Localised residency and inter-annual fidelity to coastal foraging areas may place sea bass at risk to local depletion
Bibliographic record
Abstract
For many marine migratory fish, comparatively little is known about the movement of individuals rather than the population. Yet, such individual-based movement data is vitally important to understand variability in migratory strategies and fidelity to foraging locations. A case in point is the economically important European sea bass (Dicentrarchus labrax L.) that inhabits coastal waters during the summer months before migrating offshore to spawn and overwinter. Beyond this broad generalisation we have very limited information on the movements of individuals at coastal foraging grounds. We used acoustic telemetry to track the summer movements and seasonal migrations of individual sea bass in a large tidally and estuarine influenced coastal environment. We found that the vast majority of tagged sea bass displayed long-term residency (mean, 167 days) and inter-annual fidelity (93% return rate) to specific areas. We describe individual fish home ranges of 3 km or less, and while fish clearly had core resident areas, there was movement of fish between closely located receivers. The combination of inter-annual fidelity to localised foraging areas makes sea bass very susceptible to local depletion; however, the designation of protected areas for sea bass may go a long way to ensuring the sustainability of this species.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".