Elevational Gradient in Species Richness Pattern of Epigaeic Beetles and Underlying Mechanisms at East Slope of Balang Mountain in Southwestern China
Bibliographic record
Abstract
We report on the species richness patterns of epigaeic beetles (Coleoptera: Carabidae and Staphylinidae) along a subtropical elevational gradient of Balang Mountain, southwestern China. We tested the roles of environmental factors (e.g. temperature, area and litter cover) and direct biotic interactions (e.g. foods and antagonists) that shape elevational diversity gradients. Beetles were sampled at 19 sites using pitfall traps along the studied elevational gradient ranging from 1500 m-4000 m during the 2004 growing season. A total of 74416 specimens representing 260 species were recorded. Species richness of epigaeic beetles and two families showed unimodal patterns along the elevational gradient, peaking at mid-elevations (c. 2535 m), and the ranges of most beetle species were narrow along the gradient. The potential correlates of both species richness and environmental variables were examined using linear and second order polynomial regressions. The results showed that temperature, area and litter cover had strong explanatory power of beetle species richness for nearly all richness patterns, of beetles as a whole and of Carabidae and Staphylinidae, but the density of antagonists was associated with species richness of Carabidae only. Multiple regression analyses suggested that the three environmental factors combined contributed most to richness patterns for most taxa. The results suggest that environmental factors associated with temperature, area and habitat heterogeneity could account for most variation in richness pattern of epigaeic beetles. Additionally, the mid-elevation peaks and the small range size of most species indicate that conservation efforts should give attention to the entire gradient rather than just mid-elevations.
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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 | 0.000 |
| 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".