ABSTRACT Arctic ground squirrels (Urocitellus parryii) rely primarily on dietary protein derived from plants to fuel gluconeogenesis during hibernation, yet fungal sporocarps may be an important, yet overlooked, protein source. Fungivory levels depend on sporocarp productivity, which varies with the dominant plant species and is higher on acidic than on non-acidic soils. To test whether these factors altered fungal consumption, we used stable isotopes to investigate arctic ground squirrel diets at two sites in northern Alaska, Toolik (primarily moist acidic tundra) and Atigun (primarily moist non-acidic tundra). Radiocarbon estimates can also indicate fungivory levels because ectomycorrhizal fungi assimilate soil-derived organic nitrogen whose 14C levels are higher than current photosynthesis. We measured radiocarbon in hair and δ13C and δ15N in hair, feces, ectomycorrhizal sporocarps, graminoids, and dicots. Feces were higher in δ13C and δ15N at Toolik than at Atigun, and fecal δ15N increased in August at Toolik, coincident with sporocarp production and fungal spores in feces. Mixing models indicated that graminoids contributed 64%, dicots 35%, and sporocarps 1% to Atigun hair protein, whereas graminoids contributed 37%, dicots 16%, and sporocarps 47% to Toolik hair protein. Acidic soils appeared to correlate with higher sporocarp production and fungivory at Toolik than at Atigun. Atigun hair resembled atmospheric CO2 in 14C, whereas Toolik hair had higher 14C, consistent with greater fungal consumption at Toolik. Late-season sporocarps may be a key protein source for some squirrels and may provide an integrated signal of the soil organic nitrogen assimilated by ectomycorrhizal fungi.

Long-term measurements of ecological effects of warming are often not statistically significant because of annual variability or signal noise. These are reduced in indicators that filter or reduce the noise around the signal and allow effects of climate warming to emerge. In this way, certain indicators act as medium pass filters integrating the signal over years-to-decades. In the Alaskan Arctic, the 25-year record of warming of air temperature revealed no significant trend, yet environmental and ecological changes prove that warming is affecting the ecosystem. The useful indicators are deep permafrost temperatures, vegetation and shrub biomass, satellite measures of canopy reflectance (NDVI), and chemical measures of soil weathering. In contrast, the 18-year record in the Greenland Arctic revealed an extremely high summer air-warming of 1.3 °C/decade; the cover of some plant species increased while the cover of others decreased. Useful indicators of change are NDVI and the active layer thickness.

Understanding microbial transformations in soils is important for predicting future carbon sequestration and nutrient cycling. This review questions some methods of assessing one key microbial process, the uptake of labile organic compounds. First, soil microbes have a starving-survival life style of dormancy, arrested activity, and low activity. Yet they are very abundant and remain poised to completely take up all substrates that become available. As a result, dilution assays with the addition of labeled substrates cannot be used. When labeled substrates are transformed into 14CO2, the first part of the biphasic release follows metabolic rules and is not affected by the environment. As a consequence, when identical amounts of isotopically substrates are added to soils from different climate zones, the same percentage of the substrate is respired and the same half-life of the respired 14CO2 from the labeled substrate is estimated. Second, when soils are sampled by a variety of methods from taking 10 cm diameter cores to millimeter-scale dialysis chambers, amino acids (and other organic compounds) appear to be released by the severing of fine roots and mycorrhizal networks as well as from pressing or centrifuging treatments. As a result of disturbance as well as of natural root release, concentrations of individual amino acids of ~10 µM are measured. This contrasts with concentrations of a few nM found in aquatic systems and raises questions about possible differences in the bacterial strategy between aquatic and soil ecosystems. The small size of the hyphae (2-10 μm diameter) and of the fine roots (0.2 to 2 mm diameter), make it very difficult to sample any volume of soil without introducing artifacts. Third, when micromolar amounts of labeled amino acids are added to soil, some of the isotope enters plant roots. This may be an artifact of the high µM concentrations applied.

In radioisotope studies in plankton, bacteria turn over the nanomolar ambient concentrations of dissolved amino acids within a few hours. Uptake follows Michaelis–Menten kinetics. In contrast, within minutes the very abundant bacteria and fungi in soil take up all labeled amino acids added at nanomolar to millimolar final concentrations; uptake kinetics accordingly cannot be measured. This rapid uptake agrees with earlier findings that soil microbes exist in a starving or low-activity state but are able to keep their metabolism poised to take up amino acids as they become available. How can this rapid uptake of added amino acids be reconciled with persistent soil concentrations of 10–500 μM of total dissolved amino acids? Although respiration of added amino acid carbon has been used to deduce uptake kinetics, the data indicate that in both soil and in eutrophic natural waters constant percentages of individual amino acids are respired; this percentage varies from less than 10% of the amount taken up for basic amino acids to more than 50% for acidic amino acids. We conclude that relatively fixed internal metabolic processes control the percent of amino acid respired and that the μM concentrations of amino acid measured in water extracts from soil are unavailable to microbes. Instead, these relatively high concentrations reflect amino acids in soils that are chemically protected, hidden in pores, or released from fine roots and microbes during sample preparation.

The Organized Oral Session 27, organized by Yuko Hasegawa and John E. Hobbie, was held during the 2009 ESA Annual Meeting at Albuquerque, New Mexico, on 5 August 2009. Christopher W. Schadt, Oak Ridge National Laboratory, “Functional gene microarrays and other techniques for studying the functional capabilities of microbial communities at the level of DNA and mRNA and their relationship to environmental processes” Matthew D. Wallenstein, Colorado State University, “Time to focus on function: Linking microbial community structure to novel aspects of microbial function” Yuko Hasegawa1,2, Jessica Mark Welch1, Alex M. Valm1,2, Christopher Rieken1, Mitchell L. Sogin1,2, and Gary G. Borisy1, (1) Marine Biological Laboratory, (2) Brown University, “Applying fluorescence in situ hybridization (FISH) and spectral imaging to visualize bacteria in the environment” Kurt A. Smemo1, Christopher B. Blackwood2, David J. Burke1, and Mark W. Kershner2, (1) The Holden Arboretum, (2) Kent State University, “Linking litter decomposition processes and microbial community composition in northern hardwood forest soils” Jeremy Rich1, Zoe G. Cardon2, and Julie Huber2, (1) Brown University, (2) Marine Biological Laboratory, “Linking microbial community structure to ecosystem functioning” Michael N. Weintraub, University of Toledo, “Creation of decomposition models that include different microbial groups and enzymes and application of modeling approaches to link microbial community composition to ecological processes” Steven D. Allison, University of California, Irvine, “Integration of microbial communities into largescale ecosystem models” Jennifer W. Edmonds1, Matthew J. Church2, David M. Karl2, and Samuel T. Wilson2, (1) University of Alabama, (2) University of Hawaii at Manoa, “Explaining the oceanic methane paradox through bacterial utilization of refractory organic p compounds” Joseph C. von Fischer, Craig R. Judd, and Colleen T. Webb, Colorado State University, “A traits-based framework to study the intersection of methanotroph community ecology and methane biogeochemistry” Joseph H. Vineis1, Thomas R. Horton1, and Erik A. Hobbie2, (1) State University of New York - College of Environmental Science and Forestry, (2) Complex Systems Research Center, “Ectomycorrhizal communities in temperate forest ecosystems: Does diversity decrease along an increasing natural nitrogen gradient?” Microbes are the drivers of decomposition and nutrient cycling both in aquatic and terrestrial environments. Microbes may also be dominant primary producers, as is the case with cyanobacteria in both polluted and ultraoligotrophic aquatic systems. Ecosystem ecologists have long studied microbial processes by measuring concentration changes of either products or substrates; this is the black box approach. In recent years, molecular biology techniques have been used to explore microbial communities in the environment, with recent breakthroughs and insights into the tremendous diversity in almost every habitat examined. The next step in microbial ecology is to link microbial community composition with the ecosystem processes measured in the environment (Zak et al. 2006). Eventually, the community and process information will be measured at a variety of sites that make up an ecosystem. Only then will scientists be able to predict how microbes and ecosystems respond to changes. Molecular biology techniques have played an extremely important role in the field of microbial ecology. In particular, approaches based on the comparisons of small subunit ribosomal RNA genes (SSU rRNA) (Pace 1985) have uncovered an extraordinary level of microbial phylogenetic diversity, allowing detection of both cultivable and uncultivable microbes in a variety of environments. Traditionally, taxonomic classification of bacteria has been conducted based on morphological and physiological characteristics. However, these features are inadequate to categorize most microorganisms, as phylogenetically distant species often share the same morphological or physiological characteristics. Furthermore, difficulties associated with cultivation have prevented identification and characterization of most microbes in the environment. In order to understand the impacts of processes carried out by microbes, however, it is necessary to understand their functional capabilities as well as activities in their native environments. Molecular biology techniques have also been utilized to examine microbial functions and activities. For example, the presence of known housekeeping or functional genes associated with certain microbial groups indicates potential functional capabilities of the microbial communities examined. In addition, detection of gene expressions at mRNA and protein levels allows us to link microbial responses in certain physicochemical conditions. While these molecular analyses provide useful information about microbial communities and their potential roles in the environment, the actual degree of involvement of these microbes in specific ecosystem processes is difficult to estimate from molecular data. For example, a large number of distinct bacterial types all had a sulfate reductase gene in a single salt marsh sediment sample (Bahr et al. 2005), but it is unclear how the sulfate reduction rates of these different bacteria differ under different conditions. Even when the actual microbial actors can be identified, it is difficult to determine interactions within the microbial community, or the factors controlling the abundance and activity of the microbes. Ultimately, molecular information on microbial activity will be most useful if it can be represented by mathematical models of the natural process or of a complete ecosystem. The major goals of this session were to discuss challenges associated with molecular and process-based measurements in nature and to explore how to integrate taxonomic, functional, and activity information. Furthermore, a number of speakers also discussed whether or not the integration of microbial community and functional information into ecosystem models would improve models' predictive capabilities, and if so, what would be the specific approaches that could be taken to achieve the integration. During the session, speakers also presented their research and demonstrated the utility of approaches used in their studies. We believe that communications between scientists concerning the same problems but with different expertise could greatly contribute to the advancement of the microbial ecology field as well as to the development of ecosystem models, simulating biogeochemical processes that are profoundly influenced by microbial communities. The measurement of enzymatic activities upon addition of fluorogenic substrates has been long used to estimate in situ microbial activities. However, it must be kept in mind that this method actually measures a potential activity (usually a Vmax) of an enzyme while the actual enzyme activity will range from zero to the Vmax. One example of problems associated with measuring the potential enzyme activity is that enzyme activity may be out of synchrony with enzyme release from microbes. Kurt A. Smemo (The Holden Arboretum) and colleagues analyzed patterns of multiple soil enzyme potential activities related to litter decomposition in northern hardwood forest soils, and showed that most activity is in the litter layer, with the highest activity in the colder months, particularly when snow cover is present (Fig. 1). How do such potential activities of soil enzymes measured in vitro relate to microbial community structure in the environment? In order to explore the links between the potential enzyme activities and microbial community composition, Smemo and colleagues performed terminal restriction fragment length polymorphism (T-RFLP), a DNA fingerprinting technique, to compare composition of SSU rRNA genes from different soil samples. Interestingly, their multi-variate analyses showed that the spatial variation in potential enzyme activities could best be explained by bacterial but not fungal community structure. Parallel analyses of the physicochemical parameters of the soil samples indicated that the soil properties controlling spatial variability in enzymes also explain variation in the bacterial community, but not the fungal community. Thus, if bacteria and fungi are autocorrelated at different scales, linking microbial communities using current enzyme measurement techniques might be challenging. Monthly potential activities of four extracellular enzymes important for litter decomposition in a northern hardwood forest soil. (a) β-glucosidase, (b) cellobiohydrolase, (c) α-glucosidase, and (d) β-xylosidase activity levels in the forest floor (FF) as well as 2 cm and 10 cm below the surface. Data from K. A. Smemo. Matthew D. Wallenstein (Colorado State University) pointed out problems associated with our traditional approaches of measuring microbial activities at optimal conditions. While the traditional process-based measurement allows for comparisons across space or time, or in response to experimental treatments, these measurements provide little information on the rate of activities under in situ conditions (Fig. 2). Wallenstein's study of microbial community structures and enzyme activities in soils collected near Toolik Lake, Alaska, showed that the potential activity of lignocellulose-degrading enzyme was higher in samples collected in late winter, and the enzymes sampled in winter were more sensitive to temperature than those sampled in summer (Wallenstein et al. 2008). Wallenstein and colleagues also performed analyses of microbial community structure in soils collected in different times of the year. Comparisons of total 16S rRNA gene compositions in different soil samples revealed very similar community structure, at least at higher taxonomic (phylum or class) levels (Wallenstein et al. 2007). As the total community SSU RNA analysis at the DNA level does not allow differentiation of active members of the community, Wallenstein and colleagues then selectively isolated and analyzed actively growing cells using BrdU (bromodeoxyuridine) labeling and immunocapture methods. BrdU is a synthetic thymidine analog that can be incorporated into DNA of actively dividing cells during DNA replication. Availability of antibody against BrdU enables separation of the growing cells from the rest of the community members using methods such as an immunomagnetic separation. Their experiment demonstrated that not only the soil enzyme activities but also actively growing microbial community members are changing seasonally at their study site. Effect of community composition on activity rate under optimal (top panel) and in situ (bottom panel) conditions. Traditionally, rates of soil processes (e.g., respiration, ammonia oxidation, denitrification) are measured under optimal conditions. The processes being measured typically result from the activities of many different microbial taxa. Yet, under optimal conditions (top panel), most of the activity is due to a small subset of the total community. As a result, we are not likely to find strong relationships between microbial community composition and maximum activity rates. On the other hand, in-situ conditions (bottom panel) are rarely optimal for activity rates and often fluctuate through time and space. Under these conditions, the great diversity of microbial communities contributes to the maintenance of activity, especially in the face of stress or perturbation. When processes are measured under a range of nonoptimal conditions, we are much more likely to find relationships between community composition and function. Jeremy Rich (Brown University) and colleagues performed molecular and process measurements in parallel to study the relative importance of denitrification and anaerobic ammonium oxidation (anammox), and to identify organisms responsible for these processes in the Arabian Sea and the Peru upwelling (Ward et al. 2009). While both processes utilize NO2− and produce N2 as an end product, denitrification is performed by heterotrophic bacteria utilizing organic carbon as their energy sources, and anammox is performed by autotrophic bacteria performing NH4+ oxidation for energy production. In order to identify bacterial groups responsible for N2 production in each site, Rich and colleagues performed quantitative PCR analyses of functional genes required for the two processes. Their parallel analyses of N2 production, performed by incubating seawater with 15NO2− and by measuring the ratio of 30N2 to 29N2 to 28N2, indicated that denitrification was the dominant process in the Arabian Sea while anammox occurred more predominantly in the Peruvian waters. Jennifer W. Edmonds (University of Alabama) and colleagues explored microbial activity as well as communities that are potentially responsible for utilization of methylphosponate (MPn). The uptake of Mpn has previously been shown to be coupled with methane production, contributing to the documented methane supersaturation in many aerobic ocean surface waters (the methane paradox). Edmonds combined measurements of methane production in enrichment samples with molecular analysis of bacterial communities to investigate heterotrophic bacterial utilization of MPn in the oligotrophic North Pacific Subtropical Gyre. The comparison of the methane production rates by heterotrophic bacteria in their enrichments to those by autotrophic microbes reported in a previous study showed that methane production rates were similar for heterotrophs and autotrophs when incubated with MPn. Their phylogenetic studies indicated that heterotrophic bacterial groups, including Vibrio, Roseobacter,and Alteromonas species can be responsible for MPn utilization in the surface ocean. Joseph H. Vineis (State University of New York) and co-authors studied the diversity of ectomycorrhizal fungi (EMF) along a foliar nitrogen (N) gradient in the Bartlett Experimental Forest, New Hampshire. To do this they sequenced the internal transcribed spacer (ITS) of the nuclear rDNA repeat. Based on previous observations demonstrating changes in EMF diversity in response to experimental additions of nitrogen, they expected to see a similar pattern along a natural nitrogen gradient in their field site. Interestingly, their study showed that the overall species richness did not change along the N availability gradient, even though species assemblages shifted along the same gradient. By following the dominant taxa in their data set, they identified some genera that respond to the N availability gradient (changes in richness). They also showed that grouping the EMF into broad physiological categories revealed an interesting pattern: fungi that produce lots of hydrophobic hyphae dominated in the low-nitrogen sites, and belowground carbon allocation in these sites was presumably also greater than in high-nitrogen sites. Their study illustrates difficulties associated with field manipulations; microorganisms may respond to sudden N additions as a disturbance rather than to elevated N levels per se. Technical challenges, which are the most immediate concerns to microbial ecologists using molecular approaches, were addressed by Jennifer Edmonds and Tom Horton (an author on the Vineis presentation). For example, it is important to consider spatial and temporal scales appropriate for questions. In addition, we need to be aware that disruption during sample collection and processing can potentially introduce biases and alter the microbial community we measure. Horton specifically discussed some of the challenges associated with DNA sequencing-based approaches. He pointed out that conclusions about community composition should be drawn carefully, especially when the number of DNA sequences analyzed is not large enough to reach the asymptote in the species accumulation curves. Processes of reading DNA sequences and delineating what microbes are represented by the sequences bring up different challenges. Along with the advancement of DNA sequencing technology, the amount of microbial DNA sequence information deposited into databases has increased dramatically. However, many of the deposited sequences have not been characterized or are misnamed, and therefore comparisons of newly acquired sequences to those without description will continue to be a challenge (Bidartondo 2008, Horton et al. 2009). One emerging approach to explore microbial function and activities in their native environment is to screen genes and gene products linked to known microbial processes in a high-throughput manner. Christopher W. Schadt (Oak Ridge National Laboratory) introduced functional gene microarrays for studying the functional capabilities of microbial communities at the level of DNA and mRNA, and discussed their relationship to environmental processes. The arrays described by Schadt allow us to detect the presence and/or expression of genes required for a variety of functional processes, including nitrogen, carbon, and sulfur cycling, as shown in Table 1 (He et al. 2007). Schadt discussed advantages and challenges associated with experiments using the functional microarray. For example, high-throughput analyses of community dynamics are possible, as the technique potentially allows simultaneous analysis of functional genes required for many different functions from the sample in a single hybridization. Challenges associated with the technique include designing specific probes against intended target genes, achieving high sensitivity, and developing effective means of visualizing and analyzing broad-spectrum data. As an example of microarray technique applications, Schadt and his colleague, J. Reeve of Utah State University, compared diversity and abundance of functional genes in strawberry crops under organic and conventional agricultural practices, including repeated methyl bromide fumigation. Their results showed that management regime can have a larger impact on functional microbial community structure, diversity, and abundance than does the variety of original soil types. Furthermore, for many functional genes, correlations can be made between the gene abundance levels and biogeochemical rate and process measurements. Molecular imaging techniques can also be a valuable tool for understanding microbial community structure and analysis. In contrast to most molecular biology techniques as well as process-based measurements, fluorescence in situ hybridization (FISH) allows analysis of specific microbial cells in environmental samples. In a FISH experiment, fluorescently labeled oligonucleotide probes are designed to hybridize to target-specific regions such as ribosomal RNA (rRNA) molecules, and successful hybridization reactions indicate the presence of microbial groups of interest. In contrast to DNA sequencing-based assays, FISH likely indicates whether cells are alive and/or active because active cells tend to produce more rRNA molecules. Yuko Hasegawa (Marine Biological Laboratory/Brown University) discussed the utility of FISH combined with spectral imaging to visualize environmental bacterial cells. These cells have been previously identified in DNA sequencing-based studies. Examples of the challenges associated with FISH image analysis include differentiation of probe-conferred fluorescence from autofluorescence, as well as detection of target cells that are not abundant in given communities. Analysis of specific probe-conferred spectra in each cell facilitates more accurate detection of specific target cells labeled with fluorescent probes, even when the abundance of target cells is very low in environmental samples. Microbes and their enzymes have traditionally not been included in ecosystem models. Instead, processes such as the degradation of organic compounds have been described using physical and chemical parameters. Several studies presented in this session, however, indicated that incorporation of microbial information can potentially improve the predictive power of mathematical models. Michael N. Weintraub (University of Toledo) pointed out that traditional decomposition models have included neither microbes nor their enzymes as explicit drivers of decomposition. The decomposition processes in many biogeochemical models have been described as the change in the amount of carbon over time as a function of a first-order rate constant, as well as the carbon pool size, moisture, and temperature under the given field conditions. Weintraub, however, indicated alteration of the behavior of the models as well as the conclusions that can be drawn from them when microbial function and enzyme parameters were incorporated. The first model introduced by the speaker was the Schimel and Weintraub Model (Table 2), containing enzymes as one of the agents of decomposition in soil (Schimel and Weintraub 2003). Weintraub pointed out that the amount of energy that microbes spend to produce enzymes could affect the growth of microbes as well as the fate of carbon and nitrogen in the system. Weintraub then introduced the Guild Decomposition Model (GDM) from Moorhead and Sinsabaugh (2006), which included three guilds of decomposers, each equipped with different set of enzymes specialized to different types of carbon sources (Moorhead and Sinsabaugh 2006). In the model, decay rates were optimized depending on the decomposition stage as the proportion of recalcitrant carbon sources increased over the course of decomposition. In the future, Weintraub and colleagues will be testing the hypothesized three-guild structure of the GDM against field measurements. Steven D. Allison (University of California, Irvine) introduced previously observed changes in soil respiration in response to warming treatments (Melillo et al. 2002) and discussed the importance of carbon use efficiency (CUE) in a model to recreate the original field observation. In the model simulations (Fig. 3), reducing CUE in response to warming in the model prevented the loss of carbon from the soil even though model parameters were temperature sensitive. Incorporation of CUE acclimation most closely represented the transient increase in CO2 emission observed in the field. An experiment with constant CUE in the model resulted in large losses of soil organic carbon over 30 years as a result of the warming treatment. The study indicated that incorporating biological processes such as acclimation of CUE may significantly improve the predictive power of ecosystem models. Allison also discussed how models can distill the complexity of microbial communities into a manageable set of relevant parameters using characteristics such as phylogeny, functional traits, biomass, and contribution of each group to specific processes. Finally, the speaker pointed out that those microbial processes could be modeled at multiple scales, ranging from the single-cell, population/community, or ecosystem level to global scales. Soil carbon model based on Schimel and Weintraub (2003). Temperature-sensitive processes are denoted in red text and include the Michaelis-Menten parameters for extracellular enzymes and uptake enzymes, as well as microbial carbon use efficiency (CUE). The key feature of this model structure is the direct role of extracellular enzymes as microbial catalysts of soil organic carbon (SOC) conversion to dissolved organic carbon (DOC). Joseph C. von Fischer (Colorado State University) identified four hurdles for determining if biogeochemical transformations will be more predictable by considering microbial community composition; he used methane uptake in dry grasslands as an example. The four hurdles are: (1) The process must have strong biological control, and not be primarily controlled by physical factors; (2) Researchers must document the ecophysiology, identifying factors that regulate rates of biological activity; (3) Ecophysiological responses must differ with microbial community composition; (4) the biogeography of microbes must be predictable. For the case of methane uptake in dry grasslands, von Fisher showed that the process can be controlled by physical factors (diffusion of methane into the soil) or by biological factors (activity of methanotrophic bacteria). From field measures of uptake and diffusivity, interpreted with a reaction diffusion model, von Fischer showed that both physical and biological factors are important for regulating methane uptake rates. These measures revealed that desiccation stress was an important ecophysiological response for determining rates of methanotroph activity (von Fischer et al. 2009). Next, the speaker presented DNA sequencing analysis of genes required for methanotrophic activities (Judd et al. 2009) to demonstrate that types of genes identified in methanotrophic communities varied in grassland field sites with different physicochemical characteristics (e.g., soil moisture, pH, NH4+ concentration). Furthermore, analysis of methane uptake by enzymes from each field site in response to changes in methane concentration suggested that enzyme kinetics could also vary among different methanotrophic bacterial communities (Judd et al. 2009). Such results indicated that the ecophysiological responses of methanotrophs (e.g., reduction in methane uptake in response to the loss of soil moisture) vary among different communities, as each community would produce enzymes with different kinetics. Finally, von Fischer concluded the presentation by stating that biogeography of methanotrophic bacteria could eventually become predictable from environmental properties that control microbial distributions. Together, these results suggest that methane uptake might be better modeled in the future by explicitly considering the composition of the methanotroph community. Applying available microbial information to models is challenging. Yet, understanding microbial community structure and functional capability can reveal potential activities that may dominate the system in response to future changes in the environment. One of the major challenges in developing models is that there are few standard approaches for identifying the processes and parameters that must be represented. When selecting these processes, the extreme complexity of microbial communities becomes an obstacle. Microbial communities in most environments are extremely diverse, and therefore the communities can potentially respond to environmental changes in many different ways. For example, the GDM (Moorhead and Sinsabaugh 2006) discussed by Weintraub includes three guilds of decomposers, each with a different set of enzymes specializing on carbon sources with different recalcitrance (Table 2). Simulating different groups of microbes can make the model extremely complex as each microbial group responds to environmental changes differently, and different processes carried out by the microbes can occur simultaneously. The challenges identified in molecular studies or process measurements can directly affect the models. For example, not all field and laboratory measurements can properly capture patterns of microbial processes in nature. For example, systems experiencing natural variability in chemical or physical conditions may not respond in the same manner as those in experimental sites, as seen in the studies presented by Vineis et al. Furthermore, as discussed by Edmonds and Horton (an author on the Vineis presentation), spatial and temporal scales selected for data collections may not reveal important patterns at other scales. Obtaining long-term data from field experiments can be extremely challenging but such information is critical for predicting future changes in ecosystem processes. In recent years, a variety of tools to explore microbial community structure, activity, and functional capability have become available; many, including those discussed in this session, have already made significant contributions to the field. Edmonds points out that many ecosystem ecologists have been unable to open the “black box” of the microbial community structure/activity. Projects have often been multidimensional, but not truly collaborative and integrated. In order to effectively employ traditional and newly emerging techniques and to integrate acquired information to understand overall ecosystem functions, we must be explicit about how and when the microbial community structure and activity measurements will elevate understanding of an ecosystem. Understanding challenges associated with approaches used in different fields would facilitate communication among scientists and could eventually result in successful collaborations. We believe that this session provided a great opportunity for such discussions. The session was also significant as speakers with different backgrounds and areas of e RI 02912xpertise contributed to share ideas.

Symbiotic fungi's role in providing nitrogen to host plants is well-studied in tundra at Toolik Lake, Alaska, but little-studied in the adjoining boreal forest ecosystem. Along a 570 km north-south transect from the Yukon River to the North Slope of Alaska, the 15N content was strongly reduced in ectomycorrhizal and ericoid mycorrhizal plants including Betula, Salix, Picea mariana (P. Mill.) B.S.P., Picea glauca Moench (Voss), and ericaceous plants. Compared with the 15N content of soil, the foliage of nonmycorrhizal plants (Carex and Eriophorum) was unchanged, whereas content of the ectomycorrhizal fungi was very much higher (e.g., Boletaceae, Leccinum and Cortinarius). It is hypothesized that similar processes operate in tundra and boreal forest, both nitrogen-limited ecosystems: (i) mycorrhizal fungi break down soil polymers and take up amino acids or other nitrogen compounds; (ii) mycorrhizal fungi fractionate against 15N during production of transfer compounds; (iii) host plants are accordingly depleted in 15N; and (iv) mycorrhizal fungi are enriched in 15N. Increased N availability for plant roots or decreased light availability to understory plants may have decreased N allocation to mycorrhizal partners and increased delta15N by 3-4 parts per million for southern populations of Vaccinium vitis-idaea L. and Salix. Fungal biomass, measured as ergosterol, correlated strongly with soil organic matter and attained amounts similar to those in temperate forest soils.

Oak Ridge National Laboratory, “Functional gene microarrays and other techniques for studying the functional capabilities of microbial communities at the level of DNA and mRNA and their relationship to environmental processes”Matthew D. Wallenstein, Colorado State University, “Time to focus on function: Linking microbial community structure to novel aspects of microbial function”Yuko Hasegawa