Factors affecting persistent organic pollutant (POP) accumulation in British Columbia grizzly bears (Ursus arctos horribilis)
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".