This is only part of the story, however. 



In figure 4 the relative value of information with respect to time is 

 illustrated. It depends very much on the time horizon of concern how valuable 

 the information from each of these system variables may be. The effects of 

 predation and disease, vis-a-vis the fisherman, both tend to peak 3 to 4 years 

 ahead. In the case of environment, it becomes very important as you look 

 further ahead, and finally the environment concerns dominate when one is looking 

 ahead as much as 10 years. The highest value for the natural environmental 

 information at the zero time horizon, for example, is related to the fact that 

 this information is useful to those pursuing, for example, tunas, or any other 

 resource which tends to congregate along ocean fronts near the surface. Because 

 one of our overriding scientific questions is that of the significance of 

 temporal and spatial variability of environmental parameters, knowing what is 

 going on at the time the research vessels are on the scene is also important. 

 Again that new technology - remote sensing - pops up as a significant and cost 

 effective tool to be invoked as soon as possible. 



To return now to the more complicated segment of this figure, that referring 

 to fishing, there are three curves. If one wishes to maximize "now," or to put 

 it into other words, discount the future, then fishing data is very important. 

 If, however, one wishes to maximize both revenue and stability (longer term 

 parameters) from existing populations of fish, then the curve is far different. 

 Since most of the resources have moderately long lives in the fishery, a haddock 

 year class, for example, can last several years , then other information becomes 

 important, as for example, predation and disease. Since recruitment to popula- 

 tions of fishes is controlled by many variables, at the present time it is 

 almost impossible to predict recruitment much more than 3 or 4 years ahead. 

 This curve thus tapers off quickly after 6 years. If one desires to maximize 

 ecosystem revenue, the curve falls somewhere in between these last 2 or at 

 about 5 years. Going back to the sausage machine analogy, such activity would 

 require directing the fleet to harvest certain species as opposed to others, 

 with the consequence of somewhat less ability to pass judgments on fishing 

 data alone, but required far more reliance on knowledge of the recent history 

 and future consequences of predation and disease. 



Figure 5 is simply a single graph combining all the information in figure 

 4. The most desirable mix of programs is thus indicated in terms of the relative 

 value of information against the time horizon. When one puts this information 

 into the computer and evaluates the overall information return, given various 

 budgetary levels, a new series of curves is generated. These are presented in 

 figure 6. 



To generate figure 6 we assumed that we had 1.5 full-time research vessels 

 available and a capacity to use satellite derived products. Actually we have 

 the full time use of two vessels and cannot as yet effectively use remotely 

 sensed data. It should be noted that these figures do not precisely reflect 

 our actual budget situation. The computer was asked to evaluate the relative 

 information return from each of the four system variables, given that each 

 time horizon was equally important. We are incidentally, at about the IX 

 level. 



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