Urban scavenging: vertebrates display greater sensitivity to land-cover and garden vegetation cover than invertebrates
Bibliographic record
Abstract
Abstract Scavenging removes carrion or littered food waste from the environment, promoting nutrient cycling, and reducing waste management costs. These ecosystem services are important in urban environments, where high human population densities result in increased littered food waste. It is unclear how the magnitude of scavenging across urban-rural gradients is influenced by agent, land-cover type, and patch size. We investigated scavenging provision by vertebrates and invertebrates across a gradient of urbanisation, based on impervious surface cover, in woodlands and gardens in Liverpool, UK. The percentage dry weight loss of bait after 48 h, deployed within vertebrate exclusion cages or exposed to vertebrates and invertebrates, was used to quantify scavenging provision. General linear mixed effects models were used to assess the relative contributions of vertebrates and invertebrates across an urban-rural gradient, variation in scavenging between woodlands and gardens, and the effects of semi-natural vegetation cover on scavenging in gardens. We consider patch size as a preliminary assessment of how fragmentation influences scavenging. Vertebrates contributed substantially more to scavenging than invertebrates across the urbanisation gradient. Vertebrate scavenging was greater in woodlands than gardens, while invertebrate scavenging remained consistent. Scavenging increased with patch size in gardens, but not woodlands. Vertebrate scavenging increased with patch size and garden semi-natural vegetation cover. Urban woodlands and gardens make important contributions to scavenging-mediated ecosystem services. There is a need to increase the cover of semi-natural vegetation in gardens to increase their contributions, and protect and expand woodlands, especially in areas with a high demand for scavenging-mediated ecosystem services.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".