Determinants of global variation in taxonomic and phylogenetic diversity of invasive plants

Information on the determinants of taxonomic and phylogenetic diversity of invasive plant species is crucial for managing invasive plants. With globalization, most countries have experienced substantial economic losses and environmental damage due to biological invasions. We analyzed the determinants of variation in the diversity and phylogenetic structure of invasive plants among countries worldwide. 
To do so, we used a comprehensive checklist of invasive plants in 152 countries worldwide to calculate taxonomic and phylogenetic diversity (i.e., Faith’s PD metric) and phylogenetic structure, using Mean Phylogenetic Distance (MPD) and Mean Nearest Taxon Distance (MNTD). We then combined these data in minimum adequate models with data on geographic, climatic, socio-economic, and international trade variables. We also conducted randomization tests to determine whether phylogenetic diversity of invasive plants in these countries was clustered or overdispersed. 
Taxonomic and phyl..., , ## README：Data and R code for “Determinants of global variation in taxonomic and phylogenetic diversity of invasive plants”

Edited by Bi-Cheng Dong
27 April 2025

This README file lists and describes the files used for the analyses in the manuscript, \"Determinants of global variation in taxonomic and phylogenetic diversity of invasive plants\", by Bi-Cheng Dong, Lan-Hui Wang, Li-Yuan Gao, Mark van Kleunen, Fei-Hai Yu, published in *Journal of Ecology*.

#### Files Included

#### Dataset

**20250425.global_diversity_data.v18.csv**: This dataset is the primary source used in all analyses. Each row represents a geographic unit (i.e., country). For each unit, associated geographic, environmental, socioeconomic, trade, and biodiversity data are provided. The CSV file is encoded in UTF-8. Please see the manuscript itself and the Supporting Information for additional details regarding this analysis.

**Data Description**: below are detailed descriptions of each column in the dataset. Missing v...,