Method for evaluating contribution rate of exogenous carbon in mangrove forest environment

Abstract

The invention belongs to the technical field of ecological environment monitoring and tracing, and particularly relates to a method for evaluating the contribution rate of exogenous carbon in a mangrove forest. According to the method, two technologies of stable isotope analysis and eDNA analysis are combined, and the limitation of a single method in tracing the carbon source of the mangrove forest is overcome. The stable isotope analysis can evaluate the relative contribution of different sources from the perspectives of elemental composition and isotope ratio, the environmental DNA can provide high-resolution species information and determine the contribution of specific plant species or class groups to carbon, the two complement each other, the contribution rate of mangrove forest exogenous carbon is quantitatively evaluated, the accuracy of carbon source tracing is greatly improved, and the carbon source tracing efficiency is improved. The technical problem of carbon source analysis in a complex environment with multi-source carbon input is solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of ecological environment monitoring and tracing, and particularly relates to a method for evaluating contribution rate of exogenous carbon in mangrove. BACKGROUND

[0002] Mangrove is one of the forests with the highest carbon density on earth, and plays a key role in global carbon cycle, and is regarded as a key ecological system for mitigating and adapting to climate change. The carbon cycle capacity of mangrove mainly depends on plant photosynthesis and exogenous carbon input. Among them, exogenous carbon input includes carbon input from rivers and oceans, direct input of human activities and input from adjacent ecosystems, and it is of great significance to study the sources of exogenous carbon in mangrove and its contribution rate to mangrove organic carbon.

[0003] The tracing of exogenous carbon in mangrove is not only the core link of analyzing the "high carbon sink" mechanism, but also a key scientific problem linking global carbon cycle, climate change response, ecosystem management and blue carbon economy. At present, the technical means for tracing mangrove organic carbon include stable isotope, biomarker and remote sensing. Among them, stable isotope tracing is a common method, which is often combined with other technologies to evaluate the source of organic carbon. For example, through the combination of carbon and nitrogen stable isotope technology and multi-linear mixing model, the source of mangrove sediment organic carbon can be analyzed, 50.3% of the organic carbon is derived from marine source, 33.4% is derived from terrestrial source, and the rest is derived from tree source (Mangrove Sediment Organic Carbon Burial Characteristics Analysis in Mao'an Island[J]. China Environmental Science, 2024, 44(8): 4539-4546.). In addition, there are also studies on the spatial and temporal distribution of mangrove sediment organic matter and the source of organic carbon by using stable isotope technology and molecular biology technology (Study on the Source of Mangrove Sediment Organic Carbon and Microbial Carbon Assimilation[D]. Xiamen University, 2018.). Although stable isotope technology is very mature in the application of organic carbon tracing, there are still limitations in distinguishing different sources of carbon, such as the overlap of isotope values of mangrove, salt marsh and terrestrial vegetation, which makes it difficult to accurately evaluate their respective contributions to mangrove organic carbon.

[0004] Environmental DNA (eDNA) is the DNA left by organisms interacting with the environment, which is distributed in the environment such as soil, sediment and natural water, and is a mixture of complete and fragmented DNA of organisms. The eDNA technology has the potential to identify species based on high-throughput biological detection, and is a new technology applied to mangrove ecological restoration and resource utilization in recent years. CN101148676A discloses a method for constructing a large fragment metagenomic library of mangrove soil, which relies on the eDNA technology and solves the problem that the small fragment eDNA library cannot capture the complete gene cluster of substances such as antibiotics, thereby providing a powerful tool for developing medicinal resources of mangrove soil microorganisms. In addition, the prior art reports that the eDNA technology is used to qualitatively evaluate the plant organic carbon source in the mangrove soil from the molecular level, and the relative quantitative contribution rate of plants to local organic carbon is explored (Blue Carbon Tracing Research Based on Environmental DNA of Mangrove Sediment[D]. Xiamen University, 2022.). However, this method can only qualitatively describe the biological community structure and cannot directly quantify the carbon source contribution rate.

[0005] The method for analyzing the source of carbon in mangroves mainly monitors C and N elements (%) and stable isotopes (δ 13 C and δ 15 N) in mangrove sediments, however, the organic matter in the coastal ecosystem has complex sources, and the isotope values of plants, tissues, microhabitats, seasons and growth cycles are variable and overlapping, which makes it difficult to accurately evaluate the source and fate of organic carbon using a large number of C and N isotope ratios, and therefore new methods are needed to make up for these deficiencies. SUMMARY

[0006] Based on the defects in the above-mentioned research on the contribution rate of exogenous carbon in mangroves, the present application ingeniously combines stable isotope analysis and eDNA technology to develop a method for evaluating the contribution rate of exogenous carbon in mangroves based on stable isotopes and eDNA. The method mainly uses stable isotope analysis, and after weighting the relative abundance of eDNA, the contribution rate of exogenous carbon in mangroves is quantitatively analyzed to achieve precise tracing and realize the dual constraints of "chemical tracing" and "biological response". Based on this, the present application is completed.

[0007] In a first aspect, the present application provides a method for evaluating the contribution rate of exogenous carbon in mangroves, which comprises the following steps:

[0008] S01, sampling and drawing a site map;

[0009] S02, pretreating the samples collected in S01);

[0010] S03, performing stable isotope analysis on the pretreated samples in S02) to obtain δ13C and δ 15N value;

[0011] S04, the δ13C and δ 15 N value is input into the mixed model to calculate the contribution ratio;

[0012] S05, the sample pretreated in S02) is subjected to eDNA sequencing, and the obtained data is aligned to calculate the species abundance;

[0013] S06, the data in S04) and S05) are integrated to quantitatively analyze the contribution rate of exogenous carbon in mangrove.

[0014] Further, in step S01), multiple sampling points are selected when collecting the sample, including one or more types of true mangrove, semi-mangrove, salt marsh and light beach.

[0015] Further, the sample is selected from one or more of soil / sediment samples, vegetation samples, planktonic algae samples and Spartina alterniflora samples.

[0016] Further, in step S01), the sampling site map can be drawn using one of ArcGIS, QGIS, GlobalMapper, Google Earth Pro, R language or Python.

[0017] Further, in step S02), the pretreatment refers to freezing treatment.

[0018] Further, in step S04), the mixed model is a stable isotope mixing model.

[0019] Further, the stable isotope mixing model can be SIMMR or SIAR.

[0020] In an embodiment of the present application, when the SIMMR stable isotope mixing model is used, the carbon source contribution ratio is obtained by loading the isotope data into the SIMMR model using the "simmr_load" function and analyzing by the "simmr_mcmc" function.

[0021] Further, the carbon source contribution ratio is the exogenous carbon contribution rate (f 外源 ) calculated by the Bayesian mixing model, and the formula for calculating the carbon source contribution rate is as follows:

[0022]

[0023] Wherein, f ext，iso is the exogenous carbon contribution rate under the stable isotope model, %; δ 13 C int is the endogenous carbon isotope characteristic value, ‰; δ 13 C sedδC is the sediment carbon isotope value, ‰; Δ is the fractionation correction term, ‰; δ 13 C ext is the exogenous carbon isotope characteristic value, ‰.

[0024] Further, the exogenous carbon includes suspended organic particles, phytoplankton and the like in the water body, which are transported and settled into the mangrove sediment by the water flow; the endogenous carbon refers to the endogenous organic carbon absorbed by the mangrove plants through photosynthesis and converted.

[0025] Further, in step S05, the species abundance calculation step comprises:

[0026] S051, extracting sample eDNA, eDNA amplification and DNA product sequencing to obtain DNA sequence information;

[0027] S052, comparing the sequencing results in S051) with a plant species database, clustering analysis and species annotation;

[0028] S053, calculating the plant species abundance based on the proportion of aligned sequence numbers.

[0029] Further, in step S053), the calculation of the plant species abundance needs to be corrected by one and / or more methods of qPCR, internal standard method and / or PICRUSt2.

[0030] Further, the calculation method of the species abundance is as follows:

[0031]

[0032] Wherein, F ext is the effective sequence number of exogenous species (the total number of sequences attributed to exogenous species after quality control and annotation); F int is the effective sequence number of endogenous species (the total number of sequences attributed to mangrove itself or local species).

[0033] Further, in step S06, the data integration is to calculate the isotope value obtained in step S05) and the eDNA species abundance data obtained in step S06) by using a Bayesian stable isotope mixing model.

[0034] Further, the calculation formula of the exogenous carbon contribution rate is:

[0035]

[0036] Wherein, f ext is the exogenous carbon contribution rate, (%) ; δ 13 C int is the endogenous carbon isotope characteristic value, (‰); δ 13 Csed is the sediment carbon isotope value, (‰); delta is the fractionation correction term, (‰); delta 13 C ext is the exogenous carbon isotope characteristic value, (‰); eDNA ext is the relative abundance of eDNA of the exogenous species; omega eDNA is the weight coefficient of the eDNA model; omega iso is the weight coefficient of the stable isotope model.

[0037] In a second aspect, the present application provides a system for evaluating the contribution rate of exogenous carbon in mangrove, which comprises a data input module, a data processing module and a data output module;

[0038] The data input module is to input stable isotope values and eDNA sequencing data;

[0039] The data processing module comprises calculating the contribution rate of exogenous carbon, calculating the relative abundance of eDNA and comprehensive evaluation of the contribution rate of exogenous carbon;

[0040] The data output module is to output the comprehensive evaluation value of the contribution rate of exogenous carbon.

[0041] Further, in the data input module, the stable isotope values are obtained by stable isotope analysis of collected samples, and the eDNA sequencing data are obtained by eDNA extraction and sequencing of collected samples.

[0042] Still further, the collected samples refer to one or more of soil / sediment samples, vegetation samples, planktonic algae samples and / or Spartina alterniflora samples collected at multiple sampling points in the measured environment.

[0043] Still further, the stable isotope analysis comprises drawing a sampling point map.

[0044] Further, in the data processing module, the calculation of the contribution rate of exogenous carbon is performed by using a stable isotope Bayesian mixture model, and the calculation formula is as follows:

[0045]

[0046] wherein, f ext，iso is the contribution rate of exogenous carbon under the stable isotope model, (%) ; delta 13 C int is the endogenous carbon isotope characteristic value, (‰); delta 13 C sed is the sediment carbon isotope value, (‰); delta is the fractionation correction term, (‰); delta 13 C ext is the exogenous carbon isotope characteristic value, (‰).

[0047] Further, in the data processing module, the relative abundance of eDNA species is calculated by comparing the eDNA sequencing results with a plant species database, clustering and analyzing the annotated species, and calculating the relative abundance of plant species based on the sequence proportion, the calculation method being as follows:

[0048] Filtering low-quality sequences, aligning effective sequences with a reference database, and determining sequences belonging to exogenous species through taxonomic annotation, i.e., F ext and endogenous species, i.e., F int .

[0049] Further, the calculation formula of the relative abundance of plant species is as follows:

[0050]

[0051] wherein, F ext is the number of effective sequences of exogenous species (the total number of sequences belonging to exogenous species after quality control and annotation);F int is the number of effective sequences of endogenous species (the total number of sequences belonging to mangrove itself or local species)。

[0052] Further, the relative abundance of eDNA species needs to be corrected by one and / or more of qPCR, internal standard method and / or PICRUSt2.

[0053] Further, in the data processing module, the comprehensive evaluation formula of the exogenous carbon contribution rate is as follows:

[0054]

[0055] wherein, f ext is the exogenous carbon contribution rate, (%);δ 13 C int is the endogenous carbon isotope characteristic value, (‰);δ 13 C sed is the sediment carbon isotope value, (‰);Δ is the fractionation correction term, (‰);δ 13 C ext is the exogenous carbon isotope characteristic value, (‰);eDNA ext is the relative abundance of eDNA of exogenous species;ω eDNA is the weight coefficient of the eDNA model;ω iso is the weight coefficient of the stable isotope model.

[0056] In a third aspect, the present application provides a computer readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps of the method provided in the second aspect.

[0057] Further, the steps include data input, data processing and data output.

[0058] Advantages

[0059] The present application realizes accurate tracing of mangrove sources by stable isotopes and eDNA, which helps to understand the carbon cycle process of mangrove ecosystems and provides more reliable data support for evaluating the role of mangroves in global carbon balance. This has important significance for formulating scientific and reasonable mangrove protection and management strategies and promoting the implementation of blue carbon sink related projects.

[0060] The present application combines stable isotope and eDNA analysis techniques, overcoming the limitations of single methods in tracing mangrove carbon sources. Stable isotope analysis can evaluate the relative contribution of different sources from the perspective of element composition and isotope ratio, while environmental DNA can provide high-resolution species information to determine the contribution of specific plant species or groups to carbon. The two complement each other to quantitatively evaluate the contribution rate of exogenous carbon in mangroves, greatly improving the accuracy of carbon source tracing and solving the technical problems of carbon source analysis in complex multi-source carbon input environments. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 Flow chart of method for evaluating exogenous carbon contribution rate in mangroves based on stable isotopes and eDNA.

[0062] Figure 2 For sampling point map.

[0063] Figure 3 For δ 13 C and δ 15 N value map.

[0064] Figure 4 For the carbon source proportion of the sample obtained by the stable isotope model.

[0065] Figure 5 For the carbon source proportion of the sample obtained by the eDNA model. DETAILED DESCRIPTION

[0066] The specific embodiments of the present application will be further described below. It should be noted that the description of these embodiments is used to help understand the present application, but does not constitute a limitation on the present application. In addition, the technical features involved in the following described embodiments can be combined with each other as long as they do not conflict with each other.

[0067] The experimental methods in the following examples are all conventional methods unless otherwise specified. The test materials used in the following examples are all commercially available unless otherwise specified.

[0068] TERMS EXPLANATION

[0069] Clustering: a data mining technique that groups data based on similarities or differences, used to process unclassified raw data objects into different groups, and represent these groups through information structure or pattern. Clustering algorithms can be divided into several types, specifically exclusive, overlapping, hierarchical and probabilistic.

[0070] The method can effectively break through the bottleneck of distinguishing carbon sources caused by the overlap of isotope signals. When the stable isotope ratio of exogenous carbon and endogenous carbon is close, it is difficult to accurately separate the contribution ratio only by relying on isotope. eDNA can directly confirm the biological source by detecting the characteristic sequence of the exogenous species. Even if the isotope characteristics are similar, the contribution can be independently quantified through the species-specific signal, and the non-biological carbon interference can be excluded. In addition, the method significantly enhances the real-time dynamic tracking ability of carbon input. Stable isotope reflects the comprehensive result of long-term carbon accumulation, and it is difficult to capture short-term or pulse input (such as seasonal flood-borne terrestrial organic matter). eDNA can track the physiological activity or migration event of exogenous species (such as algal outbreak after typhoon, phytoplankton input by tides) in real time, and its abundance fluctuation has higher matching degree with the timeliness of carbon input. By detecting the characteristic eDNA sequence of the exogenous species (such as Spartina alterniflora, exotic algae), the biological source of exogenous carbon can be directly confirmed. For example, the eDNA abundance of Spartina alterniflora is positively correlated with the input amount of its real carbon. Even if its δ 13 C is close to that of mangrove, the contribution can be independently quantified through eDNA signal. If the sediment δ 13 C is biased to the negative source due to inorganic carbon (such as carbonate), but eDNA does not detect exogenous organisms, then the possibility of being a biological carbon source can be excluded. By combining the "stock" data of isotope with the "flux" data of eDNA, the persistent and occasional carbon input can be distinguished.

[0071] In terms of integrating biological activity information, stable isotope can only reflect the physical and chemical properties of carbon, and cannot be associated with the biological utilization process of carbon (such as selective degradation of different carbon sources by microorganisms). For example, exogenous carbon and endogenous carbon with the same isotope may have different actual ecological effects due to differences in biological availability, but the isotope model cannot distinguish them. eDNA can detect microbial functional genes or consumer eDNA to determine whether exogenous carbon is effectively utilized and the transmission path in the food web, and correct the "static assumption" of the isotope model. In terms of anti-interference ability, environmental factors or non-carbon source substances may mislead the isotope signal, while eDNA can filter the biological signal specificity and combine with Bayesian prior constraints to verify whether the change of isotope signal is related to biological carbon input, and suppress the abnormal influence caused by non-biological factors.

[0072] For the significant spatial heterogeneity of mangrove ecosystems, eDNA can be used to quickly screen the distribution of exogenous species in different microzones through high-throughput macrobarcoding technology, targetedly guide isotope sampling, improve data efficiency, and analyze the coupling relationship between exogenous carbon input and biogeochemical processes in the microenvironment with microsensors. Typical case comparisons show that the use of stable isotopes alone relies on isotope ratio differences, has low time resolution, cannot be associated with biological processes, and is easily disturbed, while the integrated method realizes the precise splitting of "isotope similar sources", improves the time resolution and anti-interference ability, and combines with biological process analysis to optimize data cost efficiency, upgrades the "stoichiometric" model to a dynamic evaluation system of "biological-chemical coupling", and significantly improves the accuracy and biological authenticity of exogenous carbon quantification in complex ecosystems.

[0073] The use of stable isotopes alone is essentially a "black box model" based on physical and chemical mass balance, while after integrating eDNA, the evaluation system is upgraded to a "white box model" of biological-chemical coupling, and its core advantages are reflected in the following aspects:

[0074] Biological authenticity: Strengthen the biological logic of carbon source identification through "field evidence" (eDNA) of exogenous species; Dynamic adaptability: Consider both carbon stocks (isotopes) and carbon fluxes (eDNA) to adapt to dynamic carbon cycling driven by tides in mangroves; Model robustness: Multi-source data are cross-verified under the Bayesian framework to reduce the uncertainty of a single method, which is particularly suitable for precise accounting of carbon sinks in complex estuarine ecosystems.

[0075] Example 1: Stable isotope analysis of exogenous carbon contribution rate in mangroves

[0076] According to the distribution of mangroves, topography, and surrounding environmental characteristics, n representative sampling points are selected. At each sampling point, a sampler is used to collect sediments and soils at low tide, and potential carbon source plant samples are collected simultaneously. The soil sampling depth is determined according to the requirements, and 3 parallel samples of each sample are collected, a total of 3n samples. During sampling, the name and latitude and longitude of the sampling point are recorded in detail, and environmental photos are taken. The sampling point map is drawn through ArcGIS software, and a compass and scale are added to the map at appropriate positions to better understand the distance and direction.

[0077] In the southern coastal area of Guangdong Province, 8 sampling cities are marked, covering the eastern, Pearl River Delta, and western coastal areas of Guangdong (east longitude 110°-116°, north latitude 21°-23°), with neighboring provinces (Guangxi Zhuang Autonomous Region, Hunan Province, etc.) as auxiliary positioning. For example, Figure 2As shown, 8 maps correspond to 8 cities' sampling areas, which finely show the local position of the city sample through the latitude and longitude range and the scale. TD: Tidal Flat (non-mangrove habitat); NM: Natural Mangrove; RM: Restored Mangrove. Each city sets 3 sample points (1 TD + 1 NM + 1 RM), a total of 24 sample points, forming a "natural → restoration → control" habitat comparison. The three sample points in the same city are within a few hundred meters, which shows that the sampling focuses on the habitat heterogeneity in the same area (such as the microenvironment difference of mangrove inside, edge, and tidal flat), so as to control the regional background interference and accurately compare the carbon storage, community structure and other indicators of different habitats.

[0078] The collected soil column samples were layered according to the corresponding profile, and the collected soil sediment samples were packaged in sealed bags. The plant tissues and suspended particulate matter samples were collected and packaged according to the plant categories. All samples were promptly transported back to the laboratory and stored in the refrigerator. Subsequent analysis of the samples was carried out after freeze-drying and grinding.

[0079] The frozen samples were placed in an elemental analyzer-isotope ratio mass spectrometer for analysis to measure the δ 13 C and δ 15 N values of each sample. The δ 13 N value was used to exclude non-carbon source interference. Exogenous nitrogen input, such as agricultural fertilizers, industrial wastewater, and atmospheric deposition, may enter the mangrove ecosystem independently of carbon input. If only carbon isotope (δ 15 C) is used to distinguish carbon sources, it may mistakenly associate biological activities related to "pure nitrogen input" (such as algal explosive growth) with exogenous carbon contribution, leading to overestimation or underestimation of the true carbon source proportion. Different nitrogen sources have unique δ 15 N characteristics (such as δ 13 N of land-based sewage ≈ +10‰-+15‰, and δ 15 N of local mangroves ≈ +3‰-+8‰), which can help determine whether exogenous input is accompanied by carbon. Nitrogen is a key element for biological growth. If exogenous carbon and nitrogen are input synchronously in a fixed ratio (such as C / N of mangrove litter ≈ 30-50), δ 15 C and δ 13 N should show a coordinated change; if only nitrogen is input (such as fertilizer leaching), δ 15 N is abnormal but δ 15 C does not change significantly, which can be used to exclude interference. 13

[0080] The δ 15 N values of different samples were plotted using R language (see Figure 3 ), to clarify the parts where the data overlap is unclear.​

[0081] The Bayesian stable isotope mixing model was used to calculate the contribution rate of exogenous carbon. The SIMMR stable isotope mixing model was installed in R software, the isotope data was loaded into the SIMMR model using the "simmr_load" function, and the contribution proportion of endogenous carbon and exogenous carbon to soil carbon composition in mangrove was obtained by analyzing the "simmr_mcmc" function.

[0082] First, the prior knowledge of the parameter is updated by the observation data, and the formula is:

[0083]

[0084] Among them: P(θ|D) is the posterior distribution of parameter θ, the probability when the data D is known; P(D|θ) is the likelihood function, the probability of observed data when the parameter D is known; P(θ) is the prior distribution, the initial probability assumption of the parameter; P(D) is the evidence factor, the marginal probability of data, which is used for normalization.

[0085] Stable isotope fractionation refers to the ratio change caused by the difference of chemical bond energy between light and heavy isotopes in biological metabolic process. In carbon source tracing, the isotope signal of soil carbon(consumer) needs to be corrected by fractionation to match the true isotope composition of the source(mangrove endogenous / exogenous carbon).

[0086] The initial isotope values of exogenous carbon(δ 13 C ext ), endogenous carbon(δ 13 C int ) were measured before the experiment, and sediment samples in the culture system were collected regularly to measure their carbon isotope values(δ 13 C sed,exp )

[0087] According to the stable isotope mass balance equation:

[0088] δ 13 C sed,exp = f ext ·δ 13 C ext +(1-f ext )δ 13 C int +Δ

[0089] Given the mixing ratio f ext (experimental setting), the measured data was brought back to calculate Δ:

[0090] Δ = δ 13 C sed,exp -[f ext ·δ 13 C ext+(1-f ext )δ 13 C int ]

[0091] It is known that total organic carbon in sediments is composed of exogenous carbon and endogenous carbon, and its isotope balance equation is as follows:

[0092] δ 13 C sed =f ext ·δ 13 C ext +(1-f ext )δ 13 C int +Δ

[0093] The calculation of fext is carried out by shifting the terms, that is, the contribution ratio of exogenous carbon to the total organic carbon source of mangroves:

[0094]

[0095] Among them, f ext，iso is the contribution rate of exogenous carbon under the stable isotope model (%); δ 13 C int is the characteristic value of endogenous carbon isotope, (‰); δ 13 C sed is the carbon isotope value of the sediment, (‰); Δ is the fractionation correction term, (‰); δ 13 C ext is the characteristic value of exogenous carbon isotope, (‰).

[0096] The results are as follows Figure 4 As shown in the figure, this quantifies the carbon source contributions of different ecosystems using stable isotope modeling. This reveals that mangroves, as "blue carbon" ecosystems, primarily derive their carbon from their own vegetation (sampling sites 1-2, such as mangrove litter), playing a central role in coastal carbon sequestration. The ocean's carbon source relies on plankton or external inputs (sampling sites 7-8, such as organic matter carried by upwelling), reflecting the open nature of the ocean carbon cycle. Salt marshes, with their carbon source derived from both halophytes (sampling site 7, such as organic matter from Suaeda salsa) and tidally transported marine carbon (sampling site 8, such as microalgae debris), are typical representatives of the carbon cycle in the land-sea interface. This significant inter-regional variation in carbon sources provides key data for understanding the carbon sink function of coastal ecosystems and developing differentiated carbon management strategies, such as mangrove conservation, salt marsh restoration, and marine carbon sink monitoring. The figure, using stable isotope modeling, visualizes the carbon source contributions of the three major ecological regions: mangroves, oceans, and salt marshes, using colored bars (sampling sites 1 to 8). Significant differences in carbon source sources are evident between regions.

[0097] Example 2: eDNA analysis of mangrove species abundance

[0098] The pre-processed sample was taken according to the detailed instructions of the DNA extraction kit used, and the specified steps and methods were followed for DNA extraction. The extracted DNA was diluted with sterile water to the appropriate concentration, and the quality and concentration of the DNA were detected by agarose gel electrophoresis and spectrophotometer. Using polymerase chain reaction technology, specific primers were designed for red tree plants, such as ITS2 universal primers (ITS3: 5'-GCATCGATGAAGAAC GCA GC-3'; ITS4: 5'-TCC TCC GCTTATTGATATGC-3'), or red mangrove specific primers (such as RbcL-F: 5'-ATGGCTCAGATCTGC TGAAGA-3' and RbcL-R: 5'-TCATCC ACAAAC TCATCATGG-3' based on rbcL gene of Rhizophora genus), to accurately amplify the target DNA sequence. After amplification, the obtained DNA product was sequenced by modern sequencing technology to obtain its base sequence information.

[0099] After purification of the PCR product, an Illumina library was constructed, and PE300 double-end sequencing was performed using the Illumina MiSeq platform. Trimmomatic v0.39 was used to remove adapters and low-quality bases (Q<30), and sequences with a length of >200bp were retained; VSEARCH v2.23 was used to cluster sequences into operational taxonomic units (OTUs) at 97% similarity, and singleton OUTs were removed. Representative sequences were subjected to BLASTn comparison with NCBI GenBank Plant Database (https: / / blast.ncbi.nlm.nih.gov / Blast.cgi) or Mangrove eDNA Database (self-built reference library of mangrove species), with an identity of ≥97%, and non-mangrove plant sequences (such as plankton and terrestrial plants) were removed.

[0100] The number of specific sequences for each plant species was counted, and the proportion in the total number of sequences was calculated to obtain the abundance of the plant species. To further improve the accuracy and reliability of the measurement results, qPCR quantitative correction technology was introduced. qPCR (quantitative polymerase chain reaction) is a highly sensitive molecular biology detection method that can quantitatively detect target DNA fragments. In calculating the abundance of plant species, qPCR was used to quantify some species to correct the abundance calculated from sequence data, ensuring the accuracy of the results. According to the abundance of each plant species, the contribution of eDNA method to the composition of soil carbon in mangrove endogenous carbon and exogenous carbon was obtained (see Figure 5 ). The calculation method of the species abundance is as follows:

[0101]

[0102] where F ext is the number of valid sequences of the exotic species (total number of sequences attributed to the exotic species after quality control and annotation); F int is the number of valid sequences of the indigenous species (total number of sequences attributed to the indigenous species or native species of the mangrove).

[0103] Example 3 Integration of data using Bayesian algorithm based on stable isotopes and eDNA analysis

[0104] A tidal zone sediment area in a mangrove region of Guangdong Province, China was selected as the research object. Surface sediment samples (0-10 cm) and samples of mangrove plants, terrestrial organic matter (river input suspended particulate matter), and algae (mainly diatoms) were collected. Carbon isotope values were determined by isotope mass spectrometry, and the relative abundance of eDNA of the exotic species (terrestrial indicator algae) was obtained by high-throughput sequencing. The stable isotope model weight ω iso = 0.6 and the eDNA model weight ω eDNA = 0.4 were determined by cross-validation, and the Bayesian prior parameters were set as α = β = 1.

[0105] According to the mass balance equation of stable isotopes, the contribution rate of exotic carbon without eDNA constraint was calculated as:

[0106]

[0107] It was shown that based on isotope data alone, the contribution of exotic carbon (terrestrial organic matter) to sediment organic carbon was about 45.1%.

[0108] Based on the relative abundance of eDNA of the exotic species eDNA ext = 0.32, the hypothetical value of the contribution rate of exotic carbon was directly constrained as:

[0109] f ext,eDNA = eDNA ext = 0.32 (32.0%)

[0110] This result reflects the proportion of carbon input corresponding to the intensity of the biological signal of the exotic species.

[0111] By integrating the two types of data through a weighted average model, the comprehensive contribution rate of exotic carbon was obtained as:

[0112]

[0113] Further, the posterior distribution was solved by Markov Chain Monte Carlo (MCMC) algorithm, and after 10,000 iterations of convergence, the posterior mean of the contribution rate of exotic carbon was 44.2%, and the 95% confidence interval was [37.2%, 42.5%].

[0114] When the stable isotope ratio of exogenous carbon and endogenous carbon is close, it is difficult to accurately separate the contribution ratio only by relying on isotope, and eDNA can directly confirm the biological source by detecting the characteristic sequence of exogenous species. The present application takes eDNA abundance as the calculation weight on the basis of stable isotope evaluation, corrects the incorrect contribution rate, independently quantifies the contribution through the species-specific signal even if the isotope characteristics are similar, excludes non-biological carbon interference, and can effectively break through the bottleneck of carbon source division caused by isotope signal overlap.

[0115] The exogenous carbon contribution rate of the research area sediment is finally determined to be 44.2%, indicating that terrestrial organic matter (such as plant detritus and soil organic carbon input by rivers) is an important source of organic carbon in the mangrove sediment area, contributing nearly 40% of the proportion. Compared with the single stable isotope model (45.1%), the result after integrating eDNA constraint is closer to the actual correlation between biological signal and carbon input, which reflects the advantage of the Bayesian framework to improve the evaluation accuracy through multi-source data (isotope physical signal + eDNA biological signal).

[0116] The results also show that the endogenous carbon of the mangrove is still the main contributor (about 60.1%), which is consistent with the typical characteristics of the "self-source carbon dominant" of the intertidal zone ecosystem, and the quantitative analysis of the exogenous carbon input provides key parameter support for the estimation of mangrove carbon sink and the study of land-sea interaction mechanism.

Claims

1. A method for assessing the exogenous carbon contribution of mangroves, comprising the following steps: S01, sampling and plotting site maps; S02, pre-processing the sample collected in S01); S03, the sample pretreated in S02) was subjected to stable isotope analysis to obtain δ13C and δ 15 N value; S04, the δ13C and δ 15 The N value is input into the hybrid model to calculate the contribution ratio; S05, performing eDNA sequencing on the sample pre-treated in S02), and calculating species abundance after comparing the obtained data; S06: Integrate the data in S04) and S05) to quantitatively analyze the exogenous carbon contribution rate of mangroves.

2. The method according to claim 1, wherein in step S01), multiple sampling points are selected when collecting samples, including one or more types such as true mangroves, semi-mangroves, salt marshes and light beaches.

3. The method according to claim 1, wherein in step S01), the sample is selected from one or more of a soil / sediment sample, a vegetation sample, a phytoplankton sample, and a Spartina alterniflora sample.

4. The method according to claim 1, wherein in step S04), the hybrid model calculates the contribution ratio by using a Bayesian hybrid model to calculate the exogenous carbon contribution rate (f 外源 ), the carbon source contribution rate is calculated as follows: in, f ext，iso is the contribution rate of exogenous carbon under the stable isotope model (%); δ 13 C int is the characteristic value of endogenous carbon isotope, (‰); δ 13 C sed is the carbon isotope value of the sediment, (‰); Δ is the fractionation correction term, (‰); δ 13 C ext is the characteristic value of exogenous carbon isotope, (‰).

5. The method according to claim 1, wherein in step S05), the species abundance calculation step comprises: S051, extracting sample eDNA, amplifying the eDNA, and sequencing the DNA product to obtain its DNA sequence information; S052, comparing the sequencing results in S051) with the plant species database, performing cluster analysis, and annotating species; S053, calculate plant species abundance based on the proportion of aligned sequences.

6. The method according to claim 3, wherein the calculation formula for species abundance is as follows: in, F ext is the effective sequence number of the exogenous species; F int is the effective sequence number of the endogenous species.

7. The method of claim 1, wherein in step S06, the data integration is performed by calculating and integrating the isotope values ​​obtained in step S05) and the cDNA species abundance data obtained in step S06) using a Bayesian stable isotope mixing model.

8. The method according to claim 1, wherein in step S06, the formula for calculating the exogenous carbon contribution rate of mangroves is: in, f ext is the contribution rate of exogenous carbon, (%); δ 13 C int is the characteristic value of endogenous carbon isotope, (‰); δ 13 C sed is the carbon isotope value of the sediment, (‰); Δ is the fractionation correction term, (‰); δ 13 C ext is the characteristic value of exogenous carbon isotope, (‰); eDNA ext is the relative abundance of eDNA of exogenous species; ω eDNA is the weight coefficient of the eDNA model; ω iso is the weight coefficient of the stable isotope model.

9. A system for evaluating the exogenous carbon contribution of mangroves, comprising a data input module, a data processing module, and a data output module: The data input module obtains stable isotope values ​​and eDNA sequencing data according to the method of claim 1 and inputs the stable isotope values ​​and eDNA sequencing data; The data processing module calculates the exogenous carbon contribution rate, calculates the relative abundance of eDNA and comprehensively evaluates the exogenous carbon contribution rate according to the method of claim 1; The data output module outputs a comprehensive evaluation value of the exogenous carbon contribution rate.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps of claim 1 are implemented, wherein the steps include data input, data processing, and data output.

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