Assessing the relative importance of human and spatial pressures on non-native plant establishment in urban forests using citizen science
Bibliographic record
Abstract
Since 2007, more people in the world live in urban than in rural areas. The development of urban areas has encroached into natural forest ecosystems, consequently increasing the ecological importance of parks and fragmented forest remnants. However, a major concern is that urban activities have rendered urban forests susceptible to non-native species incursions, making them central entry sites where non-native plant species can establish and spread. We have little understanding of what urban factors contribute to this process. Here we use data collected by citizen scientists to determine the differential impacts of spatial and urban factors on non-native plant introductions in urban forests. Using a model city, we mapped 18 urban forests within city limits, and identified all the native and non-native plants present at those sites. We then determined the relative contribution of spatial and socioeconomic variables on the richness and composition of native and non-native plant communities. We found that socioeconomic factors rather than spatial factors (e.g., urban forest area) were important modulators of overall or non-native species richness. Non-native species richness in urban forest fragments was primarily affected by residential layout, recent construction events, and nearby roads. This demonstrates that the proliferation of non-native species is inherent to urban activities and we propose that future studies replicate our approach in different cities to broaden our understanding of the spatial and social factors that modulate invasive species movement starting 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".