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Record W3162736316 · doi:10.3390/land10050507

Weak Effects of Owned Outdoor Cat Density on Urban Bird Richness and Abundance

2021· article· en· W3162736316 on OpenAlexaffabout
Genevieve C. Perkins, Amanda E. Martin, Adam C. Smith, Lenore Fahrig

Bibliographic record

VenueLand · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsEnvironment and Climate Change CanadaCarleton UniversityMinistry of Forests
Fundersnot available
KeywordsSpecies richnessPredationAbundance (ecology)CATSEcologyFelis catusGeographyHabitatFeral catFelisBiology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.153

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.188
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2021
Admission routes2
Has abstractyes

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