Factors Influencing Legacy Pollutant Accumulation in Alpine Osprey: Biology, Topography, Or Melting Glaciers?
Bibliographic record
Abstract
Persistent organic pollutants (POPs) can be transported long distances and deposited into alpine environments via cold trapping and snow scavenging processes. Here we examined biotic and abiotic factors determining contaminant variability of wildlife in alpine ecosystems. We measured POPs in eggs and plasma of an apex predator, the osprey (Pandion haliaetus) breeding in 15 mountainous watersheds across a broad latitudinal, longitudinal and altitudinal range in western Canada. After accounting for proximate biotic factors such as trophic level (δ(15)N) and carbon source (δ(13)C), variability in contaminant concentrations, including ΣDDT (sum of trichlorodiphenylethane-related compounds), toxaphene, hexachlorobenzene (HCB), total chlordane, and ΣPCBs (polychlorinated biphenyls) in osprey tissues was explained by interactions among relative size of watersheds, water bodies, elevation, and glacial input. ΣDDT in nestling plasma, for example, decreased with lake elevation, probably as a result of local past inputs from agricultural or public health usage at lower altitude sites. In contrast, toxaphene, never used as an insecticide in western Canada, increased with elevation and year-round snow and ice cover in both plasma and eggs, indicating long-range atmospheric sources as dominant for toxaphene. Lower chlorinated PCBs in plasma tended to decrease with elevation and ice cover consistent with published data and model outcomes. Temporal trends of POPs in osprey eggs are coincident with some modeled predictions of release from melting glaciers due to climate change. Currently we suggest that contaminants largely are released through annual snowpack melt and deposited in large lower elevation lakes, or some smaller lakes with poor drainage. Our study highlights the importance of understanding how biological processes integrate physical when studying the environmental chemistry of wildlife.
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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 | 0.001 | 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".