Literature DB >> 11919381

Population genetics and sociobiology: conflicting views of evolution.

James Schwartz1.   

Abstract

This article explores the tension between the population genetics and sociobiological approaches to the study of evolution. Whereas population geneticists, like Stanford's Marc Feldman, insist that the genetic complexities of organisms cannot be overlooked, sociobiologists (many of whom now prefer to call themselves "behavioral ecologists") rely on optimization models that are based on the simplest possible genetics.These optimization approaches have their roots in the classical result known as the fundamental theorem of natural selection, formulated by R. A. Fisher in 1930. From the start there was great uncertainty over the proper interpretation of Fisher's theorem, which became confused with Sewall Wright's immensely influential adaptive landscape concept. In the 1960s, a new generation of mathematical biologists proved that Fisher's theorem did not hold when fitness depended on more than one locus. Similar reasoning was used to attack W. D. Hamilton's inclusive fitness theory. A new theory, known as the theory of long-term evolution, attempts to reconcile the rigorous population genetics approach with the long-standing sociobiological view that natural selection acts to increase the fitness of organisms.

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Year:  2002        PMID: 11919381     DOI: 10.1353/pbm.2002.0039

Source DB:  PubMed          Journal:  Perspect Biol Med        ISSN: 0031-5982            Impact factor:   1.416


  3 in total

Review 1.  The simplest formal argument for fitness optimization.

Authors:  Alen Grafen
Journal:  J Genet       Date:  2008-12       Impact factor: 1.166

2.  Animal Behavior, Population Biology and the Modern Synthesis (1955-1985).

Authors:  Jean-Baptiste Grodwohl
Journal:  J Hist Biol       Date:  2019-12       Impact factor: 1.326

3.  "The Theory was Beautiful Indeed": Rise, Fall and Circulation of Maximizing Methods in Population Genetics (1930-1980).

Authors:  Jean-Baptiste Grodwohl
Journal:  J Hist Biol       Date:  2017-08       Impact factor: 1.326

  3 in total

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