Urban and rural gradient plant community configuration method based on leaf functional character response model

Abstract

The invention discloses an urban and rural gradient plant community configuration method based on a leaf functional character response model, and the method comprises the steps: dividing a target research region into a plurality of gradient regions based on a plurality of preset indexes; collecting plant leaves of different plant types meeting a preset condition in the quadrat of each gradient region, and obtaining a leaf function character index of each plant leaf; performing dimension reduction processing on the leaf functional character indexes through principal component analysis, and extracting a plurality of main leaf functional characters in each gradient region; based on the functional traits of the main guide leaves, obtaining the adaptability evaluation value of each plant species to each gradient region; and according to the adaptability evaluation value, screening the plant species meeting the adaptability condition in each gradient region, and constructing a multi-level plant configuration mode in combination with the lifestyle and landscape function of the plant species. According to the method, a scientific basis is provided for ecological adaptive planting by evaluating the adaptability of different plant varieties to different urban and rural gradient environments.

Description

Technical Field

[0001] This invention relates to the field of ecological analysis technology, and more specifically to a method for configuring urban and rural gradient plant communities based on a leaf functional trait response model. Background Technology

[0002] With the accelerating pace of global urbanization, urban ecological and environmental problems are becoming increasingly prominent. It is projected that by 2030, 60% of the world's population will live in urban areas, and this proportion will further increase to 66% by 2050. Urbanization has complex impacts on regional biodiversity, and environmental stressors such as urban heat islands, air pollution, and the expansion of impermeable surfaces seriously affect plant growth and ecological functions.

[0003] Plant leaf functional traits are important indicators reflecting plant resource utilization strategies and environmental adaptability. In 2004, Wright et al. proposed the "Leaf Economics Spectrum" (LES) theory, applying the "investment-return" model of economics to the analysis of plant leaf traits, providing a theoretical basis for understanding plant responses to environmental changes.

[0004] Currently, existing garden plant configurations have the following shortcomings: 1) Garden plant configurations are still mainly based on ornamental value and traditional experience, without fully considering the functional adaptability of plants to urban and rural environments; 2) There is a lack of a plant selection and ecological configuration system based on the quantitative and scalable functional traits of plants.

[0005] Therefore, how to adapt plant configurations to different urban and rural environmental gradients in order to enhance the ecological adaptability and service functions of urban plant communities is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of the above problems, the present invention provides a method for configuring urban and rural gradient plant communities based on a leaf functional trait response model, so as to at least solve some of the technical problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] This invention provides a method for configuring urban and rural gradient plant communities based on a leaf functional trait response model, characterized by the following steps:

[0009] S1. Based on multiple preset indicators, the target research area is divided into multiple gradient regions;

[0010] S2. Collect plant leaves of different plant species that meet the preset conditions in the quadrat of each gradient region, and obtain the leaf functional trait index of each plant leaf.

[0011] S3. The leaf functional trait indexes are reduced in dimensionality by principal component analysis, and multiple dominant leaf functional traits are extracted in each gradient region.

[0012] S4. Based on the aforementioned dominant leaf functional traits, obtain the adaptation evaluation values ​​of each plant species to each gradient region;

[0013] S5. Based on the aforementioned adaptability evaluation values, select plant species that meet the adaptability conditions in each gradient region, and construct a multi-level plant configuration pattern by combining their life forms and landscape functions.

[0014] Furthermore, the preset indicators include: population density, proportion of impervious surface, nighttime light index, and distance from the city center.

[0015] Furthermore, the leaf functional trait indicators include: morphological and structural traits, leaf photosynthetic physiological traits, and leaf chemical nutritional traits;

[0016] The morphological and structural traits include: leaf fresh weight, leaf dry weight, leaf area, specific leaf area, leaf dry matter content, and specific leaf weight;

[0017] The photosynthetic physiological traits of the leaves include: chlorophyll content, net photosynthetic rate, stomatal conductance, transpiration rate, stomatal area, and stomatal density.

[0018] The chemical nutritional characteristics of the leaves include: leaf carbon content per unit mass, leaf nitrogen content per unit mass, leaf phosphorus content per unit mass, and leaf potassium content per unit mass.

[0019] Furthermore, in S3, before performing principal component analysis, the leaf functional trait indices are tested using KMO and Bartlett's sphericity test.

[0020] Furthermore, S3 specifically includes:

[0021] Principal component analysis was used to obtain the loading coefficients of each leaf functional trait index in each gradient region.

[0022] For each gradient region, the leaf functional trait index corresponding to the load coefficient with an absolute value greater than a preset value is taken as the dominant leaf functional trait of that gradient region.

[0023] Furthermore, S4 specifically includes:

[0024] S41. Based on the dominant leaf functional traits extracted in each gradient region, obtain the principal component scores of each plant species in that gradient region.

[0025] S42. For the principal component scores of all plant species in each gradient region, calculate the membership function values ​​of each plant species on different principal components;

[0026] S43. Obtain the weight values ​​of each principal component;

[0027] S44. For each plant species, based on the corresponding membership function value and weight value, the fitness evaluation value of the plant species for the corresponding gradient region is obtained through weighted calculation.

[0028] Furthermore, the adaptability evaluation value is expressed as:

[0029]

[0030] Where D represents the fitness evaluation value; m represents the total number of principal components; μ(Z) i W represents the membership function value corresponding to the i-th principal component; i This represents the weight value corresponding to the i-th principal component.

[0031] Furthermore, in S5, the multi-level plant configuration pattern includes a combination of species in a tree layer, a shrub layer, and a herbaceous vine layer.

[0032] Furthermore, in S5, the multi-level plant configuration mode is configured based on fast investment return type and slow investment return type.

[0033] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method for configuring urban and rural gradient plant communities based on a leaf functional trait response model, which has the following beneficial effects:

[0034] This invention provides a scientific basis for ecologically adaptive planting by evaluating the adaptability of different plant species to different urban and rural gradient environments;

[0035] This invention constructs a plant screening and configuration model based on leaf functional traits to enhance the ecological function and landscape sustainability of urban green spaces; and provides a quantitative and operable plant configuration solution for landscaping in urbanized areas.

[0036] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0038] Figure 1This is a schematic diagram of the process for configuring urban and rural gradient plant communities based on a leaf functional trait response model, as provided in an embodiment of the present invention.

[0039] Figure 2 A schematic diagram of the first gradient city center area factor load provided for an embodiment of the present invention.

[0040] Figure 3 This is a bi-labeled schematic diagram of leaf functional traits in the first gradient city center region provided in an embodiment of the present invention.

[0041] Figure 4 This is a schematic diagram of the second gradient suburban region factor loading provided in an embodiment of the present invention.

[0042] Figure 5 This is a schematic diagram of the second gradient suburban region leaf functional traits provided in an embodiment of the present invention.

[0043] Figure 6 This is a schematic diagram of the third gradient rural region factor loading provided in an embodiment of the present invention.

[0044] Figure 7 This is a schematic diagram of the double-labeled leaf functional traits in the third gradient rural region provided in an embodiment of the present invention.

[0045] Figure 8 This is a schematic diagram of the first gradient urban area adaptability evaluation value results provided in an embodiment of the present invention.

[0046] Figure 9 This is a schematic diagram of the second gradient suburban area adaptability evaluation value results provided in an embodiment of the present invention.

[0047] Figure 10 This is a schematic diagram of the three-gradient rural area adaptability evaluation value results provided in an embodiment of the present invention.

[0048] Figure 11 This is a schematic diagram illustrating the adaptability evaluation results of evergreen trees provided in an embodiment of the present invention.

[0049] Figure 12 This is a schematic diagram illustrating the adaptability evaluation results of deciduous trees provided in an embodiment of the present invention.

[0050] Figure 13 This is a schematic diagram illustrating the adaptability evaluation results of evergreen shrubs provided in an embodiment of the present invention.

[0051] Figure 14 This is a schematic diagram illustrating the adaptability evaluation results of deciduous shrubs provided in an embodiment of the present invention.

[0052] Figure 15 This is a schematic diagram illustrating the adaptability evaluation results of herbs provided in an embodiment of the present invention.

[0053] Figure 16 This is a schematic diagram illustrating the adaptability evaluation results of vines provided in an embodiment of the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] This invention discloses a method for configuring urban and rural gradient plant communities based on a leaf functional trait response model, such as... Figure 1 As shown, it includes the following steps:

[0056] S1. Based on multiple preset indicators, the target research area is divided into multiple gradient regions;

[0057] S2. Collect plant leaves of different plant species that meet the preset conditions in the quadrats of each gradient region, and obtain the leaf functional trait index of each plant leaf.

[0058] S3. Principal component analysis was used to reduce the dimensionality of leaf functional trait indices and extract multiple dominant leaf functional traits in each gradient region.

[0059] S4. Based on multiple dominant leaf functional traits, obtain the adaptation evaluation values ​​of each plant species to each gradient region;

[0060] S5. Based on the adaptability evaluation value, select plant species that meet the adaptability conditions in each gradient area, and construct a multi-level plant configuration pattern by combining their life form and landscape function.

[0061] Next, each of the above steps will be explained in detail.

[0062] In step S1 above, the target study area is divided into multiple gradient regions based on multiple preset indicators. The preset indicators include: population density, proportion of impervious surface, nighttime light index and distance from the city center. The gradient regions can be city center areas, suburbs and rural areas, etc.

[0063] In step S2 above, plant leaves of different plant species meeting preset conditions are collected within the quadrats of each gradient region, and leaf functional trait indices of each plant leaf are obtained; wherein:

[0064] (1) The above-mentioned pre-set conditions include: 1) all of them are dominant tree species in the sample plot, and they are growing vigorously and have similar diameters at breast height, so as to ensure that the growth status and age of the plants are similar; 2) all of them are tree species that have been planted for many years and have adapted to the local environment and have stable growth; 3) the life forms include evergreen and deciduous, trees, shrubs, herbs and vines.

[0065] (2) Leaf functional traits include: morphological and structural traits, photosynthetic physiological traits, and chemical nutritional traits; specifically:

[0066] Morphological and structural traits include: leaf fresh weight (LFW), leaf dry weight (LDW), leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), and specific leaf weight (LMA). Among these, leaf fresh weight (LFW), leaf dry weight (LDW), and leaf area (LA) are the most basic components. Specific leaf area (SLA) reflects the trade-off between light resource acquisition and leaf formation costs for plants. Leaf dry matter content (LDMC) reflects the degree of hydration and stress resistance of plants. Specific leaf weight (LMA) can quantitatively present the input cost per unit area of ​​plant and reflect the ecological adaptation strategies of plants to different habitats.

[0067] Leaf photosynthetic physiological traits include: chlorophyll content SPAD, net photosynthetic rate Pn, stomatal conductance Gs, transpiration rate Tr, stomatal area SS, and stomatal density SD. Among these, chlorophyll content SPAD, net photosynthetic rate Pn, stomatal conductance Gs, and transpiration rate Tr directly reflect the plant's growth and metabolism. Stomatal area SS and stomatal density SD are the main stomatal traits of plant leaves and can directly reflect the plant's ability to exchange water and air.

[0068] Leaf chemical nutritional traits include: carbon content per unit mass of leaf (C). mass Leaf nitrogen content per unit mass (N) mass Phosphorus content per unit mass of leaves (P) mass Potassium content (K) per unit mass of leaves mass These four chemical nutritional traits can effectively reflect the efficiency of leaves in capturing and utilizing natural resources.

[0069] The above 16 leaf functional traits play an important physiological role in the growth and development of plants.

[0070] In step S3 above, leaf functional trait indices are tested using KMO and Bartlett's test of sphericity to ensure their applicability. Generally, principal component analysis is suitable when the KMO value is >0.5 and the significance P-value of the Bartlett's test of sphericity is less than 0.05.

[0071] After the test is completed, the leaf functional trait indexes are dimensionality reduced by principal component analysis, and multiple dominant leaf functional traits under each gradient region are extracted. Specifically, this includes: obtaining the loading coefficient of each leaf functional trait index under each gradient region through principal component analysis; for each gradient region, the leaf functional trait index corresponding to the loading coefficient with an absolute value greater than a preset value is taken as the dominant leaf functional trait of that gradient region.

[0072] In step S4 above, based on multiple dominant leaf functional traits, the fitness evaluation values ​​of each plant species for each gradient region are obtained; specifically including:

[0073] S41. Based on the dominant leaf functional traits extracted under each gradient region, obtain the principal component scores for each plant species under that gradient region; represented as:

[0074]

[0075] Among them, Z i The score of the i-th principal component is represented by ; n represents the total number of dominant leaf functional traits; a ik X represents the loading of the functional trait of the k-th dominant leaf corresponding to the i-th principal component; k This represents the standardized value of the functional trait of the k-th dominant leaf;

[0076] S42. For the principal component scores of all plant species within each gradient region, calculate the membership function values ​​of each plant species on different principal components; expressed as:

[0077]

[0078] Where, μ(Z) i Z represents the membership function value; imin Z represents the minimum score of the i-th principal component; imin This represents the maximum score of the i-th principal component;

[0079] S43. Obtain the weight values ​​of each principal component; represented as:

[0080]

[0081] Among them, W i P represents the weight value of the i-th principal component, used to reflect the relative importance of the dominant leaf functional trait index represented by this principal component in the fitness evaluation of each gradient region; i represents the contribution rate of the i-th principal component; m represents the total number of principal components selected (eigenvalues ​​> 1). This represents the sum of the eigenvalues ​​of all selected principal components.

[0082] S44. For each plant species, based on the corresponding membership function value and weight value, a weighted calculation is performed to obtain the fitness evaluation value of that plant species for the corresponding gradient region; expressed as:

[0083]

[0084] Where D represents the adaptability evaluation value.

[0085] In step S5 above, plant species that meet the adaptation conditions in each gradient area are selected based on the adaptability evaluation value. Combining their life form and landscape function, a multi-level plant configuration pattern is constructed. This multi-level plant configuration pattern includes species combinations of tree layer, shrub layer, and herbaceous vine layer. The multi-level plant configuration pattern is configured based on fast investment return type and slow investment return type.

[0086] Next, a specific embodiment will be used to describe in detail the method for configuring urban and rural gradient plant communities based on a leaf functional trait response model provided by the present invention.

[0087] 1. Urban-Rural Gradient Division and Sampling Design:

[0088] Based on a combination of factors including population density, the proportion of impervious surfaces, nighttime light index, and distance from the city center, the target study area was divided into three gradients: Gradient 1: the city center area; Gradient 2: the suburbs; and Gradient 3: the rural area. Within these three gradient areas, considering the aforementioned urban development factors, plant species diversity, and sampling convenience, three green space environments were randomly selected in each gradient area, totaling nine sampling sites. Using a random sampling method, 15 quadrats (10×10m) were selected in each gradient for vegetation surveys and leaf samples were collected. The vegetation at the sampling sites consisted of subtropical evergreen and deciduous broad-leaved mixed forests, with plant community structures including tree-shrub-herbaceous ground cover, tree-shrub, tree-herbaceous ground cover, and shrub-herbaceous ground cover types.

[0089] 2. Determination of experimental tree species and research indicators:

[0090] Based on a plant survey of the quadrats within the sample plots, 42 vigorous plant species of similar age were selected for the study. Leaf functional traits of each plant were obtained, including:

[0091] Morphological and structural traits: leaf fresh weight (LFW), leaf dry weight (LDW), leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), and specific leaf weight (LMA);

[0092] Leaf photosynthetic physiological traits: chlorophyll content SPAD, net photosynthetic rate Pn, stomatal conductance Gs, transpiration rate Tr, stomatal area SS, and stomatal density SD.

[0093] Leaf chemical nutritional traits: Carbon content per unit mass of leaf (C) mass Leaf nitrogen content per unit mass (N) mass Phosphorus content per unit mass of leaves (P) mass Potassium content (K) per unit mass of leaves mass .

[0094] 3. Sampling and measurement methods:

[0095] During summer, plant metabolism is more vigorous, resulting in greater sedimentation, adsorption, and absorption of urban pollutants. Pollutant accumulation in plants peaks in autumn. To ensure sufficient nutrient accumulation in the leaves, sampling for this invention was conducted during sunny weather in early September to early October 2023.

[0096] Three mature, healthy, and uniformly growing standard plants of each species were randomly selected within each quadrat. Using 10m high-altitude pruning shears and gardening shears, healthy branches in the east, south, west, and north directions were cut from each plant, and fully sun-exposed leaves were randomly collected from these branches. A total of 42 plant species were collected from nine quadrats across three gradients, with sufficient leaf samples collected from each species at each sampling site. The leaf surfaces were wiped clean with absorbent cotton, placed in sealed bags lightly moistened with water, and stored in a portable refrigerator (4℃ freezer). The samples were then brought back to the laboratory and analyzed using instruments including a leaf area scanner, analytical balance, handheld chlorophyll meter, photosynthesis apparatus, optical microscope, elemental analyzer, UV-Vis spectrophotometer, flame photometer, digester, forced-air drying oven, oven, and plant shredder.

[0097] 4. Data Analysis and Model Building:

[0098] The trait index within each principal component is determined by the loading coefficients of the rotation factors in the component matrix after principal component extraction. The larger the absolute value of the loading coefficient of the leaf economic spectrum trait in the nth principal component, the more likely that the trait index is located in the nth principal component and is the dominant leaf trait index in that principal component.

[0099] (1) Ensure data applicability through KMO and Bartlett tests.

[0100] Before performing principal component analysis, KMO and Bartlett's test of sphericity are performed to ensure the applicability of the trait data. Generally, principal component analysis is suitable when the KMO value is >0.5 and the significance p-value of the Bartlett's test of sphericity is less than 0.05, and the trait data of the three gradients all meet the analysis conditions.

[0101] (2) Principal component analysis (PCA) was used to reduce the dimensionality of the trait data and extract the dominant traits at each gradient:

[0102] First-tier city center area: Corresponding factor loading plot and leaf functional trait bigraph as follows Figure 2 and Figure 3 As shown in Table 1, the leaf functional trait loading coefficients for the first gradient city center area are as follows: The first principal component mainly includes LDMC (leaf dry matter content), SLA (specific leaf area), LMA (specific leaf weight), Gs (stomatal conductance), Tr (transpiration rate), Nmass (leaf nitrogen content per unit mass), and Kmass (leaf potassium content per unit mass), with absolute values ​​of loading coefficients of 0.542, 0.761, 0.662, 0.716, 0.667, 0.545, and 0.538, respectively; the second principal component mainly includes... The first principal component mainly includes LFW (fresh leaf weight), LDW (dry leaf weight), and LA (leaf area), with absolute loading coefficients of 0.915, 0.862, and 0.882, respectively; the third principal component mainly includes SS (stomatal area) and SD (stomatal density), with absolute loading coefficients of 0.770 and 0.708, respectively; the fourth principal component mainly includes SPAD (chlorophyll value), Pn (net photosynthetic rate), and Tr (transpiration rate), with absolute loading coefficients of 0.545, 0.537, and 0.598, respectively; the fifth principal component mainly includes C mass (Carbon content per unit mass of leaves), and the absolute value of the loading coefficient is 0.875.

[0103] Table 1: Leaf Functional Trait Loading Coefficients in the First-Tier City Center Area

[0104]

[0105] Second-tier suburban region: Corresponding factor loading plots and bigraphies of leaf functional traits are as follows Figure 4 and Figure 5 As shown in Table 2, the leaf functional trait loading coefficients for the second-gradient suburban region are as follows: The first principal component mainly includes LFW (fresh leaf weight), LDW (dry leaf weight), LA (leaf area), LDMC (dry leaf content), SLA (specific leaf area), and Kmass (potassium content per unit mass of leaf), with absolute values ​​of loading coefficients of 0.763, 0.584, 0.796, 0.660, 0.611, and 0.678, respectively; the second principal component mainly includes LDW (dry leaf weight), SLA (specific leaf area), Pn (net leaf area), and Kmass (potassium content per unit mass of leaf). The absolute values ​​of the loading coefficients for the photosynthetic rate, stomatal conductance (Gs), transpiration rate (Tr), and nitrogen content per unit mass of leaves were 0.597, 0.564, 0.619, 0.538, 0.616, and 0.585, respectively. The third principal component mainly included Gs (stomatal conductance) and Tr (transpiration rate), with absolute values ​​of loading coefficients of 0.725 and 0.659, respectively. The fourth principal component mainly included SS (stomatal area) and SD (stomatal density), with absolute values ​​of loading coefficients of 0.658 and 0.502, respectively.

[0106] Table 2: Leaf Functional Trait Loading Coefficients in Suburban Areas of the Second Gradient

[0107]

[0108] Third-tier rural areas: corresponding factor loading plots and bigraphies of leaf functional traits are as follows Figure 6 and Figure 7 As shown in Table 3, the leaf functional trait loading coefficients for the third-gradient rural region are as follows: The first principal component mainly includes LFW (leaf fresh weight), LDW (leaf dry weight), LA (leaf area), LDMC (leaf dry matter content), Pn (net photosynthetic rate), Gs (stomatal conductance), Tr (transpiration rate), SD (stomatal density), and Kmass (leaf potassium content per unit mass), with absolute values ​​of loading coefficients of 0.636, 0.515, 0.643, 0.563, 0.604, 0.745, 0.711, 0.645, and 0.508, respectively; the second principal component mainly includes LFW (leaf fresh weight), LDW (leaf dry weight), and S... The absolute values ​​of the loading coefficients for LA (specific leaf area) were 0.680, 0.760, and 0.608, respectively; the third principal component mainly included SPAD (chlorophyll value), Pn (net photosynthetic rate), and Nmass (leaf nitrogen content per unit mass), with absolute values ​​of loading coefficients of 0.615, 0.622, and 0.516, respectively; the fifth principal component mainly included LMA (specific leaf weight) and SD (stomatal density), with absolute values ​​of loading coefficients of 0.522 and 0.531, respectively; and the sixth principal component mainly included Cmass (leaf carbon content per unit mass) and Pmass (leaf phosphorus content per unit mass), with absolute values ​​of loading coefficients of 0.645 and 0.519, respectively.

[0109] Table 3: Leaf functional trait loading coefficients in rural areas of the third tier

[0110]

[0111] (3) Based on multiple dominant leaf functional traits, the adaptation evaluation value D of each plant species to each gradient region was obtained:

[0112] To evaluate the adaptability of plants to different urban-rural gradient environments, a model was constructed using a combination of principal component analysis and fuzzy membership functions. In the steps described above, principal component analysis was used to select the dominant leaf functional traits in each gradient environment. Based on this, the weights of different leaf functional traits in evaluating adaptability to urban-rural gradient environments were calculated. Then, fuzzy membership functions were used to calculate the membership function values ​​for each plant species. Finally, a weighted average was used to obtain the adaptation evaluation value of each plant species to urban-rural gradient environments, and the adaptability of plants in different gradient environments was ranked according to the evaluation values.

[0113] The results of the fitness evaluation values ​​at each gradient are as follows: Figures 8-10 As shown in the figure. According to the adaptability evaluation results, in the first-tier city center area, the three plants with the highest adaptability evaluation values ​​are *Fattya japonica*, *Ginkgo biloba*, and *Polygonum multiflorum*, while the three with the lowest are *Celtis sinensis*, *Acer palmatum*, and *Rubus idaeus*. In the second-tier suburban area, the three plants with the highest adaptability evaluation values ​​are *Fattya japonica*, *Calathea chinensis*, and *Cercis chinensis*, while the three with the lowest are *Cinnamomum camphora*, *Photinia serratifolia*, and *Nandina domestica*. In the third-tier rural area, the three plants with the highest adaptability evaluation values ​​are *Fattya japonica*, *Pontederia cordata*, and *Calathea chinensis*, while the three with the lowest are *Rubus idaeus*, *Celtis sinensis*, and *Koelreuteria paniculata*. Overall, *Acer palmatum* shows poor adaptability in all three environmental tiers. *Celtis sinensis* shows poor adaptability in the first-tier city center area and the third-tier rural area, but good adaptability in the second-tier suburban area.

[0114] The results of the adaptability evaluation of evergreen trees are as follows: Figure 11 As shown, among all living plant species, evergreen trees exhibited a moderate average adaptability. In the first tier of urban areas, Podocarpus macrophyllus showed the best adaptability, while Camphor tree showed the worst. In the second tier of suburban areas, Loquat showed the best adaptability, while Camphor tree showed the worst. In the third tier of rural areas, Loquat showed the best adaptability, while Podocarpus macrophyllus showed the worst. Overall, Camphor tree showed poor adaptability in all three environmental tiers, while Loquat showed good adaptability in all three. Podocarpus macrophyllus showed completely opposite adaptability in the first tier of urban areas and the third tier of rural areas.

[0115] The results of the adaptability evaluation of deciduous trees are as follows: Figure 12 As shown, among all life-type plants, deciduous trees exhibited the worst average adaptability. In the first tier, urban areas, ginkgo was the most adaptable, while hackberry was the least adaptable; in the second tier, suburban areas, hackberry was the most adaptable, while Japanese maple was the least adaptable; in the third tier, rural areas, chinaberry was the most adaptable, while hackberry was the least adaptable. Overall, hackberry showed poor adaptability in all three tiers, with its adaptability in the second tier (suburban areas) and the third tier (rural areas) showing completely opposite trends.

[0116] The results of the adaptability evaluation of evergreen shrubs are as follows: Figure 13As shown. Among all life-type plants, evergreen shrubs showed the best average adaptability. In the first tier of urban areas, Loropetalum chinense was the best adaptable, and Hydrangea chinensis was the worst; in the second tier of suburban areas, Forsythia suspensa was the best adaptable, and Photinia serratifolia was the worst; in the third tier of rural areas, Forsythia suspensa was the best adaptable, and Hydrangea chinensis was the worst. Overall, Hydrangea chinensis showed poor adaptability in the first tier of urban areas and the third tier of rural areas, but performed relatively well in the second tier of suburban areas. Rhododendron simsii showed poor adaptability in all three tiers of environments, while Forsythia suspensa and Fatsia japonica showed better adaptability in the second tier of suburban areas and the third tier of rural areas.

[0117] The results of the adaptability evaluation of deciduous shrubs are as follows: Figure 14 As shown, among all life-type plants, deciduous shrubs exhibited the best average adaptability. Crape myrtle adapted well to urban environments in the first tier and rural environments in the third tier, while Bauhinia adapted well to suburban environments in the second tier.

[0118] The results of the herb's adaptability evaluation are as follows: Figure 15 As shown. Among all life-type plants, herbaceous plants showed a relatively moderate average adaptability. In the first tier, the best adaptability was found in urban areas, while the worst was found in variegated liriope. In the second tier, the best adaptability was found in suburban areas, while the worst was found in variegated liriope. In the third tier, the best adaptability was found in rural areas, while the worst was found in liriope. Overall, variegated liriope showed poor adaptability in all three tiers, while variegated liriope and canna lily showed good adaptability in all three tiers.

[0119] The results of the adaptability evaluation of vines are as follows Figure 16 As shown, among all life-type plants, vines generally showed the lowest adaptability. *Trachelospermum jasminoides* adapted best in the first tier of urban areas and the third tier of rural areas, while *Wisteria jasminoides* adapted best in the second tier of suburban areas.

[0120] 5. Plant adaptability evaluation and configuration strategy:

[0121] Based on the adaptability evaluation value, the plant species with the best adaptability at each gradient were selected. Combining their life form, ecological function and landscape effect, multi-level plant configuration patterns were proposed, including species combination suggestions for tree layer, shrub layer and herbaceous vine layer.

[0122] (1) Adaptive-based optimization of plant species selection:

[0123] Based on the results of the adaptive selection of plants for different urban and rural gradient environments, in the first-tier urban center area, priority can be given to planting plants such as Podocarpus macrophyllus, Ginkgo biloba, Loropetalum chinense, Lagerstroemia indica, Pickerelweed, and Trachelospermum jasminoides; in the second-tier suburban area, priority can be given to planting plants such as Eriobotrya japonica, Celtis sinensis, Forsythia suspensa, Cercis chinensis, Tamarix chinensis, and Wisteria sieboldii; in the third-tier rural area, priority can be given to planting plants such as Eriobotrya japonica, Chinaberry, Forsythia suspensa, Lagerstroemia indica, Pickerelweed, and Trachelospermum jasminoides.

[0124] (2) Plant community construction:

[0125] Plant community structure reflects the distribution pattern between individuals within the community, mainly including the degree of aggregation in quantity, spatial combination relationships, and morphological differences. It is the result of the combined effects of different ecological processes at different temporal and spatial scales. Based on prioritizing plants with good adaptability to different urban and rural gradient environments, and according to design concepts and actual needs, appropriate combinations of plants with certain densities, different horizontal and vertical structures, and different sizes are used to maximize landscape effects and ecological benefits.

[0126] (3) Plant optimization strategies based on different urban-rural gradient environments:

[0127] 1) When optimizing plant configuration in the city center area of ​​the first tier:

[0128] The recommended tree layer is a combination of plants including Podocarpus macrophyllus, Ligustrum lucidum, Magnolia grandiflora, Eriobotrya japonica, Ginkgo biloba, Prunus serrulata, Zelkova serrata, and Koelreuteria paniculata. Among these, Magnolia grandiflora and Zelkova serrata are considered "fast-return" plants, while Podocarpus macrophyllus, Ligustrum lucidum, and Ginkgo biloba are considered "slow-return" plants. Podocarpus macrophyllus has a beautiful shape; Ligustrum lucidum has dense foliage, a neat shape, and is tolerant of pruning, and is highly resistant to air pollution; Magnolia grandiflora has large, fragrant flowers and can absorb various harmful gases; Eriobotrya japonica has plump fruit and auspicious symbolism; Ginkgo biloba is golden and vibrant in autumn, widely found in the streets and alleys of the target research area, perfectly matching the city's cultural atmosphere; Prunus serrulata is large and beautiful, like a rosy sunset; Zelkova serrata has vibrant autumn colors, can reduce noise, resist pollution and typhoons, and the name "Zelkova" is a homophone for "passing the imperial examinations," symbolizing success in the imperial examinations; Koelreuteria paniculata is valued for its leaves in spring, flowers in summer, and leaves and fruits in autumn, offering excellent landscape effects and strong resistance to pollutants.

[0129] For the shrub layer, it is recommended to combine plants such as "Loropetalum chinense + Ivy + Buxus macrocarpa + Buxus macrocarpa 'Aureomarginata' + Coral Tree + Lagerstroemia indica". Among them, Loropetalum chinense, Ivy, and Lagerstroemia indica are "fast-return" plants, while Buxus macrocarpa and Buxus macrocarpa 'Aureomarginata' are "slow-return" plants. Loropetalum chinense has bright flower and leaf colors, is tolerant of both high and low temperatures, and can create rich plant landscape shapes with good visual effects; Ivy is evergreen, does not require much light, can purify the air, and can be used to create vertical landscapes; Buxus macrocarpa and Buxus macrocarpa 'Aureomarginata' are suitable for plant landscapes with different shapes, can purify the air, and can be selected and combined according to the landscape design requirements; Coral Tree is evergreen, has bright red fruit, and has strong adaptability and resistance; Lagerstroemia indica has a smooth and twisted trunk, dense and fragrant flowers, can resist pollution, and has a good symbolic meaning.

[0130] For the herbaceous vine layer, it is recommended to combine plants such as "pickpox + canna + water caltrop + liriope + star jasmine". Among them, water caltrop and star jasmine are "fast-return" plants, while pickpox and liriope are "slow-return" plants. Pickpox has large leaves, beautiful flowers, and a long flowering period, and is often used for greening wetlands and water bodies, which helps ecological restoration, improve water quality, and maintain aquatic ecosystems. Canna has large, brightly colored flowers and comes in a variety of varieties, and can absorb harmful substances from the air. Water caltrop has a long flowering period and is tolerant of waterlogging, making it suitable for planting in water bodies. Liriope is low-growing and can provide dense coverage with low maintenance costs, making it suitable for lawn planting. Star jasmine has fragrant flowers, a long flowering period, and is cold and heat tolerant, making it a good ground cover or climbing plant.

[0131] 2) When optimizing plant configuration in suburban areas of the second gradient:

[0132] For the tree layer, it is recommended to combine plants such as loquat, podocarpus, magnolia, privet, hackberry, tallow tree, weeping willow, and purple-leaf plum. Among them, magnolia and hackberry are "fast-return" plants, while podocarpus and privet are "slow-return" plants. Hackberry has a beautiful shape, vibrant autumn colors, strong resistance, can absorb and retain dust, is highly adaptable, grows rapidly, and has a high survival rate. Tallow tree has high ornamental value in both its fruit and leaves, and has strong resistance to sulfur dioxide and chlorine, making it suitable for planting near water. Weeping willow has a beautiful swaying posture in the wind, making it suitable for embankment protection and factory greening. Purple-leaf plum has purplish-red leaves all year round, can be planted in various ways, can resist high concentrations of chlorine, and has high landscape value.

[0133] For the shrub layer, it is recommended to combine plants such as "Wild Forsythia + Fatsia japonica + Variegated Periwinkle + Hydrangea + Buxus macrocarpa + Coral Tree + Bauhinia". Among them, Wild Forsythia, Variegated Periwinkle, and Hydrangea are "fast-return investment" plants, while Coral Tree and Bauhinia are "slow-return investment" plants. Wild Forsythia blooms in early spring with drooping branches and is often planted near water. Fatsia japonica has large, thick leaves, making it excellent as understory and helping to trap dust and resist pollution. Variegated Periwinkle has beautiful flowers and is evergreen, making it suitable for ground cover or wall mulch. Hydrangea has full, colorful flowers and an excellent landscape effect. Bauhinia has a long flowering period, dense flowers, and a rich cultural heritage.

[0134] For the herbaceous vine layer, it is recommended to combine plants such as "water calathea + canna lily + liriope muscari + variegated liriope muscari + wisteria". Among them, water calathea and wisteria are "fast-return" plants, while variegated liriope muscari is a "slow-return" plant. Variegated liriope muscari has beautiful foliage, is cold and heat tolerant, and is an excellent colorful foliage ground cover plant; wisteria has a long lifespan, extremely beautiful flowers, and vigorous growth, making it suitable for creating a beautiful vine landscape.

[0135] In summary, the method for configuring urban and rural gradient plant communities based on a leaf functional trait response model provided by this invention has the following beneficial effects:

[0136] In this invention, the plant leaf economic spectrum, as an important theory in plant functional ecology, studies the relationship between plants and the environment from the individual plant to the entire ecosystem, effectively revealing the plant's response to environmental changes. Landscape architecture, with its fundamental mission of harmonizing the relationship between humans and nature, considers plant landscape configuration as a crucial component. By approaching the issue from the perspective of the plant leaf economic spectrum and focusing on the urban-rural gradient scale, it is possible to better address numerous problems faced by urban ecosystems, improve the quality of the living environment, and provide a scientific basis for urban ecological protection and sustainable development. Furthermore, by comprehensively considering multiple factors influencing urban development, it is possible to more rationally divide the urban-rural gradient, maximizing the effectiveness of experiments and research and ensuring the targeted nature of the proposed strategies.

[0137] In this embodiment of the invention, the relevant research on leaf economic spectrum is mostly carried out from a macro perspective, and the relevant research is mostly approached from an ecological perspective. The relevant research results are carried out around plant adaptability and specific configuration, and the research content is more in-depth and expanded than before.

[0138] In this embodiment of the invention, while the original plant configuration focused more on aesthetic value, the ecological function and environmental adaptability of plants were explored in detail. Based on the research results, a plant selection system suitable for different urban and rural gradients was constructed.

[0139] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0140] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for configuring urban and rural gradient plant communities based on a leaf functional trait response model, characterized in that, Includes the following steps: S1. Based on multiple preset indicators, the target research area is divided into multiple gradient regions; S2. Collect plant leaves of different plant species that meet the preset conditions in the quadrat of each gradient region, and obtain the leaf functional trait index of each plant leaf. S3. The leaf functional trait indexes are reduced in dimensionality by principal component analysis, and multiple dominant leaf functional traits are extracted in each gradient region. S4. Based on the aforementioned dominant leaf functional traits, obtain the adaptation evaluation values ​​of each plant species to each gradient region; S5. Based on the aforementioned adaptability evaluation values, select plant species that meet the adaptability conditions in each gradient region, and construct a multi-level plant configuration pattern by combining their life forms and landscape functions.

2. The method for configuring urban and rural gradient plant communities based on a leaf functional trait response model according to claim 1, characterized in that, The preset indicators include: population density, proportion of impermeable surfaces, nighttime light index, and distance from the city center.

3. The method for configuring urban and rural gradient plant communities based on a leaf functional trait response model according to claim 1, characterized in that, The leaf functional trait indicators include: morphological and structural traits, photosynthetic physiological traits, and chemical nutritional traits. The morphological and structural traits include: leaf fresh weight, leaf dry weight, leaf area, specific leaf area, leaf dry matter content, and specific leaf weight; The photosynthetic physiological traits of the leaves include: chlorophyll content, net photosynthetic rate, stomatal conductance, transpiration rate, stomatal area, and stomatal density. The chemical nutritional characteristics of the leaves include: leaf carbon content per unit mass, leaf nitrogen content per unit mass, leaf phosphorus content per unit mass, and leaf potassium content per unit mass.

4. The method for configuring urban and rural gradient plant communities based on a leaf functional trait response model according to claim 1, characterized in that, In S3, the leaf functional trait indices are tested using KMO and Bartlett's sphericity test before principal component analysis.

5. The method for configuring urban and rural gradient plant communities based on a leaf functional trait response model according to claim 1, characterized in that, S3 specifically includes: Principal component analysis was used to obtain the loading coefficients of each leaf functional trait index in each gradient region. For each gradient region, the leaf functional trait index corresponding to the load coefficient with an absolute value greater than a preset value is taken as the dominant leaf functional trait of that gradient region.

6. The method for configuring urban and rural gradient plant communities based on a leaf functional trait response model according to claim 1, characterized in that, S4 specifically includes: S41. Based on the dominant leaf functional traits extracted in each gradient region, obtain the principal component scores of each plant species in that gradient region. S42. For the principal component scores of all plant species in each gradient region, calculate the membership function values ​​of each plant species on different principal components; S43. Obtain the weight values ​​of each principal component; S44. For each plant species, based on the corresponding membership function value and weight value, the fitness evaluation value of the plant species for the corresponding gradient region is obtained through weighted calculation.

7. The method for configuring urban and rural gradient plant communities based on a leaf functional trait response model according to claim 6, characterized in that, The adaptive evaluation value is expressed as: Where D represents the fitness evaluation value; m represents the total number of principal components; μ(Z) i W represents the membership function value corresponding to the i-th principal component; i This represents the weight value corresponding to the i-th principal component.

8. The method for configuring urban and rural gradient plant communities based on a leaf functional trait response model according to claim 1, characterized in that, In S5, the multi-level plant configuration pattern includes a combination of species in a tree layer, a shrub layer, and a herbaceous vine layer.

9. A method for configuring urban and rural gradient plant communities based on a leaf functional trait response model according to claim 1, characterized in that, In S5, the multi-level plant configuration mode is configured based on fast investment return type and slow investment return type.

Images