Phenological responses to multiple environmental drivers under climate change: insights from a long‐term observational study and a manipulative field experiment
Bibliographic record
Abstract
Climate change has induced pronounced shifts in the reproductive phenology of plants, yet we know little about which environmental factors contribute to interspecific variation in responses and their effects on fitness. We integrate data from a 43 yr record of first flowering for six species in subalpine Colorado meadows with a 3 yr snow manipulation experiment on the perennial forb Boechera stricta (Brassicaceae) from the same site. We analyze shifts in the onset of flowering in relation to environmental drivers known to influence phenology: the timing of snowmelt, the accumulation of growing degree days, and photoperiod. Variation in responses to climate change depended on the sequence in which species flowered, with early-flowering species reproducing faster, at a lower heat sum, and under increasingly disparate photoperiods relative to later-flowering species. Early snow-removal treatments confirm that the timing of snowmelt governs observed trends in flowering phenology of B. stricta and that climate change can reduce the probability of flowering, thereby depressing fitness. Our findings suggest that climate change is decoupling historical combinations of photoperiod and temperature and outpacing phenological changes for our focal species. Accurate predictions of biological responses to climate change require a thorough understanding of the factors driving shifts in phenology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.017 | 0.001 |
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 teacher head, 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".