**THE CAUSES AND CONSEQUENCES OF MICROBIAL COMMUNITY STRUCTURE**

**Topic Editors Diana R. Nemergut, Ashley Shade and Cyrille Violle**

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**ISSN** 1664-8714 **ISBN** 978-2-88919-361-5 **DOI** 10.3389/978-2-88919-361-5

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# **THE CAUSES AND CONSEQUENCES OF MICROBIAL COMMUNITY STRUCTURE**

Topic Editors: **Diana R. Nemergut,** Duke University, USA **Ashley Shade,** Michigan State University, USA **Cyrille Violle,** Université Paul-Valéry Montpellier, France

The causes and consequences of differences in microbial community structure, defined here as the relative proportions of rare and abundant organisms within a community, are poorly understood. Articles in "The Causes and Consequences of Microbial Community Structure", use empirical or modeling approaches as well as literature reviews to enrich our mechanistic understanding of the controls over the relationship between community structure and ecosystem processes. Specifically, authors address the role of trait distributions and tradeoffs, species-species interactions, evolutionary dynamics, community assembly processes and physical controls in affecting 'who's there' and 'what they are doing.'

# Table of Contents

Carol Arnosti, Bernhard M. Fuchs, Rudolf Amann and Uta Passow

*33 Grappling with Proteus: Population-Level Approaches to Understanding Microbial Diversity*

Mallory J. Choudoir, Ashley N.Campbell and Daniel H. Buckley

	- Andrew J. King, Emily C. Farrer, Katharine N. Suding and Steven K. Schmidt

Joseph E. Knelman and Diana R. Nemergut

*92 Evolutionary History, Immigration History, and the Extent of Diversification in Community Assembly*

Matthew L. Knope, Samantha E. Forde and Tadashi Fukami

*100 Microbial Biogeography of Arctic Streams: Exploring Influences of Lithology and Habitat*

Julia R. Larouche, William B. Bowden, Rosanna Giordano, Michael B. Flinn and Byron C. Crump

# When, where and how does microbial community composition matter?

## *Diana R. Nemergut 1,2,3\*, Ashley Shade4 and Cyrille Violle5*

*1 Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA*

*2 Environmental Studies Program, University of Colorado, Boulder, CO, USA*

*4 Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, USA*

*\*Correspondence<EMAIL_ADDRESS>
#### *Edited by:*

*Lisa Y. Stein, University of Alberta, Canada*

## *Reviewed by:*

*Ming Nie, University of Aberdeen, UK*

**Keywords: structure-function, biodiversity-ecosystem function, trait-based approaches, species-species interactions, ecological trade-offs, microbial community assembly, trait distributions**

Our planet is experiencing rates of environmental change unprecedented in modern times, and an understanding of how microbes both mediate and respond to these shifts is an important research challenge (De Vries and Shade, 2013). Because of the temporal and spatial scales over which microbes function as well as their extreme diversity, dynamics in microbial structure and processes are typically examined at the community level. However, the factors that drive patterns in microbial structure and function, and the links between them, remain widely debated (Prosser et al., 2007). In this issue, such patterns in microbial communities are further documented for soils, lakes, streams and ocean provinces (Arnosti et al., 2012; Jones et al., 2012; King et al., 2012; Larouche et al., 2012). Additionally, the importance of spatial and temporal dynamics (Armitage et al., 2012; Arnosti et al., 2012; Jones et al., 2012; Larouche et al., 2012) and interactions with macrobiota (King et al., 2012) in driving these patterns is demonstrated. Yet, a central but unanswered question is: "does knowing who is there help us to better understand what they are doing?" Indeed, as shown here by Salles et al. (2012), links between structure and function can often be weak, both at the level of the individual and at the level of the community. Several papers in this special issue, "The Causes and Consequences of Microbial Community Structure," use empirical or modeling approaches as well as literature reviews to enrich our mechanistic understanding of the controls over the relationship between community structure and ecosystem processes. Specifically, authors address the role of trait distributions and trade-offs, species-species interactions, evolutionary dynamics, community assembly processes and physical controls in affecting "who's there" and "what they are doing."

Trait-based approaches can provide mechanistic links between community structure and function, and are gaining popularity in microbial ecology (Krause et al., 2014). Importantly, the distribution of traits within a community may affect the relationship between structure and function (Webb et al., 2010). Thus, as highlighted in this issue by Comte et al. (2013), traits can be considered at both the individual and the community level, where trait distributions may have important implications for emergent properties (e.g., redundancy). Indeed, Shade et al. (2012) highlight a variety of traits that may govern the stability of individual organisms, populations and communities including plasticity, tolerance and dormancy. Folse and Allison (2012) used a multinutrient, multi-genotype model of enzyme activity, and showed that trait distributions could yield insight into the relationships between biodiversity and ecosystem function. They found that generalists dominated at low levels of community diversity when rates of enzyme production and enzyme diffusion were lowest. Matias et al. (2013) used a simple microcosm experiment and examined the response of assembled communities to fluctuations in salinity. Their results were somewhat different from Folse and Allison (2012), as they found that community diversity was positively related to productivity and that generalists were more productive and less variable over time. Their work also showed that there did not appear to be a fitness trade-off associated with generalization. Comte et al. (2013) took a novel approach to examine plasticity and redundancy in freshwater bacterioplankton communities, and described explicit metrics to track these traits within community transplant experiments. They showed that plasticity appeared to be an intrinsic community property while redundancy was affected by external environmental factors. Their work also revealed strong relationships between community plasticity and redundancy, with no evidence for trade-offs and a possible co-selection of these attributes.

As well, species-species interactions can affect the relationship between communities and processes. In the model presented by Folse and Allison (2012), the importance of both "coalitions" of complementary organisms and the abundance of "cheaters," or organisms that use a public good without contributing to its production, increased under high levels of enzyme production. They also found that the presence of cheaters could affect the relationship between biodiversity and function. Fox (2012) offered a cautionary tale in terms of our ability to interpret relationships

*3 Department of Biology, Duke University, Durham, NC, USA*

*5 CEFE UMR 5175, CNRS - Université de Montpellier - Université Paul-Valéry Montpellier – EPHE, Montpellier, France*

between abundance and "adaptedness" because of organismal interactions. He used a consumer-resource model to demonstrate that, at medium levels of niche overlap, outcomes of competition can be unpredictable, decoupling relationships between abundance and adaptation.

Evolutionary dynamics can also alter relationships between structure and function. In a Perspectives Article, Choudoir et al. (2012) advocate for population-level approaches to examining microbial community diversity, emphasizing that organisms with exactly the same 16S rRNA gene sequence can exhibit very different ecological dynamics. Indeed, Salles et al. (2012) examined the links between rates of denitrification and phylogenies and highlighted the potential importance of horizontal gene transfer (HGT) by showing that similarity in *nirK* genes, which are thought to be subject to HGT, is not related to N2O accumulation rates. Furthermore, for *nirS* and 16S rRNA genes, Salles et al. (2012) showed that there was more explanatory power between structure and function at finer scales of phylogenetic resolution for denitrification and metabolic profiles respectively. Pearce et al. (2012) used metagenomics to examine a soil microbial community from Mars Oasis, Antarctica, and showed that while genera-level diversity was limited, species-level diversity was high. They proposed that this suggests strong selection on the types of taxa that can inhabit this extreme environment combined with high rates of diversification within those lineages. Related, Knope et al. (2012) used a microcosm approach to examine the importance of evolutionary history for diversification in bacteria. They showed that prior exposure to an environmental challenge led to higher rates of diversification. These studies suggest that understanding the coupling of ecological and evolutionary processes is key for interpreting microbial community patterns of structure and function.

Community assembly processes may also alter the relationship between "who's there" and "what they do" (Nemergut et al., 2013). Knope et al. (2012) found that arriving in a community first led to a greater degree of diversification within bacteria, likely because of niche-preemption. Pholchan et al. (2013) used a variety of manipulations to alter microbial community assembly in sludge reactors and showed that relationships between biodiversity and ecosystem function in these systems were unpredictable. They hypothesized that the relative importance of stochastic vs. deterministic assembly processes could change the relationship between biodiversity and ecosystem function. In their comment on the Pholchan manuscript, Knelman and Nemergut (2014) provide a conceptual framework illustrating how assembly, biodiversity and function may be related. Together, these studies provide growing evidence for the importance of assembly processes in determining microbial community properties.

Physical dynamics may also be key in regulating the relationship between structure and function. Schimel and Schaeffer (2012) propose a conceptual framework that highlights a requirement that biological processes need to be rate limiting or fate determining in order for community structure to matter for ecosystem function. For example, they propose that structure is not likely to be relevant for organic matter breakdown in mineral soils, where diffusion is limited and organic particles may be occluded or sorbed to soil surfaces. Likewise, Folse and Allison (2012) demonstrate that rates of diffusion of enzymes can affect community diversity and the relative proportion of generalists to specialists. Their work also showed high rates of diffusion coupled to high rates of production can lead to community bottlenecks and increases in stochasticity. As well, King et al. (2012) found that physical dynamics may also affect biotic relationships. They found that associations between plants and microbial community composition were less pronounced at higher elevations, likely due to an increase in the influence of physical harshness on community composition.

Together, the studies in this special issue highlight the role of a variety of ecological, evolutionary and physical dynamics in microbial community structure and function (**Figure 1**). This body of work emphasizes the importance of emergent, aggregate community properties and the role of community dynamics in variations in the strength of the structure-function relationships. As Schimel wrote in 1995 "At a small enough scale, microbial community structure must be a dominant control on ecological processes, but as we move up in scale toward the ecosystem and integrate across many individual communities, the influence of microbial community structures decreases." Predicting when, where, how, and at what scale microbial communities may

reviews to address a suite of controls over the relationship between community structure and ecosystem function.

respond to environmental changes remains a research priority and these papers present new insights into this challenge.

## **ACKNOWLEDGMENTS**

Cyrille Violle was supported by a Marie Curie International Outgoing Fellowship within the 7th European Community Framework Program (DiversiTraits project, no. 221060).

## **REFERENCES**

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 06 August 2014; accepted: 03 September 2014; published online: 26 September 2014.*

*Citation: Nemergut DR, Shade A and Violle C (2014) When, where and how does microbial community composition matter? Front. Microbiol. 5:497. doi: 10.3389/ fmicb.2014.00497*

*This article was submitted to Terrestrial Microbiology, a section of the journal Frontiers in Microbiology.*

*Copyright © 2014 Nemergut, Shade and Violle. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Millimeter-scale patterns of phylogenetic and trait diversity in a salt marsh microbial mat

#### **DavidW. Armitage1\*, Kimberley L. Gallagher 2 , Nicholas D.Youngblut 3 , Daniel H. Buckley 4 and Stephen H. Zinder 5**

1 Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA

2 Department of Marine Sciences, University of Connecticut, Groton, CT, USA

3 Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

4 Department of Crop and Soil Sciences, Cornell University, Ithaca, NY, USA

5 Department of Microbiology, Cornell University, Ithaca, NY, USA

#### **Edited by:**

Cyrille Violle, Centre National de la Recherche Scientifique, France

#### **Reviewed by:**

James T. Hollibaugh, University of Georgia, USA Susannah Green Tringe, Department of Energy Joint Genome Institute, USA

#### **\*Correspondence:**

David W. Armitage, Department of Integrative Biology, University of California Berkeley, 1005 Valley Life Sciences Building, Berkeley, CA 94720-3140, USA.

e-mail<EMAIL_ADDRESS>
Intertidal microbial mats are comprised of distinctly colored millimeter-thick layers whose communities organize in response to environmental gradients such as light availability, oxygen/sulfur concentrations, and redox potential. Here, slight changes in depth correspond to sharp niche boundaries.We explore the patterns of biodiversity along this depth gradient as it relates to functional groups of bacteria, as well as trait-encoding genes. We used molecular techniques to determine how the mat's layers differed from one another with respect to taxonomic, phylogenetic, and trait diversity, and used these metrics to assess potential drivers of community assembly. We used a range of null models to compute the degree of phylogenetic and functional dispersion for each layer. The SSU-rRNA reads were dominated by Cyanobacteria and Chromatiales, but contained a high taxonomic diversity. The composition of each mat core was significantly different for developmental stage, year, and layer. Phylogenetic richness and evenness positively covaried with depth, and trait richness tended to decrease with depth. We found evidence for significant phylogenetic clustering for all bacteria below the surface layer, supporting the role of habitat filtering in the assembly of mat layers. However, this signal disappeared when the phylogenetic dispersion of particular functional groups, such as oxygenic phototrophs, was measured. Overall, trait diversity measured by orthologous genes was also lower than would be expected by chance, except for genes related to photosynthesis in the topmost layer. Additionally, we show how the choice of taxa pools, null models, spatial scale, and phylogenies can impact our ability to test hypotheses pertaining to community assembly. Our results demonstrate that given the appropriate physiochemical conditions, strong phylogenetic, and trait variation, as well as habitat filtering, can occur at the millimeter-scale.

**Keywords: microbial mat, community assembly, biodiversity, phylogenetics, null models, metagenomics, salt marsh**

## **INTRODUCTION**

The mounting evidence that biodiversity *per se* positively affects the emergent functions of an ecosystem justifies further studies of the mechanisms by which taxa coexist (Loreau et al., 2001; Hooper et al., 2005). Much of this theory is built on data from eukaryotes, due mainly to our inability to survey bacterial, archaeal, and viral (henceforth microbial) assemblages in their natural environments (but see Bell et al., 2005). Beyond driving a number of critical biogeochemical functions, microbes encompass a tremendous pool of undescribed biodiversity on earth (Curtis and Sloan, 2004; Quince et al., 2008). Censuses of microbial diversity commonly encounter staggering levels of genetic and taxonomic information, and often lead to the discovery of novel biological functions (Cowan et al., 2005). In particular, the metagenomic and targeted-gene amplicon approaches to microbial ecology can be combined to visualize and statistically compare multiple dimensions of biodiversity within and

between environmental samples (Tyson et al., 2004; Sogin et al., 2006).

Biodiversity within a microbial community can be defined in three fundamental ways. Taxonomic diversity, or the number of arbitrarily similar units and their abundance distributions, is the traditional metric by which communities are defined and compared. Trait diversity, or the breadth of phenotypic, rather than genotypic, differences among individuals also has a rich history of use in ecology. Certain traits are used to signify the role an organism plays in the context of biotic and abiotic interactions. These traits are termed "functional" in the sense that they influence the properties of the greater ecosystem. Because traits are a product of evolutionary dynamics within a population, a phylogenetic perspective on diversity is a useful bridge between taxonomic and trait diversity (Faith, 1992).

It is now recognized that communities are dynamic, arbitrarily bounded assemblages whose members are products of both contingent, historical processes, and semi-deterministic assembly rules (Ricklefs, 2006). Historical processes that structure communities include the constraints on diversification imposed by biogeography (e.g., dispersal limitation) and evolution (e.g., adaptive radiation, Red Queen dynamics; MacArthur andWilson, 1967; Vermeij, 1987; Losos et al., 1998; Gillespie, 2004). In contrast, assembly rules are generally defined as contemporary mechanisms which either permit or prohibit an individual or taxon from occupying a particular local habitat (Diamond, 1975; Weiher and Keddy, 1999). While phylogenetic approaches have been a cornerstone in evolutionary biogeography for some time, ecologists have only recently adopted a phylogenetic perspective to tease apart community assembly mechanisms (Webb et al., 2002; Cavender-Bares et al., 2004). Most commonly, the phylogenetic relatedness (or trait similarity) of taxa within a particular habitat is compared to the averaged relatedness of randomized communities whose taxa are sampled from a pool of potential colonizers (called a *regional pool*). If the observed phylogenetic relatedness of a community is significantly lower or higher than the mean (or median) of the randomized communities, then the community is said to be either phylogenetically clustered or overdispersed, respectively (Webb, 2000). If the assumption of phylogenetic niche conservatism is justified, then the differences between two organisms' traits should positively covary with phylogenetic distance (Peterson et al., 1999). Thus,if habitat filtering is a dominant community assembly mechanism, we expect to find phylogenetic and trait clustering in that habitat due to trait-driven niche conservatism. Alternatively, in a scenario involving character displacement, competition between sister taxa will result in divergent selection on their traits (and hence their realized niches). In this case, habitat filtering results in independence between the phylogenetic diversity and trait diversity of a community, and manifests as functional clustering with no pattern to the phylogenetic structure of the community.

The assumption of phylogenetic niche conservatism appears robust at very broad taxonomic levels (e.g., among all Angiosperms), but can break down within smaller clades (e.g., oak species; Cavender-Bares et al., 2006). In microbial communities, horizontal gene transfer (HGT) among distantly related taxa weakens such an assumption at all taxonomic levels. Conversely, certain mono- and polyphyletic clades do possess a suite of traits which make certain habitats much more favorable. For example, the Cyanobacteria require both light and oxygen to carry out photosynthesis and respiration, and thus should generally be found in oxic, photic habitats. Likewise, the microaerophilic and anaerobic non-oxygenic phototrophs require light of particular wavelengths, as well as reduced inorganic sulfur and hydrogen for photosynthesis. Many obligate anaerobes (e.g., order Clostridiales) can similarly be found in habitats satisfying certain abiotic conditions.

Metagenomic shotgun and targeted-gene amplicon sequencing are two complementary culture-independent approaches to assessing microbial biodiversity. The former offers a relatively unbiased view of the suite of genomic information in an environmental sample, provided adequate sequencing depth and assembly steps. Assembled sequence fragments can then be compared against reference databases to predict their structures and potential functions. The downsides to this approach include erroneous predictions of gene function and limitations assessing overall diversity due to inadequate sequencing depth. To overcome the second caveat, phylogenetically informative genes (e.g., SSU-rRNA) can be amplified by PCR and the resulting amplicon pool sequenced alongside or independent of a shotgun metagenome. Depending on the quality and length of shotgun contigs and amplicon sequences, both metagenomic and amplicon approaches can yield information on the taxonomic, phylogenetic, and functional aspects of microbial biodiversity (Burke et al., 2011). Consequently, these data can also be used to inform our understanding of the processes structuring microbial communities. For instance, many studies have found evidence for phylogenetic and functional clustering in microbial assemblages in marine (Barberán and Casamayor, 2010; Kembel et al., 2011; Bryant et al., 2012; Pontarp et al., 2012), freshwater (Horner-Devine and Bohannan, 2006; Newton et al., 2007; Amaral-Zettler et al., 2010; Barberán and Casamayor, 2010), and terrestrial habitats (Horner-Devine and Bohannan, 2006; Bryant et al., 2008; Wang et al., 2012). Although these results are often taken as evidence for habitat filtering, many are based on comparisons of samples collected at distances which are often orders-of-magnitude greater than the scale at which cells are known to interact (Long and Azam, 2001; Dechesne et al., 2006). Therefore, quantification of niche-based community assembly may be confounded by historical biogeographic and evolutionary processes, such as adaptive radiation, genetic drift, and serial founder effects (Ricklefs, 2006; Vamosi et al., 2009; Fine and Kembel, 2011). By measuring the phylogenetic and functional properties of adjacent habitats at a scale permissive of genetic admixture (so-called microhabitats), such results can be more reliably attributed to trait-driven differences in habitat specialization (Webb et al., 2008).

Microbial mats are one of the more conspicuous and wellstudied microbial communities (Stal and Caumette, 1994; Seckbach and Oren, 2010). These mats typically form in habitats too extreme to support plant growth, such as hypersaline soils, geothermal springs, and tidal flats. Their laminated appearance is due to vertical segregation of particular guilds of bacteria and diatoms, which assemble in response to millimeter-scale gradients in both light intensity and redox potential (Jørgensen et al., 1979;Revsbech et al., 1983; van Gemerden, 1993). In temperate environments, the top layer is often dominated by oxygenic cyanobacteria and eukaryotic algae and takes on a green hue due to its chlorophyll *a* content. During daylight hours, the oxygen concentration of this layer is equal to or higher than atmospheric levels and decreases with depth to trace levels at 5 mm. Thus, this layer also supports a rich community of aerobic heterotrophs. The production of extracellular polysaccharides (EPS) in the upper layers, however, probably limits the efficacy of larger eukaryotic grazers in capturing prey (Awramik, 1984). Light becomes more diffuse past 3 mm depth, but can still drive non-oxygenic photosynthesis in groups such as purple sulfur bacteria and green sulfur bacteria, provided the appropriate reducing agents are available (Jørgensen and Des Marais, 1986; Pierson et al., 1990). At depths greater than 10 mm, light is absent at wavelengths <1µm and photosynthesis does not occur. Here, the microbial community primarily consists of anaerobic sulfate-reducers, although this form of respiration also

occurs in the mats' photic zones (Pierson et al., 1987; Risatti et al., 1994).

Despite the historical significance of microbial mats, such as their role in the ecology of early Earth (Des Marais, 2003), there have been few molecular surveys of such communities, and even fewer focusing on temperate salt marsh habitats (Ley et al., 2006; Buckley et al., 2008; Kunin et al., 2008; Bolhuis and Stal, 2011; Burow et al., 2012). Our aim was twofold: (1) to present the results of a shotgun metagenomic and targeted-gene amplicon survey of a particularly well-studied salt marsh microbial mat and (2) determine if patterns in the taxonomic, phylogenetic, and functional diversities of the microbial mat show evidence for non-random community assembly. The extreme biotic stratification and abiotic gradients evident in microbial mats led us to predict systematic differences in microscale biodiversity. For instance, because light, oxygen, and sulfur gradients in the mat favor particular metabolic strategies, and since many of these metabolic (particularly photosynthetic) strategies are phylogenetically conserved, taxa present within each layer should be more related to one another than expected by chance, or phylogenetically clustered, when measured over the entire bacterial domain. Under the assumption of phylogenetic niche conservatism, if trait-based habitat filtering is a dominant mechanism of community assembly, functional traits should also be clustered. Alternatively, if HGT is not a product of phylogenetic distance, a widespread prevalence of non-homologous recombination should decouple phylogenetic and trait diversity patterns, and manifest as clustered traits with random phylogenetic dispersion.

## **MATERIALS AND METHODS STUDY SITE**

The Great Sippewissett salt marsh is located west of Falmouth,MA, on Buzzard's Bay (N41˚350 13.3400, W70˚380 29.1000). The habitat is typical of New England salt marshes, with braided tidal creeks running around dense stands of *Spartina*. Microbial mats form in sandy intertidal sediments which lack colonization by plants, and are identifiable by the leathery, green/gold-colored top layer (Nicholson et al., 1987; Pierson et al., 1987; **Figure 1**).

We collected samples from the Great Sippewissett salt marsh on two occasions: June 23, 2010 and July 6, 2011. In 2010, two cores measuring 2 cm (diameter) × 5 cm were collected from two mat habitats. One of these cores was chosen as an example of an early successional mat. The early successional mat consisted of a wide light-green band of cyanobacteria descending from the surface to approximately 1 cm depth in unconsolidated sandy sediment. The early successional mat lacked a leathery surface layer, conspicuous bands attributable to anoxygenic phototrophs, and the cohesiveness which are all characteristic of climax mat communities in this system (Nicholson et al., 1987). The early successional mat was likely initiated at the end of winter and was developing in close proximity (1–3 m) to an area containing mature mats. Our second core from 2010 was taken from this climax mat system which consisted of a thick leathery surface layer and sharply defined dark green, pink, and brown cohesive layers (as described in Nicholson et al., 1987). Our assumption that these mat sections were of different ages is based on (1) their qualitative differences (slight

**FIGURE 1 | Greater Sippewissett salt marsh microbial mat showing typical lamination (photo credit: NDY).**

green banding vs. multicolored layering, loose vs. stabilized sediment) and (2) their close proximity (similar biotic and abiotic characteristics). These definitions are consistent with descriptions of coastal mat development elsewhere (Stal et al., 1985; Mir et al., 1991; Stal and Caumette, 1994). Henceforth, we refer to the two cores from 2010 as "young mat" (YM-10) and "old mat," (OM-10). We collected an additional core from Great Sippewissett salt marsh in 2011. This core was representative of a climax microbial mat community and is referred to as OM-11. Intact mat sections (20 cm2 ) were returned to the lab for processing. Cores were taken from the center of mat sections, the cores sectioned, and DNA extracted within 2–3 h of collection. The developed mats were sectioned with a sterile razor blade at the boundaries of their colored layers, and the young mat at corresponding depth.

Oxygen concentration and pH measurements were performed in 2011 using OX-50 and a pH-probes attached to a Microsensor Monometer (Unisense, Denmark).*In situ* depth profiling was conducted with a Unisense micromanipulator MM33. We also used abiotic data from previously published studies of the Sippewissett microbial mat including chlorophyll *a*, bacteriochlorophyll *a*, and sulfide (Pierson et al., 1987; Buckley et al., 2008). While these data are not perfectly matched to our samples, the descriptions and locations of the mats used in each study closely resemble our own.

## **DNA EXTRACTION AND SEQUENCING**

DNA was extracted from each sectioned layer using the MoBio PowerSoil kit according to the manufacturer's instructions. This extraction was prepared for sequencing at the Bay Paul Center (Marine Biological Laboratory, Woods Hole, MA, USA) and the procedure is detailed elsewhere (Huber et al., 2007 Supplementary Materials). Briefly, this involved PCR amplification of the SSU-rRNA v4 through v6 hypervariable regions using degenerate primer sets 518F (50 -CCAGCAGCYGCGGTAAN-30 ) and 1064R (consensus: 50 -CGACRRCCATGCANCACCT-30 ) for the 2010 cores and primers 515F (50 -GTGYCAGCMGCCGCGGTAA-3 0 ) and 907R (50 -CCGYCAATTCMTTTRAGTTT-30 ) for the 2011 cores (Morrison and Sogin, Unpublished). The forward primers were synthesized to include (from 50 to 30 ) Roche A-adapters, a unique 8-base barcode specific to each sample, a 2-base linker sequence, and the forward primer. Reverse primers were synthesized with the Roche B-adapter linked to the primer via 2-base linker. Template concentration among all extractions was normalized to 15 ng/µl. PCR templates were amplified using 2× Phusion HF polymerase with 8% DMSO for 32–36 cycles, the first 10 cycles using a touchdown annealing temperature from 68 to 58˚C followed by 12 cycles of three-step and 10–14 cycles of two step PCR. We used the PicoGreen assay to quantify PCR product and concentrate it to 100µl. It was then gel-purified using the Montage Kit (Millipore) and shipped to Penn State Genomics Core Facility and sequenced on the Roche GS-FLX Titanium platform.

In addition to the SSU-rRNA amplicon libraries, we performed metagenomic sequencing on the layers of the old mat in 2010. All five samples were given unique multiplex identifier (MID) tags prior to pooling on the microtiter plate. Pyrosequencing was carried out as described above.

#### **SSU-rRNA PROCESSING**

All analyses of the SSU-rRNA amplicons were conducted within the QIIME software package (Caporaso et al., 2010a). Raw 454 reads were quality filtered, dereplicated, and demultiplexed by removing Roche adapters, linkers, primers, and sample barcodes. Sequences shorter than 350 bp and longer than 550 bp were discarded. Next, to expedite chimera detection, we clustered identical reads using uclust (Edgar, 2010). One representative sequence of each cluster was aligned to the Greengenes core set with PyNAST 1.1 (DeSantis et al., 2006; Caporaso et al., 2010b). Chimeric sequences (7,804 total) were detected using the ChimeraSlayer algorithm and subsequently removed (Haas et al., 2011). To examine the effect of phylogenetic resolution on resulting diversity metrics, we used uclust to bin SSU-rRNA sequences into operational taxonomic units (OTUs) using a 97% similarity cutoff using a greedy algorithm. Singleton OTUs (those appearing in only one sample, or represented by only one sequence) were discarded. We assigned taxonomic identities to a sequence representing a particular OTU using the RDP naïve Bayesian classifier (Cole et al., 2009). These representative sequences were once again aligned to the Greengenes core set using PyNAST. Any sequences identified as belonging to archaea, eukarya, or chloroplasts were removed, as were singleton OTUs. The sequences were masked and used to build a phylogenetic tree with FastTree 2.1.4 using the GTR + CAT approximation for substitution rate heterogeneity (Stamatakis, 2006;Price et al.,2010). This software is optimizedfor phylogenetic inference from large datasets and makes use of nearest-neighbor interchange algorithms to significantly decrease computation time (Price et al., 2010).

#### **METAGENOME PROCESSING**

Shotgun sequences from the 2010 old mat layers were quality filtered and demultiplexed using QIIME's split\_libraries procedure. Sequences shorter than 75 bp were discarded. This filtering resulted in approximately 243.8 Mbp. These sequences were then submitted to MG-RAST for dereplication and screening against model organismal DNA (Meyer et al., 2008). Protein prediction was carried out in the MG-RAST pipeline using FragGeneScan (Rho et al., 2010). We used the categorization of the Kyoto Encyclopedia of Genes and Genomes (KEGG) for annotating our sequences (Kanehisa et al., 2012).

### **DIVERSITY ESTIMATION**

Rather than using a univariate index to characterize the taxonomic richness and evenness of each mat layer, we computed the series of effective numbers, *qD* Z (**p**), recently presented by Leinster and Cobbold (2012). This family of metrics is a simple extension of Hill numbers (Hill, 1973) that also account for the similarity between taxa, represented by a matrix *Zij*. These numbers are generalizations of particular diversity indices, and can be plotted against the parameter *q*, the sensitivity to rare taxa. At *q* = 0, the number equals the taxonomic richness *S*, if a naïve similarity matrix (the identity) is used, otherwise, it is a measure of phylogenetic diversity (Faith, 1992; Chao et al., 2010). At *q* =∞, the naïve solution is the reciprocal of the Berger–Parker index and gives the inverse proportional abundance (*p*) of the most dominant taxa. To measure the phylogenetic diversity of a community, we used the equation.

$$^q D^z(\pi) = \left(\sum\_{\stackrel{i,b:\,i\in I\_{b\cdot}}{\pi\_{(i,b)}}} \pi\_{(i,b)} (Z\pi)^{q-1}\_{(i,b)}\right)^{q/(1-q)}$$

where π(*i,b*) is the relative abundance of "historical species" (*i,b*), where *b* is a particular branch terminating in the subset of taxa present in the community: *i* ∈ {1, 2, 3,. . ., *S*}, *Ib* ⊆ {1, 2, 3,. . ., *S*}, and *Z(i,b)(j,c)* is an asymmetric relatedness matrix (details in Leinster and Cobbold, 2012, Appendix). Additionally, we calculated 0*D* **Z** (**p**) with *Z* = *I*, which is simply the taxonomic richness of the community. For each layer, these values were calculated over a range of *q* (0–5) averaged over 100 subsamples rarefied to the smallest number of sequences in a layer (Old mat 2010, 10–15 mm, 1071 sequences).

Mean phylogenetic diversity (MPD) was estimated by calculating the average phylogenetic distance of all pairwise branch lengths. For examination of particular mono- and polyphyletic guilds within the overall phylogeny (e.g., Cyanobacteria, sulfur oxidizing bacteria), we trimmed the tree to only include particular clades and recalculated the distance matrix. These values were then averaged over 100 rarefactions normalized to the smallest sample.

#### **NULL MODEL ANALYSES**

We estimated the standard effect size of each community's rarefied mean phylogenetic distance (MPDSES) by comparing it to the rarefied values of 999 randomized communities generated using null models. Because the statistical power to detect niche-based community assembly varies with the choice of null model, we assessed the agreement of three different randomization routines: (1) the independent swap algorithm,which shuffles the taxa/sample table; (2) the phylogeny.pool algorithm, which draws random samples from the phylogenetic distance matrix to populate the taxa/sample table; and (3) the taxa.labels algorithm, which shuffles the labels of a phylogenetic distance matrix (Kembel and Hubbell, 2006; Kembel,2009). In null model three,we calculated phylogenetic diversity both by weighting the relative number of sequences obtained for each OTU as either 0 or 1 (presence/absence) or as *pij*, the proportional relative abundance of the OTU in its respective sample. We acknowledge that measuring proportional abundance using sequence counts is not optimal, but it still provides a more realistic view of a community compared to presence/absence data. We assessed the robustness of our results by first comparing the results of null model three (taxa.labels) using three definitions of "taxa pools": (1) the entire set of OTUs detected in all samples during all years, (2) the set of OTUs detected in each year, separately, and (3) the OTUs only found in each particular core. We did this with the expectation that phylogenetic dispersion is sensitive to the spatiotemporal scale of the taxa pool (Kembel and Hubbell, 2006; Swenson et al., 2006; Lessard et al., 2012). The first, and least conservative species pool assumes that all OTUs detected in the study are equally likely to colonize all mat layers, independent of year. The last, and most conservative pool assumes that the only OTUs capable of colonizing all three mat cores are those that were detected within all three cores to begin with. Additionally, because weak bifurcation support values in our phylogeny might bias our estimates of standard effect size, we tested the robustness of our findings by collapsing bifurcating nodes into polytomies if the nodes' support values were below a certain threshold (50 and 80%). Samples were considered significantly overdispersed or clustered (α = 0.05) if they fell above or below 95% of the randomized communities' values, respectively. We ran these tests for all OTUs detected in the sample, and for three functional guilds: (1) phylum Cyanobacteria (oxygenic phototrophs), (2) order Chromatiales (purple sulfur bacteria), and (3) orders Desulfobacterales, Desulfovibrionales, Syntrophobacteriales, and Clostridiales (anaerobic sulfate-reducers; Risatti et al., 1994).

Trait richness and dispersion of the OM-10 sample was measured using the function-level KEGG ortholog (KO) group, excluding hypothetical proteins at an *e*-value equal to or less than 10−5 with a minimum alignment length of 50 bp. To correct for sample size bias, we rarefied KO richness to the lowest number of KO genes detected in our sample (0–2 mm; 20,121). We followed the methods of Bryant et al. (2012) in re-sampling without replacement 1,000 simulated sets of KO genes for each community from the pool of KO genes detected throughout the mat core. We compared our observed KO richness to those of our simulated communities to assess whether or not layers contained more or fewer trait-encoding genes than would be expected by

chance. We verified our findings by performing the same routine on non-supervised orthologous groups (NOGs), which are annotated algorithmically rather than by hand (Jensen et al., 2008), and clustered orthologous groups (COGs),which are manually curated (Tatusov et al., 1997). We also measured rarefied KO richness patterns for different groups of genes which may be ecologically important in structuring the microbial mat and compared these values to a randomized expectation. These gene categories included (1) total metabolism, (2) photosynthesis, (3) carbohydrate metabolism, (4) sulfur metabolism, (5) ABC transporters, and (6) oxidative phosphorylation. Significance was assessed using the rank test described for MPDSES.

### **STATISTICAL ANALYSES**

To statistically compare phylogenetic richness between communities, we calculated the unweighted SSU-rRNA UniFrac distances between samples, rarefied to the sample with the lowest number of sequences, and clustered using Ward's minimum variance method (Lozupone and Knight, 2005). Statistical significance was assigned to clusters as determined by approximately unbiased (AU) *p*values calculated from 10,000 multiscale bootstrap dendrograms (Shimodaira, 2004). We used the permutation-based analysis of variance ADONIS to test the null hypotheses of whether differences existed in community OTU composition between years, sites, and depth (Anderson, 2001). We used linear regression to test for a relationship between our Hill diversity measures and pH, depth, chl *a*, Bchl *a*, sulfide, and oxygen concentrations. Because our data appeared non-linear in exploratory analyses, we compared linear and power law (log–log-transformed) fits for the same data and selected the top model based on its coefficient of determination. We calculated regressions both with and without covariates for the age of the mat sample and the year in which it was collected. All quantitative analyses were carried out in R 2.14 (R Development Core Team, 2011) using the packages "vegan 2.0" (Oksanen et al., 2012), "picante 1.3" (Kembel et al., 2010), and custom scripts for rarefaction and diversity profiling (Bryant1 ; Cobbold2 ; Armitage3 ).

## **RESULTS**

Our measurements of variation in abiotic conditions with depth demonstrate clear gradients. Our oxygen profile showed the predicted spike in concentration between 2 and 3 mm, and decreased to trace levels past 5 mm (**Figure 2**). Likewise, pH declined with depth to 3 mm and remained constant to 6 mm (**Figure 2**).

After quality filtering, dereplication, and singleton removal, we were left with a total of 91,392 SSU-rRNA sequences representing 3,503 OTUs clustered at 97% sequence similarity. The top layers of each cluster were dominated by Cyanobacteria of the family Oscillatoriales, while the lower clusters were heavily dominated by Proteobacteria, including many OTUs classified to the family Chromatiales (**Figure 3**). UniFrac clustering revealed that samples were clustered by year and age (**Figure 4**). Two statistically significant clusters were identified: one containing all but

1http://openwetware.org/wiki/User:Jessica\_A.\_Bryant

2http://www.maths.gla.ac.uk/∼cc/supplements/diversity.html 3http://github.com/darmitage

the top layers of the old mats, and one containing the top layers of all mats and the young mat (AU test; *p* < 0.001; **Figure 4**). We rejected the null hypotheses that communities were compositionally identical (α = 0.05) across years (*F*1,13 = 4.20, *p* < 0.005), ages (*F*1,13 = 3.34, *p* < 0.005), and depth (*F*1,13 = 2.72, *p* < 0.005). However, these three effects only explained 50.6% of the variance in our data.

Taxonomic richness, 0*D*(**p**), and phylogenetic richness, 0*D* **Z** (π), were both positively associated with depth on the log– log scale, and this result was significant (*p* < 0.001, *R* 2 = 0.64; *p* < 0.0001, *R* 2 = 0.75). Profiles of *qD Z* (π) show that the top layer of each mat had the lowest phylogenetic richness (*q* = 0) and evenness (*q* > 0). Overall phylogenetic richness was greatest in the young mat,with the exception of the top layer,which had the lowest phylogenetic and taxonomic richness of the three cores (**Figure 5**).

Phylogenetic clustering increased linearly with depth when mat age was included as a covariate (*p* < 0.05,*R* 2 = 0.65; **Figure 6**). The old mat samples from 2010 to 2011 showed similar patterns in phylogenetic dispersion, with positive MPDSES values in the top layer and significantly clustered communities in the underlying layers. Additionally, we found evidence for an increase in phylogenetic clustering with mat age for depths >2 mm (**Figure 6**). The young mat was similarly patterned, but the significance of the clustering was less than that of the older mats. Our results did not change when phylogenetic bifurcations were collapsed into polytomies at 50 and 80% support values. Our choice of null model did not change most of our results, but the independent swap algorithm dampened the strength of statistical significance compared to the phylogeny-shuffling approaches (**Table A1** in Appendix). Compared to our first taxa pool (all OTUs in all years and samples), pools 2 (within-year), and 3 (within-core) altered magnitude of statistical significance for many of the samples (**Table A1** in Appendix). There were no differences in outcomes between relative abundance-weighted and censored randomizations (**Table A1** in Appendix). Chromatiales (purple sulfur bacteria) exhibited no significant phylogenetic dispersion in any layer. The same was true for Cyanobacteria, except for one significantly clustered community in the top layer of the young mat (MPDSES = −2.11, rank = 15, *p* < 0.05). Sulfate reducing bacteria were phylogenetically clustered in the young mat, but only significantly so for the 2–5 mm (pink) layer (MPDSES = −3.79, rank = 7, *p* < 0.01).

Trait richness, measured by rarefied KO and COG annotations, decreased with depth, and appeared to negatively covary with taxonomic and phylogenetic richness. Using the NOG gene annotation, functional richness appeared to slightly increase in the third and fourth layers, but could not be statistically evaluated with only four data points. Total trait richness was significantly lower than expected under a randomized sample for all layers and all ortholog databases, indicating fewer traits were present in each layer than expected by chance (**Figure 7**). Photosynthesis-related KO genes decreased with depth, and were significantly greater than expected by chance in the top layer, and reduced in the underlying layers (**Figure 8**). Richness of metabolic KO genes decreased with depth. Genes related to sulfur metabolism and carbohydrate metabolism remained relatively constant with depth, though the former category had very few KO hits. Genes encoding ABC transporters decreased in richness to the third (brown) layer, and then increased in the bottom layer. Genes coding for oxidative phosphorylation steadily declined with depth.

## **DISCUSSION**

#### **COMMUNITY COMPOSITION**

Using a combination of bacterial SSU-rRNA amplicons and metagenomics, we quantified patterns in taxonomic, phylogenetic, and trait diversity within a salt marsh microbial mat. Our results, taken in concert with other studies, suggest cyanobacterial mats contain a very diverse community with large variation at the millimeter-scale (Ley et al., 2006; Villanueva et al., 2007; Kunin et al., 2008; Dillon et al., 2009). This variation appeared to be primarily driven by light limitation with depth. At the surface, light drives oxygenic photosynthesis in organisms possessing chlorophyll *a* and favors the oxidation of water to oxygen. As light becomes limited at depths greater than 2 mm, phototrophs possessing bacteriochlorophylls dominate, such as the purple bacteria (e.g., orders Rhodospirillales and Chromatiales) and the green

sulfur bacteria (order Chlorobiales). These organisms use sulfide, hydrogen, and organics as reducing agents, scavenging it from the anaerobic reduction of sulfate carried out in the underlying layers. Our finding that all of the young mat's layers most closely resembled the top green layers of the developed mats is not surprising, considering that in the absence of thick EPS layers, light probably penetrates much deeper into the mat.While the young mat sample did not display laminae, it still contained numerous taxa identified as sulfate-reducers, as well as potential sulfur oxidizing bacteria. This result is in agreement with other studies of intertidal mats, which demonstrate patterns of succession initiated by *Oscillatoria* spp. and other oxygenic phototrophs (Stal et al., 1985; Bolhuis and Stal, 2011). These early colonizers contribute to the formation of the underlying layers by stabilizing the sediment and making available organic carbon to the heterotroph communities, whose activities in turn create anoxic conditions and permit dissimilatory sulfate reduction and photosynthetic oxidation of hydrogen sulfide as life history strategies (Herbert and Welsh, 1994).

## **DIVERSITY PATTERNS**

In all three mat cores, phylogenetic and taxonomic richness was lowest in the top layer. Similar patterns have been found in marine systems (Stevens and Ulloa, 2008; Kembel et al., 2011; but see Bryant et al., 2012), hypersaline lakes (Humayoun et al., 2003), subtidal sand (Böer et al., 2009), and hypersaline microbial mats (Dillon et al., 2009). Because the sharpest increase in diversity occurred during the transition between the first and second layers, we hypothesize that the top layer of the mat may be a habitat unfavorable to the majority of OTUs detected in our samples due to a combination of UV irradiation, temperature, and flood scouring (Calkins and Thordardottir,1980;Garcia-Pichel and Castenholz, 1994; Ibelings and de Winder, 1994; Bossio and Scow, 1998). Because these factors fluctuate on a daily cycle, and because these communities undergo massive diel migrations, the daytime top layer of the mat is likely composed of stress-tolerant aerobes, while at night, microaerophiles and UV-sensitive taxa temporarily migrate to the top layer (Villanueva et al., 2007; Dillon et al., 2009).

dashed lines are results using taxa pool 3 (individual cores) as the regional

the shaded region indicate statistically significant phylogenetic clustering. Values above this region indicate phylogenetic overdispersion.

Niche theory predicts that areas of higher resource heterogeneity will support a greater diversity of taxa (MacArthur and Levins, 1967; but see Stevens and Carson, 2002). Thus, if a particular mat layer has a greater diversity of resources, we might expect an equally proportional diversity of genes relating to resource use. However, we did not find evidence of a decreasing diversity with depth of metabolic strategies measured by overall metabolic KO genes, nor genes encoding for carbohydrate metabolism, and ABC transporters, suggesting that an increase in resource heterogeneity does not explain the correlation between diversity and depth. Competition theory presents an alternative mechanism to explain the increase in diversity with mat depth.We speculate that the pool of nutrients available for assimilation (primarily *N* and *P*) may be limiting to heterotrophs in the oxic layer due to the large biomass of primary producers such as heterocystous Cyanobacteria and diatoms (Herbert and Welsh, 1994; Camacho and de Wit, 2003; Jonkers et al., 2003; Yannarell and Paerl, 2007). If competition for limiting nutrients, rather than competition for sources of C and energy, are resulting in competitive exclusion then we might expect competitive exclusion should not be as dominant a force deeper in

the mat, allowing more taxa to coexist (Hutchinson, 1961). Indeed, we found indirect support for competition being greater in the top mat layer than in underlying layers based on phylogenetic dispersion measurements. At the domain level, the top layers of each core often showed phylogenetic overdispersion, meaning OTUs detected in this layer were less related than would be expected by chance. These results match those of Bryant et al. (2012), who found significant phylogenetic overdispersion only in the uppermost photic layers of their pelagic depth series, a region also dominated by primary production and aerobic metabolism. In soils, Horner-Devine and Bohannan (2006) found a negative relationship between total organic carbon and the phylogenetic relatedness of the community. If the assumption of phylogenetic niche conservatism holds for the domain bacteria, and if competitive interactions are greatest among sister clades, then phylogenetic overdispersion can be taken as evidence for limiting similarity within a set of interacting taxa (MacArthur and Levins, 1967; Webb et al., 2002). Currently, there is limited evidence for either assumption in microbial communities, and this remains a fertile area for research (Boyd et al., 2010; Mayfield and Levine, 2010; Violle et al., 2011).

The qualitatively negative relationship between taxonomic/phylogenetic richness and trait richness measured via KO and COG pathways was unexpected. The KEGG and COG databases we used to annotate gene function in our samples were obtained from sequenced genomes, which are heavily biased toward model organisms and those from well-studied environments. Thus, the decrease in trait richness we observe may be due to annotation bias if deeper mat layers contain more organisms with poorly characterized genes and pathways. Our data do not support this claim, however, as we did not observe either a decrease in the proportion of unassigned reads nor an increased mean *e*-value with depth. Differences in OTU abundance between layers might also impact the observed pattern of trait richness. If the layer with the highest taxonomic richness is also the least even of the samples, the set of genes identified in that community will be biased toward the dominant taxa. Our diversity profiles clearly show that both diversity and evenness increase with depth, and so trait bias toward dominant OTUs cannot explain the decrease in trait richness that occurs with mat depth. Finally, our omission of archaeal reads from the amplicon library may explain the pattern, since Archaea are probably proportionally more abundant in the anaerobic layers. However, they do not exhibit as wide a physiological breadth as the domain Bacteria and are not expected to strongly bias measurements of trait diversity.

That our data show clear patterns of phylogenetic clustering with depth suggests that niche-based habitat filtering impacts community assembly as oxygen and light become limited. This claim is strengthened by our finding of significantly fewer KO genes in each layer than are expected by chance. When particular functional guilds of taxa were examined, however, this pattern dissolved, indicating that this clustering may only be apparent at very broad taxonomic levels, or in clades which we did not investigate. Other studies have also found an effect of taxonomic resolution on community clustering patterns (Horner-Devine and Bohannan, 2006;Pontarp et al.,2012). Habitat filtering in this community may be driven by the phylogenetic conservatism of phenotypes capable of anaerobic respiration. However, the metagenomic approach does not allow us to test this prediction directly due to both our inability to match taxon and gene, and our inability to match genotype with phenotype. Nonetheless, phylogenetic clustering patterns dominate the literature, and appear to covary with a number of factors, such as chlorophyll *a* content, organic carbon, nitrate (Horner-Devine and Bohannan, 2006), ocean depth (Kembel et al., 2011; Bryant et al., 2012), elevation (Bryant et al., 2008; Wang et al., 2012), and salinity (Barberán and Casamayor, 2010). More generally, the principle of habitat filtering has been the basis for enrichment culturing of microbes since the early days of microbiology, and it is not surprising that different habitats, characterized by different metabolic substrates and abiotic conditions, will selectively filter out all but a subset of taxa in the same way as a defined medium in a culture flask. One model of phylogenetic succession predicts that if the traits of early colonizers

are phylogenetically conserved, then habitat filtering should give way to facilitative and competitive interactions as succession progresses, which should manifest as a switch from phylogenetically clustered to random/overdispersed communities with time (Verdú et al., 2009). In our samples, the increased phylogenetic clustering with mat age at depths below 2 mm, along with the compositional clustering among the young mat and the top layers of the old mats, suggests the increased importance of habitat filtering with succession, as poorly adapted initial colonizers are lost from underlying layers while taxa more suited to the changing abiotic conditions are recruited into the habitat.

### **NULL MODEL CONSIDERATIONS**

In many cases, our choice of null model affected the interpretation of our analyses. By decreasing the size of our regional taxa pools from the entire suite of OTUs in a sample to the set of OTUs in one particular mat layer, we were limiting the dispersal potential for taxa not shared among samples. Little is known about the Armitage et al. Biodiversity of microbial mats

scale at which microbes disperse (Whitaker et al., 2003), but we found relatively similar results between the two most extreme taxa pools (**Table A1** in Appendix). It is likely that no two taxa have the same likelihood of occupying a habitat, and future studies should consider a spatially explicit metacommunity approach to identifying more realistic taxa pools (Lessard et al., 2012; Peres-Neto et al., 2012). Likewise, we found that the independent swap algorithm decreased the effect sizes of MPD. This null model has been identified as being the most conservative in detecting nichebased community assembly in simulations (Kembel, 2009). This randomization routine, compared with the two phylogeny-based approaches, did not identify overdispersion in our 0–2 mm layers. The statistical power to detect limiting similarity in communities requires testing, as do alternative null models which incorporate the effects of real ecological processes (Gotelli and Ulrich, 2012). By weighting our randomizations by a taxon's relative abundance, we were assuming that the probability of colonization in a mat layer was proportional to the taxon's abundance in the community, while presence/absence data give all taxa an equal probability (Kembel and Hubbell, 2006). In reality, the true dispersal probability for a taxon lies somewhere in between these two extremes, but has yet to be quantified so that an appropriate null model can be developed. In the absence of more sophisticated, semimechanistic null models, we advocate a pluralistic view of null modeling schemes focusing efforts on interpretation of a suite of models in light of their constraints and assumptions. Additionally, most, if not all, of the assumptions surrounding null models and community assembly (e.g., dispersal scale, niche conservatism) have yet to be assessed for the majority of organisms, microbial or otherwise, and we strongly urge ecologists to interpret their results in the light of these knowledge gaps.

## **REFERENCES**

## **CONCLUSION**

Microbial mats exhibit marked variation in diversity at scales much smaller than typically considered in ecological studies. Our observations of phylogenetic and trait clustering within the layers of a salt marsh microbial mat indicate habitat filtering as the main driver for community assembly. However, the effects of biotic interactions such as competition and syntrophy cannot be dismissed as alternative factors affecting community structure, especially in the mat's topmost Cyanobacteria-dominated layer. Nevertheless, until rigorous manipulative studies are carried out to test the assumptions of phylogenetic niche conservatism and dispersal limitation in bacteria, results from community phylogenetic approaches must be interpreted with caution.We suggest that these types of studies be carried out using microbial mat communities, which have historically proven tractable to both laboratory and field experimentation.

## **ACKNOWLEDGMENTS**

We thank C. Pepe-Ranney and L. Kelly for help with data processing. H. Morrison and the staff of the MBL Bay Paul center provided assistance with sequencing. This research was performed by participants in the MBL Microbial Diversity Course in Woods Hole, MA, and was supported in part by The Howard Hughes Medical Foundation, The Gordon and Betty Moore Foundation under grant No. (2493), the National Science Foundation under grant No. (DEB-0917499), the US Department of Energy under grant No. (DE-FG02-10ER13361), and the NASA Astrobiology Institute. David W. Armitage was supported by an NSF Graduate Research Fellowship. The authors thank the NSF Dimensions of Diversity distributed graduate seminar (DEB-1050680) and the Sousa Lab for valuable input.

on mountainsides: contrasting elevational patterns of bacterial and plant diversity. *Proc. Natl. Acad. Sci. U.S.A.* 105, 11505–11511.

Slough estuary, Monterey Bay. *ISME J.* 6, 863–874.

performance of community phylogenetic structure tests. *Ecol. Lett.* 12, 949–960.

for comparing microbial communities. *Appl. Environ. Microbiol.* 71, 8228–8235.

marsh, Massachusetts. *FEMS Microbiol. Lett.* 45, 365–376.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 12 June 2012; paper pending published: 04 July 2012; accepted: 24 July 2012; published online: 10 August 2012. Citation: Armitage DW, Gallagher KL, Youngblut ND, Buckley DH and Zinder SH (2012) Millimeter-scale patterns of phylogenetic and trait diversity in a salt marsh microbial mat. Front. Microbio. 3:293. doi: 10.3389/fmicb.2012.00293*

*This article was submitted to Frontiers in Aquatic Microbiology, a specialty of Frontiers in Microbiology.*

*Copyright © 2012 Armitage, Gallagher, Youngblut, Buckley and Zinder. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

## **APPENDIX**

**Table A1 | Rarefied MPDSES values for all bacteria, Cyanobacteria, purple sulfur bacteria, and sulfate reducing bacteria.**

(Continued)

#### **Table A1 | Continued**

Values are presented for three species pools, and four null models, including abundance-weighted randomizations (see text for details). \*Indicates significant (p < 0.05) phylogenetic clustering (negative values) or overdispersion (positive values) based on rank test.

# Contrasting extracellular enzyme activities of particle-associated bacteria from distinct provinces of the North Atlantic Ocean

## *Carol Arnosti 1\*, Bernhard M. Fuchs 2, Rudolf Amann2 and Uta Passow3†*

1 Department of Marine Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

2 Department of Molecular Ecology, Max Planck Institute for Marine Microbiology, Bremen, Germany

3 Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany

#### *Edited by:*

Diana R. Nemergut, University of Colorado at Boulder, USA

#### *Reviewed by:*

David C. Smith, University of Rhode Island, USA Steven Allison, University of California at Irvine, USA

#### *\*Correspondence:*

Carol Arnosti, Department of Marine Sciences, University of North Carolina at Chapel Hill, CB #3300, 3117A Venable Hall, Chapel Hill, NC 27599-3300, USA. e-mail<EMAIL_ADDRESS>
#### *†Present address:*

Uta Passow, Marine Sciences Institute, University of California at Santa Barbara, Santa Barbara, CA, USA.

Microbial communities play a key role in the marine carbon cycle, processing much of phytoplankton-derived organic matter. The composition of these communities varies by depth, season, and location in the ocean; the functional consequences of these compositional variations for the carbon cycle are only beginning to be explored. We measured the abilities of microbial communities in the large-particle fraction (retained by a 10-μm pore-size cartridge filter) to enzymatically hydrolyze high molecular weight substrates, and therefore initiate carbon remineralization in four distinct oceanic provinces: the boreal polar (BPLR), the Arctic oceanic (ARCT), the North Atlantic drift (NADR), and the North Atlantic subtropical (NAST) provinces. Since we expected the large-particle fraction to include phytoplankton cells, we measured the hydrolysis of polysaccharide substrates (laminarin, fucoidan, xylan, and chondroitin sulfate) expected to be associated with phytoplankton. Hydrolysis rates and patterns clustered into two groups, the BPLR/ARCT and the NADR/NAST. All four substrates were hydrolyzed by the BPLR/ARCT communities; hydrolysis rates of individual substrate varied by factors of ca. 1–4. In contrast, chondroitin was not hydrolyzed in the NADR/NAST, and hydrolytic activity was dominated by laminarinase. Fluorescence in situ hybridization of the large-particle fraction post-incubation showed a substantial contribution (15–26%) of CF319a-positive cells (Bacteroidetes) to total DAPIstainable cells. Concurrent studies of microbial community composition and of fosmids from these same stations also demonstrated similarities between BPLR and ARCT stations, which were distinct from the NADR/NAST stations. Together, these data support a picture of compositionally as well as functionally distinct communities across these oceanic provinces.

**Keywords: extracellular enzymes, biogeography, particles-associated bacteria, hydrolysis, carbon cycling**

"fmicb-03-00425" — 2012/12/11 — 21:00 — page 1 — #1

## **INTRODUCTION**

Heterotrophic microbial communities collectively process a large fraction of the organic matter biosynthesized in the ocean (Azam, 1998), remineralizing, repackaging, and respiring a variety of substrates, and thus playing a central role in the marine carbon cycle. These communities have been shown recently to exhibit distinct biogeographic patterns in the ocean, with community composition differing by location, season, water mass, and depth (e.g., Kan et al., 2006; Agogue et al., 2011; Gilbert et al., 2012; Hanson et al., 2012). The consequences of these compositional differences for microbial community function are only beginning to be explored, however. The distribution of functional genes among communities at different locations, depths, and times in the ocean demonstrates the potential for distinct functionalities among these communities (DeLong et al., 2006; Teeling et al., 2012), but the conditions under which and extent to which potential differences in function might be expressed are still largely unknown. Although transcriptomic investigations have yielded insight into broad categories of genes that are active in marine microbial communities

(Poretsky et al., 2009; Shi et al., 2011), this approach has major limitations with respect to carbon cycling, due to the wide range of potential substrates in ocean waters and our very limited abilities to identify specific functional genes related to cycling of these substrates (e.g., Rebuffet et al., 2011).

Direct measurements of carbon processing by microbial communities demonstrate their abilities to take up specific low molecular weight substrates labeled with 14C or 3H (e.g., Rich et al., 1997; Vila-Costa et al., 2007; Alonso-Saez et al., 2008), but these studies yield only indirect information about the rate at which most natural marine organic matter is remineralized, since most marine organic matter is initially biosynthesized as macromolecules such as polysaccharides, proteins, and lipid complexes. Although marine macromolecules are present in much higher concentrations than low molecular weight organic matter, microbial access to these substrates requires hydrolysis by extracellular enzymes to produce substrates that can be transported across cell membranes for further processing (Arnosti, 2011). The activities of these enzymes thus initiate carbon cycling by heterotrophic

microbial communities, and determine the types of organic matter that can serve as substrates.

Efforts to measure activities of microbial extracellular enzymes also – paradoxically – usually rely on a small number of low molecular weight substrate proxies. These commercially available proxies consist of a monomer such as glucose or leucine linked to a fluorophore whose fluorescence increases greatly upon hydrolysis of the monomer-fluorophore bond (Hoppe, 1983). This experimental approach has been used to compare potential hydrolysis rates at a wide range of depths and locations in the water column (e.g., Huston and Deming, 2002; Baltar et al., 2009; Piontek et al., 2011), and results have frequently been extrapolated to the degradation of carbohydrates and proteins in general (Christian and Karl, 1995; Fukuda et al., 2000). These substrate proxies, however, do not adequately mimic the three-dimensional structure of organic macromolecules in solution, so the relationship between hydrolysis rates measured with these proxies and the hydrolysis rates of their putative macromolecular counterparts are highly uncertain (Warren, 1996).

In order to measure the activities of enzymes responsible for hydrolysis of high molecular weight organic matter, alternative methods have been developed. These methods are intended also to detect the activities of endo-acting enzymes that cleave macromolecules mid-chain, an essential step in microbial degradation of macromolecular organic matter (Weiner et al., 2008; McBride et al., 2009). These approaches require synthesis of specific labeled substrates: fluorescently labeled polysaccharides and phytoplankton extracts (Arnosti, 1995, 2003; Arnosti et al., 2005b) or peptides (Pantoja et al., 1997, 2009; Pantoja and Lee, 1999), and chromatographic separation of the hydrolysis products, and thus requires considerably more work pre- and post-experiment. The additional effort is compensated for by the fact that these substrates can be used to investigate differences in hydrolysis rates and patterns for substrates with closely related structures, yielding new insight into the specific enzymatic capabilities of heterotrophic microbial communities in the water column and sediments. Using this approach, major functional differences among heterotrophic microbial communities at different depths and locations in the ocean have been identified (Arnosti et al., 2005a; Arnosti, 2008; Steen et al., 2012), showing that specific complements of enzyme activities are found not just for individual organisms, but among entire microbial communities. Moreover, these patterns of differences in enzyme activities have been shown to extend along large spatial gradients in the ocean (Arnosti et al., 2011), perhaps reflecting large-scale changes in microbial community composition along latitudinal gradients (Pommier et al., 2007; Fuhrman et al., 2008).

Given previous evidence of substantial differences in microbial communities in the North Atlantic (Schattenhofer et al., 2009), we tested whether organisms from distinct oceanic provinces within near-surface waters of the North Atlantic differed in their abilities to hydrolyze high molecular weight substrates. We focused in particular on a fraction of seawater enriched in large particles, since particle-associated heterotrophic communities are believed to be well-equipped to metabolize high molecular weight substrates, with enzymes that are sufficiently active so as to provide hydrolysate to the surrounding water column community as well as to the attached community (Smith et al., 1992; Simon et al., 2002; Grossart, 2010). At the conclusion of our incubation experiments, we carried out fluorescence *in situ* hybridization (FISH) staining to compare communities from the same station that had been incubated with different substrates, as well as communities obtained from different locations that were incubated with the same substrate. Concurrently conducted investigations of microbial community composition (Gomez-Pereira et al., 2010; Schattenhofer et al., 2011) and *Bacteroidetes*-associated fosmids (Gomez-Pereira et al., 2012) from the same locations provided us with the opportunity to examine the link between microbial community composition and function, and thus provided a larger context within which to interpret our measurements of microbial community activity. Together, these data provide new insight into the functional capabilities as well as the composition of microbial communities in distinct oceanic provinces of near-surface waters of the North Atlantic.

## **MATERIALS AND METHODS**

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## **SAMPLE COLLECTION AND WATER MASS IDENTIFICATION**

Water was collected at a depth of 20 m using Niskin bottles mounted on a rosette equipped with a CTD at four stations (S3, S6, S12, and S19) during the VISION cruise of the R/V *Maria S. Merian* (September/October 2006). The stations represented distinct oceanic provinces on a N–S gradient along the 30◦W meridian. As discussed in detail in Gomez-Pereira et al. (2010), water mass provinces were defined via satellite-derived parameters including Advanced Very High Resolution Radiometer (AVHRR), sea surface temperature and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) water leaving radiance, according to Oliver and Irwin (2008). By these definitions, S3 was in the boreal polar (BPLR), S6 was within the Arctic oceanic (ARCT) province, S12 was within the North Atlantic drift (NADR), and S19 was within the North Atlantic subtropical (NAST) province (Gomez-Pereira et al., 2010).

## **SAMPLE PREPARATION AND MEASUREMENT OF EXTRACELLULAR ENZYMATIC HYDROLYSIS**

At each station, ca. 100 l of seawater (combined contents of five Niskin bottles) were passed through a stainless steel cartridge filter (Wolf Technik, Weil der Stadt, Germany), resulting in approximately 1 l retentate with particles larger than 10 μm (**Table 1**). The microbial community of the retentate is operationally defined as the large-particle-associated fraction; this fraction was used for incubation experiments. We note that this operationally defined fraction necessarily contained some bacteria present in the surrounding seawater, since the particles in the retentate were not removed from surrounding water. Microscopy of the retentate revealed that protists (diatoms, dinoflagellates, ciliates, and many others) and ample debris of unknown origin were collected. Since large-particle-associated bacteria concentrated from the euphotic zone may preferentially be associated with phytoplankton cells, which are typically carbohydrate-rich (Parsons et al., 1961), we focused on measuring the potential of the bacteria retained within this fraction to hydrolyze high molecular weight substrates that would likely be associated

\*Data from Gomez-Pereira et al. (2010).

with phytoplankton. Such substrates would include carbohydrates exudates, carbohydrate-containing cellular components, and phytoplankton-derived transparent exopolymeric particles (TEP; Painter, 1983; Passow et al., 1994; Myklestad, 1995). Four different fluorescently labeled polysaccharides (laminarin, xylan, fucoidan, and chondroitin sulfate) were therefore used to measure the activity of extracellular enzymes, after the method of Arnosti (1995, 2003). The polysaccharides were purchased from Sigma or Fluka, labeled with fluoresceinamine (Sigma; Isomer II), and characterized as described in Arnosti (2003). Laminarin (β(1,3) glucose), xylan (β(1,4) xylose), and fucoidan (sulfated fucose) are components of marine algae and phytoplankton (Parsons et al., 1961; Painter, 1983); fucoidan additionally has a chemical composition consistent with TEP (Zhou et al., 1998). Chondroitin sulfate is a marine-derived polysaccharide (a sulfated polymer of galactosamine and glucuronic acid (β-GlcA (1,3)-GalNAc (1,4)) that is rapidly hydrolyzed by heterotrophic microbial communities in seawater and sediments (Arnosti, 2008; Arnosti et al., 2009). Most of these polysaccharides are produced by marine phytoplankton and algae (Painter, 1983; Alderkamp et al., 2007). Moreover, activities of enzymes hydrolyzing all of these substrates have been measured in seawater and sediments (Arnosti et al., 2005a; Arnosti, 2008; Teske et al., 2011), and gene sequences corresponding to enzymes that would hydrolyze these substrate have also been identified in the genomes of recently sequenced marine bacteria (Glöckner et al., 2003; Bauer et al., 2006; Weiner et al., 2008).

At each station, four sets of substrate incubations were prepared: duplicate live incubations (15 ml each) and single killed control incubations (10 ml seawater + 3.5 ml formalin) for each substrate. Each substrate was added at a final concentration of 3.5 μM monomer-equivalent to the incubations. Incubations from S3 and S6 were incubated at 4◦C in the dark in a temperaturecontrolled room; incubations from S12 and S19 were initially incubated at 18–23◦C (in flowing seawater in the lab) and then were incubated at 20◦C (temperature-controlled room) in the dark. Subsamples (ca. 2.5 ml) were removed from each incubation at 0, 5, and 15 days, filtered through a sterile 0.2-μm pore-size filter, the first 1 ml of filtrate was discarded, and the remaining 1.5 ml of the filtrate was stored frozen until analysis. In brief, substrate hydrolysis was determined from the changes in polysaccharide molecular weight with time, as measured using gel permeation chromatography and fluorescence detection, described in detail in Arnosti (2003). Gel columns consisted of a 20 cm × 1 cm column of Sephadex G-50 gel, connected in series to a 18.5 cm × 1 cm column of Sephadex G-75 gel. Mobile phase (phosphate buffer, pH 8.0) was pumped at 1 ml min−1 by a Shimadzu LC-10AT pump; the column outflow passed through a Hitachi L-7480 fluorescence detector, set to excitation and emission wavelengths of 490 and 530 nm, respectively. Hydrolysis rates were calculated from differences in substrate molecular weight at the different time points, as described in detail in Arnosti (2003).

### **FLUORESCENCE** *IN SITU* **HYBRIDIZATION AND CELL COUNTS**

At the end of the 15-day incubation, the remaining incubation water (ca. 7.5 ml) from one of the two live replicates was fixed with particle-free formaldehyde, filtered through polycarbonate 0.2-μm pore-sized filters, and the filters were stored frozen (−20◦C) until analysis. *In situ* identification with the standard catalyzed reporter deposition (CARD) FISH protocol (Pernthaler et al., 2002) and cell counting relative to total cell counts (DAPI) was carried out using the general bacterial probe mix EUB338 I– III (Daims et al., 1999) as well as the group-specific probes CF319a (Manz et al., 1996) and PLA46 (Neef et al., 1998; for a recent update of probe specificity see Amann and Fuchs, 2008) targeting *Bacteroidetes* and *Planctomycetales*, respectively. For two samples, insufficient volume was obtained to make accurate FISH counts, so relative abundances were estimated.

## **RESULTS**

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#### **ENZYMATIC HYDROLYSIS RATES AND PATTERNS**

The four stations sampled represent different North Atlantic provinces, as characterized by distinctive temperature and salinity signatures, as well as nutrient and chlorophyll concentrations (**Table 1**; see Gomez-Pereira et al., 2010 for further details). They also showed distinct patterns and rates of extracellular enzymatic activities. **Figure 1** shows the maximum hydrolysis rate of each substrate at each station, which was measured after either 5 or 15 days incubation. Maximal rates were observed after 5 days of incubation for fucoidan from all stations, for laminarin from all stations except S6, and for xylan from S12 and S19. The maximal hydrolysis rates of xylan from S3 and S6, of laminarin from S6, and of chondroitin at S3 and S6 were observed after 15 days of incubation. Data from both time points (5 and 15 days) are plotted in **Figure 1** because in cases where substrates are hydrolyzed rapidly (e.g., maximum values at 5 days), later time points (e.g., 15 days) typically show a lower calculated hydrolysis rate, reflecting the fact that hydrolysis was nearly complete at the earlier time.

The differences in timescales over which hydrolysis rates reach a maximum rate reflect the activity and distribution of extracellular enzymes within a given community. Since hydrolysis of fluorescently labeled polysaccharides is detected as a change in the molecular weight distribution of the entire pool of added substrate (see Arnosti, 1995, 2003 for more details), extended incubations (timescales of days) are typically required to measure activity in pelagic samples. An enzyme activity that is low or is uncommon among a community is first observable after a more extended period of incubation, while enzyme activities that are intrinsically rapid or are widespread among a given community may be observed at an earlier time point. Since these timescales of measurement (days) allow sufficient time for community growth as well as enzyme expression, enzyme activities measured using this technique represent the potential of a community to react to substrate input – e.g., by induction of genes and/or by growth of a numerically small or slow-growing members of the community – rather than reflecting only the activity of enzymes already present at the time substrate is added to the sample. We note also that in this incubation, the time points were quite widely spaced; maximum hydrolysis rates might have been

**FIGURE 1 | Enzymatic hydrolysis rates of four polysaccharides in 10** μ**m retentate from each station.** Chon, chondroitin sulfate; fu, fucoidan; lam, laminarin; xyl, xylan. Bars show standard deviation of two replicate incubations. Hydrolysis rates are maximum rates that were measured after 5 days incubation for all substrates except chondroitin, laminarin at S6, and xylan at S3 and S6, which were from 15 days incubation (see text).

reached prior to 5 or 15 days, so the rates reported here could be underestimated. We also note that these measurements necessarily represent the potential of the microbial community present in the sample at the time of collection. Samples collected at different seasons could show different patterns, depending on larger-scale factors that could drive seasonal variation in microbial community composition.

## **HYDROLYSIS PATTERNS BY STATION**

As a pair, S3 and S6 resembled each other in hydrolysis rates and patterns. At these stations, all four substrates were hydrolyzed by the large-particle-associated microbial fraction. Hydrolysis rates of chondroitin were quite high; the hydrolysis rates of the other substrates were for the most part within a factor of ca. 1–4 of the chondroitin hydrolysis rate. At S12 and S19, in contrast, hydrolytic activity was highest for laminarin, which was very rapidly hydrolyzed (14.6 and 14.9 nmol l−1 h−1, respectively). Hydrolysis rates of fucoidan and xylan at S12 and S19 were 5–20 times lower than that of laminarin, and there was no indication of chondroitin hydrolysis in the large-particle-associated fraction from these stations.

#### **MICROBIAL COMMUNITY COMPOSITION**

The composition of the microbial communities at the conclusion of the incubation was investigated using CARD-FISH. The general bacterial probe mixture EUB338 I–III hybridized to 75–99% of all DAPI-stained cells. Of these bacteria, a substantial proportion was assigned to *Bacteroidetes* with probe CF319a: on average, 26, 22, 15, and 17% for S3, S6, S12, and S19, respectively (**Table 2**). Counts with the probe PLA46 specificfor *Planctomycetes*were low or below the detection limit at all stations (data not shown), serving also as a negative control for unspecific staining and autofluorescence. For any given station, there was no consistent relationship between hydrolysis rate of a specific substrate and fraction of cells detected by CF319a.

### **DISCUSSION**

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The enzymatic complement of heterotrophic bacteria is variable even among closely related organisms (Gao et al., 2003;

**Table 2 | CF319a hybridized cells, as % DAPI-stained cells, after 15-day incubation with substrate.**

Lam, laminarin; xyl, xylan; fu, fucoidan; chon, chondroitin sulfate.

\*Estimated abundance: volume too low to count precisely (see text). These values not included in the average.

\*\*Bulk water: CF319a-stained cells (as % of DAPI-stained cells) from a depth of 20 m at each station; data from Gomez-Pereira et al. (2010).

Gomez-Pereira et al., 2012), so the nature and type of substrate that can be accessed by a given organism is quite specific, as has been demonstrated by microbiological and genomic investigations of a variety of prokaryotes (e.g., Martinez et al., 1996; Ensor et al., 1999; Glöckner et al., 2003; Bauer et al., 2006; Weiner et al., 2008). Some bacteria are able to use low molecular weight hydrolysis products although they cannot effectively hydrolyze the initial substrates (Cotta, 1992), demonstrating the necessity for close interactions among specific bacteria within a community. The extent to which substrate preference patterns extend from specific organisms to entire microbial communities, however, is only beginning to be explored in marine environments. Previous studies of polysaccharide hydrolysis by pelagic microbial communities demonstrate site-specific differences in extracellular enzymatic hydrolysis rates and patterns (Arnosti et al., 2005a; Steen et al., 2008) as well as large-scale latitudinal gradients in the spectrum of polysaccharide hydrolases activities in surface ocean waters (Arnosti et al., 2011). The observation that enzymatic hydrolysis patterns measured here clustered into two groups, consisting of the northern BPLR/ARCT stations S3 and S6, and the more temperate NADR/NAST stations S12 and S19 (**Figure 1**) suggests that there are biogeographic patterns in enzymatic capabilities also among the large-particle-associated fraction of microbial communities.

These functionally distinct communities also differed in composition, as shown by FISH staining of whole water (unsorted) as well as flow cytometry-sorted samples collected concurrently with the present study. Although members of the SAR11 were the single largest constituents of the unsorted population, contributing 25–32% of the total stained cells at a depth of 20 m at all four stations, the contribution of other prokaryotic cells was quite variable by station (Schattenhofer et al., 2011; **Figure 2**). CF319astainable cells (*Bacteroidetes*) contributed a large fraction of the identifiable cells, Syn405 (*Synechococcus*) counts were significant only in S3 and S6, while Pro405 (*Prochlorococcus*) counts were significant only at S12 and S19 (**Figure 2**; Schattenhofer et al., 2011). The fundamental factors shaping these differences in microbial community composition remain to be determined (Hanson et al., 2012); as noted by Schattenhofer et al. (2011), specific groups of bacterioplankton were statistically correlated with physical, chemical, and biological parameters, in agreement with investigations of other microbial communities at other locations (Kan et al., 2006; Gilbert et al., 2012). In any case, the general trend of a north to south decrease in nutrient and chlorophyll *a* concentrations as well as picoeukaryotic abundance and prokaryotic counts at S3, S6, S12, and S19 tracks the changes in prokaryotic community composition and extracellular enzyme activities measured at the four stations (**Table 1**; **Figure 2**).

Compositional distinctions among the microbial communities at these four stations were supported also by more detailed analysis of bacteroidetal communities, carried out on the filtrate that had passed through the 10 μm cartridge. A focus on members of the class Flavobacteria of the phylum *Bacteroidetes* is especially relevant due to their proven abilities to degrade organic macromolecules in marine waters (Kirchman, 2002; Bauer et al., 2006), as well as their abundance at these stations and in our incubations (**Table 2**). Analysis of 16S rRNA gene clone libraries and direct

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cell counts via CARD-FISH demonstrated that the flavobacterial composition of S3 and S6 had considerable overlap, sharing 25– 40% of flavobacterial phylotypes, while S3 and S18 were mostly unrelated, sharing just 2–5% of flavobacterial phylotypes (Gomez-Pereira et al., 2010). Flavobacteria are a major target of FISH probe CF319a, which comprised a substantial proportion (5–31%) of cells in bulk seawater (Gomez-Pereira et al., 2010) as well as 15–26% of the post-incubation large-particle-associated fraction from these stations (**Table 2**). Since the complement of glycosyl hydrolase genes in fully sequenced members of the *Bacteroidetes* differs substantially (Gomez-Pereira et al., 2012), these differences in flavobacterial community composition indicate the potential for functional differences.

Evidence of biogeographic patterns in polysaccharide hydrolase activities (**Figures 1 and 2**) is also supported by the results of a metagenomic investigation based on analysis of *Bacteroidetes*associated fosmids obtained from S3 and S18, the same samples Gomez-Pereira et al. (2010) used for clone libraries and CARD-FISH staining. Fifteen glycosyl hydrolase-associated genes were identified in the fosmids, some of which included signal sequences that predicted enzyme export to the outer membrane (Gomez-Pereira et al., 2012), where they could hydrolyze extracellular polysaccharides. At S3, genes corresponding to sulfatases (used to remove sulfate groups from polysaccharides) were also identified; most of these enzymes were likewise predicted to be exported from the cytoplasm (Gomez-Pereira et al., 2012), and thus to play a role in extracellular hydrolysis. Sulfatase genes were also identified at S18, in lower numbers than at S3. Overall, the relative content of glycosyl hydrolases and sulfatases at S3 was higher than at S18; differences in gene abundance were correlated with the abundance of the organisms from which the fosmid sequences were derived (Gomez-Pereira et al., 2012). The current study thus provides evidence of functional differences that are implied by the genomic and community population data from samples collected concurrently from these same stations (Gomez-Pereira et al., 2010, 2012; Schattenhofer et al., 2011).

Although differences in patterns of enzymatic hydrolysis (**Figures 1 and 2**) suggest fundamental differences in function of the large-particle-associated fraction of the community, information about relative gene abundance at different stations (Gomez-Pereira et al., 2012) cannot be linked directly to specific rates of hydrolysis. The extent and conditions under which a given gene is expressed in the environment are unknown; moreover, the kinetic characteristics of the enzymes themselves as well as the quantity of enzymes produced contribute to observed hydrolysis rates. Furthermore, the hydrolysis rates measured in this study may be affected by the fraction of large particles retained by the 10 μm cartridge filter, as well as by the extent of particle colonization by bacteria at each station. The DNA extractions of Gomez-Pereira et al. (2010, 2012) were carried out on the fraction of water that passed through the 10 μm cartridge, i.e., on the filtrate, rather than the retentate, and thus specifically excluded the large-particle-associated fraction used to measure enzyme activities in the current study. The retentate fraction, however, was isolated within a background of retained seawater (i.e., particles were not isolated from their surrounding solution), and therefore also contained some cells that were present in the last fraction

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to pass into the cartridge filter. Substrate hydrolysis rates showed no systematic relationship with concentration factor (**Table 1**), also indicating that the differences observed among stations were not an outcome of sample manipulation. Furthermore, Schattenhofer et al. (2011) did not prefilter their samples, and the population they studied (**Figure 2**) would thus also include the fraction defined here as large-particle-associated bacteria. In any case, any differences between stations in bacterial cell numbers of the large-particle-associated fraction would be reflected primarily in differences in the rates of hydrolysis, and not in the patterns of substrates hydrolyzed. Different patterns of substrate hydrolysis point instead at functionally different communities.

The patterns evident at these stations additionally demonstrate that enzymatic hydrolysis rates in pelagic waters are not a simple function of environmental temperature. Although *in situ* as well as incubation temperatures at S12 and S19 were substantially warmer than at S3 and S6, only laminarin hydrolysis rates appear to track temperature. Hydrolysis rates of fucoidan, chondroitin, and xylan, in contrast, were comparable to or higher at the colder stations (S3/S6) than at the warmer stations (S12/S19). A correlation of laminarin hydrolysis with temperature, and little temperature correlation for other polysaccharides, is in fact a pattern consistent with enzyme activities in surface ocean waters on latitudinal gradients (Arnosti et al., 2011).

The lack of measurable chondroitin hydrolysis at S12 and S19 is particularly interesting in light of the observation that it is one of the activities frequently measured in surface ocean waters (Arnosti et al., 2011). At these stations, hydrolytic activity may be associated with the free living or small-particle-associated microbial fraction that would not have been retained by a 10-μm filter. This activity may also simply be missing in this biogeographic province, since evidence to date suggests that enzymatic capabilities are non-uniformly distributed in the surface ocean (Arnosti et al., 2005a, 2011; Steen et al., 2008), and polysaccharide hydrolase gene distribution varied among these stations (Gomez-Pereira et al., 2012). Conversely, fucoidan hydrolysis at all four stations is also notable, since it is an activity that is relatively infrequently measured in surface ocean waters (Arnosti et al., 2011), and perhaps is primarily associated with large-particle-associated bacteria. Both chondroitin and fucoidan are sulfated polysaccharides, and their rates and extent of utilization may also be related to the activities of sulfatase enzymes whose genes were identified in the fosmids from these stations (Gomez-Pereira et al., 2012). The pattern of more rapid chondroitin hydrolysis upon extended incubation (i.e., 15 days) is consistent with previous observations of chondroitin hydrolysis in marine waters and sediments, as well as experiments indicating that chondroitin hydrolysis is induced in marine bacteria (Arnosti, 2004).

Our CARD-FISH identifications focused on the potential enrichment of two bacterial phyla that had been linked to degradation of (sulfated) polysaccharide, *Planctomycetales* (Glöckner et al., 2003; Woebken et al., 2007) and *Bacteroidetes* (Kirchman, 2002; Bauer et al., 2006). Whereas we could not detect significant numbers of *Planctomycetales* with probe PLA46, there was consistently a strong contribution of CF319a-positive bacteroidetal cells in the large-particle-associated fraction post-incubation (**Table 2**). This result is consistent with the observation that members of the phylum *Bacteroidetes* – and in particular those of the class *Flavobacteria* – are frequently associated with phytoplankton blooms (Gomez-Pereira et al., 2012; Teeling et al., 2012). FISH staining showed somewhat higher average counts of CF319astainable cells at S3/S6 compared to S12/S19, a result likely due

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to initial differences in phytoplankton (**Figure 2**; Gomez-Pereira et al., 2010) as well as to microbial growth in response to substrate addition. Specific differences in functional potential of these organisms, however, became evident only through direct measurement of extracellular enzyme activities, using an experimental approach that provides information about structural specificities of these enzymes. This investigation is the first to specifically focus on the hydrolytic capabilities of large-particle-associated bacteria to hydrolyze these substrates; it is also the first investigation to combine FISH staining directly with these measurements. These communities evidently exhibit distinct patterns in enzyme activities, as has been observed for unfiltered surface and subsurface waters (Arnosti et al., 2005a, 2011; Steen et al., 2008, 2012). The extent to which patterns of enzyme activities may differ for large particles vs. whole water should be a focal point of further work, as should the extent to which such patterns may change through annual cycles of phytoplankton and bacterial succession (Allison et al., 2012; Gilbert et al., 2012). Such investigations will help us define more precisely at a functional level the contributions of specific microbial communities to carbon processing in the ocean.

## **ACKNOWLEDGMENTS**

We thank the captain, crew, and scientific party of R/V *Maria S. Merian* for a successful cruise, and Antje Wichels and Gunnar Gerdts help in sample collection. We are particularly grateful to Jörg Wulf and Birgit Rattunde for their meticulous efforts to obtain FISH data from minimal sample volume. This project was supported by the Max Planck Society. Additional support to Carol Arnosti was provided by the U.S. National Science Foundation (OCE-0323975 and OCE-0848703), as well as by the Alfred Wegener Institute for Polar and Marine Research, and the Hanse-Wissenschaftskolleg (Delmenhorst). Additional support to Uta Passow was provided by the U.S. National Science Foundation (OCE-0926711 and OCE-1041038).

and seawater microbial communities: contrasts in microbial hydrolysis of complex substrates. *FEMS Microbiol. Ecol.* 66, 343–351.

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bacterioplankton in response to dissolved organic carbon. *Environ. Microbiol.* 12, 616–627.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 29 July 2012; accepted: 27 November 2012; published online: 13 December 2012.*

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*Citation: Arnosti C, Fuchs BM, Amann R and Passow U (2012) Contrasting extracellular enzyme activities of particle-associated bacteria from distinct provinces of the North Atlantic Ocean. Front. Microbio. 3:425. doi: 10.3389/ fmicb.2012.00425*

*This article was submitted to Frontiers in Aquatic Microbiology, a specialty of Frontiers in Microbiology.*

*Copyright © 2012 Arnosti, Fuchs, Amann and Passow. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

#### *Mallory J. Choudoir***--***| , Ashley N. Campbell* **--***| and Daniel H. Buckley\**

Department of Crop and Soil Sciences, Cornell University, Ithaca, NY, USA

#### *Edited by:*

Ashley Shade, Yale University, USA

#### *Reviewed by:*

Jennifer F. Biddle, University of Delaware, USA Maureen Coleman, University of Chicago, USA David Baltrus, University of Arizona, USA

#### *\*Correspondence:*

Daniel H. Buckley, Department of Crop and Soil Sciences, Cornell University, 705 Bradfield Hall, Ithaca, NY 14853, USA. e-mail<EMAIL_ADDRESS>

The emerging fields of microbial population genetics and genomics provide an avenue to study the ecological rules that govern how communities form, function, and evolve. Our struggle to understand the causes and consequences of microbial diversity stems from our inability to define ecologically and evolutionarily meaningful units of diversity. The 16S rRNA-based tools that have been so useful in charting microbial diversity may lack sufficient sensitivity to answer many questions about the ecology and evolution of microbes. Examining genetic diversity with increased resolution is vital to understanding the forces shaping community structure. Population genetic analyses enabled by whole genome sequencing, multilocus sequence analyses, or single-nucleotide polymorphism analyses permit the testing of hypotheses pertaining to the geographic distribution, migration, and habitat preference of specific microbial lineages. Furthermore, these approaches can reveal patterns of gene exchange within and between populations and communities. Tools from microbial population genetics and population genomics can be used to increase the resolution with which we measure microbial diversity, enabling a focus on the scale of genetic diversity at which ecological processes impact evolutionary events. This tighter focus promises to improve our understanding of the causes and consequences of microbial community structure.

#### **Keywords: microbial population, ecology, biogeography, evolution, microbial diversity**

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## **INTRODUCTION**

According to Greek mythology, Proteus knew of all things past, present, and future, but only shared this knowledge if captured. When pursued he would change shape, and the closer his pursuer the more quickly he changed, his knowledge just beyond grasp. Our efforts to define the causes and consequences of microbial community structure are like grappling with Proteus. A rigorous framework for understanding microbial community structure remains beyond our grasp because our concepts of what constitutes a community, a species, or a population are ill-developed and fungible. We argue that our comprehension of microbial communities is severely hampered by our inability to recognize ecologically and evolutionarily meaningful units of diversity. We believe observations from microbial population genetics and genomics promise to change the manner in which we define microbial diversity. Population genetics and population genomics both focus on the evolutionary dynamics of populations. While these approaches employ different analytical techniques, conceptually they represent points along a continuum, the differences being the amount and type of genetic information used to make evolutionary inference. Population-based approaches represent a powerful new paradigm for exploring the fundamental units of community structure.

The ribosomal RNA (rRNA) paradigm has been invaluable in charting the diversity of the microbial world by providing both a phylogenetic framework for understanding microbial diversity and tools for characterizing microbial communities without the biases imposed by cultivation. The remarkable conservation of

rRNA gene sequences makes them excellent for determining phylogenetic relationships between diverse microbial assemblages. However, as a result of their high conservation these molecules are insensitive to evolutionary changes that occur in response to ecological dynamics. As the field of microbial ecology matures, we increasingly seek to understand the ecological rules that govern how communities assemble, function, and evolve. The rRNAbased tools that have fueled the growth and development of our field may be poorly suited to answering many of the ecological questions we now face.

The most common unit of diversity employed in analyses of microbial community structure is the operational taxonomic unit (OTU) based on the 16S rRNA gene. An OTU is generally defined as a group of sequences that differ by less than 3% of nucleotide positions in the 16S rRNA gene (OTU0.03; Hughes et al., 2001). This cutoff is based on current criteria for defining microbial species (Wayne et al., 1987). In other fields of biology, a "species" is considered a distinct and coherent evolutionary unit in terms of ancestry, range, or ecological function (Coyne and Orr, 2004). The current microbial species concept, however, is primarily based on the needs of taxonomic analysis. This taxonomic species concept was established before the use of molecular sequence data and before phylogenetic analysis of microorganisms was possible. Sequence data was applied to the microbial species framework in a *post hoc* manner as a way of supporting the pre-existing species definition. Hence, the foundation that underpins the OTU0.03 does not have a firm ecological basis and we should consider carefully the range of ecological and evolutionary hypotheses

that can be addressed effectively on the basis of this unit of diversity.

The nascent field of microbial biogeography provides a compelling example of the limitations that the current microbial species concept imposes on our understanding of microbial diversity. As demonstrated by *Escherichia coli* and *Salmonella enterica*, 2.8% divergence of 16S rRNA between species can take approximately 63–120 million years (Ochman et al., 1999). Hence, strains within an OTU0.03 may have shared a most recent common ancestor during the early Cretaceous, a period when many of the Earth's continents were still joined. Consider for a moment the consequences if this unit of diversity were employed in the study of plants and animals. For example, all species in the tortoise family have diverged in the last 50 million years, and thus, if studied using units of diversity that have the same sensitivity as the OTU0.03, would represent a single globally distributed taxonomic unit. Were Darwin to have used this definition of diversity, he would have observed only one type of tortoise in the Galapagos, and we would all be the poorer for it. It should not surprise us that this unit of diversity is poorly suited to resolve current patterns of microbial biogeography.

The use of rRNA-based OTUs obscures the recent evolutionary history of microbial lineages. We know that organisms with exactly the same 16S rRNA gene sequence can share as few as 38% of the genes in their genomes (Welch et al., 2002; Tettelin et al., 2005; Hall et al., 2010), and that organisms with the same 16S rRNA gene sequence can have different ecological characteristics (Jaspers and Overmann, 2004). Units of diversity defined by rRNA genes are valuable in terms of discovering and characterizing new lineages, charting the scope of microbial diversity, and resolving evolutionary relationships at temporal scales ranging approximately from 107 to 109 years. However this unit of diversity is not well suited to addressing ecological and evolutionary processes, such as dispersal and speciation, which operate at timescales of less than 106 years.

A focus on genetic diversity at a smaller phylogenetic scale than the OTU0.03 is vital to understanding the forces that govern community structure. Rather than attempting to define the appropriate units for studying microbial diversity *a priori*, we should currently be making observations of the patterns of genetic diversity that exist in nature. This can be achieved by focusing on groups of closely related strains and adopting a flexible and methods-free concept of microbial populations: a group of organisms characterized by a genetic, spatial, temporal, or ecological boundary. Populations of isolates can be studied through multilocus analyses which provide greater phylogenetic resolution than 16S rRNA sequence analyses and which allow for estimation of recombination rates. Genomic analyses, including analyses of single-nucleotide polymorphisms (SNPs) or whole genome sequences, provide even greater phylogenetic resolution and the ability to explore patterns of gene exchange and signatures of selection. Population genetics and genomics now provide a solid foundation to study evolutionary dynamics at the sale of ecological interactions and a framework for addressing specific questions such as: (1) How are microbial lineages distributed spatially, and what are the roles of migration and local adaptation in defining the genetic and functional characteristics of communities? (2) How do patterns of gene flow vary with respect to the genetic and geographic distance between strains? (3) How do population dynamics influence community dynamics and ecological processes? The following is a brief overview of how our understanding of microbial ecology may be enhanced by taking a population genomics approach.

## **IMPLEMENTING POPULATION-LEVEL APPROACHES**

The forces governing microbial biogeography can be best evaluated at fine scales of genetic diversity (Pearson et al., 2009; Vogler et al., 2009), and *Bacillus anthracis* provides a case study to demonstrate this point (Kenefic et al., 2009). The potential use of *Bacillus anthracis* in terror attacks created a need to distinguish naturally occurring strains from those used as biological weapons and to understand the genetic diversity within this species. Multiple *Bacillus anthracis* genome sequences were used to identify canonical SNPs that resolve branching points in the phylogeny of the species, and these SNPs were subsequently used to explore the origins of *Bacillus anthracis* in North America (Kenefic et al., 2009). The introduction of anthrax to North America was hypothesized to have occurred along the US Gulf Coast by infected European cattle during the colonial period of American history. However, analysis of SNPs in 285 geographically diverse isolates from North America indicates a Eurasian ancestor originating from the north, likely entering the continent along the Bering land bridge and introduced by ungulate migrations during the last ice age (Kenefic et al., 2009). Through this approach, we see that discernible patterns of microbial biogeography were established as a result of dispersal at a temporal scale of thousands of years. These patterns could only be observed through genome-level analyses.

In another example, Pearson et al. (2009) reconstructed the evolutionary history of *Burkholderia pseudomallei* and *Burkholderia mallei* using >14,000 orthologous SNPs from 33 whole genome sequences of *Burkholderia pseudomallei* and *Burkholderia mallei* sampled across Australia, Southeast Asia, and the rest of the world. This phylogeny was supplemented with data from multilocus sequence analysis (MLSA) of >1,700 global *Burkholderia* isolates. Population structure of these isolates supports the existence of two geographically distinct *Burkholderia pseudomallei* subpopulations, originating in Australia and Southeast Asia and separated by the Wallace Line, a geographical pattern well-documented in macroorganisms. The deeply branching Australian *Burkholderia pseudomallei* group was determined to be the most genetically diverse lineage, thus, representative of the ancestral gene pool. Molecular clock estimates suggest these *Burkholderia pseudomallei* populations diverged between 16,000 and 225,000 years ago (Pearson et al., 2009). The biogeographical patterns observed in *Burkholderia* underlie patterns of genome differentiation and ultimately govern the origins of diversity within the genus. These genetic boundaries would be overlooked if sequence analyses were restricted to rRNA genes.

*Helicobacter pylori* inhabits the stomachs of over half the world's human population and provides another useful case study. MLSA of 769 *H. pylori* isolates representing 51 distinct human populations identified six extant subpopulations of *H. pylori*. These ancestral populations correlate well with geographical regions at a global scale. Extant strains of *H. pylori* demonstrate distinct

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patterns of ancestral admixture influenced by the geographic origin and mixing of hosts (Linz et al., 2007). There is an inverse relationship between genetic diversity and geographic distance from East Africa in both *H. pylori* and its human host, and thus *H. pylori* dispersal patterns are believed to mirror human migration patterns from East Africa approximately 58,000 years ago (Linz et al., 2007).

These studies demonstrate that patterns of microbial biogeography, veiled in analysis of 16S rRNA genes, become evident through more sensitive genetic analyses. In the case of *Bacillus anthracis*, we learn that European strains did not routinely colonize and persist in North America, despite multiple introductions over hundreds of years during the time of European settlement (Kenefic et al., 2009). This raises interesting new questions about the factors that govern the competitive fitness of strains introduced to new habitats. Issues of dispersal and colonization are critical for understanding constraints on community structure. An advantage of population genetics and genomics is that they provide a route for investigating microbial biogeography and also provide data that can be used to explore the ecological adaptations that impact colonization success and ultimately the environmental distribution of species.

Analyses of strain collections spanning discrete sites make it possible to determine how ecological traits map onto the evolutionary history of a lineage (Connor et al., 2010; Becraft et al., 2011; Preheim et al., 2011). For example, using an approach to map habitat traits onto microbial phylogeny, ecological populations within coastal *Vibrio* isolates can be predicted based on seasonal occurrence and particulate size fractionation (Hunt et al., 2008; Preheim et al., 2011). Populations adapted to a free-living lifestyle can be distinguished from those adapted to living on the surface of organic matter particulates, or on the surface of phytoplankton (Preheim et al., 2011). This approach has also been used to identify ecological populations in *Bacillus* (Connor et al., 2010) and *Synechococcus* (Becraft et al., 2011). Both solar exposure and soil texture are important predictors of ecological populations among the *Bacillus subtilis*–*Bacillus licheniformis* clade (Connor et al., 2010), while ecological populations of *Synechococcus* correspond with gradients of temperature and depth in microbial mats (Becraft et al., 2011). Recognizing the existence of meaningful ecological units is the first step to understanding both the ecological factors that govern the spatial and temporal dynamics of microbial communities and the evolutionary dynamics that govern the origins and maintenance of microbial diversity.

An advantage of using population genomics over single or multilocus methods is the ability to evaluate the impact of horizontal gene transfers (HGTs) on microbial evolution and ecology. HGT can blur the lines of ancestry between lineages, shuffling adaptive genes, and HGT may prevent the development of genetically and ecologically cohesive populations (Fraser et al., 2009; Shapiro et al., 2012). Genomic studies provide evidence that patterns of gene exchange may be controlled by propinquity, with local adaptation facilitated by sampling genes from the environment. For example, environmental co-localization governs exchange of integron cassettes in *Vibrio* species regardless of species boundaries (Boucher et al., 2011), while, interspecies exchange of core genes is not observed (Boucher et al., 2011). Likewise, Caro-Quintero et al. (2011) demonstrated that *Shewanella baltica* isolates in the Baltic Sea exchange genes more frequently with isolates found at similar depths than with isolates at different depths. Lastly, gene flow in the thermoacidophilic crenarchaeon *Sulfolobus islandicus* is influenced by geographical isolation, driving patterns of speciation (Whitaker et al., 2003; Reno et al., 2009). These observations suggest that evolutionary processes may vary between microbial lineages and even between core and auxiliary genes (Riley and Lizotte-Waniewski, 2009; Léfebure et al., 2010). In the case of *Shewanella baltica*, as much as 20% of the genome was inherited from co-localized strains (Caro-Quintero et al., 2011), suggesting that ecological interactions can have strong impacts on genome dynamics.

At a gross level, it is clear that the composition of a microbial community has strong impacts on environmental biogeochemistry, though the biotic and abiotic mechanisms that link community structure and function remain poorly described. If we want to understand community structure–function relationships at a fundamental level, we need to start with measurements of diversity that capture adaptive differences within and between lineages. We need to understand how the genomic diversity within a lineage impacts ecological function and is distributed in the environment, how evolutionary and ecological forces regulate gene exchange, and how patterns of gene exchange within and between lineages impact community function. These inquiries can be enabled by studying populations as the fundamental units from which communities are constructed.

## **WHERE DO WE GO FROM HERE?**

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Microbial population genetics and genomics are opening avenues for understanding the ecological and evolutionary mechanisms governing microbial diversity. However, a focus on the dynamics of populations and species still faces several formidable obstacles. Foremost is the current lack of coherent and pragmatic definitions for populations and species. Without such criteria it is difficult to objectively compare results between studies. As our understanding of microbial populations improves, it will be important to develop objective criteria for defining lineages that will be relevant and applicable across a wide range of microorganisms.

Another challenge to the application of microbial population genomics is a lack of information about how to properly sample the genetic diversity of microbial populations, both in terms of spatial scale and numbers of strains. Rigorous population studies require robust genetic sampling across appropriate spatial, temporal, or habitat scales in order to achieve the ultimate goal of accurately depicting patterns of biodiversity existing in nature. Sufficient individuals must be sampled to represent the breadth of genetic diversity across an organism's geographic range and to capture genetically informative loci that reflect its evolutionary history. What spatial scales best capture the genomic diversity of microbial populations? Over what spatial and temporal scales does a microbial cell sample genetic material from its environment? What scales are most suitable for inferring adaptive traits from an organism's habitat distribution? How do we define a microhabitat, or an ecological niche? What environmental parameters should be measured as part of the sampling design? All of these questions are fundamental and must be addressed as we move forward.

An obvious limitation to population genomic approaches is the need for strains to be cultivated in isolation. While most microorganisms have yet to be cultivated, much progress can still be made with organisms that we can cultivate now. Model systems can be developed and used to explore the evolutionary and ecological mechanisms that regulate microbial diversity. Once revealed, these mechanisms can be used to make predictions relevant to organisms more recalcitrant to cultivation. In addition, a solution to the cultivation problem is available through application of single-cell genomics. Single-cell methods of genome analysis can be used to perform genome sequencing or multilocus analysis on individual microbial cells without the need for cultivation (Stepanauskas and Sieracki, 2007; Swan et al., 2011; Tadmor et al., 2011; Martinez-Garcia et al., 2012). Metagenomics offers another avenue through which theory developed through microbial population genetics and genomics may be applied to more complex communities without the need for cultivation (Allen and Banfield, 2005; Simmons et al., 2008; Dick et al., 2009; Morowitz et al., 2011; Denef and Banfield, 2012; Narasingarao et al., 2012).