M I N I R E V I E W Modeling microbial community structure and functional diversity across time and space Peter E. Larsen1, Sean M. Gibbons1,2,3 & Jack A. Gilbert1,2 1Argonne National Laboratory, Lemont, IL, USA 2Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA and 3The Graduate Program in Biophysical Sciences, University of Chicago, Chicago, IL, USA Correspondence: Peter E. Larsen, Argonne National Laboratory, Department of Biosciences, 9700, South Cass Avenue, Lemont, IL 60439, USA. Tel.: +1 630 252 3984 fax: +1 630 252 9155 e-mail: plarsen@anl.gov Received 30 December 2011 revised 16 April 2012 accepted 18 April 2012. Final version published online 28 May 2012. DOI: 10.1111/j.1574-6968.2012.02588.x Editor: Simon Silver Keywords modeling microbial ecology systems biology microbial diversity community function. Abstract Microbial communities exhibit exquisitely complex structure. Many aspects of this complexity, from the number of species to the total number of interac- tions, are currently very difficult to examine directly. However, extraordinary efforts are being made to make these systems accessible to scientific investiga- tion. While recent advances in high-throughput sequencing technologies have improved accessibility to the taxonomic and functional diversity of complex communities, monitoring the dynamics of these systems over time and space – using appropriate experimental design – is still expensive. Fortunately, model- ing can be used as a lens to focus low-resolution observations of community dynamics to enable mathematical abstractions of functional and taxonomic dynamics across space and time. Here, we review the approaches for modeling bacterial diversity at both the very large and the very small scales at which microbial systems interact with their environments. We show that modeling can help to connect biogeochemical processes to specific microbial metabolic pathways. Introduction To understand microbial systems, it is necessary to con- sider the scales at which they interact with their environ- ment. These scales range spatially from microns to kilometers and temporally from eons to hours. Account- ing for 350–550 billion tons of extant biomass (Whitman et al., 1998), microorganisms are the principal form of life on Earth, and they have dominated Earth’s evolution- ary history. Prokaryotes, the oldest lineage on the tree of life, first appeared about 3.8 billion years ago (Mojzsis et al., 1996) and have been detected in virtually every environment that has been investigated, from boiling lakes (Barns et al., 1994 Hugenholtz et al., 1998), to the atmosphere (Fierer et al., 2008 Bowers et al., 2009), to deep in the planet’s crust (Takai et al., 2001 Fisk et al., 2003 Edwards et al., 2006 Teske & Sorensen, 2008). Microbial metabolism contributes to biogeochemical cycles (O’dor et al., 2009 Hoegh-Guldberg, 2010) and has both direct and indirect impacts on Earth’s climate (Bardgett et al., 2008 Graham et al., 2012). Indeed, mar- ine microbial activity has even been implicated as a corre- late in earlier mass species extinction events (Baune & Bottcher, 2010). The concept that living processes drive changes the physical environment at the global scale is not new. The ‘Gaia Hypothesis’, which postulates that living processes help maintain atmospheric homeostasis, was published nearly 40 years ago (Lovelock et al., 1974), and there is mounting evidence that this is indeed the case (Charlson et al., 1987 Cicerone & Oremland, 1988 Gorham, 1991). Use of next-generation high-throughput data, however, has only recently made possible direct investigations of the specific molecular mechanisms and microbial consortia responsible for the planet’s dynamic equilibrium. While their effects may be global, microbial systems interact with their environments at microscopic scales. A single gram of soil might contain around 109 microbial units (Torsvik & Ovreas, 2002), and an average milliliter of seawater will contain approximately a million bacterial cells. The wide taxonomic diversity of these populations (Pedros-Alio, 2006) is fostered, at least in part, by myr- iad microenvironments accessible to the bacteria. In soil and marine systems, the majority of microbial diversity is represented in the minority of biomass (Pedros-Alio, 2006 Sogin et al., 2006 Ashby et al., 2007 Elshahed FEMS Microbiol Lett 332 (2012) 91–98 ª 2012 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved MICROBIOLOGY LETTERS

et al., 2008). Generally, in highly diverse microbial com- munities, a few abundant taxa predominate, with a long tail of low abundance taxa (Sogin et al., 2006). These low abundance taxa in particular are crucial to our understanding of microbial ecosystems, as they represent the vast functional diversity that can rapidly blossom to high abundance under the appropriate environmental conditions (e.g. Caporaso et al., 2011a, b, c Gilbert et al., 2011). Microbial systems can be described using environmen- tal DNA sequence information and contextual metadata, which reveal dynamic taxonomic and functional diversity across gradients of natural or experimental variation (Tyson et al., 2004 Venter et al., 2004 DeLong et al., 2006 Gilbert et al., 2010 Delmont et al., 2011). Taxo- nomic diversity is a measure of the community species composition, which is maintained or altered via interac- tions and adaptations between each species and its envi- ronment. Functional diversity is a measure of the frequency and the type of predicted enzyme functions encoded in a community’s metagenome, and represents the potential to express a phenotype that interacts with a particular environmental state. Increasing depth from continuing advances in sequencing technologies has enabled whole genomes to be reassembled from metage- nomic data, which permits appropriate descriptions of the taxonomic and functional potential of individual spe- cies imbedded within each community (Woyke et al., 2010 Hess et al., 2011 Iverson et al., 2012). While the goal of this mini-review is not to highlight the impact of these studies on defining the relationships between micro- bial communities and their environments [which is cov- ered in other reviews, e.g. (Torsvik & Ovreas, 2002 Fierer & Jackson, 2006 Falkowski et al., 2008 Wooley et al., 2010 Gilbert & Dupont, 2011)], it is important to state that each community, whether embedded in a desiccated soil particle or in a biofilm attached to a hermit crab in a coral sea, presents a potentially unique set of interactions with the ecosystem. Here, we summarize current approaches used to generate predictive models that incor- porate taxonomic and functional diversity at the meta- bolic, microbial interaction, community composition, and ecosystem scales of microbial ecology. Microbial community sampling efforts Metagenomics is the capture and analysis of genomic information from a volume of environmental sample (Fig. 1 Handelsman et al., 1998 Gilbert & Dupont, 2011). Recent advances in direct sequencing of DNA from an environmental sample have generated prodigious amounts of sequence information, resulting in a data bonanza (Field et al., 2011). Equally important as the col- lection of metagenomic data, however, is the concurrent collection of associated metadata (i.e. the chemical and physical characteristics of the environment undergoing metagenomic analysis). To generate hypotheses regarding the interactions within a community that result in observed patterns in diversity and richness, the relevant physical, chemical and biological factors must be mea- sured. Probes can quantify various parameters, such as temperature, pH, ammonia, silicate, and oxygen concen- tration, at approximately the scale experienced by individ- ual microorganisms (Debeer et al., 1992 Zhang et al., 1995 Rani et al., 2007 Stewart & Franklin, 2008). Meta- bolomic techniques such as near- and mid-infrared diffuse reflectance spectroscopy (Forouzangohar et al., 2009), nuclear magnetic resonance, or gas chromatogra- phy-mass spectrometry (Viant et al., 2003 Viant, 2008 Wooley et al., 2010) can provide measurements for very small volumes of environmental samples, but they only provide for a fraction of the thousands of metabolites potentially present (Viant, 2008). At the opposite end of the physical scale, remote sensing, recognized as the only tool for gathering data over extensive spatial and tempo- ral scales (Graetz, 1990), collects data measuring electro- magnetic radiation reflected or emitted from earth’s surface, without direct physical contact with objects or phenomena under investigation. Remotely sensed imagery can provide a synoptic view of landscapes, enabling data acquisition over large expanses and/or physically inacces- sible areas. Recent technological advances permit acquisi- tion of imagery with spatial resolution as fine as 60 cm2 and temporal resolution as high as once a day when using a satellite platform. Ongoing environmental monitoring projects that focus on using high-throughput sequencing techniques and continuous collection of contextual metadata to explore microbial life (e.g. The Global Ocean Survey (http://www. jcvi.org/cms/research/projects/gos), Tara Oceans (http:// oceans.taraexpeditions.org/), the Hawaiian Ocean Time Series (http://hahana.soest.hawaii.edu/hot), the Bermudan Ocean Time Series (http://bats.bios.edu), Western Channel Observatory (http://www.westernchannelobservatory.org. uk/), and The National Ecological Observatory Network (NEON http://www.neoninc.org)) are generating huge quantities of data on the dynamics of microbial communi- ties in ecosystems across local, continental, and global scales. Recently, studies of coastal marine systems (Gilbert et al., 2010, 2011 Caporaso et al., 2011a, b, c), the human microbiome (Caporaso et al., 2011a, b, c), animal rumen (Hess et al., 2011), and Arctic tundra (Graham et al., 2011 Mackelprang et al., 2011) provide examples of the data density (both sequencing-based and contextual metadata) required to characterize microbial community structure in complex ecosystems. ª 2012 Federation of European Microbiological Societies FEMS Microbiol Lett 332 (2012) 91–98 Published by Blackwell Publishing Ltd. All rights reserved 92 P.E. Larsen et al.