JOHNSON N.Pdf

GENETIC INVESTIGATIONS REVEAL NEW INSIGHTS INTO THE DIVERSITY, DISTRIBUTION, AND LIFE HISTORY OF FRESHWATER MUSSELS (BIVALVIA: UNIONIDAE) INHABITING THE NORTH AMERICAN COASTAL PLAIN

NATHAN ALLEN JOHNSON

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

UNIVERSITY OF FLORIDA

2017

To all my collaborators, colleagues, family, and friends who helped make this endeavor a success

ACKNOWLEDGMENTS

This work would not have been completed without the guidance, friendship, support and assistance of my advisors, committee members, colleagues, and loved ones. My advisor, Dr. Jim Austin, provided continuous guidance and stimulus throughout my graduate career and above all, gave me total freedom to pursue a research project that piqued my interests. I also express my gratitude to Dr. Jim

Williams for sharing his wealth of knowledge on aquatic fauna and for all his time and effort traveling around the “southeast corner” of the US making collections for this project and connecting me to a network of aquatic biologists around the world. Many thanks to the rest of my graduate committee, Dr. Mark Brenner, Dr. Tom Frazer, and Dr.

Gustav Paulay who were always available for important discussions regarding my research and extremely helpful through all the steps of my graduate career.

I give special thanks to Dr. Ken Rice, Howard Jelks, and Gary Mahon who provided continued support for my dissertation research, publications, and development of my freshwater mussel research program at the U.S. Geological Survey Wetland and

Aquatic Research Center in Gainesville, Florida. I also give special thanks to my fellow graduate students and laboratory mates (Andrew Barbour, Jason Butler, John

Hargrove, Aria Johnson, Matt Lauretta, James Nifong, Wade Ross, Emily Saarinen,

Matt Shirley, and Joe Townsend), and dozens of mussel biologists, laboratory technicians, USGS colleagues, and museum staff who provided logistical support or helped with specimen collection, laboratory data collection, and curation for my projects

(Caitlin Beaver, Mandy Bemis, Amy Benson, Tim Boozer, Ben Bosman, Sherry Bostick,

Mike Buntin, Lyuba Burlakova, Bob Butler, Patricia Caccavale, Celine Carneiro, Janet

Clayton, Mike Cordova, Kevin Cummings, Gerry Dinkins, Drew Dutterer, Scott Faiman,

Todd Fobian, Paul Freeman, Mike Gangloff, Jeff Garner, John Harris, Mike Hart, Paul

Hartfield, Libby Hartfield, Karen Herrington, Jordan Holcomb, Bob Howells, Maggie

Hunter, Ben Hutchins, Jaclyn Irwin, John Johansen, Matt Johnson, Paul Johnson, Bob

Jones, Jess Jones, Ben Lundeen, Steve McMurray, John Moran, Bruce Moring, Cheryl

Morrison, Particia Morrison, Eric Nagid, Susan Oetker, Michael Perkins, Heather Perry,

John Pfeiffer, Emma Pistole, Tracey Popejoy, Jeff Powell, Sandy Pursifull, Morgan

Raley, Charles Randklev, Clint Robertson, Kevin Roe, Matt Rowe, Shane Ruessler,

Sara Seagraves, Colin Shea, Shawna Simpson, Joe Skorupski, Todd Slack, John

Slapcinsky, Chase Smith, Charrish Stevens, Carson Stringfellow, Jeremy Tieman, Eric

Tsakris, Travis Tuten, Brian Watson, Carla Wieser, Jason Wisniewski, and Craig

Zievis). Funding for this project was provided by Florida Fish and Wildlife Conservation

Commission, U.S. Fish and Wildlife Service, and U.S. Geological Survey.

Ultimately, support from my loved ones provided me the strength and motivation to complete such a large, selfish undertaking. My parents, John Johnson and Karen True, nurtured my hunger for knowledge from birth and continue to this day. I also give special thanks to my siblings for always pushing me to achieve my dreams. Last, and most importantly, to my amazing wife, Antonia, who provided unwavering love, support, and inspiration during the purist of my professional aspirations.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 8

LIST OF FIGURES ...... 9

LIST OF ABBREVIATIONS ...... 11

ABSTRACT ...... 12

CHAPTER

1 INTRODUCTION ...... 14

2 USING DNA BARCODES TO RECALIBRATE TAXONOMY, TEST MISIDENTIFICATION RATES, AND UNCOVER PATTERNS OF GENETIC DIVERSITY IN FRESHWATER MUSSELS (BIVALVIA: UNIONIDAE) ...... 23

Methods ...... 27 Taxon Sampling and Data Collection ...... 27 Data Analyses ...... 30 Results ...... 32 Misidentifications ...... 33 Barcode Gap Analyses ...... 34 Shallow Interspecific Divergence and Non-monophyletic Species ...... 35 Cases of Deep Intraspecific Divergence and Putative Cryptic Diversity ...... 37 Anodontini ...... 37 Lampsilini ...... 39 Pleurobemini ...... 41 Quadrulini...... 41 Discussion ...... 42

3 APPLYING DNA BARCODES TO INVESTIGATE ECOLOGICAL HOST ASSOCIATIONS AND SPECIES BOUNDARIES FOR FRESHWATER MUSSELS ...... 82

Methods ...... 85 Specimen Collection ...... 85 DNA Sequencing and Data Analyses ...... 86 Results ...... 88 Reference DNA Barcode Library ...... 88 Juvenile Mussel Identification and Host Fish Characterization ...... 90 Discussion ...... 93 DNA Barcode Reference Library ...... 93

Importance of DNA Reference Libraries ...... 94 Shallow Interspecific Divergence ...... 94 Deep Intraspecific Divergence ...... 95 Misidentifications ...... 95 Fish Hosts ...... 96

4 INTEGRATIVE TAXONOMY RESOLVES GENERIC PLACEMENT AND SPECIES BOUNDARIES FOR IMPERILED FRESHWATER MUSSELS ...... 114

Methods ...... 117 Taxon Sampling and Molecular Data ...... 117 Phylogenetic and Phylogeographic Analyses ...... 118 Morphometric Analyses ...... 120 Results ...... 121 Taxon Sampling and Molecular Analyses...... 121 Morphometric Analyses ...... 123 Discussion ...... 124 Implications for Taxonomy and Conservation ...... 126 Discussion of Generic-level Relationships...... 127

5 CONCLUSIONS ...... 138

LIST OF REFERENCES ...... 142

BIOGRAPHICAL SKETCH ...... 159

LIST OF TABLES

Table page

2-1 The number and percentage of freshwater mussel misidentifications revealed using F-cox1 and M-cox1 DNA barcodes...... 46

2-2 Frequency of occurrence for each original morphology-based identification corrected using F-cox1 barcodes...... 47

2-3 Sample sizes (n), mean and maximum uncorrected p-distance (%), and the distance to the nearest neighbor (NN) species based on 1551 Fcox1 sequences for 57 currently recognized freshwater mussel species...... 48

2-4 Sample sizes (n), mean and maximum uncorrected p-distance (%), and the distance to the nearest neighbor (NN) species based on 377 M-cox1 sequences representing 37 currently recognized freshwater mussel species. ... 50

2-5 BIN assignments based on 1551 F-cox1 DNA barcodes representing 57 freshwater mussel species in southeastern United States ...... 51

3-1 Collection sites (site abbreviations) and sampling dates for the five fish surveys where metamorphosed juveniles were recovered and identified using DNA barcodes...... 99

3-2 Sample identifiers, museum catalog number, and collection coordinates (latitude and longitude) for 124 freshwater mussel specimens...... 100

3-3 Sample sizes (n), mean and maximum intraspecific p-distances (d), and distance to nearest neighbor species (NN) shown as percentages for taxa included in the F-cox1 barcode library...... 104

3-4 Barcode index number (BIN) assignments based on 124 F-cox1 DNA barcode sequences representing 24 freshwater mussel species known from central Texas ...... 105

3-5 Naturally infested host fishes that produced juvenile freshwater mussels. Site numbers follow Table 3-1. The total number of juveniles for each mussel species is shown in parenthesis...... 106

4-1 Taxa sampled, drainage of collection, and number of sequences for all individuals included in molecular analyses...... 131

4-2 Analysis of molecular variance (AMOVA) among members of the Cyclonaias (Quadrula) petrina and Cyclonaias (Quadrula) pustulosa species complexes.. 132

LIST OF FIGURES

Figure page

1-1 Map delineating the North American Coastal Plain...... 22

2-1 Collection sites in the southeastern United States for the 1571 specimens analyzed in this study. Each point may represent several collection sites and multiple taxa collected from the same locality...... 53

2-2 Circular phylogram based on 1551 F-cox1 gene sequences representing 23 genera and 57 species of freshwater mussels collected from the southeastern United States...... 54

2-3 Neighbor-joining tree based on 1551 F-cox1 sequences...... 55

2-4 Neighbor-joining tree based on 373 M-cox1 sequences...... 72

2-5 Frequency distribution histogram of uncorrected pairwise genetic distance based on 1551 Fcox1 sequences assigned to 57 currently recognized freshwater mussel species in the southeastern United States...... 77

2-6 Neighbor-joining subtrees illustrating BIN sharing between Fusconaia burkei and Fusconaia escambia (left) and Elliptio chipolaensis and Elliptio nigella (right)...... 78

2-7 Neighbor-joining subtrees illustrating examples of shallow interspecific divergence for which two or more species formed a single genetic cluster...... 79

2-8 Neighbor-joining subtrees illustrating examples of deep intraspecific divergence for which conspecifics were split into two or more BINS, indicating possible cases of cryptic diversity...... 80

2-9 Neighbor-joining subtrees illustrating examples of high intraspecific divergence in which conspecific individuals were assigned to two or more BINS, indicating possible cases of cryptic diversity...... 81

3-1 Sampling locations in central Texas for fishes that produced juvenile freshwater mussels identified using DNA barcodes...... 107

3-2 Neighbor-joining tree based on 124 F-cox1 sequences...... 108

3-3 Most likely topology generated in the BI analysis with indications of clades containing juvenile mussels recovered from naturally infested fishes...... 111

3-4 Intraspecific and interspecific uncorrected p-distances with cases of high intraspecific variation and low interspecific divergence indicated...... 112

3-5 Scatterplot illustrating the overlap of maximum intraspecific p-distances with the nearest neighbor distances. Points above the diagonal line indicate species with a barcode gap...... 113

4-1 Map showing sampled localities (dots) for members of the Cyclonaias (Quadrula) pustulosa species complex (left) and Cyclonaias (Quadrula) petrina species complex (right)...... 133

4-2 Maximum likelihood (ML) phylogeny based on concatenated mtDNA and nDNA datasets for Quadrulini...... 134

4-3 Comparison of results for members of the Cyclonaias (Quadrula) petrina species complex...... 135

4-4 Comparison of results for members of the Cyclonaias (Quadrula) pustulosa species complex...... 136

4-5 Histograms illustrating the distribution of all intraspecific and interspecific pairwise uncorrected-p distances for Cyclonaias (Quadrula) petrina complex (top) and Cyclonaias (Quadrula) pustulosa complex (bottom) ...... 137

LIST OF ABBREVIATIONS

ACF Apalachicola-Chattahoochee-Flint River Basin

DUI Doubly uniparental inheritance

ESA Endangered Species Act

EYC Escambia-Yellow-Choctawhatchee River Basin

F-cox1 Maternal copy of cytochrome oxidase subunit 1 gene

ITS1 Internal transcriber subunit 1 gene

M-cox1 Paternal copy of cytochrome oxidase subunit 1 gene

ND1 NADH dehydrogenase subunit 1 gene

PCR Polymerase Chain Reaction

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

GENETIC INVESTIGATIONS REVEAL NEW INSIGHTS INTO THE DIVERSITY, DISTRIBUTION, AND LIFE HISTORY OF FRESHWATER MUSSELS (BIVALVIA: UNIONIDAE) INHABITING THE NORTH AMERICAN COASTAL PLAIN

Nathan Allen Johnson

December 2017

Chair: James D. Austin Major: Fisheries and Aquatic Sciences

There is an urgent need to reevaluate species diversity in North American freshwater mussels (Bivalvia: Unionidae) because of high rates of imperilment and inherent difficulties with the delineation of species boundaries. Molecular data, specifically mitochondrial gene sequences, are common in systematic studies of freshwater mussels, but DNA barcoding methods have received little attention. As the basis for my dissertation, I initiated a new biodiversity assessment for freshwater mussels by developing a comprehensive barcode reference library as an important taxonomic first-step toward an integrative and unified taxonomy. This effort resulted in two regionally comprehensive DNA barcode libraries representing approximately 80 species and 1,700 specimens collected from nearly 300 locations across 54 river basins. The vast majority of the collections were made from Gulf Coast rivers draining the North American Coastal Plain (NACP), which is a known biodiversity hotspot. My analyses of DNA barcodes revealed high levels of misidentification rates for several genera, including Elliptio (44.3%), Villosa (23.9%), Utterbackia (18.2%), Uniomerus

(15.9%), and Toxolasma (6.8%). At the species-level, 15 taxa were genetically

indistinguishable, including several that were either federally listed or being considered for listing under the US Endangered Species Act. In contrast, 16 species exhibited high intraspecific divergence, suggesting possible cases of overlooked species-level diversity that warrant further investigation. I subsequently used the DNA library to determine ecological hosts for freshwater mussel larvae (glochidia) by providing molecular identifications for juvenile mussels that completed metamorphoses on in situ parasitized fishes. Using members of the Quadrulini as a case example, I demonstrated how DNA barcodes represent an important taxonomic first-step where multiple independent lines of evidence (e.g. morphology, genetics, behavior, geography) can be integrated to make holistic decisions regarding evolutionary relationships. Specifically, I used data from 3 genes (CO1, ND1, ITS1), external morphometrics, and geographic distributions to revise generic placement of 11 taxa, synonymize 4 taxa, and provide evidence for a previously unrecognized species. Finally, I advocated for expansion of the DNA library to include other regional mussel faunas to assist with future evolutionary studies, biodiversity assessments, accurate identifications, and development of properly targeted conservation management programs.

CHAPTER 1 INTRODUCTION

A major focus of evolutionary biology involves the explanation of how, when, and where new species evolve. This is an important challenge because understanding the status of various groups of taxa underlies the need to predict responses to global change and to identify schemes for in situ conservation (Gaston 2000). Global biodiversity is not heterogeneously distributed and 36 biodiversity hotspots are formally recognized (Noss et al. 2015), including the North American Coastal Plain (NACP)

(Figure 1-1). The NACP once harbored approximately 6200 plant, 424 freshwater fish,

291 reptile (Noss et al. 2015), and 225 freshwater mussel species (Williams et al. 1993;

2017). However, large-scale habitat modification and other anthropogenic activities have caused extinction rates to increase, triggering a biodiversity crisis (Wake and

Vredenburg 2008; Burkhead 2012). New species are still being discovered and many groups, especially invertebrates, are still poorly known. This scenario creates an urgency to protect remaining diversity while racing to discover and describe existing diversity before undocumented species go extinct.

Freshwater mussels of the family Unionidae are one of the best studied groups of freshwater mollusks in the NACP. However, high rates of imperilment coupled with difficulties discriminating between intraspecific variability and interspecific diversity using traditional methods (e.g. conchology, soft anatomy characters, reproductive structures) may impede conservation of remaining diversity. In recent years, analyses of mtDNA sequence datasets have successfully resolved a number of important questions regarding the validity of some unionid groups (Mulvey et al. 1997; King et al 1999;

Gangloff et al 2006; Jones et al 2006; Campbell et al. 2008; Pfeiffer et al. 2016; Perkins

et al. 2017), and helped identify cryptic species in some southeastern watersheds

(Mulvey et al. 1997; King et al. 1999; Jones and Neves 2010; Lane et al. 2016).

Integration of the molecular data generated by these studies with other character-based datasets (e.g. shell morphology, soft anatomy, glochidial and conglutinate morphology, periods of gravidity, fish host specificity, mantle displays) has greatly benefitted recent taxonomic revisions and has led to a more holistic understanding of the biogeography and conservation genetics of some freshwater mussel groups (King et al. 1999; Roe and Hartfield 2005; Jones et al. 2006; Zanatta and Murphy 2008; Lane et al. 2016).

The principal goal of my dissertation research was to reevaluate previous hypotheses regarding the distribution, classification, and diagnosis of unionids using an integrative taxonomic framework with inferences drawn from multiple independent lines of evidence (e.g. morphology, geography, genetics). The study focused on freshwater mussels inhabiting the NACP, which is characterized by a high degree of endemism and a rich geologic history, making it an excellent area to focus my systematic studies and phylogeographic research on this highly imperiled group of animals.

The species problem as it relates to unionids has evolved throughout the taxonomic history of this group. Early taxonomists followed a typological species concept in which descriptions were based on a single or only a few specimens. Poor understanding of intraspecific variation in morphological characters resulted in the description of more than 4000 taxa (Haas 1969). As systematics advanced and more collections were made, biogeographic patterns began to emerge and taxonomists realized many descriptions were based on subtle differences and probably represented variation within the same species (Ortmann 1920; Johnson 1970). Studying variation in

morphological characters triggered the application of a morphological species concept that aimed to consider both between- and within-species variation. However, several confounding factors still make application of this species concept difficult. For example, the environment is known to impact the shell morphology of unionids, making it difficult to distinguish between intraspecific variability, phenotypic plasticity, and interspecific similarity (Ortmann 1920; Eagar 1954; Zieritz et al. 2010; Inoue et al. 2013; Bourdeau et al. 2015; Fassatoui et al. 2015; Zajac et al 2017).

Interpretation and quantification of morphological variation is subjective and extremely difficult because freshwater bivalves lack consistent landmarks for informative and reproducible morphometric analyses. In other groups, geographic distributions are sometimes helpful in delineating species boundaries. Unfortunately, the dispersal ranges for most mussels were (and still are) largely unknown and difficult to predict through space and time. Cryptic diversity and issues of convergence are additional limitations that must be considered when relying solely on morphological characters.

The morphological species concept served as a temporary remedy to the typological concept, but synonymy issues resulting from “over-splitting” and subsequent scrutiny of

“over-lumping” revisions still plague the taxonomy of this group today (Williams et al.

2014; 2017). Recent ecological, physiological, and molecular studies are being used to develop a more integrative taxonomy. Researchers now recognize how these new findings can provide a more holistic understanding of the evolutionary relationships within and among taxa.

The problem now lies in how to interpret these new data within the established taxonomy built on non-molecular characters. Is this left to the expert taxonomist or to

ecologists, physiologists, or geneticists? The immediate solution has been to adopt a taxonomic species concept and let the expert taxonomist make the call. This is a conservative approach that relies on authoritative interpretation by a regional expert who typically has the most experience with the group. The adaptive nature of this concept is attractive and allows the expert to consider multiple lines of evidence before making the decision. However, there are several problems with this approach to resolving our current taxonomic problems. First, it does not provide options for specimens that experts are unable to identify, or for cases in which experts can’t agree on the appropriate taxonomic classification. Second, it often forces taxonomists to interpret findings from studies outside their area of expertise. Lastly, the highly subjective nature of diagnosing species still remains and the taxonomic uncertainties continue to fester.

To date, at least 32 species concepts have been proposed (Zachos 2016), all of which have limitations, from demonstrating reproductive isolation (Mayr 1942) to a lack of phylogenetic resolution despite clear morphological, ecological, or physiological divergence (Funk and Omland 2003; McVay and Carstens 2013; Whelan and Strong

2015). In recent years, there has been a shift towards delimiting and describing taxa by integrating information from different methods and data types. This multisource framework, known as “integrative taxonomy” (Dayrat 2005; Will et al. 2005), increases opportunities for linking decades of morphological scrutiny and other observations in malacology with recent advancements in science and technology.

Here, I outline a new foundation for the future of mussel taxonomy that utilizes a

DNA barcoding approach to bring experts from multiple disciplines together to

objectively characterize species and delineate their boundaries objectively. The objective criteria and vision of DNA barcoding has sparked biodiversity inventory initiatives across the globe. Since 2003, several thousand papers related to DNA barcoding have been published with a large number directly related to taxonomy and systematics. Many are campaigns to inventory the biological diversity within a particular taxonomic group (e.g. FISH-BOL, Mosquitoes of the World, ABBI-All birds barcoding initiative). Another large body of literature revolves around interpretation of molecular barcode data (and molecular data in general) debating the strengths and weaknesses of delineating species using a limited amount of molecular data (Tautz et al. 2003; Blaxter

2004; Will et al. 2004; 2005; Hickerson et al. 2006; Kohler 2007; Packer et al. 2009) .

However, when coupled with complementary perspectives (e.g. morphology, ecology, life history), the method provides a powerful approach for solving taxonomic problems

(Ward et al. 2009; Puillandre et al. 2012; Kekkonen and Hebert 2014; Hendrich et al.

2015; Chambers and Hebert 2016). To date, the method has seen little application for freshwater mussel research and our efforts are the first to build comprehensive DNA barcode reference libraries.

The DNA barcoding method differs from previous approaches using mtDNA by incorporating standardized data collection to meet compliance of community standards for DNA barcoding (Ratnasingham and Hebert 2007; Benson et al. 2012). Under this framework, results can be combined and analyzed using combinations of distance- and phylogenetic-based approaches. Identification and current taxonomy can be evaluated objectively and distinct lineages revealed by DNA barcode analyses can be compared against other independent lines of evidence (e.g. morphology, geography, ecology, etc.)

to determine the appropriate rank of classification. DNA barcoding is an attractive application for refining our understanding of the diversity and distributions of North

American freshwater mussels.

In Chapter 2, I introduce the species problem as it pertains to freshwater mussels and describe how I’ve created and applied a dual DNA barcoding approach (F-cox1 and

M-cox1) and phylogenetic analyses to identify and characterize unionid diversity in

Florida and contiguous drainages in Alabama and Georgia. This area is known as the

Greater Floridan Region (GFR) (Williams et al. 2014). Diversity in shell shapes has resulted in >100 original species descriptions from specimens collected throughout the state, yet only 61 species and 23 genera are currently recognized (Williams et al. 2014).

The current distribution of many species within the state remains unresolved because of taxonomic ambiguities and poor understanding of conchological variation within and among populations. Our analysis of 1551 maternal and 373 paternal cox1 sequences

(and counting) representing 57 species from 23 genera indicate that morphological characters used in previous taxonomic assessments are largely congruent with results from DNA barcode analysis. However, there are exceptions, especially within the genus

Elliptio. Results also reveal deeply diverged populations within several species, suggesting overlooked cryptic diversity in the study area. Results from DNA barcoding were compared to recent taxonomic assignments based largely on morphological characters, and directions toward a unified taxonomy based on an integrative taxonomic framework were outlined.

In Chapter 3, I expand spatial and taxonomic coverage of the DNA library to incorporate freshwater mussels of central Texas. Specifically, I generated F-cox1

sequences for 124 adult specimens, which represent all 17 genera and 24 species of mussels known from the Colorado and Guadalupe river basins in central Texas. In most cases, mussel species exhibited low intraspecific variation (mean p-distance 0.50%) when compared with distance to nearest neighbor species (mean p-distance 8.44%).

However, DNA barcode analyses highlighted three cases of misidentification and other instances in which current taxonomy both overestimated and underestimated diversity in this region. We subsequently used the mussel DNA barcode library to characterize ecological host use by generating F-cox1 sequences for 137 juveniles recovered from

12 fish species infected in situ. All newly transformed juveniles were identified by assignment to monophyletic clades corresponding to 8 mussel species, including 4 of 5 federal candidate species (Lampsilis bracteata, Quadrula aurea, Quadrula houstonensis, and Quadrula petrina) that occur in the sampled drainages. Our efforts to recalibrate taxonomy and characterize ecological hosts provide insights into population processes such as recruitment (e.g. availability of suitable hosts), dispersal (e.g. vagility of host), and resiliency (e.g. host generalist vs. specialist). This information is critical to managers working to assess conservation status, extinction risks, and recovery options for remaining freshwater mussel populations.

In Chapter 4, I focus on the taxonomic issues within the genus Quadrula and demonstrate the utility of an integrative approach to species delimitation that considers molecular, distribution, and morphology data to evaluate evolutionary relationships.

Specifically, I examined genetic relationships using three genes (CO1, ND1, and ITS1) representing 8 genera and 20 species in the Quadrulini and evaluated morphological variation throughout the ranges of 8 species in two species complexes. My results

support the assignment of 12 nominal taxa to the genus Cyclonaias: C. aurea, C. asperata, C. houstonensis, C. infucata, C. kleiniana, C. mortoni, C. nodulata, C. petrina,

C. pustulosa, C. refulgens, C. succissa, and C. tuberculata. Additionally, congruence across all lines of evidence (i.e. morphology, geography, and genetics) indicated that current taxonomy overestimates species-level diversity in the C. pustulosa species complex while underestimating diversity in the C. petrina species complex. I revised species-level classifications by synonymizing four taxa (C. aurea, C. houstonensis, C. mortoni, and C. refulgens) considered either species or subspecies under Cyclonaias pustulosa and provide evidence for a previously unrecognized species from the

Cyclonaias petrina complex that is endemic to the Guadalupe River basin. These findings have important implications regarding the conservation assessments and pending legislative protections for several freshwater mussel species within western

Gulf of Mexico drainages.

In my fifth and final chapter, I present a synopsis of the major findings within my dissertation and present a plan for continuing to move the field of systematic malacology and conservation genetics forward through education, outreach, and collaborative research. Finally, I announce my goal to lead the effort to build a comprehensive DNA barcode library (UNIO-BARCODE) for all freshwater mussels in

North America and discuss several applications for such a dataset.

Figure 1-1. Map delineating the North American Coastal Plain.

CHAPTER 2 USING DNA BARCODES TO RECALIBRATE TAXONOMY, TEST MISIDENTIFICATION RATES, AND UNCOVER PATTERNS OF GENETIC DIVERSITY IN FRESHWATER MUSSELS (BIVALVIA: UNIONIDAE)

Accurate and precise identification and classification provide the foundation for understanding evolutionary relationships, are key to identifying biogeographic processes, and facilitate the targeting of conservation programs (Dexter et al. 2010;

Shea et al. 2011). Our ability to identify distinct evolutionary lineages objectively in some groups remains an important challenge for modern systematics research (Fujita et al. 2012). Urgency increases for assessments involving imperiled species, particularly those fraught with taxonomic instability, because conservation efforts are typically based on species-level designations (Chambers and Hebert 2016; Huang and Knowles

2016; Sukumaran and Knowles 2017).

Freshwater mussels of the family Unionidae, also known as naiads, pearly mussels, freshwater clams, or unionids, are a diverse group of bivalve mollusks that are distributed on every continent except Antarctica. Approximately 300 species are known from the United States, with the majority of this diversity residing in rivers of the

Southeast where many endemic taxa have evolved (Turgeon et al. 1988; 1998; Williams et al. 1993; 2008; 2014; 2017). This fauna is highly imperiled, with about 78% of currently recognized species considered either extinct, endangered, threatened, or of special concern (Williams et al. 1993; Turgeon et al. 1998; Lydeard et al. 2004; Haag

2012; Haag and Williams 2014). At least 10% of North American mussel taxa became extinct in the past 100 years (Neves et al. 1997; Haag 2012; Haag and Williams 2014), which is comparable to extinction rates observed in the rainforest (Ricciardi and

Rasmussen 1999) and for other freshwater organisms (e.g. Burkhead 2012). Despite

the realization that native unionid populations are in peril, taxonomic uncertainties remain for many species and limit the development of effective conservation management strategies (Pfeiffer et al. 2016). Furthermore, the identification of many unionid species is complicated by the lack of discrete morphological characters that are useful for diagnosing species accurately, delimiting species boundaries, or determining evolutionary relationships (Shea et al. 2011; Pfeiffer et al. 2016; Perkins et al. 2017).

The conservation of remaining North American mussel populations would benefit greatly from improved methods for identification and delineation of species boundaries, which would also enable a better understanding of the biogeographic processes responsible for creating this biodiversity.

DNA barcoding has gained popularity in recent years as an effective method for rapid biodiversity assessment, specimen identification, and taxonomic exploration

(Hebert et al. 2003). DNA barcoding differs from past applications of genetics regarding identification and taxonomic revision by encouraging a transparent and reproducible method based on a well-curated collection and reference barcode library, which allows the testing of falsifiable hypotheses using an objective foundation. The intention is to supplement and test conventional taxonomic classifications, not discard evidence presented in studies relying on traditional approaches. This creates an alliance between molecular and morphological taxonomists to offer a more objective, reliable, and rapid species identification and classification scheme.

In additional to high levels of diversity, imperilment, and taxonomic uncertainty, freshwater mussels have several other intrinsic characteristics that make them an attractive group for broad-scale DNA barcode efforts. Perhaps the most captivating is

the parasitic larval stage that requires attachment to a host fish before metamorphosis into a free-living individual. Adult mussels are relatively sedentary and the larval stage represents the primary mechanism for dispersal, making it difficult to determine distribution limits when host fishes are unknown. DNA barcodes have the potential to link different life stages and have been used to provide species-level identifications of larvae encysted on fishes (Boyer et al. 2011; Chapter 3). Additionally, freshwater mussels, along with at least six marine bivalve families, have a peculiar way of mtDNA inheritance called doubly uniparental inheritance (DUI). The DUI system is characterized by two independently inherited mtDNA genomes: the female-transmitted

(F-genome), which is carried in somatic tissues of both sexes, and the male-transmitted

(M-genome), which is transcribed not only in male gonads, but recently discovered to be heteroplasmic in male and female soma as well (for review, see Breton et al. 2007;

Breton et al. 2017). The F genome exhibits strict maternal inheritance (SMI), as in all other members of the animal kingdom, but M genomes are transmitted paternally (i.e. from fathers to male offspring). Studies investigating intra/interspecific variation have consistently shown that levels of differentiation were higher in M genomes compared to

F genomes indicating the M genome evolves at a faster rate (Liu et al. 1996; Krebs

2004; Doucet-Beaupré et al. 2012; Krebs et al. 2013). This allows creation of dual DNA barcode libraries based on independently evolving mtDNA genomes, which alleviates some limitations associated with single-gene approaches (Rubinoff and Holland 2005;

Toews and Brelsford 2012).

The effectiveness of DNA barcoding as a tool for identification of freshwater mussels has received relatively little attention. Despite using DNA barcoding to describe

the methods in previous studies (Campbell et al. 2008; Boyer et al. 2011; Moyer and

Diaz-Ferguson 2012), none have included required documentation to meet community standards for DNA barcode compliance. This is problematic given the limitations and errors of mussel identifications (Shea et al. 2011) and need for reproducible results in biological research (Vink et al. 2012). The potential for errors associated with using data from public repositories is well known (Harris 2003), especially for groups in which species are difficult to identify or taxon sampling is sparse (Santos and Branco 2012).

Multiple studies have highlighted errors in GenBank accessions for freshwater mussels

(Campbell et al. 2005; Boyer et al. 2011; Campbell and Lydeard 2012; Williams et al.

2017).

Here, we establish a comprehensive DNA barcode library for freshwater mussels using both male- and female-specific copies of the mtDNA gene COI (F-cox1 and M- cox1). We analyzed the barcode sequences to compare molecular and morphological based species assignments and evaluated rates of misidentification. Specifically, we tested the effectiveness of the barcode genes to delineate intraspecific variation from interspecific divergence while identifying cases for which: 1) barcode clusters showed high congruence with morphology-based identification and current taxonomy; 2) morphologically indistinguishable, but genetically and geographically diagnosable lineages suggested overlooked or cryptic diversity; and 3) groups of currently recognized species exhibited low genetic divergence that prevents their discrimination using DNA barcodes. Finally, we discuss the implications of our findings and utility of the DNA reference library for facilitating future research and conservation by providing the accurate identification of unidentified specimens, discovering potentially cryptic taxa,

and formally incorporating molecular divergences with past and present taxonomic assessments.

Methods

Taxon Sampling and Data Collection

To build our DNA barcode library, we aimed to collect and sequence at least 3 individuals for all 57 extant freshwater mussel species recently recognized as occurring in the Greater Floridan Region (GFR), comprised of Florida and contiguous drainages in

Alabama and Georgia (Williams et al. 2011; 2014). Sampling focused on type localities and drainages from where species were originally described (Figure 2-1). In some cases, sampling type drainages required collecting individuals outside the GFR. Sample size for each species varied as a function of rarity and distribution. We analyzed additional individuals from multiple watersheds throughout the range of each species whenever possible.

All specimens were sampled either non-lethally using DNA swabs (Henley et al.

2006) or preserved in 95-100% non-denatured ethanol (EtOH) after both anterior and posterior adductor muscles were severed. Specimens were preserved in volumes of

EtOH approximately five times the volume of tissue (Nagy 2010; Evans and Paulay

2012). The concentration of EtOH was measured after 24 hours using a hydrometer

(Fisherbrand 11-590). In most cases, a total volume change of EtOH was required to maintain concentrations ≥ 95%. After initial preservation, a subsample of mantle tissue was placed in 95% EtOH and stored at -80°C for long-term preservation. To ensure the highest levels of reproducibility of our findings, we deposited all vouchered specimens in public museums and provided all metadata related to the voucher specimens, including collection locality data, specimen images, nucleotide sequences, PCR primers, and

.ab1 trace files, to the Barcode of Life Data (BOLD) system (www.barcodinglife.org;

Ratnasingham and Hebert 2007) under the project ‘UNIO-BARCODE’. Specimens sampled using swabs were tagged externally with cyanoacrylic adhesive and individually coded Hallprint shellfish tags (Hallprint Inc., Hindmarsh Valley, South

Australia), photographed, and released to the site of capture. Specimens were considered ‘barcode compliant’ when DNA sequences were > 500 nucleotides and all metadata, photographs, and trace files were supplied.

Initial specimen identifications were based on formal diagnostics using a combination of morphological characters, including shell and soft anatomy, and the geographic location consistent with original species descriptions from recent taxonomic treatments (Williams et al. 2011; 2014). Regional freshwater malacologists assigned specimens to the lowest taxonomic level possible after consulting the primary literature.

The initial identifications and taxonomic assignments were recorded and captured in the

10-character sample ID as follows: first character represents the first letter of the genus, characters 2-4 correspond to the first three letters of the species epithet, characters 5-7 represent the drainage of collection, and 8-10 are numeric values to make each sample

ID unique. We labeled ambiguous generic or species-level designations as “Unknown” or “species.” Each specimen was measured, labeled with the unique sample ID, and photographed to capture morphological characters contained within the inside of the left valve and outside of the right valve. Only external images were available for swabbed specimens.

DNA was isolated from tissue (mantle or gonadal) and Isohelix swabs (Boca

Scientific) using one of the following three protocols: (1) Puregene extraction kit

(Qiagen); (2) organic extractions at the Smithsonian Institution using robotic facilities; and (3) a modified plate extraction protocol (Ivanova et al. 2006). Stock and diluted DNA were stored at -80°C for long-term preservation. For the maternal or female genome copy of COI (F-cox1), we first used the primer sets dgLCO-1490 5’-

GGTCAACAAATCATAAAGAYATYGG-3’ and dgHCO-2198 5’

TAAACTTCAGGGTGACCAAARAAYCA-3’ (Meyer 2003) and used a variety of other primers and primer cocktails, including COIFCamp 5’-

GTTCCACAAATCATAAGGATATTGG-3’ and COIRCamp 5’-

TACACCTCAGGGTGACCAAAAAACCA-3’ (Campbell et al. 2005), all of which amplify the same gene segment. For the paternal or male genome COI (M-cox1), we used

HCO700dy2 5’-TCAGGGTGACCAAAAAAYCA-3’ and MCO1_22F 5’-

RTGCGTTGRRYDTTTTCBACTA-3’ (Walker et al. 2007). PCR amplifications were conducted in 96-well plates containing 10 µL reactions with the following reagents and volumes: 1 µL of DNA template (~20 ng), 0.25 µL BSA (10mg/mL), 0.5 µL dNTPs

(10mM; 2.5 mM each dNTP), 2µL 5X PCR buffer, 0.3 µL of each primer (10 µM), 0.3 µL

MgCL2 (2mM), 0.1 µL GoTaq DNA Polymerase (Promega), and 5.25 µL dH2O. Thermal cycling profiles were as follows: for F-COX1, an initial denaturation at 95ºC for 3 min followed by 5 cycles of 95°C for 30 s, 45°C for 40 s, 72°C for 45 s, then 35 cycles of

95°C for 30 s, 51°C for 40 s, 72ºC for 45 s, with a final elongation at 72°C for 10 min, and hold at 4°C for 30 min followed by 15°C forever; for M-COX1, we used an initial denaturation at 95°C for 2 min followed by 40 cycles of 95°C for 60 s, 45°C for 60 s,

72°C for 60 s, with a final elongation at 72°C for 10 min, and hold at 4°C for 30 min followed by 15°C forever.

All PCR products were visualized on 1.5% agarose gels stained with ethidium bromide. Unincorporated PCR products were removed using 1µL of ExoSAP-IT (USB,

Santa Clara, CA, USA) per 10 µL of PCR product, following manufacturer’s protocols.

Cycle sequencing was performed on both forward and reverse strands using the BigDye

Terminator v3.1 Cycle Sequencing Kit. Sequencing reactions of 5 µL were cleaned using spin column filtration through Sephadex pellets and Millipore plates before electrophoresing products on an ABI 3130xl or ABI 3730 DNA analyzer (Applied

Biosystems, Inc). Complementary DNA sequences were assembled, edited, and exported as consensus sequences using Geneious v6.1.2 (http://www.geneious.com,

Kearse et al. 2012). DNA sequences were aligned to published sequences in Mesquite v 3.04 (Madison and Madison 2015) using MUSCLE (Edgar 2004) and translated to protein using standard invertebrate mitochondrial code amino acid to ensure no gaps or stop codons were present in the alignments. Both DNA sequence alignments were submitted to BOLD.

Data Analyses

We assessed misidentification rates and tested the ability of gender-associated mtDNA barcodes to distinguish between currently recognized freshwater mussel species, using a combination of genetic distances, phylogenetic methods, and Barcode

Index Numbers (BINs). For all distance-based analyses, we calculated the uncorrected p-distance instead of various models of sequence evolution to avoid high levels of variance that often result from parameterized models (Lefebure et al. 2006; Collins et al.

2012) and assumptions related to nucleotide evolution models (Ratnasingham and

Hebert 2013). Initial neighbor-joining (NJ) trees were calculated to evaluate morphology-based identifications, and to assign unidentified specimens to haplotype

clusters for subsequent analyses. Identifications were assessed for all haplotypes that nested within a monophyletic cluster containing specimens with a priori morphology- based assignment to the species level. Misidentifications were not counted for individuals representing species in shared BINs because in these cases currently recognized species could not be delineated using DNA barcodes (see section 3.3).

Additionally, labeling errors or incorrect use of names in synonymy (e.g. Elliptio hazelhurstianus synonymized under Elliptio ahenea) were not counted as misidentifications. Based on these conditions, we consider our calculations of misidentification rates to be fair and conservative.

After correcting identifications, intra/interspecific pairwise genetic distances

(hereafter referred to as genetic distances) were calculated using Mega 7 (Kumar et al.

2016) and distance to nearest neighbor (NN) species was calculated using the ‘Barcode

Gap Analysis’ tool in BOLD (Ratnasingham and Hebert 2007). We assessed the number of instances when maximum intraspecific divergence exceeded the nearest neighbor distance, which represent the absence of a barcode gap.

In addition to assessing putative species limits based on monophyly, all sequences were assigned to a BIN using the 3-step Refined Single Linkage (RESL) algorithm (Ratnasingham and Hebert 2013) implemented in BOLD. The BIN system was used to establish an interim taxonomic system to identify operational taxonomic units (OTUs) and reveal conflicts between morphological and molecular assignments.

Each taxon was scored into one of four categories (MATCH, MERGE, SPLIT, or

MIXTURE) as defined by Ratnasingham and Hebert (2013). For example, BINs containing multiple species may indicate the need for taxonomic revision or highlight

cases in which barcode divergence is insufficient to allow species discrimination using

COI alone (MERGES). Alternatively, a signal for cryptic diversity occurs when members of a species are assigned to multiple BINs, revealing overlooked species-level diversity during application of traditional (morphology-based) taxonomic approaches (SPLITS).

After alignment of the input sequences, all individual sequences with < 2.2% genetic distance were clustered into initial BINS. Any sequence or set of sequences with a genetic distance to NN > 4.4% were assigned to a newly formed BIN. In the final

‘cluster refinement’ step, clusters that exhibit high sequence variation and clear partitions within the BIN were further split into separate clusters, even when genetic distance was < 2.2%. Clusters of sequences without a clear demarcation and <2% genetic distance remained as a single OTU. The RESL method benefits from not relying on a single distance-based threshold and is computationally less demanding when compared to other methods used to evaluate the ‘barcode gap’ (Meyer and Paulay

2005; Meier et al. 2006; Pons et al. 2006; Puillandre et al. 2012; Yang and Rannala

2017).

Results

Newly-generated mtDNA barcode entries were created for 1571 freshwater mussels representing 1 family, 2 subfamilies, 5 tribes, 23 genera, and 57 species currently recognized from the southeastern United States (Figure 2-2). The dataset represents 56 of the 57 extant taxa known from the Greater Floridan Region (Williams et al. 2011; 2014), including 12 species protected under the US Endangered Species

Act (ESA). The dataset also includes two species, Ligumia subrotunda, not previously recognized from the study area, and Toxolasma parvum, both of which have been introduced outside their native range (Williams et al. 2014). The only extant species not

included was Medionidus simpsonianus, which is federally designated as endangered and was recently rediscovered in the Ochlockonee River (Holcomb et al. 2015).

Geographic coverage included 257 localities sampled within 25 independent river drainages that flow directly into the Gulf of Mexico or Atlantic Ocean (Figure 2-1). Most species were represented by multiple specimens (mean specimens per species for F- cox1 = 27.3; M-cox1 = 10.2) and broad geographic sampling to better characterize intraspecific variability. Date of collection ranged from 1934 to 2013 (mean = 2008) for specimens from which full barcode sequences were recovered. Both DNA alignments were free of indels and stop codons. A total of 1924 DNA sequences were analyzed for

1571 specimens in the library; 1551 were represented by F-cox1 and 373 for M-cox1.

Mean sequence length of F-cox1 was 644 nucleotides (range: 522-648) and included sequences for 98.3% of currently recognized taxa. For M-cox1, the mean sequence length was 656 nucleotides (range: 561-678) and provide coverage for 63.8% of taxa.

Both Fcox1 and Mcox1 sequences were generated for 353 specimens. Barcode compliance was achieved for 96.7% of individuals (1519 of 1571) with 8 specimens lacking successful trace files, 29 lacking photographs, and 20 represented only by M- cox1 sequences.

Misidentifications

All sequenced individuals nested within monophyletic clusters containing specimens with a priori morphology-based assignment to the species level (Figure 2-3;

Figure 2-4). This included 172 specimens without species designations prior to DNA sequencing. F-cox1 barcodes revealed that morphology-based identifications were incorrect at the generic and species level at rates of 1.9% and 5.7%, respectively (Table

2-1), for a total of 88 misidentified specimens (Table 2-2). The number of

misidentifications revealed using M-cox1 barcodes was slightly lower (genus 1.3%; species 2.4%). Misidentifications involved 27 species belonging to 13 genera. Members of Elliptio were involved with 44.3% of the misidentifications, including 19 individuals misidentified as either E. chipolaensis or E. nigella. High percentages of misidentifications also included members of Villosa (23.9%), Utterbackia (18.2%),

Uniomerus (15.9%), and Toxolasma (6.8%).

Barcode Gap Analyses

The majority of both sex-linked COI barcodes were congruent with current taxonomic designations; however, intra/interspecific genetic distances showed the absence of a barcode gap (Figure 2-5). In fact, 11 species (19.3%) had F-cox1 barcode sharing in which two or more currently recognized species had matching haplotypes and exhibited a NN distance of zero (Table 2-3). We observed 7 additional cases for whichF-cox1 NN distances were < 2.2% and max intraspecific distance exceeded the distance to NN. The remaining 39 taxa (68.4%) could be unambiguously assigned using

F-cox1. Intraspecific genetic distance for F-cox1 ranged from 0% to 12.65% and distance to the nearest neighbor ranged from 0% to 11.42%. Performance of the M- cox1 gene for discriminating between currently species was similar, although samples sizes were much lower. A total of 7 currently recognized species shared M-cox1 haplotypes and 6 additional species were characterized by NN distances < 1% (Table 2-

4). NN distances ranged from 3.53-18.44 for the 24 remaining species represented by

M-cox1 barcodes.

The 1551 F-cox1 barcodes were represented by 72 BINs (Table 2-5), 6 of which were considered to be taxonomically discordant, impacting 426 UNIOBARCODE records among 18 currently recognized species. These shared BINs highlight cases for

which barcode divergence was insufficient to allow species discrimination using F-cox1 alone and warrant further investigation. In contrast, a total of 66 BINs were considered taxonomically concordant (1125 records), 5 of which were represented by only a single record. These concordant BINs involved 39 currently recognized species with 24 BINs scored as Match and 39 as Split. High intraspecific divergence was observed in 15 cases in which conspecific individuals were assigned to 2 or more BINs, suggesting possible cryptic diversity in these lineages (Table 2-5).

Shallow Interspecific Divergence and Non-monophyletic Species

Analyses of our barcode data exposed several cases in which morphologically described species exhibited low levels of interspecific divergence or shared haplotypes.

Specifically, 18 currently recognized species were indistinguishable using Fcox1 barcodes (Table 2-3; Table 2-4) and were recovered as non-monophyletic in our NJ tree. Two species (Fusconaia burkei and Fusconaia escambia) were characterized by only 0.78% sequence divergence at F-cox1 (Table 2-3) and shared a BIN (Table 2-5;

Figure 2-3). However, these two species are clearly separable morphologically and geographically (Figure 2-6), and are genetically diagnosable by 5 nucleotides. These results align with previous phylogenetic studies that have included these taxa and suggest a recent evolutionary origin (e.g. Campbell and Lydeard 2012; Pfeiffer et al.

2016). Similarly, Quadrula infucata and Quadrula kleiniana shared a BIN with a 2.04% sequence divergence. These taxa are morphologically similar, but allopatric and molecularly diagnosable by 11 nucleotides, and likely represent valid species.

Individuals within the genus Elliptio showed the greatest inconsistency between morphology and genetic signature, with 12 currently recognized species exhibiting overlap between intra- and inter-specific divergences (Table 2-3; Table 2-4). These

Elliptio BINs were scored as MERGE (10) or MIXTURE (2). Elliptio has been considered the most diverse freshwater mussel genus in North America, with 38 currently recognized species occurring in Atlantic and Gulf of Mexico drainages (Turgeon et al.

1998; Williams et al. 2014). However, the genus has a tangled history of taxonomic treatments due to the subjectivity of using morphological characters to separate intraspecific variability from interspecific similarity (Simpson 1892; Clench and Turner

1956; Johnson1970; 1972; Williams et al. 2008; 2014). Most malacologists recognize 3 or 4 morphological groups in the genus (Johnson 1970; Johnson 1972; Williams et al.

2008; Williams et al. 2014). Our analyses of F-cox1 barcodes assigned members of the genus Elliptio to 4 BINs; however, BIN membership did not follow current taxonomy or morphological groups (Table 2-3; Table 2-4). The Elliptio BIN (ACY9226) included two imperiled taxa, E. chipolaensis (N=19) and E. nigella (N=21) (Figure 2-5), both endemic to the ACF basin, but considered allopatric (Williams et al. 2014). These taxa also shared M-cox1 barcodes (Figure 2-4) and no diagnostic nucleotides were identified to separate these two taxa at either locus. Although the two taxa superficially resemble each other morphologically and occur in the same Gulf drainage (Figure 2-6), we suggest a more comprehensive investigation to validate the relationships between these two imperiled species.

Overall, barcode sequences did not provide a strong signal for biogeographic patterns and allopatric taxa within the genus Elliptio. In fact, only one Elliptio BIN

(AAX7569) formed a geographically isolated group, which contained specimens collected from the Alabama River identified as E. arctata (Figure 2-4; Figure 2-7). The remaining E. arctata shared BIN AAB3266 with nine other species of Elliptio (E. arctata,

E. crassidens, E fraterna, E. fumata, E. jayensis, E. mcmichaeli, E. monroensis, E. occulta, E. pullata, and E. purpurella), which included 206 specimens collected from throughout the study area (Figure 2-4; Figure 2-7). A fourth BIN (ACZ0072) was comprised of specimens identified as Elliptio jayensis collected from the Withlacoochee

(Tampa Bay drainage) (N=4) and St. Johns (N=2) rivers and E. pullata from Spring

Creek in the Chipola River Basin (N=2) (Figure 2-4; Figure 2-7). A congruent signal was observed at Mcox1, indicating that these Elliptio BINs do not recover currently recognized morphospecies as monophyletic clades (Figure 2-4; Figure 2-7), highlighting the need for future investigations to elucidate the taxonomic validity of morphospecies in the genus Elliptio.

Cases of Deep Intraspecific Divergence and Putative Cryptic Diversity

At the other end of the spectrum were species that showed deep intraspecific divergence. Included were 15 currently recognized species in 10 genera that were divided into 39 BINs (Table 2-5). In this section, we provide details for two classes of potential cryptic diversity organized by tribe. The first includes cases of high intraspecific divergence between conspecifics that resulted in monophyletic clades that were found to be allopatric. These BINs were both genetically and geographically diagnosable. We also describe cases in which multiple BINs were identified for a given species, but the sequence clusters revealed sharing among river drainages.

Anodontini

Anodontoides radiatus occurs in Gulf of Mexico drainages from western Florida to Louisiana. Among the three drainages sampled during this study, two allopatric BINs were delineated; one for the Apalachicola-Chattahoochee-Flint (ACF) Basin and another for the Choctawhatchee and Escambia river basins (Figure 2-3; Figure 2-8) with

3.52% genetic distance separating the two BINs. The fact that high intraspecific divergence was observed within only a portion of the species’ range suggests additional cryptic diversity may exist. These findings are particularly important given A. radiatus is currently under review for ESA protection (USFWS 2011).

Pyganodon grandis has been considered the most widespread freshwater mussel in North America, occurring throughout the Gulf of Mexico drainages from

Florida to Mexico and throughout the Interior Basin and Great Lakes drainage (Williams et al. 2008). We included samples from four eastern Gulf of Mexico drainages and recovered 3 geographically isolated and monophyletic BINs (Figure 2-3; Figure 2-8).

Members from the Escambia and Choctawhatchee clustered together in one BIN, which was 8.92% and 9.61% divergent from the Ochlockonee and Apalachicola clades. These levels of divergence suggest that diversity within this species may be severely underestimated and more extensive phylogeographic sampling will likely lead to the recovery of additional cryptic lineages.

Utterbackia peninsularis is endemic to Gulf of Mexico drainages from the

Suwannee River south to the Tampa Bay drainage. We recovered two BINs with strong geographic structuring for U. peninsularis (Figure 2-3; Figure 2-8). One BIN was based on two individuals from the upper Santa Fe River, a tributary to the Suwannee River.

These animals were collected above a portion of the Santa Fe that flows completely underground for ~ 5 km, representing a presumed natural barrier to dispersal of freshwater mussels at all life stages. The second BIN included animals collected from the middle Suwannee River, Withalcoochee River, and Tampa Bay drainages (Figure 2-

3; Figure 2-8). Additional fine-scale phylogeographic sampling in the Suwannee River

basin is needed to verify whether the subterranean portion of the Santa Fe River is the true boundary between these two cryptic lineages.

Lampsilini

The genus Hamiota is endemic to Gulf of Mexico drainages with two species occurring in the study area, both of which are federally listed under ESA. DNA barcodes for one species, H. subangulata, align with current taxonomy. However, Hamiota australis, was split into two BINs with specimens from the Choctawhatchee River in both

BINs (Figure 2-3; Figure 2-8). Additional investigation is needed to determine whether taxonomic revisions, following these BIN designations, are warranted.

Lampsilis is a widespread genus distributed throughout Atlantic, Gulf of Mexico, and Interior drainages. Two of the three species in the genus Lampsilis were represented by multiple BINs. For L. floridensis, two monophyletic, allopatric clades were recovered with 2.11% sequence divergence separating Suwannee individuals from the Choctawhatchee/ACF/Ochlockonee clade (Figure 2-3). Specimens of L. straminea were delineated into five BINs, three of which corresponded to geographically isolated sequence clusters (Figure 2-3). The remaining two BINs contained individuals from the Escambia drainage; one with individuals from both the Escambia and Yellow and the other only with individuals from the Escambia (Figure 2-3). Divergence levels ranged from 1.19 – 3.19% with adjacent drainages showing lower levels of divergence relative to drainages separated by larger geographic distances, except for the two BINs containing Escambia individuals, which were 2.91% divergent. This was the only example of this phylogeographic pattern in our dataset.

Toxolasma is another widespread genus known to occur in Atlantic, Gulf of

Mexico, and Interior drainages with three species recognized from the GFR. In our

assessment, two species were resolved as paraphyletic (Figure 2-3) with one BIN being shared between Toxolasma. sp cf. corvunculus, an undescribed species recognized by

Williams et al. (2008; 2014) as endemic to EYC, and T. paulum. Interestingly, the phylogeographic break between these two taxa appears to be in the Choctawhatchee

Basin, with specimens from the upper Choctawhatchee sharing a BIN with specimens from the upper Chipola. This relationship was supported by both F-cox1 and M-cox1 barcodes (Figure 2-3; Figure 2-4), suggesting a possible stream capture or sharing of individuals across drainage divides via human-mediated dispersal. We are unaware of other aquatic taxa that exhibit the same phylogeographic pattern and highlight this anomaly for future investigation. The third species, T. parvum, was resolved in a single

BIN and reported as introduced to the study region (Williams et al. 2014).

The genus Villosa is highly diverse and widespread throughout the Atlantic, Gulf, and Interior basins with four species occurring within the study area. Three species exhibited a strong signal for allopatrically distributed cryptic diversity. The highest level of intraspecific divergence was observed in Villosa lienosa, which was split into five monophyletic clades that corresponded to allopatric BINs (Figure 2-3; Figure 2-9).

Divergence between these clades ranged from 1.33% (Choctawhatchee vs Escambia and Yellow) to 5.23% (Suwannee vs Escambia and Yellow). Divergence levels were similar between the three clades recovered for V. villosa (Figure 2-3; Figure 2-9), with

2.12% genetic distance between individuals sampled from Apalachicola and

Ochlockonee basins compared with specimens from the Suwannee Basin. The highest divergence (4.99%) was observed between the Suwannee and Choctawhatchee basins.

The maximum intraspecific divergence between the two BINs containing V. vibex was

3.91%, which separated the Escambia/Yellow clade from the remaining drainages that were sampled (Figure 2-3; Figure 2-9).

Pleurobemini

Two species of the genus Pleurobema are known from the GFR, both federally listed under the ESA. DNA barcodes for both species exhibited high intraspecific divergence and were split into two BINs (Figure 2-3). Pleurobema pyriforme were assigned to two allopatric BINs (Apalachicola vs Suwannee basins). Pleurobema pyriforme was described from the Apalachicola Basin (Lea, 1857) and a name is available for the population in the Suwannee River (Pleurobema reclusum; Wright,

1898). However, specimens from the intervening Ochlockonee River Basin were unavailable for our study and should be included in future assessments. BINs assigned to P. strodeanum were not exclusively allopatric with specimens from the Escambia showing membership in both BINs. Additional sampling for P. strodeanum should focus on the intervening Yellow River Basin to determine whether a signal for significant phylogeographic structure exists within the range of this imperiled species.

Quadrulini

Megalonaias nervosa is one of the most widespread species in North America, occupying rivers of the east and west Gulf of Mexico from Florida to Guatemala, and the

Interior Basin including the Great Lakes (Williams et al. 2008; 2014; Watters et al.

2009). Barcoded specimens were split into two BINs. The most populous BIN included individuals from multiple drainages (Figure 2-3). However, the other BIN was a singleton, represented by one specimen from the Ochlockonee River.

Uniomerus is found in Atlantic, Gulf, and Interior Basin drainages with three species occurring within the GFR. Two of these species exhibited high levels of

intraspecific divergence (Figure 2-3). Uniomerus columbensis split into two allopatric

BINs (Choctawhatchee vs Apalachicola). Barcodes for U. tetralasmus were also assigned to two BINs; one exclusive to the Escambia River and the other with individuals from both the Escambia and Yellow rivers. All U. carolinianus were assigned to a single BIN.

Discussion

This study provided the first comprehensive DNA barcode reference library for freshwater mussels globally. Here, we showed that both sex-linked COI barcodes provide an efficient, non-biased tool for identification and calibration of current taxonomic hypotheses. The strong correspondence between morphological- and molecular-based assignments indicates that prior morphological studies have been effective in species recognition. However, DNA barcodes indicate diversity in the group has been both overlooked and in some cases overestimated. Such discrepancies are not surprising, given the tangled taxonomic history of freshwater mussels.

The mean level of intraspecific divergence at F-cox1 across all mussel species was 1.18%, which was approximately two to four times greater than observed values for other comprehensively sampled barcoded groups, including North American birds

(0.23% - Kerr et al. 2009), eastern North American Lepidoptera (0.43% - Hebert et. al

2010), and North American freshwater fishes (0.73% - April et al. 2011). The highly fragmented network of freshwater ecosystems, coupled with the limited dispersal capabilities that rely on host fish distributions, are likely explanations for the high levels of intraspecific divergence observed within freshwater mussels. In some cases, barcodes enabled the assignment of individuals to drainage of origin or even place them within subwatersheds (e.g. Toxolasma sp. cf. corvunculus in the upper vs lower

Choctawhatchee). However, we observed multiple instances in which currently recognized species were not separable using either F-cox1 or M-cox1. This was the case for nearly all members of the genus Elliptio.

High levels of unassigned and misidentified specimens underscore the need for reliable and objective tools to complement morphological identification, even when performed by specialized taxonomists. Accurate identification becomes increasingly important for imperiled taxa. In this study, 28.4% of all misidentified were either federally protected under the ESA (E. chipolaensis, H. australis, M. penicillatus, O. choctawensis, and P. pyriforme) or under review for federal listing (A. radiatus and E. ahenea). These findings are significant, especially for imperiled taxa, given that false positive error rates as low as 5% can substantially bias species presence models (Royle and Link 2006;

Shea et al. 2011). All identifications in this study were conducted by malacologists with at least ten years of experience; therefore, our results should be considered a conservative estimate of misidentification rates in freshwater mussel surveys. All specimens involved in conflicts between initial morphology-based identifications and barcode results were re-examined, which showed that DNA-based identifications were correct. This exercise also helped researchers identify additional diagnostic morphological characters useful for separating similar species. For example, sculpturing on the umbo portion of the shell proved useful for distinguishing between members of the genera Villosa and Toxolasma, both of which had high occurrences of misidentification (23.9% and 6.8%, respectively). Umbo sculpture also helped separate

Elliptio from Uniomerus, which had high occurrences of misidentification (44.3% and

15.9%, respectively). However, the umbo portion of the shell is prone to erosion,

especially in older specimens, making DNA barcodes the only reliable method of identification in some cases.

Recent studies applying molecular tools show promise for providing a more objective method to clarify taxonomic assignments of unionids (Mulvey et al. 1997; Roe and Lydeard 1998; King et al. 1999; Jones et al. 2006; Grobler et al. 2006; Elderkin et al. 2008; Grobler et al. 2011; Inoue et al. 2013; Pfeiffer et al. 2016; Perkins et a. 2017), several of which have detected and described broad-scale genetic differences between allopatric populations of federally protected species (Roe and Lydeard 1998; King et al.

1999; Serb et al. 2003; Jones et al. 2006; Jones et al. 2015). However, DNA barcoding differs from past applications of genetics regarding identification and taxonomic revision by encouraging a transparent and reproducible method based on a well-curated collection. Additionally, the capacity of our DNA barcode libraries to enable the identification of otherwise taxonomically ambiguous specimens (e.g. larvae, juveniles, introduced species) represents a major advance for future ecological studies and monitoring efforts focused on freshwater mussels and their vertebrate hosts (Boyer et al. 2011; Chapter 3). Although several previous studies involving freshwater mussels mention the use of DNA barcoding (e.g. Campbell et al. 2008), none aimed to satisfy the rigorous standards implemented here to ensure reproducibility of results and barcode compliance.

By demonstrating the utility of DNA barcode libraries in freshwater mussel research, we hope to encourage future researchers involved with molecular systematics of freshwater mussels to contribute to the DNA barcoding effort by submitting metadata needed for barcode compliance. This includes both existing and future DNA

sequencing efforts. The main goal is to build an alliance between molecular and morphology-based taxonomy to offer a more objective, reliable, and rapid species identification and classification scheme, not to discard conventional classifications and morphological studies. Expanding our DNA barcode libraries will also enhance biodiversity assessments by facilitating discovery of overlooked species.

Table 2-1. The number and percentage of freshwater mussel misidentifications revealed using F-cox1 and M-cox1 DNA barcodes. Original COIF COIM Identification Genus Genus % Species Species % Genus Genus % Species Species % Correct 1519/1551 97.9% 1303/1551 84.0% 367/373 98.4% 319/373 85.5% Incorrect 29/1551 1.9% 88/1551 5.7% 5/373 1.3% 9/373 2.4% Unassigned 3/1551 <1% 160/1551 10.3% 1/373 <1% 45/373 12.1%

Table 2-2. Frequency of occurrence for each original morphology-based identification corrected using F-cox1 barcodes. Original Identification Corrected Identification # Misidentifications Anodonta suborbiculata Anodonta hartfieldorum 2 Elliptio ahenea Elliptio s.s. 7 Elliptio chipolaensis Elliptio s.s. 8 Elliptio jayensis Villosa amygdalum 1 Elliptio nigella Elliptio s.s. 11 Hamiota australis Villosa vibex 2 Lampsilis straminea Villosa vibex 1 Medionidus penicillatus Villosa lienosa 1 Obovaria choctawensis Villosa lienosa 1 Pleurobema pyriforme Anodontoides radiatus 1 Pleurobema pyriforme Quadrula infucata 2 Pleurobema pyriforme Toxolasma paulum 1 Quadrula infucata Elliptio crassidens 1 Toxolasma paulum Toxolasma parvum 3 Uniomerus carolinianus Elliptio s.s. 6 Uniomerus carolinianus Villosa lienosa 1 Uniomerus columbensis Elliptio s.s. 5 Uniomerus columbensis Pleurobema pyriforme 2 Utterbackia imbecillis Utterbackia peggyae 12 Utterbackia imbecillis Utterbackia peninsularis 1 Utterbackia peninsularis Utterbackia imbecillis 3 Villosa amygdalum Villosa vibex 1 Villosa lienosa Toxolasma sp. cf. corvunculus 1 Villosa lienosa Toxolasma paulum 2 Villosa lienosa Villosa vibex 8 Villosa lienosa Villosa villosa 1 Villosa vibex Villosa amygdalum 1 Villosa vibex Villosa lienosa 2

Table 2-3. Sample sizes (n), mean and maximum uncorrected p-distance (%), and the distance to the nearest neighbor (NN) species based on 1551 Fcox1 sequences for 57 currently recognized freshwater mussel species in the southeastern United States. Dashes (-) for p-distances represent cases for which the species was only represented by a single specimen Mean Max Distance Species n p-dist p-dist Nearest Neighbor (NN) to NN Alasmidonta triangulata 2 1.32 1.32 Utterbackia imbecillis 10.03 Amblema neislerii 24 0.41 1.16 Amblema plicata 4.56 Amblema plicata 1 - - Amblema neislerii 4.56 Amphinaias infucata 35 0.38 1.11 Amphinaias kleiniana 2.01 Amphinaias kleiniana 7 0.96 1.39 Amphinaias infucata 2.01 Amphinaias succissa 27 0.95 2.16 Amphinaias infucata 7.72 Anodontoides radiatus 32 1.83 4.08 Alasmidonta triangulata 11.42 Elliptio ahenea 18 0.41 2.62 Elliptio jayensis 3.41 Elliptio arctata 9 2.32 4.09 Elliptio fraterna 1.69 Elliptio chipolaensis 19 0.41 0.94 Elliptio nigella 0.00 Elliptio crassidens 29 0.44 1.23 Elliptio pullata 0.00 Elliptio fraterna 11 0.80 1.60 Elliptio pullata 0.00 Elliptio fumata 33 1.43 3.40 Elliptio crassidens 0.00 Elliptio jayensis 40 1.72 3.86 Elliptio pullata 0.00 Elliptio mcmichaeli 12 0.37 0.77 Elliptio crassidens 0.00 Elliptio monroensis 6 0.93 1.58 Elliptio jayensis 0.00 Elliptio nigella 21 0.59 1.23 Elliptio chipolaensis 0.00 Elliptio occulta 13 1.21 2.82 Elliptio jayensis 0.00 Elliptio pullata 51 1.04 3.55 Elliptio jayensis 0.00 Elliptio purpurella 15 1.60 3.07 Elliptio jayensis 0.00 Elliptoideus sloatianus 11 0.28 0.97 Elliptio ahenea 8.49 Fusconaia burkei 24 0.03 0.31 Fusconaia escambia 0.77 Fusconaia escambia 29 0.08 1.39 Fusconaia burkei 0.77 Fusconaia rotulata 2 0.00 0.00 Villosa amygdala 10.19 Glebula rotundata 75 0.27 0.80 Villosa villosa 9.57 Hamiota australis 18 1.70 3.35 Hamiota subangulata 1.85 Hamiota subangulata 4 0.00 0.00 Hamiota australis 1.85 Lampsilis floridensis 41 1.21 2.66 Lampsilis straminea 6.76 Lampsilis ornata 5 0.00 0.00 Villosa villosa 7.52 Lampsilis straminea 55 2.05 3.54 Villosa villosa 6.03 Ligumia subrostrata 4 0.44 0.93 Villosa amygdala 6.83 Medionidus penicillatus 7 0.35 0.77 Medionidus walkeri 2.93 Medionidus walkeri 6 0.24 0.49 Medionidus penicillatus 2.93 Megalonaias nervosa 11 0.91 3.09 Elliptio fumata 9.48 Obovaria choctawensis 15 0.53 1.39 Villosa villosa 6.22 Plectomerus dombeyanus 5 0.25 0.63 Villosa amygdala 9.59 Pleurobema pyriforme 13 1.03 2.47 Pleurobema strodeanum 5.97 Pleurobema strodeanum 27 1.94 4.48 Pleurobema pyriforme 5.97 Ptychobranchus jonesi 3 0.21 0.31 Villosa villosa 7.50 Pyganodon grandis 19 5.10 10.34 Utterbackia peninsularis 10.65 Toxolasma sp. cf. corvunculus 72 5.54 12.65 Toxolasma paulum 0.77 Toxolasma parvum 26 0.24 1.73 Elliptio ahenea 9.41 Toxolasma paulum 186 3.44 9.03 Toxolasma sp. cf. corvunculus 0.77 Uniomerus caroliniana 50 1.67 3.40 Uniomerus columbensis 9.98 Uniomerus columbensis 11 3.78 8.18 Uniomerus tetralasmus 8.33

Table 2-3. Continued Mean Max Distance Species n p-dist p-dist Nearest Neighbor (NN) to NN Uniomerus tetralasmus 13 0.72 1.90 Uniomerus columbensis 8.33 Utterbackia couperiana 10 0.37 1.13 Utterbackia heardi 5.82 Utterbackia hartfieldorum 15 0.06 0.31 Utterbackia suborbiculata 3.09 Utterbackia heardi 6 0.10 0.31 Utterbackia couperiana 5.82 Utterbackia imbecillis 43 0.53 1.85 Utterbackia peninsularis 9.10 Utterbackia peggyae 42 1.63 3.40 Utterbackia imbecillis 10.03 Utterbackia peninsularis 10 1.17 2.78 Utterbackia imbecillis 9.10 Utterbackia suborbiculata 1 - - Utterbackia hartfieldorum 3.09 Villosa amygdala 39 0.37 0.77 Villosa villosa 2.01 Villosa lienosa 80 2.53 6.17 Lampsilis straminea 6.13 Villosa vibex 117 1.85 5.09 Villosa villosa 6.70 Villosa villosa 52 2.50 5.56 Villosa amygdala 2.01

Table 2-4. Sample sizes (n), mean and maximum uncorrected p-distance (%), and the distance to the nearest neighbor (NN) species based on 377 M-cox1 sequences representing 37 currently recognized freshwater mussel species in the southeastern United States. Dashes (-) for p-distances represent cases for which the species is a singleton. Mean Max Distance Species Nearest Neighbor (NN) n p-dist p-dist to NN Amblema neislerii 6 0.90 1.67 Toxolasma paulum 12.01 Amphinaias infucata 3 0.10 0.15 Amphinaias succissa 5.83 Amphinaias succissa 5 0.62 0.89 Amphinaias infucata 5.83 Elliptio arctata 1 - - Elliptio crassidens 3.53 Elliptio chipolaensis 7 0.20 0.61 Elliptio nigella 0.00 Elliptio crassidens 4 0.49 0.59 Elliptio mcmichaeli 0.29 Elliptio fraterna 2 0.15 0.15 Elliptio fumata 0.00 Elliptio fumata 3 0.42 0.62 Elliptio fraterna 0.00 Elliptio jayensis 3 - - Elliptio purpurella 0.00 Elliptio mcmichaeli 1 - - Elliptio crassidens 0.29 Elliptio nigella 13 0.02 0.15 Elliptio chipolaensis 0.00 Elliptio pullata 13 2.50 4.82 Elliptio fraterna 0.00 Elliptio purpurella 4 1.47 2.74 Elliptio jayensis 0.00 Elliptoideus sloatianus 2 0.00 0.00 Pleurobema strodeanum 10.85 Fusconaia burkei 2 1.09 1.09 Pleurobema strodeanum 8.63 Glebula rotundata 23 0.43 1.23 Toxolasma cf. corvunculus 12.73 Hamiota australis 1 - - Hamiota subangulata 0.88 Hamiota subangulata 3 0.10 0.15 Hamiota australis 0.88 Lampsilis floridensis 20 0.34 0.88 Hamiota subangulata 8.85 Lampsilis straminea 16 1.54 3.27 Villosa vibex 9.66 Ligumia subrostrata 1 - - Hamiota subangulata 12.77 Medionidus penicillatus 1 - - Ptychobranchus jonesi 11.33 Obovaria choctawensis 5 0.00 0.00 Hamiota australis 10.32 Pleurobema pyriforme 1 - - Pleurobema strodeanum 5.59 Pleurobema strodeanum 7 2.51 5.12 Pleurobema pyriforme 5.59 Ptychobranchus jonesi 3 0 0 Medionidus penicillatus 11.33 Pyganodon grandis 2 0.15 0.15 Utterbackia peggyae 18.44 Toxolasma cf. corvunculus 38 4.27 11.15 Toxolasma paulum 0.32 Toxolasma paulum 88 1.89 5.02 Toxolasma cf. corvunculus 0.32 Uniomerus tetralasmus 1 - - Amphinaias infucata 12.56 Utterbackia couperiana 2 0.00 0.00 Utterbackia heardi 6.44 Utterbackia hartfieldorum 4 0.07 0.15 Utterbackia heardi 7.82 Utterbackia heardi 3 0.10 0.15 Utterbackia couperiana 6.44 Utterbackia peggyae 2 0.00 0.00 Pyganodon grandis 18.44 Villosa lienosa 47 1.71 3.24 Hamiota subangulata 9.03 Villosa vibex 27 0.78 2.11 Hamiota subangulata 6.35 Villosa villosa 11 2.20 4.28 Hamiota australis 6.64

Table 2-5. BIN assignments based on 1551 F-cox1 DNA barcodes representing 57 freshwater mussel species in southeastern United States. Each taxon was assigned one of four scores (match, merge, split, or mixture). Sample sizes (n), mean and maximum pairwise uncorrected p-distances, and distance to nearest neighbor for each BIN are provided. Mean Max NN Taxa BIN Score n Distance Distance Distance Alasmidonta triangulata BOLD:ACZ0306 Match 2 1.32 1.32 10.03 Amblema neislerii BOLD:ACY9806 Match 24 0.41 1.16 4.56 Amblema plicata BOLD:AAA8507 Match 1 0.00 0.00 4.56 Amphinaias infucata BOLD:ACY9539 Merge 35 1.10 3.68 7.72 Amphinaias kleiniana BOLD:ACY9539 Merge 7 1.10 3.68 7.72 Amphinaias succissa BOLD:ACY9599 Match 27 0.93 2.02 7.72 Anodontoides radiatus BOLD:ACZ0233 Split 24 0.21 0.48 0.97 BOLD:ACZ0234 8 0.41 0.77 3.54 Elliptio ahenea BOLD:ACZ0145 Split 1 0.00 0.00 2.29 BOLD:ACZ0146 17 0.15 0.31 2.29 Elliptio arctata BOLD:AAB3266 Mixture 4 0.42 0.93 1.69 BOLD:AAX7569 5 0.11 0.31 2.56 Elliptio chipolaensis BOLD:ACY9226 Merge 19 0.50 1.23 6.21 Elliptio crassidens BOLD:AAB3266 Merge 29 0.97 3.70 1.57 Elliptio fraterna BOLD:AAB3266 Merge 11 0.97 3.70 1.57 Elliptio fumata BOLD:AAB3266 Merge 33 0.97 3.70 1.57 Elliptio jayensis BOLD:AAB3266 Mixture 33 0.97 3.70 1.57 BOLD:ACZ0072 6 0.21 0.62 2.06 Elliptio mcmichaeli BOLD:AAB3266 Merge 12 0.97 3.70 1.57 Elliptio monroensis BOLD:AAB3266 Merge 6 0.97 3.70 1.57 Elliptio nigella BOLD:ACY9226 Merge 21 0.50 1.23 6.21 Elliptio occulta BOLD:AAB3266 Merge 13 0.97 3.70 1.57 Elliptio pullata BOLD:AAB3266 Mixture 49 0.97 3.70 1.57 BOLD:ACZ0072 2 0.21 0.62 2.06 Elliptio purpurella BOLD:AAB3266 Merge 15 0.97 3.70 1.57 Elliptoideus sloatianus BOLD:ACY9367 Match 11 0.28 0.97 8.49 Fusconaia burkei BOLD:ABZ1530 Merge 24 0.46 2.01 6.37 Fusconaia escambia BOLD:ABZ1530 Merge 29 0.46 2.01 6.37 Fusconaia rotulata BOLD:AAI7254 Match 2 0.00 0.00 10.19 Glebula rotundata BOLD:AAF5442 Match 75 0.27 0.80 9.57 Hamiota australis BOLD:AAE2928 Split 9 0.39 0.77 2.47 BOLD:AAE2929 9 0.23 0.50 1.85 Hamiota subangulata BOLD:AAE2926 Match 4 0.00 0.00 1.85 Lampsilis floridensis BOLD:ACY9565 Split 30 1.21 2.66 6.76 BOLD:ACY9566 11 1.21 2.66 6.76 Lampsilis ornata BOLD:AAE6092 Match 5 0.00 0.00 7.52 Lampsilis straminea BOLD:AAJ3103 Split 6 0.00 0.00 1.12 BOLD:ACY9495 13 0.23 0.63 1.81 BOLD:ACY9929 15 0.19 0.62 1.81 BOLD:ACY9930 16 0.15 0.81 1.12 BOLD:ACZ0141 5 0.13 0.34 2.47 Ligumia subrostrata BOLD:ACZ0248 Match 4 0.45 0.94 6.92 Medionidus penicillatus BOLD:ACY9215 Match 7 0.35 0.77 2.93 Medionidus walkeri BOLD:ACY9837 Match 6 0.24 0.49 2.93 Megalonaias nervosa BOLD:AAX3278 Split 10 0.46 0.97 2.63 BOLD:ADC9398 1 0.00 0.00 2.63 Obovaria choctawensis BOLD:ACY9767 Match 15 0.53 1.39 6.22

Table 2-5. Continued Mean Max NN Taxa BIN Score n Distance Distance Distance Plectomerus dombeyanus BOLD:AAF2933 Match 5 0.25 0.63 9.59 Pleurobema pyriforme BOLD:AAY4325 Split 11 1.03 2.47 5.97 BOLD:ACZ0226 2 1.03 2.47 5.97 Pleurobema strodeanum BOLD:AAH9181 Split 20 0.74 1.96 3.38 BOLD:AAH9184 7 0.74 1.96 3.38 Ptychobranchus jonesi BOLD:ACZ0255 Match 3 0.21 0.31 7.50 Pyganodon grandis BOLD:ACY9175 Split 6 0.00 0.00 8.66 BOLD:ACY9846 4 0.23 0.46 2.31 BOLD:ACY9847 9 0.14 0.47 2.31 Toxolasma sp. cf. corvunculus BOLD:ACY9376 Mixture 50 0.66 2.31 8.66 BOLD:ACY9683 22 0.76 1.70 4.78 Toxolasma parvum BOLD:AAC9832 Match 26 0.04 0.33 1.16 Toxolasma paulum BOLD:ACY9683 Mixture 56 0.76 1.70 4.78 BOLD:ACZ0276 130 1.52 5.42 4.78 Uniomerus caroliniana BOLD:ACZ0018 Match 50 1.67 3.40 9.98 Uniomerus columbensis BOLD:ACY9630 Split 1 0.00 0.00 3.55 BOLD:ACZ0052 7 0.12 0.32 6.29 BOLD:ACZ0090 3 0.21 0.31 3.55 Uniomerus tetralasmus BOLD:ACY9741 Split 10 0.11 0.32 1.49 BOLD:ACZ0285 3 0.21 0.33 1.49 Utterbackia couperiana BOLD:ACZ0235 Match 10 0.37 1.13 5.82 Utterbackia hartfieldorum BOLD:ACZ0069 Match 15 0.06 0.31 3.09 Utterbackia heardi BOLD:ACZ0261 Match 6 0.10 0.31 5.82 Utterbackia imbecillis BOLD:ACD2688 Match 43 0.52 1.70 9.10 Utterbackia peggyae BOLD:ACH3909 Match 42 0.28 1.39 2.31 Utterbackia peninsularis BOLD:ACD2152 Split 8 0.42 0.93 2.47 BOLD:ACH4097 2 0.00 0.00 2.47 Utterbackia suborbiculata BOLD:ACY9286 Match 1 0.00 0.00 3.09 Villosa amygdala BOLD:ACY9237 Match 39 0.37 0.77 2.01 Villosa lienosa BOLD:ACY9446 Split 7 0.29 0.62 2.01 BOLD:ACY9447 35 0.20 0.62 2.01 BOLD:ACY9448 15 0.26 1.39 2.01 BOLD:ACY9737 7 0.23 0.33 1.23 BOLD:ACZ0030 16 0.20 0.46 1.23 Villosa vibex BOLD:ACZ0322 Split 25 0.27 0.81 3.43 BOLD:ACZ0323 92 0.78 2.62 3.43 Villosa villosa BOLD:AAH7736 Split 10 0.14 0.47 1.75 BOLD:ACY9238 29 0.53 1.23 1.75 BOLD:ACY9794 13 0.14 0.46 4.31

Figure 2-1. Collection sites in the southeastern United States for the 1571 specimens analyzed in this study. Each point may represent several collection sites and multiple taxa collected from the same locality.

Figure 2-2. Circular phylogram based on 1551 F-cox1 gene sequences representing 23 genera and 57 species of freshwater mussels collected from the southeastern United States.

Figure 2-3. Neighbor-joining tree based on 1551 F-cox1 sequences. Taxonomy, specimen identifiers, and Barcode Index Numbers (BIN) assigned to each taxon are given as BOLD:XXXXXXX. Drainages of collection are abbreviated as follows: Apalachicola (Apa), Aucilla (Auc), Chattahoochee (Cha), Chipola (Chi), Choctawhatchee (Cho), Econfina (Eco), Escambia (Esc), Everglades (Eve), Fenhalloway (Fen), Flint (Fli), Mississippi (Mis), Mobile (Mob), Mayakka (Mya), Ochlockonee (Och), Peace (Pea), St. Johns (StJ), St. Marys (StM), Steinhatchee (Ste), Suwannee (Suw), Tampa Bay (Tam), Tomoka (Tom), Withlacoochee (Wit), and Yellow (Yel).

Figure 2-3. Continued

Figure 2-4. Neighbor-joining tree based on 373 M-cox1 sequences. Taxonomy, specimen identifiers, and Barcode Index Numbers (BIN) assigned to each taxon are given as BOLD:XXXXXXX. Drainages of collection are abbreviated as follows: Apalachicola (Apa), Aucilla (Auc), Chattahoochee (Cha), Choctawhatchee (Cho), Econfina (Eco), Escambia (Esc), Everglades (Eve), Fenhalloway (Fen), Mississippi (Mis), Mobile (Mob), Mayakka (Mya), Ochlockonee (Och), Peace (Pea), St. Johns (StJ), St. Marys (StM), Steinhatchee (Ste), Suwannee (Suw), Tampa Bay (Tam), Tomoka (Tom), Withlacoochee (Wit), and Yellow (Yel).

Figure 2-4. Continued

Figure 2-5. Frequency distribution histogram of uncorrected pairwise genetic distance based on 1551 Fcox1 sequences assigned to 57 currently recognized freshwater mussel species in the southeastern United States. The intraspecific and interspecific distances are displayed using black and gray columns, respectively.

Figure 2-6. Neighbor-joining subtrees illustrating BIN sharing between Fusconaia burkei and Fusconaia escambia (left) and Elliptio chipolaensis and Elliptio nigella (right).

Figure 2-7. Neighbor-joining subtrees illustrating examples of shallow interspecific divergence for which two or more species formed a single genetic cluster.

Figure 2-8. Neighbor-joining subtrees illustrating examples of deep intraspecific divergence for which conspecifics were split into two or more BINS, indicating possible cases of cryptic diversity.

Figure 2-9. Neighbor-joining subtrees illustrating examples of high intraspecific divergence in which conspecific individuals were assigned to two or more BINS, indicating possible cases of cryptic diversity.

CHAPTER 3 APPLYING DNA BARCODES TO INVESTIGATE ECOLOGICAL HOST ASSOCIATIONS AND SPECIES BOUNDARIES FOR FRESHWATER MUSSELS

North America is the epicenter of freshwater mussel diversity with nearly 300 species from 55 genera in the United States and Canada (Williams et al. 2017).

Anthropogenic disturbances, including broad-scale habitat modification and degradation of water quality, have had detrimental impacts on mussel diversity; recent assessments are that at least 70% of species in North America are imperiled or extinct (Williams et al.

1993, Turgeon et al. 1998, Lydeard et al. 1999, Haag 2012; Haag and Williams 2014).

Much of this diversity resides within isolated river basins that drain into the Gulf of

Mexico where high numbers of endemic taxa have evolved (Haag 2010). Taxonomic uncertainties and incomplete understanding of basic ecological and life history requirements for many mussel species of conservation concern are impediments to the protection and recovery of remaining populations (Haag and Williams 2014; Johnson et al. 2016; Pfeiffer et al. 2016; McLeod et al. 2017).

Freshwater mussel taxa differ in many aspects of their biology and life history traits, but nearly all have a highly specialized larval stage (glochidia) that must parasitize the gills or fins of freshwater fishes to complete metamorphosis to the juvenile stage. Host use varies widely among species, from ‘host specialist’ that rely on one species of fish to ‘host generalists’ with larvae that may complete metamorphosis on a wide variety of fishes (Barnhart et al. 2008; Haag 2012). This difference in host fish specificity has a profound effect on several important population processes including recruitment (e.g. availability of suitable hosts), dispersal (e.g. vagility of host), and resiliency (e.g. host generalist vs. specialist) (Berg et al. 2008; Strayer 2008).

Information on mussel-host relationships becomes especially critical for managers

working to protect or recover remaining mussel populations using options that include captive propagation, augmentation, and reestablishment (Neves 1997; Jones et al.

2006; Haag and Williams 2014; McMurray and Roe 2017).

Over the past century, the study of unionid-host associations has been extensive

(Lefevre and Curtis 1910; Barnhart et al. 2008; Johnson et al. 2016), yet remains incomplete, as detailed information is lacking or potentially erroneous for many species.

What information is available has been collated into a comprehensive on-line database

(Freshwater Mussel Host Database 2017). Most studies characterized mussel hosts by ex situ infestations using laboratory inoculation trials that immerse a suite of host fishes in a glochidia-bath under controlled conditions (Neves et al. 1985; Johnson et al. 2016).

Inoculation trials can be replicated and enable distinction between glochidia that are rejected from non-suitable hosts and glochidia that complete metamorphosis to the juvenile life stage. This approach has led to the identification of so called ‘physiological hosts’ and may not represent host-use under natural conditions (Berg et al. 2008;

Levine et al. 2012). For example, many mussel species have host attraction strategies that mimic prey items to aid infection of host fishes within specific feeding guilds (e.g. piscivore vs. insectivore) (Barnhart et al. 2008). During inoculation trials, exposure of glochidia is human-mediated and does not test ecological compatibility in situ (Hoggarth

1992). Other sources are based on in situ infestations of host fishes to identify

‘ecological hosts’ (Berg et al 2008; Levine et al. 2012). The majority of these sources rely on observing encysted glochidia on the gills, fins, or skin of fishes infected with mussel larvae in the wild (Wilson 1916; Coker et al. 1921; Boyer et al. 2011; Levine et al. 2012). Results from host experiments are routinely categorized by evidence type

following Hoggarth (1992). Laboratory-based observations that lack confirmation of metamorphosis are scored as laboratory infestation (LI), whereas laboratory transformation (LT) indicates observations of metamorphosis from glochidia to juveniles under controlled conditions. Similarly, field based observations are designed as natural infestation (NI) or natural transformation (NT), with the latter based on evidence of metamorphosis following in situ infestations of glochidia on host fishes.

The laboratory inoculation and in situ approaches have inherent shortcomings and observations from any study (laboratory or field-based) in which metamorphosis was not documented should be considered tenuous because glochidia will attach to nonanimal objects and non-hosts without completing metamorphosis (Lefevre and

Curtis 1910; Haag and Warren 2003; Lellis et al. 2013). Additionally, observations based on natural infestations are problematic, given difficulties with species-level identification of encysted glochidia or metamorphosed juveniles (Haag and Warren

2003; Kennedy and Haag 2005). However, recent advancements in molecular techniques, such as DNA barcoding, have improved our ability to identify mussels of all life stages (White et al. 1994; Gerke and Tiedemann 2001; Kneeland and Rhymer 2008;

Boyer et al. 2011; Ziertiz et al. 2012; Chapter 2).

Gulf Coast Rivers of the southwestern United States harbor a considerable portion of the overall mussel diversity in North America and at least 52 species have been reported from the inland waters of Texas (Howells et al. 1996; Williams et al.

2017). Rivers of the Western Gulf Province (Rio Grande to Brazos River) historically supported at least 31 species, including 11 endemics (Haag 2010). Nine of these endemic species, however, are considered threatened by the State of Texas and also

are being considered for listing under the US Endangered Species Act (ESA) as threatened or endangered with critical habitat designation. Taxonomic uncertainties coupled with limited understanding of ecological hosts, however, complicate listing decisions and conservation actions. To help guide management efforts for the candidate species, we evaluated current taxonomy and characterized ecological fish hosts using DNA barcodes. Specifically, we developed a comprehensive library of DNA barcodes derived from expert-identified reference material and used it to assign species-level identifications of metamorphosed juvenile mussels recovered from naturally parasitized fishes. The goal was to develop a reliable approach for understanding ecological host-fish requirements for freshwater mussels, using a central

Texas example in which six candidate species were being considered for listing under the ESA (False Spike, Fusconaia mitchelli; Texas Fatmucket, Lampsilis bracteata;

Texas Fawnsfoot, Truncilla macrodon; Golden Orb, Quadrula aurea; Smooth

Pimpleback, Quadrula houstonensis; and Texas Pimpleback, Quadrula petrina). Finally, we used DNA barcodes to explore current classification based largely on shell morphology and biogeography and highlight taxa that warrant further investigation.

Methods

Specimen Collection

Adult freshwater mussels were collected July 2008 – August 2013 using a combination of tactile searches, hand rakes, and visual underwater searches (e.g.

SCUBA). We aimed to collect ≥ 3 individuals for each of the 24 species known to occur in the Colorado and Guadalupe River drainages of central Texas (Figure 3-1). We also sampled outside the drainages to obtain hard to find species that have been reported in these two drainages. Specimens were identified to the lowest taxonomic unit possible

by regional freshwater malacologists using a combination of morphological characters and geographic location consistent with original species descriptions and primary literature. All specimens were photographed, preserved in 95% ethanol, assigned catalog numbers, and deposited at the Florida Museum of Natural History (FLMNH).

Potential host fish were collected from four sites in the Colorado River drainage and from two sites in the Guadalupe River drainage, in areas where the six ESA candidate species were known to occur (Table 3-1; Figure 3-1). Fishes were sampled from all available habitat types using seines or electrofishing by boat or barge and captured in close proximity to mussel beds identified during previous surveys (Braun et al. 2015). A sample of fishes representing members of different families and species were selected to provide a diverse array of potential host fishes. These fish were transported in aerated coolers to the US Fish and Wildlife Service hatchery in San

Marcos, Texas and separated by species into holding tanks held at 21-24°C. The bottom of each tank was siphoned daily for 28 days to recover metamorphosed juveniles or sloughed glochidia. The siphonate was filtered through individual 100 um mesh sieves and contents were examined using a dissecting microscope.

Metamorphosed juveniles were distinguished from sloughed glochidia by the presence of soft parts between the valves or observation of individuals actively pedal feeding.

Each metamorphosed juvenile was photographed, measured, and preserved in individual wells on a 96-well plate containing 95% ethanol.

DNA Sequencing and Data Analyses

Tissue samples and whole juveniles were extracted using a modified plate extraction protocol (Ivanova et al. 2006). Primers for amplification and bidirectional sequencing of the maternal copy of cytochrome c oxidase subunit I (F-cox1) were: COI

dgLCO-1490 - GGTCAACAAATCATAAAGAYATYGG and COI dgHCO-2198 –

TAAACTTCAGGGTGACCAAARAAYCA (Meyer 2003). The PCR amplifications were conducted in 27-µl reactions with the following reagents and volumes: H20 (14.74 µl),

5X TaqMaster PCR enhancer (5.4 µl) (5 Prime, Inc.) magnesium solution (2.7 µl @ 25 mM) (5 Prime, Inc.), DNTP (0.54 µl @ 10 mM), Primers (0.54 µl @ 10 mM), Taq (0.54 µl

@ 5 U/µl), and DNA template (2.0 µl). Both positive and negative controls were included with each PCR reaction. PCR products were verified on 1% agarose gels stained with ethidium bromide and successfully amplified PCR products were purified and bidirectionally sequenced on ABI 3730 (Life Technologies). Chromatograms were cleaned and assembled using Geneious v 6.1.8 (http://geneious.com, Kearse et al.

2012).

Sequences from adult specimens were aligned in Mesquite v 2.7.5 (Maddison and Maddison 2011) using ClustalW (Larkin et al. 2007). The F-cox1 alignment was translated into amino acids to confirm the absence of stop codons. The final alignment was uploaded to BOLD under the project UNIOBARCODE–TXI. Intra- and interspecific distances and Neighbor-joining trees were calculated with MEGA 7 (Kumar et al. 2016) using uncorrected p-distance with pairwise deletions to account for missing data in the alignment. Distance to nearest neighbor (NN) species based on uncorrected p-distance was calculated using the ‘Barcode Gap Analysis’ tool in BOLD (Ratnasingham and

Hebert 2007). The presence of a barcode gap (Meyer and Paulay 2005) was assessed by comparing overlap between intraspecific, interspecific, and NN p-distances.

Additionally, all sequences were clustered using the 3-step Refined Single Linkage

(RESL) algorithm (Ratnasingham and Hebert 2013) and assigned Barcode Index

Numbers (BIN) using the “Cluster Analysis” tool in BOLD (Ratnasingham and Hebert

2007). The BIN system was used to identify conflicts between morphological and molecular assignments and establish operational taxonomic units (OTUs) using barcode sequence clusters. Current taxonomy was evaluated by assigning each BIN to one of four categories (match, merge, split, or mixture) as defined by Ratnasingham and

Hebert (2013).

For a more robust phylogenetic reconstruction and to identify juvenile freshwater mussels recovered from naturally infested fishes, sequences from both adults and juveniles were combined into a single alignment. The resulting molecular matrix was divided into three partitions, one for each codon position and jModelTest v 2.1.4

(Darriba et al. 2012) was used to find the best fit model of nucleotide substitution for each partition according to the Akaike information criterion (AIC). The F-cox1 matrix was analyzed using Maximum Likelihood (ML) in RAxML v 8.0.0 (Stamatakis 2014) and

Bayesian Inference (BI) in MrBayes v 3.2.2 (Ronquist et al. 2012) using the CIPRES

Science Gateway (Miller et al. 2010). ML analyses were conducted using 1000 tree searches and nodal support was measured using 2000 rapid bootstraps. BI analyses were implemented using 2 runs of 8 chains for 24 x 106 generations, sampling every

1000 trees and omitting the first 8000 as burn-in. Convergence of the two runs was determined by standard deviation of split frequencies < 0.0001 and average potential scale reduction factors (PRSF) of 1.00.

Results

Reference DNA Barcode Library

We generated a comprehensive F-cox1 barcode library containing 124 adult mussels representing all 17 genera and 24 species known to occur in the Colorado and

Guadalupe river drainages in central Texas (Table 3-2; Figure 3-2). Representatives of six species could not be located from the target drainages and specimens from outside the study area were included to represent these species. Average length of F-cox1 sequences was 646 bp (min: 539; max 656) and no insertions, deletions, or stop codons were present in the alignment. Geographic coverage included 26 sampling sites located within 5 major river drainages (Colorado, Galveston Bay, Guadalupe, Pascagoula, and

Sabine) across three states (Louisiana, Mississippi, and Texas). The average number of

F-cox1 sequences representing each species was 5.17 (min: 1; max: 19) (Table 3-3).

All sequences generated for adult specimens were ‘barcode compliant’ in accordance with BOLD (Ratnasingham and Hebert 2007) and included the required fields for barcode data standards on GenBank (Benson et al. 2012). Additional metadata

(collection and locality information, photographs, sequences, trace files, museum numbers, etc.) associated with each specimen and DNA sequence can be found on

BOLD (www.boldsystems.org/) under the project UNIOBARCODE–TXI.

Analyses of F-cox1 barcodes demonstrate high congruence with current taxonomy and 91.67% of individuals were recovered in well-supported clades containing other conspecifics. Close inspection of sequence clusters in all three phylogenetic analyses (BI, ML, and NJ), however, revealed three conflicts with morphology-based identification (Figure 3-2; Figure 3-3). Specifically, the only two individuals originally identified as Q. couchiana from the Guadalupe River grouped within a monophyletic clade containing a Q. petrina specimen from the Guadalupe

River. Another individual morphologically identified as L. bracteata from the Guadalupe

River was recovered sister to L. hydiana instead of grouping with other conspecifics. We

reexamined morphological characters for all three problematic specimens and determined the original identifications to be incorrect and DNA-based identifications were followed during subsequent analyses.

The overall BIN count was nearly equal to the current species count (25 vs 24) with 72.58% of the barcode records assigned to 20 matching BINs that aligned with current taxonomy (Table 3-4). Analyses of intraspecific, interspecific, and NN genetic distances, however, indicated the absence of a clear barcode gap in our F-cox1 dataset

(Table 3-3; Figure 3-4; Figure 3-5). Specifically, maximum intraspecific p-distances exceeded NN distances for Q. aurea and Q. houstonensis (Table 3-3). These two taxa were merged into a single taxonomically discordant BIN (Table 3-4), which impacted

15.32% of the barcode records. In contrast, 12.10% of DNA barcodes revealed high intraspecific variation in four currently recognized species. The highest intraspecific p- distances separated populations of Q. petrina in the Guadalupe and Colorado rivers

(Table 3-3) and each allopatric population was split into a separate OTU BIN (Table 3-

4). Maximum intraspecific divergence was relatively high for F. mitchelli (2.22%), L. hydiana (2.13%), and U. imbecillis (2.29%) (Table 3-3), but only the latter was split into two separate BINs (Table 3-4). These cases represent the potential for cryptic diversity and warrant further investigation.

Juvenile Mussel Identification and Host Fish Characterization

We successfully amplified F-cox1 sequences for 137 metamorphosed juveniles recovered from naturally parasitized fish (Table 3-5; Figure 3-3). All juveniles clustered in well-supported monophyletic clades containing adult specimens representing eight freshwater mussel species in our reference DNA barcode library (Figure 3-3). The highest number of juvenile mussels was recovered for A. plicata (n=75), followed by L.

hydiana (n=18), Q. aurea (n=15), U. imbecillis (n=12), T. texasiense (n=8), Q. petrina

(n=6), Q. houstonensis (n=2), and L. bracteata (n=1) (Table 3-1; Figure 3-3). Four of these taxa are candidates for ESA listing (L. bracteata, Q. aurea, Q. houstonensis, and

Q. petrina).

A total of 12 freshwater fish species were confirmed to be ecological hosts for eight freshwater mussel species (Table 3-5; Figure 3-3). The highest diversity of host fishes was characterized for A. plicata (n=6 species), followed by L. hydiana (n=5), U. imbecillis (n=5), T. texasiense (n=3), Q. aurea (n=2), L. bracteata (n=1), Q. houstonensis (n=1), and Q. petrina (n=1) (Table 3-5; Figure 3-3). Total numbers of juveniles recovered from each species of fish were as follows: Micropterus salmoides

(n=47); Lepomis megalotis (n=41); Lepomis macrochirus (n=6); Pimephales vigilax

(n=1); Macrhybopsis marconis (n=1); Ictalurus punctatus (n=24); Lepomis cyanellus

(n=3); Percina carbonaria (n=1); Etheostoma spectabile (n=1); Lepomis auritus (n=4);

Etheostoma gracile (n=7); and Micropterus punctulatus (n=7) (Table 3-5; Figure 3-3).

We ranked fish hosts depending on the number of transformed juveniles recovered for a specific mussel species (Figure 3-3). For example, a total of 30 A. plicata, 13 L. hydiana, and 2 U. imbecillis juveniles were recovered after completing metamorphosis and naturally releasing from M. salmoides.

The number of fishes identified as hosts for each mussel species differed considerably and host relationships for most species were concomitant with previous observations. Similar to previous studies, we categorized each mussel species as a

‘host specialists’ or ‘host generalists’ depending on the taxonomic breadth of fish host diversity (Barnhart et al. 2008; Haag 2012). For host specialists in our study, we

documented I. punctatus as an ecological host for Q. aurea, Q. houstonensis, and Q. petrina, which is consistent with existing NI and LT observations on closely related species (Q. pustulosa: Coker et al. 1921). In addition, we identified M. punctulatus as an ecological host for Q. aurea. This fish had not been previously reported as a host for any congeners. In general, Quadrula species in the Pustulosa group (Simpson 1900;

Serb et al. 2003; Chapter 4) have been considered host specialists that primarily infect ictalurid catfishes via reflexive release of glochidia (Barnhart et al. 2008; Sietman et al.

2012). Hosts identified for L. bracteata and L. hydiana were primarily centrarchids, consistent with previous studies on congeners (Howells 1997; Johnson et al. 2012).

Females of the genus Lampsilis typically have modified mantle tissue, which serve as mimetic lures to aid in host attraction (Barnhart et al. 2008). Our study revealed that L. hydiana also utilize several small-bodied fishes of the Cyprinidae (M. marconis) and

Percidae (E. spectabile and P. carbonaria), albeit each of these relationships was based on recovery of a single metamorphosed juvenile. This finding contradicts the conventional hypothesis that mussels with large mantle-flap displays utilize only large- bodied, predatory fishes as ecological hosts (e.g. M. salmoides) (Kraemer 1970;

Barnhart et al. 2008). For T. texasiense, we identified three novel hosts from two families (Centrarchidae: L. auritus and L. megalotis; Percidae: E. gracile). Prior to our study, only two hosts were identified for T. texasiense, both centrarchids based on NI evidence (M. macrochirus and L. gulosus: Stern and Felder 1978).

Our study included two host generalists, A. plicata and U. imbecillis, with at least

29 species (8 families) and 20 species (6 families) of known host fishes, respectively

(Freshwater Mussel Host Database 2017). For A. plicata, we identified six host fishes,

three of which were not previously reported (E. gracile, L. auritus, and M. punctulatus).

We confirmed three previously reported host fishes and identified two novel hosts (L. auritus and P. vigilax) for U. imbecillis.

Discussion

The primary objectives of our project were to evaluate current taxonomy and investigate ecological host fishes for freshwater mussels in central Texas using our reference DNA barcode library. We demonstrated that results from DNA barcode analyses align with current taxonomy for most taxa and highlight cases of incongruence.

We provide information on the ecological hosts for eight freshwater mussel species, including four of the six federal candidates known from the study area. Our recalibration of taxonomy and characterization of ecological fish hosts provides information critical for managers who are working to protect or recover remaining freshwater mussel populations.

DNA Barcode Reference Library

Our study represents the first comprehensive molecular survey of freshwater mussel diversity within two river basins in central Texas and joins a growing body of literature using DNA barcoding to evaluate taxonomy and answer ecological questions

(e.g. Chapter 2). All 24 species of mussels currently recognized from this region were assessed using F-cox1 barcodes. Our study includes the first BOLD entries for 17 of the

24 species and the first F-cox1 sequences reported for 2 species (L. bracteata and Q. aurea). The observed distribution of intra- and interspecific genetic distances was within the range of previously reported values for unionids (Campbell et al. 2008; Boyer et al.

2011; Pfeiffer et al. 2016; Perkins et al. 2017; Chapter 2). DNA barcode clusters were generally congruent with current taxonomy, indicating agreement with existing

approaches used to delineate freshwater mussel taxa in this region. The lack of a barcode gap (Meyer and Paulay 2005) for some taxa and a few misidentifications, however, highlight cases that require additional scrutiny.

Importance of DNA Reference Libraries

To ensure reproducibility of our findings and facilitate future research, we followed DNA barcode data standards (Ratnasingham and Hebert 2007; Benson et al.

2012; Chapter 2) when generating our DNA reference library by including the required metadata for sequences linked to voucher specimens cataloged in public museums.

These steps are essential to provide a reliable molecular framework for future ecological, systematic, and conservation studies in light of ongoing taxonomic issues

(Williams et al. 2017), high occurrences of misidentification (Shea et al. 2011; Chapter

2), and inaccuracies of existing mussel sequence data on GenBank (Boyer et al. 2011;

Campbell and Lydeard 2012).

Shallow Interspecific Divergence

Our F-cox1 sequences enabled clear assignment of currently recognized species to barcode clusters except for Q. aurea and Q. houstonensis (Figure 3-2; Figure 3-4;

Figure 3-5), which are two of six species in the study area currently being considered for listing under the ESA. The observed overlap between intra- and interspecific genetic distance values limits our ability to rely on F-cox1 barcodes to distinguish between these two taxa. As a result, Q. aurea and Q. houstonensis were considered members of the same OTU and merged into a single BIN (Table 3-4). Current classification is largely based on the allopatric distribution of these two taxa, with Q. aurea considered endemic to the Guadalupe and Nueces river basins and Q. houstonensis endemic to the Brazos and Colorado river basins (Howells et al. 1996). These two taxa are similar

morphologically and may represent the same species. Further research is needed to assess species boundaries among these two imperiled taxa (see Chapter 4).

Deep Intraspecific Divergence

Results obtained in this work and previous studies (Chapter 2) reveal the existence of cryptic diversity in several species, suggesting freshwater mussel diversity is still underestimated in some lineages. For example, we observed high intraspecific p- distances separating allopatric populations of Q. petrina in the Guadalupe and Colorado rivers (Table 3-3), with each allopatric population representing a distinct OTU BIN

(Table 3-4). In addition, high intraspecific F-cox1 divergence (2.29%) was observed among U. imbecillis specimens from the Pascagoula River (Table 3-4). These cases represent the potential for cryptic diversity and warrant further investigation using additional molecular markers and other independent datasets before formal recognition and description.

Misidentifications

Misidentification rates of freshwater mussels have been documented in previous studies (Shea et al. 2011; Chapter 2) and we revealed two cases of misidentification in our dataset using DNA barcodes. Misidentification of congeners L. hydiana and L. bracteata was not surprising because these taxa are morphologically similar with overlapping geographic distributions. For example, both taxa exhibit sexually dimorphic shell shapes and typically have dark rays that radiate from the umbo to the ventral margin of the shell (Howells et al. 1996). Similarly, Q. couchiana and Q. petrina are morphologically similar congeners. The current and historical distribution of Q. couchiana, however, remains unclear and it is likely that this species is extinct (Williams et al. 1993; 2017; Howells et al. 1996; Turgeon et al. 1998; Serb et al. 2003).

Lack of morphological distinctiveness increases risks of misidentification, especially for inexperienced malacologist (Shea et al. 2011). This is cause for concern for imperiled taxa like L. bracteata and Q. petrina, which are being considered for listing under the ESA. False positive and false negative errors represent sources of error in survey data and may lead to erroneous abundance estimates, distributional information, and conservation assessments (Royle and Link 2006; Shea et al. 2011). For example, instances in which L. bracteata are mistaken for L. hydiana represent false negatives that underestimate the abundance and distribution of L. bracteata. In contrast, false positives for L. bracteata may inflate demographic and distribution estimates and mislead conservation efforts. Our DNA barcode library is a valuable tool for identification of closely related species and can help resolve identification issues that arise from a reliance on morphology-based taxonomy.

Fish Hosts

We used our DNA barcode library to provide reliable evidence about ecological hosts for eight freshwater mussel species, including four of the six federal candidates known from the study area. Our results were categorized as NT, based on observations of metamorphosis from glochidia to juveniles on host fishes that were collected after in situ infestation. Prior to our study, host relationships based on NT observations were limited to only 11 unionids and 4 host fishes (Howard, 1914; Boyer et al. 2011; Hove et al. 2012; Freshwater Mussel host Database 2017). Existing host information was available for five of the eight mussel species, including one of the federal candidates (L. bracteata). Using our approach, we identified novel ecological hosts for seven unionids, including the first reported hosts for three federal candidates (Q. aurea, Q. houstonensis, and Q. petrina).

Our approach to characterizing host fish requirements for freshwater mussels using naturally infested fishes and DNA-based identification of metamorphosed juveniles circumvents deficiencies inherent with previous methods. Regardless of the method used, it is critical that host relationships be based on observations of encysted glochidia that complete metamorphosis and are naturally released from the host, not merely on observations of encysted glochidia. A replicated inoculation host trial revealed that reported host-mussel relationships based on the occurrence of encysted glochidia on naturally infected fishes were erroneous (Fritts et al. 2012). We agree with

Fritts et al. (2012) and urge caution with respect to observations that lack confirmation of metamorphosis (i.e. LI and NI evidence types), given that glochidia attach to nonanimal objects and non-hosts without completing metamorphosis (Lefevre and

Curtis 1910; Lellis et al. 2013; Johnson et al. 2016; McLeod et al. 2017). In contrast, a field study based on observations of encysted glochidia showed that laboratory trials may overestimate the number of ecological hosts (Levine et al. 2012). Field-based studies that rely on morphological characters to identify encysted glochidia or metamorphosed juveniles however, are problematic, given difficulties with identification to the species-level (Haag and Warren 2003; Kennedy and Haag 2005), except in cases of extremely low diversity (Levine et al. 2012). These conflicts raise questions about the reliability of both field and laboratory-based studies and support the use of replicated LT experiments to confirm the results of NT studies, especially those that lack molecular- based identifications of metamorphosed juveniles (Boyer et al. 2011; Fritts et al. 2012).

As with many other metrics used to assess conservation status and extinction risks, there is often a lack of detailed information on the life histories of species that are

of conservation concern. Intuitively, one may hypothesize that mussels relying on rare, narrowly distributed fishes are more susceptible to imperilment than those that depend on common, widespread fishes. However, our findings, along with previous studies

(Haag and Warren 2003; Haag 2012; Haag and Williams 2014), contradict this assumption, in that four of the five candidate species use common, widespread fishes as hosts (e.g. I. punctatus and L. macrochirus). These data provide insights on important population processes including recruitment (e.g. availability of suitable hosts), dispersal (e.g. vagility of host), and resiliency (e.g. host generalist vs. specialist), making this information critical for managers who are working to protect or recover remaining freshwater mussel populations. Furthermore, the greater understanding of reproductive traits and requirements of these animals provides an opportunity for future recovery efforts that may include propagation and culture, which is a common method used to recover mussel species and protect populations from extinction (Neves 1997;

Jones et al. 2006; Haag and Williams 2014; McMurray and Roe 2017).

Table 3-1. Collection sites (site abbreviations) and sampling dates for the five fish surveys where metamorphosed juveniles were recovered and identified using DNA barcodes. Site Location Latitude Longitude Drainage Collection Juvenile identifications Dates 1 San Saba River 30.8995 -99.9107 Colorado 22 May 2012 Lampsilis bracteata (1) Bois D'Arc Road 4 April 2013 Utterbackia imbecillis (8) Menard, Texas 2 San Saba River 31.2231 -98.7846 Colorado 21 August 2012 Lampsilis hydiana (1) County Road 208 1 April 2013 San Saba, Texas 3 Colorado River 31.2057 -98.5689 Colorado 29 April 2013 Quadrula petrina (6) Highway 190 Utterbackia imbecillis (4) San Saba, TX Lampsilis hydiana (1) 4 Colorado River 29.6779 -96.5173 Colorado 11 June 2013 Quadrula houstonensis (2) Kleinman Road Amblema plicata (3) Columbus, Texas 5 Guadalupe River 28.8311 -97.059 Guadalupe 30 May 2012 Quadrula aurea (8) Highway 77 4 April 2013 Lampsilis hydiana (3) Victoria, Texas 10 June 2013 Amblema plicata (57) Toxolasma texasiense (2) 6 Guadalupe River 30.05 -99.1592 Guadalupe 29 May 2012 Quadrula aurea (7) Concho Drive 2 April 2013 Lampsilis hydiana (13) Kerrville, Texas 30 April 2013 Amblema plicata (15) Toxolasma texasiense (6)

Table 3-2. Sample identifiers, museum catalog number, and collection coordinates (latitude and longitude) for 124 freshwater mussel specimens. Catalog Lat. Long. Taxon Sample ID Number Amblema plicata ApliCol019 UF440996 29.454 -96.396 Amblema plicata ApliCol021 UF440996 29.454 -96.396 Amblema plicata ApliCol039 UF441153 31.191 -98.903 Amblema plicata ApliCol040 UF441154 31.262 -98.595 Amblema plicata ApliGua012 UF440983 29.470 -97.491 Amblema plicata ApliGua013 UF440999 28.831 -97.061 Amblema plicata ApliGua014 UF440999 28.831 -97.061 Amblema plicata ApliGua015 UF440999 28.831 -97.061 Amblema plicata ApliGua016 UF440999 28.831 -97.061 Arcidens confragosus AconSab001 UF441199 30.355 -96.142 Cyrtonaias tampicoensis CtamCol001 UF438302 30.526 -98.160 Cyrtonaias tampicoensis CtamCol007 UF441144 31.483 -99.031 Cyrtonaias tampicoensis CtamGua002 UF438301 29.470 -97.491 Cyrtonaias tampicoensis CtamGua003 UF438301 29.470 -97.491 Cyrtonaias tampicoensis CtamGua004 UF441000 28.831 -97.061 Cyrtonaias tampicoensis CtamGua005 UF441000 28.831 -97.061 Fusconaia mitchelli FmitCol010 UF438155 30.659 -99.324 Fusconaia mitchelli QmitCol004 UF438010 29.484 -97.448 Fusconaia mitchelli QmitGua001 UF441081 29.484 -97.448 Fusconaia mitchelli QmitGua002 UF441082 29.484 -97.449 Fusconaia mitchelli QmitGua005 Swab 29.484 -97.449 Fusconaia mitchelli QmitGua006 Swab 29.484 -97.449 Fusconaia mitchelli QmitGua007 Swab 29.484 -97.449 Fusconaia mitchelli QmitGua008 Swab 29.484 -97.449 Fusconaia mitchelli QmitGua009 Swab 29.484 -97.448 Glebula rotundata GrotGua104 UF440905 28.511 -96.819 Glebula rotundata GrotGua105 UF440905 28.511 -96.819 Glebula rotundata GrotGua106 UF440905 28.511 -96.819 Glebula rotundata GrotGua107 UF440905 28.511 -96.819 Glebula rotundata GrotGua108 UF440905 28.511 -96.819 Glebula rotundata GrotGua109 UF440905 28.511 -96.819 Glebula rotundata GrotGua110 UF440905 28.511 -96.819 Glebula rotundata GrotGua111 UF440905 28.511 -96.819 Glebula rotundata GrotGua112 UF440905 28.511 -96.819 Glebula rotundata GrotGua113 UF440905 28.511 -96.819 Glebula rotundata GrotGua114 UF440905 28.511 -96.819

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Table 3-2. Continued Catalog Lat. Long. Taxon Sample ID Number Glebula rotundata GrotGua115 UF440905 28.511 -96.819 Glebula rotundata GrotGua117 UF440905 28.511 -96.819 Glebula rotundata GrotGua118 UF440905 28.511 -96.819 Glebula rotundata GrotGua119 UF440905 28.511 -96.819 Glebula rotundata GrotGua120 UF440905 28.511 -96.819 Glebula rotundata GrotGua121 UF440905 28.511 -96.819 Glebula rotundata GrotGua122 UF440905 28.511 -96.819 Glebula rotundata GrotGua123 UF440905 28.511 -96.819 Lampsilis bracteata LbraCol001 UF441140 30.901 -99.916 Lampsilis bracteata LbraCol005 UF438020 30.901 -99.917 Lampsilis bracteata LbraCol007 UF438104 30.659 -99.324 Lampsilis bracteata LbraCol008 UF438104 30.659 -99.324 Lampsilis bracteata LbraCol009 UF438104 30.659 -99.324 Lampsilis bracteata LbraCol010 UF438104 30.659 -99.324 Lampsilis hydiana LbraGua002 UF438018 30.065 -99.179 Lampsilis hydiana LhydGua006 UF441001 29.470 -97.491 Lampsilis teres LterCol024 UF440997 29.454 -96.396 Lampsilis teres LterCol025 UF440997 29.454 -96.396 Lampsilis teres LterCol026 UF440997 29.454 -96.396 Lampsilis teres LterCol027 UF440997 29.454 -96.396 Lampsilis teres LterGua017 UF440982 28.831 -97.061 Lampsilis teres LterGua018 UF440982 28.831 -97.061 Lampsilis teres LterGua020 UF440982 28.831 -97.061 Lampsilis teres LterGua023 UF440982 28.831 -97.061 Leptodea fragilis LfraCol005 UF441225 31.468 -99.160 Ligumia subrostrata LsubPas001 UF438305 30.632 -88.652 Ligumia subrostrata LsubPas002 UF438305 30.632 -88.652 Ligumia subrostrata LsubPas003 UF438305 30.632 -88.652 Ligumia subrostrata LsubPas004 UF438305 30.632 -88.652 Ligumia subrostrata LsubPas005 UF438305 30.632 -88.652 Megalonaias nervosa MnerGua022 UF438300 29.470 -97.491 Megalonaias nervosa MnerGua025 UF438300 29.470 -97.491 Potamilus purpuratus PpurCol002 UF441141 31.483 -99.031 Pyganodon grandis PgraGal017 UF438299 29.790 -95.624 Pyganodon grandis PgraSab037 UF441215 31.372 -93.516 Pyganodon grandis PgraSab038 UF441219 31.187 -93.554 Quadrula apiculata QapiCol044 UF441088 31.207 -98.688 Quadrula aurea QaurGua001 UF440968 28.531 -97.043 Quadrula aurea QaurGua004 UF440981 28.831 -97.061

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Table 3-2. Continued Catalog Lat. Long. Taxon Sample ID Number Quadrula aurea QaurGua011 UF441085 29.671 -97.696 Quadrula aurea QaurGua012 UF441085 29.671 -97.696 Quadrula aurea QaurGua013 UF441085 29.671 -97.696 Quadrula aurea QaurGua014 UF441085 29.671 -97.696 Quadrula aurea QaurGua015 UF441085 29.671 -97.696 Quadrula aurea QaurGua016 UF441085 29.671 -97.696 Quadrula aurea QaurGua017 UF441085 29.671 -97.696 Quadrula aurea QaurGua018 UF441085 29.671 -97.696 Quadrula aurea QaurGua020 UF441142 29.670 -97.697 Quadrula aurea QaurGua021 UF441257 28.651 -97.433 Quadrula aurea QaurGua022 UF441257 28.651 -97.433 Quadrula houstonensis QhouCol003 UF440989 29.454 -96.396 Quadrula houstonensis QhouCol004 UF441087 31.207 -98.688 Quadrula houstonensis QhouCol005 UF441087 31.207 -98.688 Quadrula houstonensis QhouCol006 UF441087 31.207 -98.688 Quadrula houstonensis QhouCol007 UF441087 31.207 -98.688 Quadrula houstonensis QhouCol009 UF441087 31.207 -98.688 Quadrula petrina QcouGua001 UF441143 29.670 -97.697 Quadrula petrina QcouGua002 UF441143 29.670 -97.697 Quadrula petrina QpetCol022 UF441224 31.468 -99.160 Quadrula petrina QpetCol023 UF441224 31.468 -99.160 Quadrula petrina QpetCol024 UF441226 31.483 -99.031 Quadrula petrina QpetCol025 UF441226 31.483 -99.031 Quadrula petrina QpetCol060 UF438105 30.659 -99.324 Quadrula petrina QpetCol061 UF438105 30.659 -99.324 Quadrula petrina QpetGua001 UF440979 28.831 -97.061 Strophitus undulatus SundCol004 UF438106 30.659 -99.324 Strophitus undulatus SundCol005 UF438106 30.659 -99.324 Toxolasma texasiense TtexGua032 UF440980 29.470 -97.491 Toxolasma texasiense TtexGua035 UF440978 28.831 -97.061 Tritigonia verrucosa QverCol009 UF441208 31.483 -99.031 Tritigonia verrucosa QverCol011 UF441210 31.191 -98.903 Truncilla macrodon TmacCol001 UF440984 29.454 -96.396 Truncilla macrodon TmacCol002 UF440984 29.454 -96.396 Truncilla macrodon TmacCol005 UF441137 31.239 -98.600 Uniomerus declivis UdecSab004 UF438312 30.665 -93.658 Uniomerus declivis UdecSab006 UF441203 31.500 -93.373 Uniomerus tetralasmus UtetCol005 UF438303 30.365 -97.620 Uniomerus tetralasmus UtetCol007 UF438303 30.365 -97.620

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Table 3-2. Continued Catalog Lat. Long. Taxon Sample ID Number Uniomerus tetralasmus UtetCol008 UF438303 30.365 -97.620 Uniomerus tetralasmus UtetCol009 UF438303 30.365 -97.620 Uniomerus tetralasmus UtetCol010 UF438303 30.365 -97.620 Utterbackia imbecillis UimbPas057 UF438304 30.632 -88.652 Utterbackia imbecillis UimbPas058 UF438304 30.632 -88.652 Utterbackia imbecillis UimbPas059 UF438304 30.632 -88.652 Utterbackia imbecillis UimbPas060 UF438304 30.632 -88.652 Utterbackia imbecillis UimbPas061 UF438304 30.632 -88.652 Utterbackia imbecillis UimbPas062 UF438304 30.632 -88.652 Utterbackia imbecillis UimbPas057 UF438304 30.632 -88.652

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Table 3-3. Sample sizes (n), mean and maximum intraspecific p-distances (d), and distance to nearest neighbor species (NN) shown as percentages for taxa included in the F-cox1 barcode library. Species n mean d max d Nearest Species NN Amblema plicata 9 0.44 0.77 Fusconaia mitchelli 10.91 Arcidens confragosus 1 - - Strophitus undulatus 12.23 Cyrtonaias tampicoensis 6 0.47 0.92 Glebula rotundata 10.29 Fusconaia mitchelli 9 0.85 2.22 Amblema plicata 10.91 Glebula rotundata 19 0.05 0.31 Cyrtonaias tampicoensis 10.29 Lampsilis bracteata 6 0.09 0.19 Leptodea fragilis 8.53 Lampsilis hydiana 2 2.13 2.13 Lampsilis teres 7.83 Lampsilis teres 8 0.35 0.61 Lampsilis hydiana 7.83 Leptodea fragilis 1 - - Potamilus purpuratus 6.88 Ligumia subrostrata 5 0.06 0.15 Leptodea fragilis 8.94 Megalonaias nervosa 2 0.15 0.15 Fusconaia mitchelli 11.46 Potamilus purpuratus 1 - - Leptodea fragilis 6.88 Pyganodon grandis 3 0.31 0.46 Arcidens confragosus 12.69 Quadrula apiculata 1 - - Quadrula petrina 8.45 Quadrula aurea 13 0.41 1.03 Quadrula houstonensis 1.25 Quadrula houstonensis 6 0.54 1.63 Quadrula aurea 1.25 Quadrula petrina 9 2.24 4.25 Quadrula aurea 4.47 Strophitus undulatus 2 0 0 Utterbackia imbecillis 11.28 Toxolasma texasiense 2 0.15 0.15 Leptodea fragilis 10.86 Tritigonia verrucosa 2 0.31 0.31 Quadrula petrina 9.55 Truncilla macrodon 3 0.1 0.16 Leptodea fragilis 9.17 Uniomerus declivis 2 0 0 Uniomerus tetralasmus 4.62 Uniomerus tetralasmus 6 0.05 0.15 Uniomerus declivis 4.62 Utterbackia imbecillis 6 1.22 2.29 Strophitus undulatus 11.28 Average 5.17 0.50 0.89 Average 8.44

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Table 3-4. Barcode index number (BIN) assignments based on 124 F-cox1 DNA barcode sequences representing 24 freshwater mussel species known from central Texas. Each taxon was assigned to one of four categories (match, merge, split, or mixture). Sample sizes (n), mean and maximum pairwise uncorrected p-distances, and distance to nearest neighbor species (NN) for each BIN are provided. Mean Max NN Taxa BIN Score n Distance Distance Distance Amblema plicata BOLD:AAA8507 Match 9 0.43 1.32 10.03 Arcidens confragosus BOLD:ACQ0671 Match 1 0.00 0.00 12.23 Cyrtonaias tampicoensis BOLD:AAE7911 Match 6 0.47 0.93 10.24 Fusconaia mitchelli BOLD:ATZ1101 Match 9 0.85 2.23 10.80 Glebula rotundata BOLD:AAF5442 Match 19 0.05 0.31 10.34 Lampsilis bracteata BOLD:ATZ1105 Match 6 0.09 0.19 8.57 Lampsilis hydiana BOLD:ATZ1118 Match 2 2.14 2.14 7.72 Lampsilis teres BOLD:AAF4542 Match 8 0.34 0.62 7.72 Leptodea fragilis BOLD:ADC8698 Match 1 0.00 0.00 6.88 Ligumia subrostrata BOLD:ACZ0248 Match 5 0.06 0.15 8.98 Megalonaias nervosa BOLD:AAX3278 Match 2 0.15 0.15 11.36 Potamilus purpuratus BOLD:AAE3348 Match 1 0.00 0.00 6.88 Pyganodon grandis BOLD:ACY9847 Match 3 0.31 0.45 12.69 Quadrula apiculata BOLD:ACH7152 Match 1 0.00 0.00 8.45 Quadrula aurea BOLD:AAI0636 Merge 13 0.92 2.01 4.46 Quadrula houstonensis BOLD:AAI0636 Merge 6 0.92 2.01 4.46 Quadrula petrina BOLD:ATZ1107 Split 3 0.55 0.86 3.40 BOLD:ATZ1115 6 0.64 1.07 3.40 Strophitus undulatus BOLD:AAI0012 Match 2 0.00 0.00 11.31 Toxolasma texasiense BOLD:AAK1891 Match 2 0.15 0.15 10.86 Tritigonia verrucosa BOLD:AAW7935 Match 2 0.31 0.31 9.41 Truncilla macrodon BOLD:ACH2404 Match 3 0.10 0.16 9.17 Uniomerus declivis BOLD:AAW8486 Match 2 0.00 0.00 4.67 Uniomerus tetralasmus BOLD:ACY9741 Match 6 0.05 0.16 4.67 Utterbackia imbecillis BOLD:ATZ1123 Split 2 0.00 0.00 2.29 BOLD:ACD2688 4 0.00 0.00 2.29

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Table 3-5. Naturally infested host fishes that produced juvenile freshwater mussels. Site numbers follow Table 3-1. The total number of juveniles for each mussel species is shown in parenthesis.

Family and species Common Name Site Collection Date Number of Juveniles Centrarchidae Lepomis auritus Redbreast Sunfish 1 22 May 2012 U. imbecillis (2) 6 29 May 2012 A. plicata (1) T. texasiense (1) Lepomis cyanellus Green Sunfish 1 22 May 2012 U. imbecillis (3) Lepomis macrochirus Bluegill 1 22 May 2012 L. bracteata (1) U. imbecillis (3) 3 29 April 2013 U. imbecillis (1) 6 29 May 2012 A. plicata (1) Lepomis megalotis Longear Sunfish 4 11 June 2013 A. plicata (3) Q. houstonensis (2) 5 30 May 2012 A. plicata (9) T. texasiense (5) 6 29 May 2012 A. plicata (21) T. texasiense (1) Micropterus punctulatus Spotted Bass 5 30 May 2012 A. plicata (4) L. hydiana (2) Q. aurea (1) Micropterus salmoides Largemouth Bass 2 21 Aug 2012 L. hydiana (1) 3 29 April 2013 U. imbecillis (2) 6 29 May 2012 A. plicata (30) L. hydiana (12) Cyprinidae Macrhybosis marconis Burrhead Chub 5 30 May 2012 L. hydiana (1) Pimephales vigilax Bullhead Minnow 3 29 April 2013 U. imbecillis (1) Ictaluridae 3 29 April 2013 Q. petrina (6) Ictalurus punctatus Channel Catfish 5 30 May 2012 Q. aurea (7) 6 29 May 2012 Q. aurea (7) Percidae Etheostoma gracile Slough Darter 5 30 May 2012 A. plicata (2) T. texasiense (1) 6 29 May 2012 A. plicata (4) Etheostoma spectabile Orangethroat Darter 6 29 May 2012 L. hydiana (1) Percina carbonaria Texas Logperch 3 29 April 2013 L. hydiana (1)

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Figure 3-1. Sampling locations in central Texas for fishes that produced juvenile freshwater mussels identified using DNA barcodes.

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Figure 3-2. Neighbor-joining tree based on 124 F-cox1 sequences. Taxonomy, specimen identifiers, and Barcode Index Numbers (BIN) assigned to each taxon are given as BOLD:XXXXXXX. Drainages of collection are abbreviated as follows: Colorado (Col), Galveston Bay (Gav), Guadalupe (Gua), Pascagoula (Pas), and Sabine (Sab).

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Figure 3-2. Continued.

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Figure 3-2. Continued.

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Figure 3-3. Most likely topology generated in the BI analysis with indications of clades containing juvenile mussels recovered from naturally infested fishes. Values above and below the branch lengths equal BI posterior probability and ML bootstrap support, respectively. Colors indicate the number of juvenile mussels recovered and identified from each fish species.

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Figure 3-4. Intraspecific and interspecific uncorrected p-distances with cases of high intraspecific variation and low interspecific divergence indicated.

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Figure 3-5. Scatterplot illustrating the overlap of maximum intraspecific p-distances with the nearest neighbor distances. Points above the diagonal line indicate species with a barcode gap.

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CHAPTER 4 INTEGRATIVE TAXONOMY RESOLVES GENERIC PLACEMENT AND SPECIES BOUNDARIES FOR IMPERILED FRESHWATER MUSSELS

Accurate taxonomy is critical to accurately define and classify biodiversity. In addition, good taxonomy has profound implications for biological inferences regarding biological characteristics, ecological responses, and conservation priorities, among other pursuits (Barraclough and Nee 2001; Mace 2004). Methods used to delineate genera and species continue to evolve and conflicts often reflect different interpretations of available data types (e.g. morphological vs. molecular). Model-based approaches such as multispecies coalescent models (MSC) are also powerful methods (Rannala and Yang 2003) that have been utilized to delimit species boundaries (Leaché and

Fujita 2010; Fujita and Leaché 2011). Recent empirical studies, however, have criticized

MSC models for identifying population structure rather than species boundaries (Hedin

2015; Pfeiffer et al. 2016; Sukumaran and Knowles 2017; Willis 2017), which suggests caution is prudent when basing species hypotheses solely on MSC models. Integrative approaches that draw inference from multiple independent lines of evidence have been called for increasingly (Dayrat 2005; Leache et al. 2009; Knowles and Carstens 2007;

Padial et al. 2010; Schlick-Steiner et al. 2010; Carstens et al. 2013) and reveal that morphological characters or geographic distributions alone are not necessarily diagnostic at the generic and species levels (Jones et al. 2006; Huang and Knowles

2016; Pfeiffer et al. 2016; Perkins et al. 2017).

An example of a high-diversity taxonomic group that has been characterized historically based on distributional patterns and phenotypic diagnostics is freshwater mussels (Bivalvia: Unionidae), which is among the most critically endangered faunas on

Earth. At least 10% of the unionid taxa in the United States are extinct, and 65% of the

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remaining species are considered imperiled (Williams et al. 1993; Haag 2012; Haag and

Williams 2014). Conservation efforts focused on freshwater mussels are complicated by taxonomic uncertainty that stems from limited discrete morphological characteristics that would enable species diagnosis or determination of evolutionary lineages. Molecular phylogenetics has changed the delimitation of freshwater mussel species boundaries dramatically by revealing morphologically cryptic diversity (Roe and Lydeard 1998; King et al. 1999; Lydeard et al. 2000; Jones et al. 2006; Pfeiffer et al. 2016) and demonstrating that morphology-based assessments alone can misguide classification and conservation (Mulvey et al. 1997; Serb et al. 2003; Pfeiffer et al. 2016; Perkins et al.

2017).

New molecular tools, analytical methods, and studies diagnosing other intrinsic traits offer opportunities to further recalibrate unionid taxonomy. This work is urgent when taxonomic uncertainties complicate the development and implementation of conservation and recovery programs. North American unionids in the tribe Quadrulini have been the focus of several generic, species, and population level genetic studies

(Berg et al. 1998; Serb et al. 2003; Roe and Boyer 2015) but a comprehensive sampling using multiple, independently evolving molecular markers is lacking. Recent efforts to compile and expand life history information for several members of Quadrulini (Hove et al. 2012; Sietman et al. 2012; Harriger et al. 2015) coupled with several members of the genus Quadrula being considered for protection under the Endangered Species Act

(ESA) has stimulated interest in revisiting the systematics of the Quadrulini.

Systematics of the Quadrulini has long been a source of taxonomic debate and confusion and remains in a chaotic state at the generic and species level (Simpson

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1900; 1914; Ortmann 1912; Frierson 1927; Vidrine 1993; Howells et al. 1996; Serb et al.

2003; Graf and Cummings 2007; Campbell and Lydeard 2012). Recent taxonomic treatments recognized between five and nine genera in Quadrulini based on authoritative interpretation of morphological and life history characters (Williams et al.

1993; 2008; 2014; 2017; Turgeon et al. 1988; 1998; Serb et al. 2003; Graf and

Cummings 2007; Campbell and Lydeard 2012; Lopez-Lima et al. 2017). Species boundaries are complicated by a variety of morphological and geographic forms that have perplexed systematists for decades, a consequence of high intraspecific variation in shell morphology that often overlaps between species (Valentine and Stansbery,

1971; Neck, 1982; Vidrine et al. 1993; Howells et al. 2002; Serb et al. 2003; Williams et al. 2008). Several taxa that occupy Gulf of Mexico drainages and lower sections of the

Interior Basin of North America were recognized as either distinct species or subspecies of Q. pustulosa by recent taxonomic treatments (Turgeon et al. 1988; 1998; Vidrine

1993; Williams et al. 1993; 2008; 2014; Howells et al. 1996; Graf and Cummings 2007).

Phylogenetic studies placed Q. aurea, Q. mortoni, Q. refulgens, Q. pustulosa, and Q. succissa together within a species complex (i.e. the Q. pustulosa species complex)

(Serb et al. 2003; Szumowski et al. 2012), but the relationship of Q. houstonensis remains untested. These studies also revealed the close relationship between Q. nodulata and Q. petrina and advocated for denser phylogeographic sampling before delineating species boundaries within this species complex (i.e. Q. petrina species complex). Of particular importance is the taxonomic validity of three species being considered for protection under the ESA (USFWS 2011), Q. aurea, Q. houstonensis, and Q. petrina.

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In this study, we investigated relationships within Quadrulini to resolve taxonomic incongruences that have become problematic for conservation efforts. Specifically, we implemented an integrative taxonomic approach that utilized multilocus sequence data, morphometric analyses, and geographic distributions to investigate supraspecific relationships within Quadrulini and species boundaries in both the Q. pustulosa and Q. petrina species complexes. Our findings support the revision of generic-level classification, the synonymy of several geographically isolated taxa, and diagnosis of previously undescribed diversity. Our study highlights the utility of combining multiple lines of evidence with broad taxonomic and phylogeographic sampling to appraise existing taxonomy and discover cryptic species for a highly imperiled group of animals.

Methods

Taxon Sampling and Molecular Data

Sampling concentrated on the following recognized taxa: Q. asperata, Q. aurea,

Q. houstonensis, Q. infucata, Q. kleiniana, Q. mortoni, Q. nodulata, Q. petrina, Q. pustulosa, Q. refulgens, and Q. succissa. Efforts were made to sample throughout the range of each species including type localities. Outgroups from within Quadrulini and two closely related tribes (Amblemini and Pleurobemini) were selected based on relationships resolved in previous phylogenetic studies (Serb et al. 2003; Campbell and

Lydeard 2012; Lopes-Lima et al. 2017). All specimens involved with DNA analyses were sacrificed for vouchering in museum collections.

We utilized two protein-coding mitochondrial (mtDNA) genes and one nuclear

(nDNA) gene for phylogenetic reconstruction: cytochrome c oxidase subunit 1 (CO1),

NADH dehydrogenase subunit 1 (ND1), and internal transcribed spacer 1 (ITS1). Tissue samples were preserved in 95% ethanol and DNA was extracted using a modified plate

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extraction protocol (Ivanova et al. 2006). Primers used for polymerase chain reaction

(PCR) and sequencing were as follows: CO1 dgLCO-1490-

GGTCAACAAATCATAAAGAYATYGG and CO1 dgHCO-2198-

TAAACTTCAGGGTGACCAAARAAYCA (Meyer, 2003); ND1 Leu-uurF-

TGGCAGAAAAGTGCATCAGATTAAAGC and LoGlyR-

CCTGCTTGGAAGGCAAGTGTACT (Serb et al. 2003); ITS1-18S-

AAAAAGCTTCCGTAGGTGAACCTGCG and ITS1-5.8S-

AGCTTGCTGCGTTCTTCATCG (King et al. 1999). The PCR protocol for plate amplifications was conducted in a 12.5 µl mixture: distilled deionized water (4.25 µl),

MyTaqTM Red Mix (6.25 µl) (Bioline), primers (0.5 µl) and DNA template (20 ng).

Bidirectional sequencing was performed at the Interdisciplinary Center for

Biotechnology Research at the University of Florida on an ABI 3730 (Life Technologies).

Geneious v 9.1.5 (Kearse et al. 2012) was used to edit chromatograms and assemble consensus sequences. The mtDNA genes were aligned in Mesquite v 3.2.0 (Maddison and Maddison, 2017) using the L-INS-i method in MAFFT v 7.299 (Katoh and Standley,

2013) and translated into amino acids to ensure absence of stop codons and gaps. The

ITS1 alignment was performed using the E-INS-i method in MAFFT, because of the presence of indels.

Phylogenetic and Phylogeographic Analyses

We estimated phylogenetic relationships using a three gene concatenated dataset (i.e. CO1, ND1, ITS1) for members of Quadrulini using maximum likelihood

(ML) searches in IQ-TREE v 1.5.2 (Nguyen et al. 2015) and Bayesian inference (BI) in

BEAST v 2.4.4 (Bouckaert et al. 2014). Partitions and substitution models for IQ-TREE and BEAST2 were determined by PartitionFinder v1.1.1 (Lanfear et al. 2012). ML

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analyses included an initial tree search before implementing 1000 ultrafast bootstrap

(BS) replicates to estimate nodal support (Minh et al. 2013). BI analyses executed a total of 2x108 generations sampling trees every 1000 generations with an initial 25% burn-in. A relaxed log-normal molecular clock was used on all partitions considering the standard deviation of log rate on branches and the coefficient of variance were greater than 0.1 for all partitions (Drummond and Bouckaert 2015). The relaxed log-normal molecular clock was fixed at 0.34 for the 1st codon position of CO1 (Marko, 2002) and remaining partitions were estimated by BEAST2. Yule process was used as the species tree prior. To ensure adequate sampling, effective sample size (ESS) of all parameters was assessed in Tracer v.1.6 (Rambaut et al. 2014). We used SumTrees in DendroPy v

4.2.0 (Sukumaran and Holder 2010) to estimate a consensus tree with an initial 25% burn-in. We tested for a significant difference between ML and BI topologies in IQ-TREE v 1.5.2 (Nguyen et al. 2015) using K-H (Kishino and Hasegawa 1990), S-H (Shimodaira and Hasegawa 2000), and approximately unbiased (AU) tests (Shimodaira and

Goldman 2002). A significance level of α=0.05 was assumed when interpreting output.

Phylogeographic structure was assessed to visualize the geographic distribution of genetic diversity within and between the members of two species complexes: the Q. pustulosa species complex (Q. aurea, Q. houstonensis, Q. mortoni, Q. pustulosa, Q. refulgens, and Q. succissa) and the Q. petrina species complex (Q. nodulata and Q. petrina). TCS haplotype networks were generated from mtDNA and nDNA independently for each group using PopART 1.7 (Clement et al. 2002). We included samples lacking ITS sequences in the mtDNA haplotype networks, along with

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previously published data on GenBank, to increase sample sizes and to expand phylogeographic coverage.

To further investigate evolutionary relationships, we calculated uncorrected pairwise genetic distances in MEGA7 (Kumar et al. 2016) for CO1, ND1, and ITS1 independently. Sequences were grouped according to drainage as follows: Q. aurea

(Guadalupe and Nueces), Q. houstonensis (Colorado and Brazos), Q. mortoni (Trinity,

Neches, and Sabine), Q. nodulata (Neches, Red, Sabine, and Mississippi), Q. petrina

(Colorado), Q. sp. cf petrina (Guadalupe), Q. pustulosa (Red and Mississippi), Q. refulgens (Pascagoula and Pearl), Q. succissa (Escambia, Yellow, and

Choctawhatchee) (Figure 4-1). Gaps and missing data were treated by pairwise deletion between taxa and each taxon was evaluated for diagnostic nucleotides at each mtDNA locus. Additionally, we conducted an analysis of molecular variance (AMOVA, Excoffier et al. 1992) following 1000 permutations to evaluate inter- and intra-population diversity among members of both the Q. pustulosa and Q. petrina species complexes using

ARLEQUIN (Schneider et al. 1997). These groupings align with the null hypothesis based on current taxonomy (Howells et al. 1996) and were not based on distinct genetic groups or phylogeographic results.

Morphometric Analyses

We collected morphometric data for members of the Q. pustulosa and Q. petrina species complexes by measuring external shell dimensions on all specimens used in genetic analyses and on additional individuals encountered during field surveys. Three morphological measurements were made to the nearest 0.01 mm using digital calipers: maximum length, height, and width. Measurement values were loge-transformed to produce a scale-invariant matrix while preserving information about allometry (Jolicoeur

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1963; Strauss 1985; Kowalewski et al. 1997). Loge-transformed variables were converted into three ratios: height/length, width/length, and width/height. We examined morphological variation using principal components analyses (PCA) in the ggbiplot package (Vu 2011) and canonical variates analyses (CVA) in the package Morpho

(Schlager and Jefferis 2016) using R v 3.3.1. The PCA analyses were performed to test whether morphological groupings were apparent without a priori assignment to a specific group. Canonical variate scores were used for cross-validated discriminant analyses (DA) to test whether morphometric data could assign individuals to geographic groups for the Q. petrina complex or currently recognized species for the Q. pustulosa complex. Additionally, we analyzed morphological variation of loge-transformed variables between the two Q. petrina clades (Colorado and Guadalupe drainages) using a permutational multivariate analysis of variance (MANOVA) in the R package vegan

(Oksanen et al. 2016) using 1000 iterations. A significance level of α=0.05 was assumed when assessing the statistical significance of all tested hypotheses.

Results

Taxon Sampling and Molecular Analyses

Our three-gene molecular matrix consisted of 217 individuals representing 8 genera and 20 species (Table 4-1). Each taxon was represented by CO1 (avg. ≈ 642 nucleotides [nt]), ND1 (avg. ≈ 797 nt), and ITS1 (951 nt with avg. ≈ 49.13% gaps) and the concatenated three gene alignment consisted of 2397 nt. Protein coding mtDNA genes did not contain any gaps or stop codons. The large proportion of gaps in the ITS1 alignment was a consequence of partial duplication in the gene region (294-298 nt) found in Cyclonaias tuberculata, which was previously reported (Campbell et al. 2012).

Five partitions and nucleotide substitution models were selected by Partitionfinder for

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implementation in both IQ-TREE and BEAST: CO1 and ND1 1st position- TrNef+I+G,

CO1 and ND1 2nd position- HKY+I+G, CO1 3rd position- HKY+G, ND1 3rd position-

TrN+G, and ITS1- K80+I+G. Convergence of BEAST runs was supported by ESS>200 for all parameters except ITS1 likelihood (ESS=168) and proportion of invariant sites at

CO1 and ND1 2nd position (ESS=55). All topological tests (KH, SH, and AU) found significant support for the ML topology (p<0.05) compared to the BI topology. A qualitative visual comparison revealed minor topological differences, mostly caused by varied placement of individuals within poorly supported nodes. Comparing topologies of the 50% consensus trees revealed a slight shift in the placement of C. tuberculata, which was basal to clades containing all members of both the Q. petrina and Q. pustulosa species complexes in the BI reconstruction but sister to the Q. petrina species complex for ML. We view this as a minor incongruence that has no impact on the resulting nomenclature and we present ML phylogenetic reconstruction of the concatenated 3-gene matrix containing ML and BI nodal support values (Figure 4-2).

Phylogenetic analyses resolved a paraphyletic Q. petrina, with Q. nodulata nested between two reciprocally monophyletic and geographically isolated Q. petrina clades (Colorado and Guadalupe drainages) (Figure 4-3). In contrast, five of the six recognized species in the Q. pustulosa species complex were not monophyletic in the optimal topology (Figure 4-4). Specifically, Q. succissa was sister to a clade containing

Q. aurea, Q. houstonensis, Q. mortoni, Q. pustulosa, and Q. refulgens. For the Q. petrina complex, totals of 80 and 55 individuals were included in the mtDNA and ITS1 haplotype networks, respectively (Figure 4-3). Three groups are clearly depicted in both networks: Q. petrina from the Colorado River, Q. petrina from the Guadalupe River, and

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Q. nodulata. For the Q. pustulosa species complex, 263 and 114 individuals were included in the mtDNA and ITS1 haplotype networks, respectively (Figure 4-4).

Quadrula succissa was molecularly diagnosable from other taxa and clearly divergent in both the mtDNA and ITS1 haplotype networks. All other species shared ITS1 haplotypes and showed weak phylogeographic structuring among mtDNA haplotypes.

We observed no overlap between intraspecific variation and interspecific divergence in genetic distance among members of the Q. petrina complex (Figure 4-5).

Additionally, all three clades contained diagnostic nucleotides: Q. petrina from the

Colorado River (CO1/ND1 = 4/16), Q. petrina from the Guadalupe River (CO1/ND1 =

4/16), and Q. nodulata (CO1/ND1 = 6/5). However, uncorrected p-distances show a high degree of overlap between intraspecific variation and interspecific divergence among members of the Q. pustulosa complex, with the exception of Q. succissa (Figure

6), which also exhibited diagnostic nucleotides (CO1/ND1 =3/4). None of the other taxa were molecularly diagnosable. The AMOVA results parallel the levels of genetic distances observed in each species complex. The AMOVA for members of the Q. pustulosa complex indicated that genetic variation within species was roughly equal to variation between species, with 52.42% and 51.32% of the variation between all species, and 47.58% and 48.68% within species for COI and ND1, respectively. In contrast, AMOVA between members of the Q. petrina complex revealed high levels of genetic structuring, with 87.45% and 88.98% of the variation between the three species groups and 12.55% and 11.02% within species groups for CO1 and ND1, respectively.

Morphometric Analyses

We measured a total of 3800 individuals from museum and field collections, representing members of the Q. petrina (1387) and Q. pustulosa (2413) complexes: Q.

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petrina from the Colorado (527), Q. petrina from the Guadalupe (849), Q. nodulata (11),

Q. aurea (868), Q. houstonensis (604), Q. mortoni (796), Q. pustulosa (95), Q. refulgens

(10), and Q. succissa (40). PCA eigenvalues explained 99.6% and 100% of the total variability between members of the Q. petrina and Q. pustulosa complexes, respectively

(Figure 4-3; Figure 4-4). The PCA for the Q. petrina complex revealed high levels of morphological variation among individuals within three distinct groups: Colorado River

Q. petrina; Guadalupe River Q. petrina; Q. nodulata. Cross-validated DA scores provided an overall classification accuracy of 80.1% (Colorado River Q. petrina =

77.8%; Guadalupe River Q. petrina = 81.3%; Q. nodulata = 100%). Additionally, permutational MANOVA depicted significant differentiation between C. petrina from the

Colorado and Guadalupe Rivers (α=0.000999). PCA for the Q. pustulosa complex illustrated high levels of morphological overlap between currently recognized species.

Cross-validated DA scores provided an overall classification accuracy of

50.48%. Visualization of the PCA plot and DA scores provided a marginal signal for two groups: Q. houstonensis (47.2%), Q. mortoni (25.9%), Q. pustulosa (61.1%), and Q. refulgens (40.0%); and Q. aurea (74.3%) and Q. succissa (50.0%).

Discussion

Our primary goal was to investigate boundaries among members of two species complexes, using multiple molecular-based analyses and additional lines of evidence

(e.g. morphometrics) to delimit species within an integrative taxonomic framework

(Dayrat 2005; Will et al. 2005). Our broad geographic sampling and phylogenetic analyses identified nine well-supported species-level clades, including two species complexes containing taxa of immediate conservation concern (Figure 4-2; Figure 4-3;

Figure 4-4). Both BI and ML analyses resolved Q. petrina as paraphyletic with regard to

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Q. nodulata. The two divergent Q. petrina clades correspond to individuals sampled from the Colorado and Guadalupe rivers, with the Colorado River clade being sister to

Q. nodulata. This provides credible evidence that species-level diversity is underestimated in this complex. mtDNA sequence divergence exhibited a clear gap between intraspecific variation and interspecific divergence among the three geographically isolated clades (Figure 4-5), indicative of species-level divergence and similar to values reported for several other freshwater mussel species (Roe and

Lydeard 1998; Serb et al. 2003; Jones et al. 2006; Campbell et al. 2008; Inoue et al.

2014; Pfeiffer et al. 2016; Perkins et al. 2017). Sequence divergence at ITS1 was lower relative to both mtDNA loci but consistent with patterns observed in previous studies utilizing these genes (Pfeiffer et al. 2016; Perkins et al. 2017). Morphometric analyses also suggest clear separation of Q. nodulata and the Colorado and Guadalupe Q. petrina clades (Figure 4-3).

Prior to our study, little information was available regarding phylogenetic relationships between members of the Q. pustulosa complex. Previous researchers questioned the validity of taxa in the Q. pustulosa complex because of difficulties distinguishing between morphological forms, geographic variants, and distinct species

(Strecker 1931; Turgeon et al. 1988; 1998; Vidrine 1993; Williams et al. 1993; 2008;

2014; Howells et. al 1996; Graf and Cummings 2007; Watters et al. 2009). For our assessment, we allowed geographic distributions based on current taxonomy to represent the null species hypotheses. Our molecular and morphometric data indicate that current taxonomy overestimates species-level diversity in the Q. pustulosa complex. In fact, our data show greater genetic divergence and morphological

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distinctiveness between the two geographically isolated populations of Q. petrina than between all Q. aurea, Q. houstonenis, Q. mortoni, Q. pustulosa, and Q. refulgens sampled. All five taxa previously recognized as species or subspecies in the Q. pustulosa species complex exhibited extensive paraphyly (Figure 4-4), with no clear distinction between intraspecific variation and interspecific divergence at mtDNA loci or clear signals for diagnosis using morphological characters (Figure 4-4). With the exception of Q. succissa, relationships among mtDNA haplotypes show weak associations with currently recognized taxonomy and several nominal taxa share ITS1 haplotypes (Figure 4-5). Additionally, morphometric analyses illustrated limited ability to distinguish between members of the Q. pustulosa complex using shell measurements.

Specifically, Q. houstonensis, Q. mortoni, Q. pustulosa, and Q. refulgens were indistinguishable. Both Q. aurea and Q. succissa were found to be significantly more compressed than other members of the complex yet only 74% of individuals identified morphologically as Q. aurea were binned correctly, with 25% assigned to Q. succissa.

Our molecular-based analyses, however, do not support the recognition of Q. aurea as a distinct species and we suspect that the observed morphological differences in Q. aurea may be a product of ecophenotypic variation, a common phenomenon in freshwater mussels (Ortmann 1920; Eagar 1954; Zieritz et al. 2010; Inoue et al. 2013;

Bourdeau et al. 2015; Fassatoui et al. 2015; Zajac et al. 2017).

Implications for Taxonomy and Conservation

Our study is the first to analyze extensive phylogeographic and morphometric variation in the Q. pustulosa and Q. petrina species complexes, joining a growing number of empirical studies that show patterns of diversity in freshwater mussels are complex and do not always match expectations based on morphological characters

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(Inoue et al. 2013; 2014; Pfeiffer et al. 2016; Perkins et al. 2017). Considering the lack of diagnosable units across multiple independent lines of evidence, we suggest that Q. aurea, Q. houstonensis, Q. mortoni, and Q. refulgens be designated as synonyms of Q. pustulosa. This expands the distribution of Q. pustulosa from the Pascagoula River drainage west to the Nueces River drainage in south Texas (Figure 4-1). Our phylogeographic assessment shows geographic structuring of populations within Q. pustulosa sensu lato, which provides resource managers with valuable information for future recovery efforts, especially those involving propagation, augmentation, translocation, and reintroduction (Jones et al. 2006; McMurray and Roe 2017).

Additionally, our findings provide compelling evidence for recognition of an undescribed species in the Q. petrina species complex that is endemic to the Guadalupe River.

These taxonomic treatments may impact listing decisions by resource management agencies considering our findings suggest that two species (Q. aurea and Q. houstonensis) may be synonyms of Q. pustulosa, and another species (Q. petrina) contains a cryptic lineage that may represent an undescribed species.

Discussion of Generic-level Relationships

Several recent molecular phylogenies have helped resolve the supraspecific relationships of the Quadrulini (Serb et al. 2003; Campbell and Lydeard 2012), but interpretations of these relationships have led to several incongruent generic-level classifications (Serb et al. 2003; Graf and Cummings 2007; Williams et al. 2008; 2014;

2017; Campbell and Lydeard 2012; Lopez-Lima et al. 2017; Williams et al. 2017). Our phylogenetic analyses support the recognition of six genera within Quadrulini:

Cyclonaias, Megalonaias, Quadrula, Theliderma, Tritogonia, and Uniomerus (Table 4-1;

Figure 4-2). Similar to previous molecular studies, Theliderma was recovered as sister

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to Tritogonia verrucosa and Quadrula s.s. (Serb et al. 2003; Campbell and Lydeard

2012). Tritogonia has been treated either as a synonym of Quadrula s.s. (Serb et al.

2003; Williams et al. 2008) or as a monotypic genus (Graf and Cummings 2007;

Watters et al. 2009; Lopez-Lima et al. 2017; Williams et al. 2017). Our results support the two most recent assessments, which recognize Tritogonia as a monotypic genus distinct from Quadrula s.s. (Watters et al. 2009; Lopez-Lima et al. 2017; Williams et al.

2017).

The genus Cyclonaias has long been considered monotypic and distinguished from Quadrula, Theliderma, and Tritogonia by only brooding larvae in the outer two gills

(Simpson 1900; 1914; Ortmann 1912; 1919; Walker 1918; Williams et al. 2008; Watters et al. 2009). However, Frierson (1927) reported C. tuberculata to brood larvae in all four gills and subsequently described the genus Cyclonaias as “recalcitrant” and playing

“havoc with classification” because of variability in brooding morphology. Furthermore, at least three other species, Q. apiculata, Theliderma cylindrica, and T. verrucosa, have been reported to brood larvae in two or four gills (Simpson 1914; Yeager and Neves

1986; Williams et al. 2008). Phylogenetic relationships do not support previous classifications based on larval brooding morphology indicating that the number of gills involved in larval brooding can vary and may not represent shared ancestral states among genera and species of the Quadrulini (Figure 4-2; Campbell and Lydeard 2012).

We recovered C. tuberculata within a well-supported clade (BS/PP=97/94) that included taxa previously assigned to Amphinaias (Graf and Cummings 2007), Quadrula

(Simpson 1914; Williams et al. 1993; Turgeon et al. 1988; 1998; Serb et al. 2003;

Williams et al. 2008; 2014), Quincuncina (Graf and Cummings 2007), and Rotundaria

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(Campbell and Lydeard 2012) (Figure 4-2). Several genus- or subgenus-level names have been used recently for this group but no consensus has been reached. Graf and

Cummings (2007) resurrected Amphinaias based on the molecular phylogeny of Serb et al. (2003) and the morphological groups of Simpson (1900). Campbell and Lydeard

(2012) resolved C. tuberculata nested within a paraphyletic Amphinaias, and subsequently resurrected the epithet Rotundaria (Agassiz 1852) to represent this clade based on statements in Valenciennes (1827). However, Valenciennes (1827) did not explicitly state that C. tuberculata was the type of Rotundaria. Ortmann and Walker

(1922) clarified this issue pointing out that Herrmannsen (1848) designated Obovaria subrotunda as the type of Rotundaria, relegating Rotundaria to a junior synonym of

Obovaria and recognized Cyclonaias tuberculata. Therefore, treatment of Rafinesque’s type of Unio tuberculata as the type species of Rotundaria is invalid.

The type species of Amphinaias, A. couchiana (Lea 1860), could not be included in this analysis and is thought to be extinct (Williams et al. 1993; Howells et al. 1996;

Turgeon et al. 1998; Serb et al. 2003; Williams et al. 2017). Morphologically, A. couchiana most closely resembles members of Quadrula s.s. and has been allied with this group in previous assessments (Simpson 1900; 1914; Strecker 1931) and we follow

Williams et al. (2017) by supporting the combination Quadrula couchiana. Regardless of the generic placement of Unio couchiana, the inclusion of C. tuberculata in the clade representing 12 taxa, including Q. pustulosa, makes Cyclonaias the oldest name available. The priority of Cyclonaias applies to the generic epithet Pustulosa (Frierson,

1927) as well. Accordingly, we propose that the following 12 species included in this study be assigned to the genus Cyclonaias: C. aurea, C. asperata, C. houstonensis, C.

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infucata, C. kleiniana, C. mortoni, C. nodulata, C. petrina, C. pustulosa, C. refulgens, C. succissa, and C. tuberculata (Table 4-1). This finding follows closely the most recent taxonomic revision of North American unionids (Williams et al. 2017).

Conclusions

In this study, we used an integrative approach that considered molecular, distribution, and morphology data to evaluate relationships within and among several genera of the Quadrulini. Our phylogenetic analyses revealed that morphological and anatomical characters considered to be synapomorphic at the genus-level may have misled prior taxonomy. We used our findings to revise generic-level classifications

(Table 4-1; Figure 4-2). At the species level, congruence across all lines of evidence indicates that current taxonomy overestimates diversity in the Cyclonaias (Quadrula) pustulosa species complex, while underestimating diversity in the Cyclonaias

(Quadrula) petrina species complex. These findings may affect future conservation and management efforts, especially for the three species (C. aurea, C. houstonensis, and C. petrina) under consideration for listing by the US Fish and Wildlife Service (USFWS

2011).

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Table 4-1. Taxa sampled, drainage of collection, and number of sequences for all individuals included in molecular analyses. * denotes taxa within the Cyclonaias (Quadrula) petrina complex and ** denotes taxa within the Cyclonaias (Quadrula) pustulosa complex. Taxa Drainage CO1 & ND1 ITS1 Tribe Amblemini Amblema plicata Colorado 2 2 Tribe Pleurobemini Elliptio crassidens Ohio 1 1 Pearl 1 1 Tribe Quadrulini Cyclonaias (Quadrula) aurea** Guadalupe 30 9 Nueces 39 7 Cyclonaias (Quadrula) asperata Mobile 6 6 Cyclonaias (Quadrula) houstonensis** Brazos 18 12 Colorado 14 7 Cyclonaias (Quadrula) infucata Apalachicola 16 16 Ochlockonee 5 5 Cyclonaias (Quadrula) kleiniana Suwannee 4 4 Cyclonaias (Quadrula) mortoni** Neches 26 10 Sabine 8 6 San Jacinto 9 0 Trinity 15 9 Cyclonaias (Quadrula) nodulata* Mississippi 5 1 Neches 3 0 Ouachita 4 4 Red 1 0 Cyclonaias (Quadrula) petrina* Colorado 33 23 Cyclonaias (Quadrula) sp. cf petrina* Guadalupe 33 27 Cyclonaias (Quadrula) pustulosa** Neosho 4 2 Ohio 9 5 Osage 4 2 Ouachita 16 8 Red 26 11 St. Croix 5 3 St. Francis 12 5 Cyclonaias (Quadrula) refulgens** Pascagoula 5 3 Pearl 5 2 Cyclonaias (Quadrula) succissa** Choctawhatchee 33 9 Escambia 13 2 Yellow 3 3 Cyclonaias tuberculata Tennessee 3 3 Megalonaias nervosa Guadalupe 1 1 Ohio 1 1 Quadrula apiculata Rio Grande 1 1 Quadrula quadrula Ohio 1 1 Theliderma metanevra Ohio 1 1 Tennessee 1 1 Tritigonia verrucosa Ohio 3 1 Red 1 1 Uniomerus tetralasmus Bayou Pierre 1 1 Colorado 1 1

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Table 4-2. Analysis of molecular variance (AMOVA) among members of the Cyclonaias (Quadrula) petrina and Cyclonaias (Quadrula) pustulosa species complexes. Samples were grouped according to current taxonomy. All values were significant (P < 0.0001). Percentage of variance Source of variation CO1 ND1 Cyclonaias (Quadrula) petrina complex Among groups 87.45 88.98 Within groups 12.55 11.02 Cyclonaias (Quadrula) pustulosa complex Among groups 47.58 48.68 Within groups 52.42 51.32

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Figure 4-1. Map showing sampled localities (dots) for members of the Cyclonaias (Quadrula) pustulosa species complex (left) and Cyclonaias (Quadrula) petrina species complex (right). Colors correspond to groups within each complex: C. pustulosa complex - red (C.aurea), green (C. houstonensis), purple (C. mortoni), yellow (C. pustulosa), blue (C. refulgens), and cyan (C. succissa); C. petrina complex - red (C. nodulata), green (C. petrina), and blue (C. sp. cf. petrina).

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Figure 4-2. Maximum likelihood (ML) phylogeny based on concatenated mtDNA and nDNA datasets for Quadrulini. Nodes are collapsed into single species-level clades with revised taxonomy (old generic names in parentheses). Asterisks above and below nodes represent ≥ 99% bootstrap and 0.99 posterior probability support, respectively. Number in parentheses after taxon name indicates sample size.

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Figure 4-3. Comparison of results for members of the Cyclonaias (Quadrula) petrina species complex. Clockwise from the top-left panel: Fully-resolved and expanded phylogeny based on COI, NDI, and ITS1 sequences; COI+ND1 haplotype network; PCA plots with 95% CI ellipses and arrows for biplot variables (HL, height/length; WL, width/length; WH, width/height); and ITS1 haplotype network. Colors indicate the following taxa: Red (Cyclonaias nodulata); Green (Cyclonaias petrina); Blue (Cyclonaias sp. cf. petrina). Black dots on the networks represent missing or unsampled haplotypes.

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Figure 4-4. Comparison of results for members of the Cyclonaias (Quadrula) pustulosa species complex. Clockwise from top-left panel: Fully resolved and expanded phylogeny based on COI, NDI, and ITS1 sequences; COI+ND1 haplotype network; PCA plots with 95% CI ellipses and arrows for biplot variables (HL, height/length; WL, width/length; WH, width/height); and ITS1 haplotype network. Colors indicate the following taxa: Red (Cyclonaias aurea); Green (Cyclonaias houstonensis); Purple (Cyclonaias mortoni); Yellow (Cyclonaias pustulosa); Blue (Cyclonaias refulgens); Cyan (Cyclonaias succissa). Black dots on the networks represent missing or unsampled haplotypes.

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Figure 4-5. Histograms illustrating the distribution of all intraspecific and interspecific pairwise uncorrected-p distances for Cyclonaias (Quadrula) petrina complex (top) and Cyclonaias (Quadrula) pustulosa complex (bottom) based on COI (left) and ND1 (right).

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CHAPTER 5 CONCLUSIONS

In this dissertation, I aspired to initiate a new epoch of biodiversity assessments for freshwater mussels. By utilizing DNA barcoding as a method for i) testing current taxonomy, ii) identifying cases of cryptic diversity, and iii) accounting for misidentifications, I have developed a viable model for modern taxonomic treatment of freshwater mussels that overcomes some of the historical issues associated with dependency on difficult morphological traits. My utilization of the DNA barcoding approach to answer important ecological questions related to the complex life history of freshwater mussels not only expands our knowledge with respect to host fish requirements, but also provides timely information needed to make conservation management decisions. Finally, I demonstrated how DNA barcodes represent an important taxonomic first-step and that multiple independent lines of evidence (e.g. morphology, genetics, behavior, geography) can be integrated to make holistic decisions regarding evolutionary relationships. My results have important implications for the systematics and conservation of a highly imperiled group of freshwater mussels, and have the potential to catalyze a unified effort toward taxonomy and conservation at regional, national, and global scales.

This project began as my Ph.D. dissertation and has evolved into a federal research program focused on understanding the diversity and distributions of unionids.

My efforts to advance science and conservation of freshwater mussels provide more than only publications and scientific reports. For example, beyond publishing my findings in peer-reviewed journals, results of my research have been disseminated through professional workshops, guest lectures, webinars, presentations at professional

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meetings, and online resources (e.g. Barcode of Life Database, US Geological Survey homepage, and ResearchGate). Such outlets have allowed me to connect with a diverse audience of scientists, resource managers, consultants, students, and other members of the general public. This is an important contribution given that many resource managers and non-scientists have difficulties understating the value of characterizing patterns of speciation or quantifying biodiversity within a highly imperiled group of organisms.

Until recently, taxonomic assessments that address unionids relied primarily on morphological characters and biogeographic patterns to delineate species boundaries

(Turgeon et al. 1988; 1998; Williams et al. 1993). Since 1998, modern taxonomy and systematics have routinely utilized molecular characters along with other lines of evidence (e.g. soft anatomy, behavior, geography), which has reopened the question of how many species of freshwater mussels currently exist. In fact, a recent account of

North American freshwater mussels identified > 100 taxonomic changes for 298 species

(Williams et al. 2017), the majority resulting from studies that included DNA evidence.

This underscores the value of taxonomic assessments that incorporate molecular data.

Unfortunately, the vast majority of molecular data for unionids lack associated voucher specimens and adherence to DNA barcode community standards for maintenance of records, voucher specimens, and supporting information. This is what sets the BOLD database apart from GenBank (Ransasingham and Hebert 2007). As of

October 2017, a total of 5,659 COI sequences were available for the Unionidae in

GenBank. The percentage of these with linkable voucher specimens is unknown, but most seem to lack a definitive association with any voucher information. Ambiguous

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DNA sequences linked to vouchers specimens can be evaluated easily to determine whether issues are based on misidentification, laboratory contamination, or taxonomic problems. Without vouchers, the issue remains dubious and can mislead future research. For example, a study that used DNA barcodes to identify juvenile mussels encountered issues with identifications using GenBank data (Boyer et al. 2011). Another case is based on a contaminated sequence has impacted a variety of studies and misguided generic assignments of several taxa within the tribe Quadrulini (Serb et al.

2003; Graf and Cumming 2007; Campbell and Lydeard 2012; Williams et al. 2017;

Chapter 4).

My combined efforts in this dissertation provided the first DNA-compliant barcodes for nearly 1700 specimens representing 81 currently recognized species. This is nearly one-third of the extant unionid diversity known from the United States and Canada.

Moving forward, my goal is to coordinate the creation of a comprehensive DNA barcode library for all North American unionids known as UNIO-BARCODE. The completion of this task will offer powerful tools for expanding our understanding of the diversity, distribution, and ecology of freshwater mussels. This DNA barcode initiative will facilitate fast, reliable species-level identification by specialists and non-specialists alike, and remove a major impediment to the conservation and recovery of remaining freshwater mussel diversity.

Organizing the effort through BOLD provides online access to large-scale collections with high-resolution digital images and comprehensive DNA libraries. It provides an online identification guide that puts results of accurate taxonomy and systematics in the hands of scientists and non-scientists, including the public and

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conservation managers. The online framework provides a resource for teaching workshops and guiding future taxonomic assessments. It also facilitates access to reliable, up-to-date information by allowing collections to be easily curated following changes in taxonomy. Finally, it facilitates collaboration among experts in multiple disciplines (e.g. taxonomists, ecologists, geneticists).

The potential applications for such a DNA library are extensive. Perhaps the most important benefit is to provide a reliable, objective, and rapid approach to mussel identification. Without DNA evidence, identifications rely on expert opinion for identification, creating the so called ‘taxonomic impediment’ (de Carvalho et al. 2007).

Morphology, however, can be difficult to interpret, even by experts, often creating conflicting opinions regarding the identification of a particular individual. Therefore, each regional library has the potential to accomplish the discovery of cryptic species, resolve longstanding taxonomic uncertainties, characterize diagnostic morphological features, and identify all specimens to the species level at any life stage. Given the taxonomic breadth of this endeavor, no single researcher can be expected to carry out this project.

The task, however, can be completed through a network of regional collaborations. The goal is that all researchers involved with freshwater mussel research and conservation management will benefit from this project.

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BIOGRAPHICAL SKETCH

Nathan (Nate) Johnson was born in Salem Ohio in August 1979, the first of three children of John Lewis Johnson, Jr. and Karen May True. He was raised in a rural area in the outskirts of Richmond, Virginia. Nate spent his childhood summers exploring the shorelines of the Rappahannock River at the river home of his grandparents, John

Lewis Johnson, Sr. and Hester Ann Johnson. This is where Nate developed his fascination with aquatic organisms, as he learned to swim, fish, trap blue crabs, and find freshwater mussels. By the age of 16, Nate was spending the majority of his free time exploring forests, swamps, and waterways in eastern Virginia and volunteering at a local U.S. Fish and Wildlife National Fish Hatchery in Charles City, Virginia. He graduated with honors from Highland Springs High School outside Richmond, Virginia in

1997 and then enrolled in the Department of Fisheries and Wildlife Sciences at Virginia

Tech. In 2001, he graduated with a Bachelor of Science degree in fisheries science, with a minor in biology. From 2002 to 2003, Nate researched under the supervision of

Dr. Richard Neves and Dr. Jess Jones at the Freshwater Mollusk Conservation Center in Blacksburg, Virginia, conducting freshwater biological surveys and propagating endangered freshwater mussels for recovery of populations throughout the

Southeastern United States. From 2003-2005, he worked full-time in the Conservation

Genetics Laboratory of Dr. Eric Hallerman at Virginia Tech on a variety of population genetics projects, focusing on conservation of native freshwater fish and shellfish.

During the summer of 2005, he conducted an internship at the National Center for Cool and Coldwater Aquaculture (NCCCWA) in Kearneysville, WV under the direction of Dr.

Yniv Palti. Beginning in the fall of 2005, he enrolled at Virginia Tech as a graduate research assistant, where he pursued a Master of Science degree in fisheries and

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wildlife sciences. He received his master’s degree in August of 2007 and moved to

Gainesville, Florida to pursue his doctorate degree, studying the systematics, phylogeography, and conservation genetics of freshwater mussels at the University of

Florida, under the direction of Dr. James Austin and Dr. James Williams. In August of

2010, Nate accepted a Pathways Student Internship with the U.S. Geological Survey in

Gainesville, Florida, where he worked full-time to build his freshwater mussel research program and continue his graduate studies as time allowed. On April 25, 2015, he married Antonia Francesca Brewster and they became the proud parents of Bryce Alan

Johnson on December 29, 2016. Nate received his Doctor of Philosophy in fisheries and aquatic sciences from the University of Florida in fall 2017 and continued his career with the U.S. Geological Survey Wetland and Aquatic Research Center in Gainesville,

Florida.

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