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
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
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
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.
This study was conducted along
Location of Rajang River within Sarawak, Malaysia (inset). Panel
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
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.
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,
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).
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
ANOSIM global test scores based on various parameters.
Results of permutational multivariate analysis of variance (PERMANOVA). DOF represents degrees of freedom.
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
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.
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
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
(
The calculated
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 (
The calculated
The relative abundance of predicted functional profiles in the four source types across two seasons based on KEGG pathways.
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.
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 (
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.
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.
The majority of bacterial taxa were restricted to a relatively small number
of assemblages. Dominant phyla typically found in Malaysian peat swamps such
as
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
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 (
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
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
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,
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
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
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
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
Raw sequences have been deposited with the NCBI BioSample database under BioProject ID PRJNA565954.
The supplement related to this article is available online at:
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.
The authors declare that they have no conflict of interest.
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.
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.
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).
This paper was edited by Palanisamy Shanmugam and reviewed by two anonymous referees.