DOI: 10.5061/dryad.sxksn031z
title: Data from: Quantifying (non)parallelism of microbial community change using multivariate vector analysis

description: 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 parallelism.
 We exemplify its use by reanalyzing gut microbiota data from multiple fish
 species that exhibit parallel shifts in trophic ecology. This approach
 provides an analytical framework for quantitative comparisons across host
 lineages, thereby providing the potential to advance our capacity to
 predict microbiota changes. Hence, we encourage the development and
 application of quantitative measures, such as multivariate vector
 analysis, to better understand the role of microbiota dynamics during
 their hosts’ adaptive evolution, particularly in settings of parallel
 evolution.
description: 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 quality control with the QIIME2 plugin DADA2 (Callahan
 et al. 2016). A bacterial phylogenetic tree was produced with FastTree
 2.1.3 (Price et al. 2010). We calculated different phylogenetic (weighted
 and unweighted UniFrac) and non-phylogenetic (Bray-Curtis dissimilarity)
 metrics for bacterial community composition (Lozupone et al. 2011).
 Comparing different metrics allowed us to infer the robustness of our
 vector analyses. Based on distance matrices, principal coordinate analyses
 (PCoA) were performed and PCoA scores were used as input for vector
 analyses. MetaCyc pathway abundances, Kyoto Encyclopedia of Genes and
 Genomes (KEGG) orthologs and Enzyme Commission numbers were predicted with
 the PICRUSt2 plugin in QIIME2 (Douglas et al. 2020; Kanehisa et al. 2012).
 As recommended by the developers, we used a maximum nearest-sequenced
 taxon index (NSTI) cutoff of 2 to exclude unreliable predictions based on
 poorly characterized bacterial taxa. We used different rarefaction depths
 for the analyses of composition and inferred function of the gut
 microbiota, depending on sequencing depth for each dataset.
description: 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.