Emergence and Stability of Population Structure and Biological Aggregates Across Scales

eSMB2020 eSMB2020 Follow Wednesday at 9:30am EDT
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Olivia Chu & Daniel Cooney


Ecological and evolutionary dynamics are fundamentally driven by and intertwined with the structure of populations across a wide range of organization levels, from the origins of multicellular organisms to swarming and flocking behaviors of animal groups or cooperation and collective decision-making in human populations. In particular, the clustering of populations into localized group or network structures can facilitate the evolution of cooperative behaviors [1]. In addition, the emergence and persistence of group structure can constitute a major evolutionary transition to higher levels of biological organization [2]. The coevolutionary dynamics of population structure and genetic, behavioral, and cultural traits plays out over a range of time scales, from short-time ecological competition, to intermediate-term replicator dynamics, to long-term adaptive dynamics. In our session, we will present a survey of recent research focusing on collective and cooperative behav- iors using a variety of mathematical frameworks to study a broad range of evolutionary questions. From the mathematical perspective, our speakers will detail models using tools from the theories of dynamical systems, stochastic processes, and adaptive networks, and will study questions using approaches from evolutionary game theory, consensus and flocking dynamics, and aggregation-fragmentation processes. In term of biological applications, our speakers will present on research on the onset of multicellular life-cycles (Yuriy Pichugin), collective information-processing in animal groups (Pawel Romanczuk), the balance between homophily and heterophily in human interactions (Olivia Chu), and the co-evolution of homophily and cooperation from microbial communities to human societies (Feng Fu). With this variety of talks, we hope to bring together a diverse group of speakers, who are typically associated with different research communities, by uniting under the common theme of cooperative behavior and the emergence of group structure across scales.

Olivia Chu

Princeton University
"An Adaptive Voter Model in Heterogeneous Environments"
In human social systems, it is natural to assume that individuals’ opinions influence and are influenced by their interactions. Mathematically, it is common to represent such systems as networks, where nodes are individuals and edges denote connections. Adaptive network models explore the dynamic relationship between node properties and network topology. For opinion dynamics, adaptive voter models provide two mechanisms through which changes can occur within the network. First, through homophily, an edge forms between two individuals who already agree; second, through social learning, an individual adopts one of their neighbor’s opinions. Central to these models is assortative mixing: individuals more frequently attach to those who are similar to them, which facilitates the formation of sub-communities of like-minded individuals. However, it is not always the case that individuals want to cluster into homogeneous groups. Instead, they might attempt to surround themselves with individuals who both agree and disagree with them, in an effort to attain a balance of inclusion and distinctiveness in their social environments. In this work, we explore the effects that such preferences for heterogeneous environments have on the dynamics of the adaptive voter model.

Feng Fu

Dartmouth College
"How phenotypic similarity begets cooperation"
Tag-based cooperation, or cooperation based on phenotypic similarity, has long been seen as a potent mechanism of cooperation. The evolutionary origin and variability of tag-based cooperation has yet to be fully answered. Here we show analytically and by means of simulations that tag-based cooperation can always evolve by natural selection in the presence of sufficient tag diversity. Our work provides fundamental insights into understanding the widespread of tag-based cooperation in the real world from microbial populations to complex human societies.

Yuriy Pichugin

Max Planck Institute for Evolutionary Biology
"Evolution of clonal life cycles: recipes for multicellularity, equal split, and single-cell bottleneck"
There is a huge variety in reproduction modes observed even among the simplest organisms. Many species are unicellular but some form simple colonies. Some of the colonies reproduce by splitting into equal parts others produce unicellular propagules. What is the driving force, which shapes the evolution of life cycles? What are the conditions promoting uni- or multi-cellular life cycles? We developed the stage-structured matrix population model of the growth and reproduction of unstructured multicellular organisms. Using this model, we investigated the conditions favoring the evolution of diverse life cycles: unicellular, with an equal split of a colony, and with the reproduction via single-cell bottleneck. We identified the set of profiles of size-dependent growth and death rates promoting each of these life cycles. We found that the conditions promoting a single-cell bottleneck are the steady improvement in the performance of the colony with its size. At the same time, the equal splits require the sudden rise in growth rate (drop in the death rate) at the size of the newborn offspring. Altogether, our findings demonstrate the patterns behind the evolution of multicellular life cycles.

Pawel Romanczuk

Hombolt University of Berlin
"Flocking in complex environments – attention trade-offs in collective information processing"
The ability of biological and artificial collectives to outperform solitary individuals in a wide variety of tasks depends crucially on the efficient processing of social and environmental information at the level of the collective. Here, we model collective behavior in complex environments with many potentially distracting cues. Counter-intuitively, large-scale coordination in such environments can be maximized by strongly limiting the cognitive capacity of individuals, where due to self-organized dynamics the collective self-isolates from disrupting information. We observe a fundamental trade-off between coordination and collective responsiveness to environmental cues. Our results offer important insights into possible evo- lutionary trade-offs in collective behavior in biology and suggests novel principles for design of artificial swarms exploiting attentional bottlenecks.

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