Sharing is caring? Measurement error and the issues arising from combining 3D morphometric datasets
| Autor: | Tom Dooley, Carmelo Fruciano, Kaylene Butler, Matthew J. Phillips, Vera Weisbecker, Melina Anouche Celik |
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| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
0301 basic medicine Inference Biology computer.software_genre photogrammetry 010603 evolutionary biology 01 natural sciences Statistical power 03 medical and health sciences Limit (mathematics) geometric morphometrics Ecology Evolution Behavior and Systematics Nature and Landscape Conservation Original Research Observational error Ecology Dimensionality reduction phylogenetic signal 030104 developmental biology Photogrammetry Sample size determination Data mining Scale (map) computer measurement error |
| Zdroj: | Ecology and Evolution Fruciano, C, Celik, M A, Butler, K, Dooley, T, Weisbecker, V & Phillips, M J 2017, ' Sharing is caring? Measurement error and the issues arising from combining 3D morphometric datasets ', Ecology and Evolution, vol. 7, no. 17, pp. 7034-7046 . https://doi.org/10.1002/ece3.3256 |
| ISSN: | 2045-7758 |
| DOI: | 10.1002/ece3.3256 |
| Popis: | Geometric morphometrics is routinely used in ecology and evolution and morphometric datasets are increasingly shared among researchers, allowing for more comprehensive studies and higher statistical power (as a consequence of increased sample size). However, sharing of morphometric data opens up the question of how much nonbiologically relevant variation (i.e., measurement error) is introduced in the resulting datasets and how this variation affects analyses. We perform a set of analyses based on an empirical 3D geometric morphometric dataset. In particular, we quantify the amount of error associated with combining data from multiple devices and digitized by multiple operators and test for the presence of bias. We also extend these analyses to a dataset obtained with a recently developed automated method, which does not require human‐digitized landmarks. Further, we analyze how measurement error affects estimates of phylogenetic signal and how its effect compares with the effect of phylogenetic uncertainty. We show that measurement error can be substantial when combining surface models produced by different devices and even more among landmarks digitized by different operators. We also document the presence of small, but significant, amounts of nonrandom error (i.e., bias). Measurement error is heavily reduced by excluding landmarks that are difficult to digitize. The automated method we tested had low levels of error, if used in combination with a procedure for dimensionality reduction. Estimates of phylogenetic signal can be more affected by measurement error than by phylogenetic uncertainty. Our results generally highlight the importance of landmark choice and the usefulness of estimating measurement error. Further, measurement error may limit comparisons of estimates of phylogenetic signal across studies if these have been performed using different devices or by different operators. Finally, we also show how widely held assumptions do not always hold true, particularly that measurement error affects inference more at a shallower phylogenetic scale and that automated methods perform worse than human digitization. |
| Databáze: | OpenAIRE |
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