As clear as mud: Turbidity induces behavioral changes in the African cichlid Pseudocrenilabrus multicolor
Bibliographic record
Abstract
Abstract Aquatic biodiversity is being lost at an unprecedented rate. One factor driving this loss is increased turbidity, an environmental stressor that can impose behavioral, morphological, and/or physiological costs on fishes. Here we describe the behavioral response of a widespread African cichlid, Pseudocrenilabrus multicolor victoriae, to turbidity. We used a split-brood rearing design to test if F1 offspring reared in turbid water, originating from river (turbid) and swamp (clear) populations, behave differently than full-sibs reared in clear water. We examined two facets of behavior: (1) behaviors of fish in full sib groups, including activity level and social dynamics collected during the rearing period; and (2) male aggressive behavior directed at potential male competitors after fish had reached maturity; this was done in an experimental set-up independent of the rearing aquaria. Regardless of population of origin, fish reared in turbid water were marginally less active and performed fewer social behaviors than those reared in clear water. On the other hand, when tested against a competitor in turbid water, males performed more aggressive behaviors, regardless of population of origin or rearing environment. Our results suggest a plastic behavioral response to turbidity that may allow P. multicolor to persist over a range of turbidity levels in nature by decreasing activity and general social behaviors and intensifying reproductive behaviors to ensure reproductive success.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".