Biology subjects

Vilette, C.

Publications and source records attributed to Vilette, C..

3 recordsLinked to original sources

bioRxiv2019

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↗

bioRxiv2019

Ranking the Ranking Methodologies: Determining Temporal Stability in Dominance Hierarchies

The importance of social hierarchies has led to the development of many techniques for inferring social ranks, leaving researchers with an overwhelming array of options to choose from. Many of our research questions involve longitudinal analyses, so we were interested in a method that would provide reliable ranks across time. But how does one determine which method performs best?\n\nWe attempt to answer this question by using a training-testing procedure to compare 13 different approaches for calculating dominance hierarchies (seven methods, plus 6 analytical variants of these). We assess each methods performance, its efficiency, and the extent to which the calculated ranks obtained from the training dataset accurately predict the outcome of observed aggression in the testing dataset.\n\nWe found that all methods tested performed well, despite some differences in inferred rank order. With respect to the need for a \"burn-in\" period to enable reliable ranks to be calculated, again, all methods were efficient and able to infer reliable ranks from the very start of the study period (i.e., with little to no burn-in period). Using a common 6-month burn-in period to aid comparison, we found that all methods could predict aggressive outcomes accurately for the subsequent 10 months. Beyond this 10-month threshold, accuracy in prediction decreased as the testing dataset increased in length. The decay was rather shallow, however, indicating overall rank stability during this period.\n\nIn general, a training-testing approach allows researchers to determine the most appropriate method for their dataset, given sampling effort, the frequency of agonistic interactions, the steepness of the hierarchy, and the nature of the research question being asked. Put simply, we did not find a single best method, but our approach offers researchers a valuable tool for identifying the method that will work best for them.\n\nHighlightsO_LIAll ranking methods tested performed well at predicting future aggressive outcomes, despite some differences in inferred rank order.\nC_LIO_LIAll ranking methods appear to be efficient in inferring reliable ranks from the very start (i.e., with little to no burn-in period), but all showed improvement as the burn-in period increased.\nC_LIO_LIUsing a common 6-month burn-in period, we found that all methods could predict aggressive outcomes accurately for the subsequent 10 months. Beyond this threshold, accuracy in prediction decreased as the testing dataset increased in length.\nC_LIO_LISwitching to a data-driven approach to assign k-values, via the training/validation/testing procedure, resulted in a marked improvement in performance in the modified Elo-rating method.\nC_LI

animal behavior and cognition↗

bioRxiv2019

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↗