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Title: Can dynamic network modelling be used to identify adaptive microbiomes?
Abstract

In recent times, interest has grown in understanding how microbiomes – the collection of microorganisms in a specific environment – influence the survivability or fitness of their plant and animal hosts. The profound diversity of bacterial and fungal species found in certain environments, such as soil, provides a large pool of potential microbial partners that can interact in ways that reveal patterns of associations linking host–microbiome traits developed over time. However, most microbiome sequence data are reported as a community fingerprint, without analysis of interaction networks across microbial taxa through time.

To address this knowledge gap, more robust tools are needed to account for microbiome dynamics that could signal a beneficial change to a plant or animal host. In this paper, we discuss applying mathematical tools, such as dynamic network modelling, which involves the use of longitudinal data to study system dynamics and microbiomes that identify potential alterations in microbial communities over time in response to an environmental change. In addition, we discuss the potential challenges and pitfalls of these methodologies, such as handling large amounts of sequencing data and accounting for random processes that influence community dynamics, as well as potential ways to address them.

Ultimately, we argue that components of microbial community interactions can be characterized through mathematical models to reveal insights into complex dynamics associated with a plant or animal host trait. The inclusion of interaction networks in microbiome studies could provide insights into the behaviour of complex communities in tandem with host trait modification and evolution.

A freePlain Language Summarycan be found within the Supporting Information of this article.

 
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NSF-PAR ID:
10457037
Author(s) / Creator(s):
 ;  ;
Publisher / Repository:
Wiley-Blackwell
Date Published:
Journal Name:
Functional Ecology
Volume:
34
Issue:
10
ISSN:
0269-8463
Page Range / eLocation ID:
p. 2065-2074
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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