Specimen‐tailored ‘lived’ climate reveals precipitation onset and amount best predict specimen phenology, but only weakly predict estimated reproduction across a clade
Bibliographic record
Abstract
Herbarium specimens are widely distributed in space and time, thereby capturing diverse conditions. We reconstructed specimen 'lived' climate from knowledge of germination cues and collection dates for 14 annual species in the Streptanthus (s.l.) clade (Brassicaceae) to ask: which climate attributes best explain specimen phenological stage and estimated reproduction? Are climate effects on phenology and reproduction evolutionarily conserved? We used climate data geolocated to collection sites to reconstruct the climate experienced by specimens and to ask which aspects of climate best explain specimen reproductive traits. We mapped slopes of climate relationships with these traits on the phylogeny to explore evolutionary constraint and models of evolution. Precipitation amount and onset, more than temperature, best predicted specimen phenology, but weakly predicted reproduction. Earlier rainfall was associated with more phenological advancement, a relationship that showed phylogenetic signal. Few climate predictors explained specimen reproduction. Phenological compensation, interactions with other species, or challenges in estimating total reproduction from specimens may reduce the signal between climate and reproduction. We highlight the value of specimen-tailored growing season estimates for reconstructing climate, incorporating evolutionary relationships in assessing responses to climate. We propose supplemental collection protocols to increase the utility of specimens for understanding climate impacts.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".