Cross-taxon correlation and effectiveness of indicator taxa in nature reserves of China
Bibliographic record
Abstract
Indicator taxa have been widely used in biological conservation when data on the taxa of conservation interest are lacking. However, most studies have focused on common taxa (e.g., vascular plants, birds, reptiles, amphibians, and mammals) and found little consistent correlation between taxa. In this study, we extended the investigation to cover 10 taxonomic groups from 361 nature reserves in China. We assessed the strength of competing mechanisms hypothesized to promote indicator taxa correlations. We detected significant positive correlations in species richness among most of the 10 taxonomic groups except for macrofungi-insect and bryophyte-bird pairs. Yet, we found no single taxon satisfying the criteria to be used as an indicator for other taxa, although angiosperms had the potential to predict species richness of several other groups (e.g., ferns, gymnosperms, mammals, reptiles, amphibians), and ferns were useful for indicating reptiles and amphibians. Macrofungi and insects are the two groups that cannot be effectively indicated by any other groups. We also found that energy and climatic stability played the most significant role in regulating species richness of most study taxa, and possibly their between-taxon correlations. Our study provides new evidence for understanding cross-taxon congruence and the underlying mechanisms in the nature reserves of China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.015 | 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 teacher head, 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".