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Home , Agatasa, Agrias amydon, Archaeoprepona, Archaeoprepona demophon, Palla (butterfly), Palla decius

INSIGHTS INTO THE ORIGIN AND EVOLUTION OF PREPONINE BUTTERFLIES

ELENA ORTIZ ACEVEDO

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2017

To my husband and son, who make my life worth living

ACKNOWLEDGMENTS

I thank the McGuire Center for Lepidoptera and Biodiversity and the Florida Museum of

Natural History for supporting this study. I also thank my graduate committee for their invaluable help throughout my graduate program. I am grateful to everyone who made tissue available for me to process, and I thank the museums and public and private collections that allowed me to access their data. I am also grateful to Dr. L. Xiao for support in the molecular laboratory and Dr. J. P. Gomez for his assistance with analyses and the artwork. I am grateful to everyone involved in fieldwork and in permit processing in Ecuador and Colombia. I thank everyone who offered valuable comments, suggestions and feedback. I also acknowledge the other institutions, individuals and funding sources that assisted collaborators in my research. I acknowledge funding from Sigma Xi (Grants-in-Aid of Research G20100315153261), the

Center for Systematic Entomology, the Council of the Linnean Society and the Systematics

Association for the Systematics Research Fund, the William C. and Bertha M. Cornett

Fellowship, the College of Agricultural and Life Sciences, the Florida Foundation, the AMNH collections study grant, and COLCIENCIAS. Lastly, I am deeply grateful to my family, who supported me all these years, you are my foundation and my motivation to become a better person.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 9

ABSTRACT ...... 11

CHAPTER

1 INTRODUCTION ...... 13

2 MOLECULAR PHYLOGENY OF PREPONINI ...... 16

Background ...... 16 Materials and Methods ...... 18 Taxon and Gene Sampling ...... 18 DNA Study ...... 18 Data Partitioning and Phylogenetic Analyses ...... 19 Morphological Study ...... 21 Distribution Maps ...... 22 Results...... 22 Phylogenetic Relationships ...... 22 Morphological Analyses ...... 25 Discussion ...... 25 Molecular Approach to Species Boundaries ...... 25 Taxonomic Changes Based on Molecules and Morphology ...... 28

3 ORIGIN, BIOGEOGRAPHY AND EVOLUTION OF COLOR IN PREPONINES ...... 38

Background ...... 38 Materials and Methods ...... 40 Phylogeny Reconstruction and Dating ...... 40 Inference of Biogeographic History ...... 42 Diversification Rates ...... 44 Phenotypic Evolution ...... 45 Results...... 48 Phylogenetic Reconstruction and Dating ...... 48 Inference of Biogeographic History ...... 49 Diversification Rates and Phenotypic Evolution ...... 50 Discussion ...... 51 Origin and Biogeographical Patterns ...... 51 Diversification of Preponines ...... 55 Phenotypic Evolution ...... 57

4 PHYLOGEOGRAPHY AND SPECIES DELIMITATION IN PREPONA LAERTES ...... 65

Background ...... 65 Materials and Methods ...... 68 Specimen Collection, Storage and Preparation ...... 68 DNA Extraction ...... 69 COI Barcoding ...... 69 RADseq Data ...... 69 Data Analysis ...... 71 Results...... 76 Phylogenetic Relationships ...... 76 Alternative Approaches ...... 78 Discussion ...... 79 Inference of Relationships ...... 79 COI Barcoding vs. RADseq ...... 85 Effect of Missing Data ...... 86 Alternative Approaches to Species Delimitation ...... 87

5 CONCLUSIONS ...... 100

APPENDIX

A PHYLOGENY OF PREPONINI - SUPPLEMENTARY FIGURES ...... 104

B PHYLOGENY OF PREPONINI - SUPPLEMENTARY TABLES ...... 115

C ORIGIN, BIOGEOGRAPHY AND EVOLUTION OF COLOR IN PREPONINES - SUPPLEMENTARY FIGURES ...... 121

D ORIGIN, BIOGEOGRAPHY AND EVOLUTION OF COLOR IN PREPONINES - SUPPLEMENTARY TABLES ...... 127

E PHYLOGEOGRAPHY AND SPECIES DELIMITATION IN PREPONA LAERTES - SUPPLEMENTARY FIGURES ...... 136

F PHYLOGEOGRAPHY AND SPECIES DELIMITATION IN PREPONA LAERTES - SUPPLEMENTARY TABLES ...... 148

LIST OF REFERENCES ...... 158

BIOGRAPHICAL SKETCH ...... 176

LIST OF TABLES

Table page

2-1 Preponine species sensu Lamas (2004)...... 33

2-2 Wing pattern and genitalic characters for the three groups found within Prepona pylene and treated here as distinct species. Characters are illustrated and identified in Figure 2-2...... 34

2-3 Genitalic characters for Mesoprepona pheridamas. Characters are illustrated and identified in Figure 2-3...... 34

3-1 Evolutionary rates for the mean color of the different areas of the wing measured...... 61

3-2 Evolutionary rates for the mode color of the different areas of the wing measured...... 62

4-1 Specimens included in the COI Barcoding and RADseq study...... 92

4-2 Samples included in the missing-data analysis...... 95

4-3 Genetic distance for the four clusters identified by NJ. Groups 4 and 5 correspond to the outgroup species...... 96

B-1 Specimens included in the phylogenetic study ...... 116

D-1 Specimens included in the phylogenetic hypothesis...... 127

D-2 Regions used in the biogeographical analysis...... 129

D-3 Areas allowed in the biogeographical analysis. Top matrix corresponds to the first four time slices (from present to past). Bottom matrix corresponds to the older time slice...... 130

D-4 Probability of movement among regions. Top matrix corresponds to the first four time slices (from present to past). Bottom matrix corresponds to the older time slice. ...131

D-5 Partition finder partitions and molecular clock designations for the three molecular clock tests...... 132

D-6 Path sampling results for the three molecular clock tests...... 133

D-7 Bayes factors for model comparison of the molecular clock tests...... 134

D-8 Akaike Information Criterion values for the different models tested in the biogeographical reconstruction...... 135

F-1 Percentage similarity among the topologies recovered for different thresholds of missing data computed with Compare2Trees...... 148

F-2 Raw PH85 distance values for the different topologies (above diagonal) and normalized PH85 distance (nPH85)...... 149

F-3 Grouping schemes for Neighbor Joining. Outgroup species are LEP-02477 and LEP- 16418...... 150

F-4 Genetic distances found for the grouping scheme of 17 groups for Prepona laertes identified by NJ. Groups 4 and 5 correspond to the outgroup species. Gr: Group ...... 152

F-5 Within group genetic divergence for the four groups recovered by Neighbor Joining for the COI Barcoding gene...... 153

F-6 bPTP ML partition supports for the delineated groups...... 154

F-7 BIC values for the different values of k explored...... 156

F-8 Fst fixation index values for the four clusters identified by the multivariate analysis. ...157

LIST OF FIGURES

Figure page

2-1 Bayesian Inference with unpartitioned data phylogenetic tree for Preponini. Numbers above nodes correspond to posterior probabilities. Species names are accompanied by their voucher code...... 35

2-2 Wing pattern and genitalia of Prepona specimens. Size bar = 1cm. Letters correspond to characters in Table 2-2...... 36

2-3 Genitalia of preponine genera...... 36

2-4 Distribution map for Mesoprepona pheridamas...... 37

3-1 Biogeographical range estimation for the tribe Preponini based on the BAYAREALIKE + J model...... 63

3-2 Ancestral reconstruction of the mode RGB red channel for the Cell 1 and lineage- through-time plot...... 64

4-1 FastTree topology for the COI Barcoding gene. Node numbers correspond to bootstrap values. Color code follows the text. Vertical lines in gray scale correspond to the different species delimitation methods used...... 97

4-2 ML RAxML reconstruction for the single SNP dataset. Numbers on nodes correspond to bootstrap values. Color code follows text...... 98

C-1 Collection dates for the specimens measured in the study of color...... 121

C-2 Wing regions measured in the study of color...... 122

C-3 Correlation between each of the cells and wing area...... 123

C-4 Dated tree for Preponini showing confidence intervals bars for each node...... 124

C-5 Dated tree for Preponini showing confidence intervals range for each node...... 125

C-6 Marginal probability for all possible ranges of origin of the biogeographical estimation. The nodes correspond to the ones labeled in Figure 3-1...... 126

E-1 W-IQ-Tree ML reconstruction for the COI Barcoding gene. Numbers in nodes correspond to bootstrap values...... 136

E-2 RAxML ML reconstruction for the COI Barcoding gene. Numbers in nodes correspond to bootstrap values...... 137

E-3 Consensus topology computed with Dendroscope for the COI Barcoding reconstructions using FastTree, W-IQ-Tree and RAxML...... 138

E-4 RAxML ML reconstruction for the concatenated loci RADseq dataset. Numbers below nodes correspond to bootstrap values...... 139

E-5 RAxML ML reconstruction for the 50% missing-data dataset. Numbers below nodes correspond to bootstrap values...... 140

E-6 RAxML ML reconstruction for the 70% missing-data dataset. Numbers below nodes correspond to bootstrap values...... 141

E-7 RAxML ML reconstruction for the 90% missing-data dataset. Numbers below nodes correspond to bootstrap values...... 142

E-8 Neighbor Joining topology for the COI Barcoding gene. Numbers below nodes correspond to bootstrap values...... 143

E-9 Automatic Barcode Gap Discovery topology recovered for the COI Barcoding gene....144

E-10 Support values for the GMYC species delimitation model...... 145

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

INSIGHTS INTO THE ORIGIN AND EVOLUTION OF PREPONINE BUTTERFLIES

Elena Ortiz Acevedo

December 2017

Chair: Keith R. Willmott Major: Entomology and Nematology

A more robust and comprehensive phylogenetic hypothesis for the butterfly tribe

Preponini (Nymphalidae: Charaxinae) is proposed. This new hypothesis is based on six molecular makers and includes 83 preponine samples that broadly represent the distribution range and diverse color forms. Morphological data was also used as a complement to molecular data and proved extremely useful to inform about the most appropriate classification scheme.

The study revealed that the tribe is more diverse than currently conceived, with a new genus described and three taxa raised to species status (Archaeoprepona priene (Hewitson, 1859),

Prepona eugenes Bates, 1865 and Prepona gnorima Bates, 1865), with the possibility of species diversity further increasing since some taxa showed high genetic divergence. The updated phylogeny was used to explore the tempo and mode of diversification of the tribe, genera and species in order to better understand the evolutionary history of the group. A combination of approaches including phylogenetics and biogeography was used to test whether geological events and/or species’ traits influenced the evolution of this colorful group of butterflies.

Preponines illustrate an out-of-the-Amazon biogeographical model, where subsequent colonizations, possibly due to newly created niches resulting from geological events, allowed for their diversification. Diversification was also tested from the perspective of species’ traits, where

color pattern was identified as a potential driver of evolution, especially the red and blue channels of the RGB color spectrum. Further studies regarding the potential reasons why such channels might be influencing the evolution of the group are encouraged, in particular exploring the hypothesis that preponines are involved in mimicry rings with other butterfly groups

(Nymphalidae: Biblidinae). Lastly, a more detailed study is presented for an apparently unusually variable taxon, Prepona laertes. This species was the subject of taxonomic scrutiny using two different molecular approaches. COI barcoding and RADseq analyses proved useful to reveal that, as debated by earlier authors, the widespread and polymorphic species Prepona laertes is actually a cryptic species complex within which at least two additional species are hidden: P. pseudomphale and P. philipponi. This result adds to the evidence provided by the six- gene phylogenetic hypothesis in suggesting that the tribe Preponini is more diverse than currently conceived.

CHAPTER 1 INTRODUCTION

Understanding the origin and maintenance of biodiversity has been a longstanding field of scientific research, especially in the world’s tropical regions (Hutchinson 1959, Ricklefs 2004,

Graham et al. 2014). The tropics contain a disproportionate number of biodiversity hotspots due to their extraordinary species richness and high levels of endemism (Mittermeier et al. 1998,

Myers et al. 2000), and thus have attracted much attention. Multiple hypotheses have been proposed to explain the high biodiversity and taxonomic richness seen in the tropics, and despite being fundamentally different, they all benefit from research using a combination of disciplines such as taxonomy, systematics, evolution, ecology, and biogeography (Wiens and Donoghue

2004, Wiens and Graham 2005. Mittelbach et al. 2007).

Butterflies are a great model system to explore the origins of biodiversity for multiple reasons: i) They are amongst the most speciose groups on earth and also are abundant in nature, ii) they show a richness peak in the tropics, where the Neotropical region accounts for significant richness, in particular the Andean countries of Colombia, Perú, Ecuador and Bolivia, iii) collection practices and protocols are well studied and standardized, iv) the preservation of specimens and tissue samples is relatively straightforward since they can be kept in glassine envelopes and cryogenic tubes until ready to process, v) morphological data can be accessed readily and for multiple individuals, vi) molecular data can also be sampled with ease and from a small tissue sample that usually does not result in significant damage to the specimen, and vii) they are strongly represented in museum and historical collections, thus offering extensive sources of information on their spatial and temporal distribution, among others.

One of the most charismatic butterfly groups is the Neotropical tribe Preponini. Besides exhibiting the qualities mentioned above, significant advances have recently been made in

clarifying their taxonomy, providing a firmer foundation to explore ecological and evolutionary questions. Preponini also show dramatically different color patterns that could potentially have influenced their evolution, a trait that has also attracted the undivided attention of collectors and naturalists and has resulted in the description of hundreds of names whose scientific value has been challenged in recent years. Preponines are therefore great candidates for the study of the origin and maintenance of diversity in one of the most species-rich regions in the world. In this dissertation, I aim to resolve several taxonomic issues by exploring in more detail the genetic and morphological diversity revealed by previous studies. With this improved taxonomic understanding, I go on to investigate the tempo and mode of diversification, from a biogeographic perspective as well as exploring whether color pattern change might have influenced the evolution of the group. Lastly, I move from the macroevolutionary perspective to investigate the evolutionary history of a preponine taxon whose taxonomy has been debated due to its highly variable color pattern.

In the first chapter of my dissertation I build on the existing species-level phylogeny for the tribe. I include additional markers and samples to provide a better and more comprehensive phylogenetic hypothesis. I also complement the molecular data with morphological information to further support the results and provide a more robust taxonomy.

In the second chapter, I use the above mentioned phylogenetic hypothesis to explore the timing of origin of the tribe, genera and species. I also take a biogeographical perspective and investigate whether geological events might have influenced and shaped the evolution of the group. Lastly, I focus on color pattern as a species trait that might have also driven the diversification patterns in preponines.

In the last chapter, I change evolutionary scales and focus on a taxon, Prepona laertes, whose high variability in coloration pattern has generated contrasting taxonomic hypotheses.

These hypotheses are reflected in the differing number of species that this name is regarded as representing. The current taxonomic hypothesis suggests a single polymorphic, widespread species, but previous authors argued that what is now called P. laertes actually contains several species. Thus, I take two different approaches to compare and contrast these opposing ideas. I use the more traditional molecular approach, COI barcoding, and the more recent whole genome screening approach, RADseq, to investigate and attempt to clarify the longstanding taxonomic dilemma in this variable taxon.

CHAPTER 2* MOLECULAR PHYLOGENY OF PREPONINI

Background

The tribe Preponini is one of the most easily recognized Neotropical butterfly groups.

These large, forest-canopy butterflies are characterized by having robust bodies, erratic flight, and some of the most brilliant wing color patterns of all butterflies. The tribe occurs in Central and South America, from Mexico to Argentina, with a species richness peak in the Amazon basin. The immature stages are known for several species, including Archaeoprepona amphimachus (Fabricius, 1775), A. camilla (Godman & Salvin, [1884]), A. demophon (Linnaeus,

1758), Prepona laertes (Hübner, [1811]), P. hewitsonius Bates, 1860, P. amydon Hewitson,

1854, P. claudina (Godart, [1824]), and P. pheridamas (Cramer, [1777]) (Muyshondt 1973b, a,

1974, Casagrande and Mielke 1985, Furtado 2001, Teshirogi 2004, Dias et al. 2011, Janzen and

Hallwachs 2017), but otherwise the biology of the remaining taxa in the tribe is largely unknown. Many preponines are rarely encountered in nature, complicating studies of their immature and adult biology, and most of our knowledge of their immature stages comes from several long term studies of butterflies in Costa Rica and Brazil. Despite host plant record for preponines being scarce, the available literature suggests that they feed on a variety of plant families, but with the taxonomic breadth of plant species on which they feed perhaps varying among preponine species (Beccaloni et al. 2008, Janzen and Hallwachs 2017). The caterpillars are characterized by having projections on their cephalic capsule, and being dull in color, in contrast to the brightly colored adults.

* This chapter is published in the scientific journal Insect Systematics and Biodiversity, Volume 1, Issue 1, 25 October 2017, Pages 48–56.

The higher taxonomy of the tribe remained reasonably stable from the early 1970s until recently. As a result of new molecular and morphological studies (Ortiz-Acevedo and Willmott

2013, Bonfantti, 2014) the genus Anaeomorpha Rothschild,1984 was transferred to its own, monotypic tribe. Two further genera were sunk to synonymy, so that Preponini now contains only two genera, Prepona Boisduval, [1836] and Archaeoprepona Fruhstorfer, 1915 (Table 2-1).

In addition, these two studies revealed that the species diversity of the tribe is likely underestimated in the most recent classification (Lamas, 2004). Although the beauty and conspicuousness of preponine color patterns, which range from bright blue to red and orange on the upperside to cryptic and potentially mimetic undersides, have attracted the attention of collectors and naturalists for centuries, the species-level taxonomy has remained chaotic. With marked geographic and local variation in color patterns, hundreds of species-level names have been published (Lamas 2004), making not only the delimitation of species, but also determining the correct nomenclature, a real challenge.

Although the current molecular phylogeny for the tribe enabled certain taxonomic issues to be addressed, several areas remained to be resolved. Here, I focus on the taxonomic status of both subspecies of Archaeoprepona chromus (Guérin-Méneville, [1844]), on the generic status of the isolated taxon Prepona pheridamas, and on the apparent paraphyly found for several other taxa such as P. pylene Hewitson, 1854. I build on the existing species-level molecular phylogeny

(Ortiz-Acevedo and Willmott 2013) to answer these questions by: i) complementing the dataset with three additional markers and almost twice the number of sequenced individuals to help resolve unclear relationships and improve node support and ii) exploring relevant morphological data within particular taxa to complement the molecular results. This study also emphasizes the importance of integrating different sources of information and highlights the key role of

traditional morphology-based taxonomy to provide a more robust classification as a foundation for exploring ecological and evolutionary questions.

Materials and Methods

Taxon and Gene Sampling

The analysis included DNA sequence data available in Genbank from Ortiz-Acevedo and

Willmott (2013), in addition to new sequence data. The existing three gene (COI, COII and

EF1a) and 45 individual dataset was complemented with three additional nuclear markers that have been shown useful for phylogenetic studies in butterflies: CAD (a protein coding gene involved in pyrimidine biosynthesis, which expresses carbamoylphosphate synthetase, aspartate carbamyltransferase and dihydroorotase), GAPDH (Glyceraldehyde 3-phosphate dehydrogenase), and RpS5 (ribosomal protein S5 coding gene) (Regier et al. 2008, Wahlberg and

Wheat 2008, Mutanen et al. 2010). In addition, I added 38 individuals for the whole six-gene dataset. The final dataset contained 87 individuals, including four outgroup species which represented the related Neotropical charaxine tribe Anaeini (Table B-1). Fresh butterfly tissue was obtained in the field by collecting individuals using butterfly nets and baited traps.

Additional tissue was obtained from museum specimens and through donations from national and international collaborators. One or two legs were placed in 96% EtOH and stored in a -80 freezer until ready to process.

DNA Study

Genomic DNA was extracted using the Qiagen DNEasy Extraction Kit. I followed the manufacturer’s protocol, except for some old tissue where I followed Iudica et al. (2001). I used standard polymerase chain reaction (PCR) to amplify the mitochondrial and nuclear regions using published primers (Table B-2). I designed novel internal primers for CAD and primers for the complete GAPDH gene (Table B-2). The PCR cycling profiles for COI, COII and EF-1a and

reaction mix are described in Ortiz-Acevedo and Willmott (2013), the remaining cycling profiles are outlined in Table B-3. DNA sequencing was carried out by the Interdisciplinary Center for

Biotechnology Research Sanger Sequencing Group (ICBR) at the University of Florida,

Gainesville, Florida. I manually edited both strands of each gene and also checked peak calls using Geneious v7 (http://www.geneious.com, Kearse et al. 2012) and the resulting sequences were aligned using ClustalW (Larkin et al. 2007), checked by eye and a consensus sequence was produced for each sample and gene. I created a matrix for each gene and then concatenated them in a single matrix. The final edited and aligned sequences comprised 618bp for COI, 826bp for

COII, 951bp for EF-1α, 984bp for CAD, 535bp for GAPDH, and 540bp for RpS5. The concatenated alignment totaled 4454bp.

Data Partitioning and Phylogenetic Analyses

I conducted phylogenetic analyses on the concatenated dataset and on each individual gene matrix. I used Partition Finder v2.1.1 (Lanfear et al. 2012) to select the best substitution model and partitioning scheme for the concatenated dataset according to the Akaike Information

Criterion (AIC) and using a greedy searching scheme. Maximum Likelihood (ML) and Bayesian

Inference (BI) analyses were conducted in RAxML v8.2.10 (Stamatakis 2014) and MrBayes v3.2.2 (Huelsenbeck et al. 2001, Ronquist and Huelsenbeck 2003), respectively, available through the CIPRES portal (https://www.phylo.org). For the concatenated matrix I conducted

1000 ML searches starting from a random tree; this analysis was done for the best-fit partitioned scheme found by Partition Finder and for the dataset without partitions in which case GTR + G was the model selected (Stamatakis 2014). ML analyses were followed by a 1000-replicate rapid bootstrap analysis (Stamatakis 2014). BI analyses were also performed for the partitioned and unpartitioned dataset, using the models selected by Partition Finder. I ran four independent

Markov Chain Monte Carlo (MCMC) runs of 20 million generations sampling every 1000 steps

with a burn-in of 25% of trees and tested convergence by examining that the average standard deviation of split frequencies fell below 0.01, visually inspecting the trace files in Tracer v1.6

(http://tree.bio.ed.ac.uk/software/tracer/) and checking that the effective sample size values for the trace was higher than 200. I used FigTree v1.3.1 (tree.bio.ed.ac.uk/software/figtree/) to edit the resulting trees and the software Compare2Trees (Nye et al. 2006) to compare the different topologies obtained with the different methods and partitions. To test for differences among the topologies obtained by the different methods and partition schemes, I calculated the distance among all possible pairs of trees using the method of Penny and Hendy (1985). To test for the significance of this metric, I simulated 1000 random trees with same number of tips as the observed one and calculated the distance metric for the random trees, I repeated this procedure

1000 times to obtain a null distribution of the distance metrics given the number of tips observed.

Subsequently, I calculated a standardized effect size for the observed distance by subtracting the observed value from the mean of the null distribution and dividing it by the standard deviation of the null distribution. If the observed trees have a lower distance than expected by chance then I expect the standardized effect size to be larger than +1.96 (reference value).

To test for discrepancies between gene trees and species trees I inferred a species tree using the *BEAST (Heled and Drummond 2010) template in BEAUTI, and analyses were run in

BEAST2 v2.4.5 (Bouckaert et al. 2014). I used Partition Finder v2.1.1 (Lanfear et al. 2012) to select the best *BEAST partitioning scheme according to the Akaike Information Criterion

(AIC) and using a greedy searching scheme. Since species limits in some cases are uncertain, specimens were a priori grouped according to subspecies. Site models were unlinked for all codon positions and genes, while clock models and tree models were linked for all codon position within genes, but unlinked between genes, except for COI and COII, where clock and

tree models were linked since both are mitochondrial and share the same evolutionary history.

Substitution models were inferred using the bModelTest plugin (Bouckaert and Drummond

2017). The tree prior was set to a Yule model, and the clock prior set to relaxed lognormal

(Drummond et al. 2006). The population function was set to constant with linear root. The popmean prior and the birth rate prior were set to gamma distributions with alpha and beta of

0.05 and 100, and 0.5 and 1.0, respectively. Other priors were kept as default. Three individual runs were performed, each with 200 million generations and sampling every 20,000 generations.

The runs were combined in LogCombiner v2.3.0 and subsampled to a total of 10,000 trees, which were used to infer a maximum credibility tree in TreeAnnotator v2.4.5. Convergence was assessed in Tracer v1.6 (Rambaut et al. 2014). Individual gene tree analyses were inferred using the *BEAST (Heled and Drummond 2010) template in BEAUTi and run in BEAST v2.4.5

(Bouckaert et al. 2014) as above.

I used MEGA v7.0 (Kumar et al. 2016) to estimate genetic divergence within and among species using the Kimura 2-parameter substitution model and partial deletion of sites with missing data.

Morphological Study

A total of 22 specimens of Prepona pylene were examined, including eight females and

14 males (Table B-4), which represent all of the subspecies recognized in Lamas (2004). For

Prepona pheridamas, a total of eleven specimens were examined (Table B-5). For the wing shape analysis, Nijhout’s (1991) nymphalid ground plan was followed.

Abdomens were detached and immersed in a test tube with 10% potassium hydroxide solution (KOH 10%). The test tube was heated in a water bath for approximately 10 minutes, then the abdomen was dissected and the genitalia removed. A Leica stereomicroscope coupled with a camera lucida was used to visualize the genitalia and prepare illustrations.

High resolution photographs were used to trace the wing venation and patterns.

Preponines have large veins that are easily observed, and thus no damage to the wings was required, even though in some instances the removal of the scales was needed. In such cases I the wings were treated with 70% alcohol, followed by sodium hypochlorite until cleared and then neutralized in a 70% alcohol wash.

Low vacuum SEM was used to obtain photomicrographs of specific structures; these were fixed on the support with double-sided tape and scanned using a JEOL JSM-6360LV scanning electron microscope.

Distribution Maps

Locality data for Prepona pheridamas, the type species of the new genus described in this paper, were gathered from specimens deposited in five major collections (Table B-6).

Localities were georeferenced using coordinate data from specimen labels or using Google Earth to obtain approximate coordinates when such data were not present. Resulting data were used to construct distribution maps using DIVA-GIS v.7.4.0 (Hijmans et al. 2004).

Results

Phylogenetic Relationships

Individual analyses of the genes found both genera to be monophyletic (Figure A-1 to

Figure A-6). The topology of the species tree analysis (Figure A-7) was highly similar to the topology obtained from the concatenated dataset, in which the monophyly of both genera was well supported. Partition Finder split the 4454 bp dataset into 15 partitions for ML, 16 partitions for BI, and 17 partitions for *BEAST (Table B-7). ML and BI unpartitioned and partitioned analyses resulted in almost identical topologies, except for the placement of Archaeoprepona chalciope (Hübner, [1823]), the relationships of Archaeoprepona amphimachus + A. meander

(Cramer, [1775]), and intraspecific relationships for some taxa such as Prepona praeneste

Hewitson, 1859, P. amydon, P. hewitsonius, P. narcissus Staudinger, [1885], and P. claudina

(Figure A-8 to Figure A-10). The overall topology scores obtained with the different methods and partitions were highly similar, ranging from 94.7% to 98.3% (Table B-8), suggesting that the results are robust to the phylogenetic inference method used and model assumptions. In addition, the significance test shows that all the resulting trees were more similar than expected by chance

(Table B-9).

Support for relationships among Archaeoprepona increased after the inclusion of more individuals and genes (Figure 1-1). Even though the overall topology did not change substantially, some nodes that lacked support in the previous study (Ortiz-Acevedo and Willmott

2013) were highly supported in the new analyses, such as that containing Archaeoprepona phaedra (Godman & Salvin, [1884]) + (A. demophoon (Hübner, [1814]) + (A. camilla + A. demophon)). The remaining nodes were consistent with the previous phylogeny (Ortiz-Acevedo and Willmott 2013). I also found increased support for a node for which I did not include additional taxa, where the inclusion of the additional three nuclear markers helped to resolve the paraphyletic relationship among Archaeoprepona meander and A. amphimachus. A newly included, second individual of the very rare taxon Archaeoprepona chromus priene Hewitson,

1859 grouped with the single currently available individual of that taxon. Based on the concatenated dataset, the genetic divergence within these two subspecies was 1% and 0% within

Archaeoprepona chromus chromus and A. c. priene respectively, and between subspecies was

3%. Genetic distances were also assessed for the COI barcoding gene region, and were found to be 1.5% and 0% within Archaeoprepona chromus chromus and A. c. priene respectively, and

4.6% between subspecies.

As in the previous study (Ortiz-Acevedo and Willmott 2013), Prepona pheridamas was sister to the remainder of Prepona, followed by P. dexamenus Hopffer, 1874 and P. laertes, and their placement was highly supported by posterior probability and bootstrap values. In contrast to previous results, the relationships among species in the P. werneri Hering & Hopp, 1925 + P. pylene + P. deiphile (Godart, [1824]) + P. praeneste + former Agrias Doubleday, 1844 clade were more robust, showing higher BI and ML bootstrap values (albeit a few nodes were still weakly supported). All species formerly classified within Prepona were found monophyletic except P. deiphile and P. pylene. Both of those species were found to be paraphyletic and closely related to each other. The Transandean Prepona pylene clustered with South American Prepona deiphile, and together these two clades were sister to Central American Prepona deiphile. The

South American subspecies of Prepona pylene were found to be sister to the remaining P. pylene and P. deiphile representatives. Results also helped to clarify the sister species to the former genus Agrias. Ortiz-Acevedo and Willmott (2013) suggested that Prepona praeneste was a likely candidate, and the expanded phylogeny here corroborated that suggestion and also confirmed the placement of former Agrias within Prepona. The only species in the former Agrias clade that was found monophyletic after adding taxa and DNA data was Prepona aedon Hewitson, 1848, while relationships within this clade were found to be more complex than previously thought.

The addition of two individuals of Prepona claudina did not help clarify the paraphyly recovered in the previous study (Ortiz-Acevedo and Willmott 2013). Similarly, P. amydon and P. hewitsonius were found to be paraphyletic. In this case, central Amazonian Prepona amydon individuals clustered with P. hewitsonius, also an Amazonian species. In contrast, the west

Amazonian/east Andean foothill Prepona amydon amydonius Staudinger, 1886 did not cluster

with the remaining Amazonian taxa but was placed with the red/yellow Central

American/Transandean P. amydon taxa.

Morphological Analyses

Morphological study of Prepona pylene resulted in identification of 12 characters that showed variation among taxa currently treated within that species (Table 2-2, Figure 2-2), supporting their division into three distinct groups. The pattern and coloration of both wing surfaces provided the most characters (7), followed by male genitalia (3) and female genitalia

(2). Characters were not observed to vary among specimens of the same taxon.

Morphological study of Prepona pheridamas showed that the taxon lacks a number of synapomorphies that unite the remaining species of Prepona. Four characters of the male and female genitalia differentiate this taxon from the remaining preponine genera (Table 2-3, Figure

2-3).

Discussion

Addition of nuclear markers and taxa proved successful in clarifying phylogenetic relationships that were formerly unresolved and allowed the exploration of several species-level issues within Preponini. The new dataset also improved overall node support throughout the phylogeny. Furthermore, the importance of complementing molecules with morphology was illustrated in unveiling hidden taxonomic diversity.

Molecular Approach to Species Boundaries

The species tree supported the results found by BI and ML analyses and the topologies obtained with the different methods were congruent with each other. Species in the genus

Archaeoprepona were all found to be monophyletic, with high node support. The widespread

Archaeoprepona chromus chromus is known to be sympatric (or elevationally parapatric) with A. c. priene in the northeastern Andes (Willmott & Hall, unpublished data), and the two taxa were

treated as species prior to Lamas (2004). Nevertheless, the slight wing pattern differences between the taxa left their taxonomic status open to question. Inclusion of a second specimen of

Archaeoprepona chromus priene corroborated the hypothesis that both subspecies deserve species status. I found that both subspecies were monophyletic with genetic distance comparable to that between other sympatric sister species (e.g. 3.16% genetic distance between A. camilla and A. demophon for the concatenated dataset and 6% for the COI region) (Figure 2-1). Despite the fact that a genetic distance of 2% or more has typically been found between sister species across animal groups (Johns and Avise 1998, Hebert et al. 2003a), studies of Lepidoptera have advised a 3% genetic distance as a typical threshold (Hebert et al. 2003b, Hajibabaei et al. 2006).

Based on the sympatry and genetic divergence between these two former A. chromus taxa, I therefore restore Archaeoprepona priene (stat. rev.) to species status.

Adding more markers was also beneficial in confirming the monophyly of the closely related, sympatric species Archaeoprepona amphimachus and A. meander. They both were found paraphyletic based on the previous dataset of three markers and six specimens, but the expanded data set of six markers for the same specimens was able to resolve their relationships with moderate to high support.

The phylogeny of the genus Prepona, on the other hand, remains less well resolved than that of Archaeoprepona. The topology near the base of the genus inferred from molecular data was consistent with morphology, and showed high node support. More apical clades, however, often showed disagreement between relationships inferred from molecular data and the former classification (Lamas, 2004), thus challenging the current species concepts of such taxa. Prepona deiphile formed two clusters coinciding with geography, with P. deiphile ibarra Beutelspacher,

1982 as the Central American representative and P. d. sphacteria Fruhstorfer, 1916 + P. d.

neoterpe Honrath, 1884 as the South American counterparts. This apparent deep split within the species is also supported by other sources of data such as morphology and ecology (Bonfantti

2014, Llorente-Bousquets et al. in preparation, Garcia-Jimenez et al. 2017), and the species taxonomy will need to be revised after careful study, so we do not make any taxonomic changes here.

The genus Prepona is characterized by short branch lengths, especially at the apical nodes, which is indicative of recent speciation events where taxa are rapidly differentiating, as seen in Prepona claudina and P. narcissus. These two are morphologically well-defined species that are partially sympatric, so I believe that the lack of monophyly for both is most likely explained by incomplete lineage sorting, in which genes fail to sort at the speciation event

(Maddison 1997, Johns and Avise 1998).

Several interesting results emerged within the highly variable Prepona amydon. In the previous phylogenetic hypothesis, Prepona amydon was represented by just a single specimen from Ecuador. This new study included ten specimens of Prepona amydon, representing at least seven of the 17 recognized subspecies and comprising a broad range of wing pattern phenotypes.

In the analysis, Prepona amydon was paraphyletic with respect to P. hewitsonius. There are two clades, one that includes the Central American/Transandean P. amydon subspecies and east

Andean foothill subspecies, and another that includes the central Amazonian and Guianan P. amydon subspecies plus P. hewitsonius. This result raises the intriguing possibility of recent gene flow between P. hewitsonius and sympatric, central Amazonian P. amydon, but since this part of the tree is characterized by short branch lengths and support for many of the internal nodes is lacking, additional molecular and morphological study is needed.

The study confirmed that Prepona praeneste is the sister taxon to the clade formerly called Agrias. Interestingly, Prepona praeneste is the only representative of the genus Prepona

(sensu Lamas 2004) that has red in the wing pattern, suggesting that there has been a single appearance of the red color in the dorsal forewing in the genus. In contrast, the blue and green dorsal coloration of basal Prepona is also present in Prepona hewitsonius and in some subspecies of Prepona amydon.

Taxonomic Changes Based on Molecules and Morphology

Species level. Prepona pylene is another case where the current species concept is challenged by the results, which suggest that the seven described subspecies within P. pylene

(Lamas 2004) comprise several species, with those seven subspecies forming two clear clusters.

The first contains the Amazonian + eastern South American subspecies (i.e. eugenes and

Brazilian subspecies) and the second contains the central American + Transandean subspecies

(Figure 2-1). Examination of genitalic characters as well as wing venation and pattern supported the split of Prepona pylene into three allopatric species: i) Prepona pylene, ii) Prepona eugenes stat. rev. with the subspecies eugenes, laertides Staudinger, 1898, and bahiana Fruhstorfer,

1897, and iii) Prepona gnorima stat. rev. with the subspecies gnorima, philetas Fruhstorfer,

1904 and jordani Fruhstorfer, 1905. The molecular dataset supports the split of Prepona gnorima from P. pylene + P. eugenes while it provides no additional support for recognition of P. pylene as a distinct species since it includes only a single individual of that species. Although some subspecies within Prepona gnorima were also found to be polyphyletic, monophyly is not expected at the subspecies level since there are no strong geographic barriers to gene flow.

Identification of the three species using dorsal wing pattern is relatively straightforward, but the ventral wing pattern provides even more characters to differentiate them (Figure 2-2,

Table 2-2). The presence of a two-tone ventral band and the absence of the submarginal

cordiform spots on the ventral side of the hind wing differentiate Prepona gnorima from P. pylene and P. eugenes. These patterns differences, coupled with the disjunction in their geographic ranges and the 3.33% mean genetic distance, support the revalidation of P. gnorima as a species instead of a subspecies of P. pylene. Prepona pylene and P. eugenes differ in the extent of the dorsal blue band and the overall ventral coloration, with the pattern elements of P. pylene being much darker and contrasting than in P. eugenes, as well as P. pylene having a submarginal band on the hind wing. Prepona pylene also lacks conspicuous ‘pearl’-colored areas in the 'central symmetry system' (CSS) of the wing and presents a more uniformly colored basal region on the ventral hind wing (Figure 2-2, Table 2-2). Male genitalic characters are also useful to segregate the three species. The uncus of Prepona pylene and P. gnorima is rugose in texture while in P. eugenes it presents small dorsal spines. The gnathos of the three species is covered with spines but in P. gnorima the spines cluster in the dorso-apical region. In addition, the gnathos of Prepona gnorima is tapered while in P. pylene it presents a strong medial-ventral projection and in P. eugenes it resembles a spine. Differences in female genitalia such as the presence/absence of the lamella postvaginalis and the length of the signa were found, but more studies are needed to confirm that these are consistent.

Generic level. Complementing traditional morphology-based taxonomy with molecular data supports a further generic-level revision. Prepona pheridamas was sister to the remainder of

Prepona, with the data here providing stronger support for this relationship than in the previous study (Ortiz-Acevedo and Willmott 2013). The three specimens of Prepona pheridamas included in the study formed a highly supported clade but also showed long internal branch lengths, which suggests high genetic divergence among the samples and could be indicative of hidden diversity within the clade.

Prepona pheridamas lacks a number of distinctive synapomorphies that all remaining

Prepona species possess, such as the conspicuous eyespots on the ventral hind wing (Figure A-

11). Additional morphological characters support the recognition of a new genus to contain P. pheridamas. The distal portion of the gnathos in the male genitalia of Prepona pheridamas is tapered (without a club) and has few striations with a rough texture, whereas in the rest of

Prepona it is clubbed with spines, and in Archaeoprepona it has multiple striations (Figure 2-3)

(Bonfantti et al. 2015). The aedeagus of Prepona pheridamas has its ventral side completely membranous, whereas in the rest of Prepona it is only two thirds sclerotized and in

Archaeoprepona it is completely sclerotized. The female genitalia of Prepona pheridamas also provides important characters that contrast with Archaeoprepona and the rest of Prepona. In

Prepona pheridamas, the ductus bursae and corpus bursae are very similar in width, while in the rest of Prepona and in Archaeoprepona the ductus bursae is considerably thinner. On the other hand, Prepona pheridamas lacks signa, which contrasts with the rest of preponines where signa are present (Figure 2-3).

The immature stages of Prepona pheridamas are not strikingly different from either

Archaeoprepona or the remaining Prepona. In fact, the egg is very similar to Prepona laertes laertes Brown & Mielke, 1967 (Dias et al. 2011) and the larvae are very similar to Prepona laertes laertes and P. claudina godmani Fruhstorfer, 1895 (Furtado 2001, Dias et al. 2011), mainly in the shape of the cephalic capsule, which is a relevant trait for the diagnosis and differentiation of the genera of Preponini. In contrast, Archaeoprepona caterpillars have two separate horns, which when seen frontally resemble the aspect of the letter ‘M’. In Prepona the horns are united, giving the aspect of a triangle (Muyshondt 1973b).

Based on the differences in morphological and molecular characters described above, the taxon Prepona pheridamas is removed from the genus Prepona and placed in a new monotypic genus Mesoprepona Bonfantti, Casagrande and Mielke, n. gen.. The utility of monotypic taxa has been addressed in the literature (e.g. Platnick 1976, Rana and Ranade 2009), with some authors recommending against their use because they provide no information to identify a taxon

(e.g. Farris 1976), and others advocating their use since they denote morphological distinctiveness that can be used for identification purposes (e.g. Fritz et al. 2011). The latter opinion better applies to the case of Mesoprepona pheridamas, where the name does convey information regarding its unique combination of morphological characters when compared to the remaining genera in the tribe, thus making the taxon readily identifiable. In particular, most of the morphological characters that distinguish Archaeoprepona from Prepona (excluding P. pheridamas) are also different in P. pheridamas, and otherwise consistent within each of these groups. Mesoprepona pheridamas n. comb. as currently proposed is a monotypic genus but the molecular results revealed high intraspecific divergence and are an incentive to focus attention in collecting efforts to explore in more detail the intraspecific diversity. Prepona pheridamas phila

Fruhstorfer, 1904 and Prepona pheridamas attalis Fruhstorfer 1916 are retained as synonyms of

Mesoprepona pheridamas (Lamas 2004).

Mesoprepona pheridamas (Figure A-11) is widely distributed in South America (Figure

2-4), occurring in the Guianas, the Amazon and southern Brazil. Adults fly throughout the year in humid forests and have a broad elevational range. Males prefer to fly during the middle of the day and are most commonly found in light gaps and forest edges (Furtado, 2001). Adults feed on sap from trees, on fermented fruits, and are attracted to feces (Furtado 2001) and carrion (pers.

obs.). The immature stages feed on Hirtella gracilipes (Hook. f.) (Chrysobalanaceae) (Furtado

2001).

Table 2-1. Preponine species sensu Lamas (2004). Archaeoprepona Prepona amphimachus aedon camilla amydon chalciope claudina demophon deiphile demophoon dexamenus licomedes hewitsonius meander laertes phaedra narcissus pheridamas praeneste pylene werneri

Table 2-2. Wing pattern and genitalic characters for the three groups found within Prepona pylene and treated here as distinct species. Characters are illustrated and identified in Figure 2-2. Character Prepona pylene Prepona eugenes Prepona gnorima DFW and DHW Bright blue Iridescent blue Deep purple band color (a) DFW and DHW Occupies one fifth of Occupies one third of the width Occupies one fourth-one fifth band size (b) the width of the wings of the wings (varies in size) of the width of the wings DFW internal dark Absent Absent Present (absent in female) blue band (c) VFW and VHW Basal region pearl- Pearl and ochre Basal region light brown with surface coloration colored with interlaced interlaced dark brown scales, (d) dark brown scales, the the rest of the wing is brown rest of the wing is brown VFW light colored Absent Presence of large pearl-colored Presence of small pearl- spots external to spots colored spots the central symmetry system (CSS) in forewing (e) VHW ocellar Cordiform spots Cordiform spots Invaginations of the band symmetry system (f) VHW marginal Absent Absent Present band (g) Male genitalia: Rugose With dorsal small spines Rugose uncus texture (h) Male genitalia: Strong medial-ventral Strong medial-ventral spine Tapered gnathos shape (i) projection projection Male genitalia: Spines Spines Spines restricted to the dorso- gnathos texture (j) apical region Female genitalia: Present Absent Absent lamella postvaginalis (k) Female genitalia: Two thirds of the length Slightly shorter than the corpus Slightly shorter than the length of signa (l) of the corpus bursae bursae corpus bursae

Table 2-3. Genitalic characters for Mesoprepona pheridamas. Characters are illustrated and identified in Figure 2-3. Character Mesoprepona Prepona Archaeoprepona Male genitalia: gnathos Few striations with rough Clubbed with spines Multiple striations distal shape (a) texture Male genitalia: Completely membranous Two thirds sclerotized Completely aedeagus ventral sclerotized texture (b) Female genitalia: Similar in width Ductus bursae thinner Ductus bursae thinner ductus bursae and than corpus bursae than corpus bursae corpus bursae width (c) Female genitalia: signa Absent Present Present (d)

Figure 2-1. Bayesian Inference with unpartitioned data phylogenetic tree for Preponini. Numbers above nodes correspond to posterior probabilities. Species names are accompanied by their voucher code. The line weight indicates whether the clade was recovered in one, two, three or four topologies: BI with unpartitioned data, BI with partitioned data, ML with unpartitioned data, and ML with partitioned data. Gray boxes highlight areas where taxonomic changes are proposed.

Figure 2-2. Wing pattern and genitalia of Prepona specimens. Size bar = 1cm. Letters correspond to characters in Table 2-2. A) Prepona pylene male. B. Prepona pylene female. C) Prepona eugenes male. D) Prepona eugenes female. E) Prepona gnorima male. E) Prepona gnorima female.

Figure 2-3. Genitalia of preponine genera. Letters correspond to characters in Table 2-3. A) Mesoprepona pheridamas male. B) Prepona laertes male. C) Archaeoprepona licomedes male. D) Mesoprepona pheridamas female. E) Prepona laertes female. F) Archaeoprepona licomedes female.

Figure 2-4. Distribution map for Mesoprepona pheridamas.

CHAPTER 3 ORIGIN, BIOGEOGRAPHY AND EVOLUTION OF COLOR IN PREPONINES

Background

Understanding the biogeography of multiple Neotropical groups is key for developing an understanding of broad current biodiversity patterns. The field of biogeography now sheds light on the dynamic nature of species diversification (Mittlebach et al. 2007, Weir and Schluter 2007,

Wiens et al 2009) and its role in the maintenance of biodiversity (Qian and Ricklefs 2012,

Ricklefs and Renner 2012). It has also provided insights to help make conservation decisions concerning critically endangered species and ecosystems (e.g. Atlantic Forest, Carnaval et al.

2009). Combining knowledge of phylogenetic relationships with distribution data and timing of diversification is proving useful in disentangling evolutionary histories of organisms (Haffer

1969, Weir et al. 2009, Pinto-Sanchez et al. 2012), testing competing hypotheses about their origin and evolution (Mullen et al. 2011), and investigating how species’ traits might have had a key role in the evolutionary history of the study group (Losos et al. 1997, Gillespie 2004).

It has been proposed that some traits serve as key innovations that allow rapid diversification, thus influencing the evolutionary origin of entire lineages (Schluter 2000,

Salzburger et al. 2005, Mayhew 2007, Nicholson et al. 2014). In butterflies, for example, wing color pattern is a trait that has attracted the attention of naturalists since Darwin and Wallace’s observations in the XIX century, and it has proven to be key in intra- and interspecific interactions and speciation processes (Fordyce et al. 2002, Jiggins et al. 2004, Mallarino et al.

2005, Jiggins et al. 2006, Mavaréz et al. 2006). There are thus numerous studies focused on the evolution of color patterns and the possible roles they might play in the evolution of these colorful insects (e.g. Silberglied and Taylor 1973, Mallet and Gilbert 1995, Jiggins et al. 2001,

Berthier 2005, Kemp 2007).

Butterflies are an excellent model system for the study of the evolution of color, with their brilliantly colored wings being one of the most notable features of these highly visible insects. As noted by Nijhout (1991), color pattern alone can be used to distinguish most of the

12,000 described species of butterflies and 6,000 skippers. Some functions of color patterns have attracted particular attention, such as mimicry, defense from predators, mate recognition and sexual selection (e.g. Obara and Majerus, 2000; Kemp, 2007, Jiggins, 2008). The increasing availability of comprehensive phylogenetic hypotheses in this insect group (e.g. Wahlberg et al.

2005, Wahlberg et al. 2009) now allows more rigorous study of how different, and potentially conflicting, functions of color have interacted over evolutionary time to generate the patterns that butterflies display (Finkbeiner et al. 2014). In addition, such phylogenies also enable tests of how shifts in color pattern, potentially in concert with changes in geographic range and habitat, have influenced speciation and diversification (Jiggins et al. 2006; Chazot et al. 2014).

The Neotropical butterfly tribe Preponini (Nymphalidae: Charaxinae) is very well known among lepidopterists because of the large size and conspicuous color patterns of its species.

Preponines inhabit the forest canopy and are characterized by having robust bodies and erratic flight. They are distributed from Mexico to Argentina, with a species richness peak in the

Amazon basin. The wing color patterns of preponines exhibit a dramatic transition from dorsally blue and ventrally brown to dorsally red/orange and ventrally multicolored, with this transition being the reason why some Prepona species have long been considered to belong to different genera (the former genus Agrias; see Ortiz-Acevedo and Willmott 2013 and Ortiz-Acevedo et al. in press). These bright color patterns have attracted the attention of collectors and naturalists since the XIX century, resulting in hundreds of names for the 24 recognized species. The tribe’s taxonomy is under revision, recently changing from 21 species in five genera to 24 species in the

three genera Mesoprepona, Prepona and Archaeoprepona (Ortiz-Acevedo and Willmott, 2013;

Ortiz-Acevedo et al. 2017), and ongoing work aims to clarify whether the tribe is potentially more diverse (Llorente et al. in preparation). Nevertheless, no study has focused on understanding the biogeographic origin of the tribe and the role of color in its diversification patterns. Consequently, in the present study potential explanations for current patterns in color and geographic distribution are investigated.

In this study, the biogeographic origin of the tribe is studied by estimating ancestral geographical ranges to understand how major geographic events in North, Central and South

America have shaped the taxonomic diversity of the tribe. Due to the higher diversity of preponines in the Amazon region, I hypothesize this is the most likely place of origin. Second, the role of coloration in preponine diversification is evaluated by reconstructing the ancestral states for color pattern and estimating rates of phenotypic evolution along the phylogenetic tree.

Finally, the extent to which shifts in phenotypic evolution are related to major changes in the biogeographic history is explored, in an attempt to propose further hypotheses about the origin of preponine color patterns and their potential role in mimetic rings.

Materials and Methods

Phylogeny Reconstruction and Dating

The analysis included DNA sequence data available in Genbank from Ortiz-Acevedo and

Willmott (2013) and Ortiz-Acevedo et al. (in press). The phylogenetic hypothesis is based on six genes, two mitochondrial and four nuclear, and includes a total of 25 preponine specimens. Due to the uncertainty regarding the phylogenetic relationships among Preponini and other tribes within the subfamily Charaxinae, 14 additional samples were included that represented the remaining five charaxine tribes (Table D-1).

Partition Finder v2.1.1 (Lanfear et al. 2012) using a greedy searching scheme and the

Akaike Information Criterion (AIC) was used to select the BEAST partitioning scheme. A dated tree was inferred using BEAST 2.4.5 (Bouckaert et al. 2014). The clock prior was set to relaxed log normal, and site models were co-estimated with the phylogeny using reversible jump using the bModelTest 0.3.3 plugin (Bouckaert 2017). Site models were unlinked and tree models were linked. I ran three different analyses that differed in the number of molecular clocks used. The tree prior was set to a Yule model. The birth rate prior was set to uniform with the lower value of

0 and upper of 1,000 and the uncorrelated lognormal relaxed clock means for each partition were set to a gamma distribution with alpha of 0.01 and beta of 1,000. I used a secondary calibration point from Wahlberg et al. (2009) to date the tree and implemented this as a normal prior. The estimated age for the common ancestor node for all charaxines was set at 48.7 Ma (44.5 – 56.7

Ma) with a mean of 48.7 and sigma of 4.0. The value of sigma was selected to include the error associated with the primary dating study and incorporating the confidence interval estimated for the Charaxinae node in the normal prior calibration as suggested by Forest (2009). Three individual runs of 100 million generations were performed, sampling every 10,000 generations.

The runs were combined in LogCombiner 2.3.0 and convergence was assessed in Tracer v1.6

(Rambaut et al. 2014). I subsampled a total of 10,000 trees using LogCombiner 2.4.5 to infer a maximum credibility tree in TreeAnnotator 2.4.5. I used path sampling analysis available in the

BEAST2 package to compare the marginal likelihood of the analyses with different clocks and used Bayes Factor (Fan et al. 2011) to select the model that performed best for the data, which has been shown to outperform other marginal likelihood estimators (Baele et al. 2012, Baele et al. 2013a, b). I used 100 steps with a chain length of 1 million generations and other settings were kept as default and tested convergence by examining that the average standard deviation of

split frequencies fell below 0.01, visually inspecting the trace files in Tracer v1.6

(http://tree.bio.ed.ac.uk/software/tracer/) and checking that the effective sample size values for the trace was higher than 200.

Inference of Biogeographic History

I recorded locality data for preponine specimens in a Microsoft Access database at the following collections: the McGuire Center for Lepidoptera and Biodiversity – Florida Museum of Natural History, University of Florida (Gainesville, FL, USA), National Museum of Natural

History – Smithsonian Institution (Washington D.C., USA), American Museum of Natural

History (New York, NY, USA), Instituto Alexander von Humboldt (Villa de Leyva, Colombia),

Instituto de Ciencias Naturales - Universidad Nacional de Colombia Sede Bogotá (Bogotá,

Colombia). Additional data was obtained from the literature and from a photographic database shared by a Colombian collaborator. I georeferenced localities from various sources including

Google Earth, literature and published/unpublished gazetteers. Subsequently, I cleaned the final database to remove erroneous localities as well as localities that did not reach a certain threshold of precision, referred to multiple possible locations, or could not be located. By analyzing this data in graphical form I was able to identify priority taxa that lacked information, in which case I went back to the literature to attempt fill gaps in the database.

To classify the biogeographic regions occupied by preponines I used NatureServe's classification of the Neotropics into 'Ecological Systems' (Josse et al. 2003) and made some modifications in consideration of previous knowledge of the group. I imported locality information and biogeographic regions to DIVA-GIS (Hijmans et al. 2001) to visualize distributions, assign species to biogeographic regions and code taxa as present or absent by region. I generated a dataset that included all preponine species plus representatives of the

remaining charaxine tribes for a total of ten areas, eight in the Neotropical region plus

Afrotropical and Indo-Malayan regions (Table D-2).

I used the R package BioGeoBEARS 0.2.1(Matzke 2013, 2014) to simultaneously estimate the ancestral range for Preponini using the inference methods Dispersal-Extinction

Cladogenesis (DEC) (Ree and Smith 2008), and the Maximum Likelihood implementation of

Dispersal-Vicariance analysis (DIVALIKE) (Ronquist 1997), and Bayesian biogeographical inference (BAYAREALIKE) (Landis et al. 2013). In addition, I tested the above methods also including the founder-event parameter, + J, where a daughter lineage can occupy an area outside of the parental range (Matzke 2014). I tested a time-stratified model in which I took geology into account by following Ree and Smith's (2008) and Condamine et al.'s (2012) specification of geological time and implemented a paleo-geographical model following five time slices: (1)

Origin of Preponini - 52 Ma, (2) 32 - 23 Ma, (3) 23 - 10 Ma, (4) 10 - 7 Ma, and (5) 7 Ma - present. I allowed different configurations of areas in each time slice by either assigning a 0 or 1 based on whether the area was absent or present in each time bin, respectively (Table D-3). I also allowed the probability of movement across areas to change in time (values between 0.0001 and

1), accounting for geographic position and for barriers to dispersal, and penalized accordingly.

The maximum number of areas any species may occupy was set to eight since this is the maximum number of areas observed to be occupied in the present by any single preponine species. By fixing the number of allowed ancestral areas I attempted to circumvent likely biases that have been evidenced for at least one of the tested biogeographical methods (e.g. Ree et al.

2005), and therefore follow Buerki et al. (2011) and Sanmartín (2003) and assume the ancestral range was not larger than the current one. I used likelihood ratio tests and AIC to compare the models and identify which had the best fit for the data.

Diversification Rates

Using the dated tree, I estimated the rate of diversification for the tribe. First, I visualized the accumulation of lineages through time using a lineage-through-time plot to qualitatively evaluate the rate of diversification. Second, to test if there has been a change in diversification rates through the evolutionary history of preponine butterflies, I used the Constant-Rates (CR) test by calculating the gamma statistic (Pybus and Harvey 2000). The CR test evaluates if the position of the internal nodes is closer or further from the root than expected under a pure birth process by calculating the gamma statistic. If the internal nodes are closer to the root then the value for gamma is expected to be significantly smaller than 0, suggesting rapid radiation close to the root and a decrease towards the tips of the tree. Conversely, if internal nodes are closer to the tips of the tree, the gamma value is expected to be significantly larger than 0, suggesting an increase in diversification rates towards recent times. Under a pure birth model, gamma is expected to follow a standard normal distribution, where -1.96 and 1.96 are the critical values at alpha = 0.05 level and a two-tailed test. Finally, I estimated the speciation rate assuming a pure birth process and given the clade age and species richness using the estimator of Magallón and

Sanderson (2001).

In addition to estimating the gamma statistic and the speciation rate under a pure birth model, I fitted a MEDUSA model that estimates shifts in diversification rates across the tree. The model adds a series of diversification breakpoints which estimates the parameters under a Yule, a birth-death process or mixed models. It then returns the best combination of diversification breakpoints by comparing the model with increased number of parameters to the simpler constant diversification model (Alfaro et al. 2009).

Phenotypic Evolution

To reconstruct ancestral color patterns and estimate their rate of evolution I photographed a minimum of one museum specimen for every species in the tribe deposited at the McGuire

Center for Lepidoptera and Biodiversity, Florida Museum of Natural History (MGCL-FLMNH) collection. The number of individuals sampled averaged 11 per species, but some species were represented by a higher number of individuals because of their significant higher variation in broad color pattern such as in Prepona amydon (n=61). All species were represented by at least four individuals except for Archaeoprepona priene, for which only one specimen was available.

Even though species in the tribe are not considered to be sexually dimorphic, I selected only male specimens for analyses because of their better representation in collections and to avoid the possibility of slight variations in color between the sexes. I selected recent specimens to reduce the possibility of changes in coloration of specimens resulting from preservation conditions, although in some cases it was necessary to use older specimens because of low species representation. The collection date of specimens photographed ranged between 1910 to 2014, with almost 70% of the specimens collected after 1980. The collection date varied among species but I attempted to maintain an approximately similar distribution of collection dates so that error due to potential reduction in color intensity was similar across the tips of the tree (Figure C-1).

Photographs in JPEG format were taken using a light box with a set of four daylight fluorescent Sylvania light bulbs and a Nikon D5300 camera body coupled with a Nikon 60mm f/2.8G ED Auto Focus-S Micro-Nikkor Lens. I used a Kodak color separation guide and gray scale with a ruler included to calibrate the camera and the images before analysis. Color was measured at three independent locations on the forewing as well as over the forewing as a whole.

The three forewing locations included the discal cell (Cell 1), and the regions delimited by the discal cell and the veins M1 and M2 (Cell 2), and CuA1 and CuA2 (Cell 3) (Figure C-2). Those

regions were selected to represent the most significant observed variation in color and pattern across the forewing. Since the wing veins which delineate the three regions are homologous across species, the same region in the wing was measured consistently, irrespective of wing size or shape. Similarly, since wing cell area scales with wing size, a similar proportion of the wing area was measured for all specimens. The correlation between the area of each of the cells and total wing area was larger than 0.92 for every cell and represents on average 10% of the total wing area (Figure C-3; Cell 1 – R = 0.92, Cell 1 Area = 10.3 + 0.08*Wing Area; Cell 2 – R =

0.97, Cell 2 Area = -14.4 + 0.09*Wing Area; Cell 3 – R = 0.95, Cell 3 Area = 17.2 + 0.11*Wing

Area) (Figure C-3). On each of the wing regions and for the whole wing I measured the mean and mode of the red, green, and blue channels (RGB) as well as total RGB values using ImageJ

(Rasband, 1997-2014, Abramoff et al. 2004, Schneider et al. 2012).

To test for changes in evolutionary rates in color across lineages, I used a Bayesian approach using a reversible jump Markov chain Monte Carlo (rjMCMC) process to compare among different models of changes in evolutionary rates. All models tested follow a Brownian motion (BM) model of evolution, the most commonly used model for continuous traits.

Brownian motion model of evolution is the most simple and parsimonious model that has good statistical properties and few parameters that are easy to estimate given the restricted amount of data. It also performs well for traits evolving under wide range of scenarios. The first model fitted was a single rate Brownian motion (BM) in which phenotypic rate evolution was constant across the tree. Next, I fitted a relaxed rate BM (rBM1) in which the evolutionary rate of the BM mode was allowed to shift a single time along the trees (Revell et al. 2012). Subsequently, I fitted a second relaxed BM model (rBM2) in which evolutionary rates were allowed to shift multiple times across the trees. In the fourth model evaluated I kept evolutionary rates constant (no shift),

but traits were allowed to pulse rapidly, representing jumps in the mean of the trait (jump-BM).

In the last model, I combined rBM2 and jump-BM to allow phenotypic rates to shift several times and the mean trait to jump across the tree (jump-rBM, Eastman et al. 2011). Model selection was performed using Akaike Information Criterion (AIC) for MCMC samples (Raftery

2007) by calculating the difference in AIC (AIC) between the model with lowest AIC and any other model fitted to the data. I assumed a model to be significantly better than another if AIC

> 2. In the case in which AIC < 2, I selected the simplest model in the following order: 1) BM,

2) rBM1, 3) rBM2 4) jump-BM and 5) jump-rBM. I did not perform model averaging in cases in which -2 < deltaBIC < 2, because it has been suggested to be misleading (Taper and Ponciano

2016). Since I wanted to test for differences in evolutionary rates and the signature of evolution in the three different cells as well as the overall wing, I did not attempt to reduce the variable set by means of principal component analysis. Additionally, since Preponini consists of three genera in two clades, one mainly blue and the other blue and red, I wanted to evaluate the rates of evolution of the different color channels independently. For each model in each cell and measurement, I ran a single chain for 500000 generations sampling every 100. I assessed convergence of the MCMC by visually inspecting the trace of each parameter and determining if the effective size of the chain was above 100. Before running the models, I calibrated the rjMCMC to estimate a reasonable proposal width to initiate sampling of the Markov chain.

Subsequently, I visually inspected the congruence in the timing of the shifts and jumps in phenotypic evolutionary and diversification rates. In case where shifts or jumps in phenotypic rates were congruent or predated shifts in diversification rates, I used a Binary State Speciation and Extinction (BiSSE) model to evaluate if such shifts or jumps in evolutionary rates had an influence on diversification rates (Maddison et al. 2007). BiSSE models test if speciation and

extinction rates are associated with transitions in a binary trait. Such a model is particularly useful for the analyses since I wanted to test if changes in phenotypic evolutionary rates from low to high or high to low could potentially drive higher speciation or extinction in the clade after the shift. Consequently, I assigned each of the tips of the trees either a 0 or a 1 depending on their placement respective to the node in which phenotypic rates shifted. Tips descending from the shift node were coded with 1 and the rest with 0. Subsequently, I estimated one diversification rate and one extinction rate for each of the sub-trees leading to each group of tips and a transition rate from 0 to 1 and vice versa. The total number of parameters estimated by this model was six, with two speciation, two extinction and two transition rates (Maddison et al.

2007, FitzJohn et al. 2009). I compared this model to a simpler model in which I constrained speciation and extinction rates to be equal among sub-trees and allowed transition rates to vary.

The two models were then compared using BIC and a difference of 2 or more indicated significantly stronger support in favor of the model with the lowest BIC. I repeated the above protocol for each of the nodes in which a shift in phenotypic evolutionary rate was identified from different cells and color channels. Finally, to visualize the ancestral states of the character I used a maximum likelihood approach to estimate the state of the trait at each node (Felsenstein

2004). All analyses were performed in R using the packages ‘phytools’ (Revell et al. 2012), ‘ape’

(Paradis et al. 2004), ‘Geiger 2.0’ (Harmon et al. 2008) and ‘diversitree’ (FitzJohn et al. 2012).

Results

Phylogenetic Reconstruction and Dating

Partition finder found 15 partitions for the dataset (Table D-5). The estimates for the marginal likelihood obtained by the path sampling analysis (Table D-6) were compared using

Bayes Factors and the three analyses with different clocks were equally supported in both datasets (BF = ~1, Table D-7). The origin of Preponini was around 31.5 Ma (Figure 3-1) in the

Oligocene. In contrast, the genera appeared in the mid-Miocene, around 14.6 Ma for

Mesoprepona + Prepona and 14 Ma for Archaeoprepona. Confidence intervals are considerable as expected by the use of secondary calibration points (Figures C-4, C-5).

Inference of Biogeographic History

I recorded 4050 preponine specimens, producing 1121 species/locality records after removing duplicate or ambiguous records or localities with unreliable coordinates.

Model selection. The analyses in BioGeoBEARS identified the model BAYAREALIKE

+ J as the best model based on AIC and AICc (Table D-8). Likelihood ratio tests allowed further exploration of the model selection by comparing BAYAREALIKE + J with its nested model

BAYAREALIKE (model that yielded the smaller AIC and AICc of all comparisons) and confirmed that BAYAREALIKE + J is the best model for this dataset (p-value = 0.013).

Ancestral range estimation. The topology resulting from the best model for the data is illustrated in Figure 3-1. The tribe most likely originated in the Amazon, and despite other ranges being likely, most include the Amazon region (Figure C-6). The genus Archaeoprepona most likely originated within a range comprising Central America, western South America, Andes and

Amazon. The other ranges with high probability for this node are characterized by being broader than the ranges for the whole tribe, whereas the Andes and Amazon are included in most of the ranges. Similarly, for Mesoprepona + Prepona, the estimates recover broader ranges than for the tribe but narrower than for Archaeoprepona, identifying the region comprising the Andes,

Amazon, and Atlantic forest as the most likely origin of the sister genera. Here again, the Andes and Amazon are found in most ranges but the Atlantic forest is also recovered as a likely ancestral range (Figure C-6). The clade that corresponds to the transition of color patterns from mostly blue to red species shows larger differences of marginal probabilities than the previous

nodes and a tendency to narrow the ancestral distribution, where only two ranges recovered >

80% of the marginal probability estimates. For this clade, I found that the most likely area of origin comprises the Amazon and Andes.

Diversification Rates and Phenotypic Evolution

According to the gamma statistic, diversification rates have been constant through the evolutionary history of preponines (gamma statistic = -0.34, p = 0.73). Using the estimator of

Magallón and Sanderson (2001), the diversification rate in preponine butterflies is 0.08 species/Ma. In contrast, the MEDUSA analysis marginally suggests an increase in diversification rates from 0.07 (95% CI: 0.04 - 0.12) to 0.22 (95% CI: 0.11 – 0.39) at 26.8 Ma from the preponine most recent common ancestor, which corresponds to the clade sister to Prepona werneri (AIC = 1.9, lnLik one rate = -74.88, lnLik two rates = -71.70; Figure 3-1, Figure 3-2).

The lineage through time plot (Figure 3-2) also visually departs from the expectation under the pure birth model.

Color evolutionary rates along the preponine phylogeny have mostly remained constant

(Table 3-1, Table 3-2,). Both mean and mode metrics yielded similar results. For most of the cells and most of the color channels the preferred model of evolution was the simple model with one evolutionary rate across the tree. Although the overall signature of evolution suggests constant phenotypic evolutionary rates, I identified two jumps in evolutionary rate in the red channel Cell 1 in the red Prepona clade; the first one is at the base of the clade and the second is along the branch leading to Prepona hewitsonius (Table 3-1, Table 3-2, Figure 3-2).

Furthermore, I found that the blue channel measured in Cell 2 showed on average six jumps but only four of them have high posterior probability. These jumps are located at the base of the P. hewitsonius + P. amydon clade, at the tip leading to P. hewitsonius, at the tip leading to

P. deiphile (CA) and at the tip leading to A. priene (Figure 3-2). The blue channel in Cell 3 was estimated to have on average two jumps and six shifts across the tree. Only the jump leading to

A. priene and none of the shifts in the rate of evolution had posterior probability larger than 0.5.

Although I calibrated the initiation of the Markov chain, varied the proposal width accordingly, and tested different lengths of the Markov chain, some of the cell measurements did not converge nor yielded reasonable estimates of the evolutionary rate parameter. For such measurements, the evolutionary rate was consistently estimated to be either 0 or too large (Table

3-1, Table 3-2).

Differences in phenotypic evolutionary rates did not influence diversification rates since the shifts in phenotypic rates were not congruent or did not predate the marginal shift in diversification rate. Consequently, I did not further explore this option nor show any results of the BiSSE analysis.

Discussion

Origin and Biogeographical Patterns

The tempo of diversification for the tribe Preponini is congruent with the age proposed by

Wahlberg et al. (2009), which is expected given the use of a secondary calibration point extracted from their study. However, the results are also consistent with the dates found for the tribe in another study that used an independent dataset to infer the timing of diversification (Peña and Wahlberg 2008). Preponine evolution is estimated to follow an out-of-the-Amazon biogeographical model with subsequent dispersal to Central and southern South America where new regions became available for colonization as a result of major geological events. Early speciation events in the tribe are characterized by an expansion of range range that is maintained for most of subsequent speciation events in Archaeoprepona but not in Mesoprepona + Prepona.

On the other hand, Mesoprepona and Prepona show a more complex biogeographical history

where multiple instances of contraction/expansion of distributions seem to have played a key role in evolution. Preponine genera thus exhibit contrasting biogeographical histories, suggesting that they might have evolved under different evolutionary pressures and scenarios.

Phylogeny calibration can be achieved by using primary or secondary calibration points.

Primary calibration points are extracted from the fossil record, but many groups of organisms have a poor or non-existent fossil record, as is the case for Lepidoptera. In these cases, secondary calibration points serve as means to date phylogenies by using indirect information to infer dates of origin. Multiple sources of information can be used in these cases, such as some biogeographical events, already published age estimates from independent dating studies, or known DNA substitution rates (e.g. Richardson 2001, Ho 2007, Ho and Phillips 2009, Kozak et al. 2015). It is unsurprising that secondary calibration points are widely used and the literature is well-supplied with these kinds of studies given the poor fossil record for many groups (Hipsley and Muller 2014). The use of secondary calibration points has not been immune to debate, with their use being highly questioned in the literature (Shaul and Graur 2002, Graur and Martin

2004, Sauquet et al. 2012), but despite potentially not being the most accurate approach, it is the only possibility for groups which lack primary calibration points (Hedges and Kumar 2004,

Forest 2009), such as preponines.

Using BioGeoBEARS, where all models are implemented under a maximum likelihood framework allows for model testing, comparison and selection using either likelihood or

Bayesian approaches (Lawing and Matzke 2014). This permitted the identification of the best biogeographic scenario for Preponini and also the likely important processes that had effect in the evolution of the group. The biogeographical estimation using the six models selected

BAYAREALIKE + J as the best fitting model for our data. The results agree to what has been

suggested in the scientific literature (Lawing and Matzke 2014), where DEC and

BAYAREALIKE outperformed DIVALIKE (Table D-8). The inclusion of the J parameter had a positive effect in the estimations, however, not all models that accounted for the + J parameter outperformed the traditional ones (Table D-8). This finding is congruent with results of other studies were the J parameter is found to dramatically improve model fit (Matzke 2013, Harris et al. 2014, Matzke 2014, Voelker et al. 2014, Toussaint and Balke 2016, Zhang et al. 2017), as evidenced here by the AIC (a AIC >2 is considered significant). The J parameter allows a daughter lineage to jump to a new area. This parameter is labelled in BioGeoBEARS as founder- event speciation, a term coined by Carson (1983). This parameter was initially used in studies of island biogeography, but it has also proved useful to explain patterns in non-island scenarios

(Matzke 2014). A number of recent studies highlight the potentially important effect of long range dispersal in the distribution patterns of multiple groups of organisms (de Queiroz 2005,

Baguette 2003, Rota et al. 2016, Toussaint et al. 2016), for which reason I opted to include this parameter and its inclusion indeed improved biogeographical estimates. The identification of the

BAYAREALIKE + J model as the best model suggests that changes in distribution of preponines occurred along the branches either by dispersal or extinction instead of being associated with cladogenetic events as allowed in DEC, DEC + J, DIVALIKE and DIVALIKE + J. This suggests that changes along the branches had more influence than cladogenetic events in the biogeographical history of preponines.

The results show that the tribe Preponini most likely evolved in the Amazon region during the early Oligocene around 31.5 Ma. During this epoch, South America detached from

Antarctica and moved north towards North America, a movement that was accompanied with a worldwide marine regression and the spread of humid forests in South America. These

geological changes presumably increased available niches facilitated the dispersal, colonization and divergence of different groups of organisms.

Preponine genera originated in the mid Miocene about 14.3 Ma. Their appearance was associated with an expansion in their distribution from the single Amazonian region of their ancestor. This is congruent with the hypothesis that preponines dispersed, colonized and diverged in niches newly created by geological events on the Oligocene and Miocene. In addition to the changes mentioned above, new niches were also created by two additional major geological events that have been demonstrated to have played major roles in the diversification of multiple groups of organisms: i) the uplift of the Andes, a global biodiversity hotspot that is currently viewed as a major driver of biological diversification (Graham et al. 2004, , Orme et al.

2005, Hughes and Eastwood 2006, Brumfield and Edwards 2007, Hoorn et al 2010, Smith et al.

2014, De-Silva et al. 2017), and ii) the appearance of the land bridge connecting North and South

America that, even though considered a recent event (3 Ma), has recently been suggested to be as old as 23-15 Ma (Bacon et al. 2015, Montes et al. 2015).

The three preponine genera show different biogeographic histories. The genus

Archaeoprepona appeared about 14 Ma and began immediate diversification. Its ancestor is recovered as a broadly distributed species that, in most cases, did not show a change in distribution range as it underwent speciation events (Figure 3-1). Some of the most recent speciation events, on the other hand, exhibited changes in distribution by either contracting, as for A. chromus, A. priene and A. licomedes which are restricted to northwestern South America and the Amazon, or by an expansion as shown for A. amphimachus and A. meander which are amongst the most widely distributed preponine species. The ancestor of Mesoprepona + Prepona originated around 14.6 Ma most likely in a region comprising the Andes, Amazon and the

Atlantic forest. In contrast to Archaeoprepona, diversification in Prepona did not happen immediately but with a delay of approximately 5 Ma. Then, the genus experienced rapid speciation events that were associated with a reduction in distribution size. The early branching

Prepona species, P. dexamenus and P. laertes, in addition to Mesoprepona, are widely distributed, but distribution patterns change for the remaining Prepona species where a reduction in range was estimated as the group underwent speciation events. The ancestor of P. werneri + remaining Prepona is recovered as a restricted species distributed only in the region west of the

Andes. Subsequent speciation events were correlated with an expansion of the distribution range, leaving P. werneri restricted to its original range possibly due to an intensified Andean uplift period around 4.5 Ma (Hoorn et al. 2010). The remaining Prepona species show contrasting biogeographic histories. The first group includes a clade that most likely originated in the

Amazon around 4.3 Ma with a subsequent dispersal to North and Central America most likely aided by the large-scale formation of the Panama Isthmus around 3 Ma (Saito 1976, Keigwin

1978, O’Dea et al. 2016). The second group, which contains the red Prepona species, retained the ancestor’s distribution range throughout evolutionary time until the most recent splits where species have become either restricted to a single area, as in P. hewitsonius and P. narcissus, or broadly distributed as in P. claudina, P. aedon and P. amydon. The most recent speciation events, as seen for P. narcissus and P. claudina, were potentially driven by the climatic instability characteristic of the Pleistocene as seen for other groups (Haffer 1969, Johnson and

Cicero 2004, Carnaval et al. 2014, Garzón-Orduña et al. 2014).

Diversification of Preponines

This study shows that the diversification rate in preponine butterflies has been not significantly different from constant over 30 Ma of evolution. The gamma statistic suggests that preponines have a balanced evolutionary pattern without an early or late burst of diversification.

The results suggest that the observed branching pattern follows one of a pure-birth (Yule) model where the birth rate is constant in time and extinction is unlikely. Nonetheless, the lineage through time plot does suggest a deviation from the pure birth expectation. Such a deviation was marginally confirmed by the MEDUSA model, where a marginal increase in diversification rates in the clade sister to P. werneri was identified and in which the varying diversification rates model was better by 1.9 AICc points than the constant model.

In studies of other butterfly groups, palaeoclimatic events, natural history traits or biogeographical events have been shown to have shaped diversification rates by influencing speciation and/or diversification (Wahlberg et al. 2013, De-Silva et al. 2016, Sahoo et al. 2017).

In preponine butterflies, diversification rate marginally increased around 6.2 Ma, the time of major uplift in the northern Andes, the final formation of the Amazon river and the presence of a proto-connection between Central and South America. In the clade sister to P. werneri, it is likely that the closing of the Panama isthmus had a stronger influence on the diversification patterns than the formation of the Amazon river, given that ancestral state estimation shows a migration from Amazonia to Central America shortly after the node in which diversification rate increased.

Assuming constant diversification, preponine butterflies seem to have a slower rate of diversification than other nymphalid groups that have been studied (Peña and Espeland 2015).

Conversely, the MEDUSA model estimated that diversification rates in the clade sister to P. werneri is much higher than for the rest of the tree, comparable with high diversification rates in other charaxines (Peña and Espeland 2015). The case of Mesoprepona is interesting because despite of having had the same time to diversify and presumably comparable ecological opportunities as the other two genera, and exploiting the same larval hostplants as some Prepona

species, Mesoprepona is significantly less diverse. Although it is possible that this monotypic genus contains hidden diversity, given the long branch lengths of the individuals included in previous phylogenetic analyses (Ortiz Acevedo et al. in press), phenotypic diversity is clearly very low in comparison with Prepona. Focusing on increasing sampling effort could help clarify the true diversity of this genus. Alternatively, the long branch lengths recovered could be indicative of higher extinction rates in this lineage.

Phenotypic Evolution

During the course of their evolution Prepona underwent a drastic change in the color pattern of both wing surfaces. This feature was the key character used by previous taxonomists to classify Prepona species (sensu Ortiz-Acevedo and Willmott 2013) in two genera. The former genus Agrias contained mostly yellow, orange, blue and red species and the former genus

Prepona contained mostly blue species. Evidence for the former being nested well within the latter has come not only from molecular data but also morphology (Ortiz-Acevedo and Willmott

2013, Bonfantti 2014, Ortiz-Acevedo et al. in press,). Here, I provide evidence that the color pattern jumped to a different value in the ancestor of the red Prepona clade. Such jump in phenotype caused the genus Prepona to include butterflies with such strikingly different coloration patterns.

Tests performed here identified more changes in phenotypic rate for the red and blue

RGB channels (Table 3-2) than for the green and total RGB. The changes identified are mostly jumps in the mean of the evolutionary process and are located around the red Prepona clade. The analysis consistently identified the jump from blue to red in Cell 1 with no shift in the rate of evolution. Despite the red Prepona clade being strikingly different in morphology, the rate at which this phenotype changes has been constant through time. These jumps are consistent with previous findings, reporting that drastic changes in color patterns of butterfly wings and other

organisms have a relatively simple genetic basis and can appear relatively fast in evolutionary time (e.g. Reed et al. 2011). Differential genetic expression in particular regions of the wing are also responsible for localized changes which might be a plausible explanation for the results I found in Cell 1. These localized changes are not always visible under analysis of the entire wing, thus, isolating these regions that are likely to change quickly increases the likelihood of detecting shifts and jumps in the evolution of phenotype.

Jumps and shifts in the blue channel are restricted to the tips and might be associated with recent speciation events. For example, both Cell 2 and Cell 3 identified a jump in the blue channel leading to P. hewitsonius. This species is phenotypically more diverse in combining different colors in the forewing. Another consistent jump in the blue channel of the Cell 2 and

Cell 3 was identified for A. priene, suggesting that A. priene inherited the same pattern observed in the genus but not the same color blue.

Although similar signatures of trait evolution were recovered by the mean and the mode of color channels, I identified less phenotypic rate changes across wing regions and RGB color channels when using mean color than mode color (Table 3-1, Table 3-2). In skewed distributions the mode is a much more accurate proxy of the central tendency than the mean. Using the mode of color measurement better describes color as humans visually perceive it. The mode in a skewed distribution describes better where the bulk of the density is concentrated while the mean may deviate from the mode because of large numbers in the tails of the distribution. This is particularly true when measuring color channels separately in areas of the wing, such as Cell 1, for example, in which one group might be totally red and others might have sparse red patches embedded in a largely dark background.

It is worth noting that for some channels and regions the models did not converge or yield realistic parameter estimates. The first explanation for this issue is the violation in the normality assumption of Brownian motion. RGB data are bounded between 0 and 255 and thus the data is not normally distributed. Although I attempted to fit the models with transformed data, the results were similarly unsatisfactory. The second explanation is that such traits are adaptively evolving, violating the simple diffusion assumption of the Brownian motion model of evolution.

In such cases, models such as the Orstein-Uhlenbeck (OU) model in which trait variability is bounded by a selection parameter and species have an optimum trait value might be best suited. I explored such a possibility by fitting constant and variable rates OU models, but, again, the models yielded a variance of either 0 or too large for the data set I collected. Third, it is also possible that high intraspecific variability (or measurement error) is responsible for the bad fit of the BM and OU models by being similar or exceeding interspecific variability in the data set. I believe that in this particular case, measurement error is minimal and thus I attribute this intraspecific variability to real biological variability among individuals. A test of such a hypothesis requires a careful revision of the species boundaries in several polymorphic species, to ensure that intraspecific variability is not inflated by incorrect classification. It has been shown that some stochastic methods commonly used in ecology and evolution might suffer from parameter identifiability when including two different sources of variation (Dennis et al. 2006).

This means that when measurement error is estimated from the data, the model has two different parameter values for which the model is equally likely. In the first, the evolutionary rate is estimated to be high and measurement variability low. Alternatively, the opposite scenario might also occur in which measurement error is high and evolutionary rates are estimated to be low.

The above mentioned scenarios are additional potential explanations for the poor fit the models

to some of the measurements. When I allowed the model to estimate measurement error, evolutionary rates were estimated to be high. In contrast, when I constrained measurement error by the high intraspecific variability the model estimated the evolutionary rate as zero. By definition, evolutionary rates cannot be zero otherwise all the tips in tree would be expected to have the same trait value.

This study serves as a preliminary approach to studying the evolution of color patterns in

Preponini and as a generator of hypotheses for the observed evolutionary shifts in color patterns.

A likely explanation for the dramatic change of color patterns in preponines is their potential involvement in mimicry rings with genera such as Callicore and Asterope (Nymphalidae:

Biblidinae), which are documented to have remarkably similar color patterns (Descimon 1977,

Jenkings 1987). This hypothesis, however, has remained untested to date. Mimicry is plausible since species in both groups feed on toxic plants, including the families Sapindaceae and

Erythroxylaceae, but whether they sequester plant toxins as caterpillars and retain them after eclosing as adults is still unknown, as is their palatability in general. Preliminary geographical distribution data shows a correspondence in coloration and distribution of both groups (Ortiz-

Acevedo, unpublished data). With this study, I hope to provide a solid phylogenetic foundation and open the door to novel studies in examining in detail the potential roles and significance of coloration patterns in the evolution of preponines.

Table 3-1. Evolutionary rates for the mean color of the different areas of the wing measured. α is defined as the estimated value of the character at the root and 훔2 as the evolutionary rate (or variance). Asterisks make reference to cases where models did not converge nor yield realistic parameter estimates (see text for details). Measurement α 훔2 Jumps Shifts lnL AIC Model Mean Wing Total 102.63 12.89 -97.78 197.43 BM Red 129.49 49.55 -115.85 234.34 BM Green 99.53 14.81 -100.37 202.86 BM Blue* Cell 1 Total 74.43 16.93 2 -105.17 218.87 jump-BM Red 112.14 37.41 3 -123.02 254.95 jump-BM Green 70.60 23.13 -107.15 216.37 BM Blue* Cell 2 Total* Red 129.37 37.33 -112.95 228.50 BM Green* Blue* Cell 3 Total 113.79 15.12 -100.36 202.55 BM Red 127.85 87.85 -124.92 253.23 BM Green 109.65 26.38 -106.98 216.37 BM Blue*

Table 3-2. Evolutionary rates for the mode color of the different areas of the wing measured. α is defined as the estimated value of the character at the root and 훔2 as the evolutionary rate (or variance). Asterisks make reference to cases where models did not converge nor yield realistic parameter estimates (see text for details). Measurement α 훔2 Jumps Shifts lnL AIC Model Mode Wing Total 84.64 47.84 -113.22 228.23 BM Red* Green 71.49 12.71 -100.53 203.35 BM Blue* Cell 1 Total 67.98 19.14 1.22 -107.65 218.88 jump-BM Red 98.97 32.53 2.17 -122.55 255.51 jump-BM Green 58.23 27.65 -111.64 225.32 BM Blue 43.57 30.96 -119.93 242.37 BM Cell 2 Total 89.80 25.91 -114.18 230.52 BM Red* Green 76.58 24.73 -111.27 224.78 BM Blue 46.51 2.79 5.92 -100.30 214.74 jump-BM Cell 3 Total 99.16 59.53 -120.70 244.26 BM Red* Green 80.33 38.95 -117.00 236.59 BM Blue 76.92 94.65 1.73 5.50 -137.59 301.18 jump-rBM

Figure 3-1. Biogeographical range estimation for the tribe Preponini based on the BAYAREALIKE + J model. Dotted gray lines delimit the time bins used in the stratified analysis. Black triangles denote periods of increased Andean uplift (Hoorn et al. 2010). Numbers in nodes make reference to the main text. Abbreviations follow: PL: Pliocene, P: Pleistocene.

Figure 3-2. Ancestral reconstruction of the mode RGB red channel for the Cell 1 and lineage- through-time plot. Red dotted line denotes expectation of lineage accumulation under a pure birth process. Red and blue arrows highlight branches where a jump in color was identified for the blue and red channel in Cells 1, 2, and 3 (see text for details).

CHAPTER 4 PHYLOGEOGRAPHY AND SPECIES DELIMITATION IN PREPONA LAERTES

Background

Butterfly taxonomy and systematics is a field that has been the focus of numerous studies since the earlier observations of naturalists in the XIX century. Historically, the field has depended mainly in the adult morphology, but recent studies have also emphasized the importance of immature stages and life history traits, advocating the need for comprehensive studies that combine different sources of information (e.g. Wahlberg et al. 2005). More recently, the field has shifted to include DNA data as an alternative source of information that allows for more rigorous and complete studies, facilitating the task of species discovery and description

(Hajibabaei et al. 2006).

The use of DNA data has proven useful in resolving the taxonomy of species where traditional morphological studies have proven unsuccessful, with the study of cryptic species being a textbook example (Hebert et al. 2004). The most commonly used gene region for animals lies within the mitochondrial gene Cytochrome Oxidase subunit I, also known as the barcoding gene, and it has been used across multiple taxonomic groups and proven to be a key tool in understanding hidden diversity (Hebert et al. 2003a, b, Hebert et al. 2004). It has also been demonstrated to be helpful in studies where degraded DNA is the only source of information

(Hajibabaei et al. 2006), therefore, it is the go-to method for researchers that can only rely on these types of data. DNA barcoding is efficient, cheap, can be done without a state-of-the-art laboratory or sophisticated equipment, produces data that are simple to analyze and share, and shows high (although obviously not complete) accuracy. Protocols for obtaining the data are readily available in the scientific literature and the downstream analyses are somewhat standardized but evolving at a fast pace. However, use of the method has not grown without

criticism (e.g. Will and Rubinoff 2004, Brower 2006). Critics find troubling that such a short region of DNA, form only one locus, is used as a single tool to elucidate the relationships and evolutionary histories of taxa, advocating instead for the use of “integrative taxonomy” with additional complementary sources of information (Will et al. 2005). Current research and barcoding initiatives now almost always use other independent sources of information to make results more robust.

More recently, the availability of next generation sequencing (NGS) techniques and whole genome screening methods have become more widely used because their efficiency in providing a much greater quantity of data (Metzker 2010). These methods can work well with museum specimens and in cases where the DNA quality has been compromised (Bi et al. 2013,

Tin et a. 2014, Burrell et al. 2015). However, a major disadvantage over a single gene approach is the need for special equipment and facilities that can handle massive computer power, which translates into high costs, and the downstream difficulties in sharing and analyzing data.

Restriction-site–associated-DNA sequencing (RADseq) is the preferred method for population genetic research but has been increasingly used in phylogeography and in studies of species complexes and boundaries (Davey and Blaxter 2011, McCormack et al. 2013, Wagner et al.

2013). RADseq entails using restriction enzymes to digest the genome, then, using a series of adaptors, the whole genome is screened in search of genetic variations called single nucleotide polymorphisms (SNPs). The SNPs are then used to reconstruct the patterns of ancestry among samples and to draw evolutionary histories and also to address population genetics questions. Its high associated costs and the need of specific equipment is compensated by large amounts of data, and the process is less labor-intensive. Still, DNA barcoding and PCR targeted approaches are still being used in phylogeography and phylogenetics studies across animal groups, despite

the availability of newer genomic approaches (e.g. Fernandes et al. 2013, Graham Reynolds et al.

2014, Medina et al. 2014, Mayer et al. 2015).

Butterflies are used as model taxa to explore questions in ecology and evolution. They are charismatic, easy to collect, and often abundant in nature. Their taxonomy is well studied and there is a considerable relevant scientific literature. Some groups of butterflies attract more attention than others, of which nymphalids lead the way. This group includes butterflies showing great variation in size, shape and color, from large owl butterflies to tiny fritillaries. The family

Nymphalidae also includes the conspicuous genus Prepona (including the former genus Agrias).

Preponines (Nymphalidae: Charaxinae: Preponini) have caught the attention of naturalists and collectors for the last couple of centuries, and despite recent efforts to clarify their phylogenetic relationships (Ortiz-Acevedo and Willmott 2013, Bonfantti 2014, Ortiz-Acevedo et al. in press), research is still needed in particular at the species level. Prepona laertes is one of the most outstanding of such cases, being among the best known and locally most abundant preponine species. It is a medium sized, robust and fast flying butterfly, whose bright blue dorsal coloration makes it very conspicuous in its natural habitat. The species is distributed in the Neotropics, from Mexico to Argentina, and inhabits primary and secondary forests where it patrols the canopy, flying down to feed on rotten fruit or carrion. Recently, its distribution range expanded to the United States through accidental colonization and establishment of a population in

Broward County, Florida (Hayden 2013, 2017). Eugene LeMoult, the famous French naturalist and entomologist, had a strong fascination with this butterfly that resulted in the description of dozens of names which have been under debate ever since their description. Lamas (2004) lumped all available names into a single polymorphic species with four recognized subspecies, but this taxonomy has been questioned. In particular, Neild (1996) suggested that Prepona

laertes sensu Lamas should be split into at least three species, Prepona laertes, P. pseudomphale, and P. philipponi. Although Neild (1996) described several characters in the wing pattern and androconial scales of the male dorsal hind wing to differentiate the species, these characters also show some local and geographic variation, and the true species diversity, the association of populations with one another, and the correct assignment of names to species, are all open to debate. For these reasons, the more conservative approach of Lamas (2004) has not been conclusively disproved, and despite the attention of many researchers (Neild 1996, Janzen pers. com.), a detailed study addressing the opposing taxonomic arrangements is lacking.

With this study I therefore explore the conflicting taxonomic hypotheses for Prepona laertes and attempt to clarify whether it is a highly variable, polymorphic species, or if it should be split into two or more species, by using a DNA barcoding approach as well as the whole genome screening method RADseq. I explore different methods to delimit species using molecular data to better understand the true diversity within P. laertes. Lastly, I test how the results obtained from the two different molecular methods compare to each other, asking whether they yield the same signal, complementing each other, or differ in conclusions.

Materials and Methods

Specimen Collection, Storage and Preparation

Specimens were collected in Colombia and Ecuador using traps baited with fermenting banana and rotting fish as well as using entomological nets. Two legs were removed and preserved in 96% ethyl alcohol, and the remaining material was placed in a glassine envelope and stored in containers with silica gel until ready to be spread in the lab. Additional tissue was obtained by visiting entomological collections in the USA and abroad, and through collaborations with researchers in other institutions.

DNA Extraction

I used the Qiagen DNEasy Extraction Kit to extract genomic DNA from one leg of each specimen following the manufacturer’s protocol. I decreased the amount of volume for the final elution to 50 ul to increase DNA concentration. Extraction success, DNA quality and quantity were tested on 1.2% agarose gels.

COI Barcoding

Amplification and sequencing. I used standard PCR to amplify the COI barcode region using the primers LepF1/LepR1 and LCO/HCO (Folmer et al. 1994, Hebert et al. 2004). PCR cycling profiles and master mix are described in Ortiz-Acevedo and Willmott (2013). DNA sequencing was carried out by ICBR CORE at University of Florida and Eurofins Genomics,

LLC, Louisville, KY.

Data processing. I used Geneious R7 (http://www.geneious.com, Kearse et al. 2012) to manually edit the data and check peak calls and used ClustalW (Larskin et al. 2007) to align the resulting sequences. The final dataset consisted of sequences from 60 samples, of which two corresponded to the outgroup species (Table 4-1), with a total length of 634 bp.

RADseq Data

Library preparation. A Qubit 3.0 Fluorometer was used to quantify DNA concentration to identify samples for RADseq analyses. A total of 35 samples out of the 60 included in the barcode study were suitable for library preparation based on DNA quantity and quality (Table 4-

1). Two outgroup species were included in the analysis. Each sample was run twice for a total of

74 libraries. The modified ddRADseq protocol by Peterson et al. (2012) was followed, using the restriction enzymes EcoRI and MseI, with the barcodes hybridized on the EcoRI adaptor.

Individual complementary single stranded oligos were hybridized to EcoRI to form double

stranded adaptors that contain overhanging enzyme cut-site sequences, the associated barcode, and the Illumina flow cell priming site and sequencing primer.

The DNA (normalized at 20 ng/ul concentration for every individual in 6 ul of volume) was digested by the restriction enzymes in a single reaction, and incubated for 8 hours at 37C.

Unique barcodes on the EcoRI adaptor and the universal MseI adaptor were used across all samples, and incubated at 16°C for 6 hours. Library construction and adaptor ligation were verified with a PCR reaction, and the PCR products were visually examined on agarose gel; the same gel was also used to examine the fragment range that produced the highest density of fragments to determine the fragment range to capture during the size selection. Restriction- ligation samples that produced a successful PCR product were combined into a single pool, and the pooled sample was cleaned up and concentrated using a Qiagen PCR cleanup spin column to remove digestion and ligation enzymes and buffers prior to size selection. The final pooled sample was eluted in water.

Sequencing. The ICBR CORE facility performed the concentration of restriction-ligation product, the Pippin ELF size selection and Bioanalyzer evaluation to get a precise concentration estimate of each fraction as well the evaluation for the success of the size selection step. The fragment size selected was 200-450 bp. The flow-cell binding sequences were incorporated using a second PCR reaction to the size-selected fractions, and verified on agarose gel. A pool of successful PCR reactions was combined in a single tube, concentrated and cleaned up, and concentration was quantified using Qbit (119 ng/ul in 40 ul volume). The final highly multiplexed library (74 individuals) was sequenced on Illumina NextSeq500 Mid Output, spiked with 15% PhiX, with a decreased target cluster density by about 20% to a 160,000/mm cluster density using 1x150 cycles on a single lane, single flow-cell.

Data processing. Illumina sequence data was initially edited by Illumina’s BaseSpace software, where the Illumina adaptors were trimmed for all reads. All following analyses were run using the UF Research Computing’s HiperGator 2.0. I used FASTX toolkit

(http://hannonlab.cshl.edu/fastx_toolkit/) to filter, demultiplex and trim the Illumina reads. For the ‘FASTX quality filter’ step I selected a phred score of 20 across 90% of the read. Then I ran

‘FASTX barcode splitter’ to create a fasta/q file per library and then combined the data from the two libraries per sample in a single fasta/q file. Then, I removed the sequences from barcodes and restriction sites using ‘FASTX trimmer’ leaving the remaining sequence untouched. Lastly, I ran the FASTX demultiplexed, filtered and trimmed data through the IPYRAD denovo pipeline using the parameters defined in the supplementary information file.

Data Analysis

Phylogenetic relationships. I used different methods for analysis of the COI barcode region to investigate the relationships in Prepona laertes. I used a maximum likelihood approach where I explored different methods such as FastTree (Price et al. 2009, 2010) implemented as a plug-in for Geneious R7, W-IQ-Tree (Nguyen et al. 2015, Trifinopolous et al. 2016) available online in its own standalone portal (http://iqtree.cibiv.univie.ac.at/), and RAxML v8 (Stamatakis

2014) available in the CIPRES portal (https://www.phylo.org/, Miller et al. 2010). I used

Dendroscope 3 (Huson et al. 2007, Huson and Scornavacca 2012) to generate a majority rule consensus tree.

For the RADseq data I used the resulting output files from the IPYRAD pipeline to infer the phylogenetic relationships under a maximum likelihood approach with a GTR+G model of evolution in RAxML v8 (Stamatakis 2014) available in the CIPRES portal

(https://www.phylo.org/, Miller et al. 2010). I used 1000 rapid bootstrapping replicates as a measure of node support. I defined several data matrices for analysis based on two different

criteria. I first selected two datasets where the number of sites included for analysis differed: i) concatenated loci for all samples and ii) single SNP per locus. Due to the invariant sites definition discrepancy between IPYRAD and ML analyses, I used the R (CRAN project) package ‘phrynomics’ (Banbury and Leaché 2014) to remove sites where non-missing data were assumed invariant by ML and used an ascertainment bias correction model (Lewis 2001, Leaché et al. 2015) for the reconstruction. Then, for the single SNP per locus dataset, I identified different thresholds of missing data to compare the effect of missing data in the analyses: i) 50% of samples has data for each SNP, ii) 70% of samples has data for each SNP, iii) 90% of samples has data for each SNP and used the ascertainment bias correction model (Table 2).

I also evaluated the congruence of the topologies using different methods. First I explored overall topological similarity using Compare2Trees (Nye et al. 2006). In addition, I statistically tested whether the PH85 distance (Penny and Hendy 1985) between topologies was significantly different in the R package ‘ape’ (Paradis et al. 2004).

Alternative approaches. In addition to examining potential species within Prepona laertes using the phylogenetic methods as described above, I explored other approaches. For the

COI barcode data, I performed a Neighbor Joining (NJ) analysis with a 3% barcoding gap as suggested to be a typical minimum divergence between species in Lepidoptera (Hebert et al.

2004, Hajibabaei et al. 2006). I selected the uncorrected p-distance model, with partial deletion of missing data, and remaining default settings in MEGA7 (Kumar et al. 2007). Evidence for better performance of the uncorrelated p-distance model over K2P model has been documented in the scientific literature (Srivathsan and Meier 2011), thus, I selected the former over the otherwise widely used latter. I also tested different grouping schemes based on monophyly of

clusters, then calculated between- and within-group pairwise genetic distances using the same parameters as above.

Secondly, I used the Automatic Barcode Gap Discovery (ABGD) method (Puillandre et al. 2012) implemented in the web interface (http://wwwabi.snv.jussieu.fr/public/abgd/) with the

COI barcode data. This method uses a local slope function on the pairwise distances between samples to detect a barcoding gap and defines groups accordingly. Subsequently, the process is repeated until no additional gaps are identified. I recorded assignments for intraspecific divergence (P) values between 0.001 and 0.100 and tested different parameter values for the relative gap width and the distance metric (X=1 and X=1.5, and Jukes Cantor and K2P, respectively).

Lastly, I applied the Poisson Tree Processes (PTP) method (Zhang et al. 2013), the multi- rate Poisson Tree Processes (mPTP) method (Kapli et al. 2017), and the Generalized Mixed Yule

Coalescent (GMYC) model (Pons et al. 2006, Fontaneto et al. 2007) to the COI barcode data.

These methods use a phylogenetic tree as input instead of the sequence alignment to infer species limits. PTP uses the number of substitutions per site to model speciation rate, assuming that the number of substitutions per site between species is larger than within species. It delimits species in the input tree by determining and adjusting the transition point between intra- and interspecific processes (i.e. speciation and coalescence). bPTP is a Bayesian Inference implementation of the original maximum likelihood PTP where the species delineated by the maximum likelihood solution are given BI support. It is a method intended for single locus molecular data and uses the phylogenetic species concept as a basis. I uploaded the FastTree output to the bPTP web interface (http://species.h-its.org) where I ran the analysis for 500.000 MCMC generations and other defaults settings.

Based on PTP, mPTP also uses MCMC to calculate support values for the species delineated by ML, but it implements a faster and more accurate method that can handle larger datasets. In contrast to PTP, mPTP allows multiple rates of intra- and interspecific processes which improves the estimate in cases where these conditions are present. I ran mPTP using the web interface (http://mptp.h-its.org/#/tree) and used their default settings.

In contrast to bPTP and mPTP, the Generalized Mixed Yule Coalescent model (GMYC) requires a time-calibrated ultrametric tree and uses it to identify the location on the tree where intraspecific processes shift to interspecific processes. To obtain an ultrametric tree, I used

BEAST2 v.2.4.6 (Bouckaert et al. 2014). I partitioned the data by codon position, unlinked site models and linked clock and tree models. I used the bModelTest 0.3.3 plugin (Bouckaert, 2017) to estimate site models, which were co-estimated with the phylogeny. The clock prior was set to relaxed log normal. The tree prior was set to a Yule model, the birth rate prior was set to gamma with the lower value of one and upper of six and the monophyly of Prepona laertes was constrained. The GMYC analysis was run in R (CRAN project) using the packages ‘ape’

(Paradis et al. 2004), ‘paran’ (Dinno 2012), ‘splits’ (Ezard et al. 2017), and ‘rncl’ (Michonneau et al. 2016). I used a single threshold as initially proposed by (Pons et al. 2006) as well as the multiple thresholds extension (Monaghan et al. 2009).

For the RADseq data I used the single SNP per locus data to study the population structure of Prepona laertes and evaluate putative evolutionary units by identifying genetic patterns in the absence of model-based assumptions. First, I reduced the number of variables through a Principal Component Analysis (PCA), and then inferred the number of clusters for the data by using the function find.clusters in the R package ‘adegenet’ (Jombart 2008, 2010). The find.clusters function runs successive K-means which is a clustering algorithm that is designed to

find the optimal number of groups (k) by sequentially increasing the number of groups and maximizing the variation between them. The different values of k are then compared using

Bayesian Information Criterion (BIC), where the optimal k corresponds to the lower BIC. The strength of the evidence against a higher BIC can be tested by calculating BIC. There is evidence against a higher BIC when BIC is between 2-6 and such a difference is significantly stronger when BIC is between 6-10. Then, I used the function dapc in the R package ‘adegenet’

(Jombart 2008, 2010) to visualize whether there was any genetic structure in the data. The function dapc, which stands for Discriminant Analysis of Principal Components, is a multivariate method that uses the between-group and within-group variance to designate grouping combinations. It allows visualization of clusters of individuals where the distance among samples is indicative of gene flow and migration, and also provides the membership probability of each sample, where the probability of membership to a particular group is higher when the distance to that particular centroid is minimized. I used this multivariate approach to investigate population structure because it is faster, less computationally intensive, does not require model selection for identification of clusters and performs better than other available software (Jombart et al. 2010). In addition, despite the main focus of the study not being the population genetics of

Prepona laertes, I calculated the fixation index Fst as a means to explore if the clusters identified by the ML topology showed evidence of genetic structure. I used the R packages ‘vcfR’ (Knaus and Grunwald 2017) and ‘hierfstat’ (Goudet 2005) to manipulate the RADseq data and calculate

Weir and Cockerman’s Fst which is specifically useful for cases with small datasets and thus is unbiased by sample size (Weir and Cockerman 1984).

Results

Phylogenetic Relationships

COI barcoding. The COI dataset included individuals from throughout the distribution of Prepona laertes, representing at least 31 localities in 10 countries (Table 4-1). I attempted to cover as much of the geographic distribution as possible in order to include as much genetic variation as possible that would allow for a more comprehensive COI analysis. I used a color code to reflect the Neild (1996) hypothesis of multiple species within P. laertes, namely P. laertes, P. philipponi and P. pseudomphale, and to ease the comparison to the currently conceived hypothesis (Lamas 2004) of a single polymorphic species. The topologies obtained by

FastTree, W-IQ-Tree and RAxML show high similarity, ranging from 82.1% to 91% topological identity (Figures 4-1, Figure E-1, and Figure E-2 respectively). The majority rule consensus tree for the three ML methods is shown in Figure E-3, this topology has several polytomies across the tree which are indicative of the discrepancies among topologies. All the topologies are equally supported by bootstrap values, but for discussion purposes I selected the topology obtained with

FastTree since it has slightly higher support and should prove to be a better and more parsimonious hypothesis if taxonomic changes are to be made. The FastTree topology found multiple groups that show only some structure based on geography. Three major groups were identified. The earliest branching group corresponds to P. laertes samples that represent the

Interandean Valleys and eastern Andes foothills. The second group corresponds to two sister groups, one from northern Central America/North America and the other from the western Andes foothills, with a single specimen from Meta in the eastern foothills of the Andes in Colombia.

The third group is the largest one and contains samples corresponding to the putative species P. philipponi and P. pseudomphale. These taxa branched out early within this group and monophyly was only recovered for P. pseudomphale. Prepona philipponi, on the other hand, was

found to be paraphyletic but showed some correspondence with geography, where the samples clustered by locality. The remaining samples in this last group form a clade and correspond to individuals identified as P. laertes and that represent localities east of the Andes and are mostly distributed in the Amazon and Guianan shield.

RADseq analyses. The RADseq analysis generated a total of approximately 111 million raw reads. I processed 35 samples for the RADseq analyses, but I discarded one sample due to the lack of high quality data after sequencing, filtering, demultiplexing and trimming, leaving a final set of 34 samples that were analyzed using IPYRAD. The IPYRAD pipeline found a total of 69240 loci for 34 individuals with coverage ranging from 135 to 35555. The topologies found by RAxML for the two datasets with different number of sites included, all loci and single SNPs, show 83.8% topological similarity, are equally supported by bootstrap support, and have a

Robinson-Foulds (RF) value of 18 (Figures E-4, Figure 4-2, Table F-2). Both topologies showed two major groups. One corresponds to representatives of Central America, western Andes foothills, interandean valleys and northeastern Andes foothills. The second group contains twice the number of representatives and includes the two putative species Prepona philipponi and P. pseudomphale. The only major discrepancy between both topologies occurred in this second group, where the single SNP topology recovered P. philipponi as monophyletic and samples clustered by geography. In contrast, the concatenated loci dataset failed to identify P. philipponi as monophyletic but found that samples from the same geographic region did cluster together.

The remaining samples of the second group represent specimens distributed in eastern Andes foothills, Amazonia and the Guiana Shield. Both topologies showed the same relationships at deeper nodes within this group but differed slightly in some of the most recent nodes. The other

putative species, P. pseudomphale, is represented by three specimens, and is placed basally within the second group and closely related to P. philipponi.

The topologies for the datasets with different thresholds of missing data show moderate similarity and have varying PH85 distances ranging from 18 to 36 (Figures E-5 to E-7, Table F-

1). Here again, the major discrepancy regards Prepona philipponi, which is recovered as monophyletic only in the 90% dataset. Bootstrap support for the three topologies is generally low, with higher support for datasets with more missing data.

Alternative Approaches

Traditional DNA barcoding suggests that Prepona laertes should be split into four species, using a NJ approach and the 3% threshold suggested for Lepidoptera (Figure E-8). The putative four species have a genetic distance that ranges from 3.9% to 10.1% (Table 4-3, Table

F-3, groups 4 and 5 correspond to the outgroup species). Another grouping scheme was explored to identify other potential sub-clusters that would meet the 3% threshold. I found that under an extreme scenario of 17 different groups (Table F-3, Table F-4), there are several groups that have between-group divergence over 3%. I then explored the within-group genetic distance under the four group scenario for the group that would comprise the additional subgroupings identified in the 17 group scenario (group 6) and found it is well below the 3% genetic divergence threshold

(Table F-5).

For the ABGD, I tested different sets of parameters and considered that the initial partition of a relative barcoding gap of X=1 and JC69 distance model produced assignments that are concordant with the remaining methods. The ABGD also found four groups which are completely congruent with the NJ groups, albeit with some differences in topology within groups.

The maximum likelihood partition of the bPTP method delineated 10 species for the COI dataset (Figure 4-1, Table F-6). The range of species delimited under this method is 10 to 60 based on the MCMC exploration. The highest Bayesian supported solution recovered only a few groups with support > 0.9 (results not shown) and none corresponded to the ten delineated species. In contrast to bPTP, mPTP delineated four species (Figure 4-1). GMYC suggested 16 species using the single threshold and 20 species for the multiple thresholds extension.

The multivariate dapc analysis identified four clusters that are congruent with the ones identified by the ML reconstruction of the single SNP dataset (with the ascertainment bias correction) (Figure 4-2). Here, I followed the color code as in the COI section but changed the shade of green to reflect the multivariate analyses results. The membership probability plot shows two distinct groups (dark and light green), another group that has some structure (purple) and the last group that is not clearly defined (blue). The clustering algorithm supported the designation of four groups (k=4) based on the lowest BIC value, but the ∆BIC shows that there is no significant difference between k=3 and k=4 (Table F-7). The Fst values for the four clusters show relatively small values ranging from 0.08 to 0.16 (Table F-8).

Discussion

Inference of Relationships

Prepona laertes is currently considered a polymorphic species, where color patterns on both wing surfaces show variability. A competing hypothesis suggests that P. laertes is an example of a cryptic species complex where at least three species are hidden under such variability (Neild 1996), namely P. laertes, P. philipponi and P. pseudomphale. However, the lack of consistent, strong morphological characters corresponding to each of these putative species has made this hypothesis difficult to evaluate. Multiple approaches can be taken to

evaluate such contrasting hypotheses, and here I take advantage of two, COI barcoding and

RADseq, to hopefully clarify the true taxonomic diversity.

Firstly, I focused on evaluating the use of DNA barcoding as it has been proven to be a useful method for refining the taxonomy of otherwise difficult taxa. As seen in the topologies recovered by the different methods for the COI gene reconstruction, and using a color code that reflects the hypothesis of three putative species, it is shown that there is a correspondence between taxa identified on the basis of morphological characters outlined by Neild (1996) and

DNA (Figure 4-1, Figure E-1, Figure E-2). The major discrepancy among them regards the placement of the putative species Prepona philipponi and P. pseudomphale. Regardless of its placement and the method of reconstruction, Prepona philipponi is found paraphyletic, but individuals cluster by geography with one group from western Ecuador and the other from the

Guianan shield. The western Ecuador cluster of Prepona philipponi, though, clustered with a sample from the same geographic area but that has the morphology of Prepona laertes.

Similarly, P. pseudomphale samples were also recovered as monophyletic in the three topologies. The remaining samples, representing the taxon laertes, were split into three major clusters; one consisting of individuals from Interandean Valleys, eastern Andes foothills and

Guianan shield, the next consisting of two sister groups that represent Central America/North

America and the western Andes foothills respectively, and the last comprising individuals from localities from eastern South America, including the Andes foothills, Amazonia and the Guianan shield. The geographic distribution of group one plus group two more or less matches the distribution of the currently recognized taxon P. laertes octavia. The third group broadly matches the distribution of another subspecies, P. laertes demodice. Despite having samples throughout the distribution of P. laertes, it was not clear whether samples of the remaining two

recognized subspecies, P. laertes louisa and P. laertes laertes, were included, due to the lack of a clear definition of the diagnostic characters or distribution of these taxa in historical literature.

The COI barcoding data suggest that P. laertes as currently conceived is a taxon whose morphological variability obscures its real taxonomic diversity.

In the second approach, I screened the whole genome to increase considerably the amount of molecular data. I focused on a subset of individuals for which DNA quantity and quality met the technique’s standards and compensated the reduction in the number of samples with a higher coverage per sample by processing each specimen twice. RADseq pipelines provide multiple types of output data depending on the analyses of interest. For phylogeny reconstruction, it outputs a concatenated matrix with all the loci, including SNPs and invariable sites, and a matrix with a single variable site per locus. In the literature there is contrasting support for the use of different type of RADseq output data. Some authors use the concatenated loci, which include SNPs plus invariable sites, while others use datasets that include only a single variable site per locus (e.g. Eaton and Ree 2013, Massatti et al. 2015, Herrera and Shank,

2016). I used both datasets to infer the relationships in Prepona laertes but used the single SNP dataset for the downstream analyses due to the potential of obtaining misleading results given the inclusion of the invariant sites (Steel et al. 2000). I found both topologies to be highly supported by bootstrap values and very similar overall. As with the barcoding data, I found that there was correspondence between morphology and DNA data for at least three putative species. Prepona philipponi was initially thought to be restricted to east of the Andes (Neild 1996), but western

Ecuadorian specimens have been collected in the last decade (Checa, unpublished data).

Specimens in collections confirm that the species is broadly distributed in South America, including Colombia, Venezuela, Brazil and Bolivia. The results showed that P. philipponi is a

strong genetic entity that also shows geographic structure, showing high divergence between both geographical clusters (west and east of the Andes) in both topologies. This could be indicative of the existence of either two subspecies or two species. However, the high divergence, at least in the single SNP topology (Figure 4-2), could also be an artifact of the lack of sampling for the rest of the distribution which could potentially break the strong difference seen between both geographical clusters. From a morphological perspective, this taxon shows some consistent differences in wing pattern when compared to the remaining P. laertes and P. pseudomphale, including the color of the underside, the shape of wings and the straight black line that divides the light coloration on the basal half of the ventral hind wing from the darker distal half (Neild 1996) (Figure E-11). In addition, both geographical groups show differences in color pattern that would help support considering them different taxa, but the inclusion of additional samples from intervening regions would be useful to further explore whether the high genetic divergence is maintained and the observed morphological differences are stable. Prepona pseudomphale was consistent in both topologies, being recovered as monophyletic with high bootstrap support. Prepona pseudomphale also showed some consistent morphological characters, although these seemed to show some geographical variation. The specimens I examined were characterized by a darker androconial 'hair pencil' at the inner margin of the dorsal hind wing (hair pencil only present in males) and dark blue scaling adjacent and anterior of the light greenish-blue band discal band, which in turn is located closer to the wing margin than in sympatric P. laertes (Figure E-12). The DNA data confirm that P. pseudomphale merits recognition as a distinct species. The remaining samples belong to P. laertes, and as with COI barcoding, I found two groups that broadly match the distributions of the currently conceived subspecies P. laertes octavia and P. laertes demodice, and in contrast to the COI reconstruction,

both were found to be monophyletic (Figure 4-1, Figure 4-2). Importantly, however, the results show that two clades within P. laertes are sympatric in the western Amazon, and therefore that these clades might be considered as distinct species. Work is underway to attempt to identify diagnostic morphological characters that might support such a hypothesis.

As a complement to the phylogeny reconstruction, the multivariate analyses allowed me to study the genetic structure and visualize it in graphical form (Figure 4-2). The dapc shows that the samples clustered in four groups that corresponded to the clades found in the ML reconstruction for the single SNP dataset: i) Central America, western Andes foothills, interandean valleys and eastern Andes foothills clade, ii) Prepona philipponi, iii) P. pseudomphale, and iv) eastern Andes foothills, Amazonia and the Guiana Shield clade. I tested different values of k in search for the optimum value. Under BIC, I found the optimum at k=4, but BIC suggests that k=4 is not significantly different from k=3, which means that having three groups also fits the data. I further explored this result by addressing the membership probability, which allowed visualization of the probability of each sample to belong to each of the four different groups, being thus indicative of genetic structure and allowing identification of admixed samples. The data showed overall high genetic structure, with approximately 60% of the samples having membership probability > 0.90. The remaining samples showed evidence of admixture. Most of them had > 0.6 membership probability to a particular group which is indicative of genetic proximity to that particular group as opposed to complete admixture (i.e.

0.25 for each cluster), thus suggesting some signal of genetic differentiation. Different thresholds for membership probabilities have been used in the scientific literature to designate “pure” from

“mixed” samples ranging from 0.6 to 0.95 in a wide variety of organisms, including insects (e.g.

Andres et al. 2013, Roullier et al. 2013, Therkildsen et al. 2013, Dornburg et al. 2015, Basto et

al. 2016). I found these thresholds to be arbitrary and refrain from defining one for this study, and instead I discuss the overall patterns seen for each cluster. There are two groups that are clearly defined and show high genetic structure, corresponding to the two clades for P. laertes

(dark and light green). Prepona philipponi it is not as clearly defined as the P. laertes showing some admixture (purple). Lastly, P. pseudomphale shows mixed genetic constitution except for a single sample that shows high genetic structure. The discriminant Principal Component Analysis

(Jombart et al. 2010) was my preferred method to explore genetic structure for several reasons.

Its performance is comparable to STRUCTURE which is the preferred method for this type of analysis (Campoy et al. 2016, Dupuis et al. 2017, Vollmer and Rosel 2017), it is free of assumptions regarding population genetics models, it can be applied to large datasets in little computational time, and it generates a graphical representation to visualize genetic patterns in addition to the membership probabilities, among other reasons (Jombart et al. 2010). dapc has also been found to perform better than STRUCTURE in some specific cases (Kraus et al. 2016).

Therefore, I believe that the results of the dpac provide an accurate picture of the genetic structure of Prepona laertes (sensu Lamas). Despite the main objective of the study not being to explore the population genetics of P. laertes, I used the fixation index Fst to explore the admixture in the different clades. There is lack of consensus in the scientific literature about the significance of the values of this index, and though some designate discrete classes (Hartl and

Clark 1997, Frankham et al. 2000), it is acknowledged that Fst values should be interpreted case by case and that different population processes might give the same value of Fst. All of the Fst values fell into the “moderate genetic differentiation” category, but I believe that using such a classification might obscure the patterns seen and therefore prefer to compare them against each other to obtain a more reliable picture of the patterns. The Fst values for the four clades show

evidence of admixture for all the comparisons (all Fst values <0.25). Cluster one (P. laertes CA-

SA) shows the highest values of Fst when compared to the remaining clusters, this is indicative that it shares less genetic information with the remaining clusters than the remaining clusters among themselves. The smaller Fst values correspond to cluster four (P. pseudomphale) which is congruent with its phylogenetic placement (Figure 4-2).

COI Barcoding vs. RADseq

Comparison of the RADseq data topology with the COI barcoding topology showed that although the RADseq topology was better resolved and more highly supported, it showed the same overall patterns as that for COI barcoding (Figure 4-3). The major difference found between both methods concerned Prepona philipponi, where RADseq recovered it as a monophyletic entity while COI barcoding failed to do so. The data suggests that, although COI barcoding has been widely criticized, it still gives an accurate general picture of the overall patterns in this species, although it might lack the power to resolve some relationships in the absence of other data. Thus, using COI barcoding as a first approach to screen difficult groups should be encouraged, especially where funding and equipment are limited, to provide data to complement other sources of information. It is well known that costs associated with next generation sequencing techniques pose a challenge for many research initiatives and groups (us included), but I found that using barcoding to select a subset of samples to process using these methods is a fast, reliable and more cost-effective way to improve understanding beyond the sometimes weak phylogenetic signal provided by COI barcoding alone. Many of the samples came from specimens collected in the field whose tissue was conserved appropriately for DNA analyses, but some samples came from spread museum specimens that were hydrated in the curation process and whose DNA quality and quantity was compromised. Here, I have

demonstrated that both methods, COI barcoding and RADseq, are useful for museum specimens and illustrate their value for obtaining molecular data for phylogenetic reconstruction.

Effect of Missing Data

By setting different thresholds I was able to test the effect of missing data on the phylogenetic reconstruction. The results showed that the overall bootstrap support dropped as the percentage of missing data decreased (Figures E-5 to E-7). The dataset that contained the least amount of missing data was the only one that recovered Prepona philipponi as monophyletic.

The only other dataset that recovered this relationship was the single SNP dataset that included the full set of individuals (Figure 4-2). It seems, though, that having a smaller amount of missing data helps recover the putative species but at the expense of bootstrap support. There is no consensus as to whether loci with missing data should be included or not, and if so, which threshold should be applied. Some authors take a more conservative approach and remove missing data (e.g. McCormack et al. 2012) while others include all missing data regardless of its proportion (e.g. Emerson et al. 2010). More recently, empirical studies have taken into account the potential effects of missing data therefore acknowledging how such decisions might affect the resulting topologies. Some studies have found that the level of missing data has minimal effect in the relationships recovered (e.g. Hipp et al. 2014, Darwell et al. 2016), while in other studies the inclusion of missing data did have an influence either in terms of the resolution of the tree or in the bootstrap support (e.g. Eaton and Ree 2013, Wagner et al. 2013, Leaché et al.

2015b). The results here agree with the latter study, I observed a negative trade-off between the amount of missing data and topological support. Finding a generally applicable rule-of-thumb for tolerance of missing data is unlikely and missing data thresholds should be analyzed in a case- by-case fashion where simulation data could provide useful (Huang and Knowles 2014). In addition, there is evidence that the inclusion of missing data increases the bootstrap support of

not only shallow divergences (Huang and Knowles 2014), but also on long branches (Leaché et al. 2015a), as seen in this study for Prepona laertes. Therefore, I believe that in this case, characters with missing data seems to provide useful information to clarify the relationships within Prepona laertes.

Topology comparison using percentage of similarity shows that all recovered topologies are moderately to highly similar (Table F-1). However, due to the potential subjectivity of this using a percentage value, I statistically tested whether the observed distance between all possible pairs of topologies was significantly different using the PH85 distance. PH85 distance follows a

Poisson probability distribution with mean 0.5 (Penny and Hendy 1985) and for the current dataset, distances between any two topologies below 59 are not considered statistically different.

All of the possible comparison pairs show distances less than 59, therefore, all our topologies are more similar to each other than expected by chance (Table F-2). Therefore, the inclusion of missing data seemed to aid in the resolution of some relationships and increase overall bootstrap support, but such improvement was not significant from a statistical perspective.

Alternative Approaches to Species Delimitation

I used other approaches to infer species boundaries to test for congruence among them, since congruence among methods can be considered a proxy of robustness (Carstens et al. 2013).

Similarly, some of these methods have been suggested in the scientific literature to produce a better estimate of taxonomic diversity than the commonly used NJ approach (Collins and

Cruickshank 2013). Figure 4-1 allows easier visualization of how the species delimitation methods compare to each other, and to explore whether they are congruent or not.

The traditional barcoding method, where a NJ tree is used to explore intraspecific variation and interspecific divergence using a 3% barcoding gap as a measure to delineate species boundaries, found four species for the 60 sequence dataset (Figure 4-1). This topology

showed most of the relationships found in the ML reconstruction but there were major discrepancies in the deeper nodes as well for some internal nodes (Figure E-8). Regardless of the fact that the placement of the putative species Prepona philipponi and P. pseudomphale in the NJ tree did not match the ML reconstruction, both were found to cluster putatively conspecific individuals based on phenotype. I explored different grouping schemes to identify potential subclusters, more specifically in the larger cluster, which comprised 41 sequences. Despite the fact that genetic divergence supported possible subdivisions of this cluster (i.e. P. philipponi), evaluation of genetic divergence within this large cluster suggested that there is no support to over-split this group (within group genetic distances < 3%, Table S4). Therefore, I believe the most likely grouping scheme based on traditional barcoding supports four species within P. laertes, consistent with the major clades recovered by the ML reconstruction but different from the putative species hypothesized. These clades correspond to a group distributed in the western and eastern Andes foothills and Guianian Shield; another group that contains samples from

Central and North America which is sister to a group containing samples from the eastern slope of the Andes; and a last group containing samples from localities all east of the Andes, mostly from Amazonia and the Guianan Shield, although this last group did not form a well-defined cluster.

Proponents of ABGD have shown that the method is successful in recovering a very similar number of species than the number originally reported when using empirical datasets that are publicly available and with a value of intraspecific divergence (P) ~ 0.01 (Puillandre et al.

2012). When setting this threshold for the current dataset (P = 0.0129), I found that the most likely grouping for the sequences in the primary partition was N = 4. In contrast, the recursive partition recovered N = 7. The recursive and primary partitions handle heterogeneity differently,

the recursive partition being better in this sense, whereas primary partitions are more stable in cases where prior values have wider ranges, and are usually better at recovering a number of species closer to that reported by taxonomists in the literature (Puillandre et al. 2012). For these reasons I considered the results of the primary partition at the P ~ 0.01 value to yield the most likely grouping for the sequences (Figure 4-1). The ABGD findings were completely congruent with the traditional barcoding approach in the major groups recovered. Nonetheless, deeper nodes and within group relationships were not consistent between topologies (Figure E-8, Figure

E-9), the ABGD being better resolved in deeper nodes and recovering more similar relationships as found in the ML reconstruction than to the NJ topology. Here, the putative species P. pseudomphale was recovered as monophyletic while P. philipponi was not.

The bPTP method was more labile and delineated ten species, which contrasts to the four species recovered by the previous methods. bPTP proponents suggest that 0.95 values of posterior probability are indicative of strong support for that particular species delineation, whereas, values under 0.95 make the delineations very difficult. Thus, delineations should aim for posterior probability values over 0.95 to confidently say that all descendants from that particular node were delimited as a single species. In this study, most of the posterior probability values from the MCMC process fell below the 0.95 threshold and the ten delineations failed to have support higher than 0.95 (Table F-5), suggesting that relatively infrequently were sequences below that particular node delimited as a single species. The MCMC chain sampled the smallest delimitation for Prepona laertes with ten clusters and 60 for the largest with mean of 35.46. The wide range could be indicative of lack of strong signal and high levels of uncertainty. There was little congruence of the groups found by bPTP and the previous methods discussed (Figure 4-1), since it tended to over-split in the deeper nodes. bPTP only recovered one of the groups as

previously delineated by NJ and ABGD and also recovered as a major clade in the ML reconstruction, while the remaining clusters found by the previous methods were split by bPTP.

The degree to which bPTP over-split the three groups ranged from subdividing into two species to subdividing into five species. This over-splitting resulted in the delineation of singleton groups closer to the base of the tree, in some cases corresponding to localities represented by a single sample (i.e. Caldas in Colombia and San Cristóbal in Venezuela), but in others corresponding to localities that were well represented throughout the tree. bPTP is the first species delimitation method that recognized the Ecuadorian P. philipponi samples as a distinct species, a result that could also be inferred from the ML reconstruction. These samples, besides clustering with a morphologically distinct P. laertes representative (albeit distributed in the same region), failed to form a group with the other P. philipponi samples from French Guiana.

The mPTP method delineated four species, a number that is consistent with the most conservative results of the traditional NJ barcoding approach and ABGD, but they only matched in a single group (Figure 4-1). The group considered as single species by NJ and ABGD was split in two species in mPTP, as found by also by bPTP. This group recovered two clusters, one for the Ecuadorian Prepona philipponi + western Andes P. laertes and another representing localities from the eastern Andes foothills, Amazonia and the Guiana shield. In contrast, the remaining two groups considered as two different species by NJ and ABGD were merged by mPTP, and samples that represent North and Central America were joined with their sister group distributed in the western and eastern slope of the Andes, which seems plausible from a geographical perspective.

The GMYC method recovered different results for the single and multiple threshold settings. As suggested by Fujisawa and Barraclough (2013), the multiple threshold approach

usually performs poorly and tends to overestimate the number of species. I did find that the multiple threshold estimated more species than the single threshold (16 and 20 respectively), but even so, the single threshold was the species delineation method that predicted more species

(Figure 4-1). The topology was weakly supported and there was not a single node with high support (Figure E-10). As in the case of bPTP, lack of support implies uncertainty and results should be carefully interpreted. This method appeared to be the most sensitive of all the methods explored given the high number of species delineated, where several of the new groupings represented localities that were well sampled in the tree.

Table 4-1. Specimens included in the COI Barcoding and RADseq study. Taxon MolVoucher Locality Year Prepona laertes demodice LEP-00528 Ecuador: Orellana: Yarina 2003 Prepona laertes demodice LEP-00529 Ecuador: Esmeraldas: Lita-San Lorenzo Rd. Km. 44 1996 Prepona laertes demodice LEP-00535 Ecuador: Orellana: Napo Wildlife Center Napo trail 2005 Prepona laertes demodice LEP-00536 Ecuador: Orellana: Boca del Río Añangu 2005 Prepona laertes demodice LEP-01322 Ecuador: Orellana: Tiputini Biodiversity Station 2002 Prepona laertes demodice LEP-01323 Ecuador: Orellana: Tiputini Biodiversity Station 2002 Prepona laertes demodice LEP-01330 Ecuador: Orellana: Tiputini Biodiversity Station 2002 Prepona laertes demodice LEP-01331 Ecuador: Orellana: Tiputini Biodiversity Station 2002 Prepona laertes demodice LEP-03340 Brazil: Rondônia: 62 Km. S Ariquemes linha C-10 5 Km. S of Cacaulândia 1994 Prepona laertes demodice LEP-16339 Peru: Cuzco: Atalaya 2011 Prepona laertes demodice LEP-16342 Peru: Cuzco: Quebrada Quitacalzón 2013 Prepona laertes laertes LEP-03511 Colombia: Meta: Rey Zamuro 2010 Prepona laertes laertes LEP-03523 Colombia: Meta: Rey Zamuro 2010 Prepona laertes octavia LEP-00529 Ecuador: Orellana: Boca del Río Añangu 1996 Prepona laertes octavia LEP-00530 Ecuador: Pichincha: Palmito Pamba 1998 Prepona laertes octavia LEP-00531 Ecuador: Pichincha: Río Tanti 1993 Prepona laertes octavia LEP-01799 El Salvador: No data: No data 2009 Prepona laertes octavia LEP-01800 El Salvador: No data: No data 2009 Prepona laertes octavia LEP-03421 Mexico: Tabasco: Cerro de Cocona 1995 Prepona laertes philipponi LEP-03355 Ecuador: Esmeraldas: Reserva Canandé No data Prepona laertes philipponi LEP-03356 Ecuador: Esmeraldas: Reserva Canandé No data Prepona laertes philipponi LEP-04215 France: French Guiana: Saul 2010

Table 4-1. Continued Taxon MolVoucher Locality Year Prepona laertes philipponi LEP-04220 France: French Guiana: pk 27 Piste de Kaw 2010 Prepona laertes philipponi LEP-04226 France: French Guiana: Matoury 2009 Prepona laertes philipponi LEP-04228 France: French Guiana: Matoury 2010 Prepona laertes pseudomphale LEP-00520 Ecuador: Napo: Finca San Carlos 1996 Prepona laertes pseudomphale LEP-00608 Ecuador: Orellana: Tiputini Biodiversity Station 2002 Prepona laertes pseudomphale LEP-03348 Ecuador: Orellana: Yasuní Estacion Científica No data Prepona laertes pseudomphale LEP-03544 Ecuador: Orellana: Yasuní Estacion Científica 2010 Prepona laertes pseudomphale LEP-04223 France: French Guiana: Trinité 2010 Prepona laertes pseudomphale LEP-04225 France: French Guiana: Laussat 2010 Prepona laertes ssp. LEP-03337 Brazil: Rondônia: 62 Km. S Ariquemes linha C-10 5 Km. S of Cacaulândia 1997 Prepona laertes ssp. LEP-03338 Brazil: Rondônia: 62 Km. S Ariquemes linha C-10 5 Km. S of Cacaulândia 1997 Prepona laertes ssp. LEP-03350 Ecuador: Orellana: Yasuní Estacion Científica No data Prepona laertes ssp. LEP-03351 Ecuador: Orellana: Yasuní Estacion Científica 2002 Prepona laertes ssp. LEP-03352 Ecuador: Manabí: No data No data Prepona laertes ssp. LEP-03353 Ecuador: Manabí: No data No data Prepona laertes ssp. LEP-03359 Ecuador: Esmeraldas: Reserva Canandé No data Prepona laertes ssp. LEP-03370 Ecuador: Napo: Tena-Puyo Road 13 km SE of Tena 2010 Prepona laertes ssp. LEP-03371 Ecuador: Napo: Campo Anaconda 2010 Prepona laertes ssp. LEP-03440 Ecuador: Esmeraldas: San Francisco ridge No data Prepona laertes ssp. LEP-03441 Ecuador: Esmeraldas: San Francisco ridge No data Prepona laertes ssp. LEP-03449 Venezuela: San Cristobal: PNN Paramillo 2010 Prepona laertes ssp. LEP-03495 Colombia: Caldas: Río Manso 2010

Table 4-1. Continued Taxon MolVoucher Locality Year Prepona laertes ssp. LEP-03519 Colombia: Meta: Rey Zamuro 2010 Prepona laertes ssp. LEP-03521 Colombia: Meta: Rey Zamuro 2010 Prepona laertes ssp. LEP-03540 Ecuador: Orellana: Yasuní Estacion Científica 2010 Prepona laertes ssp. LEP-03541 Ecuador: Napo: Sacha Lodge 2010 Prepona laertes ssp. LEP-03542 Ecuador: Orellana: Yasuní Estacion Científica 2010 Prepona laertes ssp. LEP-03543 Ecuador: Napo: Yarina 2010 Prepona laertes ssp. LEP-04207 France: French Guiana: pk 27 Piste de Kaw 2010 Prepona laertes ssp. LEP-04208 France: French Guiana: Petit-Saut 2010 Prepona laertes ssp. LEP-04209 France: French Guiana: Petit-Saut 2010 Prepona laertes ssp. LEP-04210 France: French Guiana: Matoury 2009 Prepona laertes ssp. LEP-04211 France: French Guiana: Approuague No data Prepona laertes ssp. LEP-04213 France: French Guiana: Saul 2010 Prepona laertes ssp. LEP-04221 France: French Guiana: Matoury 2009 Prepona laertes ssp. LEP-04222 France: French Guiana: pk 27 Piste de Kaw 2010 Prepona laertes ssp. LEP-04224 France: French Guiana: Petit-Saut 2010 Prepona laertes ssp. LEP-04229 France: French Guiana: pk 27 Piste de Kaw 2010 Prepona laertes ssp. LEP-11521 USA: Florida: Fort Lauderdale 2013 Prepona dexamenus LEP-02477 Honduras: Atlántida: Pico Bonito Lodge 2010 Prepona werneri LEP-16418 Ecuador: Esmeraldas: Río Chuchuví 2011

Table 4-2. Samples included in the missing-data analysis. Missing data (%) Samples included Total SNP 50 16 3351 70 23 1599 90 30 499

Table 4-3. Genetic distance for the four clusters identified by NJ. Groups 4 and 5 correspond to the outgroup species. Group 6 Group 1 Group 2 Group 4 Group 3 Group 5 Group 6 - 0.013 0.012 0.016 0.012 0.015 Group 1 0.064 - 0.012 0.017 0.011 0.016 Group 2 0.056 0.052 - 0.016 0.01 0.016 Group 4 0.092 0.101 0.088 - 0.016 0.016 Group 3 0.059 0.048 0.039 0.091 - 0.015 Group 5 0.086 0.094 0.086 0.086 0.084 -

Figure 4-1. FastTree topology for the COI Barcoding gene. Node numbers correspond to bootstrap values. Color code follows the text. Vertical lines in gray scale correspond to the different species delimitation methods used. Abbreviations include: NJ: for Neighbor Joining, ABGD: Automatic Barcode Gap Discovery, bPTP: Poisson Tree Process, mPTP : multi-rate Poisson Tree Process, and GMYC: Generalized Mixed Yule Coalescent.

Figure 4-2. ML RAxML reconstruction for the single SNP dataset. Numbers on nodes correspond to bootstrap values. Color code follows text.

Figure 4-3. Comparison of topologies recovered with the RADseq single SNP data and COI Barcoding. Color code follows text.

CHAPTER 5 CONCLUSIONS

This study has broadened our knowledge of the systematics and evolution of preponine butterflies, which are amongst the most recognizable groups of Neotropical butterflies. I clarified their taxonomy and used phylogenetic relationships, based on molecular data complemented with morphology, to improve our understanding of the true diversity in the group. Subsequently, this taxonomic hypothesis served as a foundation for exploring the tempo and mode of diversification of preponines, investigating how morphological traits might have had an effect in their evolution, and studying in detail a taxon that has been problematic given the high color pattern variability, for which competing taxonomic hypotheses have been proposed but for which no study had yet attempted to test their validity.

Initially, I reconstructed the patterns of ancestry for the tribe. The molecular phylogeny based on six genes allowed me to further clarify the taxonomic diversity of the tribe Preponini. I identified taxa that needed taxonomic modifications and proceeded to make such changes using the molecular data combined with morphological data. These changes concerned different taxonomic levels, from splitting one species into multiple to the description of a new genus. The tribe Preponini was then found to contain 24 species of which one, Prepona deiphile, was found to be paraphyletic and shows high divergence suggesting hidden diversity. I also highlight the need for additional detailed molecular and morphological studies in specific clades of the tribe for which relationships have not yet been clarified, with Prepona amydon as an example.

Consequently, I support the statement that future phylogenetic studies should include multiple sources of independent information since they provide a more comprehensive picture of evolutionary history.

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Using the phylogeny as a framework, I was able to study the tempo and mode of evolution of preponines. My study adds to the growing number of studies of biological diversification in the Neotropical region, where the Andes mountain range and Amazon have played an important role. From this biogeographical perspective, I was also able to reveal how geological changes had an effect in the distribution patterns currently found in preponines and whether such changes might have played an important role in speciation. Preponine genera exhibit different biogeographical histories which could be indicative of dissimilar evolutionary pressures and trajectories. I also explored overall diversification rates and color pattern trait evolutionary rates, and investigated how these might have shaped preponine evolution. I explored these results in the light of biogeographical analyses in an attempt to find potential geological events that have driven the observed phenotypic evolution. The phenotypic evolution analysis was carried out on the forewing as a whole and in different wing cells delimited by veins. By selecting different parts of the wing, using different color measures and different RGB channels, I was able to study how this phenotypic trait changed over evolutionary time and hypothesize the potential reasons for the patterns seen. I complemented this analysis by also studying phenotypic evolutionary rates over the wing as a whole, and show that treating the wing as a whole evolutionary unit might obscure patterns that would otherwise be detected at a finer scale. It has been shown that genetic control of butterfly color patterns depends on the wing region, with some genes being turned on/off depending on selection for the fittest phenotype; by investigating different parts of the wing I attempted to identify regions that might be driving the evolution of the color pattern and speculate the underlying reasons why such regions are the drivers.

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Lastly, I explored the evolutionary history of one preponine taxon that displays highly variable color pattern, Prepona laertes. Contrasting hypotheses have been proposed for this taxon, from considering it as a single species to considering it as an example of a species complex. I used different molecular methods to investigate these contrasting views and propose appropriate taxonomic changes. The study tested the usefulness of COI barcoding for clarifying relationships in a difficult group. Despite not having enough power to clearly resolve all relationships, I found that the COI barcode region had important phylogenetic signal in this case, and that was a useful first approach as a foundation for more detailed studies. I went on to use

RADseq data to attempt to resolve relationships where COI was not entirely successful, and showed that whole genome screening even in a smaller dataset (i.e. 60 samples for COI vs. 32 samples for RADseq) was extremely beneficial for this purpose. Maximum likelihood phylogenies from both COI and RADseq recovered the putative species Prepona pseudomphale as monophyletic, hence I suggest this taxon should be reinstated as a species. For P. philipponi, only the single SNP RADseq data was able to recover monophyly, but all datasets suggested a clear geographic structure that should be further studied by the inclusion of additional specimens from localities that were not represented in the current dataset. I suggest restoring P. philipponi to species status, but the status of the west Andean samples examined, that diverge in geography and color pattern, requires further study. Consequently, I show that COI barcoding is still an extremely useful method and confirm its validity in cases where funding is limited and there is a lack of the sophisticated equipment required by next generation sequencing practices and whole genome screening methods. My study also showed that different species delimitation methods yielded different conclusions and, despite some tendency to over-split relationships within P. laertes, there is congruence to some degree. The ML reconstruction provided support for the

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validity of the putative species, though some might object that it is a slightly subjective approach since the criterion for delimitation is monophyly, therefore, any monophyletic group could potentially be a species and such decisions might vary from author to author. I encourage complementing molecular data and the results from different species delineation methods with information from independent sources such as geographical distributions and wing pattern morphological characters to make the most informed decisions regarding species boundaries and potential taxonomic changes. I therefore selected the most informative groupings from each method in the light of geography, molecules, and morphology. My data suggest that P. laertes as currently conceived contains four species, but a detailed morphological and color pattern analysis is needed to clarify two of the distinct groups found: one distributed in North and

Central America, interandean valleys, and the western and eastern slopes of the Andes, and a second species distributed east of the Andes in the foothills, Amazon and the Guianan shield.

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APPENDIX A PHYLOGENY OF PREPONINI - SUPPLEMENTARY FIGURES

This section contains the supplementary figures referenced in Chapter 2.

Figure A-1. *BEAST tree for COI.

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Figure A-2. *BEAST tree for COII.

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Figure A-3. *BEAST tree for EF1a.

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Figure A-4. *BEAST tree for CAD.

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Figure A-5. *BEAST tree for GAPDH.

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Figure A-6. *BEAST tree for RpS5.

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Figure A-7. *BEAST species tree reconstruction.

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Figure A-8. BI unpartitioned tree. Node numbers correspond to posterior probability values.

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Figure A-9. ML partitioned tree. Node numbers correspond to bootstrap values.

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Figure A-10. ML unpartitioned tree. Node numbers correspond to bootstrap values.

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Figure A-11. Mesoprepona pheridamas wing color pattern, dorsal (top) and ventral (bottom).

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APPENDIX B PHYLOGENY OF PREPONINI - SUPPLEMENTARY TABLES

This section contains the supplementary tables referenced in Chapter 2.

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Table B-1. Specimens included in the phylogenetic study Taxon Voucher Origin COI COII EF1a CAD GAPDH RpS5 Archaeoprepona amphimachus LEP-00501 Ecuador: Napo: Pimpilala - KC132915 KC133017 KY080791 KY080935 KY081013 amphimachus Archaeoprepona a. Colombia: Meta: Rey LEP-03515 KY080855 KC132945 KC133044 KY080824 KY080973 KY081050 amphimachus Zamuro Archaeoprepona a. Ecuador: Sucumbíos: Sacha LEP-03545 KC132998 KC132948 KC133046 - KY080976 KY081053 amphimachus Lodge Ecuador: Manabí: Cerro de Archaeoprepona a. amphiktion LEP-00564 Achi, Km. 12 Jipijapa-Pto. KC132974 KC132929 KC133027 KY080802 KY080947 KY081027 Cayo rd. Archaeoprepona chromus Ecuador: Loja: Ridge S LEP-00563 KC132973 KC132928 KC133026 KY080801 - KY081026 chromus Sozoranga Ecuador: Zamora Chinchipe: Archaeoprepona c. chromus LEP-00554 San Francisco, Casa de KC132972 KC132927 KC133025 KY080800 - KY081025 Arcoiris Ecuador: Napo: Km. 49 Tena Archaeoprepona c. priene LEP-00598 KC132976 - - KY080804 KY080949 - - Loreto Archaeoprepona c. priene LEP-04533 Ecuador: No data: No data KY080857 KY080886 KY080919 KY080835 KY080985 KY081060 Ecuador: Esmeraldas: El Archaeoprepona camilla LEP-00521 KC132966 KC132922 KC133021 KY080794 KY080940 KY081018 Durango Costa Rica: Guanacaste: Archaeoprepona camilla LEP-01571 Area de Conservación de KC132979 KC132933 KC133030 KY080806 KY080951 KY081030 Guanacaste Brazil: Santa Catarina: São Archaeoprepona chalciope LEP-04034 KC133002 KC132950 KC133050 KY080829 KY080979 - Bento do Sul Brazil: Rio Grande do Sul: Archaeoprepona chalciope LEP-04136 KC133004 - - - - - Santa Cruz do Sul Archaeoprepona chalciope LEP-16441 Brazil: Sao Paulo: No data - KY080910 - - KY081008 - Archaeoprepona demophoon Ecuador: Orellana: Yasuní LEP-11164 KY080859 KY080888 KY080921 KY080837 - - andicola Estacion Científica Ecuador: Napo: Archaeoprepona d. andicola LEP-00522 KC132967 - - KY080795 KY080941 KY081019 Chichicorrumi Colombia: Meta: Rey Archaeoprepona d. demophoon LEP-03505 KC132992 KC132942 KC133041 KY080821 KY080970 KY081047 Zamuro Mexico: Quintana Roo: Archaeoprepona d. ssp. LEP-03378 Reserva San Felipe de KY080852 KY080882 KY080916 KY080811 KY080959 - Bacalar

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Table B-1. Continued Taxon Voucher Origin COI COII EF1a CAD GAPDH RpS5 Archaeoprepona demophon Honduras: La Ceiba: Pico LEP-01159 KC132978 KC132932 KC133029 KY080805 KY080950 KY081029 centralis Bonito Ecuador: Manabí: Reserva Archaeoprepona d. muson LEP-00568 Lalo Loor, Pedernales-Jama KC132975 KC132930 KC133028 KY080803 KY080948 KY081028 rd. Surinam: Silawesi: Nassau Archaeoprepona d. ssp LEP-11380 - - - - KY080991 KY081062 Mountain Ecuador: Napo: Tena-Puyo Archaeoprepona licomedes LEP-03369 KC132983 KC132916 KC133032 KY080810 KY080958 KY081036 Road, 13 km SE of Tena Archaeoprepona licomedes LEP-03462 Peru: Loreto: Madre Selva - KC132917 KC133039 - KY080968 KY081044 Surinam: Silawesi: Nassau Archaeoprepona licomedes LEP-11378 - KY080891 - - KY080989 KY081061 Mountain Perú: Cosñipata: Quebrada Archaeoprepona licomedes LEP-16336 KY080867 KY080899 KY080924 KY080843 KY080996 KY081067 Quitacalzón Ecuador: Orellana: Boca del Archaeoprepona m. meander LEP-00542 KC132970 KC132925 KC133024 KY080798 KY080945 KY081023 Río Anañgu Colombia: Meta: Rey Archaeoprepona m. meander LEP-03513 KC132993 KC132943 KC133042 KY080822 KY080971 KY081048 Zamuro Archaeoprepona phaedra aelia LEP-03411 Mexico: Oaxaca: Choapán - KC132936 KC133033 KY080812 KY080961 KY081038 Archaeoprepona phaedra aelia LEP-03435 Mexico: Oaxaca: La Soledad - KC132938 KC133036 KY080815 KY080964 KY081041 Mexico: Oaxaca: 3 Km. Archaeoprepona phaedra aelia LEP-03436 - KC132939 KC133037 KY080816 KY080965 KY081042 Pluma Hidalgo Prepona aedon aedon LEP-04033 Ecuador: Imbabura: No data KC133001 MF197448 KC133049 KY080828 KY080978 KY081055 Prepona aedon aedon LEP-04512 Ecuador: No data: No data KY080856 KY080885 - - - - Costa Rica: Heredia: Prepona aedon rodriguezi LEP-16391 Estacion Biologica La KY080871 KY080902 KY080927 KY080845 KY081000 KY081071 Tirimbina Costa Rica: Heredia: Prepona aedon rodriguezi LEP-16392 Estacion Biologica La KY080872 KY080903 KY080928 KY080846 KY081001 KY081072 Tirimbina Colombia: Boyacá: Otanche Prepona amydon amydon LEP-16423 KY080877 KY080909 - - KY081007 - Sector Quebrada El Cofre Prepona amydon amydonius LEP-00504 Ecuador: Orellana: Yarina - KY080879 - KY080937 KY081015

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Table B-1. Continued Taxon Voucher Origin COI COII EF1a CAD GAPDH RpS5 Venezuela: San Cristobal: Prepona amydon bogotana LEP-03450 KY080853 KY080883 KY080917 - KY080967 - PNN Paramillo Surinam: Silawesi: Nassau Prepona amydon aurantiaca LEP-11379 KY080862 KY080892 - KY080840 KY080990 - Mountain Ecuador: Esmeraldas: El Prepona amydon frontina LEP-00515 KC132964 KC132920 KC133019 KY080793 KY080938 KY081016 Encanto Prepona amydon phalcidon LEP-11493 Brazil: Amazonas: Maues KY080866 KY080897 - - KY080995 KY081066 Prepona amydon phalcidon LEP-11504 Brazil: Amazonas: Rio Marau - KY080898 - - - - Colombia: Vaupés: Rio Prepona amydon rubella LEP-11489 KY080864 KY080895 - - KY080994 - Vaupes - Mitu Prepona amydon rubella LEP-11490 Brazil: Amazonas: Rio Negro KY080865 KY080896 - KY080842 - KY081065 Prepona amydon smalli LEP-16404 Panama: Panamá: Bayano KY080874 KY080906 KY080929 KY080847 KY081004 KY081075 Ecuador: Sucumbíos: Cerro Prepona claudina lugens LEP-00502 - KC132919 KC133018 KY080792 KY080936 KY081014 Lumbaqui Norte Ecuador: Orellana: Boca del Prepona claudina lugens LEP-00546 KC132971 KC132926 KY080913 KY080799 KY080946 KY081024 Río Anañgu Prepona claudina ssp LEP-16417 Ecuador: Napo: No data KY080875 KY080907 KY080930 - KY081005 KY081076 Prepona claudina ssp LEP-16446 Brazil: Bahia: Una Reserve - KY080911 - - KY081009 * Mexico: Oaxaca: Finca Prepona deiphile ibarra LEP-03427 KC132986 MF197449 KC133034 KY080813 KY080962 KY081039 Copalita Mexico: Oaxaca: San Isidro Prepona deiphile ibarra LEP-03428 KC132987 KC132937 KC133035 KY080814 KY080963 KY081040 Aurora Ecuador: Morona-Santiago: Prepona deiphile neoterpe LEP-11367 KY080860 KY080889 KY080922 KY080838 KY080987 - Pablo Sexto Ecuador: Morona-Santiago: Prepona deiphile neoterpe LEP-11368 KY080861 KY080890 KY080923 KY080839 KY080988 - Pablo Sexto Perú: Cuzco: Quedrada Prepona deiphile sphacteria LEP-16350 KY080869 KY080900 KY080925 KY080844 KY080998 KY081069 Quitacalzón Ecuador: Orellana: Boca del Prepona dexamenus LEP-00539 KC132969 KC132924 KC133023 KY080797 KY080943 KY081021 Río Anañgu Honduras: La Ceiba: Pico Prepona dexamenus LEP-02477 KY080850 KY080880 KY080914 - KY080953 KY081032 Bonito

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Table B-1. Continued Taxon Voucher Origin COI COII EF1a CAD GAPDH RpS5 Colombia: Meta: Rey Prepona dexamenus LEP-03514 KC132994 KC132944 KC133043 KY080823 KY080972 KY081049 Zamuro Prepona dexamenus LEP-16367 Ecuador: No data: No data KY080870 KY080901 KY080926 - KY080999 KY081070 Colombia: Amazonas: Km Prepona hewitsonius beatifica LEP-03467 KC132991 KC132941 KC133040 KY080819 KY080969 KY081046 25, Otraparte Ecuador: Morona Santiago: Prepona hewitsonius beatifica LEP-03552 KC133000 KC132949 KC133048 KY080827 KY080977 KY081054 Yaupi Prepona hewitsonius beatifica LEP-16400 Peru: Loreto: Picuroyacu KY080873 KY080905 - - KY081003 KY081074 Prepona laertes demodice LEP-00528 Ecuador: Orellana: Yarina KC132968 KC132923 KC133022 KY080796 KY080942 KY081020 Perú: Madre de Dios: Prepona laertes demodice LEP-16340 KY080868 - - - KY080997 KY081068 Amazonia Lodge Colombia: Meta: Rey Prepona laertes ssp. LEP-03519 KC132996 KC132946 KC133063 KY080825 KY080974 KY081051 Zamuro Prepona laertes ssp. LEP-04215 France: French Guiana: Saul - KY080884 KY080918 KY080832 KY080982 - Prepona narcissus LEP-03396 Peru: No data: No data KC132984 KC132935 - - KY080960 KY081037 France: French Guiana: Prepona narcissus LEP-04218 KC133005 KC132952 KC133053 KY080833 KY080983 KY081058 Roura, Route de Kaw pk27 Brazil: Rondônia: 62km. S Prepona pheridamas LEP-03341 Ariquemes, linha C-10, 5km. KC132980 KC132934 - - KY080954 - S of Cacaulandia Prepona pheridamas LEP-03347 Ecuador: Napo: Yasuní KC132982 - - KY080808 KY080955 KY081033 Brazil: Rondonia: Jirau Prepona pheridamas LEP-16448 - - KY080933 - KY081011 KY081078 Project, Porto Velho, Caicara Prepona praeneste abrupta LEP-11459 Perú: Junin: Satipo - KY080894 - KY080841 KY080993 KY081064 Ecuador: Napo: Km. 49 Tena Prepona praeneste praeneste LEP-00499 KC132963 KC132914 - KY080790 KY080934 KY081012 - Loreto Ecuador: Morona-Santiago: Prepona praeneste praeneste LEP-03438 KC132990 KC132940 KC133038 KY080817 KY080966 KY081043 Condor Mirador Ecuador: Esmeraldas: Prepona pylene gnorima LEP-03360 KY080851 KY080881 KY080915 KY080809 KY080956 KY081034 Canandé Colombia: Santander: Finca Prepona pylene gnorima LEP-03524 KC132997 KC132947 KC133045 KY080826 KY080975 KY081052 El Galapo

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Table B-1. Continued Taxon Voucher Origin COI COII EF1a CAD GAPDH RpS5 Panamá: Colón: Pipeline Prepona pylene gnorima LEP-11392 KY080863 KY080893 - - KY080992 KY081063 Road Ecuador: Esmeraldas: El Prepona pylene jordani LEP-00517 KC132965 KC132921 KC133020 - KY080939 KY081017 Durango Guatemala: Tikal: Petén Prepona pylene philetas LEP-04356 KC133006 - - KY080834 KY080984 KY081059 National Park Costa Rica: Colón: El Rodeo- Prepona pylene philetas LEP-16396 - KY080904 - - KY081002 KY081073 Candelaria Prepona pylene pylene LEP-16447 Brazil: Sao Paulo: Atibaia KY080878 KY080912 KY080932 - KY081010 KY081077 Ecuador: Esmeraldas: No Prepona werneri LEP-04035 KC133003 KC132951 KC133051 KY080830 KY080980 KY081056 data Ecuador: Esmeraldas: San Prepona werneri LEP-04150 - MF197450 KC133052 KY080831 KY080981 KY081057 Francisco Ecuador: Esmeraldas: Rio Prepona werneri LEP-16418 KY080876 KY080908 KY080931 KY080848 KY081006 - Chuchuvi Ecuador: Orellana: Napo Prepone pylene eugenes LEP-00540 KY080849 - - - KY080944 KY081022 Wildlife Center, Napo trail Prepone pylene eugenes LEP-11138 Ecuador: Napo: Jatun Sacha KY080858 KY080887 KY080920 KY080836 KY080986 - El Salvador: No data: No Hypna clytemnestra LEP-01801 KC133015 KC132960 KC133061 KY080807 KY080952 KY081031 data Memphis polyxo LEP-03368 Ecuador: Napo: El Capricho KC133014 KC132959 KC133060 - KY080957 KY081035 Honduras: Yoro: Honduran Siderone galanthis LEP-02480 KY080854 KC132961 KC133062 KY080820 - - Emerald Reserve Colombia: Amazonas: Km. Zaretis isidora LEP-03466 KC133016 KC132962 - KY080818 - KY081045 25 Otraparte

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APPENDIX C ORIGIN, BIOGEOGRAPHY AND EVOLUTION OF COLOR IN PREPONINES - SUPPLEMENTARY FIGURES

This section contains the supplementary figures referenced in Chapter 3.

Figure C-1. Collection dates for the specimens measured in the study of color.

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Figure C-2. Wing regions measured in the study of color.

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Figure C-3. Correlation between each of the cells and wing area.

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Agatasa_calydonia_NW111_8

Prothoe_franck_NW103_5

Charaxes_berkeleyi_EV_0052

Charaxes_bipunctatus_KAP222

Charaxes_trajanus_FM_15

Charaxes_moori_NW121_24

Palla_decius_NW124_7

Palla_violinitens_NW123_19

Palla_ussheri_NW123_22

Palla_publius_NW123_24

Anaeomorpha_mirifica_LEP16998

Anaeomorpha_splendida_LEP03551

Hypna_clytemnestra_LEP01801

Memphis_polyxo_LEP03368

Siderone_galanthis_LEP03482

Zaretis_isidora_LEP03466

Archaeoprepona_amphimachus_amphimachus_LEP03515

Archaeoprepona_meander_meander_LEP00542

Archaeoprepona_camilla_LEP01571

Archaeoprepona_demophon_muson_LEP00568

Archaeoprepona_demophoon_ssp_LEP03378

Archaeoprepona_phaedra_aelia_LEP03435

Archaeoprepona_chromus_LEP00554

Archaeoprepona_priene_LEP04533

Archaeoprepona_licomedes_ssp_LEP16336

Archaeoprepona_chalciope_LEP04034

Prepona_aedon_ssp_LEP04033

Prepona_claudina_lugens_LEP00546

Prepona_narcissus_ssp_LEP04218

Prepona_amydon_frontina_LEP00515

Prepona_hewitsonius_beatifica_LEP03552

Prepona_praeneste_praeneste_LEP03438

Prepona_deiphile_(CA-SA)_LEP03427

Prepona_deiphile_(SA)_LEP16350

Prepona_gnorima_LEP03360

Prepona_eugenes_LEP11138

Prepona_pylene_LEP16447

Prepona_werneri_LEP04035

Prepona_laertes_demodice_LEP00528

Prepona_dexamenus_LEP03514

Prepona_pheridamas_LEP03347

5.0

Figure C-4. Dated tree for Preponini showing confidence intervals bars for each node.

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Figure C-5. Dated tree for Preponini showing confidence intervals range for each node.

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Figure C-6. Marginal probability for all possible ranges of origin of the biogeographical estimation. The nodes correspond to the ones labeled in Figure 3-1.

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APPENDIX D ORIGIN, BIOGEOGRAPHY AND EVOLUTION OF COLOR IN PREPONINES - SUPPLEMENTARY TABLES

This section contains the supplementary tables referenced in Chapter 3.

Table D-1. Specimens included in the phylogenetic hypothesis. Tribe Taxon Molecular Voucher COI COII Efa CAD GAPDH RpS5 Archaeoprepona Preponini LEP-03515 KY080855 KC132945 KC133044 KY080824 KY080973 KY081050 amphimachus Archaeoprepona Preponini LEP-01571 KC132979 KC132933 KC133030 KY080806 KY080951 KY081030 camilla Archaeoprepona Preponini LEP-04034 KC133002 KC132950 KC133050 KY080829 KY080979 - chalciope Archaeoprepona Preponini LEP-00554 KC132972 KC132927 KC133025 KY080800 - KY081025 chomus Archaeoprepona Preponini LEP-00568 KC132975 KC132930 KC133028 KY080803 KY080948 KY081028 demophon Archaeoprepona Preponini LEP-03378 KY080852 KY080882 KY080916 KY080811 KY080959 - demophoon Archaeoprepona Preponini LEP-16336 KY080867 KY080899 KY080924 KY080843 KY080996 KY081067 licomedes Archaeoprepona Preponini LEP-00542 KC132970 KC132925 KC133024 KY080798 KY080945 KY081023 meander Archaeoprepona Preponini LEP-03435 - KC132938 KC133036 KY080815 KY080964 KY081041 phaedra Preponini Archaeoprepona priene LEP-04533 KY080857 KY080886 KY080919 KY080835 KY080985 KY081060 Preponini Prepona aedon LEP-04033 KC133001 MF197448 KC133049 KY080828 KY080978 KY081055 Preponini Prepona amydon LEP-00515 KC132964 KC132920 KC133019 KY080793 KY080938 KY081016 Preponini Prepona claudina LEP-00546 KC132971 KC132926 KY080913 KY080799 KY080946 KY081024 Preponini Prepona deiphile CA LEP-03427 KC132986 MF197449 KC133034 KY080813 KY080962 KY081039 Preponini Prepona deiphile SA LEP-16350 KY080869 KY080900 KY080925 KY080844 KY080998 KY081069 Preponini Prepona dexamenus LEP-03514 KC132994 KC132944 KC133043 KY080823 KY080972 KY081049 Preponini Prepona gnorima LEP-03360 KY080851 KY080881 KY080915 KY080809 KY080956 KY081034 Preponini Prepona hewitsonius LEP-03552 KC133000 KC132949 KC133048 KY080827 KY080977 KY081054

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Table D-1. Continued Tribe Taxon Molecular Voucher COI COII Efa CAD GAPDH RpS5 Preponini Prepona laertes LEP-00528 KC132968 KC132923 KC133022 KY080796 KY080942 KY081020 Preponini Prepona narcissus LEP-04218 KC133005 KC132952 KC133053 KY080833 KY080983 KY081058 Preponini Prepona pheridamas LEP-03347 KC132982 - - KY080808 KY080955 KY081033 Preponini Prepona praeneste LEP-03438 KC132990 KC132940 KC133038 KY080817 KY080966 KY081043 Preponini Prepona pylene LEP-16447 KY080878 KY080912 KY080932 - KY081010 KY081077 Preponini Prepona werneri LEP-04035 KC133003 KC132951 KC133051 KY080830 KY080980 KY081056 Preponini Prepone eugenes LEP-11138 KY080858 KY080887 KY080920 KY080836 KY080986 - Anaeini Hypna clytemnestra LEP-01801 KC133015 KC132960 KC133061 KY080807 KY080952 KY081031 Anaeini Memphis polyxo LEP-03368 KC-133014 KC132959 KC133060 KY080810 KY080957 KY081035 Anaeini Siderone galanthis LEP-03482 KY080854 KC132961 KC133062 KY080820 - - Anaeini Zaretis isidora LEP-03466 KC133016 KC132962 - KY080819 - KY081045 Anaeomorphini Anaeomorpha mirifica LEP-16998 KY609921.1 - - - - - Anaeomorphini Anaeomorpha splendida LEP-03551 KC132999.1 KC132918.1 KC133047.1 - - - Charaxini Charaxes berkeleyi EV-0052 GQ256776.1 - GQ256909.1 - - GQ257111.1 Charaxini Charaxes bipunctatus KAP222 GQ256780.1 - GQ256912.1 - - GQ257114.1 Charaxini Charaxes moori NW121-24 EU528325.1 - EU528302.1 GQ864702.1 GQ865022.1 EU528459.1 Charaxini Charaxes trajanus FM-15 GQ256888.1 - GQ257003.1 - - GQ257208.1 Pallini Palla decius NW124-7 DQ338576.1 - DQ338884.1 EU141311.1 - EU141389.1 Pallini Palla violinitens NW123-19 GQ256894.1 - GQ257008.1 - - GQ257214.1 Pallini Palla ussheri NW123-22 GQ256892.1 - GQ257006.1 - - GQ257212.1 Pallini Palla publius NW123-24 GQ256891.1 - GQ257005.1 - - GQ257211.1 Prothoini Agatasa calydonia NW111-8 EU528310.1 - EU528288.1 GQ864598.1 - EU528420.1 Prothoini Prothoe franck NW103-8 EU528327.1 - EU528304.1 GQ864703.1 GQ865023.1 EU528462.1

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Table D-2. Regions used in the biogeographical analysis. Code Region A Amazonia B Atlantic Forest C Cerrado D Chaco E Central America F West slope, coast of the Andes and Interandean valleys G Caribbean H Andes I Afrotropical J Indo-Malayan

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Table D-3. Areas allowed in the biogeographical analysis. Top matrix corresponds to the first four time slices (from present to past). Bottom matrix corresponds to the older time slice. A B C D E F G H I J A 1 1 1 1 1 1 1 1 1 1 B 1 1 1 1 1 1 1 1 1 1 C 1 1 1 1 1 1 1 1 1 1 D 1 1 1 1 1 1 1 1 1 1 E 1 1 1 1 1 1 1 1 1 1 F 1 1 1 1 1 1 1 1 1 1 G 1 1 1 1 1 1 1 1 1 1 H 1 1 1 1 1 1 1 1 1 1 I 1 1 1 1 1 1 1 1 1 1 J 1 1 1 1 1 1 1 1 1 1

A 1 1 1 1 1 1 1 0 1 1 B 1 1 1 1 1 1 1 0 1 1 C 1 1 1 1 1 1 1 0 1 1 D 1 1 1 1 1 1 1 0 1 1 E 1 1 1 1 1 1 1 0 1 1 F 1 1 1 1 1 1 1 0 1 1 G 1 1 1 1 1 1 1 0 1 1 H 0 0 0 0 0 0 0 0 0 0 I 1 1 1 1 1 1 1 0 1 1 J 1 1 1 1 1 1 1 0 1 1

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Table D-4. Probability of movement among regions. Top matrix corresponds to the first four time slices (from present to past). Bottom matrix corresponds to the older time slice. A B C D E F G H I J A 1 1 1 1 0.375 1 0.5 0.75 0.5 0.1875 B 1 1 1 1 0.25 0.05 0.375 0.5625 0.5 0.1875 C 1 1 1 1 0.25 0.75 0.375 0.375 0.375 0.125 D 1 1 1 1 0.25 0.75 0.375 0.75 0.375 0.125 E 0.375 0.25 0.25 0.25 1 0.5 0.25 0.28125 0.5 0.1875 F 1 0.05 0.75 0.75 0.5 1 0.5 0.75 0.5 0.1875 G 0.5 0.375 0.375 0.375 0.25 0.5 1 0.28125 0.5 0.1875 H 0.75 0.5625 0.75 0.75 0.28125 0.75 0.28125 1 0.25 0.025 I 0.5 0.5 0.375 0.375 0.5 0.5 0.5 0.25 1 0.5 J 0.1875 0.1875 0.125 0.125 0.1875 0.1875 0.1875 0.025 0.5 1

A 1 1 1 1 0.375 1 0.5 0.00001 0.5 0.1875 B 1 1 1 1 0.25 0.05 0.375 0.00001 0.5 0.1875 C 1 1 1 1 0.25 0.75 0.375 0.00001 0.375 0.125 D 1 1 1 1 0.25 0.75 0.375 0.00001 0.375 0.125 E 0.375 0.25 0.25 0.25 1 0.5 0.25 0.00001 0.5 0.1875 F 1 0.05 0.75 0.75 0.5 1 0.5 0.00001 0.5 0.1875 G 0.5 0.375 0.375 0.375 0.25 0.5 1 0.00001 0.5 0.1875 H 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 I 0.5 0.5 0.375 0.375 0.5 0.5 0.5 0.00001 1 0.5 J 0.1875 0.1875 0.125 0.125 0.1875 0.1875 0.1875 0.00001 0.5 1

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Table D-5. Partition finder partitions and molecular clock designations for the three molecular clock tests. * Due to the combined nature of this partition, Gene2_pos2 from mtDNA and Gene4_pos2 from Nuclear DNA, an independent clock for this partition was used. Analysis 1 - Analysis 2 - mtDNA and Analysis 3 - Clock per Partition Partitions Best model Subset partitions Subset sites mtDNA vs Nuclear multiple nuclear DNA clocks Finder partition DNA clocks 1 bModelTest Gene1_pos3 1-618\3 mtDNA clock mtDNA clock clock Gene1_pos3 clock 2 bModelTest Gene1_pos1 2-618\3 mtDNA clock mtDNA clock clock Gene1_pos1 clock 3 bModelTest Gene1_pos2 3-618\3 mtDNA clock mtDNA clock clock Gene1_pos2 clock 4 bModelTest Gene2_pos3 619-1444\3 mtDNA clock mtDNA clock clock Gene2_pos3 clock 5 bModelTest Gene2_pos1 620-1444\3 mtDNA clock mtDNA clock clock Gene2_pos1 clock 6 bModelTest Gene2_pos2 621-1444\3 NuclearDNA Gene2_pos2 clock Gene2_pos2 clock 7 bModelTest Gene3_pos3 1445-2395\3 NuclearDNA Gene3_pos3 clock Gene3_pos3 clock 8 bModelTest Gene5_pos1, Gene3_pos1 3382-3914\3 1446-2395\3 NuclearDNA Gene5_pos1, Gene3_pos1 clock Gene5_pos1, Gene3_pos1 clock 9 bModelTest Gene6_pos2, Gene3_pos2 3915-4454\3 1447-2395\3 NuclearDNA Gene6_pos2, Gene3_pos2 clock Gene6_pos2, Gene3_pos2 clock 10 bModelTest Gene4_pos3 2396-3379\3 NuclearDNA Gene4_pos3 clock Gene4_pos3 clock 11 bModelTest Gene4_pos1 2397-3379\3 NuclearDNA Gene4_pos1 clock Gene4_pos1 clock 12 bModelTest Gene4_pos2 2398-3379\3 NuclearDNA Gene4_pos2 clock Gene4_pos2 clock 13 bModelTest Gene5_pos2 3380-3914\3 NuclearDNA Gene5_pos2 clock Gene5_pos2 clock 14 bModelTest Gene5_pos3, Gene6_pos3 3381-3914\3 3916-4454\3 NuclearDNA Gene5_pos3, Gene6_pos3 clock Gene5_pos3, Gene6_pos3 clock 15 bModelTest Gene6_pos1 3917-4454\3 NuclearDNA Gene6_pos1 clock Gene6_pos1 clock

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Table D-6. Path sampling results for the three molecular clock tests. Analysis 1 - mtDNA vs Analysis 2 - mtDNA and Analysis 3 - Clock per Nuclear DNA clocks multiple nuclear DNA clocks Partition Finder partition -25645.55 -25482.39 -25396.79

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Table D-7. Bayes factors for model comparison of the molecular clock tests. Model 0 corresponds to the mtDNA vs Nuclear DNA clocks analysis, model 1 corresponds to the mtDNA and multiple nuclear DNA clocks analysis, and model 3 corresponds to the clock per Partition Finder partition analysis. Model 0/Model 1 Model 0/Model 2 Model 1/Model 2 1.00495 1.00895 1.00398 1.00640 1.00979 1.00337

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Table D-8. Akaike Information Criterion values for the different models tested in the biogeographical reconstruction. Model AIC AICc deltaAIC deltaAICc DEC 407.1 407.4 21.2 12.1 DEC+J 403.5 404.2 14.2 8.9 DIVALIKE 409.0 409.3 20.5 14 DIVALIKE+J 409.7 410.4 21 15.1 BAYAREALIKE 398.8 399.1 3.3 3.8 BAYAREALIKE+J 394.6 395.3 0 0

135

APPENDIX E PHYLOGEOGRAPHY AND SPECIES DELIMITATION IN PREPONA LAERTES - SUPPLEMENTARY FIGURES

Figure E-1. W-IQ-Tree ML reconstruction for the COI Barcoding gene. Numbers in nodes correspond to bootstrap values.

136

Figure E-2. RAxML ML reconstruction for the COI Barcoding gene. Numbers in nodes correspond to bootstrap values.

137

Figure E-3. Consensus topology computed with Dendroscope for the COI Barcoding reconstructions using FastTree, W-IQ-Tree and RAxML.

138

Figure E-4. RAxML ML reconstruction for the concatenated loci RADseq dataset. Numbers below nodes correspond to bootstrap values.

139

Figure E-5. RAxML ML reconstruction for the 50% missing-data dataset. Numbers below nodes correspond to bootstrap values.

140

Figure E-6. RAxML ML reconstruction for the 70% missing-data dataset. Numbers below nodes correspond to bootstrap values.

141

Figure E-7. RAxML ML reconstruction for the 90% missing-data dataset. Numbers below nodes correspond to bootstrap values.

142

Figure E-8. Neighbor Joining topology for the COI Barcoding gene. Numbers below nodes correspond to bootstrap values.

143

Figure E-9. Automatic Barcode Gap Discovery topology recovered for the COI Barcoding gene.

144

Figure E-10. Support values for the GMYC species delimitation model.

145

Figure E-11. Dorsal (top) and ventral (bottom) coloration patterns of Prepona philipponi.

146

Figure E-12. Dorsal (top) and ventral (bottom) coloration patterns of Prepona pseudomphale.

147

APPENDIX F PHYLOGEOGRAPHY AND SPECIES DELIMITATION IN PREPONA LAERTES - SUPPLEMENTARY TABLES

Table F-1. Percentage similarity among the topologies recovered for different thresholds of missing data computed with Compare2Trees. Missing data (%) 50 70 90 all samples 50 - 81.4 69.1 80 70 - - 65.9 69.4 90 - - - 68.1 all samples - - - -

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Table F-2. Raw PH85 distance values for the different topologies (above diagonal) and normalized PH85 distance (nPH85). In both cases, smaller numbers suggest more similarity between topologies, while larger numbers suggest the topologies are more dissimilar. PH85 distance ranges from 0 to 62 while nPH85 ranges from 0 to 1. Concatenated Single 50% missing 70% missing 90% missing

loci SNP data data data Concatenated - 18 24 32 36 loci Single SNP 0.29 - 28 38 36 50% missing 0.39 0.45 - 24 34 data 70% missing 0.52 0.61 0.39 - 36 data 90% missing 0.58 0.58 0.55 0.58 - data

149

Table F-3. Grouping schemes for Neighbor Joining. Outgroup species are LEP-02477 and LEP- 16418. Sample 4 Group Scheme 17 Group Scheme LEP-00520 6 17 LEP-00528 6 7 LEP-00529 1 1 LEP-00530 1 1 LEP-00531 1 1 LEP-00535 6 12 LEP-00536 6 12 LEP-00608 6 19 LEP-01322 6 15 LEP-01323 6 14 LEP-01330 6 8 LEP-01331 6 12 LEP-01799 2 2 LEP-01800 2 2 LEP-02477 4 4 LEP-03337 6 9 LEP-03338 6 14 LEP-03340 6 6 LEP-03348 6 19 LEP-03350 6 8 LEP-03351 3 3 LEP-03352 1 1 LEP-03353 6 18 LEP-03355 6 18 LEP-03356 6 18 LEP-03359 1 1 LEP-03370 6 12 LEP-03371 3 3 LEP-03421 2 2 LEP-03440 1 1 LEP-03441 1 1 LEP-03449 3 3 LEP-03495 3 3 LEP-03511 6 9 LEP-03519 1 1 LEP-03521 6 7

150

Table F-3 Continued Sample 4 Group Scheme 17 Group Scheme LEP-03523 6 7 LEP-03527 1 1 LEP-03540 3 3 LEP-03541 6 12 LEP-03542 6 9 LEP-03543 6 14 LEP-03544 6 19 LEP-04207 6 13 LEP-04208 6 9 LEP-04209 6 9 LEP-04210 3 3 LEP-04211 6 11 LEP-04213 6 7 LEP-04215 6 16 LEP-04220 6 16 LEP-04221 6 10 LEP-04222 6 13 LEP-04223 6 19 LEP-04224 3 3 LEP-04225 6 19 LEP-04226 6 16 LEP-04228 6 16 LEP-04229 6 11 LEP-11521 2 2 LEP-16339 6 11 LEP-16342 6 7 LEP-16418 5 5

151

Table F-4. Genetic distances found for the grouping scheme of 17 groups for Prepona laertes identified by NJ. Groups 4 and 5 correspond to the outgroup species. Gr: Group

Gr. 17 Gr. 7 Gr. 1 Gr. 12 Gr. 19 Gr. 15 Gr. 14 Gr. 8 Gr. 2 Gr. 4 Gr. 9 Gr. 6 Gr. 3 Gr. 18 Gr. 13 Gr. 11 Gr. 16 Gr. 10 Gr. 5

Gr. 17 - 0.007 0.013 0.008 0.003 0.007 0.008 0.008 0.013 0.016 0.007 0.007 0.013 0.009 0.008 0.008 0.008 0.008 0.016

Gr. 7 0.014 - 0.013 0.006 0.006 0.006 0.007 0.005 0.012 0.016 0.003 0.003 0.012 0.011 0.007 0.006 0.008 0.005 0.015

Gr. 1 0.055 0.055 - 0.014 0.013 0.014 0.015 0.013 0.012 0.017 0.013 0.013 0.011 0.014 0.013 0.014 0.013 0.013 0.016

Gr. 12 0.021 0.014 0.069 - 0.007 0.007 0.008 0.008 0.013 0.017 0.007 0.007 0.013 0.01 0.007 0.004 0.009 0.007 0.016

Gr. 19 0.004 0.011 0.058 0.018 - 0.006 0.008 0.007 0.013 0.016 0.007 0.007 0.013 0.009 0.007 0.007 0.007 0.007 0.016

Gr. 15 0.014 0.011 0.065 0.018 0.011 - 0.007 0.007 0.013 0.017 0.007 0.007 0.013 0.011 0.007 0.007 0.007 0.007 0.016

Gr. 14 0.029 0.024 0.078 0.027 0.026 0.023 - 0.008 0.014 0.019 0.008 0.008 0.013 0.011 0.008 0.007 0.01 0.007 0.017

Gr. 8 0.018 0.007 0.061 0.022 0.015 0.015 0.029 - 0.012 0.016 0.003 0.005 0.012 0.011 0.007 0.007 0.008 0.007 0.015

Gr. 2 0.061 0.053 0.052 0.059 0.059 0.056 0.061 0.052 - 0.016 0.013 0.012 0.01 0.013 0.012 0.013 0.012 0.012 0.016

Gr. 4 0.089 0.086 0.101 0.097 0.086 0.093 0.107 0.08 0.088 - 0.017 0.016 0.016 0.018 0.016 0.017 0.016 0.017 0.016

Gr. 9 0.018 0.004 0.058 0.018 0.014 0.014 0.029 0.004 0.056 0.089 - 0.005 0.012 0.011 0.008 0.007 0.009 0.006 0.015

Gr. 6 0.018 0.004 0.058 0.017 0.014 0.014 0.029 0.009 0.051 0.082 0.007 - 0.012 0.011 0.007 0.007 0.008 0.006 0.015

Gr. 3 0.055 0.048 0.048 0.062 0.059 0.059 0.065 0.053 0.039 0.091 0.052 0.052 - 0.014 0.012 0.013 0.013 0.012 0.015

Gr. 18 0.027 0.042 0.069 0.036 0.031 0.042 0.046 0.046 0.063 0.106 0.045 0.044 0.071 - 0.01 0.01 0.01 0.011 0.018

Gr. 13 0.02 0.016 0.063 0.017 0.016 0.016 0.027 0.015 0.047 0.084 0.02 0.016 0.056 0.039 - 0.006 0.008 0.007 0.016

Gr. 11 0.018 0.011 0.065 0.005 0.014 0.014 0.021 0.018 0.054 0.096 0.014 0.014 0.058 0.038 0.013 - 0.008 0.006 0.016

Gr. 16 0.027 0.03 0.072 0.031 0.023 0.021 0.041 0.031 0.056 0.091 0.034 0.03 0.07 0.043 0.026 0.027 - 0.009 0.016

Gr. 10 0.021 0.007 0.062 0.016 0.018 0.018 0.024 0.015 0.053 0.089 0.011 0.011 0.056 0.042 0.016 0.011 0.03 - 0.015

Gr. 5 0.089 0.075 0.094 0.089 0.086 0.086 0.095 0.078 0.086 0.086 0.079 0.071 0.084 0.104 0.087 0.086 0.095 0.079 -

152

Table F-5. Within group genetic divergence for the four groups recovered by Neighbor Joining for the COI Barcoding gene. Group Within group genetic divergence Standard error 1 0.007 0.003 2 0.013 0.004 3 0.012 0.004 4 NA NA 5 NA NA 6 0.021 0.004

153

Table F-6. bPTP ML partition supports for the delineated groups. Group Sample Support 1 philipponi_Esmeraldas_(ECD)_LEP03356 0.256 laertes_Manabi_(ECD)_LEP03353 philipponi_Esmeraldas_(ECD)_LEP03355 2 0.011 pseudomphale_Napo_(ECD)_LEP00520 laertes_Orellana_(ECD)_LEP00528 laertes_Orellana_(ECD)_LEP00535 laertes_Orellana_(ECD)_LEP00536 pseudomphale_Orellana_(ECD)_LEP00608 laertes_Orellana_(ECD)_LEP01322 laertes_Orellana_(ECD)_LEP01323 laertes_Orellana_(ECD)_LEP01330 laertes_Orellana_(ECD)_LEP01331 laertes_Rondonia_(BRA)_LEP03337 laertes_Rondonia_(BRA)_LEP03338 laertes_Rondonia_(BRA)_LEP03340 pseudomphale_Orellana_(ECD)_LEP03348 laertes_Orellana_(ECD)_LEP03350 laertes_Napo_(ECD)_LEP03370 laertes_Meta_(COL)_LEP03511 laertes_Meta_(COL)_LEP03521 laertes_Meta_(COL)_LEP03523 laertes_Napo_(ECD)_LEP03541 laertes_Orellana_(ECD)_LEP03542 laertes_Napo_(ECD)_LEP03543 laertes_Orellana_(ECD)_LEP03544 laertes_FrenchGuiana_(FRG)_LEP04207 laertes_FrenchGuiana_(FRG)_LEP04208 laertes_FrenchGuiana_(FRG)_LEP04209 laertes_FrenchGuiana_(FRG)_LEP04211 laertes_FrenchGuiana_(FRG)_LEP04213 philipponi_FrenchGuiana_(FRG)_LEP04215

154

Table F-6. Continued

Group Sample Support

philipponi_FrenchGuiana_(FRG)_LEP04220

laertes_FrenchGuiana_(FRG)_LEP04221

laertes_FrenchGuiana_(FRG)_LEP04222

laertes_FrenchGuiana_(FRG)_LEP04223

pseudomphale_FrenchGuiana_(FRG)_LEP04225

philipponi_FrenchGuiana_(FRG)_LEP04226

philipponi_FrenchGuiana_(FRG)_LEP04228

laertes_FrenchGuiana_(FRG)_LEP04229

laertes_Cuzco_(PER)_LEP16339 laertes_Cuzco_(PER)_LEP16342 3 laertes_Meta_(COL)_LEP03519 0.155 pseudomphale_Santander_(COL)_LEP03527 laertes_Manabi_(ECD)_LEP03352 laertes_Pichincha_(ECD)_LEP00530 laertes_Esmeraldas_(ECD)_LEP00529 laertes_Esmeraldas_(ECD)_LEP03359 laertes_Pichincha_(ECD)_LEP00531 laertes_Nodata_(ECD)_LEP03441 laertes_Nodata_(ECD)_LEP03440 4 laertes_Tabasco_(MEX)_LEP03421 0.856 5 laertes_Florida_(USA)_LEP11521 0.287 laertes_Nodata_(ELS)_LEP01799 laertes_Nodata_(ELS)_LEP01800 6 laertes_Napo_(ECD)_LEP03371 0.749 7 laertes_Orellana_(ECD)_LEP03540 0.56 8 laertes_Orellana_(ECD)_LEP03351 0.193 laertes_FrenchGuiana_(FRG)_LEP04224 laertes_FrenchGuiana_(FRG)_LEP04210 9 laertes_SanCristobal_(VE)_LEP03449 0.731 10 laertes_Caldas_(COL)_LEP03495 0.731

155

Table F-7. BIC values for the different values of k explored. k BIC ∆BIC 1 214.6 3.5 2 213.7 2.6 3 212.5 1.4 4 211.1 0

156

Table F-8. Fst fixation index values for the four clusters identified by the multivariate analysis. Numbers correspond to the following groups: 1: laertes CA-SA (dark green), 2: philipponi (purple), 3: laertes SA (light green), and 4: pseudomphale (blue). 1 3 2 4 1 - 0.16 0.13 0.15 3 - - 0.12 0.09 2 - - - 0.08

157

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BIOGRAPHICAL SKETCH

Elena Ortiz Acevedo is a biologist broadly interested in ecology and evolution, using diurnal butterflies as a model to understand the mechanisms that generate and maintain species diversity. She is particularly interested in understanding the causes of speciation and diversification, and as a consequence she has lately focused her work on phylogenetics and species delimitation. She graduated in 2007 from Universidad de Los Andes in Bogotá,

Colombia, where butterflies first attracted her attention during her undergraduate thesis.

In 2009, she became a master’s student at the Department of Entomology and

Nematology, and McGuire Center for Lepidoptera and Biodiversity at the Florida Museum of

Natural History, at the University of Florida. There, she became interested in the butterfly tribe

Preponini, an interest which drove her to propose and execute a project in which she aimed to resolve the phylogenetic relationships of the tribe and clarify the number of species in the group.

After graduation in 2011, she continued working on Neotropical butterflies at the McGuire

Center for Lepidoptera and Biodiversity until she started her PhD program in 2012. The knowledge and experiences she has gained in the last seven years have maintained her interest in butterfly research, improved her skills in planning and executing research projects, enhanced her abilities for critical thinking and data analysis, and finally have increased her commitment to working with biological collections, a commitment which she hopes to keep developing for many years.

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