Limnol. Oceanogr., 40(7), 1995, 1236-1242
8 1995, by the American Society of Limnology and Oceanography, Inc.
Viruses and protists cause similar bacterial mortality in
coastal seawater
Jed A. Fuhrman and Rachel T. Noble
Department of Biological Sciences, University of Southern California, Los Angeles 90089-037 1
Abstract
Mesocosms filled with 80 liters of coastal seawater from Santa Monica, California, were used twice (June
and November) to budget bacterial production and loss, as well as to assessthe relative significance of viral
lysis and Protist grazing in bacterial mortality. Bacterial abundance was -6 x 1O9cells liter-l in June and
2 x 10” in November, with viral abundances -2 x lOlo particles liter-l in June and 1.5 x IO’O in November.
Incorporation of [3H]thymidine and leucine yielded essentially identical production estimates and allowed
calculation of total bacterial mortality in these closed systems. Bacterial growth rates were l-2 d--l in June
and l-3 d-l in November. Three independent lines of evidence indicated that bacterial mortality attributed
to grazing by protists was about equal to that attributed to viruses: size fractionation of disappearance of
labeled DNA, with a 50% reduction after protists were removed; comparison of Protist grazing rates estimated
with fluorescently labeled bacteria and virus production-based bacterial lysis rates, with 40-50% of the total
ascribed to viruses; and model-based interpretation ofthe 3.3-4.6% ofbacteria visibly infected with assembled
intracellular viruses, suggesting that 24-66% of loss is due to infection. Redundant production and loss
measurements as well as the independent loss process estimates agreed within -30%, yielding a reasonably
balanced budget. We believe the loss of bacteria to viruses reflects a significant dissipation of energy in this
ecosystem and that viruses and protists contribute similarly to bacterial mortality.
It is well established that a significant fraction of the
total carbon flux in marine planktonic systems passes
through free-living bacteria (Azam et al. 1983; Cole et al.
1988; Fuhrman 1992). It was earlier thought that virtually
all of this production passes on to higher trophic levels
via protists, primarily small flagellates, grazing on bacteria (see McManus and Fuhrman 1988; Pace 1988).
However, in recent years it has been recognized that infection by viruses may contribute significantly to bacterial
loss (Bergh et al. 1989; Proctor and Fuhrman 1990; see
also Fuhrman and Suttle 1993). The action of bacterial
viruses in planktonic systems can have several far-reaching impacts on the system as a whole. It has been theorized
that viral infection of bacteria leads to a “futile cycle” or
“short circuit” of C flow between the bacterial, viral, and
dissolved organic C (DOC) compartments, with substantial respiratory losses as the cycle turns (Bratbak et al.
1990; Fuhrman 1992).
As an example of the potential impact of bacterial viruses, a quantitative model of such a cycle embedded in
a steady state food web suggested that inclusion of a bacterial loss term from viruses equivalent to the loss from
protists leads to 27% increases in bacterial production
and respiration but a 37% decrease in export of bacterial
carbon to protistan grazers and a 7% decrease in macrozooplankton production (Fuhrman 1992). Thus, viral
activity shifts more production and respiration into the
bacteria and away from other heterotrophs. Viruses also
Acknowledgments
Thanks to Robin M. Wilcox, Alison A. Davis, and Ximcna
A. Hernandez for assistance with measurements and ideas for
improvements on the mesocosms and to M. Weinbauer for
suggestions regarding the manuscript.
This work was supported by NSF grant OCE 92-l 8234 and
USC Sea Grant.
affect the species composition and diversity of the bacterial community because viruses are usually highly specific for certain hosts (Fuhrman and Suttle 1993), but a
given Protist can graze on a variety of bacterial species.
Overall, the impact of the viruses on these food-web processesdepends on how the bacterial losses from viruses
compare to those from protists.
Recent studies have attempted to quantify the extent
of bacterial mortality caused by viruses and the relative
significance of viruses and protists to the loss of bacteria;
the results have suggested losses from viral lysis are in
the same range as those attributed to Protist grazing (Proctor and Fuhrman 1990, 1992; Suttle et al. 1992). However, these studies rely on models or other indirect measures, and individual studies have each used only a single
method and have been unconfirmed by other means; as
a result, these previous studies are not as convincing as
they might have been. Also, Bratbak et al. (1992) tried
to balance bacterial production against estimates of loss
due to viruses, but their study yielded loss estimates that
were inexplicably several times higher than production.
Clearly, there is no consensus on this issue. Our purpose
was to make as complete a budget as possible of bacterial
growth and loss by independent, redundant measurements and to provide direct comparisons of losses attributable to viruses and protists. We found that such
budgets balanced reasonably well and that viruses and
protists were responsible for similar amounts of bacterial
loss in the southern California coastal environment we
examined.
Methods
Sample collection -Samples were collected by acidrinsed bucket from Santa Monica Pier (34”05’N,
1236
Causes of bacterial mortality
118”3O’W), transported to our lab, and put (within 1 h)
into a Nalgene polyethylene 80-liter carboy that had been
rinsed with 5% HCl and then rinsed several times with
seawater. A long-shaft plastic propeller (3-cm diam) was
mounted through the cap and rotated at 200-300 rpm in
the middle of the carboy. The carboy was placed outside
in a seawater-temperature-controlled polyethylene bath
in full sunlight. Mesocosm 1 was started on 15 June 1993
at 1745 hours and mesocosm 2 on 17 November 1993
at 1200 hours. Samples were removed through a medicalgrade silicone siphon that was cleared of several tubing
volumes before each use.
Bacterial growth rates -Thymidine and leucine incorporation methods were modified from Fuhrman and Azam
(1982), Kirchman et al. (1985), and Simon and Azam
(1989). At each time point, duplicate 42-ml samples and
1% Formalin-killed controls were subsampled into wellrinsed sterile 50-ml polypropylene tubes (VWR brand).
Samples were inoculated with 5 nM [methyZ-3H]thymidine
or [3,4,5-3H]leucine (both from DuPont New England
Nuclear). Subsamples were incubated in the lab at seawater temperature in a fluorescent-lighted (during daytime, dark at night) incubator. After 30-min incubations,
duplicate 20-ml samples from each tube were passed
through HAWP Millipore filters (mixed cellulose acetate
and cellulose nitrate, 0.45-pm nominal pore-size) in cold
stainless steel filtration funnels in a lo-place manifold
(Hoefer Scientific). Filtration valves were then closed, and
2 ml of ice-cold 5% trichloroacetic acid (TCA) was added.
After 2 min, the TCA was filtered through, and the filters
and funnels were rinsed 3 times with 1 ml of cold 5%
TCA; the funnels were then removed, and the edge of the
filters was rinsed 3 times with 1 ml of 5% TCA. Filters
were placed in a glass 20-ml vial, and 1 ml of 1 N HCl
was added; the filters were heated to 90-100°C for 1 h
(to hydrolyze the nucleic acids). After the vials cooled, 5
ml of Ecoscint (National Diagnostics) was added, and the
samples were counted by liquid scintillation with quench
correction (Packard). Conversion factors used to calculate
production from the moles of thymidine or leucine incorporated were the averages reported by Fuhrman and
Azam (1982) at 2 x 1Oi* cells produced per mole thymidint incorporated and those reported by Chin-Leo and
Kirchman (1988) and Kirchman (1992) at 1.5 x 10” cells
produced per mole leucine incorporated.
Virus counts-Viruses were counted by ultracentrifuging (120,000 x g, 3 h, 20°C) 4-ml seawater samples onto
carbon-stabilized Formvar-coated 200-mesh copper grids
(Borsheim et al. 1990; Cochlan et al. 1993) and subsequently staining the grids with 1% uranyl acetate for 30
s. Viruses were counted on a JEOL 100 CXII transmission
electron microscope (TEM), and taper corrections were
implemented into final calculations (Suttle et al. 1992).
Viruses were typically counted at 27,000 x and the bacteria at 10,000 x at 80 keV. The percentage of bacteria
infected with visible viruses was determined two ways.
In the first method, thin sections were prepared from
bacteria on filters that had been preserved and embedded,
1237
then sectioned, as described by Hennes and Simon (1995).
This method involves filtering 100 ml of preserved buffercd (0.05 M sodium barbital, 0.025 M sodium acetate,
pH 8.2, 1% glutaraldehyde) seawater onto a 13-mm-diameter Irgalan black-stained Sartorius cellulose-nitrate
filter with the filtration area restricted to 20 mm2 to concentrate the bacteria in one location. The filter is then
enrobed in ultralow-melting agarose, sliced into 0.5-mm
strips, postfixed with 2% 0~0, for 2 h, dehydrated with
a graded ethanol series, infiltrated and embedded in Spurrs
low-viscosity embedding medium, cut into ultrathin sections, stained in 1% uranyl acetate and 1% lead citrate
for 20 min each, and viewed by TEM at 80 keV. Cells
with three or more visible viruslike particles inside were
counted as infected (Proctor and Fuhrman 1990).
The second method used TEM to look through the
bacteria at the relatively high acceleration voltage of 100
keV to enumerate the number of bacteria in the sample
that contained virus-shaped particles, as described by
Weinbauer et al. (1993). Only those bacteria with resolution great enough to enable visualization of the interior
of the cells were counted. Bacteria containing five or more
viruses were counted as infected.
Cell counts-Bacteria were directly counted from 2%
Formalin-preserved samples by epifluorescence microscopy with acridine orange stain (Hobbie et al. 1977) and
an Olympus Vanox microscope. Bacteria were also counted by TEM from the samples prepared for virus counts.
Protists were counted from 1% glutaraldehyde-preserved
samples by epifluorescence with proflavine stain (Haas
1982); counts included estimates of size (to the nearest
pm) and presence or absence of chlorophyll fluorescence.
Virus production -Virus production was estimated by
thymidine incorporation into TCA-insoluble < 0.2 pm
DNAse-resistant material by a method modified from
Steward ct al. (1992b). Duplicate 50-ml samples were
collected into conical 50-ml tubes, and [3H]thymidine
was added to 5 nM final concentration. Subsamples (7
ml) were collected at 0, 6, 15, and 24 h. From each subsample, 5 ml was filtered through a 0.2~pm Acrodisc (Gelman), and the filtrate was split into duplicate 2-ml samples. Nucleases (10 ~1 each from stocks containing 1 unit
DNase I, 1 unit RNase, or 5 units Micrococcal nuclease
per ~1) were added to the 2-ml subsamples, which were
then incubated at room temperature for 1 h. Then 40 ~1
of Formalin was added to each to stop the enzymes, and
the samples were refrigerated. Within 1 d, each tube was
subsampled into duplicate 900~~1volumes in 2-ml microfuge tubes on ice, and carrier solution (50 pg ml- l each
DNA, RNA, and BSA) was added to each. To each subsample, 300 ~1of cold 20% TCA was added; one duplicate
remained on ice, and the other was incubated at 100°C
for 1 h. After the l-h incubations, the hot sample was
cooled on ice for 10 min. Tubes were shaken hard to
resuspend precipitates and the suspensions passedthrough
25-mm HA Millipore filters. Tubes were rinsed with 1
ml of 5% TCA that was then poured through the filter.
Filters were rinsed and counted as described above for
1238
Fuhrman and Noble
I
Mesocosm 1
30
A
n
I
Viruses
:
Bacteria
r\
”
20
30
40
50
20
30
40
50
a
3
Mesocosm 2
16~10~
10
time (hours)
Fig. 1. Bacterial and viral abundances in mesocosm 1 and
mesocosm 2.
thymidine incorporation. Control samples were killed with
1% Formalin. Conversion of DNA incorporation values
(cold TCA-hot TCA) into virus production used the factor
of 6.17 x 1020 viruses produced per mole thymidine incorporation previously determined for coastal southern
California (Steward et al. 1992a).
Mortality rates -The total bacterial mortality rate (and
its size fractionation) was estimated by the method of
Servais et al. (1985), in which bacterial DNA is “pulselabeled” and then the decline in labeled bacterial DNA
is monitored over a 2-d period. The pulse labeling was
done by adding a low level of [3H]thymidine (0.5 nM) to
a I-liter subsample in a polypropylene bottle which was
incubated in the water bath next to the large carboy. .At
periodic intervals, duplicate 40-ml samples were taken
and extracted with TCA (as for thymidine incorporation).
Radioactivity was counted to determine when the incorporation peaked (an indication that labeled substrate was
exhausted). Further changes in incorporation represent
degradation of DNA due to mortality. When the incorporation peak was confirmed by successive measurements (after - 10-20-h total incubation), the sample was
divided, and half was gravity filtered through a 47-mmdiameter Nuclepore filter (1 .O-pm pore-size in June, 0.6pm in November) to remove protists. The two half-samples were incubated side-by-side, subsampled, and ex-
tracted with TCA periodically. Mortality was calculated
from the decay rate of TCA-insoluble label determined
by linear regression of the decline of the log of radioactivity with time.
Bacterial mortality from Protist grazing was determined twice in each mesocosm by disappearance of fluorescently labeled bacteria (FLB; Sherr et al. 1987) as per
Pace et al. (1990). FLB were produced from Santa Monica
Bay seawater by concentration with an Amicon 30-kD
spiral cartridge unit and heat treatment at 60°C to kill
and stain the cells with 5-([4, 6-dichlorotriazin-2YL]amino) fluorescein. FLB were added to a 1-liter subsample taken from the mesocosm, with initial FLB concentration of 0.9-1.3 X lo5 cells ml-l as determined by
unstained epifluorescence counts (blue excitation). Slides
were prepared within 1 d of sampling. FLB concentrations
were measured every 6-8 h. Controls were alternately
killed with 1% Formalin and prefiltered through a Nuclepore filter (1.0 pm in mesocosm 1 and 0.6 pm in mesocosm 2). The mortality rate was estimated from the
best fit (linear or exponential) to the decline of the FLB
abundance with time, corrected by subtracting the decline
in controls. Because FLB are nonmotile (heat killed), their
removal by grazers underestimates grazing rates when the
actual prey are motile (Landry et al. 199 1; Gonzalez et
al. 1993). This underestimate ranges from a factor of 2.2
for slow-moving prey to 2.7 for fast-moving prey (with
natural assemblages of grazers). Because we do not know
the relative abundances of nonmotile, slow, and fast bacteria in natural assemblages, we have assumed an equal
proportion of the three types and have applied a correction factor of 2 (i.e. the FLB disappearance rates were
doubled to estimate Protist grazing of natural bacteria).
Live stained bacteria (Landry et al. 1991) were not used
because they could divide or become infected with viruses
over the 2-d experiments.
Results
Bacterial, viral, and nonpigmented nanoplankton abundance-In mesocosm 1, bacterial abundance stayed relatively constant at 5-7 x lo9 cells liter-l (avg, 6.5 x 109).
Viruses were more dynamic, ranging from 1.5 to 3 x 1O’O
particles liter - I, and tended to be lowest in the middle
of the experiment (Fig. 1). In mesocosm 2, the pattern
was opposite; bacterial abundance ranged from 1 to
2.5 x lo9 cells liter-’ (avg, 1.88X 109) and viruses were
more constant, ranging from 1.2 to 1.8 x lOlo particles
liter-l (Fig. 1). In both experiments, bacterial counts by
epifluorescence were statistically indistinguishable from
counts by TEM (t-test, P > 0.05). Potentially bacterivorous Protist counts at the beginning of the experiments
showed 5.9 x 1O6nonpigmented nanoplankton liter- l with
an average equivalent spherical diameter of 3.2 pm in
mesocosm 1 and 6.1 x lo6 nonpigmented nanoplankton
liter-l with an average equivalent spherical diameter of
3.6 ym in mesocosm 2.
Bacterial production - In both mesocosms, bacterial
production as measured by thymidine incorporation was
very close to that measured by leucine incorporation (Fig.
1239
Causes of bacterial mortality
2). Bacterial specific growth rates (production divided by
bacterial abundance) were - l-2 d- * in mesocosm 1 and
-0.5-3 d- 1 in mesocosm 2. Average production rates
were 3.08 -t 0.14 x 1O8cells liter-l h-l (mean + SE of the
mean) in mesocosm 1 and 1.48 +0.09 x lo8 cells liter- L
h-l in mesocosm 2. Calculated as average hourly specific
rates to compare with loss estimates, these correspond to
4.7% h-l in mesocosm 1 and 7.9% h-l in mesocosm 2.
Bacterial loss-The loss of bacteria was calculated several ways. First, because the mesocosms were closed systems, we could calculate a loss rate from our knowledge
of production and changes in cell abundance from the
equation
Loss = production - (Nt - No).
(1)
N, and No are bacterial abundance at the end and beginning of the time period over which the production and
loss occur. These loss rates tended to follow the production rates because changes in bacterial abundance were
generally smaller than the bacterial production rate. The
average loss rates were 4.4+0.3% h-l in mesocosm 1 and
8.3 +0.8% h-l in mesocosm 2 (Table 1). When compared
to the average production estimates, these rates correspond to the observed small net increase in bacterial
abundance in mesocosm 1 (i.e. production slightly exceeded loss) and a small net decrease in mesocosm 2.
Second, we estimated total loss and size-fractionated
rates for the experiments that measured the disappearance of labeled DNA. In mesocosm 1, the decline in the
unfiltered sample was twice that seen in the l-pm filtrate
(Table 1, Fig. 3). Therefore, removal of protists eliminated half of the loss, suggesting roughly equal contributions of protists and viruses (or other nonprotist loss
mechanisms). In mesocosm 2, it was more difficult to
estimate the rates because the radioactivity was initially
flat, then declined rapidly, followed by a slower decline
(Fig. 3). This pattern does not follow the expected model,
so its application is questionable. If one uses the sharp
2-point decline between 25 and 29 h only, it was 8.2%
h-l in the unfiltered water and about the same in the 0.6pm filtrate. The similarity of the two rates in mesocosm
2 suggeststhat most loss is not due to protists, but we do
not have much confidence in this result because the filtration removed 75% of the label, declines were short
lived, and the estimates were based on only two data
points for each calculation.
6.0 x 108,
Mesocosm
t
1
0
0
10
20
0
10
20
30
40
50
30
40
50
0
time (hours)
Fig. 2. Bacterial production in mesocosm 1 and mesocosm
2 as determined by incorporation
of [3H]thymidine
and
[3H]leucine.
Third, we estimated loss due to Protist grazing directly
from the corrected decline in FLB, which was -2% h-l
Eigm;;ocosm 1 and - 3.1% h- ’ in mesocosm 2 (Table 1,
. .
Fourth, once each day we estimated loss due to viruses
directly from virus production. These production rates
were 1.78+0.12 and 1.61-r-0.1 1 x 10” viruses liter-’ h-l
in mesocosm 1 and 1.05+0.07 and 1.03*0.05x log in
mesocosm 2. These rates require an estimate of burst size
to convert them to bacterial mortality estimates. We estimate from our TEM observations of infected bacteria
that the burst size in these samples is about 20 virus
Table 1. Bacterial loss rates in % h-l, expressed as mean k SE (ND-not
determined).
Process
“Total” loss rate
Minimum loss estimate
Minimum loss without protists
Protist-caused loss
Method
1. Production (TdR and Leu) - A AODC
2A. Disappearance of labeled DNA
2B. Same < 1 pm
3. Disappearance of FLB
Virus-caused loss
4. Virus production/burst size
Protists + viruses
Sumof
+ 4
* No SE calculated, estimate is from line generated by only two time points.
Mesocosm 1
4.4-10.3
2.4kO.2
1.2kO.2
1.8kO.02 day 1
2.2kO.02 day 2
1.4kO.l day 1
1.2kO.l day 2
3.3(75% of total)
Mesocosm 2
8.3kO.8
8.2”
ND
2.8f0.2 day 1
3.4kO.2 day 2
2.8kO.2 day 1
2.7kO.l day 2
5.9(7 1% of total)
1240
Fuhrman and Noble
5.9
Mesocosm
LE
B
a
Mesocosm
1
57
0
-7gy-7
I.
0
0
0
E
A
2
E
1 .O-pmfiltered
control
Formalinkilled
control
5
5.5
1
4.9
A
5.3
t
I
4.8
10
20
30
40
’
0
50
10
20
30
40
50
5.1
1
4.9
E
2
4.7
8
4.5
4.8 1
0
10
20
30
time
40
50
(hours)
Fig. 3. Decline in tritiated TCA-insoluble material (largely
bacterial [7H]DNA) produced by bacterial incorporation from
a tracer level of[JH]thymidinc added at time zero and exhausted
from the media before declines could bc measured. Mesocosm
1-whole seawater and < 1-pm size fraction. Mesocosm 2whole scawatcr and <0.6-pm size fraction. In mesocosm 2, only
the sharp decline between 25 and 29 h was used for calculation
(see tcw).
particles, which implies that lysis of the bacterial population was 1.3% h-* in mesocosm 1 and 2.8% h-l in
mesocosm 2 (Table 1).
Fifth, we could also estimate the proportion of loss due
to viruses from the fraction of bacteria containing mature
assembled viruses. The embedded samples from mesocosm 1 were unusable due to a problem with the embedding medium. At the beginning of mesocosm 2, 3.3% of
the bacteria appeared infected; about midway (25.8 h)
through mesocosm 2, 4.6% of the bacteria appeared infected. The model of Proctor and Fuhrman (1990) and
Proctor et al. (1993) suggeststhat the proportion of total
mortality that can be ascribed to viruses is about equal
to the percentage of visibly infected bacteria multiplied
by a factor ranging from 7.4 to 14.3. Therefore, the initial
proportion of infected bacteria in mesocosm 2 is estimated to be 24-47%, and in the middle of the experiment,
34-66%.
43
0
10
20
30
40
50
60
time (hours)
Fig. 4. Disappearance of fluorescently labeled bacteria (FLB).
Mesocosm 1-in the first experiment (O-22 h), Formalin-kilicd
control (0) vs. untreated seawater(A). In the second experiment
(22-46 h), 1.0~pm-filtered control (0) vs. untreated seawater
(A). Mesocosm 2-in the first experiment (O-35 h), Formalinkilled control (0) vs. untreated seawater (A). In the second cxperiment (28-52 h), 0.6-pm filtered control (0) vs. untreated
seawater (A).
Because of the difficulty with embedding medium in
mesocosm 1, we tried the alternative method of looking
through whole bacteria (Weinbauer et al. 1993). With
these samples, we found that the visual resolution of this
approach was much lower than when we used thin sections, and it was more difficult to identify cellular inclusions as viruses. Therefore, we used a different criterion
to consider a cell “infected”: five rather than three viruses
per bacterium. The observed proportions of visibly infected bacteria were 1.8-2.9% in mesocosm 1 and 0.71.5% in mesocosm 2 (counted at the same sample times
as the thin sections). It is likely that some infected cells
were missed in the whole-cell approach because of variations in staining and cell thickness. Also, some infected
cells may disrupt at the centrifugation speed used here
(M. Weinbauer pers. comm.). Because of potential differences between analysis of thin sections and whole cells
Causes of bacterial mortality
as well as the different criteria in scoring cells, the model
of Proctor and Fuhrman (1992) was not applied to wholecell results.
Virus turnover-Although
it was not one of our primary
goals, our data permit estimation of virus turnover rates
in these samples. The virus production rates imply virus
turnover rates averaging 9% h-l in mesocosm 1 and 7%
in mesocosm 2.
Discussion
Agreement from redundant independent measures lends
considerable strength to a conclusion, and we have incorporated such redundancy into our study. The epifluorescence and TEM counts of bacteria were essentially
identical, confirming the accuracy of the method by which
we converted TEM observations into abundances; because such counts are straightforward, we assume they
are accurate. The bacterial production estimates from
thymidine and leucine were also essentially identical, and
each used independently determined conversion factors;
therefore, we believe these values are correct. However,
because the accuracy of bacterial production measurements is not always obvious (see Ducklow and Carlson
1992), it is still possible that both measurements are incorrect by exactly the same amount. We will still assume,
however, that the bacterial production measurements are
accurate. The bacterial production values determined over
time allow direct calculation of bacterial losses from the
mesocosms (method 1 above) which we use as a baseline
to compare the other methods (Table 1).
One other method yielded direct estimates of total bacterial mortality (disappearance of labeled DNA- method
2A and B), but this method is expected to produce an
underestimate because the death of bacteria does not necessarily entail degradation of its DNA (Servais et al. 1989).
Grazers and even viruses can retain the label in TCAinsoluble macromolecules in a way that masks the mortality from measurement by this method. Indeed, this
approach yielded lower values than the baseline method
1 in both mesocosms; the mortality from mesocosm 1
was only 55% of the “total,” and mortality from mesocosm 2 was a nearly identical 99% (but only from two
data points).
Size fractionation of the DNA disappearance showed
that in mesocosm 1, the rate of mortality in the l-pmfiltered sample was half the rate of the unfiltered sample.
Filtration removes all but a tiny portion of the protists
(and those that pass are extremely small and unlikely to
bc significant grazers). If we make the simplifying assumption that the mortality processes of the < l-pm and
> 1-pm bacteria are similar, this suggeststhat protists are
responsible for about half the mortality and that agents
passing a 1-pm filter, presumably viruses, are responsible
for the other half. In mesocosm 2, the decline did not
follow the expected model (the rate declined drastically
with time), and the slopes were similar in unfiltered and
filtered samples over the short time frame. However, be-
1241
cause only 25% of the label passed the 0.6-pm filter and
only two data points were used, it does not seem reasonable to extrapolate this rate to the entire bacterial community. It does appear that viruses were important mortality agents in this experiment as well.
Servais et al. (1985, 1989) developed and used the disappearance of labeled DNA to estimate mortality; they
used 2-pm filtrations to partition loss between protists
and other causes. Typically, they found about half the
mortality was removed by the filtrations in coastal Belgian and Mediterranean waters, similar to our results.
However, because protists sometimes include individuals
that can squeeze through 2-pm filters, especially in less
productive waters, Servais et al. were not certain how to
interpret their size-fractionation results. They also concluded from a lab experiment (with lysogenic Escherichia
co/i) that lysis by phage is not detectable by this method.
However, we suggestthat due to the great variety in phage
nutrition and regulation of host nucleic acids (Ackermann
and DuBow 1987), some phage-induced mortality is
probably detectable immediately and some may require
degradation of the phage before detection. With the rapid
phage turnover we observed, the results probably still
indicate much=of the mortality. Therefore, this method
probably underestimates the contribution of phages to
bacterial mortality by an unknown amount.
The other mortality measurements were for specific
mechanisms, and we can sum them together so as to
budget the total and attribute specific fractions of the
mortality to particular mechanisms (Table 1). The FLB
disappearance should be attributed to Protist grazing (the
FLBs were heat killed to prevent infection), and the virus
production measurements have been converted directly
into bacterial mortality estimates. In mesocosm 1, these
two estimates add up to 75% of the “total” mortality
calculated from method 1. Of the subtotal, Protist grazing
is estimated to make up 6 1% and viral lysis 39%. In
mesocosm 2, the FLB + viral production-based estimated add up to 7 1% of the “total,” with the grazing
making up 53% of the subtotal and viral lysis 47%. Therefore, we can specifically account for 70-75% of the total
apparent mortality with these two mechanisms, and each
mechanism appears to contribute a comparable amount.
Given the uncertainties in such measurements, we believe
the results are surprisingly consistent. The underestimate
in accounting for the total could be due to errors in our
determinations or to possible other mortality mechanisms, such as antibiosis, about which we have no data.
Three independent lines of evidence lead us to conclude
that viruses and protists cause comparable rates of bacterial mortality in this system. First, the size fractionation
of labeled DNA disappearance, indicating 50% ascribed
to protists and 50% to viruses. Second, the partitioning
of total mortality between grazers (measured by FLB) and
viruses (measured by virus production), indicating 4050% due to viruses. Third, the model-based interpretation of the percentage of bacteria seen in thin sections
with assembled viruses inside, indicating 24-66% due to
viruses. It therefore seems clear that mortality from viral
1242
Fuhrman and Noble
activity must be considered in studies of bacterioplankton
processes.
References
ACKERMANN, H.-W., AND M. S. DuBow.
1987. Natural groups
of bacteriophages, p. 202. Viruses of prokaryotes, V. 2.
CRC.
A MEYER-REIL, ANDT.
AZAM, F.,T. FENCHEL, J.G.GRAY,L.
THINGSTAD. 1983. The ecological role of water-column
microbes in the sea. Mar. Ecol. Prog. Ser. 10: 257-263.
BERGH, O., K. Y. BBRSHEIM, G. BRATBAK, AND M. HELDAL.
1989. High abundance of viruses found in aquatic environments. Nature 340: 467-468.
BBRSHEIM, K.Y.,G.
BRATBAK, AND M. HELDAL. 1990. Enumcration and biomass estimation of planktonic bacteria
and viruses by transmission electron microscopy. Appl.
Environ. Microbial. 56: 352-356.
BRATBAK,G., M. HELDAL,S.NORLAND, AND T.F. THINGSTAD.
1990. Viruses as partners in spring bloom microbial
trophodynamics. Appl. Environ. Microbial. 56: 1400-1405.
T. F. THINGSTAD, B. RIEMANN, AND 0. H.
HASLUND. ’ 1992. Incorporation of viruses into the budget
of microbial C-transfer. A first approach. Mar. Ecol. Prog.
Ser. 83: 273-280.
CHIN-LEO, G., AND D. L. KIRCHMAN. 1988. Estimating bacterial production in marine waters from the simultaneous
incorporation of thymidine and leucine. Appl. Environ.
Microbial. 54: 1934-1939.
COCHLAN, W.P.,J. WIKNER,G. F. STEWARD, D.C. SMITH,AND
F. AZAM. 1993. Spatial distribution of viruses, bacteria
and chlorophyll a in neritic, oceanic and estuarine environments. Mar. Ecol. Prog. Ser. 92: 77-87.
COLE, J. J., S. FINDLAY, AND M. L. PACE. 1988. Bacterial
production in fresh and saltwater ecosystems: A cross-system overview. Mar. Ecol. Prog. Ser. 43: l-l 0.
DUCKLOW,H. W., AND C. A. CARLSON. 1992. Oceanic bacterial
production, p. 113-l 8 1. In K. C. Marshall [ed.], Advances
in microbial ecology. Plenum.
FUHRMAN, J. A. 1992. Bacterioplankton roles in cycling of
organic matter: The microbial food web, p. 36 l-383. In P.
G. Falkowski and A. D. Woodhead [eds.], Primary productivity and biogeochemical cycles in the sea. Plenum.
AND F. AZAM. 1982. Thymidine incorporation as a
measure of heterotrophic bacterioplankton production in
marine surface waters: Evaluation and field results. Mar.
Biol. 66: 109-l 20.
AND C. A. SUTTLE. 1993. Viruses in marine planktonic
systems. Oceanography 6: 5 l-63.
GONZALEZ, J. M., E. B. SHERR, AND B. F. SHERR. 1993. Differential feeding by marine flagellates on growing versus
starving, and on motile versus nonmotile, bacterial prey.
Mar. Ecol. Prog. Ser. 102: 257-267.
HAAS, L. W. 1982. Improved epifluorescence microscopy for
observing planktonic micro-organisms. Ann. Inst. Oceanogr. 58: 261-266.
HENNES, K. P., AND M. SIMON. 1995. Significance of bacteriophages for controlling bacterioplankton growth in a mesotrophic lake. Appl. Environ. Microbial. 61: 333-340.
HOBBIE, J. E., R. J. DALEY, AND S. JASPER. 1977. Use ofNuclepore filters for counting bacteria by fluorescence microscopy. Appl. Environ. Microbial. 33: 1225-1228.
KIRCHMAN, D. L. 1992. Incorporation of thymidine and leu-
tine in the subarctic Pacific: Application to estimating bacterial production. Mar. Ecol. Prog. Ser. 82: 301-309.
-,
E. K'NEEs, AND R. E. HODSON. 1985. Leucine incorporation and its potential as a measure of protein synthesis
by bacteria in natural aquatic systems. Appl. Environ. Microbiol. 49: 599-607.
LANDRY, M.R.,J.M.
LEHNER-FOURNIER, J.A. SI-JNDSTROM,V.
L. FAGERNESS, AND K. E. SELPH. 1991. Discrimination
between living and heat-killed prey by a marine zooflagellate, Paraphysomonas vestita (Stokes). J. Exp. Mar. Biol.
Ecol. 146: 139-151.
MCMANUS, G. B., AND J. A. FUHRMAN. 1988. Controlofmarine bacterioplankton populations: Measurement and significance of grazing. Hydrobiologia 159: 5 l-62.
PACE, M. L. 1988. Bacterial mortality and the fate of bacterial
production. Hydrobiologia 159: 4 l-49.
-,G.
B. MCMANUS,ANDS. E.G. FINDLAY. 1990. Planktonic community structure determines the fate of bacterial
production in a temperate lake. Limnol. Oceanogr. 35: 795808.
PROCTOR, L. hi., AND J. A. FUHRMAN. 1990. Viral mortality
of marine bacteria and cyanobacteria. Nature 343: 60-62.
-,
AND -.
1992. Mortality of marine bacteria in
response to enrichments of the virus size fraction from
seawater. Mar. Ecol. Prog. Ser. 87: 283-293.
-,
A. OKUBO, AND J. A. FUHRMAN. 1993. Calibrating
estimates of phage induced mortality in marine bacteria:
Ultrastructural studies of marine bacteriophage developmcnt from one-step growth experiments. Microb. Ecol. 25:
’ 161-182.
SERVAIS, P.,G. BILLEN, J. MARTINEZ, AND J. VIVES-REGO. 1989.
Estimating bacterial mortality by the disappearance of 3Hlabeled intracellular DNA. FEMS (Fed. Eur. Microbial. Sot.)
Microbial. Ecol. 62: 119-126.
AND J. V. REGO. 1985. Rate of bacterial mor-,
tahty in aquatic environments. Appl. Environ. Microbial.
49: 1448-1454.
SHERR, B.F.,E.B.
SHERR,AND R.D. FALLON. 1987. Use of
monodispersed, fluorescently labeled bacteria to estimate
in situ protozoan bacterivory. Appl. Environ. Microbial.
53: 958-965.
SIMON, M., AND F. AZAM. 1989. Protein content and protein
synthesis rates of planktonic marine bacteria. Mar. Ecol.
Prog. Ser. 51: 201-213.
STEWARD,G. F.,J. WIKNER, W.P. COCHLAN, D.C. SMITH,AND
F. AZAM. 1992a. Estimation of virus production in the
sea. 2. Field results. Mar. Microb. Food Webs 6(2): 79-90.
D. C. SMITH, W. P. COCHLAN, AND F. AZAM.
19b2b. Esiimation of virus production in the sea. 1. Method development. Mar. Microb. Food Webs 6(2): 57-78.
SUTTLE, C. A., F. CHEN, AND A. M. CHAN. 1992. Marine
viruses: Decay rates, diversity and ecological implications,
p. 153-163. In Int. Mar. Biotechnol. Conf. “IMBC-91.”
Develop. Microbial. Ser. W. Brown.
WEINBAUER, M. G., D. FUKS, AND P. PEDUZZI. 1993. Distribution ofviruscs and dissolved DNA along a coastal trophic
gradient in the northern Adriatic Sea. Appl. Environ. Microbiol. 59: 4074-4082.
Submitted: 14 March I995
Accepted: 9 May 1995
Amended: 19 July 1995