Reef sharks: recent advances in ecological understanding to inform conservation
Bibliographic record
Abstract
Sharks are increasingly being recognized as important members of coral-reef communities, but their overall conservation status remains uncertain. Nine of the 29 reef-shark species are designated as data deficient in the IUCN Red List, and three-fourths of reef sharks had unknown population trends at the time of their assessment. Fortunately, reef-shark research is on the rise. This new body of research demonstrates reef sharks' high site restriction, fidelity and residency on coral reefs, their broad trophic roles connecting reef communities and their high population genetic structure, all information that should be useful for their management and conservation. Importantly, recent studies on the abundance and population trends of the three classic carcharhinid reef sharks (grey reef shark Carcharhinus amblyrhynchos, blacktip reef shark Carcharhinus melanopterus and whitetip reef shark Triaenodon obesus) may contribute to reassessments identifying them as more vulnerable than currently realized. Because over half of the research effort has focused on only these three reef sharks and the nurse shark Ginglymostoma cirratum in only a few locales, there remain large taxonomic and geographic gaps in reef-shark knowledge. As such, a large portion of reef-shark biodiversity remains uncharacterized despite needs for targeted research identified in their red list assessments. A research agenda for the future should integrate abundance, life history, trophic ecology, genetics, habitat use and movement studies, and expand the breadth of such research to understudied species and localities, in order to better understand the conservation requirements of these species and to motivate effective conservation solutions.
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 | 0.001 | 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) | 0.001 | 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 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".