# Quantification of the biological control of aphids by their natural enemies in sugar beet crops

Léna Barascou, Marianne Doehler, Ségolène Buzy, Aurore Arnoult, Eloi

Salembier, Valentin Richard, Loïc Daniel, Frank Duval, Anne Le Ralec

► To cite this version:

Léna Barascou, Marianne Doehler, Ségolène Buzy, Aurore Arnoult, Eloi Salembier, et al. Quantification of the biological control of aphids by their natural enemies in sugar beet crops. Biological Control, 2025, 205, pp.105773. 10.1016/j.biocontrol.2025.105773. hal-05046020

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Submitted on 24 Apr 2025

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Perspective

Quantification of the biological control of aphids by their natural enemies
in sugar beet crops

Lena Barascou<sup>a,b,*</sup>, Marianne Doehler<sup>c</sup>, Ségolène Buzy<sup>a</sup>, Aurore Arnoult<sup>d</sup>, Eloi Salembier<sup>a</sup>, Valentin Richard<sup>a</sup>, Loïc Daniel<sup>a</sup>, Frank Duval<sup>a</sup>, Anne Le Ralec<sup>a,c,**</sup>

HIGHLIGHTS

* Ground-dwelling predators reduced aphid populations by up to 80%.

* A high parasitism rate was detected early in the season.

* Field margin could impact the activity-density of generalist predators.

* For sugar beet crop, early ground-dwelling predators were key to biological control.

ARTICLE INFO

Keywords:

Aphids

Natural enemies

Exclusion devices

Sugar beet

Biological control

A B S T R A C T

Conservation biological control system involves a diverse community of natural enemies that significantly regulate pests in crops. However, the relative contribution of these different functional groups of natural enemies to aphid regulation is poorly known in major crop-pest systems. Moreover, there is a need to identify the timing of this contribution, especially when aphids are able to transmit pathogenic viruses to plants, such as in sugar beet crops. In this study, we quantified the effect of ground dwelling predators (carabids, spiders, rove beetles) and parasitoids on aphids' abundance in sugar beet crops, using two experiments conducted over two successive years (2022 and 2023). Based on physical exclusion devices (pitfall traps, barriers, insect-proof cages), we showed that ground dwelling predators significantly lowered the abundance of aphids (*Aphis fabae* and *Myzus persicae*). Ground dwelling predators made a significant contribution to natural pest control, as they lowered aphids' populations by up to 80%, especially as they were present early in the season. Around 48% parasitism rate was measured in 2023. Our results suggest that both groups of natural enemies appear to play a particularly important role in aphid regulation, especially ground dwelling predators. This suggests that agricultural practices, as a vegetated field margin, favouring generalist predators overwintering or colonization could lead to fewer damage and losses for farmers.

# 1. Introduction

Conservation biological control (CBC) is based on practices designed to increase populations and effectiveness of natural enemies (Eilenberg et al., 2001; Stenberg et al., 2021), and is one of the most well-studied ways to control crop pests without chemical insecticides. However, despite numerous works on this method in the last twenty years (Gurr

et al., 2017), its adoption by farmers remains slow (Shields et al., 2019). One of the main reasons is that the efficiency of CBC is not widely demonstrated (Begg et al., 2017), as the impact of new management practices, such as implementation of flower strips can be highly variable (Karp et al., 2018, Albrecht et al., 2020). Moreover, very few studies assess the impact of CBC on crop damage or yield, and even less their economic value (Johnson et al., 2021; Mesmin et al., 2022). As

<footnote><sup>a</sup> INRAE, IGEPP, 35650 Le Rheu, France</footnote>

<footnote><sup>b</sup> Entomology and Nematology Department, University of Florida, Gainesville, FL, USA</footnote>

<footnote><sup>c</sup> Institut Agro, Univ Rennes, INRAE, IGEPP, 35000 Rennes, France</footnote>

<footnote><sup>d</sup> CNRS, ECOBIO, Université de Rennes 1, France</footnote>

<footnote>* Corresponding author at: INRAE, IGEPP, 35650 Le Rheu, France.</footnote>

<footnote>** Corresponding author at: INRAE, IGEPP, 35650 Le Rheu, France.</footnote>

<footnote>E-mail addresses: lenabarascou@ufl.edu (L. Barascou), anne-le.ralec@inrae.fr (A. Le Ralec).</footnote>
emphasized by Shields et al. (2019) and Iuliano and Gratton (2020), expanding scientific knowledge on the impacts of CBC in specific agricultural contexts is essential.

Aphids are among the most significant crop pests (Dedryver et al., 2010), as they not only feed on plants sap but also transmit phytoviruses, causing 10 – 15 % of agricultural production losses worldwide (Loebenstein, 2008). In Europe, the sugar beet crop (Beta vulgaris L.), provides about 25 % of the sugar supply, but faces considerable threats from aphid infestations. Several foliar feeding aphid species are reported to feed on sugar beet, with two key species, the green peach aphid (Myzus persicae) and the black bean aphid (Aphis fabae), considered the most harmful (Dewar and Cooke, 2006). M. persicae is considered economically important primarily due to its role in pathogen transmission of phytoviruses, while A. fabae, also a virus vector, has a more direct impact on sugar beet productivity and yield as it easily reaches high population densities, resulting in a high level of sap spoliation (Pretorius, 2014). Many viruses can infect sugar beet and cause important damage, including an intense yellowing of leaves, which results in a reduction of sucrose yield by 8 to 47 % (Smith and Hallsworth, 1990). Additionally, the ban on neonicotinoid seed treatments in Europe (European Commission, 2013), despite multiple derogations, has made aphid control more challenging, increasing yield losses from 11 to 15 % (Hossain et al., 2021).

Therefore, there is a need to find alternatives to control aphids and virus transmission, and CBC emerges as a promising solution. Indeed, aphids have very diverse and numerous natural enemies. Many studies have demonstrated that it is possible to enhance their abundance in crop systems through agroecological approaches (Hassan et al., 2016; Zehnder et al., 2007), potentially resulting in better control of aphids in crops. However, as even low numbers of colonizing aphids can initiate virus transmission, limitation of aphid populations by natural enemies must begin early in the season to target colonizing individuals and limit the spread of aphids and associated diseases.

Natural enemies (NEs) which regulate aphid populations can be classified in two distinct functional groups: (i) specialist predators (e.g. coccinellids, lacewings, hoverflies) including parasitoids (specialized on single or few aphid species as prey or host; Desneux et al., 2009), and (ii) generalist predators (e.g. spiders, carabid or rove beetles) which feed on aphids along with other prey. (Birkhofer and Wolters, 2012). These groups differ in their predatory behavior and interactions with pests (Symondson et al., 2002). Specialists are highly efficient at finding their prey and can rapidly reduce pest populations (Sabelis, 1992). However, their density varies greatly over time, as they only develop after their specific host has colonized the crop (Sigsgaard, 2002). Then, the numbers of specialist predators decline quickly once pest abundances become low (Murdoch et al., 1985). Conversely, generalists, like ground-dwelling predators, maintain stable populations in crops due to their ability to feed on alternative prey (Kendall, 2003); thus, they can survive in the environment in the absence of abundant pest populations. Moreover, their presence from the onset of the infestation may ensure early control in the pest infestation dynamics (Ekbom et al., 1992; Symondson et al., 2002). Overall, studies have suggested that these two NEs groups together may enhance aphid suppression (Mesmin et al., 2022; Thies et al., 2011), even if intraguild predation can sometimes occur (Polis and Myers, 1989; Snyder and Ives, 2003). However, as methods to enhance populations of each functional group could be quite different (e.g. flower strips vs grassy strips), it is necessary to measure their relative contribution to aphid regulation and to identify the timing of this contribution. Moreover, the quantification of such services is crucial to convince farmers that CBC can be a beneficial practice and an efficient tool to control aphid populations and thus reduce vector-borne diseases in their crops.

Therefore, assessing the effectiveness of CBC relies first on quantifying the reduction of pest populations by their various NEs. This is often processed by correlation methods. A more precise method consists of physical exclusion using cages or buried barriers restricting the

movement of individuals according to their dispersal traits and/or size (Luck et al., 1988). The abundance of natural enemies can be reduced experimentally with the use of exclusion devices. For example, the exclusion of flying arthropods can be achieved using fine-mesh nets (Rusch et al., 2013), and the exclusion of ground-dwelling arthropods can be managed with partially buried barriers (Gontijo et al., 2015; Hopper et al., 1995). These methods allow researchers to isolate the effect of different functional groups and thus compare their respective impacts on pest densities.

In the present study, such exclusion devices were used to determine the timing and effectiveness of two groups of natural enemies in controlling aphids and associated viruses in sugar beet crops. Our final goal was to provide sugar beet producers with quantitative data to support decision-making regarding CBC adoption on their farms. For this purpose, we quantified the relative contribution of aerial and epigeous natural enemies to aphid control over two separate years. We hypothesized that generalist ground-dwelling predators would be present in sugar beets earlier in the season and could reduce aphid populations more effectively than aerial predators (specialists). As wild vegetation on field edges is known to influence aphids NEs (Olson and Wäckers, 2007; Tschumi et al., 2016), we measured the effect of proximity to a vegetated field border on aphid reduction.

## 2. Materials and methods

### 2.1. Study site

Experiments were performed from April 24 to June 6, 2022, and from April 5 to June 14, 2023, in north-western France. Sugar beet (ARUM variety) was grown in a 1 ha field of the INRAE's experimental station (UE 0787, "Domaine expérimental de la Motte") in Le Rheu (48°121 N – 1°795 W). For agronomic reasons, a different field was used each year. Sugar beet was sown in a 50 cm inter-row spacing, on March 28, 2022, and April 6, 2023. No pesticides were applied throughout the experiments to ensure natural enemy populations were not disrupted.

### 2.2. Experimental design

To quantify aphid regulation by natural enemies, we used different exclusion devices for ground-dwelling predators (generalist predators: carabids, spiders, rove beetles) and flying natural enemies (parasitoids and specialist predators: ladybugs, hoverflies, lacewings).

At the edge of the sugar beet crop, a flower strip (FS) was planted as our vegetated field border to assess its potential influence on NEs. Three blocks of four subplots (3 m x 3 m, 25 plants each) were arranged at two distances from the FS: near (< 5 m) and far(50 m). All subplots were placed at least 3 m apart from each other in the same block; each block was placed at least 4 m apart from each other (Fig. 1; Fig. S1). The flower strip was composed mainly of phacelia in 2022 (15 m x 75 m) and was a mix of nine plant species in 2023 (Pollifauriflor mix, "SARL Ferme Saint Hubert" Society, France; Table S1) (3 m x 60 m), sown in September 2021 and 2022, respectively. We assessed natural pest control provided by natural enemies by comparing aphid density in exclosures and unmanipulated controls. The exclosures of generalist predators involved a combination of semi-underground buried barriers (Greenborder®, 40 cm below and 20 cm above ground) and 19 pitfall traps per subplot (BB treatment), which is efficient in reducing ground-dwelling predators' density (Luck et al., 1988; Mesmin et al., 2022). Insect-proof cages (L x l x h = 3 x 3 x 2 m; mesh: 300 x 300 µm; Diatex®) were used to exclude flying insects (Ca treatment). A combination of barriers, pitfall traps, and insect-proof cages was used to exclude all-natural enemies (CBB treatment). The unmanipulated controls (C treatment) were subplots of the same dimensions as the exclosures, delimited by stakes and without traps, cages, or barriers.

In 2023, additional subplots of the same dimensions (3 x 3 m) with 19 pitfall traps only (no cages and barriers; Cb treatment) were
<figure><img src="image_2.png" /><figcaption>Fig. 1. Experimental set-up in 2022 (A) and 2023 (B): sugar beet crop with a vegetated border (flower strips) as a field margin. In 2022, *Cb* treatment wasn't set up.</figcaption></figure>

introduced near (< 5 m) and far (50 m) from the FS. This enables the
assessment of the effectiveness of the epigeous predator exclusion
method: we compared the change in ground dwelling predators' abun-
dance in subplots without barriers (where they can move around natu-
rally – *Cb*) to their abundance in subplots with barriers (which prevent
predators from reaching the subplots – *BB* and *CBB*). A lower abundance
of ground-dwelling predators in subplots with barriers compared to
those without barriers strongly suggests that the combination of barriers
and pitfall traps effectively limits predator abundance.

All exclusion devices were set up right after crop sowing, ensuring
that NEs were excluded before aphid colonization. Each year, six sub-
plots were randomly assigned to the different treatments (*C*, *BB*, *CBB*, *Ca*
and *Cb*) throughout the crop's length and width. In subplots with insect-
proof cages (*Ca* and *CBB* treatments), plants were artificially inoculated
with aphids of both species, *M. persicae* and *A. fabae*, reared on sugar
beet in the laboratory. Beforehand, random sampling of the plot was
carried out to estimate the average aphid density per plant and the
proportion of infested plants under the nets. The average density of
M. persicae and A. fabae in the crop was estimated at the crop's 2-leaf stage (start of the colonization). The day after, infestation of beet plants under the cages was carried out manually at the same densities (2 M. persicae on 40 plants and 120 A. fabae on 20 plants in 2022; 1 M. persicae on 8 plants and 8 A. fabae on 21 plants in 2023). However, in 2023, a second aphid introduction was carried out (2 M. persicae on 5 plants and 10 A. fabae on 8 plants), due to heavy rainfall affecting initial aphid settlement.

## 2.3. Data collection

### 2.3.1. *Aphid populations*

Aphid densities were monitored weekly from April 27 to June 6, 2022, and from May 4 to June 14, 2023. Each week, the number of individual aphids was counted on five sugar beet plants in each subplot for each treatment except *Cb*. The five randomly selected beet plants were uprooted and immediately individually bagged. The aphids were then counted and identified to species level under a binocular microscope. They were then preserved in 100 % ethanol and kept at 4 °C.

### 2.3.2. *Natural enemies' populations*

Predator populations were monitored weekly, beginning before the beet seed germination, from April 5 to June 1, 2022, and from April 13 to May 25, 2023. Ground-dwelling predators were captured using pitfall traps (Barber traps) buried in the ground of *BB*, *CBB*, and *Cb* subplots. Each trap contained a preservative solution of salt water (30 g/l) with a few drops of soap to reduce the surface tension and thus facilitate insect immersion (Hassan, 2012). Arthropods trapped were then stored in 70 % ethanol, sorted into major groups (carabid beetles, spiders, and rove beetles), and counted. Captured carabids were identified to species level using a dichotomous key (Roger et al., 2016).

Flying insects (ladybugs, hoverflies, lacewings, and parasitoids) were monitored in the field during aphid sampling by visually inspecting the leaves of the five sampled plants before uprooting them. We re-examined the leaves after bringing them to the lab to identify any specialist predators that had been missed during field observations. For parasitoids, mummies (i.e., parasitized aphids) observed on the same beet plants were collected, counted, and isolated in tubes to allow parasitoid adult emergence and subsequent identification of species. Moreover, subsamples of the collected aphids in *C* and *Ca* treatments were analysed using a molecular method (Derocles et al., 2012) to detect the presence of an aphidiid parasitoid in live aphids and compare the parasitism rate in the different treatments. DNA extraction was carried out using a salting-out protocol (Miller et al., 1988). The extracted DNA was amplified using the HCO2198 - LCO1490 (COI) (Folmer et al., 1994) and the 16S-F-16S-Rspe (16S) primer pairs, specific to *Aphidiinae* (Derocles et al., 2012). The generalist COI primer pair allowed for to verification of the presence of DNA in the extracted sample, while the 16S primer pair allowed for to detection of *aphidiin* parasitoid DNA. Parasitism rates were estimated as the ratio of the number of aphids parasitized to the total number of aphids analyzed.

## 2.4. Data analysis

Data were analysed using the statistical software R v4.0.3 (R Core Team, 2020). Considering the differences in experimentation set-up, as well as the variation in both absolute and relative abundances of the two aphid species between years, the analyses were carried out separately for each year. For each variable and each model, the error distribution and link function were selected based on the type of response and the evaluation of model residuals for correct distribution, dispersion, outliers, and overall model fit(function *simulateResiduals*, package *DHARMa*, Hartig, 2020). Overdispersion was assessed by comparing Poisson models to negative binomial models, and when overdispersion was detected, a negative binomial distribution was used (*glm.nb* function from the MASS package; Venables and Ripley, 2002). We selected the

best model based on the second-order Akaike's Information Criterion (AICc) (Burnham and Anderson, 2003) by using backward model selection from the full model. In all models, the significance of the fixed effects was tested with deviance analyses (Type II, function 'Anova', package 'car'; Fox and Weisberg, 2019). Analyses were followed by post-hoc Tukey multiple comparison tests (function *emmeans*, package *emmeans*; Lenth, 2016) to assess significant differences between variable levels.

To validate the epigeous predator exclusion method, the activity-density of carabids, rove beetles, and spiders per subplot (*Cb* vs *BB* vs *CBB* treatments) was analysed using a negative binomial distribution. Treatment and sampling date were included as fixed factors, along with their interaction (treatment × sampling date).

The parasitism rate was analysed using a Generalized Linear Mixed Model (GLMM), with a binomial distribution (no parasitism detection: 0 and parasitism detection: 1) for each year. The distance from the field margin and the sampling date were included as fixed factors (with interaction between both factors).

The number of aphids per plant in each treatment was analysed separately (*Cb* vs *BB* and *Ca* vs *CBB*) using a GLMM, with a negative binomial distribution to account for overdispersion. Exclusion devices and sampling date were included as fixed factors, along with their interaction.

The effect of distance from the field margin on aphid abundance was analysed using a GLMM with a negative binomial distribution to account for overdispersion. The distance from the field margin and the sampling date were included as fixed factors, as well as the interaction between them.

The effect of distance from the field margin on ground-dwelling predator abundance was analysed using a GLMM with a negative binomial distribution. Distance from the field margin, sampling date, and aphid abundance were included as fixed factors, along with their two-way interactions: distance × date, distance × aphid abundance, and date × aphid abundance.

In all models, the individual subplot was included as a random factor to account for the repeated measures taken from the same spatial units (Faraway, 2006). This helps partition out the variance attributable to differences between subplots, thereby avoiding pseudo-replication and ensuring unbiased inference.

# 3. Results

## 3.1. Efficiency of exclusion devices

The efficiency of the exclusion devices on ground-dwelling predators was analysed only in 2023. Overall, we trapped 2,232 rove beetles, 6,449 spiders and 3,062 carabid beetles in "Cb treatment", "BB treatment", and "CBB treatment" subplots. There was a significant interaction between the treatment (*Cb* vs *BB* vs *CBB*) and the sampling date ($\chi^2 = 22.81$, df = 12, p = 0.03; Table S2) on the activity-densities (AD) of carabid beetles. The AD of carabids was significantly higher in the *Cb* treatment than in *BB* and *CBB* treatments across all sampling dates (Tukey post hoc, p < 0.001; Fig. 2), except on May 4, May 16 and May 25, where no significant difference was found between *Cb* and *CBB* treatments (Tukey post hoc, p = 0.13, p = 0.07 and p = 0.25, respectively). Overall, carabid AD were at least 2.5 and 1.6 times higher in *Cb* subplots than in *BB* and *CBB* subplots, respectively (Fig. S2). There was a significant interaction between the treatment (*Cb* vs *BB* vs *CBB*) and the sampling date ($\chi^2 = 115.92$, df = 12, p < 0.001; Table S2) on the AD of spiders. The AD of spiders was significantly higher in the *Cb* treatment than in *BB* and *CBB* treatments across most of the sampling dates (Tukey post hoc, p < 0.001; Fig. 2). However, there was no significant difference found between *Cb* and *CBB* treatments on May 16 and May 25 (Tukey post hoc, p = 0.13, p = 0.34, respectively), as well as no significant difference between *Cb* and *BB* treatments on June 1 (Tukey post hoc, p = 0.29; Fig. 2). There were significant differences in spider AD between
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Activity-density of ground dwelling predators (A), carabids (B), rove beetles (B), and spiders (C) (mean ± se) collected per trap across sampling dates in the "Cb treatment" (green colour), "BB treatment" (orange colour) and "CBB treatment" (red colour).</figcaption></figure>

BB and CBB treatments only on April 20 (Tukey post hoc, p = 0.02), with higher AD observed in the CBB treatment. Overall, the AD of spiders were at least 2.4 and 1.7 times higher in Cb subplots than in BB and CBB subplots during the first dates (Fig. S2). There was a significant interaction between the treatment (Cb vs BB vs CBB) and the sampling date (γ² = 92.60, df = 12, p < 0.001; Table S2) on the AD of rove beetles. The AD of rove beetles differed significantly only during the first two sampling dates, with AD being at least 3.4 times higher in Cb treatment compared to CBB (Tukey post hoc, p < 0.001; Fig. 2), and 1.2 times higher in BB treatments compared to CBB (Tukey post hoc, p < 0.001; Fig. 2). Though the difference between Cb, BB and CBB treatments significantly fluctuated through time for all taxa, the AD inside Cb subplots was always higher (at least in tendency) than that in BB and CBB subplots (Fig. S2).

The efficiency of the exclusion devices on aerial natural enemies was analysed for both years. Overall, we observed few aerial natural enemies

in all treatments (Ca, CBB, C and BB). Under cages (Ca and CBB treatments), only 4 and 14 ladybugs, 0 and 5 hoverflies, 0 and 1 lacewing were observed in 2022 and 2023, respectively. In subplots without cages (C and BB treatments), 8 and 41 ladybugs, 1 and 14 hoverflies, 1 and 1 lacewing were counted in 2022 and 2023, respectively. In both years, only eight mummies (dead parasitized aphids) were observed under cages (both Ca and CBB treatments) throughout the sampling season whereas 14 mummies were sampled in both C and BB treatments. In 2022, a low level of parasitism was confirmed by the molecular analysis under the cages (Ca treatment only): 4 aphids were detected parasitised on 82 sampled, i.e. a parasitism rate of 4.88 % (1.58 % A. fabae and 15.78 % M. persicae parasitised). However, higher parasitism rates were detected in 2023: 10 aphids were detected parasitised on 39 sampled, i.e. a parasitism rate of 25.64 % (27.03 % A. fabae and 0 % M. persicae parasitised).
## 3.2. Effect of aerial natural enemies on aphid density

Although we observed some reduction in aerial natural enemy abundance under the cages, we were not able to measure the impact on aphid populations due to difficulties in aphid settlement under the cages. Heavy rainfall just after the artificial infestation prevented us from comparing the population trends in the Ca treatment with those of control subplots without cages. The only estimation of this impact was obtained through the parasitism rates.

Parasitism rates obtained from the number of mummies sampled were very low throughout the sampling season (< 1 %) for both treatments (Ca and C treatments). The parasitoids emerging from the few mummies are mainly composed of *Aphidius avenae*, *Aphidius ervi*, *Aphidius matricariae*, and *Diaeretiella rapae* on *M. persicae*. Other species have also been identified, such as *Binodoxys* sp. on *A. fabae*.

In 2022, a low level of parasitism was confirmed by the molecular analysis in subplots without insect-proof nets (C treatment; Table S3): 26 aphids were detected parasitized out of the 189 sampled, i.e., a parasitism rate of 13.76 ± 2.51 % (12.40 ± 2.51 % *A. fabae* and 16.67 ± 4.85 % *M. persicae* parasitized). There was no interaction out of the cages ($\chi^2$ = 8.22, df = 5, p = 0.14). No effect of each factor individually was observed (sampling session: $\chi^2$ = 14.63, df = 5, p = 0.12; distance: $\chi^2$ = 1.43, df = 1, p = 0.23). Under the cages (Ca treatment), the level of parasitism was lower than outside: 4 aphids were detected parasitized out of the 82 sampled, i.e., a parasitism rate of 4.87 ± 2.39 % (1.59 ± 1.59 % *A. fabae* and 15.79 ± 8.59 % *M. persicae* parasitized). There was also no interaction between the sampling date and the distance on the parasitism rate ($\chi^2$ = 3.22, df = 3, p = 0.36). Likewise, no effect of the sampling session and the distance, each factored individually, was observed (sampling session: $\chi^2$ = 9.35, df = 4, p = 0.053; distance: $\chi^2$ =

0.003, df = 1, p = 0.96).

In 2023, the parasitism rate was higher than in 2022 (Table S3): 41 aphids were detected parasitized out of the 85 sampled in the C treatment, i.e., a parasitism rate of 46.82 ± 3.80 % (42.28 ± 4.47 % *A. fabae* and 58.00 ± 7.05 % *M. persicae* parasitized). Under the cages, 10 aphids were detected parasitized out of the 39 sampled, i.e., a parasitism rate of 25.64 ± 7.08 % (27.03 ± 7.40 % *A. fabae* parasitized and no *M. persicae* parasitized). No interaction between the sampling date and the distance was observed on the parasitism rate in either the C treatment ($\chi^2$ = 3.40, df = 5, p = 0.64) and the Ca treatments ($\chi^2$ = 5.20, df = 3, p = 0.16). Sampling session had a significant effect on parasitism detected in the aphids sampled in the C treatment (Fig. 2B, $\chi^2$ = 62.08, df = 6, p < 0.001) but not the distance from the field margin ($\chi^2$ = 2.24, df = 1, p = 0.13). Before decreasing, the parasitism rate was significantly higher at the beginning of the season (May 4 and May 11). No effect of sampling session and distance was observed on the parasitism detected in the aphids sampled under cages ($\chi^2$ = 4.06, df = 3, p = 0.25; $\chi^2$ = 0.13, df = 1, p = 0.72).

## 3.3. The effect of generalist predators on aphid density

Three aphid species were found on beets during both years. *Aphis fabae* was the most abundant species, comprising 87.93 % and 98.36 % of the total aphids sampled, compared to *M. persicae*, making up 9.99 % and 1.06 % of aphids sampled, and *Macrosiphum euphorbiae*, making up only 2.08 % and 0.57 % of total aphids, in 2022 and 2023, respectively. Hereafter, only the results obtained for all the aphids counted are presented with all taxa considered together.

In 2022, there was a significant interaction between the treatment (C vs BB) and the sampling date ($\chi^2$ = 36.22, df = 5, p < 0.001; Fig. 3A).

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Temporal evolution of aphids per plant (mean ± se) in 2022 (A) and 2023 (B) per treatment: BB = ground dwelling predators excluded with a combination of buried barrier and pitfall traps, C = ground dwelling predators not excluded. Asterisks depict the statistical significance of treatment effects as follows: for asterisks, 0 < *** < 0.001 < ** < 0.010 < * < 0.050 < non-significant (n.s.).</figcaption></figure>
The effect of treatment fluctuated with time: the reduction of epigeous predators had no significant effect at the start of the aphid population growth but only during the growth peak in May 18. The total number of aphids per plant was 89 times lower in the C treatment than in BB treatment (Tukey post hoc, p < 0.001). In 2023, there was also a significant interaction between the treatment (C vs BB) and the sampling date ($\chi^2 = 16.64$, df = 6, p = 0.011; Fig. 3B). The exclusion of ground-dwelling predators had a significant effect on the reduction of aphid population at different sampling dates: the total number of aphids per plant was 5.2, 3.7, 14.3, and 56.3 times lower in the C treatment than in BB treatment respectively in May 17 (Tukey post hoc, p = 0.01), May 31 (Tukey post hoc, p = 0.04), June 7 (Tukey post hoc, p < 0.01) and June 14 (Tukey post hoc, p < 0.01).

In 2022, there was a significant interaction between the treatment (Ca vs CBB) and the sampling date ($\chi^2 = 14.39$, df = 4, p < 0.01; Fig. 4A). The effect of the exclusion of all-natural enemies was not significant at the start of the aphid population growth, but was significant in May 25: the total number of aphids per plant was 8 times lower in the Ca treatment than in CBB treatment (Tukey post hoc, p < 0.001). In 2023, there was also a significant interaction between the treatment (Ca vs CBB) and the sampling date ($\chi^2 = 27.15$, df = 3, p < 0.01; Fig. 4B). The effect of the exclusion of all-natural enemies is significant at the first sampling date, May 17, and at the last one, June 14: the total number of aphids per plant was 17 times and 23 times lower in the Ca treatment than in CBB treatment, respectively for these two dates (Tukey post hoc, p < 0.01 and p = 0.03, respectively for May 17 and June 14).

### 3.4. Characterization of ground dwelling predator communities during the occurrence of aphids

The activity-density of the ground dwelling predator communities observed in Cb treatment is shown in Fig. 5. From the 13th of April to the 4th of May, i.e. the period without aphids, the ground dwelling predators were already present in the sugar beet crop. During this period, rove and carabid beetles dominated the ground dwelling predators' composition, representing 35.45 % and 34.57 % respectively. Spiders represented 29.98 % of predators. Among carabid beetles, a total of 1,532 carabid individuals belonging to 49 species were captured in pitfall traps (Cb treatment) for the whole period. *Poecilus cupreus* was dominant during this first period, representing 17.92 % of the carabid beetles (Fig. 6). Then, from the 16th of May to the 1st of June, when aphid populations were growing, spiders were the most abundant taxa of ground dwelling predators (72.23 %), followed by carabid beetles representing 22.14 % of the ground dwelling predators. Rove beetles were of minor importance during this second period (5.63 %). Among carabid beetles, *P. cupreus* was again dominant during this period (21.07 %) followed by the newly emerging species *Anchomenus dorsalis* and *Pterostichus melanarius* (4.41 % and 3.8 % respectively).

### 3.5. Effect of distance from a field margin on aphid density and ground dwelling predators

In 2022, there was no interaction between the distance and the sampling date ($\chi^2 = 10.76$, df = 5, p = 0.056), but on May 11, the number of aphids per plant seemed higher at 50 m than close to the field margin. No main effect of the distance from the field margin was

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Temporal evolution of aphids per plant (mean ± se) in 2022 (A) and 2023 (B) per treatment: CBB = all-natural enemies excluded with a combination of cages, buried barrier and pitfall traps; Ca = flying natural enemies excluded with cages. Asterisks depict the statistical significance of treatment effects as follows: for asterisks, $0 < \*\*\* < 0.001 < \*\* < 0.010 < \* < 0.050 < \text{non-significant (n.s.)}$.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Numbers of carabids, spiders and rove beetles (mean ± se) collected per subplot and per week in the Cb treatment. The arrow shows the presence period of aphid population, and the black rectangle highlights the period of high aphid population.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Variations of cumulative abundance of carabid species each representing at least 0.5% of the total abundance in the sugar beet crop in 2023 related to the sampling session (Cb treatment).</figcaption></figure>

observed ($\chi^2$ = 0.70, df = 5, *p* = 0.40) in the number of aphids per plant. Only the sampling date had a significant effect on the number of aphids per plant ($\chi^2$ = 43.45, df = 5, *p* < 0.001; Fig. 7A): aphid abundance was 14.9 times higher on May 11 compared to April 27 (Tukey post hoc, *p* < 0.001), and 7.9 times higher than on May 18 (Tukey post hoc, *p* = 0.02). In 2023, there was a significant interaction between the distance and the sampling date ($\chi^2$ = 13.18, df = 6, *p* = 0.04): on May 24, the number of aphids per plant was 13.83 times lower at 50 m from the edge than close to the edge (Fig. 7B).

The effect of distance from a field margin on ground-dwelling predators was analysed only in 2023. There was a significant interaction between aphid abundance and both sampling date ($\chi^2$ = 11.91, df = 5, *p* = 0.036) and distance from the field margin ($\chi^2$ = 6.79, df = 1, *p* < 0.01) on the activity-density of ground-dwelling predators. On May 4, carabid beetle activity-density increased dramatically with rising aphid numbers, far more than on any other date (Fig. 8A). At low aphid densities, the AD of carabids was significantly higher near to the field margin than at 50 m. However, as aphid abundance increased, the AD of carabids rose more steeply at 50 m (Fig. 8B). There was a significant interaction between sampling date and distance from the field margin on the AD of spiders ($\chi^2$ = 21.20, df = 4, *p* < 0.001). On May 11 and June 1, significantly more spiders were recorded at 50 m compared to < 5 m (Tukey post hoc, *p* < 0.001, for both dates; Fig. 9A). No significant differences were observed on other dates (May 4, May 16, and May 25),

although the trend was generally consistent with higher activity at 50 m. There was also a significant effect of aphid abundance on spider activity-density ($\chi^2$ = 4.32, df = 1, *p* = 0.038): spider activity-density decreased significantly with increasing aphid abundance (Fig. 9B). There is a significant interaction between sampling date and distance on the AD of rove beetles ($\chi^2$ = 50.52, df = 6, *p* < 0.001; Fig. 10): their AD was 5.86 and 5.26 times higher at 50 m on April 13 (Tukey post hoc, *p* < 0.001) and May 25 (Tukey post hoc, *p* = 0.03), respectively.

## 4. Discussion

Using a partial exclusion method, these two years of experiments demonstrate that ground-dwelling predators are highly effective in regulating aphid populations, reducing aphid abundance by up to 80 %. Moreover, these natural enemies are present early in the season, even before sugar beet emergence (under our experimental conditions), which can be crucial for natural pest control. Even though we found few aphid mummies, the molecular analysis detected a high parasitism rate: in 2023, 48.23 % of the sampled aphids were parasitized outside the cages.

The partially buried barriers in combination with the Barber traps significantly reduced the abundance of all ground dwelling predators, resulting in two situations with homogeneous pest infestations but different predation pressure. This confirms earlier studies on carabids
<figure><img src="image_8.png" /><figcaption>**Fig. 7.** Number of aphids per plant (mean ± se) related to sampling dates in 2022 (A) and related to the distance from the field margin in 2023 (B). Different letters indicate significant differences (α < 0.05).</figcaption></figure>

(Carrillo et al., 2007; Lee and Edwards, 2012; Mesmin et al., 2022), but also for less-studied spiders and rove beetles (Birkhofer et al., 2017; Mesmin et al., 2022). However, despite the combined use of barriers and pitfall traps, a residual population of each predators group remained inside the subplots with barriers and those with cages and barriers. This could be explained by species turnover, related to seasonal variation: some species overwinter as adults (e.g. *P. cupreus*, Ribera et al., 1999), and thus are active early in the season, before they are progressively succeeded or joined by those that overwinter as larvae in the soil and emerge in May-June (e.g. *P. melanarius*). There is continuous intra-subplot emergence: colonization is stopped by our exclusion method but not intra emergence, even if individuals are quickly trapped. This may also be true for spiders which can overwinter as eggs, immatures, or adults (Schaefer, 1977), and therefore may emerge at various periods in the spring. Moreover, we can't rule out the possibility that some individuals may colonize the subplots by climbing the barriers or from the air. This is probably the case for rove beetles, for which a significant reduction was only observed in the complete exclusion treatment. These residual populations lead to an underestimation of the impact of the ground-dwelling predators on aphid regulation. Nevertheless, the constant trapping of these arthropods limits their predation within our system.

Similarly, for aerial natural enemies, the molecular analysis allowed the detection of parasitized aphids for both years under the cages. Some parasitoids were therefore able to penetrate into the cages which

nevertheless limited colonization: we observed a parasitism rate that was twice as high for both years when parasitoids were not restricted by the cages.

With these partial exclusion methods, we have shown that excluding ground-dwelling predators led to an increase in the number of aphids per plant. A maximum reduction of 80 % in aphids per plant was observed when predators had access to the plants, with the effect varying across the season. This was mainly attributed to these predators because specialist predators as syrphid larvae, coccinellid adults and larvae, were rarely observed on plants as well as dead parasitized aphids (mummies) in both years. Moreover, we observed a greater reduction of aphid density per plant with a complete exclusion of natural enemies, compared to when only flying natural enemies were removed (*Ca* treatment) (*CBB* treatment). This result aligns with findings from a study carried out on root flies and aphids in *Brassica* crops (Mesmin et al., 2022). They make it possible to generalize the role of ground-dwelling predators in the regulation of crop pests, which until now has been studied only on cereal aphids (Kendall, 2003).

The relative abundances of carabid beetles suggest that *P. cupreus* and *P. obtusa* may have been important contributors to the control of aphid populations (Chiverton, 1988, 1987; Roubinet et al., 2017; Symondson et al., 2002). In our study, the natural aphid control by these predators could be due to a direct predation of aphids on the leaves. Even if carabids are generally considered as mainly ground dwellers, some studies reported them climbing on crop canopy (Dunning et al., 1975;
<figure><img src="image_9.png" /><figcaption>**Fig. 8.** Activity-density (mean ± se) of carabid beetles per trap in relation to aphid abundance and (A) distance from the field margin, or (B) sampling dates. Thick lines represent the model predictions with shaded areas indicating 95 % confidence intervals (CI 95 %).</figcaption></figure>

Kamenova et al., 2015; Mesmin et al., 2022). Large species like those that were dominant in the present study, such as *P. cupreus* throughout the season and *P. melanarius* at the end of the season, may climb on sugar beet plants (Chiverton, 1988). The rigid structure of beet leaves can make them easier to climb and directly reach the aphids. Moreover, a direct consumption of aphids that have fallen on the ground could also happen, reducing the rate at which aphids return to the crop canopy (Winder, 1990). Aphid dropping is a frequent behaviour and can be associated with disturbance by specialized aerial predators (Humphreys, 2022; Losey and Denno, 1998). This is the main explanation for the control of aphid populations by ground terrestrial arthropods on cereals (Winder, 1990), but should be confirmed in sugar beet crops by visual observation.

Moreover, carabid beetles, spiders, and rove beetles were present and active early in the season in 2023 (April 13th), before sugar beet plant emergence and thus before the colonization of aphids in the crop. The presence of ground-dwelling predators at that time is crucial for the early control of aphid populations and to limit vector dissemination and disease transmission to sugar beets, which are more susceptible in the early stages (Kift et al., 1998; Watson and Watson, 1953). Ground-dwelling arthropods are mostly generalist and opportunistic predators

(Ekschmitt et al., 1997; Roubinet et al., 2017; Symondson et al., 2002), so they may survive inside the fields relying on several prey species, making their population dynamics globally independent from that of the aphids. For insect pests with continuous population growth after the arrival of the first individuals, like aphids, this characteristic is probably of major importance: early active generalist predators may be efficient at limiting pest population growth, and even in the case of low infestations. However, we observed a general decline in the activity-density of these predators on May 11th in 2023, which can be explained by the rainfall at this period: the average rainfall the day before reached 17 ml (Fig. S3).

The rainfall was also probably responsible for issues in settling aphids under the cages, which made it impossible to reliably measure the effect of aerial NEs exclusion on aphid populations. Rainfalls occurred after both artificial infestations of aphids in the cages in 2022 (17.5 ml and 12.5 ml of rain on May 22nd and May 23rd respectively;) and 2023 (3.5 ml and 17 ml of rain on May 4th and May 10th respectively; figure S3), which may have washed away the aphids deposited on the sugar beet plants. Similar results were reported by Karungi et al. (2000) and Melesse and Singh (2012), suggesting that aphid populations were killed by heavy rainfall.

This rainfall could also have hidden the parasitism activity of the
<figure><img src="image_10.png" /><figcaption>**Fig. 9.** Activity-density (mean ± se) of ground spiders per trap in relation to (A) distance from the field margin across sampling dates and (B) aphid abundance. Asterisks depict the statistical significance of treatment effects as follows: for asterisks, 0 < *** < 0.001 < ** < 0.010 < * < 0.050 < non-significant (n.s.).</figcaption></figure>

aphidiids in 2023. While in 2022 there was some consistency between the rate of parasitism estimated by molecular detection and the number of mummies observed, this was not the case in 2023. The average rate of parasitism reached near 50% even though very few mummies have been observed. We could assume that the parasitized aphids were eliminated by the rain before mummification, with this assumption being supported by the collapse of the parasitism rate after mid-May. This difference in the parasitism rate between both years can be explained by the difference in the density of aphids. The density of aphids per plant was 2 times lower in 2023 than in 2022. Higher rates of parasitism were often observed at low aphid densities, with a negative density-dependence (Khatri et al., 2021; Zhao et al., 2012). This may be due to the tendency of many aphidid parasitoids not to parasitise all the aphids in high-density colonies and to disperse their eggs to limit the risks of predation and hyperparasitism (Mackauer and Völkl, 1993).

Consequently, the parasitism rate was high early in the season when aphid population density was still low, indicating that parasitoids could significantly contribute to an early aphid regulation. The few parasitoids that emerged from the aphid mummies belong to parasitoid species

often found on other crops such as Brassicaceae (cabbage, rapeseed) (Derocles et al., 2020). Parasitoids, being more specialized, are often considered more effective in regulating aphid populations (Schmidt et al., 2003), but in our study we can hypothesize a complementarity between the two types of natural enemies, since they seem to be active at the same time in the sugar beet crop. Mesmin et al. (2022) found no evidence of intraguild interference between predators and parasitoids of the cabbage aphid *Brevicoryne brassicae*, and many studies have shown complementarity between predators and parasitoids of other pests such as the woolly aphid *Eriosoma lanigerum* (Gontijo et al., 2015) or the pollen beetle *Meligethes aeneus* larvae (Dainese et al., 2017).

In this study, we also tested the hypothesis that distance to a vegetated field margin influences aphid regulation by the two types of NEs, as such agroecological infrastructures (AEI) could be used to enhance biological control. We observed different effects of the AEI on the population of pests and predators, which were modulated also by aphid population. The presence of the field margin only had an effect in 2023 on aphid populations: aphids were more abundant close to the AEI than in field. The field margin could represent refuge areas for these
<figure><img src="image_11.png" /><figcaption>**Fig. 10.** Activity-density (mean ± se) of rove beetles per trap in relation to distance from the field margin across sampling dates. Asterisks depict the statistical significance of treatment effects as follows: for asterisks, 0 < *** < 0.001 < ** < 0.010 < * < 0.050 < non-significant (n.s.).</figcaption></figure>

phytophagous insects. Aphids first colonize the field from the margin, where they often develop more quickly. However, studies on the distribution of aphids in a field show conflicting results. Sometimes aphids are abundant near the edges (Winder, 1990). Sometimes, no spatial structure in their distribution is observed (Winder, 2005). In most studies, field margins are not surveyed when sampling aphids, precisely to avoid this edge effect (Andrade et al., 2015). Another explanation for the high density of aphids close to the field margin could be explained by a lower efficiency of natural enemies there, due to the abundance of alternative prey. We did not observe an effect of the edge distance on parasitism rate. Parasitoids are flying natural enemies capable of moving between fields, which could reduce the impact of the margin on their numbers. Moreover, carabid beetles showed a strong and positive relation with aphid density, especially near the field edge and early in the season. However, as aphid abundance increased, carabid activity increased more steeply at 50 m, which can explain the low density of aphids at 50 m. Carabid beetles are present all over the field due to their migration during the season (Wallin and Ekbom, 1988). These arthropods move from overwintering sites from the margin to the centre of the field, to find more nutritious resources and more prey such as springtails, dipterans, and small beetles. Moreover, we observed a different carabid community between the two distances (Fig. S4), which could be due to the openness of the environment (Honěk, 1988; Mei et al., 2023). Certain species can be influenced by the field margin, particularly when these habitats are covered, while others prefer more open environments, rather than the center of the crop field. Compared to the carabids, spiders responded negatively to aphid levels and exhibited shifting edge preferences over time. We can hypothesize that, as generalist predator, spiders may shift to alternative prey (springtails, dipteran larvae, colembolans) that are more abundant in the litter or soil-vegetation matrix, particularly away from heavily infested aphid patches. Rove beetles displayed the most pronounced date-distance interaction. The strength of this interaction suggests that the edge effect was not consistent across the season; in some dates, beetles may have been more abundant at the edge, while on others, the difference may have diminished or even reversed. This result is consistent with their high mobility compared to carabids (Holland et al., 2004), thanks to their ability to fly. These results do not allow us to draw a conclusion on the ability of vegetated field margins to enhance NEs populations, because the study was done on a single field without a paired control. To robustly assess the influence of vegetated borders on NE populations and pest control, future studies should be specifically designed to compare bordered fields with

appropriate control borders and flower strips across multiple sites. Additionally, increasing the number of experimental fields and extending the research over multiple years would greatly improve the generalizability and strength of the conclusions drawn.

In conclusion, this study demonstrates that natural enemies can greatly reduce aphid populations in sugar beet. Ground dwelling predators, an ubiquitous functional group that reduce pests damage on a wide range of crops, appear to play a particularly important role in aphid regulation. Their early presence in the field crop could offer an effective regulation of disease transmission to sugar beets, which are more susceptible in the early stages. This has downstream impacts on overall crop health by limiting infection by the main aphid vectors of the beet yellow virus. The contribution of parasitism to aphid regulation could also be significant, particularly in situations of low aphid density. It is noteworthy that the generalist predators, but also some parasitoid species, involved in aphid regulation could also attack aphids on the main other crops of the sugar beet rotations, especially wheat and rapeseed.

The next steps toward the development of a conservation biological control strategy against vectors are first to compare the communities of natural enemies in European areas of sugar beet production, which differ from that where the present study took place, especially in the density of semi-natural habitats and the climate. This could lead to less abundant or diverse communities of ground dwelling predators or parasitoids, and then subsequently to a lower level of biocontrol. Second, the impact of AEIs, like flower strips, on natural enemy communities, and on the enhancement of biological control, needs to be assessed in this context of large open fields with very few natural habitats. Third, we need to establish the relationship between the level of control of aphid populations by the NEs, and the prevalence of the disease and the associated crop losses. Economic arguments are needed to encourage a change in growers' practices in favour of conservation biological control (Courtois and Baret, 2022; Dedryver et al., 2010; Mesmin et al., 2022). The data required for the two first steps have been collected over two years, 2022 and 2023, in the French sugar beet production areas (Tougeron et al., in prep.). These results provide empirical evidence demonstrating the efficacy of CBC, in association with other non-chemical methods, and may encourage further use of CBC in crops beyond sugar beets.

### CRediT authorship contribution statement

Lena Barascou: Writing – review & editing, Writing – original draft,
**Methodology, Formal analysis, Data curation, Conceptualization.**
**Marianne Doehler:** Writing – review & editing, Methodology.
**Ségolène Buzy:** Writing – review & editing, Methodology.
**Aurore Arnoult:** Writing – review & editing, Methodology.
**Eloi Salembier:** Methodology.
**Valentin Richard:** Methodology.
**Loïc Daniel:** Methodology.
**Frank Duval:** Methodology.
**Anne Le Ralec:** Writing – review & editing, Writing – original draft, Supervision, Methodology, 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.

Acknowledgments

This work was supported by the French IAE-B project as part of the French National Plan for Research and Innovation (PNRI) and funded by the French Ministry of Agriculture (the Ministry of Agriculture cannot be held responsible). The authors would like to thank Dr. Yann Tricault for helping with the experiment design protocol and Dr. Kaitlin R. Deutsch and Ayla M. Babanikos for providing language help, and Dr. Amélie Noël for providing statistical analysis help.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.biocontrol.2025.105773.

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