Behavioural acclimation to cameras and observers in coral reef fishes
Bibliographic record
Abstract
Abstract Observer presence can bias behavioural studies of animals in both the wild and the laboratory. Despite existing evidence for significant observer effects across several taxa, little is known about the minimum periods of acclimation that should precede behavioural observations. To date, most studies either do not report any acclimation periods or include a non‐specific period without empirically quantifying its appropriateness. Here, we conducted in situ behavioural observations of two species of demersal coral reef fishes using cameras and/or observers to examine the biases associated with either approach. For both treatments, we generated 25 min time series of a number of vigilance‐associated behaviours (i.e., distance from shelter and mate, time out of shelter, swimming activity) and estimated the point of acclimation using changepoint analysis. In the camera trials, acclimation in both species appeared to occur between 2 and 7 min for different behaviours. When an observer was present, however, no apparent acclimation occurred until the observer left the area. Overall, our findings demonstrate that (i) behavioural studies of wild fishes conducted by an observer may be biased due to permanent observer effects, and (ii) when using video equipment, a species‐ and behaviour‐specific acclimation period should precede behavioural scoring.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".