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Author |
Couzin, I.D.; Krause, J.; Franks, N.R.; Levin, S.A. |
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Title |
Effective leadership and decision-making in animal groups on the move |
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Journal Article |
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2005 |
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Nature |
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433 |
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7025 |
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513-516 |
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0028-0836 |
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10.1038/nature03236 |
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Equine Behaviour @ team @ |
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4827 |
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Author |
Croft, D. P.; James, R..; Krause, J. |
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Title |
Comparing Networks |
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Book Chapter |
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Year |
2008 |
Publication |
Exploring Animal Social Networks |
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141-162 |
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Social network analysis is used widely in the social sciences to study interactions among people, groups, and organizations, yet until now there has been no book that shows behavioral biologists how to apply it to their work on animal populations. Exploring Animal Social Networks provides a practical guide for researchers, undergraduates, and graduate students in ecology, evolutionary biology, animal behavior, and zoology.
Existing methods for studying animal social structure focus either on one animal and its interactions or on the average properties of a whole population. This book enables researchers to probe animal social structure at all levels, from the individual to the population. No prior knowledge of network theory is assumed. The authors give a step-by-step introduction to the different procedures and offer ideas for designing studies, collecting data, and interpreting results. They examine some of today's most sophisticated statistical tools for social network analysis and show how they can be used to study social interactions in animals, including cetaceans, ungulates, primates, insects, and fish. Drawing from an array of techniques, the authors explore how network structures influence individual behavior and how this in turn influences, and is influenced by, behavior at the population level. Throughout, the authors use two software packages--UCINET and NETDRAW--to illustrate how these powerful analytical tools can be applied to different animal social organizations.
Darren P. Croft is lecturer in animal behavior at the University of Wales, Bangor. Richard James is senior lecturer in physics at the University of Bath. Jens Krause is professor of behavioral ecology at the University of Leeds.
Reviews:
“Exploring Animal Social Networks shows behavioral biologists how to apply social network theory to animal populations. In doing so, Croft, James, and Krause illustrate the connections between an animal's individual behaviors and how these, in turn, influence and are influenced by behavior at the population level. . . . Valuable for readers interested in using quantitative analyses to study animal social behaviors.”--Choice
“[T]his volume provides an engaging, accessible, and timely introduction to the use of network theory methods for examining the social behavior of animals.”--Noa Pinter-Wollman, Quarterly Review of Biology
“The book is a useful 'handbook' providing detailed, stepwise procedures sufficient to allow the reader to address a broad range of questions about social interactions. . . . The book includes numerous examples of the kind of research questions one might ask, and, thus, it allows the reader to find the analysis that best fits the data set to be analyzed. Thus, even readers with minimal prior knowledge of social network analysis will be able to apply this approach. And if further assistance is needed, the authors provide numerous references to specific procedures that have been used by others.”--Thomas R. Zentall, PsycCRITIQUES
Endorsements:
“An important and timely addition to the literature. This book should be readily accessible to researchers who are interested in animal social organization but who have little or no experience in conducting network analysis. The book is well-written in an engaging style and contains a good number of examples drawn from a range of taxonomic groups.”--Paul R. Moorcroft, Harvard University
More Endorsements
Table of Contents:
Preface vii
Chapter 1: Introduction to Social Networks 1
Chapter 2: Data Collection 19
Chapter 3: Visual Exploration 42
Chapter 4: Node-Based Measures 64
Chapter 5: Statistical Tests of Node-Based Measures 88
Chapter 6: Searching for Substructures 117
Chapter 7: Comparing Networks 141
Chapter 8: Conclusions 163
Glossary of Frequently Used Terms 173
References 175
Index 187
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* Biological Sciences |
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Princton University Press |
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Princeton, NY |
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Equine Behaviour @ team @ |
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4955 |
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Croft, D. P.; James, R..; Krause, J. (eds) |
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Title |
Exploring Animal Social Networks |
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2008 |
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Princton University Press |
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Princton |
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Croft, D. P.; James, R..; Krause, J. |
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9780691127521 |
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no |
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Equine Behaviour @ team @ |
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5139 |
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Krause, J.; Bumann, D.; Todt, D. |
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Title |
Relationship between the position preference and nutritional state of individuals in schools of juvenile roach (Rutilus rutilus) |
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Journal Article |
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1992 |
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Behavioral Ecology and Sociobiology |
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Behav. Ecol. Sociobiol. |
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30 |
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3 |
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177-180 |
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Position preferences of well-fed and food-deprived juvenile roach were investigated in schools of 2 and 4 fish in the laboratory. Food-deprived fish appeared significantly more often in the front position than their well-fed conspecifics. For fish at the same hunger level, individuals at the front of the school had the highest feeding rate. These results represent the first evidence for a relationship between the nutritional state of individual fish and their positions in a school and suggest a functional advantage of the preference. |
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Equine Behaviour @ team @ |
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5140 |
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Author |
Couzin, I.D.; Krause, J. |
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Title |
Self-Organization and Collective Behavior in Vertebrates |
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2003 |
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Advances in the Study of Behavior |
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32 |
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1-75 |
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Academic Press |
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Peter J. B. Slater, J.S.R., Charles T. Snowdon and Timothy J. Roper |
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0065-3454 |
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Equine Behaviour @ team @ |
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5144 |
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Bode, N.W.F.; Faria, J.J.; Franks, D.W.; Krause, J.; Wood, A.J. |
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How perceived threat increases synchronization in collectively moving animal groups |
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2010 |
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Proceedings of the Royal Society B: Biological Sciences |
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Proc. Roy. Soc. Lond. B Biol. Sci. |
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277 |
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1697 |
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3065-3070 |
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Nature is rich with many different examples of the cohesive motion of animals. Previous attempts to model collective motion have primarily focused on group behaviours of identical individuals. In contrast, we put our emphasis on modelling the contributions of different individual-level characteristics within such groups by using stochastic asynchronous updating of individual positions and orientations. Our model predicts that higher updating frequency, which we relate to perceived threat, leads to more synchronized group movement, with speed and nearest-neighbour distributions becoming more uniform. Experiments with three-spined sticklebacks (Gasterosteus aculeatus) that were exposed to different threat levels provide strong empirical support for our predictions. Our results suggest that the behaviour of fish (at different states of agitation) can be explained by a single parameter in our model: the updating frequency. We postulate a mechanism for collective behavioural changes in different environment-induced contexts, and explain our findings with reference to confusion and oddity effects. |
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10.1098/rspb.2010.0855 |
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Equine Behaviour @ team @ |
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5188 |
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Ward, A.J.W.; Sumpter, D.J.T.; Couzin, I.D.; Hart, P.J.B.; Krause, J. |
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Quorum decision-making facilitates information transfer in fish shoals |
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Journal Article |
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2008 |
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Proceedings of the National Academy of Sciences of the United States of America |
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Proc. Natl. Acad. Sci. U.S.A. |
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105 |
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19 |
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6948-6953 |
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Despite the growing interest in collective phenomena such as “swarm intelligence” and “wisdom of the crowds,” little is known about the mechanisms underlying decision-making in vertebrate animal groups. How do animals use the behavior of others to make more accurate decisions, especially when it is not possible to identify which individuals possess pertinent information? One plausible answer is that individuals respond only when they see a threshold number of individuals perform a particular behavior. Here, we investigate the role of such “quorum responses” in the movement decisions of fish (three-spine stickleback, Gasterosteus aculeatus). We show that a quorum response to conspecifics can explain how sticklebacks make collective movement decisions, both in the absence and presence of a potential predation risk. Importantly our experimental work shows that a quorum response can reduce the likelihood of amplification of nonadaptive following behavior. Whereas the traveling direction of solitary fish was strongly influenced by a single replica conspecific, the replica was largely ignored by larger groups of four or eight sticklebacks under risk, and the addition of a second replica was required to exert influence on the movement decisions of such groups. Model simulations further predict that quorum responses by fish improve the accuracy and speed of their decision-making over that of independent decision-makers or those using a weak linear response. This study shows that effective and accurate information transfer in groups may be gained only through nonlinear responses of group members to each other, thus highlighting the importance of quorum decision-making. |
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10.1073/pnas.0710344105 |
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Equine Behaviour @ team @ |
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5252 |
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Couzin, I.D.; Krause, J.; James, R.; Ruxton, G.D.; Franks, N.R. |
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Collective Memory and Spatial Sorting in Animal Groups |
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Journal Article |
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2002 |
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Journal of Theoretical Biology |
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J. Theor. Biol. |
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218 |
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1 |
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1-11 |
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We present a self-organizing model of group formation in three-dimensional space, and use it to investigate the spatial dynamics of animal groups such as fish schools and bird flocks. We reveal the existence of major group-level behavioural transitions related to minor changes in individual-level interactions. Further, we present the first evidence for collective memory in such animal groups (where the previous history of group structure influences the collective behaviour exhibited as individual interactions change) during the transition of a group from one type of collective behaviour to another. The model is then used to show how differences among individuals influence group structure, and how individuals employing simple, local rules of thumb, can accurately change their spatial position within a group (e.g. to move to the centre, the front, or the periphery) in the absence of information on their current position within the group as a whole. These results are considered in the context of the evolution and ecological importance of animal groups. |
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0022-5193 |
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Equine Behaviour @ team @ |
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5310 |
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Krause, J.; James, R.; Franks, D.W.; Croft, D. P. |
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Title |
Animal Social Networks. |
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2015 |
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Oxford University Press |
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Oxford |
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Equine Behaviour @ team @ |
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5883 |
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Gaunitz, C.; Fages, A.; Hanghøj, K.; Albrechtsen, A.; Khan, N.; Schubert, M.; Seguin-Orlando, A.; Owens, I.J.; Felkel, S.; Bignon-Lau, O.; de Barros Damgaard, P.; Mittnik, A.; Mohaseb, A.F.; Davoudi, H.; Alquraishi, S.; Alfarhan, A.H.; Al-Rasheid, K.A.S.; Crubézy, E.; Benecke, N.; Olsen, S.; Brown, D.; Anthony, D.; Massy, K.; Pitulko, V.; Kasparov, A.; Brem, G.; Hofreiter, M.; Mukhtarova, G.; Baimukhanov, N.; Lõugas, L.; Onar, V.; Stockhammer, P.W.; Krause, J.; Boldgiv, B.; Undrakhbold, S.; Erdenebaatar, D.; Lepetz, S.; Mashkour, M.; Ludwig, A.; Wallner, B.; Merz, V.; Merz, I.; Zaibert, V.; Willerslev, E.; Librado, P.; Outram, A.K.; Orlando, L. |
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Title |
Ancient genomes revisit the ancestry of domestic and Przewalski's horses |
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2018 |
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Science |
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360 |
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6384 |
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111-114 |
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The Eneolithic Botai culture of the Central Asian steppes provides the earliest archaeological evidence for horse husbandry, ~5,500 ya, but the exact nature of early horse domestication remains controversial. We generated 42 ancient horse genomes, including 20 from Botai. Compared to 46 published ancient and modern horse genomes, our data indicate that Przewalski's horses are the feral descendants of horses herded at Botai and not truly wild horses. All domestic horses dated from ~4,000 ya to present only show ~2.7% of Botai-related ancestry. This indicates that a massive genomic turnover underpins the expansion of the horse stock that gave rise to modern domesticates, which coincides with large-scale human population expansions during the Early Bronze Age. |
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Admin @ knut @ |
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