Method and system for regulating heavily logged forest based on soil microbial sampling data

Abstract

The application discloses a heavy cutting woodland regulation method and system based on soil microbial sampling data, comprising the following steps: selecting a sampling area, collecting soil samples at different soil layer depths to obtain soil microbial sampling data at each layer depth; determining a cutting index and a community recovery index of the sampling area; establishing a soil microbial recovery evaluation model to determine a soil microbial health index and a regulation index of the sampling area; and determining and executing a regulation measure according to the soil microbial health index and the regulation index. The application quantifies the influence degree of root aging and decay caused by cutting on the microbial state of different soil layer depths by constructing a cutting index, characterizes the recovery progress of the microbial community in the time dimension relative to the undisturbed state by constructing a community recovery index, and establishes a recovery evaluation model accordingly, so that the evaluation and regulation of the soil microbial recovery state of the heavy cutting woodland are realized.

Description

Technical Field

[0001] This invention relates to the field of ecological restoration data processing technology, specifically to a method and system for regulating heavily logged forest land based on soil microbial sampling data. Background Technology

[0002] Severe logging has severely damaged the structure and function of forest soil microbial communities, thereby affecting soil nutrient cycling and vegetation restoration.

[0003] Existing control methods are mostly based on static detection of soil microbial indicators and single threshold judgment, failing to fully consider the spatiotemporal heterogeneity of the impact of logging history on soil. Specifically, after logging, the distribution and aging and decay process of roots at different soil depths vary significantly among forest stands of different developmental levels, resulting in different degrees of impact on deep and shallow microorganisms. However, current technologies lack a means to jointly quantify and assess the impact of logging intensity, post-logging duration, and the degree of root influence at different soil depths.

[0004] Furthermore, existing technologies for evaluating the recovery status of microbial communities tend to focus on comparing the absolute values ​​of microbial indicators, without considering their recovery progress relative to the undisturbed state over a post-harvest time dimension. This makes it difficult to accurately determine whether the community is in a phase of accelerated recovery, normal recovery, or stagnant recovery, resulting in a lack of precise basis for the selection of control measures and a high risk of insufficient or excessive intervention.

[0005] Therefore, this paper provides a method and system for regulating heavily logged forest land based on soil microbial sampling data. Summary of the Invention

[0006] To address the above problems, this invention provides a method and system for regulating heavily logged forest land based on soil microbial sampling data.

[0007] A first aspect of the present invention provides a method for regulating heavily logged forest land based on soil microbial sampling data, comprising the following steps:

[0008] S1. Select the sampling area, collect soil samples at different soil depths, and obtain soil microbial sampling data at each depth after performing microbial testing.

[0009] S2. Determine the logging index and community recovery index of the sampling area;

[0010] S3. Based on the soil microbial sampling data at each depth, the harvesting index, and the community recovery index, establish a soil microbial recovery evaluation model to determine the soil microbial health index and the regulation index of the sampling area;

[0011] S4. Determine control measures based on the soil microbial health index and the control index;

[0012] S5. Implement the aforementioned control measures.

[0013] As a preferred method, the harvesting index is determined through the following steps:

[0014] The initial breast height sectional area of ​​the sampling area before logging and the current remaining breast height sectional area are obtained, and the breast height sectional area attenuation is obtained based on the difference between the two.

[0015] Obtain the logging time limit of the sampling area from the time of logging to the current time;

[0016] The forest development level of the sampling area before logging is determined based on the initial basal area at breast height, and the typical root distribution depth range of the area is determined based on the forest development level.

[0017] Using the attenuation of the basal area at breast height, the logging time limit, and the typical distribution depth range as input parameters, the logging index is obtained through a preset root system influence attenuation model. The root system influence attenuation model includes a combined spatiotemporal relationship in which the attenuation of the basal area at breast height increases with the extension of the logging time limit, and the influence on soil microorganisms decreases from the typical root distribution depth range to the shallow and deep layers. The logging index represents the degree of influence of root aging and decay caused by logging on the current microbial state at different soil depths.

[0018] As a preferred method, the community recovery index is determined through the following steps:

[0019] The Shannon index and Chao1 index of the microbial community were obtained from soil microbial sampling data at different soil depths.

[0020] The Shannon index and Chao1 index of the current sampling area are compared with the reference values ​​of Shannon index and Chao1 index of the control forest land to obtain the Shannon recovery rate and Chao1 recovery rate.

[0021] The community recovery rate is calculated based on a weighted combination of the Shannon recovery rate and the Chao1 recovery rate.

[0022] Obtain the logging time limit of the sampling area from the logging occurrence to the current time, and fit the community restoration degree with the logging time limit to obtain the community restoration time limit;

[0023] The community recovery index is determined based on the ratio of the community recovery time to the expected natural recovery time of the control forest.

[0024] The community recovery index represents the recovery progress of the current microbial community relative to its undisturbed state over time. The control forest is a forest with similar site conditions to the sampling area and a logging degree lower than a preset heavy logging threshold.

[0025] As a preferred approach, the number of control forest lands is multiple, and each control forest land has a different initial basal area at breast height and a corresponding pre-harvest forest development level;

[0026] The method further includes the following steps:

[0027] The reference values ​​of Shannon Index and Chao1 Index for each of the control forest lands were obtained, as well as the expected natural recovery time for each of the control forest lands.

[0028] Based on data from multiple control forest lands, a database of correspondences was established between the initial height-to-breast-area, the logging time limit, the Shannon index reference value, and the Chao1 index reference value.

[0029] When determining the community recovery index of the sampling area, the control forest data that is closest to the initial height-to-breast-area of ​​the sampling area is selected from the corresponding relationship database as a reference benchmark.

[0030] As a preferred method, the soil microbial health index and the regulatory index are determined through the following steps:

[0031] A multiple regression evaluation model was constructed with the logging index and the community recovery index as independent variables and the soil microbial health index as the dependent variable.

[0032] The actual detection values ​​of each microbial index in the soil microbial sampling data at each depth are compared with the expected recovery values ​​determined based on the harvesting index and the community recovery index to obtain the depth deviation of each microbial index.

[0033] The soil microbial health index is obtained by weighting and fusing the depth deviations at each depth location;

[0034] The regulation index is calculated based on the difference between the soil microbial health index and the preset health threshold, and the difference between the community recovery index and the preset recovery threshold.

[0035] The regulation index represents the degree to which the sampling area currently requires human intervention to restore soil microorganisms.

[0036] As a preferred approach, the microbial indicators in the soil microbial sampling data include microbial biomass, diversity index, and relative abundance of key functional groups; the key functional groups include nitrogen-fixing bacteria, phosphate-solubilizing bacteria, and arbuscular mycorrhizal fungi; the diversity index includes the Shannon index and the Chao1 index.

[0037] As a preferred approach, the control measures determined in step S4 include:

[0038] When the regulation index is higher than the preset strong intervention threshold, a first regulation measure is generated with the application of organic fertilizer and inoculation of microbial agents as the first priority.

[0039] When the regulation index is between the preset weak intervention threshold and the strong intervention threshold, a second regulation measure with the application of biochar and the replanting of nitrogen-fixing tree species as the first priority is generated.

[0040] When the regulation index is lower than or equal to the weak intervention threshold, a third regulation measure with enclosure and management as the first priority is generated;

[0041] The combination of microbial strains in the first control measure is determined based on the depth deviation between the relative abundance of key functional groups at each depth position and the corresponding expected recovery value.

[0042] As a preferred method, after S5 implements the control measures, after a preset monitoring cycle, soil samples at each depth are collected again and microbial tests are performed to re-determine the soil microbial health index and the control index;

[0043] If the re-determined control index is still higher than the preset weak intervention threshold, supplementary control measures are generated and implemented.

[0044] A second aspect of the present invention provides a control system for heavily logged forest land based on soil microbial sampling data, comprising:

[0045] The data acquisition module is used to acquire soil microbial sampling data at different soil depths in the sampling area;

[0046] The model evaluation module is used to determine the logging index and community recovery index of the sampling area. Based on the soil microbial sampling data at each depth, the logging index and the community recovery index, a soil microbial recovery evaluation model is established to determine the soil microbial health index and the regulation index of the sampling area.

[0047] The decision-making module is used to determine control measures based on the soil microbial health index and the control index;

[0048] The execution and feedback module is used to execute the control measures and, after execution, trigger the data acquisition module to reacquire data to re-determine the control index. If the control index does not fall to the target range, the decision module generates supplementary control measures.

[0049] Compared with the prior art, the present invention has the following advantages:

[0050] This invention constructs a logging index, incorporating the decline in basal area at breast height, logging time, and typical root distribution depth range into a unified spatiotemporal model. This quantifies the impact of root aging and decay caused by logging on the microbial state at different soil depths, providing an accurate background for interpreting stratified sampling data.

[0051] By constructing community recovery indices, the Shannon index and Chao1 index were compared with reference values ​​of control forests at multiple developmental levels. The relationship between community recovery and harvesting time was fitted to obtain the community recovery time in the time dimension, thereby accurately characterizing the recovery progress of the microbial community relative to the undisturbed state.

[0052] The soil microbial restoration evaluation model, based on the logging index and community restoration index, can comprehensively assess the spatiotemporal impact of logging and the community restoration process, quantify and integrate the deviation of microbial indicators at different depths, and obtain the soil microbial health index and regulation index, thereby achieving a comprehensive assessment of the forest restoration status.

[0053] Based on the hierarchical relationship between the regulation index and the preset threshold, differentiated regulation measures with clear priorities are generated. Furthermore, the combination of microbial agents in the first regulation measure can be accurately determined based on the deviation of key functional groups at each depth, ensuring that the intervention measures match the degree and type of degradation.

[0054] By introducing multi-control forest land data to establish a reference benchmark library, and selecting the closest control forest land as the evaluation benchmark based on the initial chest height basal area of ​​the sampling area, the evaluation accuracy and comparability of forest land with different developmental backgrounds are improved. Attached Figure Description

[0055] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0056] Figure 1 This is a flowchart illustrating the method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the system provided in an embodiment of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] In one specific embodiment of the present invention, a method for regulating heavily logged forest land based on soil microbial sampling data is provided, such as... Figure 1 As shown, this invention aims to address the problem in existing technologies that fail to jointly quantify and assess the impact of logging intensity, post-logging duration, and root system at different depths, and that it is difficult to accurately determine the recovery stage of the microbial community, resulting in a lack of precise basis for the selection of control measures. This invention aims to achieve differentiated control based on the recovery status of microorganisms.

[0059] Specifically, the method includes the following steps:

[0060] S1. Select the sampling area, collect soil samples at different soil depths, and obtain soil microbial sampling data at each depth after performing microbial testing.

[0061] In this embodiment, the selection criteria for the sampling area include: the historical logging intensity of the area reaching a preset heavy logging standard, the number of years since logging, and the preliminary characteristics of current vegetation recovery. The heavy logging standard is defined by the removal ratio of the horizontal area at breast height (HB), which typically refers to a single logging operation removing more than 60% of the total horizontal area at HB before logging.

[0062] After determining the sampling area, stratified sampling is conducted. The sampling depth range is determined based on the forest development level corresponding to the initial basal area at breast height (DBH) before logging. Specifically, the relationship between forest development level and initial DBH is determined through a pre-defined grading table: the initial DBH is divided into several intervals, with the upper and lower limits of each interval corresponding to different forest development levels. For example, when the initial DBH is greater than or equal to 35m… 2 When the range is / ha, it is classified as a mature forest; when it is greater than or equal to 20m 2 / ha and less than 35m 2 When the forest height is within the range of / ha, it is classified as a middle-aged forest; when it is less than 20m... 2When the area falls within a certain range (e.g., 0-60 cm), it is classified as a young forest. Each forest development level corresponds to a different typical root distribution depth range: for example, 0-60 cm for mature forests, 0-40 cm for middle-aged forests, and 0-25 cm for young forests. During sampling, within this typical root distribution depth range, several sampling layers are divided at preset intervals, which can be set to 5 cm or 10 cm, thereby obtaining soil samples at different soil depths.

[0063] Microbial testing was performed on soil samples collected from various depths using at least one of the following methods: phospholipid fatty acid analysis, high-throughput sequencing, or real-time quantitative PCR. After testing, soil microbial sampling data for each depth were obtained. The microbial indicators in this data included microbial biomass, diversity index, and relative abundance of key functional groups. The diversity index included the Shannon index and the Chao1 index; the key functional groups included nitrogen-fixing bacteria, phosphate-solubilizing bacteria, and arbuscular mycorrhizal fungi. The formula for calculating the Shannon index is as follows:

[0064]

[0065] in, The number of operation classification units, For the first The relative abundance of each operational taxonomic unit. The formula for calculating the Chao1 index is:

[0066]

[0067] in, The number of operational classification units actually observed. The number of operation classification units containing only one sequence. This refers to the number of operational taxonomic units containing only two sequences. The relative abundance of the key functional groups is obtained through functional gene quantification or functional annotation, and is expressed as a percentage of the number of sequences in the target functional group out of the total number of sequences.

[0068] S2. Determine the logging index and community recovery index of the sampling area.

[0069] In S2, the harvesting index is determined. In this embodiment, this is specifically achieved through the following steps:

[0070] First, the initial basal area at breast height (DBH) before logging and the current remaining DBH in the sampling area were obtained. The initial DBH was obtained by consulting historical forest survey archives, logging area survey design records, or by extrapolating from growth models. The remaining DBH was obtained through on-site measurement of each tree. The difference between the two was used to calculate the DBH attenuation.

[0071]

[0072] in, This represents the initial cross-sectional area at chest height. The current remaining cross-sectional area at breast height. This represents the decrease in cross-sectional area at breast height, in meters. 2 / ha.

[0073] Secondly, the logging time limit of the sampling area from the time of logging to the current time is obtained. The logging timeframe was obtained by reviewing logging records, tree-ring analysis, or time-series analysis of remote sensing images, and the unit is years.

[0074] Next, the forest development level of the sampling area before logging is determined based on the initial basal area at breast height, and the typical root distribution depth range of the area is determined based on the forest development level, as described above.

[0075] Then, the logging index is obtained by using the attenuation amount of the cross-sectional area at breast height, the logging time limit, and the typical distribution depth range as input parameters and through a preset root system influence attenuation model.

[0076] The root system impact attenuation model is a mathematical model used to quantify the spatiotemporal distribution characteristics of the impact of root aging and decay on soil microorganisms after logging. This model is constructed based on the following principles: after logging, the death and decay of tree roots proceed over time, and the degree of decay increases with the extension of the logging period; simultaneously, the intensity of the impact of root decay on soil microorganisms is not uniformly distributed spatially, but rather decreases from the typical root distribution depth range towards shallower and deeper layers. Based on this, the root system impact attenuation model in this embodiment adopts the following form:

[0077]

[0078] in, Indicates depth The impact of logging at that location; This is a scale coefficient related to forest development level; is the root decay rate constant, which represents the rate at which root decay approaches saturation over time, and is expressed in units of 1 / year; For logging time limits; This represents the central depth of the typical root distribution range. , and These are the upper and lower limits of the typical root distribution depth range, respectively; To influence the standard deviation of diffusion, This characterizes the extent of the influence's diffusion in the depth direction.

[0079] For parameters and The calibration method is as follows:

[0080] Multiple reference plots belonging to the same climate zone and similar tree species as the sampling area, with known and precise logging years, were selected. The logging years of these reference plots covered at least three different time points: the initial stage (1-3 years after logging), the middle stage (5-10 years after logging), and the late stage (15 years or more after logging). In each reference plot, soil microbial sampling data at each depth were obtained using the same stratified sampling method as in S1, with microbial biomass as the response variable.

[0081] Calibration When the value is given, the center depth of the typical root distribution depth range within the same reference plot is considered. At this location, the microbial biomass change sequence under different logging years was obtained. With logging year as the independent variable and the decrease in microbial biomass relative to the unlogged control as the dependent variable, the exponential saturation function was fitted using the nonlinear least squares method.

[0082]

[0083] in, For logging The percentage decrease in microbial biomass over the years This represents the maximum asymptotic value of the decrease ratio. (The value obtained from the fitting) This is the root decay rate constant under this climate zone and tree species composition, with a typical value range of 0.05-0.3 / year.

[0084] Calibration When the value is set, the already calibrated value will be... Substituting the values ​​into the root system impact attenuation model, the objective is to minimize the sum of squared residuals between the calculated logging index at each depth in the reference plot and the measured percentage decrease in microbial biomass. This is achieved using either gradient descent or grid search methods. Different forest development levels correspond to different Value, mature forest grade The value is usually greater than that of the middle-aged forest class, and the middle-aged forest class has a higher value. The value is usually greater than that of the young forest grade, and the specific value is stored in a parameter lookup table indexed by climate zone and forest development grade.

[0085] The model at different layer depths The influence components calculated at the location This constitutes a logging impact vector, which represents the specific values ​​of the logging index at each depth. The complete form of the logging index is:

[0086]

[0087] in This represents the total number of sampling depths. This logging index quantifies the impact of root aging and decay caused by logging on the current microbial state at different soil depths; a higher value indicates a stronger residual impact of logging at that depth.

[0088] In S2, the community recovery index is determined. In this embodiment, this is specifically achieved through the following steps:

[0089] First, Shannon indices of microbial communities at different soil depths were extracted from soil microbial sampling data obtained from S1. and Chao1 index The Shannon index and Chao1 index at each depth were averaged, or a weighted average was calculated based on the soil volume weights at each depth, to obtain the comprehensive Shannon index representing the entire sampling area. and the comprehensive Chao1 index .

[0090] Secondly, data from control forest lands are obtained. In this embodiment, the control forest lands are forest lands with similar site conditions to the sampling area and a logging degree lower than a preset heavy logging threshold, preferably natural forest land or long-term closed forest land with similar site conditions that has not been logged. There are multiple control forest lands, each with a different initial basal area at breast height and a corresponding pre-logging forest development level. The Shannon Index reference value for each control forest land is then obtained. Chao1 Index Reference Value And the expected natural recovery time for each control forest plot. The expected natural recovery timeframe refers to the estimated time it would take for the microbial community of the control forest to recover to its current control state after assuming the same degree of logging. This timeframe is obtained through long-term locational observation data or time series reconstruction.

[0091] Based on data from multiple control forest sites, a correspondence database is established between the initial basal area at breast height (BHB), the logging timeframe, the Shannon Index reference value, and the Chao1 Index reference value. This correspondence database is a multidimensional lookup table or interpolation model, using the initial BHB as the index key to store the corresponding reference values ​​and expected natural recovery timeframes.

[0092] Then, when determining the community recovery index of the sampling area, the control forest data that is closest to the initial breast height basal area of ​​the sampling area is selected from the corresponding relationship database as a reference benchmark.

[0093] Next, the composite Shannon index for the current sampling area will be... and the comprehensive Chao1 index , respectively with the Shannon index reference value in the reference benchmark Chao1 Index Reference Value By comparison, the Shannon recovery rate was obtained. and Chao1 recovery rate :

[0094]

[0095]

[0096] The community recovery rate is calculated based on a weighted combination of the Shannon recovery rate and the Chao1 recovery rate. :

[0097]

[0098] in, and Let be the weighting coefficient, satisfying The weighting coefficients are determined based on the indicative importance of the Shannon index and the Chao1 index for ecosystem function recovery. In this embodiment, The value range is 0.4-0.6. The corresponding value is 0.6-0.4.

[0099] Then, the logging time limit of the sampling area from the time of logging to the current time is obtained. The community restoration degree With the aforementioned logging time limit By performing fitting, the community recovery time was obtained. The fitting method can employ nonlinear least squares, and the fitting function takes the form of a logistic growth curve:

[0100]

[0101] in, This represents the theoretical upper limit of the recoverability, and is typically taken as 1. and These are the fitting parameters. Based on the fitted curve, solve for the... The time corresponding to reaching the preset recovery completion judgment value (e.g., 0.95). This is the community recovery timeframe. If there is sampling data from multiple time points, it can be directly substituted into the fitting data; if there is only data from the current time point, the following constraint fitting method is used to determine the community recovery time limit. :

[0102] First, several control forest plots with different initial breast height and basal area were selected from the corresponding database. Each control forest plot has a known expected natural recovery time. And the corresponding community recovery time series data, which describes the entire recovery trajectory from logging to completion of recovery.

[0103] Secondly, shape feature parameters of the recovery trajectories were extracted from the time series data of each control forest land, specifically the relative growth rate of the logistic growth curve of the recovery trajectory. and inflection point position Multiple control forest plots and Corresponding initial chest height cross-sectional area and expected natural recovery time Establishing regression relationships:

[0104]

[0105] in and This is a regression function.

[0106] Then, substituting the initial chest height cross-sectional area of ​​the sampling area into the above regression relationship, the estimated relative growth rate of the recovered trajectory of the sampling area is obtained. and estimated inflection point location Based on this, the logistic growth curve is constrained as follows:

[0107]

[0108] Let the current moment time Equal to the currently calculated community recovery degree Solve for the actual value of the inflection point:

[0109]

[0110] Finally, solve for when The preset recovery completion threshold has been reached. (When taken as 0.95) The value represents the community recovery time. :

[0111]

[0112] Finally, based on the community recovery time limit... Expected natural recovery time of control forest land in the reference benchmark The ratio of the two values ​​is used to determine the community recovery index. :

[0113]

[0114] in, This is the community recovery index. This index characterizes the recovery progress of the current microbial community relative to its undisturbed state over time. When the value equals 1, it indicates that the expected time required for the current community to reach the recovery level of the control forest is comparable to the natural recovery time of the control forest itself; when... A value greater than 1 indicates that the current community is recovering slower than expected and will require a longer time to recover; when... A value less than 1 indicates that the current community is recovering faster than expected.

[0115] S3. Based on the soil microbial sampling data at each depth, the logging index, and the community recovery index, establish a soil microbial recovery evaluation model to determine the soil microbial health index and the regulation index of the sampling area.

[0116] In this embodiment, S3 is specifically implemented through the following steps:

[0117] First, a multiple regression evaluation model is constructed, with the logging index and the community recovery index as independent variables and the soil microbial health index as the dependent variable. This model is based on statistical analysis of historical monitoring data to establish a quantitative relationship between the logging index, the community recovery index, and the expected recovery values ​​of each microbial indicator. The general form of the model is as follows:

[0118]

[0119] in, In the first Each layer depth position First Expected recovery value of each microbial indicator; For the logging index at The component at the location; Community recovery index; In order to target the The regression function for each microbial indicator can be established using multiple linear regression, support vector regression, or neural network regression methods.

[0120] The expected recovery value refers to the theoretically expected value of this microbial index at this depth in a forest on a normal recovery trajectory, given the intensity of logging impact and the stage of community recovery. This value is obtained by fitting data from a large number of plots at different recovery stages.

[0121] Then, the actual detection values ​​of each microbial index in the soil microbial sampling data at each depth were obtained. The corresponding expected recovery value determined based on the logging index and the community recovery index. By comparison, the depth deviation of each microbial indicator was obtained. :

[0122]

[0123] in, For the first The microbiological indicator in the first The depth deviation at each depth position, a positive value indicates that the actual value is better than the expected value, and a negative value indicates that the actual value is worse than the expected value.

[0124] Subsequently, the depth deviations at each depth location are weighted and fused to obtain the soil microbial health index. :

[0125]

[0126] in, This represents the total number of microbial indicators; This represents the total number of sampling layer depths; For the first The weight of each microbial indicator reflects its importance to soil health and meets the following requirements. ; For the first The depth weight of each soil layer reflects the difference in the contribution of different soil layers to overall soil health, and satisfies... The weighting of root depth is determined based on the proportion of root biomass at each depth, and the determination method is as follows:

[0127] For the sampling area, since the original tree roots had died and decayed after heavy logging, the root biomass proportion was estimated based on the initial basal area at breast height and forest development level before logging. Specifically, according to the typical root distribution depth range corresponding to the forest development level, the root vertical distribution model for that tree species or tree species group was invoked. This root vertical distribution model describes the relative distribution ratio of root biomass with depth, and its functional form is:

[0128]

[0129] in, For depth The relative density of root biomass at the location satisfies ; The root distribution attenuation coefficient is determined based on the tree species group, such as deep-rooted tree species. Take 0.05-0.10 g / cm, for shallow-rooted tree species. Take 0.10-0.20 / cm; This represents the maximum depth within the typical root distribution depth range.

[0130] For the Each sampling layer depth Take its depth range The root biomass integral value within the layer is taken as the proportion of root biomass at that depth, where The sampling layer spacing is used. After normalizing the root biomass proportion at each layer depth, the layer depth weight is obtained. :

[0131]

[0132] If there is actual data on the current residual root biomass measured by the excavation profile method in the sampling area, the measured data will be used instead of the model estimate. When there are a lot of newly grown roots of renewing vegetation at certain depths, the biomass of the newly grown roots will not be included in the depth weighting calculation to avoid interfering with the assessment of the logging legacy.

[0133] In this embodiment, the index weights of each microbial indicator are... The soil microbial health index was determined using principal component analysis or expert scoring; the sum of the weights of microbial biomass, Shannon index, Chao1 index, relative abundance of nitrogen-fixing bacteria, relative abundance of phosphate-solubilizing bacteria, and relative abundance of arbuscular mycorrhizal fungi was 1. The value range is usually between -1 and 1. A positive value indicates that the overall health condition is better than the expected level of the current recovery stage, a negative value indicates that it is worse than the expected level, and a zero value indicates that it is on the expected recovery trajectory.

[0134] Finally, based on the soil microbial health index and the preset health threshold... The difference between them, and the community recovery index and the preset recovery threshold. The difference between them is used to calculate the regulation index. :

[0135]

[0136] in, and These are the weighting coefficients. , representing the relative importance of microbial health deviation and recovery lag in regulatory decisions, respectively. Health threshold and recovery threshold The setting method is as follows:

[0137] Health threshold This represents the acceptable lower limit of the soil microbial health index. The method for setting this threshold is as follows: Soil microbial data are collected from multiple reference forest plots under closed restoration and proven good restoration conditions within the same climatic zone and under similar site conditions. The soil microbial health index is calculated for each reference forest plot using the method described above. The lower quartile or mean of the health index for all reference forest plots is then subtracted from one standard deviation to obtain the health threshold. If there is insufficient reference forest land data, then a conservative estimate should be taken. This means that the actual value is allowed to be slightly lower than the expected recovery value by up to 10%.

[0138] Recovery threshold This represents the upper limit of the acceptable lag in community recovery progress. The method for setting this threshold is as follows: Select logged forests within the same climate zone that have been proven to have successfully recovered to a near-natural state through natural restoration; calculate the community recovery index series at different recovery stages; and take the 95th percentile of the community recovery index during the recovery process as the recovery threshold. If the data is insufficient, take a conservative approach. This means that the recovery period is allowed to be up to 20% longer than the expected natural recovery period of the control forest.

[0139] The above thresholds may be adjusted appropriately according to the restoration goals and regional ecological policy requirements, but the adjusted thresholds and the basis for adjustment must be clearly recorded in the regulation plan.

[0140] The regulation index The value is dimensionless; the larger the value, the more urgent and intense the need for human intervention in the sampling area.

[0141] S4. Determine control measures based on the soil microbial health index and the control index.

[0142] In this embodiment, S4 is based on the control index. Based on the relationship with preset multi-level thresholds, hierarchical control measures are generated, as follows:

[0143] When the regulation index Higher than the preset strong intervention threshold This indicates that the soil microbial community is in a state of severe degradation and its recovery is severely delayed, leading to the development of a first regulatory measure prioritizing the application of organic fertilizer and inoculation with microbial agents. The combination of microbial agents used in this first regulatory measure is determined based on the depth deviation between the relative abundance of key functional groups at each depth location and the corresponding expected recovery value. Specifically, from all depth locations and all key functional group types, several items with the largest negative depth deviation are selected, and microbial agents corresponding to these items at the target depth locations and target key functional group types are preferentially added. For example, if the depth deviation of nitrogen-fixing bacteria at a depth of 20-30 cm is -0.6, and the depth deviation of arbuscular mycorrhizal fungi at a depth of 0-10 cm is -0.5, then the agent combination should preferentially include nitrogen-fixing bacteria and arbuscular mycorrhizal fungi, and they should be applied primarily to the corresponding depths. The application rate and depth of organic fertilizer are determined based on the deviation between the soil organic matter content and the expected value in that area.

[0144] When the regulatory index exceeds the strong intervention threshold, the soil microbial community has severely deviated from its normal recovery trajectory, with the abundance of key functional groups significantly lower than expected, indicating that the soil's nutrient cycling function and symbiotic network have been structurally damaged. Under these circumstances, relying solely on natural recovery is insufficient to return the community to its normal successional trajectory. Applying organic fertilizer can provide readily available carbon sources for residual and replenished microorganisms, rapidly increasing soil basal respiration and microbial biomass, and rebuilding the energy base of the microbial community. Inoculation with microbial agents targets the most severely deficient key functional groups, directly repairing damaged nitrogen fixation, phosphorus activation, and symbiotic absorption functions. The combined use of these two methods allows for simultaneous intervention from both energy supply and functional reconstruction dimensions, creating a synergistic recovery effect that enables severely degraded microbial communities to quickly cross the initial recovery threshold and re-enter the positive successional process.

[0145] When the regulation index Given the preset weak intervention threshold and the strong intervention threshold When the soil microbial community exhibits moderate degradation or delayed recovery, a second regulatory measure is implemented, prioritizing the application of biochar and the replanting of nitrogen-fixing tree species. The amount of biochar applied is determined based on the deviation of soil bulk density and water retention capacity, while the density and species of nitrogen-fixing tree species replanted are determined based on the depth deviation of the relative abundance of nitrogen-fixing bacteria.

[0146] When the regulatory index is in the moderate range, although the microbial community has deviated from the normal recovery trajectory, its basic structure and function have not been completely lost, and the community still retains a certain self-recovery potential. At this time, high-intensity direct microbial intervention is not advisable; instead, the main strategy should be to improve microbial habitat conditions and enhance the driving force of natural recovery. Biochar has high porosity and a large specific surface area. After being applied to the soil, it can significantly improve soil aeration and water retention capacity, providing a stable microenvironment for microorganisms. At the same time, its surface functional groups can adsorb and slowly release nutrients, reducing nutrient leaching and creating suitable habitat conditions for the autonomous recovery of the microbial community. Replanting nitrogen-fixing tree species involves introducing host plants that can form a symbiotic relationship with nitrogen-fixing microorganisms. The photosynthetic products of the plants continuously input carbon sources into the soil, and the nitrogen-fixing bacteria are selectively enriched through root exudates, thereby driving the gradual reconstruction of nitrogen-fixing function in a biological manner. Both of these measures belong to habitat regulation and biologically driven interventions, with moderate intervention intensity, which can accelerate the recovery process without destroying the community's self-organizing ability.

[0147] When the regulation index Less than or equal to the weak intervention threshold When this occurs, it indicates that the soil microbial community is on a normal recovery trajectory or close to recovery. This leads to the generation of a third regulatory measure with enclosure and management as the first priority, reducing human interference and relying on natural recovery forces to complete the recovery.

[0148] When the regulatory index is below the weak intervention threshold, the structure and abundance of each functional group of the microbial community are basically at or close to the expected recovery trajectory, and the community's self-repair mechanism and internal feedback regulation function are operating effectively. At this stage, additional human intervention not only fails to further accelerate recovery, but may also interfere with or even reverse the achieved recovery results due to negative effects such as physical disturbance of soil structure, disruption of the original community competitive balance by the input of exogenous substances, and competitive exclusion of local adaptive microorganisms by exogenous microorganisms introduced by human activities. Therefore, adopting enclosure and management measures to eliminate external disturbance factors such as grazing, trampling, and illegal logging, so that the recovering microbial community-vegetation-soil system gradually tends towards a steady state under natural feedback regulation, is the optimal regulatory strategy at this stage.

[0149] The above thresholds and Based on statistical data from a large number of historical recovery cases, for example... Set to 0.5. Set to 0.15.

[0150] S5. Implement the aforementioned control measures.

[0151] In this embodiment, the control measures determined in S4 are implemented. For the first control measure, microbial inoculants and organic fertilizers are applied to the corresponding target depth through injection, furrow application, or broadcasting and compaction. For the second control measure, biochar is mixed into the soil through tillage, and nitrogen-fixing tree species are replanted at the appropriate time. For the third control measure, fences or signs are erected to restrict human activities.

[0152] After the control measures are implemented in S5, a closed-loop monitoring and feedback step is also included. After a preset monitoring period, which is set based on the generational turnover time and recovery response rate of the microbial community (e.g., one year or one growing season), soil samples are collected again from each depth and subjected to microbial testing to re-determine the soil microbial health index. and the aforementioned regulatory index .

[0153] If the aforementioned regulatory index is re-determined Still higher than the preset weak intervention threshold If so, supplementary control measures are generated and implemented. These supplementary control measures redetermine the intervention intensity and type based on the updated layer depth deviation. For example, if the control index decreased after the previous implementation of the first control measure but remains higher than the previous value, then... If the inoculant combination and organic fertilizer application rate are updated based on the new layer depth deviation, supplementary regulation is implemented. If the regulation index does not decrease significantly after the previous implementation of the second regulation measure, it is determined that there are deeper soil obstacle factors, and the supplementary regulation measure is upgraded to the first regulation measure or a combination of the first and second regulation measures. If the regulation index is determined again... It has been reduced to the threshold of weak intervention. The third control measure then begins, entering the stage of enclosure and management.

[0154] In another specific embodiment of the present invention, a control system for heavily logged forest land based on soil microbial sampling data is provided, such as... Figure 2The diagram illustrates a method for regulating heavily logged forest land based on soil microbial sampling data, as described in the above embodiments. The system includes: a data acquisition module for acquiring soil microbial sampling data at different soil depths within a sampling area; a model evaluation module for determining the logging index and community recovery index of the sampling area, establishing a soil microbial recovery evaluation model based on the soil microbial sampling data at each depth, the logging index, and the community recovery index, and determining a soil microbial health index and a regulation index for the sampling area; a decision-making module for determining regulation measures based on the soil microbial health index and the regulation index; and an execution and feedback module for executing the regulation measures, triggering the data acquisition module to reacquire data after execution to re-determine the regulation index, and generating supplementary regulation measures by the decision-making module if the regulation index does not fall below the target range.

[0155] Specifically, the data acquisition module includes a sampling unit and a detection unit. The sampling unit is equipped with a stratified soil sampler for collecting soil samples at preset soil depths; the detection unit is equipped with a microbial analysis instrument for performing microbial detection on the soil samples and outputting data on microbial biomass, Shannon index, Chao1 index, and relative abundance of each key functional group.

[0156] The model evaluation module includes an index calculation unit and a model operation unit. The index calculation unit calculates the harvesting index at each depth based on a pre-defined root system impact attenuation model, and calculates the community recovery index by comparing the Shannon index and Chao1 index with control forest data. The model operation unit executes a multiple regression evaluation model, calculates the expected recovery value and depth deviation, and weights and fuses these values ​​to obtain the soil microbial health index and regulation index. The index calculation unit internally stores a database of correspondences between multiple control forests and model parameters.

[0157] The decision-making module includes a threshold comparison unit and a measure generation unit. The threshold comparison unit is used to compare the control index with the strong intervention threshold and the weak intervention threshold; the measure generation unit is used to generate the first control measure, the second control measure, or the third control measure in order of priority according to the comparison results, and in the first control measure, the combination of microbial agents and species is determined according to the depth deviation of each layer of key functional groups.

[0158] The execution and feedback module includes an execution management unit and a periodic triggering unit. The execution management unit is used to issue control commands to fertilization equipment, microbial agent application equipment, or replanting equipment and record the execution status; the periodic triggering unit is used to trigger the data acquisition module to resample and detect when the preset monitoring period arrives, and to feed the new data back to the model evaluation module.

[0159] This embodiment constructs a logging index, incorporating the decline in basal area at breast height, logging duration, and the typical root distribution depth range into a unified spatiotemporal mathematical model. This quantifies the impact of root aging and decay caused by logging on the microbial state at different soil depths. The model fully considers the depth differences in root distribution under different forest development levels, as well as the spatiotemporal pattern of root decay impact saturating with prolonged logging time and decreasing from the typical root distribution depth range towards shallow and deep layers. This provides accurate quantitative evidence of interference background for interpreting stratified sampling data, solving the problem in existing technologies of being unable to jointly quantify and assess the impact of logging intensity, post-logging duration, and the degree of root influence at different depths.

[0160] This embodiment constructs a community recovery index, compares the Shannon index and Chao1 index with reference values ​​of control forests at multiple developmental levels, obtains the community recovery degree through weighted combination, and fits the community recovery degree with the harvesting time to obtain the community recovery time limit. Finally, the ratio of the community recovery time limit to the expected natural recovery time limit of the control forest is used as the evaluation index, achieving a quantitative characterization of the progress of microbial community recovery over time. This method can accurately determine whether the community is currently in an accelerated recovery, normal recovery, or stagnant recovery stage, providing a scientific basis for differentiated regulation decisions.

[0161] This embodiment presents a soil microbial recovery evaluation model based on the logging index and community recovery index. This model comprehensively considers the spatiotemporal impacts of logging and the community recovery process. It compares the actual measured values ​​of various microbial indicators at different depths with the expected recovery values ​​to obtain the depth deviation. Furthermore, by weighted fusion of different depths and indicators, a soil microbial health index is obtained. This index considers both the depth heterogeneity of logging impacts and the recovery progress of the community over time, achieving a multi-dimensional and accurate evaluation of the soil microbial recovery status of heavily logged forest land.

[0162] This embodiment calculates a regulation index by comprehensively considering the deviations of the soil microbial health index from the health threshold and the community recovery index from the recovery threshold. Based on a tiered comparison of the regulation index with strong and weak intervention thresholds, three levels of differentiated regulation measures are generated. When the regulation index is higher than the strong intervention threshold, a synergistic recovery strategy combining organic fertilizer and microbial agents for energy supply and functional reconstruction is adopted. When the regulation index is in the medium range, a habitat regulation and bio-driven strategy involving biochar and nitrogen-fixing tree replanting is employed. When the regulation index is lower than the weak intervention threshold, a natural recovery strategy of enclosure and management is used. Each level of regulation measure has a clear ecological theoretical basis, achieving a precise match between intervention intensity and the degree of degradation. This avoids both recovery stagnation due to insufficient intervention and damage to the community's self-organizing capacity due to excessive intervention.

[0163] In the first control measure, this embodiment accurately determines the combination of microbial agents and the application depth based on the depth deviation of the relative abundance of key functional groups at each depth position. It prioritizes supplementing the target depth position and target functional group type with the largest deviation, thereby achieving accurate diagnosis and targeted supplementation of functional deficiencies and significantly improving the pertinence and effectiveness of microbial agent application.

[0164] This embodiment establishes a database of correspondences between initial basal area at breast height, logging time, Shannon index reference value, and Chao1 index reference value by introducing data from multiple control forests at different developmental levels. When determining the community recovery index, the control forest data that is closest to the initial basal area at breast height of the sampling area is selected as the reference benchmark. This solves the problem of differences in expected recovery of forests with different developmental backgrounds and improves the accuracy and comparability of the evaluation.

[0165] In this embodiment, after the control measures are implemented, a closed-loop feedback mechanism with a preset monitoring cycle is used to resample and detect and re-determine the control index. The intensity and type of subsequent control measures are dynamically adjusted according to the changing trend of the control index, thereby realizing dynamic adaptive control based on the microbial recovery status and ensuring that the recovery process always progresses along the desired trajectory.

[0166] The control system provided in this embodiment integrates sampling and detection, index calculation, model evaluation, measure generation, execution management and closed-loop feedback into a unified process through the coordinated operation of the data acquisition module, model evaluation module, decision-making module and execution and feedback module. It can implement precise, dynamic and hierarchical control based on the spatiotemporal restoration characteristics of soil microorganisms for the differentiated degradation state of forest land of different development levels after heavy logging, which greatly improves the scientificity and automation level of the restoration management of heavily logged forest land.

[0167] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent variations only. Individual components and functions are optional unless explicitly required, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed combinations. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0168] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to achieve the described functions, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the described devices, apparatuses, and units can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0169] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, function, and operation of implementations of apparatus, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based device that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for regulating heavily logged forest land based on soil microbial sampling data, characterized in that, Includes the following steps: S1. Select the sampling area, collect soil samples at different soil depths, and obtain soil microbial sampling data at each depth after performing microbial testing. S2. Determine the logging index and community recovery index of the sampling area; S3. Based on the soil microbial sampling data at each depth, the harvesting index, and the community recovery index, establish a soil microbial recovery evaluation model to determine the soil microbial health index and the regulation index of the sampling area; S4. Determine control measures based on the soil microbial health index and the control index; S5. Implement the aforementioned control measures.

2. The method according to claim 1, characterized in that, The logging index is determined through the following steps: The initial breast height sectional area of ​​the sampling area before logging and the current remaining breast height sectional area are obtained, and the breast height sectional area attenuation is obtained based on the difference between the two. Obtain the logging time limit of the sampling area from the time of logging to the current time; The forest development level of the sampling area before logging is determined based on the initial basal area at breast height, and the typical root distribution depth range of the area is determined based on the forest development level. Using the attenuation of the basal area at breast height, the logging time limit, and the typical distribution depth range as input parameters, the logging index is obtained through a preset root system influence attenuation model. The root system influence attenuation model includes a combined spatiotemporal relationship in which the attenuation of the basal area at breast height increases with the extension of the logging time limit, and the influence on soil microorganisms decreases from the typical root distribution depth range to the shallow and deep layers. The logging index represents the degree of influence of root aging and decay caused by logging on the current microbial state at different soil depths.

3. The method according to claim 2, characterized in that, The community recovery index is determined through the following steps: The Shannon index and Chao1 index of the microbial community were obtained from soil microbial sampling data at different soil depths. The Shannon index and Chao1 index of the current sampling area are compared with the reference values ​​of Shannon index and Chao1 index of the control forest land to obtain the Shannon recovery rate and Chao1 recovery rate. The community recovery rate is calculated based on a weighted combination of the Shannon recovery rate and the Chao1 recovery rate. Obtain the logging time limit of the sampling area from the logging occurrence to the current time, and fit the community restoration degree with the logging time limit to obtain the community restoration time limit; The community recovery index is determined based on the ratio of the community recovery time to the expected natural recovery time of the control forest. The community recovery index represents the recovery progress of the current microbial community relative to its undisturbed state over time. The control forest is a forest with similar site conditions to the sampling area and a logging degree lower than a preset heavy logging threshold.

4. The method according to claim 3, characterized in that, The number of control forest lands is multiple, and each control forest land has a different initial basal area at breast height and a corresponding pre-harvest forest development level; The method further includes the following steps: The reference values ​​of Shannon Index and Chao1 Index for each of the control forest lands were obtained, as well as the expected natural recovery time for each of the control forest lands. Based on data from multiple control forest lands, a database of correspondences was established between the initial height-to-breast-area, the logging time limit, the Shannon index reference value, and the Chao1 index reference value. When determining the community recovery index of the sampling area, the control forest data that is closest to the initial height-to-breast-area of ​​the sampling area is selected from the corresponding relationship database as a reference benchmark.

5. The method according to claim 4, characterized in that, The soil microbial health index and the regulation index are determined through the following steps: A multiple regression evaluation model was constructed with the logging index and the community recovery index as independent variables and the soil microbial health index as the dependent variable. The actual detection values ​​of each microbial index in the soil microbial sampling data at each depth are compared with the expected recovery values ​​determined based on the harvesting index and the community recovery index to obtain the depth deviation of each microbial index. The soil microbial health index is obtained by weighting and fusing the depth deviations at each depth location; The regulation index is calculated based on the difference between the soil microbial health index and the preset health threshold, and the difference between the community recovery index and the preset recovery threshold. The regulation index represents the degree to which the sampling area currently requires human intervention to restore soil microorganisms.

6. The method according to claim 5, characterized in that, The microbial indicators in the soil microbial sampling data include microbial biomass, diversity index, and relative abundance of key functional groups; the key functional groups include nitrogen-fixing bacteria, phosphate-solubilizing bacteria, and arbuscular mycorrhizal fungi; the diversity index includes the Shannon index and the Chao1 index.

7. The method according to claim 6, characterized in that, The control measures determined in S4 include: When the regulation index is higher than the preset strong intervention threshold, a first regulation measure is generated with the application of organic fertilizer and inoculation of microbial agents as the first priority. When the regulation index is between the preset weak intervention threshold and the strong intervention threshold, a second regulation measure with the application of biochar and the replanting of nitrogen-fixing tree species as the first priority is generated. When the regulation index is lower than or equal to the weak intervention threshold, a third regulation measure with enclosure and management as the first priority is generated; The combination of microbial strains in the first control measure is determined based on the depth deviation between the relative abundance of key functional groups at each depth position and the corresponding expected recovery value.

8. The method according to claim 7, characterized in that, After S5 implements the control measures, after a preset monitoring cycle, soil samples at each depth are collected again and microbial tests are performed to re-determine the soil microbial health index and the control index. If the re-determined control index is still higher than the preset weak intervention threshold, supplementary control measures are generated and implemented.

9. A control system for heavily logged forest land based on soil microbial sampling data, characterized in that, include: The data acquisition module is used to acquire soil microbial sampling data at different soil depths in the sampling area; The model evaluation module is used to determine the logging index and community recovery index of the sampling area. Based on the soil microbial sampling data at each depth, the logging index and the community recovery index, a soil microbial recovery evaluation model is established to determine the soil microbial health index and the regulation index of the sampling area. The decision-making module is used to determine control measures based on the soil microbial health index and the control index; The execution and feedback module is used to execute the control measures and, after execution, trigger the data acquisition module to reacquire data to re-determine the control index. If the control index does not fall to the target range, the decision module generates supplementary control measures.

Images