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Bonnell, T. R.

Publications and source records attributed to Bonnell, T. R..

2 recordsLinked to original sources

Network reaction norms: taking into account network position and network plasticity in response to environmental change.

Recent studies have highlighted the link between consistent inter-individual differences in behaviour and consistency in social network position. There is also evidence that network structures can show temporal dynamics, suggesting that consistency in social network position across time does not preclude some form of plasticity in response to environmental variation. To better consider variation in network position and plasticity simultaneously we introduce the network reaction norm (NRN) approach. As an illustrative example, we used behavioural data on chacma baboons, collected over a period of seven years, to construct a time series of networks, using a moving window. Applying an NRN approach with these data, we found that most of the variation in network centrality could be explained by inter-individual differences in mean centrality. There was also evidence, however, for individual differences in network plasticity. These differences suggest that environmental conditions may influence which individuals are most central, i.e., they lead to an individual x environment interaction. We suggest that expanding from measures of repeatability in social networks to network reaction norms can provide a more temporally nuanced way to investigate social phenotypes within groups, and lead to a better understanding of the development and maintenance of individual variation in social behaviour.

animal behavior and cognition

Analyzing time-aggregated networks: the role of bootstrapping, permutation, and simulation.

Networks are often used to describe adaptive social systems, where individual (node) behaviour generates network-level structures that influence subsequent individual-level behaviour. To address questions about the dynamics of network structure in these systems, there is a need to analyze networks through time. Various statistical methods exist for estimating the behaviour of networks in time, in terms of both time-ordered and time-aggregated networks. In this paper, we discuss three main analytical steps for the analysis of time-aggregated network data: 1) aggregation choices, 2) null-model comparisons, and 3) constructing, parameterizing, and making inferences from time series models. We then present a custom R package, netTS, which facilitates these steps. Observed grooming data from a group of vervet monkeys, a highly social primate species, is used as an example to highlight three potential analyses: 1) quantifying the stability of network-level social structures through time, 2) identifying keystone nodes driving/maintaining network structures, and 3) quantifying the interdependence between node behaviour through time. In particular, we highlight the role of bootstrapping, permutation, and simulation as critical components in the analysis of time-aggregated networks.

animal behavior and cognition