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  • 1. Aplin, Lucy M.
    et al.
    Farine, Damien R.
    Mann, Richard P.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Sheldon, Ben C.
    Individual-level personality influences social foraging and collective behaviour in wild birds2014In: Proceedings of the Royal Society of London. Biological Sciences, ISSN 0962-8452, E-ISSN 1471-2954, Vol. 281, no 1789, p. 20141016-Article in journal (Refereed)
    Abstract [en]

    There is increasing evidence that animal groups can maintain coordinated behaviour and make collective decisions based on simple interaction rules. Effective collective action may be further facilitated by individual variation within groups, particularly through leader-follower polymorphisms. Recent studies have suggested that individual-level personality traits influence the degree to which individuals use social information, are attracted to conspecifics, or act as leaders/followers. However, evidence is equivocal and largely limited to laboratory studies. We use an automated data-collection system to conduct an experiment testing the relationship between personality and collective decision-making in the wild. First, we report that foraging flocks of great tits (Parus major) show strikingly synchronous behaviour. A predictive model of collective decision-making replicates patterns well, suggesting simple interaction rules are sufficient to explain the observed social behaviour. Second, within groups, individuals with more reactive personalities behave more collectively, moving to within-flock areas of higher density. By contrast, proactive individuals tend to move to and feed at spatial periphery of flocks. Finally, comparing alternative simulations of flocking with empirical data, we demonstrate that variation in personality promotes within-patch movement while maintaining group cohesion. Our results illustrate the importance of incorporating individual variability in models of social behaviour.

  • 2. Farine, Damien R.
    et al.
    Aplin, Lucy M.
    Garroway, Colin J.
    Mann, Richard P.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Sheldon, Ben C.
    Collective decision making and social interaction rules in mixed-species flocks of songbirds2014In: Animal Behaviour, ISSN 0003-3472, E-ISSN 1095-8282, Vol. 95, p. 173-182Article in journal (Refereed)
    Abstract [en]

    Associations in mixed-species foraging groups are common in animals, yet have rarely been explored in the context of collective behaviour. Despite many investigations into the social and ecological conditions under which individuals should form groups, we still know little about the specific behavioural rules that individuals adopt in these contexts, or whether these can be generalized to heterospecifics. Here, we studied collective behaviour in flocks in a community of five species of woodland passerine birds. We adopted an automated data collection protocol, involving visits by RFID-tagged birds to feeding stations equipped with antennae, over two winters, recording 91576 feeding events by 1904 individuals. We demonstrated highly synchronized feeding behaviour within patches, with birds moving towards areas of the patch with the largest proportion of the flock. Using a model of collective decision making, we then explored the underlying decision rule birds may be using when foraging in mixed-species flocks. The model tested whether birds used a different decision rule for conspecifics and heterospecifics, and whether the rules used by individuals of different species varied. We found that species differed in their response to the distribution of conspecifics and heterospecifics across foraging patches. However, simulating decisions using the different rules, which reproduced our data well, suggested that the outcome of using different decision rules by each species resulted in qualitatively similar overall patterns of movement. It is possible that the decision rules each species uses may be adjusted to variation in mean species abundance in order for individuals to maintain the same overall flock-level response. This is likely to be important for maintaining coordinated behaviour across species, and to result in quick and adaptive flock responses to food resources that are patchily distributed in space and time.  

  • 3.
    Forsman, Jonas
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Physics Didactics.
    Mann, Richard P.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Linder, Cedric
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Physics Didactics.
    Van den Bogaard, Maartje
    Delft University.
    Sandbox University: Estimating Influence of Institutional Action2014In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 7, p. e103261-Article in journal (Refereed)
    Abstract [en]

    The approach presented in this article represents a generalizable and adaptable methodology for identifying complexinteractions in educational systems and for investigating how manipulation of these systems may affect educationaloutcomes of interest. Multilayer Minimum Spanning Tree and Monte-Carlo methods are used. A virtual Sandbox Universityis created in order to facilitate effective identification of successful and stable initiatives within higher education, which canaffect students’ credits and student retention – something that has been lacking up until now. The results highlight theimportance of teacher feedback and teacher-student rapport, which is congruent with current educational findings,illustrating the methodology’s potential to provide a new basis for further empirical studies of issues in higher educationfrom a complex systems perspective.

  • 4. Freeman, Robin
    et al.
    Mann, Richard
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Guilford, Tim
    Biro, Dora
    Group decisions and individual differences: route fidelity predicts flight leadership in homing pigeons (Columba livia)2011In: Biology Letters, ISSN 1744-9561, E-ISSN 1744-957X, Vol. 7, no 1, p. 63-66Article in journal (Refereed)
    Abstract [en]

    How social-living animals make collective decisions is currently the subject of intense scientific interest, with increasing focus on the role of individual variation within the group. Previously, we demonstrated that during paired flight in homing pigeons, a fully transitive leadership hierarchy emerges as birds are forced to choose between their own and their partner's habitual routes. This stable hierarchy suggests a role for individual differences mediating leadership decisions within homing pigeon pairs. What these differences are, however, has remained elusive. Using novel quantitative techniques to analyse habitual route structure, we show here that leadership can be predicted from prior route-following fidelity. Birds that are more faithful to their own route when homing alone are more likely to emerge as leaders when homing socially. We discuss how this fidelity may relate to the leadership phenomenon, and propose that leadership may emerge from the interplay between individual route confidence and the dynamics of paired flight.

  • 5. Herbert-Read, James E.
    et al.
    Perna, Andrea
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Mann, Richard P.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Schaerf, Timothy M.
    Sumpter, David J. T.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Ward, Ashley J. W.
    Inferring the rules of interaction of shoaling fish2011In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 108, no 46, p. 18726-18731Article in journal (Refereed)
    Abstract [en]

    Collective motion, where large numbers of individuals move synchronously together, is achieved when individuals adopt interaction rules that determine how they respond to their neighbors' movements and positions. These rules determine how group-living animals move, make decisions, and transmit information between individuals. Nonetheless, few studies have explicitly determined these interaction rules in moving groups, and very little is known about the interaction rules of fish. Here, we identify three key rules for the social interactions of mosquitofish (Gambusia holbrooki): (i) Attraction forces are important in maintaining group cohesion, while we find only weak evidence that fish align with their neighbor's orientation; (ii) repulsion is mediated principally by changes in speed; (iii) although the positions and directions of all shoal members are highly correlated, individuals only respond to their single nearest neighbor. The last two of these rules are different from the classical models of collective animal motion, raising new questions about how fish and other animals self-organize on the move.

  • 6.
    Kolm, Niclas
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Animal Ecology.
    Amcoff, Mirjam
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Animal Ecology.
    Mann, Richard P.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Arnqvist, Göran
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Animal Ecology.
    Diversification of a Food-Mimicking Male Ornament via Sensory Drive2012In: Current Biology, ISSN 0960-9822, E-ISSN 1879-0445, Vol. 22, no 15, p. 1440-1443Article in journal (Refereed)
    Abstract [en]

    The evolutionary divergence of sexual signals is often important during the formation of new animal species, but our understanding of the origin of signal diversity is limited [1, 2]. Sensory drive, the optimization of communication signal efficiency through matching to the local environment, has been highlighted as a potential promoter of diversification and speciation [3]. The swordtail characin (Corynopoma riisei) is a tropical fish in which males display a flag-like ornament that elicits female foraging behavior during courtship. We show that the shape of the male ornament covaries with female diet across natural populations. More specifically, natural populations in which the female diet is more dominated by ants exhibit male ornaments more similar to the shape of an ant. Feeding experiments confirm that females habituated to a diet of ants prefer to bite at male ornaments from populations with a diet more dominated by ants. Our results show that the male ornament functions as a "fishing lure" that is diversifying in shape to match local variation in female search images employed during foraging. This direct link between variation in female feeding ecology and the evolutionary diversification of male sexual ornaments suggests that sensory drive may be a common engine of signal divergence.

  • 7.
    Mann, R. P.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Herbert-Read, James E.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Animal ecology.
    Ma, Q.
    Jordan, L. A.
    Sumpter, David J. T.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Ward, A. J. W.
    A model comparison reveals dynamic social information drives the movements of humbug damselfish (Dascyllus aruanus)2014In: Journal of the Royal Society Interface, ISSN 1742-5689, E-ISSN 1742-5662, Vol. 11, no 90, p. 20130794-Article in journal (Refereed)
    Abstract [en]

    Animals make use a range of social information to inform their movement decisions. One common movement rule, found across many different species, is that the probability that an individual moves to an area increases with the number of conspecifics there. However, in many cases, it remains unclear what social cues produce this and other similar movement rules. Here, we investigate what cues are used by damselfish (Dascyllus aruanus) when repeatedly crossing back and forth between two coral patches in an experimental arena. We find that an individual's decision to move is best predicted by the recent movements of conspecifics either to or from that individual's current habitat. Rather than actively seeking attachment to a larger group, individuals are instead prioritizing highly local and dynamic information with very limited spatial and temporal ranges. By reanalysing data in which the same species crossed for the first time to a new coral patch, we show that the individuals use static cues in this case. This suggests that these fish alter their information usage according to the structure and familiarity of their environment by using stable information when moving to a novel area and localized dynamic information when moving between familiar areas.

  • 8.
    Mann, Richard P.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Bayesian Inference for Identifying Interaction Rules in Moving Animal Groups2011In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 6, no 8, p. e22827-Article in journal (Refereed)
    Abstract [en]

    The emergence of similar collective patterns from different self-propelled particle models of animal groups points to a restricted set of "universal'' classes for these patterns. While universality is interesting, it is often the fine details of animal interactions that are of biological importance. Universality thus presents a challenge to inferring such interactions from macroscopic group dynamics since these can be consistent with many underlying interaction models. We present a Bayesian framework for learning animal interaction rules from fine scale recordings of animal movements in swarms. We apply these techniques to the inverse problem of inferring interaction rules from simulation models, showing that parameters can often be inferred from a small number of observations. Our methodology allows us to quantify our confidence in parameter fitting. For example, we show that attraction and alignment terms can be reliably estimated when animals are milling in a torus shape, while interaction radius cannot be reliably measured in such a situation. We assess the importance of rate of data collection and show how to test different models, such as topological and metric neighbourhood models. Taken together our results both inform the design of experiments on animal interactions and suggest how these data should be best analysed.

  • 9.
    Mann, Richard P.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Armstrong, Chris
    Meade, Jessica
    Freeman, Robin
    Biro, Dora
    Guilford, Tim
    Landscape complexity influences route-memory formation in navigating pigeons2014In: Biology Letters, ISSN 1744-9561, E-ISSN 1744-957X, Vol. 10, no 1, p. 20130885-Article in journal (Refereed)
    Abstract [en]

    Observations of the flight paths of pigeons navigating from familiar locations have shown that these birds are able to learn and subsequently follow habitual routes home. It has been suggested that navigation along these routes is based on the recognition of memorized visual landmarks. Previous research has identified the effect of landmarks on flight path structure, and thus the locations of potentially salient sites. Pigeons have also been observed to be particularly attracted to strong linear features in the landscape, such as roads and rivers. However, a more general understanding of the specific characteristics of the landscape that facilitate route learning has remained out of reach. In this study, we identify landscape complexity as a key predictor of the fidelity to the habitual route, and thus conclude that pigeons form route memories most strongly in regions where the landscape complexity is neither too great nor too low. Our results imply that pigeons process their visual environment on a characteristic spatial scale while navigating and can explain the different degrees of success in reproducing route learning in different geographical locations.

  • 10.
    Mann, Richard P.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Faria, Jolyon
    Sumpter, David J. T.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Krause, Jens
    The dynamics of audience applause2013In: Journal of the Royal Society Interface, ISSN 1742-5689, E-ISSN 1742-5662, Vol. 10, no 85, p. 20130466-Article in journal (Refereed)
    Abstract [en]

    The study of social identity and crowd psychology looks at how and why individual people change their behaviour in response to others. Within a group, a new behaviour can emerge first in a few individuals before it spreads rapidly to all other members. A number of mathematical models have been hypothesized to describe these social contagion phenomena, but these models remain largely untested against empirical data. We used Bayesian model selection to test between various hypotheses about the spread of a simple social behaviour, applause after an academic presentation. Individuals' probability of starting clapping increased in proportion to the number of other audience members already 'infected' by this social contagion, regardless of their spatial proximity. The cessation of applause is similarly socially mediated, but is to a lesser degree controlled by the reluctance of individuals to clap too many times. We also found consistent differences between individuals in their willingness to start and stop clapping. The social contagion model arising from our analysis predicts that the time the audience spends clapping can vary considerably, even in the absence of any differences in the quality of the presentations they have heard.

  • 11.
    Mann, Richard P.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Perna, Andrea
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Strömbom, Daniel
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Garnett, Roman
    Herbert-Read, James E.
    Sumpter, David J. T.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Ward, Ashley J. W.
    Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection2012In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 8, no 1, p. e1002308-Article in journal (Refereed)
    Abstract [en]

    Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical 'phase transition', whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have 'memory' of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture fine scale rules of interaction, which are primarily mediated by physical contact. Conversely, the Markovian self-propelled particle model captures the fine scale rules of interaction but fails to reproduce global dynamics. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects.

  • 12.
    Ranganathan, Shyam
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Spaiser, Viktoria
    Mann, Richard P.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Sumpter, David J. T.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Bayesian Dynamical Systems Modelling in the Social Sciences2014In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 1, p. e86468-Article in journal (Refereed)
    Abstract [en]

    Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach.

  • 13. Sasaki, Takao
    et al.
    Granovskiy, Boris
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Mann, Richard P.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Sumpter, David J. T.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Pratt, Stephen C.
    Ant colonies outperform individuals when a sensory discrimination task is difficult but not when it is easy2013In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 110, no 34, p. 13769-13773Article in journal (Refereed)
    Abstract [en]

    "Collective intelligence" and "wisdom of crowds" refer to situations in which groups achieve more accurate perception and better decisions than solitary agents. Whether groups outperform individuals should depend on the kind of task and its difficulty, but the nature of this relationship remains unknown. Here we show that colonies of Temnothorax ants outperform individuals for a difficult perception task but that individuals do better than groups when the task is easy. Subjects were required to choose the better of two nest sites as the quality difference was varied. For small differences, colonies were more likely than isolated ants to choose the better site, but this relationship was reversed for large differences. We explain these results using a mathematical model, which shows that positive feedback between group members effectively integrates information and sharpens the discrimination of fine differences. When the task is easier the same positive feedback can lock the colony into a suboptimal choice. These results suggest the conditions under which crowds do or do not become wise.

  • 14. Spaiser, Viktoria
    et al.
    Ranganathan, Shyam
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Mann, Richard P.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Sumpter, David J. T.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    The Dynamics of Democracy, Development and Cultural Values2014In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 6, p. e97856-Article in journal (Refereed)
    Abstract [en]

    Over the past decades many countries have experienced rapid changes in their economies, their democratic institutions and the values of their citizens. Comprehensive data measuring these changes across very different countries has recently become openly available. Between country similarities suggest common underlying dynamics in how countries develop in terms of economy, democracy and cultural values. We apply a novel Bayesian dynamical systems approach to identify the model which best captures the complex, mainly non-linear dynamics that underlie these changes. We show that the level of Human Development Index (HDI) in a country drives first democracy and then higher emancipation of citizens. This change occurs once the countries pass a certain threshold in HDI. The data also suggests that there is a limit to the growth of wealth, set by higher emancipation. Having reached a high level of democracy and emancipation, societies tend towards equilibrium that does not support further economic growth. Our findings give strong empirical evidence against a popular political science theory, known as the Human Development Sequence. Contrary to this theory, we find that implementation of human-rights and democratisation precede increases in emancipative values.

  • 15.
    Strömbom, Daniel
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Mann, Richard P.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Wilson, Alan M.
    University of London.
    Hailes, Stephen
    University College of London.
    Morton, A. Jennifer
    University of Cambridge.
    Sumpter, David J. T.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    King, Andrew J.
    Swansea University.
    Solving the shepherding problem: Heuristics for herding autonomous, interacting agents2014In: Journal of the Royal Society Interface, ISSN 1742-5689, E-ISSN 1742-5662, Vol. 11, no 100, p. 20140719-Article in journal (Refereed)
    Abstract [en]

    Herding of sheep by dogs is a powerful example of one individual causing many unwilling individuals to move in the same direction. Similar phenomena are central to crowd control, cleaning the environment and other engineering problems. Despite single dogs solving this 'shepherding problem' every day, it remains unknown which algorithm they employ or whether a general algorithm exists for shepherding. Here, we demonstrate such an algorithm, based on adaptive switching between collecting the agents when they are too dispersed and driving them once they are aggregated. Our algorithm reproduces key features of empirical data collected from sheep-dog interactions and suggests new ways in which robots can be designed to influence movements of living and artificial agents.

  • 16.
    Sumpter, David J. T.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Mann, Richard P.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Perna, Andrea
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    The modelling cycle for collective animal behaviour2012In: INTERFACE FOCUS, ISSN 2042-8898, Vol. 2, no 6, p. 764-773Article, review/survey (Refereed)
    Abstract [en]

    Collective animal behaviour is the study of how interactions between individuals produce group level patterns, and why these interactions have evolved. This study has proved itself uniquely interdisciplinary, involving physicists, mathematicians, engineers as well as biologists. Almost all experimental work in this area is related directly or indirectly to mathematical models, with regular movement back and forth between models, experimental data and statistical fitting. In this paper, we describe how the modelling cycle works in the study of collective animal behaviour. We classify studies as addressing questions at different levels or linking different levels, i.e. as local, local to global, global to local or global. We also describe three distinct approaches-theory-driven, data-driven and model selection-to these questions. We show, with reference to our own research on species across different taxa, how we move between these different levels of description and how these various approaches can be applied to link levels together.

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