Download Linking fungal communities to wood density loss after 12 years of

FEMS Microbiology Ecology, 91, 2015, fiv032
doi: 10.1093/femsec/fiv032
Advance Access Publication Date: 31 March 2015
Research Article
RESEARCH ARTICLE
Linking fungal communities to wood density loss
after 12 years of log decay
Ariana Kubartová∗ , Elisabet Ottosson and Jan Stenlid
Department of Forest Mycology and Plant Pathology, SLU, Uppsala Biocenter, Box 7026, 570 07 Uppsala, Sweden
∗ Corresponding author: Department of Forest Mycology and Plant Pathology, SLU, Uppsala Biocenter, Box 7026, 570 07 Uppsala, Sweden.
Tel: +46 18 672725; Fax: +46 (0) 18-67-35-99; E-mail: [email protected]
One sentence summary: The manuscript documents importance of fungal communities, as the major wood decomposers, in dead-wood decay, which
should be taken into account when modeling carbon cycle and storage in forests.
Editor: Ian C Anderson
ABSTRACT
Changes in biodiversity might alter decomposition processes and, consequently, carbon and nutrient cycling. We examined
fungal diversity and density loss in experimental Norway spruce logs after 12 years of decay in a hemiboreal forest.
Between 28 and 50% of the original wood biomass remained, depending on the fungal community composition in the log,
operational taxonomic unit (OTU) richness had only a minor effect on the log biomass. Although the communities were
OTU rich (190–340 OTUs per log), the majority of OTUs were infrequent or rare; wood degradation therefore depended
mostly on the most abundant OTUs and their decomposing abilities. The least decayed logs were characterized by
continuous dominance of an earlier colonizer and by high within-log community diversity, which was significantly related
to sample variables (position in log, density and moisture). In the most decayed logs, the earlier colonizers were generally
replaced by white-rot species able to exploit the highly decomposed wood. The communities were relatively spatially
uniform within whole logs, independent of the sample variables, whereas among-log diversity was high. Importance of
fungal community composition in decomposition processes should be taken into account when studying and modeling
carbon dynamics in forest ecosystems.
Keywords: wood decomposition; Norway spruce logs; fungal diversity; high-throughput sequencing; diversity–function
relationship
INTRODUCTION
Decomposing wood contributes to biodiversity by providing substrate and habitat for a wide range of organisms. In addition, it
is a major pool of organic carbon, which is released as the wood
decays. Fungi have coevolved with woody plants for more than
350 million years (Stubblefield, Taylor and Beck 1985) and during this time, they have become specialized in many ways, including the production of diverse wood-degrading extracellular
enzymes (Lundell, Makela and Hilden 2010; Floudas et al. 2012).
The most wood-decaying species with heterogeneous modes of
wood degradation are Basidiomycetes (Riley et al. 2014), but there
are also numerous cellulolytic species among Ascomycetes, and
lignin breakdown has been reported in fungi belonging to the
order Xylariales (Pointing, Parungao and Hyde 2003). Fungi with
the enzymatic ability to degrade intact cell wall components
have been described as structural wood decayers (Stokland,
Siitonen and Jonsson 2012). By breaking down cell walls into
smaller components, the structural wood decayers are able to
provide a substrate for the numerous residual wood decayers,
including many ascomycetes, agarics and yeasts (Rayner and
Boddy 1988; Wells and Boddy 2002; Botha 2011; Fukasawa, Osono
and Takeda 2011). In addition to the wood decayers, fungi with
other ecological roles have also been reported in decomposing
wood (Rajala et al. 2011; Kubartova et al. 2012).
Received: 17 December 2014; Accepted: 18 March 2015
C FEMS 2015. All rights reserved. For permissions, please e-mail: [email protected]
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Fungal colonization and decomposition starts in living trees:
for example, in wounds, dead branches or as heart-rot (Boddy
2000; Parfitt et al. 2010). When the tree dies, many other fungi
are then also able to colonize the wood (Boddy 2001; Vasiliauskas
et al. 2005; Jönsson, Edman and Jonsson 2008; Persson, Ihrmark
and Stenlid 2011). The wood undergoes both physical and chemical changes as it decomposes; however, the decay process and
the rate of decay are not constant over time (Makinen et al. 2006;
Aakala 2010). Initial and intermediate decay stages are characterized by effective cellulose and lignin breakdown thanks to
structural decayer activity (Klotzbucher et al. 2011; Olsson et al.
2011; Rajala et al. 2011; van der Wal et al. 2013). When the wood
is in an advanced stage of decay, another suite of fungi takes
over, including the residual decayers. In the final stage of decomposition, the wood completely disintegrates into small fragments, which become pieces of organic matter in the humus or
soil layer. The exact dynamics in the succession of wood decayers is not fully understood and some early colonizers may reside
in the wood and remain active over extended periods of time
(Rayner and Boddy 1988).
Fungi interact both in combative and facilitative ways at
all stages of decay (Holmer and Stenlid 1997; Boddy 2000;
Ovaskainen, Hottola and Siitonen 2010; Dickie et al. 2012; Ottosson et al. 2014). Early-arriving species may have a greater ability to establish and have a greater resource pool with which to
support metabolically expensive interactions later on (Holmer,
Renvall and Stenlid 1997; Holmer and Stenlid 1997; Wells and
Boddy 2002). The presence of specific fungi at the earlier stages
of colonization can affect community composition at the later
stages of decay (Renvall 1995; Cox, Wilkinson and Anderson
2001; Heilmann-Clausen and Boddy 2005; Fukami et al. 2010;
Dickie et al. 2012; van der Wal et al. 2013). Although certain
species tend to colonize the wood earlier than others (Jönsson,
Edman and Jonsson 2008), the colonization history and community assembly are highly stochastic and can proceed via many
alternative pathways owing to different random aspects (Boddy
2001).
Knowledge of how fungal community composition is linked
to wood-decay rates, especially in the long term, is essential to
modeling carbon dynamics as well as for assessing the consequences of biodiversity change on the carbon cycle. Most of the
studies examining these effects have taken place in laboratory
microcosms (e.g. Cox, Wilkinson and Anderson 2001; Deacon
et al. 2006; Toljander et al. 2006; Fukami et al. 2010; Valentı́n et al.
2014). Although these studies enabled a high level of experimental control, they were limited in terms of a realistic species pool
and were carried out over a relatively short period of time. The
field-based studies were usually limited by species identification
methods (Kubartova et al. 2009; Lindner et al. 2011; Dickie et al.
2012), but nowadays high-throughput sequencing has opened
up novel ways to address classic questions (Peay, Kennedy and
Bruns 2008; Hibbett, Ohman and Kirk 2009; Andrew et al. 2013;
Lindahl et al. 2013). It has revealed that fungal communities in
decomposing wood were richer and more diverse than previously expected (Ovaskainen et al. 2010; Kubartova et al. 2012;
Ovaskainen et al. 2013). Though, these studies measured wooddecay stages in the field in a way that did not enable detailed
investigation of wood decay.
The aim of this study was to link fungal community composition to variation in wood density loss in naturally colonized logs
with the help of a long-term field-based experiment. Twelve cut
Norway spruce logs of equal size were used: eight logs were uninoculated controls, two logs were inoculated with the brownrot fungus Fomitopsis pinicola and two logs were inoculated with
the white-rot fungus Resinicium bicolor. The logs were left to decompose for 12 years in the same hemiboreal forest plot. The
logs were then destructively harvested, which enabled extended
sampling with accurate density and moisture measures. A detailed view of the fungal communities was obtained by 454sequencing of the fungal DNA.
MATERIALS AND METHODS
Study site, experimental design and sampling
Twelve experimental logs were freshly cut from two healthy Norway spruce trees, growing at the same forest site (approximately
N 59.9, E 18.2). Logs included bark, were 1 m long and ranged between 25 and 35 cm in diameter. They were placed within 200 m
of each other in an old-grown Norway spruce-dominated forest
adjacent to Fiby nature reserve in central Sweden (N 59.881, E
17.353) in the summer of 1997. Two of the logs were initially inoculated with the brown-rot fungus F. pinicola, two were inoculated with the white-rot fungus R. bicolor and the remaining eight
logs were left uninoculated. For details about the inoculations
and placement, see Lindner et al. (2011). In August 2009, after 12
years of exposure, the logs were harvested in the forest, placed
in black plastic bags and transported to the laboratory. In the laboratory, three 5-cm thick discs were cut from each log according
to the schema in Fig. 1. Each of the discs was divided into four
outer and four inner fragments (i.e. 24 fragments from each log;
288 samples in total) (Fig. 1). Wood samples for DNA extraction (4
cm−2 ) were removed by a sterilized drill with diameter 10 mm or
by tweezers from the central part of each disc fragment (Fig. 1),
homogenized and then kept frozen at −21◦ C in a plastic zip bag.
The remaining part of the disc fragment was weighed, inserted
undisturbed into a plastic bag and then its volume was measured in a water column. The disc fragment was then removed
from the bag and dried to constant weight at 60◦ C. Wood density was calculated from the volume and dry weight values. The
initial wood density at the time of log creation was 0.45 g m−3 ,
which was used to calculate the remaining mass after 12 years
of decay.
Laboratory analyses
One and half cubic centimeters of each frozen wood sample was
transferred to a 2-ml screw-cap tube with five glass beads prior
to the DNA extraction. The samples were then homogenized
using a Fast-prep shaker (Precellys 24 Bertin Technologies),
and DNA was obtained by adding lysis CTAB buffer, extracting by chloroform and precipitating by isopropanol; for details,
see Kubartova et al. (2012). DNA concentration was measured
using NanoDrop (Thermo Scientific). Samples with a DNA concentration greater than 20 ng μl−1 were further purified using a
JetQuick DNA purification kit (Genomed GmbH). DNA from each
sample was amplified by PCR using gITS7–ITS4 and fITS9-ITS4
primer pair (Ihrmark et al. 2012) (Appendix S5, Supporting Information). The ITS4 primer was tagged with one of 144 identifiers
(8 bp long, designed at the department, listed in Appendix S3,
Supporting Information). PCRs were conducted in 50 μl reactions
(5 ng of the template, 200 μM of each nucleotide, 200 nM of each
primer, 0.025 U μl−1 of DreamTaq Green polymerase (Thermo Scientific) in buffer; 5 min at 94◦ C, 27 cycles of 30 s at 94◦ C, 30 s at
56◦ C, 60 s at 72◦ C, 8 min at 72◦ C). PCR products were purified by
AMPure kit (Beckman Coulter) and their concentrations established by Qubit fluorometer (Life Technologies). Next, 200 ng of
each PCR product was mixed into two pooled samples, which
Kubartová et al.
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Figure 1. Diagram of sampling within individual logs: entire log (left) and one of the three discs (upper-right) divided into four outer and four inner fragments; drill
samples are represented by the dots.
were further purified on agarose gel using QIAquick Gel Extraction Kit (Qiagen). The two pools were subjected to ligation of sequencing adaptors and sequenced by 454-sequencing technology on one whole plate using GL FLX Titanium system (Roche).
The ligation and sequencing were performed by LGC Genomics,
Germany.
Four samples with the highest numbers of Heterobasidion
parviporum sequences (see identification below) were selected
from each of the four H. parviporum-dominated logs. To detect potential genet variability within and among logs, these
H. parviporum samples were subjected to molecular fingerprinting using microsatellites, which was performed using five
microsatellite loci (Ha-ms1, Ha-ms2, Ha-ms6, Ha-ms9 and Hams10) according to the methodology described in Johannesson and Stenlid (2004) and in Oliva, Bendz-Hellgren and Stenlid
(2011).
Bioinformatics and OTU identifications
The sequencing resulted in 661 723 reads passing quality control in the SCATA pipeline (http://scata.mykopat.slu.se/, 7 April
2015, date last accessed) (i.e. 45% from the 1468 278 sequences in
total; minimum lengths 200 bp; minimum average quality score
20; minimum allowed base quality 5; barcode mismatch 0.1; no
chimera-test since eventual chimeras would end up as singletons in the single-linkage clustering). The reads were clustered
in SCATA using single-linkage clustering based on a clustering
distance of 0.02 of the high-quality region extracted from .fna
files (minimum alignment to consider clustering 0.7; collapse
homopolymers longer than 3 bp; mismatch penalty 1; gap open
penalty 0). The most common genotype was used to represent
each operational taxonomical unit (OTU, cf. species). OTUs represented by at least five reads were retained in the dataset, resulting in 547 fungal OTUs across 288 samples, based on 654
709 high-quality sequences. The OTU sequences were compared
with known sequences in several databases: UNITE (Abarenkov
et al. 2010), SAF (Ovaskainen, Hottola and Siitonen 2010) and curated sequence selection from GenBank (NCBI). They were also
aligned using ClustalW and organized in a neighbor-joining tree
using MEGA software (Tamura et al. 2011) to determine their relatedness and to remove non-fungal sequences (4512 of reads;
0.7%). Results from all these sources were combined when setting the OTU identities. The ITS homology for delimiting taxa
was set to 98–100% at species level, 94–97% at genus level and
80–94% at order level. The term ‘dominant’ was used for an OTU
with the greatest number of sequences detected in a log, ‘abundant’ for OTUs with a high number of sequences detected in a
log, i.e. hundreds or thousands of sequences, many times higher
than could be caused solely by PCR biases (Ihrmark et al. 2012).
The logs were named after the dominant OTU in order to present
the results in a comprehensive way. Term ‘widespread’ was used
for OTUs that were present in the majority of samples (Table S3,
Supporting Information). For the data matrices (OTUs by samples and sample variables), sequence and sample counts, identifications and annotation of the ecological roles of all OTUs,
see Supporting Information. The sequences are deposited in
GenBank with accession numbers KM492941–KM493488.
Statistics
Multivariable analyses of the fungal communities were performed using ordination techniques in the CANOCO 4.5 software
package (Microcomputer Power). Detrended correspondence
analysis (DCA), describing maximum variability among the samples, was used to ordinate the logs and samples within individual logs. Canonical correspondence analysis (CCA) followed by
a Monte Carlo permutation test was used to calculate the significance of the sample variables (position, density, moisture)
to fungal community differences (Table S3, Supporting Information Fig. 2). CCA first-axis species scores described the correlations of OTU occurrences with the continuous variables density
and moisture. The inverted Simpson diversity index (i.e. 1/D; D
= sum p i ˆ 2; the higher the value, the greater the diversity) for
each sample was calculated using the ‘invsimpson’ function in
the R vegan package (R Development Core Team 2010; Oksanen
et al. 2013). Indicator species analysis (Dufrene and Legendre
1997), which indicated the OTUs that tend to be found in one of
the logs versus the others, was counted using the ‘duleg’ function in labdsv R package (Roberts 2012). P-values of 0.05 and less
were considered significant in all the analyses performed.
RESULTS
OTU distributions
The majority of the 547 OTUs (43.1%) belonged to the Ascomycota, whereas the majority of the sequences belonged to the
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FEMS Microbiology Ecology, 2015, Vol. 91, No. 5
Figure 2. CCA plot illustrating that within the whole dataset: (i) moisture content had a greater influence on community composition than wood density, being more
correlated with the first axis (i.e. x-axis) then the density (see also Table 3), and (ii) the variables were partly negatively correlated. OTUs identified to species level
with the greatest weight values are shown (for more information, see Table 2, for abbreviations, see Appendix S1 (Supporting Information), sheet OTU identifications,
column D).
Basidiomycota (85.8%, Table 1). Only 0.6% of sequences remained unassigned to any phylum. With regard to the ecological roles of the fungi inhabiting the logs, the known wooddecaying fungi were by far the most abundant, accounting for
78.2% of the sequences, even though they only comprised 8.2%
of the OTUs. The numerous OTUs with unknown ecological roles
(76.2%) comprised only 16.8% of all the sequences (Table 1). The
number of OTUs in the 24 samples collected from each individual log ranged from 190 to 340 (Table S1, Supporting Information). A total of 59 OTUs (10.4%) were detected in all 12 logs,
whereas 52 OTUs (9.5%) were present in 1 log only. Resinicium
bicolor was both the most abundant (represented by the highest number of sequences) and the most ‘widespread’ (present
in the most samples) of all the OTUs. The Ascomycetes Can-
Table 1. Number of OTUs and sequences belonging to different fungal phyla and their ecological role. ‘Wood decaying OTUs’ are those
restricted to dead wood, while ‘saprotrophic OTUs’ are known from
litter and soil substrates.
Phyllum
Basidiomycota
Ascomycota
Zygomycota
Chytridiomycota
Unassigned
Ecology
Wood decaying
Saprotrophic
Mycorrhizal
Lichenised
Unknown
Number of OTUs (%)
Number of
sequences (%)
185 (33.8)
236 (43.1)
18 (3.3)
3 (0.5)
105 (19.2)
561 693 (85.8)
88 506 (13.5)
804 (0.1)
104 (0.02)
3602 (0.6)
Number of OTUs (%)
Number of
sequences (%)
45 (8.2)
49 (9.0)
32 (5.9)
4 (0.7)
417 (76.2)
511 697 (78.2)
23 772 (3.6)
9382 (1.4)
53 (0.01)
109 805 (16.8)
dida paludigena and Phialophora sp. 3; and the Basidiomycetes H.
parviporum and F. pinicola were the next most ‘abundant’ (see
OTU list in the Supporting Information for all the data). Within
the whole dataset, fungal communities differed significantly between samples with diverse moisture content (F = 4.930, logs as
covariables), but not between samples with distinct wood density (F = 1.390, logs as covariables, Fig. 2). The OTUs that correlated best with the samples with a low moisture content were H.
parviporum, Helotiales sp. 6 and Megacollybia platyphylla, whereas
Basidiomycete 27, Kneiffiella alutacea and Hyphodontia sp. 19 correlated best with samples with a high moisture content. In the
least decayed samples, Ascocoryne cylichnium, Basidiomycete 43
and H. parviporum were the most common OTUs, whereas Leotiomycetes 51, Junghuhnia luteoalba and K. alutacea were detected
mainly in the most decayed samples. See Table 2 for the list of
correlations for the 30 widespread and well-identified OTUs and
Table S2 (Supporting Information) for the complete list for the
60 most widespread OTUs.
Community composition among logs
Among the 12 logs, 4 were dominated by OTU assigned as
H. parviporum, including one of the logs initially inoculated
with R. bicolor (henceforth these logs are referred to as Het 1,
Het 2, Het 3 and Res in1, respectively; Fig. 3a, Table S3, Supporting Information). Each of these four logs was colonized
by a different H. parviporum genet, and a single or two heterokaryotic genets were established within the individual logs
(Table S4, Supporting Information). Another four logs were
dominated by OTU assigned as R. bicolor, including the second log initially inoculated with R. bicolor and one log inoculated with F. pinicola (henceforth these logs are referred to as
Res in2, Res 1, Res 2 and Fom in1, respectively). Fomitopsis pinicola assigned OTU dominated the second log (Fom in2) that
had been inoculated with this species, and R. bicolor was the
Kubartová et al.
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Table 2. Top 30 widespread OTUs (present in 70 or more samples and identified to genus or species level or among the 10 the most widespread
OTUs), sorted according to their correlation with wood density (left, starting with OTUs present mainly in the least decayed samples) and
with wood moisture content (right, starting with OTUs present in the driest samples). The correlations are expressed as canonical axis species
scores from a CCA with wood density/moisture content as environmental variables. Less-decayed samples were drier (i.e. the variables were
negatively correlated, Fig. 4). For each OTU, its classification, ecological strategy and abundance is shown on the right of the correlation value.
The 10 most widespread OTUs are highlighted in bold.
next most abundant OTU. In the remaining three logs, the
most abundant OTUs were assigned as Conferticium ochraceum,
K. alutacea and J. luteoalba, all white-rot fungi (henceforth these
logs are referred to as Con, Kne and Jun, respectively). Overall, the dominating species accounted for between 24.8% (Jun)
and 75.4% (Res in2) of all the sequences detected in each log.
The 10 most abundant species accounted for between 77.6%
(Het 1) and 95.5% (Res 2) of all the sequences in an individual
log, whereas the other OTUs only comprised between 4.5 and
22.4% of the sequences (276 OTUs in Het 1, 197 OTUs in Res 2;
Fig. 3a). Community composition differed significantly among
the logs (F = 5.914). OTUs with the highest indicator values varied even among logs dominated by the same OTU (Table S3,
Supporting Information).
The two F. pinicola inoculated logs had the highest average
OTU-richness values (Fig. 4a), whereas Jun, Res 2 and Con had
the lowest level of OTU richness. Res 2, Jun and Kne logs were
the most decayed logs, with only 28–32% of the original mass
remaining (Fig. 4b); these logs also had the most distinct com-
munity composition (Fig. 3b and c). The least decayed logs were
Het 1, Het 2, Het 3 and Res in2 (45–50% of the original mass
remained) with intermediate-richness values and resembling
communities. Wood density might differ in samples with similar communities, particularly in the logs that had a different
dominant species (Fig. 3d). The highest moisture content was
recorded in logs Kne and Jun, followed by Res 1 and Res 2 (between 71.6 and 79.3%; Fig. 4c), Kne, Jun and Res 2 were also the
three most decayed logs. The lowest moisture content, about
45%, was recorded in Het 1, Het 2 and Res in1 (Het 1 and Het 2
were also the least decayed logs, see above). The number of OTUs
with specified ecological roles was similar among all the logs
and was independent of wood density (Fig. S1a, Supporting Information). The abundance of wood-decaying and saprotrophic
OTUs was lowest in the highly decayed logs Con, Kne and Jun,
and in the least decayed log Het 2 (72–76%; Fig. S1b, Supporting
Information). Mycorrhizal OTUs were only abundant in the Kne
log, where Suillus variegatus and Piloderma sp. 45 were among the
most abundant OTUs (Table S3, Supporting Information).
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FEMS Microbiology Ecology, 2015, Vol. 91, No. 5
Figure 3. Differences in fungal community compositions in individual logs: (a) the prevalence of the 10 most abundant OTUs and of the other OTUs; (b) DCA ordination
plot, displaying similarities/differences in log fungal community compositions based on the 24 samples from each log; (c) DCA plot visualizing communities in each
individual sample (different colors for the 12 logs, 288 samples in total); and (d) DCA attribute plot where circle size and color-gradient indicate sample density (small
green circles indicate more decayed samples, large blue circles indicate less decayed samples). A few of the most abundant OTUs accounted for majority of all log
reads. Communities differed significantly among logs, although there was some overlap. Samples with similar communities might differ in wood density, particularly
if originating from logs with different dominant OTU. Log names were assigned on the basis of the most prevalent OTU detected or after the inoculated species in the
case of the inoculated logs, see notes of Table 3.
Kubartová et al.
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Within individual logs, differences among the fungal communities correlated with variations in the moisture content (significant in eight logs; Table 3; Fig. S2, Supporting Information).
The second most influencing variable was whether the sample
had been taken from the outer or inner position of a log (seven
logs): communities in the outer samples were more diverse compared with the inner samples (Fig. S2, Supporting Information
where the filled symbols for outer samples are more spread in
the DCA plots compared to the empty symbols for inner samples). The fungal communities detected in the three discs taken
at different horizontal positions were not significantly different, except log Fom in1 (Table 3). Overall, the communities were
most diverse in the least decayed logs, whereas OTUs converged
within the whole log in the most decayed Res 2 and Jun (Table 3,
the differences in these logs were not significant; Fig. S2, Supporting Information all samples clustered together in the DCA
ordination space). Neither OTU richness nor Simpson diversity
was correlated with wood moisture or density in individual logs
(Fig. S2, Supporting Information).
DISCUSSION
Experimental design
This study took advantage of a long-term field-based experiment, where the design and scale made it possible to investigate
the variables of interest and where other factors that could influence microbial colonization of wood were kept to a minimum.
All the experimental logs were exposed to the local species pool
at the same time point. The incubation of the logs in the same
forest plot further minimized dissimilarities caused by vegetation and by mesoclimate. The logs were created as part of the
experimental design, which not only eliminated the effect of
different log size and the effect of a tree mortality agent but
also facilitated accurate density and moisture measurements.
Thus, all the described differences in fungal communities and
wood densities are expected to reflect alternative pathways of
community development. To achieve such data over an ecologically realistic time frame is challenging. It was not realistic to
obtain the time zero information when the experiment started
in 1997. The evidence of which species previously inhabited the
logs (Lindner et al. 2011) is also skewed as different molecular
techniques were used due to their rapid development. Further,
there had been more inoculated logs placed in the forest site,
in order to have equal number of inoculated and uninoculated
logs, but they were destroyed during the experiment run.
Community development and log decay
Figure 4. Average values and standard error bars for individual logs of (a) OTU
richness; (b) wood density (the lower the value, the more decayed the wood; the
original wood density was 0.45 g cm−3 , the remaining mass after 12 years decay
ranged from 28 to 50%); and (c) moisture content. Moisture tended to increase as
decay proceeded. The OTU richness peaked in the transition phase between the
intermediate and advanced stages of decay and then declined. Log names were
assigned on the basis of the most prevalent OTU detected or after the inoculated
species in the case of the inoculated logs, see notes of Table 3.
We observed important differences in wood density in the different logs after 12 years of decay (0.126–0.224 g cm−3 ; i.e. 28–50% of
the original mass remained, corresponding to decay class 4–2 on
the traditional knife 5-class decay scale). Most likely, the underlying mechanism that accounts for these differences is based on
the composition of the fungal communities. In general, the less
decayed logs had lower moisture content, were still dominated
by OTU assigned as a highly common species (Kubartova et al.
2012) and displayed high within-log community diversity. The
most decayed logs had a high moisture content (above 75%), low
OTU richness, were dominated by white-rot OTUs and the community structure became more uniform within the whole logs,
whereas among-log variability increased.
The least decayed logs were those dominated by H. parviporum, a root rot fungus pathogenic to living trees, known as early
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FEMS Microbiology Ecology, 2015, Vol. 91, No. 5
Table 3. Significance of sample position in log [horizontally among discs 1–3 and among inner and outer samples (In x out)], sample wood
density and moisture content on fungal community composition, determined using permutation tests following CCA. Significant P-values
(≤0.05) are highlighted in bold, based on 999 permutations. The logs are named after the dominant OTU or named after the inoculated species
in the case of the inoculated logs (see the text).
In × out
Disc
H. parviporum
dominated logs
R. bicolor
dominated logs
Logs dominated
by other species
Density
Moisture
Log
F
P-value
F
P-value
F
P-value
F
P-value
Het 1
Het 2
Het 3
Res in1
Res in2
Res 1
Res 2
Fom in1
Fom in2
Con
Kne
Jun
0.99
1.14
0.93
1.16
1.06
1.17
1.38
1.60
0.66
1.18
1.22
0.96
0.494
0.242
0.489
0.288
0.362
0.170
0.122
0.025
0.817
0.212
0.091
0.492
2.23
2.67
6.95
1.86
2.25
1.08
0.43
0.99
4.11
1.73
1.49
1.42
0.001
0.005
0.001
0.059
0.014
0.332
0.888
0.009
0.001
0.069
0.025
0.128
1.85
2.14
1.50
2.07
1.73
1.73
1.03
2.05
0.16
1.31
1.29
0.78
0.006
0.014
0.170
0.034
0.099
0.079
0.343
0.082
0.977
0.170
0.159
0.728
2.78
3.27
4.24
2.21
2.27
2.28
1.97
1.12
5.70
3.54
1.41
2.31
0.001
0.001
0.001
0.021
0.016
0.007
0.116
0.304
0.001
0.001
0.099
0.120
Notes: Log names: Het 1, Het 2, Het 3 – logs dominated by Heterobasidion parviporum; Res in1 and Res in2 – logs inoculated by Resinicium bicolor; Res 1 and Res 2 – logs
dominated by Resinicium bicolor; Fom in1 and Fom in2 – logs inoculated by Fomitopsis pinicola; Con, Kne, Jun – logs dominated by Conferticium ochraceum, Kneiffiella alutacea
and Junghuhnia luteoalba.
successional species (Korhonen and Stenlid 1998; Olson et al.
2012). Each of the logs was colonized by a different H. parviporum genet, which confirmed that the logs had been infected by
air-borne spores and that the logs had not been infected prior to
the start of the experiment. The presence of a single genet per
log (or two genets in one case) indicated that the species can successfully colonize and establish in a whole log from two spores
forming a heterokaryon in a freshly cut log which consequently
results in a low rate of decay in the whole log.
Another species that colonized all the logs and was the dominant OTU in several of the logs was R. bicolor, a strong competitor
and a slow decayer, able to respond to enrichment disturbances
such as fresh woody substrates (Kirby, Stenlid and Holdenrieder
1990; Holmer and Stenlid 1993, 1996). In two logs, it occupied significantly more wood than any of the other dominating OTUs in
the other logs. One of these two logs was the most decayed of all
the surveyed logs; the second, R. bicolor-inoculated log was the
least decayed of the four R. bicolor-dominated logs. These different levels of decay might be explained by the presence of a powerful decayer at an earlier stage of decay in the more decayed log.
Conversely, in the inoculated log, R. bicolor had possibly become
well established at the start of the experiment, preventing other
early-decay species from becoming abundant and decaying the
wood effectively. Resinicium bicolor is known to establish naturally both by mycelial cords growing along the forest floor and
by basidiospores (Kirby, Stenlid and Holdenrieder 1990). In the
latter case, numerous competing genets in a log would result in
a decreased rate of decay. We did not attempt to identify genets
of R. bicolor and, hence, the possible influence of its population
structure on the decay rate remains unresolved.
Fomitopsis pinicola was abundant only in the inoculated logs
although it was present in all the logs. It is known as early successional species and not a strong competitor (Holmer, Renvall and Stenlid 1997; Holmer and Stenlid 1997). This might
also partly explain the high level of OTU richness in the two F.
pinicola-inoculated logs. A second contributing factor behind the
OTU richness in these logs was that these logs were in a transition phase between an intermediate and an advanced stage
of decay (about 40% of remaining mass), and hence the log environment was suitable for species of both stages. Fomitopsis
pinicola is a more efficient decayer than H. parviporum, as con-
firmed by the lower wood density in the F. pinicola-inoculated
logs.
The logs dominated by C. ochraceum, K. alutacea and J. luteoalba
were the most decayed ones. The earlier colonizers had probably declined and OTU richness had decreased. The K. alutaceaand J. luteoalba-dominated logs were initially brown-rotted and
thus more decayed than the white-rotted C. ochraceum log (FT-IR
spectroscopy data, not shown). White-rot species that follow a
brown-rot species are able to exploit the remaining lignin resulting in an acceleration in the rate of decay following the species
replacement (Renvall 1995). These three OTUs were all present
in all 12 logs and were detected even in the least decayed samples, although at low levels of abundance, which indicated that
they had been present in the wood since an earlier stage of decay and were not newly colonizing the logs from the surrounding
environment.
Community richness, composition and density loss
relationships
It has been hypothesized that greater species richness would result in an enhanced decay rate because of functional niche complementarity, enzyme variability and more intense resource exploitation. However, although such an effect has been observed,
mainly under laboratory conditions in species-poor communities, it has been less clear or negative in more diverse communities (Ekschmitt et al. 2001; Loreau et al. 2001; Hattenschwiler,
Tiunov and Scheu 2005; Gessner et al. 2010; Hattenschwiler,
Fromin and Barantal 2011; Nielsen et al. 2011; van der Wal et al.
2013). In a recent 454-sequencing-based study, OTU richness per
sample was positively related with sapwood decay in five years
old oak stumps (van der Wal, Ottosson and de Boer 2015). In
the present study, OTU richness and the Simpson diversity index were not correlated with sample density loss. Moreover,
species richness or diversity indexes weight species equally regardless of their functional diversity or phylogenetic relatedness (Cadotte, Albert and Walker 2013). Indeed, the results of
this study confirmed that community composition influenced
the decay rate more than the OTU richness itself.
The communities in all logs were dominated by a few
highly abundant OTUs, whereas the numerous other OTUs were
Kubartová et al.
infrequent or scarce; hence, the highly abundant OTUs were also
the key OTUs responsible for overall log density loss, in accordance with the expectations of Boddy (2001) and Vetrovsky et al.
(2011). Moderate changes in community composition are likely
to have a rather minor effect on density loss compared with
the replacement of the dominant OTU or a few highly abundant
OTUs. The OTUs with a low level of prevalence might occupy
specific niches and, thus, not compete with the other OTUs, or
they might be functionally redundant with other OTUs, allocating energy to competitive interactions to enable them to survive
instead of to grow and spread. At a within-log scale, the effect
of community composition on density loss decreased as the decay process proceeded. However, the increased community variation observed among the most decayed logs might contribute
to variability in the decomposition dynamics at a larger scale
within the forest stand.
If community composition is partially the product of species
interactions (besides neutral processes), closely related and/or
functionally similar species should hardly co-exist (Chesson
2000; Crisp and Cook 2012). The two most abundant OTUs (accounting for between 45 and 86% of the sequences) did not belong to the same order (Table S3, Supporting Information) in
any of the studied logs, although they were often both whiterot fungi. This could indicate competitive exclusion among close
relatives.
The first set of 12 logs created at this field site was sampled
after six years of decay (Lindner et al. 2011). That study used isolation and DNA-cloning techniques which has to be considered
when comparing the results. Four logs inoculated by R. bicolor,
four logs inoculated by F. pinicola and four un-inoculated control
logs were sampled in six years. Inoculation with F. pinicola led to
significantly greater mass loss (to average density 0.225 g cm−3 ,
50% of the original mass decayed, compared to the least decayed
R. bicolor logs with 0.256 g cm−3 , 39%) and lowest richness (19
OTUs per log, compared to 25 and 51 OTUs in R. bicolor and control logs, respectively). It is in contrast with the 12-years sampling and confirms the decline of F. pinicola in the inoculated logs.
Resinicium bicolor was present in both control and inoculated
logs in both samplings, confirming its high colonization potential. Calocera viscosa, Botryobasidium subcoronatum and Sistotrema
brinkmannii were abundant in the control logs in six years, while
H. parviporum was detected in R. bicolor inoculated logs only. It
was in contrast to the 12-years data where this species was detected in all 12 logs and dominated three of the eight control
logs.
Wood density decreased more rapidly during the first six
years, resulting in a density of 0.204–0.273 g cm−3 in individual
logs, corresponding to 55–40% of the original mass decayed. During the next six years, the wood decayed by a further 32% (maximum), if calculated as the difference between the least decayed
log after six years and the most decayed log after 12 years (0.126–
0.224 g cm−3 ). The density of the least decayed logs after 12 years
was comparable to the density of the most decayed log after six
years of decay (0.224 and 0.204 g cm−3 , respectively, see above).
Given that all 24 experimental logs were decomposed in the
same environmental context, this study showed that the fungal communities had a pronounced impact on the wood-decay
rate.
Decomposition of organic substrates represents an important part of the global carbon cycle and affects global change
through CO2 release. Implementing fungal community composition to carbon dynamics models would made carbon sequestration estimates more reliable, when compared to models based
on chemical properties and environmental factors only. Though,
9
it would be complicated to include the whole fungal community in such models. Luckily enough, their reliability could be
highly improved even if implementing only the most abundant
species that govern the density loss. Identifying fungi responsible for high wood-decay rates might also help to find enzymes that would facilitate production of lignocellulose-derived
biofuels.
SUPPLEMENTARY DATA
Supplementary data is available at FEMSEC online.
ACKNOWLEDGEMENTS
We are also thankful to Hanna Johannesson, Mårten Gustafsson
and Lillian Holmer for helping with the initial inoculation and
placement of logs and to Tomáš Kubart for helping with the R
scripts.
FUNDING
The financial support from a SLU excellence grant to J.S. is acknowledged. E. och T. Westins stiftelse för lantbruksforskning
provided fellowships to A.K.
DATA ACCESSIBILITY
DNA sequence assembly, OTU identifications, OTU distribution
matrix and sample data, tag and primer sequences and raw-data
sequencing .sff files are uploaded as online supporting information. The sequences are also deposited in GenBank with accession numbers KM492941—KM493490.
AUTHOR CONTRIBUTION
AK contributed to field sampling, laboratory work, data analyses and manuscript writing. EO contributed to field sampling,
laboratory work, OTU identifications and ecology, figures, result
discussions and manuscript reading. JS contributed to field sampling, result discussions and manuscript reading.
Conflict of interest. None declared.
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