Heterogeneous patterns of availability for detection during visual surveys: spatiotemporal variation in sea turtle dive–surfacing behaviour on a feeding ground
Bibliographic record
Abstract
Summary 1. During aerial or boat‐based surveys for large‐bodied diving taxa (e.g. marine mammals and marine turtles), a proportion of animals present will be missed because they are submerged and out of view, leading to ‘availability bias’ in abundance indices. Information on dive–surfacing patterns can improve corrections for availability bias. However, as dive data are typically limited, availability correction factors are often based on poorly resolved dive and surface times, and diving heterogeneity is not considered. 2. We collected dive records for green turtlesChelonia mydas,Linnaeus 1758, and loggerhead turtlesCaretta caretta, Linnaeus 1758, on a foraging ground in Shark Bay, Western Australia to quantify dive–surfacing patterns and assess potential correlations with easily measured environmental features: habitat depth and water temperature. Bayesian regression models were used to predict dive and surface interval durations across temperature–depth gradients and assess their uncertainty. We used these predictions to quantify variation in availability correction factors, which were multipliers designed, in this case, to adjust surface sightings data to incorporate diving animals. 3. Dive and surface interval durations for both species varied positively with depth and negatively with temperature, consistent witha prioriexpectations, although temperature effects were not always significant. Dive metrics were predictable, although uncertainty increased in deeper habitat with few observed dives. 4. Availability correction factors were highly heterogeneous, with larger corrections necessary in colder, deeper conditions (long‐diving, infrequent surfacing behaviour) and smaller corrections required in warmer, shallower conditions (short‐diving, frequent‐surfacing behaviour). 5. Predictable variation in the diving behaviour of chelonid sea turtles across environmental gradients on a foraging ground reveals that site‐specific knowledge of dive–surfacing patterns can be important to mitigate the effects of availability bias during population surveys. Accounting for such trends may improve the reliability of ecological inferences (e.g. spatiotemporal distribution trends) and the efficacy of applications (e.g. conservation planning) based on survey data.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".