Latitudinal Diversity Gradients and Rapoport Effects in Chinese Endemic Woody Seed Plants
Bibliographic record
Abstract
The distribution of plant species diversity has long been a major focus on biogeography. Yet, the universality of the popular Rapoport’s rule remains controversial for endemic plants, as previous studies have focused more on broad-ranged species. Here, we collected data for 4418 endemic woody seed plant species across China, including trees, shrubs, and lianas, to explore the latitudinal patterns of species range size and richness, and test the relevant biogeographic law. The species range size distribution was examined for conformity with Rapoport’s rule using four methods (i.e., Steven’s, Pagel’s, the mid-point, and the across-species method). Spatial patterns of species richness along latitudinal gradient were also investigated by parabolic regression. Results showed that species range size increased with latitude for all species as well as by trees, shrubs and lianas, especially assessed by Pagel’s method. Species richness was highest at low latitude, where species range size was smallest, and decreased with increasing latitude. The species range size and richness of shrubs were maximum, followed by trees then lianas. These findings prove that Rapoport’s rule is strongly supported by latitudinal patterns of species distribution in Chinese endemic woody seed plants.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".