Complexifying the urban lawn improves heat mitigation and arthropod biodiversity
Bibliographic record
Abstract
Urban green infrastructures (GI) are important features of cities which provide many ecosystem services promoting citizens’ well-being. As space is often limited in cities for establishing new GI, it is important to optimize the contribution of ecosystem services of existing GI. The objective of this paper is to compare the performance of lawns to three more complex types of recently established common low-height urban green infrastructures (LHGI) in relation to two ecosystem services: heat mitigation and habitat for biodiversity. We collected data from 48 plots in a semi-controlled context in the Greater Montreal area (Canada) where we compared unmanaged sowed indigenous herbaceous vegetation (flower meadow), medium-sized hedgerow (hedgerow), highly maintained lawn (lawn) and naturally regenerated unmanaged shrub vegetation (natural). We quantified the contribution of plant structure and species diversity to the two ecosystem services, using surface temperature and arthropods morphospecies richness as indicators of heat mitigation and habitat for biodiversity. We also tested the use of the Mean Information Gain (MIG) computed from photos, a measure of complexity, as a possible indicator of LHGI performance. There were major differences in both surface temperature and arthropod morphospecies richness between lawns and the other three LHGI. Results showed that plant structure and diversity improved LHGI performance. Finally, MIG was not found to be usable as good LGHI indicator in our experimental context. This study shows that increasing plant structural complexity and/or diversity increases heat mitigation and habitat for arthropod biodiversity of LHGI. Given its extent in North America, complexifying the omnipresent urban lawns holds considerable potential for GI improvement.
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 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 | 0.001 | 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) | 0.001 | 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 teacher head, 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".