Prediction of planktonic protistan grazing rates

Transcription

1 Notes 195 GLIBERT, P. M., C. GARSIDE, J. A. FUHRMAN, AND M. R. LAWS, E Isotope dilution models and the mystery ROMAN Time-dependent coupling of inor- of the vanishing 15N. Limnol. Oceanogr. 29: ganic and organic nitrogen uptake and regeneration PROBYN, T., AND S. J. PAINTING Nitrogen uptake in the plume of the Chesapeake Bay estuary and its by size-fractionated phytoplankton populations in regulation by large heterotrophs. Limnol. Oceanogr. Antarctic surface waters. Limnol. Oceanogr. 30: : , F. LIPSCHULTZ, J. J. MCCARTHY, AND M. A. AL- SYRE~~, P. J Uptake and utilization of nitrogen TABET Isotope dilution model of uptake and compounds, p Zn L. J. Rogers and J. R. Galon remineralization of ammonium by marine plankton. [eds.], Biochemistry of the algae and cyanobacterium. Limnol. Oceanogr. 27: Oxford. GOLDMAN, J. C., D. CARON, AND M. R. DENNETT WARD. B. B.. K. A. KILPATRICK. E. H. RENGER, AND R. Regulation of gross growth efficiency and ammonium w. EPPLEY Biological nitrogen cycling in the regeneration in bacteria by substrate C : N ratio. Lim- nitracline. Limnol. Oceanogr. 34: nol. Oceanogr. 32: 1239-l 252. WHEELER, P. A., AND D. L. KIRCHMAN Utilization HOBBIE, J. E., R. J. DALEY, AND S. JASPER Use of of inorganic and organic nitrogen by bacteria in ma- Nuclepore filters for counting bacteria by fluorescence rine systems. Limnol. Oceanogr. 31: microscopy. Appl. Environ. Microbial. 33: 1225-l 228. HORRIGAN, S. G., A. HAGSTR~M, I. KOIKE, AND F. AZAM Inorganic nitrogen utilization by assemblages of marine bacteria in seawater culture. Mar. Ecol. Prog. Ser. 50: Submitted: 10 November 1992 Accepted: 24 March 1993 Amended: 24 May 1993 Limnol. Oceanogr., 39(l), 1994, , by the American Society of Limnology and Oceanography, Inc. Prediction of planktonic protistan grazing rates Abstract-Data on planktonic protistan feeding though these did not show the saturation of a typical were gathered from the literature and multiple re- hyperbolic relationship, most likely due to the nature gression statistics used to find a model that would of the data and the statistical model itself. Larger predict ingestion rates over a wide range of biological predator concentrations as well as larger prey cell and environmental conditions. The proposed model volumes resulted in decreased ingestion rates. (with an R2 = 0.75) includes temperature and cell volumes and concentrations of both prey and predator as explanatory variables. Data from a lotic sys- Planktonic heterotrophic protists are ubiqtern departed from the rest of the data set, but marine uitous in aquatic environments. Although their and lentic systems were indistinguishable. Whether the grazing experiments were done with direct or ecological role as an energy link to higher troindirect methods was not important. All continuous phic levels or a sink (Pomeroy 199 1) and their variables presented a power relationship with respect importance as nutrient regenerators (Caron to ingestion rate with the exception of temperature, 199 1) are debated, they are a fundamental part which had an exponential relationship. Prey concentration had a direct effect on ingestion rates, al- of aquatic food webs (with the possible exclusion of stream ecosystems). Planktonic protists are thought to have a high washout rate in Acknowledgments streams (Fenchel 1987) but there are excep- I thank Thelma Richardson for computer and SAS as- tions like the Ogeechee River (Georgia) where sistance and the staff of the Statistical Consulting Center Protist abundance is comparable to that of othof the University of Georgia, especially Toshihide Hama- er aquatic habitats (Carlough and Meyer 1989). zaki, for their assistance with multiple regression statistics; To gain a more quantitative grasp of phagohowever, any errors of fact or interpretation remain my sole responsibility. I also thank Susan Bennet, David Bird, trophic protistan impact, it is important to Mike Pace, Karel Simek, Suzanne Strom, and Peter Verity know who eats whom and how much and for sharing unpublished data. Comments by Larry Pom- what biological and environmental variables eroy, Josep M. Gasol, Mary Ann Moran, Peter Verity, and affect dinner and how. Adequate data have three anonymous reviewers greatly improved earlier drafts been gathered on protistan grazing rates from of this paper. This work was supported by NSF grant OCE 91-l 5673 both lab and field experiments over a range of to L. R. Pomeroy and W. J. Wiebe. conditions. Perhaps the most well-known vari-

2 196 Notes able to affect ingestion rates is prey concentration, which in most cases adjusts to a functional response curve of type 2 and can be modeled by a hyperbolic function analogous to that of Michaelis-Menten for enzymatic reactions (Curds and Cockburn 1968). The size of the predator is another factor as shown by the power relationship between maximum ingestion rates and body mass (Moloney and Field 1989). There are few studies on the effect of temperature on grazing rates, but the relationship tends to follow an exponential curve characterized by a Qlo (Rassoulzadegan 1982; Verity 1985; Caron et al. 1986; Choi and Peters 1992) although independence of grazing rates and temperature has also been reported (Stoecker and Guillard 1982). Prey quality also affects grazing rates. Prey geometry has been invoked frequently as a parameter affecting food selectivity in phagotrophic protists (Verity 199 l), although we do not know the relationship of these parameters to grazing rates. Nutritional aspects of prey quality are being considered as food selectivity parameters (Verity 199 l), but we do not even know what chemical cue, if any, is responsible for differences in grazing rates. How all these variables interplay has not been studied. This complexity of interactions and natural histories forces use of criteria such as guilds or size classes of planktonic protists rather than taxonomy (Capriulo et al ) to study grazing rates. My purpose here is twofold. First, to present a model, using multiple regression statistics on data from the literature, that predicts grazing rates of planktonic heterotrophic protists based on size. Second, to determine the relationships of grazing rates to the different predictor variables. The literature was examined for grazing experiments (lab and field studies) dealing with planktonic protistan predators. Grazing rates are reported in many different ways, from feeding or ingestion rates estimating prey ingested per predator per unit time to clearance rates estimating volumes of water swept clear of prey per predator per unit time. Data were gathered from 47 sources accounting for 804 observations that included some type of grazing rate and other variables. Observations were considered on the basis of the values being directly reported or easily calculated from information in the source. No extraneous conversion factors were used. In some cases cell volumes were calculated from reported sizes by assuming a spherical shape. When data had to be read from graphs, these were enlarged to gain accuracy. The goal was to elucidate what possible variables affect the consumption of prey particles by different predators and how those variables function. Ingestion rate is an immediate measure of the feeding process, while clearance is the result of this process. On the basis of this reasoning, ingestion rate was chosen as the response or dependent variable. Temperature, concentration of prey and predator, and cell volumes of prey and predator were chosen as possible explanatory variables. The type of aquatic system (marine, lentic, and lotic), the type of method used to measure ingestion rate (direct vs. indirect), and the type of environment where the experiments were performed (lab vs. field) were used as qualitative variables (a list of nomenclature is provided). In the case of field studies, an additional constraint was set: only sources that used direct methods (use of tracer particles as prey surrogates) were considered due to the diffi- culty of assigning grazing rates to different organisms when using indirect methods (decrease in prey, inhibition, filtration, etc.). Observations that contained information for all the variables totaled 329 (Table 1). Exploratory analysis showed the need to logtransform all the continuous variables, except temperature, to correct for distribution skew- ness and inequality of variance over the range of observations. A base- 10 log was used and the transformed variables were named LNIR, LNVPY, LNVPD, LNCPY, and LNCPD (Fig. 1 gives an example). Nomenclature IR Ingestion rate, No. prey predator- h-l TEMP Temp., C VPY Prey cell vol., pm3 VPD Predator cell vol., pm3 CPY Prey concn, No. prey mlkl CPD Predator concn, No. predator ml- SYST System METH Method ENV Environment

3 Table 1. Data sources used in calculating multiple regression models (n-number of observations obtained from a specific source). Reference n Predator System Methodological procedure Lab studies Scott 1985 Goldman and Caron 1985 Choi and Peters 1992 Sherr et al Pace and Bailiff 1987 Goldman and Dennett 1990 Verity 1985 Chrzanowski and Simek 1990 Wikner et al Sherr et al Fenchel 1982 Grover 1990 Goldman et al Goldman et al Bloem and Bar-Gilissen 1989 Caron et al Nakamura et al Strom 1991 Verity 1991 Hansen 1992 Field studies Sherr et al Bennett et al Simek and StraSkrabova 1992 McManus and Fuhrman 1988* Andersen and Fenchel 1985* Carlough and Meyer t Ciliates Ciliates Ciliates Ciliates Dinoflagellates Dinoflagellates Dinoflagellates Ciliates Dinoflagellates Ciliates Ciliates Ciliates Dinoflagellates Cyanobacteria Lentic Lentic Lentic Lentic Continuous culture Fluorescently labeled bacteria Continuous culture Decrease in dry weight Fluorescently labeled bacteria Genetically marked bacteria Decrease in dry weight Ingested algae Lentic Lentic * Used only in the estimation of the field subset multiple regression model because these sources lack information on the cell volume of bacteria. t Not used for the estimation of any model. Lotic Fluorescently labeled algae Fluorescent beads Fluorescently labeled bacteria Fluorescent microspheres Fluorescently labeled bacteria

4 198 Notes r = LNVPY Fig. 1. Log-log plot of ingestion rate vs. prey cell volume, where r is Pearson s correlation coefficient Model Estimate of LNIR Fig. 2. Plot of residual values vs. predicted ingestion rates for model 2 (see Table 2).. The multiple form regression model used is of the Y = (30 + p,x, &X, + c. Y is the dependent variable, the p terms are parameters for the independent variables (x), and the error term E is assumed to average 0. I determined which variables were important for this model with a stepwise selection procedure with a significance to enter the model of 0.05 and a significance to stay of 0.1. This procedure was used in combination with a maximum R2 improvement technique, which evaluates all variable switches before any switch is made (SAS Inst. Inc. 1988). Both techniques gave the same models. Residual and influence plots were obtained to check for outliers and highly influential observations. Residuals had a normal distribution, as determined by the Shapiro-Wilk test and normal probability plots, and a random scatter when plotted against the predicted values of LNIR (Fig. 2). Several statistical outliers and large leverage points were inspected. After determining that there was no reason to doubt the accuracy of the measurement of those entries and that they showed no statistical trend, model selection procedures were performed dropping one outlier or large leverage point at a time. When no more of these points appeared, the resulting model was inspected against the original. These models were always the same in terms of variable selection and parameter sign. Slight changes in the parameter estimate values were not significantly different at the 95% CL. The model without outliers had a slightly higher R2. Based on these analyses, I determined to keep the outliers in the data set. It was impossible to cross-validate the model with an independent data set because no additional data are available. However, there is a procedure for both selecting the best model and internally validating it. It involves the predicted residuals sum-of-squares (PRESS), which has been used to validate a model with similar statistical characteristics (White et al. 1991). A predicted residual is obtained by dropping an observation, recalculating parameter estimates, estimating the omitted observation, and subtracting it from the observed value. The process is repeated for all observations and the sum-of-squares calculated. The model with the lowest PRESS has the highest predictability value and, in this case, it coincided with the model selected by the other criteria. All continuous variables were selected into the model. Lotic systems appeared to be sub- stantially different from the rest of the data set, presenting consistently lower ingestion rates. The data entries for lotic systems all come from a single source and because the number of points is low (34), no further statistical anal-

5 yses were performed on this data subset. For the other data, marine systems were not different from lentic systems. The choice of method used to determine ingestion rates (METH) was not an important variable. However, ENV (lab vs. field study) was important (Table 2, Eq. 1). In the field data subset (Table 2, Eq. 4) the volume of prey is not selected as an explanatory variable, most likely because bacteria are the prey particles in all the sources so that there is very little prey cell-volume variability. Further inspection of the data set suggested that the differences between the laboratory and the field environment were not due to the environment per se but to the fact that direct ingestion experiments with large ciliates and dinoflagellates have not been performed in situ. Unfortunately, studies using indirect methods, although giving good estimates of community grazing, are poor at identifying the organisms that do the actual grazing and consequently had to be omitted. ENV is the last variable entered in the model represented by Eq. 1, and the predictive power gained by its inclusion is very small. The remainder of the discussion will focus on analysis of Eq. 2. (The model represented by Eq. 3 for the laboratory subset is not discussed here.) As can be seen from the R2 value (Table 2) the model explains 75% of the variability in the data set, which is quite high when the heterogeneity of organisms and their feeding mechanisms, habitats, physiological states of predators, and methods used are considered. When the model is transformed back into the original variables, we obtain an expression of the type: IR = 0.002Vpy-0.344VpD0.477Cpy0.489.CpD lo(o.033 TEMP) (5) Notes 199 The confidence interval of an individual or mean predicted observation will be slightly skewed (i.e. not centered around the mean in Eq. 5 due to transformation back into the original variables). Also, larger predicted values will have larger confidence intervals, as they reflect the nature of the original data where variances increase with means. This behavior is illustrated in Fig. 3 where means of predicted

6 200 Notes a o Temperature ( C) Temperature ( C) Fig. 3. Predicted ingestion rate and 95% confidence bands vs. temperature for the untransformed (a) and transformed (b) dependent variable. Variable settings are VPY = 0.5, VPD = 500, CPY = 106, and CPD = 103. IR and the 95% confidence bands are shown responding to temperature with values that would be typical for a heterotrophic microflagellate. Figure 3a and b are identical except for the y-axis. The regression equation has predictability value as shown by the high R2, especially when variable settings remain in the ranges of the original data. Caution is advised when extrapolating beyond the range of observations or when using combinations that are not represented in the entries. For instance, although there are some reports of protozoa ingesting particles larger than themselves (Suttle et al and references therein), they are not included in the data set and predicted rates might be misleading. Table 3 shows minimum and maximum values for the different variables to give an idea of the range of values from which this model was derived. These ranges are by no means intended to have a strict biological meaning because trends in experimental design and the relatively small number of sources could bias them. Taking the above points into consideration and having values for the explanatory variables for a particular system should enable prediction of the order of magnitude of any kind of grazing rate for planktonic phagotrophic protists. The model was used to predict ingestion rates for a range of freshwater and marine ecosystems and characteristic predator-prey interactions (Table 4). Some values had to be chosen arbitrarily while others represent only seasonal occurrences, and the predictions are not to be taken as spatial nor temporal aver- Table 3. Observed ranges for untransformed variables partitioned by predator biovolume. IR TEMP VPY VPD CPY CPD VPD I lo2 Min x (N = 81) Max x lo8 7x 104 lo2 < VPD I 103 Min (N = 90) Max X x lo5 IO3 < VPD I lo4 Min x (N = 33) Max 1.09x x lo x x x lo3 lo4 < VPD I 105 Min x x (N = 76) Max x x x lo5 < VPD Min X (N = 94) Max x lo X All Min (N = 329) Max 1.09x x x lo X x lo5

7 ages for the systems. Predicted ingestion rates fit well within the values reported in the literature and the reader can easily calculate other grazing rates, including community clearance rates. The largest discrepancies between observed and predicted ingestion rates are less than an order of magnitude and could easily result from an arbitrary choice for a specific variable. As new studies are conducted, greater ranges of values for predictor variables will become available and the model can be improved. Researchers in protozoan feeding are urged to report concentrations and cell volumes of both prey and predator as well as temperature measurements because all these variables seem to be important in determining grazing rates. Also, there is a need for more grazing studies, especially in the field, involving direct measurements of ciliate grazing on an array of prey sizes and taxa and of heterotrophic dinoflagellates (Table 1)-a potentially important but poorly studied group of organisms in marine systems (Strom 199 1). The response of ingestion rate to temperature is exponential (Fig. 3) as has been shown by empirical studies. Temperature is the last variable in entering the model, although few would argue that temperature is an unimportant controller of any metabolically related process. Its importance could be masked by other variables that are highly influential because of their wide sampling ranges. Part of the problem might be the scarcity of protistan feeding studies with temperature as a manipulated variable. Several field studies had to be discarded because water temperature was not reported. The Qlo parameter, implicit in the partial coefficient for temperature, has a value of In the case of ciliates, Verity (1985) reported a range of for Titinnopsis, and Rassoulzadegan (1982) reported a value of 3.29 for Lohmaniella spiralis. Different ecotypes of the microflagellate Paraphysomonas imperforata showed Qlo values of 3.7 (Caron et al. 1986) 2.05, and (Choi and Peters 1992). The average of these values is 2.82, relatively close to the value of 2.14 (1.9 l-2.40) derived from the statistical model. This fact would strengthen the assumption that the parameter estimates are very close to their true values. The nature of the response of ingestion rate Notes 201

8 202 Notes vs. the other variables is a power function of Q the form % M Rate = a variableb where a and b are constants. These relation- & ships were, in a sense, determined a priori by Q the choice of variable transformations used, and their functionality might not always have ; & a biological meaning. On the other hand, if the 5 CPD=lOOO true relationships were substantially different, 3 the log transformation would not have linear- *s ized the data and equalized the variances as successfully as it did. Whether they have a true Y 2 biological meaning cannot be discerned here, but we can try to find points in common with Prey concentration (#prey ml-l) x106 known functionalities. The relationship between maximum inges- Fig. 4. Predicted ingestion rate vs. prey concentration for different predator concentrations. Variable settings are tion rates and predator cell volumes has long VPY = 0.5, VPD = 500, and TEMP = 15. been documented as following a power curve. Estimated b coefficients have been reported in the range of for a wide variety of aquat- present the high prey concentrations necessary ic organisms, with most values being close to to be in the region of constant ingestion rate (Moloney and Field 1989). In the present The true relationship could well have an ascase, b is 0.477, showing that the ingestion rate ymptote at large prey concentrations; however, increases with increased predator cell volume to test this relationship in the multiple regresin a decelerate way (when linearized the slope sion model, with many of the variables being is < 1). I did not use maximum ingestion rates, log transformed, is difficult. which do not allow one to find a relationship Predator concentration with b = has that holds for nonsaturating prey concentra- the opposite effect (compared to prey concentions and varying temperatures. This discrep- tration) on ingestion rate. Larger predator conancy makes the values of the coefficients dif- centrations result in a lower ingestion rate. ficult to compare but shows that the power Some sort of chemical interference among the relationship holds for changing temperatures predators as they become crowded could exand concentrations of prey. plain the observed trend. However, the rela- Prey concentration and predator concentration also present power relationships with respect to ingestion rate. Higher values of prey concentrations will produce higher ingestion rates (b = 0.489) provided that the remaining variables stay constant. This result is logical because prey concentration should have a direct effect on the encounter rate of predator and prey. However, the relationship should have an upper limit, since ingestion rate should be restrained ultimately by some biochemical parameter such as the rate of membrane production for food vacuole formation (Fenchel 1987; Capriulo et al ). In cultures, the relationship is usually adjusted to a hyperbolic function, but Porter et al. (1982) showed that a suite of mathematical relationships, including a power function, would fit their experi- mental data for the cladoceran Daphnia. Moreover, in the present data set, few entries tionship holds for a wide range of organisms, so it seems more likely that it has some relation with the amount of prey available per predator, showing exploitative competition for a food resource. The effect of predator concentration on the functional response curve is shown in Fig. 4. Curds and Cockbum (1968) found a very similar relationship showing that the concentration of the ciliate Tetrahymena pyriformis actually affected its functional response curve when fed the bacterium Klebsiella aerogenes. They suggested that mutual interference between the feeding currents of the ciliates could be the cause. There is a need for experiments of this kind to be repeated to confirm the effect of predator concentration alone. The response to cell volume of prey is another power function, with b = This variable is the first one selected in the different models with the exception of the model for the

9 Notes % , A % #prey b ----_ Pm3 prey a z a a. 2 b,) / # 20- /, 36) z S g VJ I G 2 0, 0, , 30., 40 p 5o Prey cell volume (prr-3) Fig. 5. Predicted ingestion rate as No. prey predator I hp I and as pm3 prey predator hp vs. prey cell volume. Variable settings are VPD = 500, CPY = 106, CPD = 103, and TEMP = B 3.6pm m 71w.I Prey ESD (pm) Fig. 6. Data set-derived ranges of prey size grazed by different predator size categories. Predator size labels are means for the different predator categories (see Table 3). ESD- Equivalent spherical diameter. field data subset for reasons mentioned above. Larger prey cell volumes result in a decreased ingestion rate. To my knowledge, this relationship has not been studied before, although it has been suggested to be important, at least for flagellates (Capriulo et al ). Figure 5 shows IR as No. prey predator-i h-l and as pm3 prey predator-l h-l vs. VPY for a typical flagellate. There is an increase in No. prey ingested per unit time as prey cell volume decreases. This increase is intuitively sound since a predator has to ingest more particles to obtain the same amount of food. On the other hand, total prey volume ingested increases with increasing prey cell volume. Can we predict the effect of an increased volume of prey ingested on the predator s metabolism? Consider the following equality: SGR (d- ) = SIR (d-i) GGE. (6) SGR is the specific growth rate of the predator, SIR the specific ingestion rate calculated as the volume of prey ingested per volume of predator per unit time, and GGE the conversion efficiency of prey volume into predator volume. If SGR remains constant, higher SIR should be accompanied by lower efficiencies, but if GGE remains constant, an increase in SIR translates into a higher growth rate. During balanced growth, constancy of GGE vs. growth rate is supported by empirical evidence (Fenchel 1987) provided temperature and prey quality are held constant. Alternatively, both SGR and GGE could change in a complicated way with respect to ingestion rate, which is likely to be the case in this data set with variability in temperature, prey quality, physiological states, and a range of predator types. Food selectivity studies based on prey size point toward an optimum prey particle size (Rassoulzadegan and Etienne 198 1; Jonsson 1986). Other studies have been done with only a small range of prey particle sizes, showing preferred grazing on extreme sizes. It is significant in such cases that when only small prey particles such as bacteria are offered, preference is toward larger particles (Andersson et al. 1986; Chrzanowski and Simek 1990; Gonzalez et al. 1990), while preference is toward smaller particles when prey size is comparable to predator size (Goldman and Dennett 1990). Again, these preferences point toward an optimum prey size class. If the predator is actively selecting prey, some energetic advantage is the ultimate selective advantage. As prey size increases, it would seem that there is a fine trade-off between growth rates and conversion efficiencies that maximizes the predator s en- ergetics. Although it should not affect this discussion, the fact that protists change their size depending on different variables complicates the conclusions if an actual organism is con- sidered rather than an imaginary predator size class. Values for maximum and minimum prey cell sizes were obtained for each predator cell

10 204 Notes category (see Table 3). These were plotted against the mean predator size in each category (Fig. 6) to obtain the range of reported prey sizes for the different predator classes. The smallest predator size class is represented entirely by microflagellates, which also dominate the next larger size class. Ciliates are represented in all size classes except the smallest, and they heavily dominate the two largest size classes. Dinoflagellates can be found in the intermediate classes, mostly in the 1,000-l O,OOOpm3 classes. If we assume that a mean predator size is representative of the taxa in a predator size category and that reported ranges are representative of true prey ranges rather than of experimental design bias, bacteria-sized particles are normally ingested by predators < 50- pm ESD. The range of prey sizes in smaller predators seems to be limited by predator size itself, while predators >50-pm ESD seem to use a narrower range of prey sizes. It must be noted that in this latter case, the data come from only three sources. Large predators could have an upper prey size limit set by a short axis or mouth part and a lower prey size limit set by the particle retention effectiveness of their feeding structures. In contrast, some small phagotrophic protists have been shown to have a great morphological plasticity, varying their sizes depending on the prey size offered. The microflagellate P. imperforata feeding on different-sized algae showed a 5-fold change in cell volume (Goldman and Dennett 1990), whereas Suttle et al. (1986) reported microflagellates ingesting diatoms 6 times longer than their own diameter. As mentioned earlier, prey cell volume is the first variable entered in the models, explaining, on its own, 20% of the variance in the data set, yet our understanding of the effect of this parameter on protozoan grazing and physiology and the energetical trade-offs involved in prey selection is incomplete. More insight should be gained when this model is incorporated into more elaborate dynamic models but, in general, there is a need for more population studies, including optimal foraging studies (Pomeroy 199 l), to see the effect of highly understudied variables such as prey size and temperature and to extend the ranges of variable values tested. Moreover, there is an obvious need to gather more information on potentially important factors in protistan feeding, such as prey selectivity based on nutritional characteristics. So far, the model will enable researchers to estimate protistan grazing impact in particular systems even when time or manpower constraints make grazing experiments impractical. Institute of Ecology University of Georgia Athens References Francesc Peters ANDERSEN, P., AND T. FENCHEL Bacterivory by microheterotrophic flagellates in seawater samples. Limnol. Oceanogr. 30: ANDERSSON, A., U. LAR~SON, AND A. HAGSTR~M Size-selective grazing by a microflagellate on pelagic bacteria. Mar. Ecol. Prog. Ser. 33: BENNEIT,S.J.,R.W. SANDERS,ANDK.G.PORTER Heterotrophic, autotrophic, and mixotrophic nanoflagellates: Seasonal abundances and bacterivory in a eutrophic lake. Limnol. Oceanogr. 35: 182 l-l 832. BLOEM, J., AND M-J. B. BWR-GILISSEN l activity and protozoan grazing potential in a stratified lake. Limnol. Oceanogr. 34: CAPRIULO, G. M., E. G. SHERR, AND B. F. SHERR Trophic behaviour and related community feeding activities of heterotrophic marine protists, p In P. C. Reid et al. [eds.], Protozoa and their role in marine processes. Springer. CARLOUGH, L. A., AND J. L. MEYER Protozoans in two southeastern blackwater rivers and their importance to trophic transfer. Limnol. Oceanogr. 34: , AND Bacterivory by sestonic protists in a southeastern blackwater river. Limnol. Oceanogr. 36: CARON, D. A Evolving role of protozoa in aquatic nutrient cycles, p In P. C. Reid et al. [eds.], Protozoa and their role in marine processes. Springer. -, J. C. GOLDMAN, AND M. R. DENNETT Effect of temperature on growth, respiration, and nutrient regeneration by an omnivorous microflagellate. Appl. Environ. Microbial. 52: 1340-l 347. CHOI, J. W., AND F. PETERS Effects of temperature on two psychrophilic ecotypes of a heterotrophic nanoflagellate, Puraphysomonas imperforuta. Appl. Environ. Microbial. 58: CHRZANOWSKI, T. H., AND K. SIMEK Prey-size selection by freshwater flagellated protozoa. Limnol. Oceanogr. 35: CURDS, C. R., AND A. COCKBURN Studies on the growth and feeding of Tetrahymena pyriformis in axenic and monoxenic culture. J. Gen. Microbial. 54: FENCHEL, T Ecology of heterotrophic microflagellates. 2. Bioenergetics and growth. Mar. Ecol. Prog. Ser. 8: Ecology of protozoa. Sci. Tech. GARRISON, D. L.,K. R. BUCK,ANJJ M. W. SILVER

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12 206 Notes HARRISON Grazing of planktonic diatoms by WIKNER, J., A. ANDERSSON, S. NORMARK, AND A. microflagellates. J. Plankton Res. 8: HAGSTR~M Use of genetically marked mini- VERITY, P. G Grazing, respiration, excretion, and cells as a probe in measurement of predation on bacgrowth rates of tintinnids. Limnol. Oceanogr. 30: teria in aquatic environments. Appl. Environ. Micro biol. 52: Feeding in planktonic protozoans: Evidence for non-random acquisition of prey. J. Protozool. 38: WHITE,P.A.,J.KALFF,J.B.RASMUSSEN,ANDJ.M.GASOL The effect of temperature and algal biomass on bacterial production and specific growth rate in freshwater and marine habitats. Microb. Ecol. 21: WRIGHT, R. T., AND R. B. COFFIN Measuring microzooplankton grazing on planktonic marine bacteria by its impact on bacterial production. Microb. Ecol. 10: Submitted: 9 September 1992 Accepted: 10 May 1993 Amended: 2 June 1993 Limnol. Oceanogr., 39(l), 1994, C 1994, by the American Society of Limnology and Oceanography, Inc. Increasing the sensitivity of a FACScan flow cytometer to study oceanic picoplankton Abstract-Existing limitations on the sensitivity of commercially available flow cytometers do not permit the detection of extremely dim picoplankton cells, particularly Prochlorococcus marinus in nearsurface oligotrophic ocean waters. This problem was overcome by making some simple modifications to a FACScan (Becton Dickinson) flow cytometer to increase its sensitivity by about fivefold. The fluorescence and side scatter sensitivity was increased by replacing the existing focusing lens and beamsteering mirror with a 3 x expansion telescope and a slightly shorter focusing lens, resulting in a smaller laser spot size at the interrogation point. Forward scatter sensitivity was increased by replacing the existing photodiode with a photomultiplier tube, which has a background noise level about an order of magnitude lower. The fluorescence sensitivity of the modified instrument, measured with a quantitative fluorescence microbead standards kit, is 74 molecules of equivalent soluble fluorochrome for fluorescein. The instrument can now detect extremely dim P. marinus from open-ocean surface waters. Sea-going flow cytometry has been used to study oceanic picoplankton for some time (Yentsch et al. 1983; Olson et al. 1985; Li and Wood 1988). These measurements have been Acknowledgments We thank Bob Hoffman, Rob Olson, Brian Binder, and Penny Chisholm for helpful discussions, and the captain and crew of the RV Oceanus. This work was supported by NSF (BSR and OCE to S. W. Chisholm, OCE , OCE , and DIR 9 1-O to S.W.C. and R. J. Olson), ONR (N K-0007 to S.W.C. and R. J. Olson), and M.I.T. Sloan Funds (to S. W.C.). J. A.D. was supported in part by an NSF graduate student fellowship. made almost exclusively with the use of commercial instruments designed for biomedical use. High sensitivity measurements (needed to detect low fluorescence picoplankton such as Prochlorococcus marinus) typically require large instruments that use high-power watercooled lasers (e.g. Epics V, Coulter Corp.), making shipboard use inconvenient. Even these instruments require modifications to enable them to detect dimly fluorescent picoplankton (Olson et al. 1990). Smaller air-cooled clinical instruments typically lack the flexibility and sensitivity needed for oceanographic use, although their relative portability and ease of use would otherwise make them ideal. For example, the FACScan (Becton Dickinson) is quite sensitive, but it cannot always detect P. marinus in near-surface oligotrophic waters where cellular fluorescence can be quite low. The unmodified FACScan uses a 15mW argon ion laser, a set of prisms to expand the beam in the vertical dimension, a 6%mm plano-convex lens to focus the laser beam down to a spot size of -20 pm (vertical) x 60 pm (horizontal), and a beam-steering plate to finetune the position of the spot (Fig. 1). Five signal parameters are collected as cells pass through the laser beam: forward light scatter, right-angle light scatter, and green (530 nm, bandwidth at half-maximum of 30 nm), orange (585 nm, bandwidth at half-maximum of 42 nm), and red (>650 nm) fluorescence. For studying picoplankton, only the two scatter parameters, the orange (phycoerythrin) and the