the decisionmaker for any particular developing pollution situation, steps 2-8 

 represent generic needs for information and predictive methodologies that will 

 be very similar in most cases. These generic needs are schematicized in figure 

 2. A major intended implication of the outlined objectives is that research 

 and information products not contributing directly to these objectives are 

 likely to be of little direct value to the decision process leading to the 

 selection of optimal waste management alternatives. The principal processes 

 and relationships that must be modeled to support the successive predictions 

 outlined in figure 2 are discussed below. 



The source characteristics, which are sometimes controllable decision 

 variables, serve as input data for trajectory and dispersion models. These 

 models should include subroutines that account for physical and chemical 

 transformations and transport of different phases. Relevant processes to be 

 modeled may include: biodegradation, adsorption-desorption, sedimentation, bed- 

 load movement, bioturbation, etc. The resultant time and space scales for 

 physical contaminant distributions can be coupled with information on demography 

 and susceptibilities to generate the measures of outcome for human health and 

 recreational values. Coupled with information on resource distribution and 

 life histories, the physical distribution of contaminants leads through 

 bio accumulation or uptake models to predicted tissue concentrations in key 

 organisms. These tissue concentrations are used in computing exposure for 

 humans and other predators, like birds and mammals. Coupled with information 

 on effects, especially on direct mortality or rep-roductive success, the 

 bio accumulation information provides input for population dynamics models for 

 key species, which lead to predictions of stock size and production. Many of 

 the measures of outcome in table 2 can be predicted or estimated directly through 

 these computational or modeling steps. The specific modeling requirements must 

 be driven, however, by the measures of outcome, which must in turn be convertible 

 to measures of value. Thus, all-encompassing ecosystem modeling is probably 

 not required, and many potential input variables may be suppressed after appro- 

 priate sensitivity analysis. When time scales of concern exceed the predictive 

 capacity of existing models, the decisionmaker must rely more heavily on 

 associated research-monitoring programs to test the adequacy of the predictions. 



The one class of resource values for which comprehensive ecosystem modeling 

 may be most applicable is wilderness values. In this instance, however, the 

 valuation approaches are not at all defined, and the suggested measures of out- 

 come may ultimately prove to be of little use. In this case, greatest initial 

 emphasis should be placed on development of acceptable and useful valuation 

 approaches . 



Most of the needs outlined above and in figure 2 have been recognized for 

 some time as essential to understanding and predicting pollutant fates and 

 effects on marine ecosystems (Wolfe and Rice 1972, Wolfe 1975, Warlen et al . 

 1977). The particular point emphasized here is that these predictions can be 

 much more highly focused on the problem at hand by explicit consideration of 

 the management alternatives and the specific values at risk. Even though full 

 and complete marine ecological understanding Is never achieved, decisions 

 impacting the marine ecosystem will continue to be made. These decisions can 

 be strengthened by careful documentation of a structured decision-oriented 

 analysis that considers alternatives, values, and uncertainties. Formal 

 techniques for such analysis have become well established in operations research 



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