Lepidoptera species richness and community composition in urban street trees
Bibliographic record
Abstract
The triple threats of climate change, habitat loss, and environmental pollution have stimulated discussion on how urban areas can be modified to mitigate heat increases and provide habitat for wildlife such as insects. The strategy of using trees to reduce temperatures has been adopted by numerous cities. However, the majority of street trees planted around the world are non-native. Studies conducted in non-urban areas have demonstrated in comparison to native plants, non-native plants are less likely to support native insect diversity. Here, we use a database approach to quantify the number of native Lepidoptera species associated with 76 of the most common street tree species planted in Vancouver, Canada. We tested the prediction that compared to non-native trees, native street trees will support a higher diversity and unique community of native Lepidoptera. As predicted, native street trees were associated with five times as many native Lepidoptera species, and the Lepidoptera communities supported by native versus non-native street trees were distinct. There was no difference in native Lepidoptera associations between broadleaf versus coniferous street trees. These results are consistent with studies that have used active sampling techniques to investigate insect richness on a smaller subset of native and non-native tree species. Collectively, these data provide good evidence that planting native instead of non-native trees will help stem the loss of insect diversity in urban areas.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".