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Department of Biological Sciences, Texas Tech University, Mailstop 3131, Lubbock, Texas 79401
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONLITERATURE CITED |
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Ascertaining the effects of anthropogenic disturbance on belowground diversity is of paramount importance because pollution from agricultural practices and industrialization are increasing worldwide. Although we have methods for evaluating soil microbial function with respect to carbon use our ability to evaluate use of other compounds is limited. Because N cycling is of paramount importance in ecosystem stability, evaluation of the ability of saprophytic soil fungi to use a variety of N sources would provide important information on possible alterations in ecosystem stability with disturbance. Herein is described a procedure (soil Nitrolog) for evaluating fungal functional diversity on a suite of 95 different N substrates. The soil Nitrolog procedure was evaluated by testing fungal functional diversity at two sites in Big Bend National Park (Chihuahuan Desert), differing in elevation and plant community composition. The soil Nitrolog procedure distinguished between the two sites based on overall use of the 95 N substrates. In addition the procedure detected differences in individual substrate use based on site specific plant compounds in response to changes in the amount of N entering these ecosystems from anthropogenic inputs.
Key words: Big Bend National Park, Biolog, Chihuahuan Desert, functional diversity, soil fungi, soil nitrogen
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONLITERATURE CITED |
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). Therefore changes in decomposer community structure and activities as a consequence of disturbance, climate change or anthropogenic inputs can have important repercussions on ecosystem functioning (Perry et al 1989). As the primary decomposers of litter and soil organic matter in terrestrial ecosystems soil fungi are vital for the maintenance of biogeochemical cycling. Biogeochemical cycling returns nutrients to the soil solution for uptake by plants, bacteria and other fungi (Cook and Rayner 1984). Kandeler (1996) noted that, although carbon cycling may not be altered by changes in community composition, nitrogen dynamics are less resilient. Because the supply of nutrients is important "bottom up" controls of ecosystem structure and dynamics ( Jenny 1980) a disturbance altering resource availability would affect other ecosystem processes (Chapin et al 1997). Understanding how saprophytic soil fungi control decomposition rates and participate in seasonal patterns of nutrient availability may prove crucial in predicting immediate and lasting effects of multiple stressors, such as climate change and anthropogenic disturbances on ecosystem process and stability. Providing a community level assessment of the ability of soil fungi to catabolize an array of nitrogenous compounds is critical to linking the functional diversity of soil fungi and effects of disturbance or environmental stressors.
Effects of atmospheric N deposition on saprophytic soil fungal biodiversity and activity has received little attention with most of the research focused on changes to mycorrhizal taxa and patterns of fruit body production (Hawksworth and Colwell 1992, Vogt et al 1992). Our understanding of the interactions among soil N dynamics, atmospheric N inputs and saprophytic soil fungi is limited primarily to fertilization effects in agricultural systems (Donnison et al 2000). In one of the few studies of N effects on soil saprophytic fungi in nonagricultural systems Arnebrant et al (1990) observed a direct decline in decomposer soil fungi in response to ammonium nitrate or urea applications to a Scots pine forest. This result is mirrored in studies from agricultural systems that also report changes in fungal biodiversity and a shift to a bacterial dominated system with increased N inputs (Bardgett and Leemans 1995, Bardgett et al 1996). More recently Bardgett et al (1999) suggest that in grassland ecosystems, as N levels increase, the role of soil fungi in decomposition and nutrient cycling decreases substantially. Changes that occur in soil fungal species composition and functional diversity in response to altered precipitation patterns, coupled with anthropogenic inputs of N, should have lasting effects on ecosystem functioning, plant success and successional trajectories. Ecosystem response to concurrent changes in below- and aboveground processes and attendant fungal biodiversity is complex and difficult to predict. Such interactions include effects on food web processes and antagonistic, symbiotic and mutualistic relationships among plants and soil microbes (Wolters et al 2000).
The ability to evaluate fungal functional diversity with respect to organic and inorganic N use, either separately or in conjunction with C use, would be useful in determining differences in the functional ability of fungi between ecosystems as they are influenced by vegetation structure, land use, disturbance history, agriculture, anthropogenic effects of N and historical amounts of soil N. The Fungilog (Dobranic and Zak 1999) and soil Fungilog method (Sobek and Zak 2003) were introduced as a means of relating ecosystem processes to the functional diversity of an assemblage of litter and soil fungi as evaluated from the in vitro catabolic profile of the attendant fungi. Fungal functional diversity as evaluated by these methods is defined as the rate at which C compounds are used (by substrate activity) and the numbers of compounds catabolized (affecting substrate richness) from a group of 95 C compounds by an assemblage of fungi colonizing plant litter or soil organic matter (Dobranic and Zak 1999, Sobek and Zak 2003). Previous studies describing functional diversity in microbial communities have focused on C use (Garland and Mills 1991, Zak et al 1994, Haack et al 1995, Zhang and Zak 1995, Guckert et al 1996, Willig et al 1996, Pennanen et al 1998, Smalla et al 1998, Dobranic and Zak 1999, Sobek and Zak 2003) to define functional differences among ecosystems.
Biolog Inc. recently introduced a microtiter plate (PM3) that allows for the evaluation of inorganic and organic nitrogen use by strains of bacteria. Described here is a method for evaluating soil fungal functional diversity of N substrates (Nitrolog) found on PM3® microtiter plates. The soil Nitrolog method is based on the soil Fungilog method described by Sobek and Zak (2003). The objectives of this study were: (i) develop a protocol that would let one use the Biolog PM3 microtiter plates to evaluate the N use capabilities of soil fungi and (ii) evaluate the sensitivity of the method to detect differences in the ability of soil fungi to use a range of N compounds between two elevation zones along an elevational gradient in the Chihuahuan Desert and in response to nitrogen additions.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONLITERATURE CITED |
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) in Big Bend National Park (BBNP). BBNP is located in southwestern Texas within the Chihuahuan Desert. BBNP comprises arid and semiarid environments and is characterized by low annual precipitation (330 mm), of which the majority occurs in the summer. Seasonal temperature fluctuations are characterized by hot summers and cool winters. The average winter temperature is 10 C, and the average summer temperature is 27 C (Staff 1985).
Soil samples were collected from the high elevation oak-pine forest on Lost Mine Peak and the midelevation sotol grassland. The oak-pine forest (LM, elevation 2098 m) is the highest point in the Pine Canyon Watershed and is dominated by Quercus emoryi (a species of live oak), Pinus cembroides (pinyon pine), several Juniperus species and several genera of bunch grasses. The sotol grassland (SG, elevation 1526 m) is a grama grassland characteristic of midelevation grassland in this part of the Chihuahuan Desert. These grasslands are dominated by Dasylirion leiophyllum (sotol), Nolina texana (bear grass), several species of Opuntia (prickly pear) and Bouteloua curtipendula (side-oats grama) (Dobranic and Zak 1999). The two study sites are in the northeastern portion of the Chisos Mountains and are separated by approximately 550 m elevation and 1.75 km.
The sites used to evaluate the effectiveness of the Nitrolog procedure in detecting landscape level patterns in the ability of soil fungi to use nitrogen compounds are part of a larger effort to understand the affects of anthropogenic N deposition and climatic change on soil microbial dynamics within an arid landscape.
Method development.— –
Biolog® PM3 plates, which contain 95 different nitrogen compounds, estimate N use in a minimally defined medium and as such contain no C source. Therefore a C source must be added to the PM3 plates (Biolog instructions for PM3 plates). An appropriate C source and concentration of C needed to be determined to allow for a gradual reduction of the tetrazolium dye over time to ensure that slowly growing fungi would contribute to color development in individual wells. Glucose was chosen as a standard C source for addition to the microtiter plates because it is readily catabolized by soil fungi (Griffin 1994). A series of glucose concentrations, 0.0 g L–1, 0.1 g L–1, 1.0 g L–1 and 5.0 g L–1, were evaluated to determine the appropriate amount of glucose to be added with the soil organic matter. A concentration of glucose would be unacceptable if: (i) the tetrazolium indicator was maximally reduced within 24 h after incorporation of fungal inoculum to the microtiter plate because slow growing fungi would not have contributed to the catabolism in the well; and (ii) concentrations resulted in an absorbance reading greater than 3.00 in any well before the end of the 5 d incubation period (the maximum absorbance reading of the plate reader is 3.0). In addition the concentration of glucose should provide an absorbance value that was similar to that observed for the glucose well in the SFN-2 Biolog plates used for C evaluation (Sobek and Zak 2003). With levels of fungal activity similar between the two types of Biolog microtiter plates using glucose as the standard, one then could directly compare activity on the C vs. the N source plates.
For evaluation of the optimal levels of glucose, five 300 g soil samples were collected along two 100 x 30 m belt transects established in the sotol grassland from Pine Canyon at Big Bend National Park in Jan 2003. Soil samples were sieved (2 mm screen) in the field to remove stones, roots and other debris. Soils were stored no more than 1 wk at 4 C before processing. In the lab soil organic matter particles (SOMP) were collected from each sample by shaking approximately 50 g of soil in 100 mL of RO water with a 10 µL Tween 80 solution for 1 h. This is an essential step because it removes spores allowing for inoculation of only those fungi actively growing in the soil (Harley and Waid 1955, Bisset and Widden 1972, Baath 1988, Sobek and Zak 2003). Each soil sample subsequently was washed through 500 µm and 250 µm sieves for several minutes with tap water. SOMP were collected in a beaker from the 250 µm sieve and filtered through a vacuum filtration system. SOMP were collected on P8 filter paper (Fisher Scientific) and stored in a Petri dish at 4 C overnight and used to inoculate the PM3 plates.
Fifty mg of SOMP, previously determined to be the density which maximized substrate activity levels but caused the least OD distortion (Sobek and Zak 2003), from each soil samtple were added to 20 mL of 0.2% H2O agar in a 25 mL vial that contained 125 µL of a MTT tetrazolium dye (stock solution is 160 mg of tetrazolium dissolved in 10 mL of sterile H2O) and glucose (0 µg, 10 µg, 100 µg and 500 µg/microplate well respectively) to produce one of the four concentrations of glucose listed above. Full details for dissolving the tetrazolium are provided in Sobek and Zak (2003). Two hundred µL of an antibiotic solution containing streptomycin sulfate (40 mg) and chlortetracycline HCL (30 mg/40 mL) are added to each vial to reduce bacterial contamination (10 µg of streptomycin and 5 µg of chlortetracycline/microplate well). The H2O agar suspension is vortexed to evenly distribute the inoculum, tetrazolium dye, glucose and antibiotics. The suspension then is poured into V-shaped Biolog® reservoirs. An eight-channel micropipetter fitted with large-orifice pipette tips (1–200 µL capacity) was used to deposit 150 µL into each well of a Biolog® PM3 microtiter plate. Plates were placed in a Ziploc bag to conserve moisture, incubated at 25 C and scanned with a computer-controlled microplate reader at 590 nm every 24 h for 120 h. Readings were not collected at 96 h due to investigator error.
Method evaluation.— – Once an appropriate glucose concentration was chosen the procedure was field tested with SOMP collected from the grassland and oak-pine forest sites in Aug 2003 along the Pine Canyon Watershed at Big Bend National Park. Soil samples were collected from a series of plots at these two locations that were initiated to examine short-and long-term effects of increasing atmospheric deposition of N to these desert ecosystems. In each site an area 40 m x 33 m was divided into a grid containing a total of 30, 5 x 5 m plots having 2 m walkways between each plot. Plots were divided into three treatments with 10 plots randomly assigned to each treatment. Treatments were chosen to evaluate a simulated increase in anthropogenic N deposition. Since 1980 the National Atmospheric Deposition Program (NADP) has been measuring N deposition from rainfall in Big Bend National Park. The average N deposition over this period is 3.99 kg/h/y. The treatments for each plot are: X, receiving no additional N, 2X receiving 2.5 g NH4NO3 and 8.24 g CaNO3 (2 x annual N deposition), and 4X receiving 5.0 g NH4NO3 and 16.48 g CaNO3 per year (4 x annual N deposition). Treatments were applied in Jun and Jul 2003 as half the annual amount during each month. Soil samples to a depth of 15 cm were collected in Aug 2003 for the evaluation component of the study. SOMP were collected and inoculated into microtiter plates as described above. Plates were incubated at 25 C and scanned on the plate reader every 24 h for 5 d.
Analytical approaches.— –
Functional diversity..
Functional diversity was evaluated as substrate activity (SA, sum of optical densities in all wells/plate), which is a measure of the rates at which fungal assemblages can catabolize the various N substrates, and substrate richness (SR, total number of N substrates catabolized), which is a measure of the diversity of an fungal assemblage with respect to the substrates that can be used (Dobranic and Zak 1999, Sobek and Zak 2003). Guilds of related compounds (TABLE I) also can be used to evaluate differences between groups (Zak et al 1994, Willig et al 1996, Sobek and Zak 2003). Utilization patterns of individual substrates can be used to discriminate between sites.
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Statistical analysis and site discrimination. SA and SR..
Differences in SA and SR between glucose concentrations were evaluated with repeated measures ANOVA. Before conducting each ANOVA, normality of error terms was evaluated via Kolmogorov-Smirnov test for goodness of fit and homoscedasticity was evaluated via Levene’s test for equality of variances. For the field assessment differences in overall SA and SR were evaluated via factorial ANOVA with site and treatment as factors. A posteriori Fishers LSD tests were performed if ANOVA indicated a significant effect of date or treatment. Sequential Bonferroni corrections (Sokal and Rohlf 1995) were applied to control experiment wise error rate.
Individual substrate data..
Analysis of substrate data employed a factorial design with each of the 95 nitrogen substrates being a dependant variable and site and treatment as factors. To determine which N substrates maximized separation between site and N addition and their interaction, a variable reduction step was necessary. Principal Components Analysis (PCA) was chosen to determine whether a single set of variables maximized group separation for the combined effects of the factors and their interaction. PCA requires that the number of observations be greater than the number of variables. To meet this assumption univariate two-way ANOVA were performed on each of the 95 nitrogen substrates. The resulting F statistics for site, treatment and interaction were used as the three variables in the PCA. For each PCA 5000 bootstrap iterations were performed to build a sampling distribution against which the observed data were compared. Confidence intervals then were generated from the point estimates for each of the variable loadings based on the sampling distribution. The percentage variance explained by principal component 1 was low (39%) and confidence intervals for each loading were large and encompassed zero (TABLE II) suggesting that a single solution does not exist for the combination of factors and their interaction. Because a single solution does not appear to exist stepwise procedures were performed for each factor and their interaction independently.
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), with individual substrate activities as dependant variables. Because maximum discrimination between main effects and their interaction was desired we chose Rao’s V, which selects variables based on their contribution to the overall separation of groups (Tabachnick and Fidell 1996, Sobek and Zak 2003). Five thousand bootstrap iterations were performed for variable selection. Stepwise Discriminant Function Analysis (DFA) was performed on the grassland and oak-pine forest data, again with individual substrate activities as dependent variables and N treatment as the independent variable. These DFA results are presented as a tool for visualizing the separation of treatments in multivariate space only as a visual complement to the MANOVA results. For each DFA 5000 bootstrap iterations were performed to select variables and to build a sampling distribution against which the observed data were compared. Confidence intervals then were generated from the point estimates based on the sampling distribution.
Criteria for evaluating and dealing with violations to multivariate normality and homogeneous variance-covariance matrices were adopted from Tabachnick and Fidell (1996). Furthermore significance of MANOVA was determined with Pillai’s trace, the most robust, of the four test statistics used by SPSS to calculate MANOVA P -values (Field 2000, Tabachnick and Fidell 1996, Zar 1999). However test statistics obtained with Wilk’s lambda and Hotelling’s trace produced the same results. Post hoc analyses on significant MANOVA either were obtained via all possible combinations of two group MANOVA or via univariate ANOVA. Bock (1975) suggested that these Univariate ANOVA were protected by the original MANOVA, but to be conservative type I error rate was corrected with the sequential Bonferroni method (Sokal and Rohlf 1995). When an ANOVA was found to be significant it was followed up with Fisher’s LSD post hoc tests. Type I error rate was controlled via sequential Bonferroni method (Sokal and Rohlf 1995). All tests are considered significant at 
=0.05 unless otherwise stated.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONLITERATURE CITED |
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). According to LSD tests maximum SR was reached with 1.0 g L–1 and 5.0 g L–1 of glucose at 120 h incubation (FIG. 1b).
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). There were no significant differences in SA among N application treatments for the oak-pine forest site and the control plots in the grassland (FIG. 2a). Furthermore SA is greatest overall in the grassland. Substrate richness differed between sites (P = 0.009) and was highest in the grassland. Substrate richness did not differ among N additions at either location (P = 0.099) (FIG. 2b).
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Based on the two-way ANOVA that examined N guild response, SR for inorganic N (P < 0.001), amides (P = 0.040), nucleotides and nucleosides (P = 0.047), L-amino acids (P = 0.011), Di-amino acids (P < 0.001) and miscellaneous (P < 0.001) guilds were significantly different between sites. Substrate richness for each of these guilds was highest in the grassland.
Site discrimination..
Results of stepwise MANOVA showed a significant difference in fungal functional diversity on nitrogen substrates between sites (P < 0.001) with the grassland having the highest N use (FIG. 5). Seven substrates were determined, by stepwise procedure to account for differences between the sites, 
-amino-N-butyric acid, L-pyro-glutamic acid, ethanolamine, agmatine, D-asparagine, xanthosine and uridine. No guild was dominant in the site analysis. Differences among N additions were determined to be significant (P < 0.001) based on stepwise MANOVA. These seven substrates accounted for differences between N treatments: L-histidine, L-cystine, L-methionine, L-arginine, L-asparagine, uracil and thymine. L-amino acids were the dominate guild in the analysis. Across-site each of the N treatments were significantly different based on two group MANO-VAs with 4X having the highest SA followed by the 2X treatment. There was not a significant interaction between site and treatment (P = 0.204). Only three substrates, L-pyroglutamic acid, -amino-N-butyric acid and ethanolamine, were used in the MANOVA to evaluate the interaction between site and amount of N addition because addition of subsequent substrates in the analysis did not change the outcome of the analysis.
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). Discriminant function (DF) 1 accounted for 88.4% of the variation in the data, while DF 2 accounted for 11.6%. Five variables were included in the analysis: uric acid, glucuronamide, L-homoserine, N-acetyl galactosamine and D-glutamic acid. Each of the five substrates represented a different guild, however two amino acids were included in the analysis (L-homoserine and D-glutamic acid). Uric acid and L-homoserine both were significantly correlated with DF2 (TABLE III, FIG. 3b).
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). Discriminant function (DF) 1 accounted for 80.0% of the variation in the data, while DF 2 accounted for the remaining 20.0%. Five variables were included in the analysis: L-cystine, N-acetyl galactosamine, L-methionine, L-homoserine and adenosine. L-amino acids were the dominant guild accounting for differences in substrate use in the Oak-Pine forest. L-cystine was significantly correlated with DF1 and N-acetyl galactosamine with DF2. (TABLE III
, FIG. 4b).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONLITERATURE CITED |
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) and has been used extensively as a means for activating the microbial biomass when employing the glucose stimulation technique for estimating microbial biomass in soils. The amount of glucose added to the wells should be only that amount necessary to promote metabolic activity without rapid luxuriant growth occurring. The goal during method development and evaluation was to provide sufficient carbon to stimulate fungal activity that would promote the uptake of the N source in the well if fungi in the inoculum were capable. We chose 1.0 g L–1 glucose for our field trials because maximum SR occurred with both 1.0 and 5.0 g L–1 of glucose, but SA was lower with 1.0 g L–1 than with 5.0 g L–1. Therefore we hoped to minimize the effect of glucose catabolism on color development in the wells. The need to add a C source to the PM3 microtiter plates does provide the opportunity to evaluate differences among sites or treatments not only in response to N use but also with respect to the impact of C type on fungal N use. For example, while we added glucose as the activating C substrate, other carbohydrates, carboxylic acids or recalcitrant substrates could be added and differences in N use evaluated in response to C availability.
Total SA and SR.— – The field test at two sites showed that the PM3 plates can distinguish differences in fungal functional diversity between two vegetation zones based the rate of substrate use (SA) and the number of N compounds used by each fungal assemblage (SR). Furthermore the procedure allowed for detection of shifts in function of fungal communities between differing experimental N inputs within each site. The initial addition of NH4+ and NO3– has not altered the fungal community’s ability to use more compounds. However the rate at which compounds were metabolized in the 2X and 4X treatments in the grassland was enhanced by the initial nitrogen application. This suggests that N could be a limiting factor for fungal growth in the grassland site and the additional N inputs have stimulated fungal metabolism.
Nitrogen guilds SA and SR.— –
The Nitrolog approach distinguished between sites based on N guild use data. Soil fungi must rely on the vegetation at each site to supply substrates for growth (Cromack and Caldwell 1992, Kjoller and Struwe 1992). Moreover vegetation affects N availability and subsequent C : N ratios, which controls decomposition rates. Therefore the observed differences in SA and SR are likely due to the differences in plant species composition between the sites.
Site, treatment and within-site discrimination.— –
The Nitrolog procedure provided sufficient resolution to discriminate between sites and treatments for N substrate use by soil fungi at BBNP. The grassland and forest sites were determined to be significantly different from each other with respect to N use. Of those compounds contributing to maximum variation between sites several are either commonly found in grasses (L-pyroglutamic acid) or accumulate in drought-stressed plants. -amino-N-butyric acid is a compound that has been found to accumulate in plants during drought stress, especially in the nodules of those associated with nitrogen fixing bacteria (Merck 1996). Agmatine is an intermediate in the putrescine synthesis pathway using the enzyme arginine decarboxylase. This pathway would be active during cell elongation and in nondividing mature tissues in plants subjected to drought stress (Rastogi et al 1993, Perez-Amador and Carbonell 1995). Mean activity on each of these compounds was highest in the SG. Because grasses are a dominant vegetation in the SG site and drought stress is a common occurrence there as well, especially during the summer, these results suggest that the Nitrolog procedure can discriminate between functional differences of soil fungi that reflect specific physiological adaptation to plants, and the effects of chemical compounds exuded by them, to the soil C and N pool within sites.
The Nitrolog method has applications to systems where nitrogen metabolism is an important determinate of fungal functional profiles, such as where nitrogen is a limiting resource or where nitrogen deposition is a concern. Furthermore combining N-use data with C-use data should let the researcher gain a more complete understanding of the overall condition of an ecosystem. Defining a ratio of C vs. N use would let the researcher monitor shifts in requirements for each nutrient as disturbance alters community resource needs.
Using Biolog plates to evaluate functional diversity in microbial communities has generated some debate based on limitations of and concerns with the procedure (Preston-Mafham et al 2002). While the technique cannot characterize the species composition of microbial communities, the usefulness of the approach is in its ability to evaluate functional aspects of microbial communities at different sites, subjected to different treatments, or to follow responses to perturbations over time. Furthermore, while Preson-Mafham et al (2002) commented on the potential problems related to inoculum densities in the microtiter plates, this procedure is designed to maintain consistent fungal inoculum, (Dobranic and Zak 1999, Sobek and Zak 2003). Functional diversity of any ecosystem is an emergent property that is determined by species richness and species densities. Therefore changes in functional diversity and the results of the Nitrolog assessment will be determined by changes in species density and composition over time.
To further examine the relationship between carbon and nitrogen use by soil fungi and the effects on ecosystem dynamics, other carbon substrates in addition to glucose could be added to the PM3 plates. In this manner one could evaluate how patterns of root exudates for example could affect the ability of the soil fungal assemblage to use root-derived C and organic N over the growing season in a quantifiable manner.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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2 E-mail: john.zak{at}ttu.edu. 
1 Corresponding author. E-mail: hgrizzle{at}gmail.com
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denotes 0.1 g glucose. 
denotes 1.0 g glucose. 
denotes 5.0 g glucose. n = 5.
