The chemical biogeography of a widespread aromatic plant species shows both spatial and temporal variation
Bibliographic record
Abstract
Abstract Plants produce a wide variety of secondary metabolites, but intraspecific variation in space and time can alter the ecological interactions these compounds mediate. The aim of this work was to document the spatial and temporal chemical biogeography ofMonarda fistulosa. I collected leaves from 1587M. fistulosaindividuals from 86 populations from Colorado to Manitoba, extracted and analyzed their terpenes with gas chromatography, mapped monoterpene chemotypes, and analyzed chemical variation with principal component analysis. I also measured the amounts of terpenes in different plant tissues to examine intraplant variation and monitored leaf terpene chemistry over a single growing season to examine temporal patterns. Finally, I extracted terpenes from herbarium samples up to 125 years old and compared the chemotypes with recent samples from the same sites.M. fistulosapopulations consisted mostly of thymol (T) and carvacrol (C) chemotypes, but geraniol (G) and (R)‐(−)‐linalool (L), a chemotype new to this species, were also present. A principal component analysis showed that most of the chemical variation across populations was due to the amounts of the dominant terpene in plants. Intraplant tissue chemistry revealed that leaves mostly had the greatest amounts of terpenes, followed by floral structures, stems, and roots. Short‐term temporal variation in leaf chemistry of T and C plants over a growing season showed that plants produced the highest levels of biosynthetic precursors early in the season and their dominant monoterpenes peaked in mid‐summer. Plant chemotype was discernable in the oldest herbarium samples, and 15 of 18 historic samples matched the majority chemotype currently at the site, indicating that population chemotype ratios may remain stable over longer time scales. Overall, the results show that plant species' secondary chemistry can vary both spatially and temporally, which may alter the biological interactions that these compounds mediate over space and time.
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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 | 0.001 | 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) | 0.001 | 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".