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Spatiotemporal ecological chaos enables gradual evolutionary diversification without niches or tradeoffs.
Mahadevan, Aditya; Pearce, Michael T; Fisher, Daniel S.
Afiliação
  • Mahadevan A; Department of Physics, Stanford University, Stanford, United States.
  • Pearce MT; Department of Physics, Stanford University, Stanford, United States.
  • Fisher DS; Department of Applied Physics, Stanford University, Stanford, United States.
Elife ; 122023 04 28.
Article em En | MEDLINE | ID: mdl-37114771
ABSTRACT
Ecological and evolutionary dynamics are intrinsically entwined. On short timescales, ecological interactions determine the fate and impact of new mutants, while on longer timescales evolution shapes the entire community. Here, we study the evolution of large numbers of closely related strains with generalized Lotka Volterra interactions but no niche structure. Host-pathogen-like interactions drive the community into a spatiotemporally chaotic state characterized by continual, spatially-local, blooms and busts. Upon the slow serial introduction of new strains, the community diversifies indefinitely, accommodating an arbitrarily large number of strains in spite of the absence of stabilizing niche interactions. The diversifying phase persists - albeit with gradually slowing diversification - in the presence of general, nonspecific, fitness differences between strains, which break the assumption of tradeoffs inherent in much previous work. Building on a dynamical-mean field-theory analysis of the ecological dynamics, an approximate effective model captures the evolution of the diversity and distributions of key properties. This work establishes a potential scenario for understanding how the interplay between evolution and ecology - in particular coevolution of a bacterial and a generalist phage species - could give rise to the extensive fine-scale diversity that is ubiquitous in the microbial world.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ecossistema / Evolução Biológica Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ecossistema / Evolução Biológica Idioma: En Ano de publicação: 2023 Tipo de documento: Article