Stock discrimination of Lake Winnipeg walleye based on Fourier and wavelet description of scale outline signals

Abstract

Abstract We introduce wavelet analysis, an alternate signal processing method to Fourier analysis, to provide variables from scale morphology that discriminate among different spawning aggregations of fish. Fourier and wavelet analyses were used to transform averaged walleye ( Sander vitreus ) scale outline's into shape variables. These variables violated the discriminant analysis’ assumption of multi-variate normality. Therefore, non-parametric statistics were used to assess the significance of the discriminant functions. Age-class effects were significant at one of the sampling locations, restricting all subsequent analyses by age-class. Variables from the wavelet decompositions formed significantly better discriminant functions than those based upon Fourier analysis for most comparisons. There was no detectable difference between the different orders of wavelets used to analyze the signals.