Evolutionary regime transitions in structured populations

• Published in: PloS one 2018-11, Vol.13 (11), p.e0200670-e0200670
• Main Authors: Alcalde Cuesta, Fernando, González Sequeiros, Pablo, Lozano Rojo, Álvaro
• Format: Article
• Language: English
• Subjects: Algorithms; Amplifiers; Animals; Apexes; Biological Evolution; Biology; Biology and Life Sciences; Computer and Information Sciences; Computer Graphics; Computer Simulation; Engineering; Evolution; Fitness; Graphical representations; Graphs; Humans; Models, Biological; Mutation; Offspring; Physical Sciences; Population; Population Dynamics; Probability; Reproductive fitness; Research and Analysis Methods; Suppressors; Theory
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0200670

The evolutionary dynamics of a finite population where resident individuals are replaced by mutant ones depends on its spatial structure. Usually, the population adopts the form of an undirected graph where the place occupied by each individual is represented by a vertex and it is bidirectionally linked to the places that can be occupied by its offspring. There are undirected graph structures that act as amplifiers of selection increasing the probability that the offspring of an advantageous mutant spreads through the graph reaching any vertex. But there also are undirected graph structures acting as suppressors of selection where this probability is less than that of the same individual placed in a homogeneous population. Here, firstly, we present the distribution of these evolutionary regimes for all undirected graphs with N ≤ 10 vertices. Some of them exhibit transitions between different regimes when the mutant fitness increases. In particular, as it has been already observed for small-order random graphs, we show that most graphs of order N ≤ 10 are amplifiers of selection. Secondly, we describe examples of amplifiers of order 7 that become suppressors from some critical value. In fact, for graphs of order N ≤ 7, we apply computer-aided techniques to symbolically compute their fixation probability and then their evolutionary regime, as well as the critical values for which they change their regime. Thirdly, the same technique is applied to some families of highly symmetrical graphs as a mean to explore methods of suppressing selection. The existence of suppression mechanisms that reverse an amplification regime when fitness increases could have a great interest in biology and network science. Finally, the analysis of all graphs from order 8 to order 10 reveals a complex and rich evolutionary dynamics, with multiple transitions between different regimes, which have not been examined in detail until now.