Integrated assessment of west coast of South Korea by use of benthic bacterial community structure as determined by eDNA, concentrations of contaminants, and in vitro bioassays
Notice bibliographique
Résumé
During the past few decades, contamination of sediments by persistent toxic substances (PTSs) has been observed in estuarine and coastal areas on the west coast of South Korea. The contaminants are suspected to cause toxicities in aquatic biota, but little is known about their ecological effects, particularly on benthic microbial communities. In this study, an eDNA-based assessment was applied along with classic assessments of exposure, such as chemistry and in vitro bioassays, to evaluate condition of benthic bacterial communities subjected to PTSs. Two strategies were adopted for the study. One was to conduct a comprehensive assessment in space (by comparing seawater and freshwater sites at five coastal regions) and in time (by following change over a 5-y period). Although we found that bacterial composition varied among and within years, some phyla, such as Proteobacteria (28.7%), Actinobacteria (13.1%), Firmicutes (12.7%), and Chloroflexi (12.5%) were consistently dominated across the study regions. Certain bacterial groups, such as Firmicutes and Verrucomicrobia have been linked to contamination at some sites in the study area and at specific points in time. Bacterial communities were not significantly correlated with salinity or AhR- and ER-mediated potencies, whereas concentrations of PAHs, APs, and certain metals (Cd and Hg) exhibited significant associations to the structure of bacterial communities at the phylum level. In fact, the relative abundance of microbes in the phylum Planctomycetes was significantly and negatively correlated with concentrations of PAHs and metals. Thus, the relative abundance of Planctomycetes could be used as an indicator of sedimentary contamination by PAHs and/or metals. Based on our correlation analyses, Cd and ER-mediated potencies were associated more with bacterial abundances at the class taxonomic level than were other PTSs and metals. Overall, the eDNA-based assessment was useful by augmenting more traditional measures of exposure and responses in a sediment triad approach and has potential as a more rapid screening tool.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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 tête enseignante, 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 ».