Effects of climate and topography on the diversity anomaly of plants disjunctly distributed in eastern Asia and eastern North America
Bibliographic record
Abstract
Abstract Aim Differences in physiography have been proposed to explain the diversity anomaly for vascular plants between environmentally similar regions of eastern Asia (EAS) and eastern North America (ENA). Here, we use plant species within disjunct genera to examine whether differences in topography contribute to the diversity anomaly and whether the richness–environment relationships differ between regions. Disjunct plants are used to ensure that the diversity anomaly relates to post‐disjunction evolution and diversification rather than regional differences in clade ages or immigration. Location EAS and ENA. Time period Current. Major taxa studied Plant taxa disjunctly distributed in EAS and ENA. Methods We compiled county‐level plant distribution data, and calculated species richness and variables of topography and climate within unit grid cells. We compared estimated coefficients of region effects among models, where richness was fitted with or without topography and climate. Topography and climate were also used to separately model within‐region spatial diversity patterns using spatial simultaneous autoregressive error models. Results The coefficients of region effects varied from −.776 for the model only including region to −.309 when topography was controlled for, but remained significant. Climate dominated the spatial diversity patterns in ENA. In contrast, the influence of climate (14.2%) on species richness was weaker than that of topography (18.3%) in the warm area of EAS. Relations to elevation and temperature varied between regions, shifting between positive and negative relationships in several cases. Main conclusions Our results demonstrate that variability in local topography contributes to the strong regional anomaly in plant species richness between EAS and ENA. Nevertheless, the diversity anomaly persists after controlling for local topography and climate. EAS and ENA also exhibit contrasting richness–environment relationships, providing another divergent aspect between the EAS–ENA disjunct floras. Our findings highlight that regional differences in topography or other environmental factors may underlie the diversity anomaly.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".