Emergent behavioral organization in heterogeneous groups of a social insect

Abstract

The composition of social groups has profound effects on their function, from collective decision-making to foraging efficiency. But few social systems afford sufficient control over group composition to precisely quantify its effects on individual and collective behavior. Here we combine experimental and theoretical approaches to study the effect of group composition on individual behavior and division of labor (DOL) in a social insect. Experimentally, we use automated behavioral tracking to monitor 120 colonies of the clonal raider ant, Ooceraea biroi, with controlled variation in three key correlates of social insect behavior: genotype, age, and morphology. We find that each of these sources of heterogeneity generates a distinct pattern of behavioral organization, including the amplification or dampening of inherent behavioral differences in colonies with mixed types. Theoretically, we use a well-studied model of DOL to explore potential mechanisms underlying the experimental findings. We find that the simplest implementation of this model, which assumes that heterogeneous individuals differ only in response thresholds, could only partially recapitulate the empirically observed patterns of behavior. However, the full spectrum of observed phenomena was recapitulated by extending the model to incorporate two factors that are biologically meaningful but theoretically rarely considered: variation among workers in task performance efficiency and among larvae in task demand. Our results thus show that different sources of heterogeneity within social groups can generate different, sometimes non-intuitive, behavioral effects, but that relatively simple models can capture these dynamics and thereby begin to elucidate the basic organizational principles of DOL in social insects.Significance StatementWhen individuals interact in an aggregate, many factors that are not known a priori affect group dynamics. A social group will therefore show emergent properties that cannot easily be predicted from how its members behave in isolation. This problem is exacerbated in mixed groups, where different individuals have different behavioral tendencies. Here we describe different facets of collective behavioral organization in mixed groups of the clonal raider ant, and show that a simple theoretical model can capture even non-intuitive aspects of the behavioral data. These results begin to reveal the principles underlying emergent behavioral organization in social insects. Importantly, our insights might apply to complex biological systems more generally and be used to help engineer collective behavior in artificial systems.

Integrating real-time data analysis into automatic tracking of social insects

“…In the last decade, automatic video tracking systems have emerged as ideal tools to investigate the behaviour of ants [4][5][6][7][8][9][10]. These live in compact societies that thrive in the laboratory and easily adapt to experimental set-ups.…”

“…Researchers usually employ tracking systems to analyse groups of ants moving in two-dimensional arenas, tagging each individual with unique identity markers (e.g. QR or ArUco matrix codes [ 7 , 8 , 11 , 12 ] or combinations of painted colour dots [ 4 – 6 , 13 ]) and using cameras to take images from top and/or bottom [ 14 ]. Many video trackers rely on inferring the position and orientation of insects via the detection of individual tags in images taken at given time intervals [ 7 , 12 ].…”

Integrating real-time data analysis into automatic tracking of social insect behavior

“…In the last decade, automatic video tracking systems have emerged as ideal tools to investigate the behavior of ants [4][5][6][7][8][9][10] . These live in compact societies that thrive in the laboratory and easily adapt to experimental setups, performing a relatively narrow repertoire of individual actions and engaging in simple social interactions.…”

“…Automatic trackers can therefore easily scrape a significant amount of information just by scanning images of their colonies. Researchers usually employ tracking systems to analyze groups of ants moving in two-dimensional arenas, tagging each individual with unique identity markers (e.g., QR or ArUco matrix codes 7,8,11,12 or combinations of painted color dots [4][5][6]13 ) and using cameras to take images from top and/or bottom 14 . Many video trackers rely on inferring the position and orientation of insects via the detection of individual tags in images taken at given time intervals 7,12 .…”

The collective behavior of ant groups depends on group genotypic composition

“…Additionally, it is unclear exactly how variation in individual-level traits leads to variation in group-level traits [12–14]. For example, it is not clear whether each group member contributes equally in determining group-level behavior or if instead some individuals have a disproportionately large effect (i.e.…”