Phylogenies and the New Evolutionary Synthesis

  • Francesco Santini


The neo-Darwinian theory of evolution is based on the concept that the processes of genetic variation and natural selection, as seen in modern populations, are sufficient to explain the large-scale patterns of diversification of life on Earth. While a neo-Darwinist model treats polygenic traits statistically, as if they resulted from the additive effects of a large number of genes of equivalent phenotypic impact, it describes large-scale phenomena primarily in a historical manner, such that the forces responsible for the origin and persistence of basic body plans, the major changes in structures and ways of life, and the influence of abiotic factors on critical events in the history of life receive scant consideration. This model persists despite evidence from the fossil record and geological dating demonstrating that large-scale patterns and past rates of evolution are not compatible with those extrapolated back from modern populations. In addition an examination of molecular development shows that long-term evolution is not the result of selection of alternative alleles controlling specific traits, or the progressive accumulation of new mutations in an additive fashion, as models used in quantitative genetics suggest. Evolutionary developmental biology and historical biogeography are two disciplines that can allow us to move past this erroneous neo-Darwinian view of evolution. Their success depends on the availability of robust phylogenetic hypotheses and a rigorous application of the comparative method. It is now finally being recognized as the only way to fully explain the patterns and processes that have led to the present diversity of life is a more holistic approach based on robust phylogenies, sound biogeographical data, a good fossil record and molecular developmental information.


Fossil Record Historical Biogeography Modern Synthesis Evolutionary Synthesis Modern Population 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Francesco Santini
    • 1
  1. 1.Department of ZoologyUniversity of TorontoTorontoCanada

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