DOI: 10.5061/dryad.q2bvq83w1
title: Native plant species are more resistant than invasive aliens to escalating environmental change factors

description: The interplay between invasive alien plant species and various
 environmental change factors can lead to unpredictable ecosystem impacts.
 Existing research predominantly examines isolated or specific
 environmental factors, leaving the effects of complex, multifaceted
 environmental changes on the growth of both invasive alien and native
 plant species inadequately explored. Here, we investigated the biomass
 responses of ten congeneric pairs of invasive and native species to six
 individual and combined environmental change factors. Our results revealed
 a significant reduction in biomass for both invasive and native species as
 the number of environmental change factors increased, with invasive
 species demonstrating heightened sensitivity. Notably, drought and
 salinity exhibited particularly severe negative effects across different
 environmental combinations, highlighting their critical role in driving
 these effects. Our findings underscore the importance of understanding and
 predicting how intensified environmental changes impact plant invasions
 and overall ecosystem stability.
description: Data were collected through greenhouse experiments and processed
 with R software, involving linear mixing model and post-hoc multiple
 comparisons of estimated marginal means.
description: # Native plant species are more resistant than invasive aliens to
 escalating environmental change factors Authors: Yang Zhao, Yu-Han Xu, Kun
 Guo, Wen-Yong Guo, Yong-Jian Wang \* Corresponding author: Wen-Yong Guo,
 [wyguo@des.ecnu.edu.cn](mailto:wyguo@des.ecnu.edu.cn),Yong-Jian Wang,
 [yongjianwang@126.com](mailto:yongjianwang@126.com) ## Description of the
 data and file structure This repository archives the data and code
 associated with the manuscript titled, “Native plant species are more
 resistant than invasive aliens to escalating environmental change
 factors”. The materials are organized into two main components to support
 transparency and reproducibility: "data.csv": The data contains
 information on all species and biomass data under different treatments.
 "liner_mixed_models.R":A linear mixed model R script running the
 results of Figures 2 and 3 described in the paper. ### The abbreviation
 and description for the variables of data.csv: Rand_1: Pot number. Origin:
 The different origins of species are classified as native and invasive.
 Species: Species names. Genus: Genus information of the species. Family:
 Family information of the species. Life_form: The different life forms of
 species are classified as annual and perennial. Factor_Number: The number
 of treatments for different environmental change factors (zero, one, two,
 four and six),. Factor_descrip: Combined treatment of different
 environmental change factors. Drought: Treatment containing only drought
 factors. Salinity: Treatment containing only salinity factors. Herbicide:
 Treatment containing only herbicide factors. Nutrient: Treatment
 containing only nutrient factors. Heat: Treatment containing only heat
 factors. Microplastic: Treatment containing only microplastic factors.
 Above_Biomass: Aboveground biomass (g) of species under different
 treatments. Below_Biomass: Belowground biomass (g) of species under
 different treatments. Total_Biomass: Total biomass (g) of species under
 different treatments. Effet: The effect value of various environmental
 change factors on the total biomass of species. LN: The effect size to
 assess specific individual and combined impacts of various environmental
 change factors on the total biomass of species. All "NA" in our
 files are missing datas ### Description of the liner\_mixed\_models R Code
 : Our two-step analytical approach (i.e., first treating the number of
 environmental change factors as a continuous variable, followed by
 analyses of individual factors) was intended to address two complementary
 research questions: (1) how cumulative environmental change factors impact
 plant responses, and (2) how specific factors contribute individually to
 those responses. #### Code/software All statistical analyses were
 conducted in R 4.1.1 (R Core Team, 2022). Each script is thoroughly
 annotated to guide through the workflow. The following R packages were
 loaded before runing the scripts: library(Rmisc) library(MASS)
 library(tidyr) library(car) library(dplyr) library(lme4) library(nlme)
 library(data.table) #### Reference R Core Team. (2022). R: A language and
 environment for statistical computing. R Foundation for Statistical
 Computing. Retrieved from
 [https://www.R-project.org/](https://www.R-project.org/).