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Recent Advances in the Theory and Application of Fitness Landscapes pp 509–526Cite as

Predicting Evolution and Visualizing High-Dimensional Fitness Landscapes

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Part of the book series: Emergence, Complexity and Computation ((ECC,volume 6))

Abstract

The tempo and mode of an adaptive process is strongly determined by the structure of the fitness landscape that underlies it. In order to be able to predict evolutionary outcomes (even on the short term), we must know more about the nature of realistic fitness landscapes than we do today. For example, in order to know whether evolution is predominantly taking paths that move upwards in fitness and along neutral ridges, or else entails a significant number of valley crossings, we need to be able to visualize these landscapes: we must determine whether there are peaks in the landscape, where these peaks are located with respect to one another, and whether evolutionary paths can connect them. This is a difficult task because genetic fitness landscapes (as opposed to those based on traits) are high-dimensional, and tools for visualizing such landscapes are lacking. In this contribution, we focus on the predictability of evolution on rugged genetic fitness landscapes, and determine that peaks in such landscapes are highly clustered: high peaks are predominantly close to other high peaks. As a consequence, the valleys separating such peaks are shallow and narrow, such that evolutionary trajectories towards the highest peak in the landscape can be achieved via a series of valley crossings.

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Author information

Authors and Affiliations

  1. Department Microbiology and Molecular Genetics & BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, 48824, U.S.A.

    Bjørn Østman & Christoph Adami

Authors
  1. Bjørn Østman
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  2. Christoph Adami
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Correspondence to Bjørn Østman .

Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering & Information Technology, HTWK Leipzig University of Applied Sciences, Leipzig, Germany

    Hendrik Richter

  2. Department of Computer Science, University of Pretoria, Pretoria, South Africa

    Andries Engelbrecht

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Østman, B., Adami, C. (2014). Predicting Evolution and Visualizing High-Dimensional Fitness Landscapes. In: Richter, H., Engelbrecht, A. (eds) Recent Advances in the Theory and Application of Fitness Landscapes. Emergence, Complexity and Computation, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41888-4_18

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  • DOI: https://doi.org/10.1007/978-3-642-41888-4_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41887-7

  • Online ISBN: 978-3-642-41888-4

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