Invasional meltdown in plants: The reduction of litter allelochemical promoted positive alien biotic interactions
Data files
Mar 06, 2024 version files 3.28 MB
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Meng_et_al_Bacteria_OTU.csv
2.32 MB
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Meng_et_al_Fungi_OTU.csv
859.28 KB
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Meng_et_al_litter_C_and_N.csv
1.31 KB
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Meng_et_al_Litter_total_phenolics.csv
788 B
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Meng_et_al_PA_Total_biomass.csv
97.07 KB
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README.md
3.55 KB
Mar 03, 2026 version files 3.31 MB
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Meng_et_al_Bacteria_OTU.csv
2.32 MB
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Meng_et_al_Fungi_OTU.csv
859.28 KB
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Meng_et_al_litter_C_and_N.csv
1.31 KB
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Meng_et_al_Litter_total_phenolics.csv
788 B
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Meng_et_al_PA_Total_biomass.csv
110.44 KB
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README.md
19.15 KB
Abstract
One core focus of ecology is how the differences in community invasion resistance are established. An invasional meltdown suggests that the establishment of one alien invader might promote further invasions through soil microbes. However, whether a similar effect might be mediated by plant litter and whether such a situation might depend on environmental factors condition is still poorly understood. We grew Phytolacca americana as target plants in pot-mesocosms under two interaction species origin (alien vs native). In each species origin, we produced 36 combinations of two litter treatments, two soil microbe treatments, three soil phosphorus levels, and three P. americana density treatments. We found that although microbial communities differed by species origin, microbial inoculation had no effect on P. americana growth. Litter origin did affect plant performance;P. americana growth was greater under alien litter treatment. This phenomenon may come from the reduction in allelochemical (e.g., total phenolics) rather than nutrients, and these processes were significantly affected by P. americana density but not by soil phosphorus content.
Synthesis: Our study provides novel insights into the invasion process showing that biotic interactions between alien plants may mediate the invasional meltdown process through the action of litter. The main result highlights the importance of bio-control in areas invaded by multiple alien species.
Dataset DOI: 10.5061/dryad.2fqz612vd
Description of the data and file structure
Data Collection Date(s)
- Soil and litter collection: April – May 2021
- Greenhouse experiment: May – September 2021
Geographic location of data collection
- Sample collection site: Kunyu Mountain National Nature Reserve, Shandong Province, China (Latitude: 37.10–37.19° N, Longitude: 121.37–121.48° E)
- Experiment execution site: Greenhouse at Ludong University, Yantai, Shandong Province, China.
Experimental Design Overview
This dataset is derived from a full-factorial greenhouse experiment designed to investigate the belowground mechanisms (soil microbes and plant litter) driving the potential invasional meltdown involving Phytolacca americanaL. The experiment systematically manipulated five factors:
- Interaction Species Origin: Plants were grown in soil and litter derived from communities containing either alien or native dominant tree species (3 species per origin, 6 species total).
- Soil Microbe Treatment: With or without living soil microbial inoculum.
- Litter Treatment: With or without surface-applied plant litter.
- Soil Phosphorus (P) Concentration: Low (approx. half of background), Medium (~0.3 mg kg⁻¹), and High (~0.65 mg kg⁻¹) levels, established by adding NaH₂PO₄ solution.
- P. americanaPlanting Density: Low (1), Medium (2), and High (4) plants per pot.
Number of Units: The experiment comprised 1512 pots (2 origins x 2 microbes x 2 litter x 3 P levels x 3 densities x 7 biological replicates). Each pot is an experimental unit.
Files and variables
File: Meng_et_al_litter_C_and_N.csv
Description: This file contains the initial chemical characteristics of the plant litter used in the experiment, specifically the total nitrogen (N) content, total carbon (C) content, and the calculated carbon-to-nitrogen (C/N) ratio. These data characterize the substrate quality of litter from different source species.
Variables
- Sample_ID: Unique identifier for each litter sample.
- origin: The origin category of the interaction species from which the litter was derived. Categorized as
alien(alien species) ornative(native species). - N_content: Nitrogen content of the litter, expressed as a mass percentage (%).
- C_content: Carbon content of the litter, expressed as a mass percentage (%).
- C/N_rate: The carbon-to-nitrogen ratio of the litter, a dimensionless value (calculated as C_content / N_content).
File: Meng_et_al_Bacteria_OTU.csv
Description: This file contains the taxonomic classification for bacterial Operational Taxonomic Units (OTUs) identified through 16S rRNA gene sequencing of soil samples. It provides the curated taxonomy for each OTU, serving as a reference for analyzing bacterial community composition associated with different experimental treatments.
Variables
- Domain: The highest taxonomic rank (e.g.,
Bacteria). - Kingdom: The kingdom-level classification.Phylum:
- Phylum: The phylum-level classification.
- Class: The class-level classification.
- Order: The order-level classification.
- Family: The family-level classification.
- Genus: The genus-level classification.
- Species: The lowest level of taxonomic assignment, when possible.
- OTU: The unique identifier for the Operational Taxonomic Unit (e.g.,
OTU_1). This corresponds to the OTU identifiers used in the accompanying sequence abundance table. - Robinia pseudoacacia L._1: The following columns, named with plant species identifiers and a replicate number (e.g.,
Robinia pseudoacacia L._1), contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns. - Robinia pseudoacacia L._2: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Robinia pseudoacacia L._3: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Pinus densiflora Sieb.et Zucc_1: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Pinus densiflora Sieb.et Zucc_2: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Pinus densiflora Sieb.et Zucc_3: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Pinus thunbergii Parl_1: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Pinus thunbergii Parl_2: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Pinus thunbergii Parl_3: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Lespedeza bicolor Turcz._1: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Lespedeza bicolor Turcz._2: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Lespedeza bicolor Turcz._3: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Quercus acutissima Carruth_1:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Quercus acutissima Carruth_2: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Quercus acutissima Carruth_3: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Amorpha fruticosa Linn_1: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Amorpha fruticosa Linn_2: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Amorpha fruticosa Linn_3: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
File: Meng_et_al_Fungi_OTU.csv
Description: This file contains the taxonomic classification for fungal Operational Taxonomic Units (OTUs) identified through ITS gene sequencing, along with their abundance (e.g., read counts or relative abundance) across individual soil samples from the six interaction plant species. Each species has three biological replicate samples.
Variables
- Domain:The highest taxonomic rank.
- Kingdom:The kingdom-level classification.
- Phylum:The phylum-level classification.
- Class:The class-level classification.
- Order:The order-level classification.
- Family:The family-level classification.
- Genus:The genus-level classification.
- Species:The lowest level of taxonomic assignment, when possible.
- OTU: The unique identifier for the fungal Operational Taxonomic Unit.
- Robinia pseudoacacia L._1: The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Robinia pseudoacacia L._2:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Robinia pseudoacacia L._3:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Pinus densiflora Sieb.et Zucc_1:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Pinus densiflora Sieb.et Zucc_2:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Pinus densiflora Sieb.et Zucc_3:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Pinus thunbergii Parl_1:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Pinus thunbergii Parl_2:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Pinus thunbergii Parl_3:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Lespedeza bicolor Turcz._1:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Lespedeza bicolor Turcz._2:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Lespedeza bicolor Turcz._3:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Quercus acutissima Carruth_1:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Quercus acutissima Carruth_2:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Quercus acutissima Carruth_3:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Amorpha fruticosa Linn_1:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Amorpha fruticosa Linn_2:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
- Amorpha fruticosa Linn_3:The following columns, named with plant species identifiers and a replicate number, contain the abundance value (e.g., sequence read count) of each OTU in that specific soil sample. Each of the six plant species has three replicate sample columns.
File: Meng_et_al_Litter_total_phenolics.csv
Description: This file contains data on the total phenolic content in the plant litter used in the experiment. Phenolics are secondary metabolites, and their concentration is a key indicator of litter quality, potentially influencing decomposition rates and soil biological processes.
Variables
- Sample_ID: Unique identifier for each litter sample.
- origin:The origin category of the interaction species from which the litter was derived. Categorized as
alien(alien species) ornative(native species). - total_phenolics_content:The concentration of total phenolic compounds in the litter. (mg/kg).
File: Meng_et_al_PA_Total_biomass.csv
Description: This file contains the key response variable data from the main greenhouse experiment: the total dry biomass of the invasive plant Phytolacca americana(PA) harvested from each experimental pot. It includes the full set of experimental treatments applied to each pot, allowing for the analysis of treatment effects on plant growth.
Variables
- Pot_ID:Unique identifier for each experimental pot.
- Microbe_Interaction:Soil microbe treatment applied to the pot. Categories:
with(living microbial inoculum added) orNone(sterilized inoculum added). - Litter_Interaction:Litter treatment applied to the pot. Categories:
with(10 g of litter added) orNone(no litter added). - Phosphorus:Soil phosphorus concentration level. Categories:
Low,Medium, orHigh. - density:Planting density of P. americanaseedlings per pot. Values:
Low,Medium, orHigh. - origin:he origin category of the interaction species from which the soil and litter were derived. Categories:
alienornative. - Interaction_species:The specific plant species that provided the soil and litter for a given pot (e.g., Robinia pseudoacacia).
- life_form:The life form or functional group of the interaction species.
- Total_biomass(g):The total oven-dry biomass (aboveground + belowground) of all P. americanaplants harvested from the pot, measured in grams (g).
Code/software
The data analysis for this study was performed using publicly available software tools and standard bioinformatics/statistical workflows. No custom code was generated. The specific software, versions, and workflows are described below.
1. Microbial Community Analysis (16S rRNA and ITS sequencing data):
- Primary Platform: QIIME 2 (version 2021.4) was used for the core analysis of amplicon sequence variants (ASVs).
- Key Steps & Plugins:
- Demultiplexing and quality control:
q2-demuxandq2-dada2(Denoising) with standard parameters. - Taxonomy assignment: The
q2-feature-classifierplugin was used with theclassify-sklearnmethod against the SILVA 138 database (for 16S) and the UNITE 8.3 database (for ITS). - Phylogenetic tree construction:
q2-phylogenywithalign-to-tree-mafft-fasttreepipeline. - Diversity analysis: Alpha and beta diversity metrics were calculated using
q2-diversityafter rarefying sequences to an even depth.
- Demultiplexing and quality control:
- Downstream Statistical Analysis & Visualization: The resulting feature table, taxonomy, and diversity matrices were exported from QIIME 2 and analyzed in R.
2. Statistical Analysis and Visualization:
- Primary Software: R (version 4.1.0).
- Key R Packages:
- Data manipulation:
dplyr,tidyr. - Statistical modeling:
lme4(for linear mixed-effects models),lmerTest(for p-values). - Multivariate analysis:
vegan(for PERMANOVA on community distance matrices). - Visualization:
ggplot2,ggpubr.
- Data manipulation:
- Workflow: All statistical tests (e.g., ANOVA, linear mixed models, PERMANOVA) and figures presented in the associated manuscript were generated using the aforementioned R packages. Specific model formulas and graphical parameters are detailed in the manuscript's methods section.
3. General Data Processing:
- Data Organization & Preliminary Calculations: Microsoft Excel (Office 365) was used for initial data entry, file organization, and basic calculations (e.g., biomass summation, C/N ratio calculation).
- Litter Chemistry Data: Total carbon and nitrogen content were determined by an elemental analyzer, and phenolic content was measured by spectrophotometry. The instrument-specific software provided by the manufacturers was used to acquire the raw concentration data.
