Methods and Systems for Assessing the Impact of Construction Disturbance on Biodiversity in Nature Reserves

Abstract

This invention provides a method and system for assessing the impact of construction disturbance on biodiversity in nature reserves, relating to the field of computer technology. The method includes: acquiring species distribution data, construction disturbance data, and environmental data within the nature reserve; constructing an ecological association network based on the species distribution data to obtain a species interaction network; extracting network structure features from the species interaction network to obtain a set of key species; modeling the disturbance propagation dynamics based on the set of key species and the construction disturbance data to obtain an impact propagation path map; predicting biodiversity responses based on the impact propagation path map and environmental data to obtain trends in changes in biological community composition; and conducting a comprehensive impact assessment based on these trends, outputting an impact level index by comparing the degree of deviation from historical baseline states. This invention effectively improves the overall accuracy and predictive precision of the assessment of the impact of construction disturbance on biodiversity in nature reserves.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a method and system for assessing the impact of construction disturbance on the biodiversity of nature reserves. Background Technology

[0002] In areas where various development and construction activities (such as transportation infrastructure, mining, and tourism facility construction) intersect with nature reserves (such as forests, grasslands, wetland ecosystems, and wildlife habitats), the physical encroachment, fragmentation effects, environmental pollution, and continuous human disturbance caused by construction activities pose complex threats to the survival and succession of protected objects (such as rare and endangered wild animals and plants, typical natural ecosystems, natural relics, and natural landscapes). The unique aspect of these impacts lies in the fact that they not only cause direct habitat loss but also trigger unpredictable indirect effects and systemic risks by altering interspecies interactions and disrupting the integrity of ecological processes.

[0003] However, most existing ecological impact assessment methods rely on comparative monitoring of the quantity or distribution range of key species before and after construction, or primarily on qualitative judgments based on expert experience. This traditional approach, based on direct observation and qualitative analysis, is inherently unable to characterize the cascading propagation paths of disturbances in complex ecological networks, nor can it simulate the nonlinear responses and critical point mutations that ecosystems may experience under the combined effects of natural environmental fluctuations and construction disturbances. Consequently, the assessment results are insufficient to effectively support the accurate prediction and full-process management of ecological risks in large-scale, long-term projects.

[0004] Given the shortcomings of the existing technologies, there is an urgent need for a method and system for assessing the impact of construction disturbance on the biodiversity of nature reserves. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for assessing the impact of construction disturbance on the biodiversity of nature reserves, thereby addressing the aforementioned problems. To achieve this objective, the technical solution adopted by this invention is as follows:

[0006] Firstly, this application provides a method for assessing the impact of construction disturbance on the biodiversity of nature reserves, including:

[0007] Acquire data on species distribution, construction disturbance, and environmental data within nature reserves;

[0008] An ecological association network is constructed based on the species distribution data, and a species interaction network is obtained by quantifying the relationship between spatial co-occurrence patterns among species.

[0009] Based on the species interaction network, network structure features are extracted, and key species sets are identified by calculating the fusion index of node betweenness centrality and feature vector centrality.

[0010] Based on the set of key species and the construction disturbance data, disturbance propagation dynamics modeling is performed. By simulating the cascading diffusion process of disturbance signals in the network, an impact propagation path diagram is obtained.

[0011] Based on the impact propagation path map and the environmental data, biodiversity response prediction is performed. By coupling the community succession model driven by internal disturbance pressure and external environment, the trend of changes in biological community composition is predicted.

[0012] A comprehensive impact assessment is conducted based on the changing trends in the biological community composition. By comparing the degree of deviation from the historical baseline state, an impact level index is output.

[0013] Secondly, this application also provides a method and system for assessing the impact of construction disturbance on the biodiversity of nature reserves, including:

[0014] The acquisition module is used to acquire species distribution data, construction disturbance data, and environmental data within nature reserves.

[0015] The construction module is used to construct an ecological association network based on the species distribution data, and obtain the species interaction network by quantifying the relationship between spatial co-occurrence patterns among species;

[0016] The extraction module is used to extract network structure features based on the species interaction network and identify the key species set by calculating the fusion index of node betweenness centrality and feature vector centrality.

[0017] The modeling module is used to perform disturbance propagation dynamics modeling based on the set of key species and the construction disturbance data, and to obtain the influence propagation path diagram by simulating the cascading diffusion process of disturbance signals in the network;

[0018] The prediction module is used to predict biodiversity response based on the impact propagation path map and the environmental data. By coupling the community succession model driven by internal disturbance pressure and external environment, it predicts the trend of changes in biological community composition.

[0019] The assessment module is used to conduct a comprehensive impact assessment based on the changing trends of the biological community composition, and outputs an impact level index by comparing the degree of deviation from the historical baseline state.

[0020] The beneficial effects of this invention are as follows:

[0021] This invention identifies key species sets by constructing a species interaction network and simulates the dynamic propagation process of construction disturbance in the network. It then couples environmental driving factors to predict the nonlinear response of biological communities, realizing the transformation from static local assessment to dynamic system risk assessment. This effectively improves the overall and predictive accuracy of the assessment of the impact of construction disturbance on the biodiversity of nature reserves. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a method for assessing the impact of construction disturbance on biodiversity in nature reserves, as described in an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the structure of an impact assessment system for construction disturbance on biodiversity in nature reserves, as described in an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of the structure of an equipment for assessing the impact of construction disturbance on the biodiversity of nature reserves, as described in an embodiment of the present invention.

[0026] The diagram is labeled as follows: 800, an assessment device for the impact of construction disturbance on biodiversity in nature reserves; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, acquisition module; 902, construction module; 903, extraction module; 904, modeling module; 905, prediction module; 906, assessment module. Detailed Implementation

[0027] 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, not all, of the embodiments of the present invention. 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. Therefore, the following detailed description of the embodiments of the present 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 present invention without inventive effort are within the scope of protection of the present invention.

[0028] It should be noted that similar reference numerals 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. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] Example 1:

[0030] This embodiment provides a method for assessing the impact of construction disturbance on the biodiversity of nature reserves.

[0031] See Figure 1 The figure shows that the method includes steps S100 to S600.

[0032] Step S100: Obtain species distribution data, construction disturbance data, and environmental data within the nature reserve;

[0033] Step S100 involves acquiring the necessary foundational data for the assessment. Specifically, species distribution data includes the location and abundance of species occurrences, as well as their temporal changes, collected through field surveys, infrared camera monitoring, acoustic recorders, and satellite remote sensing. This aims to construct a base map reflecting the spatial distribution pattern of organisms within the protected area. Construction disturbance data covers the type of engineering activity (e.g., road construction, visitor center construction), spatial extent (construction area boundaries, impact assessment area), intensity (e.g., noise levels, vibration intensity, dust concentration), and their dynamic changes over time. Environmental data includes continuous monitoring data on climatic factors (temperature, precipitation), hydrological factors (water level, flow velocity), and soil factors (pH, nutrients) obtained from meteorological stations, hydrological stations, and environmental sensor networks.

[0034] Step S200: Construct an ecological association network based on species distribution data, and obtain a species interaction network by quantifying the relationship between spatial co-occurrence patterns among species;

[0035] The core of step S200 is to transform discrete species distribution information into a network structure that reflects potential ecological relationships. This step analyzes the co-occurrence and exclusion patterns of species at multiple scales and infers indirect interactions connected by intermediary species, thereby constructing a species interaction network that not only contains direct spatial associations but also implies the possibility of functional dependence. This provides a structured model for understanding the complex interspecific relationships within protected areas.

[0036] Step S300: Extract network structure features based on the species interaction network, and identify the key species set by calculating the fusion index of node betweenness centrality and feature vector centrality.

[0037] Step S300 involves identifying a set of species from the aforementioned network structure that are crucial for maintaining network stability and function. This process comprehensively assesses the structural role of species in network connectivity and their bridging value in connecting different functional modules, thereby identifying key species that, if disturbed, could have a disproportionately large impact on the entire ecosystem. This allows assessment resources to focus on the weakest links in the ecosystem.

[0038] Step S400: Based on the key species set and construction disturbance data, perform disturbance propagation dynamics modeling, and obtain the influence propagation path diagram by simulating the cascading diffusion process of disturbance signals in the network;

[0039] Step S400 aims to dynamically simulate the propagation process of construction disturbances in the ecological network. This step uses a set of key species as the initial impact point, combines the spatiotemporal characteristics of construction disturbances, defines the rules for the transmission of disturbances among species, simulates how disturbance signals spread, attenuate, or amplify in the network like waves, and finally generates a map depicting the expected path and intensity of the disturbance's impact, thus combining static network structure analysis with the dynamic process of disturbance propagation.

[0040] Step S500: Based on the impact propagation path map and environmental data, predict the biodiversity response. By coupling the community succession model driven by internal disturbance pressure and external environment, predict the trend of changes in biological community composition.

[0041] Step S500 couples internal disturbance pressure with external environmental change data, and constructs a dynamic model to simulate how species fitness changes and interspecific interactions jointly drive the succession trajectory of the community. It pays particular attention to nonlinear changes or critical point phenomena that may occur in the system, thereby enabling the prediction of medium- and long-term ecological risks.

[0042] Step S600: Conduct a comprehensive impact assessment based on the changing trends of biological community composition, and output the impact level index by comparing the degree of deviation from the historical baseline state.

[0043] Step S600 is the final assessment and output stage. This step compares the predicted community change trends with the historical baseline state, quantifies the expected deviation of key ecological structures and functions, and, with reference to preset ecological protection thresholds, transforms the complex prediction results into intuitive impact level indicators, providing clear scientific basis for managers' decision-making.

[0044] Further, step S200 includes steps S210 to S230.

[0045] Step S210: Based on the species distribution data, perform spatial distribution heterogeneity analysis. By identifying the clustering and exclusion patterns of species distribution hotspots at multiple scales, obtain the multi-scale spatial associations between species.

[0046] Step S220: Based on the multi-scale spatial correlation, perform indirect interaction inference processing. By analyzing the statistical correlation shown by the preset species pairs when there is a third species mediating the connection in the non-overlapping region, the indirect co-occurrence relationship regulated by the mediating species is obtained.

[0047] Step S230: Based on multi-scale spatial relationships and indirect co-occurrence relationships, perform interaction network integration processing. By superimposing direct spatial relationships and indirect mediation relationships into a unified interaction intensity tensor, the species interaction network is obtained.

[0048] Understandably, species distribution within nature reserves often exhibits high spatial heterogeneity, and interspecific relationships, besides direct competition or symbiosis, include numerous indirect associations arising from shared predators and reliance on common resources. Traditional methods struggle to effectively capture this complex spatial dependence pattern. Step S210 first analyzes the spatial heterogeneity of species distribution data, focusing on identifying clustering and exclusion patterns of species distribution hotspots at different spatial scales. For example, it analyzes the distribution associations of specific tree species and understory plants along slope gradients. This aims to reveal direct spatial associations between species based on habitat filtering and biological interactions, providing a foundational connectivity matrix for network construction. Building upon this, step S220 performs indirect interaction inference processing. The technical approach involves statistically analyzing whether species pairs with non-overlapping distribution areas exhibit statistical correlation due to significant associations with the same intermediary species (such as a pollinator or predator). For instance, it analyzes whether two non-coexisting plants exhibit synergistic changes due to reliance on the same pollinator, thereby identifying indirect co-occurrence relationships regulated by intermediary species. The interaction network integration process in step S230 integrates the direct spatial associations and indirect co-occurrence relationships obtained in the previous steps. Its core is to construct a unified interaction intensity tensor to simultaneously represent these two relationships. For example, different weights are assigned to direct adjacency relationships and indirect relationships connected through intermediary species and then superimposed. This results in a species interaction network that can more comprehensively reflect the possibility of real ecological interactions within the protected area. This network not only includes spatial proximity but also embeds functional associations inferred from ecological processes.

[0049] Preferably, in step S210, the input data is first processed through multi-scale spatial correlation analysis, using a model based on bivariate kernel density estimation at different spatial scales and bandwidths (e.g., h). fine =100m, h medium =500m, hcoarse =1000m, where m represents the fine-scale bandwidth, meso-scale bandwidth, and coarse-scale bandwidth, respectively. The formula for calculating the spatial association strength of each pair of species is as follows:

[0050] ;

[0051] In the formula, n represents the strength of the direct spatial association between species i and j at a spatial scale s; i n j The number of occurrence points for species i and j; l p , l q h is the spatial coordinate vector of the p-th occurrence point of species i and the q-th occurrence point of species j; (s) The bandwidth of the kernel function is used to define the spatial scale *s*. This step outputs a three-dimensional tensor containing the direct correlation intensities across multiple scales. Based on this, step S220 performs indirect interaction inference, the process of which involves first setting a threshold based on the result of S210 (e.g., ...). >0.05) Construct a binary direct association network, and then, for species pairs (i, j) that do not co-occur directly, use a partial correlation model to analyze the strength of their indirect associations through the intermediary species k, and screen out indirect associations with significant levels. The partial correlation model is expressed as:

[0052] ;

[0053] In the formula, This represents the partial correlation coefficient between species i and j after controlling for the intermediate species k, i.e., the strength of the indirect association; r ij The Pearson correlation coefficient for the distribution of species i and j across all sampling points (calculated using 0 / 1 occurrence data or continuous abundance data); r ik r jk Let be the Pearson correlation coefficient between the distributions of species i and k, and between species j and k.

[0054] Step S230 then performs interactive network integration, which first involves processing the direct association strength A. ij and the indirect correlation strength I after aggregation ij (For example, take all possible intermediate k corresponding to) The maximum value of the matrix is ​​normalized, and finally, the final network connection weights are calculated using a weighted fusion formula, outputting a weighted species interaction network adjacency matrix as the final result of this process. The weighted fusion formula is:

[0055] ;

[0056] In the formula, W IJIn the species interaction network, α represents the connection weight from node I to node J; β represents the fusion weight coefficients. This represents the strength of direct spatial association calculated and standardized at the optimal scale; This represents the standardized indirect correlation strength of the aggregation.

[0057] Further, step S300 includes steps S310 to S330.

[0058] Step S310: Based on the species interaction network, perform network stability simulation processing based on phylogenetic signal strength. By simulating the impact of removing species nodes with different phylogenetic uniqueness on network connectivity, a preliminary species list is obtained.

[0059] Step S320: Based on the species interaction network and the preliminary species list, perform functional trait complementarity module analysis and obtain the key node set by quantifying the bridging role of nodes in connecting species modules with different functional traits.

[0060] Step S330: Based on the set of key nodes, a comprehensive judgment process is performed. By filtering out species nodes that exist in the preliminary species list and whose functional bridge role index exceeds the average value of their respective modules, and multiplying the two indicators, the set of key species is obtained.

[0061] It should be noted that in the ecosystems of nature reserves, the identification of key species requires not only considering their structural position in the current ecological network, but also assessing the profound impacts of their loss, such as the loss of evolutionary history information and the decline in functional diversity. Step S310 first performs network stability simulation processing based on phylogenetic signal strength. The core of this process is to simulate the selective removal of nodes that are unique in the phylogenetic lineage (i.e., have unique evolutionary history and lack closely related species), and compare this with the removal of common nodes in the phylogenetic lineage, observing the different impacts on the overall network connectivity. This processing aims to identify species that are irreplaceable in maintaining the community's evolutionary heritage, and whose disappearance would significantly disrupt network stability, thereby obtaining a preliminary species list focusing on evolutionary uniqueness and network structural robustness. Step S320 shifts to the functional dimension, performing functional trait complementarity module analysis. This process first divides the network into different functional modules based on functional traits (such as seed dispersal methods, diet, phenology, etc.), and then quantifies the bridging role of each node in connecting these functionally different modules. For example, it assesses whether a predator species connects different trophic functional modules dominated by ground rodents and forest birds, thereby screening out the set of key nodes that are crucial to maintaining the functional connectivity and diversity of the entire system. Step S330, the comprehensive judgment process, is the final fusion step. Its technical means lies in setting a clear integration rule: only species that appear in both the preliminary species list obtained in step S310 (possessing evolutionary uniqueness and basic structural importance) and the set of key nodes obtained in step S320 (possessing important functional bridging roles) are retained. Furthermore, their functional bridging role index must be higher than the average level of their respective functional modules. Finally, the phylogenetic uniqueness influence index and the functional bridging role index of each candidate species are multiplied, and the species are ranked and screened accordingly. This method ensures that the final set of key species is those that are highly important in terms of both phylogenetic historical representativeness and ecological functional connectivity, and that could have a profound and irreversible impact on the ecosystem if disturbed by construction, thus providing clear targets for precise assessment and conservation.

[0062] Preferably, in step S310, the phylogenetic signal strength network stability simulation first calculates the phylogenetic uniqueness index for each node, using the following formula:

[0063] ;

[0064] In the formula, PDI I d is the phylogenetic uniqueness index of node I; IJ f represents the evolutionary distance between nodes I and J on the phylogenetic tree; JLet be the relative abundance of node J; λ be the decay coefficient (set to 0.5 to balance the contributions of rare and common species); and n be the total number of nodes. Next, the simulation sequentially removes the node groups with the highest and lowest PDI values ​​(each accounting for 20% of the total), and calculates the relative change in the network's global efficiency after each removal:

[0065] ;

[0066] Among them, ΔGE I GE represents the relative change in the global efficiency of the network after removing node I; original For network global efficiency; GE removed(I) The global efficiency after removing node I is calculated. Then, the phylogenetic importance index (SPI) is calculated. I =PDI I ×ΔGE I The nodes with the highest SPI values ​​were selected to form a preliminary species list. Step S320 involves functional trait complementarity module analysis. First, based on the functional trait data, spectral clustering is used to divide the network into K functional modules (K is optimally determined based on the silhouette coefficient). Then, the participation coefficient of each node is calculated using the following formula:

[0067] ;

[0068] Among them, P I Let k be the participation coefficient of node I. (I,m) Let k be the sum of the connection weights between node I and the nodes within module m. I The total connection weight of node I; simultaneously calculate the inter-module connectivity strength:

[0069] ;

[0070] Among them, B I The strength of the connection between different modules at node I is then calculated, and finally the functional bridge performance index (FBI) is calculated. I =P I ×B I The nodes with the highest FBI values ​​(top 20%) are selected as the key node set. Step S330 involves a comprehensive assessment: first, candidate nodes are selected from the intersection of the preliminary species list and the key node set; then, the average FBI value of the functional module to which each candidate node belongs is calculated. module ; Retain PDI satisfaction I >1.2×FBI module The nodes are then used to calculate the Key Composite Score (KSI). I =SPI I FBI I The system sorts nodes by their scores and selects the top 10% as the final set of key species for output.

[0071] Further, step S400 includes steps S410 to S430.

[0072] Step S410: Based on the key species set and construction disturbance data, perform disturbance propagation rule definition processing. By establishing a disturbance transmission probability matrix based on species functional group division and introducing a species tolerance threshold as a critical condition for disturbance propagation, a nonlinear disturbance propagation model is obtained.

[0073] Step S420: Based on the nonlinear disturbance propagation model, perform multi-stage disturbance diffusion simulation processing. By combining the spatiotemporal characteristics of construction activities, simulate the dynamic process of disturbance propagating along network connections in an asynchronous and non-uniform manner to obtain the spatiotemporal diffusion map of disturbance.

[0074] Step S430: Based on the spatiotemporal diffusion map of the disturbance, perform influence path integration processing. By identifying the main channels and secondary paths of the disturbance wavefront propagation and quantifying the cumulative effect of the disturbance intensity on each path, an influence propagation path map is obtained.

[0075] Understandably, when faced with construction disturbances, the impact of the disturbance does not spread uniformly, but rather propagates along the inherent interaction networks between species. This propagation process is constrained by both the species' own tolerance capacity and the spatiotemporal dynamics of the construction activities. The disturbance propagation rule definition in step S410 forms the basis for constructing the dynamic model, its core being the establishment of a nonlinear disturbance propagation model. This process first divides the key species set into different functional groups based on their functional traits (such as trophic level, diet, and range). Based on this, a disturbance propagation probability matrix is ​​constructed, defining the likelihood and intensity of disturbance propagation between different functional groups. More importantly, the model introduces a specific tolerance threshold for each species node. Only when the cumulative disturbance intensity exceeds this threshold will the node be "activated" and continue to propagate the disturbance to connected nodes. This effectively simulates the ecosystem's buffering capacity against disturbances and the critical effect of disturbance propagation, making the model more consistent with ecological mechanisms.

[0076] Step S420, the multi-stage disturbance diffusion simulation, utilizes the aforementioned model for dynamic extrapolation. This process takes construction disturbance data (such as noise intensity and vibration levels at different construction stages and in different areas) as input, simulating the process by which the disturbance signal propagates asynchronously and non-uniformly along the species interaction network from the initial affected point (i.e., key species nodes). For example, species nodes closer to the core construction area are activated first, and the disturbance may then be transmitted to species in more distant areas through predator-prey, competitive, or symbiotic relationships. This simulation process generates a spatiotemporal diffusion map of the disturbance that shows how it gradually spreads in both time and space.

[0077] Step S430, the impact path integration process, aims to extract key information from complex dynamic simulation results. This process analyzes the generated spatiotemporal diffusion map of the disturbance, identifying the primary channels (i.e., key species linkages along which the disturbance propagates rapidly and attenuates slowly) and secondary paths upon which the disturbance wavefront relies for propagation. Furthermore, it quantifies the cumulative effect of the disturbance intensity along each path—whether the disturbance strengthens, weakens, or remains stable during propagation along the path. Finally, by integrating the primary and secondary paths and their cumulative effects, a clear impact propagation path map is obtained. This map not only reveals the scope of the impact but, more importantly, identifies the critical routes and relative intensities of the impact propagation.

[0078] Preferably, the perturbation propagation rule definition process in step S410 first uses the k-means clustering algorithm based on functional trait data to divide key species into G functional groups (the number of groups G is determined by the elbow rule) to capture the influence of functional similarity on perturbation propagation; then, it constructs a perturbation transmission probability matrix Q, whose elements Q gb Let represent the probability that a disturbance is propagated from group g to group b, and the formula is:

[0079] ;

[0080] Among them, D gb The Euclidean distance for functional traits between groups g and b (calculated based on standardized trait values) is given, with γ being the attenuation coefficient; a tolerance threshold T is defined for each species node i. i The calculation formula is as follows:

[0081] ;

[0082] Where M is the maximum disturbance intensity in the construction disturbance data, and c is the strategy correlation coefficient (c is 0.7 for K-strategy species and 0.3 for r-strategy species), which is used to simulate the critical points of different responses of species to disturbances; finally, a nonlinear disturbance propagation model is obtained, which includes a Q matrix and a T vector. Step S420 performs multi-stage disturbance diffusion simulation, using a discrete-time step model, where S... i(t) Let S be the cumulative disturbance intensity of node i at time t; during initialization, for node i located in the construction area, S... i(0) The initial strength L is directly taken from the construction disturbance data. i (e.g., normalize the noise decibel value to the range [0,1]); for each time step t, check each node: if S i(t) >T i If a node is activated, it will propagate the perturbation to its neighboring node j. The propagation amount is determined by the propagation probability P. ij Decision (P) ij(Obtained from matrix Q based on the groups to which i and j belong); the update rule is:

[0083] ;

[0084] in, Let S be the set of nodes activated at time t. j (t+1) represents the cumulative perturbation intensity of node i at time t+1; the simulation continues until no new nodes are activated or the maximum time step T is reached. max (Set to 50 steps, covering the construction cycle); Output a spatiotemporal diffusion map of the disturbance, which is a three-dimensional array recording the S of each node at each time step. i (t). Step S430 performs influence path integration, first extracting the perturbation wavefront (i.e., the first activation time t of each node) from the diffusion map. i The Dijkstra algorithm is used to identify the shortest time path (primary path) and the path with the maximum cumulative intensity (secondary path) from the construction origin to each node. For each path, the cumulative effect is calculated and the final output is an influence propagation path graph, which is a weighted directed graph where nodes are key species and edge weights are the cumulative effect values ​​of the path.

[0085] Further, step S500 includes steps S510 to S530.

[0086] Step S510: Based on the impact propagation path map and environmental data, perform niche dynamic response modeling. By coupling the disturbance pressure gradient with the environmental factor gradient, construct a spatiotemporally changing species fitness surface to obtain the initial population response model.

[0087] Step S520: Based on the initial response model of the population, perform population dynamic simulation processing of interspecific relationship regulation. By introducing the regulation function of interspecific interaction on the population growth rate, a dynamic model of multi-species co-evolution is obtained.

[0088] Step S530: Based on the dynamic model, perform community state transition prediction processing. By identifying the system stability boundary and critical point, simulate the nonlinear transformation process of community composition from quantitative to qualitative change under different disturbance-environment combination scenarios, and predict the trend of biological community composition change.

[0089] It should be noted that the impact of construction disturbance on biodiversity does not occur in isolation, but rather is intertwined and interacts with the natural fluctuations of environmental factors such as temperature, precipitation, and hydrology. Traditional linear models struggle to capture this complex interaction and the potential ecosystem disruptions it may trigger. Step S510, the niche dynamic response modeling, aims to characterize the initial response of species to changing environments. Its core is to couple the spatially heterogeneous disturbance pressure gradient revealed by the impact propagation path map with the corresponding environmental factor gradients provided by environmental data, dynamically constructing a species fitness surface that varies with time and space. This surface quantifies the degree to which a specific species' survival and reproduction are favorable or unfavorable at a particular spatiotemporal location, considering local disturbance intensity and environmental conditions, thus obtaining an initial response model reflecting the potential distribution and abundance changes of each species' population. Step S520, the population dynamics simulation of interspecific relationship regulation, further enhances the model's realism. It combines the initial model, which only considers environmental selection, with species interaction network information reflecting interspecific competition, predation, mutualism, and other relationships. By introducing a function that regulates the basic population growth rate through interspecific interactions, it simulates that population dynamics are not only limited by the environment and disturbances but also profoundly affected by the presence and abundance of other species, thus obtaining a dynamic model that reflects multi-species co-succession. Step S530, the community state transition prediction, focuses on assessing the consequences under long-term and extreme scenarios. Based on the simulation results of the dynamic model, this process focuses on analyzing whether the system has a stability boundary or critical point. By simulating combinations of different construction intensities and future climate scenarios, it predicts whether the composition of the biological community will undergo smooth, gradual quantitative changes or structural, irreversible qualitative changes after crossing a certain threshold, such as the replacement of key functional groups or a fundamental shift in dominant species types. Ultimately, it predicts the possible trends in the composition of the biological community, providing early warning for risk management.

[0090] Preferably, step S510 first defines the basic niche function N for each species i. i (E), where E represents the environmental factor vector, and a Gaussian response model is used:

[0091] ;

[0092] Among them, E v Let μ be the value of the v-th environmental factor. (i,v) σ represents the optimal value of species i for factor v. (i,v) Tolerance range; then, the disturbance pressure P (from the influence propagation path diagram) is coupled with environmental factors to construct a fitness surface:

[0093] ;

[0094] Among them, Z i(x,y,t) represents the combined fitness of species i at spatial location (x,y) and time t, N i (E(x,y,t)) represents the basic niche suitability of species i at spatial location (x,y) and time t, λ i Let P(x,y,t) be the sensitivity coefficient of species i to disturbance (values ​​range from 0.1 to 0.5, with higher values ​​for endangered species), and let P(x,y,t) be the disturbance intensity at location (x,y) at time t. Finally, the initial population response model is obtained, describing the potential distribution of species in the absence of interspecific interactions. Step S520 performs population dynamics simulation of interspecific relationship regulation using an improved Lotka-Volterra model:

[0095] ;

[0096] Where, N i Let r be the population density of species i. i K represents the intrinsic growth rate. i For environmental carrying capacity, α iV Z represents the interspecific interaction coefficient (competition is negative, mutualism is positive, and predation is determined by trophic level). i (t) represents the fitness at time t; the model is discretized on a spatial grid with a time step Δt of 1 month and a simulation duration of 10 years, resulting in a dynamic model of multi-species co-succession. Step S530 predicts community state transitions, first calculating the community stability index: resilience. , where λ max The largest eigenvalue of the community matrix; resistance This indicates the community change caused by the disturbance; then, by continuously increasing the disturbance pressure P, the change in dR / dP or The critical perturbation intensity PC corresponding to the abrupt change point (slope change exceeding the threshold θ=0.8) is determined; finally, different perturbation-environment combinations are simulated to predict the trend of community composition changes, especially the replacement of dominant species and the change of functional group proportions.

[0097] Further, step S600 includes steps S610 to S630.

[0098] Step S610: Based on the trend of changes in the composition of the biological community, conduct an assessment of the loss of ecosystem integrity. By calculating the decrease in the trophic level structure integrity index caused by changes in the abundance of species in functional groups, the structural impact coefficient is obtained.

[0099] Step S620: Based on the structural impact coefficient, conduct an ecological function sustainability assessment. By analyzing the coupling relationship between the extinction rate of endemic species and the decline in the supply capacity of ecosystem services, the functional impact coefficient is obtained.

[0100] Step S630: Based on the structural impact coefficient and the functional impact coefficient, a comprehensive impact level determination process is carried out. By establishing a dual-coefficient fusion model based on the ecological red line threshold of nature reserves, when any coefficient exceeds its ecological red line threshold, the corresponding impact level warning is triggered, and the impact level index is output.

[0101] The final step aims to transform the predicted trends in biological community change into a comprehensive impact level that can be used for management decisions. Step S610 first assesses the loss of ecosystem integrity. Its core processing involves analyzing how predicted changes in biological community composition affect the structural stability of the ecosystem. Specifically, this is done by quantifying the expected changes in the abundance of key functional groups at each trophic level (e.g., producers, primary consumers, higher predators) to calculate the degree of decline in the trophic level structural integrity index. This decline is the structural impact coefficient, reflecting the risk of ecosystem simplification or disintegration due to construction disturbance. Step S620 then assesses the sustainability of ecological functions. This processing is not directly based on the structural impact coefficient, but rather, also based on the trends in biological community composition change, it deeply analyzes two key types of information: first, the expected disappearance rate of endemic or endangered species within the protected area; and second, the estimated decline in the supply capacity of specific ecosystem service functions (e.g., water conservation, pollination, seed dispersal) directly associated with such species changes. By establishing a coupling model between these two, a functional impact coefficient characterizing the risk of ecological function impairment is obtained. The comprehensive impact level determination in step S630 is the final decision-making stage. This process establishes a dual-coefficient fusion model based on the pre-set ecological red line threshold of nature reserves. This model sets threshold limits for both structural impact coefficients and functional impact coefficients. The determination logic is that as long as one coefficient exceeds its corresponding ecological red line threshold, regardless of the value of the other coefficient, a higher-level impact warning will be triggered. Through this "one-vote veto" fusion mechanism, complex ecological prediction results are integrated into a comprehensive impact level indicator that is intuitive and has clear warning significance, thereby providing a key basis for taking different levels of protection intervention actions.

[0102] Example 2:

[0103] like Figure 2 As shown in the figure, this embodiment provides a system for assessing the impact of construction disturbance on the biodiversity of nature reserves. The system includes:

[0104] The acquisition module 901 is used to acquire species distribution data, construction disturbance data, and environmental data within nature reserves.

[0105] Module 902 is used to construct an ecological association network based on species distribution data, and obtain the species interaction network by quantifying the relationship between spatial co-occurrence patterns among species.

[0106] The extraction module 903 is used to extract network structure features based on the species interaction network and identify the key species set by calculating the fusion index of node betweenness centrality and feature vector centrality.

[0107] Modeling module 904 is used to perform disturbance propagation dynamics modeling based on key species set and construction disturbance data. By simulating the cascading diffusion process of disturbance signals in the network, an impact propagation path diagram is obtained.

[0108] The prediction module 905 is used to predict biodiversity response based on the impact propagation path map and environmental data. By coupling the community succession model driven by internal disturbance pressure and external environment, it predicts the trend of changes in biological community composition.

[0109] The assessment module 906 is used to conduct a comprehensive impact assessment based on the changing trends of biological community composition. By comparing the degree of deviation from the historical baseline state, it outputs an impact level index.

[0110] In one specific embodiment of this application, the construction module 902 includes:

[0111] The first building unit is used to perform spatial distribution heterogeneity analysis based on species distribution data. By identifying the clustering and exclusion patterns of species distribution hotspots at multiple scales, the multi-scale spatial associations between species are obtained.

[0112] The second building block is used to perform indirect interaction inference processing based on multi-scale spatial correlation. By analyzing the statistical correlation shown by the preset species pairs when there is a third species mediating the connection in non-overlapping regions, the indirect co-occurrence relationship regulated by the mediating species is obtained.

[0113] The third building block is used to integrate the interaction network based on multi-scale spatial relationships and indirect co-occurrence relationships. By superimposing direct spatial relationships and indirect mediation relationships into a unified interaction intensity tensor, the species interaction network is obtained.

[0114] In one specific embodiment of this application, the extraction module 903 includes:

[0115] The first extraction unit is used to perform network stability simulation processing based on phylogenetic signal strength according to the species interaction network. By simulating the difference in network connectivity caused by removing species nodes with different phylogenetic uniqueness, a preliminary species list is obtained.

[0116] The second extraction unit is used to perform functional trait complementarity module analysis based on the species interaction network and the preliminary species list. By quantifying the bridging role of nodes in connecting species modules with different functional traits, a set of key nodes is obtained.

[0117] The third extraction unit is used to perform comprehensive judgment processing based on the key node set. It filters species nodes that exist in the preliminary species list and whose functional bridge role index exceeds the average value of their respective modules, and performs a product operation on their two indicators to obtain the key species set.

[0118] In one specific embodiment of this application, the modeling module 904 includes:

[0119] The first modeling unit is used to define disturbance propagation rules based on the key species set and construction disturbance data. By establishing a disturbance transmission probability matrix based on species functional group division and introducing a species tolerance threshold as a critical condition for disturbance propagation, a nonlinear disturbance propagation model is obtained.

[0120] The second modeling unit is used to perform multi-stage disturbance diffusion simulation processing based on the nonlinear disturbance propagation model. By combining the spatiotemporal characteristics of construction activities, it simulates the dynamic process of disturbance propagating along network connections in an asynchronous and non-uniform manner, and obtains the spatiotemporal diffusion map of disturbance.

[0121] The third modeling unit is used to integrate the influence paths based on the spatiotemporal diffusion map of the disturbance. By identifying the main and secondary channels of the disturbance wavefront propagation and quantifying the cumulative effect of the disturbance intensity on each path, the influence propagation path map is obtained.

[0122] In one specific embodiment of this application, the prediction module 905 includes:

[0123] The first prediction unit is used to perform niche dynamic response modeling based on the impact propagation path map and environmental data. By coupling the disturbance pressure gradient with the environmental factor gradient, a spatiotemporally varying species fitness surface is constructed to obtain the initial response model of the population.

[0124] The second prediction unit is used to perform population dynamics simulation processing based on the initial response model of the population and the regulation of interspecific relationships. By introducing the regulation function of interspecific interaction on the population growth rate, a dynamic model of multi-species co-evolution is obtained.

[0125] The third prediction unit is used to predict community state transitions based on the dynamic model. By identifying the system stability boundary and critical point, it simulates the nonlinear transformation process of community composition from quantitative to qualitative change under different disturbance-environment combinations, and predicts the trend of biological community composition change.

[0126] In one specific embodiment of this application, the evaluation module 906 includes:

[0127] The first assessment unit is used to assess the loss of ecosystem integrity based on the changing trend of biological community composition. It obtains the structural impact coefficient by calculating the decrease in the trophic level structure integrity index caused by changes in the abundance of species in functional groups.

[0128] The second assessment unit is used to conduct ecological function sustainability assessment based on the structural impact coefficient. It obtains the functional impact coefficient by analyzing the coupling relationship between the extinction rate of endemic species and the decline in the supply capacity of ecosystem services.

[0129] The third assessment unit is used to determine the comprehensive impact level based on the structural impact coefficient and the functional impact coefficient. By establishing a dual-coefficient fusion model based on the ecological red line threshold of nature reserves, when any coefficient exceeds its ecological red line threshold, the corresponding impact level warning is triggered and the impact level index is output.

[0130] Example 3:

[0131] Corresponding to the above method embodiments, this embodiment also provides an assessment device for evaluating the impact of construction disturbance on the biodiversity of nature reserves. The assessment device for evaluating the impact of construction disturbance on the biodiversity of nature reserves described below can be referred to in correspondence with the assessment method for evaluating the impact of construction disturbance on the biodiversity of nature reserves described above.

[0132] Figure 3 This is a block diagram illustrating an assessment device 800 for evaluating the impact of construction disturbance on biodiversity in nature reserves, according to an exemplary embodiment. Figure 3 As shown, the device 800 for assessing the impact of construction disturbance on the biodiversity of nature reserves may include: a processor 801 and a memory 802. The device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0133] The processor 801 controls the overall operation of the device 800 for assessing the impact of construction disturbance on the biodiversity of nature reserves, thereby completing all or part of the steps in the aforementioned method for assessing the impact of construction disturbance on the biodiversity of nature reserves. The memory 802 stores various types of data to support the operation of the device 800. This data may include, for example, instructions for any application or method operating on the device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the construction disturbance impact assessment device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0134] In an exemplary embodiment, an impact assessment device 800 for construction disturbance on biodiversity in nature reserves may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the aforementioned method for assessing the impact of construction disturbance on biodiversity in nature reserves.

[0135] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, these program instructions implement the steps of the above-described method for assessing the impact of construction disturbance on the biodiversity of nature reserves. For example, the computer-readable storage medium may be the memory 802 including the program instructions, which may be executed by a processor 801 of a device 800 for assessing the impact of construction disturbance on the biodiversity of nature reserves to complete the above-described method for assessing the impact of construction disturbance on the biodiversity of nature reserves.

[0136] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the influence of construction disturbance on the biodiversity of a nature conservation site, characterized in that, The method comprises the following steps: acquiring species distribution data, construction disturbance data and environmental data in the nature reserve; constructing an ecological correlation network according to the species distribution data, quantifying the relationship between the spatial co-occurrence patterns of species, and obtaining a species interaction network; extracting network structure features according to the species interaction network, identifying a key species set by calculating a fusion index of node betweenness centrality and eigenvector centrality, and obtaining the key species set; modeling disturbance propagation dynamics according to the key species set and the construction disturbance data, simulating the cascading diffusion process of the disturbance signal in the network, and obtaining an influence propagation path diagram; predicting biodiversity response according to the influence propagation path diagram and the environmental data, coupling the community succession model under the internal disturbance pressure and the external environmental driving, and predicting the change trend of the biological community composition; comprehensively evaluating the influence according to the change trend of the biological community composition, comparing the deviation degree of the historical baseline state, and outputting an influence level index; wherein, by simulating the cascading diffusion process of the disturbance signal in the network, the influence propagation path diagram is obtained, comprising: according to the key species set and the construction disturbance data, performing disturbance propagation rule definition processing, establishing a disturbance transmission probability matrix based on the division of species functional groups, and introducing a species tolerance threshold as a critical condition for disturbance propagation, to obtain a nonlinear disturbance propagation model; according to the nonlinear disturbance propagation model, performing multi-stage disturbance diffusion simulation processing, combining the spatio-temporal characteristics of construction activities, simulating the dynamic process of disturbance propagation along the network connection in an asynchronous and non-uniform manner, and obtaining a disturbance spatio-temporal diffusion graph; according to the disturbance spatio-temporal diffusion graph, performing influence path integration processing, identifying the main channel and the secondary path of the disturbance wavefront propagation, and quantifying the cumulative effect of disturbance intensity on each path, to obtain the influence propagation path diagram; wherein, according to the influence propagation path diagram and the environmental data, the biodiversity response is predicted by coupling the community succession model under the internal disturbance pressure and the external environmental driving, and the change trend of the biological community composition is predicted, comprising: according to the influence propagation path diagram and the environmental data, performing niche dynamic response modeling processing, coupling the disturbance pressure gradient and the environmental factor gradient, constructing a species suitability surface that changes with time and space, and obtaining a population initial response model; according to the population initial response model, performing population dynamic simulation processing of interspecific relationship adjustment, introducing an adjustment function of interspecific interaction relationship on population growth rate, and obtaining a dynamic model of multi-species coordinated succession; according to the dynamic model, performing community state transition prediction processing, identifying the system stability boundary and critical point, simulating the nonlinear conversion process from quantitative change to qualitative change of community composition under different disturbance-environment combination scenarios, and predicting the change trend of the biological community composition.

2. The method of claim 1, wherein the construction disturbance is a road construction. According to the species distribution data, the ecological correlation network is constructed, the relationship between the spatial co-occurrence patterns of species is quantified, and the species interaction network is obtained, comprising: According to the species distribution data, spatial distribution heterogeneity analysis processing is performed, the aggregation and repulsion patterns of species distribution hotspots at multiple scales are identified, and the multi-scale spatial correlation between species is obtained; According to the multi-scale spatial correlation, indirect interaction inference processing is performed, and the statistical correlation of the preset species pair in the presence of a third species intermediate connection in a non-overlapping area is analyzed to obtain an indirect co-occurrence relationship regulated by the intermediate species; According to the multi-scale spatial correlation and the indirect co-occurrence relationship, interactive network integration processing is performed, and direct spatial correlation and indirect intermediate correlation are superimposed into a unified interaction strength tensor to obtain a species interaction network.

3. The method of claim 1, wherein the construction disturbance is a road construction. According to the species interaction network, network structure feature extraction is performed, a fusion index of node betweenness centrality and eigenvector centrality is calculated, and a key species set is identified, including: According to the species interaction network, network stability simulation processing based on phylogenetic signal strength is performed, and the difference in network connectivity caused by removing species nodes with different phylogenetic uniqueness is simulated to obtain a preliminary species list; According to the species interaction network and the preliminary species list, functional trait complementarity module analysis processing is performed, and the bridge role of nodes in connecting species modules with different functional traits is quantified to obtain a key node set; According to the key node set, comprehensive judgment processing is performed, species nodes that exist in the preliminary species list and have a functional bridge role index exceeding the average value of their own module are screened, and the two indexes are multiplied to obtain a key species set.

4. A system for evaluating the influence of construction disturbance on the biodiversity of a nature conservation site, characterized in that It includes: An acquisition module is configured to acquire species distribution data, construction disturbance data, and environmental data in a nature reserve; A construction module is configured to construct an ecological correlation network based on the species distribution data, quantify the relationship between spatial co-occurrence patterns of species, and obtain a species interaction network; An extraction module is configured to extract network structure features from the species interaction network, calculate a fusion index of node betweenness centrality and eigenvector centrality, and identify a key species set; A modeling module is configured to model disturbance propagation dynamics based on the key species set and the construction disturbance data, simulate the cascading diffusion process of disturbance signals in the network, and obtain an influence propagation path diagram; A prediction module is configured to predict biodiversity response based on the influence propagation path diagram and the environmental data, couple an internal disturbance pressure with an external environmental-driven community succession model, and predict a biodiversity composition change trend; An evaluation module is configured to evaluate the comprehensive impact based on the biodiversity composition change trend, compare the deviation degree of the historical baseline state, and output an impact level index; The modeling module includes: A first modeling unit is configured to define disturbance propagation rules based on the key species set and the construction disturbance data, establish a disturbance transmission probability matrix based on species functional group division, introduce a species tolerance threshold as a critical condition for disturbance propagation, and obtain a nonlinear disturbance propagation model. The second modeling unit is configured to perform a multi-stage disturbance diffusion simulation process according to the nonlinear disturbance propagation model, simulate a dynamic process of disturbance propagation along network connections in an asynchronous and non-uniform manner by combining time-space characteristics of construction activities, and obtain a disturbance space-time diffusion atlas. The third modeling unit is configured to perform an influence path integration process according to the disturbance space-time diffusion atlas, identify main channels and secondary paths of disturbance wavefront propagation, and quantify cumulative effects of disturbance intensity on each path, so as to obtain an influence propagation path diagram. The prediction module includes: The first prediction unit is configured to perform a niche dynamic response modeling process according to the influence propagation path diagram and the environmental data, couple disturbance pressure gradients and environmental factor gradients, construct a species suitability surface that changes with time and space, and obtain a population initial response model. The second prediction unit is configured to perform a population dynamic simulation process of interspecific relationship adjustment according to the population initial response model, introduce an adjustment function of interspecific interaction relationship on population growth rate, and obtain a dynamic model of multi-species collaborative succession. The third prediction unit is configured to perform a community state transition prediction process according to the dynamic model, identify system stability boundaries and critical points, simulate a nonlinear conversion process of community composition from quantitative change to qualitative change under different disturbance-environment combination scenarios, and predict a trend of biological community composition change.

5. The system for evaluating the influence of construction disturbance on the biodiversity of a natural protected area according to claim 4, characterized in that, The construction module includes: The first construction unit is configured to perform a spatial distribution heterogeneity analysis process according to the species distribution data, identify aggregation and repulsion patterns of species distribution hotspots at multiple scales, and obtain a multi-scale spatial correlation relationship between species. The second construction unit is configured to perform an indirect interaction inference process according to the multi-scale spatial correlation relationship, analyze statistical correlations of a preset species pair when a third species exists as an intermediate connection in a non-overlapping area, and obtain an indirect co-occurrence relationship adjusted by an intermediate species. The third construction unit is configured to perform an interaction network integration process according to the multi-scale spatial correlation relationship and the indirect co-occurrence relationship, superimpose direct spatial correlation and indirect intermediate correlation into a unified interaction strength tensor, and obtain a species interaction network.

6. The system for evaluating influence of construction disturbance on biodiversity in a natural reserve according to claim 4, characterized in that, The extraction module includes: The first extraction unit is configured to perform a network stability simulation process based on phylogenetic signal strength according to the species interaction network, simulate differences in network connectivity caused by removing species nodes with different phylogenetic uniqueness, and obtain a preliminary species list. The second extraction unit is configured to perform a functional trait complementarity module analysis process according to the species interaction network and the preliminary species list, quantify the bridging role of nodes when connecting species modules with different functional traits, and obtain a key node set. The third extraction unit is configured to perform a comprehensive judgment process according to the key node set, filter species nodes that exist in the preliminary species list and have a functional bridge role index exceeding an average value of their own module, and perform a product operation on the two indexes, so as to obtain a key species set.

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