Marine hotspots of activity inform protection of a threatened community of pelagic species in a large oceanic jurisdiction in the South Atlantic
Bibliographic record
Abstract
Remote oceanic islands harbour unique biodiversity, especially of species that rely on pelagic resources around their breeding islands. Identifying marine areas used by such species is important to reduce or limit threats that may put these species at risk. The Tristan da Cunha group of islands in the South Atlantic Ocean hosts several endemic and globally threatened seabirds and pinnipeds; how they use the waters surrounding the islands must be considered when planning industrial activities in the entire Exclusive Economic Zone (EEZ). We identified hotspots of activity by collating animal tracking data from nine breeding seabirds and one marine mammal to inform marine management in the Tristan da Cunha EEZ.To detect statistically significant areas of concentrated activity, we calculated the time-spent-in-area that tracked individuals (breeding adults) of 10 focal species (mainly breeding adults of nine seabirds and adult female Subantarctic fur seals Arctocephalus tropicalis) invested in a grid of regular 10 × 10 km cells within the EEZ, for each of four seasons to account for temporal variability in space use. Applying a spatial aggregation statistic over these grids by each species we detected areas that are used more than expected by chance. Most of the activity hotspots were either within 100 km of the islands or were associated with seamounts being spatially constant across several seasons. Moreover, some species spend a large proportion of their time-at-sea inside the EEZ during certain breeding stages, rendering the sites we identified critical for their fitness. Our approach provides a simple and effective tool to highlight important areas for pelagic biodiversity that will benefit Tristan da Cunha’s conservation planning and marine management strategies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".