Soil Science Society of America Journal

Soil Sci. Soc. Am. J. 77:133–144 doi:10.2136/sssaj2012.0086 Received 13 Mar. 2012. *Corresponding author (markwill@vt.edu). © Soil Science Society of America, 5585 Guilford 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.

Soil Type Modestly Impacts Bacterial Community Succession Associated

with Decomposing Grass Detrituspheres

Soil Biology & Biochemistry

Soil bacteria play indispensable roles in the biogeochemical cycles of the biosphere and assume a vital role in the decomposition of organic materi-als (Kennedy, 1999; Griffiths et al., 2003). Decomposition is an important

ecological process, which controls nutrient cycling and carbon sequestration where litter chemistry and microbial activity jointly control the amount of C mineralized at different stages of decay (Rinkes et al., 2011). When viewed as a general process with the outcome of mass loss and carbon-dioxide release, the process and rate of residue decomposition is known to be controlled by environmental variables such as temperature and water availability. However, the ways in which different micro-bial taxa, especially bacteria, impact decomposition are not well described. Broadly

Himaya P. Mula-MichelDep. of Plant and Soil SciencesMississippi State Univ.117 Dorman HallMississippi State, MS 39759

Mark A. Williams*Rhizosphere and Soil, Microbial Ecology Lab.Virginia Polytechnic Inst. and State Univ.301 Latham HallBlacksburg, VA, 24061

Decomposition of plant residues has been widely studied; however, there is a lack of information on the dynamics of residue-associated microbial taxa during decomposition and how it is affected by soil and residue types. It was hypothesized that distinct microbial communities from different soils would result in colonization of residues by different bacterial taxa and that there would be a shift in bacterial community structure during substrate degrada-tion. A 2 × 2 factorial experiment with three replications was conducted con-sisting of Switchgrass (Panicum virgatum L.) and Rice (Oryza sativa L.) straw; two soil types (Sharkey and Marietta series); and four incubation periods (3, 23, 48, and 110 d; 25°C). Clone libraries were constructed from the unamend-ed soils, pre-incubation residues, and detritusphere (residues and adhering soil). Non-metric multidimensional scaling of the detritusphere communities showed a large shift in the structure of the residue-associated community fol-lowing 3 d of decomposition. This shift coincided with disappearance of sol-uble C. Labile C availability appeared to be important for driving bacterial community succession during early colonization. At later stages of decomposi-tion (Days 23–110), bacterial communities were less dynamic; however, there was partial segregation into two groups according to soil type. The relative abundance of Acidobacteria and Beta-Proteobacteria were primarily respon-sible for community differences between the detrituspheres of the two soils. No effect on bacterial community dynamics and diversity due to residue type was observed. More striking was the relative similarity in bacterial types found to dominate the detrituspheres and point to the possibility that key function-al community members are found widely in nature and that despite the huge diversity of soil bacteria, and some variation based on soil, a few dominant and widespread members came to the forefront during residue decomposition.

Abbreviations: DGGE, Denaturing Gradient Gel Electrophoresis; EDTA, ethylenediami-netetraacetic acid; LB, Luria Bertani; MRPP, multiresponse permutation procedure; NMS, nonmetric multi-dimensional scaling; OTUs, operational taxonomic units; PCR, polymerase chain reaction; PLFA, Phospholipid Fatty-acid Analysis; RDP, Ribosomal Database Project.

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speaking, distinct microbial communities could carry out rela-tively similar activities that result in similar functional outcomes during residue decomposition (Hollister et al., 2010).

Temporal change in microbial community composition is thought to occur during decomposition as a result of preferential substrate use of decomposers in response to the changing sub-strate chemistry in decaying residues (Moorhead and Sinsabaugh, 2006). Fingerprinting methods such as Phospholipid Fatty-acid Analysis (PLFA) (Klamer and Bååth, 1998; McMahon et al., 2005; Baumann et al., 2009) and Denaturing Gradient Gel Elec-trophoresis (DGGE) (Dilly et al., 2004) have provided relevant information on structural changes in community composition that occur during residue decomposition. Microbial succession, for example, on decomposing residue has been hypothesized to be a function of recalcitrant rather than soluble substrates. In contrast, Baumann et al. (2009) found no relationship between carbon chemistry of residues and community structure. To un-derstand how community phylogeny changes during the func-tionally important process of decomposition, more studies using phylotype specific methods are required.

Decomposition of residues have been linked to bacterial ac-tivities (Deschamps et al., 1980; Odier et al., 1981; Kerr et al., 1983; Berrocal et al., 1997), but only a few studies have observed the dynamics of bacteria using the 16S rRNA gene (Bernard et al., 2001; Aneja et al., 2006; Nicolardot et al., 2007; Pascault et al., 2010). A study by Nicolardot et al. (2007) noted that residue type: wheat (Triticum aestivum L.) versus rye (Secale cereale L.) and residue placement are important factors influencing commu-nity development. Nevertheless, distinct community responses among studies have made it difficult to observe consistent pat-terns of community change during plant residue decomposition.

Soil type has been shown, generically, to be a major determi-nant of microbial community composition (Bossio et al., 1998; Girvan et al., 2003; Garbeva et al., 2004; Wakelin et al., 2008). Organic C, for example, has been frequently shown to correlate with changes in microbial community structure (Grayston et al., 2004; Franklin and Mills, 2009). Soil pH has been shown to have profound effects on bacterial composition (Fierer and Jackson, 2006; Rousk et al., 2009). Because soil characteristics so often in-fluence microbial communities, it was expected that community structure and composition during the decay of plant residues might vary with soil type; however, few studies have attempted to answer this question. Lundquist et al. (1999) observed dif-ferences in the active counts of bacteria and fungi across three soil types amended with rye residues. Similarly, the variability in DGGE-based bacterial diversity during decomposition of rye was reported across varying soils (Dilly et al., 2004). In both studies, however, there were limited taxonomic characterizations that could be used to determine whether specific soil-derived shifts in community structure had occurred.

This study aims to determine how two soils with different dominant bacterial community members influence the coloniza-tion and development of bacterial communities during residue decomposition. The effects of residue type and succession dur-

ing residue aging and decomposition were also studied to assess their influence on bacterial communities. We hypothesized that: (i) different soils with markedly different microbial communities would result in the colonization and development of phylogeneti-cally distinctive but functionally similar residue-affiliated bacterial communities, (ii) bacterial community succession would follow shifts in decomposition rate related to changes in residue chemis-try, and that (iii) residue type would have a smaller but discernible influence on bacterial community composition and structure.

MATERIALS AND METHODSSoils and Residues

Soils differed in a number of characteristics including their textural properties, mineralogy, management histories, parent material, and exchangeable base contents. Marietta is a fine-loamy siliceous, active, thermic Fluvaquentic Eutrudept devel-oped from soils that formed in alluvium along streams that drain areas of the Blackland Prairie and Southern Coastal Plain Major Land Resource Areas in eastern Mississippi. The sample from this soil treatment was taken from an experimental field near Starkville, MS planted to switchgrass for 15 yr. Sharkey on the other hand, is a very-fine, smectitic, thermic Chromic Epiaquert formed from the slack-water clays on flood plains and low ter-races of the Mississippi River. The sample for this soil was col-lected from a field managed for rice–soybean [Glycine max (L.) Merr.] rotation for 7 yr and was formerly forested before the soil was cultivated. The soils were chosen because each had their own respective histories of growing rice and switchgrass and are geo-graphically isolated and independent soil types.

Surface soils were collected at a 0- to 20-cm depth and were composited and sieved at a 4.0-mm mesh size to homogenize and remove large pieces of organic matter. Soil pH was determined by 1:1 soil: 0.01 M CaCl2 solution. Exchangeable bases and ex-tractable P were measured by inductively coupled plasma (ICP) spectrophotometer (Thermo, Franklin, MA) after Mehlich 3 extraction (CH3COOH, NH4NO3, HNO3, NH4F, and eth-ylenediaminetetraacetic acid, EDTA) (Elrashidi et al., 2003) of soil. Total C and N in soils were analyzed using CHN Auto-analyzer (Vario EC III, Elemental Inc., Mat Laurel, NJ). More description of the soils and their physical and chemical charac-teristics are presented in Table 1. Sharkey had significantly higher pH, total C, exchangeable Ca, and Mg than Marietta soil. Soils did not differ in total N and K content; however, the former had significantly lower extractable P than the latter.

Rice and switchgrass are both in the Poaceae family but have significant differences in chemistry (lignin and silica content) that will affect the decomposability and perhaps the microbial communities that colonize the residues. Cellulose and lignin frac-tions of rice and switchgrass straw (Table 2) were determined us-ing acid-detergent fiber methods (Rowland and Roberts, 1994). Switchgrass had a significantly wider C to N ratio and higher lig-nin content than rice straw while cellulose did not significantly differ between the two residues. Cellulose and lignin fractions of the straws were within the range observed from other studies

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ranging from 32 to 47% and 5 to 24% for cellulose and lignin, respectively, for rice, (Saha, 2003) and 28 to 37% and 15 to 18%, respectively, for switchgrass (Zhu et al., 2005).

Treatments and Experimental LayoutThe experiment was set-up in 2 ´ 2 ´ 4 factorial arranged

in complete randomized design with three replications, two resi-due treatments (rice and switchgrass straw), two soil types (Shar-key and Marietta series), and four incubation periods (3, 23, 48, and 110 d). The time points were selected based on the change in CO2 production as decomposition proceeded and indicated a transition in microbial activity resulting from the changing bio-chemistry of decomposing straw.

IncubationThe incubation experiment was conducted in 2.0-L canning

jars filled with 200 g of soil (oven-dry weight basis). Mature straw residues of rice and switchgrass were used in this study. Plant ma-terials were dried at 65°C for approximately 24 to 36 h (until constant weight was achieved) and stored in dry and sealed con-tainers before use. Residues were cut at ~2.0 mm long, and 4 g of each residue (air-dry) were separately mixed into each of the soil types. This high residue rate was chosen to mimic the hotspots (locations enriched with organic matter) that commonly occur in the field (Ronn et al., 1996; Henriksen and Breland, 1999). Soils without residue control treatments were also included. Sterile distilled water was added homogenously over the soil in drop wise increments to bring the soil and residue to −33 KPa and maintained at −33 KPa throughout the incubation. The jars were sealed and fitted with lids containing rubber septa to allow for head space gas sampling of CO2. Jars were kept in incubation chambers at 25°C, aerated every 6 h during the first 3 d of incu-bation, every 24 h during the next 7 d, every 72 h the following 2 wk, and weekly thereafter until Day 110. The length of time of CO2 sampling and aeration was determined based on a trial 120-d incubation. Aerobic conditions and removal of CO2 were accomplished by opening and purging of the jars.

Sampling StrategyAt the termination of each incubation period, the detri-

tusphere (residue and tightly bound soil) was gently separated from the bulk soil. This was done by carefully picking the resi-dues from the bulk soil using ethanol-cleaned (70%) tweezers for

each replicate. The residue and the adhering soil were defined as detritusphere. The samples were stored at −80°C until analyses. For the pre-incubation residues and unamended soils, each was homogenized and was stored at −80°C until analyses.

Soil DNA Extraction and Preparation of the 16S rRNA Gene Libraries

Clone libraries were constructed from the detritusphere, the unamended soils, and the pre-incubation residues. Commu-nity DNA was directly extracted from 0.25 g of detritusphere and pre-incubation residues using PowerMax Soil DNA Isola-tion kit (MO Bio Laboratories Inc., Carlsbad, CA) following the manufacturer’s instructions with slight modifications. Ten grams was used for the soil to extract enough DNA for downstream ap-plications. Shaking of the samples with bead beaters was done for 45 min in a shaker incubator set at 60°C to increase DNA yield. The 16S rRNA genes were amplified, cloned, and the libraries were prepared and stored following the procedure described by Tarlera et al. (2008) with slight modifications. Extraction of ge-nomic DNA from the straw and detritusphere included grinding of the sample in liquid nitrogen. Soils were vortexed for 10 min to disperse soil particles before DNA extraction. The 16S rRNA genes were amplified using Taq Ready-to-go beads (GE-Health-care, Buckinghamshire, UK) following the manufacturer’s pro-tocol with 27 F and 1492 R primers. Amplification of target gene was done in thermocycler (Biorad PTC-100, Hercules, CA) with the following polymerase chain reaction (PCR) conditions: 3 min denaturation at 95.0°C followed by 15 cycles of 1.0 min at 94.0°C, 45 sec at 58.0°C, 2 min at 72.0°C, and final extension of 4.0 min at 72.0°C. Cloning was done using the TOPO TA clon-ing Kit (Invitrogen, Carlsbad, CA) according to the manufactur-er’s specification. Two plates of clone libraries were constructed for each treatment replicate where 16 white colonies were ran-domly picked from each plate. There were 32 clones per replicate

Table 1. Selected soil characteristics.

Soil type (USDA Taxonomic name) Parent material Textural

classification pHTotal Exchangeable

Ext PC N Ca Mg K

——— % ——— —————————— mg g-1 ——————————

Marietta (Aquic Fluventic Eutrochrepts) Slack-water clays Fine sandy loam 3.7* (0.3)‡ 2.2* (0.0) 0.22† (0.0) 937* (64.2) 129* (9.0) 111† (9.4) 151* (4.4)

Sharkey (Thermic Chromic Epiaquerts) Marl or chalk Expansive clay 5.1* (0.1) 2.6* (0.16) 0.23† (0.0) 2684* (511)687* (126.4) 195† (32.5) 36.1* (8.4)

* Significantly different (a = 0.05).† Not significantly different (a = 0.05). ‡ Values in parenthesis are standard deviation (n = 3).

Table 2. Chemical characteristics of the rice and switchgrass straw before incubation.

Residue C/N ratio Cellulose Lignin

————— % ————— Rice 66* (3.6) ‡ 37† (1.4) 8.7* (6.9)Switchgrass 79* (4.7) 35† (0.6) 18* (0.4)* Significantly different at a = 0.05.† Not significantly different at a = 0.05.‡ Values in parenthesis are standard deviation (n = 3).

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(96 for all three replicates), which were stored in Luria Bertani (LB) agar with 10% glycerol (v/v) at −80°C until sequencing.

Sequencing and EditingThe plates with clones were shipped on dry ice to the US-

DA-ARS MSA Genomics Laboratory, Stoneville, MS. Sequence chromatograms were trimmed and edited using the Codon-Code Aligner (LI COR Inc.). The sequences were aligned using Clustal W for chimera check by Mallard and Pintail programs (Ashelford et al., 2006). The chimera-free sequences were then aligned using Greengenes (DeSantis et al., 2006) before analyses. The sequences were submitted to GeneBank and assigned the ac-cession numbers JQ356896–JQ358561.

Data AnalysesThe distance matrices were calculated using the Jukes–Can-

tor algorithm in DNADIST, a program that calculates distance matrices from nucleotide sequences. DNADIST is part of PHY-LIP (PHYLogeny Inference Package), a software package of pro-grams for inferring (http://evolution.gs.washington.edu/phylip/general.html) phylogeny. The resulting distance matrices were then used in testing whether the set of libraries were significantly different using LIBSHUFF, v1.2 (Singleton et al., 2001). Binning of the clones into operational taxonomic units (OTUs) and the consequent estimate for the fraction of OTUs shared between two communities were done using the average neighbor algorithm in MOTHUR (http://www.mothur.org/), an open-source compre-hensive software package that analyzes community sequence data (Schloss et al., 2009). Coverage and rarefaction analyses were cal-culated and graphed (Kemp and Aller, 2004; see supplementary data). The analyses for Chao1, Shannon and Simpson’s recipro-cal index estimates were done on OTUs at 0.03 evolutionary dis-tance (about 97% sequence similarity). Taxonomic assignment of OTUs was done using the ribosomal database project (RDP) clas-sifier and sequence match query features of the RDP (http://rdp.cme.msu.edu/) using both environmental and cultured clones as reference (Maidak et al., 2001). RDP provides an online quality-controlled bacterial and archaeal small subunit rRNA alignments and analysis tools (Cole et al., 2009). The dominant 22 OTUs were relativized and analyzed using nonmetric multi-dimensional scaling (NMS) and statistically analyzed using the multiresponse permutation procedure (MRPP) with Sorensen (Bray–Curtis) distance. PC-ORD version 4, a software package for multivari-ate statistical analysis of ecological communities was used (MjM Software design; McCune et al., 2002). To test for differences in soil and residue properties, t tests were conducted. Analysis of variance with repeated measures were done on diversity indices, relative abundance of the three most abundant OTUs, bacterial biomass, cumulative residue-C, and water-soluble C using IBM SPSS (Statistical Package for the Social Sciences) software pack-age (Armonk, New York).

Bacterial BiomassBacterial biomass was estimated by converting the total

amount of bacterial PLFAs (17:0 cyclo, 15:0 iso, 16:0 iso, 15:0 anteiso, 17:0 anteiso, 17:0 iso, 17:0, 15:0) to cellular C using the conversion factor: 1 nmol of bacterial PLFA = 10 µg cellu-lar C. This conversion factor was calculated based on the report of Frostegård and Bååth (1996) that a bacterium cell contains 1.4 ´ 10-17 mol of PLFA, weighs 2.8 ´ 10-13 g (Neidhardt et al., 1990), and that C comprised half of a cell’s dry weight (Loferer-Krossbacher et al., 1998). To avoid interference from eukaryotic PLFA within plant straw residues, the microbial community was first separated from the straw detritusphere. This was done using 15 mL of sterile 0.9% NaCl added to 0.50 g (dry wt.) detritusphere into a 50 mL sterile centrifuge tubes. The sus-pension was shaken for 30 min at 250 rpm. Soil suspension was separated from the residue by passage through a 10.0-mm nylon filter (Spectrum Laboratories, Inc.). The filtrate was centrifuged at 12,000 ´ g for 10 min, and the pellet was used for PLFA ex-traction. PLFAs were isolated using a single-phase extraction method (White and Ringelberg, 1998) as modified by Butler et al. (2003). PLFA profiles were obtained with the MIDI Sherlock Microbial Identification System (MIDI Inc., Newark, DE). The Rapid Sherlock Calibration Mix 13000-A as standards, which include the following PLFAs: 9:0, 10:0, 11:0, 20 2OH, 10:0 3OH, 12:0, 13:0, 14:0, 15:0, 14:0 2OH, 14:3OH, 16:0, 17:0, 16:0 2OH, 18:0, 19:0, 20:0 16:1 iso.

Microbial Respiration and DecompositionMicrobial activity and the decomposition of the residues

were determined by monitoring CO2 production. Carbon diox-ide evolution was measured to assess the dynamics of microbial activities where increased CO2 flux indicated microbial metabo-lism and concurrent oxidation of the residues. Carbon dioxide evolved from the soil without residue treatments were first de-ducted from the CO2 derived from residue-amended treatments to obtain net CO2 derived from residue. In calculating the per-centage of residue-derived C respired, the cumulative C respired at Day 110 was divided by the total amount of residue-added C into the soil and multiplied by 100. Changes in rates of CO2 evo-lution during residue decomposition indicate change in substrate use. Carbon dioxide was sampled from each of the mason jars using a 10-mL syringe, and the gas samples were kept in vacuum vials before measurement. Carbon dioxide concentrations were measured using a gas chromatograph equipped with a Thermal Conductivity Detector (Varian 3600, Varian Instrument Group, Walnut Creek, CA). A jar containing 3 mL of DI water was also included to estimate non-soil CO2 production.

Water Soluble Carbon (0.01 M K2SO4)Soluble C was extracted by mixing 10 mL of 0.01 M K2SO4

and 0.50 g detritusphere (fresh weight) into 50 mL centrifuge tubes. The mixture was shaken for 1.0 h at 250 rpm and was cen-trifuged at 10,000 rpm for 10 min. The supernatant was filtered with a Whatman #2 filter and then syringe-filtered with a 0.2-mm

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mesh into a sterile container. This resulting extract is referred to as water-soluble C. The samples were then freeze dried and then re-dissolved in 500 mL of DI water. An aliquot of 100 mL was loaded into the aluminum foil, and the solution was dried in an oven at 50°C. The residue that remained on the foil after the dry-ing process was subjected to CHN Autoanalyzer (Vario EC III, Elementar Inc, Mt Laurel, NJ). A similar calculation described above for respired-C was used to calculate the soluble residue-C after determining the mass of residue in the detritusphere. This was done by oven-drying a detritusphere subsample (0.5 g, fresh weight) from each treatment triplicate at 65°C. The soil fraction was then separated from the residue by washing the former with DI H2O. Soil and residue fractions were oven-dried at 105°C and 65°C, respectively, and the weight of the residue was used in the calculations for soluble residue-C. It was assumed that the extracted soluble-C was derived from the straw and not the soil portion of the detritusphere.

RESULTSMicrobial Respiration, Soluble Carbon Release, and Bacterial Biomass

The rate of CO2 evolution was used to estimate microbial respiration and to calculate the amount of residue-C respired. Cumulative residue-C respired increased significantly with de-composition (p = 0.00) and was greater in Marietta than Sharkey soil (p = 0.00), and greater in switchgrass compared to rice straw residues (p = 0.00; Fig. 1). Despite the differences in respiration between soils and residues, a comparable release of soluble C was observed in both soils and residue types. Moreover, the greatest change in soluble C was observed in the first 3 d of decomposi-tion in both soil and residues. The highest reduction of residue-derived soluble C was noted between pre-incubation residues (Day 0) and Day 3 (Fig. 2), which coincided with the increased respiration (Fig. 1). Concentrations of soluble C remained low (<1% of total residue C) throughout the remainder of the study.

Bacterial biomass significantly increased with incubation with residue addition (P > 0.05) where differences were detected only between pre-incubation residues (Day 0) and all other incu-bation times. Bacterial biomass stayed relatively constant at Day 3 through Day110 (Fig. 3). Variability was high among samples, which may have masked some differences among treatments.

Bacterial Community Composition in Soils and Residue before Incubation

LIBSHUFF showed that replicate libraries were reproduc-ible (not significantly different; p > 0.05) between treatment replicates. Bacterial communities were statistically different be-tween the two soils and between the two residues at an evolu-tionary distance of £0.03 (p = 0.0019). Likewise, the Venn dia-gram (Fig. 4) shows a clear representation of the low community overlap between the soil and residue habitats at the same level of taxonomic resolution (D < 0.03). While we cannot know wheth-er the two soils might share vary rare members, bacterial commu-nities had clear differences in dominant community members. In

Marietta and Sharkey soils, for example, only 5% of the OTUs were shared. The bacterial communities on the residues were much simpler and shared ~20% of their OTU. No OTU overlap was observed between the soils and residues.

Phylum-level characterization of the bacterial communities in each soil and residue before incubation is shown in Table 3, providing a glimpse of the differences between the communities. Acidobacteria was the most dominant phylum in both soils (34 and 55%, for Sharkey and Marietta, respectively), a result consis-tent with the wide-spread dominance of this phylum (Barns et al., 1999; Quaiser et al., 2002). In contrast, fresh straw residues were dominated by alpha- and gamma-Proteobacteria, but fol-lowing residue aging through decomposition there was a shift to dominance by Firmicutes (Fig. 5). Other groups that were de-tected in the soils but were not observed in the straw were Bac-teriodetes, Planctomycetes, delta-Proteobacteria and the “other

Fig. 1. Percentage of residue-C respired at 3, 23, 48, and 110 d of residue decomposition. Solid and broken line denotes Marietta and Sharkey, respectively. Squares and circles represent rice straw and switchgrass treatments, respectively. Some error bars are very small and hidden behind the symbol. *significant at a = 0.05.

Fig. 2. Percentage of total residue-C extracted as water-soluble (0.01 M K2SO4) from the developing detrituspheres associated with pre-incubation residues (Day 0) and 3, 23, 48, and 110 d of residue decomposition. Solid and broken line denotes Marietta and Sharkey, respectively. Squares and circles represent rice straw and switchgrass treatments, respectively. Error bars depict the standard deviation of the mean. Some error bars are very small and hidden behind the sym-bol. *significant at a = 0.05; ns: not significant at a = 0.05.

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groups” (OP10, unclassified bacteria, Cyanobacteria, Gemmati-modetes, Verrumicrobia, and Nitrospira). Firmicutes was found associated with the residues but not the soils. Actinobacteria were relatively rare or absent across the soil and residue habitats. Alpha- and gamma-Proteobacteria, in contrast, were ubiquitous across soil and residue types (Table 3).

Structural Distribution of Bacterial Taxa throughout Decomposition

Many bacterial groups that were found in the developing detrituspheres were previously undetected in soils and residues (Table 3, Fig. 5). Firmicutes, for example, were not detected in soil and were rare members of the residues, but largely dominated (21–71%) during residue decomposition. Gamma-Proteobacteria, in contrast, was most abundant in pre-incubation residues and then declined at the later stages of decomposition. Planctomycetes and delta-Proteobacteria appeared only at low frequencies (1–6%) and

sporadically throughout the later stages of decomposition (Days 23 and 110). Actinobacteria and Acidobacteria tended to be rare members, but the latter increased up to ~12% of OTU during late decomposition. Moreover, beta-Proteobacteria and Acidobacteria were more abundant in the acidic Marietta compared to the less acidic Sharkey soil. Bacteriodetes showed a peak in abundance around Days 23–48 then eventually decreased at Day 110. The dy-namics in bacterial community structure defined at a broad-level of taxonomic classification was observed to be largely temporal and less impacted by change related to soil types.

Bacterial Community Development during Early Residue Decomposition

While both unamended soil and residue communities were characterized, for assessing changes in communities, the bacteria on the residue were assumed to reflect the communities at pre-in-cubation residues. Heterotrophic community change was assumed to be reflective, predominantly, of carbon derived from residues and not soil. Communities were expected to have derived from soil; however, they could have also originated from the residues.

NMS was used to characterize bacterial community change with incubation, soil, and residue types. Residue-associated com-munities at pre-incubation residues were very different from the communities in the developing detritusphere (Day 3–110) (Fig. 6a). To highlight these important differences, a separate NMS was conducted on the detritusphere communities (Fig. 6b). The largest change in community composition and structure oc-curred during early decomposition between pre-incubation resi-dues and Day 3. Following this early change, two distinct com-munity groupings were observed based on soil type. No segrega-tion was noted between communities based on residue type. Not surprisingly, MRPP analysis detected highly significant effects on bacterial community composition due to incubation and soil type but not due to residue (Within group average distance: Day 3 = 0.31, Day 23 = 0.46, Day 48 = 0.54, Day 110 = 0.54; A = 0.12; T = - 10; P < 0.00000).

Fig. 3. Bacterial biomass on the developing detrituspheres at pre-in-cubation residues (0), 3, 23, 48, and 110 d of residue decomposition. Error bars depict the standard deviation of the mean. *significant at a = 0.05.

Fig. 4. Venn diagram of observed operational taxonomic units (OTUs) (D £ 0.03) in the soils and residues before incubation.

Table 3. Broad-level (phylum-class) phylogenetic distribution of bacterial phylotypes before incubation.

Phylum/ClassUnamended Soil Unincubated Straw

Marietta Sharkey Rice straw Switchgrass

———— % of the total clones ———— Bacteroidetes 4.4 8.1 N.D. N.D.

Planctomycetes 6.6 6.9 N.D. N.D.

Alpha-Proteobacteria 7.7 8.0. 23 18

Beta-Proteobacteria 18 18 N.D. 4.0

Delta-Proteobacteria 2.2 8.0 N.D. N.D.

Gamma-Proteobacteria 4.4 1.2 65 72

Acidobacteria 55 35 N.D. N.D.

Actinobacteria N.D. 3.6 9.7 N.D.

Firmicutes N.D. N.D. 3.2 6.1Other group 2.2 12 N.D. N.D.

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Relative Abundance of the Three Most Abundance OTUs and Bacterial Biomass

Bacterial phylotypes closely related to Bacillus megaterium [99.1% Ribosomal Database Project (RDP) sequence similarity] were consistently the most domi-nant taxon in both soils. These taxa were not detected on the pre-incubated residues, occurred at the high-est frequency on Day 3 and remained dominant, but, nevertheless, tended to decline in relative abundance with decomposition (P < 0.05) (Fig. 7). Clones closely related to Pantoea agglomerans (99.6% RDP sequence similarity) and Chitinophaga ginsengisoli (97.2% RDP sequence similarity) were the next most dominant taxa observed, following a similar pattern of abundance in both soil types throughout decomposition (P < 0.05).

Diversity IndicesThere was a significant effect of incubation time

on all diversity indices measured while the type of resi-due did not influence any of these indices (Table 4). A significant soil by incubation-time interaction was also detected using the Reciprocal Simpson’s and Shannon indices as response variables, respectively. The average number of taxa associated with the detrituspheres, es-timated by Chao1 ranged from 34 to 129, where low-est and highest bacterial richness was found at Days 3 and 110, respectively (Table 5). Results of repeated measures analysis on Chao1 indicated that in general, bacterial richness increased significantly with decom-position (p < 0.05).

The Shannon index, which is sensitive to change in the abundance of rare groups (Hill et al., 2003), varied from 1.7–3.1 and increased linearly with time (Table 5). Significant difference according to repeated measures analysis, however, was observed only between Day 3 and the rest of the time points (p < 0.05). The reciprocal Simpson’s index, which is more weighted toward the ef-fects of community dominance, ranged widely between 3.9 and 79 (Table 5). Significant increase in this index was evident between Day 3 and the remaining time points (p < 0.05).

DISCUSSIONThis study characterized the structure and dynam-

ics of bacterial communities during the decomposition of two plant residues in two different soils. The impetus for the work stems from the knowledge that soils are extraordinarily rich microbial systems. Yet, soils are oligotrophic environments that can only support, instantaneously, the growth of a small propor-tion of the diverse microbial biomass. It was of interest to under-stand whether decomposition occurs predominantly through the activities of a dominant few or a diverse many and to assess whether two different soils dominated by different microorganisms would result in the development of different residue-associated bacterial

communities during decomposition. Broadly speaking, the results indicated that community development on residues in two differ-ent soils were compositionally very similar, but as the residue aged and the detritusphere developed, community structure shifted modestly for several phyla. Concomitant with these community shifts, the rate and extent of decomposition diverged between the soils (Fig. 6). It is not known if the small community structure shifts are related to differences in decomposition.

Fig. 5. Broad-level (Phylum-Class) abundance of bacterial communities observed in the developing detrituspheres at pre-incubation residues, 3, 23, 48, and 110 d of decomposi-tion. Solid line with open square symbol denotes Marietta, and broken lines with closed circles denotes Sharkey soil. Error bars depict standard deviation of the mean. *signifi-cant at a = 0.05; ns: not significant at a = 0.05.

140 Soil Science Society of America Journal

Bacterial Diversity Change with Decomposition

Overall, the richness and diversity of the bacterial commu-nity increased during decomposition (Table 5). Though not the major focus of study, change in the richness and diversity of the residue communities showed interesting ecological trends typi-cal of transitions from high to low substrate availability that were associated with shifts in residue C (Dilly et al., 2004; Bernard et al., 2007; Nicolardot et al., 2007) and more like the oligotrophic conditions of typical whole soils (Tarlera et al., 2008; Rousk et al., 2009). However, the decline in readily available substrate as decomposition progressed (Fig. 2) reduced the relative abun-dance of the dominant bacteria and would have increased the likelihood of sampling rarer members. Hence, while an increase in the actual richness of heterotrophic decomposers, such as slow growing oligotrophs remains a possible explanation for the increase in Chao1, Simpson’s 1/D, and Shannon’s indices, it is straightforward to see how declining populations of Bacilli with-out increases from other bacterial taxa could also result in a more diverse and rich community associated with decomposition of

residues. The results can also be interpreted to support the idea that a diversity of slow growing oligotrophs increasingly be-comes important during the decomposition of carbon-rich and heterogeneous decomposing plant residues (Dilly et al., 2004). The extent to which bacterial diversity is important during the residue decomposition still remains an unanswered question.

Bacterial Community Structure Change with Decomposition

Microbial community succession has been documented during the decomposition of a variety of plant substrates (Mou-gel et al., 2006; Williams et al., 2007; Pascault et al., 2010; Rinkes et al., 2011); however, only a few studies have focused on charac-terizing the phylogenetic composition of bacterial communities during decomposition (Bernard et al., 2007; Lee et al., 2011). In general, the results agree with the hypotheses that communities

Fig. 7. Number of clones of the three most abundant operational tax-onomic units (OTUs) on the developing detrituspheres at pre-incu-bation residues, 3, 23, 48, and 110 d of residue decomposition. Solid line with open square symbol denotes Marietta, and broken lines with closed circles denotes Sharkey soil. Error bars depict the standard deviation of the mean. * significant at a = 0.05; ns: not significant at a = 0.05.

Fig. 6. Non-metric multidimensional scaling (NMS) based on the rela-tive abundance of the 22 most abundant operational taxonomic units (OTUs) associated with: (a) Pre-incubation residues (triangles) and Days 3 through 110 (squares and circles) and (b) the developing de-tritusphere. Segregation between Day 3 and Days 23 through 110 (black symbols vs. gray) and between Marietta (squares) and Sharkey (circles) soils. Error bars depict standard deviation of the mean.

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are dynamic during decomposition; however, the dynamics were strongly tied to changes that occurred during the early coloni-zation stages of residue decomposition (Fig. 5–7). Community change also occurred during later stages of decomposition, but they were muted compared to dynamics during early coloniza-tion. Microorganisms that colonize readily available substrates may gain a selective advantage and continue to prosper during the latter stages of decomposition.

The first 3 d of residue incorporation and decomposition, as noted, was followed by tremendous changes to the bacterial com-munities, overwhelming or replacing the bacteria initially associ-ated with the straw (Fig. 6). Members of the Firmicutes accounted for ~30 to 70% of the dominant bacterial census on the decom-posing residues (Fig. 5). Gram-positive type bacteria, such as Fir-micutes, have been shown previously to dominate during the early stages of residue decomposition (McMahon et al., 2005; Williams et al., 2007). The addition of labile C has also similarly been ob-served to increase the proportion of Firmicutes in soil (Cleveland et al., 2007). The consistency of these results suggests that Fir-micutes are important sinks of carbon during early decomposition of residues and support the contention that changing resource availability, particularly soluble organics, are determinants of bac-terial community abundance on decomposing residues.

Following the increase in bacterial biomass associated with bacterial colonization of the detritus during the first 3 d of incu-bation (Fig. 3), biomass reached a steady state throughout the remainder of the study. As labile substrate declined, microbial

respiration also tended to decline (Fig. 1). Therefore, it is sus-pected that the relative decline in Firmicutes and the increase in alpha-Proteobacteria, Bacteroidetes, and Acidobacteria (Fig. 5) were likely a consequence of cell shrinkage, turnover, and death in the former population, that were balanced somewhat by cell growth in the latter populations. Bacteroidetes peaked between Days 23 and 48 in both soils (Fig. 5), which agrees with previ-ous observation of their abundance within this time frame dur-ing the decomposition of wheat straw (Bastian et al., 2009) and across a broad range of decomposing plant residues (Pascault et al., 2010). However, this result is somewhat contradictory to the claim that the frequency of this group is positively correlated with C mineralization rates (Fierer et al., 2007), whereby Bac-teroidetes increased after but not during the flush and remained dominant during latter stages of residue decomposition despite relatively low soluble C and C mineralization (Fig. 1–2). How-ever, the results agree with other observations that this group can grow on a variety of complex substrates and are adapted to low substrate availability (Kirchman, 2002; Rui et al., 2009).

Alpha-Proteobacteria increased in the later stage of decom-position (Fig. 5), which fits with previous observations (Pascault et al., 2010) of their abundance during relatively slow microbial growth on resistant substrates. However, it is less certain if the maintenance of Firmicutes throughout the latter stages of the study is a result of active metabolism of detritusphere substrates or if these taxa have transitioned into maintenance and survival modes that help to maintain cellular biomass. Though the tran-sition of the bacterial communities during the latter stages of decomposition are not as dynamic as early stages, the results sup-port the hypothesis that bacterial community dynamics track and respond to broad changes in residue chemistry/availability as decomposition progresses (Cleveland et al., 2007; Bernard et al., 2009; Jenkins et al., 2010).

Soil effects on Bacterial CommunitiesDuring the transition of bacterial communities between

Days 23 and 110, there was a modest but significant separation into two distinct groups according to soil type (Fig. 6b). This re-sult supports the hypothesis that soil properties (e.g., soil type) influences community structure; however, in the context of the large community differences initially shown between the soils, there is a subtle context to this comparison. First, the significant-ly greater proportion of Acidobacteria, and Beta-Proteobacteria

Table 4. Summary of ANOVA (p-values) testing the effects of incubation time, soil, and residue types on diversity indices.

Source of Variation

Degrees of Freedom Chao 1 Shannon

Simpson’s (1/D)

Incubation time 3 0.02* 3.25e-10* 0.00*Soil 1 0.13† 0.80† 0.04*

Residue 1 0.72† 0.06† 0.55†Incubation ´ Soil 2 0.73† 0.01* 0.06†I ncubation ´ Residue

2 0.35† 0.21† 0.71†

Soil ´ Residue 2 0.93† 0.26† 0.95†

I ncubation ´ Soil ´ Residue

3 0.43† 0.72† 0.94†

ErrorTotal 48* Significantly different at a = 0.05.† Not significantly different at a = 0.05.

Table 5. Diversity indices of bacterial communities associated with developing detritusphere.

Diversity Index

Marietta soil Sharkey soilRice straw Switchgrass Rice straw Switchgrass

Day3 Day23 Day48 Day110 Day3 Day23 Day48 Day110 Day3 Day23 Day48 Day110 Day3 Day23 Day48 Day110Chao 1 34.3 42.7 77.3 129 37.7 105 66.6 85.7 36.9 46.6 46.7 78.4 39.8 52.8 66.6 70.6

(5.3) (5.4) (9.7) (64) (15.9) (47) (47) (19) (8.1) (9.4) (17) (21) (5.3) (13.9) (9.1) (17) Shannon 2.03 2.68 3.05 2.89 1.68 2.32 2.95 2.83 2.23 2.72 2.49 2.81 1.82 2.74 2.60 2.85

(0.14) (0.06) (0.05) (0.14) (0.22) (0.28) (0.05) (0.14) (0.13) (0.08) (0.17) (0.02) (0.10) (0.07) (0.11) (0.22)

Reciprocal Simpson’s

5.21 9.69 44.5 54.1 3.98 13.8 36.2 78.90 8.54 16.23 11.5 19.3 4.52 23.0 13.4 29.8(0.17) (4.7) (6.7) (26) (1.1) (6.7) (4.5) (48) (1.5) (3.3) (2.9) (2.2) (0.53) (3.6) (3.1) (12)

142 Soil Science Society of America Journal

in the Marietta compared to the Sharkey soil (Fig. 5) appears to be both statistically and biologically significant. In contrast, Fir-micutes appear to maintain greater abundance in the Sharkey soil during later decomposition. These differences tend to occur dur-ing late succession and may thus be associated with the changing rates of decomposition observed between the two soils. Second, while there were shifts in community structure, the composition-al membership between residues in the two soils were, overall, very similar. Acidobacteria, for example accounted for 2–4% of the OTU in Sharkey- and ~8% in Marietta- incubated residues (Fig. 5). Importantly, there was thus no indication that specific bacteria adapted to the unique conditions of each soil came to dominate during residue decomposition. These results provide context to the importance of soil type for impacting community processes and structure (Bossio et al., 1998; Marschner et al., 2001; Steenwerth et al., 2002; Garbeva et al., 2004; Marschner et al., 2004; Garbeva et al., 2008).

It is notable that Marietta was considerably more acidic than Sharkey, which may have contributed to the dominance of Acidobacteria in the former compared to the latter residues in each soil (Fig. 5). This result speaks to a number of results indi-cating that pH contributes to community structure in soil (Frost-egård et al., 1993; Blagodatskaya and Anderson, 1998; Bååth and Anderson, 2003, Högberg et al., 2007; Lauber et al., 2009); how-ever, the changes in community composition noted on the de-tritusphere during the functionally important and metabolically active process of residue decomposition were relatively modest compared to effects observed previously for pH. Acidobacteria abundance on the developing detritusphere was also consider-ably lower than is typically documented in whole-soils (Tarlera et al., 2008). In this regard, Firmicutes and Bacteroidetes, which tend to be relatively low in abundance in whole-soil, dominated during residue decomposition. The highly active process of de-composition on residues may result in different patterns of com-munity development than that found in unamended soil.

Residue Effects on Bacterial CommunitiesPlant residues possessing a wide range of C/N ratio and lig-

nin contents are often used to study the effects of residue quality on rates of decomposition, for example, among eucalypt (Euca-lyptus globulus Labill.), wheat, and vetch (Vicia sativa L.) residues (Baumann et al., 2009); legumes vs. grasses (Martens, 2000) or between shoot and root of the same residue (Fujii and Takeda 2010). Silica content (Van Soest, 2006) more than lignin is con-sidered a primary limiting factor in rice straw decomposition. On this note, rice, which typically contains 2 to 7´ as much sili-ca as switchgrass, (Lanning and Eleuterius, 1987; Bae et al., 1997; Marschner, 1988; Van Soest, 2006) decomposed ~10 to 20% more than switchgrass residue (Fig. 1). Residue-type, however, played a small role in the development of bacterial community structure and diversity. Both switchgrass and rice are members of the grass family and have a number of similarities; however, the high silica content of rice appears to have had less of an im-pact than the higher C to N ratio and 2´ the lignin content of

Switchgrass. The results suggest that functions, such as decom-position, may be more sensitive to changes in residue chemistry than bacterial community composition and structure.

The lack of community differences between residues are somewhat contrary to previous reports; however, this may be due to the relatively small range of residue-types used compared to other studies (Pascault et al., 2010). Indeed, the shift in com-munities that occurred during succession on the developing de-tritusphere may be responding to this larger change in chemistry during residue decomposition. The community differences pre-viously reported (Pascault et al., 2010), however, showed a great-er diversity of genera dominated by the occurrence of singletons, thus making it difficult to compare to our current study.

Soluble carbon appears to be a more influential driver of bacterial community dynamics than the more typical indicators of residue decomposition (e.g., C to N ratio). This conclusion is based on the observation that the largest change in community structure was associated with the first 3 d of incubation when soluble carbon and respiration were highest. Previous studies have shown that microbial community succession was depen-dent on recalcitrant rather than soluble substrates (McMahon et al., 2005). However, these results were strongly impacted by fun-gal community change. While fungal community structure may be strongly regulated by recalcitrant pools, bacterial community structure is hypothesized to be more reliant on soluble pools.

CONCLUSIONSThis study confirms the process of succession of bacterial

communities during residue decomposition and further expands on the influence of soil and residue type on the colonization and development of bacterial communities on decomposing residues. Community dynamics and diversity were strongly impacted dur-ing the colonization of the residues, which coincided with the major stage of soluble carbon utilization. Although this pool of carbon was <15% of the total C mineralized during the study, it had by far the largest and most lasting impact on communities. The effects of soil type were smaller than hypothesized and did not have overt impacts on the types of bacterial taxa found on the detritusphere. However, a few differences in the abundance of specific bacterial taxa may, nevertheless, impact the rate and extent of decomposition. Despite the potential for the colonization of a huge diversity of bacteria from soil, community dynamics on the residues was also comprised of many dominant members that were relatively simple and straightforward. It was hypothesized that dif-ferent but functionally redundant communities would develop in the two soils; however, it was noted that soil effects on the bac-terial communities are best described as compositionally redun-dant. These results suggest that although soils may have incredible richness and diversity, many of the key players are relatively well known taxa that have been described for decades.

ACKNOWLEDGMENTSThanks to Barny Whitman (University of Georgia) and Kamlesh Jangid for help and insights on the methods utilized in this study.

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