Herbaceous plant community monitoring method and application thereof

Abstract

The invention provides a herbaceous plant community monitoring method and application thereof, and belongs to the technical field of ecological environment monitoring. The method comprises the following steps: dividing a quadrat, sequentially marking epiphytic points of plant individuals in the quadrat, and measuring character indexes of the plant individuals; and representing the position information of the epiphytic point location of each plant individual in the quadrat in a coordinate form in a coordinate system, and correspondingly marking the character index information of each plant individual to obtain monitoring of the plant population individuals and the character thereof in the horizontal space distribution. According to the method, the character index of each plant individual of each population in the community is matched with the corresponding spatial position, and the difference of the individual characteristics of the population in the spatial position can be quantified, so that an important ecological process is revealed; the method has a good application prospect in biodiversity evaluation, community structure analysis, community succession dynamic monitoring and ecological system function research.

Description

Technical Field

[0001] This invention belongs to the field of ecological environment monitoring technology, specifically relating to a method for monitoring herbaceous plant communities and its application. Background Technology

[0002] In ecological research, the quadrat method is one of the most fundamental methods for investigating herbaceous plant communities and is widely used by ecologists. This method typically involves establishing standardized sampling units (quadrases) within the study area, with a 1m × 1m quadrat being the most common unit scale. It generally involves statistically analyzing indicators such as plant species, population size, aboveground biomass, individual plant height, and cluster width within the 1m × 1m sampling area, and calculating indices such as species diversity, dominance, and cover. These statistically averaged indicators can reflect the basic structure and ecological function of the community to a certain extent. However, this traditional quadrat method has certain limitations. It ignores the overall or average characteristics of plant populations within a quadrat, neglects the differences in the spatial location of individuals within the quadrat, and struggles to reveal the impact of inter-individual interactions on community structure.

[0003] Therefore, how to effectively obtain the spatial distribution characteristics of different individuals in a herbaceous plant community while maintaining operational feasibility has become an urgent problem to be solved in current ecological research and application. To this end, there is a pressing need to develop a herbaceous plant community monitoring method that can take into account both quantitative statistics and spatial distribution information, in order to improve monitoring accuracy and the completeness of ecological information acquisition. Summary of the Invention

[0004] To address the problems existing in the prior art, the primary objective of this invention is to provide a method for monitoring herbaceous plant communities that can quantify the spatial differences in individual characteristics of a population, thereby revealing important ecological processes.

[0005] The second objective of this invention is to provide applications of the above-mentioned methods in biodiversity assessment, community structure analysis, community succession dynamic monitoring, and ecosystem function research.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0007] This invention provides a method for monitoring herbaceous plant communities, comprising the following steps:

[0008] The quadrats were divided, and the attachment points of individual plants in each quadrat were marked sequentially. The phenotypic indicators of the individual plants were then measured. The location information of the attachment point of each individual plant in the quadrat was represented in the form of coordinates in a coordinate system, and the phenotypic information of each individual plant was marked accordingly. This allowed for the monitoring of the horizontal spatial distribution of individual plants and their traits.

[0009] Preferably, the quadrats include 0.5m×0.5m quadrats, 1m×1m quadrats, and 2m×2m quadrats.

[0010] Preferably, the trait indicators include plant height, clump width, fresh biomass weight, and dry biomass weight.

[0011] Preferably, different markers are used to mark the attachment sites of different plant individuals.

[0012] Preferably, after marking the attachment sites of individual plants, the quadrats are photographed, and the coordinates of the control points are uniformly placed in the same coordinate system by inputting the R2V software. The coordinates of the control points are the vertices of the quadrats.

[0013] Preferably, a corresponding layer is created for each population in the quadrat in the R2V software.

[0014] Preferably, the layer of each population is converted to shp format, and the position information of the attachment point of each plant individual in each population is digitized to obtain the spatial coordinates of all plant individuals in each population.

[0015] This invention also provides the application of the above method in biodiversity assessment.

[0016] This invention also provides the application of the above method in community structure analysis and dynamic monitoring of community succession.

[0017] This invention also provides the application of the above method in ecosystem function research.

[0018] Compared with the prior art, the beneficial effects of the technical solution of the present invention are as follows:

[0019] This invention matches the trait indicators of each plant individual within a community with its corresponding spatial location. It not only measures the average state of these trait indicators but also determines their spatial distribution based on individual plant populations. Furthermore, it analyzes the spatial correlations of these trait indicators using a label correlation function. The spatial correlations of individual plant trait indicators can reveal the trade-off between competition and positive interactions among individuals, thereby helping to understand key ecological processes influencing populations and communities. This invention shows promising applications in biodiversity assessment, community structure analysis, community succession dynamics monitoring, and ecosystem function research. Attached Figure Description

[0020] Figure 1 : A 0.5m × 0.5m quadrat after division;

[0021] Figure 2 : Mark the location of each individual in each population;

[0022] Figure 3 : Small quadrats after all individuals in the population have been labeled;

[0023] Figure 4 : Coordinates of the control points in a 1m×1m quadrat;

[0024] Figure 5 Spatial distribution of individual biomass of *Stipa macrocarpa* within a 1m × 1m sampling area;

[0025] Figure 6 Spatial correlation of plant height among different individuals in a Stipa grandis population;

[0026] Figure 7 Spatial correlation of biomass among different individuals in a Stipa population;

[0027] Figure 8 Spatial correlation of individual cluster widths in *Stipa grandis* populations;

[0028] Figure 9 Spatial correlation of individual plant height in Leymus chinensis populations;

[0029] Figure 10 Spatial correlation of individual biomass in Leymus chinensis populations;

[0030] Figure 11 Spatial correlation of individual plant height among Leymus chinensis and Stipa grandis populations;

[0031] Figure 12 Spatial correlation of individual biomass among sheepgrass-stipa populations. Detailed Implementation

[0032] This invention provides a method for monitoring herbaceous plant communities, comprising the following steps:

[0033] The quadrats were divided, and the attachment points of individual plants in each quadrat were marked sequentially. The phenotypic indicators of the individual plants were then measured. The location information of the attachment point of each individual plant in the quadrat was represented in the form of coordinates in a coordinate system, and the phenotypic information of each individual plant was marked accordingly. This allowed for the monitoring of the horizontal spatial distribution of individual plants and their traits.

[0034] The preferred method for dividing the quadrats in this invention is to use bamboo chopsticks to delineate quadrats within the community to be tested. In actual ecological studies, researchers can choose quadrats of appropriate sizes according to their needs. The quadrats preferably include 0.5m × 0.5m, 1m × 1m, and 2m × 2m quadrats. As an example, this invention uses bamboo chopsticks to set up a 1m × 1m quadrat in the community, and then divides it into four smaller 0.5m × 0.5m quadrats for subsequent operations.

[0035] This invention sequentially marks the attachment points of individual plants in a quadrat and measures the phenotypic indicators of the individual plants. In actual ecological research, ecologists can select the corresponding phenotypic indicators according to actual needs. The phenotypic indicators described in this invention preferably include plant height, clump width, fresh biomass weight, and dry biomass weight.

[0036] The attachment point described in this invention refers to the point where a plant individual contacts the soil and fixes its position; that is, the specific spatial location where the plant naturally grows and takes root on the soil surface. For herbaceous plants, this point is usually the point where its base or root collar contacts the ground. As an optional implementation, this invention involves cutting the plant individual close to the ground, collecting the samples for measuring phenotypic indicators, and marking the attachment point of the individual with corresponding numbered markers.

[0037] In this invention, different markers are used to mark the attachment sites of different plant individuals. As an optional implementation, when marking the attachment sites of individuals within a population, different species are distinguished using markers of different colors or different types.

[0038] This invention involves photographing quadrats after marking the attachment sites of individual plant individuals. Preferably, after measuring the relevant indicators and marking all individuals of all species within the quadrat, the quadrat is photographed using a digital camera.

[0039] This invention measures the spatial coordinates of individual plants within a population. Preferably, after photographing the quadrats, the coordinates of control points are unified into a single coordinate system using R2V software, with the control points representing the vertices of the quadrats. A corresponding layer is created for each population within the quadrats using the R2V software. This invention converts each population layer to .shp format and opens the attribute table using Geographic Information System (GIS) software to obtain the spatial coordinates of that population. This invention digitizes the location information of the attachment points of each plant individual within each population, obtaining the spatial coordinates of all plant individuals in each population. Then, the trait indicators of each plant individual in each population are matched with its spatial coordinates to monitor the horizontal spatial distribution of individual traits within the population.

[0040] This invention also provides applications of the above methods in biodiversity assessment, community structure analysis, community succession dynamic monitoring, and ecosystem function research.

[0041] The technical solutions of this invention will be clearly and completely described below with reference to the embodiments thereof. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0042] Unless otherwise specified, the following embodiments are all conventional methods.

[0043] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.

[0044] Example 1

[0045] A method for monitoring herbaceous plant communities:

[0046] (1) Use bamboo chopsticks to set up a 1m×1m quadrat, and then divide it into four 0.5m×0.5m smaller quadrats, see... Figure 1 .

[0047] (2) Starting from the lower left corner of the 1m×1m quadrat, number each individual of each population within the first 0.5m×0.5m quadrat and measure its phenotypic indicators (plant height and / or clump width). After measurement, cut the individual close to the ground and place it in a numbered envelope (for measuring the aboveground biomass fresh weight and / or dry weight of the individual). At the same time, mark the basal position of the individual in the population with a numbered marker (for measuring the spatial coordinates of the individual in the population). See Figure 2 When marking the location of individuals within a population, different species are distinguished by different colored markers.

[0048] (3) After measuring the relevant indicators and marking all individuals of all species within the first 0.5m × 0.5m quadrat, photograph the quadrat with a digital camera. See below. Figure 3 .

[0049] (4) Follow the steps above to complete the measurement of all plant population individuals in the four 0.5m×0.5m quadrats.

[0050] (5) Determination of spatial coordinates of individual plant populations: ① The four 0.5m × 0.5m quadrats photographed were unified into the same coordinate system by inputting the coordinates of control points using R2V software. The coordinates of the control points are the vertices of the 0.5m × 0.5m quadrats. See [link to control point coordinates]. Figure 4 ① “·” indicates a control point; ② In R2V software, establish a corresponding layer for each population in each small quadrat, and then digitize the marker points of each individual in the population by adding points; ③ Convert each layer of each species to shp. format, and open the attribute table with geographic information system software (ArcGIS software) to obtain the spatial coordinates of the population; ④ Match the trait index of each individual in each population with its spatial coordinates to complete the monitoring of the horizontal spatial distribution of individual traits in the population.

[0051] Example 2

[0052] The herbaceous plant community monitoring method was used to monitor Stipagrandis.

[0053] (1) Use bamboo chopsticks to set up a 1m×1m quadrat and divide it into 4 smaller quadrats of 0.5m×0.5m.

[0054] (2) Starting from the lower left corner of the 1m×1m quadrat, number each individual of Stipa macrocarpa in the first 0.5m×0.5m quadrat, cut the individual close to the ground and put it into the corresponding numbered envelope (to determine the aboveground biomass of the individual: dry weight). At the same time, mark the attachment point of the individual of Stipa macrocarpa with the corresponding numbered marker (to determine the spatial coordinates of the individual of Stipa macrocarpa).

[0055] (3) After measuring the relevant indicators and marking all individuals of *Stipa grandis* in the first 0.5m×0.5m quadrangle, take a picture of the quadrangle with a digital camera.

[0056] (4) Follow the steps above to complete the measurement of all individual needlegrass individuals in the four 0.5m×0.5m sample plots.

[0057] (5) Determination of individual spatial coordinates of *Stipa macrocarpa*: ① Four 0.5m × 0.5m quadrats were photographed and their control point coordinates were unified into the same coordinate system using R2V software. The control point coordinates were the vertices of the 0.5m × 0.5m quadrats. ② In R2V software, a corresponding layer of *Stipa macrocarpa* was created for each quadrat. Then, the marker points of each individual *Stipa macrocarpa* were digitized by adding points. ③ Each layer of *Stipa macrocarpa* was converted to shp. format, and the attribute table was opened using geographic information system software (ArcGIS software) to obtain the spatial coordinates of *Stipa macrocarpa*. The individual population coordinates of *Stipa macrocarpa* within the 1m × 1m sampling range are shown in Table 1. ④ The phenotypic indicators of each individual *Stipa macrocarpa* were matched with their spatial coordinates. The correspondence between the individual population spatial coordinates and their individual biomass (dry weight) within the 1m × 1m sampling range of *Stipa macrocarpa* is shown in Table 1. The monitoring of the horizontal spatial distribution of individual biomass of *Stipa macrocarpa* within the 1m × 1m sampling range is completed. The spatial distribution of individual biomass of *Stipa macrocarpa* within the 1m × 1m sampling range is shown in Table 1. Figure 5 As shown, the size of the circle represents the level of individual biomass.

[0058] Table 1. Individual coordinates and corresponding biomass of *Stipa grandis* within a 1m × 1m sampling area.

[0059]

[0060]

[0061] Application Example 1

[0062] 1. Detection results of traditional monitoring methods

[0063] Traditional monitoring methods were used: within a 1m×1m quadrat, individuals of higher, medium, and lower heights were selected to measure their plant height, thereby obtaining the average plant height of the population; the number of individuals in the 1m×1m quadrat was counted and their total biomass was measured, thereby obtaining the biomass of individual plants (clumps) in the population; for clump species, individuals of larger, medium, and smaller clump widths were selected to measure their clump width, thereby obtaining the average clump width of the population.

[0064] Traditional monitoring methods were used to monitor the average values ​​of plant height, single-plant biomass (dry weight), and other phenotypic indicators of *Leymus chinensis* and *Stipa grandis* populations in typical grassland communities of Inner Mongolia, as shown in Table 2. The average plant height of the *Stipa grandis* population was 40.9 cm, the average biomass per clump was 2.3 g, and the average clump width was 6.6 cm. 2 The average height of individual plants in the Leymus chinensis population was 28.6 cm, and the biomass of a single plant was 0.4 g.

[0065] Table 2. Monitoring values ​​of different traits in individuals of typical grassland sheepgrass and needlegrass populations.

[0066] Species Plant height / cm Biomass / g <![CDATA[Cluster width / cm 2 > large needlegrass 40.9±17.9 2.3±3.5 6.6±12.4 sheepgrass 28.6±7.3 0.4±0.3 /

[0067] 2. Application of the monitoring results of this invention

[0068] The monitoring method proposed in Example 1 was used to monitor the populations of Leymus chinensis and Stipa grandis in a typical grassland community in Inner Mongolia (the only difference from the method and steps in Example 1 is that the individual traits of the populations are monitored within a sampling range of 2m×2m), and the label-correlation function was applied to analyze the spatial correlation of different traits.

[0069] In addition to obtaining the research results of traditional methods, the monitoring method of this invention can also obtain the spatial distribution of related traits of different individuals in the population and analyze the spatial correlation of these traits.

[0070] (1) Spatial association of individual traits in Stipa grandis population

[0071] The horizontal spatial distribution of plant height among different individuals in the *Stipa grandis* population within a 2m × 2m sampling area is shown below. Figure 6 In the diagram, the shade of color A represents the height of an individual plant. Based on the kmm(r) function, the spatial correlation curve of individual plant height in the *Stipa grandis* population initially rises with increasing distance, then slowly declines and stabilizes, deviating from the lower limit of the confidence interval in the distance range of 0–0.13 m. Figure 6 B in the results showed that the plant height of individual plants in the *Stipa grandis* population was significantly negatively correlated over short distances.

[0072] The distribution of biomass of different individuals in the *Stipa grandis* population in horizontal space within a 2m × 2m sampling area is shown in the figure. Figure 7 In the diagram, A represents the size of the circle, indicating the level of individual biomass. Based on the kmm(r) function, the spatial correlation curve of individual biomass in the *Stipa grandis* population first increases and then stabilizes with increasing distance. The curve does not deviate significantly from the confidence interval over the entire distance. Figure 7 B in the figure. Judging from the trend of the curve, the biomass of individual *Stipa grandis* populations remains negatively correlated over short distances.

[0073] The horizontal spatial distribution of clump width of different individuals in the *Stipa grandis* population within a 2m × 2m sampling area is shown below. Figure 8 In the diagram, A represents the size of the individual cluster amplitude. Based on the kmm(r) function, the spatial correlation curve of the individual cluster amplitude of the *Stipa grandis* population first fluctuates and rises with increasing distance, then falls and basically stabilizes. The curve does not deviate significantly from the confidence interval over the entire distance. Figure 8 In section B, the curve trend shows that the individual cluster amplitude of the *Stipa grandis* population is also negatively correlated over short distances.

[0074] (2) Spatial correlation of individual traits in Leymus chinensis population

[0075] The horizontal spatial distribution of plant height among different individuals in the Leymus chinensis population within a 2m × 2m sampling area is shown below. Figure 9 In the diagram, the shade of color A represents the height of an individual plant. Based on the kmm(r) function, the spatial correlation curve of individual plant height in the Leymus chinensis population first decreases and then slowly increases with increasing distance, deviating from the lower limit of the confidence interval within the range of 0.03–0.69 m. Figure 9 The trend of curve B in the figure indicates that there is a weak positive correlation between individual plant height of Leymus chinensis population at smaller distances, which turns into a negative correlation at smaller distances as the distance increases.

[0076] The distribution of biomass of different individuals in the Leymus chinensis population in horizontal space within a 2m × 2m sampling area is shown in the figure. Figure 10 In the diagram, A represents the size of the circle, indicating the level of individual biomass. Based on the kmm(r) function, the spatial correlation curve of individual biomass in the Leymus chinensis population first increases and then decreases with increasing distance, exhibiting fluctuating changes. The curve does not deviate from the confidence interval over the entire distance, as shown in the figure. Figure 10 The trend of curve B in the figure indicates a weak negative correlation between individual biomass of Leymus chinensis populations over short distances.

[0077] (3) Spatial association of individual traits among Leymus chinensis and Stipa grandis populations

[0078] Within a 2m × 2m sampling area, the horizontal spatial distribution of plant height among different individuals in the Leymus chinensis-Stipa grandis population is shown below. Figure 11In the diagram, A represents the *Stipa grandis* population in red and the *Leymus chinensis* population in blue, with the shade of color indicating the height of individual plants. Based on the km(r) function, the spatial correlation curve of different individual plant heights between the *Leymus chinensis* and *Stipa grandis* populations initially rises and then slowly declines with increasing distance, deviating from the upper limit of the confidence interval in the distance range of 0.1–0.29 m. Figure 11 The B curve in the figure indicates a negative correlation in plant height at small distances between individual species of Leymus chinensis and Stipa grandis.

[0079] The horizontal spatial distribution of biomass among different individuals in the Leymus chinensis-Stipa grandis population within a 2m × 2m sampling area is shown in the figure. Figure 12 In the diagram, A represents the *Stipa grandis* population in red and the *Leymus chinensis* population in blue, with the size of the circle indicating the level of individual biomass. Based on the km(r) function, the spatial correlation curve of different individual biomass between the *Leymus chinensis* and *Stipa grandis* populations first increases and then decreases with increasing distance, exhibiting fluctuating changes. The curve does not deviate from the confidence interval across the entire distance. Figure 12 The B curve variation indicates a negative correlation in individual biomass over short distances between Leymus chinensis and Stipa grandis populations.

[0080] The results show that traditional grassland community monitoring methods can only measure the average state of population or community-related trait indices; while the grassland community monitoring method proposed in this invention can not only measure the average state of related trait indices, but also measure the spatial distribution of related trait indices based on individual populations, and analyze the spatial correlation of related trait indices through labeled correlation functions. The spatial correlation of individual trait indices can reveal the trade-off between competition and positive interactions among individuals, thereby helping to understand the key ecological processes affecting populations and communities.

[0081] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for monitoring herbaceous plant communities, characterized in that, Includes the following steps: The quadrats were divided, and the attachment points of individual plants in each quadrat were marked sequentially. The phenotypic indicators of the individual plants were then measured. The location information of the attachment point of each individual plant in the quadrat was represented in the form of coordinates in a coordinate system, and the phenotypic information of each individual plant was marked accordingly. This allowed for the monitoring of the horizontal spatial distribution of individual plants and their traits.

2. The method according to claim 1, characterized in that, The quadrats include 0.5m×0.5m quadrats, 1m×1m quadrats, and 2m×2m quadrats.

3. The method according to claim 1, characterized in that, The trait indicators include plant height, clump width, fresh biomass weight, and dry biomass weight.

4. The method according to claim 1, characterized in that, Different markers are used to mark the attachment sites of different plant individuals.

5. The method according to claim 4, characterized in that, After marking the attachment sites of individual plants, the quadrats are photographed, and the coordinates of the control points are uniformly placed in the same coordinate system by inputting the R2V software. The coordinates of the control points are the vertices of the quadrats.

6. The method according to claim 5, characterized in that, In the R2V software, a corresponding layer is created for each population in the quadrat.

7. The method according to claim 6, characterized in that, The layer of each population is converted to shp format, and the position information of the attachment point of each plant individual in each population is digitized to obtain the spatial coordinates of all plant individuals in each population.

8. The application of the method according to any one of claims 1 to 7 in biodiversity assessment.

9. The application of the method described in any one of claims 1 to 7 in community structure analysis and dynamic monitoring of community succession.

10. The application of the method according to any one of claims 1 to 7 in ecosystem function research.

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