Cat-exclusion zones in rural and urban-fringe landscapes: how large would they have to be?
Bibliographic record
Abstract
Context. The process of urban sprawl brings the human population and their domestic cats (Felis catus) in close contact with wildlife in areas that were previously remote, including reserves and conservation areas created to protect populations of vulnerable or threatened species. Various mitigation measures have been proposed, including devices designed to hinder cat hunting ability, desexing to reduce wandering and nuisance behaviours, containment at night or at all times and regulations governing cat ownership. Such regulations may aim to reduce cat densities by limiting the number of cats per household, or they may define zones around sensitive conservation areas where cat ownership is prohibited. Aims. The present study sought to establish the necessary size of cat-exclusion zones in rural and urban-fringe landscapes where vulnerable prey species may also reside. Methods. With GPS collars, we tracked 38 domestic cats at three sites (one rural, two urban fringe) where small reserves contained threatened lizard species. Key results. Home ranges (95% kernel density estimates) were considerably larger for cats at the rural site (0.3–69 ha) than at urban-fringe sites (0.35–19 ha at Kaitorete Spit and 0.2–9 ha at Otago Peninsula), and were larger at night than day. Resource selection ratios indicated avoidance of open areas with little cover, such as cultivated areas (farmland), tussock grassland and duneland, whereas sources of cover such as trees and buildings were preferred. Maximum distances moved and large variability between individual cats suggest buffers in rural landscapes would need to be at least 2.4 km wide, whereas those in urban-fringe habitat could be half as large. Conclusions. Despite significant home-range size differences exhibited by cats living in rural v. urban-fringe habitats, exclusion zones would need to be wide to account for considerable inter-cat variation in movement behaviour. Implications. The size of an effective cat-exclusion zone should represent the specific landscape, amount of residential development and substantial variability between individual cats.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".