Spatial scale and conservation options for carpet sharks
Bibliographic record
Abstract
Although there is considerable momentum for expanding protected area coverage, a key criticism is the lack of connection to species-specific conservation outcomes. Conservation action needs to be tailored to species characteristics, such as behaviour, life-history traits, migratory range, as well as the scale and intensity of existing threats. Due to their small average size (<10 km 2 ), Marine Protected Areas (MPAs) are likely to be more beneficial for species with smaller geographic ranges than for larger pelagic migratory species. We developed a conservation classification scheme for the 28 Critically Endangered ( n = 1), Endangered ( n = 4), Vulnerable ( n = 12), Near Threatened ( n = 6) and Data Deficient ( n = 5) carpet sharks (Order: Orectolobiformes) based on their distribution, biology, and threats. Carpet sharks exhibit large diversity in their life histories and ecology; ranging from the Halmahera Epaulette Shark that reaches a 70 cm (total length, TL) with a geographic range of 14,446 km 2 to the Whale Shark that reaches 21 m TL and geographic range of 171,000,000 km 2 . We find that none of these carpet sharks would benefit exclusively from a single site-scale MPA protection; instead MPA networks or a combination of MPAs with broad-scale threat mitigation measures are needed for over half of the species due to the broad geographic ranges and widespread overfishing. The choice of measures would depend on (a) fraction of the population moving between sites, (b) how diffuse are the inter-site movements, and (c) the presence or otherwise of significant barriers or threats to connectivity. Hence, MPAs on their own will not solve widespread overfishing, and instead diagnosis and a tailored treatment is likely to be more effective than a single cure-all. Fishing mortality will also need to be reduced around, as well as eliminated within, MPAs to ensure maximum conservation benefit. • Single MPAs are currently unlikely to be large enough to benefit Carpet Sharks. • MPAs networks or in combination with broad-scale conservation is likely to benefit half of at-risk carpet sharks. • Tackling broad-scale threats, particularly overfishing, is an unavoidable need alongside the MPA growth agenda
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".