Data from: Collective dynamical regimes predict invasion success and impacts in microbial communities
Data files
Oct 25, 2024 version files 1.93 GB
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Invasion_data.zip
1.93 GB
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README.md
2.02 KB
Abstract
Invasions of microbial communities by species such as pathogens can have significant impacts on ecosystem services and human health1–9. Predicting the outcomes of these invasions, however, remains a challenge. Various theories propose that these outcomes depend on either characteristics of the invading species10–12 or attributes of the resident community13–16, including its composition and biodiversity3,17–19. Here we used a combination of experiments and theory to show that the interplay between dynamics, interaction strength, and diversity determine the invasion outcome in microbial communities. We found that the communities with fluctuations in species abundance are both more invasible and more diverse than stable communities, leading to a positive diversity-invasibility relationship among communities assembled in the same environment. As predicted by theory, increasing interspecies interaction strength and species pool size leads to a decrease of invasion probability in our experiment. Although diversity-invasibility relationships are qualitatively different depending upon how the diversity is changed, we provide a unified perspective on the diversity-invasibility debate by showing a universal positive correspondence between invasibility and survival fraction of resident species across all conditions. Communities composed of strongly interacting species can exhibit an emergent priority effect in which invader species are less likely to colonize than species in the original pool. However, in this regime of strong interspecies interactions, if an invasion is successful, it causes larger ecological effects on the resident community than when interactions are weak. Our results demonstrate that the invasibility and invasion effect are emergent properties of interacting species, which can be predicted by simple community-level features.
https://doi.org/10.5061/dryad.8gtht76xz
Description of the data and file structure
Data from: Collective dynamical regimes predict invasion success and impacts in microbial communities
Contents of the Compressed Folder
This folder contains the sequencing data in FASTQ format and an accompanying **Sample Sheet **(Zenodo). Below is a detailed description of each component:
1. FASTQ Files
The folder includes raw sequencing data in the form of FASTQ files. Each FASTQ file represents the sequencing reads for a specific sample. The files are named according to the following convention:
Naming Convention:
<SampleID>_L<lane number>_R<read direction>_001.fastq.gz
- SampleID: Unique identifier for each sample.
- L: The lane number used in sequencing. This indicates the sequencing lane from which the data was generated (e.g., L001 for lane 1).
- R: Read direction, where:
- R1 represents the forward read,
- R2 represents the reverse read.
- 001: A sequential number that indicates the file part (usually '001' if there’s only one part).
Example:
Sample1_L001_R1_001.fastq.gz Sample1_L001_R2_001.fastq.gz
This example represents the forward (R1) and reverse (R2) reads of Sample1 from lane 1.
2. Sample Sheet
The Sample Sheet contains metadata and detailed information regarding the samples used in the sequencing run. It includes the following columns:
- SampleID: The unique identifier for each sample, corresponding to the file names in the FASTQ files.
- Additional Metadata: Any additional information about each sample, such as group, treatment, or experimental conditions (depending on the specific study).
This folder provides all the raw sequencing data necessary for downstream analysis, as well as a sample sheet for reference.