A method, device, equipment and medium for carbon source analysis of mangrove ecosystem

Abstract

The present invention discloses a method, apparatus, equipment, and medium for analyzing carbon sources in a mangrove ecosystem. The method obtains fatty acid information for multiple mangrove food web components based on a target sample; the mangrove food web components include multiple consumed substances and multiple consumers. A non-metric multidimensional scaling analysis is performed based on the fatty acid information to obtain a first analysis result; the first analysis result includes information on the distribution differences between the multiple consumed substances and the multiple consumers at the level of long-chain unsaturated fatty acids. A similarity analysis is performed based on the fatty acid information to obtain a second analysis result; the second analysis result includes similarity parameters for the fatty acid composition between the consumed substance and each mangrove food web component other than the consumed substance itself. A target carbon source supporting the mangrove ecosystem is determined based on the first and second analysis results. The present invention effectively determines the carbon source supporting the mangrove ecosystem by analyzing the fatty acid information of each mangrove food web component.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method, device, equipment and medium for analyzing carbon sources in a mangrove ecosystem. Background Art

[0002] Ecological stoichiometry is a commonly used technique for identifying the nutritional quality of carbon sources. This involves determining the carbon source based on the carbon (C), nitrogen (N), and phosphorus (P) content of an organism. C, N, and P are essential elements that make up organisms. In most terrestrial ecosystems, N and P are the most important limiting factors for biological growth. The stoichiometric ratios of C, N, and P in plants can, to some extent, be used to indicate the dynamics of carbon accumulation and the pattern of N and P nutrient limitation in the ecosystem in which the plant resides. Foods with high C:N or C:P ratios are generally considered low-quality carbon sources, while foods with low C:N or C:P ratios are considered high-quality carbon sources.

[0003] However, for ecosystems that support extremely high biodiversity, existing ecological stoichiometry methods struggle to accurately measure the carbon, nitrogen, and phosphorus content of consumed substances in such ecosystems. For example, in mangrove ecosystems, existing ecological stoichiometry methods struggle to accurately measure the carbon, nitrogen, and phosphorus content of consumed substances, making it difficult to determine the carbon sources supporting the mangrove food web. Consequently, these methods are no longer applicable to mangrove ecosystems. For example, when sampling benthic algae growing on the surface of mangrove ecosystems, the carbon in the topsoil comes not only from the algae but also from the decomposition of mangrove litter. A high carbon content from decomposed litter increases the sample's carbon:n ratio, leading to misjudgment of the nutritional quality of the benthic algae.

[0004] Therefore, a new carbon source analysis method for mangrove ecosystems is urgently needed to determine the carbon sources supporting the mangrove food web. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method, apparatus, device and medium for analyzing carbon sources in a mangrove ecosystem to address the problem that existing ecological stoichiometry methods are difficult to accurately determine the carbon sources supporting the mangrove food web.

[0006] According to a first aspect, an embodiment of the present invention provides a method for analyzing carbon sources in a mangrove ecosystem, the method comprising:

[0007] Obtaining fatty acid information of multiple mangrove food web components based on a target sample; wherein the mangrove food web components include multiple consumed substances and multiple consumers; the target sample includes a first sample and a second sample, the first sample being a sample obtained by sampling consumed substances in the mangrove ecosystem, and the second sample being a sample obtained by sampling consumers in the mangrove ecosystem;

[0008] Performing a non-metric multidimensional scaling analysis based on the fatty acid information to obtain a first analysis result; the first analysis result includes: distribution difference information of the plurality of consumed substances and the plurality of consumers at the level of long-chain unsaturated fatty acids;

[0009] Performing a similarity analysis based on the fatty acid information to obtain a second analysis result; the second analysis result includes: similarity parameters of fatty acid compositions between the consumed substance and each of the mangrove food web components except the consumed substance itself;

[0010] A target carbon source supporting the mangrove ecosystem is determined based on the first analysis result and the second analysis result.

[0011] In some embodiments, determining the target carbon source supporting the mangrove ecosystem based on the first analysis result and the second analysis result includes:

[0012] determining, based on the distribution difference information, a first consumed substance that is distributed in the same preset area as the consumer;

[0013] Determining, based on the second analysis result, a second consumed substance whose similarity parameter to the fatty acid composition of the consumer meets a preset parameter condition;

[0014] An intersection is taken from the first consumed substance and the second consumed substance to determine a target carbon source supporting the mangrove ecosystem.

[0015] In some embodiments, the first analysis result further includes: a plurality of target long-chain unsaturated fatty acids that are significantly correlated between the consumed substance and the consumer; and after determining the target carbon source supporting the mangrove ecosystem based on the first analysis result and the second analysis result, the method further includes:

[0016] Determining the content of each of the target long-chain unsaturated fatty acids in the consumed substance based on the fatty acid information and the first analysis result;

[0017] A transmission path of the consumed substance in the consumer is predicted based on the content.

[0018] In some embodiments, predicting the transmission path of the consumed substance in the consumer based on the content includes:

[0019] Performing a multiple regression tree analysis based on the content to generate a third analysis result;

[0020] A transmission path of the consumed substance in the consumer is predicted based on the third analysis result.

[0021] In some embodiments, obtaining fatty acid information of a plurality of mangrove food web components according to a target sample comprises:

[0022] For each of the first samples, pre-processing the first sample, and extracting the fatty acid information from the pre-processed first sample; the pre-processing includes freeze-drying and grinding;

[0023] For each second sample, muscle tissue is extracted from the second sample, the muscle tissue is preprocessed, and the fatty acid information is extracted from the preprocessed muscle tissue.

[0024] In some embodiments, the consumed substances include multiple species of fresh mangrove leaves, fallen leaves, benthic algae, seaweed, and floating algae; and the consumers include multiple species of benthic animals, zooplankton, and fish.

[0025] In some embodiments, the target long-chain unsaturated fatty acids include multiple species of docosahexaenoic acid, eicosapentaenoic acid, linoleic acid, α-linolenic acid, arachidonic acid, and bacterial fatty acids.

[0026] According to a second aspect, an embodiment of the present invention provides a carbon source analysis device for a mangrove ecosystem, the device comprising:

[0027] a sample processing module for obtaining fatty acid information of a plurality of mangrove food web components based on a target sample; wherein the mangrove food web components include a plurality of consumed substances and a plurality of consumers; the target sample includes a first sample and a second sample, the first sample being a sample obtained by sampling consumed substances in the mangrove ecosystem, and the second sample being a sample obtained by sampling consumers in the mangrove ecosystem;

[0028] A first analysis module is configured to perform a non-metric multidimensional scaling analysis based on the fatty acid information to obtain a first analysis result; the first analysis result includes: distribution difference information of the plurality of consumed substances and the plurality of consumers at the level of long-chain unsaturated fatty acids;

[0029] a second analysis module configured to perform a similarity analysis based on the fatty acid information to obtain a second analysis result; wherein the second analysis result includes similarity parameters of fatty acid compositions between the consumed substance and each of the mangrove food web components other than the consumed substance;

[0030] A result determination module is used to determine a target carbon source supporting the mangrove ecosystem based on the first analysis result and the second analysis result.

[0031] According to the third aspect, an embodiment of the present invention provides a computer device, characterized in that it includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the carbon source analysis method for mangrove ecosystems as described in the first aspect.

[0032] According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the carbon source analysis method for mangrove ecosystems as described in the first aspect are implemented.

[0033] The technical solution of the present invention has the following advantages.

[0034] The present invention provides a method, apparatus, device, and medium for analyzing carbon sources in a mangrove ecosystem. The method obtains fatty acid information for multiple mangrove food web components based on a target sample, wherein the mangrove food web components include multiple consumed substances and multiple consumers. The target sample includes a first sample and a second sample, wherein the first sample is obtained by sampling the consumed substances in the mangrove ecosystem, and the second sample is obtained by sampling the consumers in the mangrove ecosystem. A non-metric multidimensional scaling analysis is performed based on the fatty acid information to obtain a first analysis result, which includes distribution difference information at the level of long-chain unsaturated fatty acids between the multiple consumed substances and the multiple consumers. A similarity analysis is performed based on the fatty acid information to obtain a second analysis result, which includes similarity parameters for the fatty acid composition between the consumed substance and each mangrove food web component other than the consumed substance itself. A target carbon source that supports the mangrove ecosystem is determined based on the first and second analysis results. The present invention introduces long-chain unsaturated fatty acids as discriminants of whether the consumed substance supports the mangrove ecosystem. By analyzing the fatty acid information of each mangrove food web component, the carbon source that supports the mangrove ecosystem can be effectively determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 This is a flow chart of a method for analyzing carbon sources in a mangrove ecosystem provided by an embodiment of the present invention.

[0037] Figure 2 This is an example diagram of a first analysis result provided by an embodiment of the present invention.

[0038] Figure 3 A flow chart of a method for determining a target carbon source supporting a mangrove ecosystem provided by an embodiment of the present invention.

[0039] Figure 4 This is a flow chart of another method for carbon source analysis of a mangrove ecosystem provided by an embodiment of the present invention.

[0040] Figure 5 This is an example diagram of visualization of the content of a target long-chain unsaturated fatty acid in a consumed substance provided by an embodiment of the present invention.

[0041] Figure 6 This is an example diagram of visualization of the content of a target long-chain unsaturated fatty acid in consumers provided by an embodiment of the present invention.

[0042] Figure 7 This is an example diagram of a third analysis result provided by an embodiment of the present invention.

[0043] Figure 8 A schematic structural diagram of a carbon source analysis device for a mangrove ecosystem provided by an embodiment of the present invention.

[0044] Figure 9 A schematic structural diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0046] Mangrove ecosystems are located at the interface between land and sea, and are typically characterized by high temperatures, high salinity, and a lack of oxygen, making them extremely unfavorable for biological growth and reproduction. However, mangrove ecosystems support extremely high biodiversity and serve as important habitats for many benthic animals, fish, birds, and mammals. The types of substances consumed in mangrove ecosystems are diverse, primarily including fresh and fallen mangrove leaves, phytoplankton, and benthic algae growing on the surface. Due to their large biomass, fresh and fallen mangrove leaves are generally considered an important foundation for supporting the biodiversity of mangrove ecosystems. However, the nutritional quality of these leaves is very low (such as nitrogen and essential fatty acids), which cannot meet the needs of animal growth and development. Therefore, animals in mangroves must actively seek out other nutrients. Effective analysis of the carbon sources of mangrove ecosystems can promote various research areas related to mangroves.

[0047] Ecological stoichiometry is currently the most commonly used technique for identifying the nutritional quality of carbon sources. However, for mangrove ecosystems, which support extremely high biodiversity, existing ecological stoichiometry methods struggle to accurately measure the C, N, and P content of consumed substances within such ecosystems. For example, during mangrove sampling, benthic algae are collected directly from the surface to collect topsoil. This topsoil contains not only benthic algae but also carbon from the decomposition of mangrove litter. When the carbon content of decomposed litter is high, the sample's C:N ratio increases, potentially reaching levels comparable to those found in other mangrove litter, leading to a misjudgment of its nutritional quality. This means that using ecological stoichiometry to indicate nutritional quality when a sample is impure is a significant drawback. However, impure algal samples are a common problem during sampling in mangrove ecosystems. Therefore, ecological stoichiometry methods are not suitable for analyzing the carbon sources supporting mangrove ecosystems.

[0048] To address the above problems, embodiments of the present invention provide a method, apparatus, device, and medium for analyzing carbon sources in a mangrove ecosystem to address the problem that existing ecological stoichiometry methods are difficult to accurately determine the carbon sources supporting the mangrove food web.

[0049] Figure 1 The flowchart of a carbon source analysis method for a mangrove ecosystem provided by an embodiment of the present invention is as follows. Figure 1 As shown, the carbon source analysis method of the mangrove ecosystem includes: steps S1 to S4.

[0050] Step S1: Obtain fatty acid information of multiple mangrove food web components based on target samples.

[0051] The mangrove food web components refer to the components of the food web that constitutes the mangrove ecosystem. The mangrove food web components include a variety of consumed substances and a variety of consumers. The consumed substances refer to substances that can provide carbon sources in the mangrove ecosystem.

[0052] In some embodiments, the consumed substances include, but are not limited to, fresh mangrove leaves, litter, benthic algae, seaweed, and phytoplankton.

[0053] In some embodiments, the consumers include, but are not limited to, benthic animals, zooplankton, and fish.

[0054] The target samples are samples obtained by sampling the components of the mangrove food web. The target samples include a first sample and a second sample, wherein the first sample is a sample obtained by sampling the consumed substances in the mangrove ecosystem, and the second sample is a sample obtained by sampling the consumers in the mangrove ecosystem.

[0055] In some embodiments, individual mangrove food web components can be pre-sampled to obtain target samples. Conventional sampling methods can be used, and are not specifically limited in this embodiment. For example, benthic algae can be collected by scraping 1 cm of topsoil from the mangrove floor to obtain target samples corresponding to benthic algae.

[0056] In one embodiment, the step of obtaining fatty acid information of multiple mangrove food web components based on the target sample (the above step S1 ) includes the following steps 1 and 2.

[0057] Step 1: For each first sample, preprocess the first sample and extract fatty acid information from the preprocessed first sample.

[0058] The first sample is obtained by sampling the consumed substances in the mangrove ecosystem. The pretreatment includes freeze-drying and grinding.

[0059] Step 2: For each second sample, extract muscle tissue from the second sample, pre-process the muscle tissue, and extract fatty acid information from the pre-processed muscle tissue.

[0060] The second sample is obtained by sampling consumers in the mangrove ecosystem. The pretreatment includes freeze-drying and grinding.

[0061] In this embodiment, fatty acid information refers to the fatty acid composition data of each mangrove food web component. This fatty acid information includes information on docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), arachidonic acid (ARA), α-linolenic acid (ALA), linoleic acid (LIN), long-chain saturated fatty acids (LC-SAFA), long-chain saturated fatty acids (BAFA), saturated fatty acids (SAFA), and monounsaturated fatty acids (MUFA).

[0062] In the embodiment of the present invention, the carbon source is no longer determined by the content of carbon, nitrogen and phosphorus, but the carbon source analysis of the mangrove ecosystem is realized by analyzing the fatty acid information of each mangrove food web component. Therefore, even if the sample is impure, it will not affect the carbon source analysis method of the present application. For example, for benthic algae, even if the sample of the benthic algae collected contains litter, because the fatty acid composition of the two is inconsistent, therefore, when analyzing based on the fatty acid information, the impact caused by litter can also be effectively filtered.

[0063] Step S2: performing non-metric multidimensional scaling analysis based on the fatty acid information to obtain a first analysis result.

[0064] Non-metric multidimensional scaling (NMDS) is a data analysis method that simplifies research objects in a multidimensional space into a bottom-dimensional space for location, analysis, and classification, while preserving the original relationships between objects. In embodiments of the present invention, this NMDS can be used to analyze the content characteristics of various long-chain unsaturated fatty acids within organisms in mangrove ecosystems and to compare the variations in long-chain unsaturated fatty acids in different organisms.

[0065] The first analysis results include: information on the distribution differences of various consumed substances and various consumers at the level of long-chain unsaturated fatty acids.

[0066] In one embodiment, the step of performing non-metric multidimensional scaling analysis based on fatty acid information to obtain a first analysis result includes: using the fatty acid information as input data, calling the metaMDS() function (a function for implementing NMDS) of the vegan package (an R language program package specifically used for community ecology data analysis) in R language (a programming language used for statistical analysis and drawing) to perform NMDS.

[0067] Figure 2 This is an example diagram of a first analysis result provided by an embodiment of the present invention.

[0068] In one embodiment, non-metric multidimensional scaling analysis is performed based on fatty acid information, and the first analysis result obtained is as follows: Figure 2 As shown. Figure 2 In the , NMDS1 is used to represent the horizontal direction, and NMDS2 is used to represent the vertical direction. The horizontal and vertical coordinates are only used to indicate the position and there is no actual unit.

[0069] The stress function value (stress) on the graph is a parameter used to measure the degree of fit of the non-metric multidimensional scaling analysis. Generally, when stress is less than 0.2, the results of the non-metric multidimensional scaling analysis are of reference significance.

[0070] The groups in the figure show a variety of consumed substances and consumers, including fish (crosses), invertebrates (crosses), fresh mangrove leaves (triangles), litter (circles), periphyton (diamonds), and phytoplankton (squares). It should be noted that in the mangrove food web, except for fish, all consumers are invertebrates.

[0071] In some embodiments, the Figure 2 A predetermined area is determined for the consumers in the food. For example, the predetermined area may include all consumers or a predetermined proportion (e.g., 95%) of consumers. Consumed substances distributed in the same predetermined area as the consumers are then determined. The consumed substances distributed in the same predetermined area as the consumers are considered to have a composition similar to that of the consumers' long-chain unsaturated fatty acids.

[0072] Step S3: perform similarity analysis based on the fatty acid information to obtain a second analysis result.

[0073] ANOSIM (Analysis of Similarities) is a nonparametric test used to analyze similarities between groups of high-dimensional data. It first calculates distance measures (or similarities) between objects using variables, then calculates relationship rankings. Finally, it uses the rankings to perform a permutation test to determine whether the differences between object groups are significantly different from the differences between groups.

[0074] The second analysis result includes a similarity parameter for the fatty acid composition between the consumed substance and various mangrove food web components other than the consumed substance itself. This similarity parameter includes an R value and a p-value. The R value measures the degree of difference between two groups, ranging from 0 to 1. An R value close to 1 indicates complete dissimilarity between the two groups, while an R value close to 0 indicates no difference between the two groups. The p-value indicates whether the similarity analysis results are significant. Lower p-values ​​indicate more significant results. A significance level of 0.05 is generally used; a p-value greater than 0.05 indicates no significant results.

[0075] In one embodiment, the step of performing similarity analysis based on fatty acid information includes: using the fatty acid information as input data, calling the anosim() function of the vegan package in the R language (a function for implementing similarity analysis) to execute ANOSIM.

[0076] The following table is an example diagram of a second analysis result provided by an embodiment of the present invention.

[0077]

[0078]

[0079] In one embodiment, after performing a similarity analysis based on fatty acid information, the second analysis results obtained are shown in the table above. In the table, the similarity parameters (R value and p value) of the fatty acid composition between fresh mangrove leaves, litter, periphyton, and phytoplankton are listed, respectively, and each mangrove food web component other than itself. Among them, the invertebrates in the mangrove food web component are refined into: fiddler crabs, sesame crabs, other crabs, Potamid gastropods (a type of gastropod), Neritid gastropods (another type of gastropod), other gastropods, shrimps, and bivalves.

[0080] The table above contains 42 similarity parameters between the two groups. Among these 38 similarity parameters, p values ​​are less than 0.05, indicating that the similarity analysis results are significant. The R values ​​for these 38 similarity parameters with p values ​​less than 0.05 are for reference only.

[0081] It should be noted that in the similarity parameter of the fatty acid composition of the consumed substance and the consumer, when p is less than 0.05, the R value is approximately close to 0, indicating that the more similar the fatty acid composition of the consumed substance and the consumer corresponding to the similarity parameter is, the more capable the consumed substance is of providing the consumer with the necessary long-chain unsaturated fatty acids.

[0082] In some embodiments, the R value can be used to measure whether the consumed substance is a high-quality carbon source, that is, whether it can provide consumers with a large amount of essential long-chain unsaturated fatty acids. A preset R threshold can be used. When p is less than 0.05 in the similarity parameter, if the R value is less than the preset R threshold, the consumed substance corresponding to the similarity parameter is considered to be a high-quality carbon source. If the R value is greater than or equal to the preset R threshold, the consumed substance corresponding to the similarity parameter is considered not to be a high-quality carbon source. The preset R threshold can be set based on actual conditions and is not specifically defined in this embodiment.

[0083] Step S4: determining a target carbon source supporting the mangrove ecosystem based on the first analysis result and the second analysis result.

[0084] The target carbon source refers to a nutrient that can provide an essential carbon source for consumers in the mangrove ecosystem. In this embodiment, the essential carbon source refers to long-chain unsaturated fatty acids that are necessary for consumers in the mangrove ecosystem.

[0085] In some embodiments, the target carbon source may refer only to the aforementioned high-quality carbon source (when p is less than 0.05, the R value is less than the preset R threshold), that is, nutrients that can provide a large amount of essential carbon sources for consumers in the mangrove ecosystem.

[0086] An embodiment of the present invention provides a method for analyzing carbon sources of a mangrove ecosystem. First, fatty acid information of multiple mangrove food web components is obtained based on a target sample. The mangrove food web components include multiple consumed substances and multiple consumers. The target sample includes a first sample and a second sample, the first sample being a sample obtained by sampling the consumed substances in the mangrove ecosystem, and the second sample being a sample obtained by sampling the consumers in the mangrove ecosystem. Then, a non-metric multidimensional scaling analysis is performed based on the fatty acid information to obtain a first analysis result. The first analysis result includes distribution difference information of the multiple consumed substances and the multiple consumers at the level of long-chain unsaturated fatty acids. A similarity analysis is performed based on the fatty acid information to obtain a second analysis result. The second analysis result includes similarity parameters of the fatty acid composition between the consumed substance and each mangrove food web component other than the consumed substance itself. Finally, a target carbon source supporting the mangrove ecosystem is determined based on the first and second analysis results. The present invention introduces long-chain unsaturated fatty acids as discriminants of whether the consumed substance can support the mangrove ecosystem. By analyzing the fatty acid information of each mangrove food web component, the carbon source supporting the mangrove ecosystem can be effectively determined.

[0087] Figure 3 A flow chart of a method for determining a target carbon source supporting a mangrove ecosystem provided by an embodiment of the present invention. Figure 3 As shown, the step of determining the target carbon source supporting the mangrove ecosystem according to the first analysis result and the second analysis result (the above-mentioned step S4) includes: steps S41 to S43.

[0088] Step S41: Determine, based on the distribution difference information, a first consumed substance that is distributed in the same preset area as the consumer.

[0089] The distribution difference information is information included in the first analysis result, and the distribution difference information can be presented in a visual manner.

[0090] In one embodiment, a preset area can be determined based on the distribution difference information. For example, the preset area can be an area that includes all consumers or an area that includes a predetermined proportion (95%) of consumers. Then, a first consumed substance that is distributed in the same preset area as the consumers is determined. The first consumed substance can be considered to be a consumed substance that is close to the long-chain unsaturated fatty acids of the consumers.

[0091] In this embodiment, the first consumed substance is a consumed substance distributed in the same preset area as the consumer. Figure 2 In the illustrated example of the first analysis result, the first consumed substance may be benthic algae and phytoplankton.

[0092] Step S42: According to the second analysis result, determine a second consumed substance whose similarity parameter with the consumer's fatty acid composition meets the preset parameter conditions.

[0093] The second analysis result includes a similarity parameter of fatty acid composition between the consumed substance and each mangrove food web component except the consumed substance itself, and the similarity parameter includes an R value and a p value.

[0094] The preset parameter conditions include: the p value is greater than 0.05 and the R value is less than a preset R threshold. The preset R threshold can be set according to actual conditions and is not specifically limited in this embodiment.

[0095] In this embodiment, the second consumed substance is a consumed substance whose similarity parameter with the fatty acid composition of the consumer meets the preset parameter conditions. In the second analysis result example diagram shown in the table above, the preset R threshold can be set to 0.3, and the second consumed substances determined are benthic algae (R = 0.284, p = 0.009 in the similarity parameter with fiddler crabs) and phytoplankton (R = 0.286, p = 0.019 in the similarity parameter with fish).

[0096] Step S43: Take the intersection of the first consumed substance and the second consumed substance to determine the target carbon source supporting the mangrove ecosystem.

[0097] Among them, the first consumed substance is a consumed substance determined from the visualization results and has a composition close to the consumer's long-chain unsaturated fatty acids, and the second consumed substance is a consumed substance determined from the data results and has a composition close to the consumer's fatty acids. Taking the intersection of the first consumed substance and the second consumed substance can improve the accuracy of the carbon source analysis of the mangrove ecosystem and effectively determine the target carbon source supporting the mangrove ecosystem.

[0098] In some embodiments, by the embodiments of the present invention Figure 2 The first consumed substance determined by the first analysis result example diagram shown is benthic algae and phytoplankton. The second consumed substance determined by the second analysis result example diagram shown in the aforementioned table is also benthic algae and phytoplankton. After taking the intersection of the two, it is determined that the target carbon source supporting the mangrove ecosystem is still benthic algae and phytoplankton.

[0099] In one embodiment, non-metric multidimensional scaling analysis is performed based on fatty acid information, and the first analysis results obtained further include: a plurality of target long-chain unsaturated fatty acids that are significantly correlated between the consumed substance and the consumer.

[0100] It should be noted that after performing non-metric multidimensional scaling analysis based on fatty acid information, correlation coefficients for each fatty acid between the consumed substance and the consumer are obtained, for example, the correlation coefficients for DHA, EPA, ARA, ALA, LIN, LC-SAFA, BAFA, SAFA, and MUFA between the consumed substance and the consumer. This correlation coefficient is used to indicate whether the fatty acids between the consumed substance and the consumer are significantly correlated. Based on this correlation coefficient, long-chain unsaturated fatty acids that are significantly correlated between the consumed substance and the consumer can be selected from a variety of fatty acids as target long-chain unsaturated fatty acids. Further analysis of these target long-chain unsaturated fatty acids can facilitate the determination of the transfer process of the target carbon source supporting the mangrove ecosystem within the mangrove food web.

[0101] In one embodiment, the target long-chain unsaturated fatty acids include multiple types of docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), linoleic acid (LIN), α-linolenic acid (ALA), arachidonic acid (ARA), and bacterial fatty acid (BAFA).

[0102] Figure 4 Flowchart of another carbon source analysis method for mangrove ecosystem provided by an embodiment of the present invention. Figure 4 As shown, after the target carbon source supporting the mangrove ecosystem is determined according to the first analysis result and the second analysis result (the above step S4), the method further includes: steps S5 and S6.

[0103] Step S5: determining the content of each target long-chain unsaturated fatty acid in the consumed substance based on the fatty acid information and the first analysis result.

[0104] The content can be expressed as a percentage, such as the proportion of each target long-chain unsaturated fatty acid in all fatty acids.

[0105] In one embodiment, the content of each target long-chain unsaturated fatty acid in each mangrove food web component (including consumed substances and consumers) can be determined by one-way ANOVA and Tukey's HSD post-hoc test, and the content of each target long-chain unsaturated fatty acid in each mangrove food web component can be visualized.

[0106] Figure 5This example visualization shows the content of a target long-chain unsaturated fatty acid in a consumed substance, as provided in an embodiment of the present invention. The consumed substances include fresh mangrove leaves, litter, periphyton, and phytoplankton. Note that the T-shaped lines on the columns corresponding to each consumed substance in the figure represent error bars.

[0107] Figure 6 This example visualization shows the content of a target long-chain unsaturated fatty acid in a consumer, provided by an embodiment of the present invention. The consumers include shrimp, potamid (a type of gastropod), other mollusks, neritids (another type of gastropod), sesarmids, other gastropods, fish, fiddler crabs, and bivalves. It should be noted that the T-shaped lines on the columns corresponding to each consumer in the figure represent error bars.

[0108] Step S6: predicting the transmission path of the consumed substance in the consumer based on the content.

[0109] In this embodiment, the consumed substance exists in the consumer in the form of fatty acids. Therefore, the transmission path of the consumed substance in the consumer can be predicted by the content of the target long-chain unsaturated fatty acid in the consumed substance.

[0110] In one embodiment, predicting the transmission path of the consumed substance in the consumer based on the content includes the following steps 1 and 2.

[0111] Step 1: Perform a multiple regression tree analysis based on the content of each target long-chain unsaturated fatty acid in the consumed substance to generate a third analysis result.

[0112] Among them, the third analysis result includes a visualized multiple regression tree.

[0113] Figure 7 This is an example diagram of a third analysis result provided by an embodiment of the present invention. Figure 7 As shown, the regression tree is first divided using DHA content as the parent node. A DHA content less than 2.55% leads to the child node on the left, and a DHA content greater than or equal to 2.55% leads to the child node on the right. In the left child node, whether the SAFA content is less than 36.95% leads to the next child node on the left. In the right child node, whether the ARA content is less than 4.75% leads to the next child node on the right. And so on. In the last child node, consumers who meet this transmission path are predicted, forming a visual multiple regression tree.

[0114] Step 2: predicting the transmission path of the consumed substance in the consumer based on the third analysis result.

[0115] In this embodiment, Figure 7 As illustrated in the example graph of the third analysis results, DHA clearly distinguishes filter feeders (such as bivalves), shrimp, fish, and sesame crabs from sediment feeders (such as gastropods, fiddler crabs, and other crabs). Bivalves and fish have higher DHA levels and lower ARA levels, while shrimp and sesame crabs have higher levels of both DHA and ARA. All gastropods contain only trace amounts of DHA. Compared to other consumers, snails (gastropods) contain more BAFA%, fiddler crabs have higher levels of EPA and ARA, and sesame crabs have higher levels of DHA%, EPA%, ARA%, ALA%, and LIN% than other crabs.

[0116] Using one-way ANOVA, Tukey's HSD post hoc, and regression tree analysis, we can predict the transport pathways of consumed substances among consumers. For example, benthic animals that primarily feed on fresh leaf litter and litter (rich in ARA), such as seed crabs; benthic animals that primarily feed on small benthic algae (rich in EPA), such as fiddler crabs and herbivorous gastropods; bivalves that primarily feed on small phytoplankton (rich in DHA); and benthic and zooplankton animals (rich in BAFA) that primarily feed on bacteria.

[0117] In the carbon source analysis method for mangrove ecosystems provided in an embodiment of the present invention, long-chain unsaturated fatty acids are used as a measure of food nutritional quality in the study of food webs in mangrove ecosystems. The full spectrum of fatty acids in basic food sources and mangrove fauna is analyzed, and the high-quality carbon sources (plankton and benthic algae) that support mangrove biodiversity are explained. This method further indicates that in complex mangrove ecosystems, the primary production of high-quality carbon sources in mangroves is shifted to higher nutritional levels, providing a new method for the study of food webs in mangrove ecosystems.

[0118] Figure 8 The following is a schematic diagram of a carbon source analysis device for a mangrove ecosystem provided by an embodiment of the present invention. Figure 8 As shown, the device includes: a sample processing module 81 , a first analysis module 82 , a second analysis module 83 and a result determination module 84 .

[0119] The sample processing module 81 is used to obtain fatty acid information of multiple mangrove food web components based on the target sample.

[0120] The mangrove food web components include a plurality of consumed substances and a plurality of consumers. The target samples include a first sample and a second sample. The first sample is a sample obtained by sampling the consumed substances in the mangrove ecosystem, and the second sample is a sample obtained by sampling the consumers in the mangrove ecosystem.

[0121] In one embodiment, the sample processing module 81 includes a first processing submodule and a second processing submodule.

[0122] The first processing submodule is used to preprocess each first sample and extract fatty acid information from the preprocessed first sample; the preprocessing includes freeze-drying and grinding.

[0123] The second processing submodule is used for extracting muscle tissue from each second sample, preprocessing the muscle tissue, and extracting fatty acid information from the preprocessed muscle tissue.

[0124] The first analysis module 82 is configured to perform non-metric multidimensional scaling analysis based on the fatty acid information to obtain a first analysis result.

[0125] The first analysis results include: information on the distribution differences of various consumed substances and various consumers at the level of long-chain unsaturated fatty acids.

[0126] The second analysis module 83 is used to perform similarity analysis based on the fatty acid information to obtain a second analysis result.

[0127] The second analysis results include similarity parameters of fatty acid composition between the consumed substance and each mangrove food web component except itself.

[0128] The result determination module 84 is used to determine the target carbon source supporting the mangrove ecosystem based on the first analysis result and the second analysis result.

[0129] In one embodiment, the result determination module 84 includes a first determination submodule, a second determination submodule, and a selection submodule.

[0130] The first sub-determining sub-module is used to determine the first consumed substance that is distributed in the same preset area as the consumer according to the distribution difference information.

[0131] The second determination submodule is used to determine, based on the second analysis result, a second consumed substance whose similarity parameter with the fatty acid composition of the consumer meets the preset parameter conditions.

[0132] The selection submodule is used to obtain the intersection of the first consumed substance and the second consumed substance to determine the target carbon source supporting the mangrove ecosystem.

[0133] In one embodiment, the carbon source analysis device for a mangrove ecosystem further includes a content determination module and a pathway analysis module.

[0134] The content determination module is used to determine the content of each target long-chain unsaturated fatty acid in the consumed substance based on the fatty acid information and the first analysis result.

[0135] The path analysis module is used to predict the transmission path of the consumed substance in the consumer based on the content

[0136] In some embodiments, the path analysis module is specifically used to: perform a multivariate regression tree analysis based on the content to generate a third analysis result; and predict the transmission path of the consumed substance in the consumer based on the third analysis result.

[0137] An embodiment of the present invention provides a carbon source analysis device for a mangrove ecosystem. The sample processing module is configured to obtain fatty acid information of multiple mangrove food web components based on a target sample. The mangrove food web components include multiple consumed substances and multiple consumers. The target sample includes a first sample and a second sample, the first sample being a sample obtained by sampling consumed substances in the mangrove ecosystem, and the second sample being a sample obtained by sampling consumers in the mangrove ecosystem. The first analysis module is configured to perform a non-metric multidimensional scaling analysis based on the fatty acid information to obtain a first analysis result. The first analysis result includes distribution difference information of the multiple consumed substances and the multiple consumers at the level of long-chain unsaturated fatty acids. The second analysis module is configured to perform a similarity analysis based on the fatty acid information to obtain a second analysis result. The second analysis result includes similarity parameters of the fatty acid composition between the consumed substance and each mangrove food web component other than the consumed substance itself. The result determination module is configured to determine a target carbon source that supports the mangrove ecosystem based on the first and second analysis results. The present invention introduces long-chain unsaturated fatty acids as discriminants of whether a consumed substance can support the mangrove ecosystem. By analyzing the fatty acid information of each mangrove food web component, the carbon source that supports the mangrove ecosystem can be effectively determined.

[0138] Figure 9 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Figure 9 As shown, the computer device may include a processor 901 and a memory 902, wherein the processor 901 and the memory 902 may be connected via a bus or other means. Figure 9 The bus connection is taken as an example.

[0139] The processor 901 may be a central processing unit (CPU). The processor 901 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0140] Memory 902, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the method for carbon source analysis of mangrove ecosystems in the embodiments of the present invention. Processor 901 executes the non-transitory software programs, instructions, and modules stored in memory 902 to perform various processor functions and data processing, thereby implementing the method for carbon source analysis of mangrove ecosystems in the aforementioned method embodiments.

[0141] The memory 902 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created by the processor 901, etc. In addition, the memory 902 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 902 may optionally include a memory remotely located relative to the processor 901, and these remote memories may be connected to the processor 901 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0142] One or more modules are stored in the memory 902 and when executed by the processor 901, the execution is as follows: Figure 1 The carbon source analysis method of the mangrove ecosystem in the illustrated embodiment.

[0143] For details of the above computer equipment, please refer to Figure 1 The corresponding descriptions and effects in the embodiments shown can be understood and will not be repeated here.

[0144] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD). The storage medium can also include a combination of the above-mentioned types of memory.

[0145] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for carbon source analysis of mangrove ecosystems, characterized in that: The method comprises: Obtaining fatty acid information of multiple mangrove food web components based on a target sample; wherein the mangrove food web components include multiple consumed substances and multiple consumers; the target sample includes a first sample and a second sample, the first sample being a sample obtained by sampling consumed substances in the mangrove ecosystem, and the second sample being a sample obtained by sampling consumers in the mangrove ecosystem; Performing a non-metric multidimensional scaling analysis based on the fatty acid information to obtain a first analysis result; the first analysis result includes: distribution difference information of the plurality of consumed substances and the plurality of consumers at the level of long-chain unsaturated fatty acids; Performing a similarity analysis based on the fatty acid information to obtain a second analysis result; the second analysis result includes: similarity parameters of fatty acid compositions between the consumed substance and each of the mangrove food web components except the consumed substance itself; determining a target carbon source supporting the mangrove ecosystem based on the first analysis result and the second analysis result; Determining the target carbon source supporting the mangrove ecosystem based on the first analysis result and the second analysis result includes: determining, based on the distribution difference information, a first consumed substance that is distributed in the same preset area as the consumer; Determining, based on the second analysis result, a second consumed substance whose similarity parameter to the fatty acid composition of the consumer meets a preset parameter condition; Taking an intersection of the first consumed substance and the second consumed substance to determine a target carbon source supporting the mangrove ecosystem; The first analysis result further includes: a plurality of target long-chain unsaturated fatty acids that are significantly correlated between the consumed substance and the consumer; after determining the target carbon source supporting the mangrove ecosystem based on the first analysis result and the second analysis result, the method further includes: Determining the content of each of the target long-chain unsaturated fatty acids in the consumed substance based on the fatty acid information and the first analysis result; predicting a transmission path of the consumed substance in the consumer based on the content; Obtaining fatty acid information of a plurality of mangrove food web components according to the target sample includes: For each of the first samples, pre-processing the first sample, and extracting the fatty acid information from the pre-processed first sample; the pre-processing includes freeze-drying and grinding; For each second sample, muscle tissue is extracted from the second sample, the muscle tissue is preprocessed, and the fatty acid information is extracted from the preprocessed muscle tissue.

2. The method according to claim 1, characterized in that The predicting of the transmission path of the consumed substance in the consumer according to the content includes: Performing a multiple regression tree analysis based on the content to generate a third analysis result; A transmission path of the consumed substance in the consumer is predicted based on the third analysis result.

3. The method according to any one of claims 1-2, characterized in that The consumed substances include a variety of fresh mangrove leaves, fallen leaves, benthic algae, seaweed, and phytoplankton; and the consumers include a variety of benthic animals, zooplankton, and fish.

4. The method according to claim 1, wherein The target long-chain unsaturated fatty acids include various kinds of docosahexaenoic acid, eicosapentaenoic acid, linoleic acid, α-linolenic acid, arachidonic acid, and bacterial fatty acids.

5. A carbon source analysis device for mangrove ecosystems, characterized in that: The device comprises: a sample processing module for obtaining fatty acid information of a plurality of mangrove food web components based on a target sample; wherein the mangrove food web components include a plurality of consumed substances and a plurality of consumers; the target sample includes a first sample and a second sample, the first sample being a sample obtained by sampling consumed substances in the mangrove ecosystem, and the second sample being a sample obtained by sampling consumers in the mangrove ecosystem; A first analysis module is configured to perform a non-metric multidimensional scaling analysis based on the fatty acid information to obtain a first analysis result; the first analysis result includes: distribution difference information of the plurality of consumed substances and the plurality of consumers at the level of long-chain unsaturated fatty acids; a second analysis module configured to perform a similarity analysis based on the fatty acid information to obtain a second analysis result; wherein the second analysis result includes similarity parameters of fatty acid compositions between the consumed substance and each of the mangrove food web components other than the consumed substance; A result determination module, configured to determine a target carbon source supporting the mangrove ecosystem based on the first analysis result and the second analysis result; Wherein, the result determination module includes a first determination submodule, a second determination submodule and a selection submodule; a first determining submodule, configured to determine, based on the distribution difference information, a first consumed substance distributed in the same preset area as the consumer; A second determining submodule is configured to determine, based on the second analysis result, a second consumed substance whose similarity parameter with the fatty acid composition of the consumer meets a preset parameter condition; A selection submodule, configured to take an intersection of the first consumed substance and the second consumed substance to determine a target carbon source supporting the mangrove ecosystem; The first analysis result also includes: a plurality of target long-chain unsaturated fatty acids that are significantly correlated between the consumed substance and the consumer; The device also includes a content determination module and a pathway analysis module; a content determination module, configured to determine the content of each of the target long-chain unsaturated fatty acids in the consumed substance based on the fatty acid information and the first analysis result; a path analysis module, configured to predict a transmission path of the consumed substance in the consumer based on the content; The sample processing module includes a first processing submodule and a second processing submodule; A first processing submodule is configured to preprocess each of the first samples and extract the fatty acid information from the preprocessed first samples; the preprocessing includes freeze-drying and grinding; The second processing submodule is configured to extract muscle tissue from each second sample, pre-process the muscle tissue, and extract the fatty acid information from the pre-processed muscle tissue.

6. A computer device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the carbon source analysis method for mangrove ecosystems as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the carbon source analysis method for a mangrove ecosystem are implemented as described in any one of claims 1 to 4.

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