Data from: Global warming and environmental heterogeneity drive species richness pattern of Picea in China
Data files
Jul 22, 2026 version files 5.99 MB
-
Actual_Grid_SpeciesRichness_VariableValues.csv
626.39 KB
-
code.R
1.79 KB
-
Historical_baseline_potential_Grid_SpeciesRichness_VariableValues.csv
626.47 KB
-
README.md
3.27 KB
-
Species_distribution_records.csv
130.70 KB
-
SSP126_2050s_Grid_SpeciesRichness_VariableValues.csv
642.32 KB
-
SSP126_2070s_Grid_SpeciesRichness_VariableValues.csv
792.72 KB
-
SSP245_2050s_Grid_SpeciesRichness_VariableValues.csv
791.78 KB
-
SSP245_2070s_Grid_SpeciesRichness_VariableValues.csv
792.29 KB
-
SSP585_2050s_Grid_SpeciesRichness_VariableValues.csv
791.89 KB
-
SSP585_2070s_Grid_SpeciesRichness_VariableValues.csv
791.07 KB
Abstract
Global warming has emerged as a key driver of global biodiversity loss, substantially impacting on the geographical distribution and richness patterns of species. Picea, a crucial component of China's forest ecosystem, plays a vital role in ecological functioning and conservation. This study employed the MaxEnt model and integrated distribution data of 22 Picea species in China to predict historical and future diversity patterns and trends. Additionally, using structural equation modeling (SEM), this study revealed how global warming and environmental heterogeneity drive the richness patterns of Picea species in China, and identified relevant diversity hotspots and conservation gaps. Results showed that climate change contracts suitable habitats for some Picea species and drives most taxa upward to higher latitudes and elevations. Environmental energy and water availability directly control Picea richness, whereas environmental heterogeneity dominates richness spatial patterns via strong indirect effects and the largest total effect. Furthermore, the analysis of protection effectiveness revealed that Picea species are well protected within the existing conservation network; however, certain conservation gaps remain. Notably, hotspot regions distributed in the Hengduan Mountains demand focused attention. This study investigated the response patterns of Picea species diversity in China to global warming and environmental heterogeneity under a multi-climate scenario framework. It not only offers insights into the evolutionary mechanisms underlying the distribution patterns of coniferous forests but also provides a scientific foundation for the strategic planning of biodiversity conservation priority areas in the context of climate change.
Description of the data and file structure
This dataset consists of two tables and an R code,one containing the distribution coordinates of each Picea species, while the other documents the species richness and corresponding variable values of each grid cell across different periods. The R code provides the necessary code for statistical analysis conducted in this research.
Coordinate Generalization for Conservation Protection
In accordance with Dryad and GBIF guidelines for sensitive threatened plant species, all geographic coordinates in the Species_distribution_records.csv file have been spatially generalized prior to public release to avoid illegal harvesting and disturbance of wild Picea populations.
Processing standard adopted:
All Critically Endangered (CR), Endangered (EN), Vulnerable (VU) and Near Threatened (NT) Picea species are rounded to integer 1-degree precision for longitude and latitude
- only Least Concern (LC) species retain coordinates with two decimal places.
Original high-precision raw coordinate data will not be published in this public repository to mitigate conservation risks.
File: code.R
Description: This R document provides the code involved in the statistical analysis conducted in this study.
Files:
- Actual_Grid_SpeciesRichness_VariableValues.csv
- Historical_baseline_potential_Grid_SpeciesRichness_VariableValues.csv
- SSP126_2050s_Grid_SpeciesRichness_VariableValues.csv
- SSP126_2070s_Grid_SpeciesRichness_VariableValues.csv
- SSP245_2050s_Grid_SpeciesRichness_VariableValues.csv
- SSP245_2070s_Grid_SpeciesRichness_VariableValues.csv
- SSP585_2050s_Grid_SpeciesRichness_VariableValues.csv
- SSP585_2070s_Grid_SpeciesRichness_VariableValues.csv
Description: Species richness and corresponding variable values for each grid cell at a resolution of 50 × 50 km.
Variables
- Grid cells_id: Number of grid cells
- species_SR: Species richness values for species
- BIO1: Annual Mean Temperature (°C)
- BIO2: Mean Diurnal Range (°C)
- BIO3: Isothermality (°C)
- BIO4: Temperature Seasonality
- BIO5: Max Temperature of Warmest Month (°C)
- BIO6: Min Temperature of Coldest Month (°C)
- BIO7: Temperature Annual Range (°C)
- BIO8: Mean Temperature of Wettest Quarter (°C)
- BIO9: Mean Temperature of Driest Quarter (°C)
- BIO10: Mean Temperature of Warmest Quarter (°C)
- BIO11: Mean Temperature of Coldest Quarter (°C)
- BIO12: Annual Precipitation (mm)
- BIO13: Precipitation of Wettest Month (mm)
- BIO14: Precipitation of Driest Month (mm)
- BIO15: Precipitation Seasonality
- BIO16: Precipitation of Wettest Quarter (mm)
- BIO17: Precipitation of Driest Quarter (mm)
- BIO18: Precipitation of Warmest Quarter (mm)
- BIO19: Precipitation of Coldest Quarter (mm)
- BIO1_RANGE: Annual Mean Temperature Range (°C)
- BIO12_RANGE: Annual Precipitation Range (mm)
- NST: Numnber of soil types
- ELER: Elevational range (m)
File: Species_distribution_records.csv
Description: Coordinates of species distribution points.
Variables
- Species: Species name
- Longitude: Longitude
- Latitude: Latitude
