An evaluation method for quantifying the influence of rice field ridge functional plant belt on arthropod community in rice field based on eDNA technology

Abstract

The application discloses an evaluation method for quantifying the influence of a functional plant strip on the arthropod community in a rice field based on an eDNA technology and application thereof. The evaluation method comprises the following steps: constructing a functional plant-rice complex ecosystem and a control system, collecting two groups of environment samples at a key phenophase of the rice, using arthropod universal primers to perform PCR amplification on mlCOlintF and jgHCO2198, sequentially performing library construction and high-throughput sequencing on the amplification products, and then performing quality control, Operational Taxonomic Unit (OTU) clustering and species annotation through a bioinformatics process to generate an OTU abundance table; based on the two OTU abundance tables, combining Alpha diversity, Beta diversity and difference significance analysis, the conservation effect of the functional plant strip on the natural enemy insect community, the spatial gradient change of the effect and the control effect on the pest population are quantitatively evaluated. The application realizes non-invasive, high-throughput and high-resolution precise monitoring of the biological community in a farmland, and provides a reliable molecular tool and decision basis for the optimal design of ecological agriculture.

Description

Technical Field

[0001] This invention relates to the field of agricultural ecology and biological control technology, specifically to a method for accurately and quantitatively evaluating the ecological functions of functional plants (such as zinnia, alfalfa, and spider flower) planted at the edge of paddy fields in terms of nurturing natural enemy insects and regulating pest populations, using environmental DNA (eDNA) macrobarcoding technology combined with spatial gridded sampling design. Background Technology

[0002] Natural enemy insects play a central role in the natural control of pest populations in rice paddy ecosystems. In ecological agriculture practices, functional plants (nectar plants and habitat plants) are often planted on the ridges of rice paddies. The aim is to "nourish" natural enemies by providing resources such as pollen, nectar, alternative hosts, and shelter, thereby increasing their population numbers and enhancing the biological control of rice paddy pests (i.e., the "push-pull" strategy or ecological regulation).

[0003] However, traditional methods for assessing the efficacy of functional plants have significant limitations: Spatial limitations: Reliance on physical capture methods such as Marshall nets, trap containers, and sweep nets. These methods can only capture individuals that reach a specific device and cannot effectively distinguish whether natural enemies come from the functional vegetation zone or migrate from a more distant area. Therefore, it is difficult to accurately quantify the "recruitment" and "maintenance" effects of functional vegetation zones on natural enemy populations and the actual range of their impact.

[0004] Limitations of identification: Traditional methods rely heavily on morphological identification, which cannot identify the remains of pests after they have been eaten, their eggs, or tiny nymphs. This leads to a serious underestimation of the pressure on pest populations and the actual control capabilities of natural enemies.

[0005] Efficiency and throughput limitations: Morphological identification is time-consuming and labor-intensive, has low throughput, and requires deep taxonomic expertise, making it difficult to conduct large-scale, dynamic monitoring of complex arthropod communities.

[0006] eDNA technology enables non-invasive, high-throughput, and high-resolution biodiversity monitoring by detecting shed genetic material in the environment (such as skin cells, secretions, excrement, and debris). However, this technology has previously been primarily used for aquatic biodiversity surveys, and a systematic approach has yet to be established for complex terrestrial agricultural ecosystems, particularly for quantifying the ecological effects of specific agricultural management practices (such as planting functional plants).

[0007] Therefore, there is an urgent need in this field for a method that can overcome spatial limitations and accurately and with high throughput quantify the real impact of functional plants on paddy field arthropod communities (especially the interaction between natural enemies and pests). Summary of the Invention

[0008] To address the aforementioned problems, this invention discloses an assessment method and its application for quantifying the impact of functional vegetation zones along paddy field ridges on arthropod communities within paddy fields based on eDNA technology. This solves the core problem of the difficulty in quantifying the true extent, degree, and dynamic changes of the impact of functional vegetation zones along paddy field ridges on arthropod communities within paddy fields.

[0009] The technical solution adopted in this invention is as follows: The method for assessing the impact of functional vegetation zones along paddy field ridges on arthropod communities in paddy fields based on eDNA technology, as described in this invention, includes the following steps: S1. Spatial gradient system construction and sampling: Functional plant-rice complex ecosystem and control system were constructed sequentially. Then, based on the spatial grid method, two sets of sampling points were set in the paddy fields of the functional plant-rice complex ecosystem and control system, respectively. Then, during the key phenological period of rice, two sets of environmental samples were collected from each set of sampling points of the two systems.

[0010] Specifically, it consists of the following three sub-steps: S11. Plant functional plant belts on the ridges of the paddy field ecosystem to construct a functional plant-rice composite ecosystem. The paddy field ecosystem includes the paddy field ridges and the interior of the paddy field. The interior of the paddy field is separated by crisscrossing ridges. The paddy field ecosystem without functional plant belts planted on the ridges is used as a control system. Preferably, functional plants (including but not limited to zinnia, spider flower, and alfalfa) are planted on the field ridges at the optimal planting density (row spacing 30cm, plant spacing 20cm) to construct a functional plant-rice complex ecosystem, and a weed-rice system is set up as a control.

[0011] S12. In the functional plant-rice complex ecosystem, a two-dimensional rectangular grid with a tight array is established on the paddy field plane. The two-dimensional rectangular grid is formed by dividing the interior of the paddy field into equal intervals along the length and width directions. Sampling points are set at each intersection of the two-dimensional rectangular grid. In the control system, sampling points are set at the same spatial locations. In practice, within the functional plant-rice complex ecosystem, a Cartesian coordinate system is established with the corner of the paddy field as the origin, the long side of the paddy field as the X-axis, and the short side of the paddy field as the Y-axis. n points are evenly spaced along the X-axis, and m points are evenly spaced along the Y-axis, forming an n×m spatial sampling grid. Multiple sampling points are then set within the paddy field of the functional plant-rice complex ecosystem according to this spatial sampling grid to analyze the spatial gradient changes of the functional plant belt effect. In the control system, n×m sampling points are also set at the same spatial locations. Preferably, in the functional plant-rice complex ecosystem, a Cartesian coordinate system is established with the corner inside the paddy field as the origin (marked as A0), the long side inside the paddy field as the X-axis, and the short side inside the paddy field as the Y-axis. Three points are set at equal intervals along the X-axis and three points are set at equal intervals along the Y-axis to form a 3×3 spatial sampling grid with sampling points A0, A1, A2; B0, B1, B2; C0, C1, C2. By comparing the eDNA data of different sites from the edge of the paddy field (A0 / B0 / C0) to the center of the paddy field (A2 / B2 / C2), the gradient change of biological distribution can be quantitatively analyzed.

[0012] S13. During the key phenological stages of rice, multiple environmental samples were collected from sampling points at different spatial locations in the functional plant-rice complex ecosystem and the control system.

[0013] Among them, functional plants include, but are not limited to, zinnias ( Zinnia elegans ), Spider Flower ( Cleome thorny ) and alfalfa ( Medicago sativa One or more of the following: Key phenological periods of rice include any day during the rice heading stage, the day before rice harvest, and the day after rice harvest; Environmental samples include one or more of the following: soil samples, rice plant washing solution samples, and functional plant pollen samples.

[0014] In practice, soil samples were taken from the top 0-10cm layer using a 60mL ring cutter and stored at -20°C; paddy field water samples were collected from the surface water using 50mL centrifuge tubes, transported immediately in an ice bath, and stored at 4°C; rice plant washing solution samples were obtained by rinsing the entire rice plant with 1000mL of deionized water, collecting the washing solution and filtering it through a 0.22μm filter membrane, which was then stored at -80°C; and functional plant pollen samples were collected using a white porcelain dish after 24 hours of pollen collection and stored at -20°C.

[0015] S2. eDNA extraction and amplification: eDNA was extracted from each group of environmental samples collected in step S1. Then, the extracted eDNA was amplified by PCR using the universal arthropod primers mlCOlintF and jgHCO2198 to obtain the corresponding amplification products. Specifically, eDNA of the functional plant-rice complex ecosystem and eDNA of the control system were extracted from environmental samples of the functional plant-rice complex ecosystem and environmental samples of the control system, respectively. Then, the universal arthropod primers mlCOlintF and jgHCO2198 were used to perform PCR amplification of the eDNA of the functional plant-rice complex ecosystem and the control system, respectively, to obtain the amplification products of the functional plant-rice complex ecosystem and the control system.

[0016] The nucleotide sequence of the universal arthropod primer mlCOlintF is shown in SEQ ID No. 1, and the nucleotide sequence of primer jgHCO2198 is shown in SEQ ID No. 2. 5′-GGWACWGGWTGAACWGTWTAYCCYCC-3′, SEQ ID No.1; 5′-TANACYTCNGGRTGNCCRAARAAYCA-3′, SEQ ID No. 2.

[0017] In practice, during eDNA extraction, commercial kits (such as QIAGEN DNeasy PowerSoilPro Kit for soil, DNeasy PowerWater Kit for water) were used to extract eDNA according to the operating procedures. All operations were performed under sterile conditions to prevent cross-contamination. PCR amplification was performed using the universal arthropod primer pair mlCOlintF / jgHCO2198 to amplify the COI barcode region. The reaction system (25 μL) consisted of 12.5 μL of 2×Taq PCR MasterMix, 0.5 μL each of forward and reverse primers (10 μM), 2 μL of template DNA, and ddH2O added to bring the total to 25 μL. The reaction program was 12.5 cycles of 94°C for 30 s, 52°C for 30 s, and 72°C for 45 s, followed by 72°C for 5 min. The PCR product was purified using AMPure XPbeads.

[0018] S3, High-throughput sequencing: Each amplification product obtained in step S2 is sequentially subjected to library construction and high-throughput sequencing to obtain the raw sequencing data of each amplification product. Specifically, the amplification products of the functional plant-rice complex ecosystem and the control system obtained in step S2 were sequentially subjected to library construction and high-throughput sequencing to obtain the raw sequencing data of the functional plant-rice complex ecosystem and the raw sequencing data of the control system.

[0019] High-throughput sequencing is a technology that can simultaneously sequence a large number of DNA molecules. Its basic principle is to cut the DNA sample to be tested into small segments, then amplify and immobilize these segments onto a sequencing chip using specific methods. Next, by adding fluorescently labeled bases or using other detection methods, the type of base at each position is determined, thereby completing the determination of the entire DNA sequence.

[0020] In practice, high-throughput sequencing was performed using the Illumina MiSeq platform to perform paired-end sequencing (PE250) on the purified PCR products; library construction was performed using the TruSeq DNA PCR-Free Library Prep Kit.

[0021] S4. Bioinformatics Analysis: The raw sequencing data obtained are sequentially subjected to quality control, splicing, and filtering to obtain high-quality sequences corresponding to each environmental sample. Then, OTU clustering is performed at a certain similarity level, and the data is compared with taxonomic databases to complete species annotation and obtain OTU tables. Specifically, the multiple raw sequencing data obtained in step S3 are sequentially subjected to quality control, splicing, and filtering to obtain multiple high-quality sequences. Then, at a similarity level of 97%, OTU clustering is performed on the multiple high-quality sequences to obtain multiple OTU representative sequences. The OTU abundance of the multiple OTU representative sequences is then calculated. Finally, the multiple OTU representative sequences are sequentially compared with taxonomic databases to complete the species annotation of the multiple OTU representative sequences, resulting in an OTU table containing species classification information and OTU abundance.

[0022] Specifically, the raw sequencing data of the functional plant-rice complex ecosystem and the control system obtained in step S3 were sequentially subjected to quality control, splicing, and filtering processes to obtain high-quality sequences for the functional plant-rice complex ecosystem and the control system, respectively. Then, at a 97% similarity level, OTU clustering was performed on the high-quality sequences of the functional plant-rice complex ecosystem and the control system to obtain representative OTU sequences for the functional plant-rice complex ecosystem and the control system, respectively. The representative OTU sequences of the functional plant-rice complex ecosystem and the control system were then statistically analyzed. The number of sequences contained in each OTU in the representative OTU sequence of the system was used to obtain the OTU abundance of the functional plant-rice complex ecosystem and the OTU abundance of the control system. Then, the representative OTU sequences of the functional plant-rice complex ecosystem and the OTU sequences of the control system were compared with taxonomic databases to complete the species annotation of the representative OTU sequences of the functional plant-rice complex ecosystem and the control system, respectively, resulting in an OTU table of the functional plant-rice complex ecosystem containing species taxonomic information and OTU abundance and an OTU table of the control system containing species taxonomic information and OTU abundance.

[0023] The taxonomic database used is the NCBI NT database (Non-Redundant Nucleic Acid Database). This database is a comprehensive and widely covered public sequence database that does indeed contain a large number of COI gene sequences of insects (including natural enemy insects). Therefore, it can be used for preliminary species identification and classification analysis of natural enemy insects.

[0024] In practice, the raw sequencing data is used to distinguish samples according to barcodes. Software such as Trimmomatic and FLASH is used for quality filtering and splicing to obtain high-quality sequences (Clean Tags) for each sample. Usearch software is used to perform OTU clustering on the high-quality sequences at 97% similarity and obtain representative OTU sequences. Then, the Uclust algorithm is used to compare the representative sequences with databases such as PR2, iBOL, and mitogenomes to complete species taxonomic annotation and generate an OTU abundance table.

[0025] S5. Quantitative assessment of biological control effectiveness: Based on the OTU table, the biodiversity index and relative abundance of target groups in the functional plant-rice complex ecosystem and the control system were calculated respectively. Combined with statistical analysis, the conservation effect of functional plant belts on natural enemy insect communities, the spatial gradient change of functional plant belt effects, and the control effect of functional plant belts on pest populations were quantitatively assessed.

[0026] Specifically, based on the OTU table, the entire analysis process can be integrated as follows: First, by calculating the Alpha diversity index and using analysis of variance to compare the differences between the functional plant zone and the control area, the conservation effect can be directly and quantitatively assessed. Next, the community structure is visualized using PCoA / NDMS, and the correlation between community dissimilarity and spatial distance is analyzed using dbRDA or Mantel tests to quantify the spatial gradient changes in the functional plant effect. Finally, a multiple regression or structural equation model is constructed to correlate key biodiversity indicators characterizing the conservation effect with field-measured pest population data, thereby quantitatively verifying the pest control effect of the functional plant zone by regulating the natural enemy community. This coherent analytical framework can systematically deduce ecological functions from changes in community structure, forming a complete chain of evidence.

[0027] The quantitative assessment is achieved through the following three core formulas: The natural enemy conservation effect enhancement rate is used to quantitatively assess the conservation effect of functional plant zones on natural enemy insect communities and quantify the degree of natural enemy resource aggregation. Its calculation formula is as follows: EER = (Ā t - Ā c ) / Ā c × 100% The pest control effect inhibition rate is used to quantify the control effect (inhibition efficacy) on a pest community. Its calculation formula is as follows: PCR = (P̄ c - P̄ t ) / P̄ c × 100% The spatial radius of functional plant effects is determined by fitting a decay model of community similarity and distance to quantify the spatial gradient changes of functional plant zone effects. S_d=S0·e (-kd) And calculate the distance D when the similarity drops to 50%. 50 = ln(2) / k to determine.

[0028] Among them, Ā t , Ā c P represents the mean relative abundance of natural enemy groups in the treatment and control areas, respectively. t 、P̄ c These represent the mean relative abundance of pest groups, and S0 is the theoretical maximum similarity, i.e., the community similarity when the distance d=0. In the model, the community on the field ridge (0 meters) in the treatment area is usually compared with itself, and its theoretical value is 1 (or 100%). However, in actual fitting, it can be estimated from the data as the model intercept parameter, representing the community state of the effect source. e is the natural constant, approximately equal to 2.71828, and k is the attenuation coefficient (unit: m). -1 This parameter is crucial, representing the rate at which community similarity decreases with increasing distance from the functional vegetation zone. A larger k value indicates a faster decay of the effect with distance and a narrower spatial influence range; a smaller k value indicates a slower decay of the effect and a wider influence range. d is the vertical distance (in meters) from the sampling point to the functional vegetation zone (field ridge). This is the sampling gradient, such as 0m, 5m, 10m, 15m, etc.

[0029] In the specific implementation, a paddy field ecosystem without functional plant belts planted on the field ridges was set up as a control system, and multiple sampling points were set up in the same spatial location. Steps S2-S5 were repeated for the sampling results to obtain the control effect of the control group on the pest population.

[0030] The biodiversity analysis includes Alpha diversity analysis and Beta diversity analysis. Alpha diversity analysis includes calculating the Chao1 index, Shannon index, Simpson index, and Coverage index. Beta diversity analysis includes PCA analysis, PCoA analysis, or NMDS analysis. In step S5, statistical analysis includes ANOSIM analysis, Adonis analysis, LEfSe analysis, ANOVA analysis of variance, or Wilcoxon rank-sum test analysis to determine whether there are significant differences in the composition of natural enemy or pest communities between the functional plant-rice complex ecosystem and the control system. The quantitative assessment specifically involves comparing the relative abundance and diversity index of natural enemy OTUs and the relative abundance of pest OTUs in environmental samples from the functional plant-rice complex ecosystem and the control system to calculate the enhancement rate of the functional plant's natural enemy conservation effect, the spatial effect range, and the inhibition rate of its pest control effect.

[0031] In practice, Alpha diversity was assessed using Mothur software to calculate Chao1, Shannon, Simpson, and Coverage indices, and to plot dilution curves and Rank-Abundance curves to evaluate species richness and diversity within the sample. Beta diversity was analyzed using Bray-Curtis and UniFrac equidistant distance algorithms, including PCA, PCoA, and NMDS analyses, supplemented by ANOSIM and Adonis statistical tests to compare community structure differences between different sample groups (e.g., functional plant areas vs. control areas, different distance sites). Differential analysis was performed using LEfSe, ANOVA, and Wilcoxon tests to screen for indicator species (e.g., specific natural enemies or pests) with significant abundance differences between different groups.

[0032] This invention captures fragments of genetic material (eDNA) scattered throughout the environment through systematic grid sampling, enabling "comprehensive" monitoring and spatiotemporal dynamic analysis of biological activities within paddy fields. The method of this invention is used to evaluate the conservation effect of functional plant zones on natural enemies and their spatiotemporal dynamic effects on pest control.

[0033] Based on the above analysis results, a quantitative interpretation is provided: Improvement rate of conservation effect: Compare the number, abundance and alpha diversity index of natural enemy groups (such as ladybugs, lacewings and spiders) in the functional plant area and the control area to calculate the improvement rate of conservation efficiency.

[0034] Spatial effect range: By comparing the similarity (Beta diversity) of natural enemy communities and the abundance changes of key natural enemy species at different distance sites (e.g., A0 vs A2) in the grid, the influence range and decay gradient of functional plant effects are quantified.

[0035] Inhibition rate of pest control effect: Compare the changes in OTU abundance of pest groups (such as planthoppers and leafhoppers) in the functional plant area and the control area; at the same time, by detecting the pest eDNA signal on rice plants, the actual predation pressure of natural enemies can be assessed more directly, and the control efficiency of pest population can be calculated.

[0036] The quantitative assessment method constructed in this invention forms a standardized indicator system that can be used to objectively compare and optimize the application of functional plants in agricultural ecosystems. The core of this method lies in transforming ecological functions into calculable and comparable key indicators. Specific applications include: assessing and optimizing functional plant configuration patterns: by comparing different configuration patterns (such as monoculture, mixed cultivation, and different spatial layouts), the calculated natural enemy conservation effect enhancement rate (EER) and effect spatial radius (D) are obtained. 50 This allows for a quantitative assessment of the strength and range of predator attraction in each planting pattern. For example, the EER and DER of mixed planting patterns... 50 If both are significantly higher than those of monoculture, it proves that the configuration is superior and can provide direct data for field ecological engineering design; Screening superior functional plant varieties: Under uniform cultivation conditions, compare the pest control effect inhibition rate (PCR) and natural enemy community Alpha diversity index of different candidate plant varieties. The higher the PCR value, the stronger the ability of the variety to suppress the pest community by cultivating natural enemies; Combined with high natural enemy diversity, superior functional plant varieties with both strong pest control function and ecological compatibility can be screened; Optimize rice paddy biological control strategies: Use the quantitative indicators of this invention as prediction and monitoring tools. Before formulating control strategies, the EER and PCR thresholds that can be achieved after introducing specific functional plants can be estimated based on historical data or models. After the strategy is implemented, by continuously monitoring the dynamic changes of these indicators, the efficacy of biological control can be evaluated in real time, and scientific decisions can be made on management measures such as mowing time and pesticide reduction, so as to achieve precise ecological regulation based on data. This invention solves the problem of the spatiotemporal dynamics of the spatiotemporal effects of functional plants (such as zinnia, alfalfa, and spider flowers) on the conservation of natural enemy communities in paddy fields and their control of pests, which cannot be accurately assessed by traditional methods. It also achieves non-invasive, high-throughput, and high-resolution accurate monitoring of farmland biological communities, providing reliable molecular tools and decision-making basis for the optimal design of ecological agriculture.

[0037] The beneficial effects of this invention are: Breakthrough in spatial assessment: By using eDNA technology to achieve full-area monitoring, combined with gridded sampling design, it is possible for the first time to accurately quantify the spatial range and gradient changes of the ecological effects of functional plants, which is impossible with traditional capture methods.

[0038] It reveals hidden ecological processes: it can detect organisms that are difficult to identify by morphology (such as tiny natural enemies and pest remains), and more realistically and comprehensively reflects the interaction between natural enemies and pests and the actual effectiveness of biological control.

[0039] It achieves high throughput and precision: overcoming the shortcomings of low efficiency and strong subjectivity in morphological identification, it can simultaneously perform non-invasive, efficient and objective identification and quantification of the entire arthropod community in hundreds or thousands of samples.

[0040] It provides reliable decision-making tools: a systematic, scientific, and quantifiable evaluation system for the field, used to screen superior functional plant varieties, optimize field configuration patterns, and assess the effectiveness of ecological regulation measures, ultimately serving the development of green agriculture. Attached Figure Description

[0041] Figure 1 This is a flowchart for constructing a quantitative evaluation index system for the function of natural enemies in rice paddies. Detailed Implementation

[0042] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments in Qianshanxia Village, Wuxing District, Huzhou City. The following embodiments are used to fully demonstrate the technical details and implementation effects of the present invention, but are not intended to limit the scope of the present invention.

[0043] Example 1: Quantitative analysis of the impact of the zinnia belt in Qianshanxia Village on the arthropod community in paddy fields Step 1: Overview of the test site and system construction Time: 2024 rice growing season.

[0044] Location: Qianshanxia Village, Wuxing District, Huzhou City, Zhejiang Province (120°06′E, 30°52′N). This plot of land is a typical rice-growing area in the Taihu Lake Basin, with flat terrain, convenient irrigation, and a single-season rice planting system.

[0045] Field design: Select two adjacent paddy fields, each with an area of ​​2 mu (approximately 0.33 hectares), a field ridge width of approximately 0.5 meters, and identical soil fertility, water management, and rice variety ('Zhejing 99').

[0046] Treatment group (Zinnia system): In mid-to-late April 2024, zinnias were manually sown in rows on the east and west ridges of the southern paddy field. Zinnia elegans The row spacing is about 30 cm. After emergence, the seedlings are thinned to a plant spacing of about 20 cm. No pesticides are used, and the plants are allowed to grow naturally.

[0047] Control group (weed system): The east and west ridges of the northern paddy field were maintained in their original natural vegetation state without any artificial sowing or removal intervention. The naturally growing weed community on the ridges consisted mainly of barnyard grass, which is common in the area. Echinochloa chicken leg), Qianjinzi ( Leptochloa chinensis ) and Hollow Lotus ( Alternaria philoxeroid () is the dominant species, and its growth and succession follow the natural process completely.

[0048] Uniform agricultural management: The field management (fertilization, irrigation, weeding) of the two paddy fields was completely identical, and no chemical pesticides were applied throughout the entire rice growing season, in order to purely evaluate the ecological effects of different field ridge vegetation on natural enemy communities and pest control functions.

[0049] Step 2, eDNA spatial gradient sampling Sampling time: Sampling will be conducted on September 25, 2024 (rice heading stage). The sampling will be conducted between 9:00 AM and 11:00 AM after a period of consecutive sunny weather.

[0050] Sampling Layout: A linear gradient sampling method was used. A center point (G0, i.e., the center of the zinnia strip or the control field ridge) was determined at the middle of each field ridge. Using a 50-meter measuring rope, three gradient sampling points were set at 5 meters (G5), 10 meters (G10), and 15 meters (G15), respectively. Three biological replicates were set up at each gradient point (i.e., one sample was taken at 5-meter intervals along the direction of the field ridge), for a total of 2 field ridges (4 sites, 3 replicates) = 24 sampling points / system.

[0051] Sample types and collection methods: Rhizosphere soil samples: At each sampling point, five mixed rice rhizosphere soil samples (0-10 cm depth) were collected using a stainless steel soil auger. After thorough mixing, approximately 50 g of the samples were placed into 50 mL sterile centrifuge tubes (Corning, USA). Sterile gloves were worn throughout the process. After each sample collection, the sampling tools were wiped with 75% alcohol and then flame-sterilized. The samples were immediately flash-frozen in a portable liquid nitrogen container and then transferred to a -80°C ultra-low temperature freezer for storage until DNA extraction.

[0052] Plant surface eDNA sampling: At each sampling point, three rice plants were randomly selected, and approximately 10g of the middle leaves were cut using sterile scissors. At point G0, approximately 10g of complete inflorescences and leaves from three additional zinnia plants were randomly collected. All plant samples were placed in sterile sampling bags. Subsequently, in the laboratory, each plant sample was placed in a sterile beaker containing 500 mL of sterile deionized water (pre-cooled to 4°C) and shaken at 200 rpm for 10 minutes on a shaker to elute bioparticles and DNA attached to the surface. The washing solution was then filtered and enriched using a vacuum pump (Millipore, USA) through a 0.22 μm mixed cellulose ester filter membrane (Millipore, USA). The filter membrane was removed with sterile forceps, folded in half, placed in a 2 mL sterile cryovial, immediately flash-frozen in liquid nitrogen, and then stored at -80°C.

[0053] Step 3: eDNA extraction, PCR amplification and sequencing eDNA extraction: Soil eDNA extraction: The DNeasy PowerSoil Pro Kit (QIAGEN, Germany) was used, strictly following the instructions. 0.25 g of frozen soil was weighed for lysis, and the DNA was finally eluted with 50 μL of Elution Buffer.

[0054] eDNA extraction from the filter membrane: The DNeasy PowerWater Kit (QIAGEN, Germany) was used. The frozen filter membrane was cut into pieces and placed into the lysis tube provided by the kit. Subsequent steps were performed according to the instructions. Finally, the DNA was eluted with 60 μL Solution EB.

[0055] All DNA extractions included a negative control (using sterile water instead of samples). DNA concentration was quantified using a Qubit 4.0 fluorometer (Thermo Fisher, USA).

[0056] PCR amplification and library preparation: The COI gene fragment was amplified using universal arthropod primer pairs mlCOlintF (5'-GGWACWGGWTGAACWGTWTAYCCYCC-3') and jgHCO2198 (5'-TANACYTCNGGRTGNCCRAARAAYCA-3'). The PCR reaction system (25 μL) was as follows: 2x PCR Master Mix: 12.5 μL, forward primer (10 μM): 1.0 μL, reverse primer (10 μM): 1.0 μL, template DNA: 2.0 μL (20-100 ng), sterile ultrapure water: 8.5 μL Reaction program: 95°C for 3 min; 35 cycles of (95°C for 30 s, 52°C for 30 s, 72°C for 45 s); 72°C for 5 min.

[0057] The amplified products were purified using AMPure XP beads (Beckman Coulter, USA) and then sent to Shanghai Biotechnology Co., Ltd. for PE250 sequencing using the Illumina NovaSeq 6000 platform.

[0058] Step 4: Bioinformatics Analysis Data quality control: Fastp v0.23.4 was used to trim and remove connector sequences from the raw data. FLASH v1.2.11 was used to stitch the paired sequences together.

[0059] OTU Clustering and Species Annotation: Sequence denoising and chimera removal were performed using the USEARCH v11.0.667 pipeline, and OTU clustering was conducted at 97% similarity. Representative OTU sequences were compared with the iBOL COI database (Release 10.0) and the NCBInt database, and species annotation was performed using a threshold of ≥97%.

[0060] Statistical analysis: Alpha diversity: The Chao1 index and Shannon index were calculated using QIIME 2, and differences between groups were compared using the Wilcoxon rank-sum test.

[0061] Beta diversity: Based on the Bray-Curtis distance matrix, principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) were performed using the R language vegan package, and the significance of differences in community structure between groups was tested using ANOSIM (Analysis of Similarities).

[0062] Differential species analysis: The LEfSe (LDA Effect Size) algorithm was used to identify significantly enriched indicator organisms in the zinnia system, with LDA Score > 3.5 and p < 0.05 as criteria.

[0063] Step 5, Results and Analysis Sequencing data overview: This sequencing effort yielded 3,215,487 high-quality sequences, which were clustered into 2,856 OTUs. Annotated, 1,892 of these OTUs belong to the phylum Arthropoda, covering multiple groups including insects, arachnids, and crustaceans.

[0064] Alpha diversity: The Shannon index of natural enemy groups (including ladybugs, lacewings, and arachnids) at point G0 in the treatment group (4.52 ± 0.21) was significantly higher than that in the control group at point G0 (3.01 ± 0.18) (p < 0.01). Furthermore, the natural enemy diversity in the treatment group gradually decreased from G0 to G15 (G0: 4.52 → G5: 3.98 → G10: 3.45 → G15: 3.12), clearly demonstrating spatial decay of the effect.

[0065] Beta diversity: NMDS analysis (Stress = 0.098) showed that the sample points of the treatment group and the control group were significantly separated (ANOSIM, R = 0.821, p = 0.001), indicating that the zinnia strip significantly altered the arthropod community structure of the surrounding paddy fields.

[0066] Gradient effect and indicator species: Ladybugs (e.g., Heterochromis heterochromis) Harmonia axyridis The relative abundance of *Heteromorpha heteromorpha* and *Heteromorpha spp.* was highest at point G0 in the treatment group (1.85%), significantly higher than that at point G0 in the control group (0.32%). Its abundance decreased with increasing distance, but at point G10 (0.78%) it was still significantly higher than that at the same locus in the control group (0.29%) (p < 0.05), and there was no significant difference between the two at point G15. LEfSe analysis further showed that *Heteromorpha heteromorpha* and *Heteromorpha spp.* Japanese propylaea ) and grass drill spider ( Hylyphantes graminicola It is the most prominent indicator predator species in the zinnia system (LDA Score > 4.0).

[0067] Pest control efficacy assessment: Brown planthoppers at each gradient point in the treatment group ( Nilaparvata is mourning. The average relative abundance of OTUs was 0.95%, significantly lower than that of the control group (1.65%) (p < 0.05), with a pest control efficiency of 42.4%. Meanwhile, higher abundance of ladybug DNA was detected in the eDNA of the flushing solution of rice plants in the treatment group, indirectly proving the enhanced predation activity of natural enemies.

[0068] In summary, this embodiment successfully applied the method of the present invention. In the paddy field ecosystem of Qianshanxia Village, Wuxing District, Huzhou, through a strict comparison between the "zinnia system" and the "weed system", the following precise and quantitative conclusions were obtained: Planting zinnias on the paddy field ridges can significantly improve the biodiversity of natural enemies in the surrounding paddy fields (compared with natural weed paddy field ridges, the Shannon index at point G0 increased by 50.2%).

[0069] Zinnia has a clear spatial gradient in its conservation effect on natural enemies, and its effective ecological radiation range can reach 10 meters, providing direct data support for optimizing the layout of ecological field ridges in this region (such as setting up a functional plant belt every 10-15 meters).

[0070] Compared to natural weed ridges, zinnia strips, by specifically cultivating dominant natural enemies (ladybugs and spiders), can more effectively reduce the population pressure of brown planthopper, a core pest in rice paddies, with a pest control efficiency of over 40%, significantly better than natural vegetation conditions. This demonstrates the outstanding biological control efficacy of artificially constructed plant strips with specific functions.

[0071] In summary, this embodiment successfully applied the method of the present invention to obtain the following precise and quantitative conclusions in the paddy field ecosystem of Qianshanxia Village, Wuxing District, Huzhou: The results of this embodiment show that planting zinnias on paddy field ridges can significantly enhance the diversity of natural enemy communities in surrounding paddy fields (Shannon index increased by 50.2%) and alter their structure, and this effect has a spatial gradient (effective range of approximately 10 meters). More importantly, this enhancement of the natural enemy community occurred simultaneously with a significant decrease (42.4%) in the relative proportion of brown planthoppers in the community, forming a complete ecological chain of evidence: "functional plants conserve natural enemies → changes in natural enemy community structure → reduction in pest pressure". This invention provides a complete and reliable technical solution that successfully solves the problem that traditional methods cannot accurately quantify the ecological efficacy of functional plants, providing powerful molecular tools and decision-making basis for the practice and promotion of ecological agriculture.

[0072] The sequence involved in this invention is as follows: SEQ ID No. 1: Name: mlCOlintF primer DNA type: other DNA Biological origin: synthetic construct 5'-GGWACWGGWTGAACWGTWTAYCCYCC-3' SEQ ID No. 2: Name: jgHCO2198 primer DNA type: other DNA Biological origin: synthetic construct 5′-TANACYTCNGGRTGNCCRAARAAYCA-3′.

Claims

1. A method for assessing the impact of functional vegetation zones along paddy field ridges on arthropod communities within paddy fields, based on eDNA technology, characterized in that... Includes the following steps: S1. Spatial gradient system construction and sampling: Functional plant-rice complex ecosystem and control system were constructed in sequence. Then, based on the spatial grid method, two sets of sampling points were set in the paddy fields of the functional plant-rice complex ecosystem and control system respectively. Then, during the key phenological period of rice, two sets of environmental samples were collected from each set of sampling points of the two systems respectively. S2. eDNA extraction and amplification: eDNA was extracted from each group of environmental samples collected in step S1. Then, the extracted eDNA was amplified by PCR using the universal arthropod primers mlCOlintF and jgHCO2198 to obtain the corresponding amplification products. S3, High-throughput sequencing: Each amplification product obtained in step S2 is sequentially subjected to library construction and high-throughput sequencing to obtain the raw sequencing data of each amplification product. S4. Bioinformatics Analysis: The original sequencing data were sequentially subjected to quality control, splicing, and filtering to obtain high-quality sequences corresponding to each environmental sample. Then, at a certain similarity level, Operational Taxonomic Unit (OTU) clustering was performed. After comparison with taxonomic databases, species annotation was completed to obtain OTU tables for functional plant-rice complex ecosystems and OTU tables for control systems. S5. Quantitative assessment of biological control effectiveness: Based on the two OTU tables, the biodiversity index and relative abundance of target groups in the functional plant-rice complex ecosystem and the control system were calculated respectively. Combined with statistical analysis, the conservation effect of the functional plant zone on the natural enemy insect community, the spatial gradient change of the functional plant zone effect, and the control effect of the functional plant zone on the pest population were quantitatively assessed.

2. The method for assessing the impact of functional vegetation zones along paddy fields on arthropod communities based on eDNA technology, as described in claim 1, is characterized in that... Step S1 specifically involves: S11. Plant functional plant strips on the ridges of the paddy field ecosystem to construct a functional plant-rice composite ecosystem, and use the paddy field ecosystem without functional plant strips on the ridges as a control system. S12. In the functional plant-rice complex ecosystem, a two-dimensional rectangular grid is established on the paddy field plane. The two-dimensional rectangular grid is formed by dividing the interior of the paddy field into equal intervals along the length and width directions. Sampling points are set at each intersection of the two-dimensional rectangular grid. In the control system, sampling points were set at the same spatial locations; S13. During the key phenological stages of rice, multiple environmental samples were collected from multiple sampling points in the functional plant-rice complex ecosystem and the control system, respectively.

3. The method for assessing the impact of functional vegetation zones along paddy fields on arthropod communities based on eDNA technology, as described in claim 2, is characterized in that: In step S11, the functional plant is one or more of zinnia, spider flower, and alfalfa.

4. The method for assessing the impact of functional plants on paddy field ridges on arthropod communities in paddy fields based on eDNA technology, as described in claim 1, is characterized in that: In step S1, the key phenological period of rice includes any day during the rice heading period, the day before rice harvest, and the day after rice harvest; the environmental samples include one or more of soil samples, rice plant cleaning solution samples, and functional plant pollen samples.

5. The method for assessing the impact of functional vegetation zones along paddy fields on arthropod communities based on eDNA technology, as described in claim 1, is characterized in that... Step S2 specifically involves: eDNA of the functional plant-rice complex ecosystem and eDNA of the control system were extracted from environmental samples of the functional plant-rice complex ecosystem and environmental samples of the control system, respectively. Then, PCR amplification of the eDNA of the functional plant-rice complex ecosystem and the control system was performed using universal arthropod primers mlCOlintF and jgHCO2198, respectively, to obtain the amplification products of the functional plant-rice complex ecosystem and the control system.

6. The method for assessing the impact of functional vegetation zones along paddy fields on arthropod communities based on eDNA technology, as described in claim 1, is characterized in that... Step S3 specifically involves: The amplification products of the functional plant-rice complex ecosystem and the control system obtained in step S2 were sequentially subjected to library construction and high-throughput sequencing to obtain the raw sequencing data of the functional plant-rice complex ecosystem and the raw sequencing data of the control system.

7. The method for assessing the impact of functional vegetation zones along paddy fields on arthropod communities based on eDNA technology according to claim 1, characterized in that, Step S4 specifically involves: The multiple raw sequencing data obtained in step S3 are sequentially subjected to quality control, splicing, and filtering to obtain multiple high-quality sequences; then, at a similarity level of 97%, OTU clustering is performed on the multiple high-quality sequences to obtain multiple OTU representative sequences; and the OTU abundance of the multiple OTU representative sequences is calculated. Then, multiple OTU representative sequences were compared with taxonomic databases in turn to complete species annotation of multiple OTU representative sequences, resulting in an OTU table containing species classification information and OTU abundance.

8. The method for assessing the impact of functional vegetation zones along paddy fields on arthropod communities in paddy fields based on eDNA technology, as described in claim 1, is characterized in that... In step S2, the nucleotide sequence of the universal arthropod primer mlCOlintF is shown in SEQ ID No. 1, and the nucleotide sequence of primer jgHCO2198 is shown in SEQ ID No.

2. 5′-GGWACWGGWTGAACWGTWTAYCCYCC-3′, SEQ ID No.1; 5′-TANACYTCNGGRTGNCCRAARAAYCA-3′, SEQ ID No.

2.

9. The method for assessing the impact of functional vegetation zones along paddy fields on arthropod communities based on eDNA technology, as described in claim 1, is characterized in that: In step S4, the taxonomic database is the NCBI NT database.

10. The method for assessing the impact of functional vegetation zones along paddy fields on arthropod communities based on eDNA technology according to claim 1, characterized in that: In step S5, the biodiversity analysis includes Alpha diversity analysis and Beta diversity analysis; the Alpha diversity analysis includes calculating the Chao1 index, Shannon index, Simpson index, and Coverage index; the Beta diversity analysis includes performing PCA analysis, PCoA analysis, or NMDS analysis; the statistical analysis in step S5 includes ANOSIM analysis, Adonis analysis, LEfSe analysis, ANOVA variance analysis, or Wilcoxon rank-sum test analysis; the quantitative assessment specifically involves comparing the relative abundance and diversity index of natural enemy OTUs and the relative abundance of pest OTUs in environmental samples from the functional plant-rice complex ecosystem and the control system, to calculate the enhancement rate of the functional plant's natural enemy conservation effect, the spatial effect range, and the inhibition rate of its pest control effect.

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