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Record W4412438343 · doi:10.24124/2025/30506

Seasonal abundance and habitat associations of wandering cats and birds in a temperate zone biodiversity hotspot

2025· dissertation· en· W4412438343 on OpenAlexfundaboutno aff
Olivia Wilson

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
FundersEnvironment and Climate Change CanadaLiber Ero FoundationMitacsUniversity of Northern British Columbia
KeywordsTemperate climateBiodiversity hotspotBiodiversityGeographyHabitatEcologyHotspot (geology)Abundance (ecology)BiologyGeology

Abstract

fetched live from OpenAlex

,The interaction between birds and wandering domestic cats is an ongoing challenge for both wildlife conservation and cat welfare, particularly in regions where high avian diversity overlaps with dense human development and wandering cats. I examined the abundance, richness and community structure of birds and the abundance of wandering domestic cats (Felis catus), in the temperate biodiversity hotspot of the south Okanagan Valley, British Columbia, Canada, between Okanagan Falls and Osoyoos, across an entire annual period. I did this by pairing point counts and photos from trail cameras from 123 locations across five seasonal periods between March 2022 and March 2023, assessing the habitat associations of birds and cats across a variety of land use types, including urban, peri-urban, agricultural, and natural. I conducted a total of 2380 point counts and used hierarchical modelling and unconstrained ordination to examine bird abundance and species richness, and community composition, respectively. My results revealed distinct seasonal patterns of bird abundance and richness with these metrics being the highest during spring migration and the breeding season. Urbanization and human development impacted the distribution of birds year-round, especially in the non-breeding seasons when a large diversity of species used urban areas. Using the same locations as the point counts, but shifting cameras every 28 days, I examined local abundance of wandering cats. I showed that wandering cats were found in high abundances in urban habitats year-round but overall had the highest abundances during the early winter, spring, and summer. Wandering cats were detected at 100% of peri-urban sites, 97% of urban sites, 65% of agricultural sites and 42% of natural sites. I estimated an annual average of 6,557 wandering cats within the study area with up to 82% of them being unowned cats, equating to one cat for every two to three people. Overall, I demonstrate the importance of identifying where birds are and what habitats they are using across the entire year and not only during distinct periods within the annual cycle (e.g. breeding). The high numbers of wandering cats, combined with the diversity of birds and other wildlife, suggests that cats likely have significant impacts on birds and other wildlife year-round in the study region and likely elsewhere. In the south Okanagan Valley, management actions such as outreach initiatives should take a seasonal and habitat-based approach. Outreach should focus on encouraging urban residents to keep their cats indoors during the winter because many species move into urban areas at this time. Resident in peri-urban and agricultural habitats should be encouraged or incentivised to spay and neuter cats on their property and keep cats inside during spring, summer, and fall when high cat numbers overlap with high bird abundance and richness. Given the high abundance and richness of birds along with the highest currently reported abundances of wandering cats per capita, these results stress the urgent need for collaborative efforts among municipalities, stakeholders, and residents to mitigate the ecological impact of wandering cats and help preserve the biodiversity of this unique region.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.124
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.319
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes2
Has abstractyes

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