Including information about soil microbial communities
into global decomposition models is critical for predicting and
understanding how ecosystem functions may shift in response to global
change. Here we combined a standardised litter bag method for estimating
decomposition rates, the Tea Bag Index (TBI), with high-throughput sequencing of
the microbial communities colonising the plant litter in the bags. Together
with students of the Federal College for Viticulture and Fruit
Growing, Klosterneuburg, Austria, acting as citizen scientists, we used this
approach to investigate the diversity of prokaryotes and fungi-colonising
recalcitrant (rooibos) and labile (green tea) plant litter buried in three
different soil types and during four seasons with the aim of (i) comparing
litter decomposition (decomposition rates (k) and stabilisation factors (S))
between soil types and seasons, (ii) comparing the microbial communities
colonising labile and recalcitrant plant litter between soil types and
seasons, and (iii) correlating microbial diversity and taxa relative abundance
patterns of colonisers with litter decomposition rates (k) and stabilisation
factors (S). Stabilisation factor (S), but not decomposition rate (k),
correlated with the season and was significantly lower in the summer,
indicating a decomposition of a larger fraction of the organic material
during the warm months. This finding highlights the necessity to include
colder seasons in the efforts of determining decomposition dynamics in order
to quantify nutrient cycling in soils accurately. With our approach, we
further showed selective colonisation of plant litter by fungal and
prokaryotic taxa sourced from the soil. The community structures of these
microbial colonisers differed most profoundly between summer and winter, and
selective enrichment of microbial orders on either rooibos or green tea
hinted at indicator taxa specialised for the primary degradation of
recalcitrant or labile organic matter, respectively. Our results
collectively demonstrate the importance of analysing decomposition dynamics
over multiple seasons and further testify to the potential of the
microbiome-resolved TBI to identify the active component of the microbial
community associated with litter decomposition.
This work demonstrates the power of the microbiome-resolved TBI to give a
holistic description of the litter decomposition process in soils.
Introduction
Litter decomposition is one of the most important terrestrial ecosystem
functions. Soil microorganisms drive this process by breaking down plant
material, leading to the release of carbon into the atmosphere as CO2
or into the soil, where it either gets sequestered or further degraded
(Talbot and Treseder, 2011; Allison et al., 2013; Cotrufo et al., 2013).
Other factors governing litter decomposition include the molecular structure
of the litter, its physical (dis)connection from the decomposer community,
and its organo–mineral associations (Schmidt et al., 2011). Litter
decomposition, therefore, directly affects both Earth's atmosphere and soil
health. Understanding this process holds the key to better agricultural
management, mitigating greenhouse gas emissions, and predicting future soil
carbon storage levels. However, because of the processes' complexity and our
inadequate understanding of how biodiversity affects it, the application of the litter
decomposition process to agricultural practices and global decomposition
models is not performing currently at its full potential. Seasonality, mainly
through temperature and moisture effects, has been demonstrated to affect
litter decomposition directly and indirectly (Prescott, 2010). Direct effects
occur due to the high sensitivity of biological processes to temperature and
water availability, and indirect effects may be the consequence of
phenotypic or community shift responses of the soil biota. Soil microbial
community structure has been hypothesised to influence process rates in
soils (McGuire and Treseder, 2010; Graham et al., 2016). In a few models
that linked microbial diversity and decomposition (e.g. MIMICS, Wieder et al.,
2014, and MEMS, Cotrufo et al., 2013), microbial diversity positively
affected nutrient-cycling efficiency and ecosystem processes through either
greater intensity of microbial exploitation of organic matter or functional
niche complementarity.
Litter quality has been shown to be important for microbial community
structure since both bacteria and fungi respond to litter physicochemical
changes during the decay process (Aneja et al., 2006; Purahong et al.,
2016). Litter biochemical traits, such as the C/N ratio and the fraction of
lignin, are considered good indicators of litter quality, as they are
related to nutrient availability and decomposition stage (Prescott, 2010;
Talbot and Treseder, 2012). Different litter types can thus select microbial
taxa that are more specialised in degrading their components. Fungi are
known to produce a suite of oxidative enzymes that degrade the recalcitrant
biopolymers of litter (Mathieu et al., 2013; Hoppe et al., 2015). In
contrast, only a few types of bacteria degrade all lignocellulosic polymers,
while most typically target simple soluble compounds (de Boer and van der
Wal, 2008). Therefore, the role of bacteria in the decomposition of more
recalcitrant material is still debated (Wilhelm et al., 2019). However,
still little is known on how microbial communities specialise in litter
types with different physical and chemical traits (Freschet et al., 2011;
Pioli et al., 2020).
The community structure and functioning of microbial communities are further
affected by biotic interactions (Daebeler et al., 2014; Garbeva et al.,
2014; Ho et al., 2016; Wilpiszeski et al., 2019). In natural communities,
such interactions generally involve competition for space and resources
(Boddy, 2000, and Faust and Raes, 2012, and references therein) but may also be
mutualistic. For example, it has been suggested that bacteria can facilitate
the activity of decaying fungi by providing important nutrients such as
nitrogen (N) and phosphorus (P) (Purahong et al., 2016). Likewise, fungi
have been demonstrated to help improve the accessibility of litter for
bacteria (de Boer et al., 2005). Therefore, it is likely that decomposition
dynamics depend not only on microbial community structure, but also on the
facilitative or competitive interactions among microbial species (Liang et
al., 2017; Aleklett et al., 2021). Despite the indications of the high
importance of species interactions for soil organic matter decomposition
dynamics, they are not well understood, and more studies under natural
conditions are needed.
Linking microbial diversity to function across different environments
represents a key aspect of ecology. In this study, we used high-throughput
sequencing in combination with the Tea Bag Index (TBI) – a cost-effective
method to study litter decomposition using commercially available teabags
(Keuskamp et al., 2013). This combination of methods allows insight to be gained
into the effect of litter traits on the community structure of microbial
decomposers and soil organic matter decomposition rates to be linked with
microbial population dynamics. The microbial diversity in soils is immense
(Thompson et al., 2017); yet large parts of it are dormant (Lennon and
Jones, 2011) or simply do not participate in litter degradation (Falkowski
et al., 2008). Therefore, the buried teabags used in this study serve as
“traps” for microbial litter degraders and assure that the diversity we
observe is composed only of its active litter degrading and associated taxa.
Specifically, in this citizen-science-aided project, we used
barcoded-amplicon high-throughput sequencing of the SSU rRNA gene (for
bacteria and archaea) and the internal transcribed spacer region (ITS) for
fungi to compare the microbial community structure in two different
standardised litter types (rooibos and green tea) with the microbial
community in three different local soil types and during four seasons, over
the course of a year. We tested the extent to which litter types with
different traits represent selective substrates for microbial community
colonisation. Finally, we related the microbial diversity and species
relative abundance patterns with two proxies (decomposition rate (k) and
stabilisation factor (S)) that describe the decomposition of labile material
(Keuskamp et al., 2013). We generally assumed that litter decomposition
dynamics and soil microbial communities will differ between the seasons.
Soil types were expected to be similar to each other given the small,
geographic scale and given that management practices are the same. More
specifically we hypothesised that (i) microbial diversity in the teabags
will correlate positively with decomposition rates and negatively with
stabilisation factors and that (ii) different subsets of local soil microbiota
will colonise labile and more recalcitrant litters, and thus each litter
type will select a different, specialised community. To the best of our
knowledge, this is the first study utilising the Tea Bag Index method in
combination with a microbiome analysis to investigate how litter
decomposition is linked with microbial community structure and diversity.
Materials and methodsDescription of the study sites
The study site is located at the Agneshof vineyards of the agricultural
college for Viticulture and Pomology in Klosterneuburg, just north of
Vienna, Austria (Fig. 1). The mean annual temperature was 11.1 ∘C, and the mean total annual precipitation was 689 mm between 2010 and 2019
at the Agneshof weather station. We selected three study plots with three
different soil types, Fluvisol, Cambisol, and Luvisol (Anjos et al., 2015), to
maximise the difference in soil characteristics. The Fluvisol and Luvisol
sites were cultivated with wine, whereas the Cambisol site was a set-aside
grassland between vineyards. At Fluvisol and Luvisol sites, we selected one
inter-row sampling area in the middle of the vineyard at least 5 m distance
from the vineyard edge. In contrast, at the Cambisol, we selected a sampling
area that was at least 5 m distance from the neighbouring vineyard.
Composite soil samples of 10–12 individual soil cores were taken from 0–10 cm depth in two (winter and spring) or three (summer and autumn) field
replicates every 3 months between March 2018 and December 2018 to
characterise the sites. Soils were sieved through a 2 mm stainless sieve and
air-dried prior to further analyses. Soil pH was measured electrochemically
(pH/mV Pocket Meter pH 340i, WTW) in 0.01 M CaCl2 at a soil-to-solution
ratio of 1 : 5 (ÖNORM L1083). Plant-available phosphorus (P) and
potassium (K) were determined by calcium–acetate–lactate (CAL) extraction
(ÖNORM L1087). Total soil organic C concentrations of the soil samples
were analysed by dry combustion in a LECO RC-612 TruMac CN at 650 ∘C (ÖNORM L1080; LECO Corp.). Total N was determined
according to ÖNORM L1095 with elemental analysis using a CNS (carbon,
nitrogen, sulfur) 2000 SGA-410–06 at 1250 ∘C. KMnO4
determination of labile carbon was analysed according to Tatzber et al. (2015). Potential nitrogen mineralisation was measured by the anaerobic
incubation method (Keeney, 1982), as modified according to Kandeler (1993).
The soil texture was determined according to ÖNORM L1061-1 and L1062-2.
For testing statistical differences in the chemical parameters between
plots, samples from different seasons were pooled.
Tea Bag Index (TBI)
The Tea Bag Index (i.e. the decomposition rate (k) and stabilisation factor (S))
was assessed according to Keuskamp et al. (2013) between December 2017 and
December 2018. In short, commercially available green tea (EAN 87 22700 05552 5) and rooibos tea (EAN 87 22700 18843 8) in non-woven, polypropylene
mesh bags produced by Lipton (Unilever) were used as standardised litter
bags. Green and rooibos teabags in eight replicate pairs (four replicate
pairs for calculating the TBI parameters and four replicate pairs for
molecular analysis) were weighed and buried pairwise at a depth of 8 cm for
3 months (Winter: December 2017–March 2018, spring: March 2018–June 2018, summer: June 2018–September 2018, autumn: September 2018–December 2018).
There was a 15 cm distance between the green and rooibos teabags within a
replicate pair and at least 75 cm between the replicate pairs. Subsequently,
the teabags used for calculating the TBI were retrieved, cleaned of adhering
soil particles, and dried for at least 3 d on a warm, dry location
before reweighing. Values for k and S were calculated using the mass losses
of green and rooibos teas, as described in Keuskamp et al. (2013). The
teabags used for molecular analysis were frozen on-site on dry ice after
being dug out and cleaned and transported in a frozen state to the Anaerobic
and Molecular Microbiology lab at SoWa, BC CAS, České
Budějovice, Czechia, for further analysis.
Molecular analyses of prokaryotic and fungal community structuresDNA extraction and amplicon sequencing
DNA was extracted from 0.25 g of frozen tea material or soil using the
DNeasy PowerSoil DNA Isolation Kit (Qiagen) according to the manufacturer's
instructions. Amplicon sequencing was done using a two-step barcoding
approach (Naqib et al., 2019). DNA was also extracted from two unburied
green tea and rooibos teabags to estimate the microbial load and diversity
already present on the tea material. The DNA extracts were then quantified
using a Quant-iT™ PicoGreen™ dsDNA Assay Kit
(Thermo) and diluted to 10 ng µL-1 for use as templates for PCR
amplification. The V4 region of the 16S rRNA gene (16S hereafter) was
amplified using primers 515F-mod-CS1 (aca ctg acg aca tgg ttc tac aGT GYC
AGC MGC CGC GGT AA) and 806-mod-CS2
(tacggtagcagagacttggtctGGACTACNVGGGTWTCTAAT; Walters et al., 2016) while ITS
was amplified using primers ITS1f-CS1
(acactgacgacatggttctacaCTTGGTCATTTAGAGGAAGTAA) and ITS2-CS2
(tacggtagcagagacttggtctGCTGCGTTCTTCATCGATGC; Gardes and Bruns, 1993).
Amplification was done in a T100 thermal cycler (Biorad) with amplification
cycles ranging from 25 to 28 for 16S and 29 to 32 cycles for ITS, depending
on the amount of template. The full protocol is available online (Angel,
2021). Before processing the samples from the experiment, we performed a
preliminary test using the chloroplast blocker pPNA (PNA Bio). For this
purpose, DNA from the unburied teabags, two buried teabags, and a soil
sample was used for amplification with or without PNA (0.25 µM, final
conc.). If PNA was used, an additional PCR step of PNA clamping (75 ∘C
for 10 s) was added in each cycle between the denaturation and primer
annealing step, according to the manufacturer's instructions. In addition,
no-template PCR control (NTC) and “blank extraction control” (DNA
extraction and amplification without a sample) were sequenced in each batch
(season). A mock community (ZymoBIOMICS Microbial Community DNA Standard II;
Zymo Research) was also amplified and sequenced. Library construction and
sequencing were performed at the DNA Services Facility at the University of
Illinois, Chicago, using an Illumina MiniSeq sequencer (Illumina) in the 2×250 cycle configuration (V2 reagent kit).
Sequence data processing and classification
Primer regions were trimmed off the amplicon sequence data using cutadapt
(V2.3; Martin, 2011). Downstream sequencing processing steps were done in R
(V4.0.3; R Core Team, 2020). Quality trimming and clustering into amplicon
sequence variants (ASVs) were done using the DADA2 pipeline (Callahan et
al., 2016). For 16S, the following quality filtering options were used: no
truncate, maxN = 0, maxEE = c(2, 2), and truncQ = 2. For ITS, the
following options were used: minLen = 50, maxN = 0, maxEE = c(2, 2), and
truncQ = 2. Chimera sequences were removed with removeBimeraDenovo() using
the “consensus” method “allowOneOff”. Taxonomic classification of the
16S ASVs was done with assignTaxonomy() against the SILVA database (Ref NR 99; V138.1; Quast et al., 2012), while for ITS, it was done against the
UNTIE database (Nilsson et al., 2018). Potential contaminant ASVs were
removed using decontam (Davis et al., 2018), employing the default options.
Unclassified taxa and those classified as either “eukaryota”,
“chloroplast”, or “mitochondria” (in the 16S dataset) or as “bacteria”
or “archaea” (in the ITS dataset) were removed. In addition, for
beta-diversity analysis, ASVs, appearing in <5 % of the samples,
were removed.
Statistical analysis
All statistical analysis was performed in R (V4.0.3; R Core Team, 2020).
Significant differences between the decomposition rates (k), stabilisation
factors (S), and the chemical parameters were determined using ANOVA followed
Tukey's HSD on the estimated marginal means (Lenth, 2021), functions
emmeans() and contrast()). To increase the statistical power, samples from
different seasons were pooled for testing the differences in soil
parameters. Spearman rank (ρ) correlations were performed to
investigate the relationship between soil properties and the decomposition
rates, stabilisation factors, and microbial amplicon sequence variant (ASV)
richness and diversity. Sequence data handling and manipulation were done
using the phyloseq package (McMurdie and Holmes, 2013). For alpha-diversity
analysis, all samples were subsampled (rarefied) to the minimum sample size
using a bootstrap subsampling with 1000 iterations to account for library
size differences, while for beta-diversity analysis, library size
normalisation was done by converting the data to relative abundance and
multiplying by median sequencing depth. The inverse Simpson index, the Shannon H
diversity index, and the Berger–Parker dominance index were calculated
using the function EstimateR() in the vegan package (Oksanen et al., 2018)
and tested using ANOVA in the stats package, followed by a post hoc Tukey HSD test
on the estimated marginal means. Variance partitioning and testing were done
using the PERMANOVA (Mcardle and Anderson, 2001) function adonis() using
Horn–Morisita distances (Horn, 1966). Differences in order composition
between the soil or litter types were tested similarly to STAMP (Parks et
al., 2014) but written in R. Briefly, the relative abundance of the orders
was compared between samples using the Kruskal–Wallis rank-sum test
(kruskal.test() in the stats package), followed by the Mann–Whitney
post hoc test (wilcox.test() in the stats package), and false discovery rate (FDR) corrected using
the Benjamini–Hochberg method (Benjamini and Hochberg, 1995); p.adjust() in
the stats package). Differentially abundant ASVs were detected using ALDEx2
(Fernandes et al., 2013) using the option denom = iqlr for the aldex.clr()
function. Differences in composition between the two teabag types and the
soil were tested in a pairwise manner. Plots were generated using ggplot2
(Wickham, 2016).
Results and discussionSoil chemical characteristics and Tea Bag Index
The weather during the study was typical for the region, with mean seasonal
air temperatures ranging from 2 ∘C in the winter to 22.5 ∘C in the summer and most of the rain occurring summer and
autumn (Table S1 in the Supplement).
To be able to draw conclusions about the connections between the community
structures of microbial litter decomposers, litter types, and the
decomposition process, we first determined the basic soil characteristics of
the three study plots. The Luvisol plot exhibited the lowest contents of P,
K, TOC, and sand and the highest pH (Table S2).
Next, we determined the decomposition rate, k (which reflects the rate by
which the labile fraction of the litter is decomposed), and the
stabilisation factor, S (which reflects the proportion of non-decomposed,
hydrolysable labile fraction that is remaining after the incubation), for
all three study plots and during four seasons. Despite decomposition rates
ranging from 0.008 d-1 at the Cambisol site to 0.025 d-1 at the
Fluvisol site (in the summer), no significant differences could be found
between sites or seasons, neither when pooled (P=0.15), nor at each site
(P=0.45; Fig. 2). The results are well within the range of summer data
previously reported from Austria (Buchholz et al., 2017; Keuskamp et al.,
2013; Sandén et al., 2020, 2021). However, to the
best of our knowledge, we are the first ones to report data during four
seasons. There was also no correlation of any measured soil properties with
the decomposition rates (data not shown), as also reported in Sandén et
al. (2020). These results were surprising, given the differences in the
determined soil characteristics (Table S2) and temperature between the
seasons, and point to the possibility that micro conditions in the soil play
a larger role than expected in determining decomposition rates.
Decomposition rate (k) and stabilisation factor (S)
determined at three sites with different soil types during four seasons.
Values shown are averages (n=4, lower in case a teabag broke
during incubation or in case the rooibos was not in the first phase of
decomposition anymore and the main assumptions for calculation of
k were violated; thus,
k could not be calculated) and the interquartile
range (violin shapes). Lowercase letters indicate significant differences
between samples (seasons and sites together;
padjusted≤0.05).
The stabilisation factors in general were higher than presented in Keuskamp
et al. (2013); however, this is the first time that the stabilisation
factors are presented for different seasons, and it seems logical that the S
would be higher in the colder seasons than over the summer. The summer
values are well in agreement with Keuskamp et al. (2013) and the other
seasons with Sarneel et al. (2020) from tundra environments and Sandén
et al. (2021) from urban Austrian sites that both reported stabilisation
factors up to 0.4, which gives further support for our findings. We did,
however, observe significantly lower stabilisation factors in summer than in
all other seasons (padjusted≤0.001). Specifically, the
stabilisation factors determined at the Cambisol and Luvisol sites were
significantly lower in summer than those determined for the other three
seasons (padjusted≤0.05). These results indicate that the
conditions during the summer months favour the decomposition of a larger
fraction of the organic material but not necessarily in a faster manner.
This is in apparent contrast to the well-known relationship between
temperature and decomposition rates (see Kirschbaum, 1995, and references
therein). However, one should consider that (1) temperature sensitivity of
the decomposition rate is much higher at low temperature and that (2) the
decomposition rate is also heavily affected by moisture, which could
counterbalance the effect of temperature. Similarly, Elumeeva et al. (2018)
also found a correlation of S but not of k to various edaphic factors. Notably,
soil moisture, pH, and altitude did not affect k (but were correlated with
S). Since S measures “how much” (rather than “how fast”) of the labile
litter fraction is stabilised material is remaining after incubation, it can
be assumed that S is more sensitive than k to the composition of microbial
communities that are active at the time of decomposition. This is because
the underlying processes amounting to S involve the concerted (discrete)
action of several microbial guilds, each specialising in decomposing one or
more substrates sequentially (Zheng et al., 2021; Glassman et al., 2018).
Establishment of an amplicon sequencing protocol to be used with the TBI
Before investigating the microbial communities colonising the teabags, we
extracted DNA from unburied teabags serving as negative controls and
performed rRNA gene amplicon sequencing (16S and ITS regions). DNA yields
were similar between buried and the unburied teabags and averaged 6.1±12.9 and 23.0±14.0 mg g-1, probably because, as expected, most of
the DNA originated from the plant cells (data not shown). A preliminary
study using the chloroplast blocker pPNA was carried out to estimate the
effect of plant chloroplast on the sequencing output (what proportion of the
sequences is dominated by chloroplasts). As Table S3 shows, only the
unburied tea material had a significant portion of chloroplast reads
(64.5 % and 15.4 % for green tea and rooibos, respectively), while after
3 months in the ground, no chloroplast reads were detected (most likely
due to a combination of microbial colonisation and chloroplast DNA
degradation). Using the pPNA blocker nearly eliminated the number of
chloroplast reads while at the same time not biasing the community
composition in the samples (Fig. S1 in the Supplement). Nevertheless, since chloroplasts were
undetected in the buried teabags, pPNA was not used in subsequent sample
processing. Figure S1 also shows that, as expected, the microbial load on the
unburied tea material was minimal and yielded only about 5 % of the reads
after quality filtering. Such a low microbial load is expected for
above-ground plant parts (e.g. Chen et al., 2020; Knorr et al., 2019).
To then study the prokaryotic and fungal communities potentially associated
with litter degradation, we extracted DNA from soil and triplicate rooibos
and green tea teabags buried for 3 months for each season at each site
and performed rRNA gene amplicon sequencing.
Richness and diversity of prokaryotic and fungal communities in teabags and soils
Sequencing the prokaryotic 16S rRNA gene and the fungal ITS region and
downstream data processing yielded a total of 16 100 16S ASVs and 3685 ITS
ASVs. After removing all ASVs with a prevalence of <5 % of the
samples, 4217 16S ASVs and 402 ITS ASVs remained. As expected, archaea were
rare and comprised only about 0.9 % of the reads. At all three sites, the
observed prokaryotic and fungal ASV richness (i.e. the observed number of
prokaryotic and fungal ASVs) was roughly twice as high in the soil samples
than in teabag samples (Fig. S2). Likewise, the microbial diversity, as
estimated by the inverse Simpson, Shannon, and Berger–Parker indexes, was
generally significantly higher in the soil samples than in the teabag
samples. There was, however, no significant difference between rooibos and
green teabags in terms of prokaryotic and fungal ASV richness or diversity.
These findings indicate selective microbial colonisation of both the labile
and the more recalcitrant litter sourced from the surrounding soil during
all seasons.
Both the Fluvisol and the Cambisol site teabags buried in summer harboured a
significantly richer prokaryotic community than those buried in other
seasons. Such seasonal differences were not observed with the fungal
communities. Similar to the observed differences in richness, the
biodiversity of prokaryotic communities detected in the teabags was often
significantly higher in summer samples than in all other seasons. This hints
that there are more distinct prokaryotic species capable of colonising and
degrading litter in summer than in the other seasons. An explanation for
this observation could be the activation of decomposing prokaryotic soil
populations by summer conditions such as elevated temperature (Kirschbaum,
1995; Allison et al., 2010). Finally, the prokaryotic ASV richness and
diversity of the communities that colonised the teabags were negatively
correlated with the stabilisation factor S (p<0.01; correlation
factor ρ=-0.60 and p<0.01; correlation factor ρ=-0.54, respectively). These findings suggest that less inhibition of
litter decomposition occurred in the summer season, in which richer and more
diverse prokaryotic communities were associated with the degradation. With
these data, we can partially confirm our hypothesis of a positive
correlation between microbial diversity and litter decomposition. Possibly,
a positive relationship between microbial diversity and litter decomposition
is prevalent in many soil types, as it has also been experimentally
established in a Cambisol (Maron et al., 2018).
The richness and diversity of fungal ASVs were generally comparable across
the seasons at all sites, except for a higher richness at the Cambisol site
in winter compared to the other seasons. This could be explained by the
hyphae growth form of many fungi, which allows them to span various
microsites in the heterogeneous soil environment and makes them less
sensitive to chemical gradients and seasonal changes (Yuste et al., 2010).
Selective colonisation of teabags by prokaryotic and fungal soil populations
After 3 months of burial, the community structures of prokaryotes and
fungi detected in teabags were significantly different from those detected
in soil (Table S4, Fig. 3). Moreover, the differential abundance analysis
clearly showed enrichment of about a third of the 16S ASVs and 14 %–20 % of
the ITS ASVs in the teabags compared to the soil (Fig. S4). Therefore, as
has been observed before in various soil systems and with a range of
different litter types (Aneja et al., 2006; Bray et al., 2012; Albright and Martiny, 2018; Yan et al., 2018; Wei et al., 2020), we conclude that the litter
material in the teabags selected for colonisation by a sizeable portion of
the bacterial, archaeal, and fungal population acting as the active litter
decomposers and associated populations. While fungi colonise new patches
using hyphal growth, prokaryotes depend on mechanisms such as adhesion to
hyphae, motility, or passive diffusion in water. Interestingly, Albright et
al. (2019) have shown that unicellular bacteria and fungi do not differ in
their dispersal abilities. Successful colonisation of the teabags is related
to microbial traits such as growth rate, mobility, and adhesive and competitive
ability (Albright and Martiny, 2018) and does not exclusively indicate the
degrading ability of a microbe. The results presented here therefore may be
biased towards the more fast-growing, strong competitors which disperse
easily and may overlook contributions of slower degraders in soil. Comparing
the green tea with the rooibos samples, we could detect a subgroup of ca.
4.2 % and 2.5 % of the 16S rRNA gene ASVs that preferentially colonised
either the green tea or rooibos teabags. In contrast, only four ITS ASVs
differed between the green tea and rooibos samples, indicating
non-preferential colonisation. For both prokaryotes and fungi, soil or
litter type explained the largest fraction of the variance in the community
composition (38 % and 23 %, respectively; Table S4), although, not
surprisingly, this was driven mainly by the differences between soil and
teabag communities. However, the difference between green tea and rooibos
was not negligible and explained 13 % and 9 % of the variance in a pairwise
comparison for prokaryotes and fungi, respectively (Table S4, Fig. 3). The
season was also a major factor, explaining 20 % and 22 % of the variance,
respectively, and driven mostly by the difference between summer and winter
(27 % and 29 % of the variance, respectively; Table S4, Fig. S4). In
contrast, soil type had only little effect, explaining only 3 % and 5 % of
the variance, respectively. This contrasts with the recent findings of
Pioli et al. (2020), in which a large effect of soil type on the community
composition was reported, though this is not surprising considering that this study
was performed on a very local scale, with identical climate and similar soil
types and management practices.
Principal coordinate analyses plots of prokaryotic (a) and fungal (b) community compositions based on Morisita–Horn distances of 16S rRNA gene
and ITS region ASVs derived from soil, green tea, and rooibos teabags. The
models were obtained using the formula: distance matrix ∼ field + sample type × season.
Further interested in the identity and possible colonisation preferences of
the microbial decomposers, we compared the relative abundance of dominant
orders between the different teabags and the soil types and between the teabag
communities in the summer vs winter, in a pairwise manner. This analysis
allowed us to identify specific bacterial and fungal lineages that exhibited
distinct colonisation patterns and were enriched in samples from the more
labile green tea or the more recalcitrant rooibos bags. In contrast to our
hypothesis (different litter types will select different microbial
colonisers), many of the detected microbes were either equally selected or
deselected by both tea types.
Most strikingly, members of the Pseudomonadales and Sphingobacteriales were
the most enriched in both green tea and rooibos teabags as compared to the
soil from the same sites with differences of 11.4 % and 9.4 % for
Pseudomonadales and 8.2 % and 8.3 % for Sphingobacteriales,
respectively (Fig. 4a, c). Additionally, Flavobacteriales, Micrococcales,
and Burkholderiales also showed higher relative abundances than the
surrounding soil (Fig. 4a). Similar increases in relative abundance of
Pseudomonadales, Flavobacteriales, Sphingobacteriales, and Micrococcales have
been observed in straw decomposing soil microcosms (Jiménez et al.,
2014; Guo et al., 2020). Members of Pseudomonadales, Flavobacteriales, and
Rhizobiales are well known to have the capacity to degrade plant lignin,
(hemi-)cellulose, or carboxymethyl cellulose (Koga et al., 1999; McBride et
al., 2009; Wang et al., 2013; Talia et al., 2012; Jackson et al., 2017).
Furthermore, Pseudomonadales and Flavobacteriales can be involved in the
degradation of furanic compounds (Jiménez et al., 2013; López et
al., 2004). The increased presence of Sphingobacteriales may be explained by
their capability to produce β-glucosidases that remove the
cello-oligosaccharides produced by polymer degraders (Matsuyama et al.,
2008) and is considered a critical and rate-limiting step in cellulose
degradation (du Plessis et al., 2009). Indeed, soils with high β-glucosidase activity were also found to be dominated by members of
the Sphingobacteriales (particularly from the Chitinophagaceae family;
Bailey et al., 2013). Merely the bacterial orders Cytophagales,
Enterobacteriales, and Rhizobiales exhibited relative abundance patterns
indicative of a selection only by rooibos or green tea, as we hypothesised
(Fig. 4a, c).
Comparative analysis of the relative abundance based on bacterial
and fungal order profiles between soil or litter types and seasons. Samples
were compared using Kruskal–Wallis rank-sum test, followed by the
Mann–Whitney post hoc test, and FDR-corrected using the Benjamini–Hochberg
method. Only orders with a median relative abundance of >5 %
are shown.
We further observed significant, selective enrichments of fungal orders in
both teabag types. Members of the Hypocreales had a 20 % higher relative
abundance in green tea than in soil but were not enriched in the rooibos
teabags (Fig. 4b). In contrast, Helotiales were only enriched in rooibos
teabags by 12 % (Fig. 4d). Hypocreales are common saprophytic fungi and
are known to produce cellobiohydrolases and endoglucanases necessary for the
depolymerisation of celluloses (Lynd et al., 2002; Martinez et al., 2008).
Helotiales are abundant, cellulolytic soil fungi that have been shown to
specialise in degrading recalcitrant organic carbon (Newsham et al., 2018;
MGnify, 2020). Our findings could point towards members of the
exclusively enriched orders being specialised primary degraders of either
labile of recalcitrant organic matter and not associated scavengers of
degradation products. Since the green tea litter contains a higher
hydrolysable fraction and a lower C : N ratio (Keuskamp et al., 2013), we
assume the Enterobacteriales and Hypocreales, which were exclusively
enriched in green tea samples, were involved in the degradation of more
labile compounds such as cellulose. Cytophagales, Rhizobiales, and
Helotiales, which were exclusively selected by rooibos tea on the other
hand, may be important primary degraders of more recalcitrant organic
matter. Members of these five orders could therefore possibly serve as
indicator species in future decomposition studies.
Conclusions
In contrast to our expectations, the different seasons did not display
different litter decomposition rates, but they did differ in the extent to
which the hydrolysable litter fraction was degraded. At the same time,
microbial richness and diversity of litter-colonising degraders were
positively correlated with the fraction of degraded organic carbon,
indicating a positive relationship between litter degradation and microbial
biodiversity in soil. Microbial colonisation of the tea litter was
substrate-selective and season-dependent for prokaryotes and fungi. On
average, about 28 %–30 % of the prokaryotic ASVs and 14 %–19 % of the fungal
ASVs preferentially colonised the rooibos and green tea, respectively.
However, their exact identity varied between seasons, especially between the
summer and winter. A total of five microbial orders were identified as
possible indicator species through their exclusive colonisation of either
recalcitrant (rooibos) or labile (green tea) litter. This work demonstrates
the power of the microbiome-resolved TBI to give a holistic description of the
litter decomposition process in soils.
Code availability
The scripts to reproduce this and all downstream statistical analysis steps
are available online under https://github.com/roey-angel/TeaTime4Schools (last access: 9 March 2022) and https://doi.org/10.5281/zenodo.6340082 (Angel, 2022).
Data availability
Raw sequence reads have been deposited in the Sequence Read Archive under
BioProject accession no. PRJNA765214 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA765214, NCBI, 2022).
The supplement related to this article is available online at: https://doi.org/10.5194/soil-8-163-2022-supplement.
Team list
Group I: Fabian Bauer, Lorenz Baumgartner, Lisa Brandl, Michael
Buchmayer, Karl Daschl, Rene Dietrich, Stefan Ebner, Margarete Jäger,
Michaela Kiss, Lea Kneissl, Katja Langmann, Elias Mauerhofer, Jakob
Paschinger, Tobias Rabl, Sophie Schachenhuber, Josef Schmid, Marlene
Steinbatz. Group II: Mathias Ettenauer, Lorenz Hauck, Mathias Herl,
Johannes Honsig, Thomas Gangl, Anja Glaser, Gabriel Hümer, Georg
Lenzatti, Stefan Lichtscheidl, Lena Mayer, Valentin Oppenauer, Jacob
Rennhofer, Katharina Schönner, Ciara Seywald. Group III: Jan
Gamzunov, Anna Hess, Christoph Schurm, Klaus Stacher, Lena Ungersböck,
Florian Valachovich, Mirjam Weissmann, Marius Wittek, Jennifer Wosak,
Valentin Zahel, Lucas Züger.
Author contributions
TS, RA, and AD designed the study. TS, HB, SG, and EK performed sampling with
support from students of biology projects groups I, II, and III. EK
developed lab protocols, and EP optimised the lab protocols and performed the
DNA extraction and PCR amplification. TS, RA, and AD analysed the data. The
manuscript was written by AD, TS, and RA with contributions from all
co-authors.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank Michael Schwarz for preparing the map in Fig. 1.
Financial support
This work was supported by the Austrian Ministry of Education, Science and
Research (BMBWF) through a Sparkling Science Grant to Taru Sandén, Helene Berthold, Elena Kinz, Anne Daebeler, Roey Angel, and
Susanne Grausenburger (SPA 06/044 TeaTime4Schools). In addition, Eva Petrová and Roey Angel were supported by
the Czech MEYS (EF16_013/0001782 – SoWa Ecosystems Research).
Anne Daebeler was supported by the FWF grant T938.
Review statement
This paper was edited by Ingrid Lubbers and reviewed by Thomas Reitz and one anonymous referee.
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