Science of the Total Environment 470–471 (2014) 1173–1183
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Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
Microbial mechanisms coupling carbon and phosphorus cycles in
phosphorus-limited northern Adriatic Sea
F. Malfatti a,⁎,1, V. Turk b,1, T. Tinta b, P. Mozetič b, M. Manganelli c,i, T.J. Samo a, J.A. Ugalde a, N. Kovač b,
M. Stefanelli c,i, M. Antonioli d, S. Fonda-Umani d, P. Del Negro e, B. Cataletto e, A. Hozić f, N. Ivošević DeNardis f,
V. Žutić f, V. Svetličić f, T. Mišić Radić f, T. Radić g, D. Fuks h, F. Azam a
a
Scripps Institution of Oceanography, University California San Diego, 92037 La Jolla, USA
National Institute of Biology, Marine Station Piran, 66330 Piran, Slovenia
c
Istituto Superiore per la Prevenzione e la Sicurezza del Lavoro (ISPESL), DIPIA, 00040 Monteporzio Catone, Roma, Italy
d
Universita' degli Studi di Trieste, Dipartimento di Biologia, 34127 Trieste, Italy
e
Istituto Nazionale di Oceanografia e Geofisica Sperimentale, Sezione Bio, 34151 Santa Croce, Trieste, Italy
f
Ruđer Bošković Institute, Division for Marine and Environmental Research, 10000 Zagreb, Croatia
g
Institute for Adriatic Crops and Karst Reclamation, Split, Croatia
h
Ruđer Bošković Institute, Center for Marine Research, Rovinj, Croatia
i
Istituto Superiore di Sanità, Dept. of Environment and Primary Prevention, Roma, Italy
b
H I G H L I G H T S
•
•
•
•
Study of marine bacterial phosphorus regeneration with 64-d mesocosm experiment.
Induction of phytoplankton bloom by adding phosphorus in phosphorus-limited water.
After the bloom crash, DOC accumulated despite high bacterial carbon-demand.
Bacteria colonized aggregates expressing intense ectohydrolase activities. High carbon and phosphorus turnover due to bacterial activity.
a r t i c l e
i n f o
Article history:
Received 23 June 2013
Received in revised form 11 October 2013
Accepted 13 October 2013
Available online xxxx
Keywords:
Marine carbon biogeochemistry
Bacterial alkaline phosphatase
ELF-enzyme
Hydrolyses
DOC accumulation
Laser Scanning Confocal Microscope
a b s t r a c t
The coastal northern Adriatic Sea receives pulsed inputs of riverine nutrients, causing phytoplankton blooms and
seasonally sustained dissolved organic carbon (DOC) accumulation—hypothesized to cause episodes of massive mucilage. The underlying mechanisms regulating P and C cycles and their coupling are unclear. Extensive biogeochemical parameters, processes and community composition were measured in a 64-day mesocosms deployed off Piran,
Slovenia. We followed the temporal trends of C and P fluxes in P-enriched (P+) and unenriched (P−) mesocosms.
An intense diatom bloom developed then crashed; however, substantial primary production was maintained
throughout, supported by tightly coupled P regeneration by bacteria and phytoplankton. Results provide novel
insights on post-bloom C and P dynamics and mechanisms. 1) Post-bloom DOC accumulation to 186 μM remained
elevated despite high bacterial carbon demand. Presumably, a large part of DOC accumulated due to the bacterial
ectohydrolytic processing of primary productivity that adventitiously generated slow-to-degrade DOC; 2) bacteria
heavily colonized post-bloom diatom aggregates, rendering them microscale hotspots of P regeneration due to
locally intense bacterial ectohydrolase activities; 3) Pi turnover was rapid thus suggesting high P flux through the
DOP pool (dissolved organic phosphorus) turnover; 4) Alpha- and Gamma-proteobacteria dominated the bacterial
communities despite great differences of C and P pools and fluxes in both mesocosms. However, minor taxa showed
dramatic changes in community compositions. Major OTUs were presumably generalists adapted to diverse productivity regimes.We suggest that variation in bacterial ectohydrolase activities on aggregates, regulating the
rates of POM → DOM transition as well as dissolved polymer hydrolysis, could become a bottleneck in P
regeneration. This could be another regulatory step, in addition to APase, in the microbial regulation of P cycle
and the coupling between C and P cycles.
Published by Elsevier B.V.
⁎ Corresponding author at: Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive MC0202, La Jolla, CA 92093 USA. Tel.:858-822-4688; Istituto
Nazionale di Oceanografia e di Geofisica Sperimentale (OGS) Oceanography Section (OCE) Via Auguste Piccard 54 34151 S. Croce - Trieste. Tel.: +390402249732.
1
These authors contributed equally to the work.
0048-9697/$ – see front matter Published by Elsevier B.V.
http://dx.doi.org/10.1016/j.scitotenv.2013.10.040
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F. Malfatti et al. / Science of the Total Environment 470–471 (2014) 1173–1183
1. Introduction
Spatial-temporal patterns of elemental cycling affecting primary
productivity and carbon cycling and storage in the ocean are regulated
by interactions among phytoplankton, bacteria and various organic
and inorganic pools and processes (Falkowski et al., 2008). The nature
and strength of bacteria-phytoplankton coupling vary in time and
space and are modulated by nutrient supply (Azam and Malfatti,
2007). Models predict that bacterial ecosystem roles can shift fundamentally in different productivities and nutrient regimes, from net
nutrient regenerator to competitor for limiting nutrients (Thingstad
et al., 1998). Under nutrient limitation, phytoplankton C fixation may
outpace bacterial use of organic matter and further bacterial use of
organic matter may be constrained by bacterial population control.
Such situations may result in accumulation of dissolved organic matter
(DOM) (Thingstad et al., 1997).
The northern Adriatic Sea is a P limited basin (Zavatarelli et al., 1998)
that has been experiencing episodes of intense accumulation of mucilage during the last two decades (Fonda Umani et al., 1989). In the
recent decades, the northern Adriatic Sea has been following a trend towards oligotrophy (see articles in Estuarine Coastal and Shelf Science
(edited by Michele Giani et al., 2012; Mozetic et al., 2010)) and it has
been suggested that this biogeochemical trend might even increase
the severity of the ecosystem response to allochtonous inputs of P and
other nutrients from sources such as the Po river, rain and urban discharges (Cossarini et al., 2012; Degobbis et al., 1995; Malej et al., 1997).
This environment experiences pulsed nutrient inputs stimulating
phytoplankton blooms interspersed with periods of Pi limitation of
productivity, but seasonally persistent high summertime DOC (Fonda
Umani et al., 2007). The persistence of DOC at high levels might involve
sustained C fixation and rapid cycles of bacterial processing of DOC and
sustained C fixation would require mechanisms for rapid P regeneration
and its availability to the phytoplankton. Over time, under nutrient limitation, the organic matter pool becomes less utilizable by bacteria, consequently bacteria by recycling nutrients support new phytoplankton
production of organic matter that is readily utilizable (Azam et al.,
1983).
Several mesocosm studies conducted in the northern Adriatic Sea
have shown that P enrichment causes phytoplankton and bacteria to
bloom (Fajon et al., 1999; Larato et al., 2010; Malej et al., 2003; Puddu
et al., 2003; Žutić et al., 2004). We conducted a mesocosm experiment
focusing on microbial pathways and mechanisms regulating P and C
fluxes and how their interaction may lead to DOC accumulation and
persistence. While enclosing the plankton may cause significant departure of the model system from the natural system, mesocosms enabled
us to examine the responses of C and P cycles to define nutrient perturbations and to infer the underlying mechanisms. We note that the complexity of the system in the northern Adriatic Sea does not allow a direct
extrapolation of the results of this experiment to the basin. However the
conclusion regarding the mechanisms should be applicable to the larger
scale system. The DOC accumulation and persistence is of broad interest
in relation to the fate of the post-bloom carbon in natural or human
induced pulsed blooms. Our goal was to understand the mechanism
of P regeneration and how it influences C cycle, particularly DOC
accumulation.
Table 1
Nutrients and DOP pool in the P− and P+ (units μM). Ntot sample on day 64 was spoiled.
Day
PO3−
4
Ptot.
DOP
N0−
2
NO−
3
NH4+
SiO4−
4
Ntot.
0.16
0.12
0.63
0.11
0.50
0.11
0.27
0.12
0.28
0.13
0.22
0.12
0.18
0.13
0.20
0.16
0.25
0.08
0.24
0.12
0.17
0.10
0.22
0.11
0.20
0.12
0.20
0.11
0.18
0.12
0.17
0.13
0.18
0.15
0.24
0.08
0.04
0.07
0.08
0.09
0.09
0.15
0.09
0.17
0.09
0.01
0.15
0.02
0.12
0.02
0.17
0.02
0.17
0.04
0.13
0.01
0.67
19.69
20.75
18.32
18.70
18.08
14.29
18.31
0.75
17.78
0.10
17.47
1.37
17.65
0.87
17.48
b0,01
12.63
0.08
0.32
0.44
0.26
0.41
0.42
0.44
0.42
0.59
4.49
0.41
0.50
1.22
0.41
0.60
0.57
0.90
0.37
0.29
0.50
4.5
26.1
22.4
29.3
26.3
25.1
19.5
23.7
1.9
24.7
3.4
25.8
5.6
22.9
3.6
29.8
21.6
12.4
2.2
16.0
39.5
38.0
32.3
32.4
29.3
26.2
31.6
16.6
28.0
14.4
28.8
15.3
28.1
12.7
29.0
15.9
28.2
–
μM
sw
P−
P+
P−
P+
P−
P+
P−
P+
P−
P+
P−
P+
P−
P+
P−
P+
P−
P+
0
2
4
6
8
10
13
22
64
b0.01
b0.01
0.46
0.01
0.28
b0.01
0.07
b0.01
0.08
0.02
0.04
b0.01
0.01
b0.01
0.02
0.01
0.01
b0.01
0.20
water was brought into the laboratory. The d0 sampling was done
before distributing seawater into experimental carboys (20 L acidwashed polycarbonate carboys; Nalgene®). Then nutrients, except P,
were added at F/10 concentration ((Guillard and Ryther, 1962);
Table 1), to ensure that no other nutrient was limiting in both treatments. After that, we added 0.5 μM Pi, orthophosphate, to the three replicate carboys (P+) while no Pi was added to the other three (P−)
carboys. The resulting N/P ratios after enrichment were 45 (in P+)
and 2000 (in P−). The six carboys were incubated and submerged
below the surface and attached to a line-pulley system.
We sampled on days 0, 2, 4, 6, 8, 10, 13, 22, 29 and 64. Equal volume
(1 L) from each of triplicate carboys was combined before analysis; our
data therefore represent the system average response to Pi amendment.
The initial plan was to sample intensively for 10 days. However the
experimental results prompted us to extend the experiment in order
to assess the DOC dynamic for a longer period of time. In order to do
this, on d10, we combined the water from the three carboys into a single
carboy for each treatment. This was done in order to have enough water
to enable us to extend the experiment to 64 days.
2.2. Nutrient analyses
2. Material and methods
Samples (300 mL) were filtered through a combusted GF/F filter
(pressure ≤ 12.5 cm Hg). Filtrates were analyzed immediately for total
−
dissolved nitrogen (TDN), ammonium (NH+
4 ), nitrite (NO2 ), nitrate
),
silicic
acid
(H
SiO
),
total
dissolved
phosphorus
(TDP)
and
(NO−
3
4
4
orthophosphate (PO3−
4 ; Pi, inorganic P, in the text), using standard protocols (Parsons et al., 1984). For each sample, 5 replicate measurements
were performed. The CV for N salts was ≤8% and for silicic acid it was
≤0.1%. For TDP and Pi the CV was ≤15% for the samples below
0.02 μM and ≤3% for the samples above 0.02 μM. The detection limit
−
−
for NH+
4 , NO2 , NO3 , H4SiO4, Pi and TDP was 0.01 μM; whereas for TDN
it was 0.2 μM. Dissolved organic phosphorus (DOP) was calculated as
[TDP] − [PO3−
4 ]. In all samples DOP was substantial or major fraction
of TDP. We therefore have high confidence in DOP values.
2.1. Mesocosm experimental design
2.3. Chlorophyll a and primary production
Two hundred liter of seawater was collected at the oceanographic
buoy Vida of Marine Biology Station, Piran (45° 32′ 55, 68″ N, 13° 33′
1, 89″ E) in the Gulf of Trieste, northern Adriatic Sea, at 5 m depth on
October 16, 2007 on R/V Sagita. The seawater was pre-filtered through
50 μm acid-washed Nitex to remove larger herbivores, kept at environmental temperature (19 °C), and protected from light. Within 1 h, the
Chlorophyll a (Chl a) was determined fluorometrically (Holm-Hansen
et al., 1965) in triplicates. Samples (100–200 mL) were filtered onto
Whatman GF/F filters, extracted in 90% acetone, and fluorescence measured and corrected for phaeopigments.
Primary production (PP) was measured by the 14C technique
(Steeman-Nielsen, 1951). Seventy-five microliter seawater was poured
F. Malfatti et al. / Science of the Total Environment 470–471 (2014) 1173–1183
into two light and one dark polycarbonate bottles and incubated for 3 h
with 15 μCi NaH14CO3, in situ and at the same depth as the mesocosms.
Sampling started at about 9 in the morning and the incubation was
setup from 9 to 12. Samples were then filtered onto 0.6 μm polycarbonate filters. The acidified filters (250 μL of 0.1 M HCl) were radioassayed
by liquid scintillation counting in a Liquid Scintillation Analyzer
(Canberra Packard TriCarb). Carbon assimilation was calculated as in
Gargas (1975) assuming 5% isotope discrimination (for details of method description see also Engel et al., 2013). On d6, PP was not measured;
we have approximated the d6 PP value based on the increase of measured POC from d4 to d6, since the phytoplankton was still in healthy
bloom phase. The PP values overall are minimum estimates since we
measured net PP, not accounting for DOC produced during PP or C loss
in bacterial carbon demand and phytoplankton respiration. We computed the daily rate assuming 12 h light period.
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into two aliquots; the “total fraction” (unfractionated seawater) and
“unattached bacteria fraction” (operationally defined as the filtrate
through an acid washed 6 μm Nitex mesh. This Nitex size was chosen
in order to gently retain aggregates, however, bacteria associated with
≤6μm particles would pass into the “unattached” fraction). These samples were processed following the protocol of Smith and Azam (1992).
BCP was calculated from 3H-leucine incorporation: 1 mol Leu−1 incorporated=3.1kg BCP (Simon and Azam, 1989) assuming 2-fold intracellular isotope dilution. BCD was computed assuming that BGE was 30% of
BCD (Del Giorgio and Cole, 1988; Hoppe et al., 2002); BCD = BCP / 0.3.
Cell specific growth rate was calculated from BCP and assuming
20 fg C per cell (Lee and Fuhrman, 1987). We computed the BCP and
BCD due to “particle-attached bacterial fraction” by subtracting the
“unattached bacteria fraction” values from the “total fraction” values.
These values would be the minimum estimates of particle-attached bacteria if b 6 μm particle-associated bacteria was significant.
2.4. Dissolved and particulate organic carbon (DOC and POC)
2.4.1. DOC
Seawater samples, in triplicate, were filtered through combusted
GF/F filters in a combusted metal-glass filtration tower system with a
hand-pump (pressure differential 12.5 cm Hg; filters changed frequently to minimize cell breakage). The filtrate was fixed with trace metal
clean 12 N HCl (final pH 2). Samples were stored at 4 °C in the dark
and shipped refrigerated to Scripps Institution for analysis on Shimadzu
TOC 5000A (Cauwet, 1994).
2.4.2. DOC turnover rate
The DOC pool turnover rate was estimated on the basis of bacterial
carbon demand (BCD) and assuming a bacterial growth efficiency
(BGE) of 30%: DOC turnover rate (d−1) = (BCD / [DOC] × BGE).
2.4.3. POC
Subsamples were filtered through Whatman GF/F glass-fiber filters
(precombusted 3 h at 480 °C to eliminate organic contaminants). After
filtration, the filters were rinsed thoroughly with distilled water to
remove salts. All samples were freeze-dried and weighed. The particulate organic carbon (POC) and total particulate nitrogen (PN) content
of freeze-dried and acid-washed filters (to remove carbonates)
(Hedges and Stern, 1984) were determined with a commercial Carlo
Erba elemental analyser 1108.
2.8. Phosphorus pool turnover and P demand for bacteria and
phytoplankton
Phosphorus uptake was measured using 33P orthophosphate
(3000 Ci/mmol; MP Biomedicals). 33P stock was diluted 1000× in
Milli-Q PCR grade water. Working solution (17 μL) was added to
1.7 mL of total or b 1 μm filtered seawater. Negative controls first received 100 μM unlabeled (“cold”) Pi. Triplicate samples were incubated
in the dark at seawater temperature for 30min and spun down (10 min,
10,000 ×g). The supernatant was aspirated and UltimaGold (Perkin
Elmer) scintillation cocktail was added to the tube before radioassay
in a Liquid Scintillation Analyzer (Canberra Packard TriCarb). Uptake
(dpm) was used to calculate the pool turnover time, T (in h), using
the formula T = −t / ln f, where t is the incubation time (Thingstad
et al., 1993, 1996): f=(dpmsample −dpmblank)/dpm added. Background
due to liquid retention during aspiration was found to be negligible
(not shown).
We roughly estimated P demand (nmol L−1 h−1) for phytoplankton
and bacteria on the bases of measured PP and BCP assuming C:P = 106
for phytoplankton (Redfield et al., 1963) and C:P = 50 for bacteria
(Fagerbakke et al., 1996). We recognize that C:P ratio of phytoplankton
(and perhaps also of bacteria) can vary several-folds depending on the
physiological state ((Deutsch and Weber, 2012; Goldman et al., 1979)
see also reference in (Sterner and Elser, 2002)). Our use of C:P of 50
for bacteria likely yielded conservative estimates of bacterial P demand.
2.5. Prokaryotic and viral abundance
2.9. Bulk and individual cell ectohydrolytic activities
Samples were fixed with 0.02 μm-filtered formaldehyde (2% final).
After 1 h at 4 °C, samples were filtered onto 0.02 μm pore-size Anodisc filter (Whatman) and stained with SYBRGold (Invitrogen) (Noble and
Fuhrman, 1998). Duplicate filters were examined on an Olympus BX51
epifluorescence microscope at 1000× magnification with a 480/520 nm
filter-set. More than 200 cells or viruses were counted in random fields
(cyanobacteria abundance method and results are in SI).
2.6. Grazing on bacteria
On d0 we determined heterotrophic nanoplankton grazing on bacteria using the dilution method (Landry and Hassett, 1982). This measurement was not done at later samplings due to sample volume constraint.
The results are shown in SI.
2.7. Bacterial carbon production and bacteria carbon demand
Bacterial carbon production (BCP) was measured by the incorporation of 3H-leucine into newly synthesized proteins (Kirchman et al.,
1985; Simon and Azam, 1989) using the centrifugation protocol
(Smith and Azam, 1992). Seawater was incubated with 20 nM 3H leucine for 1 h in the dark at in situ temperature. The sample was divided
The bulk ectohydrolytic activities for total and b6 μm fractions were
measured using fluorogenic substrate analogs at saturating concentrations (Hoppe, 1983): Alkaline phosphatase (APase) was measured
with 4-Methyl-umbelliferone-phosphate (125 μM, final); aminopeptidase was measured with L-Leucine-7-amino-4-methyl-coumarin
(250 μM, final); lipase was measured with 4-methyl-umbelliferone
oleate (100 μM final), α- and β-glucosidases were measured with
4-Methyl-umbelliferone-α and β-glucosaminide (125 μM) and
chitinase was measured with 4-methyl-umbelliferone-N-acetyl-βD-glucosaminide (100 μM, final). Assays were performed in microtiter plates incubated in the dark at in situ temperature between 1
and 2 h. Fluorescence was measured immediately after adding substrates and then at the end of the incubation (Danovaro et al., 2005),
at 355/460 nm (excitation/emission; with Multilabel counter, Perkin
Elmer). The difference of the blank-corrected (two blanks) fluorescence, average of 8 replicates was divided by the time of incubation
and calibrated against 4-MUF and 7-AMC standards. Enzymatic activities were computed as nmol substrate hydrolyzed L−1 h−1. We adopted
the same saturating concentration for phosphatase previously used in
studies in this area (Radić et al., 2006; Zoppini et al., 2005). It has
been observed that in some oligotrophic environments, with very low
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F. Malfatti et al. / Science of the Total Environment 470–471 (2014) 1173–1183
APA activities, the substrate concentration could be inhibiting (Ivančić
et al., 2009; Sebastián and Niell, 2004). However, the highest Vmax
measured by Sebastián and Niell (2004) was 0.69 nmol L−1 h−1 with
an oversaturating substrate concentration of 100 μM of MUF-P. Our velocities were in the range of ~10–700nmolL−1 h−1, much higher, in the
range of other values measured in Adriatic Sea (Danovaro et al., 2005;
Larato et al., 2010; Celussi and Del Negro, 2012) and in the low range
of values measured by Ivančić et al (2009, 2010). All these studies
used 50 μM substrate. The potential inhibitory effect of higher substrate
concentration does not allow the comparison of our study with previous studies. However our protocol is internally consistent to demonstrate the temporal variability of AP during our mesocosm study
(detection limit 10–20 nmol L−1 h−1).
We developed a protocol for relative quantification of individual cell
APase activity of bacteria and phytoplankton by Enzyme Labeled Fluorescence (ELF®, Invitrogen) at the laser confocal microscope. We slightly modified the staining protocol reported in several studies (Carlsson
and Caron, 2001; Dyhrman et al., 2002; Van Wambeke et al., 2008a).
We did not pre-incubate the samples in 70% v/v ethanol, in order to
avoid potential damage to cell membranes and to measure only the extracellular enzyme. For total seawater, we centrifuged 2 mL (4000 ×g,
10 min), concentrated it to 100 μL, and incubated for 1–2 h in the dark
at in situ temperature with 250 μM ELF® phosphate. For N 1 μm fraction,
20 mL samples were filtered onto 1 μm polycarbonate membrane, incubated in a Petri dish on a drop of 100μL ELF-buffer (5μL ELF+95μL buffer in Endogenous Phosphatase Detection Kit, Invitrogen, according to
manufacturer instructions) in the dark for 1–2 h at in situ temperature.
The reaction was stopped with 4% formaldehyde and samples stained
with DAPI (4,6-diamidino-2-phenylindole; 2 μg mL−1) for 5 min in the
dark. Negative controls were prepared as follows: no ELF control; 0 °C
control; formaldehyde-fixed control; 1 μM Pi competition control.
Images were acquired at Nikon® A1R Laser Scanning Spectral Confocal
Microscope using the Virtual Filter function to generate excitation and
emission filters with the smallest spectral overlap. Image analysis
followed published protocols (Duhamel et al., 2008; Van Wambeke
et al., 2008b). DAPI and ELF were excited at 405 nm; DAPI was read in
430–470 nm window; ELF fluorescence was read in 530–590 nm window. Relative cell ELF intensity was measured by NIS Element software
package and corrected for blanks (e.g. samples incubated with ELF buffer without ELF® phosphate).
2.10. Bacterial community structure
2.10.1. DNA extraction
Samples were filtered (0.5L whole seawater) onto 0.2 μm polyethersulfone membranes (PALL Inc.). Filters were stored in 2 mL tubes at
− 80 °C until DNA extraction. Genomic DNA was extracted following
(Boström et al., 2004) on days 0, 6, 8, 13, 22 and 64 in P− and P+. We
constructed clone libraries for days 0, 6, 8, 13 (DGGE results in SI).
2.10.2. 16S rDNA gene clone libraries and phylogenetic analysis
Bacterial 16S rDNA genes were amplified using universal primers
27F and 1492R. The PCR condition was: 95 °C for 5 min; 30 cycles of:
95 °C for 30 s, 50 °C for 30 s, 72 °C for 45 s, the last cycle was followed
with 7 min at 72 °C. Fresh PCR products were immediately cloned
using pCR® 2.1 TOPTA10 kit (Invitrogen) and sequenced at 23 ABI
3730XLs sequencer by Macrogen Inc (http://www.macrogen.com/).
Sequence quality, ambiguous base and chimera check were performed
using DNA baser software (www.DNAbaser.com) and Bellerophon software (http://foo.maths.uq.edu.au/~huber/bellerophon.pl). Sequence
taxonomic identities were done using Basic Local Alignment Search
Tool (BLAST, NCBI). Sequences were deposited in GenBank (accession
numbers in SI Table 4). Sequences were processed using QIIME version
1.5 (Caporaso et al., 2010), with operational taxonomic units (OTUs)
identified at the 97% level using the USEARCH program (Edgar 2010)
implemented in QIIME. Representative sequences for each OTU were
assigned RDP taxonomy values using RDP classifier version 2.2 (Wang
et al., 2007), with a confidence value of 0.8. A beta diversity plot was
generated via an unweighted UniFrac analysis (Lozupone and Knight,
2005) (SI Fig. 7).
3. Statistical analyses
Statistical analyses were performed to assess differences between
treatments and to detect temporal changes for each parameter. A twoway ANOVA without replication was performed for Chl a, PP, virus
and bacterial abundance, BCP and BCD, μ, DOC, ELF-based APase and
MUF-based APase. Unpaired t-test was performed on POC data while
Mann–Whitney test (unpaired, non parametric) was performed on
DOP and Pi data. All statistical analyses were run with 95% confidence
intervals, using Prism 6 statistical software (GraphPad Prism version
6.0a for Mac OS X, GraphPad Software, 2012 La Jolla California USA
www.graphpad.com). The table showing the results is reported in SI
(SI Table 3).
4. Results
4.1. Bloom dynamics and primary productivity
Enrichment with Pi caused an intense, diatom-dominated, bloom
with chlorophyll a rising from b1 to ~30 μg L−1 within 6 days (Fig. 1).
PP increased dramatically from 4 to ~200 μM C d−1 by d4—a 54-fold increase constituting a large C flux into the system. It peaked on d6 and
then dropped precipitously, by an order-of -magnitude by d8.
Chl a also declined sharply from d6 to d10 in the P+ system. Despite
sharp Chl a decline, and P depletion, the Chl a level remained quite high
(~5 μg L−1), much higher than at the start of the experiment. Likewise,
the post-bloom PP (3–20 μM C d−1) was also maintained at a high rate
and remained high for weeks post-bloom—even though Pi had been depleted to b 10 nM (Table 1). In P+, contemporaneously to the rise of the
phytoplankton bloom, there was a nitrate drawdown from 20 μM to
0.75 μM in 6 days. In P−, Pi remained near or below the detection
limit (10 nM); however, these mesocosms also experienced two
minor blooms (Fig. 1) presumably because of the removal of larger herbivores (reduced grazer control), and nitrate and silicic acid enrichment. However, in contrast to the P+ system PP that had initially
increased 4-fold returned to ~d0 value. The concentrations and trends
of Chl a, PP and Pi in P+ were significantly different in comparison to
P− (ANOVA: PChla ≤ 0.001; PPP ≤ 0.01; PPi ≤ 0.001; see SI Table 3).
4.2. Phytoplankton community composition
Initially, the phytoplankton community was comprised mainly of
diatoms (66%; 5.7 × 105 cells L−1), while unidentified nanoflagellates
(27%; 2.4 × 105 cells L−1), coccolithophorids (4%; 3.1 × 104 cells L−1),
and dinoflagellates (3%; 2.3 × 104 cells L−1) were also present. On d8,
Fig. 1. Chlorophyll a (Chl a, circles) and Primary Production PP (triangles) in P+ (black
symbols) and P− (white symbols). Each point represents 3 replicates (mean ± standard
deviation); error bars (if not visible) are smaller than the symbol size.
F. Malfatti et al. / Science of the Total Environment 470–471 (2014) 1173–1183
1177
the communities became even more diatom-dominated especially in P+
(97% in P+: 1.2 × 107 diatoms L−1; in P−: 84%; 1.4 × 106 diatoms L−1).
Despite ~10-fold higher phytoplankton abundance and PP in P+,
the dominant species were essentially identical in both mesocosms:
Pseudo-nitzschia cf. calliantha, Pseudo-nitzschia cf. fraudulenta and
Cheatoceros spp. (SI Fig. 1, floristic data not presented).
4.3. DOC and POC pool dynamics
The dramatic rise and fall of diatom bloom resulted in varying biogeochemical scenarios of coupling of C and P cycles—reflected in stocks,
dynamics and time-variation of organic C and P pools. During the first
6 days DOC in P+ changed only to a minor degree even as the bloom
intensified to ~30 μg L−1 Chl a. As bloom declined DOC rose sharply,
increasing by ~100 μM, reaching 186 μM on d10 (Fig. 2). Remarkably, the
DOC increased by ~60 μM in 2 days (d6–8); and it persisted at high level
despite sustained large bacterial C demand (BCD: ~10–15 μM C d−1,
Fig. 5). So, there phases can be seen: 1) tight bacteria-PP coupling (DOC
level controlled despite intense bloom, until d6), 2) relaxed coupling
(high BCD, but unable to match DOC production), and 3) BCD equivalent
to PP yet sustained high DOC. The implications of these scenarios are
discussed later.
Net DOC decline during ~3weeks following DOC peak was very slow
(0.35 μM d−1) while BCD was 30–80-fold higher than at d0 (Fig. 5);
subsequently, the average DOC decline (d29–64) was 0.99 μM d−1
while BCD was still ~5–10-fold greater than DOC decline. Thus, after
d10, DOC → bacteria flux was maintained with only minor net DOC depletion; new DOC must have been produced at rates nearly equivalent
to the BCD (i.e. POC → DOC → bacteria was quantitatively in balance).
The DOC pool turnover rate (assuming the BCD as its sole sink) ranged
0.02–0.13 d−1 in the bloom phase, reaching 0.14 d−1 during bloom decline; some DOC components were probably processed rapidly resulting
in decreasing lability—consistent with DOC persistence at high level. In
P−, DOC accumulated but to a lesser degree (27.9 μM).
The initial POC pool increased, due to rapid phytoplankton growth
and detritus accumulation (Fig. 2), 14.6-fold (to 117 μM) in P+, and
2.3-fold in P−. POC remained high even after the bloom had crashed,
and despite rapid, sustained C loss to the DOC pool. Presumably, it was
sustained by the observed continued PP at quite high level. The concentrations and their temporal trends of DOC and POC in P+ were significantly different than in P− (ANOVA: PDOC ≤ 0.01; PPOC ≤ 0.01; see SI
Table 3).
4.4. DOP pool dynamic
The DOP pool (Table 1) in P+ remained within 0.17–0.24 μM
and 0.10–0.15 μM in P−, during the first 22 days. The observed high
DOC/DOP (Fig. 2) could be due to preferential removal of P or due to
the low DOP production. In P+, DOC/DOP overall increased from 475
to 1035 in 13 days (the largest increase was from d6 to d8) presumably
Fig. 2. Dissolved organic carbon (DOC, squares), particulate organic carbon (POC, triangles)
and DOC/DOP pool (circles) dynamics in P+ (black symbols) and P− (white symbols).
Each point for DOC values represents 3 replicates (mean ± standard deviation); error
bars (if not visible) are smaller than the symbol size.
Fig. 3. P pool turnover of total fraction (circles) and b1 μm fraction (triangles) in P+ (black
symbols) and P− (white symbols). The scale of the x axes is logarithmic. Each point represents an average of 3 replicates; error bars (if not visible) are smaller than the symbol
size.
due to efficient P-selective removal of DOP and formation of P-depleted
dissolved organic matter (DOM) as well as possibly due to cell storage of
P as POP. The concentrations and temporal trend of DOP in P+ were significantly different in comparison to P− (ANOVA: PDOP ≤ 0.01; see SI
Table 3).
4.5. Pi turnover
After first 2 days, (as Pi pulse declined) the Pi pool in P+ was highly
dynamic (turnover time 0.8–10 h). Size fractionation showed that bacterial (b 1μm fraction) Pi uptake generally explained N50% of Pi turnover
for all time points with the exception of d0 (Fig. 3). (Turnover time in
one mesocosm at d0 was 670 h; we cannot explain this outlier but parallel d0 mesocosm had low values, as in all other time-points and
conditions).
4.6. Bacterial and viral assemblage dynamics
In both treatments, heterotrophic bacteria abundance dropped sharply within 2 days from d0 value of 2 × 109 cells L−1 (Fig. 4). In P+ the bacterial abundance then rose steadily in parallel with the development of
the bloom and reached 6 × 109 cells L−1 on d8 as the bloom declined. Bacteria cell-specific growth rates (μ; Fig. 5) however, increased sharply as
the bloom crashed, to ~4 d−1 on d13. In P+, bacterial abundance peaked
on d8 following the phytoplankton bloom and its crash; it then declined to
remain around pre-bloom values (~2 × 109 cells L−1). In P−, bacteria
abundance declined and stayed below d0 value.
We performed dilution experiments to assess grazing pressure on d0
showing that bacteria and cyanobacteria were actively grazed by
nanoflagellates in both mesocosms (SI Table 1 and cyanobacteria
Fig. 4. Abundance dynamic of viruses (triangles) and heterotrophic bacteria (HB, diamonds)
in P+ (black symbols) and P− (white symbols). Each points represents 3 replicates
(mean ± standard deviation); error bars (if not visible) are smaller than the symbol size.
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F. Malfatti et al. / Science of the Total Environment 470–471 (2014) 1173–1183
Fig. 5. Bacteria carbon production (BCP), bacteria carbon demand (BCD) and bacterial specific growth rate (μ, triangles) for the total fraction in P+ (black symbols) and P− (white symbols). Each point represents 3 replicates (mean ± standard deviation); error bars (if not visible) are smaller than the symbol size. Note BCP and BCD are marked with the same symbol
(squares) but are presented on different scales (BCD = BCP/0.3).
abundance SI Fig. 2). However, dilution experiments were not repeated
at other time points nor did we measure any other potential source of
bacterial mortality.
There was an overall modest increase in virus abundance N 46%
(P+) and N69% (P−) in the first 13 days, then they dramatically decreased (Fig. 4). Bacterial abundance and trends were significantly different in P+ in comparison to P− (ANOVA: P ≤ 0.05; see SI Table 3).
4.7. Bacteria carbon production (BCP) and carbon demand (BCD)
In P+, BCP (in the total fraction, TOT) increased by an-order-ofmagnitude by d8 (Fig. 5), following the same temporal trend as PP.
BCP remained high (~3× d0 rate) even to d64, apparently at the expense
of the accumulated DOC. Size-fractionation of BCP showed that in P −,
the majority of the BCP was due to free-living bacteria (b 6 μm) but in
P+, free-living and attached bacteria were both important contributors
to BCP. On d13, BCP of particle-attached bacteria was ~2-fold the BCP of
free-living bacteria. This may suggest that attached-bacteria were actively degrading POM consistent with the observation of the hydrolytic
enzyme activities for the N6 μm fraction (Fig. 7). The trends and the
rates of BCP (and BCD) in total and in b 6μm fractions in P+ were significantly different in comparison to P− (ANOVA: PTOT ≤ 0.01;
Pb6 μm ≤ 0.001; see SI Table 3). When comparing the total and the
b6 μm fraction, in each treatment, BCP was significantly higher in the
attached bacteria fraction (e.g. attached bacteria = (TOT − (b 6 μm));
ANOVA PP+ ≤ 0.01 and PP− ≤ 0.05; see SI Table 3).
4.8. Ectohydrolytic activities
The motivation for measuring a suite of hydrolytic enzymes (and not
only APase) was that both C and P cycles involve POM and DOM
processing by diverse bacterial ectohydrolases, for instance POP may
also be converted to DOP before becoming accessible to APase. Leucine
aminopeptidase activity in P+ rose sharply as bloom developed
(Fig. 7a) intensifying to d13 (total activity N2500 nM h−1). This represented a two orders of magnitude increase from d0 level. High activity
was present in both b6 μm and N 6 μm size fractions. Lipase activity in
P+ increased 20-fold from d4 to d6 with major contribution of N6 μm
fraction but the b6 μm fraction subsequently became dominant
(Fig. 7b). Alkaline phosphatase (APase) was low until d6 then increased
40-fold (600 nM h−1). The APase activity temporal trend was consistent
with Pi repression of APase (low activities at high Pi) (Fig. 7c). α- and βglucosidases and N-acetyl-glucoaminidase (chitinase) were generally
low compared with other enzyme activities. β-glucosidases and Nacetyl-glucosaminidase, in P+, varied in parallel with the decline of
the bloom (Fig. 7d, e, f). In P−, the ectohydrolases did not change substantially over time in both fractions. In P+, APase activities and trends
of N 6 μm fraction (attached bacteria) were significantly higher than
b6 μm fraction (free-living bacteria) (ANOVA, P ≤ 0.0001, see SI Table 3).
4.9. Individual cell APase activity
Quantification of ELF signal enabled us to microscopically identify
major contributors to P regeneration at the individual cell level
(Fig. 8b, c, d). As expected, in P− most (but not all) diatoms (N50%;
Fig. 8e) displayed high cell surface alkaline phosphatase activity after
d2. Cylindrotheca closterium, Pseudo-nitzschia sp. and Cheatoceros sp.,
dominating the phytoplankton community structure, were expressing
APase activity. In P+ the ELF-positive diatom fraction was b20%,
indicating that the orthophosphate enrichment inhibited the cellsurface expression of APase. Surprisingly, the low percentage of ELFpositive diatoms was maintained even after the Pi had been essentially
depleted. Over time, ELF-positive senescent diatom fraction became
associated with detritus. The free-living bacteria ELF-positive fraction
in P− was mostly between 32 and 100%; it was between 67 and 100%
for the attached bacterial fraction. In P+, the free-living bacteria ELFpositive fraction was first low then increased over time, whereas the
attached bacteria ELF-positive fraction was always high (N67%). Importantly, individual cell signal quantification showed that APase
activity was always higher in attached bacteria compared with freeliving bacteria in both P+ and P− (Fig. 8f). When comparing the
total and the b6 μm fraction, in each treatment, ELF-based APase
(relative cell fluorescence intensity) was significantly higher in the
attached bacteria fraction (ANOVA PP+ ≤ 0.01 and PP− ≤ 0.05; see SI
Table 3).
4.10. Bacteria community structure
Fig. 6. Bacteria carbon production (BCP) for the total fraction (TOT: black P+ and dashed
P−) and the b6 μm fraction (b6 μm: gray P+ and white P−) in P+ and P−. Each point
represents 3 replicates (mean ± standard deviation); error bars (if not visible) are smaller
than the symbol size.
We followed bacterial community structure dynamics in P + and
P − with two complementary techniques: 16S rDNA gene libraries
and Denaturing Gradient Gel Electrophoresis (DGGE; SI). The 16S
F. Malfatti et al. / Science of the Total Environment 470–471 (2014) 1173–1183
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Fig. 7. Hydrolysis rate (nM h−1) for leucine aminopeptidase (a), lipase (b), alkaline phosphatase (c), b-glucosidase (d), ®-glucosidase (e) and N-acetyl-glucosaminidase (f) for N6 μm (solid
lines) and b6 μm size fraction (dash lines) in P+ (green) and in P− (blue). Each point represents 8 replicates (mean ± standard error); error bars (if not visible) are smaller than the
symbol size.
rDNA clone libraries provided information on community
composition on days 0, 6, 8 and 13 (Fig. 8, SI Table 4). At d0, bacterial community was dominated by Alphaproteobacteria (65%)
followed by Gammaproteobacteria (14%) and fewer Flavobacteria
(7%), Verrucomicrobia (7%), Betaproteobacteria (2%), and
Deltaproteobacteria (2%). The majority of sequences over the
course of the experiment were Alphaproteobacteria and
Gammaproteobacteria. But dramatic changes happened among the
minor taxa over time and within the Alphaproteobacteria and
Gammaproteobacteria. Deltaproteobacteria disappeared immediately
while Betaproteobacteria and Verrucomicrobia disappeared over the
course of the experiment. These variations in clone abundance for
those taxa suggest that they were not favored in a high DOC and high
PP system. Flavobacteria were more highly represented in P+ than P−
, in agreement with the associations of this taxon with particle and senescent phytoplankton blooms. On d13, Planctomycetes appeared in P−
and P+ and Sphingobacteria appeared in P−. Cyanobacteria sequences
appeared in the library on d8 and d13 despite being counted at the microscope initially and throughout the experiment in both treatments (SI
Fig. 3).
We clustered the sequences at the 97% identity level and could identify 97 clusters from 273 sequences (SI Table 2). No OTU specific to
either the P+ or P− treatments or any sequence clusters were found.
This suggests that the more dominant OTU clusters Halomonadaceae
(Gammaproteobacteria, 63 OTUs), Rhodobacteraceae and Rickettsiales
(Alphaproteobacteria, 28 OTUs and 101 OTUs respectively) were generalists able to adapt to different biogeochemical scenarios during the
course of the mesocosm experiment.
Although we do not have a rigorous representative view of total
diversity of bacterial community (given insufficient number of clones),
the species diversity in each sample was different enough to distinguish
between the treatments in a principal coordinate plot (SI Fig. 5).
5. Discussion
Phytoplankton blooms – whether seasonal, episodic or humaninduced e.g. due to coastal eutrophication – are a common phenomenon
in the coastal ocean, including the Northern Adriatic sea (Fuks et al.,
2012; Gilmartin et al., 1990; Godrijan et al., 2012; Revelante and
Gilmartin, 1976). In P limited systems receiving episodic nutrient
input P regeneration is crucial, especially post-bloom, since it can sustain C fixation after Pi has been depleted, and consequently influence
C cycling and sequestration. We examined the coupling of the processes
related to C and P cycles in the northern Adriatic Sea, particularly the
mechanisms that underlie and regulate the seasonal C sequestration in
the dissolved phase (Fonda Umani et al., 2007). The accumulation of
DOC is also of interest as a potential source of episodic mucilage.
Further, our findings may be broadly relevant to a mechanistic
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Fig. 8. ELF-alkaline phosphatase assays. a) Microphotographs of ELF-alkaline phosphatase in P+ and P−. Diatoms and bacteria present bright ELF-green crystals at the alkaline phosphatase hydrolysis site. a) Free-living heterotrophic bacteria in P− on day 6, scale bar 5 μm; b) diatoms in P+ on day 4, scale bar 15 μm; c) diatoms in P− on day 4, scale bar 15 μm;
d) aggregate in P+ on day 10, scale bar 15 μm; e) EFL-alkaline phosphatase positive fraction of diatom and heterotrophic bacteria (FREE HB & ATT HB) in both mesocosms; f) relative
cell fluorescence unit (RCFU) of ELF-alkaline phosphatase for free-living (FREE HB) and attached (ATT HB) heterotrophic bacterial fraction. Each point represents 3 replicates (mean ±
standard deviation); error bars (if not visible) are smaller than the symbol size.
understanding of the coupling between C and P cycles and the biogeochemical fate of C in phytoplankton blooms.
5.1. DOC accumulated and persisted despite highly dynamic DOC pool
In P+ system, DOC pool remained essentially unchanged during the
development of the bloom but swelled to a very high level as the bloom
declined (Fig. 2; d6–10). It is noteworthy that the DOC pool throughout
all phases of the bloom and post bloom continued to have a rapid
turnover rate (conservatively calculated assuming BCD as sole DOC
sink). The enhanced level of DOC persisted past d10, and throughout
the ~2 month experiment, despite rapid flux through the DOC pool.
Turnover rates of DOC in P+ were 0.13–0.14 d−1, comparable to
0.25–0.36 d−1 found in the North Atlantic Bloom Experiment
(Kirchman et al., 1991). Presumably, the DOC pool was continually fed
by new DOC production due to aggregated as well as free diatoms. Similar results have been observed in a P stimulated mesocosm experiment
(Pečar et al., 2004).
F. Malfatti et al. / Science of the Total Environment 470–471 (2014) 1173–1183
The dominant mechanisms of DOC flux to bacteria were likely different in the bloom and post bloom phases. Healthy and rapidly growing
diatoms during the bloom phase may have exuded DOM (Cole et al.,
1982; Larsson and Hagström, 1979) that bacteria were able to use in a
tightly coupled manner. Massive DOC release during the crash of the
bloom may have involved diatom lysis as well as hydrolytic processing
of diatom detritus by bacteria. The DOC input during the two phases
were likely qualitatively different and had different liabilities to bacterial utilization. An important possibility is that the sustained high DOC
may have been subjected to repeated cycles of processing as new inputs
admix with the existing pool, P (and N) removal, and gradual enrichment with respect to slow-to-degrade and refractory DOC. The turnover
of bacterial biomass itself could be another source of slow-to-degrade
DOC (e.g. capsular polysaccharides). Counter-intuitively, post-bloom
high bacterial action on DOC may have caused the DOC pool to persist
at a high level, by accelerating its processing (and that of new inputs
from sustained PP) to slow-to-degrade molecules. This would also create dynamically varying scenarios of structural composition and complexity of DOC pool potentially affecting its biogeochemical behavior—
including cross-linking to form gels.
We hypothesized that during the demise of bloom, intense bacterial
hydrolytic activity caused the DOC to become more recalcitrant, even if
the DOC pool was receiving fresh and probably more labile DOC from PP,
viral lysis, protozoa grazing and POC dissolution. If we consider the
decrease of DOC pool (from d10 to d64) in the P+ as a measure of
semi-labile DOC (SLDOC) fraction, then ~20% of the fresh DOC was
SLDOC, and over time the DOC became increasingly enriched with
SLDOC. Other studies have shown similar abundance of SLDOC (Meon
and Kirchman, 2001; Norrman et al., 1995) and change in C/P ratio of
organic matter (Ivančić et al., 2004).
The above mechanisms of DOC accumulation may underlie the episodes of P depleted mega-gels in north Adriatic Sea. A prerequisite is
pulsed nutrient inputs causing periodic blooms and intermediate periods of P limitation but sustained C input. This would sustain DOC
flux as well as subject the DOC pool to repeat cycles of DOC processing
by nutrient limited bacteria. Microgels may then be produced by particle erosion including at the molecular scale, by enzymes, or DOC crosslinking. They would increase the residence time of organic matter subject to bacterial processing as well as serve as loci of extension of the
gel phase and growth to mega-gels. While macro-gels were not seen
in our experiment, Atomic Force Microscopy images of the DOC fraction
in P+ mesocosm showed evidence of microgel formation (Mišić Radić
et al., 2011). The gel organization corresponded structurally to the
mucilage gel phase (Svetličić et al., 2005).
5.2. Tightly coupled P fluxes
In P+, the DOP concentration was from 240 to 110 nM throughout
the experiment (with the exception on d64) and did not increase despite
the rise and fall of a major bloom (Chl a: ~2 → ~ 30 → ~5 μg L−1) and despite sustained, large fluxes of C and P through the DOM pool (accumulation of N100μM DOC). The 0.5 μM Pi pulse to the P+, once taken up by
phytoplankton and bacteria, presumably circulated largely within in the
particulate phase throughout the experiment. Despite intense organic
matter processing—reflected in high activities of multiple ectolydrolases
and rapid P pool regeneration—there was no large accumulation of Pi
after the bloom. The DOP is molecularly diverse (including phospholipids, phosphoproteins, nucleic acids and P-esters) so not all DOP is
directly APase hydrolyzable; multiple enzymes would be required to remove P from DOP. In addition to APase of both bacteria and phytoplankton a number of bacterial hydrolases were present at high levels. As
further discussed below, multiple hydrolase activities were available
with potential to transform various organic matter pools into molecules
accessible to APase and other hydrolases that are the terminal step in P
regeneration. This proposed scenario is consistent with the observed
efficient P regeneration and the accumulation of P-depleted DOC.
1181
Estimated Pi regeneration, based on P demand, was on average ~19
and 4 μM d−1 in P+ and P−. Thus, P cycling was highly dynamic but
also tightly coupled within the transformation from POP to DOP and
from DOP to Pi. We directly determined the dynamic state of the Pi
pool by measuring 33Pi turnover rate. Results indicated rapid cycling;
Pi pool turnover time was short (1–10 h; generally ~1 h) with bacteria
and larger size fraction responsible for comparable Pi uptake rates.
Since phytoplankton P pool was larger than bacteria P pool, this also
suggests rapid turnover of bacterial biomass, which is consistent with
BCP in the absence of decrease in bacterial abundance. Since bacteria
and phytoplankton P demand could have been met only (or mostly
some component of DOP can be taken up without prior hydrolysis) by
Pi generated from DOP hydrolysis, the DOP pool turnover must also
have been highly dynamic with turnover comparable to Pi pool turnover
rate. Our findings are consistent with previous studies that have shown
the importance of DOP in our study area during periods of P limitation
(Ivančić et al., 2009, 2010).
5.3. Colonized aggregates are hydrolysis and P regeneration hotspots
The massive post-bloom aggregation in P+ was associated with extensive bacterial colonization. The degree of colonization was difficult to
accurately quantify because of possible entrainment of free-bacteria
during filtration. However the BCP of attached bacteria was high (on
d13 6 μM C d−1, Fig. 6), thus suggesting that bacteria were active on
the aggregates. These attached bacteria (and N 6 μm particles) displayed
intense multiple, functionally diverse, cell-surface hydrolases: aminopeptidase, lipase, glucosidase, N-acetylglucosaminidase and APase.
Aminopeptidase, lipase, glucosidase, N-acetylglucosaminidase activities
were extremely high on N 6 μm fraction, comparable to marine snow
(Smith et al., 1992) and comparable to data reported for this area during
mucilage and phytoplankton bloom events (Caruso, 2010; Celussi et al.,
2011; Ivančić et al., 2004; La Ferla et al., 2002; Zaccone et al., 2002).
Intense enzyme activities (one to two orders of magnitude higher
volume specific activity than in b 6 μm fraction) render the aggregates
hotspots of organic matter solubilization (POC → DOC phase transformation) as well as act on various phosphorylated particulates and
supra-polymeric constituents of phytoplankton and other microbes'
biomass and detritus, including phosphoproteins, phospholipids, ribosomes and nucleic acids.
We propose that intense activities of hydrolases, in addition to
APase, on aggregates are essential steps in P regeneration, and modulation of these activities could regulate P regeneration without variation
in APase activity. This could be kinetically similar to the findings in
Feuillade and Dorioz (1992) but provide additional mechanistic
insights. These enzymes could potentially become a bottleneck in P
regeneration. Future studies and models of mechanism of regulation
of P regeneration should therefore consider as variables not only
APase activity by also other (mostly bacterial) hydrolases that mediate
the supply of APase substrates.
APase mediates the final step of P regeneration, and this activity was
also intensified on aggregates. Individual cell measurements enabled us
to distinguish APase in aggregate associated and free bacteria. The relative cell specific APase of particle-associated bacteria was generally
higher than free bacteria (Fig. 8f, SI Table 3). As has previously being
proposed, bacterial APase activity may be expressed as a strategy to liberate and use the organic moieties of the DOP despite Pi sufficiency
(Sebastián and Niell, 2004).
5.4. Generalist ecotypes in diverse C–P biogeochemical regimes
Our results suggest the hypothesis that the dominant taxa were
“generalists” capable of thriving in highly diverse biogeochemical scenarios. The dramatic changes in the minor taxa that we saw between
and within the two nutrient regimes could be ecologically important.
Bacterial community composition was dominated by Alpha- and
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F. Malfatti et al. / Science of the Total Environment 470–471 (2014) 1173–1183
Fig. 9. Bacterial community composition percent abundance histogram based on 16S r
DNA gene sequence in P− and in P+ on 0, 6, 8 and 13 days.
Gamma-proteobacteria that present high diversity and high plasticity in
response to environmental conditions (e.g. diatom bloom (Pinhassi
et al., 2004; Teeling et al., 2012; Teira et al., 2008)) (Fig. 9). Studies
have shown that Alpha-, Beta- and Gamma-proteobacteria, in P-limited
ocean, can take up 32P-phosphate (Longnecker et al., 2010; Sebastián
et al., 2012) and that within the GOS database pho operon (APA) is
widely spread with diverse cellular localizations (Luo et al., 2009)
suggesting a large range of strategies for P uptake. Therefore, within
the same groups, we can find adaptations to respond to different
microniches based on the metabolic repertoire. We cannot rule out
that they might have been specialist with high metabolic activity but
subject to population control. Our findings suggest that the biogeochemical function is preserved in the environment even if the players
might belong to different ecotypes and or have different levels of metabolic expression. This hypothesis fits with the data reported in a 3 yearstudy in the northern Adriatic Sea (Celussi and Cataletto, 2007; Celussi
et al., 2011) where DGGE band similarity analysis did not show the
same bacterial community even if the functional seasonality of the ecosystem was preserved in terms of BCP and enzymatic activities.
Supplementary data to this article can be found online at http://dx.
doi.org/10.1016/j.scitotenv.2013.10.040.
Acknowledgments
We thank the staff of the Marine Biology Station of Piran and the R/V
Sagita crew for the support. DOC analyses were performed by FM in the
laboratory of Dr. L. Aluwihare at SIO. We thank Mr. T. Makovec and Dr.
M. Grego for their assistance with image processing and Primer Software. This work was supported by grants from NSF CREICO (0132677)
and Gordon and Betty Moore Foundation MMI to FA, by the Ministry
of Higher Education, Science and Technology of Slovenia (PI-0237) to
VT, and by the Croatian Ministry of Science, Education and Sports
(Project No. 098-0982934-2744) to VS.
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