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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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6212 |
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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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Krause, J.; Lusseau, D.; James, R. |
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Title |
Animal social networks: an introduction |
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2009 |
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Behavioral Ecology and Sociobiology |
Abbreviated Journal |
Behav. Ecol. Sociobiol. |
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63 |
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7 |
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967-973-973 |
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Biomedical and Life Sciences |
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Network analysis has a long history in the mathematical and social sciences and the aim of this introduction is to provide a brief overview of the potential that it holds for the study of animal behaviour. One of the most attractive features of the network paradigm is that it provides a single conceptual framework with which we can study the social organisation of animals at all levels (individual, dyad, group, population) and for all types of interaction (aggressive, cooperative, sexual etc.). Graphical tools allow a visual inspection of networks which often helps inspire ideas for testable hypotheses. Network analysis itself provides a multitude of novel statistical tools that can be used to characterise social patterns in animal populations. Among the important insights that networks have facilitated is that indirect social connections matter. Interactions between individuals generate a social environment at the population level which in turn selects for behavioural strategies at the individual level. A social network is often a perfect means by which to represent heterogeneous relationships in a population. Probing the biological drivers for these heterogeneities, often as a function of time, forms the basis of many of the current uses of network analysis in the behavioural sciences. This special issue on social networks brings together a diverse group of practitioners whose study systems range from social insects over reptiles to birds, cetaceans, ungulates and primates in order to illustrate the wide-ranging applications of network analysis. |
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Springer Berlin / Heidelberg |
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0340-5443 |
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Equine Behaviour @ team @ |
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5209 |
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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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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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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
Subject Area:
* 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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Couzin, I.D.; Krause, J.; Franks, N.R.; Levin, S.A. |
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Effective leadership and decision-making in animal groups on the move |
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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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Croft, D. P.; James, R..; Krause, J. (eds) |
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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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Equine Behaviour @ team @ |
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5139 |
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James, R.; Bennett, P.G.; Krause, J. |
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Geometry for mutualistic and selfish herds: the limited domain of danger |
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2004 |
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Journal of Theoretical Biology |
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J. Theor. Biol. |
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228 |
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1 |
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107-113 |
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Aggregation; Selfish herd; Limited domains |
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We present a two-dimensional individual-based model of aggregation behaviour in animals by introducing the concept of a “limited domain of danger”, which represents either a limited detection range or a limited attack range of predators. The limited domain of danger provides a suitable framework for the analysis of individual movement rules under real-life conditions because it takes into account the predator's prey detection and capture abilities. For the first time, a single geometrical construct can be used to analyse the predation risk of both peripheral and central individuals in a group. Furthermore, our model provides a conceptual framework that can be equally applied to aggregation behaviour and refuge use and thus presents a conceptual advance on current theory that treats these antipredator behaviours separately. An analysis of individual movement rules using limited domains of danger showed that the time minimization strategy outcompetes the nearest neighbour strategy proposed by Hamilton's (J. Theor. Biol. 31 (1971) 295) selfish herd model, whereas a random strategy confers no benefit and can even be disadvantageous. The superior performance of the time minimization strategy highlights the importance of taking biological constraints, such as an animal's orientation relative to its neighbours, into account when searching for efficient movement rules underlying the aggregation process. |
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refbase @ user @ |
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552 |
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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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Faria, J.J.; Dyer, J.R.G.; Tosh, C.R.; Krause, J. |
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Leadership and social information use in human crowds |
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2010 |
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Animal Behaviour. |
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Anim. Behav. |
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79 |
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4 |
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895-901 |
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collective animal behaviour; group; human; inadvertent social cue; information; leadership |
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One of the big challenges for group-living animals is to find out who in a group has pertinent information (regarding food or predators) at any moment in time, because informed individuals may not be obviously recognizable to other group members. We found that individuals in human groups were capable of identifying those with information, and this identification increased group performance: the speed and accuracy of groups in reaching a target. Using video analysis we found how informed individuals might have been identified by other group members by means of inadvertent social cues (such as starting order, time spent following and group position). Furthermore, we were able to show that at least one of these cues, the group position of informed individuals, was indeed correlated with group performance. Our final experiment confirmed that leadership was even more efficient when the group members were given the identity of the leader. We discuss the effect of information status regarding the presence and identity of leaders on collective animal behaviour. |
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0003-3472 |
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Equine Behaviour @ team @ |
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5192 |
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