Collective predator evasion: Putting the criticality hypothesis to the test

PLoS Comput Biol. 2021 Mar 15;17(3):e1008832. doi: 10.1371/journal.pcbi.1008832. eCollection 2021 Mar.

Abstract

According to the criticality hypothesis, collective biological systems should operate in a special parameter region, close to so-called critical points, where the collective behavior undergoes a qualitative change between different dynamical regimes. Critical systems exhibit unique properties, which may benefit collective information processing such as maximal responsiveness to external stimuli. Besides neuronal and gene-regulatory networks, recent empirical data suggests that also animal collectives may be examples of self-organized critical systems. However, open questions about self-organization mechanisms in animal groups remain: Evolutionary adaptation towards a group-level optimum (group-level selection), implicitly assumed in the "criticality hypothesis", appears in general not reasonable for fission-fusion groups composed of non-related individuals. Furthermore, previous theoretical work relies on non-spatial models, which ignore potentially important self-organization and spatial sorting effects. Using a generic, spatially-explicit model of schooling prey being attacked by a predator, we show first that schools operating at criticality perform best. However, this is not due to optimal response of the prey to the predator, as suggested by the "criticality hypothesis", but rather due to the spatial structure of the prey school at criticality. Secondly, by investigating individual-level evolution, we show that strong spatial self-sorting effects at the critical point lead to strong selection gradients, and make it an evolutionary unstable state. Our results demonstrate the decisive role of spatio-temporal phenomena in collective behavior, and that individual-level selection is in general not a viable mechanism for self-tuning of unrelated animal groups towards criticality.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Biological Evolution
  • Computational Biology
  • Models, Biological*
  • Predatory Behavior / physiology*
  • Social Behavior*

Grants and funding

P.P.K and P.R. acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, https://www.dfg.de) Emmy Noether Programm - RO 4766/2-1. P. R. acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, https://www.dfg.de) under Germany’s Excellence Strategy – EXC 2002/1 “Science of Intelligence” – project number 390523135. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.