Foraging behaviour and activity of a marine benthivorous fish estimated using tri-axial accelerometer biologgers
Bibliographic record
Abstract
Fine-scale behaviour such as foraging is difficult to quantify in free-swimming wild fish yet has important basic and applied implications. Here, we used tri-axial accelerometer biologgers to determine accelerometric predictors of bonefish Albula vulpes behaviours (resting, swimming, bursting, coasting, and foraging) in a wetland mesocosm in Eleuthera, The Bahamas. We also used a swim flume to estimate the relationship between acceleration and swimming speed for bonefish (n = 9). In the wetland study, 5 bonefish were tagged externally with accelerometer loggers and monitored for a 5 d period during which visual behavioural observations were conducted for 4 h. Classification tree models were used to identify accelerometric criteria for bonefish behaviours, and a classification algorithm was applied to estimate behavioural frequencies for bonefish in the wetland for the 5 d period. Bonefish spent the majority of time resting (57%), followed by swimming (26%) and coasting (17%), and foraged an average of 11 times h -1 . Bonefish exhibited primarily slow swimming speeds (average 0.18 m s -1 ) while in the wetland, with occasional burst swimming events (14 h -1 ) to maximum swimming speeds ranging from 4.3 to 6.4 m s -1 across individuals. Swimming and foraging behaviours varied among individuals and over the diel cycle. Bonefish generally swam most at dawn and foraged most at night. Temperature and tide were also significant predictors of swimming behaviour, and fish were generally most active at lower temperatures (~24C) and during ebbing tidal periods despite the fact that the wetland was disconnected from the ocean. The ability to estimate fine-scale behaviours such as foraging and activity levels has important implications for understanding energy dynamics, which is fundamental to the fitness of wild fish.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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 teacher head, 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".