Complex Molecular Evolutionary Models and Information Theoretic Approaches Provide Genomic Perspectives on Amphibian Evolution
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1 Complex Molecular Evolutionary Models and Information Theoretic Approaches Provide Genomic Perspectives on Amphibian Evolution Paul M. Hime 16 May, 2017 Blue Waters Symposium
2 The Evolution of Life on Earth All life traces its origins back to a single common ancestor nearly 4 billion years ago But today, there are tens of millions of species! Reconstructing the genealogy of life is fundamental to nearly all areas of modern biology.
3 The Evolution of Life on Earth Nothing in biology makes sense, except in light of evolution Dobzhansky Nothing in evolutionary biology makes sense, except in light of phylogeny
4 All Organisms on Earth Trace Their Origins Back to a Single Common Ancestor
5 Genomes Are Documents of Evolutionary History
6 Organisms Genomes Evolve through Time
7 Phylogenetic Reconstruction Phylogenies are hypotheses about ancestor - descendent relationships. These can be estimated from genetic data (in the context of a model). Simple case: enumerate all possible trees, pick the best. Tree space explodes factorially with increasing numbers of taxa. Use heuristic search strategies to explore tree- and parameter-space.
8 Models in Evolutionary Biology Evolutionary biology is an inherently historical discipline. In evolutionary biology, one cannot replay the tape of life... We use statistical approaches to compare competing sets of models, in the light of data which we collect. All models are wrong. Some are useful. George Box
9 Data Information (Except in the Context of an Appropriate Model) Species 1 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 2 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 3 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 4 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 5 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 6 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 7 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 8 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 9 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 10 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 11 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 12 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 13 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 14 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 15 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 16 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 17 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 18 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 19 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 20 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 21 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 22 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 23 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Species 24 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
10 Data Information (Except in the Context of an Appropriate Model) Species 1 ACCGAGGGCATCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 2 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTACGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 3 ACCGAGGGCATCGATCGACTACCTTAGGGCTCTAGCCTGTTACGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 4 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTACGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 5 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTACGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 6 ACCGAGGGCATCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 7 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTCCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 8 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 9 ACCGAGGGCATCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 10 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 11 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 12 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTCCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 13 ACCGAGGGCATCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 14 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 15 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 16 ACCGAGGGCCTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 17 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTCCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 18 ACCGAGGGCCTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 19 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 20 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTGCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 21 ACCGAGGGCCTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 22 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTGCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 23 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 24 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG * * informative site informative site
11 Models of Nucleotide Substitution Species 1 ACCGAGGGCATCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 2 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTACGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 3 ACCGAGGGCATCGATCGACTACCTTAGGGCTCTAGCCTGTTACGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 4 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTACGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 5 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTACGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 6 ACCGAGGGCATCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 7 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTCCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 8 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 9 ACCGAGGGCATCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 10 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 11 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 12 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTCCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 13 ACCGAGGGCATCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 14 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 15 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 16 ACCGAGGGCCTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 17 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTCCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 18 ACCGAGGGCCTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 19 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 20 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTGCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 21 ACCGAGGGCCTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 22 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTGCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 23 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG Species 24 ACCGAGGGCTTCGATCGACTACCTTAGGGCTCTAGCCTGTTTCGTGCTAGCTGACTGATCGTAGTGTAGCTGACTGTGTG * * informative site informative site Multi-sequence Alignment General time-reversible substitution matrix Gamma-distributed rate heterogeneity across sites (discretized in practice)
12 Codon-Based Models of Molecular Evolution
13 The Multispecies Coalescent Model Gene divergences always predate species divergences Stochastic coalescent processes can lead to gene tree / species tree discordance Modified from Leliaert et al. 2014
14 Gene Tree - Species Tree Discordance
15 Population-Level Processes Affect The Expected Distributions of Gene Coalescence Luay Nakhleh Conflicting phylogenetic signal from different loci is expected, especially for more recent divergence events and large effective population sizes. What about at deep scales? Many loci (regions of the genome) may be needed for difficult questions.
16 The Genomics Revolution in Evolutionary Biology It has never been easier to collect genomic data in non-model organisms.
17 The Genomics Revolution in Evolutionary Biology It has never been easier to collect genomic data in non-model organisms. It has never been easier to collect genomic data in non-model organisms.
18 Data Information (Except in the Context of an Appropriate Model)
19 Data Information (Except in the Context of an Appropriate Model)
20 Data Information (Except in the Context of an Appropriate Model)
21 Data Information (Except in the Context of an Appropriate Model)
22 Some of the Many Tradeoffs in Phylogenomics The genomic revolution is now offering unprecedented opportunities to tackle thorny questions in evolutionary biology. But these opportunities bring analytical and computational costs.
23 Some of the Many Tradeoffs in Phylogenomics The genomic revolution is now offering unprecedented opportunities to tackle thorny questions in evolutionary biology. But these opportunities bring analytical and computational costs.
24 Genomic perspectives on the amphibian tree of life Amphibians provide a rich system for testing phylogenetic hypotheses at both deep and shallow scales of divergence. What is the topology of the amphibian Tree of Life??? How confident are we in that topology? What are the inter-ordinal relationships? What is the nature of support across the genome? What are best practices with large phylogenomic data sets???
25 Extant Amphibian Diversity Amphibians - 7,660 species as of 28 April, 2017 ( The three amphibian orders likely diverged 300+ MYA. Map from BiodiversityMapping.org
26 Extant Amphibian Diversity Caecilians (Gymnophiona) species, 33 genera, 10 families Map from BiodiversityMapping.org
27 Extant Amphibian Diversity Salamanders (Caudata) species, 68 genera, 10 families Map from BiodiversityMapping.org
28 Extant Amphibian Diversity Frogs and Toads (Anura) - 6,760 species, 448 genera, families Map from BiodiversityMapping.org
29 Amphibian Relationships Hundreds of studies have addressed phylogenetic affinities of amphibians...
30 Amphibian Relationships Hundreds of studies have addressed phylogenetic affinities of amphibians... This project aims to sample hundreds of nuclear genes for hundreds of amphibian species
31 Inter-Ordinal Amphibian Relationships Three main hypotheses for phylogenetic relationships among the three amphibian orders (assuming that Amphibia is monophyletic...) Batrachia Acauda Procera The potential resolutions of these deep branches each have very different implications for our understanding of amphibian evolution.
32 Inter-Ordinal Amphibian Relationships There are 15 possible topologies for the three amphibian orders and amniotes, assuming that Amphibia may or may NOT be monophyletic.. Batrachia Acauda Procera
33 Taxonomic Sampling across Amphibia We targeted 325 amphibian taxa (296 worked ). 276 genera (> 50% of recognized genera)... 96% of recognized families (and most subfamilies)... Taxa were sampled roughly in proportion to species richness.
34 Taxonomic Sampling across Amphibia We targeted 325 amphibian taxa (296 worked ). 276 genera (> 50% of recognized genera)... 96% of recognized families (and most subfamilies)... Taxa were sampled roughly in proportion to species richness. Multiple outgroups were included to root the phylogeny. Anolis Chrysemys Gallus Homo Latimeria
35 Generating Genomic Data in Amphibians We developed an amphibian-specific gene capture system which targets 366 semi-conserved nuclear exons. Probes were designed from genomic/transcriptomic * resources for: 1 Caecilian: Ichthyophis 4 Salamanders: Ambystoma* Cryptobranchus* Desmognathus Notophthalmus* 6 Frogs: Ascaphus Gastrophryne Mixophyes Pseudacris Rana (Lithobates) Xenopus (Silurana)
36 Nuclear Gene Tree Estimation Independently estimated ML gene trees for all genes in RAxML. Used separate (best-fit) partitioning schemes and nucleotide substitution models for each gene (GTR+G). Conducted 500 non-parametric bootstrap replicates (across sites) to assess support for branches in these gene trees. Identified outlier taxa for each gene (and removed those taxa). Conducted analyses with unconstrained and constrained topological backbones.
37 Species Tree Estimation Use multiple algorithms to estimate the topology of the species tree. Shortcut methods attempt to reconcile collections of gene trees into an estimate of the species tree (Astral and MulRF) Also implemented a gene-tree-free approach (SVDQuartets). Lastly, estimated a concatenated ML tree in RAxML, using a best-fit partitioning scheme of 76 distinct partitions (separate GTR+G models). Used the non-parametric bootstrap across sites to assess support (sites and genes for Astral).
38 Brief Overview of Results Nearly all families are recovered as monophyletic. Shallow-scale relationships are largely in line with previous studies. Most higher-order clades are recovered. Deep branches receive strong bootstrap support in species tree analyses...
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I9339_TP24839_Caudata_Salamandridae_Paramesotriton_hongkongensis_seq High bootstrap support across the tree Inter-ordinal support is for Batrachia (BS = ) We re done, right??!! Scrutinize phylogenetic signal across the genome...
40 Inter-Ordinal Support Varies across Loci (RAxML Best Gene Trees) Acauda n = 45 Procera n = 51 Batrachia n = 98
41 Support across 500 Bootstrap Replicates Acauda Batrachia Procera
42 Unbounded Support Using the AIC AIC Acauda Batrachia Procera
43 Unbounded Support Using the AIC Constrain the basal topology, then calculate AIC for each model... AIC Acauda Batrachia Procera
44 Unbounded Support Using the AIC Think of these as measuring the strength of support against alternative models. AIC Acauda Batrachia Procera
45 Backbone of the Amphibian Tree of Life These topologies are largely concordant with previous studies... With a few distinct exceptions...
46 Backbone of the Amphibian Tree of Life These topologies are largely concordant with previous studies... With a few distinct exceptions...
47 Conflict in the Frog Tree: Nasikabatrachus
48 Conflict in the Frog Tree: Nasikabatrachus
49 Conflict in the Frog Tree: Nasikabatrachus
50 Different Methods Yield Different Topologies ASTRAL Tree RAxML Tree * 78
51 A Framework for Testing Neobatrachian Relationships
52 A Framework for Testing Neobatrachian Relationships
53 Conclusions The illusion of support for topological hypotheses depends on how hard one looks. The bootstrap can help determine the direction of support, but may not be informative about its magnitude. Substantial discordance across loci exists at the base of the amphibian tree (and may not all be noise!). Genomic data and new statistical models are providing novel insights into evolutionary relationships of amphibians. More data easy answers (that are credible...).
54 Why Blue Waters? Rigorously testing competing topological models across large numbers of genes is computationally demanding. Even embarrassingly parallel approaches (gene-by-gene AIC) overwhelm typical HPC clusters resources. MCMC sampling for rugged likelihood surfaces can be improved with large numbers of Metropolis coupled chains. For Bayes factor tests (with N taxa): Markov chain Monte Carlo scales as N 2 Hamiltonian Monte Carlo scales as N 1.2
55 Acknowledgments Co-Authors/Collaborators Alan R. Lemmon Emily C. Moriarty Lemmon Elizabeth Scott-Prendini Jeremy M. Brown Robert C. Thomson Brice P. Noonan R. Alex Pyron Pedro L. V. Peloso Michelle Kortyna Justin D. Kratovil J. Scott Keogh Stephen C. Donnellan Rachel L. Mueller Christopher J. Raxworthy Krushnamegh Kunte Tissue Loans from Institutions: American Museum of Natural History (Darrel Frost, David Kizirian, Julie Feinstein) California Academy of Sciences (David Blackburn, Jens Vindum) Florida Museum of Natural History (Pamela Soltis) University of Kansas Biodiversity Institute and Natural History Museum (Rafe Brown, Linda Trueb, Andrew Campbell) Louisiana State University Museum of Natural Science (Robb Brumfield, Donna Dittmann) Museum of Comparative Zoology (Jim Hanken, José Rosado, Breda Zimkus) Museum of Vertebrate Zoology (Jim McGuire, Carol Spencer, Ted Papenfuss, Marvalee Wake, Sima Bouzid) Museum Victoria (Jane Melville, Joanna Sumner) National Museum of Natural History (Kevin De Queiroz, Addison Wynn) South African National Biodiversity Institute (Zoe Davids) Saint Louis Zoological Park (Jeffrey Ettling, Mark Wanner, Randall Junge) University of Michigan Museum of Natural History (Ronald A. Nussbaum, Gregory Schneider) Yale Peabody Museum (Gregory Watkins-Colwell) Tissue Loans from Individuals: J.J. Apodaca, Alan Channing, Becky Chong, Guarino Colli, Tyler Frye, S. Blair Hedges, Elizabeth Jockusch, Christopher McNamara, Eric O'Neill, Todd Pierson, Steve Richards, Kelly Zamudio Fellowships: NSF Graduate Research Fellowship ( ), Blue Waters Graduate Research Fellowship (NSF/NCSA) Funding: SSB Graduate Student Research Award, DEB (DWW), DEB (DWW, PMH) Santiago Ron Sandeep Das Nikhil Gaitonde David M. Green Jim Labisko David W. Weisrock
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