A method for formulating a wetland ecological restoration reference target

Abstract

The application relates to a method for formulating a wetland ecological restoration reference target in the technical field of wetland ecological restoration, and the method for formulating the wetland ecological restoration reference target is based on a palaeoecological technology and an ecological system elasticity theory to construct a wetland ecological restoration reference target, simultaneously considers a wetland natural succession process, self-recovery capability and steady state conversion characteristics, can lock a high-elasticity and stable reference state, restores a damaged wetland to a high-elasticity and stable state, and improves the stability of a wetland ecological system restoration result.

Description

Technical Field

[0001] This application relates to the field of wetland ecological restoration technology, and in particular to a method for developing reference targets for wetland ecological restoration. Background Technology

[0002] Wetlands are unique ecosystems formed by the interaction of land and water on Earth, playing a vital role in regulating runoff, purifying water quality, beautifying the environment, improving climate, and maintaining regional ecological balance. However, since the Industrial Revolution, with the development of science and technology and the rapid increase in population, global wetland ecosystems have suffered varying degrees of damage. Currently, government departments and researchers have recognized the importance of wetland ecosystem protection and restoration, and many countries and regions around the world have made some progress in wetland ecological restoration practices. The key to ecological restoration lies in formulating feasible goals and incorporating these goals into strategic plans for resource management and environmental protection.

[0003] Currently, commonly used techniques for setting reference targets for wetland ecological restoration include the historical records approach, the reference sites approach, and the paleoecology approach. However: ① The historical records approach relies on historical information such as photographs, maps, and hydrological records from before wetland disturbance to construct restoration targets. However, due to the lack or incompleteness of historical information in most areas, this method is not widely applicable and cannot form effective restoration reference targets, thus limiting its application scope; ② The reference sites approach uses existing natural wetlands with the least alteration as reference points to set targets. However, because it does not consider the natural succession process of the ecosystem, it cannot determine whether the ecosystem at the reference point is in a stable state. Furthermore, it cannot clarify whether the reference point has suffered severe disturbance in the past, and even if the current state is good, there may be potential flaws. This leads to a lack of scientific rigor and reliability in the established restoration targets. The method ensures the stability of the restoration effect; ③ Although the paleoecological method can provide a long-term ecological background and overcome some limitations of the historical record method and the reference point method, it does not take into account the resilience characteristics of the ecosystem and the law of steady-state transition. The resilience of the ecosystem has a threshold. When the disturbance exceeds the threshold, it will enter the "release" and "reorganization" stage. Once "reorganization" occurs, it is difficult to restore to the original steady state. The paleoecological method only selects the ecological characteristics of the period with less disturbance as the target, without distinguishing whether the period is in a highly elastic and stable state, and without avoiding the risk of steady-state transition. This may lead to the fact that the set restoration target cannot restore the wetland to a stable state with self-repair ability, or even the problem of degradation again after restoration.

[0004] Therefore, this invention is proposed. Summary of the Invention

[0005] In view of the problems existing in the background technology, this application provides a method for formulating reference targets for wetland ecological restoration. Specifically, it constructs reference targets for wetland ecological restoration based on paleoecology and ecosystem resilience theory, taking into account both the long-term ecological evolution background and the dynamic stability characteristics of the wetland system. This effectively solves the problems of narrow applicability, insufficient scientific rigor of targets, failure to avoid the risk of steady-state transition, and unstable restoration effects of historical record method, reference point method, paleoecology method, etc. in the formulation of reference targets for wetland ecological restoration. This enables damaged wetlands to be restored to a highly resilient and stable state, and improves the stability of wetland ecosystem restoration results.

[0006] According to one aspect of the present invention, a method for formulating reference targets for wetland ecological restoration is provided, comprising the following steps: S1. Based on wetland biological community data and environmental factor data, construct a transformation function between wetland biological community and environmental factors.

[0007] S2. Obtain biological community data from different historical periods using sediments from different strata in the wetland, and then reconstruct environmental factor data from different historical periods by substituting them into the transformation function.

[0008] S3. Test the specified materials in different sediment layers in the wetland, and combine them with archaeological and historical documents from different historical periods to obtain the intensity changes of human activities affecting the wetland ecosystem.

[0009] S4. Perform principal component analysis on biological community data from different historical periods, extract the first principal component score PC1, assess the changes in ecosystem resilience based on the first principal component score PC1, and determine whether the ecosystem has undergone a steady-state transition and the timing of the steady-state transition.

[0010] Based on the intensity of human activities affecting wetland ecosystems in different historical periods, if a steady-state transition occurs, the ecosystem is divided into an expansion phase, a maintenance phase, a release phase, and a reorganization phase according to the timeline. If no steady-state transition occurs, the ecosystem is divided into an expansion phase and a maintenance phase according to the timeline.

[0011] S5. Select biological community data and environmental factor data corresponding to the highly resilient states during the expansion and maintenance phases as reference targets for restoration.

[0012] In some embodiments of the present invention, in step S1, the environmental factors used to construct the transformation function are the main environmental factors.

[0013] In some embodiments of the present invention, step S1, constructing the transformation function between wetland biological communities and main environmental factors, includes the following steps: acquiring wetland biological community data and environmental factor data; processing the biological community data using trend-reversed correspondence analysis to calculate the gradient length of the biological community; sorting the biological community data and environmental factor data according to the gradient length of the biological community; extracting the main environmental factors affecting the biological community based on the sorting results; and selecting a mathematical model to construct the transformation function between the biological community and the main environmental factors.

[0014] In some embodiments of the present invention, the mathematical model is selected from multiple methods including weighted average, partial least squares, maximum likelihood and machine learning, and each mathematical model is selected to construct a transformation function between the biological community and the main environmental factors, and then one of them is selected as the optimal transformation function.

[0015] In some embodiments of the present invention, the selection of the optimal transformation function includes: performing cross-validation on each transformation function using a bootstrap method, judging the validation results based on the error between the predicted value and the observed value, the correlation coefficient between the predicted value and the observed value, and the maximum deviation, and selecting the optimal transformation function based on the error, the correlation coefficient, and the maximum deviation.

[0016] In some embodiments of the present invention, if the gradient length is greater than 4, the constraint sorting method of the linear model is selected; if the gradient length is less than 3, the constraint sorting method of the unimodal model is selected; if the gradient length is less than or equal to 4 but greater than or equal to 3, either the constraint sorting method of the linear model or the constraint sorting method of the unimodal model is selected.

[0017] In some embodiments of the present invention, step S2, obtaining biological community data from different historical periods using sediments from different strata in the wetland, includes the following steps: first, drilling a wetland sediment core, and then using... 210 Pb / 137 Cs、 14 The C-dating method establishes a chronological framework, tests the bulk density and loss on ignition of different strata, identifies biological community indicators in sediments of different strata, and obtains biological community data for different historical periods.

[0018] In some embodiments of the present invention, in step S3, the selected substance is selected from one or more of heavy metals, microplastics and organic pollutants.

[0019] In some embodiments of the present invention, step S3, obtaining the intensity change of human activity disturbance to wetland ecosystems, includes the following steps: testing each set substance in sediments at different strata in the wetland, calculating the enrichment factor of each set substance in sediments at different strata, and determining the disturbance timeline by combining archaeological and historical documents.

[0020] In some embodiments of the present invention, step S4, assessing the changes in ecosystem resilience based on the first principal component score PC1 and determining whether a steady-state transition has occurred and the period of the steady-state transition, includes the following steps: applying lag-autocorrelation analysis, variance analysis, and skewness analysis to the first principal component score PC1 to assess the changes in wetland resilience; determining the strength of ecosystem resilience in different historical periods based on the calculation results of the three analyses for different historical periods; and using sequence T-test, F-statistics, and Mann-Kendall sequence mutation analysis to assess whether a steady-state transition has occurred in different historical periods and to confirm the period of the steady-state transition.

[0021] In some embodiments of the present invention Compared with the prior art, the present invention achieves the following technical effects: 1. This invention breaks through the limitation of relying on historical information: by reconstructing the characteristics of historical biological communities and environmental factors in wetlands through paleoecological techniques, it does not need to rely on limited historical records, thus broadening the scope of application of the method and making it applicable to most wetland areas lacking complete historical data.

[0022] 2. This invention enhances the scientific rigor of restoration targets: by integrating ecosystem resilience theory, it clarifies the changes in resilience and steady-state transition nodes of wetland ecosystems through multi-dimensional analysis, avoiding the target bias caused by the failure to consider succession processes and historical disturbances in the reference point method, and making the restoration targets more aligned with the natural ecological attributes of wetlands.

[0023] 3. This invention avoids the risk of degradation after restoration: It takes the biological community and environmental factors corresponding to the highly elastic state (expansion / maintenance phase) before the "release" phase or before the steady-state transition as the restoration target, thereby fundamentally avoiding the problem of irreversible steady-state after restoration caused by the paleoecological method not considering the elastic threshold, and ensuring the stability of the restoration results.

[0024] 4. This invention provides practical technical support by constructing a complete technical chain of "data reconstruction - resilience assessment - target setting", which deeply integrates paleoecological reconstruction data with resilience theory, solves the problem of data being disconnected from restoration practice in existing technologies, provides clear and practical reference targets for wetland ecological restoration, and improves the scientific nature and feasibility of ecological restoration work. Attached Figure Description

[0025] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. In the drawings: Figure 1 This is a flowchart of the method for formulating reference targets for wetland ecological restoration according to the present invention. Detailed Implementation

[0026] It should be understood that the described embodiments are merely some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application. The implementation methods described in the following exemplary embodiments do not represent all implementation methods consistent with this application. Rather, they are merely examples of methods consistent with some aspects of this application as detailed in the appended claims.

[0027] In the description of this application, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0028] Currently, the commonly used techniques for setting reference targets for wetland ecological restoration include the historical records approach, the reference sites approach, and the paleoecology approach.

[0029] Historical records, such as photographs, historical maps, and hydrological survey records, are often used to establish reference targets for ecosystems before disturbance or damage. If sufficient historical information is available, this method is ideal for setting reference targets (Interagency Workgroup on Wetland Restoration, 2003). However, in most cases, due to a lack of historical information, there are insufficient detailed historical records to construct reference targets for wetland restoration.

[0030] The reference point method relies on the existing, least altered natural wetland landscape as a reference point to establish restoration targets (Cui et al., 2009; Leira et al., 2006). The ecological characteristics of the reference point are usually a reflection of the local natural state, but this method does not take into account the natural succession process of the ecosystem and it is unclear whether the referenced ecosystem is in a relatively stable state. In addition, it is unclear whether the reference point has been subjected to any serious disturbances in the past, so even if the current ecological state is good, the reference point method is not suitable.

[0031] The European Union's Water Framework Committee proposed using paleoecological methods to identify a period unaffected by industrialization, urbanization, and agricultural activities, where the physicochemical properties and biological composition of the ecosystem were minimally disturbed, and to establish reference targets based on the ecological characteristics of that period (European Union, 2000). Paleoecological methods provide long-term ecological background support for the evolution of ecosystems, overcoming the limitations of the aforementioned two techniques. However, ecosystems exhibit a certain degree of resilience to external disturbances; when disturbances are minor, ecosystems can recover naturally (Linkov et al., 2014). But the resilience of ecosystems is limited. When external disturbances are too strong, exceeding the system's resilience range, the system enters a "release" or "reorganization" phase and cannot recover naturally (Walker and Salt, 2006). Furthermore, once an ecosystem undergoes "reorganization," even with artificial intervention, it is difficult to restore it to its original state; instead, it operates under a different steady-state condition (May, 1977; Scheffer et al., 2009). Therefore, using paleoecological methods to select ecological characteristics of periods with minimal disturbance to establish reference targets for wetland ecological restoration also has its shortcomings.

[0032] Therefore, this application provides a method for formulating reference targets for wetland ecological restoration, which integrates paleoecological techniques and ecosystem resilience theory. It considers both the natural succession process of wetland ecosystems and the self-recovery capacity and steady-state transition characteristics of ecosystems, enabling damaged wetlands to recover to a highly resilient and stable state, improving the stability of wetland ecosystem restoration results, and effectively solving the technical defects of historical record method, reference point method, paleoecological method and other methods in the formulation of reference targets for wetland ecological restoration, such as "narrow scope of application, insufficient scientific nature of targets, failure to avoid the risk of steady-state transition, and unstable restoration effect".

[0033] This application discloses a method for establishing reference targets for wetland ecological restoration. For example... Figure 1 As shown, the method for establishing reference targets for wetland ecological restoration includes the following steps: Step 1: Construction of the transformation function between wetland biological community and environmental factors.

[0034] First, a modern ecological survey of the biological communities and their surrounding environmental factors is conducted in the wetland, covering the ecological radiation of each biological community.

[0035] Then, trend-reversed correspondence analysis (DCA) was used to process the biological community data. Based on the biological community gradient length, either linear (redundancy analysis, RDA) or unimodal (canonical correspondence analysis, CCA) models were selected for constrained ordination to rank the biological community data and environmental factor data. If the gradient length was greater than 4, CCA was selected; if the gradient length was less than 3, RDA was selected; if it was between 3 and 4, either model was acceptable. Based on the ordination results, the main environmental factors influencing the biological community were extracted.

[0036] Finally, mathematical models such as weighted average, partial least squares, maximum likelihood, and machine learning were selected to construct the transformation function between wetland biological communities and main environmental factors. The bootstrapping method was used to cross-validate the model simulation results, based on the error between predicted and observed values ​​(RMSEP) and the correlation coefficient between predicted and observed values ​​(R²). 2 The test results are judged by RMSEP and maximum bias, and the smaller RMSEP and maximum bias are selected. 2 The corresponding model is used as the optimal transformation function.

[0037] This invention establishes a standardized process for constructing biological community-environmental factor transformation functions: Species gradient length is determined through trend-reduction correspondence analysis (DCA), and dominant environmental factors are selected using either redundancy analysis (RDA) or canonical correspondence analysis (CCA) models. Finally, the RMSEP and R... are cross-validated using the bootstrap method. 2 The maximum deviation index determines the optimal model, ensuring the accuracy of the correlation analysis between environmental factors and biological communities.

[0038] In some embodiments of the present invention, the extended form of Redundancy Analysis (RDA), db-RDA, or the Generalized Additive Model (GAM), can be used to replace the RDA / CCA model. db-RDA can handle non-Euclidean distance data, while GAM can fit nonlinear species-environment relationships and is suitable for complex wetland ecological data.

[0039] In some embodiments of this invention, generalized linear mixture models (GLMM), enhanced regression trees (BRT), and random forests (RF) can be used to replace the original weighted average, partial least squares, and other models. These models can better handle spatial autocorrelation and data heterogeneity, improving the simulation accuracy of complex wetland systems.

[0040] In some embodiments of the present invention, mean absolute error (MAE) or Akaike Information Criterion (AIC) may be added as auxiliary verification indicators, along with RMSEP and R... 2 Max bias is used to jointly select the best model, thereby enhancing the reliability of the model.

[0041] Step 2: Reconstruct the paleoecological characteristics of the wetland.

[0042] First, wetland sediment cores were drilled, and then... 210 Pb / 137 Cs、 14 The C-dating method establishes a chronological framework, tests the bulk density and loss on ignition of different strata, and identifies biomes such as phytofossils, diatoms, cladocerans, and gnats in sediments from different strata, thus obtaining the characteristics of biome changes over historical periods, i.e., biome data for different historical periods. Then, based on the biome-major environmental factor transformation function, the changes in major environmental factors over historical periods are reconstructed.

[0043] This invention proposes a method for reconstructing wetland paleoecological features based on sediment core samples: combining 210 Pb / 137 Cs、 14 C-dating technology establishes a chronological framework, and through sediment bulk density and loss on ignition tests, as well as identification of biological indicators such as plant macrofossils and diatoms, combined with the constructed transformation function, it achieves accurate inversion of the main environmental factors of wetlands in historical periods.

[0044] In some embodiments of the present invention, short timescales (within a century) are available. 210 Pb and 7 Be-linked dating; for long-term timescales (thousands of years or more), optically stimulated luminescence (OSL) dating can be used as an alternative. 14 C-dating is suitable for wetland sediment cores with low organic matter content.

[0045] In some embodiments of this invention, pollen, ostracods, foraminifera (coastal estuarine wetlands), or soil microbial communities (rRNA sequencing) can be used to replace the original plant phagocytic fossils, diatoms, and other indicators. Pollen and ostracods are well preserved in various wetland deposits, and microbial communities can reflect short-term ecological changes, broadening the applicable scenarios of the technology.

[0046] Step 3: Identification of wetland disturbance intensity.

[0047] The test measures different layers of the sample, such as heavy metals, microplastics, and organic pollutants, and combines them with archaeological and historical documents from different historical periods, such as population changes, to reflect the intensity of human activities disturbing wetland ecosystems.

[0048] This invention constructs a multi-dimensional wetland disturbance intensity identification system: by comprehensively analyzing the physicochemical indicators of heavy metals, microplastics, and organic pollutants in different sediment layers and combining them with population change data, the system comprehensively quantifies the changes in the intensity of human activities' disturbance to wetland ecosystems.

[0049] In some embodiments of the present invention, perfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), and heavy metal forms (such as exchangeable cadmium and lead) can be used to replace the original heavy metals, microplastics, and other indicators. These pollutants are specific markers of human activities and can more accurately reflect the intensity of disturbances caused by industrial and agricultural activities.

[0050] In some embodiments of the present invention, land use change data (such as changes in reclaimed area interpreted by remote sensing) and watershed nutrient input data (such as nitrogen and phosphorus emissions) can be used to replace or supplement population change data, directly linking them to the core driving sources of wetland disturbance.

[0051] Step 4: Assessment of changes in wetland elasticity.

[0052] First, principal component analysis (PCA) was performed on the historical data of biological community changes (slow variables) to extract the first principal component score, PC1.

[0053] Then, lag-1 autocorrelation, variance analysis, and skewness analysis were used to assess the changes in wetland resilience in PC1. The larger the values ​​of these three analyses, the weaker the resilience. Furthermore, the sequence T-test (STARS), F-statistics, and Mann-Kendall sequence mutation analysis were used to assess whether the system underwent a steady-state transition in PC1. The release phase and the reorganization phase of the ecosystem's adaptive cycle were defined before and after the steady-state transition, respectively.

[0054] In this invention, the intensity of human activities affecting wetland ecosystems in different historical periods is considered. If a steady-state transition occurs, the ecosystem is divided into an expansion phase, a maintenance phase, a release phase, and a reorganization phase according to the timeline. If no steady-state transition occurs, the ecosystem is divided into an expansion phase and a maintenance phase according to the timeline.

[0055] This invention establishes a method for assessing wetland resilience and steady-state transition based on multi-index analysis: the first principal component (PC1) of the historical changes of the biological community is extracted by principal component analysis (PCA), and resilience is assessed by combining lag-autocorrelation, variance, and skewness analysis. The steady-state transition is determined by using sequence T-test (STARS), F-statistics, and Mann-Kendall mutation analysis, thus clarifying the stage of the ecosystem's adaptive cycle.

[0056] In some embodiments of the present invention, the original hysteresis-autocorrelation, variance, and skewness analysis can be replaced by the stochastic cusp model (Cusp model) or the integrated resilience assessment (IRA) method. The Cusp model quantifies the probability of the system crossing a critical point, while the IRA can identify the steady state under multiple driving factors and is suitable for wetland systems with multiple superimposed disturbances.

[0057] In some embodiments of the present invention, Bayesian change point detection, accumulation and control chart (CUSUM) can be used to replace the original STARS and Mann-Kendall analyses. The Bayesian method quantifies the transformation probability, and CUSUM is more sensitive to short-term mutations, making it suitable for wetlands with frequent disturbances.

[0058] Step 5: Establish reference targets for wetland ecological restoration.

[0059] If the ecosystem undergoes a steady-state transition, the biological community and environmental factors corresponding to the highly resilient state before the release phase—the expansion and maintenance phase—are used as the restoration targets (excluding the equally resilient reorganization phase); if the ecosystem does not undergo a steady-state transition, the biological community and environmental factors corresponding to the highly resilient state—the expansion and maintenance phase—are used as the restoration targets.

[0060] In some embodiments of the present invention, when formulating reference targets for wetland ecological restoration, climate models (such as CMIP6) can be used in conjunction with ecosystem models (such as DSSAT) to predict the changing trends of key environmental factors (such as temperature, precipitation, and sea-level rise) of wetlands in the coming years, such as 50 years. Combining historical high resilience and adaptability under future scenarios, combinations of biological communities and environmental factors that can match historical health characteristics and resist future disturbances can be selected as restoration targets.

[0061] In some embodiments of the present invention, to address the complexity of the above-mentioned wetland paleoecological feature reconstruction, the complexity of paleoecological reconstruction can be simplified by strengthening the multi-method verification of flexible assessment, thereby ensuring the scientific nature of target setting and lowering the technical application threshold. Specifically, as follows: Simplified paleoecological reconstruction: Using pollen and heavy metals as two key indicators, we can quickly obtain information on historical organisms and disturbances.

[0062] Simplified transformation function: The weighted average partial least squares (WA-PLS) model is directly adopted, eliminating the need for DCA gradient length determination, which is suitable for scenarios with small amounts of data.

[0063] Enhanced elasticity assessment: Three methods are used to cross-validate the elastic force and steady-state transition results: hysteresis-autocorrelation analysis, Cusp model, and Bayesian change point detection.

[0064] Set goals: Following the rules of the original step five, aim for a highly resilient state after cross-validation to ensure the reliability of the results.

[0065] This invention establishes a differentiated restoration target determination rule based on the steady-state transition judgment result: that is, regardless of whether the ecosystem undergoes a steady-state transition, the biological community and environmental factors corresponding to the high-elasticity state (expansion / maintenance phase) are used as reference targets; that is, if a steady-state transition occurs, the high-elasticity state before the release phase is used as the benchmark, rather than the high-elasticity state during the reorganization phase, thus providing a clear and feasible target basis for wetland ecological restoration.

[0066] This invention innovatively integrates paleoecological technology with ecosystem resilience theory: it constructs a complete method for setting reference targets for wetland ecological restoration, taking into account the natural succession process, self-recovery capacity, and steady-state transition characteristics of wetlands. It couples paleoecological reconstruction (millennial scale) with resilience assessment (centennial scale), and by identifying the critical point of the "release" stage of the ecosystem's adaptive cycle, it reversely locks in a highly resilient reference state, breaking through the limitation of traditional restoration targets relying solely on historical data or reference points.

[0067] Compared to paleoecological methods: This invention breaks through the limitations of single technology and achieves the integration of multi-dimensional theory and technology: the core of paleoecology technology focuses on the reconstruction and retrospection of the historical ecological characteristics of wetlands, while this invention deeply integrates it with the theory of ecosystem resilience. It can not only restore the historical ecological state, but also simultaneously consider the natural succession process, self-recovery capacity and steady-state transformation characteristics of wetlands, making up for the shortcomings of paleoecology technology that only focuses on "historical state" and ignores "system dynamic recovery potential".

[0068] This invention clarifies the elastic orientation of restoration goals and enhances the pertinence of restoration practices: Paleoecological techniques can provide historical ecological baselines, but cannot directly serve the core need of "high-stability restoration"; this invention relies on the theory of ecosystem resilience, takes "high-resilience state" as the restoration orientation, and can accurately target the state with strong self-recovery ability and not prone to catastrophic degradation.

[0069] This invention ensures the stability and sustainability of restoration results: Paleoecological techniques lack consideration for the steady-state transition of ecosystems, and restoration targets based on them may easily lead to re-degradation after restoration due to failure to avoid system elasticity thresholds; This invention, by integrating elasticity theory, clarifies the characteristics of steady-state transitions and ensures that restoration targets correspond to highly elastic stages of ecosystems such as "expansion / maintenance", thereby fundamentally improving the stability of wetland restoration results and overcoming the shortcomings of paleoecological techniques in ensuring the long-term effectiveness of restoration.

[0070] The method for setting reference targets for wetland ecological restoration according to this application will be further explained below with reference to specific embodiments.

[0071] Example 1 Setting goals for the restoration of the Sanjiang Plain wetland ecosystem Step 1: Modern Ecological Baseline Survey (1.1) Sample site selection and testing: Eight typical wetlands, such as floodplains, valleys, and lakeshores, were selected in the Sanjiang Plain to ensure coverage of different ecological gradients. Thirty-one sampling points were set up in each wetland to collect surface sediments and water samples. The diatom community in the surface sediments was identified, and environmental factors such as organic matter content, electrical conductivity, available nitrogen, and available phosphorus in the water samples were tested.

[0072] (1.2) Key Relationship Ranking Analysis: DCA analysis was performed on the diatom community. The DCA gradient length of diatoms was <3. RDA was used to rank diatoms and environmental factors. It was found that water level and available nitrogen were more influential in determining the distribution of diatom species than other environmental factors.

[0073] (1.3) Diatom-environment transition function: Construct diatom combination and water level (WA-PLS model, R) respectively. 2 =0.22), NH4-N (WA_Inv model, R 2 The quantitative transformation function (=0.19) provides a tool for quantitative reconstruction of paleoecology.

[0074] Step 2: Multi-indicator paleoecological reconstruction (2.1) Core collection: Peat cores were collected from the Honghe Wetland, which is representative and has continuous sedimentation. Russian peat drills and Wardenaar samplers were used to collect 148 samples at 1-cm intervals.

[0075] (2.2) High-precision dating: 210 Pb dating: Establishing a chronological sequence of the last 100-150 years (top layer 0-42cm).

[0076] AMS 14 C-Dating: Select plant macrofossils / organic matter samples, calibrate using the Bacon model, and establish a millennial-scale chronological framework (depth 50-148cm).

[0077] Linear interpolation: Interconnecting the intermediate layers by age to finally establish a continuous time series dating back 6543 years.

[0078] (2.3) Identification of biological indicators in sedimentary rock cores: Plant fossils: Identify root, stem, and leaf remains to determine the vegetation community succession over the past 6,500 years, from horsetail to sicklebush to hairy sedge.

[0079] Diatom analysis: 300 diatoms / sample were counted and identified according to the Krammer classification system. The dominant species succession sequence was identified as: Cyclops rotundi → Dipteros Libyanis → Pinnatifida brevipedimenta.

[0080] (2.4) Reconstruction of water level / available nitrogen change history: Based on the constructed diatom assemblage and water level (WA-PLS model, R 2 =0.22), NH4-N (WA_Inv model, R 2 The quantitative conversion function (=0.19) was used to reconstruct the water level and NH4-N variation characteristics since 6543 years ago.

[0081] Step 3: Quantitative Inversion and Stage Division of Historical Human Activity Disturbance (3.1) Geochemical index testing: Determine heavy metals (Cu / Zn / Pb) and polycyclic aromatic hydrocarbons (PAHs), calculate enrichment factors (EF), and identify signals of human activity. Combine archaeological and historical documents, such as ban policies, the Chinese Eastern Railway, immigration and land reclamation, and population size, to determine the timeline of interference.

[0082] 1920s: The first wetlands began to be disturbed, and EF rose slightly.

[0083] 1950s: Large-scale reclamation affected almost all wetlands.

[0084] Late 1970s: Agricultural modernization led to a sharp increase in all geochemical indicators (heavy metals, PAHs).

[0085] Step 4: Ecosystem resilience assessment and identification of mechanism transition points (4.1) Extraction of slow variables: PCA analysis was performed on the plant and diatom communities, and PC1 (which explained 41% and 30% of the variance, respectively) was extracted as a slow variable of system state.

[0086] (4.2) Elastic force monitoring: The Lag-1 autocorrelation coefficient of PC1 was calculated, and it was found that the autocorrelation value reached its maximum around 1990, and the system elasticity dropped to its minimum.

[0087] (4.3) Transition Point Detection: The STARS algorithm is used to identify transition points. Diatom communities: First response in 1950 and major shift in 1990.

[0088] Plant communities: Synchronous transformation in 1990.

[0089] (4.4) Adaptation cycle phase division: Expansion and maintenance phase – before the 1970s: The system was stable and highly resilient, with a water level of 11-13 cm.

[0090] Release Phase – 1970s-1990: Functionality degraded, elasticity decreased, and resources were rapidly released.

[0091] Reorganization phase – post-1990s: Ecological functions were damaged, and a new steady state was entered.

[0092] Step 5: Reconstructing the Reference Target System (5.1) Determination of restoration target: The ecological characteristics of the expansion and maintenance phase (before the 1970s) are selected as the restoration target, rather than the undisturbed original state, reflecting the concept of "resilient restoration".

[0093] (5.2) Quantitative parameter setting: Hydrological conditions: Water level 11–13 cm (>current status), NH4-N 0.8–1.2 mg / L.

[0094] Biology: The dominant vegetation is the *Drepanocladus aduncus* community, and the diatoms are the *Pinnularia brevicostata* community.

[0095] Environmental quality: Cu enrichment factor <1.3, Zn <1.1, Pb <1.2.

[0096] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for establishing reference targets for wetland ecological restoration, characterized in that, Includes the following steps: S1. Based on wetland biological community data and environmental factor data, construct a transformation function between wetland biological community and environmental factors; S2. Obtain biological community data from different historical periods using sediments from different strata in wetlands, and reconstruct environmental factor data from different historical periods by substituting them into the transformation function. S3. Test the specified materials in different sediment layers in the wetland, and combine them with archaeological and historical documents from different historical periods to obtain the intensity changes of human activities affecting the wetland ecosystem; S4. Perform principal component analysis on biological community data from different historical periods, extract the first principal component score PC1, assess the changes in ecosystem resilience based on the first principal component score PC1, and determine whether the ecosystem has undergone a steady-state transition and the timing of the steady-state transition. Based on the intensity of human activities affecting wetland ecosystems in different historical periods, if a steady-state transition occurs, the ecosystem is divided into an expansion phase, a maintenance phase, a release phase, and a reorganization phase according to the timeline; if no steady-state transition occurs, the ecosystem is divided into an expansion phase and a maintenance phase according to the timeline. S5. Select biological community data and environmental factor data corresponding to the highly resilient states during the expansion and maintenance phases as reference targets for restoration.

2. The method for formulating reference targets for wetland ecological restoration according to claim 1, characterized in that, In step S1, the environmental factors used to construct the transformation function are the main environmental factors.

3. The method for formulating reference targets for wetland ecological restoration according to claim 2, characterized in that, In step S1, constructing the transformation function between wetland biological communities and main environmental factors includes the following steps: To acquire data on biological communities and environmental factors in wetlands; Trend correspondence analysis was used to process the biological community data, thereby calculating the gradient length of the biological community. The biological community data and environmental factor data are sorted according to the gradient length of the biological community. Extract the main environmental factors affecting biological communities based on the ranking results; We will choose a mathematical model to construct a transformation function between the biological community and the main environmental factors.

4. The method for formulating reference targets for wetland ecological restoration according to claim 3, characterized in that, The mathematical models are selected from multiple methods including weighted average, partial least squares, maximum likelihood and machine learning. Each mathematical model is selected to construct a transformation function between the biological community and the main environmental factors, and then one of them is selected as the optimal transformation function.

5. The method for formulating reference targets for wetland ecological restoration according to claim 4, characterized in that, The selection of the optimal transformation function includes: The bootstrap method is used to perform cross-validation on each transformation function. The validation results are judged based on the error between the predicted and observed values, the correlation coefficient between the predicted and observed values, and the maximum deviation. The optimal transformation function is selected based on the error, correlation coefficient, and maximum deviation.

6. The method for formulating reference targets for wetland ecological restoration according to claim 3, characterized in that, If the gradient length is greater than 4, the constraint sorting method for the linear model is selected; if the gradient length is less than 3, the constraint sorting method for the unimodal model is selected; if the gradient length is less than or equal to 4 but greater than or equal to 3, either the constraint sorting method for the linear model or the constraint sorting method for the unimodal model is selected.

7. The method for formulating reference targets for wetland ecological restoration according to claim 1, characterized in that, In step S2, obtaining biological community data from different historical periods using sediments from different strata in the wetland includes the following steps: First, a wetland sediment core was drilled, and then... 210 Pb / 137 Cs、 14 The C-dating method establishes a chronological framework, tests the bulk density and loss on ignition of different strata, identifies biological community indicators in sediments of different strata, and obtains biological community data for different historical periods.

8. The method for formulating reference targets for wetland ecological restoration according to claim 1, characterized in that, In step S3, the selected substance is selected from one or more of heavy metals, microplastics, and organic pollutants.

9. The method for formulating reference targets for wetland ecological restoration according to claim 8, characterized in that, In step S3, obtaining the intensity changes of human activities' disturbance to the wetland ecosystem includes the following steps: Test various target substances in sediments at different strata in wetlands, calculate the enrichment factors of each target substance in sediments at different strata, and determine the interference timeline by combining archaeological and historical documents.

10. The method for formulating reference targets for wetland ecological restoration according to claim 1, characterized in that, In step S4, the assessment of ecosystem resilience changes based on the first principal component score PC1, and the determination of whether a steady-state transition has occurred and the timing of such a transition, include the following steps: The first principal component score PC1 was analyzed using lag-autocorrelation analysis, variance analysis, and skewness analysis to assess changes in wetland resilience. Based on the calculation results of the three methods at different historical periods, the strength of ecosystem resilience at different historical periods was determined. Sequence T-test, F-statistics, and Mann-Kendall sequence mutation analysis were used to assess whether the ecosystem underwent a steady-state transition at different historical periods and to identify the periods in which the steady-state transition occurred.

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