University of Florida Thesis Or Dissertation

THE USE OF MULTIPLE DATA TYPES FOR ELUCIDATING DRIVERS OF SPECIATION IN THE BUTTERFLY GENUS HERMEUPTYCHIA (NYMPHALIDAE: SATYRINAE: EUPTYCHIINA)

DENISE TAN

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

2018

To my Grandmother and Uncle William

ACKNOWLEDGMENTS

I am extremely grateful to members of my graduate committee, Dr. Akito

Kawahara, Dr. Andrea Lucky, Dr. Colette St. Mary and Dr. Jacqueline Miller for their encouragement and guidance. I would especially like to thank my supervisor, Dr. Keith

Willmott for his patience, advice and mentoring throughout my Ph.D. program.

I would like to express my gratitude to Dr. Jaret Daniels and Dr. Thomas Emmel, the current and founding directors of the McGuire Center for Lepidoptera and

Biodiversity (Florida Museum of Natural History), as well as the Dean of the College of

Agricultural and Life Sciences (University of Florida) for allowing me to pursue my Ph.D. degree via a Graduate Research Assistantship and the Grinter Fellowship. I thank also the Office of Graduate Diversity Initiatives for providing tuition assistance during my final semester via the Supplemental Retention Scholarship.

I thank the National Science Foundation (Grant No. DEB-1256742 and DEB-

1416127, Richard Glider Graduate School (American Museum of Natural History;

Theodore Roosevelt Memorial Fund), Dr. Norm Leppla, the Entomology and

Nematology Student Organisation (ENSO Travel Grant) and Dr. Rudolf Meier (National

University of Singapore) for funding my research, fieldwork and trips for professional development.

I am deeply appreciative of Dr. Nick Grishin (University of Texas Southwestern

Medical Center) and members of his lab, Qian Cong, Wenlin Li and Jinhui Shen, as well as Dr. Frank Rheindt (National University of Singapore) and members of his research group, Keren Sadanandan, Elize Ng and Nathaniel Ng, for so graciously hosting me at their institutions and for generously sharing their knowledge and expertise.

Additionally, I thank Dr. Andrew Warren for advice on collection sites and techniques. For assistance pertaining to the rearing of Hermeuptychia butterflies, as well as the generous provision of laboratory space, I am grateful to Dr. Jaret Daniels,

Dr. Andrei Sourakov and Matthew Standridge. I am also appreciative of Stacey Huber and Jonathan Bremer for their guidance on the usage of the imaging systems found at the McGuire Center for Lepidoptera and Biodiversity.

I would also like to acknowledge the incredible contributions of my undergraduate assistants and volunteers, Anamaria Parus, Michelle Dunbar, Mario Abels, Alana

Pacheco and Addison Eggert – Thank you for your enthusiasm and hard work!

On a more personal note, I would like to express my deepest appreciation to

Marianne Espeland, Elena Ortiz, Lei Xiao, Jose Martinez and all my brilliant colleagues at McGuire Center for Lepidoptera and Biodiversity and the Department of Entomology and Nematology. It is unbelievably inspiring to be able to work alongside like-minded individuals who devote themselves so entirely to their vocations. To Qinwen Xia, Chao

Chen, Kayla Chen, Mengyi Gu, Chia-Yin Tsai, Yao Xu, Claudia Pagano, Kok Ben Toh,

Gaurav Vaidya, Matthew Standridge, Kristin Rossetti and members of SEAEC, you have my heartfelt gratitude for your friendship and for making Gainesville feel like home for the past four and a half years.

To Heather Chan and Leonard Chan, thank you for sharing my love for animals

(especially insects!) and Disney movies. I look forward to learning, exploring and laughing with you for many years to come.

The unwavering support I receive from my friends and family is why I feel empowered to see the world and cultivate my passions. So, to my parents, David Tan

and Brenda Ho, Bernice Tan, Nathaniel Ng and Siti Yaakub, I honestly can’t thank you enough for embracing all my eccentricities and being there every step of the way.

Lastly, I am proud to dedicate this dissertation to two people whose absence has been deeply felt, my grandmother, Ming Geok Chin and uncle, William Ho.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 10

LIST OF FIGURES ...... 11

LIST OF OBJECTS ...... 14

LIST OF ABBREVIATIONS ...... 15

ABSTRACT ...... 18

CHAPTER

1 INTRODUCTION ...... 20

The Genus Hermeuptychia ...... 21 Research Aims ...... 25

2 USING COI-BASED SPECIES DELIMITATION METHODS TO ESTIMATE DIVERSITY AND GENERATE PRELIMINARY SPECIES HYPOTHESES ...... 30

Materials and Methods...... 33 Sampling ...... 33 DNA Extraction, Amplification and Sequencing ...... 33 Inferring Phylogenetic Relationships ...... 34 Species Delineation Based on Genetic Variation ...... 36 Results ...... 38 Molecular Phylogenetic Analyses ...... 38 Defining Putative Species Boundaries ...... 39 Discussion ...... 41 Updated Molecular Phylogeny ...... 41 Preliminary Species Diversity Estimates ...... 42

3 USING GENOME-WIDE, SINGLE-NUCLEOTIDE POLYMORPHISMS TO RIGOROUSLY INVESTIGATE PHYLOGENETIC RELATIONSHIPS AND SPECIES LIMITS IN ECUADORIAN HERMEUPTYCHIA ...... 49

Materials and Methods...... 53 Hermeuptychia Genome Assembly ...... 53 Sampling and DNA Extraction ...... 54 Double-Digest RAD-Seq Library Preparation ...... 55 Pipeline for SNP-Based Analyses ...... 55 Quality filtering and SNP calling ...... 55

Investigating population substructure ...... 57 Inferring species tree from biallelic markers ...... 58 Pipeline Resulting in Supermatrix of ddRAD-Seq Loci ...... 59 Results ...... 60 Genetic Structure within Putative Species ...... 60 ‘Atalanta’ group ...... 61 ‘Harmonia’ group ...... 62 ‘Gisella’ and ‘Clara’ groups ...... 62 Species 3 group ...... 63 ‘Maimoune’ and ‘Hermes’ groups ...... 63 Phylogeny Inferred from Genome-Wide Variation ...... 64 Discussion ...... 65 Investigating Species Limits with ddRAD-Seq ...... 65 Phylogenomic Relationships across Ecuadorian Hermeuptychia ...... 68

4 RE-SURVEY OF WING AND MALE GENITALIA MORPHOLOGY IN ECUADORIAN HERMEUPTYCHIA ...... 95

Methods ...... 97 Ventral Wing Images ...... 97 Male Genitalia Images ...... 97 Results ...... 98 ‘Atalanta’ Group ...... 98 ‘Maimoune’ Group ...... 99 ‘Clara’ Group ...... 100 Species 3 Group ...... 100 ‘Hermes’ Group ...... 100 ‘Gisella’ Group ...... 101 ‘Harmonia’ Group ...... 101 ‘Pimpla’ Group ...... 102 Discussion ...... 103

5 CONCLUSIONS ...... 120

APPENDIX

A SPECIMEN INFORMATION ...... 124

Collection Locality Data for All Specimens ...... 124 Genbank Accession Numbers for Previous Published Sequences ...... 143 Information on Specimens Selected for ddRAD-Seq ...... 150

B SPECIES DELIMITATION RESULTS ...... 153

Putative Species Classifications Based on the GMYC Approach ...... 153 Putative Species Classifications Based on the mPTP Approach ...... 158 Putative Species Classifications Based on the bPTP Approach ...... 162 Putative Species Classifications Based on the ABGD Approach ...... 166

C PRELIMINARY SNAPP TREE FOR ECUADORIAN HERMEUPTYCHIA ...... 170

D SUPPLEMENTARY VENTRAL WING AND MALE GENITALIA IMAGES ...... 171

LIST OF REFERENCES ...... 173

BIOGRAPHICAL SKETCH ...... 182

LIST OF TABLES

Table page

2-1 Information on primers used in this study ...... 44

2-2 A summary of the results and estimated number of Hermeuptychia species generated by the various methods tested in this investigation ...... 44

3-1 Filtering conditions and resulting SNP counts for individual datasets ...... 71

A-1 Locality information for the Hermeuptychia specimens investigated ...... 124

A-2 Genbank accession numbers of previously published sequences that have been utilized in this investigation ...... 143

A-3 Putative species groupings, specimen ID, collection year and DNA concentrations of the samples used for double-digest RAD-Seq...... 150

B-1 Results from GMYC, multiple thresholds analysis ...... 153

B-2 Results from mPTP, single-rate analysis ...... 158

B-3 Results from bPTP, maximum likelihood partitions ...... 162

B-4 Results of ABGD, with recursive partitioning ...... 166

LIST OF FIGURES

Figure page

1-1 Type specimen images for Hermeuptychia taxa found in North America...... 27

1-2 Diagnostic characters that differentiate H. intricata (A, C & E) from H. sosybius males (B, D, & F)...... 28

1-3 Hermeuptychia clara specimens characterized by a pale band on the ventral hind wings formed by a straight dark post-discal line bordered by white scales...... 29

2-1 Distribution map of barcoded Hermeuptychia specimens. White dots indicate localities from which previously published specimens have been collected. Black dots indicate the new localities incorporated in this study...... 46

2-2 Bayesian inference (BEAST2) tree with posterior probabilities (top) > 0.5 and bootstrap values (bottom) > 50 indicated. Sequences generated in this study have a LEP/IN prefix and are in blue for easy visualization...... 47

2-3 Left - The unresolved relationships of the ‘H. cucullina’ and ‘H. gisella’ complex from the phylogeny presented in Seraphim et al., (2013)...... 48

3-1 Principle component analysis (PCA) and STRUCTURE results for Species 4. .. 72

3-2 Principle component analysis (PCA) and STRUCTURE results for ‘Pimpla’ ...... 73

3-3 Principle component analysis (PCA) and STRUCTURE results for Species 5. .. 74

3-4 Principle component analysis (PCA) and STRUCTURE results for the ‘Atalanta’ group...... 75

3-5 Molecular phylogenies for the ‘Atalanta’ group ...... 76

3-6 Principle component analysis (PCA) and STRUCTURE results for the ‘Harmonia’ group...... 77

3-7 Molecular phylogenies for the ‘Harmonia’ group...... 78

3-8 Principle component analysis (PCA) and STRUCTURE results for the ‘Gisella’ group...... 79

3-9 Molecular phylogenies for the ‘Gisella’ group...... 80

3-10 Principle component analysis (PCA) and STRUCTURE results for the ‘Clara’ group...... 81

3-11 Molecular phylogenies for the ‘Clara’ group...... 82

3-12 Principle component analysis (PCA) and STRUCTURE results for the Species 3 group...... 83

3-13 Molecular phylogenies for the Species 3 group...... 84

3-14 Principle component analysis (PCA) and STRUCTURE results for the ‘Maimoune’ group...... 85

3-15 Molecular phylogenies for the ‘Maimoune’ group...... 86

3-16 Principle component analysis (PCA) and STRUCTURE results for the ‘Hermes’ group...... 87

3-17 Molecular phylogenies for the ‘Hermes’ group...... 88

3-18 Maximum likelihood (RAxML) tree based on 11,000 concatenated ddRAD- Seq loci. Bootstrap values >50 are indicated. The colors correspond to clusters identified by STRUCTURE...... 89

3-19 Geographic distributions of Species 4 (blue), Species 5 (Orange) and ‘Pimpla’ (Green)...... 90

3-20 Overlapping geographic distributions of the two distinct clusters identified within ‘Atalanta’...... 91

3-21 Overlapping geographic distributions of the four distinct clusters identified within ‘Harmonia’...... 92

3-22 Geographic distributions of the two distinct clusters identified within ‘Gisella’. ... 93

3-23 Geographic distributions of the distinct clusters identified within ‘Maimoune’, ‘Hermes’, ‘Clara’ and Species 3...... 94

4-1 Ventral wing patterns of specimens representing two morphology groups within ‘Atalanta’...... 105

4-2 Lateral view of male genitalia of specimens representing two morphology groups within ‘Atalanta’...... 106

4-3 Ventral wing patterns of specimens representing different genomic clusters within ‘Maimoune’...... 107

4-4 Lateral view of male genitalia of specimens representing genomic clusters within ‘Maimoune’...... 108

4-5 Ventral wing patterns of specimens representing different genomic clusters within ‘Clara’...... 109

4-6 Lateral view of male genitalia of specimens representing genomic clusters within ‘Clara’...... 110

4-7 Ventral wing patterns of specimens representing different genomic clusters within Species 3...... 111

4-8 Lateral view of male genitalia of specimens representing genomic clusters within Species 3...... 112

4-9 Ventral wing patterns of specimens representing different genomic clusters within ‘Hermes’...... 113

4-10 Lateral view of male genitalia of specimens representing genomic clusters within ‘Hermes’ ...... 114

4-11 Ventral wing patterns of specimens representing different genomic clusters within ‘Gisella’...... 115

4-12 Lateral view of male genitalia of specimens representing genomic clusters within ‘Gisella’...... 116

4-13 Ventral wing patterns of specimens representing different genomic clusters within ‘Harmonia’...... 117

4-14 Lateral view of male genitalia of specimens representing genomic clusters within ‘Harmonia’...... 118

4-15 Ventral wing patterns of specimens representing ‘Pimpla’. The diffused patch of white scales in the posterior half of the hind wings is distinctive amongst known Hermeuptychia species...... 119

4-16 Lateral view of male genitalia of specimens from the ‘Pimpla’ group. Tracings of the shape of the valva are provided for greater clarity...... 119

C-1 The preliminary tree topology obtained from the species tree method SNAPP...... 170

D-1 Ventral wing patterns of specimens representing Species 1, Species 2, Species 4 and Species 5...... 171

D-2 Lateral view of male genitalia of specimens representing Species 1, Species 4 and Species 5. All Species 2 specimens were female specimens...... 172

LIST OF OBJECTS

Object page

2-1 Figure 2-2 as a PDF document (.pdf file 839kb)...... 47

3-1 Figure 3-18 as a PDF document (.pdf file 11.5 mb) ...... 89

LIST OF ABBREVIATIONS

ABGD Automatic Barcode Gap Discovery

AC Amphidecta calliomma

APE Analyses of phylogenetics and evolution

BEAST Bayesian evolutionary analysis sampling trees

BEAUti Bayesian evolutionary analysis utility bp Base pairs bPTP Bayesian implementation of Poisson tree process model

BSA Bovine serum albumin

BWA Burrows-Wheeler aligner

CIPRES Cyberinfrastructure for phylogenetic research

CLUMPP Cluster matching and permutation program

COI Cytochrome C oxidase I

CPU Central processing unit ddH2O Double distilled water ddRADseq Double-digest restriction-site associated deoxyribonucleic acid sequencing

DNA Deoxyribonucleic acid dsDNA Double-stranded deoxyribonucleic acid

EO Euptychia ordinata

EOS Electro-optical system gb Gigabyte

GC-MS Gas chromatography–mass spectrometry gDNA Genomic deoxyribonucleic acid

GIS-NGSP Genome Institute of Singapore Next-Generation Sequencing

Platform

GM Godartiana muscosa

GMYC Generalized Mixed Yule Coalescent

GTRGAMMA General time reversible gamma distribution

HCV Hepatitis c virus

ISO International organization of standardization kb Kilo bases

KOH Potassium hydroxide

MAFFT Mass alignment using fast Fourier transform

MAPQ Mapping quality mb Megabyte

MCMC Markov chain Monte Carlo

MEGA Molecular Evolutionary Genetics Analysis

ML Maximum likelihood

MOTU Molecular operational taxonomic units

MP Maximum parsimony mPTP Multi-rate Poisson tree processes mtDNA Mitochondrial deoxyribonucleic acid

NGS Next-generation sequencing

PC Principle component

PCA Principle component analysis

PCR Polymerase chain reaction

PI Pharneuptychia innocentia

PTP Poisson Tree Process

RAD-Seq Restriction-site associated deoxyribonucleic acid sequencing

RAxML Randomized axelerated maximum likelihood

RC Rareuptychia clio

RNA ribonucleic acid

RNA-seq ribonucleic acid sequencing

SAMtools Sequence alignment/map tools

SB Splendeuptychia boliviensis

SEM Scanning electron microscopy

SI Splendeuptychia itonis

SLR Single-lens reflex

SNAPP Single nucleotide polymorphism and amplified fragment length polymorphism package for phylogenetic analysis

SNP Single nucleotide polymorphism

SPLITS Species limits by threshold statistics

USA United States of America

XML Extensible markup language

ZP Zischkaia pacarus

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

THE USE OF MULTIPLE DATA TYPES FOR ELUCIDATING DRIVERS OF SPECIATION IN THE BUTTERFLY GENUS HERMEUPTYCHIA (NYMPHALIDAE: SATYRINAE: EUPTYCHIINA)

Denise Tan

May 2018

Chair: Keith R. Willmott Major: Entomology and Nematology

In order to elucidate the forces generating diversification, we must first be able to classify biodiversity. Determining if a population constitutes a separately evolving lineage is challenging for taxa with broad ranges due to the effects of localized selection pressures on allopatric populations. This is more so if body sizes are small and few distinguishing characters are available for identification. One such problematic group is the poorly understood butterfly genus Hermeuptychia Forster, 1964. Preliminary attempts at investigating species boundaries within Hermeuptychia have been very valuable, but data have been geographically limited and the use of a single mitochondrial gene (COI) does not allow for fully resolving evolutionary relationships.

I tripled the number of specimens and significantly improved geographic coverage of barcoded Hermeuptychia. To demonstrate that COI datasets are independently useful for estimating diversity, without the subjectivity of relying on branch support of reconstructed phylogenies, I applied three species delimitation methods: GMYC, PTP and ABGD. Since another motivation of this compendium of

studies is to synthesize analytical pipelines for addressing similar questions in related groups, I identify the best performing versions of alternative approaches throughout.

Additionally, I use ddRADseq to survey genome-wide variation in Ecuadorian

Hermeuptychia. This sample-set is particularly robust in that numerous habitats, altitudes and localities from both sides of the Andes are represented. Thousands of

SNPs were identified, allowing me to probe genomic structure within putative taxa and provide resolution of deeper phylogenetic relationships within Hermeuptychia. More importantly, I directly compared species inferences from COI versus ddRADseq and determined congruence with geographic distributions as well as morphology. Taking all data types into consideration I find that diversity is very severely underestimated in

Hermeuptychia and that the proper assignment of species names (through the study and placement of type specimens) needs to be prioritized in order to make significant progress in producing a reliable classification for Hermeuptychia.

Lastly, this multi-faceted assessment produced better-defined species hypotheses and enabled identification of meaningful divergences in the commonly sequenced COI. The latter should have extensive implications for biodiversity research where funding, computational capabilities, and/or ability to acquire appropriately- preserved specimens for NGS, are limited.

CHAPTER 1 INTRODUCTION

Identifying and classifying biodiversity is an increasingly urgent challenge with accelerating threats to natural environments from climate change and anthropogenic factors. Nowhere is this challenge greater than in the insects, which are vital for ecosystem health, particularly sensitive to environmental change, and dominate biodiversity. Determining whether a population constitutes a separately evolving lineage can be particularly challenging for actively radiating, recently diverged groups. This is particularly so for taxa with broad geographic ranges, since allopatric populations can also be expected to evolve independently through localized selection pressures, accumulating variances in ecologically relevant traits. DNA sequencing has provided a new avenue for re-evaluating the boundaries of these widespread species, and cryptic speciation is often inferred whenever large genetic variation is discovered between geographically structured populations that are, at least initially, indistinguishable

(Bickford et al., 2007). However, few studies follow up by examining morphological characters in greater detail, sequencing more genes or evaluating novel information

(such as geographic distribution, ecology, life history, behavior etc.) that could further clarify species status. Indeed, interesting underlying processes that may explain incongruent molecular and morphological datasets will only be evident if there are multiple sources of data to place inconsistencies into context (Lipscomb et al., 2003;

Yeates et al., 2011). Furthermore, erroneous classification of independently evolving lineages has critical implications for estimates of species richness and endemism, affecting conservation prioritization, management and our fundamental understanding of speciation mechanisms in these groups (Lohman et al., 2010).

The terms ‘integrative' or 'iterative taxonomy’ have now become synonymous with species-level classifications that aim to incorporate all character information for greater rigor (Dayrat, 2005; Will et al., 2005; Dupuis et al., 2012). The emerging consensus is that the combination of diverse, independent data types better accounts for inherent failure rates of each information source (i.e. there is a greater possibility of resolving the ‘true’ set of species lineages) (Padial et al., 2010; Pires & Marinoni, 2010).

Essentially, taxa proposed based on multiple lines of evidence are better defined and better supported hypotheses for the development of further studies. Concordance amongst information types may indicate greater reliability but cannot be demanded a priori because of the heterogeneity of evolutionary processes operating within and between populations and across varying spatio-temporal scales. Consequently, it is in acknowledging and addressing this incongruence that we might also gain greater insights on lesser known and understudied drivers of diversification.

The Genus Hermeuptychia

The butterfly genus Hermeuptychia (Nymphalidae, Satyrinae, Euptychiina) currently consists of eleven recognised species with an extensive distribution in forest and grassland habitats from southeastern USA to northern Argentina (Lamas, 2004;

Cong & Grishin, 2014; Nakahara et al., 2016). The taxonomy of the group has been highly problematic because these butterflies are small with very similar external morphologies and high intraspecific variability in wing ocelli patterns (Figure 1-1). Adults of all known Hermeuptychia species are uniformly brown dorsally, with only a few darker lines and a series of small ocelli that occur along the ventral wing margin to provide clues to species identities. Additionally, there are twice as many species names as there are currently recognised taxa, but many type specimens are old, missing or

damaged, making it difficult to re-examine diagnostic characters and properly assign existing names. Since Forster (1964) first proposed the genus, based on distinctive male genitalia morphology (generally elongated valvae with a thin and long aedeagus), little revisionary work has been performed on these seemingly ubiquitous yet unassuming butterflies.

Within the subtribe Euptychiina, the placement of Hermeuptychia has remained contentious. Analyzing a 4447 bp dataset from five genes, Peña et al., (2010) recovered

Hermeuptychia as being sister to Rareuptychia and (Amphidecta + Euptychia) under

Bayesian inference, but this was inconsistent with the maximum parsimony reconstruction where it was sister to the monotypic genus Cercyeuptychia and

Godartiana instead (Cercyeuptychia has since been synonymized with Godartiana;

Zacca et al., 2017) . In 2017, Marín et al., utilized over a hundred morphological characters to examine phylogenetic relationships and found Hermeuptychia to be closely related to (Prenda + Taydebis), (Pindis + Zischkaia) and part of Splendeuptychia instead. Notably, these relationships were not well supported. Most recently, in a phylogeny based on 386 nuclear loci (sequenced using anchored hybrid enrichment),

Hermeuptychia was no longer recovered within any of the aforementioned groups and was sister to the rest of Euptychiina (excepting the ‘Megisto’, ‘Cyllopsis’ and ‘Euptychia’ clades) with moderate support (Espeland et al., in review).

Due to poor taxonomic knowledge, many specimens are assigned by default to the type species of the genus, Hermeuptychia hermes, rendering it apparently the most common and widely distributed taxon, although this assumption remains to be verified by comprehensive geographic sampling. In 2014, Seraphim et al. obtained cytochrome

C oxidase I (COI) gene sequences for 188 specimens (primarily from Brazil and

Colombia) and compared male genitalia morphology for all distinct molecular groups in a pilot attempt to clarify species boundaries within Hermeuptychia. Although geographic sampling was limited, this analysis produced several significant outcomes including ascertaining the monophyly of the genus and tentatively associating names with the eight genetic groups based on overall similarity to existing types. It should be noted, however, that these names have not been properly resolved and I henceforth utilize single quotation marks to indicate that the sequence names will require further verification and should not be accepted as correct. Further findings can be summarized as so:

 For five of the eight genetic groups identified, genitalia morphology and COI variation yielded similar inferences about species limits.

 Marked intraspecific genetic structure was detected within ‘H. atalanta’, some of which corresponded to differences in habitat or altitude.

 Two morphotypes with subtle yet consistent differences were identified within ‘H. maimoune’. Interestingly, this divergence also corresponded to geographic isolation by the Andes.

 Despite low genetic distances, three distinctive morphotypes exist within the ‘H. cucullina’ group.

 Two of 30 downloaded Genbank sequences (specimens from Mexico) failed to cluster with sequences generated from that study, suggesting that these may represent new/undescribed taxa.

The authors therefore concluded that broader sampling, especially in terms of number of loci (particularly nuclear genes) and geographic populations, was necessary to comprehensively evaluate current species hypotheses and resolve phylogenetic relationships within Hermeuptychia.

A follow-up study by Cong & Grishin (2014) surveyed COI variation in samples collected across Texas, leading to the discovery and description of two new species -

Hermeuptychia intricata and H. hermybius. The latter can only be found in the southernmost part of Texas and is identifiable from wing pattern and genitalia morphology. The former occurs sympatrically with H. sosybius and is remarkably similar in terms of external morphology (Figure 1-1). Although the authors stated at the time that they “failed to find strong diagnostic differences that would hold against individual variation”, it was eventually determined that a dorsal wing character could be used to distinguish male specimens: dark androconial scales present in the basal two-thirds of the forewings and areas surrounding the discal cell on the hind wings gives H. sosybius males a two-toned appearance, whereas the wings of H. intricata lack androconial scales and are uniformly colored (Figure 1-2) (Warren et al., 2014). The two species also exhibit distinct differences in male and female genitalia. In order to avoid taxonomic confusion, since neither the original Fabricius type nor any lectotypes could be located, a neotype for Papilio sosybius was designated from Savannah, Georgia, USA. Lastly, the authors also concluded that the name “H. hermes” should not be applied to US populations of Hermeuptychia on account of significant dissimilarity in genitalic characters between US specimens and Brazilian specimens (the type locality for hermes being Brazil) and that DNA barcodes are at least 4% divergent between these regions.

These pilot molecular studies made significant headway in estimating species numbers in Hermeuptychia. However, the ability of data from a single mitochondrial locus (COI) to effectively resolve species boundaries or deeper level phylogenetic

relationships in this widely distributed genus has remained untested. Geographic sampling could also be expanded (existing sampling has focused on Brazil, Colombia and Texas (USA) thus far) and all evidence suggests that species diversity is severely underestimated in Hermeuptychia. In fact, a novel species with readily distinguishable wing patterns found in montane forest habitats of Ecuador, was only described very recently (Figure 1-3) (Nakahara et al., 2016). Little is known about Hermeuptychia life history, although basic descriptions of immature stages are available for H. atalanta

(Cosmo et al., 2014), H. sosybius and H. hermybius (Cong & Grishin, 2014). In terms of behavioral investigations, only one study, investigating the effect of prior residency and body mass on male territorial contests involving H. fallax (Peixoto & Benson, 2011), has been published. Similarly, nothing is known of population-level divergences (i.e., evidence of hybridization, introgression or genetic admixture) that could explain current patterns of distribution and determine the extent to which geographical isolation causes diversification or maintains reproductive isolation. While these butterflies are not presently considered endangered, the recent discovery of cryptic species within

Hermeuptychia could mean that they are also not as common as once thought. Such dearth of knowledge will undoubtedly impede our ability to conserve and manage

Hermeuptychia populations if the need so arises in the future.

Research Aims

Consequently, my first objective is to improve geographic sampling so that genetic variation within the ‘barcoding gene’, COI, can be better characterized. Then, I incorporate findings from additional data types: genome-wide single nucleotide polymorphisms, distribution data and morphology (dorsal wing patterns and male genitalia) in order to more fully resolve evolutionary relationships and determine species

boundaries in Hermeuptychia. Agreement amongst the various lines of evidence will undoubtedly result in better defined species hypotheses, but it is in addressing incongruence that we are more likely to gain a better understanding of the processes that drive speciation in this enigmatic genus. For example, if genitalia morphology is particularly divergent in sympatric taxa then we might suspect that conspecific recognition and/or other sexually selected processes might be initiating cladogenesis.

On the other hand, if genetic variation is consistent with geographic isolation in the absence of genitalic differences, then one might surmise that physical barriers and/or niche segregation is a prevalent speciation force.

With the exception of Heliconius butterflies, such a comprehensive approach is novel for a Neotropical butterfly genus. The resulting protocols, insights and findings will significantly advance our ability to address similar questions in closely related groups

(such as the subtribe Euptychiina for which extensive taxonomic revision is currently in progress). Therefore, another objective of this dissertation is to rigorously compare various methods for objectively identifying evolutionarily significant units/putative species as well as the treatment and analysis of large SNP-based datasets resulting from next-generation sequencing protocols that are broadly suitable for existing collections of poorly understood groups.

H. sosybius H. intricata H. hermybius H. sosybius H. intricata H. hermybius

Figure 1-1. Type specimen images for Hermeuptychia taxa found in North America. Dorsal wings are brown without obvious markings (top row). Ventral surfaces are a lighter brown with two dark vertical lines and dashes in addition to a series of eyespots that occur along the wing margin (bottom row). Source: Warren, A.D., Davis, K.J., Stangeland, E.M., Pelham, J.P. & Grishin, N. V. 2013. Illustrated Lists of American Butterflies, http://butterfliesofamerica.com (July 18, 2017)

Figure 1-2. Diagnostic characters that differentiate H. intricata (A, C & E) from H. sosybius males (B, D, & F). A & B) Dorsal images of male wings, C & D) ventral patterns of male wings and E & F) full dorsal view of male specimens. Source: Warren, A.D., Davis, K.J., Stangeland, E.M., Pelham, J.P. & Grishin, N. V. 2013. Illustrated Lists of American Butterflies, http://butterfliesofamerica.com (July 18, 2017)

Figure 1-3. Hermeuptychia clara specimens characterized by a pale band on the ventral hind wings formed by a straight dark post-discal line bordered by white scales. A & B) Dorsal and ventral wing patterns of the holotype male. C & D) Dorsal and ventral wing patterns of the paratype female. Source: Nakahara, S., Tan, D., Lamas, G., Parus, A. & Willmott, K.R. 2016. A distinctive new species of Hermeuptychia Forster, 1964 from the eastern tropical Andes (Lepidoptera: Nymphalidae: Satyrinae). Trop. Lepid. Res. 26: 77–84.

CHAPTER 2 USING COI-BASED SPECIES DELIMITATION METHODS TO ESTIMATE DIVERSITY AND GENERATE PRELIMINARY SPECIES HYPOTHESES

DNA barcoding was first introduced as a means of automating the assignment of unidentified individuals to species and facilitating species discovery using differences within standardized gene regions such as mitochondrial cytochrome C oxidase I (COI;

Hebert et al., 2003). Although the initial proposal to implement a fixed threshold for species diagnosis using COI sequence dissimilarity has been heavily debated (Meyer &

Paulay, 2005; Meier et al., 2006; Wiemers & Fiedler, 2007), it is widely acknowledged that the incorporation of genetic information overcomes several inherent limitations of traditional taxonomy such as: detecting speciation events that occur independently of observable morphological divergence (e.g. in extreme environments that impose stabilizing selection on morphology or where genetic incompatibility arises through allopatry and drift, and reproductive isolation involves non-morphological characters), informing of species boundaries despite high phenotypic plasticity, enabling identification of damaged or poorly developed specimens and producing comparative data from samples across different sexes and developmental stages (in contrast, morphological keys that are based on male genitalic characters cannot be applied to females and immatures) (Valentini et al., 2009). In this manner, COI ‘barcodes’ are particularly useful for broadly surveying diversity in groups where taxonomic knowledge is initially lacking (i.e. poorly understood taxa or when only a few morphological characters are available for species delimitation), facilitating further investigation once preliminary species hypotheses are available (Rock et al., 2008; Jansen et al., 2009;

Derycke et al., 2010; Decaëns et al., 2013).

In pilot investigations of species diversity in Hermeuptychia, high branch support was stated as the main criterion for defining molecular species boundaries (Cong &

Grishin, 2014; Seraphim et al., 2014). However, defining a threshold is subjective and can vary depending on a multitude of factors including but not limited to model selection, resampling strategy and even choice of out-group. More importantly, this approach is difficult to implement when a priori hypotheses about species identities

(such as additional information from morphology and/or distribution) are not yet available. Instead, several clustering- and phylogeny- based approaches have shown great effectiveness for objectively delineating putative species based solely on single locus molecular datasets. The Generalized Mixed Yule Coalescent (GMYC) method separately models the fit of Yule (inter-specific branching) and coalescent (intra-specific branching) processes to an ultrametric phylogenetic tree in order to determine a transition point/threshold (by maximizing the likelihood score) used to delimit species boundaries (Pons et al., 2006; Fontaneto et al., 2007; Fujisawa & Barraclough, 2013).

The requirement of an ultrametric gene tree (fully bifurcating with any two sequences always having the same distance to their common ancestor) for input is a major limitation since available methods for correcting rate heterogeneity in order to generate such clocklike trees are not straightforward (Tang et al., 2014). The Poisson Tree

Process (PTP) method is more recent and achieves a similar goal of identifying meaningful differences between branching events on a molecular phylogeny but does so by modeling speciation using the number of substitutions rather than time. Therefore, it only requires an accurately rooted standard phylogenetic tree, circumventing the potentially error-prone process of generating time-calibrated ultrametric trees. Heuristic

algorithms are then applied to identify the most likely classification of branches into population- and species level processes (Zhang et al., 2013). Experimental comparisons of these approaches have led to the recommendation that both be used together, given the differences in their underlying assumptions (Tang et al., 2014).

Clustering-based approaches, on the other hand, have capitalized on the premise that in a distribution of pairwise differences for a set of COI sequences, a ‘barcode gap’ potentially exists between the range of intraspecific and interspecific variation. The

Automatic Barcode Gap Discovery (ABGD) method is a two-step process whereby the barcode gap is first statistically inferred from the dataset and used to initially partition the dataset, and then the process is recursively applied onto primary partitions until no further splitting occurs. Recursive partitioning helps to account for variation in mutation rate and instances where intra- and inter-specific variation is overlapping (i.e. there is no real barcode gap) (Puillandre et al., 2012). Unlike GMYC and PTP, the ABGD method does not require a phylogeny, only a sequence alignment in fasta format or a distance matrix.

In this chapter, I first sought to expand geographic representation for

Hermeuptychia by including specimens from several formerly unrepresented countries as well as a robust sample set representing numerous localities and habitats in Ecuador

(Figure 2–1). The latter country's unique geographical landscape (with the Andes acting as a physical barrier separating the eastern and western lowlands), extraordinary range of habitats, and rich butterfly diversity resulting from diverse local faunas as well as contributions from three of the Neotropical region's four main biogeographic regions, make it a valuable region to include in any taxonomic study of a diverse Neotropical

group. The specimens studied here are a result of long-term surveys in Ecuador conducted by collaborators and made available to me for this study. Furthermore, I compare the utility of three analytical approaches (GMYC, PTP and ABGD) for allowing large barcoding datasets to be independently useful for, at least preliminarily, estimating diversity and identifying putative species for which further investigation of other data types can ensue.

Materials and Methods

Sampling

A total of 612 Hermeuptychia specimens (dried or preserved in 95% ethanol) were sampled, collected from 128 localities in Ecuador and 21 localities in Brazil, Costa

Rica, French Guiana, Guatemala, Mexico, Panama and Peru, between the years 2001

– 2016 (further specimen information can be found in Table A-1). Specimens used in this study are mostly deposited in the McGuire Center for Lepidoptera and Biodiversity

(Florida Museum of Natural History, University of Florida, USA), although a few individuals came from other institutions or private collections.

DNA Extraction, Amplification and Sequencing

Genomic DNA was extracted either by pulverizing two legs from dried specimens

(manually with a pestle or using a bead beater homogenizer) or overnight digestion of the intact thorax of alcohol-preserved specimens, using the DNeasy Blood & Tissue Kit

(Qiagen, Valencia, CA, USA). In order to maximize final DNA concentration while minimizing compromise to overall yield, DNA was eluted twice using only 50 μl of Buffer

AE each time. The COI ‘barcoding’ region was amplified using Nymphalidae-specific primers, LCO_nym and HCO_nym. In cases of failure (such as when dealing with older

and/or degraded specimens), two primer pairs targeting shorter gene fragments within the same region were used instead (for primer information see Table 2-1).

Polymerase chain reactions (PCRs) were prepared in 25 μl reactions consisting of 1 μl template DNA, 0.7 μl of each primer (10 μM), 0.5 μl bovine serum albumin (BSA,

20mg/mL), 9.6 μl ddH2O and 12.5 μl Taq polymerase mastermix (2X Taq Master Mix

(Omega Bio-tek, Norcross, GA, USA) or OneTaq Hot Start Quick-load 2x Master Mix with standard buffer (New England Biolabs, Ipswich, MA, USA)). The PCR thermocycling profile was as follows: initial denaturation for 1 min at 94°C, four cycles of

94°C for 30 seconds, 48°C for 40 seconds and 72°C for 1 min followed by 36 cycles of

94 °C for 30 seconds, 51°C for 40 seconds, 72°C (or according to user specification for

Taq polymerase) for 1 min and a final extension of 72°C for 5 mins. When the COI_24F and COI_396R primer pair was used, annealing temperature was set at 58°C for all 40 cycles.

PCR products were examined on 1.2% agarose gels stained with 0.5 μg/ml ethidium bromide. PCR purification and sequencing were performed at the

Interdisciplinary Center for Biotechnology, University of Florida (Gainesville, FL, USA) or

Eurofins Genomics (Louisville, KY, USA). Shorter fragments were combined, and all sequences were inspected and manually edited using Geneious v. 9.1.3 (Biomatters,

Auckland, New Zealand).

Inferring Phylogenetic Relationships

For a more comprehensive examination of relationships within Hermeuptychia,

292 previously published sequences were also included in this analysis (Genbank accession numbers are listed in Table A-2). The chosen outgroups were identical to

Seraphim et al., (2014):

1. Godartiana muscosa (GM) 2. Splendeuptychia itonis (SI) 3. Amphidecta calliomma (AC) 4. Pharneuptychia innocentia (PI) 5. Splendeuptychia boliviensis (SB) 6. Rareuptychia clio (RC) 7. Zischkaia pacarus (ZP) 8. Euptychia ordinata (EO)

The combined dataset consisted of 912 sequences that were aligned using the

FFT-NS-2 progressive algorithm in MAFFT v 7.310 (Katoh et al., 2002; Katoh &

Standley, 2013) and trimmed to minimize missing data (final alignment was 620 base pairs in length). Additionally, amino acid sequences were manually inspected to ensure there were no stop codons present. Minimum and maximum pairwise differences were calculated using MEGA v 7 (Kumar et al., 2016) using p-distance and partial deletion of missing data (50% site coverage cut-off). Since zero length terminal branches hamper likelihood estimations, non-unique haplotypes (i.e. identical sequences) were removed from the alignment using the ElimDupes tool on the HCV sequence database (Los

Alamos National Security, LLC), with the option to consider sub-sequences as duplicates. Phylogenies were inferred based on maximum likelihood (ML), maximum parsimony (MP) and Bayesian inference using a dataset partitioned by codon positions.

The maximum likelihood analysis was performed using RAxML v 8.2.10, 1000 rapid bootstrap replicates were conducted and the best-scoring ML tree was evaluated under the GTRGAMMA model (Stamatakis, 2014), implemented on the CIPRES

Science Gateway portal (Miller et al., 2010). Bayesian inference trees were produced using the BEAST 2 package v 2.4.5 (Bouckaert et al., 2014). XML files were generated using BEAUti, with the following settings: model selection was performed via BEAST model test (bModeltest package) using empirical frequencies, the relaxed clock log

normal model, and the birth rate prior was set to Gamma (Alpha 1.0, Beta 6.0) to improve convergence. All other priors were kept at default setting. Markov chain Monte

Carlo (MCMC) chain length was set at 100 × 106, logging every 10 × 103 samples. All

BEAST analyses were executed using the HiPerGator 2.0 computing cluster at the

University of Florida (Gainesville, Florida, USA). The resultant trace files were evaluated in Tracer v 1.6.0 (http://tree.bio.ed.ac.uk/software/tracer/). Three independent BEAST runs were combined using LogCombiner v 2.4.5 with 10% burn-in and the option to resample states at a lower frequency to generate 10 × 103 trees. Maximum clade credibility trees with posterior probability limit set at 0.5 and mean node heights were constructed in TreeAnnotator v 2.4.5. Resulting phylogenetic trees were visualized and edited in Figtree v 1.4.3 (http://tree.bio.ed.ac.uk/software/figtree/) and Adobe Illustrator

CS6 v 16.0.4 (Adobe Systems Inc., San Jose, California, USA).

Species Delineation Based on Genetic Variation

Three analytical methods that did not require prior information on species identities and were appropriate for a single-locus dataset were used to delineate putative species and designate molecular operational taxonomic units (MOTU) for our dataset: (a) Tree-based, model-based approaches, Generalized Mixed Yule Coalescent

(GMYC) and Poisson Tree Processes (PTP); as well as (b) a clustering-based approach, Automatic Barcode Gap Discovery (ABGD).

As recommended by Tang et al., (2014) in their investigation on the effects of phylogenetic reconstruction methods on the robustness of GMYC and PTP estimates, I utilized the Bayesian Inference gene tree, constructed using BEAST, on the dataset comprising only of unique haplotypes (see ‘Inferring Phylogenetic Relationships’ section for details) for running GMYC and a rooted maximum likelihood (RAxML) tree for

running PTP. The single and multiple threshold version of GMYC was implemented in

RStudio v 1.10.136 (https://www.rstudio.com) using the APE v. 4.1 (Paradis et al., 2004) and SPLITS v 1.0-19 (http://r-forge.r-project.org/projects/splits/) packages. All available versions of PTP was executed: The Bayesian implementation of PTP (bPTP) (Zhang et al., 2013) was run using the bPTP python package v. 0.51

(https://github.com/zhangjiajie/PTP) with 10 × 106 Markov chain Monte Carlo (MCMC) iterations (with burn-in set at 0.2) to achieve convergence. Within this package, a maximum likelihood solution is also provided as part of the standard output set. The multi-rate PTP (mPTP) v 0.2.2 package uses a dynamic programming algorithm to compute the ML delimitation and MCMC sampling to generate support values for each clade that can be used to assess confidence of inferred delimitation (Kapli et al., 2017).

Due to this method’s ability to handle large datasets, 50 × 106 MCMC iterations were performed using both the single- and multi-rate parameters. Unlike GMYC and PTP, the

ABGD method does not require a phylogeny, only a sequence alignment in fasta format or a distance matrix. The analysis was performed on the ABGD website

(http://wwwabi.snv.jussieu.fr/public/abgd/abgdweb.html) with default settings (Pmin =

0.001, Pmax = 0.1, Steps = 10, X (relative gap width) = 1.5) and simple distances.

The species delineation methods were then evaluated based on the following criteria, whether or not:

1. Outgroup taxa are recovered as distinct species-units

2. Previously published sequences representing specimens with similar genitalia are split into separate species instead

3. Sequences representing specimens with dissimilar genitalia morphology are recovered as the same species instead.

To clarify, a comparative analysis of genitalia morphology is not within the scope of this chapter since the primary goal is to explore the utility of these objective approaches on a vastly expanded molecular dataset for which little taxonomic knowledge is available.

However, I am able to initially evaluate congruence based on corresponding morphological information from previous investigations and recent species descriptions without making any assumptions about the morphology of newly sequenced specimens

(Cong & Grishin, 2014; Seraphim et al., 2014; Nakahara et al., 2016). Instead, the assignment of the same specimens that were previously segregated into morphology groups is evaluated.

Results

The combined dataset of 904 sequences (612 generated in this study + 292 previous published sequences downloaded from Genbank) comprised 374 unique sequences. The genetic distances (i.e. proportion of nucleotide sites at which two sequences being compared were different) ranged from 0.00% – 15.80%, with an average pairwise distance of 4.21% (i.e. average difference of 26 bp).

Molecular Phylogenetic Analyses

The resultant phylogeny is highly structured with moderate support. Where there is high branch support, there is repeated congruence between the maximum likelihood

(bootstrap resampling) and Bayesian (posterior probability) methods (Figure 2-2, Object

2-1). The genus Hermeuptychia is recovered as monophyletic with high support, with a previously published sequence (representing a Brazilian specimen with distinct genitalia, “Hermeuptychia sp. n. 1 NS-2013 voucher MT17”) (Seraphim et al., 2014) once again emerging as sister to the rest of the group (Figure 2-2, Object 2-1). Notably, many of the new sequences generated in our investigation were recovered as distinct,

highly supported clades that did not contain any previously published sequences

(Figure 2-2, Object 2-1).

Defining Putative Species Boundaries

The GMYC approach produced the largest diversity estimates with the single threshold version (maximum likelihood of model = 3298.333) recovering 98 species and the multiple threshold version (maximum likelihood of model = 3308.878) recovering 82 species. All eight outgroups were recovered as distinct taxa in the single-threshold analysis, whereas under the multiple threshold model, outgroups were merged into three species instead (1 - Euptychia ordinata (EO), 2 - Rareuptychia clio (RC) +

Zischkaia pacarus (ZP) and 3 - Godartiana muscosa (GM) + Splendeuptychia itonis (SI)

+ Amphidecta calliomma (AC) + Pharneuptychia innocentia (PI) + Splendeuptychia boliviensis (SB)). In both implementations of GMYC, specimens with similar genitalia morphology were frequently delineated as multiple species instead. However, the multiple threshold analysis performed better by splitting the ‘H. atalanta’ and ‘H. harmonia’ morphology groups into fewer species and recovering the ‘H. pimpla’ group as a single taxon (Table B-1).

Conversely, the mPTP approach yielded the lowest diversity estimates: 25 species under the single-rate analysis (best score = 749.733) and 22 species under the multi-rate analysis (best score = 585.915). In both implementations of mPTP, specimens with dissimilar genitalia morphology were placed within the same species (H. sosybius + H. hermybius and H. intricata + ‘H. gisella’  + ‘H. gisella’  + ‘H. cucullina’

; note that these names and shapes correspond to molecular and morphology group designations in Seraphim et al., (2014)). The single-rate analysis performed better by recovering the ‘H. atalanta’ morphology group as the same species whereas the multi-

rate analysis delineated these specimens as two species instead. Additionally, all eight outgroups were recovered as different species in the single-rate analysis but were merged into five species in the multi-rate analysis (1 – EO, 2 - RC + ZP, 3 – GM, 4 - SI

+ AC and 5 - PI + SB; abbreviated outgroup names are used here for brevity) (Table B-

2).

The bPTP implementation of the PTP model produced 135 species in the delimitation with the highest posterior probability and 47 species in the maximal likelihood implementation. All outgroups were defined as distinct taxa in both analyses, but the maximum likelihood partitions fared significantly better in recovering specimens from the same morphology groups, together. Specifically, the ‘H. atalanta’ morphology group was split into two species instead of four and the ‘H. gisella’, ‘H. cucullina’ and ‘H. pimpla’ groups were individually recovered as a single unit instead of being split into two separate taxa. The ‘H. harmonia’ group was also recovered as one taxon instead of four genetic species under the bayesian approach (Table B-3).

Finally, the ABGD method recovered 53 species after initial partitioning (P =

1.00e-03) and 56 species through recursive partitioning (P = 1.67e-03). The only difference being that recursive partitioning process further splits a primary partition into four species, in a manner that better corresponds to the differences in morphology discovered within the ‘H. cucullina’ and ‘H. gisella’ complex (Seraphim et al., 2014)

(Table B-4). A summary of results from the better-performing version of each method is provided in Table 2-2.

Discussion

Updated Molecular Phylogeny

The Neotropical butterfly genus Hermeuptychia is often referred to as complex and in need of taxonomic and phylogenetic revision (e.g. Marín et al., 2011; Seraphim et al., 2014; Nakahara et al., 2016). Here, we build upon pioneering molecular studies and generate another 612 COI sequences, thereby tripling the number of

Hermeuptychia specimens for which molecular information is now available.

Furthermore, while previous datasets consisted primarily of specimens from USA, Brazil and Colombia, geographic sampling is now expanded to include a robust sample set collected from numerous localities and habitats across Ecuador. There is also improved representation of genetic variation from Costa Rica, French Guiana, Guatemala,

Mexico, Panama and Peru. As with previous phylogenetic analyses, Hermeuptychia is recovered as monophyletic with strong support (Figure 2-2, Object 2-1). Overall the reconstructed phylogeny is highly structured with moderate support amongst recently diverged clades, whilst many deeper phylogenetic relationships remain unresolved, as is to be expected since only one mitochondrial gene is being investigated here. Notably, numerous specimens sequenced as part of our investigation formed distinct and highly supported clades that did not otherwise contain previously published sequences. These may represent several novel species and this result is further evidence of the need for expanded sampling given the group’s vast distribution and highly cryptic diversity (see red vertical lines in Figure 2-2, Object 2-1).

Unfortunately, none of our sequences was recovered together with specimen

MT17 from Alto Araguaia, Brazil (previously established to be sister to the rest of

Hermeuptychia). Nevertheless, the new information has provided additional context and

some resolution for the ‘H. cucullina’ and ‘H. gisella’ complex, where previously three different morphotypes (‘H. gisella’  + ‘H. gisella’  + ‘H. cucullina’ ) were placed in the same molecular group, recovered as a polytomy (Seraphim et al., 2014). The authors observed “marked morphological variation that is not congruent with the

‘barcode’ molecular analysis”. In our phylogeny, the ‘H. gisella’  group is polyphyletic with only some members being recovered as sister to the ‘H. cucullina’  clade. There is further evidence of the ‘H. gisella’  group being rather distinct from all other ‘H. gisella’ and ‘H. cucullina’ individuals as it is recovered as sister to the clade containing

H. intricata instead (Figure 2-2, Object 2-1 & 2-3). Additionally, the two Mexican specimens (represented by Hermeuptychia sp. hermes ECO02 MAL 02840 in our phylogeny) that did not fall into any of the previously established genetic groups are now recovered as sister to one of the aforementioned distinct clades consisting only of newly sequenced individuals from north-western Ecuador (Figure 2-2, Object 2-1).

Consequently, the species identities and relationships of these specimens should be clarified once further investigation on morphological differences and genome wide variation are completed.

Preliminary Species Diversity Estimates

Earlier investigations into species boundaries in Hermeuptychia relied on branch support of reconstructed molecular phylogenies to demarcate species boundaries based on genetic information. However, deciding on an appropriate level of support is somewhat arbitrary since it can vary depending on phylogeny-building methodology, model selection and choice of out-group. More importantly, this approach is not feasible when taxonomic knowledge is lacking, and little is known about the group being investigated. Here we utilized several molecular phylogeny- and clustering- based

species delimitation methods to more objectively differentiate intraspecific population substructure from interspecific divergences (e.g. Kekkonen et al., 2015). Using a fixed set of criteria (see materials and methods) to evaluate suitability of each method for our sizeable dataset and, more importantly, what boundaries will best inform additional analyses, I find that the GMYC approach placed individuals from the same morphology groups into multiple, distinct species or molecular operational taxonomic units (MOTUs) most frequently (Table 2-2). Our findings echo the sentiment that whilst it remains a very popular approach, GMYC delivers a much higher species count than traditional taxonomy and/or other such approaches (e.g. Carstens et al., 2013; Talavera et al.,

2013). Also, the requirement of ultrametricity limits the ways an input tree can be generated, especially since post-hoc branch-smoothing methodologies can potentially introduce bias and tend not to be straightforward (Tang et al., 2014). In contrast, the

PTP method only requires an accurately rooted standard phylogenetic tree and the maximum likelihood solution produced a diversity estimate that was comparable to results from the clustering-based, ABGD method (Table 2-2). Both approaches are in agreement with respect to the recognition of the outgroups as distinct species as well as

15 putative species including the following represented by: Hermeuptychia sp. n. 1 NS-

2013 voucher MT17, Hermeuptychia sp. hermes ECO02 MAL02840, Hermeuptychia sp

NS2013CO01, the H. clara group, the ‘H. cucullina’ group, the H. sosybius group excluding Hermeuptychia sosybius GSM 299, 44 sequences from the ‘H. atalanta’ group, Hermeuptychia sp. hermes ECO01 MAL02845 and multiple potential novel taxa comprising only new sequences generated in this study (Figure 2-2, Object 2-1; Table

B-3 & Table B-4).

Nevertheless, the more pressing issue at hand is that under the best performing methods, diversity in the genus Hermeuptychia is severely underestimated (the average amongst all four methods is 45 species, as compared to the eleven currently recognised species) (Table 2-2). Given that much of its distribution remains under-sampled, our findings certainly indicate that further examinations need to occur with urgency. The vastly expanded dataset (both in terms of number of specimens and geographic representation) and results from several objective means of species delimitation based on COI further reinforces initial beliefs that much more information needs to be acquired before we can gain a fuller understanding of diversity, evolutionary relationships and the factors that drive diversification within Hermeuptychia. However, I have shown here that a robust COI dataset can certainly inform about which clades require attention when re- examining morphology and probing other lines of evidence. Hopefully, this dataset and accompanying framework for generating putative species hypotheses will encourage further work on widely distributed, highly cryptic genera such as Hermeuptychia.

Table 2-1. Information on primers used in this study Primer Direction Sequence (5’ – 3’) Reference LCO_nym F TTTCTACAAATCATAAAGATATTGG - HCO_nym R TAAACTTCAGGGTGACCAAAAAATCA - COI_24F 1st F YCGAATAGAATTAGGWAAYCCAGG - COI_396R Half R WGCTCCTAAAATTGADGAAATWCC - Ron 2nd F GGATCACCTGATATAGCATTCC Monteiro & Pierce, 2001 Nancy Half R CCTGGTAAATTAAAATATAAACTTC Monteiro & Pierce, 2001

Table 2-2. A summary of the results and estimated number of Hermeuptychia species generated by the various methods tested in this investigation Outgroups Same Morphology Different Morphology Putative Method Version Separated Group but Split Group but Lumped Species ‘H. atalanta' 4 Multiple ‘H. hermes' 4 GMYC No - 79 thresholds ‘H. maimoune'  2 ‘H. maimoune'  2

Table 2-2. Continued Outgroups Same Morphology Different Morphology Putative Method Version Separated Group but Split Group but Lumped Species ‘H. gisella'  3 H. intricata 3

H. sosybius 3 ‘H. harmonia' 3 ‘H. maimoune'  1 ‘H. maimoune'  ‘H. gisella'  mPTP ‘H. gisella'  PTP Yes - 1 17 (Single rate) ‘H. cucullina'  H. intricata H. hermybius 1 H. sosybius bPTP ‘H. atalanta' 2 ‘H. maimoune'  PTP (Maximum Yes H. intricata 2 1 39 ‘H. maimoune'  likelihood) H. sosybius 2 ‘H. atalanta' 4 ‘H. maimoune'  2 Recursive ‘H. gisella'  2 ABGD Yes - 48 partitioning H. sosybius 2 ‘H. harmonia' 3 ‘H. pimpla’ 2 *Numbers listed under putative species exclude outgroup taxa

Figure 2-1. Distribution map of barcoded Hermeuptychia specimens. White dots indicate localities from which previously published specimens have been collected. Black dots indicate the new localities incorporated in this study. Within the red box, an expanded view of Ecuador with robust population-level representation is provided. Data and Image from Keith Willmott.

Figure 2-2. Bayesian inference (BEAST2) tree with posterior probabilities (top) > 0.5 and bootstrap values (bottom) > 50 indicated. Sequences generated in this study have a LEP/IN prefix and are in blue for easy visualization. Species boundaries as indicated by bPTP (black), ABGD (green) and GMYC (grey) are illustrated as vertical colored bars on the side. * Indicates groups that were recovered as one putative species but appear separated due to the underlying phylogeny. Red vertical bars denote putative species that do not include any previously published sequences. Horizontal colored bars and shapes appearing alongside specimens denote morphology groupings detailed in Seraphim et al., (2014).

Object 2-1. Figure 2-2 as a PDF document (.pdf file 8kb).

H. clara + new sequences (ns) 1 98 ‘H. cucullina’ ¢ 1 ‘H. gisella’ A + ns 74 0.93 ‘H. gisella’ B + ns 51

1 H. intricata + ns

93 ‘H. gisella’ p + ns

Figure 2-3. Left - The unresolved relationships of the ‘H. cucullina’ and ‘H. gisella’ complex from the previous COI phylogeny. Source: Seraphim, N., Marín, M. A, Freitas, A. V.L. & Silva-Brandão, K.L. 2014. Morphological and molecular marker contributions to disentangling the cryptic Hermeuptychia hermes species complex (Nymphalidae: Satyrinae: Euptychiina). Mol. Ecol. Resour. 14: 39–49. Right – Relationships involving the same morphology groups from our phylogenetic analyses based on Bayesian inference. Support values > 50 are indicated with posterior probability values indicated above the branch and bootstrap values indicated below the branch.

CHAPTER 3 USING GENOME-WIDE, SINGLE-NUCLEOTIDE POLYMORPHISMS TO RIGOROUSLY INVESTIGATE PHYLOGENETIC RELATIONSHIPS AND SPECIES LIMITS IN ECUADORIAN HERMEUPTYCHIA

Inferences in systematics based on a single marker can be complicated by the influence of intracellular endosymbiotic bacteria (e.g., Wolbachia) on mitochondrial DNA

(mtDNA) variation (Hurst & Jiggins, 2005; Whitworth et al., 2007; Zabal-Aguirre et al.,

2014), the possibility of co-amplification of nuclear pseudogenes (non-functional copies of mtDNA) (Bensasson et al., 2001; Song et al., 2008), the discovery of differential levels of introgression for uni-parentally inherited genes (Petit & Excoffier, 2009), instances of heteroplasmy (Magnacca & Brown, 2010) and potential discordance between individual gene trees and species trees (Nichols, 2001). Most authors, therefore, now preferentially incorporate data from multiple independent genetic markers to reduce misidentifications, overestimation of species numbers and erroneous inferences about evolutionary histories (Caterino et al., 2000; Rubinoff, 2006; Edwards,

2009). The availability of high-throughput, massively parallel sequencing technologies

(i.e., next-generation sequencing; NGS) has revolutionised the scale at which molecular data can be generated, and thus the questions that can be addressed in non-model organisms with limited or no existing genomic information. However, many NGS-based protocols have stricter requirements for DNA quality and quantity in order to maximize utility, data robustness and overall cost effectiveness.

The term ‘restriction-site associated DNA sequencing’ or RAD-Seq refers to a reduced representation libraries, NGS technique where genome-wide variation is sampled by targeting regions that flank the recognition sites of restriction enzymes

(Lemmon & Lemmon, 2013). With this approach, genomic DNA (gDNA) is first digested

with one or more restriction enzymes. The restriction fragments then undergo size selection followed by library preparation and high-throughput sequencing of pooled samples from multiple individuals (Baird et al., 2008; Peterson et al., 2012). The availability of a wide array of restriction enzymes (differing in methylation sensitivities and nature of recognition sites) adds to the versatility of this method as an assay tool.

Since the fragmentation of DNA is inherent to this technique, it is also more ‘tolerant’ of degradation in older specimens. Assembling the full genomes of multiple individuals within a population is very costly and often unnecessary since many biological questions can be satisfactorily addressed using polymorphisms from a subset of genomic regions. Therefore, a major appeal of RAD-Seq is that variation across the genome is evaluated at a fraction of the cost of whole-genome sequencing. As compared to other restriction-enzyme-based methods for marker discovery and genotyping, such as restriction fragment length polymorphisms (RFLP) and amplified fragment length polymorphisms (AFLP), RAD-Seq makes it possible to simultaneously identify, validate and score hundreds of thousands of genomic markers in hundreds of individuals, avoiding an otherwise costly and laborious process (Davey et al., 2011).

Reinvigorating the field of population genomics, RAD-seq has been widely applied towards linkage or quantitative trait locus mapping (e.g. Baxter et al., 2011; Gonen et al., 2014) as well as genotyping, phylogeography and the characterization of gene flow between wild populations (e.g. Hohenlohe et al., 2013; Silva et al., 2018).

More recently, RAD-Seq has demonstrated promising scalability and has been effective for inferring broader interspecific phylogenetic relationships. In silico studies using data from Drosophila, mammals and yeast have demonstrated sufficient retention

of orthologous restriction sites across species (Rubin et al., 2012), and genome-wide

RAD tags have also proven useful for resolving species-level relationships using empirical data from all major lineages including flowering plants (Eaton & Ree, 2013), ground beetles (Cruaud et al., 2014; Takahashi et al., 2014), freshwater fishes (Jones et al., 2013; Wagner et al., 2013), deep sea octocorals (Pante et al., 2015), neotropical

Adelpha butterflies and their temperate sister genus Limenitis (Ebel et al., 2015) and

Heliconius butterflies (Nadeau et al., 2013).

In this chapter, I focus on putative species of Hermeuptychia occurring in

Ecuador, due to the availability of specimens suitable for double-digest RAD-Seq

(ddRADseq) and detailed collection information that will facilitate the mapping of geographic distributions. In ddRADseq, the single restriction enzyme digest coupled with secondary random fragmentation is replaced by a more precise two-enzyme double digest which facilitates the elimination of genomic regions flanked by recognition sites that are either too close or too distant, via size selection (libraries will consist of fragments that are closer to the target size). This also allows researchers to better adjust the number of genomic markers and fraction of the genome that is to be sampled

(Peterson et al., 2012). The sizeable amount of resulting SNP data will be used to simultaneously achieve several aims: First, I aim to rigorously test species limits inferred from DNA barcoding and the application of delimitation methods by characterizing intraspecific genetic structure. Second, I will generate robust phylogenies based on genome-wide molecular variation, that provide resolution of deeper relationships within

Hermeuptychia. Additionally, I will compare two emergent methods for clustering, filtering and analyzing SNP datasets:

1. Inferring phylogeny directly from biallelic markers (SNPs) using a Bayesian coalescence model.

2. Phylogeny reconstruction based on a supermatrix of concatenated ddRAD-Seq loci, using maximum likelihood tree inference.

These approaches function under different sets of assumptions, and thus have their own limitations. The SNP and AFLP package for phylogenetic analysis (SNAPP) approach assumes low levels or no gene flow between terminal branches and may produce misleading results when applied onto deeper species divergences and/or minimally-informative genes (Lanier & Knowles, 2015; Xi et al., 2015). Concatenation methods, on the other hand, have been found to be susceptible to a wide range of potential errors due to the inability to accommodate heterogeneity in gene trees, substitution parameters and evolutionary rates (Edwards et al., 2007; Salichos & Rokas,

2013; Simmons & Goloboff, 2014; Roch & Steel, 2015; Song et al., 2015). Therefore, it is preferable to implement both approaches and seek congruent topologies.

Investigating genome-wide variation by surveying SNPs is generally acknowledged to be a more rigorous molecular approach than examining just a single rapidly evolving marker. However, despite the increased affordability of NGS technologies, DNA barcoding will remain as a cheaper, simpler (especially in terms of bioinformatics skills required to handle and analyze NGS datasets) and more accessible

(with less stringent requirements for sample quality and DNA quantity) approach for rapidly identifying species and preliminarily assessing diversity. In this study, I take advantage of a unique opportunity to directly compare the two approaches (single locus,

COI versus thousands of SNPs), using the same robust sample set, for a group of cryptic species with genuinely minimal a priori understanding of the species-level taxonomy. Since multiple kinds of data will be obtained from the same specimens, a

level of agreement may then be established and used to calibrate/extrapolate the signal from DNA barcoding. In this manner, we will be able to more accurately identify taxonomically meaningful divergence in the commonly sequenced COI in related groups under study.

These resources combined will help facilitate research where only older or improperly preserved rare specimens are available and it is neither feasible nor prudent to re-acquire fresh specimens of taxa that are already well-represented in existing collections. It will also allow long-term biodiversity surveys, for which numerous specimens have already been DNA barcoded, to remain relevant for informing further research. Furthermore, DNA barcoding allows new data to quickly be included and analyzed in a growing dataset, and 'democratizes' science since many smaller institutions have the resources to do DNA barcoding but not genomic work. Such issues are pertinent for biodiversity researchers having to adapt to the large shift in scale at which molecular data can be generated and my results should therefore aid our ability to discover, classify and conserve biodiversity.

Materials and Methods

Hermeuptychia Genome Assembly

Having a reference genome, especially that of a closely related taxon, is advantageous for several aspects of RAD-Seq including the ability to closely predict restriction enzyme cut sites and sequencing coverage. Reference genomes can be used for the alignment of sequencing reads to provide additional context for gene identity and function since it confers greater confidence in the assessment of gene orthology. In the summer of 2015, Florida specimens were provided as a contribution towards an on-going effort to sequence and assemble genomes for all USA

Hermeuptychia at Dr Nick Grishin’s laboratory (University of Texas Southwestern).

Using specimens collected from localities in Texas, paired-end (250 bp and 500 bp) and mate-pair (2, 6 & 15 kb) libraries had already been constructed. I constructed 400 bp paired-end libraries (for H. intricata, H. sosybius and two potential new species from

Ecuador) as well as a RNA-seq library for H. hermybius in order to complement existing data (library prep and genome assembly methodology is detailed in Cong et al., 2015,

2017). Preliminary results demonstrated low genome coverage which was determined to be due to a larger than expected genome size (unpubl. data). This vital information has had great impact on the design of my study since genome size directly determines the optimal number of specimens to include in a sequencing lane.

Sampling and DNA Extraction

A total of 110 specimens representing all evolutionarily significant units (i.e. putative species) implied by the DNA barcoding study described in Chapter 2 were selected in a manner that maximized the representation of COI haplotypes (additional information, including starting DNA concentrations, can be found in Table A-3).

Whenever possible, such as where there were multiple specimens from the same population with identical barcodes, genomic DNA was re-extracted via overnight digestion of the intact thorax of alcohol-preserved specimens, using the DNeasy Blood

& Tissue Kit (Qiagen, Valencia, CA, USA). In order to maximize final DNA concentration while minimizing compromise to overall yield, DNA was eluted twice using only 50 μl of

Buffer AE each time. The wings and abdomens of these specimens were then set aside for morphological survey so that multiple data types were available from the same specimens. Where re-extractions were not possible, whole genome amplification was performed using the REPLI-g Mini Kit (Qiagen, Valencia, CA, USA).

Sample quality (amount of intact, high molecular weight DNA) was determined by running 2 μl genomic DNA (gDNA) on 0.7% agarose gel at 120 volts for 30 minutes.

DNA concentration was quantified using Qubit dsDNA Broad Range Assay Kit

(Invitrogen, USA).

Double-Digest RAD-Seq Library Preparation

Libraries were prepared using a modified version of the Peterson et al., (2012) protocol. The restriction enzymes, EcoRI and MspI were used for double digestion.

Sera-Mag magnetic beads (Thermo Scientific, USA) were used for post restriction and adapter ligation clean up. Ligated samples were pooled in equimolar concentration and size selection for 350 bp fragments was performed using the Pippin Prep system (Sage science, USA). Clean up steps performed after size selection and PCR enrichment was performed with Agencourt AMPure XP beads (Beckman Coulter Inc., USA). Throughout the library preparation process, DNA concentration was quantified using Qubit dsDNA

High Sensitivity DNA Assay Kit (Invitrogen, USA). A final quality check for fragment size was performed using the Fragment Analyzer (Advanced Analytical). A single, 150 bp paired-end Illumina Hi-seq 4000 run was performed at the Genome Institute of

Singapore Next-Generation Sequencing Platform (GIS-NGSP).

Pipeline for SNP-Based Analyses

Quality filtering and SNP calling

The process radtags program in STACKS v 1.45 (Catchen et al., 2011, 2013) was used to demultiplex the resulting genomic data. Low quality reads (with phred+33 scores less than 10) and reads containing one or more uncalled base were discarded.

The procedure allowed for one mismatch in the barcodes. Demultiplex reads were truncated to 145 bp. The draft genome of a H. intricata specimen (NVG-3318, female,

San Jacinto CO. TX) was used as a reference genome. The reference genome was indexed and aligned against demultiplexed reads using the Burrows-Wheeler aligner

(BWA) v 0.7.16a (Li & Durbin, 2009) and SAMtools v 1.6 (Li et al., 2009). All reads with

MAPQ scores lower than 20 were discarded.

Single nucleotide polymorphisms (SNP) calling was performed using the ref map.pl and populations programs in STACKS v 1.45 (Catchen et al., 2011, 2013). The minimum number of identical, raw reads required to create a stack was set at 10 with all other settings left at default.

Several different genomic datasets were generated for downstream analyses.

First, SNPs were called for each of the putative species groups, including two outgroup sequences, generating ten individual datasets. This allowed for a better evaluation of

COI-based species delimitations (i.e. bPTP vs ABGD vs GMYC) as the focus is placed on shallower divergences that occurred at more recent evolutionary timescales. Then, another dataset was generated by calling for SNPs using all sequenced specimens in order to investigate deeper evolutionary relationships across Ecuadorian

Hermeuptychia taxa. The final number of SNPs in each dataset was determined by two main factors: (a) the minimum number of populations a locus must be present in for it to be retained (designated as p value and must be an integer) and (b) the minimum percentage of individuals in a population required to process the locus (designated as r value and can include a decimal place). A single random SNP was retained from each qualifying locus. A range of values, for p and r, were tested in order to minimize missing data while maximizing SNP count: final settings for each dataset are summarized in

Table 3-1.

Within each dataset, SNPs were then filtered to remove markers that demonstrated strong linkage disequilibrium (pairs of variants with squared correlation, r2, greater than 0.9) using the indep-pairwise command in PLINK v 1.9

(http://pngu.mgh.harvard.edu/purcell/plink/; Purcell et al., 2007). Finally, the program

BayeScan v 2.1 (Foll & Gaggiotti, 2008; Foll et al., 2010; Fischer et al., 2011) was used to test for the neutrality of SNPs, so that markers under strong selection might be removed.

All analyses were performed on the HiperGator2 computing cluster at the

University of Florida.

Investigating population substructure

Two different methods were applied to each of the ten putative species groups in order to detect and visualize genetic structure among populations: Principle component analysis (PCA) and Bayesian clustering.

PCA was performed using the Ade4 v 1.7-10 (Dray & Dufour, 2007) and factoextra v 1.0.5 (http://www.sthda.com/english/rpkgs/factoextra/) packages in R.

The program STRUCTURE v 2.3.4 (Pritchard et al., 2000) was used to implement a Bayesian algorithm for assigning individuals to a predefined number of genomic clusters (K), based on differences in allele frequency. The admixture model of correlated allele frequencies was utilized, with 100,000 burn-in and 500,000 Monte

Carlo Markov Chain (MCMC) repetitions. Ten iterations were performed for each value of K, where K ranged from 2 – 8 (if number of specimens < 8, then K was set to the number of specimens). STRUCTURE results were then compiled and examined to identify the most statistically supported value for K (via the Evanno method), using

STRUCTURE HARVESTER (Earl & vonHoldt, 2012) and CLUMPP (Jakobsson &

Rosenberg, 2007). For easy visualization of STRUCTURE/CLUMPP output, plots were generated in R. STRUCTURE and CLUMPP analyses were performed on the

HiperGator2 computing cluster at the University of Florida.

Inferring species tree from biallelic markers

The SNAPP algorithm computes the likelihood of a species tree topology directly from unlinked biallelic markers (such as well-spaced SNPs generated by ddRAD-Seq) under a ‘finite-sites’ model for mutation. The multispecies coalescent model is implemented using a Bayesian Markov chain Monte Carlo (MCMC) sampler and is able to infer branch lengths (representing divergence times) and population sizes where sufficient mutations have occurred throughout the species tree (Bryant et al., 2012).

Parameters for a SNAPP analysis are set up using the BEAST v 2.4.7 package

(Bouckaert et al., 2014). A XML file was prepared using BEAUti (Drummond et al.,

2012). Samples being investigated were grouped according to genomic distinctiveness as indicated by Bayesian clustering (see previous section) for investigating population- level divergences. Due to computational constraints, samples representing different populations were merged for the larger analysis involving all Ecuadorian Hermeuptychia samples. A direct estimate for mutation rates (U and V) was obtained by using the option to “Calc mutation rates”. All other priors were kept at default settings and Markov chain Monte Carlo (MCMC) chain length was set at 5 × 106, logging every 1 × 103 sample. Resultant log files were evaluated in TRACER v 1.6.0

(http://beast.bio.ed.ac.uk/Tracer) to ensure convergence had been achieved and that the Bayesian runs had reached an effective sample size > 200 after burn-in.

Independent runs were combined using LogCombiner v 2.4.5 with 10% burn-in.

Maximum clade credibility trees with posterior probability limit set at 0.5 and mean node

heights were constructed in TreeAnnotator v 2.4.5. Resulting phylogenetic trees were visualized and edited in Figtree v 1.4.3 (http://tree.bio.ed.ac.uk/software/figtree/) and

Adobe Illustrator CS6 v 16.0.4 (Adobe Systems Inc.).

Pipeline Resulting in Supermatrix of ddRAD-Seq Loci

The iPyRAD toolkit (https://github.com/dereneaton/ipyrad) was utilized to filter and cluster demultiplexed reads (sequence reads that have already been sorted according to sample indexes) to produce a supermatrix of concatenated loci. An advantage of iPyRAD is that it is designed to accommodate data from disparate taxa

(representing evolutionary relationships at deeper phylogenetic scales) using global alignment clustering, which allows for indel variation when determining homology. This helps to recover more shared loci from distantly related samples (Eaton, 2014). The draft genome of the H. intricata specimen (NVG-3318, female, San Jacinto CO. TX) was again used as a reference genome for the mapping of reads based on sequence similarity. Paired reads were merged if they were determined to overlap partially. The final ddRAD-Seq loci alignment was assembled by adjusting the minimum number of samples required to possess a locus before it is retained (i.e. the min_samples_locus parameter). For reconstructing phylogenetic relationships across Hermeuptychia, the minimum number of samples was set at six. Due to computational constraints, several values were tested in order to attain a final dataset that was between 1 – 3 million bp in length. For assessing population substructure within putative species, minimum number of samples was set at four instead (since the putative species groups were individually represented by a smaller number of specimens). An R script was written to extract information for a subset of individuals for each putative species group. All other parameters were left at default.

Molecular phylogenies were generated from the resulting supermatrix (the concatenation of RAD-Seq loci that passed all filtering thresholds) using maximum likelihood (ML). The ML analysis was performed using RAxML v 8.2.8 (Stamatakis,

2014). The best scoring ML tree was evaluated under the GTRGAMMA model and 1000 rapid bootstrap replicates were conducted. All analyses were implemented on the

HiperGator2 computing cluster at the University of Florida

Results

Using the STACKS pipeline, approximately 532 million reads were obtained from

109 individuals after quality-filtering to remove low quality reads and reads with ambiguous barcodes (99.30% retained reads). There was an average of 4.9 million reads per individual, with an overall range of 1.1 – 15.7 million reads per individual. A total of 432,427 loci was obtained after alignment with the H. intricata reference genome.

When a minimum of four samples were required to possess a locus before it was retained, iPyrad retained 28,722 loci producing a concatenated alignment that was

7.5 million bp in length.

Genetic Structure within Putative Species

For each of the ten species groups, population sub-structure was first investigated using PCA and Bayesian clustering (using STRUCTURE), implemented on individual SNP datasets. When consistent genomic structure was observed, branch lengths within phylogenies derived from two independent tree inference methods were compared (i.e. genomic distance between outgroups were used as ‘yardsticks’ to further assess distinctiveness). Since there are no fixed thresholds for teasing apart intra- from

inter- specific variation, the outcomes of all four approaches are evaluated collectively for a more comprehensive perspective.

No overt genomic structure was present within three of the ten putative species investigated: Species 4, Species 5 and ‘Pimpla’. The former two groups were so named because they did not consist of any previously sequenced specimens that were already associated with existing names. For all three putative taxa, the most statistically supported value of K (i.e. number of clusters) as identified by the STRUCTURE analyses, was K = 2. Under this value of K, individuals of each group appeared to be genetically homogenous. For Species 4 and ‘Pimpla’, this pattern was also true for all other K values tested (Figure 3-1, 3-2). In Species 5 however, three distinct groups were observed under maximum value of K (= total number of samples), 3 (Figure 3-3).

Similarly, I was unable to identify any combination of principle components (PCs) that could consistently distinguish specimens in these groups.

‘Atalanta’ group

As the ‘Atalanta’ group was represented by eight specimens, K values between 2 and 8 were tested in STRUCTURE. The value of K with the highest statistical support was K = 2, clustering the specimens into two distinct groups (Figure 3–4). There was no further splitting at all higher values of K (i.e. two distinct groups were produced at K = 3

– 8). Providing additional support, the combination of PC1 and PC2 (accounting for

38.56% of the observed variation) as well as PC1 and PC3 (accounting for 35.78% of observed variation) separated the samples in the same manner (Figure 3-4). The observed branch lengths between the two ‘Atalanta’ groups were also comparable to inter-specific branch lengths between outgroups, Species 2 and 3 (Figure 3-5).

‘Harmonia’ group

Applying the STRUCTURE analysis on the ‘Harmonia’ group, the value of K with the highest statistical support was identified as K = 4. Under this setting, four clusters were produced via Bayesian clustering with no further splitting occurring at higher values of K. Together, all three principle components accounted for 41.91% of the observed variation (PC1: 16.37%, PC2: 14.23% and PC3: 11.31%) and consistently divided the samples in the same manner (forming four distinct clusters) (Figure 3-6). In both the SNAPP and RAxML phylogenies, the branch lengths between the ‘Harmonia’ groups were also longer than the intra-specific variation observed within the outgroup,

Species 6 (Figure 3–7).

‘Gisella’ and ‘Clara’ groups

For both the ‘Gisella’ and ‘Clara’ groups, K = 2 was the most well supported number of clusters in the STRUCTURE analysis. For the ‘Gisella’ group, the combination of PC1 and PC2 (accounting for 38.35% of observed variation) as well as

PC1 and PC3 (36.00% of observed variation) separated the nine specimens in the same manner as Bayesian clustering (Figure 3-8). Interestingly, in the ‘Gisella’ species tree (generated using SNAPP) these two clusters are paraphyletic (‘Gisella’ 2 is sister to

Species 4 instead of ‘Gisella’ 1). In the ML tree, the ‘Gisella’ clusters are recovered as sisters but with very low bootstrap support (50; Figure 3-9). Here, the branch lengths observed were similar to the inter-specific branch lengths observed between the outgroups (Species 4 and ‘Clara’).

The PCA analysis for the ‘Clara’ group was not as informative due to low sample size (n = 3) (Figure 3-10). In both the STRUCTURE analysis and molecular phylogenies, one sample (LEP 37596) was identified as being distinct from the rest. In

the SNAPP tree, the branch lengths separating LEP 37596 from the rest of ‘Clara’ were comparable to the interspecific branch lengths between the outgroup taxa, Species 4 and ‘Gisella’ (Figure 3-11). In the ML phylogeny however, the ‘Gisella’ branch was much longer.

Species 3 group

The most statistically supported value of K in the STRUCTURE analysis for

Species 3 is K = 3. At this value of K, three distinct groups are produced, and no further splitting occurs at K = 4. In the PCA analysis, the combination of PC1 and 2 (which accounts for ~75% of the observed variation), in particular, delineates these groups in the same manner (Figure 3-12). Looking at the reconstructed phylogenies, the inter- specific branch lengths (that are indicative of genomic distance) between outgroups,

Species 1 and Species 2, are more pronounced than the branching within the Species 3 clusters (Figure 3-13).

‘Maimoune’ and ‘Hermes’ groups

Both the ‘Hermes’ and ‘Maimoune’ groups are separated into three clusters under the most supported value of K, K = 3. Again, no further splitting occurs at all larger values of K (4 – 8). Together PC1, PC2 and PC3 only explains 34.18% of the observed variation in ‘Maimoune’ and 34.81% in the ‘Hermes’ group. In both instances, the combination of PC1 and PC2 as well as PC1 and PC3, only separates the samples into two groups (Figure 3-14, Figure 3-16). Looking at branch lengths, the branches representing ‘Maimoune’ groups are longer than the intra-specific variation of the two outgroup sequences (LEP 14859 and LEP 14861 are from the same STRUCTURE clusters in ‘Hermes’) (Figure 3-15). This ‘yardstick’ approach is less helpful for the

‘Hermes’ group where there is conflict between the SNAPP and RAxML trees.

Specifically, the ‘Hermes’ branches are much differentiated in the species tree, but opposite is true under ML inference (Figure 3-17).

Phylogeny Inferred from Genome-Wide Variation

A minimum of six samples were required to possess a locus before it was retained to investigate phylogenomic relationships in Ecuadorian Hermeuptychia. Under this condition, iPyrad retained 11,095 loci resulting in a final supermatrix that was 2.9 million bp in length. This ddRAD-Seq loci alignment had 189,844 distinct alignment patterns and the combined bootstrap and ML search took a total of 82.15 hours to complete (multi-threaded version, 30 threads).

As compared to the phylogeny based on the single mitochondrial gene, COI, the resultant phylogeny based on genome-wide SNPs is more well-resolved and with moderately high branch support (final ML optimization likelihood: -4965824.473538)

(Figure 3-14, Object 3-1). One singleton specimen, LEP 17683 was previously recovered in the ‘H. fallax’ clade that was sister to the rest of Hermeuptychia (moderate support, posterior probability: 0.66 and bootstrap: 71). Here, the same specimen was placed sister to the rest of Ecuadorian Hermeuptychia, with the exception of the clade consisting of ‘Pimpla’ and ‘Harmonia’ instead (bootstrap support: 100). The shallower phylogenetic relationships that were well-supported in the COI phylogeny remain well- supported here. Furthermore, the previously uncertain placement and evolutionary relationships of several groups (including Species 1, Species 4, ‘Gisella’, ‘Hermes’,

Species 2 and ‘Maimoune’) were now better resolved with strong support. The reconstructed phylogenetic relationships were also concordant with the population substructure detection methods (see corresponding cluster coloration). Notably, there were two specimens (LEP 14843 from ‘Pimpla’ and LEP 18126 from ‘Atalanta’) that

were not recovered with their respective putative species groups. These same specimens were excluded from the SNAPP species tree analysis due to having large proportions of missing data during SNP calling.

Discussion

Double digest RAD-seq proved to be an effective tool for surveying genome-wide nucleotide polymorphisms in Hermeuptychia. As compared to the average pairwise distance of 4.21% (difference of 26 bp) in COI, thousands of loci were used here to test species limits as well as reconstruct phylogenomic relationships at multiple levels

(within putative taxa and across Ecuadorian Hermeuptychia). A key advantage of having such large and robust datasets is that multiple intra- and inter- specific evolutionary questions can be addressed simultaneously with appropriate specimen choice.

Investigating Species Limits with ddRAD-Seq

It should be noted that one property of the methods available for species assignment, such as STRUCTURE or PCA, is that with sufficient genome coverage

(number of loci) they are capable of detecting very subtle differences between populations (even populations with recent or ongoing gene flow that would not normally be classified as a separate species). This sensitivity reemphasizes the need for the subsequent validation of putative species using other data types and methods

(Rittmeyer & Austin, 2012).

Nevertheless, these above-mentioned approaches provided support for genomic homogeneity in three putative species: Species 4, Species 5 and ‘Pimpla’. This finding was in complete agreement with results from the Automatic Barcode Gap Discovery

(ABGD) species delimitation method (whereas GMYC divided Species 4 into three

species-units and bPTP split Species 5 into two). Notably, the geographic distributions of all three groups are restricted to eastern Ecuador (Figure 3-15).

The same approaches identified two divergent clusters within the ‘Atalanta’ group. The distinctiveness of these clusters is further reinforced by branch lengths that are comparable to the inter-specific branch lengths of the outgroups from Species 2 and

Species 3 (Figure 3-5). It is important to note that this ‘yardstick’ approach informs of relative (instead of absolute) genome-wide differences as compared to the selected outgroups. Also, longer branch lengths do not necessarily mean that a clade should be elevated to species status since there are many factors (such as evolutionary history, age, size of geographic range, population size and a variety of other biotic or abiotic selective pressures with varied effects on the genome) that can lead to intra-specific variation being greater than inter-specific variation even between closely related taxa.

Perhaps a more compelling evidence that ‘Atalanta’ is likely to comprise of two evolutionary significant species units is that the geographic distributions of these two clusters overlap (i.e. there is genomic heterogeneity despite sympatry) (Figure 3-16).

It is a similar situation for the ‘Harmonia’ group but in this case four distinct clusters are identified by STRUCTURE with every combination of PC1, PC2 and PC3 segregating the samples in the same manner (Figure 3-6). Tree inferences using

SNAPP and RAxML are also consistent in that the branch lengths within ‘Harmonia’ are much longer than the intra-specific distances observed within the two outgroup sequences from ‘Pimpla’. Whilst the ‘Harmonia’ 1 (grey) and ‘Harmonia’ 2 (green) clusters are reliably recovered as sisters with high support, the relationship between the other two clusters is more uncertain. The better-supported outcome is the ML tree

where ‘Harmonia’ 4 (blue) is sister to ‘Harmonia’ 1 + ‘Harmonia’ 2 (grey + green) and

‘Harmonia’ 3 (orange) is sister to this larger clade (Figure 3-7). Incorporating distribution data, it seems likely that the observed variation between the grey and blue genomic groups as well as the orange and green clusters may be attributed to geographical isolation from being on either side of the Andes (Figure 3-17). Taking everything into consideration, ‘Harmonia’ could consist of as many as four evolutionary significant units or more conservatively, two sympatric taxa (taxon one: green and grey ‘Harmonia’, taxon two: orange and blue ‘Harmonia’) with prominent population-level variation due to the presence of physical barriers.

Furthermore, two allopatric clusters were identified within the ‘Gisella’ group

(Figure C-5, C-6). The validity of the ‘Gisella’ clusters finds support in the PCA results

(Figure 3-8) and branch lengths in both phylogenies. Notably, in the species tree inferred directly from SNPs (SNAPP), one of the ‘Gisella’ clusters (‘Gisella’ 2, blue) is recovered as sister to Species 4 (outgroup) with high support (posterior probabilities:

0.93). This relationship is also recovered in the ML tree generated for all Ecuadorian

Hermeuptychia samples albeit with lower support (Figure 3-14, bootstrap support: 53).

Despite the marked genomic variation observed, specimens from these ‘Gisella’ clusters are distributed on different sides of the Andes (Figure-3-22). Therefore, it is hard to distinguish, from genomic data alone, if these do represent different species or are populations demonstrating high genomic variation due to geographic isolation.

The same challenge is true for the remaining putative species groups:

‘Maimoune’, ‘Hermes’, ‘Clara’ and Species 3, where the allopatry of identified clusters

has made interpretation of the substructure discovery methods less straightforward

(Figures 3-23).

Comparing the species discovery methods implemented in this Chapter, I find that PCA is not very suitable for use with only a small number of individuals. Having too few data points makes determining if clustering patterns are consistent very challenging.

Conversely, the results of Bayesian clustering implemented in the program,

STRUCTURE (coupled with the Evanno et al., (2005) K statistic) is much easier to interpret and is highly concordant with branching patterns in all resultant phylogenies.

However, having to perform 5-15 replicates (with different random seeds) of each value of K to be tested is time consuming and computationally tedious.

Phylogenomic Relationships across Ecuadorian Hermeuptychia

A major limitation of the COI phylogeny (as discussed in Chapter 2) was that many deeper phylogenetic relationships remained unresolved. Here, major improvements are made as several groups are finally placed with moderate to high branch support:

 The ‘Gisella’ group is recovered as sister to Species 4 (bootstrap value: 100)

 Species 1 is sister to ‘Gisella’ + Species 4 (bootstrap value: 75)

 Species 2 is recovered as sister to ‘Atalanta’ (bootstrap value: 100)

 ‘Maimoune’ is sister to Species 2 + ‘Atalanta’ (bootstrap value: 83)

 ‘Hermes’ was previously sister to ‘Maimoune’ with very low branch support (posterior probability: 0.52, no bootstrap support). Now, ‘Hermes’ is recovered as sister to a large clade consisting of Species 3, Species 5, ‘Clara’, ‘Maimoune’, (Species 2 + Atalanta) (bootstrap value: 94)

The Species 3, Species 5 and ‘Clara’ clades are each monophyletic with 100 bootstrap support (as they were in the COI phylogeny) but their positions amongst Ecuadorian

Hermeuptychia remain unclear.

At present there is only a limited number of tools for the inference of species phylogenies based on SNP data from multiple samples per species. The most commonly used software package is BEAST (Bouckaert et al., 2014) with its template

SNAPP (Bryant et al., 2012) which implements a Bayesian coalescent analysis.

Unfortunately, SNAPP treats each SNP as having its own ‘gene tree’ and requires that at least one lineage/population per species must have data at that site for it to be retained. If one or more species is missing data at a particular site, this SNP is removed from the dataset completely. In this manner, SNAPP can only tolerate small proportions of missing data and this is problematic for analyses involving very divergent groups (i.e. deeper phylogenetic relationships). Even after merging all distinct clusters for each putative species group to increase the probability of retaining a site, SNAPP removed

1030 sites (only 287 sites were retained).

Therefore, a significant advantage of the concatenation approach (where a supermatrix of ddRAD-Seq loci is generated) is that samples with large portions of missing data can still be included in the analysis without affecting SNP calling for the rest of the sample set. In the RAxML phylogeny for Ecuadorian Hermeuptychia, three of the five samples that had to be excluded from SNP-calling in STACKS, due to having large proportions of missing data, were appropriately recovered with other specimens from the same putative species group. However, LEP 14843 (‘Pimpla’) and LEP 18126

(‘Atalanta’) were recovered amongst ‘Atalanta’ and ‘Maimoune’ respectively, instead.

Therefore, while there is some evidence that minimal filtering of sequencing reads and the inclusion of more loci by relaxing cut-offs for missing data can help improve phylogeographic resolution (e.g Hodel et al., (2017)), extreme caution should be taken to ensure that this compromise does not lead to inaccurate conclusions.

Further comparing the two popular approaches for analyzing SNP datasets and generating phylogenies, I found that SNAPP is computationally very demanding and severely more time consuming than RAxML (and other ML based algorithms for tree reconstruction such as IQ-TREE). Our dataset comprised of 287 retained sites (from

108 sequences designated into 14 ‘taxa’ by merging distinct Bayesian clusters) and it took a month to generate 265,000 states (number of tasks = 10, CPUs per task = 3 and memory = 5 gb). It helped that multiple parallel runs could be eventually be combined but this is still an immense investment of time and resources. Our preliminary SNAPP tree (single run and 1.16 × 106 states) demonstrated strong support for three sister relationships that were in agreement with the RAxML topology:

 ‘Harmonia’ and ‘Pimpla’ (posterior probability: 1.00)  ‘Atalanta’ and ‘Maimoune’ (posterior probability: 0.84)  ‘Gisella’ and Species 4 (posterior probability: 0.94)

LEP 17683 was recovered as sister to ‘Harmonia’ + ‘Pimpla’ (posterior probability: 0.82) and this concurred with the COI phylogeny instead (Figure C-1).

In general, the exploration of genetic variation using COI (i.e. DNA barcoding), did a fairly good job of accurately capturing the patterns of genome-wide variation. As mentioned, the well-supported phylogenetic relationships inferred from COI remained stable even as genome-wide SNPs were investigated. Unsurprisingly, individuals representing different localities but sharing the same DNA barcode were always

recovered in the same ddRAD-Seq cluster. The distinctiveness of the clusters identified by STRUCTURE and PCA were most accurately predicted by the ABGD method

(whereas GMYC tended to over split the groups). However, the efficacy of the various species delimitation methods that were implemented and discussed in Chapter 2 will be better known once the statuses of these distinct clusters are further resolved after examining morphological characters.

Table 3-1. Filtering conditions and resulting SNP counts for individual datasets Proportion SNP Species group Outgroup sequences p value r value missing data count LEP18185 (Species 2) ‘Atalanta’ 3 0.7 0 – 0.11 1859 LEP17666 (Species 3) LEP42216 (‘Maimoune’) ‘Hermes’ 2 0.75 0 – 0.19 1989 LEP37424 (‘Maimoune’) LEP14859 (‘Hermes’) ‘Maimoune’ 2 0.75 0 – 0.17 1814 LEP14861 (‘Hermes’) All sequences from Species 3 3 0.75 0 – 0.10 1189 Species 1 & 2 All sequences from ‘Clara’ 2 0.7 0 – 0.06 1261 Species 4 All sequences from Species 4 2 0.7 0 – 0.06 1261 ‘Clara’ LEP18157 (‘Clara’) ‘Gisella’ 3 0.7 0 – 0.09 1609 LEP18131 (Species 4) LEP14803 (‘Clara’) Species 5 2 0.6 0 – 0.08 1807 LEP18157 (‘Clara’) LEP14838 (‘Pimpla’) ‘Harmonia’ 2 0.6 0 – 0.22 1976 LEP14839 (‘Pimpla’) LEP42048 (‘Harmonia’) ‘Pimpla’ 2 0.6 0 – 0.10 1511 LEP55527 (‘Harmonia’) All specimens - 20 0.1 0.11 – 0.52 1333

Figure 3-1. Principle component analysis (PCA) and STRUCTURE results for Species 4. The numbers in parentheses indicate the percent genomic variation explained by each principle component (PC). A) PCA plot for PC1 against PC2, B) PCA plot for PC1 against PC3, C) PCA plot for PC2 against PC3 and D) STRUCTURE plot for the most statistically supported value of K, K=2.

Figure 3-2. Principle component analysis (PCA) and STRUCTURE results for ‘Pimpla’ The numbers in parentheses indicate the percent genomic variation explained by each principle component (PC). A) PCA plot for PC1 against PC2, B) PCA plot for PC1 against PC3, C) PCA plot for PC2 against PC3 and D) STRUCTURE plot for the most statistically supported value of K, K=2.

Figure 3-3. Principle component analysis (PCA) and STRUCTURE results for Species 5. The numbers in parentheses indicate the percent genomic variation explained by each principle component (PC). A) PCA plot for PC1 against PC2, B) STRUCTURE plot for the most statistically supported value of K, K=2 and C) STRUCTURE plot for K = 3.

Figure 3-4. Principle component analysis (PCA) and STRUCTURE results for the ‘Atalanta’ group. The numbers in parentheses indicate the percent genomic variation explained by each principle component (PC). A) PCA plot for PC1 against PC2, B) PCA plot for PC1 against PC3, C) PCA plot for PC2 against PC3 and D) STRUCTURE plot for the most statistically supported value of K, K=2.

Figure 3-5. Molecular phylogenies for the ‘Atalanta’ group. A) The phylogeny inferred from multispecies coalescence using SNAPP with posterior probabilities indicated and B) The phylogeny inferred from maximum likelihood using RAxML, with bootstrap values indicated.

Figure 3-6. Principle component analysis (PCA) and STRUCTURE results for the ‘Harmonia’ group. The numbers in parentheses indicate the percent genomic variation explained by each principle component (PC). A) PCA plot for PC1 against PC2, B) PCA plot for PC1 against PC3, C) PCA plot for PC2 against PC3 and D) STRUCTURE plot for the most statistically supported value of K, K=4.

Figure 3-7. Molecular phylogenies for the ‘Harmonia’ group. A) The phylogeny inferred from multispecies coalescence using SNAPP with posterior probabilities indicated and B) The phylogeny inferred from maximum likelihood using RAxML, with bootstrap values indicated.

Figure 3-8. Principle component analysis (PCA) and STRUCTURE results for the ‘Gisella’ group. The numbers in parentheses indicate the percent genomic variation explained by each principle component (PC). A) PCA plot for PC1 against PC2, B) PCA plot for PC1 against PC3, C) PCA plot for PC2 against PC3 and D) STRUCTURE plot for the most statistically supported value of K, K=2.

Figure 3-9. Molecular phylogenies for the ‘Gisella’ group. A) The phylogeny inferred from multispecies coalescence using SNAPP with posterior probabilities indicated and B) The phylogeny inferred from maximum likelihood using RAxML, with bootstrap values indicated.

Figure 3-10. Principle component analysis (PCA) and STRUCTURE results for the ‘Clara’ group. The numbers in parentheses indicate the percent genomic variation explained by each principle component (PC). A) PCA plot for PC1 against PC2, B) STRUCTURE plot for the most statistically supported value of K, K=2.

Figure 3-11. Molecular phylogenies for the ‘Clara’ group. A) The phylogeny inferred from multispecies coalescence using SNAPP with posterior probabilities indicated and B) The phylogeny inferred from maximum likelihood using RAxML, with bootstrap values indicated.

Figure 3-12. Principle component analysis (PCA) and STRUCTURE results for the Species 3 group. The numbers in parentheses indicate the percent genomic variation explained by each principle component (PC). A) PCA plot for PC1 against PC2, B) PCA plot for PC1 against PC3, C) PCA plot for PC2 against PC3 and D) STRUCTURE plot for the most statistically supported value of K, K=3.

Figure 3-13. Molecular phylogenies for the Species 3 group. A) The phylogeny inferred from multispecies coalescence using SNAPP with posterior probabilities indicated and B) The phylogeny inferred from maximum likelihood using RAxML, with bootstrap values indicated.

Figure 3-14. Principle component analysis (PCA) and STRUCTURE results for the ‘Maimoune’ group. The numbers in parentheses indicate the percent genomic variation explained by each principle component (PC). A) PCA plot for PC1 against PC2, B) PCA plot for PC1 against PC3, C) PCA plot for PC2 against PC3 and D) STRUCTURE plot for the most statistically supported value of K, K=3.

Figure 3-15. Molecular phylogenies for the ‘Maimoune’ group. A) The phylogeny inferred from multispecies coalescence using SNAPP with posterior probabilities indicated and B) The phylogeny inferred from maximum likelihood using RAxML, with bootstrap values indicated.

Figure 3-16. Principle component analysis (PCA) and STRUCTURE results for the ‘Hermes’ group. The numbers in parentheses indicate the percent genomic variation explained by each principle component (PC). A) PCA plot for PC1 against PC2, B) PCA plot for PC1 against PC3, C) PCA plot for PC2 against PC3 and D) STRUCTURE plot for the most statistically supported value of K, K=3.

Figure 3-17. Molecular phylogenies for the ‘Hermes’ group. A) The phylogeny inferred from multispecies coalescence using SNAPP with posterior probabilities indicated and B) The phylogeny inferred from maximum likelihood using RAxML, with bootstrap values indicated.

Figure 3-18. Maximum likelihood (RAxML) tree based on 11,000 concatenated ddRAD- Seq loci. Bootstrap values >50 are indicated. The colors correspond to clusters identified by STRUCTURE. * indicates low coverage samples that were left out of the SNAPP analysis. Samples that had undergone genome amplification prior to ddRAD-Seq library prep have ‘GA’ appended to the end of the specimen ID.

Object 3-1. Figure 3-18 as a PDF document (.pdf file 11.5 mb)

Figure 3-19. Geographic distributions of Species 4 (blue), Species 5 (Orange) and ‘Pimpla’ (Green). Distribution maps generated using Google My Maps, map data: Google.

‘ATALANTA’

Figure 3-20. Overlapping geographic distributions of the two distinct clusters identified within ‘Atalanta’. Colors correspond to STRUCTURE results in Figure 3-4. Distribution maps generated using Google My Maps, map data: Google.

‘HARMONIA’

Figure 3-21. Overlapping geographic distributions of the four distinct clusters identified within ‘Harmonia’. Colors correspond to STRUCTURE results in Figure 3-6. Distribution maps generated using Google My Maps, map data: Google.

‘GISELLA’

Figure 3-22. Geographic distributions of the two distinct clusters identified within ‘Gisella’. Colors correspond to STRUCTURE results. Distribution maps generated using Google My Maps, map data: Google.

‘MAIMOUNE’ ‘HERMES’

‘CLARA’ SPECIES 3

Figure 3-23. Geographic distributions of the distinct clusters identified within ‘Maimoune’, ‘Hermes’, ‘Clara’ and Species 3. Colors correspond to STRUCTURE results. Distribution maps generated using Google My Maps, map data: Google.

CHAPTER 4 RE-SURVEY OF WING AND MALE GENITALIA MORPHOLOGY IN ECUADORIAN HERMEUPTYCHIA

In Seraphim et al., (2014), ‘morphology groups’ were designated based on differences in male genitalia. Wing patterns were also documented but neither high quality images nor detailed description of differences were included in that publication.

In fact, specimens had not been spread and portions of the forewings remained obscured. The authors stated, “note that wing morphology is very similar between species and that ocelli pattern can vary within species, between geographical regions”.

Indeed, when H. intricata was described by Cong & Grishin in 2014, these authors were also initially unable to identify any wing pattern characters that could be useful for reliably differentiating it from sympatric H. sosybius (despite strong disparities in male and female genitalia and in COI sequence). It was again noted that wing patterns were highly variable in both taxa with ventral eyespots varying from large to almost absent.

Subsequently, it was discovered that H. sosybius males could be distinguished by the presence of a dense patch of dark androconial scales (specialized scales commonly associated with pheromone dissemination) on the dorsal surface of most of the forewing and part of the hind wing, but this sexually dimorphic character was limited in its utility

(since it could not be applied to female or severely worn/damaged specimens) (Warren et al., 2014).

Despite evidence that Hermeuptychia wing patterns might often lack reliable, useful characters for species delimitation, a distinctive new species, Hermeuptychia clara, that could be readily distinguished based on ventral wing pattern was described shortly after (Nakahara et al., 2016). In Ecuador, H. clara occurs from 1000–1800 m in montane forest habitats in the eastern tropical Andes. Interestingly, it differs from all

other Hermeuptychia species not in wing ocelli patterns but in bearing a straight dark post-discal line on the ventral hind wing that is bordered by white scaling, forming an evident pale, even band (Figure 1-3). Undoubtedly, it is due to broad inadequacies in geographic sampling and limited taxonomic study of the group that such an obviously novel species remained undescribed until recently.

Unfortunately, too little is currently known about female genitalic characters to facilitate meaningful comparisons between potentially novel species with other known taxa. However, male genitalic characters (e.g. shape of the uncus, shape of the valvae and length of cucullus) have proven to be quite divergent even among closely related taxa (e.g. Cong & Grishin, 2014; Seraphim et al., 2014). The rapid evolutionary divergence in shape and complexity of genitalia have long been ascribed to the effects of pre- and post- copulatory selection pressures (Arnqvist, 1998; Shamloul et al., 2010;

Frazee & Masly, 2015). Therefore, whether or not consistent differences in genitalic characters can be identified amongst closely related taxa might also indicate whether sexual selection is a significant driver of diversification in the genus Hermeuptychia.

The main aim of this chapter is to determine if the clustering of specimens based on genome-wide SNPs (i.e. the groups identified by STRUCTURE in the previous chapter), is also supported by consistent differences in morphology. In particular, within allopatric clusters the genomic heterogeneity observed could simply be due to the effects of geographic separation elevating intra-specific variation. Here, ventral wing patterns and male genitalia are extensively re-investigated in order to provide additional clarification on the species status of these ‘genomic clusters’.

Methods

Ventral Wing Images

Dissected wings were photographed in the imaging station located at the

McGuire Center for Lepidoptera and Biodiversity. The set-up consists of a Canon EOS

7D digital SLR camera fitted with a Canon EF-S 60mm f/2.8 macro lens, mounted on copy stand kit and tethered to a Dell OptiPlex 990 desktop computer (Dell Inc, USA) for live view shooting via Canon EOS Utility software v 2. All four wings and associated labels were arranged on a piece of glass sitting atop a standard light box, along with a scale and a color calibration target (ColorGauge Nano Target, Image Science

Associates LLC, USA). Images were taken using autofocus and the following settings:

1/15 shutter speed, F16 aperture and ISO 250. All images were collated and edited using the Digital Photo Professional software v 3.14.40. 0 (Canon Inc, Japan).

Male Genitalia Images

Dissected abdomens were prepared for dissection by soaking in hot, 10% potassium hydroxide (KOH) solution for 15 – 40 minutes. After dissection, specimens were stored in glycerol before being photographed.

Dissected male genitalia were imaged using the motorized Microptics system at the McGuire Center for Lepidoptera and Biodiversity. This set-up consists of a Canon

EOS 6D digital SLR camera mounted on a StackShot macro rail system (Cognisys Inc,

USA) and fitted with the Model K2 DistaMax long-distance microscope (Infinity Photo-

Optical Company, USA) and CF-4 lens. The camera was tethered to a Dell OptiPlex

9020 desktop computer to enable live view shooting. All images were captured at the following settings: 1/125 shutter speed, aperture manually adjusted and ISO 100. In order to extend depth-of-field, several images were taken over a range of focus

distances then stacked/combined using the Helicon Focus Pro software v 6.7.1 using either the method B (depth map) or method C (pyramid) focus stacking algorithm. All genitalia images were edited using Adobe Photoshop CS6 v 13.0.6 (Adobe Systems

Inc.).

Ventral wing patterns and male genitalia morphology were re-investigated in the light of genomic structure described in the previous chapter.

Results

‘Atalanta’ Group

Based on the molecular data, the ‘Atalanta’ group consists of two distinct clusters of specimens with over-lapping geographic distributions. Correspondingly, two morphotypes can be identified within ‘Atalanta’. Specifically, specimens from the orange cluster bear a series of hind wing eyespots that are smaller and more uniform in size as compared to specimens from the blue cluster (Figure 4-1). In Hermeuptychia, the second, fifth and sixth (counting from the top of the hind wing, i.e. those in cells M2-M1,

Cu2-Cu1 and 2A-Cu2, respectively) ocelli are typically more prominent owing to a well- defined black center pupiled with white/pale blue scales. In specimens from the blue cluster, these ocelli (particularly the second one) are larger and slightly more oblong in shape as compared to the rest of the series (that are more circular). These differences appear consistent despite obvious color variation (all images were taken with the same set-up and color calibrated using the same target). Additionally, there are several differences in male genitalia (Figure 4-2):

 Blue-cluster specimens bear a series of small spines on the socius (crescent shaped structure just anterior of the gnathos)

 In lateral view, the base of the gnathos appears more right-angled and robust in the orange-cluster specimens

 The ‘elbow’ of the valva is more pronounced in blue-cluster specimens

 The aedeagus is longer and more slender in blue-cluster specimens

‘Maimoune’ Group

The ‘Maimoune’ group consists of three clusters of specimens. The geographic distribution of the orange-cluster specimens is restricted to south-eastern Ecuador, green-cluster specimens can be found from the north to mid-eastern Ecuador and the blue-cluster specimens are located in the north-west. Looking at wing morphology, specimens from the orange and blue clusters have prominent eyespots that are larger than the rest of the series (much like the specimens from the blue cluster in ‘Atalanta’;

Figure 4-1). What further differentiates the specimens from the orange cluster is the presence of a diffused patch of pale brown/white scales on the hind wings that is most prominent in the specimen LEP 14800. The hind wing eyespots in specimens from the green cluster are more uniform in size, with the second eyespot being more circular as compared to specimens from other clusters (Figure 4-3). As expected, males of the orange and green clusters have very similar genitalia morphology except that the phallobase is much longer in specimens from the green cluster whilst the aedeagus is much longer in specimens from the orange cluster. Specimens from the blue cluster differ from the rest in two ways (Figure 4-4):

 The valva has a more prominent ‘elbow’ and has ‘arms’ (terminal projections) that are much longer (extending far beyond the gnathos).

 In lateral view, where the gnathos is connected to the socius, specimens from the blue cluster lack the obvious notch that is present in specimens from the green and orange clusters.

‘Clara’ Group

In the case of the ‘Clara’ group, LEP 37596 is consistently separated from the rest of the group based on both COI and genome-wide variation. In terms of geographic distribution, it is unique in being found in the lowlands whereas H. clara was described from high elevation forests. The pale white band that is most distinctive in H. clara specimens is less obvious in this specimen (Figure 4-5). The genitalia of LEP 37596 can be differentiated from the others by having a series of ‘teeth’ or small bumps at the base of the socius, having a more rounded gnathos base and having valvae ‘arms’ that are much longer, slender and tapering towards the tips (Figure 4-6).

Species 3 Group

Continuing with trend in observed differences amongst wing ocelli patterns, the green-cluster specimen (LEP 17666) in Species 3 has prominent eyespots that are more similar in shape and size as compared to the rest of the hind wing ocelli. The second hind wing eyespot in the other two specimens appear to be fully oblong in shape

(as if a circle was stretched lengthwise) (Figure 4-7). In terms of genitalic characters, the base of the gnathos is more curved in the specimens from the orange cluster whereas specimens from the green cluster possess a more prominent bump at the valvae ‘elbow’. Perhaps the most striking difference is in the shape of the uncus (from dorsal view). The tip of the uncus is more pointed (tapers more drastically) in specimens from the green cluster (Figure 4-8).

‘Hermes’ Group

The ‘Hermes’ group is divided into three geographically isolated clusters. Based on wing patterns alone, the specimens from the green and orange clusters appear to be the most different in terms of size of eyespots. The specimens from the blue cluster

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seem to fall somewhere in between the range of phenotypes set by specimens from the other two clusters (Figure 4-9). In terms of male genitalic characters, specimens from the orange cluster are most easily distinguished via the deep curvature on the ventral edge of the valva (anterior to the ‘elbow’). Also, the gnathos appears to be much shorter than specimens from the other ‘Hermes’ clusters. Male genitalia in specimens from the blue and green clusters appear to be very similar except that the ‘elbow’ in the valvae of specimens from the blue cluster is very pronounced and the saccus appears to be much shorter in specimens from the green cluster (Figure 4-10).

‘Gisella’ Group

Likewise, the ‘Gisella’ group consists of two allopatric clusters that are very genomically distinct (such that the blue cluster was occasionally recovered as sister to the outgroup, Species 4 instead). The ventral wing patterns are very similar. However, in specimens from the blue cluster, the post-medial dark brown line on the hind wings curves outwards at the vein M1 and then bulges outwards significantly again just before it meets the anal margin (see red ticks in Figure 4-11). It is more straightforward to differentiate specimens from the two clusters using genitalic characters since orange- cluster specimens have the following traits (Figure 4-12):

 Valva is deeply curved inwards at the base (next to the ‘elbow’). The base of the valva in specimens from the blue cluster is almost straight with no curvature.

 The gnathos appears to be broader and much more strongly angled.

 The carina is less irregular/jagged than in specimens from the blue cluster.

‘Harmonia’ Group

Four distinct genomic clusters are identified within ‘Harmonia’. The blue and green clusters have a somewhat restricted geographic distribution in north-western

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Ecuador whilst the orange and green clusters are broadly distributed across eastern

Ecuador (Figure 3-21). The green and grey clusters are consistently recovered as sister species whereas the orange and blue clusters appear to be more genetically distinct (in the better supported ML phylogeny, orange is sister to grey + green and blue is sister to all three other clusters). Looking at ventral wing patterns, specimens from sympatric clusters appear more similar. Specimens from the blue and grey clusters have a very enlarged second hind wing eyespot that appears to have fused with the third, more reduced, eyespot. The second eyespot is much less prominent in specimens from the green and orange clusters. The dark brown, post-medial line on the hind wings is also very wavy (whereas it is mostly straight, with the exception of one bulge at the anal margin in the orange- and green- cluster specimens) (Figure 4-13). Notably, male genitalia morphology groups the clusters in a different manner. Specimens from the grey cluster are very distinct because the ‘elbow’ on the valva is sharper and more triangular as compared to the rounded bumps in the other three groups. Specimens in the orange cluster can be distinguished by the longer and more gracile gnathos (lacking a thick base in lateral view) as well as significantly shorter tips of the valvae (referred to here as ‘arms’). Male genitalia of specimens from the blue and green clusters appear similar despite differences in wing patterns (Figure 4-14).

‘Pimpla’ Group

The ‘pimpla’ group is genomically homogenous (in that no further intra-specific sub-structuring was discovered) but is highlighted here because of the distinctiveness of its ventral hind wing pattern. Specifically, the presence of a diffused patch of white scales in the middle of the posterior half of the wing, particularly towards the anal margin, distinguishes it from all other ‘genomic clusters’ investigated in this chapter

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(Figure 4-15). The valvae in the genitalia of ‘Pimpla’ males appear to almost completely lack an ‘elbow’ and are very slender (much like specimens from the various ‘Harmonia’ clusters) (Figure 4-16).

The ventral wing patterns and male genitalia morphology for Species 1, Species

2, Species 4 and Species 5 (all genomically homogenous) can be found in Figure D-1 and Figure D-2.

Discussion

Given that the SNP-based analyses, STRUCTURE and PCA, are capable of detecting very subtle differences between populations (even those experiencing ongoing gene flow), the expectation is that at least some of the genomic clusters

(specimens clustered based on genome-wide variation) might simply represent allopatric populations with similar morphologies. For the ‘Atalanta’ group, where the two distinct clusters are sympatric, I find corresponding differences in both wing ocelli pattern and male genitalia as expected. Rather surprisingly, I also find that a combination of wing and genitalic character is almost always useful for splitting specimens in a manner that is in strong agreement with clustering patterns in most other groups. A sole exception exists in the ‘Hermes’ group, where the morphological differences between the green and blue clusters is somewhat subtle and weak (i.e. these characters may not hold up if additional sampling is conducted beyond this study).

Whereas most of the findings in this chapter are straightforward, the results from the

‘Harmonia’ group are most intriguing. Since the sympatric clusters (blue + green and orange + green) demonstrated genomic heterogeneity despite overlapping distributions,

I expected these to be morphologically dissimilar as well. However, the sympatric clusters appeared to have similar ventral wing patterns but differed in male genitalic

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characters (this is reminiscent of the H. sosybius and H. intricata species complex). In fact, it is the specimens from the allopatric, blue and green, clusters that appear to be quite identical in terms of male genitalia. Based on the data generated here, it seems that four evolutionarily significant units are present in the ‘Harmonia’ group, but further investigations into the biogeography of this group are needed and should yield fascinating results.

I find that male genitalic characters were arguably more useful than wing patterns for the purposes of delimiting species in Hermeuptychia. That being said, the orange cluster in ‘Maimoune’, the blue and grey clusters in ‘Harmonia’ and the ‘Pimpla’ groups all possess ventral wing patterns that are very distinctive (much like H. clara).

Remarkably, within seven putative species groups, 17 morpho-groups (that are also supported by variation in COI and genome-wide SNPs) are identified. Therefore, these likely represent 17 distinct species. Since the patterns of divergence seem to coincide with geographic isolation and divergent male genitalic characters, I have to assume that some combination of both types of selective forces must be responsible for the driving speciation in Hermeuptychia. The studies detailed here really just scraps the surface of what is actually shaping evolutionary relationships in these butterflies. I briefly discuss general findings and future directions in the following chapter.

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Figure 4-1. Ventral wing patterns of specimens representing two morphology groups within ‘Atalanta’. The color of the specimen ID labels corresponds to STRUCTURE clustering results in Chapter 3. Photographs by Denise Tan.

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A LEP37615

B LEP14854

Figure 4-2. Lateral view of male genitalia of specimens representing two morphology groups within ‘Atalanta’. Tracings of the shape of the valva are provided for greater clarity. The colors of the tracings correspond to STRUCTURE grouping detailed in Chapter 3. Arrows indicate structures that most differ. Photographs by Denise Tan.

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Figure 4-3. Ventral wing patterns of specimens representing different genomic clusters within ‘Maimoune’. The color of the specimen ID labels corresponds to STRUCTURE clustering results in Chapter 3. Photographs by Denise Tan.

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Valva ‘arm’

LEP17675 Valva ‘elbow’ B

Notch in socius

LEP18261 C

Phallobase

LEP18149

Figure 4-4. Lateral view of male genitalia of specimens representing genomic clusters within ‘Maimoune’. Tracings of the shape of the valva are provided for greater clarity. The colors of the tracings correspond to STRUCTURE grouping detailed in Chapter 3. Arrows indicate structures that most differ. Photographs by Denise Tan.

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Figure 4-5. Ventral wing patterns of specimens representing different genomic clusters within ‘Clara’. The color of the specimen ID labels corresponds to STRUCTURE clustering results in Chapter 3. Note that color variation in LEP 37596 might be due to differences in lighting since this is the only pinned specimen. Photographs by Denise Tan.

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LEP18157 B

Gnathos

LEP37596

Figure 4-6. Lateral view of male genitalia of specimens representing genomic clusters within ‘Clara’. Tracings of the shape of the valva are provided for greater clarity. The colors of the tracings correspond to STRUCTURE grouping detailed in Chapter 3. Arrows indicate structures that most differ. Photographs by Denise Tan.

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Figure 4-7. Ventral wing patterns of specimens representing different genomic clusters within Species 3. The color of the specimen ID labels corresponds to STRUCTURE clustering results in Chapter 3. Photographs by Denise Tan.

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Dorsal view of uncus

LEP18418 B

LEP17666

Figure 4-8. Lateral view of male genitalia of specimens representing genomic clusters within Species 3. Tracings of the shape of the valva are provided for greater clarity. The colors of the tracings correspond to STRUCTURE grouping detailed in Chapter 3. Arrows indicate structures that most differ. Photographs by Denise Tan.

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Figure 4-9. Ventral wing patterns of specimens representing different genomic clusters within ‘Hermes’. The color of the specimen ID labels corresponds to STRUCTURE clustering results in Chapter 3. Photographs by Denise Tan.

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LEP17668 B

LEP18096 C

LEP14861

Figure 4-10. Lateral view of male genitalia of specimens representing genomic clusters within ‘Hermes’. Tracings of the shape of the valva are provided for greater clarity. The colors of the tracings correspond to STRUCTURE grouping detailed in Chapter 3. Arrows indicate structures that most differ. Photographs by Denise Tan.

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Figure 4-11. Ventral wing patterns of specimens representing different genomic clusters within ‘Gisella’. The color of the specimen ID labels corresponds to STRUCTURE clustering results in Chapter 3. Photographs by Denise Tan.

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LEP37636 B

LEP18153

Figure 4-12. Lateral view of male genitalia of specimens representing genomic clusters within ‘Gisella’. Tracings of the shape of the valva are provided for greater clarity. The colors of the tracings correspond to STRUCTURE grouping detailed in Chapter 3. Arrows indicate structures that most differ. Photographs by Denise Tan.

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Figure 4-13. Ventral wing patterns of specimens representing different genomic clusters within ‘Harmonia’. The color of the specimen ID labels corresponds to STRUCTURE clustering results in Chapter 3. Photographs by Denise Tan.

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LEP55527

LEP18460

LEP14816

LEP18248

Figure 4-14. Lateral view of male genitalia of specimens representing genomic clusters within ‘Harmonia’. Tracings of the shape of the valva are provided for greater clarity. The colors of the tracings correspond to STRUCTURE grouping detailed in Chapter 3. Arrows indicate structures that most differ. Photographs by Denise Tan.

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Figure 4-15. Ventral wing patterns of specimens representing ‘Pimpla’. The diffused patch of white scales in the posterior half of the hind wings is distinctive amongst known Hermeuptychia species. Photographs by Denise Tan.

LEP14833

Figure 4-16. Lateral view of male genitalia of specimens from the ‘Pimpla’ group. Tracings of the shape of the valva are provided for greater clarity. Photograph by Denise Tan.

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CHAPTER 5 CONCLUSIONS

This compendium of studies was designed to not only increase knowledge of taxonomy and systematics in Hermeuptychia, but also to provide guidelines about the best performing practices (in terms of specimen choice, experimental methodology and analyses etc.) for further work in the genus and related groups. These are my major findings:

 DNA barcoding (the exploration of variation in the COI gene) is highly predictive of genome-wide variation. Specimens that share the same barcode are always placed in the same clusters based on identified SNPs. Highly supported clades in the COI phylogeny are similarly recovered in the more well-resolved and robust phylogeny based on thousands of SNPs.

 The best performing species delimitation method appears to be ABGD (with recursive partitioning). The GMYC approach also correctly predicted many of the evolutionary significant units (morphologically distinct, genomic clusters) but also tended to drastically over-split the putative species groups and inflate diversity estimates.

 ABGD is, by far, the fastest and simplest species delimitation method to perform (since it only requires a nucleotide alignment or a distance matrix). Therefore, when dealing with COI datasets, the most effective sampling strategy would be to focus on boundaries set by ABGD while making sure that most well-supported clades/branches are represented.

 If funds are available, ddRAD-Seq is the preferred strategy to assess genome- wide variation since it is cost-effective (when considering the massive amount of data generated) and allows for a broad range of questions (intra-specific gene flow, species assignment or phylogenomics) to be simultaneously addressed just by adjusting specimen choice criteria.

 For the purposes of reconstructing evolutionary relationships using genome-wide SNPs, I find it hard to state a preference since concatenation methods are susceptible to many biases (and may thus produce conflicting topologies) but generating species trees using SNAPP is overwhelmingly time consuming and computationally intensive.

 Multiple phylogenomic inference methods should always be utilized to assess congruence. So-called ‘fast methods’ for generating species trees are readily becoming available from extensive testing in a wide variety of organisms (e.g. ASTRAL; (Mirarab et al., 2014; Mirarab & Warnow, 2018) and should similarly be

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tested in order to determine if there is congruence with the RAxML phylogeny discussed here.

 Differences in male genitalic characters could be identified in almost all distinct genomic clusters. This finding warrants greater efforts to characterize the extent of intra-specific variation in Hermeuptychia genitalia and generate more comparative information on female genitalia.

 Although consistent differences could be identified in ventral wing patterns within individual putative species groups, looking broadly across the whole genus, wing morphology is mostly non-distinctive (with the exception of a few clusters bearing patches of white/paler scales and larger than average eyespots).

 Taking all data types into consideration, I find that diversity within Hermeuptychia is very severely underestimated (by at least 50%). The need to locate, barcode and photograph type specimens is urgent and should be prioritized so that species names can be properly assigned, and truly novel taxa can be described (instead of remaining in an unresolved/ambiguous taxonomic state).

Across the broad geographic range of Hermeuptychia, arguably only two countries (USA and now, Ecuador) are currently, well sampled. Brazil and Colombia are much larger countries, as compared to Ecuador, and can only be considered to have been preliminarily studied. It is apparent that each time population-level representation is improved for a geographic region, novel species are discovered (e.g. Cong & Grishin,

2014; Nakahara et al., 2016). Some, like H. clara, can be readily distinguished whilst cryptic species complexes will require additional and careful scrutiny. This dissertation should serve to facilitate and encourage further efforts to resolve the taxonomic challenge that is Hermeuptychia.

Moving forward, I think Hermeuptychia butterflies also have great potential as a study system for sexual selection. For sympatric taxa demonstrating evident genetic and/or morphological dissimilarity, reproductive isolation is inferred, but virtually never demonstrated experimentally. However, I have had success in using wild-caught

Hermeuptychia butterflies to establish laboratory populations. My experience is that, it is

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very feasible to conduct laboratory-based hybridization experiments in order to determine if genetically distinct Hermeuptychia populations are also valid biological species (i.e. reproductively isolated). Since very little is currently known about the courtship and mating behaviors of Hermeuptychia butterflies, these kinds of studies will also facilitate documentation of mating behavior and enable careful evaluation of behavioral differences that may be acting as pre-zygotic reproduction barriers. Such experiments should produce data that is not only complementary to molecular analyses inferring gene flow and hybridization, but that is also typically overlooked due to the difficulties in establishing live cultures and observing behavior in captivity.

During my pilot studies of mating behavior in Hermeuptychia, I observed interesting female ‘resistance’ behavior (where females use hind legs to push/kick at males while seemingly pulling their abdomens away) have been observed to occur after prolonged copulation (unpubl. data). Such behavior could be relevant to studies of sexual conflict (and could be initially explored by determining if copulation frequency and/or duration is significantly correlated to female longevity). Yet another significant

‘by-product’ of rearing Hermeuptychia butterflies under a controlled laboratory setting, is the opportunity to determine if there are differences in developmental time and/or larval morphology. Variation in such ecologically relevant life history traits could result from divergent selective pressures from contrasting environmental (i.e. microhabitat) preferences and should further help with species delimitation.

Finally, since the presence of dark androconial scales on wings appears to be the only external morphological character for differentiating between males of sympatrically occurring H. intricata and H. sosybius, it would be fascinating to gain

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some insights regarding the function of these specialized scales in Hermeuptychia butterflies. A relevant and initial query would be whether these scales play an active role in inter- and intra-specific communication (e.g. conspecific recognition and mate choice). The structure and arrangement of these scales could be studied in detail, via techniques such as scanning electron microscopy (SEM), in order to identify potential inter-specific differences and possibly relate it to function. Although commonly associated with the dissemination of pheromones, this dense scale patch could also be involved in visual signaling. Therefore, chemical analyses (such as gas chromatography–mass spectrometry; GC-MS) could be used to determine if male- specific pheromones can be found at the androconial regions of the fore and hind wings. If present, differences in the chemical composition of pheromones could provide additional evidence for species delimitation. The presence of androconia is not ubiquitous in Hermeuptychia. Therefore, one could also map this trait onto a well- resolved phylogeny in order to explore character evolution. This might eventually lead us to a better understanding of the factors promoting divergence in such cryptic species complexes.

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APPENDIX A SPECIMEN INFORMATION

Collection Locality Data for All Specimens

Table A-1. Locality information for the Hermeuptychia specimens investigated Specimen ID Locality (Decimal Latitude and Longitude) MGCL-LOAN-568 Brazil: São Paulo: Serra do Japi (-23.25, -46.917) MGCL-LOAN-569 Brazil: São Paulo: Serra do Japi (-23.25, -46.917) MGCL-LOAN-570 Brazil: São Paulo: Serra do Japi (-23.25, -46.917) Costa Rica: Alajuela: Res. Biol. Alb. M. Berenes, UCR, San IN057 Ramón, 900-1000m (10.218, -84.599) Costa Rica: Heredia: Finca Bernal, 1600m, 4Km NE of San IN058 Rafael (10.040, -84.077) IN054 Costa Rica: Puntarenas: Parque Nacional Carara IN055 Costa Rica: Puntarenas: Parque Nacional Carara Costa Rica: San José: Cerro Escazu, 1650m, 5Km E of IN059 Palmichal (9.840, -84.159) LEP 04347 Ecuador: Carchi: Chical 'primera cordillera' (0.929, -78.178) LEP 04336 Ecuador: Carchi: Chical 'segunda cordillera' (0.924, -78.188) LEP 04339 Ecuador: Carchi: Chical 'segunda cordillera' (0.924, -78.188) LEP 55612 Ecuador: Carchi: Chical-Gualchán rd. (0.896, -78.217) LEP 55614 Ecuador: Carchi: Chical-Gualchán rd. (0.896, -78.217) LEP 55615 Ecuador: Carchi: Chical-Gualchán rd. (0.896, -78.217) LEP 55617 Ecuador: Carchi: Chical-Gualchán rd. (0.896, -78.217) LEP 04345 Ecuador: Carchi: E of Maldonado (0.887, -78.096) LEP 04342 Ecuador: Carchi: Finca San Francisco (0.803, -78.171) LEP 18410 Ecuador: Carchi: Finca San Francisco (0.803, -78.171) LEP 18454 Ecuador: Carchi: Finca San Francisco (0.803, -78.171) LEP 18469 Ecuador: Carchi: Finca San Francisco (0.803, -78.171) LEP 18470 Ecuador: Carchi: Finca San Francisco (0.803, -78.171) LEP 18455 Ecuador: Carchi: km 14 Gualchán-Chical rd. (0.836, -78.227) LEP 18456 Ecuador: Carchi: km 14 Gualchán-Chical rd. (0.836, -78.227) LEP 17696 Ecuador: Carchi: Lita, ridge east of Río Baboso (0.888, -78.438) LEP 18235 Ecuador: Carchi: Lita, ridge east of Río Baboso (0.888, -78.438) LEP 18236 Ecuador: Carchi: Lita, ridge east of Río Baboso (0.888, -78.438) LEP 18243 Ecuador: Carchi: Lita, ridge east of Río Baboso (0.888, -78.438) LEP 37640 Ecuador: Carchi: Lita, ridge east of Río Baboso (0.888, -78.438) LEP 37641 Ecuador: Carchi: Lita, ridge east of Río Baboso (0.888, -78.438) LEP 37642 Ecuador: Carchi: Lita, ridge east of Río Baboso (0.888, -78.438) LEP 17685 Ecuador: Carchi: Río Chorro Blanco (0.805, -78.145) LEP 17686 Ecuador: Carchi: Río Chorro Blanco (0.805, -78.145) LEP 18249 Ecuador: Carchi: Río Chorro Blanco (0.805, -78.145)

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Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) LEP 18250 Ecuador: Carchi: Río Chorro Blanco (0.805, -78.145) LEP 18251 Ecuador: Carchi: Río Chorro Blanco (0.805, -78.145) LEP 18457 Ecuador: Carchi: Río Chorro Blanco (0.805, -78.145) LEP 18458 Ecuador: Carchi: Río Chorro Blanco (0.805, -78.145) LEP 18473 Ecuador: Carchi: Río Chorro Blanco (0.805, -78.145) LEP 18471 Ecuador: Carchi: Santa Cecilia (0.778, -78.329) LEP 18472 Ecuador: Carchi: Santa Cecilia (0.778, -78.329) LEP 18476 Ecuador: Esmeraldas: Angostura (0.888, -78.848) LEP 55493 Ecuador: Esmeraldas: c. 11 km NE Chamanga (0.331, -79.881) LEP 55529 Ecuador: Esmeraldas: c. 11 km NE Chamanga (0.331, -79.881) LEP 55530 Ecuador: Esmeraldas: c. 11 km NE Chamanga (0.331, -79.881) LEP 55533 Ecuador: Esmeraldas: c. 11 km NE Chamanga (0.331, -79.881) LEP 55534 Ecuador: Esmeraldas: c. 11 km NE Chamanga (0.331, -79.881) LEP 55452 Ecuador: Esmeraldas: c. 11 km S Selva Alegre (0.863, -78.856) LEP 55503 Ecuador: Esmeraldas: c. 11 km S Selva Alegre (0.863, -78.856) LEP 55504 Ecuador: Esmeraldas: c. 11 km S Selva Alegre (0.863, -78.856) LEP 18477 Ecuador: Esmeraldas: El Cerro (0.973, -78.922) LEP 18478 Ecuador: Esmeraldas: El Cerro (0.973, -78.922) LEP 37633 Ecuador: Esmeraldas: El Cerro (0.973, -78.922) LEP 37634 Ecuador: Esmeraldas: El Cerro (0.973, -78.922) LEP 18210 Ecuador: Esmeraldas: El Durango (1.046, -78.635) LEP 18211 Ecuador: Esmeraldas: El Durango (1.046, -78.635) LEP 18212 Ecuador: Esmeraldas: El Durango (1.046, -78.635) LEP 42059 Ecuador: Esmeraldas: El Durango (1.046, -78.635) LEP 42060 Ecuador: Esmeraldas: El Durango (1.046, -78.635) LEP 42061 Ecuador: Esmeraldas: El Durango (1.046, -78.635) LEP 42062 Ecuador: Esmeraldas: El Durango (1.046, -78.635) LEP 42063 Ecuador: Esmeraldas: El Durango (1.046, -78.635) LEP 42064 Ecuador: Esmeraldas: El Durango (1.046, -78.635) LEP 42065 Ecuador: Esmeraldas: El Durango (1.046, -78.635) LEP 42066 Ecuador: Esmeraldas: El Durango (1.046, -78.635) LEP 42083 Ecuador: Esmeraldas: El Durango (1.046, -78.635) LEP 42220 Ecuador: Esmeraldas: El Durango (1.046, -78.635) LEP 42221 Ecuador: Esmeraldas: El Durango (1.046, -78.635) LEP 18484 Ecuador: Esmeraldas: Estero Tachina (0.9, -79.565) LEP 17658 Ecuador: Esmeraldas: Finca Cypris (1.011, -78.609) LEP 17659 Ecuador: Esmeraldas: Finca Cypris (1.011, -78.609) LEP 17687 Ecuador: Esmeraldas: Finca Cypris (1.011, -78.609) LEP 17695 Ecuador: Esmeraldas: Finca Cypris (1.011, -78.609)

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Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) LEP 18230 Ecuador: Esmeraldas: Finca Cypris (1.011, -78.609) LEP 18233 Ecuador: Esmeraldas: Finca Cypris (1.011, -78.609) LEP 18234 Ecuador: Esmeraldas: Finca Cypris (1.011, -78.609) LEP 18479 Ecuador: Esmeraldas: Gualto (0.978, -79.31) Ecuador: Esmeraldas: km 11-12 Lagarto-Anchayacu rd. (1.007, - LEP 18480 79.211) Ecuador: Esmeraldas: km 11-12 Lagarto-Anchayacu rd. (1.007, - LEP 18481 79.211) Ecuador: Esmeraldas: km 11-12 Lagarto-Anchayacu rd. (1.007, - LEP 18482 79.211) Ecuador: Esmeraldas: km 11-12 Lagarto-Anchayacu rd. (1.007, - LEP 18483 79.211) Ecuador: Esmeraldas: km 18.5 San Lorenzo-Lita rd. (1.152, - LEP 37631 78.745) Ecuador: Esmeraldas: km 18.5 San Lorenzo-Lita rd. (1.152, - LEP 37632 78.745) LEP 18489 Ecuador: Esmeraldas: Playa Escondida (0.818, -80.005) LEP 18490 Ecuador: Esmeraldas: Quingüe (0.713, -80.073) LEP 18491 Ecuador: Esmeraldas: Quingüe (0.713, -80.073) LEP 18492 Ecuador: Esmeraldas: Quingüe (0.713, -80.073) LEP 18411 Ecuador: Esmeraldas: Quingüe (0.718, -80.084) LEP 18486 Ecuador: Esmeraldas: Quingüe (0.718, -80.084) LEP 18487 Ecuador: Esmeraldas: Quingüe (0.718, -80.084) LEP 18488 Ecuador: Esmeraldas: Quingüe (0.718, -80.084) LEP 18493 Ecuador: Esmeraldas: Reserva Canandé Lodge (0.521, -79.208) LEP 18494 Ecuador: Esmeraldas: Reserva Canandé Lodge (0.521, -79.208) LEP 37423 Ecuador: Esmeraldas: Reserva de Tigrillo, lodge (0.85, -78.777) LEP 37424 Ecuador: Esmeraldas: Reserva de Tigrillo, lodge (0.85, -78.777) Ecuador: Esmeraldas: Reserva de Tigrillo, Peñon del Santo trail LEP 37422 (0.851, -78.778) LEP 17252 Ecuador: Esmeraldas: Reserva Río Canandé (0.483, -79.201) LEP 17253 Ecuador: Esmeraldas: Reserva Río Canandé (0.483, -79.201) LEP 55481 Ecuador: Esmeraldas: Río Chuchuví (0.881, -78.515) LEP 55482 Ecuador: Esmeraldas: Río Chuchuví (0.881, -78.515) LEP 55517 Ecuador: Esmeraldas: Río Chuchuví (0.881, -78.515) LEP 55525 Ecuador: Esmeraldas: Río Chuchuví (0.881, -78.515) LEP 55527 Ecuador: Esmeraldas: Río Chuchuví (0.881, -78.515) LEP 17688 Ecuador: Esmeraldas: San Francisco ridge (1.107, -78.699) LEP 17689 Ecuador: Esmeraldas: San Francisco ridge (1.107, -78.699) LEP 17690 Ecuador: Esmeraldas: San Francisco ridge (1.107, -78.699) LEP 17691 Ecuador: Esmeraldas: San Francisco ridge (1.107, -78.699) LEP 18098 Ecuador: Esmeraldas: San Francisco ridge (1.107, -78.699)

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Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) LEP 18099 Ecuador: Esmeraldas: San Francisco ridge (1.107, -78.699) LEP 37635 Ecuador: Esmeraldas: San Francisco ridge (1.107, -78.699) LEP 37636 Ecuador: Esmeraldas: San Francisco ridge (1.107, -78.699) LEP 37637 Ecuador: Esmeraldas: San Francisco ridge (1.107, -78.699) LEP 37638 Ecuador: Esmeraldas: San Francisco ridge (1.107, -78.699) LEP 55453 Ecuador: Esmeraldas: Tongora (0.671, -80.087) LEP 55458 Ecuador: Esmeraldas: Tongora (0.671, -80.087) LEP 55472 Ecuador: Esmeraldas: Tongora (0.671, -80.087) LEP 55491 Ecuador: Esmeraldas: Tongora (0.671, -80.087) LEP 55492 Ecuador: Esmeraldas: Tongora (0.671, -80.087) LEP 55505 Ecuador: Esmeraldas: Tongora (0.671, -80.087) LEP 55506 Ecuador: Esmeraldas: Tongora (0.671, -80.087) LEP 55510 Ecuador: Esmeraldas: Tongora (0.671, -80.087) LEP 55515 Ecuador: Esmeraldas: Tongora (0.671, -80.087) LEP 17661 Ecuador: Esmeraldas: Tundaloma Lodge (1.178, -78.748) LEP 17698 Ecuador: Esmeraldas: Tundaloma Lodge (1.178, -78.748) LEP 17699 Ecuador: Esmeraldas: Tundaloma Lodge (1.178, -78.748) LEP 18475 Ecuador: Esmeraldas: Tundaloma Lodge (1.178, -78.748) LEP 37639 Ecuador: Esmeraldas: Tundaloma Lodge (1.178, -78.748) LEP 18485 Ecuador: Esmeraldas: Zapatta (0.885, -79.54) LEP 18095 Ecuador: Imbabura: above Getsemaní (0.799, -78.355) LEP 18096 Ecuador: Imbabura: above Getsemaní (0.799, -78.355) LEP 18466 Ecuador: Imbabura: Chontal Alto (0.297, -78.701) LEP 18467 Ecuador: Imbabura: Chontal Alto (0.297, -78.701) LEP 17692 Ecuador: Imbabura: Finca Fénix (0.816, -78.406) Ecuador: Imbabura: Santa Rita de Cachaco, ridge to S (0.773, - LEP 18097 78.371) Ecuador: Imbabura: Santa Rita de Cachaco, ridge to S (0.773, - LEP 18100 78.371) Ecuador: Imbabura: Santa Rita de Cachaco, ridge to S (0.773, - LEP 55460 78.371) Ecuador: Imbabura: Santa Rita de Cachaco, ridge to S (0.773, - LEP 55467 78.371) Ecuador: Imbabura: Santa Rita de Cachaco, ridge to S (0.773, - LEP 55485 78.371) Ecuador: Imbabura: Santa Rita de Cachaco, ridge to S (0.773, - LEP 55521 78.371) Ecuador: Imbabura: Santa Rita de Cachaco, ridge to S (0.773, - LEP 55522 78.371) Ecuador: Loja: Cangochara-Quebrada Canutal track (-4.529, - LEP 14825 79.379)

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Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) Ecuador: Loja: Cangochara-Quebrada Canutal track (-4.529, - LEP 14847 79.379) Ecuador: Loja: Cangochara-Quebrada Canutal track (-4.529, - LEP 14864 79.379) LEP 14859 Ecuador: Loja: km 9 Amaluza-Quilanga rd. (-4.532, -79.425) LEP 14865 Ecuador: Loja: km 9 Amaluza-Quilanga rd. (-4.532, -79.425) LEP 04343 Ecuador: Loja: Río Chonta (-4.204, -79.327) LEP 14861 Ecuador: Loja: Río Chonta (-4.204, -79.327) LEP 14862 Ecuador: Loja: Río Chonta (-4.204, -79.327) Ecuador: Manabí: Reserva Lalo Loor, Pedernales-Jama rd. (- LEP 17348 0.091, -80.149) Ecuador: Manabí: Reserva Lalo Loor, Pedernales-Jama rd. (- LEP 17349 0.091, -80.149) LEP 04344 Ecuador: Morona-Santiago: 'Sopladora ridge' (-2.598, -78.456) LEP 17664 Ecuador: Morona-Santiago: 2 km N San Isidro (-2.198, -78.157) LEP 17672 Ecuador: Morona-Santiago: 2 km N San Isidro (-2.198, -78.157) LEP 17673 Ecuador: Morona-Santiago: 2 km N San Isidro (-2.198, -78.157) LEP 18231 Ecuador: Morona-Santiago: 2 km N San Isidro (-2.198, -78.157) LEP 18232 Ecuador: Morona-Santiago: 2 km N San Isidro (-2.198, -78.157) Ecuador: Morona-Santiago: 2.5 km N Puerto Morona (-2.902, - LEP 18130 77.742) Ecuador: Morona-Santiago: 2.5 km N Puerto Morona (-2.902, - LEP 18131 77.742) Ecuador: Morona-Santiago: 2.5 km N Puerto Morona (-2.902, - LEP 18132 77.742) LEP 04338 Ecuador: Morona-Santiago: Chupianza Grande (-2.741, -78.335) LEP 54351 Ecuador: Morona-Santiago: Condor Mirador (-3.635, -78.39) LEP 04290 Ecuador: Morona-Santiago: Condor Mirador (-3.641, -78.393) LEP 54350 Ecuador: Morona-Santiago: Condor Mirador (-3.644, -78.406) Ecuador: Morona-Santiago: E of Mision de Bomboiza (-3.435, - LEP 18238 78.505) Ecuador: Morona-Santiago: E of Mision de Bomboiza (-3.435, - LEP 18239 78.505) Ecuador: Morona-Santiago: E of Mision de Bomboiza (-3.435, - LEP 18240 78.505) Ecuador: Morona-Santiago: forest ridge nr. Yaupi (-2.854, - LEP 18506 77.947) Ecuador: Morona-Santiago: Guarumales/Hidropaute (-2.569, - LEP 04350 78.514) Ecuador: Morona-Santiago: Guarumales/Hidropaute (-2.571, - LEP 04352 78.516) Ecuador: Morona-Santiago: Guarumales/Hidropaute (-2.576, - LEP 04360 78.513)

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Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) Ecuador: Morona-Santiago: Guarumales/Hidropaute (-2.578, - LEP 04333 78.513) Ecuador: Morona-Santiago: Guarumales/Hidropaute (-2.578, - LEP 04349 78.513) Ecuador: Morona-Santiago: Guarumales/Hidropaute (-2.578, - LEP 04361 78.513) LEP 18148 Ecuador: Morona-Santiago: Isla de las Conchas (-3.036, -77.975) LEP 18149 Ecuador: Morona-Santiago: Isla de las Conchas (-3.036, -77.975) LEP 18150 Ecuador: Morona-Santiago: Isla de las Conchas (-3.036, -77.975) LEP 18151 Ecuador: Morona-Santiago: Isla de las Conchas (-3.036, -77.975) LEP 18152 Ecuador: Morona-Santiago: Isla de las Conchas (-3.036, -77.975) LEP 37615 Ecuador: Morona-Santiago: Isla de las Conchas (-3.036, -77.975) Ecuador: Morona-Santiago: km 14 Chigüinda-Gualaquiza rd. (- LEP 14817 3.262, -78.651) Ecuador: Morona-Santiago: km 14 Chigüinda-Gualaquiza rd. (- LEP 14819 3.262, -78.651) Ecuador: Morona-Santiago: km 14 Chigüinda-Gualaquiza rd. (- LEP 14849 3.262, -78.651) Ecuador: Morona-Santiago: km 3 Chigüinda-Gualaceo rd. (-3.219, LEP 14804 -78.739) Ecuador: Morona-Santiago: km 3 Puerto Morona-San José de LEP 37613 Morona (-2.912, -77.707) Ecuador: Morona-Santiago: km 3 Puerto Morona-San José de LEP 37614 Morona (-2.912, -77.707) Ecuador: Morona-Santiago: km 3 Puerto Morona-San José de LEP 37616 Morona (-2.912, -77.707) Ecuador: Morona-Santiago: km 3 Puerto Morona-San José de LEP 37617 Morona (-2.912, -77.707) Ecuador: Morona-Santiago: km 3 Puerto Morona-San José de LEP 37618 Morona (-2.912, -77.707) Ecuador: Morona-Santiago: km 32.8 Santiago-Puerto Morona rd. LEP 17655 (-2.982, -77.802) Ecuador: Morona-Santiago: km 32.8 Santiago-Puerto Morona rd. LEP 17683 (-2.982, -77.802) Ecuador: Morona-Santiago: km 47.6 Santiago-Puerto Morona rd. LEP 17650 (-2.937, -77.747) Ecuador: Morona-Santiago: km 47.6 Santiago-Puerto Morona rd. LEP 17668 (-2.937, -77.747) Ecuador: Morona-Santiago: km 47.6 Santiago-Puerto Morona rd. LEP 17669 (-2.937, -77.747) Ecuador: Morona-Santiago: km 47.6 Santiago-Puerto Morona rd. LEP 17670 (-2.937, -77.747) Ecuador: Morona-Santiago: km 47.6 Santiago-Puerto Morona rd. LEP 17671 (-2.937, -77.747)

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Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) Ecuador: Morona-Santiago: km 47.6 Santiago-Puerto Morona rd. LEP 17684 (-2.937, -77.747) Ecuador: Morona-Santiago: km 5 Santiago-Méndez rd. (-3.028, - LEP 18147 78.045) Ecuador: Morona-Santiago: km. 9.5 Chigüinda-Gualaquiza rd., LEP 14818 river (-3.244, -78.671) Ecuador: Morona-Santiago: km. 9.5 Chigüinda-Gualaquiza rd., LEP 14834 river (-3.244, -78.671) LEP 54659 Ecuador: Morona-Santiago: Puerto Yaupi (-2.981, -77.802) LEP 18153 Ecuador: Morona-Santiago: Río Abanico (-2.255, -78.2) LEP 18154 Ecuador: Morona-Santiago: Río Abanico (-2.255, -78.2) LEP 18229 Ecuador: Morona-Santiago: Río Abanico (-2.255, -78.2) LEP 18237 Ecuador: Morona-Santiago: Río Abanico (-2.255, -78.2) LEP 14848 Ecuador: Morona-Santiago: Río Aguilar (-3.296, -78.634) LEP 04340 Ecuador: Morona-Santiago: Río Wawaime (-2.771, -78.352) LEP 18133 Ecuador: Morona-Santiago: Río Yapapas (-3.012, -78.064) LEP 18134 Ecuador: Morona-Santiago: Río Yapapas (-3.012, -78.064) LEP 04387 Ecuador: Morona-Santiago: San José de Morona (-2.882, -77.67) LEP 04386 Ecuador: Morona-Santiago: Santiago (-3.036, -78.033) LEP 11187 Ecuador: Napo: 'near Baeza' [=El Arrayán] (-0.476, -77.873) LEP 11189 Ecuador: Napo: 'near Baeza' [=El Arrayán] (-0.476, -77.873) LEP 17662 Ecuador: Napo: Apuya (-1.105, -77.778) LEP 17663 Ecuador: Napo: Apuya (-1.105, -77.778) LEP 17665 Ecuador: Napo: Apuya (-1.105, -77.778) LEP 17666 Ecuador: Napo: Apuya (-1.105, -77.778) LEP 17667 Ecuador: Napo: Apuya (-1.105, -77.778) LEP 17681 Ecuador: Napo: Apuya (-1.105, -77.778) LEP 17682 Ecuador: Napo: Apuya (-1.105, -77.778) LEP 18177 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 18178 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 18180 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 18182 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 18183 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 18184 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 18185 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 42051 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 42052 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 42053 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 42054 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 42055 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 42056 Ecuador: Napo: Apuya (-1.115, -77.777)

130

Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) LEP 42057 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 42058 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 18176 Ecuador: Napo: Apuya (-1.115, -77.777) LEP 54538 Ecuador: Napo: Cordillera Galeras (-0.817, -77.582) LEP 10383 Ecuador: Napo: Jatun Sacha (-1.05, -77.586) Ecuador: Napo: km 10 El Chaco-El Reventador rd. (-0.275, - LEP 17660 77.76) LEP 37425 Ecuador: Napo: km 34 Baeza-Lumbaquí rd. (-0.266, -77.755) LEP 18179 Ecuador: Napo: Pimpilala (-1.075, -77.937) LEP 18181 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18186 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18187 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18188 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18189 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18190 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18191 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18192 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18193 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18194 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18195 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18196 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18197 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18198 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18199 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18200 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18201 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18202 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18203 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18204 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18205 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18206 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18207 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18208 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18209 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18213 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 42048 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 42049 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 42050 Ecuador: Napo: Wildsumaco Biological Station (-0.671, -77.599) LEP 18119 Ecuador: Orellana: Bogi 2 W stream (-0.712, -76.475) LEP 18120 Ecuador: Orellana: Bogi 2 W stream (-0.712, -76.475) LEP 18121 Ecuador: Orellana: Bogi 2 W stream (-0.712, -76.475)

131

Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) LEP 18122 Ecuador: Orellana: Bogi 2 W stream (-0.712, -76.475) LEP 18123 Ecuador: Orellana: Bogi 2 W stream (-0.712, -76.475) LEP 18124 Ecuador: Orellana: Bogi 2 W stream (-0.712, -76.475) LEP 18167 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 18168 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 18169 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 18170 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 18173 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 18174 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 18175 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42067 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42068 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42069 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42070 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42071 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42072 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42073 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42074 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42075 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42076 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42077 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42078 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42079 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42081 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42082 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42208 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42209 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42210 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42211 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42212 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42213 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42214 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42215 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42216 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42217 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42218 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 42219 Ecuador: Orellana: Estación Científica Yasuní (-0.671, -76.401) LEP 17458 Ecuador: Orellana: Estación Científica Yasuní (-0.674, -76.397) LEP 17467 Ecuador: Orellana: Estación Científica Yasuní (-0.674, -76.397) LEP 17700 Ecuador: Orellana: Estación Científica Yasuní (-0.674, -76.397)

132

Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) LEP 18116 Ecuador: Orellana: Estación Científica Yasuní (-0.674, -76.397) LEP 18117 Ecuador: Orellana: Estación Científica Yasuní (-0.674, -76.397) Ecuador: Orellana: Estación Científica Yasuní, camino torre (- LEP 17701 0.679, -76.4) Ecuador: Orellana: km 16.5 Loreto-Ávila Viejo rd. (-0.443, - LEP 37593 77.398) Ecuador: Orellana: km 16.5 Pompeya-Estación Científica Yasuní LEP 18110 rd. (-0.539, -76.53) Ecuador: Orellana: km 16.5 Pompeya-Estación Científica Yasuní LEP 18111 rd. (-0.539, -76.53) Ecuador: Orellana: km 16.5 Pompeya-Estación Científica Yasuní LEP 18112 rd. (-0.539, -76.53) Ecuador: Orellana: km 16.5 Pompeya-Estación Científica Yasuní LEP 18113 rd. (-0.539, -76.53) Ecuador: Orellana: km 16.5 Pompeya-Estación Científica Yasuní LEP 18128 rd. (-0.539, -76.53) Ecuador: Orellana: km 16.5 Pompeya-Estación Científica Yasuní LEP 18129 rd. (-0.539, -76.53) Ecuador: Orellana: km 35 Pompeya-Estación Científica Yasuní rd. LEP 18125 (-0.631, -76.461) Ecuador: Orellana: km 35 Pompeya-Estación Científica Yasuní rd. LEP 18126 (-0.631, -76.461) Ecuador: Orellana: Parque Nacional Yasuní, 10 km E Guardianía LEP 37590 Pindo (-0.718, -76.652) Ecuador: Orellana: Parque Nacional Yasuní, 10 km E Guardianía LEP 37591 Pindo (-0.718, -76.652) Ecuador: Orellana: Parque Nacional Yasuní, 10 km E Guardianía LEP 37592 Pindo (-0.718, -76.652) Ecuador: Orellana: Reserva Biológica del Río Bigal (-0.53, - LEP 54674 77.425) Ecuador: Orellana: Reserva Biológica del Río Bigal (-0.53, - LEP 54684 77.425) Ecuador: Orellana: Reserva Biológica del Río Bigal (-0.53, - LEP 54685 77.425) Ecuador: Orellana: Reserva Biológica del Río Bigal, main LEP 11191 campsite (-0.525, -77.418) Ecuador: Orellana: Reserva Biológica del Río Bigal, main LEP 11192 campsite (-0.525, -77.418) Ecuador: Orellana: Reserva Biológica del Río Bigal, main LEP 11195 campsite (-0.525, -77.418) Ecuador: Orellana: Reserva Biológica del Río Bigal, main LEP 11196 campsite (-0.525, -77.418) Ecuador: Orellana: Reserva Biológica del Río Bigal, main LEP 11200 campsite (-0.525, -77.418)

133

Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) Ecuador: Orellana: Reserva Biológica del Río Bigal, main LEP 11202 campsite (-0.525, -77.418) LEP 18108 Ecuador: Orellana: Río Cocaya cabañas (-0.918, -75.254) LEP 18109 Ecuador: Orellana: Río Cocaya cabañas (-0.918, -75.254) LEP 18118 Ecuador: Orellana: Río Cocaya cabañas (-0.918, -75.254) LEP 18114 Ecuador: Orellana: Río Indillama (-0.469, -76.587) LEP 18115 Ecuador: Orellana: Río Indillama (-0.469, -76.587) LEP 18104 Ecuador: Orellana: Tambococha (-0.978, -75.426) LEP 18105 Ecuador: Orellana: Tambococha (-0.978, -75.426) LEP 18106 Ecuador: Orellana: Tambococha (-0.978, -75.426) LEP 18107 Ecuador: Orellana: Tambococha (-0.978, -75.426) LEP 55448 Ecuador: Orellana: Tiputini Biodiversity Station (-0.703, -76.008) LEP 18140 Ecuador: Pastaza: 10.5 km SW Palora (-1.756, -78.03) LEP 18141 Ecuador: Pastaza: 10.5 km SW Palora (-1.756, -78.03) LEP 18142 Ecuador: Pastaza: 10.5 km SW Palora (-1.756, -78.03) LEP 18143 Ecuador: Pastaza: 10.5 km SW Palora (-1.756, -78.03) LEP 18144 Ecuador: Pastaza: 10.5 km SW Palora (-1.756, -78.03) LEP 18145 Ecuador: Pastaza: 10.5 km SW Palora (-1.756, -78.03) LEP 18146 Ecuador: Pastaza: 10.5 km SW Palora (-1.756, -78.03) LEP 37594 Ecuador: Pastaza: Consuelo (-1.922, -77.821) LEP 37595 Ecuador: Pastaza: Consuelo (-1.922, -77.821) LEP 37596 Ecuador: Pastaza: Consuelo (-1.922, -77.821) LEP 04315 Ecuador: Pastaza: Kapawi Lodge (-2.542, -76.859) LEP 04316 Ecuador: Pastaza: Kapawi Lodge (-2.542, -76.859) LEP 04325 Ecuador: Pastaza: Kapawi Lodge (-2.542, -76.859) LEP 04318 Ecuador: Pastaza: Kapawi village (-2.538, -76.836) LEP 04319 Ecuador: Pastaza: Kapawi village (-2.538, -76.836) LEP 04321 Ecuador: Pastaza: Kapawi village (-2.538, -76.836) LEP 04327 Ecuador: Pastaza: Kapawi village (-2.538, -76.836) LEP 18135 Ecuador: Pastaza: km 11 Mera-Río Anzu rd. (-1.421, -78.052) LEP 18136 Ecuador: Pastaza: km 11 Mera-Río Anzu rd. (-1.421, -78.052) LEP 18137 Ecuador: Pastaza: km 11 Mera-Río Anzu rd. (-1.421, -78.052) LEP 18138 Ecuador: Pastaza: km 11 Mera-Río Anzu rd. (-1.421, -78.052) LEP 18127 Ecuador: Pastaza: km 16 Mera-Río Anzu rd. (-1.386, -78.054) LEP 18139 Ecuador: Pastaza: km 8 Mera-Río Anzu rd. (-1.429, -78.066) LEP 34301 Ecuador: Pastaza: Yutsuntsa (-2.351, -76.454) LEP 18412 Ecuador: Pichincha: 12 km SW Las Tolas (0.051, -78.838) LEP 18413 Ecuador: Pichincha: 12 km SW Las Tolas (0.051, -78.838) LEP 18414 Ecuador: Pichincha: 12 km SW Las Tolas (0.051, -78.838) LEP 18415 Ecuador: Pichincha: 12 km SW Las Tolas (0.051, -78.838) LEP 18416 Ecuador: Pichincha: 12 km SW Las Tolas (0.051, -78.838)

134

Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) LEP 18417 Ecuador: Pichincha: 12 km SW Las Tolas (0.051, -78.838) LEP 18418 Ecuador: Pichincha: 12 km SW Las Tolas (0.051, -78.838) LEP 18419 Ecuador: Pichincha: 12 km SW Las Tolas (0.051, -78.838) LEP 18460 Ecuador: Pichincha: 12 km SW Las Tolas (0.051, -78.838) LEP 18496 Ecuador: Pichincha: 12 km SW Las Tolas (0.051, -78.838) LEP 18459 Ecuador: Pichincha: 3 km SW Las Tolas (0.055, -78.801) LEP 18463 Ecuador: Pichincha: 7 km SW Las Tolas (0.057, -78.818) LEP 18464 Ecuador: Pichincha: 7 km SW Las Tolas (0.057, -78.818) LEP 18103 Ecuador: Pichincha: Bellavista Lodge 'D trail' (-0.012, -78.68) LEP 18102 Ecuador: Pichincha: Bellavista Lodge ridge road (0.017, -78.689) LEP 18465 Ecuador: Pichincha: Cerro de Las Chontillas (0.116, -78.836) LEP 18421 Ecuador: Pichincha: km 18 Pacto-Guayabillas rd. (0.174, -78.854) LEP 18422 Ecuador: Pichincha: km 20 Pacto-Guayabillas rd. (0.193, -78.858) LEP 18423 Ecuador: Pichincha: km 20 Pacto-Guayabillas rd. (0.193, -78.858) LEP 18424 Ecuador: Pichincha: km 20 Pacto-Guayabillas rd. (0.193, -78.858) LEP 18499 Ecuador: Pichincha: km 20 Pacto-Guayabillas rd. (0.193, -78.858) LEP 17675 Ecuador: Pichincha: km 7 Pacto-Guayabillas rd. (0.15, -78.815) LEP 17676 Ecuador: Pichincha: km 7 Pacto-Guayabillas rd. (0.15, -78.815) LEP 17680 Ecuador: Pichincha: km 7 Pacto-Guayabillas rd. (0.15, -78.815) LEP 18468 Ecuador: Pichincha: km 9 Pacto-Guayabillas rd. (0.155, -78.821) LEP 18497 Ecuador: Pichincha: km 9 Pacto-Guayabillas rd. (0.155, -78.821) LEP 18101 Ecuador: Pichincha: Reserva Mangaloma (0.121, -78.994) LEP 18461 Ecuador: Pichincha: Tandayapa Bird Lodge (0.002, -78.678) LEP 18462 Ecuador: Pichincha: Tandayapa Bird Lodge (0.002, -78.678) LEP 37588 Ecuador: Sucumbíos: 15 km N Sevilla (0.207, -77.116) LEP 37589 Ecuador: Sucumbíos: 15 km N Sevilla (0.207, -77.116) LEP 15311 Ecuador: Sucumbíos: Cerro Lumbaqui Norte (0.028, -77.317) LEP 15312 Ecuador: Sucumbíos: Cerro Lumbaqui Norte (0.028, -77.317) LEP 17674 Ecuador: Sucumbíos: Cerro Lumbaqui Norte (0.028, -77.317) LEP 37426 Ecuador: Sucumbíos: Cerro Lumbaqui Norte (0.028, -77.317) LEP 37427 Ecuador: Sucumbíos: Cerro Lumbaqui Norte (0.028, -77.317) LEP 37428 Ecuador: Sucumbíos: Cerro Lumbaqui Norte (0.028, -77.317) LEP 37429 Ecuador: Sucumbíos: Cerro Lumbaqui Norte (0.028, -77.317) LEP 37430 Ecuador: Sucumbíos: Cerro Lumbaqui Norte (0.028, -77.317) LEP 37431 Ecuador: Sucumbíos: Cerro Lumbaqui Norte (0.028, -77.317) LEP 37432 Ecuador: Sucumbíos: Cerro Lumbaqui Norte (0.028, -77.317) Ecuador: Sucumbíos: km 10.5 Lumbaqui-Baeza rd. (-0.004, - LEP 15307 77.417) Ecuador: Sucumbíos: km 10.5 Lumbaqui-Baeza rd. (-0.004, - LEP 37433 77.417) LEP 11190 Ecuador: Sucumbíos: Lodge at Quijos (-0.423, -77.85)

135

Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) Ecuador: Tungurahua: km 20 Puyo-Baños rd. (-1.422, -78.175) LEP 18086 1250 m Ecuador: Tungurahua: km 20 Puyo-Baños rd. (-1.422, -78.175) LEP 18087 1250 m Ecuador: Tungurahua: km 20 Puyo-Baños rd. (-1.422, -78.175) LEP 18157 1250 m Ecuador: Tungurahua: km 20 Puyo-Baños rd. (-1.422, -78.175) LEP 18158 1250 m Ecuador: Tungurahua: km 20 Puyo-Baños rd. (-1.422, -78.175) LEP 18156 1250 m Ecuador: Zamora-Chinchipe: 15 km SW above Zurmi (-4.168, - LEP 17651 78.699) LEP 04328 Ecuador: Zamora-Chinchipe: c. 3 km S Shaime (-4.35, -78.658) Ecuador: Zamora-Chinchipe: c. 4 km N Guayguayme Alto (-3.897, LEP 18241 -78.89) Ecuador: Zamora-Chinchipe: Cabañas Ecológicas Copalinga, Río LEP 04331 Bombuscaro (-4.091, -78.959) Ecuador: Zamora-Chinchipe: Cabañas Ecológicas Copalinga, Río LEP 04332 Bombuscaro (-4.091, -78.959) LEP 17653 Ecuador: Zamora-Chinchipe: Cabañas Yankuam (-4.249, -78.659) LEP 17679 Ecuador: Zamora-Chinchipe: Cabañas Yankuam (-4.249, -78.659) LEP 17693 Ecuador: Zamora-Chinchipe: Cabañas Yankuam (-4.249, -78.659) LEP 17694 Ecuador: Zamora-Chinchipe: Cabañas Yankuam (-4.249, -78.659) Ecuador: Zamora-Chinchipe: Cascada Tres Chorros (-3.545, - LEP 14805 78.965) Ecuador: Zamora-Chinchipe: Cascada Tres Chorros (-3.545, - LEP 14829 78.965) Ecuador: Zamora-Chinchipe: Cascada Tres Chorros (-3.545, - LEP 14851 78.965) Ecuador: Zamora-Chinchipe: Cascada Tres Chorros (-3.545, - LEP 14855 78.965) LEP 14801 Ecuador: Zamora-Chinchipe: Corral Pamba (-3.572, -78.961) LEP 14802 Ecuador: Zamora-Chinchipe: Corral Pamba (-3.572, -78.961) LEP 14803 Ecuador: Zamora-Chinchipe: Corral Pamba (-3.572, -78.961) Ecuador: Zamora-Chinchipe: Destacamento Paquisha Alto (- LEP 54488 3.908, -78.485) Ecuador: Zamora-Chinchipe: Destacamento Paquisha Alto (- LEP 54489 3.909, -78.486) Ecuador: Zamora-Chinchipe: Destacamento Paquisha Alto (- LEP 54492 3.909, -78.486) Ecuador: Zamora-Chinchipe: Destacamento Paquisha Alto (-3.91, LEP 04296 -78.486) Ecuador: Zamora-Chinchipe: Destacamento Paquisha Alto (-3.91, LEP 54486 -78.486)

136

Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) Ecuador: Zamora-Chinchipe: Destacamento Paquisha Alto (-3.91, LEP 54487 -78.486) Ecuador: Zamora-Chinchipe: Destacamento Paquisha Alto (-3.91, LEP 54490 -78.486) Ecuador: Zamora-Chinchipe: Destacamento Paquisha Alto (-3.91, LEP 54494 -78.486) Ecuador: Zamora-Chinchipe: Destacamento Paquisha Alto (- LEP 54493 3.912, -78.489) Ecuador: Zamora-Chinchipe: Filo de Sanguinuma (-4.707, - LEP 04337 79.114) LEP 04348 Ecuador: Zamora-Chinchipe: Finca San Carlos (-4.798, -79.309) LEP 18255 Ecuador: Zamora-Chinchipe: Finca San Carlos (-4.798, -79.309) Ecuador: Zamora-Chinchipe: hill above Quebrada Maycú (-4.206, LEP 17657 -78.636) LEP 14828 Ecuador: Zamora-Chinchipe: Juyapa (-3.58, -78.951) LEP 14831 Ecuador: Zamora-Chinchipe: Juyapa (-3.58, -78.951) LEP 14832 Ecuador: Zamora-Chinchipe: Juyapa (-3.58, -78.951) LEP 14833 Ecuador: Zamora-Chinchipe: Juyapa (-3.58, -78.951) LEP 14838 Ecuador: Zamora-Chinchipe: Juyapa (-3.58, -78.951) Ecuador: Zamora-Chinchipe: km 13 Los Encuentros-Zarza (- LEP 04289 3.809, -78.606) Ecuador: Zamora-Chinchipe: km 13 Los Encuentros-Zarza (- LEP 04310 3.809, -78.606) Ecuador: Zamora-Chinchipe: km 13 Los Encuentros-Zarza (- LEP 04313 3.809, -78.606) Ecuador: Zamora-Chinchipe: km 13 Los Encuentros-Zarza (- LEP 04324 3.809, -78.606) Ecuador: Zamora-Chinchipe: km 14 Zumba-San Andrés rd. (- LEP 17678 4.887, -79.173) Ecuador: Zamora-Chinchipe: km 14 Zumba-San Andrés rd. (- LEP 18242 4.887, -79.173) Ecuador: Zamora-Chinchipe: km 14 Zumba-San Andrés rd. (- LEP 18244 4.887, -79.173) Ecuador: Zamora-Chinchipe: km 14 Zumba-San Andrés rd. (- LEP 18245 4.887, -79.173) Ecuador: Zamora-Chinchipe: km 14 Zumba-San Andrés rd. (- LEP 18246 4.887, -79.173) Ecuador: Zamora-Chinchipe: km 14 Zumba-San Andrés rd. (- LEP 18247 4.887, -79.173) Ecuador: Zamora-Chinchipe: km 14 Zumba-San Andrés rd. (- LEP 18248 4.887, -79.173) Ecuador: Zamora-Chinchipe: km 14 Zumba-San Andrés rd. (- LEP 18257 4.887, -79.173)

137

Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) Ecuador: Zamora-Chinchipe: km 14 Zumba-San Andrés rd. (- LEP 18260 4.887, -79.173) Ecuador: Zamora-Chinchipe: km 2.6 El Pite-Río Mayo rd. (-4.866, LEP 17649 -79.094) Ecuador: Zamora-Chinchipe: km 2.6 El Pite-Río Mayo rd. (-4.866, LEP 18256 -79.094) Ecuador: Zamora-Chinchipe: km 2.6 El Pite-Río Mayo rd. (-4.866, LEP 18261 -79.094) Ecuador: Zamora-Chinchipe: km 21 Zumba-Los Sungas rd. (- LEP 14796 4.936, -79.172) Ecuador: Zamora-Chinchipe: km 21 Zumba-Los Sungas rd. (- LEP 14846 4.936, -79.172) Ecuador: Zamora-Chinchipe: km 3.5 El Tambo-San Juan del Oro LEP 18252 (-3.952, -79.059) Ecuador: Zamora-Chinchipe: km 3.5 El Tambo-San Juan del Oro LEP 18253 (-3.952, -79.059) Ecuador: Zamora-Chinchipe: km 4.3 San Andrés-Jimbura rd. (- LEP 04341 4.8, -79.305) Ecuador: Zamora-Chinchipe: km 4.3 San Andrés-Jimbura rd. (- LEP 04353 4.8, -79.305) Ecuador: Zamora-Chinchipe: km 4.3 San Andrés-Jimbura rd. (- LEP 04362 4.8, -79.305) Ecuador: Zamora-Chinchipe: km 5 Zumba-Chito rd. (-4.888, - LEP 14793 79.124) Ecuador: Zamora-Chinchipe: km 5 Zumba-Chito rd. (-4.888, - LEP 14797 79.124) Ecuador: Zamora-Chinchipe: km 5 Zumba-Chito rd. (-4.888, - LEP 14798 79.124) Ecuador: Zamora-Chinchipe: km 5 Zumba-Chito rd. (-4.888, - LEP 14844 79.124) Ecuador: Zamora-Chinchipe: km 5 Zumba-Chito rd. (-4.888, - LEP 14845 79.124) Ecuador: Zamora-Chinchipe: km 5 Zumba-Chito rd. (-4.888, - LEP 14854 79.124) Ecuador: Zamora-Chinchipe: km 8 Zumba-Chito rd. (-4.891, - LEP 14795 79.115) Ecuador: Zamora-Chinchipe: km 8.5 Valladolid-Zumba rd. (-4.603, LEP 04335 -79.129) LEP 04299 Ecuador: Zamora-Chinchipe: La Libertad (-3.798, -78.607) LEP 04300 Ecuador: Zamora-Chinchipe: La Libertad (-3.798, -78.607) LEP 04301 Ecuador: Zamora-Chinchipe: La Libertad (-3.798, -78.607) LEP 04303 Ecuador: Zamora-Chinchipe: La Libertad (-3.798, -78.607) LEP 04304 Ecuador: Zamora-Chinchipe: La Libertad (-3.798, -78.607) LEP 04307 Ecuador: Zamora-Chinchipe: La Libertad (-3.798, -78.607)

138

Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) LEP 04311 Ecuador: Zamora-Chinchipe: La Libertad (-3.798, -78.607) LEP 04312 Ecuador: Zamora-Chinchipe: La Libertad (-3.798, -78.607) LEP 04326 Ecuador: Zamora-Chinchipe: La Libertad (-3.798, -78.607) LEP 04330 Ecuador: Zamora-Chinchipe: La Libertad (-3.798, -78.607) LEP 16774 Ecuador: Zamora-Chinchipe: La Libertad (-3.798, -78.607) LEP 14850 Ecuador: Zamora-Chinchipe: Pucapamba (-4.937, -79.106) Ecuador: Zamora-Chinchipe: Quebrada de los Rubies (-4.877, - LEP 18258 79.209) Ecuador: Zamora-Chinchipe: Quebrada de los Rubies (-4.877, - LEP 18259 79.209) Ecuador: Zamora-Chinchipe: Quebrada Huanchunangui (-4.92, - LEP 04334 79.165) Ecuador: Zamora-Chinchipe: Quebrada San Ramón, power LEP 04293 station (-3.97, -79.062) LEP 14839 Ecuador: Zamora-Chinchipe: Quebrada Troya (-4.792, -79.312) LEP 14840 Ecuador: Zamora-Chinchipe: Quebrada Troya (-4.792, -79.312) LEP 14841 Ecuador: Zamora-Chinchipe: Quebrada Troya (-4.792, -79.312) Ecuador: Zamora-Chinchipe: Quebrada Zurita, old Loja-Zamora LEP 18254 rd. (-3.967, -79.115) Ecuador: Zamora-Chinchipe: Quimi-Condor Mirador rd. (-3.6, - LEP 04294 78.475) Ecuador: Zamora-Chinchipe: Quimi-Condor Mirador rd. (-3.616, - LEP 04287 78.444) Ecuador: Zamora-Chinchipe: Quimi-Condor Mirador rd. (-3.616, - LEP 04292 78.444) LEP 17648 Ecuador: Zamora-Chinchipe: Reserva Maycú (-4.212, -78.643) LEP 17652 Ecuador: Zamora-Chinchipe: Reserva Maycú (-4.212, -78.643) LEP 17654 Ecuador: Zamora-Chinchipe: Reserva Maycú (-4.212, -78.643) LEP 17697 Ecuador: Zamora-Chinchipe: Reserva Maycú (-4.212, -78.643) Ecuador: Zamora-Chinchipe: ridge above San Luís (-3.921, - LEP 14816 78.914) LEP 04305 Ecuador: Zamora-Chinchipe: ridge E San Roque (-3.703, -78.593) LEP 04308 Ecuador: Zamora-Chinchipe: ridge E San Roque (-3.703, -78.593) LEP 04329 Ecuador: Zamora-Chinchipe: ridge E San Roque (-3.703, -78.593) LEP 34291 Ecuador: Zamora-Chinchipe: Río Bombuscaro (-4.113, -78.965) LEP 14799 Ecuador: Zamora-Chinchipe: Río Isimanchi (-4.828, -79.262) LEP 14800 Ecuador: Zamora-Chinchipe: Río Isimanchi (-4.828, -79.262) LEP 14821 Ecuador: Zamora-Chinchipe: Río Isimanchi (-4.828, -79.262) LEP 14822 Ecuador: Zamora-Chinchipe: Río Isimanchi (-4.828, -79.262) LEP 14852 Ecuador: Zamora-Chinchipe: Río Isimanchi (-4.828, -79.262) LEP 14853 Ecuador: Zamora-Chinchipe: Río Isimanchi (-4.828, -79.262) LEP 14856 Ecuador: Zamora-Chinchipe: Río Isimanchi (-4.828, -79.262)

139

Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) LEP 14857 Ecuador: Zamora-Chinchipe: Río Isimanchi (-4.828, -79.262) LEP 14858 Ecuador: Zamora-Chinchipe: Río Isimanchi (-4.828, -79.262) LEP 14860 Ecuador: Zamora-Chinchipe: Río Isimanchi (-4.828, -79.262) LEP 14863 Ecuador: Zamora-Chinchipe: Río Isimanchi (-4.828, -79.262) LEP 14823 Ecuador: Zamora-Chinchipe: San Andrés (-4.821, -79.289) LEP 04346 Ecuador: Zamora-Chinchipe: Soñaderos (-4.052, -79.018) LEP 14820 Ecuador: Zamora-Chinchipe: Tres Aguas (-4.892, -78.993) LEP 14824 Ecuador: Zamora-Chinchipe: Tres Aguas (-4.892, -78.993) LEP 14826 Ecuador: Zamora-Chinchipe: Tres Aguas (-4.892, -78.993) LEP 14827 Ecuador: Zamora-Chinchipe: Tres Aguas (-4.892, -78.993) LEP 14830 Ecuador: Zamora-Chinchipe: Tres Aguas (-4.892, -78.993) LEP 14835 Ecuador: Zamora-Chinchipe: Tres Aguas (-4.892, -78.993) LEP 14836 Ecuador: Zamora-Chinchipe: Tres Aguas (-4.892, -78.993) LEP 14842 Ecuador: Zamora-Chinchipe: Tres Aguas (-4.892, -78.993) LEP 14843 Ecuador: Zamora-Chinchipe: Tres Aguas (-4.892, -78.993) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 34593 54.067) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 34598 54.067) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 34599 54.067) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 37169 54.067) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 37170 54.067) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 37174 54.067) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 37176 54.067) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 37179 54.067) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 37180 54.067) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 37182 54.067) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 37184 54.067) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 37186 54.067) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 37187 54.067) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 37188 54.067)

140

Table A.1. Continued Specimen ID Locality (Decimal Latitude and Longitude) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 37189 54.067) French Guiana: St-Laurent du Maroni: Moutochi Lodge (5.324, - LEP 37190 54.067) LEP 34353 French Guiana: St-Laurent du Maroni: Saül (3.855, -53.302) Guatemala: Baja Verapaz: Km 160.5 La Cumbre-Purulha Rd, IN044 1650-1850m (15.216, -90.221) Guatemala: Baja Verapaz: Km 160.5 La Cumbre-Purulha Rd, IN045 1650-1850m (15.216, -90.221) Guatemala: Baja Verapaz: Km 160.5 La Cumbre-Purulha Rd, IN046 1650-1850m (15.216, -90.221) LEP 18525 Mexico: Veracruz: c. 1 km S Jalcomulco (19.324, -96.757) LEP 18542 Mexico: Veracruz: c. 1 km S Jalcomulco (19.324, -96.757) LEP 18555 Mexico: Veracruz: c. 1 km S Jalcomulco (19.324, -96.757) LEP 18535 Mexico: Veracruz: Cascada Texolo (19.401, -96.99) LEP 18537 Mexico: Veracruz: Cascada Texolo (19.401, -96.99) LEP 18538 Mexico: Veracruz: Cerro de la Galaxia (19.567, -96.934) Mexico: Veracruz: Jardín Botánico Francisco Javier Clavijero LEP 18532 (19.508, -96.94) Mexico: Veracruz: Jardín Botánico Francisco Javier Clavijero LEP 18547 (19.508, -96.94) Mexico: Veracruz: Jardín Botánico Francisco Javier Clavijero LEP 19298 (19.508, -96.94) LEP 18533 Mexico: Veracruz: km 11 Jalcomulco-Coatepec (19.386, -96.869) LEP 18541 Mexico: Veracruz: km 11 Jalcomulco-Coatepec (19.386, -96.869) LEP 18545 Mexico: Veracruz: km 11 Jalcomulco-Coatepec (19.386, -96.869) LEP 18556 Mexico: Veracruz: km 11 Jalcomulco-Coatepec (19.386, -96.869) LEP 19300 Mexico: Veracruz: km 11 Jalcomulco-Coatepec (19.386, -96.869) LEP 18528 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18530 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18531 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18534 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18536 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18539 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18540 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18543 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18544 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18548 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18550 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18551 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18553 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18554 Mexico: Veracruz: Montecillo (19.425, -96.952)

141

Table A-1. Continued Specimen ID Locality (Decimal Latitude and Longitude) LEP 18557 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18558 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18559 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18560 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18561 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 19297 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 19299 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 19301 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 19302 Mexico: Veracruz: Montecillo (19.425, -96.952) LEP 18526 Mexico: Veracruz: Rancho Escondido (19.475, -96.97) LEP 18529 Mexico: Veracruz: Rancho Escondido (19.475, -96.97) LEP 18546 Mexico: Veracruz: Rancho Escondido (19.475, -96.97) LEP 18549 Mexico: Veracruz: Rancho Escondido (19.475, -96.97) LEP 18552 Mexico: Veracruz: Rancho Escondido (19.475, -96.97) Panama: Chiriquí: Above Ojo de Agua, end of W fork, 1713m IN064 (8.874, -82.741) Panama: Herrera: 2.5Km S of Las Minas, Azuero Peninsula, IN065 337m (7.781, -80.753) Peru: Cuzco: Río Cosñipata valley on rd. to Pilcopata (-13.071, - IN068 71.568) Peru: Cuzco: Río Cosñipata valley on rd. to Pilcopata, 2050- IN069 1950m Peru: Junín: Río Ulcumayo valley NW San Ramón, trail up river (- IN072 10.984, -75.438) Peru: Junín: Río Ulcumayo valley NW San Ramón, trail up river (- IN073 10.984, -75.438) LEP 37405 Peru: San Martín: Rumi Jepelacio, Ruta del Agua LEP 37407 Peru: San Martín: Rumi Jepelacio, Ruta del Agua LEP 37410 Peru: San Martín: Rumi Jepelacio, Ruta del Agua LEP 37413 Peru: San Martín: Rumi Jepelacio, Ruta del Agua LEP 37402 Peru: San Martín: Urahuasha (-6.47, -76.338) LEP 37403 Peru: San Martín: Urahuasha (-6.47, -76.338)

142

Genbank Accession Numbers for Previous Published Sequences

Table A-2. Genbank accession numbers of previously published sequences that have been utilized in this investigation Sequence Definition Genbank Accession No. Hermeuptychia atalanta voucher AC01 KF466003.1 Hermeuptychia atalanta voucher AC02 KF466004.1 Hermeuptychia atalanta voucher B01 KF466005.1 Hermeuptychia atalanta voucher BA01 ST BA JN109057.1 Hermeuptychia atalanta voucher BA02 ST BA JN109058.1 Hermeuptychia atalanta voucher BA03 ST BA JN109059.1 Hermeuptychia atalanta voucher C01 KF466006.1 Hermeuptychia atalanta voucher C02 KF466007.1 Hermeuptychia atalanta voucher C12 KF466008.1 Hermeuptychia atalanta voucher C13 KF466009.1 Hermeuptychia atalanta voucher C23 KF466010.1 Hermeuptychia atalanta voucher CE01 KF466011.1 Hermeuptychia atalanta voucher CE02 KF466012.1 Hermeuptychia atalanta voucher CJ01 KF466013.1 Hermeuptychia atalanta voucher CJ02 KF466014.1 Hermeuptychia atalanta voucher CJ03 KF466015.1 Hermeuptychia atalanta voucher CJ04 KF466016.1 Hermeuptychia atalanta voucher CJ05 KF466017.1 Hermeuptychia atalanta voucher DF02 KF466060.1 Hermeuptychia atalanta voucher DF03 KF466061.1 Hermeuptychia atalanta voucher DF04 KF466062.1 Hermeuptychia atalanta voucher DF10 KF466063.1 Hermeuptychia atalanta voucher DF11 KF466064.1 Hermeuptychia atalanta voucher DF12 KF466065.1 Hermeuptychia atalanta voucher ES01 KF466079.1 Hermeuptychia atalanta voucher GO01 KF466085.1 Hermeuptychia atalanta voucher GSM-297 KF466058.1 Hermeuptychia atalanta voucher H-GSM-7 KF466027.1 Hermeuptychia atalanta voucher J01 JU SP JN109048.1 Hermeuptychia atalanta voucher J04 JU SP JN109049.1 Hermeuptychia atalanta voucher J06 JU SP JN109050.1 Hermeuptychia atalanta voucher J07 KF466087.1 Hermeuptychia atalanta voucher J27 KF466091.1 Hermeuptychia atalanta voucher L04 KF466094.1 Hermeuptychia atalanta voucher M02 KF466105.1 Hermeuptychia atalanta voucher M18 KF466106.1 Hermeuptychia atalanta voucher M24 KF466107.1 Hermeuptychia atalanta voucher MG11 KF466109.1 Hermeuptychia atalanta voucher MG12 KF466110.1 Hermeuptychia atalanta voucher MG21 KF466111.1 Hermeuptychia atalanta voucher MG22 KF466112.1

143

Table A-2. Continued Sequence Definition Genbank Accession No. Hermeuptychia atalanta voucher MS02 KF466113.1 Hermeuptychia atalanta voucher MS03 KF466114.1 Hermeuptychia atalanta voucher MS04 KF466115.1 Hermeuptychia atalanta voucher MS05 KF466116.1 Hermeuptychia atalanta voucher MS06 KF466117.1 Hermeuptychia atalanta voucher MS07 KF466118.1 Hermeuptychia atalanta voucher MS08 KF466119.1 Hermeuptychia atalanta voucher MT03 PA MT JN109051.1 Hermeuptychia atalanta voucher MT04 PA MT JN109052.1 Hermeuptychia atalanta voucher MT05 PA MT JN109053.1 Hermeuptychia atalanta voucher MT06 KF466121.1 Hermeuptychia atalanta voucher MT08 KF466122.1 Hermeuptychia atalanta voucher MT09 KF466123.1 Hermeuptychia atalanta voucher MT10 KF466124.1 Hermeuptychia atalanta voucher MT11 KF466125.1 Hermeuptychia atalanta voucher P06 KF466131.1 Hermeuptychia atalanta voucher P07 KF466132.1 Hermeuptychia atalanta voucher P14 KF466133.1 Hermeuptychia atalanta voucher P15 KF466134.1 Hermeuptychia atalanta voucher P21 KF466135.1 Hermeuptychia atalanta voucher PA01 KF466136.1 Hermeuptychia atalanta voucher PA03 KF466138.1 Hermeuptychia atalanta voucher PA07 KF466141.1 Hermeuptychia atalanta voucher R01 CA SP JN109039.1 Hermeuptychia atalanta voucher R10 CA SP JN109040.1 Hermeuptychia atalanta voucher R15 CA SP JN109041.1 Hermeuptychia atalanta voucher RS11 CA RS JN109045.1 Hermeuptychia atalanta voucher RS32 CA RS JN109046.1 Hermeuptychia atalanta voucher RS34 CA RS JN109047.1 Hermeuptychia atalanta voucher RS36 KF466150.1 Hermeuptychia atalanta voucher RS42 PM RS JN109054.1 Hermeuptychia atalanta voucher RS70 KF466151.1 Hermeuptychia atalanta voucher RS74 KF466152.1 Hermeuptychia atalanta voucher RS75 KF466153.1 Hermeuptychia atalanta voucher RS77 PM RS JN109055.1 Hermeuptychia atalanta voucher RS80 PM RS JN109056.1 Hermeuptychia atalanta voucher RS85 KF466154.1 Hermeuptychia atalanta voucher RS95 KF466155.1 Hermeuptychia atalanta voucher RS97 KF466156.1 Hermeuptychia atalanta voucher RS98 KF466157.1 Hermeuptychia atalanta voucher SI14 CA SP2 JN109042.1 Hermeuptychia atalanta voucher SI18 CA SP2 JN109043.1 Hermeuptychia atalanta voucher SI23 CA SP2 JN109044.1 Hermeuptychia atalanta voucher SII10 KF466158.1

144

Table A-2. Continued Sequence Definition Genbank Accession No. Hermeuptychia atalanta voucher SII12 KF466159.1 Hermeuptychia atalanta voucher TO02 KF466161.1 Hermeuptychia atalanta voucher TO03 KF466162.1 Hermeuptychia atalanta voucher TO04 KF466163.1 Hermeuptychia atalanta voucher TO05 KF466164.1 Hermeuptychia cucullina voucher CP04-11 GU205840.1 Hermeuptychia cucullina voucher PE03 KF466142.1 Hermeuptychia cucullina voucher PE04 KF466143.1 Hermeuptychia cucullina voucher PE05 KF466144.1 Hermeuptychia fallax voucher J02 KF466086.1 Hermeuptychia fallax voucher J08 KF466088.1 Hermeuptychia fallax voucher J17 KF466089.1 Hermeuptychia fallax voucher L06 KF466095.1 Hermeuptychia fallax voucher L08 KF466096.1 Hermeuptychia fallax voucher L09 KF466097.1 Hermeuptychia fallax voucher L10 KF466098.1 Hermeuptychia fallax voucher L11 KF466099.1 Hermeuptychia fallax voucher L14 KF466100.1 Hermeuptychia fallax voucher L17 KF466101.1 Hermeuptychia fallax voucher L20 KF466102.1 Hermeuptychia fallax voucher L21 KF466103.1 Hermeuptychia fallax voucher L22 KF466104.1 Hermeuptychia fallax voucher R18 KF466145.1 Hermeuptychia fallax voucher R23 KF466146.1 Hermeuptychia fallax voucher R25 KF466147.1 Hermeuptychia fallax voucher V01 KF466166.1 Hermeuptychia gisella voucher CO05 KF466022.1 Hermeuptychia gisella voucher H-GSM-3 KF466024.1 Hermeuptychia gisella voucher J19 KF466090.1 Hermeuptychia gisella voucher J29 KF466092.1 Hermeuptychia gisella voucher kw-090605-16 KF466073.1 Hermeuptychia gisella voucher kw-090605-17 KF466074.1 Hermeuptychia gisella voucher L01 KF466093.1 Hermeuptychia gisella voucher MT12 KF466126.1 Hermeuptychia gisella voucher MT13 KF466127.1 Hermeuptychia gisella voucher MT15 KF466128.1 Hermeuptychia gisella voucher MT16 KF466129.1 Hermeuptychia harmonia voucher CO06 KF466023.1 Hermeuptychia harmonia voucher CP06-93 GU205842.1 Hermeuptychia harmonia voucher GSM-235 KF466040.1 Hermeuptychia harmonia voucher GSM-237 KF466041.1 Hermeuptychia harmonia voucher GSM-448 KF466048.1 Hermeuptychia harmonia voucher GSM-450 KF466049.1 Hermeuptychia harmonia voucher GSM-452 KF466044.1

145

Table A-2. Continued Sequence Definition Genbank Accession No. Hermeuptychia harmonia voucher GSM-453 KF466045.1 Hermeuptychia harmonia voucher GSM-454 KF466050.1 Hermeuptychia harmonia voucher GSM-455 KF466043.1 Hermeuptychia harmonia voucher GSM-458 KF466046.1 Hermeuptychia harmonia voucher GSM-459 KF466047.1 Hermeuptychia harmonia voucher GSM-482 KF466053.1 Hermeuptychia harmonia voucher GSM-485 KF466051.1 Hermeuptychia harmonia voucher kw-090605-10 KF466070.1 Hermeuptychia harmonia voucher kw-090605-14 KF466071.1 Hermeuptychia harmonia voucher kw-090605-15 KF466072.1 Hermeuptychia harmonia voucher kw-090605-21 KF466077.1 Hermeuptychia harmonia voucher kw-090605-22 KF466078.1 Hermeuptychia harmonia voucher kw-090605-8 KF466068.1 Hermeuptychia hermes voucher CO02 KF466019.1 Hermeuptychia hermes voucher DNA96-016 AY508548.1 Hermeuptychia hermes voucher GSM-231 KF466042.1 Hermeuptychia hermes voucher GSM-254 KF466055.1 Hermeuptychia hermes voucher GSM-283 KF466056.1 Hermeuptychia hermes voucher GSM-314 KF466057.1 Hermeuptychia hermes voucher H-GSM-1 KF466167.1 Hermeuptychia hermes voucher H-GSM-15 KF466033.1 Hermeuptychia hermes voucher H-GSM-16 KF466034.1 Hermeuptychia hermes voucher H-GSM-17 KF466035.1 Hermeuptychia hermes voucher H-GSM-26 KF466039.1 Hermeuptychia hermes voucher H-GSM-5 KF466025.1 Hermeuptychia hermes voucher H-GSM-8 KF466028.1 Hermeuptychia hermes voucher kw-090605-18 KF466075.1 Hermeuptychia hermes voucher MG08 KF466108.1 Hermeuptychia hermes voucher MT01 KF466120.1 Hermeuptychia hermes voucher PA02 KF466137.1 Hermeuptychia hermes voucher PA04 KF466139.1 Hermeuptychia hermes voucher TO06 KF466165.1 Hermeuptychia hermybius voucher 13385H10 KJ025587.1 Hermeuptychia hermybius voucher NVG-1603 KJ025569.1 Hermeuptychia hermybius voucher NVG-1607 KJ025570.1 Hermeuptychia hermybius voucher NVG-1609 KJ025571.1 Hermeuptychia hermybius voucher NVG-1610 KJ025572.1 Hermeuptychia hermybius voucher NVG-1611 KJ025573.1 Hermeuptychia hermybius voucher NVG-1612 KJ025574.1 Hermeuptychia hermybius voucher NVG-1628 KJ025575.1 Hermeuptychia hermybius voucher NVG-1635 KJ025586.1 Hermeuptychia hermybius voucher NVG-1695 KJ025576.1 Hermeuptychia hermybius voucher NVG-1698 KJ025577.1 Hermeuptychia hermybius voucher NVG-1699 KJ025578.1

146

Table A-2. Continued Sequence Definition Genbank Accession No. Hermeuptychia hermybius voucher NVG-1712 KJ025579.1 Hermeuptychia hermybius voucher NVG-1714 KJ025580.1 Hermeuptychia hermybius voucher NVG-1726 KJ025581.1 Hermeuptychia hermybius voucher NVG-1727 KJ025582.1 Hermeuptychia hermybius voucher NVG-1735 KJ025583.1 Hermeuptychia hermybius voucher NVG-1737 KJ025584.1 Hermeuptychia hermybius voucher NVG-1747 KJ025585.1 Hermeuptychia intricata voucher 13385G07 KJ025600.1 Hermeuptychia intricata voucher 13385G08 KJ025604.1 Hermeuptychia intricata voucher 13385G09 KJ025605.1 Hermeuptychia intricata voucher 13385G11 KJ025606.1 Hermeuptychia intricata voucher 13385H01 KJ025601.1 Hermeuptychia intricata voucher 13385H02 KJ025602.1 Hermeuptychia intricata voucher 13386A02 KJ025607.1 Hermeuptychia intricata voucher 13386A03 KJ025603.1 Hermeuptychia intricata voucher NVG-1541 KJ025588.1 Hermeuptychia intricata voucher NVG-1548 KJ025589.1 Hermeuptychia intricata voucher NVG-1551 KJ025590.1 Hermeuptychia intricata voucher NVG-1554 KJ025591.1 Hermeuptychia intricata voucher NVG-1555 KJ025592.1 Hermeuptychia intricata voucher NVG-1556 KJ025593.1 Hermeuptychia intricata voucher NVG-1558 KJ025594.1 Hermeuptychia intricata voucher NVG-1560 KJ025595.1 Hermeuptychia intricata voucher NVG-1563 KJ025596.1 Hermeuptychia intricata voucher NVG-1565 KJ025597.1 Hermeuptychia intricata voucher NVG-1629 KJ025598.1 Hermeuptychia intricata voucher NVG-1631 KJ025599.1 Hermeuptychia maimoune voucher CO03 KF466020.1 Hermeuptychia maimoune voucher CO04 KF466021.1 Hermeuptychia maimoune voucher H-GSM-10 KF466030.1 Hermeuptychia maimoune voucher H-GSM-13 KF466031.1 Hermeuptychia maimoune voucher H-GSM-14 KF466032.1 Hermeuptychia maimoune voucher H-GSM-24 KF466037.1 Hermeuptychia maimoune voucher H-GSM-25 KF466038.1 Hermeuptychia maimoune voucher H-GSM-6 KF466026.1 Hermeuptychia maimoune voucher H-GSM-9 KF466029.1 Hermeuptychia maimoune voucher kw-090605-5 KF466067.1 Hermeuptychia maimoune voucher kw-090605-9 KF466069.1 Hermeuptychia maimoune voucher PA05 KF466140.1 Hermeuptychia maimoune voucher TO01 KF466160.1 Hermeuptychia pimpla voucher CP04-10 GU205843.1 Hermeuptychia pimpla voucher GSM-487 KF466052.1 Hermeuptychia pimpla voucher GSM-489 KF466054.1 Hermeuptychia pimpla voucher kw-090605-20 KF466076.1

147

Table A-2. Continued Sequence Definition Genbank Accession No. Hermeuptychia pimpla voucher kw-090605-4 KF466066.1 Hermeuptychia sosybius voucher 13385G10 KJ025564.1 Hermeuptychia sosybius voucher 13385G12 KJ025560.1 Hermeuptychia sosybius voucher 13385H03 KJ025555.1 Hermeuptychia sosybius voucher 13385H04 KJ025553.1 Hermeuptychia sosybius voucher 13385H05 KJ025556.1 Hermeuptychia sosybius voucher 13385H06 KJ025557.1 Hermeuptychia sosybius voucher 13385H07 KJ025558.1 Hermeuptychia sosybius voucher 13385H08 KJ025559.1 Hermeuptychia sosybius voucher 13385H09 KJ025565.1 Hermeuptychia sosybius voucher 13385H11 KJ025554.1 Hermeuptychia sosybius voucher 13386A01 KJ025566.1 Hermeuptychia sosybius voucher 13386A04 KJ025567.1 Hermeuptychia sosybius voucher 13386A06 KJ025568.1 Hermeuptychia sosybius voucher 13386A07 KJ025561.1 Hermeuptychia sosybius voucher 15609E04 KJ025563.1 Hermeuptychia sosybius voucher DNA-ATBI-0799 GU089906.1 Hermeuptychia sosybius voucher DNA-ATBI-0847 GU089907.1 Hermeuptychia sosybius voucher DNA-ATBI-0848 GU089908.1 Hermeuptychia sosybius voucher DNA-ATBI-0849 GU089909.1 Hermeuptychia sosybius voucher DNA-ATBI-4109 GU088394.1 Hermeuptychia sosybius voucher DNA-ATBI-4110 GU088393.1 Hermeuptychia sosybius voucher EUA02 KF466080.1 Hermeuptychia sosybius voucher EUA03 KF466081.1 Hermeuptychia sosybius voucher EUA06 KF466082.1 Hermeuptychia sosybius voucher EUA07 KF466083.1 Hermeuptychia sosybius voucher EUA08 KF466084.1 Hermeuptychia sosybius voucher GSM-299 KF466059.1 Hermeuptychia sosybius voucher NVG-1537 KJ025532.1 Hermeuptychia sosybius voucher NVG-1538 KJ025533.1 Hermeuptychia sosybius voucher NVG-1539 KJ025534.1 Hermeuptychia sosybius voucher NVG-1540 KJ025535.1 Hermeuptychia sosybius voucher NVG-1542 KJ025536.1 Hermeuptychia sosybius voucher NVG-1543 KJ025537.1 Hermeuptychia sosybius voucher NVG-1544 KJ025538.1 Hermeuptychia sosybius voucher NVG-1545 KJ025539.1 Hermeuptychia sosybius voucher NVG-1546 KJ025540.1 Hermeuptychia sosybius voucher NVG-1547 KJ025541.1 Hermeuptychia sosybius voucher NVG-1549 KJ025542.1 Hermeuptychia sosybius voucher NVG-1550 KJ025543.1 Hermeuptychia sosybius voucher NVG-1552 KJ025544.1 Hermeuptychia sosybius voucher NVG-1553 KJ025545.1 Hermeuptychia sosybius voucher NVG-1557 KJ025546.1 Hermeuptychia sosybius voucher NVG-1559 KJ025547.1

148

Table A-2. Continued Sequence Definition Genbank Accession No. Hermeuptychia sosybius voucher NVG-1561 KJ025548.1 Hermeuptychia sosybius voucher NVG-1562 KJ025549.1 Hermeuptychia sosybius voucher NVG-1564 KJ025550.1 Hermeuptychia sosybius voucher NVG-1566 KJ025551.1 Hermeuptychia sosybius voucher NVG-1567 KJ025552.1 Hermeuptychia sosybius voucher NVG-1606 KJ025527.1 Hermeuptychia sosybius voucher NVG-1630 KJ025525.1 Hermeuptychia sosybius voucher NVG-1632 KJ025524.1 Hermeuptychia sosybius voucher NVG-1633 KJ025526.1 Hermeuptychia sosybius voucher NVG-1845 KJ025562.1 Hermeuptychia sosybius voucher NVG-696 KJ025523.1 Hermeuptychia sosybius voucher NVG-783 KJ025528.1 Hermeuptychia sosybius voucher NVG-784 KJ025529.1 Hermeuptychia sosybius voucher NVG-785 KJ025530.1 Hermeuptychia sosybius voucher NVG-786 KJ025531.1 Hermeuptychia sp. 1YB voucher YB-BCI6182 HM406618.1 Hermeuptychia sp. 1YB voucher YB-BCI6980 HM406623.1 Hermeuptychia sp. hermesECO01 voucher MAL-02845 HM431618.1 Hermeuptychia sp. hermesECO02 voucher MAL-02840 GU659464.1 Hermeuptychia sp. hermesECO02 voucher MAL-02846 GU659462.1 Hermeuptychia sp. hermesECO03 voucher MAL-02839 GU659471.1 Hermeuptychia sp. hermesECO03 voucher MAL-02841 GU659465.1 Hermeuptychia sp. hermesECO03 voucher MAL-02843 GU659467.1 Hermeuptychia sp. hermesECO03 voucher MAL-02847 GU659463.1 Hermeuptychia sp. hermesECO03 voucher MAL-02848 GU659457.1 Hermeuptychia sp. n. 1 NS-2013 voucher MT17 KF466130.1 Hermeuptychia sp. n. 2 NS-2013 voucher RS108 KF466148.1 Hermeuptychia sp. n. 2 NS-2013 voucher RS109 KF466149.1 Hermeuptychia sp. NS-2013 voucher CO01 KF466018.1 Hermeuptychia sp. NS-2013 voucher H-GSM-23 KF466036.1 Outgroups:

Godartiana muscosa DQ338582.1 Pharneuptychia innocentia voucher CP12-06 DQ338808.1 Zischkaia pacarus voucher CP14-02 GQ864819.1 Splendeuptychia boliviensis voucher CP02-48 GU205866.1 Splendeuptychia itonis voucher CP02-44 DQ338811.1 Rareuptychia clio voucher CP01-23 DQ338810.1 Amphidecta calliomma voucher NW126-21 DQ338879.1 Euptychia ordinata voucher CP01-14 GU205835.1

149

Information on Specimens Selected for ddRAD-Seq

Table A-3. Putative species groupings, specimen ID, collection year and DNA concentrations of the samples used for double-digest RAD-Seq Putative Species Specimen ID Collection Year DNA Conc. (ng/ul) ‘Atalanta’ LEP 42053 2016 44.2 ‘Atalanta’ LEP 18174 2016 65.2 ‘Atalanta’ LEP 17700 2014 168.0 ‘Atalanta’ LEP 14856 2013 96.2 ‘Atalanta’ LEP 42056 2016 24.0 ‘Atalanta’ LEP 18176 2016 67.6 ‘Atalanta’ LEP 42072 2016 110.0 ‘Atalanta’ LEP 37615 2016 161.0 ‘Atalanta’ LEP 37617 2016 105.0 ‘Atalanta’ LEP 18126 2015 135.0 ‘Clara’ LEP 14803 2013 137.0 ‘Clara’ LEP 18157 2016 104.0 ‘Clara’ LEP 37596 2016 62.6, genome amplified ‘Gisella’ LEP 18493 2011 84.8 ‘Gisella’ LEP 17696 2014 143.0 ‘Gisella’ LEP 18475 2011 106.0 ‘Gisella’ LEP 14864 2013 116.0 ‘Gisella’ LEP 18478 2011 83.6 ‘Gisella’ LEP 42066 2016 50.4 ‘Gisella’ LEP 18153 2015 108.0 ‘Gisella’ LEP 18202 2016 61.6 ‘Gisella’ LEP 18198 2016 48.4 ‘Gisella’ LEP 17651 2014 105.0 ‘Gisella’ LEP 18150 2015 59.6, genome amplified ‘Harmonia’ LEP 18213 2016 30.4 ‘Harmonia’ LEP 18245 2014 63.6 ‘Harmonia’ LEP 14817 2013 64.5 ‘Harmonia’ LEP 18142 2015 71.2 ‘Harmonia’ LEP 14816 2013 50.4 ‘Harmonia’ LEP 18154 2015 57.0 ‘Harmonia’ LEP 18207 2016 39.8 ‘Harmonia’ LEP 37427 2016 55.6 ‘Harmonia’ LEP 14822 2013 76.0 ‘Harmonia’ LEP 18178 2016 61.4 ‘Harmonia’ LEP 18236 2014 59.8 ‘Harmonia’ LEP 18097 2015 58.8

150

Table A-3. Continued Putative Species Specimen ID Collection Year DNA Conc. (ng/ul) ‘Harmonia’ LEP 55527 2015 55.0 ‘Harmonia’ LEP 18457 2011 116.0 ‘Harmonia’ LEP 18460 2011 109.0 ‘Harmonia’ LEP 18458 2011 113.0 ‘Harmonia’ LEP 18462 2011 131.0 ‘Harmonia’ LEP 55467 2015 117.0 ‘Harmonia’ LEP 18238 2014 72.2 ‘Harmonia’ LEP 18209 2016 52.4 ‘Harmonia’ LEP 42048 2016 26.8 ‘Harmonia’ LEP 14832 2013 88.0 ‘Harmonia’ LEP 18248 2014 83.4 ‘Harmonia’ LEP 14826 2013 66.0 ‘Hermes' LEP 42061 2016 35.6 ‘Hermes' LEP 18480 2011 48.8 ‘Hermes' LEP 42059 2016 35.0 ‘Hermes' LEP 55530 2015 49.0 ‘Hermes' LEP 37640 2016 63.0 ‘Hermes' LEP 55533 2015 53.8 ‘Hermes' LEP 18490 2015 47.0 ‘Hermes' LEP 18096 2014 66.8 ‘Hermes' LEP 55458 2015 54.8 ‘Hermes' LEP 42221 2016 32.0 ‘Hermes' LEP 42055 2016 32.0 ‘Hermes' LEP 42071 2016 51.4 ‘Hermes' LEP 17668 2015 42.2 ‘Hermes' LEP 42081 2016 27.2 ‘Hermes' LEP 18108 2015 67.4 ‘Hermes' LEP 17679 2014 76.4 ‘Hermes' LEP 42052 2016 32.8 ‘Hermes' LEP 18183 2016 36.2 ‘Hermes' LEP 14859 2013 86.2 ‘Hermes' LEP 14861 2013 61.6 ‘Maimoune' LEP 18261 2014 106.0 ‘Maimoune' LEP 14793 2013 118.0 ‘Maimoune' LEP 14800 2013 96.0 ‘Maimoune' LEP 37616 2016 89.2 ‘Maimoune' LEP 18179 2016 47.6 ‘Maimoune' LEP 18140 2015 99.8

151

Table A-3. Continued Putative Species Specimen ID Collection Year DNA Conc. (ng/ul) ‘Maimoune' LEP 18143 2015 110.0 ‘Maimoune' LEP 17673 2015 100.0 ‘Maimoune' LEP 42216 2016 52.8 ‘Maimoune' LEP 42075 2016 16.7 ‘Maimoune' LEP 18149 2015 92.2 ‘Maimoune' LEP 18175 2016 44.2 ‘Maimoune' LEP 14851 2013 126.0 ‘Maimoune' LEP 55504 2015 98.0 ‘Maimoune' LEP 37424 2016 97.8 ‘Maimoune' LEP 17686 2014 98.2 ‘Maimoune' LEP 55506 2015 113.0 ‘Maimoune' LEP 17675 2015 90.4 Species 1 LEP 18243 2014 98.6 Species 1 LEP 37637 2016 59.8, genome amplified Species 5 LEP 42069 2016 40.0 Species 5 LEP 42077 2016 24.4 Species 5 LEP 18106 2015 118.0 Species 6 LEP 14838 2013 110.0 Species 6 LEP 14839 2013 156.0 Species 6 LEP 14843 2013 161.0 Species 6 LEP 18253 2014 120.0 Species 6 LEP 14833 2013 148.0 Species 6 LEP 14842 2013 138.0 Species 2 LEP 18185 2016 53.0 Species 2 LEP 37410 2015 99.8 Species 3 LEP 17666 2015 57.8, genome amplified Species 3 LEP 42062 2016 30.4 Species 3 LEP 18418 2011 71.8 Species 3 LEP 34291 2014 40.2, genome amplified Species 4 LEP 18131 2015 81.4 Species 4 LEP 18133 2015 103.0 Species 4 LEP 42058 2016 53.2 Species 4 LEP 37593 2016 95.8 Species 4 LEP 18120 2015 90.2 Species 7 LEP 17683 2014 99.2, genome amplified Species 8 LEP 17650 2015 95.4, genome amplified

152

APPENDIX B SPECIES DELIMITATION RESULTS

Putative Species Classifications Based on the GMYC Approach

Table B-1. Results from GMYC, multiple thresholds analysis Morphology Group Species Specimens DQ338582.1 Godartiana muscosa, DQ338879.1 Amphidecta calliomma NW126 21, DQ338808.1 1 Pharneuptychia innocentia CP12 06, GU205866.1 Splendeuptychia boliviensis CP02 48, DQ338811.1 Outgroups Splendeuptychia itonis CP02 44 DQ338810.1 Rareuptychia clio CP01 23, GQ864819.1 2 Zischkaia pacarus CP14 02 3 GU205835.1 Euptychia ordinata CP01 14 Hermeuptychia atalanta AC02, Hermeuptychia atalanta PA01, Hermeuptychia atalanta CE01, LEP17678, LEP54538, LEP42056, Hermeuptychia atalanta voucher BA03 ST BA, Hermeuptychia atalanta ES01/RS32/MG12/PA03, LEP04319, Hermeuptychia atalanta DF10, Hermeuptychia atalanta CE02, Hermeuptychia atalanta DF12, Hermeuptychia atalanta MT06, Hermeuptychia atalanta voucher MT03 PA MT, Hermeuptychia atalanta MT09, Hermeuptychia atalanta MT08, Hermeuptychia atalanta PA07, Hermeuptychia atalanta voucher MT05 PA MT, Hermeuptychia atalanta 4 L04, Hermeuptychia atalanta M18, Hermeuptychia atalanta TO03, Hermeuptychia atalanta MS03, Hermeuptychia atalanta voucher C23/RS80/RS85, Hermeuptychia atalanta ‘H. atalanta' MT11, Hermeuptychia atalanta M24, Hermeuptychia atalanta MS05, Hermeuptychia atalanta MS04, Hermeuptychia atalanta MS06, Hermeuptychia atalanta MS06, Hermeuptychia atalanta MS08, Hermeuptychia atalanta TO02, Hermeuptychia atalanta SII10, Hermeuptychia atalanta MS02, Hermeuptychia atalanta voucher RS34 CA RS, MGCL LOAN 570, Hermeuptychia atalanta DF11 Hermeuptychia atalanta CJ01, Hermeuptychia atalanta 5 CJ02, Hermeuptychia atalanta CJ04, Hermeuptychia atalanta CJ05, Hermeuptychia atalanta CJ03 Hermeuptychia atalanta GSM 297, Hermeuptychia atalanta 6 CO12 Hermeuptychia atalanta MG11, Hermeuptychia atalanta 7 MG21, Hermeuptychia atalanta MG22 Hermeuptychia cucullina PE03, Hermeuptychia cucullina ‘H. cucullina' 8 PE05, Hermeuptychia cucullina PE04

153

Table B-1. Continued Morphology Group Species Specimens

Hermeuptychia fallax J02, Hermeuptychia fallax L10, MGCL LOAN 569, Hermeuptychia fallax V01, Hermeuptychia fallax ‘H. fallax' 9 L17, Hermeuptychia fallax L06, Hermeuptychia fallax L09, Hermeuptychia fallax L11, Hermeuptychia fallax L20, Hermeuptychia fallax L21, Hermeuptychia fallax L14

Hermeuptychia sp n RS108, Hermeuptychia sp n RS109, ‘H. gisella' ▲ 10 MGCL LOAN 568 Hermeuptychia gisella J19, Hermeuptychia gisella L01, 11 Hermeuptychia gisella MT13, LEP54684, LEP54685, LEP18150 Hermeuptychia gisella kw 090605 16, LEP17661, ‘H. gisella' ● 12 Hermeuptychia gisella CO05, LEP17252, LEP17253, LEP17685 Hermeuptychia atalanta MT10, Hermeuptychia gisella 13 MT15, Hermeuptychia gisella MT12, Hermeuptychia gisella MT16

Hermeuptychia harmonia GSM 235, Hermeuptychia harmonia GSM 237, Hermeuptychia harmonia kw 090605 10, Hermeuptychia harmonia kw 090605 14, LEP18127, LEP11191, Hermeuptychia pimpla CP04 10, LEP18154, LEP37402, LEP54659, LEP18142, Hermeuptychia 14 harmonia kw 090605 15, LEP04341, Hermeuptychia harmonia EQ22, LEP17663, LEP18178, Hermeuptychia harmonia kw 090605 8, LEP11195, LEP37432, ‘H. harmonia' Hermeuptychia harmonia CO30/CO31/CO33/CO06, LEP54350, LEP15307, LEP18410, LEP18234, LEP18422, LEP18417, LEP18233

Hermeuptychia harmonia EQ21, LEP37407, LEP14832, 15 LEP17660, LEP37405, LEP04299 LEP18454, LEP18251, LEP18467, LEP18468, LEP55615, 16 Hermeuptychia harmonia CO38, LEP55460, LEP55522, LEP55521, LEP55485 Hermeuptychia hermes GSM 231, LEP18482, Hermeuptychia hermes GSM 283, LEP18479, Hermeuptychia hermes CO07, LEP18485, LEP55530, Hermeuptychia hermes CO10, Hermeuptychia hermes GSM 17 314, LEP18483, Hermeuptychia sp. 1YB YB BCI6980, ‘H. hermes’ LEP55510, LEP55505, LEP18492, LEP17689, LEP17691, LEP55493, LEP17687, LEP42221, LEP55492, LEP55453, LEP55491, LEP55529, LEP55534, LEP18486, Hermeuptychia hermes H GSM 26, LEP18494 LEP17671, LEP17697, Hermeuptychia hermes CO02, 18 LEP34353, LEP37169, LEP37182

154

Table B-1. Continued Morphology Group Species Specimens Hermeuptychia hermes H GSM 15, LEP37180, LEP37189, LEP37589, LEP37590, LEP18115, Hermeuptychia hermes 19 H GSM 17, LEP37170, Hermeuptychia hermes MT01, ‘H. hermes’ LEP17701, Hermeuptychia hermes MG08, LEP42212, LEP18104, LEP18113 20 Hermeuptychia hermes PA02, Hermeuptychia hermes PA04 Hermeuptychia hermes intri DNA96 016, Hermeuptychia 21 intricata 13385G07 22 Hermeuptychia intricata 13385G08, IN058, IN059 H. intricata Hermeuptychia intricata 13385H01, Hermeuptychia intricata 13385H02, Hermeuptychia intricata 13386A02, 23 Hermeuptychia intricata 13386A03, Hermeuptychia intricata NVG 1541 Hermeuptychia hermybius NVG 1603, Hermeuptychia H. hermybius 24 hermybius NVG 1628 Hermeuptychia maimoune CO18, LEP37423, LEP37422, 25 LEP55503, LEP17658, LEP17690

‘H. maimoune' ◼ Hermeuptychia maimoune H GSM 24, LEP18470, LEP55482, LEP18471, LEP55617, LEP04342, 26 Hermeuptychia maimoune CO11, LEP17692, LEP55517, Hermeuptychia maimoune H GSM 9, LEP55614

Hermeuptychia maimoune EQ05, LEP37413, LEP37616, 27 ‘H. maimoune' ● LEP04386, LEP18152, IN072, IN073, LEP55448 28 Hermeuptychia maimoune EQ09 Hermeuptychia pimpla GSM 487, Hermeuptychia pimpla GSM 489, Hermeuptychia pimpla kw 090605 20, LEP54494, LEP14843, Hermeuptychia pimpla kw 090605 4, LEP14838, ‘H. pimpla' 29 LEP18252, LEP54487, LEP54351, LEP54492, LEP54486, LEP54489, LEP14833, LEP54488, LEP54493, LEP54490, LEP14842, LEP11189, LEP15311 Hermeuptychia sosybius 13385G10, Hermeuptychia sosybius EUA07, Hermeuptychia sosybius DNA ATBI 0849, 30 Hermeuptychia sosybius NVG 1559, Hermeuptychia sosybius EUA08, Hermeuptychia sosybius EUA03 H. sosybius Hermeuptychia sosybius 13385G12, Hermeuptychia sosybius NVG 1845, Hermeuptychia sosybius 15609E04, 31 Hermeuptychia sosybius EUA06, Hermeuptychia sosybius EUA02 32 Hermeuptychia sosybius GSM 299 33 Hermeuptychia sp n 1NS 2013 34 Hermeuptychia sp NS 2013CO01 Hermeuptychia sp. hermes ECO01 MAL 02845, LEP18525, 35 LEP18545, LEP18540, LEP18528, LEP18555, LEP18541 36 Hermeuptychia sp. hermes ECO02 MAL 02840

155

Table B-1. Continued Morphology Group Species Specimens Hermeuptychia sp. hermes ECO03 MAL 02839, LEP18531, 37 LEP18551, LEP18560 38 Hermeuptychia sp. hermes ECO03 MAL 02848, LEP18538 Hermeuptychia atalanta RS74, Hermeuptychia atalanta 39 RS98 Hermeuptychia maimoune CO04, Hermeuptychia maimoune 40 H GSM 14 Hermeuptychia maimoune PA05, Hermeuptychia maimoune 41 TO01 42 Hermeuptychia hermes kw 090605 18 43 Hermeuptychia hermes TO06 44 Hermeuptychia harmonia CP06 93 45 Hermeuptychia fallax J08 Hermeuptychia atalanta DF03, Hermeuptychia atalanta 46 GO01 47 Hermeuptychia gisella kw 090605 17, LEP18493, LEP18491 48 IN044, IN045 49 IN054, IN055 50 IN057 51 IN064 52 IN065, LEP18499 53 IN068, IN069 54 LEP04290, LEP18158, LEP18156, LEP18086, LEP18087 55 LEP04325 56 LEP17349, LEP14865 LEP17458, LEP18120, LEP17467, LEP37431, LEP18133, 57 LEP18147, LEP37428, LEP17667 58 LEP17648, LEP18107 59 LEP17650 LEP17652, LEP18126, LEP37615, LEP18151, LEP18134, 60 LEP18129 LEP17653, LEP14849, LEP04326, LEP18229, LEP37429, 61 LEP18149, LEP37426, LEP18175, LEP42216, LEP18144 62 LEP17666 63 LEP17681, LEP18143, LEP18146 64 LEP17682, LEP18183, LEP10383 65 LEP17683, LEP37179 66 LEP17693, LEP18116, LEP34599, LEP37186 67 LEP18122 68 LEP18185, LEP37618, LEP37403, LEP11196

156

Table B-1. Continued Morphology Group Species Specimens LEP18196, LEP18153, LEP04311, LEP18232, LEP04346, 69 LEP37425, LEP54674 70 LEP18239 71 LEP18240, LEP04330, LEP14855 72 LEP18256, LEP14798, LEP18258, LEP14800 LEP18414, LEP18415, LEP18416, LEP18419, LEP17659, 73 LEP18412, LEP37430 74 LEP18466 75 LEP18506 76 LEP18526, LEP18529, LEP18537 77 LEP18543, LEP18544, LEP18550, LEP18558 78 LEP19299 79 LEP34291, LEP04305, LEP18088 80 LEP37188, LEP37190 81 LEP37596 82 LEP37637, LEP18243 Note: Specimens for which male genitalia were evaluated have their names underlined. Information from Cong & Grishin, (2014), Seraphim et al., (2014) and Nakahara et al., (2016).

157

Putative Species Classifications Based on the mPTP Approach

Table B-2. Results from mPTP, single-rate analysis Morphology Group Species Specimens 1 DQ338582.1 Godartiana muscosa 2 DQ338808.1 Pharneuptychia innocentia CP12 3 DQ338810.1 Rareuptychia clio CP01 23 4 DQ338811.1 Splendeuptychia itonis CP02 44 Outgroups 5 DQ338879.1 Amphidecta calliomma NW126 21 6 GQ864819.1 Zischkaia pacarus CP14 02 7 GU205835.1 Euptychia ordinata CP01 14 8 GU205866.1 Splendeuptychia boliviensis CP02 Hermeuptychia maimoune EQ09, LEP04330, LEP18240, LEP14855, LEP55448, IN072, IN073, LEP17653, LEP14849, LEP04326, LEP18144, LEP18175, LEP42216, LEP18146, LEP17681, LEP18143, LEP18149, LEP18229, LEP37426, LEP37429, Hermeuptychia maimoune EQ05, Hermeuptychia maimoune H GSM 14, Hermeuptychia maimoune CO04, Hermeuptychia maimoune TO01, ‘H. maimoune' ◼ Hermeuptychia maimoune PA05, LEP14798, LEP18256, 9 ‘H. maimoune' ● LEP18258, LEP14800, LEP18152, LEP37413, LEP37616, LEP04386, LEP04325, LEP18499, IN065, LEP55517, LEP55617, Hermeuptychia maimoune H GSM 9, LEP18470, LEP18471, LEP55614, Hermeuptychia maimoune CO11, LEP04342, LEP55482, LEP17692, Hermeuptychia maimoune H GSM 24, LEP17658, LEP17690, Hermeuptychia maimoune CO18, LEP55503, LEP37423, LEP37422 LEP15311, Hermeuptychia pimpla CO39, Hermeuptychia pimpla GSM 489, Hermeuptychia pimpla EQ20, LEP14843, LEP54494, LEP54487, Hermeuptychia pimpla kw 090605 4, ‘H. pimpla 10 LEP18252, LEP14838, LEP54490, LEP14833, LEP14842, LEP11189, LEP54492, LEP54488, LEP54493, LEP54489, LEP54351, LEP54486 LEP17683, LEP37179, Hermeuptychia fallax J08, Hermeuptychia fallax L14, Hermeuptychia fallax J02, Hermeuptychia fallax V01, Hermeuptychia fallax L10, MGCL ‘H. fallax 11 LOAN 569, Hermeuptychia fallax L17, Hermeuptychia fallax L21, Hermeuptychia fallax L09, Hermeuptychia fallax L11, Hermeuptychia fallax L20, Hermeuptychia fallax L06

158

Table B-2. Continued Morphology Group Species Specimens Hermeuptychia atalanta H GSM 7, Hermeuptychia atalanta GSM 297, Hermeuptychia atalanta MG11, Hermeuptychia atalanta MG21, Hermeuptychia atalanta MG22, Hermeuptychia atalanta RS74, Hermeuptychia atalanta RS98, Hermeuptychia atalanta CJ04, Hermeuptychia atalanta CJ05, Hermeuptychia atalanta CJ03, Hermeuptychia atalanta CJ01, Hermeuptychia atalanta CJ02, Hermeuptychia atalanta L04, Hermeuptychia atalanta DF12, Hermeuptychia atalanta DF10, LEP42056, Hermeuptychia atalanta CE02, Hermeuptychia atalanta GO01, Hermeuptychia atalanta DF03, Hermeuptychia atalanta DF11, LEP54538, Hermeuptychia atalanta voucher MT03 PA MT, Hermeuptychia atalanta PA07, Hermeuptychia atalanta voucher MT05 PA MT, Hermeuptychia atalanta MT06, Hermeuptychia atalanta ‘H. atalanta' 12 MT08, Hermeuptychia atalanta MT09, Hermeuptychia atalanta CE01, Hermeuptychia atalanta voucher BA03 ST BA, LEP04319, Hermeuptychia atalanta ES01, RS32,MG12,PA03, LEP17678, Hermeuptychia atalanta PA01, Hermeuptychia atalanta AC02, LEP18122, LEP18129, LEP17652, LEP18126, LEP18151, LEP18134, LEP37615, Hermeuptychia atalanta MT11, Hermeuptychia atalanta SII10, Hermeuptychia atalanta MS02, Hermeuptychia atalanta C23/RS80/RS85, MGCL LOAN 570, Hermeuptychia atalanta MS06, Hermeuptychia atalanta MS04, Hermeuptychia atalanta MS08, Hermeuptychia atalanta MS03, Hermeuptychia atalanta voucher RS34 CA RS, Hermeuptychia atalanta M18, Hermeuptychia atalanta M24, Hermeuptychia atalanta TO02, Hermeuptychia atalanta TO03, Hermeuptychia atalanta MS05 Hermeuptychia hermes kw 090605 18, LEP17349, LEP14865, Hermeuptychia hermes TO06, Hermeuptychia hermes PA04, Hermeuptychia hermes PA02, LEP17697, LEP17671, LEP37590, LEP18115, LEP42212, LEP10383, LEP17682, LEP37189, LEP37170, Hermeuptychia hermes H GSM 17, LEP37169, LEP37182, LEP37180, Hermeuptychia hermes H GSM 15, LEP37589, LEP34353, ‘H. hermes' 13 Hermeuptychia hermes MG08, LEP18183, Hermeuptychia hermes CO02, LEP18104, LEP18113, LEP17701, Hermeuptychia hermes MT01, Hermeuptychia sp. 1YB YB BCI6980, LEP18492, Hermeuptychia hermes CO10, Hermeuptychia hermes H GSM 1, LEP18479, LEP18483, Hermeuptychia hermes H GSM 26, Hermeuptychia hermes GSM 231

159

Table B-2. Continued Morphology Group Species Specimens Hermeuptychia hermes GSM 283, LEP18485, LEP18482, Hermeuptychia hermes GSM 314, LEP55530, LEP18494, ‘H. hermes’ 13 LEP55534, LEP55492, LEP18486, LEP17689, LEP55453, LEP55491, LEP17691, LEP55510, LEP17687, LEP42221, LEP55505, LEP55493, LEP55529 LEP37432, LEP17663, Hermeuptychia harmonia CO30,CO31,CO33,CO06, LEP04341, LEP15307, LEP11195, Hermeuptychia harmonia kw 090605 15, Hermeuptychia harmonia EQ22, Hermeuptychia harmonia kw 090605 8, LEP54350, LEP18178, LEP18154, Hermeuptychia pimpla CP04 10, LEP18127, LEP18142, LEP37402, LEP11191, LEP54659, Hermeuptychia harmonia kw 090605 10, Hermeuptychia harmonia kw ‘H. harmonia' 14 090605 14, LEP18410, LEP18233, LEP18422, LEP18417, LEP18234, Hermeuptychia harmonia GSM 235, Hermeuptychia harmonia GSM 237, LEP18239, LEP04299, LEP17660, LEP14832, Hermeuptychia harmonia EQ21, LEP37405, LEP37407, LEP55615, Hermeuptychia harmonia CO38, LEP18466, LEP18454, LEP18251, LEP18468, LEP18467, LEP55485, LEP55460, LEP55522, LEP55521 IN045, IN044, Hermeuptychia sosybius CO53, Hermeuptychia sosybius 15609E04, Hermeuptychia sosybius NVG 1845, Hermeuptychia sosybius 13385G12, Hermeuptychia sosybius EUA06, Hermeuptychia sosybius EUA02, Hermeuptychia sosybius NVG 1559, Hermeuptychia sosybius DNA ATBI 0849, Hermeuptychia sosybius EUA03, H. sosybius 15 Hermeuptychia sosybius EUA07, Hermeuptychia sosybius H. hermybius 13385G10, Hermeuptychia sosybius EUA08, LEP18538, Hermeuptychia sp. hermes ECO03 MAL 02839, Hermeuptychia hermybius NVG 1628, Hermeuptychia hermybius NVG 1603, LEP18550, LEP18558, LEP18543, LEP18531, LEP18560, LEP19299, LEP18551, Hermeuptychia sp. hermes ECO03 MAL 02848, LEP18544 16 Hermeuptychia harmonia CP06 93, IN069, IN068 LEP37596, LEP18086, LEP18087, LEP18156, LEP04290, 17 LEP18158 18 Hermeuptychia sp n 1NS 2013 19 Hermeuptychia sp. hermes ECO02 MAL 02840 20 IN054, IN055 21 LEP11196, LEP18185, LEP37403, LEP37618 22 LEP17648, LEP18107 LEP17666, LEP34291, LEP18088, LEP04305, LEP37430, 23 LEP18412, LEP17659, LEP18415, LEP18416, LEP18419, LEP18414

160

Table B-2. Continued Morphology Group Species Specimens 24 LEP18243, LEP37637 MGCL LOAN 568, Hermeuptychia gisella RS108, Hermeuptychia gisella RS109, LEP18506, LEP18116, LEP34599, LEP37190, LEP37186, LEP37188, LEP17693, LEP17458, LEP17467, LEP17667, LEP37428, LEP18147, LEP37431, LEP18120, LEP18133, IN064, LEP17650, LEP18526, LEP18537, LEP18529, Hermeuptychia sp NS 2013CO01, Hermeuptychia intricata 13385G08, Hermeuptychia intricata 13385G07, Hermeuptychia hermes intri DNA96 016, Hermeuptychia intricata 13385H01, Hermeuptychia intricata 13385H02, Hermeuptychia intricata NVG 1541, Hermeuptychia intricata 13386A03, ‘H. gisella' ▲ Hermeuptychia intricata 13386A02, Hermeuptychia sp. ‘H. gisella' ● 25 hermes ECO01 MAL 02845, IN057, IN058, IN059, 'H. cucullina' LEP18555, LEP18545, LEP18528, LEP18525, LEP18540, H. intricata LEP18541, Hermeuptychia cucullina PE05, Hermeuptychia cucullina PE04, Hermeuptychia cucullina PE04 11, LEP17253, LEP17252, LEP18493, LEP18491, LEP17661, LEP17685, Hermeuptychia gisella CO05, Hermeuptychia gisella kw 090605 17, Hermeuptychia gisella kw 090605 16, Hermeuptychia gisella MT15, Hermeuptychia atalanta MT10, Hermeuptychia gisella MT12, Hermeuptychia gisella MT16, Hermeuptychia gisella J19, Hermeuptychia gisella MT13, LEP18150, LEP54684, Hermeuptychia gisella L01, LEP54685, LEP54674, LEP37425, LEP04346, LEP18232, LEP04311, LEP18153, LEP18196 Note: Specimens for which male genitalia were evaluated have their names underlined. Information from Cong & Grishin, (2014), Seraphim et al., (2014) and Nakahara et al., (2016).

161

Putative Species Classifications Based on the bPTP Approach

Table B-3. Results from bPTP, maximum likelihood partitions Morphology Group Species Specimens 1 DQ338582.1 Godartiana muscosa 2 DQ338808.1 Pharneuptychia innocentia CP12 3 DQ338810.1 Rareuptychia clio CP01 23 4 DQ338811.1 Splendeuptychia itonis CP02 44 Outgroups 5 DQ338879.1 Amphidecta calliomma NW126 21 6 GQ864819.1 Zischkaia pacarus CP14 02 7 GU205835.1 Euptychia ordinata CP01 14 8 GU205866.1 Splendeuptychia boliviensis CP02 Hermeuptychia cucullina PE05, Hermeuptychia cucullina ‘H. cucullina' 9 PE04, Hermeuptychia cucullina PE03 Hermeuptychia hermes kw 090605 18, LEP17349, LEP14865, Hermeuptychia hermes TO06, Hermeuptychia hermes PA04, Hermeuptychia hermes PA02, LEP17697, LEP17671, LEP37590, LEP18115, LEP42212, LEP10383, LEP17682, LEP37189, LEP37170, Hermeuptychia hermes H GSM 17, LEP37169, LEP37182, LEP37180, Hermeuptychia hermes H GSM 15, LEP37589, LEP34353, Hermeuptychia hermes MG08, LEP18183, Hermeuptychia hermes CO02, LEP18104, LEP18113, LEP17701, ‘H. hermes' 10 Hermeuptychia hermes MT01, Hermeuptychia sp. 1YB YB BCI6980, LEP18492, Hermeuptychia hermes CO10, Hermeuptychia hermes CO07, LEP18479, LEP18483, Hermeuptychia hermes H GSM 26, Hermeuptychia hermes GSM 231, Hermeuptychia hermes GSM 283, LEP18485, LEP18482, Hermeuptychia hermes GSM 314, LEP55530, LEP18494, LEP55534, LEP55492, LEP18486, LEP17689, LEP55453, LEP55491, LEP17691, LEP55510, LEP17687, LEP42221, LEP55505, LEP55493, LEP55529 Hermeuptychia intricata 13385G07, Hermeuptychia hermes intri DNA96 016, Hermeuptychia intricata 13385H01, 11 Hermeuptychia intricata 13385H02, Hermeuptychia intricata H. intricata NVG 1541, Hermeuptychia intricata 13386A03, Hermeuptychia intricata 13386A02 12 Hermeuptychia intricata 13385G08 Hermeuptychia atalanta CO12, Hermeuptychia atalanta GSM 297, Hermeuptychia atalanta MG11, Hermeuptychia atalanta MG21, Hermeuptychia atalanta MG22, Hermeuptychia atalanta RS74, Hermeuptychia atalanta ‘H. atalanta’ 13 RS98, Hermeuptychia atalanta CJ04, Hermeuptychia atalanta CJ05, Hermeuptychia atalanta CJ03, Hermeuptychia atalanta CJ01, Hermeuptychia atalanta CJ02

162

Table B-3. Continued Morphology Group Species Specimens Hermeuptychia atalanta L04, Hermeuptychia atalanta DF12, Hermeuptychia atalanta DF10, LEP42056, Hermeuptychia atalanta CE02, Hermeuptychia atalanta GO01, Hermeuptychia atalanta DF03, Hermeuptychia atalanta DF11, LEP54538, Hermeuptychia atalanta voucher MT03 PA MT, Hermeuptychia atalanta PA07, Hermeuptychia atalanta voucher MT05 PA MT, Hermeuptychia atalanta MT06, Hermeuptychia atalanta MT08, Hermeuptychia atalanta MT09, Hermeuptychia atalanta CE01, Hermeuptychia atalanta voucher BA03 ST BA, LEP04319, Hermeuptychia atalanta RS32/MG12/PA03/ES01, ‘H. atalanta’ 14 LEP17678, Hermeuptychia atalanta PA01, Hermeuptychia atalanta AC02, LEP18122, LEP18129, LEP17652, LEP18126, LEP18151, LEP18134, LEP37615, Hermeuptychia atalanta MT11, Hermeuptychia atalanta SII10, Hermeuptychia atalanta MS02, Hermeuptychia atalanta C23/RS80/RS85, MGCL LOAN 570, Hermeuptychia atalanta MS06, Hermeuptychia atalanta MS04, Hermeuptychia atalanta MS08, Hermeuptychia atalanta MS03, Hermeuptychia atalanta voucher RS34 CA RS, Hermeuptychia atalanta M18, Hermeuptychia atalanta M24, Hermeuptychia atalanta TO02, Hermeuptychia atalanta TO03, Hermeuptychia atalanta MS05 Hermeuptychia maimoune EQ09, LEP04330, LEP18240, LEP14855, LEP55448, IN072, IN073, LEP17653, LEP14849, LEP04326, LEP18144, LEP18175, LEP42216, LEP18146, LEP17681, LEP18143, LEP18149, LEP18229, LEP37426, LEP37429, Hermeuptychia maimoune EQ05, Hermeuptychia maimoune H GSM 14, Hermeuptychia maimoune CO04, Hermeuptychia maimoune TO01, ‘H. maimoune' ◼ 15 Hermeuptychia maimoune PA05, LEP14798, LEP18256, ‘H. maimoune' ● LEP18258, LEP14800, LEP18152, LEP37413, LEP37616, LEP04386, LEP04325, LEP18499, IN065, LEP55517, LEP55617, Hermeuptychia maimoune H GSM 9, LEP18470, LEP18471, LEP55614, LEP04339, LEP04342, LEP55482, LEP17692, Hermeuptychia maimoune H GSM 24, LEP17658, LEP17690, Hermeuptychia maimoune CO18, LEP55503, LEP37423, LEP37422 16 Hermeuptychia sosybius CO53 Hermeuptychia sosybius 15609E04, Hermeuptychia sosybius NVG 1845, Hermeuptychia sosybius 13385G12, H. sosybius Hermeuptychia sosybius EUA06, Hermeuptychia sosybius 17 EUA02, Hermeuptychia sosybius NVG 1559, Hermeuptychia sosybius DNA ATBI 0849, Hermeuptychia sosybius EUA03, Hermeuptychia sosybius EUA07, Hermeuptychia sosybius 13385G10, Hermeuptychia sosybius EUA08 18 Hermeuptychia sp n 1NS 2013

16 3

Table B-3. Continued Morphology Group Species Specimens 19 Hermeuptychia sp NS 2013CO01

20 Hermeuptychia sp. hermes ECO01 MAL 02845

21 Hermeuptychia sp. hermes ECO02 MAL 02840

22 IN044

23 IN045

24 IN054, IN055

25 IN057, IN058, IN059

26 IN064

27 IN069, IN068

28 LEP11196, LEP18185

LEP15311, Hermeuptychia pimpla CO39, Hermeuptychia pimpla GSM 489, Hermeuptychia pimpla EQ20, LEP14843, LEP54494, LEP54487, Hermeuptychia pimpla kw 090605 4, ‘H. pimpla' 29 LEP18252, LEP14838, LEP54490, LEP14833, LEP14842, LEP11189, LEP54492, LEP54488, LEP54493, LEP54489, LEP54351, LEP54486 LEP17253, LEP17252, LEP18493, LEP18491, LEP17661, LEP17685, Hermeuptychia gisella CO05, Hermeuptychia gisella kw 090605 17, Hermeuptychia gisella kw 090605 16, Hermeuptychia gisella MT15, Hermeuptychia atalanta ‘H. gisella' ● 30 MT10, Hermeuptychia gisella MT12, Hermeuptychia gisella MT16, Hermeuptychia gisella J19, Hermeuptychia gisella MT13, LEP18150, LEP54684, Hermeuptychia gisella L01, LEP54685, LEP54674, LEP37425, LEP04346, LEP18232, LEP04311, LEP18153, LEP18196 31 LEP17648 32 LEP17650 LEP17666, LEP34291, LEP18088, LEP04305, LEP37430, 33 LEP18412, LEP17659, LEP18415, LEP18416, LEP18419, LEP18414 34 LEP18107 35 LEP18243, LEP37637 36 LEP18525, LEP18540, LEP18541 37 LEP18526, LEP18537, LEP18529 LEP17683, LEP37179, Hermeuptychia fallax J08, Hermeuptychia fallax L14, Hermeuptychia fallax J02, Hermeuptychia fallax V01, Hermeuptychia fallax L10, MGCL ‘H. fallax' 38 LOAN 569, Hermeuptychia fallax L17, Hermeuptychia fallax L21, Hermeuptychia fallax L09, Hermeuptychia fallax L11, Hermeuptychia fallax L20, Hermeuptychia fallax L06 LEP18538, Hermeuptychia sp. hermes ECO03 MAL 02839, Hermeuptychia hermybius NVG 1628, Hermeuptychia H. hermybius 39 hermybius NVG 1603, LEP18550, LEP18558, LEP18543, LEP18531, LEP18560, LEP19299, LEP18551, Hermeuptychia sp. hermes ECO03 MAL 02848, LEP18544

164

Table B-3. Continued Morphology Group Species Specimens 40 LEP18545, LEP18528 41 LEP18555 42 LEP37403 LEP37432, LEP17663, Hermeuptychia harmonia CO30/CO31/CO33/CO06, LEP04341, LEP15307, LEP11195, Hermeuptychia harmonia kw 090605 15, Hermeuptychia harmonia EQ22, Hermeuptychia harmonia kw 090605 8, LEP54350, LEP18178, LEP18154, Hermeuptychia pimpla CP04 10, LEP18127, LEP18142, LEP37402, LEP11191, LEP54659, Hermeuptychia harmonia kw 090605 10, Hermeuptychia harmonia kw ‘H. harmonia' 43 090605 14, LEP18410, LEP18233, LEP18422, LEP18417, LEP18234, Hermeuptychia harmonia GSM 235, Hermeuptychia harmonia GSM 237, LEP18239, LEP04299, LEP17660, LEP14832, Hermeuptychia harmonia EQ21, LEP37405, LEP37407, LEP55615, Hermeuptychia harmonia CO38, LEP18466, LEP18454, LEP18251, LEP18468, LEP18467, LEP55485, LEP55460, LEP55522, LEP55521 44 Hermeuptychia harmonia CP06 93 LEP37596, LEP18086, LEP18087, LEP18156, LEP04290, 45 LEP18158 46 LEP37618 MGCL LOAN 568, Hermeuptychia gisella RS108, Hermeuptychia gisella RS109, LEP18506, LEP18116, ‘H. gisella' ▲ 47 LEP34599, LEP37190, LEP37186, LEP37188, LEP17693, LEP17458, LEP17467, LEP17667, LEP37428, LEP18147, LEP37431, LEP18120, LEP18133 Note: Specimens for which male genitalia were evaluated have their names underlined. Information from Cong & Grishin, (2014), Seraphim et al., (2014) and Nakahara et al., (2016).

165

Putative Species Classifications Based on the ABGD Approach

Table B-4. Results of ABGD, with recursive partitioning Morphology Group Species Specimens 1 DQ338582.1 Godartiana muscosa 2 DQ338808.1 Pharneuptychia innocentia CP12 3 DQ338810.1 Rareuptychia clio CP01-23 4 DQ338811.1 Splendeuptychia itonis CP02-44 Outgroups 5 DQ338879.1 Amphidecta calliomma NW126-21 6 GQ864819.1 Zischkaia pacarus CP14-02 7 GU205835.1 Euptychia ordinata CP01-14 8 GU205866.1 Splendeuptychia boliviensis CP02 Hermeuptychia cucullina PE03, Hermeuptychia cucullina ‘H. cucullina' 9 PE04, Hermeuptychia cucullina PE05 LEP14832, LEP17660, LEP37405, LEP37407, 10 Hermeuptychia harmonia EQ21, LEP04299, LEP18239 Hermeuptychia harmonia CO38, LEP18251, LEP18454, 11 LEP18466, LEP18468, LEP18467, LEP55485, LEP55615, LEP55460, LEP55521, LEP55522 LEP18154, LEP18142, LEP18127, LEP37402, LEP11191, ‘H. harmonia' LEP54659, Hermeuptychia harmonia kw-090605-10, Hermeuptychia harmonia kw-090605-14, Hermeuptychia pimpla CP04-10, Hermeuptychia harmonia 12 CO30/CO31/CO33/CO06, LEP11195 maimoune, LEP54350, Hermeuptychia harmonia kw-090605-15, Hermeuptychia harmonia EQ22, LEP17663, Hermeuptychia harmonia kw-090605-8, LEP18178, LEP37432, LEP15307, LEP04341 Hermeuptychia hermybius NVG-1603, Hermeuptychia H. hermybius 13 hermybius NVG-1628 Hermeuptychia intricata NVG-1541, Hermeuptychia intricata 13386A02, Hermeuptychia intricata 13386A03, Hermeuptychia intricata 13385H01, Hermeuptychia intricata H. intricata 14 13385H02, Hermeuptychia intricata 13385G08, Hermeuptychia intricata 13385G07 Hermeuptychia hermes intri DNA96-016 Hermeuptychia atalanta CJ01, Hermeuptychia atalanta CJ02, Hermeuptychia atalanta CJ05, Hermeuptychia 15 atalanta CJ04, Hermeuptychia atalanta RS74, ‘H. atalanta’ Hermeuptychia atalanta RS98, Hermeuptychia atalanta CJ03 Hermeuptychia atalanta CO12, Hermeuptychia atalanta 16 GSM-297

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Table B-4. Continued Morphology Group Species Specimens LEP18151, LEP18134, LEP37615, LEP18126, LEP18129, LEP17652, LEP18122, LEP18139, LEP17678, LEP42056, Hermeuptychia atalanta CE02, Hermeuptychia atalanta voucher BA03 ST BA, Hermeuptychia atalanta CE01, Hermeuptychia atalanta L04, LEP54538, Hermeuptychia atalanta DF11, MGCL LOAN 570, Hermeuptychia atalanta C23/RS80/RS85, Hermeuptychia atalanta M24, Hermeuptychia atalanta TO02, Hermeuptychia atalanta SII10, Hermeuptychia atalanta M18, Hermeuptychia atalanta voucher RS34 CA RS, Hermeuptychia atalanta TO03, Hermeuptychia atalanta AC02, Hermeuptychia atalanta 17 PA01, LEP04319, Hermeuptychia atalanta MS02, ‘H. atalanta' Hermeuptychia atalanta GO01, Hermeuptychia atalanta voucher MT05 PA MT, Hermeuptychia atalanta MT06, Hermeuptychia atalanta PA07, Hermeuptychia atalanta MT09, Hermeuptychia atalanta voucher MT03 PA MT, Hermeuptychia atalanta MT08, Hermeuptychia atalanta DF03, Hermeuptychia atalanta DF12, Hermeuptychia atalanta MS03, Hermeuptychia atalanta MT11, Hermeuptychia atalanta MS08, Hermeuptychia atalanta MS04, Hermeuptychia atalanta MS06, Hermeuptychia atalanta DF10, Hermeuptychia atalanta MS05 Hermeuptychia atalanta MG11, Hermeuptychia atalanta 18 MG21, Hermeuptychia atalanta MG22 LEP17692, Hermeuptychia maimoune CO11, LEP55482, LEP18471, Hermeuptychia maimoune H-GSM-9, LEP55614, LEP04342, LEP55517, LEP55617, LEP55503, ‘H. maimoune' ◼ 19 LEP37422, LEP37423, Hermeuptychia maimoune CO18, LEP17690, LEP17658, LEP18499, Hermeuptychia maimoune H-GSM-24, LEP18470, IN065 ‘H. maimoune' ● 20 Hermeuptychia maimoune EQ09 LEP18152, LEP37413, LEP04386, LEP37616, Hermeuptychia maimoune CO04, Hermeuptychia maimoune H-GSM-14, Hermeuptychia maimoune EQ05, LEP18149, LEP18229, LEP37426, LEP37429, LEP18144, LEP42216, 21 LEP18175, LEP14855, LEP18240, LEP04326, LEP17653, LEP18146, LEP18143, LEP17681, LEP14849, IN072, IN073, LEP14800, LEP18258, LEP18256, LEP14798, Hermeuptychia maimoune PA05, LEP55448, LEP04330 Hermeuptychia pimpla CO39, Hermeuptychia pimpla GSM- 22 489 LEP15311, LEP14843, LEP54494, LEP14838, LEP18252, ‘H. pimpla’ LEP54487, Hermeuptychia pimpla kw-090605-4,

23 Hermeuptychia pimpla EQ20, LEP14842, LEP14833, LEP54492, LEP54351, LEP54489, LEP54488, LEP54493, LEP54486, LEP54490, LEP11189

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Table B-4. Continued Morphology Group Species Specimens LEP18531, LEP18543, LEP18551, LEP18550, LEP19299, Hermeuptychia sp. hermes ECO03 MAL-02839, 24 Hermeuptychia sp. hermes ECO03 MAL-02848, LEP18544, LEP18538, LEP18560, LEP18558, IN044, IN045, Hermeuptychia sosybius CO53 H. sosybius Hermeuptychia sosybius DNA-ATBI-0849, Hermeuptychia sosybius NVG-1559, Hermeuptychia sosybius EUA08, Hermeuptychia sosybius EUA03, Hermeuptychia sosybius 25 EUA07, Hermeuptychia sosybius EUA06, Hermeuptychia sosybius EUA02, Hermeuptychia sosybius 13385G12, Hermeuptychia sosybius 13385G10, Hermeuptychia sosybius NVG-1845, Hermeuptychia sosybius 15609E04 MGCL LOAN 568, Hermeuptychia gisella RS108, ‘H. gisella' ▲ 26 Hermeuptychia gisella RS109 LEP18153, LEP18196, LEP04311, LEP37425, LEP18150, LEP54684, LEP54685, Hermeuptychia gisella J19, Hermeuptychia gisella L01, Hermeuptychia atalanta MT10, 27 Hermeuptychia gisella MT12, Hermeuptychia gisella MT15, LEP54674, LEP18232, Hermeuptychia gisella MT16, ‘H. gisella' ● Hermeuptychia gisella MT13, LEP04346 Hermeuptychia gisella CO05, LEP17685, LEP17661, LEP18491, LEP18493, Hermeuptychia gisella kw-090605- 28 16, Hermeuptychia gisella kw-090605-17, LEP17253, LEP17252 MGCL LOAN 569, Hermeuptychia fallax L10, Hermeuptychia fallax V01, Hermeuptychia fallax J02, Hermeuptychia fallax L14, Hermeuptychia fallax J08, ‘H. fallax' 29 Hermeuptychia fallax J17, Hermeuptychia fallax L20, Hermeuptychia fallax L06, Hermeuptychia fallax L09, Hermeuptychia fallax L21, Hermeuptychia fallax L11 LEP18486, LEP17691, LEP17689, LEP42221, LEP55453, LEP17687, LEP55510, LEP55505, LEP55493, LEP55529, LEP55492, LEP18492, LEP55491, Hermeuptychia hermes CO07, LEP55530, LEP18482, Hermeuptychia hermes GSM- 231, Hermeuptychia hermes GSM-283, Hermeuptychia hermes GSM-314, LEP18485, LEP18479, LEP18483, LEP18494, Hermeuptychia hermes H-GSM-26, LEP55534, Hermeuptychia hermes CO10, Hermeuptychia sp. 1YB YB- ‘H. hermes' 30 BCI6980, LEP18115, LEP37590, LEP18113, Hermeuptychia hermes CO02, LEP18104, LEP42212, LEP17701, Hermeuptychia hermes MT01, LEP37180, Hermeuptychia hermes H-GSM-15, LEP37170, LEP37189, Hermeuptychia hermes H-GSM-17, LEP37169, LEP37182, LEP37589, LEP17697, LEP17671, Hermeuptychia hermes MG08, LEP18183, LEP10383, LEP17682, LEP34353, Hermeuptychia hermes TO06, Hermeuptychia hermes PA04, Hermeuptychia hermes PA02

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Table B-4. Continued Morphology Group Species Specimens 31 Hermeuptychia sp n 1NS-2013 32 Hermeuptychia sp NS-2013CO01 33 Hermeuptychia sp. hermes ECO01 MAL-02845 34 Hermeuptychia sp. hermes ECO02 MAL-02840 35 IN054, IN055 36 IN057, IN059, IN058 37 IN064 38 IN068, IN069, Hermeuptychia harmonia CP06-93 39 Hermeuptychia hermes kw-090605-18 40 LEP04325 41 LEP11196, LEP18185, LEP37403, LEP37618 42 LEP14865, LEP17349 43 LEP17650 Hermeuptychia harmonia GSM-235, Hermeuptychia 44 harmonia GSM-237 45 LEP17683 LEP18087, LEP18086, LEP04290, LEP18158, LEP18156, 46 LEP37596 47 LEP18107, LEP17648 LEP18147, LEP18120, LEP18133, LEP37431, LEP17667, 48 LEP37428, LEP17458, LEP17467, LEP18116, LEP34599, LEP37186, LEP37188, LEP17693, LEP37190 49 LEP18243, LEP37637 LEP18412, LEP17659, LEP18414, LEP18416, LEP18415, 50 LEP18419, LEP37430, LEP18088, LEP04305, LEP34291, LEP17666 51 LEP18422, LEP18233, LEP18410, LEP18417, LEP18234 52 LEP18506 LEP18525, LEP18555, LEP18528, LEP18545, LEP18540, 53 LEP18541 54 LEP18526, LEP18529, LEP18537 55 LEP37179 56 Hermeuptychia maimoune TO01 Note: Specimens for which male genitalia were evaluated have their names underlined. Information from Cong & Grishin, (2014), Seraphim et al., (2014) and Nakahara et al., (2016).

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APPENDIX C PRELIMINARY SNAPP TREE FOR ECUADORIAN HERMEUPTYCHIA

Figure C-1. The preliminary tree topology obtained from the species tree method SNAPP.

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APPENDIX D SUPPLEMENTARY VENTRAL WING AND MALE GENITALIA IMAGES

Figure D-1. Ventral wing patterns of specimens representing Species 1, Species 2, Species 4 and Species 5. The color of the specimen ID labels corresponds to STRUCTURE clustering results in Chapter 3. Photographs by Denise Tan.

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SPECIES 1 LEP18243

SPECIES 4 LEP37593

SPECIES 5 LEP18106

Figure D-2. Lateral view of male genitalia of specimens representing Species 1, Species 4 and Species 5. All Species 2 specimens were female specimens. Photographs by Denise Tan.

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BIOGRAPHICAL SKETCH

Denise Tan grew up in the urban, city-state of Singapore with a great affection for nature, especially animals. In order to learn more about natural history and conservation, she majored in life sciences, concentrating in biology, at the National

University of Singapore. In her third year of college, she conducted her first independent research project, pertaining to primate conservation, under the supervision of Dr. Rudolf

Meier. At the Evolutionary Biology Laboratory, she discovered and was immensely fascinated by the sexual dimorphism and intricate mating behaviors exhibited by black scavenger flies (Diptera: Sepsidae). Eventually she became proficient in rearing live cultures, allowing her to design a series of sexual selection experiments incorporating molecular systematics and traditional taxonomy. She obtained her B.Sc. (with Honors) in 2008, continued with this area of research and graduated with a M.Sc. in 2011.

Deciding on a career in entomological research, she began her Ph.D. at the

University of Florida where she was supported by a Graduate Research Assistantship jointly funded by the Department of Entomology and Nematology as well as the

McGuire Center for Lepidoptera and Biodiversity (Florida Museum of Natural History).

This appointment allowed her to combine her love for teaching with her interests for museum-based biodiversity research. Drawing on her previous experience and training, she developed a project on the poorly understood butterfly genus Hermeuptychia

(Nymphalidae: Satyrinae: Euptychiina) under the mentorship of Dr. Keith Willmott.

Denise received her doctorate in the Spring of 2018 and plans to return to

Singapore where she will continue cultivating her passion for teaching, outreach and

Lepidoptera research.

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