Introduction
Forest plantations are widely used to mitigate carbon dioxide (CO2)
concentrations in the atmosphere (IPCC, 2013). Worldwide, about 68 000 ha
of non-forest land have been planted to forest during the past 15 years,
which has been recognised as a management tool for stocking carbon (C) in
soils, thereby contributing toward climate change mitigation (Keenan et al.,
2015). Afforestation with different tree species has been accepted as an
effective measure for increasing soil C stocks. However, considering the
demand for the timber of certain tree species and to optimise the
productivity of stemwood, forest farmers usually select certain tree species
such as coniferous tree species (Lu et al., 2012). The effects of
afforestation with different tree species on soil C stocks have been widely
studied (Nave et al., 2013; Vesterdal et al., 2013), showing that different
tree species can greatly affect soil C sequestration (Vesterdal et al.,
2013). On the other hand, the sustainability of afforestation has received
widespread attention. It is necessary to investigate the effects of
afforestation with different tree species on soil microbial diversity and
ecosystem functions, such as methane (CH4) oxidation capacity, as these
functions drive soil biogeochemical cycling. However, the community assembly
of complex soil microorganisms remains largely unexplored at a fine taxonomic
resolution, although these microorganisms catalyse soil functional processes
in close association with soil C transformation.
A large fraction of soil C and nitrogen (N) contents are arguably derived
from tree litter, and thus differences in the chemistry of plant traits may
be a key driving force in shaping the abundance and composition of soil
bacterial and fungal communities to various extents (Prescott and Grayston,
2013). In fact, strong correlations between microbial communities and
dominant trees have been observed, implying that the composition of fungal and
bacterial communities in both soil and forest litter is largely determined by
the tree plantation (Urbanová et al., 2015). In turn, microbial
communities are responsible for organic matter mineralisation and providing
nutrients for tree growth, and are thus an integral component of global C and
N cycles. Generally, soil with higher C : N ratios under different tree
species has more recalcitrant biopolymers, which are represented by the
polysaccharides and cellulose that are used by decomposer microorganisms.
Because of their filamentous form, fungi tend to be more involved in the
decomposition of polymeric compounds (de Boer et al., 2005). Bacteria that
preferentially use organic compounds with a low molecular mass may rely on
the products of fungal-biopolymer decomposition for nutrition (Tedersoo et
al., 2008). Soils with lower C : N ratios, which tend to have more organic
compounds with a low molecular mass, have more bacteria than fungi. Although
much research has focused on the effects of tree plantations on soil C
storage, as an overall measure for soil ecosystem services, the ecologically
important processes of microbial functional guilds are rarely investigated
because of the low resolution of conventional culture-dependent techniques.
Atmospheric CH4 is the second most important greenhouse gas after
CO2, contributing about 25 % to global warming, and CH4 has
about 25–30 times the warming potential of CO2 on a molecular basis.
Therefore, CH4 productions and oxidation in soils have received
widespread attention (IPCC, 2013). Upland forest soils mostly function as
significant CH4 sinks (Kolb, 2009), but CH4 oxidation capacity in
these soils is sensitive to land uses such as afforestation with different
tree species (Tate, 2015). Soil CH4-oxidising bacteria are considered to
be the sole catalyst for consumption of CH4 in the atmosphere by forest
soils (Knief et al., 2005). Intriguingly, the diversity of CH4-oxidising
bacteria appears to explain the CH4 oxidation capacity in grassland
ecosystems (Zhou et al., 2008) or in forest ecosystems (Levine et al., 2011).
However, these studies are largely based on the correlation of flux
measurements to functional biomarker pmoA genes encoding particulate
CH4 monooxygenase in various soils, and these generalised conclusions
warrant further investigation. Furthermore, the underlying mechanisms of how
abiotic factors select for specific microbial taxa at different taxonomic
ranks remains largely uncertain.
Previous studies have shown that the community structure and diversity of
soil microorganisms are mainly influenced by soil pH (Rousk et al., 2010),
soil organic C (Sul et al., 2013) and/or soil nutrient concentrations (Lauber
et al., 2008). Previously we introduced the key factor limitation hypothesis
to explain the differences in soil microbial diversity and found that, in a
given habitat, when a certain key influencing factor becomes more and more
constrained, microbial diversity will increase (Zhou et al., 2011). Here, we
selected 78-year-old tree plantations that were developed on the same soil
parent material in order to test whether soil microbial diversity increased
or not under progressively limited conditions of key physiochemical factors
resulting from different tree species regimes. We used bacterial-specific
primers and eukaryotic-specific primers to investigate changes in the
community structure and diversity of soil bacteria and fungi, respectively,
in response to long-term afforestation with different tree species.
Soil physiochemical properties in 78-year-old forest plantations
with different tree species.
Properties
Slash pine
Hoop pine
Kauri pine
Eucalyptus
Moisture (g kg-1 dry soil)
42.6 ± 2.2b
31.1 ± 5.3b
30.9 ± 6.7b
76.9 ± 16.6a
pH
4.58 ± 0.03b
5.64 ± 0.22a
6.01 ± 0.23a
4.49 ± 0.04b
Total C (g kg-1)
13.81 ± 0.81b
10.13 ± 1.52b
8.92 ± 1.57b
26.11 ± 4.37a
Total N (g kg-1)
0.44 ± 0.03b
0.43 ± 0.05b
0.42 ± 0.09b
0.87 ± 0.14a
C : N
31.8 ± 0.7a
23.1 ± 1.3b
21.2 ± 0.7b
29.8 ± 0.6a
EOC (mg kg-1)
340 ± 41b
341 ± 31b
360 ± 30b
625 ± 77a
EON (mg kg-1)
14.7 ± 2.9b
18.4 ± 1.9ab
23.1 ± 1.5a
22.4 ± 1.8a
EOC : EON
24.17 ± 1.81a
18.79 ± 1.48b
15.66 ± 0.87b
27.64 ± 1.64a
C, carbon; N, nitrogen; EOC, extractable organic C; EON, extractable organic
N. Different letters in the same row indicate significant differences at
P<0.05 between tree species.
Based on these hypothesis, the objectives of this study were to
(1) investigate the selective effects of long-term tree plantations on the
community assembly of soil bacteria and eukaryotes; (2) elucidate the
functional efficiency of soil CH4 oxidation processes and
CH4-oxidising bacterial communities under different tree species; and
(3) determine the key factors influencing the community structure and
diversity of soil bacteria and eukaryotes under different tree species.
Materials and methods
Experimental site
We selected a 78-year-old forest plantation with different tree species which
was established in 1935 on a site that was originally a banana farm. The
forest plantation site is located at Cooloola, Tin Can Bay, Southeast
Queensland, Australia (25∘56′49′′ S, 153∘5′27′′ E).
The altitude is 43 m above sea level with a mean annual rainfall of 1287 mm.
Winter temperatures range from 7 to 23 ∘C over June–August and
summer temperatures range from 18 to 30 ∘C over December–February
(Lu et al., 2012). Four tree species were selected, including an exotic
coniferous species (slash pine, Pinus elliottii Engelm. Var.
elliottii) and two native conifer species (hoop pine, Araucaria cunninghamii Ait, and kauri pine, Agathis robusta C. Moore,), as
well as a Eucalyptus species (Eucalyptus grandis W Hill ex
Maiden). All of them were planted adjacently on a broad, gently undulating
plain with a gentle slope of less than 5∘. The plot size of each tree
species was 1.087, 0.308, 0.428 and 0.60 ha, respectively (Lu et al., 2012).
Four subplots of 10 m × 20 m in each tree plantation were randomly
selected for soil sampling, resulting in a total of 16 subplots. The
thicknesses of the litter and fermentation layers were 5–6 and 1–2 cm for
slash pine, respectively, whereas the corresponding values were 4–5 cm and
1–2 cm for the hoop pine and kauri pine plots. The Eucalyptus plot
had a thicker litter layer of 8–10 cm and a similarly thick fermentation
layer of 1–2 cm.
Soil sampling and measurement of soil physiochemical properties
Soil samples were collected in August 2013 using a diagonal sampling pattern
(i.e. one point at each corner and one in the centre of each plot) using a
soil auger (8 cm in diameter) at 0–10 cm depth within each plot. The soil
cores were immediately mixed thoroughly and kept in a cooler (4 ∘C).
After passing the samples through a 2 mm sieve to remove roots and stones,
the soil samples were stored at 4 ∘C prior to analysis. Part of each
fresh sample was stored at 4 ∘C for analysis of soil moisture and
pH. The other parts were air dried and stored at room temperature for soil
extractable organic C (EOC) and N (EON) analysis via hot water extraction
(X. Q. Zhou et al., 2013). After air-dried soil samples were finely ground,
soil total C and N contents were determined using an Isoprime isotope ratio
mass spectrometer with a Eurovector elemental analyzer (Isoprime-EuroEA 3000)
(Zhou et al., 2012). Soil moisture content was determined after samples were
oven dried at 105 ∘C overnight. The particle size of these soils was
dominated by the sand fraction (∼ 96 %). All soil biochemical
properties are shown in Table 1.
Soil genomic DNA extraction and amplification
Nucleic acids were extracted from 0.5 g of each sample by using the
procedures described previously elsewhere (Zhou et al., 2010). In brief, soil
was placed in a 2 mL screwcap tube containing a mixture of ceramic and
silica particles (Bio101, Carlsbad, Calif.); the mixture was homogenised for
30 s in a FastPrep bead beater cell disrupter (Bio101). After the nucleic
acids had been precipitated and washed twice in 75 % (vol / vol)
ethanol, the final DNA was resuspended in 100 µL double-distilled
water. Furthermore, the crude extract was purified with the QIAquick Gel
Extraction Kit (Qiagen Inc, Shanghai, China). DNA concentrations were
measured with a Biospec-mini spectrophotometer (Shimadzu, Kyoto, Japan).
Selected statistics of the Miseq sequencing output from 78-year-old
forest plantations with different tree species.
Treatment
Slash pine
Hoop pine
Kauri pine
Eucalyptus
Total numbers of prokaryotes sequences
79 665 ± 22 306b
86 640 ± 4379b
83 370 ± 3207b
116 776 ± 1812a
Bacteria (%) of total sequences
99.73 ± 0.08a
99.55 ± 0.04b
99.49 ± 0.09b
99.75 ± 0.03a
Archaea (%) of total sequences
0b
0b
0b
0.02 ± 0.01a
Unclassified (%) of bacterial sequences
0.27 ± 0.04b
0.45 ± 0.04a
0.51 ± 0.09a
0.23 ± 0.02b
Total numbers of Eukaryotes
57 768 ± 10 653
65 719 ± 5317
58 195 ± 6964
64 436 ± 5930
Different letters indicate significant differences at P<0.05
between the treatments.
The bacterial-specific primers of 530F and 907R (Poulsen et al., 2013) were
used to amplify the bacterial 16S rRNA gene and the eukaryotic-specific
primers of Euk528F–Euk706R (Urbanová et al., 2015) were used to amplify
the eukaryotic 18S rRNA gene. The final concentration of different components
in the polymerase chain reaction (PCR) amplification mixture was as follows:
0.4 µM of each primer, 200 µM of each deoxynucleoside
triphosphate, 1.5 mM MgCl2, 1 × thermophilic DNA polymerase
and 10 × reaction buffer (MgCl2-free), 1.25 U per
50 µL of Taq DNA polymerase (Promega, Madison, WI), and DNAse- and
RNAse-free filter-sterilised water. All reactions were performed in a
PTC-200 thermal cycler (MJ Research Co., New York, USA). The PCR cycle
conditions were an initial denaturation/activation of the hot start
polymerase at 98 ∘C for 30 s, followed by 30 cycles of denaturation
at 98 ∘C for 5 s, annealing at 53 ∘C for 20 s and
elongation at 72 ∘C for 20s, and then a final elongation step at
72 ∘C for 5 min. The PCR products were run on a gel and the
appropriate fragments were cut and purified using Qiagen Gel Extraction kit
(Qiagen). The amplicons were sequenced on an Illumina Miseq machine
(Illumina, Nanjing, China).
High-throughput sequencing data analysis and statistical
analysis
The sequence data were processed mainly using QIIME (Caporaso et al., 2010)
and UPARSE (Edgar, 2013). The default filter settings were used: no ambiguous
bases were allowed (Huse et al., 2007) and the minimum read length was 150 bp.
The raw sequences were demultiplexed by using unique barcodes that allowed no
mismatch. Pair-end data of each sample were joined with FLASH (Magoč and
Salzberg, 2011) using the default parameters. Reads with average quality
scores below 20 were discarded. The high-quality reads were clustered at
similarity of 97 % to generate operational taxonomic units (OTUs) using
UPARSE. Simultaneously, chimeras were filtered. For each OTU, one
representative sequence was picked. The Shannon–Wiener index, evenness and
chaos scores of soil bacteria and eukaryotes of each tree species were
calculated based on OTU data. A taxonomic annotation was assigned to each
representative sequence via the assign_taxonomy.py tool in QIIME, using
SILVA 123 (Quast et al., 2013) as a reference database. To avoid any
potential uncertainty caused by sampling error (J. Zhou et al., 2013), each
sample was rarefied to the same sequence number: 39 480 reads for 16S rRNA
and 28 967 reads for 18S rRNA. The Pearson correlation between soil
physiochemical properties and microbial diversity was calculated. All
results were tested statistically using ANOVA and Tukey's test, and
significance was considered to be at P<0.05 for the tree species
treatments.
Rarefaction curves were plotted to compare α diversity at different
sequencing depths. The Bray–Curtis distance was calculated, and principal
coordinate analysis (PcoA) was applied to the distance matrix. Canonical
correlation analysis (CCA) was used to elucidate the relationship between
soil physiochemical properties and the patterns of bacterial and eukaryotic
community structure among the tree species treatments. The CCA was performed
with the vegan package in R software. Structural equation modelling
was performed using R software with the lavaan package to explore
the causal links between soil pH and nutrient quality, as well as soil
bacterial and eukaryotic diversities. The model considered soil pH, C : N,
EOC : EON and microbial diversity. We considered that path analysis was
most appropriate for data sets with large sample sizes but that the samples
for the number of variables in every model were too small (n=16).
However, small sample sizes generally result in conservative fitting
estimates (Shipley, 2000). In structural equation modelling, a χ2 test
is used to determine whether the covariance structures implied by the model
adequately fit the actual covariance structures of the data. A
non-significant χ2 test (P>0.05) indicates an adequate model
fit. The coefficients of each path, taken as the calculated standardised
coefficients, were determined by analysing the correlation matrices. Paths in
this model were considered to be significant at P<0.05. These
coefficients indicate by how many standard deviations the effect variable
would change if the causal variable was changed by 1 standard deviation
(Shipley, 2000).
Estimation of operational taxonomical units (OTUs), Shannon
diversity index (H), evenness and chaos scores of soil bacterial and
eukaryotes in 78-year-old forest plantations with different tree species.
Slash pine
Hoop pine
Kauri pine
Eucalyptus
Bacteria
OTUs
1400 ± 146b
2486 ± 175a
2751 ± 151a
1538 ± 37b
H
6.89 ± 0.12b
8.33 ± 0.38a
8.92 ± 0.23a
6.87 ± 0.11b
Evenness
0.9747 ± 0.0042b
0.9883 ± 0.0035a
0.9937 ± 0.0011a
0.9768 ± 0.0022b
Chaos
1934 ± 140b
2968 ± 172a
3211 ± 132a
2084 ± 79b
Eukaryotes
OTUs
528 ± 24c
792 ± 53ab
891 ± 46a
768 ± 8.7b
H
4.61 ± 0.19b
4.84 ± 0.35b
5.82 ± 0.35a
5.17 ± 0.21ab
Evenness
0.9027 ± 0.0131ab
0.8838 ± 0.0232b
0.9392 ± 0.0143a
0.9089 ± 0.0221ab
Chaos
705 ± 29b
1001 ± 72a
1069 ± 55a
932 ± 15ab
Different letters within each row indicate significant differences at
P<0.05.
Results
Soil bacterial and eukaryotic community structure and diversity
The total number of bacterial sequences and their relative abundance in soils
under different tree species are shown in Table 2. Of the total number of
sequences from each treatment, only a small percentage (< 1 %) were
unclassified or from Archaeal taxa, whereas the majority (> 99 %)
belonged to bacterial taxa. The bacterial sequences at different phylogenetic
levels revealed that the number of orders and families represented an
unimodal mode, first increasing and then decreasing with increasing
numbers of sequences, whereas the number of genera show a relatively flat
curve (Fig. S1 in the Supplement).
Kauri pine and hoop pine had a significantly higher bacterial Shannon
diversity index than slash pine and Eucalyptus, whereas kauri pine
and hoop pine had significantly higher eukaryotic diversity indices than
slash pine, and there were no significant differences in soil eukaryotic
diversity index between slash pine and Eucalyptus (Table 3). We
calculated the mean OTUs for each tree species and the results are shown in
Fig. S2. For soil bacterial taxa, kauri pine had the highest bacterial OTUs,
followed by hoop pine, while Eucalyptus and slash pine had the
lowest bacterial OTUs. Kauri pine also had high soil eukaryotic OTUs,
followed by hoop pine and Eucalyptus, whereas slash pine had the
lowest soil eukaryotic OTUs.
Through PcoA analysis of soil bacterial taxa, we found that PcoA1 and PcoA2
explained 61.4 and 9.8 % of the variations in soil bacterial communities,
respectively, for the tree species treatments (Fig. S3a). Slash pine and
Eucalyptus formed a cluster, which was clearly separated from the
cluster of hoop pine and kauri pine. In addition, PcoA1 and PcoA2 explained
20.5 and 19.1 % of the soil eukaryotic community, respectively, for the
tree species treatments (Fig. S3b). There was a clear separation between slash
pine, kauri pine and Eucalyptus, whereas hoop pine did not have a
clear separation from the other coniferous tree species.
Relative abundance of soil bacteria and eukaryotes
The relative abundance of specific bacterial taxa in the samples was
investigated at different taxonomical levels (phyla and genera). The most
frequent phyla for all treatments were Acidobacteria (32–58 %),
Proteobacteria (19–36 %), Actinobacteria (15–19 %) and
Planctomycetes (5–10 %), followed by varying occurrence of
WD272, Firmicutes, Bacteroidetes, Cyanobacteria, Armatimonadetes and
Chloroflexi (Fig. 1). Long-term tree plantations apparently imposed strong
selection on the microbial community structure. Slash pine and
Eucalyptus had significantly higher Acidobacteria, and
WD272, and markedly higher Actinobacteria proportions than hoop pine
and kauri pine, whereas Alphaproteobacteria and Planctomycetes,
Bacteroidetes, Armatimonadetes, Chloroflexi, Verrucomicrobia,
Gemmatimonadetes and Nitrospirae were apparently stimulated under slash pine
and Eucalyptus (Fig. 2).
Most abundant bacterial phyla in soils under different tree
species.
By comparison, eukaryotic taxa in the samples came from five major phyla and
eight major genera (Fig. 3). Slash pine and Eucalyptus had
significantly higher proportions of Ascomycota fungi (59.1–61.6 %) than
hoop pine (45.0 %) and kauri pine (52.1 %) (Fig. 3a).
Eucalyptus had a significantly higher relative abundance of
Leotiomycetes spp. (22.4 %) than the other tree species, whereas
slash pine had a significantly higher relative abundance of
Pezizomycotina spp. (18.9 %) than the other tree species
(Fig. 3b). Hoop pine had significantly higher unassigned phyla (23.0 %)
and unassigned genera (22.9 %) than the other three treatments (Fig. 3).
Most abundant bacterial genera in soils under different tree
species.
Most abundant eukaryotic phyla (a) and genera (b) in soils under
different tree species.
Key factors influencing soil bacterial and eukaryotic
communities
CCA eigenvalues indicated that Axes 1 and 2 accounted for 59.2 and 10.3 %
of the overall variance of soil bacterial communities (Fig. 4a), whereas
Axes 1 and 2 explained 20.0 and 15.0 % of the overall variance in soil
eukaryotic communities (Fig. 4b). The species–environment correlations of
soil bacterial and eukaryotic taxa were 0.94 and 0.95, respectively,
indicating that soil microbial community patterns were strongly correlated
with soil physiochemical properties. The arrows for soil pH, C : N and EOC : EON
were longer than those of the other variables, indicating that these factors
accounted for the greatest proportion of the variance in soil bacterial taxa
among different tree species treatments. Similarly, soil pH, C : N and EOC : EON
accounted for the greatest proportion of variance in soil eukaryotic
community patterns.
Canonical correlation analysis (CCA) of the relationships between
soil physiochemical properties and bacterial (a) and
eukaryotic (b)
communities under different tree species.
The soil bacterial community patterns under slash pine and under
Eucalyptus were mainly influenced by C : N and EOC : EON,
whereas those under hoop pine and under kauri pine were largely influenced by
pH and EON. Soil eukaryotic communities under Eucalyptus were mainly
influenced by TC, TN and EOC, whereas those under hoop pine and under kauri
pine were largely influenced by pH and EON (Fig. 4). We calculated the
correlation between the key physiochemical properties and the soil bacterial
and eukaryotic diversity index, and found that there was a negative
relationship between soil pH and the bacterial and eukaryotic diversity
indices, whereas soil C : N and EOC : EON had a positive influence on the
soil bacterial and eukaryotic diversity index (Table 4). Through calculating
the relative contributions of soil pH and nutrient quality, such as C : N
ratios and EOC : EON ratios, to soil bacterial and eukaryotic diversities,
we found that soil pH played a predominant role in determining soil microbial
diversity (Fig. 5).
Path diagrams representing the final model showing the
contributions of soil pH and nutrient quality to soil bacterial diversity (a)
and eukaryotic diversity (b) under different tree species. Numbers
associated with single-headed arrows are partial regression coefficients of
multiple regressions. C : N, ratio of soil total C to total N contents;
EOC : EON, ratio of soil extractable organic C to extractable organic N
contents. Solid arrows denote the directions and effects that were
significant (P<0.05); the numbers on these pathways are the
coefficients. Dashed arrows represent the directions and effects that were
not significant (P>0.05). ∗, ∗∗ and
∗∗∗ indicate significant differences at P<0.05, P<0.01 and P<0.001, respectively.
Correlation between key physiochemical factors and operational
taxonomical units (OTUs) numbers, Shannon diversity index (H) and evenness
of soil bacteria and eukaryotes in 78-year-old forest plantations with
different tree species.
r
Bacterial OTUs
Bacterial H
Bacterial evenness
Eukaryotic OTUs
Eukaryotic H
Eukaryotic evenness
C : N
-0.951*
-0.948*
-0.856*
-0.755*
-0.571*
-0.287
EOC : EON
-0.831*
-0.869*
-0.742*
-0.49
-0.446
-0.261
pH
0.949*
0.962*
0.852*
0.709*
0.619*
0.394
∗ Indicates significance at P<0.01.
C, carbon; EOC, extractable organic carbon; EON, extractable organic
nitrogen; N, nitrogen.
Methanotrophic activities and community composition
Eucalyptus had the highest potential methanotrophic activity, which
was significantly higher than that of hoop pine and kauri pine (Fig. 5).
There were no significant differences in potential methanotrophic activity
between slash pine and Eucalyptus. As revealed by high-throughput
sequencing, methanotrophic communities were dominated by members of the
Methylosinus genus. Slash pine and Eucalyptus had a
significantly higher abundance of Methylosinus spp. than hoop pine
and kauri pine. Apart from Methylosinus, we also detected other
methylotrophic genera: Methylophilum, Microvirga and
Candidatus methylacidiphilum. Given that the Methylosinus
genus is a group of high-affinity methanotrophs that can use CH4 at
extremely low levels of appropriately 2.0 parts per million
(volume / volume) in the atmosphere (Kolb, 2009), they may play an
important role as a biological sink of atmospheric CH4 in this region.
Discussion
Afforestation is a useful management practice for mitigating CO2
emissions derived from anthropogenic activities, and many studies have
reported that afforestation can increase soil organic C contents (Nave et
al., 2013). In this study, the adjacent plantation forests have relatively
flat terrain that was developed from the same parent soil, so the microbial
divergence of community assemblies in forest soils was considered to be a net
effect resulting from the niche segregation imposed by 78 years of being
planted to different tree species. Our results provide compelling evidence
for how the microbial community structure depends on soil pH and nutrient
quality such as C : N ratios and EOC : EON ratios, which are largely
determined by tree plantations.
Community structure and relative abundance of soil bacteria and
eukaryotes under tree species
Trees may influence soil microbial communities through direct effects such as
root exudation and interactions with root symbiosis and root-associated
microorganisms, or through indirect effects such as litter production and
alteration of the microclimate (e.g. temperature and moisture) (Prescott and
Grayston, 2013). Regardless of the direct or indirect pathways, the specific
tree species plays an important role in determining community structure and
the abundance of soil microorganisms (Urbanová et al., 2015). In this
study, we found that slash pine and Eucalyptus had significantly
lower soil pH than hoop pine and kauri pine, which may favour the
significantly higher relative abundance of Acidobacteria (Fig. 1). Given that
Acidobacteria are considered to be indicators of soil pH status (Lauber et
al., 2009), soil pH at the study site (below 6.5) could be considered acidic
(Table 1), which was supported by the dominant Acidobacteria phylum in soil
bacterial communities across the tree species treatments (Fig. 1). The
relative abundance of Acidobacteria in this study was higher than those in
other alkaline soils of terrestrial ecosystems (Rousk et al., 2010;
Urbanová et al., 2015).
Similarly, slash pine and Eucalyptus seem to favour a higher relative
abundance of Ascomycota fungi than hoop pine and kauri pine (Fig. 3), which
could be explained by lower soil pH and higher C : N ratios (Fig. 4).
Previous studies have stated that fungal, rather than bacterial, communities
are more subject to stronger selection pressure under different tree
plantation regimes (de Boer et al., 2005). A reason for this could be that
the root-associated filamentous fungi may extend from the tree rhizosphere
and carry the legacy of plant traits to the bulk soil. Filamentous fungi are
stronger decomposers of litter production with lower nutrient quality and can
better cope with environmental stresses such as low pH and a dry environment
with limited connectivity (de Boer et al., 2005; Urbanová et al., 2015).
It has been reported that soil microbial communities are strongly influenced
by soil pH, soil organic C contents or the C : N ratio in terrestrial
ecosystems (Fierer and Jackson, 2006; Fierer et al., 2009; Rousk et al.,
2010). However, we found that EOC : EON ratios could also result in
differences in soil bacterial and eukaryotic communities between the tree
species treatments (Fig. 4), in addition to soil pH and C : N ratios as
previously demonstrated. It thus seems plausible that microorganisms with a
specific life strategy for labile C and N use could have been preferentially
stimulated. Meanwhile, it should be emphasised that a large fraction of
Acidobacteria remained unculturable, and our results provide strong hints
towards substrate optimisation to cultivate the as-yet-uncultivated microbes
in soils.
Diversity of soil bacteria and eukaryotes under tree species
The negative correlation between the EOC : EON ratio and the microbial
diversity index provides further support for our key factor limitation
hypothesis, namely that when the key factors influencing soil microbial
communities become limiting, microbial diversity will increase (Zhou et al.,
2011). A small fraction of microorganisms might have propagated rapidly as a
result of the elevation of easily available EOC in slash pine and
Eucalyptus plantations. This unbalanced growth of microbial
communities thus resulted in significantly lower bacterial OTU numbers,
Shannon diversity index and evenness than hoop pine and kauri pine (Table 3).
This discrepancy could have resulted from anthropogenic enrichment via these
78-year-old tree plantations.
However, it could not be ruled out that other key soil physiochemical factors
may play a substantial role in determining the soil microbial community
patterns (Fig. 4). The relationships between soil microbial diversity and
these key factors were calculated. We found that nutrient quality indicators
such as C : N ratios had significantly negative relationships with the soil
bacterial and eukaryotic OTU numbers and Shannon diversity index. EOC : EON
ratios had significantly negative relationships with the bacterial OTU
numbers and the diversity index, but only had a negative relationship with
the eukaryotic OTU numbers and diversity index. This could be attributed to
how the dominant eukaryotic taxa were filamentous microorganisms, which can
get nutrients from plant roots or other places via hyphae in the
heterogeneous environment. Interestingly, we found that soil pH had
significantly positive relationships with the bacterial and eukaryotic OTU
numbers and diversity index, which may outweigh the positive role of soil
nutrient quality (Fig. 5), resulting in lower soil bacterial and eukaryotic
diversity under slash pine and Eucalyptus.
Methanotrophic activity and abundance
Oxidising CH4 from the atmosphere is an important forest ecosystem
function (Tate, 2015). It is well known that soil CH4 uptake is mediated
by a specific group of soil bacteria (i.e. methane-oxidising bacteria –
methanotrophs) that can only use CH4 as their sole source of C and
energy, with only a few exceptions (Hanson and Hanson, 1996). Soil
methanotrophs can be divided into two groups (low- and high-affinity
methanotroph) based on differences in their phylogenetic affiliation, C
assimilation pathway and phospholipid fatty acid composition (Hanson and
Hanson, 1996). In forest soils, methanotrophs are known to be dominated by
high-affinity methanotrophs such as Methylosinus spp. (Kolb, 2009).
In our study, the higher Methylosinus abundances under slash pine
and Eucalyptus may be responsible for the higher potential
methanotrophic activities (Fig. 6). This is most likely to represent the
naturally occurring process of microbial CH4 oxidation under field
conditions because high-throughput sequencing of the total microbial
communities is unlikely to favour the detection of a certain functional group.
The high frequency of Methylosinus thus suggests these
microorganisms have been well adapted to CH4-depleted environments and
played an important role in atmospheric CH4 oxidation. However, the
as-yet-cultivated USCα and USCγ genera are also considered to
be important high-affinity methanotrophs in forest soils (Levine et al.,
2011). These microorganisms can be detected only through their pmoA
genes, which encode for an active subunit of the key enzyme of particulate
CH4 monooxygenase. Comparative analysis of both 16S rRNA and
pmoA genes would provide useful insights into the adaptation of
microbially mediated CH4 oxidation to long-term plantation regimes in
forested soils.
Soil potential methanotrophic activity (a) and methylotrophic
genus reads detected by high-throughput sequencing (b) under different tree
species.