The Tibetan Plateau harbors the largest number of
glaciers outside the polar regions, which are the source of several major
rivers in Asia. These glaciers are also major sources of nutrients for
downstream ecosystems, while there is a little amount of data available on the nutrient
transformation processes on the glacier surface. Here, we monitored the
carbon and nitrogen concentration changes in a snowpit following a snowfall
in the Dunde Glacier of the Tibetan Plateau. The association of carbon and
nitrogen changes with bacterial community dynamics was investigated in the
surface and subsurface snow (depth at 0–15 and 15–30 cm, respectively)
during a 9 d period. Our results revealed rapid temporal changes in
nitrogen (including nitrate and ammonium) and bacterial communities in both
surface and subsurface snow. Nitrate and ammonium concentrations increased
from 0.44 to 1.15 mg L-1 and 0.18 to 0.24 mg L-1 in the surface snow and
decreased from 3.81 to 1.04 and 0.53 to 0.25 mg L-1 in the subsurface
snow over time. Therefore, we suggest that the surface snow is not
nitrogen-limited, while the subsurface snow is associated with nitrogen
consumption processes and is nitrogen-limited. The nitrate concentration
co-varied with bacterial diversity, community structure, and the predicted
nitrogen fixation and nitrogen assimilation/denitrification-related genes
(narG), suggesting nitrogen could mediate bacterial community changes. The
nitrogen limitation and enriched denitrification-related genes in subsurface
snow suggested stronger environmental and biotic filtering than those in
surface snow, which may explain the lower bacterial diversity, more
pronounced community temporal changes, and stronger biotic interactions.
Collectively, these findings advance our understanding of bacterial
community variations and bacterial interactions after snow deposition and
provide a possible biological explanation for nitrogen dynamics in snow.
Introduction
The Tibetan Plateau is the world's third-largest ice reservoir after those
in Antarctica and Greenland (Qiu, 2012). These glaciers are the source of
several large rivers in Asia, such as the Yellow, Yangtze, Mekong, Salween,
Brahmaputra, and Indus rivers (Immerzeel et al., 2010). Glaciers are major
sources of nutrients (carbon and nitrogen) for the downstream ecosystems
(Singer et al., 2012; Hood et al., 2015; Liu et al., 2021). It has been
estimated that 80 Gg of dissolved organic carbon and 27–43 Gg of
nitrogen are exported from the Greenland Ice Sheet (Bhatia et al., 2013;
Wadham et al., 2016). These nutrients are subjected to complex accumulation
and transformation processes in the glacier snow before being released into
downstream ecosystems, and microorganisms are the drivers of these processes
(Anesio and Laybourn-Parry, 2012; Hell et al., 2013; Hodson et al., 2008).
Several studies on snowpacks revealed vital knowledge of the nutrient and
microbial community dynamics in the Arctic (Hell et al., 2013; Larose et
al., 2013a, 2013b; Maccario et al., 2014,
2019), Antarctic (Antony et al., 2016), and Alps (Lazzaro et al., 2015).
However, such knowledge is rarely available for the Tibetan Plateau,
constraining our understanding of the nutrient accumulation, transformation,
and release processes, which is urgently needed under the enhanced warming
and glacier retreat on the Tibetan Plateau.
Autochthonous (microbial origin) and allochthonous (wet and dry atmospheric
depositions) are the major sources of nutrients in supraglacial snow, and
the contribution of allochthonous sources was much greater in Arctic
glaciers (Larose et al., 2013a). Microorganisms are highly involved in the
transformation of both autochthonous and allochthonous nutrients. Several
studies investigated the dynamics of nutrient and bacterial changes in
supraglacial snow during the ablation period. Larose et al. (2013a) revealed
that the form of nitrogen varied as a function of time in supraglacial snow
during a 2-month field study in Svalbard, and fluctuations in microbial
community structure were reported with the relative abundance of fungi and
bacteria (such as Bacteroidetes and Proteobacteria) increasing and decreasing,
respectively. Seasonal shifts in snowpack bacterial communities were
reported in the mountain snow in Japan, where rapid microbial growth was
observed with increasing snow temperature and meltwater content (Segawa et
al., 2005). However, the results of these studies are likely the consequence
of several precipitation events due to the long study period. During
precipitation, a new snow layer forms above the previous ones, which is
responsible for the stratified snowpack structure. These different snow
layers have distinct physical and chemical characteristics, and their age
also differs substantially (Lazzaro et al., 2015). Thus, while the microbial
process across the aged snowpack can be complex, focusing on supraglacial
snow from a single snowfall event could provide unique insights into the
bacterial and nutrient dynamics. For instance, Hell et al. (2013) reported
bacterial community structure changes during the ablation period across 5 d in the high Arctic, but the bacterial and nutrient dynamics during the
snow accumulation period remain elusive.
Surface and subsurface snow typically harbors distinct bacterial community
structures (Xiang et al., 2009; Møller et al., 2013; Carey et al., 2016).
For example, algae (chloroplasts), Proteobacteria, Bacteroidetes, and
Cyanobacteria were more abundant in surface snow, while Firmicutes and
Fusobacteria were more abundant in the deeper snow layer (Møller et al.,
2013). A previous study had proposed that nitrogen availability could also
be a driver of microbial community structure and function in snow (Larose et
al., 2013b), in which the NO3- and NH4+ concentrations
drove the community composition in Ny-Ålesund snowpack. A dissolved
inorganic nitrogen addition experiment also showed a clear community
response with the bacterial abundance being elevated and genera richness declining
in the final time point compared to the initial time point, suggesting
potential specialization of heterotrophic communities (Holland et al.,
2020).
Differences in physicochemical conditions can also indirectly influence
bacterial community structure through impacts on the types of biotic
interactions (Friedman and Gore, 2017; Khan et al., 2018; Bergk Pinto et
al., 2019). For example, the addition of organic carbon shifted bacterial
interactions from collaboration to competition in Arctic snow (Bergk Pinto
et al., 2019), with complex organic carbon degradation and mineralization
requiring intensive microbial collaborations (Krug et al., 2020), which are
particularly important for oligotrophic environments, such as glaciers.
Collaboration is also known to be essential to biological processes such as
ammonia oxidation and denitrification, in which various organisms carry out
different steps of these processes (Henry et al., 2005; Madsen, 2011; Yuan
et al., 2021). These changes in interactions and network complexity can
favor or disadvantage certain bacterial groups, thereby changing the
bacterial community structure (i.e., biofiltering).
Several studies have investigated the nutrient and bacterial community
changes in supraglacial snow across the winter (Brooks et al., 1998; Liu et
al., 2006), but the bacterial and nutrient dynamics of freshly fallen snow
have been largely overlooked. These short temporal changes will influence
the following post-depositional processes after it is buried by the next
snowfall and will ultimately determine the physicochemical properties of
the stratified snow in the following year. In the present study, we
investigated the bacterial community and snow physiochemical property
changes in the surface and subsurface supraglacial snow during a 9 d
period after a single snowfall event at the Dunde Glacier on the northeastern
Tibetan Plateau. We aimed to answer the following key questions: (1) do the bacterial community and nutrients change at a short temporal scale;
(2) do the bacterial communities in different snow layers exhibit similar
community temporal changes; and (3) are the temporal changes in the surface
and subsurface snow related to environmental filtering, biotic interactions,
or both?
Materials and methodsSite description and sample collection
Snow samples were collected from the ablation zone at the Dunde Glacier
(38∘06′ N, 96∘24′ E; 5325 m above the
sea-level) during October and November 2016 (Fig. S1 in the Supplement). Dunde
Glacier is located in the Qilian mountain region on the northeastern
Tibetan Plateau, and it is continuously monitored by the Institute of
Tibetan Plateau Research, Chinese Academic of Sciences. No supraglacial snow
was observed on the glacier surface on 10 October when we first
arrived at the camp. Snowfall started on 18 October and ended on
23 October. Sampling was conducted over a 9 d period after
the snowfall stopped on a small, flat 5 m × 3 m area to reduce the
impact of sample heterogeneity due to spatial variations. Snow samples were
collected on 24, 25, 26, 27, and 29 October and 2 November (which are referred to as day 1, 2, 3,
4, 6, and 9) until the next snowfall started. This enabled us to monitor the
succession of bacterial communities and the chemical changes in snow through
time after deposition. The ambient air temperature during the sampling period
averaged -8∘C (data available through the European Centre for
Medium-Range Weather Forecasts; Fig. S2), and no snow melting
was observed over the 9 d period.
On each day, three snow pits were randomly dug within the 5 m × 3 m
area, and any two snow pits were 30–50 cm apart. Each snow pit was
approximately 30 cm deep, and the snow was further divided equally into the
surface and subsurface layers (approximately 15 cm deep for each layer) to
get enough snow for DNA extraction, according to Carey et al. (2016). For
each snow pit, the top 1 cm in contact with the air was removed using a
sterile spoon to avoid contamination, and then surface and subsurface snow
were collected using a sterilized Teflon shovel into 3 L sterile sampling
bags separately. Approximately 100 mL were used for physicochemical
analyses, whereas the rest was used for DNA extraction. A total of 36
samples were collected. Tyvek bodysuits and latex gloves were worn during
the entire sampling process to minimize the potential for contamination, and
gloves were worn during all subsequent handling of samples. Samples were
kept frozen during the transportation to the laboratory and stored at -20∘C until analysis.
Environmental characterization of snow
The 100 mL snow sample allocated for physicochemical analysis was melted at
room temperature for 3 h before being analyzed. For dissolved organic
carbon (DOC) and major ion measurements, 100 mL of snow meltwater was
syringe-filtered through a 0.45 µm polytetrafluoroethylene (PTFE)
membrane filter (Macherey–Nagel) into 20 mL glass bottles. The membrane was
pre-treated with 1 % HCl, deionized water rinsed, and 450 ∘C combusted for >3 h to remove any potential carbon and nitrogen on
the membrane, and the initial 10 mL of the filtrate was discarded before
collecting the sample for analysis to eliminate any residual compound on the
membrane. The DOC concentrations were measured with a TOC-VCPH analyzer
(Shimadzu Corp., Japan). Major ions (NH4+, NO3-,
Na+, K+, and SO42-) were analyzed using a Thermo-Fisher ICS-900 (ion chromatography system) as described previously (Rice et al., 2012).
The precision and accuracy of the TOC-VCPH analyzer were both <3 %, and the limit of detection was 0.05 mg L-1. The precision and
accuracy of the ICS-900 were <5 % and 0.1 mg L-1, and the limit of detection was 0.01 mg L-1 (Fig. S3).
DNA extraction
For assessing the bacterial community composition, snow samples (3 L) were
melted at 4 ∘C overnight and filtered onto a sterile 0.22 µm
polycarbonate membrane (Millipore, USA) with a vacuum pump (Ntengwe, 2005).
Bacterial community DNA was extracted from the biomass retained on the
filters using a FastDNA® SPIN Kit for Soil (MP Biomedicals,
Santa Ana, CA, USA) according to the manufacturer's instructions. DNA
extraction with no sample added was performed in parallel and used as a
negative control.
The raw DNA was checked by electrophoresis in 1 % (w/v) agarose gel and
purified from the gel using an Agarose Gel DNA purification kit (Takara,
Japan). The concentration and purity of the DNA extracts were measured using
a NanoDrop 1000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA).
The extracted DNA was stored at -80∘C until amplification.
Bacterial 16S rRNA amplification and Illumina MiSeq sequencing
In total, 36 DNA samples and one negative control were subjected to amplicon
sequencing. Universal primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R
(5′-GGACTACHVGGGTWTCTAAT-3′) (Caporaso et al., 2012), with 12 nt unique
barcodes, were used to amplify the V4 hyper-variable regions of the
bacterial 16S rRNA gene. Polymerase chain reaction (PCR) was performed under
the following conditions: 94 ∘C for 5 min, 30 cycles of
94 ∘C for 30 s, 52 ∘C for 30 s, and
72 ∘C for 30 s, followed by a final cycle of 10 min at
72 ∘C. Each PCR reaction contained 12.5 µL2× Premix Taq DNA
polymerase (Takara Biotechnology and Dalian Co. Ltd., China), 1 µL
primer (0.4 µM final concentration), and 8.5 µL nuclease-free
water, as well as 2 µL DNA template (20 ng µL-1) or 2 µL sterile
water for the PCR negative controls. PCR products were confirmed using
agarose gel electrophoresis, and no PCR band was detected in the PCR
negative controls. To minimize PCR batch-to-batch variations and maximize
the quantity of PCR product, triplicate PCR reactions were performed for
each sample, and PCR products were pooled for purification using the OMEGA
Gel Extraction Kit (Omega Bio-Tek, Norcross, GA, USA) following
electrophoresis. PCR products from different samples were pooled in equal
molar amounts and then used for 2×250 bp paired-end sequencing
on a MiSeq machine (Illumina, San Diego, CA).
Processing of Illumina sequencing data
MiSeq sequence data were processed using the QIIME 2 pipeline version 2018.8
(Bolyen et al., 2019), following the recommended procedures
(https://docs.qiime2.org/2022.2/, last access: 4 March 2020) and using the plugin demux to
visualize interactive quality diagrams and check read quality. Plugin DADA2
(Callahan et al., 2016) was applied to remove primers, truncate poor-quality
bases, conduct de-replication, identify chimeras, and merge paired-end
reads. Commands included in the feature table (McDonald et al., 2012)
generated the summary statistics of sequences related to the samples.
Further, we trained a naïve Bayes classifier with the
feature-classifier plugin using the 16S rRNA gene database at 99 %
similarity of the SILVA 132 QIIME release and based on the 515F–806R primer
pair as used for the PCR. Finally, the taxa plugin was used to filter
mitochondrial and chloroplast sequences, as well as to generate absolute
read count tables of all taxa for each sample. Data were analyzed at the
level of amplicon sequence variant (ASV), in which ASVs are delineated by
100 % sequence identity (Callahan et al., 2017).
After removing singletons, a total of 1 685 186 high-quality reads were
obtained, representing 9178 ASVs. Before statistical analysis, the dataset
was rarefied to 45 000 reads per sample, which is the lowest read count
among samples. Rarefaction curves reached an asymptote before the
subsampling, which confirmed that this depth was sufficient to detect the
diversity present (Fig. S4).
Network analysis
The ASV–ASV associations within the surface and subsurface bacterial
communities were explored using Molecular Ecological Network Analysis
Pipeline (http://ieg4.rccc.ou.edu/mena, last access: 10 May 2021) (Deng et al., 2012). The ASVs that
occurred in at least 50 % of the samples from the surface or subsurface
group were selected to construct the network. Spearman's rank correlation
coefficient (ρ) was calculated to reflect the strength of association
between species. The false discovery rates (q values) were calculated from
the observed p-value distribution. The resulting correlation matrix was
analyzed with the random matrix theory (RMT)-based network approach to
determine the correlation threshold for network construction, and the same
threshold was used for both the surface and subsurface network, so the
topological properties of the surface and subsurface networks are
comparable.
Statistical analysis
Shannon–Wiener and Chao1 indices, which were used to estimate the species
richness in the snow community, were calculated using the diversity function in
the R package vegan (Oksanen et al., 2010). Functional profiling of bacterial
taxa was carried out using the package Tax4Fun2 in R (Wemheuer et al., 2020).
While the application of functional profiles predicted from 16S rRNA
gene-based community composition data is limited by the functional
information available in databases, we present these data as one possible
interpretation of the patterns detected and note that the Tax4Fun2 package
performed well compared to older widely used programs (Wemheuer et al.,
2020). The pairwise Wilcoxon rank-sum test was used to compare the
depth-horizon differences in environmental variables, alpha diversity, and
the relative abundance of taxonomic groups at the phylum level. Linear
regression modeling was implemented in R using the lm function to estimate
the trend of environmental characteristics, alpha diversity, and microbial
community composition changes. Multiple linear regression analysis was
performed to determine the contribution and significance of the
environmental characteristics to the alpha diversity using the lm function
in R. We use the stepwise Akaike information criterion (AIC) method for
variable selection by the step function in R. The best model was chosen
based on the lowest AIC value (Wagenmakers and Farrell, 2004). The bacterial
community structure was subjected to principal coordinate analysis (PCoA)
carried out using the pcoa function of the ape package in R. The
significance of dissimilarity of community composition among samples was
tested using permutational multivariate analysis of variance (PERMANOVA)
based on Bray–Curtis distance metrics with the adonis function in the R
package vegan (Oksanen et al., 2010). Test results with p<0.05 were
considered statistically significant. The Mantel test based on Spearman's rank
correlations was performed using the bacterial dissimilarity and
environmental dissimilarity matrices, calculated based on the Bray–Curtis
distance metrics and Euclidean distance metrics in the vegan R package,
respectively. The normalized stochasticity ratio (NST) based on the
Bray–Curtis dissimilarity was calculated using the NST package in R to
estimate the determinacy and stochasticity of the bacterial assembly
processes with high accuracy and precision (Ning et al., 2019). The NST
index used 50 % as the boundary point between more deterministic
(<50 %) and more stochastic (>50 %) assembly
processes. All environmental variables were normalized before the
calculation. All statistical analyses were executed in R version 3.4.3 (R
Core Team, 2017).
The pattern of environmental factor changes in the surface
and subsurface snow layers. (a) Environmental factor comparisons in the surface and subsurface snow
layers. Each dot represents an individual sample. Significantly higher
concentrations of NO3-, NH4+, K+, and
SO42- were observed in the subsurface layer based on Wilcoxon
rank-sum test. (b) Temporal changes in environmental factors in the surface
and subsurface layers. The solid and dashed lines indicate significant and
non-significant temporal changes, respectively. The concentration of
NO3- and NH4+ in the surface layer significantly
increased with time, while the concentration of NO3-, and
NH4+ in the subsurface layer, significantly decreased with time.
Significance is based on linear regression. Grey shading indicates the
95 % confidence interval of regression.
ResultsEnvironmental characteristics of the snowpack
The concentrations of NO3- and NH4+ ranged from 0.44 to
5.09 and 0.17 to 0.62 mg L-1, respectively (Fig. 1a,
Table S1 in the Supplement), and they were both significantly higher in the
subsurface than in the surface snow (Wilcoxon rank-sum test: all p<0.001; Fig. 1a). K+ and SO42- ions in the subsurface snow
were also significantly higher (0.29±0.13 and 6.09±3.18 mg L-1, respectively) than those in the surface snow (0.12±0.08
and 3.71±1.64 mg L-1; Wilcoxon rank-sum test: p<0.001
and p=0.015, respectively). The concentrations of DOC ranged from 0.46 to
5.89 mg L-1 and exhibited no significant difference between the surface
and subsurface snow (Wilcoxon rank-sum test: p=0.310). The concentrations
of Na+ ions ranged from 0.35 to 7.34 mg L-1, and there was no significant
difference between the surface and subsurface snow (Wilcoxon rank-sum test:
p=0.079). The concentration of NO3- and NH4+ ions in
the surface snow exhibited a weak but significantly positive association
with time (F1,16=5.97, p=0.027, and R2=0.27 and F1,16=8.58, p=0.010, and R2=0.35, respectively; Fig. 1b). On the
other hand, stronger negative associations were found between inorganic
nitrogen and time in the subsurface snow (F1,16=40.66, p<0.001, and R2=0.72 and F1,16=50.74, p<0.001, and R2=0.76, respectively). Other environmental parameters exhibited no
significant changes with time.
Taxonomic composition of bacterial community in snow. Only dominant
phyla are presented (relative abundance >1 %). The snow
community are dominated by Alphaproteobacteria, Actinobacteria,
Cyanobacteria, Gammaproteobacteria, Bacteroidetes, Firmicutes, Chloroflexi,
Gemmatimonadetes, Planctomycetes, Acidobacteria, Deltaproteobacteria, and
Deinococcus-Thermus.
Diversity and composition of bacterial community from the snowpack
The surface and subsurface snow were both dominated by Alphaproteobacteria,
Actinobacteria, Cyanobacteria, Gammaproteobacteria, Bacteroidetes,
Firmicutes, Chloroflexi, Gemmatimonadetes, Planctomycetes, Acidobacteria,
Deltaproteobacteria, and Deinococcus-Thermus (Fig. 2). The relative
abundance of most of these phyla was not significantly different in the two
snow layers except the Gemmatimonadetes, Planctomycetes, and Acidobacteria,
which were significantly more abundant in the surface layer than in the
subsurface layer (all p<0.05, Wilcoxon rank-sum test;
Fig. S5). In the surface layer, weak but significant negative trends were
observed between the relative abundances and ASV number of
Alphaproteobacteria, Gammaproteobacteria, and Firmicutes and time
(F1,16=6.97, p=0.018, and R2=0.30; F1,16=23.8,
p<0.001, and R2=0.60; and F1,16=22.28, p<0.001, and R2=0.58 in relative abundance; F1,16=7.56, p=0.014, and R2=0.32; F1,16=27.12, p<0.001, and R2=0.63; and F1,16=16.68, p=0.001, and R2=0.51 in ASV
number, respectively), while weak positive correlations were observed
between the relative abundances and ASV number of Cyanobacteria and
Deinococcus-Thermus and time (F1,16=6.94, p=0.018, and R2=0.30 and F1,16=13.10, p=0.002, and R2=0.45 in
relative abundance; F1,16=3.42, p=0.083, and R2=0.18 and
F1,16=4.07, p=0.061, and R2=0.20 in ASV number,
respectively; Figs. S6 and S7 in the Supplement). Relative to the surface snow,
the subsurface layer had a stronger negative correlation between the relative
abundance and ASV number of Alphaproteobacteria and Firmicutes and time
(F1,16=15.17, p=0.001, and R2=0.49 and F1,16=15.43, p=0.001, and R2=0.49 in relative abundance; F1,16=18.98, p=0.083, and R2=0.54 and F1,16=15.17, p=0.001, and
R2=0.53 in ASV number, respectively; Figs. S6 and S7),
while weak correlations were observed between the relative abundance and ASV
number of Cyanobacteria and Chloroflexi and time (F1,16=5.62,
p=0.031, and R2=0.26 and F1,16=12.81, p=0.003, and R2=0.44 in relative abundance; F1,16=5.34, p=0.034, and R2=0.25 and F1,16=14.49, p=0.002, and R2=0.47 in ASV
number, respectively).
Bacterial alpha diversity in snow layers. (a) Bacterial
alpha diversity comparison between the surface and subsurface layers. Each
dot represents an individual sample. For both Shannon and Chao1 indices, no
significant difference was observed between the surface and subsurface snow
layers. The comparison is based on Wilcoxon rank-sum test. (b) Temporal changes
in the alpha diversity indices in the surface and subsurface snow layers.
For the surface layer, no significant correlation was observed, while both
Shannon and Chao1 showed a significantly reduction with time in the
subsurface layer. Significance is based on linear regression. Grey shading
indicates the 95 % confidence interval of regression.
The influence of environmental factors on bacterial
diversity. Correlations of Shannon (a, c) and Chao1 (b, d) diversity
indices with environmental factors in the surface and subsurface layers.
Each dot represents an individual sample. The solid and dashed lines
indicate significant and nonsignificant changes, respectively. Significance
is based on linear regression. Grey shading indicates the 95 % confidence
interval of regression.
The bacterial Shannon and Chao1 indices in the surface snow were 5.61±0.39 and 744±199, respectively, and were not significantly
different from those in the subsurface layer (5.52±0.68 and 705±269, respectively) (p=0.81 and 0.57, respectively) (Fig. 3a). In
the surface snow, the Shannon and Chao1 indices were similar across the 9 d (F1,16=0.37, p=0.553, and R2=0.02 and F1,16=0.01, p=0.939, and R2=0.001, respectively; Fig. 3b).
Besides, weak positive associations of Shannon and Chao1 indices with the
DOC and sodium ions were detected (F1,16=4.90, p=0.042, and R2=0.23 and F1,16=4.91, p=0.042, and R2=0.24, respectively; Fig. 4a and b). In contrast, although weak,
significant negative correlations were observed in both Shannon and Chao1
indices with time in the subsurface snow (F1,16=12.33, p=0.003, and R2=0.44 and F1,16=8.73, p=0.009, and R2=0.35, respectively). Weak but significant positive associations of Shannon
and Chao1 indices with the concentrations of NO3- and
NH4+ were detected (Shannon diversity: F1,16=9.13, p=0.008, and R2=0.36 and F1,16=5.17, p=0.037, and R2=0.24, respectively; Chao1 index: F1,16=8.60, p=0.009, and
R2=0.36 and F1,16=5.32, p=0.035, and R2=0.25,
respectively; Fig. 4c and d). This is consistent with the multiple linear
regression results, which consistently identified the concentrations of
NO3- and NH4+ as the significant determinants of
bacterial Shannon diversity in the subsurface layer (Table S2).
Principal coordinate analysis (PCoA) of microbial communities
in the surface and subsurface snow. (a) Bray–Curtis distance-based PCoA
ordination plot. The microbial community structures of the surface and
subsurface snows are significantly different (PERMANOVA, p<0.001).
(b) Pairwise regression analysis between PCoA scores and sampling time. The
solid and dashed lines indicate significant and insignificant changes (based
on linear regression), respectively. The PCoA1 scores for the bacterial
community in the surface layer exhibit no significant correlation with time,
while the PCoA2 scores significantly correlated with time. The PCoA1 and
PCoA2 are both significantly correlated with time in the subsurface layer.
Grey shading indicates the 95 % confidence interval of regression.
Bacterial community structure and functional genes
The bacterial community structure at the ASV level significantly differed in
the surface and subsurface snow (PERMANOVA, F=2.78, p<0.001; Fig. 5a), as well as among the different sampling times (PERMANOVA, F=3.31 and p<0.001 and F=2.17 and p<0.001, respectively).
Additionally, a significant interactive effect was detected between the
depth and time (PERMANOVA, F=2.68, p<0.001), indicating that the
depth influenced the temporal pattern of bacterial community structure
changes. Specifically, only the second principal coordinate (PCoA2) values
of the surface snow significantly varied with time (F1,16=141.8, p<0.001, R2=0.89; Fig. 5b), while the PCoA1 values of the surface
snow did not (F1,16=0.04, p=0.840, R2=0.003; Fig. 5b).
Furthermore, PCoA1 and PCoA2 of the surface snow exhibited no significant
correlation with the measured environmental factors (all p>0.05; Figs. S8 and S9). In comparison, both PCoA1 and PCoA2 values of
the subsurface, albeit weakly, co-varied with time (F1,16=6.35, p=0.023, and R2=0.28 and F1,16=8.38, p=0.011, and R2=0.34, respectively; Fig. 5b), while the PCoA2 also demonstrated a significant
association with nitrate, ammonium, potassium, sulfate, and DOC
concentrations (all p<0.05; Fig. S9).
Normalized stochasticity ratio (NST) was used to examine the relative
contributions of stochasticity and determinism in shaping bacterial
communities. The average NST values were 74 % and 46 % in the surface
and subsurface snow layers, and the contribution of stochasticity was
significantly higher in the surface than in the subsurface layers (p<0.001; Fig. S10).
Results of Mantel test showing the relationships between
bacterial community composition and environmental factors in the surface and
subsurface snow. Significant correlations are in bold.
Mantel tests were performed to evaluate the effects of environmental factors
on bacterial community structure for each layer. No significant correlation
was identified between the measured environmental factors and the bacterial
community structure in the surface snow. However, weak positive associations
were apparent in the subsurface snow with the concentrations of
NO3- and NH4+ (p=0.005 and 0.01, respectively) (Table 1). The relative abundance of nitrogen-cycling-associated functional genes
was predicted in the surface and subsurface snow. The relative abundance of the
nitrogen-fixation marker gene (nifH) positively associated with time in the
surface layer, while no clear pattern was observed in the subsurface layer
(F1,16=7.76, p=0.013, and R2=0.33 and F1,16=0.57, p=0.461, and R2=0.01, respectively; Fig. S11). The
relative abundance of the narG gene, which is involved in the nitrate reduction and
denitrification process, exhibited negative and positive associations with
time in the surface and subsurface, respectively (F1,16=4.69, p=0.046, and R2=0.23 and F1,16=11.24, p=0.004, and R2=0.41, respectively). The nirK gene, which is also involved in the
denitrification process, decreased with time in the surface layer, while no
significant change was detected in the subsurface layer (F1,16=10.39, p=0.005, and R2=0.39 and F1,16=1.98, p=0.179, and
R2=0.05, respectively).
Bacterial co-occurrence networks for the surface and subsurface
layer communities. Each node represents a bacterial amplicon sequence
variant (ASV). The solid red lines represent positive correlations, and the
solid blue lines represent negative correlations. Nodes are colored by
taxonomy at the phylum level. The subsurface community networks are more
complex with a higher positive-to-total correlation ratio.
Topological properties of the empirical networks for the
surface and subsurface bacterial communities.
SurfaceSubsurfaceNo. of nodes197140No. of edges436523Number of edges per node2.213.73Positive links363500Negative links7322Ratio of positive-to-total interactions83 %95 %Modularity0.650.40No. of modules2312Average connectivity4.417.36Average clustering coefficient (avgCC)0.310.39Average path distance (GD)5.514.72Average degree (avgK)4.437.57Graph density0.020.06Transitivity (trans)0.450.49Connectedness (con)0.710.86Interspecies interactions at the surface and subsurface layers
Co-occurrence networks were constructed for the surface and subsurface
bacterial communities to infer the biotic interactions among species (Fig. 6). The surface network comprised a higher number of nodes (each indicating
one ASV, node number =197) but a lower number of edges (each indicating
a significant association between two ASVs; edge number =436) than the
subsurface network (node number =140 and edge number =523, Table 2).
The network in the subsurface snow, relative to surface snow, demonstrated a
higher number of edges per node (3.73 and 2.21, respectively), higher
average connectivity (avgK; 7.57 and 4.43, respectively), and lower average
path distance (GD; 4.72 and 5.51, respectively), which indicate a
substantially more complex network topology. Both networks were dominated by
positive (co-presence) relationships, and the subsurface network exhibited a
higher positive-to-total interaction ratio (95 %) than the surface network
(83 %).
Modularity, average clustering coefficient (avgCC), and graph density of the
surface and subsurface bacterial community networks were all higher than
those of random networks (Table S3), indicating that snowpack
bacterial networks showed non-random assemblage and exhibited modular
structures. The subsurface networks showed higher values of avgCC (0.39),
transitivity (0.49), and connectedness (0.86) than the surface bacterial
community network (0.31, 0.45, and 0.71, respectively), indicating a greater
degree of connectivity (Table 2).
DiscussionRapid shifts of bacterial community structure across a short temporal
scale
The surface and subsurface snow was dominated by Alphaproteobacteria,
Actinobacteria, Cyanobacteria, Gammaproteobacteria, and Bacteroidetes (Fig. 2). Despite differences in sampling season, the bacterial taxa detected were
consistent with previous studies on snow in the Arctic and Antarctic (Larose
et al., 2010; Carpenter et al., 2000; Amato et al., 2007; Lopatina et al.,
2013; Møller et al., 2013). Bacterial richness and diversity exhibited
little change throughout the 9 d in the surface snow layer, while they
exhibited a reduction trend in the subsurface snow layer (Fig. 3b). This
indicates that the microbiome in the subsurface snow may be subjected to
greater environmental filtering than those in the surface snow (Xiang et
al., 2009). Among all environmental factors measured, nitrate and ammonium
were the only measured environmental factors that changed across the 9 d. The nitrate and ammonium concentrations in the subsurface snow both
exhibited an R2 value of greater than 0.7 and reduced with time,
therefore indicating a consumption process (Fig. 1b). Despite the R2
value being weak, both nitrate and ammonium concentrations co-varied with
bacteria richness and diversity in subsurface snow, which was not observed
in the surface snow (Fig. 4). Furthermore, multiple linear regression
analyses also identified nitrate and ammonium to be the dominant driver of
bacterial Shannon diversity in the subsurface snow (Table S2).
Thus, these results suggest that nitrate and ammonium could play a more
important role in influencing bacterial diversity in subsurface snow than
that in surface snow. Nitrogen is an essential nutrient for microbial growth,
and it plays an important role in controlling microbial diversity and ecosystem
productivity (Vitousek et al., 2002; Xia et al., 2008; Sun et al., 2014).
The positive associations between nitrogen concentration and alpha diversity
indices have been typically inferred as nitrogen limitation (Telling et al.,
2011). Thus, these results hint that nitrogen limitation could occur in
subsurface snow and influence bacteria diversity. In comparison, the surface
layer is unlikely to be subjected to nitrogen limitation, and the nitrogen in
the surface snow slightly increased. This is consistent with previous
studies on the Greenland ice sheet, where nitrate additions to surface ice
did not alter the cryoconite community cell abundance and 16S rRNA
gene-based community composition (Cameron et al., 2017).
The bacterial community structure also exhibited temporal changes in the
subsurface layer. Furthermore, associations between nitrogen and the
microbial community structure were observed to a certain degree (Table 1 and
Fig. 5), again indicating some level of environmental filtering (Kim et al.,
2016). This is consistent with the finding in the Arctic that nitrogen
influences snow bacterial community composition via regulating algae
metabolism (Lutz et al., 2017). This is also consistent with the higher
contribution of deterministic processes in the subsurface layer than in the
surface layer (Fig. S10). Deterministic processes could be due
to environmental filtering or biotic interactions, whereas stochastic
processes include dispersal limitation, community drift, and speciation
(Stegen et al., 2012). The surface layer could receive nitrogen input
through aeolian deposition processes (Björkman et al., 2014), whereas
the subsurface snow could only receive limited external microbial and
nutrient input through supraglacial meltwater. The latter could be
particularly limited during the glacier deposition period when the glacier
surface temperature is below 0 ∘C (Fig. S2).
Our results suggest that both bacteria and snow physiochemical properties
experience changes across the 9 d during the snow deposition period for
the Tibetan glacier investigated here, and those changes were stronger
in the subsurface layer than in the surface layer. Traditionally,
supraglacial snow is recognized as a cold oligotrophic environment with a
very slow metabolism rate (Quesada and Vincent, 2012; Marshall and Chalmers,
1997), but increasing evidence has suggested that bacterial community
changes can occur on a short temporal scale. For example, Hell et al. (2013)
reported changes in the dominant bacterial phylum Proteobacteria across 5 d, and active bacterial metabolism has been observed in the Greenland Ice
Sheet supraglacial ice (Nicholes et al., 2019). In addition, active bacteria
affiliated with Proteobacteria have been identified in the Antarctic
(Lopatina et al., 2013) and Arctic (Holland et al., 2020) snow at
temperatures below 0 ∘C, therefore supporting the present study that
bacterial community changes in 9 d could be possible. This indicates
that supraglacial snow can harbor an active bacterial community, which in
turn can have an impact on nutrient transformation.
Distinct nitrogen-transformation processes in surface and subsurface
snow
Both ammonium and nitrate concentrations showed a weak increasing trend with
time in the surface snow (Fig. 1). The weak increase in ammonium could be
explained by biogenic emissions due to local plant and animal sources
(Filippa et al., 2010), while the increase in nitrate has been largely
attributed to atmospheric deposition (Björkman et al., 2014). Nitrogen
deposition occurs at a rate of 282 kg N km-2 yr-1 in the region
of our investigation (Lü and Tian, 2007), which equals 0.19 mg N for
the 0.5 m × 0.5 m area sampled each day (assuming nitrogen
deposition occurred evenly across the year). Another potential source of
nitrogen input could be the nitrogen fixation process (Telling et al., 2011).
Bacteria are the only microorganisms that are capable of fixing atmospheric
nitrogen (Bernhard, 2010). Potential nitrogen input from microbial processes
is supported by the increase in the nitrogen-fixing Cyanobacteria
(Fig. S6) and nifH gene (Fig. S11). Cyanobacteria
are known as free-living phototrophs capable of nitrogen fixation,
especially in extreme environments (Chrismas et al., 2018; Makhalanyane et
al., 2015; Levy-Booth et al., 2014). For example, Cyanobacteria were found
to be the main group of potential nitrogen fixers determined by quantitative
PCR with three sets of specific nifH primers on the surface of the Greenland Ice
Sheet (Telling et al., 2012). The nitrogen fixation rate was not quantified
in the present study, but the present study suggests that microbial nitrogen
fixation could be an overlooked source of nitrogen in Tibetan glacier snow.
Further transcriptomic and nitrogen-isotope analyses may provide additional
evidence on the microbial activity in nitrogen fixation.
In contrast with the surface layer, nitrogen concentrations (nitrate and
ammonium) significantly decreased in the subsurface snow with time (Fig. 1).
A possible explanation for this might be the microbial utilization and
photochemical degradation of nitrogen compounds (Björkman et al., 2014).
The microbial processes, i.e. nitrate reduction and denitrification process,
are evidenced by the increase in the narG gene (Fig. S11) (Telling et
al., 2011; Zhang et al., 2020). Alternatively, microorganisms may carry out
assimilatory nitrate reduction, which is used to incorporate nitrogen into
biomolecules (Larose et al., 2013a; Richardson and Watmough, 1999). The
assimilatory process is performed by a range of microorganisms including
bacteria, algae, yeasts, and fungi (Huth and Liebs, 1988). Thus, further
studies on eukaryotes, including algae, may provide a full understanding of
the nitrogen consumption mechanisms in subsurface snow. The denitrification
process converts nitrate to N2 and generates nitrite, nitric oxide
(NO), and nitrous oxide (N2O) intermediates (Kuypers et al., 2018). A
previous study detected microbial-specific phylogenetic probes that targeted
genera whose members are able to carry out denitrification reactions such as
Roseomonas in a snowpack of Spitsbergen Island of Svalbard, Norway (Larose
et al., 2013a). Amoroso et al. (2010) also proposed that denitrification can
explain the microbial isotopic signature observed in winter snow at
Ny-Ålesund. Although the oxygen level in the subsurface snow was not
measured, the occurrence of anaerobic denitrification reactions in
subsurface snow has been reported in Arctic snowpacks (Larose et al.,
2013a). Lastly, photochemical degradation of nitrogen compounds is the most
well-known nitrogen degradation pathway, and the release of both NO and
NOx by NO3- photolysis on natural snow has been reported in
European high Arctic snowpack (Amoroso et al., 2010; Beine et al., 2003). In
a snow reactive nitrogen oxide (NOy) survey in Greenland, NOy
flux was reported to exit snow in 52 out of 112 measurements (Dibb et al.,
1998). Further metatranscriptomic analyses targeting the genes associated
with nitrogen cycling are required to confirm the distinct nitrogen
transformation processes between the surface and subsurface layers.
Subsurface snow exhibits greater complexity in biotic interactions
Biotic interactions can explain a substantial proportion of the community
structure variations (Hacquard et al., 2015; Dang and Lovell, 2016). Our
results indicated that the subsurface community network was more complex as
evidenced by the higher average connectivity and a shorter path length (GD)
compared to the surface community network (Table 2). This is likely due to
the enhanced environmental filtering, as has been observed in other systems
subjected to environmental stresses (Ji et al., 2019; Wang et al., 2018). A
higher ratio of positive-to-total interactions, but lower modularity, was
identified in the subsurface snow network (Table 2). In general, higher
positive interactions indicate increased microbial cooperation (Ju et al.,
2014; Scheffer et al., 2012), whereas a reduction in modularity indicates
microbial niche homogenization (Ji et al., 2019). The enhanced biotic
associations and cooperation in the subsurface layer may be attributed to
the occurrence of denitrification processes, as denitrification is a
multi-step process that involves multiple bacterial cohorts to complete the
process (Henry et al., 2005; Madsen, 2011; Yuan et al., 2021). The enhanced
collaboration and deterministic succession were previously reported in the
bacterial community associated with the anoxic decomposition of microcystis
biomass (Wu et al., 2020), while cross-feeding was shown to enhance positive
interactions among the different members of the community (Borchert et al.,
2021).
The path lengths of the subsurface network were lower than that of the
surface layer (Table 2). The shorter path length has been proposed to be
associated with a higher transfer efficiency of information and materials
across the microorganisms in the network (Du et al., 2020) which are
required for complex biological processes that require extensive bacterial
collaboration, such as denitrification (Yuan et al., 2021). Thus, the short
path length is consistent with the dominance of denitrification processes in
the subsurface layer. Previous studies have proposed microbial interactions
as biotic drivers that impact microbial diversity (Calcagno et al., 2017;
Hunt and Ward, 2015). Thus, those microorganisms which are not adapted to the
subsurface environment would be excluded from the environment, which
provides an alternative explanation for the reduction in diversity (Scheffer
et al., 2012; Ziegler et al., 2018; Bergk Pinto et al., 2019).
Conclusion
Our results showed the dynamics of nitrogen and the bacterial community in
supraglacial snow over 9 d. Inorganic nitrogen was unchanged or
slightly increased in the surface snow, while it decreased in subsurface
snow. Due to atmospheric nitrogen deposition and potential bacterial
nitrogen fixation activities, nitrogen limitation is unlikely to occur in
the surface snow. In contrast, nitrogen consumption was inferred in the
subsurface snow. Nitrogen is traditionally recognized to be released from
the supraglacial environment due to photolysis, whereas this study hints
that nitrogen assimilation and denitrification could be alternative routes.
Therefore, the increased nitrogen deposition due to anthropogenic activities
may enhance the nitrogen consumption in the subsurface snow, which reduces
the impact of increased nitrogen discharge on downstream glacier-fed rivers.
In summary, our results provide a new perspective on the nutrients and
bacterial community dynamics in supraglacial snow of the Tibetan Plateau.
Further studies based on metagenome and metatranscriptome can enhance the
understanding of bacterial functions.
Data availability
Sequence data generated in the present study have been deposited at the
National Center for Biotechnology Information (NCBI) Sequence Read Archive
under the ID PRJNA649151 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA649151, last access: 28 July 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/tc-16-1265-2022-supplement.
Author contributions
YL and MJ conceived the study and developed the idea. YC performed DNA
extraction. YC and FW performed the environmental characterization measure.
YC conducted the data statistical analysis. YC and KL wrote the first draft
of the paper, and MJ, TJVM, and YL revised the paper substantially. All
authors read and approved the final paper.
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 Alexandre Magno Barbosa Anesio, Zhengquan Gu, Paudel Adhikari Namita, Zhihao Zhang, and Xuezi Guo for their valuable input related to writing, revising, or providing maps of the sampling sites.
Financial support
This work was supported by the National Key Research and Development Program of China (grant no. 2019YFC1509103), the National Natural Science Foundation of China (grant no. 91851207), the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (grant
no. 2019QZKK0503), and the Strategic Priority Research Program (A) of the Chinese Academy of Sciences (grant no. XDA20050101).
Review statement
This paper was edited by Elizabeth Bagshaws and reviewed by two anonymous referees.
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