Data from: Functional traits mediate the interactive effects of climate and human disturbance on non-native plant diversity
Data files
May 07, 2026 version files 233.86 KB
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012_Liu_et_al_traitenv_2025.rds
177.34 KB
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Analysis_figures_code_Liu_2025_traitenv.R
49.56 KB
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R_packages_required_Liu_2025_traitenv.R
1.09 KB
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README.md
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Abstract
Global climate change and expanding human disturbance are reshaping plant invasion dynamics. While both climatic variables and human disturbance independently affect invasions, their interactive effects, particularly as mediated by plant functional traits, remain underexplored. To address this gap, we leveraged continental-scale organismal data from the National Ecological Observatory Network (NEON) to examine how trait differences between native and non-native species interact with climate and human disturbance to shape non-native plant diversity. Our findings show that non-native species diversity peaks at intermediate temperatures. Furthermore, human disturbance interacted strongly with precipitation and aridity: high disturbance increased non-native diversity in low-precipitation plots and stabilized diversity across aridity gradients. Functional trait differences between non-native and native plants significantly mediated these environmental interactions. Specifically, human disturbance promoted the diversity of non-native species with less resource-acquisitive traits (e.g., lower plant height or SLA than native species) in humid environments, while resource-acquisitive non-native species (e.g., higher plant height or SLA than native species) consistently exhibited higher diversity in arid regions. These findings offer insights into how trait-mediated interactions between climate and disturbance shape non-native plant diversity, contributing to improved prediction and management of invasions under global environmental change.
Dataset DOI: 10.5061/dryad.9s4mw6mtz
Description of the data and file structure
Description of Data Collection Efforts
This dataset was compiled to investigate how functional trait differences between native and non-native plant species mediate the interactive effects of climate and human disturbance on plant invasion patterns.
Species composition data were obtained from the National Ecological Observatory Network (NEON), which conducts standardized, long-term ecological monitoring across the United States. We used herbaceous plant presence and abundance data collected from 1,411 vegetation plots across 47 terrestrial NEON sites. These sites represent a broad range of environmental conditions and land-use gradients.
Environmental data, including annual mean temperature, precipitation, and aridity index, were derived from site-level climatic records associated with NEON. Human disturbance was quantified using the Human Footprint Index (HFP).
Functional trait data for each species—including plant height, specific leaf area (SLA), leaf nitrogen content, and diaspore mass—were obtained from multiple global trait databases (TRY, BIEN, and GIFT) and harmonized using phylogenetic imputation methods to account for missing values.
The processed dataset supports analyses of species diversity (e.g., Shannon index) and trait-based ecological filtering in response to multivariate environmental gradients. No direct experimental manipulation was conducted; rather, the data represent observational synthesis from multiple open-access ecological data sources.
Files and variables
File: 012_Liu_et_al_traitenv_2025.rds
Description: Processed plot-level dataset used in the analysis, including species diversity metrics, trait differences between native and non-native species, climate variables, and human footprint values across 1,411 NEON vegetation plots from 47 sites.
Variable descriptions
Each row represents a vegetation plot.
- plotID: Unique NEON vegetation plot identifier.
- siteID: NEON site identifier.
- latitude: Plot latitude (decimal degrees).
- longitude: Plot longitude (decimal degrees).
Trait difference variables
All trait differences are calculated as:
mean(native species) − mean(non-native species)
- sla_diff: Specific leaf area difference.
- height_diff: Plant height difference.
- leaf_N_diff: Leaf nitrogen content difference.
- wood_density_diff: Wood density difference.
- diaspore_mass_diff: Diaspore mass difference.
Positive values indicate higher trait values in native species, while negative values indicate higher values in non-native species.
Diversity and richness variables
- native_richness: Number of native plant species in the plot.
- native_diversity: Hill-Shannon diversity (q = 1) of native species.
- non_native_richness: Number of non-native plant species in the plot.
- non_native_diversity: Hill-Shannon diversity (q = 1) of non-native species.
Environmental variables
- annual_temp: Standardized annual mean temperature (center = 12.0, scale = 6.63).
- annual_ppt: Standardized annual precipitation (center = 925, scale = 490).
- annual_aridity_index: Standardized aridity index (center = 0.711, scale = 0.463).
- HFP_value: Human Footprint Index value; higher values indicate greater human disturbance.
Transformed variables
- log.native.richness: Natural log-transformed native richness.
- log.hfp: Natural log-transformed Human Footprint Index.
- log.native.diversity: Natural log-transformed native diversity.
- log.nonnative.diversity: Natural log-transformed non-native diversity.
- HFP_value_log: Standardized log-transformed HFP (center = 1.64, scale = 1.16).
- native_log: Standardized log-transformed native richness (center = 3.19, scale = 0.842).
- native_diversity_log: Standardized log-transformed native diversity (center = 2.96, scale = 0.821).
Missing values (NA) indicate unavailable data for a given plot.
File: Analysis_figures_code_Liu_2025_traitenv.R
Description: Main R script for reproducing all statistical analyses and figures presented in the manuscript, including model fitting, effect visualization, and interaction plots.
File: R_packages_required_Liu_2025_traitenv.R
Description: Standalone script listing all required R packages for running the analysis, including installation and loading commands to ensure reproducibility.
Code/software
All analyses and figure generation were conducted using the R programming language (v4.2.1)
Access information
Other publicly accessible locations of the data:
- The processed data file (
012_Liu_et_al_traitenv_2025.rds) is derived from publicly accessible sources listed below. The raw data can be accessed directly from these repositories:- NEON (https://data.neonscience.org/)
- TRY Plant Trait Database (https://www.try-db.org/)
- BIEN (https://bien.nceas.ucsb.edu/bien/)
- GIFT (https://gift.uni-goettingen.de/)
- Human Footprint Index (Venter et al. 2016; https://www.nature.com/articles/sdata201667)
Data was derived from the following sources:
- NEON (National Ecological Observatory Network): Provided species occurrence and site-level environmental data across 47 U.S. sites.
- TRY, BIEN, and GIFT trait databases: Supplied plant functional traits (height, SLA, leaf nitrogen, diaspore mass) used to calculate trait differences between native and non-native species.
- Human Footprint Index (Venter et al., 2016): Used to quantify human disturbance at each site.