ENVIRONMENTAL MICROBIOLOGY

Fungal and Bacterial Community Succession Differs for ThreeWood Types during Decay in a Forest Soil

Lynn Prewitt & Youngmin Kang &

Madhavi L. Kakumanu & Mark Williams

Received: 6 August 2012 /Accepted: 11 February 2014# Springer Science+Business Media New York 2014

Abstract Wood decomposition by soil microorganisms isvital to carbon and nutrient cycles of forested ecosystems.Different wood types decompose at different rates; however,it is not known if there are differences in microbial communitysuccession associated with the decay of different wood types.In this study, the microbial community associated with thedecay of pine (decay-susceptible wood), western red cedar(decay resistant) and ACQ-treated pine (Ammoniacal CopperQuaternary, preservative-treated pine for decay resistance) inforest soil was characterized using DNA sequencing, phos-pholipid fatty acid (PLFA) analysis, and microbial activityover a 26-month period. Bray–Curtis ordination using aninternal transcribed spacer (ITS) sequence and PLFA dataindicated that fungal communities changed during successionand that wood type altered the pattern of succession.Nondecay fungi decreased over the 26 months of succession;however, by 18 months of decay, there was a major shift in thefungal communities. By this time, Trametes elegans

dominated cedar and Phlebia radiata dominated pine andACQ-treated pine. The description of PLFA associated withACQ-treated pine resembled cedar more than pine; however,both PLFA and ITS descriptions indicated that fungal com-munities associated with ACQ-treated pine were less dynam-ic, perhaps a result of the inhibition by the ACQ preservative,compared with pine and cedar. Overall, fungal communitycomposition and succession were associated with wood type.Further research into the differences in community composi-tion will help to discern their functional importance to wooddecay.

Introduction

Microbial decomposition of wood plays a key role in regulat-ing forest carbon and nutrient cycles [1–3]. The activity ofthese microorganisms is dependent upon an array of environ-mental factors that include water availability and temperature.Wood chemistry is also an important predictor of decomposi-tion, with rates varying across a broad range of wood types.For example, rates are influenced by wood density and thecontent of soluble wood extractives, cellulose, and lignin [4,5]. These differences are likely to influence the dominantmicrobial community associated with wood decomposition.Different types of microbes filling different ecological nichescould result in feedbacks that influence the rate and types ofbiochemical processes of decay [2, 3]. While a general modelof microbial succession during the decomposition of woodhas been described for decades, details about the specific typesof microbes associated with the process remain unpredictable.

Fungi are considered dominant members during the de-composition of wood; however, bacteria are often the initialcolonizers [6], feeding on available sugars and increasing thepermeability of wood [7–9]. During this time, the so-called“nondecay” fungi, such as certain molds and sapstain fungi,

L. PrewittDepartment of Forest Products, Forest andWildlife Research Center,College of Forest Resources, Mississippi State University,P.O. Box 9820, Starkville, MS 39762, USA

M. L. KakumanuHorticulture, Rhizosphere-Soil Microbial Ecology andBiogeochemistry Lab, Virginia Polytechnic and State University, 311Latham Hall, Blacksburg, VA 24061, USA

Y. KangThe Basic Herbal Medicine Research Group, Herbal MedicineResearch Division, Korea Institute of Oriental Medicine (KIOM),Daejeon 305-811, Republic of Korea

M. Williams (*)Horticulture, Rhizosphere-Soil Microbial Ecology andBiogeochemistry Lab, Virginia Polytechnic Institute and StateUniversity, 311 Latham Hall, Blacksburg, VA 24061, USAe-mail: markwill@vt.edu

Microb EcolDOI 10.1007/s00248-014-0396-3

also utilize freely available nonstructural wood substrates suchas sugars [10]. The true wood-decay fungi (soft, brown, andwhite-rot fungi) then cause loss in wood strength and gener-ally appear during the mid to late stages of wood decay [11,12]. This general model of fungal community successionduring the decomposition of wood has also been shown topredict broad shifts from soft-rot, brown-rot, and then towhite-rot fungi [13–15] and is thought to be related to thestage of decomposition and the availability of substrate [16,17].While ultimately limited by the immigration and occurrenceof available fungal taxa, changes have been linked to the abilityof fungi to compete and dominate substrate use during eachsuccessional stage. Hence, successional change in fungal com-munities during wood decomposition can vary partially due toresource competition [18, 19], wood chemistry, and other factorsrelated to microbial inhibition. The details of microbial commu-nity change, however, are less well known. Fungal taxa differ inthe capacity and efficiency of wood catabolism, and so, anunderstanding of the natural dynamics of fungal communitysuccession during the decomposition of wood will help describesome of these functional changes [19, 20].

The structural framework of wood consists of cellulose, hemi-celluloses, and lignin and comprises approximately 95 % of thewood’s total composition [20].Wood extractives (organic solublematerials) represent a smaller percentage of the wood’s mass(~5 %) but might have a significant effect on wood-decay fungiand activity. In particular, cedrol and thujaplicins, the extractivesfound in cedars and that are known to have antifungal propertiesprovide cedar with a highly durable wood compared with mostother softwoods such as pine [21]. Pines, which contain differentextractives, are generally considered more susceptible to micro-bial decay than cedars and junipers [22]. Though the extractiveshave been shown to modify fungal activity, questions remainabout the changes that occur tomicrobial communities during thedecay and decomposition of wood [23].

To better describe the succession of microorganisms asso-ciated with the decomposition of wood in forest ecosystems,with the objectives to observe (a) the community membershipof decomposing wood and (b) fungal community changeassociated with three different wood types with different sus-ceptibilities to degradation over >2 years of decomposition.Fungal gene expression related to lignin degradation wasreported to be very different among the three woods [24,25]. The microbial community structure during decomposi-tion of the three wood types pine, (a relatively decay-susceptible wood), cedar (decay-resistant wood), and ACQ-treated pine (ammonium copper quaternary, chemically treat-ed to be decay resistant) was characterized. The hypothesiswas that fungal communities would follow a pattern of suc-cession from early colonizing nondecay fungi to wood-decayfungi during the decomposition of wood. It was also hypoth-esized that membership and structure of fungal communitieswould differ during decomposition based on wood type. The

term “decomposition” is used to broadly describe catabolism,whether it originates from structural or nonstructural wood.

Materials and Methods

Preparation of Wood Stakes for Soil Incubation

Pine (Pinus taedaL.) andwestern red cedar (Thuja plicata.Donnex D.Don ) boards (5.1 cm×10.2 cm×180 cm and 2.5 cm×10.2 cm×180 cm, respectively) used for this study were pur-chased from Lowes’s Home Improvement Center, Starkville,Mississippi. One set of pine stakes were later treated with awood preservative ACQ to 0.15 pcf by the full cell method[24]. Each board was cut into strips measuring 14 mm×14 mm×115 mm (T × R × L) and numbered for identification.Samples were wrapped in Saran™ plastic wrap to equilibrate for7 days, air-dried for 1 week, and equilibrated to approximately12 % moisture content (MC). Afterward, the samples weresoaked for 7 days with daily water changing. The stakes werethen air-dried for several days until they reached 60 % MC asdetermined by weight loss, based on fresh weight [25].

Thewood stakes were placed in plastic containers (250mm×365 mm×220 mm) filled with sieved silty clay soil collectedfrom the top 7.6 cm of undisturbed forested soil at DormanLake, Oktibbeha County, Mississippi. Eight circular holes (5-mm diameter) were made in the bottom of each container fordrainage. A screen (250 mm×365 mm) was placed at thebottom of each container followed by gravel (20 mm deep)and the Dorman soil (100 mm deep). The MC of soil in eachcontainer was adjusted to 90 % of its field capacity and moni-tored weekly. Six unsterilized stakes each of pine, cedar, andACQ-treated pine were inserted 8 cm deep into the soil inreplicate containers for each sampling time for a total of 84stakes per wood type. The containers were placed in a green-house at 25 °C with a relative humidity of 30–50 % fromNovember to March and outside from April to October (exper-iment was conducted between December 2007 through April2010). Two stakes per container were covered with nylon stock-ing material and used to monitor 40–80 % MC on the woodsamples [26]. Stakes were weighed every week, and water wasadded to the soil if needed. Random sampling of the wood wasconducted on day 0 and bimonthly over 26 months.

Modulus of Elasticity

Dynamic modulus of elasticity (MOE) provided a metricof wood decay and was measured for each sampledstake on a bimonthly schedule. The average percentageof MOE change was calculated using the formula [(ini-tial MOE − current MOE)/initial MOE] × 100 % [25].The percent MOE loss of the wood was used as an

L. Prewitt et al.

indication of decay: The higher the % MOE loss, themore decayed the wood.

Sample Collection

Three of the six randomly harvested wood stakes showing themost decay (based on MOE results) were selected at 0, 4, 10,18, and 26 months and then individually cut into 16 equalsections. Four sections were combined and ground for fungalgenomic DNA and phospholipid fatty acid (PLFA) extraction,while four unground sections were used each for bacterialgenomic DNA extraction and CO2 respiration. The remainingsamples were immediately frozen in liquid nitrogen and storedat −70 °C.

Extraction of Fungal and Bacterial Genomic DNA fromWood

The fungal genomic DNA was extracted from 50-mggrounded wood in CTAB (1,000 μ l , 2 % w /vhexadecyltrimethylammonium bromide, 100 mM Tris,20 nM Na2EDTA, and 1.4 mM NaCl. The resultingmixture was processed according to the Macherey-Nagel Nucleospin Plant DNA extraction kit protocol(Easton, PA, USA) as previously described [25].

Bacterial genomic DNA was extracted by placing fourunground wood sections in 5 mL of nutrient broth (OIDCO,Becton Dickinson) overnight at 28 °C with shaking. Followingovernight incubation, the cell cultures were transferred in 1-mLaliquots to 1.5-mL microcentrifuge tubes and centrifuged forcell separation. The liquid portion was removed from eachsample, and 10 μL of RNase A was added to each tube andincubated for 2 h at 65ºC for cell lysis, mixing every 15–20 min by inverting the tubes. The mixture was transferred toa Nucleospin® spin column and centrifuged for 5 min at11,000×g to filter the lysate. The filtrate was mixed with850 μL of binding buffer and passed through a second spincolumn containing a silica membrane for 1 min at 11,000×g,binding the genomic DNA. The silica membrane was washedand dried by centrifugation at 13,000×g for 2 min. The DNAwas eluted from the silica membrane by adding 50 μL of 65 °Celution buffer, then incubated at 25 °C for 5 min, and centri-fuged at 8,000×g for 1 min to collect the eluted DNA.

The quality and quantity of the extracted fungal and bacte-rial genomic DNAs were determined by UV absorbance at260 and 280 nm using the NanoDrop spectrophotometer ND-1000 (NanoDrop Technologies, Inc.). Extracted genomicDNAs were stored at −70 °C.

Amplification of ITS and 16s rRNA Genes

Bacterial and fungal DNA associated with each wood typewere amplified using 16s ribosomal RNA (rRNA) gene andthe internal transcribed spacer (ITS) region, respectively.

Amplification of each gene was conducted with the followingthermocycler settings: initial denaturation at 94 °C for 2 min,followed by 35 cycles at 94 °C for 30 s, annealing at 60 °C for30 s, extension at 72 °C for 30 s, and a final extension at 72 °Cfor 10 min. PCR products were visualized by agarose gelelectrophoresis stained with ethidium bromide. Primers usedfor amplification were 5′-CTTGGTCATTTAGAGGAAGTAA-3′ (ITS-F) and 5′-TCCTCCGCTTATTGATATGC-3′(ITS-R) for general fungi ITS and 5′-AGACTCGATCCTGGCTCAG-3′ (16s-F) and 5′-GGTTACCTTGTTACGACTT-3′(16s-R) for general bacterial 16s rRNA gene [27].

Cloning and Sequencing of Amplified DNA Productsfor Taxa Identification

Amplified PCR products from decaying stakes were trans-formed into Escherichia coli plasmids using the TOPO-cloning kit for sequencing (K4575-40 Invitrogen, Co.,Carlsbad, CA, USA). The plasmids of positively transformedE. coli were isolated and extracted using the PureLink™Quick Plasmid Miniprep Kit (K2100-11, Invitrogen,Carlsbad, CA, USA). Plasmids were analyzed for inserts byrestriction digest using EcoRI, gel electrophoresis, and pre-pared for sequencing using the Dye Terminator CycleSequencing with Quick Start Kit (608120, Beckman CoulterCo, Brea, CA, USA). Automated sequencing was performedusing a Beckman CEQ 8000 DNA Analysis System.Sequences were edited by EditSeq™ (DNASTAR Inc.).

Phospholipid Fatty Acid (PLFA) Extraction

Total lipids were extracted from 2 g of ground wood at 0, 4,10, 18, and 26 months of wood aging using a modified Blighand Dyer method [28]. The phospholipid fraction was recov-ered and converted to fatty acidmethyl esters for analysis [29].Fatty acid methyl esters were separated, quantified, and de-tected by an Agilent 6890 series gas chromatograph (SantaClara, CA, USA) equipped with a flame ionization detector,an Ultra-2 column (19091B-102; 0.2 mm by 25m), controlledby computerized ChemStation and Sherlock software. Ultra-high-purity H2 was the carrier gas at a column head pressureof 20 kPa, septum purge of 5 mL min−1, a split ratio of 40:1,injection temperature of 300 °C, and an injection volume of2 μL. The oven temperature increased from 170 to 288 °C at28 °C min−1, and the analysis time of each sample was 6 min.Peak identification was carried out by the MicrobialIdentification System (MIDI, Inc., Newark DE, USA) follow-ing calibration with a standard mixture of 17 fatty acid methylesters (1,300 A calibration mix). The PLFA markers used todetermine the fungal population were 18:2ω6c and 18:1w9cand for the bacterial population were i15:0, a15:0, 15:0, i16:0,16:1ω7c, i17:0, a17:0, cy17:0, 17:0, and 18:1ω7c andcy19:0.

Fungal and Bacterial Community Succession on Three Wood Types

Microbial Respiration

Four small wood blocks weighing ~2 g from each treatmentwere placed in a separate 40-mL sterilized serum bottle and0.5 mL of sterile water was added. The bottles were sealedwith a crimp cap and incubated at 25 °C. Wood from eachsampling time (0, 4, 10, 18, and 26 months) was assayed forCO2 production, providing a level of microbial activity anddecomposability of the wood at each time interval. Headspacesamples were taken following 1 week of incubation.Accumulated CO2 in the headspace was measured using aVarian 3600 gas chromatograph (Varian, Inc., Palo Alto, CA,USA) equipped with a 2-m Porapak Q column, oven temper-ature of 110 °C, and a thermal conductivity detector. Theamount of CO2 measured from each sample was correctedby subtracting the CO2 measured from a serum bottle contain-ing no wood and only water.

Data Analyses

The statistical analysis of MOE was performed by two-wayanalysis of variance (ANOVA) and Tukey's test (α=0.05) forrandomized complete block design (RCBD) using SAS pro-gram (SAS 9.1, SAS Institute Inc., Cary, NC, USA).

Multivariate analysis of the PLFA data was conductedusing PC-ORD (version 4.2) software (Gleneden Beach,OR, USA). The dominant fatty acids were relativized andanalyzed by nonmetric multidimensional scaling (NMS) usingSorenson distance, as previously described [30]. NMS is anonparametric method that provides graphical ordination ofthe experimental data [31]. Fungal sequences were separatelyaligned using Clustal W and analyzed by Mallard Software tocheck for chimeras and anomalies. Sequences were groupedby the computer program DOTUR [32] at 97 % evolutionarydistance (D=0.03) to generate operational taxonomic units(OTUs). The relative abundance of the OTU was then ana-lyzed by NMS using Bray–Curtis ordination. Sequences withthe closest match (>98 %) were used for identification ofbacterial and fungal species. Analysis of variance with repeat-ed measures was conducted to analyze for differences inrespiration and PLFA abundances. The multiresponse permu-tation procedure (MRPP), a nonparametric test, was used toassess differences in fungal community structure across woodtype and incubation.

Results

The decay of wood based onMOEwas significantly greater inpine compared with cedar and ACQ-treated pine, beginning at6 months of aging (Fig. 1, P<0.001). Cedar and ACQ pinewere not significantly different from one another. A second-order polynomial was fit to pine and to the combined

cedar–ACQ-pine MOE data, with polynomials showing astrong fit to the data (R2>0.90; P<0.001). MOE was alsosignificantly different from zero (fresh wood, no decay) at6 months for pine and at 8 months for both cedar and ACQ-treated pine (P<0.05). Decay was thus measurable at 6 and8 months, respectively.

Respiration rates, used as an index of microbial activity andwood decay and decomposition, were significantly differentamong wood types (P<0.01) and averaged 45, 23, and 12 μgCO2-Cg

−1wood on pine, ACQ-treated pine, and cedar, respec-tively and showed agreement with measures of the wooddecay process (Fig. 2).

Fig. 1 Wood decay as determined by loss in % modulus of elasticity(MOE) on pine (denoted by x), cedar (circle), and ACQ-treated pine(square) over 26 months of decay. MOE losses were significantly greaterfor pine than cedar and ACQ pine (P<0.05). Cedar and ACQ pine werenot significantly different from one another. A second-order polynomialwas fit to pine and to the combined cedar–ACQ-pine MOE data, withpolynomials showing a strong fit to the data (R2>0.90). MOE wassignificantly different from 0 at 6 months for pine and at 8 months forboth cedar and ACQ-treated pine. Decay was thus measurable at 6 and8 months, respectively

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0 5 10 15 20 25

g C

O2-C

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woo

d d

-1

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20

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60

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100 PineCedarACQ-Pine

Fig. 2 Respiration rates from three wood types following 0, 4, 10, 18,and 26 months of wood decomposition in soil contact field. Rates arebased on 1-week laboratory incubation of moist wood samples on a perday basis (25 °C). Respiration from pine wood was significantly greaterthan that for cedar and ACQ pine (P<0.05). Symbols represent theaverage of three replicates and bars represent standard error

L. Prewitt et al.

Compared with respiration rates in cedar, both pine andACQ-treated pine had considerably more temporal variation.Pine, except at day 0, had the highest respiration rate overall,which was four to six times greater than the respiration rate onACQ-treated pine or cedar woods. On day 0, the respirationfrom ACQ-treated pine was the highest among the treatments.However, ACQ-treated pine and western red cedar showedsimilarly low rates of respiration thereafter.

Fungal Community Structure Based on ITS Sequences

The fungal distribution over the four sampling times and onthe three wood types is shown in Table 1. Of the total 297fungal sequences collected across all wood types, 85–91 % ofthe sequences were most closely related to wood-decay fungi.The majority of wood-decay fungi (75–98 %) were mostclosely related to white-rot, the remainder to brown-rot fungi.White-rot fungi were represented by members most closelyrelated to Trametes elegans, Phlebia radiata, and Cf. Phlebiasp., and brown-rot fungi were represented by Gloeophyllumsubferrugineum and G. sepiarium. The early colonizers weremost closely related to seven fungal species: Blastosporellazonata, Boletaceae sp., and unclassified taxa with endophyticmembers, Lecythophora sp., Volutella ciliata, Cryotococcusgatti, and Polyporus umbellatus.

At 4 months, the relative abundance of clones related toP. radiata accounted for 50–73 % on pine, 31–53 % on ACQ-treated pine, and undetected to 40 % on cedar (Fig. 3). Incontrast, T. eleganswas not detected at 4 months on any of thewood types. However, at 10months, T. elegans occupied 36%on cedar, 25% on pine, and 10% onACQ-treated pine. By theend of the study, T. elegans increased to 80 % on cedar butdeclined to 22 and 25 % on pine and ACQ-treated pine,respectively. An increase in Phlebia spp., in contrast, wasobserved in association with pine and ACQ-treated pine dur-ing latter stages of wood decomposition and aging.

Overall, the percentage of brown-rot fungi represented bymembers most closely related to G. subferrugineum andG. sepiarium were lower (13.9 %) compared with white-rotfungi (86.1 %) and equal to that of nondecay fungi. Allgroups, however, were dynamic through decomposition.Gloeophyllum-related taxa remained undetected on any woodtype at 4 months. At 10, 18, and 26 months of aging, thepercentage of Gloeophyllum spp. were 0–4 % on pine, 10–20 % on ACQ-treated pine, and 20–24 % on western redcedar.

Early colonizing nondecay fungi represented 50–70 % ofthe sequences at 4 months of decay on the three wood types.The abundance of these fungal species decreased to 4–34% at10 months and was not detected on any of the wood types at18 and 26 months (Fig. 3).

Table 1 Relative abundance of fungal members identified on pine (P), western red cedar (C), and ACQ-treated pine (A) at 4-, 10-, 18-, and 26-monthdecay in forest soil at 0.03 evolutionary distance

Closest cultured match Fungal group 4 monthsa 10 months 18 months 26 months

P C A P C A P C A P C A

Cf. Phlebia sp. WD-WR 0 0 0 13.8 26.0 31.1 30.7 0 16.6 20.7 0 16.6

Phlebia radiata WD-WR 50 40 30.7 41.3 11.1 13.7 46.2 0 36.7 55.1 0 36.7

Total Phlebia sp. 50.0 40.0 31 55.1 37.1 44.8 76.9 0 53.3 75.8 0 53.3

Trametes elegans WD-WR 0 0 0 24.1 33.3 10.3 19.2 75.8 36.6 24.1 68.9 26.6

Total T. elegans 0 0 0 24.1 33.3 10.3 19.2 75.8 36.6 24.1 68.9 26.6

Total white-rot fungi 50 40 31 79 70 55 96 76 90 100 69 80

Gloeophyllum sepiarium WD-BR 0 0 0 3.4 7.4 0 3.8 0 0 0 13.8 0

Gloeophyllum subferrugineum WD-BR 0 0 0 0 18.5 10.3 0 24.1 10 0 17.2 20.0

Total Gloeophyllum spp. 0 0 0 3.4 25.9 10.3 3.8 24.1 10 0 31.0 20.0

Total wood-decay fungi 50 40 31 83 96 66 100 100 100 100 100 100

Unclassified fungal endophyte ND 0 0 0 0 0 17.2 0 0 0 0 0 0

Lecythophora sp. ND 0 0 0 0 3.7 17.2 0 0 0 0 0 0

Volutella ciliata ND 0 0 0 17.2 0 0 0 0 0 0 0 0

Blastosporella zonata ND 0 25 38.5 0 0 0 0 0 0 0 0 0

Boletaceae sp. ND 0 15 30.7 0 0 0 0 0 0 0 0 0

Cryptococcus gatti ND 50 0 0 0 0 0 0 0 0 0 0 0

Polyporus umbellatus ND 0 20 0 0 0 0 0 0 0 0 0 0

Total nondecay fungi 50.0 60.0 69 17 4 34 0 0 0 0 0 0

Total fungal clones 6 20 13 29 27 29 26 29 30 29 29 30

Fungal and Bacterial Community Succession on Three Wood Types

Bray–Curtis ordination of the relative abundance of the 38OTUs showed patterns indicating that fungal communitiesassociated with cedar shifted considerably more than thoseassociated with pine and ACQ-treated pine (Fig. 4). Thispattern was confirmed by statistical analysis of the principalordinates (two-dimensional) using MRPP (P<0.01). At10 months, the fungal communities on each wood type weredifferent with fungal communities on cedar and ACQ-pinemore closely related than on pine. At 18 months, there werelarge shifts in fungal communities on cedar and pine but noton ACQ-treated pine. For cedar, this shift was strongly asso-ciated with the increasing dominance of T. elegans, which washighly correlated to a change along axis 1 (r=−0.72). At the26th month, the fungal community changed very little on thewood types compared with 18 months. By the end of thestudy, the fungal communities associated with pine and

ACQ-treated pine were not that different from one anotherbut were very different from cedar.

Bacterial Identification

Bacterial rRNA genes were observed for only a few bacterialtaxa. Burkholderia sp. Ellin and Oxalicibacteriumfaecigallinarum were the predominant bacteria across thethree wood types. Burkholderia sp. was found primarily onpine, while O. faecigallinarum was found predominantly oncedar and ACQ-treated pine (data not shown).

Phospholipid Fatty Acid (PLFA) Analysis

The mass of PLFAs on nonincubated wood ranged from 10 to200 times greater in ACQ-treated pine than in cedar andapproximately five times more than in pine (Fig. 5).Cedar, containing the lowest amount of extractablePLFAs, showed a general increase in PLFAs throughoutmost of the incubation.

Bray–Curtis ordination of microbial community structurebased on PLFAs (Fig. 6) and statistical analysis of the ordi-nates using MRPP indicated that wood type (P=0.0001) andincubation time (P=0.008) influenced microbial communitystructure. The pattern of change in PLFA associated with eachwood type was similar with incubation time and indicative ofa successional pattern of change. This pattern is visible alongaxis 2 of the ordination plot, accounting for 12 % of thevariation in the original data. Pine and ACQ-treated pinecommunities showed patterns indicating that they became

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P radiataT. elegensGloeophyllum spNon-decay

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Fig. 3 Relative percentage of white-rot fungi (P. radiata and T. elegans),brown-rot fungi (Gloeophyllum spp.) and nondecay fungi on pine, cedar,and ACQ-treated pine at 4, 10, 18, and 26 months of decay in forest soil.No significant differences between the abundance of taxa were detected at4 and 10 months. The abundance of P. radiatawas significantly greater inpine and ACQ pine than in cedar, while T. elegans was significantlygreater in cedar than in pine and ACQ pine (P<0.05). Symbols representthe average of three replicates and bars represent standard error

Axis 1(41%)

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0.0

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10P

26P

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18C

10C

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Fig. 4 Bray–Curtis ordination of microbial community structure basedon the relative abundance of the 38 identified fungal operational taxo-nomic units (OTUs). OTUs were calculated at D=0.03 using the com-puter program DOTUR (35). The alphanumeric designations representtime of incubation in months and based on wood type (P Pine (star), CCedar (circle), A ACQ pine (square)). Blocked MRPP analysis indicatedsignificant effects of both wood type and sampling time (P<0.01).Percentages denote the amount of variability associated with each axis.Standard errors are noted for the variation along each axis (n=3)

L. Prewitt et al.

more similar with incubation time. A bacterial fatty acid,19:0cy, was positively correlated with axis 1, accounting for41 % of the variation, while 18:0, a likely eukaryotic markerindicative of both plants and fungi, was negatively correlatedalong axis 1. The decline was likely indicative of losses ofplant fatty acids during wood decay. Despite successionaltrends, PLFA associated with cedar and pine at the 18-monthsampling tended to cluster separately from other time points.In contrast, ACQ-treated pine communities at 18 monthstended to cluster with 10-and 26-month samplings, indicatingthat community succession on ACQ-treated pine was lessdynamic than those on cedar and pine.

Discussion

Microbial Community Composition during Early WoodDecomposition

The microbial community was largely dominated by fungi;however, a few bacterial taxa were observed. The occurrenceof these taxa is consistent with the role that bacteria can playduring early decomposition of wood; however, the very lowabundance of DNA suggests that this role was likely limited.As hypothesized, however, wood type was associated withchange in the composition of the fungal communities duringwood decomposition and decay [33–35]. Some of the mostobvious changes occurred during later sampling times andwere associated with the dominant fungal taxa P. radiata andT. elegans. Before the dominance of these white-rot fungiduring succession, however, there was a trend for dominanceby nondecay fungi (50–70 %) early in decomposition(4 months) compared with the wood-decay fungi. This obser-vation is fairly consistent with the idea that nondecay fungi areearly colonizers [17, 36]. These nondecay taxa were dominantmembers of the fungal community, but the exact numbers andbiomass of these organisms are not known. It is also notknown to what extent these organisms are growing. There isno evidence indicating that the colonization process bynondecay fungi deviated substantially between the chemicallydiverse wood types.

Duringmid to latter stages of decomposition (>10months),wood-decay fungi increasingly dominated the fungal commu-nity, while nondecay fungi declined to levels below detection.This strong shift in community membership is generally sup-portive of the typical successional model of wood decay inforest soil [37]. However, these data provide more detail intolower taxonomic ranks associated with succession and differ-ences in the type of fungi associated with the aging anddecomposition of different wood types.

Fungi related to brown rot (Gloeophyllum sp.) were initial-ly undetected but tended to increase during wood decompo-sition, as expected. One exception to this was the low abun-dance of brown-rot fungi associated with pine during decom-position. Brown-rot fungi are typically aggressive decayers ofcellulose and hemicellulose in softwoods such as pine duringearly succession [38]. One possible explanation is related tosampling effort, which may have been too infrequent to cap-ture the occurrence of brown-rot fungi during early succes-sion. If brown-rot activity was highest in decay susceptiblepine between sampling intervals, the occurrence of thesebrown-rot fungi may have been missed. The overall greaterrate of microbial activity associated with pine compared withthe other wood types supports this possibility. Many types ofwhite-rot fungi can also degrade cellulose, so the lack ofobserved brown-rot fungi may also suggest that the habitatassociated with pine wood, perhaps related wood chemistry,

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mol

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ood

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mol

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A g

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ood

Fig. 5 Abundance of total PLFAs from pine, cedar, and ACQ-treatedpine following decay over 26 months. Abundances differed significantlyacross wood types (P<0.05). Symbols represent the average of threereplicates and bars represent standard error

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1026

Axis 1 (67%)

Fig. 6 Bray–Curtis ordination of microbial community structure basedon relative abundance of the dominant PLFA. The alphanumericdesignations represent time of incubation in months and based on woodtype (stars denote pine, circles denote cedar, and squares denote ACQ-treated pine). Arrows provide a depiction of the change in communitieswith time. Percentages denote the amount of variability associated witheach axis. Standard errors are small and generally hidden behind symbols(n=3). 19:0cy and 18:0 were positively and negatively correlated (r>0.70) along axis 1, respectively

Fungal and Bacterial Community Succession on Three Wood Types

favors the colonization and degradation capabilities of white-rot over that of brown-rot fungi. If this observation of white-rot dominance is related to lignin degradation and the relativeenrichment of cellulose, as sometimes observed [39], it is animportant observation relevant to the conversion of celluloseto sugars for purposes such as ethanol production. As theprocess of wood decay and associated microbial communitysuccession are better understood, it is expected that ways tobetter control and manage microbial communities and theproducts of decomposition will be possible.

The basidiomycete white-rot fungus P. radiata was com-mon, appearing early and becoming dominant with wooddecay on pine and ACQ-treated pine. On cedar, however, taxamost closely related to the white-rot fungus T. elegans weredominant during later stages of decomposition (18–26 months). This outcome fits with the hypothesis that woodextractives and secondary compounds such as thujaplicansassociated with cedar affect fungal community establishmentand succession. It cannot be ruled out that other factors such asdifferences in the structural and chemical properties of thewood influenced the success of different fungal taxa, however.The role of these white-rot fungi and whether they playfunctionally redundant roles in the decomposition processare not known, but determining why they show preferencefor particular wood habitats or affect the wood decay processwill help to explain their wood-associated dynamics.

Fungal dynamics, rates of wood mass loss, and microbialactivity can be used to understand possible linkages betweencommunity structure and decomposition. Preservative-treatedpine (ACQ pine) showed low levels of decay and microbialactivity similar to that for cedar but significantly lower thanuntreated pine, throughout most of the study. Fungal commu-nity structure was different between all three wood types butwith cedar diverging from those of ACQ pine and pine in thelater periods of wood decay. The differences between thesecommunities were primarily explained by the abundances ofTrametes elegans and Phlebia radiata. ACQ-pine fungal com-munity structure and pine were dominated by P. radiata; how-ever, the former changed very little during the latter samplingmonths (10–28), perhaps an indication that the communitieswere suppressed by the ACQ preservative. Community dy-namics thus indicated that all three wood types support differ-ent types of fungal taxa.

A proposed mechanism of fungal suppression in ACQ-treated wood is related to the ability of copper to form met-al–enzyme complexes that interfere with enzyme activity[40–42]. The relatively high rates of respiration associatedwith ACQ pine initially, representing high microbial activity,could be the result of at least two possibilities. First, inresponse to lowered enzyme activity in the presence ofACQ,microbes may continuously upregulate enzyme produc-tion for the purposes of transport and catabolism of availablesugars and starches. As enzyme activity continues to be

suppressed and microbes continue to respond to availablecarbon, more enzymes are produced and respiration is in-creased. This effect would continue to occur at the expenseof microbial biomass but would presumably come to a halt asmicrobial biomass and energy reserves are depleted. Anotherexplanation of temporarily high respiratory activity in theACQ-treated pine wood might be the result of the ACQtreatment process. The process involves the addition of analkaline solution under pressure, which could have resulted ina temporary increase in bioavailable sugars and starches thatfuel a burst of respiration. Eventually, the reduced capacity toutilize bioavailable organics and along with decreased enzymefunction would suppress microbial activity, wood decay, andsuccession. Overall, the differences are consistent with wood-type-influencing fungal communities and their successionaltrajectories during wood decomposition and decay.

PLFA-Based Description of Wood-Associated MicrobialCommunities

Several patterns of change and the appearance of previouslyundetected PLFAs associated with wood aging and decaywere helpful in understanding structural and physiologicalchanges in the wood-associated microbial communities. Themicrobial communities associated with each wood clusteredseparately from one another; however, more of the variation inthe multivariate plot was accounted for by the separation ofthe decay-susceptible pine relative to the more resistant cedarand ACQ-pine woods. Differences along axis 1 were stronglycorrelated with two PLFAs, one of which was an indicator ofbacterial biomass (19:0cy). The bacterial specific PLFAs,though in relatively low concentrations, were observed acrosswood types. The occurrence of this bacterial marker associat-ed with the more decomposable pine wood is consistent withthe role that bacteria are thought to play during the earlieststages of wood aging and decomposition and help to prime theprocess of decomposition for fungi [8]. The greater decay ofpine compared with the other two wood types was associatedwith greater relative abundance of bacteria, possibly a result ofgreater available substrate for bacterial colonization andgrowth. Another PLFA, usually an indicator of eukaryotesand fungi (18:0), was also strongly correlated with Axis 1and had a much greater relative abundance in cedar and ACQ-treated pine woods. However, because this PLFA is found inboth plants and fungi, the abundance is difficult to interpret.The results, nevertheless, support the idea that different com-munities develop on these wood types.

PLFA profiles showed change over time (Fig. 6; Axis 2),but the temporal dynamics were often less predictable thanthose described using rRNA genes. PLFA-based communitydynamics associated with ACQ-treated pine, however, wererelatively similar at 10, 18, and 26 months and agree withsimilar observations based on ITS-based fungal community

L. Prewitt et al.

structure. Moreover, the change along Axis 1 describesthe variation between the two relative resistant woodtypes and decay susceptible pine, which tends to sup-port ITS-based observations of community differencesacross wood types. Compared with the ITS data, therewas a more pronounced pattern of change using PLFAin pine compared with ACQ pine and cedar wood types.The different patterns of succession are not surprising,perhaps representing the methodological differences inmicrobial community profiling. The overall outcomes,however, support the hypothesis that wood type affectsmicrobial community structure.

PLFA is a much broader method of characterization ofmicrobial communities than ITS. PLFA represents all mi-crobes and likely plant biomass, while ITS is fungal specific.PLFAs are also very sensitive to environmental and habitatchange. For example, the bacterial marker 19:0cy is formedonly from a precursor fatty acid (18:1ω7). The highabundance of 19:0cy relative to 18:1ω7 is consistentwith unbalanced growth [43], as expected to occurduring wood decay. Overall, the changes in PLFA aresupportive of wood type and stage of decomposition asdeterminants of microbial community structure. Thebroader description provided by PLFA, however, is like-ly to include information on physiological in addition tostructural changes in microbial communities.

Conclusion

Fungal community dynamics during wood aging and decom-position, with some exceptions, generally followed succes-sional patterns previously documented using broad groupingsof nondecay, brown-rot, and white-rot wood-decay fungi. Theresults furthermore indicate that there are specific alterationsin fungal communities that occur during decay of differentwood types. In particular, decay was dominated by differentwhite-rot fungi in cedar (Trametes elegans) compared with thepine wood types (Phlebia radiata). After the first few monthsof wood aging and decomposition, the ACQ treatment ofwood appeared to suppress successional community change.Whether differences in phyla and successional patterns be-tween wood types are indicative of specific adaptations byfungi during the decay and decomposition process needsfurther investigation. Shifts in communities need to be under-stood in terms of their functional relevance to ecosystemprocesses.

Acknowledgments The authors acknowledge the support for this workprovided by the National Science Foundation (MCB-0641788) and theLucas BiodeteriorationCenter. Also, special thanks toMr.Min Lee for hishelp in this research.

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