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Strömbom D, Crocker A, Gery A, Tulevech G, Sands A, Ward K, Pandey S. Modelling the emergence of social-bird biological controls to mitigate invasions of the spotted lanternfly and similar invasive pests. R Soc Open Sci 2024; 11:231671. [PMID: 38384778 PMCID: PMC10878819 DOI: 10.1098/rsos.231671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/19/2024] [Indexed: 02/23/2024]
Abstract
The spotted lanternfly is an emerging global invasive insect pest. Due to a lack of natural enemies where it is invasive, human intervention is required. Extensive management has been applied but the spread continues. Recently, the idea of bird-based biological controls has re-emerged and shown effective in studies. However, it is questionable, if birds are able to effectively control unfamiliar and occasionally toxic invasive pests in short timeframes. Unless, perhaps, the birds are effective social learners and toxicity of the invaders is rare. Here, we introduce a mathematical model for social learning in a great tit-like bird to investigate conditions for the emergence of a collective biological control of a pest that is occasionally toxic, like the lanternfly. We find that the social observation rate relative to the proportion of toxic lanternfly dictate when collective biological controls will emerge. We also implement the social learning model into a model of collective motion in bird-like animals, and find that it produces results consistent with the mathematical model. Our work suggests that social birds may be useful in managing the spotted lanternfly, and that removing the toxicity-inducing preferred host of the lanternfly should be a priority to facilitate this.
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Affiliation(s)
- Daniel Strömbom
- Lafayette College, Department of Biology, Easton, PA 18042, USA
| | - Amanda Crocker
- Lafayette College, Department of Biology, Easton, PA 18042, USA
| | - Alison Gery
- Lafayette College, Department of Biology, Easton, PA 18042, USA
| | - Grace Tulevech
- Lafayette College, Department of Biology, Easton, PA 18042, USA
| | - Autumn Sands
- Lafayette College, Department of Biology, Easton, PA 18042, USA
| | - Kelly Ward
- Lafayette College, Department of Biology, Easton, PA 18042, USA
| | - Swati Pandey
- Lafayette College, Department of Biology, Easton, PA 18042, USA
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2
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Crocker A, Strömbom D. Susceptible-Infected-Susceptible type COVID-19 spread with collective effects. Sci Rep 2023; 13:22600. [PMID: 38114694 PMCID: PMC10730724 DOI: 10.1038/s41598-023-49949-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023] Open
Abstract
Many models developed to forecast and attempt to understand the COVID-19 pandemic are highly complex, and few take collective behavior into account. As the pandemic progressed individual recurrent infection was observed and simpler susceptible-infected type models were introduced. However, these do not include mechanisms to model collective behavior. Here, we introduce an extension of the SIS model that accounts for collective behavior and show that it has four equilibria. Two of the equilibria are the standard SIS model equilibria, a third is always unstable, and a fourth where collective behavior and infection prevalence interact to produce either node-like or oscillatory dynamics. We then parameterized the model using estimates of the transmission and recovery rates for COVID-19 and present phase diagrams for fixed recovery rate and free transmission rate, and both rates fixed. We observe that regions of oscillatory dynamics exist in both cases and that the collective behavior parameter regulates their extent. Finally, we show that the system exhibits hysteresis when the collective behavior parameter varies over time. This model provides a minimal framework for explaining oscillatory phenomena such as recurring waves of infection and hysteresis effects observed in COVID-19, and other SIS-type epidemics, in terms of collective behavior.
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Affiliation(s)
- Amanda Crocker
- Department of Biology, Lafayette College, Easton, PA, 18042, USA
| | - Daniel Strömbom
- Department of Biology, Lafayette College, Easton, PA, 18042, USA.
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King AJ, Portugal SJ, Strömbom D, Mann RP, Carrillo JA, Kalise D, de Croon G, Barnett H, Scerri P, Groß R, Chadwick DR, Papadopoulou M. Biologically inspired herding of animal groups by robots. Methods Ecol Evol 2023. [DOI: 10.1111/2041-210x.14049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Andrew J. King
- Department of Biosciences, Faculty of Science and Engineering Swansea University Swansea UK
| | - Steven J. Portugal
- Department of Biological Sciences, School of Life and Environmental Sciences Royal Holloway University of London Egham UK
| | - Daniel Strömbom
- Department of Biology Lafayette College Easton Pennsylvania USA
| | - Richard P. Mann
- Department of Statistics, School of Mathematics University of Leeds Leeds UK
| | | | - Dante Kalise
- Department of Mathematics Imperial College London London UK
| | - Guido de Croon
- Faculty of Aerospace Engineering Delft University of Technology Delft The Netherlands
| | - Heather Barnett
- Central Saint Martins University of the Arts London London UK
| | - Paul Scerri
- Perceptronics Solutions Los Angeles California USA
| | - Roderich Groß
- Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK
| | - David R. Chadwick
- Environment Centre Wales, School of Natural Sciences Bangor University Bangor UK
| | - Marina Papadopoulou
- Department of Biosciences, Faculty of Science and Engineering Swansea University Swansea UK
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4
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Strömbom D, Pandey S. Modeling the life cycle of the spotted lanternfly (Lycorma delicatula) with management implications. Math Biosci 2021; 340:108670. [PMID: 34302819 DOI: 10.1016/j.mbs.2021.108670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 06/02/2021] [Accepted: 07/14/2021] [Indexed: 11/25/2022]
Abstract
The spotted lanternfly (SLF) is an invasive pest that emerged in the US less than a decade ago. With few natural enemies and an ability to feed on a wide variety of readily available plants the population has grown rapidly. It is causing damage to a wide range of natural and economically important farmed plants and at present there is no known way to stop the growth and spread of the population. However, a number of control measures have been proposed to limit the growth and the effectiveness of some of these have been assessed via empirical studies. Studies to estimate the natural mortality rate of the lanternfly's different life stages and other properties of its life cycle are also available. However, no attempt to integrate this empirical information to estimate population level characteristics such as the population growth rate and the potential effects of proposed control measures can be found in the literature. Here, we introduce a simple population dynamics model parameterized using available information in the literature to obtain estimates of this type. Our model suggests that the annual growth rate of the SLF population in the US is 5.47, that only three out of six proposed control measures considered here have the potential to decrease the population even if we can find and treat each SLF in every stage, and that even with a combined strategy involving the most effective proposed control measures about 35% of all SLF in the relevant stages must be found and treated to turn the current population growth into decline. Suggesting that eradication of the spotted lanternfly over larger geographical areas in the US will be challenging, and we believe that the modeling framework presented here may be useful in providing estimates to inform feasibility assessment of proposed management efforts.
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Affiliation(s)
- Daniel Strömbom
- Department of Biology, Lafayette College, Easton, PA 18042, USA.
| | - Swati Pandey
- Department of Biology, Lafayette College, Easton, PA 18042, USA
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5
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Strömbom D, Hassan T, Hunter Greis W, Antia A. Asynchrony induces polarization in attraction-based models of collective motion. R Soc Open Sci 2019; 6:190381. [PMID: 31183154 PMCID: PMC6502356 DOI: 10.1098/rsos.190381] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 03/15/2019] [Indexed: 05/14/2023]
Abstract
Animal groups frequently move in a highly organized manner, as represented by flocks of birds and schools of fish. Despite being an everyday occurrence, we do not fully understand how this works. In particular, what social interactions between animals give rise to the flock structures we observe? This question is often investigated using self-propelled particle models where particles represent the individual animals. These models differ in the social interactions used, individual particle properties, and various technical assumptions. One particular technical assumption relates to whether all particles update their headings and positions at exactly the same time (synchronous update) or not (asynchronous update). Here, we investigate the causal effects of this assumption in an attraction-only model and find that it has a dramatic impact. Polarized groups do not form when synchronous update is used, but are produced with asynchronous update, and this phenomenon is robust with respect to variation in particle displacements and inclusion of noise. Given that many important models have been implemented with synchronous update only, we speculate that our understanding of the social interactions on which they are based may be incomplete. Perhaps previously unobserved phenomena will emerge if other potentially more realistic update schemes are used.
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Affiliation(s)
- Daniel Strömbom
- Department of Mathematics, Uppsala University, Uppsala 75601, Sweden
- Department of Biology, Lafayette College, Easton 18042, PA, USA
- Department of Biosciences, College of Science, Swansea University, Swansea SA2 6PP, UK
- Author for correspondence: Daniel Strömbom e-mail:
| | - Tasnia Hassan
- Department of Biology, Lafayette College, Easton 18042, PA, USA
| | - W. Hunter Greis
- Department of Biology, Lafayette College, Easton 18042, PA, USA
| | - Alice Antia
- Department of Mathematics and Statistics, Carleton College, Northfield 55057, MN, USA
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6
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Strömbom D, Dussutour A. Self-organized traffic via priority rules in leaf-cutting ants. PLoS Comput Biol 2018; 14:e1006523. [PMID: 30307942 PMCID: PMC6198993 DOI: 10.1371/journal.pcbi.1006523] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 10/23/2018] [Accepted: 09/21/2018] [Indexed: 11/18/2022] Open
Abstract
Ants, termites and humans often form well-organized and highly efficient trails between different locations. Yet the microscopic traffic rules responsible for this organization and efficiency are not fully understood. In previous experimental studies with leaf-cutting ants (Atta colombica), a set of local priority rules were isolated and it was proposed that these rules govern the temporal and spatial organization of the traffic on the trails. Here we introduce a model based on these priority rules to investigate whether they are sufficient to produce traffic similar to that observed in the experiments on both a narrow and a wider trail. We establish that the model is able to reproduce key characteristics of the traffic on the trails. In particular, we show that the proposed priority rules induce de-synchronization into clusters of inbound and outbound ants on a narrow trail, and that priority-type dependent segregated traffic emerges on a wider trail. Due to the generic nature of the proposed priority rules we speculate that they may be used to model traffic organization in a variety of other ant species.
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Affiliation(s)
- Daniel Strömbom
- Department of Mathematics, Uppsala University, Uppsala, Sweden
- Department of Biosciences, Swansea University, Swansea, United Kingdom
- Department of Biology, Lafayette College, Easton, Pennsylvania, United States of America
| | - Audrey Dussutour
- Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, Toulouse, France
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7
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Abstract
Animals as diverse as ants and humans are faced with the tasks of collecting, transporting or herding objects. Sheepdogs do this daily when they collect, herd, and maneuver flocks of sheep. Here, we adapt a shepherding algorithm inspired by sheepdogs to collect and transport objects using a robot. Our approach produces an effective robot collection process that autonomously adapts to changing environmental conditions and is robust to noise from various sources. We suggest that this biomimetic process could be implemented into suitable robots to perform collection and transport tasks that might include – for example – cleaning up objects in the environment, keeping animals away from sensitive areas or collecting and herding animals to a specific location. Furthermore, the feedback controlled interactions between the robot and objects which we study can be used to interrogate and understand the local and global interactions of real animal groups, thus offering a novel methodology of value to researchers studying collective animal behavior.
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Affiliation(s)
- Daniel Strömbom
- Department of Mathematics, Uppsala University, Uppsala, Sweden.,Department of Biosciences, Swansea University, Swansea, United Kingdom
| | - Andrew J King
- Department of Biosciences, Swansea University, Swansea, United Kingdom
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8
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Krause J, Herbert-Read JE, Seebacher F, Domenici P, Wilson ADM, Marras S, Svendsen MBS, Strömbom D, Steffensen JF, Krause S, Viblanc PE, Couillaud P, Bach P, Sabarros PS, Zaslansky P, Kurvers RHJM. Injury-mediated decrease in locomotor performance increases predation risk in schooling fish. Philos Trans R Soc Lond B Biol Sci 2017; 372:20160232. [PMID: 28673910 PMCID: PMC5498294 DOI: 10.1098/rstb.2016.0232] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/06/2017] [Indexed: 11/12/2022] Open
Abstract
The costs and benefits of group living often depend on the spatial position of individuals within groups and the ability of individuals to occupy preferred positions. For example, models of predation events for moving prey groups predict higher mortality risk for individuals at the periphery and front of groups. We investigated these predictions in sardine (Sardinella aurita) schools under attack from group hunting sailfish (Istiophorus platypterus) in the open ocean. Sailfish approached sardine schools about equally often from the front and rear, but prior to attack there was a chasing period in which sardines attempted to swim away from the predator. Consequently, all sailfish attacks were directed at the rear and peripheral positions of the school, resulting in higher predation risk for individuals at these positions. During attacks, sailfish slash at sardines with their bill causing prey injury including scale removal and tissue damage. Sardines injured in previous attacks were more often found in the rear half of the school than in the front half. Moreover, injured fish had lower tail-beat frequencies and lagged behind uninjured fish. Injuries inflicted by sailfish bills may, therefore, hinder prey swimming speed and drive spatial sorting in prey schools through passive self-assortment. We found only partial support for the theoretical predictions from current predator-prey models, highlighting the importance of incorporating more realistic predator-prey dynamics into these models.This article is part of the themed issue 'Physiological determinants of social behaviour in animals'.
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Affiliation(s)
- J Krause
- Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany
- Albrecht Daniel Thaer-Institute, Faculty of Life Sciences, Humboldt-Universität zu Berlin, Invalidenstrasse 42, 10115 Berlin, Germany
| | - J E Herbert-Read
- Department of Mathematics, Uppsala University, Uppsala, Sweden
- Department of Zoology, Stockholm University, Stockholm, Sweden
| | - F Seebacher
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales 2006, Australia
| | - P Domenici
- IAMC-CNR, Istituto per l'Ambiente Marino Costiero, Consiglio Nazionale delle Ricerche, Località Sa Mardini, 09170 Torregrande, Oristano, Italy
| | - A D M Wilson
- Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany
- Department of Zoology, Stockholm University, Stockholm, Sweden
| | - S Marras
- IAMC-CNR, Istituto per l'Ambiente Marino Costiero, Consiglio Nazionale delle Ricerche, Località Sa Mardini, 09170 Torregrande, Oristano, Italy
| | - M B S Svendsen
- Marine Biological Section, University of Copenhagen, Strandpromenaden 5, 3000 Helsingør, Denmark
| | - D Strömbom
- Department of Mathematics, Uppsala University, Uppsala, Sweden
- Department of Biology, Lafayette College, Easton, 18042 PA, USA
| | - J F Steffensen
- Marine Biological Section, University of Copenhagen, Strandpromenaden 5, 3000 Helsingør, Denmark
| | - S Krause
- Department of Electrical Engineering and Computer Science, Lübeck University of Applied Sciences, 23562 Lübeck, Germany
| | - P E Viblanc
- Albrecht Daniel Thaer-Institute, Faculty of Life Sciences, Humboldt-Universität zu Berlin, Invalidenstrasse 42, 10115 Berlin, Germany
| | - P Couillaud
- Département de la Licence Sciences et Technologies, Université Pierre et Marie Curie, 4 place Jussieu, 75005 Paris, France
| | - P Bach
- Institut de Recherche pour le Développement, UMR 248 MARBEC, Ob7, Avenue Jean Monnet, CS 30171, 34203 Sète Cedex, France
| | - P S Sabarros
- Institut de Recherche pour le Développement, UMR 248 MARBEC, Ob7, Avenue Jean Monnet, CS 30171, 34203 Sète Cedex, France
| | - P Zaslansky
- Julius Wolff Institute for Biomechanics and Musculoskeletal Regeneration, Charité - Universitätsmedizin Berlin, Philippstraße 13, Haus 11, 10115 Berlin, Germany
| | - R H J M Kurvers
- Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
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Herbert-Read JE, Romanczuk P, Krause S, Strömbom D, Couillaud P, Domenici P, Kurvers RHJM, Marras S, Steffensen JF, Wilson ADM, Krause J. Correction to ‘Proto-cooperation: group hunting sailfish improve hunting success by alternating attacks on grouping prey’. Proc Biol Sci 2016; 283:rspb.2016.2586. [DOI: 10.1098/rspb.2016.2586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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10
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Herbert-Read JE, Romanczuk P, Krause S, Strömbom D, Couillaud P, Domenici P, Kurvers RHJM, Marras S, Steffensen JF, Wilson ADM, Krause J. Proto-cooperation: group hunting sailfish improve hunting success by alternating attacks on grouping prey. Proc Biol Sci 2016; 283:20161671. [PMID: 27807269 PMCID: PMC5124094 DOI: 10.1098/rspb.2016.1671] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 10/12/2016] [Indexed: 12/13/2022] Open
Abstract
We present evidence of a novel form of group hunting. Individual sailfish (Istiophorus platypterus) alternate attacks with other group members on their schooling prey (Sardinella aurita). While only 24% of attacks result in prey capture, multiple prey are injured in 95% of attacks, resulting in an increase of injured fish in the school with the number of attacks. How quickly prey are captured is positively correlated with the level of injury of the school, suggesting that hunters can benefit from other conspecifics' attacks on the prey. To explore this, we built a mathematical model capturing the dynamics of the hunt. We show that group hunting provides major efficiency gains (prey caught per unit time) for individuals in groups of up to 70 members. We also demonstrate that a free riding strategy, where some individuals wait until the prey are sufficiently injured before attacking, is only beneficial if the cost of attacking is high, and only then when waiting times are short. Our findings provide evidence that cooperative benefits can be realized through the facilitative effects of individuals' hunting actions without spatial coordination of attacks. Such 'proto-cooperation' may be the pre-cursor to more complex group-hunting strategies.
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Affiliation(s)
- James E Herbert-Read
- Department of Mathematics, Uppsala University, 75106, Uppsala, Sweden
- Department of Zoology, Stockholm University, 10691, Stockholm, Sweden
| | - Pawel Romanczuk
- Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, Berlin, Germany
- Faculty of Life Sciences, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton 08544, NJ, USA
| | - Stefan Krause
- Department of Electrical Engineering and Computer Science, Lübeck University of Applied Sciences, 23562 Lübeck, Germany
| | - Daniel Strömbom
- Department of Mathematics, Uppsala University, 75106, Uppsala, Sweden
- Department of Biology, Lafayette College, Easton 18042, PA, USA
| | - Pierre Couillaud
- Département de la Licence Sciences et Technologies, Université Pierre et Marie Curie, 75005 Paris, France
| | - Paolo Domenici
- IAMC-CNR, Istituto per l'Ambiente Marino Costiero, Consiglio Nazionale delle Ricerche, Località Sa Mardini, 09170 Torregrande, Oristano, Italy
| | - Ralf H J M Kurvers
- Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, Berlin, Germany
- Center for Adaptive Rationality, Max Planck Institute for Human Development, 14195 Berlin, Germany
| | - Stefano Marras
- IAMC-CNR, Istituto per l'Ambiente Marino Costiero, Consiglio Nazionale delle Ricerche, Località Sa Mardini, 09170 Torregrande, Oristano, Italy
| | - John F Steffensen
- Marine Biological Section, University of Copenhagen, Helsingor 3000, Denmark
| | - Alexander D M Wilson
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales 2006, Australia
- School of Life and Environmental Sciences, Deakin University, Waurn Ponds, Victoria 3216, Australia
| | - Jens Krause
- Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, Berlin, Germany
- Faculty of Life Sciences, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
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11
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Strömbom D, Mann RP, Wilson AM, Hailes S, Morton AJ, Sumpter DJT, King AJ. Solving the shepherding problem: heuristics for herding autonomous, interacting agents. J R Soc Interface 2015; 11:20140719. [PMID: 25165603 PMCID: PMC4191104 DOI: 10.1098/rsif.2014.0719] [Citation(s) in RCA: 108] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
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.
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Affiliation(s)
- Daniel Strömbom
- Department of Mathematics, Uppsala University, Uppsala 75106, Sweden
| | - Richard P Mann
- Department of Mathematics, Uppsala University, Uppsala 75106, Sweden
| | - Alan M Wilson
- Structure and Motion Laboratory, The Royal Veterinary College, University of London, Hatfield, Hertfordshire AL9 7TA, UK
| | - Stephen Hailes
- Department of Computer Science, University College of London, Gower Street, London WC1E 6BT, UK
| | - A Jennifer Morton
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
| | - David J T Sumpter
- Department of Mathematics, Uppsala University, Uppsala 75106, Sweden
| | - Andrew J King
- Department of Biosciences, College of Science, Swansea University, Swansea SA2 8PP, UK
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12
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Sircova A, Karimi F, Osin EN, Lee S, Holme P, Strömbom D. Simulating irrational human behavior to prevent resource depletion. PLoS One 2015; 10:e0117612. [PMID: 25760635 PMCID: PMC4356575 DOI: 10.1371/journal.pone.0117612] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Accepted: 12/29/2014] [Indexed: 11/19/2022] Open
Abstract
In a situation with a limited common resource, cooperation between individuals sharing the resource is essential. However, people often act upon self-interest in irrational ways that threaten the long-term survival of the whole group. A lack of sustainable or environmentally responsible behavior is often observed. In this study, we examine how the maximization of benefits principle works in a wider social interactive context of personality preferences in order to gain a more realistic insight into the evolution of cooperation. We used time perspective (TP), a concept reflecting individual differences in orientation towards past, present, or future, and relevant for making sustainable choices. We developed a personality-driven agent-based model that explores the role of personality in the outcomes of social dilemmas and includes multiple facets of diversity: (1) The agents have different behavior strategies: individual differences derived by applying cluster analysis to survey data from 22 countries (N = 10,940) and resulting in 7 cross-cultural profiles of TP; (2) The non-uniform distribution of the types of agents across countries; (3) The diverse interactions between the agents; and (4) diverse responses to those interactions in a well-mixed population. As one of the results, we introduced an index of overall cooperation for each of the 22 countries, which was validated against cultural, economic, and sustainability indicators (HDI, dimensions of national culture, and Environment Performance Index). It was associated with higher human development, higher individualism, lower power distance, and better environmental performance. The findings illustrate how individual differences in TP can be simulated to predict the ways people in different countries solve the personal vs. common gain dilemma in the global limited-resource situation. This interdisciplinary approach to social simulation can be adopted to explain the possible causes of global environmental issues and to predict their possible outcomes.
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Affiliation(s)
- Anna Sircova
- Department of Psychology, Umeå University, Umeå, Sweden
- Independent researcher, Copenhagen, Denmark
- * E-mail: (AS); (FK); (PH)
| | - Fariba Karimi
- IceLab, Department of Physics, Umeå University, Umeå, Sweden
- * E-mail: (AS); (FK); (PH)
| | - Evgeny N. Osin
- International Laboratory of Positive Psychology of Personality and Motivation, National Research University Higher School of Economics, Moscow, Russia
| | - Sungmin Lee
- IceLab, Department of Physics, Umeå University, Umeå, Sweden
| | - Petter Holme
- IceLab, Department of Physics, Umeå University, Umeå, Sweden
- Department of Energy Science, Sungkyunkwan University, Suwon, Korea
- * E-mail: (AS); (FK); (PH)
| | - Daniel Strömbom
- Department of Mathematics, Uppsala University, Uppsala, Sweden
- Department of Biology, Lafayette College, Easton, Pennsylvania, United States of America
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13
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Mann RP, Perna A, Strömbom D, Garnett R, Herbert-Read JE, Sumpter DJT, Ward AJW. Multi-scale inference of interaction rules in animal groups using Bayesian model selection. PLoS Comput Biol 2013; 9:e1002961. [PMID: 23555206 PMCID: PMC3605063 DOI: 10.1371/journal.pcbi.1002961] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2012] [Accepted: 01/15/2013] [Indexed: 11/28/2022] Open
Abstract
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 the observed locality of interactions. Traditional self-propelled particle models fail to capture the fine scale dynamics of the system. 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, while maintaining a biologically plausible perceptual range. 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. The collective movement of animals in a group is an impressive phenomenon whereby large scale spatio-temporal patterns emerge from simple interactions between individuals. Theoretically, much of our understanding of animal group motion comes from models inspired by statistical physics. In these models, animals are treated as moving (self-propelled) particles that interact with each other according to simple rules. Recently, researchers have shown greater interest in using experimental data to verify which rules are actually implemented by a particular animal species. In our study, we present a rigorous selection between alternative models inspired by the literature for a system of glass prawns. We find that the classic theoretical models do not accurately predict either the fine scale or large scale behaviour of the system. Instead, individual animals appear to be interacting even when completely separated from each other. To resolve this we introduce a new class of models wherein prawns ‘remember‚ their previous interactions, integrating their experiences over time when deciding to change behaviour. These show that the fine scale and large scale behaviour of the prawns is consistent with interactions only between individuals who are close together.
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Affiliation(s)
- Richard P Mann
- Department of Mathematics, Uppsala University, Uppsala, Sweden.
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Mann RP, Perna A, Strömbom D, Garnett R, Herbert-Read JE, Sumpter DJT, Ward AJW. Multi-scale inference of interaction rules in animal groups using Bayesian model selection. PLoS Comput Biol 2012; 8:e1002308. [PMID: 22241970 PMCID: PMC3252267 DOI: 10.1371/journal.pcbi.1002308] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2011] [Accepted: 10/31/2011] [Indexed: 11/29/2022] Open
Abstract
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. The collective movement of animals in a group is an impressive phenomenon whereby large scale spatio-temporal patterns emerge from simple interactions between individuals. Theoretically, much of our understanding of animal group motion comes from models inspired by statistical physics. In these models, animals are treated as moving (self-propelled) particles that interact with each other according to simple rules. Recently, researchers have shown greater interest in using experimental data to verify which rules are actually implemented by a particular animal species. In our study, we present a rigorous selection between alternative models inspired by the literature for a system of glass prawns. We find that the classic theoretical models can accurately capture either the fine-scale behaviour or the large-scale collective patterns of movement of the prawns. However, none are able to reproduce both levels of description at the same time. To resolve this conflict we introduce a new class of models wherein prawns ‘remember’, their previous interactions, integrating their experiences over time when deciding to change behaviour. These outperform the traditional models in predicting when individual prawns will change their direction of motion and restore consistency between the fine-scale rules of interaction and the global behaviour of the group.
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Affiliation(s)
- Richard P Mann
- Department of Mathematics, Uppsala University, Uppsala, Sweden.
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Abstract
Many animal groups, for example schools of fish or flocks of birds, exhibit complex dynamic patterns while moving cohesively in the same direction. These flocking patterns have been studied using self-propelled particle models, most of which assume that collective motion arises from individuals aligning with their neighbours. Here, we propose a self-propelled particle model in which the only social force between individuals is attraction. We show that this model generates three different phases: swarms, undirected mills and moving aligned groups. By studying our model in the zero noise limit, we show how these phases depend on the relative strength of attraction and individual inertia. Moreover, by restricting the field of vision of the individuals and increasing the degree of noise in the system, we find that the groups generate both directed mills and three dynamically moving, 'rotating chain' structures. A rich diversity of patterns is generated by social attraction alone, which may provide insight into the dynamics of natural flocks.
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Affiliation(s)
- Daniel Strömbom
- Mathematics Department, Uppsala University, Box 480, 751 06 Uppsala, Sweden.
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