Explaining intraspecific diversity in plant secondary metabolites in an ecological context
Bibliographic record
Abstract
Summary Plant secondary metabolites ( PSM s) are ubiquitous in plants and play many ecological roles. Each compound can vary in presence and/or quantity, and the composition of the mixture of chemicals can vary, such that chemodiversity can be partitioned within and among individuals. Plant ontogeny and environmental and genetic variation are recognized as sources of chemical variation, but recent advances in understanding the molecular basis of variation may allow the future deployment of isogenic mutants to test the specific adaptive function of variation in PSM s. An important consequence of high intraspecific variation is the capacity to evolve rapidly. It is becoming increasingly clear that trait variance linked to both macro‐ and micro‐environmental variation can also evolve and may respond more strongly to selection than mean trait values. This research, which is in its infancy in plants, highlights what could be a missing piece of the picture of PSM evolution. PSM polymorphisms are probably maintained by multiple selective forces acting across many spatial and temporal scales, but convincing examples that recognize the diversity of plant population structures are rare. We describe how diversity can be inherently beneficial for plants and suggest fruitful avenues for future research to untangle the causes and consequences of intraspecific variation. Contents Summary 733 I. Introduction 733 II. PSM variation in time: ontogeny, phenology and induced defences 734 III. PSM variation through space: the role of environment 735 IV. Genes and biosynthetic pathways underlying PSM variation 735 V. Mechanisms for diversification of PSMs 735 VI. Examples of diversity from specific biosynthetic pathways 737 VII. How and why is chemical diversity maintained? 739 VIII. Evolvability of PSM variation 740 IX. Evolutionary strategies and diversity in plant chemical defence 742 X. Conclusions and future directions 744 Acknowledgements 745 References 745
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".