Modulation of dog-owner relationship and dog social and cognitive behavior by owner temperament and dog breed group

Variations in Canine Behavioural Characteristics across Conventional Breed Clusters and Most Common Breed-Based Public Stereotypes

Dog breeds are grouped based on scientific agreement, whether for traditional reasons or specific tasks during their domestication. Discrepancies may occur between public views of breed behaviour and actual evidence. This research aims to investigate differences in five behavioural traits (aggression towards people, aggression towards animals, fearfulness, responsiveness to training, and activity/excitability) by using the Dog Personality Questionnaire (DPQ) across six conventional groups/clusters of dog breeds (herding, hunting, guarding, companion dogs, potentially aggressive breeds, and mixed-breed dogs) and to assess hypotheses derived from common public presumptions. A cohort of 1309 dog owners sourced through diverse online platforms took part in the study. Contrary to stereotypes, the findings indicate that breeds labelled as "potentially aggressive" display lower levels of aggression compared to guarding breeds (χ2 (5) = 3.657, p = 0.041) and mixed-breeds (χ2 (5) = 3.870, p = 0.002). Additionally, mixed-breed dogs exhibited the highest levels of fearfulness among the six conventional clusters. In terms of aggression and gender, males demonstrated higher aggression levels towards both humans and animals compared to females (p = 0.001). These results challenge established assumptions and emphasise the necessity of evidence-based methodologies in the assessment of canine behaviour.

Canine Euthanasia's Trend Analysis during Thirty Years (1990-2020) in Italy: A Veterinary Hospital as Case Study

This study aimed to investigate changes in the number of, and reasons for, requests for dogs' euthanasia over the last thirty years. Data (breed, age, gender, neuter status, manner, and cause of death) from dogs' euthanasia registered between 1990 and 2020 in a small animals' veterinary hospital were analyzed. The overall period examined has been split into two terms (T1 = 1990-2004 and T2 = 2005-2020) considering the introduction and enforcement of Law 189/2004. During the whole period examined, a significant increasing trend in euthanasia cases has been recorded (p = 0.027). Comparing the two terms, we observed significant differences regarding variables such as age, breed, reproductive status, and ownership. The number of euthanized dogs was significantly higher in T1 than in T2. Dogs euthanized in T2 were older than dogs in T1. A high percentage of the euthanized dogs were crossbred and stray dogs. Additionally, the number of neutered/spayed dogs was significantly higher. Regarding the cause of death, a significantly higher percentage of neoplastic processes was detected in T2. The data here reported suggest a potential influence of Law 189/2004. This law in Italy has proven to be a legal milestone that has influenced the decision-making process between euthanasia and natural death.

Olfaction in the canine cognitive and emotional processes: From behavioral and neural viewpoints to measurement possibilities

Domestic dogs (Canis familiaris) have excellent olfactory processing capabilities that are utilized widely in human society e.g., working with customs, police, and army; their scent detection is also used in guarding, hunting, mold-sniffing, searching for missing people or animals, and facilitating the life of the disabled. Sniffing and searching for odors is a natural, species-typical behavior and essential for the dog's welfare. While taking advantage of this canine ability widely, we understand its foundations and implications quite poorly. We can improve animal welfare by better understanding their olfactory world. In this review, we outline the olfactory processing of dogs in the nervous system, summarize the current knowledge of scent detection and differentiation; the effect of odors on the dogs' cognitive and emotional processes and the dog-human bond; and consider the methodological advancements that could be developed further to aid in our understanding of the canine world of odors.

An artificial intelligence approach to predicting personality types in dogs

Canine personality and behavioural characteristics have a significant influence on relationships between domestic dogs and humans as well as determining the suitability of dogs for specific working roles. As a result, many researchers have attempted to develop reliable personality assessment tools for dogs. Most previous work has analysed dogs' behavioural patterns collected via questionnaires using traditional statistical analytic approaches. Artificial Intelligence has been widely and successfully used for predicting human personality types. However, similar approaches have not been applied to data on canine personality. In this research, machine learning techniques were applied to the classification of canine personality types using behavioural data derived from the C-BARQ project. As the dataset was not labelled, in the first step, an unsupervised learning approach was adopted and K-Means algorithm was used to perform clustering and labelling of the data. Five distinct categories of dogs emerged from the K-Means clustering analysis of behavioural data, corresponding to five different personality types. Feature importance analysis was then conducted to identify the relative importance of each behavioural variable's contribution to each cluster and descriptive labels were generated for each of the personality traits based on these associations. The five personality types identified in this paper were labelled: "Excitable/Hyperattached", "Anxious/Fearful", "Aloof/Predatory", "Reactive/Assertive", and "Calm/Agreeable". Four machine learning models including Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naïve Bayes, and Decision Tree were implemented to predict the personality traits of dogs based on the labelled data. The performance of the models was evaluated using fivefold cross validation method and the results demonstrated that the Decision Tree model provided the best performance with a substantial accuracy of 99%. The novel AI-based methodology in this research may be useful in the future to enhance the selection and training of dogs for specific working and non-working roles.

Dog owner mental health is associated with dog behavioural problems, dog care and dog-facilitated social interaction: a prospective cohort study

Despite numerous qualitative and cross-sectional studies investigating how dog-related factors may impact owners' well-being, empirical studies to test these causal effects are lacking. This prospective cohort study examined the correlation and potential causal effect of 17 dog-related factors with six well-being outcomes (depression, anxiety, loneliness, suicidal ideation, hedonic well-being and eudaimonic well-being) in dog owners. Over a four-week period, 709 adult dog owners reported their weekly well-being and occurrence of each dog-related factor (e.g. how many times they ran with their dogs). A random intercept cross-lagged panel model (RI-CLPM) with significance threshold set at 0.001 was used. Six factors correlated with poorer owner well-being (i.e. aggressive dog behaviour, fearful dog behaviour, poor dog health, failure to provide for the dog, lack of control over the dog, and dog presence). Only 'friendly conversation with others due to the dog' correlated with better well-being. Purposeful reductions in the frequency of dog behavioural and health-related issues are likely to improve owner well-being, as well as greater consistency in dog care (i.e. provide for the dog) and more engagement in friendly dog-facilitated social interactions. No potential causal effects were significant. Further studies investigating causal relationships are essential to improve people's well-being through dog ownership.