Impact of Holocene environmental change on the evolutionary ecology of an Arctic top predator
Bibliographic record
Abstract
Abstract The Arctic is among the most climatically sensitive environments on Earth, and the disappearance of multiyear sea-ice in the Arctic Ocean is predicted within decades. As apex predators, polar bears are sentinel species for addressing the impact of environmental variability on Arctic marine ecosystems. By integrating genomics, isotopic analysis, morphometrics, and ecological modelling, we investigate how Holocene environmental changes affected the evolutionary ecology of polar bears around Greenland. We show that throughout the last ∼11,000 years, Greenlandic polar bears have been heavily influenced by changes in sea-surface temperature (SST) and sea-ice cover. Most notable are major reductions in effective population size at the beginning of the Holocene and during the Holocene Thermal Maximum ∼6 kya, which coincide with increases in annual mean SST, reduction in sea-ice covers, declines in suitable habitat, and shifts in suitable habitat northwards. Furthermore, we show how individuals sampled from west and east Greenland are genetically, morphologically, and ecologically distinct. We find bears sampled in west Greenland to be larger, more genetically diverse and have diets dominated by ringed seals, whereas bears from east Greenland are smaller and less diverse with more varied diets, putatively driven by regional biotic differences. Taken together, we provide novel insights into the vulnerability of polar bears to environmental change, and how the Arctic marine ecosystem plays a vital role in shaping the evolutionary and ecological trajectories of its inhabitants. Teaser Multivariate investigations of the environment’s role in the evolutionary ecology of Greenlandic polar bears.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".