Habitat selection and 3D space use partitioning of resident juvenile hawksbill sea turtles in a small Caribbean bay
Bibliographic record
Abstract
Abstract Understanding how aquatic animals select and partition resources provides relevant information about community dynamics that can be used to help manage conservation efforts. The critically endangered hawksbill sea turtle ( Eretmochelys imbricata ) spends an extended part of its juvenile development in coastal waters. A strong proclivity to remain resident in small areas, often in high density, raises questions about how juveniles partition resources including selection of habitat and spatial overlap among conspecifics. Using between 36 and 41 acoustic receivers in the 1.5 km 2 study site, this study quantified day-and-night habitat selection, as well as 2D and 3D space use of 23 juvenile hawksbills within two adjacent Caribbean foraging grounds—Brewers Bay and Hawksbill Cove, St. Thomas, US Virgin Islands—between 2015 and 2018. We found that coral reef, rock, and the artificial dolosse forming an airport runway, were the most strongly selected habitats based on resource selection indices. Individual activity spaces in 2D and 3D were both larger during the day compared to night, although the same parts of the bay were used by each individual during both periods. The 3D approach also showed deeper space use during the day. Weekly comparisons of activity space between individuals showed limited overlap (mean 95% UD overlap; day: 0.15 (2D) and 0.07 (3D), night: 0.11 (2D) and 0.03 (3D)), suggesting some degree of resource partitioning or territoriality. Results from this study provide relevant space use information for resource management of juvenile hawksbills, in which many populations are facing habitat degradation and population declines.
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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 | 0.000 |
| 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 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".