Mountain forest ecosystem multi-target operation condition evaluation and optimization method

Abstract

The invention discloses a mountain forest ecosystem multi-target operation condition evaluation and optimization method, which comprises the following steps: carrying out vegetation investigation and soil sampling on each sample plot, and respectively calculating ecosystem service indexes of each sample plot; classifying the sample plots according to forest ages, operation modes, vegetation types and environmental gradients; comparing the differences of various ecological system services under different forest ages, operation modes and vegetation types through statistical test, and analyzing the change trend and fitting relationship of various ecological system services along with the forest ages and environment gradients; calculating correlation coefficients between every two ecological system services, and analyzing changes of the correlation coefficients on different forest ages and different environment gradients; identifying key driving factors by using a machine learning method; and synthesizing the results to generate a multi-target operation strategy result. The problems that in the prior art, cognition of various ecological system services and complex relations thereof on the real mountain environment gradient is insufficient, and the operation strategy lacks quantitative basis are solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of forest ecological management and resource management, and specifically relates to a mountain forest ecosystem multi-objective management condition evaluation and optimization method. BACKGROUND

[0002] Mountain forest ecosystems provide humans with biodiversity maintenance, carbon sequestration, water conservation, soil conservation, and other ecosystem services. Existing research has focused on the evaluation of single vegetation types, management modes, or a small number of services, and has relied heavily on remote sensing or model simulation, lacking systematic analysis based on on-site multidimensional observation. The understanding of the variation of various ecosystem services of forests and their synergies and trade-offs at different forest ages (young, middle-aged, near-mature, mature, and over-mature forests), forest management modes (such as natural forests / artificial forests, pure forests / mixed forests, coniferous / broadleaved / needle-broadleaf), vegetation types, and environmental gradients (such as altitude, slope, and aspect) and their driving mechanisms is still insufficient, especially in the rugged mountain environmental gradient (such as altitude, slope, and aspect), resulting in a lack of practical reference guidelines for the implementation of diversified ecosystem service management of mountain forests, and the management measures are not strongly targeted, making it difficult to achieve multi-objective synergistic optimization. SUMMARY

[0003] The purpose of the present application is to provide a mountain forest ecosystem multi-objective management condition evaluation and optimization method, which solves the problem of insufficient understanding of various ecosystem services and their complex relationships in real mountain environmental gradients and the lack of quantitative basis for management strategies in the prior art.

[0004] The technical solution adopted by the present application is a mountain forest ecosystem multi-objective management condition evaluation and optimization method, comprising the following steps: Step 1, setting up survey plots and quadrats, conducting vegetation per-tree surveys and soil sampling, and calculating the ecosystem service indicators of each plot; Step 2, according to the survey results, classifying the plots by forest age, management mode, vegetation type, and environmental gradient; Step 3, comparing the differences in each ecosystem service under different forest ages, management modes, and vegetation types through statistical tests, and analyzing the trends and fitting relationships of each ecosystem service with forest age and environmental gradient; Step 4, calculating the correlation coefficients between each pair of ecosystem services, and analyzing the changes in these correlation coefficients at different forest ages and different environmental gradients; Step 5, using machine learning methods to identify key driving factors; Step 6, generating multi-objective management strategy results based on the results of steps 3-5.

[0005] The present application is characterized in that, Ecosystem service indicators include: species diversity, tree biomass, carbon storage, soil quality index, and soil water storage.

[0006] Species diversity is measured by species richness (R) and the Pielou evenness index (E), calculated as follows: Species richness: R=S; where S represents the total number of tree, shrub, and grass species within the quadrat; Pielou evenness index: ; Shannon-Wiener Index: ; In the formula, Pi represents relative importance, Pi = (relative height + relative coverage + relative frequency) / 3, and i represents the i-th species in the sample plot.

[0007] The tree growth is calculated as follows: B=aD 2 In the formula, B represents biomass (kg); D represents the actual measured diameter at breast height (DBH) of each tree trunk in the quadrat (cm); and a is the regression parameter. Carbon reserves are calculated as follows: Aboveground tree carbon storage = Tree trunk biomass × Trunk carbon content coefficient Soil organic carbon storage

[0008] C 总 = Carbon storage in tree trunks + Carbon storage in soil In the formula, Soil organic carbon storage, t / ha 2 , It is the first j Soil organic carbon content in the first 0-20cm layer, g / kg Soil bulk density, g / cm³ 3 , It is the first j The thickness of each soil layer is in cm, and 0.1 is the area conversion factor.

[0009] The formula for calculating soil water storage capacity is as follows: Po(%) = (Soil maximum water holding capacity % - Soil capillary water holding capacity %) × ρ / (1.0g) cm -3 )

[0010] In the formula, ρ represents soil bulk density, Si is forest area (ha), h is soil layer thickness (m), Po is non-capillary porosity (%), and W is soil water storage capacity (t / ha). The soil quality index is evaluated using principal component analysis to assess soil fertility quality.

[0011] Step 2 specifically involves: based on the survey results, systematically classifying the sample plots according to forest age, management model (forest origin, tree species composition, life form) and vegetation type, and classifying them according to key environmental gradients.

[0012] Key environmental gradients include: altitude, slope, and aspect.

[0013] In step 3, the classification results of steps 1 and 2 are tested for normality and homogeneity of variance. Multiple comparisons of the differences in various ecosystem services under different forest ages, management patterns and vegetation types are conducted using independent samples t-tests and one-way ANOVA. Linear fitting is used to fit the variation patterns of various ecosystem services with forest age and environmental gradient.

[0014] Step 4 specifically involves: Calculate the Pearson correlation coefficient matrix among all services across all sample plots as a whole, and calculate and compare the correlation coefficients within different forest ages and environmental gradient subsets.

[0015] Step 5 specifically involves selecting species composition, forest stand structure, ecological processes, and environmental elements as candidate driving factors. Random forest and relative importance machine learning methods are used to quantify the explanatory contribution of each driving factor to the variation of each ecosystem service and to identify key driving factors.

[0016] The beneficial effects of this invention are: The present invention provides a multi-objective assessment and optimization method for the management of mountain forest ecosystems. This method integrates and assesses various ecosystem services, environmental gradients, different forest ages, management models, and vegetation types simultaneously, overcoming the limitations of traditional methods that rely on single or indirect data sources. It analyzes the dependence of ecosystem services and their trade-offs / synergies on environmental gradients such as altitude and aspect; it quantitatively identifies the dominant driving factors affecting various services, providing a target for precise management; and based on the quantitative analysis results, it forms a differentiated and dynamic multi-objective management strategy system, significantly improving the scientific nature and ecological benefits of forest management. Attached Figure Description

[0017] Figure 1 This is the sample design drawing of the present invention; Figure 2 This invention is a comparative diagram of ecosystem service differences under different forest ages and vegetation types; Figure 3 This is a comparative chart of ecosystem service differences under different business models; Figure 4 It is a fitted graph showing the trend of ecosystem services changing with environmental gradients; Figure 5 It is a correlation diagram of the interrelationships of ecosystem services on the environmental gradient; Figure 6 This is a diagram analyzing the relative importance of four types of driving factors to various ecosystem services. Detailed Implementation

[0018] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0019] Example 1 The multi-objective management assessment and optimization method for mountain forest ecosystems of this invention consists of six interrelated steps forming a complete analysis-decision closed loop. The specific steps are as follows: Step 1: Set up survey plots and quadrats, conduct vegetation surveys and soil sampling, and calculate the ecosystem service indicators for each plot. Step 2: Based on the survey results, classify the sample plots according to forest age, management mode, vegetation type, and environmental gradient; Step 3: Compare the differences in various ecosystem services under different forest ages, management models and vegetation types through statistical tests, and analyze the changing trends and fitting relationships of various ecosystem services with forest age and environmental gradient; Through multiple comparisons and trend fitting, the static differences and dynamic evolution patterns of service supply were revealed.

[0020] Step 4, Service Trade-offs and Synergies Analysis: Calculate the correlation coefficients between each pair of ecosystem services and analyze the changes of these correlation coefficients at different forest ages and environmental gradients; Step 5: Use machine learning methods to identify key driving factors; Step 6: Combining the results of steps 3-5, a multi-objective business strategy is generated: The analysis results from steps 3-5 are integrated and transformed to form differentiated and actionable multi-objective forest management and optimization strategies tailored to different forest age stages, environmental gradients, and vegetation types. For example, for the scenario of "middle-aged forests, sunny slopes, and monoculture coniferous forests," the strategy might be: "Focus on replanting drought-resistant native broad-leaved tree species to improve stand structure, alleviate the trade-off between carbon storage and water retention, and improve soil quality." The final output includes management plan recommendations based on zoning, classification, and specific objectives, which can directly guide practice.

[0021] Example 2 Based on Example 1, step 1 of this invention involves setting up survey plots and quadrats according to standard methods, and conducting vegetation surveys and soil sampling. Multiple ecosystem service indicators, including species richness, evenness, tree biomass, carbon storage, soil water storage, and soil quality index, are calculated for each plot.

[0022] The survey data in this invention are based on standardized calculations performed using field sampling, ensuring the directness and reliability of the data and laying a solid foundation for subsequent analysis.

[0023] The field sampling survey methods are as follows: Taking typical mountain forests in western Sichuan, China as an example, 5-10 survey points were randomly set up according to the distribution of different vegetation types in the protected area, totaling 248 survey plots. The survey points should include as many vegetation types as possible in western Sichuan with different forest ages, management patterns (origin, tree species composition, life forms) and environmental gradients, in order to comprehensively and objectively reflect the current status of forest ecosystem services in western Sichuan.

[0024] The study quadrats were set up based on 20 m × 20 m research plots. Tree quadrats were the same size as the plots, 20 m × 20 m, totaling 248 tree quadrats. Shrub quadrats were 5 m × 5 m, with three replicates evenly spaced along the diagonal of each tree quadrat, totaling 248 × 3 shrub quadrats. Herbaceous quadrats were 1 m × 1 m, with three replicates evenly spaced along the other diagonal of each tree quadrat, totaling 248 × 3 herbaceous quadrats. Soil samples were collected from the 0–20 cm topsoil layer in the middle of the 5 m × 5 m shrub quadrats, totaling 284 × 3 samples. Soil physicochemical properties were measured indoors. Soil bulk density and moisture content were collected and measured: at the soil sampling profile, a 100 cm... 3 Two undisturbed soil samples were taken using a ring sampler for determining soil porosity, soil bulk density, and other parameters. The sample plot design is as follows: Figure 1 As shown.

[0025] In the field quadrat survey, the study first collected basic information about the quadrats, recording in detail the latitude, longitude, altitude, slope, aspect, vegetation type, and canopy closure. Tree survey: Each tree was monitored, recording canopy closure, species name, diameter at breast height (DBH), tree height, crown width, tree survival status, and pest and disease status. For trees, a DBH ≥ 5 cm was used as the baseline, and the diameter at breast height at 1.3 m above the ground was taken as the tree DBH. Shrub survey: Total shrub cover, species name, number of individual plants (clumps), and average shrub height (five plants of the same species were randomly selected, their heights measured, and the average was calculated). Herbaceous survey: Total herbaceous quadrat cover, species name, number of individual plants (clumps), and average herbaceous height (five plants of the same species were randomly selected, their heights measured, and the average was calculated).

[0026] The following are calculations of various ecosystem service indicators: 1) Species diversity Based on actual plot surveys, species diversity is measured using species richness (R) and Pielou evenness (E), calculated using the following formulas: R=S(1) E=H / LnS(2) (3) Pi = (relative height + relative coverage + relative frequency) / 3 (4) Where S represents the total number of tree, shrub, and grass species in the quadrat, Pi represents relative importance, H represents the Shannon-Wiener index, Pi represents relative importance, and i represents the i-th species in the quadrat.

[0027] 2) Tree biomass B=aD 2 (5) In the formula, B represents biomass (kg); D represents the actual measured diameter at breast height (DBH) of each tree trunk in the quadrat (cm); and a is the regression parameter, with specific values ​​referenced in the book "Carbon Storage-Biomass Equation of Chinese Forest Ecosystems".

[0028] 3) Carbon storage Forest carbon storage is characterized by the sum of aboveground tree trunk carbon storage and soil organic carbon storage. Tree trunk carbon storage is estimated by multiplying forest biomass by a carbon content coefficient, which is set to a fixed value of 0.50. Forest underground soil carbon storage is calculated based on soil bulk density, organic carbon content, soil layer thickness, and forest stand area, using the following formula: Carbon storage of tree trunks above ground = Tree trunk biomass × 0.5 (carbon content coefficient of trunk) (6) (7) C 总 = Carbon storage of tree trunks above ground + Organic carbon storage of soil (8) In the formula, Soil organic carbon storage, t / ha 2 , It is the first j Soil organic carbon content in the first 0-20cm layer, g / kg Soil bulk density, g / cm³ 3 , It is the first j The thickness of each soil layer is in cm, and 0.1 is the area conversion factor.

[0029] 4) Soil water storage capacity Forest water storage capacity consists of two parts: the water-holding capacity of aboveground vegetation and the water-holding capacity of underground soil. However, due to the significant manpower and resources required for field measurements of aboveground vegetation water holding capacity, coupled with the large sample size, soil saturation water storage capacity, which reflects the soil's potential for water storage and regulation, is chosen as the indicator for forest water storage capacity. The calculation formula is as follows: Po(%) = (Soil maximum water holding capacity % - Soil capillary water holding capacity %) × ρ / (1.0g) cm -3 (9) (10) In the formula, ρ represents soil bulk density, Si is forest area (ha); h is soil layer thickness (m); Po is non-capillary porosity (%); and W is soil water storage capacity (t / ha). 5) Soil quality index The Soil Quality Index (SQI) is an effective tool for assessing soil quality. Principal component analysis was used to assess the fertility quality of forest soils in the study area.

[0030] Example 3 Based on Example 2, Step 2 specifically involves: according to the survey results, systematically classifying the sample plots according to forest age, management mode (forest origin, tree species composition, life form) and vegetation type, and classifying them according to key environmental gradients, as shown in Table 1 below.

[0031] Table 1. Classification by vegetation type, management model, and environmental gradient

[0032] The vegetation types include nine categories: artificial evergreen coniferous pure forest, artificial broad-leaved deciduous pure forest, artificial coniferous and broad-leaved mixed forest, artificial coniferous mixed forest, natural evergreen coniferous pure forest, natural broad-leaved deciduous pure forest, natural broad-leaved and deciduous mixed forest, natural coniferous and broad-leaved mixed forest, and natural coniferous mixed forest.

[0033] Forests are classified into natural forests and plantations according to their origin; into pure forests and mixed forests according to their tree species composition; and into coniferous forests, broad-leaved forests, and coniferous-broad-leaved forests according to the leaf life of the dominant tree species.

[0034] Based on the survey results, the forests were classified according to age, altitude, slope, and aspect. Altitude was determined using an equidistant method, establishing six gradients: <1600m, 1600-2000m, 2000-2400m, 2400-2800m, 2800m-3200m, and >3200m. Slope was classified according to forestry classification standards into five levels: ≤5°, 5-15°, 16-25°, 26-35°, and >36°. Slope aspect was classified into four levels: shaded slope (0-45° and 315-360°), semi-shaded slope (45-135°), semi-sunny slope (225-315°), and sunny slope (135-225°).

[0035] Determination of forest age: The study first measured the diameter at breast height (DBH) of trees in the research plots. Then, by analyzing the average annual growth of the corresponding tree species, the growth factors of those species were identified, and the forest age was calculated. Finally, based on the origin of the plots, the age of the dominant tree species, and the age classification table of major tree species in the "Technical Regulations for Forest Resource Planning and Design Survey" (GB / T26424—2010), the surveyed plots were divided into five age groups: young forests, middle-aged forests, near-mature forests, mature forests, and over-mature forests.

[0036] In step 2, both natural gradients and human management models were considered to construct an analytical framework that comprehensively reflects the heterogeneity of mountain forests.

[0037] Example 4 Based on Example 3, in step 3, the classification results of steps 1 and 2 are tested for normality and homogeneity of variance. Multiple comparisons of forest ecosystem services under different forest ages, origins, tree species composition, life forms and vegetation types are conducted using independent samples t-test and one-way ANOVA. Linear or nonlinear regression models are used to fit the variation patterns of various ecosystem services with mountain spatiotemporal gradients such as forest age, altitude, slope and aspect.

[0038] In step 4, the Pearson correlation coefficient matrix among various services across all sample plots is calculated. Then, the correlation coefficients are calculated separately for different age groups and spatiotemporal gradient subsets such as different elevation zones, and the changes in these relationships are revealed through comparison.

[0039] Example 5 Based on Example 4, step 5 specifically includes: Indicators such as species composition, stand structure, ecological processes, and environmental factors were selected as candidate driving factors. Random forest and relative importance machine learning methods were used to quantify the explanatory contribution of each driving factor to the variation of each ecosystem service and to identify key driving factors.

[0040] Table 2 Vegetation Survey and Environmental Elements Table

[0041] Candidate driving factors are shown in Table 2 above. Species composition includes species richness (R), proportion of native tree species (PNT), proportion of invasive alien species (PIAS), and rare and endemic species (R&E). Stand structure includes canopy density (CD), tree density (TD), maximum tree height (MHT), maximum tree diameter at breast height (AHT), maximum shrub height (MHS), shrub cover (SP), and stand age (Age). Ecological processes include mortality (M), soil erosion degree (DSE), soil carbon-nitrogen ratio (C / N), base area (BA), and bare land (BG). Environmental factors included elevation (HH), slope (S), aspect (A), and distance from the sampling point to the nearest road (DIS). Machine learning methods such as random forest and relative importance were used to quantify the explanatory contribution of each driving factor to the variation of each ecosystem service and to identify key driving factors.

[0042] By delving deeper into the underlying mechanisms (driving forces) from the surface (services) to identify key regulatory factors, management measures can be targeted and effective.

[0043] Example 6 Taking a typical mountain forest in western Sichuan, China as an example, the implementation steps of the method described in this invention are as follows: Step 1: Set up survey plots and quadrats, conduct vegetation surveys and soil sampling, and calculate the ecosystem service indicators for each plot. Based on the distribution of different vegetation types in the protected area, 5-10 survey points were randomly set up, totaling 248 survey plots. The survey points should, as far as possible, include multiple vegetation types of different forest ages, management patterns (origin, tree species composition, life forms), and environmental gradients in western Sichuan, in order to comprehensively and objectively reflect the current status of forest ecosystem services in western Sichuan.

[0044] Survey plots and quadrats were set up according to standard methods, and vegetation surveys and soil sampling were conducted for each tree. Species richness, evenness, tree biomass, carbon storage, soil water storage, and soil quality index were calculated for each plot.

[0045] The study quadrats were set up based on 20 m × 20 m research plots. Tree quadrats were the same size as the plots, 20 m × 20 m, totaling 248 tree quadrats. Shrub quadrats were 5 m × 5 m, with three replicates evenly spaced along the diagonal of each tree quadrat, totaling 248 × 3 shrub quadrats. Herbaceous quadrats were 1 m × 1 m, with three replicates evenly spaced along the other diagonal of each tree quadrat, totaling 248 × 3 herbaceous quadrats. Soil samples were collected from the 0–20 cm topsoil layer in the middle of the 5 m × 5 m shrub quadrats, totaling 284 × 3 samples. Soil physicochemical properties were measured indoors. Soil bulk density and moisture content were collected and measured: at the soil sampling profile, a 100 cm... 3 Two undisturbed soil samples were taken using a ring sampler for determining soil porosity, soil bulk density, and other parameters. The sample plot design is as follows: Figure 1 As shown.

[0046] In the field quadrat survey, the study first collected basic information about the quadrats, recording in detail the latitude, longitude, altitude, slope, aspect, vegetation type, and canopy closure. Tree survey: Each tree was monitored, recording canopy closure, species name, diameter at breast height (DBH), tree height, crown width, tree survival status, and pest and disease status. For trees, a DBH ≥ 5 cm was used as the baseline, and the diameter at breast height at 1.3 m above the ground was taken as the tree DBH. Shrub survey: Total shrub cover, species name, number of individual plants (clumps), and average shrub height (five plants of the same species were randomly selected, their heights measured, and the average was calculated). Herbaceous survey: Total herbaceous quadrat cover, species name, number of individual plants (clumps), and average herbaceous height (five plants of the same species were randomly selected, their heights measured, and the average was calculated).

[0047] Step 2: Construction of the classification system: Based on the survey records, the sample plots were classified into grades according to forest age group (young, middle, near-mature, mature, overmature), altitude zone (every 400m interval), slope grade (≤5°, etc.) and aspect class (shady, semi-shady, semi-sunny, sunny); at the same time, they were systematically classified according to origin (natural / artificial), tree species composition (pure / mixed), life form (coniferous / broadleaf / coniferous-broadleaf) and vegetation type.

[0048] The vegetation types in this invention include nine types: artificial evergreen coniferous pure forest, artificial broad-leaved deciduous pure forest, artificial coniferous and broad-leaved mixed forest, artificial coniferous mixed forest, natural evergreen coniferous pure forest, natural broad-leaved deciduous pure forest, natural broad-leaved and deciduous mixed forest, natural coniferous and broad-leaved mixed forest, and natural coniferous mixed forest.

[0049] Step 3, Data Analysis: The classification results of steps 1 and 2 were tested for normality and homogeneity of variance. Independent samples t-tests and one-way ANOVA were used to compare the differences in various ecosystem services under different forest ages, management models, and vegetation types. The comparison of ecosystem service differences under different forest ages and vegetation types is as follows: Figure 2 As shown, a comparison of ecosystem service differences under different business models is as follows: Figure 3 As shown. Linear or nonlinear regression models are used to analyze the changing trends of various services with continuous gradients such as forest age and altitude, such as... Figure 4 As shown.

[0050] Step 4, Relationship Resolution: The Pearson correlation strength among various services across all sample plots was calculated using SPSS, a professional statistical analysis software. The results are as follows: Figure 5 As shown in a. Furthermore, correlation coefficients were calculated for different age groups and gradient subsets at different altitudes, and changes in the relationships were revealed through comparison, such as... Figure 5 As shown in bd.

[0051] This indicates the salience of the trade-off-synergy between the two services. This indicates that P < 0.05. This indicates that P < 0.01; This indicates that P < 0.001. The strength of the relationship is judged by the magnitude of the correlation coefficient. A correlation coefficient above 0.7 indicates a very strong relationship; a correlation coefficient between 0.4 and 0.7 indicates a strong relationship; and a correlation coefficient between 0.2 and 0.4 indicates a moderate relationship.

[0052] Step 5, Driver Recognition: Species composition, stand structure, ecological processes, and environmental factors (such as elevation and aspect) were selected as candidate driving factors. Machine learning methods, such as random forest, were used to quantify the explanatory contribution of each driving factor to the variation of each ecosystem service, and key driving factors were identified, such as... Figure 6 As shown.

[0053] Step 6, Strategy Generation and Output: Based on the results of steps 3-5, a structured management strategy table is generated. For example, for the scenario of "middle-aged forests, sunny slopes, and monoculture coniferous forests," the strategy might be: "Focus on replanting drought-resistant native broad-leaved tree species to improve stand structure, alleviate the trade-off between carbon storage and water retention, and improve soil quality." The final output includes management plan recommendations based on zoning, classification, and objectives.

[0054] In step 6, the classification results from step 2 and the analysis results from step 3 are used as the basis for the classification results. Figure 2 , Figure 3 and Figure 4 This leads to the conclusion that: differentiated management based on forest age and vegetation type should be implemented; and near-natural forest management models should be promoted. Step 4 involves correlation coefficient analysis. Figure 5 It can be seen that forest spatial allocation is optimized based on mountain environmental gradient. Step 5 is the driving factor. Figure 6 It can be seen that service synergy is promoted through structural adjustment and key process regulation.

[0055] Based on a multi-objective business strategy, management units are divided, and differentiated business plans are formulated: 1) Implement differentiated management based on forest age and vegetation type. Strengthen the protection and regeneration of native tree species in natural broad-leaved forests and mixed coniferous and broad-leaved forests during the middle-aged to mature forest stage to enhance biodiversity, carbon storage and soil quality; optimize the structure of plantations through thinning and replanting of native tree species to enhance their comprehensive service functions.

[0056] 2) Promote near-natural forest management models, introduce native tree species into artificial pure forests, and gradually form mixed tree species and uneven-aged stands; in new afforestation or renovation projects, give priority to designing mixed coniferous and broad-leaved or mixed broad-leaved models to improve species richness, carbon sequestration and soil conservation.

[0057] 3) Optimize forest spatial configuration based on mountain environmental gradients. For example, at altitudes of 2000–2400m, prioritize the protection or restoration of stands with high species richness; at altitudes >2400m, focus on protecting stands with high tree biomass and carbon storage; at altitudes of 1600–2000m, emphasize the maintenance of soil water retention capacity, and appropriately retain or configure vegetation types with high soil water retention capacity, such as PBDP or PCBM. On shady and semi-shady slopes, prioritize the protection or development of stands with high carbon storage, biomass, and soil quality (such as natural coniferous and broad-leaved mixed forests). On sunny and semi-sunny slopes, prioritize the configuration of drought-resistant tree species or stands with strong water retention capacity, taking into account both soil water retention capacity and ecological stability. Within a slope range of 16–25°, multi-objective management can be carried out to synergistically improve multiple services. In steep slopes >35°, focus on soil and water conservation and ecological protection, strictly restrict logging, and implement forest closure for natural regeneration.

[0058] 4) Promoting service synergy through structural adjustment and key process regulation: Adjusting forest stand structure by thinning, replanting, and other measures to increase tree density, maximum tree height and diameter at breast height, and increasing shrub cover, thereby synergistically improving carbon storage, biomass, and soil quality. Regulating key ecological processes by protecting understory vegetation and litter layer, controlling invasive alien species, and promoting the regeneration of native tree species enhances the ecosystem's resilience to disturbance.

[0059] The analysis of sample plots in western Sichuan using the method of this invention showed that: The method of this invention successfully quantifies the advantages and disadvantages of different management models: natural forests are significantly superior to plantations in terms of biomass, carbon storage, and species richness, while plantations have higher soil water retention, such as... Figure 3 As shown, the functional complementarity and trade-offs between the modes are clearly defined.

[0060] The environmental dependence patterns are clearly revealed: tree biomass is strongly positively correlated with altitude, and species richness shows a unimodal relationship with altitude, providing a direct basis for altitude-based zoned management, such as... Figure 4 .

[0061] Key driving factors were accurately identified: for example, such as Figure 6 As shown, 82.6% of the variation in the soil quality index can be explained by both stand structure and environmental factors, with soil carbon-nitrogen ratio and altitude being key factors. This service can be improved by soil improvement and site consideration.

[0062] The generated strategies are targeted: for example, based on the finding that carbon storage and water storage are more balanced at lower altitudes, the strategies recommend specific tree species configurations in the region to balance the two, thus improving the precision of management measures.

[0063] Example 7: In a nature reserve, the multi-objective management assessment and optimization method for mountain forest ecosystems of this invention was applied. Steps 1-5 revealed that middle-aged natural mixed coniferous and broad-leaved forests (NCBM) in this region exhibited outstanding performance in carbon storage and biomass, but the trade-off between carbon storage and soil water retention intensified with increasing slope. Step 6 generated the following strategy: In NCBM stands with a slope >25°, conservation management was implemented, clear-cutting was prohibited, light thinning was adopted, and deep-rooted shrubs were replanted to enhance water retention, thereby achieving synergy between carbon sequestration and water conservation.

[0064] Example 8: In a certain artificial forest farm, the multi-objective management status assessment and optimization method for mountain forest ecosystems of this invention was applied. Analysis revealed that the soil quality index of mature artificial coniferous pure stands (PECP) was low and showed a positive correlation with stand age. Driving analysis indicated that the soil carbon-nitrogen ratio was a key limiting factor. Step 6: Generation strategy: Implement near-natural transformation of this type of forest stand, replant native broad-leaved tree species with strong nitrogen-fixing capacity at forest gaps, and retain litter to gradually improve the soil carbon-nitrogen ratio and overall soil quality.

[0065] Example 9: Comprehensive regional analysis shows that in overmature forests on shady slopes at altitudes of 2000-2400m, multiple ecosystem services exhibit high synergy. Step 6 generates the strategy: designate this type of area as an ecological protection core zone, prioritizing natural succession and strictly limiting human interference, serving as an anchor point for the supply of regional ecosystem services.

[0066] The method for multi-objective management status assessment and optimization of mountain forest ecosystems of the present invention has the following advantages: This approach integrates and evaluates various ecosystem services, environmental gradients, and different management models simultaneously, considering multiple dimensions of services, environment, and management to avoid biased decision-making. It breaks through the limitations of traditional single-dimensional or indirect data sources, analyzes the dependence of ecosystem services and their trade-offs / synergies on key environmental gradients such as forest age, altitude, and aspect, quantitatively identifies the dominant driving factors affecting various services, and provides targeted solutions for precision management. Based on the quantitative analysis results, a differentiated and dynamic multi-objective management strategy system is formed, significantly improving the scientific nature and ecological benefits of forest management.

Claims

1. A method for assessing and optimizing the multi-objective management status of mountain forest ecosystems, characterized in that, Includes the following steps: Step 1: Conduct vegetation surveys and soil sampling at each plot, and calculate the ecosystem service indicators for each plot. Step 2: Classify the sample plots according to forest age, management model, vegetation type, and environmental gradient; Step 3: Compare the differences in various ecosystem services under different forest ages, management models and vegetation types through statistical tests, and analyze the changing trends and fitting relationships of various ecosystem services with forest age and environmental gradient; Step 4: Calculate the correlation coefficients between each pair of ecosystem services and analyze the changes in these correlation coefficients at different forest ages and environmental gradients; Step 5: Use machine learning methods to identify key driving factors; Step 6: Combine the results of steps 3-5 to generate a multi-objective business strategy result.

2. The method for multi-objective management status assessment and optimization of mountain forest ecosystems according to claim 1, characterized in that, The ecosystem service indicators include: species diversity, tree biomass, carbon storage, soil quality index, and soil water storage.

3. The method for multi-objective management status assessment and optimization of mountain forest ecosystems according to claim 2, characterized in that, The species diversity is measured by species richness R and Pielou evenness index E, and is calculated as follows: Species richness: R=S; where S represents the total number of tree, shrub, and grass species within the quadrat; Pielou evenness index: ; Shannon-Wiener Index: ; In the formula, Pi represents relative importance, Pi = (relative height + relative coverage + relative frequency) / 3, and i represents the i-th species in the sample plot.

4. The method for multi-objective management status assessment and optimization of mountain forest ecosystems according to claim 2, characterized in that, The tree biomass was calculated as follows: B=aD 2 In the formula, B represents biomass (kg); D represents the actual measured diameter at breast height (DBH) of each tree trunk in the quadrat (cm); and a is the regression parameter. The carbon storage is calculated as follows: Aboveground tree carbon storage = Tree trunk biomass × Trunk carbon content coefficient Soil organic carbon storage C 总 = Carbon storage of tree trunks above ground + Carbon storage of soil In the formula, Soil organic carbon storage, t / ha 2 , It is the first j Soil organic carbon content in the first 0-20cm layer, g / kg Soil bulk density, g / cm³ 3 , It is the first j The thickness of each soil layer is in cm, and 0.1 is the area conversion factor.

5. The method for multi-objective management status assessment and optimization of mountain forest ecosystems according to claim 2, characterized in that, The formula for calculating soil water storage capacity is as follows: Po(%) = (Soil maximum water holding capacity % - Soil capillary water holding capacity %) × ρ / (1.0g) cm -3 ) In the formula, ρ represents soil bulk density, Si is forest area (ha), h is soil layer thickness (m), Po is non-capillary porosity (%), and W is soil water storage capacity (t / ha). The soil quality index is evaluated using principal component analysis to assess soil fertility quality.

6. The method for multi-objective management status assessment and optimization of mountain forest ecosystems according to claim 1, characterized in that, Step 2 specifically involves: based on the survey results, systematically classifying the sample plots according to forest age, management model, and vegetation type, and classifying them into levels according to key environmental gradients; The management model includes forest origin, tree species composition, and lifestyle.

7. The method for assessing and optimizing the multi-objective management status of mountain forest ecosystems according to claim 1, characterized in that, The environmental gradient includes: forest age, altitude, slope, and aspect.

8. The method for assessing and optimizing the multi-objective management status of mountain forest ecosystems according to claim 1, characterized in that, In step 3, the classification results of steps 1 and 2 are tested for normality and homogeneity of variance. Multiple comparisons of the differences in various ecosystem services under different forest ages, management patterns and vegetation types are conducted using independent samples t-tests and one-way ANOVA. Linear fitting is used to fit the variation patterns of various ecosystem services with forest age and environmental gradient.

9. The method for assessing and optimizing the multi-objective management status of mountain forest ecosystems according to claim 1, characterized in that, Step 4 specifically involves: Calculate the Pearson correlation coefficient matrix among all services across all sample plots as a whole, and calculate and compare the correlation coefficients within different forest ages and environmental gradient subsets.

10. The method for assessing and optimizing the multi-objective management status of mountain forest ecosystems according to claim 1, characterized in that, Step 5 specifically involves selecting species composition, forest stand structure, ecological processes, and environmental factor indicators as candidate driving factors, using random forest and relative importance machine learning methods to quantify the explanatory contribution of each driving factor to the variation of each ecosystem service, and identifying key driving factors.

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