Helen Kettle

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

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.

Modelling the emergent dynamics and major metabolites of the human colonic microbiota

We present here a first attempt at modelling microbial dynamics in the human colon incorporating both uncertainty and adaptation. This is based on the development of a Monod-equation based, differential equation model, which produces computer simulations of the population dynamics and major metabolites of microbial communities from the human colon. To reduce the complexity of the system, we divide the bacterial community into 10 bacterial functional groups (BFGs) each distinguished by its substrate preferences, metabolic pathways and its preferred pH range. The model simulates the growth of a large number of bacterial strains and incorporates variation in microbiota composition between people, while also allowing succession and enabling adaptation to environmental changes. The model is shown to reproduce many of the observed changes in major phylogenetic groups and key metabolites such as butyrate, acetate and propionate in response to a one unit pH shift in experimental continuous flow fermentors inoculated with human faecal microbiota. Nevertheless, it should be regarded as a learning tool to be updated as our knowledge of bacterial groups and their interactions expands. Given the difficulty of accessing the colon, modelling can play an extremely important role in interpreting experimental data and predicting the consequences of dietary modulation.

pH feedback and phenotypic diversity within bacterial functional groups of the human gut.

Microbial diversity in the human colon is very high with apparently large functional redundancy such that within each bacterial functional group there are many coexisting strains. Modelling this mathematically is problematic since strains within a functional group are often competing for the same limited number of resources and therefore competitive exclusion theory predicts a loss of diversity over time. Here we investigate, through computer simulation, a fluctuation dependent mechanism for the promotion of diversity. A variable pH environment caused by acidic by-products of bacterial growth on a fluctuating substrate coupled with small differences in acid tolerance between strains promotes diversity under both equilibrium and far-from-equilibrium conditions. Under equilibrium conditions pH fluctuations and relative nonlinearity in pH limitation among strains combine to prevent complete competitive exclusion. Under far-from-equilibrium conditions, loss of diversity through extinctions is made more difficult because pH cycling leads to fluctuations in the competitive ranking of strains, thereby helping to equalise fitness. We assume a trade-off between acid tolerance and maximum growth rate so that our microbial system consists of strains ranging from specialists to generalists. By altering the magnitude of the effect of the system on its pH environment (e.g. the buffering capacity of the colon) and the pattern of incoming resource we explore the conditions that promote diversity.

Rice ecosystem services in South-east Asia

Josef Settele · Kong Luen Heong · Ingolf Kühn · Stefan Klotz · Joachim H. Spangenberg · Gertrudo Arida · Alexis Beaurepaire · Silke Beck · Erwin Bergmeier · Benjamin Burkhard · Roland Brandl · Jesus Victor Bustamante · Adam Butler · Jimmy Cabbigat · Xuan Canh Le · Josie Lynn A. Catindig · Van Chien Ho · Quoc Cuong Le · Kinh Bac Dang · Monina Escalada · Christophe Dominik · Markus Franzén · Oliver Fried · Christoph Görg · Volker Grescho · Sabine Grossmann · Geoff M. Gurr · Buyung A. R. Hadi · Huu Hai Le · Alexander Harpke · Annika L. Hass · Norbert Hirneisen · Finbarr G. Horgan · Stefan Hotes · Yuzuru Isoda · Reinhold Jahn · Helen Kettle · Anika Klotzbücher · Thimo Klotzbücher · Fanny Langerwisch · Wai‐Hong Loke · Yu‐Pin Lin · Zhongxian Lu · Keng‐Yeang Lum · Damasa B. Magcale‐Macandog · Glenn Marion · Leonardo Marquez · Felix Müller · Hung Manh Nguyen · Quynh Anh Nguyen · Van Sinh Nguyen · Jürgen Ott · Lyubomir Penev · Hong Thai Pham · Nico Radermacher · Beatriz Rodriguez‐Labajos · Christina Sann · Cornelia Sattler · Martin Schädler · Stefan Scheu · Anja Schmidt · Julian Schrader · Oliver Schweiger · Ralf Seppelt · Kukiat Soitong · Pavel Stoev · Susanne Stoll‐Kleemann · Vera Tekken · Kirsten Thonicke · Bianca Tilliger · Kai Tobias · Y. Andi Trisyono · Thanh Truong Dao · Teja Tscharntke · Quang Tuan Le · Manfred Türke · Tomáš Václavík · Doris Vetterlein · Sylvia ’Bong’ Villareal · Kim Chi Vu · Quynh Vu · Wolfgang W. Weisser · Catrin Westphal · Zengrong Zhu · Martin Wiemers

stagePop: modelling stage‐structured populations in r

Summary We present an r-package, stagePop, which is a tool for predicting the deterministic dynamics and interactions of stage-structured populations (i.e. where the life cycle consists of distinct stages, for example eggs, juveniles and reproductive adults). The continuous-time formulation enables stagePop to easily simulate time-varying stage durations, overlapping generations and density-dependent vital rates. The package can be used to predict predator–prey interactions, host–parasitoid interactions, resource competition, intra-specific competition and the effects of environmental change on stage-structured (and non-stage structured) species. It can be used for predicting the effects of bio-control and climate change, investigating mechanisms for maintaining diversity, predicting the dynamics of complex food webs following perturbation and so on. The code is based on the formulation by Nisbet and Gurney (Theoretical Population Biology, 23, 1983, 114) using delay-differential equations, which are solved using the r-packages deSolve or PBSddesolve.

microPop: Modelling microbial populations and communities in R

We thank the Scottish Goverment’s Rural and Environment Science and Ana-lytical Services Division (RESAS) for funding this research. Also many thanks to Rafael Munoz-Tamayo for sharing his matlab code for the rumen model