Literature DB >> 21680445

Performance surfaces and adaptive landscapes.

Stevan J Arnold1.   

Abstract

In an earlier characterization of the relationship between morphology, performance and fitness, I focused only on directional selection (Arnold, 1983). The aim of this article is to extend that characterization to include stabilizing and other forms of nonlinear selection. As in the earlier characterization, this more general description of the morphology-performance-fitness relationship splits empirical analysis into two parts: the study of the relationship between morpholgy and performance, and the study of the relationship between performance and fitness. From a conceptual standpoint, my goal is to specify the relationship of performance studies to the adaptive landscape. I begin by reviewing the adaptive landscape concept and its importance in evolutionary biology. A central point emerging from that review is that that key descriptors of the adaptive landscape can be estimated by measuring the impact of selection on the means, variances and covariances of phenotypic traits. Those descriptors can be estimated by making a quadratic (regression) approximation to the selection surface that describes the relationship between the phenotypic traits of individuals and their fitness. Analysis of the effects of morphology on performance follows an analogous procedure: making a quadratic approximation to the individual performance surface and then using that approximation to solve for the descriptors of the performance landscape. I conclude by discussing the evolution of performance and adaptive landscapes. One possibility with biomechanical justification is that the performance landscape evolves along performance lines of least resistance.

Year:  2003        PMID: 21680445     DOI: 10.1093/icb/43.3.367

Source DB:  PubMed          Journal:  Integr Comp Biol        ISSN: 1540-7063            Impact factor:   3.326


  22 in total

1.  Biomechanical trade-offs bias rates of evolution in the feeding apparatus of fishes.

Authors:  Roi Holzman; David C Collar; Samantha A Price; C Darrin Hulsey; Robert C Thomson; Peter C Wainwright
Journal:  Proc Biol Sci       Date:  2011-10-12       Impact factor: 5.349

Review 2.  The generation of variation and the developmental basis for evolutionary novelty.

Authors:  Benedikt Hallgrímsson; Heather A Jamniczky; Nathan M Young; Campbell Rolian; Urs Schmidt-Ott; Ralph S Marcucio
Journal:  J Exp Zool B Mol Dev Evol       Date:  2012-05-30       Impact factor: 2.656

3.  Reconciling strong stabilizing selection with the maintenance of genetic variation in a natural population of black field crickets (Teleogryllus commodus).

Authors:  John Hunt; Mark W Blows; Felix Zajitschek; Michael D Jennions; Robert Brooks
Journal:  Genetics       Date:  2007-07-29       Impact factor: 4.562

4.  Predation shapes sperm performance surfaces in guppies.

Authors:  Alessandro Devigili; Jonathan P Evans; John L Fitzpatrick
Journal:  Proc Biol Sci       Date:  2019-06-26       Impact factor: 5.349

Review 5.  Speciation through the lens of biomechanics: locomotion, prey capture and reproductive isolation.

Authors:  Timothy E Higham; Sean M Rogers; R Brian Langerhans; Heather A Jamniczky; George V Lauder; William J Stewart; Christopher H Martin; David N Reznick
Journal:  Proc Biol Sci       Date:  2016-09-14       Impact factor: 5.349

6.  Hydrodynamic Simulations of the Performance Landscape for Suction-Feeding Fishes Reveal Multiple Peaks for Different Prey Types.

Authors:  Karin H Olsson; Christopher H Martin; Roi Holzman
Journal:  Integr Comp Biol       Date:  2020-11-01       Impact factor: 3.326

7.  Hybridization alters the shape of the genotypic fitness landscape, increasing access to novel fitness peaks during adaptive radiation.

Authors:  Austin H Patton; Emilie J Richards; Katelyn J Gould; Logan K Buie; Christopher H Martin
Journal:  Elife       Date:  2022-05-26       Impact factor: 8.713

8.  Simplification, Innateness, and the Absorption of Meaning from Context: How Novelty Arises from Gradual Network Evolution.

Authors:  Adi Livnat
Journal:  Evol Biol       Date:  2017-03-11       Impact factor: 3.119

Review 9.  Imaging structural co-variance between human brain regions.

Authors:  Aaron Alexander-Bloch; Jay N Giedd; Ed Bullmore
Journal:  Nat Rev Neurosci       Date:  2013-03-27       Impact factor: 34.870

10.  Testing adaptive hypotheses of convergence with functional landscapes: a case study of bone-cracking hypercarnivores.

Authors:  Zhijie Jack Tseng
Journal:  PLoS One       Date:  2013-05-29       Impact factor: 3.240

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