Microb Ecol (2014) 68:212–221
DOI 10.1007/s00248-014-0396-3
ENVIRONMENTAL MICROBIOLOGY
Fungal and Bacterial Community Succession Differs for Three
Wood Types during Decay in a Forest Soil
Lynn Prewitt & Youngmin Kang &
Madhavi L. Kakumanu & Mark Williams
Received: 6 August 2012 / Accepted: 11 February 2014 / Published online: 13 March 2014
# Springer Science+Business Media New York 2014
Abstract Wood decomposition by soil microorganisms is
vital 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 community
succession associated with the decay of different wood types.
In this study, the microbial community associated with the
decay of pine (decay-susceptible wood), western red cedar
(decay resistant) and ACQ-treated pine (Ammoniacal Copper
Quaternary, preservative-treated pine for decay resistance) in
forest soil was characterized using DNA sequencing, phospholipid fatty acid (PLFA) analysis, and microbial activity
over a 26-month period. Bray–Curtis ordination using an
internal transcribed spacer (ITS) sequence and PLFA data
indicated that fungal communities changed during succession
and 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 the
fungal communities. By this time, Trametes elegans
L. Prewitt
Department of Forest Products, Forest and Wildlife Research Center,
College of Forest Resources, Mississippi State University,
P.O. Box 9820, Starkville, MS 39762, USA
M. L. Kakumanu
Horticulture, Rhizosphere-Soil Microbial Ecology and
Biogeochemistry Lab, Virginia Polytechnic and State University, 311
Latham Hall, Blacksburg, VA 24061, USA
Y. Kang
The Basic Herbal Medicine Research Group, Herbal Medicine
Research Division, Korea Institute of Oriental Medicine (KIOM),
Daejeon 305-811, Republic of Korea
M. Williams (*)
Horticulture, Rhizosphere-Soil Microbial Ecology and
Biogeochemistry Lab, Virginia Polytechnic Institute and State
University, 311 Latham Hall, Blacksburg, VA 24061, USA
e-mail: [email protected]
dominated cedar and Phlebia radiata dominated pine and
ACQ-treated pine. The description of PLFA associated with
ACQ-treated pine resembled cedar more than pine; however,
both PLFA and ITS descriptions indicated that fungal communities associated with ACQ-treated pine were less dynamic, perhaps a result of the inhibition by the ACQ preservative,
compared with pine and cedar. Overall, fungal community
composition and succession were associated with wood type.
Further research into the differences in community composition will help to discern their functional importance to wood
decay.
Introduction
Microbial decomposition of wood plays a key role in regulating forest carbon and nutrient cycles [1–3]. The activity of
these microorganisms is dependent upon an array of environmental factors that include water availability and temperature.
Wood chemistry is also an important predictor of decomposition, with rates varying across a broad range of wood types.
For example, rates are influenced by wood density and the
content of soluble wood extractives, cellulose, and lignin [4,
5]. These differences are likely to influence the dominant
microbial community associated with wood decomposition.
Different types of microbes filling different ecological niches
could result in feedbacks that influence the rate and types of
biochemical processes of decay [2, 3]. While a general model
of microbial succession during the decomposition of wood
has been described for decades, details about the specific types
of microbes associated with the process remain unpredictable.
Fungi are considered dominant members during the decomposition of wood; however, bacteria are often the initial
colonizers [6], feeding on available sugars and increasing the
permeability of wood [7–9]. During this time, the so-called
“nondecay” fungi, such as certain molds and sapstain fungi,
Fungal and Bacterial Community Succession on Three Wood Types
also utilize freely available nonstructural wood substrates such
as sugars [10]. The true wood-decay fungi (soft, brown, and
white-rot fungi) then cause loss in wood strength and generally appear during the mid to late stages of wood decay [11,
12]. This general model of fungal community succession
during the decomposition of wood has also been shown to
predict broad shifts from soft-rot, brown-rot, and then to
white-rot fungi [13–15] and is thought to be related to the
stage of decomposition and the availability of substrate [16,
17]. While ultimately limited by the immigration and occurrence
of available fungal taxa, changes have been linked to the ability
of fungi to compete and dominate substrate use during each
successional stage. Hence, successional change in fungal communities during wood decomposition can vary partially due to
resource competition [18, 19], wood chemistry, and other factors
related to microbial inhibition. The details of microbial community change, however, are less well known. Fungal taxa differ in
the capacity and efficiency of wood catabolism, and so, an
understanding of the natural dynamics of fungal community
succession during the decomposition of wood will help describe
some of these functional changes [19, 20].
The structural framework of wood consists of cellulose, hemicelluloses, and lignin and comprises approximately 95 % of the
wood’s total composition [20]. Wood extractives (organic soluble
materials) represent a smaller percentage of the wood’s mass
(~5 %) but might have a significant effect on wood-decay fungi
and activity. In particular, cedrol and thujaplicins, the extractives
found in cedars and that are known to have antifungal properties
provide cedar with a highly durable wood compared with most
other softwoods such as pine [21]. Pines, which contain different
extractives, are generally considered more susceptible to microbial decay than cedars and junipers [22]. Though the extractives
have been shown to modify fungal activity, questions remain
about the changes that occur to microbial communities during the
decay and decomposition of wood [23].
To better describe the succession of microorganisms associated with the decomposition of wood in forest ecosystems,
with the objectives to observe (a) the community membership
of decomposing wood and (b) fungal community change
associated with three different wood types with different susceptibilities to degradation over >2 years of decomposition.
Fungal gene expression related to lignin degradation was
reported to be very different among the three woods [24,
25]. The microbial community structure during decomposition of the three wood types pine, (a relatively decaysusceptible wood), cedar (decay-resistant wood), and ACQtreated pine (ammonium copper quaternary, chemically treated to be decay resistant) was characterized. The hypothesis
was that fungal communities would follow a pattern of succession from early colonizing nondecay fungi to wood-decay
fungi during the decomposition of wood. It was also hypothesized that membership and structure of fungal communities
would differ during decomposition based on wood type. The
213
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 taeda L.) and western red cedar (Thuja plicata.Donn
ex 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 purchased from Lowes’s Home Improvement Center, Starkville,
Mississippi. One set of pine stakes were later treated with a
wood 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 for
7 days, air-dried for 1 week, and equilibrated to approximately
12 % moisture content (MC). Afterward, the samples were
soaked for 7 days with daily water changing. The stakes were
then air-dried for several days until they reached 60 % MC as
determined by weight loss, based on fresh weight [25].
The wood stakes were placed in plastic containers (250 mm×
365 mm×220 mm) filled with sieved silty clay soil collected
from the top 7.6 cm of undisturbed forested soil at Dorman
Lake, Oktibbeha County, Mississippi. Eight circular holes (5mm diameter) were made in the bottom of each container for
drainage. A screen (250 mm×365 mm) was placed at the
bottom of each container followed by gravel (20 mm deep)
and the Dorman soil (100 mm deep). The MC of soil in each
container was adjusted to 90 % of its field capacity and monitored weekly. Six unsterilized stakes each of pine, cedar, and
ACQ-treated pine were inserted 8 cm deep into the soil in
replicate containers for each sampling time for a total of 84
stakes per wood type. The containers were placed in a greenhouse at 25 °C with a relative humidity of 30–50 % from
November to March and outside from April to October (experiment was conducted between December 2007 through April
2010). Two stakes per container were covered with nylon stocking material and used to monitor 40–80 % MC on the wood
samples [26]. Stakes were weighed every week, and water was
added to the soil if needed. Random sampling of the wood was
conducted on day 0 and bimonthly over 26 months.
Modulus of Elasticity
Dynamic modulus of elasticity (MOE) provided a metric
of wood decay and was measured for each sampled
stake on a bimonthly schedule. The average percentage
of MOE change was calculated using the formula [(initial MOE − current MOE)/initial MOE] × 100 % [25].
The percent MOE loss of the wood was used as an
214
indication of decay: The higher the % MOE loss, the
more decayed the wood.
Sample Collection
Three of the six randomly harvested wood stakes showing the
most decay (based on MOE results) were selected at 0, 4, 10,
18, and 26 months and then individually cut into 16 equal
sections. Four sections were combined and ground for fungal
genomic DNA and phospholipid fatty acid (PLFA) extraction,
while four unground sections were used each for bacterial
genomic DNA extraction and CO2 respiration. The remaining
samples were immediately frozen in liquid nitrogen and stored
at −70 °C.
Extraction of Fungal and Bacterial Genomic DNA from Wood
The fungal genomic DNA was extracted from 50-mg
grounded wood in CTAB (1,000 μl, 2 % w/v
hexadecyltrimethylammonium bromide, 100 mM Tris,
20 nM Na2EDTA, and 1.4 mM NaCl. The resulting
mixture was processed according to the MachereyNagel Nucleospin Plant DNA extraction kit protocol
(Easton, PA, USA) as previously described [25].
Bacterial genomic DNA was extracted by placing four
unground wood sections in 5 mL of nutrient broth (OIDCO,
Becton Dickinson) overnight at 28 °C with shaking. Following
overnight incubation, the cell cultures were transferred in 1-mL
aliquots to 1.5-mL microcentrifuge tubes and centrifuged for
cell separation. The liquid portion was removed from each
sample, and 10 μL of RNase A was added to each tube and
incubated for 2 h at 65ºC for cell lysis, mixing every 15–
20 min by inverting the tubes. The mixture was transferred to
a Nucleospin® spin column and centrifuged for 5 min at
11,000×g to filter the lysate. The filtrate was mixed with
850 μL of binding buffer and passed through a second spin
column containing a silica membrane for 1 min at 11,000×g,
binding the genomic DNA. The silica membrane was washed
and dried by centrifugation at 13,000×g for 2 min. The DNA
was eluted from the silica membrane by adding 50 μL of 65 °C
elution buffer, then incubated at 25 °C for 5 min, and centrifuged at 8,000×g for 1 min to collect the eluted DNA.
The quality and quantity of the extracted fungal and bacterial genomic DNAs were determined by UV absorbance at
260 and 280 nm using the NanoDrop spectrophotometer ND1000 (NanoDrop Technologies, Inc.). Extracted genomic
DNAs were stored at −70 °C.
Amplification of ITS and 16s rRNA Genes
Bacterial and fungal DNA associated with each wood type
were amplified using 16s ribosomal RNA (rRNA) gene and
the internal transcribed spacer (ITS) region, respectively.
L. Prewitt et al.
Amplification of each gene was conducted with the following
thermocycler settings: initial denaturation at 94 °C for 2 min,
followed by 35 cycles at 94 °C for 30 s, annealing at 60 °C for
30 s, extension at 72 °C for 30 s, and a final extension at 72 °C
for 10 min. PCR products were visualized by agarose gel
electrophoresis stained with ethidium bromide. Primers used
for amplification were 5′-CTTGGTCATTTAGAGGAAGT
AA-3′ (ITS-F) and 5′-TCCTCCGCTTATTGATATGC-3′
(ITS-R) for general fungi ITS and 5′-AGACTCGATCCTGG
CTCAG-3′ (16s-F) and 5′-GGTTACCTTGTTACGACTT-3′
(16s-R) for general bacterial 16s rRNA gene [27].
Cloning and Sequencing of Amplified DNA Products
for Taxa Identification
Amplified PCR products from decaying stakes were transformed into Escherichia coli plasmids using the TOPOcloning kit for sequencing (K4575-40 Invitrogen, Co.,
Carlsbad, CA, USA). The plasmids of positively transformed
E. coli were isolated and extracted using the PureLink™
Quick Plasmid Miniprep Kit (K2100-11, Invitrogen,
Carlsbad, CA, USA). Plasmids were analyzed for inserts by
restriction digest using EcoRI, gel electrophoresis, and prepared for sequencing using the Dye Terminator Cycle
Sequencing with Quick Start Kit (608120, Beckman Coulter
Co, Brea, CA, USA). Automated sequencing was performed
using 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 Bligh
and Dyer method [28]. The phospholipid fraction was recovered and converted to fatty acid methyl esters for analysis [29].
Fatty acid methyl esters were separated, quantified, and detected by an Agilent 6890 series gas chromatograph (Santa
Clara, CA, USA) equipped with a flame ionization detector,
an Ultra-2 column (19091B-102; 0.2 mm by 25 m), controlled
by computerized ChemStation and Sherlock software. Ultrahigh-purity H2 was the carrier gas at a column head pressure
of 20 kPa, septum purge of 5 mL min−1, a split ratio of 40:1,
injection temperature of 300 °C, and an injection volume of
2 μL. The oven temperature increased from 170 to 288 °C at
28 °C min−1, and the analysis time of each sample was 6 min.
Peak identification was carried out by the Microbial
Identification System (MIDI, Inc., Newark DE, USA) following calibration with a standard mixture of 17 fatty acid methyl
esters (1,300 A calibration mix). The PLFA markers used to
determine the fungal population were 18:2ω6c and 18:1w9c
and 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 and
cy19:0.
Fungal and Bacterial Community Succession on Three Wood Types
215
Microbial Respiration
Data Analyses
The statistical analysis of MOE was performed by two-way
analysis of variance (ANOVA) and Tukey's test (α=0.05) for
randomized complete block design (RCBD) using SAS program (SAS 9.1, SAS Institute Inc., Cary, NC, USA).
Multivariate analysis of the PLFA data was conducted
using PC-ORD (version 4.2) software (Gleneden Beach,
OR, USA). The dominant fatty acids were relativized and
analyzed by nonmetric multidimensional scaling (NMS) using
Sorenson distance, as previously described [30]. NMS is a
nonparametric method that provides graphical ordination of
the experimental data [31]. Fungal sequences were separately
aligned using Clustal W and analyzed by Mallard Software to
check for chimeras and anomalies. Sequences were grouped
by the computer program DOTUR [32] at 97 % evolutionary
distance (D=0.03) to generate operational taxonomic units
(OTUs). The relative abundance of the OTU was then analyzed by NMS using Bray–Curtis ordination. Sequences with
the closest match (>98 %) were used for identification of
bacterial and fungal species. Analysis of variance with repeated measures was conducted to analyze for differences in
respiration and PLFA abundances. The multiresponse permutation procedure (MRPP), a nonparametric test, was used to
assess differences in fungal community structure across wood
type and incubation.
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 greater
for pine than cedar and ACQ pine (P<0.05). Cedar and ACQ pine were
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 a strong fit to the data (R2 >0.90). MOE was
significantly different from 0 at 6 months for pine and at 8 months for
both cedar and ACQ-treated pine. Decay was thus measurable at 6 and
8 months, respectively
cedar–ACQ-pine MOE data, with polynomials showing a
strong fit to the data (R2 >0.90; P<0.001). MOE was also
significantly different from zero (fresh wood, no decay) at
6 months for pine and at 8 months for both cedar and ACQtreated pine (P<0.05). Decay was thus measurable at 6 and
8 months, respectively.
Respiration rates, used as an index of microbial activity and
wood decay and decomposition, were significantly different
among wood types (P<0.01) and averaged 45, 23, and 12 μg
CO2-Cg−1wood on pine, ACQ-treated pine, and cedar, respectively and showed agreement with measures of the wood
decay process (Fig. 2).
g CO2 -C g-1 wood d -1
Four small wood blocks weighing ~2 g from each treatment
were placed in a separate 40-mL sterilized serum bottle and
0.5 mL of sterile water was added. The bottles were sealed
with a crimp cap and incubated at 25 °C. Wood from each
sampling time (0, 4, 10, 18, and 26 months) was assayed for
CO2 production, providing a level of microbial activity and
decomposability of the wood at each time interval. Headspace
samples were taken following 1 week of incubation.
Accumulated CO2 in the headspace was measured using a
Varian 3600 gas chromatograph (Varian, Inc., Palo Alto, CA,
USA) equipped with a 2-m Porapak Q column, oven temperature of 110 °C, and a thermal conductivity detector. The
amount of CO2 measured from each sample was corrected
by subtracting the CO2 measured from a serum bottle containing no wood and only water.
Pine
Cedar
ACQ-Pine
100
80
60
40
20
0
Results
0
5
10
15
20
25
Months of Incubation
The decay of wood based on MOE was significantly greater in
pine compared with cedar and ACQ-treated pine, beginning at
6 months of aging (Fig. 1, P<0.001). Cedar and ACQ pine
were not significantly different from one another. A secondorder polynomial was fit to pine and to the combined
Fig. 2 Respiration rates from three wood types following 0, 4, 10, 18,
and 26 months of wood decomposition in soil contact field. Rates are
based on 1-week laboratory incubation of moist wood samples on a per
day basis (25 °C). Respiration from pine wood was significantly greater
than that for cedar and ACQ pine (P<0.05). Symbols represent the
average of three replicates and bars represent standard error
216
L. Prewitt et al.
At 4 months, the relative abundance of clones related to
P. radiata accounted for 50–73 % on pine, 31–53 % on ACQtreated pine, and undetected to 40 % on cedar (Fig. 3). In
contrast, T. elegans was not detected at 4 months on any of the
wood types. However, at 10 months, T. elegans occupied 36 %
on cedar, 25 % on pine, and 10 % on ACQ-treated pine. By the
end of the study, T. elegans increased to 80 % on cedar but
declined to 22 and 25 % on pine and ACQ-treated pine,
respectively. An increase in Phlebia spp., in contrast, was
observed in association with pine and ACQ-treated pine during latter stages of wood decomposition and aging.
Overall, the percentage of brown-rot fungi represented by
members most closely related to G. subferrugineum and
G. sepiarium were lower (13.9 %) compared with white-rot
fungi (86.1 %) and equal to that of nondecay fungi. All
groups, however, were dynamic through decomposition.
Gloeophyllum-related taxa remained undetected on any wood
type at 4 months. At 10, 18, and 26 months of aging, the
percentage of Gloeophyllum spp. were 0–4 % on pine, 10–
20 % on ACQ-treated pine, and 20–24 % on western red
cedar.
Early colonizing nondecay fungi represented 50–70 % of
the sequences at 4 months of decay on the three wood types.
The abundance of these fungal species decreased to 4–34 % at
10 months and was not detected on any of the wood types at
18 and 26 months (Fig. 3).
Compared with respiration rates in cedar, both pine and
ACQ-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 on
ACQ-treated pine or cedar woods. On day 0, the respiration
from ACQ-treated pine was the highest among the treatments.
However, ACQ-treated pine and western red cedar showed
similarly low rates of respiration thereafter.
Fungal Community Structure Based on ITS Sequences
The fungal distribution over the four sampling times and on
the three wood types is shown in Table 1. Of the total 297
fungal sequences collected across all wood types, 85–91 % of
the sequences were most closely related to wood-decay fungi.
The majority of wood-decay fungi (75–98 %) were most
closely related to white-rot, the remainder to brown-rot fungi.
White-rot fungi were represented by members most closely
related to Trametes elegans, Phlebia radiata, and Cf. Phlebia
sp., and brown-rot fungi were represented by Gloeophyllum
subferrugineum and G. sepiarium. The early colonizers were
most closely related to seven fungal species: Blastosporella
zonata, Boletaceae sp., and unclassified taxa with endophytic
members, Lecythophora sp., Volutella ciliata, Cryotococcus
gatti, and Polyporus umbellatus.
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-month
decay in forest soil at 0.03 evolutionary distance
Closest cultured match
Fungal group
Cf. Phlebia sp.
Phlebia radiata
Total Phlebia sp.
Trametes elegans
Total T. elegans
Total white-rot fungi
Gloeophyllum sepiarium
Gloeophyllum subferrugineum
Total Gloeophyllum spp.
Total wood-decay fungi
Unclassified fungal endophyte
Lecythophora sp.
WD-WR
WD-WR
Volutella ciliata
Blastosporella zonata
Boletaceae sp.
Cryptococcus gatti
Polyporus umbellatus
Total nondecay fungi
Total fungal clones
ND
ND
ND
ND
ND
WD-WR
WD-BR
WD-BR
ND
ND
4 monthsa
10 months
18 months
26 months
P
C
A
P
C
A
P
C
A
P
C
A
0
50
50.0
0
0
50
0
0
0
50
0
0
0
40
40.0
0
0
40
0
0
0
40
0
0
0
30.7
31
0
0
31
0
0
0
31
0
0
13.8
41.3
55.1
24.1
24.1
79
3.4
0
3.4
83
0
0
26.0
11.1
37.1
33.3
33.3
70
7.4
18.5
25.9
96
0
3.7
31.1
13.7
44.8
10.3
10.3
55
0
10.3
10.3
66
17.2
17.2
30.7
46.2
76.9
19.2
19.2
96
3.8
0
3.8
100
0
0
0
0
0
75.8
75.8
76
0
24.1
24.1
100
0
0
16.6
36.7
53.3
36.6
36.6
90
0
10
10
100
0
0
20.7
55.1
75.8
24.1
24.1
100
0
0
0
100
0
0
0
0
0
68.9
68.9
69
13.8
17.2
31.0
100
0
0
16.6
36.7
53.3
26.6
26.6
80
0
20.0
20.0
100
0
0
0
0
0
50
0
50.0
6
0
25
15
0
20
60.0
20
0
38.5
30.7
0
0
69
13
17.2
0
0
0
0
17
29
0
0
0
0
0
4
27
0
0
0
0
0
34
29
0
0
0
0
0
0
26
0
0
0
0
0
0
29
0
0
0
0
0
0
30
0
0
0
0
0
0
29
0
0
0
0
0
0
29
0
0
0
0
0
0
30
Fungal and Bacterial Community Succession on Three Wood Types
217
0.8
Pine
80
40
20
0.6
Axis 2 (12%)
P radiata
T. elegens
Gloeophyllum sp
Non-decay
60
10P
26C
0.4
18P
0
18C
26A
26P
18A
0.2
Relative abundance (%)
Cedar
10A
80
10C
0.0
60
0.0
0.2
0.4
0.6
0.8
1.0
Axis 1(41%)
40
Fig. 4 Bray–Curtis ordination of microbial community structure based
on the relative abundance of the 38 identified fungal operational taxonomic units (OTUs). OTUs were calculated at D=0.03 using the computer program DOTUR (35). The alphanumeric designations represent
time of incubation in months and based on wood type (P Pine (star), C
Cedar (circle), A ACQ pine (square)). Blocked MRPP analysis indicated
significant 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)
20
0
ACQ-Pine
80
60
40
ACQ-treated pine were not that different from one another
but were very different from cedar.
20
0
0
5
10
15
20
25
30
Months of Incubation
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 at
4 and 10 months. The abundance of P. radiata was significantly greater in
pine and ACQ pine than in cedar, while T. elegans was significantly
greater in cedar than in pine and ACQ pine (P<0.05). Symbols represent
the average of three replicates and bars represent standard error
Bacterial Identification
Bacterial rRNA genes were observed for only a few bacterial
taxa. Burkholderia sp. Ellin and Oxalicibacterium
faecigallinarum were the predominant bacteria across the
three wood types. Burkholderia sp. was found primarily on
pine, while O. faecigallinarum was found predominantly on
cedar and ACQ-treated pine (data not shown).
Phospholipid Fatty Acid (PLFA) Analysis
Bray–Curtis ordination of the relative abundance of the 38
OTUs showed patterns indicating that fungal communities
associated with cedar shifted considerably more than those
associated with pine and ACQ-treated pine (Fig. 4). This
pattern was confirmed by statistical analysis of the principal
ordinates (two-dimensional) using MRPP (P < 0.01). At
10 months, the fungal communities on each wood type were
different with fungal communities on cedar and ACQ-pine
more closely related than on pine. At 18 months, there were
large shifts in fungal communities on cedar and pine but not
on ACQ-treated pine. For cedar, this shift was strongly associated with the increasing dominance of T. elegans, which was
highly correlated to a change along axis 1 (r=−0.72). At the
26th month, the fungal community changed very little on the
wood types compared with 18 months. By the end of the
study, the fungal communities associated with pine and
The mass of PLFAs on nonincubated wood ranged from 10 to
200 times greater in ACQ-treated pine than in cedar and
approximately five times more than in pine (Fig. 5).
Cedar, containing the lowest amount of extractable
PLFAs, showed a general increase in PLFAs throughout
most of the incubation.
Bray–Curtis ordination of microbial community structure
based on PLFAs (Fig. 6) and statistical analysis of the ordinates using MRPP indicated that wood type (P=0.0001) and
incubation time (P=0.008) influenced microbial community
structure. The pattern of change in PLFA associated with each
wood type was similar with incubation time and indicative of
a successional pattern of change. This pattern is visible along
axis 2 of the ordination plot, accounting for 12 % of the
variation in the original data. Pine and ACQ-treated pine
communities showed patterns indicating that they became
L. Prewitt et al.
30
25
20
15
10
5
0
ACQ-Pine
Discussion
Pine
Microbial Community Composition during Early Wood
Decomposition
0.8
0.6
Cedar
0.4
0.2
0.0
0
5
10
15
20
Months of Incubation
25
mol PLFA g-1 wood
mol PLFA g-1 wood
218
30
Fig. 5 Abundance of total PLFAs from pine, cedar, and ACQ-treated
pine following decay over 26 months. Abundances differed significantly
across wood types (P<0.05). Symbols represent the average of three
replicates and bars represent standard error
more similar with incubation time. A bacterial fatty acid,
19:0cy, was positively correlated with axis 1, accounting for
41 % of the variation, while 18:0, a likely eukaryotic marker
indicative of both plants and fungi, was negatively correlated
along axis 1. The decline was likely indicative of losses of
plant fatty acids during wood decay. Despite successional
trends, PLFA associated with cedar and pine at the 18-month
sampling tended to cluster separately from other time points.
In contrast, ACQ-treated pine communities at 18 months
tended to cluster with 10-and 26-month samplings, indicating
that community succession on ACQ-treated pine was less
dynamic than those on cedar and pine.
0.5
0
Cedar
0.4
4
10
Axis 2 (13%)
26
0.3
18
0
0
18
0.2
ACQ-Pine
4
4
26
18
0.1
Pine
10
10
26
0.0
0.0
Axis 1 (67%)
0.5
Fig. 6 Bray–Curtis ordination of microbial community structure based
on relative abundance of the dominant PLFA. The alphanumeric
designations represent time of incubation in months and based on wood
type (stars denote pine, circles denote cedar, and squares denote ACQtreated pine). Arrows provide a depiction of the change in communities
with time. Percentages denote the amount of variability associated with
each 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
The microbial community was largely dominated by fungi;
however, a few bacterial taxa were observed. The occurrence
of these taxa is consistent with the role that bacteria can play
during early decomposition of wood; however, the very low
abundance of DNA suggests that this role was likely limited.
As hypothesized, however, wood type was associated with
change in the composition of the fungal communities during
wood decomposition and decay [33–35]. Some of the most
obvious changes occurred during later sampling times and
were associated with the dominant fungal taxa P. radiata and
T. elegans. Before the dominance of these white-rot fungi
during succession, however, there was a trend for dominance
by nondecay fungi (50–70 %) early in decomposition
(4 months) compared with the wood-decay fungi. This observation is fairly consistent with the idea that nondecay fungi are
early colonizers [17, 36]. These nondecay taxa were dominant
members of the fungal community, but the exact numbers and
biomass of these organisms are not known. It is also not
known to what extent these organisms are growing. There is
no evidence indicating that the colonization process by
nondecay fungi deviated substantially between the chemically
diverse wood types.
During mid to latter stages of decomposition (>10 months),
wood-decay fungi increasingly dominated the fungal community, while nondecay fungi declined to levels below detection.
This strong shift in community membership is generally supportive of the typical successional model of wood decay in
forest soil [37]. However, these data provide more detail into
lower taxonomic ranks associated with succession and differences in the type of fungi associated with the aging and
decomposition of different wood types.
Fungi related to brown rot (Gloeophyllum sp.) were initially undetected but tended to increase during wood decomposition, as expected. One exception to this was the low abundance of brown-rot fungi associated with pine during decomposition. Brown-rot fungi are typically aggressive decayers of
cellulose and hemicellulose in softwoods such as pine during
early succession [38]. One possible explanation is related to
sampling effort, which may have been too infrequent to capture the occurrence of brown-rot fungi during early succession. If brown-rot activity was highest in decay susceptible
pine between sampling intervals, the occurrence of these
brown-rot fungi may have been missed. The overall greater
rate of microbial activity associated with pine compared with
the other wood types supports this possibility. Many types of
white-rot fungi can also degrade cellulose, so the lack of
observed brown-rot fungi may also suggest that the habitat
associated with pine wood, perhaps related wood chemistry,
Fungal and Bacterial Community Succession on Three Wood Types
favors the colonization and degradation capabilities of whiterot over that of brown-rot fungi. If this observation of whiterot dominance is related to lignin degradation and the relative
enrichment of cellulose, as sometimes observed [39], it is an
important observation relevant to the conversion of cellulose
to sugars for purposes such as ethanol production. As the
process of wood decay and associated microbial community
succession are better understood, it is expected that ways to
better control and manage microbial communities and the
products of decomposition will be possible.
The basidiomycete white-rot fungus P. radiata was common, appearing early and becoming dominant with wood
decay on pine and ACQ-treated pine. On cedar, however, taxa
most closely related to the white-rot fungus T. elegans were
dominant during later stages of decomposition (18–
26 months). This outcome fits with the hypothesis that wood
extractives and secondary compounds such as thujaplicans
associated with cedar affect fungal community establishment
and succession. It cannot be ruled out that other factors such as
differences in the structural and chemical properties of the
wood influenced the success of different fungal taxa, however.
The role of these white-rot fungi and whether they play
functionally redundant roles in the decomposition process
are not known, but determining why they show preference
for particular wood habitats or affect the wood decay process
will help to explain their wood-associated dynamics.
Fungal dynamics, rates of wood mass loss, and microbial
activity can be used to understand possible linkages between
community structure and decomposition. Preservative-treated
pine (ACQ pine) showed low levels of decay and microbial
activity similar to that for cedar but significantly lower than
untreated pine, throughout most of the study. Fungal community structure was different between all three wood types but
with cedar diverging from those of ACQ pine and pine in the
later periods of wood decay. The differences between these
communities were primarily explained by the abundances of
Trametes elegans and Phlebia radiata. ACQ-pine fungal community structure and pine were dominated by P. radiata; however, the former changed very little during the latter sampling
months (10–28), perhaps an indication that the communities
were suppressed by the ACQ preservative. Community dynamics thus indicated that all three wood types support different types of fungal taxa.
A proposed mechanism of fungal suppression in ACQtreated wood is related to the ability of copper to form metal–enzyme complexes that interfere with enzyme activity
[40–42]. The relatively high rates of respiration associated
with ACQ pine initially, representing high microbial activity,
could be the result of at least two possibilities. First, in
response to lowered enzyme activity in the presence of
ACQ, microbes may continuously upregulate enzyme production for the purposes of transport and catabolism of available
sugars and starches. As enzyme activity continues to be
219
suppressed and microbes continue to respond to available
carbon, more enzymes are produced and respiration is increased. This effect would continue to occur at the expense
of microbial biomass but would presumably come to a halt as
microbial biomass and energy reserves are depleted. Another
explanation of temporarily high respiratory activity in the
ACQ-treated pine wood might be the result of the ACQ
treatment process. The process involves the addition of an
alkaline solution under pressure, which could have resulted in
a temporary increase in bioavailable sugars and starches that
fuel a burst of respiration. Eventually, the reduced capacity to
utilize bioavailable organics and along with decreased enzyme
function would suppress microbial activity, wood decay, and
succession. Overall, the differences are consistent with woodtype-influencing fungal communities and their successional
trajectories during wood decomposition and decay.
PLFA-Based Description of Wood-Associated Microbial
Communities
Several patterns of change and the appearance of previously
undetected PLFAs associated with wood aging and decay
were helpful in understanding structural and physiological
changes in the wood-associated microbial communities. The
microbial communities associated with each wood clustered
separately from one another; however, more of the variation in
the multivariate plot was accounted for by the separation of
the decay-susceptible pine relative to the more resistant cedar
and ACQ-pine woods. Differences along axis 1 were strongly
correlated with two PLFAs, one of which was an indicator of
bacterial biomass (19:0cy). The bacterial specific PLFAs,
though in relatively low concentrations, were observed across
wood types. The occurrence of this bacterial marker associated with the more decomposable pine wood is consistent with
the role that bacteria are thought to play during the earliest
stages of wood aging and decomposition and help to prime the
process of decomposition for fungi [8]. The greater decay of
pine compared with the other two wood types was associated
with greater relative abundance of bacteria, possibly a result of
greater available substrate for bacterial colonization and
growth. Another PLFA, usually an indicator of eukaryotes
and fungi (18:0), was also strongly correlated with Axis 1
and had a much greater relative abundance in cedar and ACQtreated pine woods. However, because this PLFA is found in
both plants and fungi, the abundance is difficult to interpret.
The results, nevertheless, support the idea that different communities develop on these wood types.
PLFA profiles showed change over time (Fig. 6; Axis 2),
but the temporal dynamics were often less predictable than
those described using rRNA genes. PLFA-based community
dynamics associated with ACQ-treated pine, however, were
relatively similar at 10, 18, and 26 months and agree with
similar observations based on ITS-based fungal community
220
structure. Moreover, the change along Axis 1 describes
the variation between the two relative resistant wood
types and decay susceptible pine, which tends to support ITS-based observations of community differences
across wood types. Compared with the ITS data, there
was a more pronounced pattern of change using PLFA
in pine compared with ACQ pine and cedar wood types.
The different patterns of succession are not surprising,
perhaps representing the methodological differences in
microbial community profiling. The overall outcomes,
however, support the hypothesis that wood type affects
microbial community structure.
PLFA is a much broader method of characterization of
microbial communities than ITS. PLFA represents all microbes and likely plant biomass, while ITS is fungal specific.
PLFAs are also very sensitive to environmental and habitat
change. For example, the bacterial marker 19:0cy is formed
only from a precursor fatty acid (18:1ω7). The high
abundance of 19:0cy relative to 18:1ω7 is consistent
with unbalanced growth [43], as expected to occur
during wood decay. Overall, the changes in PLFA are
supportive of wood type and stage of decomposition as
determinants of microbial community structure. The
broader description provided by PLFA, however, is likely to include information on physiological in addition to
structural changes in microbial communities.
Conclusion
Fungal community dynamics during wood aging and decomposition, with some exceptions, generally followed successional patterns previously documented using broad groupings
of nondecay, brown-rot, and white-rot wood-decay fungi. The
results furthermore indicate that there are specific alterations
in fungal communities that occur during decay of different
wood types. In particular, decay was dominated by different
white-rot fungi in cedar (Trametes elegans) compared with the
pine wood types (Phlebia radiata). After the first few months
of wood aging and decomposition, the ACQ treatment of
wood appeared to suppress successional community change.
Whether differences in phyla and successional patterns between wood types are indicative of specific adaptations by
fungi during the decay and decomposition process needs
further investigation. Shifts in communities need to be understood in terms of their functional relevance to ecosystem
processes.
Acknowledgments The authors acknowledge the support for this work
provided by the National Science Foundation (MCB-0641788) and the
Lucas Biodeterioration Center. Also, special thanks to Mr. Min Lee for his
help in this research.
L. Prewitt et al.
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