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Cattle develop preferential relationships with other individuals in the herd. These social interactions between individuals have a significant impact on both animal welfare and production. Given the relevance of social behaviour in dairy cattle, scientific studies have focused on understanding social interactions among cattle. These may also be influenced by individual area preferences, particularly when animals are housed in confined spaces. Therefore, investigating the relationship between individual area preferences and social interactions is essential for understanding social behaviour in dairy cattle. Real-time location systems provide the opportunity to monitor individual area preferences and social contacts at the same time. This study aims to assess the impact of dairy cows' area preferences on their daily social contacts and to determine the potential implications of overlooking individual area preferences in social behaviour studies. The individual position of the lactating cows was automatically collected once per second for two months on a Swedish commercial farm housing dairy cows inside a free-stall barn. The location data of 243 lactating cows was used to construct the social networks and to estimate the similarity of the area utilisation distributions between these individuals. The effect of utilisation distribution similarity in social networks was investigated by applying separable temporal exponential random graph mixed models. The role of different cow characteristics in the similarity of the utilisation distributions was assessed through a linear mixed model. Our analyses stressed the importance of similarity of area preference, parity, kindergarten effect, and filial relatedness in shaping daily social contacts in dairy cattle. The kindergarten effect refers to the effect on cow behaviour of being grouped together in the early stages of their lives. Similarity of area preference was influenced by the kindergarten effect and relatedness by pedigree, which favoured interactions between these individuals. The described approach allowed to disassociate the area preference from the social contacts between cows, providing more accurate results of the importance of the cow's characteristics on their social behaviour.

Social interactions between cows play a fundamental role in the daily activities of dairy cattle. Real-time location systems provide on a continuous and automated basis information about the position of individual cows inside barns, offering a valuable opportunity to monitor dyadic social contacts. Understanding dyadic social interactions could be applied to enhance the stability of the social structure promoting animal welfare and to model disease transmission in dairy cattle. This study aimed to identify the impact of different cow characteristics on the likelihood of the formation and persistence of social contacts in dairy cattle. The individual position of the lactating cows was automatically collected once per second for 2 weeks, using an ultra-wideband system on a Swedish commercial farm consisting of nearly 200 dairy cows inside a free-stall barn. Social networks were constructed using the position data of 149 cows with available information on all characteristics during the study period. Social contacts were considered as a binary variable indicating whether a cow pair was within 2.5 m of each other for at least 10 min per day. The role of cow characteristics in social networks was studied by applying separable temporal exponential random graph models. Our results revealed that cows of the same parity interacted more consistently, as well as those born within 7 d of each other or are closely related by pedigree. The repeatability of the topological parameters indicated a consistent short-term stability of the individual animal roles within the social network structure. Additional research is required to elucidate the underlying mechanisms governing the long-term evolution of social contacts among dairy cattle and to investigate the relationship between these networks and the transmission of diseases in the dairy cattle population.

Dairy cattle are frequently housed in freestalls with limited space, affecting social interactions between individuals. Social behavior in dairy cattle is gaining recognition as a valuable tool for identifying sick animals, but its application is hampered by the complexities of analyzing social interactions in intensive housing systems. In this context, precision livestock technologies present the opportunity to continuously monitor dyadic spatial associations on dairy farms. The aim of this study is to evaluate the accuracy of predicting social behavior of dairy cows using social network analysis. Daily social networks were built using the position data from 149 cows over 14 consecutive days of the study period. We applied the separable temporal exponential random graph models to estimate the likelihood of formation and persistence of social contacts between dairy cows individually and to predict the social network on the subsequent day. The correlation between the individual degree centrality values, the number of established social contacts per individual, between the predicted and observed networks ranged from 0.22 to 0.49 when the structural information from network triangles was included in the model. This study presents a novel approach for predicting animal social behavior in intensive housing systems using spatial association information obtained from a real-time location system. The results indicate the potential of this approach as a crucial step toward the larger goal of identifying disruptions in dairy cows' expected social behavior. © 2024

In modern freestall barns where large groups of cows are housed together, the behavior displayed by herd mates can influence the welfare and production of other individuals. Therefore, understanding social interactions in groups of dairy cows is important to enhance herd management and optimize the outcomes of both animal health and welfare in the future. Many factors can affect the number of social contacts in a group. This study aimed to identify which characteristics of a cow are associated with the number of contacts it has with other group members in 2 different functional areas (feeding and resting area) to increase our understanding of the social behavior of dairy cows. Inside 2 herds housed in freestall barns with around 200 lactating cows each, cow positions were recorded with an ultra-wideband real-time location system collecting all cows' positions every second over 2 wk. Using the positioning data of the cows, we quantified the number of contacts between them, assuming that cows spending time in proximity to one another (within a distance of 2.5 m for at least 10 min per day) were interacting socially. We documented in which barn areas these interactions occurred and used linear mixed models to investigate if lactation stage, parity, breed, pregnancy status, estrus, udder health, and claw health affect the number of contacts. We found variation in the number of contacts a cow had between individuals in both functional areas. Cows in later lactation had more contacts in the feeding area than cows in early lactation. Furthermore, in one herd, higher parity cows had fewer contacts in the feeding area than first parity cows, and in the other herd, cows in third parity or higher had more contacts in the resting area. This study indicates that cow characteristics such as parity and days in milk are associated with the number of contacts a cow has daily to its herd mates and provides useful information for further research on social interactions of dairy cows.

Phenotypic variability of a genotype is relevant both in natural and domestic populations. In the past two decades, variability has been studied as a heritable quantitative genetic trait in its own right, often referred to as inherited variability or environmental canalization. So far, studies on inherited variability have only considered genetic effects of the focal individual, that is, direct genetic effects on inherited variability. Observations from aquaculture populations and some plants, however, suggest that an additional source of genetic variation in inherited variability may be generated through competition. Social interactions, such as competition, are often a source of Indirect Genetic Effects (IGE). An IGE is a heritable effect of an individual on the trait value of another individual. IGEs may substantially affect heritable variation underlying the trait, and the direction and magnitude of response to selection. To understand the contribution of IGEs to evolution of environmental canalization in natural populations, and to exploit such inherited variability in animal and plant breeding, we need statistical models to capture this effect. To our knowledge, it is unknown to what extent the current statistical models commonly used for IGE and inherited variability capture the effect of competition on inherited variability. Here, we investigate the potential of current statistical models for inherited variability and trait values, to capture the direct and indirect genetic effects of competition on variability. Our results show that a direct model of inherited variability almost entirely captures the genetic sensitivity of individuals to competition, whereas an indirect model of inherited variability captures the cooperative genetic effects of individuals on their partners. Models for trait levels, however, capture only a small part of the genetic effects of competition. The estimation of direct and indirect genetic effects of competition, therefore, is possible with models for inherited variability but may require a two-step analysis. © 2022 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd.

Introduction: Although breast and prostate cancers arise in different organs and are more frequent in the opposite sex, multiple studies have reported an association between their family history. Analysis of single nucleotide polymorphism data, based on distant relatives, has revealed a small positive genetic correlation between these cancers explained by common variants. The estimate of genetic correlation based on close relatives reveals the extent to which shared genetic risks are explained by both common and rare variants. This estimate is unknown for breast and prostate cancer. Method: We estimated the relative risks, heritability, and genetic correlation of breast cancer and prostate cancer, based on the Minnesota Breast and Prostate Cancer Study, a family study of 141 families ascertained for breast cancer. Results: Heritability of breast cancer was 0.34 (95% credible interval: 0.23-0.49) and 0.65 (95% credible interval: 0.36-0.97) for prostate cancer, and the genetic correlation was 0.23. In terms of odds ratios, these values correspond to a 1.3 times higher odds of breast cancer among probands, given that the brother has prostate cancer. Conclusion: This study shows the inherent relation between prostate cancer and breast cancer; an incident of one in a family increases the risk of developing the other. The large difference between estimates of genetic correlation from distant and close relatives, if replicated, suggests that rare variants contribute to the shared genetic risk of breast and prostate cancer. However, the difference could steam from genotype-by-family effects shared between the two types of cancers. ©; 2021 The Author(s).

Position data from real-time indoor positioning systems are increasingly used for studying individual cow behavior and social behavior in dairy herds. However, missing data challenges achieving reliable continuous activity monitoring and behavior studies. This study investigates the pattern of missing data and alternative interpolation methods in ultra-wideband based real-time indoor positioning systems in a free-stall barn. We collected 3 months of position data from a Swedish farm with around 200 cows. Data sampled for 6 days from 69 cows were used in subsequent analyzes to determine the location and duration of missing data. Data from 20 cows with the most reliable tags were selected to compare the effects of four different interpolation methods (previous, linear interpolation, cubic spline data interpolation and modified Akima interpolation). By comparing the observed data with the interpolations of the simulated missing data, the mean error distance varied from around 55 cm, using the previously last observed position, to around 17 cm for modified Akima. Modified Akima interpolation has the lowest error distance for all investigated activities (rest, walking, standing, feeding). Larger error distances were found in areas where the cows walk and turn, such as the corner between feeding and cubicles. Modified Akima interpolation is expected to be useful in the subsequent analyses of data gathered using real-time indoor positioning systems.

The cerebellum has a highly conserved neural structure across species but varies widely in size. The wide variation in cerebellar size (both absolute and in proportion to the rest of the brain) among species and populations suggests that functional specialization is linked to its size. There is increasing recognition that the cerebellum contributes to cognitive processing and emotional control in addition to its role in motor coordination. However, to what extent cerebellum size reflects variation in these behavioral processes within species remains largely unknown. By using a unique intercross chicken population based on parental lines with high divergence in cerebellum size, we compared the behavior of individuals repeatedly exposed to the same fear test (emergence test) early in life and after sexual maturity (eight trials per age group) with proportional cerebellum size and cerebellum neural density. While proportional cerebellum size did not predict the initial fear response of the individuals (trial 1), it did increasingly predict adult individuals response as the trials progressed. Our results suggest that proportional cerebellum size does not necessarily predict an individual's fear response, but rather the habituation process to a fearful stimulus. Cerebellum neuronal density did not predict fear behavior in the individuals which suggests that these effects do not result from changes in neuronal density but due to other variables linked to proportional cerebellum size which might underlie fear habituation.

To ensure milk quality and detect cows with signs of mastitis, visual inspection of milk by prestripping quarters before milking is recommended in many countries. An objective method to find milk changed in homogeneity (i.e., with clots) is to use commercially available inline filters to inspect the milk. Due to the required manual labor, this method is not applicable in automatic milking systems (AMS). We investigated the possibility of detecting and predicting changes in milk homogeneity using data generated by AMS. In total, 21,335 quarter-level milk inspections were performed on 5,424 milkings of 624 unique cows on 4 farms by applying visual inspection of inline filters that assembled clots from the separate quarters during milking. Images of the filters with clots were scored for density, resulting in 892 observations with signs of clots for analysis (77% traces or mild cases, 15% moderate cases, and 8% heavy cases). The quarter density scores were combined into 1 score indicating the presence of clots during a single cow milking and into 2 scores summarizing the density scores in cow milkings during a 30-h sampling period. Data generated from the AMS, such as milk yield, milk flow, conductivity, and online somatic cell counts, were used as input to 4 multilayer perceptron models to detect or predict single milkings with clots and to detect milking periods with clots. All models resulted in high specificity (98-100%), showing that the models correctly classified cow milkings or cow milking periods with no clots observed. The ability to successfully classify cow milkings or cow periods with observed clots had a low sensitivity. The highest sensitivity (26%) was obtained by the model that detected clots in a single milking. The prevalence of clots in the data was low (2.4%), which was reflected in the results. The positive predictive value depends on the prevalence and was relatively high, with the highest positive predictive value (72%) reached in the model that detected clots during the 30-h sampling periods. The misclassification rate for cow milkings that included higher-density scores was lower, indicating that the models that detected or predicted clots in a single milking could better distinguish the heavier cases of clots. Using data from AMS to detect and predict changes in milk homogeneity seems to be possible, although the prediction performance for the definitions of clots used in this study was poor.