A Method for Multi-Dimensional Analysis and Evaluation of the Molecular Diet of Coral Reef Herbivorous Fishes

Abstract

The present invention relates to the field of marine biological feeding, in particular to a method for multi-dimensional analysis and evaluation of the molecular feeding habits of coral reef herbivorous fish. Specific primers are determined for the complex characteristics of mixed food residues in gastric content samples, and multiplex PCR amplification is performed on target genes to obtain DNA barcode sequence data of the food sources of target fish; the DNA barcode sequence data is subjected to multi-level alignment analysis with the sequence data in the DNA reference database; a molecular characteristic sequence map of different species in the gastric content is constructed according to the analysis results; feeding preference indexes of target fish are determined according to the molecular characteristic sequence map to evaluate the regulation ability of the target fish population structure on the growth of macroalgae in the coral reef ecosystem. The present invention can comprehensively analyze the feeding habits of coral reef herbivorous fish, provide a scientific basis for the protection and management of coral reef ecosystems, and effectively improve the efficiency of feeding habit analysis and the accuracy of ecological evaluation.

Description

Technical Field

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

[0002] Coral reef ecosystems are known as the "tropical rainforests of the ocean" due to their extremely high biodiversity and ecological functions, and play an irreplaceable role in protecting coastlines, maintaining marine biodiversity, and promoting the global carbon cycle. However, in recent years, the problem of coral reef degradation has become increasingly serious globally, and the outbreak of macroalgae has become one of the major threats faced by coral reef ecosystems. The overgrowth of macroalgae not only competes with corals for light and space resources, but also inhibits the growth of corals through allelopathy, leading to significant changes in the structure and function of coral reef ecosystems. Research shows that herbivorous fish play a key role in controlling macroalgal communities, and the imbalance of their population structure may lead to the transformation of corals to macroalgae, thus accelerating the degradation of coral reefs. Therefore, analyzing the feeding habits of coral reef herbivorous fish and evaluating their regulatory ability on macroalgal communities are of great significance for understanding the growth mechanism of macroalgae in coral reef ecosystems and formulating effective protection strategies. However, traditional feeding habit analysis methods (such as microscopic examination of stomach contents) have problems such as low efficiency and limited information content, and are difficult to meet the needs of large-scale and multi-dimensional feeding habit analysis. With the development of DNA sequencing technology, high-throughput sequencing technology based on DNA barcoding provides a new solution for fish feeding habit research, which can quickly and accurately identify the food composition in stomach contents and provides important technical support for 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 deficiencies of the prior art and provides a method for multi-dimensional analysis and evaluation of the molecular feeding habits of coral reef herbivorous fish.

[0004] The technical solution adopted by the present invention to achieve the above object is as follows:

[0005] The present invention discloses a method for multi-dimensional analysis and evaluation of the molecular feeding habits of coral reef herbivorous fish, which is characterized by including the following steps:

[0006] Collect stomach content samples of target fish with spatio-temporal representativeness and construct a multi-dimensional sample set including different ecological regions and seasonal variations;

[0007] Determine specific primers according to the complex characteristics of mixed food residues in the stomach content samples and perform multiplex PCR amplification on the target gene to obtain DNA barcode sequence data of the food sources of the target fish;

[0008] Perform multi-level alignment analysis on the DNA barcode sequence data and the sequence data in the DNA reference database to obtain the analysis results; construct a molecular characteristic sequence map of different species in the stomach contents according to the analysis results;

[0009] Determine the feeding preference index of the target fish according to the molecular characteristic sequence map to evaluate the regulation ability of the target fish population structure on the growth of macroalgae in the coral reef ecosystem.

[0010] Preferably, collect stomach content samples of the target fish with spatio-temporal representativeness and construct a multi-dimensional sample set including different ecological regions and seasonal variations, specifically:

[0011] According to the spatial heterogeneity of the coral reef ecosystem, divide the target area into different ecological units, including near shore, far shore, shallow water area and deep water area;

[0012] Combine historical ecological data to determine the density distribution value of the target fish in different ecological units, and mark the ecological units with density distribution values greater than the preset density threshold as target ecological units;

[0013] Within the target ecological unit, design preset sampling times and preset sampling points according to the seasonal variation law, and capture the target fish at the preset sampling points at the preset sampling times;

[0014] Use non-invasive sampling technology to obtain the stomach content samples of the target fish, and record the environmental parameters and fish biological characteristics of the preset sampling points;

[0015] Perform standardized pretreatment on the collected stomach content samples, including removing impurities, cryopreservation and homogenization treatment;

[0016] Store the pretreated samples according to ecological units and seasons, and construct a multi-dimensional sample set including spatial dimension, time dimension and environmental parameters.

[0017] Preferably, determine specific primers for the complex characteristics of the mixed food residues in the stomach content samples and perform multiplex PCR amplification on the target gene to obtain the DNA barcode sequence data of the food sources of the target fish, specifically:

[0018] Based on the conserved sequence region of the target gene, combined with the genomic data of coral reef macroalgae and symbiotic organisms, determine a pair of specific primers with broad-spectrum coverage;

[0019] Extract DNA from the pretreated stomach content samples, and detect the concentration and quality of the extracted DNA;

[0020] Perform multiplex PCR amplification on the target gene according to the specific primers, and simultaneously perform gel electrophoresis detection on the amplification products to verify the amplification effect until the amplification effect meets the requirements;

[0021] Purify the multiplex PCR products to remove primer dimers and non-specific amplification products, and obtain DNA fragments of the target size through fragment screening;

[0022] Construct a library from the purified DNA fragments, including end repair, adapter ligation, and PCR enrichment, and perform paired-end sequencing in combination with a high-throughput sequencing platform to generate DNA barcode sequence data.

[0023] Preferably, perform multi-level alignment analysis on the DNA barcode sequence data and the sequence data in the DNA reference database to obtain the analysis results. Specifically:

[0024] Based on the local sequence alignment algorithm, perform a primary alignment of the DNA barcode sequence data and the sequence data in the DNA reference database, and mark the DNA barcode sequence data with a similarity greater than the preset similarity as the initial matching sequences;

[0025] Based on the global sequence alignment algorithm, perform a fine alignment of the initial matching sequences, and mark the initial matching sequences with a coincidence degree greater than the preset coincidence degree as the 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;

[0026] Combined with the species classification information in the DNA reference database, perform species annotation on each representative sequence to determine its corresponding species and obtain the species annotation information of each representative sequence;

[0027] Based on the species annotation information, count the number of species sequences corresponding to each representative sequence, and calculate the percentage of the number of species sequences in the total number of all sequences to obtain the relative species abundance corresponding to each representative sequence;

[0028] Generate the analysis results based on the sequence alignment score and the length of the overlapping region between the representative sequences, the species annotation information of each representative sequence, the number of species sequences, and the relative species abundance.

[0029] Preferably, construct a molecular characteristic sequence map of different species in the gastric contents according to the analysis results. Specifically:

[0030] According to the species annotation information of the representative sequences in the analysis results, construct an initial node set, where each node represents a species, and assign its taxonomic characteristics and sequence characteristics as node attributes;

[0031] Based on the sequence alignment score and the length of the overlapping region between the representative sequences, calculate the edge weights between the nodes, construct a weighted edge set, and form an initial graph structure;

[0032] Map the nodes and edges in the initial graph structure to a low-dimensional vector space, preserving the sequence similarity and taxonomic relevance between species, and generate the embedded representations of the nodes and edges;

[0033] Input the embedded representations into a graph neural network model, extract the high-order features of the nodes through multiple graph convolution operations, and combine the attention mechanism to dynamically adjust the importance of different nodes and edges, optimizing the representation ability of the graph structure;

[0034] Utilize the output results of the graph neural network, combine the species sequence quantity and relative abundance information, construct the molecular feature sequence map of different species in the gastric contents, where the node size represents the species abundance, the edge weight represents the sequence similarity between species, and present the topological structure and key features of the map through visualization techniques.

[0035] Preferably, determine the feeding preference index of the target fish according to the molecular feature sequence map to evaluate the regulation ability of the target fish population structure on the growth of macroalgae in the coral reef ecosystem. Specifically:

[0036] Based on the node size and edge weight information in the molecular feature sequence map, extract the relative abundance of each macroalgae species and its centrality index in the fish feeding network, quantify the feeding preference of the target fish for different macroalgae species, and obtain the feeding preference index of the target fish;

[0037] Combine the functional characteristics of the macroalgae community in the coral reef ecosystem, construct a macroalgae functional feature matrix, and conduct correlation analysis between the feeding preference index and the functional feature matrix to determine the feeding contribution degree of the target fish to the key functional groups;

[0038] Use the ecological network analysis method to analyze the trophic level position and feeding niche breadth of the target fish in the coral reef food web, and evaluate its regulation ability on the macroalgae community and its functional importance in the ecosystem;

[0039] Combine historical ecological data and environmental parameters to establish an association model between the target fish population density and the macroalgae community dynamics, and simulate the succession trend of the macroalgae community and the growth changes of the macroalgae in the coral reef ecosystem under different population densities;

[0040] By setting ecological thresholds, evaluate the regulation ability of the target fish population density on the coral reef ecosystem and generate an evaluation report.

[0041] Among them, the environmental parameters include water temperature, salinity, and light intensity; the fish biological characteristics include species, body length, and body weight. The functional characteristics include growth rate, competition ability, and influence on corals. The target genes include the rbcL gene and the 18S rRNA gene. The ecological thresholds include macroalgae coverage rate and coral survival rate.

[0042] The present invention solves the technical defects existing in the background art, and the present invention has the following beneficial effects: Through multi-dimensional sample collection, specific primer design, high-throughput sequencing and multi-level alignment 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 index is further quantified to evaluate the regulation ability of the target fish population on the coral reef ecosystem, providing a scientific basis for the protection and management of the coral reef ecosystem and effectively improving the efficiency of diet analysis and the accuracy of ecological assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0044] Figure 1 It is the first method flow chart of a multi-dimensional analysis and evaluation method for the molecular diet of coral reef herbivorous fish;

[0045] Figure 2 It is the second method flow chart of a multi-dimensional analysis and evaluation method for the molecular diet of coral reef herbivorous fish. DETAILED DESCRIPTION OF THE INVENTION

[0046] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0047] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0048] As Figure 1 shown, the present invention discloses a multi-dimensional analysis and evaluation method for the molecular diet of coral reef herbivorous fish, including the following steps:

[0049] S102. Collect stomach content samples of target fish with spatio-temporal representativeness and construct a multi-dimensional sample set including different ecological regions and seasonal variations;

[0050] S104. Determine specific primers for the complex characteristics of the mixed food residues in the stomach content samples and perform multiplex PCR amplification on the target genes to obtain the DNA barcode sequence data of the food sources of the target fish;

[0051] S106. Perform multi-level alignment analysis on the DNA barcode sequence data with the sequence data in the DNA reference database to obtain the analysis results; construct the molecular characteristic sequence maps of different species in the stomach content according to the analysis results;

[0052] S108. Determine the feeding preference indexes of the target fish according to the molecular characteristic sequence maps to evaluate the regulation ability of the target fish population structure on the growth of large seaweeds in the coral reef ecosystem.

[0053] Through multi-dimensional sample collection, specific primer design, high-throughput sequencing and multi-level alignment analysis, the present invention can comprehensively and accurately analyze the diet composition of herbivorous fish in coral reefs and construct molecular characteristic sequence maps; further quantify the feeding preference indexes based on the maps and evaluate the regulation ability of the target fish population on the coral reef ecosystem, providing a scientific basis for the protection and management of the coral reef ecosystem and effectively improving the efficiency of diet analysis and the accuracy of ecological assessment.

[0054] Preferably, collect the stomach content samples of the target fish with spatio-temporal representativeness and construct a multi-dimensional sample set including different ecological regions and seasonal changes, specifically:

[0055] According to the spatial heterogeneity of the coral reef ecosystem, divide the target area into different ecological units, including nearshore, offshore, shallow water area and deep water area;

[0056] Combine the historical ecological data to determine the density distribution values of the target fish in different ecological units, and mark the ecological units with density distribution values greater than the preset density threshold as target ecological units;

[0057] In the target ecological units, design the preset sampling times and preset sampling points according to the seasonal change rules, and capture the target fish at the preset sampling points at the preset sampling times;

[0058] Adopt non-invasive sampling techniques to obtain the stomach content samples of the target fish, and record the environmental parameters and fish biological characteristics of the preset sampling points;

[0059] Perform standardized pretreatment on the collected stomach content samples, including removing impurities, cryopreservation and homogenization treatment;

[0060] Classify and store the pretreated samples according to ecological units and seasons, and construct a multi-dimensional sample set including spatial dimension, time dimension and environmental parameters.

[0061] Among them, the environmental parameters include water temperature, salinity, and light intensity; the fish biological characteristics include species, body length, and body weight.

[0062] In summary, this method scientifically divides ecological units, accurately locates target ecological units by combining historical data, and designs sampling times and sampling points according to seasonal laws to ensure the spatio-temporal representativeness of samples. At the same time, non-invasive sampling techniques and standardized preprocessing are adopted to ensure the integrity and consistency of samples. Finally, a multi-dimensional sample set including space, time, and environmental parameters is constructed, which can comprehensively cover the heterogeneity of the coral reef ecosystem and provide a high-quality and multi-dimensional data basis for subsequent diet analysis.

[0063] Preferably, specific primers are determined for the complex characteristics of the mixed food residues in the stomach content samples, and multiplex PCR amplification is performed on the target gene to obtain DNA barcode sequence data of the food sources of the target fish, as Figure 2 shown, specifically:

[0064] S202. Based on the conserved sequence region of the target gene, combined with the genomic data of coral reef macroalgae and symbiotic organisms, determine a pair of specific primer pairs with broad-spectrum coverage;

[0065] S204. Extract DNA from the preprocessed stomach content samples, and use a spectrophotometer or a fluorescence quantitative instrument to detect the concentration and quality of the extracted DNA;

[0066] S206. Perform multiplex PCR amplification on the target gene according to the specific primer pairs, and at the same time perform gel electrophoresis detection on the amplification products to verify the amplification effect until the amplification effect meets the requirements;

[0067] S208. Purify the multiplex PCR products, remove primer dimers and non-specific amplification products, and obtain DNA fragments of the target size through fragment screening;

[0068] S210. Construct a library for the purified DNA fragments, including end repair, adapter ligation, and PCR enrichment, and perform paired-end sequencing in combination with a high-throughput sequencing platform to generate DNA barcode sequence data.

[0069] Among them, the specific primer is a pair of short single-stranded DNA fragments designed to amplify a specific region of the target gene. Its sequence highly matches the conserved region of the target gene and can specifically recognize and amplify the target fragment in the PCR reaction. The design of the specific primer is based on the conserved sequence characteristics of the target gene to ensure that it can cover the diversity of the target species while avoiding non-specific amplification.

[0070] Exemplarily, genomic data of macroalgae and symbiotic organisms in the coral reef ecosystem are obtained from public genomic databases (such as NCBI, EMBL), and conserved sequence regions of target genes (such as rbcL gene and 18S rRNA gene) are screened to ensure high consistency and specificity of the selected regions in the target species. Bioinformatics tools (such as Primer3, OligoCalc) are used to design primers for the screened conserved sequence regions to ensure the specificity and amplification efficiency of the primer pair. When designing primers, the primer length is set to 18 - 25 bases, the GC content is 40% - 60%, the annealing temperature is 50°C - 65°C, and the formation of primer dimers and hairpin structures is avoided. The designed primer pair is preliminarily verified. The genomic DNA of known seaweed species is used for PCR amplification, and the amplification products are detected by gel electrophoresis to ensure that the primer pair can specifically amplify the target gene fragment and there are no non-specific amplification products. Finally, a broad-spectrum coverage test is conducted on the verified primer pair. Representative seaweed and symbiotic organism samples in the coral reef ecosystem are selected for PCR amplification to ensure that the primer pair can cover the diversity of target species and generate high-quality DNA barcode sequence data.

[0071] In summary, through specific primers with broad-spectrum coverage, combined with multiplex PCR amplification and high-throughput sequencing technologies, this method can efficiently and accurately obtain DNA barcode sequence data of the food sources of target fish from complex mixed food residues, effectively solve the problem of food fragments that are difficult to identify by traditional methods, and provide more comprehensive and accurate food composition information.

[0072] Preferably, the DNA barcode sequence data is subjected to multi-level alignment analysis with the sequence data in the DNA reference database to obtain the analysis results. Specifically:

[0073] Based on a local sequence alignment algorithm (such as BLAST), the DNA barcode sequence data is initially aligned with the sequence data in the DNA reference database, and the DNA barcode sequence data with a similarity greater than the preset similarity is marked as the initial matching sequence;

[0074] It should be noted that the DNA barcode sequence data to be aligned and the sequence data in the DNA reference database are input into the BLAST algorithm, and candidate sequences similar to the target sequence are 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 sequence alignment scores (such as E value and alignment length), filters out candidate sequences with a similarity greater than the preset similarity threshold (such as 90%); finally, the screened candidate sequences are marked as the initial matching sequence, and their alignment scores and matching region information are recorded.

[0075] Perform a fine alignment on the initial matching sequences based on a global sequence alignment algorithm (such as Needleman-Wunsch), and mark the initial matching sequences with a coincidence degree greater than the preset coincidence degree as representative sequences;

[0076] It should be noted that the initial matching sequences and the corresponding sequences in the DNA reference database are input into the Needleman-Wunsch algorithm, and the global alignment score between the sequences is calculated through the dynamic programming method, including a comprehensive evaluation of matching, mismatch, and gap penalties. Calculate the coincidence degree of the sequences according to the alignment result, that is, the percentage of the length of the matching region in the total length of the sequence, and screen out the sequences with a coincidence degree greater than the preset coincidence degree threshold (such as 95%). Mark the screened sequences as representative sequences, and record their alignment scores and coincidence region information.

[0077] Compare each representative sequence one by one to obtain the sequence alignment score and the length of the overlapping region between the representative sequences;

[0078] Combined with the species classification information in the DNA reference database, perform species annotation on each representative sequence to determine its corresponding species, and obtain the species annotation information of each representative sequence;

[0079] Based on the species annotation information, count the number of species sequences corresponding to each representative sequence, and calculate the percentage of the number of species sequences in the total number of all sequences to obtain the relative abundance of species corresponding to each representative sequence; among them, the number of species sequences is the number of algal species sequences;

[0080] Generate an analysis result based on the sequence alignment score and the length of the overlapping region between the representative sequences, the species annotation information of each representative sequence, the number of species sequences, and the relative abundance of species.

[0081] Among them, the DNA reference database is a structured database containing genomic sequence information of various biological species, usually obtained from public resources (such as NCBI, EMBL, etc.) or self-sequencing data, covering the DNA barcode sequences of target organisms (such as coral reef macroalgae and symbiotic organisms) and their species classification information. Through standardized storage and indexing, this database provides a reliable reference basis for DNA sequence alignment and species annotation, ensuring the accuracy and comprehensiveness of diet analysis.

[0082] In summary, through the multi-level analysis of local and global sequence alignment algorithms, this method can accurately identify the species information in the DNA barcode sequence data, annotate it in combination with the species classification information, and finally generate an analysis result including the number of species sequences, relative abundance, and sequence similarity relationship.

[0083] Preferably, construct a molecular characteristic sequence map of different species in the stomach content according to the analysis result, specifically:

[0084] Construct an initial node set according to the species annotation information of the representative sequences in the analysis results, where each node represents a species, and assign its taxonomic characteristics and sequence characteristics as node attributes;

[0085] Among them, the taxonomic characteristics of the species include the taxonomic hierarchy (such as kingdom / phylum / class / order / family / genus / species) and the ecological functional group attributes (such as macroalgae, microalgae, detritus, etc.); the sequence characteristics include molecular marker data such as the target gene type, sequence length, GC content, alignment score, SNP sites, and genetic distance.

[0086] Calculate the edge weights between nodes based on the sequence alignment scores and the lengths of the overlapping regions between representative sequences, construct a weighted edge set, and form an initial graph structure;

[0087] It should be noted that the calculation formula for the edge weights between nodes based on the sequence alignment scores and the lengths of the overlapping regions between representative sequences is:

[0088]

[0089] In the formula, is the edge weight between node i and node j (the value range is [0,1]); is the alignment score of sequence i and sequence j (generated by sequence alignment algorithms such as BLAST or Needleman-Wunsch); is the theoretical maximum alignment score when the two sequences are completely matched; is the actual length (base / amino acid unit) of the effective overlapping region in the alignment of sequence i and j; is the total length of sequence i; is the total length of sequence j.

[0090] Map the nodes and edges in the initial graph structure to a low-dimensional vector space, retain the sequence similarity and taxonomic relevance between species, and generate the embedding representations of nodes and edges;

[0091] Input the embedding representations into a graph neural network model, extract the high-order features of nodes through multi-layer graph convolution operations, and combine the attention mechanism to dynamically adjust the importance of different nodes and edges, and optimize the representation ability of the graph structure;

[0092] Utilize the output results of the graph neural network, combine the species sequence quantity and relative abundance information, construct a molecular feature sequence map of different species in the stomach content, where the node size represents the species abundance, the edge weight represents the sequence similarity between species, and present the topological structure and key features of the map through visualization techniques.

[0093] It should be noted that this method can efficiently extract the high-order features among species, and combine the information of the species sequence quantity and relative abundance to generate the molecular feature sequence map of different species in the gastric contents. The molecular feature sequence map visually displays the species abundance and sequence similarity relationship, providing a comprehensive and accurate visualization tool for the diet analysis of coral reef herbivorous fish.

[0094] Preferably, the feeding preference index of the target fish is determined according to the molecular feature sequence map to evaluate the regulation ability of the target fish population structure on the growth of macroalgae in the coral reef ecosystem. Specifically:

[0095] Based on the size of the nodes and the edge weight information in the molecular feature sequence map, the relative abundance of each macroalgae species and its centrality index in the fish feeding network are extracted, and the feeding preference of the target fish for different macroalgae species is quantified to obtain the feeding preference index of the target fish;

[0096] Among them, the centrality index is specifically the betweenness centrality (propagation influence) and the degree centrality (direct association strength); the betweenness centrality is a measure of the mediation ability of a species to control the flow of information / energy in the feeding network; the degree centrality reflects the quantity and weight of the direct feeding associations of a species with other species.

[0097] It should be noted that the node attributes of the molecular feature sequence map are analyzed, and the relative abundance of each macroalgae species is calculated by normalizing with the node diameter (corresponding to the species sequence quantity). A directed weighted feeding network graph is constructed based on the edge weight (sequence similarity), and then the propagation influence and direct association strength of the species in the feeding network are obtained; finally, after the relative abundance and the two types of centrality indexes are Z-score standardized, the Z-score value of the relative abundance and the Z-score values of the two types of centrality indexes (that is, the average value after individual standardization) are added according to the ratio of 0.6 and 0.4, so as to generate a comprehensive feeding preference index and complete the quantitative expression. Specifically, 60% of the weight is assigned to the relative abundance (reflecting the feeding magnitude), reflecting the basic feeding intensity of the fish for specific macroalgae; 40% of the weight is assigned to the centrality index (reflecting the ecological regulation potential in the feeding network), comprehensively evaluating the functional importance of the species in the food web.

[0098] Combined with the functional characteristics of the macroalgae community in the coral reef ecosystem (such as growth rate, competition ability and influence on corals), a macroalgae functional feature matrix is constructed, and the feeding preference index is correlated with the functional feature matrix to determine the feeding contribution degree of the target fish to the key functional groups;

[0099] It should be noted that first, the functional characteristic data of coral reef algal species (including growth rate, competition ability index, coral inhibition coefficient, etc.) are collected, and an algal functional characteristic matrix F with dimensions of n×m is constructed (n is the number of species, and m is the functional parameter). Among them, the functional parameters include growth rate (biomass increment / time), competition ability index (quantification value of resource utilization efficiency), and coral inhibition coefficient (negative impact intensity on coral growth); and each parameter is standardized; subsequently, the feeding preference index and the algal functional characteristic matrix are multiplied to obtain the weighted contribution degree vector of each functional characteristic:

[0100]

[0101] In the formula, is the functional characteristic contribution degree vector with dimensions of 1×m; is the transpose of the feeding preference index vector with dimensions of 1×n; is the algal functional characteristic matrix;

[0102] Finally, the key functional groups (such as high competition - strong inhibition type) are extracted through the principal component analysis method, and the cumulative contribution degree of the target fish to them is calculated:

[0103]

[0104] In the formula, is the cumulative feeding contribution degree of the target fish to the key functional group, reflecting the comprehensive influence intensity of the fish on the functional characteristics of this group; is the set of key functional groups (such as high competition - strong inhibition type algal groups) screened through the principal component analysis; represents traversing a single functional group or species in the set K of key functional groups; represents the weighted contribution degree value of the th functional group; is the functional weight of the

[0105] Using the ecological network analysis method, analyze the trophic level position and feeding niche width of the target fish in the coral reef food web, and evaluate its regulatory ability on the algal community and its functional importance in the ecosystem;

[0106] It should be noted that based on the feeding preference index of the target fish species and the species composition of the coral reef food web, a food web topological structure is constructed, where nodes represent species and edges represent feeding relationships. Secondly, a trophic level calculation method (such as an algorithm based on the shortest path) is used to determine the trophic level position of the target fish in the food web and quantify its position in the food chain. Then, the feeding niche breadth of the target fish is analyzed using a niche breadth index (such as the Shannon-Wiener index) to 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 indices (such as degree centrality and betweenness centrality) to evaluate its regulatory ability on the seaweed community and its key role in the ecosystem.

[0107] Combined with historical ecological data and environmental parameters, an association model between the population density of the target fish and the dynamics of the seaweed community is established to simulate the succession trend of the seaweed community and the growth changes of macroalgae in the coral reef ecosystem under different population densities.

[0108] It should be noted that by collecting historical ecological data of the target area, including the population density of the target fish, the coverage rate of the seaweed community, the coral survival rate, and environmental parameters (such as water temperature, salinity, light intensity, etc.), and performing standardized processing. Then, a quantitative relationship model between the population density of the target fish and the coverage rate of the seaweed community is constructed using multiple regression analysis or a generalized additive model (GAM), and environmental parameters are introduced as covariates to improve the accuracy of the model. The dynamic change trend of the seaweed community under different population densities is simulated using the model to predict the responses of the seaweed coverage rate and the coral survival rate. Finally, the influence of key parameters on the model results is evaluated through sensitivity analysis to verify the stability and reliability of the model.

[0109] By setting ecological thresholds (such as the seaweed coverage rate, the coral survival rate, etc.), the regulatory ability of the population density of the target fish on the coral reef ecosystem is evaluated to generate an evaluation report.

[0110] It should be noted that through the health standards of the coral reef ecosystem and related research, key ecological indicators (such as the seaweed coverage rate not exceeding 30% and the coral survival rate not less than 50%) are set as evaluation indicators for the regulatory ability. Secondly, using the association model between the population density of the target fish and the dynamics of the seaweed community, the change trends of the seaweed coverage rate and the coral survival rate under different population densities are simulated to determine whether they exceed or approach the set ecological thresholds. Then, the relationship between the population density of the target fish and the degradation risk is quantified through statistical analysis methods (such as logistic regression or decision tree) to determine the critical population density value. Finally, the analysis results are organized into a structured evaluation report, including the degradation risk level, the critical population density value, and protection suggestions.

[0111] It should be noted that this method integrates fish feeding preferences, seaweed functional characteristics, and ecological network analysis to quantify the feeding contribution of target fish to key algae and their regulatory efficacy in the food web, revealing the dynamic regulatory mechanism of fish populations on the growth of macroalgae in coral reef ecosystems. At the same time, combined with the dynamic model of historical data and environmental parameters, it can simulate the succession of seaweed and the change process of coral reefs under the change of fish density, and predict the degradation risk by setting ecological thresholds, providing a quantitative evaluation tool for scientific decision-making in coral reef ecological protection.

[0112] In this embodiment, the multi-dimensional analysis and evaluation method for the feeding habits of the coral reef herbivorous fish may further include the following steps:

[0113] Perform whole-genome resequencing on the target fish population to obtain genomic data with a coverage rate greater than the preset coverage rate;

[0114] Use bioinformatics tools (such as GATK) to detect variations in the genomic data, screen out single nucleotide polymorphism sites (SNP sites) with a variation degree less than the preset variation degree threshold, and annotate their functional regions (such as coding regions, regulatory regions);

[0115] Combined with the feeding phenotype data of the target fish (such as feeding preference indicators, feeding niche width, etc.), use the genome-wide association study (GWAS) method to screen out SNP sites significantly related to the feeding phenotype, and establish a genotype-feeding phenotype association map;

[0116] Perform functional enrichment analysis on the screened feeding habit-related SNP sites, identify the biological pathways and functional modules they participate in, and reveal their potential ecological adaptation mechanisms;

[0117] Based on the genotype-feeding phenotype association map and the results of functional enrichment analysis, construct a population feeding habit adaptive evolution potential prediction model to evaluate the contribution of different genotypes to the feeding phenotype and their adaptive response ability under environmental changes.

[0118] It should be noted that through whole-genome resequencing, variation detection, and genome-wide association analysis, this method can accurately screen out SNP sites related to the feeding phenotype, establish a genotype-feeding phenotype association map, and reveal the biological pathways and ecological adaptation mechanisms they participate in. Finally, the constructed population feeding habit adaptive evolution potential prediction model can be used to evaluate the contribution of different genotypes to the feeding phenotype and their adaptive response ability under environmental changes.

[0119] In this embodiment, the multi-dimensional analysis and evaluation method for the feeding habits of the coral reef herbivorous fish may further include the following steps:

[0120] Collect energy content data (such as calorific value or carbon content per unit biomass) of the target fish and its feeding objects, and combine the feeding preference index to obtain the energy intake of the target fish for different feeding objects;

[0121] Based on the species composition and feeding relationships of the coral reef food web, construct a food web topological structure, where nodes represent species and edges represent energy transfer relationships;

[0122] Using the energy intake and the food web topological structure, calculate the energy transfer flux of each edge, construct an energy transfer flux matrix, and quantify the intensity of energy flow between species;

[0123] Adopt network analysis methods (such as node centrality, betweenness centrality) to analyze the hub effect of the target fish in the energy transfer network and evaluate its key role in energy flow;

[0124] Combined with environmental parameters (such as water temperature, light intensity) and historical ecological data, analyze the spatio-temporal variation characteristics of the energy hub effect of the target fish, and reveal its functional stability in the coral reef ecosystem and its ability to regulate energy flow.

[0125] In summary, by constructing an energy transfer flux matrix, the intensity of energy flow between species in the coral reef food web can be accurately quantified, the regulatory ability of the target fish on energy flow and its functional stability in the ecosystem can be comprehensively analyzed, thereby providing a basis for the study of energy flow and the formulation of protection strategies in the coral reef ecosystem.

[0126] In 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 illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, 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 with each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be electrical, mechanical, or other forms.

[0127] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0128] In addition, each functional unit in the embodiments of the present invention may be entirely integrated into one processing unit, or each unit may be individually regarded as one unit, or two or more units may be integrated into one unit; the above integrated unit may be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0129] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs and other various media that can store program codes.

[0130] Alternatively, if the above integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the various embodiments of the present invention. And the foregoing storage medium includes: removable storage devices, ROM, RAM, magnetic disks, or optical discs and other various media that can store program codes.

[0131] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention.

Claims

1. A method for multi-dimensional analysis and evaluation of the molecular diet of coral reef herbivorous fish, characterized in that, It includes the following steps: Collect stomach content samples of target fish with spatio-temporal representativeness and construct a multi-dimensional sample set including different ecological regions and seasonal variations; Determine specific primers according to the complex characteristics of the mixed food residues in the stomach content samples and perform multiplex PCR amplification on the target gene to obtain DNA barcode sequence data of the food sources of the target fish; Perform multi-level alignment analysis on the DNA barcode sequence data and the sequence data in the DNA reference database to obtain the analysis results; construct a molecular characteristic sequence map of different species in the stomach content according to the analysis results; Determine the feeding preference index of the target fish according to the molecular characteristic sequence map to evaluate the regulation ability of the target fish population structure on the growth of macroalgae in the coral reef ecosystem; It also includes the following steps: Perform whole-genome resequencing on the target fish population to obtain genome data with a coverage rate greater than the preset coverage rate; Perform variant detection on the genome data, screen out single nucleotide polymorphism sites with a variability less than the preset variability threshold, and annotate their functional regions; Combined with the feeding phenotype data of the target fish, use the genome-wide association analysis method to screen out SNP sites significantly related to the feeding phenotype and establish a genotype-feeding phenotype association map; Perform functional enrichment analysis on the screened diet-related SNP sites, identify the biological pathways and functional modules they participate in, and reveal their potential ecological adaptation mechanisms; Based on the genotype-feeding phenotype association map and the results of functional enrichment analysis, construct a population diet adaptation evolution potential prediction model to evaluate the contribution of different genotypes to the feeding phenotype and their adaptive response ability under environmental changes.

2. The molecular diet multi-dimensional analysis and evaluation method of a coral reef herbivorous fish according to claim 1, characterized in that Collect stomach content samples of target fish with spatio-temporal representativeness and construct a multi-dimensional sample set including different ecological regions and seasonal variations. Specifically: According to the spatial heterogeneity of the coral reef ecosystem, divide the target area into different ecological units, including nearshore, offshore, shallow water area and deep water area; Combine historical ecological data to determine the density distribution values of the target fish in different ecological units, and mark the ecological units with density distribution values greater than the preset density threshold as target ecological units; Within the target ecological unit, design preset sampling times and preset sampling points according to the seasonal variation law, and capture the target fish at the preset sampling points at the preset sampling times; Use non-invasive sampling technology to obtain the stomach content samples of the target fish, and record the environmental parameters and fish biological characteristics of the preset sampling points; Perform standardized pretreatment on the collected stomach content samples, including removing impurities, cryopreservation and homogenization treatment; Classify and store the pretreated samples according to ecological units and seasons, and construct a multi-dimensional sample set including spatial dimension, time dimension and environmental parameters.

3. The molecular diet multi-dimensional analysis and evaluation method of a coral reef herbivorous fish according to claim 1, characterized in that Determine specific primers according to the complex characteristics of the mixed food residues in the stomach content samples and perform multiplex PCR amplification on the target gene to obtain DNA barcode sequence data of the food sources of the target fish. Specifically: Based on the conserved sequence region of the target gene, combined with the genome data of coral reef macroalgae and symbiotic organisms, determine a pair of specific primers with broad-spectrum coverage; Extract DNA from the preprocessed gastric content samples, and detect the concentration and quality of the extracted DNA; Perform multiplex PCR amplification on the target gene according to the specific primers, and simultaneously detect the amplification products by gel electrophoresis to verify the amplification effect until the amplification effect meets the requirements; Purify the multiplex PCR products, remove primer dimers and non-specific amplification products, and obtain DNA fragments of the target size through fragment screening; Construct a library for the purified DNA fragments, including end repair, adapter ligation, and PCR enrichment, and perform paired-end sequencing in combination with a high-throughput sequencing platform to generate DNA barcode sequence data.

4. The molecular diet multi-dimensional analysis and evaluation method of a coral reef herbivorous fish according to claim 1, characterized in that Perform multi-level alignment analysis on the DNA barcode sequence data and the sequence data in the DNA reference database to obtain the analysis results. Specifically: Perform primary alignment on the DNA barcode sequence data and the sequence data in the DNA reference database based on the local sequence alignment algorithm, and mark the DNA barcode sequence data with a similarity greater than the preset similarity as the initial matching sequences; Perform fine alignment on the initial matching sequences based on the global sequence alignment algorithm, and mark the initial matching sequences with a coincidence degree greater than the preset coincidence degree as the representative sequences; Compare each representative sequence one by one to obtain the sequence alignment score and the overlapping region length between the representative sequences; Combined with the species classification information in the DNA reference database, perform species annotation on each representative sequence to determine its corresponding species, and obtain the species annotation information of each representative sequence; Based on the species annotation information, count the number of species sequences corresponding to each representative sequence, and calculate the percentage of the number of species sequences in the total number of all sequences to obtain the relative abundance of species corresponding to each representative sequence; Generate the analysis results based on the sequence alignment score and overlapping region length between the representative sequences, the species annotation information of each representative sequence, the number of species sequences, and the relative abundance of species; 5. The molecular diet multi-dimensional analysis and evaluation method of a coral reef herbivorous fish according to claim 1, characterized in that Construct a molecular characteristic sequence map of different species in the gastric content according to the analysis results. Specifically: Construct an initial node set according to the species annotation information of the representative sequences in the analysis results, where each node represents a species, and assign its taxonomic characteristics and sequence characteristics as node attributes; Calculate the edge weights between the nodes based on the sequence alignment score and overlapping region length between the representative sequences, construct a weighted edge set, and 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 relevance between species, and generate the embedded representations of the nodes and edges; Input the embedded representations into a graph neural network model, extract the high-order features of the nodes through multi-layer graph convolution operations, and dynamically adjust the importance of different nodes and edges in combination with the attention mechanism to optimize the representation ability of the graph structure; Utilize the output results of the graph neural network, combined with the number of species sequences and relative abundance information, to construct a molecular characteristic sequence map of different species in the gastric content, where the node size represents the species abundance, the edge weight represents the sequence similarity between species, and present the topological structure and key features of the map through visualization techniques.

6. The molecular diet multi-dimensional analysis and evaluation method of a coral reef herbivorous fish according to claim 1, characterized in that Determine the feeding preference index of the target fish based on the molecular characteristic sequence map to evaluate the regulation ability of the target fish population structure on the growth of macroalgae in the coral reef ecosystem. Specifically: Based on the size of the nodes and the edge weight information in the molecular characteristic sequence map, extract the relative abundances of each seaweed species and their centrality indices in the fish feeding network, quantify the feeding preferences of the target fish for different seaweed species, and obtain the feeding preference index of the target fish; Combined with the functional characteristics of the seaweed community in the coral reef ecosystem, construct a seaweed functional characteristic matrix, and conduct correlation analysis between the feeding preference index and the functional characteristic matrix to determine the feeding contribution of the target fish to the key functional groups; Using the ecological network analysis method, analyze the trophic level position and feeding niche width of the target fish in the coral reef food web, and evaluate its regulation ability on the seaweed community and its functional importance in the ecosystem; Combined with historical ecological data and environmental parameters, establish an association model between the target fish population density and the seaweed community dynamics, and simulate the succession trend of the seaweed community and the growth changes of macroalgae in the coral reef ecosystem under different population densities; By setting ecological thresholds, evaluate the regulation ability of the target fish population density on the coral reef ecosystem, and generate an evaluation report.

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

8. The molecular diet multi-dimensional analysis and evaluation method of a coral reef herbivorous fish according to claim 6, characterized in that: The functional characteristics include growth rate, competition ability, and influence on corals.

9. The molecular diet multi-dimensional analysis and evaluation method of a coral reef herbivorous fish according to claim 1, characterized in that: The target genes include the rbcL gene and the 18S rRNA gene.

10. The molecular diet multi-dimensional analysis and evaluation method of a coral reef herbivorous fish according to claim 6, characterized in that: The ecological thresholds include seaweed coverage rate and coral survival rate.

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