A method for analyzing the stability of aquatic food webs based on river connectivity
By establishing a river connectivity-aquatic food web stability model and combining it with fatty acid analysis and ecological network assessment, the shortcomings of food web stability assessment in aquatic ecosystems were addressed, scientific assessment and prediction of aquatic ecosystems were achieved, and an understanding of ecosystem response mechanisms and protection strategies were provided.
Patent Information
- Application Number
- CN202411911758.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-24
- Publication Date
- 2025-09-30
- Estimated Expiration
- 2044-12-24
AI Technical Summary
Existing technologies lack methods that comprehensively consider the relationships between organisms at different trophic levels, resulting in insufficient understanding of complex interactions and overall stability in ecosystem assessments, and making it impossible to scientifically and comprehensively evaluate the response of aquatic ecosystems to environmental pressures.
A river connectivity-aquatic food web stability model was established. The stability of the aquatic food web was evaluated by calculating the total connectivity score and fatty acid analysis combined with ecological network analysis. The model was fitted using nonlinear least squares method, integrating the research framework of terrestrial landscape and aquatic nutrient quality.
It has achieved a scientific assessment of the stability of aquatic food webs, breaking through the limitations of traditional ecosystem research, providing an understanding and prediction of the response mechanism of ecosystems under environmental pressure, and providing a scientific basis for protecting biodiversity and managing natural resources.
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Figure CN119940702B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of ecology and environmental science and technology, and in particular to a method for analyzing the stability of an aquatic food web based on river connectivity. Background Art
[0002] Existing research on aquatic ecosystems typically focuses on how individual communities (such as fish and aquatic plants) are affected by environmental change. However, relatively few studies have examined the impact of environmental change on the structure of the entire food web, and methods that comprehensively consider the relationships between organisms at different trophic levels are lacking. This results in a lack of understanding of complex interactions and overall stability in ecosystem assessments. Therefore, a new approach is urgently needed that integrates food web structure with community changes to more scientifically and comprehensively assess the responses of aquatic ecosystems to environmental stress. 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 response to the shortcomings of the existing technology.
[0004] 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. Specifically, the present invention includes 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 , annual runoff variation coefficient of river section The coefficient of variation of the monthly average runoff of the Hehe River 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 reach;
[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 , annual runoff variation coefficient of river section The coefficient of variation of the monthly average runoff of the Hehe River ,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 a river section at period t. The riparian zone is a strip extending 100 to 500 meters on both sides of the river section. The built-up land area of the riparian zone is the area of human-built areas developed into buildings, roads, and agricultural land within 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 average annual flow of the river section in period t; m is the month number, is the measured monthly discharge volume in the mth month of 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 each river section in the target area is calculated based on six parameter indicators, specifically:
[0024] Calculate 6 parameter indicators of river sections The Pearson correlation coefficient between the two values was 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 was normalized, and the average value of each row was 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) According to the total score of river section connectivity 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 connectivity through ecological network analysis (ENA).
[0035] The Spearman correlation between the FA profiles of plankton, macroinvertebrates, and fish in each food quality group within 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 network nodes. j The number of nodes filtered out.
[0036] The food web stability is calculated as follows:
[0037]
[0038] is the food web stability of the j-th river reach, is the number of nodes screened out in the j-th river section visual ecological network, is the total number of nodes in the visualized ecological network of the j-th 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 can 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 effects of the present invention are that it combines the study of landscape connectivity with that of aquatic food webs, making up for the shortcomings of previous studies that only focused on single terrestrial or water 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 analyzing the stability of the aquatic food web in several sections of the Dongjiang River;
[0044] Figure 2 This is a comprehensive connectivity analysis map 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 number of embodiments.
[0047] It should be clear that the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this 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", "an", "the" 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 otherwise.
[0049] This paper 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, this paper breaks through the limitations of traditional ecosystem research and innovatively integrates the research framework of terrestrial landscape and aquatic nutrient quality, providing a new approach to understanding and predicting the response mechanism of ecosystems under environmental pressure.
[0050] Food web stability refers to the ability of an ecosystem's network of interconnected species through food relationships to maintain its structure and function in the face of internal or external disturbances. This stability is a key factor in ecosystem health and sustainability. Studying food web stability helps us understand how ecosystems respond to global challenges such as climate change and pollution, and provides a scientific basis for biodiversity conservation and the development of effective natural resource management strategies.
[0051] 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 are used to track the flow of substances through the food chain. Fatty acid analysis (FAA) identifies food sources by analyzing the specific fatty acid types within an organism. Different producers, such as algae or plants, produce unique fatty acid profiles, and these signatures can be passed down the food chain to consumers.
[0052] This paper establishes a river connectivity-aquatic food web stability model based on connectivity analysis and fatty acid analysis, which can efficiently determine food web stability. Its scientific rationality is verified using fatty acid analysis. The details are as follows:
[0053] (1) The Dongjiang River, with a total length of 560 km, was divided into 56 sections with each section being 10 km long. The stability of the aquatic food web was calculated for 37 sections where fatty acid samples were 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 connectivity.
[0057] The Spearman correlation between the FA profiles of plankton, macroinvertebrates, and fish in each food quality group within 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 network nodes. j The number of nodes filtered out.
[0058] The food web stability is calculated as follows:
[0059]
[0060] is the food web stability of the j-th river reach, is the number of nodes screened out in the j-th river section visual ecological network, is the total number of nodes in the visualized ecological network of the j-th 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 and 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 , annual runoff variation coefficient of river section The coefficient of variation of the monthly average runoff of the Hehe River ,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 a river section at period t. The riparian zone is a strip extending 100 to 500 meters on both sides of the river section. The built-up land area of the riparian zone is the area of human-built areas developed into buildings, roads, and agricultural land within 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 average annual flow of the river section in period t; m is the month number, is the measured monthly discharge volume in the mth month of 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 parameter indicators of river sections The Pearson correlation coefficient between the two values was 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 was normalized, and the average value of each row was 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 i-th 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 the 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] As can be seen from the above examples, 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 land changes in rivers and riparian zones 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 specification and practicing the contents disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely as exemplary, 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 exact structures 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 method for analyzing the stability of an aquatic food web 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 , annual runoff variation coefficient of river section The coefficient of variation of the monthly average runoff of the Hehe River 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 reach; ； in, is the proportionality constant, is the power index, is the exponential decay coefficient; 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 , annual runoff variation coefficient of river section The coefficient of variation of the monthly average runoff of the Hehe River The details are as follows: ； 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 riparian zone area of the river reach at time t. The riparian zone is a strip extending 100 to 500 meters on either side of the river reach. The built-up land area of the riparian zone is the area of human-built land developed into buildings, roads, and agricultural land within 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; ； in, is the average annual flow of the river section during the reference period, The average annual flow of the river section in period t; m is the month number, is the measured monthly discharge volume in the mth month of the evaluation period T, is the measured monthly average discharge volume 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.
2. 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 six parameter indicators, specifically: Calculate 6 parameter indicators of river sections The Pearson correlation coefficient between the two values was calculated, and a 6-order judgment matrix that meets the consistency test was established using Saaty's "1-9 scaling method". Each column of the matrix was normalized, and the average value of each row was 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 in order: 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 , annual runoff variation coefficient of river section The coefficient of variation of the monthly average runoff of the Hehe River ; is the weight of the i-th parameter indicator.
3. The analysis method according to claim 1, characterized in that The connectivity-aquatic food web stability model was fitted using the following method: (2.1) For each river section, calculate the stability of its aquatic food web based on fatty acids ; (2.2) According to the total score of river section connectivity and food web stability of river reaches , fitting a connectivity-aquatic food web stability model.
4. The analysis method according to claim 3, characterized in that Based on fatty acids, the stability of aquatic food webs is calculated as: (2.11) Select key fatty acid functional groups: linoleic acid, α-linoleic acid, arachidonic acid, eicosapentaenoic acid, docosahexaenoic acid, total saturated fatty acids, total monounsaturated fatty acids, and total bacterial fatty acids; 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 connectivity through ecological network analysis; The Spearman correlation between the FA profiles of plankton, macroinvertebrates, and fish in each food quality group within the river section was calculated to generate a visual ecological network. Only data with correlation |r| > 0.5 and correlation "two-tailed" P < 0.05 were selected to form 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 j-th river reach, is the number of nodes screened out in the j-th river section visual ecological network, is the total number of nodes in the visualized ecological network of the j-th river section.
5. The analysis method according to claim 4, characterized in that According to the total connectivity score of the river segment 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 can get the effect of connectivity on food web stability: ； in, is the proportionality constant, is the power index, is the exponential decay coefficient.