Using stable-isotope analysis and acoustic telemetry data to infer broad-scale migration patterns of Port Jackson sharks (Heterodontus portusjacksoni)
Bibliographic record
Abstract
Context Understanding migratory species’ habitat selection is complicated by variation in movement strategies. Stable-isotope analysis provides a powerful tool to investigate such variation. Aims We used acoustic telemetry and stable-isotope analysis to better understand the movement strategies of Port Jackson sharks. Methods We compared the d13C and d15N values of fin tissue from acoustically tracked individuals that undertook three distinct movement strategies. Hierarchical cluster analysis was then used to cluster movement strategies of a larger sample of sharks on the basis of d13C and d15N values. Key results Tracked individuals that remained in Jervis Bay were enriched in 13C, compared with those that migrated south after the breeding season. Individuals were assigned to six clusters and d13C and d15N values indicated that migrating males and females may utilise different geographical areas or niches during the non-breeding season. Conclusions By using stable isotope analysis and acoustic telemetry, we identified distinct groups of Port Jackson sharks with similar broad-scale movement strategies. Implications These variable movement strategies may lead to different reproductive fitness advantages on an individual and population level, having implications for the broader ecosystem, given the important role mesopredators play in southern reef marine ecosystems.
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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) | 0.000 | 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".