# Off-target: Herbicides in cereal fields favour competitive weeds over non-target species

Ségolène Humann-Guilleminot, Vincent Bretagnolle, Sabrina Gaba

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## To cite this version:

Ségolène Humann-Guilleminot, Vincent Bretagnolle, Sabrina Gaba. Off-target: Herbicides in cereal fields favour competitive weeds over non-target species. Agriculture, Ecosystems & Environment, 2026, 397, pp.110026. 10.1016/j.agee.2025.110026. hal-05326098

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Submitted on 22 Oct 2025

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# Off-target: Herbicides in cereal fields favour competitive weeds over non-target species

Ségolène Humann-Guilleminot<sup>a,b,*</sup> <sup>ID</sup>, Vincent Bretagnolle<sup>b,c</sup> <sup>DD</sup>, Sabrina Gaba<sup>b,d</sup>

## ARTICLE INFO

**Keywords:**

Agricultural intensification

Agroecology

Pesticides

Plant communities

Winter wheat

Insecticides

Fungicides

## ABSTRACT

Weed diversity plays an important role in maintaining resilient agroecosystems, yet agricultural practices, such as pesticide applications, significantly shape weed communities. Previous studies have primarily focused on comparing organic and conventional farming, with a particular emphasis on land-use intensification. However, less attention has been given to the specific effects of herbicides, insecticides, and fungicides on weed communities, including the species affected and the mechanisms driving these changes, based on farmer-recorded application data. Our study examined the impact of herbicide, fungicide, and insecticide use – both in terms of Treatment Frequency Index (TFI), i.e., intensity of application, and quantity (QA) – on weed communities in 96 non-organic cereal fields over 4 years. We did not detect any effect of insecticide metrics on weed communities. Interestingly, our results indicate that TFI of fungicide decreased weed abundance, whereas QA did not. In contrast, for herbicides, QA had a stronger negative impact on weed communities than TFI. This suggests that TFI of fungicide, which may reflect low-dose but frequent applications, could exert indirect and long-term effects on weed suppression. In comparison, QA of herbicide more directly reflects the toxic load delivered to weeds and is therefore a better predictor of their suppression. Our results reveal that herbicides are the main factor shaping weed communities by decreasing the abundance of non-target/non-competitive species while replacing them with competitive species. These findings point out the potential ineffectiveness of herbicide application on problematic weeds and emphasise the need to reconsider the use of herbicides to maintain weed diversity in agricultural landscapes.

## 1. Introduction

Weeds play a critical ecological role within agroecosystems, by providing habitats and food for wildlife, supporting plant-pollinator interactions, enhancing soil health, facilitating nitrogen fixation, and preventing erosion (Altieri, 1999; Marshall et al., 2003; Storkey et al., 2011; Bretagnolle and Gaba, 2015). However, the ability of weed communities to provide these ecosystem services can be strongly affected by frequent disturbances in agroecosystems (Emmerson et al., 2016). Unlike natural ecosystems, agroecosystems face frequent and severe disturbances such as pesticide use, soil tilling, and crop harvesting, which significantly impact their dynamics (Tilman et al., 2002; Gaba et al., 2014; Duru et al., 2015). Herbicides, along with mechanical

weeding and tillage, are key methods used to manage weeds and minimise crop competition to protect yields, although the relationship between herbicide use and yield is not straightforward (Tilman et al., 2002; Gaba et al., 2016; Benaragama et al., 2016; Zimdahl, 2018; Das et al., 2024). This frequent disturbance alters the plant community's typical structure and diversity, often leading to fields dominated by one or two weed species (Borgy et al., 2012; Storkey and Neve, 2018). Consequently, weed populations develop unique structures and traits influenced by both competition with dominant crops and farming-induced disturbances, which vary depending on crop type and associated practices (Gaba et al., 2014; Mahaut et al., 2020).

Numerous studies have explored how agricultural practices influence weed communities, particularly comparing organic and

<footnote><sup>a</sup> Department of Environmental Science, Radboud Institute for Biological and Environmental Sciences (RIBES), Faculty of Science, Radboud University, Nijmegen 6500, the Netherlands</footnote>

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

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

<footnote><sup>d</sup> USC 1339 Centre d'Etudes Biologiques de Chizé, INRAE, CNRS & La Rochelle Université, Villiers-En-Bois 79360, France</footnote>

<footnote>* Corresponding author at: Department of Environmental Science, Radboud Institute for Biological and Environmental Sciences (RIBES), Faculty of Science, Radboud University, Nijmegen 6500, the Netherlands.</footnote>

<footnote>E-mail addresses: segolene.humann@gmail.com, segolene.humann-guilleminot@ru.nl (S. Humann-Guilleminot).</footnote>
conventional farming. Organic farming, which avoids synthetic pesticides and nitrogen inputs, generally promotes greater weed species richness (e.g., Bengtsson et al., 2005; Henckel et al., 2015; Carrié et al. 2022; Gong et al., 2022). However, differences between organic and conventional farming extend beyond pesticide use; organic farming also involves more diverse crop rotations and semi-natural habitats, which contribute to increased weed diversity (Tuck et al., 2014). This complexity makes it difficult to attribute differences in weed diversity solely to pesticide use. To disentangle the effects of specific management practices, other research has focused on categorising the impacts of farming disturbances such as herbicides, nitrogen inputs, and tillage on weed abundance and diversity (Fried et al., 2008; José-María et al., 2010; Storkey et al., 2011; Campiglia et al., 2018; Fonderflick et al., 2020). At the species level, herbicides reduce weed abundance and species richness, with potential impact on non-target plants (Boutin et al., 2014; Andert et al., 2022; Tavella et al., 2025). However, at the community level, we still ignore whether community shifts resulting from herbicide use, are gradual or threshold-based, an important distinction for anticipating ecosystem responses to anthropogenic pressures and providing effective management strategies (Hillebrand et al., 2020; Spake et al., 2022). This raises questions about the role of herbicides as environmental filters that select for herbicide-resistant weed species that might disrupt key biotic interactions, such as competition, resulting in species turnover or nested changes along an herbicide gradient. Although much less studied, fungicides may indirectly influence weed communities by modifying plant-microbiome interactions and soil fungal networks (e.g., Trognitz et al., 2016; Saleem et al., 2019; Simonin et al., 2022). Similarly, insecticides can alter weed communities by affecting seed predators, potentially allowing herbicide-resistant weeds that are normally consumed by these predators to persist (Rowen et al., 2025), although direct evidence of effects on weed diversity remains scarce. Therefore, studying the gradients of fungicides and insecticides alongside herbicides offers a more holistic perspective on how pesticide use affects weed communities both directly (herbicides) or indirectly (other pesticides) by, for example, altering soil microbiome and reducing pollination.

In this study, we focused on the effect of herbicides, fungicides, and insecticides on weed communities in 96 conventional winter cereal fields (2017–2020) located within a Long-Term Socio-Ecological Research platform in west of France. To our knowledge, this is the first time that the three main classes of pesticides have been studied using actual field-level application data provided by farmers, gathered through years of close collaboration. Because organic farming systems involve multiple, distinctly different management practices that may introduce more variability than the sole use of pesticides, we chose to focus our study exclusively on non-organic cereal fields to isolate and quantify the specific effects of pesticides on weed communities. Based on a combination of multivariate and univariate analyses, our aim was to address four key questions: 1) How do herbicide, fungicide, and insecticide influence weed communities at different scales, from species level to communities? 2) Do changes in weed community response to herbicide use occur gradually, or do they shift abruptly once a certain herbicide application threshold is reached? These patterns naturally lead to species-specific considerations, motivating two additional questions: 3) Which species exhibit key pesticide-driven patterns in weed community structure, revealing both the species most susceptible to pesticide use and those that thrive under increased application intensity? 4) What are the underlying mechanisms of community change, particularly in terms of nestedness and turnover components along the pesticide gradient? Together, these questions and the large-scale empirical dataset provide a novel and comprehensive assessment of how pesticide use shapes weed communities across multiple ecological scales, identifying both the affected species and the mechanisms driving these changes.

We conducted all analyses using both the Treatment Frequency Index (TFI), which reflects an intensity of use, and the quantity of active

ingredient applied. Although not independent of one another, the two indices represent different aspects of pesticide use allowing us to contrast the effects of disturbances related to pesticide use. TFI is an indicator of pesticide use intensity, considering the number of treatments, the dose applied relative to a standard reference dose, and the proportion of the treated area, while QA reflects the total chemical load applied per hectare (Möhring et al., 2019). Both indices have been widely used in agronomic and ecological studies to assess pesticide pressure and its impacts on weed communities and other agroecosystem components, with both QA and TFI showing a good average fit as proxies for the potential environmental risks of pesticides (Hossard et al., 2017; Möhring et al., 2019; Eurostat. 2020; MAA, 2023; Masson et al., 2024; de Mol et al., 2025).

Based on these complementary indicators, we can hypothesize that pesticide use may influence weed communities differently. Notably, we predicted that higher intensity of application (TFI) would lead to gradual changes in weed species richness and abundance by consistently disrupting recovery, whereas higher quantity of active ingredient would cause more abrupt shifts due to its stronger direct impact on weed survival.

Our study primary focuses on field cores, acknowledging that fields centres and field margins harbour different weed communities due to edge and landscape effects, as well as differential exposure to pesticides (Bourgeois et al., 2019; Carrié et al. 2022). Our primary focus was to investigate the impact of pesticides on weed communities, thus only data collected in the centre of each field are presented in the main part of this paper. Nonetheless, results obtained from field margins are also given and used for contextualization.

## 2. Material and methods

### 2.1. Study area

The study was conducted between 2017 and 2020 in the Long-Term Socio-Ecological Research (LTSER) platform "Zone Atelier Plaine & Val de Sèvre" (hereafter referred to as ZA PVS, Bretagnolle et al., 2018a). The study focuses on conventional winter cereals fields, which account for ca. 60 % of the cultivated crops in the study site, with an average field size of 4.5 ha. The study area encompasses 435 km² of agricultural land located in the southern region of the city of Niort, within the Deux-Sèvres department in France.

### 2.2. Farmers' survey

Information on farming practices, including pesticide and fertilizer applications (product names, dates and quantities of each application), tillage techniques and the mechanical weed control operations (number, depth), were obtained through farmers' interviews performed at the end of each cropping season. In France, farmers are required to record all information related to pesticide and fertiliser applications on their fields. Each field in which relevés and interviews were performed was randomly selected before collecting the data. More importantly, the persons in charge of doing the vegetation relevés were blind to the amount of pesticides applied in the fields. Based on the collected information, we derived two widely used quantitative pesticide indicators and nitrogen input values (see (Catarino et al., 2019) for details on calculation). The first is the Treatment Frequency Index (TFI) that is a measure of pesticide use intensity, i.e., the number of registered doses applied, for each pesticide, per hectare and per crop season, calculated as follows: $TFI = \frac{\text{Application rate} \times \text{Treated surface}}{\text{Recommended dose} \times \text{Plot surface area}}$. The second is the Quantity of Active Ingredients applied (QA), as the sum of the amount, in grams, of each active ingredient applied per hectare. The calculation and use of both indices are thoroughly explained in Möhring et al. (2019), with a clear illustration provided in their Fig. 1.
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Weed abundance and species richness in relation to intensity (TFI) and quantity (QA) of herbicides (represented by green dots) and fungicides (represented by brown triangles). A log scale is used on the y-axis to reflect that the response variable was log-transformed in the model. Both TFI and QA values were centered and scaled (i.e., standardised). A change of one unit (x-axis) corresponds to an increase or a decrease of one standard deviation from the mean of TFI or QA of herbicides or fungicides. Linear regression lines are added based on the statistical evidence: no linear regression is displayed in the absence of evidence of correlation, while one or two linear regression lines are presented when there is evidence supporting a relationship between one or more variables. Grey area represents the 95 % confidence interval for the fitted line derived from predictions of the linear mixed-effects model, accounting for the effects of other variables in the model.</figcaption></figure>

## 2.3. Weed surveys

Identity and occurrence of weeds were recorded each year from April to July in a total of 96 different winter cereals fields, including winter wheat and winter barley (see Bretagnolle et al., 2018b for general methods). Within each field, 20 quadrats (1 m² each) - each subdivided into four 0.25 m² subquadrats - are positioned along two parallel transects from field border to centre (10 per transect). Additionally, 5 quadrats (again, subdivided into four 0.25 m²) located at one of the field margins are also surveyed (refer to Figure S1 in Bretagnolle et al., 2018b for more details). Number of quadrats and their size were selected to optimally represent the plant community while minimising survey time (Bretagnolle et al., 2018b). Weed species and abundance were recorded by direct counting with a presence/absence scale within each 0.25 m² sub-quadrat of each plant present. The abundance scale was defined as follows: 0 for absence; 1 when a single individual of a species was present; and 2 if a species was represented by more than one individual. Weed abundance was first calculated at 1 m² quadrat, as the sum of abundances of all plants per species present in the four subquadrats, taking the geometric mean (3.33) when abundance was coded as 2 in a sub-quadrat (thus, in a quadrat, for a species, abundance varied from 0 to 13.33). Weed abundance was then defined and calculated as the average between the 20 1 m² quadrats in the centre of the field and as the average between the five 1 m² quadrats at the field margins.

## 2.4. Statistical analyses

All statistical analyses were performed using the R v. 4.2.2 software (R Core Team, 2022). They were performed separately for field centres and field margins.

All analyses investigating the impact of TFI and QA for all pesticides were conducted separately for each index due to their strong correlation and the sensitivity of the results to small changes in the data (all R² > 0.5, all p < 2e-16).

We also assessed potential correlations among pesticide use variables

(herbicides, fungicides, and insecticides) and nitrogen input using Spearman's rank correlation coefficients for both Treatment Frequency Index (TFI) and Quantity of Active Ingredient (QA). Correlation matrices were visualized with the *ggcorrplot* package (Kassambara, 2023) and are presented in Figure S1 of the supplementary material. In all subsequent models, continuous variables were standardised (centered and scaled) to ensure comparability across predictors, reduce collinearity, and improve the reliability of model estimates.

### 2.4.1. Influence of pesticide application on total weed abundance and species richness

We first examined whether the TFI and QA for herbicides, fungicides, and insecticides influenced weed abundance and species richness. For this purpose, we ran four linear mixed-effects models (LMMs), with log-transformed weed abundance and log-transformed species richness as the response variables. The explanatory fixed factors included TFI or QA for herbicides, fungicides, and insecticides, as well as nitrogen input and the Julian date (continuous) of the botanical survey. Year was included as a random factor to account for inter-annual variability. LMMs were performed using the *lmer* function from the R package *lmerTest* (Kuznetsova et al., 2017).

### 2.4.2. Influence of pesticide application at community level

We addressed the effects of both TFI and QA of herbicides, fungicides and insecticides at the community level. To do this, we used partial distance-based redundancy analyses (partial dbRDA, an extension of dbRDA; Legendre and Anderson, 1999) models including either TFI or QA of herbicides, fungicides and insecticides, nitrogen input and Julian date as explanatory fixed factors using *dbrda* function. As a constrained ordination method, dbRDA directly relates species composition to environmental variables, making it well-suited for hypothesis-driven studies. Unlike unconstrained methods (e.g., NMDS, PCA), which do not incorporate explanatory variables, dbRDA are reliable for analysing species-environment relations. It also accommodates the non-Euclidean and zero-inflated nature of species abundance data through appropriate
dissimilarity measures, which makes it preferable to standard RDA in such contexts. Compared to canonical correspondence analysis (CCA), which assumes unimodal species responses, dbRDA assumes linear relationships, making it more suitable for analysing moderate-length gradients like our pesticide gradient (Jupke and Schäfer, 2020). We controlled for the sampling year by adding the year as a conditional variable to the model to account for the variation associated with it. Weed communities were characterized by a response matrix, which contained dissimilarity values across the sites (fields). All explanatory variables were standardised (centered and scaled). We applied a Hellinger transformation on the weed community matrix, i.e., it converts species abundances into the square-root of relative abundance within each site, to better linearise the distances among sites and reduce the effect of dominant species using the *decostand* function (Legendre and Legendre, 2012). Dissimilarity index selection was based on a Spearman rank correlation among seven dissimilarity indices, using the *rankindex* function. This function computes the distance indices between all pairs of samples and correlates these distances with the environmental variables included in our final model using the Spearman correlation method. The purpose was to find a distance measure that accurately represents the original distances in reduced-dimensional space. Among the tested distances, Canberra distance was identified as the most suitable for our data since it is adapted to heterogeneous scales and positive values. We assessed the significance of each explanatory variable by conducting an ANOVA using the *anova.cca* function with 999 permutations. All the functions used for these analyses were done using the R *vegan* package (Oksanen et al., 2022).

### 2.4.3. Influence of pesticide application on the abundance of competitive and non-competitive species

After identifying the species responsible for community changes via dbRDA described in the previous section, we grouped them based on their association with high and low herbicide inputs into competitive and non-competitive species, respectively. We then assessed the relative abundance of these groups, exploring how the Treatment Frequency Index (TFI) and the Quantity of Active Ingredient (QA) of pesticides affected them. To do this, we conducted four linear mixed-effects models (LMMs) using the *lmer* function from the R package *lmerTest*. Weed abundance was log-transformed in all models. To avoid issues with log(0) for zero abundance values, we added 1 to all values prior to log-transformation. Our models included TFI or QA for herbicides, fungicides, and insecticides, along with nitrogen input and the continuous Julian date of the botanical survey as fixed factors, and Year as a random factor to adjust for inter-annual variability. All continuous variables were standardised.

### 2.4.4. Underlying mechanisms under weed community changes

To identify the underlying ecological mechanisms of community change as revealed by the dbRDA, we analysed how weed communities responded to herbicide use by calculating abundance-based dissimilarity indices of β-diversity, split into β-turnover and β-nestedness components using the *beta pair.abund* function from the *betapart* package (Baselga, 2017). In this methodology, the total dissimilarity between sample pairs (βBC) is divided into balanced variation (βBal) and abundance gradient (βGra). βBal measures the turnover of species abundance between samples, while βGra reflects changes in total abundance between communities, indicating nestedness (Baselga, 2017). To assess the influence of herbicides, quantified either as TFI or QA, on these dissimilarities, we applied partial Mantel tests with the *mantelpartial* function from the *vegan* package. The analysis involves a predictor matrix of Euclidean distances based on TFI or QA differences between fields, and response matrices consisting of the β-diversity dissimilarities (βBC, βBal, βGra). Additionally, we controlled for nitrogen input differences using a third matrix to isolate the specific effect of herbicides. The significance of the relationships is evaluated using Pearson's coefficient and 999 permutations in the partial Mantel test setup. For measuring dissimilarities, we

chose the Bray-Curtis index, determined to be highly suitable for our data by the *rankindex* function, second only to the Canberra index (the latter not being available in the package). This index was applied to a matrix of species abundances across sample sites, transformed using Hellinger to normalise the data.

### 2.4.5. Modelling assumptions

We chose not to include additional environmental variables or interaction terms between the three pesticides to avoid model complexity and instability (Clark et al., 2020). However, to ensure the validity of this decision, we followed the recommendations of Sutherland et al., (2023) and tested a selection of models with and without additional agricultural practices added alone or in interactions (i.e., mechanical weeding, tillage operations, interactions between pesticide input and nitrogen). The models that included only pesticides, nitrogen inputs and Julian date were consistently selected as the best fit. Modelling assumptions of LMMs (normality and homoscedasticity of residuals, homogeneity of variance, collinearity) were validated by visual inspection using the *check_model* function of the performance R package (Lüdecke et al., 2021). All variance inflation factors were below three.

Multivariate models were validated through a visual inspection of residuals to ensure compliance with assumptions of multivariate normality and homogeneity. This included examining the residual distributions and graphically plotting the residuals against the fitted values.

Following recommendations in the ASA statement on p-values (Wasserstein and Lazar, 2016), we do not interpret our results based on an arbitrary threshold for statistical significance (*p* < 0.05). Instead, we use *p*-values to measure the amount of statistical evidence against the null hypothesis and further examine the estimated coefficients of the model to assess the strength of the associations and the confidence we should have in those estimates. Effect sizes, i.e., correlation coefficient (*r*), are provided for each Linear-mixed effect models (LMM) and indicate the strength and direction of the association. The common interpretation is based on Cohen (1977) guidelines: small (*r* < 0.30), medium (*r* < 0.50), large (*r* > 0.50).

## 3. Results

We recorded a total of 182 weed species over the four years, with 122 species occurring in the field centres and 165 in the field margins. Descriptive statistics (median, mean ± standard deviation, minimum and maximum) are shown in Table S1.

### 3.1. Effects of pesticides and nitrogen on weed abundance and species richness

The linear mixed-effects model (LMM) with the Treatment Frequency index (i.e., intensity of application; TFI) as the predictor variable indicates no statistical evidence that herbicides influence weed abundance but some evidence that it decreases weed species richness (*p* = 0.10 and *p* = 0.09, respectively, effect size *r* = -0.18 for weed species richness; Table 1). However, we found that fungicides influence both weed abundance and species richness (*p* = 0.01 and *r* = -0.27 for both variables; Table 1, Fig. 1). By contrast, no effect of insecticides is detected (*p* > 0.45; Table 1), though their use on wheat in our study area is rather marginal. Weak statistical evidence suggests that the quantity of active ingredient (QA) of herbicides influences weed abundance (*p* = 0.05 and *r* = -0.21), while it negatively impacts weed species richness (*p* = 0.02 and *r* = -0.24, respectively; Table 1; Fig. 1). Additionally, nitrogen input increased weed abundance (*p* = 0.02, *r* = 0.25; Table 1) but not weed species richness (for both TFI and QA models; *p* = 0.1 and *p* = 0.05, respectively; Table 1). Lastly, the sampling date did not influence weed abundance nor species richness.

In field margins, none of the pesticides, whether measured by TFI or QA, impact weed abundance and species richness. However, there is
<caption>**Table 1**</caption>

Summary of the linear mixed-effects model investigating the effect of Treatment Frequency Index and Quantity of Active Ingredient of herbicide, fungicide, insecticide, nitrogen input and Julian date (all variables standardised) on log-transformed weed abundance and log-transformed species richness in field centres using a restricted maximum likelihood function. The table shows model estimates ± standard error (Est. ± s.e.) and associated 95 % confidence intervals (C.I.). The table also shows multiplicative estimates for untransformed variables (Est. mult.) and associated multiplicative confidence intervals (C.I. mult.). Additionally, effect sizes r are provided, along with p-values that are derived from a t-test testing the null hypothesis that the estimate is 0.

<table><thead><tr><th rowspan="3">Explanatory variables</th><th colspan="3">Log-transformed weed abundance</th><th colspan="3">Log-transformed species richness</th></tr><tr><th colspan="3">Treatment Frequency Index</th><th colspan="3">Est. Mult.</th></tr><tr><th>Estimate ± s.e.</th><th>C.I.</th><th>Est. Mult.</th><th>C.I. mult.</th><th>Estimate ± s.e.</th><th>C.I.</th><th>Est. Mult.</th><th>C.I. mult.</th></tr></thead><tbody><tr><td>Intercept</td><td>3.13 ± 0.28</td><td>[2.58 - 3.68]</td><td>22.85</td><td>[13.16 - 39.68]</td><td>-</td><td>[1.96 - 2.35]</td><td>8.65</td><td>[7.11 - 10.51]</td><td>-</td><td><i>p</i> &lt; 0.001</td></tr><tr><td>Herbicide</td><td>-0.13 ± 0.08</td><td>[-0.28 - 0.03]</td><td>0.88</td><td>[0.76 - 1.03]</td><td>-0.17</td><td>[0.20 - 0.01]</td><td>0.91</td><td>[0.82 - 1.01]</td><td>-0.18</td><td>0.087</td></tr><tr><td>Fungicide</td><td>-0.22 ± 0.08</td><td>[-0.39 - 0.06]</td><td>0.8</td><td>[0.68 - 0.95]</td><td>-0.27</td><td>[-0.28 - 0.04]</td><td>0.85</td><td>[0.76 - 0.96]</td><td>-0.27</td><td>0.01</td></tr><tr><td>Insecticide</td><td>-0.00 ± 0.08</td><td>[-0.16 - 0.15]</td><td>1</td><td>[0.86 - 1.16]</td><td>-0.01</td><td>[-0.15 - 0.07]</td><td>0.96</td><td>[0.86 - 1.07]</td><td>-0.08</td><td>0.455</td></tr><tr><td>Nitrogen input</td><td>0.21 ± 0.09</td><td>[0.04 - 0.39]</td><td>1.24</td><td>[1.04 - 1.48]</td><td>0.25</td><td>[-0.02 - 0.23]</td><td>1.11</td><td>[0.98 - 1.26]</td><td>0.18</td><td>0.1</td></tr><tr><td>Julian date</td><td>-0.07 ± 0.09</td><td>[-0.25 - 0.12]</td><td>0.93</td><td>[0.78 - 1.12]</td><td>-0.08</td><td>[-0.14 - 0.11]</td><td>0.98</td><td>[0.87 - 1.12]</td><td>-0.03</td><td>0.8</td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Quantity of Active Ingredient</td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Log-transformed weed abundance</td></tr><tr><td>Intercept</td><td>3.14 ± 0.27</td><td>[2.60 - 3.69]</td><td>23.1</td><td>[13.39 - 39.85]</td><td>-</td><td>[1.99 - 2.34]</td><td>8.71</td><td>[7.29 - 10.42]</td><td>-</td><td><i>p</i> &lt; 0.001</td></tr><tr><td>Herbicide</td><td>-0.15 ± 0.08</td><td>[-0.30 - 0.00]</td><td>0.86</td><td>[0.74 - 1.00]</td><td>-0.21</td><td>[-0.24 - 0.02]</td><td>0.88</td><td>[0.79 - 0.98]</td><td>-0.24</td><td>0.02</td></tr><tr><td>Fungicide</td><td>-0.07 ± 0.09</td><td>[-0.25 - 0.10]</td><td>0.93</td><td>[0.78 - 1.10]</td><td>-0.09</td><td>[-0.23 - 0.02]</td><td>0.90</td><td>[0.79 - 1.02]</td><td>-0.17</td><td>0.1</td></tr><tr><td>Insecticide</td><td>0.10 ± 0.08</td><td>[-0.05 - 0.26]</td><td>1.11</td><td>[0.95 - 1.30]</td><td>0.14</td><td>[-0.07 - 0.15]</td><td>1.04</td><td>[0.93 - 1.17]</td><td>0.07</td><td>0.49</td></tr><tr><td>Nitrogen input</td><td>0.23 ± 0.09</td><td>[0.04 - 0.42]</td><td>1.26</td><td>[1.05 - 1.52]</td><td>0.25</td><td>[0.00 - 0.26]</td><td>1.14</td><td>[1.00 - 1.30]</td><td>0.23</td><td>0.05</td></tr><tr><td>Julian date</td><td>-0.05 ± 0.10</td><td>[-0.25 - 0.15]</td><td>0.95</td><td>[0.78 - 1.16]</td><td>-0.05</td><td>[-0.11 - 0.16]</td><td>1.02</td><td>[0.89 - 1.17]</td><td>0.04</td><td>0.76</td></tr></tbody></table>

Quantitative of Active Ingredient (total contribution of all variables = 6.52 %)

<table><thead><tr><th rowspan="3">Explanatory variables</th><th colspan="4">Treatment Frequency Index (total contribution of all variables = 7.08 %)</th></tr><tr><th colspan="4">Coefficient associated with each explanatory variable on each axis</th></tr><tr><th>dbRDA1</th><th>dbRDA2</th><th>Effect size</th><th><i>p</i></th></tr></thead><tbody><tr><td>Herbicide</td><td>0.74</td><td>0.46</td><td>1.20</td><td>0.001</td></tr><tr><td>Fungicide</td><td>-0.14</td><td>0.55</td><td>1.04</td><td>0.11</td></tr><tr><td>Insecticide</td><td>-0.17</td><td>0.41</td><td>0.28</td><td>0.10</td></tr><tr><td>Nitrogen input</td><td>-0.48</td><td>0.02</td><td>0.87</td><td>0.009</td></tr><tr><td>Julian date</td><td>-0.28</td><td>0.56</td><td>1.07</td><td>0.06</td></tr><tr><td colspan="5">Proportion explained of each axis:</td></tr><tr><td colspan="5">dbRDA 1 = 32.31 % (2.29 % of variance explained by dbRDA1 overall)</td></tr><tr><td colspan="5">dbRDA 2 = 22.12 % (1.57 % of variance explained by dbRDA2 overall)</td></tr><tr><th colspan="5">Coefficient associated with each explanatory variable on each axis</th></tr><tr><th></th><th>dbRDA1</th><th>dbRDA2</th><th>Effect size</th><th><i>p</i></th></tr><tr><td>Herbicide</td><td>-0.60</td><td>-0.47</td><td>1.36</td><td>0.005</td></tr><tr><td>Fungicide</td><td>0.04</td><td>-0.66</td><td>0.87</td><td>0.24</td></tr><tr><td>Insecticide</td><td>0.14</td><td>-0.10</td><td>0.67</td><td>0.74</td></tr><tr><td>Nitrogen input</td><td>0.51</td><td>-0.20</td><td>0.74</td><td>0.003</td></tr><tr><td>Julian date</td><td>0.16</td><td>-0.66</td><td>0.98</td><td>0.11</td></tr><tr><td colspan="5">Proportion explained of each axis:</td></tr><tr><td colspan="5">dbRDA 1 = 34.44 % (2.24 % of variance explained by dbRDA1 overall)</td></tr><tr><td colspan="5">dbRDA 2 = 24.25 % (1.57 % of variance explained by dbRDA2 overall)</td></tr></tbody></table>

statistical evidence of a positive effect of nitrogen addition on weed species richness (Table S2; supporting information).

## 3.2. Weed community response to pesticide use

The multivariate analysis using distance-based redundancy analysis (dbRDA) provides strong statistical evidence that variation in weed community composition across winter cereal fields is explained by both TFI and QA of herbicides, and nitrogen input (Table 2, Fig. 2), with a stronger effect of herbicide compared to nitrogen and from QA compared to TFI of herbicide (Table 2, Figure S2; supporting information). The model using QA explains slightly more variance in community composition (7.08 %) than the TFI model (6.52 %). In both models, the first dbRDA axis accounts for most of the variance, mainly driven by herbicides and nitrogen inputs (Table 2, Figure S2). Herbicide impact on weed communities is stronger than that of nitrogen input in both models (see effect sizes; Table 2). There is also a wide variation in weed communities across fields, with a gradual change in communities as herbicide use (both TFI and QA) increases (Fig. 2).

To investigate which species were most affected by herbicide use through the dbRDA analysis (TFI or QA) and contributed to shaping weed communities, we extracted and projected species scores along the two dbRDA axes. Because herbicide use was the main driver of community changes in both models, we reoriented the dbRDA reference frame (represented by red arrows in Figure S2) to align it with the herbicide vectors (TFI and QA), allowing a clearer projection of species scores along these gradients. After identifying the species most strongly associated with herbicide gradients, we searched a *posteriori* different sources (e.g., scientific literature and agricultural reference sites

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

Summary statistics from the dbRDA model and ANOVA using Canberra distance to test the influence of TFI/QAs of herbicide, fungicide, insecticide, nitrogen inputs, and Julian date on weed community composition in field centres. The table shows the total contribution of each variable to the variance in community composition and the proportion of variance explained by each axis. It also includes the coefficients for each variable on the ordination axes, representing their coordinates along each axis. Additionally, effect sizes, indicating the strength of the influence of each variable on the ordination, are provided, along with p-values. Effect sizes correspond to the length of each arrow in the biplot and are calculated as the Euclidean distance from the origin to the variable's coordinates on dbRDA1 and dbRDA2 (Figure S2). All variables were standardised (centered and scaled).
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** dbRDA biplot projections displaying weed community composition along herbicide input gradients based on the intensity of application of herbicide (Treatment Frequency Index, TFI; left) and the quantity of active ingredient applied (QA; right). Each point represents a community from a cereal field, with the colour gradient (light to dark green) indicating increasing herbicide input. In both models, herbicide input contributes most strongly to the first dbRDA axis (dbRDA1). The models explain 6.52% (TFI) and 7.08% (QA) of the variance in community composition.</figcaption></figure>

https://wikiagri.fr/) to determine whether these species are generally considered competitive or non-competitive in cereal cropping systems – regardless of their specific association with high or low herbicide input. The species classified as competitive or non-competitive based on agronomic knowledge are highlighted in the table associated with Fig. 3. A few species – *Mercurialis annua*, *Polygonum aviculare*, *Atriplex patula*, *Chenopodium album* and *Veronica persica* (later named non-competitive species; Fig. 3) are associated with low herbicide input (either TFI or QA of herbicide). In contrast, Lolium multiflorum, Galium aparine, Alopecurus myosuroides, Bromus hordeaceus, and Fallopia convolvulus (later named as competitive species; Fig. 3) are associated with higher herbicide use (Fig. 3, Figure S2).

In field margins, neither pesticide use measured as TFI nor nitrogen input affect weed communities. However, there is weak statistical evidence suggesting that the QA of herbicides has an impact on weed communities. Additionally, the sampling date influences weed communities in both models (Table S3, Figure S3; supporting information).

### 3.3. Impact of pesticides on the abundance of competitive and non-competitive weed species

Our analysis provides strong statistical evidence that both the Treatment Frequency Index (TFI) and the Quantity of Active Ingredient (QA) of herbicides affect the abundance of non-competitive with crops weed species (both $p < 0.001$ and $r = -0.41$ and $r = -0.44$, respectively). Conversely, we found no impact of herbicide input on the abundance of competitive weeds ($p = 0.31$ and $p = 0.93$, for TFI and QA, respectively; Table 3, Fig. 4). Additionally, while nitrogen input is found to influence the abundance of non-competitive species, it has no effect on competitive species (Table 3). This influence of nitrogen is comparatively weaker than that of herbicides, with effect sizes of $r = 0.29$ and $r = 0.37$ in the TFI and QA models, respectively. We report no statistical evidence that any other variables, including fungicides and insecticides, affect the abundance of any weed species subgroup (Table 3).

### 3.4. Nestedness versus turn-over in β-diversity response patterns

Since both herbicides and nitrogen influence weed communities, we further analysed β-diversity patterns in response to increasing herbicide while controlling for nitrogen input. Results indicate that TFI of

herbicide does not explain total dissimilarity between pairwise fields ($\beta$total; partial Mantel statistic $r = 0.02, p = 0.32$), nor does it account for turnover ($\beta$Bal; $r = 0.02, p = 0.36$) or nestedness ($\beta$Gra; $r = -0.001, p = 0.49$). In contrast, the QA of herbicides affects both turnover ($\beta$Bal; $r = 0.12, p = 0.003$) and total dissimilarity ($\beta$total; $r = 0.12, p = 0.005$), but not nestedness ($\beta$Gra; $r = -0.06, p = 0.96$).

We do not find any difference in β-diversity in field margin communities, regardless of whether TFI or QA of herbicides is used (all $r < 0.07, p > 0.10$).

## 4. Discussion

In this study, we investigated the impact of varying levels of herbicide, fungicide, and insecticide use on weed communities across 96 conventionally managed cereal fields. Our findings reveal that both the intensity and quantity of herbicide applications influence changes in weed communities, with quantity having a stronger impact than intensity on all parameters studied. We also find that the intensity of fungicide applications appears to reduce both weed abundance and species richness, whereas the quantity applied does not. Lastly, insecticides are found to be unrelated to any of the parameters describing weed communities.

Concerning insecticides, a floor effect due to their presence in only a small proportion of the studied fields may explain the lack of notable impacts on weed communities. Additionally, their mode of application and the type of data collected in this study may also contribute to blurring any potential effect of insecticides on weeds. Indeed, our study only includes data about direct aerial applications, although some products, particularly until 2018, could be applied as seed coatings. Therefore, we lack an important piece of information about the intensity and quantity of insecticides applied.

The fact that the intensity of application of fungicides, i.e., higher or lower doses relative to the recommendations, rather than the quantity applied, decreased abundance and species richness of weeds suggests that fungicide application intensity, which may reflect low-dose but frequent applications, could exert indirect and long-term effects on weed suppression and highlights the necessity to reduce the application intensity and not exceed the recommended doses. Such an effect of fungicides may be caused by an alteration of symbiotic arbuscular mycorrhizal fungi in the soil and interference with nutrient uptake by the plants, which may allow certain weed species to dominate, thus
<figure><img src="image_6.png" /></figure>

<table><caption>**Fig. 3.** Species associations with the Treatment Frequency Index (TFI) and Quantity of Active Ingredient (QA) of herbicides represented by projecting species scores onto the two herbicide input vectors from the distance-based redundancy analysis (dbRDA). We reoriented the orthonormal reference frame (shown in Fig. S2) to use the TFI and QA herbicide vectors as reference axes for projecting species scores associated with these metrics. Species associated with low herbicide input appear in the bottom left (either TFI or QA), while those associated with high herbicide input are positioned in the top right. The red colour gradient indicates the degree of association with the herbicide input vectors, with deeper red reflecting a stronger association with high or low herbicide sensitivity. The table below accompanies Fig. 3 and classifies species at the two ends of the herbicide input gradient, i.e., the species that contribute most strongly to the analysis, as either competitive or non-competitive in cereal cropping systems. This classification was done independently of their scores on the gradient. (Ahmad et al., 2021; Beckie and Jasieniuk, 2021; Costea and Tardif, 2005; Eslami and Ward, 2021; Harker and O'Sullivan, 1993; Keshavarzi and Mosaferi, 2020; Ribeiro et al., 2023; Stalter and Lonard, 2024).</caption><thead><tr><th>Species name</th><th>Competitiveness to cereal crops</th><th>Source</th></tr></thead><tbody><tr><td>Lolium multiflorum</td><td>High competitiveness</td><td><ul><li>Beckie and Jasieniuk, 2021</li></ul></td></tr><tr><td>Galium aparine</td><td>High competitiveness</td><td><ul><li>Wilson and Wright, 1987</li></ul></td></tr><tr><td>Alopecurus myosuroides</td><td>High competitiveness</td><td><ul><li>https://wikiagri.fr/articles/le-gaillet-gratteron/20106/</li><li>Ahmad et al., 2021</li></ul></td></tr><tr><td>Bromus hordeaceus</td><td>High competitiveness</td><td><ul><li>https://wikiagri.fr/articles/le-vulpin-des-champs/19821/</li><li>Ribeiro et al., 2023</li></ul></td></tr><tr><td>Elytrigia repens</td><td>High competitiveness</td><td><ul><li>https://wikiagri.fr/articles/les-graminees-annuelles/20047/</li><li>Harker and O'Sullivan, 1993</li></ul></td></tr><tr><td>Fallopia convolvulus</td><td>Moderate competitiveness</td><td><ul><li>https://wikiagri.fr/articles/le-chiendent-rampant/20277/</li><li>Keshavarzi and Mosaferi, 2020</li></ul></td></tr><tr><td>Veronica persica</td><td>Low competitiveness</td><td><ul><li>https://wikiagri.fr/articles/les-graminees-annuelles/20047/</li><li>https://wikiagri.fr/articles/le-cheron-des-champs/20043/</li><li>https://wikiagri.fr/articles/la-veronique-de-perse/20334/</li></ul></td></tr><tr><td>Chenopodium album</td><td>Competitive to other crops than cereals</td><td><ul><li>https://croprotect.com/articles/how-competitive-are-different-weed-species</li></ul></td></tr><tr><td>Atriplex patula</td><td>Non-competitive</td><td><ul><li>Eslami and Ward, 2021</li></ul></td></tr><tr><td>Polygonum aviculare</td><td>Low competitiveness (non-competitive in cereals)</td><td><ul><li>https://wikiagri.fr/articles/le-chenopode-blanc/20145/</li><li>Stalter and Lonard., 2024</li></ul></td></tr><tr><td>Mercurialis annua</td><td>Competitive to other crops than cereals</td><td><ul><li>Costea and Tardif, 2005</li><li>https://wikiagri.fr/articles/la-renouee-des-oiseaux/20136/</li><li>https://wikiagri.fr/articles/la-mercuriale-annuelle/20048/</li></ul></td></tr></tbody></table>

resulting in lower overall species richness (Francis and Read, 1995; Bennett and Cahill Jr, 2016; Han et al., 2021). For example, in nutrient-poor dry grasslands, the suppression of arbuscular mycorrhizal fungi through fungicide application has been shown to significantly alter plant species composition, leading to decreased richness and increased dominance by specific grass species (Dostálek et al., 2013). This suggests that fungicides can indirectly shape weed community dynamics by disrupting mutualistic fungal associations. In addition, frequent fungicide applications – particularly during early growth stages – may also exert direct effects on plant growth by inhibiting through inhibition of regulators enzymes (Nordmeyer and Koch, 2014).

We find an impact of both TFI and QA of herbicide on weed community composition through the multivariate analyses. We acknowledge that the variables included in our models explain only a modest proportion of the variance, which is common in community ecology due to the influence of numerous unmeasured or stochastic factors (Greenacre and Primicerio, 2014). However, we did not find any clusters of weed communities, suggesting that communities gradually change as

herbicide use increases. This effect may occur through herbicides selectively eliminating more sensitive, non-target weed species while allowing others to proliferate (Gaba et al., 2016). In fact, although not rare, weed species that we classified as non-competitive generally do not compete with crops even under low herbicide input (apart from Chenopodium album which competes with crops other than winter wheat) and therefore are unlikely to be responsible for yield loss (see associated Table of Fig. 3). In contrast, species found to be associated with high herbicide input are known to be competitive weeds. Competitive weeds compete directly with crops, including cereals, for light, nutrients, and water, but may also release allelopathic toxins that hinder crop growth, act as vectors for diseases and therefore impact harvest quality (e.g., increasing moisture in harvested grains) and reduce yields (Tilman et al., 2002; Zimdahl, 2008, 2018). While farmers may adjust herbicide application in response to the presence of competitive weeds, our models examining the relationship between herbicide input and weed abundance in competitive and non-competitive weeds indicate that greater herbicide intensity and quantity are associated with a lower
<table><caption>**Table 3** Summary of the linear mixed model investigating the effect of Treatment Frequency Index and Quantity of Active Ingredient of herbicide, fungicide, insecticide, nitrogen input and Julian date (all variables standardised) on weed abundance of competitive and non-competitive species using a restricted maximum likelihood function. The table shows model estimates ± standard error (Est. ± s.e.) and associated 95 % confidence intervals (C.I.). P-values are derived from a t-test testing the null hypothesis that the estimate is 0. The table also shows multiplicative estimates for untransformed variable (Est. mult.) and associated multiplicative confidence intervals (C.I. mult.). Additionally, effect sizes *r* are provided, along with *p*-values that are derived from a t-test testing the null hypothesis that the estimate is 0.</caption><thead><tr><th rowspan="3"></th><th colspan="6">Treatment Frequency Index</th><th colspan="6">Log-transformed weed abundance of non-competitive species</th></tr><tr><th colspan="3">Estimate ± s.e.</th><th colspan="3">Est. Mult.</th><th colspan="3">C.I. mult.</th><th colspan="3">Estimate ± s.e.</th><th colspan="3">Est. Mult.</th><th colspan="3">C.I. mult.</th></tr><tr><th></th><th>C.I.</th><th>Est. Mult.</th><th></th><th>C.I. mult.</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>C.I.</th><th>Est. Mult.</th><th></th><th></th><th></th><th></th><th></th></tr><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Intercept</td><td>1.81 ± 0.26</td><td>[1.29 - 2.33]</td><td>6.13</td><td></td><td>[3.64 - 10.32]</td><td></td><td></td><td></td><td></td><td></td><td>1.50 ± 0.20</td><td>[1.09 - 1.90]</td><td>4.46</td><td>[2.98 - 6.69]</td><td></td><td></td><td></td><td></td></tr><tr><td>Herbicide</td><td>0.09 ± 0.09</td><td>[-0.09 - 0.28]</td><td>1.1</td><td></td><td>[0.92 - 1.32]</td><td></td><td>0.11</td><td>0.310</td><td></td><td>-0.37 ± 0.09</td><td></td><td>[-0.55 - -0.20]</td><td>0.69</td><td>[0.58 - 0.82]</td><td>-0.41</td><td></td><td></td><td>0.001</td></tr><tr><td>Fungicide</td><td>-0.11 ± 0.10</td><td>[-0.31 - 0.09]</td><td>0.9</td><td></td><td>[0.74 - 1.10]</td><td></td><td>-0.11</td><td>0.291</td><td></td><td>-0.15 ± 0.1</td><td></td><td>[-0.34 - 0.04]</td><td>0.86</td><td>[0.71 - 1.04]</td><td>-0.16</td><td></td><td></td><td>0.001</td></tr><tr><td>Insecticide</td><td>-0.08 ± 0.09</td><td>[-0.26 - 0.11]</td><td>0.93</td><td></td><td>[0.77 - 1.11]</td><td></td><td>-0.09</td><td>0.413</td><td></td><td>0.01 ± 0.09</td><td></td><td>[-0.17 - 0.19]</td><td>1.01</td><td>[0.85 - 1.21]</td><td>0.01</td><td></td><td></td><td>0.915</td></tr><tr><td>Nitrogen input</td><td>-0.03 ± 0.11</td><td>[-0.19 - 0.24]</td><td>1.03</td><td></td><td>[0.83 - 1.27]</td><td></td><td>0.03</td><td>0.813</td><td></td><td>0.30 ± 0.1</td><td></td><td>[0.09 - 0.50]</td><td>1.35</td><td>[1.10 - 1.65]</td><td>0.29</td><td></td><td></td><td>0.005</td></tr><tr><td>Julian date</td><td>-0.12 ± 0.11</td><td>[-0.34 - 0.10]</td><td>0.89</td><td></td><td>[0.71 - 1.11]</td><td></td><td>-0.12</td><td>0.280</td><td></td><td>0.12 ± 0.11</td><td></td><td>[-0.09 - 0.33]</td><td>1.12</td><td>[0.91 - 1.39]</td><td>0.12</td><td></td><td></td><td>0.271</td></tr><tr><td>Quantity of Active Ingredient</td><td colspan="16">Log-transformed weed abundance of competitive species</td></tr><tr><td></td><td colspan="3">Estimate ± s.e.</td><td colspan="3">Est. Mult.</td><td colspan="3">C.I. mult.</td><td colspan="3">Estimate ± s.e.</td><td colspan="3">Est. Mult.</td><td colspan="3">C.I. mult.</td></tr><tr><td>Intercept</td><td>1.82 ± 0.27</td><td>[1.28 - 2.35]</td><td>6.14</td><td></td><td>[3.60 - 10.49]</td><td></td><td></td><td></td><td></td><td></td><td>1.51 ± 0.20</td><td>[1.12 - 1.90]</td><td>4.52</td><td>[3.06 - 6.70]</td><td></td><td></td><td></td><td></td></tr><tr><td>Herbicide</td><td>-0.01 ± 0.09</td><td>[-0.19 - 0.17]</td><td>0.99</td><td></td><td>[0.83 - 1.19]</td><td></td><td>-0.01</td><td>0.927</td><td></td><td>-0.40 ± 0.09</td><td></td><td>[-0.57 - -0.23]</td><td>0.67</td><td>[0.56 - 0.80]</td><td>-0.44</td><td></td><td></td><td>0.001</td></tr><tr><td>Fungicide</td><td>-0.05 ± 0.1</td><td>[-0.26 - 0.16]</td><td>0.95</td><td></td><td>[0.77 - 1.18]</td><td></td><td>-0.05</td><td>0.656</td><td></td><td>-0.13 ± 0.10</td><td></td><td>[-0.32 - 0.07]</td><td>0.88</td><td>[0.72 - 1.07]</td><td>-0.14</td><td></td><td></td><td>0.201</td></tr><tr><td>Insecticide</td><td>0.07 ± 0.1</td><td>[-0.12 - 0.26]</td><td>1.07</td><td></td><td>[0.89 - 1.29]</td><td></td><td>0.08</td><td>0.483</td><td></td><td>0.09 ± 0.09</td><td></td><td>[-0.09 - 0.27]</td><td>1.09</td><td>[0.92 - 1.31]</td><td>0.11</td><td></td><td></td><td>0.317</td></tr><tr><td>Nitrogen input</td><td>0.03 ± 0.11</td><td>[-0.20 - 0.25]</td><td>1.03</td><td></td><td>[0.82 - 1.29]</td><td></td><td>0.02</td><td>0.821</td><td></td><td>0.39 ± 0.11</td><td></td><td>[0.18 - 0.60]</td><td>1.48</td><td>[1.20 - 1.83]</td><td>0.37</td><td></td><td></td><td>0.001</td></tr><tr><td>Julian date</td><td>-0.15 ± 0.12</td><td>[-0.38 - 0.09]</td><td>0.86</td><td></td><td>[0.68 - 1.10]</td><td></td><td>-0.13</td><td>0.224</td><td></td><td>0.21 ± 0.11</td><td></td><td>[-0.01 - 0.43]</td><td>1.23</td><td>[0.99 - 1.54]</td><td>0.20</td><td></td><td></td><td>0.062</td></tr></tbody></table>

abundance of non-competitive species, but are unrelated to the abundance of competitive weeds – the primary targets of herbicide. This finding challenges the hypothesis that higher herbicide input simply reflects a response to higher competitive weed pressure. The lack of relationship between competitive species and the intensity and quantity of herbicide further hints at their potential tolerance to herbicides and/or their ability to thrive in environments where competitors have been suppressed by herbicides. Overall, it appears that higher herbicide application gradually eliminates weeds, disproportionately impacting weed species with low competitiveness against crops while favouring those that are highly competitive against crops (Gaba et al., 2016; Andert et al., 2022).

We can think of several mechanisms to explain the observed changes in weed communities. Changes in species community composition can occur through species replacement (captured by the balanced variation of β-diversity, i.e., species turnover), shifts in the abundance of pre-existing species (represented by gradient variation of β-diversity, i.e., nestedness), or a combination of both (reflected in total β-diversity dissimilarity). We do not find any relationship between any indices of β-diversity and the intensity of herbicide application, suggesting that TFI only reduces species richness without altering dissimilarity across communities. In contrast, the quantity of herbicide applied appears to notably influence species turnover, overall β-diversity dissimilarity, weed abundance and species richness. This pattern describes both a decline in non-competitive species and their replacement by persistent, more abundant, competitive species as herbicide use increases.

The persistence of these problematic species under high herbicide input may be attributed to the evolution of herbicide resistance mechanisms, a well-recognized issue in modern agriculture. Herbicides remain the primary method of weed control in most conventional cropping systems, yet numerous cases of resistance have emerged, with 273 weed species confirmed to exhibit resistance across 75 countries worldwide (Heap, 2025). Herbicide resistance is particularly pronounced among competitive weeds like *Lolium multiflorum* and *Alopecurus myosuroides*, both associated with high herbicide use in our study (Gaines et al., 2020; Franco-Ortega et al., 2021; Brunharo and Tranel, 2023). The evolution of herbicide resistance is influenced by multiple interacting factors, including the herbicide's mode of action, the biological and genetic characteristics of the weed species, and agricultural practices (e.g., herbicide dose, frequency, mixtures; Bagavathiannan and Davis, 2018; Moss et al., 2019). These factors can either accelerate or inhibit resistance development, shaping shifts in weed species richness, evenness, and community composition over time (Oreja et al., 2022, 2024). In our study, the persistence of competitive weeds under high herbicide inputs, both in terms of quantity and intensity, likely reflects inherent tolerance and selection for resistant genotypes while susceptible species are potentially eradicated or reduced, sometimes leading to local extinction (Booth et al., 2003; Ryan et al., 2010).

Beyond their direct effects on weed communities, our findings resonate with recent modelling work showing that herbicide-induced shifts in plant communities can cascade through trophic networks, including plant-pollinator interactions (Tavella et al., 2025). Such models have demonstrated that the replacement of non-competitive species by herbicide-tolerant or competitive weeds not only reduces plant diversity but also increases network fragility, lowers robustness, and amplifies the risk of extinction cascades in pollinators and other interacting species (Egan et al., 2014; Leguizamón et al., 2025).

While herbicide application is the major factor shaping weed communities, our findings also indicate that nitrogen input influences community composition, though to a lesser extent than herbicides, by increasing weed abundance without reducing species richness. Indeed, nitrogen levels are known to modify species composition without decreasing species richness (Jiang et al., 2018; Humann-Guilleminot et al., 2023; Kordbacheh et al., 2023). Several weed species are known to thrive in nitrogen-rich environments, where they outcompete other species, leading to shifts in overall community composition (Kim et al.,
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Abundance of competitive and non-competitive weed species in relation to standardised TFI and QA of herbicides. A log scale is used on the y-axis to reflect that the response variable was log-transformed in the model. A change of one unit (x-axis) corresponds to an increase or a decrease of one standard deviation from the mean of TFI or QA of herbicides. Linear regression lines are presented when there is evidence supporting a relationship between weed abundance and herbicide input. Grey area represents the 95 % confidence interval for the fitted line derived from predictions of the linear mixed-effects model, accounting for the effects of other variables in the model. The partial correlation coefficient, *r*, indicates the strength and direction of the association between two variables while accounting for other variables in the model. It is displayed for each association. To avoid issues with log(0) for zero abundance values, we added 1 to all values prior to log-transformation. As a result, a value of 1 on the log-transformed scale corresponds to an observed abundance of 0.</figcaption></figure>

2006; Berger et al., 2007; Moreau et al., 2014). Our results also confirm that nitrogen is a weaker driver of β-diversity than herbicides (Carrié et al. 2022).

This study, which focuses on the impact of herbicides, fungicides and insecticides in conventional cereals fields, reveals how the quantity, and to a lesser extent the intensity, of herbicides applied alters weed communities at all levels from weed species richness to community composition. In particular, herbicides decrease the abundance of non-competitive weeds while replacing them with competitive to crops species. Furthermore, our results indicate that weed abundance and species richness in cereals fields decrease with increasing intensity of fungicide application. We acknowledge that this study is limited to cereal crops and similar analyses across other cropping systems – especially those with higher pesticide inputs – are needed to assess the broader applicability of our findings. Furthermore, our study is observational and does not allow for direct causal inferences; experimental approaches would be necessary to confirm the causal effects of herbicide use on competitive and non-competitive weeds. Nonetheless, our study relies on a large sample size and realistic, up-to-date, agricultural practices. Importantly, our results point at a potential limitation of the use of herbicides to control highly competitive weed species and the need to limit their application in favour of agroecological practices that promote the diversity of weeds beneficial to agroecosystems.

### CRediT authorship contribution statement

**Ségolène Humann-Guillememinot:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Formal analysis, Data curation, Conceptualization. **Sabrina Gaba:** Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Data curation, Conceptualization. **Vincent Bretagnolle:** Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Data curation, Conceptualization.

### Funding

The study has received funding from the CNRS project SeeLife for long-term biodiversity monitoring and the Nouvelle Aquitaine Region (project Transform' Actions). SG and VB were funded by INRAE and CNRS. SHG received funding from CNRS and an SNSF-Postdoc.Mobility grant (#P500PB_222110).

### Declaration of Competing Interest

The authors declare no competing interests.

### Acknowledgements

We would like to thank all farmers who participated in the survey for devoting their time and for their confidence expressed by letting us monitor weed flora within their fields. We are deeply grateful to Adrien Berquer, Sébastien Boinot, Valentin Cornet, Stanilas de Closets, Thierry Fanjas-Mercere, Jonathan Millaud and Ségolène Rousselet for their investment in weed flora monitoring. We also would like to thank Ambroise Lahut and Niklas Mörhing for data curation and computation of agricultural practices metrics; and Fabien Vialoux and Claire Lamare for conducting farmers' interviews.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.agee.2025.110026.

### Data availability

All plant data and R code generated to conduct the analyses described in that study are available in a public database: https://doi.org/10.48579/PRO/9CKQC6. Please note that pesticides and fertilizer data are confidential data under the French data protection law (CNIL). Please contact the authors if you want to obtain this data.
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