HOW DO GEOLOGICAL SAMPLING BIASES AFFECT STUDIES OF MORPHOLOGICAL EVOLUTION IN DEEP TIME? A CASE STUDY OF PTEROSAUR (REPTILIA: ARCHOSAURIA) DISPARITY
Article first published online: 20 AUG 2011
© 2011 The Author(s). Evolution © 2011 The Society for the Study of Evolution.
Volume 66, Issue 1, pages 147–162, January 2012
How to Cite
Butler, R. J., Brusatte, S. L., Andres, B. and Benson, R. B. J. (2012), HOW DO GEOLOGICAL SAMPLING BIASES AFFECT STUDIES OF MORPHOLOGICAL EVOLUTION IN DEEP TIME? A CASE STUDY OF PTEROSAUR (REPTILIA: ARCHOSAURIA) DISPARITY. Evolution, 66: 147–162. doi: 10.1111/j.1558-5646.2011.01415.x
- Issue published online: 3 JAN 2012
- Article first published online: 20 AUG 2011
- Accepted manuscript online: 22 JUL 2011 02:19AM EST
- Received February 16, 2011, Accepted July 12, 2011
- missing data;
- sampling biases
A fundamental contribution of paleobiology to macroevolutionary theory has been the illumination of deep time patterns of diversification. However, recent work has suggested that taxonomic diversity counts taken from the fossil record may be strongly biased by uneven spatiotemporal sampling. Although morphological diversity (disparity) is also frequently used to examine evolutionary radiations, no empirical work has yet addressed how disparity might be affected by uneven fossil record sampling. Here, we use pterosaurs (Mesozoic flying reptiles) as an exemplar group to address this problem. We calculate multiple disparity metrics based upon a comprehensive anatomical dataset including a novel phylogenetic correction for missing data, statistically compare these metrics to four geological sampling proxies, and use multiple regression modeling to assess the importance of uneven sampling and exceptional fossil deposits (Lagerstätten). We find that range-based disparity metrics are strongly affected by uneven fossil record sampling, and should therefore be interpreted cautiously. The robustness of variance-based metrics to sample size and geological sampling suggests that they can be more confidently interpreted as reflecting true biological signals. In addition, our results highlight the problem of high levels of missing data for disparity analyses, indicating a pressing need for more theoretical and empirical work.