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

• Published in: The ISME Journal Vol. 10; no. 11; pp. 2557 - 2568
• Main Authors: Widder, Stefanie, Allen, Rosalind J, Pfeiffer, Thomas, Curtis, Thomas P, Wiuf, Carsten, Sloan, William T, Cordero, Otto X, Brown, Sam P, Momeni, Babak, Shou, Wenying, Kettle, Helen, Flint, Harry J, Haas, Andreas F, Laroche, Béatrice, Kreft, Jan-Ulrich, Rainey, Paul B, Freilich, Shiri, Schuster, Stefan, Milferstedt, Kim, van der Meer, Jan R, Groβkopf, Tobias, Huisman, Jef, Free, Andrew, Picioreanu, Cristian, Quince, Christopher, Klapper, Isaac, Labarthe, Simon, Smets, Barth F, Wang, Harris, Soyer, Orkun S
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.11.2016; Nature Publishing Group
• Subjects: 631/326/2565; Air Microbiology; Animals; Biogeochemical cycles; Biomedical and Life Sciences; Computer Science; Data collection; Ecological function; Ecology; Ecosystem; Evolutionary Biology; Humans; Life Sciences; Marine ecosystems; Microbial activity; Microbial Ecology; Microbial Genetics and Genomics; Microbiology; Microbiology and Parasitology; Mini Review; Modeling and Simulation; Models, Theoretical; Oceans; Prediction models; Seawater - microbiology; Soil Microbiology; predictive; challenge; ecology; microbial; challenge in microbial
• ISSN: 1751-7362; 1751-7370
• DOI: 10.1038/ismej.2016.45

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.