Predicting free‐roaming cat population densities in urban areas
Bibliographic record
Abstract
Abstract Although free‐roaming cats can have a significant impact on the environment, and substantial resources have been invested to find humane alternatives for managing free‐roaming cat populations, there are no empirical estimates of free‐roaming cat population size in medium to large cities. In addition, little is known about factors limiting free‐roaming cat population size and distribution. Using Guelph, ON, Canada (pop: 120 000; 86.7 km2) as a case‐study, we apply replicated distance transect sampling and likelihood‐based hierarchical modelling to compare human‐mediated landscape patterns of land use, distance to roads, distance to wooded areas, building density and socio‐economic status to explain the abundance of free‐roaming cats. We then derive an empirical estimate of total population size and present a spatially explicit prediction of free‐roaming cat density across an entire city. Cat abundance was highest in residential areas and lowest in commercial and institutional areas, negatively related to median household income, and positively related to distance from woods and building density. Total population size was estimated to be 7662 (95% bootstrap CI: 6145–9966) for Guelph; free‐roaming cat density varied from 0 to 49.4 cats per ha. Our estimate overlapped with an independent estimate of indoor‐outdoor cats (11 927; 95% CI: 6361–20 989) derived from random surveys of city residents, which implies our distance transect methodology was relatively robust and unbiased. Our approach used simple geographical information that is readily available for most urban areas in North America and can be applied broadly to inform cat management in urban areas. Finally, our results suggest that free‐roaming cat density in cities could be determined by bottom‐up processes (e.g. enhanced food availability in residential areas) as well as top‐down processes (e.g. enhanced susceptibility to coyote predation near wooded areas) which are typically reserved to explain wildlife populations in natural environments.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 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 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".