Macroevolutionary history predicts flowering time but not phenological sensitivity to temperature in grasses
Bibliographic record
Abstract
PREMISE: Long-term observations show that flowering phenology has shifted in many lineages in response to climate change. However, it remains unclear whether these results can be generalized to predict the presence, direction, or magnitude of responses in lineages for which we lack long time-series data. If phenological responses are phylogenetically conserved, we can extrapolate from species for which we have data to predict the responses of close relatives. While several studies have found that closely related species flower at similar times, fewer have evaluated whether phylogenetically proximal species respond to environmental change similarly. METHODS: We paired flowering time data from 3161 manually scored herbarium specimens of 72 species of grasses (Poaceae) with historical climate data and analyzed the phylogenetic signal and phylogenetic half-life of phenological sensitivity. We also ran these analyses on a subset of species showing statistically significant sensitivities, in order to assess the role of sampling bias on phylogenetic signal. RESULTS: Closely related grass species tend to flower at similar times, but flowering times respond to temperature changes in species-specific ways. We also show that only including species for which there is strong evidence of phenological shifts results in overestimating phylogenetic signal. CONCLUSIONS: In agreement with other recent studies, our results suggest caution in extrapolating from evidence of phylogenetic similarity to predicting shared responses in this ecologically relevant trait. Future work is needed to better understand the discrepancy between the phylogenetic signal in observed phenological shifts and absence of such signal in sensitivity.
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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) | 0.000 | 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 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".