Influence of past climate change on phylogeography and demographic history of narwhals, <i>Monodon monoceros</i>
Bibliographic record
Abstract
The Arctic is warming at an unprecedented rate, with unknown consequences for endemic fauna. However, Earth has experienced severe climatic oscillations in the past, and understanding how species responded to them might provide insight into their resilience to near-future climatic predictions. Little is known about the responses of Arctic marine mammals to past climatic shifts, but narwhals ( Monodon monoceros ) are considered one of the endemic Arctic species most vulnerable to environmental change. Here, we analyse 121 complete mitochondrial genomes from narwhals sampled across their range and use them in combination with species distribution models to elucidate the influence of past and ongoing climatic shifts on their population structure and demographic history. We find low levels of genetic diversity and limited geographic structuring of genetic clades. We show that narwhals experienced a long-term low effective population size, which increased after the Last Glacial Maximum, when the amount of suitable habitat expanded. Similar post-glacial habitat release has been a key driver of population size expansion of other polar marine predators. Our analyses indicate that habitat availability has been critical to the success of narwhals, raising concerns for their fate in an increasingly warming Arctic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".