Evidence of the Impacts of Pharmaceuticals on Aquatic Animal Behaviour (EIPAAB): a systematic map and open access database
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,084 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,005 |
| Bibliométrie | 0,111 | 0,091 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».