The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.

31 32The use of linear mixed effects models (LMMs) is increasingly common in the analysis 33 of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of 34 data types, ecological data are often complex and require complex model structures, 35 and the fitting and interpretation of such models is not always straightforward. The 36 ability to achieve robust biological inference requires that practitioners know how and 37 when to apply these tools. Here, we provide a general overview of current methods for 38 the application of LMMs to biological data, and highlight the typical pitfalls that can be 39 encountered in the statistical modelling process. We tackle several issues relating to the 40 use of information theory and multi-model inference in ecology, and demonstrate the 41 tendency for data dredging to lead to greatly inflated Type I error rate (false positives) 42 and impaired inference. We offer practical solutions and direct the reader to key

Despite the fact that many animals live in groups, there is still no clear consensus about the ecological or evolutionary mechanisms underlying colonial living. Recently, research has suggested that colonies may be important as sources of social information. The ready availability of information from conspecifics allows animals to make better decisions about avoiding predators, reducing brood parasitism, migratory phenology, mate choice, habitat choice and foraging. These choices can play a large part in the development and maintenance of colonies. Here we review the types of information provided by colonial animals and examine the different ways in which decision-making in colonies can be enhanced by social information. We discuss what roles information might take in the evolution, formation and maintenance of colonies. In the process, we illustrate that information use permeates all aspects of colonial living.

Social interactions present opportunities for both information and infection to spread through populations. Social learning is often proposed as a key benefit of sociality, while infectious disease spread are proposed as a major cost. Multiple empirical and theoretical studies have demonstrated the importance of social structure for the transmission of either information or harmful pathogens and parasites, but rarely in combination. We provide an overview of relevant empirical studies, discuss differences in the transmission processes of infection and information, and review how these processes have been modelled. Finally, we highlight ways in which animal social network structure and dynamics might mediate the tradeoff between the sharing of information and infection. We reveal how modular social network structures can promote the spread of information and mitigate against the spread of infection relative to other network structures. We discuss how the maintenance of long‐term social bonds, clustering of social contacts in time, and adaptive plasticity in behavioural interactions, all play important roles in influencing the transmission of information and infection. We provide novel hypotheses and suggest new directions for research that quantifies the transmission of information and infection simultaneously across different network structures to help tease apart their influence on the evolution of social behaviour.

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The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues relating to the use of information theory and multi-model inference in ecology, and demonstrate the tendency for data dredging to lead to greatly inflated Type I error rate (false positives) and impaired inference. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.

Natural disasters can cause rapid demographic changes that disturb the social structure of a population as individuals may lose connections. These changes also have indirect effects as survivors alter their within-group connections or move between groups. As group membership and network position may influence individual fitness, indirect effects may affect how individuals and populations recover from catastrophic events. Here we study changes in the social structure after a large predation event in a population of wild house mice ( Mus musculus domesticus ), when a third of adults were lost. Using social network analysis, we examine how heterogeneity in sociality results in varied responses to losing connections. We then investigate how these differences influence the overall network structure. An individual's reaction to losing associates depended on its sociality prior to the event. Those that were less social before formed more weak connections afterwards, while more social individuals reduced the number of survivors they associated with. Otherwise, the number and size of social groups were highly robust. This indicates that social preferences can drive how individuals adjust their social behaviour after catastrophic turnover events, despite the population's resilience in social structure.

Ambient noise can affect the availability of acoustic information to animals, altering both foraging and vigilance behaviour. Using captive zebra finches Taeniopygia guttata, we examined the effect of ambient broadband noise on foraging decisions. Birds were given a choice between foraging in a quiet area where conspecific calls could be heard or a noisy area where these calls would be masked. Birds foraging in noisy areas spent a significantly more time vigilant than those in quiet areas, resulting in less efficient foraging. Despite this there was no significant difference in the amount of time spent in the two noise regimes. However there did appear a preference for initially choosing quiet patches during individuals’ second trial. These results emphasise how masking noise can influence the foraging and anti-predation behaviour of animals, which is particularly relevant as anthropogenic noise becomes increasingly prevalent in the natural world.