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Applied and Environmental Microbiology, May 2005, p. 2695-2704, Vol. 71, No. 5
0099-2240/05/$08.00+0     doi:10.1128/AEM.71.5.2695-2704.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.

Distribution and Stability of Sulfate-Reducing Prokaryotic and Hydrogenotrophic Methanogenic Assemblages in Nutrient-Impacted Regions of the Florida Everglades{dagger}

Hector Castro,1 Susan Newman,1,2 K. R. Reddy,1 and Andrew Ogram1,3*

Soil and Water Science Department,1 Microbiology and Cell Science Department, University of Florida, Gainesville, Florida,3 South Florida Water Management District, Everglades Division, West Palm Beach, Florida2

Received 11 July 2004/ Accepted 2 December 2004


   ABSTRACT
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Although the influence of phosphorus loading on the Everglades ecosystem has received a great deal of attention, most research has targeted macro indicators, such as those based on vegetation or fauna, or chemical and physical parameters involved in biogeochemical cycles. Fewer studies have addressed the role of microorganisms, and these have mainly targeted gross informative parameters such as microbial biomass, enzymatic activities, and microbial enumerations. The objectives of this study were to characterize the dynamics of sulfate-reducing and methanogenic assemblages using terminal restriction fragment length polymorphism (T-RFLP) targeting the dissimilatory sulfite reductase (dsrA) and methyl coenzyme M reductase (mcrA) genes, respectively, and assess the impact of nutrient enrichment on microbial assemblages in the northern Everglades. T-RFLP combined with principal component analysis was a powerful technique to discriminate between soils from sites with eutrophic, transitional, and oligotrophic nutrient concentrations. dsrA T-RFLP provided a higher level of discrimination between the three sites. mcrA was a relatively weaker system to distinguish between sites, since it could not categorically discriminate between eutrophic and transition soil samples, but may be useful as an early indicator of phosphorus loading which is altering hydrogenotrophic methanogenic community in the transition zones, making them more similar to eutrophic zones. Clearly, targeting a combination of different microbial communities provides greater insight into the functioning of this ecosystem and provides useful information for understanding the relationship between eutrophication effects and microbial assemblages.


   INTRODUCTION
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Anthropogenic phosphorus additions into the Everglades, an historically low-nutrient ecosystem, resulted in undesirable changes from the original ecosystem (26, 31). The ability to correct these problems, establish recovery endpoints, and monitor the success or failure of restoration strategies depends on finding a suitable battery of ecological indicators. The influence of phosphorus loading in the Everglades has received a great deal of attention at the process level; however, most research has targeted macro indicators, such as those based on vegetation or fauna, or chemical and physical parameters involved in biogeochemical cycles (29). Fewer studies have addressed the role of microorganisms, and these primarily targeted gross parameters such as microbial biomass, enzymatic activities, and microbial enumerations (12, 15, 37), but they provided little insight into the microbial groups responsible for the biogeochemical processes. Microbial communities dynamically react to environmental changes and may be used as ecological indicators to assess the impact of human activities on different ecosystems (1).

Mineralization of organic matter in anaerobic environments is a complex and synchronized chain of events involving several microbial trophic groups (30). Methanogenesis is generally considered the dominant terminal carbon mineralization process in freshwater wetlands with low sulfate input (35). However, some reports demonstrated that sulfate reduction is important in freshwater systems, and a significant amount of carbon mineralized follows this pathway (2, 6, 17, 22). Natural and anthropogenic sulfate inputs in the Everglades resulted in relatively high levels of sulfate, the primary electron acceptor of sulfate-reducing prokaryotes (SRP) (32). In addition, a major environmental concern in the Everglades is mercury methylation, a process regulated by SRP, and subsequent biomagnification of this toxic compound in Everglades fauna (3).

We previously reported differences in microbial assemblages of sulfate-reducing prokaryotes and methanogens in Everglades soils (7, 9, 10). Dissimilatory sulfite reductase (dsrA) clone libraries constructed from DNA taken from soils of eutrophic regions were dominated by Desulfotomaculum-like sequences related to those species capable of complete oxidation of organic substrates. dsrA clone libraries representative of oligotrophic regions were dominated by Desulfotomaculum-like sequences related to species unable to carry out complete oxidation (7). In addition, clone libraries of the archaeal 16S rRNA and methyl coenzyme M reductase (mcr) genes of methanogens were dominated by sequences related to the hydrogenotrophic members of the Methanomicrobiales family (9). However, these studies were limited to two sampling times and did not yield information on the stability of these assemblages with time.

In this study, terminal restriction fragment length polymorphism (T-RFLP) targeting dsr and mcr genes in soil samples collected along the nutrient gradient in the northern Everglades over more than an entire hydroperiod (April 2001 to August 2002) were used to determine the robustness of inferences made regarding selection of specific metabolic types of SRP and methanogens in response to eutrophication. Information gained in the present study provides insight into the distribution and stability of microbial assemblages responsible for the terminal steps of carbon mineralization in freshwater wetlands and how those microbial assemblages respond both to eutrophication and to changes in hydrology, a major regulator of carbon mineralization in wetlands.


   MATERIALS AND METHODS
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Soil samples.
T-RFLP studies were conducted on samples obtained from Water Conservation Area 2A (WCA-2A) at sampling locations described previously (7). All studies were conducted in the 0- to 10-cm soil layer of triplicate soil core samples collected monthly from April 2001 to August 2002, with the exception of August 2001 to November 2001, when high water levels prevented sample collection. Soil samples included eutrophic (F1), transition (F4), and oligotrophic (U3) regions of WCA-2A, making a total of 36 samples per site. Soil samples were kept at –80°C until DNA was extracted. Biogeochemical data for these samples were obtained according to Castro et al. (7) and are provided in Table 1. Water depths recorded during the sampling times are presented in Fig. 1.


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  		TABLE 1. Selected chemical and microbial characteristics of Everglades soils along the eutrophication gradientb

 


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  		FIG. 1. Water levels of eutrophic (F1) and oligotrophic (U3) sites of the Everglades WCA-2A.

 
Nucleic acid extraction and PCR amplification.
One nucleic acid extraction was performed for each sample, using 0.25 g of soil per core with UltraClean Soil DNA kits (MoBio, Solana Beach, CA) according to the manufacturer's instructions. PCR was conducted in 50-µl reaction mixtures using the primer set designed by Wagner et al. (34) for dsrA and the primer set designed by Luton et al. (25) for mcrA. Primers were labeled with the fluorophore 6-carboxyfluorescein in the 5' position (Invitrogen, Carlsbad, CA). PCR conditions were similar to those described in Castro et al. (7) for the dsrA gene and Luton et al. (25) for the mcrA gene, with the exception that the annealing temperature was lowered to 54°C for dsrA and to 53°C for mcrA PCR analysis. PCRs were conducted in 1:10 dilutions for the dsrA primer set and 1:1 for the mcrA primer set. PCR products were electrophoresed through 0.7% and 1.5% agarose gels for DSR (ca. 1.9-kb fragment) and MCR (ca. 465- to 490-bp fragment) amplicons, respectively, to confirm products of the expected sizes. After PCR amplification the products were cleaned and concentrated using a QIAquick PCR purification kit (QIAGEN, Valencia, CA) to a final volume of 30 µl following the manufacturer's instructions.

Enzymatic digestion.
Several restriction enzymes were tested in silico using sequence information gained from the clone libraries and previously published studies of MCR (25). Sequences were digested in silico using CloneMap version 2.11 (GCG Scientific Inc., Ballwin, MO). The restriction enzymes that produced the greatest degree of discrimination were RsaI for dsrA analysis and Sau96I for mcrA analysis (data not shown). Approximately 100 to 150 ng of PCR product (estimated by visual inspection of gels after PCR kit purification) was digested with the appropriate restriction enzyme. The enzymatic digestion reaction mixture consisted of 5 units of restriction enzyme (Promega, Madison, WI), 1x restriction buffer, 1 µg bovine serum albumin, and deionized water to a final volume of 10 µl. Enzymatic digestions were incubated at 37°C overnight.

T-RFLP analysis.
Digested product (1.5 µl) was used for terminal restriction fragment (T-RF) detection by the DNA Sequencing Core Laboratory at the University of Florida. Briefly, digested products were mixed with 2.5 µl deionized formamide, 0.5 µl ROX-labeled GeneScan 500-bp internal size standard (Applied Biosystems, Perkin Elmer Corporation, Norwalk, CT), and 0.5 µl of loading buffer (50 mM EDTA, 50 mg/ml blue dextran). Samples were denatured by heating at 95°C for 3 min and subsequently transferred to ice until loading of the gel. A total of 1 µl was electrophoresed through a 36-cm, 5% polyacrylamide gel containing 7 M urea at 3 kV on an ABI 377 genetic analyzer (Applied Biosystems). T-RFLP profiles were analyzed using GeneScan version 2.1 software (Applied Biosystems). Sizes in base pairs of T-RFs were calculated using internal standards. Peak sizes in base pairs and peak areas were exported to Excel 97 SR-1 (Microsoft Corporation, Redmond, WA) for data analysis. Peaks with heights lower than 50 fluorescent units were filtered out from the final data matrix, a standard practice when analyzing T-RFLP data. Many reports have set these filter value to 25 units (20) or 100 units (24, 27); under our experimental conditions, 50 units was a reasonable filter value.

T-RFLP data analysis.
Data analysis was conducted in two ways: (i) qualitatively, scoring the presence or absence of a particular T-RF as 1 or 0 to produce a binary data matrix, and (b) quantitatively, using the relative abundance of a particular T-RF normalized by the total area of all T-RFs. Principal components analysis (PCA) was performed using the relative abundance of individual peaks with a Multivariate Statistical Package (MSVP version 3.12d; Kovach Computing Services, Wales, UK). Cluster analysis using the T-RFLP binary data matrix was performed using the genetic distance (Gd) calculation of Link et al. (21) as follows: Gdxy = (Nx + Ny)/(Nx + Ny + Nxy), where Nx is number of T-RF in profile x but not in profile y; Ny is the number of T-RF in profile y but not in profile x; and Nxy is the number of T-RFs shared by profiles x and y. Dendrograms were constructed using the unweighted-pair group method using average linkages (UPGMA) analysis and Treecon software version 1.3b (33).


   RESULTS
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Biogeochemical characterization.
The biogeochemical data are in agreement with historical records previously described for this ecosystem (12, 13, 28, 29). Total phosphorus (TP) was higher in the eutrophic F1 site, followed by the transition F4 site and oligotrophic U3 site (Fig. 2, Table 1). Total inorganic phosphorus followed the same trend as TP and represented 35% of the TP in F1, 40% in F4, and 25% in U3. Microbial biomass P (MBP) did not follow this trend; MBP was higher in F4, followed by F1 and U3. Microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN), like MBP, were higher in F4, followed by F1 and U3.



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  		FIG. 2. Total phosphorus (TP), total inorganic phosphorus (Tpi), and microbial biomass phosphorus (MBP) levels (in mg/kg) in (A) eutrophic (F1), (B) transition (F4), and (C) oligotrophic (U3) sites of the Everglades WCA-2A.

 
C/N ratios were 15 for eutrophic F1 and 13 for transition F4 and oligotrophic U3 sites. C/P ratios ranged from 1,403 in U3 to 310 in F1, with F4 in the middle with a C/P ratio of 482. The N/P ratio was higher in U3 (106), followed by F4 (36) and F1 (31). MBC/TC ratios, related to carbon availability and the propensity of a soil to accumulate organic carbon, were similar to previously reported values (29). The highest value was found in F4 (0.029), with 0.017 for F1 and 0.007 for U3.

MBN/TN followed a similar trend, with the highest value observed in transition F4 (0.06) followed by eutrophic F1 (0.03) and oligotrophic U3 (0.008) sites, suggesting high nitrogen demand, but not nitrogen deficiency, since the N/P ratio for F4 is relatively high (N/P of 36).

MBP/TP, an indicator of the efficiency of P assimilation by microbial populations, was higher in F4 (0.30) than in either F1 (0.11) or U3 (0.16). Overall, C, N, and P ratios may indicate that the eutrophic zones are N limited and that the oligotrophic regions are P limited, with no significant limitation of N or P in the transition regions, resulting in higher microbial biomass.

T-RFLP data analysis.
Several problems faced by other researchers were encountered with the T-RF data. First, some irreproducible peaks with no clear presence pattern were observed in one of the triplicate samples and were removed from the analysis (16, 19). In most cases, these peaks were minor (area lower than 1% of the total area) and of no known phylogenetic affiliation. Removal of these peaks did not result in major changes to the final outcome of principal component analysis but facilitated data analysis and interpretation. Second, in the case of the dsr T-RFLP, approximately 15 samples were removed from the final set of 108 samples. T-RFLP data for these removed samples were dominated by only one or two peaks and showed a total area 100 times lower than the total area for the rest of analyzed samples. Optimization of PCR conditions for these samples probably would result in inclusion of these samples in the data set, but because T-RFLP was intended to be a rapid tool to assess microbial diversity, rigorous optimization of single samples was not attempted. This is a possible drawback of the method as a high-throughput technique. Third, in silico digestions of some mcrA sequences indicated the possibility of T-RFs with differences of 1 bp. Such T-RFs were not resolved in this study and were considered a single peak in the data analysis (18). Principal component analyses that considered these as either individual peaks or combined peaks did not alter the final outcome of the analyses.

T-RFLP analysis of SRP assemblages.
For the approximately 20 restriction enzymes tested, digestion with RsaI provided the best possible discrimination between phylogenetic groups. Although it is desirable that a single peak should represent a group of clones with the same phylogenetic affiliation, this was not always the case in this study. Of a total of 23 T-RFs, 13 did not have a known phylogenetic affiliation (Table 2). A total of 30% of these unassigned T-RFs were primarily found in samples from transition F4 site, a site for which clone libraries were not constructed. Further work is required to identify these T-RFs.


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  		TABLE 2. Phylogenetic affiliations of selected dsrA T-RFs for eutrophic (F1) and oligotrophic (U3) soil samples

 
T-RF relative frequencies are presented using the average for three replicate soil cores for eutrophic F1, transition F4, and oligotrophic U3 sites (Fig. 3). For F1 and F4, the T-RF distribution was uniform and stable between sampling times (the coefficient of variance for F1 was 64 ± 34% and for F4 was 57 ± 30%). T-RFLP profiles from U3 were more variable (coefficient of variance, 122 ± 76%). These results are in good agreement with seasonal variability observed in previously described clone libraries from these sites (7). Of a total of 26 T-RFs, 9 T-RFs were observed in all sites; 3 T-RFs were present exclusively in F1, and 5 were present exclusively in F4. Some T-RFs were shared between two sites; five T-RFs were shared by F1 and F4 sites, three T-RFs were shared by by F1 and U3, and one was shared by F4 and U3. No T-RFs were exclusive of the oligotrophic U3 site, with the possible exception of T-RF 113, which was present in U3 and F1, but the relative proportion in F1 profiles was lower than 1% (Table 2).



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  		FIG. 3. Community dynamics for dsrA in (A) eutrophic, (B) transition, and (C) oligotrophic soils determined by T-RFLP analysis. Samples are labeled according to the season, month, year that were taken. SP, spring; SU, summer; FA, fall; WI, winter.

 
PCA and UPGMA analysis of SRP assemblages.
PCA has been extensively used to compare complex microbial communities (14). To apply PCA to these data, the area of an individual peak was normalized to the total area of the peaks. In this study, PCA analysis proved to be a powerful method to discriminate between the three levels of eutrophication (Fig. 4). PCA axis 1 explained 30.4% of the variability and PCA axis 2 explained 17.5%, with a cumulative percentage of 47.9%. A total of six axes were required to explain a cumulative percentage of 72.2%. These values are comparable to other T-RFLP study results (19, 20).



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  		FIG. 4. PCA ordering generated from T-RFLP profiles for dsrA of eutrophic (F1), transition (F4), and oligotrophic (U3) soils.

 
UPGMA dendrograms of the genetic distances using binary ordination revealed the same three clusters as in PCA (data not shown). Three clusters were observed: one for eutrophic F1; one for transition F4; and one for oligotrophic U3 soil samples. UPGMA analysis suggested that eutrophic assemblages were more similar to transition soil samples than to oligotrophic soil samples.

T-RFLP analysis of methanogenic assemblages based on mcrA.
Most-likely phylogenetic affiliations of T-RFs are presented in Table 3. Sau96I in silico digestions provided the best discrimination among sequences from the MCR clone library. As with dsrA T-RFLP profiles, some T-RFs represented members of different phylogenetic groups. Certain T-RFs did not have a known phylogenetic affiliation; however, these T-RFs were present in low frequencies, with the exception of T-RF 392 bp, which was present in most samples in considerable amounts.


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  		TABLE 3. Phylogenetic affiliation of mcrA T-RFs for eutrophic (F1) and oligotrophic (U3) soil samples

 
T-RFLP profiles for eutrophic F1, transition F4, and oligotrophic U3 sites were uniform and stable among samples (Fig. 5). The relative stability of T-RFLP profiles for U3 samples contradicted the more variable distribution of clones previously found in mcr clone libraries (9).



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  		FIG. 5. Community dynamics for the mcrA in (A) eutrophic (F1), (B) transition (F4), and (C) oligotrophic (U3) soils determined by T-RFLP analysis. Samples are labeled according to the season, month, and year that they were taken. SP, spring; SU, summer; FA, fall; WI, winter.

 
In summary, T-RFLP profiles from F1 and F4 were dominated by T-RF 129 bp (clusters MCR-4, -6, and -7), 229 bp (clusters MCR-5 and-7), and 398 bp (clusters MCR-5 and -7), which is in relatively good agreement with results obtained with clone libraries (9). T-RFLP profiles from U3 were in good agreement with clone library results and were dominated by T-RF 129 bp (clusters MCR-4, -6 and -7), 375 bp (clusters MCR-1, -2, and -4), 392 bp (unknown phylogenetic affiliation), and 398 bp (clusters MCR-5 and -7).

In contrast to dsrA T-RFLP profiles, where a selective distribution of T-RFs was observed in relation to site, most mcrA T-RFs were present in all samples (18 of a total of 22 T-RFs). One T-RF was shared by F1 and F4, one was shared by F4 and U3, one was shared by F1 and U3, and one T-RF was exclusively present in U3; however, their relative proportions were low.

PCA and UPGMA analysis of methanogenic community.
PCA analysis proved useful in discriminating oligotrophic sites (U3) from the other two levels of eutrophication (eutrophic F1 and transition F4) (Fig. 6). Although two possible different clusters can be described for F1 and F4, categorical discrimination between F1 and U3 was not achieved. PCA axis 1 explained 29.5% of the variability, and PCA axis 2 explained 11.1%, with a cumulative percentage of 40.6%. A total of seven axes were required to explain a cumulative percentage of 72.0%.



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  		FIG. 6. PCA ordering generated from T-RFLP profiles for mcrA of eutrophic (F1), transition (F4), and oligotrophic (U3) soils.

 
Cluster analysis of the T-RFLP binary data matrix using UPGMA confirmed the PCA results. Four clusters were observed, comprising a cluster for each level of eutrophication, and a fourth cluster composed of a combination of eutrophic F1 and transition F4 was also observed. This corresponded with the overlap observed for these two sites in the PCA analysis.


   DISCUSSION
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Several studies conducted with WCA-2A of the northern Everglades have shown changes in numerous chemical, physical, and microbial parameters related to eutrophication levels. The present report is the first to relate eutrophication level to composition of microbial communities during an entire hydroperiod in WCA-2A.

T-RFLP profiles targeting dsrA were in good agreement with results previously obtained from clone libraries, with some discrepancies for the oligotrophic U3 site (7). Discrepancies between clone library and T-RFLP profile data have previously been reported (11, 23), and discrepancies were attributed to biases in cloning amplification products or to differences in annealing temperatures between PCRs used to develop clone libraries and those used for T-RFLP analysis.

T-RFs possibly related to Desulfotomaculum spp. in eutrophic F1 and transition F4 sites were dominated by T-RF 185, corresponding to cluster DSR-8 (Desulfotomaculum-like complete oxidizer sequences). T-RF 185 was also present in oligotrophic U3 sites, but this cluster was not observed in U3 clone libraries. T-RF 188, corresponding to cluster DSR-4 (Desulfotomaculum-like sequences, incomplete oxidizers) and cluster DSR-5 (uncultured SRP), was observed in considerable number in T-RFLP from oligotrophic sites but was either a minor component or not present in T-RFLP profiles of the eutrophic or transition sites, respectively. Assuming that T-RF 188 represents incomplete oxidizing Desulfotomaculum-like sequences, this may indicate that oligotrophic Desulfotomaculum assemblages are comprised of a combination of complete and incomplete oxidizing Desulfotomaculum sequences and not solely of incomplete oxidizers as suggested by sequence analysis of clone libraries.

Since PCA analysis was based on peak area and the possibility that T-RF areas may not be quantitative due to biases inherent in PCR-based methods, PCA analyses were corroborated using cluster analysis with a binary data matrix. The two types of analyses, PCA using relative abundance of T-RFs and UPGMA using presence or absence of T-RFs, confirmed the clustering of the three levels of eutrophication.

T-RFLP of these functional genes was a powerful method to discriminate between soils with different eutrophication levels. dsrA T-RFLP provided a higher level of discrimination between the three sites than did mcrA. mcrA proved to be a weaker system for differentiation between different eutrophication levels, since it could not categorically discriminate between eutrophic and transition soil samples.

T-RFLP analyses differentiated between the microbial assemblages according to eutrophication level, but no discrimination was achieved within a particular eutrophication level with respect to season. This indicates that hydroperiod did not significantly affect the composition of microbial assemblages responsible for terminal carbon mineralization. This relative stability with seasonality could be beneficial when these microbial groups are used as ecological indicators, since they respond to eutrophication levels rather than exhibit a variable response to seasonal changes.

Sulfate concentrations and electron donor type and concentration control the activity of SRP in freshwater marshes (36). Sulfate concentrations in these marshes were similar between eutrophic and oligotrophic sites (8), and total sulfur concentrations in the 0- to 10-cm soil layer soils are higher than total sulfur content in lower soil layers in both eutrophic and oligotrophic sites, indicating sulfur accumulation in the ecosystem (4). The total sulfur content is relatively similar in the 0- to 10-cm soil layers of eutrophic soils (1.26 to 1.36%) and oligotrophic soils (1.57 to 1.74%) (5). However, higher amounts of sulfur are present as organic sulfur (70 to 80%) in eutrophic sites compared to oligotrophic sites, which have lower organic sulfur content (50 to 55%). Wright and Reddy (37) did not observe differences in arylsulfatase activities along the eutrophication gradient. This may indicate that some of the sulfate entering the northern region of the Everglades is fixed in the organic fraction of eutrophic soils due to higher microbial activity and that the remainder moves southward to more oligotrophic sites. Sulfate input due to atmospheric deposition or groundwater recharge is minor in these ecosystems (4). If sulfate is not driving the differences between SRP assemblages along the phosphorus gradient, the greater number and activity of SRP previously reported and differences in SRP assemblages reported in this study could be explained by differences in the amount and types of electron donors. Results from enrichment cultures and MPN enumerations suggest greater metabolic diversity in eutrophic zones than in oligotrophic zones (8). This possible separation on the basis of metabolism suggests selection by the type and amount of electron donor, which may be characteristic of the eutrophication status of the ecosystem.

T-RFLP data obtained using mcrA clearly discriminated methanogenic populations in oligotrophic sites from those in eutrophic and transition sites but were not sufficiently strong to discriminate between eutrophic and transition sites. Methanogens are able to use only a limited number of substrates in freshwater ecosystems (mainly acetate, H2-CO2, and formate) such that it was not expected that much discrimination would be observed by targeting methanogenic assemblages. However, it is clear that hydrogenotrophic methanogens are responding in different ways to the environmental conditions along the gradient. Different Km values for growth on hydrogen have been reported for different species of hydrogenotrophic methanogens (35), which may explain differences between methanogen assemblages in the eutrophic and transition zones and those in more oligotrophic sites. It may be that hydrogen levels in the eutrophic and transition zones are higher due to greater microbial activity and that this selected populations with higher Km and that the opposite would happen in oligotrophic sites due to lower microbial activity. Further research is required to verify the possibility of physiological difference among hydrogenotrophic methanogenic populations inhabiting sites with different nutrient levels.

The results from dsrA T-RFLP were useful for distinguishing between the three sites with different levels of impact, but the results of the mcrA T-RFLP may indicate that phosphorus loading is altering the methanogenic population in the transition zones, making it more similar to those in eutrophic zones, and may be an early indication of phosphorus impact on microbial communities and processes in southern WCA-2A. Targeting a combination of different microbial populations provides greater insight into the functioning of this ecosystem and may provide useful information for planning and implementation of ecosystem restoration technologies.


   ACKNOWLEDGMENTS
 
This study was supported by grant DEB-0078368 from the National Science Foundation.

We are grateful to Joe Prenger and Yu Wang, Wetland Biogeochemistry Laboratory, Soil and Water Science Department, for sampling coordination and providing geochemical data. We also thank Ron Corstanje and Kenneth Portier for helpful discussions on T-RFLP data analysis.


   FOOTNOTES
 
* Corresponding author. Mailing address: University of Florida, P.O. Box 110290, Gainesville, FL 32611-0290. Phone: (352) 392-1951. Fax: (352) 392-3902. E-mail: avo{at}mail.ifas.ufl.edu. Back

{dagger} Florida Agricultural Experimental Station Journal Series no. R-10548. Back


   REFERENCES
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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Applied and Environmental Microbiology, May 2005, p. 2695-2704, Vol. 71, No. 5
0099-2240/05/$08.00+0     doi:10.1128/AEM.71.5.2695-2704.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.



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