Health evaluation method and system for mountain river ecosystem based on food web

Abstract

The invention provides a health evaluation method and system for a mountain river ecosystem based on a food web, and relates to the technical field of ecological environment science and aquatic ecology, and the method comprises the steps: collecting data of the mountain river ecosystem; constructing an Ecopath model database according to the mountain river ecosystem data; according to the Ecopath model database, an Ecopath model is constructed; and establishing an ecological system evaluation method based on an Ecopath model, and performing health evaluation. The method comprehensively considers the interaction, physical and chemical environmental factors and unique hydrological characteristics of the multi-trophic-level biocenosis of the mountainous river, and solves the problem that the traditional evaluation method neglects the structure and function of the food web and is difficult to accurately quantify the ecological health condition. And a new technical means is provided for protecting and managing a river ecosystem in a mountainous area under the interference of climate change and human activities.

Description

Technical Field

[0001] This invention relates to the fields of ecological environment science and aquatic ecology, and more specifically to a method and system for health assessment of mountain river ecosystems based on food webs. Background Technology

[0002] In mountain river ecosystems, trophic level structure and food web characteristics play a crucial role in maintaining river biodiversity and ecological balance. Due to significant topographical differences and rapid water flow, mountain rivers often possess high species diversity and unique biological community structures. Furthermore, changes in watershed runoff patterns, water temperature increases, and altered nutrient loads can all affect the efficiency of energy transfer and nutrient conversion between primary producers (such as algae) and intermediate- to higher-level consumers (such as aquatic insects and fish), thereby disrupting the health of the river ecosystem. Especially in high-altitude or cold regions, surface runoff is mainly composed of multiple water sources, including precipitation, glacial meltwater, and snowmelt. Water temperature and flow velocity vary greatly with the seasons, and hydrological processes are complex and variable, bringing uncertainty and challenges to watershed water ecological management and protection.

[0003] Existing methods for assessing mountain river ecosystems typically focus on monitoring and analyzing single biological communities or certain physicochemical indicators. For example, they may only consider water quality physicochemical parameters or use single indicators such as benthic animal indices or fish biodiversity integrity indices, lacking a comprehensive consideration of interactions between different trophic levels and energy flow characteristics. Due to the complex hydrological conditions and significant spatiotemporal heterogeneity of biodiversity in mountain rivers, traditional methods often struggle to accurately describe the dynamic characteristics of food web structure and function, leading to biased assessments of river health and hindering the provision of sufficiently accurate and comprehensive data for subsequent ecological restoration and management decisions.

[0004] Therefore, how to propose a health assessment method and system for mountain river ecosystems based on food webs, and how to introduce the food web structure and functional elements of multi-trophic-level biological communities on the basis of comprehensive physicochemical indicators, and quantitatively consider key parameters such as nutrient transfer efficiency, so as to more objectively and accurately reflect the health status of mountain river ecosystems, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for health assessment of mountain river ecosystems based on food webs. Combining a zonal and stratified analysis strategy, it fully considers the uncertainties brought about by hydrological processes and climate change in mountain rivers. Based on comprehensive physicochemical indicators, it introduces the food web structure and functional elements of multi-trophic-level biological communities, quantitatively considering key parameters such as nutrient transfer efficiency, thus more objectively and accurately reflecting the health status of mountain river ecosystems. To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for assessing the health of mountain river ecosystems based on food webs, comprising:

[0007] Collect data on mountain river ecosystems;

[0008] An Ecopath model database was constructed based on the mountain river ecosystem data.

[0009] Construct an Ecopath model based on the Ecopath model database;

[0010] An ecosystem assessment method was established based on the Ecopath model, and a health assessment was conducted.

[0011] Optionally, the mountain river ecosystem data includes: fish data, biomass data, trophic level data, fish food composition data, hydrological and water temperature data, and water quality data.

[0012] Optionally, the Ecopath model database includes a spatial database and an attribute database.

[0013] Optionally, the Ecopath model includes:

[0014] The mass conservation relationship between the functional groups:

[0015] P i =Y i +M2 i ×B i +E i +BA i +MO i ×B i ;

[0016] Energy balance relationships between functional groups:

[0017] Q i =P i +R i +U i ;

[0018] Among them, P i Y represents the total productivity of functional group i; i M2 represents the total catch rate of functional group i; i B represents the instantaneous predator mortality rate of functional group i; i Represents biomass; E i Represents the net migration rate of functional group i; BA i The biomass accumulation rate of functional group i; MO i Other mortality rates representing functional group i; Q i R represents the consumption of function group i; i Represents the respiratory volume of functional group i; Ui This represents the amount of undigested food in functional group i.

[0019] Optionally, the Ecopath model can also be optimized based on other mortality rates and instantaneous predation mortality rates:

[0020] MO i =P i (1-EE i );

[0021]

[0022] Among them, EE i Represents the ecotrophic efficiency of functional group i; DC ji The proportion of functional group i in the food composition of functional group i;

[0023] Substituting the above equation into the functional groups, the mass conservation relationship simplifies to:

[0024]

[0025] in, The ratio of production to biomass represents functional group i. B represents the ratio of consumption to biomass for functional group i. i For biomass, B j Predator biomass.

[0026] Optionally, the P / B calculation process includes:

[0027]

[0028]

[0029] F = C / B;

[0030] Where Z represents the total mortality rate, F represents the total fishery mortality rate, and M represents the natural mortality rate; K represents the curvature of the von Bertalanffy growth equation; L ∞ T represents the asymptotic length of the von Bertalanfy growth equation. c Represents the annual average temperature of the aquatic environment; L represents the average total length of the fish; L′ represents the average body length of the fish; C represents the annual catch; B represents the biomass.

[0031] Optionally, the Q / B calculation process includes:

[0032] log(Q / B) = 7.964 - 0.204logW ∞ -1.965T′+0.532h+0.398d;

[0033] T′=1000 / (T+273.15);

[0034] A = H 2 / a;

[0035] Among them, W ∞ denoted by von Bertalanfy growth equation, asymptotic body weight; T′ represents lake average temperature; h is a dummy variable representing food type; d is also a dummy variable representing food type; A represents caudal fin shape parameter; H represents caudal fin height; a represents caudal fin area; and T is temperature.

[0036] Optionally, the method for establishing an ecosystem evaluation based on the Ecopath model includes:

[0037] Select ecological network analysis indicators that describe ecosystem function, maturity, and food web structure;

[0038] The weights of multi-level indicators of an ecosystem are determined using the analytic hierarchy process (AHP).

[0039] Standardize each indicator to eliminate differences in indicator magnitude and the direction of ecosystem response.

[0040] Optionally, the standardization process for each indicator includes:

[0041] Standardization of positive indicators:

[0042]

[0043] Standardization of negative indicators:

[0044]

[0045] Standardization of indicators that stabilize at ideal values:

[0046]

[0047] In the formula, r i Let max(R) be the standardized value of the i-th index. i ) represents the maximum value of the i-th indicator, min(R) i R is the minimum value of the i-th index. i P represents the true value of the i-th indicator. i This is the ideal value for this indicator.

[0048] Optionally, a food web-based system for assessing the health of mountain river ecosystems includes:

[0049] Data Acquisition Module: Used to collect data on mountain river ecosystems;

[0050] Model database construction module: used to construct the Ecopath model database based on the mountain river ecosystem data;

[0051] Model building module: used to build Ecopath models based on the Ecopath model database;

[0052] Evaluation module: Used to establish ecosystem evaluation methods and conduct health assessments based on the Ecopath model.

[0053] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and system for health assessment of mountain river ecosystems based on food webs, which has the following beneficial effects:

[0054] This invention proposes a food web-based method and system for assessing the health of mountain river ecosystems, comprising: collecting mountain river ecosystem data; constructing an Ecopath model database based on the mountain river ecosystem data; constructing an Ecopath model based on the Ecopath model database; and establishing an ecosystem assessment method based on the Ecopath model and conducting a health assessment. This invention comprehensively considers the interactions of multi-trophic-level biological communities in mountain rivers, physicochemical environmental factors, and unique hydrological characteristics, solving the problems of traditional assessment methods neglecting food web structure and function and being unable to accurately quantify ecological health status. It provides a new technical means for the protection and management of mountain river ecosystems under the influence of climate change and human activities. Attached Figure Description

[0055] 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0056] Figure 1 This is a schematic diagram of a method for assessing the health of a mountain river ecosystem based on a food web, provided by the present invention.

[0057] Figure 2 A schematic diagram of energy flow in the upper section of the Laomuhe Dam provided for this invention.

[0058] Figure 3 A schematic diagram of energy flow in the lower section of the Laomuhe Dam provided for this invention.

[0059] Figure 4 A schematic diagram of the food web structure in the upper section of the Laomuhe Dam provided for this invention.

[0060] Figure 5A schematic diagram of the food web structure in the lower section of the Laomuhe Dam provided for this invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] This invention discloses a method for assessing the health of mountain river ecosystems based on food webs, comprising:

[0063] Collect data on mountain river ecosystems;

[0064] An Ecopath model database was constructed based on the mountain river ecosystem data.

[0065] Construct an Ecopath model based on the Ecopath model database;

[0066] An ecosystem assessment method was established based on the Ecopath model, and a health assessment was conducted.

[0067] Furthermore, the mountain river ecosystem data includes: fish data, biomass data, trophic level data, fish food composition data, hydrological and temperature data, and water quality data.

[0068] Furthermore, the Ecopath model database includes a spatial database and an attribute database.

[0069] Furthermore, the Ecopath model includes:

[0070] The mass conservation relationship between the functional groups:

[0071] P i =Y i +M2 i ×B i +E i +BA i +MO i ×B i ;

[0072] Energy balance relationships between functional groups:

[0073] Q i =P i +R i +U i ;

[0074] Among them, Pi Y represents the total productivity of functional group i; i M2 represents the total catch rate of functional group i; i B represents the instantaneous predator mortality rate of functional group i; i Represents biomass; E i Represents the net migration rate of functional group i; BA i The biomass accumulation rate of functional group i; MO i Other mortality rates representing functional group i; Q i R represents the consumption of function group i; i Represents the respiratory volume of functional group i; U i This represents the amount of undigested food in functional group i.

[0075] Furthermore, this includes optimizing the Ecopath model based on other mortality rates and instantaneous predator mortality rates:

[0076] MO i =P i (1-EE i );

[0077]

[0078] Among them, EE i Represents the ecotrophic efficiency of functional group i; DC ji The proportion of functional group i in the food composition of functional group i;

[0079] Substituting the above equation into the functional groups, the mass conservation relationship simplifies to:

[0080]

[0081] in, The ratio of production to biomass represents functional group i. B represents the ratio of consumption to biomass for functional group i. i For biomass, B j Predator biomass.

[0082] Furthermore, the P / B calculation process includes:

[0083]

[0084]

[0085] F = C / B;

[0086] Where Z represents the total mortality rate, F represents the total fishery mortality rate, and M represents the natural mortality rate; K represents the curvature of the von Bertalanffy growth equation; L ∞T represents the asymptotic length of the von Bertalanfy growth equation. c Represents the annual average temperature of the aquatic environment; L represents the average total length of the fish; L′ represents the average body length of the fish; C represents the annual catch; B represents the biomass.

[0087] Furthermore, the Q / B calculation process includes:

[0088] log(Q / B) = 7.964 - 0.204logW ∞ -1.965T′+0.532h+0.398d;

[0089] T′=1000 / (T+273.15);

[0090] A = H 2 / a;

[0091] Among them, W ∞ denoted by von Bertalanfy growth equation, asymptotic body weight; T′ represents lake average temperature; h is a dummy variable representing food type; d is also a dummy variable representing food type; A represents caudal fin shape parameter; H represents caudal fin height; a represents caudal fin area; and T is temperature.

[0092] Furthermore, the ecosystem evaluation method based on the Ecopath model includes:

[0093] Select ecological network analysis indicators that describe ecosystem function, maturity, and food web structure;

[0094] The weights of multi-level indicators of an ecosystem are determined using the analytic hierarchy process (AHP).

[0095] Standardize each indicator to eliminate differences in indicator magnitude and the direction of ecosystem response.

[0096] Furthermore, the standardization process for each indicator includes:

[0097] Standardization of positive indicators:

[0098]

[0099] Standardization of negative indicators:

[0100]

[0101] Standardization of indicators that stabilize at ideal values:

[0102]

[0103] In the formula, r iLet max(R) be the standardized value of the i-th index. i ) represents the maximum value of the i-th indicator, min(R) i R is the minimum value of the i-th index. i P represents the true value of the i-th indicator. i This is the ideal value for this indicator.

[0104] In a specific implementation, a food web-based health assessment system for mountain river ecosystems includes:

[0105] Data Acquisition Module: Used to collect data on mountain river ecosystems;

[0106] Model database construction module: used to construct the Ecopath model database based on the mountain river ecosystem data;

[0107] Model building module: used to build Ecopath models based on the Ecopath model database;

[0108] Evaluation module: Used to establish ecosystem evaluation methods and systems based on the Ecopath model and to conduct health assessments.

[0109] In a specific implementation, a method for assessing the health of a mountain river ecosystem based on a food web includes the following steps:

[0110] (1) Construct the Ecopath model database, including a spatial database and an attribute database;

[0111] Ecopath is the foundational model of the EwE software. It's a static model reflecting the energy flow balance among the components of an ecosystem. It allows for in-depth exploration of food web structure and energy flow at the ecosystem level, leading to a better understanding of ecosystem energy balance. It describes an ecosystem as a complex system composed of multiple interconnected functional components that interact according to trophic dynamics, forming a complete system. These functional components are called functional groups. Functional groups primarily consist of species with similar ecological habits, important species (composed of different body lengths or ages), and also include various detritus, benthic flora and fauna, and plankton. All functional groups should encompass all aspects of energy transport within the system.

[0112] By establishing a model, it can be inferred that when the various parts of an ecosystem remain relatively stable, the energy exchange between them will also remain balanced. The basic idea of ​​the Ecopath model is to achieve the conservation of ecosystem mass through two sets of governing equations. Equation (1) can be used to describe the mass conservation relationship between the various functional groups, specifically expressed as:

[0113] Pi =Y i +M2 i ×B i +E i +BA i +MO i ×B i (1);

[0114] That is, production = catch + predation + net migration + bioaccumulation + other mortality;

[0115] Formula (2) is used to describe the energy balance of each functional group, and is expressed as:

[0116] Q i =P i +R i +U i (2);

[0117] That is, consumption = production + respiration + undigested food.

[0118] Where P represents the total productivity of functional group i; Y i M2 represents the total catch rate of functional group i; i B represents the instantaneous predator mortality rate of functional group i; i Represents biomass; E i The net migration rate (migration-immigration rate) representing functional group i; BA i Biomass accumulation rate representing functional group i; EE i Represents the ecotrophic efficiency of functional group i; ecotrophic efficiency (EE) has no unit; MO i Other mortality rates representing functional group i; Q i R represents the consumption of function group i; i Represents the respiratory volume of functional group i; U i This represents the amount of undigested food in functional group i.

[0119] Furthermore, in addition to being preyed upon and caught by fish, MO i (Other mortality rates) can be used to measure other mortality rates, such as deaths caused by old age, poor health, starvation, etc., M2 i (Predator mortality rate) can better link predators and prey, as specifically expressed in formulas (3) and (4):

[0120] MO i =P i (1-EE i (3);

[0121]

[0122] Among them, DC ji This represents the proportion of functional group i in the food composition of functional group i.

[0123] Therefore, substituting into formulas (3) and (4), formula (1) can be further simplified to:

[0124]

[0125] in, The ratio of production to biomass represents functional group i. This represents the ratio of consumption to biomass for functional group i. B i For biomass, B j Predator biomass.

[0126] By using the Ecopath model, the biological characteristics and energy flows of different functional groups in an ecosystem can be quantitatively studied, and the interactions between different trophic levels can be explored. These characteristics can be represented by formula (5), which can be used to represent each functional group.

[0127] The core parameters of the Ecopath model include: biomass B. i The ratio of production to biomass Food intake to biomass ratio and ecological nutrition efficiency (EE) i Furthermore, it also requires inputting the food composition matrix parameter DC. ji This information is used to ensure the accuracy and reliability of the model. To build the model, the basic input for each functional group requires DC... ji And at least three of the four parameters (B, P / B, EE, and Q / B), and the model can estimate the uninputted parameters using Ecopath's algorithm. Because EE in the model... i A value less than 1 is required for the entire system to function normally, so EE needs to be adjusted by appropriately modifying the parameters. i This continues until the Ecopath model reaches a steady state.

[0128] In a specific embodiment, biomass (B) represents the total amount of organisms per unit area (or volume) at a specific location and time. The biomass calculation method of the Ecopath model is based on the wet biomass weight (t / km) of the study area. 2 The data was calculated, and the time limit was one year. In this embodiment, the biomass of phytoplankton, benthic flora and fauna, and macroaquatic plants were all obtained as average values ​​from field sampling data in the study area. The biomass of phytoplankton was converted from mg / L to t / km by multiplying the average water depth at each sampling point. 2 The unit for biomass of submerged plants, emergent plants, and benthic animals is g / m³.2 With t / km 2 The values ​​represented are completely consistent, so they can be directly applied to the model calculation. The biomass of fish is estimated from the annual catch, survey reports, and published papers. The biomass of detritus is calculated using an empirical formula, as shown in formula (6):

[0129] logD=0.954logPP+0.863logE-2.41(6);

[0130] Where D represents the biomass of detritus (gC / m³) 2 ); PP represents primary production quantity (gC / m³) 2 / y); E represents average transparency (m).

[0131] In a specific embodiment, productivity (P) measures the total increase in organisms per unit time and per unit area (or volume) within an ecosystem, thus measuring the overall productivity of the ecosystem, expressed in t / (km²). 2 •Year). Productivity / Biomass (P / B) refers to the ratio of total productivity to total biomass over a specific period (usually one year), also known as biomass turnover, expressed as the reciprocal of the year (1 / year). In this embodiment, the productivity of aquatic plants and phytoplankton can be directly calculated from survey data. The P / B and Q / B values ​​of other organisms can be partially calculated using the Ecoempire empirical formula estimation function in EwE software, or partially by referring to values ​​from other similar ecosystem models.

[0132] In specific embodiments, consumption (Q) and consumption / biomass (Q / B) are two independent concepts. The former represents the amount of food consumed by a certain organism within a certain time period, while the latter represents the ratio of the organism's food consumption to its biomass within a certain time period. In the ecopath model, its unit is 1 / year.

[0133] The P / B value of fish is calculated based on empirical relationships, as shown in formulas (7), (8), and (9):

[0134]

[0135]

[0136] F = C / B (9);

[0137] Where Z represents the total mortality rate (1 / year), F represents the total fishery mortality rate (1 / year), and M represents the natural mortality rate (1 / year); K represents the curvature of the von Bertalanffy growth equation; L ∞The asymptotic length (cm) representing the von Bertalanfy growth equation; T c L represents the average annual temperature of the aquatic environment (°C); L represents the average total length of the fish (cm); L′ represents the average body length of the fish (cm); C represents the annual catch (t / (km²)). 2 / year); B represents biomass (t / km²) 2 (K, L) ∞ , L′ and M can be obtained from fishery resources databases.

[0138] The Q / B value of fish is estimated by empirical equations, as shown in equations (10), (11), and (12):

[0139] log(Q / B) = 7.964 - 0.204logW ∞ -1.965T ′ +0.532h+0.398d(10);

[0140] T′=1000 / (T+273.15)(11);

[0141] A = H 2 / a(12);

[0142] Among them, W ∞ denoted by von Bertalanfy growth equation, asymptotic body weight (g); T′ represents the average lake temperature (T′ = ℃ + 273.15); h is a dummy variable representing food type (1 represents herbivorous fish, 0 represents carnivorous and detritivorous fish); d is also a dummy variable representing food type (1 represents detritivorous fish, 0 represents herbivorous and carnivorous fish); A represents the caudal fin shape parameter (the faster the fish moves, the larger the A value, generally 1.32 for fish); H represents the caudal fin height; a represents the caudal fin area; and T is the temperature.

[0143] In a specific embodiment, diet composition (DC) is a crucial input parameter in the Ecopath model. It measures the proportion of catches or biomass of multiple organisms within the same functional group and is expressed as a weighted average. The construction of the diet composition matrices for different functional groups is based on an integrated analysis of data obtained from stomach contents analysis in relevant literature and partial fish data published in the Fishbase fisheries database.

[0144] In a specific implementation, the steps for establishing and validating an ecosystem assessment method include:

[0145] Indicator selection and calculation: Four ecological network analysis (ENA) indicators that can describe the function, maturity and food web structure of an ecosystem were selected: total primary production / total respiration (TPP / TR), total primary productivity / total biomass (TPP / TB), system omnivorous index (SOI), and connectivity index (CI).

[0146] Determining Indicator Weights: The Analytic Hierarchy Process (AHP) is a systematic analysis method commonly used to determine the weights of evaluation indicators when constructing comprehensive evaluation systems. It is one of the most frequently used methods for determining weights. In this embodiment, AHP is used to determine the weights of four indicators across three levels of the ecosystem (ecosystem function, ecosystem maturity, and food web structure). The determined indicator weights are: TPP / TR: 0.1, TPP / TB: 0.1, SOI: 0.1, and CI: 0.3.

[0147] Indicator standardization: There are significant differences in magnitude between different indicators, and the response directions of each indicator to the ecosystem are inconsistent. For example, in this embodiment, the TPP / TB value ranges from 23.23 to 81.05, which is a negative indicator. That is, the smaller the value of this indicator, the more mature the ecosystem. The SOI indicator ranges from 0.058 to 0.082, which is a positive indicator. That is, the larger the value of this indicator, the more stable the food web structure of the ecosystem.

[0148] To construct a comprehensive assessment index and eliminate differences in the magnitude of indicators and the direction of response to the ecosystem, this embodiment uses the range standardization method to standardize each indicator. There are three types of responses of ENA indicators to the ecosystem, of which SOI and CI are positive indicators, TPP / TR is a negative indicator, and TPP / TB is an indicator that is stable at the ideal value. The standardization methods for different types of indicators are shown in formulas (13), (14), and (15).

[0149] Standardization of positive indicators:

[0150]

[0151] Standardization of negative indicators:

[0152]

[0153] Standardization of indicators that stabilize at ideal values:

[0154]

[0155] Where, r i Let max(R) be the standardized value of the i-th index. i ) represents the maximum value of the i-th indicator, min(R)i R is the minimum value of the i-th index. i P represents the true value of the i-th indicator. i This represents the ideal value for the indicator. The indicators are standardized using the methods described above, ensuring they are distributed within the range of [0, 1]. The closer the standardized value of an indicator is to 1, the better the ecosystem condition.

[0156] In a specific embodiment, such as Figure 1 As shown, a method for assessing the health of a mountain river ecosystem based on a food web mainly includes the following steps:

[0157] (1) Collection of relevant data on the Heishui River. Four sampling points were set up near the Laomuhe Dam, two above and two below the dam. Data on water quality, hydrology, benthic organisms, phytoplankton, zooplankton and fish before and after the dam was removed were collected through multiple field samplings on the Heishui River and data provided by the Three Gorges Group, and then analyzed.

[0158] Water quality indicators mainly included temperature, dissolved oxygen (DO), pH, total nitrogen (TN), nitrate nitrogen, ammonia nitrogen, total phosphorus (TP), and permanganate index. Phytoplankton and zooplankton were analyzed primarily for changes in species number, species density, and overall population. Fish resources in the Heishui River from the Songxin Dam to the downstream estuary were continuously monitored from 2017 to 2022 using a combination of traditional fishing methods and eDNA testing. Changes in fish species in each section of the Heishui River before and after dam removal and impoundment were statistically analyzed. The Pielou's evenness index was used to analyze the relative evenness of fish distribution in the Heishui River from the Songxin Dam to the downstream estuary. The Shannon diversity index was used to reflect the species diversity of the fish community structure in the Heishui River from the Songxin Dam to the downstream estuary.

[0159] (2) Data on benthic organisms, phytoplankton, zooplankton, and fish in the Heishui River were collected and processed using the Ecopath module. An Ecopath model of the Heishui River ecosystem before and after the dam removal was established to analyze energy flow, food composition, and food networks, such as... Figures 2-5 As shown.

[0160] (3) Evaluation of the effectiveness of the evaluation method for mountain river ecosystems:

[0161] Key indicators (TPP / TB, TPP / TR, SOI, CI, etc.) reflecting ecosystem structure and function were selected based on Ecological Network Analysis (ENA) method, adhering to basic principles such as availability, independence, and sensitivity to ecosystem changes. Subsequently, the Analytic Hierarchy Process (AHP) was used to assign weights to each indicator.

[0162] First, a multi-level evaluation system was established, categorizing the indicators. Then, pairwise comparisons were performed based on expert scores or literature references to calculate the final weight of each indicator. To standardize the dimensions and values ​​of each indicator, methods such as range standardization were used to normalize the indicator values. The ecosystem health status was evaluated based on the standardized results, as shown in Table 1.

[0163] Table 1. Results of sensitivity parameter values

[0164] reach River Estuary Reservoir Section Laomuhe Dam Laomuhe Dam Total primary production / Total respiration 2.92 6.87 21.50 System connectivity index 0.30 0.32 0.36 Systemic Omnivorous Index 0.19 0.16 0.17

[0165] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0166] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for assessing the health of mountain river ecosystems based on food webs, characterized in that, include: Collect data on mountain river ecosystems; An Ecopath model database was constructed based on the mountain river ecosystem data. Construct an Ecopath model based on the Ecopath model database; An ecosystem assessment method was established based on the Ecopath model, and a health assessment was conducted.

2. The method for assessing the health of a mountain river ecosystem based on a food web, as described in claim 1, is characterized in that... The mountain river ecosystem data includes: fish data, biomass data, trophic level data, fish food composition data, hydrological and water temperature data, and water quality data.

3. The method for assessing the health of a mountain river ecosystem based on a food web, as described in claim 1, is characterized in that... The Ecopath model database includes a spatial database and an attribute database.

4. The method for assessing the health of a mountain river ecosystem based on a food web, as described in claim 1, is characterized in that... The Ecopath model includes: The mass conservation relationship between the functional groups: P i =Y i +M2 i ×B i +E i +BA i +MO i ×B i ; Energy balance relationships between functional groups: Q i =P i +R i +U i ; Among them, P i Y represents the total productivity of functional group i; i M2 represents the total catch rate of functional group i; i B represents the instantaneous predator mortality rate of functional group i; i Represents biomass; E i Represents the net migration rate of functional group i; BA i The biomass accumulation rate of functional group i; MO i Other mortality rates representing functional group i; Q i R represents the consumption of function group i; i Represents the respiratory volume of functional group i; U i This represents the amount of undigested food in functional group i.

5. The method for assessing the health of a mountain river ecosystem based on a food web, as described in claim 4, is characterized in that... This also includes optimizing the Ecopath model based on other mortality rates and instantaneous predation mortality rates: KNOW i =P i (1-EE i ); Among them, EE i Represents the ecotrophic efficiency of functional group i; DC ji The proportion of functional group i in the food composition of functional group i; Substituting the above equation into the functional groups, the mass conservation relationship simplifies to: in, The ratio of production to biomass represents functional group i. B represents the ratio of consumption to biomass for functional group i. i For biomass, B j Predator biomass.

6. The method for assessing the health of a mountain river ecosystem based on a food web, as described in claim 5, is characterized in that... The P / B calculation process includes: F = C / B; Where Z represents the total mortality rate, F represents the total fishery mortality rate, and M represents the natural mortality rate; K represents the curvature of the von Bertalanffy growth equation; L ∞ T represents the asymptotic length of the von Bertalanfy growth equation. c Represents the annual average temperature of the aquatic environment; L represents the average total length of the fish; L′ represents the average body length of the fish; C represents the annual catch; B represents the biomass.

7. The method for assessing the health of a mountain river ecosystem based on a food web, as described in claim 5, is characterized in that... The Q / B calculation process includes: log(Q / B)=7.964-0.204logW ∞ -1.965T′+0.532h+0.398d; T′=1000 / (T+273.15); A=H 2 / a; Among them, W ∞ denoted by von Bertalanfy growth equation, asymptotic body weight; T′ represents lake average temperature; h is a dummy variable representing food type; d is also a dummy variable representing food type; A represents caudal fin shape parameter; H represents caudal fin height; a represents caudal fin area; and T is temperature.

8. The method for assessing the health of a mountain river ecosystem based on a food web, as described in claim 1, is characterized in that... The ecosystem evaluation method based on the Ecopath model includes: Select ecological network analysis indicators that describe ecosystem function, maturity, and food web structure; The weights of multi-level indicators of an ecosystem are determined using the analytic hierarchy process (AHP). Standardize each indicator to eliminate differences in indicator magnitude and the direction of ecosystem response.

9. A method for assessing the health of a mountain river ecosystem based on a food web, as described in claim 8, is characterized in that... The standardization process for each indicator includes: Standardization of positive indicators: Standardization of negative indicators: Standardization of indicators that remain stable at ideal values: In the formula, r i Let max(R) be the standardized value of the i-th index. i ) represents the maximum value of the i-th indicator, min(R) i R is the minimum value of the i-th index. i P represents the true value of the i-th indicator. i This is the ideal value for this indicator.

10. A health assessment system for mountain river ecosystems based on food webs, characterized in that, include: Data Acquisition Module: Used to collect data on mountain river ecosystems; Model database construction module: used to construct the Ecopath model database based on the mountain river ecosystem data; Model building module: used to build Ecopath models based on the Ecopath model database; Evaluation module: Used to establish ecosystem evaluation methods and conduct health assessments based on the Ecopath model.

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