The Stochastic Life-Cycle 

 Model (SLCM): Simulating 

 the Population Dynamics 

 of Anadromous Salmonids 



Danny C. Lee 

 Jeffrey B. Hyman 



INTRODUCTION 



Models are commonly used as exploratory tools to help fishery managers 

 and planners estimate the potential effects of alternative harvest, mitiga- 

 tion, and enhancement strategies on target fish populations. Examples in- 

 clude Goodyear (1977), Taylor (1981), Walters (1981), Jensen and Hamilton 

 (1982), Peterman (1982), and MacCall and others (1983). Within the 

 Columbia River Basin, the Northwest Power Planning Council relies 

 heavily on the System Planning Model (McConnaha 1992) for analysis of 

 mitigation and enhancement opportunities for Pacific salmon and steel- 

 head. Like many other models used in fisheries science, the System Plan- 

 ning Model is a deterministic model that is designed to mimic population 

 dynamics. Within a deterministic model, simulated populations rigidly fol- 

 low mathematical rules that govern survival through each life stage and 

 from one generation to the next. Intrapopulation variability plays no role; 

 interannual variation can be introduced only by changing annual inputs 

 such as river flows. 



The caricature of reality portrayed in a deterministic model is sufficient 

 to guide management decisions in some cases, but can be dangerously mis- 

 leading in others. To understand why, first consider their conceptual un- 

 derpinnings. The more commonly used models offish population dynamics 

 were derived from surplus production models developed by fisheries scien- 

 tists in the 1950's (Beverton and Holt 1957; Ricker 1954). These models 

 were derived from the gross dynamics of major fish stocks observed over 

 several years. The general response of these stocks to increased levels of 

 fishing harvest led pioneering researchers to postulate simple models de- 

 scribing the population dynamics observed in the field. Paradoxically, 

 these models have found wide acceptance among fisheries scientists despite 

 the models' generally abysmal fit to empirical data (Rothschild 1986). 



At best, deterministic surplus-production models capture only the cen- 

 tral tendencies of a population. When combined with models that link 

 management actions to model parameters, deterministic models can pro- 

 vide a normative ranking of alternative strategies if three conditions hold. 

 First, the model must be valid for the population in question, that is, the 

 model should faithfully mimic the population's underlying dynamics. Sec- 

 ond, the population must be relatively robust so annual deviations from the 

 expected behavior do not have long-term repercussions. And third, model 

 parameters must be estimated accurately. 



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