Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 496194
Title Inferring social structure from temporal data
Author(s) Psorakis, Ioannis; Voelkl, Bernhard; Garroway, C.J.; Radersma, Reinder; Aplin, L.M.; Crates, R.A.; Culina, Antica; Farine, D.R.; Firth, J.A.; Hinde, C.A.; Kidd, Lindall R.; Milligan, Nicole D.; Roberts, Stephen J.; Verhelst, Brecht; Sheldon, Ben C.
Source Behavioral Ecology and Sociobiology 69 (2015)5. - ISSN 0340-5443 - p. 857 - 866.
Department(s) Behavioural Ecology
Publication type Refereed Article in a scientific journal
Publication year 2015
Keyword(s) Flocks - Gathering events - Great tits - Group detection - Social networks

Social network analysis has become a popular tool for characterising the social structure of populations. Animal social networks can be built either by observing individuals and defining links based on the occurrence of specific types of social interactions, or by linking individuals based on observations of physical proximity or group membership, given a certain behavioural activity. The latter approaches of discovering network structure require splitting the temporal observation stream into discrete events given an appropriate time resolution parameter. This process poses several non-trivial problems which have not received adequate attention so far. Here, using data from a study of passive integrated transponder (PIT)-tagged great tits Parus major, we discuss these problems, demonstrate how the choice of the extraction method and the temporal resolution parameter influence the appearance and properties of the retrieved network and suggest a modus operandi that minimises observer bias due to arbitrary parameter choice. Our results have important implications for all studies of social networks where associations are based on spatio-temporal proximity, and more generally for all studies where we seek to uncover the relationships amongst a population of individuals that are observed through a temporal data stream of appearance records.

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