Methods and Systems for Insect Species Identification in Green Manure Orchards Based on eDNA

Abstract

This invention relates to the field of biological gene detection technology, and proposes a method and system for insect species identification in green manure orchards based on eDNA. The method includes: collecting eDNA information from several collection points in the green manure orchard over a period of time, and obtaining several orchard DNA sequences at several times from each collection point; obtaining the gene sequence similarity between each orchard DNA sequence and the DNA sequences of each species, screening to obtain several suspected and known species in the green manure orchard, and constructing a food web in the green manure orchard; obtaining the corresponding currently analyzed insect node in the food web for suspected insect species; obtaining the energy flow matching degree of the currently analyzed insect node at the current time period; obtaining the insect category assessment degree of the suspected insect species at the current time period; and determining the insect identification indicators for each suspected insect species, thereby identifying the insect species in the green manure orchard. This invention aims to solve the problem of the impact of the existence of the food web in green manure orchards on the accuracy of insect species identification.

Description

Technical Field

[0001] This invention relates to the field of biological gene detection technology, specifically to a method and system for identifying insect species in green manure orchards based on eDNA. Background Technology

[0002] Green manure orchards have significant advantages over traditional cultivated orchards in terms of insect ecology and pest control. By protecting orchard insects, especially their natural enemies, green manure effectively controls the impact of planting on these insects, ensuring superior pest control capabilities. In contrast, traditional cultivated orchards excessively consume natural resources, leading to a loss of biodiversity and a decline in the natural regulatory functions of the ecosystem. This negatively impacts the habitat of natural enemies, reducing their populations and causing a surge in pest populations, drastically increasing the risk of pests. Green manure orchards effectively maintain the stability of the orchard ecosystem and control pests by controlling the populations of their natural enemies. Therefore, identifying insect species in green manure orchards and monitoring the effectiveness of green manure planting is essential.

[0003] Identifying insect species in green manure orchards allows us to understand their habits, damage levels, and community behavior, enabling us to take targeted control measures in advance and reduce agricultural losses. Identifying orchard insect species based on eDNA technology is a non-invasive, highly sensitive biomonitoring method, particularly suitable for identifying insects with strong concealment or similar morphology. Existing methods often employ DNA macrobarcoding technology, i.e., identification technology based on environmental eDNA, to identify insect species. This involves first obtaining eDNA from various collection points in the green manure orchard, then converting the eDNA into DNA sequences using techniques such as PCR amplification, and finally comparing the results with a gene database to obtain the insect species identification results.

[0004] Traditional eDNA-based insect species identification only compares the amplified DNA sequence with a fixed short DNA fragment in a gene database to determine similarity, without considering the base expression of the entire sequence for comprehensive analysis. This results in insufficient accuracy when comparing with the gene database. Furthermore, traditional eDNA insect species identification only considers gene expression and neglects the food web characteristics of insects in green manure orchards. This can lead to situations where multiple similar insect categories are identified after comparison with the gene database, making further screening impossible. This affects the accuracy of insect species identification in green manure orchards, hindering accurate monitoring of insect conservation effects and consequently impacting the accuracy of green manure orchard ecosystem stability monitoring results. Summary of the Invention

[0005] This invention provides a method and system for insect species identification in green manure orchards based on eDNA, to address the problem that the presence of food webs in existing green manure orchards affects the accuracy of insect species identification. The specific technical solution adopted is as follows:

[0006] This invention proposes a method for identifying insect species in green manure orchards based on eDNA. The method includes the following steps:

[0007] eDNA information was collected from several sampling points in the green manure orchard over a period of time, and several orchard DNA sequences were obtained at several sampling points at several time points.

[0008] The differences in control region length and the number of various bases between the DNA sequences of each orchard and the DNA sequences of each species in the gene database were analyzed to obtain the gene sequence similarity between the DNA sequences of each orchard and the DNA sequences of each species. Then, several suspected and known species of green manure orchards were screened, and a food web of green manure orchards was constructed. For suspected insect species, the current insect node corresponding to them in the food web was obtained, and their local network segments were extracted to obtain several known nodes at the next higher level and the next lower level.

[0009] Based on the temporal correlation of the eDNA information content of the current insect node and the known nodes at the next and next levels, combined with the number of undetermined nodes and the total number of suspected species in the local network segment, the energy flow matching degree of the current insect node in the current time period is obtained; combined with the distribution of known nodes and the current insect node in the local network segment at the collection points in the green manure orchard, and the gene sequence similarity of the suspected insect species, the insect category assessment degree of the suspected insect species in the current time period is obtained.

[0010] Based on the fluctuations in the insect category assessment of suspected insect species at different time periods, insect identification indicators for each suspected insect species were determined to identify insect species in green manure orchards.

[0011] Optionally, the specific method for obtaining the gene sequence similarity between each orchard DNA sequence and each species DNA sequence includes:

[0012] The differences in control region length between each orchard DNA sequence and the DNA sequences of each species in the gene database were analyzed to obtain the gene control region difference factor between each orchard DNA sequence and the DNA sequences of each species in the gene database.

[0013] The base content consistency between the orchard DNA sequence and the species DNA sequence is obtained based on the differences in the content of various bases between any orchard DNA sequence and any species DNA sequence. The base content consistency is negatively correlated with the difference in base content.

[0014] Based on the base content consistency and gene control region difference factor between the DNA sequence of the i-th orchard and the DNA sequence of the j-th species in the gene database, the gene sequence similarity between the DNA sequence of the i-th orchard and the DNA sequence of the j-th species is obtained. The gene sequence similarity is positively correlated with the base content consistency and negatively correlated with the gene control region difference factor.

[0015] Optionally, the specific method for obtaining the gene control region difference factor between each orchard DNA sequence and the DNA sequences of each species in the gene database includes:

[0016] The same orchard DNA sequence can be used as one orchard DNA sequence to obtain several different orchard DNA sequences.

[0017] The control region length of each orchard DNA sequence is obtained. The absolute value of the difference between the control region length of any orchard DNA sequence and the species DNA sequence of any species in the gene database is used as the gene control region difference factor between the orchard DNA sequence and the species DNA sequence.

[0018] Optionally, the specific methods for screening to obtain several suspected and known species in the green manure orchard include:

[0019] Species whose DNA sequences in the gene database have a similarity greater than the similarity threshold with the orchard DNA sequences are considered as several suspected species in the green manure orchard.

[0020] Obtain the gene sequence similarity and corresponding orchard DNA sequence for each suspected species. Then, identify the suspected species corresponding to the maximum gene sequence similarity among all suspected species as known species in the green manure orchard.

[0021] Optionally, the specific method for obtaining the current insect node corresponding to the suspected insect species in the food web and extracting its local network segment to obtain several known upper-level nodes and known lower-level nodes includes:

[0022] Other species besides the known species among the suspected species are denoted as several undetermined suspected species. The undetermined suspected species corresponding to the insect species are denoted as several insect suspected species. The nodes corresponding to the known species are denoted as known nodes. The nodes corresponding to each insect suspected species in the food web are obtained. The insect suspected species with known nodes in their neighboring positions are extracted as the insect suspected species to be analyzed.

[0023] The node corresponding to any suspected insect species to be analyzed in the food web is taken as the current insect node. All neighboring nodes of the current insect node are extracted to form a local network segment of the current insect node. The known nodes in the local network segment whose energy transfer direction points to the current insect node are taken as its next-level known nodes. The known nodes in the local network segment whose energy transfer direction points to the current insect node are taken as its next-level known nodes.

[0024] Optionally, the specific method for obtaining the energy flow matching degree of the currently analyzed insect node in the current time period includes:

[0025] Based on the temporal correlation of the number of eDNA information of the current insect node and the known nodes at the next and next levels, the energy matching degree of the current insect node in the current time period is obtained.

[0026] The nodes corresponding to unidentified suspected species in the food web are designated as unidentified nodes. The number of unidentified nodes, excluding the currently analyzed insect node, is obtained in the local network segment of the currently analyzed insect node. The product of the number of unidentified nodes and the number of unidentified suspected species in the food web is obtained. The ratio of the energy matching degree of the currently analyzed insect node to the product in the current time period is taken as the energy flow matching degree of the currently analyzed insect node in the current time period.

[0027] Optionally, the specific method for obtaining the energy matching degree of the currently analyzed insect node in the current time period includes:

[0028] Using a week as a time period, and taking the most recent week as the current time period, the daily eDNA information corresponding to the orchard DNA sequence of the current insect node under the current time period is obtained as the daily eDNA content of the current insect node.

[0029] A two-dimensional coordinate system was constructed with time-series sampling time as the x-axis and eDNA content as the y-axis. The daily eDNA content of the current insect node under the current time period was mapped, and the eDNA content change curve of the current insect node under the current time period was obtained by fitting the least squares method. The eDNA content change curves of each known node at the previous level and each known node at the next level were also obtained.

[0030] Obtain the Pearson correlation coefficient between the current insect node being analyzed and the eDNA content change curves of each known node at the next higher and lower levels during the current time period. Sum all Pearson correlation coefficients, add them to the total number of known nodes at the next higher and lower levels, and add 1 to obtain the sum, which is used as the eDNA content difference coefficient of the current insect node being analyzed during the current time period. The ratio of 1 to the eDNA content difference coefficient is used as the energy matching degree of the current insect node being analyzed during the current time period.

[0031] Optionally, the specific method for obtaining the insect category assessment degree of the suspected insect species in the current time period includes:

[0032] Obtain several collection points for the eDNA information corresponding to the orchard DNA sequence of the insect node being analyzed on any day within the current time period. Use the spatial centroid of each collection point as the collection centroid of the insect node being analyzed on that day. Obtain the collection centroids of each known node at the next higher level on that day. Use the average Euclidean distance between the insect node being analyzed on that day and the collection centroids of each known node at the next higher level on that day as the energy flow distribution difference of the insect node being analyzed on that day.

[0033] The method for calculating the Insect Category Assessment Degree (IH) of the suspected insect species to be analyzed corresponding to the current insect node in the current time period is as follows:

[0034]

[0035] Where IS represents the gene sequence similarity of the suspected insect species to be analyzed corresponding to the currently analyzed insect node, and Q represents the energy flow matching degree of the currently analyzed insect node in the current time period. This represents the mean of the differences in energy flow distribution among the currently analyzed insect nodes for each day of the current time period; exp() represents an exponential function with the natural constant as the base.

[0036] Optionally, the insect identification indicators for each suspected insect species are obtained using the following method:

[0037] The ratio of the mean to the standard deviation of the insect category assessment score of the suspected insect species to be analyzed corresponding to the current insect node in each time period is used as the insect identification index for the suspected insect species to be analyzed.

[0038] The present invention also proposes an eDNA-based insect species identification system for green manure orchards. The system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the above method.

[0039] The beneficial effects of this invention are as follows: This invention obtains suspected and known species in green manure orchards by matching the similarity of eDNA information with species DNA sequences in a gene database and constructs a food web. Based on the food web, the authenticity of energy flow in suspected species is analyzed, thereby determining the insect category assessment degree for insect species identification. Specifically, the differences in gene control region length and base content between orchard DNA sequences and species DNA sequences in the gene database are analyzed to quantify similarity based on the DNA sequence performance itself. Similar gene control region lengths and base contents are used to match orchard DNA sequences to obtain suspected and known species. Then, a food web for the green manure orchard ecosystem is constructed based on these suspected and known species. Local segments of suspected insect species are extracted for subsequent insect species analysis and identification within the food web. Based on these local segments, the analysis of the current insect node and the known nodes at the next higher level is performed. The negative correlation between the temporal changes in the amount of eDNA information reflects the authenticity of energy flow in the food web. Simultaneously, the energy flow matching degree is corrected by the number of undetermined suspected species, reducing interference from energy flow branches at undetermined nodes. Further analysis of the spatial distribution of eDNA information collection points corresponding to nodes in local network segments, considering the invariant spatial location of energy flow, and combining the gene sequence similarity of suspected insect species, comprehensively quantifies the insect category assessment degree capable of classifying and identifying suspected insect species. Based on traditional eDNA information base matching for identification, the impact of energy flow in the food web on species is considered, ultimately improving the accuracy of insect species identification in green manure orchard ecosystems. This accurate identification of insect species allows for a more precise assessment of the population sizes of various insects in green manure orchards, as well as the conservation effect of green manure planting on orchard insects and their natural enemies, thereby achieving pest control and ecosystem stability monitoring in green manure orchards. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a schematic diagram of the process for identifying insect species in green manure orchards based on eDNA, according to an embodiment of the present invention.

[0042] Figure 2 Flowchart for identifying insect species using eDNA technology;

[0043] Figure 3Schematic diagram of food web construction in green manure orchards;

[0044] Figure 4 This is an example diagram of a local network segment of the insect node currently being analyzed;

[0045] Figure 5 This is a schematic diagram of the changes in eDNA content at various levels of nodes in a local network segment. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Please see Figure 1 The diagram illustrates a flowchart of an eDNA-based method for identifying insect species in green manure orchards, according to an embodiment of the present invention. The method includes the following steps:

[0048] Step S001: Collect eDNA information from several collection points in the green manure orchard for a period of time, and obtain several orchard DNA sequences at several collection points at several times.

[0049] The purpose of this embodiment is to identify insect species based on eDNA collected from green manure orchards. This requires collecting eDNA information from multiple collection points within the orchard over a period of time. Based on this eDNA information, orchard DNA sequences are obtained through amplification and transcription. These sequences are then matched and analyzed with the DNA sequences of various species in a gene database. Figure 2 As shown, it illustrates a flowchart of the eDNA technology for insect species identification in green manure orchards.

[0050] Specifically, collection points were evenly distributed in the green manure orchard, including areas such as water bodies, soil, and air. eDNA information was collected at each collection point once a day for nearly three months. eDNA was extracted and purified using the phenol-chlorine method or a silica membrane kit to obtain DNA fragments. The purified DNA fragments were amplified using PCR amplification technology to obtain the complete DNA sequence corresponding to the eDNA, which was used as the orchard DNA sequence.

[0051] Furthermore, the length of the control region in the DNA sequence is read using regulatory element prediction tools such as Promoter 2.0 and EPD; and the content of each base (A, T, C, and G) in the DNA sequence is read using bioinformatics tools such as BioEdit software.

[0052] Step S002: Analyze the differences in control region length and the number of various bases between the DNA sequences of each orchard and the DNA sequences of each species in the gene database to obtain the gene sequence similarity between the DNA sequences of each orchard and the DNA sequences of each species. Then, screen out several suspected and known species in the green manure orchard and construct a food web for the green manure orchard. For suspected insect species, obtain the current insect node corresponding to them in the food web and extract their local network segments to obtain several known nodes at the next higher level and the next lower level.

[0053] It should be noted that after obtaining the orchard DNA sequence, that is, the DNA sequence obtained by amplification and transcription through eDNA information, it is necessary to conduct a similarity assessment with the DNA sequences in the gene database in order to obtain the species that may exist in the green manure orchard, and to construct a biological network based on the obtained species, so as to provide a basis for the final identification of suspected species in the subsequent biological network.

[0054] Preferably, in one embodiment of the present invention, the differences in control region length and the number of various bases between each orchard DNA sequence and the DNA sequences of various species in the gene database are analyzed to obtain the gene sequence similarity between each orchard DNA sequence and the DNA sequences of various species, thereby screening out several suspected and known species of green manure orchards, and constructing a food web for green manure orchards. The specific methods include:

[0055] It should be noted that a single cell contains hundreds or thousands of mitochondrial DNA molecules, while each cell contains only two copies of nuclear DNA. Therefore, after eDNA is converted into a DNA sequence, it mainly exists as a mitochondrial DNA sequence. For mitochondrial genes of ecosystem species such as insects, the control region is the non-coding region with the highest AT content (usually greater than 90%) in the mitochondrial genome, responsible for replication and transcription regulation. Variation in control region length is the main cause of interspecies differences. For example, the control region of the genus Cazira in the family Stink Bugs is as long as 1206-1213 bp, while the control region of the worm-like moth is 417 bp long. Therefore, by taking advantage of the characteristic that the length of the DNA sequence control region varies little within the same species but greatly between different species, we can calculate the gene control region difference factor between orchard DNA sequences and species DNA sequences.

[0056] Specifically, if there are identical orchard DNA sequences at various collection points at certain times, and the same orchard DNA sequence is considered as one orchard DNA sequence, then several orchard DNA sequences are obtained. The control region length of each orchard DNA sequence is obtained, and the absolute value of the difference between the control region length of any orchard DNA sequence and the species DNA sequence of any species in the gene database is used as the gene control region difference factor between the orchard DNA sequence and the species DNA sequence.

[0057] It should be further noted that DNA sequences contain a large number of bases, mainly divided into four categories: adenine (A), guanine (G), cytosine (C), and thymine (T). The base ratios in DNA sequences exhibit significant species specificity, primarily manifested in differences in AT content and base shifts. For example, the AT base ratio in chironomids is approximately 79.45%, while in leafhoppers it is approximately 75%. Regarding base shifts, most insect mtDNA (mitochondrial DNA) shows a higher A content than T (positive AT shift) and a higher C content than G (negative GC shift), but groups such as braconid bees in the order Hymenoptera exhibit reverse shifts. Since the differences in the number of the same bases in different DNA sequences can simultaneously reflect AT content and base shift levels, the species specificity of the base ratios in insect mitochondrial DNA can be used to quantify the base content consistency between orchard DNA sequences and species DNA sequences.

[0058] Specifically, based on the differences in the content of various bases between any orchard DNA sequence and any species DNA sequence, the base content consistency between the orchard DNA sequence and the species DNA sequence is obtained, and the base content consistency is negatively correlated with the difference in base content.

[0059] As an example, the base content similarity U between the DNA sequence of the i-th orchard and the DNA sequence of the j-th species in the gene database is... i,j The calculation method is as follows:

[0060]

[0061] Where M represents the number of base types, namely A, G, C, and T, a total of four base types; R i,m R represents the number of bases of type m in the DNA sequence of the i-th orchard. j,m The number of bases of type m in the DNA sequence of species j is shown; || represents the absolute value function, and exp() represents the exponential function with the natural constant as the base. In this embodiment, the exp(-x) model is used to present the inverse proportional relationship and normalization processing. x is the input of the model. The implementer can set the inverse proportional function and normalization function according to the actual situation.

[0062] It should be noted that the smaller the difference in the content of the same type of bases between the orchard DNA sequence and the species DNA sequence, and the smaller the difference in the content of all types of bases, the more consistent the base content becomes.

[0063] Furthermore, the ratio of the base content similarity to the gene control region difference factor between the i-th orchard DNA sequence and the j-th species DNA sequence in the gene database is used as the gene similarity coefficient between the i-th orchard DNA sequence and the j-th species DNA sequence. The gene similarity coefficient between each orchard DNA sequence and the species DNA sequences of each species in the gene database is linearly normalized, and the result is used as the gene sequence similarity between each orchard DNA sequence and the species DNA sequences of each species in the gene database.

[0064] Furthermore, a preset similarity threshold is set. In this embodiment, the similarity threshold is described as 0.88. Species whose DNA sequences in the gene database have a similarity greater than the similarity threshold with orchard DNA sequences are considered as several suspected species in the green manure orchard. It should be noted that if there is gene sequence similarity between the gene sequence of any species and multiple orchard DNA sequences, the maximum value is taken as the gene sequence similarity corresponding to that species. At the same time, the gene sequence similarity corresponding to each suspected species and the corresponding orchard DNA sequence are obtained. Several suspected species corresponding to the maximum value of the gene sequence similarity of all suspected species are considered as several known species in the green manure orchard.

[0065] It should be noted that the greater the consistency of base content, and the smaller the difference factor of the gene control region between the orchard DNA sequence and the species DNA sequence, the greater the similarity between the orchard DNA sequence and the species DNA sequence, thus obtaining the gene sequence similarity.

[0066] Furthermore, a food web is constructed based on all suspected species in the green manure orchard, where all suspected species are considered to exist in the green manure orchard ecosystem and construct a food web. The food web construction is a prior art technique and will not be elaborated upon in this embodiment; for example... Figure 3 As shown, Figure 3 The square represents known species, the circle represents undetermined suspected species, that is, other suspected species besides known species, and the arrow indicates the direction of energy transfer.

[0067] It should be further explained that a food web consists of producers and consumers, and consumers are composed of consumers at various levels with predator-prey relationships. Energy flows and is transferred among the nodes of the food web at each level. The direction of energy transfer is determined by the predator-prey relationships at each node. Therefore, the predator-prey relationships in a food web can reflect the survival impact of node species to a certain extent. The increase or decrease in the number of DNA sequences collected over time can reflect the increase or decrease in the number of species expressing that gene. Thus, it is possible to infer the neighboring unidentified insect nodes from the known species nodes in the food web. Therefore, it is necessary to first extract local segments of the food web for unidentified suspected species for subsequent analysis.

[0068] Preferably, in one embodiment of the present invention, the method for obtaining the current analyzed insect node corresponding to the suspected insect species in the food web and extracting its local network segment to obtain several known nodes at the upper and lower levels includes:

[0069] Other species besides the known species in the suspected species list are categorized as several undetermined suspected species. Undetermined suspected species corresponding to insect species are categorized as several insect suspected species. Nodes corresponding to known species are categorized as known nodes. Nodes corresponding to each insect suspected species in the food web are obtained. Insect suspected species with known nodes in their vicinity (where energy transfer relationships exist between nodes) are extracted and designated as the insect suspected species to be analyzed. The node corresponding to any insect suspected species in the food web is designated as the current insect node to be analyzed. Simultaneously, all neighboring nodes of the current insect node are extracted to form a local network segment of the current insect node. Known nodes in the local network segment whose energy transfer direction points to the current insect node are designated as their parent-level known nodes. Known nodes in the local network segment whose energy transfer direction points to the current insect node are designated as their next-level known nodes, such as... Figure 4 As shown.

[0070] To this end, we first analyzed the differences in gene control region length and base content between the orchard DNA sequence and the species DNA sequence in the gene database. We then quantified the similarity based on the DNA sequence itself. Species matching was performed on the orchard DNA sequence based on similar gene control region length and base content to obtain suspected and known species. Subsequently, a food web of the green manure orchard ecosystem was constructed based on the suspected and known species. Local segments of the suspected insect species were extracted for subsequent insect species analysis and identification under the food web.

[0071] Step S003: Based on the temporal correlation of the eDNA information content of the current insect node and the known nodes at the next and next levels, and combined with the number of undetermined nodes and the total number of suspected species in the local network segment, the energy flow matching degree of the current insect node in the current time period is obtained; combined with the distribution of known nodes and the current insect node in the local network segment at the collection points in the green manure orchard, and the gene sequence similarity of the suspected insect species, the insect category assessment degree of the suspected insect species in the current time period is obtained.

[0072] It should be noted that if the number of eDNA samples collected from the next level known nodes increases sharply in a short period of time, while the number of eDNA samples collected from the current insect node decreases in a short period of time, it indicates that the current insect node satisfies the mutual influence relationship between the number of predators and prey, that is, it satisfies the energy flow relationship in the food web. This suggests that the suspected insect species of the current insect node is more likely to exist in the actual green manure orchard ecology. The same applies to the known nodes at the next higher level that represent prey. This is used to analyze the energy matching degree. At the same time, the number of nodes of suspected species and undetermined suspected species in local network segments is combined to comprehensively quantify the energy flow matching degree.

[0073] Preferably, in one embodiment of the present invention, based on the temporal correlation of the number of eDNA information of the currently analyzed insect node and the known nodes at the next higher level, combined with the number of undetermined nodes and the total number of suspected species in the local network segment, the energy flow matching degree of the currently analyzed insect node in the current time period is obtained, including the following specific methods:

[0074] Using a week as a time period, and the most recent week as the current time period, the daily eDNA information corresponding to the orchard DNA sequence of the currently analyzed insect node is obtained. This is the total number of eDNA information corresponding to the orchard DNA sequence collected daily at each sampling point, and is taken as the daily eDNA content of the currently analyzed insect node. A two-dimensional coordinate system is constructed with the time-series sampling time as the x-axis and the eDNA content as the y-axis to map the daily eDNA content of the currently analyzed insect node in the current time period. The least squares method is used for fitting to obtain the eDNA content change curve of the currently analyzed insect node in the current time period. Similarly, the eDNA content change curves of each known node at the previous level and each known node at the next level are obtained, such as... Figure 5 As shown.

[0075] Furthermore, the Pearson correlation coefficient between the current insect node being analyzed and the eDNA content change curves of each known node at the next higher level is obtained in the current time period. The sum of all Pearson correlation coefficients is added to the total number of known nodes at the next higher level, and then 1 is added to obtain the sum value as the eDNA content difference coefficient of the current insect node being analyzed in the current time period. The ratio of 1 to the eDNA content difference coefficient is taken as the energy matching degree of the current insect node being analyzed in the current time period.

[0076] It should be noted that the closer the eDNA content change curves are to a negative correlation, the closer the Pearson correlation coefficient is to -1. This better matches the energy flow characteristics between predators and prey in a food web, and the smaller the eDNA content difference coefficient will be, and the closer it will be to 1. The purpose of adding 1 is not only to avoid the eDNA content difference coefficient being 0, which would make the ratio meaningless, but also to avoid the uncertainty in cases where the insect node being analyzed only has a known node at the previous level or only has a known node at the next level. The better it matches the energy flow characteristics, the greater the energy matching degree.

[0077] It should be further noted that the number of unidentified nodes in a local network segment represents branches of energy transfer relationships. The more nodes there are, the less effective the energy matching degree becomes. At the same time, the more unidentified suspected species there are in the food web, the greater the interference of nodes in the external links of the local network segment on the energy flow matching degree of the currently analyzed insect nodes. Therefore, the energy flow matching degree is obtained based on the energy matching degree.

[0078] Specifically, the nodes corresponding to unidentified suspected species in the food web are designated as unidentified nodes. The number of unidentified nodes, excluding the currently analyzed insect node, is obtained in the local network segment of the currently analyzed insect node. The product of the number of unidentified nodes and the number of unidentified suspected species in the food web is obtained. The ratio of the energy matching degree of the currently analyzed insect node to the product in the current time period is taken as the energy flow matching degree of the currently analyzed insect node in the current time period.

[0079] Preferably, in one embodiment of the present invention, the insect category assessment degree of the suspected insect species in the current time period is obtained by combining the distribution of known nodes in the local network segment and the current insect analysis node at the collection points in the green manure orchard, as well as the gene sequence similarity corresponding to the suspected insect species. The specific method includes:

[0080] To obtain several collection points for the eDNA information corresponding to the orchard DNA sequence of the insect node being analyzed on any day within the current time period, the centroid of the spatial location of each collection point is taken as the collection centroid of the insect node being analyzed on that day. The collection centroids of all known nodes at the next higher level on that day are also obtained. The average Euclidean distance between the insect node being analyzed on that day and the collection centroids of all known nodes at the next higher level is taken as the energy flow distribution difference of the insect node being analyzed on that day. The method for calculating the insect category assessment degree (IH) of the suspected insect species to be analyzed corresponding to the insect node being analyzed in the current time period is as follows:

[0081]

[0082] Where IS represents the gene sequence similarity of the suspected insect species to be analyzed corresponding to the currently analyzed insect node, and Q represents the energy flow matching degree of the currently analyzed insect node in the current time period. This represents the mean of the energy flow distribution differences of the currently analyzed insect nodes across all days in the current time period; exp() represents an exponential function with the natural constant as the base. In this embodiment, the exp(-x) model is used to present the inverse proportional relationship and normalization processing, where x is the input of the model. Implementers can set the inverse proportional function and normalization function according to the actual situation; norm represents a linear normalization function, and the normalization object is the corresponding node of all suspected insect species to be analyzed in the food web in each time period.

[0083] It should be noted that the greater the gene sequence similarity, the greater the likelihood that the suspected species corresponds to the orchard DNA sequence, and the greater the possibility of classifying the orchard DNA sequence based on the suspected species. Simultaneously, the energy flow distribution difference is used as a weight to restrict the energy flow matching degree. That is, the closer the known nodes at the next higher level are to the current insect analysis node in terms of collection point location, the more it conforms to the spatial invariance characteristic of energy flow, and the greater the reference value of the energy flow matching degree. A higher energy flow matching degree indicates a more realistic reflection of the green manure orchard ecosystem in the food web, and a greater likelihood that the suspected species actually exist. It should be specifically noted that insects that are not to be analyzed among the suspected insect species are not included in the insect category assessment at this step; that is, they are awaiting further processing because local network segments cannot be obtained.

[0084] Thus, based on local network segments, the negative correlation between the temporal changes in the amount of eDNA information between the currently analyzed insect nodes and the known nodes at the next higher level is analyzed to reflect the authenticity of energy flow in the food web. At the same time, the energy flow matching degree is obtained by correcting for the number of undetermined suspected species, reducing the interference of energy flow branches of undetermined nodes. Furthermore, the spatial distribution of the collection points corresponding to the eDNA information of the nodes in the local network segments is analyzed. Considering the invariant spatial location of energy flow, combined with the gene sequence similarity corresponding to the suspected insect species, the insect category assessment degree that can classify and identify the suspected insect species is comprehensively quantified.

[0085] Step S004: Based on the fluctuation of the insect category assessment degree of suspected insect species in each time period, determine the insect identification index of each suspected insect species, and identify the insect species in the green manure orchard.

[0086] Specifically, for the current insect analysis node, the insect category assessment scores for each time period in the past three months are obtained (the past three months correspond to 13 weeks, with a total of 13 time periods). The ratio of the mean to the standard deviation of the insect category assessment score of the suspected insect species to be analyzed corresponding to the current insect analysis node in each time period is used as the insect identification index for the suspected insect species to be analyzed.

[0087] It should be noted that the higher the mean and the smaller the standard deviation of the insect category assessment score over the past three months, the higher the overall insect category assessment score remains and the smaller the fluctuation, resulting in greater accuracy in identification and classification.

[0088] Furthermore, the three insect species with the highest insect identification indicators among all suspected insect species to be analyzed are designated as known species and identified as actual insect species existing in the green manure orchard ecosystem. It should be noted that if the number of suspected insect species to be analyzed is less than three, all insect species to be analyzed are designated as known species, and their corresponding nodes in the food web are designated as known nodes. This process continues to acquire local segments of other suspected insect species not to be analyzed in the updated food web, following the above method, until three insect species are identified as actual insect species existing in the green manure orchard ecosystem. Insect species identified among the known species when acquiring suspected species are all identified as actual insect species existing in the green manure orchard ecosystem.

[0089] Furthermore, since eDNA information is collected daily in the green manure orchard, the insect species actually existing in the green manure orchard ecosystem can be updated in real time using the methods described above after the eDNA information is obtained each day, thereby achieving insect species identification in the green manure orchard. By identifying insect species in the green manure orchard, the population size of various insect species in the green manure orchard can be estimated. Based on the estimation results, the conservation effect of planting green manure on orchard insects and their natural enemies can be analyzed. At the same time, the population control results of various pests can be obtained. Based on the conservation effect of orchard insects and their natural enemies, as well as the population control results of pests, the effective control of the green manure orchard on the stability of the ecosystem can be reflected, thus avoiding the problem of excessive consumption of natural resources in green manure orchards, which affects the stability of the ecosystem and leads to a surge in pest populations. This effectively achieves pest control through planting green manure.

[0090] Thus, by matching the similarity of eDNA information with the DNA sequences of species in the gene database, suspected and known species in green manure orchards are obtained and a food web is constructed. Based on the food web, the authenticity of energy flow in suspected species is analyzed, and the insect category assessment degree is determined for insect species identification. In addition to identification based on traditional eDNA information base matching, the impact of energy flow in the food web on species is considered, ultimately improving the accuracy of insect species identification in green manure orchard ecosystems. Through accurate insect species identification, a more accurate population of various insects in green manure orchards can be obtained, as well as the conservation effect of planting green manure on orchard insects and natural enemies, thereby achieving pest control and ecosystem stability monitoring in green manure orchards.

[0091] Another embodiment of the present invention provides an eDNA-based system for identifying insect species in green manure orchards. The system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the above-described method steps S001 to S004.

[0092] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying insect species in orchards based on eDNA, characterized by, The method comprises the following steps: Collecting eDNA information of a green manure orchard for a period of time at a plurality of collection points in the green manure orchard, and obtaining a plurality of orchard DNA sequences at a plurality of time points of each collection point; Analyzing the difference in control region length between each orchard DNA sequence and the DNA sequence of each species in the gene database, and the difference in the number of each type of base, to obtain the gene sequence similarity between each orchard DNA sequence and the DNA sequence of each species, and then screening to obtain a plurality of suspected species and known species of the green manure orchard, and constructing a food web of the green manure orchard; obtaining the corresponding current analysis insect node of the insect suspected species in the food web, and extracting the local network segment to obtain a plurality of upper and lower known nodes; Based on the time sequence change correlation of the eDNA information content of the current analysis insect node and the upper and lower known nodes, and combining the number of undetermined nodes in the local network segment and the total number of suspected species, the energy flow matching degree of the current analysis insect node in the current period is obtained; combining the distribution of the known nodes and the current analysis insect node at the collection points in the green manure orchard, and the gene sequence similarity corresponding to the insect suspected species, the insect category evaluation degree of the insect suspected species in the current period is obtained; Based on the fluctuation of the insect category evaluation degree of the insect suspected species in each period, the insect identification index of each insect suspected species is determined to identify the insect species in the green manure orchard.

2. The eDNA-based green manure orchard insect species identification method according to claim 1, characterized in that, The method for obtaining the gene sequence similarity between each orchard DNA sequence and the DNA sequence of each species comprises the following specific method: Analyzing the difference in control region length between each orchard DNA sequence and the DNA sequence of each species in the gene database to obtain the gene control region difference factor between each orchard DNA sequence and the DNA sequence of each species in the gene database; According to the difference in the content of each type of base between any one orchard DNA sequence and the DNA sequence of any one species, the base content consistency degree between the orchard DNA sequence and the species DNA sequence is obtained, and the base content consistency degree is negatively correlated with the difference in base content; According to the base content consistency degree and the gene control region difference factor between the i-th orchard DNA sequence and the DNA sequence of the j-th species in the gene database, the gene sequence similarity between the i-th orchard DNA sequence and the DNA sequence of the j-th species is obtained, and the gene sequence similarity is positively correlated with the base content consistency degree, and the gene sequence similarity is negatively correlated with the gene control region difference factor. 3.The eDNA-based green manure orchard insect species identification method according to claim 2, characterized in that, The method for obtaining the gene control region difference factor between each orchard DNA sequence and the DNA sequence of each species in the gene database comprises the following specific method: The same orchard DNA sequence is used as an orchard DNA sequence to obtain a plurality of orchard DNA sequences; Obtaining the control region length of each orchard DNA sequence, and taking the absolute value of the difference in control region length between any one orchard DNA sequence and the DNA sequence of any one species in the gene database as the gene control region difference factor between the orchard DNA sequence and the species DNA sequence. 4.The eDNA-based green manure orchard insect species identification method according to claim 1, characterized in that, The screening obtains several suspected species and known species of green manure orchard, including the specific method that: The species corresponding to the species DNA sequence with the similarity greater than the similarity threshold between the gene database and the orchard DNA sequence is taken as several suspected species of green manure orchard; The gene sequence similarity corresponding to each suspected species and the corresponding orchard DNA sequence are obtained, and the maximum value of the gene sequence similarity corresponding to all suspected species is taken as several known species of green manure orchard.

5. The eDNA-based green manure orchard insect species identification method according to claim 4, characterized in that, The corresponding current analysis insect node of the suspected species in the food web is obtained, and the local network segment is extracted to obtain the upper and lower known nodes, including the specific method that: The other species in the suspected species except the known species are recorded as several uncertain suspected species, and the uncertain suspected species corresponding to the insect species are recorded as several insect suspected species; the node corresponding to the known species is recorded as a known node, the node corresponding to each insect suspected species in the food web is obtained, and the insect suspected species with the known node in the adjacent position are extracted as the insect suspected species to be analyzed; The node corresponding to any insect suspected species to be analyzed in the food web is taken as the current analysis insect node, all the nodes in the adjacent position of the current analysis insect node are extracted to form the local network segment of the current analysis insect node, and the known node with the energy transfer direction pointing to the current analysis insect node in the local network segment is taken as the upper known node; the known node with the energy transfer direction pointing to the current analysis insect node in the local network segment is taken as the lower known node. 6.The eDNA-based green manure orchard insect species identification method according to claim 5, characterized in that, The energy flow matching degree of the current analysis insect node in the current period is obtained, including the specific method that: Based on the time sequence change correlation of the eDNA information quantity of the current analysis insect node and the upper and lower known nodes, the energy matching degree of the current analysis insect node in the current period is obtained; The node corresponding to the uncertain suspected species in the food web is taken as an uncertain node, and the number of uncertain nodes in the local network segment of the current analysis insect node except the current analysis insect node is obtained; The product of the number of uncertain nodes and the number of uncertain suspected species in the food web is obtained, and the ratio of the energy matching degree of the current analysis insect node in the current period to the product is taken as the energy flow matching degree of the current analysis insect node in the current period.

7. The eDNA-based green manure orchard insect species identification method according to claim 6, characterized in that, The energy matching degree of the current analysis insect node in the current period is obtained, including the specific method that: A week is taken as a period, the last week is taken as the current period, the eDNA information quantity of the orchard DNA sequence corresponding to the current analysis insect node in each day of the current period is obtained as the eDNA content of the current analysis insect node in each day; A two-dimensional coordinate system is constructed with the time sequence sampling time as the abscissa and the eDNA content as the ordinate, the eDNA content of the current analysis insect node in each day of the current period is mapped, and the eDNA content change curve of the current analysis insect node in the current period is obtained by least square fitting; the eDNA content change curves of each upper known node and each lower known node are obtained; The Pearson correlation coefficient between the eDNA content change curve of the current analysis insect node and each upper and lower known node in the current period is obtained, the sum of all Pearson correlation coefficients is added to the total number of upper and lower known nodes, and then 1 is added to obtain a sum value, which is taken as the eDNA content difference coefficient of the current analysis insect node in the current period; the ratio of 1 to the eDNA content difference coefficient is taken as the energy matching degree of the current analysis insect node in the current period. 8.The eDNA-based green manure orchard insect species identification method according to claim 5, characterized in that, The specific method for obtaining the insect category evaluation degree of the insect suspected species in the current period includes: Obtain the eDNA information of a plurality of collection points of the orchard DNA sequence corresponding to the current analysis insect node on any day in the current period, and take the spatial position centroid of each collection point as the collection centroid of the current analysis insect node on the day; Obtain the collection centroid of each upper and lower known node on the day, and take the average of the Euclidean distances between the collection centroid of the current analysis insect node and the collection centroid of each upper and lower known node on the day as the energy flow distribution difference of the current analysis insect node; The calculation method of the insect category evaluation degree of the insect suspected species corresponding to the current analysis insect node in the current period is as follows: Wherein, IS represents the genetic sequence similarity of the insect suspected species corresponding to the to-be-analyzed insect corresponding to the current analysis insect node, Q represents the energy flow matching degree of the current analysis insect node in the current period, represents the mean value of the energy flow distribution difference of the current analysis insect node in each day of the current period; exp() represents the exponential function with the natural constant as the base. 9.The eDNA-based green manure orchard insect species identification method according to claim 5, characterized in that, The specific method for obtaining the insect identification index of each insect suspected species is as follows: The ratio of the average of the insect category evaluation degrees of the insect suspected species corresponding to the current analysis insect node in each period to the standard deviation is taken as the insect identification index of the insect suspected species to be analyzed.

10. An eDNA-based green manure orchard insect species identification system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to realize the steps of the green manure orchard insect species identification method based on eDNA according to any one of claims 1-9.

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