Method for evaluating influence of ampullaria gigas on structure and function of water ecosystem

Abstract

The invention discloses a method for evaluating the influence of ampullaria gigas on the structure and function of a water ecosystem, and the method comprises the steps: constructing cross-character dispersion based on the plant height, biomass, chlorophyll content and carbon, nitrogen and phosphorus content of a submerged plant, and generating an initial disturbance quantity of the plant; then forming an ingestion structure quantity according to the proportion of a scraping type, a collecting type, a filtering type, a preying type and a tearing type in the benthic ingestion type group, and performing reverse treatment on the initial disturbance quantity of the plants to obtain a first disturbance group; further forming a floating structural quantity according to the water pH, dissolved oxygen, oxidation-reduction potential, suspended solids, nutritive salt concentration, turbidity and plankton diversity index, and performing reverse treatment on the first disturbance group to obtain a second disturbance group. And on the basis, constructing a system-level functional structure by using the plant initial disturbance quantity, the ingestion structure quantity and the floating structure quantity according to a multi-level corresponding relation, and generating the ecological influence quantity of the ampullaria gigas on the structure and the function of the water ecosystem according to the functional structure.

Description

Technical Field

[0001] This invention relates to the field of aquatic ecological health assessment technology, specifically to a method for assessing the impact of the golden apple snail on the structure and function of aquatic ecosystems. Background Technology

[0002] With the acceleration of globalization, the ecological risks posed by invasive alien species to freshwater ecosystems are becoming increasingly prominent. The invasive golden apple snail (Pomacea canaliculata), as one of the most dangerous invasive species, has spread rapidly across many parts of my country, both north and south, due to its wide environmental adaptability, strong feeding preferences, and rapid reproduction rate. It has become a significant disturbance factor affecting the structural stability and ecological function of aquatic ecosystems. Studies have shown that the golden apple snail mainly feeds on submerged plants, and its feeding and excretion activities significantly alter the physicochemical properties, nutrient content, and primary production structure of water bodies. This, in turn, damages aquatic vegetation communities, benthic habitats, and plankton structures, inducing a series of chain reactions of ecosystem degradation.

[0003] Current research on *Pomacea canaliculata* primarily focuses on reproductive ecology, dispersal behavior, and agricultural control. However, a systematic assessment method for its overall impact on the structure and function of aquatic ecosystems after invasion is significantly lacking. Existing research is insufficient to comprehensively quantify its combined disturbance effects on submerged plant functional traits, benthic animal diversity, plankton community structure, and key ecosystem functions (such as primary production, material cycling, and water purification). The lack of a scientific and standardized ecological impact assessment system makes it difficult to classify the risk level posed by *Pomacea canaliculata* invasion and fails to provide reliable support for ecological restoration, monitoring, and management decisions.

[0004] In view of this, the present invention proposes a method for assessing the impact of the golden apple snail on the structure and function of aquatic ecosystems. Summary of the Invention

[0005] The purpose of this invention is to provide a method for assessing the impact of golden apple snails on the structure and function of aquatic ecosystems, and to solve the technical problem of the lack of a systematic and quantifiable comprehensive assessment of changes in submerged plants, benthic organisms, plankton, and ecosystem functions caused by the invasion of golden apple snails in the existing technology.

[0006] In a first aspect, the present invention provides a method for assessing the impact of the golden apple snail on the structure and function of aquatic ecosystems, comprising the following steps:

[0007] S101: Perform cross-trait dispersion calculation to generate a trait dispersion set based on the plant height, biomass, chlorophyll content, carbon content, nitrogen content and phosphorus content of the submerged plant, and form the initial plant disturbance amount based on the trait dispersion set.

[0008] S102: The feeding structure quantity is formed according to the proportion of scrapers, collectors, filter feeders, predators and rippers in benthic organisms, and the initial disturbance quantity of plants is reversed according to the feeding structure quantity to form the first disturbance group;

[0009] S103: The planktonic structure quantity is formed based on the water body's pH, dissolved oxygen, oxidation-reduction potential, suspended solids, nutrient concentration, turbidity, and planktonic biodiversity index, and the first disturbance group is reverse-processed based on the planktonic structure quantity to form the second disturbance group;

[0010] S104: Construct a system-level functional structure by establishing a multi-level correspondence between the initial disturbance amount, feeding structure amount, and planktonic structure amount of plants;

[0011] S105: Generate the ecological impact of the golden apple snail on the structure and function of the aquatic ecosystem based on the system-level functional structure.

[0012] As a preferred embodiment of the present invention, the step of generating a trait dispersion set by performing cross-trait dispersion calculation based on the plant height, biomass, chlorophyll content, carbon content, nitrogen content, and phosphorus content of submerged plants includes:

[0013] The first trait set was formed based on the plant height, biomass, and chlorophyll content of submerged plants in the sampling points, and the amplitude normalization was performed on the difference items of each trait in the first trait set to form the first normalized set.

[0014] A second trait set was formed based on the carbon, nitrogen, and phosphorus content of submerged plants in the sample points, and amplitude normalization was performed on the difference terms of each trait in the second trait set to form a second normalized set.

[0015] The first normalized set and the second normalized set are matched to form trait crossover pairs, and Euclidean distance is calculated for each trait crossover pair to form a primary discreteness group.

[0016] The primary discrete groups are grouped according to their trait origins to form a trait discrete set.

[0017] As a preferred embodiment of the present invention, the step of forming the initial perturbation amount of the plant based on the trait dispersion set includes:

[0018] Arrange all discrete items in the set of discrete traits in a preset weight order to form a discrete sequence;

[0019] Perform sequential difference processing on adjacent discrete terms in the discreteness sequence to form a difference group;

[0020] The difference group is subjected to amplitude normalization to form a normalized difference group;

[0021] The normalized difference groups are combined according to a fixed arrangement rule to form the initial disturbance of the plant.

[0022] As a preferred embodiment of the present invention, the amplitude normalization processing of the difference group includes:

[0023] Extract the absolute values ​​of all difference items from the difference set to form an amplitude set;

[0024] Based on the largest amplitude item in the amplitude set, perform scaling processing on all difference items in the difference group;

[0025] A normalized difference set is formed based on the scaled difference terms.

[0026] As a preferred embodiment of the present invention, the step of forming the feeding structure quantity based on the proportions of scrapers, collectors, filter feeders, predators, and rippers in benthic organisms includes:

[0027] Feeding quantity groups were formed based on the sample size of each benthic feeding group;

[0028] Perform a total amount merging calculation on the aforementioned food intake groups to form a food intake ratio group;

[0029] The feeding ratio groups are arranged into feeding arrangement groups according to the position of specific tax groups;

[0030] The feeding structure quantity is formed by performing a preset weight allocation based on the feeding arrangement group.

[0031] As a preferred embodiment of the present invention, the step of performing reverse processing on the initial disturbance amount of plants based on the amount of food intake includes:

[0032] Extract all structural items from the aforementioned ingestion structural quantity to form a structural item group;

[0033] The structural item groups are matched with the initial perturbation amount of the plant in a fixed order to form perturbation correspondence groups;

[0034] Perform reverse interpolation processing on the corresponding group of disturbances to form a reverse interpolation group;

[0035] The first disturbance group is formed by performing structural term reduction calculations based on the inverse difference group.

[0036] As a preferred embodiment of the present invention, the step of forming the planktonic structure quantity based on water pH, dissolved oxygen, oxidation-reduction potential, suspended solids, nutrient concentration, turbidity, and planktonic biodiversity index includes:

[0037] A first water quality group is formed based on the pH, dissolved oxygen, and redox potential of the water body.

[0038] A second water quality group is formed based on the suspended solids, nutrient concentration, and turbidity of the water body.

[0039] The first water quality group and the second water quality group are combined in a preset order to form a water quality structure group;

[0040] The water quality structure group and the planktonic biodiversity index are combined according to their correspondence to form the planktonic structure quantity.

[0041] As a preferred embodiment of the present invention, the reverse processing of the first disturbance group based on the amount of floating structure includes:

[0042] Extract all structural terms from the aforementioned floating structural quantities to form a floating term group;

[0043] The floating term groups are matched with the first disturbance group in a fixed order to form a secondary disturbance correspondence group;

[0044] Perform reverse interpolation processing on the corresponding group of the second-level disturbance to form a second-level reverse interpolation group;

[0045] The second disturbance group is formed by performing disturbance structure reconstruction calculations based on the second-level reverse difference group.

[0046] As a preferred embodiment of the present invention, the step of forming a second disturbance group based on a second-level reverse difference group includes:

[0047] Amplitude compression is performed on all difference items in the second-level reverse difference group to form a compression group;

[0048] The compression groups are arranged in a preset fixed order to form an arrangement group;

[0049] The perturbation order is adjusted according to the aforementioned permutation group to form a second perturbation group.

[0050] As a preferred embodiment of the present invention, the step of constructing a system-level functional structure based on a multi-level correspondence between the initial disturbance amount of plants, the amount of feeding structures, and the amount of planktonic structures includes:

[0051] The initial perturbation of the plant is used as the input of the first functional layer;

[0052] The amount of ingested structures and the amount of planktonic structures are used as inputs to the second functional layer;

[0053] The first functional layer input and the second functional layer input are correlated and calculated using a preset weight allocation matrix;

[0054] A system-level functional structure is constructed based on the correlation calculation results.

[0055] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0056] This invention constructs cross-trait dispersion by integrating the structural and nutritional traits of submerged plants, and generates hierarchical perturbation quantities under the combined influence of benthic feeding group proportions and planktonic-related water quality factors. This allows the shifts in different ecological dimensions to be preserved and propagated layer by layer within a unified numerical system. Since the initial plant perturbation quantity reflects the trait shifts of submerged plants under the feeding and damage pressure of *Pomacea canaliculata*, the benthic feeding structure quantity reflects the energy conversion changes of the benthic system under invasion pressure, and the planktonic structure quantity reflects the changes in the physicochemical state of the water body and the planktonic community, the system-level functional structure obtained after inverse superposition processing contains comprehensive shift information of the three components: plants, benthic, and planktonic. The ecological impact quantity generated based on this system-level functional structure can characterize the overall disturbance degree of *Pomacea canaliculata* invasion on the primary production layer, the benthic functional layer, and the aquatic environmental layer within the same framework. This makes the quantification of ecological impact no longer dependent on single indicators or single ecological levels, thus establishing a stable one-to-one correspondence between the assessment results and ecological processes, facilitating invasion risk assessment and ecological management decisions in actual water bodies. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0058] Figure 1 This is a schematic diagram of the assessment framework for the impact of the golden apple snail on the structure and function of aquatic ecosystems according to the present invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings.

[0060] Throughout the accompanying drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions. The described embodiments are only a part of the embodiments of this application, not all of them. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0061] Example 1

[0062] Please see Figure 1As shown, this embodiment provides a method for assessing the impact of *Pomacea canaliculata* on the structure and function of aquatic ecosystems. By precisely analyzing the phenotypic characteristics, community structure, and species diversity disturbance patterns of submerged plants (plant height, biomass, chlorophyll, community cover), plankton (zooplankton, phytoplankton), and benthic organisms (scrapers, gatherers, predators), it clarifies the impact mechanism on core ecological functions such as primary productivity, material cycling, and water purification. This method overcomes the technical bottleneck in analyzing the impact of invasive species in complex aquatic ecosystems, constructs an assessment system that is scientific, practical, and operable, and provides reliable technical support and scientific basis for early warning of *Pomacea canaliculata* invasion risks, formulation of control strategies, quantitative assessment of control effects, and management. The method includes the following steps:

[0063] S101: Perform cross-trait dispersion calculation to generate a trait dispersion set based on the plant height, biomass, chlorophyll content, carbon content, nitrogen content and phosphorus content of the submerged plant, and form the initial plant disturbance amount based on the trait dispersion set.

[0064] This can be understood as follows: investigators set up several underwater quadrats in the slow-flowing areas of lakes or rivers where golden apple snails have invaded, and recorded the average plant height, biomass per unit area, chlorophyll content per unit area, carbon content per unit dry weight, nitrogen content per unit dry weight, and phosphorus content per unit dry weight of the dominant submerged plants in each quadrat. These six types of trait values ​​are used as input data for the subsequent S101 treatment.

[0065] The step of generating a trait dispersion set by performing cross-trait dispersion calculation based on the plant height, biomass, chlorophyll content, carbon content, nitrogen content, and phosphorus content of submerged plants includes:

[0066] The first trait set was formed based on the plant height, biomass, and chlorophyll content of submerged plants in the sampling points, and the amplitude normalization was performed on the difference items of each trait in the first trait set to form the first normalized set.

[0067] This can be understood as follows: Using a single sample point as a unit, a set of primary traits is constructed for that sample point, including plant height, biomass, and chlorophyll content. This generates a first normalized set for subsequent dispersion calculations. For each sample point, plant height, biomass, and chlorophyll content are first denoted as H, B, and C_chl, forming the primary trait set {H, B, C_chl}. Then, within the same sample point, the difference terms between these three traits are calculated, for example:

[0068] The difference between plant height and biomass is D_HB = H − B;

[0069] The difference between plant height and chlorophyll content is D_HC = H − C_chl;

[0070] The difference between biomass and chlorophyll content is D_BC = B − C_chl.

[0071] In other words, after obtaining the three difference terms, the absolute values ​​of these three difference terms are used as the original amplitude values. Using the largest amplitude value as the benchmark, amplitude normalization is performed on the three difference terms. For example, each difference term can be divided by the largest amplitude value to ensure that the normalized difference values ​​fall within the same order of magnitude, forming the first normalized set {D_HB′, D_HC′, D_BC′}. This embodiment does not limit the specific normalization formula; those skilled in the art can choose from conventional normalization methods, as long as the relative magnitude relationship between the three difference terms remains unchanged.

[0072] A second trait set was formed based on the carbon, nitrogen, and phosphorus content of submerged plants in the sample points, and amplitude normalization was performed on the difference terms of each trait in the second trait set to form a second normalized set.

[0073] This can be understood as follows: for the same trait, the plant's carbon content, nitrogen content, and phosphorus content are denoted as C, N, and P, respectively, forming a set of secondary traits {C, N, P}. Then, the differences between these three nutritional traits are calculated, for example:

[0074] The difference between carbon content and nitrogen content, D_CN = C − N;

[0075] The difference between carbon content and phosphorus content, D_CP = C − P;

[0076] The difference between nitrogen and phosphorus content is D_NP = N − P.

[0077] In other words, the absolute values ​​of the three difference terms are used to form a set of amplitude values. The maximum value in this set of amplitude values ​​is used as the benchmark to perform amplitude normalization on the three difference terms, resulting in a second normalization set {D_CN′, D_CP′, D_NP′}, which is used to characterize the internal offset relationship between nutrient elements.

[0078] The first normalized set and the second normalized set are matched to form trait crossover pairs, and Euclidean distance is calculated for each trait crossover pair to form a primary discreteness group.

[0079] It should be noted that, firstly, the three normalized differences in the first normalized set are paired with the three normalized differences in the second normalized set in the corresponding order, for example:

[0080] D_HB′ and D_CN′ form a first trait crossover pair;

[0081] D_HC′ and D_CP′ are paired to form a crossover pair for the second trait;

[0082] D_BC′ and D_NP′ are paired to form a crossover pair for the third trait.

[0083] This can be understood as follows: for each trait crossover pair (x, y), Euclidean distance is used as the dispersion calculation method, that is, √(x² + y²) is calculated for each pair, thus obtaining three primary dispersion values ​​L_1, L_2, and L_3. The three primary dispersion values ​​together constitute the primary dispersion set, which is used to characterize the linkage and migration strength between structural traits and trophic traits within the same sample point.

[0084] The primary discrete groups are grouped according to their trait origins to form a trait discrete set.

[0085] It should be noted that, according to the trait source or pairing logic, L_1, L_2, and L_3 in the primary dispersion group can be directly combined to form the trait dispersion set {L_1, L_2, L_3}. Here, L_1 can be understood as reflecting the dispersion of both plant height and carbon / nitrogen shift, L_2 as reflecting the dispersion of both plant height and carbon / phosphorus shift, and L_3 as reflecting the dispersion of both biomass and nitrogen / phosphorus shift. In this embodiment, L_1 to L_3 are not further grouped; it is only necessary to ensure that this set maintains a stable order identifier in subsequent steps.

[0086] Specifically, the method of forming the initial perturbation of the plant based on the set of trait dispersions includes:

[0087] Arrange all discrete items in the set of discrete traits in a preset weight order to form a discrete sequence;

[0088] It should be noted that, firstly, according to a preset weight order, L_1, L_2, and L_3 in the trait dispersion set are arranged to form a dispersion sequence. The weight order can be set according to the ecological importance of different trait intersection pairs; for example, dispersions that better reflect structural changes can be placed first, and dispersions that better reflect trophic shifts can be placed later. After sorting, an ordered dispersion arrangement is obtained, such as {L_a, L_b, L_c}.

[0089] Perform sequential difference processing on adjacent discrete terms in the discreteness sequence to form a difference group;

[0090] It should be noted that the order difference is performed on adjacent discrete terms in the discrete sequence, for example, to calculate:

[0091] The difference term Δ_1 = L_b − L_a;

[0092] The difference term Δ_2 = L_c − L_b.

[0093] In other words, the difference processing does not change the relative magnitude of the original dispersion, but extracts the "change between dispersions" so that the subsequent perturbation quantity can better highlight the mutual differences between the dispersions of traits.

[0094] The difference group is subjected to amplitude normalization to form a normalized difference group;

[0095] More specifically, the amplitude normalization process performed on the difference group includes:

[0096] Extract the absolute values ​​of all difference items from the difference set to form an amplitude set;

[0097] Based on the largest amplitude item in the amplitude set, perform scaling processing on all difference items in the difference group;

[0098] A normalized difference set is formed based on the scaled difference terms.

[0099] This can be understood as providing directly implementable computational logic for normalizing the magnitude of the difference set. In actual programming, the difference set can be stored as a list of values ​​of length 2. First, the absolute value of each element in the list is taken to form a magnitude list. Then, the original difference is scaled based on the maximum value in this list. Finally, the scaled result is returned as a normalized difference set. This process does not depend on a specific software environment or hardware platform and can be implemented in common scientific computing software or programming languages.

[0100] The normalized difference groups are combined according to a fixed arrangement rule to form the initial plant disturbance.

[0101] It should be noted that Δ_1′ and Δ_2′ in the normalized difference set are merged according to a fixed arrangement rule, for example, maintaining their order in the original difference calculation, and combined into a perturbation quantity containing two perturbation components, which serves as the initial perturbation quantity for the plant. For each sample point, a corresponding set of initial perturbation quantities for the plant can be obtained through the aforementioned steps, which can then be synthesized and reverse-processed with benthic structure quantities and planktonic structure quantities in subsequent steps.

[0102] S102: The feeding structure quantity is formed according to the proportion of scrapers, collectors, filter feeders, predators and rippers in benthic organisms, and the initial disturbance quantity of plants is reversed according to the feeding structure quantity to form the first disturbance group;

[0103] This process can be understood as follows: During specific monitoring, investigators set up benthic animal sampling points at locations identical to or adjacent to submerged plant quadrats. Benthic animal samples were obtained using quantitative samplers or mud samplers. Under laboratory conditions, the benthic animals at each sampling point were identified to the genus or species level, and then classified into five functional groups according to their feeding methods: scrapers, collectors, filter feeders, predators, and rippers. Subsequently, the number of individuals in each group at each sampling point was counted, and a feeding structure quantity was constructed based on this quantity. The feeding structure quantity was then paired with the initial plant disturbance quantity at the corresponding sampling point to obtain the first disturbance group after considering benthic feeding.

[0104] The method of determining the feeding structure based on the proportions of scrapers, collectors, filter feeders, predators, and rippers among benthic organisms includes:

[0105] Feeding quantity groups were formed based on the sample size of each benthic feeding group;

[0106] It should be noted that, for each sampling point, the number of individuals of scrapers, gatherers, filter feeders, predators, and rippers was counted separately and denoted as G_num (number of scrapers), C_num (number of gatherers), F_num (number of filter feeders), P_num (number of predators), and S_num (number of rippers), respectively. These five values ​​were organized into a five-dimensional data set, denoted as the feeding data set {G_num, C_num, F_num, P_num, S_num}, which served as the basic input for subsequent proportion calculations.

[0107] Perform a total amount merging calculation on the aforementioned food intake groups to form a food intake ratio group;

[0108] It should be noted that, firstly, the sum of the number of individuals in the five categories in the food intake group is calculated. Under the premise that the sum of the number of individuals in the five categories is greater than zero, the proportion of the five functional groups in this sample point is extracted. These five proportion values ​​constitute the food intake proportion group {G_ratio, C_ratio, F_ratio, P_ratio, S_ratio}, which is used to characterize the relative proportion of different food intake functional groups in this sample point.

[0109] The feeding ratio groups are arranged into feeding arrangement groups according to the position of specific tax groups;

[0110] This can be understood as follows: in order to keep the positions of various groups fixed in subsequent calculations, the feeding ratio groups are arranged in a preset order, such as in the order of "scraping-collecting-filtering-predating-tearing-gnawing". The five ratio values ​​are arranged in sequence to form feeding arrangement groups, so that the feeding ratio data between different points are comparable in dimension and position, and avoid confusion in subsequent weight processing and synthesis processes caused by inconsistent order.

[0111] The feeding structure quantity is formed by performing a preset weight allocation based on the feeding arrangement group.

[0112] This can be understood as follows: in order to highlight functional groups such as scraping and collecting that directly overlap with the feeding behavior of golden apple snails, the proportion values ​​in the feeding arrangement group can be linearly combined according to preset weights, compressing the five proportions into two comprehensive indicators.

[0113] For example, feeding pressure indices directly related to vegetation and feeding pressure indices related to non-direct feeding pathways can be set. The specific values ​​can be calibrated based on benthic food web research or field observation results. The original five-category proportion information is compressed into two structural indices combined into a feeding structure quantity. This feeding structure quantity reflects both the direct feeding pressure on submerged plants and the indirect energy distribution transmitted through other functional pathways in two-dimensional space, providing a quantitative basis for subsequent reverse correction of the initial disturbance amount of plants.

[0114] Specifically, the reverse processing of the initial disturbance amount of plants based on the amount of food intake includes:

[0115] Extract all structural items from the aforementioned ingestion structural quantity to form a structural item group;

[0116] The structural item groups are matched with the initial perturbation amount of the plant in a fixed order to form perturbation correspondence groups;

[0117] Perform reverse interpolation processing on the corresponding group of disturbances to form a reverse interpolation group;

[0118] The first disturbance group is formed by performing structural term reduction calculations based on the inverse difference group.

[0119] This can be understood as follows: After obtaining the feeding structure data, two structural indices from the feeding structure data are directly extracted as a structural term group. This structural term group is then mapped one-to-one with the two perturbation components of the initial plant perturbation data at fixed positions, resulting in two corresponding pairs for each sample point. Based on this, the operation of "subtracting the corresponding structural term from the plant perturbation component" is performed on each pair, causing benthic feeding to numerically correct the plant perturbation intensity. Subsequently, the two corrected values ​​are combined in a fixed order to form the first perturbation group, and its amplitude is uniformly scaled as needed to maintain a numerical scale consistent with the initial perturbation data. Through the above overall reverse processing, the first perturbation group can simultaneously reflect the combined effect of plant trait dispersion and benthic feeding structure, providing a stable input for subsequent secondary reverse processing based on planktonic structure data.

[0120] S103: The planktonic structure quantity is formed based on the water body's pH, dissolved oxygen, oxidation-reduction potential, suspended solids, nutrient concentration, turbidity, and planktonic biodiversity index, and the first disturbance group is reverse-processed based on the planktonic structure quantity to form the second disturbance group;

[0121] This can be understood as follows: after obtaining the amount of planktonic structure, the structural terms are combined with the perturbation components in the first perturbation group at fixed positions. For each pair, the inverse operation of "subtracting the corresponding structural term from the perturbation component of the previous layer" is performed. This allows the physicochemical conditions and planktonic characteristics of the sampled water to numerically regulate the perturbation amount resulting from the combined effects of plants and benthic organisms. The two corrected values ​​obtained through this inverse process are combined in a fixed order and scaled uniformly as needed to form the second perturbation group. This group reflects the comprehensive perturbation transmission of three types of ecological factors: plant trait dispersion, benthic structure, and planktonic structure, providing a stable input for the construction of system-level functional structures.

[0122] The method of determining the amount of planktonic structure based on water pH, dissolved oxygen, redox potential, suspended solids, nutrient concentration, turbidity, and planktonic biodiversity index includes:

[0123] A first water quality group is formed based on the pH, dissolved oxygen, and redox potential of the water body.

[0124] A second water quality group is formed based on the suspended solids, nutrient concentration, and turbidity of the water body.

[0125] The first water quality group and the second water quality group are combined in a preset order to form a water quality structure group;

[0126] The water quality structure group and the planktonic biodiversity index are combined according to their correspondence to form the planktonic structure quantity.

[0127] This can be understood as follows: in the monitoring of sampled water bodies, by simultaneously measuring the above six physicochemical indicators, pH, dissolved oxygen, and oxidation-reduction potential are classified as the first water quality dimension, while suspended solids, nutrient concentration, and turbidity are classified as the second water quality dimension. Subsequently, the two water quality groups are combined in a fixed order to form a water quality structure group, and the planktonic biodiversity index is incorporated as an additional item. This planktonic structure quantity simultaneously covers the physicochemical characteristics of the water body and the planktonic community status, and can be directly used for subsequent reverse disturbance treatment.

[0128] Specifically, the reverse processing of the first disturbance group based on the amount of floating structure includes:

[0129] Extract all structural terms from the aforementioned floating structural quantities to form a floating term group;

[0130] The floating term groups are matched with the first disturbance group in a fixed order to form a secondary disturbance correspondence group;

[0131] Perform reverse interpolation processing on the corresponding group of the second-level disturbance to form a second-level reverse interpolation group;

[0132] The second disturbance group is formed by performing disturbance structure reconstruction calculations based on the second-level reverse difference group.

[0133] This can be understood as follows: all structural terms in the planktonic structure are paired with two perturbation components of the first perturbation group at fixed positions to form a second-level perturbation correspondence group; and an inverse operation is performed on each pair to weaken or amplify the effect of the planktonic structure on the previous perturbation layer. Subsequently, the resulting second-level inverse difference groups are reorganized in a fixed order, and amplitude scaling is performed as necessary to maintain scale consistency, forming a second perturbation group that can reflect the cumulative perturbation results of the three ecological dimensions.

[0134] More specifically, the formation of the second disturbance group based on the second-level reverse difference group includes:

[0135] Amplitude compression is performed on all difference items in the second-level reverse difference group to form a compression group;

[0136] The compression groups are arranged in a preset fixed order to form an arrangement group;

[0137] The perturbation order is adjusted according to the aforementioned permutation group to form a second perturbation group.

[0138] This can be understood as follows: after obtaining the second-level inverse difference set, the absolute values ​​of all difference terms are used as amplitude inputs, and a uniform scaling is performed based on the largest amplitude term, thus forming a compressed set for subsequent combination. The compressed set is then arranged in a preset order to ensure that the perturbation components at different points maintain positional consistency. Based on this, the positional order of the perturbation components is adjusted according to the arrangement set, so that the final second perturbation set maintains continuity with the previous layer's processing results in both numerical scale and internal structure, and can serve as the final perturbation input for constructing the system-level functional structure.

[0139] S104: Construct a system-level functional structure by establishing a multi-level correspondence between the initial disturbance amount, feeding structure amount, and planktonic structure amount of plants;

[0140] This can be understood as follows: after obtaining the initial plant disturbance, feeding structure, and planktonic structure quantities, the initial plant disturbance is used as the input for the first functional layer, and the feeding and planktonic structure quantities are used as the input for the second functional layer, maintaining their correspondence within the same point. Subsequently, based on a preset weighting matrix, the two disturbance components of the first functional layer are weighted and linked with multiple structural terms of the second functional layer, enabling the disturbance effects of different ecological dimensions to achieve additive or cancelling properties within a unified numerical system. Through this correlation calculation, a set of functional vectors containing comprehensive structural information can be obtained, which serves as the system-level functional structure to characterize the overall coupling state between plant trait shifts, benthic functional structures, and planktonic structures.

[0141] The construction of a system-level functional structure based on a multi-level correspondence between initial plant disturbance, feeding structure, and planktonic structure includes:

[0142] The initial perturbation of the plant is used as the input of the first functional layer;

[0143] The amount of ingested structures and the amount of planktonic structures are used as inputs to the second functional layer;

[0144] The first functional layer input and the second functional layer input are correlated and calculated using a preset weight allocation matrix;

[0145] Based on the correlation calculation results, a system-level functional structure is constructed.

[0146] In one specific implementation, the two components of the initial plant disturbance are input as the first functional layer; two structural indices from the feeding structure and all structural terms from the planktonic structure are input as the second functional layer, giving the two layers of input structural information with different ecological dimensions. Subsequently, a pre-defined weighting matrix is ​​used to perform correlation operations on the two layers of input. The weighting coefficients can be determined based on field surveys or ecological mechanisms and remain fixed within the same study. The results are compiled into a set of system functional vectors, forming a system-level functional structure, providing a unified ecological comprehensive quantity for the next step.

[0147] S105: Generate the ecological impact of the golden apple snail on the structure and function of the aquatic ecosystem based on the system-level functional structure.

[0148] This can be understood as follows: after obtaining the system-level functional structure, an ecological impact quantity is constructed based on the combination relationships of the components within the functional vector. This ecological impact quantity reflects, in a quantifiable way, the common shifts in plant trait dispersion, benthic functional structure, and planktonic structure within the overall ecosystem under the invasion of *Pomacea canaliculata*. By combining different components in the system-level functional structure according to preset rules, such as using a linear weighting method or a structure ratio method, a final index reflecting the comprehensive disturbance degree of the multi-layered ecological structure can be obtained, serving as the ecological impact quantity of *Pomacea canaliculata* on the structure and function of the aquatic ecosystem. This embodiment does not limit the specific calculation formula; it only requires that the calculation method used remains consistent across different sample points, which can be directly reproduced by those skilled in the art.

[0149] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for assessing the impact of the golden apple snail on the structure and function of aquatic ecosystems, characterized in that, Includes the following steps: S101: Perform cross-trait dispersion calculation to generate a trait dispersion set based on the plant height, biomass, chlorophyll content, carbon content, nitrogen content and phosphorus content of the submerged plant, and form the initial plant disturbance amount based on the trait dispersion set. S102: The feeding structure quantity is formed according to the proportion of scrapers, collectors, filter feeders, predators and rippers in benthic organisms, and the initial disturbance quantity of plants is reversed according to the feeding structure quantity to form the first disturbance group; S103: The planktonic structure quantity is formed based on the water body pH, dissolved oxygen, oxidation-reduction potential, suspended solids, nutrient concentration, turbidity and planktonic biodiversity index, and the first disturbance group is reverse-processed based on the planktonic structure quantity to form the second disturbance group. S104: Construct a system-level functional structure by establishing a multi-level correspondence between the initial disturbance amount, feeding structure amount, and planktonic structure amount of plants; S105: Generate the ecological impact of the golden apple snail on the structure and function of the aquatic ecosystem based on the system-level functional structure.

2. The method for assessing the impact of the golden apple snail on the structure and function of aquatic ecosystems according to claim 1, characterized in that, The process of generating a trait dispersion set by performing cross-trait dispersion calculation based on the plant height, biomass, chlorophyll content, carbon content, nitrogen content, and phosphorus content of submerged plants includes: The first trait set was formed based on the plant height, biomass, and chlorophyll content of submerged plants in the sampling points, and the amplitude normalization was performed on the difference items of each trait in the first trait set to form the first normalized set. A second trait set was formed based on the carbon, nitrogen, and phosphorus content of submerged plants in the sample points, and amplitude normalization was performed on the difference terms of each trait in the second trait set to form a second normalized set. The first normalized set and the second normalized set are matched to form trait crossover pairs, and Euclidean distance is calculated for each trait crossover pair to form a primary discreteness group. The primary discrete groups are grouped according to their trait origins to form a trait discrete set.

3. The method for assessing the impact of the golden apple snail on the structure and function of aquatic ecosystems according to claim 2, characterized in that, The initial perturbation of the plant based on the set of trait dispersions includes: Arrange all discrete items in the set of discrete traits in a preset weight order to form a discrete sequence; Perform sequential difference processing on adjacent discrete terms in the discreteness sequence to form a difference group; The difference group is subjected to amplitude normalization to form a normalized difference group; The normalized difference groups are combined according to a fixed arrangement rule to form the initial disturbance of the plant.

4. The method for assessing the impact of the golden apple snail on the structure and function of aquatic ecosystems according to claim 3, characterized in that, The amplitude normalization process performed on the difference group includes: Extract the absolute values ​​of all difference items from the difference set to form an amplitude set; Based on the largest amplitude item in the amplitude set, perform scaling processing on all difference items in the difference group; A normalized difference set is formed based on the scaled difference terms.

5. The method for assessing the impact of the golden apple snail on the structure and function of aquatic ecosystems according to claim 1, characterized in that, The feeding structure quantity, determined based on the proportions of scrapers, collectors, filter feeders, predators, and rippers among benthic organisms, includes: Feeding quantity groups were formed based on the sample size of each benthic feeding group; Perform a total amount merging calculation on the aforementioned food intake groups to form a food intake ratio group; The feeding ratio groups are arranged into feeding arrangement groups according to the position of specific tax groups; The feeding structure quantity is formed by performing a preset weight allocation based on the feeding arrangement group.

6. The method for assessing the impact of the golden apple snail on the structure and function of aquatic ecosystems according to claim 5, characterized in that, The reverse processing of the initial disturbance amount of plants based on the amount of food intake includes: Extract all structural items from the aforementioned ingestion structural quantity to form a structural item group; The structural item groups are matched with the initial perturbation amount of the plant in a fixed order to form perturbation correspondence groups; Perform reverse interpolation processing on the corresponding group of disturbances to form a reverse interpolation group; The first disturbance group is formed by performing structural term reduction calculations based on the inverse difference group.

7. The method for assessing the impact of the golden apple snail on the structure and function of aquatic ecosystems according to claim 1, characterized in that, The method for determining planktonic structure based on water pH, dissolved oxygen, redox potential, suspended solids, nutrient concentration, turbidity, and planktonic biodiversity index includes: A first water quality group is formed based on the pH, dissolved oxygen, and redox potential of the water body. A second water quality group is formed based on the suspended solids, nutrient concentration, and turbidity of the water body. The first water quality group and the second water quality group are combined in a preset order to form a water quality structure group; The water quality structure group and the planktonic biodiversity index are combined according to their correspondence to form the planktonic structure quantity.

8. The method for assessing the impact of the golden apple snail on the structure and function of aquatic ecosystems according to claim 7, characterized in that, The reverse processing of the first disturbance group based on the amount of floating structure includes: Extract all structural terms from the aforementioned floating structural quantities to form a floating term group; The floating term groups are matched with the first disturbance group in a fixed order to form a secondary disturbance correspondence group; Perform reverse interpolation processing on the corresponding group of the second-level disturbance to form a second-level reverse interpolation group; The second disturbance group is formed by performing disturbance structure reconstruction calculations based on the second-level reverse difference group.

9. The method for assessing the impact of the golden apple snail on the structure and function of aquatic ecosystems according to claim 8, characterized in that, The process of forming a second disturbance group based on a second-level reverse difference group includes: Amplitude compression is performed on all difference items in the second-level reverse difference group to form a compression group; The compression groups are arranged in a preset fixed order to form an arrangement group; The perturbation order is adjusted according to the aforementioned permutation group to form a second perturbation group.

10. The method for assessing the impact of the golden apple snail on the structure and function of aquatic ecosystems according to claim 1, characterized in that, The method of constructing a system-level functional structure based on a multi-level correspondence between initial plant disturbance, feeding structure, and planktonic structure includes: The initial perturbation of the plant is used as the input of the first functional layer; The amount of ingested structures and the amount of planktonic structures are used as inputs to the second functional layer; The first functional layer input and the second functional layer input are correlated and calculated using a preset weight allocation matrix; A system-level functional structure is constructed based on the correlation calculation results.

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