bioRxiv Science⌕ Search

bioRxiv · 10.1101/545020

Development of a fixed list of descriptors for the qualitative behavioural assessment of shelter dogs

Abstract

The shelter environment may have a severe impact on the quality of life of dogs, and there is thus a need to develop valid tools to assess their welfare. These tools should be sensitive not only to the animals physical health but also to their mental health, including the assessment of positive and negative emotions. Qualitative Behaviour Assessment (QBA) is an integrative whole animal measure that captures the expressive quality of an animals demeanour, using descriptors such as relaxed, anxious, and playful. In this study, for the first time, we developed and tested a fixed-list of qualitative QBA descriptors for application to dogs living in kennels. A list of 20 QBA descriptors was developed based on literature search and an expert opinion survey. Inter-observer reliability was investigated by asking 11 observers to use these descriptors to score 13 video clips of kennelled dogs. Principal Component Analysis (PCA) was used to extract four main dimensions together explaining 70.9% of the total variation between clips. PC1 characterised curious/playful/excitable, sociable demeanour, PC2 ranged from comfortable/relaxed to anxious/nervous/stressed expression, PC3 described fearful demeanour, and PC4 characterized bored/depressed demeanour. Observers agreement on the ranking of video clips on these four expressive dimensions was good (Kendalls W: 0.60-0.80). ANOVA showed a significant effect of observer on mean clip score on all PCs (p<0.05) due to a few observers scoring differently from the rest of the group. These results indicate the potential of the proposed list of QBA terms for sheltered dogs to serve as a non-invasive, easy-to-use assessment tool. However, the observers effect on mean scores points towards the need for adequate observer training. The QBA scoring tool can be integrated with existing welfare assessment protocols for shelter dogs and strengthen the power of those protocols to assess and evaluate the animals experience in shelters.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Arena, L., Wemelsfelder, F., Messori, S., Ferri, N., Barnard, S.. 2019-02-08. Development of a fixed list of descriptors for the qualitative behavioural assessment of shelter dogs. https://doi.org/10.1101/545020

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Deconstructing Boldness: Latent behavioural structure across novelty, predation, and exploration in an endangered species translocation program

Risk-related behaviours including neophobia, exploration, and predator avoidance are often assumed to covary along a general boldness axis. Although some translocation studies have linked one or more of these behaviours with fitness, it is possible that the underlying behavioural structure varies under different selective environments. Conservation breeding programs provide a unique opportunity to evaluate the structure of behavioural correlations in a non-domestic species under artificial conditions. We tested whether risk-related behaviours represent a unified boldness syndrome in a captive population of the endangered Attwaters prairie chicken (APC; Tympanuchus cupido attwateri). We quantified the repeatable behaviours of 257 juveniles and compared 11 models of behavioural covariation structure using multivariate mixed models and confirmatory factor analysis. There was no support for a single risk-taking axis; instead, we found three dimensions representing responses to novelty and environmental stimuli (Engagement), predator avoidance (Threat Response), and maze exploration (Maze Progression). Our results indicate that these risk-related behaviours are not organized along a single axis in captive APCs, challenging assumptions that risk-taking represents a universal behavioural syndrome. To anticipate post-release success accurately, conservation managers should understand the exact behavioural architecture of the captive population rather than assume that the classical bold-shy structure occurs in all contexts.

animal behavior and cognition↗

Shared brain transcriptome signatures of social evolution and plasticity in shell-dwelling cichlid fishes

Inter-individual variability in phenotypic traits is widespread in nature both across and within species, with similar patterns of variation often occurring between and within species. Given that these two types of variation are generated by evolutionary and plasticity processes respectively, it raises the question to what extent the causal mechanisms underlying such variability at these two different levels are shared or not. To address this question, we studied the variation in brain transcriptomes in Tanganyikan cichlids that exhibit both intra- and interspecific variation in social behavior. We compared two facultatively social cichlid species, each containing naturally occurring solitary and social-living individuals, with a closely related obligatory solitary resembling the ancestral state. Differential gene expression and Gene Set Enrichment Analyses were performed in the telencephalon and diencephalon transcriptomes to investigate the molecular basis of both behavioral plasticity and the evolution of sociality. Behavioral plasticity involved few differentially expressed genes but extensive changes in biological pathways, supporting a highly polygenic architecture of social behavior. Across both species, social-living individuals consistently upregulated pathways involved in translation, protein homeostasis, and energy metabolism, whereas solitary individuals showed enrichment of neuronal morphogenesis, cell adhesion, and structural plasticity. Remarkably, these same functional programs were associated with the independent evolution of sociality, revealing a strong overlap between evolutionary and plastic transcriptional responses. This convergence was largely restricted to the telencephalon, while the diencephalon showed limited and inconsistent functional signatures. Together, our results support the repeated recruitment of gene regulatory programs during the evolution of sociality and demonstrate that behavioral plasticity and evolutionary transitions share common neurogenomic responses, consistent with a conserved genetic toolkit for social behavior.

animal behavior and cognition↗

Inductive bias anchors a scaling law for biological learning

The speed of animal learning varies substantially across individuals and task domains, and does not always track intuitive notions of task complexity. A nominally simple binary decision task can take thousands of rewards to reach proficiency, whereas seemingly complex naturalistic tasks can be learned within dozens. A long-standing explanation is that learning speed reflects the alignment between an animal's inductive biases and the task at hand. However, without a precise notion of alignment, it is difficult to translate this idea into quantitative predictions. Here, modeling an animal as a Bayesian learner that improves through iterative reward feedback, we use statistical learning theory to derive a scaling law for biological learning. The law makes two predictions. First, inductive bias multiplicatively sets the speed of learning through a single quantity, the effective complexity. This quantity measures how well the task aligns with the animal's prior, and corresponds to the number of bits required to represent the task solution under that prior. We demonstrate this multiplicative scaling in a human motor learning dataset, where behavioral measurements before learning can precisely predict subsequent learning speed. Second, task performance improves as a power law in experience. We validate this power law in a large-scale mouse learning dataset and show that a two-parameter rescaling collapses individual learning curves onto a universal function predicted by the theory. These results suggest that behavioral measurements before learning can be used to predict task-dependent learning speeds, and they explain why learning speed reflects alignment with prior knowledge rather than nominal task complexity.

animal behavior and cognition↗