"Estimation of Divergence Times in Asparagales in the Presence of Hybridization," presented in symposium "Insights and Benefits from Monocot Palaeobiology: Fossils, DNA, and Phylogenies" at Monocots V (5th International Conference on Comparative Biology of Monocotyledons, The New York Botanical Garden, July 2013).

1. Estimation of divergence times in Asparagales in the
presence of hybridization
Kate L. Hertweck, Michael R. McKain
Monocots V
Blog:
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Twitter @k8hert
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Plantzafrica.comErica Wheeler

2. Objectives
1) Describing lynchpins of monocot evolution
2) Informing Asparagales divergence times with lessons from
monocots
3) Applications of dating estimates
What are the most important considerations in determining
the context of diversification in monocots?
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

3. Kate L. Hertweck, Michael S. Kinney, Sarah Mathews, Mark W. Chase,
M. A. Gandolfo, J. Chris Pires
Divergence time estimation in monocots
●
Improved Chase et al. (2006) dataset with 8 genes (11459 bp)
●
1 mt, 4 cp, 2 nrDNA, 1 LCNG (PHYC)
●
151 total taxa (27 outgroup, including 10 eudicots)
●
MUSCLE, RAxML/MrBayes (partitioned, GTRGAMMA)
●
r8s (13 fossil constraints), PL (TN) with cross validation, BEAST
●
Does a dataset representing all three genomic partitions accurately
reconstruct evolutionary relationships?
●
Can we improve divergence time estimations in monocots using
comprehensive taxon sampling?
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

4. Fossil calibrations for Asparagales
constraint Age (Ma) taxon
1 CG angiosperms 110.4 (Early-Middle Albian) Carpestella lacunata
2 SL Nymphaeales 110.4 (Early-Middle Albian) Microvictoria svitkoana
3 SL Schisandraceae 89.3 (Cenomanian-Turonian) Illiciospermum pusillum
4 SL Chloranthaceae 130 (Hauterivian, Helez Fm.) Clavatipollenites minutus
5 SL Magnoliales 112 (Late Aptian) Endressinia brasiliana
6 SL Calycanthaceae 118.5 (Late Barremian-Early Aptian) Jerseyanthus calycanthoides
7 SL Lactoridaceae 70.6 (Early Turonian-Campanian) Lactoripollenites africanus
8 CG eudicots 99.6 (Late Albian) Spanomera marylandensis
9 SL Araceae 118.5 (Late Barremain-Early Aptian) Cobbania corrugata
10 CG Pandanales 89.3 (Turonian) Mabelia, Nuhliantha
11 SL Arecales 70.6 (Campanian) Sabalites magothiensis,
Palmoxylon cliffwoodensis
12 SL Zingiberales 72.5 (Campanian, absolute age) Tricostatocarpon silvapinedae,
Striatornata sanantoniensis
13 SL Poaceae 65.5 (Maastrichtian) phytoliths
(Gandolfo, pers. comm.)
K. Hertweck, NESCent, Asparagales divergence 1. Challenges 2. Data 3. Applications

5. Monocot timetree
Single data partitions fail to
reconstruct deep relationships
Combined dataset yields high
confidence at most nodes
(except commelinids)
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

6. Monocot timetree: Maxage of angiosperms at 170 Ma
Outgroups constrained for
proper fossil placement
Confidence intervals:
Maxage of angiosperms=
200 and 150 Ma
CG monocots:
153 Ma (134-127)
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

7. Lynchpins in monocot divergence time estimation
● Taxonomic sampling
● Even sampling across lineages
● Represent early diverging taxa in each lineage
● Sample lineages which are both species rich and poor
● Select optimal lineages for fossil calibrations
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

8. Lynchpins in monocot divergence time estimation
● Taxonomic sampling
● Even sampling across lineages
● Represent early diverging taxa in each lineage
● Sample lineages which are both species rich and poor
● Select optimal lineages for fossil calibrations
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

9. Lynchpins in monocot divergence time estimation
● Taxonomic sampling
● Molecular sequence sampling
● Combining data partitions more reliably estimates
phylogeny, although constraining topology may still be
necessary
● Variation in life history may affect rates of molecular
evolution
● Multiple independent data partitions may smooth rate
variation in individual genes
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

10. Lynchpins in monocot divergence time estimation
● Taxonomic sampling
● Molecular sequence sampling
● Fossil calibrations
● Incongruence between molecular and fossil dates for
divergence
● Most variation in divergence time estimation comes
from implementation of fossil calibrations
● Calibrations at shallow nodes can be influential
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

11. Lynchpins in monocot divergence time estimation
● Taxonomic sampling
● Molecular sequence sampling
● Fossil calibrations
● Some lynchpins are exacerbated in Asparagales
● What lessons learned from dating monocots can assist in
improving divergence time estimates in Asparagales?
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

12. Objectives
1) Describing lynchpins of monocot evolution
2) Informing Asparagales divergence times with lessons from
monocots
3) Applications of dating estimates
How can studies in monocots improve divergence time
estimates in Asparagales?
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

13. Basic Asparagales divergence times
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications
Monocot 8 gene dataset
(Hertweck et al., unpub.)
CG Asparagales: 113 Ma (125-105)
CG “Core” Asparagales: 61 Ma (67-58)
CG Asparagaceae: 52 Ma (58-49)
CG Amaryllidaceae: 50 Ma (55-47)
CG Xanthorrhoeaceae: 47 Ma (52-44)
Broad taxonomic divergence
time analyses
(Janssen and Bremer 2004,
Magallon and Castillo 2009,
Bell et al. 2010)
CG Asparagales: 92-127 my
Datasets:
Chen et al. 2013: many taxa,
4 cpDNA loci
Seberg et al. 2013: many taxa,
3 cpDNA and 2 mtDNA loci
* McKain plastome: some
taxa, complete plastomes
* McKain orthologs: fewer
taxa, 19 nuclear orthologs

14. Lynchpin 1: Taxonomic sampling in Asparagales
Chen S, Kim D-K, Chase MW, Kim J-H (2013) Networks
in a Large-Scale Phylogenetic Analysis: Reconstructing
Evolutionary History of Asparagales (Lilianae) Based on
Four Plastid Genes. PLoS ONE 8(3): e59472.
doi:10.1371/journal.pone.0059472
http://www.plosone.org/article/info:doi/10.1371/journal.p
one.0059472
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

15. ●
Chen et al. 2013:
●
253 species, 201 genera, all families in Asparagales
●
29 species from monocot outgroups
●
Three calibrations: CG Asparagales and two outgroups
Effect of many taxa
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications
Monocot 8 gene Chen BEAST
CG Asparagales 113 Ma (125-105) 93 Ma (101-93)
CG “Core” Asparagales 61 Ma (67-58) 63 Ma (75-53)
CG Asparagaceae 52 Ma (58-49) 56 Ma (65-48)
CG Amaryllidaceae 50 Ma (55-47) 51 Ma (62-42)
CG Xanthorrhoeaceae 47 Ma (52-44) 56 Ma (66-48)
More taxa increases accuracy of dating estimates?

16. • Variation in genome size
• Karyotype (chromosome structure, bimodality)
• Polyploidy (auto-, allo-, aneu- ploidy)
• Hybridization (and other historically confounding
evolutionary events)
How do these genomic phenomena affect our ability
to reconstruct phylogeny and other evolutionary
patterns?
Lynchpin 2: Molecular sequence sampling
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

17. Application of nuclear data to divergence time estimates
• ITS
• Low copy nuclear genes
• Transcriptomes
• PHYTOCHROME C (PHYC)
– Well characterized; red/far red light sensing
– Single copy in almost all groups studied; orthology established
– Phylogenetically informative
– Degenerate primers in exon 1 of PHYC (Mathews et al., 1995)
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

18. PHYC reconstructs relationships between families
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

19. Intrafamilial relationships from PHYC are messy
Androstephium (Brodiaeoideae)
Brodiaea (Brodiaeoideae)
Drimia (Scilloideae)
Dianella (Hemerocallidoideae)
Geitonoplesium (Hemerocallidoideae)
Arthropodium (Laxmanniaceae)
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

20. Intrafamilial relationships from PHYC are messy
Androstephium (Brodiaeoideae)
Brodiaea (Brodiaeoideae)
Drimia (Scilloideae)
Dianella (Hemerocallidoideae)
Geitonoplesium (Hemerocallidoideae)
Arthropodium (Laxmanniaceae)
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications
• Hybridization and other types of reticulate networks may require a different
approach to divergence time estimation
• No methods explicitly incorporating networks for divergence times

21. Lynchpin 3: Fossil calibrations
constraint Age (Ma) taxon
CG Orchidaceae 15 (Early-Middle Miocene) Meliorchis caribea
CG Agavoideae 12 (Middle Miocene) Protoyucca
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications
● Most botanists aren't paleobotanists
● Applying secondary calibrations might be problematic
● Evidence for additional fossil calibrations in Asparagales lineages
● Emerging support for both deep and shallow fossil calibrations across
the phylogeny (except this requires representative taxon sampling)
Ramirez et al. 2007

22. Secondary calibrations can have unexpected results!
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications
Monocot 8 gene McKain 19 single
copy nuclear genes
(two applicable
fossils)
McKain 19 single
copy nuclear genes
CG Asparagales 113 Ma (125-105) 87 Ma (102-72) 113 Ma (fixed age)
CG “Core”
Asparagales
61 Ma (67-58) 63 Ma (75-53) 78 Ma (74-81)
CG Asparagaceae 52 Ma (58-49) 52 Ma (62-43) 63 Ma (60-66)
CG Amaryllidaceae 50 Ma (55-47) 48 Ma (57-40) 59 Ma (56-61)
CG Xanthorrhoeaceae 47 Ma (52-44) 56 Ma (66-46) 69 Ma (66-71)

23. Recommendations for dating estimates in Asparagales
● Can any of uncertainty in three lynchpins be accommodated by
analytical techniques?
– Easier to make comparisons with r8s (or other non-Bayesian
algorithms)
– *BEAST: explicitly incorporates coalescent to infer “species” tree,
divergence times, population sizes
– Different approach to prior assignment of fossils (exponential
distribution, Chen et al. 2013)
– Uncertainty better managed by Bayesian methods, but this benefit
nullified by priors which are too complex!
– Congruification (Eastman 2013): an alternative to running
complete analysis
– Look at relative diversification rather than absolute
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

24. Objectives
1) Describing lynchpins of monocot evolution
2) Informing Asparagales divergence times with lessons from
monocots
3) Applications of dating estimates
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications
Where do we go from here?

25. Genomic changes occur repeatedly in Asparagales phylogeny
Hertweck,
Genome, 2013
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

26. Dating transposable element proliferation
Hertweck,
Genome, 2013
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications
Black: copia
White: gypsy
Rectangles: total repeats

27. Divergence times provide the context for inferring evolutionary patterns
● From monocot 8 gene dataset:
● SymmeTREE (Chan et al. 2005): tree topology and tree-wide
species diversity
● One significant and one marginally significant shift in diversification,
both in subfamily Agavoideae
● Other methods
● Incorporating divergence times
● Exploring relationships with morphological or life history traits
● Compounding uncertainty
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

28. Summary
●
My personally favored estimate for CG Asparagales:
113 Ma (125-105)
●
Problems with dating in monocots are exacerbated in
Asparagales
●
Taxonomic sampling
●
Molecular sequence sampling
●
Fossil calibrations
●
Can any of these problems be accommodated by analytical
techniques?
●
Careful balance between appropriate sampling and
possibly compounding problems
●
Think creatively, and try multiple approaches
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications

29. Basic Asparagales divergence times
K. Hertweck, NESCent, Asparagales divergence 1. Monocots 2. Asparagales 3. Applications
Monocot 8 gene dataset
CG Asparagales: 113 Ma (125-105)
CG “Core” Asparagales: 61 Ma (67-58)
CG Asparagaceae: 52 Ma (58-49)
CG Amaryllidaceae: 50 Ma (55-47)
CG Xanthorrhoeaceae: 47 Ma (52-44)

30. Acknowledgements
• J. Chris Pires and lab (U of Missouri)
• Victoria Docktor
• NESCent science and bioinformatics folks
• Fossil consultation
• S. Magallon
• E. Friis
• M. Beilstein
• N. Nagalingum
• Matt Dorrance
K. Hertweck, NESCent, Asparagales divergence
Repetitive elements in Asparagales:
Poster 46!
Blog:
k8hert.blogspot.com
Twitter @k8hert
Google+ k8hertweck@gmail.com
Slides available:
http://www.slideshare.net/katehertweck