Invasive pests and pathogens as potential drivers of urban forest distributional inequalities and inequities
Bibliographic record
Abstract
Environmental injustices are influenced by socio-political and environmental legacies. Urban forest inequalities and inequities are often attributed to drivers like systemic racism and segregation. However, in recent decades, invasive pests and pathogens have substantially changed urban forests. It is not known how these invasive pests and pathogens act as a driver of urban forest inequalities and inequities. At the western range of Dutch elm disease ( Ophiostoma spp.) and emerald ash borer ( Agrilus planipennis ), we examined how the loss of localized street tree monocultures of elm ( Ulmus spp.) and ash ( Fraxinus spp.) will result in changes to distributional justice. We examined street tree count and basal area distributions, applying the Gini Index to measure inequality under current conditions and hypothetical pest-induced loss scenarios. Findings reveal that DED-related elm losses could improve distributional equality, likely due to the high density of elm in already greener areas, while EAB-related losses of ash could increase inequalities. These street tree losses would disproportionately affect areas of high economic dependency, ethno-cultural composition, and situational vulnerability. Our results indicate that pest-induced urban forest losses do not merely reduce canopy cover but may reshape distributional equality and equity in ways that align with socioeconomic disparities. This research highlights the need to incorporate principles of environmental justice in pest management approaches and replanting efforts, particularly prioritizing systemically marginalized communities. These findings underscore the critical role of diversity and strategic planning in urban forest resilience, advocating for practices that mitigate the social and ecological impacts of invasive pests and pathogens. • Street tree distribution is influenced by socio-political and environmental legacies • Invasive pests and pathogens change distributional equality and equity of urban forests • In the study cities, DED causes improved distributional equality while EAB worsens inequalities • Tree losses are inequitable, impacting systemically marginalized communities • Pest management and replanting efforts must consider environmental injustices
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".