Huelsenbeck, John P; Nielsen, Rasmus; Bollback, Jonathan PIST Austria
Many questions in evolutionary biology are best addressed by comparing traits in different species. Often such studies involve mapping characters on phylogenetic trees. Mapping characters on trees allows the nature, number, and timing of the transformations to be identified. The parsimony method is the only method available for mapping morphological characters on phylogenies. Although the parsimony method often makes reasonable reconstructions of the history of a character, it has a number of limitations. These limitations include the inability to consider more than a single change along a branch on a tree and the uncoupling of evolutionary time from amount of character change. We extended a method described by Nielsen (2002, Syst. Biol. 51:729-739) to the mapping of morphological characters under continuous-time Markov models and demonstrate here the utility of the method for mapping characters on trees and for identifying character correlation.
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Huelsenbeck J, Nielsen R, Bollback JP. Stochastic mapping of morphological characters. Systematic Biology. 2003;52(2):131-158. doi:10.1080/10635150390192780
Huelsenbeck, J., Nielsen, R., & Bollback, J. P. (2003). Stochastic mapping of morphological characters. Systematic Biology. Oxford University Press. https://doi.org/10.1080/10635150390192780
Huelsenbeck, John, Rasmus Nielsen, and Jonathan P Bollback. “Stochastic Mapping of Morphological Characters.” Systematic Biology. Oxford University Press, 2003. https://doi.org/10.1080/10635150390192780.
J. Huelsenbeck, R. Nielsen, and J. P. Bollback, “Stochastic mapping of morphological characters,” Systematic Biology, vol. 52, no. 2. Oxford University Press, pp. 131–58, 2003.
Huelsenbeck J, Nielsen R, Bollback JP. 2003. Stochastic mapping of morphological characters. Systematic Biology. 52(2), 131–58.
Huelsenbeck, John, et al. “Stochastic Mapping of Morphological Characters.” Systematic Biology, vol. 52, no. 2, Oxford University Press, 2003, pp. 131–58, doi:10.1080/10635150390192780.