Genomic analyses capture the human-induced demographic collapse and recovery in a wide-ranging cervid
Bibliographic record
Abstract
Abstract The glacial cycles of the Quaternary heavily impacted species through successions of population contractions and expansions. Similarly, populations have been intensely shaped by human pressures such as unregulated hunting and land use changes. White-tailed and mule deer survived in different refugia through the Last Glacial Maximum, and their populations were severely reduced after the European colonisation. Here, we analysed 73 re-sequenced deer genomes from across their North American range to understand the consequences of climatic and anthropogenic pressures on deer demographic and adaptive history. We found a strong signal of glacial-induced vicariance and demographic decline; notably, there is a severe decline in white-tailed deer effective population size (N e ) at the end of the Last Glacial Maximum. We found robust evidence for colonial overharvest in the form of a recent and dramatic drop in N e in all analysed populations. Historical census size and restocking data show a clear parallel to historical N e estimates, and temporal N e /N c ratio shows patterns of conservation concern for mule deer. Signatures of selection highlight genes related to temperature, including a cold receptor previously highlighted in woolly mammoth. We also detected immune-genes that we surmise reflect the changing land-use patterns in North America. Our study provides a detailed picture of anthropogenic and climatic-induced decline in deer diversity, and clues to understanding the conservation concerns of mule deer and the successful demographic recovery of white-tailed deer.
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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 | 0.001 |
| 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".