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Studying animal behavior as collective phenomena is a powerful tool for understanding social processes, including group coordination and decision-making. However, linking individual behavior during group decision-making to the preferences underlying those actions poses a considerable challenge. Optimal foraging theory, and specifically the marginal value theorem (MVT), can provide predictions about individual preferences, against which the behavior of groups can be compared under different models of influence. A major strength of formally linking optimal foraging theory to collective behavior is that it generates predictions that can easily be tested under field conditions. This opens the door to studying group decision-making in a range of species; a necessary step for revealing the ecological drivers and evolutionary consequences of collective decision-making. A growing body of evidence emerging from the analysis of advanced animal tracking data shows that moving groups make shared decisions about where to go, with each group member influencing the outcome. How groups coordinate departure decisions (when to go), however, remains poorly understood. Classic models from optimal foraging theory, specifically the marginal value theorem (MVT), are well-established tools that can generate quantitative predictions about when individuals should prefer to leave a food patch, given patch quality and the distribution of patches in the environment. Integrating optimal foraging theory into studies of animal collectives provides rich opportunities for gaining new insights from both empirical and theoretical studies. Specifically, the MVT can be used to make predictions about conflict of interests among group members, how consensus costs vary under different models of collective decision-making, and under what environmental conditions shared decision-making may be favored or disfavored. [ABSTRACT FROM AUTHOR]

Animal social groups are complex systems that are likely to exhibit tipping points--which are defined as drastic shifts in the dynamics of systems that arise from small changes in environmental conditions--yet this concept has not been carefully applied to these systems. Here, we summarize the concepts behind tipping points and describe instances in which they are likely to occur in animal societies. We also offer ways in which the study of social tipping points can open up new lines of inquiry in behavioural ecology and generate novel questions, methods, and approaches in animal behaviour and other fields, including community and ecosystem ecology. While some behaviours of living systems are hard to predict, we argue that probing tipping points across animal societies and across tiers of biological organization--populations, communities, ecosystems--may help to reveal principles that transcend traditional disciplinary boundaries. [ABSTRACT FROM AUTHOR]

Social network analysis is quickly becoming an established framework to study the structure of animal social systems. To explore the social network of a population, observers must capture data on the interactions or associations between individuals. Sampling decisions significantly impact the outcome of data collection, notably the amount of data available from which to construct social networks. However, little is known about how different sampling methods, and more generally the extent of sampling effort, impact the robustness of social network analyses. Here, we generate proximity networks from data obtained via nearly continuous GPS tracking of members of a wild baboon troop ( Papio anubis ). These data allow us to produce networks based on complete observations of interindividual distances between group members. We then mimic several widely used focal animal sampling and group scanning methods by subsampling the complete data set to simulate observational data comparable to that produced by human observers. We explore how sampling effort, sampling methods, network definitions and levels and types of sampling error affect the correlation between the estimated and complete networks. Our results suggest that for some scenarios, even low levels of sampling effort (5–10 samples/individual) can provide the same information as high sampling effort (>64 samples/individual). However, we find that insufficient data collected across all potentially interacting individuals, certain network definitions (how edge weights and distance thresholds are calculated) and misidentifications of individuals in the network can generate spurious network structure with little or no correlation to the underlying or ‘real’ social structure. Our results suggest that data collection methods should be designed to maximize the number of potential interactions (edges) recorded for each observation. We discuss the relative trade-offs between maximizing the amount of data collected across as many individuals as possible and the potential for erroneous observations. [ABSTRACT FROM AUTHOR]

Collective decision-making is a daily occurrence in the lives of many groupliving animals, and can have critical consequences for the fitness of individuals. Understanding howdecisions are reached, includingwho has influence and the mechanisms by which information and preferences are integrated, has posed a fundamental challenge. Here, we provide a methodological framework for studying influence and leadership in groups. We propose that individuals have influence if their actions result in some behavioural change among their group-mates, and are leaders if they consistently influence others.We highlight three components of influence (influence instances, total influence and consistency of influence), which can be assessed at two levels (individual-toindividual and individual-to-group). We then review different methods, ranging from individual positioning within groups to information-theoretic approaches, by which influence has been operationally defined in empirical studies, as well as how such observations can be aggregated to give insight into the underlying decision-making process.We focus on the domain of collective movement,with a particular emphasis on methods that have recently been, or are being, developed to take advantage of simultaneous tracking data. We aimto provide a resource bringing together methodological tools currently available for studying leadership in moving animal groups, as well as to discuss the limitations of current methodologies and suggest productive avenues for future research. This article is part of the theme issue 'Collective movement ecology'. [ABSTRACT FROM AUTHOR]

The article reports that technological innovations integrating high-resolution global positioning system and other detectors into miniaturized tracking tags has significantly improved the ability of researchers to describe animal movement. It comments on the parallels with big data transformations in other research fields. It talks about insights on species interactions as a result of simultaneous tracking of multiple animals and the use of remote sensing and new analytical approaches.

A correction is presented to the article "Locomotor compromises maintain group cohesion in baboon troops on the move" which appeared in the 28 July 2021 issue.

A correction is presented to the article "Animal lifestyle affects acceptable mass limits for attached tags" which appeared in the 27 October 2021 issue.