Determinants of global variation in taxonomic and phylogenetic diversity of invasive plants
Data files
May 12, 2025 version files 198.98 KB
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20250425.global_diversity_data.v18.csv
78.74 KB
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Rcode_for_Fig.s1.R
14.28 KB
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Rcode_for_Fig.s2.R
13.46 KB
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Rcode_for_Fig.s3.R
4.99 KB
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Rcode_for_Fig.s4.R
4.77 KB
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Rcode_for_Figs.1.2.6.R
23.21 KB
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Rcode_for_Tables1.2.and.Figs.2.3.5.R
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README.md
6.75 KB
Abstract
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.
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).
