Background: Matrices of morphological characters are frequently used for dating species divergence times in systematics. In some studies, morphological and molecular character data from living taxa are combined, whereas others use morphological characters from extinct taxa as well. We investigated whether morphological data produce time estimates that are concordant with molecular data. If true, it will justify the use of morphological characters alongside molecular data in divergence time inference.
Results: We systematically analyzed three empirical datasets from different species groups to test the concordance of dates of species divergence inferred using molecular and discrete morphological data from extant taxa as test cases. We found a high correlation between their divergence time estimates, despite a poor linear relationship between branch lengths for morphological and molecular data mapped onto the same phylogeny. This was because node-to-tip distances showed a much higher correlation than branch lengths, because of an averaging effect over multiple branches. We found that nodes with a large number of taxa often benefit from such averaging, but some considerable discordance between time estimates from molecules and morphology may still occur because some deeper branches show large difference from two types of data.
Conclusions: Our findings suggest that node- and tip-calibration approaches may be better suited for nodes with a large number of taxa. Nevertheless, we highlight the importance of evaluating the concordance of time structure in morphological and molecular data before any dating analysis using combined datasets.
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This preprint is available for download as a PDF.
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On 31 Jan, 2021
On 31 Jan, 2021
On 31 Jan, 2021
On 15 Jan, 2021
Received 22 Dec, 2020
Invitations sent on 17 Dec, 2020
On 17 Dec, 2020
On 15 Dec, 2020
On 15 Dec, 2020
On 15 Dec, 2020
Posted 22 May, 2020
On 26 Oct, 2020
On 31 Aug, 2020
Received 14 Jun, 2020
On 06 Jun, 2020
Invitations sent on 05 Jun, 2020
On 14 May, 2020
On 13 May, 2020
On 13 May, 2020
On 12 May, 2020
On 31 Jan, 2021
On 31 Jan, 2021
On 31 Jan, 2021
On 15 Jan, 2021
Received 22 Dec, 2020
Invitations sent on 17 Dec, 2020
On 17 Dec, 2020
On 15 Dec, 2020
On 15 Dec, 2020
On 15 Dec, 2020
Posted 22 May, 2020
On 26 Oct, 2020
On 31 Aug, 2020
Received 14 Jun, 2020
On 06 Jun, 2020
Invitations sent on 05 Jun, 2020
On 14 May, 2020
On 13 May, 2020
On 13 May, 2020
On 12 May, 2020
Background: Matrices of morphological characters are frequently used for dating species divergence times in systematics. In some studies, morphological and molecular character data from living taxa are combined, whereas others use morphological characters from extinct taxa as well. We investigated whether morphological data produce time estimates that are concordant with molecular data. If true, it will justify the use of morphological characters alongside molecular data in divergence time inference.
Results: We systematically analyzed three empirical datasets from different species groups to test the concordance of dates of species divergence inferred using molecular and discrete morphological data from extant taxa as test cases. We found a high correlation between their divergence time estimates, despite a poor linear relationship between branch lengths for morphological and molecular data mapped onto the same phylogeny. This was because node-to-tip distances showed a much higher correlation than branch lengths, because of an averaging effect over multiple branches. We found that nodes with a large number of taxa often benefit from such averaging, but some considerable discordance between time estimates from molecules and morphology may still occur because some deeper branches show large difference from two types of data.
Conclusions: Our findings suggest that node- and tip-calibration approaches may be better suited for nodes with a large number of taxa. Nevertheless, we highlight the importance of evaluating the concordance of time structure in morphological and molecular data before any dating analysis using combined datasets.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This preprint is available for download as a PDF.
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