Bacterial diversity along a 2600 km river continuum

Bacterial diversity along a 2600 km river continuum
Environmental Microbiology (2015) 17(12), 4994–5007
Bacterial diversity along a 2600 km river continuum
Domenico Savio,1,2 Lucas Sinclair,3 Umer Z. Ijaz,4
Juraj Parajka,1,5 Georg H. Reischer,2,6
Philipp Stadler,1,7 Alfred P. Blaschke,1,5
Günter Blöschl,1,5 Robert L. Mach,2
Alexander K. T. Kirschner,6,8
Andreas H. Farnleitner1,2,6 and Alexander Eiler3*
Centre for Water Resource Systems (CWRS), Vienna
University of Technology, Vienna, Austria.
Research Group Environmental Microbiology and
Molecular Ecology, Institute of Chemical Engineering,
Vienna University of Technology, Vienna, Austria.
Institute of Hydraulic Engineering and Water Resource
Management, Vienna University of Technology, Vienna,
Institute for Water Quality, Resource and Waste
Management, Vienna University of Technology, Vienna,
Department of Ecology and Genetics, Limnology,
Science for Life Laboratory, Uppsala University,
Uppsala, Sweden.
School of Engineering, University of Glasgow,
Glasgow, UK.
Interuniversity Cooperation Centre Water and Health,
Institute for Hygiene and Applied Immunology, Water
Hygiene, Medical University of Vienna, Vienna, Austria.
The bacterioplankton diversity in large rivers has thus
far been under-sampled despite the importance of
streams and rivers as components of continental
landscapes. Here, we present a comprehensive
dataset detailing the bacterioplankton diversity along
the midstream of the Danube River and its tributaries.
Using 16S rRNA-gene amplicon sequencing, our
analysis revealed that bacterial richness and evenness gradually declined downriver in both the freeliving and particle-associated bacterial communities.
These shifts were also supported by beta diversity
analysis, where the effects of tributaries were negligible in regards to the overall variation. In addition,
the river was largely dominated by bacteria that
Received 20 August, 2014; accepted 21 April, 2015. *For correspondence. E-mail [email protected]; Tel. (+46) 18 471 2700;
Fax (+46) 18 53 1134.
are commonly observed in freshwaters. Dominated
by the acI lineage, the freshwater SAR11 (LD12)
and the Polynucleobacter group, typical freshwater
taxa increased in proportion downriver and were
accompanied by a decrease in soil and groundwateraffiliated bacteria. Based on views of the metacommunity and River Continuum Concept, we
interpret the observed taxonomic patterns and
accompanying changes in alpha and beta diversity
with the intention of laying the foundation for a
unified concept for river bacterioplankton diversity.
Streams and rivers link terrestrial and lentic systems with
their marine counterparts and provide numerous essential
ecosystem services. They supply drinking water, are used
for irrigation, industry and hydropower and serve as transport routes or for recreation. Of general importance is the
role of lotic systems in biogeochemical nutrient cycling.
Until recently, rivers and streams were mainly considered
as pipes shuttling organic material and nutrients from the
land to the ocean (Cole et al., 2007). This view has begun
to change as lotic and lentic systems are now considered
more akin to ‘leaky funnels’ in regard to the cycling of
elements. Indeed, they play an important role in the temporary storage and transformation of terrestrial organic
matter (Ensign and Doyle, 2006; Cole et al., 2007;
Withers and Jarvie, 2008; Battin et al., 2009). As a
result of recognizing the specific role of streams and rivers
in the carbon cycle (Richey et al., 2002; Battin et al.,
2009; Raymond et al., 2013), the study of the diverse
processes ongoing in the water column and sediments of
lotic networks has received increasing interest (Kronvang
et al., 1999; Beaulieu et al., 2010; Seitzinger et al., 2010;
Aufdenkampe et al., 2011; Benstead and Leigh, 2012;
Raymond et al., 2013).
When attempting to model the mechanisms of nutrient
processing in freshwater systems, bacteria are regarded
as the main transformers of elemental nutrients and
viewed as substantial contributors to the energy flow
(Cotner and Biddanda, 2002; Battin et al., 2009; Findlay,
2010; Madsen, 2011). However, in the case of open lotic
systems such as rivers, there remains a lack of knowledge concerning the diversity of bacterial communities
(Battin et al., 2009). For example, up until the present day
there is no agreement on the distinctness of river
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and
reproduction in any medium, provided the original work is properly cited.
River bacterioplankton diversity
Fig. 1. Overview and detailed map of the Danube river catchment showing all sampling sites during the Joint Danube Survey 2; red dots
indicate sampling points in the midstream of the Danube River; blue dots represent sampling points in tributaries before merging
with the Danube River. Blue-shaded font indicates official numbering of river kilometres, starting with rkm 2600 at the uppermost site to rkm 0
at the river mouth. Country abbreviations and large cities are written in black. The map was created using Quantum GIS (Quantum GIS
Development Team, 2011).
bacterioplankton communities from that of other freshwater systems or the variability of its diversity along entire
river networks.
Until recently, it was only known that the most abundant
taxa comprising riverine bacterioplankton seem to resemble lake bacteria and can thus be regarded as ‘typical’
freshwater bacteria (Zwart et al., 2002; Lozupone and
Knight, 2007; Newton et al., 2011). In particular, bacteria
affiliated with the phyla Proteobacteria (particularly
Cyanobacteria and Verrucomicrobia were found to dominate the bacterial communities in rivers (Crump et al.,
1999; Zwart et al., 2002; Cottrell et al., 2005; Winter
et al., 2007; Lemke et al., 2008; Mueller-Spitz et al.,
2009; Newton et al., 2011; Liu et al., 2012). A recent
metagenome study corroborates a general dominance
of the phyla Proteobacteria and Actinobacteria, and
more specifically the clear dominance of the cosmopolitan
freshwater lineage acI of the phylum Actinobacteria
in the Amazon river (Ghai et al., 2011). The dominance
of Actinobacteria and Proteobacteria in riverine
bacterioplankton was also confirmed in three recent highthroughput sequencing studies on the Upper Mississippi
River (USA; Staley et al., 2013), the Yenisei River (RUS;
Kolmakova et al., 2014) and the River Thames (UK; Read
et al., 2015). Staley and colleagues (2013) were the first
to suggest a persistent and ubiquitous ‘core bacterial
community’ along a river stretch. Read and colleagues
(2015) examined the longitudinal development of the
bacterioplankton community at 23 sites along the river
network of the 9948 km2 Thames basin. They found a shift
from a Bacteroidetes-dominated community in the headwaters to an Actinobacteria-dominated community in the
lower reaches near the river mouth and location of the
sampling point in the river network to be the most predictive parameter. These patterns they interpreted as evidence for ecological succession along the river continuum.
However, the existing studies focused on relatively small
river basins and/or a small number of sampling sites. In
large river basins, however, one would expect that the
spatial patterns of bacterial community compositions manifest themselves more clearly than in small ones due to the
larger contrast in environmental conditions. In this paper,
we analyse the results from a second-generation sequencing experiment by separately investigating the free-living
and particle-associated bacterioplankton communities
along 96 sites in the network of the entire Danube basin
(Fig. 1). The Danube River is 2780 km in length and drains
a catchment area of 801 000 km2 with 83 million inhabitants (Schmedtje et al., 2004; Sommerwerk et al., 2010).
Based on our results, we propose that the bacterioplankton
communities in the midstream of such a large river develop
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd,
Environmental Microbiology, 17, 4994–5007
4996 D. Savio et al.
Fig. 2. Gradual development of the read proportions assigned to
the operationally defined ‘core communities’ of the free-living and
particle-associated fraction (all OTUs occurring in at least 90% of
all Danube River samples of the respective size fraction) along
‘mean dendritic stream length’. Round symbols represent samples
from the free-living size fraction (0.2–3.0 μm); squares represent
samples from the particle-associated size fraction (> 3.0 μm). Red
dots indicate samples from the free-living size fraction (0.2–3.0 μm)
of the Danube River only; blue squares indicate samples from the
particle-associated fraction (> 3.0 μm) of the Danube River; open
dots and squares represent tributary samples from the free-living
and particle-associated size fraction, respectively; dark blue lines
indicate fitted linear models with confidence intervals of 0.95 in red
and blue for the respective size fraction of the Danube River
samples. Detailed regression statistics for the core community
development in the Danube River (exclusive tributary samples) are
shown in the figure.
gradually and increasingly independent from tributary and
riparian influence as a result of the interplay between
dispersal-facilitated (‘mass effects’) and environmental
condition-based sorting (‘species sorting’; Leibold et al.,
2004; Crump et al., 2007; 2012). Moreover, we argue that
these processes, represented in the meta-community
concept, can be linked to the River Continuum Concept
(RCC; Vannote et al., 1980), which explains the role of the
hydrological flow conditions, the riparian zone, substrate
and food as important factors in determining community
structures along entire river systems.
Selected environmental and geomorphological
In total, more than 280 individual parameters, including
chemical, microbiological, ecotoxicological, radiological
and biological parameters were investigated within the
Joint Danube Survey 2 (Liska et al., 2008). Alkalinity, pH,
concentration of nitrate as well as dissolved silicates
exhibited a gradually decreasing trend along the river as
previously described by Liska and colleagues (2008) and
illustrated in Fig. S1D–G. Total phytoplankton biomass
(Chla) showed a peak between river kilometre 1481 and
1107 (sites 38–55) with total bacterial production following
a similar trend, whereas total suspended solid concentration increased considerably in the last 900 kilometres
before reaching the Black Sea (Fig. S1H–J).
Several geomorphological measures were calculated
such as ‘river kilometre’, ‘catchment area’, ‘mean dendritic
stream length’ and ‘cumulative dendritic distance
upstream’ and were compared with each other. For
example, ‘mean dendritic stream length’ correlated very
closely with ‘river kilometre’ (as defined by the distance to
mouth; linear model R2 = 0.98; P < 0.001; Fig. S1A). In
contrast, ‘cumulative dendritic distance upstream’ correlated almost perfectly with ‘catchment area’ (linear model
R2 > 0.99; P < 0.001; Fig. S1C). From a hydrological point
of view, it can be argued that ‘mean dendritic stream
length’ is a better proxy of stream residence time than
‘cumulative dendritic distance upstream’, as it represents
the average travel time of a drop of water, assuming
randomly distributed spring discharges and constant flow
velocities in the river system (Rodriguez-Iturbe et al.,
Core microbial community
In total, sequencing resulted in 1 572 361 sequence reads
(further referred to as ‘reads’) after quality filtering and
clustered into clustering into 8697 bacterial operational
taxonomic units (OTUs). The majority of bacteriaassigned OTUs (4402 out of 8697) were only represented
by less than 10 reads in the entire dataset. As a consequence, 3243 of 8697 OTUs (∼37%) were present in only
one to four samples, and an additional 2219 OTUs (∼26%)
were present in as few as five to nine samples. Besides
these rare OTUs, the core community of the Danube
River, as operationally defined by all OTUs that appeared
in at least 90% of all Danube River samples, comprised
89 OTUs in the free-living bacterioplankton (0.2–3.0 μm)
and 141 OTUs in the particle-associated fraction
(> 3.0 μm). On average, 81% of all reads of the free-living
river community and 63% of all reads of the particleassociated river community were part of their respective
core community. The relative abundance of the core communities in both fractions increased significantly towards
the river mouth with similar slopes revealed by regression
analysis (Fig. 2).
Variability in river bacterioplankton alpha diversity
To investigate alpha diversity, we calculated the Chao1
richness estimator and Pielou’s evenness index for both
size fractions after rarefying all samples down to 7000
reads and discarding 36 samples with fewer reads. We
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd,
Environmental Microbiology, 17, 4994–5007
River bacterioplankton diversity
Fig. 3. The gradual development of (A) the bacterial richness (Chao1) and (B) Pielou’s evenness (J) along the ‘mean dendritic stream length’
at each sampling site; red dots indicate samples from the free-living size fraction (0.2–3.0 μm) of the Danube River only (n = 27); blue squares
indicate samples from the particle-associated fraction (> 3.0 μm) of the Danube River (n = 40); open dots and squares represent tributary
samples from the free-living and particle-associated size fraction, respectively; dark blue lines indicate fitted linear models with confidence
intervals of 0.95 in red and blue for the respective fraction of Danube River samples. Detailed regression statistics for Danube River samples
(exclusive tributary samples) are shown in the figure.
observed the highest diversity of all samples in the
upstream part of the Danube River, representing mediumsized stream reaches according to the RCC definition.
Richness and evenness then gradually decreased downstream in both size fractions (Fig. 3A and B) as confirmed
by regression analysis using ‘mean dendritic stream
length’ as well as ‘river kilometre’, ‘cumulative dendritic
distance upstream’ and ‘catchment area’ (Table S1). For
bacterial richness, the regressions revealed very similar
slopes for the free-living and the particle-associated fractions (Fig. 3A). Besides the observed trends in the midstream communities of the Danube River, the tributary
communities frequently formed outliers with considerably
lower richness and evenness (Fig. 3A and B).
Among the two size fractions, the estimated richness
was consistently higher in the particle-associated communities than in the free-living fraction (Wilcoxon rank sum
test; P-value < 0.001) with means of 2025 OTUs and 1248
OTUs, respectively (Fig. 3A). Similar observations were
reported in studies on (coastal) marine environments as
well as lentic freshwater environments (Bižić-Ionescu
et al., 2014; Mohit et al., 2014), ascribing the higher alpha
diversity in the particle-associated communities to the
high heterogeneity in the particle microenvironment. A
large spectrum of niches can be provided by the high
heterogeneity among particles in rivers including mobilized sediments, living organisms such as planktonic
algae or zooplankton and detritus derived from terrestrial
and aquatic sources. Bižić-Ionescu and colleagues (2014)
even suggested that the presence of diversely colonized
particles of different age, origin and composition can be
described based on the observation of a higher richness
in the particle-associated fraction.
Variability in river bacterioplankton beta diversity
While communities of both size fractions in the Danube
River covary significantly with alkalinity, nitrate concentration and dissolved silicates, the particle-associated communities additionally covaried highly significantly with total
bacterial production, nitrite concentrations, phytoplankton
biomass and total suspended solids (Table 1). These correlations indicate that chemical properties may determine
bacterioplankton community composition. Nevertheless,
the observed significant relationship between community
composition and ‘mean dendritic stream length’ (Table 1)
emphasizes the underlying role of stream travel times.
Still, as tributary samples were scattered in the multidimensional space (Fig. 4), the midstream Danube River
communities are suggested to develop independently
from tributary communities, which is further supported
when depicting tributaries according to their position of
confluence within the Danube River (see Fig. S2).
In addition to the observation that (i) tributary
bacterioplankton communities did not follow the general
patterns of midstream Danube River communities and
often formed outliers in the ordination space, other visual
impressions from the non-metric multidimensional scaling
(NMDS) are that (ii) there is a distinction in community
composition between the two size fractions as confirmed
by permutational multivariate analysis of variance
(PERMANOVA) analysis (R2 = 0.156, P-value < 0.01),
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd,
Environmental Microbiology, 17, 4994–5007
4998 D. Savio et al.
Table 1. Summary statistics of correspondence between environmental variables and the projections of bacterioplankton community samples in
the NMDS ordination based on either free-living or particle-associated size fractions for the Danube River samples only (left) and tributary samples
included (right). The results were obtained using the function ‘envfit’ included in the R-package ‘vegan’ (Oksanen et al., 2013).
Danube River only
Official Danube River kilometrea (for tributaries rkm at confluence)
Official Danube River kilometrea (calculated for tributariesb)
Mean dendritic stream length (water residence time)
Median dendritic length
Catchment size
Cumulated dendritic distance
Silicates dissolved
Total bacterial production
Bacterial production filtered fraction
Phytoplankton biomass (Chla)
Total suspended solids
Water temperature
Organic nitrogen
Orthophosphate phosphorus
Total phosphorus
Dissolved oxygen
Danube River and tributary samples
Free living
Free living
Significant codes: ***≤ 0.001 **≤ 0.01
*≤ 0.05.
a. rkm 2600 = upstream region [near Ulm (DE)]; rkm 0 = river mouth (Black Sea).
b. To obtain the corresponding ’Danube river kilometres’ according to the official numbering (see a) for tributaries, the length of tributaries was
calculated by subtracting the official length of tributaries (Schmedtje et al., 2004) from the official length of the Danube River (2780 km; Schmedtje
et al., 2004).
Fig. 4. Non-metric multidimensional scaling
plot of the compositional dissimilarities
between communities (Bray–Curtis
dissimilarities) of all samples of the Danube
River and its tributaries. The stress value of
NMDS was 0.17. Dots represent free-living
bacterial communities (0.2–3.0 μm); triangles
display particle-associated bacterial
communities (> 3.0 μm). Open symbols
represent tributary samples, whereas full
symbols indicate communities in the Danube
River. The gradient from orange to blue via
purple indicates the ‘mean dendritic stream
length’ at the respective sampling site in the
Danube River.
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd,
Environmental Microbiology, 17, 4994–5007
River bacterioplankton diversity
Danube River
Free-living fraction
Particle-associated fraction
16 %
14 %
12 %
10 %
Flow direction
(rkm 2600)
Freshwater taxa
Flow direction
(rkm 0)
(rkm 2600)
(rkm 0)
Freshwater 'clade' or 'lineage'
non-typical freshwater taxa
Fig. 5. A heat map (A) revealing the dynamics of the eight most abundant typical freshwater tribes along the Danube River defined according
to Newton and colleagues (2011). The gradient from black red via yellow to white indicates the relative quantitative contribution of the
respective tribe to all sequence reads in any one sample, with a maximum of 16%.
Panel (B) displays the overall contribution of typical freshwater tribes (dark blue) as well as clades and lineages (turquoise) according to the
definition by Newton and colleagues (2011) to the river bacterioplankton amplicon sequences along the river; black bars represent reads that
could not be matched to sequences of the used freshwater database (Newton et al., 2011) neither on tribe, clade or lineage-level (named
‘non-typical freshwater taxa’). ‘Freshwater taxa’ and ‘freshwater clade or lineage’ represent all reads that could be matched to sequences of
the used freshwater database at the respective similarity level. Samples from the Danube River as well as the investigated tributaries are
arranged from left to the right, with increasing distance from the source and separated according to the respective size fractions.
and (iii) there appears to be synchrony in the community
changes of the two size fractions along the river’s course,
which we statistically verified using a Procrustes test
(r = 0.96, P < 0.001). Furthermore, (iv) a permutation test
on the beta dispersion values between the samples of
each size fraction revealed a higher variability (by a factor
of 0.06) in the > 3.0 μm fraction when compared with the
0.2–3.0 μm fraction (P-value = 0.002) (see Fig. S3). This
supports the idea of a larger heterogeneity in niche availability in the particle-associated communities based on
higher diversity in particle age and origin.
Typical river bacterioplankton
Along the river, the bacterioplankton community
was dominated by Actinobacteria, Proteobacteria,
Bacteroidetes, Verrucomicrobia and candidate division
OD1, with an increasing proportion of reads assigned to
the phylum Actinobacteria in the free-living size fraction
downriver (Fig. S4). In contrast, reads assigned to
Bacteroidetes decreased significantly along the river in
the free-living fraction. In the particle-associated fraction,
these trends in phylum composition were less pronounced. In addition to assigning reads to the phylum
level, we taxonomically annotated all 9322 OTUs using
similarity searches against the database of freshwater
bacteria developed by Newton and colleagues (2011).
The analysis revealed that up to 80% of the free-living and
more than 65% of the particle-associated bacterial population inhabiting the midstream Danube River communities could be assigned to previously described freshwater
taxa (Fig. 5B). In particular, these included representatives of the LD12-tribe belonging to the subphylum of
Alphaproteobacteria, as well as the acI-B1-, acI-A7- and
acI-C2-tribes belonging to the phylum Actinobacteria.
Such dominance of few freshwater taxa in riverine
bacterioplankton communities (see also Zwart et al.,
2002; Lozupone and Knight, 2007; Newton et al., 2011;
Read et al., 2015) corroborates the idea that river
bacterioplankton resembles those of lakes.
Interestingly, in the free-living size fraction, we
observed a clear increase in the relative abundance of the
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd,
Environmental Microbiology, 17, 4994–5007
5000 D. Savio et al.
these OTUs may originate from non-aquatic sources. In
the particle-associated fraction, typical freshwater taxa
were less common (Fig. 5B).
To confirm the non-aquatic origin of certain OTUs, we
first blasted a representative for each of the 8697 bacterial
OTUs against the NCBI-Nucleotide database; next, any
environmental descriptive terms occurring in the search
results were retrieved and classified according to the
Environmental Ontology (EnvO; Buttigieg et al., 2013) terminology. Permutational analysis of variance of the EnvOclassified data revealed a significant difference in variance
between the two size fractions (PERMANOVA; R2 = 0.42,
P < 0.0001), supporting a high variability in origin and
particle age. Restricting the analysis to particular EnvO
terms such as ‘groundwater’ and ‘soil’ suggests that the
proportion of bacteria potentially originating from these
sources decreased towards the river’s mouth (Fig. 6A and
B), while ‘river’, ‘lake’ and ‘epilimnion’ terms exhibited
increasing trends downriver (Fig. S5A–C). In particular, we
could only identify four OTUs receiving a classification that
was dominated by the ‘river’ term.
As with every homology-based assignment, SEQENV
results are affected by database entries; for example the
under-representation of entries from rivers compared with
lakes likely biases against the ‘river’ term. Thus, a larger
number of typical river bacterioplankton may exist than
detected by our analysis.
Fig. 6. Results from the SEQENV analysis scoring sequences
according to their environmental context (EnvO; environmental
ontology). The Y-axis represents the proportion of (A) ‘groundwater’
and (B) ‘soil’ terms associated with sequence reads per sample
along the ‘mean dendritic stream length’ at each sampling site
(X-axis). Red dots indicate Danube River samples of the
0.2–3.0 μm size fraction (n = 42), and blue squares indicate
samples from the > 3.0 μm size fraction (n = 52). Open dots and
squares represent tributary samples of the free-living and
particle-associated size fractions respectively. Dark blue lines
represent fitted linear models for the Danube River samples with
confidence intervals of 0.95 in red and blue for the respective
fractions. Detailed regression statistics are given in Table S1.
four above-mentioned tribes towards the river mouth
(Fig. 5A), contributing up to 35% of the community. The
increasing relative abundance of these four tribes was
accompanied by a general increase in relative abundance
of OTUs matching other freshwater tribes, lineages or
clades according to Newton and colleagues (2011) as
depicted in Fig. 5B. In contrast, the number of OTUs not
matching any sequence of the freshwater database either
at tribe, clade or at lineage-level decreased (Fig. 5B;
labelled ‘non-typical freshwater taxa’), suggesting that
The tremendous diversity within the microbial communities inhabiting all types of environments is being revealed
by a rapidly increasing number of studies applying highthroughput sequencing technologies (e.g. Sogin et al.,
2006; Andersson et al., 2009; Galand et al., 2009; Eiler
et al., 2012; Peura et al., 2012). At the same time, many
mechanisms modulating this diversity have been suggested including ‘mass effects’ and ‘species sorting’,
which vary widely in importance depending on the environment (Leibold et al., 2004; Besemer et al., 2012;
Hanson et al., 2012; Lindström and Langenheder, 2012;
Szekely et al., 2013).
Regarding bacterioplankton in large river networks, we
propose that the highest diversity exists in headwaters,
and from thereon decreases towards river mouths. This,
we argue, results from the inoculation of bacterioplankton
by advection from surrounding environments (i.e. soil and
groundwater) in the headwaters as supported by our
SEQENV results and previous studies (Crump et al., 2007;
2012; Besemer et al., 2012; 2013). This initial pervasive
impact from the riparian zone on headwaters (‘mass
effects’) can simply be justified as follows: (i) in lotic environments, bacterioplankton is transported primarily passively, (ii) the large contact zone of small headwaters
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd,
Environmental Microbiology, 17, 4994–5007
River bacterioplankton diversity
(large surface-area-to-volume ratio) with the surrounding
environment (soil and groundwater) facilitates the contribution of allochthonous bacteria to the river community
(Crump et al., 2007; 2012; Besemer et al., 2012), (iii)
these source environments of inoculation (soils and
groundwater) harbour a much higher diversity than
aquatic communities (e.g. Crump et al., 2012), and (iv)
these newly introduced allochthonous bacteria should be
at least temporarily capable of proliferating in their new
lotic environment, making them constitutive members of
the community. Overall, this process of allochthonous
input can be described by the so-called ‘mass-effect’,
where dispersal of organisms exceeds the rate of local
extinction (Leibold et al., 2004; Crump et al., 2007;
Flowing downriver, with increasing river width and
decreasing ‘riparian influence’, we propose that ‘speciessorting’ progressively prevails over ‘mass effects’ in
shaping the bacterioplankton composition. This is supported by the increase of the core communities’ relative
abundance in both size fractions (Fig. 2) as well as the
rapidly decreasing number of first-time occurrences of
OTUs from upstream to downstream (Fig. S6). The associated progressive rise of few and more competitive
taxa is further supported by the observed simultaneous
decrease in evenness together with bacterial richness in
both size fractions downriver (Fig. 3A and B). Finally,
the decrease of cell volumes along the Danube River
(Velimirov et al., 2011) as well as a rise of typical freshwater bacteria (Fig. 5A and B), representing small cells
with oligotrophic lifestyles (Salcher et al., 2011; Garcia
et al., 2013), provides further evidence for the increasing
importance of ‘species-sorting’. The increasing resemblance with lake communities (Fig. S5B and C) further
corroborates the idea that lake and river bacterioplankton
resemble each other.
The interplay between ‘species sorting’ and ‘mass
effects’ is fundamental to the meta-community concept
(Leibold et al., 2004) and has previously been used to
explain the decreasing diversity along a path from
upslope soils via headwater streams to a final lake
(Crump et al., 2012). The latter study suggested advection of soil bacteria to strongly influence the receiving
water bodies due to the ‘mass effect’ of dispersing organisms exceeding the rate of ‘species sorting’ (Crump et al.,
2012). For large river networks, however, this raises the
question of when ‘species sorting’ will exceed ‘mass
effects’. In contrast to ‘mass effects’, ‘species sorting’
presumes that bacterial growth rates have to be shorter
than the residence time in the stream. Assuming a
maximum bacterial growth rate of about 1d−1 (as calculated for the bulk communities along the Danube River
with a maximum and mean of 0.92d−1 and 0.18d−1, respectively) and considering an estimated in-stream residence
time for the Danube River of 32 days (Velimirov et al.,
2011), this provides sufficient time for ‘successful’ species
In contrast to Crump and colleagues (2012), Read and
colleagues (2015) put little emphasis on the ‘mass effects’
of the riparian zone and, instead, explained the downstream shift in the bacterial communities along the River
Thames as the result of ecological succession. They
argued that sampling sites not only represented a spatial
distribution but also a time series where downstream sites
with longer water residence times contain older river water
and a planktonic community that is in the later stages of
ecological succession. However, a concept like ecological
succession might not be the most suited to describe
bacterioplankton diversity patterns in the Danube basin
which is much larger, and the residence times are about
five times as long when compared with the River Thames.
First, we could not observe an initial active growth and
propagation of new pioneer species (often r-strategists)
as it would be expected during ecological succession,
but solely a decrease in proportions of Bacteroidetesaffiliated cells. Second, the data clearly showed a
decrease in alpha diversity, both evenness and richness,
downriver. Assuming a dominance of fast-growing
r-strategists in the highly dynamic communities of the
upstream reaches, one would expect low evenness in
these reaches. Moreover, highest production rates, as
anticipated for a dominance of r-strategists, were not
observed in the upstream reaches, but the middle section
of the Danube River [between river kilometre (rkm) 16321071; Velimirov et al., 2011]. Besides, elevated production
rates in the uppermost section were largely assignable to
particle-associated bacteria (Velimirov et al., 2011), indicating high-quality particulate substrate originating from
the riparian zone.
Instead of viewing the community assembly in headwaters as the colonization of a lifeless area (primary succession) or the development following a disturbance of an
established community (secondary succession), we
simply regard streams as part of the hydrological cycle
such as soil waters and interstitial groundwaters, in which
bacteria are transported passively. Thus, we conclude
that dispersal-based concepts are more appropriate for
describing the community assembly at the transition
between the different compartments. The Danube data
suggest that the ‘mass effects’ of soil and groundwater
bacteria across the streambed contact zone are important
processes, as reflected in the decreasing proportion of
soil and groundwater-associated bacteria. This decrease
of the effect of the streambed contact zone can be illustrated by comparing the uppermost and lowest reaches of
the survey, using the ratio of wetted perimeter and crosssectional area as a measure of river bed contact zone. At
the Upper Danube near Ulm, Germany, an average dis-
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd,
Environmental Microbiology, 17, 4994–5007
5002 D. Savio et al.
charge of 40 m3 s−1 translates into a ratio of about
0.83 m−1 while at the Lower Danube near Reni, Romania,
an average discharge of 6300 m3 s−1 translates into a ratio
of 0.056 m−1. This means that within the sampled reach,
the effect of the contact zone decreases by more than
10-fold downriver.
Taken together, the findings of this paper and the existing
literature (Besemer et al., 2012; 2013; Crump et al., 2012;
Staley et al., 2013) suggest that the diversity of
bacterioplankton decreases from headwaters to the river
mouth due to the decreasing importance of the ‘riparian
influence’. This is consistent with the important role the
RCC assigns to the riparian zone as well as to the physical
drivers such as river flow and wetted perimeter. Moreover,
by referring to dissolved organic matter (DOM) in terms of
quality, proposing highest DOM diversity in headwaters
and a downstream export of more refractory compounds,
the RCC also allows us to incorporate a proposed DOM
(‘food’)-based ‘species sorting’, leading to an oligotrophic
freshwater bacteria-dominated community with increasing
water residence time. Analogously, an increase in the
abundance of a few, more competitive species along the
river is implied in the RCC for macroorganisms from
medium-sized reaches towards river mouths (Vannote
et al., 1980). However, to further develop and incorporate
bacterioplankton into the RCC, future studies should
address the role of DOM quality changes as a function of
catchment characteristics as well as seasonal changes in
physical and chemical features of riverine systems.
In summary, the data from the Danube survey along a
2600 km river continuum indicated that bacterial richness
and evenness gradually declined downriver in both the
free-living and particle-associated bacterial communities,
resulting in an increase in relative abundance of typical
freshwater taxa downstream. The decreasing influence of
soil and groundwater bacteria downstream suggests the
RCC as a valid interpretive framework which stipulates a
continuous gradient of physical conditions that elicit a
series of biological responses, resulting in consistent patterns of community structure and function along the river
Experimental procedures
Supporting data
Within the frame of the second Joint Danube Survey (JDS2), a
wide range of chemical and biological parameters was collected (Liska et al., 2008). All data, sampling methods as well
as analytical methods, are made publicly available via the
official website of the International Commission for the Protection of the Danube River ( and
data used in this study is provided in Table S3.. Selected data
from Joint Danube Survey (JDS) 1 and 2 were published
previously in several studies (Kirschner et al., 2009; Janauer
et al., 2010; Velimirov et al., 2011; Von der Ohe et al., 2011).
Geomorphological parameters including ‘catchment area’,
‘mean dendritic stream length’ and, for comparison, ‘cumulative dendritic distance upstream’ were calculated based on
data from the Catchment Characterisation and Modelling
(CCM) River and CATCHMENT DATABASE, version 2.1 (De Jager
and Vogt, 2010). The ‘mean dendritic stream length’ was
calculated by first identifying the stream paths from all springs
in the catchment area upstream a sampling site to that sampling site. The lengths of these paths were than averaged. This
implies that the shared reaches were counted multiple times
consistent with the movement of water drops in the river basin.
This parameter gives the average flow distance (assuming the
spring discharges are randomly distributed in the catchment)
and therefore the average residence time in the stream
(assuming constant flow velocities). In contrast, the ‘cumulative dendritic distance upstream’ was calculated as
the sum of the blue lines on the map (counting the lengths
below confluences only once) which gives a geometric parameter indicative of the drainage density, but not of residence
Additionally, as a measure of the river bed contact zone, the
ratio of wetted perimeter and cross-sectional area was calculated for the long-term average discharge. Both the wetted
perimeter and the areas are obtained from bathymetric
surveys. The product of the area and the flow velocity gives the
discharge. Here, typical values of the ratio were chosen for a
short reach upstream the sampling sites.
Study sites and sample collection
Samples were collected within the frame of the JDS 2
project in 2007. The overall purpose of the Joint Danube
Surveys is to produce a comprehensive evaluation of the
chemical and ecological status of the entire Danube River
on the basis of the European Union Water Framework Directive (Liska et al., 2008). During sampling from 15 August to
26 September 2007, 75 sites were sampled along the mainstream of the Danube River along its navigable way from
river kilometre (rkm) 2600 near Ulm (DE) to the river mouth
at rkm 0 (Kirschner et al., 2009) as shown in Fig. 1. In addition, 21 samples from the Danube’s major tributaries and
branches were included. At the most upstream sites, the
Danube River is representative of a typical stream of the
rhithron and characterized by its tributaries Iller, Lech and
Isar (Kavka and Poetsch, 2002). The trip took 43 days which
is a similar time period as the travel time of water in the
Danube over this reach (see Velimirov et al., 2011). Samples
were collected with sterile 1 L glass flasks from a water
depth of approximately 30 cm. Glass flasks were sterilized
by rinsing with 0.5% HNO3 and autoclaving them. For
deoxyribonucleic acid (DNA) extraction of the particleassociated bacterioplankton depending on the biomass
concentration, 120–300 ml river water was filtered through
3.0 μm pore-sized polycarbonate filters (Cyclopore,
Whatman, Germany) by vacuum filtration. The filtrate, which
represented the bacterioplankton fraction smaller than
3.0 μm (later referred to as ‘free-living’ bacterioplankton),
was collected in a sterile glass bottle and subsequently filtered through 0.2 μm pore-sized polycarbonate filters
(Cyclopore, Whatman, Germany). The filters were stored at
−80°C until DNA extraction.
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd,
Environmental Microbiology, 17, 4994–5007
River bacterioplankton diversity
DNA extraction and quantification of bacterial DNA
using quantitative polymerase chain reaction
Genomic DNA was extracted using a slightly modified protocol of a previously published phenol-chloroform, beadbeating procedure (Griffiths et al., 2000) using isopropanol
instead of polyethylene glycol for DNA precipitation. Total
DNA concentration was assessed applying the Quant-iT
PicoGreen dsDNA Assay Kit (Life Technologies Corporation,
USA), and 16S rRNA genes were quantified using Bacteriaspecific quantitative polymerase chain reaction (qPCR).
Quantitative PCR reactions contained 2.5 μl of 1:4 and 1:16
diluted DNA extract as the template, 0.2 μM of primers 8F
and 338 (Frank et al., 2007; Fierer et al., 2008) targeting the
V1-V2 region of most bacterial 16S rRNA genes and iQ
SYBR Green Supermix (Bio-Rad Laboratories, Hercules,
USA). All primer information are available in Table S2. The
ratios of measured 16S rRNA gene copy numbers in the
different sample dilutions that deviated markedly from one
after multiplication with the respective dilution factor were
interpreted as an indicator for PCR inhibition.
Preparation of 16S rRNA gene amplicon libraries
For the preparation of amplicon libraries, 16S rRNA genes
were amplified and barcoded in a two-step PCR procedure to
reduce PCR bias that is introduced by long primers and
sequencing adaptor overhangs (Berry et al., 2011). We followed the protocol as described by Sinclair and colleagues
(2015). In short, 16S rRNA gene fragments of most bacteria
were amplified by applying primers Bakt_341F and
Bakt_805R (Herlemann et al., 2011; Table S2) targeting the
V3-V4 variable regions. In 25 μl reactions containing 0.5 μM
primer Bakt_341F and Bakt_805R, 0.2 μM dNTPs
(Invitrogen), 0.5 U Q5 HF DNA polymerase and the provided
buffer (New England Biolabs, USA), genomic DNA was
amplified in duplicate in 20 cycles. To use equal amounts of
bacterial template DNA for increased comparability and
reduction of PCR bias, the final volume of environmental
DNA extract used for each sample was calculated based on
16S rRNA gene copy concentration in the respective sample
determined earlier by qPCR (see above). For 105 samples,
the self-defined optimum volume of environmental DNA
extract corresponding to 6.4 × 105 16S rRNA genes was
spiked into the first step PCR reactions; however, for 27
samples, lower concentrations were used due to limited
amounts of bacterial genomic DNA or PCR inhibition
detected by quantitative PCR (see above). These 132
samples included eight biological replicates. Prior to the
analysis, we removed four samples due to their extremely low
genomic DNA concentrations and 16S rRNA gene copy
numbers. Duplicates of PCR products were pooled, diluted to
1:100 and used as templates in the subsequent barcoding
PCR. In this PCR, diluted 16S rRNA gene amplicons were
amplified using 50 primer pairs with unique barcode pairs
(Sinclair et al., 2015; Table S2). The barcoding PCRs for
most samples were conducted in triplicates analogous to the
first PCR (n = 100). The remaining 32 samples that had weak
bands in first step PCR due to low genomic template DNA
concentrations or high sample dilution were amplified in 6–9
replicates to increase amplicon DNA yield. Barcoded PCR
amplicons were pooled in an equimolar fashion after purifi-
cation using the Agencourt AMPure XP purification system
(Beckman Coulter, Danvers, MA, USA) and quantification of
amplicon concentration using the Quant-iT PicoGreen
dsDNA Assay Kit (Life Technologies Corporation, USA).
Finally, a total of 137 samples including five negative controls
resulted in four pools for sequencing.
Illumina sequencing
The sequencing was performed on an Illumina MiSeq at the
SciLifeLab SNP/SEQ sequencing facility hosted by Uppsala
University. For each pool, the library preparation was performed separately following the TruSeq Sample Preparation
Kit V2 protocol (EUC 15026489 Rev C, Illumina) with the
exception of the initial fragmentation and size selection procedures. This involves the binding of the standard sequencing adapters in combination with separate Illumina-specific
multiplex identifier (MID) bar codes that enables the combination of different pools on the same sequencing run (Sinclair
et al., 2015). After pooling, random PhiX DNA was added
(5%) to provide calibration and help with the cluster generation on the MiSeq’s flow cell.
16S rRNA gene amplicon data analysis
The sequence data were processed as outlined by Sinclair
and colleagues (2015). After sequencing the libraries of 16S
rRNA gene amplicons, the read pairs were de-multiplexed
and joined using the PANDASEQ software v2.4 (Masella
et al., 2012). Next, sequence reads (further referred to as
‘reads’) that did not bear the correct primer sequences at the
start and end of their sequences were discarded. Reads were
then filtered based on their PHRED scores. Chimera removal
and OTU clustering at 3% sequence dissimilarity was performed by pooling all reads from all samples together and
applying the UPARSE algorithm v7.0.1001 (Edgar, 2013).
Here, any OTU containing less than two reads was discarded. Each OTU was subsequently taxonomically classified by operating a similarity search against the SILVAMOD
database and employing the CREST assignment algorithm
(Lanzén et al., 2012). Plastid, mitochondrial and archaeal
OTUs were removed. In addition, OTUs were also taxonomically annotated against the freshwater database (Newton
et al., 2011) using the same method. If necessary, OTU rarefying for the purpose of standardizing sequence numbers
was performed using the ‘rrarefy’-function implemented in the
R package vegan (Oksanen et al., 2013). For alpha diversity
analysis (Chao1 richness estimator and Pielou’s evenness),
we rarefied down to 7000 reads per sample. This was based
on one study revealing that for water samples, a sequencing
depth of 5000 16S rRNA gene reads per sample captured
more than 80% of the trends in Chao1 richness and Pielou’s
evenness (Lundin et al., 2012). Furthermore, this study could
show that for water samples, 1000 reads per sample
explained to 90% the trends in beta diversity (Bray–Curtis
dissimilarity index). By rarefying down to 2347, which was the
read number of the sample with the lowest reads, all samples
could be included in the beta-diversity analysis. Diversity
measures, statistical analyses and plot generation were conducted in R (R Core Team, 2013) and using python scripts.
The habitat index for the top 5000 OTUs was determined
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd,
Environmental Microbiology, 17, 4994–5007
5004 D. Savio et al.
using the SEQENV pipeline (
seqenv.html). The SEQENV pipeline retrieves hits to highly
similar sequences from public repositories (NCBI Genbank)
and uses a text mining module to identify EnvO (Buttigieg
et al., 2013) terms mentioned in the associated contextual
information records (‘Isolation Source’ field entry for genomes
in Genbank or associated PubMed abstracts). At the time of
running SEQENV on our dataset (version 0.8), there were
approximately 1200 EnvO terms organized into three main
branches (namely, environmental material, environmental
feature and biome). However, we used SEQENV to retrieve a
subset of these terms, i.e. those that contain ‘Habitat’
(EnvO:00002036). Raw sequence data were submitted to the
NCBI Sequence Read Archive under accession number
General description of sequences
In total, DNA was extracted and sequenced from 132 filtered
water samples originating from the Danube River and its
tributaries. In addition, the same procedure was applied to five
negative control samples. The sequencing yielded two
030 029 read pairs ranging from 3451 to 24 873 per sample.
After quality filtering and mate pair joining as outlined in
Sinclair and colleagues (2015), 1 572 361 sequence reads
were obtained. The OTU clustering resulted in 8697
OTUs after the removal of all Plastid-, Mitochondrion-,
Thaumarchaeota-, Crenarchaeota- and Euryarchaeotaassigned OTUs. Archaea-assigned OTUs were removed
because of the use of bacteria-specific primers not giving a
representative picture of the targeted Archaea community. The
undesirable Plastid, Mitochondrion and Archaea sequences
represented 19.1% of the reads and accounted for 625 OTUs.
Next, for the alpha diversity analysis, we excluded any sample
with less than 7000 reads, resulting in 8648 OTUs in the
remaining 88 samples. By contrast, for the beta diversity
analysis, which is less affected by rare OTUs, all samples were
randomly rarefied to the lowest number of reads in any one
sample in order to include a maximum number of samples in
the analysis. This brought every sample down to 2347 reads,
and any OTU containing less than two reads was discarded,
which brought the total OTU count to 5082.
This study was supported by the Austrian Science Fund
(FWF) as part of the ‘Vienna Doctoral Program on Water
Resource Systems’ (DKplus W1219-N22) and the FWF projects P25817-B22 and P23900-B22, as well as the research
project ‘Groundwater Resource Systems Vienna’ in cooperation with Vienna Water (MA31). AE and LS are funded by the
Swedish Foundation for Strategic Research (ICA10-0015).
Infrastructure (cruise ships, floating laboratory) and logistics
for collecting, storing and transporting samples were provided by the International Commission for the Protection of
the Danube River (ICPDR). The analyses were performed
using resources provided by the SNIC through the Uppsala
Multidisciplinary Center for Advanced Computational Science
(UPPMAX) under project ‘b2011035’. This product includes
data licensed from ICPDR.
Conflict of interest
The authors declare no conflict of interest.
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Supporting information
Additional Supporting Information may be found in the online
version of this article at the publisher’s web-site:
Fig. S1. Development of selected environmental parameters
along the Danube River from the upstream region (rkm 2600;
left) to the river mouth at the Black Sea (rkm 0; right). Left
panel: alkalinity, pH, total bacterial production (TBP), total
suspended solids (TSS); Right panel: nitrate (NO3-), dissolved silicates (SiO2 diss) and phytoplankton biomass
(Chl-a) [PP biomass (Chl-a)].
Fig. S2. Non-metric multidimensional scaling plot of the
compositional dissimilarities between communities (Bray–
Curtis dissimilarities) of all samples of the Danube River and
its tributaries. The stress value of the NMDS was 0.17. Dots
represent free-living bacterial communities (0.2–3.0 μm); triangles display particle-associated bacterial communities
(> 3.0 μm). Open symbols represent tributary samples,
whereas full symbols indicate communities in the Danube
River. The gradient from orange to blue via purple indicates
the official Danube River kilometre assignment [rkm
2600 = upstream region near Ulm (DE), rkm 0 = river mouth
at Black Sea] at the respective sampling site in the Danube
River and for tributaries at the site (official rkm) of confluence
with the Danube River, independent of its length.
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd,
Environmental Microbiology, 17, 4994–5007
River bacterioplankton diversity
Fig. S3. Box plot of variability in bacterial communities in
different size fractions (0.2–3.0 μm and > 3.0 μm) based on
beta-dispersion of Bray–Curtis dissimilarities. Left: Variability
(distance from centroid) in the free-living bacterial community; Right: Variability in the attached bacterial community.
Fig. S4. Phylum-level taxonomic composition of the bacterial communities along the Danube River. The Y-axis shows
the read proportions assigned to the five most abundant
phyla in the free-living fraction (left) and the particleassociated fraction (right). Lower abundant phyla were
included in the fraction ‘Others’ due to their low proportions.
Samples are arranged from left to the right representing
sequence from upstream (rkm 2600) to river mouth at the
Black Sea (rkm 0).
Fig. S5. Results from the SEQENV analysis scoring
sequences according to their environmental context (EnvO;
environmental ontology). The Y-axis represents the proportion of (A) ‘river’, (B) ‘lake’ and (C) ‘epilimnion’ terms associated with sequence reads per sample along the ‘mean
dendritic stream length’ at each sampling site (X-axis). Red
dots indicate Danube River samples of the 0.2–3.0 μm size
fraction (n = 42), and blue squares indicate samples from the
> 3.0 μm size fraction (n = 52). Open dots and squares represent tributary samples of the free-living and particleassociated size fractions respectively. Dark blue lines
represent fitted linear models for the Danube River samples
with confidence intervals of 0.95 in red and blue for the
respective fractions. Detailed regression statistics are given
in Table S1.
Fig. S6. First occurrence plot of OTUs along the Danube
River. Plotted are the numbers of OTUs occurring for the first
time at the respective rkm of each sampling sites.
Table S1. Summary of regression statistics (slope, intercept,
multiple R-squared and P-value) for fitted linear models
between the proportion of different EnvO terms associated
with sequence reads per sample and ‘mean dendritic stream
length’ at the respective sampling site (upper section), and
between alpha diversity measures [Chao1, Pielou’s Evenness (J)] and geomorphological parameters (lower section).
From the SEQENV analysis, regression statistics are given for
the EnvO-terms ‘groundwater’ (Fig. 6A), ‘soil’ (Fig. 6B), ‘river’
(Fig. S5A), ‘lake’ (Fig. S5B) and ‘epilimnion’ (Fig. S5C). FL:
free-living community of the Danube River (0.2–3.0 μm); PA:
particle-associated Danube River community (> 3.0 μm).
Table S2. List of used primers and barcodes for Illumina
Table S3. Results of all measured environmental and chemical parameters during the JDS 2. Copy of the online available
JDS2-database-content. Export date: 2013-11-07.
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd,
Environmental Microbiology, 17, 4994–5007
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