Relationships between species richness of vascular plants and terrestrial vertebrates in China: analyses based on data of nature reserves
Bibliographic record
Abstract
ABSTRACT The relationship between plant diversity and animal diversity on a broadscale and its mechanisms are uncertain. In this study, we explored this relationship and its possible mechanisms using data from 186 nature reserves across China on species richness of vascular plants and terrestrial vertebrates, and climatic and topographical variables. We found significant positive correlations between species richness in almost all taxa of vascular plants and terrestrial vertebrates. Multiple regression analyses indicated that plant richness was a significant predictor of richness patterns for terrestrial vertebrates (except birds), suggesting that a causal association may exist between plant diversity and vertebrate diversity in China. The mechanisms for the relationships between species richness of plants and animals are probably dependent on vertebrate groups. For mammals (endothermic vertebrates), this relationship probably represents the integrated effects of plants on animals through trophic links (i.e. providing foods) and non‐trophic interactions (i.e. supplying habitats), whereas for amphibians and reptiles (ectothermic vertebrates), this may be a result of the non‐trophic links, such as the effects of plants on the resources that amphibians and reptiles require.
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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 | 0.002 | 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".