# Exposure to pesticides is correlated with gut microbiota alterations in a farmland raptor

Léa Bariod, Elva Fuentes, Maurice Millet, Joël White, Samuel Jacquiod, Jérôme Moreau, Karine Monceau

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Léa Bariod, Elva Fuentes, Maurice Millet, Joël White, Samuel Jacquiod, et al.. Exposure to pesticides is correlated with gut microbiota alterations in a farmland raptor. Environment International, 2025, 199, pp.109436. 10.1016/j.envint.2025.109436. hal-05057925

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Short communication

# Exposure to pesticides is correlated with gut microbiota alterations in a farmland raptor

Léa Bariod<sup>a</sup>, Elva Fuentes<sup>a</sup>, Maurice Millet<sup>b</sup>, Joël White<sup>c</sup>, Samuel Jacquiod<sup>d</sup>, Jérôme Moreau<sup>a</sup>, Karine Monceau<sup>a,e,∗</sup>

ARTICLE INFO

Keywords:

Bacterial communities

Contaminants

Dysbiosis

Farmland bird

Microbiome

ABSTRACT

The gut microbiota is crucial for host health and can be impacted by various environmental disruptions, yet the effects of multiple pesticide exposures on farmland organisms' microbiomes remain largely unexplored. We assessed microbiota changes in a wild apex predator exposed to multiple pesticides in agricultural landscapes. Pesticides, including acetochlor and quinoxyfen, which are supposed to be banned, were significantly positively correlated with certain key bacteria from Actinobacteria, Alphaproteobacteria and Gammaproteobacteria classes. Our results light up the potential collateral effect of pesticides on gut bacterial assemblages through unknown mechanisms. These effects could result in dysbiosis and the promotion of potential pathogens and/or the selection of bacteria that might allow the organism to detoxify the organism. Although formal metagenomic analyses would be required soon, these microbial shifts underline the broader ecological consequences of pesticide exposure, emphasising the need for integrated biodiversity conservation and ecosystem management to protect environmental and public health.

# 1. Introduction

The gut microbiota is an essential modulator of host physiology and plays a key role in host health. Its importance is such that, in organisms, including humans, its disruption is associated with several pathologies, including diabetes, cancer, cardiovascular events, and neurodegenerative diseases (Hou et al., 2022). Among the possible sources of disruption, pesticides represent a major potential threat (Yadav, 2010; Sharma et al., 2020). Mostly ingested orally through water and food, pesticides are thought to alter the structure of the microbiota, which plays a key role in supporting the intestinal barriers that regulate the entry of substances into the body. While much research has focused on the effects of a single pesticide molecule on microbiota composition—primarily in humans, laboratory or domestic animal models (Yuan et al., 2019; Garcia-Mazcorro et al., 2021)—the impact on wildlife remains less explored. Wild birds, particularly apex predators such as raptors, serve as vital bio-sentinels for ecosystem health (Bodawatta et al., 2022; Handy et al., 2023). As top-level predators in the food web, they are

especially vulnerable to pesticide exposure due to biomagnification (Badry et al., 2020). Studying the effects of pesticides on the microbiota of wild birds not only provides insights into the health of these organisms but also highlights broader ecological consequences, making them relevant biological models for understanding environmental and animal health impacts.

In the present study, we sampled Montagu's harrier (Circus pygargus) nestlings to examine the effects of pesticides on the composition and structure of the gut microbiota of an avian apex predator. In nestlings, gut colonisation occurs predominantly in the first days after hatching (Těsický et al., 2024) and is influenced by the environment and parental feeding (Diez-Méndez et al., 2023). Montagu's harriers are especially interesting in this context as they nest on the ground of cereal crops, the majority of which have been treated with pesticides before the breeding period, i.e., before May. The development of the nestlings' gut microbiota could, therefore, be influenced mostly by the local remanence from the soil and food provided by adults. Here, we describe the relationship of the concentration of pesticides measured in blood and in the

<footnote><sup>a</sup> UMR 7372, Centre d'Études Biologiques de Chizé, La Rochelle Université & CNRS, 79360 Villiers en Bois, France</footnote>

<footnote><sup>b</sup> Université de Strasbourg, CNRS-UMR 7515, ICPEES, 67087 Strasbourg cedex 2, France</footnote>

<footnote><sup>c</sup> Centre de Recherche sur la Biodiversité et l'Environnement, UMR 5300, CNRS-IRD-UT3-INPT, 118 Route de Narbonne, F-31062 Toulouse, France</footnote>

<footnote><sup>d</sup> INRAE, Institut Agro, Université de Bourgogne, Université de Bourgogne Franche-Comté, Agroécologie, 21000 Dijon, France</footnote>

<footnote><sup>e</sup> LTSER "Zone Atelier Plaine & Val de Sevre", CNRS, 79360 Villiers-en-Bois, France</footnote>

<footnote>* Corresponding author at: UMR 7372, Centre d'Études Biologiques de Chizé, La Rochelle Université & CNRS, 79360 Villiers en Bois, France.
E-mail address: karine.monceau@univ-lr.fr (K. Monceau).</footnote>
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Nest locations of nestlings (n = 22) studied in the Long-Term Socio-Ecological Research Zone Atelier Plaine & Val de Sèvre (LTSER ZAPVS). Landscape features are derived from 2022 data in our GIS database, including infrastructures such as buildings, sports fields, cemeteries, locks, and bridges.</figcaption></figure>

composition of the gut microbiota measured in cloacal samples con-
trolling for nestling body condition and sex. We considered both the
diversity of pesticides (i.e., the so-called cocktail effect) and the diversity
of the gut microbiota.

## 2. Materials & Methods

2.1. *Ethical consideration*

All captures and experiments conformed to the French guidelines for
the ethical use of animals in research (APAFIS#36725-
2022041910139000 v6). Bird handling was allowed and licensed by the
"Centre de Recherches sur la Biologie des Populations d'Oiseaux"
(CRBPO; Museum National d'Histoire Naturelle; licence #1308).

## 2.2. *Studied area and biological model*

The study site "Zone Atelier Plaine & Val de Sèvre" (ZAPVS there-
after) is located in the Deux-Sèvres department (Nouvelle-Aquitaine,
France; Fig. 1) and covers 450 km². It is an area of intensive farming,
whose landscape is dominated by winter cereal crops (i.e., on average
41.5 % of the area between 2009 and 2016), with also crops including
corn, sunflowers, and oilseed rape (Bretagnolle et al., 2018; Fig. 1).
Since 1994, Montagu's harriers have been monitored in the ZAPVS
during the breeding period, after their return from migration mainly
from Africa, from late April to early September (depending on the year).
This threatened, protected species nests on the ground, mainly in cereal
crops, making chicks and adults directly subject to agricultural activity
(Arroyo et al., 2002; Millon et al., 2002). In France, the populations of
this bird species are decreasing, probably due to the intensification of
agricultural work in the last decades, making them of conservational
priority but also limiting our sample size (Comolet-Tirman et al., 2015).
On average, 2.05 fledglings per nest survive and fly out of the nest; this
productivity depends mainly on available resources and especially their
main prey (Salamolard et al., 2000). As a top predator, Montagu's
harriers feed mostly on common voles (Microtus arvalis) and, in years of
poor abundance, on orthopterans, making them prone to bio-
magnification and thus to high exposure to pesticides. Generally, males

provide a food supply for incubating females and chicks and then fe-
males also participate during the chick-rearing period. Feeding areas in
the ZAPVS are approximately 14 km² (Guixé and Arroyo, 2011).

2.3. *Sampling*

In 2022, professional ornithologists located and visited Montagu's
harrier nests in the study area (Fig. 1). The details concerning nest
monitoring can be found in Fuentes et al. (2023). Briefly, 22 chicks (11
males, 11 females) from 13 nests were visited before the eggs hatched
and subsequently every week. After the hatching, chicks were head-
marked with a water-based coloured pen to allow individual identifi-
cation, and 15 days later, they were banded with a unique coded
aluminium ring provided by the Museum National d'Histoire Naturelle
de Paris (France). They were also sexed according to their iris colour (i.
e., brown for females, grey for males, Leroux and Bretagnolle, 1996).
Blood and microbiota sampling occurred when nestlings were 26 ± 2
days old, between June 27 and July 19, as Montagu's harrier pairs
reproduce asynchronously over the breeding period. The chicks were
carefully handled in a short time to collect blood and microbiota samples
frozen at -20 °C and -80 °C, respectively, before analyses. Blood
samples were used to assess pesticide load by taking 50 µL from the
brachial vein using a sterile needle and heparinised capillaries and then
placed in Eppendorf tubes. The pesticide load in the blood was quanti-
fied as a measure of their recent exposure (Espín et al., 2016). Gut
bacterial communities were sampled by gently inserting a sterile nee-
dleless syringe into the cloaca of each bird, injecting 1 mL of sterile
phosphate-buffered saline, and then drawing it out again. Samples were
immediately placed in sterile vials, kept in aocolbox in the field, and
later stored at -80 °C. While each part of the digestive tract harbours
specific bacterial communities, microbial shifts incurred in the higher
intestine of birds lead to concurrent shifts in cloacal communities,
making cloacal sampling a reliable non-invasive technique to study
inter-individual variability in gut communities (e.g., on various bird
species, van Dongen et al., 2013; Teyssier et al., 2020).
<table><caption>**Table 1**

Description of the 8 MOTUs correlated with pesticides, including results from NCBI Blast and putative identification.</caption><thead><tr><th>Phylum</th><th>Class</th><th>Order</th><th>Family</th><th>Genus</th><th>MOTUs</th><th>NCBI blast assignment (%)</th><th>Putative species</th></tr></thead><tbody><tr><td rowspan="2"><strong>Actinobacteria</strong></td><td>Actinobacteria</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Corynebacteriales</td><td></td><td></td><td>Corynebacterium sp.</td><td>OTU_048</td><td>100%</td><td>C. jeddahense</td></tr><tr><td></td><td>Corynebacteriaceae</td><td></td><td></td><td>Turicella sp.</td><td>OTU_040</td><td>100%</td><td>T. otiitidis (syn. Corynebacterium otiitidis)</td></tr><tr><td></td><td>Micrococcales</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Microbacteriaceae</td><td></td><td></td><td>Leifsonia sp.</td><td>OUT_059</td><td>100%</td><td>L. xyli subsp. Cynodontis, L. quatic, L. shinshuensis, L. naganoensis</td></tr><tr><td></td><td></td><td></td><td></td><td>Micrococcus sp.</td><td>OUT_102</td><td>100%</td><td>M. luteus (3 strains), M. aloeverae, M. endophyticus, M. yunnanensis, M. cohnii, M. antarcticus, M. flavus</td></tr><tr><td rowspan="2"><strong>Proteobacteria</strong></td><td>Alphaproteobacteria</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Caulobacterales</td><td></td><td></td><td>Phenylobacterium sp.</td><td>OUT_136</td><td>98.42-97.63%</td><td>P. aquaticum, P. glaciei, P. koreense</td></tr><tr><td></td><td>Caulobacteraceae</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Gammaproteobacteria</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Burkholderiales</td><td></td><td></td><td></td><td>Ralstonia sp.</td><td>OUT_003</td><td>100%</td><td>R. syzygii (4 strains), R. insidiosa</td></tr><tr><td></td><td>Burkholderiaceae</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pseudomonadales</td><td></td><td></td><td></td><td>Alcaligenes sp.</td><td>OTU_020</td><td>93.28%</td><td>A. faecalis, A. aquatilis, A. parafaecalis, A. ammonioxydans</td></tr><tr><td>Pseudomonadaceae</td><td></td><td></td><td></td><td>Pseudomonas sp.</td><td>OTU_099</td><td>100%</td><td>More than 10 different Pseudomonas spp. such as P. argentiniensis, P. parafulva, P. flavescens, P. hunanensis, P. punonensis, P. cuatrociengasensis, P. putida, P. fulva, P. faucium, P. promysalinigenes</td></tr></tbody></table>

## 2.4. Pesticide analysis

Pesticide analysis follows the method developed by Rodrigues et al. (2023). Using the whole blood samples, including red blood cells and plasma, the analytical method allowed the detection and quantification of 116 compounds of pesticide used in the ZAPVS and more globally in France, including active ingredients (fungicides, herbicides and insecticides) and synergists (which improve the action of pesticides) or safeners.

Each blood sample was thawed, weighed, mixed with 2 mL dichloromethane and ethyl acetate (1:1), and homogenised by vortexing for 1 min, followed by three rounds of sonication for 10 min each time. After this, centrifugation of samples was done for 5 min to pool supernatants before evaporating them under a fume hood until having a final volume of 500 µL. Then, the samples were concentrated and injected simultaneously into liquid chromatography (LC-MS/MS) or gas chromatography (GC-MS/MS, for more volatile compounds) for quantification. The LC-MS/MS analyses were carried out in MRM (Multiple Reaction Monitoring) mode with two mass transitions for each compound, one for quantification and one for qualification. The GC-MS/MS analyses were preceded by a thermal desorption step, and a MRM mode was also used for detection and quantification. The concentration of each pesticide detected in the blood of chicks was obtained in pg·mg⁻¹. Analytical quality assurance and control procedures are provided in the detailed description of the method by Rodrigues et al. (2023).

## 2.5. Gut microbiota analysis

The microbiota in cloacal samples was assessed using a metabarcoding approach with the bacterial 16S ribosomal RNA gene, present in all bacteria and archaea (Mizrahi-Man et al., 2013), at the Plateforme Genomique, Genopole Toulouse (France).

### 2.5.1. DNA extraction and amplification

Bacterial DNA was extracted following the method of DNeasy® PowerSoil® Pro Kit and the standard protocol designed for the purification of total DNA from Gram-positive bacteria (Qiagen, Venlo, Netherlands). Bacterial DNA was then amplified by PCR targeting the V3-V4 hypervariable regions of 16 s rRNA in a room dedicated solely to metabarcoding procedures. The 20 µL PCR solution contained 2X Master Mix AmpliTaq Gold (Fisher), 0.008X BSA, 0.25 µM forward primer

(GTGYCAGCMGCCGCGGTAA), 0.25 µM reverse primer (GGAC-TACNVGGGTWTCTAAT) and 2 µL DNA extract. First, a denaturation at 95 °C for 10 min took place, followed by 35 cycles of denaturation at different temperatures: 95 °C for 1 min, 50 °C for 1 min, 72 °C for 1 min 35 s and at 72 °C for 10 min. Then, nine negative and nine positive controls were included in all amplifications to, respectively, avoid potential contamination and monitor the amplification's performance. Three PCR replicates were done for each sample, and the primers were tagged using a system of 36 octamers with at least five differences between them to obtain a unique tag combination for each PCR product.

### 2.5.2. Bioinformatics and data curation

Illumina sequencing data were processed and filtered using the OBITools package. The sequences were demultiplexed, dereplicated, and filtered according to their length (100 base pairs minimum) and their alignment quality (40 % minimum, Taberlet et al., 2018). Sequences were then assigned to samples using the “ngsfilter” command, allowing 0 and 2 errors on the tags and primers, respectively, with identical sequences merged with the obiuniq command. Low-quality sequences, i.e., less than 1 read in the whole dataset, shorter than expected (<80 bp) or with ambiguous bases, were filtered out. After this cleaning, sequences were grouped into MOTUs (Molecular Taxonomic Operational Units), with sumaclust, according to a similarity threshold of 97 %. For each MOTU, the most abundant sequence was considered its representative sequence, its abundance corresponding to the sum of the abundance of its members. The taxonomic assignment for each MOTU was carried out with the SILVAngs pipeline v.1.9.10 and the SILVA SSU database v138.1 (Quast et al., 2012). In addition to the obitools program, other artifactual sequences (PCR/sequencing errors, contaminant sequences, tag-jumps and dysfunctional PCRs) were identified using the R package MetabarR. MOTUs that did not belong to the targeted clade (i.e., Bacteria), with maximum abundance in the negative controls or with a similarity below 80 % to the reference sequence were excluded. To standardise differences among samples, rarefaction was applied to 4300 reads.

## 2.6. Statistical analyses

We tested the concordance between the gut microbiota community structure and the concentration of pesticides measured on chicks' blood using a sparse partial least square discriminant analysis with blocks
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Pearson's correlation tests between the axes of the principal component analyses (PCA) on MOTU abundance and pesticide concentrations, (a) correlation between the first axes of each PCA [PC1] and (b) correlation between the second axes of each PCA [PC2].</figcaption></figure>

(block sPLS-DA) implemented in the 'mixOmics' package ('block.spllda' function, Rohart et al., 2017). To address potential biases in the data, we first applied a series of data quality control steps. Variables in each dataset were filtered to retain only those that were both abundant and frequently detected, defined as those occurring in at least 5 nestlings. This filtering step aimed to reduce the impact of rare or low-abundance variables, which could introduce noise into the analysis. Additionally, we performed normalization on both datasets to control for the body condition effect. We also tested the effect of sex, but the model failed to correctly classify chick sex based on MOTU and pesticide profiles, showing that the sex effect is not a major determinant of MOTU/pesticide correlations. Next, we constructed the block sPLS-DA model to test the level of congruence between the two datasets. The relationships between components 1 and 2 from each dataset were evaluated by calculating correlations of the eigenvalues (principal components) of the samples. The sPLSDA method automatically implements a Pearson's correlation test (method included in MixOmics package) based on the position of samples in the components of choice (here PC1 of both

datasets). This method has previously been applied to large datasets, with correlation scores as low as 0.4, particularly when the number of replicates is high and the model classification is robust (see Jacquiod et al., 2022). Given the limited number of replicates and the fact that the method is not used for classification purposes, only correlations with an absolute strength (|strength|) greater than 0.6 were considered relevant. This threshold was chosen to focus on meaningful and reliable correlations between microbiota taxa (MOTUs) and pesticide concentrations, minimizing the influence of weak or spurious correlations. The MOTUs retained by the sPLSDA were then assigned to the nearest known organisms using NCBI nBLAST optimized for highly similar sequences (megablast, see Table 1, Altschul et al., 1990; Morgulis et al., 2008).

### 3. Results

In the blood of the 22 chicks, 36 pesticides and 139 MOTUs were detected, but we only kept those observed in at least five different chicks for analyses to ensure robustness. Thus, sPLS-DA included the 35 most prevalent MOTUs and 25 pesticides, which were found in the blood of at least five chicks and controlled for the chick's body condition as an essential component of their quality.

The eigenvalues for the first and second components of pesticide and microbiota matrices were highly correlated (Person's correlation test, 1st component: r = 0.82, P < 0.0001 and 2nd component: r = 0.75, P < 0.0001), indicating important underlying correlations between variables across each dataset (Fig. 2). The strongest correlations (>|0.60|) involved eight different MOTUs (4 Actinobacteria and 4 Proteobacteria, see Table 1) and 12 pesticides (two fungicides, seven herbicides, three insecticides), among which three were banned in Europe (acetochlor: 2013, chlorpyrifos: 2020, quinoxyfen: 2019) (Fig. 3). Most MOTUs were positively correlated with pesticides except *Turicella* sp., which was negatively correlated with acetochlor (Fig. 3).

### 4. Discussion

The MOTUs were phylogenetically close to groups known for different roles that might be important to consider in the present case, with either beneficial or adverse effects on birds. These groups might be involved in bird metabolism, may induce pathogenicity, and/or be involved in bioremediation. For example, *Corynebacterium* sp. is commonly found in birds (Katsukawa et al., 2016; Pereira et al., 2024) and has been observed in higher abundance in migratory than in resident birds, which suggests a role in fat deposition metabolism (Zhang et al., 2021). Members of the Pseudomonadaceae family, such as *Alcaligenes* sp. or *Pseudomonas* sp., might be responsible for sanitary issues, such as respiratory disease in poultry for *A. faecalis* (Berkhoff et al., 1983) or be the vector of resistant genes that might be problematic in the current multiple drug-resistant crisis for *P. putida* (Li et al., 2021) (Table 1). In the meantime, these bacteria might also be helpful mediators of bird detoxification in being able to degrade some pesticide molecules such as chlorpyrifos (Ajaz et al., 2012) or, more widely, other organosphorous and neonicotinoid insecticides (review in Gilani et al., 2016; Książek-Trela and Szpyrka, 2022). *Ralstonia* sp. (R. syzygii and R. insidiosa, Table 1) are commonly found in soils and plants and are also known to degrade organophosphorus pesticides such as diazinon (Wang and Liu, 2016).

Overall, MOTUs we detected were poorly known in birds and deserve further investigation to understand their effects as well as how pesticides may act on gut microbiota. Based only on the short and conserved 16S RNA, it is, however, difficult to formally identify bacteria that might be a major concern for nestling health. The functional activity of these bacteria could be extrapolated from our 16S rRNA gene sequences coupled with metagenomics databases using programs such as Picrust2 (Douglas et al., 2020) or Tax4Fun (Aßhauer et al., 2015). However, these computational tools must require reliable genome annotation, i.e., known bacterial gene references, and their use is subject to cautious
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Heatmap representing correlation levels between pesticides and MOTUs. Pesticides in bold red are banned (see text for details). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

outside the human case (Sun et al., 2020; Matchado et al., 2024, see
Garcia-Mazcorro et al., 2021 for an example in birds). In the present
case, we believe that a metabolomic approach would be more relevant to
assess the functional profiling due to the lack of a reliable bird micro-
biota database. Complex multivariate analyses might be performed,
including life history traits and/or physiological parameters (see an
example in Fuentes et al., 2023 in the same species). The effects of body
condition have already been tested, showing a relationship between gut
bacteria richness and diversity and pesticide load (Bariod et al., 2025).
Further analyses would require a higher sample size as it would involve
three different matrices to be correlated (one for MOTUs, one for pes-
ticides and one for life and physiological life history traits). Nonetheless,
our results are an important contribution in several respects. First, we
show a significant correlation between the composition of the gut
microbiota of an organism exposed to a cocktail of different pesticides
found in its blood, suggesting that the processes linking microbiota and
pesticide effects cannot be drawn from a single molecule nor a bacterial
species, but rather has to be appreciated at the group level (for both
pesticides and MOTUs). Second, our results suggest that pesticides may
modulate the development of key bacterial taxa at early stages of the gut
microbiota ontogeny, which may have later implications (due to priority
effects) on the development of the adult microbiota and its health. Third,
over and above the presence of banned molecules per se (see Fuentes
et al., 2024), our results shed light on an overlooked collateral effect of
pesticides, i.e., a general modification of gut bacterial assemblages. This
can either lead to dysbiosis and the promotion of potential pathogens
and/or the selection of bacteria that detoxify the organism, thus
providing a form of acclimatisation to a polluted environment (at the
expense of health or not; these two hypotheses are not exclusive).

**5. Conclusion**

Our study provides evidence of pesticide impacts on wildlife gut
microbiota, highlighting links between pesticide exposure and changes
in microbiota composition, although the functional/physiological
pathways have to be identified properly. Additionally, our findings
support the 'One Health' framework, stressing the interconnectedness of
wildlife, ecosystem, and human health, particularly in pesticide-affected
agricultural areas. These results underline the need for integrated ap-
proaches to biodiversity conservation and ecosystem management to
protect both environmental and public health.

CRediT authorship contribution statement

**Léa Bariod:** Writing – original draft, Formal analysis, Conceptualization. **Elva Fuentes:** Writing – review & editing. **Maurice Millet:** Writing – review & editing, Formal analysis. **Joël White:** Writing – review & editing, Formal analysis, Conceptualization. **Samuel Jacquiod:** Writing – review & editing, Formal analysis. **Jérôme Moreau:** Writing – review & editing, Supervision, Conceptualization. **Karine Monceau:** Writing – review & editing, Supervision, Project administration, Funding acquisition, Conceptualization.

Funding

This study was funded by La Rochelle université (ACI Actions LUDI grant 2022), the French National Research Agency (ANR JCJC Pesti-Stress, grant #19-CE34-0003-01), the Contrat de Plan État-Région (CPER) Econat and the region Nouvelle-Aquitaine (BioBird project).

**Declaration of competing interest**

The authors declare the following financial interests/personal re-
lationships which may be considered as potential competing interests:
Karine Monceau reports financial support was provided by French Na-
tional Research Agency. Karine Monceau reports financial support was
provided by Nouvelle-Aquitaine Regional Council. Karine Monceau re-
ports financial support was provided by La Rochelle University. If there
are other authors, they 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 the farmers who allowed access to their fields, the
people who contributed to data collection in the field, and Vincent
Bretagnolle for his involvement in the Montagu's project. Thanks also to
Marie-Amélie Pussacq-Caillet for her preliminary work on the
microbiota.

Data availability

Data will be made available on request.
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