Data from: Evaluating the vulnerability of Tetracentron sinense habitats to climate-induced latitudinal shifts
Data files
Jul 09, 2024 version files 196.92 MB
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Datasets.rar
196.92 MB
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README.md
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Abstract
Objective: Exploring the changing process of the geographical distribution pattern of Tetracentron sinense Oliv. and its main influencing factors since the last interglacial period can provide a scientific basis for the effective protection and management of the species.
Methods: The MaxEnt model was used to construct the potential distribution areas of T. sinense in different periods such as the last interglacial (LIG), the last glacial maximum (LGM), the Mid-Holocene (MID), the current and future (2050s, 2070s). On the premise of discussing the influence of dominant environmental factors on its distribution model, the suitable area changes of T. sinense under different ecological climate situations were quantitatively analyzed.
Results: (1) The AUC and TSS values predicted by the optimized model were 0.959 and 0.835, respectively, indicating a good predictive effect by the MaxEnt model; the potential suitable areas for T. sinense in the current period are mainly located in southwest China, which are wider compared to the actual habitats. (2) Jackknife testing showed that the lowest temperature in the coldest month (Bio6), elevation (Elev), seasonal variation coefficient of temperature (Bio4) and surface calcium carbonate content (T-CACO3) are the dominant environmental factors affecting the distribution of T. sinense. (3) From the last interglacial period to the current period, the total suitable area of T. sinense showed a decreasing trend; the distribution points of T. sinense populations in Mid-Holocene period may be the origin of the postglacial population, and Southwest China may be its glacial biological refuge. (4) Compared with the current period, the total suitable area ranges of T. sinense in China in the 2050s and 2070s decreased, and the centroid location of its total fitness area all migrated to the northwest, with the largest migration distance in 2070s under the SSPs 7.0 climate scenario.
Conclusion: Temperature was the principal factor influencing the geographical distribution of T. sinense. With the global warming, the range of T. sinense suitable areas will show a shrinking trend, with a shift towards higher-latitude regions. Ex-situ conservation measures could be taken to preserve its germplasm resources.
https://doi.org/10.5061/dryad.vx0k6dk0m
Datasets
Utilizing the MaxEnt model and ArcGIS spatial analysis technology, this study examines potentially suitable areas for T. sinense across historical periods (the last interglacial period, the last glacial maximum, and the Middle Holocene) as well as current and future periods (2050s and 2070s). The objectives of this study are to (1) analyze the dynamic changes in potentially suitable areas, (2) investigate the main environmental factors driving changes in the distribution pattern of Tetracentron sinense, and (3) suggest a scientific basis for the effective protection and management of Tetracentron sinense.
Occurrence points: The distribution records of Tetracentron sinense are sourced from various data platforms such as Flora Reipublicae Popularis Sinicae (http://iplant.cn/), Plant Photo Bank of China, and the Global Biodiversity Information Facility (GBIF, Additionally, In addition, our team also conducted The Convert the coordinate trees, of aquatic trees represented latitude, need to be converted longitude and latitude into decimal numbers using specific by ARCGIS table should formulas. contain The table species, contains three columns: spcious, should be saved latitude, and save it file. Add CSV format file; from the distribution of the ARCGIS 10.2, format utilize to ARCGIS10.2, “Data/Display and data, Data/Export data” obtains the vector file of the water data. Define distribution point data; defines the projected geographical coordinate employ as WGS1984, and uses ENMTools software to filter out each grid in the raster layer 2.5m. Retain only accuracy of 2.5m distribution point model, eliminate retained in sites, reduce error bias, and use bias is reduced, and distribution points are used for modeling.
Environmental variables: We gathered environmental variables associated with bioclimatic, soil, and topographic factors as potential predictors of species distribution. 19 climate variables spanning the last glacial, last glacial maximum, Mid-Holocene, current periods, and future scenarios were sourced from the World Climate Database (http://worldclim.org). Future scenarios included low-concentration emissions (SSPs1-2.6) and high-concentration emissions (SSPs5-8.5) of greenhouse gases. 16 soil variables pertaining to the soil surface were acquired from the Chinese Soil Dataset within the Harmonized World Soil Database (HWSD, http://www.fao.org/faostat/en/#data), while elevation data were obtained from the same source. The spatial resolution was set at 2.5 m. Map data were represented in SHP format based on a 1:1 million scale Chinese map obtained from the National Center for Basic Geographic Information.
| To mitigate multicollinearity and potential model overfitting, we conducted Spearman’s correlation analysis within ArcGIS to examine relationships among environmental factors. Variables demonstrating a correlation coefficient | r | ≥ 0.8 were considered highly correlated, and the less influential factor was excluded from subsequent analysis. Consequently, a total of 16 environmental factors were retained for calculation and analysis within the MaxEnt model. |
Climate and elevation variable data were clipped according to the vectograph of a 1:1 million scale Chinese administrative map and then converted to ASC format using ArcGIS software. Soil variable data were integrated by importing the China soil file and HWSD DATA file into ArcGIS software. Subsequently, the grid layer comprising the 16 soil variables within the MU_GLOBAL layer was extracted and converted into ASC format. Finally, all environmental variable layers underwent batch processing using ArcGIS software, resulting in environment layers with non-overlapping extents.
Environmental variables used in the study
| Type | Variables | Description | UNITS |
|---|---|---|---|
| Bioclimatic variables | Bio1 | Annual Mean Temperature | ℃ |
| Bio2 | Mean Diurnal Range | ℃ | |
| Bio3 | Isothermality | 1 | |
| Bio4 | Temperature Seasonality | 1 | |
| Bio5 | Max Temperature | ℃ | |
| Bio6 | Min Temperature of Coldest Month | ℃ | |
| Bio7 | Temperature Annual Range | ℃ | |
| Bio8 | Mean Temperature of Wettest | ℃ | |
| Bio9 | Mean Temperature of Driest Quarter | ℃ | |
| Bio10 | Mean Temperature of Warmest Quarter | ℃ | |
| Bio11 | Mean Temperature of Coldest Quarter | ℃ | |
| Bio12 | Annual Precipitation | mm | |
| Bio13 | mm | ||
| Bio14 | Precipitation of Driest Month | mm | |
| Bio15 | Precipitation Seasonality | 1 | |
| Bio16 | Precipitation of Wettest Quarter | mm | |
| Bio17 | Precipitation of Driest Quarter | mm | |
| Bio18 | Precipitation of Warmest Quarter | mm | |
| Bio19 | Precipitation of Coldest Quarter | mm | |
| Soil Variable | T_GRAVEL | Topsoil Gravel Content | %vol. |
| T_SAND | Topsoil Sand Fraction | % wt. | |
| T_SILT | Topsoil Silt Fraction | % wt. | |
| T_CLAY | Topsoil Clay Fraction | % wt. | |
| T_USDA_TEX_CLASS | Topsoil USDA Texture Classification | name | |
| T_REF_BULK_DENSITY | Topsoil Reference Bulk Density | kg/dm3 | |
| T_OC | Topsoil Organic Carbon | % wt. | |
| T_PH_H2O | Topsoil pH (H2O) | -log(H+) | |
| T-ESP | Topsoil Sodicity (ESP) | % | |
| T_CEC_CLAY | Topsoil CEC (clay) | cmol/kg | |
| T_BS | Topsoil Base Saturation | % | |
| T_TEB | Topsoil TEB | cmol/kg | |
| T_CACO3 | Topsoil Calcium Carbonate | % wt | |
| T_CASO4 | Topsoil Gypsum | % wt. | |
| T_ECE | Topsoil Salinity (Elco) | dS/m | |
| T_CEC_SOIL | Topsoil CEC (soil) | cmol/kg | |
| Terrain | Elev | Elevation | m |
ENMeval package: To avoid overfitting due to the high complexity of the model constructed with default parameters, which may cause the predicted distribution of the potential habitat of Tetracentron sinense to deviate too much from the actual situation, this study used the ENMeval package, and adjusted the two important parameters, namely the regularization multiplier (RM) and feature combination (FC), to improve the prediction accuracy of the model.
File folder list (files found within Data.zip)
Species records —the occurrence records for the Tetracentron sinense
LGM —36 the last glacial maximum environmental variables
LIG —36 the last interglacial environmental variables
MID —36 the Mid-Holocene environmental variables
Current —36 current environmental variables
2050s —36 future environmental variables for SSPs2.6, SSPs4.5, SSPs7.0 and SSPs8.5 for 2050s
2070s —36 future environmental variables for SSPs2.6, SSPs4.5, SSPs7.0 and SSPs8.5 for 2070s