Basin ecological health threshold evaluation method based on landscape fragmentation and microbial network modularity

Abstract

The invention discloses a watershed ecological health threshold evaluation method based on landscape fragmentation and microbial network modularity, relates to the technical field of crossing of watershed ecology and microbiomics, and solves the problems that in the prior art, a landscape fragmentation and microbial network stability association mechanism is not clear, and a restoration strategy lacks threshold early warning. The method comprises the steps of sampling point arrangement and sample collection, landscape fragmentation index calculation, sampling point clustering and grouping, microbial symbiosis network construction, modular index calculation, landscape fragmentation and modular relation modeling and critical threshold recognition. According to the method, the landscape fragmentation and the microbial network modularity are dynamically associated, the relationship between the landscape fragmentation and the microbial network modularity is quantified through a scientific and rigorous method, a new view angle and a new method are developed for river basin ecological research, and a clear quantitative basis is provided for river basin ecological health early warning and land utilization management based on critical threshold recognition of landscape fragmentation.

Description

Technical Field

[0001] The present invention relates to the field of the intersection of watershed ecology and microbiome, and in particular to a watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity. Background Art

[0002] The health of watershed ecosystems is increasingly disturbed by human activities. Urban expansion, agricultural development and road network construction have led to an increase in the density of natural landscape patches, increased edge complexity and reduced connectivity, forming a highly fragmented spatial pattern. This change in landscape heterogeneity not only hinders material circulation and energy flow, but also directly affects the habitat suitability of microbial communities by changing microhabitat conditions, thereby threatening the functional stability of watershed ecosystems. As the core driver of biogeochemical cycles, the community structure and interaction network of microorganisms have highly sensitive response characteristics to ecosystem health and are important indicators for ecosystem health assessment.

[0003] Existing watershed health assessment methods only use microbial diversity index or metabolic function indicators, which have certain limitations and are difficult to fully reflect the status of ecosystems affected by external interference. Traditional landscape ecological models mostly focus on the impact of landscape pattern on water quality or macro-biodiversity, while ignoring its role in the stability of microbial networks. Microorganisms are the core executors of ecosystem functions. The modularity of their symbiotic networks can characterize the anti-interference ability and functional coordination of the community, but existing technologies have not yet established a quantitative correlation between landscape fragmentation index and modularity dynamics. In watershed ecological health management, there is a lack of effective methods to assess the impact of landscape fragmentation on microbial communities and even the entire watershed ecosystem, and it is difficult to accurately determine the critical threshold for significant changes in ecosystem health. Therefore, developing a watershed ecological health threshold assessment method that can comprehensively consider landscape fragmentation and microbial network modularity is of great significance for scientifically guiding watershed ecological protection and land use planning. Summary of the invention

[0004] The purpose of the present invention is to provide a watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity, so as to solve the problems in the prior art that the association mechanism between landscape fragmentation and microbial network stability is unclear and the restoration strategy lacks threshold warning.

[0005] The present invention provides a watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity, comprising: Step 1: Arrange sampling points according to the land use type, topographic division and river hydrological characteristics in the basin, record the longitude and latitude coordinates and collect sediment samples; Step 2: Process the satellite remote sensing images of the watershed, extract the patch density, edge density and aggregation degree based on the sampling point locations, and construct a comprehensive landscape fragmentation index for each sampling point; Step 3: Standardize and preprocess the comprehensive landscape fragmentation index of all sampling points, use the elbow rule to determine the optimal number of clusters, and execute the K-means clustering algorithm to divide the sampling points into several fragmentation level groups; Step 4: The microbial species composition of all sediment samples was determined, the Spearman correlation between operational taxonomic units was calculated based on the species abundance matrix, significant correlations were screened, and the microbial symbiosis network was constructed using the R language igraph package; Step 5: The Louvain algorithm was used to perform modular analysis on each microbial symbiotic network and calculate the modularity index of each fragmentation level group; Step 6: For the mean of the comprehensive landscape fragmentation index of all fragmentation level groups and the modularity index of each fragmentation level group, a local weighted regression fitting model is applied to construct a nonlinear response curve of landscape fragmentation and modularity index at the global scale; Step seven: Determine the critical threshold of the comprehensive landscape fragmentation index at which modularity decreases significantly by minimizing the Akaike information criterion value.

[0006] Furthermore, the step 1 comprises: The sampling points are rationally arranged according to the land use type, topographic division and river hydrological characteristics in the basin; Use GPS positioning equipment to record the latitude and longitude coordinates of each sampling point; Riverbed sediment samples were collected at each sampling point, and surface sediments with a depth of 0-25 cm were collected. Each sampling point was repeated at least 3 times, and the amount of sediment sample collected each time was not less than 100 g. After the surface sediment samples were fully mixed, they were stored in an environment of -20°C until transported back to the laboratory.

[0007] Furthermore, the step 2 includes: Acquire and process satellite remote sensing images of the watershed; Supervised classification of remote sensing images is performed with the help of remote sensing image processing platform; The ArcGIS platform was used to divide the rivers from the source to the sampling point, and the buffer was set at a preset distance to extract the land use data within the buffer. The processed remote sensing images were imported into Fragstats to calculate the landscape pattern index. , where PD is the patch density, N is the number of patches, and A is the landscape area; , where ED is the edge density and E is the total length of the patch edge; , where AI is the degree of aggregation, is the number of adjacent patches of the same type; Constructing a comprehensive landscape fragmentation index:

[0008] Among them, FI is the comprehensive landscape fragmentation index.

[0009] Furthermore, in the step of supervised classification of remote sensing images with the help of remote sensing image processing platform, the land use classification adopts the maximum likelihood method, and the classification result is verified by the Kappa coefficient, Kappa>0.75; the isolated node deletion threshold of network construction is set to Degree=0, that is, the nodes with Degree=0 will be deleted from the network.

[0010] Furthermore, the step three comprises: The comprehensive landscape fragmentation index calculated from all sampling points is standardized to eliminate the influence of different dimensions on the clustering results; the normalization formula is used:

[0011] in, is the original FI value, is the standardized comprehensive landscape fragmentation index, and are the minimum and maximum FI values ​​of all sampling points, respectively; Use the elbow rule to determine the optimal number of clusters, execute the K-means clustering algorithm within a certain range, calculate the clustering error under each k value, draw a curve of the relationship between the number of clusters k and the sum of squares within the group, and observe the inflection point of the curve. The k value corresponding to the inflection point is the optimal number of clusters; According to the determined optimal number of clusters, the K-means clustering algorithm is executed, and the standardized comprehensive landscape fragmentation index is used as input data. Through iterative calculation, each sampling point is assigned to the cluster that is most similar to its comprehensive landscape fragmentation index. Finally, all sampling points are divided into several fragmentation level groups, and the sampling points in each group have similar landscape fragmentation characteristics.

[0012] Furthermore, the step 4 includes: The 16S rRNA gene V4 region was sequenced using the Illumina NovaSeq platform, and the operational taxonomic unit abundance table was generated using the QIIME2 pipeline; The Hmisc package of R language was used to calculate the Spearman correlation between operational classification units of each group, and the correlation coefficient threshold was | |>0.6, significance level p<0.01, significant edges were screened; the igraph package was used to construct an undirected weighted network and isolated nodes were deleted.

[0013] Furthermore, the step five comprises: The Leuven algorithm is used to divide the modules and calculate the modularity index. The formula is:

[0014] Where Q is the modularity index, is the ratio of the inner edge of module i, is the edge ratio of module i.

[0015] Furthermore, in step five, the significance of the modularity index is evaluated by a null model test, with a significance level of p<0.05, so as to verify the reliability of the module division result.

[0016] Furthermore, the step six comprises: Based on the mean of the comprehensive landscape fragmentation index of all fragmentation level groups and the modularity index data set of the corresponding fragmentation level groups, the local weighted regression function of the stats package of R language was used to fit the nonlinear relationship. The model formula is:

[0017] Among them, Q is the modularity index, FI is the comprehensive landscape fragmentation index, is a local weighted polynomial function, is the error term; The optimal fitting curve is obtained by iteratively weighting and adjusting the local weight function, which describes the response of the modularity index to changes in the degree of landscape fragmentation.

[0018] Furthermore, the step seven comprises: In the R language environment, the segmented package was called to perform segmented regression analysis on the comprehensive landscape fragmentation index and modularity index data; During the segmented regression process, the segmentation points of the model are continuously adjusted to find the segmentation scheme that minimizes the Akaike information criterion value; the segmentation point determined by minimizing the Akaike information criterion value is the critical threshold of the comprehensive landscape fragmentation index where the modularity decreases significantly.

[0019] The present invention has the following beneficial effects: the watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity of the present invention dynamically associates landscape fragmentation with microbial network modularity, quantifies the relationship between the two through a scientific and rigorous method, and opens up new perspectives and methods for watershed ecological research. Based on the identification of critical thresholds of landscape fragmentation, it provides a clear quantitative basis for watershed ecological health early warning and land use management. When the landscape fragmentation index approaches or exceeds the threshold, timely measures are taken to prevent the degradation of the watershed ecosystem, which has important practical guiding significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a flow chart of the watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity of the present invention.

[0022] Figure 2 Schematic diagram of the local weighted regression fitting curve of landscape fragmentation index and microbial network modularity index. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0024] See also Figure 1 , which is a watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity provided by the present invention, comprising: Step 1: Arrange sampling points according to the land use type, terrain division and river hydrological characteristics in the basin, record the longitude and latitude coordinates and collect sediment samples.

[0025] Specifically, according to the land use type, terrain division and river hydrological characteristics in the basin, the sampling points are reasonably arranged with comprehensive consideration, and the various information layers are superimposed and analyzed using Geographic Information System (GIS) technology to determine the location of the sampling points. Land use type refers to the distribution of different land use methods such as cultivated land, forest land, construction land, and water areas. Terrain division refers to the division of different terrain areas such as mountains, plains, and hills. River hydrological characteristics refer to characteristics such as river velocity, flow, water depth, and river width. The sampling points are reasonably arranged with comprehensive consideration so that the sampling points can reflect the characteristics of different areas in the basin as comprehensively as possible.

[0026] A high-precision GPS locator was used to measure and record the longitude and latitude coordinates at each sampling point, accurate to six decimal places, to ensure the accuracy of the sampling point location information.

[0027] At the selected sampling points, use sterile sampling tools to collect riverbed sediment samples. Insert the sampling tool vertically into the riverbed and collect surface sediments at a depth of 0-25 cm. Repeat the collection three times at each sampling point, with each collection amount of no less than 100g. Place the sample in a sterile bag and mix it thoroughly. After marking, quickly place it in a portable refrigerator, maintain a low temperature environment of about 4°C, transport it back to the laboratory as soon as possible, and transfer it to a -20°C refrigerator to prevent changes in the microbial community structure.

[0028] Step 2: Process the satellite remote sensing images of the watershed, extract the patch density, edge density and aggregation degree based on the sampling point locations, and construct a comprehensive landscape fragmentation index for each sampling point.

[0029] Specifically, high-resolution satellite remote sensing data, such as Landsat series satellite images or Sentinel satellite images, are used to obtain remote sensing image data of the study basin. The image acquisition time should be selected during the vegetation growing season when the weather is clear and there is no cloud cover to ensure the image quality. The acquired images are subjected to preprocessing operations such as radiometric calibration, atmospheric correction and geometric correction in sequence. Radiometric calibration converts the digital quantization value recorded by the sensor into the actual radiation brightness value of the surface to correct the sensor's own error; atmospheric correction is performed through the FLAASH module in the remote sensing image processing platform (The Environment for Visualizing Images, ENVI) to remove the scattering and absorption effects of the atmosphere on light, so that the image can truly reflect the reflection characteristics of the surface objects. 45 evenly distributed object points are carefully selected on the topographic map as ground control points. These points cover different terrain and object types in the basin, including landmark objects such as mountain peaks, river intersections, and road intersections. Quadratic polynomials and cubic convolution interpolation methods are used to perform geometric correction on the image to ensure the spatial position accuracy of the image and make the subsequent analysis results more reliable.

[0030] With the help of remote sensing image processing platform, the maximum likelihood method is used to supervise the classification of preprocessed remote sensing images. Referring to the "Land Use Status Classification" (GB / T 21010-2017), the land use types are divided into 9 categories: cultivated land, forest land, construction land, water area, grassland, wetland, unused land, transportation land, and special land.

[0031] After the classification is completed, 500 ground truth sample points are randomly selected, and their accurate land use types are determined through field investigations and historical data inquiries. The Kappa coefficient is calculated using the accuracy verification tool of the ENVI software. When the Kappa coefficient is greater than 0.75, it indicates that the classification result is reliable and can accurately reflect the land use status of the basin. The isolated node deletion threshold of the network construction is set to Degree=0, that is, nodes with Degree=0 will be deleted from the network to ensure the connectivity and effectiveness of the network.

[0032] The ArcGIS platform was used to divide the rivers from the source to the sampling point, and the buffer zone was set with a preset distance to extract the land use data within the buffer zone. The processed remote sensing images were imported into Fragstats to calculate the landscape pattern index.

[0033] , where PD is the patch density, N is the number of patches, and A is the landscape area in hm²; , where ED is the edge density, E is the total length of the patch edge, in m; , where AI is the degree of aggregation, is the number of adjacent patches of the same type; Constructing a comprehensive landscape fragmentation index:

[0034] Among them, FI is the comprehensive landscape fragmentation index. Substituting the patch density (PD), edge density (ED) and aggregation index (AI) calculated above into the formula, the comprehensive landscape fragmentation index (FI) is obtained. This index can comprehensively and quantitatively measure the degree of fragmentation of the watershed landscape and provide key data support for subsequent analysis.

[0035] Step three, the comprehensive landscape fragmentation index of all sampling points was standardized and preprocessed, the optimal number of clusters was determined using the elbow rule, and the K-means clustering algorithm was executed to divide the sampling points into several fragmentation level groups.

[0036] Specifically, the comprehensive landscape fragmentation index calculated from all sampling points was standardized to eliminate the influence of different dimensions on the clustering results; the normalization formula was used:

[0037] in, is the original FI value, is the standardized comprehensive landscape fragmentation index, and are the minimum and maximum FI values ​​of all sampling points, respectively; The Elbow Method is used to determine the optimal number of clusters. The K-means clustering algorithm is performed within a certain range, such as k=2~10, and the clustering error under each k value is calculated. It is usually measured by the Within-Cluster Sum of Squares (WCSS), that is, the square sum of the distance from each sampling point to the center of the cluster to which it belongs. A curve is drawn between the number of clusters k and the within-cluster sum of squares, and the inflection point of the curve is observed. The k value corresponding to the inflection point is the optimal number of clusters.

[0038] According to the determined optimal number of clusters, the K-means clustering algorithm is executed, and the standardized comprehensive landscape fragmentation index is used as input data. Through iterative calculation, each sampling point is assigned to the cluster that is most similar to its comprehensive landscape fragmentation index. Finally, all sampling points are divided into several fragmentation level groups, and the sampling points in each group have similar landscape fragmentation characteristics.

[0039] Step 4: The microbial species composition of all sediment samples was determined, the Spearman correlation between operational taxonomic units (OTUs) was calculated based on the species abundance matrix, significant correlations were screened, and the microbial symbiosis network was constructed using the igraph package in R language.

[0040] Specifically, the Illumina NovaSeq platform was used to sequence the V4 region of the 16S rRNA gene, and the OTU abundance table was generated through the QIIME2 process. First, a professional DNA extraction kit was used to extract microbial DNA from sediment samples. The extraction process was strictly carried out in accordance with the instructions of the kit to ensure that the quality and purity of the extracted DNA met the sequencing requirements. For example, QIAGEN's PowerSoil DNA Isolation Kit was used to extract microbial DNA from sediment samples. The extraction process was strictly carried out in accordance with the instructions of the kit to ensure that the quality and purity of the extracted DNA met the sequencing requirements. After the extraction was completed, the quality and concentration of the DNA were tested by agarose gel electrophoresis and a nucleic acid concentration meter.

[0041] The extracted DNA was then PCR amplified, and the amplification primers were selected as universal primers for the V4 region of the 16S rRNA gene. The amplification reaction system and conditions were optimized to ensure the specificity and efficiency of amplification. After the amplification product was purified, a sequencing library was constructed and high-throughput sequencing was performed on the Illumina NovaSeq platform. The sequencing data was processed through the QIIME2 process, including quality control, sequence splicing, denoising, species annotation and other steps, and finally an OTU abundance table was generated.

[0042] For example, the extracted DNA was subjected to PCR amplification, and the amplification primers selected were the universal primers 515F (5'-GTGCCAGCMGCCGCGGTAA-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') targeting the V4 region of the 16S rRNA gene. The amplification reaction system and conditions were optimized, such as a 25 μL reaction system containing 12.5 μL of 2×Taq PCR MasterMix, 1 μL of forward primer, 1 μL of reverse primer, 2 μL of template DNA, and 8.5 μL of sterile water; the reaction conditions were 95°C pre-denaturation for 3 min, followed by 95°C denaturation for 30 s, 55°C annealing for 30 s, and 72°C extension for 30 s, for a total of 30 cycles, and finally 72°C extension for 10 min to ensure the specificity and efficiency of the amplification. After the amplification product was purified, a sequencing library was constructed and high-throughput sequencing was performed on the Illumina NovaSeq platform. The sequencing data were processed through the QIIME2 pipeline, including quality control (removing low-quality sequences, primer sequences, etc.), sequence splicing, denoising, species annotation and other steps, and finally an OTU abundance table was generated.

[0043] The Hmisc package of R language was used to calculate the Spearman correlation between OTUs. During the calculation process, the correlation coefficient threshold was set to | |>0.6, significance level p<0.01, screen out OTU pairs with significant correlation as edges in the microbial symbiosis network. Use the igraph package to construct an undirected weighted network. Input the screened significant edges and corresponding OTU node information into the igraph package to construct the microbial symbiosis network. During the construction process, set the isolated node Degree=0 deletion threshold, that is, the nodes with Degree=0 will be deleted from the network to ensure the connectivity and effectiveness of the network.

[0044] Step 5: The Louvain algorithm was used to perform modular analysis on each microbial symbiotic network and calculate the modularity index of each fragmentation level group.

[0045] Specifically, the Louvain algorithm is used to divide the constructed microbial symbiotic network into modules. The Louvain algorithm is a community discovery algorithm based on a greedy strategy that can quickly and effectively divide the network into different modules. During the division process, multiple iterative optimizations are performed to achieve the optimal module division result. The Louvain algorithm is used to divide the modules and calculate the modularity index. The formula is:

[0046] Where Q is the modularity index, is the edge ratio in module i, which is obtained by counting the ratio of the number of edges in module i to the total number of edges in the network; is the edge ratio of module i, which is obtained by counting the ratio of the number of edges of module i to the sum of the degrees of all nodes in the network. The larger the Q value, the stronger the modularity of the microbial network and the more stable the community structure.

[0047] The significance of the modularity index Q was evaluated by the null model test. The network was randomly reconnected 100 times to maintain the degree distribution, and the modularity Q value of the network after each reconnection was calculated. The significance level p value was calculated based on the comparison between these random Q values ​​and the actual calculated Q values. The significance of the modularity Q value was tested by the null model, and the significance level p<0.05 was used to verify the reliability of the module partitioning results.

[0048] Step 6: For the mean value of the comprehensive landscape fragmentation index of all fragmentation level groups and the modularity index of each fragmentation level group, a local weighted regression fitting model is applied to construct a nonlinear response curve of landscape fragmentation and modularity index at the global scale.

[0049] Specifically, based on the mean of the comprehensive landscape fragmentation index of all fragmentation level groups and the modularity index data set of the corresponding fragmentation level groups, the local weighted regression function of the stats package of R language was used to fit the nonlinear relationship. The model formula is:

[0050] Among them, Q is the modularity index, FI is the comprehensive landscape fragmentation index, is a local weighted polynomial function, The initial value of the model span parameter was set to 0.75, and the model span value corresponding to the minimum mean square error was selected through cross-validation optimization. The polynomial order was fixed to the second order to better capture the nonlinear relationship between the landscape fragmentation index and the microbial network modularity index and the possible inflection point characteristics.

[0051] The iterative weighted least squares method is implemented to gradually adjust the local weight function, such as the Tricube kernel function, to obtain the optimal fitting curve, which describes the response of the modularity index when the degree of landscape fragmentation changes. During the model construction process, the local weight function is adjusted iteratively to continuously optimize the model, and finally the optimal fitting curve between landscape fragmentation and modularity index is obtained, which intuitively reflects the response trend of the modularity of the microbial network as the degree of landscape fragmentation changes.

[0052] Step seven: Determine the critical threshold of the comprehensive landscape fragmentation index at which modularity decreases significantly by minimizing the Akaike information criterion value.

[0053] Specifically, in the R language environment, the segmented package was called to perform segmented regression analysis on the comprehensive landscape fragmentation index and modularity index data.

[0054] In the process of segmented regression analysis, different segmentation points were continuously tried, and the Akaike Information Criterion (AIC) value of the model under each segmentation scheme was calculated. The AIC value comprehensively considers the goodness of fit and complexity of the model, and selects the segmentation scheme that minimizes the AIC value. The segmentation point determined by this scheme is the critical threshold (FIcrit) of the integrated landscape fragmentation index (FI) corresponding to a significant decrease in modularity. When the landscape fragmentation index reaches or exceeds the critical threshold, the modularity of the microbial network will change significantly, indicating that the watershed ecosystem may face the risk of structural and functional changes, providing key quantitative indicators for watershed ecological health early warning and land use management.

[0055] like Figure 2 As shown in the figure, the horizontal axis is the degree of landscape fragmentation; the vertical axis is the modularity index of the microbial network. The curve shows a downward trend that starts slowly and then quickly, clearly showing the nonlinear relationship between the two. The critical threshold determined by minimizing the Akaike information criterion value is clearly marked in the figure, providing a key quantitative basis for watershed ecological early warning and management.

[0056] The embodiment of the present invention further provides a storage medium, wherein a computer program is stored in the storage medium, and when the computer program is executed by a processor, some or all of the steps in each embodiment of the watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity provided by the present invention are implemented. The storage medium may be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0057] The above-described embodiments of the present invention do not limit the protection scope of the present invention.

Claims

1. A watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity, characterized in that: include: Step 1: Arrange sampling points according to the land use type, topographic division and river hydrological characteristics in the basin, record the longitude and latitude coordinates and collect sediment samples; Step 2: Process the satellite remote sensing images of the watershed, extract the patch density, edge density and aggregation degree based on the sampling point locations, and construct a comprehensive landscape fragmentation index for each sampling point; Step 3: Standardize and preprocess the comprehensive landscape fragmentation index of all sampling points, use the elbow rule to determine the optimal number of clusters, and execute the K-means clustering algorithm to divide the sampling points into several fragmentation level groups; Step 4: The microbial species composition of all sediment samples was determined, the Spearman correlation between operational taxonomic units was calculated based on the species abundance matrix, significant correlations were screened, and the microbial symbiosis network was constructed using the R language igraph package; Step 5: The Louvain algorithm was used to perform modular analysis on each microbial symbiotic network and calculate the modularity index of each fragmentation level group; Step 6: For the mean of the comprehensive landscape fragmentation index of all fragmentation level groups and the modularity index of each fragmentation level group, a local weighted regression fitting model is applied to construct a nonlinear response curve of landscape fragmentation and modularity index at the global scale; Step seven: Determine the critical threshold of the comprehensive landscape fragmentation index at which modularity decreases significantly by minimizing the Akaike information criterion value.

2. The watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity according to claim 1, characterized in that: The step one comprises: The sampling points are rationally arranged according to the land use type, topographic division and river hydrological characteristics in the basin; Use GPS positioning equipment to record the latitude and longitude coordinates of each sampling point; Riverbed sediment samples were collected at each sampling point, and surface sediments with a depth of 0-25 cm were collected. Each sampling point was repeated at least 3 times, and the amount of sediment sample collected each time was not less than 100 g. After the surface sediment samples were fully mixed, they were stored in an environment of -20°C until transported back to the laboratory.

3. The watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity according to claim 1, characterized in that: The second step comprises: Acquire and process satellite remote sensing images of the watershed; Supervised classification of remote sensing images is performed with the help of remote sensing image processing platform; The ArcGIS platform was used to divide the rivers from the source to the sampling point, and the buffer was set at a preset distance to extract the land use data within the buffer. The processed remote sensing images were imported into Fragstats to calculate the landscape pattern index. , where PD is the patch density, N is the number of patches, and A is the landscape area; , where ED is the edge density and E is the total length of the patch edge; , where AI is the degree of aggregation, is the number of adjacent patches of the same type; Constructing a comprehensive landscape fragmentation index: Among them, FI is the comprehensive landscape fragmentation index.

4. The watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity according to claim 3 is characterized in that: In the step of supervised classification of remote sensing images with the help of remote sensing image processing platform, the maximum likelihood method is used for land use classification, and the classification result is verified by Kappa coefficient, Kappa>0.75; the isolated node deletion threshold of network construction is set to Degree=0, that is, nodes with Degree=0 will be deleted from the network.

5. The watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity according to claim 1, characterized in that: The step three comprises: The comprehensive landscape fragmentation index calculated from all sampling points is standardized to eliminate the influence of different dimensions on the clustering results; the normalization formula is used: in, is the original FI value, is the standardized comprehensive landscape fragmentation index, and are the minimum and maximum FI values ​​of all sampling points, respectively; Use the elbow rule to determine the optimal number of clusters, execute the K-means clustering algorithm within a certain range, calculate the clustering error under each k value, draw a curve of the relationship between the number of clusters k and the sum of squares within the group, and observe the inflection point of the curve. The k value corresponding to the inflection point is the optimal number of clusters; According to the determined optimal number of clusters, the K-means clustering algorithm is executed, and the standardized comprehensive landscape fragmentation index is used as input data. Through iterative calculation, each sampling point is assigned to the cluster that is most similar to its comprehensive landscape fragmentation index. Finally, all sampling points are divided into several fragmentation level groups, and the sampling points in each group have similar landscape fragmentation characteristics.

6. The watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity according to claim 1, characterized in that: The fourth step comprises: The 16S rRNA gene V4 region was sequenced using the Illumina NovaSeq platform, and the operational taxonomic unit abundance table was generated using the QIIME2 pipeline; The Hmisc package of R language was used to calculate the Spearman correlation between operational classification units of each group, and the correlation coefficient threshold was | |>0.6, significance level p<0.01, significant edges were screened; the igraph package was used to construct an undirected weighted network and isolated nodes were deleted.

7. The watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity according to claim 1, characterized in that: The step five comprises: The Leuven algorithm is used to divide the modules and calculate the modularity index. The formula is: Where Q is the modularity index, is the ratio of the inner edge of module i, is the edge ratio of module i.

8. The watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity according to claim 7, characterized in that: The step 5 evaluates the significance of the modularity index through a null model test, with a significance level of p<0.05, to verify the reliability of the module division result.

9. The watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity according to claim 1, characterized in that: The step six comprises: Based on the mean of the comprehensive landscape fragmentation index of all fragmentation level groups and the modularity index data set of the corresponding fragmentation level groups, the local weighted regression function of the stats package of R language was used to fit the nonlinear relationship. The model formula is: Among them, Q is the modularity index, FI is the comprehensive landscape fragmentation index, is a local weighted polynomial function, is the error term; The optimal fitting curve is obtained by iteratively weighting and adjusting the local weight function, which describes the response of the modularity index to changes in the degree of landscape fragmentation.

10. The watershed ecological health threshold assessment method based on landscape fragmentation and microbial network modularity according to claim 1, characterized in that: The step seven comprises: In the R language environment, the segmented package was called to perform segmented regression analysis on the comprehensive landscape fragmentation index and modularity index data; During the segmented regression process, the segmentation points of the model are continuously adjusted to find the segmentation scheme that minimizes the Akaike information criterion value; the segmentation point determined by minimizing the Akaike information criterion value is the critical threshold of the comprehensive landscape fragmentation index where the modularity decreases significantly.

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