DOI: 10.5061/dryad.h18931zxz
title: Determinants of global variation in taxonomic and phylogenetic diversity of invasive plants

description: 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
 phylogenetic diversity of invasive plants exhibited spatial congruence,
 both positively correlated with insularity, mean annual precipitation
 (MAP), and HS-12 import values per capita, but negatively associated with
 mean annual temperature (MAT) and HS-07 import evenness. In addition,
 taxonomic diversity also increased with airport density, HS-12 import
 evenness, and lower HS-08 imports. MPD increased with greater land area
 and airport density, and fewer HS-12 exporting source countries. MNTD
 increased with MAT but declined with greater land area and insularity.
 Phylogenetic clustering occurred in 28.9–49.3% of countries, whereas
 phylogenetic overdispersion was rare, observed only in 0.6–5.3% of
 countries.  synthesis. Our study reveals that variation in
 taxonomic and phylogenetic diversity of invasive plant species among
 countries is shaped by geographic, socio-economic, climatic, and
 international trade factors. Nearly one-third of the countries showed
 phylogenetic clustering of invasive plant species, indicating a relatively
 consistent global pattern. These findings underscore the importance of
 integrating both taxonomic and phylogenetic perspectives in invasion
 ecology, emphasizing the need for regionally tailored management
 strategies that effectively account for regional geographic, climatic,
 socio-economic, and trade-related factors to mitigate future plant
 invasions.
description: ## 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 values are indicated by “NA” in the dataset. *
 **Geographic Identifiers** * `name`: Name of the geographic unit (string)
 * `alpha_3`: ISO 3166-1 alpha-3 country code (string) * `region`: Name of
 continent (string) * `sub_region`: Name of sub-continent (string) *
 `island`: Indicates if the geographic unit is an island (string: no or
 yes) * **Infrastructure and Socioeconomic Data** * `airport.n`: Number of
 airports (integer) * `seaport.n`: Number of seaports (integer) *
 `mean.population_1k`: Mean population number (continuous; unit: in
 thousands) * `mean.area_km2`: Mean land area (continuous; unit: square
 kilometers) * `mean.population.density_km2`: Mean population density
 (continuous; unit: persons per square kilometer) * `gdp.mean_cpi2016`:
 Mean Gross Domestic Product, adjusted by the 2016 Consumer Price Index
 (continuous; unit: million US dollars, 2016 value) * **Environmental
 Data** * `wc2.1_10m_bio_1`: Mean Annual Temperature (continuous; unit: °C;
 from WorldClim v2.1 at 10 arc-minute resolution, bioclimatic variable 1) *
 `wc2.1_10m_bio_12`: Mean Annual Precipitation (continuous; unit: mm; from
 WorldClim v2.1 at 10 arc-minute resolution, bioclimatic variable 12) *
 **Trade Data (HS Chapters 06, 07, 08, 12)** *
 `HS_06_total_trade_cpi2016_avg`: Average total trade value of importer
 countries for HS Chapter 06, adjusted by 2016 CPI (continuous; unit: US
 dollars, 2016 value) * `HS_07_total_trade_cpi2016_avg`: Average total
 trade value of importer countries for HS Chapter 07, adjusted by 2016 CPI
 (continuous; unit: US dollars, 2016 value) *
 `HS_08_total_trade_cpi2016_avg`: Average total trade value of importer
 countries for HS Chapter 08, adjusted by 2016 CPI (continuous; unit: US
 dollars, 2016 value) * `HS_12_total_trade_cpi2016_avg`: Average total
 trade value of importer countries for HS Chapter 12, adjusted by 2016 CPI
 (continuous; unit: US dollars, 2016 value) * `HS_06_n_exporters_avg`:
 Average number of exporter countries for HS Chapter 06 (live plants and
 cut flowers) (continuous) * `HS_07_n_exporters_avg`: Average number of
 exporter countries for HS Chapter 07 (vegetables) (continuous) *
 `HS_08_n_exporters_avg`: Average number of exporter countries for HS
 Chapter 08 (fruits and nuts) (continuous) * `HS_12_n_exporters_avg`:
 Average number of exporter countries for HS Chapter 12 (seeds, grains, and
 medicinal plants) (continuous) * `HS_06_evenness_avg`: Average trade
 evenness index of exporter countries for HS Chapter 06 (continuous; 0-1) *
 `HS_07_evenness_avg`: Average trade evenness index of exporter countries
 for HS Chapter 07 (continuous; 0-1) * `HS_08_evenness_avg`: Average trade
 evenness index of exporter countries for HS Chapter 08 (continuous; 0-1) *
 `HS_12_evenness_avg`: Average trade evenness index of exporter countries
 for HS Chapter 12 (continuous; 0-1) * **Biodiversity and Phylogenetic
 Metrics** * `Observed_SR`: Observed Species Richness (SR; integer) *
 `Observed_PD`: Observed Phylogenetic Diversity (Faith's PD)
 (continuous; unit: million years) * `Observed_MPD`: Observed Mean Pairwise
 Phylogenetic Distance (MPD) within each country (continuous; unit: million
 years) * `Observed_MNTD`: Observed Mean Nearest Taxon Phylogenetic
 Distance (MNTD) within each country (continuous; unit: million years) *
 `Null_mean_PD`: Mean Phylogenetic Diversity from null model (continuous;
 unit: million years) * `Null_mean_MPD`: Mean Mean Pairwise Distance from
 null model (continuous; unit: million years) * `Null_mean_MNTD`: Mean Mean
 Nearest Taxon Distance from null model (continuous; unit: million years) *
 `Null_sd_PD`: Standard deviation of Phylogenetic Diversity from null model
 (continuous; unit: million years) * `Null_sd_MPD`: Standard deviation of
 Mean Pairwise Distance from null model (continuous; unit: million years) *
 `Null_sd_MNTD`: Standard deviation of Mean Nearest Taxon Distance from
 null model (continuous; unit: million years) * `SES_PD`: Standardized
 Effect Size - Phylogenetic Diversity ((Observed - Null Mean) / Null SD)
 (continuous; unitless) * `SES_MPD`: Standardized Effect Size - Mean
 Pairwise Distance (continuous; unitless) * `SES_MNTD`: Standardized Effect
 Size - Mean Nearest Taxon Distance (continuous; unitless) * `P_value_PD`:
 P-value associated with SES_PD (continuous; range 0-1) * `P_value_MPD`:
 P-value associated with SES_MPD (continuous; range 0-1) * `P_value_MNTD`:
 P-value associated with SES_MNTD (continuous; range 0-1) * `n_native`:
 Number of native species within each country (integer) #### R code These R
 scripts are used to analyze global patterns of invasive plant diversity
 (taxonomic and phylogenetic), identify potential multiple drivers, and
 visualize the results. Here is the brief description of these R script
 files: * **`Rcode_for_Tables1.2.and.Figs.2.3.5.R`** * performs the core
 statistical modeling to identify significant drivers of invasive species
 diversity. * **`Rcode_for_Figs.1.4.6.R`** * creates global maps
 visualizing spatial patterns of diversity and phylogenetic structure. *
 **`Rcode_for_Fig.s1.R`** * calculates and visualizes the correlation
 matrix for all predictor variables (Supplementary Figure S1). *
 **`Rcode_for_Fig.s2.R`** * enerates stacked bar charts comparing
 phylogenetic structure patterns across continents and between
 islands/mainlands (Supplementary Figure S2). * **`Rcode_for_Fig.s3.R`** *
 creates scatter plots showing the relationship between native species
 richness and invasive species diversity (SR and PD) (Supplementary Figure
 S3). * **`Rcode_for_Fig.s4.R`** * creates scatter plots showing the
 relationship between native species richness and land area (Supplementary
 Figure S4).