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Measurement error in geometric morphometrics

Abstract

Geometric morphometrics—a set of methods for the statistical analysis of shape once saluted as a revolutionary advancement in the analysis of morphology —is now mature and routinely used in ecology and evolution. However, a factor often disregarded in empirical studies is the presence and the extent of measurement error. This is potentially a very serious issue because random measurement error can inflate the amount of variance and, since many statistical analyses are based on the amount of “explained” relative to “residual” variance, can result in loss of statistical power. On the other hand, systematic bias can affect statistical analyses by biasing the results (i.e. variation due to bias is incorporated in the analysis and treated as biologically-meaningful variation). Here, I briefly review common sources of error in geometric morphometrics. I then review the most commonly used methods to measure and account for both random and non-random measurement error, providing a worked example using a real dataset.

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Acknowledgments

My gratitude goes to Venera Ferrito for her continued support. I also thank her because—when she was acting as my supervisor—she has let me pursue my methodological interests even when they were not immediately related to the biological investigation we were carrying out. I am deeply grateful to F. James Rohlf for exposing me to the first few papers I have ever read on the fascinating subject of measurement error, for his comments to a very early version of this manuscript and for his continued support. The insightful comments of two reviewers have greatly contributed to improving this review.

Author information

Correspondence to Carmelo Fruciano.

Additional information

This article is part of the Special Issue “Size and Shape: Integration of morphometrics, mathematical modelling, developmental and evolutionary biology”, Guest Editors: Nico Posnien—Nikola-Michael Prpic.

Communicated by Nico Posnien and Nikola-Michael Prpic

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Fruciano, C. Measurement error in geometric morphometrics. Dev Genes Evol 226, 139–158 (2016). https://doi.org/10.1007/s00427-016-0537-4

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Keywords

  • Geometric morphometrics
  • Measurement error
  • Multivariate analysis
  • Bias