DOI: 10.5061/dryad.rbnzs7hmh
title: Data from: Prediction of Platycodon grandiflorus distribution in China using MaxEnt model concerning current and future climate change

description: Platycodon grandiflorus, the sole species from the genus
 Platycodon and a member of the Campanulaceae family, has been
 known for its medicinal, culinary, and ornamental uses for approximately
 2000 years. Its distribution is primarily in eastern Asia,
 including China, Korea, Japan, and Russia. With increasing market demand
 and the depletion of wild resources, understanding the distribution and
 factors affecting its habitat suitability is crucial for its conservation
 and sustainable utilization. This study aims to predict the suitable
 habitat for P. grandiflorus in China considering current and
 future climate scenarios. The MaxEnt model, with an AUC value of
 0.846, demonstrated good predictive ability for the current and future
 distribution of P. grandiflorus. It also identified the
 central, eastern, and southern regions as suitable habitats, with the
 critical environmental factors being precipitation, temperature, and
 elevation. Future scenarios under both SSP126 and SSP585 projections
 indicate an increase in suitable habitats, particularly in northeastern
 and central China, albeit with a shift in the distribution center towards
 the northeast by 2041-2060 and 2081-2100 under different
 scenarios. As a result, P. grandiflorus’ distribution is
 significantly influenced by environmental factors, with precipitation and
 temperature being pivotal. In summary, this study predicts an
 expansion of suitable habitats under future climate scenarios, suggesting
 that climate change may facilitate the growth and distribution of P.
 grandiflorus in new areas. The northward shift in the
 distribution center underlines the impact of global warming on plant
 distribution. These findings are crucial for the conservation, effective
 utilization, and strategic planning for the cultivation of P. grandiflorus
 in the face of climate change.
description: <strong>Distribution point
 collection</strong> According to the Global
 Biodiversity Information Facility (GBIF, http://www.gbif.org/) as well as Chinese Virtual Herbarium (CVH), accessed on 7 January 2024, the distribution points of <em>P. grandiflorus</em> in China were screened and recorded. Repeated locations were removed and the remaining locations were screened manually so that only one location appeared in every 10 km × 10 km grid cell. 405 known distribution locations of <em>P. grandiflorus</em> were documented through the use of ArcGIS 10.8 (Esri, Redlands, CA, USA) (Fig. 1). The data points were saved in CSV format for subsequent analysis by ArcGIS. <strong>Environmental parameters</strong> Fifty-two environmental parameters which could influence the distribution of <em>P. grandiflorus</em> were selected. This study used the latest WorldClim version 2.1 (https://worldclim.org/, accessed on 13 January 2024) to obtain current, future climate projection data as well as elevation data at a spatial resolution of 2.5 min. Climate type data used include January to December precipitation and average temperatures, as well as 19 bioclimatic factors. The Shared Socioeconomic Pathways (SSPs) are based on five narratives of socioeconomic development (Riahi et al. 2017). For this study, we selected the low forcing scenario SSP126 and the high forcing scenario SSP585 to predict the impacts on <em>P. grandiflorus</em>. SSP126 represents a low-material, low-resource, and low-energy green development pathway, while the SSP585 scenario depicts a future socioeconomic pathway with high emissions and high carbon use (O’Neill et al. 2016). In this study, we adopted BCC-CSM2-MR climate model which was reported to accurately simulates temperature and precipitation in China (Wu et al. 2019). The BCC-CSM2-MR model was developed by the National Climate Centre (Beijing, China) and participated in the International Coupled Model Comparison Program (ICMCP) and enhanced its climate simulation capability in Eastern Asia, especially for the China region. We projected the potential distribution areas of <em>P. grandiflorus</em> for 2041-2060 and 2081-2100 under the SSP126 and SSP585 scenarios. In addition to the above-mentioned variables, we considered two topographic factors and six soil factors. The topographic factors included slope and aspect. Soil factors included topsoil clay fraction, topsoil sand fraction, topsoil organic carbon, the acidity and alkalinity of the soil, the total nutrient fixing capacity of the soil, available water storage capacity of the soil unit. We obtained the topographical data from the Geospatial Data Cloud (http://www.gscloud.cn/, downloaded on 15 January 2024)，the soil data through the Harmonized World Soil Database (https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmon- ized-world-soil-database-v12/en/, downloaded on 15 January 2024). These environment variables were converted to ASCII format by the ArcGIS Conversion Tools for downstream analysis. High correlations and covariances between the extracted environmental variables can easily lead to overfitting of the model and affect the prediction results, so not all variables were needed (Hu and Hua 2014). Therefore, for every environmental factor, its contribution to the model predictions was first evaluated using the jackknife test (a module in MaxEnt (version 3.4.1)), and the environmental factors that contributed less (&lt;1 %) were eliminated. The correlation between the remaining variables was then calculated in SPSS 20.0 adopting the Pearson correlation coefficient method. We considered two variables with |r|≥0.8 to be significantly correlated, and excluded one of the variables with relatively low biological significance to minimize model overfitting (Xu et al. 2019). After screening, only 17 of the initial 52 environment variables were included for evaluation (Table 1).
description: # Data from: Prediction of Platycodon grandiflorus distribution in China
 using MaxEnt model concerning current and future climate change
 [https://doi.org/10.5061/dryad.rbnzs7hmh](https://doi.org/10.5061/dryad.rbnzs7hmh) We have submitted our raw data, including 52 environment variables converted to ASCII format (1-16_environment_variables_converted_to_ASCII_format.zip, 17-32_environment_variables_converted_to_ASCII_format.zip, 33-52_environment_variables_converted_to_ASCII_format.zip), 405 known distribution locations of *P. grandiflorus* contain latitude and longitude information (405_known_distribution_locations_of_*P._grandiflorus*.csv), a comparison of topsoil pH and organic carbon in HWSD and SoilGrid (HWSD_vs_SoilGrid.xlsx), and the probability of existence based on screening 52 environment variables (Probability_of_existence_by_screening_52_environment_variables.docx).  **Descriptions** 52 environment variables converted to ASCII format, divided into the following three parts due to file size: (1) **1-16_environment_variables_converted_to_ASCII_format.zip** aspect = Aspect /° awc_class = Available water storage capacity in mm/m of the soil unit bio1 = Annual Mean Temperature /℃ bio2 = Mean Diurnal Range (Mean of monthly (max temp - min temp)) /℃ bio3 = Isothermality (BIO2/BIO7) (×100) bio4 = Temperature Seasonality (standard deviation ×100) bio5 = Max Temperature of Warmest Month /℃ bio6 = Min Temperature of Coldest Month /℃ bio7 = Temperature Annual Range (BIO5-BIO6) /℃ bio8 = Mean Temperature of Wettest Quarter /℃ bio9 = Mean Temperature of Driest Quarter /℃ bio10 = Mean Temperature of Warmest Quarter /℃ bio11 = Mean Temperature of Coldest Quarter /℃ bio12 = Annual Precipitation /mm bio13 = Precipitation of Wettest Month /mm bio14 = Precipitation of Driest Month /mm (2) **17-32_environment_variables_converted_to_ASCII_format.zip** bio15 = Precipitation Seasonality (Coefficient of Variation) /mm bio16 = Precipitation of Wettest Quarter /mm bio17 = Precipitation of Driest Quarter /mm bio18 = Precipitation of Warmest Quarter /mm bio19 = Precipitation of Coldest Quarter /mm elev = Elevation/m slope = Slope/° t_cec_soil = Topsoil CEC (soil) / cmol/kg t_clay = Topsoil Clay Fraction /% wt. t_oc = Topsoil Organic Carbon /% weight t_ph_h2o = Topsoil pH (H2O) /-log(H+ ) t-sand = Topsoil Sand Fraction /% wt. wc2.1_2.5m_prec_01 = January precipitation /mm wc2.1_2.5m_prec_02 = February precipitation /mm wc2.1_2.5m_prec_03 = December precipitation /mm wc2.1_2.5m_prec_04 = April precipitation /mm (3) **33-52_environment_variables_converted_to_ASCII_format.zip** wc2.1_2.5m_prec_05 = May precipitation /mm wc2.1_2.5m_prec_06 = June precipitation /mm wc2.1_2.5m_prec_07 = July precipitation /mm wc2.1_2.5m_prec_08 = August precipitation /mm wc2.1_2.5m_prec_09 = September precipitation /mm wc2.1_2.5m_prec_10 = October precipitation /mm wc2.1_2.5m_prec_11 = November precipitation /mm wc2.1_2.5m_prec_12 = December precipitation /mm wc2.1_2.5m_tavg_01 = January average temperature /℃ wc2.1_2.5m_tavg_02 = February average temperature /℃ wc2.1_2.5m_tavg_03 = December average temperature /℃ wc2.1_2.5m_tavg_04 = April average temperature /℃ wc2.1_2.5m_tavg_05 = May average temperature /℃ wc2.1_2.5m_tavg_06 = June average temperature /℃ wc2.1_2.5m_tavg_07 = July average temperature /℃ wc2.1_2.5m_tavg_08 = August average temperature /℃ wc2.1_2.5m_tavg_09 = September average temperature /℃ wc2.1_2.5m_tavg_10 = October average temperature /℃ wc2.1_2.5m_tavg_11 = November average temperature /℃ wc2.1_2.5m_tavg_12 = December average temperature /℃ **405_known_distribution_locations_of_*P._grandiflorus*.csv** •species: Name of species •Longitude: Longitude in species coordinates •Latitude: Latitude in species coordinates **HWSD_vs_SoilGrid.xlsx** •No: Number •Longitude: Longitude in species coordinates •Latitude: Latitude in species coordinates •SoilGrid topsoil pH: Topsoil pH in SoilGrid •HWSD topsoil pH: Topsoil pH in HWSD •|SoilGrid pH-HWSD|&gt;1: If the absolute difference between the pH value of the SoilGrid and the pH value of the HWSD is greater than 1, the output should be 1. If the difference is less than or equal to 1, the output should be 0. •SoilGrid organic carbon content: organic carbon content in SoilGrid •HWSD organic carbon content: organic carbon content in HWSD •|SoilGrid-HWSD|&gt;1: If the absolute difference between the organic carbon content value of the SoilGrid and the organic carbon content value of the HWSD is greater than 1, the output should be 1. If the difference is less than or equal to 1, the output should be 0. **Probability_of_existence_by_screening_52_environment_variables.docx** •Variable: 52 environment variables •Percent contribution: Percentage contribution from each environment variable •Permutation importance: Permutation importance from each environment variable