Species occurrence locations and environmental parameters used for predicting the potential planting regions of Pterocarpus santalinus
Data files
May 08, 2024 version files 1.33 GB
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Environmental_Parameters_(current).zip
847.13 MB
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Environmental_Parameters_(future-BCC-CSM2-MR).zip
484.09 MB
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README.md
5.43 KB
May 08, 2024 version files 1.33 GB
-
Environmental_Parameters_(current).zip
847.13 MB
-
Environmental_Parameters_(future-BCC-CSM2-MR).zip
484.09 MB
-
README.md
4.87 KB
Abstract
This study explores the habitat distribution of Pterocarpus santalinus, a valuable rosewood species, across China, focusing on its response to current and future climate changes. Utilizing the MaxEnt model, we assess its suitable habitat under present conditions and future climate scenarios (SSPs126, SSPs245, and SSPs585). Our findings reveal that the current suitable habitat, spanning approximately 409,600 km², is primarily located in the central and southern parts of Guangdong, Guangxi, Fujian, Yunnan, as well as in the Hainan provinces, along with the coastal regions of Taiwan, and the Sichuan-Chongqing border. The habitat's distribution is significantly influenced by climatic factors such as temperature seasonality (bio4), mean temperature of the wettest quarter (bio8), annual mean temperature (bio1), and annual precipitation (bio12), while terrain and soil factors play a lesser role. Under future climate scenarios, the suitable habitat for P. santalinus is projected to expand, with a northeastward shift in its distribution center. This research not only sheds light on the geoecological characteristics and geographical distribution of P. santalinus in China but also offers a scientific basis for planning its cultivation areas and enhancing cultivation efficiency under changing climate conditions.
https://doi.org/10.5061/dryad.0p2ngf274
Overview
The supplementary dataset is an integral component of our research paper, encompassing species occurrence records and associated environmental variables utilized for predicting potential species distributions. All environmental factors are stored in the ASC file format for ease of processing and analysis. To ensure the accuracy and interpretability of the data, we have also provided XML and PRJ files as supplementary materials. These additional files contain essential metadata and spatial reference system information, which are crucial for ecologists and conservation biologists when conducting predictive analyses for the potential distribution of other species.
Data Formats
• .asc: The environmental factors' raster data is stored in ASCII format, where each pixel value represents the value of a specific environmental factor at a particular geographic location.
• .xml: The XML file provides detailed metadata for raster data, including methods of data collection, temporal coverage, and descriptions of data quality, to support the understanding and use of the data.
• .prj: The PRJ file specifies the spatial reference system for raster data, ensuring accurate geographic positioning and facilitating analysis and visualization within Geographic Information Systems (GIS).
Descriptions
Environmental Parameters (current).zip
Current environmental factors (17).
Environmental Parameters (future-BCC-CSM2-MR).zip
For future climate modeling, we chose the BCC-CSM2-MR dataset to project scenarios for the 2050s (2041-2060), 2070s (2061-2080), and 2090s (2081-2100) with a spatial resolution of 30 seconds. Three Shared Socio-economic Pathways (SSPs) - SSPs126 (lowest greenhouse gas emission scenario), SSPs245 (moderate greenhouse gas emission scenario), and SSPs585 (highest greenhouse gas emission scenario) - were selected to provide a more scientific depiction of future climate change.
location
Three Shared Socio-economic Pathways (2050s): Environmental Parameters (future-BCC-CSM2-MR)/2041-2060/SSP126; Environmental Parameters (future-BCC-CSM2-MR)/2041-2060/SSP245; Environmental Parameters (future-BCC-CSM2-MR)/2041-2060/SSP585.
Three Shared Socio-economic Pathways (2070s): Environmental Parameters (future-BCC-CSM2-MR)/2061-2080/ssp126; Environmental Parameters (future-BCC-CSM2-MR)/2061-2080/ssp245; Environmental Parameters (future-BCC-CSM2-MR)/2061-2080/ssp585.
Three Shared Socio-economic Pathways (2090s): Environmental Parameters (future-BCC-CSM2-MR)/2081-2100/ssp126; Environmental Parameters (future-BCC-CSM2-MR)/2081-2100/ssp245; Environmental Parameters (future-BCC-CSM2-MR)/2081-2100/ssp585.
Abbreviations
• Bio 1: Annual Mean Temperature, (°C×10)
• Bio 2: Mean Diurnal Range (Mean of monthly (max temp - min temp)), (°C×10)
• Bio 3: Isothermality (BIO2/BIO7) (×100), (%)
• Bio 4: Temperature Seasonality (standard deviation ×100), (°C×100)
• Bio 8: Mean Temperature of Wettest Quarter, (°C×10)
• Bio 12: Annual Precipitation, (mm)
• Bio 14: Precipitation of Driest Month, (mm)
• Bio 15: Precipitation Seasonality, (Coefficient of Variation)
• alt: altitude, (m)
• slo: slope, (°)
• asp: aspect, (/*)
• t_bden: soil bulk density, (g/cm3)
• t_ph: soil pH, (pH units)
• t_sand: sand content, (%)
• t_clay: clay content, (%)
• t_oc: organic carbon content, (%)
• t_gravel: gravel percentage, (%)
• bioc: bioclimatic variables
• prec: monthly total precipitation, (mm)
• tmax: monthly average maximum temperature, (°C)
• tmin: monthly average minimum temperature, (°C)
Key Information Sources
Environmental variables were derived from the following sources:
WorldClim database (https://www.worldclim.org/data/index.html)
Code/Software
ArcGIS (Version 10.2; https://www.esri.com/) can be used for viewing, processing, and format conversion of environmental factor data (.asc).
MaxEnt model (Version 3.4.4; https://biodiversityinformatics.amnh.org/open_source/maxent/ is required to predict the potential distribution areas of a species. Once the environmental variables (ASC file) and Species occurrence locations (CSV file) have been imported, the execution of the software is possible.
We identified the occurrence locations of P. santalinus using a three-pronged approach: (1) A comprehensive review of relevant literature; (2) Extensive field investigations; and (3) Data acquisition from online databases, specifically the Global Biodiversity Information Facility (GBIF, https://www.gbif.org) and the National Specimen Information Infrastructure (NSII, http://www.nsii.org.cn/2017/home.php).
We downloaded environmental parameters from WorldClim (https://www.worldclim.org/data/index.html) and Harmonized World Soil Database v 1.2 (HWSD; http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/), processed them using ArcGIS 10.2