A semi-automated spectral approach to analysing cyclical growth patterns using fish scales

Abstract

We introduce a new semi-automated approach to analysing growth patterns recorded on fish scales. After manually specifying the centre of the scale, the algorithm radially unwraps the scale patterns along a series of transects from the centre to the edge of the scale. A sliding-window Fourier transform is used to produce a spectrogram for each sampled transect of the scale image. The maximum frequency over all sampled transects of the average spectrogram yields a well discriminated peak frequency trace that can then serve as a growth template for that fish. The spectrogram patterns of individual fish scales can be adjusted to a common period accounting for differences in date of return or size of fish at return without biasing the growth profile of the scale. We apply the method to 147 Atlantic salmon scale images sampled from three years and contrast the information derived with this automated approach to what is obtained using classical human operator measurements. The spectrogram analysis quantifies growth patterns using the entire scale image rather than just a single transect and provides the possibility of more robustly analysing individual scale growth patterns. This semi-automated approach that removes essentially all the human operator interventions provides an opportunity to process large datasets of fish scale images and combined with advanced analyses such as deep learning methods could lead to a greater understanding of salmon marine migration patterns and responses to variations in ecosystem conditions.