An efficient method for sorting and quantifying individual social traits based on group‐level behaviour

Abstract

In social contexts, animal behaviour is often studied in terms of group‐level characteristics. One clear example of this is the collective motion of animals in decentralized structures, such as bird flocks and fish schools. A major goal of research is to identify how group‐level behaviours are shaped by the traits of individuals within them. Few methods exist to make these connections. Individual assessment is often limited, forcing alternatives such as fitting agent‐based models to experimental data. We provide a systematic experimental method for sorting animals according to socially relevant traits, without assaying them or even tagging them individually. Instead, they are repeatedly subjected to behavioural assays in groups, between which the group memberships are rearranged, in order to test the effect of many different combinations of individuals on a group‐level property or feature. We analyse this method using a general model for the group feature, and simulate a variety of specific cases to track how individuals are sorted in each case. We find that in the case where the members of a group contribute equally to the group feature, the sorting procedure increases the between‐group behavioural variation well above what is expected for groups randomly sampled from a population. For a wide class of group feature models, the individual phenotypes are efficiently sorted across the groups and thus become available for further analysis on how individual properties affect group behaviour. We also show that the experimental data can be used to estimate the individual‐level repeatability of the underlying traits. Our method allows experimenters to find repeatable variation in social behaviours that cannot be assessed in solitary individuals. Furthermore, experiments in animal behaviour often focus on comparisons between groups randomly sampled from a population. Increasing the behavioural variation between groups increases statistical power for testing whether a group feature is related to other properties of groups or to their phenotypic composition. Sorting according to socially relevant traits is also beneficial in artificial selection experiments, and for testing correlations with other traits. Overall, the method provides a useful tool to study how individual properties influence social behaviour.

Assortative interactions revealed by sorting of animal groups

“…It is reasonable to assume that consistent differences between groups are due to consistent differences in some individual trait, usually quantified by repeatability (Bell, Hankison, & Laskowski, 2009). The group consistency of global alignment was therefore fitted to a theoretical model (Szorkovszky et al, 2017), in order to estimate the repeatability of the underlying individual trait. The expected trend in the group consistency depends on the within-individual variation relative to the between-individual variation, as well as on how the group's outcome is related to its phenotypic composition .…”

“…differences between individuals account for all variation), the consistency of the rankings rapidly approaches one as sorting progresses. Using maximum-likelihood estimation, the simulated R that best fits the outcome can therefore be chosen (Szorkovszky et al, 2017).…”

“…By creating groups with consistent differences in individual behaviour, we can investigate the traits underlying the properties of shoals, and hence obtain insights into how specific group behaviours may evolve under selection (Ioannou, Guttal, & Couzin 2012). Guppies are a model species in the study of anti-predator shoaling behaviour (Farr, 1975;Dugatkin & Godin, 1992; van der Bijl, Thyselius, Kotrschal, & Kolm, 2015;Herbert-Read et al, 2017), known for fission-fusion dynamics and self-assortment according to sociability (Croft et al, 2005). We investigate the differences between these sorted groups' shoaling behaviours, to identify possible mechanisms for self-assortment.…”

“…We further predict that with an increasing number of iterations, this consistency will increase as the traits become sorted according to phenotype, and hence, within-individual variation becomes relatively less important. By fitting our experimental results to a simulated model of the sorting process, we estimate the trait repeatability R. Finding R from purely group-level data may seem counter-intuitive, but maximumlikelihood fitting is possible due to the fact that the sorting dynamics depend heavily on the underlying variation between individuals (Szorkovszky et al, 2017). We then analyse differences between the sorted groups at three scales: at the group level, at the level of subgroups (local aggregations) and at the level of basic locomotion and interactions.…”

Rapid evolution of coordinated and collective movement in response to artificial selection

“…By artificially selecting for polarization over multiple generations we tested whether, and how quickly, coordinated group movement evolved when under strong directional selection. Importantly, polarization can only be measured in a group context but we nevertheless could apply an individual-level selection approach; our recently developed sorting protocol of repeated mixing and polarization-determination concentrates the individuals with the highest polarization propensities in few groups ( 18, 19 ). Those individuals could then be bred for the selection lines.…”