2019-11, Papageorgiou, Danai, Christensen, Charlotte, Gall, Gabriella, Klarevas-Irby, James A., Nyaguthii, Brendah, Couzin, Iain D., Farine, Damien R.

Animal societies can be organised in multiple hierarchical tiers [1]. Such multilevel societies, where stable groups move together through the landscape, overlapping and associating preferentially with specific other groups, are thought to represent one of the most complex forms of social structure in vertebrates. For example, hamadryas baboons (Papio hamadryas) live in units consisting of one male and one or several females, or of several solitary males, that group into clans. These clans then come together with solitary bachelor males to form larger bands [2]. This social structure means that individuals have to track many different types of relationships at the same time [1,3]. Here, we provide detailed quantitative evidence for the presence of a multilevel society in a small-brained bird, the vulturine guineafowl (Acryllium vulturinum). We demonstrate that this species lives in large, multi-male, multi-female groups that associate preferentially with specific other groups, both during the day and at night-time communal roosts.

2019-06-10, Kao, Albert B., Couzin, Iain D.

Many animal groups exhibit signatures of persistent internal modular structure, whereby individuals consistently interact with certain groupmates more than others. In such groups, information relevant to a collective decision may spread unevenly through the group, but how this impacts the quality of the resulting decision is not well understood. Here, we explicitly model modularity within animal groups and examine how it affects the amount of information represented in collective decisions, as well as the accuracy of those decisions. We find that modular structure necessarily causes a loss of information, effectively silencing the input from a fraction of the group. However, the effect of this information loss on collective accuracy depends on the informational environment in which the decision is made. In simple environments, the information loss is detrimental to collective accuracy. By contrast, in complex environments, modularity tends to improve accuracy. This is because small group sizes typically maximize collective accuracy in such environments, and modular structure allows a large group to behave like a smaller group (in terms of its decision-making). These results suggest that in naturalistic environments containing correlated information, large animal groups may be able to exploit modular structure to improve decision accuracy while retaining other benefits of large group size. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.

2018-10, Couzin, Iain D.

From the rapidly expanding spiral waves exhibited by colonies of giant honeybees to the ripples of light that cross a turning school of fish, synchrony proves essential to the lives of group-living organisms. Here I consider what we know about the mechanisms and adaptive value of synchronization among animals, as well as outlining open questions that, if answered, could advance our understanding of the functional complexity of animal collectives.

2018-05-19, Torney, Colin J., Hopcraft, J. Grant C., Morrison, Thomas A., Couzin, Iain D., Levin, Simon A.

A central question in ecology is how to link processes that occur over different scales. The daily interactions of individual organisms ultimately determine community dynamics, population fluctuations and the functioning of entire ecosystems. Observations of these multiscale ecological processes are constrained by various technological, biological or logistical issues, and there are often vast discrepancies between the scale at which observation is possible and the scale of the question of interest. Animal movement is characterized by processes that act over multiple spatial and temporal scales. Second-by-second decisions accumulate to produce annual movement patterns. Individuals influence, and are influenced by, collective movement decisions, which then govern the spatial distribution of populations and the connectivity of meta-populations. While the field of movement ecology is experiencing unprecedented growth in the availability of movement data, there remain challenges in integrating observations with questions of ecological interest. In this article, we present the major challenges of addressing these issues within the context of the Serengeti wildebeest migration, a keystone ecological phenomena that crosses multiple scales of space, time and biological complexity.This article is part of the theme issue 'Collective movement ecology'.

2019-10-08, Sosna, Matthew M. G., Twomey, Colin R., Bak-Coleman, Joseph, Poel, Winnie, Daniels, Bryan C., Romanczuk, Pawel, Couzin, Iain D.

The need to make fast decisions under risky and uncertain conditions is a widespread problem in the natural world. While there has been extensive work on how individual organisms dynamically modify their behavior to respond appropriately to changing environmental conditions (and how this is encoded in the brain), we know remarkably little about the corresponding aspects of collective information processing in animal groups. For example, many groups appear to show increased "sensitivity" in the presence of perceived threat, as evidenced by the increased frequency and magnitude of repeated cascading waves of behavioral change often observed in fish schools and bird flocks under such circumstances. How such context-dependent changes in collective sensitivity are mediated, however, is unknown. Here we address this question using schooling fish as a model system, focusing on 2 nonexclusive hypotheses: 1) that changes in collective responsiveness result from changes in how individuals respond to social cues (i.e., changes to the properties of the "nodes" in the social network), and 2) that they result from changes made to the structural connectivity of the network itself (i.e., the computation is encoded in the "edges" of the network). We find that despite the fact that perceived risk increases the probability for individuals to initiate an alarm, the context-dependent change in collective sensitivity predominantly results not from changes in how individuals respond to social cues, but instead from how individuals modify the spatial structure, and correspondingly the topology of the network of interactions, within the group. Risk is thus encoded as a collective property, emphasizing that in group-living species individual fitness can depend strongly on coupling between scales of behavioral organization.

2019, Rahwan, Iyad, Cebrian, Manuel, Obradovich, Nick, Bongard, Josh, Bonnefon, Jean-François, Breazeal, Cynthia, Crandall, Jacob W., Christakis, Nicholas A., Couzin, Iain D., Jackson, Matthew O.

Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. Here we argue that this necessitates a broad scientific research agenda to study machine behaviour that incorporates and expands upon the discipline of computer science and includes insights from across the sciences. We first outline a set of questions that are fundamental to this emerging field and then explore the technical, legal and institutional constraints on the study of machine behaviour.

2018-09, Koblitz, Jens C., Frokosh, Oren, Nagy, Mate, Carlson, Nora V., Naik, Hemal, Couzin, Iain D.

Acoustic localization has been used to track numerous vocalizing animals, including whales, bats birds by measuring the time of arrival differences of a sound recorded by multiple receivers. This method, however, requires the species of interest to vocalize regularly to achieve decent temporal resolution. In order to track the movements of birds that do not vocalize regularly, a miniature, radio controlled ultrasonic speaker is attached to the birds and constantly emits ultrasound chirps. The chirps from various tagged animals can be discriminated based on information encoded in the signal. A 30 microphone array covering the ceiling of a large aviary (14.8 × 6.6 × 3.9 m) is used to record the chirps and provides the basis for accurate localization of the sources. As the chirps do not overlap with the frequency of the animals vocalizations, these can be localized and assigned individually in a flock of moving birds. In addition, a VICON motion capture system provides a simple and accurate method to ground truth the acoustic localizations as it records very precise movement information of the individuals while line of sight is between the marker on the animal and a number of cameras is established. The acoustic and visual tracking systems working together provides a unique and novel answer to consistent and precise localizations of non-vocalizing individuals (and groups of individuals) as they move through space.

2019-10-01, Graving, Jacob M., Chae, Daniel, Naik, Hemal, Li, Liang, Koger, Benjamin, Costelloe, Blair R., Couzin, Iain D.

Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal's body parts directly from images or videos. However, currently-available animal pose estimation methods have limitations in speed and robustness. Here we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2× with no loss in accuracy compared to currently-available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings-including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.

2018-11-27, Hein, Andrew M., Gil, Michael A., Twomey, Colin R., Couzin, Iain D., Levin, Simon A.

To evade their predators, animals must quickly detect potential threats, gauge risk, and mount a response. Putative neural circuits responsible for these tasks have been isolated in laboratory studies. However, it is unclear whether and how these circuits combine to generate the flexible, dynamic sequences of evasion behavior exhibited by wild, freely moving animals. Here, we report that evasion behavior of wild fish on a coral reef is generated through a sequence of well-defined decision rules that convert visual sensory input into behavioral actions. Using an automated system to present visual threat stimuli to fish in situ, we show that individuals initiate escape maneuvers in response to the perceived size and expansion rate of an oncoming threat using a decision rule that matches dynamics of known loom-sensitive neural circuits. After initiating an evasion maneuver, fish adjust their trajectories using a control rule based on visual feedback to steer away from the threat and toward shelter. These decision rules accurately describe evasion behavior of fish from phylogenetically distant families, illustrating the conserved nature of escape decision-making. Our results reveal how the flexible behavioral responses required for survival can emerge from relatively simple, conserved decision-making mechanisms.

2018-08, Delcourt, Johann, Miller, Noam Y., Couzin, Iain D., Garnier, Simon

Collective behaviors are observed throughout nature, from bacterial colonies to human societies. Important theoretical breakthroughs have recently been made in understanding why animals produce group behaviors and how they coordinate their activities, build collective structures, and make decisions. However, standardized experimental methods to test these findings have been lacking. Notably, easily and unambiguously determining the membership of a group and the responses of an individual within that group is still a challenge. The radial arm maze is presented here as a new standardized method to investigate collective exploration and decision-making in animal groups. This paradigm gives individuals within animal groups the opportunity to make choices among a set of discrete alternatives, and these choices can easily be tracked over long periods of time. We demonstrate the usefulness of this paradigm by performing a set of refuge-site selection experiments with groups of fish. Using an open-source, robust custom image-processing algorithm, we automatically counted the number of animals in each arm of the maze to identify the majority choice. We also propose a new index to quantify the degree of group cohesion in this context. The radial arm maze paradigm provides an easy way to categorize and quantify the choices made by animals. It makes it possible to readily apply the traditional uses of the radial arm maze with single animals to the study of animal groups. Moreover, it opens up the possibility of studying questions specifically related to collective behaviors.