Novel model‐based clustering reveals ecologically differentiated bacterial genomes across a large climate gradient

A pervasive challenge in microbial ecology is understanding the genetic level where ecological units can be differentiated. Ecological differentiation often occurs at fine genomic levels, yet it is unclear how to utilise ecological information to define ecotypes given the breadth of environmental va...

Full description

Saved in:
Bibliographic Details
Published in:Ecology letters 2019-12, Vol.22 (12), p.2077-2086
Main Authors: Simonsen, Anna K., Barrett, Luke G., Thrall, Peter H., Prober, Suzanne M., Chase, Jonathan
Format: Article
Language:English
Subjects:
Bacteria
Climate
Clustering
Differentiation
Distribution patterns
ecological differentiation
Ecology
Ecotype
Ecotypes
Environmental information
Genome, Bacterial
Genomes
Host plants
Landscape
Microorganisms
Niches
Phylogeny
soil
Soil Microbiology
Soils
symbiont
Symbionts
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A pervasive challenge in microbial ecology is understanding the genetic level where ecological units can be differentiated. Ecological differentiation often occurs at fine genomic levels, yet it is unclear how to utilise ecological information to define ecotypes given the breadth of environmental variation among microbial taxa. Here, we present an analytical framework that infers clusters along genome‐based microbial phylogenies according to shared environmental responses. The advantage of our approach is the ability to identify genomic clusters that best fit complex environmental information whilst characterising cluster niches through model predictions. We apply our method to determine climate‐associated ecotypes in populations of nitrogen‐fixing symbionts using whole genomes, explicitly sampled to detect climate differentiation across a heterogeneous landscape. Although soil and plant host characteristics strongly influence distribution patterns of inferred ecotypes, our flexible statistical method enabled us to identify climate‐associated genomic clusters using environmental data, providing solid support for ecological specialisation in soil symbionts.
ISSN:1461-023X
1461-0248
DOI:10.1111/ele.13389