We are confident that the counter-intuitive nature of our findings cannot be attributed to the limitations of DGGE. Fingerprinting methods like DGGE are regarded as inferior to the new generation of sequencing techniques. It is often forgotten that DGGE, like next generation sequencing, uses a large sample size (about 10,000 individuals). Studies with next generation sequencing have suggested this sample size is enough to make valid comparisons between datasets. The weakness of DGGE is that it can only detect those taxa that occur above an abundance threshold of about 1% of the community under consideration (Muyzer et al., 1993). The implicit assumption in this study is that a change in diversity is associated with a change in the number of taxa above this threshold. The association between evenness and the number of taxa detected supports this assumption. Moreover, some large-scale patterns first detected with fingerprinting methodologies (Fierer and Jackson, 2006) have been found to be substantially unaltered when re-examined with larger datasets (Lauber et al., 2009).

Thus it is gratifying, but not interesting, that creating temporal variation (using a SBR) and increasing the number of individuals (by increasing the organic load) both appeared to increase diversity. Both results seem intuitively reasonable from a niche and neutral perspective, respectively. That both perspectives appear valid strengthens our assertion, supported by recent modeling work, that niche effects will overlay neutral ones (Ofiteru et al., 2010). However, it is this relatively bland interpretation that emphasizes the challenge and importance of attempting to quantify the relative importance of these mechanisms, which we are unable to do with data in this format.

The failure of immigration to increase diversity is not, on reflection, surprising. The organisms in the soil were at or below the detection limit for DGGE and so will not have been directly detectable unless they increased in abundance in the system. Ecologists recognize that it is difficult to "invade"mature communities and haveformulated the monopolization hypothesis to accountfor this phenomenon (De Meester et al., 2002). There is evidence that immigration occurs at low rates in microbial communities subject to high rates of dispersal (Curtis et al., 2006; Baptista et al., in preparation). This phenomenon may well underlie the poor reputation of bio-augmentation in many quarters. The slight drop in the diversity associated with the addition of soil is harder to interpret from a neutral perspective. There are possible nichebased explanations. For example, the decreased diversity in the glucose treatment could be a result of competition from fewer, but more competitive species from the soil. Alternatively, it might be attributable the small quantity of sodium cholate used to suspend the soil.

However, the failure of a feed with a more complex chemical composition to support greater diversity is problematic. We do not believe this is an artifact, as we have also recently failed to increase diversity by increasing resource complexity in a bioelectrochemical cell (Heidrich, submitted). We are unable to conceive of a simple niche-based explanation though we admit that multiple resource can have complex effects (Lendenmann and Egli, 1998). A NCM requires that we invoke either a more diverse source community, higher rate of migration or a larger local community. We have no

reason to invoke a higher immigration rate in glucose fed reactors. However, the biomass was slightly and significantly (Pholchan et al., 2010) higher with glucose (which has a slightly higher free energy) than with complex wastes, even though the COD of the feeds were the same. This suggests that the yields are higher for organisms growing on glucose than on complex wastes. The larger amount of biomass could lead to a higher diversity. However, we have previously postulated that groups of organisms with higher yields will also have higher evolution rates and thus higher source diversities (Curtis et al., 2008). To separate these two possibilities it would be necessary to fit a neutral model to our findings. This is not possible with the data we have generated.

To interpret the ostensibly contradictory and counterintuitive findings with respect to diversity and EDC removal, we must recall that the functions rendered by a microbial community will depend on the abundance of the organisms performing that function and the environment encountered. It is clear from the simple community assembly model that a drop in evenness in a microbial community, which would be associated with a drop in observed richness, could also lead to an increase in the abundance of certain taxa. This would explain why EDC removal increased when diversity decreased in reactors fed with complex wastes or dosed with a soil suspension. The corollary that increases in diversity lead to a reduction in EDC removal was (i) perceptible at higher organic loading rates where the removal of E3 was lower (with no effect being seen with E1, E2, or EE2) but (ii) absent in higher diversity SBRs. However, SBRs are effectively plug flow reactors and thus far more hydraulically efficient than CSTRs. Consequently, the former (SBRs) may achieve higher effluent quality than the latter (CSTRs), even with an inferior removal rate.

We conclude that it is not possible to make simple blanket statements about the relationship between putative rare functions and diversity. This supports earlier insightful suggestion that ecologists should not always assume that increased biodiversity always means improved function (Jiang et al., 2008) and warn against extending findings on biomass and diversity (Tilman et al., 1997) to other functions.

If our interpretation is correct, this relationship may well depend on the proportional abundance of the organisms with that function in the metacommunity, the size of the local community and the immigration rates that connect the former and the latter. It will thus be predictable using neutral theory, but not intuitively obvious and, of course, subject to other environmental constraints. The application of this approach would require that we eschew the nebulous term rare and define the proportional abundance, and possibly activity, of the taxa responsible for the removal of a given micropollutant. This is a study of a series of pilot plants seeded from just one "mother" plant and we cannot be sure how generalizable our findings are. However, neutral theory (if applicable) would allow us to determine "a priori" how generalizable our findings by relating the probability of a function occurring to the proportional abundance of the organisms with that function in the metacommunity.

The niche and neutral perspectives are not mutually exclusive. One might also explain the patterns in these relatively rare functions by invoking the appearance and disappearance of niches for the EDC degrading organisms. However, we have no evidence to

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support this perspective which, if true, could mean the presence and absence of functions is situation bound.

In summary we suggest that, in this system, an increase in evenness equates with a decrease in abundance of some abundant taxa and an increase in abundance in some rare taxa. This is why a change in evenness can lead to a decrease in the removal of some EDC and an increase in others. Unless and until we know the proportional abundance of the EDC removing taxa we will not be

## **REFERENCES**

responses of a sewage microbial community to dilution-induced reductions in diversity. *Microb. Ecol.* 52, 280–288.

able to determine the effect of diversity on EDC removal. Unless we know the mechanism governing the diversity (niche or neutral or both) we will not be able to predict EDC removal.

#### **ACKNOWLEDGMENTS**

We thank Diana Nemergut and the excellent anonymous reviewers of this manuscript for their thoughtful and perspicacious comments.

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roles of immigration and chance in shaping prokaryote community structure. *Environ. Microbiol.* 8, 732–740.

evenness favours functionality under selective stress. *Nature* 458, 623–626.

Woodcock, S., van der Gast, C. J., Bell, T., Lunn, M., Curtis, T. P., Head, I. M., et al. (2007). Neutral assembly of bacterial communities. *Fems Microbiol. Ecol.* 62, 171–180.

Zang, K., Kurisu, F., Kasuga, I., Furumai, H., and Yagi, O. (2008). Analysis of the phylogenetic diversity of estrone-degrading bacteria in activated sewage sludge using microautoradiography-fluorescence in situ hybridization. *Syst. Appl. Microbiol.* 31, 206–214.

**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: 08 August 2012; accepted: 11 March 2013; published online: 01 May 2013.*

*Citation: Pholchan MK, Baptista JC, Davenport RJ, Sloan WT and Curtis TP (2013) Microbial community assembly, theory and rare functions. Front. Microbiol. 4:68. doi: 10.3389/fmicb.2013. 00068*

*This article was submitted to Frontiers in Evolutionary and Genomic*

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*Microbiology, a specialty of Frontiers in Microbiology.*

*Copyright © 2013 Pholchan, Baptista, Davenport, Sloan and Curtis. 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 thirdparty graphics etc.*

# Relating phylogenetic and functional diversity among denitrifiers and quantifying their capacity to predict community functioning

## *Joana Falcão Salles\*, Xavier Le Roux and Franck Poly*

INRA, CNRS, Ecologie Microbienne (UMR 5557, USC 1193), Université Lyon 1, Université de Lyon, Villeurbanne, France

#### *Edited by:*

Diana Reid Nemergut, University of Colorado, Boulder, USA

#### *Reviewed by:*

Matthew Wallenstein, Colorado State University, USA Jonathan Winston Leff, University of Colorado at Boulder, USA

#### *\*Correspondence:*

Joana Falcão Salles, Department of Microbial Ecology, Centre for Life Sciences, University of Groningen, P.O. Box 11103, 9700 CC Groningen, Netherland. e-mail<EMAIL_ADDRESS>
Genetic diversity of phylogenetic or functional markers is widely used as a proxy of microbial diversity. However, it remains unclear to what extent functional diversity (FD), gene sequence diversity and community functioning are linked. For a range of denitrifying bacteria, we analyzed the relationships between (i) the similarity of functional traits evaluated from metabolic profiles (BIOLOG plates) or from N2O accumulation patterns on different carbon sources and (ii) the similarity of phylogenetic (16S rRNA gene) or functional (nir gene) markers. We also calculated different proxies for the diversity of denitrifier community based on taxa richness, phylogenetic (16S rRNA gene) or functional similarities (based either on metabolic profiles or N2O accumulation patterns), and evaluated their performance in inferring the functioning of assembled denitrifying communities. For individual strains, the variation in the 16S rRNA gene sequence was weakly correlated with the variation in metabolic patterns (ρ = 0.35) and was not related to N2O accumulation.The latter was correlated with the similarity of nitrite reductase residues.When nir genes were analyzed separately, the similarity in amino acids coded by the nirS genes was highly correlated with the observed patterns of N2O accumulation (ρ = 0.62), whereas nirK amino acid residues were unrelated to N2O accumulation. For bacterial assemblages, phylogenetic diversity (average similarity among species in a community) and mean community dissimilarity (average distance between species) calculated using 16S rRNA gene sequences, and FD measures associated with metabolic profiles, poorly predicted the variation in the functioning of assembled communities (≤15%). In contrast, the proxies of FD based on N2O accumulation patterns performed better and explained from 23 to 42% of the variation in denitrification. Amongst those, community niche was the best metric, indicating the importance of complementarity for resources in the context of bacterial community functioning.

**Keywords: biodiversity proxies, carbon sources, denitrification, functional traits, phylogenetic diversity**

## **INTRODUCTION**

As biodiversity is threatened by global changes, understanding the relationship between diversity and the functioning of ecosystems or biological communities has received increasing attention (Hooper et al., 2005). Microbial communities are key drivers of soil ecosystem processes. Yet, as 10 g of soil might contain 104– 106 distinct taxa (Torsvik and Ovreas, 2002; Gans et al., 2005), a complete understanding of microbial diversity is still a challenge. Despite this overwhelming prokaryotic diversity, the potential loss of diversity at the microbial scale is also of concern. In this context, it remains unclear to what extent microbial community structure, especially when based on the 16S rRNA gene as phylogenetic marker, can be used to explain differences in ecosystem processes related to carbon and nitrogen cycles (Wertz et al., 2006; Fierer et al., 2007; Le Roux et al., 2008; Philippot et al., 2010). Overall, from a biodiversity-ecosystem functioning point of view, it remains unclear the extent to which microbial community composition can be used to predict its impact on ecosystem processes

and how community composition should be characterized in this context.

Given the high degree of redundancy observed for microorganisms, changes in microbial community structure might not lead to changes microbial community function. Functional redundancy is indeed commonplace and especially for broad functions such as those associated with carbon metabolism, community composition may not influence community functioning (Wohl et al., 2004; Wertz et al., 2006). In contrast, it has been recently shown that differences in microbial community structure led to changes in mineralization rates (Strickland et al., 2009). Contradictory results have also been observed for other less broad functions, such as denitrification in soils (Cavigelli and Robertson, 2000; Wertz et al., 2006; Salles et al., 2009). This inconsistency in results might be due to differences in the methodological approaches and genes targeted to predict the effect of community structure on ecosystem functioning. For some processes, the genetic diversity of the phylogenetic marker is not related to the functional traits

that influence the functioning of the ecosystem (Jones et al., 2008; Baelum et al., 2010). In that case, taxa identity might not provide enough information on whether an organism is able to carry out a given process, at what rates or under which environmental conditions. For instance, physiological studies have shown that the enzymatic activities among species from different bacterial classes might be more comparable than between species belonging to the same genus (Carlson and Ingraham, 1983) whereas very similar taxa can strongly differfrom afunctional point of view (Salles et al., 2009). Therefore, in order to better predict community functioning, it is crucial to understand the relationship between microbial phylogeny and physiology (Allison and Martiny, 2008).

In the context of bacteria associated with the nitrogen cycle, the congruence between taxonomic phylogenies, which are mostly based on 16S rRNA gene sequences, and the phylogenies based on functional gene sequences is process-dependent. For instance, ammonium oxidation is carried out by a relatively small number of prokaryotic taxa and experimental evidence suggests a congruence in the phylogeny of ribosomal (16S rRNA) and *amo* genes for both bacterial and archaeal ammonia oxidizers (Prosser and Nicol, 2008). Among denitrifiers, the enzyme nitrite reductase, which mediates the reduction of nitrite to nitric oxide, is present in all denitrifying bacterial species. Furthermore, twofunctionally redundant but structurally distinct nitrite reductases are found among denitrifiers: a copper (Cu-Nir) and a cytochrome *cd1* (Cd-Nir) nitrite reductase, coded by the *nirK* and *nirS* genes, respectively. In contrast to nitrifiers, denitrifying bacteria are phylogenetic diverse and are distributed over 60 genera (Zumft, 1997). When comparing the phylogeny of the *nir* genes with the 16S rRNA gene,it was shown that only the *nirS* and 16S rRNA gene phylogeny was congruent, suggesting that *nirK* might have been mainly acquired through horizontal gene transfer (Heylen et al., 2006).

The use of either phylogenetic or functional genes has improved our knowledge about the ecology of microbial functional groups. For instance, a good congruence seems to exist between the phylogeny (at the genus level) and functional traits of bacterial nitrite oxidizers (Attard et al., 2010). In the case of denitrification, the use of functional genes might provide evidence that *nirK-* and *nirS*harboring bacteria are ecologically distinct. For instance, a study focusing on a short-term restoration chronosequence indicated that the abundance of the major *nirS* populations vary similarly with time after disturbance, since sites that were restored at the same time shared higher similarities in *nirS* communities. Conversely, *nirK* populations were characterized by three independent response groups, suggesting higher sensitivity to environmental gradients (Smith and Ogram, 2008). Furthermore, it has been proposed that *nirK* denitrifiers respond to a range of environmental parameters, whereas denitrifiers harboring *nirS* are mainly driven by nitrate availability (Jones and Hallin, 2010). The ecologically distinct role of *nirK* and *nirS* communities has been also observed in the rhizosphere of grain legumes where *nirK* and not *nirS* gene transcripts could be detected (Sharma et al., 2005), and in cropping systems where changes in denitrification were related to the abundance of *nirK*- rather than *nirS*-harboring bacteria (Attard et al., 2011).

Despite our increasing knowledge about the ecological distribution of these functional groups and the mechanisms underlying community assembly, an issue that remains open is to what extent the information based on the genetic diversity of phylogenetic or functional markers reflect the diversity of traits among functional groups, and furthermore, how both these markers ultimately influence ecosystem functioning. In this study we used denitrifiers as a model group to address these relationships. We focused on denitrification because it is a function performed by taxonomically diverse species, whose activity is regulated in particular by the quality and quantity of organic compounds. Moreover, the two key functional genes coding for nitrite reductase (*nirK* and *nirS*) could provide different results due to their different functionalities and to the importance of horizontal transfer for *nirK*. In order to test the extent to which functional similarity (similarity of functional traits) is linked to the variation in the sequence of phylogenetic or functional marker genes, we analyzed a set of 29 denitrifying strains according to the following attributes. Genetic diversity was calculated for the phylogenetic (16S rRNA) and functional markers (nir). Additionally, functional trait diversity was determined according to the metabolic profiles of the strains by measuring their activity over a range of 95 carbon sources under denitrifying conditions and measuring their N2O accumulation patterns in presence of six different carbon sources. We then assessed to what extent the genetic similarities (16S rRNA gene or nir genes) could be used to infer the similarity in functional traits. In this context, we hypothesize that the 16S rRNA phylogenetic marker would be weakly correlated with the similarity in metabolic profiles, but not correlated with the N2O accumulation patterns. Moreover,we predict that the genetic diversity of the functional markers (nir genes) would correlate with the N2O accumulation patterns. Under this hypothesis, we also predicted that this correlation could be weaker for genes highly submitted to horizontal transfer, such as *nirK*.

In addition, to examine the link between bacterial diversity and denitrification, we calculated a range of diversity proxies widely used in general ecology (Heemsbergen et al., 2004; Petchey and Gaston, 2006; Cadotte et al., 2009; Petchey et al., 2009; Mouchet et al., 2010). We also determined which diversity proxies, based on taxa richness, phylogenetic (16S rRNA gene) or functional similarities (based either on metabolic profiles or N2O accumulation patterns), were more relevant for predicting the effect of bacterial diversity on denitrification. For that, we used recently published data (Salles et al., 2009) on denitrification rates for a range of assembled denitrifying bacterial communities. We hypothesize that diversity proxies that take into account functional attributes would be the best predictors of community functioning. The observed relationships between the functional traits and phylogenetic/genetic relatedness and a range of diversity proxies widely used in microbial ecology are discussed in the context of predicting the effect of bacterial diversity on community functioning.

## **MATERIALS AND METHODS TAXONOMIC AFFILIATION OF STRAINS**

A set of 29 denitrifying bacterial strains commonly occurring in the soil or plant rhizosphere were chosen (**Table 1**). The taxonomic identity of the strains was verified by amplifying 16S rRNA gene with primer set pA (5 AGA GTT TGA TCC TGG CTC AG 3 ) and pH (5 AAG GAG GTG ATC CAG CCG CA 3 ), which amplifies a fragment of approximately 1500 bp, corresponding to

**Table 1 | List of the denitrifying strains used in this work, indicating which functional and genetic aspects were investigated for each one.**

a Metabolic profiles were performed with BIOLOG AN plates under denitrifying conditions.

bN2O accumulation values (μgN-N2O ml culture−1) obtained on each of the six carbon sources, 66 h after the inoculation.

c Accession number corresponding to the almost full-length of the 16S rRNA gene sequence.

dAccession number corresponding to the partial sequences of the genes coding for nitrite reductase and the type of gene found. nirK: the copper (Cu-Nir) nitrite reductase; nirS, cytochrome; cd1 (Cd-Nir) nitrite reductase.

almost full-length gene sequence (Bruce et al., 1992). Details on the amplification procedure of the 16S rRNA gene are given as Appendix.

PCR products were purified using a PCR purification kit (Macherey Nagel, Düren, Germany) and cloned into the pGEM T-Easy vector system (Promega Ltd.,Southampton,UK) and inserted in JM109 supercompetent *E. coli* cells (Stratagene Inc., Maidstone, UK). Clones were sequenced by LGC genomics using ABI 3730- XL Sequencer (LGC genomics, Berlin, Germany). The taxonomic affiliation of the strains was assigned by comparing the obtained sequences against public databases (Blastn, NCBI)1. The similarity between the 16S rRNA gene sequences was determined by creating

a pairwise distance matrix based on the Jukes and Cantor substitution model (Ribosomal database project, release 10; Jukes and Cantor, 1969; Cole et al., 2007).

#### **CHARACTERIZATION OF THE NITRITE REDUCTASE IDENTITY**

Partial sequences of the genes *nirK* and *nirS*, coding for the copper (Cu-Nir) or cytochrome *cd1* (Cd-Nir) nitrite reductase respectively, were amplified from the denitrifying bacterial strains. The *nirK* genes were amplified using the primers Copper 583F and Copper 909R (Liu et al., 2003), generating a fragment of 358 bp. A 425-bp fragment from the *nirS* gene was amplified by using the primers cd3aF and R3cd (Throback et al., 2004). Details on amplification procedure for *nirK* and *nirS* genes are given as Appendix. Both *nirK* and *nirS* fragments were cloned and sequenced as described above for 16S rRNA gene.

1http://www.ncbi.nlm.nih.gov/

The nucleotide sequences were aligned and translated to amino acid sequences using BioEdit2. Differences among amino acid residues between pairs of *nirK* or *nirS* denitrifying bacterial strains were measured between pairs of *nirK-* or *nirS*-harboring denitrifying bacterial strains, using Gonnet matrix (Gonnet et al., 1992) implemented in the software DNASTAR (Madison, USA).

#### **DETERMINATION OF METABOLIC PROFILES**

BIOLOG AN plates (AES Chemunex, Bruz, France) were inoculated with strains listed on **Table 1**. Cultures representing the pre-inoculum were grown under anaerobic conditions on NB medium containing 20 mM KNO3 for 3 days at 28˚C and 200 rpm. Cells from pre-inoculum were harvested by centrifugation at 15˚C for 15 min at 10,000 *g*, and resuspended in fresh NB medium containing 20 mM KNO3 to obtain OD560 = 0.1. These fresh cell suspensions that represented the inocula were grown for 5 h under the same conditions mentioned above. After this period, cells were harvested by centrifugation as previously described, washed twice in PBS and starved for 2 h at room temperature. The inocula were then centrifuged and resuspended in PBS containing 20 mM KNO3. Cell density was adjusted to OD560 = 0.2 and 150μl of cell suspension was transferred to each well of BIOLOG AN plates (AES Chemunex, Bruz, France). Plates were placed in anaerobic incubation bags (Merck, Fontenay-sous-Bois, France). The atmosphere inside the bags was replaced by N2 and the bags were sealed with two anaeroclips (Merck). Anaerotest strip (Merck) was used as an indicator for the absence of O2 inside the bags. Plates were incubated at 28˚C for 2 days and subsequently analyzed with a spectrophotometer (Xenius, SAFAS, Monaco) at 550 nm. The data were normalized by dividing the absorbance of each well by the total absorbance of the plate. The whole analysis was performed in duplicate, meaning that two individual colonies from each strain were used to inoculate two separate pre-inocula.

A matrix representing the metabolic profiles (relative activity values on each of the 95 carbon sources) of the denitrifying strains was used to calculate the functional trait similarity among strains. We used either Euclidean distance model or Bray Curtis similarity (square root transformed data), implemented in the software PRIMER (version 6, PRIMER-E Ltd., Plymouth, UK). Two distance measures were used to evaluate to what extend strain relatedness would be dependent on the distance applied to calculate functional trait similarity.

### **QUANTIFYING N2O PRODUCTION IN PRESENCE OF DIFFERENT CARBON SOURCES**

N2O accumulation patterns on individual carbon sources were measured for a set of 22 bacterial strains (**Table 1**). Six out of the 95 carbon sources found in the BIOLOG plates were chosen, according to their ability to discriminate the selected strains (Salles et al., 2009) and their common occurrence in bulk or rhizosphere soil. Bacterial cells were inoculated in microcosms consisting of 150-ml plasma flasks sealed with rubber stoppers and containing 50 ml of minimal medium M9 (Sambrook et al., 1989) supplemented with 0.02 M KNO3 and one carbon source (1.6 mg C

L−1): d-cellobiose, maltose, l-fucose, l-malic acid, l-glutamine, or fumaric acid. We replaced the atmosphere of the flasks by a 90:10 mixture of He-C2H2 to provide anaerobic conditions for denitrification and to inhibit of N2O-reductase activity, allowing us to quantify the amount of N2O produced by the bacterial strains. Flasks were incubated at 28˚C and 160 rpm. The bacterial inoculum consisted of cells at the exponential growth phase, growing anaerobically on the same medium containing all six carbon sources (each source was present at 0.27 mg C L−1). Before inoculation, cells were harvested by centrifugation, washed with sterile PBS buffer and let to starvation for 2 h at room temperature on PBS. Bacteria were inoculated in the microcosms to obtain a final cell density of OD560 = 0.002.

In order to quantify N2O accumulation, the head space of the flasks was sampled 10 times during a period of 3 days (0, 11, 15, 19, 23, 37, 43, 46, 61, and 66 h after inoculation) and immediately analyzed by injecting 100μl of collected gas directly in a gas chromatograph (P200 MicroGC, Agilent technology, Massy, France).

This experiment was carried out in duplicate. The N2O production by each individual strain was determined as microgram N-N2O per milliliter 66 h after inoculation, when all the strains had attained the lag phase. For each culture, the number of cells was measured by flow cytometry as detailed in the Section"Appendix." A matrix containing the average N2O accumulation patterns (μgN-N2O ml culture−1) for each source and each strain was used to determine the functional similarity among strains, as previously indicated for metabolic profiles, using Euclidean distance model. We analyzed both raw data and data normalized by dividing each value by the sum of values obtained on each of the six carbon sources for a given strain.

#### **COMPUTATION OF DIVERSITY PROXIES FOR BACTERIAL ASSEMBLAGES**

The communities described by Salles et al. (2009) refer to a set of 16 denitrifying bacterial strains that were assembled in different levels of richness (*S*), varying from 1 to 8. For each richness level, community composition also varied, ranging from 24 communities with two strains, 12 communities of four strains and 8 communities with eight strains. All 16 strains were also grown in monocultures, in duplicates.

Phylogenetic diversity (PD) was calculated using similarity distances based on 16S rRNA gene sequences. Briefly, PD was calculated for each one of the bacterial communities mentioned above, by taking into account all possible pairwise combinations among strains in a given community and averaging the computed similarity distances (Jukes and Cantor substitution model). Considering that PD is dependent on species richness, we also used another diversity proxy, mean community dissimilarity (Diss) that is less affected by the number of species in a community. Thus, the 16S rRNA gene was used to calculate Diss, defined as the average distance between species (Heemsbergen et al., 2004; Jousset et al., 2011). Distances were calculated using the Maximum Composite Likelihood model, implemented in MEGA (version 5.2).

Functional diversity (FD) proxies based on physiological data were calculated using similarities obtained from metabolic profiles (BIOLOG) or N2O accumulation patterns. Two methods were applied to infer FD: one based on distance measures and

2http://www.mbio.ncsu.edu/BioEdit/bioedit.html

another on dendrograms. For the distance-based method, we used the measure of Functional Attribute Distance (FAD; Walker et al., 1999; Mouchet et al., 2010), which represents the mean distance between species present in a community (Cadotte et al., 2009; Petchey et al., 2009). The dendrogram-based method relies on the FD measure (Petchey and Gaston, 2002) and corresponds to the total branch length from a functional dendrogram, which connects members of a given community together (Cadotte et al., 2009; Petchey et al., 2009). FAD was determined by using Euclidean distance to calculate the pairwise distance between species. FD was generated using the functions "dist()," "hclust()," and "Xtree()" implemented in R (version 2.13.0; Petchey and Gaston, 2002).

We also used the measure Community Niche (CN) measure (Salles et al., 2009), which is based on the performance (N2O accumulation) of individual species when they are growing on single carbon sources. Briefly, given a community of several strains growing on a mixture of *n* C sources, CN aggregates the best performances (here highest N2O accumulation) observed on individual C sources (Salles et al., 2009). Suppose a community composed of denitrifying speciesA and B,growing on a mixture of resources 1,2, and 3.When growing on individual resources, species A has higher denitrification rate than species B for resource 1 (DA1 > DB1). For the other two resources, species B performs better (DA2 < DB2; DA3 < DB3) Thus, the CN of this two-species community represents the sum of the highest performance on each resource (CNAB = DA1 + DB2 + DB3).

#### **DATA ANALYSIS**

Principal components analyses (PCA) and redundancy analysis (RDA) based on the metabolic profiles or N2O accumulation patterns were performed with CANOCO software (version 4.52, Wageningen, The Netherlands).

The relationships between functional and (phylo)genetic similarities were tested using a non-parametric form of Mantel test (RELATE), implemented in PRIMER-E software package (version 6). The similarity matrices obtained based on metabolic profiles or N2O production patterns were compared against the matrices based on genetic information (16S rRNA gene sequence or nitrite reductase identity) using a rank correlation coefficient (here Spearman coefficient) and significance level obtained by a permutation test (5000 permutations).

Standard regressions were used to test for relationships between community functioning (N2O accumulation) and different diversity proxies. The strength, the form and significance of the relationships were assessed by regression, implemented on the SPSS software (release 16.0.2).

In order to compare strains in terms of their functional traits, we used similarity metrics based on Euclidean distance because they provide the distance between strains in a *n*-dimensional space (*n* = 95 or 6,for metabolic profiles and N2O accumulation, respectively). Since the choice of the similarity coefficient is likely to influence our results, we also computed the similarities using the Bray Curtis coefficient. Although the correlation coefficient values varied according to the similarity measure used, the overall patterns were the same and our conclusions still hold (data not shown).

## **RESULTS**

## **METABOLIC PROFILES**

The analyses of the metabolic profiles of the 29 denitrifying bacterial strains, determined with BIOLOG plates under denitrifying conditions, showed that strains varied mainly in their ability to utilize aromatic compounds, carbohydrates, carboxylic acids, amino acids, and alcohols (**Figure 1**). Redundancy analyses performed with the same data set indicated that all classes of substrates had a significant effect (*P* < 0.006) on the metabolic profiles, except for amines (*P* = 0.054) and polymers (*P* = 0.066). In order to determine the relative contribution of each class of substrates, we used variance partitioning to control for the effect of individual classes when all other classes are defined as covariables in a constrained analysis (Leps and Smilauer, 2003). From the amount of variability explained by the ordination model when all classes are considered (63.5%), 29% could be assigned to the classes of substrates, as follows: aromatic compounds, 5.9%; carbohydrates, 5.1%; organic acids, 4.7%; amino acids, 4.2%; alcohol, 3.6%; amines, 3.1%; polymers, 2.4%, whereas the remaining variation was attributed to the variance shared between individual substrates.

When considering each phylogenetic class separately (**Figure 1**), the γ-proteobacteria were functionally diverse, with representatives having very different metabolic profiles. All but two pseudomonads were able to utilize organic acid compounds. The non-pseudomonads strains differed from the other class member by their ability to use aromatic compounds, carbohydrates, and polymers and by their low metabolic activity on organic acids. The α-proteobacteria clustered closely together, except for strain G30, which showed the most divergent metabolic profiles

**FIGURE 1 | Principal components analysis showing the metabolic diversity of 29 denitrifying bacterial strains, as determined by metabolic profiles using BIOLOG AN plates under denitrifying conditions.** For each strain, the label corresponds to the code provided in **Table 1**. Symbols correspond to the phylogenetic groups of bacterial strains studied. Squares: α-proteobacteria; circles: γ-proteobacteria; diamonds: β-proteobacteria; triangles: Bacilli. Black arrows indicate the classes of carbon substrates found in the BIOLOG; gray arrows indicate the six selected individual carbons substrates used in the microcosm experiments.

together with two strains from the *Achromobacter xylosoxidans* (β-proteobacteria class). Species belonging to the genus *Ochrobactrum* showed a wide variety of profiles, the only widespread characteristic being their low efficiency in using organic acids. The two representatives of the genus *Azospirillum* were quite distinct in their metabolic profiles, varying mainly in the use of amino and organic acids. The two genera belonging to the β-proteobacteria class differed greatly among each other, mainly regarding the use of sources belonging to the carboxylic acid class. The three representatives of the Bacilli class clustered very close together and could be identified by their ability to use aromatic compounds, carbohydrates, and polymers.

## **N2O ACCUMULATION PATTERNS**

The effect of individual carbon sources on the N2O accumulation patterns by individual strains was characterized by PCA analysis. As seen on **Figure 2** (top), when using normalized values, closely related strains tended to exhibit similar patterns with a number of exceptions. More specifically, *P. aeruginosa* strains showed similar N2O accumulation when growing on glutamine but varied in their ability to produce N2O using one of the carboxylic acids (malic acid). Similarly, the two *Burkholderia* species varied mainly in their response to malic acid and glutamine. The functioning of *P. fluorescens* and *P. stutzeri* strains was alike, with N2O accumulation varying mainly in relation to fucose and cellobiose, except for one strain of *P. fluorescens* that used glutamine. Fumaric acid and cellobiose induced the largest variation in the N2O accumulation observed between the Bacilli species. The metabolic profiles of the three *Ochrobactrum* strains were different, varying mainly in the use of glutamine and malic acid. In particular, these strains varied in the absolute values of N2O production (**Figure 2**, bottom). The majority of *P. aeruginosa* strains showed high N2O production. The highest N2O accumulation values were observed for one strain of *Ochrobactrum* and for *Paracoccus denitrificans*.

## **RELATIONSHIP BETWEEN THE SIMILARITY OF METABOLIC PROFILES AND SIMILARITY OF PATTERNS OF N2O ACCUMULATION AND (PHYLO)GENETIC RELATEDNESS**

A significant correlation was observed between the metabolic profiles and phylogenetic relatedness between strains (**Figure 3**). The similarity of 16S rRNA gene sequence computed using the Euclidian distance model explained a significant but modest fraction of the variability observed in the metabolic profiles obtained on BIOLOG (ρ = 0.35; *P* = 0.002). Interestingly, when focusing on bacterial classes, higher correlations were observed between the genetic distance based on the 16S rRNA gene sequences and the percentage of the variation in metabolic profiles (ρ = 0.48, *P* = 0.005 and ρ = 0.71, *P* = 0.003 for γ-proteobacteria and αproteobacteria, respectively; **Table 2**). Considering that the metabolic profiles showed an uneven response across strains, we performed the same analysis using a matrix based on Bray Curtis coefficient instead of Euclidian distance. Although the Spearman correlation coefficients changed slightly, the conclusions were the same (all strains: ρ = 0.35, *P* = 0.001; γ-proteobacteria: ρ = 0.57, *P* = 0.004; α-proteobacteria: ρ = 0.69, *P* = 0.003).

The similarity of N2O accumulation patterns among strains could not be explained by the similarity of the 16S rRNA gene, even when considering α-and γ-proteobacterial classes separately (**Figure 4A**). Computing the pattern in the N2O accumulation per cell (i.e., patterns of specific activity) led to similar results, as no significant relationships were observed (**Figure 4B**; **Table 2**). In contrast, the identity of the nitrite reductase residues was significantly correlated with the patterns of N2O accumulation among all strains (ρ = 0.45, *P* = 0.004; **Table 2**; **Figure 5A**). When considering the identity of amino acid residues obtained from the two genes coding for nitrite reductase (*nirK* and *nirS*) separately, the similarity in amino acids coded by the *nirS* genes was highly correlated with the observed pattern of N2O accumulation (ρ = 0.62, *P* = 0.003; **Table 2**), whereas that associated with *nirK* gene was unrelated to N2O accumulation pattern (*P* = 0.735; **Table 2**; **Figure 5B**). The similarity of the patterns of N2O production per cell and the identity of the nitrite reductase residues was just above the significance level (*P* = 0.053; **Table 2**). However, when analyzing the *nirK* and *nirS* residues separately, no correlation was found between the patterns of N2O production per cell and the identity of each type of nir residues (*PnirK* = 0.505 and *PnirS* = 0.407; **Table 2**).

### **RELATIONSHIPS BETWEEN DIVERSITY PROXIES AND FUNCTIONING FOR BACTERIAL ASSEMBLAGES**

Species richness, PD and phylogenetic dissimilarity (Diss) explained similar, low percentages of variation in community functioning (from 10 to 14%; **Figures 6A–C**). FD measures

associated with metabolic diversity either lead to non-significant relationships with communityfunctioning (FDBIOLOG, **Figure 6D**) or explained a similar percentage of variation in functioning as measures associated with the phylogenetic marker (FADBIOLOG, **Figure 6E**). FD measures associated with N2O production (FDN2O and FADN2O) both had a better explanatory power (23–26%) of the variation in community functioning than measures based on metabolic diversity or phylogenetic marker (**Figures 6F,G**). Amongst all the diversity proxies, the CN index, which accounts for complementary effects among strains rather than diversity *per se*, was the best predictor of community functioning and explained 42% of the variation in N2O production (**Figure 6H**).

## **DISCUSSION**

Many studies evaluating the effects of biodiversity on ecosystem functioning have focused on species richness, despite the fact that ecosystem functioning is not governed by the phylogenetic content of its organisms but rather by the functional traits of the individuals present, the distribution and abundance of these individuals, and their biological activity (Hooper et al., 2002; Naeem and Wright, 2003; Giller et al., 2004; Salles et al., 2009). In this context, the choice of relevant functional traits for macro-organisms has been compared to the search for the holy grail (Lavorel and Garnier, 2002). When considering microorganisms, one could argue that this search for appropriate functional traits represents a more feasible task, as the functional genes coding for the well-studied functions such as denitrification are mostly known (Philippot and Hallin, 2005; Philippot et al., 2007). Defining the groups involved

**Table 2 | Relationship between (phylo) genetic similarity and functional trait distances among denitrifying strains, as determined by Mantel test.**

1Similarity between the 16S rRNA gene sequences was determined by creating a pairwise distance matrix based on the Jukes and Cantor substitution model.

2Similarities in amino acid residues were measured between pairs of nirK- or nirS-harboring denitrifying bacterial species, using Gonnet matrix.

3 Variation in functional patterns between strains were computed based on Euclidean distance.

The significance of the relationship was determined using the Spearman correlation coefficient and 5000 permutations.

in each process sets a good foundation toward the quest for the functional "micro grail," even though the sheer microbial diversity might still limit this quest. Indeed such an approach has been extensively used in microbial ecology, and the diversity within functional groups has been assessed by quantifying the genetic diversity of either phylogenetic markers such as 16S rRNA gene (e.g., Stephen et al., 1996) or functional markers such as key functional genes involved in the process of interest (e.g., Poly et al., 2008). Despite the usefulness of these approaches, the issues that remain open are (i) to what extent does the genetic variation observed for phylogenetic or functional markers correspond to the variation of functional traits within microbial groups, and (ii) what effect does this genetic variation, functional or phylogenetic, and diversity of functional traits have on community and ecosystem functioning. We have addressed these issues for a range of denitrifying bacteria using two approaches. Firstly, by looking at the relationship between the similarity of functional traits evaluated from metabolic profiles that were obtained on BIOLOG plates or from N2O accumulation patterns on different C sources, and the similarity of phylogenetic (16S rRNA gene) or functional (*nir* gene) markers. Secondly, we computed proxies of phylogenetic diversity and FD among denitrifying bacterial assemblages, which were then used to assess how well they could explain the functioning (denitrification) of assembled communities.

### **PHYLOGENETIC SIGNAL AND PHYSIOLOGICAL TRAITS**

The relationship between ecological and phylogenetic similarities defines the phylogenetic signal (Losos, 2008), and this notion was supported in our metabolic profile data: closely related strains were able to utilize carbon substrates in a similar fashion, whereas distantly related strains exhibited a greater variation in their metabolic resemblance. However, the strength of the phylogenetic signal to explain metabolic profiles was low, as the Mantel test exhibited a ρ = 0.35 when considering the whole set of strains. In fact, in some cases, unrelated strains shared higher similarities in their metabolic profiles (as shown by smaller Euclidean distances) than closely related ones. Similar conclusions have been recently reported for natural soil microbial communities, from which 39% of the catabolic profiles could be explained by the phylogenetic content of the communities, when considering two sites (Fierer et al., 2012). Interestingly, our findings indicate a higher consistency in terms of metabolic profiles among members of the α-proteobacteria class, which showed higher phylogenetic signal in metabolic profiles when compared to γ-proteobacteria class. This is consistent with previous results reporting that there is an ecological coherence of bacterial groups at deeper branches of bacterial taxonomy, such as bacterial classes (Fierer et al., 2007; Philippot et al., 2010). For instance, the comparison between the metabolic plasticity of soil bacterial communities facing addition of different C compounds and their phylogeny suggests that closely related species do not always use resources in a similar manner, although some bacterial families seem to be more consistent in their carbon metabolism (Goldfarb et al., 2011). Our results are consistent with the conclusion of Cohan (2006) who stated that bacterial systematics fails to provide species labels allowing predictions about the biology of the members of a given bacterial species, given the great diversity in the metabolic capabilities of closely related strains. Actually, our results indicate that the use of phylogeny to infer bacterial community composition or diversity might not be meaningful from an ecosystem function perspective.

This conclusion is even stronger if phylogenetic traits are used to infer denitrification (here, N2O production), a process known to be weakly related to phylogeny (Philippot and Hallin, 2005, see section below). Indeed, we observed no phylogenetic signal when evaluating the patterns of N2O production, even after correcting

these values for the number of cells (an indication of specific activity).

### **FUNCTIONAL MARKERS AND N2O PRODUCTION**

In order to evaluate the diversity of denitrifying bacterial strains in environmental sample, most studies use the genetic diversity of sequences of key genes involved in the denitrification step of interest, rather than phylogenetic markers (Patra et al., 2005; Sharma et al., 2005; Heylen et al., 2006; Smith and Ogram, 2008; Hallin et al., 2009). In our study, the patterns of N2O accumulations (for a range of carbon sources) did not show a phylogenetic signal but were highly correlated with the identity of nitrite reductase residues. Interestingly, the patterns differed among nitrite reductase genes. The variation in the identity of the partial NirS protein was highly correlated with the variation observed for N2O accumulation profiles for *nirS*-harboring bacteria; in contrast, the variation observed for N2O accumulation profiles was not significantly correlated with variation in the identity of the NirK protein for *nirK*-harboring bacteria. It has been suggested that the *nirS* gene is less prone to horizontal gene transfer than *nirK* (Heylen et al., 2006; Jones et al., 2008), which could explain the higher congruency observed between functional gene sequence similarity and functional trait similarity for *nirS*-harboring bacteria. These findings have great implications for studies that use nitrite reductase genes to link community composition and denitrification rates, indicating that although the genetic diversity associated with both *nirK* and *nirS* can be used to study shifts in the composition of denitrifying communities, the changes in the genetic structure of nirK-harboring denitrifier communities are not well related to changes in functional traits relevant for denitrification.

In our study we have opted for focusing on *nir* genes, since those are present in all denitrifying species (Mahne and Tiedje, 1995). One could argue that our analyses were performed on partial amino acid sequences from both proteins (116 and 143 amino acid residues from NirK and NirS, respectively) and that the use of the whole protein could partly influence our results. Nevertheless, the stretch of DNA coding for nitrite reductase used here corresponds to those commonly used to study the diversity of denitrifiers (Heylen et al., 2006; Smith and Ogram, 2008;

Hallin et al., 2009). Additionally, considering that other genes involved in the denitrification processes do not always explain denitrification patterns in a similar fashion as *nir* gene (Kandeler et al., 2006; Hallin et al., 2009) it would be interesting to determine how the identity of genes such as *nar*, *nor*, or *nos*, would predict denitrification rates. These comparisons might indicate the most appropriate functional marker to describe the structure of denitrifying communities. Moreover, our results show that the regulation mechanisms of targeted functions and the evolutionary history of bacterial taxa have to be accounted for to steer us towards our quest for the functional grail in microbial ecology.

## **CHARACTERIZING DENITRIFIER DIVERSITY FROM A FUNCTIONAL PERSPECTIVE**

The quest to understand and predict the effects of biodiversity on ecosystem functioning has led to the development of a range of diversity measures. These have been used to quantify the extent to which the different aspects of biodiversity, such as species richness, phylogenetic distance or FD, influence ecosystem processes, and services (Petchey and Gaston, 2002; Petchey, 2004; Mouillot, 2007; Cadotte et al., 2009; Mouchet et al., 2010). Likewise, the need to predict microbial-mediated ecosystem processes has spurred on the development of a range of methodological approaches that focus on the genetic information

contained on phylogenetic or functional gene markers, and also on physiological trait like metabolic patterns, substrate induced respiration, and enzyme activities, to mention a few. However, a range of proxies is rarely systematically studied, especially for a range of bacterial species that can then be examined as assembled communities. This limits our ability to infer which diversity proxies are the most useful to explain community functioning.

We tackled these issues by calculating indexes based on phylogenetic and functional diversity that are often used to predict the effect of the diversity of higher organisms on ecosystem processes (Petchey and Gaston, 2002; Petchey, 2004; Cadotte et al., 2009) for a range of denitrifying bacterial strains. Furthermore, we inferred how valuable each one of them was for predicting community functioning (denitrification), by relating these indexes to the level of community functioning reported by Salles et al. (2009) for denitrifying bacterial assemblages.

Despite the fact that diversity metrics based on phylogenetic distance or the number of species in a given community are often poorly related to community functioning in general (Hooper et al., 2005) and microbial (Salles et al.,2009) ecology, they are often used to assess microbial diversity in environmental samples.When used to predict the functioning of denitrifying bacterial assemblages, both the metrics based on phylogenetic marker (PD and Diss) and on species richness (S) performed poorly. Considering the issues discussed in the previous section, the results observed for PD were not surprising, and probably reflect the low phylogenetic signal associated with denitrification. The weak predictive power of species richness should be carefully considered, especially in experiments addressing the relationship between microbial diversity and functioning, in which species richness is often the explanatory variable considered (Bärlocher and Corkum, 2003; Setäla and McLean, 2004;Wohl et al., 2004;Bell et al.,2005;Tiunov and Scheu, 2005; Jiang, 2007).

Contrary to measures based on phylogenetic distance or species richness, measures associated with functional traits are more meaningful in the biodiversity-ecosystem functioning context, as they integrate organismal traits that directly influence a given process. We therefore determined functional diversity for two types of traits, metabolic diversity and N2O production patterns, each one quantifying a different aspect of denitrifier functionality. For both trait types, we used different metrics based on multivariate strategy, FAD and FD, and compared their ability to predict denitrification to the one of CN (Salles et al., 2009). Interestingly, the diversity measures based on metabolic diversity explained no or little variation in denitrification, whereas those based on N2O production patterns performed better. This suggests that both the choice of traits and diversity metric(s) are important to properly infer community functioning. More specifically, a diversity measure based on overall carbon metabolism (FADBIOLOG) poorly predicted denitrification,whereas applying the same diversity metric but based on N2O production patterns doubled its explanatory power, highlighting the importance of selecting for the appropriate traits. Functional diversity metrics also differ in their explanatory

#### **REFERENCES**

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Attard, E., Recous, S., Chabbi, A., De Berranger, C., Guillaumaud, N., power. For instance,multivariate diversity measures based on N2O production patterns (FDN2O and FADN2O) explained the denitrification rates equally well, which could be partially explained by the degree of correlation between them (*R* = 0.63). However, their performance remained inferior to CN. This could be attributed to the fact that CN reflects the complementarity effect among strains (accounting for the performance of each strain on single carbon sources, it considers the sum of the highest performance on each individual carbon source possible for a given community) rather than diversity *per se*, as calculated for FADN2O and FDN2O. Thus, the superior performance of CN as compared to the other proxies presented here supports previous evidence (Hooper et al., 2005) of the importance of complementarity for resource use among taxa in the context of bacterial community functioning.

The information retained in the phylogenetic marker might be ecologically meaningful for studies focusing on microbial community composition and distribution. However, our results confirm the often cited hypothesis that, for broad functions such as denitrification, the diversity of functional gene sequences are better predictors of functioning than the diversity of sequences of phylogenetic markers (Philippot and Hallin, 2005). Moreover, our results suggest that characterizing the genetic diversity of *nir* gene fragments, as is often done to analyze the relationship between the diversity and functioning of denitrifiers, might be more meaningful for *nirS*- than *nirK-*harboring communities. Further studies focusing on the whole denitrification machinery and considering full gene sequences are necessary to confirm our hypothesis. Nevertheless, we provide evidence that findings based on the relationship between *nir*K genetic diversity and denitrification rates should be considered with caution. More generally, our results show that when analyzing the link between the diversity and functioning of microbial communities, indexes based on the diversity of phylogenetic or functional marker genes, or on functional trait diversity, might be useful. But they might remain inferior to indexes that more explicitly reflect complementarity effects among populations rather than the diversity *per se*. Overall, our results spur the use of diversity indexes based on relevant functional traits and the development of diversity proxies that integrate complementarity effect.

#### **ACKNOWLEDGMENTS**

We thank G. Mouchiroud and Michèle Weiss (UMR 5534) for use of the flow cytometer, and the graduate students who helped in the characterization of specific activities. Joana Falcão Salles was supported by a post-doctoral grant from the Institute National de la Recherche Agronomique (INRA).

Labreuche, J., Philippot, L., Schmid, B., and Le Roux, X. (2011). Soil environmental conditions rather than denitrifier abundance and diversity drive potential denitrification after changes in land-uses. *Glob. Chang. Biol.* 17, 1975–1989.

Baelum, J., Jacobsen, C. S., and Holben, W. E. (2010). Comparison of 16S rRNA gene phylogeny and functional tfdA gene distribution in thirty-one different 2,4 dichlorophenoxyacetic acid and 4 chloro-2-methylphenoxyacetic acid degraders. *Syst. Appl. Microbiol.* 33, 67–70.

Bärlocher, F., and Corkum, M. (2003). Nutrient enrichment overwhelms diversity effects in leaf decomposition by stream fungi. *Oikos* 101, 247–252.

G. (2004). Biodiversity effects on ecosystem functioning: emerging issues and their experimental test in aquatic environments. *Oikos* 104, 423–436.

and bacteria to aerobic ammonia oxidation in the environment. *Environ. Microbiol.* 10, 2931–2941.

short-term restoration chronosequence. *Appl. Environ. Microbiol.* 74, 5615–5620.

**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: 28 February 2012; accepted: 22 May 2012; published online: 12 June 2012.*

*Citation: Salles JF, Le Roux X and Poly F (2012) Relating phylogenetic and functional diversity among denitrifiers and quantifying their capacity to predict community functioning. Front. Microbio. 3:209. doi: 10.3389/fmicb.2012.00209 This article was submitted to Frontiers*

*in Terrestrial Microbiology, a specialty of Frontiers in Microbiology.*

*Copyright © 2012 Salles, Le Roux and Poly. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits noncommercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.*

## **APPENDIX**

## **MATERIALS AND METHODS**

## *Characterization of taxonomic affiliation of strains*

In order to amplify the 16S rRNA gene, genomic DNA was isolated from the strains using the DNeasy Tissue Kit (QIAGEN, Courtaboeuf, France), following the manufacturer instructions. One microliter of genomic DNA was used as template in a 20-μl-PCR reaction containing 200μM dNTP, 0.5μM of each primer, 0.5 U Phusion hot start high fidelity DNA polymerase (Ozyme, St Quentin en Yvelines, France) and 1 × Phusion GC buffer (1.5 mM MgCl2). PCR program consisted of 30 s at 98˚C, followed by 30 cycles at 98˚C for 10 s, annealing at 68˚C for 15 s, and elongation at 72˚C for 45 s. Additionally, a final elongation step at 72˚C for 7 min was performed.

## *Characterization of nitrite reductase identity*

The *nirK* genes were amplified using the primers Copper 583F and Copper 909R (Liu et al., 2003), generating a fragment of 358 bp, in a 20-μl-PCR reaction mix containing 1μl of genomic DNA as template, consisting of 200μM dNTP, 0.5μM of each primer, 0.5 U Phusion hot start high fidelity DNA polymerase (Ozyme, St Quentin en Yvelines, France) and 1 × Phusion HF buffer (1.5 mM MgCl2). An initial denaturation step of 30 s at 98˚C was used, followed by 30 cycles of 98˚C for 10 s, annealing at 62˚C for 15 s, and elongation at 72˚C for 15 s. The final elongation step was extended for 7 min at 72˚C.

For *nirS* gene, amplification was carried out in a 50-μl-PCR reaction mix containing 1μl of genomic DNA as template, consisting of 200μM dNTP, 0.5μM of each primer, 10 ng/μl of T4 gene protein (Qbiogene, Carlsbad, USA), 1.5 mM MgCl2, 1 U of Platinum *Taq* DNA Polymerase High Fidelity (Invitrogen, Cergy Pontoise, France) and 1 × buffer. Amplification was carried out with an initial denaturation step of 2 min at 94˚C, followed by 35 cycles of 94˚C for 30 s, annealing at 57˚C for 60 s, and elongation at 72˚C for 60 s. The final elongation step was extended for 10 min at 72˚C.

## *Determining the cell density for denitrifying bacterial strains*

In order to count the number of cells, 100μl of the cultures were harvested immediately after collecting gas samples (see Materials and Methods). Cells were fixed by mixing with equal volume of 98% ethanol, and stored at −20˚C. For counting, 15–30μl of cells were mixed with PBS buffer containing 0.1% BSA to obtain a final volume of 300μl. The cell suspension was sonicated for 10 s to limit cell aggregates and 6μl of fluorescent beads (AlignFlow, Fisher Scientific, Illkirch, France) were added. The cell suspensions were then counted using a flow cytometer (BD FACSCalibur, Becton–Dickinson, San Jose, CA, USA). Two measurements were performed for each cell suspension. As mentioned in Materials and Methods, this experiment was carried out in duplicate. Cell counts were performed for 18 strains, up to 46 h after inoculation, when all the strains were in exponential phase.

**REVIEW ARTICLE** published: 26 September 2012 doi: 10.3389/fmicb.2012.00348

# Microbial control over carbon cycling in soil

## *Joshua P. Schimel\* and Sean M. Schaeffer†*

Department of Ecology, Evolution and Marine Biology, University of California at Santa Barbara, Santa Barbara, CA, USA

### *Edited by:*

Diana Reid Nemergut, University of Colorado at Boulder, USA

#### *Reviewed by:*

Anthony Yannarell, University of Illinois at Urbana-Champaign, USA Mark Bradford, Yale University, USA Kelly Sierra Ramriez, Global Soil Biodiversity Initiative, Colorado State University, USA

#### *\*Correspondence:*

Joshua P. Schimel, Department of Ecology, Evolution and Marine Biology, University of California at Santa Barbara, Santa Barbara, CA 93106, USA. e-mail<EMAIL_ADDRESS>
#### *†Current address:*

Sean M. Schaeffer, Department of Biosystems Engineering & Soil Science, University of Tennessee, 2506 E.J. Chapman Drive, Knoxville, TN 37996-4531, USA.

A major thrust of terrestrial microbial ecology is focused on understanding when and how the composition of the microbial community affects the functioning of biogeochemical processes at the ecosystem scale (meters-to-kilometers and days-to-years). While research has demonstrated these linkages for physiologically and phylogenetically "narrow" processes such as trace gas emissions and nitrification, there is less conclusive evidence that microbial community composition influences the "broad" processes of decomposition and organic matter (OM) turnover in soil. In this paper, we consider how soil microbial community structure influences C cycling. We consider the phylogenetic level at which microbes form meaningful guilds, based on overall life history strategies, and suggest that these are associated with deep evolutionary divergences, while much of the species-level diversity probably reflects functional redundancy. We then consider under what conditions it is possible for differences among microbes to affect process dynamics, and argue that while microbial community structure may be important in the rate of OM breakdown in the rhizosphere and in detritus, it is likely not important in the mineral soil. In mineral soil, physical access to occluded or sorbed substrates is the rate-limiting process. Microbial community influences on OM turnover in mineral soils are based on how organisms allocate the C they take up – not only do the fates of the molecules differ, but they can affect the soil system differently as well. For example, extracellular enzymes and extracellular polysaccharides can be key controls on soil structure and function. How microbes allocate C may also be particularly important for understanding the long-term fate of C in soil – is it sequestered or not?

#### **Keywords: microbial communities, carbon, diversity, litter, roots, soil**

"fmicb-03-00348" — 2012/9/25 — 11:39 — page 1 — #1

Interest in how the composition of soil microbial communities governs the functioning of soil and ecosystem processes goes back to the dawn of microbiology with workers such as Pasteur and Winogradsky. However, the way we think about such issues has evolved – thefocus is no longer identifying organisms with a capacity to carry out a function, e.g., characterizing the traits of specific nitrifiers (Waksman, 1927; Meyer, 1993) but on how varying the composition of a group affects the dynamics of the process it carries out – for example, how differences in the nitrifiers present affect nitrification kinetics (Braker and Conrad, 2011). This shift in the nature of the questions has been driven by three factors. The first is the development of techniques that allow us to characterize the identities of microbes *in situ* (16S and 18S rDNA), their potential (functional genes), and their physiological state (e.g., RNA/DNA ratios; stable isotope probing; BrDU incorporation; Roux-Michollet et al., 2010; Morales and Holben, 2011). The second is the growing interest in integrating evolutionary and ecological theory into microbial ecology to better understand microbial systems (Jiang,2007; Prosser et al.,2007; Peay et al.,2008; Fierer et al., 2009; Locey, 2010). The third is the societal and scientific need to better understand and model important processes that influence ecosystem functions and the global climate system (Schimel and Gulledge, 1998; Reid, 2011).

In some ways, the thinking in microbial ecology has paralleled the development of plant ecology – questions have gone from "who's there?," analogous to the work of Joseph Banks and other naturalists in the late eighteenth and nineteenth centuries, to "why are they there?," and "what are they doing?," analogous to Tansley and Clements in the early 20th centuries. A question central to much of modern microbial ecology is "does who's there matter?"

As microbial ecologists, we might wish the answer to this question to be "yes," but in fact, this is not certain (Prosser, 2012). It is likely that for some processes the composition of the community matters, while for others it does not, and that the answer changes with physical and phylogenetic scale. Schimel (1995) postulated that"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 individual community structures decreases." That paper posed the question "Is there some minimal scale necessary to adequately explain ecosystem processes at which microbial community structure still has a measurable influence on the nature and rates of those processes?"

From that question grew the argument that "narrow" processes – those that involve a specific physiological pathway or which are carried out by a phylogenetically constrained group of organisms – might be sensitive to the composition of the guild of microorganisms carrying it out, even at the ecosystem level (Schimel, 1995; Groffman and Bohlen, 1999). Examples of such processes include specific plant–microbe interactions (e.g., N-fixation, mycorrhizae, and pathogens) and trace gas emissions, notably N2O and CH4. Both the conceptual arguments and the research to back them up are increasingly well developed for such processes. For example, the ratio of N2O/N2 produced by denitrification (Bakken et al., 2012; Salles et al., 2012), the rate of nitrification and the speed with which it responds to fertilization (Isobe et al., 2011), and the sensitivity of methanogenesis to NH4 + (Bodelier et al., 2000) are all sensitive to the composition of the community of organisms carrying them out.

However, these narrow processes are generally niche players in overall biogeochemical cycles. They are often important, either for ecosystem functioning (e.g., nitrification or sulfate reduction) or for global systems (e.g., N2O and CH4 fluxes), but typically engage only a small fraction of the total microbial community and are responsible for a limited portion of the total cycle of the involved element. Most microorganisms in soil are aerobic heterotrophs involved in the "broad" or "aggregate" processes (*sensu*; Schimel et al., 2005); these are the processes that are carried out by a wide range of organisms or that we measure as a single process but are actually the sum of multiple distinct processes (e.g., soil respiration). Broad processes are responsible for the largest flows of C in soil systems: decomposition and C storage.

In this paper, therefore, we will focus on the microbial role in these large flows associated with the soil C cycle. We will briefly discuss our evolving understanding of the nature and causes of microbial diversity in soil to consider the level of phylogeny that might define meaningful functional groups for addressing "who's there" questions in C-cycling research. We will then discuss the circumstances where microbial community structure might regulate the processing of organic matter (OM) in soil, and some areas where we see a particular need for advancing this research.

## **CAUSES AND NATURE OF MICROBIAL DIVERSITY IN SOIL**

Microbial diversity in soil is high. Typical soil samples contain many thousands of individual taxa (commonly described as"operational taxonomic units"; OTU's) of Bacteria, Archaea, and Fungi. Some estimates suggest there can be more than 106 individual species-level OTUs in a single soil (Fierer et al., 2007). This poses two central, but related, questions to microbial ecologists: how there can be such great diversity, and does it have any functional significance? (Prosser, 2012).

Classical theories of biodiversity are grounded in the concept of the niche and competitive exclusion: two species cannot stably coexist in a single niche. Thus, each species *must* have some functional differentiation (Clark, 2010). Niche-based theory, however, struggles with the high biodiversity of plants, which compete for a limited suite of resources (light, water, mineral nutrients); yet there may be hundreds of species within some habitats.

To explain such anomalous patterns of high biodiversity, alternative ideas have developed. Neutral theory argues that species can coexist within a niche when the variation in fitness among individuals is as great as among species (Hubbell, 2001); but species can also coexist when competition among individuals is as intense as among species (Clark, 2010; Clark et al., 2011; Beckage et al., 2012). Such dynamics allow functionally overlapping taxa to coexist, especially when the environment is highly variable, and when organisms are sessile; conditions that likely hold true for many soil microbes (Sloan et al., 2006; Dumbrell et al., 2010; Fierer and Lennon, 2011; Prosser, 2012).

Growing evidence suggests that niche and functional differentiation explain patterns of diversity at high levels of microbial phylogeny (e.g., families and phyla) and is associated with life-history strategies (Fierer et al., 2007; Philippot et al., 2010). However, it has been hard to identify meaningful functional differentiation within more finely defined groups (e.g., within genera; Philippot et al., 2010; Prosser, 2012). This conclusion, if true, will affect how we study microbial community composition; for example, how deeply to sequence communities to analyze their structure in terms of ecologically meaningful groups. It takes far fewer sequences to quantify a community to family than to species (Barberán et al., 2012).

In soil, organisms must adapt to a complex array of substrates, physical/chemical conditions, and biotic interactions, each of which may affect community composition. Some organisms specialize on particular substrates; for example, fungi that grow best on sucrose vs. cellulose vs. lignin vs. tannin–protein complexes (Hanson et al., 2008). In other cases, organisms appear to respond to specific environmental variables. For example, O2 (Bodegom et al., 2001), moisture (Lennon et al., 2012), pH (Fierer and Jackson, 2006) and even varying levels of these parameters (DeAngelis et al., 2010) can select for specific organisms. In some cases, this selection operates at high phylogenetic levels – e.g., pH controls the relative growth of fungi vs. bacteria (Rousk et al., 2009). In other cases, selection operates atfamily or genus. For example, within the phylum Glomeromycota (the arbuscular mycorrhizal fungi), soil pH may select more strongly than host plant for the specific taxa present (Dumbrell et al., 2010); within the bacteria, *Clostridium* spp. are obligate anaerobes while most *Bacillus* spp*.* are aerobes, yet both genera are closely aligned within the Firmicutes.

However, the dominant environments that we can identify in soil are rarely defined by single characteristics (e.g., pH alone), but by combinations of characteristics that organisms must deal with in synchrony. To adapt to an environment with a suite of co-occurring conditions, an organism requires a suite of complementary traits – a life history strategy. For example, litter decomposers (e.g., many basidiomycete fungi) rely on extracellular enzymes to cut plant polymers into oligomers and monomers that may be taken up and metabolized, but they must also deal with litter drying out frequently (Schimel et al., 1999) and with the high C/N stoichiometry typical of leaf litter. Some bacteria are "rhizobacteria" that appear to depend on specific exudates released by plant roots (DeAngelis et al., 2009; Remenant et al., 2009) and are adapted to the complex environment of the rhizosphere (Bertin et al., 2003). The Acidobacteria appear to be stress tolerant oligotrophs (Fierer et al., 2007, 2011) while the Bacteroidetes and the β-Proteobacteria appear to be copiotrophs that require adequate moisture (Lennon et al., 2012).

Evolving a successful life history strategy implies deep evolutionary patterns and may explain why we observe meaningful functional groups or guilds of microbes, and that they are defined primarily at high phylogenetic levels – families or phyla rather than at species or genera (Moorhead and Sinsabaugh, 2006; Fierer et al., 2007; Philippot et al., 2010; Follows and Dutkiewicz, 2011). For example, the ability to retain high levels of rRNA through drought

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and to respond quickly to rewetting appears to be a function of bacterial phylum, with Actinobacteria and Verrucomicrobia being rapid responders, while Firmicutes were intermediate; Proteobacterial responses however differed at the class level (Placella et al., 2012). At the species level, there generally appears to be substantial functional redundancy (Prosser, 2012).

This conclusion is reinforced by the ubiquity of horizontal gene transfer (HGT) among bacteria. HGT allows organisms to transfer the genes to carry out specific processes (Kurland et al., 2003). However, HGT is most common among closely related taxa (Kurland et al., 2003), and for pathways that are simple and require few enzymes. Thus, HGT is unlikely to break down functional barriers at high levels of phylogenetic difference and so is unlikely to transfer major life-history strategies that require complex gene networks or rearranging core physiological pathways (Kurland et al., 2003). For example, while the genes for nitrogenase appears to have been transferred across taxa multiple times (Falkowski et al., 2008), transferring the full suite of genes required to form N-fixing nodules in legumes appears to have happened perhaps twice in history (Chen et al., 2003). Denitrification has spread widely across the bacterial world because it requires only a slight modification to the terminal end of the electron transport chain, branching electrons off from cytochrome *b* to one of several nitrogen reductases. It is an easy physiology to maintain as an alternate to aerobic respiration. In contrast, because the redox potential for sulfate reduction is so much higher than that of O2, organisms cannot merely insert sulfate reductase in place of cytochrome *o*; sulfate reducers have an entirely different electron transport system (Rabus et al., 2006), making it difficult for an aerobe to become a SO= 4 reducer through HGT. The ability to carry out specific biodegradation reactions has been transferred frequently (Liang et al., 2012), but a rhizobacterium is not likely to become a litter decomposer overnight.

Much soil diversity may actually reflect beta diversity – a diversity of habitats within a landscape, rather than diversity within a habitat. This argues that soil is really a complex landscape with repeatable and definable microhabitats, each of which might have more constrained diversity. No one considers it surprising that California has > 3,000 native plant species because the State spans from alpine tundra to conifer forests to arid scrubland and desert, each with its own array of species. Does soil have analogous distinct communities? Rhizospheres select for discrete and reproducible communities, based on both the chemical nature of plant rhizodeposits (Paterson et al., 2007; DeAngelis et al., 2009; Dennis et al., 2010) and the physical environment created by roots (e.g., altered O2, pH, and water availability; da Rocha et al., 2009; Hinsinger et al., 2009). Soil aggregates may also select for specific microbial groups; for example, Acidobacteria may be common in macroaggregates but not the inner microaggregate (Mummey et al., 2006). Communities may also vary based on the size pores they inhabit (Ruamps et al., 2011).

However, even if physical structure does not create repeated defined habitats that select for specific communities analogous to grassland or alpine plant communities, the physical complexity of the microbial landscape and a lack of connectivity between pores may reduce competitive interactions among taxa and allow greater overall diversity (Görres et al., 1999; Dechesne et al., 2008) analogously to how different valleys within a single mountain range may have somewhat different flora. Within a single soil, decreasing pore connectivity by reducing water content can increase bacterial species richness (Carson et al., 2010). We still only poorly understand how soil structure creates habitats and niches and how it regulates interactions to control diversity and community composition. This remains an important research area (Schmidt et al., 2011; Dungait et al., 2012).

Questions remain, however, about how many distinct niches can exist in soil, and about the level of phylogenetic resolution at which meaningful niche selection and partitioning acts. As we explore the functional significance of community composition, we need to further develop our understanding of the nature of microbial diversity and the phylogenetic levels at which distinct life-history strategies emerge and how these translate into meaningful microbial functional groups and thence into meaningful functions. We also need to better understand the "microbial landscape" and how the physical structuring of the soil system interacts with microbial communities to regulate the processes that control ecosystem functioning.

## **CONSEQUENCES OF SOIL COMMUNITY COMPOSITION**

As different groups of microorganisms have distinct functional traits with the potential to influence the processes they carry out (e.g., exoenzyme producers vs. "cheaters"; Allison, 2005), it raises the question of where and how those differences might be expressed in the environment. Just because organisms' traits differ does not mean that they necessarily function differently. There are several necessary conditions for soil microbial community composition to affect ecological processes.

	- a. The rate limiting step in a reaction sequence or,
	- b. The fate-controlling step i.e., at a branch point that channels substrates into pathways with different fates.

The first criterion is the basis of the concept of "physiologically broad" within "broad vs. narrow" theory: if all organisms carry out a process in the same way it can not matter which is active for process function. Community composition can only affect processes if organisms vary in how or when they function (Schimel, 1995; Allison and Martiny, 2008).

Even if the organisms present in a community do vary in their functional characteristics, being different is not enough to control the dynamics of C cycling. The organismal biology must also be what controls the process – either in terms of how fast a reaction proceeds or where it proceeds to: what are the products.

### **RATE LIMITING STEP: CATABOLISM VS. ACCESS?**

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Most OM transformations involve multiple steps with different inherent kinetics. For microbial community composition to play a role in controlling such a transformation, the slowest, rate limiting step, must be biological. In soil, that is not necessarily the case; rather abiotic processes can be rate-limiting (Kemmitt et al., 2008). For example, in dry and sunny ecosystems, photodegradation of aboveground litter can potentially oxidize plant-C all the way to CO2 (Austin and Vivanco, 2006). Photodegradation, however, generally accounts for only a limited amount of litter breakdown (Brandt et al., 2010). The more common way for abiotic processes to regulate OM turnover is through physical mechanisms that limit microbial access to substrate (Stevenson, 1982; Pingnatello, 1999; Kemmitt et al., 2008; Dungait et al., 2012). Thus, in considering the potential role of microbial community composition in a biogeochemical process, the first question is whether microbes have physical access to the substrate (**Figure 1**).

In fresh unprotected detritus, microbial access is not generally a constraint on decomposition. The exception is wood; it can take some time for fungal hyphae to penetrate into a log (Barker, 2008). In mineral soils, however, the situation is different. Mineral soils contain the bulk of OM in the total soil profile, and possibly in an entire ecosystem (Jobbágy and Jackson, 2000). Much of this is in protected forms, either occluded in aggregates or sorbed on mineral surfaces (**Figure 1**; Krull et al., 2003). The rate at which this C can be metabolized is limited by microbes' ability to access it (Six et al., 2004; John et al., 2005). The importance of physical protection has long been recognized as a control on soil OM (SOM) turnover (Yoo et al., 2011), but increasingly researchers have been recognizing the role of physical space and the structure of the "microbial landscape" as a specific control on the dynamics of microbial communities and of their function as well (Dungait et al., 2012).

Microbes in mineral soils are constrained by the pore networks that they live in (Young and Ritz, 2005; Donnell et al., 2007). As soils dry, bacteria become effectively immobile (Wang and Or, 2010) and must rely on diffusion to supply resources. Yet, soil is a "sticky" environment, and substrate diffusion can be slowed or prevented when molecules interact with electrically charged clay particles or OM that coats particles (Carrington et al., 2012). In other words, life in soil is like life in a chromatography column. The interactions of microbes and substrates with the physical matrix regulates how, or even whether, soil C is utilized; the critical processes are sorption/desorption, diffusion, and transport (**Figure 1**; Ekschmitt et al., 2005).

For example, in a California grassland soil, Xiang et al. (2008) showed that in deep soils (1 meter), multiple dry/wet cycles increased total respiration and microbial biomass by more than 500%; the C respired came from a pool with an estimated

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turnover time of 600–800 years (Schimel et al., 2011). This pool of chemically labile OM was only metabolized following rewetting either because rewetting caused it to desorb, or because the flow of water redistributed C and so overcame diffusion limitations.

A meta-analysis of studies evaluating microbial respiration responses to water stress (Manzoni et al., 2012) showed that across a wide range of soils, the relationship between water potential and relative respiration is linear with a consistent threshold value at which respiration effectively stops (ca. −14 MPa); this value was similar to "the water potentials where soil diffusion becomes impaired." They concluded that the only mechanism that could reasonably explain the observed relationship was diffusion control on substrate supply.

In contrast, when aggregates occlude organic detritus (Six et al., 2004), two steps are required for microbes to process the material – first a physical step of aggregate disruption, and then possibly a second step in which exoenzymes break up the polymers (Navarro-García et al., 2011). The aggregate disruption step still limits overall metabolism of the material, but once that step is overcome, there may still be a delay before material is processed and metabolized.

The conclusion that OM breakdown in mineral soils is not limited by the catabolic capacity of the OM, but rather physical factors that limit microbial access to it, is reinforced by our changing understanding of the chemical structure of SOM. The classical model of SOM was based on humic/fulvic acids, and what might be described as the "snowflake" theory of SOM, in which humic materials were thought to be large polymers (Sutton and Sposito, 2005), molecules so complex it was possible no two were identical. In such a model, molecules require extracellular enzymes to fragment them, but with no repeated structures, enzymes could not be high affinity "latch-and-key" hydrolytic enzymes but instead must be non-specific oxidative "shotgun" enzymes that produce high-energy radicals or peroxides. Thus, microbes would rely on chance to generate fragments that they can take up and metabolize (Stevenson, 1982). In this vision, SOM molecules are inherently resistant and dependent on specialized exoenzymes; decomposition is therefore potentially sensitive to the organisms involved, and whether they have the necessary traits to be able to process humic molecules.

The old humic model of SOM, however, is increasingly considered obsolete (Schmidt et al., 2011), and is being replaced by a conceptual model in which SOM is made up of aggregates of small, but chemically defined molecules (Sutton and Sposito, 2005; Schmidt et al., 2011). These small molecules can also form layers on clay surfaces, leading to a model that has been called the "onion layering" model (Sollins et al., 2006). In this new model, individual constituent molecules are small enough that they can be taken up by microorganisms and are often simple enough that they can be channeled into metabolic pathways that are common among microbes. There should therefore be a lot of redundancy among microbes' ability to use such compounds, making it unlikely that their metabolism would be limited by a lack of appropriate enzymes. Yet, the bulk mineral-phase carbon in soils is frequently thousands of years old (Trumbore, 2009). Because such long turnover times cannot be explained by chemical recalcitrance (Dungait et al., 2012), they are unlikely to result from biological constraints and are presumably insensitive to microbial community composition.

## **FATE CONTROLLING STEP: ALLOCATION**

Although we argue that for the protected OM in mineral soils, the composition of the microbial community is not important in controlling the *rate* at which SOM is processed, that does not mean community composition is not important in the *fate* of this material. While a molecule's accessibility to a microbe is controlled by physical processes, once a microbe has taken it up, its fate is entirely under the control of that organism (**Figure 1**). Ultimately therefore, while catabolic potentials have limited role in controlling the turnover of mineral SOM, anabolic processes are unquestionably important (Liang et al., 2011). What do microbes do with the carbon they access? How do they allocate it? How do microbial C transformations further affect the composition of SOM? These *are* sensitive to microbial community structure.

The sensitivity of SOM turnover to anabolism is recognized implicitly even in large-scale biogeochemical models; although these models often lack any microbial carbon pool, and even when such a pool exists it is usually just a C reservoir, rather than a driver of decomposition (Manzoni and Porporato, 2009). In these models, when C moves from one pool to another, some is lost as CO2; the proportion of C-moved relative to C-lost is essentially equivalent to carbon use efficiency (CUE). In physiology, CUE is the fraction of substrate that is taken up that is assimilated immediately into microbial biomass (Allison et al., 2010). With simple compounds, the immediate CUE – usually measured by a short-term assay with an isotopically labeled substrate – is more a function of molecular structure than microbial community composition (Sugai and Schimel, 1993). The magnitude of CUE, however, is a function of time and physiological condition; stressed microbes can have a higher maintenance energy demand which functionally reduces CUE (Allison et al., 2010). Thus, how CUE and C-turnover respond to stress may be a function of microbial life history strategy and stress tolerance.

Microbes, however, do more with substrate than just convert it to "biomass." Rather, they synthesize a variety of products that affect the functioning of ecosystems. A select few of these include:

A. Extracellular enzymes

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The fate of each group of compounds is different in soil, and patterns of allocation and production vary across microorganisms (Harris, 1981; Lennon et al., 2012). For example, the microbial products present in decomposing aspen litter differed dramatically with the site the litter was decomposed in (i.e., microbial inoculum), even though decomposition rates did not differ significantly (Wallenstein et al., 2010). Substantial differences among allocation patterns are associated with microbes' life-history strategies and hence with their phylogeny. Further, the ways in which microbes allocate C can influence soil structure and function and so alter microbial habitats and overall soil functioning.

## *Extracellular enzymes*

For those organisms that rely on detritus (plant, animal, or microbial) for their resource supply, extracellular enzymes are required to break down polymers (Sinsabaugh, 1994). Although several simple theoretical models of decomposition include a single enzyme pool (e.g., Schimel and Weintraub, 2003), in reality a suite of different enzymes are required, including substrate-specific enzymes targeting C, N, and P (e.g., cellulolytic, proteolytic, and phosphatase), and non-specific oxidative enzymes such as laccase and peroxidase (Caldwell, 2005; Sinsabaugh et al., 2009). For exoenzyme-producing decomposers, enzymes may be the first priority for C-allocation to ensure resource supply (Schimel and Weintraub, 2003). The overall microbial community is able to shift allocation among these different groups of enzymes to match production to resource demand; enzymes are selectively produced to increase the supply of the most limiting element (Sinsabaugh and Moorhead, 1994; Sinsabaugh et al., 2009) and to target the most available substrates (Sistla and Schimel, 2012). It remains unclear how much of this is due to physiological plasticity of individual organisms or reflects shifts in the composition of the microbial community. The products of exoenzyme breakdown become available to other organisms including other microbes, so called "cheaters" (Allison, 2005) or "opportunists" (Moorhead and Sinsabaugh, 2006), and to plant roots (Schimel and Bennett, 2004). Some microbial groups are dominant producers (e.g., Basidiomycetes) while cheaters appear to dominate in groups such as the β-Proteobacteria and Bacteroidetes (Fierer et al., 2007).

## *Extracellular polysaccharides*

Another important class of extracellular materials is polysaccharides (Holden, 2011; Sutherland, 2001). Some microorganisms can embed themselves in a matrix of extracellular polymeric materials (EPS) that are mostly polysaccharide but also contain DNA and protein (Or et al., 2007; Jiao et al., 2010; Holden, 2011). EPS is hygroscopic (Chenu, 1993; Henao and Mazeau, 2009), facilitating prolonged cellular hydration and nutrient resupply in drying soils (Or et al., 2007). EPS can bridge between microbes and their substrates and allow them to survive in dry soils. EPS can also protect exoenzymes (Sutherland, 2001; Holden, 2011), and can either promote (Chenu and Roberson, 1996) or constrain (Holden et al., 1997) C diffusion to microbes. EPS can alter soil structure to mediate water retention (Chenu and Roberson, 1996), hydraulic conductivity (Henao and Mazeau, 2009; Rosenzweig et al., 2009), and aggregate structure (Roberson et al., 1995; Park et al., 2007). By promoting aggregate formation, microbes can create favorable growth environments, either in the interior of macroaggregates, which may have increased water content, proximity to substrates, and physical protection from predators (Görres et al., 1999; Neher et al., 1999; Six et al., 2006), or in macropores that offer easier access to diffusing substrate (Ruamps et al., 2011). Aggregate interiors, however, can also be a constrained growth environment when accessible substrates are depleted, or if intra-aggregate pore size is small enough to prevent colonization (Chenu et al., 2001).

Microorganisms'ability to produce EPS appears correlated with their ability to grow at low water potentials (Lennon et al., 2012) and so appears to be part of a deeply rooted life-history strategy that includes the ability to tolerate low O2 (associated with being in a saturated biofilm) and a longer lag before starting exponential growth. This strategy is concentrated in a subset of phyla, notably the Firmicutes (Lennon et al., 2012). Microorganisms can also produce other chemicals that directly affect soil conditions and structure, including a variety of proteins such as hydrophobins, glomalin (produced by arbuscular mycorrhizal fungi in the Glomales), and chaplins (produced by Actinomycetes; Rillig et al., 2007).

## *Cell wall polymers*

Within a cell, microbes also have the ability to shift resource allocation among different pools; these allocation patterns affect the functioning of the cell itself and potentially that of the overall soil system. Cell wall materials are thought to be potentially important sources of C and N for long-term stabilization (Liang et al., 2011). The proportion of chitin, peptidoglycan, lipids, and other cell-wall and outer-membrane components depend on the ratio of Fungi:Bacteria:Archaea, as well as the proportion of Grampositive:Gram-negative bacteria within the bacterial community. Amino sugars, which both bacteria and fungi use as components of their cell walls (peptidoglycan and chitin, respectively) become important constituents in soil organic nitrogen pools (Dai et al., 2002; Kögel-Knabner, 2002; Joergensen and Wichern, 2008), yet are produced differentially by the different groups of organisms.

## *Stress response compounds*

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It has been argued that microorganisms physiologically acclimate to survive stresses such as low water potential or freezing by accumulating cytoplasmic constituents such as osmolytes or cryoprotectants (Schimel et al., 2007). These molecules would have to be lost or transformed rapidly when the stress ended (rewetting or thaw). While there are consistently flushes of C and N upon rewetting and thaw, recent research suggests that in soil the "osmolyte theory"for how microbes tolerate drought may be incorrect (Boot et al., 2012; Kakumanu et al., 2012) and that the substrate flushes may be instead be associated with mobilization of non-biomass SOM (Miller et al., 2005; Xiang et al., 2008). This may not be the case for freezing, however, as the organisms that are active at low temperature appear distinct from those active under warmer conditions (McMahon et al., 2011) and the nature of OM processing and nutrient balance shifts between summer and winter (Schimel and Mikan, 2005). Soil freezing may select for anaerobes (Miller et al., 2007). How the production of such material varies across the microbial world remains unclear.

## **SCALING UP IN TIME: DO MICROBIAL COMMUNITY INFLUENCES SHIFT WITH TIME SCALE?**

The importance of microbial community composition may vary with the time scale being considered, but not necessarily in an intuitive way. Generally, we assume that scales of time and space are linked – it makes no sense to think about the global carbon cycle *this second*, nor does it make sense to ask what carbon in a single square centimeter will be doing over the next century. Rather we assume that questions framed at fine spatial scales will also target short time scales – studies on soil cores rarely last longer than months. In considering spatial scales, Schimel (1995) suggested that there should be a continuum in the influence of microbial community structure, with maximal influence at the finest spatial scales, with influence fading out at progressively larger spatial scales. We suggest that this is reasonable, but the same logic might not apply to time scales.

As the scale of focus changes, the nature of the processes that are most relevant change, as do the intellectual models that we use for considering them. For example at the pore-scale, we focus on patterns of microbial growth and substrate diffusion; reaction-diffusion models are a core intellectual paradigm for understanding this scale (Or et al., 2007; Holden, 2011); these emphasize access and catabolism. A new approach in microbial ecology is Dynamic Energy Budget modeling, which focuses on how organisms allocate resources under different environmental conditions and how that translates into population growth and turnover (Klanjscek et al., 2012). At the ecosystem scale, studies frequently focus on a seasonal to interannual time scale and emphasize plant production and decomposition (the life and death histories of plants) – box-and-flow biogeochemical models are the dominant paradigm (Manzoni and Porporato, 2009) and at this scale it is difficult to show any substantial influence of microbial community composition on C cycling. Larger scales become the domain of Earth System models, but these generally have a biogeochemistry core that is based on ecosystem-scale models (Clark et al., 2011). One might therefore argue that at these larger scales, the specific dynamics of microbes would be even less important in regulating the key processes. But at the decade-and-up times scales relevant to the global climate system, plant production and litter decomposition become tightly balanced, and overall C storage and loss become increasingly a function of the big, slow pools – e.g., stabilized OM in the mineral soil (Jobbágy and Jackson, 2000). Thus, to evaluate the processes that control C-sequestration, it is not the rate of microbial growth or of litter decay that matter most – it is the production of stabilized materials, which are a small fraction of the total (Liang et al., 2011). These products result from specific anabolic pathways of microbes and on patterns of microbial community composition that are reasonably stable across time (Grandy et al., 2009). Thus, as we focus on understanding soil C dynamics overlong time-scales, the allocation patterns of specific groups of microbes that regulate the fate of OM in mineral soils becomes increasingly important.

We have briefly discussed perspectives on microbial diversity and the ways it may affect C cycling in soil systems. We have argued that our perspectives shift among time scales and specific processes, and that the roles of microbial community composition in controlling these processes shift as well. We also need to consider how these dynamics play out in different compartments of the soil system, because community influences may act differently among them. As a simple breakdown of the dominant zones within the soil landscape, we can consider (A) rhizosphere, (B) aboveground litter, (C) dead roots, and (D) mineral soil (**Figure 2**). We hypothesize that the composition of the microbial community plays a significant role in controlling C cycling in the rhizosphere and in organic detritus (litter or dead roots), but for distinctly different reasons. In the rhizosphere, microbial community composition is regulated by the specific substrates and chemical signals released by the plant root, and by the specific physical and biotic environment created by the plant root in terms of O2, pH, and other chemical variables (Jaeger et al.,1999; Hinsinger et al.,2009). These selectfor a distinct group of microbes, some of which act as plant growthpromoting rhizobacteria or as pathogens and so have powerful feedbacks to plant growth and C cycling. In physically unprotected organic detritus, chemical structures remain complex and specific to the plant, microbe, or animal that produced them; exoenzyme breakdown is necessary for microbes to metabolize them. Thus, their breakdown remains under biological control and sensitive to the specific identities of the decomposers present. Extensive work has been done on aboveground litter, linking chemistry and organisms, and decomposition dynamics. The community present in litter alters decomposition kinetics, often the community native to the litter's home site is more effective than communities from other sites; the "home-field advantage" (Sinsabaugh and Moorhead, 1994; Schimel et al., 1999; Aneja et al., 2004; Craine et al., 2007; Strickland et al., 2009; Baumann et al., 2011; Freschet et al., 2011; Baldrian et al., 2012; Schneider et al., 2012).

In contrast, comparatively little research has been done on dead roots. This is surprising because typically at least half of ecosystem net primary productivity goes into belowground structures (Chapin III et al., 2002), and root litter is likely a greater source of stabilized soil C than is aboveground litter (Rasse et al., 2005; Schmidt et al., 2011; Carrington et al., 2012). The chemistry of root litter differs from that of foliar litter (Harmon et al., 2009), and differs in decomposition dynamics. For example, while high C/N foliar litter inevitably shows a phase of N-immobilization early in decomposition, this is not the case for roots, which begin to mineralize N in parallel with mass loss (Parton et al., 2007). Relatively few studies have evaluated the microbiology of root decomposition compared to those on leaf litter (e.g., Fisk et al., 2010; Baumann et al., 2011). Because root litter is such an important source of soil C and because so little has been done on the relationships between chemistry and microbiology in dead roots, we consider this to be the most important compartment for microbial study in the soil system.

In mineral soils, as we have discussed, the influence of microbial community composition on the rate of breakdown of SOM is likely to be limited. Rather, community composition may more strongly reflect the physical environment and substrate access patterns. However, the distribution of major phylogenetic groups may control the fate of that material. Here, we need continued study on SOM chemistry to better understand the factors that regulate microbial access to substrate and we need increased study on microbial processing and production of new materials to better understand how they regulate the physical structure of soil and the long-term fate of soil C.

## **CONCLUSION**

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In their effects on soil C cycling, the influences of microbial communities appear to be associated with life history patterns that are deeply rooted in microbial phylogeny – functional groups appear at the level of families or phyla rather than species or genera. Even accepting that soil microbes have different life history strategies and comprise different functional groups however, for those

differences to influence ecosystem C dynamics, those organisms must carry out steps in OM processing that are rate-limiting in overall OM breakdown, which requires that they have physical access to the material, or they must control the fate of that material, synthesizing alternative products with important characteristics for ecosystem function. The specific compounds produced likely affect the nature of soil processes most strongly at either the shortest or the longest time scales, but least strongly at the interannual "ecosystem" scale that dominates much biogeochemical study. In the rhizosphere and in detritus, community composition likely influences C-cycling rates, while in the mineral soil, it may primarily influence the fate of C, while physical processes controlling microbial access to C regulate turnover rate. The largest uncertainty about the role of community composition probably exists for dead roots. These may constitute the largest source of C that

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## **ACKNOWLEDGMENTS**

We thank Dr. Patricia Holden for contributing to the thinking that led to this paper, and to three reviewers who offered valuable criticism and suggestion that substantially improved this paper. We thank the U.S. National Science Foundation Ecosystem Science Program for supporting this effort, both through grants from the Ecosystem Science Program (DEB-0444712; DEB-1145875) and for supporting the Enzymes in the Environment Research Coordination Network, which supported the workshop "Incorporating Enzymes and Microbial Physiology into Biogeochemical Models" in Ft. Collins Co in May 2012; participants at the workshop offered valuable insights.

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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: 25 June 2012; accepted: 09 September 2012; published online: 26 September 2012.*

*Citation: Schimel JP and Schaeffer SM (2012) Microbial control over carbon cycling in soil. Front. Microbio. 3:348. doi: 10.3389/fmicb.2012.00348*

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

*Copyright © 2012 Schimel and Schaeffer. 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.*

# Fundamentals of microbial community resistance and resilience

#### **Ashley Shade1† , Hannes Peter 2† , Steven D. Allison3 , Didier L. Baho4 , Mercè Berga5 , Helmut Bürgmann6 , David H. Huber 7 , Silke Langenheder 5 , Jay T. Lennon8 , Jennifer B. H. Martiny 3 , Kristin L. Matulich3 , Thomas M. Schmidt 9 and Jo Handelsman1\***

#### **Edited by:**

Cyrille Violle, CNRS, France

#### **Reviewed by:**

Anthony Yannarell, University of Illinois at Urbana-Champaign, USA Jack Gilbert, University of Chicago, USA

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

Jo Handelsman, Department of Molecular, Cellular and Developmental Biology, Yale University, 219 Prospect Street, New Haven, CT 06520, USA. e-mail<EMAIL_ADDRESS>
†Ashley Shade and Hannes Peter have contributed equally to this work. Microbial communities are at the heart of all ecosystems, and yet microbial community behavior in disturbed environments remains difficult to measure and predict. Understanding the drivers of microbial community stability, including resistance (insensitivity to disturbance) and resilience (the rate of recovery after disturbance) is important for predicting community response to disturbance. Here, we provide an overview of the concepts of stability that are relevant for microbial communities. First, we highlight insights from ecology that are useful for defining and measuring stability. To determine whether general disturbance responses exist for microbial communities, we next examine representative studies from the literature that investigated community responses to press (long-term) and pulse (short-term) disturbances in a variety of habitats.Then we discuss the biological features of individual microorganisms, of microbial populations, and of microbial communities that may govern overall community stability. We conclude with thoughts about the unique insights that systems perspectives – informed by meta-omics data – may provide about microbial community stability.

**Keywords: microbial ecology, disturbance, stability, sensitivity, structure-function, perturbation, community structure, time series**

## **INTRODUCTION**

In habitats as diverse as soil and the human body, key ecosystem processes are driven by microbial communities – local assemblages of microorganisms that interact with each other and their environment (Konopka, 2009). Thus, microbiology research in biomedical, environmental, agricultural, and bioenergy contexts shares a common challenge: to predict how functions and composition of microbial communities respond to disturbances (Robinson et al., 2010a; Gonzalez et al., 2011a,b; Costello et al., 2012).

Here, we introduce a breadth of topics that provide insight into the responses of microbial communities to disturbance. We first highlight key concepts from ecology that are useful in thinking about microbial stability, pointing readers to an extensive literature on the subject of disturbance and community stability. We then summarize the current state of knowledge about resistance and resilience of microbial communities inhabiting a variety of ecosystems, emphasizing overarching trends gleaned from a review of 247 representative studies. We next provide a synthesis of the properties of individual microorganisms, populations, and communities that influence microbial community stability. Finally, we discuss insights into stability that may emerge from a systems-level perspective – describing microbial communities as networks of genes, transcripts, proteins, and metabolite signals.

## **KEY CONCEPTS FROM ECOLOGY**

#### **DEFINING DISTURBANCE**

Disturbances are causal events that either (1) alter the immediate environment and have possible repercussions for a community or (2) directly alter a community (Rykiel, 1985; Glasby and Underwood, 1996). After disturbance, community members may die (mortality) or change in their relative abundances (Rykiel, 1985). A disturbance can be difficult to define, as its definition depends on scale and context. Disturbances occur at various spatial and temporal scales (Paine et al., 1998) with different frequencies (number of occurrences per unit time), intensities (magnitude of the disturbance), extents (proportion of the ecosystem affected), and periodicities (regularity of occurrences; Grimm and Wissel, 1997). Disturbances may also be defined relative to the disturbance regime of an ecosystem, such as a fire or flooding cycle.

Disturbances are often classified as *pulses* or *presses* depending on their duration (Bender et al., 1984). In general, pulse disturbances are relatively discrete, short-term events, whereas presses are long-term or continuous (**Figure 1**). However, these time scales may differ depending on the generation time of the community of interest. For instance, a tree falling in a forest may create a press disturbance to the underlying soil microorganisms, whereas the same event might be considered a pulse disturbance to nearby understory vegetation. Though the distinction between pulse and press disturbances has received much attention in the ecology literature, there is less discussion of patterns of microbial community responses to pulses and presses. However, microbial community responses to pulses and presses are important to consider in the context of global climate change. With global changes, pulse disturbances (e.g., extreme weather events) are expected to increase in frequency, and ongoing press disturbances are expected to continue (e.g., atmospheric increases in carbon dioxide, ocean acidification; Intergovernmental Panel on Climate Change, 2007). Therefore, throughout this work, we discuss microbial community responses in both pulse and press disturbance scenarios.

#### **DEFINING STABILITY**

Disturbance and community stability are necessarily related, as stability is defined as a community's response to disturbance (Rykiel, 1985). Here, we adopt definitions most similar to Pimm (1984), in which stability is comprised of resistance and resilience (**Table 1**), two quantifiable metrics that are useful for comparing community disturbance responses and have precedent in the microbial ecology literature (e.g., Allison and Martiny, 2008). However, readers should be aware that the ecology literature includes many definitions of stability, and a full examination of these definitions is available elsewhere (Grimm and Wissel, 1997). Here, resistance is defined as the degree to which a community is insensitive to a disturbance, and resilience is the rate at which a community returns to a pre-disturbance condition (Pimm, 1984). A related concept, sensitivity, is the inverse of resistance and defined as the degree of community change following a disturbance. Both resistance and resilience are usually quantified in relation to a community's level of intrinsic variability, sometimes referred to as the "normal operating range" (van Straalen, 2002). There are many methods in the literature [see a recent summary by Griffiths and Philippot (2012)] for comparing resistance or resilience across communities (Orwin and Wardle, 2004).

A community's stability can be investigated in terms of functional or compositional parameters. In microbial ecology, many studies also focus on the degree to which functional and compositional stability are related. This may depend in large part on the particular function of interest (Schimel, 1995). For functions that are carried out by many taxa (Schimel, 1995), i.e., communities harboring a high degree of functional redundancy, changes in community composition may not correspond with changes in functional rates (Allison and Martiny, 2008). Alternatively, for functions performed by only a few taxa (for example, in situations of ecological coherence of closely related taxa, Philippot et al., 2010), the sensitivity and resilience of this function may closely follow changes in the abundance of those taxa. Notably, estimates of resistance and resilience for the same microbial community may have different values depending on whether compositional or functional responses are measured and on which functions are used to assess stability.

**FIGURE 1 | Examples of quantitative definitions of resistance and resilience from ecology (Westman, 1978; Orwin andWardle, 2004; Suding et al., 2004).** A microbial community parameter of interest has a mean value of y 0 and temporal variance, illustrated here by a 95% confidence interval around the mean (though other quantifications of variance, such as standard deviation or variance ratios may be used). A pulse disturbance ends (or a press disturbance begins) at time t 0 and the parameter changes by |y 0 − yL| after a time lag tL − t 0. Resistance (RS) is an index of the magnitude of this change.

$$\mathcal{RS} = 1 - \frac{2\left|\chi\_0 - \chi\_1\right|}{\lambda^\rho + |\lambda^\rho - \lambda^\iota|}\tag{1}$$

Resilience (RL) is an index of the rate of return to y 0 after the lag period,

$$\mathcal{RL} = \left[ \frac{2|\chi\_0 - \chi\_1|}{|\chi\_0 - \chi\_1| + |\chi\_0 - \chi\_1|} - 1 \right] \div (t\_\circ - t\_\circ) \tag{2}$$

where yn is the parameter value at measurement time tn. A parameter is "recovered" when it is statistically indistinguishable from the pre-disturbance mean. Alternatively, the parameter may not recover and instead may stabilize at a new mean value representing an alternative stable state. This possibility is more likely in response to a press disturbance. Further, RS and RL could be related to normalized parameters describing the disturbance (e.g., intensity, duration, frequency of the stressor in relation to the pre-disturbance mean and variance), which is useful for cross-system comparisons.

One perspective of stability, sometimes referred to as "ecological resilience," relies on the existence of many stable states in which a community may reside (e.g., Holling, 1973, 1996; Botton et al., 2006). For instance, a community may shift to a new stable state when subjected to a press disturbance (**Figure 1**). The existence of multiple equilibria can also be illustrated by the concept of a stability landscape (Beisner et al., 2003; Scheffer and Carpenter, 2003; Collie et al., 2004; Folke et al., 2004), which can be used to conceptualize microbial community responses to disturbance (Blodau and Knorr, 2006; Mao-Jones et al., 2010; Bürgmann et al., 2011; Seto and Iwasa, 2011). In a visualization of a stability landscape, a ball represents a community that can exist in one of many different equilibrium states (basins) within the stability landscape (Beisner et al., 2003; Scheffer and Carpenter, 2003; **Figure 2**). A disturbance

event is analogous to applying a force to the ball within its basin. A community may resist the disturbance, which is represented by the ball remaining in its basin. Alternatively, the community could change but exhibit resilience, which is represented by the ball moving outward from the basin but then returning to its original location (**Figure 2A**). If resistance and resilience are low or the disturbance strong enough, the community may shift to an alternative equilibrium (also called alternative stable state), represented by the ball moving into a new basin. Once in an alternative equilibrium, the community's return to the previous composition or function may be difficult (Botton et al., 2006). Moreover, environmental conditions shape the stability landscape (**Figure 2B**). Thus, if a press disturbance permanently alters the stability landscape, this will have implications for community stability and the likelihood of community shifts to alternative stable states.

Studies of alternative stable states and regime shifts in microbial systems remain rare (Botton et al., 2006), though the conceptual framework is gaining popularity, especially among researchers interested in the human gut microbiome (Lozupone et al., 2012), as the existence of alternative stable states may provide explanation as to the immense variability observed within and among individual gut microbial communities. There is also evidence of alternative stable states in the vaginal microbiome, where eight "super-groups" of distinct microbial assemblages have been detected across hundreds of healthy women (Zhou et al., 2007).

Additionally, there are a few concrete examples of microbial communities that exhibited regime shifts. For instance, increased influx of groundwater triggered a functional regime shift from iron-reduction to sulfate-reduction in anoxic sediments of mine drainage lakes (Blodau and Knorr, 2006), and operational changes triggered a compositional andfunctional regime shift in a sequencing batch reactor for nitrogen elimination from urine (Bürgmann et al., 2011). Furthermore, regime shifts in microbial communities may have far-reaching consequences for ecosystems, as suggested by theoretical models of coral reef microbial communities that shift composition from antibiotic-producers to pathogens (Mao-Jones et al., 2010). As time series studies are extended to include more disturbance events, alternative stable states may be detected for other microbial communities. In the example in **Figure 2**, microbial community composition and function are mapped using multivariate ordination to visualize a stability landscape.

#### **MEASURING STABILITY: COMMUNITY RESPONSE TO DISTURBANCE**

Ecologists have long considered how to quantify resistance and resilience of communities and their functions. One experimental design that specifically addresses the impact of a disturbance is "before-after-control-impact" (BACI; Stewart-Oaten et al., 1992; Smith et al., 1993; Ellis and Schneider, 1997; Stewart-Oaten and Bence, 2001; Fraterrigo and Rusak, 2008). But, BACI has known limitations [discussed in Underwood (1994)], including violation

of assumptions of non-independence of samples collected over time. Thus, multivariate autoregressive moving-average models, which remove temporal autocorrelation, have been applied to understand the contribution of species and environmental interactions to the stability of communities (e.g., Ives et al., 2003). Additionally, Bayesian approaches such as dynamic linear models (DLM) account for the autocorrelation of time series data, and estimate the sensitivity of data to disturbance (e.g., Carpenter and Brock, 2006). Techniques such as DLM also make projections about future behavior of a response variable based on pre-disturbance data distribution (sometimes called intervention). There are many other techniques that quantify temporal variability to assess the impact of disturbance and measure resilience (e.g., Underwood, 1994; Ives, 1995; Ives et al., 2000; Fraterrigo and Rusak, 2008). Methods for handling temporal datasets (e.g., Lennon, 2011) will become increasingly useful to microbiologists as more and longer time series of microbial communities become available. However, the species-rich nature of many microbial datasets, especially those generated using highthroughput sequencing, present computational challenges that will likely require new methods of statistical analysis (Gonzalez et al., 2011a).

When measuring stability, it is important to distinguish between responses to pulse and press disturbances, as recovery may be quantified by slightly different methods (Glasby and Underwood, 1996). Ideally, resilience to a press disturbance should be determined after the community composition or function reaches its maximum deviation from the expected mean (**Figure 1**).With a press, there is often more uncertainty about when the disturbance has caused the maximal change in the community, especially if it is unknown when the press disturbance began and whether it has ceased. Therefore, when to establish the baseline for measuring resilience after a press disturbance is less obvious, and therefore a major research challenge. By contrast, response to a pulse can be defined immediately after the pulse ends, although there may be a time lag before the disturbance response is completed.