Weak Effects of Owned Outdoor Cat Density on Urban Bird Richness and Abundance
Bibliographic record
Abstract
Domestic cats (Felis catus) are ubiquitous predators of birds in urban areas. In addition to the lethal effect of predation, there can also be sublethal, negative effects of domestic cats on individual birds. These effects have led to the inference that reducing outdoor cat densities would benefit urban bird communities. Here we estimate the likely result of policies/programs designed to reduce densities of owned outdoor cats in urban areas, estimating relationships between bird richness/abundance and cat densities across 58 landscapes in Ottawa, Ontario, Canada. We estimate that we would most likely observe one additional bird species, and 0.003 additional individuals per species, if policies/programs reduced owned outdoor cat densities to zero in an average landscape in Ottawa (with 130.2 cats/km2). However, these effects of cat density on birds were uncertain, with 95% confidence intervals crossing zero. Our findings—in combination with those of previous studies—suggest a need for research to resolve the apparent disconnect between the strong, negative effects of cats on individual urban birds and the weak, uncertain effects of cats on bird populations. Although measures that reduce owned outdoor cat densities are justified based on the precautionary principle, evidence to date does not support prioritizing these measures over those addressing threats that have consistently strong effects on bird populations.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".