Experimental insights into the importance of aquatic

Experimental insights into the importance of aquatic
The ISME Journal (2016) 10, 533–545
© 2016 International Society for Microbial Ecology All rights reserved 1751-7362/16
Experimental insights into the importance of aquatic
bacterial community composition to the degradation
of dissolved organic matter
Jürg B Logue1,2, Colin A Stedmon3, Anne M Kellerman4, Nikoline J Nielsen5,
Anders F Andersson6, Hjalmar Laudon7, Eva S Lindström4 and Emma S Kritzberg1
Department of Biology/Aquatic Ecology, Lund University, Lund, Sweden; 2Science for Life Laboratory, Solna,
Sweden; 3National Institute for Aquatic Resources, Technical University of Denmark, Charlottenlund,
Denmark; 4Department of Ecology and Genetics/Limnology, Uppsala University, Uppsala, Sweden;
Department of Plant and Environmental Sciences, University of Copenhagen, Copenhagen, Denmark;
Division of Gene Technology, Science for Life Laboratory, School of Biotechnology, KTH Royal Institute of
Technology, Solna, Sweden and 7Department of Forest Ecology and Management, Swedish University of
Agricultural Sciences, Umeå, Sweden
Bacteria play a central role in the cycling of carbon, yet our understanding of the relationship
between the taxonomic composition and the degradation of dissolved organic matter (DOM) is still
poor. In this experimental study, we were able to demonstrate a direct link between community
composition and ecosystem functioning in that differently structured aquatic bacterial communities differed in their degradation of terrestrially derived DOM. Although the same amount of
carbon was processed, both the temporal pattern of degradation and the compounds degraded
differed among communities. We, moreover, uncovered that low-molecular-weight carbon was
available to all communities for utilisation, whereas the ability to degrade carbon of greater
molecular weight was a trait less widely distributed. Finally, whereas the degradation of either
low- or high-molecular-weight carbon was not restricted to a single phylogenetic clade, our results
illustrate that bacterial taxa of similar phylogenetic classification differed substantially in their
association with the degradation of DOM compounds. Applying techniques that capture
the diversity and complexity of both bacterial communities and DOM, our study provides new
insight into how the structure of bacterial communities may affect processes of biogeochemical
The ISME Journal (2016) 10, 533–545; doi:10.1038/ismej.2015.131; published online 21 August 2015
Carbon (C) cycling has received considerable attention in recent years, spurred by the increase of
carbon dioxide concentrations in the atmosphere
and the therewith-associated changes in climate
(Solomon et al., 2007). In the wake thereof, attempts
have been made to balance the global C budget and
to develop a mechanistic understanding of its
underlying dynamics. This has led to a revision of
the traditional view in which inland waters were
considered a passive ‘pipe’ that merely transported C
from land to sea. It is now, however, recognised that
inland waters make up an active compartment:
one that mineralises, transforms and stores C of
Correspondence: JB Logue, Department of Biology/Aquatic Ecology, Lund University, Sölvegatan 37, Lund 22362, Sweden.
E-mail: [email protected]
Received 11 December 2014; revised 13 April 2015; accepted
1 July 2015; published online 21 August 2015
terrestrial origin besides transporting it to the oceans
(Cole et al., 2007; Battin et al., 2009; Tranvik et al.,
2009). Therefore and in view of future climatic
changes, it is of great importance to comprehend
which factors influence the mineralisation and
transformation of terrestrially derived C in freshwater ecosystems.
It is the bacteria that essentially decompose this
allochthonous dissolved organic matter (DOM) and
introduce it into the aquatic food web (Pomeroy
1974; Azam et al., 1983; Jansson et al., 2007).
Bacterial degradation of DOM is carried out by
phylogenetically diverse communities, whose
composition has been shown to be affected by the
quality and quantity of DOM (for example, Logue
and Lindström, 2008). Furthermore, differences in
bulk bacterial processes (for example, bacterial
respiration or production) related to changes in
DOM quality and quantity point towards the
existence of functionally distinct bacterial groups
(for example, Kirchman et al., 2004). Yet, studies
Bacterial composition matters to functioning
JB Logue et al
investigating how the composition of bacterial
communities affects the cycling of C in fresh waters
have to date yielded inconclusive results; while
some argue to having observed a close relationship
between bacterial community composition (BCC)
and C processing (Crump et al., 2003; Kirchman
et al., 2004; Judd et al., 2006; Kritzberg et al., 2006;
Langenheder et al., 2006; Bertilsson et al., 2007),
others found inconsistent (Comte and del Giorgio,
2009, 2010; Lindström et al., 2010) or weak links
(Langenheder et al., 2005). It has to be noted, though
that rather than actually demonstrating a direct
relationship between BCC and C processing (see
Langenheder et al., 2005, 2006), most studies
illustrate that environmental parameters, such
as DOM quality and quantity, affect community
composition and functioning alike. Given the intertwined nature of BCC, the environment and bacterial
functioning, studies directly addressing the relationship between aquatic BCC and C processing are
clearly lacking.
This lack may be partly due to former methodological limitations. Despite their importance in
aquatic systems, DOM and microbial diversity yet
remain to be characterised for the most part (Curtis
and Sloan, 2005; Hertkorn et al., 2008). As DOM is
one of the most complex molecular mixtures on
Earth (Hedges et al., 2000) and microbial communities are extremely diverse (Curtis and Sloan, 2004),
studies going beyond bulk assessments of DOM as
well as the most abundant members of microbial
communities have been rather challenging. Recent
technological advances in the field of molecular
biology (for example, high-throughput sequencing)
and adopting advanced instrumental approaches
into analytical chemistry (for example, electrospray
ionisation mass spectrometry (ESI-MS)) have,
however, made it possible to obtain information of
greater resolution and depth in this respect (see
Kujawinski, 2011 for an overview and Herlemann
et al., 2014; Landa et al., 2014 and Shabarova et al.,
2014 for studies that combine the two approaches).
Such an in-depth and integrative characterisation of
both complex DOM compounds and microbial
communities is a prerequisite for exploring the
relationship between microbial community composition and the processing of DOM.
Here we studied the link between the composition of aquatic bacterial communities and the
degradation of DOM of terrestrial origin. The aim
was to examine how bacterial communities different in composition differ in their processing of
DOM. We hypothesised that bacterial assemblages
of different origin differ in their ability and
potential to degrade DOM, because they vary in
composition. We tested this hypothesis by adopting
a common garden experiment in which a uniform,
terrestrially derived yet artificially prepared DOM
medium was inoculated with aquatic bacterial
communities collected from four sites of varying
environmental character.
The ISME Journal
Materials and methods
Study sites and sampling
Study sites. The four environmental sites that were
selected for this experiment are all situated within
the Umeå River basin in the boreal zone of northern
Sweden and differed in dissolved organic carbon
(DOC) characteristics (Supplementary Table S1).
Two aquatic samples were taken within the Krycklan
catchment at the Svartberget long-term ecological
research site (Laudon et al., 2013): that is, a humic
headwater lake (EnvHL) and a groundwater (EnvGW)
sample. The two remaining aquatic samples were
collected downstream of the Krycklan catchment in
the Vindelälven River (EnvVA), one of two major
tributaries to the Umeå River, and its mouth in the
Baltic Sea (EnvBa).
Sampling. Sampling was carried out on 29 May
2012, towards the end of the spring flood. Two
samples were taken at each site: one for bacterial
abundance and community composition and one for
water chemistry analyses. Samples for bacterial
abundance and community composition were
collected in sterile 1-litre polypropylene bottles
(Nalgene, Rochester, MN, USA), whereas water
chemistry samples were collected in acid-washed
(p.a. quality HCl; Sigma-Aldrich, St Louis, MO, USA)
and Milli-Q (ion- and nuclease-free water) -rinsed
polyethylene bottles (Mellerud Plast, Mellerud,
Sweden). EnvBa, EnvHL and EnvVA were sampled
taking grab samples, whereas EnvGW was sampled
from a shallow, perforated groundwater well.
Samples were kept cold and in the dark during
transportation to the laboratory. In the laboratory,
water chemistry samples were stored at − 20 °C until
further processing, while samples for bacterial
abundance and community composition were first
pre-sieved (225 μm; nylon net filter) and filtered with
a GF/F filter (0.7 μm, pre-combusted at 400 °C for 6 h;
Whatman, Maidstone, UK) to avoid capturing larger
particles and remove grazers, respectively.
Experimental set-up
The experiment was performed as a common garden
experiment, applying a batch culture approach in
which a medium derived artificially from soil
was inoculated with bacterial cells from the four
environments (henceforth called experimental treatments: Ba, GW, HL, and VA). A control, consisting of
medium only (that is, no bacterial inoculum added),
was run alongside the four experimental treatments.
Batch cultures were prepared in 1-litre glass
bottles with a sealing constructed as follows: a
polybutylene terephthalate screw-cap with aperture,
holding a silicone rubber seal pierced with two
holes; one for a metal needle connected to a sterile
60-ml syringe to enable the withdrawal of sample
material, the other one for a sterile venting filter to
Bacterial composition matters to functioning
JB Logue et al
avoid the creation of a vacuum when withdrawing
sample material. Batch cultures were filled without
headspace and stirred continuously throughout the
experiment. Stirring was performed using magnetic
stir bars in combination with magnetic stirrers.
Magnetic stir bars were acid-washed (p.a. quality
HCl; Sigma-Aldrich), rinsed with Milli-Q and heatsterilised at 120 °C before usage in batch cultures.
The medium was prepared from soil collected
from the topsoil layer within the riparian zone 50 m
downstream of the groundwater-sampling site. The
soil was kept cold and in the dark during transportation to the laboratory, where it was stored at − 20 °C
until further processing. In the laboratory, ~ 200 g of
soil were added to 0.8 l of Milli-Q and shaken on a
rotary table in the dark for 3 h. The soil–water
mixture was then subjected to a stepwise filtration,
starting from filters with a pore size of 225 down to
0.2 μm. Coarse filters (225, 150, 75, and 50 μm;
nylon net filters) were acid-washed (p.a. quality
HCl; Sigma-Aldrich), rinsed with Milli-Q and
heat-sterilised at 120 °C, whereas filters of smaller
pore size were either combusted at 400 °C for 6 h
(20 and 8 μm; Cellulose Filters Ashless Grades;
Whatman) or heat-sterilised at 120 °C (0.7 and
0.2 μm; Supor PES Membrane Disc Filters; Pall
Corporation, Port Washington, WI, USA) before
utilisation. In a final step, tangential flow filtration
(50 kDa; Pellicon XL Filter; Merck Millipore,
Billerica, MA, USA) was performed to obtain a
sterile medium with regard to bacteria. Once the
medium was diluted to a final concentration of 17
mg C l−1, nitrogen and phosphorus were added as
NH4–NO3 (final concentration: 3.26 mg N l−1) and
NaH2–PO4 (final concentration: 0.97 mg P l−1),
respectively, thus preventing nitrogen and phosphorus limitation.
Four inocula were prepared; one each from the
respective GF/F-filtrate of the four environmental
sites. After having been stored at 4 °C and in the dark
for 6 days (that is, from sampling the bacteria in the
field to preparing the inocula in the laboratory), the
inocula were concentrated to ~ 1 × 106 cells ml−1 via
tangential flow filtration (Merck Millipore) and
subsequently added to the medium (1% of final
volume; except to the controls). Finally, at time point
zero (that is, the beginning of the experiment), each
of the four experimental treatments and the control
(5 × 1, n = 5) were divided up into three batch
cultures, respectively: that is, treatments and control
were each run in three independent triplicates (5 × 3,
n = 15) after time point zero (see Supplementary
Methods S1 for an in-detail description of inocula
preparation, inoculation and start of the experiment).
The experiment was conducted in a constant
temperature room at 15 °C and in the dark, and
terminated after 5 and a half days on the basis of
levelling off DOC concentrations. Samples were
taken for bulk DOC concentration, UV–visible
absorbance and fluorescence, ESI-MS, bacterial
abundance, and BCC. Bulk DOC concentration
and UV–visible absorbance and fluorescence were
sampled on seven (that is, 0, 30, 78, 90, 102, 114, and
126 h), whereas samples for bacterial abundance
were taken on nine occasions throughout the
experiment (that is, 0, 10, 30, 54, 78, 90, 102, 114,
and 126 h). ESI-MS samples were taken both at the
beginning and at the end, whereas BCC was only
sampled at the end of the experiment. Samples for
BCC were, moreover, also taken from the four
original environments (from the respective GF/Ffiltrate; Supplementary Methods S1 and Supplementary
Figure S1).
All glass- and plastic ware was acid-washed
overnight in HCl (p.a. quality; Sigma-Aldrich),
extensively rinsed with Milli-Q and combusted at
400 °C for 6 h or heat-sterilised (120 °C), respectively.
DOM analyses
Samples analysed for DOC and optical properties
were pre-filtered with a 0.2-μm syringe filter
(Puradisc PES; Whatman).
Dissolved organic carbon. The concentration of
DOC was recorded using a Sievers 900 Laboratory
Total Organic Carbon Analyzer (UV/persulfate
oxidation; GE Analytical Instruments, Manchester,
UK). The manner in which the medium was
prepared ensured negligible concentrations of particulate organic material; hence, total organic C is
comparable to DOC.
UV–visible absorbance and fluorescence. Absorbance
spectra were measured from 200 to 700 nm at 1-nm
intervals, with a Lambda 35 UV–visible spectrometer
(Perkin Elmer, Waltham, MA, USA). Samples were
measured in a 1-cm quartz cuvette and distilled
water was used as a blank measurement.
Excitation–emission matrices (EEMs) were collected with a FluoroMax-2 spectrofluorometer (Horiba Scientific, Edison, NJ, USA), using a 1-cm quartz
cuvette. Excitation wavelengths (λEx) spanned from
250 to 445 nm in 5-nm increments, whereas emission
wavelengths (λEm) ranged from 300 to 600 nm at
increments of 4 nm. Excitation and emission slit
widths were set to 5 nm and the integration time was
0.1 s. Blank subtraction, correction of EEMs and
calibration to Raman units was carried out according
to Murphy et al. (2010). Four individual fluorescing
components in the EEMs were identified and
validated with parallel factor (PARAFAC) analysis,
using the MATLAB and Statistics Toolbox (R2013a;
The MathWorks, Inc., Natick, MA, USA) in combination with the DOMFluor toolbox (Stedmon and Bro,
2008). The components were derived from the EEMs
of 95 samples and their fluorescence characteristics
are depicted as insets in Figure 3b.
Electrospray ionisation mass spectrometry. DOM
was first isolated via solid-phase extraction (SPE) as
described by Dittmar et al. (2008). Note that SPE—as
The ISME Journal
Bacterial composition matters to functioning
JB Logue et al
any presently available DOM isolation method—
only retains a certain fraction of the total DOM
(that is, polar compounds of low to moderate
molecular weight), yet extraction efficiency is
generally higher compared with other isolation
methods (Green et al., 2014). In brief, experimental
samples were filtered (0.2 μm; Puradisc PES Syringe
Filter; Whatman), acidified with HCl (p.a. quality;
Sigma-Aldrich) to pH 2.5 and stored at 4 °C until
SPE. SPE cartridges (Bond Elut-PPL, 1 g, 6 ml;
Agilent Technologies, Santa Clara, CA, USA) were
soaked overnight in methanol (LC-MS CHROMASOLV;
Sigma-Aldrich), rinsed in Milli-Q, re-rinsed with
methanol, and then rinsed with acidified Milli-Q
(pH = 2, p.a. quality HCl; Sigma-Aldrich). Immediately before SPE, samples were acidified one more
time with HCl (p.a. quality; Sigma-Aldrich) to pH 2.
Acidified sample aliquots (600 ml) were allowed
to pass through the SPE cartridges by gravity.
Cartridges were subsequently rinsed with acidified
Milli-Q (pH = 2, p.a. quality HCl; Sigma-Aldrich) and
dried with gaseous N2. DOC was eluted with 4 ml
methanol and stored at − 20 °C.
Mass spectra were collected on a quadrupole timeof-flight mass spectrometer, operating in scan mode
with negative ESI. Blank injections of the mobile
phase and two selected samples (henceforth called
reference samples) were each measured four times
throughout, with the latter to check for instrument
drift, measurement reproducibility and analytical
precision. A more detailed description can be found
in Supplementary Methods S2.
Data reduction of mass spectra was conducted as
follows: mass to charge ratios (m/z) were binned to
integers, which resulted in 1900 m/z ranging from
100 to 1999 (see Figure 5a for an example).
Subsequently, sample representative mass spectra
were obtained by combining spectra across the
injection profile. An average blank measurement
was calculated and subtracted from all samples. The
four separate measurements from each of the two
selected reference samples were thereafter used to
estimate and test for analytical precision. At first, the
s.d. for each m/z for both reference samples were
calculated. Next, the highest s.d. from either of the
two was selected at each m/z and multiplied by 2.
Finally, this recombined spectra was adopted to
define a threshold for indicating significant changes
in DOM mass spectra during the experiment; only
changes > 2 s.d. for each respective m/z were
considered significant and included in subsequent
Bacterial abundance
Bacterial cells were preserved in sterile-filtered,
borax-buffered formaldehyde at a final concentration of 4% w/v. Bacterial abundance was enumerated by flow cytometric determination (CyFlow
space; Partec, Münster, Germany) of SYTO
13- (Invitrogen, Carlsbad, CA, USA) stained cells,
The ISME Journal
following the method described by del Giorgio
et al. (1996).
Nucleic acid extraction, PCR and pyrosequencing
Bacterioplankton cells were collected onto 0.2 μm
membrane filters (Supor-200 Membrane Disc Filters;
Pall Corporation), filtering 0.2 l of water. Filters were
placed into sterile cryogenic vials (Nalgene) and
finally kept at − 80 °C until further processing.
Nucleic acid extraction. Nucleic acid extraction
was performed following the protocol 3 of the EasyDNA kit (Invitrogen) with an extra 0.2 g of 0.1 mm
zirconia/silica beads. Extracted nucleic acids were
sized and yields quantified by means of agarose (1%)
gel electrophoresis, GelRed staining (Biotium Inc.,
Hayward, CA, USA) and UV transillumination before
PCR amplification.
PCR amplification and template preparation. The
bacterial hypervariable regions V3 and V4 of the 16 S
rRNA gene were PCR amplified, using bacterial
forward and reverse primer 341 (5′-CCTACGGG
TCTAATCC-3′), respectively (Herlemann et al.,
2011). The primers were modified before employment according to the final configuration: Adaptor
B-341F and AdaptorA-MID-805R (AdaptorA and B
are 454 Life Sciences adaptor sequences; 454 Life
Sciences, Branford, CT, USA). Multiplex identifiers
were seven-nucleotide long, sample specific and
developed following recommendations by Engelbrektson et al. (2010). PCR reactions were performed
in a 20-μl reaction volume comprising 0.4 U Phusion
high-fidelity DNA polymerase (Finnzymes, Espoo,
Finland), 1 × Phusion HF reaction buffer (Finnzymes), 200 μM of each dNTP (Invitrogen), 200 nM
of each primer (Eurofins MWG, Ebersberg,
Germany), 0.1 mg ml − 1 T4 gene 32 protein (New
England Biolabs, Ipswich, UK) and finally
5–10 ng of extracted nucleic acid. Thermocycling
(DNA Engine (PTC-200) Peltier Thermal Cycler;
Bio-Rad Laboratories, Hercules, CA, USA) was
conducted with an initial denaturation step at 95 °C
for 5 min, followed by 27 cycles of denaturation at
95 °C for 40 s, annealing at 53 °C for 40 s, extension at
72 °C for 1 min and finalised with a 7-min extension
step at 72 °C. Four technical replicates were run per
sample, pooled after PCR amplification and purified
using the Agencourt AMPure XP purification kit
(Beckman Coulter Inc., Brea, CA, USA). Nucleic acid
yields were checked on a fluorescence microplate
reader (Ultra 384; Tecan Group Ltd, Männedorf,
Switzerland), employing the Quant-iT PicoGreen
dsDNA quantification kit (Invitrogen). Finally, PCR
amplicons were pooled in equimolar proportions to
obtain a similar number of 454-pyrosequencing
reads per sample.
Pyrosequencing. The final pooled amplicon was
sequenced unidirectionally (Lib-L chemistry) on a
Bacterial composition matters to functioning
JB Logue et al
454 GS-FLX system (454 Life Sciences) at the
Norwegian High-Throughput Sequencing Centre
(NSC, Oslo, Norway; http://www.sequencing.uio.
no), using GS-FLX Titanium reagents.
Sequence analyses
The 454-pyrosequencing errors, PCR single base
errors and chimeric sequences were removed from
the 454-pyrosequencing amplicon library employing
AmpliconNoise (v1.26; Quince et al., 2011) followed
by Perseus (Quince et al., 2011). Pyrosequencing
reads not matching multiplex identifier and/or
primer sequences were removed just as were reads
shorter than 200 bp. Reads were further truncated at
450 bp, eliminating additional noise (Mardis, 2008),
and finally trimmed off multiplex identifier and
primer sequences.
Denoised 454-pyrosequences were clustered into
operational taxonomic units (OTUs) at a level of 97%
sequence identity (AmpliconNoise, v1.29; Quince
et al., 2011) and classified based on the RDP naive
Bayesian rRNA Classifier (RDP Classifier, v2.6; Wang
et al., 2007). Representative sequences were aligned
based on the SILVA alignment (release 102; Quast
et al., 2013) using mothur (v1.33.2; Schloss et al.,
2009). Finally, pyrosequences that could neither be
aligned nor assigned, or were assigned as Archaea or
Eukaryota (for example, chloroplasts) were further
removed. The 454-pyrosequencing reads of both
experimental (Ba, GW, HL, and VA) and environmental (EnvBa, EnvGW, EnvHL, and EnvVA) samples have been deposited at the NCBI Sequence Read
Archive under accession number SRP021096.
Data analyses
Pyrosequencing sampling efforts (that is, the number
of pyrosequences obtained per sample) were normalised for statistical data analysis. Normalisation was
done across samples through sub-sampling and
analyses are based on 29 206 reads randomly drawn
from each experimental sample. To analyse differences in BCC among experimental treatments, a
multivariate generalised linear model (Wang et al.,
2012; Warton et al., 2012) was applied. The model
that is fitted is log-linear and assumes a negative
binomial distribution of data. Relationships between
bacterial assemblages at the end of the experiment
were visualised employing non-metric multidimensional scaling (Bray–Curtis distance) ordination.
To investigate whether bacterial abundances or
DOC concentrations were significantly different
between treatments at the end of the experiment, a
one-way analysis of variance (ANOVA) was carried
out. Repeated-measures ANOVAs were performed
for bacterial abundances, DOC concentrations and
fluorescent intensities of PARAFAC components
between experimental treatments, to test for treatment and time effects as well as for an interaction of
the two throughout the experiment. Permutational
ANOVAs (Euclidean distance) were computed to
examine differences between the experimental treatments with respect to fluorescence, fluorescent
(PARAFAC) components and m/z at the end of the
experiment. The relationships between experimental
treatments regarding m/z were, in addition, analysed
by principal component analysis.
Finally, associations between bacterial OTUs and
change in m/z were examined via Mantel’s test and
correlation analysis. Mantel’s testing was carried out
between distance matrices derived from the final
relative abundances of bacterial OTUs (Bray–Curtis
distance) and change in m/z from the beginning to the
end of the experiment (Euclidean distance). Correlation analysis tested for co-variation between the final
relative abundance of the most abundant bacterial
taxa and change in m/z. The most abundant taxa were
arbitrarily defined as OTUs containing 4100 reads
per OTU across all experimental samples (35 in total).
To correct for multiple correlations, P-values were
adjusted according to the false discovery rate
(Benjamini and Hochberg, 1995).
All statistical data analyses were conducted using
R (2015), in particular the vegan (Oksanen et al.,
2008) and the mvabund (Wang et al., 2012) packages,
and P-values were opposed to an α-value of 0.05.
BCC analysis
Environmental and experimental samples contained on average 1259 and 64 OTUs, respectively
(Supplementary Table S2 and Supplementary
Figure S2). Bacterial communities from the four
environmental sites were distinct from one another
in composition (Supplementary Figure S3). The
triplicate experimental bacterial assemblages were
more similar in composition to each other than to
such from other experimental treatments as both
non-metric multidimensional scaling ordination
(Figure 1) and multivariate generalised linear model
(Wald = 35.88, P = 1.00E − 03) show.
Bacterial abundance analysis
Bacterial abundances of the four treatments
increased considerably over the course of the
experiment from on average 1.6 × 104 in the beginning to between 4.1 × 107 and 1.8 × 108 cells ml−1 in
the end (Figure 2a). Abundances recorded in the
controls were at, or marginally above, the detection
limit throughout the experiment. Overall, bacterial
abundances changed significantly over time and
differently as to treatments over the course of the
experiment (Table 1). Yet, only HL differed significantly from the other treatments in terms of bacterial
abundance at the end of the experiment (ANOVA;
F = 21.67, P = 3.39E − 04; Figure 2a). The growth
curves more or less resemble the growth of a single
The ISME Journal
Bacterial composition matters to functioning
JB Logue et al
stress = 0.07
Bacterial Abundance [log10 Cells mL-1]
Figure 1 NMDS representation of bacterial communities from the
four experimental treatments. NMDS ordination was derived from
pairwise Bray–Curtis distances. Numbers depict replicates one,
two and three. Hulls were drawn to group replicates within an
experimental treatment. Abbreviations: Ba, Baltic; GW, groundwater; HL, headwater lake; VA, Vindelälven.
species batch culture (with an initial lag, an
exponential and the onset of a stationary phase).
Analyses of DOM
The concentration of DOC in all treatments
decreased by approximately two-thirds over
the course of the experiment from on average
17 mg C l−1 in the beginning to 6 mg C l−1 in the end
(Figure 2b). The control samples also experienced a
decline, albeit a considerably less pronounced one.
On the whole, DOC concentrations changed significantly over time, although only VA significantly
differed from the other three treatments over the
course of the experiment (Table 1). DOC concentrations measured at the end of the experiment did not
differ among the four treatments (ANOVA; F = 0.85,
P = 0.51).
The changes in fluorescence in the controls were
minimal compared with the four experimental
treatments (Figure 3a). Ba, GW and HL showed a
high degree of similarity in qualitative (spectral)
change with a distinct removal of fluorescence at
~ λEm 460 and 300 nm. VA, on the other hand,
exhibited a notable difference from the other experimental treatments in that a loss of fluorescence at
~ λEm 360 nm and an increase in fluorescence at
~ λEm 470 nm could be observed (Figure 3a).
Furthermore, the four treatments differed significantly from each other with regard to fluorescence at
the end of the experiment (permutational ANOVA;
R2 = 0.83, P = 2.00E − 03). PARAFAC analysis identified four distinct fluorescent components for which
the molecular structures are unknown. Components
one and two (C1 and C2) showed locations of
maximum peak intensities typical of what is referred
to as humic like, whereas component 3 (C3) exhibited
fluorescence properties similar to that of the amino
The ISME Journal
Dissolved Organic Carbon Concentration [mg C L-1]
Time [hrs]
Figure 2 Trends of bacterial abundances (a) and DOC concentrations (b). Bacterial abundances and DOC concentrations were
measured over the course of the experiment for the four treatments
and the control (mean ± s.e., n = 3 replicates; except for time point
0, where n = 1). Letters A and B in a denote significant differences
between treatments with regard to bacterial abundances at the end
of the experiment (A) or not (B), respectively, assessed by means of
Tukey’s post-hoc test on an ANOVA. Abbreviations: Ba, Baltic; C,
control; GW, groundwater; HL, headwater lake; VA, Vindelälven.
acid tryptophan (also called protein like). Component 4 (C4) depicted intermediate characteristics.
The controls, in general, showed no change in
fluorescence intensities for all four components
(Figure 3b). Compared with the other two components, C1 and C2, remained more or less unaltered in
fluorescence throughout the experiment, with only a
slight systematic removal of C1 in all treatments but
VA. C3 showed a substantial decrease in intensity,
whereas C4 experienced a marginal increase for
all four experimental communities (Figure 3b).
PARAFAC components predominantly changed significantly over time but only C3 differed significantly
throughout the experiment across all treatments
(Table 1). Permutational ANOVA, furthermore, identified significant differences in PARAFAC components among the four treatments at the end of the
experiment (R2 = 0.97, P = 9.99E − 04).
o2.00E − 16*,a
o2.00E − 16*
o2.00E − 16*
Abbreviations: ANOVA, analyses of variance; BA, Baltic; DOC, dissolved organic carbon; GW, groundwater; HL, headwater lake; PARAFAC, parallel factor; VA, Vindelälven River.
*Indicate significant P-values.
Note that experimental treatments HL and VA did not significantly differ in bacterial abundances from each other throughout the experiment (assessed by linear mixed-effects model and Tukey’s
post-hoc test).
Note that only VA significantly differed from the other treatments throughout the experiment with regard to DOC concentrations (assessed by linear mixed-effects model and Tukey’s post-hoc test).
Note that experimental treatments GW and HL did neither significantly differ in C1 nor C2 from each other throughout the experiment (assessed by linear mixed-effects model and Tukey’s posthoc test).
Note that linear mixed-effects modelling and subsequent Tukey’s post-hoc testing with respect to PARAFAC components C1, C2, C3 and C4 could only be performed starting from the second time
point, as linear mixed-effects models do not accept missing data (data for the first time point was not available for VA).
Note that BA and GW did not significantly differ in C4 from HL or VA and HL, respectively, throughout the experiment (assessed by linear mixed-effects model and Tukey’s post-hoc test).
1.48E − 08*,d,e
o2.00E − 16*
1.83E − 06*
o 2.00E − 16*,c,
2.66E − 14*
2.73E − 04*
2.26E − 06*,b
o2.00E − 16*
1.78E − 11*
o2.00E − 16*,c,d
o2.00E − 16*
4.03E − 05*
o2.00E − 16*,d
o2.00E − 16*
2.19E − 03*
Bacterial abundance
Table 1 Results from repeated measures ANOVA, testing for differences in bacterial abundances, DOC concentrations and fluorescent intensities of the four components identified by
PARAFAC analysis between the experimental treatments and over time
Bacterial composition matters to functioning
JB Logue et al
Mass spectra (see Figure 5a for an example) of the
four treatments did not differ notably from each
other in the beginning of the experiment (Figure 4);
however, they did at the end (as seen in Figure 4 and
confirmed by permutational ANOVA; R2 = 0.42,
P = 1.70E − 03). Some masses also decreased significantly in intensity in the controls (Figure 5b).
Bacterial assemblages from Ba and HL reduced the
intensity of masses from a very broad range of m/z,
whereas bacteria from GW and VA tended to
preferentially reduce the intensity of masses of
o600 m/z (Figure 5b). Further, it is only within a
very narrow mass range (~200–600 m/z; corresponding to low-molecular-weight carbon (LMWC);
Supplementary Methods S2) that bacterial assemblages from all experimental treatments were able to
degrade DOM (Figure 5c).
Associations between bacterial taxa and the
degradation of DOM
Experimental communities similar in composition
disclosed similar trends in the overall change of m/z
over time (Mantel’s test: R = 0.38, P = 0.01). Correlating the relative abundances of the most abundant
bacterial taxa at the end of the experiment (classified
predominantly as Proteobacteria and, to a far lesser
extent, Bacteroidetes) with the change in m/z
throughout the experiment showed a variety of both
positive and negative associations, and that OTUs
associated with different range intervals of change in
m/z (Figure 6). With regard to the latter, strong
positive associations between relative OTU abundance and the degradation of m/z of smaller or larger
size could be found for β-proteobacterial taxa (that is,
OTUs C1, C430 and C3), and α- (that is, OTUs C3141
and C18) and β-proteobacterial OTUs (that is, C3145
and C836), respectively. Furthermore, high relative
abundances of the γ- and α-proteobacteria C812 and
C0, respectively, associated strongly with a decrease
in m/z over almost the entire range of m/z.
Taking advantage of technological advances in
analytical chemistry and molecular biology, we
explored how the composition of aquatic bacterial
communities affected the degradation of DOM of
terrestrial origin. Having adopted an experimental
approach in which model communities were
exposed to a terrestrially derived DOM substrate,
our results highlight that although bacterial communities that differ in composition degraded the same
amount of DOM, both the temporal pattern of
degradation and, most importantly, the compounds
that were degraded significantly differed. Finally, we
observed that the most abundant bacterial taxa
differed substantially in their association with the
degradation of DOM compounds.
The ISME Journal
Bacterial composition matters to functioning
JB Logue et al
Fluorescence Spectra
Mean Change in Fluorescence
Standard Deviation of Change
C1 (humic-like)
C2 (humic-like)
Normalised Fluorescence
Normalised Fluorescence
Raman Units [R.U.]
Wavelength [nm]
C3 (protein-like)
Wavelength [nm]
Wavelength [nm]
C4 (intermediate)
Normalised Fluorescence
Normalised Fluorescence
Raman Units [R.U.]
Wavelength [nm]
Time [hrs]
Time [hrs]
Figure 3 Net changes in DOM fluorescence (a) and fluorescent intensities of components identified by PARAFAC analysis (b).
(a) Excitation–emission matrices (EEMs) at the start of the experiment (n = 1) together with the mean change and s.d. in fluorescence from
the beginning to the end of the experiment across the three replicates for each treatment (n = 3). Excitation (λEx) and emission (λEm)
wavelengths are given on the x and y axis, respectively. (b) Pictures changes of fluorescent intensities of PARAFAC components: C1, C2, C3
and C4 (mean ± s.e., n = 3; except for time point 0 and time point 2 for VA only, where n = 1). All components were normalised to zero for
time point zero, except the ones in VA, which were normalised to zero for the second time point, as the measurement at time point zero
had to be discarded owing to an erroneous reading. Insets visualise the respective spectral properties of the four fluorescent components
identified by PARAFAC analysis. Abbreviations: Ba, Baltic; C, control; GW, groundwater; HL, headwater lake; VA, Vindelälven.
The ISME Journal
Bacterial composition matters to functioning
JB Logue et al
Experimental Treatments
PC2 (17.1%)
PC1 (30.9%)
A major goal in ecology is to link the composition
of biological communities with processes occurring
in an ecosystem. Given the entwined nature of
microbial community composition, the environment
and ecosystem processes, one of the greatest challenges is to test for direct effects of composition on
functioning. Common garden experiments allow for
precisely that by standardising environmental parameters and, therefore, enabling the teasing apart of
the effects of the environment from the composition
of microbial communities on functioning (Reed and
Martiny, 2007). The downside of incubating microbial communities under batch growth conditions,
however, is that the resulting community will differ
from the composition of its original inoculum (for
example, Christian and Capone, 2002). Indeed, our
analyses identified a change from environmental to
experimental bacterial communities in both diversity and composition (Supplementary Figures S2 and
S4, respectively). It has been further suggested that
such experiments favour micro-organisms rare in
nature but featuring opportunistic, copiotrophic
qualities that allow for a more rapid adaptation to
changes in environmental conditions and, hence, to
outcompete others that are originally more abundant.
In nature, DOM varies in quality (and quantity) over
space and time (for example, Kothawala et al., 2014),
variations to which microbes need to adapt. Yet, the
exposure of different bacterial communities in
our experiment to a freshly prepared, terrestrially
derived and, hence, highly bioavailable DOM
substrate as C source possibly enhanced growth of
such naturally rare bacteria. As the communities still
differ in composition at the end of the experiment, it
can, however, be assumed that the functional
Times Reduced
Figure 4 Results from principal component analysis of mass to
charge ratios (m/z) that derived from ESI-MS. The first and second
principal component explained 30.9% and 17.1% of the variability, respectively. Samples from both the beginning (not
numbered) and end (numbered) of the experiment are visualised.
Numbers depict replicates one, two and three. Hulls were drawn
to group not only the replicates within a treatment but also the
samples at the beginning of the experiment. Abbreviations: Ba,
Baltic; C, control; GW, groundwater; HL, headwater lake; VA,
1300 1500
Mass to Charge Ratio [m/z]
Figure 5 Results from ESI-MS analyses. (a) An example for the
mass spectra of DOM obtained from an experimental sample (that
is, GW) at the beginning of the experiment. Mass to charge ratios
(m/z) that were significantly reduced in intensity throughout the
experiment are visualised in b. Numbers in brackets specify the
total number of m/z significantly reduced in intensity per sample
by the end of the experiment. (c) m/z that were significantly
reduced in intensity by none of the four experimental treatments
and their respective replicates (Times Reduced 0), all three
replicates of just one treatment (Times Reduced 1), all
three replicates of only two treatments (Times Reduced 2), all
three replicates of three and all four experimental treatments
(Times Reduced 3 and 4, respectively) by the end of the
experiment. The dashed line visualises the distinction between
LMWC (o600 m/z) and high-molecular-weight carbon masses
(4600 m/z). Abbreviations: Ba, Baltic; C, control; GW, groundwater; HL, headwater lake; VA, Vindelälven.
differences observed are the consequence of initial
compositional differences among the bacterial
Our results, thus, show a close link between BCC
and function. Going beyond a mere identification of
a link between BCC and DOM degradation, our
results further highlight that the four experimental
communities degraded different components of the
DOM pool. Although fluorescence analyses illustrate
that certain DOM components were commonly
more bioavailable than others, both fluorescence
and ESI-MS analyses demonstrate that the four
different bacterial communities differed in which
DOM components were degraded preferentially.
Most importantly, ESI-MS analysis uncovered that
community composition was of little importance
regarding the degradation of LMWC, whereas the
utilisation of masses of greater size differed among
communities. This means that the ability to use
LMWC is a functional property (trait) rather common
The ISME Journal
Bacterial composition matters to functioning
JB Logue et al
900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 1999
Mass to Charge Ratio [m/z]
Figure 6 Heatmap visualising the Spearman’s rank correlation coefficients (Spearman’s ρ) from the correlation analyses between the
relative abundance of the 35 most abundant (that is, 4100 reads per) OTUs at the end (rows) and the change in mass to charge ratios (m/z)
from the beginning to the end of the experiment (columns). A high correlation coefficient (red) stands for a strong positive correlation
between an OTU’s relative abundance and the decrease in m/z from the beginning to the end of the experiment. The dendrogram clusters
OTUs according to Spearman’s ρ, whereas the colour columns depict affiliation of OTUs in accordance with taxonomic classification
(from left to right: phylum, class and genus; colours match the legend to the right of the heatmap). The size of each bubble is proportional
to the OTU’s relative abundance.
in all of the four bacterial communities, whereas the
capability to use C of high-molecular-weight appears
to be a trait restricted to particular bacterial communities. An explanation could lie in a finding made by
Weiss et al. (1991) that compounds of up to ~ 600 Da
(that is, LMWC) can be taken up readily by microorganisms across the cell membrane (that is, through
a variety of transmembranic transport systems),
whereas larger ones require extracellular cleavage
by means of enzymatic hydrolysis (that is, via
individual or interacting ectoenzymes), an ability
that indeed not all bacterial taxa possess (for
example, Berlemont and Martiny 2013). Yet, bacterial members within a community vary not only with
regard to the ability to produce ectoenzymes but also
in their capability to express transmembranic transport systems that allow the uptake of compounds
exceeding 600 Da (for example, Teeling et al., 2012).
However, a microbial community’s toolbox of traits
is more than the sum of its parts; on the one hand,
some bacterial taxa may be needed to actually
facilitate the degradation process, allowing other
micro-organisms to either hydrolyse substrates
further or take them up, on the other the process
may only continue when some microbes act in
The ISME Journal
concert. Pedler et al. (2014), for instance, demonstrated that the readily available fraction of a coastal
DOM pool could be completely removed by a single
taxon, whereas decomposition of the less bioavailable portion required additional members of the
community. Hence, it becomes apparent that not
only the chemical composition of DOM (that is,
quality) but also the distribution of traits within
microbial communities are important when it comes
to whether or not DOM evades microbial remineralisation and transformation. As such, bioavailability can be perceived as an ongoing interaction between the chemical composition of DOM and
a microbial community’s metabolic capacity rather
than merely an inherent property of DOM (Nelson
and Wear 2014).
Although the degradation of either low- or highmolecular-weight carbon was not restricted to a
single phylogenetic clade, our results illustrate that
bacterial taxa of similar phylogenetic classification
differed substantially in their association with the
degradation of DOM compounds (both at a 97% and
99% sequence identity level; results for the latter are
not shown). This may be an indication for high
variation in the functional, and thus ecological,
Bacterial composition matters to functioning
JB Logue et al
potential among closely related populations within
microbial communities (that is, micro-diversity; see
Zimmerman et al., 2013); for example, the two as
Herminiimonas classified bacterial taxa C3 and C836
were generally associated with the degradation of
low- and high-molecular-weight carbon, respectively. Hence, our results demonstrate that the
capacity of a community to degrade DOM compounds cannot easily be predicted from phylogenetic
information alone, at least not from information
derived from the 16S rRNA gene (see also Covert and
Moran, 2001; Fuhrman and Hagström, 2008 and
Martiny et al., 2013).
Considering the associations observed between the
relative abundances of the most abundant bacteria
and the degradation of DOM compounds the question arises ‘Why do the observed degradation
patterns not look more similar, given that these
bacterial taxa were generally present in all four
communities?’. One explanation could be that the
functional gene repertoire of these bacteria varied
between experimental communities as a result of
adaptation to their original environments. Another
could be that these abundant bacteria depend on
other taxa with a different set of traits fundamental to
the degradation of certain DOM compounds (see
Pedler et al., 2014); taxa that are rarer and may not be
present in all communities. Such interplay will,
though, not be detectable via correlation analysis. In
fact, caution has to be exercised when interpreting
the results from the correlation analysis in that it
does not allow drawing conclusions about the cause
and effect, and, as such, cannot be used to unambiguously link a specific bacterial taxon to the
degradation and utilisation of a particular DOM
compound. In addition, size (for example, m/z)
represents only one property of DOM; correlating
other properties with bacterial taxa may yield more
nuanced and different associations, as well as traitspecific insights. Once associations have been
established, they may guide researchers to conduct
studies more non-generic in character, such as
controlled experiments in which the degradation
capacities of a single bacterial population are
investigated. Moreover, identifying functional genes
involved in the degradation of DOM along with
assigning the chemical composition to individual
DOM compounds via ultrahigh-resolution MS (for
example, Fourier transform ion cyclotron resonance
MS; see Hertkorn et al., 2008) could potentially
provide insight into microbial traits that may or may
not be phylogenetically constrained. Combining
such trait-based information with knowledge of the
regulation of microbial activities, the monitoring of
functional genes (metatranscriptomics or metaproteomics; for example, Moran, 2009; Teeling et al.,
2012, respectively) and/or metabolic features (single
cell genomics; for example, Rinke et al., 2013) may
offer a way to explore the use of individual organic
matter compounds by specific microbial taxa in
complex communities to an even greater depth and
improve our understanding of how microbial community composition may affect the cycling of C in
the biosphere.
Conflict of Interest
The authors declare no conflict of interest.
We gratefully acknowledge the help of Dolly Kothawala
with UV–visible absorbance and fluorescence troubleshooting. We thank Jan Johansson for help of absorbance
and fluorescence sample processing in the laboratory. We
thank Jérôme Comte, Silke Langenheder and Lars Tranvik
for constructive comments and suggestions on the manuscript. Computing resources were provided by the
Swedish National Infrastructure for Computing (SNIC)
through the Uppsala Multidisciplinary Centre for
Advanced Computational Science (UPPMAX) under
Project b2010008. Financial support to this study was
procured by the Swedish Research Council (VR; 20104081 granted to ESK, CAS and ESL; 2011-5689 supporting
AFA), the Swiss National Science Foundation (SNSF;
PA00P3_145355 granted to JBL), the strategic research
area ‘Biodiversity and Ecosystem Services in a Changing
Climate’ (BECC) and Helge Ax:sson Johnsons Foundation
(supporting JBL), the VKR Centre of Excellence on
Ocean Life (supporting CAS), the Swedish Research
Council for Environment, Agricultural Sciences and
Spatial Planning (FORMAS) via the strong research
environment ‘Color of Water’ (supporting AMK), EC
BONUS (BLUEPRINT supporting AFA), and Future
Forest, ForWater (FORMAS), the Kempe Foundation and
SITES (VR) (funding the Krycklan Catchment Study).
The funders had no role in study design, data collection
and analysis, decision to publish, or preparation of the
Author contributions
ESK conceived the study with contributions from
CAS and ESL. JBL designed the study with contributions from CAS, ESL and ESK. JBL collected
environmental samples with assistance of HL. AMK
carried out the SPE and NJN ran the ESI-MS, while
CAS performed PARAFAC and ESI-MS analyses. JBL
collected all experimental data, performed 454pyrosequencing analyses, analysed output data in
close collaboration with AFA and wrote first draft of
the manuscript to which all authors contributed in
subsequent revisions.
Azam F, Fenchel T, Field JG, Gray JS, Meyerreil LA,
Thingstad F. (1983). The ecological role of watercolumn microbes in the sea. Marine Ecol Prog Ser 10:
The ISME Journal
Bacterial composition matters to functioning
JB Logue et al
Battin TJ, Luyssaert S, Kaplan LA, Aufdenkampe AK,
Richter A, Tranvik LJ. (2009). The boundless
carbon cycle. Nat Geosci 2: 598–600.
Benjamini Y, Hochberg Y. (1995). Controlling the false
discovery rate - a practical and powerful approach to
multiple testing. J R Stat Soc B Met 57: 289–300.
Berlemont R, Martiny AC. (2013). Phylogenetic distribution of potential cellulases in bacteria. Appl Environ
Microbiol 79: 1545–1554.
Bertilsson S, Eiler A, Nordqvist A, Jørgensen NOG. (2007).
Links between bacterial production, amino-acid utilization and community composition in productive
lakes. ISME J 1: 532–544.
Christian RR, Capone DG. (2002). Overview of issues in
aquatic microbial ecology. In: Hurst CJ, Crawford RL,
Knudsen GR, McInerney MJ, Stetzenbach LD (eds).
Manual of Environmental Microbiology. 2nd edn. ASM
Press: Washington, pp 323–328.
Cole JJ, Prairie YT, Caraco NF, McDowell WH, Tranvik LJ,
Striegl RG et al. (2007). Plumbing the global carbon
cycle: Integrating inland waters into the terrestrial
carbon budget. Ecosystems 10: 171–184.
Comte J, del Giorgio PA. (2009). Links between resources,
C metabolism and the major components of bacterioplankton community structure across a range
of freshwater ecosystems. Environ Microbiol 11:
Comte J, del Giorgio PA. (2010). Linking the patterns
of change in composition and function in bacterioplankton successions along environmental gradients.
Ecology 91: 1466–1476.
Covert JS, Moran MA. (2001). Molecular characterization
of estuarine bacterial communities that use high- and
low-molecular weight fractions of dissolved organic
carbon. Aquat Microb Ecol 25: 127–139.
Crump BC, Kling GW, Bahr M, Hobbie JE. (2003).
Bacterioplankton community shifts in an arctic lake
correlate with seasonal changes in organic matter
source. Appl Environ Microbiol 69: 2253–2268.
Curtis TP, Sloan WT. (2004). Prokaryotic diversity and its
limits: microbial community structure in nature and
implications for microbial ecology. Curr Opin Microbiol 7: 221–226.
Curtis TP, Sloan WT. (2005). Exploring microbial diversity
- a vast below. Science 309: 1331–1333.
del Giorgio PA, Bird DF, Prairie YT, Planas D. (1996). Flow
cytometric determination of bacterial abundance in
lake plankton with the green nucleic acid stain
SYTO 13. Limnol Oceanogr 41: 783–789.
Dittmar T, Koch B, Hertkorn N, Kattner G. (2008). A simple
and efficient method for the solid-phase extraction of
dissolved organic matter (SPE-DOM) from seawater.
Limnol Oceanogr Methods 6: 230–235.
Engelbrektson A, Kunin V, Wrighton KC, Zvenigorodsky
N, Chen F, Ochman H et al. (2010). Experimental
factors affecting PCR-based estimates of microbial
species richness and evenness. ISME J 4: 642–647.
Fuhrman JA, Hagström Å. (2008). Bacterial and archaeal
community structure and its patterns. In: Kirchman DL
(ed). Microbial Ecology of the Oceans. 2nd edn. John
Wiley & Sons: Hoboken, pp 45–90.
Green NW, Perdue EM, Aiken GR, Butler KD, Chen HM,
Dittmar T et al. (2014). An intercomparison of three
methods for the large-scale isolation of oceanic
dissolved organic matter. Mar Chem 161: 14–19.
Hedges JI, Eglinton G, Hatcher PG, Kirchman DL,
Arnosti C, Derenne S et al. (2000). The molecularlyThe ISME Journal
uncharacterized component of nonliving organic
matter in natural environments. Organic Geochem 31:
Herlemann DPR, Labrenz M, Jürgens K, Bertilsson S,
Waniek JJ, Andersson AF. (2011). Transitions in
bacterial communities along the 2000 km salinity
gradient of the Baltic Sea. ISME J 5: 1571–1579.
Herlemann DPR, Manecki M, Meeske C, Pollehne F,
Labrenz M, Schulz-Bull D et al. (2014). Uncoupling
of bacterial and terrigenous dissolved organic matter
dynamics in decomposition experiments. PLoS One 9:
Hertkorn N, Frommberger M, Witt M, Koch BP,
Schmitt-Kopplin P, Perdue EM. (2008). Natural organic
matter and the event horizon of mass spectrometry.
Anal Chem 80: 8908–8919.
Jansson M, Persson L, De Roos AM, Jones RI, Tranvik LJ.
(2007). Terrestrial carbon and intraspecific sizevariation shape lake ecosystems. Trends Ecol Evol 22:
Judd KE, Crump BC, Kling GW. (2006). Variation in
dissolved organic matter controls bacterial production
and community composition. Ecology 87: 2068–2079.
Kirchman DL, Dittel AI, Findlay SEG, Fischer D. (2004).
Changes in bacterial activity and community structure
in response to dissolved organic matter in the Hudson
River, New York. Aquat Microb Ecol 35: 243–257.
Kothawala DN, Stedmon CA, Müller RA, Weyhenmeyer GA,
Köhler SJ, Tranvik LJ. (2014). Controls of dissolved
organic matter quality: evidence from a large-scale boreal
lake survey. Global Change Biol 20: 1101–1114.
Kritzberg ES, Cole JJ, Pace MM, Graneli W. (2006).
Bacterial growth on allochthonous carbon in humic
and nutrient-enriched lakes: Results from whole-lake
C-13 addition experiments. Ecosystems 9: 489–499.
Kujawinski EB. (2011). The impact of microbial metabolism on marine dissolved organic matter. Annu Rev
Mar Sci 3: 567–599.
Landa M, Cottrell MT, Kirchman DL, Kaiser K,
Medeiros PM, Tremblay L et al. (2014). Phylogenetic
and structural response of heterotrophic bacteria to
dissolved organic matter of different chemical composition in a continuous culture study. Environ Microbiol
16: 1668–1681.
Langenheder S, Lindström ES, Tranvik LJ. (2005).
Weak coupling between community composition and
functioning of aquatic bacteria. Limnol Oceanogr 50:
Langenheder S, Lindström ES, Tranvik LJ. (2006).
Structure and function of bacterial communities emerging from different sources under identical conditions.
Appl Environ Microbiol 72: 212–220.
Laudon H, Taberman I, Ågren A, Futter M,
Ottosson-Löfvenius M, Bishop K. (2013). The Krycklan
Catchment Study-a flagship infrastructure for hydrology,
biogeochemistry, and climate research in the boreal
landscape. Water Resour Res 49: 7154–7158.
Lindström ES, Feng XM, Graneli W, Kritzberg ES. (2010).
The interplay between bacterial community composition
and the environment determining function of inland
water bacteria. Limnol Oceanogr 55: 2052–2060.
Logue JB, Lindström ES. (2008). Biogeography of bacterioplankton in inland waters. Freshwater Rev 1: 99–114.
Mardis ER. (2008). Next-generation DNA sequencing
methods. Ann Rev Genomics Hum Genet 9: 387–402.
Bacterial composition matters to functioning
JB Logue et al
Martiny AC, Treseder K, Pusch G. (2013). Phylogenetic
conservatism of functional traits in microorganisms.
ISME J 7: 830–838.
Moran MA. (2009). Metatranscriptomics: eavesdropping
on complex microbial communities. Microbe 4:
Murphy KR, Butler KD, Spencer RGM, Stedmon CA,
Boehme JR, Aiken GR. (2010). Measurement of
dissolved organic matter fluorescence in aquatic
environments: an interlaboratory comparison. Environ
Sci Technol 44: 9405–9412.
Nelson CE, Wear EK. (2014). Microbial diversity and the
lability of dissolved organic carbon. Proc Natl Acad Sci
USA 111: 7166–7167.
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR,
O'Hara RB et al. (2008). vegan: Communiy Ecology
Package. R package version 2.2-1.
Pedler BE, Aluwihare LI, Azam F. (2014). Single bacterial
strain capable of significant contribution to carbon
cycling in the surface ocean. Proc Natl Acad Sci USA
111: 7202–7207.
Pomeroy LR. (1974). The oceans food web, a changing
paradigm. Bioscience 24: 499–504.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P
et al. (2013). The SILVA ribosomal RNA gene database
project: improved data processing and web-based
tools. Nucleic Acids Res 41: D590–D596.
Quince C, Lanzén A, Davenport RJ, Turnbaugh PJ. (2011).
Removing noise from pyrosequenced amplicons. BMC
Bioinformatics 12:38: 1–18.
R. (2015). R: A Language and Environment for Statistical
Computing. R Foundation for Statistical Computing:
Vienna, Austria. http://www.R-project.org/.
Reed HE, Martiny JBH. (2007). Testing the functional
significance of microbial composition in natural communities. FEMS Microbiol Ecol 62: 161–170.
Rinke C, Schwientek P, Sczyrba A, Ivanova NN, Anderson IJ,
Cheng JF et al. (2013). Insights into the phylogeny and
coding potential of microbial dark matter. Nature 499:
Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M,
Hollister EB et al. (2009). Introducing mothur: opensource, platform-independent, community-supported
software for describing and comparing microbial
communities. Appl Environ Microbiol 75: 7537–7541.
Shabarova T, Villiger J, Morenkov O, Niggemann J, Dittmar
T, Pernthaler J. (2014). Bacterial community structure
and dissolved organic matter in repeatedly flooded
subsurface karst water pools. FEMS Microbiol Ecol 89:
Solomon S, Qin D, Manning M, Chen Z, Marquis M,
Averyt KB et al. (2007). Climate Change 2007: The
Physical Science Basis. Cambridge University Press:
Stedmon CA, Bro R. (2008). Characterizing dissolved
organic matter fluorescence with parallel factor
analysis: a tutorial. Limnol Oceanogr Methods 6:
Teeling H, Fuchs BM, Becher D, Klockow C,
Gardebrecht A, Bennke CM et al. (2012). Substratecontrolled succession of marine bacterioplankton
populations induced by a phytoplankton bloom.
Science 336: 608–611.
Tranvik LJ, Downing JA, Cotner JB, Loiselle SA, Striegl RG,
Ballatore TJ et al. (2009). Lakes and reservoirs as
regulators of carbon cycling and climate. Limnol
Oceanogr 54: 2298–2314.
Wang Q, Garrity GM, Tiedje JM, Cole JR. (2007). Naive
Bayesian classifier for rapid assignment of rRNA
sequences into the new bacterial taxonomy. Appl
Environ Microbiol 73: 5261–5267.
Wang Y, Naumann U, Wright ST, Warton DI. (2012).
mvabund - an R package for model-based analysis of
multivariate abundance data. Methods Ecol Evol 3:
Warton DI, Wright ST, Wang Y. (2012). Distance-based
multivariate analyses confound location and dispersion effects. Methods Ecol Evol 3: 89–101.
Weiss MS, Abele U, Weckesser J, Welte W, Schiltz E,
Schulz GE. (1991). Molecular architecture and electrostatic properties of a bacterial porin. Science 254:
Zimmerman AE, Martiny AC, Allison SD. (2013).
Microdiversity of extracellular enzyme genes
among sequenced prokaryotic genomes. ISME J 7:
This work is licensed under a Creative
Attribution-NonCommercialNoDerivs 4.0 International License. The images or
other third party material in this article are included
in the article’s Creative Commons license, unless
indicated otherwise in the credit line; if the material
is not included under the Creative Commons license,
users will need to obtain permission from the license
holder to reproduce the material. To view a copy
of this license, visit http://creativecommons.org/
Supplementary Information accompanies this paper on The ISME Journal website (http://www.nature.com/ismej)
The ISME Journal
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF