Multi-dimensional analysis and evaluation method for molecular feeding habits of coral reef phytophagous fishes

Abstract

The invention relates to the field of marine organism ingestion, in particular to a multi-dimensional analysis and evaluation method for molecular feeding habits of coral reef phytophagous fishes, which comprises the following steps of: determining a specific primer aiming at complex characteristics of mixed food residues in a gastric substance sample, and performing multiple PCR (Polymerase Chain Reaction) amplification on a target gene to obtain DNA (Deoxyribose Nucleic Acid) bar code sequence data of a target fish food source; performing multi-stage comparative analysis on the DNA bar code sequence data and sequence data in a DNA reference database; constructing a molecular characteristic sequence map of different species in the gastric contents according to an analysis result; and according to the molecular characteristic sequence map, determining a feeding preference index of the target fish so as to evaluate the regulation and control capability of the population structure of the target fish on the growth of the macroalgae in the coral reef ecosystem. According to the method, the feeding habit composition of the coral reef phytophagous fishes can be comprehensively analyzed, a scientific basis is provided for protection and management of a coral reef ecological system, and the efficiency of feeding habit analysis and the accuracy of ecological evaluation are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of marine biological feeding, and in particular to a multi-dimensional analysis and evaluation method of the molecular feeding habits of coral reef herbivorous fish. Background Art

[0002] Coral reef ecosystems are known as "tropical rainforests in the ocean" due to their extremely high biodiversity and ecological functions. They play an irreplaceable role in protecting coastlines, maintaining marine biodiversity, and promoting global carbon cycles. However, in recent years, the degradation of coral reefs has become increasingly serious worldwide, and the outbreak of large algae has become one of the major threats to coral reef ecosystems. The overgrowth of large algae not only competes with corals for light and space resources, but also inhibits the growth of corals through allelopathic effects, leading to significant changes in the structure and function of coral reef ecosystems. Studies have shown that herbivorous fish play a key role in controlling large algae communities, and the imbalance of their population structure may lead to the transition from coral phase to algae phase, thereby accelerating the degradation of coral reefs. Therefore, analyzing the feeding characteristics of herbivorous fish in coral reefs and evaluating their ability to regulate algae communities are of great significance for understanding the growth mechanism of large algae in coral reef ecosystems and formulating effective protection strategies. However, traditional feeding analysis methods (such as stomach content microscopy) have problems such as low efficiency and limited information, which makes it difficult to meet the needs of large-scale and multi-dimensional feeding analysis. With the development of DNA sequencing technology, high-throughput sequencing technology based on DNA barcodes has provided a new solution for the study of fish feeding habits. It can quickly and accurately identify the food composition in stomach contents, and provides important technical support for the multi-dimensional analysis of the feeding habits and ecological function evaluation of coral reef herbivorous fish. Summary of the invention

[0003] The present invention overcomes the shortcomings of the prior art and provides a multi-dimensional analysis and evaluation method for the molecular feeding habits of coral reef herbivorous fish.

[0004] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: The present invention discloses a multi-dimensional analysis and evaluation method of the molecular feeding habits of coral reef herbivorous fish, which is characterized by comprising the following steps: Collect stomach samples of target fish that are representative in time and space and construct a multi-dimensional sample set covering different ecological regions and seasonal changes; According to the complex characteristics of mixed food residues in stomach content samples, specific primers were determined and multiplex PCR amplification of target genes was performed to obtain DNA barcode sequence data of the food source of target fish; Perform multi-level comparison analysis on the DNA barcode sequence data and sequence data in a DNA reference database to obtain analysis results; construct molecular characteristic sequence maps of different species in gastric contents according to the analysis results; The feeding preference index of the target fish is determined based on the molecular characteristic sequence map to evaluate the regulatory ability of the target fish population structure on the growth of large seaweeds in the coral reef ecosystem.

[0005] Preferably, target fish stomach content samples with temporal and spatial representativeness are collected and a multi-dimensional sample set covering different ecological regions and seasonal changes is constructed, specifically: According to the spatial heterogeneity of coral reef ecosystems, the target area is divided into different ecological units, including nearshore, offshore, shallow water and deep water areas; Determine the density distribution values ​​of target fish in different ecological units in combination with historical ecological data, and mark ecological units with density distribution values ​​greater than a preset density threshold as target ecological units; In the target ecological unit, preset sampling time and preset sampling points are designed according to seasonal variation rules, and the target fish are captured at the preset sampling points according to the preset sampling time; Use non-destructive sampling techniques to obtain stomach samples of target fish and record environmental parameters and fish biological characteristics at preset sampling points; The collected gastric contents samples were subjected to standardized pretreatment, including removal of impurities, cryopreservation and homogenization; The preprocessed samples are stored according to ecological units and seasons to construct a multidimensional sample set containing spatial dimensions, temporal dimensions and environmental parameters.

[0006] Preferably, specific primers are determined based on the complex characteristics of mixed food residues in the stomach content sample and multiple PCR amplification is performed on the target gene to obtain DNA barcode sequence data of the food source of the target fish, specifically: Based on the conserved sequence regions of the target genes and combined with the genomic data of coral reef macroalgae and symbiotic organisms, specific primer pairs with broad coverage were determined; DNA was extracted from the pre-treated gastric contents samples, and the concentration and quality of the extracted DNA were tested; Performing multiple PCR amplification on the target gene according to the specific primers, and performing gel electrophoresis detection on the amplified products to verify the amplification effect until the amplification effect meets the requirements; The multiplex PCR products were purified to remove primer dimers and non-specific amplification products, and DNA fragments of target size were obtained through fragment screening; The purified DNA fragments were used for library construction, including end repair, adapter ligation and PCR enrichment, and paired-end sequencing was performed in combination with a high-throughput sequencing platform to generate DNA barcode sequence data.

[0007] Preferably, the DNA barcode sequence data is subjected to a multi-level comparison analysis with the sequence data in the DNA reference database to obtain the analysis results, specifically: Performing a primary comparison between the DNA barcode sequence data and the sequence data in the DNA reference database based on a local sequence comparison algorithm, and marking the DNA barcode sequence data with a similarity greater than a preset similarity as an initial matching sequence; Based on the global sequence alignment algorithm, the initial matching sequences are finely aligned, and the initial matching sequences with a degree of overlap greater than a preset degree of overlap are marked as representative sequences; each representative sequence is compared one by one to obtain the sequence alignment score and the length of the overlapping region between the representative sequences; Combined with the species classification information in the DNA reference database, each representative sequence is annotated to determine its corresponding species, and the species annotation information of each representative sequence is obtained; Based on the species annotation information, the number of species sequences corresponding to each representative sequence was counted, and the percentage of the number of each species sequence in the total number of all sequences was calculated to obtain the relative abundance of the species corresponding to each representative sequence; The analysis results were generated based on the sequence alignment scores and overlapping region lengths between representative sequences, species annotation information for each representative sequence, the number of species sequences, and the relative abundance of species.

[0008] Preferably, a molecular characteristic sequence map of different species in the gastric contents is constructed according to the analysis results, specifically: According to the species annotation information of the representative sequences in the analysis results, an initial node set is constructed, where each node represents a species and is assigned species taxonomic characteristics and sequence characteristics as node attributes; The edge weights between nodes are calculated based on the sequence alignment scores and the length of the overlapping regions between representative sequences, and a weighted edge set is constructed to form an initial graph structure; Map the nodes and edges in the initial graph structure to a low-dimensional vector space, retain the sequence similarity and taxonomic association between species, and generate embedded representations of nodes and edges; The embedded representation is input into the graph neural network model, and the high-order features of the nodes are extracted through multi-layer graph convolution operations. The importance of different nodes and edges is dynamically adjusted in combination with the attention mechanism to optimize the representation ability of the graph structure. Using the output results of the graph neural network, combined with the number of species sequences and relative abundance information, a molecular characteristic sequence map of different species in gastric contents was constructed, where the node size represents the species abundance, and the edge weight represents the sequence similarity between species. The topological structure and key features of the map were presented through visualization technology.

[0009] Preferably, the feeding preference index of the target fish is determined according to the molecular characteristic sequence map to evaluate the regulatory ability of the target fish population structure on the growth of large algae in the coral reef ecosystem, specifically: Based on the size of nodes and edge weight information in the molecular feature sequence map, the relative abundance of each seaweed species and its centrality index in the fish feeding network are extracted to quantify the feeding preference of target fish for different seaweed species and obtain the feeding preference index of target fish. Combined with the functional characteristics of seaweed communities in coral reef ecosystems, a seaweed functional characteristic matrix is ​​constructed, and the feeding preference index is correlated with the functional characteristic matrix to determine the feeding contribution of target fish to key functional groups; Using ecological network analysis methods, we analyzed the trophic level position and feeding niche width of the target fish in the coral reef food web, and evaluated their regulatory ability on the seaweed community and their functional importance in the ecosystem; Combined with historical ecological data and environmental parameters, a correlation model between the target fish population density and the dynamics of the seaweed community was established to simulate the succession trend of the seaweed community under different population densities and the growth changes of large seaweed in the coral reef ecosystem; By setting ecological thresholds, the regulatory capacity of target fish population density on coral reef ecosystems is evaluated and an assessment report is generated.

[0010] The environmental parameters include water temperature, salinity and light intensity; the biological characteristics of fish include species, body length and weight. The functional characteristics include growth rate, competitiveness and influence on corals. The target genes include rbcL gene and 18SrRNA gene. The ecological thresholds include seaweed coverage and coral survival rate.

[0011] The present invention solves the technical defects existing in the background technology, and has the following beneficial effects: the present invention can comprehensively and accurately analyze the diet composition of coral reef herbivorous fish and construct a molecular characteristic sequence map through multi-dimensional sample collection, specific primer design, high-throughput sequencing and multi-level comparison analysis; based on the map, the feeding preference index is further quantified to evaluate the regulatory ability of the target fish population on the coral reef ecosystem, which provides a scientific basis for the protection and management of the coral reef ecosystem and effectively improves the efficiency of diet analysis and the accuracy of ecological assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0013] Figure 1 A first method flow chart of a multi-dimensional analysis and evaluation method of the molecular feeding habits of coral reef herbivorous fish; Figure 2 This is a flow chart of the second method of the multi-dimensional analysis and evaluation method of the molecular feeding habits of coral reef herbivorous fish. DETAILED DESCRIPTION

[0014] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0015] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0016] like Figure 1 As shown, the present invention discloses a multi-dimensional analysis and evaluation method of the molecular feeding habits of coral reef herbivorous fish, comprising the following steps: S102. Collect stomach samples of target fish that are representative in time and space and construct a multi-dimensional sample set covering different ecological regions and seasonal changes; S104. Determine specific primers based on the complex characteristics of mixed food residues in stomach content samples and perform multiplex PCR amplification on target genes to obtain DNA barcode sequence data of the food source of target fish; S106, performing multi-level comparison analysis on the DNA barcode sequence data and the sequence data in the DNA reference database to obtain analysis results; and constructing molecular characteristic sequence maps of different species in the gastric contents according to the analysis results; S108. Determine the feeding preference index of the target fish according to the molecular characteristic sequence map to evaluate the regulatory ability of the target fish population structure on the growth of large seaweeds in the coral reef ecosystem.

[0017] Through multi-dimensional sample collection, specific primer design, high-throughput sequencing and multi-level comparison analysis, the present invention can comprehensively and accurately analyze the diet composition of coral reef herbivorous fish and construct a molecular characteristic sequence map; based on the map, the feeding preference indicators are further quantified, and the regulatory ability of the target fish population on the coral reef ecosystem is evaluated, which provides a scientific basis for the protection and management of the coral reef ecosystem and effectively improves the efficiency of diet analysis and the accuracy of ecological assessment.

[0018] Preferably, target fish stomach content samples with temporal and spatial representativeness are collected and a multi-dimensional sample set covering different ecological regions and seasonal changes is constructed, specifically: According to the spatial heterogeneity of coral reef ecosystems, the target area is divided into different ecological units, including nearshore, offshore, shallow water and deep water areas; Determine the density distribution values ​​of target fish in different ecological units in combination with historical ecological data, and mark ecological units with density distribution values ​​greater than a preset density threshold as target ecological units; In the target ecological unit, preset sampling time and preset sampling points are designed according to seasonal variation rules, and the target fish are captured at the preset sampling points according to the preset sampling time; Use non-destructive sampling techniques to obtain stomach samples of target fish and record environmental parameters and fish biological characteristics at preset sampling points; The collected gastric contents samples were subjected to standardized pretreatment, including removal of impurities, cryopreservation and homogenization; The preprocessed samples are stored according to ecological units and seasons to construct a multidimensional sample set containing spatial dimensions, temporal dimensions and environmental parameters.

[0019] Among them, the environmental parameters include water temperature, salinity and light intensity; the biological characteristics of fish include species, body length and weight.

[0020] In summary, this method ensures the temporal and spatial representativeness of the samples by scientifically dividing ecological units, accurately locating the target ecological units based on historical data, and designing sampling times and sampling points according to seasonal patterns. At the same time, non-destructive sampling technology and standardized preprocessing are used to ensure the integrity and consistency of the samples. Finally, a multidimensional sample set containing spatial, temporal and environmental parameters is constructed, which can comprehensively cover the heterogeneity of coral reef ecosystems and provide a high-quality, multi-dimensional data foundation for subsequent food analysis.

[0021] Preferably, specific primers are determined for the complex characteristics of mixed food residues in the stomach content sample and multiplex PCR amplification is performed on the target gene to obtain DNA barcode sequence data of the target fish food source, such as Figure 2 As shown, specifically: S202, based on the conserved sequence regions of the target gene, combined with the genomic data of coral reef macroalgae and symbiotic organisms, determine specific primer pairs with broad spectrum coverage; S204, extracting DNA from the pretreated gastric content sample, and detecting the concentration and quality of the extracted DNA using a spectrophotometer or a fluorescence quantification instrument; S206, performing multiple PCR amplification on the target gene according to the specific primers, and performing gel electrophoresis detection on the amplification products to verify the amplification effect, until the amplification effect meets the requirements; S208, purifying the multiplex PCR products to remove primer dimers and non-specific amplification products, and obtaining DNA fragments of target size by fragment screening; S210, constructing a library of the purified DNA fragments, including end repair, adapter ligation and PCR enrichment, and performing double-end sequencing in combination with a high-throughput sequencing platform to generate DNA barcode sequence data.

[0022] Among them, specific primers are a pair of short single-stranded DNA fragments designed to amplify specific regions of target genes. Their sequences are highly matched with the conserved regions of target genes and can specifically identify and amplify target fragments in PCR reactions. The design of specific primers is based on the conserved sequence characteristics of target genes, ensuring that they can cover the diversity of target species while avoiding nonspecific amplification.

[0023] For example, the genome data of large seaweeds and symbiotic organisms in coral reef ecosystems are obtained from public genome databases (such as NCBI and EMBL), and the conserved sequence regions of target genes (such as rbcL gene and 18S rRNA gene) are screened to ensure that the selected regions are highly consistent and specific in the target species. Primers are designed for the screened conserved sequence regions using bioinformatics tools (such as Primer3 and OligoCalc) to ensure the specificity and amplification efficiency of the primer pairs. When designing primers, the primer length is set to 18-25 bases, the GC content is 40%-60%, the annealing temperature is 50℃-65℃, and the formation of primer dimers and hairpin structures is avoided. The designed primer pairs are preliminarily verified, and PCR amplification is performed using genomic DNA of known seaweed species, and the amplification products are detected by gel electrophoresis to ensure that the primer pairs can specifically amplify the target gene fragments and there are no non-specific amplification products. Finally, the verified primer pairs were tested for broad-spectrum coverage, and representative seaweed and symbiotic organism samples in coral reef ecosystems were selected for PCR amplification to ensure that the primer pairs could cover the diversity of the target species and generate high-quality DNA barcode sequence data.

[0024] In summary, this method uses specific primers with broad-spectrum coverage, combined with multiplex PCR amplification and high-throughput sequencing technology, to efficiently and accurately obtain DNA barcode sequence data of the target fish food source from complex mixed food residues, effectively solving the problem of food fragments that are difficult to identify by traditional methods, and providing more comprehensive and accurate food composition information.

[0025] Preferably, the DNA barcode sequence data is subjected to a multi-level comparison analysis with the sequence data in the DNA reference database to obtain the analysis results, specifically: Performing a primary comparison between the DNA barcode sequence data and the sequence data in the DNA reference database based on a local sequence comparison algorithm (such as BLAST), and marking the DNA barcode sequence data with a similarity greater than a preset similarity as an initial matching sequence; It should be noted that the DNA barcode sequence data to be compared and the sequence data in the DNA reference database are input into the BLAST algorithm, and the candidate sequence with similarity to the target sequence is searched in the database through the local alignment method. The BLAST algorithm calculates the similarity between the target sequence and the candidate sequence based on the sequence alignment score (such as E value and alignment length), and screens out the candidate sequences with similarity greater than the preset similarity threshold (such as 90%); finally, the screened candidate sequences are marked as initial matching sequences, and their alignment scores and matching region information are recorded.

[0026] Perform fine alignment of the initial matching sequences based on a global sequence alignment algorithm (such as Needleman-Wunsch), and mark the initial matching sequences with a degree of overlap greater than a preset degree of overlap as representative sequences; It should be noted that the initial matching sequence and the corresponding sequence in the DNA reference database are input into the Needleman-Wunsch algorithm, and the global alignment score between the sequences is calculated by the dynamic programming method, including a comprehensive evaluation of matching, mismatching and gap penalties. The overlap of the sequences is calculated based on the alignment results, that is, the percentage of the length of the matching region to the total length of the sequence, and sequences with an overlap greater than a preset overlap threshold (such as 95%) are screened out. The screened sequences are marked as representative sequences, and their alignment scores and overlap region information are recorded.

[0027] Compare each representative sequence one by one to obtain the sequence alignment score and overlapping region length between the representative sequences; Combined with the species classification information in the DNA reference database, each representative sequence is annotated to determine its corresponding species, and the species annotation information of each representative sequence is obtained; Based on the species annotation information, the number of species sequences corresponding to each representative sequence was counted, and the percentage of the number of each species sequence in the total number of all sequences was calculated to obtain the relative abundance of the species corresponding to each representative sequence; the number of species sequences was the number of algae species sequences; The analysis results were generated based on the sequence alignment scores and overlapping region lengths between representative sequences, species annotation information for each representative sequence, the number of species sequences, and the relative abundance of species.

[0028] Among them, the DNA reference database is a structured database containing genome sequence information of various biological species, usually obtained from public resources (such as NCBI, EMBL, etc.) or independent sequencing data, covering the DNA barcode sequences of target organisms (such as coral reef large algae and symbiotic organisms) and their species classification information. Through standardized storage and indexing, this database provides a reliable reference for DNA sequence comparison and species annotation, ensuring the accuracy and comprehensiveness of food analysis.

[0029] In summary, this method can accurately identify species information in DNA barcode sequence data through multi-level analysis of local and global sequence alignment algorithms, and annotate them in combination with species classification information, ultimately generating analysis results including species sequence quantity, relative abundance, and sequence similarity relationships.

[0030] Preferably, a molecular characteristic sequence map of different species in the gastric contents is constructed according to the analysis results, specifically: According to the species annotation information of the representative sequences in the analysis results, an initial node set is constructed, where each node represents a species and is assigned species taxonomic characteristics and sequence characteristics as node attributes; Among them, the species taxonomic characteristics include species classification levels (such as kingdom / phylum / class / order / family / genus / species) and ecological functional group attributes (such as large algae, microalgae, debris, etc.); sequence characteristics include molecular marker data such as target gene type, sequence length, GC content, alignment score, SNP site and genetic distance.

[0031] The edge weights between nodes are calculated based on the sequence alignment scores and the length of the overlapping regions between representative sequences, and a weighted edge set is constructed to form an initial graph structure; It should be noted that the calculation formula for calculating the edge weight between nodes based on the sequence alignment score between representative sequences and the length of the overlapping region is:

[0032] In the formula, is the edge weight between node i and node j (the value range is [0,1]); is the alignment score between sequence i and sequence j (generated by a sequence alignment algorithm, such as BLAST or Needleman-Wunsch); It is the theoretical maximum alignment score when the two sequences are completely matched; is the actual length of the effective overlapping region in the alignment of sequences i and j (in base / amino acid units); is the total length of sequence i; is the total length of sequence j.

[0033] Map the nodes and edges in the initial graph structure to a low-dimensional vector space, retain the sequence similarity and taxonomic association between species, and generate embedded representations of nodes and edges; The embedded representation is input into the graph neural network model, and the high-order features of the nodes are extracted through multi-layer graph convolution operations. The importance of different nodes and edges is dynamically adjusted in combination with the attention mechanism to optimize the representation ability of the graph structure. Using the output results of the graph neural network, combined with the number of species sequences and relative abundance information, a molecular characteristic sequence map of different species in gastric contents was constructed, where the node size represents the species abundance, and the edge weight represents the sequence similarity between species. The topological structure and key features of the map were presented through visualization technology.

[0034] It should be noted that this method can efficiently extract high-order characteristics between species, and combine the number of species sequences and relative abundance information to generate molecular characteristic sequence maps of different species in stomach contents. The molecular characteristic sequence maps intuitively display the relationship between species abundance and sequence similarity, providing a comprehensive and accurate visualization tool for the feeding habits analysis of coral reef herbivorous fish.

[0035] Preferably, the feeding preference index of the target fish is determined according to the molecular characteristic sequence map to evaluate the regulatory ability of the target fish population structure on the growth of large algae in the coral reef ecosystem, specifically: Based on the size of nodes and edge weight information in the molecular feature sequence map, the relative abundance of each seaweed species and its centrality index in the fish feeding network are extracted to quantify the feeding preference of target fish for different seaweed species and obtain the feeding preference index of target fish. Among them, the centrality indicators are specifically betweenness centrality (propagation influence) and degree centrality (direct association strength); betweenness centrality measures the intermediary ability of species to control the flow of information / energy in the feeding network; degree centrality reflects the number and weight of direct feeding associations between species and other species.

[0036] It should be noted that the node attributes of the molecular feature sequence map are analyzed and the relative abundance of each seaweed species is calculated by normalizing the node diameter (corresponding to the number of species sequences). A directed weighted feeding network diagram is constructed based on edge weights (sequence similarity), and then the propagation influence and direct correlation strength of species in the feeding network are obtained; finally, the relative abundance and the two types of centrality indicators are Z-score standardized, and then the Z-score value of the relative abundance and the Z-score value of the two types of centrality indicators (that is, the average value after each individual standardization) are added at a ratio of 0.6 and 0.4, thereby generating a comprehensive feeding preference index and completing the quantitative expression. Specifically, 60% of the weight is allocated to the relative abundance (reflecting the feeding level), reflecting the basic feeding intensity of fish on specific seaweeds; 40% of the weight is allocated to the centrality indicator (reflecting the ecological regulation potential in the feeding network), which comprehensively evaluates the functional importance of species in the food web.

[0037] Combined with the functional characteristics of seaweed communities in coral reef ecosystems (such as growth rate, competitiveness and influence on corals), a seaweed functional characteristic matrix is ​​constructed, and the feeding preference index is correlated with the functional characteristic matrix to determine the feeding contribution of target fish to key functional groups; It should be noted that the functional characteristic data of coral reef seaweed species (including growth rate, competitiveness index and coral inhibition coefficient, etc.) were first collected to construct a seaweed functional characteristic matrix F with a dimension of n×m (n is the number of species, m is the functional parameter), where the functional parameters include growth rate (biomass increment / time), competitiveness index (quantified value of resource utilization efficiency), and coral inhibition coefficient (intensity of negative impact on coral growth); and each parameter was standardized; then the feeding preference index was multiplied by the seaweed functional characteristic matrix to obtain the weighted contribution vector of each functional characteristic:

[0038] In the formula, is the functional characteristic contribution vector with dimension 1×m; Transpose the feeding preference index vector with dimension 1×n; for Matrix of seaweed functional characteristics; Finally, the principal component analysis method was used to extract key functional groups (such as high competition-strong inhibition type) and calculate the cumulative contribution of target fish to them:

[0039] In the formula, The cumulative feeding contribution of target fish to key functional groups reflects the comprehensive impact of fish on the functional characteristics of the group; It is a collection of key functional groups selected through principal component analysis (such as high competition-strong inhibition type seaweed group); It means traversing a single functional group or species in the key functional group set K; Representative The weighted contribution value of each functional group; For the The functional weight of each functional group.

[0040] Using ecological network analysis methods, we analyzed the trophic level position and feeding niche width of the target fish in the coral reef food web, and evaluated their regulatory ability on the seaweed community and their functional importance in the ecosystem; It should be noted that based on the feeding preference index of the target fish and the species composition of the coral reef food web, the food web topology is constructed, in which nodes represent species and edges represent feeding relationships; secondly, the trophic level calculation method (such as the shortest path-based algorithm) is used to determine the trophic level position of the target fish in the food web and quantify its level in the food chain. Then, the niche breadth index (such as the Shannon-Wiener index) is used to analyze the feeding niche breadth of the target fish and evaluate its feeding diversity and resource utilization range; finally, the functional importance of the target fish in the food web is analyzed in combination with network centrality indicators (such as degree centrality and betweenness centrality), and its regulatory ability on the seaweed community and its key role in the ecosystem are evaluated.

[0041] Combined with historical ecological data and environmental parameters, a correlation model between the target fish population density and the dynamics of the seaweed community was established to simulate the succession trend of the seaweed community under different population densities and the growth changes of large seaweed in the coral reef ecosystem; It should be noted that historical ecological data of the target area, including target fish population density, seaweed community coverage, coral survival rate and environmental parameters (such as water temperature, salinity, light intensity, etc.), were collected and standardized. Then, multiple regression analysis or generalized additive model (GAM) was used to construct a quantitative relationship model between target fish population density and seaweed community coverage, and environmental parameters were introduced as covariates to improve the accuracy of the model. The model was used to simulate the dynamic trend of seaweed communities under different population densities and predict the response of seaweed coverage and coral survival rate. Finally, the influence of key parameters on the model results was evaluated through sensitivity analysis to verify the stability and reliability of the model.

[0042] By setting ecological thresholds (such as seaweed coverage, coral survival rate, etc.), the regulatory capacity of target fish population density on coral reef ecosystems is evaluated and an assessment report is generated.

[0043] It should be noted that through the health standards of coral reef ecosystems and related research, key ecological indicators (such as seaweed coverage not exceeding 30% and coral survival rate not less than 50%) are set as control capacity evaluation indicators. Secondly, the correlation model between the target fish population density and the dynamics of the seaweed community is used to simulate the changing trends of seaweed coverage and coral survival rate under different population densities to determine whether they exceed or approach the set ecological thresholds; then, the relationship between the target fish population density and the risk of degradation is quantified through statistical analysis methods (such as logistic regression or decision trees) to determine the critical population density value. Finally, the analysis results are organized into a structured assessment report, including the degradation risk level, critical population density value and protection recommendations.

[0044] It should be noted that this method quantifies the feeding contribution of target fish to key algae and its regulatory efficiency in the food web by integrating fish feeding preferences, seaweed functional characteristics and ecological network analysis, revealing the dynamic regulatory mechanism of fish populations on the growth of large seaweed in coral reef ecosystems; at the same time, the dynamic model combining historical data and environmental parameters can simulate the process of seaweed succession and coral reef changes under changes in fish density, and predict degradation risks by setting ecological thresholds, providing a quantitative assessment tool for scientific decision-making for coral reef ecological protection.

[0045] In this embodiment, the multi-dimensional analysis and evaluation method of the feeding habits of coral reef herbivorous fish may also include the following steps: Perform whole genome resequencing on the target fish population to obtain genome data with a coverage greater than the preset coverage; Use bioinformatics tools (such as GATK) to detect variations in genomic data, screen out single nucleotide polymorphism sites (SNP sites) with a variation less than the preset variation threshold, and annotate their functional regions (such as coding regions, regulatory regions); Combined with the feeding phenotype data of target fish (such as feeding preference index, feeding niche width, etc.), the genome-wide association analysis (GWAS) method was used to screen SNP sites significantly associated with feeding phenotypes and establish a genotype-feeding phenotype association map; Functional enrichment analysis was performed on the selected diet-related SNP sites to identify the biological pathways and functional modules involved, revealing their potential ecological adaptation mechanisms; Based on the genotype-feeding phenotype association map and functional enrichment analysis results, a prediction model for the adaptive evolutionary potential of population diet was constructed to evaluate the contribution of different genotypes to the feeding phenotype and their adaptive response capabilities under environmental changes.

[0046] It should be noted that this method can accurately screen SNP sites related to feeding phenotypes through whole-genome resequencing, variation detection and whole-genome association analysis, establish a genotype-feeding phenotype association map, and reveal the biological pathways and ecological adaptation mechanisms involved. The final constructed population feeding adaptive evolutionary potential prediction model can be used to evaluate the contribution of different genotypes to feeding phenotypes and their adaptive response capabilities under environmental changes.

[0047] In this embodiment, the multi-dimensional analysis and evaluation method of the feeding habits of coral reef herbivorous fish may also include the following steps: Collect energy content data of target fish and their food objects (such as calorific value or carbon content per unit biomass), and combine it with feeding preference indicators to obtain the energy intake of target fish for different food objects; Based on the species composition and feeding relationships of the coral reef food web, a food web topology structure is constructed, in which nodes represent species and edges represent energy transfer relationships; Using energy intake and food web topology, the energy transfer flux of each edge is calculated, an energy transfer flux matrix is ​​constructed, and the intensity of energy flow between species is quantified; Network analysis methods (such as node centrality and betweenness centrality) were used to analyze the hub effect of target fish in the energy transfer network and evaluate their key role in energy flow; Combining environmental parameters (such as water temperature and light intensity) and historical ecological data, the temporal and spatial variation characteristics of the energy hub effect of target fish are analyzed to reveal their functional stability in coral reef ecosystems and their ability to regulate energy flow.

[0048] In summary, by constructing an energy transfer flux matrix, we can accurately quantify the intensity of energy flow between species in the coral reef food web, and comprehensively analyze the target fish's ability to regulate energy flow and its functional stability in the ecosystem, thereby providing a basis for energy flow research and protection strategy formulation in coral reef ecosystems.

[0049] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0050] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0051] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0052] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0053] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0054] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A multi-dimensional analysis and assessment method for the molecular feeding habits of coral reef herbivorous fish, characterized in that: The following steps are involved: Collect temporally and spatially representative stomach samples of target fish and construct a multidimensional sample set covering different ecological regions and seasonal changes; Based on the complex characteristics of mixed food residues in stomach content samples, specific primers were identified and multiplex PCR amplification of target genes was performed to obtain DNA barcode sequence data of the target fish food source; Performing multi-level comparison analysis on the DNA barcode sequence data and sequence data in a DNA reference database to obtain analysis results; constructing molecular characteristic sequence maps of different species in the gastric contents based on the analysis results; The feeding preference index of the target fish is determined based on the molecular characteristic sequence map to evaluate the regulatory ability of the target fish population structure on the growth of large seaweed in the coral reef ecosystem.

2. The method for multi-dimensional analysis and evaluation of molecular feeding habits of coral reef herbivorous fish according to claim 1, characterized in that: Collect temporally and spatially representative stomach samples of target fish and construct a multidimensional sample set covering different ecological regions and seasonal variations, specifically: Based on the spatial heterogeneity of coral reef ecosystems, the target area was divided into different ecological units, including nearshore, offshore, shallow water and deep water areas; Determine the density distribution of target fish in different ecological units based on historical ecological data, and mark ecological units with density distribution values ​​greater than a preset density threshold as target ecological units; Within the target ecological unit, preset sampling times and sampling points are designed according to seasonal variations, and target fish are captured at the preset sampling points and at the preset sampling times; Use non-invasive sampling techniques to obtain stomach content samples of target fish and record environmental parameters and fish biological characteristics at pre-set sampling points; The collected gastric contents samples were subjected to standardized pretreatment, including removal of impurities, cryopreservation and homogenization; The preprocessed samples are stored according to ecological units and seasons to construct a multidimensional sample set containing spatial dimensions, temporal dimensions and environmental parameters.

3. The method for multi-dimensional analysis and evaluation of molecular feeding habits of coral reef herbivorous fish according to claim 1, characterized in that: Based on the complex characteristics of mixed food residues in stomach contents, specific primers were identified and multiplex PCR amplification of target genes was performed to obtain DNA barcode sequence data of the target fish food source, specifically: Based on the conserved sequence regions of the target gene and combined with the genomic data of coral reef macroalgae and symbiotic organisms, specific primer pairs with broad coverage were identified; DNA was extracted from the pretreated gastric contents samples, and the concentration and quality of the extracted DNA were tested; Perform multiple PCR amplification on the target gene using the specific primers, and perform gel electrophoresis on the amplified products to verify the amplification effect until the amplification effect meets the requirements; The multiplex PCR products were purified to remove primer dimers and non-specific amplification products, and DNA fragments of the target size were obtained through fragment screening; The purified DNA fragments were subjected to library construction, including end repair, adapter ligation, and PCR enrichment. Paired-end sequencing was performed on a high-throughput sequencing platform to generate DNA barcode sequence data.

4. The method for multi-dimensional analysis and evaluation of molecular feeding habits of coral reef herbivorous fish according to claim 1, characterized in that: Perform multi-level comparison analysis on the DNA barcode sequence data and sequence data in the DNA reference database to obtain analysis results, specifically: Performing a primary comparison between the DNA barcode sequence data and the sequence data in the DNA reference database based on a local sequence alignment algorithm, and marking the DNA barcode sequence data with a similarity greater than a preset similarity as an initial matching sequence; Perform a detailed alignment of the initial matching sequences based on a global sequence alignment algorithm, and mark the initial matching sequences with a degree of overlap greater than a preset degree of overlap as representative sequences; compare each representative sequence one by one to obtain the sequence alignment score and the length of the overlapping region between the representative sequences; Combined with the species classification information in the DNA reference database, each representative sequence is annotated to determine its corresponding species, and the species annotation information of each representative sequence is obtained; Based on the species annotation information, the number of species sequences corresponding to each representative sequence was counted, and the percentage of the number of each species sequence in the total number of all sequences was calculated to obtain the relative abundance of the species corresponding to each representative sequence; The analysis results are generated based on the sequence alignment scores and overlapping region lengths between representative sequences, species annotation information of each representative sequence, the number of species sequences and the relative abundance of species.

5. The method for multi-dimensional analysis and evaluation of molecular feeding habits of coral reef herbivorous fish according to claim 1, characterized in that: Based on the analysis results, molecular characteristic sequence maps of different species in the stomach contents were constructed, specifically: Based on the species annotation information of the representative sequences in the analysis results, an initial node set is constructed, where each node represents a species and is assigned species taxonomic characteristics and sequence characteristics as node attributes; The edge weights between nodes are calculated based on the sequence alignment scores and overlapping region lengths between representative sequences, and a weighted edge set is constructed to form the initial graph structure. Mapping the nodes and edges in the initial graph structure into a low-dimensional vector space, preserving the sequence similarity and taxonomic association between species, and generating embedded representations of nodes and edges; The embedded representation is input into the graph neural network model, and high-level features of nodes are extracted through multi-layer graph convolution operations. The attention mechanism is combined to dynamically adjust the importance of different nodes and edges to optimize the representation ability of the graph structure. Using the output results of the graph neural network, combined with the number of species sequences and relative abundance information, a molecular characteristic sequence map of different species in gastric contents was constructed, where the node size represents the species abundance, and the edge weight represents the sequence similarity between species. The topological structure and key features of the map were presented through visualization technology.

6. The method for multi-dimensional analysis and evaluation of molecular feeding habits of coral reef herbivorous fish according to claim 1, characterized in that: The feeding preference index of the target fish is determined based on the molecular characteristic sequence map to evaluate the regulatory ability of the target fish population structure on the growth of macroalgae in the coral reef ecosystem, specifically: Based on the node size and edge weight information in the molecular feature sequence map, the relative abundance of each seaweed species and its centrality index in the fish feeding network are extracted to quantify the feeding preference of target fish for different seaweed species and obtain the feeding preference index of target fish. Combining the functional characteristics of seaweed communities in coral reef ecosystems, a seaweed functional characteristic matrix was constructed, and correlation analysis was performed between the feeding preference index and the functional characteristic matrix to determine the feeding contribution of target fish to key functional groups; Using ecological network analysis methods, we analyzed the trophic position and feeding niche breadth of target fish species in the coral reef food web, and assessed their regulatory capacity on algal communities and their functional importance in the ecosystem. Combining historical ecological data and environmental parameters, a correlation model between target fish population density and seaweed community dynamics was established to simulate the succession trends of seaweed communities and the growth changes of large seaweed in coral reef ecosystems under different population densities. By setting ecological thresholds, the regulatory capacity of target fish population density on coral reef ecosystems is evaluated and an assessment report is generated.

7. The method for multi-dimensional molecular feeding habits analysis and assessment of coral reef herbivorous fish according to claim 2, characterized in that: The environmental parameters include water temperature, salinity and light intensity; the biological characteristics of fish include species, body length and weight.

8. The method for multi-dimensional molecular feeding habits analysis and assessment of coral reef herbivorous fish according to claim 6, characterized in that: The functional properties include growth rate, competitiveness and impact on corals.

9. The method for multi-dimensional molecular feeding habits analysis and assessment of coral reef herbivorous fish according to claim 1, characterized in that: The target genes include rbcL gene and 18SrRNA gene.

10. The method for multi-dimensional molecular feeding habits analysis and assessment of coral reef herbivorous fish according to claim 6, characterized in that: The ecological thresholds include seaweed coverage and coral survival rates.

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