River connectivity-based aquatic food web stability analysis method

Abstract

The invention discloses a stability analysis method of an aquatic food network based on river connectivity. The method comprises the following steps: calculating a river reach obstacle coefficient, a longest continuous river reach ratio, a construction land coefficient, a constant water surface area coefficient, an annual runoff change coefficient and a monthly average runoff change coefficient to obtain comprehensive connectivity; based on the connectivity total score, a connectivity-aquatic food web stability model is adopted to evaluate the aquatic food web stability of the river reach; obtaining a fatty acid map through the key fatty acid functional group; based on the fatty acid spectrogram, food web stability is analyzed and evaluated through an ecological network; and in combination with the total connectivity score of the river reach and aquatic food web stability data, obtaining a comprehensive stability evaluation model. The method provided by the invention is an important tool for knowing the change of the stability of the aquatic food web due to the change of the river connectivity, and meanwhile, the change trend of the stability of the aquatic food web caused by land utilization is also predicted.

Description

Technical Field

[0001] The invention relates to the technical field of ecology and environmental science, and in particular to a stability analysis method of an aquatic food web based on river connectivity. Background Art

[0002] Existing studies on aquatic ecosystems usually focus on how individual communities (such as fish, aquatic plants, etc.) are affected by environmental changes. However, there are relatively few studies on the impact of environmental changes on the structure of the entire food web, and there is a lack of methods that comprehensively consider the relationships between organisms at different trophic levels. This has led to a lack of understanding of complex interactions and overall stability in ecosystem assessments. Therefore, there is an urgent need for a new method that combines food web structure with community changes to more scientifically and comprehensively assess the response of aquatic ecosystems to environmental pressures. Summary of the invention

[0003] The purpose of the present invention is to provide a more scientific and comprehensive method for evaluating the stability of aquatic food webs in view of the deficiencies of the prior art.

[0004] The present invention achieves a scientific assessment of the stability of an aquatic food web by establishing a river connectivity-aquatic food web stability model. Specifically, the present invention comprises the following steps:

[0005] (1) Calculate the total connectivity score of the target river section ; The total connectivity score Based on the river section obstacle coefficient , the ratio of the longest continuous river section , river bank construction land coefficient , constant water surface area coefficient of river section , Coefficient of variation of annual runoff of river section The coefficient of variation of the monthly average runoff of the river section Six indicators were calculated;

[0006] (2) Based on the total connectivity score, the connectivity-aquatic food web stability model was used to obtain the aquatic food web stability score S of the river section;

[0007]

[0008] in, is the proportionality constant, is the power index, is the exponential decay coefficient.

[0009] Furthermore, the six parameter indicators are expressed as , represents the i-th parameter index in the j-th river section, i=1,2…,6; the six parameter indexes in the j-th river section , ,……, The river section obstacle coefficient , the ratio of the longest continuous river section , river bank construction land coefficient , constant water surface area coefficient of river section , Coefficient of variation of annual runoff of river section The coefficient of variation of the monthly average runoff of the river section ,in,

[0010] 00%

[0011] is the length of the river section, is the number of type i hydropower stations in the river section, n is the total number of types of hydropower stations in the river section, is the obstacle coefficient of the i-th type hydropower station.

[0012]

[0013] is the longest adjacent distance between two hydropower stations in the river section;

[0014]

[0015] in, is the construction land area of ​​the river bank in the river section at period t, The total area of ​​the riparian zone of the river section at period t; the riparian zone is a strip area extending 100 meters to 500 meters on both sides of the river section, and the riparian zone construction land area is the area of ​​human construction area developed into buildings, roads, and agricultural land in this riparian zone.

[0016]

[0017] is the constant water surface area in the river section during period t; the constant water surface is the part of the water body that remains unchanged;

[0018] 00%

[0019]

[0020] in, is the average annual flow of the river section during the reference period, The annual average flow of the river section in period t; m is the month number, is the measured monthly discharge of the mth month in the evaluation period T, is the measured monthly average discharge during the reference period. The evaluation period T is the latest 12 months in period t.

[0021] Reference period: Flow data from 1961 to 1975 are used as reference.

[0022] Obtain 6 parameter indicators for each river section , represents the i-th parameter index in the j-th river section, i=1,2…,6.

[0023] Furthermore, the connectivity of the river sections is calculated based on six parameter indicators of each river section in the target area, specifically:

[0024] Calculate 6 parameters of river sections The Pearson correlation coefficient between them is used to establish a 6-order judgment matrix that meets the consistency test using Saaty's "1-9 scaling method". Each column of the matrix is ​​normalized, and the average value of each row is calculated as the weight of the parameter index corresponding to the row. .

[0025] Calculate the composite connectivity score:

[0026]

[0027] in is the total connectivity score of the j-th river section, is the i-th parameter index, is the weight of the i-th parameter indicator.

[0028] Furthermore, the connectivity-aquatic food web stability model was fitted by the following method:

[0029] (2.1) For each river section, calculate the stability of its aquatic food web based on fatty acids ;

[0030] (2.2) Total score based on the connectivity of the river segment and food web stability of river reaches , fitting a connectivity-aquatic food web stability model.

[0031] Furthermore, the stability of the aquatic food web was calculated based on fatty acids, specifically:

[0032] (2.11) Key fatty acid functional groups were selected: linoleic acid (LIN), α-linoleic acid (ALA), arachidonic acid (ARA), EPA, docosahexaenoic acid (DHA), total saturated fatty acids (SAFA), total monounsaturated fatty acids (MUFA), and total bacterial fatty acids (BAFA).

[0033] Based on key fatty acid functional groups, the plankton FA profile, macroinvertebrate FA profile, and fish FA profile of the river section were obtained;

[0034] (2.12) Assess the impact of changes in phytoplankton food quality on food web connections using ecological network analysis (ENA).

[0035] The Spearman correlation between the FA profiles of plankton, macroinvertebrates, and fish in each food quality group in the river section was calculated to generate a visual ecological network. Only data with strong correlation (|r|>0.5) and significant correlation ("two-tailed" P<0.05) were selected to form the network nodes. j The number of nodes filtered out.

[0036] The calculation method for food web stability is as follows:

[0037]

[0038] is the food web stability of the jth river segment, is the number of nodes screened in the j-th river section visualization ecological network, is the total number of nodes of the visualized ecological network of the jth river section.

[0039] Furthermore, according to the total connectivity score of the river section and food web stability of river reaches , fitting the connectivity-aquatic food web stability model, specifically: the total connectivity score based on multiple river sections , nonlinear least squares method is used to fit , , , we get the effect of connectivity on food web stability:

[0040]

[0041] in, is the proportionality constant, is the power index, is the exponential decay coefficient.

[0042] The beneficial effect of the present invention is that it combines the study of landscape connectivity with that of aquatic food webs, making up for the deficiency of previous studies that only focused on single terrestrial or aquatic ecosystems, and for the first time systematically evaluating the impact of terrestrial landscape diversity on the structure and function of aquatic food webs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a diagram for analyzing the stability of aquatic food webs in several sections of the Dongjiang River;

[0044] Figure 2 This is a comprehensive connectivity analysis diagram of several sections of the Dongjiang River;

[0045] Figure 3 Connectivity-stability fitting curves for several sections of the Dongjiang River. DETAILED DESCRIPTION

[0046] The embodiments of the present invention are further described below with reference to a plurality of embodiments.

[0047] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0048] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0049] The present invention establishes a river connectivity-aquatic food web stability model to achieve a scientific assessment of the stability of the aquatic food web. By introducing river connectivity analysis and fatty acid omics analysis, the present invention breaks through the limitations of traditional ecosystem research and innovatively integrates the research framework of terrestrial landscape and aquatic nutrient quality, providing a new way to understand and predict the response mechanism of ecosystems under environmental pressure.

[0050] Food web stability refers to the ability of a network structure in which species are interconnected through food relationships in an ecosystem to maintain its structure and function in the face of internal or external disturbances. This stability is one of the key factors for the health and sustainability of an ecosystem. Studying food web stability helps us understand how ecosystems respond to global issues such as climate change and pollution, and provides a scientific basis for protecting biodiversity and formulating effective natural resource management strategies.

[0051] The current food web stability analysis methods mainly include stable isotope analysis (SIA) and fatty acid analysis (FAA). Stable isotope analysis (SIA) uses the carbon (δ¹³C) and nitrogen (δ¹ 5Nitrogen (N) isotope ratios to track the flow of materials through the food chain. Fatty Acid Analysis (FAA) determines the source of food by analyzing the specific types of fatty acids in organisms. Different producers (such as algae or plants) produce unique fatty acid combinations, and these characteristics can be passed on to consumers through the food chain.

[0052] The present invention establishes a river connectivity-aquatic food web stability model based on connectivity analysis and fatty acid analysis, which can efficiently obtain food web stability. And its scientific rationality is verified by fatty acid analysis. The details are as follows:

[0053] (1) For the Dongjiang River, which is 560 km long in total, it is divided into 56 sections with a single section of 10 km. The stability of the aquatic food web is calculated for 37 sections where fatty acid samples are easily collected. The details are as follows:

[0054] Key fatty acid functional groups were selected: linoleic acid (LIN), α-linoleic acid (ALA), arachidonic acid (ARA), eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), sum of saturated fatty acids (SAFA), sum of monounsaturated fatty acids (MUFA), and sum of bacterial fatty acids (BAFA).

[0055] Based on key fatty acid functional groups, the plankton FA profile, macroinvertebrate FA profile, and fish FA profile of the river section were obtained;

[0056] (2) Ecological network analysis (ENA) was used to assess the impact of changes in phytoplankton food quality on food web connections.

[0057] The Spearman correlation between the FA profiles of plankton, macroinvertebrates, and fish in each food quality group in the river section was calculated to generate a visual ecological network. Only data with strong correlation (|r|>0.5) and significant correlation ("two-tailed" P<0.05) were selected to form the network nodes. j The number of nodes filtered out.

[0058] The calculation method for food web stability is as follows:

[0059]

[0060] is the food web stability of the jth river segment, is the number of nodes screened in the j-th river section visualization ecological network, is the total number of nodes of the visualized ecological network of the jth river section.

[0061] The stability scores of aquatic food webs in each river section are as follows: Figure 1 shown.

[0062] (2) Calculate the six parameter indicators for these 37 river sections Total connectivity score ; j = 1, 2, ..., 37;

[0063] represents the i-th parameter index in the j-th river section, i=1,2…,6;

[0064] Six parameter indicators in the jth river section , ,……, The river section obstacle coefficient , the ratio of the longest continuous river section , river bank construction land coefficient , constant water surface area coefficient of river section , Coefficient of variation of annual runoff of river section The coefficient of variation of the monthly average runoff of the river section ,in,

[0065] 00%

[0066] is the length of the river section, is the number of type i hydropower stations in the river section, n is the total number of types of hydropower stations in the river section, is the obstacle coefficient of the i-th type hydropower station.

[0067]

[0068] is the longest adjacent distance between two hydropower stations in the river section;

[0069]

[0070] in, is the construction land area of ​​the river bank in the river section at period t, The total area of ​​the riparian zone of the river section at period t; the riparian zone is a strip area extending 100 meters to 500 meters on both sides of the river section, and the riparian zone construction land area is the area of ​​human construction area developed into buildings, roads, and agricultural land in this riparian zone.

[0071]

[0072] is the constant water surface area in the river section during period t; the constant water surface is the part of the water body that remains unchanged;

[0073] 00%

[0074]

[0075] in, is the average annual flow of the river section during the reference period, The annual average flow of the river section in period t; m is the month number, is the measured monthly discharge of the mth month in the evaluation period T, is the measured monthly average discharge during the reference period. The evaluation period T is the latest 12 months in period t.

[0076] The reference period uses the flow data from 1961 to 1975 as a reference.

[0077] Obtain 6 parameter indicators for each river section , represents the i-th parameter index in the j-th river section, i=1,2…,6.

[0078] Calculate 6 parameters of river sections The Pearson correlation coefficient between them is used to establish a 6-order judgment matrix that meets the consistency test using Saaty's "1-9 scaling method". Each column of the matrix is ​​normalized, and the average value of each row is calculated as the weight of the parameter index corresponding to the row. .

[0079] Calculate the composite connectivity score:

[0080]

[0081] in is the total connectivity score of the j-th river section, is the i-th parameter index, is the weight of the ith parameter index. The comprehensive connectivity score of each river section is as follows: Figure 2 shown.

[0082] (3) Stability of aquatic food webs in each river section obtained from step (1) and total connectivity score , fitting correlation curve:

[0083]

[0084] The fitting results are as follows Figure 3 As shown. Among them, is the proportionality constant, is the power index, is the exponential decay coefficient.

[0085] (4) For the five river sections where fatty acid data were difficult to collect, their total connectivity scores were calculated according to step 2, and their stability scores S1 were calculated according to the model fitted in step 3.

[0086] (5) For the above five river sections, the fatty acid analysis method was used to analyze their stability S2, and the analysis results of step 4 were verified, as shown in the following table:

[0087] reach S1 S2 16 0.648 0.678 17 0.725 0.703 39 0.582 0.597 46 0.391 0.41 49 0.783 0.75

[0088] It can be seen from the above embodiments that the stability analysis method based on connectivity of the present invention is accurate with an error of about 4.64%. The method of the present application can more effectively and scientifically evaluate the impact of changes in rivers and riparian land on the stability of the food web.

[0089] Those skilled in the art will readily appreciate other embodiments of the present application after considering the description and practicing the contents disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The description and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0090] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A stability analysis method for aquatic food webs based on river connectivity, characterized in that: At least the following steps are included: (1) Calculate the total connectivity score of the target river section ; The total connectivity score Based on the river section obstacle coefficient , the ratio of the longest continuous river section , river bank construction land coefficient , constant water surface area coefficient of river section , Coefficient of variation of annual runoff of river section The coefficient of variation of the monthly average runoff of the river section Six indicators were calculated; (2) Based on the total connectivity score, the connectivity-aquatic food web stability model was used to obtain the aquatic food web stability score S of the river section; ； in, is the proportionality constant, is the power index, is the exponential decay coefficient.

2. The analysis method according to claim 1, characterized in that In step 1, six indicators: river section obstacle coefficient , the ratio of the longest continuous river section , river bank construction land coefficient , constant water surface area coefficient of river section , Coefficient of variation of annual runoff of river section The coefficient of variation of the monthly average runoff of the river section The details are as follows: 00%； is the length of the river section, is the number of type i hydropower stations in the river section, n is the total number of types of hydropower stations in the river section, is the obstacle coefficient of the i-th type hydropower station; ； is the longest adjacent distance between two hydropower stations in the river section; ； in, is the construction land area of ​​the river bank in the river section at period t, The total area of ​​the riparian zone of the river section at period t; the riparian zone is a strip extending 100 to 500 meters on both sides of the river section, and the construction land area of ​​the riparian zone is the area of ​​human construction areas developed into buildings, roads, and agricultural land in this riparian zone; ； is the constant water surface area in the river section during period t; the constant water surface is the part of the water body that remains unchanged; 00%； ； in, is the average annual flow of the river section during the reference period, The annual average flow of the river section in period t; m is the month number, is the measured monthly discharge of the mth month in the evaluation period T, is the measured monthly average discharge during the reference period; the evaluation period T is the latest 12 months in period t; Obtain 6 parameter indicators for each river section , represents the i-th parameter index in the j-th river section, i=1,2…,6.

3. The analysis method according to claim 1, characterized in that The total connectivity score of each river section in the target area is calculated based on the six parameter indicators, which are: Calculate 6 parameters of river sections The Pearson correlation coefficient between them is used, and Saaty's "1-9 scaling method" is used to establish a 6-order judgment matrix that meets the consistency test; each column of the matrix is ​​normalized, and the average value of each row is calculated as the weight of the parameter index corresponding to the row ; Calculate the composite connectivity score: ； in is the total connectivity score of the j-th river section, represents the i-th parameter index in the j-th river section, i=1,2…,6; the six parameter indexes in the j-th river section , ,……, There are six indicators: river section obstacle coefficient , the ratio of the longest continuous river section , river bank construction land coefficient , constant water surface area coefficient of river section , Coefficient of variation of annual runoff of river section The coefficient of variation of the monthly average runoff of the river section ; is the weight of the i-th parameter indicator.

4. The analysis method according to claim 1, characterized in that The connectivity-aquatic food web stability model was fitted by the following method: (2.1) For each river section, calculate the stability of its aquatic food web based on fatty acids ; (2.2) Total score based on the connectivity of the river segment and food web stability of river reaches , fitting a connectivity-aquatic food web stability model.

5. The analysis method according to claim 4, characterized in that Based on fatty acids, the stability of the aquatic food web is calculated as: (2.11) Select key fatty acid functional groups: linoleic acid (LIN), α-linoleic acid (ALA), arachidonic acid (ARA), eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), total saturated fatty acids (SAFA), total monounsaturated fatty acids (MUFA), and total bacterial fatty acids (BAFA); Based on key fatty acid functional groups, the plankton FA profile, macroinvertebrate FA profile, and fish FA profile of the river section were obtained; (2.12) Assess the impact of changes in phytoplankton food quality on food web connections using ecological network analysis (ENA); The Spearman correlation between the FA profiles of plankton, macroinvertebrates, and fish in each food quality group in the river section was calculated to generate a visual ecological network. Only data with strong correlation (|r|>0.5) and significant correlation ("two-tailed" P<0.05) were selected to form the network nodes. j is the number of nodes filtered out; The calculation method for food web stability is as follows: ； is the food web stability of the jth river segment, is the number of nodes screened in the j-th river section visualization ecological network, is the total number of nodes of the visualized ecological network of the jth river section.

6. The analysis method according to claim 4, characterized in that Total score based on river segment connectivity and food web stability of river reaches , fitting the connectivity-aquatic food web stability model, specifically: the total connectivity score based on multiple river sections , nonlinear least squares method is used to fit , , , we get the effect of connectivity on food web stability: in, is the proportionality constant, is the power index, is the exponential decay coefficient.

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