Qualitative Behavioural Assessment as a welfare indicator for farmed Atlantic salmon (Salmo salar) in response to a stressful challenge

Animal welfare assessments have struggled to investigate the emotional states of animals while focusing solely on available empirical evidence. Qualitative Behavioural Assessment (QBA) may provide insights into an animal's subjective experiences without compromising scientific rigor. Rather than assessing explicit, physical behaviours (i.e., what animals are doing, such as swimming or feeding), QBA describes and quantifies the overall expressive manner in which animals execute those behaviours (i.e., how relaxed or agitated they appear). While QBA has been successfully applied to scientific welfare assessments in a variety of species, its application within aquaculture remains largely unexplored. This study aimed to assess QBA's effectiveness in capturing changes in the emotional behaviour of Atlantic salmon following exposure to a stressful challenge. Nine tanks of juvenile Atlantic salmon were video-recorded every morning for 15 min over a 7-day period, in the middle of which a stressful challenge (intrusive sampling) was conducted on the salmon. The resultant 1-min, 63 video clips were then semi-randomised to avoid predictability and treatment bias for QBA scorers. Twelve salmon-industry professionals generated a list of 16 qualitative descriptors (e.g., relaxed, agitated, stressed) after viewing unrelated video-recordings depicting varying expressive characteristics of salmon in different contexts. A different group of 5 observers, with varied experience of salmon farming, subsequently scored the 16 descriptors for each clip using a Visual Analogue Scale (VAS). Principal Components Analysis (correlation matrix, no rotation) was used to identify perceived patterns of expressive characteristics across the video-clips, which revealed 4 dimensions explaining 74.5% of the variation between clips. PC1, ranging from 'relaxed/content/positive active' to 'unsettled/stressed/spooked/skittish' explained the highest percentage of variation (37%). QBA scores for video-clips on PC1, PC2, and PC4 achieved good inter- and intra-observer reliability. Linear Mixed Effects Models, controlled for observer variation in PC1 scores, showed a significant difference between PC1 scores before and after sampling (p = 0.03), with salmon being perceived as more stressed afterwards. PC1 scores also correlated positively with darting behaviours (r = 0.42, p < 0.001). These results are the first to report QBA's sensitivity to changes in expressive characteristics of salmon following a putatively stressful challenge, demonstrating QBA's potential as a welfare indicator within aquaculture.

A systematic review of the impact of housing on sow welfare during post-weaning and early pregnancy periods

Breeder animals are an important focus in farm animal welfare assessments as they typically live the longest lives and are at the greatest risk for suffering due to their longevity. For breeding pigs, the time between the end of lactation (post-weaning) and the implantation of embryos (early gestation) is very dynamic from both a physiological and husbandry perspective. However, research to date is limited on how best to house and manage sows during this critical period of their production cycle from a welfare perspective. Previous animal-based welfare outcome measures were restricted to certain health, behavioral and physiological indicators. This systematic review used Web of Science to make in-depth comparisons among welfare-based studies that focus on sow housing during the post-weaning and early pregnancy period to identify important knowledge gaps. Only a small number of studies (n = 27) were found that met our systematic search criteria. Compared to stalls, group housing requires mixing of animals and always triggers more aggression and skin lesions at the time of mixing. The predominant use of health and physiological indicators constrained the animal-based welfare outcomes in these studies. Thus, what type of housing yields the best overall welfare outcome remains to be elucidated as none of the studies found explored the mental wellbeing of sows during this period. This systematic review defines a critical knowledge gap regarding the full impact of housing on the welfare of post-weaning and early gestation sows. This gap, and thus the true welfare impact of sow housing, will only be addressed by the use of novel, more holistic assessment methods that also capture the psychological state of the sow.

Affective State Recognition in Livestock—Artificial Intelligence Approaches

Simple Summary

Emotions or affective states recognition in farm animals is an underexplored research domain. Despite significant advances in animal welfare research, animal affective state computing through the development and application of devices and platforms that can not only recognize but interpret and process the emotions, are in a nascent stage. The analysis and measurement of unique behavioural, physical, and biological characteristics offered by biometric sensor technologies and the affiliated complex and large data sets, opens the pathway for novel and realistic identification of individual animals amongst a herd or a flock. By capitalizing on the immense potential of biometric sensors, artificial intelligence enabled big data methods offer substantial advancement of animal welfare standards and meet the urgent needs of caretakers to respond effectively to maintain the wellbeing of their animals.

Abstract

Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are not scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time-consuming, interrupt farming processes and involve subjective judgments. Biometric sensor data enabled by artificial intelligence is an emerging smart solution to unobtrusively monitoring livestock, but its potential for quantifying affective states and ground-breaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and behaviour in real time are a near-term possibility.

Once bitten, twice shy: Aggressive and defeated pigs begin agonistic encounters with more negative emotions

Aggression between unfamiliar commercial pigs is common and likely invokes strong emotions in contestants. Furthermore, contest outcomes affect subsequent aggressive behaviour, suggesting a potential lasting influence on affective state. Here we used a combination of qualitative and quantitative methods to assess the emotional expression of pigs in agonistic encounters. We investigated how recent victory or defeat influences emotions expressed in a subsequent contest, and the role of aggressiveness as a personality trait in emotional expression. We observed the pre-escalation contest behaviour (second contest; age 13 wks) in animals of different aggressiveness (categorised using two resident intruder tests as Agg+ or Agg-, age 9 wks), which had recently won or lost a contest (first contest; 10 wks). We measured gaze direction and ear position. Observers watched video clips of the initial 30 s of the second contest and evaluated the emotional expression of 57 pigs (25 contest 1 winners, 32 contest 1 losers) using qualitative behavioural assessment (QBA) with a fixed list of 20 descriptive terms. QBA identified three principal components (PCs), accounting for 68% of the variation: PC1 (agitated/tense to relaxed/content), PC2 (fearful/aimless to confident/enjoying) and PC3 (listless/ indifferent). Agg- pigs and males showed a more positive emotionality (PC2). PC1 and PC3 were unaffected by first contest outcome and aggressiveness. Agg+ pigs were more likely to hold their ears back (X2 =7.8, p = 0.005) during the early contest period. Differences in attention were detected in the contest outcome × aggressiveness interaction (χ24.3, p = 0.04), whereby approaching the opponent was influenced by winning and losing in the Agg- pigs only. QBA and gaze behaviour reveal differences in emotional valence between pigs of different aggressiveness: less aggressive pigs may be more susceptible to the emotional impact of victory and defeat but overall, more aggressive pigs express more negative emotionality at the start of agonistic encounters.

Qualitative Behavioral Assessment in Juvenile Farmed Atlantic Salmon (Salmo salar): Potential for On-Farm Welfare Assessment

There is a growing scientific and legislative consensus that fish are sentient, and therefore have the capacity to experience pain and suffering. The assessment of the welfare of farmed fish is challenging due to the aquatic environment and the number of animals housed together. However, with increasing global production and intensification of aquaculture comes greater impetus for developing effective tools which are suitable for the aquatic environment to assess the emotional experience and welfare of farmed fish. This study therefore aimed to investigate the use of Qualitative Behavioral Assessment (QBA), originally developed for terrestrial farmed animals, in farmed salmon and evaluate its potential for use as a welfare monitoring tool. QBA is a “whole animal” approach based on the description and quantification of the expressive qualities of an animal's dynamic style of behaving, using descriptors such as relaxed, agitated, lethargic, or confident. A list of 20 qualitative descriptors was generated by fish farmers after viewing video-footage showing behavior expressions representative of the full repertoire of salmon in this context. A separate, non-experienced group of 10 observers subsequently watched 25 video clips of farmed salmon, and scored the 20 descriptors for each clip using a Visual Analog Scale (VAS). To assess intra-observer reliability each observer viewed the same 25 video clips twice, in two sessions 10 days apart, with the second clip set presented in a different order. The observers were unaware that the two sets of video clips were identical. Data were analyzed using Principal Component (PC) Analysis (correlation matrix, no rotation), revealing four dimensions that together explained 79% of the variation between video clips, with PC1 (Tense/anxious/skittish—Calm/mellow/relaxed) explaining the greatest percentage of variation (56%). PC1 was the only dimension to show acceptable inter- and intra-observer reliability, and mean PC1 scores correlated significantly to durations of slow and erratic physical movements measured for the same 25 video clips. Further refinements to the methodology may be necessary, but this study is the first to provide evidence for the potential of Qualitative Behavioral Assessment to serve as a time-efficient welfare assessment tool for juvenile salmon under farmed conditions.

Feasibility of a Sheep Welfare Assessment Tool in the Pre-export Phase of Australian Live Export Industry

Sheep are exposed to numerous stressors and environments during the pre-export phase of the live export industry. Establishing how animal behavior, health and demeanor reflect their experiences prior to sea transport is the first step toward testing the suitability and practicality of animal welfare measures. A total of 240 merino wethers originating from four farms were assessed at four locations in the live export chain: on farm, upon arrival to the registered export feedlot (Fe1), prior to departing the feedlot (Fe2) and 30 min post loading onto a live export vessel. Each of these locations and time points represent relevant assessment points as part of the commercial live export process. Pen-side behavioral and health measures were collected. Video footage was collected and edited to provide 48 30–45 s duration clips that were then scored by 12 assessors against 10 demeanor terms using a Qualitative Behavioral Assessment (QBA) methodology; data were analyzed using Principal Components (PC) analysis. Repeated Measures ANOVAs tested for variation in each dependent measure across each location and time point. There were low levels of health issues recorded overall; however, seven health and behavior measures significantly varied across the locations and time points. Most vocalizing was recorded on farm; most drinking, eating and resting behaviors were recorded at Fe1 and ruminating at Fe2; while the highest percentage of wethers with ocular discharge and lameness was on the vessel. For QBA, PC1 explained 30.5% of the variability, with agitated and nervous loaded to one end of the axis and calm and relaxed loaded to the opposing end. PC2 explained 24.5% of the variability, with interested, alert and sociable loaded to one end of the axis and lethargic loaded to the opposing end. Spearman's rank correlations between behavior, health and PC scores indicated that wethers eating, ruminating and resting were scored as more calm/relaxed, while those scored as more agitated/nervous or/ lethargic were also likely to vocalize. Determining how wethers respond to the different environments in the immediate pre-export phase of the journey informs on their welfare and the practicality of using a behavior tool to assess animal welfare.

Individual differences in behaviour and gut bacteria are associated in collared peccary (Mammalia, Tayassuidae)

AIMS

We tested the hypothesis that the behaviour of an individual is associated with the diversity of its gut bacteria, using the collared peccary (Pecari tajacu) as a model.

METHODS AND RESULTS

In all, 24 adult male collared peccaries received either low- (n = 12) or high-fibre diet (n = 12) to induce contrasting gut fermentation profiles. They were submitted to three short-term challenges, allowing us to rate the animals in a coping-style dimension named 'calmness'. At the end of the experimental period, we collected samples of peccaries' forestomach contents to characterize bacterial diversity. We found a significant positive association between individual 'calmness' z-scores and the bacterial evenness index in gut bacteria (and a similar trend with the Simpson's diversity index), suggesting a more homogeneous bacterial community of calmer individuals. We also found a positive association between fibres digestibility and gut bacterial diversity in the peccaries' forestomach, but no effect of the dietary fibre level.

CONCLUSIONS

Gut bacteria evenness increases with 'calmness' z-scores, suggesting a more homogeneous bacterial community of calmer individuals, compared with the more heterogeneous of the most distressed ones. Our results also suggest associations between the digestibility of ADF with the gut bacterial diversity indices and with the relative abundance of the Actinobacteria phylum.

SIGNIFICANCE AND IMPACT OF THE STUDY

Our data showed that the hosts' individual behavioural differences are potentially aligned with gut bacterial diversity. The behaviour-microbiota link is correlated with host feed efficiency and, ultimately, may have implications for animal health and welfare of farm animals.

Construction of a conceptual framework for assessment of health-related quality of life in calves with respiratory disease

Bovine respiratory disease (BRD) is one of the most prevalent diseases affecting beef and dairy calves worldwide, with implications for lifetime productivity, antimicrobial use and animal welfare. Our objective was to construct a conceptual framework for assessment of health-related quality of life (HRQL) in calves with respiratory disease, based on indicators suitable for direct pen-side visual observation. Health-related quality of life measures aim to evaluate the subjective experience of the animal rather than any related pathology. A conceptual framework graphically represents the concepts to be measured and the potential relationships between them. A multistage, mixed method approach involving diverse data sources, collection methods and stakeholders was applied to promote comprehensiveness, understanding and validity of findings. A scoping review was conducted to identify, characterize and collate evidence of behavioural indicators of BRD. The indicators identified were mapped against the principal attributes of five prominent animal welfare assessment frameworks to appraise their correspondence with different characterizations of the dimensions of welfare. Forty-two semi-structured, individual, qualitative interviews with a purposeful sample of experienced veterinarians and stockpersons from UK, USA and Canada elicited in-depth descriptions of the visual observations of HRQL they make in diagnosing and assessing the response to treatment of calves with BRD. Verbatim interview transcripts were examined using inductive thematic analysis. Respondents provided insights and understanding of indicators of HRQL in BRD such as interaction with feed source, hair coat condition, specific characteristics of eye appearance, eye contact, rumen fill and stretching (pandiculation). In an on-farm pilot study to assess the value of potential HRQL behavioural indicators, there was a moderate positive correlation between behaviour and clinical scores (r_s = 0.59) across the 5 days preceding veterinary treatment for BRD. Interestingly, the behaviours evaluated were observed a median of 1.0 (interquartile range: 1.0-3.5) days before clinical indicators used in the scoring system. The proposed conceptual framework for assessment of HRQL features 23 putative indicators of HRQL distributed across two interrelated domains - clinical signs and behavioural expressions of emotional well-being. It has potential applications to inform the development of new HRQL measures such as structured questionnaires and automated sensor technologies.