Nesting ecology of hawksbill turtles at a rookery of international significance in Australia’s Northern Territory
Bibliographic record
Abstract
Context Following centuries of intense human exploitation, the global stocks of hawksbill turtle have decreased precipitously and the species is currently considered Critically Endangered by the IUCN. Australia supports the largest breeding aggregations worldwide; however, there are no accurate estimates of population abundance and seasonality for hawksbill turtles at important nesting grounds in eastern Arnhem Land. Aims This study was designed to fill in this lack of ecological information and assist with the conservation and management of hawksbill turtles. More specifically, our overarching goals were to assess nesting seasonality, habitat preferences and provide the first estimate of annual nesting population size at a Northern Territory rookery. Methods In 2009 and 2010 we collected beach monitoring, satellite telemetry and sand temperature data over two nesting seasons at a group of three islands located 30 km off Groote Eylandt in the Gulf of Carpentaria, northern Australia. We subsequently analysed these data to unravel hawksbill nesting behaviour and reproductive outputs, and examined the vulnerability of this rookery to climate change. Key results Hawksbill turtle nesting seasonality consistently started in mid-May, peaked in mid-August and ended in late November. Annual nesting abundance showed a near 3-fold increase between 2009 and 2010, with an average of 220 and 580 hawksbill females nesting on this island group respectively. Sand temperature at 50 cm reached more than 30°C at all monitored sites during most of the peak of the incubation period. Conclusions This remote and untouched group of islands constitutes a major hawksbill turtle rookery both nationally and globally. While anthropogenic impacts and predation are low year round, climate change threatens to skew hatchling sex ratios, eventually leading to an increase in hatchling mortality. Implications Additional ground-based surveys are required to refine the accuracy of population estimates presented in this study. Given the paucity of data in the region, we recommend this island group off Groote Eylandt be used as a population-monitoring index site for the eastern Arnhem Land hawksbill turtle breeding aggregation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".