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.

Social interactions are ubiquitous across the animal kingdom. A variety of ecological and evolutionary processes are dependent on social interactions, such as movement, disease spread, information transmission, and density‐dependent reproduction and survival. Social interactions, like any behaviour, are context dependent, varying with environmental conditions. Currently, environments are changing rapidly across multiple dimensions, becoming warmer and more variable, while habitats are increasingly fragmented and contaminated with pollutants. Social interactions are expected to change in response to these stressors and to continue to change into the future. However, a comprehensive understanding of the form and magnitude of the effects of these environmental changes on social interactions is currently lacking. Focusing on four major forms of rapid environmental change currently occurring, we review how these changing environmental gradients are expected to have immediate effects on social interactions such as communication, agonistic behaviours, and group formation, which will thereby induce changes in social organisation including mating systems, dominance hierarchies, and collective behaviour. Our review covers intraspecific variation in social interactions across environments, including studies in both the wild and in laboratory settings, and across a range of taxa. The expected responses of social behaviour to environmental change are diverse, but we identify several general themes. First, very dry, variable, fragmented, or polluted environments are likely to destabilise existing social systems. This occurs as these conditions limit the energy available for complex social interactions and affect dissimilar phenotypes differently. Second, a given environmental change can lead to opposite responses in social behaviour, and the direction of the response often hinges on the natural history of the organism in question. Third, our review highlights the fact that changes in environmental factors are not occurring in isolation: multiple factors are changing simultaneously, which may have antagonistic or synergistic effects, and more work should be done to understand these combined effects. We close by identifying methodological and analytical techniques that might help to study the response of social interactions to changing environments, highlight consistent patterns among taxa, and predict subsequent evolutionary change. We expect that the changes in social interactions that we document here will have consequences for individuals, groups, and for the ecology and evolution of populations, and therefore warrant a central place in the study of animal populations, particularly in an era of rapid environmental change.

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future.

This is the author accepted manuscript. The final version is available from Elsevier Masson via the DOI in this record.The social environment is a pervasive influence on the ecological and evolutionary dynamics of animal populations. Recently, social network analysis has provided an increasingly powerful and diverse toolset to enable animal behaviour researchers to quantify the social environment of animals and the impact that it has on ecological and evolutionary processes. However, there is considerable scope for improving these methods further. We outline an approach specifically designed to model the formation of network links, exponential random graph models (ERGMs), which have great potential for modelling animal social structure. ERGMs are generative models that treat network topology as a response variable. This makes them ideal for answering questions related directly to how and why social associations or interactions occur, from the modelling of population level transmission, through within-group behavioural dynamics to social evolutionary processes. We discuss how ERGMs have been used to study animal behaviour previously, and how recent developments in the ERGM framework can increase the scope of their use further. We also highlight the strengths and weaknesses of this approach relative to more conventional methods, and provide some guidance on the situations and research areas in which they can be used appropriately. ERGMs have the potential to be an important part of an animal behaviour researcher's toolkit and fully integrating them into the field should enhance our ability to understand what shapes animal social interactions, and identify the underlying processes that lead to the social structure of animal populations.M.J.S. is funded by a NERC grant NE/M004546/1. D.N.F. is funded by the Natural Sciences and Engineering Research Council of Canada

Investigating patterns of among and within-individual trait variation in populations is essential to understanding how selection shapes phenotypes. Behavior is often the most flexible aspect of the phenotype, and to understand how it is affected by selection, we need to examine how consistent individuals are. However, it is not well understood whether among-individual differences tend to remain consistent over lifetimes, or whether the behavior of individuals relative to one another varies over time. We examined the dynamics of 4 behavioral traits (tendency to leave a refuge, shyness, activity, and exploration) in a wild population of field crickets (Gryllus campestris). We tagged individuals and then temporarily removed them from their natural environment and tested them under laboratory conditions. All 4 traits showed among-individual variance in mean levels of expression across the adult lifespan, but no significant differences in how rapidly expression changed with age. For all traits, among-individual variance increased as individuals got older. Our findings reveal seldom examined changes in variance components over the adult lifetime of wild individuals. Such changes will have important implications for the relationship between behavioral traits, life-histories, and fitness and the consequences of selection on wild individuals.