Energy flow path assessment system for seamount ecosystems based on coupling evaluation indicators

Abstract

This invention discloses a seamount ecosystem energy flow path assessment system based on coupled evaluation indicators, belonging to the field of marine ecological assessment and management technology. The system includes an assessment center, which is communicatively connected to a regional deployment module, a multi-source acquisition module, a coupled analysis module, an energy assessment module, a data management module, and a decision support module. The system achieves systematic and standardized collection of biological and environmental data in seamount areas through gridded zoning and directional equipment deployment in the regional deployment module, combined with stratified acquisition and standardized sample preprocessing in the multi-source acquisition module. The coupled analysis module constructs coupled indicators, analyzes key energy flow paths, and screens key environmental factors, providing quantitative assessment basis. The energy assessment module calculates biopump efficiency, assesses benthic food supply and spatial changes, and visualizes the results, enabling refined assessment and management of ecological energy flow in seamount areas of the western Pacific Ocean.

Description

Technical Field

[0001] This invention relates to the field of marine ecological assessment and management technology, and more specifically, to a seamount ecosystem energy flow path assessment system based on coupled evaluation indicators. Background Technology

[0002] Seamount ecosystems, as unique ecological units in the ocean, possess distinctive biological community structures and energy flow patterns. The deep-water seamounts of the western Pacific, in particular, are rich in benthic species and exhibit unique floristic characteristics, making them important vehicles for studying marine biodiversity and ecosystem function. Current research on seamount ecosystems largely focuses on single-species surveys or local environmental factor analyses, lacking coupled analyses of biological and environmental factors, making it difficult to systematically assess energy flow pathways and ecosystem functions.

[0003] Existing technologies suffer from the following shortcomings: data collection lacks a systematic design involving hierarchical and zonal divisions, making it difficult to compare ecological data from different depths and regions; a coupled evaluation index system for organisms and the environment has not been established, making it impossible to quantify the relationship between them; and the energy flow path analysis method is simplistic, ignoring complex trophic relationships among species and resulting in ambiguous identification of critical pathways. To address these shortcomings, this invention proposes a seamount ecosystem energy flow path assessment system based on coupled evaluation indicators. Through zonal deployment, hierarchical data collection, and coupled analysis, it achieves a systematic assessment of energy flow in seamount ecosystems, providing support for marine ecological protection and management. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, this invention provides a seamount ecosystem energy flow path assessment system based on coupled evaluation indicators to address the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an energy flow path assessment system for seamount ecosystems based on coupled evaluation indicators, comprising an assessment center, which is communicatively connected to a regional deployment module, a multi-source acquisition module, a coupled analysis module, an energy assessment module, a data management module, and a decision support module;

[0006] The regional deployment module is used to divide the western Pacific seamount area into grid zones and deploy water layer monitoring equipment, benthic sampling equipment and environmental sensing equipment in each zone;

[0007] The multi-source acquisition module collects biological and environmental data at preset depth levels;

[0008] The coupling analysis module extracts biological and environmental characteristic parameters, constructs a biological-environment coupling index system, analyzes key energy flow pathways, and identifies key environmental factors driving community spatial variation.

[0009] The energy assessment module calculates the efficiency of the biopump and assesses benthic food supply and spatial changes.

[0010] The data management module associates and stores various types of data and enables multi-dimensional visualization;

[0011] The decision support module generates a draft framework for the environmental management plan for the Haishan District.

[0012] Optionally, the operation process of the regional deployment module includes:

[0013] Obtain the latitude and longitude range of the Haishan area, divide it into units of 5km×5km grid, and remove units with water depth <2000m;

[0014] The remaining units are clustered into evaluation sub-regions of 50-100 square kilometers;

[0015] Record the boundary coordinates, average water depth, and terrain type of each zone;

[0016] Equipment groups are deployed in the center of each zone and in the four surrounding directions.

[0017] By adopting the above technical solutions, spatial guarantees are provided for accurately obtaining seamount ecosystem data, which helps to gain a deeper understanding of seamount topographic features and lays the foundation for subsequent analysis of the impact of topography on the ecosystem.

[0018] Optionally, the acquisition process of the multi-source acquisition module includes:

[0019] Set depth levels d (d=1,2,…,y), with the dth level ranging from (d-1)×500+1 to d×500 meters, until the maximum water depth in the seamount area is covered;

[0020] The equipment is activated in layers, with each layer taking 2-4 hours to collect data. Water temperature, salinity, ocean current velocity, and plankton data are collected through the water layer equipment; benthic species, quantity, biomass, and sediment samples are collected through the benthic equipment; and dissolved oxygen, light intensity, and nutrient concentration are collected through environmental sensors.

[0021] All data was tagged and then transmitted to the evaluation center.

[0022] By adopting the above technical solutions, it is possible to ensure that the collected data can comprehensively reflect the biological and environmental conditions at different depths of the seamount ecosystem, guarantee the integrity and representativeness of the data, and enable the collected data to accurately reflect the ecosystem characteristics at each depth level.

[0023] Optionally, the operation process of the coupling analysis module includes:

[0024] Biological and environmental parameters were extracted. Biological parameters included species richness, trophic level mean, and biomass carbon-nitrogen ratio. Environmental parameters included vertical water temperature gradient, peak to mean ratio of nutrient concentration, and ocean current velocity vector.

[0025] Construct bio-environment coupling indices: species-environment correlation degree, energy transfer efficiency coefficient, and trophic level coupling index;

[0026] Analyzing critical energy flow pathways: dividing trophic levels, constructing an energy flow matrix, determining food sources and their contribution rates, and screening critical pathways;

[0027] Identify key environmental factors: initially screen candidate factors and eliminate interfering factors.

[0028] By adopting the above technical solutions, the strength of the connection between organisms and the environment and between trophic levels has been quantified, providing a scientific quantitative basis for in-depth analysis of energy flow pathways in ecosystems. The key energy flow pathways can be analyzed comprehensively and accurately, and the key pathways with high probability and related characteristics can be identified, which helps to understand the energy flow mechanism of ecosystems.

[0029] Optionally, the operation of the energy assessment module includes calculating the efficiency of the biopump, assessing the food supply of benthic organisms, analyzing spatial variation characteristics, and generating visualization results.

[0030] By adopting the above technical solutions, the efficiency of carbon fixation and burial in seamount ecosystems can be intuitively reflected, which helps to assess the carbon cycle function of the ecosystem, is beneficial to assess the food supply of benthic organisms, and provides a reference for the protection of benthic resources.

[0031] Optionally, the operation process of the data management module includes:

[0032] Establish a correlation index for biological data, environmental data, and energy assessment results, and store them by partition number;

[0033] Set up three levels of access permissions, with different permissions corresponding to different data operation ranges;

[0034] Three-dimensional modeling technology was used to convert energy pathways into dynamic maps, and heat maps were used to show the efficiency of biopumps and the spatial distribution of food supply.

[0035] Generate interactive charts.

[0036] Optionally, the operation process of the decision support module includes: integrating biological community difference data, biological pump efficiency level, and food supply sufficiency index; setting protection priority evaluation indicators and weighting the scores to obtain S_p; dividing the area into core protection area, key control area, and general monitoring area according to S_p; formulating control indicators for different areas; and generating a draft management plan framework.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. Through the regional deployment module's grid-based zoning and targeted equipment deployment, combined with the multi-source acquisition module's layered acquisition and standardized sample preprocessing, the system achieved systematic and standardized collection of biological and environmental data in the seamount area. The coupling analysis module constructed coupling indicators, analyzed key energy flow pathways, and screened key environmental factors, providing quantitative assessment basis. The energy assessment module calculated biopump efficiency, assessed benthic food supply and spatial changes, and presented the results visually. The data management module established correlation indexes, ensured security through access control, and achieved multi-dimensional visualization. The decision support module divided protected areas based on multi-dimensional data weighted scoring and formulated control measures. Through modular design and multi-source data fusion, this system achieved refined assessment and management of ecological energy flow in the western Pacific seamount area.

[0039] 2. The coupling analysis module extracts biological and environmental characteristic parameters to construct a biological-environment coupling index system, including species-environment correlation, energy transfer efficiency coefficient, and trophic level coupling index, thereby quantifying the strength of the correlation between organisms and the environment, as well as between trophic levels. Simultaneously, it integrates multidisciplinary methods such as 16S rRNA sequencing, gastric contents analysis, stable isotope analysis, and Bayesian network models to analyze key energy flow pathways, identifying critical pathways with a probability exceeding 10% and their related characteristics. Through multi-level logical screening of key environmental factors, interference is effectively eliminated, ultimately providing a systematic, accurate, and quantifiable core analytical basis for the assessment of energy flow pathways in seamount ecosystems and subsequent ecological management. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0041] Figure 1 The flowchart provided for this invention. Detailed Implementation

[0042] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] See attached document Figure 1 The seamount ecosystem energy flow path assessment system based on coupled evaluation indicators in this embodiment includes an assessment center. The assessment center is connected to a regional deployment module, a multi-source acquisition module, a coupled analysis module, an energy assessment module, a data management module, and a decision support module. Through modular design and multi-source data fusion, it realizes the refined assessment and management of ecological energy flow in the western Pacific seamount area.

[0045] The regional deployment module is used to divide the western Pacific seamount area into gridded zones, numbered p (p=1,2,…,x), and deploy water column monitoring equipment, benthic sampling equipment, and environmental sensing equipment in each zone. The specific operation process is as follows:

[0046] Grid-based partitioning: Obtain the latitude and longitude range of the seamount area in the western Pacific Ocean (e.g., 130°E-170°E, 10°N-30°N), divide it into units of 5km×5km grid, use water depth data to remove units with water depth <2000m, and retain the deep-water seamount area;

[0047] Sub-region clustering: The remaining grid cells are clustered into evaluation sub-regions of 50-100 square kilometers based on terrain similarity, and numbered as p=1,2,…,x in the order from west to east and from north to south;

[0048] Topographic data recording: Obtain the boundary coordinates of each zone (e.g., the boundary of p=1 is 130°E-131°E, 20°N-21°N), average water depth (e.g., 2500m ± 50m), and topographic type (e.g., seamount top, slope, ocean basin) through a multibeam echo sounder.

[0049] Equipment deployment: One set of equipment is deployed in each zone center and in the four directions of east, south, west and north. Each set includes a CTD profiler, a plankton collector, a benthic sampler and an environmental sensor. The equipment is bound to the zone p through the Beidou positioning system, and an equipment-region association table is generated and stored in the evaluation center database.

[0050] The multi-source acquisition module collects biological data (species, quantity, biomass, genetic information) and environmental data (water temperature, salinity, nutrients) at preset depth levels. The specific operation process is as follows:

[0051] Depth level setting: Set the depth level d (d=1,2,…,y). The dth level is (d-1)×500+1 to d×500 meters. For example, d=1 is 1-500 meters, d=2 is 501-1000 meters, and so on, until the maximum water depth of the sea area is covered.

[0052] Data acquisition timing control: Start the equipment sequentially according to the depth level, with each level taking 2-4 hours to ensure data integrity and representativeness;

[0053] Data collection content:

[0054] (i) Aquatic equipment: The species and quantity of phytoplankton and zooplankton are obtained through a continuous plankton sampler; vertical profile data of water temperature and salinity are collected through a CTD profiler; and ocean current velocity and direction are measured through an acoustic Doppler current profiler.

[0055] (ii) Benthic equipment: collect benthic organism samples using box samplers and multi-tube samplers, and record the species, quantity and biomass; use remotely operated vehicles to capture seabed videos to assist in recording the distribution of macrobenthic organisms;

[0056] (iii) Environmental sensors: Real-time data collection of dissolved oxygen concentration, light intensity, nitrate, phosphate, silicate and other nutrient concentrations;

[0057] Data labeling and transmission: All acquired data is labeled with partition number p and depth level. The data is transmitted to the evaluation center via satellite communication or underwater acoustic communication.

[0058] The specific implementation methods for layered data acquisition include:

[0059] When the depth level d=1 (1-500 meters): start the surface plankton net (mesh size 50μm) for horizontal trawling and collect data, and simultaneously start the shallow water sensor array (including temperature, salinity and light sensors), record data every 30 minutes, focusing on capturing data related to the distribution of phytoplankton and primary productivity;

[0060] When the depth level d=2-4 (501-2000 meters): Start the mid-layer biological trawl (mesh size 200μm), operate in the "descent-stay-ascend" mode, control the descent and ascend speed at 0.5m / s, record environmental data every 10 minutes during the stay phase (each stay point is 100 meters apart), and collect zooplankton and small swimming organism samples;

[0061] When the depth level d≥5 (>2000 meters): the benthic remote sampling system is activated. Its robotic arm grabs a sediment column (length ≥50cm) and records the sediment temperature and chemical parameters such as pH and redox potential of the pore water simultaneously through the onboard sensors. Benthic macroorganisms and microorganisms are collected (if the maximum water depth is not an integer multiple of 500 meters, the endpoint of the last level is the actual maximum water depth).

[0062] Sample preprocessing:

[0063] Planktonic samples were immediately fixed with 4% formaldehyde solution at a volume ratio of 1:3 (sample:fixative).

[0064] Benthic organism samples were classified into phyla (such as polychaetes, crustaceans, and echinoderms), and morphological parameters such as body length and weight were measured.

[0065] Gene samples (such as muscle and gill tissue) are stored in a -80°C low-temperature storage box to prevent DNA degradation;

[0066] Data processing timeliness: Environmental data is sent to the assessment center via a real-time transmission link, and biological sample data is analyzed in the laboratory (such as species identification and biomass calculation) and entered into the system within 24 hours.

[0067] The coupling analysis module constructs a bio-environment coupling index system, analyzes energy flow pathways, and identifies key environmental factors driving spatial variability in communities. The specific operation process is as follows:

[0068] a. Feature parameter extraction

[0069] (i) Biological characteristic parameters: species richness, mean trophic level, and carbon-nitrogen ratio of biomass;

[0070] Species richness refers to the total number of species in a specific area, reflecting the abundance of species in that area. Suppose that in a specific area (such as a seamount sub-region), the number of species obtained through survey and statistics is S, then the species richness R = S.

[0071] A trophic level refers to the position of an organism in the food chain. The trophic mean is the average value of all trophic levels of organisms in an ecosystem, reflecting the average level of energy flow and material cycling within that ecosystem. Suppose an ecosystem has... The species, the The trophic level of each species is Then the trophic level mean The calculation formula is:

[0072] ;

[0073] This indicates the biomass of the species.

[0074] The biomass carbon-to-nitrogen ratio refers to the ratio of carbon to nitrogen content in an organism.

[0075] ;

[0076] The amount of carbon in an organism. This refers to the nitrogen content in an organism.

[0077] Species richness, trophic level mean, and biomass carbon-nitrogen ratio are used to reflect the basic structure and material cycling status of biological communities.

[0078] (ii) Environmental characteristic parameters: vertical gradient of water temperature (temperature change per 100 meters of water depth), ratio of peak to mean nutrient concentration, and ocean current velocity vector;

[0079] The formula for calculating the vertical gradient of water temperature is as follows:

[0080] ;

[0081] in, and Let represent the water temperature (in °C) measured at depths d meters and d+100 meters, respectively. This calculates the rate of temperature change between adjacent 100-meter depth layers. For example, if the water temperature is 15 °C at a depth of 100 meters and 10 °C at a depth of 200 meters, then the vertical temperature gradient is: ;

[0082] The ratio of peak to mean nutrient concentration is calculated as follows:

[0083] Set at a series of depth levels ( In the range (x, y), nutrients (such as nitrates) are present at various depths. The concentration is .

[0084] Mean concentration of nutrients :

[0085] ;

[0086] Peak nutrient concentration:

[0087] ;

[0088] The ratio of peak value to mean value is:

[0089] ;

[0090] For example, in the 5 depth levels (y=5), the nitrate concentrations are respectively =1 mg / L, =2mg / L, =3mg / L, =2mg / L, =1mg / L, then the mean =1.8mg / L, peak value =3mg / L, the peak value to mean ratio is 3 / 1.8≈1.67.

[0091] Calculation of ocean current velocity vector:

[0092] Ocean current velocity vectors are typically represented using two-dimensional coordinates. Let the component of the ocean current in the east-west direction (x-axis) be... The component in the north-south direction (y-axis) is Then the ocean current velocity vector =( ). Magnitude of ocean currents and direction (With true north as 0° and clockwise as positive) it can be calculated using the following formula:

[0093] ;

[0094] ;

[0095] For example, if the east-west component of the ocean current is measured... =0.5m / s, the component in the north-south direction =0.866m / s, then the magnitude of the ocean current velocity is... =1m / s, direction ≈30°.

[0096] b. Constructing bio-environment coupling indicators

[0097] Species-environment correlation: The correlation coefficient between each species and environmental factors is calculated through redundancy analysis (RDA). The value range is [-1, 1]. A positive number indicates that the number of species increases with the increase of the factor, i.e., a positive correlation, while a negative number indicates the opposite relationship, i.e., a negative correlation.

[0098] Energy transfer efficiency coefficient: η = (biomass of the next level / biomass of the previous level) × 100%, which reflects the efficiency of energy transfer from lower trophic levels to higher trophic levels in the food chain;

[0099] Trophic level coupling index: λ = α × |correlation coefficient| + β × η, where α + β = 1, α = 0.6, β = 0.4, which can be adjusted according to the ecosystem type, and comprehensively reflects the coupling strength between species and environment and trophic level.

[0100] c. Analyzing the critical paths of energy flow

[0101] Trophic level classification: 16S rRNA gene sequencing was performed on the collected biological samples to determine the phylogenetic relationships between species; at the same time, food residues in the digestive tract of the organisms were analyzed, and the organisms were classified into 4 trophic levels by combining the two: producers (such as phytoplankton), primary consumers (such as herbivorous zooplankton), secondary consumers (such as small carnivorous organisms), and tertiary consumers (such as large carnivorous organisms).

[0102] Constructing an energy flow matrix: Using 10 representative species as the core, construct a 10×10 energy flow matrix, where rows represent source species and columns represent destination species, for example, from producers (level 1) to primary consumers (level 2). = (Level 2 biomass / Level 1 biomass) × 100%, and so on to construct a 10×10 matrix. The matrix element M(i,j) represents the amount of energy transferred from the i-th species to the j-th species (unit: kJ / m² / d).

[0103] Determining food sources and their contribution rates: through δ¹³C (indicating carbon source) and (Indicator trophic level) Analyze stable isotopes in organisms and calculate the contribution rate r_i of each organism to different food sources. For example, a primary consumer may feed 60% on phytoplankton and 40% on organic detritus. Ensure that the sum of r_i from all sources is 1, i.e., Σr_i=1.

[0104] Key pathway selection: Input data such as total biomass, energy transfer efficiency η, and food source contribution rate r_i into the Bayesian network model. After simulation calculation, the probability of each energy pathway exists is obtained. Pathways with a probability of more than 10% are selected as key pathways, such as "phytoplankton → krill → lanternfish". Record the species identity, energy transfer amount and seasonal variation pattern in the path, such as the transfer amount in summer being higher than in winter.

[0105] d. Identify key environmental factors

[0106] Preliminary screening of candidate factors: Taking the differences in species composition of biological communities as the analysis target, environmental factors such as water temperature, salinity, nutrients, and ocean currents are used as influencing factors. Through stepwise regression analysis, the regression coefficients of each factor are calculated, and factors that have a significant impact on community differences, i.e., the absolute value of the regression coefficient > 0.3, are selected as candidate factors.

[0107] Eliminating interfering factors: Principal component analysis was performed on candidate factors to retain factor combinations that reflect major environmental changes, i.e., eigenvalues ​​> 1. At the same time, the correlation between factors was checked, and factors with strong correlations, i.e., variance inflation factors > 10, were eliminated. Finally, the key environmental factors that have the most significant impact on the spatial distribution of biological communities, such as nutrient concentration and ocean current velocity, were identified.

[0108] The coupling analysis module extracts biological and environmental characteristic parameters to construct a biological-environment coupling index system, including species-environment correlation, energy transfer efficiency coefficient, and trophic level coupling index, thereby quantifying the strength of the correlation between organisms and the environment, as well as between trophic levels. Simultaneously, it integrates multidisciplinary methods such as 16S rRNA sequencing, gastric contents analysis, stable isotope analysis, and Bayesian network models to analyze key energy flow pathways, identifying critical pathways with a probability exceeding 10% and their related characteristics. Through a multi-level logical screening process of "stepwise regression initial screening - principal component analysis dimensionality reduction - variance inflation factor decollinearity removal," key environmental factors are effectively screened to eliminate interference. Ultimately, this provides a systematic, accurate, and quantifiable core analytical basis for assessing energy flow pathways in seamount ecosystems and for subsequent ecological management.

[0109] The energy assessment module calculates the efficiency of the biopump and assesses benthic food supply and spatial changes. The specific operation process is as follows:

[0110] a. Calculate the efficiency of the biological pump

[0111] Data on phytoplankton biomass, organic carbon output, and sediment organic carbon burial at different depth levels were collected for each zone.

[0112] The biopump efficiency is calculated using the formula: Biopump efficiency = (Sediment organic carbon burial amount ÷ Total carbon fixed by phytoplankton) × 100%, where the total carbon fixed by phytoplankton is calculated by combining the photosynthetic rate with collected biomass data.

[0113] C 总 =B×P×t

[0114] Among them, C 总 B represents the total amount of carbon fixed by phytoplankton, B represents the phytoplankton biomass, and P represents the photosynthetic rate.

[0115] The efficiency values ​​of biopumps are statistically analyzed by region and classified into efficiency levels: High: ≥30%, Medium: 15%-30%, Low: <15%.

[0116] b. Assess benthic food supply

[0117] Identify the food sources of benthic organisms, including phytoplankton remains, organic detritus, and small zooplankton, through stable isotope analysis, such as δ¹³C. Calculate the contribution ratio of each source using the following formula (taking two food sources as an example):

[0118] Suppose that the stable isotope value of a certain benthic organism (consumer) is... The isotopic values ​​of the two potential food sources are and The isotopic fractionation factor is Δ (Δ refers to the isotopic difference between the consumer and the food, usually determined through literature, such as...). Given that the fractionation factor is approximately 3.4‰ and δ¹³C is approximately 0.4‰, then:

[0119] Corrected food source isotope values: ;

[0120] Contribution ratio equation: ;

[0121] in, The contribution ratio of food source 1, The contribution ratio of food source 2 (value range 0-1);

[0122] Calculate the food supply per unit area by considering the benthic biomass and metabolic requirements:

[0123] ;

[0124] in, Indicates the first i Biomass per unit area of ​​a food species Indicates the first i The proportion of carbon content in various food sources. Indicates the first i The proportion of each food source contributing to benthic organisms. k This represents the food availability coefficient (0-1). k Based on food type, such as phytoplankton remains k =0.8 ， Organic debris k =0.5, n Indicates the total variety of food sources;

[0125] Introducing a food sufficiency index: Food sufficiency index = (actual food supply ÷ total demand of benthic organisms) × 10, where an index ≥ 8 indicates sufficient supply, 5-8 indicates basic satisfaction, and < 5 indicates insufficient supply.

[0126] c. Analyze spatial variation characteristics

[0127] The average values ​​of biological pump efficiency, food supply, and sufficiency index are calculated for each zone p.

[0128] Compare data from different zones to identify patterns of spatial differences, such as the numerical differences between the area near the top of the seamount and the basin area.

[0129] By combining key environmental factors obtained from the coupling analysis module, such as nutrient concentration and ocean current velocity, we can explain the driving factors of spatial variation. For example, high nutrient areas typically have higher biopump efficiency and food supply due to high phytoplankton productivity.

[0130] The process of generating visualizations using the energy assessment module includes:

[0131] Draw a distribution map of biological pump efficiency by zone, and use three colors, red (high), yellow (medium), and blue (low), to mark the level;

[0132] Draw a spatial variation map of food supply for benthic organisms, using contour lines to represent the supply gradient and overlaying sufficiency indices such as "sufficient" and "insufficient".

[0133] By linking the distribution map with the terrain data, a three-dimensional overlay map is generated, which intuitively shows the spatial relationship between energy-related indicators and terrain.

[0134] The data management module associates and stores various types of data and enables multi-dimensional visualization. The specific operation process is as follows:

[0135] For biological data and environmental data acquired by the multi-source acquisition module, as well as results generated by the energy assessment module, such as biopump efficiency and food supply, an association index system with "partition number p" as the core is established.

[0136] Set up three levels of access permissions (administrator, analyst, visitor), with different permissions corresponding to different data operation scopes:

[0137] Administrator privileges: Can enter newly collected raw data through the system backend, modify or delete erroneous data, and export all data. They are responsible for creating and deleting user accounts, assigning user permissions, and recording user operation logs to ensure that data operations are traceable.

[0138] Analyst permissions: Can query and download data and perform analysis and modeling, but cannot modify the original data;

[0139] Visitor permissions: Visitors can only view publicly available statistical results and multi-dimensional visualization charts; they cannot access raw data.

[0140] Three-dimensional modeling technology was used to convert energy pathways into dynamic maps, and heat maps were used to show the efficiency of biopumps and the spatial distribution of food supply.

[0141] Generate interactive charts (such as line charts and bar charts), support filtering data by depth level d and partition p, and include data such as data collection time and device identification.

[0142] The decision support module generates a draft framework for the Haishan District environmental management plan. The specific operation process is as follows:

[0143] Integrate data on biological community differences, biopump efficiency levels, and food supply sufficiency indices;

[0144] Set protection priority evaluation indicators, such as biodiversity and energy pathway integrity, and calculate the weighted score S_p. S_p is calculated by scoring each of the preset protection priority evaluation indicators and combining the weight of each indicator. The weighted score of each indicator is calculated by multiplying the individual indicator score by its weight. All weighted scores are then added together to obtain S_p. For example, assuming the evaluation indicators are "biodiversity" and "energy pathway integrity" with weights of 0.6 and 0.4 respectively, if the score for biodiversity is 85 points and the score for energy pathway integrity is 75 points, then S_p = 85 × 0.6 + 75 × 0.4 = 51 + 30 = 81 points.

[0145] The zones are divided into core protection zones, key control zones, and general monitoring zones according to S_p:

[0146] Core protected area (S_p≥80 points): All development activities (such as mining and fishing) are prohibited, and scientific research is allowed only (sampling no more than once a year);

[0147] Key control areas (60≤S_p<80 points): Sampling frequency is limited (no more than 3 times per year), and large-scale engineering activities are prohibited;

[0148] General monitoring area (S_p<60 points): Set pollutant emission limits, such as petroleum ≤0.05mg / L, and require development activities to conduct environmental impact assessments.

[0149] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A seamount ecosystem energy flow pathway assessment system based on coupled evaluation indicators, including an assessment center, characterized in that, The evaluation center has the following communication connections: Regional Deployment Module: Used for grid-based zoning of the western Pacific seamount area, deploying water layer monitoring equipment, benthic sampling equipment and environmental sensing equipment in each zone; Multi-source acquisition module: Acquires biological and environmental data at preset depth levels; Coupling Analysis Module: By extracting biological and environmental characteristic parameters, a biological-environment coupling index system is constructed to analyze key energy flow pathways and identify key environmental factors driving community spatial variation; Energy assessment module: Calculates biopump efficiency and assesses benthic food supply and spatial variation; Data management module: Relates and stores various types of data and enables multi-dimensional visualization; Decision support module: Generates a draft framework for the environmental management plan of Haishan District; The operation process of the regional deployment module includes: Obtain the latitude and longitude range of the Haishan area, divide it into units of 5km×5km grid, and remove units with water depth <2000m; The remaining units are clustered into evaluation sub-regions of 50-100 square kilometers; Record the boundary coordinates, average water depth, and terrain type of each zone; Equipment groups are deployed in the center of each zone and in the four surrounding directions. The acquisition process of the multi-source acquisition module includes: Set a depth level d, where d is a positive integer, and the d-th level is [(d-1)×500+1,d×500] meters, until the maximum water depth of the seamount area is covered; The equipment is activated according to the level, with each level taking 2-4 hours to collect data. Data on water temperature, salinity, ocean current velocity, and plankton are collected using aquatic equipment; benthic organism species, quantity, biomass, and sediment samples are collected using benthic equipment; and dissolved oxygen, light intensity, and nutrient concentration are collected using environmental sensors. All data was tagged and then transmitted to the evaluation center; The process by which the coupling analysis module extracts biological and environmental characteristic parameters includes: Extraction of biological characteristic parameters: Count the total number of species in a specific area to determine species richness; calculate the weighted average of all trophic levels in the ecosystem to obtain the trophic level mean; determine the carbon and nitrogen content in organisms and calculate their ratio to obtain the biomass carbon-nitrogen ratio; Environmental characteristic parameter extraction: Calculate the temperature difference between adjacent 100-meter water depths to obtain the vertical water temperature gradient; statistically analyze the peak and mean values ​​of nutrient concentrations and calculate the ratio; calculate the magnitude and direction of ocean current velocity through east-west and north-south ocean current components to obtain the ocean current velocity vector; The process of constructing the biological-environment coupling index system by the coupling analysis module includes: Calculate species-environment association: The association coefficient between each species and environmental factors is calculated through redundancy analysis (RDA), with values ​​ranging from [-1, 1]. Calculate the energy transfer efficiency coefficient using the formula η = (Biomass of the next level / Biomass of the previous level) × 100%; Nutrient coupling index: calculated using the formula λ = 0.6 × |correlation coefficient| + 0.4 × η.

2. The seamount ecosystem energy flow path assessment system based on coupled evaluation indicators according to claim 1, characterized in that, The process by which the coupling analysis module analyzes the critical energy flow path includes: Trophic level classification: Combining 16S rRNA gene sequencing and stomach contents analysis, organisms are classified into four trophic levels: producers, primary consumers, secondary consumers, and tertiary consumers. Constructing an energy flow matrix: Using 10 representative species as the core, construct a 10×10 matrix; Determining food sources and contribution rates: through δ¹³C and Stable isotope analysis was used to calculate the contribution rate of each organism to different food sources, where δ¹³C indicates the carbon source. Indicator trophic level; Key path selection: Input the total biomass, energy transfer efficiency η, and contribution rate r_i into the Bayesian network model, and select the paths with a probability of existence of more than 10% as key paths.

3. The seamount ecosystem energy flow path assessment system based on coupled evaluation indicators according to claim 2, characterized in that, The process by which the coupling analysis module identifies key environmental factors includes: Preliminary screening of candidate factors: Taking the differences in species composition of biological communities as the target, environmental factors with absolute regression coefficients > 0.3 were selected as candidate factors through stepwise regression analysis; Eliminating interfering factors: Principal component analysis was performed on the candidate factors to retain factors with eigenvalues ​​>1 and to eliminate strongly correlated factors with variance inflation factors >10, thus finally determining the key environmental factors.

4. The seamount ecosystem energy flow path assessment system based on coupled evaluation indicators according to claim 3, characterized in that: The operation of the energy assessment module includes calculating the efficiency of the biopump, assessing the food supply of benthic organisms, analyzing spatial variation characteristics, and generating visualization results.

5. The seamount ecosystem energy flow path assessment system based on coupled evaluation indicators according to claim 4, characterized in that, The operation process of the data management module includes: Establish a correlation index for biological data, environmental data, and energy assessment results, and store them by partition number; Set up three levels of access permissions, with different permissions corresponding to different data operation ranges; Three-dimensional modeling technology was used to convert energy pathways into dynamic maps, and heat maps were used to show the efficiency of biopumps and the spatial distribution of food supply. Generate interactive charts.

6. The seamount ecosystem energy flow path assessment system based on coupled evaluation indicators according to claim 5, characterized in that: The operation process of the decision support module includes: Integrate data on biological community differences, biopump efficiency levels, and food supply sufficiency indices; Set protection priority evaluation indicators and obtain S_p by weighted scoring; The zones are divided into core protection zones, key control zones, and general monitoring zones according to S_p. Develop control indicators for different regions; Generate a draft management plan framework.

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