Estuary water ecology monitoring method based on diatom indicator species recognition and environmental response

Abstract

The invention provides an estuary water ecology monitoring method based on diatom indication species identification and environmental response. The estuary water ecology monitoring method comprises the following steps: sampling a monitored estuary to obtain water ecology monitoring data including water environment factors and diatom population data; diatom indicator species are identified by adopting a biological indicator value method; acquiring time sequence data of the same diatom indicator species in the sedimentary column by adopting a sedimentary chronology method; obtaining a mutation point of the time sequence of the diatom indicator species through MK inspection; a random forest model between water environment factors and diatom indication species is trained; setting water environment scene data to be input into the random model, obtaining an abnormal scene in which the predicted data of the diatom indication species exceeds the mutation point, and obtaining a water environment factor threshold according to the abnormal scene. The problems that in the prior art, indication species cannot be recognized by quantifying the relation between diatom population and environmental factors, and mutation points of ecological abnormality are difficult to recognize by obtaining the historical evolution law of the indication species, so that the pertinence of a monitoring result is weak are solved.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a method for monitoring estuarine aquatic ecosystems based on diatom indicator species identification and environmental response. Background Technology

[0002] Estuaries, as a crucial intersection of terrestrial and marine ecosystems, possess both freshwater and seawater mixing characteristics and serve as a core hub for material cycling, energy exchange, and biological migration. Their aquatic ecological health is directly linked to watershed ecological security and regional sustainable development. However, influenced by multiple factors such as urbanization, industrial and agricultural pollution, shipping disturbances, and climate change, estuaries face prominent ecological problems including eutrophication, habitat degradation, and reduced biodiversity. Therefore, accurate monitoring of the aquatic ecosystem's status and environmental response mechanisms has become an urgent need in the field of ecological protection.

[0003] Traditional estuarine aquatic ecosystem monitoring relies heavily on real-time detection of physicochemical indicators such as dissolved oxygen, nitrogen and phosphorus nutrients, and pH. While these methods can reflect instantaneous environmental conditions, they have limitations, including short monitoring cycles, difficulty in tracing historical changes, and an inability to quantify the long-term cumulative effects of ecosystems. In contrast, biomonitoring, with its advantages of sensitivity, cumulative response, and comprehensive responsiveness of biological communities to environmental changes, has become an important means to compensate for the shortcomings of physicochemical monitoring. Diatoms, as a widely distributed and diverse microalgae group in estuarine waters, have cell walls rich in silica and are easily preserved in sediments. Their population structure is closely related to aquatic environmental factors, and different diatom species show significant differences in tolerance and preference for specific environmental factors, giving them a unique advantage as long-term indicator organisms of the aquatic ecosystem.

[0004] Currently, some progress has been made in diatom-based ecological monitoring research, mainly focusing on the correlation analysis between diatom community structure and aquatic environmental factors, and the screening and application of single indicator species. However, existing methods lack systematic identification and validation of diatom indicator species, relying heavily on empirical screening and failing to fully integrate quantitative methods to clarify their environmental response specificity. Furthermore, short-term monitoring makes it difficult to obtain historical evolution patterns of diatom populations, resulting in an inability to accurately pinpoint key time points of ecological anomalies. Summary of the Invention

[0005] The embodiments of the present invention provide a method for monitoring estuarine aquatic ecosystems based on diatom indicator species identification and environmental response. This method aims to address the problems of existing technologies being unable to identify indicator species by quantifying the relationship between diatom populations and environmental factors, and having difficulty identifying abrupt changes in ecological anomalies by obtaining the historical evolutionary patterns of indicator species, resulting in weak targeting of monitoring results and low early warning value.

[0006] To achieve the above objectives, this invention provides a method for estuarine aquatic ecosystem monitoring based on diatom indicator species identification and environmental response, comprising the following steps: Aquatic ecological monitoring data is obtained by sampling at sampling points evenly distributed in the monitored estuary; the aquatic ecological monitoring data includes aquatic environmental factors and diatom population data; The biological indicator method was used to identify diatom indicator species that have an indicative effect on the aforementioned aquatic environmental factors; Time series data of diatom indicator species with the same species or genus name in the monitored estuarine sedimentary column were obtained using sedimentary chronology methods. By performing the MK test on the time series data of the diatom indicator species, the mutation points of the diatom indicator species data in historical periods were obtained; The random forest model was trained using the aforementioned water ecological monitoring data to quantify the response relationship between water environmental factors and diatom indicator species. Set up environmental scenario data for different water environment factors, input the environmental scenario data into the stochastic model, and output diatom indicator species prediction data for different environmental scenarios; An environmental scenario in which the predicted data of the diatom indicator species exceeds the mutation point is considered an abnormal scenario, and the management threshold of the water environment factor is obtained based on the abnormal scenario.

[0007] Furthermore, the water environmental factors include water temperature (WT), salinity (SAL), pH, total nitrogen (TN), total organic carbon (TOC), total phosphorus (TP), nitrite (NO2-N), nitrate (NO3-N), and / or ammonia nitrogen (NH3-N).

[0008] Furthermore, the biological indicator method includes the following steps: By establishing a redundancy analysis model between water environment factors and diatom population data, key water environment factors that are significantly related to diatom communities are identified. The K-means method was used to cluster key water environment factors to obtain the environmental feature types defined by each cluster. Calculate the bioindicator values ​​of each species in the diatom population for different environmental characteristics, and obtain the diatom indicator species for different environmental characteristics.

[0009] Furthermore, the redundancy analysis model is modeled with the relative abundance of each species in the diatom community as the response variable and water environmental factors as the explanatory variables. The key water environmental factors are determined based on the projection relationship and / or arrow angle between diatom species and water environmental factors in the redundancy analysis model ordination diagram.

[0010] Furthermore, the bioindicator value is calculated using the following formula: , , , In the formula, Indicates the first The first diatom species for the first Bioindicator values ​​for each type of environmental characteristic; Indicates the first The first diatom species for the first Specificity of each environmental characteristic type; Indicates the first The first diatom species for the first Specificity of each environmental characteristic type; Indicates the first The first diatom species in the Average relative abundance of each environmental characteristic type; Indicates the first The sum of the relative abundance of diatom species across all samples; Indicates the first The first diatom species in the The number of samples appearing for each environmental feature type; Indicates the first Total number of samples for each environmental feature type.

[0011] Furthermore, the random forest model uses the relative abundance of diatom indicator species as the response variable and key aquatic environmental factors as explanatory variables.

[0012] Furthermore, the sedimentary geochronology method includes the following steps: Within the monitored estuary area, sediment column sampling points with a level of human disturbance below the predetermined conditions were identified; Deposition column samples are collected through the sampling points of the deposition column, and the column body is cut at predetermined intervals to obtain multiple deposition column sub-samples. The sub-samples are numbered according to the cutting order. use 210 Pb and / or 137 Cs radiometric dating was used to determine the age of different sedimentary column samples. The diatom populations of the sedimentary column samples were identified in numerical order, and diatom indicator species were extracted to obtain time series data of diatom indicator species.

[0013] The above technical solution has the following technical effects: This invention obtains aquatic ecological monitoring data, including aquatic environmental factors and diatom population data, by sampling the monitored estuary. Diatom indicator species are identified using the biological indicator value method. Time series data of the same diatom indicator species in the sedimentary column are obtained using sedimentary chronology. Mutual abrupt changes in the historical periods of diatom indicator species are identified by performing the MK test on the time series data. A random forest model between aquatic environmental factors and diatom indicator species is trained using the aquatic ecological monitoring data. Environmental scenario data for different aquatic environmental factors are input into the random model, and predicted data for diatom indicator species under different environmental scenarios are output. Environmental scenarios where the predicted data for diatom indicator species exceeds the mutation point are considered abnormal scenarios, and monitoring thresholds for aquatic environmental factors are obtained based on these abnormal scenarios. This invention solves the problems of existing technologies being unable to identify indicator species by quantifying the relationship between diatom populations and environmental factors, and struggling to identify ecological anomalies by obtaining the historical evolutionary patterns of indicator species, resulting in weak monitoring results and low early warning value. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating an embodiment of the present invention of an estuarine aquatic ecosystem monitoring method based on diatom indicator species identification and environmental response.

[0015] Figure 2 This is a ranking diagram of diatom species and aquatic environmental factors in a redundancy analysis model according to an embodiment of the present invention.

[0016] Figure 3 This is a bar chart showing the bioindicator values ​​of diatom species according to an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the various embodiments, the present invention provides accompanying drawings. These drawings are part of the disclosure of the present invention, primarily used to illustrate the embodiments and to explain the operating principles of the embodiments in conjunction with the relevant descriptions in the specification. With reference to these drawings, those skilled in the art should be able to understand other possible implementations and the advantages of the present invention. Components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0018] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0019] Example 1: Figure 1 This is a flowchart illustrating an embodiment of the estuarine aquatic ecosystem monitoring method based on diatom indicator species identification and environmental response according to an embodiment of the present invention. Figure 1 As shown, the method of this embodiment includes the following steps: Aquatic ecological monitoring data is obtained by sampling at sampling points evenly distributed in the monitored estuary; in one specific implementation, the aquatic ecological monitoring data includes aquatic environmental factors and diatom population data. In this embodiment, 24 sampling points were evenly distributed at the monitored estuary. Sampling was conducted during the dry season, normal water season, and wet season. Each sampling session was conducted by 5-6 researchers at each site for 3-4 days. For sampling points with a water depth of <10m, surface water samples (0.1-1.0m) were collected to represent the average state of the water column. For sampling points with a water depth of 10-25m, mixed samples of surface (0.1-1.0m) and bottom (2m above the bottom) water were collected. Water temperature (WT), salinity (SAL), and pH were measured on-site using a multi-parameter water quality analyzer (YSI 6600). Collect 1L water samples, freeze and store them, and bring them back to the laboratory to determine total nitrogen (TN), total organic carbon (TOC), total phosphorus (TP), nitrite (NO2-N), nitrate (NO3-N), ammonia nitrogen (NH3-N), etc.; collect diatom population data by using a water sampler to collect 1L water samples at a depth of 0.1~1.0m from the surface of the water body, place them in HDPE bottles, fix them with 15ml of Lugol reagent, and entrust a professional institution to identify the species (to the species or genus) and count the number of planktonic diatoms, and calculate the relative abundance of each planktonic diatom.

[0020] The biological indicator method was used to identify diatom indicator species that have an indicative effect on aquatic environmental factors; In this embodiment, diatoms, as a widely distributed, rapidly reproducing, and environmentally sensitive phytoplankton group in estuarine waters, exhibit a close response relationship between their population structure (species composition, abundance, relative abundance, dominant species, etc.) and aquatic environmental factors, making them important biological indicators of aquatic environmental changes. Existing research has attempted to utilize the indicative role of diatoms for aquatic environment assessment, but most studies remain at the qualitative descriptive level, failing to establish precise correlations between diatoms and aquatic environmental factors through quantitative models. This results in the underutilization of the indicative value of diatoms and makes it difficult to infer historical aquatic environmental states based on diatom data. The core principle of using diatoms as sedimentary columns to infer historical aquatic environments lies in the superposition of their triple characteristics: environmental sensitivity, community specificity, and fossil preservation stability. Diatoms have a highly sensitive response mechanism to aquatic environmental factors such as temperature, salinity, nutrients, and pH in estuarine waters. Different aquatic environmental conditions will select specific diatom communities, and this environment-community correspondence exhibits spatiotemporal stability. Meanwhile, diatom cell walls contain a siliceous shell, exhibiting strong resistance to decomposition. They can accumulate layer by layer in estuarine sedimentary columns, remaining intact as fossils over long periods. By collecting sedimentary columns and extracting diatom fossils according to stratigraphic layers, combined with sedimentary chronology techniques, the historical years corresponding to each layer can be determined, constructing a continuous time series of diatom indicator species. In one specific implementation, the bioindicator method includes the following steps: By establishing a redundancy analysis model between water environment factors and diatom population data, key water environment factors that are significantly related to diatom communities are identified. In one specific implementation, the redundancy analysis model is modeled with the relative abundance of each species in the diatom community as the response variable and water environment factors as the explanatory variables. The significance of the model is judged by Monte Carlo permutation test (permutation times = 999) (p<0.05). The key water environment factors are determined based on the projection relationship between diatom species and water environment factors and / or the arrow angle in the redundancy analysis model ordination diagram. Figure 2 This is an ordination diagram of diatom species and aquatic environmental factors in a redundancy analysis model according to an embodiment of the present invention. Figure 2 It can be seen that the projection of the diatom population mainly falls on SAL, WT, TP, TN and pH. Therefore, these water environment factors are the key water environment factors identified in this embodiment.

[0021] The K-means method is used to cluster key water environment factors to obtain the environmental feature types defined by each cluster. In one specific implementation, the optimal number of clusters is determined by iteratively calculating the sum of squared deviations within the total group, i.e., the number of environmental feature types, which is 3 in this embodiment. Cluster 1 has the environmental feature of low WT and high TP; cluster 2 has the environmental feature of high SAL and low TP; and cluster 3 has the environmental feature of high WT and low SAL.

[0022] Calculate the bioindicator values ​​of each species in the diatom population for different environmental characteristics, and obtain the diatom indicator species for different environmental characteristics.

[0023] In one specific implementation, the bioindicator value is the IndVal index, which is calculated using the following formula: , , , In the formula, Indicates the first The first diatom species for the first Bioindicator values ​​for each type of environmental characteristic; Indicates the first The first diatom species for the first Specificity of each environmental characteristic type; Indicates the first The first diatom species for the first Specificity of each environmental characteristic type; Indicates the first The first diatom species in the Average relative abundance of each environmental characteristic type; Indicates the first The sum of the relative abundance of diatom species across all samples; Indicates the first The first diatom species in the The number of samples appearing for each environmental feature type; Indicates the first Total number of samples for each environmental feature type.

[0024] Figure 3 This is a bar chart of diatom species bioindicator values ​​according to an embodiment of the present invention, such as... Figure 3 As shown, this embodiment has 18 significant diatom indicator species, among which cluster 1 contains... Skeletonema costatum ( =0.62), Cluster 2 Lauderia annulata ( =0.58), Cluster 3 Cyclotella sp. ( =0.45) is the most representative.

[0025] Time series data of diatom indicator species with the same species or genus name in the monitored estuarine sedimentary column were obtained using sedimentary chronology methods. In this embodiment, historical information on diatom indicator species is extracted from estuarine sedimentary columns to construct complete time-series data, providing crucial evidence for tracing the evolution of aquatic ecosystems. As a transitional zone between land and sea, estuaries have formed vertically distributed sedimentary columns through long-term sediment deposition. Sediments at different depths record ecological and environmental information from corresponding historical periods; surface sediments correspond to recent times, while deeper sediments can be traced back hundreds of years or even longer. Sedimentary chronology, through… 210 Precise dating techniques such as Pb dating can pinpoint the exact formation age of each stratum in a sediment column, providing a temporal coordinate for subsequent data. Based on this, systematic sampling, laboratory separation, and identification of strata from different ages within the sediment column are conducted to screen for diatom remains (including intact shells and fragments) of the same genus or species as the diatom indicator species identified in the second step. Key indicators such as abundance, dominance, and frequency of occurrence of these diatoms at various time points are then statistically analyzed. Arranging this data chronologically creates a time-series data set of "historical age – diatom indicator species abundance / distribution characteristics." This data chain comprehensively presents the dynamic changes of diatom indicator species over a long historical period, providing indispensable foundational data support for subsequent identification of historical abrupt changes and tracing the evolution of the aquatic environment through MK testing. It is a crucial link connecting current status monitoring and historical retrospection.

[0026] In one specific implementation, the sedimentary chronology method includes the following steps: Within the monitored estuary area, sediment column sampling points with a level of human disturbance below the predetermined conditions were identified; Deposition column samples are collected through sampling points in the deposition column, and the column body is cut at predetermined intervals to obtain multiple deposition column sub-samples. The sub-samples are numbered according to the cutting order. use210 Pb and / or 137 Cs radiometric dating was used to determine the age of different sedimentary column samples. The diatom populations of the sedimentary column samples were identified in numerical order, and diatom indicator species were extracted to obtain time series data of diatom indicator species.

[0027] By performing the MK test on the time series data of diatom indicator species, the mutation points in the historical periods of the diatom indicator species data were obtained; In this embodiment, the MK test, as a non-parametric statistical method, does not require the assumption that the data follows a specific distribution. It can effectively handle outliers and missing values ​​that may exist in the time series data of diatom indicator species obtained by sedimentary chronology methods, and accurately capture significant turning points in data trends. This characteristic is highly compatible with the complex temporal dynamics of diatom populations in estuarine sedimentary environments, which are affected by natural fluctuations and human activities, and avoids the errors caused by the strict requirements of traditional parametric tests on data distribution.

[0028] At the operational level, the time-series data of diatom indicator species extracted from the sediment column must first be preprocessed to remove extreme outliers caused by sampling errors, ensuring the continuity and reliability of the data. Then, the MK test is used to calculate the Z-value and the confidence level corresponding to the mutation point. When the absolute value of the Z-value exceeds the critical value and the confidence level meets the set standard, the time point can be determined as a historical mutation point for diatom indicator species. This mutation point is essentially a direct manifestation of the significant ecological response of diatom populations after long-term accumulation of changes in estuarine water environmental factors. It reflects the key node in the transition of the water environment from a stable state to an abnormal state. For example, disturbances such as water quality deterioration and changes in hydrological conditions leave ecological imprints through changes in the abundance and dominance of diatom species, and mutation points are the concentrated manifestation of these imprints.

[0029] The core value of this step lies in transforming the time-series data of diatom indicator species into quantifiable ecological transition signals. This not only avoids the limitation of relying solely on current monitoring data to trace historical evolution, but also provides a clear benchmark threshold reference for subsequent prediction of diatom responses under different environmental scenarios using random forest models, making the final water environment monitoring threshold more historically relevant and ecologically reasonable.

[0030] A random forest model is trained using aquatic ecological monitoring data to study the relationship between aquatic environmental factors and diatom indicator species. In one specific implementation, the random forest model uses the relative abundance of diatom indicator species as the response variable and key aquatic environmental factors as explanatory variables. The random forest model aims to quantify the complex nonlinear relationship between aquatic environmental factors and diatoms, and is suitable for the complex gradient changes in estuaries.

[0031] Set up environmental scenario data for different water environmental factors, input the environmental scenario data into a stochastic model, and output diatom indicator species prediction data for different environmental scenarios; Environmental scenarios where the predicted data for diatom indicator species exceed the mutation point are considered abnormal scenarios, and management thresholds for water environmental factors are obtained based on these abnormal scenarios.

[0032] In this embodiment, these two consecutive steps constitute the core closed loop from "scenario prediction" to "threshold quantification." Essentially, it clarifies the ecological security boundary of the estuarine water environment through model extrapolation and benchmarking against historical standards. Setting environmental scenario data for different water environment factors requires basing them on the actual environmental gradient and potential disturbance scenarios of the estuary, covering natural fluctuations (such as seasonal salinity changes) and anthropogenic disturbance scenarios (such as increased nutrient discharge and water quality deterioration). It is important to note that scenario design must balance rationality and comprehensiveness, neither deviating from actual environmental characteristics nor omitting key risk scenarios. For example, gradient scenarios can be set according to nutrient concentration gradients, or scenarios simulating abrupt salinity changes under extreme hydrological conditions can be simulated to ensure coverage of various environmental states that diatom indicator species may face.

[0033] After inputting scenario data into the trained random forest model, the output diatom indicator species prediction data is essentially a simulation result of ecological response under different environmental stresses, directly reflecting the quantitative impact of changes in water environment factors on the relative abundance of species. Subsequently, by benchmarking against identified historical abrupt change points, the core logic is to correlate the "historical ecological transition benchmark" with the "future scenario response." When the predicted relative abundance of diatoms exceeds the value corresponding to the abrupt change point, it means that the environmental scenario has exceeded the ecological tolerance threshold of diatom species, and the corresponding water environment state has deviated from the historical stable range, constituting an abnormal scenario.

[0034] Ultimately, the key to transforming ecological response signals into actionable monitoring indicators lies in using reverse engineering of water environment factor management thresholds based on abnormal scenarios. This involves analyzing the specific numerical ranges of various water environment factors during abnormal scenarios to determine the critical factor levels that lead to significant mutations in diatom species, such as nutrient concentration thresholds or critical salinity gradients. This process avoids the limitations of setting thresholds solely based on physicochemical standards and, through the triple correlation of environment, species, and historical benchmarks, makes the monitoring thresholds more ecologically relevant and practically instructive, providing precise and actionable judgment criteria for estuarine aquatic ecosystem protection and risk early warning.

[0035] Although the invention has been specifically shown and described in conjunction with preferred embodiments, those skilled in the art should understand that various changes in form and detail may be made to the invention without departing from the spirit and scope of the invention as defined in the appended claims, all of which shall be within the scope of protection of the invention.

Claims

1. A method for monitoring estuarine aquatic ecosystems based on diatom indicator species identification and environmental response, characterized in that, Includes the following steps: Aquatic ecological monitoring data is obtained by sampling at sampling points evenly distributed in the monitored estuary; the aquatic ecological monitoring data includes aquatic environmental factors and diatom population data; The biological indicator method was used to identify diatom indicator species that have an indicative effect on the aforementioned aquatic environmental factors; Time series data of diatom indicator species with the same species or genus name in the monitored estuarine sedimentary column were obtained using sedimentary chronology methods. By performing the MK test on the time series data of the diatom indicator species, the mutation points of the diatom indicator species data in historical periods were obtained; The random forest model was trained using the aforementioned water ecological monitoring data to quantify the response relationship between water environmental factors and diatom indicator species. Set up environmental scenario data for different water environment factors, input the environmental scenario data into the stochastic model, and output diatom indicator species prediction data for different environmental scenarios; An environmental scenario in which the predicted data of the diatom indicator species exceeds the mutation point is considered an abnormal scenario, and the management threshold of the water environment factor is obtained based on the abnormal scenario.

2. The estuarine aquatic ecosystem monitoring method based on diatom indicator species identification and environmental response as described in claim 1, characterized in that, The water environment factors include water temperature, salinity, pH, total nitrogen, total organic carbon, total phosphorus, nitrite, nitrate and / or ammonia nitrogen.

3. The estuarine aquatic ecosystem monitoring method based on diatom indicator species identification and environmental response as described in claim 1, characterized in that, The biological indicator method includes the following steps: By establishing a redundancy analysis model between water environment factors and diatom population data, key water environment factors that are significantly related to diatom communities are identified. The K-means method was used to cluster key water environment factors to obtain the environmental feature types defined by each cluster. Calculate the bioindicator values ​​of each species in the diatom population for different environmental characteristics, and obtain the diatom indicator species for different environmental characteristics.

4. The estuarine aquatic ecosystem monitoring method based on diatom indicator species identification and environmental response according to claim 3, characterized in that, The redundancy analysis model uses the relative abundance of each species in the diatom community as the response variable and water environment factors as the explanatory variables. The key water environment factors are determined based on the projection relationship and / or arrow angle between diatom species and water environment factors in the redundancy analysis model ordination diagram.

5. The estuarine aquatic ecosystem monitoring method based on diatom indicator species identification and environmental response according to claim 3, characterized in that, The bioindicator value is calculated using the following formula: ， ， ， In the formula, Indicates the first The first diatom species for the first Bioindicator values ​​for each type of environmental characteristic; Indicates the first The first diatom species for the first Specificity of each environmental characteristic type; Indicates the first The first diatom species for the first Specificity of each environmental characteristic type; Indicates the first The first diatom species in the Average relative abundance of each environmental characteristic type; Indicates the first The sum of the relative abundance of diatom species across all samples; Indicates the first The first diatom species in the The number of samples appearing for each environmental feature type; Indicates the first Total number of samples for each environmental feature type.

6. The estuarine aquatic ecosystem monitoring method based on diatom indicator species identification and environmental response according to claim 3, characterized in that, The random forest model uses the relative abundance of diatom indicator species as the response variable and key water environment factors as explanatory variables.

7. The estuarine aquatic ecosystem monitoring method based on diatom indicator species identification and environmental response according to claim 1, characterized in that, The sedimentary geochronology method includes the following steps: Within the monitored estuary area, sediment column sampling points with a level of human disturbance below the predetermined conditions were identified; Deposition column samples are collected through the sampling points of the deposition column, and the column body is cut at predetermined intervals to obtain multiple deposition column sub-samples. The sub-samples are numbered according to the cutting order. use 210 Pb and / or 137 Cs radiometric dating was used to determine the age of different sedimentary column samples. The diatom populations of the sedimentary column samples were identified in numerical order, and diatom indicator species were extracted to obtain time series data of diatom indicator species.

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