successional changes in bacterial assemblage ... - ASLO - Wiley

Ecology, 82(2), 2001, pp. 555–566 q 2001 by the Ecological Society of America

SUCCESSIONAL CHANGES IN BACTERIAL ASSEMBLAGE STRUCTURE DURING EPILITHIC BIOFILM DEVELOPMENT COLIN R. JACKSON,1 PERRY F. CHURCHILL,

AND

ERIC E. RODEN

Department of Biological Sciences, University of Alabama, Tuscaloosa, Alabama 35487-0206 USA

Abstract. Although bacteria are often treated as one entity in ecological studies, bacterial assemblages are composed of individual species populations. Bacterial assemblages can have their own richness and structure, analogous to communities of plants and animals, although few studies have attempted to describe spatial or temporal patterns in their structure. In this study, we examined successional changes in the structure of bacterial assemblages using denaturing gradient gel electrophoresis (DGGE) analysis of polymerase chain reaction amplified 16S rDNA fragments. Bacterial biofilm assemblages developing on glass slides in a mesocosm and a small lake showed an initial increase in richness over the first week, followed by a slight decrease and a subsequent increase after two to three months. Functional changes in the bacterial community were examined using most probable number estimates and revealed decreases in the abundance of glucose- and cellulose-degraders during biofilm development, whereas benzoate-degraders became more abundant in the lake biofilms. The banding patterns observed on DGGE gels were used to derive rank-abundance profiles for each stage of biofilm development. These profiles resembled those observed for communities of macroorganisms and could usually be described by geometric series models. These models suggested greater equitability in bacterial community structure as the biofilms developed. A comparison of two successional series of biofilms separated by 30 d revealed that neither successional stage nor time of sampling was the major factor influencing bacterial assemblage structure. Our results allowed us to suggest a general model for the development of bacterial biofilm assemblages that emphasizes the interaction of species and resource diversity. This model suggests that, at least in biofilms, bacterial assemblages may not be structured by the resource competition or niche-driven patterns typical of communities of macroorganisms. Key words: bacteria; bacterial assemblage structure; biofilms; denaturing gradient gel electrophoresis; microbial communities; most probable number counts; rank-abundance distributions; succession.

INTRODUCTION Ecological succession is considered to be one of the first great theories of ecology (Tilman 1982), and numerous studies have provided theoretical and experimental insights into the phenomenon of succession and the mechanisms that cause it (Odum 1969, Connell and Slatyer 1977, Finegan 1984, Tilman 1994). Attempts have been made to apply the principles of succession to assemblages of organisms other than plants, e.g., to changes in lake phytoplankton communities (summarized by Wetzel 1983). However, changes in these communities often represent seasonal shifts in community structure rather than primary successional changes, characterized by the colonization, subsequent development, and maturation of the community of interest. Fewer studies have described succession in bacterial assemblages, and bacteria are often treated as a black box in ecological studies. This has largely been the

1 Present address: Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana 59717-3120 USA. E-mail: [email protected]

result of methodological constraints. The majority of bacteria in natural habitats are undetectable by the traditional microbiological methods of enrichment and subsequent isolation. More than 90% of the bacteria within a habitat are likely to be missed by such methods, resulting in underestimates of the complexity of natural bacterial communities (Colwell et al. 1995, O’Donnell et al. 1995, Pace 1997). Bacterial communities are, however, composed of diverse populations, and may show patterns in richness and structure similar to macroorganisms. The concept of succession could be applied to the development of attached bacterial assemblages (biofilms) in aquatic environments. The majority of bacteria in freshwater are found growing as biofilms on the surfaces of submerged substrata or sediments, and these biofilms can be complex communities with intricate architectural organization (Costerton et al. 1987, Blenkinsopp and Costerton 1991, Lock 1993). As soon as a surface is submerged, it becomes available for bacterial attachment. Within hours, an organic film forms on the surface, which can facilitate the arrival and adhesion of bacteria from the water column (Costerton et al. 1987). Within days, the surface is covered

555

556

COLIN R. JACKSON ET AL.

with bacteria, and the community continues to develop through both the growth of populations within the biofilm and additional recruitment from the water column. Studies have typically examined bacterial biofilm development in model reactor systems, often composed of one or two bacterial species (see review by Lawrence et al. 1995). In natural waters, biofilms are complex heterogeneous structures composed of bacteria, algae, and other microorganisms within an extracellular matrix (Lock 1993). The development of natural biofilm communities has generally been examined from the algal perspective, and a variety of studies have described periphyton development (Peterson 1987, Lowe et al. 1996, Johnson et al. 1997). Changes in the algal assemblage occur as the biofilm develops, and these have been reported in the context of ecological succession. Stevenson (1983) suggested that early-colonizing diatoms alter the environment by producing extracellular mucilage and slowing the rate of flow. He suggested that these changes could encourage the growth of other members of the community, following the facilitation model of Connell and Slatyer (1977). Similarly, Roemer et al. (1984) suggested that algal mucilage and stalks helped facilitate periphyton development by encouraging cell-surface adhesion and providing increased sites for colonization. By examining periphyton succession in a closed system, Johnson et al. (1997) were able to demonstrate that changes in both the dominant groups of algae, and in the species making up these groups, were a result of habitat modification within the biofilm, rather than changes in the recruitment pool within the water column. While the succession of algal populations that occurs during biofilm development has been relatively well described, little is known of the changes in bacterial populations that may occur concurrently. Those studies which have focused upon bacteria have tended to examine functional or metabolic characteristics of bacteria as a group, reporting changes in bacterial biomass, cell density, and production (Couch and Meyer 1992, Lowe et al. 1996, Sobczak 1996). Some studies have reported changes in functional aspects of the heterotrophic microbial community as biofilms develop, examining variation in extracellular enzyme activity among biofilms of different ages (Jones and Lock 1989, Sinsabaugh et al. 1991, Sabater and Romanı´ 1996). However, there is little information on how the structure of these bacterial assemblages changes during biofilm formation and maturation. The recent application of molecular techniques to microbial ecology has facilitated a more complete examination of bacterial assemblages, and allows them to be viewed from a community ecology perspective. For example, denaturing gradient gel electrophoresis (DGGE) of polymerase chain reaction (PCR) amplified 16S rRNA gene fragments can be used to compare bacterial communities from different samples by the-

Ecology, Vol. 82, No. 2

oretically separating each community into its component genome populations, each of which may appear as a discrete band in a DGGE gel (Muyzer et al. 1993, Jackson and Churchill 1999). The presence of similar bands in different samples can provide a rapid assessment of community similarity, and the number of bands present in a sample has been used to infer species richness (Muyzer et al. 1993). However, the method has its own limitations. Different bacterial populations may not differ in the 16S rDNA region of interest, and thus will produce bands that migrate to the same position in DGGE gels. It is also possible that even different sequences may comigrate, and not be separated by this method (Jackson et al. 1998). In addition, some organisms may produce more than one band in a DGGE gel because of the presence of multiple 16S rDNA operons (Nu¨bel et al. 1996). Other limitations can include an inability to detect minor members of a bacterial community, and biases involved in DNA extraction and amplification (Jackson and Churchill 1999). Despite these limitations, DGGE is a powerful tool in the analysis of prokaryote community structure, and diversity indices derived by this method have been shown to correlate closely with those derived from morphological identification (Nu¨bel et al. 1999). However, the use of molecular methods alone provides little information on the functioning of bacterial communities, for which a combination of molecular and traditional methods may be required (Jackson et al. 1998). In this study, we utilized DGGE and most probable number (MPN) enrichment culture methods to describe the changes in bacterial-assemblage structure that occur as epilithic biofilms develop in aquatic habitats. The objectives of our study were two-fold. First, we sought to demonstrate that DGGE can be used to examine structural changes in the bacterial assemblage during biofilm development. Second, we analyzed the molecular data using methods generally applied to macroorganisms, and sought to show that bacterial assemblages may behave in a manner similar to plant communities as they undergo ecological succession. In addition, we suggest some of the mechanisms that may drive bacterial succession in freshwater biofilms. METHODS

Site description Biofilm development was studied in a small lake and in a wetland mesocosm. Palmer Lake is a small, shallow lacustrine system on the University of Alabama campus, Tuscaloosa, Alabama, surrounded by grass banks and mixed deciduous cover. It receives water from two small channels that run under a nearby road before draining into the lake, and from occasional runoff from the surrounding land. Temperature in the lake was monitored at hourly intervals, and ranged from 10–308C (mean 208C) throughout the study. pH was taken every 4–5 d and ranged between 5.4 and 6.6

February 2001

BACTERIAL BIOFILM ASSEMBLAGE STRUCTURE

(mean 6.1). The mesocosm biofilms were sampled from a fiberglass mesocosm (water volume 1600 L) located in a rooftop greenhouse on the University of Alabama campus. The mesocosm was seeded with wetland sediment and water in spring 1996, and consisted of two open-water pond areas (volume 400 L each) dominated by the white water lily (Nymphaea odorata). Water slowly recirculated through the pond areas via adjacent stream channels, and the mesocosm was essentially a closed system, other than occasional supplementation with well water to offset losses from evaporation. Temperature and pH were monitored as in the lake, and ranged from 4–298C (mean 208C), and between 7.0 and 8.1 (mean 7.4), respectively.

Biofilm samples Glass microscope slides (25 3 75 mm) were chosen as a uniform surface for epilithic biofilm growth. Arrays of slides were placed within the mesocosm on 22 February 1998 by taping sets of six slides to the walls of the pond areas. Slides were submerged to a depth of 5–20 cm. Arrays of slides were placed within the lake site 2 d later. The lake arrays consisted of sets of 40 slides supported within a plastic test-tube rack, and anchored to maintain its position at a depth of 10–20 cm. Submergence depth varied due to changes in lake depth. A second set of arrays were placed within the mesocosm 30 d after the first set in order to test for reproducibility of temporal patterns in biofilm development. This second set was similar to the first, and was sampled in the same manner. A second set of lake slides was also initiated 30 d after the first. However, material from these slides became contaminated during processing so information from only one set of lake slides is reported. For each set of biofilms, slides were collected after 2, 7, 15, 30, 60, and 90 d. For the two sets of mesocosm arrays, day 60 of the first set was sampled at the same time as day 30 of the second set, and day 90 of the first set coincided with day 60 of the second set. On each sample date, a total of 24 slides was collected from the mesocosm, and the nontaped surface of each slide scraped with a sterile razor blade. Because very small amounts of material were obtained from individual slides (particularly on early sample dates), the material collected from all 24 slides was pooled for subsequent analyses. Because both surfaces of the slides that were suspended in the lake could be used, only 12 lake slides were collected on each sample date to maintain the same surface area as was sampled for the mesocosm studies. Otherwise, lake slides were processed in the same manner as the mesocosm slides. For the lake slides and the first set of mesocosm slides, a 0.5 mL subsample was taken from the material collected on days 7, 30, and 90, and used as an inoculum for MPN enrichment. The remaining material,

557

and all of the material collected on the other sample dates, was frozen for DNA extraction.

Most probable number enrichments To monitor changes in the abundance of different groups of heterotrophic bacteria during biofilm development, subsamples taken on 7, 30, and 90 d were serially diluted (ten-fold), and inoculated into MPN series with four replicate tubes per dilution (Woomer 1994). Three carbon sources were used: glucose (10 mmol/L), benzoate (10 mmol/L sodium benzoate), and cellulose (3.0 g/L crystal methyl cellulose), in order to monitor glucose-, benzoate-, and cellulose-degraders. Media consisted of the appropriate carbon source dissolved in water taken from either the lake or mesocosm (depending upon the sample) two days prior to sampling. This water was filtered (0.45 mm), amended with 5 g/L KH2PO4 and 1 g/L NH4Cl to prevent nutrient limitation during culture, adjusted to its original pH, and autoclaved prior to inoculation. After inoculation, tubes were incubated at 208C until no new growth was observed in any tubes (typically 5 d for glucose, 7 d for benzoate, and 14 d for cellulose). For each carbon source, the number of tubes showing positive bacterial growth (turbidity) at each dilution was noted and used to calculate MPN estimates of glucose-, benzoate-, and cellulose-degraders (Woomer 1994). Numbers were converted to counts per square centimeter of surface sampled.

DNA extraction and PCR amplification DNA was extracted from the material collected on each date using the method of Zhou et al. (1996), involving grinding in liquid nitrogen, freeze-thawing, and high salt, sodium dodecyl sulfate-extended heating. The solution obtained was extracted with an equal volume of phenol:chloroform (1:1), precipitated in isopropanol, and resuspended in Tris-EDTA (TE) buffer, as described previously (Jackson et al. 1997, 1998). Samples showing signs of humic contamination (a yellow-brown color) were purified using Sepharose 4B columns (Jackson et al. 1997). For each sample of whole community DNA, the V3 region of 16S rDNA corresponding to positions 341– 534 in Escherichia coli was PCR-amplified using the primers of Muyzer et al. (1993). These primers are thought to be universally conserved within Domain Bacteria, and have been used previously in DGGE analyses of bacterial DNA from environmental samples (Muyzer et al. 1993, Murray et al. 1996, Jackson et al. 1998). Amplification was performed using a touchdown procedure with a total of 22 cycles, under conditions and reactant concentrations described previously (Jackson et al. 1997). Six replicate 50 mL amplifications were performed for each sample, and the products pooled and concentrated to reduce amplification biases typical of mixed genome samples (Polz


558


and Cavanaugh 1998). Negative control amplifications were routinely performed.

Denaturing gradient gel electrophoresis The amplified products of each sample were electrophoresed along a 25–60% urea:formamide gradient in DGGE gels at 130 V and 608C for 4 h, under conditions outlined previously (Jackson et al. 1998). Approximately equal concentrations of amplified DNA from each sample were used, based on visual inspection of agarose gels. Following electrophoresis, gels were stained in ethidium bromide, and the banding patterns examined under ultraviolet light. Gels were digitally photographed, and stored as TIFF files. The intensities of the bands appearing on each gel were analyzed densitometrically using NIH Image v.1.61 (by Wayne Rasband, National Institutes of Health, Bethesda, Maryland).2 Some bands were excised from DGGE gels, and the DNA fragment eluted using the method of Muyzer et al. (1996). This DNA was reamplified, and sequenced at a dedicated DNA sequencing facility (Iowa State University, Ames, Iowa). Partial 16S rDNA sequences obtained were compared to those in GenBank using the BLAST alignment tool (Altschul et al. 1990).

Community analyses The number of bands that appeared in a DGGE gel for each sample is reported as band richness (S), an estimate of the number of 16S rDNA sequence types, which we used as a surrogate for the number of bacterial populations within a sample (i.e., species richness). Assuming that each band in a DGGE gel is representative of a distinct 16S rDNA sequence, the intensity of that band compared to others in the same sample can serve as an estimate of the relative abundance of that particular 16S rDNA sequence type. We used this abundance as a surrogate for the relative abundance of the bacterial population possessing that sequence type within a particular sample. The relative abundance of each sequence type was calculated using the band intensities obtained from NIH Image, and were used to produce rank–abundance plots, as log(10) relative abundance vs. rank (from most abundant to least), for each set of developing biofilms. In order to compare the two sets of mesocosm slides, the samples were analyzed in the same DGGE gel, and the presence of bands in the same places in different samples was noted. The dissimilarity between pairs of samples was examined as the Marczewski-Steinhaus distance (MS): MS 5 (b 1 c)/(a 1 b 1 c) where a is the number of bands present in both samples, b is the number of bands present in the first sample but not the second, and c is the number of bands present in the second sample, but not the first (Pielou 1984). 2

Freeware available from ^FTP:zippy.nimh.nih.gov&

FIG. 1. Most probable number estimates of the abundance of three different functional groups of bacteria: glucose-degraders (hatched bars), benzoate-degraders (white bars) and cellulose-degraders (black bars) at three stages of epilithic biofilm development in (a) a wetland mesocosm and (b) a small lake. Note logarithmic scales. The confidence factor is 3.80 for all.

The MS scores obtained were used to classify the samples by centroid cluster analysis, following the methods described by Pielou (1984). RESULTS

MPN enrichments Changes in the abundance of different groups of heterotrophic bacteria were apparent in both mesocosm and lake biofilms (Fig. 1). Glucose-degraders were the most abundant of the three metabolic types monitored in all samples, but tended to decrease in abundance during biofilm development. Cellulose-degraders were always from four to eight orders of magnitude less abundant than the glucose-degraders, and were virtually absent from the mesocosm site, being detected in small numbers at 7 d, but not at all thereafter (Fig. 1a). Cellulose-degraders were more abundant in the lake, and showed similar declines in numbers as the glucosedegraders (Fig. 1b). Benzoate-degraders were always present in the mesocosm samples, but no clear trend in their abundance was apparent (Fig. 1a). The lake biofilms showed an increase in the abundance of ben-

February 2001


559

FIG. 2. Changes in bacterial assemblage structure as revealed from DGGE analysis of 16S rRNA gene fragments obtained from epilithic biofilms in a wetland mesocosm (first set) and a small lake. Numbers represent the age of the biofilm in days. A and B designate bands that appeared in all assemblages analyzed, corresponding to a Pseudomonas sp. and Mycobacterium sp., respectively.

zoate-degraders as they developed, with very low numbers after 7 d, but with final numbers similar to those of cellulose-degraders (Fig. 1b).

Molecular analyses Bacterial DNA could be extracted and amplified from almost all samples. Exceptions were the material collected on day 15 of the lake samples, and day 90 of the second set of mesocosm samples. Although DNA was extracted from these samples, it amplified poorly, and DGGE gels for these samples did not give clear patterns (see appropriate lanes in Figs. 2 and 3). These samples were not included in subsequent analyses. All sets of biofilms showed changes in DGGE banding patterns as the biofilms developed (Figs. 2 and 3). Differences were apparent between the lake and mesocosm samples, for both individual samples of a specific age, and in the overall pattern of bacterial community development (Fig. 2). The lake biofilms showed greater numbers of bands in the earlier samples (day 2 and day 7), and a decrease as the biofilms developed. Differences between the final two dates (day 60 and day 90) were minor, suggesting that the biofilm community had matured at this point, and that few changes in the dominant bacterial populations were occurring. The two sets of biofilms in the mesocosm showed different trends (Fig. 3). Although both sets showed a tendency toward an increase in complexity over time, this was much more pronounced for the first set than for the second. The first set of mesocosm biofilms was amplified and analyzed twice in order to facilitate comparisons to both the lake slides and the second set of

mesocosm slides. Both amplifications showed the same DGGE profiles (compare profiles in Fig. 2 to Fig. 3), confirming that PCR amplification and DGGE analysis of samples was reproducible. Despite the overall differences in banding patterns, a number of bands appeared at the same position in DGGE gels for all samples, regardless of site or biofilm age. In particular, two bands (designated A and B) were often among the densest bands appearing in the gel (Figs. 2 and 3). Representatives of these bands (from day 2 of the first set of mesocosm samples, and day 90 of the lake samples) were sequenced. The sequence of band A corresponded to that of a Pseudomonas sp. (the closest match being to P. putida; 176/176 bases matched), while that of band B corresponded to a Mycobacterium sp. (closest matches being to M. smegmatis, M. marinum, and M. peregrinum; 160/160 bases for all). Given the short 16S rDNA sequence examined, it is not possible to say whether band B corresponds to just one of these Mycobacterium spp. or a combination. Both sequences are available in GenBank under accession numbers AF091515 (band A) and AF091516 (band B).

Community analyses Computer scanning of gels was used to more accurately detect bands, and to observe trends in band richness (S; Fig. 4). All three sets of biofilms showed initial increases in S from day 2 to day 7, and then subsequent reductions as the biofilms aged. Biofilms showed later increases in S toward the end of the study, typically with an increase of 2–5 bands between day 60 and day

560



FIG. 3. Changes in bacterial assemblage structure as revealed from DGGE analysis of 16S rRNA gene fragments obtained from two sets of epilithic biofilms in a wetland mesocosm. Numbers represent the age of the biofilm in days. The Mesocosm 1 samples were sampled exactly 30 d earlier than the corresponding Mesocosm 2 samples. A and B represent the same bands labeled in Fig. 2.

90. Despite the differences in banding patterns, the two sets of mesocosm biofilms showed similar trajectories in S, although the second set showed consistently higher richness than the first, which was about 30% less rich (typically by 3–5 bands). Rank-abundance plots showed different trends in community organization for the three sets of biofilms (Fig. 5). In terms of rank-abundance models, both sets

FIG. 4. Changes in the richness of the bacterial assemblage during epilithic biofilm succession. Band richness is the number of bands appearing in DGGE gels. Solid circles indicate lake biofilms; open symbols indicate two sets of biofilms in a wetland mesocosm (squares indicate set 1; triangles indicate set 2).

of mesocosm biofilms showed a progression from a log normal distribution in earlier samples, to a geometric series in later samples. This pattern was suggested for the lake biofilms, but the trend towards decreasing band richness with successional age made comparisons to the mesocosm samples difficult. Because there was a trend towards a geometric distribution for many samples, linear regression models were used to examine changes in the pattern of evenness during biofilm development (Table 1). The first set of mesocosm slides showed greater evenness as the biofilm developed, as indicated by lower slope values in later samples. The same trend was seen for the second set of mesocosm biofilms, but was largely influenced by a relatively steep slope on the first sample date. As would be expected from the rank-abundance plots, the goodness of fit statistic (r2) of a linear model increased as the biofilms aged, confirming that later biofilms were more similar to a geometric series. Lake biofilm rank-abundance distributions also approached linearity with increased biofilm age; however, the trend towards a more even distribution was not apparent, and these assemblages showed an increase in the slope of a linear model as the biofilms developed. A centroid cluster analysis based upon MS dissimilarity scores calculated from presence–absence data was used to examine patterns of change in the two sets of mesocosm biofilm communities (Fig. 6). No clear grouping by either sample date (e.g., grouping of Set 1 day 60 with Set 2 day 30, or Set 1 day 90 with Set


February 2001

561

FIG. 6. Classification of bacterial assemblages in mesocosm biofilms by centroid cluster analysis based upon Marczewski-Steinhaus dissimilarity scores calculated from presence–absence data, as obtained from DGGE gels. Scale indicates dissimilarity scores (low scores indicate similar assemblages). Samples are designated by letter (A 5 mesocosm set 1, B 5 mesocosm set 2) and age (2, 7, 15, 30, 60, or 90 d).

FIG. 5. Rank-abundance distributions for bacterial assemblages at different points of biofilm development (day 2 5 solid diamonds, day 7 5 open diamonds, day 15 5 solid triangles, day 30 5 open triangles, day 60 5 solid circles, day 90 5 open circles). Note logarithmic scales. Distributions from left to right reflect changes along a successional trajectory. Relative abundance was inferred from the intensity of bands in DGGE gels.

2 day 60) or biofilm age (e.g., grouping of Set 1 day 60 with Set 2 day 60) was apparent. Biofilm assemblages tended to be most similar to the community present on the previous sample date (e.g., Set 1 day 30 and day 60 were similar, as were Set 2 day 30, day 60, and day 15). The bacterial assemblages present at day 2 for each set were outliers, with no strong similarity to any other assemblage, or to each other. Otherwise, there was a separate clustering of the communities in each set. DISCUSSION

Changes in DGGE banding patterns Monitoring of epilithic biofilm communities by DGGE revealed that both bacterial assemblage structure and richness (as inferred from the number of DGGE bands) changed during biofilm development. Different banding patterns in DGGE gels were ob-

TABLE 1. Regression statistics for rank-abundance distributions of epilithic bacterial assemblages at different sites and stages of development. Mesocosm 1 Sample Day Day Day Day Day Day

2 7 15 30 60 90

Mesocosm 2 2

Slope

r

20.199 20.120 20.085 20.098 20.084 20.054

0.89 0.91 0.98 0.98 0.99 0.97

Lake 2

Slope

r

20.121 20.062 20.058 20.066 20.078 no data

0.97 0.88 0.97 0.98 0.98 no data

Slope

r2

20.039 20.033 no data 20.065 20.066 20.080

0.87 0.86 no data 0.95 0.91 0.91

Notes: Slope and r2 were determined from a geometric series model for all assemblages. All linear regressions were significant (P , 0.01; n varies with sample).

562


served for all samples, apart from the lake day 60 and day 90 samples, which were similar. Different changes in band richness were observed for each set of biofilms, although the two sets of mesocosm biofilms showed some similarities. The clearest pattern occurred in the first set of mesocosm samples, where richness gradually increased during biofilm development. An increase in richness during the early stages of succession is intuitive, and was demonstrated by Santegoeds et al. (1998) for biofilms growing in a wastewater treatment plant. They attributed this increase to a greater number of microhabitats that appear during biofilm development, which support a more diverse bacterial community. This hypothesis was not supported by the DGGE profiles of the lake biofilms, which showed a reduction in richness after the first seven days. It is difficult to explain why there would be fewer bands (and presumably fewer bacterial populations) in mature lake biofilms than in earlier stages. Almost all successional communities show increases in richness, at least in the early stages of community development. However, following initial increases in richness, later reductions may occur, possibly arising from the competitive dominance of a few populations. This could result in a transitory period of maximum richness at the middle stages of community development, a situation similar to that suggested by the intermediate disturbance hypothesis (Grime 1973, Connell 1978). Indeed, all three sets of biofilms showed a reduction in richness after the first two or three sample dates. The opposing forces of increased competition and increased number of distinct habitats may have influenced bacterial assemblage structure as the biofilms developed. Rapid colonization of surfaces by several different bacterial populations may have occurred within the first few days of submersion. Although many of these colonizing bacterial populations were detected seven days later, they were apparently unable to grow at rates high enough to be detected later in the study. Like many molecular techniques, DGGE focuses on the dominant members of a community, and is unlikely to detect organisms accounting for ,1% of the community (Muyzer et al. 1993). A band in a DGGE gel represents a portion of the community, not the absolute abundance of a given population. Populations that were initially detected may still be present in later biofilms, but rapid growth by other populations makes them more difficult to detect. As the biofilm matures, the number of available microhabitats may increase (for example, from the formation of anaerobic pockets within the biofilm), supporting a greater number of bacterial populations. In this study, we used glass slides to monitor the formation of epilithic biofilms in order to avoid random variation that would have occurred using natural substrata. Concerns have been raised over the use of artificial substrata when examining attached microbial communities. In a survey of 27 separate studies, Cattaneo and Amireault (1992) found that such substrata


can misrepresent the quantity and quality of natural algal communities. Although we used artificial substrata, the number of bands appearing in DGGE gels were similar to those that have been reported for biofilms on natural substrata such as aquatic macrophyte leaves (Jackson et al. 1998) or bivalve shells (Gillan et al. 1998), and one of the dominant 16S rRNA sequence types (that of Pseudomonas putida) had previously been detected in biofilms present in a local wetland (Jackson et al. 1998). Thus, the use of artificial substrata may not severely influence the overall nature of the bacterial assemblage that develops.

Changes in rank-abundance distributions Rank-abundance distributions can provide insights into both richness and evenness, and potentially into patterns of niche division. Although rank-abundance plots are typically derived from numbers of individuals of each species, other variables have been used. Tokeshi (1990) used biomass when comparing chironomid communities, and the proportion of total vegetation cover accounted for by different plant species has also been used as a measure of relative abundance (Bazazz 1975). In this study, we derived estimates of relative abundance from the densities of individual bands appearing in a sample when analyzed in a DGGE gel. Our abundance values are not absolute, but represent an estimate of the proportion of that sample community occupied by the bacterial population that we assume is represented by that 16S rRNA sequence type. However, it should be noted that these proportions are estimates, and could be influenced by factors not related to the size of that particular population (such as the presence of multiple copies of the amplified 16S rRNA gene, comigration of different sequences, or amplification biases for some bacterial genomes over others; Jackson and Churchill 1999). Plant community succession has been examined using rank-abundance plots, and a switch from a roughly geometric distribution in pioneer communities to a log normal distribution in later stages has been observed (Whittaker 1972, Bazazz 1975). We observed the opposite trend during bacterial community development, with rank-abundance distributions typically becoming more geometric as the biofilm developed. Tokeshi (1993) reanalyzed the work of Bazazz (1975), and showed that if a geometric series model was applied throughout that study, there was a decrease in the slope of the regression model with successional age. We observed that same trend in the mesocosm biofilms, particularly in the first set where the slope decreased from early to late biofilm communities. This reduction in slope corresponds to greater equitability of abundance between different populations, or a more evenly distributed community. The second set of mesocosm biofilms showed a similar pattern in the early stages of succession, but the slope increased slightly in the later stages of biofilm development. The lake samples ap-

February 2001


peared to show the reverse trend, with the slope of a geometric model increasing throughout the study.

Reproducibility of successional patterns One problem in studies of succession is separating autogenic changes in community structure arising from interactions between different populations, from allogenic changes, and from changes in the environment (Fisher 1990). In an attempt to distinguish between autogenic and allogenic changes in bacterial assemblages, we monitored two sets of mesocosm biofilms separated by a 30 d period. If changes in community structure were a function of changes in environmental conditions, the bacterial assemblage present on day 60 of the first set should coincide with that present on day 30 of the second set, and so forth. If changes arise because of repeatable autogenic succession within the biofilms, then samples taken at the same stage of biofilm development should show similar bacterial assemblages, regardless of the date on which they were sampled. Thus, we expected a cluster analysis to classify the mesocosm communities by either sample date (day 90 set A groups with day 60 set B, day 60 set A groups with day 30 set B) or by developmental stage (day 90 set A groups with day 90 set B, day 60 set A groups with day 60 set B, etc.). Neither of these grouping patterns emerged. A cluster analysis tended to group biofilm assemblages based upon successive samples taken from the same set. For example, day 60 set B was most similar to day 30 set B, which together were most similar to day 15 set B, with the cluster formed being most similar to day 7 set B. There was a weak separation of the two sets of mesocosm biofilms, for samples taken after day 2. The first samples (day 2) did not group with any other samples, or with each other, suggesting that these early communities bore little resemblance to those present at later dates. The length of time between sampling events did not influence community similarity: The most similar communities were those separated by 30 d, occurring towards the later stages of biofilm succession. This suggests that the initial process of biofilm development was characterized by rapid changes in bacterial community structure, with the assemblage present after a few days having little resemblance to the assemblage present a week or so later. However, as the biofilm developed, certain bacterial populations became dominant and persisted within the biofilm, stabilizing the overall community.

Functional changes in bacterial assemblages during biofilm development We used MPN counts to estimate the abundance of three groups of heterotrophic bacteria: glucose-, cellulose-, and benzoate-degraders at three stages of biofilm development. Mesocosm and lake biofilms showed a slight decrease in the abundance of glucose-degraders from early to late biofilms, and this pattern coincided

563

with changes in cellulose-degraders for the lake sample. Virtually no cellulose-degraders were detected in the mesocosm biofilms, which may be a result of the simpler environment in which these biofilms developed. While the abundance of both glucose- and cellulosedegraders declined during biofilm development in the lake, the abundance of benzoate-degraders steadily increased. This may reflect a functional shift in the bacterial assemblage from an early reliance on simple sugars and carbohydrates to the ability to use a greater variety of carbon sources such as derivatives of aromatic polymers. For both sets of samples, biofilms in the later stages of succession had a more equitable division of numbers between the different groups of heterotrophs studied. This suggests that mature bacterial assemblages may show less competition-oriented patterns of diversity and community structure through the ability of different populations to utilize different carbon sources. Other studies that have examined functional changes in attached microbial communities have monitored extracellular enzyme activity in developing biofilms (Jones and Lock 1989, Sabater and Romanı´ 1996), and have observed fluctuating patterns in enzyme activity which are difficult to compare to our changes in bacterial abundance. The MPN method, using different carbon sources, may more accurately reflect changes in the functionality of the bacterial assemblage, given that extracellular enzymes are also produced by nonbacterial microorganisms, and can remain active within the biofilm matrix for some time after they are first produced (Lock et al. 1984).

Toward a model for bacterial biofilm succession This study is one of the first to demonstrate that changes in bacterial community structure occur during freshwater epilithon development. We believe that these changes represent a successional process, and although this study was largely descriptive, some patterns in bacterial biofilm succession are suggested. Initial colonization of the substratum is likely to be stochastic, and bacterial assemblages in the early stages of biofilm succession are likely to be so dynamic that no orderly arrangement of community structure appears. Such communities typically appear as log normal distributions when examined by rank-abundance plots, and may have little similarity to the communities present at later stages. Colonization may not be entirely random, in that certain bacterial species may have greater aptitude for colonization than others, although this variability in bacteria has not been fully explored. Following the disordered nature of the early communities, the bacterial assemblage may simplify as superior competitors begin to dominate. When monitored by molecular methods, this may appear as a decrease in richness, as the less competitive populations become less abundant, and account for a small fraction of the

564


community DNA that is below detection limits. Abundance distributions may become more geometric (at least for the dominant populations), reflecting a nicheoriented structure, where resource competition drives the development of the community. However, the continuing growth of the biofilm results in a complex architecture with the formation of many microhabitats. Exchanges with the surrounding water column become less important, whereas internal cycling of resources within the biofilm, supplemented by allochthonous resources from the overlying water, becomes more important (Haack and McFeters 1982, Lawrence et al. 1995). Thus, biofilms in later successional stages effectively posses a greater number of niches, both in a sense of more physical habitats, and in terms of the variety of carbon sources available. At the level of the entire bacterial assemblage, competition in these communities may be suppressed as dominant populations utilize different resources or inhabit different regions of the biofilm. Richness and equitability may increase, reflecting both a greater number and a greater evenness of different functional groups of bacteria within the biofilm. Competition may be more important in structuring mature bacterial assemblages at a finer level of resolution, e.g., within groups of bacteria utilizing the same resources. The overall pattern of bacterial assemblage development may therefore appear to go through three major stages: (1) an initial stage characterized by colonization of different populations and lack of orderly community structure, (2) an intermediate stage characterized by a limited number of dominant populations utilizing similar resources, and (3) a late stage characterized by a mature biofilm with complex spatial structure that facilitates greater diversity through increased variation in habitat and available resources (Fig. 7). While the above model can explain the changes we observed in bacterial assemblages during this study, it is purely phenomenological. In the last two decades, mechanistic models have been proposed to explain successional patterns in plant communities, with succession being driven by the availability of limiting resources and the ability of populations or individuals to utilize these resources (Tilman 1982, 1985, Huston and Smith 1987). It is difficult to definitively state how such mechanistic models may apply to the succession of attached microbial communities, but some suggestions are possible. In bacterial communities, mechanistic models are likely to operate at the level of the population, rather than at the level of an individual bacterium. Given the nature of bacterial population growth, variation between individuals is likely to be low, and succession is unlikely at the level of individual organisms. Thus, while viewing succession at the level of the individual can explain succession in plant communities (Horn 1975, Huston and Smith 1987), this approach is less useful when describing microbial communities. Tilman (1982, 1985) suggested a resource-


FIG. 7. A conceptual illustration of the changes taking place in bacterial assemblages during biofilm succession. Early biofilms are characterized by a random assortment of many species, but with few individuals of each. More competitive species dominate the biofilm in mid-development, and competition drives down overall species diversity. As the biofilm matures, the development of a complex three-dimensional architecture increases the diversity of resources and habitats available for bacterial growth, resulting in increased species diversity in later developmental stages. In these mature biofilms, competition may operate at a finer level, within particular functional groups of bacteria utilizing the same resource.

ratio model for succession, in which changes over time reflect changing levels of different resources, and in which the community present at any given time is a result of differences in the competitive abilities of populations present at different successional stages. Such an explanation could apply to bacterial biofilm assemblages, with the caveat that resource diversity will also be increasing with time, thereby lessening competitive interactions.

Bacterial assemblages as ecological communities In ecological studies, bacteria are often lumped together as one homogenous group of organisms utilizing the same resources and functioning as one entity. Bacterial assemblages need to be viewed as separate populations of different species, which may show diverse patterns in their distribution and roles in an ecosystem. Although recent work has attempted to integrate bacteria into broad-scale ecological concepts, such as food webs and competition (e.g., Cochran-Stafira and von Ende 1998), these studies are often limited to laboratory manipulations of bacterial cultures, which may have little relevance to natural communities. Molecular techniques can facilitate the examination of the patterns

February 2001


and processes occurring in natural bacterial assemblages, although the molecular approach has its own limitations. In this study, we found that, although only a small amount of sample material (ø1 g) was required for DNA extraction, this was much more than could be obtained from individual biofilms, and it was necessary to pool material from all of the biofilms sampled on a particular date, removing the element of replication. Similarly, even after multiple slides were pooled, it took 2 d before enough material was present for our analyses. Thus, we were unable to sample changes in assemblage structure during the initial colonization period. Studies of bacterial communities should not focus solely upon the bacterial richness within a habitat, although this is an obvious starting point for such investigations. The value of explicitly reporting a richness or evenness measure of diversity for a bacterial community is limited at best. As Finlay et al. (1997) observed, the majority of bacterial populations in nature exist at low, barely detectable numbers, and form a pool of rare, cryptic species. Such minor populations may be ubiquitous, and remain undetectable until environmental conditions change, and their growth is encouraged. While the presence of many such populations may account for the high estimates of bacterial richness obtained in some studies (for example, Torsvik et al. [1990] suggested .4000 species per gram of soil), their role in the community is probably minor. To date, all studies utilizing DGGE have reported no more than fifty or so dominant genotypes within a habitat, suggesting that the bulk of the bacterial community was composed of relatively few species. It may be necessary to separate measures of bacterial diversity into two values: (1) a large fundamental diversity which reflects the entire community, including all rare species; and (2) a lower realized, or functional, diversity which reflects the dominant populations (which together may account for .99% of the bacterial community). Abundance distribution patterns may thus shed more light on bacterial community structure than diversity indices alone. In a recent paper, Ward (1998) observed that many of the viewpoints of microbiologists are derived from a history of studies upon cultivated microorganisms, and asserted that this approach is analogous to deriving ecological theories from observations of rare species in zoos. He noted that a more natural view of macroorganisms has existed for ecologists for years, and urged microbiologists to begin to view microbial, and especially bacterial, communities, from a more ecological point of view (Ward 1998). The reverse is also true: ecologists need to view bacterial communities as diverse assemblages of a variety of different bacterial populations, whose roles and functions in an ecosystem may be at least as diverse as those of macroorganisms. In addition, bacterial communities can show patterns in organization and development that operate on spatial

565

and temporal scales far more amenable to experimentation than communities of larger organisms. As the tools for the analysis of bacterial communities continue to improve, it should be possible to begin to understand the processes underlying bacterial diversity and community structure. Perhaps more importantly, it should be possible to use these bacterial communities to test general ecological theory. ACKNOWLEDGMENTS We thank Arthur Benke, Keller Suberkropp, and Wyatt Cross for helpful discussions during the design and implementation of this study. Along with Hans Paerl, and two anonymous reviewers, they also provided valuable comments on an earlier version of this manuscript. LITERATURE CITED Altschul, S. F., W. Gish, W. Miller, E. W. Myers, and D. J. Lipman. 1990. Basic local alignment search tool. Journal of Molecular Biology 215:403–410. Bazazz, F. A. 1975. Plant species diversity in old-field successional ecosystems in southern Illinois. Ecology 56:485– 488. Blenkinsopp, S. A., and J. W. Costerton. 1991. Understanding bacterial biofilms. Trends in Biotechnology 9:138–143. Cattaneo, A., and M. C. Amireault. 1992. How artificial are artificial substrata for periphyton? Journal of the North American Benthological Society 11:244–256. Cochran-Stafira, D. L., and C. N. von Ende. 1998. Integrating bacteria into food webs: studies with Sarracenia purpurea inquilines. Ecology 79:880–898. Colwell, R. R., R. A. Clayton, B. A. Ortiz-Conde, D. Jacobs, and E. Russek-Cohen. 1995. The microbial species concept and biodiversity. Pages 3–15 in D. Allsopp, R. R. Colwell, and D. L. Hawksworth, editors. Microbial diversity and ecosystem function. CAB International, Wallingford, UK. Connell, J. H. 1978. Diversity in tropical rain forests and coral reefs. Science 199:1302–1310. Connell, J. H., and R. O. Slatyer. 1977. Mechanisms of succession in natural communities and their role in community stability and organization. American Naturalist 111:1119– 1144. Costerton, J. W., K.-J. Cheng, G. G. Geesey, T. I. Ladd, J. C. Nickel, M. Dasgupta, and T. J. Marrie. 1987. Bacterial biofilms in nature and disease. Annual Review of Microbiology 41:435–464. Couch, C. A., and J. L. Meyer. 1992. Development and composition of the epixylic biofilm in a blackwater river. Freshwater Biology 27:43–51. Finegan, B. 1984. Forest succession. Nature 312:109–114. Finlay, B. J., S. C. Maberly, and J. I. Cooper. 1997. Microbial diversity and ecosystem function. Oikos 80:209–213. Fisher, S. G. 1990. Recovery processes in lotic ecosystems: limits of successional theory. Environmental Management 14:725–736. Gillan, D. C., A. G. C. L. Speksnijder, G. Zwart, and C. de Ridder. 1998. Genetic diversity of the biofilm covering Montacuta ferruginosa (Mollusca, Bivalvia) as evaluated by denaturing gradient gel electrophoresis analysis and cloning of PCR-amplified gene fragments coding for 16S rRNA. Applied and Environmental Microbiology 64:3464– 3472. Grime, J. P. 1973. Competitive exclusion in herbaceous vegetation. Nature 242:344–347. Haack, T. K., and G. A. McFeters. 1982. Nutritional relationships among microorganisms in an epilithic biofilm community. Microbial Ecology 8:115–126. Horn, H. S. 1975. Markovian properties of forest succession. Pages 196–211 in M. L. Cody and J. M. Diamond, editors.

566


Ecology and evolution of communities. Harvard University Press, Cambridge, Massachusetts, USA. Huston, M., and T. Smith. 1987. Plant succession: life history and competition. American Naturalist 130:168–198. Jackson, C. R., and P. F. Churchill. 1999. Analysis of microbial communities by denaturing gradient gel electrophoresis: Applications and limitations. Recent Research Developments in Microbiology 3:81–91. Jackson, C. R., J. P. Harper, D. Willoughby, E. E. Roden, and P. F. Churchill. 1997. A simple, efficient method for the separation of humic substances and DNA from environmental samples. Applied and Environmental Microbiology 63:4993–4995. Jackson, C. R., E. E. Roden, and P. F. Churchill. 1998. Changes in bacterial species composition in enrichment cultures with various dilutions of inoculum as monitored by denaturing gradient gel electrophoresis. Applied and Environmental Microbiology 64:5046–5048. Johnson, R. E., N. C. Tuchman, and C. G. Peterson. 1997. Changes in the vertical microdistribution of diatoms within a developing periphyton mat. Journal of the North American Benthological Society 16:503–519. Jones, S. E., and M. A. Lock. 1989. Hydrolytic extracellular enzyme activity in heterotrophic biofilms from two contrasting streams. Freshwater Biology 22:289–296. Lawrence, J. R., D. R. Korber, G. M. Wolfaardt, and D. E. Caldwell. 1995. Behavioral strategies of surface-colonizing bacteria. Advances in Microbial Ecology 14:1–75. Lock, M. A. 1993. Attached microbial communities in rivers. Pages 113–138 in T. E. Ford, editor. Aquatic microbiology. Blackwell Scientific Publications, Boston, Massachusetts, USA. Lock, M. A., R. R. Wallace, J. W. Costerton, R. M. Ventullo, and S. E. Charlton. 1984. River epilithon: towards a structural-functional model. Oikos 42:10–22. Lowe, R. L., J. B. Guckert, S. E. Belanger, D. H. Davidson, and D. W. Johnson. 1996. An evaluation of periphyton community structure and function on tile and cobble substrata in experimental stream mesocosms. Hydrobiologia 328:135–146. Murray, A. E., J. T. Hollibaugh, and C. Orrego. 1996. Phylogenetic comparisons of bacterioplankton from two California estuaries compared by denaturing gradient gel electrophoresis of 16S rDNA fragments. Applied and Environmental Microbiology 62:2676–2680. Muyzer, G., E. C. de Waal, and A. G. Uitterlinden. 1993. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Applied and Environmental Microbiology 59:695–700. Muyzer, G., S. Hottentra¨ger, A. Teske, and C. Wawer. 1996. Denaturing gradient gel electrophoresis of PCR-amplified 16S rDNA—a new molecular approach to analyse the genetic diversity of mixed microbial communities. Pages 1– 23 in A. D. L. Akkermans, J. D. van Elsas, and F. J. de Bruijn, editors. Molecular microbial ecology manual 3.4.4. Kluwer Academic Publishers, Dordrecht, The Netherlands. Nu¨bel, U., B. Engelen, A. Felske, J. Snaidr, A. Wieshuber, R. I. Amann, W. Ludwig, and H. Backhaus. 1996. Sequence heterogeneities of genes encoding 16S rRNAs in Paenibacillus polymyxa detected by temperature gradient gel electrophoresis. Journal of Bacteriology 178:5636–5643. Nu¨bel, U., F. Garcia-Pichel, M. Ku¨hl, and G. Muyzer. 1999. Quantifying microbial diversity: morphotypes, 16S rRNA genes, and carotenoids of oxygenic phototrophs in microbial mats. Applied and Environmental Microbiology 65: 422–430. O’Donnell, A. G., M. Goodfellow, and D. L. Hawksworth. 1995. Theoretical and practical aspects of the quantifica-


tion of biodiversity among microorganisms. Pages 65–73 in D. L. Hawksworth, editor. Biodiversity: measurement and estimation. Chapman & Hall, London, UK. Odum, E. P. 1969. The strategy of ecosystem development. Science 164:262–270. Pace, N. R. 1997. A molecular view of microbial diversity and the biosphere. Science 276:734–740. Peterson, C. G. 1987. Influences of flow regime on development and dessication response of lotic diatom communities. Ecology 68:946–954. Pielou, E. C. 1984. The interpretation of ecological data. John Wiley & Sons, New York, New York, USA. Polz, M. F., and C. M. Cavanaugh. 1998. Bias in templateto-product ratios in multitemplate PCR. Applied and Environmental Microbiology 64:3724–3730. Roemer, S. C., K. D. Hoagland, and J. R. Rosowski. 1984. Development of a freshwater periphyton community as influenced by diatom mucilages. Canadian Journal of Botany 62:1799–1813. Sabater, S., and A. M. Romanı´. 1996. Metabolic changes associated with biofilm formation in an undisturbed Mediterranean stream. Hydrobiologia 335:107–113. Santegoeds, C. M., T. G. Ferdelman, G. Muyzer, and D. de Beer. 1998. Structural and functional dynamics of sulfatereducing populations in bacterial biofilms. Applied and Environmental Microbiology 64:3731–3739. Sinsabaugh, R. L., S. W. Golladay, and A. E. Linkins. 1991. Comparison of epilithic and epixylic biofilm development in a boreal river. Freshwater Biology 25:179–187. Sobczak, W. V. 1996. Epilithic bacterial responses to variations in algal biomass and labile dissolved organic carbon during biofilm colonization. Journal of the North American Benthological Society 15:143–154. Stevenson, R. J. 1983. Effects of current and conditions simulating autogenically changing microhabitats on benthic diatom immigration. Ecology 64:1514–1524. Tilman, D. 1982. Resource competition and community structure. Princeton University Press, Princeton, New Jersey, USA. Tilman, D. 1985. The resource-ratio hypothesis of plant succesion. American Naturalist 125:827–852. Tilman, D. 1994. Community diversity and succession: the roles of competition, dispersal, and habitat modification. Pages 327–344 in E.-D. Schulze and H. A. Mooney, editors. Biodiversity and ecosystem function. Springer-Verlag, New York, New York, USA. Tokeshi, M. 1990. Niche apportionment or random assortment: species abundance patterns revisited. Journal of Animal Ecology 59:1129–1146. Tokeshi, M. 1993. Species abundance patterns and community structure. Advances in Ecological Research 24:111– 186. Torsvik, V., J. Goksøyr, and F. L. Daae. 1990. High diversity in DNA of soil bacteria. Applied and Environmental Microbiology 56:782–787. Ward, D. M. 1998. A natural species concept for prokaryotes. Current Opinion in Microbiology 1:271–277. Wetzel, R. G. 1983. Limnology. Saunders College Publishing, Philadelphia, Pennsylvania, USA. Whittaker, R. H. 1972. Evolution and measurement of species diversity. Taxon 21:213–251. Woomer, P. L. 1994. Most probable number counts. Pages 59–79 in R. W. Weaver, S. Angle, P. Bottomley, D. Bezdicek, S. Smith, A. Tabatabai, and A. Wollum, editors. Methods of soil analysis: part 2, microbiological and biochemical properties. Soil Science Society of America, Madison, Wisconsin, USA. Zhou, J., M. A. Bruns, and J. M. Tiedje. 1996. DNA recovery from soils of diverse composition. Applied and Environmental Microbiology 62:316–322.