Native vs. Non-Native Plants: Public Preferences, Ecosystem Services, and Conservation Strategies for Climate-Resilient Urban Green Spaces
Bibliographic record
Abstract
Climate change is reshaping urban environments, intensifying the need for resilient green space design and management that supports biodiversity, improves ecosystem services, and adapts to changing conditions. Understanding the trade-offs between native and non-native species selection is important for developing climate-resilient urban green spaces. This review examines public preferences for native versus non-native plant species and their implications for urban green space design and management. We critically analyse the ecosystem services and biodiversity benefits provided by both native and non-native plants in urban spaces, highlighting the complex trade-offs involved. Our findings indicate that while native plants can be underrepresented in urban landscapes, they offer significant ecological benefits including support for local wildlife and pollinators. Some studies have highlighted the climate resilience of native plants; however, they are likely to be more affected by climate change. Therefore, conservation strategies are needed, especially for endemic and threatened plant species. Several studies suggest a more flexible approach that integrates plant species from diverse climatic origins to improve resilience. We also explore conservation gardening (CG) as a socio-ecological strategy to integrate endangered native species into urban landscapes, promoting biodiversity and ecosystem resilience. This review stresses the importance of informed plant species selection and community involvement in creating climate-resilient urban green spaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".