Understanding Dynamics of Information Transmission in Drosophila melanogaster Using a Statistical Modeling Framework for Longitudinal Network Data (the RSiena Package)

Abstract

Social learning – the transmission of behaviors through observation or interaction with conspecifics – can be viewed as a decision-making process driven by interactions among individuals. Animal group structures change over time and interactions among individuals occur in particular orders that may be repeated following specific patterns, change in their nature, or disappear completely. Here we used a stochastic actor-oriented model built using the RSiena package in R to estimate individual behaviors and their changes through time, by analyzing the dynamic of the interaction network of the fruit fly Drosophila melanogaster during social learning experiments. In particular, we re-analyzed an experimental dataset where uninformed flies, left free to interact with informed ones, acquired and later used information about oviposition site choice obtained by social interactions. We estimated the degree to which the uninformed flies had successfully acquired the information carried by informed individuals using the proportion of eggs laid by uninformed flies on the medium their conspecifics had been trained to favor. Regardless of the degree of information acquisition measured in uninformed individuals, they always received and started interactions more frequently than informed ones did. However, information was efficiently transmitted (i.e., uninformed flies predominantly laid eggs on the same medium informed ones had learn to prefer) only when the difference in contacts sent between the two fly types was small. Interestingly, we found that the degree of reciprocation, the tendency of individuals to form mutual connections between each other, strongly affected oviposition site choice in uninformed flies. This work highlights the great potential of RSiena and its utility in the studies of interaction networks among non-human animals.

Analysing animal social network dynamics: the potential of stochastic actor‐oriented models

“…SAOMs have also been used to investigate social information transmission in Drosophila melanogaster (Pasquaretta et al . ), showing that uninformed flies tend to change social contacts faster. Boucherie et al .…”

“…; Pasquaretta et al . ). We will mainly discuss the application of SAOMs as implemented through the program SIENA (Steglich, Snijders & West ; Snijders, van de Bunt & Steglich ) and the R package RSiena (Ripley et al .…”

“…Despite the evident potential in the dynamic network analysis approach, applications in ecology remain relatively limited (but see Blonder & Dornhaus 2011;Jeanson 2012;Wilson et al 2014;Ilany, Booms & Holekamp 2015;Aplin et al 2015a;Borgeaud et al 2016;Pasquaretta et al 2016). Recent calls for the implementation of dynamic network analyses (e.g.…”

“…Alongside our guide in the Supporting Information, further information on data requirements, model fitting and statistical inference is also available in previous papers that have used SAOMs in animals (e.g. Ilany, Booms & Holekamp 2015;Borgeaud et al 2016;Pasquaretta et al 2016). We will mainly discuss the application of SAOMs as implemented through the program SIENA (Steglich, Snijders & West 2006;Snijders, van de Bunt & Steglich 2010) and the R package RSiena (Ripley et al 2015) as this program allows the full breadth of effects we describe to be implemented and comes with a large body of examples, R code and user guides (see: http://www.stats.ox.ac.uk/ snijders/siena/ for these and http://r-forge.r-project.org/R/?group_id=461 for the package itself).…”

“…The duration of each time period will be determined by the study system, the questions and processes being investigated and the resolution of the data available, and/or through pilot-analyses to determine the most appropriate resolution (e.g. Pasquaretta et al 2016). Some studies of human associations have used up to yearly censuses (e.g.…”