DOI: 10.5061/dryad.3n5tb2rkg
title: Code for: Landform and lithospheric development contribute the assembly of mountain floras in China

description: 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.
description: 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. 1. 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. 2.
 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. 3.
 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. 4. 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. 5. 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. 6. 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. 7.
 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. 8. 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](https://doi.org/10.5061/dryad.b2rbnzsk1). 9. 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. 10. 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. 11. 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.