Forest ecological restoration method based on community integrity and functional traits

Abstract

The invention belongs to the field of ecological restoration, and discloses a forest ecological restoration method based on community integrity and functional traits, and the method comprises the steps: S1, obtaining a community integrity index (CCI) of a research area; s2, whether the CCI is larger than 0 or not is judged, and if yes, species in the research area are protected; if not, entering S3; s3, acquiring dark diversity probability of species in the research area; s4, the species with the dark diversity probability smaller than a set threshold value serve as candidate species; s5, constructing an underdetermined equation set based on the expected values of the functional traits of the candidate species, and solving the relative abundance of the candidate species; and S6, performing species restoration treatment based on the relative abundance of the candidate species. According to the method, the community missing species can be quantitatively evaluated, the abundance proportion of the restoration species can be predicted, the functional restoration combination can be optimized, and the problem that potential restoration species cannot be accurately identified and function-oriented restoration configuration cannot be realized due to the fact that species selection is carried out based on a traditional method only depending on an existing community structure is solved.

Description

Technical Field

[0001] The present invention relates to the field of ecological restoration, and particularly to a forest ecological restoration method based on community integrity and functional traits. Background Art

[0002] After the current forest ecosystem is affected by natural disturbances or human destruction, problems such as the degradation of community structure and function and the decline of biodiversity often occur. Traditional restoration measures mostly select restoration species based on the characteristics of the existing community or empirical judgment, often ignoring the important role of "dark diversity" species that are potential but not actually present in the regional species pool and their functional traits in ecosystem restoration, resulting in limited restoration effects.

[0003] Therefore, there is an urgent need to develop a forest ecological restoration technical method that systematically integrates dark diversity, species functional traits, and environmental factors and has the ability of quantitative prediction to provide a more scientific and accurate restoration path for degraded forest ecosystems. Summary of the Invention

[0004] The purpose of the present invention is to disclose a forest ecological restoration method based on community integrity and functional traits to solve the technical problems raised in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] The present invention provides a forest ecological restoration method based on community integrity and functional traits, including

[0007] S1, obtaining the community integrity index CCI of the research area;

[0008] S2, determining whether CCI is greater than 0. If so, protecting the species in the research area; if not, proceeding to S3;

[0009] S3, obtaining the dark diversity probability PDD of the species in the research area;

[0010] S4, taking the species with a dark diversity probability PDD less than the set threshold as candidate species;

[0011] S5, constructing an underdetermined equation system based on the expected values of the functional traits of the candidate species and solving for the relative abundances of the candidate species;

[0012] S6, performing species restoration treatment on the research area based on the relative abundances of the candidate species.

[0013] Preferably, S1 includes:

[0014] Calculating the community integrity index CCI of the research area using the following formula:

[0015]

[0016] OD is the measured diversity index of the study area; is the dark diversity index of the study area.

[0017] Preferably, the calculation formula for the measured diversity index of the study area is:

[0018] OD =

[0019] N is the total number of species recorded in the species pool that exist in the study area; is used to indicate whether the z-th species is observed. If it is observed, then is 1; if it is not observed, then is 0.

[0020] Preferably, the process of obtaining the dark diversity index of the study area includes:

[0021] Step 1, train a prediction model;

[0022] Step 2, input the environmental data of each quadrat in the study area into the prediction model to obtain the presence probability of each species under each quadrat;

[0023] Step 3, calculate the presence probability of the species in the study area based on the presence probabilities of all quadrats;

[0024] Step 4, obtain the dark diversity index of the study area based on the presence probability of the species in the study area.

[0025] Preferably, training the prediction model includes:

[0026] The first step, prepare training data:

[0027] The training data includes a species matrix and an environmental matrix;

[0028] The second step, select a prediction model:

[0029] The prediction model is a Bayesian hierarchical model or a Markov random field model;

[0030] The third step, train the prediction model based on the training data.

[0031] Preferably, calculating the presence probability of the species in the study area based on the presence probabilities of all quadrats includes:

[0032] For species i, the probability that species i exists in quadrat k is expressed as ;

[0033] Then the probability that species i does not exist in quadrat k is 1 - ;

[0034] The probability of absence of species i in the study area is , where KU is the set of quadrats;

[0035] The probability of presence of species i in the study area is .

[0036] Preferably, the dark diversity index of the study area is obtained based on the probability of presence of species in the study area, including:

[0037] For each species i, the following calculations are performed respectively:

[0038] Determine whether the probability of presence of species i in the study area is greater than the set probability threshold. If so, store it in the set U1;

[0039] Store the species in U1 that have not been observed in any quadrat in the set U2;

[0040] Take the total number of species in U2 as the dark diversity index of the study area.

[0041] Preferably, the dark diversity probability PDD of the species in the study area is obtained, including:

[0042] Calculate the dark diversity probability of species b in the study area using the following formula :

[0043]

[0044] represents the number of times that species b belongs to the set U2 in the most recent NP observations.

[0045] Preferably, the set threshold is 0.4.

[0046] Preferably, the underdetermined system of equations is as follows:

[0047]

[0048] where represents the value of species j on the functional trait q, is the relative abundance of species j, is the expected value of the functional trait q in the community of the study area, q ∈ bU, and bU is the set of functional traits.

[0049] The present invention discloses a forest ecological restoration method based on community integrity and functional traits. Based on integrating the potential of the regional species pool and ecological functional trait information, the present invention has developed a new ecological restoration method that can quantitatively predict the optimal restoration species combination. It can be used to quantitatively evaluate the missing species in the community, predict the abundance ratio of the restoration species, and optimize the functional restoration combination, solving the technical problem that species selection based on the traditional method that only relies on the existing community structure cannot accurately identify potential restoration species and achieve function-oriented restoration configuration. It is fast, effective, convenient and practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0051] Figure 1 It is a schematic diagram of a forest ecological restoration method based on community integrity and functional traits of the present invention.

[0052] Figure 2 It is a schematic diagram of the relative abundance of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0054] For the acquisition method of the basic data of the forest area, existing conventional technologies or drones can be used for data acquisition. The specific brand of the drone is not limited, and the instruments, software, and tools involved are all obtained through commercial channels. This will not be elaborated one by one here.

[0055] Glossary: The terms involved in the embodiments are all general explanations in the art. For example:

[0056] Measured diversity: It refers to the number of species actually existing and recorded in a specific area, and can also be understood as species richness;

[0057] Community integrity: It refers to the state in which a forest ecosystem remains complete and stable in terms of species composition, ecological processes, structure, and function. It encompasses the maintenance of biodiversity, the smooth operation of material cycling, and the normal progress of energy flow. Community integrity is evaluated by measuring the ratio of the observed diversity to the dark diversity, reflecting the degree of realization of the species pool in the local community. This concept quantifies the relationship between the realized diversity and the potential diversity in the community, and more intuitively reveals the ecological state of the community and its restoration process.

[0058] Reference Figure 1 , the present invention provides a forest ecological restoration method based on community integrity and functional traits, including

[0059] S1, obtaining the community integrity index CCI of the research area;

[0060] S2, judging whether CCI is greater than 0. If so, protecting the species in the research area; if not, entering S3;

[0061] S3, obtaining the dark diversity probability PDD of the species in the research area;

[0062] S4, taking the species with the dark diversity probability PDD less than the set threshold as candidate species;

[0063] S5, constructing an underdetermined equation system based on the expected values of the functional traits of the candidate species, and solving the relative abundances of the candidate species;

[0064] S6, performing species restoration treatment on the research area based on the relative abundances of the candidate species.

[0065] Preferably, S1 includes:

[0066] Calculating the community integrity index CCI of the research area using the following formula:

[0067]

[0068] OD is the observed diversity index of the research area; is the dark diversity index of the research area.

[0069] Preferably, the calculation formula for the observed diversity index of the research area is:

[0070] OD =

[0071] N is the total number of species recorded in the species pool that exist in the research area; is used to represent whether the z-th species is observed. If it is observed, then is 1; if it is not observed, then is 0.

[0072] Preferably, the process of obtaining the dark diversity index of the study area includes:

[0073] Step 1, train a prediction model;

[0074] Step 2, input the environmental data of each quadrat in the study area into the prediction model to obtain the presence probability of each species under each quadrat;

[0075] Step 3, calculate the presence probability of the species in the study area based on the presence probabilities of all quadrats;

[0076] Step 4, obtain the dark diversity index of the study area based on the presence probability of the species in the study area.

[0077] This method makes full use of the environmental information and species co-occurrence patterns among quadrats, quantitatively identifies the species that should theoretically exist in the community but are actually missing under the current environmental conditions, and provides a more accurate basis for evaluating community integrity and conservation management.

[0078] Preferably, training the prediction model includes:

[0079] The first step is to prepare the training data:

[0080] The training data includes a species matrix and an environmental matrix;

[0081] The second step is to select a prediction model:

[0082] The prediction model is a Bayesian hierarchical model or a Markov random field model;

[0083] The third step is to train the prediction model based on the training data.

[0084] Preferably, the species matrix is an NK matrix, where NK is the total number of quadrats. In the species matrix, if the species exists in the quadrat, the value of the element corresponding to the quadrat is 1, otherwise it is 0.

[0085] Preferably, the environmental matrix is matrix, where M represents the total number of types of environmental data; the types of environmental data include temperature, precipitation, soil pH, altitude, etc.

[0086] The processing of the environmental data is as follows:

[0087] Standardize the environmental variables.

[0088] Preferably, when the prediction model is a Bayesian hierarchical model, training the prediction model based on the training data includes:

[0089] The first step is to define the model structure:

[0090] (1) Ecological process layer:

[0091] Model the true presence state of the species at the \(u\)-th site , which is usually a binary latent variable (\(0 =\) absent, \(1 =\) present).

[0092] True presence probability is usually related to environmental covariates through the \(\logit - link\) function as follows:

[0093] , , ,... are regression coefficients to be estimated.

[0094] is the random effect, used to capture spatial autocorrelation, site - specific variation, or other structured random variation (such as or the spatial Gaussian process \(GP(0,\Sigma)\)).

[0095] (2) Observation process layer:

[0096] Model the conditional probability of observing the species in the \(v\)-th survey at the \(u\)-th site , conditional on the species being truly present at the site ( \(= 1\)). If the species is absent ( \(= 0\)), the conditional probability of observing the species is 0.

[0097] The observation probability is usually also related to covariates through the \(\logit - link\) function which may include survey time, weather, observer experience, etc.:

[0098] \(\beta_0,\beta_1,\beta_2,...\) are observation regression coefficients to be estimated.

[0099] Data model:

[0100] Link the observed data (whether the species is observed in the \(v\)-th survey at the \(u\)-th site, \(0\) if not observed, otherwise \(1\)) with and as follows: | , Bernoulli( )

[0101] This means:

[0102] If the species is present ( = 1), then with probability it is observed as 1 (observed), with probability 1 - it is observed as 0 (not observed).

[0103] If the species is absent ( = 0), then it must be 0 (not observed).

[0104] Second step, prepare data:

[0105] Response variable: Observation record of the v-th survey at location u ( = 0 or 1). Note that a location usually requires multiple surveys to reliably estimate the probability of observation.

[0106] Ecological covariates : Variables describing the environmental characteristics of location u (such as altitude, slope, vegetation type, climate variables, distance to water source, etc.). These should be associated with location u rather than with a single survey v. Covariates used to predict new locations must be the same as those in the training data and processed in the same way (standardized, etc.).

[0107] Observation covariates : Variables describing the conditions of the v-th survey at location u (such as survey date, time of day, duration, cloud cover, observer, etc.). These are used to explain the variation in the observation probability.

[0108] Third step, set the prior distribution:

[0109] Specify the prior distribution for all parameters to be estimated (α0, α1, ..., β0, β1, ..., , , ...).

[0110] Fourth step, model training:

[0111] Based on the observed data y and the set prior distribution, calculate the joint posterior distribution p(θ, z | y) of the model parameters and latent variables ( ) where θ includes all parameters (α, β, σ, ...).

[0112] The above process can be implemented using Markov Chain Monte Carlo (MCMC).

[0113] Preferably, after training the prediction model based on the training data, the prediction model outputs the species - environment relationship parameters, so that the prediction model has the prediction ability.

[0114] Preferably, calculating the presence probability of a species in the study area based on the presence probabilities of all quadrats, including:

[0115] For species i, represent the probability of species i being present in quadrat k as ;

[0116] Then the probability of species i not being present in quadrat k is 1 - ;

[0117] The probability of species i not being present in the study area is , where KU is the set of quadrats;

[0118] The probability of species i being present in the study area is .

[0119] Preferably, obtaining the dark diversity index of the study area based on the presence probability of the species in the study area, including:

[0120] Perform the following calculations for each species i respectively:

[0121] Judge whether the presence probability of species i in the study area is greater than the set probability threshold (e.g., 0.5). If so, store it in set U1;

[0122] Store the species in U1 that have not been observed in any quadrat in set U2;

[0123] Take the total number of species in U2 as the dark diversity index of the study area.

[0124] Preferably, obtaining the dark diversity probability PDD of the species in the study area, including:

[0125] Calculate the dark diversity probability of species b in the study area using the following formula :

[0126]

[0127] represents the number of times that species b belongs to set U2 in the most recent NP observations. Specifically, the value of NP can be determined according to the actual number of observations.

[0128] Preferably, the set threshold is 0.4.

[0129] Preferably, the underdetermined system of equations is as follows:

[0130]

[0131] where represents the value of species j on functional trait q, is the relative abundance of species j, is the expected value of the functional trait q in the community of the study area, where q ∈ bU and bU is the set of functional traits.

[0132] It is obtained by fitting the relationship between traits and the environment using a generalized linear model and then predicting the optimal trait value in the target environment. This target trait value is used as a constraint and input into a system of linear equations to generate the species abundance distribution.

[0133] Preferably, the functional traits include the wood density, diameter at breast height, tree height, leaf nitrogen content, etc. of the species.

[0134] Preferably, solving for the relative abundances of the candidate species includes:

[0135] Establishing the objective function Y:

[0136]

[0137] is the importance weight of the functional trait q, ;

[0138] The constraint condition of Y is YS:

[0139]

[0140] Using a heuristic algorithm to perform minimization optimization on the objective function to obtain the relative abundances of each species.

[0141] Preferably, using a heuristic algorithm to perform minimization optimization on the objective function to obtain the relative abundances of each species includes:

[0142] The first step is to obtain the set of heuristic algorithms GU, and the number of heuristic algorithms included in GU is denoted as nGU;

[0143] The second step is to use each heuristic algorithm in GU to perform FM (e.g., 20) optimizations on the objective function to obtain the relative abundances of each species , represents the relative abundance of species h obtained in the c-th optimization of the b-th heuristic algorithm;

[0144] The third step is to calculate the weights of the results of each optimization of each heuristic algorithm:

[0145] Calculating the scores of the results of each optimization:

[0146]

[0147] The score representing the result of the c-th optimization of the b-th heuristic algorithm, where ms is a constant to avoid a zero denominator, for example, it can be e to the power of negative 5; Is the fitness finally obtained for the c-th optimization of the b-th heuristic algorithm;

[0148] Calculate the total score:

[0149]

[0150] Calculate the weight of the result of each optimization:

[0151]

[0152] Fourth step, respectively obtain the finally determined relative abundance of each species:

[0153]

[0154] Represents the final relative abundance of species h.

[0155] The above optimization scheme can significantly improve robustness and stability, reduce the influence of randomness, and reduce the risk of falling into local optima.

[0156] The results of multiple runs of a single algorithm may fluctuate greatly. The ensemble method smooths the fluctuations caused by randomness by fusing the results of a large number of independent runs (across different algorithms), and the final result obtained has higher reproducibility under different random seeds.

[0157] Multiple runs of different algorithms and the same algorithm may fall into different local optimal solutions. The ensemble method does not rely on "luck" to find the global optimum, but combines multiple potential "good" solutions. Even if none of the solutions is the global optimum, its weighted average result is often closer to the global optimum or a better compromise than the result of a single run. It is equivalent to exploring a wider region of the solution space.

[0158] Different algorithms are good at dealing with different types of optimization landscapes (e.g., GA is good at global exploration, PSO is good at fast convergence, SA is good at escaping local optima). The ensemble method combines the advantages of different algorithms and theoretically can find a better solution than the average result of any single algorithm.

[0159] A large number of candidate solutions contain information about the shape of the objective function and the structure of the solution space. The weighted average can be regarded as a "consensus" solution guided by this information.

[0160] Preferably, the heuristic algorithms in GU include genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm, and differential evolution algorithm.

[0161] Example:

[0162] The study area is located in C Forest Farm, B City, A Province. This area has a temperate continental climate with distinct seasons and an average annual temperature of 3.6°C. The total area of the restoration area is 150 mu, and the restoration goal is to establish a Korean pine broad-leaved forest mainly composed of native tree species. Since the area is bare land and not suitable for directly planting the ecological key species, Korean pine, in 2014, Mongolian pine, which has a fast growth rate and strong adaptability to full-light environments, was first artificially regenerated. By the end of 2024, the survival density of Mongolian pine was relatively low, but it effectively improved the habitat and prevented the degradation of forest land, creating conditions for further restoration measures. In 2025, referring to the local zonal vegetation, the species configuration for the next step of restoration was determined, and afforestation was carried out in the spring of that year accordingly.

[0163] Based on the forest survey data of the region, information on the species composition of the sample plots was obtained. Combining with public databases (such as WorldClim, SoilGrids, etc.) or field sampling data, key environmental variables such as the average annual temperature, annual precipitation, and physical and chemical properties of the soil corresponding to the sample plots were extracted. The demonstration data table is shown in Table 1.

[0164] Table 1 Demonstration Data Table

[0165] If the community integrity index of the sample plot is greater than 0, it is determined that protection measures should be implemented for the community, and the existing species should be managed according to local conditions: for example, the natural restoration method is adopted to reduce the negative impact of human intervention on the ecosystem. For areas with a relatively high vegetation coverage, a relatively low degree of degradation, and a certain natural restoration ability, a closed forest area is established. Methods such as full closure, semi-closure, or rotational closure are used. Full closure means that all human activities are prohibited during the closure period; semi-closure allows limited tending and collection activities within a certain time and scope; rotational closure is to divide the closed forest area into sections and close them in turn. Fencing, boundary markers, warning signs and other closure facilities are set at the boundary of the closed forest area to clarify the closure scope and closure requirements.

[0166] Table 2 shows the top eight commonly used tree species for afforestation ranked by the calculated probability of species dark diversity. Since the restoration area is relatively low-lying and waterlogged areas are common, according to the principle of matching trees to sites, in addition to the ecological key species, Korean pine (Pinus koraiensis), finally, suitable tree species, Juglans mandshurica and Fraxinus mandshurica, were selected as restoration species to cultivate a water-hu forest.

[0167] Table 2 Dark Diversity Probability Ranking Table

[0168] As shown in Table 2, the present invention calculated the trait mean of each species and used a generalized linear model to predict the expected trait values in the target community environment, and substituted them into an underdetermined system of equations to solve for the relative abundance of each tree species, obtaining the appendixFigure 2 。

[0169] According to the output results of the above equations, it can be determined to select Juglans mandshurica, Fraxinus mandshurica and Pinus koraiensis as the restoration species combination, and their planting proportions fall within the range shown in Figure 2 (i.e., Fraxinus mandshurica accounts for 22%, Pinus koraiensis accounts for 60%, and Juglans mandshurica accounts for 18%) as the guiding target group to implement the in-situ restoration operation.

[0170] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A forest ecological restoration method based on community integrity and functional traits, characterized in that, Including: S1. Obtain the Community Completeness Index (CCI) of the study area; S2. Judge whether CCI is greater than 0. If so, conduct protection treatment on the species in the study area; if not, proceed to S3; S3. Obtain the Probability of Dark Diversity (PDD) of the species in the study area; S4. Take the species with PDD less than the set threshold as candidate species; S5. Based on the expected values of the functional traits of the candidate species, construct an underdetermined system of equations and solve for the relative abundances of the candidate species; S6. Based on the relative abundances of the candidate species, conduct species restoration treatment on the study area.

2. The forest ecological restoration method based on community integrity and functional traits according to claim 1, wherein S1 includes: Calculate the Community Completeness Index (CCI) of the study area using the following formula: ; OD is the measured diversity index of the study area; is the dark diversity index of the study area.

3. The forest ecological restoration method based on community integrity and functional traits according to claim 1, characterized in that, The calculation formula for the measured diversity index of the study area is: OD= ; N is the total number of species present in the study area recorded in the species pool; used to indicate whether the z-th species is observed. If it is observed, then it is 1; if it is not observed, then it is 0.

4. A forest ecological restoration method based on community integrity and functional traits according to claim 1, characterized in that, The process of obtaining the dark diversity index of the study area includes: Step 1. Train a prediction model; Step 2. Input the environmental data of each quadrat in the study area into the prediction model to obtain the presence probability of each species under each quadrat; Step 3. Calculate the presence probability of the species in the study area based on the presence probabilities of all quadrats; Step 4. Obtain the dark diversity index of the study area based on the presence probability of the species in the study area.

5. A forest ecological restoration method based on community integrity and functional traits according to claim 4, characterized in that, Training the prediction model includes: The first step. Prepare training data: The training data includes a species matrix and an environmental matrix; The second step. Select a prediction model: The prediction model is a Bayesian hierarchical model or a Markov random field model; The third step. Train the prediction model based on the training data.

6. The forest ecological restoration method based on community integrity and functional traits according to claim 4, wherein Calculating the presence probability of the species in the study area based on the presence probabilities of all quadrats includes: For species i, the probability of the presence of species i in quadrat k is denoted as ; Then the probability that species i does not exist in quadrat k is 1 - ; The probability of absence of species i in the study area is , where KU is the set of quadrats; The probability of the presence of species i in the study area is .

7. A forest ecological restoration method based on community integrity and functional traits according to claim 4, characterized in that, Obtaining the dark diversity index of the study area based on the presence probability of the species in the study area includes: Conduct the following calculations for each species i respectively: Judge whether the presence probability of species i in the study area is greater than the set probability threshold. If so, store it in set U1; Store the species in U1 that have not been observed in any quadrat in set U2; Take the total number of species in U2 as the dark diversity index of the study area.

8. A forest ecological restoration method based on community integrity and functional traits according to claim 7, characterized in that, Obtaining the Probability of Dark Diversity (PDD) of the species in the study area includes: Calculate the dark diversity probability of species b in the study area using the following formula : ; indicates the number of times that species b belongs to set U2 in the most recent NP observations.

9. A forest ecological restoration method based on community integrity and functional traits according to claim 1, characterized in that The set threshold is 0.

4.

10. A forest ecological restoration method based on community integrity and functional traits according to claim 1, characterized in that, The underdetermined system of equations is as follows: ; where represents the value of species j on functional trait q, is the relative abundance of species j, is the expected value of functional trait q in the community of the study area, q ∈ bU, where bU is the set of functional traits.

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