Mongoose predation on sea turtle nests: linking behavioural ecology and conservation
Bibliographic record
Abstract
The introduced small Asian mongoose (Herpestes javanicus) is a widespread predator of sea turtle eggs and hatchlings in the Caribbean. I studied the behavioural ecology of mongoose predation on the nests of critically endangered hawksbill sea turtles (Eretmochelys imbricata) in Barbados. Combining short-term field experiments with seven years of hawksbill nesting data, I investigated how mongoose foraging behaviour, antipredator behaviour and landscape use explain the spatial and temporal patterns of sea turtle nest predation. An experiment combining artificial nests and predator tracking revealed a direct relationship between fine-scale variation in mongoose activity and nest predation risk. The combination of mongoose avoidance of open areas and the spatial distribution of hawksbill nests relative to patches of beach vegetation accurately predicted the observed peak in nest predation near the vegetation edge. Egg-burial depth by nesting hawksbills also affected predation risk, but this was primarily due to the increased digging effort required rather than any increase in nest concealment with depth. A second experiment with artificial nests confirmed the causal relationship between burial depth and predation risk and showed that substrate disturbance is a primary cue for nest detection by mongooses. At the landscape scale, mongooses tracked local nest abundance but showed a fine-scale negative response to human beach use, suggesting that human activity on nesting beaches may improve nest survival by deterring predators. Finally, an analysis of nest survival times showed that nests were most vulnerable to predation in first days following oviposition and that predation risk increased over the nesting season, providing a general framework for planning where and when predation reduction methods should be applied. I conclude that predation risk for sea turtle nests is likely to depend on: i) how predator nest-finding behaviour is modulated by nest characteristics such as cue availability and digging cost and ii) behavioural processes such as predator avoidance and resource tracking that drive patterns of landscape use and alter the contact rate between predators and nests. Despite the pessimistic views of some recent commentators, my thesis shows that Behavioural Ecology can provide unique and relevant insight into the ecological processes underlying conservation problems.
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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 | 0.001 |
| 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 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".