Analysis of narwhal tusks reveals lifelong feeding ecology and mercury exposure
Bibliographic record
Abstract
The ability of animals to respond to changes in their environment is critical to their persistence. In the Arctic, climate change and mercury exposure are two of the most important environmental threats for top predators. 1–3 Rapid warming is causing precipitous sea-ice loss, with consequences on the distribution, composition, and dietary ecology of species 4–7 and, thus, exposure to food-borne mercury. 8 Current understanding of global change and pollution impacts on Arctic wildlife relies on single-time-point individual data representing a snapshot in time. These data often lack comprehensive temporal resolution and overlook the cumulative lifelong nature of stressors as well as individual variation. To overcome these challenges, we explore the unique capacity of narwhal tusks to characterize chronological lifetime biogeochemical profiles, allowing for investigations of climate-induced dietary changes and contaminant trends. Using temporal patterns of stable isotopes (δ 13 C and δ 15 N) and mercury concentrations in annually deposited dentine growth layer groups in 10 tusks from Northwest Greenland (1962–2010), we show surprising plasticity in narwhal feeding ecology likely resulting from climate-induced changes in sea-ice cover, biological communities, and narwhal migration. Dietary changes consequently impacted mercury exposure primarily through trophic magnification effects. Mercury increased log-linearly over the study period, albeit with an unexpected rise in recent years, likely caused by increased emissions and/or greater bioavailability in a warmer, ice-free Arctic. Our findings are consistent with an emerging pattern in the Arctic of reduced sea-ice leading to changes in the migration, habitat use, food web, and contaminant exposure in Arctic top predators.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".