Abstract
Otolith shape analysis has been widely applied to study population structure and environmental influences in various fish species. However, research on American shad (Alosa sapidissima) otolith morphology remains scarce, despite its potential to provide insights into population differentiation and environmental adaptation. This study analyses otolith contour shape from 1141 American shad collected between 2000 and 2023 across eleven large rivers from Canada to Florida. Using a wavelet transform framework based on the & agrave; trous algorithm and B3-spline wavelet, we quantified otolith shape variability and assessed its effectiveness for population discrimination. Principal Component Analysis revealed significant shape variation, with key differences in the rostrum, antirostrum, and posterior region. Wavelet analysis identified two primary otolith morphologies-upper and lower rostrum-geographically structured along a latitudinal gradient. A Multilayer Perceptron neural network successfully classified individuals with 90.9% accuracy, highlighting strong population differentiation, particularly in the St. Lawrence and Delaware rivers. Cluster analysis identified five morphotypes with distinct spatial distributions, suggesting a role for local environmental conditions in shaping otolith morphology. These findings underscore the utility of otolith shape analysis in deciphering population structure and highlight potential links between environmental variation and phenotypic plasticity in American shad.