NDLI: Removing bias for fluctuating asymmetry in meristic characters

Content Provider	Springer Nature Link
Author	Young, John R.
Copyright Year	2007
Abstract	Fluctuating asymmetry (FA) in meristic characters is a random difference in count of a character between the right and left sides of an organism. Although FA has been used as an index of developmental instability for more than 30 years, it has not been closely related to the intensity of factors commonly thought to induce instability. The poor relationship could be due to use of biased FA metrics based on differences in bilateral counts, bias introduced by counting errors, low sample sizes, or a truly low correspondence between FA and stressor intensity. I demonstrate why FA metrics based on count differences are inherently biased for meristic characters, develop an unbiased maximum likelihood metric for FA ( $$\hat \sigma _A $$ ), and establish a general method to remove bias caused by counting errors. A likelihood ratio test based on the new metric is nearly as powerful as the F-test, and has a Type I statistical error closer to the nominal level. When applied to bilateral counts of sternopleural bristles in Drosophila, the new metric confirmed previous results based on count differences but removed the concern that the FA estimates were biased by the character mean. Apparent differences in pectoral fin ray FA between rainbow and cutthroat trout disappeared after correction for counting errors.
Starting Page	485
Ending Page	497
Page Count	13
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	12
Issue Number	4
e-ISSN	15372693
Language	English
Publisher	Springer-Verlag
Publisher Date	2007-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Developmental instability Maximum likelihood Stress Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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