Type I errors linked to faulty statistical analyses of endangered subspecies classifications

John R. Skalski, Richard L. Townsend, Lyman L. McDonald, John W. Kern, Joshua J. Millspaugh

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Legal issues related to subspecies identification frequently occur through the implementation of the 1973 Endangered Species Act (ESA). A listing under the ESA requires management actions to ensure the continued existence of the taxa. However, these actions often have important social, economic, and political implications. We examined the statistical methods of morphological analysis used in subspecies identification. Methods are illustrated using the California gnatcatcher (Polioptila californica), which was incorrectly listed under the ESA due to misinterpretation of morphological data. We found that inferences based on tests of sample means (i.e., t-test, Hotelling's T2-statistic), cluster analysis, and discriminant analysis were subject to high rates of false positives (identification of subspecies when none exist; Type I error). These simple tests ignore the common occurrence of spatial clines in animal tracts. Alternatively, spline-regression and step-regression procedures were found to be quite robust yet had high resolution in finding subspecies break locations.

Original languageEnglish
Pages (from-to)199-220
Number of pages22
JournalJournal of Agricultural, Biological, and Environmental Statistics
Issue number2
StatePublished - Jun 2008


  • California gnatcatcher (Polioptila californica)
  • Cluster analysis
  • Discriminant analysis
  • Multivariate statistics
  • Spatial statistics
  • Spline-regression
  • Step- regression
  • Subspecies
  • Taxonomy


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