48 



complex. As I have indicated, many major features of these systems have only recently been discovered. 

 Naturally, we now think we know it all, but probably we do not. A substantial part of the observed variability 

 in ocean systems can be filtered out by recognition of the mesoscale physical processes that drive much of the 

 primary production. Recognition of the physical variability in the system at the time of biological sampling will 

 substantially reduce variability of the data. Recognition of mesoscale features that influence biological stocks 

 and rate processes (e.g., river plumes, intrusions of underwater, eddy-induced upwelling, or storms) is an 

 important consideration. Biologists need to know in real time the physical conditions under which samples are 

 being taken, so sampling can be related to those events. If biologists cannot be with physical oceanographcrs 

 on the same cruises, then relevant physical observations, satellite imagery, CTD casts, underway S, T, 

 Chlorophyll must be a part of the biological program, so there will be assurance that the physical regime is 

 understood and documented. 



Sampling all state variables and rates in a marine food web as it is now conceived is daunting and potentially 

 costly. While we can be selective in collecting data on the basis of experience, a better approach is some 

 preliminary modeling, even of a highly condensed kind. Modeling during planning should seek (1) scant or 

 missing data that are essential and (2) state variables or rate processes that will be most sensitive and therefore 

 cost-effective to procure. Further modeling during or following the field program should be more predictive, 

 and there should be an opportunity to both refine the model and validate it with field observations. Some recent 

 biological models that incorporate microbial processes are Pace et al. (1984), Fasham (1985) and Moloney (1988). 

 These are probably more elaborate than necessary for the purposes of Minerals Management Service (MMS), 

 although they are simple enough to be run on software packages such as STELLA (High Performance Systems) 

 which require little modeling experience and no programming. 



REFERENCES 



Boesch, D.F. 1984. Field assessment of marine pollution effects: the agony and the ecstacy, pp. 643-646. In 

 H.H. White, ed. Concepts in Marine Pollution. Maryland Sea Grant Program, College Park. 



Chavez, F.P. and R.T. Barber. 1987. An estimate of new production in the equatorial Pacific. Deep-Sea Res. 

 34:1229-1243. 



Fasham, M.J.R. 1985. Flow analysis of materials in the marine euphotic zone. Jn R.E. Ulanowicz and T. Piatt, 

 eds. Ecosystem theory for biological oceanography. Canadian Bull Fish. Aquatic Sci 213:139-162. 



Griffith, P.C. 1990. Community respiration and the fate of organic matter in the waters of the South Atlantic- 

 Bight. Submitted. 



Moloney, C.L. 1988. A size-based model of carbon and nitrogen flows in plankton communities. Doctoral 

 dissertation. University of Cape Town, Rondebosch, South Africa. 256 pp. 



Pace, M.L., J.E. Glasser, and L.R. Pomeroy. 1984. A simulation analysis of continental shelf food webs. Mar. 

 Biol. 82:47-63. 



Pomeroy, L.R. and W.J. Weibe. 1988. Energetics of microbial food webs. Hydrobiologia 159:7-18. 



Reid, P.C, P.H. Burkhill, and CM. Turley, eds. 1989. Protozoa and their role in marine processes. Springer- 

 Verlag. In press. 



Singleton, F.L., D J. Grimes, and R.R. Colwell. 1984. Autochthony of bacterial communities in ocean surface 

 waters as an indicator of pollution, pp. 443-469. .In H.H. White, ed. Concepts in Marine Pollution 

 Measurements. Maryland Sea Grant Program, College Park. 



Smith, S.V. and F.T. Mckenzie. 1987. The ocean as a net heterotrophic system: implications from the carbon 

 biogeochemical cycle. Global Biogeochem. Cycles 1:187-198. 



