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Juvenile tagging and escapement enumeration was conducted during 1985-1988 in nine streams within a 150-km section on the east coast of Vancouver Island. Fourteen coho salmon (Oncorhynchus kisutch) stocks of wild, hatchery, and mixed origin were monitored for ocean survival and exploitation patterns. Estimates of smolt-to-adult survival ranged from 0.5 to 23.1%. Survival rates were highly variable across years and stocks. No stock or stock type had consistently higher survival, but one hatchery stock exhibited consistently lower survival. Average exploitation rates were about 80% each year, and were as high as 96% for some stocks. Exploitation rates were not consistently higher or lower for any stock or stock type, but hatchery reared coho tended to be subject to higher exploitation. Log-linear models were used to assess the effects of various factors on survival and exploitation. Certain hatchery rearing practices had a large influence on survival. Genetic factors, run timing, and stream location had large influences on exploitation rates. An assessment of covariation in survival and exploitation rates showed no indication of a high level of similarity among stocks from adjacent streams or among stock types in this region.

The problem of robust estimation of optimal effort levels from surplus production models is considered. A variety of models are used to generate data, for the purpose of testing estimation schemes. The result of an estimation is an estimate of the optimal effort. These efforts are compared using the expected discounted value of a deterministic stock, which corresponds to the model used to generate the data. Such a criterion takes into account not only the loss due to bias in the estimated optimal effort, but also the loss due to the variance of the estimator. Estimation is difficult if there is a lack of informative variation in effort levels or stock sizes. In such cases, the estimation scheme which maximizes the criterion described above sacrifices realism in the representation of the stock-production relationship in order to reduce the variance of the estimate of optimal effort. We present a composite estimation scheme which performs acceptably in all the cases we have examined, and whose performance degrades slowly as the amount of information in the data decreases.