How depth alters detection and capture of buried prey: exploitation of sea turtle eggs by mongooses
Bibliographic record
Abstract
Predators are an important source of mortality for animals that lay their eggs in buried nests. We asked how depth alters the process of predation for buried prey. We outlined a general model of predation risk where depth may alter both prey detection and subsequent capture: deeper prey are detected less often because the strength of olfactory cues decreases with burial depth and, once detected, are further protected by the costs of digging. Using this framework, we examined how burial depth influenced egg predation for critically endangered hawksbill sea turtles (Eretmochelys imbricata) by introduced mongooses (Herpestes javanicus) in Barbados. We tested the effects of nest depth on detection and subsequent predation using observational data on hawksbill nests over a 2-year period and an experiment with artificial nests. For both real and artificial nests, depth had little effect on nest detection by mongooses, but once detected, shallower nests were much more likely to be preyed on. Mongooses appear to use depth-invariant surface cues to detect nests but increase digging persistence in response to olfactory cues. We argue that excavation cost is an important but overlooked mechanism protecting deeply buried resources. For reptiles with buried nests, the relationship between depth and predation risk leads to important predictions about how changes to nesting habitat or the predator community will affect egg survival and offspring sex ratio. More generally, identifying the mechanisms by which burial depth protects prey is essential for understanding how resource burial strategies evolved and are maintained in predator–prey systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".