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Data from: Potential geographic distributions of endangered Opisthopappus Shih in response to environmental changes

Citation

Zhang, Hao et al. (2022), Data from: Potential geographic distributions of endangered Opisthopappus Shih in response to environmental changes, Dryad, Dataset, https://doi.org/10.5061/dryad.prr4xgxmz

Abstract

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. Opisthopappus (containing two species Opisthopappus taihangensis and Opisthopappus longilobus) 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 O. longilobus or O. taihangensis, 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, O. longilobus migrated toward south and east, while O. taihangensis moved toward west. Among all 37 environmental variables, fourteen variables (bio2, bio3, bio4, bio8, bio11, bio13, bio15, elev, T_CaCO3, T_GRAVEL, T_OC, T_SAND, T_SILT, T_TEB) were the most important factors influencing on the distribution of Opisthopappus. O. taihangensis and O. longilobus 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 O. taihangensis and O. longilobus.

Methods

The environmental factors were extracted with ARCGIS software.

Usage Notes

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 & FILE OVERVIEW

1. File List:  S_Table_2_Extract_variable.csv The environmental factors were extracted with ARCGIS software
fig.2.tif HD Figure
fig.1.tif HD Figure
fig.3C.tif HD Figure
fig.3A.tif HD Figure
fig.3B.tif HD Figure
fig.4.tif HD Figure
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

National Natural Science Foundation of China, Award: 31970358

Shanxi Scholarship Council of China, Award: 2020-090

Graduate Education Innovation Project of Shanxi, Award: 2020SY323

Graduate Education Innovation Project of Shanxi, Award: 2020SY323