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Applied and Environmental Microbiology, November 2005, p. 6998-7007, Vol. 71, No. 11
0099-2240/05/$08.00+0 doi:10.1128/AEM.71.11.6998-7007.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.
University of California, Berkeley, California 94720
Received 6 December 2004/ Accepted 23 June 2005
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ABSTRACT |
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INTRODUCTION |
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In upland humid tropical forests, soils have been found to experience rapid redox fluctuations (51). In these ecosystems, the combination of high C availability, warm temperatures, abundant rainfall, clay soils with high water-holding capacity, and high metabolic activity can lead to conditions where O2 is rapidly and completely consumed from both liquid- and gas-filled pore spaces (19). Low O2 concentrations are associated with increased labile C (38) and reductions of redox potential that are frequently sufficient for methanogenesis (28, 51). Wet upland tropical soils are less likely to exhibit the slow uniform progression from more oxidized to more reduced processes that is commonly observed in continually waterlogged sediments and subsurface aquifers (32). Instead, these tropical soils are likely to be highly dynamic, with dominant electron couples changing rapidly during frequent cycles of wetting and drying or periods of high biological activity. In these soils, high net primary productivity (62) and daily rainfall (totaling 3.5 to 4.5 m annually) lead to high availability of reductant and redox conditions that change markedly over relatively short time scales.
The physiological implications of fluctuating oxygen availability for bacterial communities have rarely been studied in upland soils. This is due to both the methodological difficulties of soil redox measurements (54) and an assumption that aerobic processes dominate upland soils. However, the frequency of redox shifts may have a strong effect on community development and function, analogous to the effects that drying and rewetting events have on soil microbial biomass and activity (18) or that temperature fluctuations have on heterotrophic respiration and methane production (59). Several recent studies have shown that shifting redox patterns can affect ecosystem processes such as methane efflux (47), glucose respiration (43), and nitrification and denitrification (16). These redox shifts can occur on very short (hourly or daily) (47) to longer (monthly or seasonally) time scales (57); however, the sensitivity of microbial communities to repeated redox cycling has not been investigated.
In high-Eh, well-oxygenated habitats, obligate aerobes depend on oxidative respiration and phosphorylation to maintain their energy and nutrient requirements. While these organisms generally do not find anoxic conditions toxic, many are inactivated or starved as O2 becomes limiting (17). Conversely, many obligate anaerobes such as methanogens, iron and sulfate reducers, find O2 toxic because they lack superoxide dismutase and catalase enzymes necessary to degrade O2– and peroxides (17, 25, 58). Free O2 toxicity may also occur due to the disruption of enzymes essential to anaerobic energy metabolism, such as hydrogenases and pyruvate:ferredoxin oxidoreductase (17). Because of the importance of soil oxygen availability to microbial metabolism, spatial shifts in microbial physiology are traditionally thought to occur along gradients of soil redox potential. As soil redox potential decreases, the dominance of functional groups shifts (1, 43, 58). Ludemann et al. (33) demonstrated such a pattern in paddy soils by using terminal restriction fragment length polymorphism (T-RFLP); others have also measured similar patterns in seasonal bacterial communities and functional changes in salt marshes (16, 27). Studies of such changes in uplands soils are rare, although Picek et al. (43) observed that microbial biomass, C mineralization, and glucose consumption were sensitive to 48-h redox shifts in a Czech field soil.
There is substantial current interest in microbial community composition and diversity in below-ground ecosystems; this is particularly true for tropical communities (5). A growing body of evidence indicates that microbial community composition significantly affects ecosystem processes (10, 60), just as above-ground plant community composition has been shown to affect processes such as N cycling (64) and decomposition (24). An important measure of the functional significance of microbial community composition is ecosystem nitrogen processing. The transformations in the nitrogen cycle are particularly susceptible to changes in soil (pE), since redox potential acts as a master switch between microbe-catalyzed processes that sequester N and those that emit N trace gases.
The objective of this study was to examine microbial community dynamics in a soil environment characterized by fluctuating redox potential. We hypothesized that the intensity and duration of soil O2 fluctuations affect the phenotypic plasticity of bacterial communities, since fluctuation should logically select for physiologies that are tolerant of extremes and resume activity rapidly. Frequent soil redox fluctuation may also make dormancy a strategy for bacteria which need to endure redox periods incompatible with their primary mechanisms of energy generation. Alternatively, a fluctuating redox system might be dominated by facultative metabolisms, such as those that can switch between O2 and NO3 or O2 and fermentation.
To determine the survival strategies important in humid tropical forest soils, we incubated soil under static aerobic and anaerobic conditions as well as fluctuating conditions and then used multiple-enzyme T-RFLP analyses to quantify bacterial community responses and diversity. We used a Web-based (in silico) tool (29) to analyze multiple T-RFLP profiles from standardized enzyme digests. By comparing results of multiple restriction enzyme digests to those of known bacterial sequences, this tool increases the specificity of putative phylogenetic assignments and was used to infer how our imposed redox treatments affected community composition. Lastly, to garner more information on the relationship between community structure and function, we compared T-RFLP profiles with gross N cycling rates.
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MATERIALS AND METHODS |
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Approximately 75 soil cores were collected from the 0- to 10-cm depth by using 6- by 12-cm polyvinyl chloride tubes; samples were collected from a 10- by 10-m area. Cores were kept intact, immediately placed in sealed ziplock bags, and transported to the University of California, Berkeley, at 
20°C. Within 24 h of sampling, cores were extruded and quickly homogenized in a 15-gallon cooler to create a single large composite sample. This sample was split into portions for initial analysis and for incubation treatments. For the initial analysis, 3-g subsamples were transferred to sterile microcentrifuge tubes and stored at –80°C for molecular community analysis. Soil moisture and microbial biomass (via chloroform fumigation incubation) were measured with the remaining soil. A 15N pool dilution was also conducted by adding 15NO3 and 15NH4 to separate soil portions and measuring 15NO3, 15NH4, 15N2O, 15N2, and 15N-microbial biomass after 0, 3, 6, and 14 h. These measurements allowed determination of inorganic N pools and rates of gross N processing, including nitrification, mineralization, denitrification, dissimilatory nitrate reduction to ammonium (DNRA), NO3/NH4 consumption, and residence time (see references 13, 22, 42, and 52 for detailed methods).
Redox incubations.
Using the composite soil, 170 g oven dry equivalent soil was added to 1,000-ml jars and divided into four treatments with five replicates each. Jars were capped with gas-tight lids fitted with a Hungate septa and a 5-in. piece of tygon tubing on the inside of the jar. Hydrated gases were delivered to the bottom of each jar (vented with a syringe needle) at a rate of 66 ml/min, giving a headspace turnover time of approximately 8 min. Soil moisture was maintained at 59% (±0.01%). Treatments included (i) "static aerobic" jars, which were constantly flushed with medical-grade air; (ii) "static anoxic" jars, which received N2 gas; (iii) "short-term fluctuation" jars, where flushing alternated between air and N2 every 12 h; and (iv) "long-term fluctuation" jars, where flushing alternated every 4 days. Jars were incubated under these treatments at 18°C and harvested after 3 weeks. At this point, each jar was subdivided; a 3-g portion was used for molecular community analysis and the remainder for 15N-based process analyses (52) as described above. The timing of flushing was designed so that jars from both fluctuating treatments were anaerobic just prior to harvesting. Periodic measurement of headspace O2, N2O, and CH4 gas concentrations indicated that N2 flushing had the desired effect in lowering soil redox, as evidenced by dramatically increased CH4 and N2O fluxes and zero O2.
T-RFLP analysis.
We extracted whole-community DNA from 0.5 g soil with the Fast DNA spin kit for soil (Bio 101 Systems, Carlsbad, CA) according to the manufacturer's instructions and pooled three extracts per sample to limit effects of random extraction bias (30). We confirmed the presence of high-molecular-weight DNA (6 to 10 kb) on 0.8% agarose gels; samples from all treatments had comparable DNA concentrations (80 ng/µl). We performed PCR amplification using 1 µl of a 1:10 dilution of soil extracts as template. PCR mixtures (50-µl final volume) contained 1x reaction buffer, 1.5 mM MgCl2, 250 µM each deoxynucleoside triphosphate, 20 µg bovine serum albumin (bovine serum albumin), 2.5 units of Taq DNA polymerase, and 400 nM primers (27F [AGAGTTTGATCCTGGCTCAG] and 1492R [TACGGYTACCTTGTTACGACTT]). The forward primer 27F was labeled with the fluorescent moiety 6-carboxyfluorescein at the 5' end for detection by capillary electrophoresis. Primers were synthesized by QIAGEN Operon (Alameda, CA), deoxynucleoside triphosphates and bovine serum albumin were obtained from Roche Molecular Systems (Alameda, CA), and all other PCR reagents were purchased from Promega (Madison, WI). PCR was performed in a Perkin-Elmer 9600 thermocycler with an initial denaturation step of 94°C for 3 min, followed by 27 cycles of 94°C for 30 s, 53°C for 30 s, and 72°C for 1 min and a final extension at 72°C for 7 min. Three replicate PCR products per sample were pooled and purified with an Ultra Clean PCR clean-up kit (MoBio, Solana Beach, CA) according to the manufacturer's instructions.
Restriction digests were standardized by using 400 ng of purified PCR product for each sample. They were performed with four separate reactions per sample, using the restriction endonucleases MspI (C
GG), AluI (AGT), RsaI (GTÂC), and HhaI (GCG) (New England Biolabs Inc.) and associated buffers under conditions recommended by the manufacturer. Digests were incubated for 24 h at 37°C, desalted, and prepared for sequencing according to established procedures (6). Terminal restriction fragment (T-RF) lengths were measured on an ABI 3100 capillary electrophoresis system, and electropherograms were analyzed with Gene Scan software for fragment lengths of between 50 and 500 bp. The precision of a simultaneously run GeneScan ROX500 (ABI) standard was consistently ±0.5 bp. Sample profiles with less than 10,000 total fluorescence units were discarded and rerun (as suggested in reference 4). Peak patterns from different samples were manually aligned by size sorting and manually grouping (binning) peaks within 0.5 base pairs of fragment length. This technique, though time-consuming, avoids errors associated with automated rounding algorithms (30) and ensures that "double peaks" are identified and accounted for only once. Peak heights were relativized based on the proportion of total sample abundance (as advocated in references 4 and 30). Only fragments occurring in at least two sample profiles were included in community analysis. Approximately 20% of identified peaks were present in only one out of five replicates and were removed from analysis. Reproducibility of profiles was between 92 and 97% for a given sample replicate (extracted, amplified, digested, and sequenced on separate occasions). The term T-RF is used to represent individual fragments, although we recognize that one T-RF may incorporate genetic material from one or several distinct bacterial ribotypes (29).
Indicator species analysis (ISA) was used to identify individual T-RFs unique to a given redox treatment(s); we analyzed both T-RF fidelity (frequency) and T-RF exclusivity (abundance). This technique, based on Dufrêne and Legendre's method (15), combines information on the concentration of "species" (in this case, a T-RF) abundance in a particular group and the faithfulness of occurrence of a species in a particular group. It produces indicator values for each species in each group; a perfect indicator species occurs within a particular group or treatment without fail and is assigned an "indicator value" of 100. Indicator value assignments are tested for statistical significance using a Monte Carlo technique. Unlike the commonly used TWINSPAN analysis, ISA is more sensitive and does not assumes the existence of a strong a priori gradient dominating the data (15). Further, ISA helps identify T-RF assemblages that best characterize treatments or groups of treatments (15).
Using T-RFLP fingerprints, phylogenetic assignments were inferred using an automated Web-based tool (PAT) that compares T-RF sizes to the University of Idaho's Microbial Community Analysis (MiCA) database of fragments produced by known 16S rRNA gene sequences from over 2,000 bacterial ribotypes (29; http://trflp.limnology.wisc.edu/index.jsp). This and other in silico approaches (35) can generate putative identifications for uncultured members of a microbial community and has been successfully compared to clone libraries in characterizing samples from lakes (29), soils (45, 46), and the human colon (36). The benefits of this technique are its capacity to rapidly generate phylogenetic assignments from submitted T-RFLP profiles; however, because experimental errors may occur in fragment size determination, these assignments may be less precise than those of a clone library (29, 30). Although many phylogenetically similar species may contribute to the same T-RF (66), the comparison of multiple enzyme digests for each sample helps to improve the specificity of PAT assignments (29). Therefore, we chose to analyze output at the level of phylum-class as opposed to the species level (where errors in assignment are more likely). As an additional conservative measure, we set the fragment bin tolerance window for the PAT algorithm at ±1 bp for fragments of 0 to 250 bp in length and ±2 bp for 250- to 500-bp fragments.
Multiple T-RFLP digests (MspI, AluI, RsaI, and HhaI) were used to increase the phylogenetic resolution of ribotype assignments (29). We used the PAT method instead of creation of a clone library due to our large sample size, the highly diverse bacterial community, and problems associated with comparing community composition among different samples' clone libraries (48, 53). The results of PAT phylogenetic assignments were not used for statistical analyses, but instead are summarized in Fig. 3 and 4 to give an overview of treatment effects on the composition and phylogenetic distribution of community members. In cases where the PAT tool identified different strains of the same bacterial species multiple times, these were consolidated into one listing for analysis. In the rare cases where a set of T-RFs matched more than one species, only the genus was included as a single listing. Species matches that occurred in only one replicate out of five for a given treatment were also excluded. Phylogenetic assignments were classified according to Bergey's Manual of Systematic Bacteriology (20).
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We chose to use an indirect gradient ordination (PCA as opposed to canonical correspondence analysis) because this analysis maximizes community variance among samples, regardless of treatment (37). While some community data do not meet PCA's assumptions, this data set does, as it has a roughly monotonic increasing linear relationship between T-RF abundance and the treatment gradient. Therefore we focus on the PCA results, as opposed to nonmetric multidimensional scaling, where the loss of statistical power yielded similar, though less interpretable, results (37, 42).
Cluster analysis and the multiresponse permutation procedure (37) were used to test for significant differences between treatment groups. The multiresponse permutation procedure "A" statistic is a measure of within-group homogeneity. When all items within a group are identical, A = 1.0. Typical values for "A" in community ecology are below 0.1; while an A value of >0.3 is considered a relatively large value (37). Diversity measures calculated for T-RFs included richness (based on the number of unique fragments that appeared in each sample) (31), Shannon's diversity index (50), and evenness (Shannon's index divided by the natural log of richness) (23). While the T-RFLP method does not give a complete estimate of sample bacterial diversity (only a fraction of bacterial species are represented in each identified terminal fragment), there should be a strong correlation between the T-RFLP terminal fragment diversity (the number of unique peaks and relative abundance of each peak) and the number of bacterial species in a given sample (31).
Analysis of variance and Tukey pairwise comparison tests were used to test for significant differences between treatments for diversity indices and PCA ordination of MspI-based T-RFLP data. To examine correlations between environmental variables/N-cycling rates and microbial community structure (represented by principal components), we used multiple regression with JMP software (SAS Institute, Inc., Cary, North Carolina) and evaluated the ability of the environmental variables to predict microbial T-RFLP profiles summarized as principal components (for further discussions of this technique, see references 2, 3, and 55). A separate multiple regression analysis was performed for each principal component. Significance was determined as a P value of <0.05 unless otherwise noted.
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RESULTS |
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Ordination of MspI-derived T-RFLP patterns shows that communities exposed to static redox treatments changed substantially compared to the original soil community (Fig. 1). Along the first principal component (PC1) (53% variance explained; eigenvalue = 0.52), soils treated with a 4-day redox fluctuation cycle retained a bacterial community composition that was statistically indistinguishable from that of the field soil, whereas soils with the anoxic/12-h and aerobic treatments moved sharply apart in ordination space (Fig. 1). Analysis of variance indicated significant treatment differences (P < 0.001), with the following pairwise differences: (anoxic/12-h) 
(field/4-day) (aerobic). Along PC2 (20% variance explained), there is an indication that the field and 4-day communities are distinct; however, the strength of this pattern, while statistically significant, is muted due to a lower eigenvalue (0.2). Examination of individual T-RFs shows that most terminal fragments present in the original community persisted in one of two treatment groupings: either the aerobic/4-day fluctuation soils or the anoxic/12-h fluctuation soils. Of the 179 total T-RFs detected, only 3 from the field soil persisted in all four incubation treatments. While 34 fragments were found only in the field soil and were not detectable following incubation, 16 T-RFs not found in the original community were promoted by the incubation treatment. The majority of these incubation-induced T-RFs were promoted by the anoxic and 12-h treatments.
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The in silico PAT analysis (Fig. 3) gives an indication of the diversity of community composition in these soils; however, due to the incomplete nature of the master ribosomal database, the results do not necessarily indicate relative abundance. After consolidation, the four restriction digests and PAT analysis generated a list of 145 species matches for T-RFs isolated from these soils. The PAT tool was unable to assign a species to many T-RFs; the percentages of unmatched fragments were 52% (MspI), 57% (HhaI), 64% (RsaI), and 71% (AluI). Addition of a fourth restriction enzyme to the analysis did not dramatically affect the composition of the output; however, the first, second, and third enzymes each significantly refined the species list. Assignments according to treatment are shown in Fig. 3, classified by phylum or class (for particularly diverse phyla). Also included are an additional number of clones, strains, and symbionts identified by the PAT program but unnamed. The community composition of these soils varied with incubation condition; the 12-h treatment had significantly fewer ribotypes identified by the PAT program (P < 0.0001), while aerobic soils and those with 4-day redox variation retained a richness and distribution of ribotypes similar to that for the field soil. While a diverse assemblage of organisms was identified, high-GC gram-positive bacteria and Proteobacteria dominated the list; the most highly represented groups included Actinobacteria (particularly the Micrococcus and Streptomycetes subgroups), Firmicutes-"Bacilli," Firmicutes-"Clostridia," and Betaproteobacteria-Burkholderia, with greater than six ribotypes each. Ordination analysis showed that the lower diversity identified in the 12-h fluctuating treatment was due to a lower proportion or complete lack of "Fusobacteria," Actinobacteria, "Clostridia," Mycoplasma, Gammaproteobacteria-enterobacteria, and Alphaproteobacteria-rhizobia. In contrast, the 4-day aerobic/anaerobic soils were matched to prominent assemblages of "Bacilli," "Clostridia," Burkholderia, cyanobacteria, Planctomycea, and a large number of unclassified organisms (Fig. 3). It is interesting to note the prominence of bacteria from the Actinobacteria in these wet, low-pH, frequently anoxic soils. Bacteria from this class have commonly been associated with dry, high-pH soil environments in the field of soil microbiology (34, 41).
To determine the relationships between community structure and soil functioning, environmental process variables were regressed against the first principal component (PC1) from the MspI-based T-RFLP PCA ordination (Table 3), followed by the second principal component (PC2). Environmental and process variables included in multiple regression analyses were soil moisture, initial field NO3 and NH4 pools, microbial biomass N, mean NO3/NH4 residence time, N gas flux, and rates of mineralization, nitrification, denitrification, DNRA, and NO3/NH4 consumption (42). All rates were measured over 0 to 24 h following 15N label addition. We removed soil moisture and residence time from the analysis, as they were nonsignificant and highly autocorrelated with other variables. Both bacterial community composition indices (PC1 and PC2) were highly correlated with initial soil NO3 concentration. There was significant correlation between PC1 and nitrification, DNRA, and denitrification (listed in order of decreasing r2). The second principal component axis (PC2) was significantly, though more weakly, correlated with nitrification, DNRA, and NH4 consumption. There was no evidence of a link between T-RFLP-based community indices and microbial biomass or 15N biomass uptake, nor was there a correlation between bacterial communities and NH4 pools or mineralization.
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DISCUSSION |
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During redox incubations, a large number of native ribotypes became undetectable via T-RFLP. This could be due to the disturbance of soil microsites or to gross-scale changes in soil pE caused by homogenization. We note, however, that the PAT output identified a greater number of species in the 4-day treatment soils than in the field soils. Four-day fluctuation soil communities retained high bacterial diversity, and after 3 weeks of lab incubation, most resembled the original field communities in terms of species diversity and identity. Ordination of these bacterial communities shows a distinct divergence between organisms exposed to static redox extremes. We suspect that this is caused by selective deletions from the original community, as organisms that are competitive in a fluctuating environment are no longer detectable when their habitat suddenly becomes static. Additionally, obligate organisms that were sheltered in semipermanent microsites may cease to function competitively as their environment segues to an incompatible redox state. Obligate anaerobes in particular may quickly drop out of a soil community as the environment is forced to become wholly aerobic, due to the effects of O2 toxicity (17). Indeed, in this experiment, a significant loss of microbial biomass occurred in the static anoxic and aerobic soils after 6 weeks of incubation (42). This may in part explain the divergence in ordination space between static redox treatments and the field soil and also the great number of T-RFs lost from the static soils relative to the original soils. In addition, individual T-RF analysis shows that different terminal fragments were lost depending on whether soil was under anoxic/12-h conditions versus aerobic conditions.
In these profiles, the anaerobic soil community consistently tended to cluster with the 12-h fluctuation soils. Previous research has shown that high C availability and high soil moisture (51, 61) cause extremely high biological O2 demand in these soils, such that any O2 that does diffuse into microsites is quickly utilized and exhausted. We speculate that 12 h is not long enough for O2 to completely resaturate microsites; however, 4 days may be long enough to allow periodic aerobic respiration. This suggests that the 4-day soil redox fluctuated in a fairly dramatic sense (beyond the microaerophilic boundary), whereas the 12-h soil likely remained poised at a reduced pE for much of the time.
In a number of cases, T-RFs that were dominant in the original soil became lower-ranking fragments after the treatment, whereas some T-RFs that were rare in the original soil came to dominate following redox treatment. While T-RFLP, being PCR based, is a largely qualitative assay, the patterns reported here were replicated over both separate samples and separate PCR amplifications. It appears that by physically disturbing and then radically changing redox regimens in these soils, the competitive balance between organisms has been altered, promoting different portions of the original community in the process. These results highlight the possibility that shifts in soil redox patterns (a result of predicted changes in tropical climate and precipitation patterns) could directly affect microbial community structure.
To our knowledge, ISA has not been previously used for T-RFLP analysis. However, it is a useful tool in data exploration, giving a sense of "representative diversity" that is perhaps a better measure than simple species richness in indicating the typical species assemblage diversity or "effective diversity" of a given treatment (15). Absence of an indicator value or a low index value is also ecologically relevant in that it may indicate a situation where a species in never present. Bacteria in field and fluctuating soils, with relatively high indicator values, occurred more consistently in treatment replicates than organisms from static redox soils. While perfect indicator T-RFs were rare, those that occurred were primarily associated with field soils and likely are organisms inhibited by lab incubation conditions. The relatively high fidelity of field and fluctuating soils, yet low exclusivity, indicates that a significant portion of the native community has evolved physiological mechanisms to contend with a variable redox environment. For example, in a variable-pE soil, microbes may have developed defense mechanisms for withstanding the redox stress due to both the presence (toxicity) and absence (starvation) of O2 (44), including antioxidant enzymes such as superoxide reductase (26, 40). They may also employ substrate storage mechanisms similar to those studied in aerobic-anaerobic activated sludge, where bacteria store polyphosphates and volatile fatty acids for delayed use as energy sources (8, 49).
We evaluated four noncompeting hypotheses regarding the physiological survival strategies of bacteria in this fluctuating redox soil. These strategies and the T-RF pattern evidence for each are presented below (Table 2).
(i) Bacteria in fluctuating redox soils are physiologically plastic and able to compete under all redox conditions. This strategy fits profile "A" (Table 2; Fig. 2) and was not supported by the data. Only 3 fragments fit this profile out of the total of 179 fragments, suggesting that few organisms in this soil are able to remain ecologically competitive under both aerobic and anaerobic conditions.
(ii) Bacteria in this soil are specifically adapted to fluctuating redox through tolerance to brief periods of unfavorable redox conditions. This is consistent with profiles "B" and "C," in which organisms persist in either a primarily aerobic or primarily anaerobic regimen, but not both. Our data show that 27% of the T-RFs found in this study followed this strategy.
(iii) Bacteria in this fluctuating redox soil are strict aerobes and anaerobes that are intolerant of incompatible redox periods in their immediate environment and likely exist in spatially isolated or discrete microsites. This incorporates profiles "D" and "E." We saw no strong evidence for this strategy in our data. Only three T-RFs were exclusive to the static treatments. However, we did find many T-RFs in the field soil that were no longer detectable in any of the treated soils (profile "G"). It is possible that some of these organisms were such strict anaerobes that they were killed by the brief physical mixing period that preceded incubation.
(iv) In this soil, bacteria either require fluctuation or are so highly adapted to redox fluctuation that they are eliminated under static conditions. This is supported by profile "F." A large proportion (50%) of the T-RFs generated in this study followed this profile.
We conclude that hypotheses ii and iv are the most likely explanations of bacterial community patterns and redox fluctuation tolerance in this soil. As there are so few T-RFs found solely in the static treatments, there is little evidence to support the presence of either highly competitive obligate anaerobes or aerobes.
The results gleaned from the PAT analysis are a preliminary step in the phylogenetic characterization of bacteria in these soils. While it is intriguing to note the diversity of organisms identified, the technique is unlikely to positively imply phylogenetic groups for this soil at any finer resolution than the order/genus level. Erroneous assignments may occur as a result of errors in fragment size calling. However, in the interest of constructing some functional implications from the species matches generated by the PAT analysis, we compiled a list of the redox preferences of each identified species according to the National Center for Biotechnology Information taxonomy website (http://www.ncbi.nlm.nih.gov/Taxonomy) and published literature (34). By this ad hoc test, approximately half the identifiable groups in the field soil were aerobes, a third were anaerobes, and the remaining groups were split between fermenters and facultative organisms (Fig. 4). By contrast, the facultative community had tripled in size in the 4-day fluctuating soils. While such results are inherently simplified and could be made more robust with the development of a clone library or microarray analysis (14), we advocate for the PAT-type analysis as an important exploratory technique that may yield further avenues of investigation. This may be particularly true in tropical soils where efficient extraction of community DNA and PCR amplification are nontrivial due to high levels of humic materials and other contaminants.
As evidenced by the large number of terminal restriction fragments that remained unmatched, PAT-type analysis is hindered by the incomplete nature of the 16S rRNA databases (e.g., the Ribosomal Database Project [35] and the Microbial Community Analysis website [29]) which form the basis for exploratory searches. Unmatched fragments may also be a result of T-RFLP artifacts. We saw little evidence that the large proportion of unmatched fragment patterns was due to insufficient sequence data to make a match (e.g., too few restriction digests) (29). Instead, we strongly suspect that these unmatched fragments represent previously uncharacterized bacteria; hence, we are pursuing development of clone libraries for these tropical soils, which as a whole remain microbiologically uncharacterized.
In an attempt to integrate community composition and function data (through multiple regression), we saw a close correlation between the bacterial community structure of these soils and N-cycling functions, including nitrification, DNRA, and denitrification. All three of these energy-conserving processes are particularly sensitive to redox; the distinction between anaerobic/12-h soils, field/4-day soils, and aerobic soils seen in ordination space may be in part driven by the selective deletion and inclusion of organisms with highly specific N-based metabolisms. Some possible examples identified in the PAT output include Nitrosospira multiformis (nitrification), Shewanella spp. and Clostridium spp. (DNRA), and Pseudomonas spp. (denitrification). As might be expected, gross N mineralization and biomass N uptake were insensitive to the imposed redox regimens, confirming that these processes proceed with relative disregard for soil redox status.
A combination of complementary community analysis techniques (ordination, indicator species analysis, and a Web-based phylogenetic assignment tool) show that soil communities experiencing anoxic/oxic fluctuation on the scale of 4 days remain most similar to native field communities. This fluctuation-adapted community is likely dominated by organisms that retain physiological tolerance mechanisms which allow them to withstand energetically unfavorable redox periods. Two analyses linking community structure and function (PAT analysis and ordination-multiple regression) suggest that climate change-induced shifts in the variability of O2 regimens may affect specific N-cycling functions such as nitrification and denitrification in tropical soils through preferential promotion of specific bacterial functional groups. Ongoing modeling and analysis of future tropical soil climate change should be alert to its effects on a bacterial community that appears to be highly adapted to the current environment.
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ACKNOWLEDGMENTS |
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This work would not have been possible with out the generous support of W. Silver, who provided site access and much assistance with 15N analysis. We also thank A. Thompson, D. Herman, E. Marin-Spiotta, M. Waldrop, K. DeAngelis, V. Eviner, M. Rusli, C. Hawkes, Eric Dubinsky, M. McGroddy, S. Baek, R. Jackson, R. Ostertag, and P. Templer for their help with laboratory analyses and technical assistance. E. Brodie provided particularly useful advice on molecular technique and manuscript preparation.
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FOOTNOTES |
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