Evidence of the Impacts of Pharmaceuticals on Aquatic Animal Behaviour (EIPAAB): a systematic map and open access database
Bibliographic record
Abstract
Background The global proliferation of pharmaceutical pollutants in aquatic ecosystems has emerged as a pressing environmental concern. These contaminants—designed to modulate biological functions at minute dosages—pose a unique threat to aquatic organisms, particularly through behavioural alterations. Recent years have seen a surge in scientific interest in the use of behavioural endpoints in chemical risk assessment and regulatory activities, underscoring their importance for fitness and survival. In parallel, research on how pollution, particularly pharmaceuticals, alters the behaviour of aquatic animals appears to have grown rapidly. Despite this, there has been a notable absence of systematic efforts to consolidate and summarise this field of study. To address this gap, our objectives are twofold: first, to systematically identify, catalogue, and synthesise primary research articles on the effects of pharmaceuticals on aquatic animal behaviour; and second, to organise the ‘Evidence of the Impacts of Pharmaceuticals on Aquatic Animal Behaviour’ (EIPAAB) into a comprehensive open-access database for scientists, policymakers, and environmental managers. Methods We systematically searched two electronic databases (Web of Science and Scopus) and supplemented these with additional article sources. The search string followed a Population–Exposure–Comparison–Outcome (PECO) framework to capture articles that used an aquatic organism (population) to test the effects of a pharmaceutical (exposure) on behaviour (outcome). Eligible articles also needed a control group (comparison). Articles were screened in two stages: an initial screening of title and abstract, followed by full-text screening alongside data extraction. Decision trees were designed a priori to appraise eligibility at both stages. Information on study validity was collected but not used as a basis for inclusion. Review findings We identified and screened 5,988 articles, of which 901 were included in the final EIPAAB database, representing 1,739 species-by-compound combinations. The database includes data collected over 48 years (1974–2022), with most articles having an environmental focus (510) and fewer relating to medical and basic research topics (233 and 158, respectively). The EIPAAB database includes 173 distinct species representing 8 phyla and 21 classes. Ray-finned fishes were by far the most common clade (75% of the evidence base). The database also includes 426 distinct pharmaceutical compounds; the most frequently investigated groups were antidepressants (28%), antiepileptics (11%), and anxiolytics (10%). The impacts of pharmaceuticals on locomotion and boldness/anxiety behaviours were most assessed out of the 10 broad behavioural categories assigned in the database (62 sub-categories in total). Generally, we detected poor reporting and/or compliance with several of our study validity criteria, including the use of experimental blinding, randomisation, reporting of compound details, and experimental treatment concentration verification. Conclusions: Our systematic map revealed a rapid increase in this research area over the past 15 years. We highlight multiple areas now suitable for quantitative synthesis and areas where evidence is lacking. We also highlight some obvious pitfalls in method reporting and practice. More detailed reporting would facilitate the use of behavioural endpoints in aquatic toxicology studies, chemical risk assessment, regulatory management activities, and improve the overall replicability of this research area. The EIPAAB database can be used as a tool for closing these knowledge and methodological gaps in the future.
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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 | 0.014 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.111 | 0.091 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".