Direct estimates of reef fish abundance across an artificial reef network
Bibliographic record
Abstract
Fisheries-independent surveys are commonly used to create indices of relative abundance. If properly designed and calibrated, these surveys may also be used to estimate absolute abundance. Here, we demonstrate the efficacy of this approach by estimating the absolute abundance of red lionfish ( Pterois volitans), gray triggerfish ( Balistes capriscus), and red snapper ( Lutjanus campechanus) across an extensive network of artificial reefs using camera counts, indices of relative abundance, calibration factors, and index-removal estimators. From 2012 to 2017, per reef estimates increased for red lionfish (20×), gray triggerfish (2.1×), and red snapper (2.2×). Network-wide absolute abundances were calculated by multiplying the average per reef estimate by the estimated number of reefs in the network. All increases were consistent with predictions of stock assessment (red snapper), management actions (gray triggerfish), or invasive species colonization (red lionfish). Our methodology demonstrates how estimates of absolute abundance can be derived from fishery-independent surveys and used to evaluate the outputs of stock assessments both in direction and magnitude and quantify critical ecosystem components.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 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".