DOI: 10.5061/dryad.8gtht76xz
title: Data from: Collective dynamical regimes predict invasion success and impacts in microbial communities

description: 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.
description: # Data from: Collective dynamical regimes predict invasion success and
 impacts in microbial communities
 [https://doi.org/10.5061/dryad.8gtht76xz](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:** `_L_R_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.