Literature DB >> 26259498

Levels and limits in artificial selection of communities.

Manuel Blouin1, Battle Karimi1,2, Jérôme Mathieu3, Thomas Z Lerch1.   

Abstract

Artificial selection of individuals has been determinant in the elaboration of the Darwinian theory of natural selection. Nowadays, artificial selection of ecosystems has proven its efficiency and could contribute to a theory of natural selection at several organisation levels. Here, we were not interested in identifying mechanisms of adaptation to selection, but in establishing the proof of principle that a specific structure of interaction network emerges under ecosystem artificial selection. We also investigated the limits in ecosystem artificial selection to evaluate its potential in terms of managing ecosystem function. By artificially selecting microbial communities for low CO2 emissions over 21 generations (n = 7560), we found a very high heritability of community phenotype (52%). Artificial selection was responsible for simpler interaction networks with lower interaction richness. Phenotype variance and heritability both decreased across generations, suggesting that selection was more likely limited by sampling effects than by stochastic ecosystem dynamics.
© 2015 John Wiley & Sons Ltd/CNRS.

Entities:  

Keywords:  Artificial selection; co-occurrence network; ecological interaction; experimental evolution; heritability

Mesh:

Substances:

Year:  2015        PMID: 26259498     DOI: 10.1111/ele.12486

Source DB:  PubMed          Journal:  Ecol Lett        ISSN: 1461-023X            Impact factor:   9.492


  14 in total

1.  Simulations reveal challenges to artificial community selection and possible strategies for success.

Authors:  Li Xie; Alex E Yuan; Wenying Shou
Journal:  PLoS Biol       Date:  2019-06-25       Impact factor: 8.029

2.  Understanding the evolution of interspecies interactions in microbial communities.

Authors:  Florien A Gorter; Michael Manhart; Martin Ackermann
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-03-23       Impact factor: 6.237

3.  Artificially selecting bacterial communities using propagule strategies.

Authors:  Chang-Yu Chang; Melisa L Osborne; Djordje Bajic; Alvaro Sanchez
Journal:  Evolution       Date:  2020-09-15       Impact factor: 3.694

4.  Deciphering links between bacterial interactions and spatial organization in multispecies biofilms.

Authors:  Wenzheng Liu; Samuel Jacquiod; Asker Brejnrod; Jakob Russel; Mette Burmølle; Søren J Sørensen
Journal:  ISME J       Date:  2019-08-27       Impact factor: 10.302

Review 5.  Directed Evolution of Microbial Communities.

Authors:  Álvaro Sánchez; Jean C C Vila; Chang-Yu Chang; Juan Diaz-Colunga; Sylvie Estrela; María Rebolleda-Gomez
Journal:  Annu Rev Biophys       Date:  2021-03-01       Impact factor: 12.981

6.  Engineering complex communities by directed evolution.

Authors:  Chang-Yu Chang; Jean C C Vila; Madeline Bender; Richard Li; Madeleine C Mankowski; Molly Bassette; Julia Borden; Stefan Golfier; Paul Gerald L Sanchez; Rachel Waymack; Xinwen Zhu; Juan Diaz-Colunga; Sylvie Estrela; Maria Rebolleda-Gomez; Alvaro Sanchez
Journal:  Nat Ecol Evol       Date:  2021-05-13       Impact factor: 15.460

7.  Microbial Community Coalescence for Microbiome Engineering.

Authors:  Matthias C Rillig; Alia Tsang; Julien Roy
Journal:  Front Microbiol       Date:  2016-12-06       Impact factor: 5.640

8.  An Experimental Framework for Generating Evolvable Chemical Systems in the Laboratory.

Authors:  David A Baum; Kalin Vetsigian
Journal:  Orig Life Evol Biosph       Date:  2016-11-18       Impact factor: 1.950

9.  Understanding microbial community dynamics to improve optimal microbiome selection.

Authors:  Robyn J Wright; Matthew I Gibson; Joseph A Christie-Oleza
Journal:  Microbiome       Date:  2019-06-03       Impact factor: 14.650

10.  Artificially selecting microbial communities: If we can breed dogs, why not microbiomes?

Authors:  Flor I Arias-Sánchez; Björn Vessman; Sara Mitri
Journal:  PLoS Biol       Date:  2019-08-30       Impact factor: 8.029

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