# Leaf litter chemistry contributes to shape the chemical footprint of macrodetritivore communities

Théo Marchand, Matthieu Chauvat, Estelle Forey, Florence

Maunoury-Danger, Mathieu Stantonja, Michaël Danger, Philippe

Usseglio-Polatera, David Baqué, Frédéric Caudaudap, Sophia Hansson, et al.

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► To cite this version:

Théo Marchand, Matthieu Chauvat, Estelle Forey, Florence Maunoury-Danger, Mathieu Stantonja, et al.. Leaf litter chemistry contributes to shape the chemical footprint of macrodetritivore communities. Applied Soil Ecology, 2026, 219, pp.106800. 10.1016/j.apsoil.2026.106800. hal-05489598

HAL Id: hal-05489598

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Submitted on 26 Feb 2026

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Research paper

# Leaf litter chemistry contributes to shape the chemical footprint of macrodetritivore communities

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Théo Marchand<sup>a,b</sup>, Matthieu Chauvat<sup>c</sup>, Estelle Forey<sup>c</sup>, Florence Maunoury-Danger<sup>d</sup>, Mathieu Santonja<sup>e</sup>, Michaël Danger<sup>d</sup>, Philippe Usseglio-Polatera<sup>d</sup>, David Baqué<sup>a,f</sup>, Frédéric Caudaudap<sup>a</sup>, Sophia V. Hansson<sup>a</sup>, Frédéric Julien<sup>a</sup>, Christophe Laplanche<sup>a</sup>, Gaël Le Roux<sup>a</sup>, Virginie Baldy<sup>e</sup>, Raphaël Gros<sup>e</sup>, Benjamin Pey<sup>a,∗</sup>

## ARTICLE INFO

**Keywords:**

Functional traits

Detritivore chemistry

Forest

Leaf litter chemistry

Soil fauna communities

Stoichiometry

## ABSTRACT

Addressing the factors underlying community patterns is a crucial endeavor as it contributes to a better understanding of the relationship between biodiversity and ecosystem functioning, as well as the associated ecosystem services. For example, detritivore communities play a major role in decomposition processes and related matter and energy fluxes in ecosystems. However, compared to living plant resources, leaf litter resources are nutritionally poor, with low macroelement concentrations. Although detritivore communities are known to depend on the local leaf litter resources, it remains unclear whether the chemical composition of detritivores depends on the locally available leaf litter. The macroelement composition of detritivores is rarely studied and is seldom compared directly to leaf litter chemical quality. Furthermore, leaf litter elements other than carbon (C), nitrogen (N), and phosphorus (P) are not systematically investigated even though large differences in elements such as calcium (Ca), potassium (K), or magnesium (Mg) can occur among both detritivore taxa and leaf litter types.

To investigate whether the chemical composition of macrodetritivore communities depends on leaf litter chemistry, we sampled 24 paired French forests sites that differed in their leaf litter chemical composition. At each site, we quantitatively sampled leaf litter transformers (Diplopoda and Isopoda) to estimate their abundance. For each morphospecies, we measured mean individual body mass and analyzed body concentrations of C, N, P, K, Ca, and Mg (hereafter called chemical traits). We also analyzed the same macroelements in the dominant leaf litter at each site. We examined the detritivore taxonomic diversity, chemical community diversity, biomass, and abundance in communities, and tested whether these parameters were influenced by leaf litter chemistry.

Results at the morphospecies level were consistent with the homeostasis hypothesis, indicating no specific physiological adaptation to the chemical composition of their trophic resources. Chemical community diversity (i.e., the FDis index based on all six chemical elements) of detritivores was higher at sites with high-quality leaf litter than at the corresponding low-quality leaf litter sites. Furthermore, community-level concentrations of P and Mg in detritivores were positively influenced by litter P and Mg concentrations, respectively.

Although effect sizes were limited, our results suggest that leaf litter chemical composition can influence detritivore chemical composition through shifts in the relative abundance of taxa. Ultimately, this may lead to a closer match between the chemical composition of detritivore communities and that of their resources.

<footnote><sup>a</sup> Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), Université de Toulouse, CNRS, IRD, Toulouse INP, Université Toulouse 3 – Paul Sabatier (UT3), Toulouse, France</footnote>

<footnote><sup>b</sup> Université Grenoble Alpes, INRAE, LESSEM, 38402, Saint-Martin-d'Hères, France</footnote>

<footnote><sup>c</sup> Normandie Univ, UNIROUEN, INRAE, ECODIV, Rouen, France</footnote>

<footnote><sup>d</sup> Laboratoire interdisciplinaire des environnements continentaux, CNRS UMR 7360, Université de Lorraine, 8, rue du général Delestraint, 57070, Metz, France</footnote>

<footnote><sup>e</sup> Aix Marseille Univ, Avignon Univ, CNRS, IRD, IMBE, Marseille, France</footnote>

<footnote><sup>f</sup> CNRS – Université Laval – Sorbonne Université – Laboratoire de recherche international Takuvik</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: benjamin.pey@toulouse-inp.fr (B. Pey).</footnote>
# 1. Introduction

Over the last decades, community ecologists have tried to address the crucial issue of identifying factors responsible for observed community patterns as this is a prerequisite to improve our understanding of the relationship between biodiversity and ecosystem functioning, as well as associated ecosystem services (Brussaard, 2012; Diamond, 1975). In particular, attention has been paid to whether community structuring is stochastic or governed by assembly rules (Connor and Simberloff, 1979; Diamond, 1975; Ulrich, 2004). Especially, environmental filters may drive community structuration under the control of assembly rules (Belyea and Lancaster, 1999; Decaëns et al., 2008). To address these questions, trait-based approaches have recently been favoured because traits inform organismal biological functioning (Pey et al., 2014; Violle et al., 2007) and could explain organism and community responses to environmental filtering.

Detritivore communities form the basis of brown trophic food chains and thus play a major role in decomposition processes and associated matter and energy fluxes within ecosystems (David, 2014; García-Palacios et al., 2013; Gessner et al., 2010). Moreover, as most detritivores have limited dispersal capacities (Gongalsky and Persson, 2013), individuals may have to cope with their very local leaf litter resources. Leaf litter influences detritivore habitats by improving water storage and providing a complex structure that insulates them from predators and abiotic parameter fluctuations such as temperature (David, 2014). Yet, this is a particularly poor nutritional resource compared to living plants used by herbivorous organisms (Li et al., 2021; Martinson et al., 2008), with notably low concentrations of macroelements that can critically impact decomposition and consumption by detritivores. This nutritional constraint makes detritivores particularly sensitive to variations in leaf litter chemical quality. From this perspective, nitrogen (N) and phosphorus (P) have been particularly studied (Evans-White and Halvorson, 2017; Hättenschwiler et al., 2005). It appears that leaf litter macroelement concentrations first depend on tree species identity (Li et al., 2021). Then, during decomposition, both physical and microbial agents generally reduce chemical deterrents and increase macroelement concentration over time (Hättenschwiler et al., 2005). A high macroelement concentration is generally associated to a high-quality resource being consumed and decomposed faster than a low-quality resource (Evans-White and Halvorson, 2017; Hättenschwiler et al., 2005).

Consequently, leaf litter quality may affect detritivore fitness (David and Gillon, 2009; Zimmer and Topp, 1997). Detritivores need to obtain minimal resources necessary for their development and growth, in the right proportions (Threshold Elemental ratios sensu Frost et al., 2006). The relative proportions between these minimal resources are supposedly relatively fixed for a given animal species (i.e., animals are assumed to be relatively homeostatic). A mismatch between consumer requirements and elements available in resources may lead to body growth constraints (Sterner and Elser, 2003) and effects at the population level, which can extend to the whole community (Beck et al., 2023). To reduce or compensate for such an elemental gap, a detritivore may adopt different strategies. It could behaviorally compensate for nutrient-poor resources by increasing its feeding or assimilation rate (Jochum et al., 2017) and/or compensate its main food resource with another more nutrient-rich one (Crenier et al., 2017). It could also adopt physiological strategies such as flexible homeostasis (i.e., a change in body chemical composition) (Cross et al., 2005; Small and Pringle, 2010; Tagliaferro et al., 2021). Detritivores may also have a reduced growth and reproduction rate because of limited macroelement availability (Rushton and Hassall, 1983; Zimmer and Topp, 1997). At the community level, other mechanisms might operate. First, detritivore species could interact, leading to competition for the highest-quality resource or, by contrast, to facilitation mechanisms (Coulis et al., 2015; Hedde et al., 2010). This could result in a bottom-up control of the whole soil food web and impact other trophic levels such as predators (Marjakangas et al., 2022). All of the above-mentioned mechanisms could lead to spatial

segregation, or competitive exclusion of detritivore species, with some species being unable to survive on a poor local resource (David et al., 2023; Ott et al., 2014).

The chemical composition of litter is known to influence macrodetritivores at multiple levels (e.g., taxonomy, biomass) (David et al., 2023; Ott et al., 2014). However, the macroelement composition of detritivores is rarely investigated (Zhang et al., 2022), and when it is, that composition is scarcely compared to litter chemical quality. Furthermore, litter elements other than C, N, and P are not systematically investigated, even though large differences in other elements (e.g., Ca, Mg) can occur among detritivorous taxa (Zhang et al., 2022) and litter types (García-Palacios et al., 2016). Macroelements can be involved in key physiological processes (e.g., signal transduction for K and Ca, enzyme and transporters cofactor for Mg, energy gestion and signalling for P, proteins and nucleic acids for N, phospholipid structure for P), and in body structures (e.g., Ca in teguments). Carbon is mostly a structural element but is considered as a reference in stoichiometry studies. Recent valuable field studies have however partially filled these knowledge gaps (Zhang et al., 2023; Warnke et al., 2023; Zhang et al., 2024). The stoichiometry of a given taxon seemed to be stable whatever the elemental composition of its available resources. Furthermore, the population biomass and abundance of a given taxon decreased with elemental mismatch between the elemental composition of the detritivore's body and of its resource. However, these recent studies considered geographically very distant sites, which makes difficult to disentangle the effect of the elemental mismatch between detritivores and their resources from the effect of other confounding factors (e.g., the influence of the pedoclimatic context or the surrounding landscape).

Here, we investigated whether the chemistry of macrodetritivore communities (hereafter called "chemical community diversity") depended on litter chemical quality (i.e., on litter macroelement concentrations). The chemical community diversity of detritivores can be defined conceptually as the sum of all detritivore species chemistry (i.e., body macroelement concentrations), weighted by the product of their respective abundances and mean individual body masses. Here, we did not study ratios between element concentrations, but rather studied each macroelement concentration as a functional trait. A set of macroelements constitute a set of functional traits describing chemical community diversity. Changes in chemical community diversity (a mass of elements at the community level) for detritivores can be due to changes in one or more of these parameters: species chemistry (a mass of elements per body mass), species abundances (number of individuals), and/or species body mass.

To test whether the chemical community diversity of macrodetritivore communities depends on the chemical quality of the litter, we selected, in different pedoclimatic contexts, pairs of geographically very close forest sites that differed only in their litter chemical quality (Fig. 1).

Under the hypothesis of strong chemical constraints, we expect detritivores to react to the chemistry of their local litter (Supplementary Fig. S1). A given morphospecies could exhibit different body element concentrations (following Hypothesis I) on sites with high- versus low-quality litter (following a flexible homeostasis hypothesis). Furthermore, if morphospecies perform better on high-quality litter, their abundance and/or mean individual body mass may be higher comparatively to those of morphospecies on sites with low-quality litter (Hypothesis II).

At the community level, the chemical composition of detritivore communities could be influenced by their local litter (Hypothesis III). Alternatively, but not exclusively, we can also expect communities to have higher productivity (i.e., higher total abundance and total biomass) (Hypothesis IV) at sites with high-quality litter due to lower chemical constraints, assuming however that litter quantity is not limiting. We also test whether the chemical quality of litter influenced taxonomic diversity at the community level, to assess how far taxonomic diversity could be a proxy of chemical composition of detritivore community (Hypothesis V). Alternatively, we can expect detritivore characteristics
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Schematic representation of the experimental design. In five French regions, several pairs of sites were identified. Each pair shared the same pedoclimatic context. One site was characterized by a priori high-quality litter ("ash" on the satellite view of one pair of sites in the Montagne Noire) and the other by a priori low-quality litter ("beech" on the same satellite view). On each site, detritivore community was collected in a quantitative way. The chemical contents of detritivores and litter were analyzed.</figcaption></figure>

to be mostly driven by geographical constraints rather than by litter quality.

**2. Material and methods**

**2.1. Sites**

We studied a total of 12 pairs of forest sites distributed in five different French regions with different pedoclimatic contexts (Pyrenean piedmont, Montagne Noire, Luberon, Vosges, Normandie) (Fig. 1, Supplementary Table S1). Each pair consisted of two nearby sites (<1 km) with highly contrasted leaf litter in the same pedoclimatic context. One site of each pair was a priori characterized by high-quality litter (litter

with a priori high macroelement content, high decomposability and palatability, based on literature data). The other site was characterized by a litter of a priori lower quality (Fig. 2, Supplementary Table S2). We selected sites with a uniform or dominant leaf litter species and a homogeneous topography between the two sites of each pair. This sampling design allowed us to specifically assess the effect of litter chemistry, while limiting the potentially confounding effects of other environmental filters acting on the detritivore community (such as biogeographic and pedoclimatic contexts). In addition, using multiple pairs of sites across a diversity of pedoclimatic contexts allowed for genericity of observed patterns.

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** PCA applied to litter chemical traits. Litter of a-priori higher quality is always higher in the graph than the corresponding litter of lower quality within each pair. The first two axes represent 64.1% of the total data variability.</figcaption></figure>
## 2.2. Soil and litter sampling

We collected soil and litter samples in the fall. At each site, we pooled five top-soil samples without litter, collected with a soil corer. Soils were then air-dried, aggregates were gently crushed by hand and sieved to 2 mm. Granulometry (percentage of clay, sand and silt), pH, organic matter, nitrogen and phosphorus content, and Cation Exchange Capacity (CEC) of the fine fraction were analyzed by the SADEF laboratory (Aspach-Le-Bas, 68700, France). We collected freshly-fallen dead leaves and air-dried them. The leaves belonged to nine species of three different families: *Alnus glutinosa*, *Betula pendula*, *Carpinus betulus*, *Corylus avellana* (Betulaceae), *Fagus sylvatica*, *Quercus ilex*, *Q. petraea*, *Q. pubescens* (Fagaceae), and *Fraxinus excelsior* (Oleaceae). Because abscission of *Q. ilex* occurs in the spring, we collected litter from pairs of sites with *Q. ilex* (Luberon sites) at the same time as the soil fauna sampling (i.e., in spring, see below).

## 2.3. Soil fauna

We characterized the detritivore community by focusing on “litter transformers” (Diplopoda and Isopoda) (Lavelle and Spain, 2001; Wardle, 2002), as they represent a homogeneous group among the soil fauna whose diet almost exclusively relies on litter material (David, 2014). We classified detritivores into morphospecies, differentiating taxa by coarse morphological characters. Detritivore morphospecies belonged to four different orders and two different classes: Glomerida, Julida, Polydesmida (Diplopoda), and Isopoda (Malacostraca). We quantitatively sampled these detritivores to estimate the abundance (number of individuals.m⁻²) of each morphospecies and we analyzed their body concentrations of macroelements: carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg). Finally, we assessed the mean individual dry body mass of morphospecies. Afterwards, a pool of detritivores was collected (either on a subsequent sampling campaign or from remaining stored individuals) to precisely identify the main species, in an informational perspective (Supplementary Table S3).

At each site, we sampled detritivores in the spring of 2021 (and 2022 in Normandie) by hand-sorting all macrodetritivore individuals from five soil blocks of 25 × 25 × 25 cm³ including litter. At the sites in Vosges, only three soil blocks were taken because the site surfaces were smaller. To obtain spatially independent samples, soil blocks were separated by at least 10 m. To avoid border effects, soil blocks were at least 10 m away from site boundaries. Individuals were assigned to coarse morphospecies and counted for total abundance assessment (Supplementary Table S4). Additional detritivore individuals were sight-hunted (collected on sight in a non-quantitative manner) throughout the site to ensure sufficient material for the chemical analyses. Detritivores were brought alive to the laboratory. There, detritivores were sorted by morphospecies, placed in plastic boxes without litter, and left overnight to purge to avoid gut contamination of chemical analyses.

## 2.4. Chemical analyses

Individuals were placed in a freezer at -20 °C until chemical analyses. After thawing, detritivores were rinsed with ultrapure water (18,2 MΩ-cm, Elga Water, PureLab, Veolia), dried at 45 °C for 4 days, weighed to the nearest 0.1 mg, and pooled into samples for chemical analyses. Each sample contained more than 20 mg of cumulated dry body mass and more than two individuals of the same morphospecies. For each morphospecies at each site, if we had sufficient biological material, we performed two samples to obtain duplicate analyses. We ground samples to powder using a bead mill (FastPrep-24™ Classic Instrument) and silica spheres. We quantified C and N concentrations from a subsample of the powder using a Total nitrogen and Organic Carbon analyzer (Thermo Scientific Flash2000 elemental analyzer) at the PAPC platform

of the CRBE laboratory (Toulouse, France). Analyses were conducted under national quality standards (NF ISO 10694; NF ISO 13878; NF EN 13137). We quantified P, K, Ca, and Mg concentrations from a subsample of the same powder, using a Quadrupole Induced Coupled Plasma – Mass Spectrometer (Q-ICPMS, NeXION 320D (Perkin Elmer), typical detection limits are 0.01 µg.kg⁻¹ with an accuracy below 5%) at the FBil platform of the CRBE laboratory (Toulouse, France). For ICP-MS analyses, 5 mg of dry powder was digested with 0.5 mL of 63–67% nitric acid overnight at 95 °C. The next morning, 10 mL (if the initial sample mass was less than 4 mg) or 15 mL of ultrapure water was added. Digestion was performed in a class 100 clean room to avoid contamination. Blanks and certified reference materials were also included for quality assurance (Supplementary Table S5). Litter chemistry (C, N, P, K, Ca, Mg) was analyzed following a similar procedure, with four analysis replicates of the litter at each site. All powder weights were measured to the nearest 0.001 mg.

## 2.5. Statistical analyses

To visualize the chemical landscape of litter and detritivores, we performed PCA analyses (Figs. 2 and 3). The PCA applied to litter described each litter on each site with C, N, P, K, Ca, and Mg concentrations. The PCA applied to detritivores described each morphospecies on each site with C, N, P, K, Ca, and Mg concentrations, and mean body mass.

Before testing our hypotheses on the effect of litter on detritivores, we tested the robustness of our sampling design by checking that i) soil parameters did not differ between sites in the same pair and that ii) litter chemistry differed between sites of the same pair.

To test for the soil effect, we performed a MANOVA analysis with soil parameters (namely Silt, Sand, pH, P, N, and C/N ratio) explained by Region, Pair-of-sites, and Litter quality variables without interactions. The percentage of Clay was excluded because Clay + Sand + Silt = 100. Organic matter and CEC variables were discarded because of multicollinearity (Pearson correlation >0.9) with other variables. P and N content variables were log-scaled to meet normal distribution assumptions.

To test for differences in litter chemistry, we performed a MANOVA analysis with litter chemistry (C, N, P, K, Ca, Mg) explained by Region, Pair-of-sites, and Litter quality variables without interactions. All variables were log-scaled to meet normal distribution assumptions.

To test the homeostasis hypothesis (hypothesis I), we performed a MANOVA analysis with site-specific morphospecies chemistry (C, N, P, K, Ca, Mg) explained by Litter quality and Pair-of-sites-specific morphospecies identity (to compare the chemistry of morphospecies between sites of the same pair). Likewise, we tested with a MANOVA analysis if morphospecies mean body mass and abundance differed with Litter quality and Pair-of-sites-specific morphospecies identity (hypothesis II). For these two analyses, only morphospecies present on both sites of each pair were selected for the analysis. Mean body mass was computed as the mean individual body mass value for each morphospecies at each site. Mean body mass and body chemical composition were computed by also considering sight-hunted individuals. All variables were log-scaled to meet normal distribution assumptions.

To test for differences in the detritivore community chemistry (hypothesis III), we performed a MANOVA with Community Weighted Means for C, N, P, K, Ca, Mg content, explained by Region, Pair-of-sites, and Litter quality variables without interactions. Community Weighted Mean (CWMₓ) values of the concentration of each X element in all sp morphospecies at each site were computed as follows:

$$CWM_{x} = \frac{\sum_{sp} Abundance_{sp} * BodyMass_{sp} * X_{sp}}{\sum_{sp} Abundance_{sp} * BodyMass_{sp}} \quad (1)$$

All variables were log-scaled to meet normal distribution
<figure><img src="image_6.png" /><figcaption>Fig. 3. PCA applied to detritivore traits. Each point is a morphospecies belonging to one site. Detritivores are grouped by orders (95% - confidence ellipses). The first two axes represent 74% of the total data variability.</figcaption></figure>

assumptions.

To test for differences in chemical diversity at the community scale we performed an ANOVA analysis with the FDis index explained by Region, Pair-of-sites, and Litter quality variables without interactions. We computed the FDis index (Laliberté and Legendre, 2010) assessing the functional dispersion of the community at each site (using the FD package on the R software (Laliberté et al., 2023)) with the C, N, P, K, Ca, and Mg body concentration of each morphospecies, weighted by its abundance multiplied by its mean individual body mass at each site. A higher functional dispersion reflects a higher abundance-weighted deviation of species trait values from the abundance-weighted centroid of the species traits of the community, i.e. a less homogeneous community from a trait perspective (Laliberté and Legendre, 2010; Mouillot et al., 2013).

To test if differences in litter chemistry influenced differences in detritivore community chemistry, we tested for a linear correlation between the ΔCWM index of each C, N, P, K, Ca, and Mg element, and the Δ index of litter concentration of the corresponding element.

Δ indexes are computed as follows:

$$ \Delta\text{index} = \frac{X_{\text{HighQualityLitter}} - X_{\text{LowQualityLitter}}}{X_{\text{HighQualityLitter}} + X_{\text{LowQualityLitter}}} \quad (2) $$

Δindex can take values between -1 ($X_{HighQualityLitter} = 0$ and $X_{LowQualityLitter} \neq 0$) and 1 ($X_{LowQualityLitter} = 0$ and $X_{HighQualityLitter} \neq 0$). Δindex = 0 when $X_{HighQualityLitter} = X_{LowQualityLitter}$ (and $\neq 0$). The closer Δindex values are to -1 and 1, the stronger the influence of litter quality. We then tested if Δindex significantly differed from zero with a one-sample, two-sided, Student t-test. We applied the Benjamini and Hochberg (1995) correction for multiple t-tests.

To test for differences in the detritivore community productivity (hypothesis IV), we performed ANOVA analyses with total abundance and biomass explained by Region, Pair-of-sites, and Litter quality variables without interactions. We initially indended to perform a MANOVA but moved to ANOVA because of multicolinearity between total abundance and biomass (Pearson correlation >0.9). Both total abundance and biomass were log-scaled to meet normal distribution assumptions. Total community biomass at each site was computed according to the following equation:

$$ \text{TotalBiomass} = \sum_{sp} \text{Abundance}_{sp} * \text{MeanBodyMass}_{sp} \quad (3) $$

Abundance index was computed using quantitative block sampling data only.

To test for differences in taxonomical diversity in the detritivore community (hypothesis V), we performed a MANOVA analysis with species richness and Shannon index explained by Region, Pair-of-sites, and Litter quality variables without interactions. Taxonomy-based indexes were computed using quantitative block sampling data only.

In all MANOVA tests, assumptions of individual normality and homoscedasticity, presence of outlier values, and multicolinearity were checked with appropriate tests. When the influence of Litter quality was significant, we computed a post-hoc ANOVA on each response variable, with the same explanatory variables as in the MANOVA. When the effect of Litter quality on a given variable was significant, we computed as follows a percentage of variation to estimate the magnitude of the “high quality” effect: 100 × (X0/X1 -1), with X0 the value of the variable on the site of low-quality litter, and X1 the value on the site with high-quality litter of the same pair. When X1 = 0, the value was a NA. We then computed a mean value ± se value. In ANOVA models, assumptions were visually checked. MANOVA and ANOVA were performed with the manova and the aov functions of the stats package (R Core Team, 4.5.0). PCA were performed with the PCA function of the FactoMineR package (Lê et al., 2008). All the analyses were performed with R version 4.0.3 (R Core Team, 2024).

## 3. Results

### 3.1. Soil and litter parameters

According to the MANOVA analysis, soil variables did not differ with litter quality (Table 1, Supplementary Fig. S2). According to the MANOVA analysis, litter chemical composition differed significantly litter quality, with C concentrations being lower (−3.8 ± 0.7%, $F_{1/11} = 29.6$, $p < 0.001$), and N, P, and Mg concentrations being higher (+44 ± 15%, +49 ± 13%, and +66 ± 20%; $F_{1/11} = 10.7$, 17.5, and 14.2; $p = 0.008$, 0.002, and 0.003, respectively) in the a priori high-quality litter (litter with a priori high macroelement content, high decomposability and palatability, based on literature data), than in the a priori low-quality litter (Table 1). K and Ca content did not show any difference between litter quality ($F_{1/11} = 1.19$, and 0.94; $p = 0.300$, and 0.352, respectively). In the MANOVA analysis, only the C content variable slightly did not comply with the normal distribution ($p_{Shapiro = 0.040}$) and the
<caption>**Table 1**</caption>

Results of analyses for soil parameters, litter chemistry, detritivore community structure, and community chemistry. The influence of Region, Pair-of-sites, and Litter quality variables was tested, without interaction. For MANOVA tests, the Pillai value, the approximate F value (with df between groups/df within groups), and the p value are given for each factor. For ANOVA tests, the F value (with df between groups/df within groups), and the p value are given for each factor. If the p value is below 0.05, parameters are in bold. MANOVAs for hypotheses I (morphospecies homeostasis) and II (morphospecies mean body mass and abundance) are not presented here because they have different explaining factors.

<table><thead><tr><th rowspan="2"></th><th rowspan="2">Model</th><th rowspan="2">Variables</th><th colspan="3">Effect of</th></tr><tr><th>Region</th><th>Pair-of-sites</th><th>Litter quality</th></tr></thead><tbody><tr><td rowspan="5">Validation of the experimental design</td><td rowspan="5">MANOVA of Soil parameters</td><td rowspan="5">Sand, Silt, pH, P, N, CN</td><td><b>Pillai</b> = 3.71</td><td>Pillai =</td><td>Pillai = 0.59</td></tr><tr><td><b>F<sub>24/36</sub></b> =</td><td>2.88</td><td><b>F<sub>6/6</sub></b> =</td></tr><tr><td><b>F<sub>24/36</sub></b> =</td><td>1.43</td><td><b>F<sub>6/6</sub></b> =</td></tr><tr><td><b>F<sub>24/36</sub></b> =</td><td>0.86</td><td><b>F<sub>6/6</sub></b> =</td></tr><tr><td><b>P</b> &lt; 0.001</td><td>0.337</td><td><b>P</b> =</td></tr><tr><td rowspan="5">MANOVA of Litter chemistry</td><td rowspan="5">C, N, P, K, Ca, Mg</td><td><b>Pillai</b> = 3.31</td><td>Pillai =</td><td>Pillai = 0.93</td></tr><tr><td><b>F<sub>24/36</sub></b> =</td><td>2.09</td><td><b>F<sub>6/6</sub></b> =</td></tr><tr><td><b>F<sub>24/36</sub></b> =</td><td>13.69</td><td><b>F<sub>6/6</sub></b> =</td></tr><tr><td><b>P</b> &lt; 0.001</td><td>0.84</td><td><b>P</b> =</td></tr><tr><td><b>P</b> = 0.724</td><td>0.003</td><td><b>P</b> =</td></tr><tr><td rowspan="5">Hypothesis III</td><td rowspan="5">MANOVA of Community chemistry</td><td rowspan="5">CWM of C, N, P, K, Ca, Mg</td><td><b>Pillai</b> = 2.73</td><td>Pillai =</td><td>Pillai =</td></tr><tr><td><b>F<sub>24/24</sub></b> =</td><td>2.66</td><td><b>F<sub>6/8</sub></b> =</td></tr><tr><td><b>F<sub>24/24</sub></b> =</td><td>0.859</td><td><b>F<sub>6/8</sub></b> =</td></tr><tr><td><b>F<sub>24/24</sub></b> =</td><td>3.04</td><td><b>F<sub>6/8</sub></b> =</td></tr><tr><td><b>P</b> = 0.034</td><td>1.06</td><td><b>P</b> =</td></tr><tr><td rowspan="5">ANOVA of community chemical diversity</td><td rowspan="5">FDis index</td><td><b>F<sub>4/8</sub></b> =</td><td><b>F<sub>6/8</sub></b> =</td><td><b>F<sub>1/8</sub></b> =</td></tr><tr><td><b>F<sub>4/8</sub></b> =</td><td>36.89</td><td><b>F<sub>1/8</sub></b> =</td></tr><tr><td><b>P</b> = 6.61</td><td>5.53</td><td><b>P</b> &lt; 0.001</td></tr><tr><td><b>P</b> = 0.012</td><td>0.015</td><td><b>P</b> =</td></tr><tr><td><b>P</b> = 0.015</td><td>0.001</td><td><b>P</b> =</td></tr><tr><td rowspan="5">Hypothesis IV</td><td rowspan="5">ANOVAs of community productivity</td><td rowspan="5">Total abundance</td><td><b>F<sub>4/8</sub></b> =</td><td><b>F<sub>6/8</sub></b> =</td><td><b>F<sub>1/8</sub></b> =</td></tr><tr><td><b>F<sub>4/8</sub></b> =</td><td>9.14</td><td><b>F<sub>1/8</sub></b> =</td></tr><tr><td><b>P</b> &lt; 13.00</td><td>3.82</td><td><b>P</b> =</td></tr><tr><td><b>P</b> = 0.001</td><td>0.014</td><td><b>P</b> =</td></tr><tr><td><b>P</b> = 0.014</td><td>0.036</td><td><b>P</b> =</td></tr><tr><td rowspan="5">Hypothesis V</td><td rowspan="5">MANOVA of community taxonomical diversity</td><td rowspan="5">Total biomass</td><td><b>F<sub>4/8</sub></b> =</td><td><b>F<sub>6/8</sub></b> =</td><td><b>F<sub>1/8</sub></b> =</td></tr><tr><td><b>F<sub>4/8</sub></b> =</td><td>5.09</td><td><b>F<sub>1/8</sub></b> =</td></tr><tr><td><b>F<sub>4/8</sub></b> =</td><td>3.46</td><td><b>F<sub>1/8</sub></b> =</td></tr><tr><td><b>P</b> = 10.62</td><td>0.054</td><td><b>P</b> =</td></tr><tr><td><b>P</b> = 0.003</td><td>0.054</td><td><b>P</b> =</td></tr><tr><td rowspan="5">Hypothesis V</td><td rowspan="5">MANOVA of community taxonomical diversity</td><td rowspan="5">Total richness and Shannon index</td><td><b>Pillai</b> = 0.85</td><td>Pillai =</td><td>Pillai = 0.72</td></tr><tr><td><b>F<sub>8/16</sub></b> =</td><td>1.27</td><td><b>F<sub>2/7</sub></b> =</td></tr><tr><td><b>F<sub>8/16</sub></b> =</td><td>2.34</td><td><b>F<sub>2/7</sub></b> =</td></tr><tr><td><b>P</b> = 1.49</td><td>2.34</td><td><b>P</b> =</td></tr><tr><td><b>P</b> = 0.235</td><td>0.012</td><td><b>P</b> =</td></tr></tbody></table>

results of the MANOVA were similar without this variable.

## 3.2. Detritivore communities

Over the five pedoclimatic regions and the 12 pairs of sites, we collected 553 detritivore individuals in a total of 112 soil blocks. Detritivore assemblages ranged from 0 individual (three sites in the Pyrenean piedmont) to 134 individuals belonging to four orders and nine morphospecies (in one site in the Luberon with *Q. pubescens* litter) (Supplementary Fig. S3, Table S4). The Luberon sites exhibited especially high abundances, all taxa combined (mean ± se: 304 ± 76 individuals.m⁻²). To characterize detritivores, we performed 160 CN

analyses, 153 ICP-MS analyses, and measured the body mass of 1281 individuals (Fig. 3). Mean detritivore concentrations were 33.56 ± 0.47% C, 9.46 ± 0.34% Ca, 5.97 ± 0.15% N, 1.56 ± 0.04% P, 0.55 ± 0.02% K, and 0.30 ± 0.01% Mg (Supplementary Table S6). The chemistry of morphospecies found on both sites of each pair of sites depended on the identity of the pair-of-sites-morphospecies (Pillai = 4.29, Approx. F<sub>204/204</sub> = 2.52, p < 0.001) (Supplementary Fig. S4), but not on litter quality (Pillai = 0.24, Approx. F<sub>6/29</sub> = 1.52, p = 0.206) (hypothesis I). Only the C content variable did not comply with the normal-distribution hypothesis (p<sub>Shapiro</sub> = 0.002), but the analysis showed convergent results without this variable. According to the MANOVA analysis, mean body mass and abundance of morphospecies were influenced by the identity of the pair-of-sites-morphospecies (Pillai = 1.68, F<sub>68/64</sub> = 4.99, p < 0.001), but not by litter quality (Pillai = 0.04, F<sub>2/31</sub> = 0.67, p = 0.520) (hypothesis II).

According to the MANOVA analysis, the detritivore community chemistry did not differ with litter quality (Hypothesis III, Table 1). Only the CWM of P and K slightly did not comply with the homogeneity of variance hypothesis (p<sub>Levene</sub> = 0.03 and 0.04, respectively), but the analysis showed convergent results without these two variables.

Detritivore chemical community diversity significantly differed with litter quality, FDis index being significantly higher (+232 ± 97%) at sites with high-quality litter (Table 1).

The correlation between ΔCWM<sub>Mg</sub> and ΔLitter<sub>Mg</sub> was significant (Table 2), indicating that the greater the difference between Mg concentrations in litters at each site, the greater the difference between the weighted mean Mg concentrations in detritivore communities. The correlation was only marginally significant for P (p-value < 0.05, but corrected p-value = 0.081). In both correlations, ΔCWM increased with ΔLitter. Mg and Ca concentrations are closely correlated in detritivores (R² = 0.63, F<sub>1,87</sub> = 148.9, p < 0.001) but not in litter (R² < 0.01, F<sub>1,22</sub> < 0.1, p = 0.944).

According to the ANOVA analyses, the detritivore community productivity (hypothesis IV) differed with litter quality (Table 1). Community total abundance significantly differed with litter quality, and was 1.4 (+138 ± 42%, not taking null values into account) times higher on sites with high-quality litter (F<sub>1/8</sub> = 14.26, p = 0.005). Community total biomass did not significantly differ with litter quality at the 0.05 threshold (Table 1), but was on average 2.8 (+279 ± 115%) times higher at sites with high-quality litter, not taking null values into account.

According to the MANOVA analysis, the detritivore community taxonomic diversity (hypothesis V) differed with litter quality (Table 1). The richness (total number of morphospecies) at the community level did not differ with litter quality by a small margin (F<sub>1/8</sub> = 4.97, p = 0.056). The Shannon-Wiener index significantly differed with litter quality (F<sub>1/8</sub> = 19.78, p = 0.002): it was on average 1.5 times higher (+150 ± 75%) at sites with high-quality litter (Table 1).

<caption>**Table 2**</caption>

Linear correlations between ΔCWM and ΔLitter concentration of each element. One asterisk (*) indicates a p-value <0.05 and two asterisks (**) indicate a p-values <0.01. A significant regression indicates that the litter chemistry influences the weighted mean chemistry of the detritivore community. The greater the difference between concentrations of an element in both litter of each site, the greater the difference between the weighted mean concentrations of this element in detritivore communities.

<table><thead><tr><th>Element</th><th>F<sub>1,7</sub></th><th>Slope</th><th>R<sup>2</sup></th><th>p-Value</th><th>p-Corrected</th></tr></thead><tbody><tr><td>C</td><td>0.00</td><td>-0.06 ± 1.18</td><td>0.00</td><td>0.959</td><td>0.959</td></tr><tr><td>N</td><td>0.55</td><td>-0.09 ± 0.11</td><td>0.07</td><td>0.481</td><td>0.577</td></tr><tr><td>P</td><td>7.80</td><td>0.59 ± 0.21</td><td>0.53</td><td>0.027*</td><td>0.081</td></tr><tr><td>K</td><td>1.49</td><td>0.27 ± 0.22</td><td>0.18</td><td>0.262</td><td>0.393</td></tr><tr><td>Ca</td><td>3.83</td><td>0.27 ± 0.14</td><td>0.35</td><td>0.091</td><td>0.182</td></tr><tr><td>Mg</td><td>28.38</td><td>0.75 ± 0.14</td><td>0.80</td><td>0.001**</td><td>0.006**</td></tr></tbody></table>
# 4. Discussion

## 4.1. Detritivore community chemical composition is influenced by litter chemistry

At the detritivore community level, we found that chemical community diversity (FDis) is significantly higher at sites with high-quality litter than at sites with low-quality litter. Changes in the FDis values indicate changes in the abundance-weighted mean distance to the abundance-weighted mean trait values of the community (Laliberté and Legendre, 2010). The higher the index, the higher the chemical community diversity (Mouillot et al., 2013). Here, it indicates that detritivore communities are chemically more diverse at sites with high-quality litter. This can be due to (1) the presence of different species with different chemical composition between sites, (2) different site abundances of each species, (3) different body mass values of particular species present at both sites, and/or (4) different chemical compositions of given species that are present at both sites. These four hypotheses are not mutually exclusive, but our results support the first and second hypotheses (trends for higher taxonomic diversity and abundance at sites with a high-quality litter), while tending to exclude the third and fourth ones (no difference in morphospecies body chemical composition and body mass with litter quality). The calculation of functional richness (FRic) would have been informative here as comparisons between FRic (abundance not included) and FDis would have indicated if the difference in chemical diversity at the community level can be explained by changes in detritivore species abundances or by changes in species functional identity (i.e., changes in species chemical composition) (Mouillot et al., 2013). Yet, due to low number of different morphospecies in some sample soils blocks (three species required, Supplementary Table S4), FRic could not be computed on several sites and is therefore not included here.

Differences in CWM of P and Mg concentrations increased with differences in litter P and Mg concentrations between sites of each pair, respectively (Table 2). The influence of litter P concentrations on detritivore P concentrations was expected because P is an element that is often limiting for consumers relying on plant resources, particularly detritivores (Danger et al., 2013; Evans-White and Halvorson, 2017; Marchand et al., 2024). Higher litter P concentrations may allow a wider range of detritivores to live there, regardless of their needs for the P element (Kaspari and Yanoviak, 2009; McGlynn et al., 2007; Ott et al., 2014). The positive correlation between ΔCWM<sub>Mg</sub> and ΔLitter<sub>Mg</sub> was surprising. Magnesium is not particularly known to be limiting for detritivores (Zhang et al., 2023; Zhang et al., 2024), and the Mg ratio between detritivore and litter concentrations is low. Two mechanisms might explain this correlation. First, this effect could be due to a non-strict homeostasis for Mg concentrations. Yet, analyses at the morphospecies level show that detritivores complied with the homeostasis hypothesis (hypothesis I). Second, Mg and Ca are chemically close (they are both alkaline earth metals and have a similar size), and Mg and Ca concentrations are closely correlated in detritivores but not in litter. Detritivores might then rely more on Mg when Ca is scarce in the resource, and vice versa. This may also partly explain why we found no correlation between ΔCWM<sub>Ca</sub> and ΔLitter<sub>Ca</sub> even though Ca is of major importance for detritivore tegumentation (Kaspari and Yanoviak, 2009). The absence of any significant difference in litter Ca concentrations between high- and low-quality litter may strengthen the assumption that Mg is especially important for detritivores to compensate for low Ca concentrations in the resource. Alternatively, our design did not allow to investigate the effect of the absolute concentration of Ca in each litter. The relative difference of Ca in paired litters may then have hidden differences between pairs of contrasted soils which could be Ca-rich or Ca-poor depending on the parent material and the pedogenesis. Santonja et al. (2020) identified the key importance of litter Ca and Mg concentrations in driving detritivore–litter interaction in streams. The lack of correlation between ΔCWM<sub>N</sub> and ΔLitter<sub>N</sub> was also surprising, as N is

considered to be a major element for detritivores (García-Palacios et al., 2013). Yet, constraints to fill N-needs may be lower than constraints to fill P- or Ca/Mg-needs (Kaspari and Yanoviak, 2009; Ott et al., 2014).

## 4.2. Detritivores are marginally influenced by litter chemistry at the morphospecies level

At the morphospecies level, we found no significant influence of litter quality on abundance, body mass, and body chemistry. Thus, our results support the mainstream hypothesis that detritivores are homeostatic (Jochum et al., 2017). They also suggest that mean individual body mass is not affected by litter quality. Lastly, detritivore abundance tended to be higher at sites with high-quality litter. The use of precise estimates of elemental needs, such as Threshold Elemental Ratios (TER) (Sterner and Elser, 2003) could have provided more information on chemical constraints. Our results are in line with recent findings of Zhang et al. (2024) who found that detritivores exhibit strict stoichio-metric homeostasis (also observed in Zhang et al., 2023) but altered population density with altitude-induced litter chemistry variation. Because of our rigorous design with paired sites, we here explicitly link these differences to the chemical composition of the available litter. Unlike Zhang et al. (2024), we did not find any effect on body size, which indicates that their findings were probably rather due to the altitudinal gradient.

## 4.3. Assets and limitations of the experimental design

Our experiment was designed to minimize the influence of pedoclimatic variables on detritivore assemblages in order to isolate as much as possible the effect of litter chemistry. Litter chemistry was significantly different between a priori high-quality and low-quality litter, which is a prerequisite to test for its effects on detritivore chemistry. Yet, differences in tree vegetation between pairwise sites could result from different long-term forest management, and such differences could also affect detritivore communities, for example by altering microhabitats such as woody debris (Topp et al., 2006). Detritivore communities might also be indirectly influenced by some soil parameters we did not consider such as soil moisture (David and Handa, 2010). Nevertheless, soil parameters were not significantly different between the sites of each pair (Supplementary Fig. S2). Using pairs of sites therefore allowed us to isolate the effect of litter chemical quality specifically. By strongly limiting the effects of pedoclimatic context on detritivore assemblages, even the weak differences observed among sites should be meaningful.

# 5. Conclusion

In this study, we showed that detritivore communities are influenced by litter chemical quality, especially through P and Mg changes. As mean individual body mass and elemental body composition of detritivores were not influenced by litter quality, abundance seems to be the main driver of detritivore community functional changes. Our results support the homeostasis hypothesis, generally assumed in ecological stoichiometry approaches, indicating no physiological adaptations to resource quality. This means that resource nutritional quality may contribute to shape consumer community chemical footprint by modifying population dynamics. One hypothesis could be that resources of better nutritional quality may remove nutritional and energy constraints for detritivores. However, abundance changes might be related to mechanisms such as limiting litter quantity, competition, facilitation, predation, parasitism, and random dispersion at the metacommunity level. Detritivore communities are then chemically influenced by the locally available litter, but the modest magnitude of this effect suggests that other mechanisms such as food complementation or species interaction should be concomitantly investigated when studying resource quality effects on consumer chemical diversity.
## Data archiving

Data are available on research.data.gouv.fr (https://doi.org/10.57745/1FCQHY).

## CRediT authorship contribution statement

**Théo Marchand:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Conceptualization.
**Matthieu Chauvat:** Writing – review & editing, Methodology, Investigation, Conceptualization.
**Estelle Forey:** Writing – review & editing, Methodology, Investigation, Conceptualization.
**Florence Maunoury-Danger:** Writing – review & editing, Methodology, Investigation, Conceptualization.
**Mathieu Santonja:** Writing – review & editing, Methodology, Investigation, Conceptualization.
**Michaël Danger:** Writing – review & editing, Methodology, Investigation, Conceptualization.
**Philippe Usseglio-Polatera:** Writing – review & editing, Validation.
**David Baqué:** Writing – review & editing, Methodology.
**Frédéric Candaudap:** Writing – review & editing, Methodology.
**Sophia V. Hansson:** Writing – review & editing, Validation, Methodology.
**Frédéric Julien:** Writing – review & editing, Methodology.
**Christophe Laplanche:** Writing – review & editing, Validation.
**Gaël Le Roux:** Writing – review & editing, Methodology.
**Virginie Baldy:** Writing – review & editing.
**Raphaël Gros:** Writing – review & editing.
**Benjamin Pey:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

## Funding statement

This work was supported by the ELEMENTARY project, funded by the French National program EC2CO (Ecosphère Continental et Côtière).

## Declaration of competing interest

The authors declare that they have no known competing interests.

## Acknowledgments

We warmly thank Mathis Flamant, Laëtitia Leroy, Lena Monceau, and Corinne Pautot for their help with the chemical analyses. We warmly thank Karolina Argote, Adriane Aupic-Samain, Charlotte Biryol, Ylona Camus, Sylvie Dupouyet, Thierry Gauquelin, Ugo Iaria, Lise Laf-fond, Chloé Lefebvre, Alice Léonard, Julien Pouget, and Thibault Dragon for their help on the field. We thank the Fast Bio-analyses of Trace Elements platform (FBil, Univ Toulouse UPS, INPT, CNRS), for the ICP analyses. We thank the Platform for physical and chemical analyses (PAPC, Univ Toulouse UPS, INPT, CNRS) for the CN analyses. We thank Estelle Renaud for her critical reading and advice. We warmly thank Jean-François David for the precise identification of detritivore species. We thank Christopher Swan for his advice on random dispersal.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.apsoil.2026.106800.

## Data availability

The data will be made available by the time the article is accepted for publication.

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