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This is the published version of a paper published in Ambio.
Citation for the original published paper (version of record):
Lindh, M V., Lefébure, R., Degerman, R., Lundin, D., Andersson, A. et al. (2015)
Consequences of increased terrestrial dissolved organic matter and temperature on
bacterioplankton community composition during a Baltic Sea mesocosm experiment.
Ambio, 44(Suppl 3): S402-S412
http://dx.doi.org/10.1007/s13280-015-0659-3
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AMBIO 2015, 44(Suppl. 3):S402–S412
DOI 10.1007/s13280-015-0659-3
Consequences of increased terrestrial dissolved organic matter
and temperature on bacterioplankton community composition
during a Baltic Sea mesocosm experiment
Markus V. Lindh, Robert Lefébure, Rickard Degerman, Daniel Lundin,
Agneta Andersson, Jarone Pinhassi
Abstract Predicted increases in runoff of terrestrial
dissolved organic matter (DOM) and sea surface
temperatures implicate substantial changes in energy
fluxes of coastal marine ecosystems. Despite marine
bacteria being critical drivers of marine carbon cycling,
knowledge
of
compositional
responses
within
bacterioplankton communities to such disturbances is
strongly limited. Using 16S rRNA gene pyrosequencing,
we examined bacterioplankton population dynamics in
Baltic Sea mesocosms with treatments combining
terrestrial DOM enrichment and increased temperature.
Among the 200 most abundant taxa, 62 % either increased
or decreased in relative abundance under changed
environmental conditions. For example, SAR11 and
SAR86 populations proliferated in combined increased
terrestrial DOM/temperature mesocosms, while the hgcI
and CL500-29 clades (Actinobacteria) decreased in the
same mesocosms. Bacteroidetes increased in both control
mesocosms and in the combined increased terrestrial
DOM/temperature mesocosms. These results indicate
considerable and differential responses among distinct
bacterial populations to combined climate change effects,
emphasizing the potential of such effects to induce shifts in
ecosystem function and carbon cycling in the future Baltic
Sea.
Keywords Terrestrial dissolved organic matter Temperature Climate change Marine bacteria Bacterial diversity
INTRODUCTION
Predicted climate change, resulting in effects such as increased sea surface temperatures and precipitation,
123
threatens the structure and function of marine communities
in many regions of the oceans, including the Baltic Sea and
coastal waters in general (Meier 2006; IPCC 2013).
Although marine bacteria play an essential role in driving
biogeochemical cycling of, e.g., carbon, knowledge on how
these microorganisms will be affected by anthropogenic
impacts is scarce. Still, increased temperature is known to
affect growth and drive compositional shifts in marine
microbial communities (Müren et al. 2005; von Scheibner
et al. 2014). In addition to temperature changes, the future
Baltic Sea is predicted to experience an increase in terrestrial nutrient runoff from rivers, including terrestrial
dissolved organic matter (tDOM), due to increased annual
levels of precipitation (Meier 2006). Such tDOM will include humic substances comprised of low- and high
molecular weight compounds like fulvic acids and lignin
(Rocker et al. 2012). Increases in terrigenous organic
matter could induce changes in food web dynamics and
energy flows in the system (Sandberg et al. 2004; Wikner
and Andersson 2012). Although the importance of DOM
composition in structuring bacterioplankton communities is
relatively well established (where phytoplankton-derived
compounds are most studied, e.g., Gomez-Consarnau et al.
2012; Teeling et al. 2012; Dinasquet et al. 2013), few
studies have considered the importance of tDOM input
(mainly humic substances derived from river runoff) in
driving bacterioplankton compositional shifts in marine
systems (but see Kisand et al. 2002; Rochelle-Newall et al.
2004; Kisand et al. 2008; Teira et al. 2009; Grubisic et al.
2012; Rocker et al. 2012). However, the impact of humic
matter on bacterioplankton composition has been extensively investigated in limnic systems (e.g., Lindström
2000; Eiler et al. 2003; Kritzberg et al. 2006). Typically,
there are few general patterns among bacterioplankton at
the phyla/class level in these studies and only a handful
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AMBIO 2015, 44(Suppl. 3):S402–S412
have analyzed the distribution of specific bacterial
populations. Nevertheless, Bacteroidetes, Gammaproteobacteria, and Betaproteobacteria seem to be prevalent in
relation to environmental conditions with high tDOM
(Kisand et al. 2002; Eiler et al. 2003; Teira et al. 2009).
Collectively these studies show that growth and community structure of bacterioplankton much depend on the
ability of bacteria to degrade and utilize tDOM. Considering the importance of DOM in shaping bacterioplankton
community structure, surprisingly few studies have investigated the potential effects of climate change-induced increases in tDOM on bacterioplankton community
composition.
In addition to changes in single environmental variables,
simultaneous shifts in both DOM composition and increased sea surface temperatures may have even larger
consequences for bacterioplankton. In the equatorial Pacific
Ocean and the Western Arctic Ocean, autochthonous dissolved organic carbon (DOC) and increased temperature
caused synergistic effects on bacterial growth (Kirchman
and Rich 1997). In the northern Baltic Sea, increased temperature regulated bacterioplankton composition to a small
extent, while high terrestrial DOM input was important in
determining community structure (Degerman et al. 2013).
However, in that study, the potential combined effect of
temperature and terrestrial DOM was not investigated.
These findings highlight the potential importance of climate
change effects in shaping the structure and function of
marine ecosystems in general and also for bacterioplankton
dynamics. Still, the potential effects of increased tDOM
concentrations and temperature on bacterial community
structure and the relative abundance of individual bacterial
populations remain largely unknown.
It is generally recognized that bacterioplankton
populations (frequently defined as operational taxonomic
units—OTUs) have a remarkable potential in responding to
environmental disturbances (Langenheder et al. 2005;
Allison and Martiny 2008; Comte and Del Giorgio 2011;
Sjöstedt et al. 2012). However, the ecological significance
of the adaptability of bacterioplankton populations, or their
physiological plasticity, in responding to synergistic environmental disturbances as highlighted above, is poorly
understood. In responding to climate change-induced environmental perturbations, bacterial populations can either
be sensitive (i.e., decrease in relative abundance), resistant
(i.e., maintain relative abundance), or responsive (i.e., increase in relative abundance) (Allison and Martiny 2008).
In addition, how individual bacterial populations differ in
their response to environmental disturbance will likely
have implications for a number of bulk community properties (e.g., bacterial production) that heavily influence
ecosystem functioning by changing the flow of carbon
(Bell et al. 2005; Comte and Del Giorgio 2011). Further
S403
knowledge on the details of gains and losses of bacterial
populations in response to environmental changes, such as
increased tDOM loading and temperature, would be desirable. This would be critically important for disentangling
the effects of climate change on bacterioplankton assemblages and their ecosystem function in the future Baltic
Sea.
Lefébure et al. (2013) showed substantial synergistic
effects of increased tDOM and temperature on different
trophic levels in the Baltic Sea. For example, both fish
production and food web efficiency were higher in mesocosms with manipulated environmental conditions compared to controls. Using samples from this experiment, we
aimed at investigating the potential future climatic effects
of changes in temperature and tDOM on bacterioplankton
community composition, specifically the dynamics of individual OTUs. We used 16S rRNA gene tag pyrosequencing analysis on samples collected from mesocosms
exposed to combined increases in temperature and tDOM
concentration as compared to controls (each in triplicates).
In addition to detecting overall changes in bacterial community composition between control mesocosms and mesocosms with increased tDOM and temperatures, we
hypothesized the experiment would allow identifying
specific bacterial populations (OTUs) that are particularly
sensitive, resistant, or responsive to the environmental
forcing.
MATERIALS AND METHODS
Experimental setup
The mesocosm experiment, to simulate the effects of increased river-bound input of tDOM and increased surface
seawater temperatures (tDOMH ? T) into the northern
Baltic Sea, was performed at Umeå Marine Research
Centre, Sweden. Each mesocosm contained 2000 l unfiltered water collected in the Bothnian Sea in October 2010
(6 °C, salinity 5), (63°340 N, 19°540 E). We used four experimental treatments with three replicates each. In this
study, our focus is on two of these treatments, tDOM addition and temperature increase vs. control conditions.
tDOM was added to increase DOC concentration from
4.5 mg l-1 in control mesocosms to 6 mg C l-1 in
tDOMH ? T mesocosms. Temperature was initially raised
to 15 °C in all mesocosms (‘‘stabilization phase’’ for
18 days) to ensure equal starting point and then by another
4 °C in the tDOMH ? T mesocosms to 19 °C during
35 days. Mesocosms were kept at a constant temperature
(±0.5 °C). For detailed descriptions of the experimental
setup and sampling of biotic and abiotic environmental
parameters, see (Lefébure et al. 2013).
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AMBIO 2015, 44(Suppl. 3):S402–S412
1.0
S404
Stress = 0.13
Control
tDOMH+T
St. phase
Control
0.5
35_6
tDOMH+T
28_12
35_12
35_11 28_6
28_4
14_11
28_11
35_1
14_6
0
NMDS2
Treatment
35_8
14_12
35_4
0_11
St. phase
0_6
0_1
28_8
14_4
0_12
0_4
14_8
−1.0
−0.5
14_1
−1.0
−0.5
0
0.5
1.0
NMDS1
Fig. 1 nMDS analysis based on 454 pyrosequencing data of control mesocosms and mesocosms of increased terrestrial dissolved matter with
increased temperature (tDOMH ? T). Circles denote distinct clusters based on visual grouping of samples. Each sample is designated by
experimental day and mesocosm number (6, 11 and 12 = control, 1, 4 and 8 = tDOMH ? T). St. phase stabilization phase prior to the experiment
Collection and extraction of community DNA
Biomass for DNA extraction was collected at the stabilization
phase (i.e., prior to the experiment start) and then at the start
(day 0), middle (day 14), and end of the experimental phase
(day 28 and 35). Samples of 0.5–1.0 l were filtered onto 0.2 lm
pore size, 47-mm diameter Supor filters (PALL Life Sciences).
The filters were immediately frozen at -80 °C in 1.8 ml TE
buffer (10 mM Tris, 1 mM EDTA, pH 8.0) until further processing. DNA extraction was performed according to the
phenol chloroform extraction protocol in Riemann et al.
(2000). Bacterial 16S rRNA genes were amplified with bacterial primers 341F and 805R, containing adaptor and barcode
following the protocol of Herlemann et al. (2011). The resulting purified barcoded amplicons were normalized in
equimolar amounts and sequenced on a Roche GS-FLX 454
automated pyrosequencer (Roche Applied Science, Branford,
CT, USA) at Science for Life Laboratory, Stockholm, Sweden.
Sequence processing and analysis
Raw sequence data generated from 454 pyrosequencing
were processed following the bioinformatical pipeline described in Lindh et al. (2015). The 454 run resulted in
80 000 reads. After denoising and chimera removal, samples
123
contained on average 2763 (±597) sequence reads for each
sample. The final OTU table, including chloroplast sequences, consisted of 1688 OTUs (excluding singletons).
DNA sequences have been deposited in the National Center
for Biotechnology Information (NCBI) Sequence Read
Archive under accession number SRP036629.
Statistical analyses and graphical outputs
All graphical outputs were performed in R 3.0.2, and statistical tests were made using the package Vegan (Oksanen
et al. 2010). Clusters in nMDS analysis were drawn based
on visual difference between samples.
RESULTS
Microbial community composition
Analysis of pyrosequencing data on microbial community
composition by non-metric multidimensional scaling
(nMDS) showed that the initial samples on day 0 clustered together and close to the sample from the end of
the stabilization phase (Fig. 1). A distinct grouping of
samples distinguishing control mesocosms from
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AMBIO 2015, 44(Suppl. 3):S402–S412
S405
Treatment response
Control (”Sensitive”)
tDOMH+T (”Responsive”)
Both (”Resistant”)
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Actinobacteria
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Fig. 2 Maximum Likelihood (ML)-based phylogenetic tree of 16S rRNA gene sequences obtained from 454 data. ML tree was calculated from
nearest neighbor interchange (NNI). Pie charts indicate average relative abundance for each major bacterial group (including all OTUs) in control
and tDOMH ? T mesocosms. The size of each pie chart is proportional to total average relative abundance. Differential response in relative
abundance of the top 200 most abundant OTUs is indicated by blue filled circles for OTUs responding in control mesocosms (‘‘sensitive’’), pink
filled circles for OTUs responding in tDOMH ? T mesocosms (‘‘responsive’’), and black filled squares for OTUs with unchanged response
(‘‘resistant’’). Arrows denote particularly important OTUs mentioned in discussion. Scale bar represents 0.1 nucleotide substitutions per site
mesocosms with increased terrestrial humic DOM and
temperature (tDOMH ? T) was observed already in the
middle of the experiment (day 14). This pattern was
consistent among replicates for control and tDOMH ? T
mesocosms, and was maintained until the end of the
experiments (days 28 and 35) (Fig. 1). The microbial
community composition in the control and tDOMH ? T
mesocosms was significantly different (PerMANOVA,
p = 0.01, n = 23).
Population dynamics
Differences between control and tDOMH ? T mesocosms
in terms of community composition resulted mainly from
the gradual increase and decrease in the relative abundance
of different bacterial populations (defined as OTUs, at
97 % of sequence identity of the 16S rRNA gene) and
much less from the presence/absence of specific OTUs.
Therefore, we investigated the distribution patterns of the
200 most abundant OTUs over the entire experiment (accounting for 88 % of total reads), summarized in Fig. 2.
Further details on the 20 most abundant OTUs are summarized in Table 1. Among the top 200 OTUs, 30 % were
more abundant in tDOMH ? T mesocosms and 32 % were
less abundant in tDOMH ? T mesocosms compared to
control mesocosms. A large proportion of the OTUs, 38 %,
were resistant to manipulation, i.e., responded similarly in
tDOMH ? T mesocosms and the controls in terms of
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Table 1 Response of the top 20 most abundant OTUs over the experiment. Phyla/Class is abbreviated; Actino.—Actinobacteria, Alpha—
Alphaproteobacteria, Bact.—Bacteroidetes, Beta—Betaproteobacteria, Gamma—Gammaproteobacteria. Sequence annotation was performed
with SINA/SILVA and also using manual BLAST showing the Accession number of the closest relative found in genbank and 16S rRNA gene
identity in percent. Asterisks (*) indicate relation to phylotypes previously found in the Baltic Sea and (§) indicates observations in past nutrient
amendment experiments. Average relative abundance and maximum relative abundance (in parenthesis) during the experiment are given in
percent. We define abundant populations as having [1 % in relative abundance and rare populations as having \0.1 %. The detection limit for
this study is around 0.03 %, based on the sequencing depth. Magnitude of response in control and TDOMH ? T mesocosms is indicated with ?
(present) or - (absent). The level of response is indicated by the number of ?, which is relative for each OTU
OTU
Taxa
(SINA/SILVA)
Rel.
abund.
(control)
Control
tDOM ? T
Rel. abund.
(tDOMH ? T) (‘‘sensitive’’) (‘‘responsive’’)
Acc. #
(GenBank)
Phyla/
class
Rel. abund.
(All)
UMU_000001 hgcI clade
FR647689.1
[100 %]*
Actino.
7.8 (20.4)
11.2 (20.4)
3.8 (7.3)
???
?
UMU_000002 CL500-29
HQ836442.1
[100 %]*§
Actino.
6.2 (11.9)
8.8 (11.9)
3.2 (10.2)
???
?
UMU_000012 CL500-29
AB831248.1
[99 %]§
Actino.
0.9 (2.5)
1.2 (2.5)
0.5 (2.0)
??
?
UMU_000028 CL500-29
DQ270295.1
[100 %]*
Actino.
1.0 (2.5)
0.9 (1.7)
1.1 (2.5)
?
??
UMU_000029 hgcI clade
AB831241.1
[100 %]§
Actino.
1.0 (3.2)
1.6 (3.2)
0.2 (0.8)
??
?
UMU_000051 hgcI clade
AB831253.1
[100 %]§
Actino.
1.0 (1.7)
1.2 (1.7)
0.8 (1.2)
??
??
UMU_000003 SAR11
JQ974826.1
[100 %]*
Alpha
2.9 (13.8)
1.6 (6.5)
4.6 (13.8)
?
???
UMU_000004 uncl. Roseobacter FR647982.1
[100 %]*
Alpha
10.2 (15.9)
10.5 (15.9)
10 (14.7)
???
???
UMU_000005 NS11-12
FR647978.1
[100 %]*
Bact.
1.7 (4.5)
2.5 (4.5)
0.7 (1.8)
??
?
UMU_000007 NS3a
KC899250.1
[100 %]
Bact.
2.0 (6.8)
3.3 (6.8)
0.5 (2.2)
???
?
UMU_000009 Owenweeksia
FJ744887.1 [100 %] Bact.
1.2 (5.2)
0.8 (1.8)
1.6 (5.2)
?
???
UMU_000013 Flavobacteriaceae DQ189595.1
[99 %]§
Bact.
1.4 (5.6)
1.7 (5.6)
1.0 (2.4)
??
??
UMU_000015 Owenweeksia
EU878165.1
[100 %]§
Bact.
1.2 (5.3)
0.8 (1.3)
1.8 (5.3)
?
???
UMU_000016 Fluviicola
FR691964.1
[100 %]§
Bact.
1.4 (3.4)
1.8 (3.4)
0.9 (3.2)
??
?
UMU_000024 Cyclobacteriaceae HQ836440.1
[100 %]*§
Bact.
1.3 (3.2)
1.7 (3.2)
0.8 (1.7)
??
?
UMU_000000 Burkholderia
JN371511.1
[100 %]
Beta
7.7 (41.5)
3.2 (6.7)
13.1 (41.5)
?
???
UMU_000006 BAL58
HQ836424.1
[100 %]*§
Beta
1.6 (4.1)
1.3 (4.1)
2.9 (3.5)
?
??
UMU_000010 Oxalobacteraceae FJ828452.1
[100 %]§
Beta
1.6 (5.9)
1.8 (5.9)
1.3 (4.8)
??
??
UMU_000011 Comamonadaceae EU167462.1
[100 %]§
Beta
1.0 (5.3)
0.0 (0.0)
2.2 (5.3)
-
??
UMU_000008 SAR86
Gamma
1.7 (9.0)
0.5 (2.3)
2.8 (9.0)
?
???
FR647697.1
[100 %]*
increasing or maintaining the relative abundance. At the
phyla/class level, several actinobacterial OTUs were less
abundant in tDOMH ? T mesocosms as in the controls,
indicating that they were sensitive to this change in growth
conditions (Fig. 2). Bacteroidetes OTUs were more diverse
in their response to tDOMH ? T, where some OTUs
123
preferred control conditions, while others were predominant in tDOMH ? T mesocosms (Fig. 2). Still, other
OTUs within Bacteroidetes were maintained or increased
in relative abundance in both control and tDOMH ? T
mesocosms. Similarly, Gammaproteobacteria contained
several OTUs responding either mostly to control or
Ó The Author(s) 2015. This article is published with open access at Springerlink.com
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AMBIO 2015, 44(Suppl. 3):S402–S412
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Table 2 Recruitment of rare OTUs. Examples of OTUs undetected during stabilization phase and day 0 of the experiment but later detected in
tDOMH ? T mesocosms. Phyla/Class is abbreviated; Bact.—Bacteroidetes, Beta—Betaproteobacteria, Delta—Deltaproteobacteria, and
Chlor.—Chloroflexi. Average relative abundance and maximum relative abundance (in parenthesis) during the experiment are given in percent
OTU
Taxa
Taxon
Rel. abund.
UMU_000020
NS9 marine group
Bact.
1.5 (3.9)
UMU_000027
Fluviicola
Bact.
1.0 (2.7)
UMU_000011
UMU_000037
Comamonadaceae
Desulfuromonadales
Beta
Delta
2.2 (5.3)
1.0 (6.7)
UMU_000060
Caldilinea
Chlor.
0.5 (2.1)
tDOMH ? T mesocosms and some that were unchanged or
increased equally between these conditions. Moreover, a large
number of Betaproteobacteria were found in high relative
abundance in our study and were also quite variable in their
response, but a majority was more abundant in tDOMH ? T
mesocosms, indicating that they were responsive. Several
Alphaproteobacteria OTUs increased in tDOMH ? T mesocosms or were unchanged between controls and simulated
climate change. Cyanobacteria and phytoplankton (chloroplast sequences) displayed higher relative abundance in control compared to tDOMH ? T mesocosms (Fig. 2).
Within Actinobacteria, most members of the hgcI clade
(for example UMU_000001, UMU_000029) were predominant in control mesocosms (Fig. 2; Table 1). Similarly,
most Actinobacteria members of the CL500-29 clade were
also more abundant in control compared to tDOMH ? T
mesocosms (for example, UMU_000002, UMU_000012).
However, one CL500-29 clade OTU (UMU_000082) responded by increasing more in tDOMH ? T mesocosms
than in controls (Fig. 2).
Among Bacteroidetes, most Owenweeksia-related OTUs
(UMU_000009, UMU_000015, UMU_000077, UMU_000
300) increased in abundance in tDOMH ? T mesocosms,
whereas NS3a clade OTUs were abundant mainly in control
mesocosms (UMU_000007, UMU_000161). Interestingly,
one OTU (UMU_000019) related to Fluviicola was abundant
in tDOMH ? T mesocosms on day 14 but decreased toward
the end of the experiment, while the same OTU in the control
mesocosms was low in abundance on day 14 but increased
toward the end (Table 2). In addition, another Fluviicola
relative (UMU_000027) was not detected in the beginning of
the experiment but later increased substantially in tDOMH ? T
mesocosms (Table 2). Similarly, an NS9 relative (UMU_
000020) was also recruited from being undetected to become
abundant in tDOMH ? T mesocosms (Table 2). Concomitantly, a member of the numerically abundant SAR86 clade
was predominantly abundant in control mesocosms
(UMU_000165) and another SAR86 OTU was higher, at
around 2.8 % in average relative abundance (reaching up to
10 %), in tDOMH ? T mesocosms (UMU 000008) (Fig. 2;
Table 1).
Most Comamonadaceae (Betaproteobacteria) were
abundant in the tDOMH ? T mesocosms on day 14, but then
decreased substantially toward the end of the experiment.
However, three OTUs (UMU_000006, UMU_000050 and
UMU_000011), closely related to BAL58 marine group,
further increased in relative abundance in tDOMH ? T
mesocosms after day 14 (Fig. 2; Table 1). Another Betaproteobacteria OTU, related to Burkholderiales
(UMU_000000), responded substantially in tDOMH ? T
mesocosms where it reached over 40 % of relative abundance (Fig. 2; Table 1). Interestingly, we note that only one
OTU in the whole experiment (UMU_000011) (Comamonadaceae) was absent in the controls while reaching up to
5.3 % of relative abundance in tDOM ? T mesocosms
(Table 1). Furthermore, the same OTU (UMU_000011) was
absent in the beginning of the experiment but was later recruited from what is frequently called the rare biosphere to
tDOMH ? T mesocosms (Table 2). All other OTUs were
always detected in both control and tDOM ? T mesocosms
(albeit sometimes in small numbers in one of the two,
\0.01 % of relative abundance).
Within Alphaproteobacteria, we note that one Roseobacter-related OTU (UMU_000004) was equally abundant, between 10 and 16 % relative abundance, in both control and
tDOMH ? T mesocosms (Fig. 2; Table 1). We also observed
that members of the numerically abundant SAR11 clade
(UMU_000067 and UMU_000003) responded positively, up
to 13.8 % in relative abundance, in the tDOMH ? T mesocosms. Concomitantly, these SAR11 OTUs was lower, up to
6.5 %, in control mesocosms (Fig. 2; Table 1). Furthermore,
an OTU (UMU_000026) closely related to Candidatus
Spartobacterium Baltica1 (Herlemann et al. 2011, 2013) was
predominantly abundant in control mesocosms and did not
respond in tDOMH ? T mesocosms (Fig. 2).
A few OTUs increased substantially from being undetected to become abundant ([1 % of total abundance), as
indicated above (UMU_000027, UMU_000011, UMU_
000020). In addition to these OTUs, a Desulfuromonadales
(Deltaprotebacteria) and a Caldilinea (Chloroflexi) also
appeared in tDOMH ? T mesocosms after being undetected at first (Table 2).
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123
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AMBIO 2015, 44(Suppl. 3):S402–S412
DISCUSSION
In this mesocosm study with water from the northern Baltic
Sea, we showed effects of increased tDOM and temperature in shaping bacterial community composition.
These findings add important understanding of the bacterioplankton population dynamics in the Baltic Sea mesocosm experiment of Lefébure et al. (2013), who established
that tDOM and temperature significantly affected bulk
microbial activities. This is in agreement with studies in as
diverse environments as the western Arctic, equatorial
Pacific Ocean, and the Baltic Sea, all showing substantial
combined effects of increased DOM and temperatures on
bacterioplankton bulk activities (Kirchman and Rich 1997;
Degerman et al. 2013) and overall general community
structure (Degerman et al. 2013). Our findings further
highlight that increased tDOM and temperatures promoted
or suppressed a spectrum of individual populations (Fig. 2;
Tables 1, 2). This study thus provides a comprehensive
analysis of which bacterial populations may respond or not
to future anthropogenic-induced shifts in environmental
conditions.
Differential response
Among the top 200 OTUs, around one-third showed
relatively similar abundances in the tDOMH ? T and
control mesocosms, suggesting that they were not affected
by the induced changes in growth conditions—at least not
within the time frame of the experiment. Another one-third
of the OTUs increased in the tDOMH ? T mesocosms.
Moreover, one-third of the OTUs were more abundant in
control mesocosms, suggesting that they were negatively
affected by increased temperature and tDOM. Overall, the
analysis thus showed that 62 % of the top 200 OTUs in the
current experiment were affected either positively or
negatively by changes in environmental conditions. This
suggests that persistent changes over periods from several
months to years in temperatures and tDOM loading have
the potential to cause profound changes in bacterioplankton
community composition.
Several major bacterial groups that are abundant in the
Baltic Sea were also abundant in our experiment. We find
for example that Alphaproteobacteria were generally responsive (Fig. 2). Also Betaproteobacteria OTUs were
mostly responsive, whereas Actinobacteria and phytoplankton were generally sensitive (Fig. 2). Still, within all
major groups, there were both responsive and sensitive
OTUs and even resistant ones. A possible reason for detecting equal increase or resistance among bacterial
populations between control and tDOMH ? T mesocosms
could potentially be the ‘‘bottle-effect’’. Such effects are
frequently seen in incubation experiments, especially
123
among Gammaproteobacteria (e.g., Dinasquet et al. 2013).
Nevertheless, despite possible ‘‘bottle-effects’’, there were
not only striking differences in terms of responsiveness,
sensitivity, and resistance at low taxonomic resolution
between major phylogenetic groups, but also differences
within each phyla/class. Thus, we conclude that a majority
of the responses observed were distinctive of the
tDOMH ? T treatment as compared to the controls.
The following is an account of distinct distribution
patterns for important individual populations. Betaproteobacterial OTUs like Burkholderia and Comamonadaceae were positively influenced by increased tDOM
and temperature (Fig. 2; Tables 1, 2). In accordance, a
study investigating the effects of continental runoff from
the Iberian Peninsula on bacterioplankton showed a strong
positive correlation between humic DOM and Betaproteobacteria (Teira et al. 2009). Although Betaproteobacteria are frequently found in small numbers in the Baltic Sea
in general, specific members can reach up to several percent of the total community in the Baltic Sea (Herlemann
et al. 2011, Lindh et al. 2015). In addition, the northern
Baltic Sea contains on average more Betaproteobacteria
than elsewhere in the Baltic Sea (Herlemann et al. 2011),
possibly related to the lower salinity and/or higher levels of
tDOM in this region. This suggests that the Burkholderia
and BAL58 OTUs, in particular, and Betaproteobacteria, in
general, may have an increased biogeochemical role in the
cycling of carbon in the Baltic Sea and estuarine environments under future predicted climate change scenarios.
Alphaproteobacteria were generally stimulated by increased tDOM and temperatures, i.e., responsive, albeit
some were found in both tDOMH ? T and control mesocosms, i.e., resistant. One particularly abundant Alphaproteobacteria was the resistant Roseobacter clade OTU
UMU_000004 (Table 1). Members of the Roseobacter clade
are often dominant (up to 25 % of total abundance) in
marine surface waters around the globe (Newton et al. 2010)
and relatives of this particular OTU have previously been
found in the Baltic Sea (Sjöstedt et al. 2012). Many
Roseobacter members contain metabolic features that allow
them to be successful in various marine environments and
are therefore of major importance for the cycling of carbon
(Wagner-Dobler and Biebl 2006; Newton et al. 2010). Regarding members of the SAR11 clade bacteria, which are
characterized as oligotrophs (Morris et al. 2002; Tripp
2013), it was surprising that two SAR11 OTUs were responsive to increased tDOM and temperatures, while a third
was found primarily in the controls (Fig. 2; Table 1). In a
previous Baltic Sea climate change experiment, a close
relative of these SAR11 OTUs was predominant in higher
temperatures (6 °C) but absent at lower temperatures (3 °C)
(Lindh et al. 2013). Thus, although SAR11 clade bacteria
are generally oligotrophs, it appears that different OTUs in
Ó The Author(s) 2015. This article is published with open access at Springerlink.com
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AMBIO 2015, 44(Suppl. 3):S402–S412
this clade have a noticeable capacity to respond to changes
in temperature and tDOM availability, considering that their
abundance in seawater can have major implications for
defining bacterioplankton community structure.
Actinobacteria are generally found in high abundance
across the Baltic Sea, particularly in the northern basins
(Bothnian Bay, Bothnian Sea) (Herlemann et al. 2011;
Dupont et al. 2014, Lindh et al. 2015). Two important
examples of sensitive actinobacterial OTUs, i.e., more
abundant in control than in tDOMH ? T mesocosms, were
members of the hgcI clade and the CL500-29 clade. The
hgcI clade has previously not been described extensively
among OTUs of the Baltic Sea, but relatives have been
detected in high abundance in experiments with Baltic
seawater (Sjöstedt et al. 2012). In lakes, members of the
hgcI clade are often dominant components of the bacterioplankton, where they have a competitive advantage in
waters with low DOC concentrations at low temperature
(Glöckner et al. 2000). Still, bacteria in the hgcI clade
remain poorly characterized and their functional traits in
marine/brackish environments are unknown. The CL50029 clade OTU found in high abundance in our control
mesocosms has previously been found to be a generalist in
terms of utilization of different carbon compounds in Baltic
Sea microcosm experiments (Gomez-Consarnau et al.
2012). Thus, our results suggest a major decrease in the
abundance of presently abundant actinobacterial populations in the northern Baltic Sea under predicted climate
change scenarios.
Within Verrucomicrobia, we found 4 distinct OTUs
related to the abundant but relatively unknown Candidatus
Spartobacterium baltica that showed different responses in
our mesocosms. This taxon is spatially widespread and
abundant in the Baltic Sea (Herlemann et al. 2011),
particularly during summer at times of cyanobacterial
blooms and high temperatures (Andersson et al. 2009;
Herlemann et al. 2011). This is likely due to the ability
to utilize phytoplankton-derived high-molecular weight
polysaccharides (Herlemann et al. 2013). In contrast, in our
tDOMH ? T mesocosms, one of the Verrucomicrobial
OTUs was outcompeted by other populations, which may
suggest that it is less adapted to degrade and utilize terrigenous carbon-like humic substances. Still, it is important
to note that other close relatives were either responsive or
resistant to the control and tDOMH ? T conditions investigated here.
A similar distribution of differential responses was seen
in the SAR86 clade, where one SAR86 OTU was responsive in tDOMH ? T mesocosms, and another was sensitive.
Different members of this clade seem to have the capacity
to degrade and utilize specific carbon compounds (Dupont
et al. 2012), suggesting a possible differentiation into ecotypes. Thus, for several taxa, ecotype-level differentiation
S409
among closely related populations is important to consider
when interpreting responses to changes in environmental
conditions.
Bacteroidetes are often abundant in the Baltic Sea
(Andersson et al. 2009; Herlemann et al. 2011, Lindh
et al. 2015), and they are generally recognized for having
an arsenal of enzymes to degrade phytoplankton-derived
polysaccharides and peptides (Kirchman 2002; Fernandez-Gomez et al. 2013). Within the Bacteroidetes, there
was substantial variation in response to the mesocosm
conditions. For example, Owenweeksia OTUs were responsive, while members of the genus Fluviicola and the
NS3a clade were sensitive to increased tDOM and temperature. Bacteroidetes often respond strongly to changes
in growth conditions, either positively or negatively depending on which specific taxon/genus they belong to
(Pinhassi et al. 2004; Andersson et al. 2009; Diez-Vives
et al. 2014; von Scheibner et al. 2014). Substantial differences within Bacteroidetes in the number of glycoside
hydrolases and peptidases are proposed to indicate a
differentiation among taxa for distinct DOM utilization
patterns (Fernandez-Gomez et al. 2013). This could account for parts of the variability among Bacteroidetes
populations in degrading humic substances found in
tDOM in our study.
In addition to major differences in the increase/decrease
of OTUs, it was also curious to note that a few OTUs increased in relative abundance from being undetected at the
onset of the experiment (Table 2). In particular, opportunistic populations, such as Comamonadaceae (Betaproteobacteria) and Desulfuromonadales (Deltaprotebacteria)
OTUs, increased substantially in tDOMH ? T mesocosms.
Rare, or initially undetected OTUs that becomes abundant
also occurs in situ in the marine environment and has been
observed in experimental incubations following environmental perturbations, emphasizing the role of the rare biosphere in responding to change in environmental conditions
(Campbell et al. 2011; Lennon and Jones 2011; Sjöstedt et al.
2012; Alonso-Saez et al. 2014). For example, change in
salinity promoted previously rare or undetected OTUs in
chemostat transplants between Skagerrak seawater and
Baltic Sea water (Sjöstedt et al. 2012).
It is also important to note that the observed changes
among bacterial populations in our experiment are the result of adaptation in a closed system. The distribution of
bacterial populations in the natural marine environment is
limited by few physical barriers and, in the perspective of
climate change, dispersal is likely an important factor for
bacterioplankton responses to environmental change.
Nevertheless, our observations highlight substantial effects
of climate change-induced shifts in the local environmental
conditions for regulating bacterioplankton community
composition
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123
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AMBIO 2015, 44(Suppl. 3):S402–S412
CONCLUSIONS
The observed shifts in bacterial community composition
that we report here link to concomitant changes in community metabolism as reported by Lefébure et al. (2013).
Notably, Lefébure et al. (2013) showed that bacterial production increased substantially in tDOMH ? T compared to
control mesocosms, indicating that the response of community metabolism under the manipulated environmental
conditions could affect ecosystem functioning in brackish
seawater. The current study thus contributes detailed insights into how the response in community metabolism was
linked to the increase/decrease in the abundance of specific
bacterial populations. Bacterioplankton composition is increasingly viewed as a factor that contributes to controlling
ecosystem functioning (Bell et al. 2005; Comte and Del
Giorgio 2011). Both adaptation and replacement of OTUs
have been observed in other aquatic systems (Langenheder
et al. 2005; Comte and Del Giorgio 2011) emphasizing the
presence of both generalist and specialist populations. Altogether, these findings suggest that environmental disturbances induced by anthropogenic activities, such as
increased precipitation and sea surface temperatures, are
liable to cause alterations in microbially mediated
ecosystem functions and carbon fluxes, ultimately promoting heterotrophy in brackish seawater systems.
Acknowledgments We thank José M. Gonzalez for his expertise in
assisting in phylogenetic analysis-related questions. We also acknowledge Sabina Arnautovic for her skillful technical assistance in
the processing of samples. This work was supported by the Swedish
Research Council VR to J.P., the Swedish Research Council FORMAS to A.A., and the Swedish governmental strong research programme EcoChange to J.P and A.A.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
made.
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AUTHOR BIOGRAPHIES
Markus V. Lindh has a PhD degree in ecology from the Linnaeus
University. He is a Marine Ecologist with an interest in climate
change-induced effects and ecological mechanisms for driving the
distribution and abundance of marine bacteria.
Address: Centre for Ecology and Evolution in Microbial model
Systems - EEMiS, Linnaeus University, 391 82 Kalmar, Sweden.
e-mail: [email protected]
Ó The Author(s) 2015. This article is published with open access at Springerlink.com
www.kva.se/en
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AMBIO 2015, 44(Suppl. 3):S402–S412
Robert Lefébure has a PhD degree in marine ecology and his research focuses on climate change-driven effects on food web production dynamics, trophic cascades, and effects of changing
environmental conditions on predator–prey interactions.
Address: Department of Ecology and Environmental Science, Umeå
University, 901 87 Umeå, Sweden.
Address: Marine Stewardship Council, 1 Snow Hill, London EC1A
2DH, UK.
e-mail: [email protected]
Rickard Degerman is a PhD student at Umeå University. He is a
Marine Ecologist studying the effects of climate change on food web
heterotrophic production, trophic cascades, and bacterial community
composition in the Baltic Sea.
Address: Department of Ecology and Environmental Science, Umeå
University, 901 87 Umeå, Sweden.
e-mail: [email protected]
Daniel Lundin is a PostDoc at the Linnaeus University. He is a
Bioinformatician with an interest in computer-based analyses in microbial ecology.
123
Address: Centre for Ecology and Evolution in Microbial model
Systems - EEMiS, Linnaeus University, 391 82 Kalmar, Sweden.
e-mail: [email protected]
Agneta Andersson is a Professor at Umeå University. She is a
Marine Ecologist with an interest in eutrophication and toxic substances and influence of increasing organic input on marine pelagic
food webs.
Address: Department of Ecology and Environmental Science, Umeå
University, 901 87 Umeå, Sweden.
e-mail: [email protected]
Jarone Pinhassi (&) is a Professor at the Linnaeus University. He is
a Marine Microbial Ecologist with an interest in the ecology, physiology, genomics, and diversity of marine bacterioplankton, focusing
on linkages between bacterioplankton community structure and
function in marine pelagic systems.
Address: Centre for Ecology and Evolution in Microbial model
Systems - EEMiS, Linnaeus University, 391 82 Kalmar, Sweden.
e-mail: [email protected]
Ó The Author(s) 2015. This article is published with open access at Springerlink.com
www.kva.se/en