Factors affecting persistent organic pollutant (POP) accumulation in British Columbia grizzly bears (Ursus arctos horribilis)
Notice bibliographique
Résumé
This thesis characterizes major factors influencing the accumulation of polychlorinated biphenyls (PCBs), organochlorine (OC) pesticides, and polybrominated diphenyl ethers (PBDEs), in grizzly bears. Dietary differences among grizzly bears have significant implications for contaminant concentrations and patterns. While salmon-eating bears were dominated by lipophilic PCBs, OC pesticides, and lower-brominated PBDEs, non-salmon-eating bears were dominated by the more volatile PCBs and OC pesticides and higher-brominated PBDEs (e.g. BDE-209). Overall, the ocean-salmon-bear pathway appeared to preferentially select for those contaminants with an intermediate log Kow ~6.5, with salmon delivering up to 70% of OC pesticides, 85% of PBDEs and 90% of PCBs to grizzly bears. Fat utilization by grizzly bears during hibernation results in significant contaminant concentration increases in residual fat (“concentration effect”). Overall, total PCBs increased by 2.21 times from pre- to post-hibernation, and total PBDEs by 1.58 times. Interestingly, the patterns of the two distinct pre-hibernation grizzly bear feeding ecologies (salmon- and non-salmon-eating) converged during hibernation, suggesting that shared metabolic capacities drive POP patterns during hibernation. Relative to salmon, grizzly bears have extremely low biomagnification factors (BMFs) for PCBs (0.147), compared to other marine mammals. Low BMF values were a result of >90% depuration (loss) of PCBs through contaminant metabolism and excretion. The results suggest that grizzly bears only metabolize PCB congeners with meta- and para- vicinal hydrogen (H) atoms, suggesting that they have active cytochrome (CYP) P450 2B/3A-like metabolic enzymes. However, congeners structurally resistant to metabolic biotransformation, and those with ortho- and meta- vicinal H atoms, were not readily metabolized, but rather were lost through excretion. This was evidenced by a significant relationship between total retention (Rtotal) of those congeners and log Kow, as well as a lack of change in that relationship during hibernation. Vegetation and the terrestrial food web were dominated by PBDEs and volatile OC pesticides and PCBs, while salmon and the marine food web were dominated by lipophilic PCBs and OC pesticides, mirroring patterns in grizzly bears within their respective food web. Following consumption of these various foods by the grizzly bears, fecal material closely resembled food in contaminant pattern, suggesting that many of the contaminants may go unabsorbed. While previous work identified major factors (e.g. age, sex, diet) influencing POP behaviour in wildlife and food webs, this research highlights the need to refine our ideas about those factors in order to better assess chemical health risk in wildlife by considering: 1) individual differences in feeding behaviour; 2) integrated dietary histories (temporal changes); 3) unique biological traits affecting POP fate; 4) modes of POP loss other than metabolism; 5) selection of the most recalcitrant congener for more robust analysis of POP behaviour; 6) use of non-invasive techniques to study diet and POP exposure; and, 7) tissue residue guidelines underestimate health risks. Our results also suggest that PBDEs show POP-type characteristics as defined under the Stockholm Convention, and thus should be regulated.
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 | 0,001 | 0,000 |
| 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) | 0,001 | 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 ».