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. 2025 May 8;101(6):fiaf052. doi: 10.1093/femsec/fiaf052

Impact of pesticides on soil health: identification of key soil microbial indicators for ecotoxicological assessment strategies through meta-analysis

Mark Swaine 1,✉, Alessandro Bergna 2, Ben Oyserman 3, Sotirios Vasileiadis 4, Panagiotis A Karas 5, Claudio Screpanti 6, Dimitrios G Karpouzas 7
PMCID: PMC12105574  PMID: 40338616

Abstract

Pesticides remain a cornerstone of modern agriculture. Despite their key role, it is well documented that pesticides can have considerable off-target effects on a range of organisms. The effects of pesticides on soil health, and more importantly on soil microbiota, are currently not well addressed at the regulatory level, despite cumulative evidence for the pivotal role of the soil microbiota on ecosystem functioning. Here, we use a meta-analysis to assess the effects of pesticides on soil health parameters identifying key biological indicators for environmental risk assessment analysis. We demonstrate that ammonia oxidizing archaeal and bacterial amoA gene abundance were the most consistent indicators for pesticide exposure, with inhibition driven by herbicides and fungicides. Our meta-analysis, combined with their key functional role and the existence of well-standardized, high-resolution methods for monitoring their abundance, highlighted the potential of ammonia-oxidizing microorganisms (AOMs) as indicators of the toxicity of pesticides on soil microbiota. AOM could serve as drivers of chemical innovation in a “benign and sustainable by design” approach where new pesticide compounds will have to meet sustainability targets and ensure soil health preservation.

Keywords: amoA gene abundance, enzyme activity, indicators, microbial endpoints, pesticides, soil

Identification of microbial endpoints as key soil health indicators for potential use as tools for soil ecotoxicological assessment strategies, and the development of upcoming plant protection products.

Introduction

Pesticides are an important management strategy in modern agriculture for stabilizing food security and reducing economic and ecological losses estimated at ∼$220 billion (US) annually (FAO 2022). However, concerns are being raised about their potential impacts on biodiversity (Bernhardt et al. 2017, Wang et al. 2020). Recent initiatives like the (i) Farm to Fork strategy (Moschitz et al. 2021), (ii) Sustainable Use of Pesticides Directive 128/2009 (EU2021/2115), (iii) Zero Pollution Action Plan (EC2021b), and (iv) Biodiversity Strategy (Atwood et al. 2024), aim to reduce the use of chemical pesticides in EU. In response to this, implementation of new approaches by industry in the early stages of their R&D pipelines aim to discover and develop new bio-actives that sustain soil health (Lehmann et al. 2020, Mammola et al. 2020), defined as “the capacity of the living soil to function, within natural or managed ecosystem boundaries, to sustain and promote plant and animal productivity and health, and to maintain or enhance water and air quality” (Screpanti 2021). The use of chemical pesticides continues to rise globally (Sharma et al. 2019) with a broad range of substances still being applied. In addition, new products, mostly belonging to the so-called group of low-risk products (e.g. microbials, botanicals), are continuously put on the market (Wang et al. 2020). To compound this, climate change is leading to a continuous increase of global surface temperature by 0.82oC between 2011 and 2020 (NOAA 2020) and projected increases of 0.9oC–1.7oC by 2050 (Forster et al. 2021) are expected to amplify the risk of increased abundance, higher rates of infection, increased prevalence, and prolonged developmental stages of soil-borne pathogens and insect pests (Chaloner et al. 2021, Romero et al. 2022). During this same period, global food demand is anticipated to increase by 35% to 56% (van Dijk et al. 2021). All these factors are expected to lead to an increased demand for pest control and highlight the need for innovation and discovery of sustainable crop protection solutions. In this respect, for the foreseeable future, it is conceivable that the rate of pesticide usage will remain at current levels, if not increase.

Despite their key role in modern agriculture, it is well documented that certain pesticides can have considerable off-target effects on a wide range of organisms, most notably birds, plants, bees, etc. (Lee et al. 2011, Sánchez-Bayo 2021), often when best agronomic practices are not followed. To ensure that pesticides reaching the market have minimal off target effects, EU, USA, and other countries and continents have put in place stringent regulatory frameworks (Donley 2019). However, the effects of pesticides on soil health and its key functional components such as the soil microbiota are not well addressed at the regulatory level and in fact are still based on the nitrogen transformation test (OECD 216 test), which has been criticized for low resolution (Pedrinho et al. 2024) and erratic performance (Sweeny et al. 2024). This despite the continuously increasing research in this area (Fig. S1), considerable methodological advances in soil microbial ecology (Karpouzas et al. 2022) and the widespread detection of pesticide residues in soils (Silva et al. 2019, Froger et al. 2023, Knuth et al. 2024).

Soil itself is a vastly heterogeneous habitat, formed by a complex mixture of minerals, organic matter, and a network of water and air-filled pore spaces that supports and regulates almost all aspects of life on our planet. Arguably, the most dominant organisms in soil are microorganisms. Made up of bacteria, fungi, archaea, protists, and viruses, the soil microbiota accounts for an estimated 3%–4% of the total biomass on earth (Bar-On et al. 2018, Anthony et al. 2023) regulating 80%–90% of soil processes (Nannipieri et al. 2003). Soil microorganisms are involved in a range of ecosystem services including, but not limited to: (i) regulation of soil fertility and nutrient cycling, (ii) biological control, (iii) maintenance of habitats for species, (iv) waste and water treatment, (v) carbon sequestration and storage, and (vi) food production (Thiele-Bruhn et al. 2021). To date, several studies have investigated the impacts of pesticides on soil biological activities (Imfeld et al. 2012, Riah et al. 2014, Wołejko et al. 2020, Shahid and Khan 2022). However, this wealth of knowledge is fragmented and must be assessed in a systematic way, allowing a path for the development of tools that may be used for preventing adverse effects on the soil microbiota and preserving soil health.

We conducted a meta-analysis of global peer-reviewed data aiming to identify the extent of pesticide effects on the soil microbiota and to prioritize microbial endpoints that are sensitive to pesticide exposure and are associated with plant growth and nutrient cycling, organic matter decomposition and regulation of GHG emissions. These could serve primarily as microbial indicators for the assessment of the potential impacts of pesticides on soil health (Ockleford et al. 2017) and further on as early screening indicators to guide the discovery of new crop protection solutions that will preserve soil health. The data gathered will be eventually implemented into a database that will facilitate safeguarding the intensity and functioning of soils in an increasingly challenging environment.

Methods

Data collection, consolidation, and establishment

A literature search was performed on studies conducted for the period 1970–2023 focusing on the effects of pesticides on soil microorganisms. The literature survey focused on studies reporting soil microbial responses to pesticides based on the following keywords: “soil microbiology” AND “soil enzyme activity” AND “soil community structure” AND “soil microbial abundance” AND “pesticides” AND “herbicides” AND “fungicides” AND “insecticides”. The databases Scopus (Elsevier B.V: Amsterdam, The Netherlands) and Web of Science (Clarivate: London, UK) were used to source articles. A secondary search of all articles was performed with R package ‘pdftools’ (R v.4.2.2) using a more focused set of selection criteria to identify articles specific to certain key soil enzyme and molecular indicators; “urease” or “β-glucosidase” or “dehydrogenase” or “alkaline phosphatase” or “acid phosphatase” or “nitrification” or “denitrification” or “CO2 respiration” or “N2O” or “ammonia oxidation” or “ammonia oxidizing bacteria” or “ammonia oxidizing archaea” or “amoA” or “AOB” or “AOA” or “narG” or “nirK” or “nirS” or “nosZ” or “nifH” or “18S rRNA” or “ITS” or “16S rRNA” or “fungal CFU” or “bacterial CFU”.

Selection criteria and data assembly

The workflow for the selection of the studies to be included in the analysis is given in Fig. 1. For each published article, general information regarding author, publication year, and title were recorded. Pesticide groups (e.g. insecticides, fungicides, herbicides) needed to be represented by three or more separate studies to be included in the analysis. Studies not providing standard deviation (SD) or standard error (SE) were excluded from the meta-analysis. To explore effects of pesticides on soil microorganisms, data were analyzed per individual soil biological indicator, or they were placed into functional groups. The implementation of the above filtering criteria resulted in the analysis of the following endpoints: 16S rRNA gene abundance, urease activity, acid phosphatase activity, alkaline phosphatase activity, arylsulfatase activity, dehydrogenase activity, CO2 respiration, β-glucosidase activity, ammonia-oxidizing archaea (AOA) and bacteria (AOB) amoA gene abundance, potential nitrification, nirK and nirS gene abundance, bacterial and fungal CFUs. Soil group nutrient cycling marker assessment was performed as follows (i) indicators related to key nitrogen cycling functions, nitrification, denitrification, and N2 fixation, (ii) plant available nutrient cycling indicators (urease, acid and alkaline phosphatase, and arylsulfatase activity), (iii) organic matter cycling (dehydrogenase, CO2 respiration and b-glucosidase). The analysis of grouped and individual soil biological indicators was performed collectively for all pesticides included in the study (total pesticides), but also per pesticide category according to their target organism: fungicides, herbicides, and insecticides.

Figure 1.

Figure 1.

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The workflow of the selection of the publications included in the meta-analysis and the criteria for exclusion.

Only studies describing the effects of individual pesticide active ingredients and not of pesticide mixtures were considered in the meta-analysis. Several of the studies reported multiple observations based on pesticide type, pesticide concentrations, soil type, land use, and experimental longevity. Data mean values, SD or SE were extracted from papers using webplot digitizer (v. 4.6) (Rohatgi. 2024).

Data preparation and statistics

To ensure consistency in data analysis, pesticide concentrations were converted into dose rates expressed as ppm, where not directly reported as such. Data that expressed responses as zero were removed from the dataset. Reported SE were subsequently converted to SD using the equation Inline graphic. Temporal dynamics from repeated measurements and their corresponding SD in all studies were aggregated into total effects (no time-dependency) (equations 1 and 2), where Mi = average mean, j = total number of means and ni = number of replications.

graphic file with name TM0002.gif (1)
graphic file with name TM0003.gif (2)

Aggregated effect sizes for each biological indicator were calculated as log response ratio (LRR) respective to control samples (Hedges et al. 1999, Gurevitch et al. 2001). Where ln is the natural log of the result of Xt (means of treatments) by Xc (means of controls) (equation 3). Variances were calculated from aggregated SDs presented in the articles from respective sample means.

graphic file with name TM0004.gif (3)
graphic file with name TM0005.gif (4)
graphic file with name TM0006.gif (5)

SDt and SDc correspond to the SDs of the indicator response and control values respectively [equation (4)]. Mean effect sizes and corresponding 95% confidence intervals (CIs) were calculated from respective log risk ratios using the rma.mv function using the “metafor” package for random-effects models (Viechtbauer 2010) producing weighted log risk ratios (LRR). Wald’s tests (Inline graphic) (equation 5) were performed to assess any significant differences between two mean effect size estimates. μ1 and μ2 are the mean effect sizes 1 and 2, respectively, and SE[μ1] and SE[μ2] denote the standard error of mean effect size 1 and 2 respectively. Publication bias was assessed using Egger's test for multilevel models (Rodgers and Pustejovsky 2021). No significant publication bias was detected (Table S1).

Pesticide effects were recorded as significant (P ≤ 0.05) when CIs of mean effect sizes did not overlap with zero. Meta-regressions were performed to identify responses of mean effect size against total pesticide, fungicide, herbicide, and insecticide dose rates for soil functional groups and individual soil biological indicators.

Results and discussion

Our literature search returned in total 1732 articles, of which 292 that contained the key microbial endpoints in relation to pesticide treatments were selected for further assessment (Fig. 1). Out of those, 80 studies detailed the necessary information were selected. However, not all studies indicated the recommended dose rates of the studied compounds, therefore a linear regression analysis was performed to assess the range of pesticide dose that would be relevant to include in our analysis (Fig. S2). It was observed that pesticide doses between 0.01 and 25 ppm displayed no significant change in observed effect sizes, therefore this range was included in our analysis. Based on this, a total of 59 studies offering 814 individual observations (K) for aggregated assessment were eventually introduced into the meta-analysis. These studies spanned five continents (Fig. 2) and consisted of both field and laboratory assessments. Effect sizes are reported as mean estimated weighted log risk ratios LRR, ± confidence intervals (CI) and P values to demonstrate significance.

Figure 2.

Figure 2.

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The global distribution of the studies considered in the meta-analysis.

Pesticide effects on microbially driven indicators of soil quality

Initially, we studied the effects of pesticides on microbial endpoints clustered according to functional groups relating to soil quality; plant nutrient cycling, organic matter decomposition, nitrification, denitrification, and N2 fixation. A collective assessment of all pesticide treatments, regardless of type, resulted in significant effects on indicators of soil quality (Fig. 3a, F(5, 725) = 5.54, P ≤ 0.001) leading to an overall negative response (LRR = −0.11 ± 0.03, P ≤ 0.001).

Figure 3.

Figure 3.

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Effect of pesticide applications on soil group markers. (a) total pesticide application, (b) fungicide application, (c) herbicide applications, and (d) insecticide applications. n = number of studies, K = total number of observations, and P = P value for each indicator. Dashed line indicates zero change and significance threshold </> for confidence intervals.

Plant nutrient cycling, organic matter decomposition, and nitrification demonstrated similar negative responses to pesticide additions to soil [Fig. 3(a)]. Pooled effect sizes for both fungicide and herbicide treated soils resulted in negative responses (F(5, 158) = 4.91, P ≤ 0.001; (LRR = −0.28 ± 0.13, P ≤ 0.001 and F(4, 309) = 7.13, P ≤ 0.001; (LRR = −0.21 ± 0.16, P = 0.08, respectively (Fig. 3b and c). For fungicide treated soils, plant nutrient cycling and organic matter decomposition functional groups responded negatively to treatment (LRR = −0.34 ± 0.18, P ≤ 0.001; LRR = −0.29 ± 0.18, P = 0.001, respectively; Fig. 3b). Soil organic matter (SOM), a primary reservoir of Carbon (C), Nitrogen (N), Phosphorus (P), and Sulphur (S), contains over 95% of the organic S and between 20% and 80% of the organic P in soil, with both relying upon soil microbial endpoints for organo-S and P mobilization (Anderson 1980, Kertz et al. 2007, Jindo et al. 2023). Microorganisms also account for a significant portion of SOM, releasing essential plant available nutrients such as C, N, and P from their necromass (Chen et al. 2019). Thereby, reductions in abundance and/or activity of microbial fractions in soil, as indicated through our analysis, may suggest that fungicide application could result in reduced concentrations of bioavailable soil nutrients, such as N, P, and S, which are vital for plant growth, adversely affecting crop productivity if unavailable (Etienne et al. 2018, Anas et al. 2020, Nanda et al. 2020, Hawkins et al. 2022). As reductions in microbial endpoints responsible for organic matter decomposition coincide with those of the plant nutrient cycling functional group for the fungicide treatment group in our study, this could indicate reduced mobilization of essential soil nutrients through either direct release from SOM decomposition by microbial activity or indirect release from microbial necromass.

Nitrification demonstrated similar reductions in both fungicide and herbicide treated soils (LRR = −0.34 ± 0.22, P ≤ 0.001; LRR = −0.34 ± 0.28, P = 0.001, respectively; Fig. 3b and c) indicating a consistent sensitivity to both fungicide and herbicide applications to soil. While inhibition of nitrification has its benefits, for example, reductions in soil nitrate leaching and decreasing N2O emissions from soil, a well-regulated nitrification cycle is essential to soil health. The presence of nitrate, released by nitrification processes, increases plant development (Hachiya and Sakakibara 2017). In addition, ammonia has a negative effect on plant, abiotic and biotic components of the soil. Excess ammonia affects the uptake of nutrients, disturbs hormonal balance, decreases soluble carbohydrates of plants and can alter photosynthesis and other metabolic pathways (Wang et al. 2016). Insecticides demonstrated no significant effect on any of the functional groups [Fig. 3(d)] suggesting limited off-target effects on the soil microbiota.

Effects of pesticides on individual microbial indicators

We further analyzed the effects of pesticides on individual microbial indicators. Eight out of the 19 indicators presented significant changes in the mean effect size in comparison to their individual controls for pooled pesticide treatments, indicating that across all studies these indicators were significantly reduced in the grouped pesticide assessment; AOA and AOB amoA gene abundance, urease, dehydrogenase, alkaline phosphatase, dehydrogenase, and β- glucosidase activities, and fungal CFU counts [Fig. 4(a)], We further explored the effects on each indicator based upon specific pesticide groups. The most consistent effects were observed for AOA and AOB, which presented significantly lower values in studies evaluating fungicide and herbicide treated soils, unlike the other indicators which were significantly affected from either the fungicide or herbicide groups and not by both [Fig. 4 (b) and (c)]; no significant effects were observed for any indicators for insecticide treatments [Fig. 4(d)]. The abundance of the amoA gene of AOA [Fig. 4(a), LRR = −0.37 ± 0.2, P = 0.003] exhibited the most significant reduction, attributed mainly to fungicide treated samples [Fig. 4b, LRR = −0.83 ± 0.33, P ≤ 0.001].

Figure 4.

Figure 4.

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Effect of pesticide applications on soil biological indicators. (a) total pesticide application, (b) fungicide application, (c) herbicide applications, and (d) insecticide applications. n = number of studies, K = total number of observations and P = P value for each indicator. Dashed line indicates zero change and significance threshold </> for confidence intervals.

Considering responses of mean effect size are representative products from each individual biological indicator, a Wald’s test was performed to understand the relative responses from other pooled effect sizes. Pooled effect size, in this study, represented an average effect of pesticide treatment on all indicators, therefore significant deviations from this can suggest whether certain biological indicators have a stronger response in comparison to the average of all other indicators. For total pesticide effects, AOA amoA gene abundance stood out as the only indicator that demonstrated a significant difference from the pooled effect [Fig. 4(a), Inline graphic= −2.4, P = 0.008]. AOA amoA gene abundance was also the most significant indicator for fungicide treatments [Fig. 4(b), Inline graphic= −3.3, P = 0.001] and although not statistically significant, a similar pattern for AOA emerged for herbicide treated soils [Fig. 4(c), Inline graphic= −0.38, P = 0.1], compared with both AOB [Fig. 4(c), Inline graphic= −0.32, P = 0.2] and alkaline phosphatase [Fig. 4(c), Inline graphic= −0.23, P = 0.4].

The more pronounced effects of fungicides and herbicides on AOM can be associated with direct toxicity, based on the mode of action of these pesticide groups, or indirect effects driven by microbial interactions in soil. While direct effects were not surprising for fungicides, considering their prime activity on fungi that share biochemical pathways with prokaryotes, the adverse effects of herbicides were not as obvious. Thiour-Mauprivez et al. (2019), though, identified several classes of herbicides (e.g. sulfonylureas, triketones, glyphosate, triazolinones, clomazone) that inhibit plant enzymes (e.g. acetolactate synthase, hydroxyphenyl pyruvate dioxygenase, 5-enolpyruvylshikimate-3-phosphate synthase, protoporphyrinogen oxidase, and 1-Deoxy-D-xylulose 5-phosphate synthase) with homologs in bacterial genomes, including nitrifiers. Indirect effects of pesticides driven by microbe–microbe interactions are expected to be important, but their true extent remain unknown. For example, inhibition of AOM activity through reduced predation by protozoa due to cycloheximide inhibition of the later has been reported for wastewater samples (Pogue and Kilbride 2007) and more recently, hymexazol, was shown to ignite strong indirect rather than direct effects on the soil bacterial community (Meyer et al. 2024). Therefore, it can be expected that similar indirect pesticidal effects could occur in soil and their future quantification will be a major advent in ecological risk assessment (Rohr et al. 2006)

Further investigation into the effects of the 65 individual active ingredients used in our meta-analysis revealed that significant reduction of AOA in this study was primarily driven by tebuconazole [Fig. 5 (b)]. Tebuconazole, recently discovered to be one of the most frequently detected pesticide compounds in soils across Europe, at levels above its Predicted Environmental Concentrations in soil (PECsoil) used for assessing the risk for in-soil organisms (Hvězdová et al. 2018, Silva et al. 2019), exhibited significant effects on AOA amoA gene abundance mean effect size (Inline graphic= −2.38, P = 0.02), when compared to the pooled effect of all active ingredients [LRR = −1.5 ± 0.83, P = 0.001, Fig. 5(b)]. Interestingly, while evidence for the presence of sterol biosynthesis genes, the site of action of tebuconazole, have been found residing in bacteria (Nakano et al. 2007) with homologs suggested for genomes of nitrifiers such as N. europaea (Desmond et al. 2009). However homologs for the site of tebuconazole are yet to be identified in archaea, so the potential mode of inhibition of this fungicide in AOA is yet unknown.

Figure 5.

Figure 5.

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Effect of active ingredients on AOB (a) and AOA (b) amoA gene abundance. Diamonds = fungicides, hexagons = herbicides, squares = insecticides and rectangles = total pooled effect of all active ingredients. Dashed line indicates zero change and significance threshold </> for confidence intervals.

Chlorothalonil, an inhibitor of glutathione pathways in bacteria, and trifluralin, an inhibitor of mitosis, treatments resulted in significant inhibition of AOB amoA abundance [Fig. 5(a)]. Interestingly, AOM do not possess glutathione pathways, however, several previous studies have identified 4-hydroxy-chlorothalonil, the main soil derivative of chlorothalonil, as the key driver of direct adverse effects on the soil microbiota, including AOM (Wu et al. 2014, Zhang et al. 2016). Trifluralin inhibition of AOB amoA could result through direct mode of action, binding on eukaryotic β-tubulin, distant homologues of which have been found in bacteria (FtsZ protein), although showing little interkingdom conservation (Carbadillo- López and Errington 2003). Alternatively, trifluralin has a chemical structure that contains di-nitroaniline, which has been demonstrated as an effective inhibitor of nitrification (Zhang et al. 2010).

The abundance of functional microbial groups, such as AOM, have been demonstrated as stronger statistical predictors of ecosystem function under anthropogenic stress than diversity metrics of the soil microbiome (Osburn et al. 2023). The high sensitivity of AOM to abiotic stressors, such as heavy metals and antibiotics (Xioafang et al. 2009, Olliver et al. 2013, Lagos et al. 2023, Tang et al. 2023), but also to agronomic practices (e.g. tillage) (Munroe et al. 2016), is well documented and their potential role as indicators of the toxicity of pesticides (Thiele-Bruhn et al. 2020) on the soil microbiota and use as bioindicators for general soil monitoring has been put forward in the past (Wessén et al. 2011, Karpouzas et al. 2016). However, this is the first time that the value of AOM as indicators of the toxicity of pesticides on the soil microbiota is convincingly demonstrated through a meta-analysis of the global literature.

Microbial endpoints associated with AOM, such as potential nitrification, exhibited no significant responses to pesticides [Fig. 4(a, b)]. Despite being a standardized method (ISO-15685, 2012), potential nitrification does not consider the different physiologies of the functional groups participating in the nitrification process (substrate preferences, ammonia affinity cell specific activity), favouring AOB over AOA and comammox bacteria (Hazard et al. 2021). This in combination with its lack of reported response in our analysis suggests that its usefulness as an indicator of pesticide toxicity may be limited. Other N cycling indicators, such as urease, an important nutrient cycling enzyme in soil and nirK, an indicator of N2O emissions, demonstrated significant reductions compared to their respective controls, only in the fungicide treatment (Fig. 4b, LRR = −0.37 ± 0.19, P = 0.002, LRR = −0.42 ± 0.40, P = 0.04). Their direct inclusion, or inclusion of molecular indicators associated with other aspects of the nitrogen cycle (i.e. ureA-G, narG, nirK, nirS, nosZ, nifH), as a key biological indicators of pesticide effects on soil health could have potential. However, their prospective utility as such is limited, considering that their response was restricted only to fungicides. In addition, the denitrification functional group demonstrated little response to pesticide applications, both in the collective pesticide treatment and for individual pesticide treatments [Fig. 3(a–d)]. The same applies for CO2 microbial respiration in which observed negative responses were also restricted to fungicides (Fig. 4b, LRR = −0.41 ± 0.24, P = 0.002), hence limiting its potential as potential indicator.

Critical assessment of the study

The main aim of this study was to assess the overall impact of pesticides on key soil health parameters and discern microbial endpoints that could be used primarily as indicators for assessing the effect of pesticides on soil microorganisms. A key finding is that AOA, primarily, and AOB amoA gene abundances, demonstrated statistically consistent reductions in mean effect sizes, compared to all other biological indicators used in this meta-analysis, being particularly sensitive to herbicides and fungicides. Other microbial indicators consistently measured in the relevant literature showed limited significant responses to specific pesticide groups, which challenges their universal applicability as microbial indicators compared to AOM. EFSA has identified arbuscular mycorrhizal fungi (AMF) as potential indicators of pesticide toxicity on soil microorganisms (Ockleford et al. 2017, Mallmann et al. 2018). Our meta-analysis verified their high sensitivity to pesticides (Fig. S3), however, they were not further considered in our study as (1) the data derived did not meet our relevance criteria, i.e. most studies did not include SD or SE information for meta-analysis and (2) the dataset was derived purely from in vitro studies of low ecological relevance, reinforcing the need for high quality pot and field assessments of the toxicity of pesticides on AMF. In addition, the obligate symbiotic lifestyle of AMF brings difficulties in discerning direct from indirect effects, as herbicides may affect AMF root colonization due to reduced root exudation. For example, glyphosate has been observed to reduce chlorophyll accumulation in a range of plants (Kitchen et al. 1981, Zobiole et al. 2011, Silva et al. 2014), a pigment which is essential for plant photosynthesis (Mandal and Dutta 2020), which accounts for up to 20% of root allocated soil C (Guyonnet et al. 2018). Overall, AMF demand delicate and precise standardized experimental setups to identify pesticide effects at the different life stages of AMF (Sweeney et al. 2022). On the other hand, the inclusion of AOM in the ecotoxicological risk assessments may serve as a workable solution

Conclusions

Our study is the first to collate high quality literature data referring to the effect of pesticides on soil microbial functioning and soil health and provides strong evidence for the potential of AOA & AOB amoA gene abundances as a candidate microbial indicator in soil microbial ecotoxicology. AOM fulfil all the major criteria that a good bioindicator should carry (i) they are responsible for a key step in soil nutrient cycling, (ii) they are sensitive to abiotic stressors like pesticides, (iii) there are high resolution and well standardized methods (ISO–17 601, 2016 “Estimation of abundance of selected microbial gene sequences by quantitative PCR from soil DNA”) available for their quantification. The use of fast-track screening assays for AOM (Beeckman et al. 2023), in the pipeline for the discovery of novel pesticides, could select chemical leads that are conducive with soil health preservation. Data derived from those assays could be used along with well-designed soil tests (Meyer et al. 2024), to discern the nature of the effect seen (direct vs indirect), in a community ecology framework. The exploration of the use of AOM as indicators of pesticide toxicity should be complemented with studies on their normal operating range in agricultural soils, that will enable the establishment of ecotoxicological test systems that could allow for their use in ecological risk assessment.

Supplementary Material

fiaf052_Supplemental_File
fiaf052_supplemental_file.docx (1.1MB, docx)

Contributor Information

Mark Swaine, University of Thessaly, Department of Biochemistry and Biotechnology, Laboratory of Plant and Environmental Biotechnology, Larissa 41500, Greece.

Alessandro Bergna, SYNGENTA A.G., Stein 4332, Switzerland.

Ben Oyserman, SYNGENTA A.G., Stein 4332, Switzerland.

Sotirios Vasileiadis, University of Thessaly, Department of Biochemistry and Biotechnology, Laboratory of Plant and Environmental Biotechnology, Larissa 41500, Greece.

Panagiotis A Karas, University of Thessaly, Department of Biochemistry and Biotechnology, Laboratory of Plant and Environmental Biotechnology, Larissa 41500, Greece.

Claudio Screpanti, SYNGENTA A.G., Stein 4332, Switzerland.

Dimitrios G Karpouzas, University of Thessaly, Department of Biochemistry and Biotechnology, Laboratory of Plant and Environmental Biotechnology, Larissa 41500, Greece.

Author contributions

Mark Swaine (Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing), Alessandro Bergna (Conceptualization, Funding acquisition, Project administration, Writing – review & editing), Ben Oyserman (Conceptualization, Funding acquisition, Project administration, Writing – review & editing), Sotirios Vasileiadis (Writing – review & editing), Panagiotis Karas (Writing – review & editing), Claudio Screpanti (Conceptualization, Funding acquisition, Project administration, Writing – review & editing), and Dimitrios G Karpouzas (Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Validation, Writing – review & editing)

Conflict of interest

The authors declare no conflict of interest.

Funding

This was work was financially supported by Syngenta through the collaborative project DIMITRA.

Data availability

Data not available.

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