Quantifying the insect and microbial diversity supported by native and introduced trees on a large urban university campus
Bibliographic record
Abstract
Urban forests, the trees that are planted and managed within built-up areas, provide valuable services to humans and wildlife, including lowering air temperature, carbon sequestration, improved quality of life for humans, and habitat and resources for insect, birds, and other animals. However, tree species planted within these forests are often selected for aesthetics or their ability to tolerate urban stressors, and are dominated by introduced tree species. Whether the selected trees can support urban biodiversity has largely been ignored. Trees are host to important plant-dependent organisms such as leaf-feeding insects and epiphytic microbes. These organisms are important for nutrient cycling, pest control, and tree health. Limited previous research suggests that tree provenance (i.e. native or introduced) might affect the diversity of insects and microbes supported by specific trees. Native trees are hypothesized to be a more palatable resource for phytophagous insects (and hence support a higher diversity and abundance of insects) but evidence linking tree provenance to microbial diversity or community composition is mixed. This thesis tests the hypothesis that native and introduced trees support unique communities of insects and microbes. In 2023 we sampled insect communities throughout the growing season from 80 trees (16 tree species) on the University of British Columbia Vancouver campus. We also sampled the leaf bacterial community once from each tree. We found the same overall abundance of insects on native and introduced trees, but native trees supported higher insect richness. Functional diversity and insect community composition also differed between native and introduced trees. Tree provenance did not affect microbial richness or community composition. Insect and microbial community composition were also unique between the coniferous and deciduous hosts. Overall, our findings indicate that native trees support higher insect diversity than their introduced counterparts, and suggest the continued replacement of native trees with introduced species may exacerbate the continued loss of insects worldwide. We recommend prioritizing native trees to maximize wildlife habitat and resource provisioning 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 | 0.000 | 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".