Solutions to Problems in Enumerating Sediment Bacteria by

Transcription

1 PPLIED ND ENVIRONMENTL MICROBIOLOGY, May 1989, p Vol. 55, No /89/ $2./ Copyright 1989, merican Society for Microbiology Solutions to Problems in Enumerating Sediment Bacteria by Direct Countst MRC SCHLLENBERG,* JCOB KLFF, ND JOSEPH B. RSMUSSEN Department of Biology, McGill University, 125 ve. Dr. Penfield, Montreal, Quebec H3 IBI, Canada Received 7 November 1988/ccepted 7 February 1989 We eamined the effect of different sediment types on the staining effectiveness of the fluorochrome DPI (4'-6-diamidino-2-phenylindole dihydrochloride) over a wide range of concentrations and on the masking effect of sediment particles on DPI-stained bacteria. Sediment type greatly affects the staining efficiency of DPI, and most published studies seem to have underestimated bacterial abundances by using suboptimal concentrations of the fluorochrome. DPI concentration of 5,ug ml-' is required to effectively stain the bacteria in most sediments that can be sampled with a gravity corer. When the sediments are diluted 687 times (a dilution factor similar to those most often used in the literature), sediment particle masking of stained bacteria is highly variable for different sediment types. By using a measure of turbidity (75) to indicate masking and the quartz-corrected water content as a measure of the initial (in situ) dilution of each sediment type, it becomes possible to show a linear relationship between masking and the integrated (initial eperimental) dilution of various sediments. This relationship allows the development of a correction procedure for masking which makes accurate and unbiased counts possible. Data so obtained show a strong relationship between bacteria (cells per milliliter of fresh sediment) and sediment organic matter (grams [dry weight] per milliliter of fresh sediment), one that is not discernable without the correction. The proposed method of staining and correction for sediment masking provides the basis for a standardized interpretation of sediment bacterial counts. The direct-count method is currently the most widely used method for enumerating bacteria in aquatic systems. The abundance of the bacteria is in turn correlated with their productivity (3). lthough the procedures of Porter and Feig (13) have been adopted as the standard methodology for enumerating bacteria in the water column, there is as yet little agreement on procedures for enumerating sediment bacteria. Sediments are highly heterogeneous environments containing varied proportions of water and organic and inorganic materials, the physical and chemical characteristics of which vary greatly (17). Furthermore, the arrangement of these materials in sediments is variable and depends on the energy regime of the depositional environment, rates of input to the sediment, bioturbation, etc. This physical compleity, in addition to the association of many microbes with particles (5, 16-18), has made it difficult to enumerate sediment bacteria. There are two technical problems with the direct-count method, as developed for the planktonic bacteria, that need to be addressed before a standard methodology for enumeration of sediment bacteria can be established. The first involves the optimal concentration of fluorochrome (in this study, DPI [4'-6-diamidino-2-phenylindole dihydrochloride]) to be used to stain the bacteria. lthough some sediment eperiments are done by the procedure of Porter and Feig (13), who used a DPI concentration of.1,ug ml-1 to count planktonic bacteria (7, 17), other researchers have to various etents increased the concentration of DPI used in the sediments (.5 jig ml-' in references 1 and 5, 1. jig ml-' in reference 16). This may have been done either in recognition of the possibility that sediments might bind DPI and thereby reduce bacterial staining or because, relative to the planktonic bacteria, the much larger sediment * Corresponding author. t Contribution no. 246 to the McGill Limnology Research Centre microbes might require higher concentrations of DPI for proper staining. The second procedural problem concerns the masking of stained bacteria by particles once the bacteria and sediment are collected on filters for enumeration. Clarke and Joint (2) proposed a method of counting planktonic bacteria which involves estimating the area of filters covered by particles to solve geometric probability equations which then give estimates of the number of bacteria that are attached to particles and are therefore hidden from view. This method, although useful for counting planktonic bacteria, is not applicable to sediment work, where particle films many layers thick can result when diluted sediments are filtered. ttempts to correct the masking problem in sediments have been varied and unsatisfactory. Bott and Kaplan (1) determined, by glycerol centrifugation, that the direct-count method underestimated the actual number of bacteria in their samples by a factor of 2.35, whereas Rublee and Dornseif (15) concluded that their direct counts underestimated real values by a factor of 1.15 by observing sediment at different concentrations and by assuming that the distribution of cells was similar on observed and hidden surfaces of particles. Yamamoto and Lopez (17) varied their sample dilutions depending on sediment type and bacterial abundance in an attempt to avoid biased counts. However, despite these attempts, the majority of researchers have not made any correction for masking. lthough the above attempts at correcting for the masking of bacteria raise the overall magnitude of direct counts, they do not help in revealing patterns of bacterial abundance because the bias in counts due to the great variation in sediment particle size and concentration between sites is not accounted for. The purpose of this study is to eamine these problems in bacterial direct-counting methodology and to provide solutions that will help establish a standardized counting procedure for sediment bacteria.

2 VOL. 55, 1989 ENUMERTING SEDIMENT BCTERI BY DIRECT COUNTS 1215 MTERILS ND METHODS Lake sediment samples were taken in late summer in a range of lakes in the Eastern Townships of southwestern Quebec by using a KB corer. The top 5 cm of sediment was etruded in the field, mied, subsampled, diluted with filtered (.2-,um pore size) distilled water, fied with Formalin to a final concentration of 3%, and refrigerated until needed. The samples were then further diluted to a final dilution of 687 times (a slight modification of the method of Duarte et al. [7] resulted in this dilution, which is similar to others that have been used by Jones et al. [1] and Deflaun and Mayer [5]) with filtered (.2-,um pore size) distilled water and gently sonicated in a Branson ultrasonic cleaner for 3 min with.1 M tetrasodium pyrophosphate (16). The samples were then vorteed for 15 s and incubated with DPI stain at 4 C in the dark for 2 min before being filtered on to prestained (Irgalan Black) membrane filters (.2-,um pore size; Nuclepore Corp.) (9). Bacteria were counted immediately at 1,25 magnification with a Zeiss epifluorescence microscope with a mercury lamp. Bacteria were assumed to be poisson distributed and were counted on randomly selected fields until either 2 fields or 4 bacteria were counted. Bacteria counts for the sediment masking eperiment were done at a DPI concentration of 5.,g ml-'. Sediment subsamples were retained for water content measurements (grams of water per gram of fresh weight after drying for 24 h at 9 C), for quartz-corrected water content measurements (water content calculated ecluding the dry ) weight of inorganic particles >63,m in diameter [6]), and for turbidity measurements (75, Bausch & Lomb Spec 1 spectrophotometer) at various dilutions. ll water content values below that are corrected for quartz content are referred to as quartz-corrected water content. To test whether sediment type had any effect on the staining efficiency of DPI or on the masking of bacteria, two sediments from different lakes, one profundal with a water content of 94% (site 1) and one littoral with a water content of 64% (site 2), were used. In our eamination of sediment masking, three additional sediments (from different lakes) that had water contents within the range represented by site 1 and site 2 were used to determine the relationship between sediment dilution and turbidity. RESULTS DPI stain concentration and direct counts. The concentration of the fluorochrome DPI used by others in enumerating sediment bacteria has ranged from.1 and 1. pug ml-1. By additionally counting bacteria in sediments at DPI concentrations beyond this range we attempted to determine the optimal DPI concentration for sediments falling within the range of water contents represented by our site 1 and site 2 sediments. s the concentration of DPI increased from.5 to 1.,ug ml-', the corresponding direct counts of bacteria increased by a factor of approimately 6 for the profundal sample (Fig. 1). In addition, there was an increase in the B) 3.5- U c 3- *c 2.5- E m.5- - C) E 7O "n 6- a 5- *r 4 Kt 3. D) Site 1 (profundal) LOG Final DPI Concentration (ug/mi) it. 2 (littoral) Site 2 (littoral) E E ~~~~~~~~~~ FG ce LOG Final DPI Concentration (ug/mi) LOG Final DPI Concentration (ug/ml) FIG. 1. ( and C) Epifluorescence bacterial counts at various DPI concentrations for site 1 (94% water content) and site 2 (64% water content). (B and D) Large bacteria as a percentage of the total counted at various DPI concentrations for site 1 and site 2.

3 1216 SCHLLENBERG ET L. U) c ('a C) Site 1 (profundal) * Site 2 (littoral) * ++ + t+j LOG Dilution Factor FIG. 2. Bacterial counts of site 1 and site 2 sediments over a range of sample dilutions. rrows denote counts corrected for masking. Vertical bars represent the standard errors of the means. apparent size of the bacteria (Fig. 1B), indicating an eponential rise in the biomass (abundance size) with increasing DPI concentration. The low-water-content, littoral sediment showed a 3-fold increase in bacterial numbers over the range of DPI concentrations tested (Fig. 1C and D). In addition, the saturation concentration of DPI (the concentration at which bacterial counts leveled off) was higher for the littoral sediment (3 pg ml-1), indicating that as the compactness and average coarseness of the sediments increases, the optimum concentration of DPI also increases. These results show not only that the concentration of fluorochrome used is of great importance in obtaining accurate estimates but also that the required concentration is much higher than those hitherto used. However, a DPI concentration of 5 [Lg ml-1 (ca..7 on the abscissae, Fig. 1) should satisfactorily stain sediment bacteria in sediments ranging in water content from somewhat lower than that for site 2 (64%) to 99%, at our dilution of 687 times. This range encompasses most sediments that can be sampled with a gravity corer. Sediment masking and direct counts. To evaluate the effect of sediment masking on direct counts of bacteria on membrane-filtered samples, bacteria were counted in site 1 and site 2 sediment samples over a wide range of sample turbidities (dilutions). compact, low-water-content sediment should be much more turbid and therefore should have a much greater masking effect at a given concentration than a dilute, high-water-content sediment. The results confirmed this by showing an initial increase in the mean number of bacteria counted with an increase in sample dilution (decrease in turbidity) (Fig. 2), followed by a leveling off at low turbidities, where sediment masking became insignificant (Fig. 3). t low sediment dilutions the counting error associated with the high water content sediment (site 1) was the } V1) -D PPL. ENVIRON. MICROBIOL. + Site 1 (profundal) 4 Site 2 (littoral) t X +, ~ I LOG Turbidity 75nm) FIG. 3. Bacterial counts of site 1 and site 2 sediments over a range of sample turbidities. Vertical bars represent the standard errors of the means. result of both high concentrations of particles and bacteria (>1 cells per microscope field) and incomplete masking of individual bacteria by the very fine particles present in this sediment. t high dilutions, great increases in coefficients of variation and, hence, the counting error resulted from the decreased average number of bacteria counted per field (Fig. 2), an effect previously noted by Montagna (12) and Kirchman et al. (11). For the low-water-content sediment the counting bias due to particle masking (turbidity) was much greater than that for the high-water-content sediment. t a sediment dilution of 1, times, which is similar to dilutions typically used in the literature (5, 7, 1), the low-water-content sediment (site 2) appeared to contain approimately 2 19 cells ml-', which is a sevenfold underestimate; when optimally diluted the sediment was shown to contain to cells ml-' (Fig. 2). This discrepancy, however, was less severe in fine sediments (less than twofold). Even more serious than the underestimations resulting from masking is the fact that at a typical dilution of 1, times the low-water-content, littoral sediment appeared to contain fewer cells per milliliter than did the high-water-content, profundal one, whereas when masking was eliminated the littoral sediment was shown to contain approimately four times more cells per milliliter than did the profundal one. Standardizing the counts per gram of dry sediment per gram of fresh sediment (gdw) would further increase this apparent disparity because the compact littoral sediments have a much higher concentration of particles displacing the water and therefore have a higher dry weight per unit volume than the profundal sediments. It is possible to correct counts for masking by correcting for sample turbidity at dilutions that yield statistically reliable counts (1, to 2,5 times). The correction is essentially an etrapolation of counts made at a relatively high 2.2 TBLE 1. Uncorrected bacterial counts, sediment data, correction factor, and corrected bacterial counts in two different sediments' Sample Bacteria % H2, quartz Initial Integrated Correction Corrected Bacteria Corrected bacteria site (cells ml-') corrected dilution dilution (fold) (fold) factor bacteria (cells gdw-') (cells gdw-') , , a Site 1 was profundal, and sediment had a high water content; site 2 was littoral, and sediment had a low water content.

4 VOL. 55, 1989 ) B) ENUMERTING SEDIMENT BCTERI BY DIRECT COUNTS E C LO rb um U 1.6-.i., 1.4- cn 1-d I.8 F- 2.4 I D'rgent(64) Lovering(78) o Brome(74) o a Waterloo(89) *Magog(94) l LOG Dilution Factor LOG Integrated Dilution Factor FIG. 4. () Sediment turbidity versus eperimental dilution factor for five lake sediments. Water content values are given within parentheses. (B) Sediment turbidity versus integrated dilution factor (in situ dilution eperimental dilution) for five lake sediments. Equation: y = I I + I I X t U. Q X + I X + I X Q + X + + +~~~ E LO _/. C 4._ -oa turbidity, where counting error is small, to a low turbidity (25 or log turbidity of 1.4), where it is possible to make estimates of those counts that are unbiased by masking (Fig. 3). We compared the turbidity-versus-dilution relationships for five different sediments (Fig. 4) representing a wide range of sediment types. The sediments all had statistically indistinguishable slopes relating turbidity to dilution factor, but their intercepts varied with their quartz-corrected water content. The quartz-corrected water content was better related to the intercepts than the water content, indicating that the particles responsible for masking are probably silt and clay (<63,um). Sand particles (>63 p.m) sink so rapidly that they are largely lost from the sample during repeated pipetting in the preparation procedure for counting. Thus, the strong negative relationship of the quartzcorrected water content to the intercepts of the turbidityversus-dilution curves indicates that high-water-content sediments can be characterized simply as being more dilute than the low-water-content ones. Our data therefore show that the sediments have to be standardized with respect to their initial (in situ) dilutions before a general linear model for the relationship between dilution factor and turbidity can be developed. The initial dilution of individual samples could be obtained by calculating the dilution factor required to dilute a hypothetical sample with a 5% (arbitrary) quartz-corrected water content to the observed quartz-corrected water content (Fig. 5). When the quartz-corrected water content was epressed as an initial dilution factor and then multiplied by the subsequent eperimental dilution to yield an integrated dilution factor (IDF), there was a tight linear relationship between the IDF and the turbidity of the sample (Fig. 4B). It is possible to correct bacterial direct counts (cells per milliliter) for masking by using both the IDF and the dilution factor at which the masking effect disappears. The masking effect in both the dilute (profundal) and the compact (littoral) sediments disappeared at a turbidity of 25 (114) (Fig. 3), which corresponds to an IDF of 11,766 (Fig. 4B). Since counts level off at this turbidity, the corrected counts for both sediments are obtained by multiplying the observed count by 11,766 and dividing by the sample IDF: corrected count = IDF at which there is no masking (sample IDF) DF [-Iog(1-%H2)] % H2 Content FIG. 5. Percent water content versus dilution factor. Based arbitrarily on 5% water content.

5 1218 SCHLLENBERG ET L. PPL. ENVIRON. MICROBIOL. TBLE 2. Regression statistics" Correction Regression equation n r F ratio Uncorrected Log(bacteria ml-') =.1777(log OM ml-') (not significant) Uncorrected Log(bacteria gdw-') = (log OM gdw--1) (P <.1) Corrected Log(bacteria ml-1) = (log OM ml-1) (P <.1) " Relationship between sediment bacterial abundance (Bact) and sediment organic matter content (OM) in lakes, comparing corrected with uncorrected counts and volumetric with gdw standardization of the data. direct counts = 11,766 [({1-' LO(1-C)I}.5) dl-' c, where q is the quartz-corrected water content of the sample, d is the eperimental dilution factor, and c is the epifluorescence direct count obtained. DISCUSSION The differential staining efficiency of DPI in dilute and compact sediments indicates that data eisting in the literature can be neither compared nor interpreted. Our data show that the majority of published sediment bacteria abundance data underestimate true numbers to various etents and that in the absence of corresponding data on the sediment water content and sample dilutions used it is impossible to correct bias in counts due to differences in the DPI concentration. However, it is now evident that a DPI concentration of 5 p.g ml-1 is sufficient to properly stain bacteria in sediments characterized by water contents of approimately 5 to 99%. It is unlikely that direct counting is useful in sediments with water contents much lower than 5%, because large sediment particles prevent proper focusing in high-magnification microscopy (personal observation). Data concerning the correction of bacterial counts for site 1 and site 2 sediments are given in Table 1. When the corrected bacteria numbers in Table 1 are compared with the values corresponding to the upper limits of the curves (Fig. 3, arrows), it is evident that the correction procedure removes the bias due to masking. Note that before the correction site 1 appears to have 5% more bacteria per milliliter than does site 2, whereas after the correction site 2 actually contains 3 times as many bacteria per milliliter as site 1. Note also that the standardization of the counts to gdw strongly influences the abundance values and that the overall effect of that standardization is in the direction opposite to that of our correction. The utility of the masking correction is supported by the strong relationship between bacterial abundance per milliliter of sediment and sediment organic matter per milliliter in data from 32 sites in 11 Ontario and Quebec lakes (Schallenberg and Kalff, manuscript in preparation) (Table 2). Regression analysis with uncorrected bacteria counts (per milliliter of fresh sediment) did not produce statistically significant patterns. The etraordinarily strong relationship between uncorrected bacterial abundance and organic matter that occurs when both variables are epressed per gdw of sediment results from the use of a common standardization factor (gdw of sediment) that is unrelated to bacterial abundance (r =.28, P >.5) and is the result of the autocorrelation of the dependent and independent variables. To avoid numerical artifacts of this type bacterial abundance estimates should be epressed per milliliter of wet sediment. Since no relationships between sediment bacterial abundance and sediment organic matter have been reported with both variables epressed volumetrically, it is not possible to compare our relationships with published ones. Furthermore, we have also been unable to find published sediment bacterial counts epressed per milliliter of sediment obtained with DPI. Studies that have used DPI underestimate the total bacterial counts (per gdw) to various etents (1, 5, 7, 16), because the concentrations of DPI used have been well under the optimal concentration of 5,ug ml-'. Only Yamamoto and Lopez (17) may have fortuitously circumvented this problem by variably diluting their sediment samples in relation to the bacterial concentration to facilitate counting. What is possible is to compare our estimates of bacterial abundance (per milliliter of fresh sediment) with the means and standard deviations of published direct counts obtained with acridine orange (Table 3). lthough the mean of our uncorrected data is quite similar to those obtained by other researchers, applying the masking correction increases the mean to well above those of the other studies, indicating that the published data appear to underestimate bacterial abundances by at least two- or threefold. Furthermore, the constant correction factor applied by Rublee and Dornseif (14) does not affect the coefficient of variation, whereas our correction, a variable correction factor applied individually TBLE 3. Comparison of corrected and uncorrected sediment bacterial abundance estimates obtained from the literature and the present study" Source Location of sampling Mean cells ml-' (19) CV n Rublee and Dornseif (15), uncorrected Salt marsh in North Carolina Rublee and Dornseif (15), corrected" Salt marsh in North Carolina Findlay et al. (8), uncorrected River in Georgia (sand) River in Georgia (mud) Rublee (14), uncorrected Estuaries, coastal marshes, and mud flats Simon (1; personal communication), uncorrected English lakes Schallenberg and Kalff (in preparation), uncorrected Quebec, Ontario lakes Schallenberg and Kalff (in preparation), corrected" Quebec, Ontario lakes a Estimates from the literature are acridine orange direct counts. CV, Coefficient of variation. b Correction factor, ('Correction factor, 11,766/IDF.

6 VOL. 55, 1989 ENUMERTING SEDIMENT BCTERI BY DIRECT COUNTS 1219 to each sample, enhances the variance between sites, thereby contributing to the ability to discern patterns in the data. In summary, the method presented herein provides a relatively simple way to obtain unbiased estimates of bacterial abundances in sediments spanning a wide range of water content. This is accomplished by adding more DPI (at least 5,ug ml-') than has been used to date to stain the bacteria in various sediments and by correcting for the masking effect of sediment particles on the bacteria counted. The utility of the new method is evident from the demonstrated pattern in the distribution of sediment bacteria in lakes, one not discernable by counting procedures used to date. CKNOWLEDGMENTS This work was supported by a Natural Sciences and Engineering Research Council operating grant to J. Kalff and by a Fonds pour la Formation de Chercheurs et ['ide al la Recherche (FCR) Equipe grant to R. H. Peters, J. Kalff, and W. C. Leggett. David Rowan and Joe Rasmussen provided assistance and company during the collection of the data, and Martin Perusse kindly provided useful comments on the manuscript. LITERTURE CITED 1. Bott, T. L., and L.. Kaplan Bacterial biomass, metabolic state, and activity in stream sediments: relation to environmental variables and multiple assay comparison. ppl. Environ. Microbiol. 5: Clarke, K. R., and I. R. Joint Methodology for estimating numbers of free-living and attached bacteria in estuarine water. ppl. Environ. Microbiol. 51: Cole, J. J., S. Findlay, and M. L. Pace Bacterial production in fresh and saltwater ecosystems: a cross system overview. Mar. Ecol. Prog. Ser. 43: Dale, N. G Bacteria in intertidal sediments: factors related to their distribution. Limnol. Oceanogr. 19: DeFlaun, M. F., and L. M. Mayer Relationships between bacteria and grain surfaces in intertidal sediments. Limnol. Oceanogr. 28: De Groot,. J., K. H. Zschuppe, and W. Salomons Standardization of methods of analysis for heavy metals in sediments. Hydrobiologia 92: Duarte, C. M., D. F. Bird, and J. Kalff Submerged macrophytes and sediment bacteria in the littoral zone of lake Memphremagog (Canada). Verh. Internat. Verein. Limnol. 23: Findlay, S., J. L. Meyer, and R. Risley Benthic bacterial biomass and production in two blackwater rivers. Can. J. Fish. quat. Sci. 43: Hobbie, J. E., R. J. Daley, and S. Jasper Use of nuclepore filters for counting bacteria by fluorescence microscopy. ppl. Environ. Microbiol. 33: Jones, J. G., M. J. L. G. Orlandi, and B. M. Simon microbiological study of sediments from the Cumbrian Lakes. J. Gen. Microbiol. 115: Kirchman, D., J. Siqda, R. Kapuscinski, and R. Mitchell Statistical analysis of the direct count method for enumerating bacteria. ppl. Environ. Microbiol. 44: Montagna, P Sampling design and enumeration statistics for bacteria etracted from marine sediments. ppl. Environ. Microbiol. 43: Porter, K. G., and Y. S. Feig The use of DPI for identifying and counting aquatic microflora. Limnol. Oceanogr. 25: Rublee, P Bacteria and microbial distribution in estuarine sediments, p In V. S. Kennedy (ed.), Estuarine comparisons. cademic Press, Inc., New York. 15. Rublee, P., and B. E. Dornseif Direct counts of bacteria in the sediments of a North Carolina salt marsh. Estuaries 1: Velji, M. I., and L. J. lbright Microscopic enumeration of attached marine bacteria of seawater, marine sediment, fecal matter, and kelp blade samples following pyrophosphate and ultrasound treatments. Can. J. Microbiol. 32: Yamamoto, N., and G. Lopez Bacterial abundance in relation to surface area and organic content of marine sediments. J. Ep. Mar. Biol. Ecol. 9: Zobell, C. E The effect of solid surfaces upon bacterial activity. J. Bacteriol. 46:39-56.