Data from: Potential geographic distributions of endangered Opisthopappus Shih in response to environmental changes
Creators: name: Zhang, Hao
name: Ye, Hang
name: Zang, En
name: Qie, Qiyang
name: He, Shan
name: Hao, Weili
name: Lan, Yafei
name: Liu, Zhixia
name: Sun, Genlou
name: Wang, Yiling
Publisher: name: Zenodo
Year: 2022

description: Environmental changes could dramatically influence the distribution area and niche of organisms. Taihang Mountains contain numerous endemic species, regarded as a center of distribution and diversity for many plant genera. It is necessary that having more comprehensive studies of test climate effects on species in this area. <i>Opisthopappus</i> (containing two species<i> Opisthopappus</i><i> taihangensis</i> and <i>Opisthopappus</i><i> longilobus</i>) is an endangered and endemic genus in the Taihang Mountains. Predicting the suitable potential distribution, exploring the niche difference between two species and determining the important environmental factors are very critical for the sustainable utilization and scientific conservation. In this study, the distribution areas of both two species decreased drastically from the LIG to LGM period. Main refugia might have been in the centre of the Taihang Mountains. Compared LGM, the distribution ranges expanded in the Mid-Holocene period whether <i>O. longilobus </i>or <i>O. taihangensis</i>, and that generally similar to the present distribution areas of two species. From 2050 to 2070, the predicted distribution area greatly increased for both two species. Moreover, <i>O. longilobus</i> migrated toward south and east, while <i>O. taihangensis</i> moved toward west. Among all 37 environmental variables, fourteen variables (bio2, bio3, bio4, bio8, bio11, bio13, bio15, elev, T_CaCO<sub>3</sub>, T_GRAVEL, T_OC, T_SAND, T_SILT, T_TEB) were the most important factors influencing on the distribution of <i>Opisthopappus</i>. <i>O. taihangensis</i> and <i>O. longilobus</i> presented a significant niche differentiation, and this change occurred gradually with the passage of time. These would provide some clues for the management and conservation for <i>O. taihangensis</i> and <i>O. longilobus</i>.
description: GENERAL INFORMATION Date of data collection (2020-10) : Geographic location of data collection ( latitude 34°N-38°N and longitude 111°E-115°E of Taihang Mountains in China) SHARING/ACCESS INFORMATION The data do not contain sensitive information, and do not have licensing conflicts with CC0. DATA &amp; FILE OVERVIEW 1. File List: S_Table_2_Extract_variable.csv The environmental factors were extracted with ARCGIS software<br> fig.2.tif HD Figure<br> fig.1.tif HD Figure<br> fig.3C.tif HD Figure<br> fig.3A.tif HD Figure<br> fig.3B.tif HD Figure<br> fig.4.tif HD Figure<br> fig.5.tif HD Figure METHODOLOGICAL INFORMATION Methods for processing the data: The environmental factors were extracted with ARCGIS 10.7 software. DATA-SPECIFIC INFORMATION FOR: [S_Table_2_Extract_variable.csv] 1. Number of variables: 41 2. Number of rows: 44 3. Variable List: From left to right, each column of the CSV file contains the species name, latitude, longitude, sampling point name, and Environmental Extend Variables. OBJECTID= species name lat= latitude long= longitude location= sampling point name T_GRAVEL=Topsoil Gravel Content T_SAND=Topsoil Sand Fraction T_SILT=Topsoil Silt Fraction T_CLAY=Topsoil Clay Fraction T_USDA_TEX=Topsoil USDA Texture Classification T_REF_BULK=Topsoil Reference Bulk Density T_OC=Topsoil Organic Carbon T_PH_H2O=Topsoil pH (H2O) T_CEC_CLAY=Topsoil CEC (clay) T_CEC_SOIL=Topsoil CEC (soil) T_BS=Topsoil Base Saturation T_TEB=Topsoil TEB T_CACO3=Topsoil Calcium Carbonate T_ESP=Topsoil Sodicity (ESP) T_ECE=Topsoil Salinity (Elco) T_TEXTURE= Topsoil Texture AWC_CLASS= Available water content Range elev= elevation DRAINAGE= Drainage class REF_DEPTH= Reference Soil Depth ADD_PROP= Other properties (gelic, vertic, petric) T_CASO4=Topsoil Gypsum 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 BIO13 = Precipitation of Wettest Month BIO14 = Precipitation of Driest Month BIO15 = Precipitation Seasonality (Coefficient of Variation) BIO16 = Precipitation of Wettest Quarter BIO17 = Precipitation of Driest Quarter BIO18 = Precipitation of Warmest Quarter BIO19 = Precipitation of Coldest Quarter Funding provided by: National Natural Science Foundation of China<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809<br>Award Number: 31970358Funding provided by: Shanxi Scholarship Council of China<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100003398<br>Award Number: 2020-090Funding provided by: Graduate Education Innovation Project of Shanxi*<br>Crossref Funder Registry ID: <br>Award Number: 2020SY323Funding provided by: Graduate Education Innovation Project of Shanxi<br>Crossref Funder Registry ID: <br>Award Number: 2020SY323
description: The environmental factors were extracted with ARCGIS software.