A method for quantifying organic carbon environmental stressors

Abstract

The application discloses a quantitative method for organic carbon environmental response force, collects environmental samples under different environmental variable conditions, determines organic carbon molecular composition and environmental variable values; the environmental samples are randomly divided into two data sets according to a preset proportion multiple times; the standard effect value of the change relationship between each organic carbon molecule and the environmental variable is calculated by using the data in the first data set, and the environmental response force of each organic carbon molecule is represented; the environmental response force of each sample is calculated based on a statistical method by using the data in the second data set and the calculation result of the first data set; the environmental response force of each sample is calculated by arithmetic mean according to the random number of each sample in the second data set in the multiple random processes and the environmental response force of the corresponding sample obtained by each random calculation. The method can accurately obtain quantitative analysis of the organic carbon molecular level and the environmental response force at the collection level under different ecological system and environmental change gradient conditions.

Description

Technical Field

[0001] This invention belongs to the field of environmental ecology, and in particular relates to a method for quantifying the environmental responsiveness of organic carbon. Background Technology

[0002] How organic carbon responds to environmental change in the context of global change is a hot topic in global change research. Quantifying the response of organic carbon to environmental change can better predict the dynamic changes of ecosystem organic carbon in the future global context and identify global organic carbon sensitive hotspots. Currently, research on the environmental response of organic carbon mainly focuses on macroscopic levels such as total organic carbon content and greenhouse gas emissions. Considering the high diversity and varied molecular properties of organic carbon molecules, understanding how organic carbon responds to environmental change at the molecular level is quite difficult. Therefore, there is currently a lack of methods to quantify the environmental responsiveness of organic carbon at the sample or ecosystem level based on the molecular characteristics of organic carbon, including the intensity and direction of the response. Summary of the Invention

[0003] The purpose of this invention is to provide a method for quantifying the environmental responsiveness of organic carbon.

[0004] The above-mentioned objective of this invention is achieved through the following technical solution:

[0005] A method for quantifying the environmental responsiveness of organic carbon includes:

[0006] Environmental samples were collected under different environmental variable conditions, and their organic carbon molecular composition and environmental variable values ​​were determined.

[0007] The environmental samples were randomly divided into a first dataset and a second dataset multiple times according to a preset ratio.

[0008] Take the first dataset generated by each random partition, calculate the standard effect value of the relationship between each organic carbon molecule and environmental variable changes, and characterize the environmental responsiveness of each organic carbon molecule.

[0009] Take the second dataset generated by each random division, and calculate the environmental response of each sample based on the calculation results of the environmental response of each organic carbon molecule in the first dataset using statistical methods.

[0010] Based on the number of times each sample in the second dataset was randomly selected during multiple random processes and the environmental responsiveness of the corresponding sample obtained from each random selection, the environmental responsiveness of each sample was calculated by arithmetic mean.

[0011] In a preferred embodiment, the environmental samples under different environmental variable conditions are either natural environmental samples under environmental variable variation gradient conditions, or environmental samples cultured under an environmental variable variation gradient. The different environmental variable conditions include a defined environmental variable gradient, which can be continuous environmental change or a specific environmental change treatment. For example, using temperature as the environmental variable, the environmental samples are either natural environmental samples under temperature gradient variation conditions, or environmental samples are cultured under temperature gradient variation conditions to obtain environmental samples cultured under different temperature conditions.

[0012] In a preferred embodiment, the organic carbon molecular composition data is obtained using high-resolution mass spectrometry.

[0013] As a preferred implementation method, the environmental samples are randomly divided into a first dataset and a second dataset according to a preset allocation ratio using a machine learning model training method.

[0014] As a preferred implementation, the number of random partitions is ≥999. The number of random partitions should be large enough to ensure that the second dataset covers all samples after multiple random partitions, meaning that each sample is randomly selected from the second dataset.

[0015] In a preferred embodiment, the standard effect value of the relationship between the organic carbon molecule and environmental variable changes is established based on statistical methods such as rank correlation coefficient, effect size, or regression analysis slope.

[0016] As a preferred implementation, the standard effect value is calculated using the rank correlation coefficient, which is based on a two-tailed hypothesis test to obtain the significant standard effect value. The calculation method is as follows:

[0017]

[0018]

[0019] P = 1 - 2 × pt(t, n - 2)

[0020] In the formula, ρ is the rank correlation coefficient, and d i denoted as , where is the difference between the relative abundance of the i-th organic carbon molecule and the position of the environmental variable data pair among samples, n is the number of samples; t is the t-hypothesis test, P is the significance test P-value, and pt is the t-distribution.

[0021] As a preferred implementation method, the standard effect value is quantitatively calculated using the effect size index, and the calculation method is as follows:

[0022]

[0023]

[0024] df = nt +n c -2

[0025] Where ES is the effect size. This represents the average relative abundance of the target organic carbon molecules under the given environmental conditions. S represents the average relative abundance of the target organic carbon molecules under control environmental conditions. pooled I represents the pooled standard deviation of the relative abundance of target organic carbon molecules under treated and control environmental conditions. t I represents the relative abundance of the target organic carbon molecules in each sample under the treated environmental conditions. c The relative abundance of target organic carbon molecules in each sample under control environmental conditions is given by Γ, where Γ is the gamma function, df is the degree of freedom, and n is the number of atoms in the sample. t n is the number of samples to be processed under environmental conditions. c This represents the number of samples under control environmental conditions.

[0026] As a preferred implementation method, the environmental responsiveness of each sample is calculated using a weighted average method, as follows:

[0027]

[0028] In the formula, iCER M ρ is the environmental response force based on the weighted average value. i and I i denoted as the environmental responsiveness and relative abundance of the i-th organic carbon molecule, respectively, and s is the number of organic carbon molecules with a significant rank correlation coefficient.

[0029] As a preferred implementation method, the environmental responsiveness of each sample is calculated using the ratio method, as follows:

[0030]

[0031] In the formula, iCER R For environmental response force based on the ratio method, P i and N i denoted as the relative abundance of the i-th organic carbon molecule with significant positive and negative rank correlation coefficients, respectively, and m and n are the number of organic carbon molecules with significant positive and negative rank correlation coefficients, respectively.

[0032] In a preferred embodiment, the environmental response force includes the environmental response intensity and environmental response direction of the target organic carbon molecule or sample. The environmental response intensity is characterized based on the magnitude of the environmental response force value, and the environmental response direction is characterized based on the positive or negative value of the environmental response force value.

[0033] In this invention, the environmental variables refer to environmental factors that may affect the ecosystem, including temperature, humidity, light, pH, nutrient concentration, primary productivity, microplastic input, heavy metal pollution, etc.

[0034] The environmental samples can be organisms, microorganisms, soil, water, etc.

[0035] To quantify the response of organic carbon to environmental change, this invention utilizes a machine learning model training method. The organic carbon molecular composition dataset is randomly divided into two parts according to a certain allocation ratio for independent calculation of the environmental responsiveness of organic carbon at two levels: the environmental responsiveness at the organic carbon molecular level and the environmental responsiveness at the organic carbon aggregate level. Based on statistical models such as rank correlation coefficients, the standard effect value of the relationship between the first dataset generated by each random partition and the change in environmental variables is calculated at the molecular level to measure the environmental responsiveness of each organic carbon molecule. Significant environmental responsiveness is obtained based on two-tailed hypothesis testing. Based on the environmental responsiveness at the molecular level in the first dataset generated by each random partition and the relative abundance of each molecule in the second dataset, the environmental responsiveness of each sample's organic carbon is quantified at the organic carbon aggregate level using statistical methods such as weighted averaging or ratio methods. Based on the environmental responsiveness at the organic carbon aggregate level generated by multiple random processes, the environmental responsiveness of all samples' organic carbon is calculated using the average method. The organic carbon environmental responsiveness includes both the intensity and direction of the environmental response.

[0036] The method of this invention can accurately obtain quantitative analysis of the environmental responsiveness of organic carbon at both the molecular and aggregate levels under different ecosystems and environmental change gradients. This method considers the characteristics and environmental responsiveness of individual organic carbon molecules, and can accurately quantify and compare the environmental responsiveness of organic carbon under different ecosystems or environmental conditions. It establishes and elucidates a research approach for accurately quantifying the response of organic carbon to environmental change in ecosystems at the molecular level. Attached Figure Description

[0037] Figure 1 This invention describes the variation of organic carbon temperature response of lake sediments in different climatic zones with culture temperature, calculated using the method of this invention.

[0038] Figure 2 This relates to the temperature responsiveness of organic carbon in lake sediments and CO2 emissions from greenhouse gases in different climate zones. Detailed Implementation

[0039] The present invention will now be described in detail with reference to specific implementation examples.

[0040] This embodiment presents an indoor culture experiment on sediment samples from Taihu Lake, setting up seven temperature gradients to study the response of sediment organic carbon to temperature changes. The study used uniform Taihu Lake sediments, inoculating them with microorganisms from three different climate zones (subtropical, subtropical-temperate transition zone, and temperate climate zone) for culture experiments, serving as three empirical case studies for demonstration and discussion.

[0041] The indoor culture experimental system was as follows: Uniform Taihu Lake sediment and sterile deionized water were added to 20mL bottles, and then microorganisms from three lakes in different climatic zones were inoculated. Culture was carried out for one month under seven temperature gradients. The three lakes in different climatic zones were an unnamed lake in Guangxi, Daputang Lake in Jiangsu, and Dongshan Lake in Heilongjiang. The seven temperature gradients were 5, 10, 15, 20, 25, 30, and 35℃, with each treatment including four replicates. After the experiment, a total of 84 samples were collected for organic carbon composition analysis. The specific implementation steps were as follows:

[0042] Step 1: Obtain data on the composition of organic carbon molecules.

[0043] Dissolved organic carbon was extracted from the sediment, and the molecular composition data of organic carbon was obtained by Fourier transform cyclotron resonance-gas chromatography-mass spectrometry (FT-ICR MS).

[0044] Step 2: Using machine learning model training methods, the organic carbon molecule composition dataset is randomly divided into two parts, A and B, according to a certain allocation ratio.

[0045] Using a machine learning model training method, the organic carbon molecule composition data was randomly divided into two parts, A and B, with an 80:20 allocation ratio. This was used to independently calculate the environmental responsiveness of organic carbon at two levels: the environmental responsiveness at the organic carbon molecule level and the environmental responsiveness at the organic carbon aggregate level. The random number of times was 999.

[0046] Step 3: Calculate the environmental responsiveness of each organic carbon molecule at the molecular level.

[0047] Selecting the data subset A generated by each random partitioning in step 2), this embodiment uses the Spearman rank correlation coefficient to quantitatively calculate the standard effect value of the relationship between each organic carbon molecule and temperature change at the molecular level, and uses a two-tailed hypothesis test to obtain the significant standard effect value. Based on the positive or negative value of the significant standard effect value, the environmental responsiveness of each organic carbon molecule is measured, including negative and positive environmental responsiveness. The calculation formulas for the rank correlation coefficient and the two-tailed hypothesis test are as follows:

[0048]

[0049]

[0050] P = 1 - 2 × pt(t, n - 2) (3)

[0051] In the formula, ρ is the rank correlation coefficient, and d i denoted as , where is the difference between the relative abundance and temperature data of the i-th organic carbon molecule in the samples, n is the number of samples; t is the t-hypothesis test, P is the significance test P-value, and pt is the t-distribution.

[0052] An alternative implementation method is to quantitatively calculate the standard effect value of the relationship between changes in organic carbon molecules and environmental variables using effect size indices. The calculation method is as follows:

[0053]

[0054]

[0055] df = n t +n c -2 (6)

[0056] Wherein, ES represents the effect size produced after environmental change (i.e., treatment environmental conditions) relative to the control environmental conditions. This represents the average relative abundance of the target organic carbon molecules under the given environmental conditions. S represents the average relative abundance of the target organic carbon molecules under control environmental conditions. pooled I represents the pooled standard deviation of the relative abundance of target organic carbon molecules under treated and control environmental conditions. t I represents the relative abundance of the target organic carbon molecules in each sample under the treated environmental conditions. c The relative abundance of target organic carbon molecules in each sample under control environmental conditions is given by Γ, where Γ is the gamma function, df is the degree of freedom, and n is the number of atoms in the sample. t n is the number of samples to be processed under environmental conditions. c This represents the number of samples under control environmental conditions.

[0057] Step 4: Calculate the environmental responsiveness of each organic carbon at the sample or ecosystem level.

[0058] The relative abundance of each molecule in the data subset B generated by each random partitioning in step 2) and the environmental responsiveness at the molecule level in data subset A are selected. In this embodiment, the environmental responsiveness of organic carbon is calculated at the organic carbon aggregate level based on a statistical method of weighted average, and its expression is as follows:

[0059]

[0060] In the formula, iCER M ρ is the environmental response force based on the weighted average value. i and Ii denoted as the environmental responsiveness and relative abundance of the i-th organic carbon molecule, respectively, and s is the number of organic carbon molecules with a significant rank correlation coefficient.

[0061] Step 5: Based on the environmental responsiveness at the aggregate level of organic carbon generated by multiple random processes, calculate the environmental responsiveness of organic carbon in all samples using the averaging method. Measure the environmental responsiveness of each sample's organic carbon based on the positive and negative values ​​of the iCER, including negative and positive environmental responsiveness. The formula for calculating the environmental responsiveness of each sample's organic carbon is as follows:

[0062]

[0063] In the formula, iCER represents the environmental responsiveness of organic carbon in each sample, and iCER' represents the iCER calculated for each random process. M or iCER R N k The number of random occurrences for each sample.

[0064] Among them, iCER R For environmental responsiveness based on the ratio method, if step 4 uses a statistical method based on the ratio method to calculate the environmental responsiveness of organic carbon, then iCER will be used. R Calculation, iCER R The calculation formula is as follows:

[0065]

[0066] In the formula, P i and N i denoted as the relative abundance of the i-th organic carbon molecule with significant positive and negative rank correlation coefficients, respectively, and m and n are the number of organic carbon molecules with significant positive and negative rank correlation coefficients, respectively.

[0067] According to the method of the present invention, the response of microbially driven lake sediment organic carbon to temperature changes in different climatic zones, and the variation of this response with culture temperature, are calculated, such as... Figure 1 As shown in the figure, the results from the three regions are consistent, indicating that organic carbon exhibits a stronger temperature response at higher temperatures. To explain the significance of this temperature response, the relationship between temperature response and greenhouse gas CO2 emissions was further analyzed, and the results are shown in the figure. Figure 2 A higher temperature response of organic carbon will lead to enhanced organic carbon degradation and increased CO2 emissions.

[0068] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Those skilled in the art can make various modifications and refinements without departing from the spirit and scope of the invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. A quantitative method for the environmental responsiveness of organic carbon, characterized in that, include: Environmental samples were collected under different environmental variable conditions, and their organic carbon molecular composition and environmental variable values ​​were determined. The environmental samples were randomly divided into a first dataset and a second dataset multiple times according to a preset ratio. The first dataset generated by each random partition is used to calculate the relationship between the relative abundance of each organic carbon molecule and environmental variables based on statistical methods such as rank correlation coefficient, effect size, or regression slope, in order to characterize the environmental responsiveness of each organic carbon molecule. Take the second dataset generated by each random partition, and based on the relative abundance of each organic carbon molecule in the second dataset and the calculation results of the environmental responsiveness of each organic carbon molecule in the first dataset, use the weighted average method or ratio method to calculate the environmental responsiveness of organic carbon at the organic carbon ensemble level as the environmental responsiveness of each sample. Based on the number of times each sample in the second dataset was randomly selected during multiple random processes and the environmental responsiveness of the corresponding sample obtained from each random selection, the environmental responsiveness of each sample was calculated by arithmetic mean.

2. The method according to claim 1, characterized in that, The environmental samples under different environmental variable conditions are natural environmental samples under environmental variable variation gradient conditions, or environmental samples that have been cultured under environmental variable variation gradient conditions.

3. The method according to claim 1, characterized in that, Using machine learning model training methods, environmental samples are randomly divided into a first dataset and a second dataset according to a preset allocation ratio.

4. The method according to claim 1 or 3, characterized in that, The number of random divisions is ≥999.

5. The method according to claim 1, characterized in that, The environmental impact of each organic carbon molecule is calculated using the rank correlation coefficient, which is based on a two-tailed hypothesis test to obtain the significant standard effect value. The calculation method is as follows: p = 1 - ; t = ρ ; P = 1 – 2 × pt(t, n-2); In the formula, ρ is the rank correlation coefficient. The difference between the relative abundance of the i-th organic carbon molecule and the position of the environmental variable data pair among the samples, where n is the number of samples; For the t-hypothesis test, P is the significance test P-value, and pt is the t-distribution.

6. The method according to claim 1, characterized in that, The environmental impact of each organic carbon molecule is quantitatively calculated using the effect size index, and the calculation method is as follows: ES = × ; = ； ； Where ES is the effect size. This represents the average relative abundance of the target organic carbon molecules under the given environmental conditions. S represents the average relative abundance of the target organic carbon molecules under control environmental conditions. pooled The pooled standard deviation of the relative abundance of target organic carbon molecules under treated and control environmental conditions. This represents the relative abundance of the target organic carbon molecules in each sample under the treated environmental conditions. The relative abundance of the target organic carbon molecules in each sample under control environmental conditions. Let df be the gamma function, n be the degrees of freedom, and n be the number of degrees of freedom. t n is the number of samples to be processed under environmental conditions. c This represents the number of samples under control environmental conditions.

7. The method according to claim 1, characterized in that, The environmental responsiveness of each sample is calculated using a weighted average method, as follows: iCER M = ; In the formula, iCER M Environmental responsiveness based on weighted average value, and denoted as the environmental responsiveness and relative abundance of the i-th organic carbon molecule, respectively, and s is the number of organic carbon molecules with a significant rank correlation coefficient.

8. The method according to claim 1, characterized in that, The environmental responsiveness of each sample is calculated using the ratio method, as follows: iCER R = ; In the formula, iCER R For environmental response based on the ratio method, and denoted as the relative abundance of the i-th organic carbon molecule with a significant positive rank correlation coefficient and the j-th organic carbon molecule with a significant negative rank correlation coefficient, respectively, and m and k are the number of organic carbon molecules with significant positive rank correlation coefficients and negative rank correlation coefficients, respectively.

9. The method according to claim 1, characterized in that, The environmental response force includes the environmental response intensity and environmental response direction of the target organic carbon molecule or sample. The environmental response intensity is characterized based on the magnitude of the environmental response force value, and the environmental response direction is characterized based on the positive or negative value of the environmental response force value.

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