Time-series models and biotelemetry identify behavioral dynamics within a spawning aggregation of a large marine predator
Bibliographic record
Abstract
Fish spawning aggregations (FSAs) may persist for months and include behaviors such as spawning and foraging. Acoustic telemetry has been used to measure the movements of fishes to and from FSAs, but behaviors within the aggregation are harder to quantify. We used multi-sensor acoustic telemetry (depth, acceleration) combined with continuous wavelet transformation analysis and hidden Markov models to identify behaviors within a spawning aggregation of the goliath grouper Epinephelus itajara, a vulnerable marine predator. Tagged fish (n = 20) exhibited periods (over 2 spawning seasons, August-October) where multiple individuals displayed variability in depth just after the new moon. These events may represent spawning, and there was always one event each season that included a greater number of individuals and more variability in depth. Grouper were more active during the new moon, in shallow water (<15 m), and at night. We also identified several events with behaviors more consistent with benthic foraging where grouper were highly active while remaining at constant depth. As such, intensive foraging by multiple individuals may occur during more sporadic time periods. Grouper remained close to the seafloor as currents exceeded 0.4 m s-1 but moved up into the water column during cold-water incursions (<24°C). Abiotic conditions may limit vertical habitat use and could potentially influence spawning and foraging behavior. Multi-sensor telemetry combined with time-series analysis can be used to remotely measure individual behavior within spawning aggregations. The timing of these behaviors within the FSA may also have implications for the ecological role groupers play as predators and, potentially, in bottom-up processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".