The Rajang River is the main drainage system for central Sarawak in
Malaysian Borneo and passes through peat domes through which peat-rich material
is being fed into the system and eventually into the southern South China
Sea. Microbial communities found within peat-rich systems are important
biogeochemical cyclers in terms of methane and carbon dioxide sequestration.
To address the critical lack of knowledge about microbial communities in
tropical (peat-draining) rivers, this study represents the first seasonal
assessment targeted at establishing a foundational understanding of the
microbial communities of the Rajang River–South China Sea continuum. This
was carried out utilising 16S rRNA gene amplicon sequencing via Illumina
MiSeq in size-fractionated samples (0.2 and 3.0 µm GF/C filter
membranes) covering different biogeographical features and sources from
headwaters to coastal waters. The microbial communities found along the
Rajang River exhibited taxa common to rivers (i.e. predominance of β-Proteobacteria) while estuarine and marine regions exhibited taxa that were common to the
aforementioned regions as well (i.e. predominance of α- and γ-Proteobacteria). This is in agreement with studies from other rivers which observed
similar changes along salinity gradients. In terms of particulate versus
free-living bacteria, nonmetric multi-dimensional scaling (NMDS) results
showed similarly distributed microbial communities with varying separation
between seasons. Distinct patterns were observed based on linear models as a
result of the changes in salinity along with variation of other
biogeochemical parameters. Alpha diversity indices indicated that microbial
communities were higher in diversity upstream compared to the marine and
estuarine regions, whereas anthropogenic perturbations led to increased
richness but less diversity. Despite the observed changes in bacterial
community composition and diversity that occur along the continuum of the Rajang River to the sea, the PICRUSt predictions showed minor variations. The results
provide essential context for future studies such as further analyses on the
ecosystem response to anthropogenic land-use practices and probable
development of biomarkers to improve the monitoring of water quality in this
region.
Introduction
Biogeochemical transformations are primarily governed by microbial
communities (Konopka, 2009), and it is crucial to
understand their dynamics in order to predict biosphere modulations in
response to a changing climate. Despite the importance of freshwater to
society and despite hosting the highest microbial diversity
(Besemer et al., 2013), microbial community composition and
diversity in freshwater habitats, especially in lotic environments, are much
less studied compared to marine and soil communities
(Kan, 2018).
Lotic environments are the interface between soil and aquatic environments
and aquatic environments as terrestrial environments seed microbes into the
adjacent water column due to surface runoff (Crump
et al., 2012). Until recently, rivers were thought to be passive channels in
the carbon (C) cycling and weathering products until it became clear that
rivers regulate for example the transfer of nutrients from land to coastal
areas (Smith and Hollibaugh, 1993). Several studies
have shown that bacteria are key players in nutrient processing in
freshwater systems (Cotner and
Biddanda, 2002; Findlay, 2010; Madsen, 2011).
Zhang et al. (2018a) stated that the
organic matter composition is strongly modified by bacteria as well as its
resistance to degradation. Bacteria strongly influence the fluvial organic
matter, hence playing a role in carbon cycle
(Dittmar et al., 2001)
and recent studies in the Rajang River have demonstrated that, as indicated
by high concentrations of D-form amino acids (Zhu et al., 2019). Moreover,
it was demonstrated by Jiang et al. (2019) that dissolved organic nitrogen was mineralised to NH4+,
again highlighting the biogeochemical activity and the importance of
microbes in the Rajang River. Until now, there has, however, been no study
on their diversity, a gap that this study aims to fill. Thus, it is
essential to understand the dynamics and structure of microbial communities
in them to assess their contribution towards biogeochemical fluxes such as
carbon and nitrogen (Battin et al., 2008;
Raymond et al., 2013), as well as phosphorus cycling
(Hall et al., 2013). In
addition, the fluxes as well as transformations of organic matter as well as
nutrients in aquatic systems are environmentally driven by parameters such
as temperature or the availability of nutrients in these ecosystems
(Welti et al., 2017). In turn,
various gradients (i.e physical, chemical, hydrological or even biological)
contribute to the changes in the microbial diversity and distribution living
within the lotic environments (Zeglin, 2015).
Next-generation sequencing technologies have enabled a better understanding
of the rare or unculturable biosphere which traditional culture methods
would not have been able to elucidate
(Boughner and Singh,
2016; Cao et al., 2017). Only a few studies assessing bacterial community
composition have been undertaken in lotic or riverine environments
(Fortunato et al.,
2012; Ladau et al., 2013; Zwart et al., 2002), with even fewer focusing on
the diversity of surface-attached biofilms in lotic environments,
particularly in comparison to biofilm studies in benthic habitats
(Zeglin, 2015). Furthermore, bacterial assemblages
on suspended particles were shown to differ from free-living
bacterioplankton in a number of studies
(Bidle and Fletcher, 1995;
Crump et al., 1999) in which the ratios between both fractions are often
influenced by the quality of suspended particulate matter (Doxaran et al.,
2012). Even fewer studies attempt to map bacterial community composition in a
river-to-sea continuum across multiple seasons and habitats
(Fortunato et al., 2012), and it was only recently reported that
the most abundant riverine bacterioplankton resemble lake bacteria and can
be regarded as “typical” freshwater bacteria
(Lozupone and Knight, 2007; Zwart
et al., 2002). Metagenomics studies substantiated the dominance of
Proteobacteria and Actinobacteria while Bacteroidetes, Cyanobacteria and Verrucomicrobia were also found to be abundant in rivers
(Cottrell
et al., 2005; Kolmakova et al., 2014; Lemke et al., 2009; Newton et al.,
2011; Read et al., 2015; Staley et al., 2013). While there are studies
related to the freshwater–marine gradients of rivers such as studies by
Crump and Hobbie (2005) and
Fortunato et al. (2013) and tropical peatlands
(Kanokratana
et al., 2011; Mishra et al., 2014; Yule et al., 2016; Too et al., 2018), to
the authors' knowledge, this is the first study which links both
freshwater–marine gradients as well as tropical peatlands as a cohesive
component (i.e. tropical peat-draining river to coastal ecosystem). Due to
their high diversity and fast generation time, microbial communities
(Hunt and Ward, 2015) are the first responders to
environmental changes (both natural and anthropogenic events such as storms,
upwelling and pollutants). Liao et
al. (2019) showed that extensive agricultural land-use in the inter-tidal
region of a watershed resulted in the prevalence of bacteria pathogen-like
sequences, whereas Bruland et al. (2008)
stated that the assemblages of microbes also vary temporally as a function
of oceanographic conditions, river discharge, tidal phase and season.
This study focuses on the Rajang River, which is the longest river in
Malaysia and one of the most socio-economically important peat-draining
rivers in South East Asia. It transports large amounts of terrestrial
material (Müller-Dum et al.,
2019), experiences two monsoonal seasons (Sa'adi et
al., 2017) and is subject to anthropogenic disturbances
(Gaveau
et al., 2016; Miettinen et al., 2016). Thus, it is fundamental to take into
consideration both seasonal and anthropogenic influences on the microbial
communities of the Rajang River. Given the rapid development in Sarawak and
the importance of microbes in several biogeochemical processes in the Rajang
River
(Jiang
et al., 2019; Martin et al., 2018; Müller-Dum et al., 2019; Zhu et al.,
2019), it is imperative to study the microbial communities to enable future
predictions and management responses. The Rajang River offers the
opportunity to study the microbial diversity along a river-to-sea continuum
and at the same time assess the influence of natural conditions such as seasons
(dry vs. wet), different soil types (peat vs. mineral soil) and
anthropogenic disturbances (e.g human settlements and plantations) on
microbial succession. This study aims to investigate (1) the microbial
community structure, diversity and probable function across wet and dry seasons in
order to (2) understand the underlying factors that may influence the
spatial and seasonal distribution of the prokaryotic communities and the
nutrient dynamics involved in the Rajang River.
MethodologyStudy area and sampling strategy
This study was conducted along ∼300 km of the Rajang River in
Sarawak, Malaysia (Fig. 1a). The region has an equatorial climate
characterised by constant temperatures, high extensive rainfall and high
humidity
(Wang
et al., 2009, 2005; see also Supplement Fig. S1). The Rajang delta system consists
of an alluvial valley, an associated coastal plain and a delta plain (Staub
and Esterle, 1993). The coastal plain is dissected into several small
tributaries, namely Igan, Lassa, Paloh and Rajang (Fig. 1a). The shoreline
experiences tides and seasonally strong waves ranging from 3–6 m with
intensity increasing from the east to the west. According to
Wetlands International (2015), the land surrounding the
study sites is characterised by land use change (Fig. 1b) and a range of
anthropogenic activities, such as oil palm and sago plantations (Fig. 1c),
human settlements, and transportation and sand dredge.
Location of Rajang River within Sarawak, Malaysia (inset). Panel (a) shows
the stations sampled during three different cruises: August 2016 (red
triangles), March 2017 (blue circles) and September 2017 (cyan diamonds).
(b) GIS data from 2010 (Sarawak Geoportal, 2018) indicating various forest
types. Red colour represents non-forest areas (2010), yellow represents
non-forest areas (2013), light green represents primary forests, teal
represents secondary forests, and dark green represents potential peat
swamp forests. (c) Digitised NREB map obtained from Wetlands
International (2015). The map shows the plantation cover as determined from
Landsat showing licensed oil palm and sago plantations (licensed).
A total of 59 water samples were collected along salinity gradients during
three cruises (Fig. 1a), covering both wet and dry seasons as well as
different source types (i.e. mineral or peat soils). Source types sampled
were grouped as follows: (1) marine, (2) brackish peat, (3) freshwater peat and
(4) mineral soils. From Sibu towards Kapit (upriver), the riparian zone is
mineral soil, whereas from Sibu downwards to the coast it consists of peat
which was then further divided into freshwater (salinity 0 to
∼1 PSU) and brackish water (salinity 2–28 PSU). The August 2016
cruise (coloured red) is classified as the dry season based on the lower
mean rainfall value as compared to the other two (March and September 2017), in which both are classified as the wet season (refer to
Fig. S1). The cruise in August 2016 represented the highest sampling
frequency in order to obtain complete coverage of representative regions,
while the cruises in March and September 2017 were aimed to obtain seasonal
representatives for each region. Approximately 250–500 mL of water was
filtered through 3.0 µm pore size track-etched membranes
(Nucleopore™, Whatman, Germany) via vacuum filtration. This was
referred to as the “Particulate-attached” fraction. The filtrate from the
3.0 µm portion was collected in a sterile glass bottle and
subsequently filtered through 0.2 µm pore size track-etched membranes
(Nucleopore™, Whatman, Germany). The smaller fraction was referred to
as “free-living” fraction. A total of 117 filters were recovered (1×3.0µm was discarded due to contamination) and immediately stored at -20 ∘C and sent to the Australian Centre for Ecogenomics (ACE),
Brisbane, for DNA extraction, library preparation and processing utilising
the Illumina platform (Bentley et al., 2008).
Illumina sequencing and bioinformatics analyses
Initial upstream processes were carried out by the Australian Centre for
Ecogenomics utilising the ACE mitag pipeline (ACE, 2016). The
primers utilised were based on the V3–V4 hypervariable regions of the 16S
rRNA gene. Briefly, fastq files generated from the Illumina platform were
quality-trimmed with fastqc, primer sequences were trimmed with Trimmomatic, and
poor quality sequences were removed using a sliding window of 4 bases with an
average base quality of more than 15. High-quality sequences were
subsequently processed using the mothur (Schloss et al.,
2009) pipeline. Sequences were aligned against the SILVA database
(Quast et al., 2013; Yilmaz et
al., 2014), a “pre.cluster” command was executed for de-noising, and chimeric
sequences were removed using the “chimera.vsearch” function. Chimera-free 16S
rRNA bacterial gene sequences were taxonomically assigned against the
EzTaxon database (Kim et al., 2012) using the
naïve Bayesian classifier with a threshold of 80 %. The
quality-filtered sequences were then clustered into operational taxonomic
units (OTUs) at 97 % similarity cutoff, with singleton OTUs being omitted.
In order to reduce bias caused by variations in sample size, high-quality
reads were randomly subsampled to 923 reads per sample. Apart from the
results and discussion shown for free-living and particle-attached bacteria,
the remaining discussion is based on the pooled results of both components.
The alpha diversity was calculated using the estimate_richness function embedded within the
plot_richness function found within the phyloseq package utilising R (v.3.5.3). For the analyses
of potential functional genes, Phylogenetic Investigation of Communities by
Reconstruction of Unobserved States (PICRUSt,
Langille et al., 2013) was utilised. The
metagenomics prediction table produced from PICRUSt was utilised to produce
pathway abundance profiles using HUMAnN2 (Franzosa et
al., 2018). It should be noted that the reconstructed functional genes were
based on the GreenGenes (DeSantis et al., 2006)
database and not the EzTaxon database used for the phylogeny.
Physico-chemical data and geochemical analyses
Monthly precipitation data for the period in between the cruises (August 2016 to
September 2017) were obtained from the Tropical Rainfall Measuring Mission
website (NASA, 2019) in order to gauge the seasonality (wet or
dry; see Fig. S1). In the laboratory, nutrients (nitrate,
NO3-; nitrite, NO2-; ammonium, NH4+;
Phosphate, PO43-; and silicate, SiO44-) were
photometrically determined utilising a SKALAR Sanplus continuous flow
analyser in the State Key Laboratory of Estuarine and Coastal Research
(SKLEC), Shanghai (details described in
Sia et al., 2019).
NH4+ and PO43- were determined manually following
Grasshoff et al. (1999). The total dissolved nitrogen
(TDN) and total dissolved phosphate (TDP) were determined indirectly by
obtaining the values for NO3- and PO43- via oxidation
with an alkaline-persulfate solution (Ebina et
al., 1983). The concentrations of dissolved organic nitrogen (DON) and
dissolved organic phosphorus (DOP) were estimated by subtraction of DIN from
TDN and PO43- from TDP, respectively. Belawai samples
(2∘13′47.16′′ N, 111∘12′19.04′′ E) were used in an
incubation experiment to study the net primary productivity and respiration
rate of the Rajang River. Technical triplicates were incubated in both light
and dark set-ups (refer to Supplement Table S1 for details).
Statistical analyses and distLM model
Ordination visualisation, non-metric multidimensional scaling (NMDS,
Kruskal–Wallis: Kruskal stress formula: 1; minimum stress: 0.01), similarity
analyses (ANOSIM) and coherence plots were executed using PRIMER 7
(Clarke and Gorley, 2015) to determine if the various terrestrial
source types or different land use impacted the bacterial community.
Permutational multivariate analysis of variance (PERMANOVA) was used based
on the Bray–Curtis dissimilarity of the Hellinger transformed resemblance matrix
to infer the impact of anthropogenic activities (land use) on the microbial
communities. By partitioning the community variation (using a Bray–Curtis
dissimilarity matrix resemblance), distance-based linear models (DistLM)
were used to determine the extent to which the bacterial community structure
can be explained by environmental variables
(Legendre and
Anderson, 1999). Normalising transformations of the environmental variables
were carried out prior to execution of DistLM analyses using the “Normalise
Variables” function in the PRIMER 7 software. A Hellinger transformed OTU
abundance table was used as the response variable for the variation
partition analysis. The authors would like to note that the distLM models
are based on only the August 2016 and March 2017 cruise as there was a lack
of physico-chemical data from the September 2017 cruise due to
malfunctioning equipment. Multi-collinearity between variables was tested
utilising the “Draftsman Plot” function in Primer 7 (Clarke and
Gorley, 2006; Fig. S1). However, it is sufficient to draw linkages
between the major drivers of microbial communities between seasons as March 2017 and September 2017 were considered wet seasons based on the average
precipitation (see Fig. S1).
ResultsClustering of samples according to ANOSIM global test scores
A total of 74 690 high-quality bacterial sequences were obtained from a
total of 117 samples, with 200 to 2615 sequence reads per sample. The
sequences were clustered into 2087 OTUs at the 97 % confidence interval.
Instead of displaying bacterial diversity by station, bacterial communities
were grouped together according to the R scores obtained from the ANOSIM
global test, with the parameters “cruise”, “source type” and “land use”
showing the highest scores (ANOSIM global R=0.737, P<0.001,
Table 1). Furthermore, multi-variate analysis showed that the microbial
community composition differed among the different land uses as well as sites
nested with land use and source type (Table 2).
ANOSIM global test scores based on various parameters.
Parameters tested, 999ANOSIMP valuepermutations, random samplingglobal test, RCruise (wet/dry season)0.4390.001Source type0.4220.001Land use0.1820.001Particle association0.0370.001Source type, land use0.4150.001Cruise, source type,0.7080.001particle associationCruise, source type, land use0.7370.001
Results of permutational multivariate analysis of variance
(PERMANOVA). DOF represents degrees of freedom.
Parameters tested, 9999 permutations,DOFFPpermutation of residualsunder a reduced modelLand use71.540.0016Site (nested with land use332.270.0001and particle attached)Site (nested with source132.600.0001type and land use)Shifts in bacterial community structure
The NMDS graph (2-D stress score: 0.18, Fig. 2) supported ANOSIM results by
clustering samples according to (i) source type and land use as well as (ii) cruises. The x axis (MDS1 scores) clearly reflects changes in terms of
salinity (river–sea continuum) while the y axis (MDS2 scores) emulates the
different cruises. It is apparent that there were seasonal variations as
shown from the lighter shade points, representing the August 2016 (dry
season) samples, compared to those with darker shades representing both
March and September 2017 (wet season) samples (Fig. 2). There were
clear overlaps of samples from mineral soil and freshwater peat origin. We
also observed a gradual shift of samples from mineral soils and freshwater
peat towards brackish and then marine samples.
Non-metric multi-dimensional scaling (NMDS) graph of samples according
to cruise, source type and land use.
Relative abundance (%) of dominant bacterial (at phylum level, top 10) along the various source types (marine, brackish peat, freshwater peat,
mineral soils) across three cruises/seasons.
Bacterial distribution according to source type and cruise
To further support that the four different source types support distinct
bacterial communities, the relative abundance was mapped into a percentage
plot (Fig. 3).
The core microbial communities along the Rajang River–South China Sea
continuum consist of Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, Deinococcus–Thermus and Cyanobacteria in varying abundances (Figs. 3, S4), indicating high variation within the system. The phylum
Deinococcus–Thermus was abundant in freshwater peat and in mineral soils, albeit at a lesser
extent compared to freshwater peat (Fig. 3). Taking
seasonality into consideration, the relative abundance (%) of Deinococcus–Thermus drastically decreased in
September 2017. Contrary, the abundance of Cyanobacteria was greater within marine as
well as brackish peat for the cruises of March and September 2017 but
not for August 2016. For the August 2016 cruise, Cyanobacteria were found throughout all
source types albeit at lower counts compared to the other cruises. Similar
changes in bacterial community were observed during different cruises but at
different sections of the river. For the freshwater peat and mineral soils,
the cruises of August 2016 and March 2017 had greater resemblance towards
each other. Furthermore, there was a distinct split in terms of the
bacterial community composition for the four source types across all
sampling cruises i.e. marine and brackish peat had similar composition and
freshwater peat and mineral soils had similar composition. In terms of a
river–sea continuum, the most apparent changes in the community composition
were observed during March 2017, which presented an almost step-wise change
in bacterial community composition.
Alpha diversity indices
Based on the observed indices (Fig. 4), mineral soils generally had the
highest counts of unique OTUs. However, during the September 2017 cruise,
the freshwater region had the highest values. Based on the Chao1 indices,
there was a significant effect of the source type on the observed richness
(p<0.001), with increasing values from marine to mineral soils. In
the March and September 2017 cruise, the Chao1 indices were found to
have greater variability as compared to the August 2017 cruise. For the
September 17 cruise, we observed increased values of Chao1 across the
brackish peat, freshwater peat and mineral soils. According to the
Shannon indices, the diversity of the microbial communities varied
significantly along the different source types (p<0.001). In the
dry season the Shannon indices were found to be higher than those of March
and September 2017 samples, except for the Brackish peat September 2017
samples. In terms of the Simpson diversity indices, the August 2016 season
was found to have the higher values as compared to the March 2017 and
September 2017 season.
The calculated α-diversity indices (observed, Chao1,
Shannon, Simpson and inverse Simpson) of the four different source type
along the salinity gradient.
Based on the effects of land use on the diversity indices (Fig. 5), the
sites which are surrounded by human settlements had higher observed indices
(regardless of the cruise), with the exception of the Shannon indices in
August 2016. Samples surrounded by secondary forest had the second-highest
values, with samples from August 2016 repeatedly higher than the other two
cruises. There were significant differences (p<0.001) between
samples from the coastal region with generally lower indices compared to
upstream samples (i.e. human settlement, oil palm and sago plantation, oil
palm plantation and secondary forest).
The calculated α-diversity indices (observed, Chao1,
Shannon, Simpson and inverse Simpson) of the land use types, which include coastal zone,
coastal zone with plantation (OP) influence, coastal zone with plantation
(sago and oil palm influence), human settlement, oil palm and sago mixed
plantation, oil palm plantation, and secondary forest.
The relative abundance of predicted functional profiles in the four
source types across two seasons based on KEGG pathways.
Functional profile of bacterial communities
Based on the potential KEGG pathways (Fig. 6), the functional profiles of
the microbial communities were predicted for the August 2016 and March 2017
samples. The main functions found were oxidative phosphorylation
(20.09 %), carbon fixation pathways in prokaryotes (19.00 %) and methane
metabolism (18.36 %), respectively. This was then followed by nitrogen
metabolism (11.50 %), carbon fixation in photosynthetic organisms
(7.67 %), and inorganic ion transport and metabolism (5.68 %). The remaining
functional groups were photosynthesis, sulfur metabolism, inositol
phosphate metabolism, phosphotransferase system (PTS), carbohydrate
metabolism, phosphonate and phosphinate metabolism, and lastly mineral
absorption (4.92 %, 4.31 %, 2.96 %, 2.34 %, 1.83 %, 1.11 % and
0.23 %, respectively). Clear differences were observed between source
types and seasons and potential KEGG pathways displayed similar composition
among samples originating from either (i) marine and brackish peat, or (ii) freshwater peat and mineral soil. In terms of gene abundances, the March 2017 samples (wet season) were found to have higher gene abundances, with the
highest counts in brackish peat followed by marine samples. However, marine
samples in August 2016 displayed slightly higher gene counts compared to the
brackish peat.
Distance-based linear model of bacterial communities and environmental
parameters
Marginal DistLM was performed in order to gauge the extent of
physicochemical parameters or environmental variables accounting for a
compelling proportion of variation in the bacterial communities. Significant
vectors of environmental variables (R2>0.3892, P<0.001) were calculated based on a linear model (DistLM) and plotted against
the bacterial community composition (Fig. 7). Salinity was the single best
predictor variable explaining bacterial community variation (15.27 %),
followed by DIP (10.57 %). The remaining physico-chemical parameters were
dissolved oxygen (DO, 9.64 %) and suspended particulate matter (SPM,
6.55 %), whereas for the biogeochemical parameters, silicate (9.27 %),
DOP (8.04 %), DON (6.37 %), dissolved organic carbon (DOC, 5.27 %) and
dissolved inorganic nitrogen (DIN, 4.29 %) respectively made up the
remaining variables (all variables P=0.001, except for DIN, P=0.002).
Distance-based redundancy analysis (dbRDA) plot of cruise, source type
and land use on a linear model (DistLM) of normalised predictor variables.
The distLM model clustered samples from the August 2016 cruise separately
from the March 2017 samples. Brackish peat, as well as marine samples from
August 2016, correlated more strongly with salinity, irrespective of land
use. On the contrary, the March 2017 samples were found to cluster
separately with DO. In addition, the August 2016 mineral soil samples
correlated with silicate.
Discussion
This study presents seasonal and spatial distribution of
particulate-attached and free-living bacteria in the longest river in
Malaysia in an attempt to map the bacterial community composition of the
water column across several habitats with relation to the riparian zones and
anthropogenic activities in a river-to-sea continuum. Our dataset develops a
comparison of the microbial community across two dimensions: spatial
biogeography from headwaters to the coastal zone as well as through time
(seasonally). The rich supporting dataset also allows us to assess
underlying nutrient dynamics influencing the microbial communities.
General diversity of core bacterial communities along the Rajang River–South China Sea continuum in comparison with global systems
The majority of bacterial taxa were restricted to a relatively small number
of assemblages. Dominant phyla typically found in Malaysian peat swamps such
as Proteobacteria (Kanokratana et
al., 2011; Too et al., 2018; Tripathi et al., 2016) are found throughout the
Rajang River, whereas Acidobacteria is not a major phylum in the Rajang River. However,
due to the heterogeneity of the Rajang River, substantial shifts in OTU
diversity were shown, while exhibiting successional changes in community
composition downstream. We observed abrupt shifts in terms of richness and
diversity as well as bacterial distribution, which were structured according
to macro-scale source types.
Staley et al. (2015)
proposed that variability in microbial communities was lower due to the
presence or absence of OTUs but likely due to shifts in their relative abundance.
While there were shifts in the community composition, overlap between the
core microbiome (i.e. free-living and particle-attached portions) of samples
were evident (Figs. S2, S8). The similar bacterial community
structure in terms of particle association was in line with studies by
Noble et al. (1997) and
Hollibough et al. (2000) in the Chesapeake Bay (winter
season) and San Francisco Bay, respectively. Hollibough et
al. (2000) demonstrated that the difference or similarity of the particle
association of bacterial community was due to the origin as well as
composition of the particles, particularly in marine snow or estuarine
particles. In the aforementioned study, there was limited metabolic
divergence and similar communities between the estuarine turbidity maxima
and the river samples. Due to the short residence time, the rapid exchange
of organisms likely reduced the divergence of phylogenetic composition. The
short residence time in the Rajang River likely reflected a similar scenario
to San Francisco Bay
(Müller-Dum et al., 2019).
When comparing with other rivers, the predominance of the Proteobacteria phylum, especially
within the brackish peat region (Figs. 3, S4), was similar to a
recent study on the Pearl River Delta
(Chen
et al., 2019). In another study by
Doherty et al. (2017) on
the main stem of the Amazon River (a blackwater-influenced river, similar to
the Rajang River), Actinobacteria were much more abundant (25.8 %) compared to the Rajang
River (11.95 %).
Factors determining bacterial community compositionSpatial and environmental drivers
As shown in Fig. 2, it can be observed that there was a continual shift in
microbial communities, suggesting mixing of the microbial communities from
the headwaters to the coast (Fortunato et al., 2012), which has
also been observed along the Upper Mississippi River
(Staley et al., 2015) and along
the Danube River (Savio et al., 2015). The
decrease in richness and evenness was similar to a study conducted by
Savio et al. (2015) in which the bacterial
evenness and richness declined downriver, which was in line with the river
continuum concept (Vannote et al., 1980). The presence of
peat did not affect the alpha diversity indices, which was reflected in the
shift in taxa occurring from freshwater (which includes freshwater peat)
towards the saline region (which includes brackish peat).
Salinity, DIP and DO are major environmental drivers of species distribution
(Peter et al., 2011;
Wilhelm et al., 2015). In this study, marine
and brackish peat samples correlated well with salinity. This was neatly
supported by the distribution of samples on the distLM fitted dbRDA graph
(Fig. 7), in which the affinity for each of the samples correlates to the
physical environment (e.g. the samples which group along the salinity vector
were the samples which correlate with the marine as well as brackish peat
region. The predominance of β-Proteobacteria in the freshwater region and the
predominance of α- and γ-Proteobacteria (Fig. S3) in the estuarine
region is typically the main group in seawaters
(Nogales et al., 2011) and similar to
findings by Silveira
et al. (2011) on the bacterioplankton community along the river-to-ocean
continuum from the Parnaioca River towards the Atlantic Ocean. This shows
that salinity exhibited a strong influence on the abundances of
Proteobacteria and Firmicutes. Furthermore, based on the linear model (Fig. 7), salinity was an
important factor in driving the shift in microbial communities (Table 3),
similar to findings by Herlemann et al. (2011) along a 200 km salinity gradient in the Baltic Sea. The dispersal of
taxa of microbial communities from fresh to marine waters faces a strong
barrier due to salinity (Fortunato and
Crump, 2015), likely explaining the reduced relative abundances of some taxa
(Fig. 3). For example, Chloroflexi has a higher relative abundance upstream while
Deinococcus–Thermus shows lower relative abundance downstream. Such dispersals are further
influenced by transitional waters such as estuaries and plumes, in which the
microbial communities are exposed to rapidly changing physico-chemical
conditions such as nutrients, temperature and sporadic anthropogenic
inputs (Crump et al., 2004).
Proportion of combined community variation based on marginal DistLM
test that is explained by each predictor variable using two cruises (August
and March 2017).
While the distribution of the core microbial communities are indicative of
the river–sea continuum, it is noteworthy that several phyla were distinctly
associated with specific source types. The distinct shift in bacterial taxa
for example from freshwater to brackish waters (and lack thereof between
freshwater peat and brackish peat; Fig. 3) indicates that peat did not have
a significant effect on the distribution of bacterial taxa. This was further
supported by the fact that DOC (as a proxy for organic matter of peat
origin) only accounts for 5.27 % of the community variation (Table 3). A
study on blackwater rivers in the Orinoco Basin, Venezuela
(Castillo
et al., 2004), showed that increased DOC resulted in higher bacterial
production; however, the change in bacterial production was not a reflection
of its influence on the community composition. This was supported based on a
simple respiration experiment conducted in August 2016 (Supplement Table S1), in which
the respiration rate (0.44±016 g DO L-1 d-1) was higher
than that of the primary production rate (0.39±0.08 g DO L-1 d-1).
Samples influenced by DO (Fig. 7) are from the estuarine region which showed
an almost anoxic zone (refer to Fig. S6). The low availability of
oxygen was mirrored in higher counts (samples belonging to the brackish peat
category showed the highest counts regardless of phyla as well as season;
Fig. S4). However, higher counts (particularly the phylum Chloroflexi and Cyanobacteria which are
normally associated with production of oxygen via primary productivity) do
not reflect higher primary production within this zone. Zones of coastal
estuaries are usually deemed to have higher primary productivity; however,
it can be inferred that the depletion in oxygen and higher pCO2
emissions (Müller-Dum et
al., 2019) within the brackish peat region of the August 2016 campaign was a
result of high bacterial productivity. This can be further supported by the
high SPM as a proxy of turbidity of the brackish peat (Fig. S6), which
may have resulted in the reduced primary productivity, which in turn can
explain the lower DO values. As aforementioned earlier, the respiration rate
(0.44±016 g DO L-1 d-1) was higher than that of the
primary production rate (0.39±0.08 DO L-1 d-1). This was
similar to a study in the Scheldt River in which the higher bacterial
production occurred in the turbidity maxima together with the depletion of
oxygen (Goosen et al., 1995).
Functional potential of major taxa according to source types
In the Rajang River, the relative abundance of bacterial OTUs were higher in
the estuary as well as marine region, reflecting that while the microbial
communities are structured by salinity, the abundance was more a reflection
of the nutrients available, especially in estuaries which exhibit
circulation patterns which can result in localised nutrient-rich conditions
(They et al., 2019). This was further supported
by the higher relative abundance of oxidative phosphorylation genes as well
as the nitrogen metabolism within the brackish peat and further supported by
Jiang et al. (2019) demonstrated
through incubation studies in which N transformations in the Rajang River
estuary mixing zone were higher than those in the Rajang River and coastal region.
In a study done by Yang et al. (2013),
the dominance of Proteobacteria influenced the nitrogen cycle via the processes of
nitrification and denitrification, in which aeration would increase its
abundance and result in higher mortality of Cyanobacteria. Hence, lower Proteobacteria abundance
resulted in the higher abundance of Cyanobacteria which occur as evidently shown in Fig. 3. Furthermore, the higher presence of Chloroflexi (Ward
et al., 2018) and Cyanobacteria (Guida et al., 2017) within the
marine and brackish peat region indicated its probable role in carbon
fixation, as reflected by the higher gene counts (carbon fixation pathways in
prokaryotes) in the marine and brackish peat regions as compared to the
freshwater peat and mineral soil (Fig. 6). Furthermore, the presence of the
genus Sphingomonas, a purple-sulfur bacterium, allowed the utilisation of carbon dioxide
(carbon fixation pathways in prokaryotes) and oxidation of hydrogen sulfide (sulfur metabolism) (Pfennig, 1975) (Fig. 6). In the
case of Firmicutes, the higher abundance of Firmicutes in the brackish region was reflective of
the overall production as opposed to selective growth of the particular
source type, as Firmicutes were found throughout all four source types. The highest
level of Deinococcus–Thermus (Fig. 3) was found in freshwater peat environments, indicating its
preference for this environment. It was interesting to note that most
studies on bacterial community composition show that the phylum
Deinococcus–Thermus occurs in a higher abundance in extreme environments such as in hot springs
(Zhang et al., 2018b) or in
studies that are analogous for Mars (Joseph et al., 2019). In
most of these studies, Deinococcus–Thermus was found in low abundance (e.g. 1 % in Antarctic
marine environments, 1.5 % in hypersaline soils;
Giudice and Azzaro, 2019;
Vera-Gargallo et al., 2019) when compared to
the Rajang River.
Seasonality as a driver of microbial community composition
While the development of unique community structures was strongly influenced
by spatial factors, seasonality also played a role. When taking into
consideration the major genera, there was a fundamental shift in bacterial
community composition along the continuum (Figs. 3, 4). The second-most
abundant taxon, Proteobacteria (β-Proteobacteria) peaked during seasons of high discharge. The
same pattern of peaking during high discharge can be observed in the Rajang
River, with considerably higher relative abundance in the wet season (Fig. 3)
which could be a result of the intense rainfall that led to the large input
of freshwater (Silveira et al., 2011), and ultimately resulting in a
“trickling” over microbial patterns from the freshwater to the brackish
region. Furthermore, there was a distinct difference in terms of bacterial
richness and diversity indices between the dry season (August 2016) and both
wet seasons, with September 2017 having higher observed indices, while
March 2017, despite being a wet season as well, had lower or variable observed
indices. This difference in the two wet seasons could be the due to different stages of phytoplankton bloom as mentioned earlier, as an algal bloom occurred in
September 2017 while March 2017 was after an
algal bloom event. This was reflected in the Simpson index as well as the
indices for September 2017 being lower than those of the August 2016 or
March 2017 samples. Similarly, Zhou et
al. (2018) demonstrated that the Simpson Indices for bacteria increased
after the onset of an algal bloom (brackish peat, September 2017), whereas
the Shannon indices was at the lowest (brackish peat, March 2017) (when
assuming that the region in which phytoplankton blooms occur was the
brackish peat region). Overall, there was greater diversity (based on
Shannon indices) in the dry season (August 2016) than the wet seasons (March
and September 2017), whereas there were greater OTUs in the wet season
(observed index).
Seasonal variability was also observed between the source types, particle
association and down to the genus level (Figs. 2, S2 and S5). Based on the precipitation as an indicator of the seasonality, a
probable “transitioning” phase was observed in the dry season (August 2016), with the microbial communities being more alike with the March 2017
samples (Fig. 8) when comparing both wet seasons (March and September 2017). Within the phylum rank (Fig. 3), the presence of Cyanobacteria during the March
and September 2017 cruises indicates the influence of seasonality. However,
while March and September 2017 were both considered to be wet seasons
based on the precipitation, in terms of the relative abundance, there are
considerable differences between the two cruises. The greater abundance of
Bacteroidetes in March 2017 may be indicative of the community composition adjusting due
to the processing of organic material caused by the higher cyanobacterial
abundance in the September 2017 cruise. This was similar to a study by
Pinhassi et al. (2004), in which the
higher abundance of Bacteroidetes follows after an algal bloom. Moreover, the shifts in
community composition from August 2016 to March 2017 and from March to
September 2017 are indicative of the influence of seasonality. While March
and September 2017 were similar in terms of climate, September 2017 had
higher precipitation during that month, which led to higher run-off from the
riparian region as compared with the March 2017 wet season. This could have
led to the increase in cyanobacteria, which was also reflected in the increase of
picoplankton size class during the wet season, as it was hypothesised that
the September 2017 might be more optimal for picoplankton proliferation
(Fig. S7). Furthermore, in comparison, August 2016 and March 2017 were
similar in terms of the proportion of the relative abundance of the
community composition (Fig. 3).
Land-use change and anthropogenic drivers
There has been little to no literature regarding the changes in microbial
community composition as a result of land-use changes that occur within this
region, particularly throughout the catchment area of the Rajang River. The
results obtained from this study suggest that the run-off from anthropogenic
activities alters the microbial community composition. The
Cytophaga–Flavobacterium–Bacteroidetes group, known as the CFB group, is commonly associated with
humans (Weller et al., 2000), reflecting anthropogenic
influences on the samples, especially within the brackish areas which have
several human settlements and plantations. This was shown in the coherence
plots in Figs. S10 and S11, in which the CFB group in the
Bacteroidetes phylum is shown to be more pronounced in areas with influence on oil palm
plantations. Lee-Cruz et al. (2013)
demonstrated that conversions of tropical forests to oil palm plantations are much more severe as compared to logged-over forests in terms of
bacterial community composition, as logged-over forests were shown to
exhibit some resilience and resistance (to a certain extent). This was shown
in the clustering of bacterial taxa adjacent to the oil palm plantation
regardless of the source type (Fig. S12), as the vector of
Flavobacteriia decreases in the samples of oil palm plantation in the brackish peat, as does, to a
certain extent, the vector of Bacteroidia in the oil palm plantation samples in the
freshwater peat. Furthermore, among the identified possible pathogenic
bacteria, according to Reza et al. (2018),
the taxa Flavobacterium is a potential fish pathogen and is commonly found in freshwater
habitats (Lee and Eom, 2017) as well as
coastal pelagic zones (Eilers et
al., 2001). In the Rajang River, it was the sixth most abundant class (Fig. S4). This is cause for concern as it was found to be high in the coastal
regions as well as brackish regions where fisheries and fishing activities
are concentrated.
Anthropogenic disturbances, in particular settlements and logging
(secondary forest), led to higher diversity indices (Fig. 6). On the
contrary, sites surrounded by oil palm plantations displayed the lowest
diversity indices, supporting results by
Mishra et al. (2014) who found
similar results in peatlands. Furthermore, the OTU overlapping of major
anthropogenic activities (i.e. settlements and oil palm plantations) in
Fig. S9 reflected the possibility of higher abundance of generalists as
compared to sensitive species
(Jordaan et al., 2019), as
microbial communities generally adapt to permanent stress events such as
increased concentrations of inorganic or organic nutrients. In another study
conducted by Fernandes et al. (2014), anthropogenically influenced mangroves had 2 times more γ-Proteobacteria compared to pristine mangroves. This was similar to the March 2017
cruise along the Rajang River, in which γ-Proteobacteria was the predominant class in
the marine and brackish peat region along with the significant increase in the aforementioned Bacteroidetes, which can be associated to anthropogenic activities. On
the other hand, during the dry season, the diversity of the
“less-disturbed” region was higher than the disturbed regions. However, it
should be noted that the coastal zone generally has the lowest richness and
diversity amongst the other regions regardless of the presence or absence of
anthropogenic activities. Hence, the extent of salinity intrusion may also
result in the loss of diversity and richness of the microbial communities
(Shen et al., 2018) in the Rajang River.
Conclusions
This study represents the first assessment of the microbial communities of
the Rajang River, the longest river in Malaysia, expanding our knowledge of
microbial ecology in tropical regions. The predominant taxa are
Proteobacteria (50.29 %), followed by Firmicutes (22.35 %) and Actinobacteria (11.95 %). The microbial
communities were found to change according to the source type, as
distinct patterns were observed as a result of the changes in salinity along
with variation of other biogeochemical parameters. Alpha diversity indices
indicate that the microbial diversity was higher upstream as compared to the
marine and estuarine regions, whereas anthropogenic perturbations (regions
with oil palm plantations and human settlements) led to increased richness
but less diversity compared to those that were less affected by
anthropogenic perturbations (coastal zone and secondary forest). The PICRUSt
results showed differences between source types. Areas surrounded by oil
palm plantations showed the lowest diversity and other signs of
anthropogenic impacts included the presence of the CFB group as well as
probable algal blooms. In order to further gauge and substantiate the
functional and metabolic capacity of the microbial communities within each
specific source type, metaproteomics as well as metabolomics should be
carried out along with mixing experiments. This approach will contribute
towards a better understanding of the response of microbial communities to
anthropogenic perturbations, as well as their role in degrading peat-related
run-off from the surrounding riparian regions.
Data availability
Raw sequences have been deposited with the NCBI BioSample database under BioProject ID PRJNA565954.
The supplement related to this article is available online at: https://doi.org/10.5194/bg-16-4243-2019-supplement.
Author contributions
ESAS, MM, JZ, and AM designed the study. ESAS, FHJ, and SJ performed the sample preparation during the campaigns. SJ and ZZ performed the nutrient measurements. WH and FKS performed the picoplankton measurements. ESAS and MM prepared the paper with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Biogeochemical processes in highly dynamic peat-draining rivers and estuaries in Borneo”. It is not associated with a conference.
Acknowledgements
The authors would like to thank the Sarawak Forestry Department and Sarawak
Biodiversity Centre for permission to conduct collaborative research in
Sarawak waters under permit numbers NPW.907.4.4(Jld.14)-161, Park Permit no. WL83/2017 and SBC-RA-0097-MM. Special mention to the boatmen who helped us
to collect samples, in particular Lukas Chin and his crew during the Rajang
River cruises. Also, the authors are very grateful to Kim Mincheol of
KOPRI for providing the mothur codes and supercomputer for processing the
sequences. We would also like to thank Patrick Martin for providing DOC
measurements and Denise Müller-Dum for providing SPM measurements.
Gonzalo Carassco and Nagur Cherukuru as well as student helpers from UNIMAS,
Swinburne Sarawak, SKLEC and NOCS greatly aided with the logistics and
fieldwork.
Financial support
This research has been supported by the MOHE FRGS 15 Grant (grant no. FRGS/1/2015/WAB08/SWIN/02/1), the SKLEC Open Research Fund (grant no. SKLEC-KF201610), and the Newton-Ungku Omar Fund (grant no. NE/P020283/1).
Review statement
This paper was edited by Palanisamy Shanmugam and reviewed by two anonymous referees.
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