Trait-environment relationships over short taxonomic and abiotic gradients on Sedum/moss roofs
Bibliographic record
Abstract
One commonly constructed green roof type, the Sedum /moss roof, usually has a substrate depth less than 5 cm and thus can only support a limited plant community, typically dominated by drought-tolerant succulents. Despite this restricted plant community, variation in succulent community composition exists, likely influenced by roof characteristics such as age, slope, and shade. Since different plant species and traits are associated with different ecosystem services, there is a need to understand how even minor variation in green roof design or environmental setting can influence community composition and trait values. In this study, we examine a chronosequence of 31 Sedum /moss extensive green roofs, built in a similar manner, in Malmö, Sweden and Helsinki, Finland. The purpose of this research was to understand how slight changes in green roof environmental/abiotic features affect (1) plant community-level traits (means and diversity), as well as (2) intraspecific trait variation, that is, how traits of individual species (here Sedum album , Phedimus spurius , and Phedimus hybridus ) vary along an environmental gradient. Based on our results, taller plant species are more likely to be observed on older roofs, with deeper substrate, less solar exposure, and on shorter buildings. Deeper substrates also promoted plants with higher specific leaf area (SLA). Furthermore, small changes in roof attributes led to intraspecific trait variation, with taller individuals of P. hybridus observed on roofs with a deeper substrate; taller individuals of S. album observed on younger roofs; and higher values of SLA for P. hybridus and P. spurius observed on roofs with higher solar exposure. Since both SLA and plant height have been associated with stormwater retention and thermal cooling, key green roof ecosystem services, our findings demonstrate the importance minor variation in environmental conditions can have on the benefits provided by vegetated rooftops.
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.
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 | 0.001 | 0.001 |
| 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".