as a vector of annual coefficients that is adjusted systematically to mini- 

 mize the sum of squared errors between observed and predicted observa- 

 tions. Users can set constraints on the annual coefficients so that they fall 

 within a given range. The mean of the fitted vector is then used as an esti- 

 mate of adtrecv. 



The calibration model also calculates the mean and variance of the prod- 

 uct of egg-to-presmolt, downstream migrant, and smolt-to-adult survival 

 for each cohort. The variance of this product is partitioned among the 

 three survival components algebraically. Since there is only one variance 

 equation but three unknowns, the user must decide the relative propor- 

 tions of the variance to be allocated to each life stage, or alternatively must 

 set one or two of the component variances to a constant and solve for the 

 remainder. 



Calibration Using The calibration process for the alternative model of juvenile production 

 the Alternative (Version 2) is similar in concept to that described above. Smolt-to-adult 



Model of Juvenile survival again is used as a scaling factor to provide the best fit to the data. 

 Production Paulsen and others (1991) provide an example using this version of the 



model. 



CONCLUSIONS 



The SLCM has proven to be a powerful and flexible tool for examining 

 the impact of changes in population parameters on the structure and size 

 of salmonid populations. While some aspects of the SLCM's behavior have 

 been difficult to understand at first glance, the model's overall behavior 

 conforms well to theory and experience with deterministic models. Some 

 of the more provocative results are those that deviate from an analogous 

 deterministic model, such as the model's sensitivity to parameter variance. 

 As our experience with the model grows, we expect new questions to arise 

 concerning the interplay of stochastic factors and population dynamics, 

 opening new areas of research. Such questions, and their answers, could 

 have significant management implications. 



REFERENCES 



Alderdice, D. F.; Bams, R. A; Velsen, F. P. J. 1977. Factors affecting 

 deposition, development, and survival of salmonid eggs and alevins: a 

 bibliography, 1965-1975. Tech. Rep. 743. Nanaimo, BC: Canada Fisheries 

 and Marine Service, Pacific Biological Station. 276 p. 



Beverton, R. J. H.; Holt, S. J. 1957. On the dynamics of exploited fish 

 populations. Fishery Investigations, Ser. 2, Vol. 19. London, UK: 

 Ministry of Agriculture, Fish and Food. 533 p. 



Boswell, M. T.; Ord, J. K; Patil, G. P. 1979. Chance mechanisms underlying 

 statistical distributions. In: Ord, J. K.; Patil, G. P.; Tallie, C., eds. 

 Statistical distributions in ecological work. Fairland, MD: International 

 Co-operative Publishing House: 3-156. 



Columbia Basin Fish and Wildlife Authority. 1990. Columbia Basin system 

 planning salmon and steelhead production plans. Portland, OR: North- 

 west Power Planning Council: 31 volumes. 



Gilpin, M. E.; Soule, M. E. 1986. Minimum viable populations: processes of 

 species extinction. In: Soule, M. E., ed. Conservation biology. Sunderland, 

 MA: Sinauer Associates: 19-34. 



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