Ecological and economic benefits of low‐intensity urban lawn management
Bibliographic record
Abstract
Abstract Intensive management of urban lawns is globally widespread, predominantly for aesthetic reasons. However, a growing body of knowledge demonstrates negative ecological and environmental effects of this practice. We present a meta‐analysis of North American and European studies from 2004 to 2019, which incorporates three previously unpublished datasets from eastern Canada, to investigate how mowing intensity impacts the ecology of urban lawns. The meta‐analysis provides aggregated evidence that invertebrate and plant diversity is lower in urban lawns under increased mowing intensity. This decline is independent of the level of contrast between mowing ‘treatment’ and ‘control’ (e.g. height or frequency of mowing), which differed considerably between studies. Intensive mowing also increases the occurrence of pest species (e.g. herbivorous beetle larvae and allergenic plants), though studies in this group were limited to northern environments. Changes in ecosystem‐level variables (soil temperature, soil moisture deficit and carbon deficit) were less evident and suggest changes in abiotic processes may take longer to become apparent. An economic case study of the mowing costs in Trois‐Rivières, Canada, suggests that cost savings of 36% may be possible with a modest reduction of mowing frequency. Synthesis and Applications. Increasing urban biodiversity and reducing greenhouse gas emissions are strong motivators for reducing lawn management intensity. We also suggest that the benefits of reducing pest species while saving lawn management costs may provide additional social and economic incentives for decision makers to review urban greenspace management practices.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".