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Bacteria, Bacterioplankton
Bacteria, Bacterioplankton R D Robarts, UNEP GEMS/Water Programme, Saskatoon, SK, Canada G M Carr, UNEP GEMS/Water Programme, Gatineau, QC, Canada ã 2009 Elsevier Inc. All rights reserved.
Introduction
Bacterial Numbers and Biomass
To the old adage of ‘out of sight, out of mind’ could be added ‘and unimportant.’ With respect to bacterioplankton in inland waters this was true for many scientists interested in aquatic ecology, but not all, and is certainly not true today. Bacteria have been known to inhabit natural waters since the seventeenth century although their numbers, biomass, distribution, and roles in these systems were poorly understood until the latter part of the twentieth century. Even in classical textbooks of the 1930s, such as P.S. Welch’s ‘Limnology,’ a whole chapter (albeit short) was devoted to bacteria and other microorganisms. Welch clearly had an intuition about the potential importance of bacteria in lakes and lamented the fact that very few lakes have received any study of their native bacteria. Indeed, E.P. Odum in his book ‘Fundamentals of Ecology’ in 1963 noted that, ‘‘Because of the technical difficulties of study, microbial ecology is, unfortunately, often completely omitted from the general college course in ecology.’’ The situation with respect to our knowledge and understanding of bacterioplankton in inland waters changed drastically beginning in the 1960s with publications describing and applying radioactively labeled compounds to measure the turnover and uptake of specific dissolved organic compounds in lakes. Thereafter, new protocols were developed to measure in situ the rates of bacterioplankton production in terms of cell numbers, biomass, and carbon. In addition, the introduction of the use of fluorescent dyes in microscopy led to the routine measurement of bacterial numbers and biomass and later to quantitative measurements of different aspects of microbial metabolism such as respiration and enzymatic activities. The methodology to study bacterioplankton has continued to evolve and thus there has been a revolutionary advance in our knowledge and understanding of the distribution and size of bacterioplankton populations in lakes, reservoirs, wetlands, rivers, and groundwaters and of their role in biogeochemical and energy cycles in these systems over the past three decades. In addition, since microbes are the first link joining the biotic and abiotic components of aquatic systems, they are excellent and sensitive indicators of human impacts on these systems.
Early methods to estimate the size of bacterioplankton populations were based on plate culturing techniques. But it had been accepted for a long time that these methods greatly underestimated the size of the populations. A number of direct-count methods (counting of stained bacteria with a microscope) were devised in conjunction with the use of centrifuges, chemical flocculation, or filtration in order to concentrate bacterial cells. Limnologists in the 1920s believed that direct counting of bacteria could not be undertaken without concentrating cells because lake populations were too low for accurate enumeration. Such concentrating mechanisms also created problems so there was some resistance to the use of direct counts. In the 1930s a concentration method was developed that involved evaporating water samples under reduced pressure. It was claimed that this method produced bacterial counts 2–4000 times higher than those from culture plates. For example, researchers reported bacterial numbers of (1–6) 106 ml1 in the Russian Lake Glubokoye. As can be imagined, such numbers for an unpolluted lake were viewed as being overly high and would produce a visible turbidity (which it did not), especially when others were reporting numbers of only 740–32 600 cells ml1 in lakes using a direct-count method at around the same time. However, a survey of bacterial numbers in Wisconsin lakes found that they ranged widely from 19 103 to 2 106 ml1 using this new method. Today with the widespread use of epifluorescence microscopy, and more recently with the use of flow cytometers, we know that bacteria occur in many unpolluted inland waters at concentrations of millions per milliliter. Bacteria in most natural aquatic systems range from 104 to 108 cells ml1 of water, although cell concentrations as high as 109 ml1 have been reported (Figure 1). In a 1984 cross-system overview of bacteria in surface waters, cell concentrations of (0.1–13.4) 106 ml1 were reported for a range of lakes. Even a very remote and pristine Arctic lake, Lake Taymyr on the Taymyr Peninsula in Russia, has bacterial numbers that varied between 1.3 106 and 4.8 106 cells ml1. Moreover, there have been several studies that found that
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Figure 1 Distribution of bacterioplankton production (top panel) and abundance (bottom panel) measurements from inland waters, as reported in the primary literature and summarized in Carr GM and Morin A (2002) Sampling variability and the design of bacterial abundance and production studies in aquatic environments. Canadian Journal of Fisheries and Aquatic Sciences 59: 930–937. Box plots show median (mid-line) and 25th and 75th percentiles. Whiskers extend to data points that fall within 1.5 times the midrange. Asterisks denote data points that extend beyond 1.5 times the midrange, and open circles are data points that extend beyond 3 times the midrange.
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(log-transformed data) with chlorophyll a concentrations for lakes in North America and Europe. This was not surprising as it had been assumed for some time that phytoplankton would be a major provider of substrates for bacterial production and growth, either at the time of their senescence and death or by the extracellular release of dissolved organic compounds during photosynthesis. However, one of the disconcerting conclusions was that the increase in the number of bacteria did not increase with the increase in chlorophyll a concentrations as lake trophy changed, since the slopes of the regressions were less than 1. As a general rule of thumb, oligotrophic waters have bacterial concentrations <1.7 106 cells ml1, mesotrophic waters (1.7–6.5) 106 cells ml1, and eutrophic waters >6.5 106 cells ml1. Results of within-system correlation analyses of bacterial numbers have been variable. In some studies, no correlations have been found whereas in others significant correlations between bacterial numbers and water temperature, primary production, and chlorophyll a concentration have been reported. This should not be surprising as total counts of bacterial cell numbers with epifluorescence microscopy techniques do not discriminate between different physiological groups, between live and dead cells, or between metabolically active and nonactive cells (Figure 3). Another factor that influences bacterioplankton dynamics in aquatic systems is losses due to processes such as grazing (protistan and invertebrate), parasitism, and sedimentation. However, even today few lakes have been sufficiently studied to provide firm conclusions on the relative importance of environmental factors on population sizes.
Metabolically Active Bacterioplankton 0.01
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Figure 2 Range in published relationships between chlorophyll a and bacterioplankton abundance. Adapted from Gasol, JM and Duarte CM (2000) Comparative analysis in aquatic microbial ecology: how far do they go? FEMS Microbiology Ecology 31: 99–106, with permission.
bacterial abundance in the anoxic hypolimnia of stratified lakes can be as much as twice that of the oxic strata. Several studies have tried to identify limnological parameters that regulate the variations in bacterial numbers between different aquatic systems (Figure 2) and seasonally in a specific system. In one such study, bacterial numbers were significantly correlated
Generally, the number of metabolically active bacteria is more variable between plankton communities than the total number of bacteria (Figure 4). As with total bacterial concentrations, the number of active cells in bacterioplankton populations has been correlated to indicators of lake trophy such as total phosphorus, chlorophyll, and dissolved organic carbon (DOC). However, the numbers of these two groups do not increase in the same way over enrichment gradients, resulting in a proportionately higher number of active cells in eutrophic versus oligotrophic waters. The mean of published numbers of active bacteria in lakes is about 22% with a rare study reporting as much as 100% active cells. For aquatic systems, generally, the numbers range from <1% in oligotrophic waters to >90% for highly productive systems. Differences in methodology may account for
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30 25 20 15 10 5 Achigan Alymer Baldwin Bowker Brome Brompton Memphremagog Connelly Coulombe Croche Cromwell d'Argent Lyster Magog Massawippi Nicolet Orford Silver St. Francoise Stukely Triton Truite Waterloo Yamaska
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Figure 3 Photomicrographs of bacterioplankton cells from Wascana Creek, Saskatchewan, filtered onto 0.2-mm pore-size cellulose nitrate filters: (a) stained with Syto 9 for total cell counts (Molecular Probes Inc., Eugene, OR, USA; cells appear green), (b) the same preparation stained with the commercial Live Dead stain for determination of ratio of live to dead cells, live cells appear green while putative dead cells red, and (c) filtered cell preparation stained with 5 mmol l1 cyanoditolyltetrazolium chloride (Polysciences Inc, Warrington, PA, USA) incubated for 60 min and counterstained with Syto 9; yellow cells are active while green cells are not metabolically active as determined by this assay. Magenta cells and filaments are cyanobacteria imaged using autofluorescence of their photosynthetic pigments. Imaging was carried out using the 522 þ 16, 598 þ 16 excitation lines of an MRC 1024 laser microscope (Zeiss, Jena, Germany). (Photos: J.R. Lawrence and G.W. Swerhone, NWRI, Environment Canada, Saskatoon, unpublished.)
Figure 4 Variation in the total number of bacterial cells, metabolically active cells and the percentage of metabolically active bacteria in 24 lakes of southern Que´bec, Canada. The average number of active cells in the lakes was 20.9%. Data are from del Giorgio P A and Scarborough G (1995) Increase in the proportion of metabolically active bacteria along gradients of enrichment in freshwater and marine plankton: implications for estimates of bacterial growth and production rates. Journal of Plankton Research 17: 1905–1924.
some of this variance, but research, first in marine systems and later in fresh waters, indicated that bacterial grazers may selectively remove larger, actively growing cells from a population leaving it dominated by small, slow-growing cells. Studies of the long-term seasonal variations in metabolically active bacteria
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in a particular system have shown that their contribution to the total bacterioplankton population can also be large, but such studies are rare. Hopefully, with the recent use of flow cytometry to determine metabolically active bacteria, more data will become available that will provide new insights on metabolically active bacteria in inland waters and the factors that influence them. Such data have significant consequences for the measurement of bacterioplankton growth and production measurements, which will be outlined here.
Bacterial Biomass Bacterial Biovolumes
Spatial and temporal changes in bacterial cell volume have been reported from a wide range of aquatic habitats, although the number of studies has been limited because of the difficulty in obtaining such data. To model energy and material flows, it is essential to know the factors that influence bacterial cell volume, as conversion of bacterial cell numbers to carbon, nitrogen, or phosphorus units is dependent on the derivation of accurate ‘numbers to volume’ conversion factors. Therefore, a major challenge for aquatic microbial ecologists is to identify the factors that control cell volume and hence bacterial biomass. Factors that have been identified include carbon supply from algae, phosphorus concentration, predation, and water temperature. To derive a value for bacterioplankton biomass, data are needed on the number of cells and their biovolume. This is not as straightforward as it might seem. The methods used to obtain biovolume include epifluorescence microscopy, scanning electron microscopy (SEM), scanning confocal laser microscropy (in conjunction with fluorescent dyes; Figure 3) and more recently flow cytometry, also using fluorescent dyes to estimate cell dimensions. These methods have problems ranging from halo effects caused by fluorescence to cell distortions caused by sample preparation for SEM, all of which can alter the calculation of biovolumes, especially with the very small cells typical of bacterioplankton. Median bacterial cell volumes usually range between 0.013 and 0.200 mm3. Although not confirmed yet, several studies that have looked at bacterial cell size have reported that this decreases with increasing trophic status. Furthermore, there have been some studies comparing bacterial cells between the upper oxic and lower anoxic parts of lakes. These have shown that bacteria from the anoxic hypolimnion can be between 2 and 10 times larger than bacteria in
the oxic layer. The reasons for this difference and its implications on lake metabolism require further investigation. Bacterial Biomass
To derive a carbon biomass value for bacterioplankton, most researchers have either used a constant value for a cell, usually between 10 and 20 fg C cell1, or a volume conversion factor that has up to a fivefold range of a commonly used value of 121 fg C mm3. Several studies have produced values in the range of 350–720 fg C mm3, whereas others reported much lower values of between 32 and 160 fg C mm3. One reason for the higher values may be the underestimation of cell volumes due to shrinkage effects caused by fixatives and air drying, although this is not applicable to all published values. Smaller cells tend to have higher carbon and lower water contents than larger cells. Therefore, what is clear is that the assumption of a constant ratio of carbon:biovolume is not correct. Some studies have shown that the carbon:biovolume ratio of bacterioplankton not only varied with cell size but also had temporal and geographical variations, indicating that factors such as species composition, nutritional state, growth rate, and other factors played a role. While it is now clear that bacterial biomass contributes a larger portion to the total planktonic biomass in unproductive freshwater systems than in more productive systems, what is not clear is why. The most widely accepted explanations include allochthonous carbon inputs being important in oligotrophic systems, decreased bacterivory, and bacterioplankton access to nutrients that are not available to phytoplankton (i.e., very low concentrations and organic forms) or a combination of these. Correct estimates of bacterioplankton biomass distribution are a fundamental requirement in aquatic microbial ecology, and more data that lead to a sound understanding of the factors that govern biomass in diverse systems are needed.
Bacterial Production The use of 14C-glucose in the early 1960s and subsequent development of methods to determine rates of bacterial uptake, respiration, and turnover of organic compounds at natural substrate concentrations later in the decade provided much data on the decomposition and flow of organic carbon through food webs in a wide range of natural systems. Since bacteria degrade a large number of organic substrates, further developments introduced the use of
Protists, Bacteria and Fungi: Planktonic and Attached _ Bacteria, Bacterioplankton
labeled sugars, amino acids, organic acids, lignocellulose, and other dissolved and particulate substrates. These early studies demonstrated that bacteria were actively metabolizing organic matter but did not provide quantitative estimates of growth rates and production. While the growth of autotrophic bacteria that fix carbon dioxide for their primary source of carbon can potentially be measured using 14CO2, until about 20 years ago there had not been a reliable method to determine the growth of natural assemblages of heterotrophic bacteria that utilize organic substrates for a carbon source. The two most commonly used methods were the incorporation of [3H-methyl]thymidine ([3H]TdR) into bacterial DNA, and the incorporation of 3H-leucine into bacterial protein. Labeled leucine is now generally more widely used than labeled thymidine because of its greater sensitivity and the simpler assumptions and calculations required to derive cell and carbon production estimates. Bacterial production has been measured in a wide range of aquatic systems from the Arctic to Antarctic. Not surprisingly, there is huge variation in the rates reported (Figure 1). Some of this variation is due to the factors used to convert the rates of label incorporation into cell and carbon production units, particularly with the more complicated conversion of thymidine incorporation rates. Several studies have concluded that there is no significant difference in production rates determined with labeled leucine and thymidine, whereas others have concluded that there is a difference with thymidine-derived rates being lower. In addition, there are potential problems associated with isotope dilution for both methods, which is usually not measured on the assumption that saturating concentrations of tracer have been added. If saturating concentrations are not used then production will be underestimated. Volumetric rates of bacterioplankton production have been reported to range from 0.4 to >900 mg C m3 d1 in hypertrophic lakes (Figure 1). Cross-system analysis of bacterial production has found significant correlations with phytoplankton production, chlorophyll a concentration, bacterial numbers, and total phosphorus concentration. Within-systems correlations have been found with water temperature, and several studies have also demonstrated that bacterioplankton can be limited by nutrients (phosphorus) and/or by the availability of dissolved organic substrates. Bacterial-specific growth rates vary between 0.017 and 8.7 day1, producing doubling times ranging from hours to weeks. Values less than 0.01 day1 have been reported for cold waters of <6 C and under winter ice, giving
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doubling times of several years. Specific growth rates have been correlated with water temperature, producing Q10 values (i.e., the rate of change in growth rate associated with a 10 C increase in temperature) of 2–4 in various studies. However, specific groups of aquatic bacteria can have much higher Q10 values so that even small water temperature changes can promote large increases in some bacterial processes. To compare bacterial production and other data, e.g., on the uptake of organic substrates, from different systems or different parts of systems, microbial ecologists normalize them by dividing by the total cell abundance. This scaling process is intended to permit a more robust comparison of data generated from waters with markedly different bacterioplankton population sizes. However, this calculation of cellspecific production, uptake, and specific growth rates (to give population doubling times) in most instances does not enhance data analysis but complicates it. This is because, as noted earlier, a varying proportion of a bacterioplankton population is metabolically inactive and may account for a significant amount of the variation in these rates as well as produce overestimates of bacterial turnover times. At least one study has concluded that if production is scaled to the number of active bacteria in a population instead of the total population abundance, then specific production rates are fairly similar between systems and may, in fact, be higher in unproductive systems that have the lowest bacterial densities. As more data become available for a wide range of aquatic systems, the veracity and implications of this tentative conclusion to aquatic system functioning will become clearer. In some lakes, rivers, and wetlands, daily heterotrophic bacterial production exceeds daily autotrophic primary production and such systems are considered to be net heterotrophic. To calculate the ratio of the daily rates of primary to bacterial production, the assumption is made that bacterial production is constant over a diel cycle, although studies have shown this not to be the case. Since most studies of bacterial production do not include diel studies, hourly production rates are multiplied by 24. It is also necessary to take into account respiration losses as production measured by labeled leucine or thymidine are considered to be net production. This is done using a growth yield factor which, like many other conversion factors in aquatic microbial ecology, has a very wide range of <0.15–0.9. These calculations provide an estimate of the amount of phytoplankton carbon production and therefore an indication of whether a system is net heterotrophic or autotrophic.
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The concept of net heterotrophic systems is still a controversial topic amongst aquatic microbial ecologists. However, evidence is mounting that net heterotrophic systems are prevalent in most rivers and in oligotrophic to mesotrophic lakes, and that these systems act as a source of CO2 to the atmosphere. Moreover, the occurrence of net heterotrophy in so many systems implies even tighter connections between biogeochemical processes in aquatic systems and adjacent watersheds, as organic carbon from a watershed is needed to fuel heterotrophic processes in the receiving waters.
Role of Bacterioplankton in Inland Waters The traditional role attributed to bacteria in aquatic ecosystems was that of decomposer of organic matter. But bacteria not only remineralize nutrients back to the water column through decomposition processes, they also store organic carbon, are a food source to other microbes, and are important in the cycling of phosphorus and nitrogen. While early scientific papers recognized the importance of the ‘microbial ooze’ in the trophic dynamics of aquatic ecosystems, it is only recently that we have begun to be able to quantify the influence of microbes on biogeochemical processes. In the last few decades, bacteria have been viewed as important components of the microbial loop, which contains multiple trophic levels and is important in the cycling of matter and in dissipating energy through respiration. The contribution of bacteria to biogeochemical cycles is only beginning to be fully appreciated. Decomposition
As decomposers, bacterioplankton degrade dissolved organic matter (DOM), assimilate the byproducts into their cell (bacterial production), and remineralize carbon and nutrients back to the water through respiration. Organic carbon that originates from phytoplankton, either through cell death or excreted during photosynthesis, is usually assumed to be the primary source of dissolved organic carbon (DOC) that fuels bacterial metabolism. Organic carbon of algal origin is typically composed of simple sugars and is rapidly remineralized by bacteria. In contrast, organic matter that enters from allochthonous sources is a complex mixture of high molecular weight and lignified organic compounds that are not as readily decomposed. Extracellular enzymes are required to mediate degradation of polymeric organic macromolecules into smaller compounds that can be assimilated by the cell. The products of the enzymatic reactions are
believed to limit rates of assimilation of organic matter by bacteria and, hence, bacterial growth. As such, estimates of extracellular enzyme activity (EEA) may be used to quantify decomposition rates. For example, studies of EEA and bacterial production found that high bacterial productivities were typically associated with the availability of simple sugars such as saccharides and that during the summer bacterial production was fueled by algal lysates and exudates generated through the microbial loop, whereas production in spring and autumn appeared to be fueled by allochthonous carbon. Such studies support the observation that algal products are the ‘preferred’ organic carbon source to fuel bacterial metabolism, but that external carbon sources may also fuel decomposition under certain conditions. Bacterial community structure has been shown to affect the rate of processing of dissolved organic matter (DOM) in aquatic systems. There are differences among major phylogenetic groups in terms of utilization of high versus low molecular weight DOM and in terms of enzyme activities, and changes in phytoplankton community structure have also been shown to produce a response in bacterioplankton community composition. Differences in bacterial composition between lakes have also been found to be correlated to variables that reflect the relative loading of allochthonous versus autochthonous carbon, whereas seasonal changes in community composition within lakes correlated to patterns in algal abundance, temperature, and DOC. Although a common observation is that sediments overlain by anoxic waters are rich in organic matter content, it is also generally thought that anoxia slows decomposition rates in lakes. There are published studies both supporting and refuting this view. Examination of this contradiction across a series of lakes showed that bacterial production was in fact greater in the anoxic hypolimnion with lower water temperatures than in the epilimnion. The mean ratio of anoxic production to aerobic production was 1.6 for lakes that ranged from ultraoligotrophic to eutrophic. The doubling time of bacteria in the colder anoxic waters was lower than in the warmer oxic waters. This was offset by the greater bacterial abundance and biomass in the anoxic waters. The significance of these findings to not only other lakes but also to our general, and still poor, understanding of the role of bacteria in anoxic zones in lake functioning is a research area requiring further investigation. It is important to bear in mind that bacteria are not the sole decomposers in aquatic systems: fungi are responsible for the decomposition of high molecular weight and/or lignified compounds, whereas bacteria are primarily responsible for the final decomposition
Protists, Bacteria and Fungi: Planktonic and Attached _ Bacteria, Bacterioplankton
of lower molecular weight polymeric compounds and polysaccharides. A recent study experimentally demonstrated an association between bacteria and fungi, where bacterial decomposition of allochthonous organic matter was dependent on intermediate decomposition products that were produced by fungi. Still, in other studies, fungi have been shown to be the dominant organisms in decomposition. Nutrient Uptake and Cycling
Bacterioplankton are typically assumed to be limited by the availability of organic carbon and, since algal carbon appears to be the preferred substrate for bacterial metabolism, bacterial production should be closely linked to algal production and/or biomass. At coarse, cross-system scales, this relationship does hold and, as noted above, reasonably good correlations have been detected between bacterial abundance and chlorophyll a and between bacterial production and net phytoplankton production. However, there have been many studies that have demonstrated an uncoupling between phytoplankton and bacterioplankton abundance and production. The reasons for the uncoupling appear to rest, at least in part, on the availability of inorganic nutrients, and of phosphorus in particular. Bacteria are efficient competitors for inorganic nutrients such as phosphorus and nitrogen (mostly as ammonium) that limit algal growth, particularly in low nutrient environments. It has been shown through size fractionation studies that bacteria are usually responsible for at least 50% of the uptake of inorganic phosphorus in lakes. The actual proportion of the inorganic phosphorus pool that is taken up by bacteria does vary, probably in part as a function of phytoplankton biomass and availability of organic carbon. The proportion of ammonium that is taken up by bacteria in freshwater systems has not been intensively studied, mostly because freshwater ecosystems are usually assumed to be phosphorus limited, but in marine systems bacteria can be responsible for ~30% of ammonium uptake. Although bacteria may be able to incorporate phosphorus more rapidly than do algae, algal biomass is higher than bacterial biomass in more enriched systems and algae are better able to store phosphorus in their cells as phospholipids. Thus, over longer time periods (days to weeks versus minutes and hours), algae may be able to incorporate more inorganic phosphorus than do bacteria. If bacterial biomass is high, as in oligotrophic systems, then algal growth may be limited because of bacterial uptake of nutrients. However, grazing on bacteria by protozoa in the microbial loop will remineralize organic matter and release
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inorganic P and N, making these elements once again available to phytoplankton. Also, short-term physicalforcing events can temporally reverse this situation if nutrients are brought up into the water column through the entrainment of P-rich bottom sediments and/or interstitial water. Similarly, annual overturn in stratified lakes can introduce nutrients to the upper water column. In a cross-system comparison of lake bacterioplankton and phytoplankton, bacterial abundance was more strongly correlated to total phosphorus than to algal abundance, suggesting a degree of uncoupling between algal and bacterial growth. At least one study has shown that inorganic phosphorus additions can stimulate bacterial but not algal production, whereas the addition of both nitrogen and phosphorus increased both algal and bacterial production, suggesting that bacteria are better scavengers for nutrients than are algae when one or more nutrients are limiting.
Effect of Environmental Perturbations on Bacterioplankton Bacterioplankton community composition, production, and abundance vary seasonally in most freshwater systems where temporal patterns have been examined, and these variations appear to track climatic variations that affect numerous biogeochemical processes. Because of their rapid turnover rates and correlation to numerous environmental variables, bacterioplankton can also be expected to be sensitive to environmental change such as climate change and variability, increases in nutrient concentrations or to pollution by a wide range of human-made chemicals. The impact of human-made chemicals on bacterioplankton metabolism and diversity is not yet well understood. The addition of herbicides to natural ecosystems, in conjunction with inorganic nutrients, has been shown to have a synergistic effect on bacterial production. Antibiotics commonly used to treat bacterial infections and that have increasingly been detected in aquatic ecosystems at low concentrations can have an inhibitory effect on bacterial production, even at very low concentrations. Long-term exposure to toxic metals can alter a bacterial community toward strains that are resistant to heavy metals. Interestingly, a number of bacterial communities that are metal resistant have also been found to be resistant to antibiotics, suggesting an indirect selection for antibiotic resistance in natural ecosystems exposed to heavy metals. This type of indirect selection for resistance is worrisome, given general public health concerns over the evolution of antibiotic resistant bacteria.
200 Protists, Bacteria and Fungi: Planktonic and Attached _ Bacteria, Bacterioplankton See also: Fungi; Microbial Food Webs.
Further Reading Bird DF and Kalff J (1984) Empirical relationships between bacterial abundance and chlorophyll concentration in fresh and marine waters. Canadian Journal of Fisheries Aquatic Sciences 41: 1015–1023. Cole JJ (1999) Aquatic microbiology for ecosystem scientists: new and recycled paradigms in ecological microbiology. Ecosystems 2: 215–225. Cole JJ and Pace ML (1995) Bacterial secondary production in oxic and anoxic freshwaters. Limnology and Oceanography 40: 1019–1027. Cole JJ, Findlay S, and Pace ML (1988) Bacterial production in fresh and salt water ecosystems: a cross-system overview. Marine Ecology Progress Series 43: 1–10. Cotner JB and Biddanda BA (2002) Small players, large role: microbial influence on biogeochemical processes in pelagic aquatic ecosystems. Ecosystems 3: 105–121.
Odum EP (1959) Fundamentals of ecology. Philadelphia: W.B. Saunders Company. Robarts RD and Zohary T (1993) Fact or fiction—Bacterial growth rates and production as determined by [methyl-3H]-thymidine? Advances in Microbial Ecology 13: 371–425. Stepanauskas R, Glenn TC, Jagoe CH, et al. (2005) Elevated microbial tolerance to metals and antibiotics in metalcontaminated industrial environments. Environmental Science and Technology 39: 3671–3678. Verma B, Robarts RD, and Headly JV (2007) Impacts of tetracycline on planktonic bacterial production in prairie aquatic systems. Microbial Ecology 54: 52–55. Welch PS (1935) Limnology. New York: McGraw-Hill Book Company, Inc. White PA, Kalff J, Rasmussen JB, and Gasol JM (1991) The effect of temperature and algal biomass on bacterial production and specific growth rate in freshwater and marine habitats. Microbial Ecology 21: 99–118.
Introduction
Bacterial Numbers and Biomass
To the old adage of ‘out of sight, out of mind’ could be added ‘and unimportant.’ With respect to bacterioplankton in inland waters this was true for many scientists interested in aquatic ecology, but not all, and is certainly not true today. Bacteria have been known to inhabit natural waters since the seventeenth century although their numbers, biomass, distribution, and roles in these systems were poorly understood until the latter part of the twentieth century. Even in classical textbooks of the 1930s, such as P.S. Welch’s ‘Limnology,’ a whole chapter (albeit short) was devoted to bacteria and other microorganisms. Welch clearly had an intuition about the potential importance of bacteria in lakes and lamented the fact that very few lakes have received any study of their native bacteria. Indeed, E.P. Odum in his book ‘Fundamentals of Ecology’ in 1963 noted that, ‘‘Because of the technical difficulties of study, microbial ecology is, unfortunately, often completely omitted from the general college course in ecology.’’ The situation with respect to our knowledge and understanding of bacterioplankton in inland waters changed drastically beginning in the 1960s with publications describing and applying radioactively labeled compounds to measure the turnover and uptake of specific dissolved organic compounds in lakes. Thereafter, new protocols were developed to measure in situ the rates of bacterioplankton production in terms of cell numbers, biomass, and carbon. In addition, the introduction of the use of fluorescent dyes in microscopy led to the routine measurement of bacterial numbers and biomass and later to quantitative measurements of different aspects of microbial metabolism such as respiration and enzymatic activities. The methodology to study bacterioplankton has continued to evolve and thus there has been a revolutionary advance in our knowledge and understanding of the distribution and size of bacterioplankton populations in lakes, reservoirs, wetlands, rivers, and groundwaters and of their role in biogeochemical and energy cycles in these systems over the past three decades. In addition, since microbes are the first link joining the biotic and abiotic components of aquatic systems, they are excellent and sensitive indicators of human impacts on these systems.
Early methods to estimate the size of bacterioplankton populations were based on plate culturing techniques. But it had been accepted for a long time that these methods greatly underestimated the size of the populations. A number of direct-count methods (counting of stained bacteria with a microscope) were devised in conjunction with the use of centrifuges, chemical flocculation, or filtration in order to concentrate bacterial cells. Limnologists in the 1920s believed that direct counting of bacteria could not be undertaken without concentrating cells because lake populations were too low for accurate enumeration. Such concentrating mechanisms also created problems so there was some resistance to the use of direct counts. In the 1930s a concentration method was developed that involved evaporating water samples under reduced pressure. It was claimed that this method produced bacterial counts 2–4000 times higher than those from culture plates. For example, researchers reported bacterial numbers of (1–6) 106 ml1 in the Russian Lake Glubokoye. As can be imagined, such numbers for an unpolluted lake were viewed as being overly high and would produce a visible turbidity (which it did not), especially when others were reporting numbers of only 740–32 600 cells ml1 in lakes using a direct-count method at around the same time. However, a survey of bacterial numbers in Wisconsin lakes found that they ranged widely from 19 103 to 2 106 ml1 using this new method. Today with the widespread use of epifluorescence microscopy, and more recently with the use of flow cytometers, we know that bacteria occur in many unpolluted inland waters at concentrations of millions per milliliter. Bacteria in most natural aquatic systems range from 104 to 108 cells ml1 of water, although cell concentrations as high as 109 ml1 have been reported (Figure 1). In a 1984 cross-system overview of bacteria in surface waters, cell concentrations of (0.1–13.4) 106 ml1 were reported for a range of lakes. Even a very remote and pristine Arctic lake, Lake Taymyr on the Taymyr Peninsula in Russia, has bacterial numbers that varied between 1.3 106 and 4.8 106 cells ml1. Moreover, there have been several studies that found that
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10−1
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103
C m−3 d−1)
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Figure 1 Distribution of bacterioplankton production (top panel) and abundance (bottom panel) measurements from inland waters, as reported in the primary literature and summarized in Carr GM and Morin A (2002) Sampling variability and the design of bacterial abundance and production studies in aquatic environments. Canadian Journal of Fisheries and Aquatic Sciences 59: 930–937. Box plots show median (mid-line) and 25th and 75th percentiles. Whiskers extend to data points that fall within 1.5 times the midrange. Asterisks denote data points that extend beyond 1.5 times the midrange, and open circles are data points that extend beyond 3 times the midrange.
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(log-transformed data) with chlorophyll a concentrations for lakes in North America and Europe. This was not surprising as it had been assumed for some time that phytoplankton would be a major provider of substrates for bacterial production and growth, either at the time of their senescence and death or by the extracellular release of dissolved organic compounds during photosynthesis. However, one of the disconcerting conclusions was that the increase in the number of bacteria did not increase with the increase in chlorophyll a concentrations as lake trophy changed, since the slopes of the regressions were less than 1. As a general rule of thumb, oligotrophic waters have bacterial concentrations <1.7 106 cells ml1, mesotrophic waters (1.7–6.5) 106 cells ml1, and eutrophic waters >6.5 106 cells ml1. Results of within-system correlation analyses of bacterial numbers have been variable. In some studies, no correlations have been found whereas in others significant correlations between bacterial numbers and water temperature, primary production, and chlorophyll a concentration have been reported. This should not be surprising as total counts of bacterial cell numbers with epifluorescence microscopy techniques do not discriminate between different physiological groups, between live and dead cells, or between metabolically active and nonactive cells (Figure 3). Another factor that influences bacterioplankton dynamics in aquatic systems is losses due to processes such as grazing (protistan and invertebrate), parasitism, and sedimentation. However, even today few lakes have been sufficiently studied to provide firm conclusions on the relative importance of environmental factors on population sizes.
Metabolically Active Bacterioplankton 0.01
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Figure 2 Range in published relationships between chlorophyll a and bacterioplankton abundance. Adapted from Gasol, JM and Duarte CM (2000) Comparative analysis in aquatic microbial ecology: how far do they go? FEMS Microbiology Ecology 31: 99–106, with permission.
bacterial abundance in the anoxic hypolimnia of stratified lakes can be as much as twice that of the oxic strata. Several studies have tried to identify limnological parameters that regulate the variations in bacterial numbers between different aquatic systems (Figure 2) and seasonally in a specific system. In one such study, bacterial numbers were significantly correlated
Generally, the number of metabolically active bacteria is more variable between plankton communities than the total number of bacteria (Figure 4). As with total bacterial concentrations, the number of active cells in bacterioplankton populations has been correlated to indicators of lake trophy such as total phosphorus, chlorophyll, and dissolved organic carbon (DOC). However, the numbers of these two groups do not increase in the same way over enrichment gradients, resulting in a proportionately higher number of active cells in eutrophic versus oligotrophic waters. The mean of published numbers of active bacteria in lakes is about 22% with a rare study reporting as much as 100% active cells. For aquatic systems, generally, the numbers range from <1% in oligotrophic waters to >90% for highly productive systems. Differences in methodology may account for
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Total bacteria (⫻106 ml−1)
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Figure 3 Photomicrographs of bacterioplankton cells from Wascana Creek, Saskatchewan, filtered onto 0.2-mm pore-size cellulose nitrate filters: (a) stained with Syto 9 for total cell counts (Molecular Probes Inc., Eugene, OR, USA; cells appear green), (b) the same preparation stained with the commercial Live Dead stain for determination of ratio of live to dead cells, live cells appear green while putative dead cells red, and (c) filtered cell preparation stained with 5 mmol l1 cyanoditolyltetrazolium chloride (Polysciences Inc, Warrington, PA, USA) incubated for 60 min and counterstained with Syto 9; yellow cells are active while green cells are not metabolically active as determined by this assay. Magenta cells and filaments are cyanobacteria imaged using autofluorescence of their photosynthetic pigments. Imaging was carried out using the 522 þ 16, 598 þ 16 excitation lines of an MRC 1024 laser microscope (Zeiss, Jena, Germany). (Photos: J.R. Lawrence and G.W. Swerhone, NWRI, Environment Canada, Saskatoon, unpublished.)
Figure 4 Variation in the total number of bacterial cells, metabolically active cells and the percentage of metabolically active bacteria in 24 lakes of southern Que´bec, Canada. The average number of active cells in the lakes was 20.9%. Data are from del Giorgio P A and Scarborough G (1995) Increase in the proportion of metabolically active bacteria along gradients of enrichment in freshwater and marine plankton: implications for estimates of bacterial growth and production rates. Journal of Plankton Research 17: 1905–1924.
some of this variance, but research, first in marine systems and later in fresh waters, indicated that bacterial grazers may selectively remove larger, actively growing cells from a population leaving it dominated by small, slow-growing cells. Studies of the long-term seasonal variations in metabolically active bacteria
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in a particular system have shown that their contribution to the total bacterioplankton population can also be large, but such studies are rare. Hopefully, with the recent use of flow cytometry to determine metabolically active bacteria, more data will become available that will provide new insights on metabolically active bacteria in inland waters and the factors that influence them. Such data have significant consequences for the measurement of bacterioplankton growth and production measurements, which will be outlined here.
Bacterial Biomass Bacterial Biovolumes
Spatial and temporal changes in bacterial cell volume have been reported from a wide range of aquatic habitats, although the number of studies has been limited because of the difficulty in obtaining such data. To model energy and material flows, it is essential to know the factors that influence bacterial cell volume, as conversion of bacterial cell numbers to carbon, nitrogen, or phosphorus units is dependent on the derivation of accurate ‘numbers to volume’ conversion factors. Therefore, a major challenge for aquatic microbial ecologists is to identify the factors that control cell volume and hence bacterial biomass. Factors that have been identified include carbon supply from algae, phosphorus concentration, predation, and water temperature. To derive a value for bacterioplankton biomass, data are needed on the number of cells and their biovolume. This is not as straightforward as it might seem. The methods used to obtain biovolume include epifluorescence microscopy, scanning electron microscopy (SEM), scanning confocal laser microscropy (in conjunction with fluorescent dyes; Figure 3) and more recently flow cytometry, also using fluorescent dyes to estimate cell dimensions. These methods have problems ranging from halo effects caused by fluorescence to cell distortions caused by sample preparation for SEM, all of which can alter the calculation of biovolumes, especially with the very small cells typical of bacterioplankton. Median bacterial cell volumes usually range between 0.013 and 0.200 mm3. Although not confirmed yet, several studies that have looked at bacterial cell size have reported that this decreases with increasing trophic status. Furthermore, there have been some studies comparing bacterial cells between the upper oxic and lower anoxic parts of lakes. These have shown that bacteria from the anoxic hypolimnion can be between 2 and 10 times larger than bacteria in
the oxic layer. The reasons for this difference and its implications on lake metabolism require further investigation. Bacterial Biomass
To derive a carbon biomass value for bacterioplankton, most researchers have either used a constant value for a cell, usually between 10 and 20 fg C cell1, or a volume conversion factor that has up to a fivefold range of a commonly used value of 121 fg C mm3. Several studies have produced values in the range of 350–720 fg C mm3, whereas others reported much lower values of between 32 and 160 fg C mm3. One reason for the higher values may be the underestimation of cell volumes due to shrinkage effects caused by fixatives and air drying, although this is not applicable to all published values. Smaller cells tend to have higher carbon and lower water contents than larger cells. Therefore, what is clear is that the assumption of a constant ratio of carbon:biovolume is not correct. Some studies have shown that the carbon:biovolume ratio of bacterioplankton not only varied with cell size but also had temporal and geographical variations, indicating that factors such as species composition, nutritional state, growth rate, and other factors played a role. While it is now clear that bacterial biomass contributes a larger portion to the total planktonic biomass in unproductive freshwater systems than in more productive systems, what is not clear is why. The most widely accepted explanations include allochthonous carbon inputs being important in oligotrophic systems, decreased bacterivory, and bacterioplankton access to nutrients that are not available to phytoplankton (i.e., very low concentrations and organic forms) or a combination of these. Correct estimates of bacterioplankton biomass distribution are a fundamental requirement in aquatic microbial ecology, and more data that lead to a sound understanding of the factors that govern biomass in diverse systems are needed.
Bacterial Production The use of 14C-glucose in the early 1960s and subsequent development of methods to determine rates of bacterial uptake, respiration, and turnover of organic compounds at natural substrate concentrations later in the decade provided much data on the decomposition and flow of organic carbon through food webs in a wide range of natural systems. Since bacteria degrade a large number of organic substrates, further developments introduced the use of
Protists, Bacteria and Fungi: Planktonic and Attached _ Bacteria, Bacterioplankton
labeled sugars, amino acids, organic acids, lignocellulose, and other dissolved and particulate substrates. These early studies demonstrated that bacteria were actively metabolizing organic matter but did not provide quantitative estimates of growth rates and production. While the growth of autotrophic bacteria that fix carbon dioxide for their primary source of carbon can potentially be measured using 14CO2, until about 20 years ago there had not been a reliable method to determine the growth of natural assemblages of heterotrophic bacteria that utilize organic substrates for a carbon source. The two most commonly used methods were the incorporation of [3H-methyl]thymidine ([3H]TdR) into bacterial DNA, and the incorporation of 3H-leucine into bacterial protein. Labeled leucine is now generally more widely used than labeled thymidine because of its greater sensitivity and the simpler assumptions and calculations required to derive cell and carbon production estimates. Bacterial production has been measured in a wide range of aquatic systems from the Arctic to Antarctic. Not surprisingly, there is huge variation in the rates reported (Figure 1). Some of this variation is due to the factors used to convert the rates of label incorporation into cell and carbon production units, particularly with the more complicated conversion of thymidine incorporation rates. Several studies have concluded that there is no significant difference in production rates determined with labeled leucine and thymidine, whereas others have concluded that there is a difference with thymidine-derived rates being lower. In addition, there are potential problems associated with isotope dilution for both methods, which is usually not measured on the assumption that saturating concentrations of tracer have been added. If saturating concentrations are not used then production will be underestimated. Volumetric rates of bacterioplankton production have been reported to range from 0.4 to >900 mg C m3 d1 in hypertrophic lakes (Figure 1). Cross-system analysis of bacterial production has found significant correlations with phytoplankton production, chlorophyll a concentration, bacterial numbers, and total phosphorus concentration. Within-systems correlations have been found with water temperature, and several studies have also demonstrated that bacterioplankton can be limited by nutrients (phosphorus) and/or by the availability of dissolved organic substrates. Bacterial-specific growth rates vary between 0.017 and 8.7 day1, producing doubling times ranging from hours to weeks. Values less than 0.01 day1 have been reported for cold waters of <6 C and under winter ice, giving
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doubling times of several years. Specific growth rates have been correlated with water temperature, producing Q10 values (i.e., the rate of change in growth rate associated with a 10 C increase in temperature) of 2–4 in various studies. However, specific groups of aquatic bacteria can have much higher Q10 values so that even small water temperature changes can promote large increases in some bacterial processes. To compare bacterial production and other data, e.g., on the uptake of organic substrates, from different systems or different parts of systems, microbial ecologists normalize them by dividing by the total cell abundance. This scaling process is intended to permit a more robust comparison of data generated from waters with markedly different bacterioplankton population sizes. However, this calculation of cellspecific production, uptake, and specific growth rates (to give population doubling times) in most instances does not enhance data analysis but complicates it. This is because, as noted earlier, a varying proportion of a bacterioplankton population is metabolically inactive and may account for a significant amount of the variation in these rates as well as produce overestimates of bacterial turnover times. At least one study has concluded that if production is scaled to the number of active bacteria in a population instead of the total population abundance, then specific production rates are fairly similar between systems and may, in fact, be higher in unproductive systems that have the lowest bacterial densities. As more data become available for a wide range of aquatic systems, the veracity and implications of this tentative conclusion to aquatic system functioning will become clearer. In some lakes, rivers, and wetlands, daily heterotrophic bacterial production exceeds daily autotrophic primary production and such systems are considered to be net heterotrophic. To calculate the ratio of the daily rates of primary to bacterial production, the assumption is made that bacterial production is constant over a diel cycle, although studies have shown this not to be the case. Since most studies of bacterial production do not include diel studies, hourly production rates are multiplied by 24. It is also necessary to take into account respiration losses as production measured by labeled leucine or thymidine are considered to be net production. This is done using a growth yield factor which, like many other conversion factors in aquatic microbial ecology, has a very wide range of <0.15–0.9. These calculations provide an estimate of the amount of phytoplankton carbon production and therefore an indication of whether a system is net heterotrophic or autotrophic.
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The concept of net heterotrophic systems is still a controversial topic amongst aquatic microbial ecologists. However, evidence is mounting that net heterotrophic systems are prevalent in most rivers and in oligotrophic to mesotrophic lakes, and that these systems act as a source of CO2 to the atmosphere. Moreover, the occurrence of net heterotrophy in so many systems implies even tighter connections between biogeochemical processes in aquatic systems and adjacent watersheds, as organic carbon from a watershed is needed to fuel heterotrophic processes in the receiving waters.
Role of Bacterioplankton in Inland Waters The traditional role attributed to bacteria in aquatic ecosystems was that of decomposer of organic matter. But bacteria not only remineralize nutrients back to the water column through decomposition processes, they also store organic carbon, are a food source to other microbes, and are important in the cycling of phosphorus and nitrogen. While early scientific papers recognized the importance of the ‘microbial ooze’ in the trophic dynamics of aquatic ecosystems, it is only recently that we have begun to be able to quantify the influence of microbes on biogeochemical processes. In the last few decades, bacteria have been viewed as important components of the microbial loop, which contains multiple trophic levels and is important in the cycling of matter and in dissipating energy through respiration. The contribution of bacteria to biogeochemical cycles is only beginning to be fully appreciated. Decomposition
As decomposers, bacterioplankton degrade dissolved organic matter (DOM), assimilate the byproducts into their cell (bacterial production), and remineralize carbon and nutrients back to the water through respiration. Organic carbon that originates from phytoplankton, either through cell death or excreted during photosynthesis, is usually assumed to be the primary source of dissolved organic carbon (DOC) that fuels bacterial metabolism. Organic carbon of algal origin is typically composed of simple sugars and is rapidly remineralized by bacteria. In contrast, organic matter that enters from allochthonous sources is a complex mixture of high molecular weight and lignified organic compounds that are not as readily decomposed. Extracellular enzymes are required to mediate degradation of polymeric organic macromolecules into smaller compounds that can be assimilated by the cell. The products of the enzymatic reactions are
believed to limit rates of assimilation of organic matter by bacteria and, hence, bacterial growth. As such, estimates of extracellular enzyme activity (EEA) may be used to quantify decomposition rates. For example, studies of EEA and bacterial production found that high bacterial productivities were typically associated with the availability of simple sugars such as saccharides and that during the summer bacterial production was fueled by algal lysates and exudates generated through the microbial loop, whereas production in spring and autumn appeared to be fueled by allochthonous carbon. Such studies support the observation that algal products are the ‘preferred’ organic carbon source to fuel bacterial metabolism, but that external carbon sources may also fuel decomposition under certain conditions. Bacterial community structure has been shown to affect the rate of processing of dissolved organic matter (DOM) in aquatic systems. There are differences among major phylogenetic groups in terms of utilization of high versus low molecular weight DOM and in terms of enzyme activities, and changes in phytoplankton community structure have also been shown to produce a response in bacterioplankton community composition. Differences in bacterial composition between lakes have also been found to be correlated to variables that reflect the relative loading of allochthonous versus autochthonous carbon, whereas seasonal changes in community composition within lakes correlated to patterns in algal abundance, temperature, and DOC. Although a common observation is that sediments overlain by anoxic waters are rich in organic matter content, it is also generally thought that anoxia slows decomposition rates in lakes. There are published studies both supporting and refuting this view. Examination of this contradiction across a series of lakes showed that bacterial production was in fact greater in the anoxic hypolimnion with lower water temperatures than in the epilimnion. The mean ratio of anoxic production to aerobic production was 1.6 for lakes that ranged from ultraoligotrophic to eutrophic. The doubling time of bacteria in the colder anoxic waters was lower than in the warmer oxic waters. This was offset by the greater bacterial abundance and biomass in the anoxic waters. The significance of these findings to not only other lakes but also to our general, and still poor, understanding of the role of bacteria in anoxic zones in lake functioning is a research area requiring further investigation. It is important to bear in mind that bacteria are not the sole decomposers in aquatic systems: fungi are responsible for the decomposition of high molecular weight and/or lignified compounds, whereas bacteria are primarily responsible for the final decomposition
Protists, Bacteria and Fungi: Planktonic and Attached _ Bacteria, Bacterioplankton
of lower molecular weight polymeric compounds and polysaccharides. A recent study experimentally demonstrated an association between bacteria and fungi, where bacterial decomposition of allochthonous organic matter was dependent on intermediate decomposition products that were produced by fungi. Still, in other studies, fungi have been shown to be the dominant organisms in decomposition. Nutrient Uptake and Cycling
Bacterioplankton are typically assumed to be limited by the availability of organic carbon and, since algal carbon appears to be the preferred substrate for bacterial metabolism, bacterial production should be closely linked to algal production and/or biomass. At coarse, cross-system scales, this relationship does hold and, as noted above, reasonably good correlations have been detected between bacterial abundance and chlorophyll a and between bacterial production and net phytoplankton production. However, there have been many studies that have demonstrated an uncoupling between phytoplankton and bacterioplankton abundance and production. The reasons for the uncoupling appear to rest, at least in part, on the availability of inorganic nutrients, and of phosphorus in particular. Bacteria are efficient competitors for inorganic nutrients such as phosphorus and nitrogen (mostly as ammonium) that limit algal growth, particularly in low nutrient environments. It has been shown through size fractionation studies that bacteria are usually responsible for at least 50% of the uptake of inorganic phosphorus in lakes. The actual proportion of the inorganic phosphorus pool that is taken up by bacteria does vary, probably in part as a function of phytoplankton biomass and availability of organic carbon. The proportion of ammonium that is taken up by bacteria in freshwater systems has not been intensively studied, mostly because freshwater ecosystems are usually assumed to be phosphorus limited, but in marine systems bacteria can be responsible for ~30% of ammonium uptake. Although bacteria may be able to incorporate phosphorus more rapidly than do algae, algal biomass is higher than bacterial biomass in more enriched systems and algae are better able to store phosphorus in their cells as phospholipids. Thus, over longer time periods (days to weeks versus minutes and hours), algae may be able to incorporate more inorganic phosphorus than do bacteria. If bacterial biomass is high, as in oligotrophic systems, then algal growth may be limited because of bacterial uptake of nutrients. However, grazing on bacteria by protozoa in the microbial loop will remineralize organic matter and release
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inorganic P and N, making these elements once again available to phytoplankton. Also, short-term physicalforcing events can temporally reverse this situation if nutrients are brought up into the water column through the entrainment of P-rich bottom sediments and/or interstitial water. Similarly, annual overturn in stratified lakes can introduce nutrients to the upper water column. In a cross-system comparison of lake bacterioplankton and phytoplankton, bacterial abundance was more strongly correlated to total phosphorus than to algal abundance, suggesting a degree of uncoupling between algal and bacterial growth. At least one study has shown that inorganic phosphorus additions can stimulate bacterial but not algal production, whereas the addition of both nitrogen and phosphorus increased both algal and bacterial production, suggesting that bacteria are better scavengers for nutrients than are algae when one or more nutrients are limiting.
Effect of Environmental Perturbations on Bacterioplankton Bacterioplankton community composition, production, and abundance vary seasonally in most freshwater systems where temporal patterns have been examined, and these variations appear to track climatic variations that affect numerous biogeochemical processes. Because of their rapid turnover rates and correlation to numerous environmental variables, bacterioplankton can also be expected to be sensitive to environmental change such as climate change and variability, increases in nutrient concentrations or to pollution by a wide range of human-made chemicals. The impact of human-made chemicals on bacterioplankton metabolism and diversity is not yet well understood. The addition of herbicides to natural ecosystems, in conjunction with inorganic nutrients, has been shown to have a synergistic effect on bacterial production. Antibiotics commonly used to treat bacterial infections and that have increasingly been detected in aquatic ecosystems at low concentrations can have an inhibitory effect on bacterial production, even at very low concentrations. Long-term exposure to toxic metals can alter a bacterial community toward strains that are resistant to heavy metals. Interestingly, a number of bacterial communities that are metal resistant have also been found to be resistant to antibiotics, suggesting an indirect selection for antibiotic resistance in natural ecosystems exposed to heavy metals. This type of indirect selection for resistance is worrisome, given general public health concerns over the evolution of antibiotic resistant bacteria.
200 Protists, Bacteria and Fungi: Planktonic and Attached _ Bacteria, Bacterioplankton See also: Fungi; Microbial Food Webs.
Further Reading Bird DF and Kalff J (1984) Empirical relationships between bacterial abundance and chlorophyll concentration in fresh and marine waters. Canadian Journal of Fisheries Aquatic Sciences 41: 1015–1023. Cole JJ (1999) Aquatic microbiology for ecosystem scientists: new and recycled paradigms in ecological microbiology. Ecosystems 2: 215–225. Cole JJ and Pace ML (1995) Bacterial secondary production in oxic and anoxic freshwaters. Limnology and Oceanography 40: 1019–1027. Cole JJ, Findlay S, and Pace ML (1988) Bacterial production in fresh and salt water ecosystems: a cross-system overview. Marine Ecology Progress Series 43: 1–10. Cotner JB and Biddanda BA (2002) Small players, large role: microbial influence on biogeochemical processes in pelagic aquatic ecosystems. Ecosystems 3: 105–121.
Odum EP (1959) Fundamentals of ecology. Philadelphia: W.B. Saunders Company. Robarts RD and Zohary T (1993) Fact or fiction—Bacterial growth rates and production as determined by [methyl-3H]-thymidine? Advances in Microbial Ecology 13: 371–425. Stepanauskas R, Glenn TC, Jagoe CH, et al. (2005) Elevated microbial tolerance to metals and antibiotics in metalcontaminated industrial environments. Environmental Science and Technology 39: 3671–3678. Verma B, Robarts RD, and Headly JV (2007) Impacts of tetracycline on planktonic bacterial production in prairie aquatic systems. Microbial Ecology 54: 52–55. Welch PS (1935) Limnology. New York: McGraw-Hill Book Company, Inc. White PA, Kalff J, Rasmussen JB, and Gasol JM (1991) The effect of temperature and algal biomass on bacterial production and specific growth rate in freshwater and marine habitats. Microbial Ecology 21: 99–118.