Acoustic telemetry captures the full annual migration of alewife between Chesapeake Bay and the Gulf of Maine
Bibliographic record
Abstract
Anadromous fish are declining at a global scale, and a more holistic approach to management is needed that addresses threats across their freshwater, estuarine, and ocean habitats. In this study, we used acoustic telemetry to track adult alewife Alosa pseudoharengus in Chesapeake Bay, USA, to evaluate, for the first time, habitat use throughout the entire annual migration cycle. Fifty adult alewife were tagged in the Choptank River, Maryland, in spring 2022, and detection data were obtained via collaborative acoustic telemetry networks along the Eastern Seaboard of the USA and Canada. Water temperature data were also collected using data loggers in the Choptank River and from satellite measurements of sea surface temperature in Chesapeake Bay and the ocean. In total, 48 tags (96%) were detected at least once, 14 tagged fish (28%) were detected in the ocean migrating north to the Gulf of Maine and Bay of Fundy, and 5 tagged fish (10%) returned to the Choptank River in spring 2023. With few exceptions, tagged fish were detected at temperatures of 7-16°C across all habitats (river to ocean and back). Alewife made extensive use of tidal and non-tidal portions of the Choptank River during spring, migrated to summer habitats that are experiencing rapid warming, and passed through areas of high incidental catch on both the outgoing and return migration. This study highlights how acoustic telemetry can refine our understanding of river-specific migrations of anadromous fish and the management implications of their movements in regions with collaborative acoustic telemetry networks.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".