Management interventions boost orchid conservation: Evidence from Guizhou, China
Bibliographic record
Abstract
Assessing how management interventions translate to tangible protection outcomes remains pivotal for optimizing conservation investments in biodiversity hotspots. We evaluate this linkage through orchids in Guizhou Province (360 species, 138 threatened) by coupling in situ PAs performance with ex situ network efficacy across 5 km × 5 km grids (13,176 records). Key results reveal: (1) Latitudinal richness declines, with core hotspots in Xingyi and Libo-Luodian-Wangmo regions, where 63 top 5 % grids contain 64.86 % species, while 145 complementarity-based priority grids cover 90.56 % species; (2) the spatial factors and environmental factors both had significant effects ( p < 0.001) on orchid species diversity, jointly explaining 14.4 % of the total variance in orchid diversity distribution; (3) checklists from nature reserves indicate that 75.27 % of orchid species are covered, and distribution point data records suggest that 68.3 % of species occur within protected areas (PAs). Notably, 84.21 % of the National Key Protected Wild Plant species are protected, yet 38.1 % (141 species) remain unprotected. (4) Guizhou has established an ex situ conservation system centered on seven botanical gardens and one orchid conservation center (7BG-1OCC Network), conserves 66.94 % threatened species (87 species) and 81.56 % (62 species) National Key Protected Wild Plant species. This integrated conservation model, combining high-efficiency in situ reserves with a multi-node ex situ network, achieves a comprehensive protection efficiency of 85 % for Guizhou's orchids – demonstrating that coordinated management directly resolves spatial mismatches. This study provides empirical support for an integrated in situ-ex situ conservation framework and contributes a Southwest China case study toward the adaptive management goals outlined in the Kunming-Montreal Global Biodiversity Framework.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".