Method for rapidly searching suitable area of garcinia pauciflora

Abstract

The invention discloses a method for rapidly exploring a proper area of garcinia pauciflora, and relates to the related technical field of garcinia pauciflora. According to existing geographical distribution data and environmental factor data of the garcinia pauciflora, an ecological region suitable for introduction under modern and future climate conditions is accurately predicted based on MaxEnt in combination with ArcGIS, namely, the garcinia pauciflora distribution is researched under the climate conditions that low-grade (SSP1-26) greenhouse gas emission and medium-grade (SSP2-45) greenhouse gas emission are used as future climate change scenes; the ecological suitability zoning of the garcinia pauciflora is preliminarily explored, and a theoretical basis is provided for introduction and cultivation, seed allocation and the like of the garcinia pauciflora, so that the method has certain significance in researching the potential geographical distribution of the garcinia pauciflora.

Description

Technical Field

[0001] The invention relates to the technical field related to golden plum, and in particular to a method for quickly exploring a suitable area for golden plum. Background Art

[0002] Garcinia paucinervis Chun et How belongs to the genus Garcinia L. of the family Clusiaceae. It is an evergreen tree with a height of 3-25 meters. It is mainly distributed in western Guangxi and southeastern Yunnan. Garcinia paucinervis Chun et How plays an important role in terms of ornamental value, medicinal value, economy and ecology.

[0003] The growing environment of Golden Silk Plum is fragile, its distribution area is very narrow and scattered, the population is small, its seeds are not easy to germinate, and it is difficult to reproduce in the natural environment in the wild. Because its wood is precious, Golden Silk Plum has suffered from over-logging and has become a rare and endangered tree species.

[0004] Predicting the potential suitable habitats of Plum japonica under the background of current and future climate change can provide a scientific basis for the effective protection of its germplasm resources, introduction and domestication.

[0005] One of the important factors that affects the survival and reproduction of golden silk plum and the cause of its endangerment is the environmental factor. At present, the use of remote sensing information technology for zoning research has been widely applied in ecological related fields. The maximum entropy (MaxEnt) model uses environmental factors and the geographical distribution location information of a species, and adopts a specific algorithm to evaluate the ecological niche of the species. It can intuitively present the probability of the existence of the species and the potential distribution area of ​​a species under certain climatic conditions. It has been widely used in the prediction of the suitable habitat of various species. The geographic information system (ArcGIS) is an effective tool for spatial information technology processing. It integrates map and geographic analysis functions with general database operations. It uses ecological principles and data analysis to predict the potential distribution of known species based on their distribution data, and the results are displayed in the form of pictures, which has the advantages of graphics and visualization. At present, the prediction of suitable habitats for wild protected plants based on the MaxEnt model and ArcGIS software analysis has become a research hotspot, but there is still room for further optimization;

[0006] Studying the response of species distribution patterns to the coming climate change has important theoretical and practical value for the protection, development and utilization of species resources. In order to avoid the investment risk of blindly expanding its potential growth area, we urgently need to evaluate its potential spatial area under climate change conditions. Summary of the invention

[0007] The purpose of the present invention is to provide a method for quickly exploring suitable areas for growing golden silk plum, so as to solve the technical problems raised in the background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: a method for quickly exploring a suitable area for golden silk plum, comprising at least the following steps:

[0009] S1: Acquire and process the geographical distribution data of Prunus mume, and obtain the corresponding data through resource sharing platforms and publicly published papers and documents;

[0010] S2: Acquisition and processing of ecological factor data, using Spearman's correlation analysis and percentage contribution to screen variables;

[0011] S3: Set the source of future environmental data;

[0012] S4: Setting and running of the MaxEnt model. The principle of the MaxEnt model is to estimate the probability distribution of species by calculating the maximum entropy of the probability distribution of the target area under certain limited conditions;

[0013] S5: Model accuracy test, test the model of S4 according to the evaluation indicators;

[0014] S6: Setting and running the ArcGIS model to obtain the distribution map of the potential suitable growth areas of Plum chinensis and to identify the future increase and decrease in the potential suitable growth areas of Plum chinensis.

[0015] Furthermore, the resource sharing platform at least includes a teaching specimen resource sharing platform, a Chinese digital herbarium and a Chinese nature reserve specimen resource sharing platform.

[0016] Furthermore, the processing of the geographical distribution data of Prunus cerasifera includes at least the following steps:

[0017] Collect the information of the collection sites and geographical distribution of golden silk plum specimens;

[0018] Remove vaguely recorded, duplicated, and ambiguous distribution point information;

[0019] At the same time, in order to avoid the large spatial autocorrelation of the obtained distribution points due to their close distance, which would affect the accuracy of the model simulation, only one existence point is retained in each pixel;

[0020] Finally, the number of distribution points with detailed information is obtained, and then the longitude and latitude of the distribution points are determined using the Baidu picking coordinate system, with longitude as x and latitude as y, and saved as a csv format file.

[0021] Furthermore, selecting Spearman's correlation analysis and percentage contribution value to screen variables in S2 at least includes the following steps:

[0022] When the absolute value of the correlation coefficient is less than 0.8, both variables are retained first. If the absolute value of the correlation coefficient between the two variables is greater than 0.8, the environmental variable with the largest percentage contribution rate is retained.

[0023] Finally, 10 environmental variables were retained, including annual average temperature (bio-1), precipitation in the wettest quarter (bio-17), precipitation in the warmest quarter (bio-18), monthly mean day-night temperature difference (bio-2), isothermality (bio-3), temperature change variance (bio-4), altitude (elev) and aspect (aspect).

[0024] Furthermore, the setting of the MaxEnt model in S4 at least includes the following steps:

[0025] The unknown probability distribution of species in the environmental space is denoted as π, and the unknown probability distribution π is defined in a finite set X, where X is the set of pixels in the study area;

[0026] x represents each network point divided by X, π(x) is the non-negative distribution probability at point x, and satisfies The distribution with the largest entropy is selected as the optimal distribution, and its probability is Then the maximum entropy of the probability distribution is:

[0027]

[0028] Specifically, the number of distribution points of the above-mentioned golden silk plum after screening and the 10 environmental variable data were imported into the "Samples" and "Enviromental layers" commands of the MaxEnt software model;

[0029] Randomly select 75% of the data as the training set and the remaining 25% as the test set. Set the maximum number of iterations to 1000 and the rest to the default values.

[0030] To ensure the stability of the model, the model was repeated 10 times by bootstrapping method, and the average value after 10 repetitions was used as the final habitat layer to obtain the habitat suitability index;

[0031] The results were exported in logistic format and ASCII type and transferred to ArcGIS10.8 for further processing to obtain the distribution map of species suitable habitats.

[0032] Furthermore, the evaluation indicators in S5 include accuracy, sensitivity, specificity, Kappa index, TSS index and ROC curve.

[0033] Furthermore, the S6 at least includes the following steps:

[0034] Import the results of MaxEnt model simulation into ArcGIS software;

[0035] Select the Reclassify tool in ArcGIS software and execute the Reclassify command to classify the suitable habitat layer of Prunus cerasifera. That is, use the natural break classification method in the Reclassification tool to divide the potential habitat of Prunus cerasifera into four parts: unsuitable habitat, increased habitat, reduced habitat and adapted habitat, and obtain the distribution map of the potential suitable habitat of Prunus cerasifera.

[0036] After processing the distribution map of the potential suitable habitat of Prunus cerasifera, the area of ​​the suitable habitat of Prunus cerasifera in different eras and different climate scenarios in the future was statistically analyzed.

[0037] Clarify the future increase and decrease in the potential suitable habitat of golden silk plum.

[0038] Further, the processing includes at least masking, projecting and projecting grids within transformation tools.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] Based on the existing geographical distribution data and environmental factor data of Golden Silk Plum, the present invention uses MaxEnt combined with ArcGIS to accurately predict the ecological zones suitable for introduction under modern and future climatic conditions, that is, the distribution of Golden Silk Plum is studied under the climatic conditions of low (SSP1-26) and medium (SSP2-45) greenhouse gas emissions as future climate change scenarios. This preliminarily explores the ecological suitability zoning of Golden Silk Plum, and provides a theoretical basis for the introduction and cultivation of Golden Silk Plum, seed allocation, etc. Therefore, it is of certain significance to study its potential geographical distribution.

[0041] After optimization, the present invention reduces the degree of overfitting and sampling bias, can improve prediction accuracy, and combines the knife-cut method to explore the most significant environmental factors affecting the growth of golden silk plum, providing further technical support for the analysis of the adaptability of improved varieties.

[0042] Through reasonable prediction and planning of suitable growing areas, we can provide a scientific basis for the effective protection, introduction and domestication of golden silk plum germplasm resources, avoid economic losses and waste of resources caused by blind introduction, and achieve good introduction effects.

[0043] At the same time, the predicted results of the present invention can also verify the accuracy of the MaxEnt model, and further promote the development of applied ecology such as conservation biology, biodiversity conservation, and the impact of global climate change on species distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0045] Figure 1 It is a schematic diagram of the response curve of the distribution probability of golden silk plum to key environmental factors of the present invention; DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0047] A method for quickly exploring a suitable area for golden silk plum comprises at least the following steps:

[0048] S1: Acquire and process the geographical distribution data of Prunus mume, and obtain the corresponding data through resource sharing platforms and publicly published papers and documents;

[0049] The resource sharing platform includes at least the teaching specimen resource sharing platform, the China Digital Herbarium and the China Nature Reserve Specimen Resource Sharing Platform.

[0050] The processing of the geographical distribution data of Prunus chinensis includes at least the following steps:

[0051] Collect the information of the collection sites and geographical distribution of golden silk plum specimens;

[0052] Remove vaguely recorded, duplicated, and ambiguous distribution point information;

[0053] At the same time, in order to avoid the large spatial autocorrelation of the obtained distribution points due to their close distance, which would affect the accuracy of the model simulation, only one existence point is retained in each pixel;

[0054] Finally, the number of distribution points with detailed information is obtained. In this embodiment, there are 141 distribution point data in total. Then, the longitude and latitude of the distribution points are determined using the Baidu coordinate system, with longitude being x and latitude being y, and saved as a csv format file.

[0055] S2: Acquisition and processing of ecological factor data;

[0056] At present, some studies have shown that the golden silk plum is mainly distributed in the limestone karst mountains in Guangxi and Yunnan, and has certain selectivity for altitude, slope, annual average temperature, etc. Therefore, two topographic factors (slope aspect and altitude), 19 bioclimatic factors, and a total of 22 environmental variables were selected to study the suitable habitat of golden silk plum. Bioclimatic variables are derived from the World Climate Database (WorldClim, http: / / www.worldclim.org), which collects monthly meteorological data from meteorological stations around the world from 1970 to 2000 and interpolates to generate global climate variable raster data with a resolution of 30" (about 1 km2). These variables reflect the characteristics of temperature and precipitation and their seasonal changes. Topographic variables include altitude and slope aspect, which are derived from the digital elevation model (DEM) of the World Climate Database and obtained through spatial analysis in ArcGIS 10.3.

[0057] In the construction of species distribution models, not all environmental variables have an impact on the distribution prediction of Prunus cerasifera, and the high spatial collinearity between environmental factor variables will have a negative impact on model accuracy, resulting in reduced accuracy of prediction results.

[0058] Spearman's correlation analysis and percentage contribution value were selected to screen variables;

[0059] When the absolute value of the correlation coefficient is less than 0.8, both variables are retained first. If the absolute value of the correlation coefficient between the two variables is greater than 0.8, the environmental variable with the largest percentage contribution rate is retained.

[0060] Finally, 10 environmental variables were retained, including annual average temperature (bio-1), precipitation in the wettest quarter (bio-17), precipitation in the warmest quarter (bio-18), monthly mean of diurnal temperature difference (bio-2), isothermality (bio-3), temperature variation variance (bio-4), altitude (elev) and aspect (aspect). The specific information is shown in Tables 1 and 2.

[0061] Table 1 Correlation analysis of environmental factors

[0062]

[0063] Table 2 Environmental variable factors and explanations used in the present invention

[0064]

[0065] S3: Set the source of future environmental data;

[0066] The representative concentration pathway (RCP) is derived from the global climate model data published in the 5th Climate Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), including four emission scenarios: RCP2.6, RCP4.5, RCP6.0 and RCP8.5 (http: / / www.worldclim.org). However, in the 6th Climate Change Assessment Report, the IPCC predicted that global atmospheric warming will exceed 1.5℃ by the end of the 22nd century, and combined RCPs with the Shared Socioeconomic Pathway (SSP) to more specifically describe future greenhouse gas emissions and development, including four emission scenarios: SSP1-26, SSP2-45, SSP3-60 and SSP5-85. Compared with the representative concentration pathways often used in previous studies, the SSPs scenario organically combines atmospheric and carbon cycle projections with emissions and socioeconomic scenarios, which is more scientific and closer to real climate change.

[0067] The present invention selects greenhouse gas emissions of low forcing scenarios (SSP1-26) and medium forcing scenarios (SSP2-45) as future climate change scenarios, where SSP1-26 is an update of the RCP2.6 scenario, indicating that the radiative forcing in 2100 is stable at 2.6W / m2, representing a low forcing scenario; SSP2-45 is an update of the RCP4.5 scenario, indicating that the radiative forcing in 2100 is stable at 4.5W / m2, representing a medium forcing scenario. At the same time, it is assumed that the variable factors such as vegetation, topography, and water system remain unchanged, and the corresponding bioclimatic variables are the average values ​​of 2040-2060.

[0068] S4: Setting and running of the MaxEnt model. The principle of the MaxEnt model is to estimate the probability distribution of species by calculating the maximum entropy of the probability distribution of the target area under certain constraints.

[0069] The unknown probability distribution of species in the environmental space is denoted as π, and the unknown probability distribution π is defined in a finite set X, where X is the set of pixels in the study area;

[0070] x represents each network point divided by X, π(x) is the non-negative distribution probability at point x, and satisfies The distribution with the largest entropy is selected as the optimal distribution, and its probability is Then the maximum entropy of the probability distribution is:

[0071]

[0072] Specifically, the number of distribution points of the above-mentioned golden silk plum after screening (141 in this embodiment) and 10 environmental variable data are imported into the "Samples" and "Enviromental layers" commands of the MaxEnt software model;

[0073] Randomly select 75% of the data as the training set and the remaining 25% as the test set. Set the maximum number of iterations to 1000 and the rest to the default values.

[0074] To ensure the stability of the model, the model was repeated 10 times by bootstrapping method, and the average value after 10 repetitions was used as the final habitat layer to obtain the habitat suitability index;

[0075] The results were exported in logistic format and ASCII type and transferred to ArcGIS10.8 for further processing to obtain the distribution map of species suitable habitats.

[0076] S5: Model accuracy test, test the model of S4 according to the evaluation indicators;

[0077] The evaluation indicators in S5 include accuracy, sensitivity, specificity, Kappa index, TSS index and ROC curve.

[0078] The ROC curve (receiver operating characteristic curve) is an effective indicator for testing the accuracy of the ecological niche model. The ROC curve uses the area under the curve, i.e., the AUC value, as a discriminant indicator. The AUC value ranges from 0.5 to 1. The closer it is to 1, the more accurate the model is. The ROC curve is considered to be one of the best evaluation indicators at present because it is not affected by the threshold. The MaxEnt software can directly draw the ROC curve and calculate the AUC value. This method is the most widely used, so this study used MaxEnt to construct it.

[0079] The AUC is 0.500 under random prediction. After 10 repeated modeling, the AUC value under the ROC curve is measured. The AUC of the test data set is compared with 0.900. If the predicted values ​​are all higher than 0.90, it means that the MaxEnt model under the sample data has a very good prediction effect. If it is lower than 0.700, the prediction result is poor.

[0080] S6: Setting and running the ArcGIS model to obtain the distribution map of the potential suitable growth areas of Plum chinensis and to identify the future increase and decrease in the potential suitable growth areas of Plum chinensis.

[0081] S6 at least comprises the following steps:

[0082] Import the results of MaxEnt model simulation into ArcGIS software;

[0083] Select the Reclassify tool in ArcGIS software and execute the Reclassify command to classify the suitable habitat layer of Prunus cerasifera. That is, use the natural break classification method in the Reclassification tool to divide the potential habitat of Prunus cerasifera into four parts: unsuitable habitat, increased habitat, reduced habitat and adapted habitat, and obtain the distribution map of the potential suitable habitat of Prunus cerasifera.

[0084] After the distribution map of the potential suitable habitat of Prunus cerasifera is processed, the area of ​​the suitable habitat of Prunus cerasifera in different eras and different climate scenarios in the future is statistically analyzed. The processing includes at least mask coverage, projection, and projection grid in the transformation tool.

[0085] Clarify the future increase and decrease in the potential suitable habitat of golden silk plum.

[0086] Based on 141 valid distribution data of golden silk plum in China and 10 screened environmental variables, the MaxEnt model was used to simulate the potential suitable growth areas of golden silk plum under current and future climate scenarios (SSP1-26, SSP2-45), and to determine the contribution rate and threshold of key environmental factors.

[0087] Based on the above embodiments, the following data and analysis are proposed:

[0088] Accuracy evaluation of MaxEnt model

[0089] The potential distribution area of ​​Golden Silk Plum under two different climate scenarios in different years was predicted to evaluate the accuracy of the model, as shown in Table 3. The results show that the training set AUC values ​​of these models are all greater than 0.97, and the test set AUC values ​​are all greater than 0.96, which are significantly higher than the random model AUC value of 0.500, that is, the prediction results of the model have reached an excellent level. These data all show that the MaxEnt model is suitable for predicting the potential distribution adaptation area of ​​Golden Silk Plum in my country.

[0090] Table 3 ROC curve of the prediction accuracy of MaxEnt maximum entropy model software

[0091]

[0092] Analysis of key environmental variables affecting the distribution of Prunus armeniaca

[0093] SPSS software was used to conduct correlation analysis on 22 environmental factors, and finally 10 effective environmental factors were obtained. In the MaxEnt model, the importance of environmental factors to species distribution was evaluated by outputting the contribution rate, permutation importance and jackknife test of environmental factors. The leading environmental variables and their corresponding contribution rate and permutation importance values ​​were screened by jackknife method, as shown in Table 3. The larger the contribution rate, the greater the contribution of the environmental factor to model training. The results of variable importance analysis showed that the contribution rate of the four environmental variables to the model reached more than 5.0%, which had a cumulative impact of 84.9% on the suitability of the habitat of golden silk plum. Among them, the contribution rate of precipitation in the wettest season (bio-17) was the highest, at 39.8%, followed by precipitation in the warmest season (bio-18), annual average temperature (bio-1) and precipitation change variance (bio-15), with contribution rates of 29.3%, 10.2% and 5.6% respectively.

[0094] Table 4 Contribution rate of each environmental variable in the maximum entropy model

[0095]

[0096] Effects of key environmental variables on the distribution probability of Prunus chinensis

[0097] The response curves of the distribution probability of golden silk plum to the changes of four key environmental factors are all single-peaked, such as Figure 1 When the precipitation in the wettest season is 70-120 mm, it is most suitable for the survival of Golden Silk Plum. When the precipitation in the wettest season is less than 50 mm or more than 250 mm, it is very unfavorable for the survival of Golden Silk Plum. Figure 1 -A); the suitable range of precipitation in the warmest season is 525-1150mm. When the precipitation reaches 725mm, the distribution probability of golden silk plum is as high as 76.5% ( Figure 1 -B); The average temperature in the wettest season is 19-29℃, which is the most suitable for the survival of golden plum. When the temperature reaches 22℃, the distribution probability of golden plum is as high as 76.0% ( Figure 1 -C); the variance of precipitation change is 64-90( Figure 1 -D).

[0098] Distribution pattern of suitable growing areas of Prunus armeniaca in China under modern climate scenario

[0099] Under modern climate conditions, the total area of ​​potential suitable habitat for Prunus chinensis in China is 6.501×10 5 km 2, accounting for 6.77% of China's total land area, mainly distributed in Yunnan, Guangxi, Guizhou, Guangdong, Sichuan, Chongqing, Hunan, Hubei, Jiangxi, Fujian, Jiangsu, Anhui, Hainan and other regions. The results show that the potential distribution area predicted by the ecological suitable area of ​​Golden Silk Plum is larger than the actual occurrence data of Golden Silk Plum, which shows that under modern climate conditions, these areas are suitable for Golden Silk Plum to survive. If Golden Silk Plum is introduced to the local area, the survival rate is relatively high, and it can be found that most of the effective distribution points of Golden Silk Plum are located in the potential suitable area, which can further confirm that the simulation results are relatively accurate.

[0100] Changes in the suitable habitat of Prunus chinensis under future climate from 2041 to 2060

[0101] Table 5 describes the corresponding area, proportion and distribution of potential suitable areas in 2041-2060 under the future scenario. By 2041-2060, i.e., 2050s, the total suitable areas of SSP1-26 and SSP2-45 are 9.308×10 5 km 2 and 8.366×10 5 km 2 The area of ​​suitable habitat for Prunus chinensis in the modern scenario has an expansion trend compared with the areas of suitable habitats in the two future climate scenarios with lower gas emissions. Its area has increased by 2.807×10 5 km 2 and 1.864×10 5 km 2 , the percentages increased by 43.17% and 28.67% respectively.

[0102] Under SSP1-26 and SSP2-45, the total area of ​​potential suitable habitat increased by 3.929×10 5 km 2 , 2.784×10 5 km 2 , their areas accounted for 60.43% and 42.82% of the suitable habitat area in the modern scenario, respectively, with the largest increase in SSP1-26; the total area of ​​its potential suitable habitat decreased by 0.890×10 5 km 2 , 0.920×10 5 km 2 , and their areas accounted for 13.69% and 14.15% of the suitable habitat area in the modern scenario respectively; the total area of ​​potential suitable habitat overlapped with the suitable habitat area in the modern scenario by 5.379×10 5 km 2 ,

[0103] 5.582×10 5 km 2 , and their areas account for 82.73% and 85.86% of the suitable habitat area under the modern scenario respectively.

[0105] Table 5 Potential suitable distribution area and percentage of Prunus armeniaca in China under future scenarios from 2041 to 2060

[0106]

[0107] Dynamic changes in the suitable habitats of Prunus armeniaca in important provinces under future climate

[0108] The potential distribution area of ​​important provinces was quantitatively calculated using the MaxEnt model prediction and ArcGIS software. Under the two future climate scenarios in 2050s, the potential suitable distribution area of ​​western and southeastern Yunnan, northeastern Guangxi, southern Guangdong, southeastern Chongqing, northwestern Hunan, northeastern Anhui and central Jiangsu increased. Among them, the western and southeastern parts of Yunnan experienced large-scale expansion, which was 0.175×10 5 km 2 , and 1.112×10 5 km 2 , 0.993×10 5 km 2 , with percentages of 17.10% and 15.28% respectively. The suitable habitats in Sichuan and Hainan are both reduced to a small extent in the future scenarios, with the largest reduction in the area in Sichuan under SSP1-26, where the original potential distribution area is 0.417×10 5 km 2 The potential suitable habitat area in Sichuan under SSP1-26 is 0.334×10 5 km 2 , a decrease of 0.083×10 5 km 2 , the percentage is 1.27%, refer to Table 6, which shows the potential suitable distribution area and percentage of golden silk plum in important provinces under the future scenario from 2041 to 2060.

[0109] Table 6

[0110]

[0111] Note: Negative numbers in the change area and area change percentage of suitable growth areas in the table indicate a decrease.

[0112] In summary:

[0113] The present invention adopts the method combining MaxEnt model and ArcGIS to predict the potential suitable distribution area of ​​golden silk plum in my country, and obtains the following conclusions: (1) The four key variables of golden silk plum distribution are respectively the precipitation of the wettest season 70-120mm, the precipitation of the warmest season 525-1150mm, the average temperature of the wettest season 19-29℃ and the variance of precipitation 64-90, and their contribution rates are 39.8%, 29.3%, 10.2% and 5.6% respectively. (2) Under modern climate conditions, golden silk plum is mainly distributed in most areas of Guangxi and Guangdong, southern Yunnan, western Chongqing and eastern Sichuan, and the total area of ​​suitable areas is 6.501×105km2, accounting for 6.77% of China's total land area. (3) There are certain differences in the area of ​​suitable habitats for golden silk plum in different periods. In 2050s, under two future climate scenarios, the potential suitable distribution areas in western and southeastern Yunnan, northeastern Guangxi, and southern Guangdong will increase. Among them, the western and southeastern parts of Yunnan will expand on a large scale, increasing by 1.112×105km2 and 0.993×105km2 respectively; while the area of ​​suitable habitats in Sichuan and Hainan will shrink slightly under the future scenarios. The largest shrinkage area is in Sichuan in SSP1-26, which decreases by 0.083×105km2. This study has laid a certain theoretical foundation for the development of golden silk plum resources and artificial cultivation and domestication, and can provide guidance for the industrial planting and directional selection of planting bases of golden silk plum in the future.

[0114] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for quickly exploring suitable areas for golden silk plum, characterized by: At least the following steps are included: S1: Acquire and process the geographical distribution data of Prunus mume, and obtain the corresponding data through resource sharing platforms and publicly published papers and documents; S2: Acquisition and processing of ecological factor data, using Spearman's correlation analysis and percentage contribution value to screen variables; S3: Set the source of future environmental data; S4: Setting and running of the MaxEnt model. The principle of the MaxEnt model is to estimate the probability distribution of species by calculating the maximum entropy of the probability distribution of the target area under certain limited conditions; S5: Model accuracy test, test the model of S4 according to the evaluation indicators; S6: Setting and running the ArcGIS model to obtain the distribution map of the potential suitable growth areas of Plum chinensis and to identify the future increase and decrease in the potential suitable growth areas of Plum chinensis.

2. A method for quickly exploring suitable areas for golden silk plum according to claim 1, characterized in that: The resource sharing platform at least includes the teaching specimen resource sharing platform, the China Digital Herbarium and the China Nature Reserve Specimen Resource Sharing Platform.

3. A method for quickly exploring suitable areas for golden silk plum according to claim 1, characterized in that: The processing of the geographical distribution data of golden silk plum at least comprises the following steps: Collect the information of the collection sites and geographical distribution of golden silk plum specimens; Remove vaguely recorded, duplicated, and ambiguous distribution point information; At the same time, in order to avoid the large spatial autocorrelation of the obtained distribution points due to their close distance, which would affect the accuracy of the model simulation, only one existence point is retained in each pixel; Finally, the number of distribution points with detailed information is obtained, and then the longitude and latitude of the distribution points are determined using the Baidu picking coordinate system, with longitude as x and latitude as y, and saved as a csv format file.

4. A method for quickly exploring suitable areas for golden silk plum according to claim 3, characterized in that: The selection of Spearman's correlation analysis and percentage contribution value to screen variables in S2 at least includes the following steps: When the absolute value of the correlation coefficient is less than 0.8, both variables are retained first. If the absolute value of the correlation coefficient between the two variables is greater than 0.8, the environmental variable with the largest percentage contribution rate is retained. Finally, 10 environmental variables were retained, including annual average temperature, precipitation in the wettest quarter, precipitation in the warmest quarter, monthly mean of diurnal temperature difference, isothermality, temperature change variance, altitude and aspect.

5. A method for quickly exploring suitable areas for golden silk plum according to claim 4, characterized in that: The setting of the MaxEnt model in S4 at least includes the following steps: The unknown probability distribution of species in the environmental space is denoted as π, and the unknown probability distribution π is defined in a finite set X, where X is the set of pixels in the study area; x represents each network point divided by X, π(x) is the non-negative distribution probability at point x, and satisfies The distribution with the largest entropy is selected as the optimal distribution, and its probability is Then the maximum entropy of the probability distribution is: Specifically, the number of distribution points of the above-mentioned golden silk plum after screening and the 10 environmental variable data were imported into the "Samples" and "Enviromental layers" commands of the MaxEnt software model; Randomly select 75% of the data as the training set and the remaining 25% as the test set. Set the maximum number of iterations to 1000 and the rest to the default values. To ensure the stability of the model, the model was repeated 10 times by bootstrapping method, and the average value after 10 repetitions was used as the final habitat layer to obtain the habitat suitability index; The results were exported in logistic format and ASCII type and transferred to ArcGIS10.8 for further processing to obtain the distribution map of species suitable habitats.

6. A method for quickly exploring suitable areas for golden silk plum according to claim 1, characterized in that: The evaluation indicators in S5 include accuracy, sensitivity, specificity, Kappa index, TSS index and ROC curve.

7. A method for quickly exploring suitable areas for golden silk plum according to claim 1, characterized in that: The S6 at least comprises the following steps: Import the results of MaxEnt model simulation into ArcGIS software; Select the Reclassify tool in ArcGIS software and execute the Reclassify command to classify the suitable habitat layer of Prunus cerasifera. That is, use the natural break classification method in the Reclassification tool to divide the potential habitat of Prunus cerasifera into four parts: unsuitable habitat, increased habitat, reduced habitat and adapted habitat, and obtain the distribution map of the potential suitable habitat of Prunus cerasifera. After processing the distribution map of the potential suitable habitat of Prunus cerasifera, the area of ​​the suitable habitat of Prunus cerasifera in different eras and different climate scenarios in the future was statistically analyzed. Clarify the future increase and decrease in the potential suitable habitat of golden silk plum.

8. A method for quickly exploring suitable areas for golden silk plum according to claim 7, characterized in that: The processing includes at least masking, projection and projecting grids within transformation tools.

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