Environment monitoring system and method based on leeches

Abstract

The invention aims to provide a leech-based environment monitoring system. The leech-based environment monitoring system comprises the following steps: identifying key species and features thereof; collecting blood data of the leeches in four seasons; obtaining diversity parameters based on the blood data; and calculating evaluation parameters of the key species through the diversity parameters, and evaluating the abundance of the species to confirm the environmental diversity. According to the technical scheme, a comprehensive evaluation model integrating static abundance, dynamic fluctuation and rare species compensation is constructed through dynamic weight calculation of'mean value-standard deviation '. According to the technical scheme, dual-condition screening is carried out in combination with dominant species and significant seasonal change class groups, and the problem of evaluation distortion caused by seasonal community succession is solved through dynamic key species selection.

Description

Technical Field

[0001] The present invention belongs to the field of environmental protection, and relates to a method and system for detecting environmental diversity, and particularly to an environmental monitoring system and method based on leeches. Background Art

[0002] With the increasing demands for ecological protection and biodiversity monitoring, environmental diversity assessment technologies have gradually become the core tools in ecological research and environmental protection practices. In the prior art, environmental diversity prediction usually relies on direct observations of species abundances in specific areas or static analyses of single diversity indices. For example, the species diversity of a community is measured by the Shannon Index or the Simpson Index. For example: the mangrove ecological restoration assessment method, device, computer equipment and storage medium disclosed in CN118095926A consider the remote sensing data of mangrove ecological restoration and habitat change data lacking in the existing ecological restoration assessment system, and realize the comprehensiveness and objectivity of mangrove ecological restoration assessment based on the ecological restoration assessment model.

[0003] However, when considering environmental diversity or the effect of ecological protection, using the characteristics of a single species or randomly selected species and static diversity indices as the benchmark for evaluating the effectiveness of environmental diversity or ecological protection has the following significant limitations:

[0004] 1. Lack of dynamic adaptability in the selection of key species

[0005] Traditional methods often use fixed criteria (such as absolute quantity dominance) to screen key species, ignoring the functional importance of species in the ecosystem and their dynamic responses to seasonal changes. For example, dominant species may not be able to reflect the true state of the environment during non-active seasons, and the impacts of species with significant seasonal migrations or reproductive characteristics (such as migratory birds, periodically breeding insects) on the stability of the ecosystem are not effectively incorporated into the evaluation system, resulting in a deviation between the prediction results and the actual environmental diversity.

[0006] 2. One-sidedness and static nature in the application of diversity indices

[0007] The prior art mostly relies on a single index (such as only using the Shannon Index) or simply superimposing multiple indices, lacking systematic integration and dynamic analysis of diversity parameters (such as ACE index, Chao1 richness index). For example, the ACE index focuses on the assessment of the richness of rare species, and the Chao1 index is more sensitive to low-abundance species. Without considering their statistical distribution characteristics (mean and standard deviation), it is difficult to quantify the impact of data fluctuations on diversity assessment. In addition, the prior methods do not correlate species abundances with environmental dynamic changes through multi-dimensional parameters, resulting in the evaluation results being unable to accurately reflect the spatio-temporal heterogeneity of environmental pressures.

[0008] 3. Ignoring the Seasonal Dynamics and Environmental Correlation Mechanism

[0009] Environmental diversity is significantly affected by seasonal factors such as climate and resource supply, while the existing technologies rarely incorporate seasonal characteristics into the key variables of data collection and model construction. For example, the composition of insect communities in temperate forests may show explosive growth in spring and summer, while traditional methods adopt a uniform sampling strategy throughout the year, resulting in the dilution of ecological signals during critical windows and making it difficult to capture the critical points of environmental changes.

[0010] In addition, on the one hand, there are differences in the understanding of those skilled in the art; on the other hand, although the inventor studied a large number of documents and patents when making this invention, due to space limitations, not all details and contents are listed in detail. However, this does not mean that this invention does not possess the features of these existing technologies. On the contrary, this invention already possesses all the features of the existing technologies, and the applicant reserves the right to add relevant existing technologies to the background art. Summary of the Invention

[0011] In view of the above technical problems, the object of the present invention is to provide a detection system for environmental diversity. The object of the present invention is also to provide a leech-based environmental monitoring system and method, which includes a data collection module for determining key species and collecting their biological data; and a data processing module that is data-connected to the data collection module.

[0012] The data processing module is configured to:

[0013] Based on a dynamic evaluation model, evaluate environmental diversity through diversity parameters including the ACE index, Chao1 richness index, and / or Shannon index, where

[0014] The selection conditions for key species are dominant species and / or taxa with significant seasonal changes.

[0015] According to a preferred embodiment, both the dominant species and the taxa with significant seasonal changes are leeches, and the biological data collected is eDNA in the environment obtained based on leech blood.

[0016] According to a preferred embodiment, the diversity parameters include the ACE index, Chao1 richness index, and / or Shannon index.

[0017] According to a preferred embodiment, calculate the mean and standard deviation of the ACE index, Chao1 richness index, and / or Shannon index to obtain evaluation parameters.

[0018] Preferably, obtain the evaluation parameters of the ACE index based on formula (1).

[0019] α ACE = μ ACE ± 2σACE …(1)

[0020] Preferably, the evaluation parameter of the Chao1 richness index is obtained based on formula (2).

[0021] α chao1 = μ chao1 ± 2σ chao1 …(2)

[0022] Preferably, the evaluation parameter of the Shannon index is obtained based on formula (3).

[0023] α S = μ S ± 2σ S …(3)

[0024] According to a preferred embodiment, the data processing module is configured to:

[0025] Based on the time series model of species abundance change, evaluate the seasonal change of the diversity parameters of the dominant species and / or the significant seasonal change taxa.

[0026] According to a preferred embodiment, the data processing module is configured to:

[0027] Based on the dynamic evaluation model, calculate the mean and / or σ of the ACE index, the Chao1 richness index, and / or the Shannon index.

[0028] According to a preferred embodiment, when the dominant species and the significant seasonal change taxa are the same species, the evaluation of environmental diversity can be based on the standard deviation of the diversity index.

[0029] Preferably, when the diversity index exceeds ± 1σ of the normal range but does not all reach ± 2σ, generate an instruction of "there is a slight abnormality in the environmental diversity of the current season".

[0030] When multiple diversity indices deviate and all exceed ± 2σ of the normal range but do not all reach ± 3σ, generate an instruction of "there is a moderate abnormality in the environmental diversity of the current season".

[0031] When multiple diversity indices deviate and all exceed ± 3σ of the normal range, generate an instruction of "there is a significant abnormality in the environmental diversity of the current season". σ represents the standard deviation of the ACE index, the Chao1 richness index, and / or the Shannon index.

[0032] According to a preferred embodiment, the data processing module is configured to:

[0033] Set up a dynamic evaluation model for calculating the total environmental diversity score, where the calculation formula of the total environmental diversity score is as follows:

[0034]

[0035] Among them, EDS is the total environmental diversity score; SSC is the seasonal stability coefficient; SDI is the seasonal difference index; DCI is the diversity comprehensive index;

[0036]

[0037] Among them, DCI is the diversity comprehensive index; ω1 is the weight ratio of the ACE index in DCI; ω2 is the weight ratio of the Chao1 index in DCI; ω3 is the weight ratio of the Shannon index (H′) in DCI.

[0038] According to a preferred embodiment, the environmental monitoring system is further provided with an information terminal that is data-connected to the data processing module, and the information terminal can adjust its monitoring object, monitoring time, monitoring frequency or monitoring range based on the instructions of the information terminal.

[0039] The present invention also relates to a leech monitoring and early warning method, which includes the following steps:

[0040] Determine the selected key species and their identified characteristics based on season or species abundance;

[0041] Collect biological data of dominant species and / or taxa with significant seasonal changes;

[0042] Obtain diversity parameters including the ACE index, Chao1 richness index, and / or Shannon index based on the biological data;

[0043] Evaluate environmental diversity based on a dynamic evaluation model through diversity parameters including the ACE index, Chao1 richness index, and / or Shannon index.

[0044] According to a preferred embodiment, the collected data includes animal blood samples in the benthic parasitic niche and their distribution quantities.

[0045] According to a preferred embodiment, for the environmental monitoring of the Chaobai River Basin, the taxa with significant seasonal changes are Macropodus concolor, Brycon orbignyanus, Probarbus jullieni, Hypostomus uruguayensis, and / or Phyllomedusa bicolor; the dominant species are Bufo gargarizans and / or Carassius auratus.

[0046] The present invention also relates to a computer device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for detecting environmental diversity of the present invention.

[0047] The present invention also relates to a storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method for detecting environmental diversity as described in the present invention are implemented.

[0048] Beneficial effects of the technical solution involved in the present invention:

[0049] In existing research, species diversity assessment usually relies on a single index (for example, only using the Shannon index to measure evenness, or only using Chao1 to estimate species richness), or simply adding two indices (such as Chao1 and Shannon). However, it is difficult for a single or dual-index combination to comprehensively characterize the multi-dimensional characteristics of an ecosystem (such as species rarity, community stability, functional redundancy). This technical solution constructs a comprehensive evaluation model with "static abundance - dynamic fluctuation - rare species compensation" through dynamic weight calculation of "mean - standard deviation" (for example: the mean reflects the overall diversity level, and the standard deviation reveals the intensity of seasonal fluctuations).

[0050] Meanwhile, traditional species diversity (which can also be expressed as environmental diversity) usually uses the abundance of fixed species (such as large mammals), ignoring the seasonal conversion of species' ecological roles (for example: the functional differences between water-dependent species in the dry season and widespread species in the rainy season; the fluctuations in the dynamic schedules of species throughout the four seasons). This technical solution combines "dominant species and significant seasonal change taxa" for dual-condition screening, preferably selecting species that are significantly driving ecosystem functions and sensitive to seasonal responses (for example: capturing the suppression effect of the explosive growth of dominant species on community structure through the ACE index; correcting the error of species number fluctuations caused by migration or hibernation through the Chao1 index), and solving the problem of evaluation distortion caused by seasonal community succession through dynamic key species selection. Description of the Drawings

[0051] Figure 1 It is a flowchart of the detection method involved in the present invention;

[0052] Figure 2 It is a result diagram of sequencing data and species statistical data, (A) a stacked histogram of the statistical distribution of sequencing read lengths for each sample; (B) a PCoA diagram of samples in four seasons; (C) a box plot of the distance between samples between groups through Anosim analysis; (D) an OTU tree based on the taxonomic relationship of species;

[0053] Figure 3Schematic diagram of species identification and seasonal changes in species composition. (A) Species-level clustering tree based on species taxonomic relationships; (B) Alluvial diagram of changes in relative abundances of species at different taxonomic levels (class - species) in four seasons; (C) Alluvial diagram of changes in relative abundances of species at different taxonomic levels (class - genus) in four seasons; (D) Radar chart of relative abundance ratios of the five most abundant species in four seasons; (E) Heatmap of abundances of the 20 most abundant species in different seasons.

[0054] Figure 4 Schematic diagram of differences in alpha diversity indices between seasons.

[0055] Figure 5 LEfSe identifies key taxa changing with seasons. (A) LEfSe plot of samples in different seasons; (B) LDA scores of the 25 taxa with the highest LDA.

[0056] Figure 6 The autocorrelation network identifies key species changing with seasons (P < 0.001). Detailed implementation mode

[0057] In the description of the present invention, the terms are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. In the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0058] Environmental diversity refers to the richness and complexity of different ecological environments, ecosystem types, and ecological processes within a certain area. Environmental diversity is an important part of biodiversity.

[0059] In the present invention, the ACE index can accurately estimate the potential number of low-abundance or unobserved species based on the species abundance distribution model.

[0060] In the present invention, the Chao1 index can correct the sampling bias of species richness through the frequencies of rare species (single-individual or double-individual species) in the sample.

[0061] In the present invention, the Shannon index can combine the number of species and evenness to quantify the stability of the community and the efficiency of niche allocation.

[0062] Example 1

[0063] This embodiment relates to a prediction system for environmental diversity. This embodiment relates to a prediction method for environmental diversity. This embodiment also relates to a system for evaluating environmental diversity based on species abundance. This embodiment also relates to a method for evaluating environmental diversity based on species abundance. This embodiment also relates to an anomaly detection model constructed using abundance monitoring data. This embodiment also relates to an ecological management method based on the anomaly detection model. This embodiment relates to an environmental monitoring system based on leeches.

[0064] The system includes a data acquisition module, a data storage module, a data processing module, and an information terminal.

[0065] The data acquisition module is used to collect data related to environmental diversity. The data acquisition module includes an image acquisition unit or a molecular detection unit. The image acquisition unit is set as a camera for acquiring images of specific species. The image acquisition unit can evaluate biomass data and distribution range data based on the number of specific species collected daily or monthly. The molecular detection unit includes a high-throughput sequencing platform. The molecular detection unit can perform amplicon sequencing based on collected species blood or other samples that can detect DNA. Real-time species composition data is obtained based on the sequencing results.

[0066] The data storage module is used to store the data information, historical data, processing models, and / or historical processing results collected by the data acquisition module. The data storage module includes its blockchain storage, cloud storage service, in-memory database (hard disk, optical disc, USB flash drive, or tape library, etc.), or data warehouse, memory.

[0067] The data processing module is used to process the collected data and generate corresponding evaluation results. The data processing module includes a server, a workstation, data analysis software, a processor, etc.

[0068] The data acquisition module is informationally and data-connected to the data storage module. The data acquisition module is data-connected to the data processing module. Based on different data connections, the data processing module can process the data collected by the data acquisition module based on the processing model stored in the data storage module; the data processing module can also process the data sent by the data storage module. For example: when the data processing module is a cloud processing device, the operator is equipped with a data acquisition module and a data storage module. The data acquisition module can send the real-time collected data to the data storage module, and the data storage module sends it to the data processing module. During field operations, data acquisition and data transmission do not affect each other. Considering the difficulty of data transmission in the field environment, the operator can directly carry a device integrating the data acquisition module, the data storage module, and the data processing module. At this time, the data collected by the data acquisition module can be directly sent to the data processing module and can also be synchronously sent to the data storage module for backup.

[0069] The information terminal is used to receive or send instructions. The information terminal is set as a device with communication, information input, and information output functions. Preferably, the information terminal can be a smart phone, a personal computer, a tablet, a smart wearable device, an industrial control terminal, a vehicle-mounted information terminal, etc.

[0070] Preferably, the information terminal is data-connected to the data acquisition module. The data processing module can adjust its monitoring object, monitoring time, monitoring frequency, or monitoring range based on the instructions of the information terminal. In this embodiment, since the influence of species abundance is different in different environments, for example, it is difficult to detect species abundance in snowy weather in some areas in winter, the information terminal needs to automatically or manually adjust its monitoring parameters. For example: based on the linkage with the weather station database, the information terminal can lower the image acquisition frequency or abort the image acquisition in snowy weather.

[0071] Preferably, the information terminal is data-connected to the data storage module. The information terminal can directly call the data information in the data storage module or query the past processed data. More preferably, the information terminal is configured with a dedicated component, and this component supports the implementation of the data calling function through methods such as instruction input or card swiping operation.

[0072] Preferably, the information terminal is data-connected to the data processing module. The information terminal can trigger the data processing module to reprocess the data based on the called historical acquisition data or directly stop the data processing process.

[0073] More preferably, the data processing module can also preprocess the data collected by the data acquisition module. The preprocessing process is for data quality control to ensure that the collected data is of reliable quality.

[0074] Based on the models or formulas stored in the data storage module, the data processing module can calculate the α-diversity index, the calculation of β-diversity, PCoA analysis, and identify key species.

[0075] Preferably, the α-diversity index includes ACE, Chao 1, and Shannon index. β-diversity is, for example, Bray-Curtis distance.

[0076] The environmental monitoring method based on leeches involved in the present invention includes the following steps:

[0077] Identification of key species and their characteristics;

[0078] Collection of biological and / or environmental diversity parameters;

[0079] Calculating the evaluation parameters of key species through the diversity parameters and using them to evaluate species abundance to confirm environmental diversity.

[0080] According to a preferred embodiment, the method for predicting environmental diversity involved in the present invention may include the following steps:

[0081] Select dominant species and significant seasonal change groups based on seasons;

[0082] Collect biomass data and distribution range data of at least two determined species;

[0083] Based on the biomass data and distribution range data, confirm the evaluation parameters of the ACE index, Chao1 richness index, and Shannon index for at least two species.

[0084] According to a preferred embodiment, calculate the mean and standard deviation of the ACE index, Chao1 richness index, and / or Shannon index to obtain evaluation parameters.

[0085] Preferably, obtain the evaluation parameters of the ACE index based on formula (1).

[0086] α ACE = μ ACE ± 2σ ACE …(1)

[0087] Preferably, obtain the evaluation parameters of the Chao1 richness index based on formula (2).

[0088] α chao1 = μ chao1 ± 2σ chao1 …(2)

[0089] Preferably, obtain the evaluation parameters of the Shannon index based on formula (3).

[0090] α S = μ S ± 2σ S …(3)

[0091] When the diversity index α exceeds ± 1σ of the normal range but does not all reach ± 2σ, generate an instruction of "There is a slight abnormality in the environmental diversity in the current season".

[0092] When multiple diversity indices α deviate and all exceed ± 2σ of the normal range but do not all reach ± 3σ, generate an instruction of "There is a moderate abnormality in the environmental diversity in the current season".

[0093] When multiple diversity indices α deviate and all exceed ± 3σ of the normal range, generate an instruction of "There is a significant abnormality in the environmental diversity in the current season".

[0094] In this embodiment, by calculating the mean (comprehensive diversity level) and standard deviation (seasonal fluctuation intensity) of the three indices, the contradictory index is transformed into a scalar that can be quantitatively compared, avoiding the problem that the output results generated by independently using the three parameters may be contradictory due to ecological scenario differences. Through error cancellation between parameters (such as ACE overestimating unobserved species and Chao1 depending on the frequency of rare species), the prediction accuracy of complex river basin ecosystems is improved.

[0095] Embodiment 2

[0096] This embodiment relates to an environmental monitoring method based on leeches. This embodiment also relates to a monitoring method for environmental diversity.

[0097] Collect blood samples of leeches in the river basin and obtain DNA data in their blood samples. Among them, outliers are removed, and missing value filling and time series smoothing processing are used to ensure data continuity;

[0098] Based on the DNA data in the blood of leeches in the river basin, the evaluation parameters of the ACE index, the evaluation parameters of the Chao1 richness index, and the evaluation parameters of the Shannon index are obtained respectively based on formulas (1), (2), and (3);

[0099] Confirm the diversity of the river basin environment according to the evaluation parameters of the ACE index, the evaluation parameters of the Chao1 richness index, and the evaluation parameters of the Shannon index of leeches.

[0100] Table 1

[0101]

[0102] The evaluation criteria are shown in Table 1. When the evaluation parameters of the ACE index, the evaluation parameters of the Chao1 richness index, and the evaluation parameters of the Shannon index do not exceed the range, it indicates that the environmental diversity in the current season is stable.

[0103] When one of the evaluation parameters of the ACE index, the evaluation parameters of the Chao1 richness index, and the evaluation parameters of the Shannon index exceeds the range, it indicates that there is a slight abnormality in the environmental diversity in the current season.

[0104] When two of the evaluation parameters of the ACE index, the evaluation parameters of the Chao1 richness index, and the evaluation parameters of the Shannon index exceed the range, it indicates that there is a moderate abnormality in the environmental diversity in the current season.

[0105] When all of the evaluation parameters of the ACE index, the evaluation parameters of the Chao1 richness index, and the evaluation parameters of the Shannon index exceed the range, it indicates that there is a significant abnormality in the environmental diversity in the current season.

[0106] In this embodiment, leeches are used as natural "biological samplers" to passively collect eDNA of various animals in the watershed by sucking the blood of different hosts, avoiding the need to capture or interfere with target species in traditional methods and significantly reducing the invasiveness to the ecosystem. Leeches are widely distributed and their activity ranges cover waters and surrounding habitats. The eDNA in their blood can reflect the diversity of fish, amphibians, mammals, and even birds in the watershed, providing more comprehensive information on biological communities.

[0107] Example 3

[0108] The system involved in the present invention includes a four-layer architecture of data acquisition layer → preprocessing layer → analysis layer → decision-making layer.

[0109] As Figure 1 shown, this system includes the following three steps:

[0110] Determine the dominant species and / or significant seasonal change groups;

[0111] Data acquisition and preprocessing;

[0112] Make a decision on anomaly diagnosis based on the dynamic evaluation model.

[0113] Specifically, determining the dominant species and / or significant seasonal change groups (target species screening and monitoring planning) includes the determination criteria for dominant species and / or seasonal species identification.

[0114] Preferably, the identification of dominant species can be determined based on the seasonal stability coefficient (SSC). The calculation method of SSC is shown in formula (4):

[0115]

[0116] where A i is the abundance in the i-th season, is the annual average abundance.

[0117] The dominant species in this application refers to species with relatively high abundance and stability in all seasons. For example: in the verification study shown in Example 4, Bufo gargarizans and Carassius auratus are used as dominant species, which can reflect the long-term trends and stability of the environment.

[0118] Preferably, the identification of significant seasonal species can be determined based on the seasonal difference index (SDI). The calculation method of SDI is shown in formula (5):

[0119]

[0120] where N peak is the seasonal variation coefficient, and A season is the seasonal abundance sequence.

[0121] Both the seasonal variation coefficient and the seasonal abundance sequence are conventional parameters in the fields of ecology and environmental monitoring.

[0122] Significantly seasonal species are defined in this application as species that exhibit significant abundance changes between seasons. For example, in the validation study shown in Example 4, Fejervarya limnocharis and crucian carp showed abundance fluctuations throughout the four seasons, and these species can be used to capture the impact of seasonal environmental changes on species distribution.

[0123] Specifically, data collection involves a multi-source data collection matrix. The multi-source data includes biological data, environmental data, and spatio-temporal data. Preferably, the biological data includes species abundance and eDNA concentration data. More preferably, the collection methods for biological data include image recognition, qPCR sequencing, and / or environmental DNA capture. For example, in the validation experiment of Example 4 involved in this application, by collecting the blood of leeches with the characteristic of sucking various organisms, eDNA concentration data was obtained from the blood, and then the abundance of various species in the environment and other biological data were confirmed. The environmental data includes temperature, humidity, and / or pH value. The environmental data can be detected based on Internet of Things sensors. The spatio-temporal data includes GPS coordinates and / or collection time. The spatio-temporal data can be detected based on the Beidou positioning system.

[0124] According to a preferred embodiment, data preprocessing can include outlier correction. The method of outlier correction is the dynamic truncation method of the 3σ principle.

[0125] According to a preferred embodiment, data preprocessing can include missing value compensation. The missing value compensation can be based on a spatio-temporal correlation imputation model.

[0126] It should be noted that the operation of data preprocessing is optional.

[0127] The dynamic assessment model described in "Decision-making for anomaly diagnosis based on a dynamic assessment model" can be directly confirmed based on seasonal abundance changes (such as the ACE index, the chao1 index), species richness indices (such as the Shannon index), and the analysis of the relationship between environmental factors and species abundance. The confirmation criteria can be as shown in the criteria of Example 2. Analysis of the relationship between environmental factors and species abundance: By correlation analysis (such as Pearson correlation coefficient or regression analysis), the relationship between species abundance and environmental factors (such as temperature, precipitation, etc.) is examined to reveal the impact of environmental changes on species abundance.

[0128] According to a preferred embodiment, the dynamic assessment model can be as shown in the following formulas (6) - (8):

[0129]

[0130] Among them, EDS is the total environmental diversity score;

[0131]

[0132] Among them, DCI is the diversity comprehensive index; ω1 is the weight ratio of the ACE index in DCI; ω2 is the weight ratio of the Chao1 index in DCI; ω3 is the weight ratio of the Shannon index (H′) in DCI. The weight ratio can be adjusted according to seasons. Preferably, the weight ratios for each season can be as shown in Table 2.

[0133] Table 2

[0134] <![CDATA[ω1]]> <![CDATA[ω2]]> <![CDATA[ω3]]> Spring 0.3 0.4 0.3 Summer 0.2 0.3 0.5 Autumn 0.4 0.3 0.3 Winter 0.5 0.3 0.2

[0135]

[0136] Among them, SPD is the seasonal parameter deviation; k ∈ {ACE, Chao1, H′}, μ k is the seasonal standard median, and σ k is the range semi-width.

[0137] The warning mechanism can prompt the operators of the system based on Table 3 below:

[0138] Table 3

[0139]

[0140]

[0141] Taking the detection in the Yangtze River Basin as an example.

[0142] 1. Model output

[0143] a. Calculation of SSC

[0144] A i = [80, 90, 85, 95]

[0145]

[0146] b. Evaluation of SDI

[0147] SDI in spring = 0.226.

[0148] c. Calculation of DCI

[0149] The weights in spring are ω1 = 0.3, ω2 = 0.4, ω3 = 0.3;

[0150] DCI = 0.3×0.8 + 0.4×0.8 + 0.3×0.875 = 0.24 + 0.32 + 0.2625 = 0.8225.

[0151] d. Calculation of EDS

[0152]

[0153] EDS = 0.68 + 0.166 + 2.03 = 2.876

[0154] e. Calculation of SPD

[0155] The measured value of the ACE index is 90, the standard median value is 105, and its deviation degree is 1.0.

[0156] The measured value of the Chao1 index is 114, the standard median value is 100, and its deviation degree is 0.93.

[0157] The measured value of the Shannon index is 3.3, the standard median value is 3.0, and its deviation degree is 1.5.

[0158] SPD is 3.43.

[0159] This result belongs to the orange warning. Since EDS > 1.2, the system can send a prompt of "maintain routine monitoring" to the operator's handheld terminal based on the data processing unit.

[0160] When the significant seasonal change taxa and the dominant species are two species, the DCI brought into EDS is the average value after separately calculating the DCI of the two species. The calculation of SPD is also the average value after separately calculating among species.

[0161] For example: As shown in Example 4, the abundance of Bufo gargarizans remains high in all four seasons, and its abundance change is small. It can be inferred that the ecological environment in this area is stable and suitable for the growth of this species. The abundance of Carassius auratus significantly decreases in autumn and winter, and the seasonal change is large. It may be due to seasonal changes in water quality, temperature, or food resources, indicating that the ecological system in this area may have seasonal pressure. At the same time, this result shows that Carassius auratus can be used as a significant seasonal species.

[0162] By monitoring the species abundance in different seasons and analyzing its change trend, the dynamic changes of the ecological system in the short term (such as seasonally) and long term (such as over the years) can be reflected. According to the diversity fluctuations of seasonal species and the abundance changes of dominant species, the impacts of environmental changes, especially climate change, habitat degradation, and species invasion, can be evaluated in a timely manner.

[0163] According to a preferred embodiment, the evaluation criteria can also be scored based on a formula. Specifically, the scoring criteria include: a score of 4 points when DCI ≥ 0.8; a score of 2 points when 0.5 ≤ DCI < 0.8; a score of 0 points when DCI < 0.5. The scoring criteria also include: a score of 4 points when SDI ≥ 0.6; a score of 2 points when 0.3 ≤ SDI < 0.6; a score of 0 points when SDI < 0.3. The scoring criteria also include: a score of 4 points when SSC ≥ 0.85; a score of 2 points when 0.7 ≤ SSC < 0.85; a score of 0 points when SSC < 0.7.

[0164] According to a preferred embodiment, the evaluation criteria can also be scored based on the evaluation parameters of the ACE index, the evaluation parameters of the Chao1 richness index, and the evaluation parameters of Shannon.

[0165] Total score calculation:

[0166] Environmental diversity score = (dominant species abundance stability score) + (significant seasonal species abundance change score) + (species diversity index score).

[0167] Preferably, the environmental diversity score = (evaluation parameters of the ACE index) + (evaluation parameters of the Chao1 richness index) + (evaluation parameters of Shannon).

[0168] The scoring range is 0 - 12 points.

[0169] The results include high diversity, medium diversity, and low diversity.

[0170] 1. High diversity (not less than 10 points)

[0171] If the total score is between 10 and 12 points, it indicates that the environment has high species diversity and seasonal adaptability. The abundance of dominant species changes stably, the abundance of significantly seasonal species fluctuates significantly, and the species diversity index is high, indicating strong ecological health and diversity of the environment.

[0172] 2. Medium diversity (5 - 9 points)

[0173] If the total score is between 5 and 9 points, it indicates that the environment has a certain species diversity, but the seasonal change is weak or the abundance of dominant species fluctuates greatly. The environment may be relatively stable, but the seasonal change is small, and it may be in a relatively stable state with weak seasonal change.

[0174] 3. Low diversity (not more than 4 points)

[0175] If the total score is between 0 and 4 points, it indicates that the seasonal change of the species composition in the environment is small, the abundance of dominant species fluctuates greatly, and the species diversity is low, which may reflect that the environment has been disturbed or degraded, resulting in extreme changes in species distribution.

[0176] For example: Based on the above example, when the evaluation parameters of the Chao1 richness index in the detection results of Bufo gargarizans are 114 (2 points), the evaluation parameters of the ACE index are 90 (0 points), and the evaluation parameters of the Shannon index are 3.3 (2 points), calculate the evaluation score according to the scoring criteria shown in Table 5, indicating that there are significant abnormalities in the summer diversity of this region.

[0177] Table 5

[0178]

[0179]

[0180] The species diversity in spring is relatively high, the species distribution is relatively uniform, and the number of species is relatively large. The ranges of both the ACE and Chao1 indices are relatively large, with the ACE index ranging from 120 to 150; the Chao1 index ranging from 110 to 140; and the Shannon index ranging from 3.5 to 4.0.

[0181] The species richness and species diversity in summer are relatively low, with the ACE index ranging from 90 to 120; the Chao1 index ranging from 85 to 115; and the Shannon index ranging from 2.8 to 3.2.

[0182] The species richness in autumn is between that in spring and summer, the species diversity is moderate, and the species distribution is relatively uniform. The ACE index ranges from 100 to 130; the Chao1 index ranges from 95 to 125; and the Shannon index ranges from 3.2 to 3.6.

[0183] The species richness in winter is similar to that in autumn, the species diversity is relatively high, and the species distribution is relatively uniform. The ACE index ranges from 110 to 140; the Chao1 index ranges from 105 to 135; and the Shannon index ranges from 3.3 to 3.8.

[0184] This embodiment reveals the temporal dynamics of species diversity, thus providing corresponding assistance for ecological protection and management decisions. Specifically, by continuously collecting animal samples in different seasons, the seasonal differences of eDNA can be analyzed, revealing the temporal patterns of species reproduction, migration, or environmental adaptation (such as the changes in species composition in the four seasons), and providing key data for ecosystem dynamics. The eDNA technology is highly sensitive to low-biomass species and can detect rare species or nocturnal species that are easily overlooked in traditional surveys, improving the integrity of the biodiversity assessment in the watershed.

[0185] Example 4

[0186] This embodiment relates to a method for evaluating the species diversity in different seasons by analyzing environmental DNA (eDNA) in the blood of leeches in the Chaobai River Basin in Tianjin. The following scheme verifies the effectiveness of the early warning method proposed by the present invention.

[0187] I. Experimental Procedures

[0188] 1. Sample Collection

[0189] Leeches from the Chaobai River in Tianjin were collected in different seasons for analysis. A total of 26 collections were made in spring, 33 in summer, 49 in autumn, and 48 in winter. The collected samples were used to extract blood for the following analyses.

[0190] 2. DNA Extraction

[0191] Total blood DNA was extracted using a blood DNA extraction kit (OMEGA, USA) provided by OMEGA BIO-TEK according to the manufacturer's instructions. The integrity of the DNA samples was detected by 1% agarose gel electrophoresis (AGE), and the concentration and purity of the samples were quantitatively detected using NanoDrop2000.

[0192] 3. PCR Amplification

[0193] PCR amplification was carried out in two steps: In the first step, 16S primers without special bases (eDNA-1-F: CGGTTGGGGTGACCTCGGA, eDNA-1-R: GCTGTTATCCCTAGGGTAA CT) were used. In the second step, 16S primers with special bases (eDNA-2-F: TCCCTACACGCTCTTCCGATCTCGGTTCGGTGTGTGTGACCTGGA, eDNA-2-R: AGACGTGTGCTCTTCCGATCTGCTTGCTTGCTTATCCCTAGGGT AACT) were used according to the requirements of the sequencing platform. All PCR reactions were performed using 2×Taq PCR Master Mix. The total reaction system was 25 μL, including 1.5 μL DNA (1 μg) template, 12.5 μL Master Mix, 1 μL (10 ng / μL) forward and reverse primers, and 9 μL nuclease-free water. The PCR reaction conditions were: 94°C for 2 min; 35 cycles, with each cycle consisting of 94°C for 30 s, 72°C for 30 s, and 72°C for 45 s; and finally 72°C for 5 min. The experiment was repeated three times, and the PCR products of the same sample were mixed and detected by 2% agarose gel electrophoresis (AGE). Database construction and sequencing were completed by Beijing Biomarker Technologies Co., Ltd.

[0194] 4 High-throughput Sequencing and Bioinformatics Analysis

[0195] In this study, high-throughput sequencing and bioinformatics analysis were used to analyze the species diversity and abundance of eDNA ingested by leeches. The PCR products were used to construct a library and sequenced on the Illumina Novaseq 6000 platform. The generated sequences were subjected to multiple steps of quality control, deduplication, and clustering to generate an initial OTU table. OTUs were aligned in the NCBI database using BLASTn, and only high-quality 16S-related results were retained. A complete taxonomic information table was generated in combination with taxonkit. Species diversity analysis evaluated Alpha diversity through the Chao2 estimator and Hill number statistical framework, and iNEXT and vegan were used to generate coverage-based diversity accumulation curves. Species diversity and abundance changes were analyzed in Microeco, and LEfSe was used for differential analysis of important species. The phylogenetic tree was constructed by Microbiota Process and visualized by ggtree. The Spearman correlation network between OTUs was calculated by WGCNA and imported into Cytoscape for display, providing an in-depth understanding of the relationships between species and ecological patterns.

[0196] II. Experimental Results

[0197] 1. Sequencing Data and Species Statistics

[0198] Amplicon sequencing was performed on leech blood samples from four seasons near the Chaobai River Basin in Tianjin. A total of 35,229,354 pairs of raw reads were obtained. After quality control, assembly, and data analysis, a total of 10,505,494 clean reads were filtered. The length distribution of the clean reads was between 47bp and 250bp ( Figure 2 A). These sequencing results were classified into 5 phyla, including Annelida, Arthropoda, Chordata, Mollusca, and Platyhelminthes, among which Chordata was the dominant community ( Figure 2 B). PCoA analysis was performed on the samples from the four seasons based on Bray-Curtis distance ( Figure 2 C). The horizontal and vertical axes contributed 40.2% and 20.1% of the explanatory power, respectively, that is, about 60.3% of the differences explained the species composition.

[0199] The results showed that there were seasonal changes in species composition. There were significant differences between summer and winter. The species compositions in spring-winter and summer-autumn were similar. In addition, the analysis of the four-season samples by Anosim also revealed significant differences in species composition among different seasons ( Figure 2 D).

[0200] 2. Seasonal Differences in Species Composition

[0201] The sequencing results were divided into 5 phyla and 20 orders, and could be further identified as belonging to 65 species (excluding Plagiorchiidae). It is worth noting that the identification of Stegastes partitus did not deviate from its order ( Figure 3 A). Among these 65 species, 13 species appeared only in one season. For example, Bellamya dispiralis, Cervus nippon, and Cercopithecus aethiops appeared only in spring; Drawida japonica, Aporectodea Rosea, Aporectodea trapezoides, and Metaphire guillelmi appeared only in autumn; Pipistrellus subflavus, Capra hircus, Rana choseniaca, Micropercops swinhonis, Cobitis taenia, and Cyclocheilichthys janthochir appeared only in winter. The above results indicate that there are seasonal differences in the species composition of the Chaobai River Basin.

[0202] To explore the influence of seasons on species composition, the present invention separately studied the relative abundances of species at different taxonomic levels (class to species) in four seasons. At the class level, Amphibia, Actinopterygii, and Mammalia were generally present in all four seasons, but the relative abundances of Amphibia and Actinopterygii were much higher than that of Mammalia. The relative abundance of mammals was the lowest in spring and the highest in autumn, while the abundance share of Clitellata increased significantly in winter. At the order level, Anura and Cypriniformes were the dominant communities in all four seasons, with abundance ratios exceeding 75%. With the change of seasons, the relative abundance of Cypriniformes gradually decreased, while the relative abundances of Rodentia, Ichthyosauria, and Fucales gradually increased. At the family level, Bufonidae and Cyprinidae had the largest numbers in all four seasons. The relative abundances of Cricetidae and Parabramidae gradually increased with seasons, while the relative abundance of Cyprinidae gradually decreased. From the genus level, Bufo and Carassius had the largest numbers in all four seasons, accounting for more than 50%. For example, Ondatra and Channa gradually became more abundant with the change of seasons, while Carassius gradually decreased ( Figure 3B, C). At the species level, *Trachicephalus myops* and *Cyprinus carpio* were the dominant species, prevalent in all four seasons. The relative abundance of *C. carpio* gradually decreased with the seasons. In contrast, the relative abundances of *Fejervarya limnocharis* and *Macropodus concolor* gradually increased. Additionally, the abundance of *Pseudorasbora parva* gradually decreased ( Figure 3 B).

[0203] Furthermore, by examining the seasonal differences in the relative abundances of the 5 most abundant species ( Figure 3 D), it was found that the seasonal differences in the relative abundances of *Pelophylax plancyi*, *Ondatra zibethicus*, and *Misgurnus anguillicaudatus* were not obvious, and their proportions of the total abundance were very low. *Bufo gargarizans* and *Carassius auratus* were dominant species in all four seasons, and there were obvious seasonal variations in their relative abundances. For example: the relative abundance of *Bufo bufo gargarizans* was the lowest in spring, while the relative abundance of *C. auratus* decreased with the seasons, and the relative abundance of *B. bufo gargarizans* was higher than that of *C. auratus* in all seasons except spring. To more clearly understand the seasonal variations in species abundance, we selected the 20 species with the highest abundances to study their seasonal abundance variations, and the results were as Figure 3 shown in E. It can be seen the abundances of these 20 species in different seasons. In spring, the abundances of *Bufo gargarizans*, *Carassius auratus*, *Misgurnus anguillicaudatus*, *Pelophylax plancyi*, and *Pelophylax nigromaculatus* were relatively high, while the abundances of *Amynthas hupeiensis*, *Fejervarya limnocharis*, and *Pygathrix nemaeus* were relatively low. In summer, the abundances of *Bufo bufo gargarizans*, *Carassius auratus*, and *Carassius auratus* were relatively high, while the abundances of *Amynthas hubeiensis*, *Trachicephalus myops*, *Carassius auratus*, *Pseudorasbora parva*, *Carassius cuvieri*, *Ovis aries*, and *Limnodromus scolopaceus* were relatively low. In autumn, the numbers of *Bufo bufo gargarizans*, *Carassius auratus*, *Misgurnus anguillicaudatus*, and *Misgurnus anguillicaudatus* were relatively large, while the numbers of *Amynthas stonei*, *Trachicephalus myops*, *Carassius auratus*, and *Misgurnus anguillicaudatus* were relatively small. In winter, the abundances of *Bufo bufo*, *Carassius auratus*, *Carassius auratus*, *Misgurnus anguillicaudatus*, and *Channa argus* were relatively high, while the abundances of *Batrachuperus hubeiensis*, *Macaca mulatta*, *Ovis aries*, and *Channa argus* were relatively low. In summary, *Bufo gargarizans* and *Carassius auratus* had relatively high abundances in all four seasons, which was consistent with the Figure 2 and 3 results. However, the abundances of *Amynthas hupeiensis* and *Pygathrix nemaeus* were relatively low in all four seasons.

[0204] In summary, the above results indicate that there are seasonal differences in the species composition of the Chaobai River Basin.

[0205] 3. Seasonal differences in species α-diversity

[0206] The α-diversity index analysis aims to estimate the species richness and diversity of the sample community through a series of statistical analysis indicators. As Figure 4 shown, the ACE and Chao 1 indices of the species in the Chaobai River Basin were significantly different among the four seasons (P < 0.01), with the highest in spring and the lowest in summer. This result indicates that there are significant seasonal variations in species richness (P < 0.01), with the highest richness in spring and the lowest in summer. The Shannon index was significantly higher in spring than in autumn and winter (P < 0.05), and it was the highest in spring and the lowest in autumn.

[0207] In contrast, the InvSimpson index was higher in summer than in other seasons, while it decreased significantly in winter (P < 0.05). The Pielou indices of the species in the four seasons were significantly different (P < 0.01). The Pielou indices in summer and autumn were significantly higher than those in spring and winter (P < 0.001), and the Pielou index was the highest in summer. This result indicates that there are significant seasonal variations in species evenness (P < 0.01), and the species community distribution is relatively uniform in summer and autumn. From the perspective of species coverage, the coverage in the four seasons was close to 1, indicating that the main species in the sample have been identified, and the sequencing data can be used for further analysis.

[0208] However, there were also seasonal differences in species coverage, which were significant in all seasons except winter (P < 0.001). The above results indicate that there are seasonal differences in the α-diversity of the species in the Chaobai River Basin.

[0209] 4. Major groups with seasonal differences

[0210] Key groups with seasonal differences To examine the groups with significant differences among the four seasons, this application uses LEfSe analysis to detect changes in the relative abundances of the groups. Figure 5Groups with significant differences in relative abundance among the four seasons are shown. At the phylum level, the relative abundances of arthropods and mollusks in spring are significantly higher than those in other seasons. At the class level, the relative abundances of insects, actinopterygii, and gastropods in spring are significantly higher than those in other seasons, and the relative abundance of mammals in autumn is significantly higher than that in other seasons. At the order level, the relative abundances of six orders, including Thysanoptera, Testudines, and Cypriniformes, are significantly higher in spring than in other seasons; the relative abundances of Trichoptera, Gobiiformes, and Siluriformes are significantly higher in autumn than in other seasons; the relative abundance of rodents in winter is significantly higher than that in other seasons. At the family level, the relative abundances of six families, including Thripidae, Cyprinidae, and Synbranchidae, are significantly higher in spring than in other seasons, and the relative abundance of Ranidae is significantly higher in summer than in other seasons; the relative abundances of Moniligastridae, Osphronemidae, and Serrasalmidae are significantly higher in autumn than in other seasons; the relative abundances of seven families, including Megascolecidae, Trionychidae, and Cobitidae, are significantly higher in winter than in other seasons. In winter, the relative abundances of seven families, including Megascolecidae, Parabramidae, and Cobitidae, are significantly higher than those in other seasons. At the genus level, the relative abundances of 10 genera, including Thrips, Parathrips, and Sikukia, are significantly higher in spring than in other seasons; the relative abundances of four genera, including Onychostoma, Squalidus, and Sclerophrys, are significantly higher in summer than in other seasons; the relative abundances of Aporectodea, Drawida, and Macropodus are significantly higher in autumn than in other seasons; there are nine genera in winter, including Amynthas, Channa, and Cobitis. At the species level, the relative abundances of 12 species, including Thrips flavus, Misgurnus anguillicaudatus, and Paramisgurnus dabryanus, are significantly higher in spring than in other seasons; there are four species in summer, including Onychhostoma barbatum, Squalidus gracilis, and Sclerophrys brauni; there are 11 species in autumn, including Coptocycla trapezoides, Channa argus, and Channa maculata. In addition, the relative abundances of 12 species, including Channa hubeiensis, Channa argus, and Channa maculata, are significantly higher than those in other seasons( Figure 5 A).

[0211] In summary, the numbers of groups with significant differences among spring, summer, autumn, and winter are 39, 9, 32, and 30, respectively.

[0212] Among the 110 significantly different groups, 25 groups with the highest linear discriminant analysis (LDA) values were selected( Figure 5B). There are 13 categories with significantly different relative abundances in spring, including the goldfish species (s_Carassius auratus), the eukaryote kingdom (k_Eukaryota), the ray-finned fish class (c_Actinopteri), etc. There are 5 categories with significantly different relative abundances in summer, including the Oriental fire-bellied toad species (s_Pelophylax plancyi), the amphibian class (c_Amphibia), and the Anura order (o_Anura). There are 5 categories with significantly different relative blockages in autumn, including the muskrat species (s_Ondatra zibethicus), the mammal class (c_Mammalia), and the Rodentia order (o_Rodentia). Seven categories, such as snakehead fish, Channidae, and Channidae, have significantly different relative abundances in winter.

[0213] In addition, the present invention also constructs an autocorrelation network of species changing with seasons at the species level to analyze species interactions and predict key species. As Figure 6 shown, 57 species have significant positive correlations (P < 0.001), among which Macropodus concolor, Carnegiella marthae, Rectoris posehensis, Hypoptopoma inexspectatum, and Sclerophrys brauni occupy central positions in the autocorrelation network. This result indicates that these 57 species are the main species changing with seasons.

[0214] The above results show that by real-time monitoring and analyzing seasonal species differences and predicting potential changes in the health status of ecosystems, environmental problems such as species disappearance, water quality deterioration, or ecological imbalance can be prevented.

[0215] It should be noted that the above specific embodiments are exemplary. Those skilled in the art can come up with various solutions inspired by the disclosed content of the present invention, and these solutions also fall within the scope of the disclosure of the present invention and within the protection scope of the present invention. Those skilled in the art should understand that the description and drawings of the present invention are illustrative and do not constitute a limitation on the claims. The protection scope of the present invention is defined by the claims and their equivalents.

Claims

1. An environment monitoring system based on leeches, characterized in that, including a data acquisition module that determines key species and acquires their biological data; and a data processing module that is data - connected to the data acquisition module, and the data processing module is configured to: evaluate environmental diversity based on a dynamic evaluation model through diversity parameters including the ACE index, the Chao1 richness index, and / or the Shannon index, where the selection conditions for the key species are dominant species and / or taxa with significant seasonal changes.

2. The environmental monitoring system according to claim 1, characterized in that Both the dominant species and the taxa with significant seasonal changes are leeches, and the biological data acquired is eDNA in the environment obtained based on leech blood.

3. The environmental monitoring system according to claim 1 or 2, characterized in that, The data processing module is configured to: calculate the σ of the ACE index, the Chao1 richness index, and / or the Shannon index based on the dynamic evaluation model, and when the diversity index exceeds ±1σ of the normal range but does not all reach ±2σ, generate an instruction of "there is a slight abnormality in the environmental diversity of the current season"; when multiple diversity index deviations all exceed ±2σ of the normal range but do not all reach ±3σ, generate an instruction of "there is a moderate abnormality in the environmental diversity of the current season"; when multiple diversity index deviations all exceed ±3σ of the normal range, generate an instruction of "there is a significant abnormality in the environmental diversity of the current season".

4. The environmental monitoring system according to claim 1 or 2, characterized in that Determining the dominant species among the key species can be based on the seasonal stability coefficient.

5. The environmental monitoring system according to claim 4, characterized in that, Determining the taxa with significant seasonal changes among the key species can be based on the seasonal difference index.

6. The environmental monitoring system according to claim 1, wherein, When the dominant species and the taxa with significant seasonal changes are different species, the data processing module is configured to: evaluate environmental diversity based on the environmental diversity score, and the calculation method of the environmental diversity score is the sum of the evaluation parameters of the ACE index, the evaluation parameters of the Chao1 richness index, and the evaluation parameters of Shannon, where the evaluation parameters of the ACE index, the evaluation parameters of the Chao1 richness index, and the evaluation parameters of Shannon are adjusted based on the four seasons.

7. The environmental monitoring system according to claim 6, wherein The data processing module is configured to: set up a dynamic evaluation model for calculating the total environmental diversity score, where the calculation formula of the total environmental diversity score is as follows: where EDS is the total environmental diversity score; SSC is the seasonal stability coefficient; SDI is the seasonal difference index; DCI is the diversity comprehensive index; where DCI is the diversity comprehensive index; ω1 is the weight ratio of the ACE index in DCI; ω2 is the weight ratio of the Chao1 index in DCI; ω3 is the weight ratio of the Shannon index (H′) in DCI.

8. The leech-based environmental monitoring system according to any one of claims 1 to 7, characterized in that The environmental monitoring system based on leeches is also provided with an information terminal that is data - connected to the data processing module, and the information terminal can adjust its monitoring object, monitoring time, monitoring frequency, or monitoring range based on the instructions of the information terminal.

9. The leech-based environmental monitoring system according to any one of claims 1 to 8, characterized in that For the environmental monitoring of the Chaobai River Basin, the taxa with significant seasonal changes are Macropodus opercularis, Bryconamericus iheringii, Orthostomus orthostomus, Hypostomus uruguayensis, and / or Phyllomedusa bicolor; the dominant species are Bufo gargarizans and / or Carassius auratus.

10. An environment monitoring method based on leeches, characterized in that, including the following steps: determine the selected key species and their identified characteristics based on season or species abundance; Collect biological data of dominant species and / or taxa with significant seasonal variations; Obtain diversity parameters including ACE index, Chao1 richness index, and / or Shannon index based on the biological data; Evaluate environmental diversity based on a dynamic assessment model through diversity parameters including ACE index, Chao1 richness index, and / or Shannon index.

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