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Published on: May 12, 2019
Ancestral state reconstructions require biological evidence to test evolutionary hypotheses: A case study examining
Oliver W Griffith1, Daniel G Blackburn2, Matthew C Brandley1
1School of Biological Sciences, Heydon Laurence Building (A08), University of Sydney, Camperdown, NSW, Australia.
Abstract:
To understand evolutionary transformations it is necessary to identify the character states of extinct ancestors. Ancestral character state reconstruction is inherently difficult because it requires an accurate phylogeny, character state data, and a statistical model of transition rates and is fundamentally constrained by missing data such as extinct taxa. We argue that model based ancestral character state reconstruction should be used to generate hypotheses but should not be considered an analytical endpoint. Using the evolution of viviparity and reversals to oviparity in squamates as a case study, we show how anatomical, physiological, and ecological data can be used to evaluate hypotheses about evolutionary transitions. The evolution of squamate viviparity requires changes to the timing of reproductive events and the successive loss of features responsible for building an eggshell. A reversal to oviparity requires that those lost traits re-evolve. We argue that the re-evolution of oviparity is inherently more difficult than the reverse. We outline how the inviability of intermediate phenotypes might present physiological barriers to reversals from viviparity to oviparity. Finally, we show that ecological data supports an oviparous ancestral state for squamates and multiple transitions to viviparity. In summary, we conclude that the first squamates were oviparous, that frequent transitions to viviparity have occurred, and that reversals to oviparity in viviparous lineages either have not occurred or are exceedingly rare. As this evidence supports conclusions that differ from previous ancestral state reconstructions, our paper highlights the importance of incorporating biological evidence to evaluate model-generated hypotheses.
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