Method for indicating phytoplankton community change and application thereof

Abstract

The application discloses a phytoplankton community change marking method and application thereof. The marking method is designed based on the ecological functional group theory and the state space theory, that is, a continuous time sequence is discretized into time periods of any time scale, a regional phytoplankton community index (APCI) of each time period is calculated to quantitatively determine the phytoplankton community state, and then the phytoplankton community change is marked according to the change of the APCI of each time period on a continuous time axis. The marking method of the phytoplankton community change does not need to determine a reference state in advance, the community state is quantitatively determined by the APCI, and the marking method has the advantages of absoluteness, quantitativeness and high sensitivity compared with a marking method using a PCI, can better mark the relationship between the phytoplankton community change and the environment, and is suitable for marking the phytoplankton community change in any water body.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of phytoplankton community change research. More specifically, it relates to a method for indicating phytoplankton community change and its application. BACKGROUND

[0002] Phytoplankton is a unique group of organisms that live in a way of drifting with the current in water bodies. The individual is small and easy to do passive movement under the action of wind and water flow, and has no or only weak swimming ability. It plays a key role in global material circulation and energy flow, and the total primary productivity of marine phytoplankton has nearly accounted for half of the total global primary productivity. Due to the short generation cycle of phytoplankton, it is highly sensitive to environmental changes, and thus can be used as an ecological state indicator. Changes in phytoplankton community structure are often used to indicate environmental changes.

[0003] The phytoplankton community structure index (PCI) established based on the community functional group theory and the state space theory is an index for indicating the degree of change of "phytoplankton group composition and abundance" in the quality element processing of phytoplankton, which can effectively respond to the influence of environmental changes on community structure (Tett P, Carreira C, Mills D K, et al. Use of a Phytoplankton Community Index to assess the health of coastal waters [J]. Ices Journal of Marine Science, 2008, 65(8): 1475-1482.). The method for indicating the change of phytoplankton community by PCI is based on the functional group theory to reduce the dimension of the sample biological abundance matrix, to accumulate the abundance of species belonging to the same functional group, and to select representative functional groups as the calculation object for pairwise combination. These functional groups are called state variables, and the two-dimensional space formed by the combination of state variables is the state variable space. However, PCI does not quantify the state variable space. The calculation method of PCI takes the selected data as a reference, constructs the inner and outer convex hulls as the reference state through the convex hull algorithm, projects the sample points on the same state space, and determines the deviation degree (percentage) of the sample from the reference by counting the number of sample points falling in the state space, so as to determine the change of community structure relative to the reference state. Its value range is between [0, 1], and the larger the value is, the more similar the sample is to the reference state.

[0004] While phytoplankton community index (PCI) can effectively respond to the impact of environmental changes on community structure, its calculation requires a reference state, resulting in relative values. The lack of a unified standard for selecting this reference state introduces significant human bias into the PCI results, making comparisons between different studies or at different times incomparable. Therefore, it is necessary to develop a more accurate method for indicating phytoplankton community changes that is more responsive to environmental changes. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defects and deficiencies of the existing technologies and to provide a method for indicating changes in phytoplankton and its application.

[0006] The purpose of this invention is to provide a method for identifying phytoplankton communities.

[0007] Another object of the present invention is to provide the application of the method in indicating changes in phytoplankton communities in rivers, lakes and / or oceans.

[0008] The above-mentioned objective of this invention is achieved through the following technical solution:

[0009] This invention provides a method for indicating changes in phytoplankton communities. The method comprises: first, determining the area to be labeled; acquiring phytoplankton abundance data from samples within the area; selecting two representative functional groups within the area; merging the abundance data of the two functional groups according to their time series; then discretizing the time series into time periods of arbitrary time scales; quantifying the phytoplankton community state by calculating the Area Phytoplankton Community Index (APCI) for each time period; and indicating phytoplankton community changes based on the changes in the APCI over a continuous time axis. The calculation of the APCI includes the following steps:

[0010] S1. Perform logarithmic transformation on the merged functional group abundance data, and project the transformed data into a two-dimensional Cartesian coordinate system with the two selected functional groups as the x and y axes according to the discrete time periods, to obtain the scatter plots of their respective state spaces and determine the convex hulls.

[0011] S2. Calculate the convex hull area separately. The obtained convex hull area is the regional phytoplankton community index for the corresponding time period.

[0012] The phytoplankton community change labeling method described in this invention is consistent with PCI, both based on functional group theory and state-space theory. Functional group theory refers to reducing the high-dimensional multivariate matrix of phytoplankton abundance to a two-dimensional space, while state-space theory refers to constructing a two-dimensional coordinate system using paired functional groups as the horizontal and vertical axes. Specifically, the PCI calculation process involves constructing a two-dimensional coordinate system using paired functional groups as the horizontal and vertical axes, and selecting a specified time period (t). r The abundance data of ) is projected onto a two-dimensional coordinate system, and a ring-shaped figure is constructed through the calculation of the inner and outer convex hulls. The inner and outer convex hulls of this figure are used as the boundaries, and other time periods (t) are included. i Pairs of abundance data undergo the same transformation and are projected onto the same coordinate system. i The amount of paired abundance data over a time period is denoted as N. i The number of points falling within the boundary is denoted as n. i Points falling within the boundary represent points relative to t. r The phytoplankton community structure did not change during the time period; points falling outside the boundary represent points relative to t. r During a specific time period, the phytoplankton community structure changed, and the quantitative expression of PCI is n. i With N i The ratio. It can be seen that the quantification of PCI requires the determination of a reference state, and the result is a relative value. However, since there is no unified standard for the selection of the reference state, the calculation results of PCI are greatly influenced by human factors, and there is no comparability between different studies.

[0013] Unlike the PCI method, which requires a reference state, the phytoplankton community labeling method described in this invention does not require a reference state. Specifically, the method quantifies the phytoplankton community state by calculating the Regional Phytoplankton Community Index (APCI) over various time periods, and labels phytoplankton community changes based on the changes in APCI over a continuous time axis. Since each time period can determine a unique convex hull, the difference in the area of ​​the convex hull across different time periods indicates differences in the phytoplankton community state. By discretizing the continuous time series into continuous time periods, the changes in the convex hull area over each time period reflect the changes in the phytoplankton community structure over time; therefore, the labeling process does not require selecting a reference state. Furthermore, APCI is an independent variable across different time periods, overcoming the shortcomings of the PCI method, which suffers from inconsistent reference state selection criteria, leading to significant human factors in the calculation results and a lack of comparability between different studies or usage times. Additionally, since the PCI method is based on point counts, which inherently make it a discrete variable, the method described in this invention is based on continuous variables, resulting in a continuous variable outcome. PCI calculates based on the number of points in a 0-dimensional space, while this invention calculates based on the area in a 2-dimensional space. Changes in the functional group will be amplified in the form of squares, thus possessing higher sensitivity.

[0014] Furthermore, according to the moderate disturbance hypothesis, ecosystems exhibit the highest diversity under moderate disturbance conditions. In an absolutely stable system, there will be no succession or evolution. For an ecosystem to develop, it must provide opportunities for opportunistic species, thus requiring a certain degree of disturbance. In the natural environment, phytoplankton communities in any aquatic body will experience some degree of disturbance, and the resulting changes in phytoplankton abundance can be characterized using the method described in this invention. Additionally, due to the theory of natural variation, phytoplankton abundance in natural systems will fluctuate randomly, a characteristic not limited by the habitat of organisms. This random variation can also be characterized using the method described in this invention.

[0015] Specifically, the area to be marked is a river, lake, or seawater.

[0016] Specifically, the number of samples in the area to be labeled is not less than 3, and the selected phytoplankton abundance data is continuous time series phytoplankton abundance data.

[0017] Furthermore, when discretizing a time series into time periods, the time series can be discretized into several time periods of arbitrary time scales based on the obtained phytoplankton abundance data.

[0018] Specifically, in step S1, when performing logarithmic transformation on the functional group abundance data, the data should be made to tend towards a normal distribution to prevent maxima and minima from affecting the results.

[0019] Specifically, in step S1, the abundance data of functional groups are incremented by 1 before logarithmic transformation to prevent the abundance from being zero.

[0020] Specifically, step S1 uses a Gaussian scan algorithm to determine the convex hull in the defined state space.

[0021] Specifically, in step S2, the discrete Green's formula is used to calculate the convex hull area.

[0022] Specifically, the determined state space is the minimum convex hull containing all points in the two-dimensional Cartesian coordinate system.

[0023] This invention also seeks to protect the application of the above method in indicating changes in phytoplankton communities in rivers, lakes and / or oceans.

[0024] The present invention has the following beneficial effects:

[0025] This invention, based on functional group theory and state-space theory, provides a method for indicating phytoplankton community changes. Specifically, it quantifies the phytoplankton community state by calculating the Regional Phytoplankton Community Index (APCI) for each time period, and then indicates phytoplankton community changes based on the changes in APCI over a continuous time axis. This method does not require a pre-determined reference state; the community state is quantified by APCI. Compared to existing methods using PCI, it offers advantages such as absoluteness, quantification, and high sensitivity, better indicating the relationship between phytoplankton community changes and the environment. It is applicable to indicating phytoplankton community changes in any water body. Attached Figure Description

[0026] Figure 1 This is a schematic diagram illustrating the calculation process of the phytoplankton community change indication method described in this invention.

[0027] Figure 2 This is a map showing the distribution of sample collection stations in the Tolo Harbour water quality control area.

[0028] Figure 3 The convex hull plotting results for the phytoplankton community status in the Tolo Harbour water quality control area.

[0029] Figure 4 The results of APCI and PCI calculations for the Tolo Harbour Water Quality Control Zone.

[0030] Figure 5 This is a map showing the distribution of sample collection stations in the southern water quality control area of ​​Hong Kong.

[0031] Figure 6 This is a graph showing the trend of total inorganic nitrogen and reactive phosphate concentrations in the southern water quality control area of ​​Hong Kong.

[0032] Figure 7 The convex hull plotting results for the phytoplankton community status in the southern water quality control zone of Hong Kong.

[0033] Figure 8 The results of APCI and PCI calculations for the Southern Water Quality Control Area of ​​Hong Kong.

[0034] Figure 9 The convex hull plotting results for the phytoplankton community status of Cheney Reservoir.

[0035] Figure 10 The results of APCI and PCI calculations for Cheney Reservoir are shown. Detailed Implementation

[0036] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the embodiments do not limit the present invention in any way. Unless otherwise specified, the reagents, methods and equipment used in the present invention are conventional reagents, methods and equipment in this technical field.

[0037] Unless otherwise specified, all reagents and materials used in the following examples are commercially available.

[0038] Example 1

[0039] Because the calculation of the Phytoplankton Community Structure Index (PCI) requires the determination of a reference state, resulting in a relative value, and the lack of a unified standard for the selection of the reference state, the results are highly subjective, lack comparability between different studies, and have low sensitivity when used to indicate changes in phytoplankton communities. To overcome this problem, this invention provides a method for indicating phytoplankton community changes. This method discretizes a continuous time series into time periods of arbitrary time scales, calculates the convex hull area of ​​each time period (i.e., the Regional Phytoplankton Community Index (APCI), and quantifies the phytoplankton community state. The change in the convex hull area of ​​each time period reflects the change in phytoplankton community structure over time on a continuous time axis. A schematic diagram of the calculation process of the phytoplankton community change indication method described in this invention is shown below. Figure 1 As shown.

[0040] This embodiment illustrates the application of the labeling method described in this invention in reflecting changes in the phytoplankton community structure in harbors. It specifically describes the phytoplankton community change labeling method of this invention, based on 28 years of phytoplankton monitoring data from four stations in Tolo Harbour, Hong Kong. The data used was provided by the Hong Kong Environmental Protection Department, specifically the monthly phytoplankton abundance data for Tolo Harbour, Hong Kong, over 28 years (1991–2018). Monitoring was conducted monthly starting in 1986. The Hong Kong Environmental Protection Department was responsible for sample collection and processing. Surface water samples (1 m) were collected using a Nansen water sampler, and 200 mL of the sample was fixed using Lugol's reagent. The samples were then brought back to the laboratory for phytoplankton identification and counting.

[0041] This embodiment illustrates the changes in phytoplankton community in the Tolo Harbour water quality control area of ​​Hong Kong. Four sample collection stations, TM3, TM4, TM6, and TM8, were selected in the Tolo Harbour water quality control area (example sample collection station distribution is shown in the figure). Figure 2 As shown, TM3 and TM4 are located at the bottom of Tolo Harbor, and TM6 and TM8 are located at the port of Tolo Harbor. The continuous time series phytoplankton abundance data are specifically the monthly phytoplankton abundance data from 1995 to 2018. Diatoms and Dinoflagellates are two important phytoplankton functional groups, dominating in this sea area; therefore, diatoms and dinoflagellates were selected as functional groups in this embodiment. The phytoplankton abundance data from each collection station were merged according to the time series to assess the changes in phytoplankton community structure across the entire region. The merged data according to the time series is highly representative of the entire region. In this embodiment, the abundance data of the merged functional groups Diatoms and Dinoflagellates were extracted and calculated, and the time series was discretized into several time periods at different time scales. When discretizing the time series into time periods, the time series can be discretized into several time periods at any time scale based on the obtained sample phytoplankton abundance data.

[0042] Logarithmic transformation was performed by adding 1 to the merged functional group abundance data (to prevent zero abundance) to make the transformed data tend towards a normal distribution and prevent maxima and minima from affecting the results. The transformed paired data were projected onto a two-dimensional Cartesian coordinate system with diatoms as the x-axis and dinoflagellates as the y-axis according to the discrete time period to obtain the scatter plots of their respective state spaces, and the convex hull was determined using the Gaussian scan algorithm. The convex hull area was calculated using the discrete Green's formula, and the calculated value is the phytoplankton community index (APCI) of the Tolo Harbor water quality control area for the corresponding time period. The APCI was used to quantify the state of the phytoplankton community during that time period. The regional phytoplankton community index (APCI) for each time period was calculated to quantify the state of the phytoplankton community. The changes in the regional phytoplankton community index on the continuous time axis for each time period were used to indicate the changes in the phytoplankton community.

[0043] To demonstrate and compare the impact of different data volumes on the results, this embodiment selected three time scales: one year, three consecutive years, and five consecutive years. The larger the time scale, the larger the data volume. To illustrate the changing trends of the phytoplankton community index (APCI) in the Tolo Harbor water quality control area during different periods, six time periods were selected for each time scale, totaling 3 × 6 = 18 time periods. Using diatoms as the x-axis and dinoflagellates as the y-axis, the paired data from the 18 logarithmically transformed time periods were projected onto a two-dimensional Cartesian coordinate system to obtain a state-space scatter plot. The convex hull was determined, and its area was calculated. The results are as follows: Figure 3 As shown in the figure, the diagonally sloping straight line represents a 1:1 ratio of Dinoflagellates to Diatoms abundance; the intersection of the dashed lines parallel to the coordinate axes is the median intersection of functional group abundances, and the relative position of the intersection point to the sloping straight line reflects the variation in the functional group abundance ratio. Figure 3 The results from different time periods show that the abundance of Dinoflagellates has been declining since 1998, while the abundance of Diatoms has remained almost unchanged. This indicates that with the implementation of a series of management measures, the phytoplankton community structure has changed, shifting from Dinoflagellates to the more dominant Diatoms. The decline in Dinoflagellate abundance has altered the shape of the convex hull, making it gradually flatter, and the corresponding convex hull area has also changed, thus reflecting the change in the phytoplankton community structure.

[0044] This embodiment uses the aforementioned labeling method to process 28 years of phytoplankton abundance data in the Tolo Harbour water quality control area. To compare with the PCI, the same data was used to calculate the PCI. Referring to the time period selected by the APCI, the reference states for the PCI were set for 2018, 2016–2018, and 2014–2018, respectively.

[0045] The APCI and PCI calculation results for the Tolo Harbour water quality control area are as follows: Figure 4 As shown in the figure, the bold lines represent the results of linear regression, indicating the trend changes of APCI and PCI. Solid lines indicate significant trend changes, while dashed lines indicate indistinct trends. Figure 4 It can be seen that the APCI and PCI calculated by the labeling method described in this invention show opposite trends. With the implementation of control measures, APCI shows a dynamic pattern of fluctuating decline and exhibits greater variability; while the PCI results show that, compared with the reference state, the phytoplankton community structure in the control area has changed significantly over time.

[0046] The Tolo Harbour water quality control area is a harbor with a slow water exchange rate, resulting in longer residence times for phytoplankton. Once pollutants enter the water, they are difficult to dilute, easily leading to high pollution levels. However, once remediation measures are implemented to reduce pollutant discharge into the sea, the pollutant concentration in the water will quickly decrease. Since 1986, Hong Kong has implemented a series of measures to improve the water quality of Tolo Harbour. Following these measures, the nutrient content in the water of this area has significantly decreased. Changes in the nutrients required for phytoplankton growth will have a certain impact on phytoplankton, manifesting microscopically as changes in phytoplankton physiological and biochemical processes, and macroscopically as changes in community structure, specifically in abundance and composition. The phytoplankton community change results obtained using APCI (Anaerobic Apparent Complex Index) are more consistent with this description.

[0047] Another Figure 4 It can be seen that compared to the changes in APCI, the changes in PCI are more gradual and have less variability, which to some extent indicates that APCI has higher sensitivity than PCI. The results also differ across different time scales; the larger the scale and the larger the data volume, the larger the APCI calculation result. The increased data volume also represents a longer time period, and longer time periods exhibit greater variability than shorter time periods. This indirectly demonstrates that APCI can indeed indicate changes in phytoplankton community structure.

[0048] In addition, such as Figure 4 As shown, there are more duplicate values ​​in the calculation results of PCI. This is caused by the discreteness of PCI, because PCI is based on the number of points. In contrast, the results of APCI have almost no duplicate values. The area-based calculation determines the continuity of APCI. This feature enables the absolute quantification of APCI.

[0049] Example 2

[0050] This embodiment demonstrates the application of the labeling method described in this invention in reflecting changes in the structure of phytoplankton communities in open sea areas. The data used are phytoplankton monitoring data from four sampling stations (SM3, SM6, SM17, and SM19) in the Southern Water Quality Control Area of ​​Hong Kong, provided by the Hong Kong Environmental Protection Department. Specifically, it includes 23 years of monthly phytoplankton abundance data from the Southern Water Quality Control Area of ​​Hong Kong, collected and monitored monthly since 1996. The sample collection and processing are the same as in Example 1.

[0051] The sea area described in this embodiment is located in the southern part of Hong Kong, adjacent to open sea areas. The distribution map of the four sample collection stations is shown below. Figure 5As shown, SM6, SM17, and SM19 are all adjacent to open waters, while SM3 is located within the strait. These four stations effectively characterize the features of open waters. Compared to Tolo Harbour, the water exchange in the southern water quality control area of ​​Hong Kong is faster, and phytoplankton and nutrients have shorter retention times, allowing land-based pollution entering this area to be diluted more quickly. Since 1986, the Hong Kong Environmental Protection Department has implemented a series of pollution control measures, resulting in a significant improvement in water quality and a significant decrease in nutrient content in Hong Kong's waters. However, compared to enclosed waters, the impact of these measures on the open waters of southern Hong Kong is relatively smaller, as pollutants entering the water are quickly transported by water currents.

[0052] The trend diagram of total inorganic nitrogen and reactive phosphate concentrations in the Southern Water Quality Control Zone of Hong Kong is shown below. Figure 6 As shown in the figure, the bolded straight line represents the result of linear regression, indicating the trend of nutrient changes. The solid line indicates a significant trend change, while the dashed line indicates an indistinct trend. The concentration of total inorganic nitrogen in this sea area did not show a significant trend change, indicating that the remediation measures implemented by Hong Kong did not affect the inorganic nitrogen concentration in this sea area. The concentration of reactive phosphate decreased significantly, with the main decrease occurring before 2004. After 2004, the concentration of reactive phosphate remained relatively stable.

[0053] This embodiment merges phytoplankton abundance data from four sampling stations (SM3, SM6, SM17, and SM19) according to time series to assess changes in phytoplankton community structure across the entire region. Diatoms and Dinoflagellates are still selected as functional groups in this embodiment. The merged abundance data is extracted and processed according to the labeling method described in Embodiment 1. To demonstrate and compare the impact of different data volumes of functional groups on the results, three time scales are selected: 1 year, 3 consecutive years, and 5 consecutive years. Six time periods are selected for each time scale, totaling 3 × 6 = 18 time periods. Diatoms are used as the x-axis and Dinoflagellates as the y-axis. The logarithmically transformed paired data from the 18 time periods are projected onto a two-dimensional Cartesian coordinate system to obtain a state-space scatter plot. The Gaussian scan algorithm is used to determine the convex hull, and the discrete Green's formula is used to calculate the area of ​​the convex hull. The obtained convex hull area represents the state of the phytoplankton community at different time periods using APCI quantification. In order to compare with PCI, the same data was used to calculate PCI in this embodiment. Referring to the time period selected by APCI, the reference state of PCI was set to 2018, 2016-2018 and 2014-2018 respectively.

[0054] The partial convex hull plotting results of the phytoplankton community status in the southern water quality control area of ​​Hong Kong are as follows: Figure 7As shown in the figure, the diagonally sloping straight line represents a 1:1 ratio of Dinoflagellates to Diatoms abundance; the intersection of the dashed lines parallel to the coordinate axes is the median intersection of functional group abundances, and the relative position of the intersection point to the sloping straight line reflects the variation in the functional group abundance ratio. Figure 7 The results show that the shape of the convex hull did not change significantly over time. The main changes occurred in the earlier years. The abundance of Dinoflagellates decreased somewhat, while the abundance of Diatoms did not change significantly. The community structure changed early on, but neither the structure nor the abundance value changed much afterward. The results also differed across time scales; the larger the scale, the larger the data volume, and the larger the calculated APCI value, consistent with the results of Example 1. However, compared to the results of Example 1, the change in APCI with the length of time was relatively gradual as the time scale increased. This is related to the relatively stable habitat in this sea area. A stable habitat reduces the variability of the phytoplankton community, which also indirectly demonstrates that APCI can indeed indicate changes in phytoplankton community structure.

[0055] The APCI and PCI calculation results for the Southern Water Quality Control Zone of Hong Kong are as follows: Figure 8 As shown in the figure, the bold lines represent the results of linear regression, indicating the trend changes of APCI and PCI. Solid lines indicate significant trend changes, while dashed lines indicate indistinct trends. Figure 8 The results show that APCI exhibits a significant upward trend in the early stages, followed by no significant trend change in the later stages. This change is similar to the pattern of reactive phosphate concentration changes, indicating that APCI can effectively indicate environmental changes. In contrast, PCI results show no significant change in phytoplankton community structure compared to the reference state. This method does not show the early pattern of decreasing reactive phosphate concentration, suggesting that APCI is more sensitive than PCI. Comparison across different time scales reveals that as the time scale increases, the results for both APCI and PCI become larger, and the trend becomes more moderate. Although there are numerical differences, the change pattern of APCI is basically consistent across the three time scales, while the change pattern of PCI shows significant differences. This reflects that APCI has higher accuracy than PCI. The figure also shows that PCI exhibits more pronounced dispersion characteristics, a drawback that APCI, due to its absolute quantitative nature, does not have.

[0056] Example 3

[0057] This example demonstrates the application of the labeling method described in this invention to inland lakes, using 12 consecutive years of phytoplankton abundance data from Cheney Reservoir in Kansas, USA. Since 2001, samples were collected monthly. The collected water samples were immediately fixed with Lugol's reagent, and phytoplankton were identified and counted using microscopic examination, with all phytoplankton identified down to the species level. Specific data are from publicly available data from the U.S. Geological Survey (https: / / www.sciencebase.gov / catalog / item / 6112b8c9d34ed11898f70426).

[0058] Cheney Reservoir, located in south-central Kansas, is Wichita's primary drinking water source and recreational water resource. Since 1990, frequent red tides have increased management costs and hindered recreational development, with cyanobacteria being the main harmful algal species. Cyanobacterial blooms can cause water pollution, including increased toxins and unpleasant odors, posing a potential health hazard as a drinking water source. Therefore, the Wichita city government, in cooperation with the U.S. Geological Survey, has established a continuous monitoring system at Cheney Reservoir to monitor algal blooms and facilitate timely response strategies. The dynamic changes in phytoplankton abundance can be characterized using the phytoplankton community change labeling method described in this invention. The most obvious characteristics of algal blooms are high algal cell abundance, low diversity, and a clearly dominant species; therefore, the phytoplankton community change labeling method described in this invention can also be applied to reflect the occurrence of algal blooms in this area.

[0059] To enable a sensitive response to harmful algal blooms, this embodiment selects two functional groups: Cyanophyta (cyanobacteria) and Chlorophyta (green algae). Abundance data for both functional groups are merged according to time series, and the abundance data for each functional group are incremented by 1 before logarithmic transformation. The APCI (Availability-Promoting Index) is then calculated using the same method as in Embodiment 1. To demonstrate and compare the impact of different data volumes on the results, this embodiment again selects three time scales: 1 year, 3 consecutive years, and 5 consecutive years. Each time scale still includes 6 time periods, totaling 3 × 6 = 18 time periods. Using Cyanophyta (cyanobacteria) as the x-axis and Chlorophyta (green algae) as the y-axis, the paired data from the 18 logarithmically transformed time periods are projected onto a two-dimensional Cartesian coordinate system to obtain a state-space scatter plot. The Gaussian scan algorithm is used to determine the convex hull, and the discrete Green's formula is used to calculate the area of ​​the convex hull. The convex hull area, i.e., the APCI, is used to quantify the state of the phytoplankton community during that time period. To compare with PCI, the same data was used to calculate PCI. Referring to the time period selected by APCI, the reference status of PCI was set to 2016, 2014-2016 and 2012-2016 respectively.

[0060] The convex hull plotting results of the phytoplankton community status in Cheney Reservoir are as follows: Figure 9 As shown in the figure, the diagonally sloping straight line represents a 1:1 ratio of Chlorophyta to Cyanophyta. The intersection of the two dashed lines parallel to the coordinate axes is the intersection of the median values ​​of Chlorophyta and Cyanophyta. When the intersection point is below the sloping line, it indicates that the abundance of Chlorophyta is less than that of Cyanophyta; when the intersection point is above the sloping line, it indicates that the abundance of Chlorophyta is greater than that of Cyanophyta. Figure 9 The results show that on a 1-year timescale, the abundance of Cyanophyta is higher than that of Chlorophyta. The abundance of Chlorophyta first decreases and then increases, and similar patterns are observed on 3-year and 5-year timescales. Different combinations of phytoplankton abundance variation patterns result in diverse variation patterns in APCI. Furthermore, on a one-year timescale, the APCI calculation result for 2006 was relatively small, but phytoplankton abundance was generally high during this period. A similar situation occurred in 2014. In 2010, phytoplankton abundance was low, but it had a relatively large APCI value. A larger APCI value indicates greater variability in the phytoplankton community. This variability may originate from habitat changes, competition, etc. A smaller APCI indicates less variability in the community structure and a more stable habitat. Algal blooms are more likely to occur in more stable habitats because phytoplankton have a longer residence time, which leads to the accumulation of algal cells and makes the dominant species more dominant. Therefore, an abnormally small APCI value is very likely to indicate the occurrence of algal blooms. This shows that APCI can indeed represent changes in phytoplankton community structure and can be applied to freshwater lake environments. It also has certain practical value in characterizing extreme events.

[0061] The APCI and PCI calculation results for Cheney Reservoir are as follows: Figure 10 As shown in the figure, the bolded straight line represents the results of linear regression, indicating the trend changes of APCI and PCI. The solid line indicates a significant trend change, while the dashed line indicates an indistinct trend. It can be seen that APCI does not show a significant trend change over time, exhibiting a relatively stable community structure in the early stages and in 2014. PCI, on the other hand, is relative, meaning it can only distinguish changes relative to a reference state and cannot be used to determine algal blooms. These advantages of APCI stem from its absolute and quantitative characteristics.

[0062] In summary, the phytoplankton community change labeling method described in this invention can effectively reflect the dynamic changes of the community, is applicable to various water bodies, and has the characteristics of absoluteness, quantification, and high sensitivity.

[0063] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for indicating changes in phytoplankton communities, characterized in that, The area to be labeled is determined, and phytoplankton abundance data of samples within the area is obtained. Two representative functional groups are selected within the area, and the abundance data of the two functional groups are merged according to their time series. The time series is then discretized into time periods of arbitrary time scales. The phytoplankton community state is quantified by calculating the regional phytoplankton community index for each time period. The changes in the regional phytoplankton community index over a continuous time axis are used to indicate changes in the phytoplankton community. The calculation of the regional phytoplankton community index includes the following steps: S1. Perform logarithmic transformation on the merged functional group abundance data, and project the transformed paired data into a two-dimensional Cartesian coordinate system with the two selected functional groups as the vertical and horizontal axes according to the discrete time periods to obtain the scatter plot of each state space and determine the convex hull. S2. Calculate the convex hull area separately. The obtained convex hull area is the regional phytoplankton community index for the corresponding time period. The area to be labeled is a river, lake, or ocean; the two representative functional groups are two functional groups that have an abundance advantage in the area to be labeled.

2. The method according to claim 1, characterized in that, The number of samples in the area to be labeled is no less than 3.

3. The method according to claim 1, characterized in that, The selected phytoplankton abundance data are continuous time series phytoplankton abundance data.

4. The method according to claim 1, characterized in that, In step S1, when performing a logarithmic transformation on the functional group abundance data, the data should be made to tend towards a normal distribution.

5. The method according to claim 1, characterized in that, In step S1, the functional group abundance data are incremented by 1 before logarithmic transformation.

6. The method according to claim 1, characterized in that, Step S1 uses the Gaussian scan algorithm to determine the convex hull in the defined state space.

7. The method according to claim 1, characterized in that, In step S2, the discrete Green's formula is used to calculate the convex hull area.

8. The method according to claim 7, characterized in that, The determined state space is the smallest convex hull containing all points in the two-dimensional Cartesian coordinate system.

9. The application of the method according to any one of claims 1 to 8 in indicating changes in phytoplankton communities in rivers, lakes and / or oceans.

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