Ecological niche change and risk regionalization method and system for invasive plant Euphorbia spp.

Abstract

The invention belongs to the technical field of ecological systems, and discloses an invasive plant European grass ecological niche change and risk zoning method, which comprises the following steps: acquiring distribution point data of European grass; environmental factors are selected and processed; carrying out MaxEnt model optimization and precision evaluation; dividing an adulteranthus spicatus risk area; predicting the change of the adulteranthus spicatus risk area; and quantifying the ecological niche of the andersonia chinensis. According to the method, a certain region is taken as a research region, environment factors such as a distribution point of the anumoea, a bioclimate variable, a topographic variable and a human activity variable are taken as basic data, a diffusion trend is predicted by utilizing MaxEnt, Zonalization and ArcGis, invasion risk regions of different grades are divided, ecological niche change is quantified by virtue of an R software ecospat packet, and the method is helpful for risk prevention and control of the anumoea.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of ecosystem technology, and in particular relates to a method and system for niche change and risk zoning of the invasive plant Pseudostoma sphaerocephalum. Background Art

[0002] Biological invasion is considered to be one of the driving factors that reshape the composition of biological species, community structure and ecosystem functions, and is also one of the main factors leading to the extinction of native species. With global integration and climate change, the invasion situation will become more severe. Risk assessment of alien species invasion is the most effective and economical measure to prevent biological invasion. Predicting the potential occurrence area of ​​invasive species is an important part of invasion risk assessment. Ecological niche model (ENM) is a tool for predicting the potential occurrence area of ​​species. It can predict the probability of occurrence of target species in the landscape based on environmental variables and species occurrence points. Some scholars have added the spatial optimization tool Zonation on the basis of niche model prediction to identify the core area of ​​species invasion and divide the potential occurrence area into risk areas of different levels, further saving the cost of control. Another research hotspot of biological invasion is whether the niche of invasive species changes in time and space. Some scholars believe that the change of niche is relatively small in a short historical period, and the niche of invasive species remains stable in time and space. However, some studies have shown that the niche of invasive species has changed significantly after colonization in a new environment, and the niche has drifted in time and space. Although the ecological niche of each invasive species will vary depending on the environment, understanding the changes in the ecological niche of invasive species is still an effective way to reveal their adaptive capacity and spread limitations.

[0003] Praxelis clematidea is an annual herb of the genus Praxelis in the family Asteraceae. It is native to South America and was accidentally introduced as an ornamental plant. It has a large number of seeds and can be spread by wind, water, and human transport. It has characteristics such as salt tolerance and drought tolerance, strong adaptability and diffusion ability, and high genetic diversity. It is distributed in 10 provinces in China. This species has strong resource competitiveness. After invading cultivated land, it is easy to form a monodominant community, consuming a large amount of nutrients such as nitrogen, phosphorus, and potassium, thereby reducing soil fertility; it often crowds out other plants, and the root secretions can have a toxic effect on surrounding plants, resulting in crop yield reduction and reduced biodiversity. Because it spreads quickly and seriously damages the agricultural economy and biodiversity, it is considered a malignant invasive weed and has been included in my country's "List of Key Alien Invasive Species". Praxelis clematidea has been introduced to China for more than 40 years. There have been many studies in China, but there are no reports on its ecological niche and invasion risk zoning. During the field investigation, the author found that the false stinkweed had invaded dry land, orchards, roads and other land types in more than 20 counties in a certain region, forming a certain scale. However, the province has not paid much attention to the false stinkweed, and there are few related research reports. Therefore, this study uses a certain region as the research area, and based on the distribution point information of the false stinkweed, it quantifies its ecological niche and predicts its diffusion trend. The purpose is to explore: (1) How does the ecological niche of the false stinkweed change after it invades Guizhou? (2) Which areas are the key invasion areas of the false stinkweed? How does the risk pattern of the invasion area change over time? Clarifying the temporal and spatial changes of the false stinkweed ecological niche and invasion risk areas can provide a reference for its monitoring, early warning, eradication and management. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a method and system for niche change and risk zoning of the invasive plant Pseudocercospora indica.

[0005] The present invention is achieved by providing a method for changing the ecological niche and zoning the risk of the invasive plant Pseudostoma sphaerocephalum, the method comprising:

[0006] S1: Acquisition of distribution point data of false stinkweed;

[0007] S2: Selection and treatment of environmental factors;

[0008] S3: MaxEnt model optimization and accuracy evaluation;

[0009] S4: Delineation of risk areas for false stinkweed;

[0010] S5: Prediction of changes in the risk area of ​​false stinkweed;

[0011] S6: Quantification of the ecological niche of Pseudostoma sutchuenensis.

[0012] Further, the S1 specifically includes:

[0013] Data collection was based on county-level administrative divisions, with collection routes set as evenly as possible in the east, south, west, north and center, and collection points set on each route according to the presence or absence of Pseudostoma sphaerocephalum distribution; the collection routes covered 88 county-level administrative divisions and 1,509 township-level administrative divisions in the province, mainly involving dry land, orchards, barren slopes and along roads with high invasion rates; origin: distribution data for South America were obtained from the Global Biodiversity Information Network; spatial refinement of species occurrence records helps solve problems related to spatial sampling bias, so the Thin function of the R package spThin was used, and a 1km interval distance was selected for spatial dilution of distribution points, ultimately obtaining 454 invasion points in a certain area and 444 points of origin.

[0014] Further, the S2 specifically includes:

[0015] The following environmental variables with a greater impact on plant distribution were selected:

[0016] (1) Historical bioclimatic variables: average values ​​from 1970 to 2000, and future climate variables: average values ​​from 2021 to 2040, i.e., 2030; average values ​​from 2041 to 2060, i.e., 2050; average values ​​from 2061 to 2080, i.e., 2070; and average values ​​from 2081 to 2100, i.e., 2090. The variables are based on the ssp126 scenario of the BCC-CSM2-MR model, which is a relatively reliable model for simulating precipitation and temperature in China. The data are from the World Meteorological Database.

[0017] (2) Topographic variables, including aspect, slope, and elevation, with data from the Chinese Academy of Sciences Geospatial Digital Cloud Platform;

[0018] (3) Human activity variables, including the human footprint index. The human footprint index data value range is 0 to 50. The higher the score, the higher the intensity of human activity. The data comes from SEDAC. The above three groups of environmental variables are resampled in GIS so that the grid size is unified to 30s and the coordinate system is WGS-1984.

[0019] There may be collinearity between environmental variables, which will reduce the accuracy of model prediction. In order to remove the collinearity between environmental variables, the above three groups of environmental variables were subjected to person correlation analysis. Among a group of highly correlated variables with |P|>0.8, only the variable with the highest contribution rate was retained to obtain environmental factors. These environmental factors were converted into ASCII format in GIS for MaxEnt analysis.

[0020] Further, the S3 specifically includes:

[0021] The MaxEnt model was used to predict the potential geographical distribution of Pseudostoma sutchuenensis. In the prediction of the potential geographical distribution of species based on the MaxEnt model, the parameters set by the software default were generally selected, but for some species, the default settings may not be optimal; the R package ENMeval was used to adjust the regularization multiplier RM and the feature combination FC, analyze the complexity of the model under different parameter combinations, select the combination with lower complexity for modeling, and optimize the model;

[0022] The RM parameters are set to 1-4, with an interval of 0.5 for each parameter, and a total of 8 RM parameters; for the FC parameter, the MaxEnt model provides five features: linear L, quadratic Q, hinge H, product P, and threshold T; six feature combinations are selected: H, L, LQ, LQH, LQHP, and LQHPT; in the ENMeval package:

[0023] (1) The modified Akaike information criterion (AICc) is used to select the best model. The best model has the lowest AICc value, i.e., ΔAICc = 0;

[0024] (2) The overfitting degree of the model was evaluated by using the difference between the area under the receiver operating characteristic curve (AUC) of training and testing and the 10% training omission rate (OR 10);

[0025] (3) The area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of the model running results. When AUC∈[0.80-0.90), it means that the prediction results are good; when AUC∈[0.90-1.0), it means that the prediction results are excellent. Finally, the combination parameters when ΔAICc=0 were selected to establish the optimized model.

[0026] The distribution points of Pseudostoma sutchuenensis species in a certain area and the screened environmental variables were imported into MaxEnt 3.4.1 software in CSV and ASC file formats, respectively, and its parameters were set according to the above model optimization results; then, 75% of the occurrence points were randomly selected as training sets, and the remaining 25% were used as test sets, and 10 repetitions were generated for each training partition; the average value of the 10 repetitions was selected as the final output result, the output file format was logistic, and the output file type was ASC.

[0027] Further, the S4 specifically includes:

[0028] The software Zonation v4.0 was used to prioritize the invasion landscapes of Pseudostoma sutchuenensis, and landscapes with different invasion priorities were considered as invasion risk areas of different levels. High-risk areas, medium-risk areas, low-risk areas and non-risk areas for Pseudostoma sutchuenensis invasion were divided according to the order of priority, in order to better manage Pseudostoma sutchuenensis.

[0029] Zonation uses different algorithms to sort landscape priorities. The potential geographic distribution obtained by running MaxEnt is used as input in ASC format. The core unit zoning algorithm CAZ is selected as the rule for spatial priority sorting. At the same time, the warping factor is set to 1 and the Zonation software is run.

[0030] Further, the S5 specifically includes:

[0031] The future trend of risk areas is predicted, and the prediction process is as follows:

[0032] (1) Based on environmental variables at different times, we predicted the potential geographical distribution of Pseudo-smellgrass at different times and divided the risk areas at different times according to the priority of invading landscapes;

[0033] (2) Use SDMtoolbox to compare the risk areas in different periods and obtain the changing trend of the risk areas.

[0034] Further, the S6 specifically includes:

[0035] Based on the distribution point information of Pseudostoma sutchuenensis in its origin and a certain region as well as environmental variables, the ecospat software package was used to evaluate the stability, expansion and unfilled ecological niche of Pseudostoma sutchuenensis after it invaded Guizhou. NS represents the niche occupied by both the origin and the invaded area; NE represents the newly occupied niche in the invaded area; NS represents the niche that can be occupied within the niche range of the invaded area but is not currently occupied. Schoener's D calculation, niche consistency test and niche similarity test were also performed. Schoener's D evaluates the degree of overlap between the niches of the origin and the invaded area, and the value range of D is 0-1, where 0 indicates no overlap and 1 indicates complete overlap. The niche consistency test includes testing whether the niches of the origin and the invaded area are equal. The niche similarity test evaluates whether the degree of overlap between the niche of the origin and the niche of the invaded area is higher than that of random introduction.

[0036] Another object of the present invention is to provide an invasive plant pseudostinkweed ecological niche change and risk zoning system based on the invasive plant pseudostinkweed ecological niche change and risk zoning method, characterized in that the system specifically includes:

[0037] A data acquisition module is used to acquire the distribution point data of the false stinkweed;

[0038] A factor processing module, connected to the data acquisition module, is used for selecting and processing environmental factors;

[0039] The optimization and evaluation module is connected with the factor processing module and is used for MaxEnt model optimization and accuracy evaluation;

[0040] The risk area division module is connected with the optimization evaluation module and is used to divide the risk area of ​​​​the false stinkweed;

[0041] The risk area prediction module is connected with the risk area division module and is used to predict the change of the risk area of ​​​​the false stinkweed;

[0042] The quantification module is connected with the risk area prediction module and is used for quantifying the ecological niche of Pseudostoma sphaerocephalum.

[0043] Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for niche change and risk zoning of the invasive plant Pseudostoma scabra.

[0044] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method for niche change and risk zoning of the invasive plant Pseudostoma sphaerocephalum.

[0045] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0046] First, the present invention uses the distribution points of Pseudostoma sutchuenensis and environmental factors such as bioclimatic variables, terrain variables and human activity variables as basic data, uses MaxEnt, Zonation and ArcGis to predict the diffusion trend, divides the invasion risk areas into different levels, and quantifies its ecological niche changes with the help of R software ecospat package. The results show that: (1) The current high-risk area for Pseudostoma sutchuenensis invasion is 21144.04 km2, mainly concentrated in the west to the south (Liupanshui, Anshun, Southwest Guizhou and South Guizhou); the medium-risk area is 21144.04 km2, mainly concentrated in the southwest (Anshun and South Guizhou); the low-risk area is 28192.05 km2, which exists in all cities in a certain region, mainly concentrated in a small area outside the high- and medium-risk areas; other areas are non-risk areas. (2) From now to 2050, the risk area of ​​​​Pseudo-stinkweed will mainly expand to the central, southeastern and northeastern regions; from 2050 to 2070, it will mainly shrink in the northeast; from 2070 to 2090, it will mainly expand in the central and northeastern regions. The overall trend is expansion. (3) The niche overlap between Pseudo-stinkweed and its origin is very low (Schoener's D = 0.12); the niche expansion, stability and unfilled are 88%, 12% and 96% respectively, showing an unstable niche. In Guizhou, Pseudo-stinkweed mainly invades areas with higher precipitation in the warmest season, and has a lower temperature preference for the coldest season than its origin, which has a certain degree of adaptation to low temperatures.

[0047] Second, the technical solutions of the present invention solve existing technical problems and represent significant technological progress.

[0048] Technical issues:

[0049] 1. Imperfect data collection and processing: In existing technologies, data collection of invasive plants is often insufficient or not detailed enough, which may lead to an incomplete or inaccurate understanding of the distribution situation. The current method can more accurately obtain the distribution point data of false stinkweed through detailed county-level administrative district data collection and spatial dilution technology, reduce spatial sampling bias, and improve data quality.

[0050] 2. Selection of environmental factors and collinearity issues: The impact of environmental factors on plant distribution is complex and diverse. Existing technologies may have overlooked the selection of important environmental factors or failed to effectively deal with the collinearity issues between environmental variables. This invention optimizes the selection of environmental factors and improves the accuracy of model prediction by systematically selecting historical and future climate variables, terrain variables, and human activity variables and using correlation analysis to remove collinearity.

[0051] 3. Insufficient MaxEnt model parameter setting and optimization: Traditional MaxEnt model parameter settings often rely on default values, which may lead to low model accuracy. The present invention uses the ENMeval package to optimize the model parameters and select the best parameter combination, thereby improving the prediction accuracy and reliability of the model.

[0052] 4. Inaccurate risk zone division and prediction: The risk zone division in the prior art may lack systematicity and the ability to predict future risk zone changes is also weak. The present invention uses Zonation software to prioritize invasive landscapes, provides a systematic risk zone division method, and uses SDMtoolbox to predict future risk zone change trends, thereby enhancing the scientificity and predictability of risk management.

[0053] 5. Incomplete niche assessment: The existing technology for the niche assessment of invasive plants may not be comprehensive enough and cannot accurately reflect the ecological adaptation of plants in new environments. The present invention uses the ecospat software package to comprehensively assess the niche stability, expansion and unfilled conditions of Pseudostoma stoloniferum, providing a more detailed niche analysis to help understand the ecological adaptation process of invasive plants.

[0054] Significant technological advancements:

[0055] 1. Refined data collection and processing: It provides detailed data collection and spatial dilution technology based on county-level administrative districts, improving the accuracy and representativeness of distribution point data.

[0056] 2. Optimize the selection and processing of environmental factors: Through systematic environmental factor selection and collinearity analysis, the processing of environmental factors is improved and the accuracy of model prediction is improved.

[0057] 3. Precise optimization of the MaxEnt model: Through careful adjustment and optimization of model parameters, the model's predictive ability is improved, making the prediction of the potential geographical distribution of Pseudostoma sutchuenensis more reliable.

[0058] 4. Scientific risk zone division and prediction: Using tools such as Zonation and SDMtoolbox, a systematic risk zone division and future risk change prediction method are provided, enhancing the risk management capabilities for invasive plants.

[0059] 5. Comprehensive ecological niche assessment: The comprehensive assessment of the ecological niche using the ecospat software package provides a more in-depth ecological adaptation analysis and helps understand the niche changes and adaptation strategies of Pseudostoma sphaerocephalum in the new environment.

[0060] Through the above-mentioned technological advances, the present invention solves the deficiencies in the prior art and achieves significant improvements in data collection, model optimization, risk management and niche assessment, providing a more scientific and accurate method for the management and control of invasive plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a flow chart of the niche change and risk zoning method of the invasive plant Pseudostoma sphaerocephalum provided by an embodiment of the present invention;

[0062] Figure 2 It is the origin and location information of the pseudo-stinky grass in a certain area provided by the embodiment of the present invention;

[0063] Figure 3 is a heat map of environmental factor correlation provided by an embodiment of the present invention;

[0064] Figure 4 is the MaxEnt evaluation index generated by ENMeval provided in the embodiment of the present invention. The dotted arrows represent the default parameters of the MaxEnt model, and the solid arrows represent the optimized model parameters;

[0065] Figure 5 A is a Zonation performance curve provided by an embodiment of the present invention; B is the current invasion risk area of ​​​​the false stinkweed;

[0066] Figure 6 The prediction of the change of the risk area of ​​​​the pseudo-stink grass provided by the embodiment of the present invention is as follows: A is the current risk area of ​​​​the invasion of pseudo-stink grass; BE represents the change trend of the risk area of ​​​​the pseudo-stink grass from 2030 to 2090;

[0067] Figure 7: This is the result of the ecological niche of Pseudostoma odoratum based on principal component analysis (PCA) provided by an embodiment of the present invention: A: principal component analysis of the background values ​​of all environmental variables; B: the ecological niches of the origin and the invasion site overlap, and the Schoener's D value is 0.12; the green area is the ecological niche occupied by Pseudostoma odoratum in the origin, the pink part is the ecological niche occupied in the invasion site, the blue area is the overlapping ecological niche, and the solid arrows and dotted arrows respectively represent the transfer of the ecological niche and the background environment centroid; CF: single factor niche distribution characteristics, the meaning of the color is consistent with B;

[0068] Figure 8 This is a structural diagram of the niche change and risk zoning system of the invasive plant Pseudostoma sphaerocephalum provided by an embodiment of the present invention;

[0069] In the figure: 1. Data acquisition module; 2. Factor processing module; 3. Optimization evaluation module; 4. Risk area division module; 5. Risk area prediction module; 6. Quantification module. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0071] Embodiment 1:

[0072] A study on the ecological niche changes and risk zoning of Pseudostoma sutchuenensis was conducted in a certain area of ​​Guizhou Province.

[0073] S1: Acquisition of distribution point data of false stinkweed

[0074] In the 88 county-level administrative regions and 1,509 township-level administrative regions in Guizhou Province, collection routes were evenly set up in the east, south, west, north and center, covering habitats such as dry land, orchards, barren slopes and along roads.

[0075] On each route, collection points were set according to the presence or absence of Pseudostoma sutchuenensis, and spatial thinning was performed using the Thin function of the R package spThin, ultimately obtaining 454 invasion points.

[0076] S2: Selection and treatment of environmental factors

[0077] Historical and future bioclimatic variables, topographic variables (aspect, slope, elevation), and human activity variables (Human Footprint Index) were selected for the years 1970-2000 and 2021-2100.

[0078] The environmental variables were resampled in GIS, with a unified grid size of 30s and a coordinate system of WGS-1984.

[0079] Person correlation analysis was performed to remove highly correlated environmental variables and retain the variables with the highest contribution rate.

[0080] S3: MaxEnt model optimization and accuracy evaluation

[0081] The MaxEnt model was used to predict the potential geographical distribution of Pseudostoma sutchuenensis, and the R package ENMeval was used to adjust the regularization multiplier RM and feature combination FC to select the optimal model parameters.

[0082] Import the distribution points and environmental variables of Pseudostoma sutchuenensis, set the training set and test set, run the MaxEnt model, and output the potential geographical distribution map.

[0083] S4: Demarcation of risk areas for false stinkweed

[0084] Zonation software was used to prioritize the invasion landscapes of Pseudostoma sutchuenensis and divide them into high-risk, medium-risk, low-risk and non-risk areas.

[0085] S5: Prediction of changes in the risk area of ​​false stinkweed

[0086] The potential geographical distribution of Pseudostoma sutchuenensis at different periods was predicted based on environmental variables at different periods, and risk areas were divided.

[0087] Use SDMtoolbox to compare risk areas in different periods and obtain the changing trend of risk areas.

[0088] S6: Quantification of the ecological niche of Pseudostoma sutchuenensis

[0089] The ecospat software package was used to assess the niche stability, expansion, and unfilled status of Pseudosphaera scabra in Guizhou Province.

[0090] Schoener's D calculation, niche consistency test and niche similarity test were performed.

[0091] Embodiment 2:

[0092] A study on the ecological niche change and risk zoning of Pseudostoma sphaerocephalum was conducted in another region of Guizhou Province, with similar steps to Example 1, but considering different environmental variables and more specific collection route settings.

[0093] S1: Acquisition of distribution point data of false stinkweed

[0094] In specific county-level administrative districts and township-level administrative districts in the area, more intensive collection routes are set up according to the distribution of false stinkweed.

[0095] The collection routes cover more habitat types, such as forest edges, riparian zones, etc.

[0096] The same spatial dilution method was used to obtain invasion sites.

[0097] S2: Selection and treatment of environmental factors

[0098] Select the same historical and future bioclimatic variables, but add more topographic variables such as soil type, distance to water bodies, etc.

[0099] The same environmental variable treatment and correlation analysis were performed.

[0100] S3: MaxEnt model optimization and accuracy evaluation

[0101] The same MaxEnt model optimization and accuracy evaluation methods were used.

[0102] S4: Demarcation of risk areas for false stinkweed

[0103] The same Zonation software was used for risk zone delineation.

[0104] S5: Prediction of changes in the risk area of ​​false stinkweed

[0105] Predict potential geographical distribution and changes in risk areas at different times in the future.

[0106] S6: Quantification of the ecological niche of Pseudostoma sutchuenensis

[0107] The niche changes of Pseudofoetida in the region were assessed and the same niche tests and calculations were performed.

[0108] A certain area is located in the karst area of ​​southwest China, on the Yunnan-Guizhou Plateau, with a geographical location of 103°36′~109°35′E, 24°37′~29°13′N, and a land area of ​​approximately 176,200 km 2 The landform types are mainly plateaus, mountains, hills and basins. The climate is generally subtropical humid monsoon climate, mild and humid, with an average annual temperature of 14-16°C in most areas. Precipitation is mainly concentrated from April to September, with an annual precipitation of 1100-1400mm, showing a decreasing trend from southeast to northwest in space.

[0109] like Figure 1 As shown, an embodiment of the present invention provides a method for niche change and risk zoning of an invasive plant, Pseudostoma sphaerocephalum, the method comprising:

[0110] S1: Acquisition of distribution point data of false stinkweed;

[0111] S2: Selection and treatment of environmental factors;

[0112] S3: MaxEnt model optimization and accuracy evaluation;

[0113] S4: Delineation of risk areas for false stinkweed;

[0114] S5: Prediction of changes in the risk area of ​​false stinkweed;

[0115] S6: Quantification of the ecological niche of Pseudostoma sutchuenensis.

[0116] The S1 specifically includes:

[0117] like Figure 2 As shown in the figure, the data of the distribution points of Pseudo-stinkweed in a certain area are collected in the field from April 2022 to December 2023. Data collection is based on county-level administrative districts, and collection routes are set as evenly as possible in the east, south, west, north and center. Collection points are set on each route according to the presence or absence of Pseudo-stinkweed distribution; the collection routes cover 1509 township-level administrative districts in 88 county-level administrative districts in the province, mainly involving dry land, orchards, barren slopes and along roads with high invasion rates; Origin: The distribution data of South America is obtained from the Global Biodiversity Information Network; The spatial refinement of species occurrence records helps to solve problems related to spatial sampling bias, so the Thin function of the R package spThin is used to select an interval distance of 1km for spatial dilution of distribution points, and finally 454 invasion points in a certain area and 444 origin points are obtained.

[0118] The S2 specifically includes:

[0119] The following environmental variables with a greater impact on plant distribution were selected:

[0120] (1) Historical bioclimatic variables: average values ​​from 1970 to 2000, and future climate variables: average values ​​from 2021 to 2040, i.e., 2030; average values ​​from 2041 to 2060, i.e., 2050; average values ​​from 2061 to 2080, i.e., 2070; and average values ​​from 2081 to 2100, i.e., 2090. The variables are based on the ssp126 scenario of the BCC-CSM2-MR model, which is a relatively reliable model for simulating precipitation and temperature in China. The data are from the World Meteorological Database.

[0121] (2) Topographic variables, including aspect, slope, and elevation, with data from the Chinese Academy of Sciences Geospatial Digital Cloud Platform;

[0122] (3) Human activity variables, including the human footprint index. The human footprint index data value range is 0 to 50. The higher the score, the higher the intensity of human activity. The data comes from SEDAC. The above three groups of environmental variables are resampled in GIS so that the grid size is unified to 30s and the coordinate system is WGS-1984.

[0123] There may be collinearity between environmental variables, which will reduce the accuracy of model prediction. In order to remove the collinearity between environmental variables, the above three groups of environmental variables are subjected to person correlation analysis ( Figure 3), only the variables with the highest contribution rate were retained from a set of highly correlated variables with |P|>0.8 to obtain environmental factors, which were converted into ASCII format in GIS for MaxEnt analysis.

[0124] The S3 specifically includes:

[0125] The MaxEnt model was used to predict the potential geographical distribution of Pseudostoma sutchuenensis. In the prediction of the potential geographical distribution of species based on the MaxEnt model, the parameters set by the software default were generally selected, but for some species, the default settings may not be optimal; the R package ENMeval was used to adjust the regularization multiplier RM and the feature combination FC, analyze the complexity of the model under different parameter combinations, select the combination with lower complexity for modeling, and optimize the model;

[0126] The RM parameters are set to 1-4, with an interval of 0.5 for each parameter, and a total of 8 RM parameters; for the FC parameter, the MaxEnt model provides five features: linear L, quadratic Q, hinge H, product P, and threshold T; six feature combinations are selected: H, L, LQ, LQH, LQHP, and LQHPT; in the ENMeval package:

[0127] (1) The modified Akaike information criterion (AICc) is used to select the best model. The best model has the lowest AICc value, i.e., ΔAICc = 0;

[0128] (2) The overfitting degree of the model was evaluated by using the difference between the area under the receiver operating characteristic curve (AUC) of training and testing and the 10% training omission rate (OR 10);

[0129] (3) The area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of the model operation results. When AUC∈[0.80-0.90), it means that the prediction results are good; when AUC∈[0.90-1.0), it means that the prediction results are excellent. Finally, the combination parameters when ΔAICc=0 were selected to establish the optimized model.

[0130] The distribution points of Pseudostoma sutchuenensis species in a certain area and the screened environmental variables were imported into MaxEnt 3.4.1 software in CSV and ASC file formats, respectively, and its parameters were set according to the above model optimization results; then, 75% of the occurrence points were randomly selected as training sets, and the remaining 25% were used as test sets, and 10 repetitions were generated for each training partition; the average value of the 10 repetitions was selected as the final output result, the output file format was logistic, and the output file type was ASC.

[0131] The S4 specifically includes:

[0132] The software Zonation v4.0 was used to prioritize the invasion landscapes of Pseudostoma sutchuenensis, and landscapes with different invasion priorities were considered as invasion risk areas of different levels. High-risk areas, medium-risk areas, low-risk areas and non-risk areas for Pseudostoma sutchuenensis invasion were divided according to the order of priority, in order to better manage Pseudostoma sutchuenensis.

[0133] Zonation uses different algorithms to sort landscape priorities. The potential geographic distribution obtained by running MaxEnt is used as input in ASC format. The core unit zoning algorithm CAZ is selected as the rule for spatial priority sorting. At the same time, the warping factor is set to 1 and the Zonation software is run.

[0134] The S5 specifically includes:

[0135] The future trend of risk areas is predicted, and the prediction process is as follows:

[0136] (1) Based on environmental variables at different times, we predicted the potential geographical distribution of Pseudo-smellgrass at different times and divided the risk areas at different times according to the priority of invading landscapes;

[0137] (2) Use SDMtoolbox to compare the risk areas in different periods and obtain the changing trend of the risk areas.

[0138] The S6 specifically includes:

[0139] Based on the distribution point information of Pseudostoma sutchuenensis in its origin and a certain region as well as environmental variables, the ecospat software package was used to evaluate the stability, expansion and unfilled ecological niche of Pseudostoma sutchuenensis after it invaded Guizhou. NS represents the niche occupied by both the origin and the invaded area; NE represents the newly occupied niche in the invaded area; NS represents the niche that can be occupied within the niche range of the invaded area but is not currently occupied. Schoener's D calculation, niche consistency test and niche similarity test were also performed. Schoener's D evaluates the degree of overlap between the niches of the origin and the invaded area, and the value range of D is 0-1, where 0 indicates no overlap and 1 indicates complete overlap. The niche consistency test includes testing whether the niches of the origin and the invaded area are equal. The niche similarity test evaluates whether the degree of overlap between the niche of the origin and the niche of the invaded area is higher than that of random introduction.

[0140] 1. Specific application fields or related products of the present invention.

[0141] like Figure 8As shown, the embodiment of the present invention provides an invasive plant pseudostinkweed ecological niche change and risk zoning system based on the invasive plant pseudostinkweed ecological niche change and risk zoning method, characterized in that the system specifically includes:

[0142] Data acquisition module 1, used for acquiring the distribution point data of false stinkweed;

[0143] The factor processing module 2 is connected to the data acquisition module 1 and is used for selecting and processing environmental factors;

[0144] The optimization and evaluation module 3 is connected with the factor processing module 2 and is used for the optimization and accuracy evaluation of the MaxEnt model;

[0145] The risk area division module 4 is connected to the optimization evaluation module 3 and is used for dividing the risk area of ​​​​the false stinkweed;

[0146] The risk area prediction module 5 is connected to the risk area division module 4 and is used to predict the change of the risk area of ​​​​the pseudo-stinky grass;

[0147] The quantification module 6 is connected to the risk area prediction module 5 and is used for quantifying the ecological niche of Pseudostoma sphaerocephalum.

[0148] An embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the method for niche change and risk zoning of the invasive plant Pseudostoma sphaerocephalum.

[0149] An embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method for niche change and risk zoning of the invasive plant Pseudostoma sphaerocephalum.

[0150] like Figure 4 As shown in the figure, according to the optimization results, when RM is set to 2.5 and FC is set to LQHPT, ΔAICc=0 is the best combination of the MaxEnt model in this study. The AUC.DIFF and OR10 of the optimized model are lower than those of the model with default parameters, indicating that the optimized model reduces the overfitting of the distribution data. The AUC value of the optimized model is significantly improved compared with the model with default parameters, indicating that the optimized model improves the accuracy of the prediction of the potential geographical distribution of Pseudo-stinkweed. The AUC value of the optimized model is 0.887, indicating that the prediction results are good.

[0151] From the MaxEnt model evaluation results (Table 2), it can be seen that there are two main environmental variables that affect the current potential geographical distribution of Pseudo-stinkweed, namely the warmest quarter precipitation (Bio18) and the coldest quarter average temperature, with contribution rates of 51.8% and 32.3% respectively. The sum of the contribution rates of these two environmental variables reaches 84.1%, indicating that the distribution of Pseudo-stinkweed is mainly affected by temperature and precipitation. In addition, the contribution rates of non-major environmental factors are all below 4.2%, which is significantly lower than the above two major environmental factors. The two environmental variables with the highest ranking importance are the warmest quarter precipitation (Bio18) and altitude (Ele), which are 56.3% and 21.9% respectively.

[0152] Table 2 Contribution of different environmental variables to the current potential geographical distribution of Pseudosphaera sutchuenensis in Guizhou

[0153]

[0154] Zonation performance curve shows ( Figure 5 A), when the total landscape was lost from 12% to 0, the remaining proportion of Pseudostinkweed decreased from 0.52 to 0, indicating that this continuous landscape had the highest priority and was considered a high-risk area for Pseudostinkweed invasion. When the total landscape was lost from 24% to 12%, the remaining proportion of Pseudostinkweed decreased by 0.27, indicating that this continuous landscape had the second highest priority and was considered a medium-risk area for Pseudostinkweed invasion. When the total landscape was lost from 40% to 24%, the remaining proportion of Pseudostinkweed decreased by 0.13, indicating that this continuous landscape had the third highest priority and was considered a low-risk area for Pseudostinkweed invasion. When the total landscape was lost from 100% to 40%, the remaining proportion of Pseudostinkweed hardly changed, indicating that this continuous landscape had the lowest priority and was considered a non-risk area for Pseudostinkweed invasion.

[0155] from Figure 5 B and Table 3 show that the high-risk area involves parts of Qianxinan, Anshun, Qiannan, and Liupanshui and Qiandongnan adjacent to these areas, with a total area of ​​21,144.04 km2. The concentrated area is mainly in the southwest of Guizhou, which is the typical occurrence area of ​​false stinkweed. The medium-risk area is outside the high-risk area, from Liupanshui-Anshun-Qiannan-Qiandongnan, mainly concentrated in Anshun and Qiannan, belonging to the diffusion frontier zone, with a total area of ​​21,144.04 km2. The low-risk area covers a wider range than the high- and medium-risk areas. In addition to the high- and medium-risk areas, there are also Bijie to the south, Tongren in the east, Zunyi in the west, Qiandongnan in the south, etc., which are potential risk areas, with a total area of ​​28,192.05 km2. The total area of ​​high, medium and low risk areas is 70,480.13 km2, accounting for 40% of the total land area of ​​Guizhou. The rest of the area is a non-risk area with an area of ​​105,720.20 km2.

[0156] Table 3 Areas of risk areas at different levels

[0157]

[0158] As shown in Table 4 and Figure 6 As shown in the data, compared with the current situation, the area of ​​the risk zone of Pseudostinctus increased by 3770.46km2 in 2030, mainly in the southwest of Bijie, the central part of Guiyang and the southeast of Qiandongnan. Compared with 2030, the area of ​​the risk zone of Pseudostinctus increased by 1298.42km2 in 2050; it mainly expanded in the north and northeast of Tongren, and also expanded in a small part of the central part of Bijie. Compared with 2050, the area of ​​the risk zone of Pseudostinctus decreased by 2613.35km2 in 2070, mainly in the north and northeast of Tongren, and a small part of the southeast of Qiandongnan also showed a reduction in the risk area. Compared with 2070, the area of ​​the risk zone of Pseudostinctus increased by 19133.68km2 in 2090; it expanded to varying degrees in the five cities and prefectures from central to northeastern Guizhou, among which Tongren and Zunyi had the largest expansion areas. By 2090, the risk zone of Pseudostinctus may occupy most of a certain area. In general, the risk areas of false stinkweed will show an expanding trend in the future, with a total diffusion area of ​​21,589.21 km2.

[0159] Table 4 Future changes in the area of ​​the risk area of ​​​​the pseudo-stinkweed

[0160]

[0161]

[0162] Principal component analysis of background values ​​of all environmental variables ( Figure 7 A) shows that PC1 and PC2 explain 39.83% and 22.55% of the overall variability, respectively, totaling 62.38%. PC1 is mainly related to Bio3 (Isothermality) and Bio11 (Mean Temperature of Coldest Quarter), and PC2 is mainly related to Bio2 (Mean Diurnal Range) and Bio18 (Precipitation of Warmest Quarter). Combined with the results of niche overlap analysis ( Figure 7 B), the ecological niche of Pseudostinax in Guizhou expanded along the PC1 direction, and the overlapping area with the origin was very small (D = 0.12). The results of the single factor niche distribution characteristics showed that Pseudostinax invaded areas with lower monthly mean temperature difference and isothermality in Guizhou, and all showed a single peak change ( Figure 7 CD). Compared with its native habitat, the requirement of Pseudostoma sutchuenensis for high temperature showed a decreasing trend. Although the average temperature niche of the coldest quarter occupied by Pseudostoma sutchuenensis in the invaded area showed three density peaks, the density peaks were all at lower temperature positions ( Figure 7E). In Guizhou, Pseudostoma sutchuenensis tends to be distributed in areas with higher precipitation in the warmest season, and its occurrence density shows a unimodal change with increasing precipitation ( Figure 7 F) The single-factor ecological niche occupied by Pseudostoma sutchuenensis in Guizhou is quite different from that in its origin, and the overlapping area is relatively low.

[0163] Similarity test and consistency test showed (Table 5) that Pseudostoma sutchuenensis can occupy similar but different ecological niches in the two regions. Compared with the origin, after Pseudostoma sutchuenensis invaded Guizhou, the expansion, stability and unfilled ecological niche were 0.88, 0.12 and 0.96 respectively, which showed that the ecological niche of Pseudostoma sutchuenensis expanded rapidly and the ecological niche was not conservative.

[0164] Table 5 Comparison of ecological niches between the origin of Pseudostoma sutchuenensis and a certain region

[0165]

[0166] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0167] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for niche change and risk zoning of the invasive plant Pseudocyptiaca sutchuenensis, characterized in that: The method includes: S1: Acquisition of distribution point data of false stinkweed; S2: Selection and treatment of environmental factors; S3: MaxEnt model optimization and accuracy evaluation; S4: Delineation of risk areas for false stinkweed; S5: Prediction of changes in the risk area of ​​false stinkweed; S6: Quantification of the ecological niche of Pseudostoma sutchuenensis.

2. The method for niche change and risk zoning of the invasive plant Pseudostoma sphaerocephalum according to claim 1, characterized in that: The S1 specifically includes: Data collection was based on county-level administrative divisions, with collection routes set as evenly as possible in the east, south, west, north and center, and collection points set on each route according to the presence or absence of Pseudostoma sphaerocephalum distribution; the collection routes covered 88 county-level administrative divisions and 1,509 township-level administrative divisions in the province, mainly involving dry land, orchards, barren slopes and along roads with high invasion rates; origin: distribution data for South America were obtained from the Global Biodiversity Information Network; spatial refinement of species occurrence records helps solve problems related to spatial sampling bias, so the Thin function of the R package spThin was used, and a 1km interval distance was selected for spatial dilution of distribution points, ultimately obtaining 454 invasion points in a certain area and 444 points of origin.

3. The method for niche change and risk zoning of the invasive plant Pseudostoma sphaerocephalum as claimed in claim 1, characterized in that: The S2 specifically includes: The following environmental variables with a greater impact on plant distribution were selected: (1) Historical bioclimatic variables: average values ​​from 1970 to 2000, and future climate variables: average values ​​from 2021 to 2040, i.e., 2030; average values ​​from 2041 to 2060, i.e., 2050; average values ​​from 2061 to 2080, i.e., 2070; and average values ​​from 2081 to 2100, i.e., 2090. The variables are based on the ssp126 scenario of the BCC-CSM2-MR model, which is a relatively reliable model for simulating precipitation and temperature in China. The data are from the World Meteorological Database. (2) Topographic variables, including aspect, slope, and elevation, with data from the Chinese Academy of Sciences Geospatial Digital Cloud Platform; (3) Human activity variables, including the human footprint index. The human footprint index data value range is 0 to 50. The higher the score, the higher the intensity of human activity. The data comes from SEDAC. The above three groups of environmental variables are resampled in GIS so that the grid size is unified to 30s and the coordinate system is WGS-1984. There may be collinearity between environmental variables, which will reduce the accuracy of model prediction. In order to remove the collinearity between environmental variables, the above three groups of environmental variables were subjected to person correlation analysis. Among a group of highly correlated variables with |P|>0.8, only the variable with the highest contribution rate was retained to obtain environmental factors. These environmental factors were converted into ASCII format in GIS for MaxEnt analysis.

4. The method for niche change and risk zoning of the invasive plant Pseudostoma sphaerocephalum as claimed in claim 1, characterized in that: The S3 specifically includes: The MaxEnt model was used to predict the potential geographical distribution of Pseudostoma sutchuenensis. In the prediction of the potential geographical distribution of species based on the MaxEnt model, the parameters set by the software default were generally selected, but for some species, the default settings may not be optimal; the R package ENMeval was used to adjust the regularization multiplier RM and the feature combination FC, analyze the complexity of the model under different parameter combinations, select the combination with lower complexity for modeling, and optimize the model; The RM parameters are set to 1-4, with an interval of 0.5 for each parameter, and a total of 8 RM parameters; for the FC parameter, the MaxEnt model provides five features: linear L, quadratic Q, hinge H, product P, and threshold T; six feature combinations are selected: H, L, LQ, LQH, LQHP, and LQHPT; in the ENMeval package: (1) The modified Akaike information criterion (AICc) is used to select the best model. The best model has the lowest AICc value, i.e., ΔAICc = 0; (2) The overfitting degree of the model was evaluated by using the difference between the area under the receiver operating characteristic curve (AUC) of training and testing and the 10% training omission rate (OR 10); (3) The area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of the model running results. When AUC∈[0.80-0.90), it means that the prediction results are good; when AUC∈[0.90-1.0), it means that the prediction results are excellent. Finally, the combination parameters when ΔAICc=0 were selected to establish the optimized model. The distribution points of Pseudostoma sutchuenensis species in a certain area and the screened environmental variables were imported into MaxEnt 3.4.1 software in CSV and ASC file formats, respectively, and its parameters were set according to the above model optimization results; then, 75% of the occurrence points were randomly selected as training sets, and the remaining 25% were used as test sets, and 10 repetitions were generated for each training partition; the average value of the 10 repetitions was selected as the final output result, the output file format was logistic, and the output file type was ASC.

5. The method for niche change and risk zoning of the invasive plant Pseudostoma sphaerocephalum as claimed in claim 1, characterized in that: The S4 specifically includes: The software Zonation v4.0 was used to prioritize the invasion landscapes of Pseudostoma sutchuenensis, and landscapes with different invasion priorities were considered as invasion risk areas of different levels. High-risk areas, medium-risk areas, low-risk areas and non-risk areas for Pseudostoma sutchuenensis invasion were divided according to the order of priority, in order to better manage Pseudostoma sutchuenensis. Zonation uses different algorithms to sort landscape priorities. The potential geographic distribution obtained by running MaxEnt is used as input in ASC format. The core unit zoning algorithm CAZ is selected as the rule for spatial priority sorting. At the same time, the warping factor is set to 1 and the Zonation software is run.

6. The method for niche change and risk zoning of the invasive plant Pseudostoma sphaerocephalum as claimed in claim 1, characterized in that: The S5 specifically includes: The future trend of risk areas is predicted, and the prediction process is as follows: (1) Based on environmental variables at different times, we predicted the potential geographical distribution of Pseudo-smellgrass at different times and divided the risk areas at different times according to the priority of invading landscapes; (2) Use SDMtoolbox to compare the risk areas in different periods and obtain the changing trend of the risk areas.

7. The method for niche change and risk zoning of the invasive plant Pseudostoma sphaerocephalum as claimed in claim 1, characterized in that: The S6 specifically includes: Based on the distribution point information of Pseudostoma sutchuenensis in its origin and a certain region as well as environmental variables, the ecospat software package was used to evaluate the stability, expansion and unfilled ecological niche of Pseudostoma sutchuenensis after it invaded Guizhou. NS represents the niche occupied by both the origin and the invaded area; NE represents the newly occupied niche in the invaded area; NS represents the niche that can be occupied within the niche range of the invaded area but is not currently occupied. Schoener's D calculation, niche consistency test and niche similarity test were also performed. Schoener's D evaluates the degree of overlap between the niches of the origin and the invaded area, and the value range of D is 0-1, where 0 indicates no overlap and 1 indicates complete overlap. The niche consistency test includes testing whether the niches of the origin and the invaded area are equal. The niche similarity test evaluates whether the degree of overlap between the niche of the origin and the niche of the invaded area is higher than that of random introduction.

8. A system for ecological niche change and risk zoning of invasive plant Pseudostoma sutchuenensis based on the method for ecological niche change and risk zoning of invasive plant Pseudostoma sutchuenensis as claimed in claims 1 to 7, characterized in that: The system specifically includes: A data acquisition module is used to acquire the distribution point data of the false stinkweed; A factor processing module, connected to the data acquisition module, is used for selecting and processing environmental factors; The optimization and evaluation module is connected with the factor processing module and is used for MaxEnt model optimization and accuracy evaluation; The risk area division module is connected with the optimization evaluation module and is used to divide the risk area of ​​​​the false stinkweed; The risk area prediction module is connected with the risk area division module and is used to predict the change of the risk area of ​​​​the false stinkweed; The quantification module is connected with the risk area prediction module and is used for quantifying the ecological niche of Pseudostoma sphaerocephalum.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for niche change and risk zoning of the invasive plant Pseudostoma sphaerocephalum as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the method for niche change and risk zoning of the invasive plant Pseudocerma odorifera as claimed in any one of claims 1 to 7.

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