Prediction of potential distribution of seven plant species of Aster (Asteraceae) based on MaxEnt model
Data files
Dec 01, 2025 version files 794.17 MB
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Distribution_points_of_7_Aster_species_in_China.zip
6.76 KB
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Environmental_factors1.zip
794.07 MB
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README.md
8.92 KB
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Table_1_Climate_variables.rtf
86.86 KB
Abstract
Aster (Asteraceae) species, as one of the traditional Tibetan medicinal plants in China, have high useful medicinal and unique ornamental value, the market demand has been gradually increasing. In this study, seven species of Aster were selected from Qinghai-Tibet Plateau, and the MaxEnt model was used to investigate their potential distribution in China and the changes in their suitable habitat under future climate conditions based on the current survey and distribution data of specimens on the site and six to eight environmental variables. The results showed that temperature and precipitation were important limiting factors affecting the distribution of Aster, and Bio2, Bio3, and Bio10 were common environmental factors influencing factors of Aster species. Under the current climate, the mainly potential distributed region of the seven Aster species in the Qinghai-Tibet Plateau. Under projected future climate scenarios, the suitable habitats of A. asteroides and A. diplostephioides will shrink significantly, while those of A. farreri, A. poliothamnus, A. souliei, A. tongolensis, and A. yunnanensis var. labrangensis will expand accordingly. Environmental factors provide a large gain in predicting the distribution of Aster species. Among the environmental variables, isothermality (Bio3) induced the largest impact on SDM and contained the most useful information for A. diplostephioides (55.9%), A. souliei (41.5%) and A. yunnanensis var .labrangensis (27.1%), while A. tongolensis (27.9%) and A. poliothamnus (26.8%) were more significantly affected by the temperature seasonality (Bio4), A. asteroides (66.3%) and A. farreri (21%) was more significantly affected by the mean temperature of warmest quarter (Bio10). The study findings suggest that the distribution range of seven species of Aster will be greatly impacted by climate change. This research helps identify the limiting factors affecting the natural distribution and potential suitable areas for Aster species, which can inform conservation efforts, plant introduction, acclimatization, domestication, and cultivation of Aster.
Dataset DOI: 10.5061/dryad.gf1vhhn08
Description of the data and file structure
1. Dataset Description
This dataset supports research on the phylogenetic relationships and adaptive evolution of seven Aster species endemic to or widely distributed on the Qinghai-Tibet Plateau (QTP). It comprises two core data types: raw species distribution records and climatic/environmental variables (covering current and future scenarios). These data are designed for direct application in species distribution modeling (SDM), phylogenetic analysis, and studies on alpine adaptation mechanisms, providing a comprehensive data foundation for evolutionary biology research and conservation of Aster species.
Files and variables
File: Distribution_points_of_7_Aster_species_in_China.zip
Description: Seven CSV files (one per Aster species) containing standardized occurrence records, compatible with mainstream data analysis software:
File: Table_1_Climate_variables.rtf
Description: Rich Text Format (.rtf) file detailing all 19 bioclimatic variables,
File: Environmental_factors1.zip
Description: All environmental data are provided as ASCII Grid (.asc) files, packaged into two ZIP archives. Coordinate system and resolution are consistent across all spatial data to ensure comparability.
Code/software
2. File Structure
The dataset is organized hierarchically by data type and temporal dimension for ease of access. All file naming and formatting follow standard academic data repository conventions.
2.1 Distribution points of 7 Aster species in China
Seven CSV files (one per Aster species) containing standardized occurrence records, compatible with mainstream data analysis software:
- Aster asteroids.csv
- Aster diplostephioides.csv
- Aster farreri.csv
- Aster poliothamnus.csv
- Aster souliei.csv
- Aster tongolensis.csv
- Aster yunnanensis var. labrangensis.csv
Each CSV file includes columns for:
- species: Species abbreviation (for data association)
- longitude: Decimal longitude (WGS84 coordinate system
- latitude: Decimal latitude (WGS84 coordinate system)
2.2 Environmental factors1
All environmental data are provided as ASCII Grid (.asc) files, packaged into two ZIP archives. Coordinate system and resolution are consistent across all spatial data to ensure comparability.
2.2.1Current_climate.zip
Contains two subfolders with current climate data (2.5-minute resolution, covering 25°–40°N, 73°–105°E, the QTP region):
2.2.1.1 Subfolder 1: Environmental factors of species
7 species-named subfolders, each containing filtered bioclimatic variable .asc files. Only variables significantly correlated with the species’ distribution (assessed via ecological niche modeling) are retained to reduce data redundancy. Detailed variable selection results for each species are provided in the supplementary file (see Section 3).
Subfolder list:
• Aster asteroids
• Aster diplostephioides
• Aster farreri
• Aster poliothamnus
• Aster souliei
• Aster tongolensis
• Aster yunnanensis var. labrangensis
2.2.1.2 Subfolder 2: Current_wc2.1_2.5m_bio
Unprocessed raw bioclimatic data in GeoTIFF format (source: WorldClim 2.1), serving as the parent dataset for the filtered .asc files. These files enable data traceability and secondary analysis, compatible with QGIS/ArcGIS.
File list (19 bioclimatic variables):
• wc2.1_2.5m_bio_1. tif
• wc2.1_2.5m_bio_2. tif
• wc2.1_2.5m_bio_3. tif
• wc2.1_2.5m_bio_4. tif
• wc2.1_2.5m_bio_5. tif
• wc2.1_2.5m_bio_6. tif
• wc2.1_2.5m_bio_7. tif
• wc2.1_2.5m_bio_8. tif
• wc2.1_2.5m_bio_9. tif
• wc2.1_2.5m_bio_10. tif
• wc2.1_2.5m_bio_11. tif
• wc2.1_2.5m_bio_12. tif
• wc2.1_2.5m_bio_13. tif
• wc2.1_2.5m_bio_14. tif
• wc2.1_2.5m_bio_15. tif
• wc2.1_2.5m_bio_16. tif
• wc2.1_2.5m_bio_17. tif
• wc2.1_2.5m_bio_18. tif
• wc2.1_2.5m_bio_19. tif
** **
2.2.2 Future_climate.zip
Future bioclimatic projection data based on CMIP6 models, organized by "SSP emission pathway × time period" (8 subfolders total). Variable types align with current climate data to facilitate cross-temporal comparison. Each subfolder contains 13 bioclimatic variables in .asc format:
Scenario Subfolders:
- ssp126_2041_2060/ (SSP1-2.6, 2041-2060)
- ssp126_2081_2100/ (SSP1-2.6, 2081-2100)
- ssp245_2041_2060/ (SSP2-4.5, 2041-2060)
- ssp245_2081_2100/ (SSP2-4.5, 2081-2100)
- ssp370_2041_2060/ (SSP3-7.0, 2041-2060)
- ssp370_2081_2100/ (SSP3-7.0, 2081-2100)
- ssp585_2041_2060/ (SSP5-8.5, 2041-2060)
- ssp585_2081_2100/ (SSP5-8.5, 2081-2100)
13 bioclimatic variables (asc files)
- bio_2.asc (Mean Diurnal Range)
- bio_3.asc (Isothermality)
- bio_4.asc (Temperature Seasonality)
- bio_6.asc (Min Temperature of Coldest Month)
- bio_8.asc (Mean Temperature of Wettest Quarter)
- bio_9.asc (Mean Temperature of Driest Quarter)
- bio_10.asc (Mean Temperature of Warmest Quarter)
- bio_11.asc (Mean Temperature of Coldest Quarter)
- bio_12.asc (Annual Precipitation)
- bio_14.asc (Precipitation of Driest Month)
- bio_15.asc (Precipitation Seasonality)
- bio_18.asc (Precipitation of Warmest Quarter)
- bio_19.asc (Precipitation of Coldest Quarter)
3. Table_1_Climate_variables.rtf
Rich Text Format (.rtf) file detailing all 19 bioclimatic variables, including:
Variables Abbreviation (e.g., bio_1)
Description (e.g., Annual Mean Temperature)
4. Methods Summary
4.1 Distribution Data Processing
- Compiled from two sources: field survey records (2018–2023) and verified herbarium records (sourced from GBIF).
- Data cleaning workflow:
1.Removed duplicate records (based on identical latitude/longitude and collection date).
2.Eliminated outliers using the Mahalanobis distance method (p < 0.05).
3.Validated records against species’ known elevation ranges (3000–5500 m a.s.l. on the QTP).
4.2 Climatic Data Processing
- Current data: Downloaded from WorldClim 2.1 (https://worldclim.org/).
- Future data: Sourced from CMIP6 multi-model ensemble projections (https://esgf-node.llnl.gov/).
- Unified processing steps:
1.Clipped to the QTP boundary (25°–40°N, 73°–105°E).
2.Resampled to 2.5-minute resolution using bilinear interpolation.
3.Converted from GeoTIFF to ASCII Grid (.asc) for cross-platform compatibility.
4.Filtered species-specific variables via Pearson correlation analysis (|r| > 0.3, p < 0.01) with occurrence records.
5. Software Requirements
5.1 Distribution Data (CSV Files)
- Recommended free/open-source tools:
- LibreOffice Calc (v7.0+): https://www.libreoffice.org/
- Apache OpenOffice Calc (v4.1.10+): https://www.openoffice.org/
- Commercial alternative: Microsoft Excel (v2016+).
Opening workflow:
- Launch the software.
- Navigate to File → Open and select the target CSV file.
- The file will load as a spreadsheet with labeled columns.
5.2 Climatic Data (ASC Files)
- Recommended free/open-source tool:
- QGIS (v3.28+): https://qgis.org/
- Commercial alternative: ArcGIS (v10.8+).
Opening workflow in QGIS:
- Launch QGIS.
- Navigate to Layer → Add Layer → Add Raster Layer.
- Select the target ASC file (e.g., Current_climate/bio_2.asc or Future_climate/ssp126_2041_2060/bio_3.asc).
- The raster will load as a geospatial layer, displaying the spatial distribution of the climatic variable.
6. Data License
This dataset is released under the CC0 1.0 Universal Public Domain Dedication (https://creativecommons.org/publicdomain/zero/1.0/):
- Distribution data: Sourced from GBIF, with all records verified to comply with CC0 via GBIF’s "Rights" metadata.
- Climatic data: Sourced from WorldClim and CMIP6, both distributed under CC0.
- No restrictions on use, modification, or redistribution. Attribution is encouraged but not required
Access information
Other publicly accessible locations of the data:
Data was derived from the following sources:
- Current data: Downloaded from WorldClim 2.1 (https://worldclim.org/).
- Future data: Sourced from CMIP6 multi-model ensemble projections (https://esgf-node.llnl.gov/).
The dataset includes 13 environmental factors: Mean Diurnal Range (Bio 2); Isothermality (Bio 2/ Bio 7) (×100) (Bio 3); Temperature Seasonality (standard deviation ×100) (Bio 4); Min Temperature of Coldest Month (Bio 6); Mean Temperature of Wettest Quarter (Bio 8); Mean Temperature of Driest Quarter (Bio 9); Mean Temperature of Warmest Quarter (Bio 10); Mean Temperature of Coldest Quarter (Bio 11); Annual Precipitation (Bio 12); Precipitation of Driest Month (Bio 14); Precipitation Seasonality (Coefficient of Variation) (Bio 15); Precipitation of Warmest Quarter (Bio 18); Precipitation of Coldest Quarter (Bio 19). The data format is an ASC file. Data for bioclimatic variables include present (1970-2000), future: 2041-2060, 2081-2100. Each future period includes four shared socioeconomic pathways (ssp_126, ssp_245, ssp _370, ssp_585).
Sharing/accessing information
The data sources are listed below:
Chinese Herbarium Resource Centre (https://www.cvh.org.cn)
Global Biodiversity Information Facility (GBIF) (https://www.gbif.org/)
Published papers.
Author's field fieldwork.
WorldClim database (https://www.worldclim.org/)
Geospatial data cloud (https://www.gscloud.cn/home)
Hardware
The dataset was filtered and validated using ArcGIS 10.4 (GIS) software. Pre-modelling was performed using the MaxEnt model.
