Inferring influence and leadership in moving animal groups

Strandburg‐Peshkin, Ariana; Papageorgiou, Danai; Crofoot, Margaret C.; Farine, Dan

doi:10.1098/rstb.2017.0006

Cited by 113 publications

(96 citation statements)

References 98 publications

(127 reference statements)

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“…For example, in group-living species or species with herding or swarming behaviour (e.g. starlings, many fishes, some ungulates), aggregations are an important means to avoid predation or to forage efficiently (Fryxell & Berdahl, 2018), and there has been much progress in understanding the fine-scale dynamics of group coordination (Couzin, Krause, Franks, & Levin, 2005;Strandburg-Peshkin, Papageorgiou, Crofoot, & Farine, 2018). For non-gregarious species, we know much less about if and how individuals seek or avoid each other.…”

Section: Introductionmentioning

confidence: 99%

Estimating interactions between individuals from concurrent animal movements

et al. 2019

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Animal movements arise from complex interactions of individuals with their environment, including both conspecific and heterospecific individuals. Animals may be attracted to each other for mating, social foraging, or information gain, or may keep at a distance from others to avoid aggressive encounters related to, e.g., interference competition, territoriality, or predation. With modern tracking technology, more datasets are emerging that allow to investigate fine‐scale interactions between free‐ranging individuals from movement data, however, few methods exist to disentangle fine‐scale behavioural responses of interacting individuals when these are highly individual‐specific. In a framework of step‐selection functions, we related movements decisions of individuals to dynamic occurrence distributions of other individuals obtained through kriging of their movement paths. Using simulated data, we tested the method's ability to identify various combinations of attraction, avoidance, and neutrality between individuals, including asymmetric (i.e. non‐mutual) behaviours. Additionally, we analysed radio‐telemetry data from concurrently tracked small rodents (bank vole, Myodes glareolus) to test whether our method could detect biologically plausible behaviours. We found that our method was able to successfully detect and distinguish between fine‐scale interactions (attraction, avoidance, neutrality), even when these were asymmetric between individuals. The method worked best when confounding factors were taken into account in the step‐selection function. However, even when failing to do so (e.g. due to missing information), interactions could be reasonably identified. In bank voles, responses depended strongly on the sexes of the involved individuals and matched expectations. Our approach can be combined with conventional uses of step‐selection functions to tease apart the various drivers of movement, e.g. the influence of the physical and the social environment. In addition, the method is particularly useful in studying interactions when responses are highly individual‐specific, i.e. vary between and towards different individuals, making our method suitable for both single‐species and multi‐species analyses (e.g. in the context of predation or competition).

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Section: Introductionmentioning

confidence: 99%

Estimating interactions between individuals from concurrent animal movements

et al. 2019

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“…This simplification might be disadvantageous for modelling certain behaviours, since humans and animals usually act neither strictly simultaneously nor sequentially, but observe the choices of each other and adjust their actions accordingly [2]. Indeed, the visibility of the partner's actions plays a crucial role in social interactions, both in laboratory experiments [3][4][5][6] and in natural environments [7][8][9][10][11].…”

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confidence: 99%

Emergence and suppression of cooperation by action visibility in transparent games

Unakafov

Schultze

Kagan

et al. 2018

Preprint

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Real-world agents, such as humans, animals and robots, observe each other during interactions and choose their own actions taking the partners' ongoing behaviour into account. Yet, classical game theory assumes that players act either strictly sequentially or strictly simultaneously (without knowing the choices of each other). To account for action visibility and provide a more realistic model of interactions under time constraints, we introduce a new game-theoretic setting called transparent game, where each player has a certain probability to observe the choice of the partner before deciding on its own action. Using evolutionary simulations, we demonstrate that even a small probability of seeing the partner's choice before one's own decision substantially changes evolutionary successful strategies. Action visibility enhances cooperation in a Bach-or-Stravinsky game, but disrupts cooperation in a more competitive iterated Prisoner's Dilemma. In both games, strategies based on the "Win-stay, lose-shift" and "Tit-for-tat" principles are predominant for moderate transparency, while for high transparency strategies of "Leader-Follower" type emerge. Our results have implications for studies of human and animal social behaviour, especially for the analysis of dyadic and group interactions.One of the most interesting questions in economics, biological, and social sciences is the emergence and maintenance of cooperation. A popular framework for studying cooperation (or the lack thereof) is Game Theory, which is frequently used to model interactions between "rational" decision-makers. In particular, a model for repeated interactions is provided by iterated games; two settings were previously used [1]:• Simultaneous games: players act at the same time without having any information about the current choice of the partners. Consequently, all players must make a decision under uncertainty concerning the choices of others.• Sequential games: players act in a certain order and the player acting later in the sequence is guaranteed to see the choices of the preceding players. Here the burden of uncertainty only applies to the first player or -if there are more than two players -becomes lighter with every turn in the sequence.Both settings place a simplifying restriction on the decisional context: either all players have no information about the choices of the partners (simultaneous game), or some players always have more information than others (sequential game). This simplification might be disadvantageous for modelling certain behaviours, since humans and animals usually act neither strictly simultaneously nor

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“…Distribution of Leadership. In animal groups decisions range from full distributed among all group members ('democratic') to dominated by a single or a few individuals ('despotic') 8,12 . It can be informative to quantify the number of individuals involved in a leadership role within the group.…”

Section: B Characteristics Of Leadershipmentioning

confidence: 99%

“…It can be informative to quantify the number of individuals involved in a leadership role within the group. Similar to 12 we refer to this as the distribution of leadership which we define on a continuum that lies between centralized and distributed leadership.…”

Section: B Characteristics Of Leadershipmentioning

confidence: 99%

Anatomy of leadership in collective behaviour

Garland

Berdahl

Sun

et al. 2018

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Understanding the mechanics behind the coordinated movement of mobile animal groups (collective motion) provides key insights into their biology and ecology, while also yielding algorithms for bio-inspired technologies and autonomous systems. It is becoming increasingly clear that many mobile animal groups are composed of heterogeneous individuals with differential levels and types of influence over group behaviors. The ability to infer this differential influence, or leadership, is critical to understanding group functioning in these collective animal systems. Due to the broad interpretation of leadership, many different measures and mathematical tools are used to describe and infer "leadership," e.g., position, causality, influence, and information flow. But a key question remains: which, if any, of these concepts actually describes leadership? We argue that instead of asserting a single definition or notion of leadership, the complex interaction rules and dynamics typical of a group imply that leadership itself is not merely a binary classification (leader or follower), but rather, a complex combination of many different components. In this paper, we develop an anatomy of leadership, identify several principal components, and provide a general mathematical framework for discussing leadership. With the intricacies of this taxonomy in mind, we present a set of leadership-oriented toy models that should be used as a proving ground for leadership inference methods going forward. We believe this multifaceted approach to leadership will enable a broader understanding of leadership and its inference from data in mobile animal groups and beyond.

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Inferring influence and leadership in moving animal groups

Cited by 113 publications

References 98 publications

Estimating interactions between individuals from concurrent animal movements

Estimating interactions between individuals from concurrent animal movements

Emergence and suppression of cooperation by action visibility in transparent games

Anatomy of leadership in collective behaviour

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