Reef fish communities are spooked by scuba surveys and may take hours to recover
Bibliographic record
Abstract
Ecological monitoring programs typically aim to detect changes in the abundance of species of conservation concern or which reflect system status. Coral reef fish assemblages are functionally important for reef health and these are most commonly monitored using underwater visual surveys (UVS) by divers. In addition to estimating numbers, most programs also collect estimates of fish lengths to allow calculation of biomass, an important determinant of a fish's functional impact. However, diver surveys may be biased because fishes may either avoid or are attracted to divers and the process of estimating fish length could result in fish counts that differ from those made without length estimations. Here we investigated whether (1) general diver disturbance and (2) the additional task of estimating fish lengths affected estimates of reef fish abundance and species richness during UVS, and for how long. Initial estimates of abundance and species richness were significantly higher than those made on the same section of reef after diver disturbance. However, there was no evidence that estimating fish lengths at the same time as abundance resulted in counts different from those made when estimating abundance alone. Similarly, there was little consistent bias among observers. Estimates of the time for fish taxa that avoided divers after initial contact to return to initial levels of abundance varied from three to 17 h, with one group of exploited fishes showing initial attraction to divers that declined over the study period. Our finding that many reef fishes may disperse for such long periods after initial contact with divers suggests that monitoring programs should take great care to minimise diver disturbance prior to surveys.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".