Inferring the rules of social interaction in migrating caribou

Social interactions are a significant factor that influence the decision-making of species ranging from humans to bacteria. In the context of animal migration, social interactions may lead to improved decision-making, greater ability to respond to environmental cues, and the cultural transmission of optimal routes. Despite their significance, the precise nature of social interactions in migrating species remains largely unknown. Here we deploy unmanned aerial systems to collect aerial footage of caribou as they undertake their migration from Victoria Island to mainland Canada. Through a Bayesian analysis of trajectories we reveal the fine-scale interaction rules of migrating caribou and show they are attracted to one another and copy directional choices of neighbours, but do not interact through clearly defined metric or topological interaction ranges. By explicitly considering the role of social information on movement decisions we construct a map of near neighbour influence that quantifies the nature of information flow in these herds. These results will inform more realistic, mechanism-based models of migration in caribou and other social ungulates, leading to better predictions of spatial use patterns and responses to changing environmental conditions. Moreover, we anticipate that the protocol we developed here will be broadly applicable to study social behaviour in a wide range of migratory and non-migratory taxa. This article is part of the theme issue ‘Collective movement ecology’.

[1]  P. Lent Calving and related social behavior in the barren-ground caribou. , 1966, Zeitschrift fur Tierpsychologie.

[2]  T. Vicsek,et al.  Hierarchical group dynamics in pigeon flocks , 2010, Nature.

[3]  I. Couzin,et al.  Inferring the structure and dynamics of interactions in schooling fish , 2011, Proceedings of the National Academy of Sciences.

[4]  A. Berdahl,et al.  Collective decision-making promotes fitness loss in a fusion-fission society. , 2017, Ecology letters.

[5]  Garrett M. Street,et al.  Space-use behaviour of woodland caribou based on a cognitive movement model. , 2015, The Journal of animal ecology.

[6]  D. Sumpter,et al.  Inferring the rules of interaction of shoaling fish , 2011, Proceedings of the National Academy of Sciences.

[7]  Justin M. J. Travis,et al.  The evolution of an ‘intelligent’ dispersal strategy: biased, correlated random walks in patchy landscapes , 2009 .

[8]  A. Gunn,et al.  Traditional behaviour and fidelity to caribou calving grounds by barren-ground caribou , 1986 .

[9]  Michael C. Hatfield,et al.  Unmanned aircraft systems in wildlife research: current and future applications of a transformative technology , 2016 .

[10]  Damien R. Farine,et al.  Both Nearest Neighbours and Long-term Affiliates Predict Individual Locations During Collective Movement in Wild Baboons , 2016, Scientific Reports.

[11]  M. Dumond,et al.  Sea Ice and Migration of the Dolphin and Union Caribou Herd in the Canadian Arctic: An Uncertain Future , 2010 .

[12]  R. Kays,et al.  Terrestrial animal tracking as an eye on life and planet , 2015, Science.

[13]  Hugh P. Possingham,et al.  Conserving mobile species , 2014 .

[14]  Peter Leimgruber,et al.  Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[15]  Damien R. Farine,et al.  Collective decision making and social interaction rules in mixed-species flocks of songbirds , 2014, Animal Behaviour.

[16]  Karen Anderson,et al.  Lightweight unmanned aerial vehicles will revolutionize spatial ecology , 2013 .

[17]  Andrew M. Hein,et al.  Challenges and solutions for studying collective animal behaviour in the wild , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[18]  Steven V. Viscido,et al.  The dilemma of the selfish herd: the search for a realistic movement rule. , 2002, Journal of theoretical biology.

[19]  P. Anderson More is different. , 1972, Science.

[20]  J. A. Schaefer,et al.  Caribou movement as a correlated random walk , 2000, Oecologia.

[21]  Iain D Couzin,et al.  From single steps to mass migration: the problem of scale in the movement ecology of the Serengeti wildebeest , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[22]  Tyson L Hedrick,et al.  Three-dimensional trajectories and network analyses of group behaviour within chimney swift flocks during approaches to the roost , 2017, Proceedings of the Royal Society B: Biological Sciences.

[23]  Robert B O'Hara,et al.  Social Learning of Migratory Performance , 2013, Science.

[24]  Sumio Watanabe,et al.  Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory , 2010, J. Mach. Learn. Res..

[25]  Leah Edelstein-Keshet,et al.  Inferring individual rules from collective behavior , 2010, Proceedings of the National Academy of Sciences.

[26]  S. Côté,et al.  Caribou, water, and ice – fine-scale movements of a migratory arctic ungulate in the context of climate change , 2016, Movement Ecology.

[27]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[28]  I. Couzin Collective minds , 2007, Nature.

[29]  G. Parisi,et al.  Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study , 2007, Proceedings of the National Academy of Sciences.

[30]  Colin R. Twomey,et al.  Visual sensory networks and effective information transfer in animal groups , 2013, Current Biology.

[31]  Arthur T. Bergerud,et al.  The Return of Caribou to Ungava , 2007 .

[32]  Craig Packer,et al.  Group formation stabilizes predator–prey dynamics , 2007, Nature.

[33]  A. Berdahl,et al.  Fitness trade-offs of group formation and movement by Thomson's gazelles in the Serengeti ecosystem , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[34]  David Moyer,et al.  Assessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Counts , 2016, PloS one.

[35]  Jean P. Gibert,et al.  Eco-evolutionary dynamics, density-dependent dispersal and collective behaviour: implications for salmon metapopulation robustness , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[36]  J. Calabrese,et al.  The Correlated Random Walk and the Rise of Movement Ecology , 2014 .

[37]  Inference of the drivers of collective movement in two cell types: Dictyostelium and melanoma , 2016, Journal of The Royal Society Interface.

[38]  Kyle G Horton,et al.  An assessment of spatio-temporal relationships between nocturnal bird migration traffic rates and diurnal bird stopover density , 2016, Movement ecology.

[39]  I. Couzin,et al.  Shared decision-making drives collective movement in wild baboons , 2015, Science.

[40]  Jonathan R. Potts,et al.  Unveiling trade-offs in resource selection of migratory caribou using a mechanistic movement model of availability , 2015 .

[41]  S. Bauer,et al.  Migratory Animals Couple Biodiversity and Ecosystem Functioning Worldwide , 2014, Science.

[42]  I. Couzin,et al.  A collective navigation hypothesis for homeward migration in anadromous salmonids , 2016 .

[43]  Jon S. Horne,et al.  Modeling Caribou Movements: Seasonal Ranges and Migration Routes of the Central Arctic Herd , 2016, PloS one.

[44]  Daniel Fortin,et al.  To follow or not? How animals in fusion-fission societies handle conflicting information during group decision-making. , 2015, Ecology letters.

[45]  Alan M. Wilson,et al.  Matching times of leading and following suggest cooperation through direct reciprocity during V-formation flight in ibis , 2015, Proceedings of the National Academy of Sciences.

[46]  L. Bernatchez,et al.  Integrative use of spatial, genetic, and demographic analyses for investigating genetic connectivity between migratory, montane, and sedentary caribou herds , 2007, Molecular ecology.

[47]  S. Wich,et al.  Dawn of Drone Ecology: Low-Cost Autonomous Aerial Vehicles for Conservation , 2012 .

[48]  Stephen P. Ellner,et al.  Detecting collective behaviour in animal relocation data, with application to migrating caribou , 2016 .

[49]  Roland Langrock,et al.  Modelling group dynamic animal movement , 2013, 1308.5850.

[50]  Correction to ‘Migration in the Anthropocene: how collective navigation, environmental system and taxonomy shape the vulnerability of migratory species’ , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[51]  E. J. Milner-Gulland,et al.  Animal Migration: A Synthesis , 2011 .

[52]  Roland Kays,et al.  Data from: Shared decision-making drives collective movement in wild baboons , 2015 .

[53]  Marco Scutari,et al.  Learning Bayesian Networks with the bnlearn R Package , 2009, 0908.3817.

[54]  Aki Vehtari,et al.  Understanding predictive information criteria for Bayesian models , 2013, Statistics and Computing.

[55]  W. Fagan,et al.  The importance of individual variation in the dynamics of animal collective movements , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[56]  D. McCauley,et al.  Migration in the Anthropocene: how collective navigation, environmental system and taxonomy shape the vulnerability of migratory species , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[57]  Chris Cosner,et al.  Leadership, social learning, and the maintenance (or collapse) of migratory populations , 2011, Theoretical Ecology.

[58]  I. Couzin,et al.  Emergent Sensing of Complex Environments by Mobile Animal Groups , 2013, Science.

[59]  Richard P. Mann,et al.  Bayesian Inference for Identifying Interaction Rules in Moving Animal Groups , 2011, PloS one.

[60]  Nikolai W. F. Bode,et al.  Copycat dynamics in leaderless animal group navigation , 2014 .

[61]  Brett T. McClintock,et al.  A general discrete‐time modeling framework for animal movement using multistate random walks , 2012 .

[62]  David Huard,et al.  PyMC: Bayesian Stochastic Modelling in Python. , 2010, Journal of statistical software.

[63]  Iain D Couzin,et al.  Synchronization, coordination and collective sensing during thermalling flight of freely migrating white storks , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[64]  Mevin B. Hooten,et al.  Dynamic social networks based on movement , 2015, 1512.07607.

[65]  Kun Liu,et al.  Rotation-Invariant HOG Descriptors Using Fourier Analysis in Polar and Spherical Coordinates , 2014, International Journal of Computer Vision.

[66]  Roman Garnett,et al.  Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection , 2012, PLoS Comput. Biol..

[67]  Iain D Couzin,et al.  Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement , 2016, eLife.

[68]  Deborah A. Jenkins,et al.  Socially informed random walks: incorporating group dynamics into models of population spread and growth , 2008, Proceedings of the Royal Society B: Biological Sciences.

[69]  Richard Schiffman,et al.  Wildlife conservation. Drones flying high as new tool for field biologists. , 2014, Science.

[70]  A. Ward,et al.  Sociality: The Behaviour of Group-Living Animals , 2016, Springer International Publishing.

[71]  Tanya Y. Berger-Wolf,et al.  Social information improves location prediction in the wild , 2015, AAAI 2015.

[72]  Damien R. Farine,et al.  Inferring influence and leadership in moving animal groups , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[73]  S. Levin,et al.  Collective behavior as a driver of critical transitions in migratory populations , 2016, Movement Ecology.

[74]  Dora Biro,et al.  Collective movement in ecology: from emerging technologies to conservation and management , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[75]  Edward A. Codling,et al.  Collective animal navigation and migratory culture: from theoretical models to empirical evidence , 2017, bioRxiv.