In the last 2 decades, the ecological role of virusesin marine microbial food webs has been well docu-mented (Fuhrman 1999, Weinbauer 2004). Viral lysisrecycles bacterial production back into the dissolvedorganic carbon pool through the viral shunt andenhances CO2 emissions by bacterial respiration (Wil-helm & Suttle 1999). It has been reported that viruses
could cause a mortality rate of picoplankton compara-ble to grazing effects in freshwater and marine sys-tems (Fuhrman & Nobel 1995, Wommack & Colwell2000). In order to better understand the role ofviruses in oceanic microbial processes, it is necessaryto have a clear understanding of viral dynamics,exemplified in viral abundance, production, decayrate, turnover, virus-induced bacterial mortality andburst size.
1Division of Environment, and 2Division of Life Science, Hong Kong University of Science and Technology,Clear Water Bay, Kowloon, Hong Kong SAR
3Microbial Ecology & Biogeochemistry Group, Université Pierre et Marie Curie-Paris 6, Laboratoire d’Océanographie de Villefranche, 06230 Villefranche-sur-Mer, France
4CNRS-INSU, Laboratoire d’Océanographie de Villefranche, 06230 Villefranche-sur-Mer, France5State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361005, PR China
6Present address: Center for Geomicrobiology, Aarhus University, Ny Munkegade 114, 8000 Aarhus C, Denmark
ABSTRACT: We investigated viral dynamics in the surface seawater at 13 stations in the westernSouth China Sea (SCS) during the summer of 2007; 2 cold eddies formed during the sampling period.We found modest viral production and viral decay rates. Colloidal and heat-labile substances wereimportant causes of viral removal (range 9.47 to 55.64% of viral production). During the samplingperiod, 26.44 to 96.08% (average 77.82%) of bacterial production was lysed by viruses, and a highlysignificant positive relationship was found between the rate of virus-induced bacterial mortality (m)and bacterial growth rate (μ). According to the hydrological conditions and station location, the 13stations investigated in the SCS were further subdivided into 4 regions: Cold Eddy I (CE I), Cold EddyII (CE II), oligotrophic oceanic water (OO water) and Mekong River plume (MR plume). Overall, viralactivities appeared more dynamic in mesotrophic cold eddies and in the river plume than in oligo -trophic SCS waters. However, a significantly lower bacterial growth rate, virus-induced bacterialmortality rate and m/μ, together with a high burst size in the MR plume compared to the CE I, CE IIand the OO water, indicates that bacterial and viral activities have distinct responses to the upwellingof cold subsurface waters and the freshwater plume. Our results demonstrate that viral lysis is animportant cause of loss of bacterial production in the SCS in summer, which may enhance CO2
emission to the atmosphere by respiratory processes.
KEY WORDS: South China Sea · Virus · Viral production · Viral decay rate · Virus-induced bacterialmortality rate · Bacterial growth rate
Resale or republication not permitted without written consent of the publisher
Published online March 31
Aquat Microb Ecol 63: 145–160, 2011
Previous studies have shown that the distribution ofviral abundance is related to hydrological features —such as temperature, salinity or water depth — and thetrophic state of the water column has been proposed asa possible driving force controlling the spatial distribu-tion of viruses (Cochlan et al. 1993, Weinbauer et al.1993, Hara et al. 1996). Viral abundance is greater inproductive and nutrient-rich environments, and it issignificantly correlated with chlorophyll a (chl a) andprimary production (Wommack & Colwell 2000). How-ever, Corinaldesi et al. (2003) has reported that chang-ing trophic and hydrodynamic conditions do notdirectly influence viral distribution, but do influencebacterial activity and host cell abundance.
An accurate evaluation of viral production and viraldecay rate is critical for determining potential changesin viral numbers in space and time (Noble & Fuhrman2000). Viral production can be high in non-steady-statesystems, such as tidally driven mixing (Wilhelm et al.2002), and various studies have shown that the highestviral production occurs in eutrophic waters and thelowest in oligotrophic coastal waters (Hewson et al.2001, Bongiorni et al. 2005). The higher viral produc-tion in eutrophic waters is caused by higher bacterialproduction and host cell metabolic activities. Manyfactors, such as ultraviolet radiation, absorption ontoparticles, temperature, colloidal and heat-labile sub-stances (extracellular enzymes), grazing, lysogenicinfection and other, unknown, factors are involved inremoving viruses from the water (i.e. viral decay) (Pay-ment et al. 1988, González & Suttle 1993, Noble &Fuhrman 1997, Wilhelm et al. 1998, Binder 1999, Bon-giorni et al. 2005, De Paepe & Taddei 2006). Viraldecay rates in the surface seawater have been re -ported to vary over 3 orders of magnitude across differ-ent methodologies (reviewed by Parada et al. 2007).Although temperature has proved to be an importantfactor determining viral decay rates (Parada et al.2007), another study also shows that viral decay ratesdecrease along the trophic gradient (eutrophic to olig-otrophic) (Bongiorni et al. 2005). The mechanisms forremoval of viruses in eutrophic and oligotrophic watersare different: colloidal and heat-labile substances,absorption onto particles and grazing have accountedfor more than 90% of viral production in eutrophicwaters, while these factors have accounted for lessthan 10% of viral production in oligotrophic waters(Bongiorni et al. 2005).
Estimation of virus-induced bacterial mortality iscrucial for quantifying the effects of viral lysis on thecycling of dissolved organic matter. Some studies indicate that virus-induced bacterial mortality stronglydepends on trophic conditions (Weinbauer et al. 1993,Noble & Fuhrman 2000, Weinbauer et al. 2003). Forexample, Noble & Fuhrman (2000) have reported a
potential increased impact of viruses on bacteria in theabsence of protists in nearshore and meso-oligotrophicwaters and a possible decreased impact of viruses inoffshore waters. However, a separate study by thesame authors indicated that virus-induced bacterialmortality was not directly related to trophic status(Noble & Fuhrman 1999). Thus, it seems that the factors determining virus-induced bacterial mortalityare still not well understood.
Viral dynamics have been less well studied in oligo -trophic open oceans than in coastal and estuarine envi-ronments. The South China Sea (SCS) is the secondlargest marginal sea in the world. It is characterized byan oligotrophic central basin and is influenced by largerivers, such as the Mekong and Pearl Rivers. Cyclonicand anticyclonic eddies, which play a very importantrole in the biogeochemical processes in the ocean(Falkowski et al. 1991, McGillicuddy et al. 1998, Bidi-gare et al. 2003), often occur in various parts of the SCS(e.g. Wang et al. 2008 and references therein).
To date, viral dynamics and the effects of viruses onbacteria have not been well studied in the SCS. Here,we report a thorough study of viral production, viraldecay, and virus-induced bacterial mortality for thefirst time in the western SCS. In particular, we focus onthe 2 eddies that developed during the cruise.
MATERIALS AND METHODS
Sampling stations. A total of 13 stations were investigated during a cruise in the SCS from 10 Augustto 14 September 2007 (Fig. 1). A major feature of thecruise was the formation of 2 cold eddies during thesampling period. The center of Cold Eddy I (CE I) waslocated at 14.25° N, 111.75° E (Stn TS-1), and CE I cov-ered Stns YS06 and YS12; the center of Cold Eddy II(CE II) was located at 12.50° N, 111.00° E, and CE IIcovered Stns Y03, Y00 and Y20. In addition, a bloom ofTricho desmium spp. was observed at Stn YS12 duringthe sampling period. At each station, 20 l of surfacesea water were collected in a bucket and then immedi-ately transferred to a 20 l acid-rinsed polycarbonatecarboy. Water samples were first taken for measure-ments of chlorophyll a (chl a) and the abundance ofhetero trophic bacteria and viruses; the remainingwater was processed within 1 h for setting up experi-ments on virus-induced bacterial mortality rate, viraldecay rate and viral production. Temperature andsalinity measurements were obtained using a Sea Bird Electronics SBE 911 conductivity-temperature-depth(CTD) sensor.
Chlorophyll a. Seawater (150 ml) was filteredthrough GF/F glass-fiber filters (Whatman) on boardship and then transferred to liquid nitrogen until
146
Chen et al.: Viral dynamics in the western South China Sea
analysis in the laboratory. Chl a was extracted using90% acetone at 4°C for 24 h and then determined byfluorescence analysis with a Shimadzu RF-5301PCspectrofluorometer (Parsons et al. 1984).
Bacterial and viral abundance. Seawater samples(2 ml) were taken for analysis of prokaryotes and viralabundance. Samples were fixed with EM-grade glutaraldehyde (1% final conc.) which had been fil-tered through an Al2O3 Anodisc membrane of pore size0.02 µm (Whatman); fixation was carried out for 15 minat 4°C in the dark, and the samples were then storedat –80°C until analysis (Vaulot et al. 1989). The proce-dures below are according to Olson et al. (1990). Theabundance of total heterotrophic bacteria and viruseswas determined using an Epics Altra II flow cytometerequipped with an air-cooled argon-ion laser (15 mw,488 nm; Beckman Coulter). The heterotrophic bacter-ial samples were stained with 0.02% SYBR Green I(Molecular Probes) for 15 min in the dark and thenanalyzed for 100 s at a rate of 0.1 to 1 ml h–1. Viral sam-ples were initially diluted with TE buffer (10 mM Tris,1 mM EDTA, pH 8.0) which had been filtered throughan Al2O3 Anodisc membrane of pore size 0.02 µm; thesamples were then incubated with 0.02% SYBR GreenI at 80°C for 10 min (Brussaard 2004). After cooling for5 min, viral samples were immediately analyzed for100 s at a rate of 0.1 to 1 ml h–1. The discriminator wasset on green fluorescence for both bacteria andviruses. All the samples were analyzed twice at anevent rate of about 200 events s–1, and more than
10 000 events were accumulated. Data were analyzedusing FCS Express V3 software (De Novo Software).
Experimental set-up. We designed the following ex-periments to estimate virus-induced bacterial mortalityrate, viral decay rate and viral production. 10 l of seawa-ter were initially filtered through a Maxi Capsule (Pall) ofpore size 0.22 µm and then processed with a spiral-wound ultrafiltration cartridge with a molecular weightcut-off of 100 kDa (Prep/Scale Spiral Wound TFF-6 Mod-ule PTHK, Millipore) to produce the virus-free seawater.
Viral lysis: The virus-induced bacterial mortalityrate was estimated by the ‘virus dilution approach’ ofJacquet et al. (2005), which is a modification of thedilution method of Landry & Hassett (1982). Seawaterwas filtered twice through polycarbonate filters of poresize 2 µm (GE Water & Process Technologies) toexclude all grazers. The 2 µm filtrates were dilutedwith the virus-free seawater at a series of dilution gra-dients, i.e. 20, 40, 70 and 100% in 150 ml polycarbon-ate bottles. A control containing only virus-free sea -water was also set up in parallel. Duplicates wereprepared and incubated in a bath cooled by runningseawater and exposed to natural sunlight for 24 h.Samples for the enumeration of bacteria were taken attimes 0 and 24 h. The apparent growth rate in 24 h (k,d–1) was calculated by:
k = [ln(N24h/N0h)]/1 d
where N0h and N24h represent the bacterial abundanceat 0 and 24 h, respectively. The intrinsic bacterial
147
Fig. 1. Sampling stations in the South China Sea in thesummer of 2007. The distribution of chlorophyll a inthe region, retrieved from SeaWiFS, is shown (leftpanel). d = stations in the northern area; s = stations in
the southern area
Aquat Microb Ecol 63: 145–160, 2011
growth rate (μ, d–1) and the virus-induced bacterialmortality rate (m, d–1) were calculated as the y-inter-cept and slope of linear regression fitted to the appar-ent growth rates (all bottles in duplicate dilution series)plotted against dilution factors (see Fig. 4). A carbonbudget was also calculated using μ combined with avalue of bacterial cellular carbon content from the liter-ature (1.1 × 10–8 µg C cell–1) (Liu et al. 2007).
Bacterial production (BP, µg C l–1 d–1) was calculatedby the formula:
BP = μ × BA × (1.1 × 10–8) × 1000 (ml l–1)
where BA is bacterial abundance (cells ml–1).Virus-induced loss of bacterial production (BPLoss, µg
C l–1 d–1) was calculated by the formula:
BPLoss = m × BA × (1.1 × 10–8) × 1000 (ml l–1)
Viral decay rate: The viral decay rate was estimatedfollowing Noble & Fuhrman (1997). Seawater (600 ml)was filtered through polycarbonate filters (pore size0.22 µm, GE Water & Process Technologies), filled intotriplicate 150 ml polycarbonate bottles, and then incu-bated in a temperature-controlled bath in the dark for12 h. Subsamples were collected to enumerate virusesat regular intervals (2.5 to 3 h). The viral decay rate ineach bottle was calculated as the slope of the linearregression fitted to the natural logarithm of viral abun-dance plotted against time (Fig. 2a), and the averagerate from triplicate bottles was used as the in situ viraldecay rate. Because grazers, bacteria and large parti-cles were excluded, and the bottles were incubated inthe dark, the major factor causing viral decay in thisstudy was colloidal and heat-labile substances.
Viral production: Viral production was estimatedusing a ‘modified virus dilution approach’ (Bongiorniet al. 2005) originating from the ‘virus reductionapproach’ developed by Wilhelm et al. (2002). Tripli-cates of 20 ml of seawater were transferred to 150 mlpolycarbonate bottles, and the bottles were then filledwith virus-free seawater and incubated in the darkfor 12 h; the bottles were cooled by running seawater.Subsamples were collected to enumerate viruses atregular intervals (2.5 to 3 h). Apparent viral produc-tion was calculated for every bottle (AVPbottle). Gener-ally, there were 5 patterns relating to the curve ofviral abundance versus incubation time (Fig. 2b,c).AVPbottle was calculated by different methods basedon the peaks of viral abundance, V, during incuba-tion. For curves with only 1 peak (Fig. 2b), AVPbottle
was calculated as follows: patterns i and iii, the slopeof linear regression fitted to viral abundance plottedagainst time; and pattern ii, the slope of the curvebetween Vmax and Vmin. For curves with 2 peaks(Fig. 2c): patterns iv and v, AVPbottle was calculatedby the formula:
where t = time.AVPbottle was corrected by multiplying by the ratio of
bacterial abundance in the station to bacterial abundance at time 0 h in a specific bottle (BAstation/
148
ln-t
ran
sfo
rmed
vir
al ab
un
dan
ce
15.0
15.5
16.0
16.5
Vir
al ab
und
an
ce (
×10
6 v
iru
ses m
l –1)
0.0
0.5
1.0
1.5
2.0
2.5
Pattern i Pattern ii Pattern iii
0.0 5.0 10.0 15.0
0.0
0.5
1.0
1.5
2.0
2.5Pattern iv Pattern v
a
b
ctmin1iv tmax1iv
tmaxiiitmaxii tmaxi
Vmin1vVmin1iv
Vmini
Vminii
tmax2iv
tmax2vtmax1v
tmin2iv
Vmin2iv
Vmin2v Vmax2iv
Vmax1iv
Vmaxiii
Vmaxii
Vmaxi
Vmax1vVmax2v
tmin2vtmin1v
Time (h of incubation)
Fig. 2. (a) Example of the development of viral abundance,over time, in the viral decay incubation bottle (Stn Y20). (b,c)Five patterns of development of viral abundance, over time, inthe viral production dilution incubation bottles, with pattern i(d) observed at Stn YS12, pattern ii (s) at Stn Y25, pattern iii(Z) at Stn Y96, pattern iv (R) at Stn Y60 and pattern v (e) atStn Y03. Viral abundance (Vmin and Vmax for patterns i, ii andiii; Vmin1, Vmax1, Vmin2 and Vmax2 for patterns iv and v) and corresponding time points (tmin and tmax for patterns i, ii andiii; tmin1, tmax1, tmin2 and tmax2 for patterns iv and v), used for
calculating viral production in bottles, are shown
Chen et al.: Viral dynamics in the western South China Sea
BAbottle0h), and the average values of triplicate bottleswere treated as apparent viral production of the station(AVPstation). Finally, in situ viral production of the station (VPstation, viruses ml–1 h–1) was acquired as thesum of AVPstation and the loss of viruses due to viraldecay during the incubation:
where VAstation is in situ viral abundance of the stationand VDR is viral decay rate.
Viral turnover and burst size: Viral turnover timewas calculated by dividing the in situ viral abundancewith viral production. Burst size (viruses cell–1) wascalculated using the following formula:
Burst size = [VPstation × 24 (h)]/[BA × m]
Statistics. A Pearson correlation matrix (SPSS version 13.0) was calculated to describe the correla-tions between temperature, salinity, chl a, SiO3
2 –, bacterial and viral abundance, ratio of viral to bacterialabundance (VBR), bacterial growth rate, virus-inducedbacterial mortality rate, viral decay rate, viral pro -duction, bacterial production, virus-induced loss ofbacterial production, viral turnover time, and burstsize. A single linear regression was conducted usingSigmaPlot 10.0. Analysis of variance (ANOVA) andLSD Posthoc tests were used to assess differences inparameters between environments using the statistical
software SPSS 13.0. A probability (p) of < 0.05 was considered significant. Data were log transformedbefore analysis when necessary to meet the require-ments of normal distribution.
RESULTS
Hydrographic features
Based on temperature and salinity, the 13 stations investigated were divided into 4 types of water: CE I(Stns TS-1, YS06 and YS12), CE II (Stns Y03, Y00 andY20), oligotrophic oceanic water (OO water; Stns Y96,Y86, Y60 and Y45) and Mekong River plume (MR plume;Stns Y05, Y23 and Y25) (Table 1, Fig. 1). CE I was evi-dent from the temperature and salinity distribution,while CE II was only partly covered by our sampling ef-forts — an intact CE II was not visible (Fig. 3a,b). The MRplume could be traced from reduced salinity and in-creased temperature compared to the surrounding wa-ters (Table 1, Fig. 3a,b). In addition, CE II should havesome residual Mekong River freshwater plume, which isillustrated by the distribution of chl a in the SCS in thesummer of 2007, retrieved from SeaWiFS (Fig. 1). Inor-ganic nitrogen and phosphorus concentrations in thesurface mixed layer were all below the detection limit ofcolorimetric methods (M. Dai pers. comm.), but the con-centration of silicate was measurable (Table 1).
149
Area Water Stn Lat. (° N) Long. (° E) Temp. (°C) Sal. (PSU) Chl a (µg l–1) SiO32– (µM)
Table 1. Physical characteristics, nutrients and chlorophyll a concentrations of the surface seawater in the western South ChinaSea; absolute values and means ±SD are shown. Lat. = latitude; Long. = longitude; Temp. = temperature; Sal. = salinity; chl a =chlorophyll a. Abbreviations for the 4 types of water studied: CE I = Cold Eddy I; OO water = oligotrophic oceanic water;
CE II = Cold Eddy II; MR plume = Mekong River plume
Aquat Microb Ecol 63: 145–160, 2011150
(a) Te
mp
era
ture
29.0
28.5
28.5
29.0
28.0 29.5
27.5
29
.0
29.5 27
.5
30.0
28.
5
29.5
30.0
29.5
29.5
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
15.5
(b)
Salin
ity
33.4
33.6
33.6
33.4 33
.2 33.0 32.8 32.
6 32.4
33.8
34.0
34
.033
.8
33.6
33.8
(c) C
hl a
.06
.08
.10
.12.10
.12
.12
.14
.14.16
.14.18
.16
.16
.08
.14
.10.0
8.0
6
.08
.14
.06
.12.0
4
.12
(d) S
iO32
–
2.0
1.5
1.02.
5
2.5
1.5
2.0
2.0 2.
0
2.5
2.5
3.0
(e) B
acte
rial ab
un
dan
ce .35
.40
.45
.50
.40
.55
.60.65
.70
.55
.65
.50
.50 .65.45
.60
.60
.45.40 .5
5
.55.35
.50
.50.30
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
15.5
(f)
Viral ab
un
dan
ce .6
.8
1.0
1.0
.8
.6
1.2
.81.4
1.0
1.2
1.2
1.2
1.01.0
1.0
1.2
.8
1.0
.61.2
1.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
15.5
(g) R
atio
of
viral/
b
acte
rial ab
un
dan
ce
16
18 2
0 22 2
426 30
282
624
22
20 18
16
28
16
18
2616
16
16
20
2414
22
2022
18
18
16
24
16
18
18
26
20
20
22
1828
28
(k) V
iral d
ecay r
ate
.02
.01
.01
. 02
.03
.04
.03
.04
.02
.03
.04
.04
.04.03
.04
.02
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
15.5
(m) V
DR
/VP
0
10
20
30
40
0
1020
0
30
40
40
-10
50
30
60
70
010
2060
3050
40
403020
100
(h) µ
1.0
.8
1.0
1.0
1.0
1.2
1.2
1.4
1.4
1.0
(i) m
1.2
1.0
.8
.6
.4
1.0.8
.6
1.2
.8
1.01.0
1.2
(j) m
/µ100
80
60
100
80
40
60
60
80
60
(n) B
acte
rial p
rod
uctio
n 4
4
5 5
6 6
7
8
9
10
9
8
7
6
6
(o) B
PL
oss
6
5
43
2
5
5
64
7
2
8
7
6
5
(p) V
iral tu
rno
ver
tim
e -100
10
30
30
20
2010
0
20
3040
10
50
-10
0
10
40
30
20
20
10
0
(q) B
urs
t siz
e
40
40
60
80
40
60
100
120
40
140
16
0
40
40
20
40
40
60
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
15.5
(l) V
iral p
rod
uctio
n 1.0
.5
1.0
1.52.0
.5
1.5
1.0
.5
1.0
1.5
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
15.5 11
0.0
111.
0
112.
0
113.
0
110.
0
111.
0
112.
0
113.
0 110.
0
111.
0
112.
0
113.
0
110.
0
111.
0
112.
0
113.
0 110.
0
111.
0
112.
0
113.
0
110.
0
111.
0
112.
0
113.
0
Fig
. 3. D
istr
ibu
tion
of (
a) te
mp
erat
ure
(°C
), (b
) sal
init
y (P
SU
), (c
) ch
loro
ph
yll a
(µg
l–1 )
, (d
) SiO
32– (
µM
), (e
) bac
teri
al a
bu
nd
ance
(106
cell
s m
l–1 )
, (f)
vir
al a
bu
nd
ance
(107
viru
ses
ml–
1 ), (
g)
rati
o of
vir
al t
o b
acte
rial
ab
un
dan
ce, (
h)
intr
insi
c b
acte
rial
gro
wth
rat
e (μ
, d–
1 ), (
i) v
iru
s-in
du
ced
bac
teri
al m
orta
lity
rat
e (m
, d–
1 ), (
j) r
atio
of
viru
s-in
du
ced
bac
teri
alm
orta
lity
to
bac
teri
al g
row
th r
ate
(m/μ
, %),
(k
) vi
ral d
ecay
rat
e (h
–1 )
, (l)
vir
al p
rod
uct
ion
(10
6vi
ruse
s m
l–1
h–
1 ), (
m)
vira
l dec
ay r
ate/
vira
l pro
du
ctio
n (
VD
R/V
P, %
), (
n)
bac
te-
rial
pro
du
ctio
n (
µg
C l
–1
d–
1 ), (
o) v
iru
s-in
du
ced
los
s of
bac
teri
al p
rod
uct
ion
(B
PL
oss,
µg
C l
–1
d–
1 ), (
p)
vira
l tu
rnov
er t
imes
(V
TT
, h),
an
d (
q)
bu
rst
size
(B
S).
m=
sta
tion
s in
th
e n
orth
ern
are
a; n
= s
tati
ons
in t
he
sou
ther
n a
rea
Chen et al.: Viral dynamics in the western South China Sea
Chlorophyll a
Surface chl a varied from 0.063 to0.171 µg l–1 among the stations investi-gated, with an average of 0.115 ±0.037 µg l–1 (n = 13; Table 1). Gener-ally, chl a was significantly lower inthe OO water than in the other 3 typesof water — CE I and II and the MRplume (ANOVA, p < 0.05, n = 13) —while chl a among the latter 3 waterswas comparable and displayed no significant difference (ANOVA, p >0.05, n = 13).
Bacterial abundance
Heterotrophic bacterial abundancewas low in the surface water duringour cruise, ranging from 0.35 to 0.70 ×106 cells ml–1 (average 0.52 ± 0.11 ×106 cells ml–1, n = 13; Table 2). Thehighest concentration was found atStn Y23 in the MR plume, and the low-est at Stn Y86 in the OO water, respectively (Table 2,Fig. 3e). Bacterial abundance in the OO water was significantly lower (ANOVA, p < 0.05, n = 13) thanin the other 3 waters (Table 2; Fig. 3e), whereas no significant differences existed between CE I, CE IIand the MR plume. Bacterial abundance was positivelyrelated to chl a (Pearson’s, r = 0.850, p < 0.01, n = 13;Table 3).
Viral abundance
Viral abundance varied from 0.76 × 107 to 1.44 × 107
viruses ml–1 (average 1.00 ± 0.23 × 107 viruses ml–1, n =13) (Table 2). The highest abundance was found atStn Y05 (1.44 ± 0.09 × 107 viruses ml–1) in the MRplume, whereas the lowest was found at Stn Y86 (0.76± 0.11 × 107 viruses ml–1) in the OO water (Table 2,Fig. 3f). Viral abundances among the 4 types of waterwere not significantly different from each other(ANOVA, p > 0.05, n = 13; Table 2). Viral abundancewas neither correlated with chl a (Pearson’s, r = 0.309,p > 0.05, n = 13; Table 3) nor with bacterial abundance(Pearson’s, r = 0.463, p > 0.05, n = 13; Table 3).
VBR ranged from 14 to 27 (average 20.38 ± 5.16, n =13; Table 2). The higher VBR was found in the OOwater (average 23.50 ± 3.70), and the lower VBR (average 17.33 ± 3.06) was found in CE I (Table 3).There were no significant differences of VBR amongdifferent waters (ANOVA, p > 0.05, n = 13; Table 2,
Fig. 3g). A positive relationship was observed betweenVBR and SiO3
2– (Pearson’s, r = 0.693, p < 0.01, n = 13;Table 3).
Bacterial growth rate, virus-induced bacterialmortality rate, bacterial production and virus-
induced loss of bacterial production
The results of viral lysis based on the ‘virus dilutionapproach’ are shown in Fig. 4. The bacterial growthrate averaged 1.05 ± 0.19 d–1, with the highest atStn Y60 (1.32 d–1, in the OO water) and lowest atStn Y25 (0.66 d–1, in the MR plume) (Table 4, Fig. 3h).Bacterial growth rates were comparable in CE I and IIand the OO water (average 1.10 ± 0.13, 1.09 ± 0.19,1.17 ± 0.10 d–1, respectively; Table 4, Fig. 3h). Bacterialgrowth rates in the MR plume (average 0.81 ± 0.13 d–1)were significantly lower than in other waters (Table 4,Fig. 3h). The bacterial growth rate was positively cor-related with salinity (Pearson’s, r = 0.635, p < 0.05, n =13; Table 3).
The virus-induced bacterial mortality rate (m) variedfrom 0.23 d–1 (Stn Y23, in the MR plume) to 1.15 d–1
(Stn Y60, in the OO water), and the average mortalityrate among the 13 stations investigated was 0.84 ±0.28 d–1 (Table 4). Mortality rates were extremely lowat Stns Y23 and Y25 (0.23 and 0.32 d–1, respectively),both of which are located in the MR plume (Table 4,Fig. 3i). Mortality rates were comparable in CE I, CE II
151
Area Water Stn BA VA VBR(106 cells ml–1) (107 viruses ml–1)
Table 2. Bacterial abundance (BA), viral abundance (VA), and ratio of viral tobacterial abundance (VBR) at sampling stations in the western South China Sea;individual values and average values, all representing means ± SD, are shown.
See Table 1 for abbreviations for the 4 types of water studied
Aquat Microb Ecol 63: 145–160, 2011
and OO waters (average 0.92 ± 0.21, 0.93 ± 0.07 and1.02 ± 0.10 d–1, respectively), while a significantlylower m was found in the MR plume (0.42 ± 0.26 d–1)(Table 4, Fig. 3i). The mortality rate decreased withbacterial abundance (Pearson’s, r = –0.550, p = 0.052,n = 13; Table 3) and was positively correlated withsalinity (Pearson’s, r = 0.771, p < 0.01, n = 13; Table 3),SiO3
2–– (Pearson’s, r = 0.624, p < 0.05, n = 13; Table 3)and bacterial growth rate (Pearson’s, r = 0.790, p <0.001, n = 13; Table 3, Fig. 5).
Among the 13 stations, bacterial production aver-aged 5.94 ± 1.51 µg C l––1 d–1. The highest bacterial pro-duction was found at Stn Y03 (9.90 µg C l––1 d––1, in CE I),and the lowest was found at StnY86 (4.17 µg C l––1 d––1,in the OO water) (Table 4; Fig. 3n). Bacterial produc-tion was higher in CE I and II (average 6.72 ± 0.50 and6.85 ± 2.64 µg C l–1 d–1, respectively) than in the OOwater and MR plume (average 5.01 ± 0.77 and 5.47 ±1.12 µg C l–1 d–1, respectively) (Table 4; Fig. 3n). Bacte-rial production was positively correlated with chl a(Pearson’s, r = 0.544, p < 0.05, n = 13; Table 3).
The average BPLoss was 4.59 ± 1.63 µg C l–1 d–1 for all13 stations investigated (Table 4). The highest BPLoss
was observed at Stn Y03 (7.63 µg C l–1 d–1) in CE II, andthe lowest at Stn Y23 (1.74 µg C l–1 d–1) in the MRplume (Table 4, Fig. 3o). BPLoss was significantly lower(ANOVA, p < 0.05, n = 13) in the MR plume (average2.69 ± 0.12 µg C l–1 d–1) than in CE I, CE II and OO wa-ter (average 5.63 ± 1.17, 5.73 ± 1.67 and 4.38 ± 0.83 µgC l–1 d–1, respectively) (Table 4, Fig. 3o). BPLoss waspositively correlated with salinity (Pearson’s, r = 0.708,p < 0.01, n = 13; Table 3) and with bacterial growth rate(Pearson’s, r = 0.724, p < 0.01, n = 13; Table 3).
Viruses caused approximately 77.82 ± 20.12% lossof bacterial biomass produced per day (i.e. m/μ ratio),with the highest loss at Stn TS-1 (96.08%) in CE I andthe lowest at Stn Y23 (26.44%) in the MR plume(Table 4, Fig. 3j). The m/μ ratio averaged 83.48 ±15.37, 85.88 ± 8.00, 87.24 ± 5.91 and 51.57 ± 26.80%in CE I, CE II, OO water and MR plume, respectively(Table 4, Fig. 3j), and was significantly lower in theMR plume than in the other 3 waters (ANOVA, p <0.05, n = 13). The m/μ ratio was positively correlatedwith salinity (Pearson’s, r = 0.716, p < 0.01, n = 13;Table 3) and with SiO3
2– (Pearson’s, r = 0.612, p <0.05, n = 13; Table 3). The m/μ ratio decreased withbacterial abundance (Pearson’s, r = –0.546, p = 0.054,n = 13; Table 3).
Viral production and viral decay rate
Viral production was about 1.03 ± 0.39 × 106 virusesml–1 h–1 in the investigated area (Table 4). The highestviral production appeared in CE I (Stn TS-1, 1.63 ± 0.51
152
TS
Ch
l a
Si
BA
VA
VB
Rμ
mm
/μV
DR
VP
VD
R/V
PB
PB
PL
oss
VT
TB
S
T1
–0.
553 *
–0.
260
–0.
280
0.19
90.
203
–0.
029
–0.
328
–0.
439
–0.
426
0.63
9 *0.
047
0.61
1 *–
0.07
1–
0.41
70.
165
0.38
9S
–0.
553 *
1–
0.10
90.
655 *
–0.
373
–0.
346
0.10
30.
635 *
0.77
1 **
0.71
6 **
–0.
185
–0.
035
–0.
272
0.13
50.
708 *
*–
0.12
5–
0.59
6 *C
hl
a–
0.26
0–
0.10
91
–0.
446
0.85
0 **
0.30
9–
0.53
2–
0.35
9–
0.41
0–
0.37
00.
202
0.27
50.
130
0.54
4–
0.02
20.
056
0.29
5S
i–
0.28
00.
655 *
–0.
446
1–
0.53
70.
119
0.69
3 **
0.45
30.
624 *
0.61
2 *–
0.26
2–
0.20
8–
0.02
2–
0.16
80.
447
0.25
2–
0.50
9B
A0.
199
–0.
373
0.85
0 **
–0.
537
10.
463
–0.
386
–0.
550
–0.
546
0.54
20.
469
0.37
50.
004
VA
0.20
3–
0.34
60.
309
0.11
90.
463
1–
0.24
6–
0.38
6–
0.39
70.
214
0.07
60.
259
–0.
208
0.32
5V
BR
–0.
029
0.10
3–
0.53
20.
693 *
*1
0.22
10.
226
0.19
2–
0.29
0–
0.30
70.
167
–0.
338
–0.
021
0.43
5–
0.21
2μ
–0.
328
0.63
5 *–
0.35
90.
453
–0.
386
–0.
246
0.22
11
0.79
0 **
–0.
341
0.00
7–
0.36
50.
724 *
*–
0.26
1–
0.64
9 *m
–0.
439
0.77
1 **
–0.
410
0.62
4 *–
0.55
0–
0.38
60.
226
0.79
0 **
1–
0.24
50.
020
–0.
385
0.09
8–
0.28
7m
/μ–
0.42
60.
716 *
*–
0.37
00.
612 *
–0.
546
–0.
397
0.19
21
–0.
154
0.02
4–
0.33
3–
0.25
3V
DR
0.63
9*–
0.18
50.
202
–0.
262
0.54
20.
214
–0.
290
–0.
341
–0.
245
–0.
154
10.
557 *
0.25
00.
000
–0.
145
0.48
7V
P0.
047
–0.
035
0.27
5–
0.20
80.
469
0.07
6–
0.30
70.
007
0.02
00.
024
0.55
7 *1
0.46
10.
274
VD
R/V
P0.
611*
–0.
272
0.13
0–
0.02
20.
375
0.16
7–
0.36
5–
0.38
5–
0.33
31
0.07
6B
P–
0.07
10.
135
0.54
4–
0.16
80.
259
–0.
338
0.09
80.
250
0.46
10.
076
10.
477
–0.
203
–0.
023
BP
Los
s–
0.41
70.
708 *
*–
0.02
20.
447
–0.
208
–0.
021
0.72
4 **
0.00
00.
274
0.47
71
–0.
335
VT
T0.
165
–0.
125
0.05
60.
252
0.00
40.
435
–0.
261
–0.
287
–0.
253
–0.
145
–0.
203
–0.
335
1B
S0.
389
–0.
596 *
0.29
5–
0.50
90.
325
–0.
212
–0.
649 *
0.48
7–
0.02
31
Tab
le 3
. P
ears
on c
orre
lati
on m
atri
x ob
tain
ed f
rom
exp
erim
ents
con
du
cted
on
sam
ple
s co
llec
ted
fro
m t
he
wes
tern
Sou
th C
hin
a S
ea i
n s
um
mer
200
7. T
= t
emp
erat
ure
; S
=sa
lin
ity;
ch
l a
= c
hlo
rop
hyl
l a;
Si
= c
once
ntr
atio
n o
f S
iO32
– ; B
A =
bac
teri
al a
bu
nd
ance
; VA
= v
iral
ab
un
dan
ce; V
Br
= r
atio
of
vira
l to
bac
teri
al a
bu
nd
ance
; VD
r =
vir
al d
ecay
rate
; VP
= v
iral
pro
du
ctio
n; μ
= i
ntr
insi
c b
acte
rial
gro
wth
rat
e; m
= v
iru
s-in
du
ced
bac
teri
al m
orta
lity
rat
e; B
P =
bac
teri
al p
rod
uct
ion
; BP
Los
s=
vir
us-
ind
uce
d l
oss
of b
acte
rial
pro
du
ctio
n; V
TT
= v
iral
tu
rnov
er t
ime;
BS
= b
urs
t si
ze. A
ster
isk
s: c
orre
lati
on is
sig
nif
ican
t at
th
e *0
.05
leve
l or
**0.
01 le
vel
Chen et al.: Viral dynamics in the western South China Sea
× 106 viruses ml–1 h–1) and CE II (Stn Y03; 1.60 ± 0.22 ×106 viruses ml–1 h–1), while the lowest viral productionwas observed at Stn Y96 (0.48 ± 0.10 × 106 viruses ml–1
h–1) in the OO water (Table 4, Fig. 3l). Viral productionin CE I was higher than in OO water (average 0.97 ±0.57 × 106 versus 0.85 ± 0.46 × 106, respectively) as wasthat in CE II compared to MR plume (average 1.35 ±0.22 × 106 versus 1.00 ± 0.07 × 106, respectively)(Table 4, Fig. 3l). Viral production did not differ signif-icantly among different waters (ANOVA, p > 0.05, n =13; Table 4, Fig. 3l). In the southern area, viral produc-tion was positively correlated with bacterial growthrate (Pearson’s, r = 0.958, p < 0.01, n = 6) and bacterial production (Pearson’s, r = 0.861, p < 0.05, n = 6). How-ever, no significant relationship between viral produc-tion and bacterial growth rate or bacterial productionwas found in the northern area.
Viral decay rates were between 0.007 and 0.051 h–1
among the stations investigated (Table 4). At Stn Y86in the OO water, the lowest viral decay rate (0.007 h–1)was observed, accompanied by the lowest viral abun-dance. Viral decay rates in the southern area (average0.043 ± 0.011 h–1 in CE II, and 0.037 ± 0.007 h–1 in theMR plume) were higher than those in the northernarea (0.027 ± 0.009 h–1 in CE I, and 0.022 ± 0.014 h–1 inthe OO water) (Table 4, Fig. 3k). No significant differ-ence (ANOVA, p > 0.05, n =13) of viral decay rates wasfound between the different waters. Viral decay rates
increased significantly with temperature (Pearson’s, r =0.639, p < 0.05, n = 13) (Table 3). In addition, there wasa correlation between viral production and viral decayrate (Pearson’s, r = 0.557, p < 0.05, n = 13) (Table 3).
The ratio of viral decay rate to viral production(VDR/VP) was about 32.23 ± 14.76%, with the lowestat Stn TS-1 (9.47%) in the OO water and the highest at
Fig. 4. Bacterial apparent growth rate against fraction of whole water for the parallel viral lysis dilution experiments conductedat 13 stations in the South China Sea in the summer of 2007. Equations describing the Model 1 linear regression for each stationare given in every plot. Plots a–c were derived from stations located in Cold Eddy I, plots h–j from stations located in Cold Eddy
II; and plots k–m were from stations influenced by the Mekong River plume
1.6
1.2
0.8
0.4
0.0
0.6 0.8 1.0 1.2 1.4
Linear regression line
95% confidence band
Bacterial growth rate (d–1)
Viral-
ind
uced
bacte
rial
mo
rtalit
y r
ate
(d
–1)
y = 1.2937x – 0.5254
(r2 = 0.7900, p < 0.001)
Fig. 5. Relationship between bacterial growth rate and virus-induced bacterial mortality rate in the South China Sea in thesummer of 2007. Linear regression line and 95% confidencebands are shown. Equations describing the Model 1 linear
regression (y = y0 + a × x) and p-value are shown
Aquat Microb Ecol 63: 145–160, 2011
Stn YS12 (55.64%) in CE I (Table 4, Fig. 3m).VDR/VP ranged from 9.47 to 55.64% in CE I,from 20.30 to 47.08% in CE II, from 9.59 to34.09% in the OO water, and from 38.02 to46.91% in the MR plume (Table 4, Fig. 3m).VDR/VP was higher in the MR plume (aver-age 41.48 ± 4.76%), while it was lower inthe OO water (average 21.91 ± 11.17%). Theaverage VDR/VP was not significantly dif -ferent among CE I and II, the MR plume andthe OO water (ANOVA, p > 0.05, n = 13).VDR/VP significantly increased with temper-ature (Pearson’s, r = 0.611, p < 0.05, n = 13)(Table 3).
Viral turnover and burst size
Viral turnover time among the stations stud-ied was between 6.59 h (Stn TS-1 in CE I) and42.30 h (Stn YS12 in CE II) and averaged 18.07± 10.15 h (Table 4, Fig. 3p). Viral turnovertimes among different waters were not signifi-cantly different (ANOVA, p > 0.05, n = 13),about 23.22 ± 17.98 h in CE I, 12.62 ± 5.80 h inCE II, 16.34 ± 8.94 h in the OO water and 20.70± 6.33 h in the MR plume (Table 4, Fig. 3p).
Burst size ranged from 23 to 177 virusescell–1 among all stations (average 64 ± 40;Table 4, Fig. 3q). The highest burst sizeoccurred at Stns Y23 and Y25 (177 and 107viruses cell–1, respectively) in the MR plume(Table 4, Fig. 3q). The burst size in CE I (29.33± 23.18 viruses cell–1) was smaller than in thesurrounding OO water (34.00 ± 13.69 virusescell–1), and the pattern was the same for CE IIand the surrounding MR plume (40.67 ± 5.69and 69.00 ± 39.15 viruses cell–1 for CE II andthe MR plume, respectively) (Table 4,Fig. 3q). Burst size was negatively correlatedwith salinity (Pearson’s, r = –0.596, p < 0.05, n= 13; Table 3) and bacterial growth rate (Pear-son’s, r = –0.604, p < 0.05, n = 13) (Table 3).
DISCUSSION
Methodological issues
Viral lysis
A modified Landry–Hassett dilution ap -proach was used to explore the impact of virallysis on bacterial mortality; this approach hasseveral limitations (Evans et al. 2003, Bau-
154
Are
aW
ater
Stn
μm
m/μ
VD
RV
PV
DR
/VP
BP
BP
Los
sV
TT
BS
(d–
1 )(d
–1 )
(%)
(h–
1 )(1
06vi
ruse
s m
l–1
h–
1 )(%
)(µ
g C
l–
1d
–1 )
(µg
C l
–1
d–
1 )(h
)
Nor
ther
nC
E I
TS
-11.
020.
9896
.08
0.01
6 ±
0.0
051.
63 ±
0.5
19.
476.
396.
166.
5962
YS
061.
251.
1088
.00
0.03
4 ±
0.0
090.
70 ±
0.0
541
.28
7.29
6.44
20.7
623
YS
121.
040.
6966
.35
0.03
0 ±
0.0
070.
59 ±
0.1
255
.64
6.48
4.29
42.3
040
OO
wat
erY
961.
140.
9078
.95
0.01
4 ±
0.0
060.
48 ±
0.1
028
.07
4.56
3.62
28.2
732
Y86
1.09
0.99
90.8
30.
007
± 0
.005
0.51
± 0
.16
9.59
4.17
3.78
16.3
632
Y60
1.32
1.15
87.1
20.
028
± 0
.010
1.45
± 0
.52
15.8
75.
484.
776.
7664
Y45
1.13
1.04
92.0
40.
038
± 0
.004
0.97
± 0
.18
34.0
95.
835.
3613
.95
46
Sou
ther
nC
E I
IY
031.
301.
0177
.69
0.03
0 ±
0.0
011.
60 ±
0.2
220
.30
9.90
7.63
8.63
49Y
001.
020.
8886
.27
0.05
1 ±
0.0
111.
18 ±
0.3
733
.17
5.24
4.50
9.96
68Y
200.
950.
8993
.68
0.04
7 ±
0.0
181.
26 ±
0.0
247
.08
5.41
5.05
19.2
870
MR
plu
me
Y23
0.87
0.23
26.4
40.
032
± 0
.007
1.08
± 0
.01
38.0
26.
691.
7419
.60
177
Y05
0.89
0.71
79.7
80.
033
± 0
.009
0.99
± 0
.15
46.9
15.
244.
1627
.51
59Y
250.
660.
3248
.48
0.04
5 ±
0.0
230.
94 ±
0.2
139
.50
4.49
2.17
15.0
010
7
Sou
th C
hin
a S
ea1.
05 ±
0.1
90.
84 ±
0.2
877
.82
± 2
0.12
0.03
1 ±
0.0
131.
03 ±
0.3
932
.23
± 1
4.76
5.94
± 1
.51
4.59
± 1
.63
18.0
7 ±
10.
1563
.77
± 4
0.37
Nor
ther
n1.
14 ±
0.1
10.
98 ±
0.1
585
.62
± 1
0.01
0.02
4 ±
0.0
120.
90 ±
0.4
727
.72
± 1
7.35
5.74
± 1
.10
4.92
± 1
.11
19.2
8 ±
12.
7042
.71
± 1
5.61
Sou
ther
n0.
95 ±
0.2
10.
67 ±
0.3
268
.72
± 2
5.81
0.04
0 ±
0.0
091.
18 ±
0.2
437
.50
± 9
.99
6.16
± 1
.97
4.21
± 2
.13
16.6
6 ±
7.0
188
.33
± 4
7.68
Nor
ther
nC
E I
1.10
± 0
.13
0.92
± 0
.21
83.4
8 ±
15.
370.
027
± 0
.009
0.97
± 0
.57
35.4
6 ±
23.
636.
72 ±
0.5
05.
63 ±
1.1
723
.22
± 1
7.98
41.6
7 ±
19.
55O
O w
ater
1.17
± 0
.10
1.02
± 0
.10
87.2
4 ±
5.9
10.
022
± 0
.014
0.85
± 0
.46
21.9
1 ±
11.
175.
01 ±
0.7
74.
38 ±
0.8
316
.34
± 8
.94
43.5
0 ±
15.
18
Sou
ther
nC
E I
I1.
09 ±
0.1
90.
93 ±
0.0
785
.88
± 8
.00
0.04
3 ±
0.0
111.
35 ±
0.2
233
.52
± 1
3.39
6.85
± 2
.64
5.73
± 1
.67
12.6
2 ±
5.8
063
.77
± 4
0.37
MR
plu
me
0.81
± 0
.13
0.42
± 0
.26
51.5
7 ±
26.
800.
037
± 0
.007
1.00
± 0
.07
41.4
8 ±
4.7
65.
47 ±
1.1
22.
69 ±
1.2
920
.70
± 6
.33
42.7
1 ±
15.
61
Tab
le 4
. Ab
solu
te a
nd
mea
n ±
SD
val
ues
of
vira
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d b
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rial
dyn
amic
par
amet
ers
at s
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ons
inve
stig
ated
in t
he
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tern
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bb
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ns
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the
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of
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Tab
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oth
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revi
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Chen et al.: Viral dynamics in the western South China Sea
doux et al. 2006, 2007, Kimmance et al. 2007), whichare discussed below.
First, the filtered seawater used to dilute samplesmight have contained nanoflagellates smaller than2 µm, which consume both bacteria and viruses(Jacquet et al. 2005). It has been reported that grazingby heterotrophic flagellate assemblages removes onlya small portion of viral abundance or production(González & Suttle 1993, Bettarel et al. 2005), and ourpreliminary experiments also showed that this propor-tion was negligible for both viruses and bacteria (datanot shown).
Second, ultrafiltration eliminates particulate organicmatter and high-molecular-weight dissolved organicmatter larger than 100 kDa. The exclusion of grazersmight also reduce the generation of inorganic matterand the supply of labile dissolved organic matter forbacteria (Nagata 2000). Thus, the bacterial growth ratemight be limited in viral lysis dilution experiments. Onthe other hand, because the virus-free water containslow-molecular-weight dissolved organic matter withno bacteria, the effect of substrate limitation caused byultrafiltration and exclusion of grazers might be partially offset.
Third, a host density threshold, below which infec-tion cannot take place (Wiggins & Alexander 1985,Weinbauer & Peduzzi 1994, Wilcox & Fuhrman 1994),might have occurred due to bacterial abundance aslow as 20% of the natural abundance in the mostdiluted bottles. If a threshold of 104 cells ml–1 (Wiggins& Alexander 1985) was used, bacterial abundance inour experiments did not fall below this threshold —even in the most diluted treatment (20%), where thelowest bacterial abundance was higher than 105 cellsml–1 (Table 2). High viral abundance (106 to 107 virusesml–1, Table 2) further demonstrated that contact rateand infection rate between bacteria and bacterio-phages were not compromised in the dilution series.Moreover, the linear responses obtained from the dilu-tion experiments in all 13 stations suggest that bacter-ial abundance was not diluted below this thresholdlevel (Fig. 4).
Viral production
The dilution and re-occurrence approach has beenincreasingly used and has proved to be useful forestimating viral production in different aquatic envi-ronments (Wilhelm et al. 2002, Winter et al. 2004,Mei & Danovaro 2004, Bongiorni et al. 2005). The‘modified virus dilution approach’ following Bon-giorni et al. (2005) in this study is much easier tomanipulate in the field, when compared to the ‘virusreduction approach’ (Wilhelm et al. 2002). However,
diluting hosts in the initial water by the ’modifiedvirus dilution approach’ would stimulate host growthrates and lytic cycles in the lysogenic infections,which might overestimate viral production. Becauseviral decay by colloidal and heat-labile substancesstill occurs in the diluted water, apparent viral pro-duction calculated from the incubated bottles shouldbe revised by the viral decay rate to acquire viralproduction in situ. Thus, if infected bacterial abun-dances in the diluted bottle are too low, the develop-ment of viral abundance over time would barely beobserved or might even show a decreasing pattern(pattern iii shown in Fig. 2b).
Five different patterns in the viral production curvewere observed (Fig. 2b,c). Patterns i and ii (Fig. 2b)showed an increasing viral abundance during incu-bation and had only 1 peak, which has commonlyappeared in experiments using the ‘virus reductionapproach’ (Wilhelm et al. 2002). However, mostcurves in our experiments had 2 peaks (patterns ivand v; Fig. 2c); similar patterns occurred in viral pro-duction dilution experiments conducted in the NorthSea (Winter et al. 2004). Because the incubations inour experiments lasted for about 12 h, some beingmainly during the day and others at night, one possi-bility might be that there was a diel signal of viralinfection and/or lysis in the SCS as in the North Sea,where viral lysis of bacteria occurred around noon toafternoon, and infection occurred mainly during thenight (Winter et al. 2004). Another explanation mightbe the delay in viral production that occurs whenusing the dilution and reoccurrence approach (Wein-bauer & Suttle 1999).
Dynamics of bacterial and viral abundances
Bacterial abundance was positively related to chl a(Table 3), which indicates that it is largely controlledby the labile dissolved organic matter produced by pri-mary producers in the oligotrophic SCS. However,there was no significant relationship between viralabundance and chl a or between viral and bacterialabundance (Table 3). This may imply that, on a rela-tively short temporal scale and limited spatial scale,viral abundance in the oligotrophic SCS is determinedneither by trophic status nor by host abundance. Thelack of relationships described above is not unex-pected because viral abundance is a dynamic combi-nation of viral production and removal, both of whichare influenced by many factors such as trophic status,mixing, UV radiation and turbidity (Suttle & Chen1992, Bongiorni et al. 2005) or by direct consumptionby heterotrophic nanoflagellates (González & Suttle1993).
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Bacterial growth rate and virus-induced bacterialmortality rate
The bacterial growth rate in the SCS was compara-ble to rates determined by dilution approaches inoceanic waters, such as 1.0 to 1.1 d–1 in the North Sea(Yokokawa et al. 2004, calculated from Eilers et al.2000) and 1.55 d–1 off Kuroshio, Japan (Yokokawa &Nagata 2005). It has been reported that dilutionapproaches do not properly measure the in situ bacte-rial growth rate because of the dramatic change of tax-onomic composition in diluted incubations (Fuchs et al.2000); however, Yokokawa et al. (2004) suggested thatartificial enhancement of bacterial growth rate wasminimal.
Bacterial growth rate was positively correlated withsalinity and SiO3
2–, as was m (Table 3). Because highsalinity and nutrients are characteristics of deep waterupwelled in cold eddies, the above relationships indi-cate that increasing trophic conditions enhance both μand m. Similar findings have been reported along atrophic gradient in the North Adriatic Sea (Bongiorniet al. 2005). However, we did not observe any relation-ship between bacterial production and hydrologicalconditions (temperature and salinity) or trophic condi-tions (SiO3
2– and chl a).We observed a very high bacterial mortality caused
by viral lysis (average 77.82 ± 20.12% of dailygrowth; i.e. m/μ; Table 4) among the investigatedstations; this indicates that viral lysis plays an impor-tant role in bacterial mortality in the SCS. However,in the MR plume, low values of m/μ and BPLoss werefound, together with a low m. All 3 parameters (m,m/μ and BPLoss) were significantly lower in the MRplume than in CE I, CE II and OO water. In addition,at Stn YS12, where there was a bloom of Tri-chodesmium spp., m and m/μ (0.69 d–1 and 66.35%)were much lower than at other stations in the north-ern area (Table 4). Thus, it can be concluded thatviral lysis was not so heavily responsible for bacterialmortality in the MR plume and in the water contain-ing the Trichodesmium spp. bloom. We speculatedthat bacterial and viral communities had shifted butwere not in a steady state under the influence of theMekong River plume and the Tricho desmium spp.bloom, and that the succession and mismatchingbetween immature bacterial and viral communitieswere responsible for the low m, m/μ and BPLoss. Inaddition, adsorption of viruses on particles (such asparticulate organic matter and transparent exopoly-meric particles) carried by the Mekong River fresh-water, or formed by Trichodesmium spp., mightreduce the viral infectivity and hence the infectionrates, and consequently reduce m, m/μ and BPLoss
(Suttle & Chen 1992, Noble & Fuhrman 1997, Wein-
bauer et al. 2009). A strong relationship between μand m observed in this study (Fig. 5) is in agreementwith the findings of Middelboe (2000), which showthat the rates of bacterial lysis are positively corre-lated with the host growth rates in chemostat cul-tures within a marine virus–host system. Further-more, a higher contact rate between bacteria andviruses, resulting from higher bacterial growth, maybe responsible for a higher m.
Viral production and viral decay rate
Using the dilution approaches, viral production wasreported to range from 4 × 106 to 107 viruses ml–1 h–1 ineutrophic waters (Wilhelm et al 2002, Bongiorni et al.2005), and 0.20 to 1.1 × 106 viruses ml–1 h–1 inmesotrophic and oligotrophic waters (Winter et al.2004, Bongiorni et al. 2005). In general, viral produc-tion in the SCS was comparable to that in oligotrophicand mesotrophic waters. The highest viral productionwas found at stations located in the cold eddies(Stns TS-1 and Y03, Table 4) and was comparable toviral production in mesotrophic waters (Wilhelm et al.2002), which we believe to be related to high primaryproduction in the cold eddies as indicated by highchl a. A previous study along a trophic gradient in theNorth Adriatic Sea reported similar findings andshowed that higher rates of bacterial production andhost cell metabolic activities can sustain higher viralproduction (Bongiorni et al. 2005). However, viral production had no apparent relationship with surfacechl a, bacterial growth rate or bacterial production inthis study (Table 3), suggesting that viral productionmay not be solely determined by trophic status in thispart of the SCS. Nevertheless, we found that viral pro-duction in mesotrophic waters (the MR plume, CE Iand II) was higher than in oligotrophic waters (the OOwater). We speculate that the lack of significant rela-tionships between viral production and water columntrophic conditions was caused by complex hydro -dynamics (cold eddies and the Mekong River plume)during our investigation.
To date, different approaches have been used toassess viral decay rates, and the data from differentstudies are thus difficult to compare (Parada et al.2007). Here, we compare results from the SCS withprevious studies in which dark-incubation of filtered orunfiltered seawater was used, as in this study. Viraldecay rates in the SCS (range 0.007 to 0.051 h–1;Table 4) are a little higher than those found in coastaland oceanic environments, such as in the coastal sea-waters of Texas (range 0 to 0.023 h–1; Suttle & Chen1992), in Santa Monica Bay (range 0.013 to 0.018 h–1;Noble & Fuhrman 1997), in the North Sea (range 0.003
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to 0.017 h–1; Winter et al. 2004) and in the Mediter-ranean Sea (0.020 h–1; Guixa-Boixereu et al. 1999). Inaddition, viral decay rates in the surface SCS wereabout 1 magnitude higher than those found in themesopelagic and bathypelagic waters of the NorthAtlantic (range 0.001 to 0.004 h–1; Parada et al. 2007).Differences in viral decay rates within different watersmight be related to hydrological and biological differ-ences. It has been reported that eutrophic systems,compared with oligotrophic systems, might promotehigher viral decay rates (Bongiorni et al. 2005), and wedid find that viral decay rates were significantly higherin the southern area, which received the Mekong Riverplume, than in the northern area (ANOVA, p < 0.05,n = 13) (Table 4, Figs. 1 & 3k). Furthermore, we foundthat viral decay rates increased with viral production,which might also imply that the trophic status deter-mined viral decay rates. A previous study indicatedthat viral decay rates increased with temperature (Cot-trell & Suttle 1995), although the temperature gradientcaused by the cold eddies and freshwater plumeseemed to be very small (ΔT = 1.9°C; Table 1, Fig. 3a).In this study, viral decay rates significantly increasedwith seawater temperature, as reported in anotherstudy (Parada et al. 2007).
Colloid and heat-labile substances are importantcauses of viral removal, and the proportion of viralremoval by these factors is different in eutrophic andoligotrophic waters; however, few data are available(Winter et al. 2004, Bongiorni et al. 2005). Combinedwith other data, such as viral removal by grazing, theproportion of viruses removed by colloid and heat-labile substances can help us to better estimatewhich pathway plays a more important role for viralremoval. From the VDR/VP, we found that the pro-portion of viruses removed by colloidal and heat-labile substances differed from station to station(Table 4). The highest removal (55.64%) was foundat Stn YS12, where there was a bloom of the nitro-gen-fixing cyanobacterium Trichodesmium duringthe sampling period. Trichodesmium is a filamentous,colonial cyanobacterium prevalent in tropical andsubtropical waters, and it is also one of the dominantred-tide phytoplankton species. Previous studieshave shown that the Trichodesmium bloom releasesextracellular substances (photosynthetic precursors)during photosynthesis (Pant & Devassy 1976), and anaverage of 50% of the N2 fixed by Trichodesmium isapparently released as dissolved organic nitrogen(Glibert & Bronk 1994). Some of the extracellularmatter and dissolved organic nitrogen released byTrichodesmium might be active enzymes or may fur-ther form colloids (i.e. colloidal and heat-labile sub-stances), which might elevate the viral decay rate atthis bloom-forming station (YS12) compared to other
stations. At stations located in CE I and II, where thetemperature was slightly lower, viral removal by col-loidal and heat-labile substances was very low. Fur-thermore, a strong relationship between the temper-ature and VDR/VP (Table 3) indicates that colloidaland heat-labile substances play a more importantrole in viral removal when the seawater temperatureis elevated. Because viral decay rates and the pro-portion of viral removal by colloidal and heat-labilesubstances were both sensitive to temperature, tem-perature fluctuation due to the cold eddies mightplay an important role in viral dynamics in the oligotrophic SCS.
Viral turnover and burst size
Viral turnover times calculated in this study areshorter (from hours to days, average 18.07 ± 10.15 h)than those reported in other studies carried out inoceanic waters (on average 6.1 d); however, they arecomparable to those in coastal/shelf waters (on aver-age 1.6 d) (Weinbauer 2004). The main reason for thediscrepancy could be that different methods wereused. Previous studies usually assessed viral turnoverfrom viral decay rate on the assumption that viraldecay rate is equal to viral production (Weinbauer2004). However, this is not necessarily the case,because viral decay rates in some studies, such as thatof Suttle & Chen (1992), accounted for only thoseviruses removed by the colloidal and heat-labile sub-stances in the water, but did not account for the totalremoval of viral production of the system, due to themethod used (Bongiorni et al. 2005). Our resultsshowed that viral turnover in the SCS in summer washigh, especially in CE II. Other studies conducted dur-ing this cruise also showed moderately higher biologi-cal turnover rates in the cold eddies (Chen et al. 2009).
The average viral burst size (64) in the SCS wasabout 3 times that of the average burst size (23) calcu-lated by Parada et al. (2006) for marine environments.A higher viral burst size was found at Stns Y23 and Y25in the MR plume. It is known that the cell size of thehost may directly determine viral burst size (Parada etal. 2006). Our results could be due to the higher dis-solved organic and inorganic nutrient input from riverwater which might induce a larger bacterial size andsequentially a higher burst size of the infected bacteriaat the above stations. Negative correlation of salinitywith burst size further supports the above explanation(Table 3). It is also suggested that viral burst sizeincreases with the trophic status of the environmentand bacterial production (Parada et al. 2006). How-ever, such trends were not apparent among our investigations (Table 3).
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Comparison of biological parameters among different waters
In this study, the Mekong River plume and coldeddies might influence the phytoplankton, bacteriaand viruses in 2 ways. First, the freshwater plume orupwelled water may change the physical and trophicconditions, such as temperature, salinity, nutrients anddissolved organic matter. Second, the plume or sub -surface bacterial and viral communities mix with thesurface ocean communities and form non-steady-statebacterial and viral communities.
The fact that no significant differences were found inchl a, bacterial and viral dynamic parameters betweenCE I and II (Tables 1, 2 & 4) implies that the cold eddiesformed in summer in the SCS may stimulate similarbiological responses. From significantly lower chl aand bacterial abundance in the OO water — comparedto the CE I, CE II and MR plume (Tables 1 & 2) — andelevated viral abundance, viral decay rates, viral pro-duction and VDR/VP with enhanced nutrients, it is evi-dent that substrate input (nutrients and organic matter)from the deep layer (cold eddies) or the river plumeenhanced the standing stock of the microbial commu-nity (primary producer, bacteria and viruses).
Significantly lower bacterial growth rates, m, m/μ,and BPLoss in the MR plume, compared to CE I, CE IIand the OO water, and no significant differencesamong the latter 3 waters (Table 4), indicate that bac-terial and viral activities have distinct responses to theupwelling of cold subsurface water (stable activity)and the freshwater plume (decreasing activity). It isknown that nutrients and dissolved organic matterupwelling from the deep sea are mineralized from theparticulate organic matter sinking from the upperlayer, and might be as homogeneous as those in theupper layer. In contrast, nutrients and dissolvedorganic matter from freshwater sources are quite heterogeneous when compared with those present inoceanic water. Because freshwater and marine envi-ronments have very different bacterial communitystructures (Cottrell & Kirchman 2004), we speculatethat the influence of freshwater on changes in the insitu bacterial community structure would be dramaticand it would take time to form a mature (steady state)community for the mixed water, while the influence ofdeep water should be a mild and equilibrated process.The adjustment of bacterial and viral communities inthe MR plume could cause low bacterial and viralactivities. Therefore, when compared with the OOwater, the homogeneousness of nutrients and dis-solved organic matter might explain the constant bac-terial and viral activities in CE I and II, whereas theheterogeneousness might explain the decrease in bac-terial and viral activities in the MR plume. Another
explanation for the constant bacterial and viral activi-ties in CE I and II is simply that the subsurface coldwater did not reach the surface, and thus had limitedimpact on bacterial community structure or physiology.In short, the stability of the environments in questionmight determine bacterial and viral activities.
CONCLUSIONS
This study provides data on comprehensive viral dy-namic parameters simultaneously, including viral pro-duction, viral decay rate, virus-induced bacterial mortal-ity rate, viral turnover time and burst size in the westernSCS; the sampling area consisted of different physicalsettings — from mesoscale cold eddies to the influencesof a freshwater plume. Modest viral production and viraldecay rates were observed, and viral production washigher in mesotrophic waters (freshwater plume andcold eddies) compared with oligotrophic SCS waters.Within the temperature gradient generated by the coldeddies or the freshwater plume, viral decay rate and theproportion of viruses removed by colloidal and heat-la-bile substances showed a significantly negative responseto low temperature. Viral lysis was one of the most im-portant causes of bacterial mortality in the western SCSin summer, and the virus-induced bacterial mortality ratewas determined by bacterial activity. We found that thebiological standing stock was largely determined by thetrophic status, while bacterial and viral activities mightbe influenced by the stability of waters. Because themechanisms controlling viral dynamics in different ma-rine environments are still not clear, more studies areneeded to better understand the role of viruses in bio-geochemical processes.
Acknowledgements. The authors thank Xiamen Universityfor providing the opportunity to attend the cruise. Thanks toProf. Minhan Dai and Jianyu Hu for providing the hydro-graphic and nutrient data and Tingwei Luo for technical sup-port of flow cytometry. Special thanks are given to the 3anonymous peer reviewers because their critical commentshave greatly improved the quality of this manuscript. Thiswork was supported by Hong Kong RGC (RGF grantsHKUST661407 and 661809) and the TUYF Charitable Trust(TUYF10SC08) to HL. We also acknowledge the support fromthe Ministry of Science and Technology of China through theNational Basic Research Program (2009CB421203).
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