CN114925329A - Method for marking phytoplankton change and application thereof - Google Patents
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Abstract
The invention discloses a method for marking phytoplankton change and application thereof. The marking method is designed based on an ecological functional group theory and a state space theory, namely, a continuous time sequence is dispersed into time periods of any time scale, the phytoplankton state is quantified by calculating the regional phytoplankton index (APCI) of each time period, and the phytoplankton change is marked according to the change of the APCI of each time period on a continuous time axis. The method for marking the phytoplankton change does not need to determine a reference state in advance, the community state is quantified by APCI, and compared with the existing method for marking by utilizing PCI, the method has the advantages of absolute property, quantification property, high sensitivity and the like, can better mark the relationship between the community change of the phytoplankton and the environment, and is suitable for marking the phytoplankton change in any water body.
Description
Technical Field
The invention belongs to the technical field of phytoplankton change research. More particularly, it relates to a method for marking phytoplankton change and its application.
Background
The phytoplankton is a unique group of living modes of camping and wave-following flow-by-flow in a water body, is tiny in individual, is easy to perform passive movement under the action of wind and water flow, and has no or only weak swimming capacity. It plays a key role in global material circulation and energy flow, and only the total primary productivity of marine phytoplankton has accounted for nearly half of the global total primary productivity. The phytoplankton has short generation period and high sensitivity to environmental change, so the phytoplankton can be used as an ecological state index. Changes in phytoplankton structure are often used to indicate environmental changes.
A Phytoplankton Community structure Index (PCI) established based on Community functional group theory and state space theory is an Index for marking the change degree of the composition and abundance of Phytoplankton Community in Phytoplankton mass element treatment, and can effectively respond to the influence of environment change on Community structure (Tett P, Carreira C, Mills D K, et al. use of a Phytoplankton Community Index access the health of biological waters [ J ]. ics Journal of Marine Science,2008,65(8): 1475-. The PCI marked phytoplankton change method is characterized in that a high-dimensional sample biological abundance matrix is subjected to dimensionality reduction treatment based on a functional group theory, population abundances belonging to the same functional group are subjected to accumulation treatment, representative functional groups are selected as calculation objects to be combined in pairs, the functional groups are called state variables, a two-dimensional space formed by the pairwise combination of the state variables is a state variable space, and the PCI does not quantify the state variable space. The PCI calculation method takes the selected data as a reference, constructs an inner convex envelope ring and an outer convex envelope ring through a convex envelope algorithm as a reference state, projects sample points in the same state space, and determines the deviation degree (percentage) of the samples and the reference by counting the number of the sample points falling in the state space, thereby judging the change condition of the community structure relative to the reference state; the value range is between 0, 1, and the larger the value is, the closer the sample is to the reference state is.
Although the PCI can effectively respond to the influence of environment change on the community structure, the calculation of the PCI needs to determine the reference state which is a relative value, and the selection of the reference state has no uniform standard, so that the calculation result of the PCI has great human factors, and the calculation result of the PCI has no comparability with different research or use time. Therefore, it is necessary to develop a method for indicating the phytoplankton change more accurately and more responsive to the environment.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art and provide a phytoplankton change marking method and application thereof.
The invention aims to provide a method for marking phytoplankton.
It is another object of the invention to provide the use of said method for marking changes in river, lake and/or marine phytoplankton populations.
The above purpose of the invention is realized by the following technical scheme:
the invention provides a method for marking phytoplankton change, which comprises the following steps: determining a region to be marked, acquiring sample Phytoplankton abundance data in the region to be marked, selecting two representative functional groups in the region to be marked, merging the abundance data of the two functional groups according to time sequences of the two functional groups, dispersing the time sequences into time periods of any time scale, quantifying Phytoplankton Community states by calculating regional Phytoplankton Community indexes (APCI) of the time periods, and marking Community changes of Phytoplankton according to the changes of the regional Phytoplankton Community indexes of the time periods on a continuous time axis; the calculation of the regional phytoplankton index comprises the following steps:
s1, carrying out logarithmic transformation on the abundance data of the combined functional groups, projecting the transformed data in a two-dimensional Cartesian coordinate system with the selected two functional groups as vertical and horizontal coordinates according to discrete time periods respectively, obtaining a scatter diagram of each state space and determining a convex hull;
and S2, respectively calculating the areas of the convex hulls, wherein the obtained areas of the convex hulls are the indexes of the regional phytoplankton communities in the corresponding time periods.
The method for marking phytoplankton change is consistent with PCI and is designed based on a functional group theory and a state space theory. The functional group theory refers to the dimensionality reduction of a high-dimensional phytoplankton abundance multivariate matrix into a two-dimensional space, and the state space theory refers to the formation of a two-dimensional coordinate system by taking paired functional groups as an abscissa and an ordinate respectively. Specifically, the PCI calculation process is to form a two-dimensional coordinate system by taking paired function groups as horizontal and vertical coordinates, and select a specified time period (t) r ) The abundance data of (a) are projected in a two-dimensional coordinate system, an annular graph is formed by calculation of an inner convex hull and an outer convex hull, the inner convex hull and the outer convex hull of the graph are used as boundaries, and other time periods (t) are divided into i ) The same conversion processing is carried out on paired abundance data, the data are projected in the same coordinate system, t i Data volume for time-segment paired abundance data is recorded as N i The number of points falling within the boundary is recorded as n i The points falling within the boundary represent the relative to t r The community structure of the phytoplankton is not changed in the time period; points falling outside the boundary represent points relative to t r Time period in which the phytoplankton structure has changed and the quantification of PCI is given as n i And N i The ratio of (a) to (b). It can be known that the quantification of PCI requires the determination of the reference state, and the result is a relative value, and since there is no uniform standard for the selection of the reference state, the calculation result of PCI has a great human factor and there is no comparability between different studies.
Unlike the calculation of PCI, which requires determination of the reference state, the phytoplankton labeling method of the present invention does not require determination of the reference state. Specifically, the method comprises the steps of quantifying the phytoplankton state by calculating an area phytoplankton index (APCI) in each time period, and marking the phytoplankton change according to the change of the APCI in each time period on a continuous time axis. Because each time period can determine a unique convex hull, the difference of the convex hull area of each time period can indicate that the state of the phytoplankton has difference, the continuous time sequence is dispersed into the continuous time periods, and the change of the convex hull area of each time period reflects the change of the phytoplankton structure on the time sequence, so that the marking process does not need to select a reference state. In each time period, APCI is an independent variable, and the defects that a calculation result has great human factors and different research or use time is not comparable due to non-uniform selection standard of a reference state of a PCI method are overcome. In addition, because the calculation method of the PCI is carried out based on the point number, the PCI is determined to be a discrete variable by the property of the point number, but the method is calculated based on a continuous variable, and the result is still the continuous variable. The PCI is calculated into a 0-dimensional space based on the number of points, the PCI is calculated into a two-dimensional space based on the area, and the change of the function group is amplified in a square mode, so that the PCI has higher sensitivity.
Furthermore, according to the moderate perturbation hypothesis, the ecosystem will have the highest diversity under moderate perturbation conditions, and if in an absolutely stable system, the system will not succeed and evolve. The development of the ecosystem requires the creation of opportunities, for which the system must be disturbed to some extent. In the natural environment, phytoplankton communities in any water body are disturbed to a certain extent, and the change of the abundance of the phytoplankton caused by the disturbance can be marked by adopting the method disclosed by the invention. In addition, due to the theory of natural variation, the abundance of phytoplankton will change irregularly in the natural system, and the characteristic is not limited by the habitat of the organism, and the random change can also be marked by the method of the invention.
Specifically, the area to be marked is a river, a lake or seawater.
Specifically, the number of samples in the area to be marked is not less than 3, and the selected phytoplankton abundance data is phytoplankton abundance data of a continuous time sequence.
In addition, when the time sequence is dispersed into time periods, the time sequence can be dispersed into a plurality of time periods of any time scale according to the data condition of the acquired phytoplankton abundance data of the sample.
Specifically, in step S1, when logarithmically transforming the abundance data of the functional groups, the data should be distributed normally, so as to prevent the maximum and minimum values from affecting the result.
Specifically, in step S1, 1 is added before logarithmic transformation is performed on the abundance data of the functional group, so as to prevent the abundance from being zero.
Specifically, step S1 determines the convex hull using a gaussian scanning algorithm in the determined state space.
Specifically, the convex hull area is calculated using the discrete green formula in step S2.
In particular, the determined state space is the smallest convex hull containing all points in a two-dimensional cartesian coordinate system.
The invention also applies to and protects the application of the method in marking the change of the phytoplankton communities in rivers, lakes and/or oceans.
The invention has the following beneficial effects:
the invention provides a method for marking phytoplankton change based on a functional group theory and a state space theory, namely, the state of the phytoplankton is quantified by calculating regional phytoplankton index (APCI) of each time period, and the phytoplankton change is marked according to the change of the APCI of each time period on a continuous time axis. The method for marking the phytoplankton change does not need to determine a reference state in advance, the community state is quantified by APCI, and compared with the conventional method for marking by utilizing PCI, the method has the advantages of absolute property, quantification, high sensitivity and the like, can better mark the relationship between the community change of the phytoplankton and the environment, and is suitable for marking the phytoplankton change in any water body.
Drawings
FIG. 1 is a schematic diagram of the calculation process of the phytoplankton change labeling method of the present invention.
FIG. 2 is a diagram of a sample collection site location profile for an example water quality control area of a port of Tu and Lu.
FIG. 3 is a convex hull drawing result of the phytoplankton population state in the water quality control area of hong Kong.
FIG. 4 shows the result of APCI and PCI calculations in the water quality control area of the open and spit harbor.
FIG. 5 is a diagram of a sample collection site location profile for an exemplary water quality control area in south hong Kong.
FIG. 6 is a graph showing the trend of the total inorganic nitrogen and active phosphate concentration in the water quality control area in south hong Kong.
FIG. 7 is a convex hull drawing result of the phytoplankton population status in the water quality control area of southern hong Kong.
FIG. 8 shows the result of APCI and PCI calculations in the water quality control area of south hong Kong.
FIG. 9 shows the convex hull mapping results of the phytoplankton population status in the Cheney reservoir.
Figure 10 is the results of the cini reservoir APCI and PCI calculations.
Detailed Description
The invention is further described with reference to the drawings and the following detailed description, which are not intended to limit the invention in any way. Reagents, methods and apparatus used in the present invention are conventional in the art unless otherwise indicated.
Unless otherwise indicated, reagents and materials used in the following examples are commercially available.
Example 1
Because the reference state needs to be determined in the calculation process of the phytoplankton structural index (PCI), the reference state is a relative value, and the selection of the reference state has no uniform standard, the result has great human factors, no comparability exists among different researches, and the sensitivity when the phytoplankton is marked to change is not high. In order to overcome the problem, the invention provides a method for marking phytoplankton change, which comprises the steps of dispersing a continuous time sequence into time periods of any time scale, calculating the convex hull area of each time period, namely the regional phytoplankton community index (APCI) quantitative phytoplankton state, and reflecting the change of the convex hull area of each time period on a continuous time axis, namely the change of the phytoplankton structure on the time sequence. The schematic diagram of the calculation process of the phytoplankton change marking method is shown in FIG. 1.
This embodiment is an application of the method for marking phytoplankton change in estuary in reflecting structural change of phytoplankton, and specifically describes the method for marking phytoplankton change in the invention in combination with phytoplankton monitoring data of 4 stations of hong kong and hong kong in 28 years. The data used are provided by the environmental protection agency of hong Kong, and specifically are phytoplankton monthly abundance data of 28 years (1991-2018) of hong Kong of open harbor. From 1986, once per month, the collection and the treatment of samples are all responsible for the hong Kong environmental protection agency, a Nansen water sampler is used for collecting surface water samples (1m), 200mL of water samples are fixed by a Luge reagent, and the samples are brought back to a laboratory for identifying and counting phytoplankton.
In this embodiment, the phytoplankton community change of the water quality control area of the hong kong open port is marked, and continuous time series phytoplankton abundance data of four sample collection stations of TM3, TM4, TM6 and TM8 (the distribution of the sample collection stations is shown in fig. 2, TM3 and TM4 are located at the bottom of the hong kong open port, and TM6 and TM8 are located at the hong kong open port) of the water quality control area of the hong kong open port are selected, specifically, the monthly phytoplankton abundance data from 1995 to 2018; diatoms and Dinoflagellates are two important phytoplankton functional groups, which are the absolute dominance in this sea area, so this example selects Diatoms and Dinoflagellates as functional groups; merging the phytoplankton abundance data of the samples of each acquisition station according to a time sequence so as to evaluate the phytoplankton structural change of the whole area, wherein the merged data according to the time sequence have higher representativeness to the whole area; in this embodiment, the abundance data of the combined functional groups Diatoms and Dinoflagellates are extracted for calculation, and the time series are respectively dispersed into a plurality of time periods with different time scales. When the time series are dispersed into time periods, the time series can be dispersed into a plurality of time periods of any time scale according to the data condition of the obtained phytoplankton abundance data of the samples.
Adding 1 to the merged functional group abundance data (preventing the abundance from being zero) to carry out logarithmic transformation, so that the transformed data tend to normal distribution, and the influence of maximum and minimum values on the result is prevented; respectively projecting the converted paired data in a two-dimensional Cartesian coordinate system with Diatoms as abscissa and Dinoflagellates as ordinate according to the discrete time period to obtain respective state space scatter diagrams, and determining convex hulls by using a Gaussian scanning algorithm; calculating the area of the convex hull by using a discrete Green formula, wherein the calculated value is an open harbor water quality control area phytoplankton index (APCI) of a corresponding time period, and quantifying the state of the phytoplankton in the time period by using the APCI; and respectively calculating the regional phytoplankton index (APCI) quantitative phytoplankton state of each time period, and marking the phytoplankton change according to the change of the regional phytoplankton index of each time period on a continuous time axis.
In addition, in order to show and compare the influence of different data volumes of the functional groups on the results, 3 time scales are respectively selected in the embodiment, namely, one year, three consecutive years and five consecutive years are selected, and the larger the time scale is, the larger the data volume is; in order to show the change trend of phytoplankton index (APCI) in the water quality control area of the hong kong at different periods, 6 time slices are selected for each time scale, and the total time is 3 multiplied by 6 to 18 time slices. And respectively projecting paired data of 18 time periods after logarithmic transformation in a two-dimensional Cartesian coordinate system by using Diatoms (Diatoms) as abscissa and Dinovellates (Dinoflagellates) as ordinate to obtain a state space scatter diagram, determining a convex hull and calculating the area of the convex hull. The results are shown in FIG. 3, where diagonal oblique lines indicate a ratio of Dinoflaellates to atoms abundance of 1: 1; an intersection of dotted lines parallel to the coordinate axes is a median intersection of the abundances of the functional groups, and a relative position of the intersection to the oblique straight line may indicate a change in the abundance ratio of the functional groups. From the results of the different time periods shown in fig. 3, it can be seen that the abundance of dications has almost not changed since 1998 with the decline of the abundance of dications, indicating that as a series of treatment measures are developed, the structure of phytoplankton populations changes, changing from dications to more dominant dications. The reduction of the abundance of Dinoflaellates changes the shape of the convex hull, gradually tends to be flat, and the area of the corresponding convex hull also changes, thereby reflecting the change of the phytoplankton structure.
The phytoplankton abundance data of the water quality control area of the hong kong in 28 years are processed by the marking method. In order to compare with the PCI, the PCI is calculated by adopting the same data, the time period selected by referring to APCI is referred, and the reference states of the PCI are respectively set to be 2018, 2016-2018 and 2014-2018.
The calculation results of APCI and PCI in the water quality control area of the open port are respectively shown in figure 4, the bold straight line in the figure is a linear regression result and represents the trend changes of APCI and PCI, the solid line represents that the trend changes remarkably, and the broken line represents that the trend changes inconspicuously. As can be seen from fig. 4, the APCI calculated by the labeling method of the present invention exhibits an opposite trend of change from PCI, and as the treatment measures are implemented, the APCI exhibits a dynamic mode of decreasing fluctuation and exhibits a large variability; the PCI results show that the phytoplankton structural status of the control zone is greatly changed on a time scale compared with the reference status.
The water quality control area of the populus is a bay, the water body exchange speed is low, phytoplankton has longer detention time, once pollutants enter the water body, the pollutants are difficult to dilute and are easy to generate high pollution, once treatment measures are carried out, the discharge of the pollutants into the sea is reduced, and the concentration of the pollutants in the water body is quickly reduced. In 1986, hong Kong adopted a series of measures to carry out water quality treatment work of hong Kong, through the development of a series of measures, the content of nutritive salt in the water body of the region is obviously reduced, the change of the nutritive salt required by the growth of phytoplankton can generate certain influence on the phytoplankton, which is microscopically expressed as the physiological and biochemical change of the phytoplankton, and macroscopically expressed as the change of community structure, and the concrete change of abundance and composition. The phytoplankton change results obtained by APCI labeling are more consistent with this description.
As can be seen from fig. 4, the change in PCI is more moderate and has less variability than the change state of APCI, which indicates that APCI has a higher sensitivity than PCI. The results obtained by different time scales are different, the larger the scale is, the larger the data volume is, the larger the calculation result of the APCI is, the increase of the data volume also represents the increase of the time period, and compared with the short time period, the long time period has larger variability, which indicates that the APCI can actually mark the change of the phytoplankton structure.
In addition, as shown in fig. 4, there are more repetitive values in the calculation result of the PCI, which is caused by the dispersion of the PCI because the PCI is an operation based on the number of points, while there is almost no repetitive value in the result of the APCI, and the continuity of the APCI is determined by the operation based on the area, which realizes the absolute quantification of the APCI.
Example 2
This example is the application of the labeling method of the present invention in reflecting the structure change of phytoplankton in open sea areas, and the used data is phytoplankton monitoring data of 4 sample collection sites (SM3, SM6, SM17, and SM19) in the water quality control area in south hong Kong, which is provided by the environmental protection agency in hong Kong. Specifically, the phytoplankton monthly abundance data of 23 years in the water quality control area in south hong Kong was collected and monitored once a month since 1996, and the collection and treatment of the samples were the same as those in example 1.
The sea area described in this example is located in the southern hong Kong, next to the open sea area, and the distribution of the 4 sample collection sites is shown in FIG. 5, where SM6, SM17, and SM19 are all located next to the open sea area, and SM3 is located in the channel, and these four sites can fully characterize the open water area. Compared with the water exchange in the water quality control area in the south of hong Kong, the water exchange in the water quality control area is fast, phytoplankton, nutritive salt and the like have shorter detention time, and land source pollution entering the sea area can be diluted in shorter time. Since 1986, the hong Kong environmental protection agency developed a series of pollution control works, the water quality in the hong Kong sea area was significantly improved, the content of nutritive salts was significantly reduced, but compared with the closed sea area, the series of measures had relatively little effect on the open sea area in south of hong Kong, and once the pollutants entered the water body, they were quickly transferred by the water flow.
The graph of the change trend of the total inorganic nitrogen and active phosphate concentration in the water quality control area in south hong Kong is shown in FIG. 6, wherein the bold straight line in the graph is the result of linear regression, which shows the trend change of the nutrient salt, the solid line shows that there is a significant trend change, and the dotted line shows that the change trend is not obvious. The concentration of the total inorganic nitrogen in the sea area has no obvious trend change, which indicates that the treatment measures developed by hong Kong do not influence the concentration of the inorganic nitrogen in the sea area; the concentration of active phosphate dropped significantly, with the major drop occurring before 2004 and the concentration of active phosphate remaining substantially constant after 2004.
This example combines the phytoplankton abundance data of the four sample collection sites SM3, SM6, SM17 and SM19 in a time series to assess the structural changes of phytoplankton populations across the area. In this example, Diatoms and Dinovilages were selected as functional groups, and the combined abundance data was extracted and processed according to the labeling method described in example 1. In order to demonstrate and compare the influence of different data volumes of the functional group on the result, in this embodiment, 3 time scales are still respectively selected, that is, 1 year, 3 consecutive years and 5 consecutive years, each time scale still selects 6 time periods, that is, 3 × 6 ═ 18 time periods in total, still uses the coordinates of diamond as abscissa and the coordinates of Dinoflagellates as ordinate, and respectively projects paired data of 18 time periods after logarithmic transformation into a two-dimensional cartesian coordinate system to obtain a state space scatter diagram, determines a convex hull by using a gaussian scanning algorithm, calculates the area of the convex hull by using a discrete green formula, so as to obtain the area of the convex hull, that is, APCI quantifies the state of the phytoplankton in different time periods. In addition, in order to compare with the PCI, the PCI is calculated by using the same data, and the reference states of the PCI are set to 2018, 2016-2018 and 2014-2018 respectively by referring to the time period selected by the APCI.
The drawing result of a part of convex hulls of phytoplankton community state in the water quality control area in south hong Kong is shown in FIG. 7, and the inclined straight line of the opposite angle in the drawing shows that the ratio of abundance of Dinovellates to abundance of Diatoms is 1: 1; the intersection point of the dotted lines parallel to the coordinate axes is a median intersection point of the abundance of the functional group, and the relative position of the intersection point and the inclined straight line may show a change in the abundance ratio of the functional group. From the results shown in fig. 7, it can be seen that the shape of the convex hull does not change significantly with time scale, the main change occurs in the earlier time period of the year, the abundance of Dinoflagellates has a certain decrease, while the abundance of diones does not change significantly, the colony structure changes in the early period, and then neither the structure nor the abundance value changes significantly. The results obtained at different time scales are different, the larger the scale is, the larger the data amount is, the larger the calculation result of the APCI is, which is consistent with the result of example 1, but compared with the result of example 1, the change value of the APCI along with the length of the time period is relatively flat, which is related to the relatively stable habitat of the sea area, the stable habitat reduces the variability of the phytoplankton, and this also indicates from the side that the APCI can actually mark the change of the phytoplankton structure.
The results of APCI and PCI calculations in the water quality control area of south hong kong are shown in fig. 8, where the bold straight line is the result of linear regression showing the trend changes of APCI and PCI, the solid line shows significant trend changes, and the dotted line shows insignificant trend changes. As can be seen from the results shown in fig. 8, APCI showed a significant upward trend in the early stage and no significant change in the later stage, which is similar to the change pattern of the active phosphate concentration, indicating that APCI is a good indicator of environmental changes. From the PCI results, it is clear that there is no significant change in the structure of the phytoplankton population compared to the reference state, and the method does not show a pattern of early decline in the active phosphate concentration, which also indicates that APCI is more sensitive than PCI. As can be seen from comparison among different time scales, the results of APCI and PCI become larger with the increase of the time scales, the change trend is more moderate, although the numerical values are different, the change modes of APCI among the three time scales are basically consistent, and the change mode of PCI has larger difference, which reflects that APCI has higher accuracy than PCI. The discrete nature of PCI is also evident in the figure, whereas APCI does not suffer from this disadvantage by having an absolute quantitative nature.
Example 3
This example is the application of the marking method of the present invention to reflection of inland lakes, using phytoplankton abundance data for 12 consecutive years in the kansas cheny reservoir of the united states. From 2001, samples are collected once a month, the collected water samples are immediately fixed by a Rouge reagent, and phytoplankton is identified and counted by a microscopic examination method, and the identification of the phytoplankton identifies the grade of the species. Specific data are derived from data published by the U.S. geological survey (https:// www.sciencebase.gov/catalog/item/6112b8c9d34ed11898f 70426).
The Cheney reservoir is located in the central south of Kansas and is the main drinking and entertainment water source in Wicurita. Since 1990, red tide frequently occurs, the management cost is increased, the development of entertainment industry is hindered, and the main harmful algae species are blue algae. The outbreak of the blue algae can cause the phenomena of water quality pollution such as the increase of water body toxin, water body peculiar smell and the like, and the blue algae serving as a drinking water source has potential harm to public health. Therefore, the Wikiotu government cooperates with the American geological survey bureau to construct a continuous monitoring system in the Cheney reservoir, so as to monitor the occurrence of the water bloom and make a timely response strategy. The dynamic change of the abundance of the phytoplankton can be characterized by using the method for marking the change of the phytoplankton community, the occurrence of the algal blooms is most obviously characterized by high abundance of algal cells, low diversity and obvious dominant species, and the method for marking the change of the phytoplankton community can also be applied to reflect the occurrence of the algal blooms in the area.
In order to make a sensitive response to harmful algal blooms, the two functional groups selected in the embodiment are Cyanophyta (blue algae) and Chlorophyta (green algae), the abundance data of the two functional groups are combined according to a time sequence, 1 is added to the abundance data of the two functional groups for logarithmic transformation, and APCI is calculated, wherein the specific calculation method is the same as that of embodiment 1. To demonstrate and compare the effect of different data volumes of the functional groups on the results, the present embodiment still selects 3 time scales, i.e. 1 year, 3 consecutive years and 5 consecutive years, respectively, and each time scale still selects 6 time segments, i.e. a total of 3 × 6 to 18 time segments. The method comprises the steps of taking Cycanophyta (blue algae) as an abscissa and Chlorophyta (green algae) as an ordinate, respectively projecting paired data of 18 logarithmically converted time periods in a two-dimensional Cartesian coordinate system to obtain a state space scatter diagram, determining a convex hull by using a Gaussian scanning algorithm, calculating the area of the convex hull by using a discrete Green formula, and quantifying the state of phytoplankton in the time period by using the area of the convex hull, namely APCI (approximate point code). In order to compare with the PCI, the PCI is calculated by using the same data, and the reference states of the PCI are respectively set to 2016, 2014-2016 and 2012-2016 by referring to the APCI selection time period.
The convex hull drawing result of the keny reservoir phytoplankton community state is shown in fig. 9, in the graph, a diagonal inclined straight line indicates that the ratio of the Chrophyta to the Cyanophyta is 1:1, the intersection point of two dotted lines parallel to the coordinate axis is the intersection point of the median values of the Chrophyta and the Cyanophyta, when the intersection point is below the inclined straight line, the abundance of the Chrophyta is smaller than that of the Cyanophyta, and when the intersection point is above the inclined straight line, the abundance of the Chrophyta is larger than that of the Cyanophyta. From the results shown in fig. 9, it can be seen that on the 1 year time scale, the abundance of cyanophylla is higher than that of Chlorophyta, and the abundance of Chlorophyta first decreases and then increases, and similar change patterns also exist on the 3 and 5 year time scales. Different combinations of patterns of change in phytoplankton abundance make APCI also diverse. In addition, on a 1-year time scale, the APCI calculation results in 2006 are smaller, but the phytoplankton abundance in this time period is higher, and a similar situation also occurs in 2014; in 2010, the abundance of phytoplankton is low, but the phytoplankton has a large APCI value, the larger the APCI value is, the phytoplankton has larger variability, the variability is possibly generated from the change, competition and the like of a habitat, the smaller APCI indicates that the variability of the community structure is small, the habitat is stable, algal bloom is easier to generate in the more stable habitat, the retention time of the phytoplankton is longer, the accumulation of algal cells can be caused, the dominant species is more dominant, so the abnormally small APCI value indicates that the algal bloom can be possibly generated, the APCI can really indicate the change of the phytoplankton structure, the phytoplankton structure can be applied to the water body environment of a freshwater lake, and a certain practical value is also realized on the characteristic extreme events.
The calculation results of the APCI and the PCI of the cheney reservoir are shown in fig. 10, the bold straight line in the graph is a linear regression result and represents the trend changes of the APCI and the PCI, the solid line represents that the trend changes remarkably, the dotted line represents that the trend changes unobviously, and it can be seen that the APCI has no remarkable trend change on a time sequence and has a stable community structure in the early stage and in 2014; PCI possesses relativity, i.e. only the change of PCI relative to the reference state can be distinguished, and can not be used for judging algal bloom. These advantages of APCI are derived from its absolute and quantitative nature.
In conclusion, the method for marking phytoplankton change can well reflect dynamic change of the phytoplankton, is suitable for various water bodies, and has the characteristics of absolute property, quantification and high sensitivity.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (10)
1. A phytoplankton change marking method is characterized by comprising the steps of determining a to-be-marked area, obtaining sample phytoplankton abundance data in the to-be-marked area, selecting two representative functional groups in the to-be-marked area, merging the abundance data of the two functional groups according to time sequences of the functional groups, dispersing the time sequences into time periods of any time scale, quantifying phytoplankton states by calculating regional phytoplankton indexes of the time periods, and marking phytoplankton change according to the change of the regional phytoplankton indexes of the time periods on a continuous time axis; the calculation of the regional phytoplankton index comprises the following steps:
s1, carrying out logarithmic transformation on the abundance data of the combined functional groups, projecting the transformed paired data into a two-dimensional Cartesian coordinate system with the selected two functional groups as vertical and horizontal coordinates according to discrete time periods respectively, obtaining a scatter diagram of each state space and determining a convex hull;
and S2, respectively calculating the areas of the convex hulls, wherein the obtained areas of the convex hulls are the indexes of the regional phytoplankton communities in the corresponding time periods.
2. The method of claim 1, wherein the area to be marked is a river, lake or sea water.
3. The method of claim 1, wherein the number of samples in the area to be marked is not less than 3.
4. The method of claim 1, wherein the phytoplankton abundance data selected is a time series of phytoplankton abundance data.
5. The method of claim 1, wherein the log transformation of the abundance data of the functional group in step S1 is performed such that the data are normally distributed.
6. The method of claim 1, wherein step S1 is performed by adding 1 to the functional group abundance data before logarithmic transformation.
7. The method according to claim 1, wherein step S1 determines the convex hull using a gaussian scanning algorithm in the determined state space.
8. The method of claim 1, wherein the convex hull area is calculated in step S2 using the discrete green' S formula.
9. The method of claim 7, wherein the determined state space is a minimum convex hull containing all points in a two-dimensional cartesian coordinate system.
10. Use of the method of any one of claims 1 to 9 for marking changes in river, lake and/or marine phytoplankton populations.
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