A method for evaluating carbon sequestration in mixed forests based on coupling of tree leaf functional traits

Abstract

The present invention relates to the technical field of forest ecology, and provides a mixed forest carbon sequestration evaluation method based on the coupling of tree leaf functional traits, comprising the following steps: determining community structure indicators in an evaluation area and determining the main tree species; obtaining leaf samples of each main tree species in the evaluation area; measuring the physical and chemical parameters of the leaf samples to calculate the significance and cumulative effect value of interspecific heterogeneity; setting a first area, a second area, and a third area with different felling gradients; obtaining evaluation factors of the physical and chemical parameters in each area, and calibrating the evaluation factors to finally obtain evaluation results. The beneficial effects achieved by the present invention are: considering the differences in stand structure and functional traits of the main tree species in the mixed forest at the same time, and combining the felling gradient control experiment, the problem of not including the tree species mixing degree and felling treatment when coupling leaf functional traits with forest carbon sequestration is solved. While reducing the workload, the accuracy and efficiency of mixed forest carbon sequestration evaluation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of forest ecology, and more specifically to a mixed forest carbon sequestration evaluation method based on tree leaf functional traits. Background Art

[0002] In exploring the contribution of forest ecosystems to the global carbon cycle, a carbon sequestration assessment method based on the coupling of tree leaf functional traits is gaining increasing attention. This method involves a detailed analysis of key functional traits of tree leaves, including but not limited to parameters such as stomatal conductance, chlorophyll content, and leaf thickness, as well as the interactions between these traits, the so-called coupling relationships, to accurately assess the carbon sequestration potential of trees or forests.

[0003] In the field of climate change ecology, the assessment of forest carbon sequestration potential has become a hot topic of research, especially in the management of mixed forests. In recent years, ecologists have begun to try to use weighted average values ​​of traits for quantitative analysis and to build models that can predict and evaluate the carbon sequestration capacity of trees or forests. However, current models mainly consider the variation of traits among species. The consideration of the impact of different management treatments on traits still mainly relies on empirical models and lacks systematic quantitative analysis.

[0004] Therefore, realizing carbon sequestration evaluation of mixed forest management plots based on controlled experiments and statistical analysis is a technical problem that technicians in this field need to solve urgently. Summary of the invention

[0005] To this end, the present invention provides a mixed forest carbon sequestration evaluation method based on the coupling of tree leaf functional traits that at least solves some of the above technical problems. The method analyzes the differences in leaf functional traits that can affect and indicate carbon sequestration capacity between different felling treatments and species, comprehensively considers the effects of management measures and community structure on trait variation, and provides a scientific basis for the optimization of forest carbon sequestration models.

[0006] One aspect of the present invention provides a method comprising the following steps:

[0007] S1. Determine the main tree species after measuring the community structure indicators in the evaluation area based on the second-class forest survey data in the evaluation area;

[0008] S2, in the first area, the second area, and the third area formed by different tending and felling intensities in the evaluation area, setting verification areas for the first area, the second area, and the third area, and obtaining leaf samples of each of the main tree species;

[0009] The first area is set as a light felling area; the second area is set as a moderate felling area, and the third area is a heavy felling area; the felling degrees of the first area, the second area, and the third area are divided by setting two groups of reference thresholds, the tending and felling intensity lower than the first reference threshold is the heavy felling area, the tending and felling intensity lower than the second reference threshold is the light felling area, and the area between the two is the moderate felling area, and the first reference threshold and the second reference threshold are both determined according to the ratio of the breast height cross-sectional area before and after felling in the sample plot;

[0010] The verification area includes at least one blank control group where no felling is performed;

[0011] S3, measuring the physicochemical parameters of the leaf sample, including a first physicochemical parameter, a second physicochemical parameter, a third physicochemical parameter, and a fourth physicochemical parameter;

[0012] S4. Calculate the significance and cumulative effect value of the overall heterogeneity of the first region, the second region, and the third region according to the physical and chemical parameters;

[0013] S5. Obtain evaluation factors of the main tree species in the first area, the second area, and the third area for each physical and chemical parameter, and simultaneously calibrate the evaluation factors through the verification areas of the first area, the second area, and the third area to obtain evaluation results after calibration.

[0014] As a preferred embodiment, when setting the second area and the third area in S2, the following steps are also included:

[0015] The second area is set as an intersection area of ​​a preset ratio between two adjacent first areas, and the preset ratio is determined according to a quantized ratio of tending and felling intensities between the two first areas;

[0016] The third area is set as an interval area of ​​the non-adjacent first area, and the tending and felling intensity of the interval area is set within a preset range.

[0017] As a preferred method, determining the main tree species in S1 specifically includes the following steps:

[0018] S11. Determine community structure indicators including species abundance, species frequency and basal area at breast height;

[0019] S12, obtaining the top 10 tree species ranked by importance value in the community structure index as the main tree species;

[0020] The calculation method of the importance value is as follows:

[0021] The important values ​​are the average of the three indicators: chest height to cross-sectional area ratio, relative abundance, and relative frequency;

[0022] in,

[0023] The basal area ratio at breast height is the percentage of the basal area at breast height of the species to the total basal area at breast height of the stand;

[0024] Relative abundance is the percentage of the number of individuals of a species to the total number of individuals in the stand;

[0025] Relative frequency is the percentage of the frequency of a species occurring in a sample plot to the total number of samples.

[0026] As a preferred embodiment, S2 includes the following steps:

[0027] S21. When selecting sample plots with different tending and felling intensities, it is necessary to simultaneously select control sample plots with similar forest stand growth conditions as verification areas for the first area, the second area, and the third area;

[0028] S22. Select five sample trees in each of the main tree species whose tree height, crown width, growth condition and other indicators are closest to the average level;

[0029] S23. During the growing season, secondary branches are collected at 1 / 4 of the canopy height of each sample tree, and the branch cut ends are inserted into clean water for underwater pruning to maintain the water potential inside the duct and prevent the leaves from losing water;

[0030] S24. Select 5 healthy, undamaged leaf samples from the secondary branches.

[0031] As a preferred embodiment, the step of measuring the physical and chemical parameters of the leaf sample in S3 specifically includes the following steps:

[0032] S31. The Li-6400 portable photosynthesis measurement system was used for measurement. The preset conditions were set to the standard light intensity provided by the red and blue light sources, the measurement room temperature was 25°C, the light intensity was about 1500 μmol / (m2·s), the air flow rate was 500 μmol / s, the mixing fan speed was 10000 r / min, the carbon dioxide gradient was 100, 50, 0, 200, 400, 600, 800, 1000, 1200, 1500, 1800, 2000 μmol / mol, and the induction time was 30 min.

[0033] S32. After the data is exported, the measured light response data is fitted according to the FvCB model, and the first physicochemical parameter, the second physicochemical parameter, the third physicochemical parameter and the fourth physicochemical parameter are calculated, which are the maximum carboxylation rate, the maximum electron transfer rate, the stomatal conductance, and the net photosynthetic rate, respectively.

[0034] As a preferred embodiment, in step S32, the FvCB model is in the form of formula a:

[0035]

[0036] Where A is the net photosynthetic rate; V c is the maximum carboxylation rate; C i is the intercellular carbon dioxide concentration; ∗ is the photorespiration compensation point; K c and K o is the Michaelis constant of CO2 and O2; O2 is the intercellular oxygen concentration; J is the electron transfer rate; R d is the respiration rate of mitochondria under light.

[0037] As a preferred embodiment, the evaluation in S4 specifically includes:

[0038] S41, in the first region, the second region and the third region, calculating the effect value y of the first physicochemical parameter, the second physicochemical parameter, the third physicochemical parameter and the fourth physicochemical parameter of each species i and intraspecific differences i , such as formula b and c:

[0039]

[0040]

[0041] where Y e is the mean of leaf functional traits of the treatment group; Y c is the mean of leaf functional traits of the control group; S e is the standard deviation of leaf functional traits of the treatment group; S c is the standard deviation of leaf functional traits in the control group; N e is the species abundance of the treatment group; N c is the species abundance of the control group;

[0042] S42. In the first region, based on the mixed effect model, use the restricted maximum likelihood method, with species as the categorical variable, to calculate the effect value y obtained in S41. i and intraspecific differences i Perform fitting and calculate the cumulative effect within groups.

[0043] As a preferred method, the S42 further includes the following steps:

[0044] Taking species as categorical variables, the mixed effect model was used to calculate the significance of intra-group heterogeneity of different species on the carbon sequestration capacity of mixed forests, i.e., p1 value, as well as the inter-species differences, and the intra-group cumulative effect was obtained:

[0045] If p1<0.05, there is significant heterogeneity among species;

[0046] If p1>0.05, the effect values ​​among species are relatively consistent;

[0047] If the cumulative effect within the group is positive, harvesting has a promoting effect on the traits of the species;

[0048] If the cumulative effect within the group is negative, harvesting has an inhibitory effect on the traits of the species.

[0049] As a preferred embodiment, when the calibration of the evaluation factor is performed through the second area and the third area in S5, it specifically includes:

[0050] S51, the calibration step in the second area includes:

[0051] Taking the spatial position of the continuous distribution of tree species formed by any physical and chemical parameter in the second area as a reference boundary, obtaining multiple sampling points at intervals within a preset distance of the reference boundary; taking each of the sampling points as a reference point, setting a calibration sampling area with the reference point as the center point;

[0052] In the verification area of ​​the second area, setting a calibration sampling area using the same physicochemical parameters, and obtaining sampling points having the same physicochemical parameters;

[0053] Calculate and obtain the first, second, third and fourth physicochemical parameter ratios;

[0054] The first physicochemical parameter ratio represents the intra-group cumulative effect in the second region.

[0055] The second, third and fourth physicochemical parameter ratios are calculated in the same manner as the first physicochemical parameter ratio.

[0056] The intra-group cumulative effect is a quantitative value at the same felling intensity under the same physical and chemical parameters.

[0057] Calculating the variances of the first physicochemical parameter ratio, the second physicochemical parameter ratio, the third physicochemical parameter ratio, and the fourth physicochemical parameter ratio, and writing the variance calibration into the cumulative effect values ​​of the corresponding physicochemical parameters;

[0058] S52, the calibration step in the third area includes:

[0059] Taking the third area as a reference area, performing the same calibration steps as those for the second area on the third area;

[0060] Then, the steps performed for the second area are continued with the adjacent area of ​​the third area as a reference, and the steps are terminated when the reference area is the first area;

[0061] Finally, the accumulated calibration variance is written into the cumulative effect value of the physicochemical parameter.

[0062] As a preferred method, the step S52 further includes the following steps: taking the felling gradient as a categorical variable, calculating the significance of the overall heterogeneity of different felling intensities on the carbon sequestration capacity of mixed forests under different felling gradients through a mixed effect model, that is, the p2 value, the inter-group effect value and the cumulative effect value, to evaluate the impact of felling activities on the carbon sequestration capacity of mixed forests:

[0063] If p2<0.05, there is significant heterogeneity between groups;

[0064] If p2>0.05, the effect sizes between groups are relatively consistent;

[0065] If the cumulative effect value is positive, harvesting has a promoting effect on the trait;

[0066] If the cumulative effect value is negative, harvesting has an inhibitory effect on the trait;

[0067] When this step is completed, an adjacent area of ​​the first area is selected as the calibration reference area, and the calibration reference area is set to perform the same calibration steps as the second area and the third area in sequence. If after calibration, the ratio of the significance and cumulative effect value of the overall heterogeneity of the calibration reference area and the first area is still within the range of the initial calculation, the calibration is completed and the result evaluation is performed; otherwise, the sampling point is replaced and re-sampling is performed.

[0068] The above technical solution of the present invention has the following advantages compared with the prior art:

[0069] The study found the significance and cumulative effect values: when the significance between species or between treatment groups is less than 0.05, it means that there is significant heterogeneity in the leaf functional traits between species or between treatment groups, and explanatory variables can be introduced to explain the trait variation; when the significance between species or between treatment groups is greater than 0.05, it means that there is homogeneity in the leaf functional traits between species or between treatment groups, and species composition or logging treatment has no effect on trait variation; when the cumulative effect value is positive, it means that the leaf functional traits of the forest increase after logging measures, and logging has a promoting effect on the traits; when the cumulative effect value is negative, it means that the leaf functional traits of the forest decrease after logging measures, and logging has an inhibitory effect on the traits.

[0070] The prediction of leaf functional traits is closely related to the community species composition, which reflects the ecosystem functional pattern of leaf functional traits. Leaf functional traits at the community scale are affected by different forest management measures, and their patterns may vary. Understanding and predicting this variation is helpful for the prediction of leaf functional traits and the evaluation of forest carbon sequestration capacity.

[0071] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a mixed forest carbon sequestration evaluation method based on the coupling relationship of tree leaf functional traits. The technical effect achieved is that the present invention has developed a new mixed forest carbon sequestration evaluation method based on the coupling relationship of leaf functional traits and community structure indicators. It can be used to quantitatively analyze the variation of leaf functional traits of mixed forests under different forest management measures. It solves the technical problem that the traditional trait community weighted average calculation method cannot be systematically quantitatively analyzed, which is fast, effective, convenient and practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A schematic diagram of the process provided by the present invention;

[0073] Figure 2 A schematic diagram of the path of the coupling relationship of leaf functional traits provided by the present invention;

[0074] Figure 3 A schematic diagram of the maximum carboxylation rate effect value analysis results provided by the present invention;

[0075] Figure 4 A schematic diagram of the maximum electron transfer rate effect value analysis result provided by the present invention;

[0076] Figure 5 A schematic diagram of the analysis results of the stomatal conductance effect value provided by the present invention;

[0077] Figure 6 This is a schematic diagram of the analysis results of the net photosynthetic carbon fixation rate effect value provided by the present invention. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] Embodiment 1

[0080] This embodiment provides a mixed forest carbon sequestration evaluation method based on leaf functional trait coupling, such as Figure 1 As shown, the following steps are included:

[0081] S1. Determine the main tree species after measuring the community structure indicators in the evaluation area based on the second-class forest survey data in the evaluation area;

[0082] S2, in the first area, the second area, and the third area formed by different tending and felling intensities in the evaluation area, setting verification areas for the first area, the second area, and the third area, and obtaining leaf samples of each of the main tree species;

[0083] The first area is set as a light felling area; the second area is set as a medium felling area, and the third area is a heavy felling area.

[0084] The felling degree division of the first area, the second area and the third area is determined according to the ratio of the cross-sectional area at breast height before and after felling in the sample plot; in addition, it should be noted in the embodiment of the present disclosure that, in the setting of the first area, the second area and the third area, the light, moderate and heavy levels are designed in a step-by-step manner, and the specific parameters are to set two groups of reference thresholds. The tending and felling intensity lower than the first reference threshold is the heavy felling area, the tending and felling intensity lower than the second reference threshold is the light felling area, and the area between the two is the moderate felling area.

[0085] In the embodiment of the present disclosure, the interval of the first reference threshold is set to 0.15-0.3, and the interval of the second reference threshold is set to 0.75-0.9. The first reference threshold and the second reference threshold represent the ratio of the cross-sectional area at breast height of the existing tree species after felling to that before felling. The higher the ratio, the fewer trees have been felled, and vice versa.

[0086] The verification area includes at least one blank control group where no felling is performed;

[0087] S3, measuring the physicochemical parameters of the leaf sample, including a first physicochemical parameter, a second physicochemical parameter, a third physicochemical parameter, and a fourth physicochemical parameter;

[0088] S4. Calculate the significance and cumulative effect value of the overall heterogeneity of the first region, the second region, and the third region according to the physical and chemical parameters;

[0089] S5. Obtain evaluation factors of the main tree species in the first area, the second area, and the third area for each physical and chemical parameter, and simultaneously calibrate the evaluation factors through the verification areas of the first area, the second area, and the third area to obtain evaluation results after calibration.

[0090] As a preferred embodiment, when setting the second area and the third area in S2, the following steps are also included:

[0091] The second area is set as an intersection area of ​​a preset ratio between two adjacent first areas, and the preset ratio is determined according to a quantized ratio of tending and felling intensities between the two first areas;

[0092] The third area is set as an interval area of ​​the non-adjacent first area, and the tending and felling intensity of the interval area is set within a preset range.

[0093] As a preferred method, determining the main tree species in S1 specifically includes the following steps:

[0094] S11. Determine community structure indicators including species abundance, species frequency and basal area at breast height;

[0095] S12, obtaining the top 10 tree species ranked by importance value in the community structure index as the main tree species;

[0096] The calculation method of the importance value is as follows:

[0097] The important values ​​are the average of the three indicators: chest height to cross-sectional area ratio, relative abundance, and relative frequency;

[0098] in,

[0099] The basal area ratio at breast height is the percentage of the basal area at breast height of the species to the total basal area at breast height of the stand;

[0100] Relative abundance is the percentage of the number of individuals of a species to the total number of individuals in the stand;

[0101] Relative frequency is the percentage of the frequency of a species occurring in a sample plot to the total number of samples.

[0102] As a preferred embodiment, S2 includes the following steps:

[0103] S21. When selecting sample plots with different tending and felling intensities, it is necessary to simultaneously select control sample plots with similar forest stand growth conditions as verification areas for the first area, the second area, and the third area;

[0104] S22. Select five sample trees in each of the main tree species whose tree height, crown width, growth condition and other indicators are closest to the average level;

[0105] S23. During the growing season, secondary branches are collected at 1 / 4 of the canopy height of each sample tree, and the branch cut ends are inserted into clean water for underwater pruning to maintain the water potential inside the duct and prevent the leaves from losing water;

[0106] S24. Select 5 healthy, undamaged leaf samples from the secondary branches.

[0107] As a preferred embodiment, the step of measuring the physical and chemical parameters of the leaf sample in S3 specifically includes the following steps:

[0108] S31. The Li-6400 portable photosynthesis measurement system was used for measurement. The preset conditions were set to the standard light intensity provided by the red and blue light sources, the measurement room temperature was 25°C, the light intensity was about 1500 μmol / (m2·s), the air flow rate was 500 μmol / s, the mixing fan speed was 10000 r / min, the carbon dioxide gradient was 100, 50, 0, 200, 400, 600, 800, 1000, 1200, 1500, 1800, 2000 μmol / mol, and the induction time was 30 min.

[0109] S32. After the data is exported, the measured light response data is fitted according to the FvCB model, and the first physicochemical parameter, the second physicochemical parameter, the third physicochemical parameter and the fourth physicochemical parameter are calculated, which are the maximum carboxylation rate, the maximum electron transfer rate, the stomatal conductance, and the net photosynthetic rate, respectively.

[0110] As a preferred embodiment, in step S32, the FvCB model is in the form of formula a:

[0111]

[0112] Where A is the net photosynthetic rate; V c is the maximum carboxylation rate; C i is the intercellular carbon dioxide concentration; ∗ is the photorespiration compensation point; K c and K o is the Michaelis constant of CO2 and O2; O2 is the intercellular oxygen concentration; J is the electron transfer rate; R d is the respiration rate of mitochondria under light.

[0113] As a preferred embodiment, the evaluation in S4 specifically includes:

[0114] S41, in the first region, the second region and the third region, calculating the effect value y of the first physicochemical parameter, the second physicochemical parameter, the third physicochemical parameter and the fourth physicochemical parameter of each species i and intraspecific differences i , such as formula b and c:

[0115]

[0116]

[0117] where Y e is the mean of leaf functional traits of the treatment group; Y c is the mean of leaf functional traits of the control group; S e is the standard deviation of leaf functional traits of the treatment group; S cis the standard deviation of leaf functional traits in the control group; N e is the species abundance of the treatment group; N c is the species abundance of the control group;

[0118] S42. In the first region, based on the mixed effect model, use the restricted maximum likelihood method, with species as the categorical variable, to calculate the effect value y obtained in S41. i and intraspecific differences i Perform fitting and calculate the cumulative effect within groups.

[0119] As a preferred method, the S42 further includes the following steps:

[0120] Taking species as categorical variables, the mixed effect model was used to calculate the significance of intra-group heterogeneity of different species on the carbon sequestration capacity of mixed forests, i.e., p1 value, as well as the inter-species differences, and the intra-group cumulative effect was obtained:

[0121] If p1<0.05, there is significant heterogeneity among species;

[0122] If p1>0.05, the effect values ​​among species are relatively consistent;

[0123] If the cumulative effect within the group is positive, harvesting has a promoting effect on the traits of the species;

[0124] If the cumulative effect within the group is negative, harvesting has an inhibitory effect on the traits of the species.

[0125] As a preferred embodiment, when the calibration of the evaluation factor is performed through the second area and the third area in S5, it specifically includes:

[0126] S51, the calibration step in the second area includes:

[0127] Taking the spatial position of the continuous distribution of tree species formed by any physical and chemical parameter in the second area as a reference boundary, obtaining multiple sampling points at intervals within a preset distance of the reference boundary; taking each of the sampling points as a reference point, setting a calibration sampling area with the reference point as the center point;

[0128] In the verification area of ​​the second area, setting a calibration sampling area using the same physicochemical parameters, and obtaining sampling points having the same physicochemical parameters;

[0129] Calculate and obtain the first, second, third and fourth physicochemical parameter ratios;

[0130] The first physicochemical parameter ratio represents the intra-group cumulative effect in the second region.

[0131] The second, third and fourth physicochemical parameter ratios are calculated in the same manner as the first physicochemical parameter ratio.

[0132] The intra-group cumulative effect is a quantitative value at the same felling intensity under the same physical and chemical parameters.

[0133] Calculating the variances of the first physicochemical parameter ratio, the second physicochemical parameter ratio, the third physicochemical parameter ratio, and the fourth physicochemical parameter ratio, and writing the variance calibration into the cumulative effect values ​​of the corresponding physicochemical parameters;

[0134] S52, the calibration step in the third area includes:

[0135] Taking the third area as a reference area, performing the same calibration steps as those for the second area on the third area;

[0136] Then, the steps performed for the second area are continued with the adjacent area of ​​the third area as a reference, and the steps are terminated when the reference area is the first area;

[0137] Finally, the accumulated calibration variance is written into the cumulative effect value of the physicochemical parameter.

[0138] As a preferred method, the step S52 further includes the following steps: taking the felling gradient as a categorical variable, calculating the significance of the overall heterogeneity of different felling intensities on the carbon sequestration capacity of mixed forests under different felling gradients through a mixed effect model, that is, the p2 value, the inter-group effect value and the cumulative effect value, to evaluate the impact of felling activities on the carbon sequestration capacity of mixed forests:

[0139] If p2<0.05, there is significant heterogeneity between groups;

[0140] If p2>0.05, the effect sizes between groups are relatively consistent;

[0141] If the cumulative effect value is positive, harvesting has a promoting effect on the trait;

[0142] If the cumulative effect value is negative, harvesting has an inhibitory effect on the trait;

[0143] When this step is completed, an adjacent area of ​​the first area is selected as the calibration reference area, and the calibration reference area is set to perform the same calibration steps as the second area and the third area in sequence. If after calibration, the ratio of the significance and cumulative effect value of the overall heterogeneity of the calibration reference area and the first area is still within the range of the initial calculation, the calibration is completed and the result evaluation is performed; otherwise, the sampling point is replaced and re-sampling is performed.

[0144] Embodiment 2

[0145] The disclosed embodiment is a specific implementation method based on the first embodiment and taking the forest land belonging to the Jiaohe Forestry Experimental Area Administration of Jilin Province as an example.

[0146] The study site is located in the forest land of the Jiaohe Forestry Experimental Zone Management Bureau of Jilin Province, which is located in the Zhangguangcai Mountains of the Changbai Mountain System and covers a large area of ​​undisturbed temperate natural forest. Due to the variation of climate environment and geographical conditions, the vegetation types in the Changbai Mountain System vary with the altitude gradient. From bottom to top, they are: deciduous broad-leaved forest belt (below 500m above sea level), coniferous and broad-leaved mixed forest belt (500–1100m), dark coniferous forest belt (1100–1700m), birch forest belt (1700–2100m) and alpine tundra belt (above 2100m).

[0147] Specifically, the forest type in the study area is a typical coniferous and broad-leaved mixed forest, with an average altitude of 459 meters. The region belongs to a temperate continental mountain climate affected by the monsoon, with an average annual temperature of 3.5°C, an average temperature of 21.7°C in July, and precipitation mainly concentrated in July and August, with an average annual precipitation of 695.9mm. The soil type is mountain dark brown forest soil, with a soil layer thickness of 20–80cm.

[0148] A method for evaluating carbon sequestration in mixed forests based on coupling of tree leaf functional traits, such as Figure 1-Figure 6 The following steps are shown:

[0149] Step 1: According to the regional forest category II survey data, measure the community structure indicators of the sample plot, define the importance value ranking of each species in the mixed forest, and select the top 10 tree species in the importance value ranking as the main tree species to be measured. See Table 1 for the main tree species.

[0150] Table 1

[0151]

[0152] It mainly includes 10 tree species. The total cross-sectional area of ​​the forest stand is 32.61m 2 / ha, the five tree species with the largest cross-sectional area at breast height are: Fraxinus mandshurica (6.44m 2 / ha)、Tilia amurensis(5.50m 2 / ha)、Acer monocotyledon(3.69m 2 / ha)、Juglans mandshurica(3.45m 2 / ha) and Quercus mongolica (1.01m 2 / ha), the sum of the basal areas at breast height of these five tree species accounted for 66.92% of the total basal area. The total number of trees in the plot was 1511, and the species abundances were: 375 Acer chromatica, 146 Tilia amurensis, 61 Fraxinus mandshurica, 80 Acer truncatum, 38 Juglans mandshurica, 53 Pinus koraiensis, 17 Quercus mongolica, 15 Amur corkwood, 20 Vitex montana, and 5 Betula platyphylla. The total number of quadrats in the plot was 400, and the species frequencies were: 199 Acer chromatica, 87 Tilia amurensis, 52 Fraxinus mandshurica, 61 Acer truncatum, 34 Juglans mandshurica, 43 Pinus koraiensis, 16 Quercus mongolica, 13 Amur corkwood, 18 Vitex montana, and 5 Betula platyphylla. The top 10 tree species in terms of importance value (% share) are: Acer chromium 28.63, Tilia amurensis 16.09, Fraxinus mandshurica 12.27, Acer truncatum 7.54, Juglans mandshurica 7.20, Pinus koraiensis 5.76, Quercus mongolica 2.74, Phellodendron amurense 2.21, Vitex trichosanthis 2.05 and Betula platyphylla 1.07.

[0153] Step 2: Obtain leaves of the main tree species in different tending and felling intensity plots. The specific steps are as follows:

[0154] When selecting plots with different tending and felling intensities, also select control plots with similar forest stand growth conditions;

[0155] Select five sample trees in each species of major tree species whose tree height, crown width, growth condition and other indicators are closest to the average level;

[0156] During the growing season, secondary branches were collected at 1 / 4 of the canopy height of each sample tree, and the branch cut ends were inserted into clean water for underwater pruning to maintain the water potential inside the duct and prevent the leaves from losing water;

[0157] Select 5 healthy, undamaged leaves from the secondary branches;

[0158] Step 3: Determine the maximum carboxylation rate, maximum electron transfer rate, stomatal conductance and net photosynthetic carbon fixation rate of the leaves of the main tree species. The specific steps are as follows:

[0159] The Li-6400 portable photosynthesis measurement system was used for measurement. The preset conditions were set to the standard light intensity provided by the red and blue light sources, the measurement room temperature was 25°C, the light intensity was about 1500μmol / (m2·s), the air flow rate was 500μmol / s, the mixing fan speed was 10000r / min, the carbon dioxide gradient was 100, 50, 0, 200, 400, 600, 800, 1000, 1200, 1500, 1800, 2000μmol / mol, and the induction time was 30min.

[0160] After the data were exported, the measured light response data were fitted according to the FvCB model to calculate the maximum carboxylation rate, maximum electron transfer rate, stomatal conductance, and net photosynthetic rate;

[0161] Step 4: Combine the maximum carboxylation rate, maximum electron transfer rate, stomatal conductance and net photosynthetic carbon fixation rate with community structure indicators to conduct statistical analysis to obtain the impact of major tree species on the carbon fixation capacity of mixed forests. The specific steps are as follows:

[0162] For the felling treatment group and the control group, the effect values ​​y of the maximum carboxylation rate, maximum electron transfer rate, stomatal conductance and net photosynthetic carbon fixation rate were calculated. i and intraspecific differences i , such as formula b and c:

[0163]

[0164]

[0165] where Y e is the mean of leaf functional traits of the treatment group; Y c is the mean of leaf functional traits of the control group; S e is the standard deviation of leaf functional traits of the treatment group; S c is the standard deviation of leaf functional traits in the control group; N e is the species abundance of the treatment group; N c is the species abundance of the control group;

[0166] According to the mixed effect model, the restricted maximum likelihood method was used, with the harvest gradient as the categorical variable, and the effect value y i and intraspecific differences i Perform fitting;

[0167] The significance of the overall heterogeneity of the main tree species on the carbon sequestration capacity of mixed forests under different logging gradients, i.e., the p1 value, and the cumulative effect value were calculated to evaluate the impact of logging activities on the carbon sequestration capacity of mixed forests:

[0168] If p1<0.05, there is significant heterogeneity among species;

[0169] If p1>0.05, the effect values ​​among species are relatively consistent;

[0170] If the cumulative effect value is positive, harvesting has a promoting effect on the trait;

[0171] If the cumulative effect value is negative, harvesting has an inhibitory effect on the trait.

[0172] Step 5: Perform calibration on the validation areas of the low, medium, and high harvesting intensity groups, and then obtain the evaluation results. The specific steps are as follows:

[0173] Taking the spatial position of the continuous distribution of tree species formed by any physical and chemical parameter in the low felling intensity group as the reference boundary, a plurality of sampling points are obtained at intervals within a preset distance of the reference boundary; taking each sampling point as a reference point, a calibration sampling area with the reference point as the center point is set;

[0174] In the verification area of ​​the low felling intensity group, a calibration sampling area is set using the same physicochemical parameters, and sampling points with the same physicochemical parameters are obtained;

[0175] Calculate and obtain the maximum carboxylation rate, maximum electron transfer rate, stomatal conductance, and the ratio of physical and chemical parameters of net photosynthetic rate;

[0176] The ratio of physical and chemical parameters represents the cumulative effect within the low felling intensity group.

[0177] The cumulative effect within this group is the quantitative value at the same felling intensity under the same physical and chemical parameters.

[0178] Calculate the variance of the maximum carboxylation rate, the maximum electron transfer rate, the stomatal conductance, and the net photosynthetic rate, and calibrate the variance and write it into the cumulative effect value of the corresponding physical and chemical parameters;

[0179] Afterwards, the medium felling intensity group is used as the reference area, and the same calibration steps as the low felling intensity group are performed on it; and the calibration ends when the reference area becomes the high felling intensity group;

[0180] Finally, the accumulated calibration variance is written into the cumulative effect value of the physicochemical parameter.

[0181] Afterwards, taking the felling gradient as the categorical variable, the mixed effect model was used to calculate the significance of the overall heterogeneity of different felling intensities on the carbon sequestration capacity of mixed forests under different felling gradients, namely the p2 value, the inter-group effect value and the cumulative effect value, to evaluate the impact of felling activities on the carbon sequestration capacity of mixed forests:

[0182] If p2<0.05, there is significant heterogeneity between groups;

[0183] If p2>0.05, the effect sizes between groups are relatively consistent;

[0184] If the cumulative effect value is positive, harvesting has a promoting effect on the trait;

[0185] If the cumulative effect value is negative, harvesting has an inhibitory effect on the trait;

[0186] When this step is completed, an adjacent area of ​​the high felling intensity group is selected as the calibration reference area, and the calibration reference area is set to sequentially perform the same calibration steps as the low felling intensity group and the medium felling intensity group. If after calibration, the ratio of the significance and cumulative effect value of the overall heterogeneity of the calibration reference area and the low felling intensity group is still within the range of the initial calculation, then the calibration is completed and the result evaluation is performed, and the evaluation result is as follows;

[0187] The cumulative effect values ​​of leaf functional traits, species significance p1, and harvest gradient significance p2 are shown in the attached Figure 2 –5.

[0188] Among them, for the maximum carboxylation rate, the overall heterogeneity among species was significant, p1<0.05; the overall difference among treatment groups was not significant, p2>0.05; felling treatment had an inhibitory effect on the maximum carboxylation rate, and the cumulative effect value was negative;

[0189] Among them, for the maximum electron transfer rate, the overall heterogeneity among species was significant, p1<0.05; the overall difference among treatment groups was not significant, p2>0.05; the felling treatment had no significant promoting or inhibiting effect on the maximum electron transfer rate, and the cumulative effect value was no different from 0;

[0190] Among them, for stomatal conductance, the overall heterogeneity among species was significant, p1<0.05; the overall difference among treatment groups was not significant, p2>0.05; felling treatment had a significant promoting effect on stomatal conductance, and the cumulative effect value was positive;

[0191] Among them, for the net photosynthetic carbon fixation rate, the overall heterogeneity among species was significant, p1<0.05; the overall difference among treatment groups was not significant, p2>0.05; the felling treatment had no significant promoting or inhibiting effect on the net photosynthetic carbon fixation rate, and the cumulative effect value was no different from 0.

[0192] The results show that the mixed forest carbon sequestration evaluation method based on the coupling of tree leaf functional traits provided by the present invention solves the technical problem that the traditional trait community weighted average calculation method cannot systematically and quantitatively analyze the impact of management measures on functional traits, and provides a theoretical basis for the optimization of forest carbon sequestration models.

[0193] The flow chart and block diagram in the accompanying drawings show the possible architecture, effect and operation of the device, method and computer program product according to the embodiment of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or code, and the part of the module, program segment or code contains one or more executable instructions for realizing the logical effect of the specification. In some alternative implementations, the effect marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can be determined by the effect involved. Each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be realized by a dedicated hardware-based device that performs the effect or action of the specification, or can be realized by a combination of special-purpose hardware and computer instructions.

Claims

1. A mixed forest carbon sequestration evaluation method based on the coupling of tree leaf functional traits, characterized in that: The steps include: S1. Determine the main tree species after measuring the community structure indicators in the evaluation area based on the second-class forest survey data in the evaluation area; S2, in the first area, the second area, and the third area formed by different tending and felling intensities in the evaluation area, setting verification areas for the first area, the second area, and the third area, and obtaining leaf samples of each of the main tree species; The first area is set as a light felling area; the second area is set as a moderate felling area, and the third area is a heavy felling area; the felling degrees of the first area, the second area, and the third area are divided by setting two groups of reference thresholds, the tending and felling intensity lower than the first reference threshold is the heavy felling area, the tending and felling intensity higher than the second reference threshold is the light felling area, and the area between the two is the moderate felling area, and the first reference threshold and the second reference threshold are both determined according to the ratio of the breast height cross-sectional area before and after felling in the sample plot; The verification area includes at least one blank control group where no felling is performed; The S2 includes the following steps: S21. When selecting sample plots with different tending and felling intensities, it is necessary to simultaneously select control sample plots with similar forest stand growth conditions as verification areas for the first area, the second area, and the third area; S22. Select five sample trees of each major tree species whose tree height, crown width and growth condition are at the average level; S23. During the growing season, secondary branches are collected at 1 / 4 of the canopy height of each sample tree, and the branch cut ends are inserted into clean water for underwater pruning to maintain the water potential inside the duct and prevent the leaves from losing water; S24, select 5 healthy, undamaged leaf samples from the secondary branches; S3, using a photosynthesis measurement system to measure the physicochemical parameters of the leaf sample, fitting the measured light response data according to the FvCB model, and calculating the first physicochemical parameter, the second physicochemical parameter, the third physicochemical parameter and the fourth physicochemical parameter, which are respectively the maximum carboxylation rate, the maximum electron transfer rate, the stomatal conductance, and the net photosynthetic rate; S4. Calculate the significance and cumulative effect value of the overall heterogeneity of the first region, the second region, and the third region according to the physical and chemical parameters; The evaluation in S4 specifically includes: S41, in the first region, the second region and the third region, calculating the effect value y of the first physicochemical parameter, the second physicochemical parameter, the third physicochemical parameter and the fourth physicochemical parameter of each species i and intraspecific differences i , such as formula b and c: in Y e is the mean of leaf functional traits of the treatment groups; Y c is the mean of leaf functional traits of the control group; S e is the standard deviation of leaf functional traits of the treatment groups; S c is the standard deviation of leaf functional traits in the control group; N e is the species abundance of the treatment group; N c is the species abundance of the control group; S42. In the first region, based on the mixed effect model, use the restricted maximum likelihood method, with species as the categorical variable, to calculate the effect value y obtained in S41. i and intraspecific differences i Fitting is performed to calculate the cumulative effect within the group, that is, p 1 The values, as well as the inter-species differences, give the cumulative effect within the group: like p 1 <0.05, there is significant heterogeneity among species; like p 1 >0.05, the effect sizes among species are relatively consistent; If the cumulative effect within the group is positive, harvesting has a promoting effect on the traits of the species; If the cumulative effect within the group is negative, harvesting has an inhibitory effect on the traits of the species; S5. Obtain evaluation factors of the main tree species in the first area, the second area, and the third area for each physical and chemical parameter, and simultaneously calibrate the evaluation factors through the verification areas of the first area, the second area, and the third area to obtain evaluation results after calibration.

2. The method according to claim 1, characterized in that When setting the second area and the third area in S2, the following steps are also included: The second area is set as an intersection area of ​​a preset ratio between two adjacent first areas, and the preset ratio is determined according to a quantized ratio of tending and felling intensities between the two first areas; The third area is set as an interval area of ​​the non-adjacent first area, and the tending and felling intensity of the interval area is set within a preset range.

3. The method according to claim 2, characterized in that Determining the main tree species in S1 specifically includes the following steps: S11. Determine community structure indicators including species abundance, species frequency and basal area at breast height; S12, obtaining the top 10 tree species ranked by importance value in the community structure index as the main tree species; The calculation method of the importance value is as follows: The important values ​​are the average of the three indicators: chest height to cross-sectional area ratio, relative abundance, and relative frequency; in, The basal area ratio at breast height is the percentage of the basal area at breast height of the species to the total basal area at breast height of the stand; Relative abundance is the percentage of the number of individuals of a species to the total number of individuals in the stand; Relative frequency is the percentage of the frequency of a species occurring in a sample plot to the total number of samples.

4. The method according to claim 3, characterized in that The step of measuring the physical and chemical parameters of the leaf sample in S3 specifically includes the following steps: S31. The Li-6400 portable photosynthesis measurement system was used for measurement. The preset conditions were set to the standard light intensity provided by the red and blue light sources, the measurement room temperature was 25°C, the light intensity was 1500μmol / (m²·s), the air flow rate was 500μmol / s, the mixing fan speed was 10000r / min, the carbon dioxide gradient was 100, 50, 0, 200, 400, 600, 800, 1000, 1200, 1500, 1800, 2000μmol / mol, and the induction time was 30min. S32. After the data is exported, the measured light response data is fitted according to the FvCB model, and the first physicochemical parameter, the second physicochemical parameter, the third physicochemical parameter and the fourth physicochemical parameter are calculated, which are the maximum carboxylation rate, the maximum electron transfer rate, the stomatal conductance, and the net photosynthetic rate, respectively.

5. The method according to claim 4, characterized in that In S32, the FvCB model is in the form of formula a: in, A is the net photosynthetic rate; V c is the maximum carboxylation rate; C i is the intercellular carbon dioxide concentration; Γ ∗ It is the photorespiration compensation point; K c and K o is the Michaelis constant of CO2 and O2; O 2 is the intercellular oxygen concentration; J is the electron transfer rate; R d is the respiration rate of mitochondria under light.

6. The method according to claim 5, characterized in that When the calibration of the evaluation factor is performed via the second area and the third area in S5, it specifically includes: S51, the calibration step in the second area includes: Taking the spatial position of the continuous distribution of tree species formed by any physical and chemical parameter in the second area as a reference boundary, obtaining multiple sampling points at intervals within a preset distance of the reference boundary; taking each of the sampling points as a reference point, setting a calibration sampling area with the reference point as the center point; In the verification area of ​​the second area, setting a calibration sampling area using the same physicochemical parameters, and obtaining sampling points having the same physicochemical parameters; Calculate and obtain the first, second, third and fourth physicochemical parameter ratios; The first physicochemical parameter ratio represents the intra-group cumulative effect in the second region; The second, third and fourth physicochemical parameter ratios are calculated in the same manner as the first physicochemical parameter ratio; The cumulative effect within the group is a quantitative value at the same felling intensity under the same physical and chemical parameters; Calculating the variances of the first physicochemical parameter ratio, the second physicochemical parameter ratio, the third physicochemical parameter ratio, and the fourth physicochemical parameter ratio, and writing the variance calibration into the cumulative effect values ​​of the corresponding physicochemical parameters; S52, the calibration step in the third area includes: Taking the third area as a reference area, performing the same calibration steps as those for the second area on it; Then, the steps performed for the second area are continued with the adjacent area of ​​the third area as a reference, and the steps are terminated when the reference area is the first area; Finally, the accumulated calibration variance is written into the cumulative effect value of the physicochemical parameter.

7. The method according to claim 6, characterized in that The step S52 further includes the following steps: Taking felling gradient as categorical variable, the significance of overall heterogeneity of different felling intensities on carbon sequestration capacity of mixed forest under different felling gradients was calculated by mixed effect model, namely: p 2 The effect values, inter-group effect values ​​and cumulative effect values ​​were used to evaluate the impact of logging activities on the carbon sequestration capacity of mixed forests: like p 2 <0.05, there was significant heterogeneity between the groups; like p 2 >0.05, the effect sizes between groups were relatively consistent; If the cumulative effect value is positive, harvesting has a promoting effect on the trait; If the cumulative effect value is negative, harvesting has an inhibitory effect on the trait; When this step is finished, selecting an adjacent area of ​​the first area as a calibration reference area, and setting the calibration reference area to sequentially perform the same calibration steps as the second area and the third area; If after calibration, the significance and cumulative effect value ratio of the overall heterogeneity of the calibration reference area and the first area are still within the range of the initial calculation, the calibration is completed and the result evaluation is performed; otherwise, the sampling point is replaced and re-sampling is performed.

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