The timing of reproduction is responding plastically, not genetically, to climate change in yellow‐bellied marmots (<i>Marmota flaviventer</i>)
Bibliographic record
Abstract
Abstract With global climates changing rapidly, animals must adapt to new environmental conditions with altered weather and phenology. The key to adapting to these new conditions is adjusting the timing of reproduction to maximize fitness. Using a long‐term dataset on a wild population of yellow‐bellied marmots (Marmota flaviventer) at the Rocky Mountain Biological Laboratory (RMBL), we investigated how the timing of reproduction changed with changing spring conditions over the past 50 years. Marmots are hibernators with a 4‐month active season. It is thus crucial to reproduce early enough in the season to have time to prepare for hibernation, but not too early, as snow cover prevents access to food. Importantly, climate change in this area has, on average, increased spring temperatures by 5°C and decreased spring snowpack by 50 cm over the past 50 years. We evaluated how female marmots adjust the timing of their reproduction in response to changing conditions and estimated the importance of both microevolution and plasticity in the variation in this timing. We showed that, within a year, the timing of reproduction is not as tightly linked to the date a female emerges from hibernation as previously thought. We reported a positive effect of spring snowpack but not of spring temperature on the timing of reproduction. We found inter‐individual variation in the timing of reproduction, including low heritability, but not in its response to changing spring conditions. There was directional selection for earlier reproduction since it increased the number and proportion of pups surviving their first winter. Taken together, the timing of marmot reproduction might evolve via natural selection; however, plastic changes will also be extremely important. Further, future studies on marmots should not operate under the assumption that females reproduce immediately following their emergence.
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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 | 0.001 | 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 | 0.001 | 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".