Fido, Fluffy, and wildlife conservation: The environmental consequences of domesticated animals
Bibliographic record
Abstract
Humans have created a strong relationship with cats and dogs by domesticating them. Whether owned by a human or living feral, modern domestic cats and dogs interact extensively with people and the environment. The negative interactions between these domesticated animals and wildlife have been discussed in several reviews, but few reports have provided an overview of both the positive and negative impacts these domesticated animals have on wildlife conservation. Here, we describe the diverse issues associated with domestic cats and dogs and wildlife including predation, competition, pathogen transmission, hybridization, behavioural modification, harvest of wild animals for pet food, and creation of human–wildlife conflict. We then discuss their role in supporting conservation efforts (e.g., use in species identification and tracking, biological control), and shaping our social values towards animals and appreciation for nature. Finally, we suggest necessary steps to harmonize our relationship with cats and dogs and the conservation of wildlife. For owned animals, there is potential for pet owners to support conservation efforts through a ‘pet tax’ adopted by veterinary clinics and pet stores to be used for wildlife conservation. Moreover, information regarding the impacts of these animals on wildlife and potential solutions (e.g., voluntarily keeping cats and dogs inside or use of “pet curfews”, use of bells to alert wildlife to cats) should be made available to owners who are most likely to have an influence on the behaviour of their companion animal.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".