Phenotypic variation in otolith shape of American shad across eastern North American rivers
Bibliographic record
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 à 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".