The Use of Analysis of Variance to Examine the Variations between Samples of Marine Bacterial Populations

Ashby, R. E.; Rhodes-Roberts, Muriel E.

doi:10.1111/j.1365-2672.1976.tb00657.x

Cited by 11 publications

(8 citation statements)

References 13 publications

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“…Probably the differences in bacterial counts were because the original sample (S1) was diluted. Such dilution was shown to cause variability in bacterial numbers enumerated in seawater samples [2][3][4][34][35][36][37][38]. However, there are no previous reports of the effect of dilution on bacterial densities in the case of enumerating bacteria from a marine biofilm.…”

Section: Distribution Of Bacterial Countsmentioning

confidence: 99%

Evaluating the Reliability of Counting Bacteria Using Epifluorescence Microscopy

Muthukrishnan

Govender

Dobretsov

et al. 2017

JMSE

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Abstract:The common practice of counting bacteria using epifluorescence microscopy involves selecting 5-30 random fields of view on a glass slide to calculate the arithmetic mean which is then used to estimate the total bacterial abundance. However, not much is known about the accuracy of the arithmetic mean when it is calculated by selecting random fields of view and its effect on the overall abundance. The aim of this study is to evaluate the accuracy and reliability of the arithmetic mean by estimating total bacterial abundance and to calculate its variance using a bootstrapping technique. Three fixed suspensions obtained from a three-week-old marine biofilm were stained and dispersed on glass slides. Bacterial cells were counted from a total of 13,924 fields of view on each slide. Total bacterial count data obtained were used for calculating the arithmetic mean and associated variance and bias for sample field sizes of 5, 10, 15, 20, 25, 30, 35 and 40. The study revealed a non-uniform distribution of bacterial cells on the glass slide. A minimum of 20 random fields of view or a minimum of 350 bacterial cells need to be counted to obtain a reliable value of the arithmetic mean to estimate the total bacterial abundance for a marine biofilm sample dispersed on a glass slide.

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Section: Distribution Of Bacterial Countsmentioning

confidence: 99%

Evaluating the Reliability of Counting Bacteria Using Epifluorescence Microscopy

Muthukrishnan

Govender

Dobretsov

et al. 2017

JMSE

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“…The occurrence of significant variation at the various levels of the direct count method (subsamples, filters, and fields) would suggest that bacteria are not distributed randomly in a water sample. Small-scale patchiness (<10 cm) has been demonstrated with the plate count method (1,12,18), but not for bacteria counted directly. The small-scale patchiness observed in plate count data is caused in part by bacteria attached to particles and by aggregation of unattached bacteria.…”

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confidence: 99%

“…The small-scale patchiness observed in plate count data is caused in part by bacteria attached to particles and by aggregation of unattached bacteria. Patchiness on a scale of meters, which arises because bacterial populations vary with depth and horizontal location, has been demonstrated with both the plate count (1,12,18) and direct count (5,7,10) methods.…”

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confidence: 99%

Statistical analysis of the direct count method for enumerating bacteria

Kirchman¹,

Sigda²,

Kapuscinski³

et al. 1982

Appl Environ Microbiol

179

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The direct count method for enumerating bacteria in natural environments is widely used. This paper analyzes the sources of variation contributed by the various levels of the method: subsamples, filters, and microscope fields. Based on a nested analysis of variance, we show that most of the variance (less than 80%) is caused by the fields and that the filters contributed nearly all of the remaining variance. The replication at each of the levels determines the total cost and error of a measurement. We compared several sampling schemes, including an optimal strategy which gives the lowest possible variance for a given cost. We recommend that preparing one filter from one subsample is adequate only if the samples are closely spaced in time or distance; otherwise, one filter should be prepared from two or preferably three subsamples. This sampling scheme emphasizes the importance of the highest level of replication. Our analysis shows that the accuracy of the direct count method can be substantially improved (by 20 to 50%) without a large increase in cost when the proper degree of replication at each level is performed.

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“…Obtaining precise and accurate estimates of bacterial populations in river water is complicated by the deficiencies of enumeration methods, such as their relative lack of precision and specificity (10,27,28), and by the highly nonrandom distribution of the bacteria. The scale of bacterial aggregations can be in the range of micrometers (when cells are sticking to detritus particles) (3,8,14,23,26,29), centimeters (1,18), or even kilometers (9,19).…”

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confidence: 99%