Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

Vocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions. We investigated if pig vocal indicators of emotions revealed in previous studies are valid across call types and contexts, and could potentially be used to develop an automated emotion monitoring tool. We performed an analysis of an extensive and unique dataset of low (LF) and high frequency (HF) calls emitted by pigs across numerous commercial contexts from birth to slaughter (7414 calls from 411 pigs). Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. Similarly, the context category affected all parameters. We then tested two different automated methods for call classification; a neural network revealed much higher classification accuracy compared to a permuted discriminant function analysis (pDFA), both for the valence (neural network: 91.5%; pDFA analysis weighted average across LF and HF (cross-classified): 61.7% with a chance level at 50.5%) and context (neural network: 81.5%; pDFA analysis weighted average across LF and HF (cross-classified): 19.4% with a chance level at 14.3%). These results suggest that an automated recognition system can be developed to monitor pig welfare on-farm.publishedVersio

Effects of caller characteristics on auditory laterality in an early primate (Microcebus murinus

Background: Auditory laterality is suggested to be characterized by a left hemisphere dominance for the processing of conspecific communication. Nevertheless, there are indications that auditory laterality can also be affected by communicative significance, emotional valence and social recognition. Methodology/Principal Findings: In order to gain insight into the effects of caller characteristics on auditory laterality in the early primate brain, 17 gray mouse lemurs were tested in a head turn paradigm. The head turn paradigm was established to examine potential functional hemispheric asymmetries on the behavioral level. Subjects were presented with playbacks of two conspecific call types (tsak calls and trill calls) from senders differing in familiarity (unfamiliar vs. familiar) and sex (same sex vs. other sex). Based on the head turn direction towards these calls, evidence was found for a right ear/left hemisphere dominance for the processing of calls of the other sex (Binomial test: p = 0.021, N = 10). Familiarity had no effect on the orientation biases. Conclusions/Significance: The findings in this study support the growing consensus that auditory laterality is not only determined by the acoustic processing of conspecific communication, but also by other factors like the sex of th

Behavioural lateralization in domestic pigs (Sus scrofa)—variations between motor functions and individuals

Motor lateralization is hypothesized to depend on the complexity of the motor function, but it might at the same time reflect hemispheric dominance within an individual across motor functions. We investigated possible motor lateralization patterns in four motor functions of different complexity (snout use in a manipulative task, foot use in two-stepping tasks and tail curling) in the domestic pig, a tetrapod species relevant as farm animal but also as a model in human neuroscience. A significant majority of our sample showed individual biases for manipulation with their snout and for curling their tail. Interestingly, the tail curling was lateralized towards the right at the population level and showed stronger lateralization patterns than the snout. Using a cluster analysis with combined tail and snout laterality, we identified groups of individuals with different lateralization patterns across motor functions that potentially reflect the individuals’ hemispheric dominance. To conclude, our results suggest that pigs show lateralization patterns that depend on the motor function and on the individual. Such individual lateralization patterns might have broader implications for animal personality and welfare. Our study lays the methodological groundwork for future research on laterality in pigs

Assessing animal individuality: links between personality and laterality in pigs

Animal individuality is challenging to explain because individual differences are regulated by multiple selective forces that lead to unique combinations of characteristics. For instance, the study of personality, a core aspect of individuality, may benefit from integrating other factors underlying individual differences, such as lateralised cerebral processing. Indeed, the approach-withdrawal hypothesis (the left hemisphere controls approach behaviour, the right hemisphere controls withdrawal behaviour), may account for differences in boldness or exploration between left and right hemispheric dominant individuals. To analyse the relationships between personality and laterality we tested 80 male piglets with established laterality patterns for two motor functions (tail curling direction and the side of the snout used for manipulation) and a combined classification integrating both motor functions using cluster analysis. We analysed basal salivary testosterone and cortisol along with their behaviour in standardized tests as pre-established indicators of different personality traits (Boldness, Exploration, Activity, Sociability and Coping). We found that the direction of the single motor biases showed significant associations with few personality traits. However, the combined laterality classification showed more, and more robust, significant associations with different personality traits compared to the single motor biases. These results supported the approach-withdrawal hypothesis because right-biased pigs were bolder and more explorative in a context of novelty. Additionally, right-biased pigs were more sociable than left-biased pigs. Therefore, the present study indicates that personality is indeed related to lateralised cerebral processing and provides insight into the multifactorial nature of individuality

Dairy Cow Behavior Is Affected by Period, Time of Day and Housing

Dairy cow behavior is affected by external and endogenous factors, including time of year, barn microclimate, time of day and housing. However, little is known about the combined effects of these factors. Data were collected on eight farms in Northern Italy during summer, winter and a temperate season. The temperature-humidity index (THI) was recorded using environmental sensors, whereas cow behavior was monitored using leg accelerometers and cameras. Period, time of day and their interaction all significantly affected lying, standing and feeding behavior. However, although THI had a significant negative effect on lying and a positive effect on standing during daytime (all p < 0.001), during nighttime, it only had a significant negative effect on lying duration and mean lying bout duration (p < 0.001 for both). There was also significant variation between farms in all behavioral parameters, as well as interactions with period and time of day. For instance, farm differences in lying duration were more pronounced during daytime than during nighttime. These findings show how housing can interact with other factors, such as period of the year and time of day, and illustrate the influence of barn structure and farm management on cow behavior and, consequently, their welfare

Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

Vocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions. We investigated if pig vocal indicators of emotions revealed in previous studies are valid across call types and contexts, and could potentially be used to develop an automated emotion monitoring tool. We performed an analysis of an extensive and unique dataset of low (LF) and high frequency (HF) calls emitted by pigs across numerous commercial contexts from birth to slaughter (7414 calls from 411 pigs). Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. Similarly, the context category affected all parameters. We then tested two different automated methods for call classification; a neural network revealed much higher classification accuracy compared to a permuted discriminant function analysis (pDFA), both for the valence (neural network: 91.5%; pDFA analysis weighted average across LF and HF (cross-classified): 61.7% with a chance level at 50.5%) and context (neural network: 81.5%; pDFA analysis weighted average across LF and HF (cross-classified): 19.4% with a chance level at 14.3%). These results suggest that an automated recognition system can be developed to monitor pig welfare on-farm

Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

Vocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions. We investigated if pig vocal indicators of emotions revealed in previous studies are valid across call types and contexts, and could potentially be used to develop an automated emotion monitoring tool. We performed an analysis of an extensive and unique dataset of low (LF) and high frequency (HF) calls emitted by pigs across numerous commercial contexts from birth to slaughter (7414 calls from 411 pigs). Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. Similarly, the context category affected all parameters. We then tested two different automated methods for call classification; a neural network revealed much higher classification accuracy compared to a permuted discriminant function analysis (pDFA), both for the valence (neural network: 91.5%; pDFA analysis weighted average across LF and HF (cross-classified): 61.7% with a chance level at 50.5%) and context (neural network: 81.5%; pDFA analysis weighted average across LF and HF (cross-classified): 19.4% with a chance level at 14.3%). These results suggest that an automated recognition system can be developed to monitor pig welfare on-farm