Impacts of anthropogenic pollutants on social group cohesion and individual sociability in fish: A systematic review and meta-analysis
Bibliographic record
Abstract
Anthropogenic pollutants are near-ubiquitous in aquatic systems. Aquatic animals such as fishes are subject to physiological stress induced by pollution present in aquatic systems, which can translate to changes in behaviour. Key adaptive behaviours such as shoaling and schooling may be subject to change as a result of physiological or metabolic stress or neurosensory impacts of pollution. This can result in fitness and ecological impacts such as increased predation risk and reduced foraging success. Here, we conducted a systematic metanalysis of the existing literature, comprising 165 studies, on the effects of anthropogenic pollution on sociability and group cohesion in fish species. Both organic (number of studies = 92, posterior mean (PM) = -0.483, p < 0.01) and inorganic (n = 24, PM = -1.453, p < 0.001) chemical pollutants, as well as light exposure (n = 21, PM = -3.038, p < 0.01) were found to reduce sociability. These pollutants did not reduce group cohesion, indicating that effects may be masked in group settings, though fewer studies were carried out on group cohesion and this is a key area for future research. Mixtures of chemical pollutants (n = 16) were found to reduce cohesion (PM = -43.71, p < 0.01), but increase sociability (PM = 44.27, p < 0.01). Evidence was found that fish may behaviourally acclimate to two forms of pollutant, namely mixed chemical pollutants (PM = -0.668, p < 0.01) and noise exposure (n = 22, PM = -4.043, p < 0.01). While aquatic systems are often subject to pollution from multiple sources and of multiple types, very few studies investigated the effects of multiple stressors concurrently. This review identifies trends in the existing literature, and highlights areas where further research is required in order to understand the behavioural and ecological impacts of anthropogenic pollutants in aquatic systems.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.022 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".