Method and device for simulating and predicting stability change of ecological system based on interference scene

Abstract

The invention provides a method and a device for simulating and predicting stability change of an ecological system based on an interference scene, and relates to the technical field of ecological environment evaluation and ecological restoration. The method comprises the following steps: constructing a species-habitat network and dividing functional groups of species; determining a habitat node deletion sequence; according to the habitat node deletion sequence, simulating a process of gradually deleting habitat types in the species-habitat network, and calculating a robustness index and a function stability index; further determining a comprehensive stability index and a stability threshold water level; the conversion from static evaluation to dynamic process simulation is realized, and the accumulated loss trajectory of species and functions in the interference process can be quantified; a more comprehensive ecological system stability evaluation index is constructed by integrating network structure robustness and functional group stability; and the stability threshold water level is automatically identified based on the curve inflection point, and an objective and systematic quantitative basis is provided for ecological water level control and restoration decision.

Description

Technical Field

[0001] This invention relates to the field of ecological environment assessment and ecological restoration technology, and in particular to a method and apparatus for predicting changes in ecosystem stability based on disturbance scenario simulation. Background Technology

[0002] Traditional ecological impact assessments primarily rely on static indicators of single species or taxa, making it difficult to reveal the complex dependencies between multiple species and habitats, and also failing to characterize the dynamic response processes of ecosystems under continuous hydrological disturbances. Existing species-habitat network studies largely focus on static structural analysis, lacking methods to transform specific water level disturbance scenarios into network node failure sequences and systematically quantify changes in network structural robustness and functional stability. Especially in determining the ecological thresholds for water level regulation, existing methods often rely on empirical judgments or single habitat indicators, lacking data-driven threshold identification mechanisms based on dynamic network responses. Summary of the Invention

[0003] The purpose of this invention is to provide a method and apparatus for predicting changes in ecosystem stability based on disturbance scenario simulation, realizing the transformation from static assessment to dynamic process simulation, and quantifying the cumulative loss trajectory of species and functions during disturbance; by integrating the robustness of network structure and the stability of functional groups, a more comprehensive ecosystem stability evaluation index is constructed; and based on the inflection point of the curve, the stability threshold water level is automatically identified, providing an objective and systematic quantitative basis for ecological water level control and restoration decisions.

[0004] In a first aspect, the present invention provides a method for predicting changes in ecosystem stability based on disturbance scenario simulation, comprising: Based on survey data of the target ecosystem, the study area was divided into multiple habitat types, and the richness of various species in each habitat type was determined to construct a species-habitat network; based on the functional traits of the species, the species were divided into multiple functional groups. Based on the preset hydrological disturbance scenario, the response relationship between different water levels and the existence or disappearance of each habitat type is determined, and an ordered habitat node deletion sequence is generated based on the response relationship. Based on the habitat deletion sequence, the process of gradual deletion of habitat types in the species-habitat network is simulated. By calculating the retention ratio of habitat types and species and plotting the secondary extinction curve, a robustness index characterizing the stability of the species-habitat network is obtained. At the same time, at each step of the deletion process, the functional retention ratio of each functional group and the functional redundancy index of that step are calculated, and the functional stability index is obtained by averaging the functional redundancy indices of all steps. The comprehensive stability index of the target ecosystem is determined by weighted summation based on the robustness index, functional stability index, and preset weighting coefficients. Based on the curve of functional redundancy index changing with water level, the slope change analysis method is used to identify the inflection point of the curve, and the water level corresponding to the inflection point is determined as the stability threshold water level of the target ecosystem.

[0005] In some preferred embodiments of the present invention, the survey data used to classify habitat types includes at least one of the following: remote sensing data, UAV imagery, and ground survey data; the step of determining the richness of multiple species in various habitat types includes: The richness of each species in each habitat type was obtained by reviewing historical survey data and supplementing the data with methods such as transects, sampling points, quadrats, and infrared camera surveys.

[0006] In some preferred embodiments of the present invention, the hydrological disturbance scenarios include at least one of a single sustained rise in water level, a periodic rise and fall in water level, and an extreme high or low water level event.

[0007] In some preferred embodiments of the present invention, the step of simulating the gradual deletion of habitat types in a species-habitat network based on the habitat node deletion sequence, and obtaining a robustness index characterizing the stability of the species-habitat network by calculating the retention ratio of habitat types and species and plotting secondary extinction curves includes: After each habitat node is deleted, the habitat retention ratio is calculated based on the initial total number of habitats and the current total number of habitats, and the species retention ratio is calculated based on the initial total number of species and the current total number of species. Plot a curve with the habitat retention rate corresponding to each step as the horizontal axis and the species retention rate as the vertical axis. The area under the curve is calculated using the trapezoidal method and used as the robustness index.

[0008] In some preferred embodiments of the present invention, the step of calculating the function retention ratio of each function group and the function redundancy index of that step at each step of the deletion process includes: After each step of deleting habitat nodes, the function retention ratio of each functional group is calculated based on the initial total number of species in the functional group and the current total number of surviving species. Calculate the average function retention ratio of all function groups in this step as the function redundancy index for this step.

[0009] In some preferred embodiments of the present invention, the method further includes: The stability threshold water level is determined by the position of the inflection point in the habitat node deletion sequence and the preset water level change step size.

[0010] In some preferred embodiments of the present invention, the target ecosystem is a river wetland, a reservoir wetland or a lake wetland; the species include at least two of the following: fish, birds, amphibians, benthic animals, aquatic plants and wetland plants.

[0011] Secondly, the present invention provides an apparatus for predicting changes in ecosystem stability based on disturbance scenario simulation, comprising: The data processing module is used to divide the study area into multiple habitat types based on survey data of the target ecosystem, and to determine the richness of multiple species in each habitat type in order to construct a species-habitat network; and to classify species into multiple functional groups based on their functional traits. The habitat node sorting module is used to determine the response relationship between different water levels and the existence or disappearance of each habitat type based on a preset hydrological disturbance scenario, and to generate an ordered habitat node deletion sequence based on the response relationship. The primary data computation module is used to simulate the process of gradually deleting habitat types in the species-habitat network based on the habitat node deletion sequence. By calculating the retention ratio of habitat types and species and plotting the secondary extinction curve, a robustness index characterizing the stability of the species-habitat network is obtained. At the same time, at each step of the deletion process, the functional retention ratio of each functional group and the functional redundancy index of that step are calculated, and the functional stability index is obtained by averaging the functional redundancy indices of all steps. The comprehensive stability index determination module is used to determine the comprehensive stability index of the target ecosystem by weighted summation based on the robustness index, functional stability index and preset weighting coefficients. The stability threshold water level determination module is used to identify the inflection point of the curve based on the functional redundancy index changing with water level, and to determine the water level corresponding to the inflection point as the stability threshold water level of the target ecosystem.

[0012] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the method for predicting ecosystem stability changes based on disturbance scenario simulation provided in the first aspect above.

[0013] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the method for predicting ecosystem stability changes based on disturbance scenario simulation provided in the first aspect.

[0014] This invention brings the following beneficial effects: This invention provides a method and apparatus for predicting ecosystem stability changes based on disturbance scenario simulation. The method includes: dividing the study area into multiple habitat types based on survey data of the target ecosystem, and determining the richness of multiple species in each habitat type to construct a species-habitat network; classifying species into multiple functional groups based on their functional traits; determining the response relationship between different water levels and the presence or disappearance of each habitat type based on a preset hydrological disturbance scenario, and generating an ordered habitat node deletion sequence based on the response relationship; simulating the gradual deletion process of habitat types in the species-habitat network according to the habitat node deletion sequence, and obtaining a robustness index characterizing the stability of the species-habitat network by calculating the retention ratio of habitat types and species and plotting secondary extinction curves; simultaneously, at each step of the deletion process, calculating the richness of each species in the habitat type and ... The system calculates the functional retention ratio of functional groups and the functional redundancy index for each step, and obtains the functional stability index by averaging the functional redundancy indices of all steps. Based on the robustness index, functional stability index, and preset weighting coefficients, a weighted summation is used to determine the comprehensive stability index of the target ecosystem. Based on the curve of functional redundancy index versus water level, slope change analysis is used to identify the inflection point of the curve, and the water level corresponding to the inflection point is determined as the stability threshold water level of the target ecosystem. This achieves a shift from static assessment to dynamic process simulation, quantifying the cumulative loss trajectory of species and functions during disturbance. By integrating the robustness of the network structure and the stability of functional groups, a more comprehensive ecosystem stability evaluation index is constructed. Furthermore, the system automatically identifies the stability threshold water level based on the curve inflection point, providing an objective and systematic quantitative basis for ecological water level control and restoration decisions. Attached Figure Description

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating a method for predicting ecosystem stability changes based on disturbance scenario simulation, provided in an embodiment of the present invention; Figure 2 A schematic diagram of a secondary extinction curve provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the water level-stability relationship provided in an embodiment of the present invention; Figure 4 A schematic diagram of a device for predicting changes in ecosystem stability based on disturbance scenario simulation, provided in an embodiment of the present invention; Figure 5This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0017] Icons: 310 - Data processing module; 320 - Habitat node sorting module; 330 - Primary data calculation module; 340 - Comprehensive stability index determination module; 350 - Stability threshold water level determination module; 400 - Memory; 401 - Processor; 402 - Bus; 403 - Communication interface. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0020] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0021] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention. In addition, the terms "first," "second," "third," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] Furthermore, terms such as "horizontal," "vertical," and "sag" do not imply that components must be absolutely horizontal or suspended, but rather that they can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal relative to "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0023] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0024] Traditional ecological impact assessments of river wetlands mainly rely on indicators such as species diversity index, changes in dominant species, and comparison of community structure. These methods are insufficient to depict the dependence of species on different habitat types and to predict the overall stability trend of the system under various disturbance scenarios.

[0025] Species-habitat networks treat "species" and "habitat types or patches" as two types of nodes. By establishing a dichotomous network based on the presence or abundance of species in different habitats, the importance of each habitat in maintaining multi-species diversity can be revealed. However, existing research mostly focuses on static structural analysis (such as nesting, modularity, and node importance), lacking a systematic predictive method for "changes in ecosystem stability under different disturbance scenarios."

[0026] In river wetland ecosystems, water level fluctuations and hydrological regulation are among the most critical anthropogenic disturbances. Water level changes directly lead to variations in the area, spatial location, and quality of habitats such as mudflats, shallow water zones, emergent vegetation zones, and deep water areas, thereby triggering habitat-species relationship reorganization. However, current assessments of water level management schemes often rely on simple water level-habitat area relationships or the responses of individual indicator species, lacking systematic assessment methods that consider species' dependencies on different habitats.

[0027] Existing methods have the following shortcomings: Current ecological impact assessments focus only on single indicators or single taxa, lacking a systematic assessment from a network perspective; existing ecological impact assessments often use the richness of a few indicator species or single biological groups (such as fish or birds) and habitat area as the basis for judgment, failing to depict the interdependent structure of "multi-taxa-multi-habitat". This leads to the difficulty in timely warning of the potential collapse risk of the entire ecosystem even if certain key habitats (such as seasonal mudflats and emergent vegetation zones) are severely weakened, as long as a few indicator species have not yet disappeared. Furthermore, it is impossible to quantitatively assess how many species and functions will be lost in a cascade due to the degradation of a certain type of habitat.

[0028] The lack of dynamic stability analysis of disturbance processes is a significant issue: traditional water level scenario analyses often focus on comparing habitat inundation areas under different scenarios, lacking simulations of gradual water level changes and even less consideration of secondary extinction processes involving the gradual loss of habitat nodes. Existing robustness analyses primarily concentrate on species-species networks (such as food webs and mutualistic networks). For species-habitat networks, especially in strongly hydrologically constrained systems like river wetlands, there is still a lack of mature and readily available engineering methods for constructing habitat node deletion sequences along water level gradients and quantifying network robustness.

[0029] Existing water level threshold identification lacks a comprehensive data-driven inflection point identification mechanism: In current engineering practice, the setting of ecological water level thresholds lacks a comprehensive data-driven threshold identification mechanism that considers multiple taxa and habitats, and lacks the ability to characterize the critical point from slow degradation to rapid collapse of the system.

[0030] The technical solution disclosed in this application classifies multiple habitats and determines the "presence / disappearance" of each habitat under different water level conditions. Under a given water level scenario, a habitat node deletion sequence is generated, and the habitat retention ratio and species retention ratio at each step are calculated. A secondary extinction curve is constructed, and the robustness is obtained by calculating the area enclosed under the extinction curve using the trapezoidal method. The cumulative impact of gradual water level changes on the species-habitat network structure is characterized. Functional groups are divided based on species functional traits, and the retention ratio and functional redundancy index of each functional group are calculated at each disturbance step, thereby calculating the functional stability index of the ecosystem throughout the disturbance process. From a "functional level," it describes which key functions are most sensitive to water level changes, supplementing the risk of functional loss that cannot be reflected by the number of species alone. By weighting the structural robustness and functional stability indices, a comprehensive ecosystem stability index is obtained. Based on the inflection point identification method of slope change, the ecosystem stability threshold water level is automatically identified from the water level-stability curve. Within a unified framework, a "comprehensive ecosystem stability score" and an "ecological water level threshold" are provided, directly serving water level control and key habitat protection decisions.

[0031] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0032] Example 1 This invention provides a method for predicting ecosystem stability changes based on disturbance scenario simulation. (See also...) Figure 1 The flowchart shown in this embodiment of the invention provides a method for predicting ecosystem stability changes based on disturbance scenario simulation. The method includes: Step S102: Based on the survey data of the target ecosystem, the study area is divided into multiple habitat types, and the richness of multiple species in each habitat type is determined to construct a species-habitat network; based on the functional traits of the species, the species are divided into multiple functional groups.

[0033] Specifically, this step aims to establish the underlying data model for the assessment. Traditional methods often view species or habitats in isolation, while this invention constructs a "species-habitat network," treating species and habitats as interconnected system nodes. This allows for a formal characterization of the complex interdependence between "one species relying on multiple habitats" and "one habitat supporting multiple species." Simultaneously, grouping species into functional groups based on functional traits (such as feeding type and trophic level) lays the foundation for subsequent assessments of functional dimension stability. Integrating discrete ecological survey data into a computable and simulable network system provides a structured digital twin model for subsequent dynamic disturbance analysis, overcoming the limitations of traditional methods that can only compare static, isolated indicators.

[0034] Furthermore, in some preferred embodiments of the present invention, the survey data used to classify habitat types includes at least one of the following: remote sensing data, UAV imagery, and ground survey data; the step of determining the richness of multiple species in each habitat type includes: obtaining the richness of each species in each habitat type by reviewing historical survey data and supplementing it with methods such as transect, sample point, quadrat, and infrared camera surveys.

[0035] Specifically, remote sensing and UAV imagery can be used to achieve large-scale, periodic, and non-invasive habitat mapping and classification. Combined with ground surveys, remote sensing interpretation results can be verified and refined, dividing the study area into several habitat types (such as deep water areas of main channels, slow-flowing shallow water zones, seasonal mudflats, emergent vegetation zones, mudflats, reed marshes, farmland, fishponds, etc.), and obtaining the spatial distribution and area of ​​each habitat patch under different water levels. Reviewing historical data, combined with multi-source survey methods such as transects, sampling points, quadrats, and infrared cameras, allows for the acquisition of the richness of target species (such as fish, benthic animals, amphibians, waterbirds, aquatic plants, and wetland plants) in each habitat type. This maximizes the integration and supplementation of biodiversity information. Using the species list as the rows of a matrix and habitat types as the columns, a species richness matrix for each habitat type is constructed, creating a species-habitat bipartite grid that is as realistic and complete as possible. By combining multi-source data fusion with standardized survey methods, the accuracy and reliability of the constructed network are ensured, enabling subsequent simulations and predictions to be based on solid field observation data.

[0036] Step S104: Based on the preset hydrological disturbance scenario, determine the response relationship between different water levels and the existence or disappearance of each habitat type, and generate an ordered habitat node deletion sequence based on the response relationship.

[0037] Specifically, habitat deletion sequences serve as a crucial bridge connecting physical disturbances (such as water level changes) with changes in the network model (such as the deletion of habitat types and / or species). Their core lies in establishing a quantitative response relationship between "water level" and "habitat state" (for example, when the water level exceeds a certain elevation, a specific type of tidal flat habitat is submerged and "disappears"). Based on this relationship, any specific hydrological disturbance scenario (such as water level rising from a first elevation to a second elevation) can be "translated" into a definite, ordered sequence of habitat deletions. This transforms continuous and complex environmental disturbance processes into discrete, step-by-step sequences of node operations in network analysis, enabling dynamic simulation and allowing analysis of the cumulative effects and process trajectories of disturbances.

[0038] Furthermore, in some preferred embodiments of the present invention, the hydrological disturbance scenarios include at least one of a single sustained rise in water level, a periodic rise and fall in water level, and an extreme high or low water level event.

[0039] Specifically, based on engineering operation plans or climate scenarios, multiple water level disturbance scenarios are set up, including but not limited to: "single continuous water level rise scenario (increase and duration)" to simulate reservoir impoundment, flooding, and other scenarios; "periodic water level rise and fall scenario (frequency and amplitude)" to simulate seasonal hydrological regulation or tidal effects; and "extreme water level events" to assess the impact of drought or flooding. In each scenario, the "presence / disappearance," "area," and "quality level" attributes of each habitat are updated according to the water level-water depth-habitat correspondence. These scenarios basically cover the main anthropogenic and natural hydrological disturbance patterns faced by river, lake, and wetland ecosystems. For each disturbance scenario, based on the water level-habitat response, multiple habitat node deletion sequences are generated, such as prioritizing the deletion of the habitat nodes that are submerged first according to the order of water level rise and submersion. By presetting these typical scenarios, the method provided by this invention has the general ability to evaluate the ecosystem response under various management schemes (such as different scheduling strategies) and climate scenarios, improving the practicality and applicability of the method.

[0040] Step S106: Based on the habitat node deletion sequence, simulate the process of gradual deletion of habitat types in the species-habitat network. By calculating the retention ratio of habitat types and species and plotting the secondary extinction curve, obtain the robustness index characterizing the stability of the species-habitat network. At the same time, at each step of the deletion process, calculate the functional retention ratio of each functional group and the functional redundancy index of that step, and obtain the functional stability index by averaging the functional redundancy indices of all steps.

[0041] Specifically, in the process of progressively deleting habitat types from the species-habitat grid based on the habitat node deletion sequence, it executes two computational threads in parallel: First, structural stability assessment, which simulates the deletion sequence to calculate the survival status of habitats and species in the network in real time, and plots a "secondary extinction curve" reflecting the gradual extinction of species due to habitat loss. The area under the curve (robustness index) intuitively reflects the network structure's resistance to disturbance. Second, functional stability assessment, which, in each deletion step, not only considers the presence or absence of species but also their functional attributes. It assesses changes in functional redundancy by calculating the survival ratio of each functional group and averages the results over the entire process to obtain the functional stability index. This achieves simultaneous and dynamic quantification of the dual stability of ecosystem "structure" and "function," revealing the detailed paths of which species are lost first and which functions are damaged first during disturbance, providing a deeper insight than simply counting single species.

[0042] Furthermore, in some preferred embodiments of the present invention, the process of gradually deleting habitat types in a species-habitat network is simulated based on the habitat node deletion sequence. A robustness index characterizing the stability of the species-habitat network is obtained by calculating the retention ratio of habitat types and species and plotting a secondary extinction curve. This includes: calculating the habitat retention ratio based on the initial total number of habitats and the current total number of habitats after each habitat node deletion step; calculating the species retention ratio based on the initial total number of species and the current total number of species; plotting a curve with the habitat retention ratio corresponding to each step as the abscissa and the species retention ratio as the ordinate; and calculating the area under the curve using the trapezoidal method as the robustness index.

[0043] Specifically, for a given sequence of habitat node deletions, the habitat retention rate is calculated at each deletion step t (t=0, 1, ..., T). and species retention ratio A series of points were obtained ( ).

[0044] The habitat retention ratio is determined by the following formula: , t=0,1,…,T; in, H represents the percentage of habitat nodes retained relative to the initial state when habitat nodes are deleted in step t, and H is the initial total number of habitats in the initial network. t represents the number of remaining habitat nodes at step t, which is also the current total number of habitats; t=T represents the state after completing all preset deletion steps.

[0045] The species retention ratio is determined by the following formula: , t=0,1,…,T; in, Let S be the proportion of species that still exist when the habitat node is deleted in step t, and S be the initial total number of species in the initial network. t represents the number of species nodes that still exist at step t, which is also the current total number of species; t=T represents the state after completing all preset deletion steps.

[0046] Robustness index R calculation: The above-calculated series of habitat retention ratios and species retention ratios (points) The secondary extinction curve is plotted, and the area enclosed under the curve is approximated by the trapezoidal method to obtain the robustness index R.

[0047] The area under the curve is obtained using the trapezoidal method and is used as the robustness index R: ; in, For the species retention rate at step t, similarly... For the first The percentage of species retained at each step; For the first The decrease in habitat retention rate between step t and step t. This invention transforms the abstract concept of network robustness into a clear and repeatable numerical indicator, enabling objective comparisons of stability across different disturbance scenarios or ecosystems.

[0048] Furthermore, in some preferred embodiments of the present invention, the step of calculating the functional retention ratio of each functional group and the functional redundancy index of that step at each step of the deletion process includes: after deleting habitat nodes at each step, calculating the functional retention ratio of each functional group based on the initial total number of species in the functional group and the current total number of surviving species; and calculating the average of the functional retention ratios of all functional groups in that step as the functional redundancy index of that step.

[0049] Specifically, based on species functional traits (such as feeding type, trophic level, ecological niche, etc.), G functional groups (such as wading birds group, benthic fish group, etc.) are divided, and the g-th functional group is denoted as g=1,2,...,n.

[0050] Functionality retention ratio calculation: When the habitat node is deleted in step t, the number of species still surviving in functional group g is denoted as . Then the percentage of function group g retained at step t is: ; in, The percentage of species that remain when functional group g is deleted at the habitat node in step t; The number of species still retained in functional group g when the habitat node is deleted in step t, which is also the current total number of surviving species; This represents the initial total number of species in the functional group g in the initial network.

[0051] Functional redundancy index calculation: This invention uses the average of the function retention ratios of each functional group as the functional redundancy index. : ; in, The value ranges from [0, 1]. A larger value indicates that each functional group still retains more species when the habitat node is deleted at step t, indicating higher functional redundancy. In some preferred embodiments of the present invention, if it is necessary to give higher weight to certain functional groups, it can be extended to a weighted form, such as the species retention ratio of a certain functional group. Set the weight to β.

[0052] Functional stability index calculate: To integrate the changes in functional redundancy throughout the entire disturbance process into a holistic index, this invention defines a functional stability index. The average value of the functional redundancy index for each step: ; in, This reflects the average level of functional preservation of each functional group under the entire disturbance scenario; a higher value indicates that the wetland can maintain a high level of functional redundancy even under long-term disturbance. This invention can monitor the decline in the ecosystem's "buffering capacity" during disturbance in real time, even if the total number of species remains unchanged. A decrease in redundancy also means a reduction in the redundancy of certain key functions, resulting in poorer system stability and thus warning of potential functional loss risks.

[0053] Step S108: Determine the comprehensive stability index of the target ecosystem by weighted summation based on the robustness index, functional stability index, and preset weighting coefficients.

[0054] Specifically, the robustness index and functional stability index are weighted by an adjustable coefficient (which can also be adjusted according to management objectives). In some preferred embodiments of the present invention, this coefficient is typically set to 0.5 to indicate equal importance. The robustness index R and the functional stability index are... By performing a weighted summation, a single comprehensive stability index SI is obtained, which is determined by the following formula: ; in, As a weight for structural stability, As a weight for functional stability; when When the coefficient of performance is 0.5, structural and functional stability have equal weights. In some preferred embodiments of the present invention, this weight can be adjusted according to management objectives (such as prioritizing functional maintenance or species diversity). The SI value is determined by the number of values; a higher SI value indicates greater stability of the overall structure and function of the wetland ecosystem under the disturbance scenario during long-term disturbance. This invention addresses the problem of traditional methods using single indicators or difficulties in integrating indicators, providing decision-makers with a unified "comprehensive score" for ecosystem health that considers both structure and function. This index facilitates horizontal comparisons across different scenarios and regions, directly contributing to the optimal selection of management solutions.

[0055] Step S110: Based on the curve of functional redundancy index changing with water level, the slope change analysis method is used to identify the inflection point of the curve, and the water level corresponding to the inflection point is determined as the stability threshold water level of the target ecosystem.

[0056] Specifically, for a given water level scenario, record the water level corresponding to each disturbance step t. In addition to the instantaneous stability index, in some preferred embodiments of the present invention, the functional redundancy index F(t) is used as the instantaneous stability index to construct the "water level-stability" relationship curve. }

[0057] The slope change analysis method is used for this curve. In order to characterize the rate of change of stability with water level, the embodiment of the present invention calculates the discrete slope between two adjacent points. : ; in, For segment t (water level from) Change to The approximate rate of change of stability with respect to water level; when A value less than 0 indicates that the overall stability of the ecosystem decreases as the water level rises.

[0058] By calculating the magnitude of the slope change between adjacent line segments (the amount of slope change), the location where the curve slope abruptly changes, i.e., the inflection point, is automatically identified. The inflection point indicates a sudden change in the system's sensitivity to water level, often manifested as a significant difference in slope between the two preceding and following segments. This embodiment of the invention determines the amount of slope change based on adjacent slopes. : ; in, In the water level range The magnitude of the change in the rate of decrease in internal stability; The larger the value, the greater the curvature of the water level-stability curve near the corresponding water level, meaning that an inflection point is more likely to exist.

[0059] After calculating all Subsequently, in this embodiment of the invention, the maximum value of the slope change is used as the criterion for determining the inflection point, that is: ; Then the first Water level after the segment This is the inflection point water level of the stability curve, i.e.: ; in, The change in slope The index of the step when the maximum value is reached; The corresponding water level value, also known as the ecosystem stability threshold water level, indicates that near this water level, the stability response to water level changes shows a significant intensification or reversal.

[0060] The water level corresponding to this inflection point is the critical point at which the stability of ecosystem functions declines sharply, also known as the stability threshold water level. The threshold water level can provide direct scientific basis for setting key management parameters such as the maximum operating water level of reservoirs and the minimum ecological water level of wetlands.

[0061] Furthermore, in some preferred embodiments of the present invention, the method further includes: determining a stability threshold water level by the position of the inflection point in the habitat node deletion sequence and a preset water level change step size.

[0062] Specifically, after identifying the water level range where stability undergoes a dramatic change using the inflection point identification method, and combining this with a pre-set water level change step size (e.g., a water level rise of 0.1 meters per step in the simulation), the specific water level value corresponding to the inflection point can be precisely located. For example, if the inflection point is located at the... Step and the first Between steps, with a step size of ΔH, the threshold water level... It can be accurately calculated as ,in, The benchmark water level generally represents the water level when the species-habitat grid remains unchanged. By transforming the theoretical inflection point into a specific water level elevation with clear engineering significance, management decisions (such as water level scheduling instructions) can be made accurate to the meter or even centimeter level, greatly improving the operability and guidance precision of the method.

[0063] This invention can output: ① a comprehensive ecosystem stability index S (considering both structural and functional stability indices) under different hydrological disturbance scenarios; ② stability change trends and stability threshold water level identification under fixed disturbance scenarios. These results can be used to: ① determine the upper limit of water level regulation for ecosystems such as rivers and wetlands; ② guide the spatial layout of river wetland habitat restoration; ③ formulate buffer zone construction and ecological compensation measures to reduce the impact of extreme water level events on ecosystem stability.

[0064] This invention provides a method for predicting ecosystem stability changes based on disturbance scenario simulation, comprising: dividing the study area into multiple habitat types based on survey data of the target ecosystem, and determining the richness of multiple species in each habitat type to construct a species-habitat network; classifying species into multiple functional groups based on their functional traits; determining the response relationship between different water levels and the presence or disappearance of each habitat type based on a preset hydrological disturbance scenario, and generating an ordered habitat node deletion sequence based on the response relationship; simulating the gradual deletion process of habitat types in the species-habitat network according to the habitat node deletion sequence, and obtaining a robustness index characterizing the stability of the species-habitat network by calculating the retention ratio of habitat types and species and plotting secondary extinction curves; simultaneously, at each step of the deletion process, calculating the number of functional groups... The system calculates the functional retention ratio of the population and the functional redundancy index for that step, and obtains the functional stability index by averaging the functional redundancy indices of all steps. Based on the robustness index, functional stability index, and preset weighting coefficients, a weighted summation is used to determine the comprehensive stability index of the target ecosystem. Based on the curve of functional redundancy index changing with water level, the slope change analysis method is used to identify the inflection point of the curve, and the water level corresponding to the inflection point is determined as the stability threshold water level of the target ecosystem. This achieves a transformation from static assessment to dynamic process simulation, quantifying the cumulative loss trajectory of species and functions during disturbance. By integrating the robustness of network structure and the stability of functional groups, a more comprehensive ecosystem stability evaluation index is constructed. Furthermore, the stability threshold water level is automatically identified based on the curve inflection point, providing an objective and systematic quantitative basis for ecological water level control and restoration decisions.

[0065] Example 2 Based on the above embodiments, in some preferred embodiments of the present invention, the target ecosystem is a river wetland, a reservoir wetland or a lake wetland; the species include at least two of the following: fish, birds, amphibians, benthic animals, aquatic plants and wetland plants.

[0066] Specifically, riverine wetlands, reservoir wetlands, and lacustrine wetlands are the system types most significantly affected by hydrological regulation and possess important ecological functions, making them areas where current ecological management needs are urgent. The listed groups of fish, birds, amphibians, benthic animals, and aquatic and wetland plants constitute the main body of biodiversity and key ecological functions in the aforementioned wetland ecosystems. The requirement to include at least two groups ensures that the constructed network possesses basic complexity and representativeness, reflecting ecological relationships across trophic levels or habitat types.

[0067] For example, taking a river wetland as the study area, the area was divided into eight typical habitat types using remote sensing, UAV imagery, and ground surveys: h1 main channel deep water area, h2 slow-flowing shallow water zone, h3 seasonal mudflats, h4 emergent vegetation zone, h5 mudflats, h6 reed marshes, h7 farmland, and h8 lakeside shrubland. Under different water level conditions, the spatial distribution and area of ​​patches for each habitat type were obtained, and the water level-habitat response relationship required for subsequent analysis was constructed accordingly.

[0068] Within the study area, the presence of 10 representative species in various habitat types was obtained by combining historical monitoring data, transect / spot / quadrat surveys, and infrared camera monitoring. These species cover functional groups including waterbirds, fish, amphibians, benthic invertebrates, aquatic plants, and terrestrial birds, denoted as s1-s10, including: s1 Diving Duck, s2 Small Waterfowl, s3 Wading Bird A, s4 Wading Bird B, s5 Fish A, s6 Fish B, s7 Amphibians, s8 Benthic Invertebrates, s9 Aquatic Vegetation, and s10 Terrestrial Birds. The functional group classification is as follows: Functional group G1 (wading birds + waterfowl): s1, s2, s3, s4, a total of four types.

[0069] Functional group G2 (fish): s5, s6, two types in total.

[0070] Functional group G3 (amphibians + benthic invertebrates + aquatic plants): s7, s8, s9, a total of three species.

[0071] Functional group G4 (terrestrial birds): s10, with one species.

[0072] Based on the acquired data, a 10 (species) × 8 (habitat) matrix A is constructed, where A = [a_sh], rows represent species, columns represent habitats (Table 1), 1 indicates that the species uses the habitat, and 0 indicates that it is not used.

[0073] Table 1. Species-Habitat Relationship Matrix A

[0074] According to the engineering operation plan, this embodiment of the invention sets up a "single continuous rise in water level" disturbance scenario. As the water level rises, low-lying habitats are submerged first, while high-lying habitats are affected later. Based on the water level-habitat response relationship, the habitat node deletion order is set as: σ = (h3, h5, h4, h6, h2, h1, h7, h8).

[0075] Let the initial number of habitats H=8 and the initial number of species S=10. Calculate the robustness index of the species-habitat network, as shown in Table 2: Table 2. Habitat preservation and species preservation rates at each deletion step

[0076] See Figure 2 The diagram shown is a schematic representation of a secondary extinction curve provided by an embodiment of the present invention, based on Table 2. Plot the habitat retention ratio versus species retention ratio curve (secondary extinction curve).

[0077] Using the trapezoidal method to approximate the area under the curve, the robustness index is calculated using the following formula: ; Substituting the values ​​from Table 2 into the calculation, we get: .

[0078] Furthermore, the calculation results of the function retention ratio and function redundancy index at each deletion step are shown in Table 3.

[0079] Table 3. Function Retention Ratio and Function Redundancy Index of Each Functional Group

[0080] The average value of the functional redundancy index for each step is used as the functional stability index. ; Substituting the values ​​from Table 3, we can obtain: This indicates that the average level of functional retention of each functional group during the entire water level rise disturbance process was 0.678.

[0081] To simplify the demonstration, the weights for structural stability and functional stability are set to be the same. Then, the comprehensive stability index of the ecosystem is: ; The overall stability index (SI) is close to 0.66, indicating that under this water level rise scenario, the river wetland species-habitat network has a certain degree of resistance to disturbance in terms of both structure and function, but there is already a clear risk of species and functional loss.

[0082] This embodiment assumes a linear relationship between the water level and the deletion step: initial water level If the water level is 0.0m, and the water level rises by 0.5m in each step, then the corresponding water level for each step is... for: ; Functional redundancy index As an instantaneous stability indicator, the "water level-stability" data is obtained for { } and draw the curve, such as Figure 3 The diagram shown in this embodiment of the invention illustrates a water level-stability relationship, with the vertical axis representing the functional redundancy index. .

[0083] The slope sequence obtained by calculating the slopes of adjacent points is as follows: .

[0084] Further calculate the absolute value of the difference between adjacent slopes: C t ≈[0, 0, 0.292, 0.167, 0.042, 0.25, 0.25].

[0085] According to C t The value can be obtained. The maximum value appears ,but: =2, then the first Water level after the segment This is the inflection point water level of the stability curve, i.e.:

[0086] That is, when the water level rises to about 1.5m, the stability of the ecosystem begins to decline from a relatively gradual stage to a rapid degradation stage (i.e., when the water level rises to about 1.5m). Figure 3 (The position indicated by the dashed line).

[0087] The embodiments of the present invention bring the following beneficial effects: The ecological impact assessment of disturbance processes has been upgraded from static comparison to dynamic evaluation, which can track the loss trajectory of species and functions during the gradual change of water level.

[0088] Ecosystem stability assessment indicators have evolved from considering only a single indicator to a comprehensive stability indicator system that takes into account both structure and function.

[0089] The data-driven ecological water level threshold identification method identifies the ecological threshold water level in the "water level-stability" curve by using the inflection point method, reducing subjectivity and having universality across various projects.

[0090] Example 3 Based on the above embodiments, this invention provides an apparatus for predicting ecosystem stability changes based on disturbance scenario simulation, see [link to relevant documentation]. Figure 4 The diagram shown is a structural schematic of a device for predicting ecosystem stability changes based on disturbance scenario simulation, according to an embodiment of the present invention. The device includes: The data processing module 310 is used to divide the study area into multiple habitat types based on the survey data of the target ecosystem, and to determine the richness of multiple species in each habitat type in order to construct a species-habitat network; and to divide species into multiple functional groups based on the functional traits of the species. The habitat node sorting module 320 is used to determine the response relationship between different water levels and the existence or disappearance of each habitat type based on a preset hydrological disturbance scenario, and to generate an ordered habitat node deletion sequence based on the response relationship. The primary data calculation module 330 is used to simulate the process of gradually deleting habitat types in the species-habitat network based on the habitat node deletion sequence. By calculating the retention ratio of habitat types and species and plotting the secondary extinction curve, a robustness index characterizing the stability of the species-habitat network is obtained. At the same time, at each step of the deletion process, the functional retention ratio of each functional group and the functional redundancy index of that step are calculated, and the functional stability index is obtained by averaging the functional redundancy indices of all steps. The comprehensive stability index determination module 340 is used to determine the comprehensive stability index of the target ecosystem by weighted summation based on the robustness index, the functional stability index and the preset weighting coefficients. The stability threshold water level determination module 350 is used to identify the inflection point of the curve based on the curve of functional redundancy index changing with water level, and to determine the water level corresponding to the inflection point as the stability threshold water level of the target ecosystem.

[0091] Furthermore, in some preferred embodiments of the present invention, the survey data used to classify habitat types includes at least one of the following: remote sensing data, UAV imagery, and ground survey data; the data processing module 310 is used to obtain the richness of each species in each habitat type by consulting historical survey data and supplementing it with methods such as transect, sample point, quadrat, and infrared camera surveys.

[0092] Furthermore, in some preferred embodiments of the present invention, the hydrological disturbance scenarios include at least one of a single sustained rise in water level, a periodic rise and fall in water level, and an extreme high or low water level event.

[0093] Furthermore, in some preferred embodiments of the present invention, the primary data calculation module 330 is used to calculate the habitat retention ratio based on the initial total number of habitats and the current total number of habitats after each step of deleting habitat nodes, and to calculate the species retention ratio based on the initial total number of species and the current total number of species; to draw a curve with the habitat retention ratio corresponding to each step as the abscissa and the species retention ratio as the ordinate; and to calculate the area under the curve as the robustness index using the trapezoidal method.

[0094] Furthermore, in some preferred embodiments of the present invention, the primary data calculation module 330 is used to calculate the functional retention ratio of each functional group based on the initial total number of species in the functional group and the current total number of surviving species after each step of deleting habitat nodes; and to calculate the average value of the functional retention ratios of all functional groups in this step as the functional redundancy index of this step.

[0095] Furthermore, in some preferred embodiments of the present invention, the stability threshold water level determination module 350 is also used to determine the stability threshold water level by the position of the inflection point in the habitat node deletion sequence and the preset water level change step size.

[0096] Furthermore, in some preferred embodiments of the present invention, the target ecosystem is a river wetland, a reservoir wetland, or a lake wetland; the species include at least two of the following: fish, birds, amphibians, benthic animals, aquatic plants, and wetland plants.

[0097] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device for predicting ecosystem stability changes based on disturbance scenario simulation described above can be referred to the corresponding process in the embodiments of the method for predicting ecosystem stability changes based on disturbance scenario simulation, and will not be repeated here.

[0098] Example 4 This invention also provides an electronic device for running a method for predicting ecosystem stability changes based on disturbance scenario simulation; see also Figure 5 The schematic diagram of an electronic device provided in the embodiment of the present invention shown includes a memory 400 and a processor 401. The memory 400 is used to store one or more computer instructions, which are executed by the processor 401 to implement the above-mentioned method for predicting changes in ecosystem stability based on disturbance scenario simulation.

[0099] Furthermore, Figure 5 The electronic device shown also includes a bus 402 and a communication interface 403. The processor 401, the communication interface 403 and the memory 400 are connected via the bus 402.

[0100] The memory 400 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 403 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 402 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0101] Processor 401 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 401 or by instructions in software form. Processor 401 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a readily available storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 400, and processor 401 reads information from memory 400 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0102] This invention also provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the above-described method for predicting ecosystem stability changes based on disturbance scenario simulation. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0103] The computer program products of the method, apparatus and electronic device for predicting ecosystem stability changes based on disturbance scenario simulation provided in the embodiments of the present invention include a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0104] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and / or device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0105] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0106] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting ecosystem stability changes based on disturbance scenario simulation, characterized in that, include: Based on survey data of the target ecosystem, the study area was divided into multiple habitat types, and the richness of multiple species in each habitat type was determined to construct a species-habitat network. The species are divided into multiple functional groups based on their functional traits; Based on a preset hydrological disturbance scenario, the response relationship between different water levels and the existence or disappearance of each habitat type is determined, and an ordered habitat node deletion sequence is generated based on the response relationship. Based on the habitat node deletion sequence, the process of gradually deleting habitat types in the species-habitat network is simulated. By calculating the retention ratio of the habitat type and the species and plotting the secondary extinction curve, a robustness index characterizing the stability of the species-habitat network is obtained. At the same time, at each step of the deletion process, the functional retention ratio of each functional group and the functional redundancy index of that step are calculated, and the functional stability index is obtained by averaging the functional redundancy index of all steps. The comprehensive stability index of the target ecosystem is determined by weighted summation based on the robustness index, the functional stability index, and the preset weighting coefficients. Based on the curve of the functional redundancy index changing with water level, the slope change analysis method is used to identify the inflection point of the curve, and the water level corresponding to the inflection point is determined as the stability threshold water level of the target ecosystem.

2. The method for predicting ecosystem stability changes based on disturbance scenario simulation according to claim 1, characterized in that, The survey data used to classify the habitat types includes at least one of the following: remote sensing data, UAV imagery, and ground survey data; The steps for determining the abundance of multiple species in each of the said habitat types include: The richness of each species in each habitat type was obtained by reviewing historical survey data and supplementing the data with methods such as transects, sampling points, quadrats, and infrared camera surveys.

3. The method for predicting ecosystem stability changes based on disturbance scenario simulation according to claim 1, characterized in that, The hydrological disturbance scenarios include at least one of the following: a single sustained rise in water level, a periodic rise and fall in water level, or an extreme high or low water level event.

4. The method for predicting ecosystem stability changes based on disturbance scenario simulation according to claim 1, characterized in that, Based on the habitat node deletion sequence, the process of simulating the gradual deletion of habitat types in the species-habitat network, and obtaining a robustness index characterizing the stability of the species-habitat network by calculating the retention ratio of habitat types and species and plotting secondary extinction curves, includes: After deleting the habitat node at each step, the habitat retention ratio is calculated based on the initial total number of habitats and the current total number of habitats, and the species retention ratio is calculated based on the initial total number of species and the current total number of species. Plot a curve with the habitat retention ratio corresponding to each step as the horizontal axis and the species retention ratio as the vertical axis. The area under the curve is calculated using the trapezoidal method and used as the robustness index.

5. The method for predicting ecosystem stability changes based on disturbance scenario simulation according to claim 1, characterized in that, The step of calculating the function retention ratio of each function group and the function redundancy index at each step of the deletion process includes: After deleting the habitat node at each step, the function retention ratio of each functional group is calculated based on the initial total number of species in the functional group and the current total number of surviving species. The average of the function retention ratios of all the function groups in this step is calculated as the function redundancy index for this step.

6. The method for predicting ecosystem stability changes based on disturbance scenario simulation according to claim 1, characterized in that, The method further includes: The stability threshold water level is determined by the position of the inflection point in the habitat node deletion sequence and the preset water level change step size.

7. The method for predicting ecosystem stability changes based on disturbance scenario simulation according to any one of claims 1 to 6, characterized in that, The target ecosystem is a river wetland, a reservoir wetland, or a lake wetland; the species include at least two of the following: fish, birds, amphibians, benthic animals, aquatic plants, and wetland plants.

8. A device for predicting ecosystem stability changes based on disturbance scenario simulation, characterized in that, include: The data processing module is used to divide the study area into multiple habitat types based on survey data of the target ecosystem, and to determine the richness of multiple species in each habitat type in order to construct a species-habitat network. The species are divided into multiple functional groups based on their functional traits; The habitat node sorting module is used to determine the response relationship between different water levels and the existence or disappearance of each habitat type based on a preset hydrological disturbance scenario, and to generate an ordered habitat node deletion sequence based on the response relationship. The primary data calculation module is used to simulate the process of gradually deleting habitat types in the species-habitat network according to the habitat node deletion sequence. By calculating the retention ratio of the habitat type and the species and plotting the secondary extinction curve, a robustness index characterizing the stability of the species-habitat network is obtained. At the same time, at each step of the deletion process, the functional retention ratio of each functional group and the functional redundancy index of that step are calculated, and the functional stability index is obtained by averaging the functional redundancy index of all steps. The comprehensive stability index determination module is used to determine the comprehensive stability index of the target ecosystem by weighted summation based on the robustness index, the functional stability index, and preset weighting coefficients. The stability threshold water level determination module is used to identify the inflection point of the curve based on the curve of the functional redundancy index changing with the water level, and to determine the water level corresponding to the inflection point as the stability threshold water level of the target ecosystem.

9. An electronic device, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the method for predicting ecosystem stability changes based on disturbance scenario simulation as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the method for predicting ecosystem stability changes based on disturbance scenario simulation as described in any one of claims 1 to 7.

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