DOI: 10.5061/dryad.gf1vhhn08
title: Prediction of potential distribution of seven plant species of <em>Aster</em> (Asteraceae) based on MaxEnt model

description: 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.
description: 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).
 <strong>Sharing/accessing
 information</strong> 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)
 <strong>Hardware</strong> The
 dataset was filtered and validated using ArcGIS 10.4 (GIS) software.
 Pre-modelling was performed using the MaxEnt model.
description: # Prediction of potential distribution of seven plant species of *Aster*
 (Asteraceae) based on MaxEnt model Dataset DOI:
 [10.5061/dryad.gf1vhhn08](https://doi.org/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 &lt; 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/](https://worldclim.org/)). * Future data: Sourced
 from CMIP6 multi-model ensemble projections
 ([https://esgf-node.llnl.gov/](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| &gt; 0.3, p &lt; 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/](https://www.libreoffice.org/) * Apache
 OpenOffice Calc (v4.1.10+):
 [https://www.openoffice.org/](https://www.openoffice.org/) * **Commercial
 alternative**: Microsoft Excel (v2016+). **Opening workflow**: 1. Launch
 the software. 2. Navigate to File → Open and select the target CSV file.
 3. 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/](https://qgis.org/) * **Commercial
 alternative**: ArcGIS (v10.8+). **Opening workflow in QGIS**: 1. Launch
 QGIS. 2. Navigate to Layer → Add Layer → Add Raster Layer. 3. Select the
 target ASC file (e.g., Current_climate/bio_2.asc or
 Future_climate/ssp126_2041_2060/bio_3.asc). 4. 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/](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/](https://worldclim.org/)). *
  Future data: Sourced from CMIP6 multi-model ensemble projections
 ([https://esgf-node.llnl.gov/](https://esgf-node.llnl.gov/)).