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Uncovering the drivers of host‐associated microbiota with joint species distribution modelling
Author(s) -
Björk Johannes R.,
Hui Francis K. C.,
O'Hara Robert B.,
Montoya Jose M.
Publication year - 2018
Publication title -
molecular ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.619
H-Index - 225
eISSN - 1365-294X
pISSN - 0962-1083
DOI - 10.1111/mec.14718
Subject(s) - biology , structuring , host (biology) , ecology , community structure , covariate , evolutionary biology , nestedness , computational biology , species richness , computer science , machine learning , finance , economics
In addition to the processes structuring free‐living communities, host‐associated microbiota are directly or indirectly shaped by the host. Therefore, microbiota data have a hierarchical structure where samples are nested under one or several variables representing host‐specific factors, often spanning multiple levels of biological organization. Current statistical methods do not accommodate this hierarchical data structure and therefore cannot explicitly account for the effect of the host in structuring the microbiota. We introduce a novel extension of joint species distribution models (JSDMs) which can straightforwardly accommodate and discern between effects such as host phylogeny and traits, recorded covariates such as diet and collection site, among other ecological processes. Our proposed methodology includes powerful yet familiar outputs seen in community ecology overall, including (a) model‐based ordination to visualize and quantify the main patterns in the data; (b) variance partitioning to assess how influential the included host‐specific factors are in structuring the microbiota; and (c) co‐occurrence networks to visualize microbe‐to‐microbe associations.