An ANIr-based methodology to determine if two sequence-discrete populations are identical and identify cosmopolitan prokaryotic populations.

Although sequence-discrete species appear to dominate microbial communities, readily distinguishing between distinct populations of a species recovered from different short-read metagenomic samples is challenging due to technical limitations associated with read length. To close this gap, we developed a novel algorithm to evaluate which reads in a metagenome belong to a target population based on the distribution of sequence identities of reads aligned to a reference sequence, which are filtered using a Kernel density estimation (KDE) as a flexible alternative to the commonly used static 95% nucleotide identity cutoff. Subsequently, we employed the average nucleotide identity of reads (ANIr) aligning above the KDE threshold, and resampling techniques for estimating the confidence intervals of ANIr values, to quantify intrapopulation sequence diversity and compare populations across globally representative marine samples. Most populations showed high ANIr in only a few samples at similar depths and decreased ANIr and increased gene-content difference between samples where a closely related population is detected (e.g. same 95% ANI-based species). Accordingly, ANIr correlated with the physical distance between the samples, and only a few truly cosmopolitan populations were identified. Among the latter,