Challenges in microbial ecology: building predictive understanding of community function and dynamics
Research output: Contribution to journal › Literature review › peer-review
- Stefanie Widder
- Rosalind J. Allen
- Thomas Pfeiffer
- Thomas P. Curtis
- Carsten Wiuf
- William T. Sloan
- Otto X. Cordero
- Sam P. Brown
- Babak Momeni
- Wenying Shou
- Helen Kettle
- Harry J. Flint
- Andreas F. Haas
- Beatrice Laroche
- Jan-Ulrich Kreft
- Paul B. Rainey
- Shiri Freilich
- Stefan Schuster
- Kim Milferstedt
- Jack R. van der Meer
- Tobias Grosskopf
- Jef Huisman
- Andrew Free
- Cristian Picioreanu
- Christopher Quince
- Isaac Klapper
- Simon Labarthe
- Barth F Smets
- Harris Wang
- Orkun S. Soyer
Journal | The ISME Journal |
---|
Date | Accepted/In press - 22 Feb 2016 |
---|
Date | Published (current) - 29 Mar 2016 |
---|
Number of pages | 12 |
---|
Original language | English |
---|
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.
© 2016 International Society for Microbial Ecology
Activity: Talk or presentation › Invited talk
Discover related content
Find related publications, people, projects, datasets and more using interactive charts.
View graph of relations