Microbial community diversity and composition across a gradient of soil acidity in spruce–fir forests of the southern Appalachian Mountains

Applied Soil Ecology 61 (2012) 60–68

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Microbial community diversity and composition across a gradient of soil acidity in spruce–fir forests of the southern Appalachian Mountains Sougata Bardhan a,∗ , Shibu Jose a , Michael A. Jenkins b , Christopher R. Webster c , Ranjith P. Udawatta a , Sarah E. Stehn c a

The Center for Agroforestry, School of Natural Resources, 203S Anheuser-Busch Natural Resources Building, University of Missouri, Columbia, MO 65211, United States Department of Forestry and Natural Resources, Purdue University, 715 West State Street, West Lafayette, IN 47907, United States c School of Forest Resources and Environmental Science, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, United States b

a r t i c l e

i n f o

Article history: Received 27 October 2011 Received in revised form 28 April 2012 Accepted 30 April 2012 Keywords: Acid rain Acidophilic bacteria Bacterial diversity Forest soil PCR-DGGE

a b s t r a c t Anthropogenic deposition of sulfur (S) and nitrogen (N) contributes substantially to soil acidity in some forest regions and hence studies have focused on modeling and quantifying depositions in landscapes. The resulting acidity can change the soil chemical balance, nutrient availability, microbial communities, and at a broader scale, ecosystem functioning. In this study, a 16S PCR-DGGE (polymerase chain reaction-denaturing gradient gel electrophoresis) approach was used to measure the bacterial diversity and identify the dominant bacterial species along a soil acidity gradient in high elevation spruce–fir forests of Great Smoky Mountains National Park (GSMNP). Sample sites were selected based upon modeled S deposition class (6–14, 15–23, 23–32, and 33–41 kg ha−1 ). Collected soils were analyzed for pH, C, N, Ca, Al, S, CEC, and base saturation. Average soil pH in the O, A, and B horizons were 3.6, 3.6 and 3.9, respectively. Modeled S deposition was found to be an unreliable predictor of soil S content as well as most other soil chemical properties. DGGE profiles of bacterial partial 16S rRNA genes revealed minor differences in bacterial diversity while communities were similar, dominated by members of phylum Actinobacteria, Acidobacteria, Planctomycetes, Proteobacteria, and Chloroflexi. Dominance of acidophilic bacterial species, often found in highly acidic environment such as acid-mine drainage and sphagnum bogs, suggests that the poorly buffered soils that are endemic to southern Appalachian spruce–fir forests are saturated with acidity. Our results suggest that stricter air quality standards have not resulted in shift to less acid-tolerant bacteria. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Although efforts have been made to reduce air pollution, many regions including eastern United States and Europe, still face serious environmental problems resulting from industrial activities and combustion of fossil fuels (Blake et al., 1999; Driscoll et al., 2001; Warby et al., 2005). Deposition of S and N, released from power plants and industries, in the form of acid rain can lead to decreases in soil basic cations (Ca2+ , Mg+ , K+ , Na+ ), and base saturation and to increase in Al3− , Mn2+ and H+ (Blake et al., 1999; Fenn et al., 2006; Driscoll et al., 2001). Though the Clean Air Act (1970) and subsequent amendments (1990) have resulted in decreased levels of acid deposition, the lasting impact of acidification on soil and surface waters is greatly debated (Wesselink et al., 1995; Rustad et al., 1996; Stoddard et al., 2003; Warby et al., 2009). The effects of acid deposition are of great importance in high elevation

∗ Corresponding author. Tel.: +1 573 340 9465; fax: +1 573 882 1977. E-mail address: [email protected] (S. Bardhan). 0929-1393/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.apsoil.2012.04.010

Appalachian Mountains where soils have low buffering capacity to acidification (Johnson and Lindberg, 1992). Warby et al. (2009) concluded that in spite of reductions in acid depositions to forest ecosystems of the northeastern United States, there is continuing loss of exchangeable Ca and accumulation of exchangeable Al. They further asserted that persistent acidification of such soils can seriously threaten the health of forests and make them vulnerable to injury. Acid deposition reduces exchangeable Ca in soils (Fenn et al., 2006), while simultaneously mobilizing and increasing its Al concentration (Blake et al., 1999). With the dissolution of soil minerals, the basic cations are leached down to deeper soil layers and Al3+ and H+ occupy the exchangeable sites, resulting in plant toxicity. High elevation spruce (Picea spp.)–fir (Abies spp.) forests in the Great Smoky Mountains are extremely vulnerable to atmospheric acid deposition and modeled deposition of S has been estimated as 41 kg ha−1 S annually (Weathers et al., 2006). These soils are highly weathered and naturally acidic and possess very limited ability to buffer additional inputs of acidity (Shubzda et al., 1995).

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Fig. 1. Location of spruce–fir study plots (n = 30) in Great Smoky Mountains National Park (GSMNP), Tennessee and North Carolina, USA. Inset: location of the Great Smoky Mountains National Park.

Soil microflora plays a critical role in ecosystem functioning and nutrient cycling. Soil physical and chemical conditions that influences plant productivity, such as soil pH, also alter soil microbial function and activity. This relationship has been studied in order to model bacterial community structure (Lauber et al., 2009; Rousk et al., 2010). These studies have found that soil bacteria, specifically bacterial groups, respond strongly to pH changes and can even be predicted over large spatial scales. Information about the composition of soil bacterial communities may provide information about how acid deposition and inherent soil characteristics influence overall nutrient cycling and ecosystem functioning. In the last few decades, identification and monitoring of bacterial communities in different complex environments has involved the use of the 16S rRNA gene. Since culture-based methods can only explore about 0.1–10% of the total microbial diversity (Torsvik and Ovreas, 2002), analysis of the 16S rRNA gene to identify individual species and communities provides a definite advantage over culture-based methods. Molecular techniques such as denaturing gradient gel electrophoresis (DGGE) community profiling (Muyzer et al., 1993; Øvreås et al., 1997), DNA cloning, probing and sequencing (Borneman et al., 1996), amplified rDNA restriction analysis (ARDRA; Vaneechoutte et al., 1992) and DNA melting and reassociation profiles (Torsvik et al., 1990, 1996) have been used by scientists to better identify the microbial community structure in different ecosystems. Studies have evaluated the impact of high elevation soil acidity on forest vegetation, surface and groundwater conditions and soil chemistry (Johnson and Siccama, 1983; DeHayes et al., 1999; Driscoll et al., 2003; Cai et al., 2010). Various researchers have evaluated forest stress and used different variables to quantify the degree of stress such as Ca:Al ratio (Cronan and Grigal, 1995), decline index (DI) (McLaughlin et al., 2000), and (Ca + K + Mg):Al ratio (Gorannsson and Eldhuset, 2001). Gorannsson and Eldhuset (2001) suggested that the ratio of base saturation to nitrate may

also be used as an indicator of stress/damage and, in certain cases could better predict forest stress. Few studies have specifically looked at the response of soil microbial communities to a gradient of soil conditions related to acidity in the Smoky Mountains National Park. Previously, the Ca:Al ratio has been used as a measure of critical loads of acidity (Hall et al., 2001) and as an excellent indicator of stress in forests (Cronan and Grigal, 1995). Since microbial communities are critical for ecosystem functioning, it provides a valuable indication of ecosystem health and recovery from disturbance (Maltby, 1975). The primary objective of our study was to investigate the influence of soil acidity and associated change in soil conditions on soil microbial community diversity and activity. We hypothesized that low soil pH and low values of Ca:Al ratio in high elevation spruce–fir forests will be positively related to dominance of acidophilic bacteria. 2. Materials and methods 2.1. Study area A total of 30 plots (Fig. 1) were selected for this study based on S deposition model classes (Weathers et al., 2006) at high elevation spruce–fir forests located within Great Smoky Mountains National Park (GSMNP). The selected plots were a subsample of plots from a large study undertaken to evaluate the relationship between modeled acid deposition class, soil acidity and vegetation and soil characteristics. Plots were selected to represent a range of modeled deposition classes and bulk soil pH. Only plots which were easily accessible (within 30–100 m from a trail and less than 80% slope) were selected for this study. Elevation across plots ranged from 1262 to 1964 m above sea level, pH of the soil A-horizon ranged from 3.1 to 4.2, and cation exchange capacity ranged from 1.3 to 23.1. Spruce–fir forests in the GSMNP receive

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Table 1 List of plots with elevation and selected soil characteristics. Elevation (m)

S deposition (kg ha−1 )

pH

C (%)

N (%)

NO3 -N (ppm)

NH4 -N (ppm)

Ca (ppm)

Mg (ppm)

K (ppm)

Na (ppm)

Al (ppm)

CEC (cmolc kg−1 )

F007 F015a F016 F017 F019 F020 F025 F028a F030a F033a F048 F104a F110 F123 F127a F156 F185 F190 F191 F246 S009a S011 S015 S016 S035a S036 S115

1811 1904 1803 1734 1805 1914 1830 1923 1885 1963 1876 1742 1932 1756 1628 1857 1856 1852 1816 1939 1504 1865 1727 1679 1265 1262 1428

15–23 33–41 24–32 6–14 24–32 33–41 33–41 33–41 6–14 6–14 6–14 24–32 6–14 6–14 15–23 33–41 33–41 33–41 33–41 24–32 15–23 24–32 24–32 24–32 6–14 6–14 6–14

3.2 3.5 3.6 3.9 3.9 3.8 3.5 3.5 3.7 4.2 3.9 3.8 3.4 4.0 3.7 3.5 3.9 3.5 3.6 3.5 3.3 3.2 3.8 3.5 3.7 3.1 4.0

43.8 23.8 44.3 41.9 47.6 41.1 35.8 38.1 45.8 39.9 10.0 18.3 37.6 44.9 36.2 40.9 43.9 39.6 34.8 33.1 45.0 42.1 44.3 46.9 45.2 44.6 12.0

2.1 1.2 1.7 1.3 2.4 2.4 1.7 1.9 1.5 1.9 0.7 1.0 1.8 2.0 1.9 1.9 1.7 1.9 1.7 1.8 1.7 2.0 1.9 2.1 2.0 2.0 0.9

21.0 9.0 6.0 21.0 10.0 16.0 20.0 17.0 16.0 7.0 46.0 7.0 7.0 7.0 9.0 15.0 7.0 6.0 9.0 8.0 3.7 3.7 10.0 9.0 12.0 5.0 19.0

127 3.70 80.0 72.0 63.0 57.0 94.0 52.0 61.0 85.0 36.0 25.0 20.0 47.0 74.0 3.70 3.70 3.70 6.00 12.0 55.0 57.0 57.0 124 153 78.0 16.0

837 287 130 217 1030 828 149 1700 3350 882 424 214 142 503 1530 515 1030 262 491 878 124 117 171 126 1220 110 460

348 104 27.7 25.2 309 320 27.1 298 527 134 126 43.9 50.4 104 340 97.9 202 54.5 63.9 72.9 30.7 24.1 30.4 24.4 157 35.5 110

444 177 68.2 64.0 668 622 74.0 285 713 157 156 112 154 245 552 210 455 146 135 188 66.8 72.0 78.2 83.1 193 57.1 242

40.6 23.8 66.4 70.9 37.0 42.4 74.6 23.9 28.1 15.4 17.5 10.8 20.2 12.8 36.8 19.0 29.4 14.7 17.8 42.7 91.1 85.9 87.0 84.8 27.1 77.3 13.3

336 950 3.40 6.0 155 676 30.7 414 49.5 22.5 226 277 240 236 139 854 25.9 48.2 382 152 30.8 28.2 7.3 16.4 118 29.5 411

8.4 2.9 1.3 1.8 9.6 8.6 1.5 11.8 23.1 6.0 3.7 1.8 1.6 4.1 12.1 4.0 8.1 2.2 3.4 5.7 1.4 1.3 1.7 1.4 8.0 1.3 3.9

a

Plots selected for identifying and sequencing the bacterial communities.

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up to 1400–2600 mm yr−1 of precipitation and summer and winter average temperatures are 22 ◦ C and 1 ◦ C, respectively (Stehn et al., 2011). Soils were generally well drained and derived from sandstone, metasedimentary slate, and Metaquartzite bedrock parent materials.

2.2. Soil sampling and analysis Soils were sampled within each of four 10 m × 10 m subplots that comprised a 20 m × 20 m vegetation plot. A pit was dug within each subplot to collect soil samples by horizon depth. The O, A, and B-horizons were sampled separately in each pit. Samples for each horizon from the four subplots were pooled for analysis. Soil chemical analysis (Table 1) was conducted on pooled samples from the plots (Stehn et al., 2011) at A&L Analytical Laboratories in Memphis, TN. Total carbon and nitrogen were determined using a LECO TruSpec carbon/nitrogen analyzer. Exchangeable acidity and cations were measured by NH4 Cl extraction. For microbial diversity analysis, three soil cores (0–10 cm), from each site, were collected using a soil core (3.175 cm dia.) and mixed together as a composite sample. The samples were placed into in Ziploc bags, labeled and transported from the field in coolers and stored at 4 ◦ C until they were analyzed. Soil samples were air-dried in the laboratory, crushed and passed through a 2 mm sieve to remove stones and plant debris.

2.3. Soil DNA extraction and PCR DNA was extracted and purified in two 250 mg soil sample aliquots from each plot by using the Power Soil DNA Kit (MoBio Laboratories, CA) following the manufacturer’s recommendations. DNA integrity was checked by electrophoresis on a 1% agarose gel. Purity of extracted DNA was further measured and quantified using a Nanodrop 1000 Spectrophotometer (Thermo Scientific, Wilmington, DE). A polymerase chain reaction (PCR) was conducted using a set of universal bacterial primers – PRBA 338 and PRUN 518R (Nakatsu et al., 2000) (Table 2) primers that amplify the 338–518 region of the 16S rDNA of bacteria. A 40 base GC clamp was added to the 5 end of the forward primer. For PCR reactions 50 ␮l of final mixture volume was used containing 1 ␮M of each primer, 20 ␮l of GoTaq Green Master Mix, 2× (Promega, Madison, WI) and 1–3 ␮l DNA (20 ng) template. The PCR reactions were performed using an automated Eppendorf Mastercycler Thermal Cycler (Perkin-Elmer, Norwalk, CT). The temperature program for the PCR reaction started with a 94 ◦ C denaturation step for 9 min. Then 30 cycles were conducted in which each cycle included a denaturing step of 94 ◦ C for 30 s, an annealing step of 55 ◦ C for 30 s and an extension step of 72 ◦ C for 30 s. The last step in the PCR program was a final extension at 72 ◦ C for 7 min. The samples were then held at 4 ◦ C until analyzed by agarose gel electrophoresis and stored in a freezer at −20 ◦ C.

Table 2 List of PCR primers used in this study with reference. Primer

Nucleotide sequence (5 –3 )

Tm (◦ C)

PRBA-f PRUN-r M13-f M13-r GC-PRBA

AC TCC TAC GGG AGG CAG CAG ATT ACC GCG GCT GCT GG GTA AAA CGA CGG CCA GTG AAT TG CAG GAA ACA GCT ATG ACC ATG ATT AC GC Clampa + PRBA-f

61.3 58.7 56.8 55.6 83.7

a

GC clamp: CGC CCG CCG CGC GCG GCG GGC GGG GCG GGG GCA CGG GGG G.

63

2.4. DGGE profiles A BioRad DCode apparatus (BioRad, Hercules, CA) was used to conduct the denaturing gradient gel electrophoresis (DGGE) analysis. An 8% (w/v) polyacrylamide gel, with denaturing gradients ranging from 35 to 65% was used for separation of PCR products obtained as described above. Urea and formamide were used as denaturants to facilitate the separation of DNA fragments. DGGE was performed using the Dcode Universal Mutation Detection System (BioRad Laboratories) and a 16 cm/16 cm gel apparatus. The gel was loaded and run in 1× TAE (20 mM Tris–Cl, 10 mM acetate, 0.5 mM Na2 EDTA) buffer at 60 ◦ C for a total of 780 V hours (constant voltage of 130 V for six hours). Gels were stained with ethidium bromide and visualized on a UV transilluminator and analyzed with the software packages GeneSnap and GeneTools (SynGene, Cambridge, UK). 2.5. Band excision, cloning, and sequencing of 16S rRNA gene fragments In this study, for identification and sequencing of bacterial communities, a second DGGE experiment was conducted with eight plots selected based on soil Ca:Al concentrations. To better understand the impact of acidity, eight plots (two plots each) were selected to cover a wide range of Ca:Al concentrations classes (low, med, high, and very high). The selected plots are marked with an asterisk in Fig. 2. Unique bands were cut from the DGGE gels using a sterile razor blade and placed in clean Eppendorf tubes. Bands were selected based on genetool comparison between the different samples. In total, 16 bands were excised from the DGGE gel (Fig. 3) from two replicated DGGE profiles for each soil sample. Although, ideally each DGGE band represents a dominant microbial community or an operational taxonomic unit (OTU), direct excision and sequencing can often lead to ambiguous and biased identification of bacterial taxa because of co-migration of DNA fragments from different taxa in the same position in a DGGE profile (Ercolini et al., 2003; Ercolini, 2004). Therefore in this study, after excision and purification of DGGE bands, the 16S rDNA fragments were cloned into Escherichia coli and multiple clones were selected and sequenced. The excised gel fragments were purified with the Qiaex II Gel Extraction Kit (QIAGEN, Valencia, CA, USA) according to the manufacturer’s protocol. The purified DNA was resuspended in 20 ␮l of nuclease free water. This DNA was used as a template for PCR reaction for cloning purposes. The PCR products generated from the DGGE gel bands were cloned into plasmid vector pCR 2.1 and the ligation product transformed into chemically competent E. coli TOP10 cells using the TA Cloning Kit (Catalog # K204040, Invitrogen, Carlsbad, CA, USA) following manufacturer’s protocols. The transformed cells were plated in LB (Luria–Bertani) agar plates (1.0% BactoTryptone, 0.5% Bacto-yeast extract, 1.0% NaCl, 1.5% Bacto agar, pH 7) containing 100 ␮g ml−1 ampicillin and 50 ␮g ml−1 X-Gal (5-bromo4-chloro-3-indolyl-␤-d-galacto-pyranoside). X-Gal was added in the plates in order to identify white-colored transformed colonies. Six random white colonies were selected from each plate representing an individual band from the DGGE gel. Thus a total of 96 individual colonies were selected and screened to confirm the presence of inserts. A colony PCR with the GoTaq Green Master Mix (Promega, Madison, WI, USA) was performed for each clone with a final volume of 25 ␮l and 0.5 ␮M of each vector specific primer M13 forward and M13 reverse (Table 2). The PCR reactions were performed using an automated Eppendorf Mastercycler Thermal Cycler (Perkin-Elmer, Norwalk, CT) as described earlier. The PCR products were analyzed by agarose gel electrophoresis and positive clones were identified based on the size of the fragments (approximately 430 bp). PCR products from the above agarose gel were purified using the Wizard SV Gel and PCR cleanup system (Promega,

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160

Sulfur Content (ppm) Ca:Al rao

Sulfur Deposion

140

80

70

120

60

100

50

80

40

60

30

40

20

20

10

0

0

Ca:Al Rao

64

Plot Locaons Fig. 2. Relationship between soil S content and Ca:Al ratio in O horizon of sampled plots in the Great Smoky Mountains National Park, TN and NC, USA. Plots marked with an asterisk were selected for identification of bacterial diversity.

Madison, WI, USA). Purified products were sent to the DNA Core facility (http://www.biotech.missouri.edu/dnacore/) located at the University of Missouri, Columbia, MO. Sequencing was performed on a single strand.

2000; Guindon and Gascuel, 2003; Edgar, 2004; Anisimova and Gascuel, 2006; Chevenet et al., 2006; Dereeper et al., 2008, 2010). The ‘One Click’ method was used to run the default programs: MUSCLE for multiple alignments, Gblocks for automatic

2.6. Statistical and phylogenetic analysis Statistical analyses were conducted using JMP (SAS Institute). A one-way ANOVA was used for comparison of response means at a predetermined level of significance (˛ = 0.05). DGGE fingerprints were converted to a binary matrix (presence 1, absence 0) based on the presence of band present in at least one profile. The binary matrix of the banding patterns was used to calculate a distance matrix using “DistMatrix” module of R statistical software (R project, http://www.r-project.org/). The distance matrix was converted into a multidimensional scaling (MDS) plot with artificial x and y axes with each DGGE profile represented as a single point in the plot, based on distance between each other. The Dice similarity index was also calculated based on the DGGE profiles obtained for the different soils (Sigler et al., 2004), SD =

2NC NQ + NT

(1)

where NQ is the number of bands in the query soil, NT is the number of bands in the test soil, and NC is the number of bands common to both soils. In this study, we used the total number of possible band positions across all the profiles as our query soil allowing us to compare the diversity index uniformly across the different samples in our sample set. The partial 16S rRNA gene sequences were subjected to the NCBI BLASTN (http://www.ncbi.nlm.gov/blast/) in order to identify sequences with maximum similarity. The sequences were aligned and visually compared using the DNA Dynamo software before creation of the phylogenetic tree. The phylogenetic tree for the bacterial population identified in the study plots were prepared using the Phylogeny.fr platform (http://www.phylogeny.fr/) (Castresana,

Fig. 3. Unique DGGE bands excised from different soil profiles. Lane 1 = 100 bp DNA ladder. Lanes 2–9 are sites selected (S009, F104, F127, F028, F015, S035, F030, and F033 respectively) from high elevation spruce–fir forests of the Smoky mountains.

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alignment curation, PhyML for tree building, and TreeDyn for tree drawing. The scope of concepts of our study did not allow for statistical analysis of impact of biotic/abiotic factors on microbial communities, as such studies require a different experimental design and data collection methods. 3. Results 3.1. Soil properties The soil pH values of our plots ranged between 3.0 and 4.6. The average pH for all plots for O, A, and B horizons along with standard deviation (in parenthesis) were 3.6 (0.27), 3.6 (0.32), and 3.9 (0.25), respectively. Soil chemical variables C, N, NO3 -N, NH4 -N, Ca, S, did not significantly differ across modeled S classes, with the exception of soil Al (Table 1). The soil S content and Ca:Al ratio in the different plots were not correlated (Fig. 2) and neither differed significantly based on the S deposition model. Among all the study plots for this current study, four plots had a Ca:Al ratio less than one, while 20 plots had a Ca:Al ratio less than 8 and eight plots had Ca:Al ratio of more than 10. Two plots with Ca:Al ratio less than one (F015 and F104) were selected along with six other plots (F028, F030, F033, F127, S009, and S035) representing four distinct Ca:Al values for identification of bacterial diversity through cloning and sequencing technique. 3.2. DNA extraction and PCR The average DNA yield from all samples was between 3 and 5 ␮g g−1 dry soil and was not significantly different based on either the acid deposition model or the Ca:Al or S content of soil. The total genomic DNA isolated from the different soil samples was of high molecular weight (>20 kb) and sufficiently pure to allow PCR amplification for downstream analysis. The concentration and purity of all extracted DNA was checked using a Nanodrop ND1000 spectrophotometer and showed an average absorbance ratio for A260/280 at 1.70 implying that the extracted DNA was highly pure and did not contain large amounts of coextracted impurities. A test PCR with universal bacterial primer sets – PRBA 338 and PRUN518R yielded a 220 bp (gene fragment + 40 base GC clamp) partial 16S rRNA gene amplicon, confirmed by agarose gel electrophoresis. Since there was no inhibition of PCR reaction in any of the samples, the genomic DNA was not further purified for the PCR DGGE experiment. 3.3. DGGE profiles and analysis of bacterial communities Duplicate analysis of the DGGE profiles revealed well-separated intense and faint bands for each treatment. Profiles for each plot were very similar and reproducible in both replicates in terms of the locations of bands in the DGGE profile. Bands with less than 1% relative intensity compared to sum total of the banding intensity were discarded. A total of 40 band positions were detected in the 35–65% denatured gradient gels. Overall, between ten (F191) and 20 (F025) discernible bands were observed in the different DGGE profiles. Some of the bands were common in many soil profiles, while others are present in only a few soil profiles. With the conditions used in the DGGE experiment for this study, most of the dominant bands were concentrated between 45 and 55% denaturing gradient. To estimate relative bacterial diversity and impact of acid deposition between the different profiles, band richness and Dice similarity (Sigler et al., 2004) indices were calculated (data not shown) for all samples. The phylotype richness (S, number of bands) was calculated for each soil and was normalized in comparison to the F025 soil that was assigned an index value of 1.00. A maximum

Fig. 4. Multi-dimensional scaling (MDS) plot showing similarity of DGGE profiles from 27 plots in the high elevation spruce–fir forests in Great Smoky Mountains National Park, TN and NC, USA.

value of 1.00 was assigned to the F025 soil due to the maximum number of bands (20) for this soil. In this evaluation of richness, the higher the value, the more diverse in terms of the number of dominant species that were in the soil sample. The phylotype richness index normalized using F025 varied between 0.55 and 0.95. The average richness index was highest in the S deposition class of 6–14 kg S ha−1 yr−1 , while the values were lower in the other three classes. However, there was no significant difference between any of the three classes. The Dice similarity values were calculated using the maximum number of band positions as the test soil, similar to that used in the creation of the binary matrix. Using the maximum number of band positions as the test soil we obtained a uniform reference against which all other soil profiles can be compared. The Dice similarity index values for all soil profiles ranged between 0.43 and 0.67 which indicated that there were considerable shift in the dominant population across the different sampling locations. However when statistically compared to the S deposition model there was no significant (p > 0.1141) difference in the Dice similarity index values for different soil profiles. In order to better visualize the similarity between the different DGGE profiles, a multi-dimensional scaling (MDS) plot with imaginary axes, using the band richness data, was constructed (Fig. 4). Based on the distances in the MDS plot analysis, sample plots displayed no clustering and had a haphazard distribution. 3.4. Sequencing and phylogenetic analysis We selected 16 bands from 8 plots out of a total of 30 plots marked by white arrows in Fig. 3, which were unique and unambiguous in the DGGE profiles. Bands that were too difficult to visualize with the naked eye or difficult to excise were not selected for cloning and sequencing experiments. The excision of DGGE bands and subsequent cloning and sequencing resulted in the identification of the bacterial population represented by the specific bands (Table 3). The phylogenetic analysis was conducted using BLAST search of the 16S rRNA gene fragments in the NCBI GenBank database. Results of the BLAST search revealed their closest relative and maximum similarity along with the environmental source. Most of the identified bacterial species belonged to the Actinobacteria, Acidobacteria, Proteobacteria, and other genuses commonly

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Table 3 Identification of selected 16S rRNA fragments from bacterial clones generated from excised bands from the DGGE profiles of different study plots. Similarities more than equal to 97% are reported. Band

Accession number

Closest match in GeneBank database

Band 1

HQ404617 (1) HQ204364 (3) HM480698 (4) EU874700 (7) EU680474 (9) HM535052 (12) HM062391 (14) HM062367 (15) HQ322881 (19) GU598914 (20) FJ166296 (21) HQ404617 (25) HM563568 (28) EF019723 (30) DQ129062 (31) HQ323001 (32) FJ570166 (33) GU016007 (39) GQ203346 (40) HM563587 (42) GU936369 (45) FJ024544 (46) EU628815 (48) FJ166858 (50) HQ322864 (52) EU628763 (55) GU936369 (56) HQ322868 (58) GU936342 (59) GU936340 (61) GQ203346 (63) HQ322869 (65) EU133606 (67) HM535052 (68) GU936392 (70) EU335436 (73) HQ322823 (74) AY775500 (75) FJ165909 (79) GU936448 (81) EF018832 (82) GU366789 (89) FN659198 (90) GU936470 (91) HM726977 (92) GQ203363 (93)

Uncultured soil bacterium Uncultured soil bacterium Uncultured actinobacterium Uncultured bacterium Uncultured soil bacterium Uncultured bacterium Acidobacteria bacterium Acidobacteria bacterium Acidobacteria bacterium Uncultured soil bacterium Uncultured soil bacterium Uncultured soil bacterium Uncultured soil bacterium Uncultured soil bacterium Uncultured soil bacterium Acidobacteria bacterium Uncultured proteobacterium Uncultured proteobacterium Uncultured actinobacterium Uncultured soil bacterium Uncultured proteobacterium Uncultured rhodoplanes Uncultured actinobacterium Uncultured acidobacterium Uncultured acidobacterium Uncultured acidobacterium Uncultured proteobacterium Uncultured acidobacterium Uncultured acidobacterium Uncultured acidobacterium Uncultured actinobacterium Uncultured acidobacterium Uncultured bacterium Uncultured bacterium Uncultured proteobacterium Uncultured bacterium Uncultured acidobacterium Uncultured bacterium Uncultured acidobacterium Uncultured plancomycete Uncultured proteobacterium Uncultured bacterium Uncultured bacterium Uncultured soil bacterium Uncultured soil bacterium Uncultured actinobacterium

Band 2

Band 3 Band 4

Band 5

Band 6

Band 7

Band 8

Band 9

Band 10

Band 11

Band 12

Band 13

Band 14

Band 15

Band 16

associated with and isolated from acidic and low pH environments. The relationship between the different bacterial groups identified in the study plots and their relationship with selected sequences from NCBI database is presented in Fig. 5. Among the clones, GRSM 80 grouped with Ferribacterium limneticum, an Fe(III)-reducing microorganism isolated from mining-impacted freshwater lake sediments.

4. Discussion This study provides the first characterization of microbial diversity in the high elevation spruce–fir forests of the Great Smoky Mountain National Park. Historically these forests have received huge amount of anthropogenic acid deposition resulting in the degradation and acidification of soils and aquatic systems. Although we did not observe any significant differences in the soil parameters in the different plots based on the S deposition models, atmospheric deposition of S has been documented to change soil chemistry by reducing exchangeable Ca and increasing the concentration of Al in soils (Blake et al., 1999; Driscoll et al., 2001). Such processes have been reported in the northeastern United States that resulted

Similarity 100% 100% 98% 99% 97% 100% 99% 99% 100% 100% 99% 100% 100% 98% 98% 100% 100% 99% 99% 98% 100% 100% 98% 100% 100% 98% 100% 99% 98% 97% 97% 100% 100% 100% 100% 100% 100% 100% 99% 97% 98% 98% 99% 100% 97% 100%

Environment Iron ore mine Forest soil Agricultural/grassland Acidic wetland Forest soil Freshwater wetland Agricultural/grassland Agricultural/grassland Acid mine sites Forest soil Agricultural/grassland Iron ore mine Cerrado soil Aspen forest Agricultural/forest Acid mine sites Alpine Tundra soils Conifer forests Acidic forest soil Cerrado soil Forest soil Forest soil Forest soil Environmental Abandoned mine sites Forest soil Forest soil Abandoned mine sites Forest soil Forest soil Acidic forest soil Abandoned mine sites Grassland/prairie Freshwater wetland Forest soil Grassland/prairie Abandoned mine sites Sphagnum bog Forest soil Forest soil Aspen forest Forest soil Earthworm gut Forest soil Antartic soils Acidic forest soil

in the destruction of spruce forests at high altitude mountains (Shortle and Smith, 1988; Schlegel et al., 1992). Among the measured variables, only soil Al content was found to show a significant positive correlation with modeled S deposition. Therefore, it is justifiable to use the Ca:Al ratio for the PCR-DGGE diversity experiment. Moreover, Ca:Al ratio has been used as an ecological indicator for measuring forest stress and damage and nutrients imbalance (Cronan and Grigal, 1995). It is plausible, that over the years the degradation of soils has reached a plateau and is not necessarily influenced by additional deposition at the present time. Another possibility could be that the deposition model simply is not suited for predicting actual S deposition impacts at a relevant scale for interpreting microbial community dynamics. Natural forests harbors the most biodiversity (Bardhan et al., 2012) and the study of composition and dynamics of microbial communities in high elevation spruce–fir forests receiving presumed different rates of acid deposition is critical for understanding the impact of such processes on soil chemistry and biology and, in turn, ecosystem functioning. Studies have suggested that reduced diversity in zooplankton communities in acidified lakes (Osborne and Jansen, 1993) may be a direct effect of low pH and related changes to the chemical environment. In certain cases, microbial populations

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Fig. 5. Phylogenetic tree of partial 16S rRNA gene (V3 region) sequences amplified from excised DGGE fingerprint bands from spruce–fir forest soils. GRSM code refers to clone numbers from this study.

can make the environment more acidic because of their activity (Gonzales-Toril et al., 2003) and create a positive feedback. This may be occurring in the high elevation acidic spruce–fir forests. Since little information is available on the microbial communities of severely acid impacted forests of the Great Smoky Mountains National Park, a PCR-DGGE approach can be a useful tool to gather valuable baseline information to better understand the function and activity of associated microbial communities. Each band in a DGGE profile is thought to be derived from a phylogenetically distinct member of the bacterial population and can be used to estimate species diversity (Choi et al., 2007). To prevent any bias due to co-migration of DNA fragments from different taxa in the same position in a DGGE profile (Ercolini et al., 2003; Ercolini, 2004). Bands were excised, purified, and cloned into E. coli and multiple clones were selected and sequenced. We believe as a result the data obtained in our experiments are more accurate and robust when compared with direct sequencing of DGGE bands. The richness and Dice similarity indices along with results of the multi-dimensional scaling plot show that there were minor variations in the DGGE profiles. However, no clustering based on the rates of presumed acid deposition was observed. These findings further emphasize that current S deposition may have very little impact on the soil chemistry, as suggested by Elliott et al. (2008). Thus it may be assumed that although wet deposition has decreased over the years, recovery of different forests is probably unrelated with current deposition and more influenced by previous acidification and related changes in soil chemistry. Previous researchers have found that once acidified, recovery may progress at varying rates and depends on soil properties and other environmental factors (Hill et al., 2002; Warby et al., 2005; Ormerod and Durance, 2009). According to Driscoll et al. (2001), the impact of acid rain in the northeastern United States persisted even after significant reduction in S deposition. Also, while S deposition has declined (Driscoll et al., 2001), a slight increase in the deposition of N in some regions has been observed, but overall nitrogen

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deposition has remained unchanged. Although the Clean Air Act has resulted in a reduction in the amount of S deposition, recovery of soil and ecosystem processes has likely lagged behind. Elliott et al. (2008) emphasized that even with significant reduction in acid deposition for long periods of time the recovery of depleted soils may take several decades. The fact that soil chemistry was not influenced by S deposition class signifies that beyond a certain extent the amount of S deposition had little influence on soil properties and cascading soil chemical processes might be more influential in the long-term. Based on the lack of dissimilarity of DGGE profiles in the different acid deposition regimes, our results suggest that actual microbial habitats and microbial communities differ little along the gradient of modeled acid deposition examined and have not yet reached a threshold suitable for non-acidophilic communities. Groups of bacteria identified on our plots have been previously associated with iron-ore mines (HQ404617), acidic wetlands (EU874700), acid mine sites (HQ322881), abandoned mine sites (HQ322868), and sphagnum bogs (AY775500) to list a few. A dominance of members of phylum Actinobacteria, Acidobacteria, Planctomycetes, and Proteobacteria, which have been previously associated with low pH environment (Gonzales-Toril et al., 2003; Hallberg et al., 2006; Diaby et al., 2007) were observed for most of the study plots. Identification of such acidophilic bacterial populations provides a snapshot of the habitat conditions for soil biota in the impacted plots. Further studies, with emphasis on soil biological functions and microbial activity, will be essential to understand the recovery of acidified forests and the long-term impact of acid deposition on ecosystem functioning. 5. Conclusions Soil microbial diversity and activity can often provide a better indication of the soil physical and chemical environment than simple physic-chemical analysis. Thus projects evaluating the recovery of such forests should also include quantification and identification of soil microbial populations along with vegetation and standard soil chemical analysis. This study was undertaken to compare the microbial diversity in the soils of the high elevation spruce–fir forests in Great Smoky Mountains National Park. Although wet acid deposition ranged from a low of (6–14 kg ha−1 ), medium (15–23 kg ha−1 ), high (24–32 kg ha−1 ), to very high (33–41 kg ha−1 ), microbial diversity and community structure among the different levels were not markedly different. Identification of bacterial species that normally thrive in severely acidic environments is an indication that in spite of the reduction of acid deposition, soil physico-chemical conditions in the high elevation forests in the north-eastern United States remains acidic. Acknowledgments This study was funded partly by the United States Department of Agriculture-Agricultural Research Service (USDA-ARS), National Park Service Air Resource Division (NPS-ARD), Michigan Technological University, and the University of Missouri-Columbia. The authors would like to thank for the logistical and technical help received from Tom Remaley, Megan Cooke, Max Lanning, Yarrow Titus, Aaron Mealy, Jack Dwyer and Luke Wilmer. We thank Thomas McDonough, Katri Morley, Brandon Potter, Nicole Samu, Jenny Stanley, Meg Walker-Milani, Bettina Uhlig, and Philip White for assistance with initial plot establishment and soil sampling, and Jennifer Boettger, Mike Foster, Maria Parisot, Bliss Sengbusch, and Aaron Wuori for laboratory assistance with soil sample processing and data entry. Becky Keller assisted with the deployment of the stratification protocol and initial plot establishment.

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