Assessing Soil Microbial Community Composition Across Landscapes

Published online April 5, 2007

Assessing Soil Microbial Community Composition Across Landscapes: Do Surface Soils Reveal Patterns? Victoria J. Allison* (current address)

SOIL BIOLOGY & BIOCHEMISTY NOTE

Landcare Research Private Bag 92170 Auckland 1142 New Zealand

Victoria J. Allison* Zhanna Yermakov R. Michael Miller Julie D. Jastrow Roser Matamala Biosciences Division Argonne National Lab. Argonne, IL 60439-4843

Soil microbial community composition changes with both position in the landscape and depth in the soil column. Depth patterns may be stronger than landscape patterns, and thus landscape-level patterns determined from surface soils may not be representative of the soil column as a whole. We asked whether patterns determined from surface soils and the integrated soil column reveal the same landscapelevel patterns, predicting that because of the preponderance of biomass in surface soil, biomass-weighted patterns in an integrated soil column will be the same as in the surface soil. We found that community composition in surface soils and in an integrated soil column revealed the same pattern of change with time, and were very highly positively correlated. We suggest that in systems where resource inputs, and thus microbial biomass, declines strongly with depth, changes in composition of microbial communities across the landscape can be adequately determined from surface soils. Abbreviations: gs, growing season; PCA, principle components analysis; PLFA, phospholipid fatty acid. This manuscript has been created by the Univ. of Chicago as operator of Argonne National Lab. under Contract no. W-31-109-ENG-38 with the U.S. Dep. of Energy. The U.S. government retains for itself, and others acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the government. Soil Sci. Soc. Am. J. 71:730–734 doi:10.2136/sssaj2006.0301N Received 27 Aug. 2006. *Corresponding author ([email protected]). © Soil Science Society of America 677 S. Segoe Rd. Madison WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.

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Environmental gradients and large-scale perturbations are commonly used as a means of determining the controls on microbial community composition. These broad-scale studies have revealed that the size and activity of the microbial community depends on site differences in climate (e.g., Insam et al., 1989; Wardle and Parkinson, 1990; McCulley and Burke, 2004), topography, and parent material (e.g., Schimel et al., 1985; Anderson and Domsch, 1993), mediated through their impacts on primary productivity and plant litter quality. In addition, these site factors can be altered by land management practices, including tillage, fertilizer and amendment applications, grazing, and restoration (e.g., Bardgett et al., 1993, 1997; Beare et al., 1993; Zelles et al., 1995; Hedlund, 2002). Although less well studied, soil sampling depth may also influence microbial community composition (Ahl et al., 1998; Ekelund et al., 2001; Blume et al., 2002; Griffiths et al., 2003; Agnelli et al., 2004), and the few studies that have simultaneously examined shifts across the landscape and with depth in the soil column have found that depth has a stronger impact than landscape position (Fierer et al., 2003; Allison et al., 2007). Depth influences microbial community composition because resource inputs are highly stratified, entering the system either at or close to the soil surface, and thus resource availability declines steeply with increasing depth (Feng et al., 2003; LaMontagne et al., 2003). In addition, mineral nutrients sourced from parent material increase with soil depth, due to weathering and loss from the soil surface. Further, surface soils are more exposed to desiccation (Van Gestel et al., 1992; Ekelund et al., 2001) and freeze– thaw cycles, and have higher levels of O2 (Agnelli et al., 2004). These factors have been found to influence microbial community composition, with deeper soils having declining abundances of fungi relative to bacteria (Zelles and Bai, 1994; Blume et al., 2002; Jörgensen et al., 2002; Taylor et al., 2002; Feng et al., 2003), and an increase in actinomycetes and Gram-positive bacteria relative to Gram-negative bacteria (Zelles and Bai, 1994; Feng et al., 2003; Fierer et al., 2003). Although depth is known to have major impacts on composition, in many cases it is not feasible to simultaneously examine response across the landscape and with depth in the soil column. As a result, research is left open to the criticism that “patterns established in the surface may not be representative of the soil column as a whole” (personal communication, 2003). We suggest, however, that surface soil may effectively represent the response of an integrated soil column in systems where microbial biomass declines strongly with depth: if the composition of the microbial community at depth is weighted by the total biomass of that depth, surface soil communities are likely to be representative of the whole. We tested this idea using soil collected from seven sites along a prairie restoration chronosequence, and at six depths in the soil column. Microbial community composition was assessed as relative abundance of phospholipid fatty acids (PLFAs), and composition of the surface soil was compared with that of an integrated soil column.

MATERIALS AND METHODS Site Description Samples were collected within the National Environmental Research Park at the Fermi National Accelerator Laboratory (Fermilab) in Batavia, IL (41°51′22.5″N, 88°14′19.0″W). The 30-yr mean air temperature and precipitation are 8.8°C and 999 mm, respectively SSSAJ: Volume 71: Number 3 • May–June 2007

(National Climatic Data Center, 2003). All sampling sites were located on Drummer series soil (fine-silty, mixed, mesic Typic Haplaquoll), a deep, poorly drained soil that is very typical of the soils of the Prairie Peninsula of Illinois and neighboring states. Fermilab was prairie before European settlement, but has been in cultivation since the 1830s. The site was under continuous corn (Zea mays L.) since at least 1969 (when Fermilab was established). Beginning in 1992, the agricultural fields have been rotated between corn and soybean [Glycine max (L.) Merr.]. The fields are chisel plowed most years, and fertilizer is applied when required. Since 1975, between 2 and 25 ha have been restored to tallgrass prairie annually (Betz, 1986; Jastrow, 1987).

Sampling Five restored prairie plots, one agricultural field, and one remnant prairie were sampled. The restored prairie plots were planted in spring 1977 (23 growing seasons [gs], Plot 3D), fall 1981 (18 gs, Plot 8D), spring 1992 (8 gs, Plot 18D), summer 1993 (7 gs, Plot N3D), and spring 1997 (3 gs, Plot PLD). The agricultural field used in the study was planted to soybean (Plot BD) during the year of sampling. The agricultural field and restored prairie samples were collected during an 11-d period from late August to early September 1999. The remnant prairie was sampled in early October 2001. Although the time delay may be of some concern, we assume that conditions at this native, never cultivated prairie site are at equilibrium, and thus there is little change in microbial community composition and soil physical variables with time. Further, the main comparison in this study is integrated vs. deep soil, and this was sampled at the same time for the prairie site. In addition, the prairie site has the strongest depthdependent patterns in microbial biomass, and thus removing this site would potentially bias our results toward finding that surface soil is representative. As such, we believe that including the remnant site here is the more conservative analytic approach. In the restored and remnant prairies, three quadrats were randomly distributed perpendicular to a 50-m transect. In the agricultural fields, two quadrats along the transect were located within rows, and one quadrat was placed between rows to capture the variation of a cultivated field. In each quadrat, three soil cores (diameter 4.8 cm) were taken to a depth of 25 cm, and three smaller diameter cores (diameter 3 cm) were taken from the bottom of the hole made by the first core to a depth of 100 cm. The cores were cut into depth increments of 0 to 5, 5 to 15, 15 to 25, 25 to 50, 50 to 75, and 75 to 100 cm, and composited by quadrat. Soil cores were frozen at the end of each day.

Laboratory Analyses Frozen soil cores were thawed overnight in a refrigerator, weighed, and passed through an 8-mm sieve. To determine bulk density (Db) and soil moisture content, a subsample of soil was weighed fresh, then dried at 105°C to constant weight. Bulk density was calculated by multiplying the fresh weight of cores by the ratio of dry/fresh weight of the subsample, then dividing by the total soil core volume. A subsample of soil was processed for PLFA analysis by passing it through a 2-mm sieve, and then freeze-drying (−50°C, 8 GPa) for 48 h in a Labconco Freezone 4.5 freeze-drier (Labconco, Kansas City, MO). Lipids were extracted from freeze-dried soil in a single-phase mixture of chloroform, methanol, and phosphate buffer (pH 7.4) in a ratio of 1:2:0.8, by an adaptation of the method described by Bligh and Dyer (1959). After 3 h, water and chloroform were added to separate the mixture into polar and nonpolar fractions, and total lipids were extracted from the nonpolar chloroform phase. The PLFAs were separated from SSSAJ: Volume 71: Number 3 • May–June 2007

other lipid classes by using silicic acid column chromatography (Vestal and White, 1989; Zak et al., 1996). The PLFAs were then methylated by using a mild alkaline solution, and the samples frozen until analysis. Before analysis, PLFAs were thawed and dissolved in a 20 mg L−1 solution of fatty acid methyl ester 19:0 (Matreya, Pleasant Gap, PA) in hexane, as an internal standard. Phospholipid fatty acid separation was by high-resolution fused-silica capillary gas chromatography, using an HP 6890 gas chromatograph, with an HP7683 autosampler (Agilent Technologies, Palo Alto, CA). A 25-m HP-5 column was used, with H2 as the carrier gas at a constant flow rate of 4.9 mL min−1. A 1-μL splitless injection was made for each sample, with the inlet temperature set at 230°C, and the inlet purged at 47.0 mL min−1, 0.75 min after injection. The oven temperature was held at 80°C for 1 min, increased at a rate of 20°C min−1 to 155°C, and then increased at 5°C min−1 to a final temperature of 270°C and held for 5 min. Detection of PLFAs was by flame ionization at 350°C. The PLFAs were identified by retention time in comparison to known standards, and quantified using 19:0 as an internal standard. Fatty acid nomenclature is in the form of A:BωC, where A is the number of C atoms in the chain, B is the number of double bonds, and C is the position of the double bond from the methyl end of the molecule; cis geometry is indicated by the suffix c. The prefixes i, a, and me refer to iso, anteiso, and midchain methyl branching, respectively, with cy indicating a cyclopropyl ring structure.

Data Analysis Individual PLFAs were summed to give a measure of total microbial biomass (μmol kg−1 soil) in each sample. To examine changes in broad microbial groups, we summed signature fatty acids into functional groups (see Table 1 for functional group designations). We also Table 1. Impact of signature phospholipid fatty acids (PLFAs) on principal component analysis (PCA) Axis 1 position in shallow soil and the integrated soil column, with impact defined as the eigenvector for each PLFA, scaled by its standard deviation. PLFA

Functional group

14:0 bacteria† a15:0 bacteria‡§ i17:0 bacteria‡§ a17:0 bacteria‡§ cy19:0a bacteria‡§ i15:0 Gram positive‡§ i16:0 Gram positive‡§ Gram positive‡§ 16:1ω7c cy17 Gram negative‡§ Gram negative‡§ 18:1ω7c 10Me16:0 actino/desulfobacter¶ 10Me18:0 actinomycetes§ AMF# 16:1ω5c 18:2ω6,9c saprophytic fungi‡§ saprophytic fungi† 18:1ω9c 15:0 general§ 16:0 general§ 18:0 general§ † Zak et al., 1996.

Shallow (PCA Axis 1) 0.0024 0.0334 0.0032 0.0012 −0.0207 0.0444 0.0018 0.0138 −0.0077 −0.0322 −0.1005 0.0105 0.0225 0.0029 −0.0082 0.0004 0.0277 0.0071

Integrated (PCA Axis 1) 0.0027 0.0313 0.0058 0.0034 −0.0062 0.0398 0.0085 0.0090 −0.0040 −0.0198 −0.0998 0.0069 0.0085 0.0000 −0.0159 0.0018 0.0222 0.0069

‡ Frostegård et al., 1993. § Zelles, 1999. ¶ Coleman et al., 1993. # Arbuscular mycorrhizal fungi; Olsson, 1999.

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to relate sample position along PCA Axis 1 in the surface soil layer (0–5cm) against the position of samples from the integrated soil column (0–100 cm) (SPSS, 2000). We also compared more coarse-scale measures of community composition, calculating ratios of fungi to bacteria, arbuscular mycorrhizal fungi to bacteria, Gram positive to Gram negative bacteria, the proportion of the bacterial community composed of actinomycetes, and total PLFA, in surface soils and an integrated soil column.

RESULTS AND DISCUSSION

Fig. 1. Microbial community composition assessed as relative abundance of signature phospholipid fatty acids in (a) surface soil and (b) an integrated soil column, with each community summarized to a single point by using a principle components analysis (PCA).

determined the functional group composition of an integrated soil column by first converting signature PLFAs to a volume basis using bulk density [PLFA (μmol kg−1 soil) × Db (g m−3 soil)/1 000 000 = PLFA (mmol m−3)], and then applying a weighted average to each depth based on depth thickness [PLFA (mmol m−3) × thickness of layer (m) = PLFA (mmol [depth increment]−1)]. These values were then summed to give a measure of each signature PLFA, with the contribution of each to the total in the integrated soil column proportional to its contribution at each depth. To examine community shifts, we summarized changes in the PLFA composition of each sample by using a principle components analysis (PCA) on relative molar abundances of signature PLFAs in surface soil and the integrated soil column (McCune and Mefford, 1999). We performed analyses separately for the surface and integrated soil column to ensure that the overall direction of Axis 1 was not determined by one or the other subsets of data. To determine the degree to which samples from the shallow soil and the integrated soil column were similar, we used Pearson product-moment correlations 732

Strong depth-related patterns can be used in conjunction with site variation to tease apart the impact of correlated environmental variables on microbial community composition (Allison et al., 2007). In general, deeper soils have lower relative abundances of Gram-negative bacteria, and higher relative abundances of actinomycetes (Zelles and Bai, 1994; Blume et al., 2002; Jörgensen et al., 2002; Feng et al., 2003; Fierer et al., 2003; Allison et al., 2007). In many cases, however, it is not feasible to simultaneously examine response across the landscape and with depth in the soil column. We suggest that broad-scale changes in soil microbial community composition occurring along environmental gradients may be adequately determined from surface soil. The composition of the community varied by site in very similar ways in the surface (Fig. 1a) and integrated soil column (Fig. 1b). Older sites were characterized by higher relative abundances of actinomycetal and fungal signatures, with declining relative abundances of general and bacterial PLFAs (Table 1, Fig. 1). The similarity in patterns generated by both the surface soils and integrated soil columns was at least partially due to the concentration of microbial biomass in the surface horizons. Total PLFA declined strongly with depth in this system (Allison et al., 2007). When averaged across sites, the top 5 cm of soil alone contained 37% of microbial biomass, with 68% found in the top 15 cm, and 89% in the top 25 cm (results not shown). Rapidly declining microbial biomass with soil depth is the result of high organic matter inputs in surface soils, and thus is a common pattern across many systems (Feng et al., 2003; LaMontagne et al., 2003). To more explicitly demonstrate the similarity in trends revealed by surface soils and the integrated soil column, we examined the correlation between the two depths for a number of microbial community measures. When microbial community composition was assessed by ordination on relative abundance of signature PLFAs, there was a very strong correlation between position on PCA Axis 1 for surface soil and the integrated soil column, and points fell out along the 1:1 line (Fig. 2a): trends revealed in the surface soils effectively predict trends in the integrated soil column. Although this may initially suggest that the community at depth is a subsample of the surface community, PLFAs are distributed across many taxonomic groups, and thus finding similar patterns at depth should not be taken as evidence that the same species are present. Further, as we have demonstrated previously (Allison et al., 2007), changes in microbial community composition with depth are in fact stronger than with site; it is because microbial biomass is concentrated in surface soils that surface samples and integrated soil columns reveal the same successional trends.

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Strong similarities between the composition of the microbial community in surface soils and an integrated soil column were also revealed by coarser measures, such as functional group ratios (Fig. 2b–2e). The weakest relationship was obtained for the ratio of Gram positive to Gram negative bacteria (Fig. 2d), possibly because this ratio was strongly related to depth, and only weakly related to age (Allison et al., 2007). This was made apparent in the high intercept of the regression line with the y axis in Fig. 2d: the Gram positive/Gram negative ratio was considerably higher in the integrated soil column than in the surface soil. Thus, patterns that were only weakly detected across the landscape, but strongly related to depth, may be less effectively represented by surface soil. Trends in total microbial biomass could also be detected very effectively from surface soil alone (Fig. 2f), although on a soil volume basis, total biomass was overestimated in the surface soil. Although on a volume basis total PLFA in the surface soil was higher than in the integrated soil column (i.e., microbial biomass was more concentrated in the surface soil), there were strong positive correlations between the two measures (Fig. 2f), and thus trends revealed in the surface soils would effectively predict trends in the integrated soil column. We conclude that when the primary interest is how microbial community composition changes along environmental gradients or in response to perturbations, surface soil alone will effectively reveal shifts in microbial community composition.

Fig. 2. Comparison of microbial community composition and biomass in surface soil and an integrated soil column, with microbial community composition assessed as (a) position on principal component analysis (PCA) Axis 1, (b) fungal/bacterial ratio, (c) arbuscular mycorrhizal fungal (AMF)/bacterial ratio, (d) Gram positive/Gram negative ratio, (e) actinomycetal/bacterial ratio, and (f) microbial biomass assessed as total phospholipid fatty acid (PLFA). Each point represents the mean (with standard deviation) of three replicate measures. The dotted line is a 1:1 line: points that fall above the 1:1 line are higher in the integrated than surface soil, while points that fall below the 1:1 line are higher in the surface than integrated soil column.

ACKNOWLEDGMENTS We thank Edward Cates, Paul Drayton, Lisa Gades, Kate Remmes, and Martha Sojka for assistance with field work. The research was supported by the U.S. Dep. of Energy, Office of Science, Office of Biological and Environmental Research, Climate Change Research Division under Contract W-31-109-Eng-38, and by a NZFRST postdoctoral fellowship to V.J. Allison.


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