Spatial and temporal variation in the natal otolith chemistry of a Hawaiian reef fish: prospects for measuring population connectivity
Bibliographic record
Abstract
One of the most compelling unanswered questions in marine ecology is the extent to which local populations are connected via larval exchange. Recent work has suggested that variation in the chemistry of otoliths (earstones) of fishes may function as a natural tag, potentially allowing investigators to determine sources of individual larvae and estimate larval connectivity. We analyzed the spatial and temporal variation in natal otolith chemistry of a benthic-spawning reef fish from the Hawaiian Islands. We found no consistent chemical variation at the largest scale (>100 km, among islands), but found significant variation at moderate scales (sites within islands, tens of kilometres) and small scales (clutches within sites), and chemistry of otoliths was not stable between years. These results imply that we may be able to use otolith chemistry to track larval dispersal only if the scales of dispersal match those of variation in natal otolith chemistry, and that separate natal otolith collections may be needed to track different cohorts of larvae. Finally, we found that elemental composition of recruit cores often did not match that of natal otoliths, suggesting that additional methodological development is required before we can effectively apply methods in otolith chemistry to the study of larval dispersal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".