Re‐Creating Missing Population Baselines for Pacific Reef Sharks
Bibliographic record
Abstract
Sharks and other large predators are scarce on most coral reefs, but studies of their historical ecology provide qualitative evidence that predators were once numerous in these ecosystems. Quantifying density of sharks in the absence of humans (baseline) is, however, hindered by a paucity of pertinent time-series data. Recently researchers have used underwater visual surveys, primarily of limited spatial extent or nonstandard design, to infer negative associations between reef shark abundance and human populations. We analyzed data from 1607 towed-diver surveys (>1 ha transects surveyed by observers towed behind a boat) conducted at 46 reefs in the central-western Pacific Ocean, reefs that included some of the world's most pristine coral reefs. Estimates of shark density from towed-diver surveys were substantially lower (<10%) than published estimates from surveys along small transects (<0.02 ha), which is not consistent with inverted biomass pyramids (predator biomass greater than prey biomass) reported by other researchers for pristine reefs. We examined the relation between the density of reef sharks observed in towed-diver surveys and human population in models that accounted for the influence of oceanic primary productivity, sea surface temperature, reef area, and reef physical complexity. We used these models to estimate the density of sharks in the absence of humans. Densities of gray reef sharks (Carcharhinus amblyrhynchos), whitetip reef sharks (Triaenodon obesus), and the group "all reef sharks" increased substantially as human population decreased and as primary productivity and minimum sea surface temperature (or reef area, which was highly correlated with temperature) increased. Simulated baseline densities of reef sharks under the absence of humans were 1.1-2.4/ha for the main Hawaiian Islands, 1.2-2.4/ha for inhabited islands of American Samoa, and 0.9-2.1/ha for inhabited islands in the Mariana Archipelago, which suggests that density of reef sharks has declined to 3-10% of baseline levels in these areas.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".