Impacts of Climate and Feeding Conditions on the Annual Accumulation (1986–2009) of Persistent Organic Pollutants in a Terrestrial Raptor
Bibliographic record
Abstract
The relationships between climate variability, feeding conditions, and the annual accumulation of polychlorinated biphenyls (PCB), 1,1-dichloro-2,2-bis (p-chlorophenyl) ethylene (p,p'-DDE) and polybrominated diphenyl ethers (PBDE) in eggs (n = 168) of tawny owls (Strix aluco) were assessed over a 24-year period (1986-2009) in Central Norway. Winter climate variables included the North Atlantic Oscillation (NAO) and snow conditions, whereas feeding conditions were assessed using vole abundance. The concentrations of all compounds declined between 79% (p,p'-DDE) and 86% (PBDE) over the time period. For PCB and p,p'-DDE, the accumulation was positively associated with snow depth, and negatively associated with NAO (i.e., high accumulation in cold and dry winters) when vole abundance was low, suggesting trade-offs between winter severity and feeding conditions. That is, females mobilize more body fat reserves in cold winters when feeding conditions are poor, which results in increased circulating contaminant concentrations and maternal transfer to the eggs. Owls may also have been forced to feed on prey with higher contaminant loads due to restricted prey availability. For the recently banned PBDEs, the accumulation was high when NAO was low, while snow depth was positively associated with PBDE accumulation only when feeding conditions were good. This suggests somewhat different dynamics of PBDE in the environment or in the owls, compared to PCB and p,p'-DDE. However, climate and feeding conditions explained as much of the annual variation in concentrations of all POPs, as the overall decreasing trend over the 24 years. Hence, such factors should be considered in monitoring programs for POPs. Moreover, to better understand the mechanisms of climate effects on POP accumulation, future studies should measure pollutants in different components of the food chain.
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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 | 0.000 | 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) | 0.000 | 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".