The Impact of Urbanization on Taxonomic Diversity and Functional Similarity among Butterfly Communities in Waterfront Green Spaces
Bibliographic record
Abstract
Urbanization has been shown to cause biodiversity loss. However, its effects on butterfly taxonomic and functional diversity still need to be studied, especially in urban waterfront green spaces where mechanisms of impact still need to be explored. We used butterflies as indicators to study how urbanization affects their taxonomic and functional diversity and identify indicator species in different urban ecological gradient areas. From July to September 2022, we surveyed 10 urban waterfront green spaces in Fuzhou City, China. We recorded 1163 butterflies of 28 species from 6 families. First, we explored the effects of urbanization on butterfly communities and made pairwise comparisons of different urban ecological gradients (α-diversity); secondly, we looked for differences between butterfly communities across urban ecological gradients (β-diversity); finally, we investigated differences in the response of butterfly functional groups to different urban ecological gradient areas and identified ecological indicative species. This study found the following: (1) Urbanization has led to the simplification of butterfly community structure, but there are also favorable factors that support the survival of individual butterflies; (2) Urbanization has led to significant differences in butterfly communities and plant-feeding polyphagous butterfly groups; (3) Urbanization has led to differences in the functional diversity of butterfly diet and activity space groups; (4) We identified five eco-indicator species in different urban ecological gradients.
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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".