A simulation method for aquatic food web disturbance specific to the characteristics of water conservancy and hydropower projects

Abstract

This invention belongs to the technical field of ecological impact assessment for water conservancy and hydropower projects, and specifically relates to a method for simulating aquatic food web disturbances specific to the characteristics of water conservancy and hydropower projects. The method includes: obtaining species baseline information, classifying feeding functional groups, constructing aquatic food webs, conducting aquatic food web disturbance attacks, monitoring the propagation of disturbance attack effects on the network, monitoring the network structure and function of aquatic food webs, and outputting simulation results. Compared with existing methods, the simulation method of this invention introduces a food web disturbance attack mechanism and a chain propagation mechanism, which can characterize cumulative ecological responses, effectively simulate the disturbance characteristics of water conservancy and hydropower projects, and enhance the engineering adaptability of disturbance simulation.

Description

Technical Field

[0001] This invention belongs to the field of ecological impact assessment technology for water conservancy and hydropower projects, and specifically relates to a method for simulating aquatic food web disturbances specific to the characteristics of water conservancy and hydropower projects. Background Technology

[0002] Hydropower projects, as core infrastructure for energy and water resource regulation, play a vital role in ensuring energy supply and the scientific allocation of water resources. However, the hydrological rhythm changes caused by their operation (such as sudden increases in flow, frequent rises and falls in water level, slowing of water flow, and discharge of low-temperature water) have become significant disturbances affecting the structural integrity and functional stability of river ecosystems. These changes not only reshape aquatic habitat structures but also disrupt energy flow processes and functional maintenance mechanisms within ecosystems by altering feeding relationships among species. Specifically, this manifests as the loss of local functional taxa, disruption of feeding pathways, and a decline in the stability of ecological networks.

[0003] In recent years, aquatic food web models have been widely used as an important tool for analyzing the structure of aquatic ecosystems, assessing the response of ecological networks to disturbances. Existing technologies (such as those described in the invention patent CN110135484 A, "A Method for Determining Key Species in a Food Web") primarily employ classic network disturbance strategies such as random attacks, degree centrality attacks, or betweenness centrality attacks. In each simulation, these strategies remove individual nodes and record changes in structural indicators to assess system robustness or identify key species in the food web. However, these strategies fail to reflect the spatial distribution characteristics, scheduling rhythms, and selective impacts of disturbances from water conservancy and hydropower projects on specific functional groups, making it difficult to meet the practical needs of ecological assessment for water conservancy and hydropower projects.

[0004] Furthermore, existing methods (such as those described in the invention patent CN 114819533 A, "A Method and Platform for Constructing and Analyzing the Structure and Function of a River Ecosystem Food Web") often assume that the system immediately reaches a new stable state after node disturbances, neglecting the transmission process and cumulative effects of disturbances within the ecological network. In reality, ecosystems have a clear hierarchical structure and species dependency chains. Local disturbance events often cascade through predator-prey relationships, leading to secondary impacts across levels and even systemic collapse. Existing technologies lack a framework of propagation rules that can explicitly simulate this chain-like diffusion mechanism of disturbances, and therefore cannot realistically reproduce the ecological pathways and cumulative effects of disturbances.

[0005] In summary, there is currently a lack of a method for simulating and analyzing aquatic food webs that addresses typical disturbance characteristics of water conservancy and hydropower projects, constructs functional group-oriented attack rules, and introduces network propagation mechanisms to simulate the cumulative effects of disturbances. This situation limits the explanatory power and applicability of existing methods in identifying ecologically vulnerable areas, key species nodes, and sensitive scheduling scenarios, making it difficult to support scientific decision-making for ecological scheduling optimization and the prioritization of protected areas. Summary of the Invention

[0006] To overcome the problems of existing technologies, this invention proposes a method for simulating aquatic food web disturbances specific to the characteristics of water conservancy and hydropower projects. The aim is to bypass the shortcomings of existing methods by transforming typical hydrological rhythm changes during the operation of water conservancy and hydropower projects into attack rules driving food web disturbances. This achieves a mechanism connection from actual engineering disturbance characteristics to simulation of ecological network functional responses. It breaks through the conventional approach of setting disturbance paths based on node degree, betweenness, or randomness, and constructs disturbance attack propagation rules based on path length to simulate the chain propagation and attenuation process of local disturbance attacks in the food web structure, characterizing the cumulative effect of disturbances. This overcomes the problem of existing technologies only performing static response analysis on the first-level disturbance nodes and ignoring propagation paths and structural chain reactions. Finally, through iterative simulation and spatial path modeling, a complete technical system of engineering disturbance-ecological attack-propagation response-judgment and identification is formed.

[0007] The objective of this invention is achieved as follows:

[0008] This invention provides a method for simulating aquatic food web disturbances specific to the characteristics of water conservancy and hydropower projects, comprising the following steps:

[0009] Step 1, Obtaining Species Baseline Information:

[0010] Based on environmental DNA technology, target water areas are monitored to identify the composition of aquatic species in the water area and obtain the species and relative abundance of existing aquatic organisms.

[0011] Step 2, Classification of feeding function groups:

[0012] Based on known feeding characteristics, the species obtained in step 1 are divided into different feeding functional groups, including apex predators, carnivorous fish, omnivorous fish, herbivorous fish, invertebrate predators, small predators, feeders, tearers, filter feeders, producers, and decomposers.

[0013] Step 3, Construction of the aquatic food web:

[0014] Based on known feeding relationships among species, a structured aquatic food web model is constructed. In the model, network nodes represent species, lines between nodes represent feeding relationships, arrows in the lines between nodes represent the direction of predation, and the color of a node represents its feeding functional group.

[0015] Step 4, Aquatic food web interference attack:

[0016] We introduce the typical operational characteristics of water conservancy and hydropower projects as disturbance driving factors, construct corresponding disturbance scenarios for feeding function groups for different engineering operation scenarios, target specific feeding function groups to weaken them, formulate targeted interference attack rules, and implement interference attacks.

[0017] The engineering operation scenarios include river dam construction, increased flow pulse, frequent rise and fall of water level, water storage leading to slowed water flow, and low-temperature water discharge.

[0018] The interference attack rule is to use the feeding functional groups directly affected by the disturbance as the initial attack nodes of the structured aquatic food web model, and to adopt a node deletion method. The disturbance attack intensity is set to randomly delete 10%-80% of the species nodes in the feeding functional groups directly affected by the disturbance.

[0019] Step 5: Interference attacks affect network propagation

[0020] Based on the food web network structure, and following preset propagation rules, the disturbance attack is propagated layer by layer from the initial attacking node to adjacent nodes until the disturbance intensity falls below a preset threshold. Stop the spread;

[0021] The propagation rule states that the intensity of the perturbation attack decreases as the propagation path length increases. When the attack propagates to a node d steps away from the initial node, the intensity of the perturbation attack is calculated according to the following formula:

[0022]

[0023] In the formula, This represents the initial perturbation attack strength. Here, d is the propagation attenuation coefficient, and d is the shortest path length from the node to the initial disturbance source. The strength of the perturbation attack when it propagates to a node d steps away from the initial node;

[0024] when Less than the preset threshold At that time, the transmission ceased;

[0025] Step 6, Monitoring the structure and function of aquatic food webs:

[0026] During a food web disturbance attack, the changes in the network robustness index and omnivorousness index are monitored and calculated in real time.

[0027] Step 7: Simulation result output:

[0028] Based on the changes in robustness index and omnivorous index obtained in step 6, we can determine the disturbed water areas, the species at the disturbed key nodes, and the high-impact disturbance scenarios.

[0029] The judgment rule is: when the robustness index of the food web in any area along the target water body decreases by more than a preset threshold T compared to the original food web. b If the omnivorousness index decreases by more than a preset threshold T0, the area is identified as a disturbed sensitive water area and designated as a priority protection zone; if the contribution of any species node to the change in the food web robustness index or omnivorousness index exceeds a preset impact threshold T, the area is considered a disturbed sensitive water area and designated as a priority protection zone. c When a node is identified as a sensitive key node species and designated as a priority protection target, the corresponding disturbance scenario is marked as a high-impact disturbance scenario requiring special attention when more than i along the route simultaneously meet the criteria for sensitive water areas.

[0030] Furthermore, in step 1, the types of aquatic organisms include: fish, protozoa, metazoa, large benthic invertebrates, phytoplankton, attached algae, and microorganisms.

[0031] Furthermore, in step 3, the structured aquatic food web model is a food web model along the target water area. It is formed by acquiring species composition information at different locations in the target water area and constructing local food web subsets, and then integrating the local food web subsets in the order along the water area.

[0032] Furthermore, in step 4, the initial attack function group is selected based on the hydrological rhythm change scenario. The selection rules for the initial attack node under the corresponding scenario include:

[0033] (1) In the scenario of river damming, migratory species among top predators, carnivorous fish, omnivorous fish and herbivorous fish are selected as the initial attack nodes to simulate the population isolation caused by damming directly blocking the migration path;

[0034] (2) Under the scenario of increased flow pulse, rippers, filter feeders, invertebrate predators, and small predators were selected to simulate the direct impact of high flow velocity scouring on benthic food sources;

[0035] (3) Under the scenario of frequent rise and fall of water level, select aquatic insects among producers, such as attached algae, feeders and small predators, to simulate the effects of repeated wet and dry alternation leading to a decrease in food source and feeding efficiency, and a decrease in the survival rate of eggs attached to rocks in the water.

[0036] (4) Under the scenario of water storage leading to slow water flow, filter feeders were selected to simulate the impact of reduced suspended solids settling leading to a scarcity of filter feeder resources;

[0037] (5) In the scenario of low temperature water discharge, apex predators, carnivorous fish and invertebrate predators were selected to simulate the effect of temperature reduction on predation metabolism and feeding activity.

[0038] Furthermore, in step 5, the initial perturbation attack strength The value range of the propagation attenuation coefficient is 10%-80%. The value range is 0.1-0.9, and the preset threshold is... The value range is 10%-30%.

[0039] Furthermore, in step 6, the robustness index is calculated as follows: network nodes are removed sequentially according to the interference attack rules and propagation rules. The number of nodes in the largest connected subgraph in the remaining network after each removal is recorded. The proportion of the nodes in the subgraph to the total number of nodes in the original network is calculated. The relationship curve between this proportion and the node removal proportion is plotted. The area under the curve is calculated using the numerical integration method as the single robustness index. The simulation is repeated 3-100 times under the same interference attack and propagation rules. The average of the obtained robustness indices is taken as the final robustness index.

[0040] Furthermore, in step 6, the omnivorousness index is calculated as follows: the number of functional groups that each predator is connected to is divided by the total number of functional groups in the food web. The simulation is repeated 3-100 times according to preset interference attack and propagation rules, and the average value of the obtained omnivorousness index is taken as the final omnivorousness index.

[0041] Furthermore, in step 7, the preset threshold T b The value range is 0.15-0.99, the preset threshold T0 value range is 0.15-0.99, and the preset influence threshold T c The value of is in the range of 0.2-0.99, and the value of i is in the range of 2-5.

[0042] The advantages of this invention compared to the prior art are as follows:

[0043] 1. Existing technologies, based on randomness or degree centering, set up interference attacks on food webs, which cannot effectively simulate the interference characteristics of water conservancy and hydropower projects, nor can they map real hydrological disturbances. In contrast, the aquatic food web interference simulation method described in this application, based on the characteristics of water conservancy and hydropower projects and the response sensitivity of different functional feeding groups to changes in hydrological conditions caused by water conservancy and hydropower projects, sets specific food web disturbance attack rules and network propagation rules, conducts direct impact simulation and cumulative effect simulation, and finally quantifies the structural and functional indicators of the food web. Based on the structural and functional changes of aquatic food webs, it realizes the prediction and identification of ecologically sensitive areas, key species and key driving factors of interference, thereby assisting in the design and operation management decisions of water conservancy and hydropower projects.

[0044] 2. Compared with existing methods, the aquatic food web disturbance simulation method of the present invention, by introducing a food web disturbance attack mechanism and a chain propagation mechanism, can characterize the cumulative ecological response, effectively simulate the disturbance characteristics of water conservancy and hydropower projects, and significantly enhance the engineering adaptability of disturbance simulation. Attached Figure Description

[0045] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0046] Figure 1 This illustrates a food web constructed at typical sampling points in the S River basin as described in Embodiment 1 of the present invention;

[0047] Figure 2 This shows the decrease in the index at each sampling point in application example 1;

[0048] Figure 3 The decrease in the index at each sampling point is shown in application example 2. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments disclosed herein will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0050] Example:

[0051] This embodiment provides a method for simulating aquatic food web disturbances specific to the characteristics of water conservancy and hydropower projects, including the following steps:

[0052] Step 1, Obtaining Species Baseline Information:

[0053] Based on environmental DNA (eDNA) technology, target water areas are monitored to identify the composition of aquatic species and obtain information on the types and relative abundance of existing aquatic organisms.

[0054] The types of aquatic organisms include: fish, protozoa, metazoa, large benthic invertebrates, phytoplankton, attached algae, and microorganisms.

[0055] Step 2, Classification of feeding function groups:

[0056] Based on known feeding characteristics, the species obtained in step 1 are classified into different feeding functional groups, including apex predators, carnivorous fish, omnivorous fish, herbivorous fish, invertebrate predators, small predators, foragers, tearers, filter feeders, producers, and decomposers.

[0057] Step 3, Construction of the aquatic food web:

[0058] Based on known feeding relationships among species, a structured aquatic food web model is constructed. In the model, network nodes represent species, the lines (edges) connecting nodes represent feeding relationships, the arrow direction of the lines (edges) connecting nodes represents the direction of predation, and the color of a node represents its feeding functional group.

[0059] The structured aquatic food web model is a food web model along the target water area. It is formed by acquiring species composition information at different locations in the target water area and constructing local food web subsets, and then integrating the local food web subsets in the order of the water area.

[0060] Step 4, Aquatic food web interference attack:

[0061] Typical operational characteristics of water conservancy and hydropower projects are introduced as disturbance driving factors. Corresponding disturbance scenarios for feeding function groups are constructed for different project operation scenarios. Specific feeding function groups are weakened in a targeted manner, and targeted interference attack rules are formulated and interference attacks are carried out.

[0062] The engineering operation scenarios include river dam construction, increased flow pulses, frequent water level fluctuations, water storage leading to slower water flow, and low-temperature water discharge.

[0063] The interference attack rule is to use the feeding functional groups directly affected by the disturbance as the initial attack nodes of the structured aquatic food web model, and to adopt a node deletion method. The disturbance attack intensity is set to randomly delete 10%-80% of the species nodes in the feeding functional groups directly affected by the disturbance.

[0064] Specifically, the rules for selecting the initial attack node in the corresponding scenario are as follows:

[0065] (1) In the scenario of river damming, migratory species among top predators, carnivorous fish, omnivorous fish and herbivorous fish are selected as the initial attack nodes to simulate the population isolation caused by damming directly blocking the migration path;

[0066] (2) Under the scenario of increased flow pulse, rippers, filter feeders, invertebrate predators, and small predators were selected as the initial attack nodes to simulate the direct impact of high flow velocity scouring on benthic food sources.

[0067] (3) Under the scenario of frequent rise and fall of water level, we selected the attached algae among the producers, the aquatic insects among the feeders and the small predators as the initial attack nodes to simulate the effects of repeated wet and dry alternation leading to a decrease in food source and feeding efficiency, and a decrease in the survival rate of eggs attached to rocks in the water.

[0068] (4) In the scenario where water storage leads to a slowdown in water flow, filter feeders are selected as the initial attack node to simulate the impact of reduced suspended solids settling on the scarcity of filter feeder resources.

[0069] (5) Under the scenario of low temperature water discharge, apex predators, carnivorous fish and invertebrate predators were selected as the initial attack nodes to simulate the effect of temperature reduction on predation metabolism and feeding activity.

[0070] Step 5: Interference attacks affect network propagation

[0071] Based on the food web network structure, and following preset propagation rules, the disturbance attack is propagated layer by layer from the initial attacking node to adjacent nodes until the disturbance intensity falls below a preset threshold. Stop the spread;

[0072] The propagation rule states that the intensity of the perturbation attack decreases as the propagation path length increases. When the attack propagates to a node d steps away from the initial node, the intensity of the perturbation attack is calculated according to the following formula:

[0073]

[0074] In the formula, This represents the initial perturbation attack strength. Here, d is the propagation attenuation coefficient, and d is the shortest path length from the node to the initial disturbance source. The initial perturbation attack strength is the strength of the attack when it propagates to a node d steps away from the initial node; the initial perturbation attack strength is... The value range of the propagation attenuation coefficient is 10%-80%. The value range is 0.1-0.9, and the preset threshold is... The value range is 10%-30%.

[0075] when Less than the preset threshold At this point, the propagation stops and it no longer continues to spread to more distant nodes.

[0076] Step 6, Monitoring the structure and function of aquatic food webs:

[0077] During a food web perturbation attack, the changes in the network robustness index and omnivorousness index are monitored and calculated in real time to reflect the structural stability and functional plasticity of the aquatic food web in response to the perturbation attack, respectively.

[0078] The robustness index calculation method is as follows: remove network nodes sequentially according to the interference attack rules and propagation rules, record the number of nodes in the largest connected subgraph in the remaining network after each removal step, calculate the proportion of the largest connected subgraph to the total number of nodes in the original network, plot the relationship curve between this proportion and the node removal proportion, and use the numerical integration method to calculate the area under the curve as the single robustness index. Repeat the simulation 3-100 times under the same interference attack and propagation rules, and take the average of the obtained robustness indices as the final robustness index.

[0079] The omnivorousness index is calculated as follows: the number of functional groups that each predator is connected to is divided by the total number of functional groups in the food web. The simulation is repeated 3-100 times according to preset interference attack and propagation rules, and the average value of the obtained omnivorousness index is taken as the final omnivorousness index.

[0080] Step 7: Simulation result output:

[0081] Based on the changes in robustness index and omnivorous index obtained in step 6, we can determine the disturbed water areas, the species at the disturbed key nodes, and the high-impact disturbance scenarios.

[0082] The judgment rule is: when the robustness index of the food web in any area along the target water body decreases by more than a preset threshold T compared to the original food web. b If the omnivorousness index decreases by more than a preset threshold T0, the area is identified as a disturbed sensitive water area and designated as a priority protection zone; if the contribution of any species node to the change in the food web robustness index or omnivorousness index exceeds a preset impact threshold T, the area is considered a disturbed sensitive water area and designated as a priority protection zone. c When a node is identified as a sensitive key node species and designated as a priority protection target, the corresponding disturbance scenario is marked as a high-impact disturbance scenario requiring special attention when more than i along the route simultaneously meet the criteria for sensitive water areas.

[0083] Wherein, the preset threshold T b The value range is 0.15-0.99, the preset threshold T0 value range is 0.15-0.99, and the preset influence threshold T c The value of is in the range of 0.2-0.99, and the value of i is in the range of 2-5.

[0084] Application Example 1: Simulation of Aquatic Food Web Disturbance Based on Flow Pulse Perturbation

[0085] This application example selects a section of the S River basin where a hydropower project is located as the research object, and provides a method for simulating aquatic food web disturbances caused by flow impulses. The specific steps include:

[0086] (1) Acquisition of baseline information on species in the target water area

[0087] Representative sampling points were established along a route from 10 km upstream to 30 km downstream of the dam. Water and sediment samples were collected at each 10 km interval, for a total of four sampling points. Species composition information for each sampling point was obtained through environmental DNA (eDNA) testing.

[0088] (2) Classification of feeding function groups

[0089] The aquatic species identified and monitored by eDNA were classified according to their feeding characteristics and feeding functional groups. The results are shown in Table 1.

[0090] Table 1. Classification of feeding functional groups in the target area of ​​the S River Basin

[0091]

[0092] (3) Construction of aquatic food web

[0093] Based on known feeding relationships among species in the literature, a structured aquatic food web model was constructed at each sampling point as a subset of the food web. The food web subsets were then sorted along the watercourse to form a riparian food web model. Network nodes represent species, the lines (edges) connecting nodes indicate feeding relationships, the arrows on these lines (edges) indicate the direction of predation, and node colors distinguish feeding functional groups, including rippers, filter feeders, foragers, invertebrate predators, small predators, carnivorous fish, omnivorous fish, producers, and decomposers. A typical food web constructed at a sampling point is shown in the attached figure. Figure 1 As shown.

[0094] (4) Disturbance settings and attack rules

[0095] The simulated scenario is a short-term surge in flow rate caused by the opening of a gate in a hydropower project. Literature and monitoring indicate that rippers are most sensitive to sudden changes in flow rate due to their dependence on sedimentary debris and relatively low mobility. Therefore, the attack rule is set as follows: in each sampling point's local network, 40% of the nodes in the ripper group are randomly removed. This group is determined by literature and a functional group database.

[0096] (5) Setting of propagation rules

[0097] The propagation rule of a food web network perturbation attack is that the attack strength decreases as the propagation path length increases. When the attack reaches a node d steps away from the initial node, the attack strength is calculated as follows:

[0098]

[0099] In the formula, The initial perturbation attack strength is 40% in this embodiment; The propagation attenuation coefficient is 0.5 in this embodiment; d is the shortest path length from the node to the initial disturbance source. The strength of the perturbation attack when it propagates to a node d steps away from the initial node, when When the number of nodes is less than 10% of the preset threshold, the propagation terminates and the message is no longer transmitted to more distant nodes.

[0100] (6) Simulation process

[0101] In this scenario, an attack-propagation simulation is performed, repeated 50 times per simulation. The number of nodes in the largest connected subgraph remaining in the network after each removal step is recorded, and its proportion to the total number of nodes in the original network is calculated. This proportion is plotted as a curve relative to the node removal proportion, and the area under the curve is calculated using numerical integration as the single-step robustness index. The average of the obtained robustness indices is taken as the final robustness index. The number of functional groups connected to each predator is divided by the total number of functional groups in the food web, denoted as the omnivorousness index. The simulation is repeated 50 times, and the average of the obtained omnivorousness indices is taken as the final omnivorousness index result obtained from the disturbance simulation of this hydropower project. The average robustness index and the decrease in the average omnivorousness index are calculated. For each sub-food web along the route, it is assessed whether the threshold for determining a disturbance-sensitive area has been reached. In this embodiment, the preset threshold T for the decrease in the food web robustness index compared to the original food web is used. b The preset threshold T0 for the decrease in the omnivorousness index of the food web is 0.3. Simultaneously, the contribution of individual nodes to the decrease in network indicators is recorded to identify sensitive species. In this application example, the preset impact threshold for contribution is 0.1. At the same time, it is determined whether a flow pulse disturbance scenario is a high-impact disturbance scenario requiring attention. In this application example, the criterion for determining a high-impact disturbance scenario is that more than three along-the-course areas simultaneously meet the criteria for determining a disturbed sensitive water area.

[0102] (7) Simulation results and judgment

[0103] like Figure 2 The results showed that the average robustness index at the 4th and 5th sampling points downstream decreased by more than 35%, and the omnivorousness index decreased by about 48%, classifying them as disturbance-sensitive water areas. The removal of nodal organisms *Chroterpes* and *Leptophlebia* contributed more than 12% to the decrease in food web indicators and were identified as key nodal species. In this embodiment, only two areas along the route were identified as disturbance-sensitive water areas, which did not meet the criteria for high-impact disturbance scenarios. Therefore, the flow pulse disturbance scenario in this embodiment is not a high-impact disturbance scenario requiring attention. This embodiment verifies that the attack rules and propagation mechanism in this invention can effectively identify sensitive areas and species under engineering disturbances.

[0104] Application Example 2: Simulation of Aquatic Food Web Disturbance Based on Frequent Water Level Fluctuations

[0105] In this application example, the simulated scenario involves frequent water level fluctuations caused by the daily operation of a small hydropower station, resulting in short-cycle diurnal water level turbulence. Such disturbances can significantly impact small predators, as their spawning on rocks in the water is easily disrupted by water turbulence. Therefore, this application example selects a section of the A River basin where a small hydropower project is located as the research object, and representative sampling points are established along a 50 km stretch from the dam downstream. Water and sediment samples are collected at 10 km intervals, for a total of 5 sampling points. Species composition information for each sampling point is obtained through environmental DNA (eDNA) detection. The initial simulated disturbance attack rule is to randomly remove 50% of the nodes in the small predator population. Other parameter settings, such as the disturbance propagation rule, are the same as in application example 1.

[0106] like Figure 3 Simulation results show that the average robustness index of sampling points 1, 2, and 3 after the dam site decreased by more than 30%, and the omnivorousness index decreased by about 45%, and they were continuously identified as sensitive water areas. The omnivorous fish nodes were indirectly weakened during the propagation process, indicating that the propagation mechanism captured the indirect impact path of the disturbance. In this scenario, three areas simultaneously met the sensitivity criteria, and the disturbance scenario was marked as a high-impact disturbance scenario, which can be used as a key focus for ecological scheduling optimization.

[0107] Finally, it should be noted that the above is only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention (such as the application of various formulas, the order of steps, etc.) without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for simulating aquatic food web disturbance specific to the characteristics of water conservancy and hydropower projects, characterized in that, The simulation method includes the following steps: Step 1, Obtaining Species Baseline Information: By monitoring target water areas using environmental DNA technology, we can identify the composition of aquatic species in the water area and obtain the species and relative abundance of existing aquatic organisms. Step 2, Classification of feeding function groups: Based on known feeding characteristics, the species obtained in step 1 are divided into different feeding functional groups, including apex predators, carnivorous fish, omnivorous fish, herbivorous fish, invertebrate predators, small predators, feeders, tearers, filter feeders, producers, and decomposers. Step 3, Construction of the aquatic food web: Based on known feeding relationships among species, a structured aquatic food web model is constructed. In the model, network nodes represent species, lines between nodes represent feeding relationships, arrows in the lines between nodes represent the direction of predation, and the color of a node represents its feeding functional group. Step 4, Aquatic food web interference attack: We introduce the typical operational characteristics of water conservancy and hydropower projects as disturbance driving factors, construct corresponding disturbance scenarios for feeding function groups for different engineering operation scenarios, target specific feeding function groups to weaken them, formulate targeted interference attack rules, and implement interference attacks. The engineering operation scenarios include river dam construction, increased flow pulse, frequent rise and fall of water level, water storage leading to slowed water flow, and low-temperature water discharge. The interference attack rules are as follows: the feeding functional groups directly affected by the disturbance are taken as the initial attack nodes of the structured aquatic food web model, and a node deletion method is adopted. The disturbance attack intensity is set to randomly delete 10%-80% of the species nodes in the feeding functional groups directly affected by the disturbance. Step 5: Interference attacks affect network propagation Based on the food web network structure, and following preset propagation rules, the disturbance attack is propagated layer by layer from the initial attacking node to adjacent nodes until the disturbance intensity falls below a preset threshold. Stop the spread; The propagation rule states that the intensity of the perturbation attack decreases as the propagation path length increases. When the attack propagates to a node d steps away from the initial node, the intensity of the perturbation attack is calculated according to the following formula: In the formula, This represents the initial perturbation attack strength. Here, d is the propagation attenuation coefficient, and d is the shortest path length from the node to the initial disturbance source. The strength of the perturbation attack when it propagates to a node d steps away from the initial node; when Less than the preset threshold At that time, the transmission ceased; Step 6, Monitoring the structure and function of aquatic food webs: During a food web disturbance attack, the changes in the network robustness index and omnivorousness index are monitored and calculated in real time. Step 7: Simulation result output: Based on the changes in robustness index and omnivorous index obtained in step 6, we can determine the disturbed water areas, the species at the disturbed key nodes, and the high-impact disturbance scenarios. The judgment rule is: when the robustness index of the food web in any area along the target water body decreases by more than a preset threshold T compared to the original food web. b If the omnivorousness index decreases by more than a preset threshold T0, the area is identified as a disturbed sensitive water area and designated as a priority protection zone; if the contribution of any species node to the change in the food web robustness index or omnivorousness index exceeds a preset impact threshold T, the area is considered a disturbed sensitive water area and designated as a priority protection zone. c When the node is identified as a sensitive key node species and designated as a priority protection target, the corresponding disturbance scenario is marked as a high-impact disturbance scenario requiring special attention when more than i along the route simultaneously meet the criteria for sensitive water areas.

2. The simulation method according to claim 1, characterized in that, In step 1, the types of aquatic organisms include: fish, protozoa, metazoa, large benthic invertebrates, phytoplankton, attached algae, and microorganisms.

3. The simulation method according to claim 1, characterized in that, In step 3, the structured aquatic food web model is a food web model along the target water area. It is formed by acquiring species composition information at different locations in the target water area and constructing local food web subsets, and then integrating the local food web subsets in the order along the water area.

4. The simulation method according to claim 1, characterized in that, In step 4, the initial attack function group is selected based on the hydrological rhythm change scenario. The selection rules for the initial attack node under the corresponding scenario include: (1) In the scenario of river damming, migratory species among top predators, carnivorous fish, omnivorous fish and herbivorous fish are selected as the initial attack nodes to simulate the population isolation caused by damming directly blocking the migration path; (2) Under the scenario of increased flow pulse, rippers, filter feeders, invertebrate predators, and small predators were selected to simulate the direct impact of high flow velocity scouring on benthic food sources. (3) Under the scenario of frequent rise and fall of water level, select aquatic insects among producers, such as attached algae, feeders and small predators, to simulate the effects of repeated wet and dry alternation leading to a decrease in food source and feeding efficiency, and a decrease in the survival rate of eggs attached to rocks in the water. (4) Under the scenario of water storage leading to slow water flow, filter feeders were selected to simulate the impact of reduced suspended solids settling leading to a scarcity of filter feeder resources; (5) In the scenario of low temperature water discharge, apex predators, carnivorous fish and invertebrate predators were selected to simulate the effect of temperature reduction on predation metabolism and feeding activity.

5. The simulation method according to claim 1, characterized in that, In step 5, the initial perturbation attack strength The value range of the propagation attenuation coefficient is 10%-80%. The value range is 0.1-0.9, and the preset threshold is... The value range is 10%-30%.

6. The simulation method according to claim 1, characterized in that, In step 6, the robustness index is calculated as follows: network nodes are removed sequentially according to the interference attack rules and propagation rules. The number of nodes in the largest connected subgraph in the remaining network after each removal is recorded. The proportion of the nodes in the subgraph to the total number of nodes in the original network is calculated. The relationship curve between this proportion and the node removal proportion is plotted. The area under the curve is calculated using the numerical integration method as the single robustness index. The simulation is repeated 3-100 times under the same interference attack and propagation rules. The average of the obtained robustness indices is taken as the final robustness index.

7. The simulation method according to claim 1, characterized in that, In step 6, the omnivorousness index is calculated as follows: the number of functional groups that each predator is connected to is divided by the total number of functional groups in the food web. The simulation is repeated 3-100 times according to the preset interference attack and propagation rules, and the average value of the obtained omnivorousness index is taken as the final omnivorousness index.

8. The simulation method according to claim 1, characterized in that, In step 7, the preset threshold T b The value range is 0.15-0.99, the preset threshold T0 value range is 0.15-0.99, and the preset influence threshold T c The value of is in the range of 0.2-0.99, and the value of i is in the range of 2-5.

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