The relationships between terrestrial vertebrate species richness in China’s nature reserves and environmental variables
Bibliographic record
Abstract
Explaining species richness patterns over broad geographic scales is a central issue of biogeography and macroecology. In this study, we took spatial autocorrelation into account and used terrestrial vertebrate species richness data from 211 nature reserves, together with climatic and topographical variables and reserve area, to explain terrestrial vertebrate species richness patterns in China and to test two climatically based hypotheses for animals. Our results demonstrated that species richness patterns of different terrestrial vertebrate taxa were predicted by the environmental variables used, in a decreasing order, as reptiles (56.5%), followed by amphibians (51.8%), mammals (42%), and birds (19%). The endothermic vertebrates (mammals and birds) were closely correlated with net primary productivity (NPP), which supports the productivity hypothesis, whereas the ectothermic vertebrates (amphibians and reptiles) were strongly associated with both water and energy variables but weakly with NPP, which supports the physiologically based ambient climate hypothesis. The differences in the dependence of endothermic and ectothermic vertebrates on productivity or ambient climate may be due in part to their different thermoregulatory mechanisms. Consistent with earlier studies, mammals were strongly and positively related to geomorphologic heterogeneity, measured by elevation range, implying that the protection of mountains may be especially important in conserving mammalian diversity.
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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 | 0.001 |
| 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 | 0.000 |
| 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".