Mobility can promote the evolution of cooperation via emergent self-assortment dynamics

PLoS Comput Biol. 2017 Sep 8;13(9):e1005732. doi: 10.1371/journal.pcbi.1005732. eCollection 2017 Sep.

Abstract

The evolution of costly cooperation, where cooperators pay a personal cost to benefit others, requires that cooperators interact more frequently with other cooperators. This condition, called positive assortment, is known to occur in spatially-structured viscous populations, where individuals typically have low mobility and limited dispersal. However many social organisms across taxa, from cells and bacteria, to birds, fish and ungulates, are mobile, and live in populations with considerable inter-group mixing. In the absence of information regarding others' traits or conditional strategies, such mixing may inhibit assortment and limit the potential for cooperation to evolve. Here we employ spatially-explicit individual-based evolutionary simulations to incorporate costs and benefits of two coevolving costly traits: cooperative and local cohesive tendencies. We demonstrate that, despite possessing no information about others' traits or payoffs, mobility (via self-propulsion or environmental forcing) facilitates assortment of cooperators via a dynamically evolving difference in the cohesive tendencies of cooperators and defectors. We show analytically that this assortment can also be viewed in a multilevel selection framework, where selection for cooperation among emergent groups can overcome selection against cooperators within the groups. As a result of these dynamics, we find an oscillatory pattern of cooperation and defection that maintains cooperation even in the absence of well known mechanisms such as kin interactions, reciprocity, local dispersal or conditional strategies that require information on others' strategies or payoffs. Our results offer insights into differential adhesion based mechanisms for positive assortment and reveal the possibility of cooperative aggregations in dynamic fission-fusion populations.

MeSH terms

  • Animals
  • Biological Evolution*
  • Cell Movement
  • Computational Biology
  • Cooperative Behavior*
  • Locomotion
  • Models, Biological*
  • Selection, Genetic

Grants and funding

VG acknowledges research support from a DBT-Ramalingaswamy fellowship (www.dbtindia.nic.in), DBT-IISc partnership program, DST Centre for Mathematical Biology at IISc Phase II (SR/S4/MS:799/12) (www.dst.gov.in), CSIR, and infrastructure support from DST-FIST. SAL’s work was supported by a grant from the Simons Foundation (#395890, Simon Levin) (https://www.simonsfoundation) and the ARO (W911NF-14-1-0431) (www.aro.army.mil). IDC acknowledges support from NSF (IOS-1355061, EAGER-IOS-1251585) (www.nsf.gov), ONR (N00014-09-1-1074, N00014-14-1-0635) (www.onr.navy.mil), ARO (W911NG-11-1-0385, W911NF-14-1-0431) (www.aro.army.mil), the “Struktur-und Innovationsfonds für die Forschung (SI-BW)“ of the State of Baden-Württemberg, and the Max Planck Society. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.