Global Diversity Hotspots and Conservation Priorities for Sharks
Bibliographic record
Abstract
Sharks are one of the most threatened groups of marine animals, as high exploitation rates coupled with low resilience to fishing pressure have resulted in population declines worldwide. Designing conservation strategies for this group depends on basic knowledge of the geographic distribution and diversity of known species. So far, this information has been fragmented and incomplete. Here, we have synthesized the first global shark diversity pattern from a new database of published sources, including all 507 species described at present, and have identified hotspots of shark species richness, functional diversity and endemicity from these data. We have evaluated the congruence of these diversity measures and demonstrate their potential use in setting priority areas for shark conservation. Our results show that shark diversity across all species peaks on the continental shelves and at mid-latitudes (30-40 degrees N and S). Global hotspots of species richness, functional diversity and endemicity were found off Japan, Taiwan, the East and West coasts of Australia, Southeast Africa, Southeast Brazil and Southeast USA. Moreover, some areas with low to moderate species richness such as Southern Australia, Angola, North Chile and Western Continental Europe stood out as places of high functional diversity. Finally, species affected by shark finning showed different patterns of diversity, with peaks closer to the Equator and a more oceanic distribution overall. Our results show that the global pattern of shark diversity is uniquely different from land, and other well-studied marine taxa, and may provide guidance for spatial approaches to shark conservation. However, similar to terrestrial ecosystems, protected areas based on hotspots of diversity and endemism alone would provide insufficient means for safeguarding the diverse functional roles that sharks play in marine ecosystems.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".