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.

SummaryWe 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.

This work presents a method to estimate mean daily lake surface water temperatures using only air temperature, theoretical clear-sky solar radiation, and lake size. Surface water temperatures were measured at a selection of lakes in southwest Greenland during the summers of 1998-2000. The lakes are small (surface area Ͻ150 ha) with maximum depths ranging from 3.5 to 47 m. An empirical model requiring only local air temperature and theoretical clear-sky solar radiation is developed to predict daily mean lake surface temperatures in summer for each lake. The model approximates the slow integrated response of water temperature to meteorological forcing by applying an exponential smoothing filter to air temperature. Exponential smoothing results in a 35% improvement in model fit compared with a model using unsmoothed air temperatures. The smoothed air temperatures and clear-sky solar radiation are linearly combined to estimate the daily mean lake surface temperatures. The smoothing parameters and the three linear coefficients of the model, obtained individually for each of 15 lakes, are found to relate to lake area and maximum depth, leading to the development of a general model. With this general model it is possible to predict the summer surface temperatures at any lake in this region where local air temperatures can be estimated. Cross-validation of the general model at each lake in turn indicated a 90% forecast skill and average standard error of prediction of 1.0ЊC. Examination of the daily prediction errors over time suggests a relation to strong wind events.Water temperature plays a significant role in the functioning of lake ecosystems. It affects thermal stratification, the solubility of dissolved oxygen, the metabolism and respiration of lake fauna and flora, and the toxicity of pollutants (Stefan et al. 1998). The area around Søndre Strømfjord in southwest Greenland contains a considerable number of

Simultaneous hourly measurements of lake surface water temperature (LSWT) during summer and early autumn 2000 in 29 lakes in the Swiss Alps revealed the presence of two altitudinally distinct thermal regimes. The threshold separating the low-altitude from the high-altitude regime was located at ϳ2,000 m above sea level during early summer 2000 but rose as summer progressed. Within the low-altitude regime, LSWTs are strongly related to altitude and surface air temperature. On crossing the threshold to the high-altitude regime, the LSWT lapse rate increases sharply, but the relationship of LSWT to both altitude and air temperature weakens considerably. A difference in the response of low-altitude and high-altitude mountain lakes to climatic forcing in early summer may have implications for climate change studies in which mountain lakes are employed either for paleoclimate reconstructions or as sensitive indicators of current climate change. Any long-term temporal change in the threshold altitude would imply that lakes close to the threshold may not always have been located in the same thermal regime, with consequences for paleolimnological climate reconstructions. Predictions of the effects of future climate warming on high-altitude mountain lakes may have to take into account the possibility of a concomitant rise in the threshold altitude.As the climate change debate continues, the importance of knowing how lakes respond to climatic forcing is becoming increasingly apparent. First, from a limnological perspective, an understanding of how lacustrine systems respond to climatic forcing is a necessary prerequisite to predicting their response to scenarios of future climate change. Second, from a climatological perspective, much of our knowledge of past climate changes is derived from the paleolimnological analysis of lake sediments. It is thus doubly important to establish the nature of the link between climatic forcing and lake response.The most direct impact of climatic forcing is on physical lake variables. Of these, the one that is arguably the most directly affected by climatic forcing during the biologically 1 Corresponding author (living@eawag.ch). AcknowledgmentsWe are grateful to Carlo Casty and Oliver Heiri for their help in deploying the thermistors. Data on air temperature and relative sunshine duration were provided by the Swiss Meteorological Institute (MeteoSchweiz). The manuscript benefited considerably from the comments and suggestions of two anonymous reviewers.

Abstract1. Microbial communities perform highly dynamic and complex ecosystem functions that impact plants, animals and humans. Here we present an R-package, microPop, which is a dynamic model based on a functional representation of different microbiota.2. microPop simulates the dynamics and interactions of microbial populations by solving a system of ordinary differential equations that are constructed automatically based on a description of the system. 3. Data frames for a number of microbial functional groups (MFG) and default functions for rates of microbial growth, resource uptake, metabolite production are provided but can be modified or replaced by the user.4. microPop can simulate growth in a single compartment (e.g. bio-reactor) or "compartments" in series (e.g. human colon) or in a simple 1D application (e.g. phytoplankton in a water column). Furthermore, an MFG may contain multiple strains in order to study adaptation and diversity or parameter uncertainty. Also simple interactions between viruses (bacteriophages) and bacteria can be included in microPop.5. microPop is hosted on CRAN and can be installed directly from within R. This paper describes version 1.3 of microPop. The code is also hosted on GitHub for future development (https://github.com/HelenKettle/microPop). K E Y W O R D Sbacteria, modelling bacteriophages, modelling colonic microbiota, modelling methane production, modelling microbial diversity, modelling phytoplankton, ODEs, population dynamics | INTRODUCTIONMicrobial communities play a crucial role in bio-geochemical cycling and perform ecosystem functions important to plants, animals and humans. Building predictive models that link microbial community composition to function is a key emerging challenge in microbial ecology (Widder et al., 2016). Here, we present microPop, an R package which is a mechanistic model using ordinary differential equations (t)X(t)