Challenges in microbial ecology: building predictive understanding of community function and dynamics

Abstract

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth’s soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

Metadata

Item Type:	Article
Authors/Creators:	Widder, S Allen, RJ Pfeiffer, T Curtis, TP Wiuf, C Sloan, WT Cordero, OX Brown, SP Momeni, B Shou, W Kettle, H Flint, HJ Hass, AF Laroche, B Kreft, JU Rainey, PB Freilich, S Shuster, S Milferstedt, K Van der Meer, JR Grosskopf, T Huisman, J Free, A Picioreanu, C Quince, C Klapper, I Labarthe, S Smets, B Wang, H Allison, SD Chong, J Lagomarsion, MC Croze, OA Hamelin, J Harmand, J Hoyle, R Hwa, TT Jin, Q Johson, DR Lorenzo, VD Mobilia, M https://orcid.org/0000-0002-1424-567X Murphy, B Peaudecerf, F Prosser, JI Quinn, RA Ralser, M Smith, AG Steyer, JP Swainston, N Tarnita, CE Trably, E Warren, PB Wilmes, P Soyer, O
Copyright, Publisher and Additional Information:	© 2016, International Society for Microbial Ecology. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Dates:	Published: November 2016 Published (online): 29 March 2016 Accepted: 22 February 2016
Institution:	The University of Leeds
Academic Units:	The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Applied Mathematics (Leeds)
Depositing User:	Symplectic Publications
Date Deposited:	04 Mar 2016 12:58
Last Modified:	11 Apr 2017 15:54
Published Version:	https://doi.org/10.1038/ismej.2016.45
Status:	Published
Publisher:	Nature Publishing Group
Identification Number:	10.1038/ismej.2016.45
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:95556

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Challenges in microbial ecology: building predictive understanding of community function and dynamics

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