Plant Species and Functional Group Combinations Affect Green Roof Ecosystem Functions
Bibliographic record
Abstract
BACKGROUND: Green roofs perform ecosystem services such as summer roof temperature reduction and stormwater capture that directly contribute to lower building energy use and potential economic savings. These services are in turn related to ecosystem functions performed by the vegetation layer such as radiation reflection and transpiration, but little work has examined the role of plant species composition and diversity in improving these functions. METHODOLOGY/PRINCIPAL FINDINGS: We used a replicated modular extensive (shallow growing- medium) green roof system planted with monocultures or mixtures containing one, three or five life-forms, to quantify two ecosystem services: summer roof cooling and water capture. We also measured the related ecosystem properties/processes of albedo, evapotranspiration, and the mean and temporal variability of aboveground biomass over four months. Mixtures containing three or five life-form groups, simultaneously optimized several green roof ecosystem functions, outperforming monocultures and single life-form groups, but there was much variation in performance depending on which life-forms were present in the three life-form mixtures. Some mixtures outperformed the best monocultures for water capture, evapotranspiration, and an index combining both water capture and temperature reductions. Combinations of tall forbs, grasses and succulents simultaneously optimized a range of ecosystem performance measures, thus the main benefit of including all three groups was not to maximize any single process but to perform a variety of functions well. CONCLUSIONS/SIGNIFICANCE: Ecosystem services from green roofs can be improved by planting certain life-form groups in combination, directly contributing to climate change mitigation and adaptation strategies. The strong performance by certain mixtures of life-forms, especially tall forbs, grasses and succulents, warrants further investigation into niche complementarity or facilitation as mechanisms governing biodiversity-ecosystem functioning relationships in green roof ecosystems.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| 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".