Genetic Characterization of Periphyton Communities Associated with Selenium Bioconcentration and Trophic Transfer in a Simple Food Chain
Notice bibliographique
Résumé
A major source of uncertainty in predicting selenium (Se) distribution in aquatic food webs lies in the enrichment factor (EF), the ratio of Se bioconcentration in primary producers and microorganisms relative to the concentration of Se in the surrounding water. It has been well demonstrated that EFs can vary dramatically among individual algal taxa, but data are lacking regarding the influence of periphyton community composition on EFs for a given geochemical form of Se. Therefore, the goals of this study were first to assess whether different periphyton communities could be established in aquaria with the same starting inoculum using different light and nutrient regimes, and second, to determine if the periphyton assemblage composition influences the uptake of waterborne Se (as selenite) and subsequent Se transfer to a model macroinvertebrate primary consumer. Periphyton biofilms were grown in aquaria containing filtered pond water (from Saskatoon, SK) spiked with approximately 20 μg Se/L (mean measured concentration 21.0 ± 1.2 μg Se/L), added as selenite. Five different light and nutrient regimes were applied to the aquaria (three replicates per treatment) to influence biofilm community development. After 6 weeks of biofilm maturation, 40 to 80 immature cultured snails (Stagnicola elodes) were added to each aquarium. The bacterial and algal members of the periphyton community were characterized by targeted metagenomic analyses before and after addition of snails to ensure the snails themselves did not significantly alter the biofilm community. Samples were collected for Se analysis of water, periphyton, and whole-body snail. The nutrient and light treatments resulted in substantially different compositions of the periphytic biofilms, with each being relatively consistent across replicates and throughout the study. Although the aqueous concentration of dissolved Se administered to treatments was constant, uptake by the different periphytic biofilms differed significantly. Both the low-light (61.8 ± 12.1 μg Se/g d.w.) and high-light (30.5 ± 4.7 μg Se/g d.w.) biofilms, which were found to have high proportions of cyanobacteria, contained statistically higher concentrations of Se relative to the other treatments. Furthermore, the concentration of Se in bulk periphyton was predictive of Se bioaccumulation in grazing snails but as an inverse relationship, opposite to expectations. The trophic transfer factor was inversely correlated with periphyton enrichment factor (r = -0.841). A number of different bacterial and algal taxa were correlated (either positively or negatively) with Se accumulation in periphyton biofilm and snails. Recent advancements in genetic methods make it possible to conduct detailed characterization of periphyton assemblages and begin to understand the influence that periphyton composition has on Se biodynamics in aquatic systems.
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 | 0,000 | 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 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 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 ».