Microb Ecol (1994) 28:201-203
Sources of Carbon for the Microbial Loop
MICROBIAL ECOLOGY © 1994Springer-VerlagNew York Inc.
The Problem of Species Aggregation in Food Webs C. Pedr6s-Ali6 Institut de Ci6ncies del Mar, CSIC, Passeig Joan de Barb6 s/n E-08039 Barcelona, Spain
The paradigm of the microbial food web, exemplified by the famous figure in Pomeroy [10], has been extremely productive during the past 20 years and is now well established. According to the workings of science proposed by T.S. Kuhn [7], now is the time to check its predictions in as wide a range of conditions as possible, in order to look for the paradoxes that may eventually lead toward a new paradigm. Many of its statements are based on aggregating all bacteria together and ignoring differences among them. This is obviously an oversimplification and illustrates the problem I will discuss: in order to measure boxes and arrows in any model of the food web we need to aggregate organisms into arbitrarily chosen groups. If we go too far in pooling organisms together, we will lose all the natural history that ultimately drives evolution of both organisms and communities. If we stop short and retain many groups, we will be confronted with a complexity that will escape analysis. Therefore, we need to recognize explicitly the criteria we use to aggregate organisms. This is a problem common to all ecologists. In microbial ecology, however, this aggregation has been absolutely empirical and the problem has been explicitly addressed in only a very limited way. Ecologists tend to pool all bacteria together in a box labeled "decomposers" or "bacterioplankton". Of course every biologist knows that this is an oversimplification, because bacteria are extremely diverse metabolically. Yet, it is often advantageous to ignore the detail in order to look for general patterns. One of the most successful ways of considering bacterioplankton in general has been the search for empirical relationships between bacteria and phytoplankton. The underlying assumptions are that all bacteria perform a similar function (converting photosynthate into CO2 and bacterial cells), that all phytoplankton perform the same function (conversion of CO: into photosynthate), and that photosynthate is the primary source of carbon for bacteria. If these three assumptions hold, there should be a significant relationship between bacteria and phytoplankton across systems. This hypothesis was tested by Aizaki et al. [1], Bird and Kalff [3], Cole et al. [4] and White et al. [11]. Cole et al. [4] also tested the significance of relationships between bacterial and phytoplankton activities. The relationship between phytoplankton and bacterial biomass was shown to be significant and this indicates that there is some truth in the initial assumptions [1,3,4,11]. It is particularly amazing that despite the disparity of methods used to determine bacterial production, its relationship to primary production is significant [4]. It is also clear, however, that in such log-log relationships there is considerable variability of data points around the calculated regression lines. If we consider the regressions as elements of the microbial food web paradigm, we must then explore
202
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this variability when looking for the paradoxes. In turn, it is precisely the existence of such relationships that allows one to look for the exceptions to the rules. Some systems that do not fit the relationships are hypersaline and humic systems. If many systems do not fit the general relationships there must be mechanisms ignored by our initial assumptions. The role of phytoplankton as primary producers seems clear, but maybe photosynthate is not the main source of carbon for bacteria in all systems. In effect, humic lakes, where a substantial portion of the carbon for bacteria is allocthonous, tend to have more bacterial biomass than would be predicted by the general relationship (see, for example, [5]). A large portion of surface water systems between 40°N and 40°S are to be found in small saline ponds which may dry up in the summer [12]. These systems also show more bacterial biomass than predicted. Kilham reported on Lake Elmenteita, a hypersaline lake in Africa [6], and we have studied different ponds in a solar saltern in Spain. These systems are characterized by an "excessive" bacterial biomass with respect to photosynthetic pigments according to the general relationship. There are two factors causing this deviation from prediction. In some of them, such as the crystallizer ponds in solar salterns, primary production is zero. Not even algae such as Dunaliella salina are able to function at such high salinities. Therefore, all the organic matter for bacteria is allocthonous (in this case, from the previous ponds in the evaporating circuit). Whenever a supply of allocthonous organic matter is important, systems are likely to move away from the general relationship. The second factor is reduction of predation pressure. Most predators cannot tolerate the high salinities in such systems. Even specialized ciliates such as Fabrea salina and others are absent from salinities above 200%0. Thus, free of predation pressure, bacterial biomass can accumulate far beyond the limits suggested by the general relationship. The general relationship, therefore, is not just a consequence of carbon transfer from autotrophic algae to heterotrophic bacteria, but also of predation on bacteria. When this pressure is decreased, as in hypersaline systems, the relationship shifts to higher bacterial biomass per unit of chlorophyll. Alternatively, the role of bacteria may not always be the same. As mentioned earlier, considering bacterioplankton as one box is an oversimplification. The diversity of bacterial metabolisms is well known. Phototrophic bacteria, for example, are closer to phytoplankton in their role and adaptations than to heterotrophic bacteria, and there are several other groups of autotrophic bacteria. But even among the heterotrophic bacteria, there are enough differences to consider many functional groups [8]. For example, different strategies are to be expected from bacteria in aggregates or attached to particles [2,8]. This calls for different aggregation criteria. I have examined this question elsewhere [8]. In conclusion, pooling all bacteria together has been useful in showing general patterns across systems. It has provided a hypothesis whose predictions could be checked in different environments. And it has allowed identification of exceptions that could reveal further details about microbial food webs. Some of these details, however, can only be studied by separating the bacteria into several groups. The apparent universality of the relationships described by Cole et al. [4] and White et al. [12] is a consequence of the fact that most microbial ecology has been carried out in the humid, temperate areas of the world [9,12]. Looking for the exceptions to the currently accepted rules will expand the microbial food web paradigm to cover a
Species Aggregation in Food Webs
203
wider and more representative array of aquatic systems or, alternatively, it will show where it is at fault and, thus, where we should look for evidence leading toward a new paradigm.
Acknowledgments. The work of the author has been supported by DGICYT grant PB91-075. References 1. Aizaki M, Otsuki A, Fukushima T, Hosomi M, Muraoka K (1984) Application of Carlson's trophic index to Japanese lakes and relationships between the index and other parameters. Verh Int Verein Limnol 21:675--681 2. Azam F, Martfnez J, Smith DC (1993) Bacteria-organic matter coupling on marine aggregates. In: Guerrero R, Pedrrs-Ali6 C (eds) Trends in microbial ecology. Spanish Soc. Microbiol., Barcelona, pp 410-414 3. Bird DF, Kalff J (1984) Empirical relationship between bacterial abundance and chlorophyll concentration in fresh and marine waters. Can J Fish Aquat Sci 41:1015-1023 4. Cole JJ, Findlay S, Pace ML (1988) Bacterial production in fresh and saltwater ecosystems: a cross-system overview. Mar Ecol Prog Ser 43:1-10 5. Hessen DO, Andersen T, Lyche A (1990) Carbon metabolism in a humic lake: pool sizes and cycling through zooplankton. Limnol Oceanogr 35:84-99 6. Kilham P (1981) Pelagic bacteria: extreme abundances in African saline lakes. Naturwissenschaften 67:380-381 7. Kuhn TS (1962) The structure of scientific revolutions. University of Chicago Press, Chicago 8. Pedr6s-Ali6 C (1989) Toward an autocology of bacterioplankton. In: Sommer U (ed) Plankton ecology: succession in plankton communities. Springer-Verlag, Berlin, pp 297-336 9. Pedr6s-Ali6 C, Guerrero R (1991) Abundance and activity of bacterioplankton in warm lakes. Verh Int Verein Limnol 24:1212-1219 10. Pomeroy LR (1974) The ocean's food web, a changing paradigm. BioScience 24:499-504 11. White PA, Kalff J, Rasmussen JB, Gasol JM (1991) The effect of temperature and algal biomass on bacterial production and specific growth rate in freshwater and marine habitats. Microb Ecol 21:99-118 12. Williams WD (1988) Liinnologic imbalances: an antipodan viewpoint. Freshwater Biol 20:40742O
Sources of Carbon for the Microbial Loop
MICROBIAL ECOLOGY © 1994Springer-VerlagNew York Inc.
The Problem of Species Aggregation in Food Webs C. Pedr6s-Ali6 Institut de Ci6ncies del Mar, CSIC, Passeig Joan de Barb6 s/n E-08039 Barcelona, Spain
The paradigm of the microbial food web, exemplified by the famous figure in Pomeroy [10], has been extremely productive during the past 20 years and is now well established. According to the workings of science proposed by T.S. Kuhn [7], now is the time to check its predictions in as wide a range of conditions as possible, in order to look for the paradoxes that may eventually lead toward a new paradigm. Many of its statements are based on aggregating all bacteria together and ignoring differences among them. This is obviously an oversimplification and illustrates the problem I will discuss: in order to measure boxes and arrows in any model of the food web we need to aggregate organisms into arbitrarily chosen groups. If we go too far in pooling organisms together, we will lose all the natural history that ultimately drives evolution of both organisms and communities. If we stop short and retain many groups, we will be confronted with a complexity that will escape analysis. Therefore, we need to recognize explicitly the criteria we use to aggregate organisms. This is a problem common to all ecologists. In microbial ecology, however, this aggregation has been absolutely empirical and the problem has been explicitly addressed in only a very limited way. Ecologists tend to pool all bacteria together in a box labeled "decomposers" or "bacterioplankton". Of course every biologist knows that this is an oversimplification, because bacteria are extremely diverse metabolically. Yet, it is often advantageous to ignore the detail in order to look for general patterns. One of the most successful ways of considering bacterioplankton in general has been the search for empirical relationships between bacteria and phytoplankton. The underlying assumptions are that all bacteria perform a similar function (converting photosynthate into CO2 and bacterial cells), that all phytoplankton perform the same function (conversion of CO: into photosynthate), and that photosynthate is the primary source of carbon for bacteria. If these three assumptions hold, there should be a significant relationship between bacteria and phytoplankton across systems. This hypothesis was tested by Aizaki et al. [1], Bird and Kalff [3], Cole et al. [4] and White et al. [11]. Cole et al. [4] also tested the significance of relationships between bacterial and phytoplankton activities. The relationship between phytoplankton and bacterial biomass was shown to be significant and this indicates that there is some truth in the initial assumptions [1,3,4,11]. It is particularly amazing that despite the disparity of methods used to determine bacterial production, its relationship to primary production is significant [4]. It is also clear, however, that in such log-log relationships there is considerable variability of data points around the calculated regression lines. If we consider the regressions as elements of the microbial food web paradigm, we must then explore
202
C. Pedr6s-Ali6
this variability when looking for the paradoxes. In turn, it is precisely the existence of such relationships that allows one to look for the exceptions to the rules. Some systems that do not fit the relationships are hypersaline and humic systems. If many systems do not fit the general relationships there must be mechanisms ignored by our initial assumptions. The role of phytoplankton as primary producers seems clear, but maybe photosynthate is not the main source of carbon for bacteria in all systems. In effect, humic lakes, where a substantial portion of the carbon for bacteria is allocthonous, tend to have more bacterial biomass than would be predicted by the general relationship (see, for example, [5]). A large portion of surface water systems between 40°N and 40°S are to be found in small saline ponds which may dry up in the summer [12]. These systems also show more bacterial biomass than predicted. Kilham reported on Lake Elmenteita, a hypersaline lake in Africa [6], and we have studied different ponds in a solar saltern in Spain. These systems are characterized by an "excessive" bacterial biomass with respect to photosynthetic pigments according to the general relationship. There are two factors causing this deviation from prediction. In some of them, such as the crystallizer ponds in solar salterns, primary production is zero. Not even algae such as Dunaliella salina are able to function at such high salinities. Therefore, all the organic matter for bacteria is allocthonous (in this case, from the previous ponds in the evaporating circuit). Whenever a supply of allocthonous organic matter is important, systems are likely to move away from the general relationship. The second factor is reduction of predation pressure. Most predators cannot tolerate the high salinities in such systems. Even specialized ciliates such as Fabrea salina and others are absent from salinities above 200%0. Thus, free of predation pressure, bacterial biomass can accumulate far beyond the limits suggested by the general relationship. The general relationship, therefore, is not just a consequence of carbon transfer from autotrophic algae to heterotrophic bacteria, but also of predation on bacteria. When this pressure is decreased, as in hypersaline systems, the relationship shifts to higher bacterial biomass per unit of chlorophyll. Alternatively, the role of bacteria may not always be the same. As mentioned earlier, considering bacterioplankton as one box is an oversimplification. The diversity of bacterial metabolisms is well known. Phototrophic bacteria, for example, are closer to phytoplankton in their role and adaptations than to heterotrophic bacteria, and there are several other groups of autotrophic bacteria. But even among the heterotrophic bacteria, there are enough differences to consider many functional groups [8]. For example, different strategies are to be expected from bacteria in aggregates or attached to particles [2,8]. This calls for different aggregation criteria. I have examined this question elsewhere [8]. In conclusion, pooling all bacteria together has been useful in showing general patterns across systems. It has provided a hypothesis whose predictions could be checked in different environments. And it has allowed identification of exceptions that could reveal further details about microbial food webs. Some of these details, however, can only be studied by separating the bacteria into several groups. The apparent universality of the relationships described by Cole et al. [4] and White et al. [12] is a consequence of the fact that most microbial ecology has been carried out in the humid, temperate areas of the world [9,12]. Looking for the exceptions to the currently accepted rules will expand the microbial food web paradigm to cover a
Species Aggregation in Food Webs
203
wider and more representative array of aquatic systems or, alternatively, it will show where it is at fault and, thus, where we should look for evidence leading toward a new paradigm.
Acknowledgments. The work of the author has been supported by DGICYT grant PB91-075. References 1. Aizaki M, Otsuki A, Fukushima T, Hosomi M, Muraoka K (1984) Application of Carlson's trophic index to Japanese lakes and relationships between the index and other parameters. Verh Int Verein Limnol 21:675--681 2. Azam F, Martfnez J, Smith DC (1993) Bacteria-organic matter coupling on marine aggregates. In: Guerrero R, Pedrrs-Ali6 C (eds) Trends in microbial ecology. Spanish Soc. Microbiol., Barcelona, pp 410-414 3. Bird DF, Kalff J (1984) Empirical relationship between bacterial abundance and chlorophyll concentration in fresh and marine waters. Can J Fish Aquat Sci 41:1015-1023 4. Cole JJ, Findlay S, Pace ML (1988) Bacterial production in fresh and saltwater ecosystems: a cross-system overview. Mar Ecol Prog Ser 43:1-10 5. Hessen DO, Andersen T, Lyche A (1990) Carbon metabolism in a humic lake: pool sizes and cycling through zooplankton. Limnol Oceanogr 35:84-99 6. Kilham P (1981) Pelagic bacteria: extreme abundances in African saline lakes. Naturwissenschaften 67:380-381 7. Kuhn TS (1962) The structure of scientific revolutions. University of Chicago Press, Chicago 8. Pedr6s-Ali6 C (1989) Toward an autocology of bacterioplankton. In: Sommer U (ed) Plankton ecology: succession in plankton communities. Springer-Verlag, Berlin, pp 297-336 9. Pedr6s-Ali6 C, Guerrero R (1991) Abundance and activity of bacterioplankton in warm lakes. Verh Int Verein Limnol 24:1212-1219 10. Pomeroy LR (1974) The ocean's food web, a changing paradigm. BioScience 24:499-504 11. White PA, Kalff J, Rasmussen JB, Gasol JM (1991) The effect of temperature and algal biomass on bacterial production and specific growth rate in freshwater and marine habitats. Microb Ecol 21:99-118 12. Williams WD (1988) Liinnologic imbalances: an antipodan viewpoint. Freshwater Biol 20:40742O
