August 20, 2008

New and Current Microbiological Tools for Ecosystem Ecologists

By Drenovsky, Rebecca E Feris, Kevin P; Batten, Katharine M; Hristova, Krassimira

ABSTRACT.- Interest in the relationships between soil microbial communities and ecosystem functions is growing with increasing recognition of the key roles microorganisms play in a variety of ecosystems. With a wealth of microbial methods now available, selecting the most appropriate method can be daunting, especially to those new to the field of microbial ecology. In this review, we highlight those methods currently used and most applicable to ecological studies, including assays to study various aspects of the carbon and nitrogen cycles (e.g., pool dilution, acetylene reduction, enzyme analyses, among others), methods to assess microbial community composition (e.g., phospholipid fatty acid analysis (PLFA), denaturing gradient gel electrophoresis (DGGE), terminal restriction fragment length polymorphism analysis (TRFLP), quantitative polymerase chain reaction (qPCR)) and methods to directly link community structure to function (e.g., stable isotope probing (SIP)). In our discussion of these methods, we describe the information each method provides, as well as some of their strengths and weaknesses. Using a case study, we illustrate how these methods can be applied to investigate relationships between microbial communities and the processes they perform in wetland ecosystems. We end our discussion with a series of questions to consider prior to designing experiments, in the hope that these questions will help guide ecologists in selecting the most appropriate method (s) for their research.


Over the past 20 y, the variety of methods available to ecosystem and microbial ecologists has dramatically increased. At the same time, interest in soil microorganisms and the ecosystem processes they drive has expanded. Despite this interest, a serious disconnect exists between ecosystem and microbial ecology, with these disciplines approaching the same questions from very different scales. Ecosystem ecologista focus on the landscape and regional scales at which processes are manifested, whereas microbial ecologists focus on the pore or plot scale at which underlying mechanisms can be discerned and manipulated. For example, most studies of nutrient or carbon cycling, processes well known to be mediated by microorganisms, do not measure the diversity and structure of communities driving these functions (e.g., Ruckauf et al, 2004; Strom and Christensen, 2007). Similarly, numerous microbial community studies do not directly investigate their ecosystem roles (e.g., Rinklebe and Langer, 2006; Dejournett et al, 2007). Failure to connect these scales may limit our basic understanding of ecosystem functioning or impede identification and/ or implementation of strategies for restoration or biodiversity preservation.

Historically, microbial communities and processes have been treated as a black box, in part due to methodological limitations. Current advances in microbiological techniques have overcome many of these constraints. Our goal is to introduce those unfamiliar with microbial ecology to various techniques that can probe ecosystems both functionally and structurally. A brief discussion of ecosystem function measurements is followed by descriptions of modern biochemical and molecular tools used to examine microbial community composition and to link ecosystem functions to microbial communities. Finally, a case study of a hypothetical wetland is used to illustrate how microbial methods can be applied to address ecosystem questions.


Microorganisms are intricately involved in global carbon and nitrogen cycles. Many methods familiar to ecologists quantify these microbially driven processes. Although these measurements provide information about microbial activity, most do not assess which microorganisms are responsible for cycling nitrogen and carbon. Here, we provide a sub-set of methods providing ecosystem-level function information; later sections will discuss means to characterize microbial communities driving these functions and how these approaches can be linked for greater understanding.


Each step of the nitrogen cycle is regulated by different bacterial functional groups. The main processes include nitrogen fixation (fixing atmospheric N^sub 2^ into NH^sub 3^), ammonia oxidation (conversion of NH^sub 4^^sup +^ to NO^sub 3^^sup -^), assimilatory nitrate reduction (conversion of NO^sub 3^^sup -^ to NH^sub 4^^sup +^), nitrogen mineralization (conversion of organic N to inorganic N) and denitrification (conversion of NO^sub 3^^sup -^ to NO, N^sub 2^O and N^sub 2^) . These processes, in addition to leaching, run-off and ammonia volatilization, regulate ecosystem nitrogen fluxes. To target specific portions of the nitrogen cycle, one can use acetylene reduction to measure nitrogen fixation rates (Hardy et al, 1968), stable isotope techniques to follow nitrogen transformations and fluxes (e.g., ammonia oxidation and nitrate reduction, denitrification, plant and microbial N uptake rates; Jackson et al, 1989; Delaune et al, 1998) and chloroform fumigation- incubation to determine potential net N mineralization (Jenkinson and Powlson, 1976).


Carbon cycle and decomposition processes are intimately linked. Carbon cycling can be assessed via enzyme activity, mass loss and respiration (Sinsabaugh et al, 1993; Jackson et al, 1995). By calculating mass loss from litter bags, decomposition rates can be determined and microbial activity can be measured as CO2 evolution (e.g., Jackson et al, 1995). In wetlands, microbial methane production and consumption also are important components of the carbon cycle and typically are measured by gas chromatography (e.g., Strom and Christensen, 2007).


Before we address means for characterizing communities, we should address the units that comprise these communities - the individual species present. Microbiologists agree that species exist (Cohan, 2002), but how these species are defined explicitly depends on the species concept applied. Historically, microbial species were classified based on characteristics such as morphology and physiology (Rossello-Mora and Amann, 2001; Johansen and Casamatta, 2005). With the advent of molecular techniques, genetic similarity began to play an increasingly important role in defining microbial species (Rossello-Mora and Amann, 2001). Using these characteristics, a pragmatic definition of species can be applied in which species are clusters of phenotypically and genetically similar organisms (Cohan, 2002). However, this definition may yield a conservative estimate of species, as it does not recognize the unique ecological roles certain ecotypes play in the environment. Thus, applying Cohan's (2002) ecotypic species concept, we recognize that within a given named species, there are many ecotypes and, thus, a named species may be more similar to a genus than a species (as defined by macroecologists).


Many well established methods reveal information about microbially driven ecosystem processes; however, to fully understand why ecosystem function changes occur, functional measurements must be linked to microbial community assays. Microbial cells contain a wealth of information that can be exploited to determine community structure and function (Fig. 1, Table 1). Cell membrane lipids provide an estimate of total microbial biomass and community composition. DNA-based methodologies can identify organisms, determine evolutionary relationships and estimate potential function and abundance of individual species and microbial groups. In addition, by measuring rRNA or functional gene expression, microbial activity can be assessed. Numerous methods are available for extracting microbial DNA and RNA from a suite of environmental matrices (i.e., soils, sediments, water, etc.) (Miller et al, 1999). However, each of these methods carries its own bias, such that the specific nucleic acid extraction method can affect which members of a microbial community are detected (Martin-Laurent et al, 2001; Luna et al, 2006). Therefore, it is important to apply the same nucleic acid extraction method consistendy during a given experiment to prevent unwanted variation between samples. Further, care should be given when choosing a nucleic acid extraction method to ensure that the method employed allows recovery of DNA/RNA from the target organisms and purifies it such that downstream analyses such as PCR are facilitated.

Often, microbial community composition is determined by examining an entire microbial community's "fingerprint". Here, a fingerprint is the presence/absence pattern, and sometimes abundance, of microbial groups in an environmental sample; fingerprint resolution can vary from species-level (DNA-based methods in conjunction with sequencing) to group-level (e.g., bacteria, fungi; cell membrane- based methods). Microbial communities can be compared among environmental samples using the same multivariate statistics as community ecologists studying macro-organisms (e.g., Feris et al, 2003; CordovaKreylos et al, 2006; Webster and Bourne, 2007). These methods can indicate if a treatment or perturbation changes overall community composition, specific groups or individual taxa. Frequently used methods include hierarchical classification measures such as cluster analysis; indirect gradient analysis methods such as principal components analysis, correspondence analysis and non- metric multidimensional scaling; and direct gradient analysis methods such as canonical correspondence analysis. Authors should be aware that different types of data are more or less appropriate for the various analyses based on model assumptions. For example, principal components analysis is not robust with datasets that are nonlinear and contain many zeroes (Ludwig and Reynolds, 1988). Additionally, proper interpretation of the output is not trivial. Thus, we recommend that authors consult the various texts available discussing these methods before analyzing their data (see Jongman et al, 1995; Legendre and Legendre, 1998; McCune and Grace, 2002; Leps and Smilauer, 2003). Three frequently used fingerprinting techniques that generate these multivariate datasets are phospholipid fatty acid analysis (PLFA), denaturing gradient gel electrophoresis (DGGE) and terminal restriction fragment length polymorphism analysis (TRFLP). Additionally, microbial abundance and identity can be determined by real-time PCR methods (qPCR). Many fingerprinting approaches as well as other DNA and RNA-based analyses of microorganisms depend on the amplification of specific gene sequences via the polymerase chain reaction (PCR). PCR is a highly specific and robust enzymatic reaction that allows one to specifically amplify a gene target of interest from low amounts present in an environmental sample to quantities large enough to perform many laboratory-based analyses. PCR has been used widely to analyze microbial community responses to perturbation, assess the metabolic potential of a community by detecting and quantifying the presence of specific structural genes and to quantify levels of specific groups or species of microorganisms in situ (Feris et al, 2003; Hristova et al, 2003; and many others). Successful amplification of a target gene sequence requires the development or selection from the literature of an appropriate set of PCR primers. While there are a multitude of PCR primers described in die literature that target large phylogenetic groups (e.g., Bacteria, Archaea, Eukarya), specific microbial groups/ species (e.g., beta- proteobacteria, cyanobacteria, etc.) or functional genes (e.g., genes necessary for nitrogen fixation, ammonia oxidation, etc.) ; the selection of die appropriate PCR primers for a given study may not be trivial. Some care should be given to ensure that the primer set chosen is either highly selective for the targeted group or general enough to capture all the organisms within a targeted group that are present in an environmental sample. The importance of proper primer choice is illustrated when we consider that PCR, while powerful and robust, can only detect genes of interest if those genes have some sequence homology with the PCR primers that are utilized in the PCR reaction.

In addition, for each of the approaches described here it is important to replicate each measure at a level appropriate for a given study to ensure that measured microbial responses can be related to ecosystem processes with a desired level of confidence. Many commercially available DNA extraction kits efficiently recover DNA from 0.5-1 g soil/sediment samples. However, it is unlikely that a single 1 g sample from a complex heterogeneous ecosystem will adequately describe the natural variation in the local microbial community. Thus, multiple samples of this size may need to be collected, extracted and possibly pooled to provide a sample that is representative of a given environment. To design an adequate sampling strategy it is important to consider the level of heterogeneity within a given environment at scales relevant to the organisms performing processes of interest. For example, scales of importance to microorganisms can range from the sub mm^sup 3^ to m^sup 3^ scales and greater (Ranjard et al, 2003; Zhou et al, 2004; Becker et al, 2006; Kan et al, 2007). Therefore, sampling at multiple scales and subsequently applying statistical analyses to determine at which scale microbial communities correlate with observed ecosystem functions may be necessary.


To analyze a community by PLFA, lipids are extracted directly from soil; then phospholipids are separated from other lipid fractions, methylated, and analyzed by gas chromatography (White et al, 1979) (Fig. 2). PLFA should not be confused with total soil fatty acid methyl ester analysis (TSFAME), in which all soil lipids are extrated and analyzed (Fig. 2; see Drenovsky et al, 2004 for a detailed comparison). In contrast to TSFAME, PLFA extracts contain only cellular membrane lipids, which decompose rapidly in die environment, and thus, more accurately represent the living microbial community (White et al, 1979; Pinkart et al, 2001).

PLFA provides information regarding microbial community composition, biomass and diversity. Since microbial groups vary in cellular membrane composition, changes in extracted PLFA can indicate shifts in community structure (Vestal and White, 1989). Although some fatty acids are considered biomarkers for certain microbial groups (e.g., fungi: 18:2 omega6,9c), many PLFAs are shared among organisms; thus, biomarkers must be interpreted with caution and with consideration of sample environmental conditions (Bossio and Scow, 1998). Although PLFA analysis can provide biomarker information regarding organisms involved in the carbon cycle (e.g., methanotrophs, fungi), widely accepted biomarkers for the N cycle have yet to be determined. Additionally, PLFA cannot describe the archaeal community (e.g., methanogens) , as these organisms have ether-linked, not ester-linked, membrane lipids. Summing all fatty acid concentrations in an environmental sample estimates total microbial biomass (less the archaeal biomass), and the number of fatty acids detected in a sample provides a diversity proxy (e.g., Ravit et al, 2003). Although there is not a one-to-one relationship between fatty acids detected and number of microbial taxa present, large differences between samples may indicate differences in microbial diversity, especially when entire groups of fatty acids are missing or in low abundance.


Compared to lipid-based approaches, DNA- and RNA-based methods provide high resolution (i.e., species-specific) microbial community characterization. Here we focus on two common DNA- and RNA-based fingerprinting methods, DGGE and TRFLP analysis. Bodi methods exploit gene sequence variation widiin and between microbial species to assess community composition and diversity. Both are PCR-based, requiring nucleic acid extraction from environmental samples and subsequent PCR amplification of target genes (Fig. 3) (Muyzer et al, 1993; Clement et al, 1998) . Based on the PCR primers employed, DGGE and/ or TRFLP can assess entire bacterial, archaeal or eukaryotic communities (Feris et al, 2003; Feris et al, 2004; Cookson et al, 2007; Yergeau et al, 2007) , target specific phylogenetic groups (Perez-Jimenez and Kerkhof, 2005) , detect individual species (Hristova et al, 2003) or identify specific functional groups (Burgmann et al, 2005). Often, ribosomal DNA (rDNA) genes are targeted because they contain bodi highly conserved and more variable regions useful in species-level assessments of community composition (Amann et al, 1995).

Although there are many molecular tools available for characterizing microbial communities, including microarrays, metagenomic and metaproteomic approaches, we have chosen to focus our review on DGGE and TRFLP. These methods require less optimization and technical expertise and less expensive instrumentation, and yet they still provide adequate resolution for characterizing microbial community responses to environmental perturbations. Additionally, although clone library creation for species-level characterization of microbial communities is a well- accepted method and can provide a great deal of information about community structure, this approach is difficult to apply to multiple samples collected across large scales, as would be of interest to ecosystem ecologists. Thus, we present approaches that can be used to characterize microbial communities at larger spatial scales and provide information to guide subsequent high resolution characterizations of specific samples, as deemed important by the initial higher throughput screening/ analytical tests we describe.

With DGGE, PCR products are separated based on sequence content via Polyacrylamide gel electrophoresis (Muyzer et al, 1998). The PCR products partially denature as they travel through a gradient of chemical denaturants incorporated in the gel matrix. Specifically, the separation of PCR fragments on a DGGE gel is dependent on the GC content of the DNA fragment, such that PCR products with higher GC contents are more difficult to denature. This difference is due to the three hydrogen bonds between a GC base pair relative to the two hydrogen bonds between an AT base pair. Thus, PCR products with higher GC contents will migrate farther in a DGGE gel before denaturing and slowing their migration rate. Denaturation of the PCR products retards further movement, resulting in DNA "band" formation. By separating PCR product mixtures from environmental samples, a community fingerprint is created (Fig. 4) . The number of DNA bands, their positions and in some cases their relative intensities can be compared by standard diversity analyses. When more specific information is required, bands can be cut from the gel, sequenced and compared to sequences of cultured organisms or other environmental samples in databases such as Ribosomal Database Project II (RDPII) ( or NCBI Blast (http:// TRFLP rapidly is becoming the community analytical method of choice due to its high resolution, reproducibility and throughput. In TRFLP, one or both PCR primers contain a 5'-terminal fluorescent tag that becomes incorporated into the PCR product. Amplification of community DNA produces PCR products with varying sequence content. Digestion (i.e., cutting) of such PCR products with sequence-specific restriction enzymes forms a mixture of terminal restriction fragments (TRFs); their sizes and amounts then are determined using an automated DNA sequencer (Clement et al, 1998). Similar to DGGE band patterns, the resulting TRF pattern can be used to characterize communities and identify the presence of unique microbial species/groups. However, to obtain precise species-specific identification requires matching TRFs of interest with TRFs in a clone library of known DNA sequences.

Commonly, each DGGE band or TRF is thought to represent a single sequence type and, thus, an individual "species". However, a single DGGE band or TRF can contain multiple sequences, leading to conservative estimates of species richness (Klappenbach et al, 2000). Also, PCR artifacts can affect band and TRF peak intensity; thus, intensity does not necessarily imply species abundance and must be used with caution in comparative analyses.

In contrast to DNA-based approaches, which characterize community composition, RNAbased measures indicate microbial activity (Burgmann et al, 2005; Holmes et al, 2005). Actively growing and dividing cells maintain higher intracellular rRNA concentrations; thus, rRNA presence indicates cellular activity (Wagner, 1994). By comparing rRNA and rDNA patterns via DGGE or TRFLP, metabolically active and inactive community members can be detected (Duineveld et al, 2001; Norris et al, 2002). However, RNA is degraded rapidly by native RNAses present in most environments. Thus, precautions must be taken during sample collection, transportation and processing to prevent RNA degradation for accurate in situ assessment of microbial activity.


One drawback of traditional molecular methods is that they provide qualitative, but not quantitative, information. Development of quantitative techniques such as fluorescent in situ hybridization (FISH), competitive reverse-transcriptase (RT)-PCR and real-time PCR has improved our ability to garner quantitative information about microbial populations in situ. Here we focus on real-time PCR, which rapidly is becoming the most commonly used quantitative molecular method in microbial ecology.

Real-time quantitative PCR (qPCR) has been used to quantify abundance of specific microbial groups and/or species in environmental samples by enumerating 16S rDNA or functional gene copy numbers (Suzuki et al, 2000; Hristova et al, 2003; Okano et al, 2004). This technique is based on either 5'-nuclease chemistry (e.g., TaqMan assay; Holland et al, 1991) or incorporation of a DNA- binding fluorescent dye (e.g., SYBRGreenI; Feris et al, 2003; Cavagnaro et al, 2007). Both approaches can provide precise quantitative measures of a specific DNA or RNA sequence (e.g., species, functional group or phylogenetic group) in an environmental sample and, depending on the standard used, absolute microbial cell densities or relative densities can be determined. Some advantages of real-time qPCR include sensitivity, a broad linear quantification range, amplification and detection homogeneity and potential for high throughput.


One pragmatic concern regarding these techniques is the associated cost. Instrumentation costs can be substantial. For example, a new gas Chromatograph and autosampler for analyzing PLFA samples can be well over $50,000, and a capillary system for TRFLP an analysis can cost over $100,000. In addition to instrumentation costs, the cost of reagents and supplies, as well as labor costs can quickly accumulate. Those new to the field will benefit from collaborating with researchers who routinely do these analyses and, thus, currently have the appropriate analytical instrumentation and reagents. Additionally, this type of interaction should foster cross- discipline interactions between microbiologists and ecosystem ecologists.


Traditionally, links between microbial community structure and function have been limited to correlative approaches. Whereas these techniques provide some insight into structure-function relationships, they offer no direct evidence linking particular microbial community member(s) to specific processes. In diis section, we describe how stable isotope probing (SIP) techniques can be used to directly link microbial community structure to a specific function.

SIP applies labeled substrates (e.g., ^sup 13^C methane) to environmental samples and monitors their incorporation into phylogenetically significant bio-molecules (PLFAs, DNA and RNA) (Radajewski, 2003; Osaka et al, 2006; Cebron et al, 2007; Neufeld et al, 2007). If die substrate is consumed and assimilated, the label is incorporated into new microbial biomass, which can be extracted and its fingerprint analyzed. Thus, it is possible to directly link a measurable function (e.g., methane oxidation, carbon consumption/ oxidation) with specific microorganisms in an environment. These techniques, in some cases, are most applicable to questions regarding incorporation of carbon-based compounds into microbial biomass, as some nitrogen cycle transformations (e.g., nitrification and denitrification) do not incorporate N into microbial biomass. However, SIP techniques for identifying organisms involved in other aspects of the N cycle (e.g., nitrogen fixation and other N- assimilation pathways) have been developed (Buckley et al, 2007a, b).

PLFA-SIP relies on gas chromatography-combustion-isotope ratio mass spectrometry (GG C-IRMS) to study isotopie composition of individual PLFA. Although PLFA-SIP is extremely sensitive and provides quantitative information about soil microbial communities, specieslevel identification is not possible, unlike DNA-based SIP, which can resolve structurefunction relationships at genus and species levels. DNA-SIP relies on separation of heavy (labeled) and light (unlabeled) DNA by density gradient centrifugation (Radajewski, 2003; Cebron et al, 2007). However, clear separation of labeled and unlabeled DNA depends on substrate isotopie enrichment, limiting this method to compounds highly enriched with a rare stable isotope (^sup 2^H, ^sup 13^C, or ^sup 15^N). DNA-SIP mainly has been demonstrated using single carbon compounds (e.g., methane or CO2); however, examples using more complicated ^sup 13^C labeled substrates exist (Jeon et al, 2003; Park et al, 2006).

Although DNA-SIP provides an enhanced level of resolution relative to PLFA-SIP, a number of factors may influence ^sup 13^C enrichment in DNA. These include: whether the assimilated substrate is used by a few or many community members, whether the operative anabolic pathway prevents incorporation in an unevenly labeled substrate and how long the ^sup 13^C substrate pulse duration is relative to how rapidly the labeled carbon is turned over, converted, and made available to secondary consumers. Thus, incubation times often must be empirically determined to maximize label incorporation while minimizing the likelihood of secondary consumption. Other considerations include relative abundance of naturally occurring unlabeled substrate, and the active microbial population's DNA syndiesis rate (Lueders et al, 2004).

RNA-based SIP avoids some of these complications. In active cells RNA synthesis rates are high, and labeling can occur without DNA synthesis or organism replication (i.e., the organism can be metabolically active but not growing/dividing). Therefore, labeled RNA may be detected more quickly than labeled DNA and, dius, may be more appropriate for environmental studies, if RNA can be extracted reliably from the environment in question (Lueders et al, 2004; Schwarz et al, 2007).


Methane production and denitrification are two ecosystem functions affected by the specific types and activities of microorganisms present (Sylvia et al, 2005). Here, we use approaches presented above to measure how pollutant loading could impact these functions. We compare two hypothetical wetlands, 20 km apart, with similar geology and hydrology but different pollutant loadings. The first is relatively pristine, with limited inputs from urban and industrial settings. The second is impacted heavily by andiropogenic activity, annually receiving significant sewage effluent rich in labile C and N. With these inputs, methane emissions and denitrification rates are five and ten times higher in the polluted wetland, respectively. In our study, we want to: (1) understand how the effluent is changing microbial community composition, (2) determine if community changes correlate with alterations in methane efflux and denitrification rates and (3) link observed functional changes to specific organisms or functional groups (Table 2).

Using both lipid and DNA-based approaches, we can examine community changes in response to sewage input at two levels of resolution while simultaneously taking advantage of these approaches' strengths. Since PLFA is relatively rapid and standardized, we can sample intensively, quickly gaining quantitative, landscape-level information about microbial groups associated with particular site characteristics. From our mock PLFA chromatograms, it is apparent that the pristine site has lower total PLFA number (a proxy for diversity) and lower total PLFA concentration (i.e., total microbial biomass) (Fig. 5). Analyzing our data using correspondence analysis (CA), we can see that the soil samples from die two sites group separately from one another, indicating that they differ in fatty acid composition (Fig. 6). In New Jersey salt marshes, PLFA revealed significantly fewer fatty acids in physically disturbed and chemically polluted soils compared to undisturbed, relatively pristine soils, suggesting chemical and physical disturbance may decrease microbial diversity (Ravit et al, 2003). Likewise, in floodplain soils from Germany, differences in lipid profiles between sites suggested that permanent flooding, compared to intermittent flooding, prevented establishment of fungi (Rinklebe and Langer, 2006). Using DGGE or TRFLP, we can focus on variation in broad functional groups (e.g., methanotrophs, denitrifiers) and target specific organisms (e.g., Methylocystis or Paracoccus denitrificans) . This greater taxonomic resolution complements the quantitative biomass and functional group information gained by our PLFA analyses. For instance, in a microcosm study of rice field soil, DGGE and TRFLP were used to assess how changes in protistan bacteriovore abundance changed bacterial diversity and composition over time (Murase et al, 2006). After applying our fingerprinting methods, we can augment our studies by using qPCR to determine whether sewage additions are altering population densities of specific microbial groups. For example, qPCR was used to detect increased ammonia oxidizing bacteria (AOB) population densities in marine sediments with high anthropogenic N inputs from wastewater plants and other urban systems in Tokyo Bay, Japan (Urakawa et al, 2006). In this study, qPCR was able to detect cell numbers near the limit of other methods (e.g., in situ hybridization and slot blot hybridization), indicating its detection sensitivity. Additionally, cell numbers determined by qPCR and immunofluorescence staining were well- correlated, suggesting that although DNA may be lost during purification and amplification, the results from this assay are still quite robust.

Our second objective is to link greenhouse gas production to changes in community composition. Using gas chromatography and stable isotope techniques, we could monitor wetland methane emission and denitrification rates. Ultimately, we want to understand direct relationships between community composition and ecosystem function; therefore, a first step would be to test whether community composition differences correlate with measured process rates (e.g., denitrification rates and methane fluxes). Understanding how fluxes of greenhouse gases change with conditions can give us important information linking community structure to community function. Using canonical correspondence analysis (CCA) , we could determine how microbial community composition (as assessed by PLFA, DGGE, or TRFLP) is related to these measured abiotic variables (Fig. 7) . From the CCA biplot we can see diat samples from the two sites group separately, indicating that they have different microbial communities. Additionally, samples from the impacted site are associated with higher methane efflux, N^sub 2^O efflux and soil nitrate concentrations, as the vectors for these environmental variables are increasing in the direction of the samples from the impacted site. In a study of California salt marsh sediments, it was determined that heavy metal concentrations were stronger drivers of microbial community composition than organic pollutants using partial canonical correspondence analysis (pCCA) (i.e., effects of spatial variables were removed) (Cordova-Kreylos et al, 2006).

Once a link between composition and process rates is established, we can explore which specific community members are involved in observed changes in ecosystem function. To target organisms responsible for elevated methane fluxes in the contaminated wetland, a ^sup 13^C labeled low molecular weight substrate (e.g., acetate) could be added to wetland sediments in situ or in microcosms. After short incubation times, ^sup 13^C levels in CO2 and CH^sub 4^ pools could be monitored and methane production and consumption rates calculated via pool-dilution by GC/MS. Following DNA or RNA extraction and 13C DNA/RNA fraction isolation, DGGE or TRFLP could be used to identify organisms that have incorporated the ^sup 13^C label. By simultaneously measuring methane efflux, characterizing CH^sub 4^ ^sup 13^C enrichment and tracking ^sup 13^C flow into microbial biomass we can directly link microbial community structure to an observed ecosystem function. Using a similar approach, Padmanabhan et al (2003) characterized the soil microbial communities involved in phenol and naphthalene biodegradation via GC/ MS and SIP.

Combining GC/MS and SIP, we could track how anthropogenic disturbance alters carbon flow and conversion through wetland food webs. Lueders et al (2004) combined RNA and DNA-based SIP with real- time PCR, TRFLP and comparative sequence analysis to trace carbon flow from methylotrophs into secondary eukaryotic consumers in rice field soil. By observing enrichment of specific methylotrophic nucleic acids over time, they identified methylotrophs actively consuming the applied ^sup 13^G-methanol and then tracked ^sup 13^C enrichment in various RNA sequences from eukaryotic organisms unable to consume methanol directly. Thus, SIP provided information about both the primary methanol oxidizers and their possible interactions with fungi and predators in a complex food web.


Recent advances in microbial ecology make it possible to ask more specific questions about how microbial community composition is linked to ecosystem function. However, with the diversity of new methods, choosing the best method to answer a particular question can be difficult. Considering the following questions may help guide this decision and develop experimental design: (1) Is the study targeting specific organisms or broad, functional group changes in microbial community composition? If the study is aimed at detecting landscape-level patterns, methods such as PLFA, which highlight changes in broad microbial groups, may be most appropriate. However, if the goal is to detect specific microorganisms, methods such as DGGE may be more appropriate. (2) What are the methodological limitations? Recognizing a technique's positive and negative aspects can guide interpretation and application of microbial data (Table 1). (3) At what scale is the question being asked, and how will soil heterogeneity affect die outcome? If the research questions focus on the role of soil microorganisms on a landscape/ecosystem scale, the sampling strategy employed must encompass the sampled system's heterogeneity as perceived on a microbial scale. As with other soil measurements, compositing sub-samples can help represent this heterogeneity. (4) How will seasonal fluctuations in precipitation, temperature, and day length influence the focal microorganisms? Seasonal changes can affect both microbial community structure and function and, thus, should be taken into consideration when developing sampling protocols.

Advancements in microbial ecology provide new avenues for exploring linkages between environmental processes and microbial communities, allowing both ecosystem and microbial ecologiste to answer questions that previously were intractable. Demystifying recent methodological developments in microbiology should spark new avenues of investigation and stimulate collaboration between microbial and ecosystem ecologista, two groups now recognized to share common goals and offer complementary perspectives in ecosystem studies.

Acknowledgments. - We thank C. Sheil for illustration of Figure 1. Financial support was provided by the NSF Microbial Observatories (NSF-MO MCB-0137210), NIEHS Molecular Structure/Function of Organisms Degrading Contaminants (1R21ES012812-01), NIEHS Superfund Basic Research Program (5 P42 ES04699-16).


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