Relationship between Plant Habitat Types and Butterfly Diversity in Urban Mountain Parks
Bibliographic record
Abstract
Butterflies serve as valuable indicators of urban ecosystem quality. Due to their accessibility, they also provide urban residents with essential opportunities to connect with nature, fulfilling social functions such as education and recreation, which significantly contribute to city dwellers’ physical and mental well-being. Urban mountain parks are critical habitats for butterflies; analyzing their spatial and temporal distribution and the impact of plant elements is crucial for enhancing plant landscape quality and butterfly diversity. The main results were as follows: (1) A monthly butterfly survey was carried out over the course of a year in the seven urban mountain parks of Fuzhou City. This survey recorded 46 species of butterflies from 36 genera across 7 families, totaling 2506 butterflies. (2) Among the seven habitat types analyzed, TS-, T-, and SG-habitats exhibited elevated levels of butterfly diversity, richness, abundance, and evenness. There were variations in butterfly evenness, diversity, richness, and abundance observed between these habitats. With the exception of N-habitat, there was a consistent seasonal pattern in butterfly diversity across different habitat types. (3) Butterfly diversity and abundance were significantly correlated with vegetation habitat factors across the tree, shrub, and herb layers. Multiple regression modeling using the Akaike information criterion revealed that arbor layer vegetation factors were present in the top four models for butterfly diversity, richness, abundance, and evenness. (4) The quality assessment of different habitat types ranked habitats as follows: TS-habitat > SG-habitat > TSG-habitat > T-habitat > TG-habitat > G-habitat = N-habitat.
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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 | 0.001 |
| 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".