In many European shelters, dogs may spend many years confined. A poor environment and inappropriate management may lead to a low quality of life. The absence of harmonised European regulatory frameworks defining the minimum requirements for shelter facilities makes the definition of welfare standards for kennelled dogs challenging. Here, a new protocol was developed and tested to help identify the main welfare issues for shelter dogs. Twenty‐six indicators were identified including management, resource and animal based measures. Accuracy and interobserver reliability were checked between four assessors. The protocol was applied in 29 shelters (n=1308 dogs) in six European countries. Overall prevalence of poor health conditions was below 10%. Test‐retest reliability and validity of the protocol were investigated with encouraging results. A logistic regression was carried out to assess the potential of the protocol as a tool to identify welfare hazards in shelter environments. Inappropriate space allowance, for example, was found to be a risk factor potentially affecting the animal's cleanliness, skin condition and body condition. The protocol was designed to be concise and easy to implement. Systematic data collection could help identify welfare problems that are likely to arise in certain shelter designs and thus determine improvement in animal care standards.

Loggerhead sea turtle (C. caretta) is the official European bioindicator of marine litter in the Mediterranean Sea. In 2019, 8 sea turtles, out of 28 specimens loggerhead on the Adriatic coast of Molise, were subjected to necropsy. The intestinal contents were collected and the microplastics until 0.45 μm were extracted. Qualitative and quantitative assessments were performed by stereomicroscope observation and spectroscopic analyses (attenuated total reflection-Fourier transform infrared spectroscopy, ATR-FTIR and Raman microspectroscopy, RMS). Moreover, the analytical quantification of polyethylene terephthalate (PET), polycarbonate (PC), para phthalic acid (PTA) and bisphenol A (BPA) in fat and liver tissues was performed by LC-MS/MS. Microparticles ranging from 0.45 μm to 1 mm were found in all turtles, for a total of 623, while plastic litter greater than 1 mm were found only in 4 specimens (ranging from 0.03 to 0.11 g). Nineteen different polymers and 10 pigments, including polyester (100% of animals), high-density polyethylene (50%) and polypropylene (50%) were identified. BPA, PTA and PET were detected in fat and liver tissues of all animals, while PC was found only in 50%. A major prevalence was registered in the abdominal fat tissue, although only PC compounds were significantly higher in abdominal tissue (p < 0.05), except for free PTA with liver tissue being the most contaminated (p < 0.05). Microplastics and additives surely impact the health status of turtles that showed gastrointestinal impairment and an important level of contamination in tissues. Graphical abstract

The shelter environment may have a severe impact on the dogs’ quality of life, 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 a ‘whole animal’ measure that captures the expressive quality of an animal’s demeanour, using descriptive terms such as ‘relaxed’, ‘anxious’, and ‘playful’. In this study, for the first time, we developed and tested a fixed-list of qualitative QBA terms for application to kennelled dogs. A list of 20 QBA terms was developed based on literature search and an expert opinion survey. Inter-observer reliability was investigated by asking 11 observers to use these terms to score 13 video clips of kennelled dogs. Principal Component Analysis (PCA) was used to extract four main dimensions 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 characterised bored/depressed demeanour. Observers’ agreement on the ranking of video clips on these four expressive dimensions was good (Kendall’s W: 0.60–0.80). ANOVA showed a significant effect of observer on mean clip score on all PCs (p<0.05), due to few observers scoring differently from the rest of the group. Results indicate the potential of the proposed list of QBA terms for sheltered dogs to serve, in alignment with other measures, as a non-invasive assessment tool. However, the observer effect on mean PC scores points towards the need for adequate observer training, particularly in live scoring conditions. The QBA scoring tool can be integrated with existing welfare assessment protocols for shelter dogs and strengthen the power of those protocols to evaluate the animals’ experience in shelters.

No data have been published on the use of infrared thermography (IRT) to evaluate sheep emotions. We assessed whether this technique can be used as a non-invasive measure of negative emotions. Two voluntary animal approach (VAA) tests were conducted (and filmed) on five ewes before and after being restrained. The restraining process was performed by a handler for five minutes. IRT was used during restraint and the VAA tests. The lacrimal caruncle temperature was significantly higher during restraint and in the VAA test after the restraint compared with the VAA test before the restraint (Wilcoxon’s test; p = 0.04). The latency period until first contact was longer in the second VAA test (132 s) than in the first one (60 s). Our preliminary results suggest that IRT, combined with behavioral data, is a non-invasive technique that can be useful to assess stress and infer about negative emotions in sheep.

Mankind directly controls the environment and lifestyles of several domestic species for purposes ranging from production and research to conservation and companionship. These environments and lifestyles may not offer these animals the best quality of life. Behaviour is a direct reflection of how the animal is coping with its environment. Behavioural indicators are thus among the preferred parameters to assess welfare. However, behavioural recording (usually from video) can be very time consuming and the accuracy and reliability of the output rely on the experience and background of the observers. The outburst of new video technology and computer image processing gives the basis for promising solutions. In this pilot study, we present a new prototype software able to automatically infer the behaviour of dogs housed in kennels from 3D visual data and through structured machine learning frameworks. Depth information acquired through 3D features, body part detection and training are the key elements that allow the machine to recognise postures, trajectories inside the kennel and patterns of movement that can be later labelled at convenience. The main innovation of the software is its ability to automatically cluster frequently observed temporal patterns of movement without any pre-set ethogram. Conversely, when common patterns are defined through training, a deviation from normal behaviour in time or between individuals could be assessed. The software accuracy in correctly detecting the dogs’ behaviour was checked through a validation process. An automatic behaviour recognition system, independent from human subjectivity, could add scientific knowledge on animals’ quality of life in confinement as well as saving time and resources. This 3D framework was designed to be invariant to the dog’s shape and size and could be extended to farm, laboratory and zoo quadrupeds in artificial housing. The computer vision technique applied to this software is innovative in non-human animal behaviour science. Further improvements and validation are needed, and future applications and limitations are discussed.