Quantifying (non)parallelism of microbial community change using multivariate vector analysis

Parallel evolution of phenotypic traits is regarded as strong evidence for natural selection and has been studied extensively in a variety of taxa. However, we have limited knowledge of whether parallel evolution of host organisms is accompanied by parallel changes of their associated microbial communities (i.e., microbiotas), which are crucial for their hosts’ ecology and evolution. Determining the extent of microbiota parallelism in nature can improve our ability to identify the factors that are associated with (putatively adaptive) shifts in microbial communities. While it has been emphasized that (non)parallel evolution is better considered as a quantitative continuum rather than a binary phenomenon, quantitative approaches have rarely been used to study microbiota parallelism. We advocate using multivariate vector analysis (i.e., phenotypic change vector analysis) to quantify direction and magnitude of microbiota changes and discuss the applicability of this approach for studying p..., We obtained 16S rRNA gene sequencing data from six published studies on teleost fishes, in which there are replicate niche shifts. For some of the study systems, we only analyzed a subset of the original dataset. Details about the populations/species analyzed from each study can be found in Table S1 of the paper. All sequencing data were downloaded from the NCBI Sequence Read Archive (SRA). For further information on the sequencing platforms, sample sizes and accession numbers for these published datasets, see Tables S1 and S2 of the paper. Data was converted from SRA to FASTQ format using the fastq-dump function of the SRA Toolkit v2.9.6-1 (https://github.com/ncbi/sra-tools).
We analyzed only forward reads truncated to different lengths, depending on read length and sequence quality. Reads were imported into the open-source bioinformatics pipeline Quantitative Insights Into Microbial Ecology (QIIME2; Bolyen et al. 2019) to analyze gut microbial communities. We performed sequence qualit..., The data include R scripts to perform all analyses included in the main part of the paper and the Supplementary Material as well as data files with information on all samples (sample id, ecotype, habitat) and PCoA scores. These data files represent the basis for all analyses. Furthermore, we provide fasta files with ASV IDs and sequences for all study systems.