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Mycological Progress

, Volume 3, Issue 2, pp 157–176 | Cite as

Molecular evolution of Agaricus species based on ITS and LSU rDNA sequences

  • József Geml
  • David M. Geiser
  • Daniel J. Royse
Article

Abstract

Phylogenetic analyses of 62 isolates of 42 Agaricus and related secotioid species (A. inapertus, Gyrophragmium dunalii and Longula texensis) were conducted based on sequence data of the internal transcribed spacers (ITS) and partial large subunit (LSU) of ribosomal DNA. Bayesian, maximum likelihood and maximum parsimony analyses were used to reveal evolutionary groups within the genus Agaricus, while molecular clock analyses were carried out to obtain more information about the divergence times during the evolution of Agaricus within the Basidiomycota. Six major distinct clades were found within the genus with varying levels of support. These monophyletic groups suggested interspecific relationships both confirming and challenging previous morphological sections in several cases. Our results show that most morphological features likely have evolved in apparently similar ways multiple times independently during evolution, and that the secotioid A. inapertus, Gyrophragmium dunalii and Longula texensis evolved from Agaricus species in Clade I. A new name Agaricus aridicola, and a new combination Agaricus texensis are suggested to replace the names Gyrophragmium dunalii and Longula texensis, respectively. Molecular clock estimates for minimal age of separation of the genus Agaricus from its closest relatives were 32.63 ± 8.06 and 15.45 ± 3.82 Ma, respectively, using calibrations based on other molecular clock studies on fungi and fossil data. However, Agaricus likely diverged much earlier (73.30 ± 18.12 Ma), as suggested by the estimate based on the most robust calibration.

Keywords

Internal Transcribe Spacer Maximum Parsimony Monophyletic Group Molecular Clock Parsimony Analysis 
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

© DGfM 2004

Authors and Affiliations

  • József Geml
    • 1
    • 2
  • David M. Geiser
    • 1
  • Daniel J. Royse
    • 1
  1. 1.Department of Plant PathologyThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Department of BotanySzent István UniversityBudapestHungary

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