Individual personalities predict social behaviour in wild networks of great tits (<i>Parus major)</i>

Aplin, Lucy M.; Farine, Dan; Morand‐Ferron, Julie; Cole, Ella F.; Cockburn, Andrew; Sheldon, Ben C.

doi:10.1111/ele.12181

Cited by 307 publications

(372 citation statements)

References 47 publications

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“…This conforms with the well-supported theoretical expectation that 'proactive' (i.e. bold, aggressive, fast-exploring) individuals tend to have lower sociability and weaker social network associations [9,31,60]. Alternatively, this pattern may reflect either BTdependent spatial preferences (i.e.…”

Section: Discussionsupporting

confidence: 88%

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When the going gets tough: behavioural type-dependent space use in the sleepy lizard changes as the season dries

et al. 2015

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Understanding space use remains a major challenge for animal ecology, with implications for species interactions, disease spread, and conservation. Behavioural type (BT) may shape the space use of individuals within animal populations. Bolder or more aggressive individuals tend to be more exploratory and disperse further. Yet, to date we have limited knowledge on how space use other than dispersal depends on BT. To address this question we studied BT-dependent space-use patterns of sleepy lizards (Tiliqua rugosa) in southern Australia. We combined high-resolution global positioning system (GPS) tracking of 72 free-ranging lizards with repeated behavioural assays, and with a survey of the spatial distributions of their food and refuge resources. Bayesian generalized linear mixed models (GLMM) showed that lizards responded to the spatial distribution of resources at the neighbourhood scale and to the intensity of space use by other conspecifics (showing apparent conspecific avoidance). BT (especially aggressiveness) affected space use by lizards and their response to ecological and social factors, in a seasonally dependent manner. Many of these effects and interactions were stronger later in the season when food became scarce and environmental conditions got tougher. For example, refuge and food availability became more important later in the season and unaggressive lizards were more responsive to these predictors. These findings highlight a commonly overlooked source of heterogeneity in animal space use and improve our mechanistic understanding of processes leading to behaviourally driven disease dynamics and social structure.

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Section: Discussionsupporting

confidence: 88%

“…These effects may explain variation in the spatial distribution of BTs (e.g. why similar BTs are clumped together in some cases but not in others; [58]), in their interaction rates and overall social network positions [60].…”

Section: Discussionmentioning

confidence: 99%

When the going gets tough: behavioural type-dependent space use in the sleepy lizard changes as the season dries

et al. 2015

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“…This method has already been successfully applied in several studies of this population (e.g. Farine et al 2012;Aplin et al 2013).…”

Section: Closing Remarksmentioning

confidence: 99%

Inferring social structure from temporal data

Psorakis

Voelkl

Garroway

et al. 2015

Behav Ecol Sociobiol

117

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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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“…Researchers have simply assumed that the same collective movement rules apply to any conspecifics detected within an individual's perceptual zone (Couzin et al, 2002;Hemelrijk & Hildenbrandt, 2008). Yet, empirical work has shown that phenotypic assortment and social preferences for particular individuals influence animal grouping patterns (e.g., Griffiths & Magurran, 1997;Croft et al, 2009;Aplin et al, 2013). Incorporating such preferences-embodied within a group's social network-into models of collective motion and decision-making will help to generate new, testable predictions for the field (Bode et al, 2011a).…”

Section: Collective Movement and Decision-makingmentioning

confidence: 99%

“…Personality types have been shown to differ in the strength and distribution of their interactions with group members and to assort with others based on personality (e.g., Pike et al, 2008;Croft et al, 2009;Aplin et al, 2013). Some personality types can occupy prominent or influential network positions, thereby wielding disproportionate influence over network dynamics compared to other group members (Flack et al, 2006;Modlmeier, Keiser, et al, 2014).…”

Section: Animal Personalitiesmentioning

confidence: 99%

Social dynamics, network structure, and information diffusion in fish shoals.

Hasenjager¹

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for always ensuring my library was well-stocked, and for fostering my enduring love of the natural world. Animal populations are often highly structured, with individuals differing in terms of whom they interact with and how frequently they do so. The resulting pattern of relationships constitutes a population's social network. In this dissertation, I examine how environmental variation can shape social networks and influence information flow within them. In Chapter I, I review the history of social network analysis in animal behavior research, and discuss recent insights generated by network approaches in behavioral ecology. I focus on the fields of: social learning, collective behavior, animal personalities, and cooperation. Animal network studies are often criticized for a lack of replication at the network level and an over-reliance on descriptive approaches in lieu of hypothesis testing. Small, shoaling fish may provide a means to address these concerns, as manipulative experiments can be conducted on replicate social groups under captive conditions. Chapters III-V examine the impacts of environmental variation on the social networks of Trinidadian guppy (Poecilia reticulata) shoals, the social dynamics from which they emerge, and information diffusion within them. In the experiments described in Chapter III, I manipulated shoal composition in terms of within-group familiarity.Mixed shoals of familiar and unfamiliar fish exhibited greater homogeneity in network vi structure relative to other groups, which likely contributed to the rapid diffusion of foraging information observed within them. In the experiments discussed in Chapter IV, Imanipulated the within-shoal mixture of personality types. In addition to impacting frequencies of partner switching and patterns of phenotypic assortment, individual-and group-level personality variation had strong effects on the initial acquisition of novel foraging information and the speed of its transmission through a group. In the experiments in Chapter V, I manipulated the ambient predation risk perceived by groups.High-risk conditions were associated with shifts in network structure consistent with attempts to minimize predation risk. High ambient risk also impeded the acquisition and subsequent transmission of foraging information, likely due to heightened neophobia and/or an increase in the perceived costs of personal sampling. I conclude in Chapter VI by considering the broader implications of my work and highlighting promising avenues for future research.vii

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Individual personalities predict social behaviour in wild networks of great tits (Parus major)

Cited by 307 publications

References 47 publications

When the going gets tough: behavioural type-dependent space use in the sleepy lizard changes as the season dries

When the going gets tough: behavioural type-dependent space use in the sleepy lizard changes as the season dries

Inferring social structure from temporal data

Social dynamics, network structure, and information diffusion in fish shoals.

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