Data from: 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 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.