Code for: Landform and lithospheric development contribute the assembly of mountain floras in China
Data files
May 15, 2024 version files 211.85 KB
Abstract
Although it is well documented that mountains tend to exhibit high biodiversity, how geological processes affect the assemblage of montane floras is a matter of ongoing research. Here, we explore landform-specific differences among montane floras based on a dataset comprising 17,576 angiosperm species representing 140 Chinese mountain floras, which we define as the collection of all angiosperm species growing on a specific mountain. Our results show that igneous bedrock (granitic and karst-granitic landforms) is correlated with higher species richness and phylogenetic overdispersion, while the opposite is true for sedimentary bedrock (karst, Danxia, and desert landforms), which is correlated with phylogenetic clustering. Furthermore, we show that landform type was the primary determinant of the assembly of evolutionarily older species within floras, while climate was a greater determinant for younger species. Our study indicates that landform type not only affects montane species richness, but also contributes to the composition of montane floras. To explain the assembly and differentiation of mountain floras, we propose the ‘floristic geo-lithology hypothesis’, which highlights the role of bedrock and landform processes in montane floristic assembly and provides insights for future research on speciation, migration, and biodiversity in montane regions.
README
This file includes ten R script and associated input data to generate the figures and tables of Zhao et al., (2024). R script were created using version 4.1.0.
Souce data.xlsx
This is the input data file of the following R scrip. In total, 11 data sheet included in this file. The variables included in related data set is as following:
mountain_ID: Sequence number of 140 mountain flora in China.
site: Acronym of mountain flora.
sp.number: Species richness, the total number of angiosperm plants in a mountain.
landform: Landform type of each mountain flora (Danxia, Karst, Karst-Gr, Granitic, Desert).
long: Longitude.
lat: Latitude.
PDI: Standardized effect size of phylogenetic diversity (PDFaith).
NRI: Standardized effect size of mean phylogenetic distance.
NTI: Standardized effect size of mean nearest taxon distance.
pd: PDFaith, Faith’s phylogenetic diversity.
MDT: Mean diversity time of species in a mountain.
MDT.youngest: mean diversity time of 25% youngest species in a mountain.
MDT.oldest: mean diversity time of 25% oldest species in a mountain.
tectonic_type: the tectonic zone which a mountain located (craton and orogenic).
Climatic variable, included bio2-bio19, is extracted from CHELSA climate data v. 1.2.
bio2: Mean Diurnal Range.
bio3: Isothermality (bio2/bio7) (*100) (Isoth).
bio7: Temperature Annual Range (TAR).
bio8: Mean Temperature of Wettest Quarter.
bio10: Mean Temperature of Warmest Quarter (TWQ).
bio11: Mean Temperature of Coldest Quarter (TCQ).
bio12: Annual Precipitation.
bio13: Precipitation of Wettest Month.
bio15: Precipitation Seasonality (Coefficient of Variation).
bio18: Precipitation of Warmest Quarter.
bio19: Precipitation of Coldest Quarter.
GBOTB.add.low: MDT.youngest based on a unpurned.add phylogeny.
GBOTB.add.high: MDT.oldest based on a unpurned.add phylogeny.
GBOTB.add.mean: MDT based on a unpurned.add phylogeny.
GBOTB.low: MDT.youngest based on a unpruned phylogeny.
GBOTB.high: MDT.oldest based on a unpruned phylogeny.
GBOTB.mean: MDT based on a unpruned phylogeny.
zwy.add.low: MDT.youngest based on a purned.add phylogeny.
zwy.add.high: MDT.oldest based on a purned.add phylogeny.
zwy.add.mean: MDT based on a purned.add phylogeny.
zwy.low: MDT.youngest based on a purned phylogeny.
zwy.high: MDT.oldest based on a purned phylogeny.
zwy.mean: MDT based on a purned phylogeny.
1_Model select & Supplementary Table 3-10.R
Purpose of this script is model select, and Supplementary Table 3-10. The input data is sheet "Supplementary Fig. 9-15" available at Souce Data.xlsx.2_Visualization of Figure 3.R
This script is used to generate the Figure 3. The input data is sheet "Figure 3" available at Souce Data.xlsx.3_Visualization of Figure 4.R
This script is used to generate the Figure 4. The input data is sheet "Supplementary Fig. 9-15" available at Souce Data.xlsx.4_Visualization of Supplementary Fig. 2.R
This script is used to generate the Supplementary Fig. 2. The input data is sheet "Supplementary Fig. 2" available at Souce Data.xlsx.5_Visualization of Supplementary Fig. 3.R
This script is used to generate the Supplementary Fig. 3. The input data is sheet "Supplementary Fig. 3" available at Souce Data.xlsx.6_Visualization of Supplementary Fig. 4.R
This script is used to generate the Supplementary Fig. 4. The input data is sheet "Supplementary Fig. 4" available at Souce Data.xlsx.7_Visualization of Supplementary Fig. 6.R
This script is used to generate the Supplementary Fig. 6. The input tree file available at https://doi.org/10.5061/dryad.b2rbnzsk1.8_Visualization of Supplementary Fig. 7.R
This script is used to generate the Supplementary Fig. 7. The input data is sheet "Supplementary Fig. 7" available at Souce Data.xlsx.9_Visualization of Supplementary Fig. 8.R\
This script is used to generate the Supplementary Fig. 8. The input data is sheet "Supplementary Fig. 8" available at Souce Data.xlsx.10_Visualization of Supplementary Fig. 9-15.R
This script is used to generate the Supplementary Fig. 9-15. The input data is sheet "Supplementary Fig. 9-15" available at Souce Data.xlsx.