each treatment. Crops were fertilized according to the recommended application rates for the region (i.e., 88 kg/ha of N, 18 kg/ha of P and 63 kg/ha of K) and the soil was tilled to a depth of 20–25 cm in April each year. For further details on the field site, see Poeplau et al. (2015).
<figure><img src="image_20.png" /><figcaption>**Table 1.** Soil characteristics. Mean values of three field replicates. Means suffixed by a different letter are significantly different at *p* < 0.05.</figcaption></figure>

<table><thead><tr><th></th><th>Soil C (%)</th><th>Soil N (%)</th><th>C-to-N ratio</th><th>Microbial biomass<br>(µg C g<sup>-1</sup> soil)</th><th>pH (H<sub>2</sub>O)</th></tr></thead><tbody><tr><td>With cover crops</td><td>2.7 A</td><td>0.15 A</td><td>18 A</td><td>140 A</td><td>6.0 A</td></tr><tr><td>Without cover crops</td><td>2.7 A</td><td>0.14 A</td><td>19 A</td><td>129 A</td><td>5.8 A</td></tr></tbody></table>

Topsoil was taken from the surface 20 cm in a w-shaped sampling scheme (10 cores per field replicate) and combined into one representative composite sample per field replicate (see Table 1 for general soil characteristics). After manual removal of large organic material and stones, the composite samples were homogenized using a trowel. The soil was then sieved (< 2 mm), mixed, and stored frozen for up to 7 months, until use.

## 2.2. Experimental design

Soil microbial activity was measured using isothermal calorimetry (Herrmann and Bölscher 2015), with the experiment being carried out in stages over a period of 3 months, as a maximum of 24 samples (i.e., three isothermal calorimeters, each with eight channels, were used) can be measured simultaneously: Soil samples were randomly selected, thawed, adjusted to 45% water-holding capacities with deionized water using a simple ‘funnel’ method (Nelson et al. 2024). Prior each measurement, samples were then pre-incubated for 10 days at 15°C to allow the microbial respiration flush to subside, which is released from organic matter due to sampling and freezing procedure (Herrmann and Witter 2002).

Soils (5 g dry soil equivalent) were placed in 20 mL glass reaction vials and amended with substrate solutions of varying degrees of chemodiversity richness,

each containing a total C content of 50 µg C g⁻¹ soil, i.e., low amount of C addition, 39% and 36% based on the amount of microbial biomass (see Table 1), to prevent microbial growth during the laboratory incubation. The substrate richness of the solutions ranged from 1 to 17 (Tables 2 and 3), and the amount of total C is of the same order of magnitude as the C supplied to the soil from root exudates (Pausch and Kuzyakov 2018), while being high enough to obtain a reliable heat signal from the isothermal calorimeters. Deionized water was used as a control. Seventeen individual organic substrates (Table 2) and seven different series of substrate richness gradients (Richness Series A–G, Table 3) were tested, each with six different C substrate molecular richness levels (i.e., six mixtures with different molecular richness per series; the organic substrates were mixed in proportion to their numbers within each richness level to obtain a total of 50 µg C g⁻¹ soil). Five substrate richness gradients (Richness Series A–E) mimicked the composition of root exudates from plants of the genera *Vigna* (Yang et al. 2021), *Trifolium* (Contreras et al. 2019), *Sorghum*, and *Festuca* (Vranova et al. 2013) as well as *Avena* (Guyonnet et al. 2017). Two additional arbitrary substrate richness gradients were also included to cover a wide range of substrate combinations (Richness Series F and G). In each of the seven series, two substrates were present in all six mixtures of the series. These two substrates and their NOSC differed among series, while

<table><caption>**Table 2.** Basic information of low molecular weight organic C substances used in the laboratory experiments as individual substrates or in mixtures.</caption><thead><tr><th>Abbreviation</th><th>Name of organic substrate</th><th>Nominal oxidation state of C</th><th>Specific energy content Δ<sub>c</sub>H (kJ g<sup>-1</sup> C)</th><th>C atoms</th></tr></thead><tbody><tr><td>Oxalic</td><td>oxalic acid</td><td>3.00</td><td>10.1</td><td>2</td></tr><tr><td>Formate</td><td>sodium formate</td><td>2.00</td><td>21.1</td><td>1</td></tr><tr><td>Citric</td><td>citric acid</td><td>1.00</td><td>27.2</td><td>6</td></tr><tr><td>Glyc</td><td>glycine</td><td>1.00</td><td>40.6</td><td>2</td></tr><tr><td>Keto</td><td>α-ketoglutaric acid</td><td>0.80</td><td>30.0</td><td>5</td></tr><tr><td>Cyst</td><td>L-Cysteine hydrochloride monohydrate</td><td>0.67</td><td>62.4</td><td>3</td></tr><tr><td>Benzoic</td><td>3,4-Dihydroxybenzoic acid</td><td>0.29</td><td>33.6</td><td>7</td></tr><tr><td>Cyclo</td><td>α-Cyclodextrin</td><td>0.00</td><td>35.5</td><td>36</td></tr><tr><td>Acetate</td><td>Sodium acetate anhydrous</td><td>0.00</td><td>36.4</td><td>2</td></tr><tr><td>Glu</td><td>D(+)-Glucose anhydrous</td><td>0.00</td><td>38.9</td><td>6</td></tr><tr><td>Fruct</td><td>D(−)–Fructose</td><td>0.00</td><td>39.0</td><td>6</td></tr><tr><td>Ala</td><td>L-Alanine</td><td>0.00</td><td>45.0</td><td>3</td></tr><tr><td>NAGA</td><td>N-acetyl-D-glucosamine</td><td>0.00</td><td>41.2</td><td>8</td></tr><tr><td>Phenyl</td><td>DL-Phenylalanine</td><td>−0.44</td><td>43.0</td><td>9</td></tr><tr><td>Lys</td><td>L-Lysine</td><td>−0.67</td><td>51.1</td><td>6</td></tr><tr><td>GABA</td><td>γ-Aminobutyric acid</td><td>−0.50</td><td>47.5</td><td>4</td></tr><tr><td>Isoleuc</td><td>DL-Isoleucine</td><td>−1.00</td><td>49.7</td><td>6</td></tr><tr><td>Leuc</td><td>DL-Leucine</td><td>−1.00</td><td>49.7</td><td>6</td></tr></tbody></table>

<footnote>*https://webbook.nist.gov/. The specific energy content or combustion enthalpy of exothermic reactions are reported as positive values.</footnote>
<figure><img src="image_21.png" /><figcaption>**Table 4.** Information on average nominal oxidation state of C (NOSC) and average specific energy content $\Delta_{cH}$ (kJ g$^{-1}$ C) of seven richness series (A–G) and six levels of substrate richness.</figcaption></figure>

<table><thead><tr><th rowspan="3">Richness series</th><th colspan="12">Substrate molecular richness</th></tr><tr><th colspan="2">Two</th><th colspan="2">Four</th><th colspan="2">Six</th><th colspan="2">Nine</th><th colspan="2">Twelve</th><th colspan="2">Seventeen</th></tr><tr><th>NOSC</th><th>Δ<sub>cH</sub></th><th>NOSC</th><th>Δ<sub>cH</sub></th><th>NOSC</th><th>Δ<sub>cH</sub></th><th>NOSC</th><th>Δ<sub>cH</sub></th><th>NOSC</th><th>Δ<sub>cH</sub></th><th>NOSC</th><th>Δ<sub>cH</sub></th></tr></thead><tbody><tr><td>A</td><td>1.64</td><td>21.9</td><td>1.32</td><td>27.9</td><td>0.88</td><td>31.6</td><td>0.85</td><td>31.5</td><td>0.64</td><td>33.4</td><td>0.26</td><td>37.6</td></tr><tr><td>B</td><td>1.00</td><td>33.9</td><td>0.57</td><td>35.1</td><td>0.49</td><td>40.3</td><td>0.64</td><td>36.6</td><td>0.69</td><td>35.6</td><td>0.36</td><td>38.4</td></tr><tr><td>C</td><td>0.00</td><td>42.0</td><td>-0.25</td><td>43.2</td><td>0.05</td><td>38.9</td><td>-0.10</td><td>40.8</td><td>0.18</td><td>39.0</td><td>0.30</td><td>39.1</td></tr><tr><td>D</td><td>-0.25</td><td>43.2</td><td>-0.05</td><td>41.2</td><td>-0.31</td><td>44.3</td><td>-0.26</td><td>42.3</td><td>0.31</td><td>37.7</td><td>0.24</td><td>39.7</td></tr><tr><td>E</td><td>-0.83</td><td>50.4</td><td>-0.67</td><td>47.4</td><td>-0.40</td><td>43.7</td><td>-0.28</td><td>42.5</td><td>-0.21</td><td>41.6</td><td>0.13</td><td>40.7</td></tr><tr><td>F</td><td>0.50</td><td>39.8</td><td>0.67</td><td>42.3</td><td>0.58</td><td>40.0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>0.36</td><td>38.4</td></tr><tr><td>G</td><td>1.00</td><td>28.8</td><td>0.70</td><td>32.2</td><td>0.39</td><td>34.5</td><td>0.56</td><td>36.6</td><td>0.32</td><td>39.4</td><td>0.29</td><td>39.3</td></tr></tbody></table>

within a series each richness level included the substrates of the previous lower richness level (Tables 3 and 4). In total, 108 samples were amended with individual organic substrates (3 field replicates × 2 field treatments × 18 substrates) and 240 substrate mixture samples (3 replicates × 2 field treatments × 40 substrate mixtures) were measured.

The vials were tightly sealed and placed into TAM Air isothermal calorimeters (TA Instruments Sollentuna, Sweden), with the thermostat set to 15°C. Heat dissipation was measured over a 24-h time interval. The cumulative heat dissipation was determined between 5 h and 24 h after the addition of the substrate mixtures. Heat dissipation measurements during the first hours were discarded as the signal was affected by the disturbance of the experimental setup. Each isothermal calorimeter run included only eight samples. To ensure that there was no drift in the analyses, each run included a control soil amended with deionized water, and samples amended with individual substrates were also regularly included. The heat dissipated from water-amended soils, i.e., control, was then subtracted from each of the samples measured during the same measurement run.

## 2.3. NOSC and measured vs. theoretically expected heat dissipation

The NOSC of individual substrates and substrate mixtures was calculated according to LaRowe and Van Cappellen (2011).

$$NOSC = 4 - \left[ \frac{(4C + H - 3N - 2O + 5P - 2S)}{C} \right] \quad (1)$$

where C, H, N, O, P, and S refer to the stoichiometric number of carbon, hydrogen, nitrogen, oxygen, phosphorus, and sulfur atoms per molecular formula. This equation assumes the oxidation states of the atoms (C = +4, H = +1, N = -3, O = -2, N = -3, p = +5, and S = -2) and the neutrality of organic molecules.

The weighted sum of heat dissipated from individual substrates was used to calculate theoretically expected heat dissipation values ($Q_{exp}$) for substrate mixtures

assuming that substrate mixtures were processed in an additive manner.

$$Q_{\text{exp}} = \sum_{1}^{n} \left( \frac{Q_{\text{Sub } n}}{n} \right) \quad (2)$$

where $n$ is the number of C compounds in a substrate mixture and $Q_{Sub}$ is heat dissipated (mJ g$^{-1}$ soil).

Subsequently, theoretically expected and measured heat dissipation were compared to determine whether the substrate mixtures were processed in an additive or non-additive manner.

The measured heat dissipation may deviate from the theoretically expected heat dissipation, and in the present study we distinguish between (i) additive, (ii) synergistic substrate interaction, and (iii) preferential use of added substrate. They are defined as follows and presented in a schematic representation (Figure 1):

(i) **Additive effect of substrates** occurs when the measured heat dissipation from the substrate mixture added to the soil is similar to the theoretically expected heat dissipation (±10 mJ g$^{-1}$ of soil). Here, no interactions or preferential use of added substrate occur, and all substrates are metabolized as they would be when added individually to the soil. In other words, substrates are used in an additive manner, proportionally to their share in the substrate mixture.

(ii) **Synergistic substrate interaction** occurs when the measured heat dissipation of the substrate mixture added to the soil exceeds the heat dissipation of the individual substrates in the substrate mixture, whereby the substrates are used in a nonadditive manner. These interactions enhance microbial activity beyond what would be expected from the processing of individual substrates.

(iii) **Preferential use of added substrate** occurs when the measured heat dissipation is different to the theoretically expected heat dissipation of all
<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /><figcaption>Figure 1. Schematic representation of potential effects when substrate mixtures are added to soils.</figcaption></figure>

substrates in the mixture. Here, measured heat dissipation of the substrate mixture can be either higher (more than +10 mJ g⁻¹ soil) or lower (more than −10 mJ g⁻¹ soil) than the theoretically expected heat dissipation. Contrasting to a synergistic substrate interaction, the measured heat dissipation is lower than the highest heat dissipation measured for individual substrates present in the substrate mixture.

## 2.4. Statistics

In order to determine whether there was an effect of cover crops on the processing of substrate mixtures, one-way analyses of similarity (ANOSIM) were conducted for each molecular richness level, using the Euclidean distance for data normalized relative to total heat dissipation. The cover crop treatment was used as a factor. The significant levels were determined from 9999 permutations. This analysis was carried out in the vegan library version 2.6-2 (Oksanen et al. 2025).

The relationships between the NOSC and specific energy content of organic inputs and the total heat dissipation were investigated by polynomial regression analysis using the lm function and the formula y ~ poly(x, degree two) from the library stats version 4.3.1 (https://search.r-project.org/R/refmans/stats/html/stats-package.html) and was graphically represented using the geom.smooth function from the ggplot2 package version 3.5.1 (Wickham 2016). Individual substrates were

not included in the regressions. This was because we wanted to compare measured and theoretically expected heat after the addition of substrate mixtures to soils and individual substrates have no values of theoretically expected heat.

To determine whether and to what extent measured and theoretically expected heat dissipation differed, Gardner-Altman estimation plots were created using the R package dabestr version 2023.9.12 (Ho et al. 2019).

# 3. Results

## 3.1. Molecular diversity does not constrain microbial activity

The molecular richness of the substrate mixtures added to soils had no significant effect on total heat dissipation (Figure 2). The decrease of the standard deviation as a function of molecular richness was significant in the cover crop treatment (*p* < 0.05) but not significant in the treatment without cover crops (*p* = 0.09). The ANOSIM analyses for each diversity level showed no significant differences in normalized total heat dissipation between cover crop treatments (Fig. S1).

## 3.2. Impact of NOSC on microbial activity

The cumulative heat dissipation as a function of the NOSC in the substrate mixture displayed a bell-shaped pattern in both soil treatments (Figure 3a and b). The maximum total heat dissipated occurred at intermediate NOSC values between −0.5 to +1, and minimum microbial activities generally occurred at very negative or very
<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /><figcaption>**Figure 2.** Relationships between the overall heat dissipation and the molecular richness of substrate mixtures added to soil (a) with cover crops and (b) without cover crops. Boxplots represent (i) the median as crossbars inside boxes, (ii) 75th and 25th percentiles as crossbars on the top and the bottom of boxes, respectively, (iii) the 99th and 1st percentiles as error bars above and below the boxes, respectively. Solid boxplots are measured values and empty boxplots are theoretically expected values. For molecular richness #1 (n = 51: 17 individual substrates x 3 field replicates), for molecular richness #2, #4, #6 and #17 (n= 21: 7 mixtures of substrate x 3 field replicates), for molecular richness #9 and #12 (n = 18: 6 mixtures x 3 field replicates).</figcaption></figure>

positive NOSC values. Overall, the NOSC of substrate mixtures added to soil explained 8% and 35% of the variation in measured heat dissipation in treatments with and without cover crops, respectively (Figure 3). Similar results were observed between the energy content of added substrates and soil microbial activity (Fig. S2).

**3.3. Differences between measured and theoretically expected microbial activities**

The measured overall heat dissipation in response to inputs of substrate mixtures were generally higher than theoretically expected heat dissipation (Figure 3c and d), the latter was estimated based on the weighted sum of heat dissipated from individual substrates (see Eq. 2). The differences were more pronounced in soils without cover crops than with cover crops (Figure 3c and d). The unpaired mean difference between measured and expected heat dissipated was 37 mJ g⁻¹ soil with cover crops and a 99% confidence interval of 12–63 and 73 mJ g⁻¹ soil without cover crops and a 99% confidence interval of 46–98 mJ g⁻¹ soil (Figure 4).

The deviation between measured and theoretically expected heat dissipation occurred over a narrow range of NOSC, with the most prominent deviations

occurring for NOSC values between +0.5 and +1 in soil with cover crops (Figure 3c), and between –0.5 and +1 in soil without cover crops (Figure 3d). In contrast, differences were less obvious for substrate mixtures with NOSC values outside these intervals. Similar results were observed with the specific energy content of substrate mixtures (Fig. S2c and d).

The differences between measured and theoretically expected heat dissipation were primarily due to preferential use of added substrate, which tended to occur across all levels of molecular richness (Table 5). Nevertheless, evidence for synergistic substrate interactions was observed in 38% and 19% of the substrate mixtures at molecular richness of two and four, respectively (Table 5), particularly when glycine or sodium formate were present in the substrate mixture.

**4. Discussion**

**4.1. Impact of molecular richness of substrate mixtures on microbial activity**

There are two contradictory views regarding the effects of substrate molecular diversity on microbial activity. One suggests that substrate diversity decreases microbial activity (Orwin, Wardle, and Greenfield 2006), based on the idea that the cost of processing diverse substrate
<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /><figcaption>**Figure 3.** Relationships between total heat dissipation and NOSC of organic inputs to soil. (a) and (b) Measured total heat dissipation from soils with cover crops and without cover crops, respectively. Each symbol represents the mean ± one standard error of the total heat dissipation of substrate mixtures (coloured dots with different shapes (n = 3 field replicates). Black lines are second order polynomial regressions with 99% confidence intervals fitted to measured heat dissipation. (c) and (d) Comparison of second order polynomial regressions for measured (black lines) and theoretically expected (magenta lines) heat dissipation from soils with cover crops and without cover crops, respectively.</figcaption></figure>

mixtures is higher than less diverse mixtures or single substrates (Kothawala et al. 2021; Lehmann et al. 2020; Nunan, Schmidt, and Raynaud 2020), e.g., due to a required broader range of enzyme synthesis. The alternative view is that substrate diversity increases microbial activity (Blagodatskaya et al. 2009; Mondini et al. 2006). This view postulates that increased substrate diversity allows a larger proportion of the microbial community to process the substrates available, due to increased niche differentiation (Loreau 2001).

In terms of substrate richness, the results obtained here, however, do not support either of these views, as no relationship between an increase in substrate richness and microbial activity was found (Figure 2). Previous studies have also observed a lack of effect of substrate diversity, in terms of composition and quantity, on soil respiration (Hernández and Hobbie 2010), even though they did find an effect on some enzyme activities. The lack of relationship can be explained by:

(I) The range of substrate richness in the present study is relatively low. Soil microorganisms have wide metabolic capacities processing a broad range of organic materials. Metabolic complementarity among community members would therefore allow the substrates to be processed regardless of substrate richness without any impact on overall microbial activity. Furthermore, the range of substrate richness in our study is unlikely to have affected the overall expression of transporters and catabolic proteins of individual members of the community, as the richness of the substrate mixtures used in the present study is narrow (i.e., max. 17 C substrates) compared to the wide range of extracellular enzymes present in soils (Loeppmann et al. 2016).

(II) Alternatively, the expectation that the composition of organic matter would limit microbial decomposition, and their associated microbial activity may be restricted to situations characterized by low
<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /><figcaption>**Figure 4.** Gardner-Altman estimation plot representing the mean differences between theoretically expected and measured heat dissipation in response to substrate mixtures added to soils. (a) With cover crops (n = 120) and (b) without cover crops (n = 120). This estimation plot uses the different vertical axis to display an effect size, here the mean difference (right vertical axis) between the measured and theoretically expected heat dissipation (left vertical axis). The blue and orange vertical lines within each panel illustrates the 99% confidence interval of the mean differences. The black horizontal lines within each panel illustrates the mean of the test group with theoretically expected heat dissipation set to zero.</figcaption></figure>

<table><caption>**Table 5.** Differences between measured and theoretically expected heat dissipation (mJ g⁻¹ soil) from soils with or without cover crops in response to the inputs of substrate mixtures with different levels of molecular richness. For molecular richness #2, #4, #6, and #17 (n = 21: 7 mixtures of substrate × 3 field replicates), for molecular richness #9 and #12 (n = 18: 6 mixtures × 3 field replicates). For each category, the average value ± standard deviation is shown with the number of observations (n) (definitions of effects, see Materials and methods section).</caption><thead><tr><th rowspan="2">Molecular richness</th><th rowspan="2">Effects</th><th colspan="2">With cover crops</th><th colspan="2">Without cover crops</th></tr><tr><th>ΔHeat dissipation (mJ g<sup>-1</sup> soil)</th><th>Number of mixtures (n)</th><th>Δ Heat dissipation (mJ g<sup>-1</sup> soil)</th><th>Number of mixtures (n)</th></tr></thead><tbody><tr><td rowspan="3">2</td><td>Additive effect</td><td>=</td><td>4</td><td>=</td><td>1</td></tr><tr><td>Synergistic interaction</td><td>132 ± 93</td><td>7</td><td>142 ± 64</td><td>9</td></tr><tr><td>Preferential substrate use</td><td>72 ± 48<br>-47 ± 22</td><td>7<br>3</td><td>48 ± 39<br>-80 ± 16</td><td>6<br>5</td></tr><tr><td rowspan="3">4</td><td>Additive effect</td><td>=</td><td>1</td><td>NOB</td><td>0</td></tr><tr><td>Synergistic interaction</td><td>368</td><td>1</td><td>216 ± 74</td><td>7</td></tr><tr><td>Preferential substrate use</td><td>62 ± 32<br>-64 ± 39</td><td>13<br>6</td><td>94 ± 3<br>-39 ± 15</td><td>10<br>4</td></tr><tr><td rowspan="3">6</td><td>Additive effect</td><td>=</td><td>2</td><td>=</td><td>2</td></tr><tr><td>Synergistic interaction</td><td>NOB</td><td>0</td><td>78 to 141</td><td>2</td></tr><tr><td>Preferential substrate use</td><td>81 ± 32<br>-103 ± 86</td><td>15<br>4</td><td>90 ± 56<br>-28</td><td>16<br>1</td></tr><tr><td rowspan="3">9</td><td>Additive effect</td><td>=</td><td>1</td><td>NOB</td><td>0</td></tr><tr><td>Synergistic interaction</td><td>NOB</td><td>0</td><td>NOB</td><td>0</td></tr><tr><td>Preferential substrate use</td><td>62 ± 28<br>-65 ± 46</td><td>11<br>6</td><td>100 ± 64<br>-67</td><td>17<br>1</td></tr><tr><td rowspan="3">12</td><td>Additive effect</td><td>=</td><td>3</td><td>NOB</td><td>0</td></tr><tr><td>Synergistic interaction</td><td>NOB</td><td>0</td><td>NOB</td><td>0</td></tr><tr><td>Preferential substrate use</td><td>46 ± 34<br>-58 to -27</td><td>13<br>2</td><td>68 ± 39<br>-24 ± 9</td><td>12<br>6</td></tr><tr><td rowspan="3">17</td><td>Additive effect</td><td>=</td><td>4</td><td>=</td><td>3</td></tr><tr><td>Synergistic interaction</td><td>NOB</td><td>0</td><td>262</td><td>1</td></tr><tr><td>Preferential substrate use</td><td>68 ± 52<br>-57 ± 47</td><td>14<br>3</td><td>79 ± 60<br>-55 ± 16</td><td>15<br>2</td></tr></tbody></table>

NOB = not observed.
<figure><img src="image_30.png" /></figure>

concentrations of organic substrates and greater chemodiversity (Kothawala et al. 2021; Lehmann et al. 2020), conditions found in bulk soils. The amount of C inputs to soils used in the present study, i.e., 50 µg C g⁻¹ soil, is likely higher than the C concentration of low molecular organic matter generally found in bulk soils (e.g., Gunina et al. 2017) and was aimed to simulate rhizosphere soil environments.

Even though there was no impact of substrate diversity on total microbial activity, the variability of activity decreased with substrate diversity (Figure 2). This may reflect an averaging effect: the ranges of NOSC and specific energy contents $Δ_cH$ in the substrate mixtures decreased as a function of substrate richness (Table 4). For example, NOSC ranged from -0.83 up to 1.00 and $Δ_cH$ ranged from 21.9 up to 50.4 when two substrates where added (substrate molecular richness of two), whereas when 17 substrates were added NOSC ranged from 0.13 to 0.36 and $Δ_cH$ ranged between 37.6 and 40.7, the latter being similar to those found in soil organic matter (Bölscher et al. 2017; Lorenz, Maskow, and Thiele-Bruhn 2024). Therefore, the reduction in variability may not be an effect of substrate richness per se but rather due to a reduction in the ranges of NOSC and specific energy contents.

## 4.2. Constraints from homeostasis of the NOSC

The NOSC in heterogeneous substrates bioavailable to the microbial community might be another constraint on the overall soil microbial activity. At the level of the microbial population, the greater the discrepancy between the NOSC of the substrate and the microorganisms, the greater the requirement for homeostatic mechanisms, resulting in lower catabolism associated with lower microbial growth (Heijnen and Kleerebezem 2010). This conceptual representation of a bell-shaped relationship between microbial activity and the NOSC state (Islam, Rumainul, and Dijkstra 2023; Manzoni et al. 2012) is consistent with our observations at the level of soil microbial community (Figure 3). A general explanation of this pattern is that microorganisms need to balance fluxes within catabolic pathways that supply C, nutrients, and electrons, with biosynthetic and polymerization pathways (Flamholz et al. 2025).

If microorganisms process molecules that are oxidized (positive values of NOSC with fewer electrons per C), microorganisms may need to reduce these compounds to match the NOSC of the soil microorganisms. As more oxidized molecules do not have a tendency to be reduced spontaneously, microorganisms need to

allocate a larger portion of cofactors to activate their half-reactions of reduction (Jinich et al. 2018). This additional resource allocation toward catabolic pathways may result in a lower level of ATP available to supply biosynthetic pathways, which would constrain microbial growth and associated catabolism.

When relatively more reduced compounds are processed (i.e., negative values of NOSC with more electrons per C), ATP can be produced in excess of the requirements of the biosynthetic pathways (Flamholz et al. 2025). This cytotoxic effect of excess ATP levels would need to be balanced (Pontes, Sevostyanova, and Groisman 2016). Another strategy for microorganisms to be relatively 'more active' on more reduced organic substrates than on more oxidized ones consists in shifting metabolic pathways that are characterized by relatively shorter length, higher rates of reactions, and lower ATP yields (Flamholz et al. 2013) and hence lower heat dissipation.

The NOSC of molecules captures their elemental stoichiometry but is agnostic with regard to their chemical structures and molecular weight (Bertz 1981; Cheng et al. 2022). The structure and size of compounds modulate several properties of molecules, such as their polarity/hydrophobicity (Stumpf et al. 2025), affinity, reactivity, stability, toxicity, and permeability (Bar-Even et al. 2012). These factors, which have not been considered in the present study, may have been at the origin of a large part of the variability of soil microbial activity. Furthermore, the general lack of difference between measured and theoretically expected heat dissipation for diversified inputs with low or high NOSC may be due to a methodological artifact. Indeed, for organic inputs with such extreme values of NOSC the number of mixtures with high substrate richness is reduced compared to those characterized by intermediate NOSC. This methodological artifact is common in diversity experiments where the number of replicates for high level of diversity is generally smaller than for low diversity levels as shown in Wang et al. (2019).

## 4.3. Synergistic substrate interactions vs. preferential use of added substrate

Contrary to our expectation, the microbial community in soil with cover crops did not show enhanced ability to process more diverse substrate mixtures. One first reason may be that, contrary to our original assumption, there were no differences in the microbial communities between the two soil treatments (Table 1 and L. Gismero-Rodríguez, personal communication), microbial physiology or amount of soil organic C (Schroeder et al.
<figure><img src="image_31.png" /></figure>

2025) that could cause differences in metabolic responses. Another explanation is that the diversity of metabolic capabilities in the microbial communities was far greater than the chemodiversity of substrates added, and we therefore may not have observed any differences. Even though there were no significant differences in overall heat dissipation between soils with or without cover crops, there were differences in the way the substrates were processed: the magnitude of the absolute differences between measured and theoretically expected heat dissipation for mixtures in soils without cover crops were almost twice as high as the differences in soils with cover crops (Figure 4).

We observed that most of the synergistic substrate interactions occurred at relatively low molecular richness (i.e., richness 2 and 4). This suggests that in a soil context, where diversity levels are generally several orders of magnitude higher than the one used in the present study, synergistic substrate interactions are not to be expected. Our results seem to be consistent with the view that the chemodiversity effect on microbial activity saturates at a low level of molecular richness (i.e., there is no interactive effects of supplying more than four organic substrates) (Langenheder et al. 2010; Wardle, Bonner, and Nicholson 1997).

Synergistic substrate interactions at low richness levels reflect the positive outcomes when a microbial community processes complementary organic compounds. These effects enhance microbial activity beyond what would be expected from the processing of individual substrates. Our observations of these synergistic substrate interactions are consistent with the view of an auxiliary substrate framework (Babel 2009). This framework suggests that, sometimes, the presence of a specific substrate in a mixture can provide additional cofactors once processed and therefore enhance some microbial traits. This is the case when formate is used with glucose (Gommers et al. 1988) or when there is an enhanced nitrogen availability (Koranda et al. 2014), as we observed in our study when glycine was present in substrate mixtures. At higher molecular richness, the lack of synergistic substrate interactions may be due to a more complex processing of organic substances by microorganisms. For example, synergistic substrate interactions may be masked by potential competition or inhibition between metabolic pathways (Hu et al. 2022; Tanentzap et al. 2019). Also, the concentration of all the substrates in the mixtures was lower than when added individually as the total amount of C was the same in all substrate additions (i.e., 50 µg C g⁻¹ soil). When evaluating spatial heterogeneity, Shi et al. (2021) showed that adding C substrate to one spot in a sample rather than to the whole sample resulted in lower heat dissipation. They

suggested that the addition of substrate to one spot resulted in much higher local substrate concentrations which could have saturated the metabolism of the microorganisms, resulting in slower decomposition rates. Similarly, the addition, in the present study, of one individual substrate at a given C concentration may have saturated metabolic pathways, but providing the same amount of C, yet in different forms, does not because there are many concurrent metabolic pathways. Yet, the current study set-up did not allow us to further explore substrate effects in mixtures in more detail.

In the present study, we distinguish between (i) additive effect, (ii) synergistic substrate interaction, and (iii) preferential use of added substrate. We used unlabeled organic C substrates and assumed that added organic C was consumed during the 24-h laboratory incubation. The addition of low amounts of organic C may also result in priming of native soil organic matter (Mondini et al. 2006) which may play a role in the observed discrepancy between measured and theoretically expected heat dissipation. Furthermore, antagonistic substrate interactions, i.e., metabolic capabilities of soil microorganisms are negatively affected, may occur when there is a discrepancy between the two. In this study, possible antagonistic substrate interactions may have been erroneously attributed to preferential use of added substrates. Since we used unlabeled organic C substrates, we were unable to assess whether and/or how much of the added C substrate was consumed, nor can we provide a detailed discussion of the interactions between added C substrate and the native soil organic matter and/or the extent of priming effects. However, Lloyd et al. (2016) emphasized that experiments based on simple, single C substrates are likely to be misleading, but the present study lays the foundation for future studies. We found that deviations from additive substrate effects were primarily due to preferential use of added substrate. Future studies could investigate soils from other terrestrial ecosystems by using a combination of (i) ¹³C-labeled soluble or solid substrates (e.g., Bölscher et al. 2017; Zhu et al. 2018), (ii) chemical characterization of organic matter (Stumpf et al. 2025), (iii) detailed investigation of the stoichiometry of organic matter in soils (Manzoni et al. 2017), and (iv) soil metabolomics (Baker et al. 2024). Such studies could provide us with insights into the processing of the substrate mixture, and their outcomes could be used to inform models evaluating the dynamics of soil organic matter.

## Acknowledgments

The Mellby Long-Term Field Experiment (R0-8403) is funded through the Faculty of Natural Resources and Agricultural Sciences of the Swedish University of
<figure><img src="image_32.png" /></figure>

Agricultural Science, SLU, Sweden. We thank Dorte Fischer for assistance in the laboratory. This work was presented at the workshop 'ISMOM2024: from soil organic-mineral interactions to soil management strategies (Ninth ISMOM: International Symposium on Interactions of Soil Minerals with Organic Components and Microorganisms)', which took place in Tsukuba, Ibaraki, Japan, on 14–18 October 2024, and which was sponsored by the OECD Co-operative Research Programme: Sustainable Agricultural and Food Systems (CRP), whose financial support made it possible for the corresponding author A.M. Herrmann to participate in the conference. The opinions expressed and arguments employed in this publication are the sole responsibility of the authors and do not necessarily reflect those of the OECD or of the governments of its Member countries.

## Disclosure statement

No potential conflict of interest was reported by the author(s).

## Funding

This work was supported by the European Joint Programme EJP SOIL under the grant agreement No. 862695 with co-funding from the Swedish Research Council for Sustainable Development FORMAS [grant numbers 2017-00932 and 2022-00307] and from the Agence National de la Recherche [grant number ANR-22-CE01-0025-1]. AMH was supported by the NordForsk-funded project AgroMixNorth (Project No. 136208).

## ORCID

Naoise Nunan http://orcid.org/0000-0003-3448-7171

<figure><img src="image_33.png" /></figure>

Anke M. Herrmann http://orcid.org/0000-0002-6273-1234

<figure><img src="image_34.png" /></figure>

## References

Ahamed, F., Y. You, A. Burgin, J. C. Stegen, T. D. Scheibe, and H.-S. Song. 2023. "Exploring the Determinants of Organic Matter Bioavailability Through Substrate-Explicit Thermodynamic Modeling." *Frontiers in Water* 5:1169701. https://doi.org/10.3389/frwa.2023.1169701.

Babel, W. 2009. "The Auxiliary Substrate Concept: From Simple Considerations to Heuristically Valuable Knowledge." *Engineering in Life Sciences* 9 (4): 285–290. https://doi.org/10.1002/elsc.200900027.

Baker, N. R., K. Zhalnina, M. Yuan, D. Herman, J. A. Ceja-Navarro, J. Sasse, J. S. Jordan, et al. 2024. "Nutrient and Moisture Limitations Reveal Keystone Metabolites Linking Rhizosphere Metabolomes and Microbiomes." *Proceedings of the National Academy of Sciences* 121 (32): e2303439121. https://doi.org/10.1073/pnas.2303439121.

Bar-Even, A., A. Flamholz, E. Noor, and R. Milo. 2012. "Rethinking Glycolysis: On the Biochemical Logic of Metabolic Pathways." *Nature Chemical Biology* 8 (6): 509–517. https://doi.org/10.1038/nchembio.971.

Basan, M., T. Honda, D. Christodoulou, M. Hörl, Y.-F. Chang, E. Leoncini, A. Mukherjee, et al. 2020. "A Universal Trade-Off Between Growth and Lag in Fluctuating Environments." *Nature* 584 (7821): 470–474. https://doi.org/10.1038/s41586-020-2505-4.

Bertz, S. H. 1981. "The First General Index of Molecular Complexity." *Journal of the American Chemical Society* 103 (12): 3599–3601. https://doi.org/10.1021/ja00402a071.

Blagodatskaya, E. V., S. A. Blagodatsky, T.-H. Anderson, and Y. Kuzyakov. 2009. "Contrasting Effects of Glucose, Living Roots and Maize Straw on Microbial Growth Kinetics and Substrate Availability in Soil." *European Journal of Soil Science* 60 (2): 186–197. https://doi.org/10.1111/j.1365-2389.2008.01103.x.

Bölscher, T., E. Paterson, T. Freitag, B. Thornton, and A. M. Herrmann. 2017. "Temperature Sensitivity of Substrate-Use Efficiency Can Result from Altered Microbial Physiology Without Change to Community Composition." *Soil Biology and Biochemistry* 109:59–69. https://doi.org/10.1016/j.soilbio.2017.02.005.

Bölscher, T., L. Wadsö, G. Börjesson, and A. M. Herrmann. 2016. "Differences in Substrate Use Efficiency: Impacts of Microbial Community Composition, Land Use Management, and Substrate Complexity." *Biology and Fertility of Soils* 52 (4): 547–559. https://doi.org/10.1007/s00374-016-1097-5.

Cheng, W.-H., C.-H. Hsieh, C.-W. Chang, F.-K. Shiah, and T. Miki. 2022. "New Index of Functional Specificity to Predict the Redundancy of Ecosystem Functions in Mi-Crobial Communities." *FEMS Microbiology Ecology* 98 (6): 1–9. https://doi.org/10.1093/femsec/fiac058.

Contreras, F., J. Díaz, A. D. Rombolà, and M. de la Luz Mora. 2019. "Prospecting Intercropping Between Subterranean Clover and Grapevine as Potential Strategy for Improving Grapevine Performance." *Current Plant Biology* 19:100110. https://doi.org/10.1016/j.cpb.2019.100110.

Dennis, P. G., A. J. Miller, and P. R. Hirsch. 2010. "Are Root Exudates More Important Than Other Sources of Rhizodeposits in Structuring Rhizosphere Bacterial Communities?" *FEMS Microbiology Ecology* 72 (3): 313–327. https://doi.org/10.1111/j.1574-6941.2010.00860.x.

Flamholz, A. I., A. Goyal, W. W. Fischer, D. K. Newman, and R. Phillips. 2025. "The Proteome Is a Terminal Electron Acceptor." *Proceedings of the National Academy of Sciences* 122 (1): e2404048121. https://doi.org/10.1073/pnas.2404048121.

Flamholz, A., E. Noor, A. Bar-Even, W. Liebermeister, and R. Milo. 2013. "Glycolytic Strategy as a Trade-Off Between Energy Yield and Protein Cost." *Proceedings of the National Academy of Sciences* 110 (24): 10039–10044. https://doi.org/10.1073/pnas.1215283110.

Fonvielle, J. A., S. R. Sandor, T. Dittmar, and A. J. Tanentzap. 2025. "Chemical Diversity Promotes Ecosystem Function." *BioRxiv:* 1–51. https://doi.org/10.1101/2025.02.16.638531.

Gommers, P. J. F., B. J. van Schie, J. P. van Dijken, and J. G. Kuenen. 1988. "Biochemical Limits to Microbial Growth Yields: An Analysis of Mixed Substrate Utilization." *Biotechnology and Bioengineering* 32 (1): 86–94. https://doi.org/10.1002/bit.260320112.

Gralka, M., S. Pollak, and O. X. Cordero. 2023. "Genome Content Predicts the Carbon Catabolic Preferences of Heterotrophic Bacteria." *Nature Microbiology* 8 (10): 1799–1808. https://doi.org/10.1038/s41564-023-01458-z.
<figure><img src="image_35.png" /></figure>

Gunina, A., A. R. Smith, Y. Kuzyakov, and D. L. Jones. 2017. "Microbial Uptake and Utilization of Low Molecular Weight Organic Substrates in Soil Depend on Carbon Oxidation State." *Biogeochemistry* 133 (1): 89–100. https://doi.org/10.1007/s10533-017-0313-1.

Guyonnet, J. P., F. Vautrin, G. Meiffren, C. Labois, A. M. Cantarel, S. Michalet, G. Comte, and F. el Zahar Haichar. 2017. "The Effects of Plant Nutritional Strategy on Soil Microbial Denitrification Activity through Rhizosphere Primary Metabolites." *FEMS Microbiology Ecology* 93 (4): fix022. https://doi.org/10.1093/femsec/fix022.

Hawkes, J. A., P. J. R. Sjöberg, J. Bergquist, and L. J. Tranvik. 2019. "Complexity of Dissolved Organic Matter in the Molecular Size Dimension: Insights from Coupled Size Exclusion Chromatography Electrospray Ionisation Mass Spectrometry." *Faraday Discussions* 218:52–71. https://doi.org/10.1039/C8FD00222C.

Heijnen, J. J., and R. Kleerebezem. 2010. "Bioenergetics of Microbial Growth." In *Encyclopedia of Industrial Biotechnology: Bioprocess, Bioseparation and Cell Technology.*, edited by M. C. Flickinger, 1–66. 1st ed. John Wiley & Sons. https://doi.org/10.1002/9780470054581.eib084.

Hernández, D. L., and S. E. Hobbie. 2010. "The Effects of Substrate Composition, Quantity, and Diversity on Microbial Activity." *Plant and Soil* 335 (1–2): 397–411. https://doi.org/10.1007/s11104-010-0428-9.

Herrmann, A. M., and T. Bölscher. 2015. "Simultaneous Screening of Microbial Energetics and CO₂ Respiration in Soil Samples from Different Ecosystems." *Soil Biology and Biochemistry* 83:88–92. https://doi.org/10.1016/j.soilbio.2015.01.020.

Herrmann, A., and E. Witter. 2002. "Sources of C and N Contributing to the Flush in Mineralization Upon Freeze-Thaw Cycles in Soils." *Soil Biology and Biochemistry* 34 (10): 1495–1505. https://doi.org/10.1016/S0038-0717(02)00121-9.

Ho, J., T. Tumkaya, S. Aryal, H. Choi, and A. Claridge-Chang. 2019. "Moving Beyond P Values: Data Analysis With Estimation Graphics." *Nature Methods* 16 (7): 565–566. https://doi.org/10.1038/s41592-019-0470-3.

Hu, A., M. Choi, A. J. Tanentzap, J. Liu, K.-S. Jang, J. T. Lennon, Y. Liu, et al. 2022. "Ecological Networks of Dissolved Organic Matter and Microorganisms Under Global Change." *Nature Communications* 13 (1): 3600. https://doi.org/10.1038/s41467-022-31251-1.

Islam, M., B. S. Rumainul, and F. A. Dijkstra. 2023. "Microbial Carbon Use Efficiency of Glucose Varies with Soil Clay Content: A Meta-Analysis." *Applied Soil Ecology* 181:104636. https://doi.org/10.1016/j.apsoil.2022.104636.

Jinich, A., A. Flamholz, H. Ren, S. J. Kim, B. Sanchez-Lengeling, C. A. Cotton, E. Noor, A. Aspuru-Guzik, and A. Bar-Even. 2018. "Quantum Chemistry Reveals Thermodynamic Principles of Redox Biochemistry." *PLOS Computational Biology* 14 (10): e1006471. https://doi.org/10.1371/journal.pcbi.1006471.

Jones, D. L., P. W. Hill, A. R. Smith, M. Farrell, T. Ge, N. C. Banning, and D. V. Murphy. 2018. "Role of Substrate Supply on Microbial Carbon Use Efficiency and Its Role in Interpreting Soil Microbial Community-Level Physiological Profiles (CLPP)." *Soil Biology and Biochemistry* 123:1–6. https://doi.org/10.1016/j.soilbio.2018.04.014.

Koranda, M., C. Kaiser, L. Fuchslueger, B. Kitzler, A. Sessitsch, S. Zechmeister-Boltenstern, and A. Richter. 2014. "Fungal and Bacterial Utilization of Organic Substrates Depends on Substrate Complexity and N Availability." *FEMS Microbiology Ecology* 87 (1): 142–152. https://doi.org/10.1111/1574-6941.12214.

Kothawala, D. N., A. M. Kellerman, N. Catalán, and L. J. Tranvik. 2021. "Organic Matter Degradation Across Ecosystem Boundaries: The Need for a Unified Conceptualization." *Trends in Ecology & Evolution* 36 (2): 113–122. https://doi.org/10.1016/j.tree.2020.10.006.

Langenheder, S., M. T. Bulling, M. Solan, and J. I. Prosser. 2010. "Bacterial Biodiversity-Ecosystem Functioning Relations Are Modified by Environmental Complexity." *PLOS ONE* 5 (5): e10834. https://doi.org/10.1371/journal.pone.0010834.

LaRowe, D. E., and P. Van Cappellen. 2011. "Degradation of Natural Organic Matter: A Thermodynamic Analysis." *Geochimica et Cosmochimica Acta* 75 (8): 2030–2042. https://doi.org/10.1016/j.gca.2011.01.020.

Lehmann, J., C. M. Hansel, C. Kaiser, M. Kleber, K. Maher, S. Manzoni, N. Nunan, et al. 2020. "Persistence of Soil Organic Carbon Caused by Functional Complexity." *Nature Geoscience* 13 (8): 529–534. https://doi.org/10.1038/s41561-020-0612-3.

Lloyd, D. A., K. Ritz, E. Paterson, and G. J. D. Kirk. 2016. "Effects of Soil Type and Composition of Rhizodeposits on Rhizosphere Priming Phenomena." *Soil Biology and Biochemistry* 103:512–521. https://doi.org/10.1016/j.soilbio.2016.10.002.

Loeppmann, S., M. Semenov, E. Blagodatskaya, and Y. Kuzyakov. 2016. "Substrate Quality Affects Microbial- and Enzyme Activities in Rooted Soil." *Journal of Plant Nutrition and Soil Science* 179 (1): 39–47. https://doi.org/10.1002/jpln.201400518.

Loreau, M. 2001. "Microbial Diversity, Producer-Decomposer Interactions and Ecosystem Processes: A Theoretical Model." *Proceedings of the Royal Society B* 268 (1464): 303–309. https://doi.org/10.1098/rspb.2000.1366.

Lorenz, M., T. Maskow, and S. Thiele-Bruhn. 2024. "Energy Stored in Soil Organic Matter is Influenced by Litter Quality and the Degree of Transformation – A Combustion Calorimetry Study." *Geoderma* 443:116846. https://doi.org/10.1016/j.geoderma.2024.116846.

Manzoni, S., P. Čapek, M. Mooshammer, B. D. Lindahl, A. Richter, and H. Šantrůčková. 2017. "Optimal Metabolic Regulation Along Resource Stoichiometry Gradients." *Ecology Letters* 20 (9): 1182–1191. https://doi.org/10.1111/ele.12815.

Manzoni, S., P. Taylor, A. Richter, A. Porporato, and G. I. Ågren. 2012. "Environmental and Stoichiometric Controls on Microbial Carbon-Use Efficiency in Soils." *New Phytologist* 196 (1): 79–91. https://doi.org/10.1111/j.1469-8137.2012.04225.x.

Marschner, B., and K. Kalbitz. 2003. "Controls of Bioavailability and Biodegradability of Dissolved Organic Matter in Soils." *Geoderma* 113 (3–4): 211–235. https://doi.org/10.1016/S0016-7061(02)00362-2.
<figure><img src="image_36.png" /></figure>

Mondini, C., M. L. Cayuela, M. A. Sanchez-Monedero, A. Roig, and P. C. Brookes. 2006. "Soil Microbial Biomass Activation by Trace Amounts of Readily Available Substrate." *Biology and Fertility of Soils* 42 (6): 542–549. https://doi.org/10.1007/s00374-005-0049-2.

Nelson, J. T., T. A. Adjuik, E. B. Moore, A. D. VanLoocke, A. R. Reyes, and M. D. McDaniel. 2024. "A Simple, Affordable, Do-It-Yourself Method for Measuring Soil Maximum Water Holding Capacity." *Communications in Soil Science and Plant Analysis* 55 (8): 1190–1204. https://doi.org/10.1080/00103624.2023.2296988.

Nunan, N., T. Z. Lerch, V. Pouteau, P. Mora, F. Changey, T. Kätterer, S. Giusti-Miller, and A. M. Herrmann. 2015. "Metabolising Old Soil Carbon: Simply a Matter of Simple Organic Matter?" *Soil Biology and Biochemistry* 88:128–136. https://doi.org/10.1016/j.soilbio.2015.05.018.

Nunan, N., H. Schmidt, and X. Raynaud. 2020. "The Ecology of Heterogeneity: Soil Bacterial Communities and C Dynamics." *Philosophical Transactions of the Royal Society B: Biological Sciences* 375 (1798): 20190249. https://doi.org/10.1098/rstb. 2019.0249.

Okano, H., R. Hermsen, and T. Hwa. 2021. "Hierarchical and Simultaneous Utilization of Carbon Substrates: Mechanistic Insights, Physiological Roles, and Ecological Consequences." *Current Opinion in Microbiology* 63:172–178. https://doi.org/10.1016/j.mib.2021.07.008.

Oksanen, J., G. Simpson, B. F. Guillaume, R. Kindt, P. Legendre, P. R. Minchin, R. B. O'Hara, et al. 2025. "Vegan: Community Ecology Package (R Package Version 2.6-6.1)." https://CRAN.R-project.org/package=vegan.

Orwin, K. H., D. Wardle, and L. Greenfield. 2006. "Ecological Consequences of Carbon Substrate Identity and Diversity in a Laboratory Study." *Ecology* 87 (3): 580–593. https://doi.org/10.1890/05-0383.

Pausch, J., and Y. Kuzyakov. 2018. "Carbon Input by Roots into the Soil: Quantification of Rhizodeposition from Root to Ecosystem Scale." *Global Change Biology* 24 (1): 1–12. https://doi.org/10.1111/gcb.13850.

Poeplau, C., H. Aronsson, Å. Myrbeck, and T. Kätterer. 2015. "Effect of Perennial Ryegrass Cover Crop on Soil Organic Carbon Stocks in Southern Sweden." *Geoderma Regional* 4:126–133. https://doi.org/10.1016/j.geodrs.2015.01.004.

Pontes, M. H., A. Sevostyanova, and E. Groisman. 2016. "When Too Much ATP Is Bad for Protein Synthesis." *Journal of Molecular Biology* 427 (16): 2586–2594. https://doi.org/10.1016/j.jmb.2015.06.021.

Quigley, L. N. M., A. Edwards, A. D. Steen, and A. Buchan. 2019. "Characterization of the Interactive Effects of Labile and Recalcitrant Organic Matter on Microbial Growth and Metabolism." *Frontiers in Microbiology* 10:493. https://doi.org/10.3389/fmicb.2019.00493.

Schroeder, J., A. König, C. Poeplau, T. Bölscher, K. H. E. Meurer, M. Toleikienė, M. Hanegraaf, et al. 2025. "The Effect of Crop Diversification and Season on Microbial Carbon Use Efficiency Across a European Pedoclimatic Gradient." *European Journal of Soil Science* 76 (2): e70078. https://doi.org/10.1111/ejss.70078.

Shi, A., A. Chakrawal, S. Manzoni, B. M. Fischer, N. Nunan, and A. M. Herrmann. 2021. "Substrate Spatial Heterogeneity Reduces Soil Microbial Activity." *Soil Biology and Biochemistry* 152:108068. https://doi.org/10.1016/j.soilbio.2020.108068.

Soares, M., and J. Rousk. 2019. "Microbial Growth and Carbon Use Efficiency in Soil: Links to Fungal-Bacterial Dominance, SOC-Quality and Stoichiometry." *Soil Biology and Biochemistry* 131:195–205. https://doi.org/10.1016/j.soilbio.2019.01.010.

Steinauer, K., A. Chatzinotas, and N. Eisenhauer. 2016. "Root Exudate Cocktails: The Link Between Plant Diversity and Soil Microorganisms?" *Ecology and Evolution* 6 (20): 7387–7396. https://doi.org/10.1002/ece3.2454.

Stumpf, K., C. Simon, A. Miltner, T. Maskow, and O. J. Lechtenfeld. 2025. "Deciphering the Energy Use Channels in Soil Organic Matter: Impacts of Long-Term Farmyard Manure Addition and Microbial Necromass Revealed by LC-FT-ICR-MS." *Soil Biology and Biochemistry* 208:109857. https://doi.org/10.1016/j.soilbio.2025.109857.

Takriti, M., B. Wild, J. Schnecker, M. Mooshammer, A. Knoltsch, N. Lashchinskiy, R. J. Eloy Alves, et al. 2018. "Soil Organic Matter Quality Exerts a Stronger Control Than Stoichiometry on Microbial Substrate Use Efficiency Along a Latitudinal Transect." *Soil Biology and Biochemistry* 121:212–220. https://doi.org/10.1016/j.soilbio.2018.02.022.

Tanentzap, A. J., A. Fitch, C. Orland, E. J. S. Emilson, K. M. Yakimovich, H. Osterholz, and T. Dittmar. 2019. "Chemical and Microbial Diversity Covary in Fresh Water to Influence Ecosystem Functioning." *Proceedings of the National Academy of Sciences* 116 (49): 24689–24695. https://doi.org/10.1073/pnas.1904896116.

Tang, J.-Y., and W. J. Riley. 2017. "UPECA Kinetics for Scaling Redox Reactions in Networks of Mixed Substrates and Consumers and an Example Application to Aerobic Soil Respiration." *Geoscientific Model Development* 10 (9): 3277–3295. https://doi.org/10.5194/gmd-10-3277-2017.

Tian, S., S. Yao, S. Zhu, P. Li, T. Zhang, X. Su, R. Huang, et al. 2025. "Evaluating Soil Dissolved Organic Matter as a Proxy for Soil Organic Matter Properties Across Diverse Ecosystems." *Soil Biology and Biochemistry* 204:109752. https://doi.org/10.1016/j.soilbio.2025.109752.

Vaziourakis, K.-M., L. Heffernan, E. Jakobsson, C. Grasset, D. Kothawala, and L. Tranvik. 2025. "Controls on the Respiratory Quotient of Organic Matter Decomposition Across Ecosystems." *Biogeochemistry* 168 (2): 24. https://doi.org/10.1007/s10533-025-01217-8.

Vranova, V., K. Rejsek, K. R. Skene, D. Janous, and P. Formanek. 2013. "Methods of Collection of Plant Root Exudates Concerning Plant Metabolism and Purpose: A Review." *Journal of Plant Nutrition and Soil Science* 176 (2): 175–199. https://doi.org/10.1002/jpln.201000360.

Wang, Y., M. W. Cadotte, Y. Chen, L. H. Fraser, Y. Zhang, F. Huang, S. Luo, N. Shi, and M. Loreau. 2019. "Global Evidence of Positive Biodiversity Effects on Spatial Ecosystem Stability in Natural Grasslands." *Nature Communications* 10 (1): 3207. https://doi.org/10.1038/s41467-019-11191-z.

Wardle, D., K. I. Bonner, and K. S. Nicholson. 1997. "Biodiversity and Plant Litter—Experimental Evidence Which Does Not Support the View That Enhanced Species Richness Improves Ecosystem Function." *Oikos* 79 (2): 247–258. https://doi.org/10.2307/3546010.

Weverka, J. R., H. V. Moeller, and J. P. Schimel. 2023. "Chemodiversity Controls Microbial Assimilation of Soil
<figure><img src="image_37.png" /></figure>

Organic Carbon: A Theoretical Model." *Soil Biology and Biochemistry* 187:109161. https://doi.org/10.1016/j.soilbio. 2023.109161.

Wickham, H. 2016. *Ggplot2: Elegant Graphics for Data Analysis*. Springer-Verlag New York.

Yang, K. M., T. Poolpak, P. Pokethitiyook, M. Kruatrachue, and P. Saengwilai. 2021. "Responses of Oil Degrader Enzyme Activities, Metabolism and Degradation Kinetics to Bean Root Exudates During Rhizoremediation of Crude

Oil Contaminated Soil." *International Journal of Phytoremediation* 24 (1): 101-109. https://doi.org/10. 1080/15226514.2021.1926912.

Zhu, Z., T. Ge, Y. Luo, S. Liu, X. Xu, C. Tong, O. Shibistova, G. Guggenberger, and J. Wu. 2018. "Microbial Stoichiometric Flexibility Regulates Rice Straw Mineralization and Its Priming Effect in Paddy Soil." *Soil Biology and Biochemistry* 121:67-76. https://doi.org/ 10.1016/j.soilbio.2018.03.003.