The impacts of pesticide exposure on fish conspecific interactions: A systematic review and meta-analysis
Bibliographic record
Abstract
The production of chemical pesticides poses a critical threat to aquatic ecosystems worldwide, with sub-lethal impacts evident at even relatively low concentrations. Historically, ecotoxicologists have ignored an organism's social context when investigating the effects of pesticide exposure and, instead, have tended to focus on individual-level impacts. Recently, however, there has been a growing interest in understanding the impacts of pesticide exposure on social behaviour. Despite this shift, a holistic understanding of how pesticides impact conspecific interactions (i.e., social behaviour towards individuals of the same species) is lacking due to the multitude of behaviours, pesticides and species currently investigated. In this meta-analysis, we examine the effects of pesticide exposure on conspecific interactions in fish by using data collected from 37 studies on 31 pesticides and 11 species. Our results indicate that pesticide exposure generally reduces the expression of conspecific interactions, but it does not affect the variability of responses between individuals. Courtship behaviour was the most impaired, suggesting that pesticide exposure could weaken how matings are partitioned among individuals in a population. Triazoles and organochlorines were the most impactful pesticide classes for mean differences in behaviour, while triazoles and organophosphates had the greatest effects on response variability. These findings indicate that endocrine-disrupting and neurotoxic pesticides can impact fish conspecific interactions, regardless of their chemical class. Unfortunately, there is a large taxonomic bias in the literature, with most studies using zebrafish as a model, which, in turn, provides scope for studies using a broader range of fish species. We found little statistical evidence of publication biases in our dataset and our results were validated by sensitivity analyses. Overall, our synthesis suggests that pesticides broadly reduce the expression of social behaviours, though effects vary across behaviours, pesticide types, and fish species.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.020 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".