# A meta-analysis reveals earthworms as mutualists rather than predators of soil microorganisms

Manuel Blouin, Aïssa Robin, Lysandre Amans, Frédérique Reverchon, Isabelle Barois, Patrick Lavelle

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Manuel Blouin, Aïssa Robin, Lysandre Amans, Frédérique Reverchon, Isabelle Barois, et al. A meta-analysis reveals earthworms as mutualists rather than predators of soil microorganisms. Geoderma, 2025, 455, pp.117238. 10.1016/j.geoderma.2025.117238. hal-05000738

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https://institut-agro-dijon.hal.science/hal-05000738v1

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Systematic review and meta-analysis

# A meta-analysis reveals earthworms as mutualists rather than predators of soil microorganisms

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Manuel Blouin<sup>a, *, †</sup>, Aïssa Robin<sup>a ‡</sup>, Lysandre Amans<sup>a</sup>, Frédérique Reverchon<sup>b</sup>, Isabelle Barois<sup>c</sup>, Patrick Lavelle<sup>d</sup>

## ARTICLE INFO

Handling editor: Maria Luz Cayuela

### Keywords:

- Annelids

- Microbes

- Microbiota

- Microbiome

- Abundance

- Richness

## ABSTRACT

Microorganisms constitute the largest biomass on Earth after plants, and earthworms are one of the main components of animal biomass. Both are critical drivers of soil functions and ecosystem services. Studies report either positive or negative effects of earthworms on soil microbial communities, leading to contrasting views on whether microorganisms serve as prey or mutualists for earthworms. This meta-analysis aimed to settle this debate, and examines how biotic and abiotic factors affect earthworm impacts on microbial abundance and diversity. Based on a selective search retaining only genuinely quantitative approaches, we kept 169 observations, showing that, on average, earthworms increased bacterial abundance by 16.5 % and fungal abundance by 31.4 %. Bacterial species richness rose by 8.5 % in the presence of earthworms, but fungal richness was not significantly affected. Epigeic and anecic earthworms had more notable effects than endogeics. Plant presence in experimental designs strongly amplified earthworms' effects on bacterial (+30 %) and fungal (+97 %) abundances. The largest earthworm-induced effects were observed in carbon- and nitrogen-rich soils and at low pH, while the addition of organic matter reduced these effects. Comparing microbial abundance in earthworm casts to control units without earthworms revealed effect sizes two- to three-fold greater (for bacteria and fungi, respectively) than when using the soil surrounding earthworm casts as a control. Our meta-analysis demonstrates that, despite ingesting some microorganisms, earthworms have a net positive effect on their abundance, positioning them as mutualists rather than predators of bacteria and fungi.

## 1. Introduction

Soil microbial communities are influenced by abiotic and biotic factors, with a decreasing relative importance from soil pH, organic carbon (C), redox status, moisture, nitrogen (N) and phosphorus (P) availability, to texture and structure, temperature, plant species identity and viral lysis (Fierer, 2017). If biotic factors play a relatively low role in structuring soil microbial communities at large spatial and temporal scales, they are recognized for their prominent influence at the soil profile scale, especially in terms of biogeochemical cycles (Sokol et al., 2022). Bacteria and fungi are highly limited in their ability to move towards new feeding sources once local ones become exhausted. As a consequence, active microorganisms very seldom exceed 5 % of the total microbial biomass, and “potentially active” microorganisms, able to

start utilization of available substrates within few hours, reach 10 to 40 % of the total microbial biomass (Blagodatskaya and Kuzyakov, 2013). Ecosystem engineers such as plants, earthworms, termites... are responsible for putting into contact microorganisms and their resources, such as exudates, mucilage or particulate- and mineral-associated organic matter (Lavelle et al., 2016), leading to hotspots of microbial activity, for short periods of time, called hot moments (Kuzyakov and Blagodatskaya, 2015). Earthworms are among the most abundant components of animal biomass on Earth (Bar-On et al., 2018) and shape the drilosphere, made of biopores and aggregates, one of the most important functional domains in most soils of temperate and tropical areas (Lavelle, 2002). Understanding the impact of earthworms on soil microbiota is thus a prerequisite in understanding, predicting and harnessing the soil microbiome, for trying to improve the delivery of

<footnote><sup>a</sup> Agroecologie, Institut Agro Dijon, INRAE, Université Bourgogne, Université Bourgogne Franche-Comté, Dijon, France</footnote>

<footnote><sup>b</sup> Red de Diversidad Biológica del Occidente Mexicano, Centro Regional del Bajío, Instituto de Ecología, A.C., Pátzcuaro, Michoacán, Mexico</footnote>

<footnote><sup>c</sup> Red de Ecología Funcional, Instituto de Ecología A. C., Xalapa Veracruz, Mexico</footnote>

<footnote><sup>d</sup> Université Paris Sorbonne, IEES, Paris, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: manuel.blouin@agrosupdijon.fr (M. Blouin).</footnote>
desired ecosystem services.

Studies on the interactions between earthworms and microorganisms reveal numerous inconsistencies regarding the effects of earthworms on microbial abundance and alpha-diversity metrics, especially richness or Shannon index. Strong differences in earthworms' ecological categories could be at the origin of these inconsistencies: (i) epigeic species live in the litter and affect soil surface state; (ii) anecic species live in vertical burrows connected with soil surface, which are used mainly as shelters; (iii) endogeic species make horizontal or randomly oriented burrows, considered to be temporary structures since they are rarely reused (Bouché, 1977) (see also Capowiez et al. (2024) for a recent classification of functional groups based on bioturbation behavior). However, positive, negative or neutral effects are observed even inside a given earthworm ecological category (Medina-Sauza et al., 2019a). Since soil properties are likely to influence the outcome of earthworm – soil microbiome interactions (Medina-Sauza et al., 2019b), a quantitative assessment is required to determine their effects. This *meta*-analysis would also constitute a unique opportunity to address two additional unsolved debates on the interactions between earthworms and microbial communities.

A first question relates to the net effect of earthworms on microbial abundances, which could be negative, positive or neutral. If negative, earthworms could be considered as predators of microorganisms. If positive, they could be considered as mutualists. A very high concentration of amino acids in earthworms (Pokarzhevskii et al., 1997), a reduced microbial abundance in the gut of earthworms (Aira et al., 2009b) and isotope probing (Potapov et al., 2019) led to the conclusion that microbes, especially fungi, can be prey for earthworms (Shan et al., 2013). However, this predatory relationship is difficult to reconcile with evidences for net positive effects of earthworms on microbial abundance (Aira et al., 2009a), and description of a mutualistic digestive system of earthworms, with the enzymes involved in organic compounds degradation being shared between earthworms and their microbiota (Trigo & Lavelle, 1993; Zhang et al., 1993; Lattaud et al., 1997; 1998; 1999). Earthworms add to the ingested soil one volume of water per volume of soil and 5 to 18 % intestinal mucus, made of low molecular weight glycoproteins specifically degraded by soil microorganisms. This mucus is likely responsible for a priming effect (Fontaine et al., 2007) allowing microbes to break down otherwise recalcitrant organic compounds during gut transit and release simpler molecules that can be absorbed by the gut wall (Barois and Lavelle, 1986).

A second question concerns the diversity of microbial taxa in earthworm casts. Earthworms possibly actively select or promote in their gut a sub-sample of the ingested soil microbial community and be responsible for a decreased microbial diversity in their casts (Aira et al., 2022). They could also have a core microbiota in their gut, as human do (Turnbaugh et al., 2009), which would enrich the microbial community observed in the casts. However, not all animals need a core microbiota (Hammer et al., 2019), and evidence of core microbiota in earthworms remain scarce, but see Medina-Sauza et al. (2023)).

In this *meta*-analysis, we first analyzed the literature corpus in order to identify a possible publication bias. We then investigated the overall effect of earthworm presence on the abundance and diversity of soil bacteria and fungi expressed as taxonomic richness and Shannon index. We finally performed sub-group analyses, to determine if this effect of earthworms was dependent on such co-variables as earthworm ecological categories, soil properties (C and N content, pH, texture), the presence/absence of plant in the experimental design or the method used to sample or quantify bacterial and fungal abundance.

# 2. Materials and methods

## 2.1. Literature search

The search was performed on December 13th 2023, on the Web Of Science Database, with the following keywords: [TITLE: earthworm* OR

annelid* OR lumbric] AND [TITLE: microorganism* OR microbiota OR microbiome* OR microbial communit* OR bacteria* OR fung* OR archea*] AND [ALL FIELDS: propert* OR characteristic* OR parameter* OR organic matter OR vermicompost* OR soil]. 437 results were obtained, ranging from February 1st 1997, date of the first result, to the search date. Based on the title, papers that were clearly not focused on earthworm-microbiota interactions were removed, and 193 articles were kept. The second filter was based on a careful reading of the abstract which allowed the removal of papers that did not mention earthworm effect on microbial abundance and community structure. For example, several papers were considering the effect of microbes on earthworms or the effect of a third factor on both earthworms and microbes. After this second filtering step, 136 papers were kept. The third filter was based on a full reading of the remaining articles, to dismiss papers that did not provide the necessary quantitative data for the *meta*-analysis, such as a proper description of the control, number of replicates, or dispersion parameters. At the difference of many *meta*-analyses on microbial communities, we excluded papers relying on data of amplicon sequences from *meta*-barcoding analyses, since end-point PCR is a qualitative or semi-quantitative method useful to know the relative abundances of organisms in a given sample but not their absolute abundances for between-samples comparisons (Harrison et al., 2021). Twenty-nine articles were kept after this triple filtering. Since there was often more than one treatment with earthworms in the publications, a total of 183 observations were finally obtained. A first examination of the dataset showed contrasted responses for bacteria and fungi in a number of cases. We thus analysed these two data sets separately. Subsequently, nine papers measuring the total abundance of bacteria + fungi without possibility to distinguish the two were not considered in the analysis, leading to a final database of n = 168 data points from 20 publications, 90 for bacteria (18 papers) and 78 for fungi (13 papers). These data points were documenting either one, two or three dependent variables: abundance (n = 88), richness (n = 101) and/or Shannon index (n = 85). These publications are cited here to avoid the absence of recorded citations in databases (Devliegher and Verstraete, 1997; Enami et al., 2001; Aira et al., 2009a; Jirout and Pizl, 2014; Chen et al., 2015; Hoeffner et al., 2018; Gong et al., 2019, 2023; Jacquiod et al., 2020; Song et al., 2020; Domínguez et al., 2021; Wang et al., 2021; Lin et al., 2022; Binglei et al., 2023; Liu et al., 2023; Lu et al., 2023; Medina-Sauza et al., 2023; Shi et al., 2023; Sofo et al., 2023; Zhang et al., 2023). The full list and data set are also presented as Supplementary Materials.

## 2.2. Nature of collected data

In order to calculate the effect size of earthworms on the abundance, richness and Shannon index of bacteria or fungi, the following four parameters were documented for each data point: (i) the mean of the dependant variable for the treatment with earthworm and the control, (ii) their dispersion value (standard deviation, standard error or confidence interval), (iii) the number of replicates and (iv) the unit of the dependant variable, which can be expressed as a number of gene copies (from qPCR on 16SrRNA gene for bacteria and ITS gene for fungi), µg of phospholipid fatty acid (PLFA) or as log(CFU) (colony forming units), all expressed per gram of soil. For diversity, observed taxonomic richness was considered, except for four studies providing only the Chao1 index and one the number of DGGE bands. Shannon index was retrieved at the exclusion of all other equitability indices. Mean and dispersion parameters (standard deviation or standard error, confidence interval or boxplot converted to standard deviation) were retrieved from the text or the figures, using the graphic measuring tool Mesurim 2 (v1 0.63, CC BY-NC SA, P. Consentino), except for three papers (Gong et al., 2023; Jacquiod et al., 2020; Medina-Sauza et al., 2023) for which raw data were supplied by the authors.

A second category group of retrieved information describes the experimental design: the earthworm species considered and their ecological category (epigeic, endogeic or anecic, Bouché (1977));
whether there were plants or not in the experimental set up; whether the experiment was conducted in the field, greenhouse or climatic chamber; whether the soil sample in the earthworm treatment was a cast or a composite sample which could comprise some bulk soil; whether the control was a pristine soil without earthworms or the bulk soil surrounding the drilosphere (Blouin and Jacquiud, 2020).

The third category of variables characterized soil properties: soil organic C content (SOC); total N content; pH; the presence/absence of mineral or organic input; soil texture, associated with the main component (clay, loam or sand). Values for SOC and pH were categorized into three classes and those for N into two classes with approximately the same amount of data.

## 2.3. Effect size calculation

Since continuous data had to be analysed, where the outcome was unlikely to be zero and various conditions could affect the absolute value, the metric used was the ratio (R) of the mean of the earthworm (E) treatment to the mean of the control without earthworms (C). Because such a ratio is defined on [0; +∞[, a log scale was applied to get it symmetrical on ]-∞; +∞[ (Borenstein et al., 2009). The effect size Y of each study i was thus calculated as:

$$Y_i = \ln\left(\frac{meanE}{meanC}\right)$$

This calculation of effect size is recommended for comparing a treatment and a control in experimental ecology (Hedges et al., 1999).

To compute the mean effect size M*, each individual effect size was weighted (W) with the reciprocal of the sum of the within study variance (V): $W_i = \frac{1}{\sqrt{Y_i}}$

$$M^* = \left( \frac{\sum_{i}^{n} W_i * Y_i}{\sum_{i}^{n} W_i^*} \right)$$

Since contradictory effects of earthworms on microbial communities are suspected to be due to changes in the environmental conditions, we were particularly interested in determining how soil parameters influenced the mean effect size in group analyses. We thus used a random effects model accounting for a stronger effect of between-study variance as compared with within-study variance in the computation of the global effect size (Borenstein et al., 2009):

$$V_{Y_i}^* = V_{Y_i} + T^2$$

$$T^2 = \frac{Q - df}{C}$$

$$\text{with } Q = \sum_{i}^{k} W_{i}^{*} \left( Y_{i} - \frac{\left( \sum_{i}^{k} W_{i}^{*} Y_{i} \right)}{\sum_{i}^{k} W_{i}} \right)^{2}, \quad df = k - 1 \text{ and } C = \sum W_{i} - \frac{\sum_{i}^{k} W_{i}^{2}}{\sum W_{i}}$$

where k is the number of studies.

The variance of the mean effect size M* was then:

$$V_M^* = \frac{1}{\sum_{i}^{n} W_i^*}$$

$$\text{with } W_i^* = \frac{1}{\sqrt{Y_i}}.$$

The effect of an environmental variable T², can be reported to the total variance Q to estimate the relative importance of the true variance I² by calculating:

$$I^2 = \left( \frac{Q - df}{Q} \right) * 100$$

At 25, 50 and 75 %, this portion of explained variance can be considered as low, moderate and high respectively (Borenstein et al., 2009). These

values of Q and I² are provided in Table S1.

Since ln(R) is expressed on a log metric basis, results presented in figures were back-transformed and reported as percentage change of the earthworm effect ([R-1]*100), in order to facilitate interpretation. A 95 % confidence interval (CI) was calculated. When the 95 % CI overlaps the 0, the earthworm effect is not significant at P-value = 0.05. When the CI of two different sub-groups overlap, the mean of these subgroups is not significantly different. The effect sizes calculations and statistical analyses were performed using MetaWin 3.0.14 beta (Rosenberg et al., 2000).

## 2.4. Data set quality and publication bias

A publication bias analysis allows to assess how much confidence we can place in these results by highlighting any potential self-censorship by authors when earthworms have a negative or non-significant effect, or censorship by editors for similar reasons. The most common method is to visually check if the funnel plot (effect size = f(sample size)) is symmetrical. In the absence of publication bias, the funnel plot should be symmetrical, with smaller studies randomly distributed around the overall estimate (at the bottom of the funnel) and larger studies clustering near the true effect size (at the top). In our meta-analysis, this symmetry was observed for bacterial and fungal abundance data (Fig. S1a and d). An asymmetric plot, with a lack of studies with small effect sizes and sample sizes, was observed for bacterial and fungal richness (Fig. S1b and e). The funnel plots for Shannon's index (Fig. S1c and f) lacked enough large-sample data to properly assess asymmetry. Another, more quantitative method involves examining the correlation between effect size and sample size (Table S2). If studies with small samples and small effects are missing, a negative correlation is expected. In our case, Spearman's test showed no significant correlation between effect size and sample size for any of the variables (p-value = 1), while Kendall's test indicated significant correlations for bacterial richness and Shannon's index, and for fungal abundance and Shannon's index. However, the correlation coefficient was positive for fungal abundance, ruling out the hypothesis of a bias toward studies with strong effects for this variable. When Kendall's test detects a significant p-value but Spearman's test does not, it may indicate that the "effect size and sample size" pairs are generally well-ordered, but the overall strength of the relationship is too weak for Spearman's test to detect. This situation is typical from a moderate or subtle bias for bacterial richness and both bacterial and fungal Shannon indices. There were no bias for bacterial or fungal abundances, nor for fungal richness. A last method estimates how many new studies would be needed (i) to lose the significance of the p-value (Rosenthal's Fail-Safe N) or (ii) to reach a given effect size threshold (here lnR < 0.2) for Orwin's Fail-Safe N. In our study, this method was irrelevant for fungal richness and both bacterial and fungal Shannon indices, where no significant effects were observed. For fungal abundance, a relatively high number of studies would be required (both Rosenthal and Orwin agree, Table S2). However, for bacterial abundance and richness, while Rosenthal's method suggested robust results, a small number of studies with contrary effects could reduce the observed effect or make the results less reliable (Orwin). In summary, the funnel plot, Spearman and Kendall correlations, and Rosenthal and Orwin's methods suggested no publication bias for bacterial and fungal abundance. There was likely a slight bias for bacterial and fungal richness, depending on the method. Shannon indices showed a bias.

# 3. Results and discussion

## 3.1. Do earthworms increase bacterial and fungal abundance, richness and equitability?

On average, earthworms were clearly increasing the abundance of bacteria by 16.5 % and the abundance of fungi by 31.4 % (cast or composite sample (E) versus pristine or surrounding bulk (C)) (Fig. 1).
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Influence of earthworm on (a) bacterial and (b) fungal communities. (n) The number of datapoints, and [x] the number of individual papers, error bars represent 95 % confidence interval.</figcaption></figure>

Our results are consistent with those of a recent global *meta*-analysis on the impact of soil ecosystem engineers on ecosystem functions, which reported a significant positive effect of earthworms on microbial biomass (considering fungi and bacteria together) (Wu et al., 2025). This indicates that, although earthworms ingest microorganisms along with soil, they are not merely predators reducing their prey populations. Instead, their net positive effect on microorganisms is more characteristic of a mutualistic partner. Release in the foregut part of an easily degradable mucus with high amount of water could be responsible for the awakening and feeding of dormant bacteria and fungal spores, a process poetically referred to as “the Sleeping Beauty” paradox (Lavelle et al., 1997; 2006; Medina-Sauza et al., 2019a). Another possible, although non-exclusive, mechanism is the selective ingestion by earthworms of patches of soil particularly rich in organic matter. Consequently, earthworm casts turn to be hotspots of soil fertility with higher total nutrient content than the bulk soil: about + 35 % for total P, + 46 % for total N, + 75 % for available P and + 240 % for available N as compared with bulk soil (van Groenigen et al., 2019). Cases of a decrease in microbial biomass in the presence of earthworms could be due to a sampling of old casts in which microbial activity is reduced. Indeed, microbial abundance and activity are very dynamic in casts. Right after the cast release, mineralization is stimulated as compared with surrounding soil, and then soon inhibited as the casts age (Lavelle et al., 1992; Le Mer et al., 2022; Zangerlé et al., 2014).

The effect of earthworms on microbial abundance was twice as high for fungi than bacteria, indicating that earthworms increased the fungal: bacterial ratio. This result was confirmed by calculating the (E/C)bacteria/(E/C)fungi ratio for studies reporting similar number (>1) of samples for bacteria and fungi. This calculation was based on the intra-study ratio of averaged abundance values for each taxon, rather than at the individual sample level, since sample identification was not reported in our data table (and generally not in primary studies), preventing direct pairing. The ratio was 0.51 (n = 4 per taxa) in Aira et al. (2009), 0.93 (n = 20) in Gong et al. (2018) and 0.60 (n = 11) in Jacquiod et al. (2020), yielding an overall unweighted average of 0.78. These findings reinforce the *meta*-analysis result, indicating that earthworms increased fungal abundance more than bacterial abundance. This apparently robust result could be counter-intuitive for several reasons. First, the fungal: bacteria ratio is reputedly influenced by several factors, among which soil physical disturbance, nutrient availability and the C:N ratio of soil organic matter, pH or humidity, but the direction of the effect is largely variable (Strickland and Rousk, 2010). Fungi can be expected to be more susceptible to soil physical disturbance than bacteria in terms of abundance, due to the disruption of mycelium (Helmason et al., 2009; Young and Ritz, 2000). As such, fungi could be negatively affected by earthworm bioturbation, which could alter the dense network of hyphae and decrease fungal biomass. However, there is no convincing evidence of this phenomenon. In a study reporting a decrease of the fungal:bacterial ratio due to earthworms, the main identified mechanism was the removal of the organic soil horizon (Dempsey et al., 2011). The breakdown of mycelium by earthworms could occur at a temporal scale which is quickly compensated by fungal growth. Earthworms could even favor ramification and densification of hyphae by bioturbation. Second, the fungal:bacterial ratio can also be expected to decrease in the presence of earthworms due to trophic relationships. Indeed, fungi have been found

in the digestive tract content of earthworms. Earthworm preference for leaves inoculated by fungi has been observed (Curry and Schmidt, 2007). In feeding trials with the geophagous earthworm *Metaphire guillermi* using ¹⁴C-labelled microbes and molecules, accumulation of fungal cells (11 %) and cell wall component chitin (29 %) in the tissue was significantly higher than that of bacterial cells (8 %) and cell wall component peptidoglycan (15 %), suggesting that fungal biomass is likely a more important food source for geophagous earthworms than bacterial biomass (Shan et al., 2013). However, bulk variations in isotope composition can be explained by compound-specific differences within an organism. More advanced methods, such as compound-specific stable isotope analyses, showed that anecic earthworms in grasslands contained high proportion of plant-derived essential amino acids, in contrast to endogeic earthworms and enchytraeids that contained high proportion of bacterial-derived essential amino acids (Larsen et al., 2016). A question remained regarding the intra- or extracellular position of these amino-acids. Another study using compound-specific stable isotope analysis revealed pronounced flux of energy directly from plants to earthworms (33–50 % of essential amino acids) and concluded that epigeic earthworm species primarily relied on plant litter and endogeic species primarily relied on bacteria and soil organic matter, with a lower contribution of fungi (Potapov et al., 2019). Recently, this underestimated contribution of “green energy” (root derived resources such as exudates) as compared with “brown energy” (dead soil organic matter) was confirmed: in a soil with a high predominance of bacteria over fungi (fungal:bacterial ratio < 1/10 from PLFA analysis), bulk ¹³C values indicated that root-derived carbon contributed to earthworm nutrition in each of the five species. This was confirmed by ¹³C values of essential amino acids, which suggested that most of the tissue carbon of earthworms was incorporated via bacterial- (≈ 60 %) and plant-derived resources (≈30 %) (Zhong et al., 2024). Even though earthworms consume bacteria, we observed here an increased bacterial abundance in their presence (+16.5 %), which suggested an increased turn-over rate responsible for a higher biomass production in the presence of earthworms. Beyond their feeding choice, earthworms could also change the fungal:bacterial ratio by a preferential passive dispersal of fungal spores. Earthworms are known to disperse mycorrhizal fungi (Pelosi et al., 2024). It is likely that other fungi, including saprotrophs, could also be dispersed by earthworms.

As far as microbial diversity is concerned, bacterial richness was increased by 8.5 % in the presence of earthworms, whereas fungal richness was not affected (Fig. 1). The absence of significant effect observed in a recent *meta*-analysis (Wu et al., 2025) can be explained by the fact that fungi and bacteria diversity were not distinguished. The observed increase in bacterial richness was not due to an increase in equitability, since the Shannon index was not affected by earthworms (Fig. 1). A review pointed that positive (e.g. Hoeffner et al., 2018), neutral (e.g. de Menezes et al., 2018) or negative (e.g. Koubova et al., 2015; Medina-Sauza et al., 2023) effects of earthworms on microbial diversity have been reported, depending on the sampled micro-habitats and the sampling time. A general pattern could be a reduction of richness in the cast, but an increase at the scale of the whole soil, composed of patches of casts, burrow walls and bulk soil. When distinguishing between casts and composite samples, we found that, on average, the ratio of observed bacterial richness in earthworm treatments compared
to the control was very close to 1 for casts (0.95, n = 16), whereas it was 2.71 for composite samples (n = 42). For fungi, the limited number of observations for casts (n = 2) prevented us from drawing conclusions. However, for composite samples, the ratio was close to 1 (0.97, n = 37), in contrast to bacteria. These results indicate that the observed increase in bacterial richness across the entire dataset was primarily due to soil heterogeneity induced by earthworms, rather than an increase in diversity within casts following gut transit.

Although this study was not dedicated to identify changes in species composition, it is interesting to note that up to 80 % of the bacterial ASVs detected in the gut and casts of *P. corethrurus* were exclusive of the earthworm and apparently not derived from the surrounding bulk soil (Medina-Sauza et al., 2023). The survival of bacteria from the posterior part of the intestine in the cast has been quantified as being equal to 27 and 30 % of the total posterior intestinal microbiota, for casts retrieved in soil planted with two coffee species (Medina-Sauza et al., 2023). Another study confirmed that a subset of the microbial community of the cast remained the same whatever the soil (Jacquiod et al., 2020). This observation supports the idea that some earthworm species could have their own core microbiota like other – but not all – animal species (Hammer et al., 2019), with a subset of this community surviving in the cast. However, it cannot be totally excluded that these bacterial taxa were already present in the control soil, but at too low a level to be detected. Earthworms could have revealed this presence, instead of inoculating the cast with intestine bacteria.

Since the number of data points and the $I^2$ were often low for richness and Shannon index in sub-group analyses (Table S1), and because of the slight publication bias discussed in the previous sub-section, only abundance will be presented for the following analyses, with an exception for earthworm ecological categories and plant presence/absence.

## 3.2. An effect dependent on earthworm ecological category

The ecological category of earthworms was an important determinant of variation in the effect of earthworms on microbial abundance (Fig. 2). For bacteria, epigeic earthworms had a strong effect (+119.5 %), anecic had a moderate one (+24.1 %) and endogeic had no

significant effect, with a percentage of explained variance $I^2$ coherent with the effect size (Table S1). For fungi, anecic and endogeic earthworms were responsible for an increase of abundance by +25 % and +26 % respectively, with a moderate and low percentage of explained variance respectively ($I^2$, Table S1). Epigeic earthworms had a strong effect size, but a very low $I^2$, likely associated with the poor dataset for the effect of epigeic earthworms on soil fungi. A consistent positive effect of anecic earthworms on soil microbial abundance has already been reported in an extensive review (Medina-Sauza et al., 2019b), likely due to the formation of burrows, which constitute habitats with a typically large microbial abundance (Li et al., 2022), although the magnitude of this positive effect may vary with soil depth (Sheehan et al., 2008). This review concluded by contrasting the effects of epigeic or endogeic earthworms on microbial abundance, but here, a positive effect of epigeic was observed on the abundance of both bacteria and fungi, while a positive effect of endogeic was observed on fungi only.

Bacterial alpha-diversity metrics (richness and Shannon index) were also influenced by the earthworm ecological category, with a significant positive effect of anecic earthworms on bacterial richness (+46 %, $I^2$ = 82 %) and Shannon index (+5.1 %, $I^2$ = 77 %) (Fig. 2). For anecic earthworms, we found that, on average, the ratio of observed bacterial richness in earthworm treatments compared to the control was 0.88 for casts (but not reliable with n = 1), whereas it was 5.27 for composite samples (n = 14), instead of 2.71 when considering all three ecological categories together. This result indicates that the particularly strong increase in bacterial richness observed with anecic earthworms was likely due to the heterogeneous sampling of casts as well as additional contrasted microhabitats, such as permanent anecic burrows. The effect of epigeic earthworms was not significant. The negative effect of endogeic earthworms on soil bacterial diversity (−3.7 %) was likely due to a sampling effect ($I^2$ = 2.8 %). No significant effect of earthworm ecological categories was found on fungal richness nor Shannon index. Overall, the division of data into three sub-groups limited our ability to detect significant effects, except for anecic earthworms on the bacterial and fungal abundances, bacterial richness and equitability.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Influence of (earthworm group) on (a-c) bacterial and (d-f) fungal (a and d) abundance, (b and e) specific richness and (c and f) Shannon index. Earthworm group include epigeics, anecics and endogeics. (n) the number of datapoints, and [x] the number of individual papers, error bars represent 95 % confidence interval.</figcaption></figure>

<figure><img src="image_5.png" /></figure>
### 3.3. The inevitable influence of plants

In field conditions, earthworms interact with microorganisms in a soil almost always covered by vegetation. However, experiments in controlled conditions or greenhouses are performed in simplified environmental conditions, in order to facilitate reproducibility. One of the potentially important simplifications is the absence of plants. We analyzed whether the presence of plants was important in determining the effect of earthworms on soil microorganisms. We found that the effect of earthworms strongly depended on the presence/absence of plants: earthworms had no significant effect on the abundance of both bacteria and fungi in the absence of plants, whereas in the presence of plants, they had an important effect on bacteria (+30%, I² = 74%) and a huge one on fungi (+97%, I² = 54%) (Fig. 3a and d). This effect, which was observed in a loamy soil for bacteria and in a clayey soil for fungi (Jacquiod et al., 2020), has been attributed to the synergistic effect of multiple macroorganisms (here ecosystem engineers sensu (Jones et al., 1994)) on soil microbial community. Hotspots and hot-moments could be more intense when benefiting from several energy sources, such as earthworm mucus and plant exudates. Thus, the strongest microbial "hotspots" and "hot moments" (Kuzyakov and Blagodatskaya, 2015) are likely due to the joined action of several macro-organisms. This result stresses the need to complexify too simple experimental set ups aiming at studying the effect of earthworms on microorganisms and ecosystem functions. In the same vein, earthworms have been found to increase greenhouse gas emission in a *meta*-analysis (Lubbers et al., 2013), although most results were based on pot experiments in which no plants were present. In the absence of plants, earthworms increased bacterial richness (+18.7%, I² = 61%) and Shannon index (+3.8%, I² = 55%) (Fig. 3b and c). But when plants were present, they had the opposite effect and slightly decreased richness and Shannon index, likely due to a co-selection of microorganisms by plants and earthworms, which has been called the "core microbiota of plant-earthworm interaction" (Jacquiod et al., 2020).

### 3.4. Strong and non-trivial effects of soil chemical parameters

A general idea about mutualistic interactions is that they have a predominant role when resources are scarce (Johnson et al., 2010; Weese et al., 2015). In the case of earthworms-microorganisms interaction, a greater positive effect of earthworms on microbial abundance could be expected in sandy soils, which are generally poorer in nutrients

than loamy or clayey soils, at low C and N content, and in soils without fertilization or amendment. Our results were more complex. We observed a positive effect of earthworms on the abundance of bacteria in clayey (+24.1%, I² = 63%) and sandy (+28.9%, I² = 73%) soils, but no significant effect in loamy soils (Fig. 4a). The same was true for fungi, which were more abundant in clayey (+53.2%, I² = 49%) and sandy (+29.2%, I² = 78%) soils in the presence of earthworms, but not in loamy soil (Fig. 4f). However, the texture-dependent effect of earthworms may not be clear. On another topic, the effect of earthworms on plant growth, contradictory results have been obtained in different *meta*-analyses, which concluded either to a stronger effect in sandy than in loamy or clayey soils (Brown et al., 2004, 1999; Laossi et al., 2010), or the opposite (van Groenigen et al., 2014). Thus, texture may not be a consistent soil property to explain and predict earthworm influence on microbial communities.

Soil C and N contents also influenced the effect of earthworms on microbial abundance, but not in the expected direction. Earthworm effect was only significant at high N content (I² = 50.0%, +13.4% for bacteria; I² = 65%, +42.1% for fungi at 0.1–1% of N) (Fig. 4c and h). It was significant at high C content for bacteria (I² = 76.8%, +42.4%) and particularly strong at intermediate C content (I² = 72%, +99.5% at 1–2%) and at high C content (I² = 24%, +54.9% at 3–15% of C) for fungi (Fig. 4d and i). The addition of fertilizer or amendment is a way to manipulate, at least temporarily, the amount of resource (including N and C) in the soil. The positive effect of earthworms on microbial abundance was higher when no organic matter was added to the soil (I² = 60%, +40.3% for bacteria; I² = 27%, +59.2% for fungi) (Fig. 4e and j). In particular, the general significant and positive effect of earthworms on bacterial abundance was lost when organic matter was added. These results could first seem to be contradictory: the effect of earthworms on microbial abundance is higher when soil N and C content is high, but it is lower after organic matter addition. A potential explanation is that a high C and N organic content could allow earthworms to be very selective in feeding on soil patches rich in organic matter, thus creating highly fertile islands in the soil, called microbial hot spots and hot moments (Blagodatskaya and Kuzyakov, 2013; Kuzyakov and Blagodatskaya, 2015). Conversely, the addition of organic matter could saturate the soil and weaken the heterogeneity-mediated effect of earthworms.

A soil parameter with a more consistent effect was pH: the lower the pH, the stronger the earthworm effect on microbial abundance (Fig. 4b and g). Fungi were very sensitive to earthworm presence at pH < 6.5,

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Influence of the presence or absence of plants in the experiment on (a-c) bacterial and (d-e) fungal (a and d) abundance, (b and e) specific richness and (c) Shannon index. There were no data for fungal Shannon index. (n) the number of datapoints, and [x] the number of individual papers, error bars represent 95% confidence interval.</figcaption></figure>

<figure><img src="image_7.png" /></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Influence of soil physico-chemical properties on (a-e) bacterial and (f-j) fungal abundances. (n) the number of datapoints, and [x] the number of individual papers, % error bars represent 95 % confidence interval.</figcaption></figure>

with a +113.6 % increase ($I^2$ = 56 %). Bacteria responded at neutral ($I^2$ = 73.6 %, +15.0 %) and acidic ($I^2$ = 33 %, +30.6 %) pH, not at basic pH. Since there was a relatively good distribution of pH across classes of texture, N and C content, pH can be recognized as a strong driver of earthworm effect on microbial abundance. This result echoes observations that soil pH is one of the most important drivers of soil microbial communities (Fierer and Jackson, 2006; Rousk et al., 2010), with low pH values generally detrimental to microbial biomass and diversity (Aciego Pietri and Brookes, 2009; 2008). Since the pH of an acidic soil increases during the gut transit, with remanent effect in the cast, earthworm gut could be a favorable microsite for chemical reactions and microorganisms which are inhibited by acidity (Lavelle et al., 1995).

### 3.5. *Set up, sampling and quantification methods matter*

Experimental and sampling choices in original studies could have influenced the results of our *meta-analysis*. We first investigated the experimental set up, in order to determine if field, greenhouse or climatic chamber settings had a specific influence. We found no or weak effects (Fig. S2), except in greenhouse but only in one or two studies.

We were also expecting an effect of the method of microbiota sampling in the control and earthworm treatments. Some researchers favor the use of composite samples (a mix of casts and bulk soil), whereas others finely sample the casts, recognizable as aggregates surrounded by

loose bulk soil. This second method was expected to be more precise since it should maximize the difference between microbial communities from the Earthworm treatment and the Control. We were surprised to observe that earthworm effect on the abundance of bacteria was lower when casts were isolated from the surrounding soil ($I^2$ = 19 %, +9.3 %) than when a composite sample was collected (but low $I^2$ = 56 %, +40.0 %) (Fig. 5 a). The same pattern, with a lower difference between effect sizes (+28.0 % for casts and +36.9 % for composite sample) and higher variance, was observed for fungi (Fig. 5c). Microbial communities from the soil surrounding the casts are strongly influenced by earthworms, likely due to (i) the mucus released through the skin of earthworms "around" the future position of the cast where it will be released after its way through the digestive tract, including the burrows, or (ii) molecules diffusing from the cast towards the surrounding soil. The lower increase in abundance in isolated casts could be due to (i) the fleeting dynamics of microbial activity in the casts, with a peak in microbial activity which can last only 24 h in casts of *Pontoscolex corethrurus* (Lavelle et al., 1992), or (ii) a desiccation of cast due to the time required to extract the casts from the surrounding soil which could alter microbial communities. Reducing at minimum the sampling time and maintaining a wet environment during cast retrieval could correct such a potential artifact. Using a composite sample or a more precise cast sample also had an influence on the diversity observed, since the composite sample was made of several micro-habitats (see the first section of Results and
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Impact of the sampling method on (a and b) bacterial and (c and d) fungal abundances, for the treatment with earthworm (a and c, with a dismantling of the soil in order to specifically retrieve aggregates corresponding to casts versus the sampling of a soil volume likely to include a mix of casts, burrows and bulk soil) and the control without earthworms (b and d, pristine bulk soil in experimental units without earthworms versus bulk soil surrounding the casts in experimental units with earthworms). (n) The number of datapoints, and [x] the number of individual papers, error bars represent 95 % confidence interval.</figcaption></figure>

Discussion).

Another important methodological choice is the type of control. Some researchers choose the soil away from casts as a control, that was named the “surrounding bulk” (Blouin and Jacquiod, 2020). This is the only solution in the field. But in pot experiment, it is also possible to set up distinct experimental units for the control, without earthworms, that was named the “pristine bulk”. It has been shown that elements such as N, and likely microorganisms, can be leached from casts (Decaëns et al., 1999), and that microbial community structure significantly differs in surrounding bulk and pristine bulk, likely due to a diffusion of micro-organisms from casts (Blouin and Jacquiod, 2020). Given this influence of earthworms on microbial communities beyond the cast, it is likely that the difference between the earthworm treatment and the control should be stronger with a pristine than with a surrounding bulk as control. The effect of earthworms on the abundance of bacteria and fungi (in casts or composite sample) was indeed twice higher when reported to the pristine bulk ($I^2 = 84$, +27.7 %) than the surrounding bulk (but very low $I^2 = 0$, +12.1 %) (Fig. 5b and d). We thus recommend to implement control units without earthworms in greenhouse and climatic chamber experiments to better reveal the effect of earthworms.

Finally, the quantification method could also have an influence. Amplicon sequencing was not considered in this *meta*-analysis, since end-point PCR is useful to know the relative abundances of organisms in a given sample, not their absolute abundances (Harrison et al., 2021). With qPCR, we were able to detect a significant positive effect of earthworms for fungi (+35 %) but not for bacteria (Fig. S3). It was the opposite for cultivation, which allowed to detect a significant effect only for bacteria (+26 %). PLFA quantification revealed a significant increase of both bacteria and fungi by +32 % and +156 % respectively (Fig. S3). Massification of molecular techniques could favor qPCR in most upcoming studies. However, there could be an effect of analytical methods on the ability to detect significant effect depending on the taxa of interest. Confounding factors could also explain our results, for example the well-known effect of soil texture on DNA extraction yield.

## 4. Conclusion and perspectives

The effect of earthworms on microbial abundance was important and positive, especially for fungi. The increase in bacterial abundance was stronger in composite samples than in isolated casts, likely due to the secretion of mucus around the cast or the diffusion of molecules from it. Even if microbes are inevitably destroyed during the gut transit, earthworms had an overall positive effect on bacterial and fungal abundance either in casts or composite samples. This result provides a clear answer to the long-standing debate on how to classify earthworms in relation to microorganisms: they should be considered mutualists rather than

predators of microorganisms.

Bacteria species richness was slightly increased in the presence of earthworms, essentially due to earthworm-induced soil heterogeneity captured in composite samples. The presence of plants had a very strong influence on this global positive effect of earthworms, especially for fungi. Fungi were also very responsive to the nutritional status of soil (C and N contents and amendments), and both fungi and bacteria were sensitive to soil pH. An interesting perspective could be to determine if this pH-dependent effect is linked with the $CaCO_3$ granule that earthworms produce, likely related to internal pH regulation (Versteegh et al., 2014). Future research on what the earthworms exactly feed on will help to understand more clearly their impact on soil microbiota, especially those based on (compound-specific) stable isotope analysis. Precise sampling of earthworm biogenic structures, such as casts, burrows and surrounding bulk soil of different ages, as well as bulk soil from other experimental units without any earthworms, would help to better understand the spatial and temporal dynamics of microbial hot spots and hot moments shaped by earthworms.

### CRediT authorship contribution statement

**Manuel Blouin:** Writing – original draft, Validation, Supervision, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization. **Aïssa Robin:** Visualization, Software, Methodology, Investigation, Formal analysis, Data curation. **Lysandre Amans:** Methodology, Investigation, Data curation. **Frédérique Reverchon:** Writing – review & editing, Validation. **Isabelle Barois:** Writing – review & editing, Validation. **Patrick Lavelle:** Writing – review & editing, Validation.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

We are grateful to Manqiang Liu and Xin Gong, Itzel A. Solís-García and Regina Medina-Sauza and Samuel Jacquiod for providing the rough data of their studies. We also thanks Kevin Austasie and Kadidja Cisse for their help in retrieving data. We thank the Institut Agro Dijon for funding.
# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.geoderma.2025.117238.

## Data availability

Data are available as Supplementary Materials.

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