Predicting nest survival in sea turtles: when and where are eggs most vulnerable to predation?
Bibliographic record
Abstract
Nest predation is an important practical challenge for the conservation of egg-laying reptiles, with the potential to reduce hatchling recruitment and slow the recovery of threatened populations. Accurately forecasting where and when predation will occur has the potential to optimize predation management. Survival analysis, a set of statistical techniques recently popularized in studies of avian nest success, provides a unique approach for modelling variation in egg mortality risk throughout development. We used Cox proportional hazards regression to model the survival of sea turtle eggs from predation by the small Asian mongoose Herpestes javanicus, a widely introduced and destructive sea turtle nest predator in the Caribbean. We evaluated the ability of models to predict egg survival using 7 years of nest predation data for critically endangered hawksbill sea turtles Eretmochelys imbracata in Barbados. Daily predation risk was the highest for freshly laid nests, decreasing rapidly with nest age but increasing again near the end of development. Predation risk was the highest in and near patches of beach vegetation, increased over the nesting season and increased with nest density on the open beach but not in vegetation. Survival models calibrated using data from 2004 to 2005 showed excellent discrimination and ≥84% accuracy when predicting the fate of nests from previous years. Our study provides the first quantification of the daily variation in predation risk for incubating turtle eggs, revealing a narrow time window early in development during which the application of predation reduction measures is likely to have the greatest impact on nest survival. More generally, we demonstrate the utility of survival analysis for generating fine-scale predictions of spatiotemporal variation in turtle egg mortality, providing a flexible tool for the conservation of sea turtles and other egg-laying reptiles.
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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 | 0.002 |
| 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 | 0.001 | 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 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".