APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Nov. 1983, p. 1032-1037 Vol. 46, No. 50099-2240/83/111032-06$02.00/0Copyright C 1983, American Society for Microbiology

Microplate Fecal Coliform Method to Monitor Stream WaterPollution

A. MAUL AND J. C. BLOCK*Centre des Sciences de 1'Environnement, 57000 Metz, France

Received 5 April 1983/Accepted 12 August 1983

A study has been camred out on the Moselle River by means of a microtech-nique based on the most-probable-number method for fecal coliform enumeration.This microtechnique, in which each serial dilution of a sample is inoculated into all96 wells of a microplate, was compared with the standard membrane filtermethod. It showed a marked overestimation of about 14% due, probably, to thelack of absolute specificity of the method. The high precision of the microtech-nique (13%, in terms of the coefficient of variation for log most probable number)and its relative independence from the influence of bacterial density allowed theuse of analysis of variance to investigate the effects of spatial and temporalbacterial heterogeneity on the estimation of coliforms. Variability among replicatesamples, subsamples, handling, and analytical errors were considered as themajor sources of variation in bacterial titration. Variances associated withindividual components of the sampling procedure were isolated, and optimalreplications of each step were determined. Temporal variation was shown to bemore influential than the other three components (most probable number,subsample, sample to sample), which were approximately equal in effect. Howev-er, the incidence of sample-to-sample variability (16%, in terms of the coefficientof variation for log most probable number) caused by spatial heterogeneity ofbacterial populations in the Moselle River is shown and emphasized. Consequent-ly, we recommend that replicate samples be taken on each occasion whenconducting a sampling program for a stream pollution survey.

Obtaining precise and accurate estimates ofbacterial populations in river water is complicat-ed by the deficiencies of enumeration methods,such as their relative lack of precision andspecificity (10, 27, 28), and by the highly nonran-dom distribution of the bacteria. The scale ofbacterial aggregations can be in the range ofmicrometers (when cells are sticking to detritusparticles) (3, 8, 14, 23, 26, 29), centimeters (1,18), or even kilometers (9, 19).The variability due to patchiness creates seri-

ous problems by hindering extrapolation of re-sults from a single sample, which is often as-sumed to be representative, to a large body ofwater. Thus, to define a sampling design forfecal coliform analysis of surface water it isnecessary to have both a good method of titra-tion and an accurate knowledge of the differentsources of titration errors, particularly thosecaused by the temporal and spatial heterogene-ity of bacterial populations.The objectives of this study were as follows:

(i) to compare the well-known membrane filtra-tion (MF) technique for fecal coliform enumera-tion with a microplate most-probable-numbermethod (FC-96) by using 96 inocula at each

serial dilution (method adapted from Prost andHugues [21]); (ii) to study the relative impor-tance of several aspects of the spatial and tem-poral variations upon fecal coliform density inthe Moselle River; and (iii) to determine thecontribution of each stage of the sampling to theincrease of variance, to develop an optimalsampling design that minimizes the total vari-ance.

MATERIALS AND METHODSSampling stations. Water samples were collected at

different stations (A, B, and C, or C only) spaced at200-m intervals along a canal supplying a thermalpower station with Moselle River water. Samples weretaken with sterile 500-ml bottles in the flow about 6 maway from the bank. Bottle samples held at ambienttemperature were transported to the laboratory within30 min after collection and analyzed immediately, inrandom order, for fecal coliform content.MF technique for fecal coliform enumeration. After

the bottles were shaken manually, serial 10-fold dilu-tions of the original samples were made in sterile 0.8%NaCl solution. One milliliter of each dilution wastransferred into 9 ml of diluent and immediately fil-tered through a sterile membrane (Millipore Corp.;HAWG, 0.45-jLm pore size). The membranes were

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deposited on lactose agar medium (Institut Pasteur,I.P. 64041) supplemented with triphenyltetrazoliumchloride (I.P. 62635) and tergitol 7 (I.P. 62655). After24 h of incubation at 44°C, the yellow-orange colonieswith lactose fermentation into the agar were countedand expressed as fecal coliforms (CFU) in 1 ml ofinitial sample. These colonies were not submitted tobiochemical tests for confirmation of their identity.FC-96. After the bottles were shaken manually, the

original samples were diluted as previously describedfrom one or two subsamples (1 or 10 ml), according tothe experimental scheme. A 4.8-ml portion of eachdilution was then distributed into the 96 wells (0.05 mlper well) of a microplate (Microtest II). Each well nextreceived 0.2 ml of lactose broth plus bromocresolpurple (I.P. 64041) as an indicator. After 24 h ofincubation at 44°C, yellow-colored wells were scoredas positive. The most probable number (MPN) of fecalcoliform organisms per 1 ml of initial sample wascalculated from the combination of positive readings inthree successive dilutions by using a programmablecalculator (Hewlett-Packard 41 C) for solving the well-known MPN equation of Cochran (7).The yellow positive wells considered to contain

fecal coliforms were not submitted to a confirmationstep (indole production and growth at 44°C in brilliantgreen bile lactose broth). However, a previous study(4) showed that the proportion of false-positive wellswas around 17.5% for the kind of water analyzed.Comparison of MF and FC-96 methods. Over a 1-

month period (April 1981), samples taken at the samestation on the canal on 8 randomly selected days (onesample per day) were numbered serially and analyzedin sextuplicate for fecal coliforms. The six indepen-dent estimates obtained for each sample and for eachanalytical method were submitted to a standard two-way analysis of variance after testing for homogeneityof variance by Bartlett's test on log-transformed data.

Characterizing spatial and temporal variations offecal coliform density. Over a 1-month period (June1981), 30 samples were taken at the three stations, A,B, and C (from downstream to upstream), on the canalon five randomly selected days at 9:00 a.m. and 3:00p.m. (one sample per station per selected hour perselected day). Each sample was analyzed for fecalcoliforms by the FC-96 method. An analysis of vari-ance was performed on the log-transformed data aftertesting for homogeneity of the variances by using theFm._, test (25). To assess the relative magnitudes of theseveral sources of variation, the total variance waspartitioned into components associated with: the ana-

lytical variance (precision of the FC-96 method), the

E

CLsee

E

4

tee.

C0

E-

LSD at the5% level

1 2 3 4 5 6 7 8

sample

FIG. 1. Comparison of FC-96 method (U) with thestandard MF method (E) for fecal coliform determina-tion. LSD, Least significant difference.

subsampling (variability between subsamples), thesampling (variability between bottle samples), diurnalvariation, and daily fluctuations.

RESULTS

Comparison of the MF technique and FC-96method. Coliform counts obtained from bothenumeration techniques are illustrated in Fig. 1.Each value represents the mean of six replicateassays performed on the same sample. The FC-96 method, with 96 inocula per dilution, giveshigher results than the MF technique in sevencases out of eight; the least significant differencebetween any two values calculated at the 5%level shows that the difference was significantfor the samples 1, 3, 5, 7, and 8. The data takenas a whole were analyzed by using a two-wayanalysis of variance (Table 1). The effect of thesample-method interaction (SM) is significant atthe 0.5% level. This means that the differencebetween coliform numbers given by the twomethods varies with the analyzed sample.The F ratio calculated for factorM (method) is

significant at the 6% level. Note that comparingthe mean square associated with factor M to the

TABLE 1. Comparison of FC-96 and MF enumeration; analysis of variance of log-transformed FC-96 andMF results

Source of variation Sums of Degrees of Mean squares Expected mean squares F ratio Psquares freedom

Factor S (sample) 5.05039 7 0.72148 or + 12cr2 272.26 0.0000aFactor M (method) 0.07393 1 0.07393 aE + 6USM + 48aM 5.40 0.0531bInteraction SM 0.09583 7 0.01369 oj + 6EKSM 5.17 0.0000aError E 0.21232 80 0.00265 oj

a Significant at the 0.5% level.b Not significant at the 5% level.

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1034 MAUL AND BLOCK

interaction variance instead of the error varianceincreases the weight of the conclusion which isthen extended beyond the eight analyzed sam-ples, that is, to all of the possible collectionsfrom which they issue.A one-sided test performed on the arithmetic

means of all of the log-transformed data ob-tained by the two enumeration techniques indi-cates that, on the average, the FC-96 methodgives higher results than does the MF procedureby a factor of about 14 ± 6%. The high false-positive rate (17.5%) can probably account forthis positive bias of FC-96 relative to that of MF.The adequacy of the Poisson distribution to fit

the data can be examined by calculating Fisher'sindex of dispersion (20) for each set of six countsobtained by the MF procedure. None of theeight values of D2 (5.91, 1.46, 3.00, 3.99, 1.57,1.87, 5.26, 0.95) was significantly higher than 1(null value) at the 5% level. Consistently thesame outcome occurred when comparing thesum of the eight D2 values to the chi-squaredistribution involving 40 degrees of freedom.This indicates that the Poisson distribution is anappropriate model to describe bacterial disper-sion in small-volume, well-stirred samples. Last,the average standard deviation calculated for theentire body of data obtained in these studies bythe MF technique is 0.0487, which amounts to11.2% when expressed in terms of the coeffi-cient of variation of the transformed bacterialdensity estimates. The reproducibility of the FC-96 method expressed in terms of the coefficientof variation amounts to 12.5% (SMPN = 0.0542).The 95% confidence limits of this coefficient ofvariation calculated for 40 degrees of freedomwere 10.3 and 16.1%.As additional support for the Poisson model

assumption, the assessment of SMPN is in fullagreement with the theoretical standard devi-ation of the log transformed MPN (alog) given byequation alog = 0.55/n- developed from Hal-vorson and Ziegler's data (11). When putting n

equal to 96, alOg is 0.0561, which corresponds toa coefficient of variation of 13%. Consequently,all uncontrolled factors (i.e., handling errors,variability of the volume of drops, etc.) do notseem to affect the results in an appreciable way.

Thus, the FC-96 method is characterized by a

specific precision (=13%) that is, moreover,

equivalent to that expected for the MF tech-nique.

Spatial and temporal variations of fecal coli-form density in water. The results of the experi-ment devised to study spatial and temporalvariation in fecal coliform density are given inTable 2. All reported values fell in the range of517 to 2,004 organisms per ml.The outcome of the analysis of variance for

the three-factor factorial experiment is present-

TABLE 2. Variation in fecal coliform densities ofriver water samples from three sampling stations on

S sampling days at 9:00 a.m. and 3:00 p.m.Sam- No. of Confidence

Date pling Sampling coliforms interval

tion hour per mla (95%)

18 May A 9:00 a.m. 648 506-8303:00 p.m. 798 623-1,022

B 9:00 a.m. 517 403-6623:00 p.m. 702 548-899

C 9:00 a.m. 532 415-6813:00 p.m. 555 433-711

26 May A 9:00 a.m. 1,421 1,110-1,8213:00 p.m. 1,388 1,084-1,778

B 9:00 a.m. 1,883 1,470-2,4113:00 p.m. 1,855 1,448-2,376

C 9:00 a.m. 1,724 1,346-2,2083:00 p.m. 1,769 1,381-2,265

29 May A 9:00 a.m. 1,523 1,189-1,9513:00 p.m. 759 593-972

B 9:00 a.m. 1,361 1,063-1,7433:00 p.m. 603 471-772

C 9:00 a.m. 2,004 1,565-2,5673:00 p.m. 541 423-693

1 June A 9:00 a.m. 1,987 1,551-2,5443:00 p.m. 1,056 824-1,352

B 9:00 a.m. 1,796 1,402-2,3003:00 p.m. 1,579 1,233-2,022

C 9:00 a.m. 1,221 953-1,5643:00 p.m. 1,223 955-1,566

5 June A 9:00 a.m. 870 679-1,1143:00 p.m. 1,099 858-1,407

B 9:00 a.m. 920 718-1,1783:00 p.m. 951 743-1,219

C 9:00 a.m. 926 723-1,1853:00 p.m. 887 692-1,136

a Geometric mean of results obtained from two 1-mlsubsamples independently taken in each 500-ml bottlesample.

ed in Table 3. The technique yields a variance ormean square for each source of variation, i.e.,the sampling day (factor D, with random levels),the sampling station (factor S, with fixed levels),and the sampling hour (factor H, with fixedlevels). When a factor had no significant effect,the corresponding mean square and the nextappropriate mean square of the table werepooled.The cUDSH component that is associated with

patchiness interdependent with turbulence andwater movements of an unpredictable nature isnot significant at the 5% level. This may beattributed to the insufficient number of degreesof freedom involved in the test in view of therelative smallness of the corresponding coeffi-cient of variation, which amounted to 12.1%.

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MONITORING STREAM WATER POLLUTION 1035

TABLE 3. Results of analysis of variance designed to separate spatial and temporal components of variance(analysis of log MPN)

Source of variation squares freedom Mean squares Expected mean squares F ratio P

Factor D (sampling day) 1.36988 4 0.34247 o2 + 12cd2 61.82 0.0000aFactor S (sampling 0.11730 10 0.01173 o2 + 4(uj2s + as2) 2.12 0.0544b

station) + DSFactor H (sampling 0.55896 5 0.11179 or2 + 6(L2H + uC4) 20.19 0.O000a

hour) + DHInteraction SH + 0.09704 10 0.00970 or + 2(u2SH + CrSH) 1.75 0.1148b

interaction DSHError E 0.16609 30 0.00554 (J2

a Significant at the 0.5% level.b Not significant at the 5% level.

The results are undoubtedly affected by thehour of sampling, which indicated diurnal varia-tion in coliform density. Particularly, differencesbetween values obtained at 9:00 a.m. and 3:00p.m. vary very significantly from one samplingday to another (day-hour interaction effect). It isinteresting to note that the sampling station doesnot have a statistically significant effect on theresults. This means that even if fixed and persist-ent spatial patchiness between stations exists,this does not, at any rate, preclude consideringthe sampling stations as equivalent in any fur-ther investigations. Hence, the section studiedmay be considered as homogeneous when takenas a whole. The latter statement is not surpris-ing, since there is no water outlet along thecanal; furthermore, the section is not longenough to allow substantial self-purification.The day effect was statistically significant at

the 0.5% probability level, and the ratio betweenextreme daily means was approximately 3. Still,referring to other experiments conducted underidentical conditions on the same site, daily fluc-tuations may vary over a wider range (i.e., asmuch as 25).Seen another way, the component of vari-

ance, cU2, mentioned in Table 3 is in fact the sumof two components, 44PN and rs2UB, which are,respectively, the analytical error variance char-acteristic of the FC-96 technique and the vari-ance between repeated subsamples taken in thesame sample, caused by patchiness within thebottle samples. The effects of this small-scaleheterogeneity can be checked by comparing theratio FSUB = S/S2 N to the appropriate Fdistribution. Putting SMPN equal to 0.00294 (esti-mation of i2MPN with 40 degrees of freedom) inthe equation, FsUB becomes 1.884, which ishigher than the F value given in tables with 30and 40 degrees of freedom at the 95% level (P =0.0305). Thereby, the component of variance,C2UB, expressing variability among subsampleshas been estimated to SUB = 0.00260, whichyields a coefficient of variation of 11.7%.

DISCUSSION

MF technique and FC-96 method. The MPNmethod has often been criticized because of thepoor precision permitted by too low a number ofreplicates per dilution (3, 5, or 10 replicates).However, using microplates with 96 wells inocu-lated at every serial dilution (21) results in somevaluable properties: (i) the logarithm of the MPNis normally distributed, (ii) the coefficient ofvariation is practically independent of the bacte-rial density, and (iii) the precision is about 13%.The first two properties are true given the condi-tion that bacterial density lies in an intervalexcluding extreme values (i.e., between 0.85 and158 CFU per inoculum at the lowest dilution).Note that the range of this interval is convenientfor practical use of this method. Furthermore,the properties mentioned are those required forthe use of the analysis of variance. It seemedinteresting to compare the results given by theFC-96 method with those obtained by the stan-dard MF method in estimating the number offecal coliforms at 44°C in river water. Our re-sults, in agreement with those of other authors(10, 16, 27, 28), showed a tendency of the MPNto overestimate the bacterial density as deter-mined by the plate count to an extent thatdepends on the origin and the nature of the waterexamined. Such an observation can be explainedby a better growth of bacteria in liquid mediumor by the sample-method interaction. As a mat-ter of fact, the variations in the profile of relativefrequencies of bacterial species (for instance, inconsideration of climatic conditions as well asphysicochemical characteristics of the water)undoubtedly influence the selectivity patterns ofthe two procedures. The results obtained byeither fecal coliform method will be greatlyaffected by the incidence of false-positive orfalse-negative findings (12, 22, 27).

Spatial and temporal variations of fecal coli-form density. Environmental microbiology is de-voted in large part to the study of spatial and

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1036 MAUL AND BLOCK

temporal variation of bacterial density. Fieldstudies most often emphasized one or the otherof these types of variation, which not onlydepend on the physical or chemical characteris-tics of the water, but are also influenced byclimatic, biogeographic, and hydrographic fac-tors (5, 6).The relative magnitudes of several sources of

variation observed in our study and expressed asa percentage of the total variance are 51.9% fordaily fluctuations, 32.8% for diurnal variation,5.4% for the FC-96 method, 5.1% for samplevariation, and 4.8% for subsample variation.Although these estimates are somewhat roughand therefore subject to appreciable distortion,they show the preponderance of temporal varia-tion over the other three components, which areapproximately equal. It must be emphasized thatsuch investigations are made easier by the math-ematical properties of the FC-96 estimator (es-pecially the high level of its specific precision).Note, for instance, that the variance of 5-5-5-tube MPN would be very high and would thenrepresent 54% of the total variance. This pointsout the lack of accuracy to be expected in anymicrobiological study conducted by such meth-ods.

In another study (15) we showed significantdifferences between the replicate subsamplesand among samples taken successively at thesame hour. This indicates, on the one hand, asmall-scale heterogeneity within the 500-ml sam-ples whereas on the other hand, variability be-tween repeated bottle samples reflects a largerscale patchiness. The estimated variance com-ponents derived from each hierarchical levelwere S2PN = 0.00294, SSUB = 0.00270, and S2 =

0.00479 (i.e., method, subsample, and sample,respectively), with associated coefficients ofvariation amounting to 12.5, 12, and 16%, re-spectively. Although these figures derive from a

single experiment, similar results were obtainedon other occasions.Optimal sampling design. The components of

variance cannot be considered constant andspecific to a given environment, since the effectof spatial heterogeneity on their magnitude un-doubtedly confers a certain variability to them,even within a single body of water and all themore when environments are different. In anycase each level of such a multistage, samplingprocess contributes to an increase in the totalvariance for the mean of all results. According toSnedecor and Cochran (24), in a sampling designconsisting of p samples, q subsamples, and r

assays per subsample, the total variance (oi) isequal to (ops/p) + (crsuSIpq) + (OAipNlpqr).

Estimates of fecal coliform density that arebased on the assay of a single sample or even asingle subsample lack weight in statistical analy-

sis; because of spatial heterogeneity, the resultscannot be extrapolated to a volume of watergreater than that which was analyzed. Thus, ifthe weight of any estimate is to be enhanced bytaking into account the effect of patchiness onfecal coliform enumeration, a three-stage (sam-ple, subsample, assay) sampling process involv-ing analysis of several samples should be de-signed. Furthermore, with variance componentsin hand and the above equation giving the totalvariance, it is possible to predict the confidenceinterval for the mean produced by any experi-mental design. Moreover the optimal allocationof sampling effort to each level for decreasingthe total variance can be determined. The resultsin Table 4 were calculated on the basis of theabove-mentioned estimates of Oj4PN, USUB, anda2s. These results emphasize the effects of vari-ous sampling designs on the precision of theestimated mean. The effectiveness of differentreplication schemes, involving four assays each,may be compared with the first three rows ofTable 4. Examination of the range of confidenceintervals shows the advantage of increasing rep-lication at the upper levels of the samplingdesign.Assessment of the error of the mean due to

analytical error alone may be obtained by com-paring the schemes in the fourth and the fifthrows of Table 4. This shows how limited are thegains in precision that can be attained even witha titration technique that is assumed to be per-fect, and it indicates that research devoted toimproving the precision of the enumeration tech-nique, without taking account of heterogeneity,can accomplish very little.The last row of Table 4 refers to a single

assay, assuming incorrectly that coliform bacte-ria are randomly distributed in the entire watermass. Note that precision to this last case(12.5%) is attained only by the first design,

TABLE 4. Comparison of several sampling designsSub- Assays Coeffi- Confidence

Sam- samples per Variance cient of limits at thepies pr sub- SI varia- 95% level'

sample sample tion (M%4 1 1 0.00261 11.8 79.4-125.92 2 1 0.00381 14.3 75.7-132.11 1 4 0.00823 21.1 66.4-150.61 1 1 0.01043 23.8 63.1-158.61 1 00b 0.00749 20.1 67.7-147.81C 1 1 0.00294 12.5 78.3-127.7a In each case the estimated mean has been stan-

dardized as 100%.b The symbol Xo indicates that no error must be

attributed to the enumeration technique.c Assessment obtained by assuming a perfect homo-

geneity of the water mass.

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MONITORING STREAM WATER POLLUTION 1037

which involved four samples. Based on theprevious statements, and in agreement with oth-er authors (2, 13, 17, 24), the most efficient andthe least costly experimental design would con-sist of taking several samples and one subsampleper sample to be analyzed only once.

It is worth noting that the proposed schemeshould be suitable for most cases in spite of thepeculiar assumptions on which these findingsare based. The number of samples to be collect-ed must be chosen to satisfy a constraint on totalvariance or cost. Yet, if three samples appear tobe a minimum, it should not be necessary to takemore than six samples to meet requirements ofmost microbiological studies. If p is the numberof analyzed samples, the logarithm of the bacte-rial density is given as X ± S/p-) t, where Xand S are the arithmetic mean and the samplestandard deviation of the p log-transformed esti-mations, respectively, when t is from Student's tdistribution and has p - 1 degrees of freedom.

ACKNOWLEDGMENTS

We have appreciated M. A. Dollard for her invaluableassistance and J. Nikes for typing this paper. We thank D. 0.Cliver and A. H. El-Shaarawi for discussion and criticisms.

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