Native vs. Non-Native Plants: Public Preferences, Ecosystem Services, and Conservation Strategies for Climate-Resilient Urban Green Spaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it