Quantifying the importance of urban trees to people and nature through tree removal experiments
Bibliographic record
Abstract
Abstract Experimentally manipulating urban tree abundance and structure can help explore the complex and reciprocal interactions among people, biodiversity and the services urban forests provide to humans and wildlife. In this study we take advantage of scheduled urban tree removals to experimentally quantify the benefits that urban trees provide to humans and wildlife. Specifically, we aim to understand how trees affect: (1) bird and mammal abundance and diversity, as well as an ecological process (predation); and (2) people's perception responses, such as the importance that people assign to the trees, wildlife and the site. We designed two independent Before‐after‐control‐impact (BACI) experiments based on two sites where tree removals were occurring (impact sites): an urban park and a residential street, both located in the Greater Melbourne Area, Australia. We selected three control sites for each impact site, or four per experiment. Ecological data were collected through field surveys, and social data on people's perceptions through intercept questionnaires among park and street users. Data were analysed using a GLMMs to determine the combined effect of time (before and after) and treatment (impact and controls). At the urban park, the abundance of nectarivorous birds and possums both declined by 62% following tree removal, while invertebrate predation increased by 82.1%. The level of importance people assigned to the urban park and to the trees at the site decreased after tree removal, and people's attitudes towards tree planting became more positive, meaning more people wanted to plant more trees at the site. None of these changes were observed in the street experiment where fewer and smaller trees were removed, suggesting that effects may be highly specific to context, where factors such as tree volume, diversity and arrangement influence the magnitude of social–ecological effects observed. By demonstrating the social–ecological effect of removing urban trees, we provide evidence that urban trees provide critical habitat to urban wildlife and are perceived as an important aspect of the human experience of urban nature. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".