Spatial phylogenetics of the North American flora
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract North America is a large continent with extensive climatic, geological, soil, and biological diversity. As biota faces threat from habitat destruction and climate change, making a quantitative assessment of biodiversity becomes critically important. Rapid digitization of plant specimen records and accumulation of DNA sequence data enable a much‐needed broad synthesis of species occurrences with phylogenetic data. In this study, the first such synthesis of a flora from such a large and diverse part of the world is attempted, all seed plants from the North American continent (here defined to include Canada, United States, and Mexico), with a focus on examining phylogenetic diversity and endemism. We collected digitized plant specimen records and chose a coarse grain for analysis, recognizing that this grain is currently necessary for reasonable completeness per sampling unit. We found that raw richness and endemism patterns largely support previous hypotheses of biodiversity hotspots. The application of phylogenetic metrics and a randomization test revealed novel results, including a significant phylogenetic clustering across the continent, a striking east–west geographical difference in the distribution of branch lengths, and the discovery of centers of neo‐ and paleoendemism in Mexico, the southwestern USA, and the southeastern USA. Finally, our examination of phylogenetic beta diversity provides a new approach to compare centers of endemism. We discuss the empirical challenges of working at the continental scale and the need for more sampling across large parts of the continent, for both DNA data for terminal taxa and spatial data for poorly understood regions, to confirm and extend these results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it