Microb Ecol (1984) 10:137-149
MICROBIAL ECOLOGY 9 1984 Springer-Verlag
Measuring Microzooplankton Grazing on Planktonic Marine Bacteria by Its Impact on Bacterial Production Richard T. Wright ~ and Richard B. Coffin2 ~GordonCollege,Wenham,Massachusetts01984, USA; and 2Collegeof Marine Studies, Universityof Delaware, Lewes,Delaware 19958, USA Abstract. Grazing on planktonic bacteria by microzooplankton was estimated by separating bacteria from the larger plankton with 1 #m pore Nuclepore filtration and measuring changes in bacteria in filtered and unfiltered samples over 24 hours. In the absence of grazers, bacteria increased linearly. The regression coefficient of linear increase was used to estimate in situ bacterial production. When grazers were present, the changes in bacteria concentration usually took the form of a linear decline, and grazing was estimated by subtracting the regression coefficient of the unfiltered sample from that of the 1 ~tm filtrate. Results from the Essex estuary-coastal system of northern Massachusetts show grazing and production at rates that indicate a daily turnover of the standing crop of bacteria, with highest values in mid-estuarine waters. Experiments on the size distribution of grazing showed that microzooplankton from 1-3/~m were responsible for most of the observed decrease in bacteria. It was suggested that the basic pattern of linear increase of the bacteria in the absence of grazing reflects density-dependent limitation by substrate present at the outset of the incubation and is indicative of a population that has been maintained around the mid-point of the logistic growth curve by grazing.
Introduction
Studies of planktonic bacteria in estuarine and coastal waters indicate that bacteria numbers vary less than an order of magnitude over the course of a year's cycle, a remarkably small range in view of the seasonal variation typically found for other members of the plankton [12, 17, 23, 33, 36]. Several workers [33, 36] have found a high correlation between temperature and bacterial numbers; however, the mechanism of the temperature effect on bacterial populations in marine systems remains to be determined. Wright and Coffin [36] suggested that the role of temperature is to influence other factors that are the primary determinants of the magnitude of a planktonic bacterial population in a given marine system. Three factors were suggested: dormancy, substrate supply, and grazing. In planktonic environments, the actual density reached by the bacteria can be determined by the way in which substrate supply and grazing interact. A population of zooplankton adapted to grazing on planktonic bacteria could
138
R.T. Wrightand R. B. Coffin
potentially keep bacterial population density well below upper limits set by substrate supply, according to well established ecological theory [35]. Some recent studies have suggested that grazing of planktonic bacteria does occur in marine systems and that the dominant grazers are heterotrophic microflagellates. A group of workers from Capetown, South Africa has demonstrated the consistent appearance of microflagellate and ciliate populations near the end of long-term incubation experiments. These experiments have employed dissolved and particulate organic matter derived from kelp [ 18-20, 30], phytoplankton debris [21], and fecal material [31] as sources of bacterial nutrients. In each study, bacteria biomass reached high densities and then declined rapidly with increases in microflagellate biomass. Other studies using transmission electron microscopy have demonstrated the presence of bacteria in microflagellate food vacuoles [5, 11]. Grazing of bacteria by microflagellates has been measured in the laboratory with cultured organisms [6, 28]. In addition, Fenchel [7, 8] examined microflagellate response to nutrient stress, and their spatial and seasonal distribution. Although several aspects of heterotrophic microflagellate ecology have now been examined, none of these studies has provided ambient grazing rates by organisms in the size range of the microflagellates. Several workers have demonstrated the grazing of planktonic bacteria by benthic and planktonic animals. By measuring changes in the concentration of bacteria over time in the presence of known numbers of zooplankton in small containers, Peterson et al. [24] showed clearance of natural bacteria by several species of Daphnia. Wright et al. [37] employed short-term measurements of clearance of natural bacterioplankton by several bivalve species to establish the potential of the ribbed mussel, Geukensia demissa, to utilize bacteria as a significant food source. Radioactive labeling of natural bacteria was employed by Hollibaugh et al. [14] to measure clearance rates in several cultured zooplankton. These authors cited problems with high blank values when attempting to use this approach to measure uptake of labeled bacteria by microzooplankton in natural waters. We experienced the same problem when we tried to apply this method to the measurement of grazing by estuarine zooplankton. This study represents an attempt at measuring natural grazing rates of microzooplankton (down to 1 #m) and their impact on bacterial production in coastal and estuarine waters, including a characterization of the spatial and size distribution of grazers. We have employed an approach adapted from Gak et al. [ 10] which eliminates grazers by differential filtration and estimates grazing by its impact on measured changes of bacterial numbers in filtered and unfiltered samples incubated for up to 24 hours. The method thus estimates both bacterial production and grazing on the bacteria, and in addition, has the advantage of being relatively simple in design and execution.
Methods
The study was conductedin the Essex River estuaryand offshorewaters locatedon the northern coast of Massachusetts,USA (42~ lat., 70042' long.). The Essex is a small unpollutedestuary extensivelybordered by a Spartina salt marsh [36]. The data presented here are from 2 transects
Microzooplankton Grazing on Planktonic Marine Bacteria
139
of the horizontal gradient of the estuary and coastal water (10 stations) on July 13 and August 11, 1982, and from a single mid-estuarine station on July 20 and 22, 1982. Water samples were taken in a 3 liter scrubbed Van Dora bottle and transferred to sterile 2.5 liter plastic containers. Samples were stored in the dark in an insulated chest containing ambient temperature water until they could be processed in the laboratory. The time between sample collection and initiation o f the incubation was never longer than 3 hours. In the laboratory, water samples were gravity-filtered through a 270/~m mesh plankton net and, for some treatments, through 3 ~m or 1 ~m Nuclepore filters, with vacuum controlled at 100 m m Hg or less. For the transect samples, duplicate 1 liter samples were taken from each of these 3 treatments and incubated for 24 hours in the dark in 1 liter Whirl-Pak bags at temperatures within 2"C o f ambient. Subsamples, taken every 6 hours, were fixed with 5% buffered, filtered formalin and refrigerated until bacterial and eucaryotic populations could be enumerated (within 1 week). Other parameters examined included chlorophyll a with fluorometric analysis [34], salinity using a salinity refractometer, and particulate carbon by weighing dried material caught in ashed glass fiber filters before and after combustion for 1 hour at 450~ The mid-estuary water samples from July 20 and 22, 1982, were passed through different sized plankton netting and filters in order to determine the impact of different plankton fractions on subsequent bacterial growth. Samples were gravity-filtered through 270, 120, and 37 um mesh plankton netting, and vacuum-filtered through 12, 8, 3, and 1 ~m Nuclepore filters at a vacuum of 100 m m Hg or less. Care was taken to prevent filters from clogging; fresh filters were used whenever the filtration rate began to slow down. Duplicates of these 7 samples and an unfiltered sample were incubated and subsampled as above. Bacteria were counted with a Zeiss Standard microscope using the acridine orange direct count technique o f Hobble et al. [ 13]. Bacteria on a given filter were counted until the accumulated tally reached a minimum of 400 cells. Eucaryotic organisms from the same samples were stained with acridine orange, caught on 1 ~m Nuclepore filters, and counted under epifluorescence illumination. Those organisms with obvious nuclei and no bright chlorophyll red fuorescence under UV light were counted. At least 200 cells were counted for each sample. Rates of bacterial production and grazing by zooplankton were derived from changes in the bacterial populations during incubation. Gak et ai. [10] suggested that bacterial production could be measured by incubating a nonfiltered sample and a filtered sample, the significant difference between them being the presence and absence o f zooplankton grazers. Thus: Pt=N2-
Nt + G
(1)
where P = production of bacteria, N t = the initial number o f bacteria in the nonfiltered sample, N2 = the final number in the same sample after incubation for t hours, and G = bacteria removed by zooplankton grazing. In the majority of samples processed by Gak, there was no change in the number of bacteria in unfiltered samples during the incubation, and therefore determination o f grazing essentially gave the bacterial production rate. Calculation o f G was taken from Romanova and Zonoff [26] and requires the assumption that bacteria in a sample are increasing exponentially. The growth constant for exponential growth, #, is determined by incubation of a filtered sample, such that: In n 2 - In nl t
(2)
where nl = the initial number of bacteria in the filtered sample and nz = the final number in the same sample after t hours. As presented in Gak et al. [10], the '+simplified" calculation of G is in error. The equation (4) from Gak et al., as properly derived from Romanova and Zonoff [26], should read:
G=
( n 2 - N2 \ ( n - ~ 0 --- I ]
(3)
and therefore, combining equations (2) and (3):
G _ ln n2 - ln n, ( n2 - N2 t \ ( n 2 / n ~ ) - 1/
(4)
140
R.T. Wright and R. B. Coffin
In adapting this approach to our studies, we have routinely sampled every 6 hours. Our data indicate that the assumption of exponential growth in the filtered samples cannot be supported; rather, the increase in numbers over time is more or less linear. When it is not, the deviations from linearity are usually in the form of little or no increase during the first 1 or 2 samplings (a lag effect), or a decline in numbers or increase rate during the last 1 or 2 samplings (a plateau). Therefore, our calculations of grazing and production rates have been based on the linear changes in bacterial density occurring during incubation and do not require any specific assumptions concerning the bacterial growth rate. To facilitate these calculations, regression coefficients representing the change in numbers over time were computed for bacteria density vs. incubation time in filtered and unfiltered samples, using a statistical software program (Stat-11) available with the Digital PDP- 11 computer. Occasionally, early or late samplings were dropped from the calculations in order to preserve the linearity of the relationship; in all cases, duplicate samples were employed and a given coefficient represents at least 4 sampling times. Significant differences in the slope of different samples were tested using Student's t test with the critical t set at the 95% confidence interval. Production rates were corrected for losses of bacteria due to filtration through 1 um and 3 um filters. The filtration correction for these samples was 1.1 (n = 17, SD = 0.12), and 1.05 (n = 21, SD = 0.09) for 1 and 3/~m filtrates, respectively. Production was calculated from the regression coefficient of the corrected I or 3 #m filtrate, and grazing was calculated by subtractingthe regression coefficient of the 270 ~zm filtrate (=unfiltered) from the production regression coefficient. If regression coefficients are expressed in units of 109 cells 1-~ hour-t, particulate carbon production by the bacteria is obtained by multiplying the regression coefficient times a factor converting bacterial cells into carbon, which for our system is 24 ug C per 109 cells [37].
Results T a b l e 1 s h o w s the d a t a f r o m t h e t r a n s e c t o f J u l y 13, 1982, w i t h the c h a n g e i n n u m b e r s w i t h t i m e c a l c u l a t e d f r o m the u n f i l t e r e d a n d 3 # m filtered s a m p l e s i n c u b a t e d o v e r 24 h o u r s . F i g u r e 1A s h o w s i n i t i a l b a c t e r i a l p o p u l a t i o n s a n d e s t i m a t e s o f 24 h o u r b a c t e r i a l p r o d u c t i o n a n d g r a z i n g o n t h e b a c t e r i a c a l c u l a t e d f r o m the r e g r e s s i o n data. I n t h e c o u r s e o f c o u n t i n g the b a c t e r i a s a m p l e s f r o m this set o f m e a s u r e m e n t s , we o b s e r v e d a large n u m b e r o f s m a l l e u c a r y o t e s i n the 3 # m filtrates. A c c o r d i n g l y , we e m p l o y e d b o t h 1 a n d 3 / z m filtrates for t h e t r a n s e c t s a m p l e d o n A u g u s t 11. D a t a for this t r a n s e c t are p r e s e n t e d i n T a b l e 2, a n d the t r e n d s for p r o d u c t i o n a n d g r a z i n g are s h o w n i n Fig. lB. G r a z i n g rates c a l c u l a t e d u s i n g s a m p l e s p a s s e d t h r o u g h 1 u m filters y i e l d e d c o n s i s t e n t l y h i g h e r v a l u e s t h a n t h o s e p a s s e d t h r o u g h 3 / ~ m filters i n t h e e s t u a r i n e s a m p l e s , i n d i c a t i n g t h a t t h e larger p o r e filters d i d n o t a d e q u a t e l y s c r e e n o u t g r a z i n g o r g a n i s m s i n s o m e waters. B o t h sets o f d a t a i n d i c a t e i n c r e a s e s i n b a c t e r i a l d e n s i t y a n d g r a z i n g o f b a c t e r i a i n a s h o r e w a r d d i r e c t i o n , w i t h h i g h e s t v a l u e s (up to 7.5 x 106 m l -~ d a y i g r a z e d i n a p o p u l a t i o n o f 7 • 106 m l - 1) i n m i d d l e a n d u p p e r e s t u a r i n e waters. B a c t e r i a l p r o d u c t i v i t y was h i g h e s t i n the e s t u a r y o n t h e J u l y 13 t r a n s e c t (up to 7 x 106 m l -~ d a y -1) a n d i n i m m e d i a t e c o a s t a l w a t e r s o n t h e A u g u s t 11 t r a n s e c t (ca. 5 x 106 m l -~ day-~), a l t h o u g h it m u s t b e p o i n t e d o u t t h a t the lack o f a 1 # m filtrate w o u l d t e n d to c a u s e p r o d u c t i v i t y a n d g r a z i n g d a t a to be u n d e r e s t i m a t e d for the e a r l i e r t r a n s e c t . T h e d a t a f r o m b o t h t r a n s e c t s i n d i c a t e d p r o d u c t i v i t y at h i g h e r levels t h a n g r a z i n g i n c o a s t a l a n d offshore waters, a n d t h e l a t t e r t r a n s e c t d a t a i n d i c a t e d g r a z i n g rates s u b s t a n t i a l l y g r e a t e r t h a n p r o d u c t i v i t y i n e s t u a r i n e waters. A t b o t h e n d s o f this t r a n s e c t , m i c r o o r g a n i s m s p a s s i n g t h r o u g h a 3 t~m filter d o m i n a t e d grazing.
Microzooplankton Grazing on Planktonic Marine Bacteria
141
9 8
A
7 6
T~ 5
5 4
~3 •
t~ 9 Initial Population
o
.}9
B
, Production Rate
88
9 Total Grazing Rate 9 1-3 ~ n Grazing Rate
r ._~ s 4
6
4
2
0
Fig. 1A. Initial bacteria density (acridine orange direct count), grazing rate, and production rate based on increases of bacteria in unfiltered vs. 3 #m filtered samples. Samples from July 13, 1982, taken from 7 km upriver from inlet (on left side of plot) out to station 10 km offshore (on far right side of plot). B. Same stations sampled on August 11, 1982. Total grazing rate and production rate based on increases of bacteria in unfiltered vs. 1 #m filtered samples; 1-3 #m grazing rate based on difference between grazing in 1 and 3 #m Nuelepore filtered samples.
2 "
Distance from Inlet (kin)
Two experiments were conducted to test the size distribution of grazing populations. Figures 2A and B show the bacteria counts from these 2 experiments. The 1 #m filtrate is indicative of bacterial production in the absence of any known grazing; all other samples indicate a reduction in the bacteria because of grazing, usually beginning in the first 6 hours of incubation. Regression coefficients indicating the change in numbers over time for each of the filtrates (calculated with the omission of all of the zero hour samplings) indicated that the 1 #m filtrate coefficient was significantly different from all other samples, on both dates. Except for the 3 ~tm filtrates, the regression coefficients from the remaining samples (from 8 ~m to unfiltered) were only occasionally significantly different from each other, showing no clear pattern. Grazing by organisms passing through a 3/~m filter was responsible for essentially all of the observed reduction in bacteria in the July 20 experiment and approximately 70% in the July 22 experiment, in agreement with the August 11 transect data. Small eucaryotes were counted in these 2 experiments at the outset of incubation and again at the conclusion (24 hours). Results from these counts are shown in Fig. 3, a plot ofeucaryotic counts vs. the sieve diameters. In agreehaent with the grazing data reported above, the time 0 counts show that the 1 ~tm filter removed most of the eucaryotes, with the 3 #m filter removing a smaller fraction and no significant reduction above that filter size. Furthermore, notable
142
R . T . Wright and R. B. Coffin
A 16
12
8
x
n
B 9~ 16
n
i
n
Sieve Diameter
~1
9 37
G3 9 8
MICROBIAL ECOLOGY 9 1984 Springer-Verlag
Measuring Microzooplankton Grazing on Planktonic Marine Bacteria by Its Impact on Bacterial Production Richard T. Wright ~ and Richard B. Coffin2 ~GordonCollege,Wenham,Massachusetts01984, USA; and 2Collegeof Marine Studies, Universityof Delaware, Lewes,Delaware 19958, USA Abstract. Grazing on planktonic bacteria by microzooplankton was estimated by separating bacteria from the larger plankton with 1 #m pore Nuclepore filtration and measuring changes in bacteria in filtered and unfiltered samples over 24 hours. In the absence of grazers, bacteria increased linearly. The regression coefficient of linear increase was used to estimate in situ bacterial production. When grazers were present, the changes in bacteria concentration usually took the form of a linear decline, and grazing was estimated by subtracting the regression coefficient of the unfiltered sample from that of the 1 ~tm filtrate. Results from the Essex estuary-coastal system of northern Massachusetts show grazing and production at rates that indicate a daily turnover of the standing crop of bacteria, with highest values in mid-estuarine waters. Experiments on the size distribution of grazing showed that microzooplankton from 1-3/~m were responsible for most of the observed decrease in bacteria. It was suggested that the basic pattern of linear increase of the bacteria in the absence of grazing reflects density-dependent limitation by substrate present at the outset of the incubation and is indicative of a population that has been maintained around the mid-point of the logistic growth curve by grazing.
Introduction
Studies of planktonic bacteria in estuarine and coastal waters indicate that bacteria numbers vary less than an order of magnitude over the course of a year's cycle, a remarkably small range in view of the seasonal variation typically found for other members of the plankton [12, 17, 23, 33, 36]. Several workers [33, 36] have found a high correlation between temperature and bacterial numbers; however, the mechanism of the temperature effect on bacterial populations in marine systems remains to be determined. Wright and Coffin [36] suggested that the role of temperature is to influence other factors that are the primary determinants of the magnitude of a planktonic bacterial population in a given marine system. Three factors were suggested: dormancy, substrate supply, and grazing. In planktonic environments, the actual density reached by the bacteria can be determined by the way in which substrate supply and grazing interact. A population of zooplankton adapted to grazing on planktonic bacteria could
138
R.T. Wrightand R. B. Coffin
potentially keep bacterial population density well below upper limits set by substrate supply, according to well established ecological theory [35]. Some recent studies have suggested that grazing of planktonic bacteria does occur in marine systems and that the dominant grazers are heterotrophic microflagellates. A group of workers from Capetown, South Africa has demonstrated the consistent appearance of microflagellate and ciliate populations near the end of long-term incubation experiments. These experiments have employed dissolved and particulate organic matter derived from kelp [ 18-20, 30], phytoplankton debris [21], and fecal material [31] as sources of bacterial nutrients. In each study, bacteria biomass reached high densities and then declined rapidly with increases in microflagellate biomass. Other studies using transmission electron microscopy have demonstrated the presence of bacteria in microflagellate food vacuoles [5, 11]. Grazing of bacteria by microflagellates has been measured in the laboratory with cultured organisms [6, 28]. In addition, Fenchel [7, 8] examined microflagellate response to nutrient stress, and their spatial and seasonal distribution. Although several aspects of heterotrophic microflagellate ecology have now been examined, none of these studies has provided ambient grazing rates by organisms in the size range of the microflagellates. Several workers have demonstrated the grazing of planktonic bacteria by benthic and planktonic animals. By measuring changes in the concentration of bacteria over time in the presence of known numbers of zooplankton in small containers, Peterson et al. [24] showed clearance of natural bacteria by several species of Daphnia. Wright et al. [37] employed short-term measurements of clearance of natural bacterioplankton by several bivalve species to establish the potential of the ribbed mussel, Geukensia demissa, to utilize bacteria as a significant food source. Radioactive labeling of natural bacteria was employed by Hollibaugh et al. [14] to measure clearance rates in several cultured zooplankton. These authors cited problems with high blank values when attempting to use this approach to measure uptake of labeled bacteria by microzooplankton in natural waters. We experienced the same problem when we tried to apply this method to the measurement of grazing by estuarine zooplankton. This study represents an attempt at measuring natural grazing rates of microzooplankton (down to 1 #m) and their impact on bacterial production in coastal and estuarine waters, including a characterization of the spatial and size distribution of grazers. We have employed an approach adapted from Gak et al. [ 10] which eliminates grazers by differential filtration and estimates grazing by its impact on measured changes of bacterial numbers in filtered and unfiltered samples incubated for up to 24 hours. The method thus estimates both bacterial production and grazing on the bacteria, and in addition, has the advantage of being relatively simple in design and execution.
Methods
The study was conductedin the Essex River estuaryand offshorewaters locatedon the northern coast of Massachusetts,USA (42~ lat., 70042' long.). The Essex is a small unpollutedestuary extensivelybordered by a Spartina salt marsh [36]. The data presented here are from 2 transects
Microzooplankton Grazing on Planktonic Marine Bacteria
139
of the horizontal gradient of the estuary and coastal water (10 stations) on July 13 and August 11, 1982, and from a single mid-estuarine station on July 20 and 22, 1982. Water samples were taken in a 3 liter scrubbed Van Dora bottle and transferred to sterile 2.5 liter plastic containers. Samples were stored in the dark in an insulated chest containing ambient temperature water until they could be processed in the laboratory. The time between sample collection and initiation o f the incubation was never longer than 3 hours. In the laboratory, water samples were gravity-filtered through a 270/~m mesh plankton net and, for some treatments, through 3 ~m or 1 ~m Nuclepore filters, with vacuum controlled at 100 m m Hg or less. For the transect samples, duplicate 1 liter samples were taken from each of these 3 treatments and incubated for 24 hours in the dark in 1 liter Whirl-Pak bags at temperatures within 2"C o f ambient. Subsamples, taken every 6 hours, were fixed with 5% buffered, filtered formalin and refrigerated until bacterial and eucaryotic populations could be enumerated (within 1 week). Other parameters examined included chlorophyll a with fluorometric analysis [34], salinity using a salinity refractometer, and particulate carbon by weighing dried material caught in ashed glass fiber filters before and after combustion for 1 hour at 450~ The mid-estuary water samples from July 20 and 22, 1982, were passed through different sized plankton netting and filters in order to determine the impact of different plankton fractions on subsequent bacterial growth. Samples were gravity-filtered through 270, 120, and 37 um mesh plankton netting, and vacuum-filtered through 12, 8, 3, and 1 ~m Nuclepore filters at a vacuum of 100 m m Hg or less. Care was taken to prevent filters from clogging; fresh filters were used whenever the filtration rate began to slow down. Duplicates of these 7 samples and an unfiltered sample were incubated and subsampled as above. Bacteria were counted with a Zeiss Standard microscope using the acridine orange direct count technique o f Hobble et al. [ 13]. Bacteria on a given filter were counted until the accumulated tally reached a minimum of 400 cells. Eucaryotic organisms from the same samples were stained with acridine orange, caught on 1 ~m Nuclepore filters, and counted under epifluorescence illumination. Those organisms with obvious nuclei and no bright chlorophyll red fuorescence under UV light were counted. At least 200 cells were counted for each sample. Rates of bacterial production and grazing by zooplankton were derived from changes in the bacterial populations during incubation. Gak et ai. [10] suggested that bacterial production could be measured by incubating a nonfiltered sample and a filtered sample, the significant difference between them being the presence and absence o f zooplankton grazers. Thus: Pt=N2-
Nt + G
(1)
where P = production of bacteria, N t = the initial number o f bacteria in the nonfiltered sample, N2 = the final number in the same sample after incubation for t hours, and G = bacteria removed by zooplankton grazing. In the majority of samples processed by Gak, there was no change in the number of bacteria in unfiltered samples during the incubation, and therefore determination o f grazing essentially gave the bacterial production rate. Calculation o f G was taken from Romanova and Zonoff [26] and requires the assumption that bacteria in a sample are increasing exponentially. The growth constant for exponential growth, #, is determined by incubation of a filtered sample, such that: In n 2 - In nl t
(2)
where nl = the initial number of bacteria in the filtered sample and nz = the final number in the same sample after t hours. As presented in Gak et al. [10], the '+simplified" calculation of G is in error. The equation (4) from Gak et al., as properly derived from Romanova and Zonoff [26], should read:
G=
( n 2 - N2 \ ( n - ~ 0 --- I ]
(3)
and therefore, combining equations (2) and (3):
G _ ln n2 - ln n, ( n2 - N2 t \ ( n 2 / n ~ ) - 1/
(4)
140
R.T. Wright and R. B. Coffin
In adapting this approach to our studies, we have routinely sampled every 6 hours. Our data indicate that the assumption of exponential growth in the filtered samples cannot be supported; rather, the increase in numbers over time is more or less linear. When it is not, the deviations from linearity are usually in the form of little or no increase during the first 1 or 2 samplings (a lag effect), or a decline in numbers or increase rate during the last 1 or 2 samplings (a plateau). Therefore, our calculations of grazing and production rates have been based on the linear changes in bacterial density occurring during incubation and do not require any specific assumptions concerning the bacterial growth rate. To facilitate these calculations, regression coefficients representing the change in numbers over time were computed for bacteria density vs. incubation time in filtered and unfiltered samples, using a statistical software program (Stat-11) available with the Digital PDP- 11 computer. Occasionally, early or late samplings were dropped from the calculations in order to preserve the linearity of the relationship; in all cases, duplicate samples were employed and a given coefficient represents at least 4 sampling times. Significant differences in the slope of different samples were tested using Student's t test with the critical t set at the 95% confidence interval. Production rates were corrected for losses of bacteria due to filtration through 1 um and 3 um filters. The filtration correction for these samples was 1.1 (n = 17, SD = 0.12), and 1.05 (n = 21, SD = 0.09) for 1 and 3/~m filtrates, respectively. Production was calculated from the regression coefficient of the corrected I or 3 #m filtrate, and grazing was calculated by subtractingthe regression coefficient of the 270 ~zm filtrate (=unfiltered) from the production regression coefficient. If regression coefficients are expressed in units of 109 cells 1-~ hour-t, particulate carbon production by the bacteria is obtained by multiplying the regression coefficient times a factor converting bacterial cells into carbon, which for our system is 24 ug C per 109 cells [37].
Results T a b l e 1 s h o w s the d a t a f r o m t h e t r a n s e c t o f J u l y 13, 1982, w i t h the c h a n g e i n n u m b e r s w i t h t i m e c a l c u l a t e d f r o m the u n f i l t e r e d a n d 3 # m filtered s a m p l e s i n c u b a t e d o v e r 24 h o u r s . F i g u r e 1A s h o w s i n i t i a l b a c t e r i a l p o p u l a t i o n s a n d e s t i m a t e s o f 24 h o u r b a c t e r i a l p r o d u c t i o n a n d g r a z i n g o n t h e b a c t e r i a c a l c u l a t e d f r o m the r e g r e s s i o n data. I n t h e c o u r s e o f c o u n t i n g the b a c t e r i a s a m p l e s f r o m this set o f m e a s u r e m e n t s , we o b s e r v e d a large n u m b e r o f s m a l l e u c a r y o t e s i n the 3 # m filtrates. A c c o r d i n g l y , we e m p l o y e d b o t h 1 a n d 3 / z m filtrates for t h e t r a n s e c t s a m p l e d o n A u g u s t 11. D a t a for this t r a n s e c t are p r e s e n t e d i n T a b l e 2, a n d the t r e n d s for p r o d u c t i o n a n d g r a z i n g are s h o w n i n Fig. lB. G r a z i n g rates c a l c u l a t e d u s i n g s a m p l e s p a s s e d t h r o u g h 1 u m filters y i e l d e d c o n s i s t e n t l y h i g h e r v a l u e s t h a n t h o s e p a s s e d t h r o u g h 3 / ~ m filters i n t h e e s t u a r i n e s a m p l e s , i n d i c a t i n g t h a t t h e larger p o r e filters d i d n o t a d e q u a t e l y s c r e e n o u t g r a z i n g o r g a n i s m s i n s o m e waters. B o t h sets o f d a t a i n d i c a t e i n c r e a s e s i n b a c t e r i a l d e n s i t y a n d g r a z i n g o f b a c t e r i a i n a s h o r e w a r d d i r e c t i o n , w i t h h i g h e s t v a l u e s (up to 7.5 x 106 m l -~ d a y i g r a z e d i n a p o p u l a t i o n o f 7 • 106 m l - 1) i n m i d d l e a n d u p p e r e s t u a r i n e waters. B a c t e r i a l p r o d u c t i v i t y was h i g h e s t i n the e s t u a r y o n t h e J u l y 13 t r a n s e c t (up to 7 x 106 m l -~ d a y -1) a n d i n i m m e d i a t e c o a s t a l w a t e r s o n t h e A u g u s t 11 t r a n s e c t (ca. 5 x 106 m l -~ day-~), a l t h o u g h it m u s t b e p o i n t e d o u t t h a t the lack o f a 1 # m filtrate w o u l d t e n d to c a u s e p r o d u c t i v i t y a n d g r a z i n g d a t a to be u n d e r e s t i m a t e d for the e a r l i e r t r a n s e c t . T h e d a t a f r o m b o t h t r a n s e c t s i n d i c a t e d p r o d u c t i v i t y at h i g h e r levels t h a n g r a z i n g i n c o a s t a l a n d offshore waters, a n d t h e l a t t e r t r a n s e c t d a t a i n d i c a t e d g r a z i n g rates s u b s t a n t i a l l y g r e a t e r t h a n p r o d u c t i v i t y i n e s t u a r i n e waters. A t b o t h e n d s o f this t r a n s e c t , m i c r o o r g a n i s m s p a s s i n g t h r o u g h a 3 t~m filter d o m i n a t e d grazing.
Microzooplankton Grazing on Planktonic Marine Bacteria
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9 8
A
7 6
T~ 5
5 4
~3 •
t~ 9 Initial Population
o
.}9
B
, Production Rate
88
9 Total Grazing Rate 9 1-3 ~ n Grazing Rate
r ._~ s 4
6
4
2
0
Fig. 1A. Initial bacteria density (acridine orange direct count), grazing rate, and production rate based on increases of bacteria in unfiltered vs. 3 #m filtered samples. Samples from July 13, 1982, taken from 7 km upriver from inlet (on left side of plot) out to station 10 km offshore (on far right side of plot). B. Same stations sampled on August 11, 1982. Total grazing rate and production rate based on increases of bacteria in unfiltered vs. 1 #m filtered samples; 1-3 #m grazing rate based on difference between grazing in 1 and 3 #m Nuelepore filtered samples.
2 "
Distance from Inlet (kin)
Two experiments were conducted to test the size distribution of grazing populations. Figures 2A and B show the bacteria counts from these 2 experiments. The 1 #m filtrate is indicative of bacterial production in the absence of any known grazing; all other samples indicate a reduction in the bacteria because of grazing, usually beginning in the first 6 hours of incubation. Regression coefficients indicating the change in numbers over time for each of the filtrates (calculated with the omission of all of the zero hour samplings) indicated that the 1 #m filtrate coefficient was significantly different from all other samples, on both dates. Except for the 3 ~tm filtrates, the regression coefficients from the remaining samples (from 8 ~m to unfiltered) were only occasionally significantly different from each other, showing no clear pattern. Grazing by organisms passing through a 3/~m filter was responsible for essentially all of the observed reduction in bacteria in the July 20 experiment and approximately 70% in the July 22 experiment, in agreement with the August 11 transect data. Small eucaryotes were counted in these 2 experiments at the outset of incubation and again at the conclusion (24 hours). Results from these counts are shown in Fig. 3, a plot ofeucaryotic counts vs. the sieve diameters. In agreehaent with the grazing data reported above, the time 0 counts show that the 1 ~tm filter removed most of the eucaryotes, with the 3 #m filter removing a smaller fraction and no significant reduction above that filter size. Furthermore, notable
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R . T . Wright and R. B. Coffin
A 16
12
8
x
n
B 9~ 16
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Sieve Diameter
~1
9 37
G3 9 8
