Editorial: Exploring cats: their behaviors and human-cat interactions
Notice bibliographique
Résumé
Over 45 million US households contain at least one companion cat (2); in the European Union, the population of pet cats is estimated to be 113 million (outnumbering the estimated 92 million dogs) (1). Despite this global popularity, research into the behavior and welfare of cats living in private homes is still limited; and arguably, even less is known about the mechanisms of human-cat interactions within the home. Outside the home, cats allowed uncontrolled outdoor access, alongside free-roaming cat colonies outside of human ownership (but not always without human care), still generate considerable controversy between animal advocates and conservationists concerned about cats' impact on wildlife. This Research Topic presents 12 new papers that shed light on these issues and more. The goal of this special issue is to improve our understanding of companion cats, with particular focus on their interactions with humans, and human attitudes towards these animals. The twelve manuscripts in this special issue on cat behaviors and the development of the human-cat bond cover a wide variety of themes.Turner presents a mini-review of the available literature on a number of topics relevant to our understanding of human-cat interactions, such as the importance of kitten socialization, how cats communicate with their humans, and the mechanics of social interactions between cats and humans (such as the influence of who initiates contact, and of symmetry in compliance, or lack of compliance, with the partner's "wishes"). Noting the importance of ensuring the animals' wellbeing during human-cat interactions (and the scarcity of research into this issue with companion cats), Haywood et al. present Human-Cat Interaction guidelines designed to improve the comfort and welfare of companion cats during such interactions. They developed and tested the efficacy of these new guidelines with 100 shelter cats, interacting with 120 novel members of the public, and report their results here. Nagasawa et al. examine physiological (urinary oxytocin and cortisol) responses to interaction with humans, by comparing these variables in cats during positive interactions with a familiar caretaker (including physical contact, play, etc.), versus. when such interactions were removed. In another study using physiological (fecal cortisol) measures of stress, along with weight and behavior, Carlisle et al. investigate stress levels of cats adopted by families of children with autism spectrum disorder (ASD). Cats in the Carlisle et al. study were specifically selected for sociability and calmness using the validated Feline Temperament Profile, and the adopters provided with education on cat behavior; the authors discuss the importance of these factors to successful adoption into these homes.Gajdoš Kmecová et al. review and seek to extend the existing research on play in cats, much of which has focused to date on object play [e.g., (3)], by looking at social play between cats. They suggest using a psychobiological approach to the study of play, which considers the motivational and emotional states of the cats; and present an ethogram (synthesized from the literature) and common terminology for use in future studies of cat social play. Khoddami et al. also seek to extend the existing literature on interactions between cats in multi-cat homes, by focusing specifically on two-cat households. They note that previous studies frequently lack focus on any particular group size, limiting our understanding of social dynamics in specific group sizes, despite the fact that most multi-cat households in the US and Canada consist of two cats (4,5).Four papers in this issue focus on free-roaming cats, with two exploring the often-contentious issue of domestic cats' impact on wildlife. Tan et al. summarize the arguments for and against allowing cats outdoor access, and identify several owner-and cat-related factors associated with allowing companion cats uncontrolled access to the outdoors. Kim et al. investigate attitudes of different demographic groups towards feral cats in Seoul, South Korea, following the establishment of government-supported cat feeding stations around that city. They report distinct and sometimes complex differences between the groups in their attitudes towards cats and their preferred management approach for feral cat populations [e.g., trap-neuter-release (TNR) vs. culling]; they also discuss the possible impact of the feeding stations on these results. Turner takes a critical look at the literature on cats' impacts on wildlife, in light of recent media reports of the "alarming predation of house cats on prey populations". Turner cautions that researchers should avoid bias and misinterpretation of field data, by considering what is known about predatory behavior in domestic cats and reporting estimates of total prey species population sizes. In their paper examining human-cat interactions involving free-living cats, Wandesforde-Smith et al. note the 'moral pluralism' involved in the emphasis (even requirement) for humane care and protection of owned companion cats, alongside the systematic culling of large numbers of cats supported by public policy.The final two papers discuss research into new use of technology in the study of domestic cats. Xu et al. apply and advocate for machine learning techniques (in contrast to the more traditional biomechanical experiments with living cats or cat cadavers) for improving our understanding of the feline 'athletic ability'. Given recent work using heart rate variability (HRV) as an indicator of emotion in non-human animals [e.g., (6)], Grigg et al. compare HRV data collected using an affordable, commercially available cardiac monitoring system (Polar H10) against data from a traditional ambulatory electrocardiogram, to assess whether the Polar monitors could be used for this purpose in unrestrained cats.This special issue tackles a broad range of topics relevant to domestic cats. Many of the papers add particular insight into our understanding of human-cat, and cat-cat, interactions. Others report on issues important to cat welfare, such as controversies surrounding outdoor cats and wildlife. Our understanding of domestic cat behavior and human-cat interactions continues to improve, as these papers demonstrate.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».