Vol. 51, No. 6APPLIED AND ENVIRONMENTAL MICROBIOLOGY, June 1986, p. 1199-12040099-2240/86/061199-06$02.00/0Copyright © 1986, American Society for Microbiology

Automatic Determination of Bacterioplankton Biomass byImage Analysist

PETER KOEFOED BJ0RNSENFreshwater Biological Laboratory, 3400 Hiller0d, Denmark

Received 15 October 1985/Accepted 24 February 1986

Image analysis was applied to epifluorescense microscopy of acridine orange-stained plankton samples. Aprogram was developed for discrimination and binary segmentation of digitized video images, taken by an

ultrasensitive video camera mounted on the microscope. Cell volumes were estimated from area and perimeterof the objects in the binary image. The program was tested on fluorescent latex beads of known diameters.Biovolumes measured by image analysis were compared with directly determined carbon biomasses in batchcultures of estuarine and freshwater bacterioplankton. This calibration revealed an empirical conversion factorfrom biovolume to biomass of 0.35 pg of C ,um-3 (+ 0.03 95% confidence limit). The deviation of this valuefrom the normally used conversion factors of 0.086 to 0.121 pg of C ,um-3 is discussed. The described systemwas capable of measuring 250 cells within 10 min, providing estimates of cell number, mean cell volume, andbiovolume with a precision of 5%.

The increasing knowledge of bacterioplankton as an im-portant and dynamic compartment of pelagic ecosystems (2,18) has stressed the need for accurate and reproduciblemeasurements of bacterial biomass. Usually, biomass isestimated from measurements of biovolume (30). Cell num-ber and mean volume of bacteria are also required for theconversion of zooplankton grazing rates on bacteria intoingestion rates (8, 19, 25) and for the calculation of bacterialnet production from [3H]thymidine incorporation (12, 13) orfrequency of dividing cells (15, 23).Enumeration of acridine orange-stained bacteria by epi-

fluorescence microscopy is now a widely used method forthe estimation of cell number and biovolume. It has beenpreferred to scanning electron micrography and biochemicaltechniques due to its specificity and ease of preparation (17,30, 32).

Cell number is usually converted into biovolume by mul-tiplication by a mean cell volume estimated from a fewsamples or obtained from the literature (30). This procedureis dubious, since the mean cell volume can vary considerablywith time and space (22, 26). Ideally, cell number and meanvolume should therefore be determined simultaneously oneach sample, but such a procedure is tedious without autom-atizing.

Microscopic counting and size measurements of bacteriaseems to be an obvious task for automatizing by imageanalysis, but only a few authors have yet made use of thisapproach. Krambech et al. (20) projected scanning electronmicrographs onto a digitizer tablet and marked each cellperiphery by a cursor. From this input cell number and meanvolume were calculated by a microcomputer program.Sieracki et al. (28) applied image analysis to digitized videoimages of diamidinophenylindole-stained bacteria. A binaryimage was segmented by an interactively chosen thresholdgrey level, and the number and area distribution of the"silhouettes" were calculated.This paper represents a further step towards an automated

determination of bacterial cell volumes and biovolumes. The

t Contribution no. 442 from the Freshwater Biological Labora-tory.

effort was focused on development of an algorithm forreproducible discrimination and segmentation of acridineorange-stained bacteria from natural plankton samples. Thesegmentation and volume determination were evaluated onfluorescent latex beads of known diameters, and as a furthercalibration biovolumes measured by image analysis werecompared with directly determined carbon biomasses ofbacterioplankton batch cultures.

MATERIALS AND METHODS

Sample preparation. The technique ofHobbie et al. (17) wasessentially followed. The samples were killed by Formalin toa final concentration of 1%. Subsamples of 1 or 2 ml werestained with acridine orange (0.02% final concentration) for 2to 5 min and filtered onto 0.2-jxm Nuclepore filters, prestainedin irgalan black. The filters were rinsed five times with 1 mlof water, dried, and mounted in Cargille B immersion oil justbefore microscopy.

Suspensions of fluorescent monodisperse latex beads(Polysciences Inc., Warrington, Pa.) were pipetted directlyonto microscope slides, allowed to dry, and mounted inimmersion oil.

Equipment. The hardware setup is outlined in Fig. 1. AZeiss Universal microscope was modified for epifluores-cence microscopy by using an HBO 100-W mercury lamp, aIIC FL 02 vertical illuminator with filter set 487711 (BP450-490 exciter filter, FT 510 beam splitter, and LP 520barrier filter), and a Planachromat 100/1.25 oil objective. Asilicon intensified target camera (SIT 733, Nighthawk; JAIInc., Glostrup, Denmark) with video amplifying (1 to 100times) was connected to the microscope by a 4x TV-tubus(Zeiss 477909).The video signal was converted to digital form by an array

processor (IBAS 2, Zeiss/Kontron) containing eight direct-access image memories of 512 x 512 pixels at 256 grayvalues plus color overlay. The array processor was con-trolled by a host computer (IBAS 1, Zeiss/Kontron) con-nected to a hard disk for program and data storage. The hostcomputer also controlled the autofocus and autoscanningstage via a microscope control processor.

Instructions to the host computer could be given through

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FIG. 1. Hardware configuration.

a keyboard or by a digitizer tablet with a cross-hair cursor,which also mediated input for interactive image editing.Each step of the image processing could be followed on amonitor and compared with the original video image dis-played on a separate monitor. A third monitor and a printerproduced the output of measuring data.An attempt to apply standard image analysis equipment to

epifluorescence microscopy of stained bacterioplanktonsamples primarily met two problems: (i) the smallest bacteriaoccupied too few pixels in the digitized image to allow aproper discrimination and segmentation of the objects, and(ii) the weak fluorescence of the bacteria raised the objectsonly a few grey levels above background. The first problemwas solved by inserting a TV-tubus with a factor 4 extramagnification. Combined with a x 100 objective, a scalefactor of 0.0521 Klm per pixel was revealed. A sphere of0.2-,um diameter would thus occupy approximately 10 pixelsin the digitized image. The silicon intensified target cameratype with video amplifying provided sufficient light sensitiv-ity (6 x 10-4 lx) for this high magnification, but alsointroduced background noise and blooming of the objects.The noise could be eliminated by averaging several succes-sive video images. The blooming was compensated for by anobject erosion procedure in the measuring algorithm. As analternative setup a Bosch TYK 9B video camera with a 1-in.(2.54-cm) Pasecon XQ 1466 tube was tested. This cameraprovided less noise and blooming, but required the additionof more than 10 video images to produce a digitized imagewith detectable objects, and even so, some weakly stainedbacteria were lost.

Procedure. A measuring program was constructed fromthe menu functions of the Zeiss/Kontron IBAS software(Fig. 2). To get results in micrometers, the microscope andvideo camera system were calibrated by an object microm-eter, using transmitted light. For each preparation, focusplane and start point of the scanning path were definedinteractively. A minimum of 15 fields and 250 cells wereevaluated. The stepwise discrimination and segmentation ofeach field are visualized in Fig. 3. An introduction to thetheory of digital image processing is given in Niblack(24).Immediately after the scanning stage had moved to a new

field and focus had been found, 10 successive video imageswere averaged, and the resulting gray level range waslinearly rescaled to the maximum interval of 256 (0 to 255)gray levels (Fig. 3A). The addition of images reduced thenoise contribution and improved the gray level truncation,while the rescaling was aimed at compensating for differ-ences in staining intensity and background fluorescence

between the evaluated fields. To compensate for unevenbackground fluorescence within the fields, a reference imagewas created by passing a rank order filter (size, 15 x 15pixels; rank 50) over each image. Shading correction wasmade by subtraction of the reference image (Fig. 3B). Acontour enhancement filter (5 x 5 pixels; eight directions)was then passed over the image (Fig. 3C), providing aselective blowup of well-demarcated objects and facilitatingthe following binary segmentation (at gray level 100) bysharpening the edges. The binary image (Fig. 3D) was

Scalefactor calibration (npm pix-1

i

ewil

Histogram printing ( output)

F1G. N.Alew preparation?

FIG. 2. Algorithm flow diagram. pix, Pixel.

Scanning and focus definition

Scanning movement and autofocusAddition of video images ( input)

RescaLing and shade correction

Contour enhancementBinary segmentationSmoothing and erosionInteractive image editingObject identification and measuringData classification and storing

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AUTOMATIZED BIOMASS DETERMINATION BY IMAGE ANALYSIS

FIG. 3. Hard copies of a digitized image after averaging of input video images (A), shade correction (B), contour enhancement (C),segmentation (D), erosion (E), and interactive editing (F). A heavily detritus-loaded sample was chosen in an acridine orange-stained planktonpreparation from Roskilde Fjord, Denmark. Hard copies were plotted by a matrix printer providing a resolution of 11 gray levels. A gray scaleof 75 pixels in width (- 3.9 p.m) was inserted into images A and B.

smoothened by a median filter (3 x 3 pixels; rank, 5), whichalso eliminated objects of less than 5 pixels (Fig. 3E). Theresulting binary image could finally be edited by the cursorand digitizer tablet during an interactive step by comparisonwith the original video image (Fig. 3F). The editing includedremoval of remaining detrital particles, separation of adjoin-ing bacteria, etc. However, >90% of the images could beaccepted for measurement without any editing. Before mea-suring, the objects were identified according to a measuringframe of 500 x 500 pixels. Only objects within the frame ortouching the lower or the left boundary were accepted. Foreach object, area (AR) and convex perimeter (CP) weremeasured, and the volume was calculated as 8.5 AR2_5 CP-2.This empirical formula is valid for spheres and rods withgood approximation.The calculated volumes were stored into 50 logarithmic

classes between 0.01 and 1 ,m3, and after an appropriatenumber of fields had been evaluated, a histogram of thevolume distribution was printed out with additional informa-tion on number of counts, mean volume, and standarddeviation.

Calibration to carbon biomass. The conversion ofbacterioplankton biovolume, as measured by image analysis,and carbon biomass was evaluated on batch-cultured bacte-ria from three Danish locations: the estuary of RoskildeFjord (1.5% salinity), oligotrophic Lake Slaaen, andeutrophic Lake Bryrup Langs0.

Samples of 500 ml of particle-free water were inoculatedwith 25 ml of 1-,um-filtered water treated with cycloheximide(25 mg liter-') for 2 h to eliminate flagellate grazing (14).Some batches were enriched with phytoplankton hydroly-sate, obtained from phytoplankton filtered onto GF/C filters,acidified to pH 2, and autoclaved for 1 h. The batch cultureswere incubated on a rotary shaker at ambient temperature,and at 50- to 70-h intervals 25-ml samples were used asinocula for new cultures. Parallel samples were taken forbiovolume determination (as described above) and for mea-surement of particulate organic carbon after the second andthird periods of batch growth. For measurement of particu-late organic carbon, 25-ml samples were filtered onto GF/Ffilters, which had been preoxidized in concentrated sulfuricacid with 0.25 N dichromate for 2 h to reduce filter back-ground. The filters were rinsed five times with 1 ml of sterilefiltered distilled water, dried at 60°C for 15 min, andcombusted at 700°C in a Heraus quartz tube furnace in anoxygen flow. The produced amount of CO2 was measured byan infrared gas analyzer (ADC model 225 MK 3). Samples(25 ml) of the initial culture medium were treated identicallyand subtracted as blanks. The particulate detritus present inthe inocula was diluted by a factor of 400 before the firstsampling, and no particulate detritus formation was ob-served by epifluorescence microscopy. The measured par-ticulate organic carbon was, therefore, assumed to reflectthe bacterial carbon biomass.

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0.01 0.1VOLUME vpm3

FIG. 4. Distribution of measured volumes on fluorescent latex beads of 0.22, 0.57, 1.0, and 2.0 ,um in diameter.

RESULTS AND DISCUSSION

Cell volume determination. The measuring program wastested on fluorescent latex beads of 0.22, 0.57, 1.0, and 2.0pum in diameter. The distribution of measured bead volumes(Fig. 4) indicates a dynamic range of 0.001 to 10 ,um3, withreasonable accordance between theoretical and measuredvolumes (Table 1).

Definition of the boundaries of the objects (i.e., objectsegm.entation) is a crucial step in image analysis of cellvolumes. Even when the microscope is perfectly optimizedand focused, the bacteria will appear blurred in the videoimage owing to the limited resolving power of light micros-copy. A simple segmentation at a fixed-threshold gray levelis therefore not appropriate for a reliable and reproduciblesize measurement of objects in the resulting binary image.The rescaling and contour enhancement steps of the mea-suring algorithm ensure a segmentation based on maximumcontrast rather than on a fixed light intensity.

Microscopic cell volume determination is based on two-dimensional images, and the third dimension must be as-sumed. Bacteria are usually considered to be rods, i.e.,cylindrical with hemispherical ends, and the volume (VOL)is calculated as follows: VOL = Ir/4 x w2 (I - w/3), where Iis length and w is width (11, 20).

Standard image analysis measuring routines include esti-mates of maximum and minimum diameters based on anumber of projections. This procedure will yield maximumestimates of width, however, and the error is squared byvolume calculation.

TABLE 1. Comparison of specified and measured mean volumesof fluorescent latex beads (cf. Fig. 4)

Specified Specified Measured Qveresti- CV (%)b(im) vol (p.m3) vol (qLm3) mation (%)a

0.22 0.0056 0.0067 19 400.57 0.097 0.100 3 111.0 0.52 0.50 -4 172.0 4.2 5.7 36 15a Overestimations of the measured volumes are given as percentages of the

specified volumes (negative value indicates underestimation).b CV is the percent standard deviation of single-bead volume measure-

ments.

I preferred a volume calculation based on the more stableand reliable measurements of area (AR) and convex perim-eter (CP). The formula VOL = 8.5 AR2_5 Cp-2 will giveerrors of <10% for spheres and rods, with length/widthratios of up to 5.

Conversion of biovolume to carbon biomass. For mostapplications, bacterioplankton biomass is desired in carbonunits. Table 2 summarizes the results from a number ofbacterioplankton batch cultures in which biovolumes mea-sured by image analysis were compared with directly deter-mined carbon biomasses. The mean cell volume ranged from0.19 to 0.34 p.m3 in the freshwater cultures and from 0.08 to0.12 p.m3 in the estuarine cultures. The conversion factorsobtained as biomass divided by biovolume were testedstatistically by single-factor anovars and t tests at a 95%significance level. A single-factor anovar of the entire dataset showed no significant differences between the conversionfactors. An overall mean value of 0.354 pg of C ,um-3 wasestimated, with 95% confidence limits of 0.318 to 0.390 pg ofC um-3. The mean values from the freshwater and estuarinebatches (0.31 and 0.41 pg of C ,um-3, respectively) differedsignificantly, but the significance depended entirely on thetwo first and the last data set; i.e., if either of these wererejected, the difference in mean conversion factor becameinsignificant. An omission of these three deviating datawould change neither the overall mean nor the confidencelimits. Based on the data from this study, a conversion factorof 0.35 pg of C p.m-3 for both freshwater and estuarinebacteria thus seems to be the best estimate. This value isbetween the normally used factors of 0.086 and 0.121 pg of C,um-3 (10, 31, 32) and a recently published empirical conver-sion factor of 0.56 pg of C um-3 (5).Watson et al. (31) derived a conversion factor of 0.121 pg

of C ,um-3 by multiplying a density of 1.1 g cm-3 (7), a drymatter content of 22% (27), and a carbon/dry weight ratio of50% (21). Considering the extensive use of this conversionfactor and its importance to the accuracy of mostbacterioplankton biomass and production estimates, aston-ishingly little effort has been made to verify the basicassumptions. Dry matter contents of about 20% have beenobtained by weighing pellets of centrifuged bacteria beforeand after drying (27). This procedure gives minimum esti-mates since much of the water in centrifuged pellets isintercellular (6). Bakken and Olsen (3), removing some of theintercellular water by suction at 91 kPa, measured a mean

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AUTOMATIZED BIOMASS DETERMINATION BY IMAGE ANALYSIS

TABLE 2. Comparison of biovolumes measured by image analysis and directly determined carbon biomasses in bacterioplankton batchcultures from two freshwater and one estuarine locationa

Batchb No. of cells Cell vol Biovolume Biomass C(t 3)Batch ~ ( SM)(106 Ml)(-SE) (~LM3) (t SEM) (t SEM) p fCF( Sm`)'SEM) (t SEM) ~~~~~~(106 jim3 liter-') (jig of C liter-')(pofCjm)Lake SlaaenU 7.3 ± 0.4 0.286 ± 0.027 2,110 ± 250 449 ± 25 0.21 ± 0.03U 7.5 ± 0.7 0.310 ± 0.033 2,280 ± 140 414 ± 20 0.18 ± 0.01U 3.8 + 0.3 0.258 ± 0.020 980 ± 110 251 ± 30 0.26 ± 0.04E 7.5 ± 0.2 0.338 ± 0.014 2,540 + 110 645 ± 30 0.25 ± 0.02E 6.3 ± 0.6 0.288 ± 0.005 1,790 ± 130 606 ± 40 0.34 + 0.03E 4.7 ± 0.3 0.188 ± 0.013 890 ± 50 334 + 25 0.38 ± 0.04

Lake BryrupU 6.7 + 0.8 0.278 ± 0.042 1,990 ± 540 684 ± 20 0.34 ± 0.09U 3.1 ± 0.2 0.249 ± 0.039 774 ± 140 335 ± 25 0.43 ± 0.08E 7.7 ± 0.6 0.257 ± 0.009 1,990 ± 160 597 ± 40 0.30 ± 0.03E 4.1 ± 0.2 0.257 ± 0.021 1,053 ± 120 403 ± 40 0.38 + 0.06

Roskilde FjordU 10.4 ± 0.5 0.101 ± 0.009 961 ± 20 323 ± 10 0.34 ± 0.01U 10.0 ± 0.8 0.085 ± 0.003 843 ± 50 297 ± 110 0.35 ± 0.13U 6.1 + 0.6 0.117 + 0.011 728 ± 115 254 + 50 0.35 ± 0.09U 6.4 ± 0.3 0.112 ± 0.003 713 ± 30 270 ± 70 0.38 ± 0.10E 13.6 ± 1.0 0.083 + 0.003 1,115 ± 70 345 ± 20 0.31 ± 0.03E 9.1 ± 0.5 0.080 ± 0.006 730 ± 70 307 ± 20 0.42 + 0.05E 7.1 ± 0.7 0.114 ± 0.013 789 ± 80 305 ± 60 0.39 ± 0.09E 6.4 ± 0.3 0.115 ± 0.009 736 ± 60 536 ± 80 0.73 + 0.12

a Mean values and standard errors (SEM) of triplicates are given.b U and E specify unenriched and phytoplankton hydrolysate enriched cultures, respectively.' The empirical conversion factor CF is the ratio of biomass to biovolume.

bacterial dry matter content of 30%. Bratbak and Dundas (6)niarked the intercellular water with ['4C]inulin and obtainedcorrected dry matter contents of 31 to 52% (average, 44%).The density is, of course, dependent on the dry mattercontent. Bakken and Olsen (3) estimated a partial density ofbacterial dry matter of 1.27 to 1.45 g cm-3. Literaturedensity values of 1.96 g cm-3 for nucleic acids (4) and 1.35 gcm-3 for protein (29) suggest that the dry matter densitycould be as high as 1.6 g cm3, since nucleic acids seem to bemajor components of bacterioplankton dry matter (13). Adry matter content of 44% (6) would then imply an overalldensity of 1.26 g cm-3. These considerations point to 0.3 pgof C ,um-3 as a more realistic theoretical conversion factor.

Bacteria cultured in particle-free water have been reportedto increase their cell volume (1, 9). This might decrease theproportion of DNA and cell wall components, and thecarbon/volume ratio of the cultured bacteria could thereforebe slightly reduced compared with natural bacteria. Thesignificance of this effect could not be evaluated, since thedescribed carbon biomass anlaysis is applicable only tocultures without detritus. A recently published X-ray tech-nique for biomass determination of individual bacteria (16)could perhaps elucidate the problem.The only previous attempt to establish an empirical con-

version factor from biovolume to carbon biomass (5) re-

vealed an average of 0.56 pg of C jim 3 (range, 0.24 to 0.93pg of C ,um-3). Assuming a partial dry matter density of 1.6g cm-3 and a 50% carbon/dry weight ratio, a dry mattercontent of 77% is required to obtain a conversion factor of0.56 pg of C um-3, and the maximum value of 0.93 pg of CIIm-3 is unattainable even at a 100% dry matter content. Theempirical conversion factor obtained in this study is realistic,assuming a dry matter content of 54%, a dry matter densityof 1.6 g cm-3, and a 50% carbon/dry matter ratio. Even if cellshrinkage occurs during sample preparation, the value of

0.35 pg of C ,Lm-3 would still be valid for the conversion ofbiovolumes measured by epifluorescence microscopy intocarbon biomass. As a consequence, previous studies onbacterioplankton might have underestimated both biomassesand production rates by a factor of three.

Application of image analysis. In this study, the applicationof image analysis to epifluorescence microscopy was testedon monodisperse latex beads and on cultured bacteria withindependently determined carbon biomasses. This doubleevaluation indicated that bacterioplankton biovolumes atdbiomasses could be measured with accuracies of 10 and 15%,respectively. The precision depends on number of cellsmeasured. The described system was capable of measuring250 cells in about 10 min, providing estimates of cell number,mean volume, and biovolume with standard errors of about5% (Table 2). This precision represents a considerableimprovement compared with manual procedures.Image analysis appears to be a promising tool for enumer-

ation and size measurement of fluorochrome-stained bacte-ria, particularly when changes in cell number or mean cellvolume are studied.

ACKNOWLEDGMENTS

This study was supported by the Danish Natural Science ResearchCouncil (journal 11-4934) and by the Danish EnvironmentalProtection Agency.

I thank Bo Riemann and Kaj Sand-Jensen for valuable commentson the manuscript and Jens Morten Hansen at the Danish GeologicalSurveys for providing me access to the image analyzer.

LITERATURE CITED1. Ammerman, J. W., J. A. Fuhrman, A. Hagstrom, and F. Azam.

1984. Bacterioplankton growth in seawater. I. Growth kineticsand cellular characteristics in seawater cultures. Mar. Ecol.Progr. Ser. 18:31-39.

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2. Azam, F., T. Fenchel, J. G. Field, J. S. Gray, L. A. Meyer-Reil,and F. Thingstad. 1983. The ecological role of water-columnmicrobes in the sea. Mar. Ecol. Progr. Ser. 10:257-263.

3. Bakken, L. R., and R. A. Olsen. 1983. Buoyant densities anddry-matter contents of microorganisms: conversion of a mea-sured biovolume into biornass. Appl. Environ. Microbiol. 45:1188-1195.

4. Birnie, G. P., D. Rickwood, and A. Hell. 1973. Buoyant densitiesand hydration of nucleic acids, proteins and nucleoproteincomplexes in metrizamid. Biochim. Biophys. Acta 331:283-294.

5. Bratbak, G. 1985. Bacterial biovolume and biomass estimations.Appl. Environ. Microbiol. 49:1488-1493.

6. Bratbak, G., and I. Dundas. 1984. Bacterial dry matter contentand biomass estimations. Appl. Environ. Microbiol. 48:755-757.

7. Doetsch, R. N., and T. M. Cook. 1973. Introduction to bacteriaand their ecobiology. University Park Press, Baltimore.

8. Fenchel, T. 1982. Ecology of heterotrophic microflagellates. IV.Quantitative occurence and importance as bacterial consumers.Mar. Ecol. Progr. Ser. 9:35-42.

9. Ferguson, R. L., E. N. Buckley, and A. V. Palumbo. 1984.Response of marine bacterioplankton to differential filtrationand confinement. Appl. Environ. Microbiol. 47:49-55.

10. Ferguson, R. L., and P. Rublee. 1976. Contribution of bacteria tostanding crop of coastal plankton. Limnol. Oceanogr. 21:141-145.

11. Fuhrman, J. A. 1981. Influence of method on the apparent sizedistribution of bacterioplankton cells: epifluorescence rnicros-copy compared to scanning electron microscopy. Mar. Ecol.Progr. Ser. 5:103-106.

12. Fuhrman, J. A., and F. Azam. 1980. Bacterioplankton second-ary production estimates for coastal waters off British Colum-bia, Antarctica, and California. Appl. Environ. Microbiol.39:1085-1095.

13. Fuhrman, J. A., and F. Azam. 1982. Thymidine incorporation asa measure of bacterioplankton production in marine surfacewaters. Mar. Biol. (Berlin) 66:109-120.

14. Fuhrman, J. A., and G. B. McManus. 1984. Do bacteria-sizedmarine eukaryotes consume significant bacterial production?Nature (London) 310:1257-1260.

15. Hagstrom, A., U. Larsson, P. Horstedt, and S. Nymark. 1979.Frequency of dividing cells, a new approach to the determina-tion of bacterial growth rates in aquatic environments. Appl.Environ. Microbiol. 37:805-812.

16. Heldal, M., S. Norland, and 0. Tumyr. 1985. X-ray microana-lytic method for measurements of dry matter and elementalcontent of individual bacteria. Appl. Environ. Microbiol.50:1251-1257.

17. Hobbie, J. E., R. J. Daley, and S. Jasper. 1977. Use of

Nucleopore filters for counting bacteria by epifluorescencemicroscopy. Appl. Environ. Microbiol. 33:1225-1228.

18. Hobbie, J. E., and P. J. IeB. Williams (ed,). 1984. Heterotrophicactivity in the sea. Plenum Publishing Corp., New York.

19. Hollibaugh, J. F., J. A. Fuhrman, and F. Azam. 1980. Radioac-tive labelling of natural assemblages of bacterioplankton for usein trophic studies. Limnol. Oceanogr. 25:172-181.

20. Krambeck, C., H.-J. Krambeck, and J. Overbeck. 1981. Micro-computer-assisted biomass determination of plankton bacteriaon scanning electron micrographs. Appl. Environ. Microbiol.42:142-149.

21. Luria, S. E. 1960. The bacterial protoplasm: composition andorganization, p. 1-34. In I. C. Gunsalus and R. Y. Stanier (ed.),The bacteria, vol. 1. Academic Press, Inc., New York.

22. Meyer-Reil, L. A., M. Bolter, G. Liebezett, and W. Schramm.1979. Short term variations in microbiological and chemicalparameters. Mar. Ecol. Progr. Ser. 1:1-6.

23. Newell, S. Y., and R. R. Christian. 1981. Frequency of dividingcells as an estimator of bacterial productivity. Appl. Environ.Microbiol. 42:23-31.

24. Niblack, W. 1985. An introduction to digital image processing.Strandberg Publishing Co., Birkeroed, Denmark.

25. Peterson, B. J., J. E. Hobbie, and J. Haney. 1978. Daphniagrazing on natural bacteria. Limnol. Oceanogr. 23:1039-1044.

26. Riemann, B., P. Nielsen, M. Jeppesen, B. Marcussen, and J. A.Fuhrman. 1984. Diel changes of bacterial biomass and growthrates in coastal environments determined by means of thymi-dine incorporation into DNA, frequency of dividing cells (FDC)and microautoradiography. Mar. Ecol. Progr. Ser. 17:227-235.

27. Roberts, R. B., P. H. Abelson, D. B. Cowie, E. T. Bolton, andR. J. Britten. 1955. Studies of biosynthesis in Escherichia coli.Carnegie Inst. Washington Publication no. 607.

28. Sieracki, M. E., P. W. Johnson, and J. M. Sieburth. 1985.Detection, enumeration, and sizing of planktonic bacteria byimage-analyzed epifluorescence microscopy. Appl. Environ.Microbiol. 49:799-810.

29. Tanford, C. 1961. Physical chemistry of macromolecules. JohnWiley & Sons, Inc., London.

30. Van Es, F. B., and L. A Meyer-Reil. 1982. Biomass andmetabolic activity of heterotrpohic bacteria. Adv. Microb. Ecol.6:111-170.

31. Watson, S. W., T. J. Novitsky, H. L. Quinby, and F. W. Valois.1977. Determination of bacterial number and biomass in themarine environment. Appl. Environ. Microbiol. 33:940-946.

32. Zimmermann, R. 1977. Estimation of bacterial number andbiomass by epifluorescence microscopy and scanning electronmicroscopy, p. 104-120. In G. Rheinheimer (ed.), Microbialecology of a brackish water environment, vol. 25. Springer-Verlag, New York.

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