Functional distinctiveness of major plant lineages
Bibliographic record
Abstract
Summary Plant traits vary widely across species and underpin differences in ecological strategy. Despite centuries of interest, the contributions of different evolutionary lineages to modern‐day functional diversity remain poorly quantified. Expanding data bases of plant traits plus rapidly improving phylogenies enable for the first time a data‐driven global picture of plant functional diversity across the major clades of higher plants. We mapped five key traits relevant to metabolism, resource competition and reproductive strategy onto a phylogeny across 48324 vascular plant species world‐wide, along with climate and biogeographic data. Using a novel metric, we test whether major plant lineages are functionally distinctive. We then highlight the trait–lineage combinations that are most functionally distinctive within the present‐day spread of ecological strategies. For some trait–clade combinations, knowing the clade of a species conveys little information to neo‐ and palaeo‐ecologists. In other trait–clade combinations, the clade identity can be highly revealing, especially informative clade–trait combinations include P roteaceae, which is highly distinctive, representing the global slow extreme of the leaf economic spectrum. M agnoliidae and R osidae contribute large leaf sizes and seed masses and have distinctively warm, wet climatic distributions. Synthesis . This analysis provides a shortlist of the most distinctive trait–lineage combinations along with their geographic and climatic context: a global view of extant functional diversity across the tips of the vascular plant phylogeny.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".