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Article

The Effect of Artificial Field Margins on Epigeic Arthropod Functional Groups within Adjacent Arable Land of Northeast China

by
Chuqiao Wang
1,2,
Zhenxing Bian
1,2,*,
Shuai Wang
1,3,
Xiaochen Liu
1,2 and
Yufei Zhang
1,2
1
College of Land and Environment, Shenyang Agricultural University, Shenyang 110161, China
2
Key Laboratory of Trinity Protection and Monitoring of Cultivated Land, Shenyang 110161, China
3
Institute of Bio- and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
*
Author to whom correspondence should be addressed.
Land 2022, 11(11), 1910; https://doi.org/10.3390/land11111910
Submission received: 22 September 2022 / Revised: 21 October 2022 / Accepted: 25 October 2022 / Published: 27 October 2022
(This article belongs to the Special Issue Arable Land System Resilience and Sustainable Use-Ways and Methods)
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Abstract

:
Providing food security to meet the growing human demand while improving the biodiversity of arable land is a global challenge. Although semi-natural field margins are known to enhance biodiversity in arable land systems globally, the role that abundant artificial field margins play in maintaining epigeic arthropod diversity within arable land remains unclear. Here, we compared epigeic arthropods within adjacent arable land with an artificial field margin (paved and dirt roads) and a semi-natural field margin (ditch, woodland, or grassland), as well as vegetation community characteristics at a field scale for identifying the ecological effects of different field margin types. Our results indicated the following: (i) Compared with semi-natural field margins, there is less epigeic arthropod diversity and less stable ecological networks within adjacent arable land with artificial field margins, with more herbivores within adjacent arable land with artificial field margins and more natural enemies within adjacent arable land with semi-natural field margins. (ii) Arable land adjacent to a dirt road (DR) maintained more resilient ecological networks than that adjacent to a paved road (PR), and there are more flowering plants at DRs, which attracts natural enemies, whereas Orthoptera is more active at PRs with abundant weeds. (iii) The main factors affecting epigeic arthropod functional groups were the tree layer cover (TC), herb layer abundance (HA), and herb layer height (HH) of the artificial and semi-natural field margins. We concluded that increasing the number of flowering plants and removing noxious weeds can eliminate negative effects on epigeic arthropod functional groups within adjacent arable land with artificial field margins. Delineating a certain percentage of vegetation strips to be a buffer zone in artificial field margins or creating a suitable vegetation community in semi-natural field margins can maintain and protect natural enemies and strengthen the ecological network stability between functional groups.

1. Introduction

The global population has increased significantly over the past few decades, and global food demand is projected to increase by at least 70% by 2050 [1,2]. Agricultural intensification and expansion are the main methods of increasing yields at local and landscape scales, which could meet the growing food demand globally. However, it is commonly accepted that habitat loss and landscape fragmentation caused by agricultural intensification are critical factors in the loss of internal biodiversity within arable land and the simplification of community structures [3,4]. To combat such biodiversity and corresponding ecosystem service declines, agri-environmental schemes (AESs) support the creation of semi-natural habitats, which can benefit many arthropod species that inhabit agroecosystems [5,6,7]. The European Union’s common agriculture policy (CAP) encourages farmers to set aside a certain proportion of their land or to provide landscape elements such as field margins, wildflower strips, and hedgerows [8]. In recent years, on the basis of fostering a community of life for man and nature, Biodiversity Conservation in China (BCC) has proposed incorporating biodiversity into the evaluation elements of arable land resources and to take in situ conservation measures for species with small natural communities and weak migration capabilities.
Epigeic arthropodscan act as the disseminators of energy and matter in agroecosystems [9], and because they are sensitive to environmental changes, they are often used as indicators for landscape change and biodiversity research [10]. The biological control service against pests is largely delivered by naturally occurring beneficial predatory or parasitic arthropods and is considered one of the most important regulation services supplied by biodiversity [11]. Recent studies have pointed out that linear structures within the agricultural patches (flower-rich field margins and hedgerows) may provide the most beneficial resources, overwintering habitats, and nesting materials for predators and influence epigeic arthropod communities in ecosystems [12,13,14,15]. For instance, wildflower-sown islands within arable fields and grassy field margins promote spider diversity [16,17]. The effect of habitat structure and neighbor linear features on carabid diversity in olive groves was more significant than in the farming system [18]. In addition to woody plants, field margins combining temporary wildflower habitats with permanent semi-natural habitats can improve epigeic arthropod diversity within the adjacent arable land [19]. In fact, in order to improve pest control and ecosystem stability, it is necessary to pay attention not only to natural enemy diversity but also to the ecological network relationship and co-occurrence among species [20]. Arthropods are typically classified into different trophic guilds: insect herbivores, natural enemies (predators and parasitoids), and neutrals (decomposers). In ecosystems, species are embedded in complex ecological networks and have interactions with many other species; analyzing these networks provides a better understanding of ecosystem functions, including pest control [21]. Based on epigeic arthropod functional groups, an ecological network was constructed to quantify the interactions between species and understand their role in different ecological processes [22], such as competition, antagonism, and symbiosis between natural enemies and insect herbivores. For instance, compared to the monoculture tea plantations, forest tea plantations had higher co-occurrences among natural enemies and insect herbivores, which can enhance insect herbivore control by increasing the diversity of natural enemies and the network structure between natural enemies and herbivores [23]. For this reason, to understand pest control in agricultural landscapes, it is necessary to clarify epigeic arthropod functional groups and ecological networks within adjacent arable land with artificial and semi-natural field margins.
Generally, typical agricultural landscapes are composed of elements such as arable land, artificial infrastructure, and natural or semi-natural habitats [24]. Among them, linear, semi-natural habitats are regarded as field margins, as they have a unique corridor function that can link different patches and habitats to promote the spread and migration of species [25]. It is commonly believed that epigeic arthropod communities may exhibit different ecological processes in different field margins [26]. For example, semi-natural habitats with high vegetation diversities are available shelters for natural enemies to overwinter when there are no crops in the adjacent arable fields [27,28]. Ditches are appropriate habitats for most orthopterans: even for rare and endangered species [29]. Artificial field margins are common in China and are beneficial for agricultural production activities, mainly including asphalt and soil roads. Artificial field margins decoupled from field management (fertilization, pesticides, etc.) are considered to harbor weeds, pests, and diseases and have negative effects on epigeic arthropods [30]. For example, some studies consider road transportation infrastructure as a corridor for the spread of exotic species, which has a significant impact on the surrounding landscape and poses a threat to biodiversity [31]. In addition, changing the environmental conditions in the areas adjacent to roads may cause changes in the species composition at road margins [32]. Nevertheless, on this basis, the effect of artificial margins on epigeic arthropod diversity is still debated. While most studies agree that landscape heterogeneity and non-crop habitats have a positive effect on epigeic arthropods [33,34], the role played by different field margin types is still under discussion [35,36].
Here, we compare epigeic arthropod functional groups and ecological networks within adjacent arable land with artificial field margins and semi-natural field margins to explore the relationships between vegetation community characteristics and epigeic arthropods. We attempted to address the following questions: (1) What are the differences in epigeic arthropod functional groups and ecological networks within adjacent arable land with artificial and semi-natural field margins? (2) What is the role of vegetation communities in field margins on epigeic arthropods within adjacent arable land?

2. Materials and Methods

2.1. Study Area

This study was conducted in Changtu county with a typical agricultural landscape (42°33′~43°29′ N, 123°32′~124°26′ E, altitude: 66–509 m), which is located in the Songliao Plain in Northeastern China. The study area is a mid-temperate continental monsoon climate with an average annual rainfall of 655 mm in the study year. It is a typical dry-farming area in the northern plain. The annual average temperature was 7 °C during the sampling year, with a range from −33 °C to 36 °C. Changtu has 2588 km2 of corn arable area, accounting for about 56% of the total arable land [37].
Considering the structural characteristics of typical field margins, we used five categories: grassland (grassy margins bordering corn fields, unmanaged fallows with diverse and near-natural vegetation), woodland (forest margins bordering corn fields and linear shelterbelts), and ditches (irrigation canals and ditches bordering corn fields, used for drainage irrigation) as semi-natural field margins, dirt roads (soil hardened roads bordering corn fields and woodland), and paved roads (cement or asphalt hardened roads bordering corn fields and woodland) as artificial field margins (Table 1; Figure 1). For each field margin type, we randomly selected 4 sample sites in 2 relatively regular corn fields (size: 500 m × 500 m) in the south of Changtu (Figure 2). The distance among 10 corn fields ranged from 0.57 to 2.02 km, with a mean distance of 1.05 km. In terms of agricultural treatments, all the selected annual crops were conventional tillage, and the management measures were basically the same. Sampling sites were generated using ArcGIS 10.2 and the land use database of 2019.

2.2. Biodiversity Sampling

In the autumn of 2021, we selected 10 corn fields based on 5 types of field margins. Epigeic arthropod diversity sampling (Figure 3): for each type of field margin, we selected 4 sampling sites, where 2 sampling sites in each sampling area (corn field) were more than 50 m apart to ensure that the data held up to statistically significant inspection standards. For field experiments, the interval between transects was 50 m, which exceeded the general range of epigeic arthropods [38]. According to previous research results in Changtu County, the radius of epigeic arthropod activity does not exceed 40–50 m [10,37], so it is considered that two groups of independent transects can be used. We set each strip transect consisting of four gradient sampling points at 0, 15, 30, and 45 m from the field margin. For each sample point, we arranged 3 pitfall traps to prevent a single pitfall trap from being easily affected by the interference of external factors, such as weather and human activities, which would affect the integrity of the data [10,37]. Each point included three pitfall traps made of plastic cups (bottom diameter: 4.5 cm; top diameter: 9.5 cm; height: 11 cm), placed into the ground with the rim level at the soil surface, filled with 60 mL of an attractant solution (1:2 ethylene glycol-water mixture with 3% detergent), and arranged at least 10 m from each other [39]. These traps were left in the field for 6 days, after which the epigeic arthropods in the trap bottles were collected into pre-coded polyethylene bottles. After collection, all samples were placed in 70% ethanol until further analysis. Vegetation diversity sampling: three quadrats sampling plots (1 m × 1 m) of herb vegetation were randomly arranged around the trap of the field margin in the sampling areas. The name, number of plants (clumps), herb layer abundance (HA), and herb layer height (HH) of the vegetation in the plots were recorded. In addition, the tree layer cover (TC) within each field margin was measured. All plants were identified to the species level, and all species information in the three quadrats was merged.

2.3. Analyses

To analyze epigeic arthropod diversity, we merged three trap data into one sample point data and calculated the species richness (S), abundance (N), the Shannon–Wiener index (H), and the Pielou index (P) as species diversity and evenness using PAST software (Hammer et al., 2001). Data were tested for normality using the Shapiro–Wilk tests [40] and were assessed for variance heterogeneity by the Bartlett test [41]. Epigeic arthropod diversity indexes passed the tests. Thus, they were analyzed by using a one-way ANOVA or epigeic arthropod diversity within the adjacent arable land with different field margin types in SPSS 20.0 and Origin 2021 software program [42]. The two indexes were computed as follows:
H = − ∑ P i   ln P i
P = H / ln S  
where H is the Shannon–Wiener index, P is the Pielou index, Pi is the ratio of the number of individuals of the ith species to the total number of individuals, S is the total number of species, and N is the total number of individuals of all species abundance.
We used ANOSIM analysis and non-metric multidimensional scaling (NMDS) with Bray–Curtis dissimilarity distances to assess the epigeic arthropod functional group community composition in the adjacent arable land with different field margin types [43,44]. We selected the first two-dimensional solution because it consistently maintained low stress (<0.2) across multiple runs. Non-metric multidimensional scaling was performed using the vegan package in R software [45]. The Bray–Curtis was computed as follows [45]:
D B r a y − C u r t i s = 1 − 2 ∑ min ( S A , i , S B , i ) / ∑ S A , i + ∑ S B , i
where S (A, i) and S (B, i) represent the counts of the ith species in communities A and B, and min refers to the minimum value of S (A, i) and S (B, i).
Ecological network analysis was carried out on each field margin to reveal the co-occurrence relationship among natural enemies, insect herbivores, and neutrals. We calculated all possible pairwise Spearman’s rank correlations (r) between the functional groups and drew the connection line in the ecological network map according to the value of the correlation coefficient. We evaluated ecological network nodes and edges and the parameters of the ecological network topology, which included the effect size (the number of edges), the average degree (the average number of links connecting one node to any other nodes), the average path length (the average shortest path between all nodes), the modularity index (interlinked subsets of species), and the clustering coefficient (proportion of nodes that can be reached through the other neighboring nodes) (Newman, 2003) to generate the ecological network relationship of epigeic arthropod groups using the “corr.test” functions in psych packages [46] and “plot” functions in igraph packages [47] in the R software. Network visualization was generated using Gephi [48].
Detrended correspondence analysis (DCA) showed that the ordering of species communities was linear (the gradient length of the ordering axis was 1.8 < 3). Therefore, we used redundancy analysis (RDA) [49,50] to examine the relationships between epigeic arthropod community compositions and vegetation characteristics. To simplify the analysis, we used 12 species (Teleogryllus emma, Loxoblemmus doenitzi, Phalangodidae, Camponotus japonicus, Formica japonica, Doliohus halensis, Amara macronota, Amara brevicollis, Chlaenius micans, Pheropsophus occiptalis, Prospirobolus joannsi, and Harpalus calceatus that together consisted of 93.56% of the total number of arthropods across the sampling sites) in the analysis. We used Hellinger transformation count data to satisfy the data independence test. Redundancy analyses were performed using the “Envfit” functions in vegan and ggplot2 packages in R software [45].

3. Results

3.1. Composition of Arthropods and Plant Communities

In total, 8074 epigeic arthropods comprising 52 species belonging to 26 families were collected (Table S1). For subsequent analysis, according to diet, we divided the arthropod species into natural enemies, insect herbivores, and neutrals. We found that the dominant insect herbivores (relative abundance accounted for at least 10% of the overall abundance) were Teleogryllus emma (18.50%) and Loxoblemmus doenitzi (43.31%); the dominant natural enemies (the relative abundance that accounted for at least 2% of the overall abundance) were Doliohus halensis (2.97%), Amara brevicollis (15.06%), Chlaenius micans (2.32%), and Phalangodidae (2.96%). The dominant neutrals of Formicidae were Camponotus japonicus (3.98%) and Formica japonica (2.01%).
In terms of herb vegetation diversity, we collected 2239 herb plants comprising 31 species belonging to 4 classes, 18 orders, and 19 families (Table S2). Poaceae Barnhart was mainly collected in this study, accounting for 57% of the total Asteraceae and Polygonaceae, accounting for 13% and 9% of the total, respectively. Cyperaceae, Commelinaceae, Moraceae, Asclepiadaceae, and five other families formed a common group. The remaining six families formed a rare group.

3.2. The Epigeic Arthropod Diversity within Adjacent Arable Land with Artificial and Semi-Natural Field Margins

The one-way ANOVA indicated that the abundance of epigeic arthropods within the adjacent arable land with DRs and PRs was significantly lower than that within the adjacent arable land with grassland (GL) (F = 2.631, p = 0.017; F = 3.942, p = 0.028) and woodland (WL) (F = 3.468, p = 0.026; F = 4.702, p = 0.037), and the species richness of epigeic arthropods was greater within the adjacent arable land with GL relative to the adjacent arable land with paved roads (PR) (F = 0.868, p = 0.048) (Figure 4A). The Shannon–Wiener index within adjacent arable land with dirt roads (DR) was significantly higher than that within the adjacent arable land with PRs and irrigation canals and ditches (CD) (F = 2.877, p = 0.028; F = 2.183, p = 0.034). However, no significant difference was found among adjacent arable land with artificial and semi-natural margins for species evenness (Figure 4B).

3.3. The Compositions and Ecological Network of Epigeic Arthropod Functional Groups within Adjacent Arable Land with Artificial and Semi-Natural Field Margins

We found differences in all the epigeic arthropods (the analysis of similarities (ANOSIM) R = 0.639, p < 0.001), natural enemies (ANOSIM R = 0.581, p = 0.03), and insect herbivores (ANOSIM R = 0.543, p < 0.001) within the arable land adjacent with the artificial and semi-natural field margins, but not in neutrals (ANOSIM R = 0.058, p = 0.763). The results of the non-metric multidimensional scaling ordinations (NMDS) analysis revealed that the compositions of all the epigeic arthropods within the arable land adjacent to PRs and DRs only partially overlapped with other field margins (Figure 5). Notably, the differences in the epigeic arthropod community composition between the PRs and other field margins are mainly reflected in insect herbivores, and the differences between DRs and other field margins are reflected in natural enemies. We did not find a significant difference among different arable lands adjacent to all field margin types for neutrals.
In general, compared to the randomized network, the artificial and semi-natural field margins had higher topological values than the ecological networks in the empirical network, which indicated that the relationship between species was dominated by positive correlations, among which the correlations between natural enemies and herbivores was strongest (Table 2).
In the empirical network (Figure 6), we found that the arable land adjacent to GL, WL, and CD had a stronger and more complex network than the arable land adjacent to the other field margin types due to the high average clustering coefficient, the average degree, and the density. However, the adjacent arable land with PRs had a smaller modularity index, indicating that the network had more sub-networks, which could reduce the occurrence of interactions among the species or environmental factors. The correlation coefficient between the natural enemies and herbivores in the ecological network of DRs, compared with PRs, had stronger positive correlations, in particular.

3.4. Effects of Vegetation Structure at Field Margins on the Distribution of the Epigeic Arthropod Community within Adjacent Arable Land

Monte Carlo test results (Table 3) showed that the first two RDA axes explained 41.26% of the variability in the species data. HH, HA, and TC variables explained 44.8% of the first two axes’ explanations. The RDA biplot (Figure 7) illustrated that the arable land adjacent to the PRs had a significant positive correlation with HH. We found that most natural enemies and Teleogryllus emma (G1) herbivores were positively correlated with HA and TC, mainly within the arable land adjacent to WL, GL, and CD. Formicidae (G5, G6) neutrals and Prospirobolus joannsi (G15) herbivores were positively correlated with HH, mainly in the arable land adjacent to PRs. Meanwhile, the arable land adjacent to DRs maintained a higher number of Loxoblemmus doenitzi (G2) herbivores and Amara brevicollis (G10), Chlaenius micans (G11), and Pheropsophus occiptalis (G13) natural enemies.

4. Discussion

Based on the recognized edge effects [51], we have provided novel insights into the effects of various field margin infrastructures in agroecosystems on epigeic arthropod functional groups within adjacent arable land. Building adjacent non-crop habitats on arable land, as in previous studies [52,53,54], is critical to influencing natural enemy biodiversity and pest control services [55,56]. The findings of the study revealed that the diversity of insect herbivores and natural enemies is the main reason for the differences in ecological networks between artificial and semi-natural field margins. Artificial field margins maintained less biodiversity and a more unstable ecological network compared with semi-natural field margins. Furthermore, we discovered that vegetation heterogeneity has a positive impact on epigeic arthropod communities within adjacent arable land. Planting herbaceous species that are ideal for natural enemy survival on the edge of artificial field margins can maximize the potential for biological control and mitigate the negative effects of artificial field margins on epigeic arthropods.

4.1. Effects of Field Margin Types on Epigeic Arthropod Diversity within Adjacent Arable Land

Within adjacent arable land, different types of field margins have different levels of epigeic arthropod diversity, with artificial field margins (paved roads and dirt roads) having less vegetation heterogeneity and less epigeic arthropod abundance and richness than semi-natural field margins (woodland, grassland, and irrigation canals and ditches). It is thought that artificial field margins make agricultural products easier to transport, operate upon with equipment, and harvest [57]. Artificial roads are typically constructed by removing a large amount of vegetation or crops in both cultivated and uncultivated habitats [58]. The materials used to construct an artificial limit, such as cement or asphalt, not only cause soil compaction but also have an impact on nearby creatures within a few tens of meters [59], obstructing both carabid movement and their spread [60].
While field margins are thought to be effective in improving landscape connectivity by allowing adjacent habitat species to spill over to the edge [61,62], habitat preference responses among epigeic arthropods are common [63]. Semi-natural field margins with rich vegetation species and plant versatility can provide a stable living environment and long-term resource supply for epigeic arthropod communities within adjacent arable land [64,65]. However, artificial field margins with more anthropogenic disturbances and more single vegetation structures will impede the spillover effect of epigeic arthropods with poor migration abilities, reducing the epigeic arthropod diversity in arable land [16].

4.2. The Influence of Artificial Field Margins on Epigeic Arthropod Functional Groups and Ecological Network Traits within Adjacent Arable Land

In the ecological environment, epigeic arthropod functional groups frequently have mutual influences and constraints [66]. Compared with artificial field margins, the species composition in semi-natural field margins is relatively uniform. This could be because dense vegetation provides better insulation than sparse vegetation [67], bringing cooler and wetter conditions to epigeic arthropods [68], and relatively consistent environmental conditions lead to diversity homogenization [69].
Moreover, differences in the composition of epigeic arthropods in the arable land adjacent to artificial and semi-natural field margins were primarily reflected in the natural enemies at DRs and the herbivores at PRs. One possible explanation is that there are more flowering plants (Aster tataricus L. f.) at DRs, which attracts natural enemies (predators and parasitic wasps) with greater mobility and search ranges, whereas Orthoptera is more active at PRs with abundant weeds, leading to different spatial distribution patterns. Neutrals account for a small proportion of the epigeic arthropods within the arable land at all field margin types, but they are still an important part of the epigeic arthropod communities, which can make up for the deficiencies of natural enemies in pest control to a certain extent [70].
Here, we found that the ecological network has stronger robustness in arable lands adjacent to DRs and can maintain original capabilities when disturbed. There is a low co-occurrence rate and an unstable ecological network within arable lands adjacent to PRs. We consider that the symbiotic relationship between natural enemies, insect herbivores, and neutrals can be used to evaluate pest control. On the one hand, this is due to the complex network of predators and herbivores of DRs, such as Shirakiacris shirakii, Traulia orientalis Ramme, and Harpalus rubefactus. On the other hand, the dynamic migration of natural enemies between arable land and non-crop habitats can promote the decomposition process and play a certain role in regulating biological pest control in adjacent arable land [71,72]. However, PRs with higher levels of human disturbance and monocropy are detrimental to the habitat reproduction and migration of epigeic arthropods, and the correlation between species is weak. Furthermore, the differences in network structure might be a result of multiple factors, such as plant communities, predator identity, parasitoids, insect herbivores, and environmental variables [73].

4.3. Effects of Vegetation Characteristics on Epigeic Arthropod Community Distribution within Adjacent Arable Land

We discovered more herbivores within the adjacent arable land with artificial field margins and a greater natural enemy diversity within the adjacent arable land with semi-natural field margins. These results may imply that a higher vegetation density and more diversity overall can benefit epigeic arthropods [74,75] and that semi-natural field margins satisfy the essential elements of overwintering and provide refuge for natural enemies, such as the most common carabid beetles and spiders in our study [8,76], including Phalangodidae, Doliohus halensis, Amara brevicollis, and Funnel weaver. To some extent, artificial field margins will hinder the migration of carabid beetles and spiders, but they are conducive to the survival of some mobile insect herbivores (Teleogryllus emma and Loxoblemmus doenitzi), the main pest affecting agricultural production ofin corn fields of China, which are extremely destructive and may cause harm to adjacent corn fields [77,78].
Setaria viridis (L.) Beauv., Carex L., Echinochloa crusgalli (L.) Beauv. and other common weeds of field margins compete with plants for resources and change the physical environment [79], which is beneficial to the concealment and foraging of neutrals and herbivores, but may hinder the movement and predation of natural enemies to a certain extent [80]. Tree layer cover (TC) and herbage layer abundance (HA) at field margins have a positive effect on the protection of natural enemies, among which traditional flowering plants (e.g., Commelina communis L., Aster tataricus L. f., and Polygonum aviculare L.) in the herb layer have a significant positive effect on carabid beetles and spiders. Consistent with previous studies, areas where crops are combined with flowering plants are favorable for carabid beetles and spider activity [81]. Our results suggest that the vegetation community structure and diversity at field margins can provide additional prey species and serve as habitats and food sources for natural enemies. Vegetation can provide necessary resources suitable for the survival of natural enemies, such as a wider ecological niche, more litter, and root exudates [82,83,84], which are beneficial for invertebrate pest suppression [85].
In the main grain-producing areas of Northern China, the plain area represented by Changtu in the study area undertakes the important task of ensuring regional and even national food security [37]. In order to improve food production and contribute to regional economic development, agricultural production is characterized by the large-scale planting of corn crops to replace the natural state of biodiversity, resulting in a wide range of environmental structures, which tend to be simplified and monolithic.
As an important corridor connecting semi-natural habitats and arable land [86], the creation and management of field margins have the potential to restore habitat diversity for the benefit of associated biodiversity in the arable land [87]. Previous studies showed that natural enemies prefer open habitats (e.g., grassy strips, hedgerows, woodlands, and forest fragments) and tend to move toward the interior of the fields [88,89]. Vegetation plays an important role and is necessary for maintaining biodiversity onsite in agroecosystems (e.g., species reservoirs, dispersal corridors, overwintering, and microclimatic shelters, water retention, and windbreak effects) [90,91,92]. We anticipate that the presence of vegetation communities may mitigate the negative effects of artificial field margins or improve the biological control of herbivores by natural enemies.
In agricultural landscape design and planning, while ensuring the function of field margins, plant characteristics should be reasonably optimized, epigeic arthropod diversity in arable land should be increased, the control of biological pests should be promoted, and the health and stability of agricultural ecosystems should be maintained. Future agricultural landscape management not only needs to focus on the improvement of biodiversity within arable land but also needs to pay attention to the interactions between species [93]. In addition, this study chose to obtain biodiversity data during the crop maturity period (September). In future research, data from different growing seasons of crops could be combined to enhance the broad applicability of the research conclusions. While maintaining epigeic arthropod diversity, it is only by ensuring the stability of the ecological network that we can ensure species have a stronger resilience (the ability to reorganize and recover after being disturbed) and that we can maximize the control effect of natural enemies on pests.

5. Conclusions

In this study, we analyzed epigeic arthropod functional groups and ecological network traits within adjacent arable lands with artificial and semi-natural field margins. We concluded that, compared to semi-natural field margins, artificial field margins maintained less biodiversity and unstable ecological networks. Importantly, the main factors affecting epigeic arthropod functional groups were the tree layer cover (TC), herb layer abundance (HA), and herb layer height (HH) of the field margins. Delineating a certain percentage of vegetation strips in the field margins as a buffer zone or creating a suitable vegetation community while planting more flowering plants and reducing harmful weeds can eliminate negative effects on the epigeic arthropod functional groups within arable lands adjacent to artificial field margins and can improve the ecological network stability in arable lands adjacent to semi-natural field margins. The related methods of this study are universal and can be applied to future research on the conservation of biodiversity by field margins. This study emphasizes the differences between artificial and semi-natural field margins in biodiversity conservation and proposes that a stable network structure and strong co-occurrence among natural enemies, insect herbivores, and neutrals is an important assessment tool for pest control, which is of great significance for agricultural landscapes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land11111910/s1, Table S1. Statistical table of types and numbers of epigeic arthropods. Table S2. Statistical table of herb vegetation. Text S1. Five types of field margin typical characteristics.

Author Contributions

Conceptualization, C.W. and Z.B.; data curation, C.W., S.W. and Y.Z.; formal analysis, X.L.; investigation, C.W. and Z.B.; methodology, C.W.; project administration, Z.B.; software, C.W.; supervision, Z.B.; writing—original draft, C.W.; writing—review and editing, Y.Z., X.L. and Z.B. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Natural Science Foundation of Liaoning Province (2019-ZD-0709).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Acknowledgments

We thank the reviewers for their valuable feedback on the manuscript as well as the team members for their hard work in field sampling.

Conflicts of Interest

The authors declare that they have no conflict of interest.

References

  1. Gaëtane, L.P.; Thiele, J.; Westphal, C.; Penone, C.; Allan, E.; Neyret, M.; van der Plas, F.; Ayasse, M.; Bardgett, R.D.; Birkhofer, K.; et al. Contrasting Responses of Above- and Belowground Diversity to Multiple Components of Land-Use Intensity. Nat. Commun. 2021, 12, 3918. [Google Scholar] [CrossRef]
  2. Izabela, D.; Rodriguez, L.O.; Smallwood, J.M.; Scharlemann, J.P.W.; Alcamo, J.; Antonarakis, A.S.; Rowhani, P.; Hazell, R.J.; Aksnes, D.L.; Balvanera, P.; et al. Actions on Sustainable Food Production and Consumption for the Post-2020 Global Biodiversity Framework. Sci. Adv. 2021, 7, eabc8259. [Google Scholar] [CrossRef]
  3. Zhao, Z.-H.; Hui, C.; He, D.-H.; Li, B.-L. Erratum: Corrigendum: Effects of Agricultural Intensification on Ability of Natural Enemies to Control Aphids. Sci. Rep. 2017, 7, 46806. [Google Scholar] [CrossRef] [Green Version]
  4. Raven, P.H.; Wagner, D.L. Agricultural Intensification and Climate Change Are Rapidly Decreasing Insect Biodiversity. Proc. Natl. Acad. Sci. USA 2021, 118, e2002548117. [Google Scholar] [CrossRef] [PubMed]
  5. Kleijn, D.; Baquero, R.A.; Clough, Y.; Díaz, M.; Esteban, J.; Fernández, F.; Gabriel, D.; Herzog, F.; Holzschuh, A.; Jöhl, R.; et al. Mixed Biodiversity Benefits of Agri-Environment Schemes in Five European Countries: Biodiversity Effects of European Agri-Environment Schemes. Ecol. Lett. 2006, 9, 243–254. [Google Scholar] [CrossRef] [PubMed]
  6. Gayer, C.; Lvei, G.L.; Magura, T.; Dieterich, M.; Batáry, P. Carabid Functional Diversity Is Enhanced by Conventional Flowering Fields, Organic Winter Cereals and Edge Habitats. Agric. Ecosyst. Environ. 2019, 284, 106579. [Google Scholar] [CrossRef] [Green Version]
  7. Batáry, P.; Báldi, A.; Ekroos, J.; Gallé, R.; Grass, I.; Tscharntke, T. Biologia Futura: Landscape Perspectives on Farmland Biodiversity Conservation. Biol. Futur. 2020, 71, 9–18. [Google Scholar] [CrossRef]
  8. Diehl, E.; Mader, V.; Wolters, V.; Birkhofer, K. Management Intensity and Vegetation Complexity Affect Web-Building Spiders and Their Prey. Oecologia 2013, 173, 579–589. [Google Scholar] [CrossRef]
  9. Molina, G.; Poggio, S.; Ghersa, C. Epigeal Arthropod Communities in Intensively Farmed Landscapes: Effects of Land Use Mosaics, Neighbourhood Heterogeneity, and Field Position. Agric. Ecosyst. Environ. 2014, 192, 135–143. [Google Scholar] [CrossRef]
  10. Guo, X.; Bian, Z.; Wang, S.; Wang, Q.; Zhang, Y.; Zhou, J.; Lin, L. Prediction of the Spatial Distribution of Soil Arthropods Using a Random Forest Model: A Case Study in Changtu County, Northeast China. Agric. Ecosyst. Environ. 2020, 292, 106818. [Google Scholar] [CrossRef]
  11. Bale, J.; van Lenteren, J.; Bigler, F. Biological Control and Sustainable Food Production. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 2008, 363, 761–776. [Google Scholar] [CrossRef] [Green Version]
  12. Bengtsson, J.; Ahnström, J.; Weibull, A.-C. The Effects of Organic Agriculture on Biodiversity and Abundance: A Meta-Analysis. J. Appl. Ecol. 2005, 42, 261–269. [Google Scholar] [CrossRef]
  13. Diekötter, T.; Wamser, S.; Wolters, V.; Birkhofer, K. Landscape and Management Effects on Structure and Function of Soil Arthropod Communities in Winter Wheat. Agric. Ecosyst. Environ. 2010, 137, 108–112. [Google Scholar] [CrossRef]
  14. Cardarelli, E.; Bogliani, G. Effects of Grass Management Intensity on Ground Beetle Assemblages in Rice Field Banks. Agric. Ecosyst. Environ. 2014, 195, 120–126. [Google Scholar] [CrossRef]
  15. Shi, X.; Xiao, H.; Luo, S.; Hodgson, J.A.; Bianchi, F.J.J.A.; He, H.; van der Werf, W.; Zou, Y. Can Landscape Level Semi-Natural Habitat Compensate for Pollinator Biodiversity Loss Due to Farmland Consolidation? Agric. Ecosyst. Environ. 2021, 319, 107519. [Google Scholar] [CrossRef]
  16. Plath, E.; Rischen, T.; Mohr, T.; Fischer, K. Biodiversity in Agricultural Landscapes: Grassy Field Margins and Semi-Natural Fragments Both Foster Spider Diversity and Body Size. Agric. Ecosyst. Environ. 2021, 316, 107457. [Google Scholar] [CrossRef]
  17. Rischen, T.; Geisbüsch, K.; Ruppert, D.; Fischer, K. Farmland Biodiversity: Wildflower-Sown Islands within Arable Fields and Grassy Field Margins Both Promote Spider Diversity. J. Insect Conserv. 2021, 26, 415–424. [Google Scholar] [CrossRef]
  18. Martin, E.A.; Dainese, M.; Clough, Y.; Báldi, A.; Bommarco, R.; Gagic, V.; Garratt, M.P.D.; Holzschuh, A.; Kleijn, D.; Kovács-Hostyánszki, A.; et al. The Interplay of Landscape Composition and Configuration: New Pathways to Manage Functional Biodiversity and Agroecosystem Services across Europe. Ecol. Lett. 2019, 22, 1083–1094. [Google Scholar] [CrossRef] [Green Version]
  19. Hoffmann, H.; Peter, F.; Herrmann, J.D.; Donath, T.W.; Diekötter, T. Benefits of Wildflower Areas as Overwintering Habitats for Ground-Dwelling Arthropods Depend on Landscape Structural Complexity. Agric. Ecosyst. Environ. 2021, 314, 107421. [Google Scholar] [CrossRef]
  20. Simmons, B.I.; Cirtwill, A.R.; Baker, N.J.; Wauchope, H.S.; Dicks, L.V.; Stouffer, D.B.; Sutherland, W.J. Motifs in Bipartite Ecological Networks: Uncovering Indirect Interactions. Oikos 2019, 128, 154–170. [Google Scholar] [CrossRef]
  21. Welch, K.D.; Harwood, J.D. Temporal Dynamics of Natural Enemy–Pest Interactions in a Changing Environment. Biol. Control 2014, 75, 18–27. [Google Scholar] [CrossRef]
  22. Tylianakis, J.M.; Binzer, A. Effects of Global Environmental Changes on Parasitoid–Host Food Webs and Biological Control. Biol. Control 2014, 75, 77–86. [Google Scholar] [CrossRef]
  23. Meng, Y.; Chen, H.; Behm, J.E.; Xia, S.; Yang, X. Effects of Different Tea Plantation Management Systems on Arthropod Assemblages and Network Structure. Ecosphere 2021, 12, e03677. [Google Scholar] [CrossRef]
  24. Holland, J.M.; Jeanneret, P.; Moonen, A.-C.; van der Werf, W.; Rossing, W.A.H.; Antichi, D.; Entling, M.H.; Giffard, B.; Helsen, H.; Szalai, M.; et al. Approaches to Identify the Value of Seminatural Habitats for Conservation Biological Control. Insects 2020, 11, 272. [Google Scholar] [CrossRef]
  25. Rand, T.A.; Veen, F.; Tscharntke, T. Landscape Complexity Differentially Benefits Generalized Fourth, over Specialized Third, Trophic Level Natural Enemies. Ecography 2012, 35, 97–104. [Google Scholar] [CrossRef]
  26. Rouabah, A.; Villerd, J.; Amiaud, B.; Plantureux, S.; Lasserre-Joulin, F. Response of Carabid Beetles Diversity and Size Distribution to the Vegetation Structure within Differently Managed Field Margins. Agric. Ecosyst. Environ. 2015, 200, 21–32. [Google Scholar] [CrossRef]
  27. Fusser, M.; Pfister, S.; Entling, M.; Schirmel, J. Effects of Field Margin Type and Landscape Composition on Predatory Carabids and Slugs in Wheat Fields. Agric. Ecosyst. Environ. 2017, 247, 182–188. [Google Scholar] [CrossRef]
  28. Torma, A.; Bozsó, M.; Tölgyesi, C.; Gallé, R. Relationship of Different Feeding Groups of True Bugs (Hemiptera: Heteroptera) with Habitat and Landscape Features in Pannonic Salt Grasslands. J. Insect. Conserv. 2017, 21, 645–656. [Google Scholar] [CrossRef]
  29. Haddaway, N.R. The Multifunctional Roles of Vegetated Strips around and within Agricultural Fields. Environ. Evid. 2018, 7, 14. [Google Scholar] [CrossRef] [Green Version]
  30. Ofosu-Bamfo, B.; Addo-Fordjour, P.; Belford, E. Does Road-Edge Affect Liana Community Structure and Liana-Host Interactions in Evergreen Rainforests in Ghana? Acta Oecologica 2019, 101, 103476. [Google Scholar] [CrossRef]
  31. Benedetti, Y.; Morelli, F. Spatial Mismatch Analysis among Hotspots of Alien Plant Species, Road and Railway Networks in Germany and Austria. PLoS ONE 2017, 12, e0183691. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Zeng, S.-L.; Zhang, T.-T.; Gao, Y.; Ouyang, Z.; Chen, J.-K.; Li, B.; Zhao, B. Effects of Road Age and Distance on Plant Biodiversity: A Case Study in the Yellow River Delta of China. Plant Ecol. 2011, 212, 1213–1229. [Google Scholar] [CrossRef]
  33. Chaplin-Kramer, R.; O’Rourke, M.E.; Blitzer, E.J.; Kremen, C. A Meta-Analysis of Crop Pest and Natural Enemy Response to Landscape Complexity: Pest and Natural Enemy Response to Landscape Complexity. Ecol. Lett. 2011, 14, 922–932. [Google Scholar] [CrossRef] [PubMed]
  34. Gagic, V.; Paull, C.; Schellhorn, N.A. Ecosystem Service of Biological Pest Control in Australia: The Role of Non-Crop Habitats within Landscapes: Native Vegetation and Biocontrol. Austral Entomol. 2018, 57, 194–206. [Google Scholar] [CrossRef] [Green Version]
  35. Mestre, L.; Schirmel, J.; Hetz, J.; Kolb, S.; Pfister, S.; Amato, M.; Sutter, L.; Jeanneret, P.; Albrecht, M.; Entling, M. Both Woody and Herbaceous Semi-Natural Habitats Are Essential for Spider Overwintering in European Farmland. Agric. Ecosyst. Environ. 2018, 267, 141–146. [Google Scholar] [CrossRef]
  36. Ganser, D.; Knop, E.; Albrecht, M. Sown Wildflower Strips as Overwintering Habitat for Arthropods: Effective Measure or Ecological Trap? Agric. Ecosyst. Environ. 2019, 275, 123–131. [Google Scholar] [CrossRef]
  37. Zhang, Y.; Yang, Y.; Bian, Z.; Wang, S. Multi-Scale Effects of Landscape Stucture on Epigaeic Arthropods Diversity in Arable Land System: A Case in Changtu County of Northern China. Land 2022, 11, 979. [Google Scholar] [CrossRef]
  38. Reay-Jones, F.P.F. Spatial Distribution of the Cereal Leaf Beetle (Coleoptera: Chrysomelidae) in Wheat. Environ. Entomol. 2010, 39, 10. [Google Scholar] [CrossRef]
  39. Saska, P.; Vodde, M.; Heijerman, T.; Westerman, P.; van der Werf, W. The Significance of a Grassy Field Boundary for the Spatial Distribution of Carabids within Two Cereal Fields. Agric. Ecosyst. Environ. 2007, 122, 427–434. [Google Scholar] [CrossRef]
  40. Cabaña, A.; Cabaña, E.M. Cabaña Tests of Normality Based on Transformed Empirical Processes. Methodol. Comput. Appl. Probab. 2003, 3, 27. [Google Scholar] [CrossRef]
  41. Bartlett, M.S. Properties of Sufficiency and Statistical Tests. Proc. R. Soc. Lond. 1937, 160, 268–282. [Google Scholar] [CrossRef]
  42. Kenneth, J. The Reliability of the Functional Independence Measure: A Quantative Review. Arch. Phys. Med. Rehabil. 1990, 71, 326–329. [Google Scholar] [CrossRef]
  43. Kruskal, J. Nonmetric Multidimensional Scaling: A Numerical Method. Psychometrika 1964, 29, 115–129. [Google Scholar] [CrossRef]
  44. Cao, Y.; Williams, W.P.; Bark, A.W. Similarity Measure Bias in River Benthic Aufwuchs Community Analysis. Water Environ. Res. 1997, 69, 95–106. [Google Scholar] [CrossRef]
  45. Oksanen, J.; Kindt, R.; Legendre, P.; O’Hara, R.B. Vegan: Community Ecology Package. R Package Version 2.0–2. 2011. Available online: https://cran.r-project.org.https://github.com/vegandevs/vegan (accessed on 15 May 2022).
  46. Revelle, W. Psych: Procedures for Personality and Psychological Research. 2008. Available online: https://CRAN.R-project.org/package=psych. (accessed on 3 March 2022).
  47. Han, W.S.; Lee, J.; Pham, M.D.; Yu, J.X. IGraph. Proc. VLDB Endow. 2010, 3, 449–459. [Google Scholar] [CrossRef]
  48. Bastian, M.; Heymann, S.; Jacomy, M. Gephi: An Open Source Software for Exploring and Manipulating Networks. In Proceedings of the Third International Conference on Weblogs and Social Media, ICWSM 2009, San Jose, CA, USA, 17–20 May 2009. [Google Scholar]
  49. Gasc, A.; Sueur, J.; Pavoine, S.; Pellens, R.; Grandcolas, P. Distance-Based ReDundancy Analysis (DbRDA). 2013. Available online: http://tinyurl.com/mv690/seminars/dbRDA. (accessed on 10 January 2022).
  50. Legendre, P.; Gallagher, E.D. Ecologically Meaningful Transformations for Ordination of Species Data. Oecologia 2001, 129, 271–280. [Google Scholar] [CrossRef]
  51. Ewers, R.; Didham, R. Pervasive Impact of Large-Scale Edge Effects on a Beetle Community. Proc. Natl. Acad. Sci. USA 2008, 105, 5426–5429. [Google Scholar] [CrossRef] [Green Version]
  52. Álvarez, H.; Morente Díaz, M.; Oi, F.; Rodríguez, E.; Campos, M.; Ruano, F. Semi-Natural Habitat Complexity Affects Abundance and Movement of Natural Enemies in Organic Olive Orchards. Agric. Ecosyst. Environ. 2019, 285, 106618. [Google Scholar] [CrossRef]
  53. Yang, L.; Xu, L.; Liu, B.; Zhang, Q.; Yunfei, P.; Li, Q.; Li, H.; Lu, Y. Non-Crop Habitats Promote the Abundance of Predatory Ladybeetles in Maize Fields in the Agricultural Landscape of Northern China. Agric. Ecosyst. Environ. 2019, 277, 44–52. [Google Scholar] [CrossRef]
  54. Pedley, S.; Dolman, P. Arthropod Traits and Assemblages Differ between Core Patches, Transient Stepping-Stones and Landscape Corridors. Landsc. Ecol. 2020, 35, 937–952. [Google Scholar] [CrossRef]
  55. Landis, D.; Wratten, S.; Gurr, G. Habitat Management to Conserve Natural Enemies of Arthropod Pests in Agriculture. Annu. Rev. Entomol. 2000, 45, 175–201. [Google Scholar] [CrossRef] [PubMed]
  56. Montgomery, I.; Caruso, T.; Reid, N. Hedgerows as Ecosystems: Service Delivery, Management, and Restoration. Annu. Rev. Ecol. Evol. Syst. 2020, 51, 81–102. [Google Scholar] [CrossRef]
  57. Greaves, M.; Marshall, E. Field Margins: Definitions and Statistics. Br. Crop Prot. Counc., Monogr. 1987, 35, 3–10. [Google Scholar]
  58. Zeng, S.-L.; Zhang, T.-T.; Gao, Y.; Li, B.; Fang, C.-M.; Flory, S.L.; Zhao, B. Road Effects on Vegetation Composition in a Saline Environment. J. Plant Ecol. 2011, 5, 206–218. [Google Scholar] [CrossRef] [Green Version]
  59. Marshall, E.J.P.; Moonen, A.C. Field Margins in Northern Europe: Their Functions and Interactions with Agriculture. Agric. Ecosyst. Environ. 2002, 89, 5–21. [Google Scholar] [CrossRef]
  60. Moor, F.C.D. Aspects of the Life History of Simulium Chutteri and S. Bovis (Diptera; Simuliidae) in Relation to Changing Environmental Conditions in South African Rivers. Research 1992, 25, 1817–1821. [Google Scholar] [CrossRef]
  61. Medeiros, H.R.; Martello, F.; Almeida, E.; Mengual, X.; Ribeiro, M.C. Landscape Structure Shapes the Diversity of Beneficial Insects in Coffee Producing Landscapes. Biol. Conserv. 2019, 238, 108193. [Google Scholar] [CrossRef]
  62. Tscharntke, T.; Grass, I.; Wanger, T.C.; Westphal, C.; Batáry, P. Prioritise the Most Effective Measures for Biodiversity-Friendly Agriculture. Trends Ecol. Evol. 2022, 37, 397–398. [Google Scholar] [CrossRef]
  63. Tougeron, K.; Couthouis, E.; Marrec, R.; Barascou, L.; Jacques, B.; Hugues, B.; Françoise, B.; Couty, A.; Doury, G.; Charlotte, F.; et al. Multi-Scale Approach to Biodiversity Proxies of Biological Control Service in European Farmlands. Sci. Total Environ. 2022, 822, 153569. [Google Scholar] [CrossRef]
  64. Albrecht, M.; Kleijn, D.; Williams, N.M.; Tschumi, M.; Blaauw, B.R.; Bommarco, R.; Campbell, A.J.; Dainese, M.; Drummond, F.A.; Entling, M.H.; et al. The Effectiveness of Flower Strips and Hedgerows on Pest Control, Pollination Services and Crop Yield: A Quantitative Synthesis. Ecol. Lett. 2020, 23, 1488–1498. [Google Scholar] [CrossRef]
  65. Windsor, F.M.; Tavella, J.; Rother, D.C.; Raimundo, R.L.G.; Devoto, M.; Guimarães, P.R.; Evans, D.M. Identifying Plant Mixes for Multiple Ecosystem Service Provision in Agricultural Systems Using Ecological Networks. J. Appl. Ecol. 2021, 58, 2770–2782. [Google Scholar] [CrossRef]
  66. Alignier, A.; Raymond, L.; Deconchat, M.; Menozzi, P.; Monteil, C.; Sarthou, J.P.; Vialatte, A.; Ouin, A. The Effect of Semi-Natural Habitats on Aphids and Their Natural Enemies across Spatial and Temporal Scales. Biol. Control 2014, 77, 76–82. [Google Scholar] [CrossRef] [Green Version]
  67. Gurr, G.; Wratten, S.; Landis, D.; You, M. Habitat Management to Suppress Pest Populations: Progress and Prospects. Annu. Rev. Entomol. 2016, 62, 91–109. [Google Scholar] [CrossRef] [PubMed]
  68. Fusser, M.S.; Pfister, S.C.; Entling, M.H.; Schirmel, J. Effects of Landscape Composition on Carabids and Slugs in Herbaceous and Woody Field Margins. Agric. Ecosyst. Environ. 2016, 226, 79–87. [Google Scholar] [CrossRef]
  69. Laliberté, E.; Zemunik, G.; Turner, B. Environmental Filtering Explains Variation in Plant Diversity along Resource Gradients. Science 2014, 345, 1602–1605. [Google Scholar] [CrossRef]
  70. Hu, W.; Mei, Z.; Liu, Y.; Yu, Z.; Duan, M. Recovered Grassland Area Rather than Plantation Forest Could Contribute More to Protect Epigeic Spider Diversity in Northern China. Agric. Ecosyst. Environ. 2021, 326, 107726. [Google Scholar] [CrossRef]
  71. Perry, K.I.; Wallin, K.F.; Wenzel, J.W.; Herms, D.A. Forest Disturbance and Arthropods: Small-scale Canopy Gaps Drive Invertebrate Community Structure and Composition. Ecosphere 2018, 9, e02463. [Google Scholar] [CrossRef] [Green Version]
  72. Haan, N.L.; Zhang, Y.; Landis, D.A. Predicting Landscape Configuration Effects on Agricultural Pest Suppression. Trends Ecol. Evol. 2020, 35, 175–186. [Google Scholar] [CrossRef] [Green Version]
  73. Gardarin, A.; Plantegenest, M.; Bischoff, A.; Valantin-Morison, M. Muriel Understanding Plant-Arthropod Interactions in Multitrophic Communities to Improve Conservation Biological Control: Useful Traits and Metrics. J. Pest Sci. 2018, 91, 943–955. [Google Scholar] [CrossRef]
  74. Botha, M.; Siebert, S.; Van den Berg, J.; Ellis, S.; Greyvenstein, B. Diversity Patterns of Selected Predaceous Arthropod Groups in Maize Fields and Margins in South African Highveld Grassland: Predaceous Arthropods of Maize Agro-Ecosystems. Agric. For. Entomol. 2018, 20, 461–475. [Google Scholar] [CrossRef]
  75. Arnold, S.; Elisante, F.; Mkenda, P.; Tembo, Y.; Ndakidemi, P.; Gurr, G.; Darbyshire, I.; Belmain, S.; Stevenson, P. Beneficial Insects Are Associated with Botanically Rich Margins with Trees on Small Farms. Sci. Rep. 2021, 11, 15190. [Google Scholar] [CrossRef]
  76. Wang, M.; Yu, Z.; Liu, Y.; Wu, P.; Axmacher, J. Taxon- and Functional Group-Specific Responses of Ground Beetles and Spiders to Landscape Complexity and Management Intensity in Apple Orchards of the North China Plain. Agric. Ecosyst. Environ. 2022, 323, 107700. [Google Scholar] [CrossRef]
  77. López, G.V.; Lara-Pérez, L.A.; Oros-Ortega, I.; Ramírez-Barajas, P.J.; Aryal, D.R. Diversity of Soil Macro-Arthropods Correlates to the Richness of Plant Species in Traditional Agroforestry Systems in the Humid Tropics of Mexico. Agric. Ecosyst. Environ. 2019, 286, 106658. [Google Scholar] [CrossRef]
  78. Deák, B.; Kovács, B.; Rádai, Z.; Apostolova, I.; Valkó, O. Linking Environmental Heterogeneity and Plant Diversity: The Ecological Role of Small Natural Features in Homogeneous Landscapes. Sci. Total Environ. 2020, 763, 144199. [Google Scholar] [CrossRef]
  79. Gallandt, E.; Weiner, J. Crop–Weed Competition. Ecological Monographse. 2007, 69, 1–24. [Google Scholar] [CrossRef]
  80. Corcos, D.; Lami, F.; Nardi, D.; Boscutti, F.; Marini, L. Cross-Taxon Congruence between Predatory Arthropods and Plants across Mediterranean Agricultural Landscapes. Ecol. Indic. 2021, 123, 107366. [Google Scholar] [CrossRef]
  81. Seree, L.; Chiron, F.; Valantin-Morison, M.; Barbottin, A.; Gardarin, A. Flower Strips, Crop Management and Landscape Composition Effects on Two Aphid Species and Their Natural Enemies in Faba Bean. Agric. Ecosyst. Environ. 2022, 331, 107902. [Google Scholar] [CrossRef]
  82. Paredes, D.; Cayuela, L.; Campos, M. Synergistic Effects of Ground Cover and Adjacent Vegetation on Natural Enemies of Olive Insect Pests. Agric. Ecosyst. Environ. 2013, 173, 72–80. [Google Scholar] [CrossRef]
  83. Rm, A.; Tt, B.; Pb, A. Increasing Landscape Complexity Enhances Species Richness of Farmland Arthropods, Agri-Environment Schemes Also Abundance—A Meta-Analysis. Agric. Ecosyst. Environ. 2022, 326, 107822. [Google Scholar] [CrossRef]
  84. Triquet, C.; Roume, A.; Tolon, V.; Wezel, A.; Ferrer, A. Undestroyed Winter Cover Crop Strip in Maize Fields Supports Ground-Dwelling Arthropods and Predation. Agric. Ecosyst. Environ. 2022, 326, 107783. [Google Scholar] [CrossRef]
  85. Kinnebrew, E.; Neher, D.A.; Ricketts, T.H.; Wallin, K.F.; Darby, H.; Ziegler, S.E.; Alger, S.A.; Galford, G.L. Cultivated Milkweed Hosts High Diversity of Surface-Active and Soil-Dwelling Arthropods in a New England Case Study. Agric. Ecosyst. Environ. 2022, 325, 107749. [Google Scholar] [CrossRef]
  86. Wimp, G.M.; Murphy, S.M. Habitat Edges Alter Arthropod Community Composition. Landsc. Ecol. 2021, 36, 2849–2861. [Google Scholar] [CrossRef]
  87. Hiron, M.; Berg, Å.; Eggers, S.; Josefsson, J.; Pärt, T. Bird Diversity Relates to Agri-Environment Schemes at Local and Landscape Level in Intensive Farmland. Agric. Ecosyst. Environ. 2013, 176, 9–16. [Google Scholar] [CrossRef]
  88. Liu, C.A.; Liang, M.Y.; Tang, J.W.; Jin, Y.Q.; Siddique, K. Challenges of the Establishment of Rubber-Based Agroforestry Systems: Decreases in the Diversity and Abundance of Ground Arthropods. J. Environ. Manag. 2021, 292, 112747. [Google Scholar] [CrossRef]
  89. Foster, C.; Neumann, J.; Holloway, G. Linking Mesoscale Landscape Heterogeneity and Biodiversity: Gardens and Tree Cover Significantly Modify Flower-Visiting Beetle Communities. Landsc. Ecol. 2019, 34, 1081–1095. [Google Scholar] [CrossRef] [Green Version]
  90. Pardon, P.; Reheul, D.; Mertens, J.; Reubens, B.; Verheyen, K. Gradients in Abundance and Diversity of Ground Dwelling Arthropods as a Function of Distance to Tree Rows in Temperate Arable Agroforestry Systems. Agric. Ecosyst. Environ. 2018, 270–271, 114–128. [Google Scholar] [CrossRef]
  91. Karamaouna, F.; Kati, V.; Volakakis, N.; Varikou, K.; Garantonakis, N.; Economou, L.; Birouraki, A.; Markellou, E.; Liberopoulou, S.; Edwards, M. Ground Cover Management with Mixtures of Flowering Plants to Enhance Insect Pollinators and Natural Enemies of Pests in Olive Groves. Agric. Ecosyst. Environ. 2019, 274, 76–89. [Google Scholar] [CrossRef]
  92. Hammond, H.; García-Tejero, S.; Pohl, G.; Langor, D.; Spence, J. Spatial and Temporal Variation of Epigaeic Beetle Assemblages (Coleoptera, Carabidae, Staphylinidae) in Aspen-Dominated Mixedwood Forests across North-Central Alberta. ZooKeys 2021, 1044, 951–991. [Google Scholar] [CrossRef]
  93. Svjr, A.; De, B.; Jlv, A.; Lrb, A.; Pg, A. Non-Crop Sources of Beneficial Arthropods Vary within-Season across a Prairie Agroecosystem. Agric. Ecosyst. Environ. 2021, 320, 107581. [Google Scholar] [CrossRef]
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Figure 1. Profile diagram of the 5 types of field margins with typical characteristics in the study area. The overall structure is semi-natural habitat-field margin-corn field. Field margins are divided into grassland, woodland, irrigation canals and ditches, dirt roads, and paved roads.
Figure 1. Profile diagram of the 5 types of field margins with typical characteristics in the study area. The overall structure is semi-natural habitat-field margin-corn field. Field margins are divided into grassland, woodland, irrigation canals and ditches, dirt roads, and paved roads.
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Figure 2. Location of sampling sites in the study area. The upper-left image shows the location of the study area in China; the lower-left image shows the location of the study area in Liaoning Province; and the right image shows the sample areas in the Changtu county. The base map is an elevation map of Changtu with 10 corn fields.
Figure 2. Location of sampling sites in the study area. The upper-left image shows the location of the study area in China; the lower-left image shows the location of the study area in Liaoning Province; and the right image shows the sample areas in the Changtu county. The base map is an elevation map of Changtu with 10 corn fields.
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Figure 3. A map of sample points for epigeic arthropods in the sampling areas and a map of pitfall trap.
Figure 3. A map of sample points for epigeic arthropods in the sampling areas and a map of pitfall trap.
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Figure 4. The abundance (A), species richness (A), species diversity (B), and evenness (B) of epigeic arthropods within arable land with different types of field margin. Different lowercase letters indicate significant differences among treatments at the p < 0.05 level. GL, WL, CD, DR, and PR represent grassland, woodland, irrigation canals and ditches, dirt roads, and paved roads.
Figure 4. The abundance (A), species richness (A), species diversity (B), and evenness (B) of epigeic arthropods within arable land with different types of field margin. Different lowercase letters indicate significant differences among treatments at the p < 0.05 level. GL, WL, CD, DR, and PR represent grassland, woodland, irrigation canals and ditches, dirt roads, and paved roads.
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Figure 5. The non-metric multidimensional scaling ordinations (NMDS) of all epigeic arthropods (a), natural enemies (b), insect herbivores (c), and neutrals (d). Non-metric multidimensional scaling (NMDS) based on Chord Measure for epigeic arthropods assemblages in different samples (n = 40 plots, 2 dimensions, Bray–Curtis distance, stress < 0.2). GL, WL, CD, DR, and PR represent grassland, woodland, irrigation canals and ditches, dirt roads, and paved roads.
Figure 5. The non-metric multidimensional scaling ordinations (NMDS) of all epigeic arthropods (a), natural enemies (b), insect herbivores (c), and neutrals (d). Non-metric multidimensional scaling (NMDS) based on Chord Measure for epigeic arthropods assemblages in different samples (n = 40 plots, 2 dimensions, Bray–Curtis distance, stress < 0.2). GL, WL, CD, DR, and PR represent grassland, woodland, irrigation canals and ditches, dirt roads, and paved roads.
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Figure 6. Ecological network structure diagram of natural enemies, insect herbivores, and neutrals within adjacent arable land with artificial and semi-natural field margins. The dots represent the species of arthropods (Table S1), and the size of the dots is proportional to the abundance. The connection between points showed a significant correlation (r > 0.8, p < 0.01). GL, WL, CD, DR, and PR represent grassland, woodland, irrigation canals and ditches, dirt roads, and paved roads.
Figure 6. Ecological network structure diagram of natural enemies, insect herbivores, and neutrals within adjacent arable land with artificial and semi-natural field margins. The dots represent the species of arthropods (Table S1), and the size of the dots is proportional to the abundance. The connection between points showed a significant correlation (r > 0.8, p < 0.01). GL, WL, CD, DR, and PR represent grassland, woodland, irrigation canals and ditches, dirt roads, and paved roads.
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Figure 7. Canonical correlation analysis (RDA) ordination plot of epigeic arthropods and vegetation characteristics of field margins. The first axis explained 32.63% of the species—environment relationship, whereas the second axis showed that the cumulative contribution rate was 8.63%. TC, HA, and HH represent tree layer cover, herbage layer abundance, and herb layer height. G1-G20 represent different families of epigeic arthropods, as detailed in Table S1. GL, WL, CD, DR, and PR represent grassland, woodland, irrigation canals and ditches, dirt roads, and paved roads.
Figure 7. Canonical correlation analysis (RDA) ordination plot of epigeic arthropods and vegetation characteristics of field margins. The first axis explained 32.63% of the species—environment relationship, whereas the second axis showed that the cumulative contribution rate was 8.63%. TC, HA, and HH represent tree layer cover, herbage layer abundance, and herb layer height. G1-G20 represent different families of epigeic arthropods, as detailed in Table S1. GL, WL, CD, DR, and PR represent grassland, woodland, irrigation canals and ditches, dirt roads, and paved roads.
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Table
Table 1. Five types of field margins, their typical characteristics, and vegetation characteristics. More detail on the field margins monitored can be found in Text S1.
Table 1. Five types of field margins, their typical characteristics, and vegetation characteristics. More detail on the field margins monitored can be found in Text S1.
Type of Field MarginTypical CharacteristicsHerb Vegetation Characteristics
Semi-natural field marginGrassland
(GL)		4 m < width < 10 m and rich herbaceous vegetationThe coverage of the herbaceous layer is about 92%, the canopy density of the tree layer is 0, and the dominant species is Eleusine indica.
Woodland
(WL)		4 m < width < 10 m, with natural vegetation and artificially planted poplar forestThe cover of the herbaceous layer is about 85%, the canopy density of the tree layer is about 92%, and the dominant species was Polygonum aviculare.
Irrigation canals and ditches
(CD)		Width < 10 m and may include tree speciesThe cover of the herbaceous layer is about 73%, the canopy density of the tree layer is about 65%, and the dominant species is Humulus scandens.
Artificial field marginDirt roads
(DR)		2 m < width < 4 m and single vegetation structureThe coverage of the herbaceous layer is about 50%, the canopy density of the tree layer is about 53%, and the dominant species is Echinochloa crusgalli.
Paved roads
(PR)		2 m < width < 4 m and dominated by weedsThe cover of the herbaceous layer is about 51%, the canopy density of the tree layer is about 50%, and the dominant species is Aster tataricus.
Table
Table 2. Topological parameters of the ecological network of arthropod communities within artificial and semi-natural field margins. GD: average path distance; avgD: average degree; D: Density; avgCC: average clustering coefficient; M: modularity. GL, WL, CD, DR, and PR represent grassland, woodland, irrigation canals and ditches, dirt roads, and paved roads.
Table 2. Topological parameters of the ecological network of arthropod communities within artificial and semi-natural field margins. GD: average path distance; avgD: average degree; D: Density; avgCC: average clustering coefficient; M: modularity. GL, WL, CD, DR, and PR represent grassland, woodland, irrigation canals and ditches, dirt roads, and paved roads.
Types of Field MarginEmpirical NetworkRandomized Network
GDavgDDavgCCMGDavgCCM
GL2.0006.8800.2870.7280.4991.8060.2820.214
WL2.2595.6550.2020.7730.4792.5850.1250.374
CD1.6025.1120.1850.7150.7272.4120.1430.343
DR1.7254.1380.1480.6640.7032.1620.1800.295
PR1.6673.6550.1310.5010.5762.0610.1970.271
Table
Table 3. Monte Carlo significance test of environmental factors in relation to the RDA models. Tree layer cover (TC), herbage layer abundance (HA), and herb layer height (HH).
Table 3. Monte Carlo significance test of environmental factors in relation to the RDA models. Tree layer cover (TC), herbage layer abundance (HA), and herb layer height (HH).
Environmental FactorExplanation Rate/%F-Valuep-Value
HH21.16.330.007
HA13.73.590.019
TC10.04.480.01
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Wang, C.; Bian, Z.; Wang, S.; Liu, X.; Zhang, Y. The Effect of Artificial Field Margins on Epigeic Arthropod Functional Groups within Adjacent Arable Land of Northeast China. Land 2022, 11, 1910. https://doi.org/10.3390/land11111910

AMA Style

Wang C, Bian Z, Wang S, Liu X, Zhang Y. The Effect of Artificial Field Margins on Epigeic Arthropod Functional Groups within Adjacent Arable Land of Northeast China. Land. 2022; 11(11):1910. https://doi.org/10.3390/land11111910

Chicago/Turabian Style

Wang, Chuqiao, Zhenxing Bian, Shuai Wang, Xiaochen Liu, and Yufei Zhang. 2022. "The Effect of Artificial Field Margins on Epigeic Arthropod Functional Groups within Adjacent Arable Land of Northeast China" Land 11, no. 11: 1910. https://doi.org/10.3390/land11111910

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