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.