SHIAH, FUH-KWO, AND HUGH W. DUCKLOW. Multiscale variability

Limnol. Oceanogr., 40(l),
0 1995, by the Am&can
1995, 55-66
Society of Limnology
and Oceanography,
Inc.
Multiscale variability in bacterioplankton abundance,
production, and specific growth rate in a temperate
salt-marsh tidal creek
Fuh-Kwo Shiahl and Hugh W. Ducklow
Horn Point Environmental
Laboratory,
Box 775, Cambridge,
Maryland
216 13
Abstract
Heterotrophic bacterioplankton
abundance, production, and specific growth rate in a salt-marsh tidal creek
were measured weekly from April 199 1 to September 1992. During the same period, tidal and diel sampling
studies were performed in May, June, and October 199 1 and May and August 1992. Seasonal variability of
bacterial abundance, production, and specific growth rate was regulated by temperature during nonsummer
seasons when temperature was <2O”C. During summer, bacterial variables were not limited by temperature.
Daily variability of bacterial abundance, production, and specific growth rate was regulated interactively by
tidal mixing, substrate supply, and temperature over several tidal cycles. Higher bacterial abundance, production, and specific growth rate observed at low tide indicated that bacterial growth rate in the tidal creek
was higher than in the adjacent river waters. This pattern might be due to larger nutrient fluxes originating
in the tidal creek. Occasionally, the tidal effect was overridden by temperature during study periods when
temperature changed dramatically over several tidal cycles. Bacterial diel patterns in production and specific
growth rate in the tidal creek occurred only when day-night temperature differences exceeded lO”C, with
maximal values during daytime. Short-term temperature manipulation experiments suggested that diel patterns in bacterial production and specific growth rate were probably caused by temperature, not light intensity.
Thus, temperature regulated both seasonal and diel variations in bacterial production.
Determining
the temporal and spatial variability
in
bacterial abundance and production has become an important issue during the last decade (Ducklow and Carlson
1992; Hoch and Kirchman 1993). However, most studies
have concentrated on longer time (i.e. annual to interannual) and larger spatial (i.e. greater than several hundred kilometers) scales. We know that bacterial properties
tend to be correlated with phytoplankton
biomass (and
productivity)
and temperature at very large scales (Cole
et al. 1988; Hoch and Kirchman 1993; White et al. 199 l),
but it has not been clear at what smaller scales the relationships would also hold. As suggested by Marrase et al.
(1992), the processes affecting microbial activity at the
daily scale might be fundamentally
different from those
at seasonal scales. Seasonal changes in bacterial activity
in a temperate environment
might bc strongly affected
by physical parameters, most notably temperature. On
the other hand, short-term changes in bacterial activity
might be less affected by temperature but may be more
responsive to biological and chemical features, such as
substrate supply, bacterivory, or both.
Estuarine bacteria fluctuate within bounds set by factors that increase (e.g. substrate supply) or decrease (e.g.
bacterivory) bacterial abundance. A prime requisite for
understanding controlling mechanisms is determination
of the spatial and time scales on which interactions take
place, and it is at the smaller scales that mechanistic
relationships are expressed (Dickie et al. 1987). To have
better insight into the relationships among bacteria and
environmental
factors, it is important to study bacterial
variability
on shorter time scales (i.e. hours, days) and
smaller spatial scales. Variability of estuarine and coastal
bacteria, with growth rates of hours to a few days (Andersen and Sorensen 1986; Newell et al. 1988), might be
concentrated at smaller scales.
Lack of correlation between bacterial and phytoplankton biomass and production at seasonal scales has been
observed in estuarine, coastal, and freshwater ecosystems
(see Shiah and Ducklow 1994). These studies suggest that
substrate supply from sources other than phytoplankton
could support bacterial growth and, more importantly,
imply that the seasonal variability in bacterial abundance,
production, and specific growth rate might be regulated
by factors other than substrate supply. Ducklow and Shiah (1993) and Shiah and Ducklow (1993, 1994) showed
that in Chesapeake Bay and a salt-marsh tidal creek adjacent to the Choptank River, bacterial growth was principally limited by temperature and not substrate supply
during the nonsummer season. High temperature dependency and the apparent lack of coupling between bacteria
and phytoplankton
suggested that bacterial growth in
1 Present address: Institute of Oceanography, National Taiwan University, Taipei, Taiwan.
2 Present address: College of William and Mary, School of
Marine Sciences, and Virginia Institute of Marine Sciences, Box
1346, Gloucester Point 23062- 1346.
Acknowledgments
This research was supported by grants from the NOAA-University of Maryland Sea Grant and the NSF Land-Margin Ecosystem Research Program. Support for F. Shiah was provided
by the Department of Education, Taiwan, Maryland Sea Grant,
Horn Point Environmental
Laboratory, and the EPA Multiscalc
Experimental Ecosystem Research Center (HPEL MEERC).
We thank H. Quinby for help with sample analysis and C.
Carlson, D. Kirchman, D. Stoecker, and two anonymous reviewers for commenting on the manuscript.
55
56
Shiah and Ducklow
temperate estuarine areas is probably regulated by temperature but not limited by in situ substrate supply.
Physical factors such as tides, temperature, and salinity
may have a strong impact on bacterial abundance and
activities over tidal and diel cycles. Many studies have
shown a strong diel pattern in bacterial abundance, mean
cell volume, percentage of active bacteria, production,
specific growth rate, and substrate uptake ability in estuarine and coastal areas (Chin-Leo and Benner 199 1;
Fuhrman et al. 1985; Riemann et al. 1984). This pattern
was tightly coupled with the biomass and production of
primary producers, suggesting that the diel pattern of bacteria was primarily driven by the diel cycles of their nutrient sources (phytoplankton,
seagrass, or both) whose
production was highly light-dependent. On the other hand,
Riemann and Sondergaard ( 1984) measured estuarine
bacterial production by [3H]thymidine incorporation into
DNA, dark 14C02 uptake, and frequency of dividing cells
simultaneously;
none of these measurements showed a
diel pattern. Riemann and Sondergaard suggested that in
coastal and estuarine areas, where nutrients might come
from many sources other than phytoplankton,
the lack
of diel pattern between bacteria and phytoplankton
was
not surprising.
Our major purpose was to explore the environmental
factors determining bacterial variability over several tidal
and diel cycles and to compare short-term and seasonal
variability.
We demonstrate that in a salt-marsh tidal
creek, the seasonal, tidal, and diel variability of bacterial
properties were all primarily controlled by temperature,
with nutrient supply playing a less important role in regulating bacterial growth. The temperature dependency of
bacterial specific growth rate was similar across different
estuarine habitats.
Materials and methods
Study site and sampling-The Horn Point salt-marsh
tidal creek is 4 km west of Cambridge, Maryland. It flows
into the Choptank River, which is the largest subestuary
on the eastern shore of Chesapeake Bay (Fig. 1). The creek
is -600 m long, and the mean tidal amplitude is 0.5 m.
The marsh is characterized by mixed vegetation zones,
ranging from Spartina patens and Spartina alterniflora
near the creek mouth to a fresh marsh dominated by
Hibiscus moscheutos at the upper reach, a few kilometers
from the river (Stevenson et al. 1977). The diurnal temperature range is 0-3°C in January and 28-3 5°C in August
(Stevenson et al. 1977). Mean salinity in the pond at
midmarsh ranged from O-4 in January-March
to 1O-l 1
in July-September
1974 (but only 4-7 in July-August
1975) (Stevenson et al. 1977).
For our seasonal study, we measured water samples
from the tidal creek weekly from April 199 1 to September
1992. We collected 56 weekly samples. During the same
period, we performed five diel and tidal studies in May,
June, and October 199 1 and May and August 1992. For
convenience, these are designated Dl D2, D3, D4, and
D5. For Dl, D2, and D3, water samples were taken with
2-liter opaque polycarbonate bottles at low, mid-, and
high tide over several tidal cycles at a 5-m-long, 2-m-wide
spillway (Fig. 1). In D3, water samples from the spillway
and the Choptank River (D3-Chop) were measured simultaneously. The Choptank samples were taken from
the Horn Point Environmental
Laboratory dock in a water depth of - 5 m. For D4 and D5, only low-tide water
samples at the spillway were measured consecutively over
several days. At the 2nd, 3rd, and 4th sampling point of
D4, extra water samples were incubated separately at the
in situ temperature and at the temperature of the previous
sampling point. Timing of the tide was based on the 199 1
and 1992 tide tables published by NOAA. Measurements
were performed immediately after sample collection.
Bacterial abundance- Bacterial abundance was determined by the acridinc orange direct-count method (Hobbie et al. 1977). Samples fixed with glutaraldehyde (final
concn, 1O/o)were stained with acridine orange (final concn,
0.0 1%) for 2 min and then passed through 0.2-pm polycarbonate filters prestained with Irgalan Black solution.
Filters mounted in Cargille type A immersion oil on slides
were enumerated at 1,605 x by epifluorescence microscopy (Zeiss Axiphot) with a 100-W mercury lamp, blue
BP 450-490 exciter filter, and LP520 barrier filter.
Bacterial production - Bacterial production was estimated from [3H]thymidine
(Fuhrman and Azam 1980)
and [3H]leucine (Chin-Leo and Kirchman 1988, Kirchman et al. 1985) incorporation rates. For thymidine (TdR)
incorporation
rates, duplicate or triplicate LO- or 20-ml
aliquots of water samples were incubated with [methyl3H] thymidine (sp act, 20-85 Ci mmol-‘, final concn, 10
nM) in opaque polycarbonate bottles at in situ temperature for 0.5-l .O h. The reaction was stopped by adding
formaldehyde
(final concn, 1%). Killed samples were
passed through 0.2-pm polycarbonate filters, then rinsed
four times with ice-cold 5% trichloroacetic acid and four
times with 80% ethyl alcohol. Ten milliliters
of scintillation cocktail (formula 963, DuPont) were added after
filters had been dried at 35°C overnight in the vials. Radioactivity
was counted by liquid scintillation
counter
(2200CA, Packard). For leucine (Leu) incorporation rates,
water samples were incubated with L-[3,4,5-3H-(N)]leucine
(final concn, 22 nM; 1 nM labeled leucine and 21 nM
unlabeled leucine). Incubation, extraction, and radioactivity measurement procedures were the same as those
for TdR incorporation.
The Leu incorporation rates covaried with TdR incorporation
rate in all studies except
Dl (P > 0.05, but see below). Correlation coefficients
between Leu and TdR ranged from 0.46 (for Dl) to 0.99
(for D2). The Leu data are presented only for the weekly
and D4-D5 studies. TdR incorporation rates normalized
by bacterial abundance were used as indices of bacterial
specific growth rate.
Other measurements-Chlorophyll
was determined
following the method of Parsons et al. (1984). Water depth
at different tidal amplitudes was measured at the spillway
sampling station by a meter stick (+O. 1 m). Salinity was
57
Diel temperature eflects on bacteria
N
I
I
I
77
76
75’
/
N
CHOPTANK
Fig. 1.
RIVER
Map of the Chesapeake Bay region, showing the Horn Point salt-marsh creek study area.
measured in the Dl study in May 199 1 only. Salinity
varied irregularly and was not correlated with tidal height.
Tidal height is used as an indicator of tidal stage.
Data analysis - ANOVA, ANCOVA, and regression
analysis including multiple linear and simple linear regression (model 2, Edwards 1985) were used to analyze
the relationships
among abundance, production,
and
growth rate and other factors, such as temperature and
chlorophyll. Statistical analysis was performed with SYSTAT (Wilkinson et al. 1992). Variances in bacterial abundance, thymidine incorporation rate, and specific growth
rate were equalized by natural-log transformation.
Normality of transformed data was tested by the KomogorovSmirnov test.
Results
Table 1 lists the range of all measured variables in these
studies. The ranges and coefficients of variation (C.V.) of
the weekly data were greater than those of diel and tidal
data. For the seasonal data, bacterial abundance varied
12-fold, and rate parameters (TdR, Leu) varied > 3 orders
of magnitude. For tidal and diel studies, day-night tempcrature difference in some studies was > 10°C (D 1, D4,
and D5). The ratio of the highest to lowest bacterial abundance within the diel experiment ranged from 1.2 (in D4)
to 1.7 (in D3) and was stable across these short-term
studies, but ratios for TdR and Leu were higher, with
values ranging from 2 to 5 for TdR and 3 to 6 for Leu.
Weekly, seasonal, and interannual variability-
Figure
2 shows the weekly changes of measured variables in the
salt-marsh tidal creek. Bacterial abundance, TdR and Leu
incorporation
rates, TdR incorporation rate per cell, and
Leu incorporation rate per cell (not shown) showed strong
seasonal signals (Fig. 2B-E). They were low in winter
(December, January, and February), started to increase
after March or April, attained maxima in summer, and
declined after mid-October. Chlorophyll
ranged from 1
to 34 pg liter- l, basically following same trends as temperature (Fig. 2A, F), except that there was a chlorophyll
maximum in November 199 1 and a bloom in April 1992.
Three points should be noted here. First, week-to-week
variability
within months sometimes was greater than
month-to-month
variability,
especially for the rate parameters. For example, the monthly average for TdR in
May 199 1 (34 l&202 pM h-l, n = 5) was not significantly
different from that for June 199 1 (525 +92 pM h-l, n =
4) (ANOVA, P > 0.05). Note the high variance of the
May data, which ranged from 145 to 64 1 pM h- l. Second,
annual variability was still higher than at the shorter time
scales. Some interannual patterns seem significant even
with data for only 17 months. Bacterial abundance in
April and May 199 1 (3-5 x lo9 cells liter-l) was -2O40% that of 1992 (8-10 x lo9 cells liter-l) (Fig. 2B). The
199 1 abundance maximum was in August (17.5 x lo9
cells liter-l), but in 1992, the maximum was in late May
and was -80% of that of 199 1. In addition, bacterial
abundance during the April-September
1992 period was
less variable than in 199 1. Third, in contrast to bacterial
58
Shish and Ducklow
Table 1. Range (minimum-maximum
observed) and C.V. (%, in parentheses) of measured variables temperature (“C), chlorophyll
(pg liter-l), bacterial abundance (cell, 10” liter-l), TdR and Leu incorporation
rates (PM h-l), and TdR and Leu incorporation
rates per cell (10 -9 pM h-l cell- I) in all studies performed in the salt-marsh creek. (Not analyzed--a.)
Variables
Weekly*
T
0.5/34
(49)
1.3/34.9
1B/27.5
(13)
2.5i5.9
(61)
(20)
Chl
Cell
TdR
Leu
Tdr cell I
Leu cell-’
* April
Dl
1.5i17.5
(47)
l/878
(89)
4/7,650
(89)
0.4/62
(70)
20/660
(75)
199 I-September
4.8/7.2
(10)
139/296
(18)
52 l/3,298
(61)
28/44
(14)
72/458
(55)
D2
D3
D3-Chop
D4
D5
27/3 1
(4)
11.7/27.6
(32)
10.3/14.6
(13)
279/640
19/23.5
(7)
6.2i44.3
(72)
8.5114.6
19/23
17/30
(27)
6.7/l 5.3
(42)
11.6/14
(7)
134/515
(59)
na
23/34
(13)
9.5/28.8
(36)
9.8/14.7
(14)
234/634
(37)
2,252/7,650
(44)
20/55
(33)
195/604
(40)
(28)
1,050/3,63 1
(38)
27/45
(17)
100/254
(27)
(18)
(6)
na
5.8/10.7
(21)
112/521
(45)
845/2,898
(32)
13/36
(27)
99/198
70/158
(33)
325/937
(18)
(22)
(28)
9/17
(21)
54/l 11
1 l/37
(57)
na
1992.
abundance, rate parameters reached minima in November 199 1 before temperature was minimal (January 1992)
and stayed low even after temperature started to increase
(Fig. 2A, C-E). This lag indicates that the rate parameters
were sensitive to low temperature and that bacteria were
growing in excess of removal in all except lowest temperature period (see below).
Table 2 shows the linear correlations among measured
variables. Temperature was positively correlated with all
other variables. Multiple regression was performed to analyze the relative impact of temperature and chlorophyll
on bacterial variables. Table 3 shows that -60% of the
variability for bacterial abundance could be explained by
temperature alone. For rate parameters, the R2 values
were higher, ranging from 70 to 82%. After including
chlorophyll
in the model, all the R2 values increased;
however, the contribution
of chlorophyll to the model in
explaining variability never exceeded 8%. The minor role
for chlorophyll
suggests that bacterial variables in the
creek were controlled primarily by temperature at weekly
to annual time scales (perhaps also true for interannual
time scales).
Table 4 shows the slopes for linear regressions of bacterial variables on temperature at four temperature ranges. The < 10°C data represents winter and spring, the 1O20°C data represents spring and fall, and the >2O”C data
represents summer data. The slopes for all bacterial variables of the < 10°C and lo-20°C data sets were about 23 times of those of the >2O”C data set (ANCOVA, P <
0.05). This difference indicates that the seasonal variability of bacterial properties in the creek was highly correlated with temperature; however, the temperature effect
on these variables was more profound during nonsummer
seasons.
Billen et al. (1990) proposed that at steady state, the
rate of utilization of substrate (i.e. dissolved organic matter) should be very close to the substrate supply rate; thus,
a strong correlation between bacterial production and
bacterial abundance is presumptive evidence for bottom-
up control. A slope ~0.7 suggests strong bottom-up con- ,
trol (Ducklow 1992). The effect of substrate (i.e. bottomup) control on bacterial abundance was analyzed with
linear regressions of log,,bacterial abundance on log,,TdR
at the four temperature ranges noted above that were
performed, and the slope was used as an index of strength
of bottom-up control. The slopes for log,,bacterial abundance on log ,,TdR for them all, < 1O”C, lo-20°C and
>20”Cdata were 0.33~0.08,0.20+0.05,0.29+0.06,
and
0.76 L 0.11. The slope of the > 20°C data was significantly
higher than the other three slopes (ANCOVA, P < 0.05).
These results suggest that temperature and substrate
effects on bacterial biomass might change in different seasons; that is, from fall to spring (cool-water seasons), bacterial biomass was positively correlated with the change
of temperature, and the strength of bottom-up control on
biomass was either weak or nonexistent. In summer, when
temperature was high (>2O”C), substrate supply might
have been the major factor regulating bacterial biomass.
To test this, we plotted the slopes of log,,bacterial abundance vs. log,,TdR (bottom-up control index) against the
slopes of In bacterial abundance vs. temperature (temperature control index). Figure 3 shows that there is a
negative relationship between the bottom-up control index and the temperature control index. The slope (- 10.7)
was very similar to the slope value (- 11.3) presented by
Shiah and Ducklow (1994) for Chesapeake Bay data (ANCOVA, P > 0.05).
Tidal and diel variability- Figures 4-6 show the results
of the diel studies. Choptank River water enters and leaves
the Horn Point salt marsh through the spillway sampling
station over a tidal cycle. Samples collected at different
tidal amplitudes represent different water masses of the
river: high-tide samples represent inflowing river waters,
and low-tide samples represent river waters that stay in
the salt marsh over a tidal cycle (i.e. 12 h). Our sampling
included spring, neap, and intermediate tides. The last
lunar quarter was 7 May 199 1, just before the D 1 study
59
Diel temperature efects on bacteria
800
8000
,
AM
0
d
J JASONDJFMAMJJASAM
1991
Month
J JASONDJFMAMJJAS
1991
1992
Month
1992
Fig. 2. Annual cycles of measured variables in the salt-marsh creek, April 199 1 to September 1992. A. Temperature. B. Bacterial
abundance. C. Thymidine incorporation
rate. D. Thymidine incorporation rate per cell. E. Leucine incorporation
rate. G. Chlorophyll.
of lo-12 May (neap). There was a full moon 27 June
before the D2 study of 30 June-July (spring tide). D5 (3 1
August-5 September 1992) was also during a neap tide.
The tide height cycles shown in Figs. 4A and 5A show
no difference in tidal amplitude between spring and neap
tides for our studies.
One common pattern is shown in the D2 and D3 (June
and October 199 1) studies (Figs. 5, 6). When measured
variables were correlated with tidal height, the correlation
was always negative (Table 5); that is, they were highest
at low tide and lowest at high tide, suggesting that bacterial abundance, production
(i.e. TdR), and specific
growth rate (i.e. TdR cell-l) in the Choptank waters that
entered and stayed in the salt marshes over 12 h were
higher than those in the river waters outside the salt
marshes. This pattern of higher abundance in the marsh
Table 2. Matrix of correlation coefficients for the measured variables (as in Table 1) for
the weekly sampling, April 199 l-September 1992. All variables were natural-log-transformed
except temperature (T). (Not analyzed--a.)
All values shown are significant at P < 0.05.
Variables
T
Cell
TdR
LCXl
Tdr cell-’
Leu cell- *
Chl
T
***
Cell
TdR
LeU
0.76
***
0.90
0.82
***
0.90
0.78
0.97
***
TdR cell-’ Leu cell-’
0.86
na
na
na
***
0.84
na
na
na
na
***
Chl
0.58
0.64
0.64
0.66
0.36
0.38
***
60
Shiah and Ducklow
Table 3. Coefficient of determination
(R2) values for multiple linear regressions of natural-log-transformed
bacterial
properties (abbreviations as in Table 1) on temperature (T) and
chlorophyll. All values are significant at P < 0.05.
Bacterial
variables
Cell
TdR
LkXl
TdR cell- I
Leu cell-’
R2 for T
R2 for T
and Chl
% increase
0.57
0.82
0.81
0.74
0.70
0.61
0.83
0.84
0.76
0.74
7
2
3
3
3
can be further confirmed by comparing D3 and D3-Chop.
Figure 6C-E shows that bacterial variables of D3-Chop
did not vary much over several tidal cycles (see also Table
6), and they were all lower than those of D3 except at
high tide, when bacterial variables were very close to each
other, as expected.
Table 5 also indicates that bacterial abundance, TdR,
and TdR cell- I of D 1 were not correlated with tidal height
(e.g. Fig. 4), suggesting that some factor other than tide
might affect bacterial variability
in this study. Multiple
regression analysis showed that bacterial abundance, TdR,
and TdR cell-l of D 1 were positively correlated with
temperature (n = 17, P < 0.05). Temperature explained
22% (bacterial abundance), 4 1% (TdR), and 27% (TdR
cell-l) of the variability of these three variables. Leu and
Leu cell-l were not correlated with temperature (n = 17,
P > 0.05, but see below). When tide was included in the
analysis, R2 values of abundance, TdR, and TdR cell-l
increased to 3 1, 50, and 30%. Tide did not contribute
much to explaining bacterial variability,
suggesting that
temperature sometimes can have an overriding effect on
bacterial growth parameters. Data on the relationship between bacterial variables and other environmental factors
besides tide must be analyzed from the same tidal regimes, because samples collected at different tidal amplitudes represent different water masses. Chlorophyll
concentration in the low-tide waters was not significantly
different from those of high-tide waters in Dl, D2, and
D3 (ANOVA, P > 0.05).
To examine whether there was a diurnal effect in the
D 1, D2, D3, and D3-Chop studies, we compared bacterial
variables measured during the day and at night. There
were two criteria for comparing these data: they had to
be at the same tidal stage, and in situ temperature difTable 4. Slopes for natural-log-transformed
bacterial variables (as in Table 1) on temperature (7’) for four temperature
ranges in the creek, April 199 l-September
1992. Not significant--s;
otherwise, significant at P < 0.05.
T range
W)
All
<lo
10-20
>20
n
56
10
17
29
Cell
TdR
Leu
TdR
cell- ’
Leu
cell- I
0.05
0.12
0.06
ns
0.16
0.50
0.21
ns
0.16
0.49
0.22
0.10
0.11
0.38
0.15
ns
0.11
0.36
0.15
0.09
0.0
0.0 -b
I
0.1
-0.2
Temperature control index
Fig. 3. The relationship between regression slopes for abundance vs. production (bottom-up control index) and abundance
vs. temperature (temperature control index). Indices are described in the text. The line of best fit is plotted.
ferences among them could not exceed 2°C. For example,
in Dl, the 3rd (day), 7th (night), 11th (day), and 15th
(night) sampling points were chosen because they were
all high-tide data and in situ temperatures for these points
were -2O-22OC. ANOVA showed that there was no daynight effect in Dl, D2, D3, and D3-Chop studies at any
tidal amplitude (P > 0.05).
Figure 7 shows the results of the D4 and D5 diel studies
conducted in May and August 1992. Note that only lowtide water samples were measured in these two studies,
so the tidal factor was excluded. That is, these observations address the diurnal variability of bacterial properties
sampled at the same tidal stage over 3-6 d. All measured
variables except bacterial abundance showed a very strong
diel pattern. Chlorophyll, TdR, and TdR cell-l values of
the day samples were -2-3 times higher than the night
samples (Fig. 7A, C, F; ANOVA, P < 0.05).
The low-tide data set of Dl (five low-tide samples) and
the whole data sets of D3-Chop, D4, and D5 were used
to determine the relationships among bacterial and environmental
variables while isolating the tidal effect on
variability.
D2 and D3 had only two and three low-tide
points and thus were not included in this analysis. D3Chop data were used for analysis because the results in
Table 5 showed that bacterial variables were independent
of tide. Among the independent factors, only temperature
was consistently and positively correlated with bacterial
variables in all studies (Table 6; P < 0.05), although there
was no correlation between bacterial abundance and temperature in D3-Chop, D4, and D5. Interestingly, chlorophyll explained much less variability of bacterial properties than temperature did. The daily variation in temperature could be large, and this variation was the most
important factor influencing short-term changes in bacterial growth.
In Dl, Leu and Leu cell-’ were not correlated with
temperature
and were negatively correlated with tidal
height when the entire data set was used (Table 6). However, after isolating the tidal influence, > 90% of the variability could be explained by temperature alone. Also,
l
61
Diel temperature efects on bacteria
1.00
- 30
1
A
B
8B0.50
!!
0.75
0.25
0.00(
- 25
c
I
Ii
I
Y
- 20
I
I
I
I
I
I
I
I
I
I
I
I
I
300
'6
.4
\\
d
.2
9 15 21 3
Sampling time
Fig. 4.
Thymidine
tide.
9 15 21 3
9
Sampling time
Tidal cycle study of lo-12 May 199 1 (Dl). A. Tidal height. B. Temperature. C. Thymidine
incorporation rate per cell. E. Abundance. F. Chlorophyll. Day samples-O;
night samples-O.
the previous analysis showed that 22% of the variability
of abundance, 41% of TdR, and 27% of TdR cells-’ in
Dl could be explained by temperature even when the
whole data set was used. After removing the tidal factor
from the analysis, these R2 values for temperature increased to 63, 68, and 43%. In D4 and D5, chlorophyll
was positively correlated with bacterial variables (except
bacterial abundance). However, chlorophyll was also positively correlated with temperature. These results indicate
that all measured variables were highly autocorrelated in
the two studies.
In D4, extra samples collected at the 2nd (night), 3rd
(day), and 4th (night) sampling points were incubated at
the temperature of the previous sampling point (i.e. lst,
2nd, and 3rd). This experiment examined the temperature effect on TdR cell-‘. More specifically, we hypothesizcd that if temperature was the major factor regulating
bacterial growth rate, then TdR cell- 1in samples collected
at time X but incubated at the temperature of previous
sampling (time X - 1) would be very close to the TdR
cell- * value at time X - 1. Figure 8 shows that changing
3
&
2.f
incorporation
rate. D.
9-10 May was a neap
incubation temperature forced TdR cell- 1at time X back
to those of time X - 1. For example, the in situ TdR
cell- 1 for sampling 2 was 10 x 1O-” pmol h- 1 cell- I, but
when incubated at the previous sample temperature, the
rate (18 x 1O-9 pmol h-l cell-l) was closer to the in situ
rate of sampling 1 (17 x 1O-” pmol h-l cell-‘). This effect
suggests that the diel variability of TdR cell-’ in D4 (and
perhaps the other studies) was driven by temperature and
was independent of light and chlorophyll conditions.
Discussion
Tidal variability-The
negative correlation between
tidal height and bacterial variables clearly indicated that
bacterial abundance, production, and specific growth rate
in the low-tide waters were higher than those in the Choptank River. Kirchman et al. (1984) observed the same
phenomenon in Great Sippewissett Salt Marsh. By plotting bacterial abundance vs. chlorinity and fitting data
into a simple exponential model, Kirchman et al. dem-
Shish and Ducklow
62
35
30
i?
25
20
- 50
800
C
-40
-30
$
‘;s
“0
g
4‘
0
M
t
I
I
I
I
I
I
I
I
I
I
I
-
1E
F
c
I
20
2
8
14
20
2
Sampling time
Fig. 5.
20
30
As Fig, 4, but for 30 June-l
20
I
I
2
-20
Tb
0
.s
2
- 10
I
I
8
I
I
14
I
I
20
I
I
2
Sampling time
July 199 1 (D2). 29-30 June was a spring tide.
onstrated that most of the tidal variation in bacterial
abundance was due to conservative mixing from tidal
exchange. By calculating the specific growth rate of bacteria in marsh water, they further showed that the difference in bacterial abundance between low and high tide
was due to bacterial growth in a water mass that remained
in the marsh.
That hypothesis can be supported for our tidal creek
by the following evidence: TdR cell- l and Leu cell-’ were
all higher at low tide in D2 and D3 when temperature
did not vary much during the study period. This pattern
indicates that bacterial specific growth rate in the lowtide waters was higher than that in the Choptank River
and can also be observed by comparing D3 with D3Chop, which shows that the differences in TdR cell - l and
Leu cell-’ between the spillway and the Choptank River
were highest at low tide. Two important questions arise.
Why are bacterial growth rates different in these two systems, and, within each system, what factors determine
temporal variability
of bacterial properties?
The tidal variability
in bacterial abundance, production, and specific growth rate in D2 and D3 may have
been determined by different conditions of nutrient supply. The tidal creek is shallow and small compared to the
Choptank River, but it is surrounded by well-developed
marsh vegetation (Stevenson et al. 1977). Additional nutrient inputs, including particulate and dissolved organic
matter (DOM) might come from detritus and sediments.
Resuspension of sediments is an important input of
both POC and DOC (particulate and dissolved organic
C) to the water column (Morales-Zamorano
et al. 199 I),
and the DOC released in the early stages of decomposition
might be ecologically more significant than POC for microheterotrophic
processes (Mann 1988). By measuring
DOC concentrations
and carbon isotopic composition
(i .e. 13C), Fry et al. (1992) suggested that DOC from both
phytoplankton
and marsh plants (Spartina) were important in supporting bacterial growth in the Parker estuary,
Massachusetts, although Coffin et al. (1990) showed that
DOM from phytoplankton
was probably the dominant
source of C for estuarine bacteria. With a higher substrate
flux, higher bacterial abundance and growth rate in the
creek would be expected. Stevenson et al. (1977) showed
that the creek system was heterotrophic (i.e. photosynthesis : respiration ratio < 1) all year, except in winter.
Furthermore, chlorophyll concentrations in the creek and
the Choptank River waters were similar in D 1, D2, and
D3. These observations suggest that nutrient supplies from
Diel temperature efects on bacteria
63
- 50
- 40
-30
YJ
-20
2
I
12
I
I
18
I
I
I
24
I
6
I
I
I
12
12
Sampling time
I
I
18
I
I
I
24
-0
1--v--
6
10
12
Sampling time
Fig. 6. As Fig. 4, but for 6-7 October 1991 (D3). Salt-marsh creek: day samples-O; night samples-o.
samples-A;
Choptank River: day
night samples-A.
nonphytoplankton
sources are important and that they
might support higher bacterial abundance and growth rate
in the creek.
However, this does not necessarily mean that other
factors are less important in determining short-term variability. The positive correlation between temperature and
TdR cell- I, over several tidal cycles (Fig. 4) indicated
that the actual limit for bacterial growth rate was set by
temperature instead of nutrient supply. In D 1, TdR cell- l
in the river waters (i.e. high-tide data) was sometimes
higher than that in the tidal creek waters (Fig. 4D) and
was also positively correlated with temperature (P < 0.05,
n = 4, R2 = 0.68). This relationship implies that bacterial
growth rates in both systems may not be limited by nutrient supply (see below). If nutrient supply is limiting
bacterial specific growth rate in either system, then a strong
and consistent correlation between temperature and TdR
cell-l would be less likely.
Diel variability-Only
D4 and D5 showed a strong diel
pattern, which was not observed in D 1, D2, D3, and D3Chop. Riemann and Sondergaard (1984) and Scavia and
Laird (1987) hypothesized that in aquatic systems where
nutrients might come from sources other than phytoplankton, the lack of diel pattern between bacteria and
phytoplankton
was predictable. Their hypothesis is applicable to Dl, D2, and D3. As mentioned earlier, the
prevailing explanation for bacterial diel pattern has been
that bacteria are regulated by their nutrient sourcesphytoplankton,
whose photosynthetic activity is strongly
controlled by light intensity. The basic discrepancy between these observations seems to depend on different
nutrient supply conditions. That is, diel bacterial patterns
might occur more often in environments
where phytoplankton are the major source of substrate. However, one
important point previous studies have not emphasized is
the possible role temperature plays in determining shortterm bacterial diel patterns. Higher temperature always
occurs in the daytime, so either of these factors could be
the driving force for bacterial diel variation.
We hypothesize that the diel pattern observed in the
tidal creek is controlled by temperature but not by light
intensity for the following reasons. Evidence comes from
the temperature manipulation experiments performed in
D4 (Fig. 8). When incubated at daytime temperature, the
TdR cell-* of night samples could be as high as those of
Shiah and Ducklow
64
ples. If bacterial growth in the river is nutrient limited,
then a strong diel pattern should be observed. Also note
that during these four studies, day-night temperature difference did not exceed 5”C, but in D4 and D5, the daytime
temperature was at least 10°C higher than at night.
Table 5. Correlation coefficients between measured variables (see Table I) and tidal height. All values shown are significant at P < 0.05. Not significant--s; data not availableNA.
Study
Dl
D2
D3
D3-Chop
Chl
ns
ns
Gi
Cell
TdR
-gns59
-0.86
-o”.“sl
-0.8 1
ns
ns
Leu
TdR
cell- I
Leu
cell- 1
-0.76
-0.81
-0.89
-on:86
-0.75
-0.81
-0.83
-0.8 1
ns
ns
ns
the day samples and vice versa. If bacterial growth in
night samples was limited by phytoplankton
production-which
was controlled by light intensitythen TdR
cell-’ would not increase when incubation temperature
increased. The second line of evidence is derived from
the results showing that there was no day-night effect on
bacterial variables in Dl, D2, D3, and particularly for
D3-Chop, which represented the Choptank River sam-
Short-term variability vs. seasonal variability- Bacterial variability
over daily scales was much smaller than
at the seasonal scale (Table 1). For example, bacterial
abundance in the diel and tidal studies varied about 2-fold,
but it varied 12-fold over the seasonal scale. Rate parameters (i.e. TdR, Leu, TdR cell- l, and Leu cell- I) showed
the same trend but with more dramatic differences be_ tween these two time scales. The most probable explanation for this is temperature. Seasonal temperature difference could be as great as 33”C, while the temperature
difference over single days was -4-l 3°C.
Although the absolute range and variability of bacterial
variables on short-term scales were lower than on the
seasonal scales, the dependency of bacterial variables on
40
A
- 30
30
-b
5 20
2
10
0
700
600
500
400
300
-25
I’
00
- 15
- 8000
- 6000
2g I1
- 4000 $
a
- 2000
Ii'\ I
4 \I
x
&
i \
a
4
I' '\
\y/
\ / '\
\
\ /8
'11 \
4
\I
\
AO
255r
0 12243648607284
Sampling time
108
0 12243648607284
108
Sampling time
night samples-O)
and 3 1 August-5 September 1992 (D5: day
Fig. 7. Diel studies of 21-23 May 1992 (D4: day samples-O;
samples-A;
night samples-A).
A. Chlorophyll. B. Temperature. C, D. Thymidine and leucine incorporation rates. E. Abundance.
F. Thymidine incorporation
rate per cell. 5 September was a neap tide.
65
Diel temperature e#ects on bacteria
Table 6. Slopes and coefficients of determination (%, in parentheses) for linear regressions
among measured variables (see Table I). All values shown are significant at P < 0.05; not
significant - ns; data not available - NA. All variables were natural-log-transformed
for analyses.
Study
Dl
Variables
T
D4
TdR cell-’
Leu cell-’
0.12 (91)
ns
ns
ns
0.04 (63)
ns
ns
0.09 (68)
0.16 (95)
0.4 :;67)
0.2:;69)
0.57 (85)
T
NA
Cell
TdR
0.17 (54)
0.47 (58)
iii
ns
ns
0.13 (34)
0.41 (41)
0.69 (52)
0.14 (81)
ns
ns
0.10 (32)
ns
ns
0.06 (38)
ns
ns
ns
-
0.08 (90)
1.47 (94)
ns
NA
NA
NA
0.08 (89)
1.46 (92)
ns
NA
NA
NA
0.06 (38)
ns
ns
ns
ns
ns
ns
ns
0.13 (66)
0.66 (3 1)
0.19
ns
0.13 (88)
0.68 (45)
ns
ns
0.12 (83)
0.63 (47)
T
T
Chl
Cell
TdR
temperature
L&U
ns
ns
ns
Chl
Cell
D5
TdR
0.04 (43)
ns
ns
ns
Chl
Cell
TdR
D3-Chop
Cell
Chl
was the same across different
time
scales and
even systems. The slopes for TdR cell-’ on temperature
in D3-Chop (0.14-1-0.02) and D5 (0.13+0.02)
are the
same as those at seasonal scales (0.15 kO.03, < 20°C data
set). These three slopes are not different from each other
(ANCOVA, P > 0.05). The reason for choosing D3-Chop
and D5 was that the former represented the Choptank
River system and the latter had the most low-tide data
points (n = lo), which might be more representative than
other short-term studies. This result clearly demonstrates
that bacterial growth in temperate ecosystems is primarily
controlled by temperature across different temporal scales,
and its effect seems to be independent of location. Shiah
and Ducklow (1993, 1994) showed that in Chesapeake
Bay areas, bacterial properties are strongly regulated by
temperature during nonsummer seasons, when temperature is ~20°C. They also showed that the temperature
dependency of bacterial growth rates was uniform over
seasonal and hourly scales. Our study suggests that temperature can be very important in determining the temporal variability of bacterial growth in the tidal creek and
the Choptank River at daily scales. Bacterial growth in
these two systems probably was not limited by in situ
nutrient supply during nonsummer seasons. More interestingly, the three slope values (0.14f0.02,
0.13 10.02,
and 0.15 kO.03) presented in our study are close to those
presented earlier (Shiah and Ducklow 1993, 1994). In
summer, the temperature effect was less important, and
some other factor must limit bacterial growth. Shiah and
Ducklow (1993) presented results of substrate manipulation experiments performed in the salt-marsh creek and
Chesapeake Bay. Substrate additions (glucose, ammonium, or dissolved free amino acids) only increased bacterial properties and rates when the temperature was
>2O”C, suggesting the potential importance of nutrient
limitation in summer.
-
0.12
0.59
0.17
0.86
(66)
(33)
(20)
(87)
ns
ns
Conclusions
Our results in a small salt-marsh creek suggest that the
relationships among bacterial growth rate, temperature,
and substrate supply rate are complex and interrelated.
Substrate sources may come from allochthonous and autochthonous inputs. These inputs were not measured, and
the annual cycle of substrate flux is still undefined. Our
major point is that both bacterial growth rate and the
substrate effects on bacterial growth are functions of temperature. We hypothesize that during nonsummer seasons, substrate stocks and supply rates are always higher
than bacterial demand and this leaves temperature regulation as the major determinant of bacterial growth rate.
During summer, when bacterial demand exceeds sub-
45
-z 4035 -
0
0
In situ
Manipulated
8
0
0
0
l
To 30 ; 25 E
a 20 T
2 15 10 5
0
0
:
0
8
I
1
I
2
I
I
3
4
Sampling Point
I
5
6
Fig. 8. Effect of temperature manipulation
on TdR cell- I
during D4. Rates at the in situ temperature-O;
rates in samples
incubated at the temperature of the previous sampling-a.
66
Shiah and Ducklow
strate supply rate, substrate supply rate may limit
terial growth.
bac-
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