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
Summary Herbarium specimens are broadly distributed in space and time, enabling investigation of climate impacts on phenology and fitness. 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, including the timing of precipitation onset, best explain specimen phenological stage and estimated reproduction? We also asked whether climate effects on phenology and reproduction were evolutionarily conserved. Precipitation amount and onset date, more than temperature, best predicted specimen phenology, but only weakly predicted reproduction. Earlier rainfall onset was associated with more phenologically advanced specimens, a relationship that showed phylogenetic signal. Few climate predictors explained variation in specimen reproduction. The lack of association between specimen reproduction and climate may arise from phenological shifts that buffer impacts of climate, interactions with other species, or challenges in estimating total reproduction from specimens. Our results highlight the value of specimen-tailored growing season conditions for reconstructing climate, incorporating evolutionary relationships in assessing responses to climate, and the complexities of estimating fitness from specimens. For the latter, we propose supplemental herbarium collections and community science protocols to increase the utility of these data for understanding climate impacts on populations.
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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 | 0.002 |
| 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) | 0.003 | 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 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".