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  Fragmentation modes and the evolution of life cycles

Pichugin, Y., Peña, J., Rainey, P. B., & Traulsen, A. (2017). Fragmentation modes and the evolution of life cycles. PLoS Computational Biology, 13(11). doi:10.1371/journal.pcbi.1005860.

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Pichugin, Yuriy1, Author           
Peña, Jorge1, Author           
Rainey, Paul B.2, Author           
Traulsen, Arne1, Author           
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1Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_1445641              
2Department Microbial Population Biology, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_2421699              

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 Abstract: Author summary Mode of reproduction is a defining trait of all organisms, including colonial bacteria and multicellular organisms. To produce offspring, aggregates must fragment by splitting into two or more groups. The particular way that a given group fragments defines the life cycle of the organism. For instance, insect colonies can reproduce by splitting or by producing individuals that found new colonies. Similarly, some colonial bacteria propagate by fission or by releasing single cells, while others split in highly sophisticated ways; in multicellular organisms reproduction typically proceeds via a single-cell bottleneck phase. The space of possibilities for fragmentation is so vast that an exhaustive analysis seems daunting. Focusing on fragmentation modes of a simple kind we parametrise all possible modes of group fragmentation and identify those modes leading to the fastest population growth rate. Two kinds of life cycle dominate: one involving division into two equal size groups, and the other involving production of a unicellular propagule. The prevalence of these life cycles in nature is consistent with our null model and suggests that benefits accruing from population growth rate alone may have shaped the evolution of fragmentation mode.

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Language(s): eng - English
 Dates: 2017-05-122017-10-312017-11-222017-11
 Publication Status: Issued
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 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1005860
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: 20 Volume / Issue: 13 (11) Sequence Number: - Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1