On the geography of big plant genera
Bibliographic record
Abstract
BACKGROUND AND AIMS: Fewer than one percent of the World's plant genera have >500 species, yet these big genera collectively account for >25% of plant species. It remains unclear how these specific big genera achieved their present-day global distributions and if they share characteristics that may have contributed to their success. We examined the distributions of big plant genera to determine: (i) if the diversity patterns of big genera are representative of overall plant diversity patterns; and (ii) if there are groups of big genera with similar geographic distributions that may help explain their success. METHODS: We mapped the distribution of each flowering plant species at the botanical country scale using data from the World Checklist of Vascular Plants and investigated the proportion of species in big genera in each botanical country across latitudes and climate zones. We used hierarchical clustering to determine whether big genera could be grouped based upon their distributions in botanical countries, aggregated into floristic realms. KEY RESULTS: Big plant genera are not distributed evenly relative to global flowering plant diversity but are particularly well-represented in continental and polar regions of the Northern Hemisphere. Big plant genera can be grouped into five clusters based upon their shared distributions, each centred around one floristic realm. Individual big genera, however, tend to occur across multiple floristic realms, with >92% occurring in two or more floristic realms and ∼33% occurring across all realms. CONCLUSIONS: We propose that pre-adaptions, ecological opportunity, long-distance dispersal, and key innovations have played a central role in the geographical evolution of big genera, and contributed to their exceptional size and distribution. Collectively, these factors have resulted in repeated radiations among different clades across big plant genera and have ultimately led to the accumulation of species diversity in these groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".