Microb Ecol (1994) 28:327-329
M o d e l i n g the M i c r o b i a l L o o p
MICROBIAL ECOLOGYInc. © 1994Springer-Verlag New York
Aggregation and Disaggregation of Microbial Food Webs R.R. Christian Biology Department, East Carolina University, Greenville, North Carolina, 27858, USA
Abstract. Models of the microbial food web have generally used compartments aggregated by general body size and gross taxonomy. It has been assumed that these also reflect guilds or holons. Generally, results of simulation or analysis based on this structure have been reasonably well validated. Herein I summarize why the aggregations may be justified and what may be learned from disaggregation. • . . neither the modelers nor the empiricists really know how many steps there are in microbial f o o d chains in natural waters or how much the length of the chain may vary in time and space (from Pomeroy and Wiebe ).
Ducklow  and Wright and Coffin  provide good focal points for discussion of the systems ecology of the microbial loop. Ducklow  provides a review of modeling and analysis of the marine, pelagic loop. He includes discussion of both construction of dynamic simulation models and network analysis. Wright and Coffin  describe the interplay between field studies and modeling. Both describe results from simulation models with highly aggregated compartments representing the microbial food web. Division into compartments is based largely on body size and/or gross taxonomic characteristics. This has been the approach of choice by others for simulation models and for network analysis (e.g., [2, 3, 11, 17]). I address the two following questions about this approach: (1) Why have the highly aggregated compartments based on size and gross taxonomy been successful in predicting the nature of the microbial food web? and (2) What more can we learn from disaggregating compartments? The compartments vary among models but generally are aggregated to include (1) bacteria, as the smallest organisms; (2) protozoans, which may be subdivided by motility style (i.e., flagellates and ciliates) or size; and (3) larger metazoan zooplankters. Each compartment is assumed to function as a guild (common feeding profile) or holon (unit within a level in a hierarchy of control) . But this is a largely untested assumption. The literature is replete with examples of substantial variability within each aggregation. For example, planktonic ciliates include hundreds of species distributed over a wide range of trophic profiles from photoautotrophy to feeding on a variety of prey sizes and with different strategies . Clearance rates by copepods feeding on ciliates range over 2 orders of magnitude . There are major distinctions in phylogeny and growth characteristics among large and small free-living bacteria and attached forms [7, 15]. Exclusion of such details
limit the predictive capabilities of aggregated models , but such models seem to be validated by field results [8, 18]. Ducklow  indicated that aggregation is warranted when turnover times of the component organisms are similar, but this is not sufficient. Turnover times of the dominant microbes within each size class probably are within a factor of 5 for any particular environment [12, 15]. Perhaps more important to the success of simulation is that the turnover times among microbial size classes are similar and wellcoupled. Thus, as seen in results provided by both Ducklow  and Wright and Coffin , lags between biomass peaks are short and follow a regular pattern. These patterns do not mean, however, that the real trophic structure is established as indicated above in the quote from Pomeroy and Wiebe . The success of prediction may indicate that, whatever the trophic structure within a compartment, it does not change significantly during the conditions simulated. By inference, the real communities that have been simulated [8, 18] may not have had major shifts in trophic structure within the gross taxonomic aggregations. Finally, the hierarchy works because body size is generally associated with physiological and ecological characteristics . Aquatic ecologists hardly give a second thought to the assumption that big organisms eat small organisms, and yet this is not necessarily a characteristic of terrestrial communities. For example, consider the trophic positions of lions, giraffes, and elephants on the Serengeti. In pelagic systems there may be a relatively even distribution of biomass per volume of water across body sizes from bacteria to nekton [9, 14], attributed in part to a size-dependent feeding cascade . This supports body-size based compartments. Concem needs be given to viruses and other small parasites, but overall, current aggregations may implement Occam's razor. What then is learned by disaggregating the compartments? Disaggregation would involve more detailed separation of size groups by feeding profile. First, from hierarchy theory, to understand the potential of a hierarchical level or holon, one needs to examine the next lower level or arrangement of holons within [ 1]. Second, prediction should improve with disaggregation under conditions where community structure would be expected to change (e.g., with the introduction of anthropogenic substances or with experimental treatment). Third, disaggregation is required to evaluate the number of trophic links within the food web and thus the controls over the link vs. sink question . Lastly, placing the microbial food web in the context of broader food web theory would benefit from disaggregation. There has been growing interest in food webs and their commonality across ecosystems [2, 5, 6]. Though controversial, generalizations have been made, such as the "scaling laws" [5, 10]. They involve constancy or patterns in the fractions among groupings such as basal, intermediate, and top predators. Yet the foodweb constructions are highly aggregated within the microbial size ranges. Would these "laws" change if we had more knowledge about the microbial food web? Are these generalizations fractal; do they hold for food webs across all body size ranges? Thus the disaggregation of the compartments of microbial food web has implications not only for better understanding of microbial ecology but also ecosystem ecology in general. Acknowledgments. This work was partially supported by NSF grant No. DEB-921172 for the VCRLTER.
Food Web Aggregation and Disaggregation
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