Gentrification and urban forest structure and stress: Lessons from two cities
Bibliographic record
Abstract
Urban trees must be both present and healthy to provide vital ecosystem services, but there are documented inequities in tree canopy cover within U.S. cities. These inequities, like cities themselves, are not static. However, the influence of urban social and physical change itself on urban tree canopy structure, and especially tree stress, is poorly understood, limiting the efficacy of long-term urban forest planning. Gentrification, the process by which investments in a neighborhood displace low-income residents, can disrupt both social relationships and the physical space of the neighborhood, and can thus influence patterns of tree planting, investment in tree care, and likelihood of tree damage. Here, we quantify patterns in the number, diversity, and stress of street trees stress across neighborhoods in two regions experiencing gentrification: the West Side of Chicago, IL and Durham, NC. We found variation in the number, diversity and stress of street trees, including numerous regions where more than a third of street trees had high stress. However, the results do not indicate a uniform relationship between social and physical disruption and the number, diversity, or stress of trees. Our results and supplementary case study analysis hint at the role of municipal policies alongside non-governmental and community actions for especially tree planting practices and potentially tree care. Research and management that considers the role of actors such as residents, local non-profits, and city governments together may be better able to adjust management based on local conditions to cultivate a more resilient urban street tree population • Proportion of trees with high stress reached as high as 59%, but varied within cities • Social and physical disruption was not uniformly associated with negative outcomes • Tree number and stress are consistent with city policies (especially for construction) • Species diversity consistent with influence of city policies and/or landowners • Mixed methods needed to clarify influence of municipal and non-governmental actors
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".