DOI: 10.5061/dryad.2ngf1vj08
title: Data from: Predicting invasion success of cultivated naturalized plants in China

description: Plant invasions pose significant threats to native ecosystems, human
 health, and global economies. However, the complex and multidimensional
 nature of factors influencing plant invasions makes it challenging to
 predict and interpret their invasion success accurately. Using a robust
 machine learning algorithm, random forest, and an extensive suite of
 characteristics related to environmental niches, species traits, and
 propagule pressure, we developed a classification model to predict the
 invasion success of naturalized cultivated plants in China. Based on the
 final optimal model, we evaluated the relative importance of individual
 and grouped variables and their prediction performance. Our study
 identified key individual variables within each of three groupings:
 climatic suitability and native range size (environmental niches),
 phylogenetic distance to the closest native taxon and vegetative
 propagation mode (species traits), and the number of botanical gardens and
 provinces where species were cultivated (propagule pressure). Remarkably,
 when grouped variables were evaluated, the relative importance of grouped
 variables increased dramatically—by 13.5 to 17.7 times—compared to the
 cumulative importance of individual variables within a category. However,
 the relative importance of one category was primarily due to the number of
 variables within each category rather than its inherent characteristics.
 Synthesis and applications. Our findings emphasize the necessity of
 developing data-driven predictive tools for effective invasion risk
 assessment using large datasets. We also highlight the importance of
 grouped variables in enhancing model interpretability. For practical
 application in China, we recommend prioritizing surveillance of alien
 plant species with large native ranges and high climatic suitability.
 Implementing a tiered risk assessment system based on our random forest
 model can allow for a more effective allocation of resources for
 monitoring and managing invasive species. Ultimately, interdisciplinary
 collaboration is crucial for implementing and applying these predictive
 tools, thereby protecting biodiversity, ecosystem services, and economic
 interests.
description: <strong>Data compilation</strong>
 We compiled a checklist of the 735 naturalized plant taxa
 introduced for cultivation in China, based on the <em>Catalogue of
 Cultivated Plants in China</em> (Lin, 2018) and <em>The
 Checklist of the Naturalized Plants in China</em> (Yan et al.,
 2019). The binomial names of these naturalized taxa were standardized
 according to The Plant List (TPL, version 1.1;
 http://www.theplantlist.org) using the R package 'Taxonstand'
 (Cayuela et al., 2021). Among these naturalized taxa, 435 were classified
 as non-invasive and 300 as invasive in China, based on information from
 Hao and Ma (2023). Non-invasive taxa are naturalized taxa that form
 self-sustaining populations outside cultivation but remain limited in
 spread. Invasive taxa are naturalized taxa that spread widely, causing
 economic, social, and ecological damage to the invaded ecosystems. These
 definitions are consistent with those used by Lin <em>et
 al</em>. (2021). To identify the characteristics
 that could potentially drive the invasion success of naturalized taxa, we
 compiled data on 12 characteristics for each taxon, resulting in 34
 variables. The 34 variables were grouped into three categories, including
 species traits and similarity to native species (life form, propagation
 mode, maximum height, phylogenetic mean pairwise distance [MPD],
 phylogenetic nearest pairwise distance [NNPD], and weighted mean pairwise
 distance [wMPD] to the native flora in China), propagule pressure (the
 number of botanical gardens, the number of provinces in which taxa were
 cultivated, economic use category and the number of economic use
 categories), and environmental niches (climatic suitability, native range
 size and continents of origin). Native range size and continents of origin
 were quantified using the Taxonomic Databases Working Group (TDWG) level-1
 (continental) and level-3 (regional) geographical classifications. A
 phylogenetic tree was constructed using 735 naturalized plant taxa and
 30,248 native plant taxa in China. Further details are provided in the
 Supplementary Material. <strong>Data
 preparation</strong> There were 38 naturalized
 taxa with missing values for some of the 34 variables. Following the
 guidance of Breiman (2003), missing data were imputed by weighting the
 frequency of the non-missing values with proximity values, using the
 'rfImpute' function in the R package 'randomForest'
 (Feng et al., 2020). To standardize the explanatory
 variables, we scaled all continuous variables to have a mean of zero and a
 standard deviation of one (Table S1). Before scaling, we also natural
 log(x + 1) transformed the number of botanical gardens, the number of
 provinces, the number of economic use categories, and native range size;
 natural log(x + 0.001) transformed climatic suitability; natural log
 transformed MPD, NNPD, wMPD, and maximum height to achieve more regular
 distributions of these variables. As maximum height is associated with
 life form, we scaled maximum height separately within each life-form
 category. To preliminarily explore the rationality of
 variable grouping, we employed Pearson correlation analysis to examine
 bivariate relationships and Self-Organizing Map (SOM) analysis to
 visualize high-dimensional data clustering patterns through dimensionality
 reduction (Kohonen, 2001). Further details are provided in the
 Supplementary Material.
description: # Data and R code for "Predicting invasion success of cultivated
 naturalized plants in China" Prepared by Bi-Cheng Dong 19 December
 2024 This README file lists and describes the files used for the analyses
 involved in the manuscript, "Predicting invasion success of
 cultivated naturalized plants in China", by Bi-Cheng Dong, Ran Dong,
 Qiang Yang, Nicole L. Kinlock, Fei-Hai Yu, and Mark van Kleunen, published
 in *Journal of Applied Ecology*. Please see the manuscript itself and the
 Supporting Information for additional details regarding this analysis.
 #### Files included #### Dataset of cultivated naturalized plants in China
 * **chklist.invasion2018_20230929_v24.csv**: this dataset is the primary
 source used in all analyses. Each row represents a seed plant taxon that
 has been naturalized non-invasive or invasive in China, standardized by
 The Plant List (TPL). For each taxon, associated data on propagule
 pressure, environmental niches, and species traits are provided. The CSV
 file is encoded in UTF-8. * **Data description**: below are detailed
 descriptions of each column in the dataset. Missing values are indicated
 by "NA" in the dataset. * **Basic information** * TPL_names:
 species names standardized by The Plant List * invasion.status.2022:
 invasion status of naturalized alien taxa (binary; 0: non-invasive; 1:
 invasive) * **Propagule pressure** * bg_num: number of Chinese botanical
 gardens where the taxon is cultivated (integer) * chklst_prov_num:  number
 of Chinese provinces where the alien taxon is cultivated (integer) *
 wcup_eco_use_AF: use as animal food (binary; 0: no; 1: yes) *
 wcup_eco_use_EU: environmental uses (binary; 0: no; 1: yes) *
 wcup_eco_use_FU: use as fuels (binary; 0: no; 1: yes) * wcup_eco_use_GS:
 use as gene sources (binary; 0: no; 1: yes) * wcup_eco_use_HF: use as
 human food (binary; 0: no; 1: yes) * wcup_eco_use_IF: use as invertebrate
 food (binary; 0: no; 1: yes) * wcup_eco_use_MA: use for materials (binary;
 0: no; 1: yes) * wcup_eco_use_ME: use as medicine (binary; 0: no; 1: yes)
 * wcup_eco_use_PO: use as poisons (binary; 0: no; 1: yes) *
 wcup_eco_use_SU: social uses (binary; 0: no; 1: yes) * wcup_eco_use_num:
 number of economic use categories provided by the WCUPS dataset (integer)
 * **Environmental niches** * hab_suit_mean: mean climatic suitability of
 the taxon in China estimated by SDMs (continuous; 0-1) * X1: native to
 Europe (binary; 0: no; 1: yes) * X2: native to Africa (binary; 0: no; 1:
 yes) * X3: native to Asia-Temp. (binary; 0: no; 1: yes) * X4: native to
 Asia-Trop. (binary; 0: no; 1: yes) * X5: native to Australasia (binary; 0:
 no; 1: yes) * X6: native to Pacific Isl. (binary; 0: no; 1: yes) * X7:
 native to North America (binary; 0: no; 1: yes) * X8: native to South
 America (binary; 0: no; 1: yes) * X9: native to Antarctic (binary; 0: no;
 1: yes) * no.NativeRange.level03: native range size calculated by the
 number of TDWG level-3 regions (integer) * **Species traits** *
 lf_short.herb: short-lived herbs (binary; 0: no; 1: yes) * lf_long.herb:
 long-lived herbs (binary; 0: no; 1: yes) * lf_woody: woody
 species (binary; 0: no; 1: yes) * prop_type.seed.v2: propagation by
 seeds (binary; 0: no; 1: yes) * prop_type.veg.v2: propagation by
 vegetative means (binary; 0: no; 1: yes) * prop_type.both.v2: propagation
 by both means (binary; 0: no; 1: yes) * max.height.new2023: maximum height
 (continuous; unit: m) * pd.mean.to_native: the mean (pairwise)
 phylogenetic distance of the alien taxon to the 30,248 native taxa in
 China (continuous; unit: mya) * pd.min.to_native: the phylogenetic
 distance of the alien taxon to the most closely related native
 taxon (continuous; unit: mya) * pd.wmean.to_native: the weighted mean
 phylogenetic distance of the alien taxon to the 30,248 native taxa in
 China, weighted by occurrence in Chinese TDWG level-3 regions (n =
 10) (continuous; unit: mya) #### R code used to conduct analyses These R
 script files form a complete machine learning analysis pipeline, covering
 everything from data preparation to model training, optimization,
 evaluation, and interpretation. The workflow primarily utilizes the caret
 package in R and encompasses all key steps in a machine learning analysis.
 Here is the brief explanation of these R script files: *
 **NEW001.data.preparation.r** * Data preparation and preprocessing script
 * format conversion and missing value handling * Feature engineering and
 data transformation * Code for figure s5 in the supplementary file. *
 **NEW002.caret.models.r** * Dataset splitting (training/test sets) *
 Building various machine learning models using caret package *
 **NEW003.caret.model.selection.r** * Model selection and evaluation *
 Performance comparison of different models * Optimal model selection *
 Code for figure s7 in the supplementary file. *
 **NEW003a.caret.optimal.som.r** * Construct Self-Organizing Map (SOM)
 model * SOM results visualization * Code for figure s8 in the
 supplementary file. * **NEW003b.caret.optimal.rf.r** * Run Random Forest
 model with case weights again * Model evaluation metrics * Code for figure
 s6 in the supplementary file. *
 **NEW004.caret.combination.of.variable.importances.step01.r** * Feature
 importance construction *
 **NEW004.caret.combination.of.variable.importances.step02v1.r** * Feature
 importance visualization * Code for figures 2 and 3 in the main text *
 Code for figures s2 and s3 in the supplementary file. *
 **NEW007.caret.pdp.plot.based.on.raw.data.v5.r** * Partial Dependence
 Plots (PDP) based on raw data * Code for figure s1 in the supplementary
 file. * **NEW007.caret.pdp.plot.based.on.transformed.data.v5.r** * Partial
 Dependence Plots based on transformed data * Code for figure 1 in the main
 text. * **NEW008.caret.randomzation.text.step01.r** * Randomization tests
 * Data shuffling/resampling * **Caution**: even with 30 cores running in
 parallel, the execution time of randomization tests is approximately five
 hours. * **NEW008.caret.randomzation.text.step02.r** * Result
 visualization of randomization test * Code for figure s4 in the
 supplementary file.