Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies

Details

• Journal Title:
  Marine Mammal Science
• Personal Author:
  Archer, Frederick I. ; Martien, Karen K. ; Taylor, Barbara L.
• NOAA Program & Office:
  NMFS (National Marine Fisheries Service) ; SWFSC (Southwest Fisheries Science Center)
• Description:
  We examine the use of an ensemble method, Random Forests, to delimit sub- species using mitochondrial DNA (mtDNA) sequences. Diagnosability, a measure of the ability to correctly determine the taxon of a specimen of unknown origin, has historically been used to delimit subspecies, but few studies have explored how to estimate it from DNA sequences. Using simulated and empirical data sets, we demonstrate that Random Forests produces classification models that perform well
• Keywords:
  Aquatic Science Ecology, Evolution, Behavior And Systematics Ecology, Evolution, Behavior And Systematics Ecology, Evolution, Behavior And Systematics
• Source:
  Marine Mammal Science, 33(S1), 101-131
• DOI:
  https://doi.org/10.1111/mms.12414
• ISSN:
  0824-0469
• Format:
  PDF
• Publisher:
  Wiley
• Document Type:
  Journal Article
• License:
  https://creativecommons.org/publicdomain/zero/1.0/
• Rights Information:
  CC0 Public Domain
• Rights Statement:
  This article is a U.S. Government work and is in the public domain in the U.S.A.
• Compliance:
  Library
• Main Document Checksum:
  urn:sha256:d128fff962db578b4b796aba03c15796b1b969f2c2eafc57eb2e861bd581c38c
• Download URL:
  https://repository.library.noaa.gov/view/noaa/52490/noaa_52490_DS1.pdf
• File Type:
  [PDF - 990.50 KB ]