Literature DB >> 16237671

Microbial experiments on adaptive landscapes.

Nick Colegrave1, Angus Buckling.   

Abstract

The adaptive landscape is one of the most widely used metaphors in evolutionary biology. It is created by plotting fitness against phenotypes or genotypes in a given environment. The shape of the landscape is crucial in predicting the outcome of evolution: whether evolution will result in populations reaching predictable end points, or whether multiple evolutionary outcomes are more likely. In a more applied sense, the landscape will determine whether organisms will evolve to lose 'costly' resistance to antibiotics, herbicides or pesticides when the use of the control agent is stopped. Laboratory populations of microbes allow evolution to be observed in real time and, as such, provide key insights into the topology of adaptive landscapes. Copyright (c) 2005 Wiley Periodicals, Inc.

Mesh:

Year:  2005        PMID: 16237671     DOI: 10.1002/bies.20292

Source DB:  PubMed          Journal:  Bioessays        ISSN: 0265-9247            Impact factor:   4.345


  18 in total

1.  Source-sink dynamics shape the evolution of antibiotic resistance and its pleiotropic fitness cost.

Authors:  Gabriel G Perron; Andrew Gonzalez; Angus Buckling
Journal:  Proc Biol Sci       Date:  2007-09-22       Impact factor: 5.349

2.  Ecological diversification of Vibrio fischeri serially passaged for 500 generations in novel squid host Euprymna tasmanica.

Authors:  William Soto; Ferdinand M Rivera; Michele K Nishiguchi
Journal:  Microb Ecol       Date:  2014-01-09       Impact factor: 4.552

3.  Mutational neighbourhood and mutation supply rate constrain adaptation in Pseudomonas aeruginosa.

Authors:  Alex R Hall; Victoria F Griffiths; R Craig MacLean; Nick Colegrave
Journal:  Proc Biol Sci       Date:  2009-11-04       Impact factor: 5.349

4.  Evolutionary constraints in fitness landscapes.

Authors:  Luca Ferretti; Daniel Weinreich; Fumio Tajima; Guillaume Achaz
Journal:  Heredity (Edinb)       Date:  2018-07-11       Impact factor: 3.821

Review 5.  Empirical fitness landscapes and the predictability of evolution.

Authors:  J Arjan G M de Visser; Joachim Krug
Journal:  Nat Rev Genet       Date:  2014-06-10       Impact factor: 53.242

6.  Efficient escape from local optima in a highly rugged fitness landscape by evolving RNA virus populations.

Authors:  Héctor Cervera; Jasna Lalić; Santiago F Elena
Journal:  Proc Biol Sci       Date:  2016-08-17       Impact factor: 5.349

7.  Effects of host plant and genetic background on the fitness costs of resistance to Bacillus thuringiensis.

Authors:  B Raymond; D J Wright; M B Bonsall
Journal:  Heredity (Edinb)       Date:  2010-06-02       Impact factor: 3.821

8.  Properties of selected mutations and genotypic landscapes under Fisher's geometric model.

Authors:  François Blanquart; Guillaume Achaz; Thomas Bataillon; Olivier Tenaillon
Journal:  Evolution       Date:  2014-11-17       Impact factor: 3.694

9.  Experimental evolution with E. coli in diverse resource environments. I. Fluctuating environments promote divergence of replicate populations.

Authors:  Tim F Cooper; Richard E Lenski
Journal:  BMC Evol Biol       Date:  2010-01-13       Impact factor: 3.260

10.  Dynamics of Campylobacter colonization of a natural host, Sturnus vulgaris (European starling).

Authors:  F M Colles; N D McCarthy; J C Howe; C L Devereux; A G Gosler; M C J Maiden
Journal:  Environ Microbiol       Date:  2008-09-29       Impact factor: 5.491

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