82 Linear mixed models for dissecting social and direct genetic effects of automatic feeder visit sequences

Abstract In this study, we delved into the feeding behaviors of 548 swine, using 114,263 records from FIRE feeders to uncover the direct and social genetic effects of feeding duration. We aimed at dissecting direct and social effects and genetics and environmental components of feeding duration using linear mixed-effects models (LMMs). To accomplish this, we fit and compared several models. The full model included direct and social genetic effects and permanent environmental effects or the social effects or both sets of effects. All models included the effect of the social group. This model facilitates a comprehensive understanding of the determinants of feeding times as it related to social interaction sequences. A novel aspect of our study is the use of sequence of feeder visits as a proxy to social interaction at the feeder, so separate social genetic and social permanent environmental effects of an individual from the ‘Social Group’ effect. The analysis reveals that the full model offers the best fit to the data, as indicated by the smallest Akaike Information Criterion (AIC). However, comparisons with reduced models suggest a partial confounding between additive genetic and permanent environmental effects at both additive and social levels. Notably, the variance explained by the social group remains consistent across models, implying minimal confounding with this factor and underscoring the distinct impact of social dynamics. The observation that social direct genetic effects do not significantly overlap (e.g., M1 vs M2) is promising, suggesting the potential to address the confounding between additive genetic (A) and permanent environmental (PE) effects by incorporating a more detailed pedigree analysis. By presenting variance components and model fit indices (Table 1), our research illuminates the intricate interactions among genetic, environmental, and social factors affecting swine behavior. The use of mixed-effects modeling in this study advances our understanding of the temporal dynamics of animal social behavior, emphasizing the importance of detailed statistical analysis in behavioral research.