Evaluation method for plant habitat suitability

Abstract

The invention belongs to the technical field of protection biology, and particularly relates to a plant habitat suitability evaluation method. According to the specific technical scheme, survival rate and growth rate indexes under different precipitation amounts and temperature gradients are evaluated through field planting experiments, a model is established, and expression and potential adaptability of research species on different climate gradients are quantified and evaluated; mapping an optimal model result to a geographic space through a geographic information system or remote sensing data, and performing time regression on past climate suitability indexes to obtain the suitability change rate of the research species; and carrying out linear regression in the future time by utilizing the suitability change rate, which is an index showing the adaptability change trend of the research species, and predicting the future climate adaptability of the research species to obtain the suitable habitat area of the research species in the future space, thereby providing support for the protection work of the research species.

Description

Technical Field

[0001] The invention belongs to the technical field of conservation biology, and in particular relates to a method for evaluating the suitability of plant habitats. Background Art

[0002] Habitat suitability assessment is an important part of biodiversity conservation. Understanding the habitat requirements and spatial distribution of plants is crucial for the protection and management of biological species.

[0003] With the dramatic climate change of recent years, many plants have struggled to adapt to the resulting shifts in suitable habitats, preventing them from timely migration and conservation efforts. This has led to habitat shrinkage and even extinction. Due to their endangered, rare, and fragile nature, protected plants are more vulnerable to habitat loss during these shifts and struggle to migrate to new, suitable habitats, resulting in population decline or even extinction.

[0004] Therefore, it is necessary to accurately estimate the current and future habitat suitability distribution of protected plants, which will help implement more comprehensive protection plans such as habitat restoration, suitable habitat protection, and optimization of suitable habitat spatial layout, thereby protecting biodiversity and maintaining the species richness and stability of the ecosystem.

[0005] Standardized species distribution models (SDMs) based on species occurrence data are the most commonly used method for assessing climate suitability. These models are constructed based on species occurrence data (i.e., the geographic coordinates of species observations) and environmental variables (such as climate, topography, and soils), and can simulate and predict species habitats. However, these models assume that the occurrence and variation of a species are in equilibrium with the environmental factors affecting its distribution. This static assumption ignores changes in a species' adaptability to its habitat, thereby losing the ability to predict potential changes in suitable habitats as a result of changes in species adaptability, leading to inaccurate simulation results.

[0006] This inaccuracy is specifically manifested as follows: (1) non-dynamic equilibrium, where the current species distribution has not fully adapted to the current climate and there is a time lag effect, that is, the model may underestimate the species' potential habitat or incorrectly predict its distribution range; (2) there is no similar climate zone, that is, subsequent combinations that do not exist in the current distribution area may appear in the future. The model makes predictions based on the current climate range and cannot accurately predict the response of short-legged species under completely new climate conditions (extrapolation risk). Species may adjust their tolerance thresholds through adaptive evolution or phenotypic plasticity, and the model cannot simulate such dynamics (niche conservatism failure). As a result, the model may overestimate or underestimate the species' habitat.

[0007] In order to overcome the limitations of the non-dynamic equilibrium and the lack of similar climate zones in the model and better estimate the current and future potential habitat distribution ranges of protected species, it is necessary to establish a more accurate plant habitat suitability assessment method to quantify the response of species distribution to climate change in order to conduct in-depth simulation and prediction, and assist managers and relevant staff in formulating effective conservation strategies. Summary of the Invention

[0008] To achieve the above-mentioned purpose, the present invention adopts the following technical solution: a method for evaluating plant habitat suitability, comprising the following steps:

[0009] S1. Select seeds from healthy, pest-free mother plants for cultivation. After cultivation, conduct field planting experiments to obtain the survival rate and growth rate of the species.

[0010] S2. Quantify the influence weights of survival rate and growth rate using the objective weighting method of principal component analysis or the objective weighting method of entropy weight method, and combine the survival rate and growth rate into a species adaptability index to comprehensively reflect the survival and growth ability of plants in complex environments;

[0011] S3. Use a general linear model to analyze the relationship between the influence of climate factors at different planting sites and the species adaptability index. Evaluate a set of candidate models for up to two of the five climate variables obtained at each site and their interactions. Select the optimal model that is closest to the species growth mechanism from the candidate model set.

[0012] S4. Map the analysis results of the optimal model to geographic space through geographic information systems or remote sensing data, perform temporal regression on past climate suitability indices, and obtain the species suitability change rate;

[0013] S5. Use the suitability change rate to perform linear regression in the future to predict the future climate adaptability of the research species and obtain the suitable habitat area of the research species in the future space.

[0014] Preferably, the step S1 includes:

[0015] S11. Obtain climate data suitable for the survival of the study species and select a site for the field planting experiment based on the climate data; the climate data includes the maximum range of annual mean temperature and annual mean precipitation, and the Pearson correlation coefficient is used to test whether the correlation between annual mean temperature and annual mean precipitation is the lowest; the site selection requires that the climate covers the entire gradient of annual mean temperature and annual mean precipitation;

[0016] S12. Deploy small climate stations at the selected sites to obtain daily climate data for each site;

[0017] S13, collecting seeds from healthy mother plants free of pests and diseases, breeding seedlings in a greenhouse, and transplanting them to the selected site after the seedlings are grown;

[0018] S14. Record species growth data and survival status during the monitoring period, and calculate the survival rate and growth rate of the study species.

[0019] Preferably, in step S3, the candidate model set of at most two variables and their interactions among the five climate variables obtained at each location is evaluated, and the candidate model set includes:

[0020] The plurality of climate variables include air temperature AT, precipitation RF, relative humidity RH, soil moisture SM, and vapor pressure difference VPD;

[0021] Univariate model: only one climate variable is used, with a total of 5 models;

[0022] Bivariate main effect model: Two climate variables were selected from the five climate variables for combination, with no interaction between the two selected variables, for a total of 10 models;

[0023] Bivariate interaction model: Two climate variables were selected from the five climate variables for combination, with no interaction between the two selected variables, for a total of 10 models;

[0024] The candidate model set includes 25 models.

[0025] Preferably, in step S3, selecting the optimal model from the candidate model set includes:

[0026] S31. Calculate the collinearity r between each variable combination and exclude models with high collinearity;

[0027] S32, calculating the modified Akaike Information Criterion (AICc) values of the remaining models, and sorting the remaining models by score;

[0028] S33, selecting the model with the minimum AICc value, traversing all remaining models, and calculating ΔAICc for each model, where ΔAICc represents the AICc value of the current model minus the minimum AICc value;

[0029] S34. Calculate the coefficient of determination R of the remaining model 2 ;

[0030] S35, with the coefficient of determination R 2 The goal is to make the ΔAICc smaller and determine the optimal model.

[0031] Preferably, step S4 includes:

[0032] S41. Divide the study area into a grid map and assign the same average temperature and average precipitation during the study period to each grid cell;

[0033] S42, comparing the site data of each plot climate station with the global climate grid data, and correcting the deviation using the linear regression relationship between the two;

[0034] S43, based on the species adaptability index of the optimal model, the continuous probability values of climate factors are converted into binary maps to define a relative climate suitability index;

[0035] S44. Generate climate grid maps for multiple field planting experiment cycles, and perform temporal regression on the climate suitability index of each grid. Use a linear model to fit the temporal trend of the climate suitability index of each grid, map the regression results of each grid to geographic space, and use the geographic information system to visualize the changes in suitable areas under current and future climate scenarios, and display the suitability change rate of the study area.

[0036] The present application also protects an electronic device, comprising: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement a plant habitat suitability assessment method.

[0037] The present application also protects a computer-readable medium, wherein the computer-readable medium stores a computer program, and wherein the computer program implements a plant habitat suitability assessment method when executed by a processor.

[0038] The present invention has the following beneficial effects:

[0039] Experimentally introduce the survival rate or growth rate of research species across climate gradients, that is, evaluate the survival rate and survival rate indicators under different precipitation and temperature gradients through field planting experiments to establish a model, quantify and evaluate the performance and potential adaptability of research species on different climate gradients; use the optimal model for spatial modeling, that is, map the optimal model results to the geographic space through geographic information system or remote sensing data, and perform time regression on the past climate suitability index to obtain the suitability change rate of the research species; use the suitability change rate, an index that shows the changing trend of the adaptability of the research species, to perform linear regression in the future time, predict the future climate adaptability of the research species, and obtain the suitable habitat area of the research species in the future space, to provide support for the protection of the research species. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flowchart of the evaluation method of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the accompanying drawings to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0042] like Figure 1 As shown, the present invention discloses a method for evaluating plant habitat suitability, comprising the following steps:

[0043] S1. Select seeds from healthy mother plants without diseases or insect pests for cultivation. After cultivation, conduct field planting experiments to obtain the survival rate and growth rate of the species.

[0044] Specifically, step S1 includes the following process:

[0045] S11. Collect historical data, review literature, and conduct field research to determine the maximum known climatic range of the species' distribution. This will determine the range of mean annual temperature (MAT) and mean annual precipitation (MAP) suitable for the species' survival. Use the Pearson correlation coefficient to test the correlation between MAT and MAP. Generally, MAT and MAP have the lowest correlations among all climate variables, effectively isolating the contributions of each variable from other potentially related climate variables.

[0046] The climate of the experimental site should cover the entire gradient of mean annual temperature (MAT) and mean annual precipitation (MAP); at the same time, the experimental site should have a low ground cover to minimize dynamic interference caused by competition or promotion among plants.

[0047] S12. Deploy a small climate station at the selected site to collect data on air temperature (AT), precipitation (RF), relative humidity (RH), and soil moisture (SM). Hourly vapor pressure difference (VPD) can be calculated from hourly air temperature (AT) and relative humidity (RH). Based on at least 23 hours of daily climate data, calculate the daily averages of air temperature (AT), relative humidity (RH), soil moisture (SM), and vapor pressure difference (VPD), as well as the daily sum of precipitation (RF). Use a gap-filling procedure to estimate missing daily averages for each station. For each climate data point, use the linear regression relationship with the station data with the highest correlation coefficient for that variable to fill in the missing data point.

[0048] The calculation process of vapor pressure difference VPD is as follows:

[0049] First, calculate the saturated water vapor pressure SVP using the cubic polynomial proposed by Lowe, which is applicable to the calculation of the saturated water vapor pressure on the surface of liquid water:

[0050] SVP=6.49+0.3495×AT+0.00781×AT2 +0.0000645×AT 3 (Unit: hPa);

[0051] The above formula has high accuracy in the range of -35℃ to +35℃.

[0052] Then calculate the actual water vapor pressure AVP based on the relative humidity RH and the saturated water vapor pressure SVP. The calculation formula is as follows:

[0053] (Unit: hPa);

[0054] The calculation formula for vapor pressure difference VPD is as follows:

[0055] VPD=SVP-AVP (unit: hPa).

[0056] In a greenhouse, seeds are sown in a medium suitable for plant growth. Typically, the ratio of nutrient soil, perlite, and vermiculite is 1:1:1. A slow-release fertilizer is added as needed. Once the seedlings are established, they are transplanted to the same soil as the original habitat, and their growth data is recorded. The goal of cultivating seedlings is to ensure that the plants have sufficient vitality to allow for observation of their growth feedback in a natural environment.

[0057] During the season suitable for the growth of the species, the cultivated plants are planted in the selected plots in a random order with an average distance of about 1 meter. After planting, water is added and no water is given afterwards.

[0058] S14. Monitor each plot at the end of the rainy and dry seasons in the distribution area of the plots. The monitoring period is generally 5 years (the monitoring period should vary depending on the life cycle of the specific species). During the monitoring, the growth data and survival status of the species are recorded. The relative growth rate (RGR) is generally used to express the changes in the plants since planting.

[0059]

[0060] W2 represents the final diameter of the plant, W1 represents the initial diameter of the plant; t2 represents the final time of the plant, t1 represents the initial time of the plant, and t2-t1 represents the number of days the plant grows. Diameter data is generally calculated from the base growth data of the plant.

[0061] S2. Plant survival was calculated as the cumulative survival rate at each site during the study period, and growth was calculated as the average relative growth rate (RGR) at each site from the time of field planting to the end of the study. Using objective weighting methods such as principal component analysis (PCA) or entropy weighting, the weights of survival and growth were quantified and combined into a species adaptability index, which comprehensively reflects the ability of plants to survive and grow in complex environments. This simplifies the subsequent multivariate analysis model, allowing the use of a simpler general linear model, focusing on the relative climatic suitability of each site. This means selecting the optimal model for the correlation between the species adaptability index and climatic variables for plants planted in the wild at each site.

[0062] S3. Use a general linear model to analyze the impact of climate factors on species adaptability index in different planting sites, evaluate a set of candidate models for up to two of the five climate variables obtained at each site and their interactions, and select the optimal model that best approximates the species growth mechanism from the candidate model set. The details are as follows:

[0063] S21. Determine the candidate model set: To study the response of species adaptability index to climatic factors, it is necessary to evaluate the candidate model set of up to two variables out of the five climate variables obtained at each location and their interactions. The five climate variables are air temperature AT, precipitation RF, relative humidity RH, soil moisture SM, and vapor pressure difference VPD. Among them, the daily climate average (or the sum of precipitation RF) is the average value during the entire study period. The arithmetic mean of the climate variables on the same date in the years covered by the study period is conducive to eliminating abnormal fluctuations in a single year, reflecting long-term climate characteristics, and improving the robustness of the model.

[0064] Original variables: air temperature AT, precipitation RF, relative humidity RH, soil moisture SM, and vapor pressure difference VPD. Each model contains at most two variables and their interactions, resulting in the following three situations:

[0065] 1) Univariate model: only one climate variable, no interaction. So there are 1 each of air temperature (AT), precipitation (RF), relative humidity (RH), soil moisture (SM), and vapor pressure deficit (VPD), for a total of 5 models;

[0066] 2) Bivariate main effect model: Two climate variables, no interaction. Air temperature (AT), precipitation (RF), relative humidity (RH), soil moisture (SM), and vapor pressure deficit (VPD) are combined in pairs, for a total of C(5,2) = 10 models.

[0067] 3) Bivariate interaction model: Two climate variables interact. Thus, there are two combinations of temperature (AT), precipitation (RF), relative humidity (RH), soil moisture (SM), and vapor pressure difference (VPD), for a total of C(5,2) = 10 models.

[0068] Therefore, the candidate model set includes 25 models.

[0069] S22. Calculate collinearity r: Calculate the collinearity r between each variable combination. Exclude models with high collinearity, and define the remaining models as the residual models. Collinearity refers to the presence of a linear correlation between independent variables in a multiple linear regression model. If two or more independent variables in a model have a very strong linear correlation, model parameter estimates may become unstable or difficult to understand.

[0070] S23. Calculate AICc: Calculate the modified Akaike Information Criterion (AICc) for the remaining models and rank them by score. The modified AICc is used to measure the balance between model goodness of fit and complexity. Lower values indicate a model with greater ability to explain the data while avoiding overfitting.

[0071]

[0072] AIC = -2ln(L) + 2k, where L is the maximum likelihood of the model, k is the number of parameters, and n is the sample size. AICc adds a correction term to AIC to mitigate potential bias in AIC with small sample sizes. AICc measures the balance between model goodness of fit and complexity. Lower values indicate a stronger model's ability to explain the data while avoiding overfitting. You can use the R packages AICcmodavg (for calculating AICc) and MuMIn (for multi-model comparison) to calculate AICc.

[0073] S24. Calculate ΔAICc: Calculate ΔAICc based on the AICc ranking. This is the AICc of each model minus the minimum AICc value among all candidate models. This value reflects the difference between each candidate model and the optimal model. This metric measures the relative difference between candidate models and the optimal model. By quantifying the balance between model goodness of fit and complexity, it helps determine which of multiple models most closely resembles the true data generation mechanism. Generally, the smaller the ΔAICc value, the better the model.

[0074] ΔAICc i =AICc i -min(AICc1,AICc2,...,AICc m );

[0075] ΔAICc<2: The model has similar explanatory power to the optimal model and can be considered as an equivalent model;

[0076] 2≤ΔAICc≤7: The model has some support, but is weaker than the optimal model;

[0077] ΔAICc>10: The model has little support and can be excluded.

[0078] S25. Calculate the coefficient of determination R 2 :R 2 The value is an indicator used to measure the ability of the regression model (linear) to explain data variation. It can determine the degree of influence of the independent variable on the dependent variable. Its value range is [0,1].

[0079] R 2 =1: The model fits the data perfectly, and all observations fall on the regression line;

[0080] R 2 =0: the model cannot explain the variation of the dependent variable at all;

[0081] R 2 =0.8: The model can explain 80% of the data variation, and the remaining 20% is caused by variables not included in the model or random errors.

[0082]

[0083] SST (total sum of squares): the sum of the differences between the actual value of the dependent variable and its mean, reflecting the total variation of the data;

[0084] SSR (sum of squares of regression): the sum of the differences between the model predictions and the mean, reflecting the variation explained by the model;

[0085] SSE (Residual Sum of Squares): The sum of the differences between the actual value and the predicted value, reflecting the variation not explained by the model.

[0086] Calculate R 2 To calculate the value, you can use statistical tools such as Excel, R language (summary(lm())), or Python's sklearn.metrics.r2_score tool.

[0087] S26. Determine the optimal model: R 2 The R value ensures sufficient fitting of known data (avoiding underfitting), while AICc constrains model complexity (avoiding overfitting). The two complement each other to form a dual verification mechanism of "explanatory power-generalization", systematically balancing model goodness of fit, complexity and explanatory power, and selecting the model that best meets the research objectives. Therefore, when selecting the optimal model, R is used to 2 When choosing, please note that there is no clear threshold for the AICc value itself. You need to compare the ΔAICc values of different models to judge their quality. For example: ① When the ΔAICc values are similar (such as the difference is <2), choose R 2 Higher model;②When R 2 The model with slightly lower ΔAICc < 2 may be better, and comprehensive comparison is performed to balance the fit and complexity.

[0088] S3. Use geographic information systems (GIS) or remote sensing data to map the optimal model analysis results onto geographic space. Regress past climate suitability indices over time to obtain the rate of change in species suitability. Spatial modeling essentially involves spatially explicitizing the optimal model of species suitability indices and climate variables, and mapping the model results onto geographic space using GIS or remote sensing data.

[0089] Specifically, step S3 includes the following process:

[0090] S31. Gridding: Divide the study area into grids ranging from a few hundred meters to a thousand meters, assigning each grid cell the same average temperature and average precipitation for the study period. High-precision climate raster data, such as WorldClim, is used.

[0091] S32. Grid data correction: In order to confirm that the average temperature AT and average precipitation RF can accurately represent the study area, the site data of each plot climate station are compared with the global climate grid data, and the linear regression relationship between the two is used to correct the deviation.

[0092] S33. Simulate habitat suitability: Based on the species adaptability index of the optimal model, the continuous probability value of the climate factor is converted into a binary map to define a relative climate suitability index with a value range of 0-1, which is convenient for simulating the suitability of the species habitat in the study area.

[0093] S34. Calculate the suitability change rate: To estimate the possible changes in relative climate suitability over the past few decades, a field planting experiment cycle is generally 5 years. A climate grid map of each consecutive 5 years is generated, and the climate suitability index of each grid is regressed over time. A linear model is used to fit the temporal trend of the climate suitability index of each grid. The regression results of each grid (such as slope, p-value) are mapped to geographic space. The changes in suitable areas under current and future climate scenarios are visualized in combination with a geographic information system, showing the suitability change rate of the study area, and the alpha value (α value) is used to test significance.

[0094] S4. Use the suitability change rate to perform linear regression in the future to predict the future climate adaptability of species and obtain the suitable habitat areas of species in the future space.

[0095] Using the suitability change rate, an index that reflects the changing trend of the species' climate adaptability, linear regression and spatial modeling were performed in the future to predict the habitat suitability of the species in the future study area.

[0096] All of the above spatial modeling can be performed using the R package terra. Using these methods, we can systematically predict the spatial distribution evolution of plants under climate change, providing a scientific basis for ecological protection and resource management.

[0097] An electronic device includes: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement a plant habitat suitability assessment method. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers.

[0098] A computer-readable medium storing a computer program that, when executed by a processor, performs a method for assessing plant habitat suitability. In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program borne on a non-transitory computer-readable medium, the computer program containing program code for executing the method illustrated in the flowcharts.

[0099] It should be noted that the computer-readable medium of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0100] In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

[0101] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0102] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0103] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0104] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0105] The units involved in the embodiments described in this disclosure may be implemented in software or hardware, wherein the name of a unit does not necessarily limit the unit itself.

[0106] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0107] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0108] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various deformations, modifications, and substitutions made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. A method for evaluating plant habitat suitability, characterized by: Through field planting experiments, the survival rate and growth rate indicators under different precipitation and temperature gradients were evaluated and models were established to quantify and evaluate the performance and potential adaptability of the research species in different climate gradients; the optimal model results were mapped to the geographic space through geographic information systems and remote sensing data, and the past climate suitability index was regressed over time to obtain the suitability change rate of the research species; the suitability change rate was used to perform linear regression in the future time to predict the future climate adaptability of the research species and obtain the suitable habitat area of the research species in the future space.

2. The method for evaluating plant habitat suitability according to claim 1, wherein: The steps include: S1. Select seeds from healthy, pest-free mother plants for cultivation. After cultivation, conduct field planting experiments to obtain the survival rate and growth rate of the species. S2. Quantify the influence weights of survival rate and growth rate using the objective weighting method of principal component analysis or the objective weighting method of entropy weight method, and combine the survival rate and growth rate into a species adaptability index to comprehensively reflect the survival and growth ability of plants in complex environments; S3. Use a general linear model to analyze the relationship between the influence of climate factors at different planting sites and the species adaptability index. Evaluate a set of candidate models for up to two of the five climate variables obtained at each site and their interactions. Select the optimal model that is closest to the species growth mechanism from the candidate model set. S4. Map the analysis results of the optimal model to geographic space through geographic information systems or remote sensing data, perform temporal regression on past climate suitability indices, and obtain the species suitability change rate; S5. Use the suitability change rate to perform linear regression in the future to predict the future climate adaptability of the research species and obtain the suitable habitat area of the research species in the future space.

3. The method for evaluating plant habitat suitability according to claim 2, wherein: The step S1 comprises: S11. Obtaining climate data suitable for the survival of the research species, and selecting a site for the field planting experiment based on the climate data; S12. Deploy small climate stations at the selected sites to obtain daily climate data for each site; S13, collecting seeds from healthy mother plants free of pests and diseases, breeding seedlings in a greenhouse, and transplanting them to the selected site after the seedlings are grown; S14. Record species growth data and survival status during the monitoring period, and calculate the survival rate and growth rate of the study species.

4. The method for evaluating plant habitat suitability according to claim 3, wherein: The climate data in step S11 includes the maximum range of the annual average temperature and the annual average precipitation, and the Pearson correlation coefficient is used to test whether the correlation between the annual average temperature and the annual average precipitation is the lowest.

5. The method for evaluating plant habitat suitability according to claim 3, wherein: The site selection requires that the climate covers the entire gradient of mean annual temperature and mean annual precipitation.

6. The method for evaluating plant habitat suitability according to claim 2, wherein: In step S3, a candidate model set of at most two variables and their interactions among the five climate variables obtained at each location is evaluated. The candidate model set includes: The plurality of climate variables include air temperature AT, precipitation RF, relative humidity RH, soil moisture SM, and vapor pressure difference VPD; Univariate model: only one climate variable is used, with a total of 5 models; Bivariate main effect model: Two climate variables were selected from the five climate variables for combination, with no interaction between the two selected variables, for a total of 10 models; Bivariate interaction model: Two climate variables were selected from the five climate variables for combination, with no interaction between the two selected variables, for a total of 10 models; The candidate model set includes 25 models.

7. A method for evaluating plant habitat suitability according to claim 6, characterized in that: In step S3, selecting the optimal model from the candidate model set includes: S31. Calculate the collinearity r between each variable combination and exclude models with high collinearity; S32, calculating the modified Akaike Information Criterion (AICc) values of the remaining models, and sorting the remaining models by score; S33, selecting the model with the minimum AICc value, traversing all remaining models, and calculating ΔAICc for each model, where ΔAICc represents the AICc value of the current model minus the minimum AICc value; S34. Calculate the coefficient of determination R of the remaining model 2 ; S35, with the coefficient of determination R 2 The goal is to make the ΔAICc smaller and determine the optimal model.

8. The method for evaluating plant habitat suitability according to claim 2, wherein: The step S4 comprises: S41. Divide the study area into a grid map and assign the same average temperature and average precipitation during the study period to each grid cell; S42, comparing the site data of each plot climate station with the global climate grid data, and correcting the deviation using the linear regression relationship between the two; S43, based on the species adaptability index of the optimal model, the continuous probability values of climate factors are converted into binary maps to define a relative climate suitability index; S44. Generate climate grid maps for multiple field planting experiment cycles, and perform temporal regression on the climate suitability index of each grid. Use a linear model to fit the temporal trend of the climate suitability index of each grid, map the regression results of each grid to geographic space, and use the geographic information system to visualize the changes in suitable areas under current and future climate scenarios, and display the suitability change rate of the study area.

9. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method for evaluating plant habitat suitability as described in any one of claims 1 to 8.

10. A computer-readable medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for evaluating plant habitat suitability according to any one of claims 1 to 8 is implemented.

Images