A method for quantifying the cooperative promotion of diversity and carbon sink of a picea crassifolia and sabina przewalskii community

Abstract

The present application relates to the technical field of forest multifunctional management, and discloses a kind of quantification management method for synergistically improving the diversity and carbon sink of Qinghai spruce and Qilian juniper community.The method is used to solve the problems of lack of synergistic quantification scheme for biodiversity conservation and carbon sink improvement and inaccurate optimization of spatial structure.The method selects full-mixed degree, size ratio and angular scale to construct a spatial structure comprehensive index, uses multivariate quadratic regression to establish biodiversity and carbon sink models, and determines the synergistic optimal parameters through multi-objective optimization: full-mixed degree 0.5830, size ratio 0.8621 and angular scale 0.4420.Under the constraints of tree species number, diameter class number and harvesting intensity, the method selects and harvests trees to carry out quantification thinning simulation.The method improves the spatial structure comprehensive index of the forest stand by 2.61% to 21.30%, the carbon sink function index reaches 237130.40, and the biodiversity comprehensive index reaches 0.9157, thereby providing a standardized quantification scheme for the multifunctional management of forests on the Qinghai-Tibet Plateau.

Description

Technical Field

[0001] This invention relates to the interdisciplinary fields of forest ecological management, biodiversity conservation, and carbon sequestration enhancement, specifically to a quantitative management method for the synergistic enhancement of biodiversity and carbon sequestration in Qinghai spruce and Qilian juniper communities. Background Technology

[0002] Forest biodiversity conservation and carbon sequestration enhancement are core ecosystem services, but the synergistic optimization of these two functions has lacked quantitative technical support. Existing research often focuses on one function in isolation, leading to management measures that are incomplete or misguided—either emphasizing carbon sequestration while neglecting species protection, or stressing diversity while reducing carbon sequestration efficiency.

[0003] The Qinghai-Tibet Plateau serves as a crucial ecological barrier for my country, with Qinghai spruce and Qilian juniper being dominant species. However, their communities face challenges such as irrational spatial structure, intense interspecific competition, and poor functional synergy. Existing management techniques suffer from three major shortcomings: 1. The spatial structure description is fragmented and lacks comprehensive quantitative indicators that integrate full mixing degree, size ratio, and angular scale; 2. The relationship between biodiversity and carbon sinks has not been quantified, and the optimal structural threshold for their synergistic effect cannot be clearly defined; 3. The selective logging measures lack precise constraints, the selection of felled trees is highly subjective, and it is difficult to achieve structure-oriented functional optimization.

[0004] While international research on forest spatial structure exists, a collaborative management plan tailored to the unique climate and vegetation characteristics of the Qinghai-Tibet Plateau is lacking. Domestic research largely focuses on correlation analysis, failing to establish a complete quantitative system encompassing "structure-function-management." Therefore, there is an urgent need to develop a quantitative management technology for the collaborative enhancement of Qinghai spruce and Qilian juniper communities. Summary of the Invention

[0005] (a) Purpose of the invention To address the problems of insufficient spatial structure quantification, poor functional synergy, and imprecise management measures in existing technologies, this invention provides a quantitative management method for the synergistic enhancement of biodiversity and carbon sequestration in Qinghai spruce and Qilian juniper communities. This method achieves a closed loop of "spatial structure optimization - dual-functional synergy - quantitative selective logging," providing a scientific basis for the multifunctional management of forests on the Qinghai-Tibet Plateau.

[0006] (II) Technical Solution The core technical solution of this invention includes four major modules: spatial structure parameter construction, single-function model establishment, cooperative optimal structure determination, and quantitative selective felling simulation, as detailed below: 1. Construction of stand spatial structure parameters Using full mixing degree (reflecting species isolation), size ratio (reflecting interspecific competition), and angular scale (reflecting spatial distribution pattern) as core parameters, the four-tree method was adopted to divide the spatial structure units. A comprehensive spatial structure index Q(x) was constructed using multiplication and division, with the formula Q(x) = (M(x)+1) / [(U(x)+1)×|W(x)-0.5|], to achieve a comprehensive quantitative evaluation of the stand's spatial structure.

[0007] 2. Establishment of a Single Dominant Function Regression Model Biodiversity composite index: The CRITIC method is used to integrate the Gini coefficient, Shannon-Wiener index, Pielou evenness index, and Simpson index to comprehensively reflect the level of community species diversity. Carbon sink function index: Based on tree species biomass equation (Qinghai spruce W) a =0.14865×D 2 . 28906 Qilian Juniper W a =0.58×D 1 . 746 Biomass was calculated, and carbon storage was obtained by combining the corresponding carbon conversion coefficients (0.4905 aboveground and 0.488 belowground for Qinghai spruce; 0.483 aboveground and 0.4608 belowground for Qilian juniper). Regression model construction: Using spatial structure parameters as independent variables and biodiversity and carbon sink function index as dependent variables, a multiple quadratic regression model is established to identify the optimal spatial structure parameters for a single function.

[0008] 3. Determination of the optimal spatial structure for collaboration A multi-objective optimization model was adopted, with the maximum value of a single function as a constraint. The weights (P1, P2) of biodiversity and carbon sink function were calculated using the CRITIC method. The objective function was to minimize the sum of the deviation variables, and the optimal spatial structure parameters were obtained. For the Qinghai spruce and Qilian juniper communities, the optimal parameters were: full mixing degree 0.5830, size ratio 0.8621, and angular scale 0.4420. At this value, the comprehensive biodiversity index was 0.9157, and the carbon sink function index was 237130.40.

[0009] 4. Quantitative selective felling simulation With the goal of achieving a cooperative optimal spatial structure, four constraints were set: the number of tree species remained constant, the number of diameter classes remained constant, the harvesting intensity was ≤30%, and the harvesting amount was ≤ the growth amount. By selectively harvesting trees (prioritizing the harvesting of diseased and weak trees and individuals with low competitive advantage), the degree of mixed forest stands was increased, the size ratio was reduced, and the angular scale was made closer to a random distribution (0.5) after selective harvesting, thus gradually approaching the cooperative optimal spatial structure.

[0010] (III) Beneficial Effects 1. Significant functional synergy: Through coordinated optimal spatial structure regulation, biodiversity and carbon sequestration functions are simultaneously enhanced, resulting in a 15%-25% increase in the overall benefits of forest stands compared to single-function management; 2. Precise Quantification of Spatial Structure: The constructed comprehensive index and regression model can quantitatively describe the structure-function relationship, solving the subjectivity problem of traditional management and improving prediction accuracy R. 2 Reaching 0.4072-0.4374; 3. Controllable logging measures: Clearly define selective logging constraints and screening criteria, control logging intensity at 6.75%-7.94%, avoid over-logging, and ensure forest stand sustainability; 4. High adaptability: Designed specifically for the ecological characteristics of Qinghai spruce and Qilian juniper communities on the Qinghai-Tibet Plateau, it can be directly applied to forest areas within Qinghai Province. After parameter adjustments, it can be extended to similar coniferous forests in western China. 5. Standardized Operation: A standardized process of "data collection - structural analysis - model building - optimization decision-selective logging" has been established, which is convenient for forestry departments to promote and apply. Attached Figure Description

[0011] Figure 1. Research technology roadmap; Figure 2. Residual plot of the species diversity model of natural forests of Qinghai spruce and Qilian juniper: showing the fitting effect between the model predictions and the measured values; Figure 3. Residual diagram of carbon sink model of Qinghai spruce natural forest; Figure 4. Residual diagram of carbon sink model of natural forest of Juniperus chinensis in Qilian Mountains; Figure 5. Distribution of selective logging locations in natural Juniperus chinensis forest plots in Qilian Mountains; Figure 6. Distribution of selective logging locations in natural spruce forest plots in Qinghai. Detailed Implementation

[0012] (I) Overview of the test area 1. Natural Conditions: The experimental area is located in Qinghai Province (31°36′-39°19′N, 89°35′-103°04′E), with a plateau continental climate. The average annual temperature ranges from -5.1 to 9.0℃, and annual precipitation varies greatly regionally, with more precipitation in the east and drier precipitation in the west. The soil types are mainly brown coniferous forest soil and dark brown soil, with a pH value of 4.5-6.0 and an organic matter content of 8-15%.

[0013] 2. Forest Resources: The province has 3.0966 million hectares of forest land. Qinghai spruce and Qilian juniper are the dominant coniferous species, accounting for more than 78% of the total number of trees in the stands, accompanied by birch, aspen, and other species. The stands are mainly middle-aged forests (40-60 years old), with an average density of 800-1500 trees / hectare and an average diameter at breast height of 12.5-28.8 cm.

[0014] 3. Experimental plots: 123 natural forest plots of Qinghai spruce and 250 natural forest plots of Qilian juniper were selected, with a total area of ​​0.04 hectares (20m×20m). There was no significant human or natural disturbance, and the mortality rate was <1%.

[0015] (II) Specific Implementation Steps 1. Data Collection Forest survey: Record the species, diameter at breast height (DBH) (accuracy 0.1cm), height, azimuth, and horizontal distance of each tree, and number and label the sample trees; Environmental survey: Collect soil samples (0-20cm, 20-40cm, and 40-60cm soil layers) to determine organic matter content and bulk density; collect plot climate data (temperature and precipitation). Data preprocessing: Excel and SPSS 26.0 were used to clean the data, remove outliers, and fill in missing values ​​(mean imputation of adjacent plots).

[0016] 2. Calculation of spatial structural parameters Core parameter calculation: Calculate the degree of mixing (M), size ratio (U), and angular scale (W) for each sample plot according to the formula described in claim 2. Comprehensive index calculation: The spatial structure comprehensive index Q(x) is calculated by Q(x) = (M(x)+1) / [(U(x)+1)×|W(x)-0.5|]. The average Q0 of the Qinghai spruce sample plot is 6.0624, and the average Q0 of the Qilian juniper sample plot is 7.5717.

[0017] 3. Construction of a Single Dominant Functional Model Biodiversity index calculation: According to the formula described in claim 3, four indicators, including the Gini coefficient and the Shannon-Wiener index, are calculated and integrated into a comprehensive biodiversity index using the CRITIC method; Carbon sink function index calculation: The biomass of a single tree is calculated using the corresponding tree species biomass equation, multiplied by the carbon conversion coefficient to obtain the carbon storage, and summarized as the carbon sink function index of the sample plot; Regression model fitting: A function-structure relationship model was constructed using multiple quadratic regression, and the model was fitted using R... 2The accuracy of the model was verified by RMSE, and the biodiversity and carbon sink regression model of Qinghai spruce and Qilian juniper was finally determined (as shown in claim 5).

[0018] 4. Solving for the optimal spatial structure through collaboration Determination of optimal parameters for a single function: With the constraint that another function reaches the average level and the spatial comprehensive index ≥ Q0, the optimal spatial structure for a single function is solved. The optimal parameters for a single function of Qinghai spruce are M=1, U=0, and W=0.34487. Multi-objective optimization solution: The multi-objective optimization model was run using Lingo software, and the cooperative optimal spatial structure parameters were calculated to be M=0.5830, U=0.8621, and W=0.4420.

[0019] 5. Quantitative selective felling simulation Constraints are set as follows: the number of tree species remains unchanged, the diameter class remains unchanged, the harvesting intensity is ≤30%, and the harvest amount is ≤ the growth amount; Timber selection and screening: Qinghai spruce sample plot: 5 selectively felled trees (3 Qilian juniper and 2 Qinghai spruce), with a felling intensity of 7.94%. After selective felling, M=0.3640 (increased by 13.40%), U=0.4838 (decreased by 0.0001%), and |W-0.5|=0.1250 (decreased by 14.85%). Qilian Juniper sample plot: 5 selectively felled trees (all Qilian Juniper) were selected, with a felling intensity of 6.75%. After selective felling, M=0 (unchanged), U=0.5063 (decreased by 0.61%), and |W-0.5|=0.0854 (decreased by 2.40%). Effect verification: After selective logging, the comprehensive index of spatial structure of the sample plots increased by 21.30% (Qinghai spruce) and 2.61% (Qilian juniper), respectively, and the carbon sink function index was significantly improved.

[0020] (III) Implementation Precautions 1. Data collection specifications: For diameter at breast height (DBH) measurement, a fixed measurement point must be set at 1.3m from the trunk, using a diameter measuring tape (accuracy 0.1cm); for azimuth and horizontal distance measurement, a GPS positioning instrument (accuracy ±5m) must be used. 2. Scope of application of the model: The regression model of this invention is applicable to natural forests of Qinghai spruce and Qilian juniper with an altitude of 700-1300m and a forest age of 40-60 years in Qinghai Province. For other areas, the parameters need to be recalibrated. 3. Requirements for logging operations: Prioritize logging of diseased or weak trees, slow-growing trees, and individuals with low competitive advantage, and avoid logging dominant trees and rare species; after logging, promptly clear branches and stack them along contour lines to prevent soil erosion; 4. Long-term monitoring: Conduct a sample plot resurvey every 2 years to update spatial structure parameters and functional indices, and adjust the next round of selective logging schemes based on the monitoring results to gradually approach the optimal spatial structure.

Claims

1. A quantitative management method for synergistically enhancing the biodiversity and carbon sequestration of Qinghai spruce and Qilian juniper communities, characterized in that, Includes the following steps: (1) Construction of stand spatial structure parameters: The degree of mixing (M), size ratio (U), and angle scale (W) were selected as core spatial structure parameters. The four-tree method was used to divide the spatial structure units and construct the stand spatial structure comprehensive index Q (x). (2) Establishment of a single dominant function regression model: Using the biodiversity comprehensive index and carbon sink function index as dependent variables and spatial structure parameters as independent variables, a multiple quadratic regression model is constructed to determine the optimal spatial structure parameters for a single function. (3) Determination of the optimal spatial structure for synergy: A multi-objective optimization model is adopted, with the maximum value of a single function as a constraint. The functional weights are calculated by combining the CRITIC weight method, and the optimal spatial structure parameters for synergy between biodiversity and carbon sink are obtained. (4) Quantitative selective logging simulation: With the goal of synergistic optimal spatial structure, the constraints of constant number of tree species, constant diameter class, logging intensity ≤30%, and logging amount ≤ growth amount are set to screen and select trees for logging and implement logging, and gradually optimize the stand spatial structure.

2. The quantitative management method according to claim 1, characterized in that, The calculation formulas for the spatial structure parameters mentioned in step (1) are as follows: Fully mixed degree (Mc i ): Mc i = [1 / 2]×(D i + n i / n)×M i = [M i / 2]×[1 - (1 / (n+1) 2 )×Σ(n j 2 (j from 1 to s) i ) + n i / n], where D i M is the Simpson index. i For simple mixing degree, n is the number of adjacent trees, s i n represents the number of tree species. i The number of neighboring trees of different tree species; Size ratio (U) i ): U i = (1 / n)×Σk ij (j ranges from 1 to n), where k ij The value is 0 (the diameter at breast height of the adjacent tree is less than that of the reference tree) or 1 (the diameter at breast height of the adjacent tree is not less than that of the reference tree). Angular scale (W) i ): W i = (1 / n)×Σz ij (j ranges from 1 to n), where z ij =1 (α angle < 72°) or 0 (α angle ≥ 72°); Spatial structure comprehensive index (Q (x)): Q (x) = (M (x)+1) / [(U (x)+1)×|W (x)-0.5|].

3. The quantitative management method according to claim 1, characterized in that, The biodiversity composite index mentioned in step (2) is obtained by integrating the Gini coefficient, Shannon-Wiener index, Pielou evenness index, and Simpson index using the CRITIC method. The calculation formulas for each index are as follows: Gini coefficient: GC = [Σ(2t-n-1)×ba t (t from 1 to n)] / [Σba t [(t from 1 to n) × (n-1)], where ba t The area of ​​the forest floor under the diameter class t; Shannon-Wiener index: H = -Σp i ×lnp i (i from 1 to s), where s is the number of species, p i The percentage of individuals of the i-th type; Pielou Evenness Index: J h = [-Σp i ×lnp i (i from 1 to s)] / lnS, where s is the number of species, p i The percentage of individuals of the i-th type; Simpson's index: D = 1 - Σp i 2 (j from 1 to s), where s is the number of species, p i Let represent the percentage of individuals of the i-th type.

4. The quantitative management method according to claim 1, characterized in that, The carbon sink function index mentioned in step (2) is calculated by the biomass-carbon conversion coefficient. The calculation formula is: C=W×BEF, where W is plant biomass and BEF is the conversion coefficient; the aboveground part of Qinghai spruce BEF=0.4905 and the underground part=0.488, and the aboveground part of Qilian juniper BEF=0.483 and the underground part=0.4608.

5. The quantitative management method according to claim 1, characterized in that, The specific multiple quadratic regression model mentioned in step (2) is as follows: Biodiversity model of natural spruce forests in Qinghai: Z1 = 0.66059 - 1.42773×U 2 + 5.04578×M 2 -0.04777×W 2 - 1.51163×M×W(R 2 =0.4374). Carbon sink model for natural spruce forests in Qinghai: Z2 = 230618 - 191568×U 2 + 230058×M 2 - 237106×W 2 - 92828×M×W(R 2 =0.4149). Carbon sink model for natural juniper forests in Qilian: Z2 = 211648 - 112474×U 2 - 1487556×M 2 - 292451×W 2 + 785457×M×W(R 2 =0.4072).

6. The quantitative management method according to claim 1, characterized in that, The multi-objective optimization model mentioned in step (3) is: f1(M,U,W) + d1 - - d1 + = Z1;f2(M,U,W) + d2 - - d2 + = Z2;MinZ = P1×(d1 - +d1 + ) + P2×(d2 - +d2 + ); Z1 and Z2 are the single maximum values ​​for biodiversity and carbon sequestration function, respectively. + d - P1 and P2 are the positive and negative deviation variables, respectively, and the functional weights are the optimal spatial structure parameters: M=0.5830, U=0.8621, and W=0.4420.

7. The quantitative management method according to claim 1, characterized in that, The constraints of the selective felling simulation in step (4) include: Number of tree species (N) (x) = Number of tree species before selective felling (N0); Diameter series (D (x) = Pre-selective cutting diameter order (D0); Post-selective logging full crossbreeding degree (M) (x) ) ≥ Pre-selective harvesting degree of full mixing (M0); Size ratio after selective felling (U) (x) ) ≤ Size ratio before selective felling (U0); | Selective felling angle scale (W) (x) -0.5|≤|Selective cutting angle scale (W0)-0.5|; The logging intensity is ≤30%, and the amount of logging is ≤ the amount of growth.

8. A quantitative management system for the synergistic improvement of Qinghai spruce and Qilian juniper communities, characterized in that, The method described in any one of claims 1-7 includes a data acquisition module, a spatial structure analysis module, a functional model construction module, a multi-objective optimization module, and a selective decision-making module. Data acquisition module: Collects data on tree species, diameter at breast height (DBH), azimuth, horizontal distance, soil, and climate of the sample plots; Spatial structure analysis module: calculates full mixing degree, size ratio, angular scale, and comprehensive spatial structure index; Functional model building module: Establishing a regression model of biodiversity and carbon sink function; Multi-objective optimization module: solves for cooperatively optimal spatial structure parameters; Logging Selection Module: Based on constraints, selects logging sites and outputs logging plans.

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