Geographic patterns and environmental correlates of taxonomic and phylogenetic beta diversity for large‐scale angiosperm assemblages in China
Bibliographic record
Abstract
A full understanding of the origin and maintenance of β‐diversity patterns in a region requires exploring the relationships of both taxonomic and phylogenetic β‐diversity (TBD and PBD, respectively), and their respective turnover and nestedness components, with geographic and environmental distances. Here, we simultaneously investigated all these aspects of β‐diversity for angiosperms in China. Specifically, we evaluated the relative importance of environmental filtering vs dispersal limitation processes in shaping β‐diversity patterns. We found that TBD and PBD as quantified using a moving window approach decreased towards higher latitudes across the whole of China, and their turnover components were correlated with latitude more strongly than their nestedness components. When quantifying β‐diversity as pairwise distances, geographic and climatic distances across China together explained 60 and 53% of the variation in TBD and PBD, respectively. After the variation in β‐diversity explained by climatic distance was accounted for, geographic distance independently explained about 23 and 12% of the variation in TBD and PBD, respectively, across China. Overall, our results suggest that environmental filtering based on climatic tolerance conserved across lineages is the main force shaping β‐diversity patterns for angiosperms in China.
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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 | 0.000 | 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".