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

Authored by David J T Sumpter, Alex Szorkovszky, Alexander Kotrschal, James E Herbert Read, Niclas Kolm, Kristiaan Pelckmans

Date Published: 2017

DOI: 10.1111/2041-210x.12813

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

Abstract

1. 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. 2. 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. 3. 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. 4. 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.

Tags

networks Leadership Decision-Making Personality Boldness Animal groups collective behaviour Fish Plasticity Guppy Artificial selection Group composition Personality-traits Artificial selection