Stock Identification of Sockeye Salmon (Oncorhynchus nerka) with Scale Pattern Recognition

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Abstract
The non-Gaussian density functions underlying polynomial discriminant functions are employed in a classification scheme designed for sockeye salmon (O. nerka). A leaving-one-out approach is used to estimate the smoothing parameters in the density functions and to obtain nearly unbiased estimates of expected actual error rates in the classification scheme. All available observations of known origin may be used to determine the discriminant rule and estimate classificaiton error rates. These are needed to obtain point estimates of the proportions of subpopulations present in areas of intermingling. Several additional improvements over the polynomial discriminant method are noted. The scheme is applied to scale measurement data of sockeye salmon from Bristol Bay, the Gulf of Alaska, and the Kamchatka Peninsula [USA].