Reservoir fish resource evaluation method and system based on data analysis

Abstract

This invention proposes a data analysis-based method and system for assessing fish resources in reservoirs, belonging to the field of resource assessment. The method includes collecting ecological characteristic data and population characteristic parameters of introduced and native fish populations in the reservoir; calculating niche overlap indices between introduced and native fish populations in both spatial and trophic dimensions, and integrating these to obtain a comprehensive niche difference index; calculating the population growth potential of each population and obtaining a fitness difference index; processing the comprehensive niche difference index and fitness difference index to obtain a coexistence stability score and determining the coexistence stability level of the two populations; acquiring time-series data of the niche overlap index, extracting fluctuation characteristic parameters, and generating a fish resource assessment report based on the fluctuation characteristic parameters and the coexistence stability level. This invention can comprehensively assess niche differences between populations.

Description

Technical Field

[0001] This invention relates to the field of resource assessment technology, and in particular to a method and system for assessing fish resources in reservoirs based on data analysis. Background Technology

[0002] With the increasing prevalence of aquaculture and biological invasions, the expansion of invasive fish populations in reservoir ecosystems poses a threat to native species. Accurately assessing the competitive relationship and coexistence mechanisms between invasive and native species is crucial for reservoir fishery resource management. Modern coexistence theory, proposed by Chesson in 2000, posits that species coexistence depends on two key factors: niche differences and fitness differences. Niche differences promote species coexistence because different species utilize different resources or spaces, reducing direct competition; fitness differences, however, can lead to competitive exclusion, with more fit species gradually replacing less fit ones.

[0003] In fish ecology research, stable isotope techniques have been widely applied to trophic niche analysis, and spatial distribution data based on sampling surveys can reflect the habitat use patterns of species. In assessing fish population dynamics, methods for estimating intrinsic growth rates based on life history parameters are relatively mature. These methods effectively assess the population growth potential of species by integrating parameters such as fertility, age at sexual maturity, and natural mortality. However, existing techniques have the following shortcomings: First, most studies are limited to single-dimensional analysis, lacking a systematic approach that integrates spatial and trophic niches; second, existing methods are mostly based on static life history parameters, failing to fully consider the long-term impact of reproductive potential on population dynamics; third, most studies remain at the qualitative descriptive level, lacking a quantitative stability evaluation system; and fourth, existing assessment methods are mostly static assessments, lacking a dynamic early warning mechanism based on time series data.

[0004] Chinese patent CN102297865B discloses a biological water quality monitoring system and method for fish behavior. This patent uses a camera to capture the behavior of a group of experimental fish in a tank, employs image processing technology to quantify behavioral parameters such as the fish's average swimming speed, distribution location, and density, and determines whether the water is polluted by comparing these parameters to preset thresholds. However, it only reflects water quality through changes in fish behavioral parameters and cannot analyze the competitive relationship between invasive and native species or diagnose coexistence mechanisms when considering the coexistence relationships between fish populations. Summary of the Invention

[0005] In view of this, the present invention proposes a data analysis-based method and system for assessing fish resources in reservoirs, which can assess the coexistence mechanism between invasive and native species, quantify the stability of the coexistence relationship, and provide dynamic early warning function.

[0006] The technical solution of this invention is implemented as follows: On the one hand, the present invention provides a method for assessing fish resources in reservoirs based on data analysis, comprising the following steps: S1. Collect ecological characteristic data and population characteristic parameters of alien fish populations and native fish populations in the reservoir. The ecological characteristic data includes spatial distribution data and trophic structure data. S2. Based on ecological characteristic data, calculate the niche overlap index between introduced fish populations and native fish populations in spatial and nutritional dimensions respectively, and integrate the niche overlap indexes in multiple dimensions to obtain a comprehensive niche difference index. S3. Calculate the population growth potential of the two populations based on the population characteristic parameters, and obtain the fitness difference index based on the difference in the population growth potential of the two populations. S4. The coexistence stability score is obtained by processing the comprehensive niche difference index and fitness difference index. The weight of the comprehensive niche difference index is greater than that of the fitness difference index. A dynamic threshold is set based on the comprehensive niche difference index. The coexistence stability level of the two populations is determined by combining the coexistence stability score and the dynamic threshold. S5. Obtain time series data of niche overlap indicators, extract fluctuation characteristic parameters from the time series data, and generate a fish resource assessment report based on the fluctuation characteristic parameters and coexistence stability level.

[0007] Based on the above technical solutions, preferably, the trophic structure data is stable isotope data, which includes carbon stable isotope data and nitrogen stable isotope data.

[0008] Based on the above technical solutions, preferably, the step of calculating the niche overlap index between introduced fish populations and native fish populations in both spatial and trophic dimensions based on ecological characteristic data specifically includes: The reservoir was divided into multiple spatial units, and the resource utilization ratios of introduced fish populations and native fish populations in each spatial unit were statistically analyzed to obtain the resource utilization ratios of the two populations in each spatial unit. Similarity was used to calculate the resource utilization ratio of two populations in each spatial unit, resulting in a spatial niche overlap index. The similarity calculation formula is as follows: ; in, This is a spatial indicator of niche overlap. The total number of spatial units. For the introduction of fish populations in the first Resource utilization ratio of each spatial unit For native fish populations in the first The resource utilization ratio of each spatial unit.

[0009] Based on the above technical solutions, preferably, the integration of multiple dimensions of niche overlap indicators to obtain a comprehensive niche difference index specifically includes: Trophic level correction was performed on the stable isotope data. Trophic niche boundaries of invasive fish populations and native fish populations were constructed in a two-dimensional coordinate space composed of carbon stable isotopes and nitrogen stable isotopes, respectively. The areas of the two trophic niche boundaries and their overlapping areas were calculated. The niche overlap index of the trophic dimension was obtained based on the ratio of the overlapping area to the area of ​​the trophic niche boundary. Spatial and trophic niche overlap indices are integrated using an information entropy-based heterogeneity integration strategy to obtain a comprehensive niche difference index, calculated as follows: ; ; ; in, This represents a comprehensive indicator of ecological niche differences. Indicates a dimension index. Indicates the number of niche dimensions. Indicates the first Niche differences in each dimension Indicates the first Dynamic weights for each dimension Represents the equilibrium sensitivity coefficient. This represents the normalized Shannon entropy. This represents the dimension index other than j. This represents the niche difference value in the k-th dimension.

[0010] Based on the above technical solutions, preferably, the population characteristic parameters include sexual maturity age, maximum age, survival rate of each age group, and reproductive capacity of each age group. Step S3 specifically includes: Net reproductive rate is calculated based on the survival rate and fertility of each age group. Net reproductive rate represents the number of offspring produced by a single individual in the lifetime of a population under ideal conditions. Generation time is calculated based on the survival rate, age of sexual maturity, and fertility of each age group. Generation time represents the average time required for a population to regenerate a new generation. Calculate the intrinsic growth rate of the population based on net reproductive rate and generation time; Using the intrinsic growth rate of a population as the population growth potential, the population growth potential of introduced fish populations and native fish populations were calculated separately, and the difference in fitness difference was obtained by calculating the difference in growth potential between the two populations.

[0011] Based on the above technical solutions, preferably, the process of processing the comprehensive niche difference index and fitness difference index to obtain the coexistence stability score specifically includes: The coexistence stability score is obtained by linearly combining the comprehensive niche difference index and the fitness difference index. The calculation formula is as follows: ; in, To score the coexistence stability, The weights of the comprehensive niche difference indicators. As the weight of the fitness difference index, This is a comprehensive indicator of ecological niche differences. For fitness difference index; Using the comprehensive niche difference index as a baseline value, combined with a competition intensity correction factor, a dynamic threshold is calculated using the Logistic response function. The calculation formula is as follows: ; ; in, For dynamic thresholds, To provide a comprehensive indicator of ecological niche differences. Indicates the benchmark scaling factor. Indicates the steepness coefficient of the response. This represents the competition intensity correction factor. This indicates the critical competition intensity. The weighting coefficients represent the spatial overlap index. Niche overlap index representing spatial dimensions. The weighting coefficients representing trophic level similarity indicators. This indicates the difference in trophic levels between two populations. Indicates the scale parameter. The weighting coefficients representing the population density balance index Indicates the relative abundance of invasive fish populations. This indicates the relative abundance of native fish populations.

[0012] Based on the above technical solutions, preferably, the step of combining coexistence stability scores and dynamic thresholds to determine the coexistence stability level of two populations specifically includes: Set a preset ecological threshold to determine whether the comprehensive ecological niche difference index exceeds the preset ecological niche threshold: If so, determine the relationship between the fitness difference index and the dynamic threshold. If the fitness difference index is less than the dynamic threshold, determine that the alien fish population and the native fish population in the reservoir are in stable coexistence. If the fitness difference index is greater than the dynamic threshold, then the classification is based on the numerical range of the coexistence stability score: When the coexistence stability score is greater than the first preset score threshold, the invasive fish population and the native fish population in the reservoir are determined to be in conditional coexistence. When the coexistence stability score is between the first preset score threshold and the second preset score threshold, the coexistence between the alien fish population and the native fish population in the reservoir is determined to be unstable. When the comprehensive niche difference index is less than the preset niche threshold, it is determined that there is competition and exclusion between the alien fish population and the native fish population in the reservoir.

[0013] Based on the above technical solutions, preferably, the step of obtaining time series data of niche overlap indicators and extracting fluctuation characteristic parameters from the time series data specifically includes: Spatial niche overlap index, trophic niche overlap index, and comprehensive niche overlap index were obtained at different time points to form three sets of time series data. Detrending processing was performed on each set of time series data to obtain residual series. The autocorrelation characteristic parameters of the residual series are calculated, and the fluctuation amplitude characteristic parameters of the residual series, including variance and kurtosis coefficient, are calculated using the moving window method. The early warning index is obtained by standardizing the autocorrelation characteristic parameter and the volatility characteristic parameter and then weighting them together.

[0014] More preferably, the process of generating a fish resource assessment report based on fluctuation characteristic parameters and coexistence stability levels specifically includes: Two warning thresholds are set based on the warning index, where the first warning threshold is less than the second warning threshold. The setting of the two warning thresholds is based on the numerical distribution range of the warning index. When the warning index is below the first warning threshold, it is judged as low risk, indicating that the coexistence relationship is stable and the autocorrelation and volatility of each time series data are at a normal level, and the future trend is flat. When the warning index is between the first and second warning thresholds, it is judged as medium risk, indicating that the coexistence relationship shows signs of fluctuation, the autocorrelation of each time series data is enhanced or the fluctuation amplitude is increased, and the coexistence relationship is close to an unstable state and needs to be continuously monitored. When the warning index is higher than the second warning threshold, it is judged as high risk, indicating that the coexistence relationship is close to the critical state, the autocorrelation and volatility of each time series data group are significantly increased, and the coexistence relationship may suddenly change. By combining the coexistence stability level with the risk level, a fish resource assessment report is generated, which includes the current coexistence status, future trends, and risk level.

[0015] On the other hand, the present invention provides a data analysis-based reservoir fish resource assessment system, employing the data analysis-based reservoir fish resource assessment method as described above, including: The data acquisition module is used to collect ecological characteristic data and population characteristic parameters of alien fish populations and native fish populations in the reservoir. The ecological characteristic data includes spatial distribution data and trophic structure data. The niche difference calculation module is used to calculate the niche overlap index between introduced fish populations and native fish populations in the spatial and nutritional dimensions based on ecological characteristic data. A nonlinear integration strategy is used to integrate the niche overlap indexes of multiple dimensions to obtain a comprehensive niche difference index. The fitness difference calculation module is used to calculate the population growth potential of two populations based on population characteristic parameters, and obtain the fitness difference index based on the difference between the population growth potential of the two populations. The coexistence relationship determination module is used to process the comprehensive niche difference index and fitness difference index using a weighted combination strategy to obtain a coexistence stability score. The weight of the comprehensive niche difference index is greater than that of the fitness difference index. A dynamic threshold is set based on the comprehensive niche difference index. The coexistence stability score and the dynamic threshold are combined to determine the coexistence stability level of the two populations. The assessment module is used to acquire time series data of niche overlap indicators, extract fluctuation characteristic parameters from the time series data, and generate a fish resource assessment report based on the fluctuation characteristic parameters and coexistence stability level.

[0016] The data analysis-based method and system for assessing fish resources in reservoirs of the present invention have the following advantages over existing technologies: (1) By integrating the niche overlap indicators of spatial and nutritional dimensions, a comprehensive niche difference indicator is obtained through multiplication operation, which can accurately reflect the degree of differentiation between two populations in multidimensional resource space and more comprehensively assess the niche differences between populations. (2) By acquiring time series data of niche overlap index, extracting time series fluctuation characteristic parameters, and generating a fish resource assessment report based on fluctuation characteristic parameters and coexistence stability level, the dynamic change trend of coexistence relationship can be captured. By analyzing the changes in autocorrelation characteristic parameters and fluctuation amplitude characteristic parameters, early warning signals of the system approaching the critical transition point can be identified, which helps to take intervention measures before ecological disasters occur, and protect the biodiversity of reservoir ecosystem and the sustainable use of fishery resources. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the data analysis-based reservoir fish resource assessment method of the present invention; Figure 2 This is a flowchart illustrating the calculation of the comprehensive niche difference index in the data analysis-based reservoir fish resource assessment method of the present invention. Figure 3 This is a flowchart illustrating the calculation of the fitness difference index in the data analysis-based reservoir fish resource assessment method of the present invention. Figure 4 This is a schematic diagram illustrating the coexistence stability assessment method for reservoir fish resources based on data analysis according to the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, this invention provides a data analysis-based method for assessing fish resources in reservoirs, comprising the following steps: S1. Collect ecological characteristic data and population characteristic parameters of alien fish populations and native fish populations in the reservoir. The ecological characteristic data includes spatial distribution data and trophic structure data. The trophic structure data is stable isotope data, which includes carbon stable isotope data and nitrogen stable isotope data.

[0021] Understandably, sampling sites should cover the main habitat types of the reservoir, including different areas such as reservoir bays, open water areas, river inlets, and dam fronts. Sampling sites should be evenly distributed across different water depth gradients, including shallow, mid-water, and deep water areas, to fully reflect the vertical distribution differences of fish. Specifically, the collection of spatial distribution data should record the precise coordinates of the body length, weight, and capture location of each individual from both introduced and native fish populations, obtaining the population distribution data of both populations at each sampling point, as well as the spatial location information of each individual. For trophic structure data, samples of potential food sources such as phytoplankton, zooplankton, and benthic animals should be collected simultaneously in the reservoir, and stable isotope analysis should be performed to obtain isotope datasets for both populations and isotope data for each food source.

[0022] like Figure 2 As shown in Figure S2, based on ecological characteristic data, the niche overlap index between the introduced fish population and the native fish population in the spatial and nutritional dimensions is calculated respectively. The niche overlap indexes in multiple dimensions are integrated to obtain the comprehensive niche difference index. Specifically, the calculation of niche overlap indicators between introduced fish populations and native fish populations in both spatial and trophic dimensions based on ecological characteristic data includes: The reservoir was divided into multiple spatial units, and the resource utilization ratios of introduced and native fish populations in each spatial unit were statistically analyzed to obtain the resource utilization ratios of the two populations in each spatial unit; the calculation formula is as follows: in, This indicates the presence of invasive fish populations in each spatial unit. The proportion of resource utilization Indicates the native fish population in each spatial unit The proportion of resource utilization This indicates the presence of invasive fish populations in each spatial unit. The number of captures in Indicates the native fish population in each spatial unit The number of captures in This represents the total number of spatial units. Indicates the summation index of the spatial cell; Similarity was used to calculate the resource utilization ratio of two populations in each spatial unit, resulting in a spatial niche overlap index. The similarity calculation formula is as follows: ; in, This is a spatial dimension indicator of niche overlap.

[0023] Understandable, when When the value is close to 0, it indicates that the two populations are almost completely separated spatially, each occupying different habitats; when... When the value is close to 1, it indicates that the two populations have a high degree of overlap in spatial distribution and utilize similar habitat resources.

[0024] This invention integrates niche overlap indicators of spatial and nutritional dimensions, and obtains a comprehensive niche difference index through multiplication. This index can accurately reflect the degree of differentiation between two populations in a multidimensional resource space, and more comprehensively assess the niche differences between populations. It overcomes the assessment bias that may be caused by insufficient information in a single dimension in traditional methods.

[0025] Furthermore, the integration of multiple dimensions of niche overlap indicators to obtain a comprehensive niche difference index specifically includes: Trophic level correction was applied to the stable isotope data. Trophic niche boundaries for invasive and native fish populations were constructed in a two-dimensional coordinate space composed of carbon and nitrogen stable isotopes, respectively. The areas of the two trophic niche boundaries and their overlapping areas were calculated. The niche overlap index for the trophic dimension was obtained based on the ratio of the overlapping area to the area of ​​the trophic niche boundary. The calculation formula is as follows: in, This represents the area of ​​the union of the two convex hulls. This represents the trophic niche boundary area of ​​an invasive fish population. This represents the area of ​​the trophic niche boundary constructed by native fish populations. The area of ​​the overlapping region of two convex hulls is denoted as . This serves as an indicator of niche overlap from a nutritional perspective. A heterogeneity integration strategy based on information entropy was used to integrate the ecological niche overlap indicators in the spatial dimension and the ecological niche overlap indicators in the trophic dimension to obtain a comprehensive ecological niche difference index.

[0026] Furthermore, the heterogeneity integration strategy based on information entropy specifically includes: Preliminary values ​​for niche differences in both the spatial and trophic dimensions were calculated separately: in, This refers to niche differences in spatial dimensions. Niche differences in nutritional dimensions.

[0027] The equilibrium index of multidimensional differentiation is calculated using the following formula: in, Normalized Shannon entropy is used to quantify the degree of equilibrium in multidimensional differentiation. The number of niche dimensions in this invention (Spatial dimension and nutritional dimension) For the first Niche differences in each dimension This represents the niche difference value in the k-th dimension. It is the sum of the differences across all dimensions. Indicates the first The percentage of differences in each dimension within the total differences. As a normalization factor, make The range of values ​​is .when When the value is close to 1, it indicates that the degree of differentiation among the dimensions is balanced; when... When the value is close to 0, it indicates that the differentiation is concentrated in a certain dimension.

[0028] The dynamic weights of each dimension are calculated based on the balance index. The calculation formula is as follows: in, For the first Dynamic weights for each dimension This is the balance sensitivity coefficient, reflecting the strength of the impact of balance on weight allocation, with a value range of [value range missing]. The exponent term in With equilibrium The decrease in the weighting of the dimension increases the weighting of the dimension with a high degree of differentiation.

[0029] The comprehensive niche difference index was calculated using a weighted geometric mean. in, To provide a comprehensive indicator of ecological niche differences. This represents a series of multiplication operations. Indicates the first Niche differences across all dimensions are calculated using a power-law operation with their dynamic weights. This formula preserves the geometric mean's sensitivity to extreme values ​​(the overall difference approaches zero when any dimension is completely undifferentiated), while simultaneously using dynamic weights... It compensates for uneven differentiation: when one dimension is significantly differentiated while other dimensions are not sufficiently differentiated, the high weight of that dimension moderately improves the comprehensive difference index, and more accurately reflects the actual coexistence potential.

[0030] Understandably, in the two-dimensional niche analysis framework of this invention, when j=1, That is (Spatial niche differences), when j=2, That is (Necological niche differences in nutritional dimension).

[0031] When the degree of differentiation between two dimensions differs significantly (e.g., one dimension is completely separate while the other highly overlaps), the balance index is low; when the degree of differentiation between two dimensions is similar, the balance index is high. The higher the balance, the more equal the weights of each dimension tend to be; the lower the balance, the higher the weight of the dimension with a greater degree of differentiation, and the lower the weight of the dimension with a lesser degree of differentiation.

[0032] By introducing normalized Shannon entropy H to quantify the degree of equilibrium of multidimensional differentiation, when In (perfect equilibrium), the exponent term degenerates to 1. Simplified to That is, weights are allocated according to the proportion of the difference values; when When the exponent term decreases, the exponent term increases, making The weighting of larger dimensions is further enhanced. This is achieved through the balance sensitivity coefficient. The controllability of the weight adjustment intensity has been achieved: The larger the value, the stronger the amplification effect of weight differences during uneven differentiation; The smaller the value, the more conservative the weight distribution tends to be.

[0033] The heterogeneous integration strategy of information entropy in this invention solves the inherent defects of the simple product method, and achieves this through information entropy. Identify the imbalance between spatial and nutritional dimensions and increase the weight of the spatial dimension. This makes the comprehensive difference index This more accurately reflects the actual potential for coexistence. Conversely, if the two dimensions are similarly differentiated ( If the weighting is relatively high, the weighting tends to be equal, and the comprehensive difference index maintains the robustness of the assessment.

[0034] In one embodiment of the present invention, taking .when At that time, the calculation yielded (This indicates that the differentiation is not balanced enough). ,but This method achieves quantitative correction of the multidimensional differentiation imbalance through dynamic weights.

[0035] Understandably, for invasive fish populations, based on the corrected isotopic values ​​of all samples, a minimal convex polygon containing all points is constructed in a two-dimensional isotopic space. This convex polygon is defined as the trophic niche space of the invasive fish population, and its area is denoted as... Similarly, the convex hull of the nutritional niche space of native fish populations is constructed, and its area is denoted as... .when When the value is close to 0, it indicates that the two populations are almost completely separated in their use of food resources; when... A value close to 1 indicates that two populations utilize highly similar food resources. When niches in any dimension are completely separated, the overall niche overlap also equals 0, indicating that the populations have achieved complete differentiation in that resource dimension. This complete differentiation in a single dimension is sufficient to eliminate competition and support population coexistence, even if overlap exists in other dimensions. When the value is close to 0, it indicates that the two populations highly overlap in the multidimensional niche space, and the degree of niche differentiation is very low; when... When the value is close to 1, it indicates that the two populations are almost completely separated in the multidimensional niche space, and the degree of niche differentiation is very high.

[0036] S3. Calculate the population growth potential of the two populations based on the population characteristic parameters, and obtain the fitness difference index based on the difference in the population growth potential of the two populations; wherein, the population characteristic parameters include sexual maturity age, maximum age, survival rate of each age group and reproductive capacity of each age group.

[0037] Understandably, population characteristic parameters of introduced and native fish populations are collected by reviewing scientific literature, fisheries resource survey reports, and conducting on-site measurements. For life history parameters lacking direct observational data, empirical formulas based on life history theory can be used for estimation. The estimation of sexual maturity age is based on body length data. According to fish life history theory, sexual maturity typically occurs when an individual reaches a specific proportion of its asymptotic body length. For most bony fish, the body length at sexual maturity is approximately 60% to 70% of the asymptotic body length. The age required to reach this body length is calculated by working backwards from the von Bertalanffy growth equation, using the following formula: in, Indicates the age of sexual maturity. The body length at sexual maturity can be set to... , To approximate body length, This represents the growth coefficient.

[0038] Estimating natural mortality requires selecting an appropriate empirical formula based on the fish's life form and environmental conditions.

[0039] like Figure 3 As shown, specifically, step S3 includes: Net reproductive rate is calculated based on the survival rate and fertility of each age group. Net reproductive rate represents the number of offspring produced by a single individual in the lifetime of a population under ideal conditions. Generation time is calculated based on survival rate, age of sexual maturity, and fertility of each age group. Generation time represents the average time required for a population to regenerate. The calculation formula is: in, For the generational time of invasive fish populations, The age of sexual maturity for introduced fish populations. This indicates the average contribution time during the breeding season of invasive fish populations. Indicates the generational time of native fish populations. Indicates the age of sexual maturity in native fish populations. This indicates the average contribution time during the breeding season of native fish populations; The intrinsic growth rate of a population is calculated based on net reproductive rate and generation time, using the following formula: in, The intrinsic growth rate of the population. Net reproductive rate, For generations; Using the intrinsic growth rate of a population as its population growth potential, the population growth potential of both introduced and native fish populations was calculated. The difference between the two population growth potentials was then used to obtain the fitness difference index, calculated using the following formula: in, As a fitness difference index, and The population growth potential of introduced fish populations and native fish populations are respectively considered. The sign of the numerator indicates the larger of the two values, and the absolute value sign of the numerator ensures that the fitness difference is always non-negative.

[0040] Understandably, fitness difference indices are used to characterize the competitive advantage of invasive fish species relative to native fish species. The absolute difference in population growth potential is transformed into a relative difference; when the population growth potential of two populations is equal, A value of 0 indicates no difference in fitness; when the growth potential of one population is much greater than that of another population, A value close to 1 indicates a significant difference in fitness.

[0041] This invention uses the difference in population growth potential between introduced and native fish populations as a fitness difference indicator to accurately reflect the competitive advantage of introduced fish over native fish.

[0042] like Figure 4As shown in S4, the coexistence stability score is obtained by processing the comprehensive niche difference index and fitness difference index. The weight of the comprehensive niche difference index is greater than that of the fitness difference index. A dynamic threshold is set based on the comprehensive niche difference index. The coexistence stability level of the two populations is determined by combining the coexistence stability score and the dynamic threshold. Specifically, the process of processing the comprehensive niche difference index and fitness difference index to obtain the coexistence stability score includes: The coexistence stability score is obtained by linearly combining the comprehensive niche difference index and the fitness difference index. The calculation formula is as follows: ; in, To score the coexistence stability, The weights of the comprehensive niche difference indicators. As the weight of the fitness difference index, To provide a comprehensive indicator of ecological niche differences. For fitness difference index; Using the comprehensive niche difference index as a baseline value, combined with a competition intensity correction factor, a dynamic threshold is calculated using the Logistic response function. The calculation formula is as follows: ; ; in, This is a dynamic threshold, representing the maximum fitness difference the system can tolerate under the current niche differences and competition intensity. To provide a comprehensive indicator of ecological niche differences. The benchmark scaling factor has a range of values. , To respond to the steepness coefficient, the sensitivity of the control threshold to changes in competition intensity is determined, and its value range is specified. , The critical competition intensity, with a value range of... It is a natural exponential function. This is a competition intensity correction factor, with a value range of... , , These are the weighting coefficients for the three indicators. This is an indicator of ecological niche overlap in the spatial dimension. The difference in trophic levels between the two populations, For scale parameters, and The relative abundances are those of introduced fish populations and native fish populations, respectively.

[0043] Understandable. This is the Logistic suppression function, used to achieve non-linear adjustment of the threshold by the competition intensity; the competition intensity correction factor is also included. Reflecting the actual competitive pressure between two populations in resource utilization, this is quantified by integrating spatial overlap, trophic level similarity, and population density balance. When resources are abundant, space is isolated, trophic levels are separated, or population densities are disparate, the Logistic function value is close to 1 (low competitive pressure, a high threshold, indicating a high tolerance for fitness differences). When there is high spatial overlap, similar trophic levels, and balanced population density, the Logistic function value decreases rapidly (high competitive pressure, threshold significantly lowered), indicating that even if there are certain differences in ecological niches, intense actual competition will still reduce the system's tolerance for fitness differences.

[0044] is a Gaussian kernel function used to transform trophic level differences into a trophic level similarity measure to quantify their contribution to competition intensity. When the value is close to 0 (highly similar trophic levels), a value close to 1 indicates high competition intensity. When the value of this function increases (trophic level separation), it rapidly decays to 0, indicating a decrease in competition intensity. As an indicator of population density balance, when the densities of two populations are close, this item is close to 1, indicating that the populations are evenly matched and the outcome of the competition is uncertain, requiring a more rigorous assessment of coexistence stability; when the density of one population is much greater than that of the other, this item is close to 0, indicating that the population density has been significantly unbalanced, there may be a competitive advantage or the competition is nearing its end, at which point the contribution of this item to the competition intensity factor decreases.

[0045] In one embodiment of the present invention, Calculated using nitrogen stable isotope data, the calculation formula is as follows: in, and The values ​​are the average nitrogen isotope values ​​for introduced and native fish populations, respectively, and 3.4‰ is the standard enrichment factor for nitrogen isotopes between trophic levels.

[0046] This dual-level regulation mechanism is more in line with the actual situation of the coexistence of alien and native species in reservoir ecosystems: under conditions of abundant resources or spatial isolation, stable coexistence can be maintained even if there is partial overlap in ecological niches; while under conditions of scarce resources or high contact, even if there is ecological niche differentiation, intense competition may lead to rejection.

[0047] In one embodiment of the present invention, taking .when At that time, the calculation yielded (If the competitive pressure is low) ;when At that time, the calculation yielded (High competitive pressure) .

[0048] It is evident that, under the same niche differences, high competition intensity lowers the dynamic threshold, significantly increases the rigor of coexistence stability assessment, and makes the assessment results more consistent with the dynamic competition process of the reservoir ecosystem.

[0049] Furthermore, the method of determining the coexistence stability level of two populations by combining coexistence stability scores and dynamic thresholds specifically includes: Set a preset ecological threshold to determine whether the comprehensive ecological niche difference index exceeds the preset ecological niche threshold: If so, determine the relationship between the fitness difference index and the dynamic threshold. If the fitness difference index is less than the dynamic threshold, determine that the alien fish population and the native fish population in the reservoir are in stable coexistence. If the fitness difference index is greater than the dynamic threshold, then the classification is based on the numerical range of the coexistence stability score: When the coexistence stability score is greater than the first preset score threshold, the invasive fish population and the native fish population in the reservoir are determined to be in conditional coexistence. When the coexistence stability score is between the first preset score threshold and the second preset score threshold, the coexistence between the alien fish population and the native fish population in the reservoir is determined to be unstable. When the comprehensive niche difference index is less than the preset niche threshold, it is determined that there is competition and exclusion between the alien fish population and the native fish population in the reservoir.

[0050] Understandably, the preset threshold for niche difference is based on empirical data from ecological studies regarding the critical value of niche overlap. In practical applications, it should be adjusted according to the specific ecological characteristics of the reservoir, the biological characteristics of the target species, and management objectives. When the comprehensive niche difference index exceeds this preset threshold, it indicates a high degree of niche differentiation and the presence of a niche basis to support coexistence.

[0051] S5. Obtain time series data of niche overlap indicators, extract fluctuation characteristic parameters from the time series data, and generate a fish resource assessment report based on the fluctuation characteristic parameters and coexistence stability level.

[0052] Specifically, the acquisition of time-series data of niche overlap indicators and the extraction of fluctuation characteristic parameters from the time-series data include: Spatial niche overlap index, trophic niche overlap index, and comprehensive niche overlap index were obtained at different time points to form three sets of time series data. Detrending processing was performed on each set of time series data to obtain residual series. The autocorrelation characteristic parameters of the residual series are calculated, and the volatility characteristic parameters of the residual series, including variance and kurtosis coefficient, are calculated using the moving window method; the calculation formula is as follows: in, This represents the autocorrelation coefficient with a lag of one period. The average value of the residual sequence. Indicates year index, Indicates the number of years observed. Indicates the first Residual after annual trend Indicates the first Residual after annual trend; The early warning index is obtained by standardizing the autocorrelation characteristic parameter and the volatility characteristic parameter and then weighting them together.

[0053] Understandably, the purpose of detrending is to separate long-term trends from short-term fluctuations. This is achieved by using locally weighted regression or sliding window smoothing to fit the long-term trend of the niche overlap index, and then calculating the residual sequence. When A value close to 0 indicates that fluctuations in adjacent years are independent of each other. A value significantly greater than 0 indicates positive autocorrelation, meaning that the system recovers slowly after being disturbed and remains in a state deviating from equilibrium for many consecutive years.

[0054] In one embodiment of the present invention, the standardization method is as follows: the parameter value of the current period is compared with the baseline period, and the first 3 to 5 years of the initial monitoring period are selected as the baseline period. The average autocorrelation coefficient and average variance of the baseline period are calculated as reference values. For the autocorrelation coefficient, the normalized increment is calculated by subtracting the baseline value from the current value and then dividing by the normalization factor; for the variance, normalization is performed using a logarithmic ratio.

[0055] Furthermore, the process of generating a fish resource assessment report based on fluctuation characteristic parameters and coexistence stability levels specifically includes: Two warning thresholds are set based on the warning index, where the first warning threshold is less than the second warning threshold. The setting of the two warning thresholds is based on the numerical distribution range of the warning index. When the warning index is below the first warning threshold, it is judged as low risk, indicating that the coexistence relationship is stable and the autocorrelation and volatility of each time series data are at a normal level, and the future trend is flat. When the warning index is between the first and second warning thresholds, it is judged as medium risk, indicating that the coexistence relationship shows signs of fluctuation, the autocorrelation of each time series data is enhanced or the fluctuation amplitude is increased, and the coexistence relationship is close to an unstable state and needs to be continuously monitored. When the warning index is higher than the second warning threshold, it is judged as high risk, indicating that the coexistence relationship is close to the critical state, the autocorrelation and volatility of each time series data group are significantly increased, and the coexistence relationship may suddenly change. By combining the coexistence stability level with the risk level, a fish resource assessment report is generated, which includes the current coexistence status, future trends, and risk level.

[0056] This invention acquires time-series data of niche overlap indicators, extracts fluctuation characteristic parameters of the time series, and generates a fish resource assessment report based on the fluctuation characteristic parameters and coexistence stability level. It captures the dynamic changing trend of coexistence relationships and identifies early warning signals of the system approaching the critical transition point by analyzing changes in autocorrelation characteristic parameters and fluctuation amplitude characteristic parameters. This helps to take intervention measures before ecological disasters occur, protecting the biodiversity of reservoir ecosystems and the sustainable use of fishery resources.

[0057] In one embodiment of the present invention, when the coexistence relationship is stable, it is recommended to maintain routine monitoring and conduct a comprehensive assessment once a year; when there are signs of fluctuation in the coexistence relationship, it is recommended to increase the monitoring frequency to once every six months, closely monitor population dynamics and niche changes, identify potential threats in a timely manner, and prepare emergency plans; when the coexistence relationship is close to a critical state, it is recommended to activate the emergency response mechanism, immediately conduct in-depth investigation and analysis of the sources of risk, take active intervention measures such as habitat improvement, control of alien species, and protection of native species, and increase the monitoring frequency to once a quarter or more frequently.

[0058] This invention also provides a data analysis-based reservoir fish resource assessment system, employing the data analysis-based reservoir fish resource assessment method described above, including: The data acquisition module is used to collect ecological characteristic data and population characteristic parameters of alien fish populations and native fish populations in the reservoir. The ecological characteristic data includes spatial distribution data and trophic structure data. The niche difference calculation module is used to calculate the niche overlap index between introduced fish populations and native fish populations in the spatial and nutritional dimensions based on ecological characteristic data. A nonlinear integration strategy is used to integrate the niche overlap indexes of multiple dimensions to obtain a comprehensive niche difference index. The fitness difference calculation module is used to calculate the population growth potential of two populations based on population characteristic parameters, and obtain the fitness difference index based on the difference between the population growth potential of the two populations. The coexistence relationship determination module is used to process the comprehensive niche difference index and fitness difference index using a weighted combination strategy to obtain a coexistence stability score. The weight of the comprehensive niche difference index is greater than that of the fitness difference index. A dynamic threshold is set based on the comprehensive niche difference index. The coexistence stability score and the dynamic threshold are combined to determine the coexistence stability level of the two populations. The assessment module is used to acquire time series data of niche overlap indicators, extract fluctuation characteristic parameters from the time series data, and generate a fish resource assessment report based on the fluctuation characteristic parameters and coexistence stability level.

[0059] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data analysis-based method for assessing fish resources in reservoirs, characterized in that: Includes the following steps: S1. Collect ecological characteristic data and population characteristic parameters of alien fish populations and native fish populations in the reservoir. The ecological characteristic data includes spatial distribution data and trophic structure data. S2. Based on ecological characteristic data, calculate the niche overlap index between introduced fish populations and native fish populations in spatial and nutritional dimensions respectively, and integrate the niche overlap indexes in multiple dimensions to obtain a comprehensive niche difference index. S3. Calculate the population growth potential of the two populations based on the population characteristic parameters, and obtain the fitness difference index based on the difference in the population growth potential of the two populations. S4. The coexistence stability score is obtained by processing the comprehensive niche difference index and fitness difference index. The weight of the comprehensive niche difference index is greater than that of the fitness difference index. A dynamic threshold is set based on the comprehensive niche difference index. The coexistence stability level of the two populations is determined by combining the coexistence stability score and the dynamic threshold. S5. Obtain time series data of niche overlap indicators, extract fluctuation characteristic parameters from the time series data, and generate a fish resource assessment report based on the fluctuation characteristic parameters and coexistence stability level.

2. The method for assessing fish resources in reservoirs based on data analysis as described in claim 1, characterized in that: The trophic structure data is stable isotope data, which includes carbon stable isotope data and nitrogen stable isotope data.

3. The method for assessing fish resources in reservoirs based on data analysis as described in claim 2, characterized in that: The calculation of niche overlap indices between introduced and native fish populations in both spatial and trophic dimensions based on ecological characteristic data specifically includes: The reservoir was divided into multiple spatial units, and the resource utilization ratios of introduced fish populations and native fish populations in each spatial unit were statistically analyzed to obtain the resource utilization ratios of the two populations in each spatial unit. Similarity was used to calculate the resource utilization ratio of two populations in each spatial unit, resulting in a spatial niche overlap index. The similarity calculation formula is as follows: ； in, This is a spatial indicator of niche overlap. The total number of spatial units. For the introduction of fish populations in the first Resource utilization ratio of each spatial unit For native fish populations in the first The resource utilization ratio of each spatial unit.

4. The method for assessing fish resources in reservoirs based on data analysis as described in claim 3, characterized in that: The integrated ecological niche difference index, obtained by integrating multiple dimensions of ecological niche overlap indicators, specifically includes: Trophic level correction was performed on the stable isotope data. Trophic niche boundaries of invasive fish populations and native fish populations were constructed in a two-dimensional coordinate space composed of carbon stable isotopes and nitrogen stable isotopes, respectively. The areas of the two trophic niche boundaries and their overlapping areas were calculated. The niche overlap index of the trophic dimension was obtained based on the ratio of the overlapping area to the area of ​​the trophic niche boundary. Spatial and trophic niche overlap indices are integrated using an information entropy-based heterogeneity integration strategy to obtain a comprehensive niche difference index, calculated as follows: ； ； ； in, This represents a comprehensive indicator of ecological niche differences. Indicates a dimension index. Indicates the number of niche dimensions. Indicates the first Niche differences in each dimension Indicates the first Dynamic weights for each dimension Represents the equilibrium sensitivity coefficient. This represents the normalized Shannon entropy. This represents the dimension index other than j. This represents the niche difference value in the k-th dimension.

5. The method for assessing fish resources in reservoirs based on data analysis as described in claim 4, characterized in that: The population characteristic parameters include sexual maturity age, maximum age, survival rate of each age group, and reproductive capacity of each age group. Step S3 specifically includes: Net reproductive rate is calculated based on the survival rate and fertility of each age group. Net reproductive rate represents the number of offspring produced by a single individual in the lifetime of a population under ideal conditions. Generation time is calculated based on the survival rate, age of sexual maturity, and fertility of each age group. Generation time represents the average time required for a population to regenerate a new generation. Calculate the intrinsic growth rate of the population based on net reproductive rate and generation time; Using the intrinsic growth rate of a population as the population growth potential, the population growth potential of introduced fish populations and native fish populations were calculated separately, and the difference in fitness difference was obtained by calculating the difference in growth potential between the two populations.

6. The method for assessing fish resources in reservoirs based on data analysis as described in claim 5, characterized in that: The process of processing the comprehensive niche difference index and fitness difference index to obtain the coexistence stability score specifically includes: The coexistence stability score is obtained by linearly combining the comprehensive niche difference index and the fitness difference index. The calculation formula is as follows: ； in, To score the coexistence stability, The weights of the comprehensive niche difference indicators. As the weight of the fitness difference index, To provide a comprehensive indicator of ecological niche differences. For fitness difference index; Using the comprehensive niche difference index as a baseline value, combined with a competition intensity correction factor, a dynamic threshold is calculated using the Logistic response function. The calculation formula is as follows: ； ； in, For dynamic thresholds, To provide a comprehensive indicator of ecological niche differences. Indicates the benchmark scaling factor. Indicates the steepness coefficient of the response. This represents the competition intensity correction factor. This indicates the critical competition intensity. The weighting coefficients represent the spatial overlap index. Niche overlap index representing spatial dimensions. The weighting coefficients representing trophic level similarity indicators. This indicates the difference in trophic levels between two populations. Indicates the scale parameter. The weighting coefficients representing the population density balance index Indicates the relative abundance of invasive fish populations. This indicates the relative abundance of native fish populations.

7. The method for assessing fish resources in reservoirs based on data analysis as described in claim 6, characterized in that: The method of determining the coexistence stability level of two populations by combining coexistence stability scores and dynamic thresholds specifically includes: Set a preset ecological threshold to determine whether the comprehensive ecological niche difference index exceeds the preset ecological niche threshold: If so, determine the relationship between the fitness difference index and the dynamic threshold. If the fitness difference index is less than the dynamic threshold, determine that the alien fish population and the native fish population in the reservoir are in stable coexistence. If the fitness difference index is greater than the dynamic threshold, then the classification is based on the numerical range of the coexistence stability score: When the coexistence stability score is greater than the first preset score threshold, the invasive fish population and the native fish population in the reservoir are determined to be in conditional coexistence. When the coexistence stability score is between the first preset score threshold and the second preset score threshold, the coexistence between the alien fish population and the native fish population in the reservoir is determined to be unstable. When the comprehensive niche difference index is less than the preset niche threshold, it is determined that there is competition and exclusion between the alien fish population and the native fish population in the reservoir.

8. The method for assessing fish resources in reservoirs based on data analysis as described in claim 4, characterized in that: The acquisition of time-series data of niche overlap indicators and the extraction of fluctuation characteristic parameters from the time-series data specifically include: Spatial niche overlap index, trophic niche overlap index, and comprehensive niche overlap index were obtained at different time points to form three sets of time series data. Detrending processing was performed on each set of time series data to obtain residual series. The autocorrelation characteristic parameters of the residual series are calculated, and the fluctuation amplitude characteristic parameters of the residual series, including variance and kurtosis coefficient, are calculated using the moving window method. The early warning index is obtained by standardizing the autocorrelation characteristic parameter and the volatility characteristic parameter and then weighting them together.

9. The method for assessing fish resources in a reservoir based on data analysis as described in claim 8, characterized in that: The fish resource assessment report generated based on fluctuation characteristic parameters and coexistence stability levels specifically includes: Two warning thresholds are set based on the warning index, where the first warning threshold is less than the second warning threshold. The setting of the two warning thresholds is based on the numerical distribution range of the warning index. When the warning index is below the first warning threshold, it is judged as low risk, indicating that the coexistence relationship is stable and the autocorrelation and volatility of each time series data are at a normal level, and the future trend is flat. When the warning index is between the first and second warning thresholds, it is judged as medium risk, indicating that the coexistence relationship shows signs of fluctuation, the autocorrelation of each time series data is enhanced or the fluctuation amplitude is increased, and the coexistence relationship is close to an unstable state and needs to be continuously monitored. When the warning index is higher than the second warning threshold, it is judged as high risk, indicating that the coexistence relationship is close to the critical state, the autocorrelation and volatility of each time series data group are significantly increased, and the coexistence relationship may suddenly change. By combining the coexistence stability level with the risk level, a fish resource assessment report is generated, which includes the current coexistence status, future trends, and risk level.

10. A data analysis-based reservoir fish resource assessment system, characterized in that: The method for assessing fish resources in reservoirs based on data analysis, as described in any one of claims 1-9, includes: The data acquisition module is used to collect ecological characteristic data and population characteristic parameters of alien fish populations and native fish populations in the reservoir. The ecological characteristic data includes spatial distribution data and trophic structure data. The niche difference calculation module is used to calculate the niche overlap index between introduced fish populations and native fish populations in the spatial and nutritional dimensions based on ecological characteristic data. A nonlinear integration strategy is used to integrate the niche overlap indexes of multiple dimensions to obtain a comprehensive niche difference index. The fitness difference calculation module is used to calculate the population growth potential of two populations based on population characteristic parameters, and obtain the fitness difference index based on the difference between the population growth potential of the two populations. The coexistence relationship determination module is used to process the comprehensive niche difference index and fitness difference index using a weighted combination strategy to obtain a coexistence stability score. The weight of the comprehensive niche difference index is greater than that of the fitness difference index. A dynamic threshold is set based on the comprehensive niche difference index. The coexistence stability score and the dynamic threshold are combined to determine the coexistence stability level of the two populations. The assessment module is used to acquire time series data of niche overlap indicators, extract fluctuation characteristic parameters from the time series data, and generate a fish resource assessment report based on the fluctuation characteristic parameters and coexistence stability level.

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