Native plant species are more resistant than invasive aliens to escalating environmental change factors
Data files
May 30, 2025 version files 324.45 KB
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data.csv
313.25 KB
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liner_mixed_models.R
7.89 KB
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README.md
3.30 KB
Abstract
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.
Authors: Yang Zhao, Yu-Han Xu, Kun Guo, Wen-Yong Guo, Yong-Jian Wang
* Corresponding author: Wen-Yong Guo, wyguo@des.ecnu.edu.cn,Yong-Jian Wang, 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/.
Data were collected through greenhouse experiments and processed with R software, involving linear mixing model and post-hoc multiple comparisons of estimated marginal means.