Manual Acoustic Tracking Reveals the Spatial Ecology of Giant Trevally at a Remote South Pacific Atoll, with Implications for Their Management
Bibliographic record
Abstract
Giant trevally (Caranx ignobilis) are important predators on the reefs of the tropical Indo-Pacific and research into their spatial ecology is needed to improve our understanding of their behavior and assist fisheries management. We used active acoustic telemetry to describe the fine-scale movements of giant trevally at Tetiaroa Atoll, including their home range, site fidelity, habitat use and spatial overlap with a small (21 km2) Marine Protected Area (MPA). The home ranges of giant trevally were small but varied among individuals (Minimum Convex Polygon 𝑋𝑋 = 3.2 ± 2.5 km2). All giant trevally exhibited site fidelity to their respective home ranges with a 31% average overlap in daily space use, but there was limited overlap in home ranges among individuals, with intra-individual spatial overlap significantly greater than inter-individual overlap (t = -4.93, df = 16.87, p-value = <0.001). There was only modest overlap (19 ± 19%) of giant trevally home ranges within the MPA and high spatial overlap of home ranges with deep lagoon habitats (90 ± 0.09 %). Our results indicate that MPAs could be an effective tool for the conservation of this species if they are implemented on an atoll-wide scale. However, in the case of managing recreational catch-and-release fisheries, rotational smallscale temporal closures could be effective in regulating the angling pressure imposed upon giant trevally, provided post-release mortality is minimized. The results of this study provide the first detailed account of habitat use in this species and highlight the need for additional research on the factors contributing to the survival of caught-and-released giant trevally in predator dominated atolls, especially as their popularity as a target of recreational fisheries continues to grow, and fishing operations and agencies are faced with the need to manage their fisheries.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".