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. 2026 May 5;82(9):8181–8188. doi: 10.1002/ps.70862

Non‐crop habitats are associated with early‐season abundance dynamics of predatory natural enemies adjacent to wheat fields

Xiaozhou Wang 1,2, Yanhong Li 1, Min Qi 1,2, Xiaoyi Liang 1, Zhuo Li 1, Shovon Chandra Sarkar 3, Xingyuan Men 1, Feng Ge 1, Xingrui Zhang 1,✉
PMCID: PMC13453166  PMID: 42087368

Abstract

BACKGROUND

Non‐crop habitats are integral components of winter wheat agroecosystems, providing food resources and refuges that may support early‐spring recovery of predatory natural enemies and contribute to early‐season biological control potential.

RESULTS

During 2024–2025, we used sticky traps to monitor four typical non‐crop habitats (woodland, grassland, village, and uncultivated land) adjacent to wheat fields, quantifying early‐season abundance dynamics of predatory natural enemies across habitat types and survey dates. Our results show that early‐season abundance dynamics varied significantly among habitat types. Eupeodes corollae was most abundant in woodland, averaging 10.36‐fold higher than in village. Chrysoperla sinica was most abundant in grassland, averaging 7.72‐fold higher than in uncultivated land. Village provided important early‐season refuges for coccinellids: Propylaea japonica abundance was on average 4.14‐fold higher than in uncultivated land, and Harmonia axyridis abundance was on average 5.32‐fold higher.

CONCLUSION

Conserving and properly managing village‐edge vegetation, grasslands, and woodland edges surrounding farmland may promote the early‐spring recovery of predatory natural enemies in wheat agroecosystems. These findings suggest potential for strengthened early‐season aphid management through enhanced availability of predatory natural enemies and reduced reliance on chemical pesticides, although aphid populations were not monitored and possible spillover into wheat fields was inferred indirectly from spatial and temporal abundance patterns. © 2026 Society of Chemical Industry.

Keywords: early‐spring recovery, non‐crop habitats, predatory natural enemies, wheat fields

The early‐spring population dynamics of predatory insects varied among non‐crop habitats.: syrphid flies ( Eupeodes corollae) was highest in woodland, lacewings ( Chrysoperla sinica) was highest in grassland, and coccinellids ( Propylaea japonica and Harmonia axyridis ) were highest in villages. Together, these habitats provide overwintering sites, dispersal corridors, and preventive suppression.

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1. INTRODUCTION

Natural enemies play a vital role in biological control of insect pests in agricultural ecosystems. 1 , 2 Among them, predatory insects represent the dominant group, characterized by high species diversity and wide distribution, and are capable of exerting strong regulatory pressure on pest populations. 3 , 4 , 5 In farmland systems such as wheat fields, predatory natural enemies contribute more than 50% of pest suppression. 6 , 7 Within this ‘crop–natural enemy–pest’ interaction system, the complex and dynamic relationships between natural enemies and pests are jointly shaped by multiple factors, including farmland structure, crop phenology, agricultural management practices, and pest density. 8 , 9 Among these, overwintering and spring emergence are two critical spatiotemporal stages in the life cycle of predatory natural enemies, strongly influencing population recovery, subsequent field colonization, and pest control potential. 10 , 11 Consequently, non‐crop habitats can function as food sources and overwintering refuges, and may also facilitate early‐spring spillover of predatory natural enemies into adjacent croplands.

As important ecological units within agricultural landscapes, non‐crop habitats typically include woodlands, shrubs, hedgerows, field margins, fallows, grasslands, roadside vegetation, and ditches. Owing to their relatively low levels of agricultural disturbance, these habitats provide predatory natural enemies with abundant resources 12 , 13 , 14 , 15 such as pollen, nectar, alternative prey, nesting materials, and overwintering refuges. 13 , 14 For instance, Harmonia axyridis and Propylaea japonica commonly overwinter in rock crevices or leaf litter, 16 whereas Eupeodes corollae and Chrysoperla sinica often overwinter as adults or eggs within bark crevices. 17 , 18 , 19 With the onset of warmer temperatures, predatory natural enemies are expected to become active and may spill over from surrounding non‐crop habitats into croplands, where they can contribute to early‐season pest suppression. 20 Such seasonal habitat complementarity is particularly pronounced in the winter wheat–summer maize rotation systems of northern China. 21

In recent years, several theories and hypotheses have been proposed regarding the role of non‐crop habitats in agricultural ecosystems. 22 The enemy hypothesis suggests that diverse vegetation structures provide more food and shelter resources, 23 thereby increasing the diversity and abundance of predatory natural enemies and enhancing their capacity for pest suppression. The associational resistance hypothesis emphasizes that when diverse plant communities occur around crops, 24 , 25 herbivores experience greater difficulty in host location, leading to reduced crop damage. A growing body of empirical evidence further demonstrates that non‐crop habitats not only increase predatory natural enemy abundance but also improve the stability and temporal reliability of pest control services. 15

In the North China wheat region, the greenbug Schizaphis graminum, the bird cherry‐oat aphid Rhopalosiphum padi, and the English grain aphid Sitobion avenae are the most serious pests threatening wheat production. 26 , 27 , 28 Although chemical control using neonicotinoid insecticides such as imidacloprid and thiamethoxam can suppress aphid populations in the short term, long‐term reliance has led to the ‘3R’ problems of resistance, resurgence, and residues. Moreover, the widespread use of pesticides exerts lethal and repellent effects on non‐target predatory natural enemies, disrupting the ecological balance between farmland and surrounding non‐crop habitats. 28 , 29 By contrast, continuous networks of non‐crop habitats—comprising villages, grasslands, and woodland edges—can function both as overwintering sources for predatory natural enemies and as ecological corridors that may facilitate early‐spring spillover into adjacent wheat fields. 30 Such networks facilitate complementary control by different predatory natural enemy guilds across temporal windows, thereby enhancing the effectiveness and resilience of pest regulation. 4 However, despite the widely recognized importance of non‐crop habitats, quantitative assessments remain limited in the North China wheat region regarding how different non‐crop habitat types supply predatory natural enemies during the early‐spring period before pest build‐up and how these supply patterns vary over time. We therefore proposed two hypotheses: predatory natural‐enemy abundance differs significantly among habitat types, with structurally more complex natural habitats (woodland and grassland) supporting higher abundance than village and uncultivated land; and predatory natural‐enemy abundance changes significantly as the season progresses, reflecting post‐winter population recovery and spatial–temporal patterns potentially consistent with inferred spillover from non‐crop habitats toward adjacent wheat fields.

In this study, we quantified early‐spring abundance dynamics of four predatory natural enemies—syrphid flies (E. corollae), coccinellids (P. japonica and H. axyridis), and lacewings (C. sinica)—across four non‐crop habitats adjacent to winter wheat fields (woodland, grassland, village, and uncultivated land). Specifically, we aimed to: (i) compare the supply potential of different habitats for predatory natural enemies in early spring and identify key habitats that provide the strongest support; and (ii) characterize early‐spring temporal dynamics (mid‐April to early May) to infer likely spillover toward adjacent wheat fields based on spatial and temporal abundance patterns. Our findings provide a basis for landscape‐scale habitat optimization to support early‐season predator conservation and to reduce reliance on insecticides, although direct effects on aphid suppression require further validation.

2. MATERIALS AND METHODS

2.1. Experimental materials and design

This study was conducted during 2024–2025 in an ecological pest management demonstration zone of approximately 667 ha (10 000 mu) in Liutuan Town, Changyi City, Shandong Province, China (119°25′ E, 37°01′ N), a typical winter wheat–summer maize rotation region (Supporting Information, Fig. S1). Four types of non‐crop habitats adjacent to wheat fields were selected as study habitats: an elm woodland (hereafter ‘woodland’), a purple alfalfa (Medicago sativa L.) grassland (‘grassland’), a village vegetation zone (‘village’), and an abandoned uncultivated land (‘uncultivated land’) (Fig. 1).

Figure 1.

Figure 1

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Diagram of the four non‐crop habitat types and the corresponding sampling sites (woodland, grassland, village, and uncultivated land).

Sampling points were established at the boundary between wheat fields and adjacent non‐crop habitats. Specifically, on the wheat field side, traps were placed at 0, 10, 20, and 30 m from the field edge, while on the non‐crop habitat side, traps were placed at 2, 5, 10, 20, and 50 m from the edge. Distances were measured perpendicularly from the wheat–habitat boundary line. At each sampling point, a 1.5 m bamboo pole was erected, and a white sticky trap (26 × 20 cm) was suspended 20 cm below the top of the pole, with the trap surface oriented toward the prevailing wind direction, which was northwest according to local meteorological records.

Each non‐crop habitat type had four replicates (n = 4). All four replicates were established along the interface between a wheat field and the adjacent non‐crop habitat, with each replicate corresponding to a spatially separated segment of the wheat–non‐crop interface. The spacing among replicates was sufficient to minimize potential interference, and each wheat field bordered only one type of non‐crop habitat. This layout was kept consistent over two consecutive years, with the same set of interface segments monitored in both years. Natural enemies captured on sticky traps were recorded every 3–4 days. To ensure data accuracy and comparability, traps were replaced after each survey to avoid bias from adhesive saturation or insect residues. The experiment was conducted over two consecutive years, with eight surveys each year, carried out on April 11, 14, 17, 21, 24, and 27, as well as on May 1 and 6. Here, each replicate was treated as an independent spatial sampling unit within each habitat type. Within each replicate, traps placed at multiple distances were considered subsamples rather than independent replicates. This sampling design was intended to quantify abundance gradients and temporal dynamics across the habitat–wheat boundary rather than to directly track individual movement direction or dispersal.

2.2. Data analysis

Natural‐enemy abundance was analyzed as count data. To minimize pseudoreplication, for each taxon and survey date we summed counts across all traps within the same replicate (including both wheat‐field and non‐crop sides of the boundary), yielding one replicate‐level observation per date for analysis. Sampling year and survey date were combined into a single temporal factor with 16 levels (2 years × 8 surveys) and included as a fixed effect. Negative binomial generalized linear models (GLMs) with a log link function were employed to evaluate the effects of three fixed effects: habitat type (4 levels), time (16 levels), and their interaction (habitat × survey date) on the abundance of each predatory natural‐enemy taxon. Pairwise comparisons among habitat types were conducted based on estimated marginal means (EMMs), with Bonferroni adjustment applied for multiple comparisons. Model adequacy was evaluated by inspecting residual patterns and dispersion statistics and by comparing the observed proportion of zeros at the earliest survey dates with that expected under the fitted negative binomial models; negative binomial models were used to accommodate overdispersion and early‐season zeros (i.e., potential zero inflation at the earliest survey dates). Based on these diagnostics, we judged that an explicit zero‐inflated model was not required, and we therefore retained the negative binomial GLMs for parsimony. All statistical analyses were performed in SPSS 27.0 (IBM, Armonk, NY, USA), and figures were generated using Origin 2024 (OriginLab, Northampton, MA, USA).

3. RESULTS

3.1. Abundance dynamics of predators across habitat types and survey dates

For all four taxa, habitat type, survey date, and their interaction had significant effects on abundance (Table 1); below we describe taxon‐specific temporal patterns and peak timing.

Table 1.

Effects of habitat type, time (survey date), and their interaction on the abundance of four predator taxa (negative binomial GLMs)

Taxon Factor df χ 2 P
Eupeodes corollae Habitat 3 289.352 <0.001
Time 15 1120.459 <0.001
Habitat × Time 45 523.381 <0.001
Propylaea japonica Habitat 3 176.469 <0.001
Time 15 185.882 <0.001
Habitat × Time 45 275.502 <0.001
Harmonia axyridis Habitat 3 248.198 <0.001
Time 15 598.697 <0.001
Habitat × Time 45 628.518 <0.001
Chrysoperla sinica Habitat 3 414.277 <0.001
Time 15 329.992 <0.001
Habitat × Time 45 440.427 <0.001
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Note: Degree of freedom (df), Wald χ2, and P‐values are presented in the table. Explanatory variables with P < 0.05 (shown in bold) were retained in the final model.

3.1.1. Eupeodes corollae

The abundance of E. corollae differed significantly among habitat types (df = 3, χ2 = 289.352, P < 0.001) (Table 1). Abundance also varied significantly among survey dates (df = 15, χ2 = 1120.459, P < 0.001) (Table 1) and increased markedly over the survey period, with the largest increases occurring between 24 April and 1 May in both years (Fig. 2). The interaction between survey date and habitat type was significant (df = 45, χ2 = 523.381, P < 0.001) (Table 1), indicating that habitat differences changed over time.

Figure 2.

Figure 2

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Abundance dynamics of Eupeodes corollae in different non‐crop habitats in 2024 (A) and 2025 (B). Data are means ± SE.

3.1.2. Propylaea japonica

The abundance of P. japonica differed significantly among habitat types (df = 3, χ2 = 176.469, P < 0.001) (Table 1). Abundance varied significantly among survey dates (df = 15, χ2 = 185.882, P < 0.001) (Table 1). From 14 April to 21 April, abundance increased markedly, followed by a decline later in the survey period (Fig. 3). The interaction between survey date and habitat type was significant (df = 45, χ2 = 275.502, P < 0.001) (Table 1), indicating that the timing of abundance peaks varied among habitats.

Figure 3.

Figure 3

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Abundance dynamics of Propylaea japonica in different non‐crop habitats in 2024 (A) and 2025 (B). Data are means ± SE.

3.1.3. Harmonia axyridis

The abundance of H. axyridis differed significantly among habitat types (df = 3, χ2 = 248.198, P < 0.001) (Table 1). Abundance also varied significantly among survey dates (df = 15, χ2 = 598.697, P < 0.001) (Table 1). Two abundance peaks were observed, with the first occurring around 14 April; a secondary rise occurred later in the survey period, and its timing differed among habitats (Fig. 4). The interaction between survey date and habitat type was significant (df = 45, χ2 = 628.518, P < 0.001) (Table 1), indicating that both temporal and habitat‐related factors jointly influenced the dynamics of H. axyridis.

Figure 4.

Figure 4

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Abundance dynamics of Harmonia axyridis in different non‐crop habitats in 2024 (A) and 2025 (B). Data are means ± SE.

3.1.4. Chrysoperla sinica

The abundance of C. sinica differed significantly among habitat types (df = 3, χ2 = 414.277, P < 0.001) (Table 1). Abundance varied significantly among survey dates (df = 15, χ2 = 329.992, P < 0.001) (Table 1). Abundance showed habitat‐specific temporal patterns (Fig. 5), with a pronounced early‐season peak in woodland around 14 April followed by a rapid decline, whereas grassland exhibited a more gradual increase to a later peak (17 April) and remained relatively higher toward early May. The interaction between survey date and habitat type was significant (df = 45, χ2 = 440.427, P < 0.001) (Table 1), indicating that both temporal variation and habitat type jointly influenced the dynamics.

Figure 5.

Figure 5

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Abundance dynamics of Chrysoperla sinica in different non‐crop habitats in 2024 (A) and 2025 (B). Data are means ± SE.

3.2. Comparison of the abundance of various natural enemies across different non‐crop habitats

Overall patterns were consistent between 2024 and 2025 (Fig. 6). E. corollae was most abundant in woodland, and to a lesser extent in grassland and uncultivated land, with the highest abundance occurring in woodlands. Woodland abundances were significantly higher than those in village (P < 0.05) and uncultivated land (P < 0.05), and the overall mean abundance in woodlands was 10.36‐fold higher than in village (Fig. 6). In contrast, coccinellids peaked in village: both P. japonica and H. axyridis were significantly more abundant in village than in uncultivated land (P < 0.05), with overall mean abundances 4.14‐fold (P. japonica) and 5.32‐fold (H. axyridis) higher, respectively (Fig. 6). C. sinica was most abundant in grasslands, which supported significantly higher abundance than village (P < 0.05) and uncultivated land (P < 0.05); the overall mean abundance in grasslands was 7.72‐fold higher than in uncultivated land (Fig. 6). Across groups, uncultivated land generally supported the lowest abundances, indicating limited habitat‐support capacity (Fig. 6).

Figure 6.

Figure 6

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Abundance of four predator taxa across non‐crop habitat types in 2024 (A) and 2025 (B). Differences among habitats were tested using negative binomial GLMs followed by EMM‐based pairwise comparisons with Bonferroni adjustment; significant pairwise differences are described in the text.

4. DISCUSSION

This study shows that both habitat type and survey date jointly shaped habitat‐specific early‐season abundance dynamics of multiple groups of predatory natural enemies across the non‐crop habitat–wheat field interface in a winter wheat system. Woodlands and grasslands generally supported higher early‐season abundance than village and uncultivated land, although responses differed among groups, with coccinellids peaking in village habitats. These patterns are consistent with previous studies identifying non‐crop habitats as important refuges for predatory natural enemies 31 , 32 , 33 and suggest functional differentiation among adjacent habitat types during the early recovery period. For example, woodland may favor E. corollae, grassland may better support C. sinica, and village habitats may provide complementary refuges for coccinellids. In villages, both vegetated areas and artificial structures such as building eaves and wall crevices may contribute to overwintering or early‐season persistence, although these explanations remain hypothetical because vegetation composition and architectural features were not quantified. By contrast, uncultivated land appeared to provide relatively limited support for predatory natural enemies. 29 , 34 More broadly, resident predators such as spiders may also respond differently to non‐crop habitat structure than more mobile flying predators. In particular, grass margins and other structurally complex habitats may provide important refuges, overwintering sites, and hunting substrates for spiders, thereby complementing the roles of flying predatory insects in early‐season pest regulation. 11 , 23 , 35

The abundance of predatory natural enemies varied across survey dates, suggesting that early‐spring recovery may be associated with wheat phenology and shifting resource availability. Greater early‐season availability supported by surrounding non‐crop habitats may therefore contribute to preventive aphid management potential in wheat systems. 21 However, because aphid dynamics were not monitored concurrently, synchrony between peaks of predatory natural enemies and aphid outbreaks cannot be inferred, and actual pest suppression could not be directly evaluated. Differences in overwintering stage and shelter use among syrphids, lacewings, and coccinellids may partly explain the habitat‐specific patterns observed here. We therefore hypothesize that woodlands and grasslands may better support some predatory natural enemies that overwinter in diapause or concealed microhabitats, whereas village vegetation and artificial structures may provide complementary support for aggregating overwintering coccinellids. These hypotheses require further validation through direct sampling during the overwintering period and explicit evidence of habitat use.

Our results further highlight group‐specific differences among predatory natural enemies in habitat‐associated abundance dynamics during the early‐spring recovery period, 36 but these patterns should be interpreted as indirect evidence compatible with spillover rather than as direct evidence of movement from non‐crop habitats into wheat fields. Because this study did not include direct movement‐tracking methods, any inference about movement of predatory natural enemies into wheat fields should be interpreted cautiously. Future studies integrating mark–recapture approaches or molecular analyses 4 , 7 would be valuable for confirming the physical flow of individuals across the habitat–wheat interface and for strengthening inference about spillover. The observed abundance patterns may also reflect alternative, non‐exclusive processes, such as differences in overwintering survival, 11 , 16 , 37 , 38 emergence timing, 17 , 39 or local microclimatic filtering, 11 , 37 , 40 rather than solely reflecting spillover from non‐crop habitats into wheat fields.

In addition to natural habitats, anthropogenic habitats may also play a complementary role in agricultural landscapes. 41 Habitat planning and management, including ecological islands and corridors, 42 may enhance the foraging, reproduction, sheltering, and overwintering capacity of predatory natural enemies and thereby improve ecological pest management. For winter wheat systems, management should prioritize conserving woodland and grassland edges, maintaining village‐edge vegetation where feasible, and minimizing broad‐spectrum insecticide use during the early‐season recovery period of predatory natural enemies. 35 , 43 , 44 , 45

Although our study was conducted in the North China winter wheat–summer maize rotation system, the broader relevance of these findings to other temperate agroecosystems should be interpreted cautiously. Comparative studies from other temperate regions, particularly European arable systems, have likewise shown that adjacent non‐crop habitats may support predatory natural enemies and contribute to biological pest regulation, 35 , 42 , 45 , 46 However, the magnitude, timing, and taxon‐specific expression of these effects are likely to vary with local climate, crop phenology, habitat composition, and management intensity. Therefore, habitat‐based recommendations should be adapted to local conditions rather than directly extrapolated across systems. Overall, our findings support habitat‐based early‐season management as a practical pathway to strengthen aphid management potential in winter wheat while potentially reducing reliance on chemical control. For agricultural managers, this means conserving village‐edge vegetation, grass margins, and woodland edges as key non‐crop habitats within conservation biological control programs. 47 , 48 , 49

FUNDING INFORMATION

This research was supported by the National Key R&D Program of China (2023YFD1400800), Introducing Top Talent Program of Shandong (2023YSYY‐006), Joint Funds of the National Natural Science Foundation of China (U24A20412), Agricultural Scientific and Technological Innovation Project of Shandong Academy of Agricultural Sciences (CXGC2025F05), Agricultural Scientific and Technological Innovation Project of Shandong Academy of Agricultural Sciences (CXGC2025C05) and the Young Talent of Lifting engineering for Science and Technology in Shandong, China (SDAST2025QTB037).

CONFLICT OF INTEREST

The authors declare that they have no conflict of interest.

ETHICS STATEMENT

This article does not include any studies involving human participants or animals performed by any of the authors.

Supporting information

Figure S1. Satellite image showing the four non‐crop habitat types (woodland, grassland, village, and uncultivated land) and the adjacent wheat fields. The red lines indicate the four replicate wheat–non‐crop habitat interface segments for each habitat type.

PS-82-8181-s001.png (1MB, png)

ACKNOWLEDGEMENTS

The authors thank the field technicians and local farmers for access to sampling sites and assistance with sampling.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1. Satellite image showing the four non‐crop habitat types (woodland, grassland, village, and uncultivated land) and the adjacent wheat fields. The red lines indicate the four replicate wheat–non‐crop habitat interface segments for each habitat type.

PS-82-8181-s001.png (1MB, png)

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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