contribution of biodegradation to overall pesticide dissipation depends on the physicochemical characteristics of both the soil and the pesticides, as well as the degradation capability of the soil microbiome (Huang et al., 2018; Satish et al., 2017; Schroll et al., 2006). In particular, soil water content and temperature affect phase partitioning and availability of pesticides and nutrients, thus controlling microbial activity (Meena et al., 2020; Mervosh et al., 1995), which includes pesticide degradation. Higher water content and temperature generally enhance pesticide availability for degrading microorganisms, thereby promoting biodegradation (Xue et al., 2017). However, these conditions may also increase the exposure of non-target soil microorganisms, potentially altering community responses and soil functions.

We recently showed that synthetic pesticides can affect interactions of non-target microorganisms in soil with potential effect on key functions, which includes the degradation of soil organic compounds (Fournier et al., 2020). This study has raised new questions regarding the effect of soil physicochemical properties and pesticide exposure on pesticide dissipation and its subsequent effect on soil microbial communities. How do variations in key soil physicochemical properties, such as water content and temperature, affect pesticide dissipation and the associated response of the exposed soil microbiota? The advent of high-throughput sequencing (HTS) of environmental DNA offers an unprecedented opportunity to assess the diversity and response of various taxonomic groups within soil microorganisms when exposed to pesticide mixtures (Fournier et al., 2020; Imfeld and Vuilleumier, 2012; Wolejko et al., 2020). HTS-based studies hold potential to examine in vineyard soil the effect of low-level pesticide mixtures on different microbial domains (Cycoń et al., 2017; Gobbi et al., 2022). Although numerous studies have focused on evaluating the effect of individual pesticides on soil bacteria, it is crucial to conduct reference experiments that consider pesticide dissipation in multi-contaminated vineyard soils. Such experiments are essential for evaluating the responses of bacterial, fungal, and protistan communities.

The primary objectives of this study were: (i) to investigate the effect of water content and temperature on the dissipation and biodegradation of individual pesticides within a mixture, and (ii) to evaluate and compare the responses of bacterial, fungal, and protistan communities as pesticide dissipation occurs in a vineyard soil contaminated with various pesticides. We conducted a laboratory-based microcosm study using both autoclaved and non-autoclaved vineyard soils. These soils were spiked with a mixture of commonly used fungicides and herbicides at concentrations relevant to field conditions. We hypothesized that the presence of multiple pesticide residues in the vineyard soil would have a more significant effect on microbial communities compared to the effects of soil water content and temperature.

## 2. Material and methods

### 2.1. Chemicals and pesticide selection

Standards of fungicides and herbicides (Analytical grade, purity >99 %), metolachlor-d₁₁, alachlor-d₁₃ (analytical grade purity: >97 %), dichloromethane (DCM), acetonitrile (ACN), ethyl acetate (EtOAc) (HPLC grade, purity: >99.9 %) and anhydrous magnesium sulfate (reagent grade: >97 %) were all purchased from Sigma-Aldrich (St. Louis, MO, USA). Primary-secondary amine (PSA) was supplied by Supelco (Bellefonte, PA, USA). Pesticides for soil spiking and standards for chromatographic analysis were prepared, respectively, in DCM and ACN as 1 g/L stock solutions. Aliquots were stored at -20°C.

Four fungicides (carbendazim, dimethomorph, rac-metalaxyl, and pyrimethanil) and five herbicides (atrazine, isoproturon, isoxaben, simazine, and S-metolachlor) were selected for soil microcosm experiments. These pesticides were chosen based on their widespread use, common application in vineyards either simultaneously or successively (carbendazim, dimethomorph, rac-metalaxyl, pyrimethanil, isoproturon, isoxaben), and frequent detection in soil and water, often as

legacy herbicides (atrazine, simazine, and S-metolachlor). They represent a broad range of physicochemical properties, potentially resulting in varied behaviors in vineyard soil (Table 1). Eleven other pesticides were initially considered in the study but excluded due to low extraction recoveries, leading to high concentration data uncertainty (data not shown).

### 2.2. Vineyard soil

About 25 kg of topsoil (0–1 cm) was collected on August 6–7, 2015 across four transects on the experimental vineyard catchment at Roufach, situated in the Alsatian foothills (47°57'9 N, 07°17'3 E) (Gregoire et al., 2010; Duplay et al., 2014). The main soil types are Cambisol (Hypereutric Clayic) and Haplic Cambisol (Calcaric Siltic). The soil (pH 8.1) contains 69 % of clay, 23 % of silt, 8 % of sand and 1.7 % of organic matter (Table S1) (Duplay et al., 2014). The herbicides were applied on soil to control grass while the fungicides were used typically on leaves and fruits to control fungal and oomycete diseases, and in particular downy mildew (Imfeld et al., 2020). Simazine, atrazine and isoproturon are legacy vineyard herbicides. Although S-metolachlor application has not been reported, it may occur in vineyards due to drift and deposits from nearby maize and wheat fields where it is massively used (Gilevska et al., 2023).

Soil samples were transported in coolers. Samples were mixed and homogenized with a riffle splitter, sieved through a 2 mm mesh and stored at 4°C until analysis. Prior to experiment, soil samples were conditioned for two weeks at room temperature.

### 2.3. Experimental set-up and operations

Three series of soil microcosm experiments were set-up: (i) non-autoclaved (biotic) soils spiked with pesticides to evaluate pesticide dissipation, (ii) autoclaved (abiotic) soils spiked with pesticides, as controls to evaluate abiotic pesticide dissipation, and (iii) non-autoclaved soils adjusted with sterile distilled water only as nonspiked controls (Figure S1). A total of 60 experiments were carried out, which included 20 experiments with nonautoclaved spiked soil, 20 experiments with autoclaved spiked soil, and 20 experiments with non-autoclaved, non-spiked control soils. Each series of experiments was performed in triplicate, resulting in a total of 180 individual soil microcosms (Figure S1).

The soil microcosms were assembled sterilely under laminar flow conditions using 20 g of air-dried soil placed in autoclaved 30 mL crimp glass vials. The vials were sealed with silicone/natural PTFE caps (Interchim®, France). To maintain oxic conditions in the vials while minimizing water loss and preventing contamination, a 0.2 µm syringe filter (Rotilabo®, Carl Roth®, France) was affixed to a syringe tip inserted through the vial cap (Figure S1).

To spike the soil, stock solutions of individual pesticides in DCM were added to distilled water and stirred until complete DCM evaporation. The resulting pesticide aqueous solutions were filter-sterilized using 0.2 µm syringe filters before soil spiking. No traces of DCM were detected in the final aqueous solutions (data not shown). Each experiment involving nonautoclaved soils was spiked with the pesticide mixture to achieve a final concentration of each individual pesticide at 5 µg g⁻¹ of soil, which corresponds to pesticide concentration in soil following pesticide use (Imfeld et al., 2020). Subsequently, the spiked solutions were adjusted to achieve a final soil water content of either 20 % or 40 % (w/w) to investigate the effect of varying soil water content on pesticide dissipation. The autoclaved control experiments involved soil autoclaving at 121°C for 30 minutes, using saturated steam under 15 psi, with three cycles performed at 24 h intervals. These autoclaved soil samples were spiked with pesticides and adjusted in the same manner as the non-autoclaved experiments. The control experiments using non-autoclaved soil were not spiked with pesticides but were adjusted to a final soil water content of 20 % or 40 % (w/w) using
<caption>**Table 1**</caption>

Physicochemical properties and half-lives of pesticides, ordered according to pesticide solubility in water (20°C). Data are from the Pesticide Properties DataBase (PPDB, 2009). H: herbicide, F: fungicide.

<table><thead><tr><th>Compound</th><th>Formula</th><th>Family</th><th>Type</th><th>Solubility [mg L<sup>-1</sup>]</th><th>K<sub>oc</sub> [mL g<sup>-1</sup>]</th><th>Theoretical DT<sub>50</sub> [d]</th></tr></thead><tbody><tr><td>Isoxaben</td><td>C<sub>18</sub>H<sub>24</sub>N<sub>2</sub>O<sub>4</sub></td><td>Benzamide</td><td>H</td><td>0.9</td><td>351</td><td>105</td></tr><tr><td>Simazine</td><td>C<sub>7</sub>H<sub>12</sub>ClN<sub>5</sub></td><td>Triazine</td><td>H</td><td>5</td><td>340</td><td>60</td></tr><tr><td>Carbendazim</td><td>C<sub>9</sub>H<sub>9</sub>N<sub>3</sub>O<sub>2</sub></td><td>Benzimidazole</td><td>F</td><td>8</td><td>223</td><td>40</td></tr><tr><td>Dimethomorph</td><td>C<sub>21</sub>H<sub>22</sub>ClNO<sub>4</sub></td><td>Morpholine</td><td>F</td><td>29</td><td>1360</td><td>57</td></tr><tr><td>Atrazine</td><td>C<sub>8</sub>H<sub>14</sub>ClN<sub>5</sub></td><td>Triazine</td><td>H</td><td>35</td><td>93</td><td>75</td></tr><tr><td>Isoproturon</td><td>C<sub>12</sub>H<sub>18</sub>N<sub>2</sub>O</td><td>Phenylurea</td><td>H</td><td>70</td><td>122</td><td>12</td></tr><tr><td>Pyrimethanil</td><td>C<sub>12</sub>H<sub>13</sub>N<sub>3</sub></td><td>Anilinopyrimidine</td><td>F</td><td>121</td><td>301</td><td>55</td></tr><tr><td>S-Metolachlor</td><td>C<sub>15</sub>H<sub>22</sub>ClNO<sub>2</sub></td><td>Chloroacetamide</td><td>H</td><td>480</td><td>185</td><td>15</td></tr><tr><td>Rac-metalaxyl</td><td>C<sub>15</sub>H<sub>21</sub>NO<sub>4</sub></td><td>Phenylamide</td><td>F</td><td>7100</td><td>163</td><td>42</td></tr></tbody></table>

sterile deionized water. To ensure uniformity within the soil microcosms, the aqueous pesticide solution, or deionized water for controls, was gradually added to small quantities of soil and mixed. The microcosms were then subjected to 1 min of vortexing and 1 h of shaking.

All experiments were maintained in the dark for a maximum duration of 200 days, during which they were incubated at temperatures of either 20°C or 30°C to explore the effect of temperature on pesticide dissipation. Sacrificial sampling was conducted on days 1 (once equilibrium was achieved), 10, 50, 100, and 200.

## 2.4. Pesticide quantification

Pesticides were extracted from the soil and quantified as described previously (Masbou et al., 2018). Briefly, the soil water content of samples was adjusted to 80 % before three successive extractions of 5 g of soil with 2.5 mL of ethyl acetate, followed by drying with MgSO₄. An additional cleanup procedure using primary secondary amine and MgSO₄ was performed before quantification (Anastassiades et al., 2003). Extraction recoveries for all pesticides exceeded 50 % and were reproducible among replicates. Simazine, dimethomorph, atrazine, pyrimethanil, S-metolachlor, and rac-metalaxyl were quantified using gas chromatography-mass spectrometry (GC-MS) with a Trace 1300 GC coupled to an ISQ MS (Thermo Fisher Scientific), equipped with a TG-5MS column (30 m × 0.25 mm ID, 0.25 µm film thickness). More polar pesticides, including isoxaben, carbendazim, and isoproturon, were quantified using liquid chromatography-mass spectrometry (LC-MS) with a TSQ Quantum Access Max (Thermo Scientific), equipped with an EC 150/3 Nucleodur C18 Pyramid 3 µm column. The mobile phase, delivered at 0.3 mL min⁻¹, consisted of a gradient of acetonitrile (30–95 %) and water (70–5 %) in positive mode. Isotope-labelled internal standards of pesticides were systematically used. Analytical error was less than 15 % and limits of quantification were greater than 0.34 µg g⁻¹ for all pesticides.

## 2.5. Bacterial, fungal and protistan community analyses

### 2.5.1. DNA extraction and sequencing

For the analysis of microbial communities, triplicate soil samples from each nonautoclaved experiment and sampling day were combined to provide a representative sample for each treatment and sampling day. To confirm the absence or low abundance of active microorganisms in the autoclaved microcosms samples four from day one and four from day 200 were also processed. Low-quality sequences for bacteria, fungi, and protists were systematically retrieved from these autoclaved samples.

DNA extraction was performed with 0.5 g of fresh soil using the FastDNA SPIN Kit for soil (MP Biomedicals, Solon, OH) and following the recommendations of the manufacturer. DNA extracts were quantified using a Quawell q9000 spectrophotometer, adjusted to 20 ng µL⁻¹ in ultra-pure water and stored at -20 °C. DNA samples (25 µL) were sent to the Centre for Comparative Genomics and Evolutionary Bioinformatics (Halifax, Canada) for PCR amplification and Illumina MiSeq sequencing.

The V4–V5 region of the 16S rDNA gene was amplified with the primers 515FB and 926 R (Walters et al., 2016) to characterize bacterial communities. Fungal internal transcribed spacer 2 region was amplified with the primer pair ITS86F and ITS4R (Walters et al., 2016). The V4 region of the 18S rRNA gene was amplified with the general eukaryotic primer pair E572F and E1009R to investigate protistan communities (Comeau et al., 2011). Information about the PCR procedures and Illumina sequencing were described previously (Comeau et al., 2017, 2011). The amplicon data are available on EMBL European Nucleotide Archive under project number PRJEB44062.

### 2.5.2. Sequence data processing and taxonomic assignment

The absence of sequencing primers in the dataset was verified using cutadapt V4.4 (Martin 2011). The reads analysis was carried out with the DADA2 software (Callahan et al., 2016). The DADA2 pipeline infers exact amplicon sequence variants (ASVs) from sequencing data with filtering, dereplication, sample inference, chimera identification and merging of paired-end reads. The QIIME2 (Bolyen et al., 2019) was used for the taxonomy assignment to the ASVs with a pre-trained Naive Bayes classifiers (Wang et al., 2007) and the UNITE database as a reference for fungi (Nilsson et al., 2019) and the Silva database (Ref NR 99, release 132) for bacteria and protists (Quast et al., 2012). ASVs from the 16S, the 18S and the ITS datasets, which were not assigned to, respectively, bacteria, fungi and protists, were discarded. Fungal and protistan sequencing failed respectively for two autoclaved (<2404 sequences per samples) and six nonautoclaved samples (<553 sequences per samples), including samples from experiment at 30°C and 40 % water content. These samples were thus discarded for downstream analyses. Since our approach relies on extracted DNA, our data might include ASVs from extracellular DNA or encysted cells.

## 2.6. Data analysis

### 2.6.1. Pesticide data analysis

Pesticide data in each experiment were fitted to a single first-order (SFO) model according to NAFTA guidelines (US-EPA) to determine the dissipation rate constants (k) and the first-order dissipation half-lives (DT₅₀), i.e., the time required for 50 % dissipation of the pesticide, whenever the model was significant (R² >0.6, p <0.05) and n ≥5 sampling points were available.

The contribution of abiotic and biotic pesticide dissipation was estimated by comparing dissipation in nonautoclaved (biotic) and autoclaved (abiotic) spiked soil experiments. The dissipated fraction of pesticide at time t (Dₜ,%) in the autoclaved experiments (Dₐutoclaved,%) defines the contribution of abiotic dissipation, which includes abiotic degradation and irreversible sorption in soil. The dissipation in nonautoclaved (biotic) experiments (Dₐutoclaved,%) additionally includes biotic degradation. Thus, the extent of biodegradation (Bₜ,%) of pesticides at time t (days) was estimated as:

$$B_t = (D_{\text{nonautoclaved},t} - D_{\text{autoclaved},t}) \quad (1)$$
In Eq. (1), it is assumed that the extent of abiotic dissipation is equal in autoclaved and nonautoclaved soil experiments.

Non-parametric Wilcoxon tests were applied at a significance level of 0.05 to compare the effect of autoclaving, temperature and water content on pesticide dissipation patterns using the R program (Copyright 2005, The R Foundation for Statistical Computing, Version 2.15.1).

### 2.6.2. Microbial data analysis

Changes of microbial diversity were analyzed under different conditions and over time. ASVs representing less than 1 % of the average sample depth were discarded to minimise the effect of random

sequencing errors and noises when estimating diversity (Schiaffino et al., 2016).

To evaluate taxon accumulation of bacteria, fungi, and protists per sample, rarefaction curves were constructed using the rarefy function from the R package vegan. The alpha diversity of bacteria, fungi, and protists was calculated using the Inverse Simpson and Shannon diversity indices. Stepwise linear regression models were computed to examine the effects of water content, temperature, incubation time, and pesticide exposure on microbial diversity. All alpha diversity analyses were performed on a rarefied dataset, consisting of 10,645 bacterial sequences, 2404 fungal sequences, and 553 protistan sequences, respectively.

<figure><img src="image_1.png" /><figcaption>**Fig. 1.** Pesticide dissipation (%), half-lives (DT<sub>50</sub>), and dissipation rate constants (k) in vineyard soil experiments under two different water content conditions: 20 % (A) and 40 % (B) over 200 days. Dashed bars denote the contribution of pesticide degradation to the overall dissipation process. The error bars associated with pesticide dissipation represent 95 % confidence intervals, accounting for error propagation. The upper 95 % confidence interval bars were capped at a maximum value of 100 %. For both DT<sub>50</sub> and k values, the error bars are either equal to or smaller than the respective symbols. A "#" symbol indicates the absence of a value due to non-significant fitting of pesticide dissipation data with the SFO model (p>0.05).</figcaption></figure>
To visualize change in the composition of bacteria, fungi and protists according to soil conditions, incubation time and pesticide dissipation, nonmetric multidimensional scaling (NMDS) were conducted with the Bray–Curtis distance using the R function “metaMDS” (Oksanen et al., 2015). The “envfit” function was used to correlate environmental variables with the communities (Oksanen et al., 2015). Concentrations of the nine pesticides were included as variables to evaluate how individual pesticides correlate with alpha and beta microbial diversity. A few (<5 %) missing pesticide concentration values were imputed with the missForest R package under default settings (Stekhoven and Buhlmann, 2012). All statistical analyses were carried using the R 3.6.1 environment (Oksanen et al., 2015).

## 3. Results and discussion

### 3.1. Effect of water content and temperature on pesticide dissipation

Pesticide half-lives (DT<sub>50</sub>) estimated in the soil experiments at 20°C and 20 % water content differed from the reference DT<sub>50</sub> values reported for the same conditions (PPDB, 2009) (Table 1 and Fig. 1). The DT<sub>50</sub> of isoxaben, simazine, atrazine, and pyrimethanil were up to 2.4 times lower than the reference PPDB values, while the DT<sub>50</sub> of dimethromorph, rac-metalaxyl, S-metolachlor, and isoproturon were higher (Fig. 1). Both soil physicochemical characteristics and the use of a pesticide mixture instead of individual pesticides likely affected pesticide sorption and degradation in the soil (Meena et al., 2020; Vischetti et al., 2020), which resulted in distinct dissipation patterns. Varying DT<sub>50</sub> values between experiments also highlights the difficulty to extrapolate soil-specific estimates of pesticide persistence and the need to evaluate determinants and processes of pesticide dissipation in multi-contaminated soils.

#### 3.1.1. Effect of water content on pesticide dissipation

The DT<sub>50</sub> values were significantly higher (p<0.05) in both the autoclaved and nonautoclaved experiments with 40 % water content compared to those with 20 % water content (Fig. 1). This indicates that pesticide dissipation occurred more slowly as the water content increased. The sorption of several pesticides, including simazine, has been shown to correlate with soil water content (García-Valcárcel and Tadeo, 1999; Ochsner et al., 2006). The carbon-water partitioning coefficient (K<sub>oc</sub>) of pesticides is expected to vary with water content. This variation is attributed to changes in the accessibility of sorption sites within smaller-sized soil fractions, specifically clay and silt, and the hydrophobicity of soil organic matter (SOM) (Mao et al., 2019; Ochsner et al., 2006). As the water content increases, the extractable fraction of pesticides may also increase, reducing the fraction of non-extractable residues (NER) and apparent dissipation. This increase in pesticide extractability and DT<sub>50</sub> values was particularly observed in experiments with 40 % water content (Fig. 1).

The effect of water content on pesticide dissipation was particularly pronounced for the most soluble pesticides, such as isoproturon, pyrimethanil, S-metolachlor, and rac-metalaxyl (Table 1). The DT<sub>50</sub> values of these pesticides were 1.2- to 5-fold higher in both autoclaved and nonautoclaved experiments with 40 % water content compared to those with 20 % water content (Fig. 1). The increased extractability of soluble pesticides at higher water content levels is expected to reduce their sorption and sequestration, primarily due to slower diffusion into soil microsites. This, in turn, may enhance their availability and mobility within vineyard soil (Meite et al., 2022).

#### 3.1.2. Effect of temperature on pesticide dissipation

Temperature significantly (p<0.05) affected pesticide dissipation in the nonautoclaved experiments. At 30°C, there was up to a 2-fold increase in dissipation rate constants (Table S2) and a 30 % reduction in half-lives compared to experiments conducted at 20°C (Fig. 1). Higher temperatures are expected to enhance the sorption-desorption rates of

pesticides in both autoclaved and nonautoclaved soil experiments, subsequently increasing pesticide availability and degradation rates (Dong and Sun, 2016). However, few studies have focused on sorption-desorption of pesticides in autoclaved soils. For instance, atrazine in its unprotonated and molecular form is expected to be weakly bonded to the soil mineral matrix given the high pH (8.1) and alumina content (Al<sub>2</sub>O<sub>3</sub>, 7.49%; Table S1) of the vineyard soil (Czaplicka et al., 2018). Accordingly, atrazine desorption increased with both temperature and pH as sorbate-sorbent bonds are weakened, while its solubility increased with temperature (Cessna et al., 2017; McGlamery and Slife, 1966).

In the nonautoclaved soil experiments, temperature likely also increased pesticide bioavailability (Ruggiero et al., 2002) and microbial activity (Cessna et al., 2017; Reedich et al., 2017a). Few studies examined the effect of soil temperature on microbial composition and associated pesticide degradation (Pietikäinen et al., 2005; Reedich et al., 2017b). However, the activity of degrading microbial populations and pesticide biodegradation processes were likely enhanced in the experiments at 30°C. Hence, both higher pesticide bioavailability and increased microbial activity contributed to a greater role of biodegradation in pesticide dissipation at 30°C.

#### 3.2. Pesticide abiotic and biotic dissipation in vineyard soil

Examining the patterns of pesticide dissipation in vineyard soil allowed the estimation of contributions of abiotic and biotic dissipation. In nonautoclaved experiments, pesticide dissipation surpassed 60 % within a 200-day period, with the sole exception being pyrimethanil (Fig. 1). Conversely, pesticide dissipation was significantly slower (p<0.05) in autoclaved soil experiments when compared to non-autoclaved experiments, except for carbendazim and isoproturon (Fig. 1). Slower pesticide dissipation in the autoclaved experiments (Ruggiero et al., 2002) indicated that biodegradation significantly contributed to the overall pesticide dissipation in the nonautoclaved experiments.

##### 3.2.1. Contribution of abiotic processes to pesticide dissipation

In the autoclaved experiments, the predominant abiotic dissipation process of pesticides in the vineyard soil was ageing. Pesticide ageing results from the gradual physical entrapment of pesticides in soil microsites formed by the coating of sand and silt particulate surfaces with clay minerals and the stabilization of aggregates with organic matter over time (Meite et al., 2022). The contribution of ageing was particularly significant (>60 % within 200 days) for less soluble and more hydrophobic pesticides (solubility <10 mg L<sup>-1</sup> and K<sub>oc</sub>>200 mL g<sup>-1</sup>; Table 1), including isoxaben, simazine and carbendazim. In this experiment, other abiotic dissipation processes, such as pesticide volatilization, photodegradation, and hydrolysis, were deemed insignificant. Indeed, the Henry's law constants for all pesticides used, measured at 25°C, were below 10<sup>-3</sup> pa m<sup>3</sup> mol<sup>-1</sup>. Additionally, the soil microcosms were conducted within a closed system without light, thereby precluding pesticide photodegradation. Moreover, considering the soil pH (8.1±0.4), hydrolysis was not expected to substantially contribute to the dissipation of the selected pesticides after 200 days (EXTOXNET, 2000).

Similar dissipation patterns during sorption and ageing in soil have been observed previously for both ionic (e.g., simazine) and non-ionic (e.g., isoproturon) pesticides, involving fast initial sorption followed by slower sequestration in soil (Loeffler et al., 2020; Wauchope et al., 2002). While less-soluble and hydrophobic pesticides aged and remained bound to soil, pesticide extractability is expected to decrease over time, resulting in higher K<sub>d</sub> values. Pesticide sequestration in soil thus reduce their desorption rates and environmental reactivity in the vineyard soil (Gevao et al., 2000), while pesticides become less available (Chung and Alexander, 1998; Yu et al., 2009) and less toxic (Silva et al., 2019) to organisms. However, ageing is a transient and uncertain natural remediation process as persistent pesticide residues are not
degraded and may desorb over time.

### 3.2.2. Contribution of biodegradation to pesticide dissipation

The contribution of biodegradation (B) to overall dissipation was assessed for individual pesticides within the mixture by comparing the dissipation fractions of pesticides in autoclaved and nonautoclaved experiments conducted over 50, 100, and 200 days (Eq. 1 and Fig. 1). Biodegradation over 200 days represented more than 30% of the dissipation of rac-metalaxyl under all conditions, as well as for isoproturon, pyrimethanil, and S-metolachlor in experiments at 30°C with 40% water content (Fig. 1). A shared characteristic among these four pesticides, which exhibited notable biodegradation in the vineyard soil, is their relatively high solubility (>60 mg L⁻¹) (Table 1). In soil, water serves as a solvent, affecting the partitioning of nutrients, salts, and soluble pesticides. Higher water content enhances solute bioavailability, thereby supporting bacterial growth and activity (Borowik and Wyszkowska, 2016; Kottler et al., 2001; Ochsner et al., 2006), which in turn can increase pesticide biodegradation in the vineyard soil.

An additional significant finding is the systematic decrease in the contribution of biodegradation for all pesticides from 100 to 200 days (Table S3). Over time, pesticides are expected to become less bioavailable, resulting in reduced biodegradation contributions, with ageing—the gradual sequestration of pesticides into soil—becoming the dominant process. After 100 days, it is likely that pesticide sequestration through sorption and slow diffusion into remote soil microsites limited pesticide bioavailability, consequently lowering biodegradation rates. This decline in biodegradation within the vineyard soil suggests that pesticide biodegradation, as a fundamental microbial activity, can serve as a proxy of pesticide bioavailability and the long-term exposure of non-target organisms to pesticides.

### 3.3. Effect of water content, temperature, pesticides and incubation time on microbial diversity and composition

In total, we obtained 891,992 high-quality sequences (644,456 16S for bacteria, 185,767 ITS for fungi, and 61,769 18S for protists) from both the nonautoclaved spiked and control experiments. These sequences corresponded to 2603 bacterial, 602 fungal, and 645 protistan ASVs. The sequencing depth was sufficient for a comprehensive evaluation of microbial diversity in the vineyard soil, as demonstrated by rarefaction curves of diversity indices reaching asymptotes (Figure S2). Among bacterial taxa, the four most abundant groups identified through 16S sequences were Proteobacteria (28.5%) and Acidobacteria (15.9%). In the fungal domain, the dominant phyla based on fungal ITS sequences were Ascomycota (45.0%) and Basidiomycota (30.0%). Within protists, the two most prevalent phyla based on 18S sequences were SAR (60.2%) and Amoebozoa (24.4%). Considering the effect of temperature and water content on pesticide dissipation processes, including biodegradation, we conducted an analysis to identify and compare the factors affecting bacterial, fungal, and protistan diversity and composition in the vineyard soil.

#### 3.3.1. Determinants of microbial diversity in the vineyard soil

Temperature, water content, incubation time and exposure to individual pesticide had different and group-specific effects on alpha diversity, including the inverse Simpson diversity index, of bacteria, fungi and protists in the vineyard soil (Table 2). Analyses based on the Shannon diversity index provided very similar results (data not shown).

The linear regression model indicated that bacterial diversity positively correlated with the simazine and negatively correlated with water content and atrazine. Although atrazine did not significantly alter the overall soil bacterial community in Brazilian Red Latosol soil (Fernandes et al., 2020), the observed correlations may be transient and related to the release of nitrogen during herbicide degradation in soil. On the other hand, higher simazine concentrations (50 µg g⁻¹) has been shown to inhibited the nitrification processes in agricultural soil (Hernández et al., 2011).

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

Effect of selected variables on the inverse Simpson diversity of bacteria, fungi and protists. Summary statistics of linear model after stepwise variables selection for bacteria: adjusted-R² = 0.71 (p < 0.001), fungi: adjusted-R² = 0.40; (p < 0.001) and protists: adjusted-R² = 0.59 (p < 0.001). SE: standard error.

<table><thead><tr><th></th><th></th><th>Estimate</th><th>SE</th><th>t-value</th><th>Pr(&gt;|t|)</th></tr></thead><tbody><tr><td rowspan="4">Bacteria</td><td>(Intercept)</td><td>159.92</td><td>8.64</td><td>18.50</td><td>&lt; 0.001 ***</td></tr><tr><td>Water content</td><td>-96.62</td><td>9.76</td><td>-9.90</td><td>&lt; 0.001 ***</td></tr><tr><td>Simazine</td><td>53.61</td><td>25.16</td><td>2.13</td><td>0.040 *</td></tr><tr><td>Atrazine</td><td>-119.51</td><td>46.12</td><td>-2.59</td><td>0.013 *</td></tr><tr><td rowspan="3">Fungi</td><td>(Intercept)</td><td>11.85</td><td>0.48</td><td>24.60</td><td>&lt; 0.001 ***</td></tr><tr><td>Temperature</td><td>-1.41</td><td>0.50</td><td>-2.82</td><td>0.009 **</td></tr><tr><td>Water content</td><td>2.09</td><td>0.62</td><td>3.38</td><td>0.002 **</td></tr><tr><td rowspan="6">Protists</td><td>(Intercept)</td><td>49.11</td><td>2.22</td><td>22.12</td><td>&lt; 0.001 ***</td></tr><tr><td>Incubation time</td><td>-8.90</td><td>2.96</td><td>-3.01</td><td>0.006 **</td></tr><tr><td>Pyrimethanil</td><td>59.74</td><td>12.10</td><td>4.94</td><td>&lt; 0.001 ***</td></tr><tr><td>S-metolachlor</td><td>-29.69</td><td>14.17</td><td>-2.10</td><td>0.046 *</td></tr><tr><td>Dimethomorph</td><td>-27.61</td><td>7.74</td><td>-3.57</td><td>0.002 **</td></tr><tr><td>Isoproturon</td><td>17.45</td><td>7.25</td><td>2.41</td><td>0.024 *</td></tr></tbody></table>

One explanation for the correlation between bacterial diversity and the presence of triazine herbicide residues is biodegradation. In this study, we observed the biodegradation of simazine and atrazine (Fig. 1), which involves pesticide transformation and the formation of transformation products. The formation of pesticide residues in soil can lead to the emergence of new ecological niches that support specialized microbial taxa, exemplified by simazine, or it can affect the abundance of vulnerable bacterial taxa, as observed with atrazine (Walder et al., 2022). Although pesticide residues did not appear to reduce bacterial alpha diversity, they may modify microbial communities, which could, in turn, have implications for soil functionality.

Fungal diversity exhibited a negative correlation with temperature and a positive one with water content. However, pesticide exposure did not have a noticeable effect on fungal diversity. These findings align with numerous prior studies indicating the sensitivity of bacteria, fungi, and protists to variations in soil moisture levels (Geisen et al., 2014; Manzoni et al., 2012). The absence of correlation between fungal diversity and individual fungicide may be surprising since fungicides may affect non-target soil fungi. However, weak associations with fungicides on fungal diversity were also observed in previous studies with synthetic fungicides (Walder et al., 2022) and copper (Keiblinger et al., 2018). One might hypothesize that fungi in the vineyard soil have become well adapted to the chronic application of fungicide mixtures over decades, possibly developing a co-tolerance to multiple fungicides at the community level (Viti et al., 2008). Consequently, the effect of fungicide exposure at environmental concentrations on fungal diversity could depend on both the soil properties that control fungicide availability in the soil and the level of adaptation of fungal communities that have been chronically exposed to fungicide applications.

Protistan diversity did not exhibit significant correlations with soil temperature or water content (Table 2). However, it did show negative correlations with incubation time, the herbicide S-metolachlor, and the fungicide dimethomorph. Conversely, protistan diversity displayed positive correlations with the fungicide pyrimethanil and the herbicide isoproturon. Bacterial and fungal biomass can be a carbon source for protists. Therefore, changes in soil organic matter (SOM) composition over the incubation period due to bacterial and fungal successions (Jiao et al., 2017) might have affected the carbon sources available to protists, thereby altering protistan diversity. Additionally, pesticides could interfere with protist metabolic pathways. For instance, S-metolachlor has been shown to inhibit the synthesis of long-chain fatty acids and cell division in protists (Zaller and Brühl, 2021). In addition, there is evidence of a non-linear relationship between S-metolachlor and the growth of the *Euglypha rotunda* testate amoeba (Amacker et al., 2018), indicating a lack of a straightforward dose-response relationship. While our findings do not establish direct causation, pesticides such as dimethomorph and isoproturon may affect competition for ecological niches
by indirectly favoring less susceptible protists, potentially displacing
niche competitors. Additionally, the dominance of a few
pesticide-tolerant protist groups could also affect overall protistan
diversity.

It is important to note that Next-Generation Sequencing (NGS),
which is based on DNA extracted from soil and high-throughput
sequencing, provides information on relative rather than absolute mi-
crobial abundance. However, pesticides can impact not only the di-
versity of bacteria, fungi, and protists but also their absolute abundance.
Therefore, future studies should consider combining NGS with quanti-
tative PCR or flow cytometry to quantify specific microbial genes,
enabling a more comprehensive evaluation of changes in microbial
abundance in multi-contaminated soils (Lee et al., 2021).

3.3.2. Determinants of microbial composition in the vineyard soil

The water content had a primary effect on the taxonomic composi-
tion of bacterial, fungal and protistan communities (Fig. 2, Tab. S6). The
community composition differed significantly in experiments with 20 %
and 40 % water content (bacteria: R² =0.62 *P* <0,05; fungi: R²=0.38,
*P*= < 0,05, protists: R²=0.35, *P* <0.05), but did not vary much with
temperature changes (bacteria: R² =0.03, *P* > 0.05; fungi: R²=0.38, *P*
>0.05; protists: R²=0.14, *P* >0.05; Tab. S6).

Another primary determinant of community change was the incu-
bation time (Fig. 2), observed in both spiked and non-spiked experi-
ments. The community composition of bacteria, fungi, and protists
showed significant differences over time, regardless of soil conditions
and exposure to pesticides (bacteria: R² = 0.59; fungi: R² = 0.20; pro-
tists: R² = 0.25; p<0.05, Table S4). Temporal changes in microbial
community composition were commonly observed in similar microcosm
experiments (Jiao et al., 2017; Sun and Ge, 2021; Torabi et al., 2020).
This temporal change may correspond to the succession of community
compositions during phases of soil organic matter (SOM) diagenesis,
characterized by time-course changes in SOM composition. Exposure to
pesticide residues in the spiked experiments could also be a contributing
factor. However, community compositions in both spiked and
non-spiked experiments did not significantly differ, indicating that
exposure to pesticides did not induce rapid or significant alterations in
the microbial communities in the spiked experiments (Fig. 2). This
supports the notion that the temporal change in microbial composition
primarily reflects SOM turnover in the soil experiment.

It is also worth noting that the temporal change was particularly
pronounced in the bacterial and fungal communities in the experiments
with 40 % water content (Fig. 2). This highlights the sensitivity of
bacterial and fungal diversity to changes in soil moisture (Yuste et al.,

<figure><img src="image_2.png" /><figcaption>**Fig. 2.** Non-metric multidimensional scaling (NMDS) plots of bacteria, fungi and protists obtained with the Bray-Curtis dissimilarity matrix. Arrows illustrate environmental variables that significantly correlated with the bacterial, fungal and protistan communities (enfit, *p* <0.05). Stress value for bacteria, fungi and protists = 0.11, 0.13 and 0.18, respectively, allowing the 2D representation.</figcaption></figure>
2011). It also suggests that bacteria and fungi were associated with the decomposition of dissolved, more bioavailable organic matter in soil as soil water content increases (Marschner and Kalbitz, 2003).

From a methodological standpoint, it should be acknowledged that microcosms with a larger amount of soil than that used in this study may reduce the heterogeneity of pesticide-related processes and the associated community responses. This, in turn, might limit temporal changes in microbial communities throughout the experiment (Rivett et al., 2021).

Despite the primary influence of water content and incubation time on microbial community compositions, pesticide exposure appeared to be a secondary factor affecting protistan community changes. Notably, pesticide exposure did not significantly alter the protistan community, as there was little difference between the spiked and non-spiked experiments (Fig. 2 and Table S4). However, minor changes in the protistan community composition were correlated with the herbicides simazine and isoxaben in the spiked experiments (Fig. 2 and Table S4).

To the best of our knowledge, this is the first study to examine the effect of simazine and isoxaben on protistan communities in soil. The response of protists to synthetic pesticides has been less studied compared to bacteria and fungi (Karpouzas et al., 2022; Wu et al., 2022). So far, the detrimental effect of simazine on protistan communities has only been reported in phototropic protistan communities in aquatic ecosystems (Magnusson et al., 2010). However, several studies have indicated that synthetic pesticides can affect protists, which may in turn affect microbial food web interactions (Fournier et al., 2020). For instance, the slime mold Dictyostelium discoideum exhibited high sensitivity when exposed to the insecticide permethrin (Amaroli et al., 2018), and chronic doses of the fungicide propiconazole affected various flagellates (Ekelund et al., 2000).

The effect of pesticide residues on protists may not solely result from direct toxic effects but could also be driven by indirect effects related to changes in bacterial and fungal communities (Wang et al., 2023). For example, the positive correlation between simazine and bacterial diversity (Table 2) suggests that the release of nitrogen and the toxic effects of simazine metabolites during simazine degradation in soil could affect predator-prey interactions between protists and bacteria in the multi-contaminated soil. Clearly, further research is needed to gain a better understanding of the direct and indirect effects of synthetic pesticides on protistan communities and their functions (Wu et al., 2022). Protists are highly diverse, abundant, play key roles in various ecosystem functions (Geisen et al., 2018), and may be more sensitive to pesticides compared to other soil organisms (Fournier et al., 2022, 2020). As a result, they hold significant potential for soil microbial ecotoxicology (Karpouzas et al., 2022).

Although our study identified factors associated with changes in the soil microbiome, our experimental design limited conclusions regarding the ecotoxic effects of individual pesticides within the mixture. The statistically inferred potential effects were based on the pesticide concentrations in the mixture rather than individual pesticide concentrations. However, further studies are required to determine how the bioavailability of individual pesticides varied in the vineyard soil, how this affected the exposure of the soil microbiome, and whether the effects of individual pesticides differed from those of the pesticide mixture. Additionally, the presence of other pesticides and dissolved organic molecules, which were not monitored in the multi-contaminated soil experiment, may also affect the soil microbiome. Hence, the conclusions drawn from this study regarding the relationship between soil variables, including pesticide concentration, remain specific to the study vineyard soil and the pesticide mixture.

## 4. Conclusions

Our exploratory study proposes a framework for examining pesticide dissipation in vineyard soils, considering the soil microbiota. It underscores the value of utilizing information from bacterial, fungal, and

protistan communities to investigate the combined effect of water content and temperature on pesticide dissipation, along with the concurrent response of the soil microbiota in a multi-contaminated vineyard soil.

The pesticide dissipation in the vineyard soil generally decreased at 30°C and increased with higher water content, which underscored the significant effect of pesticide physicochemical properties and soil conditions on pesticide dissipation. The kinetics of dissipation in both autoclaved and non autoclaved experiments indicated that a variety of processes, both degradative and non-degradative, contributed to the apparent dissipation of selected herbicides and fungicides. Notably, biodegradation played a significant role in the dissipation of more soluble pesticides, indicating higher bioavailability and microbial degradation activity with elevated soil temperature and water content. In contrast, hydrophobic pesticides ($K_{oc} >200 \text{ mL g}^{-1}$) primarily dissipated through ageing, irrespective of soil autoclaving, temperature, and water content. The dominance of specific dissipation processes was closely tied to pesticide bioavailability in soil porewater, favoring biodegradation while being affected by pesticide sorption. This knowledge, combined with an understanding of condition-specific dissipation processes, is crucial in the context of climate change for more robust predictions of pesticide losses from vineyard soils and the assessment of pesticide effects on non-target soil organisms.

Analysis of DNA sequences revealed distinct responses in the diversity and community composition of bacteria, fungi, and protists, emphasizing the importance of considering all three domains of soil microbiota to decipher the factors governing their variability in vineyard soil. Pesticide exposure emerged as a secondary factor explaining the variation in microbial communities, alongside primary drivers such as soil water. Notably, while protist communities appear more sensitive to pesticide exposure than bacterial and fungal communities, the overall impact on the protistan community remained minor. Further investigations are required to determine whether the protistan response is due to the direct toxic effects of pesticide residues or indirect effects linked to changes in bacterial and fungal communities. Protist taxa may serve as sensitive and integrative indicators in future ecotoxicological studies involving multi-contaminated soils.

## Funding

This study was supported by the Alsace Region (France), the Agence de l'Éau Rhin-Meuse (AERM) and by HES-SO, through projects 78046 (MaLDiveS) and 123353 (VITAE).

## CRediT authorship contribution statement

**Gwenael Imfeld:** Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Fatima Meite:** Writing – review & editing, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Lucas Ehrhart:** Writing – review & editing, Investigation, Formal analysis, Data curation. **Bertrand Fournier:** Writing – review & editing, Validation, Supervision, Investigation, Formal analysis, Data curation, Conceptualization. **Thierry Heger:** Writing – original draft, Validation, Supervision, Software, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## 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.
# Data Availability

Data will be made available on request.

# Acknowledgements

Fatima Meite was supported by a fellowship of the Alsace Region and the Agence de l'Éau Rhin-Meuse (AERM). This research was also funded by HES-SO (projects 78046 (MaLDiveS) and 123353 (VITAE)). The authors acknowledge Eric Pernin and Nanami Kawaguchi for support in sampling and soil treatments and Florine Degrune for her help with the bioinformatic analyses.

# Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ecoenv.2024.116994.

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