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Müller, J. ; Tellier, A.*

Life-History traits and the replicator equation.

Math. Biosci. 349:108826 (2022)
Postprint DOI PMC
Open Access Green
Due to the relevance for conservation biology, there is an increasing interest to extend evolutionary genomics models to plant, animal or microbial species. However, this requires to understand the effect of life-history traits absent in humans on genomic evolution. In this context, it is fundamentally of interest to generalize the replicator equation, which is at the heart of most population genomics models. However, as the inclusion of life-history traits generates models with a large state space, the analysis becomes involving. We focus, here, on quiescence and seed banks, two features common to many plant, invertebrate and microbial species. We develop a method to obtain a low-dimensional replicator equation in the context of evolutionary game theory, based on two assumptions: (1) the life-history traits are per se neutral, and (2) frequency-dependent selection is weak. We use the results to investigate the evolution and maintenance of cooperation based on the Prisoner's dilemma and the snowdrift game. We first consider the generalized replicator equation, and then refine the investigation using adaptive dynamics. It turns out that, depending on the structure and timing of the quiescence/dormancy life-history trait, cooperation in a homogeneous population can be stabilized. We finally discuss and highlight the relevance of these results for plant, invertebrate and microbial communities.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Adaptive Dynamics ; Life-history Traits ; Persister ; Prisoner’s Dilemma ; Quiescence ; Replicator Equation ; Seed Bank
ISSN (print) / ISBN 0025-5564
e-ISSN 1879-3134
Zeitschrift Mathematical Biosciences
Quellenangaben Band: 349, Heft: , Seiten: , Artikelnummer: 108826 Supplement: ,
Verlag Elsevier
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen International Graduate School of Science and Engineering
Deutsche Forschungsgemeinschaft