Seasonal climate drives population growth but not costs of reproduction of a perennial wildflower
Bibliographic record
Abstract
Costs of reproduction are predicted to shift under climate change, but the extent to which weaker or stronger costs influence population responses to interannual climate variation is unknown. We asked how seasonal climate, manipulated rainfall, and costs of reproduction influence vital rates and population growth in a long-lived herbaceous perennial plant, Primula hendersonii, across an ongoing rainfall manipulation experiment in oak savanna of northwestern North America. Simulated drought reduced population growth rates, and vital rates (e.g., probability of flowering, individual growth) responded individualistically to variation in winter, spring, and summer temperatures, although not to variation in seasonal precipitation. However, only warmer spring temperatures were associated with a decline in population growth rates. Although we observed a weak negative effect of past reproduction on growth and future reproduction for large individuals, these costs of reproduction ultimately did not influence population growth. Further, observational and manipulative experiments to detect costs of reproduction suggest subtle differences in cost expression. We show that direct climate drivers had a stronger effect on population growth than indirect changes in costs of reproduction and may be more important for understanding population persistence under climate change.
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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 | 0.000 | 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".