Popenaias Popeii, a Mussel That Inhabits Desert Rivers in the Southwestern United States

MIAMI UNIVERSITY The Graduate School

Certificate for Approving the Dissertation

We hereby approve the Dissertation

Todd D. Levine

Candidate for the Degree:

Doctor of Philosophy

______

David J. Berg, Advisor

______

Brian Keane, Reader

______

Ann Rypstra, Reader

______

Michael J. Vanni, Reader

______

R. James Hickey

Graduate School Representative

ABSTRACT

CONSEQUENCES OF LIFE HISTORY VARIATION IN FRESHWATER MUSSELS: DEMOGRAPHY AND HOST RELATIONSHIPS

by Todd D. Levine

The evolutionary potential of species resides at the population level. Demographic features, selective pressures, and linkages between populations all influence the evolutionary potential and trajectory of species. Because parasite population dynamics are obligately linked to those of their hosts, parasite evolutionary potential may be dictated by interactions with their hosts. Freshwater mussels in the superfamily Unionoidea are unique among parasites, exhibiting very long free-living stages and are the object of conservation efforts. The study of these mussels provides two-fold benefits: conservation of a unique taxon and provision of data from animals that contrast stereotypical parasites. To examine the population dynamics of mussels, I studied Popenaias popeii, a mussel that inhabits desert rivers in the southwestern United States. I studied the demographics of adult mussels using a 10 year mark-and- recapture dataset, with which I examined the interplay between survival and environmental variables. High flows reduced survival, an effect that may be somewhat ameliorated by the use of habitat refuges. My analysis indicates this population is stable, given that their habitat remains suitable. Second, I studied infestation of fishes by P. popeii and contrasted these results with those from laboratory studies. Many more fishes were identified as potential hosts for P. popeii when only laboratory success was used as a criterion for determining whether they were hosts. Natural barriers to infestation likely reduced the total number of individuals and species that could be infested, reflecting a marked difference between the fundamental and realized niches. Natural infestations must overcome many barriers to infestation including immunology, behavior and phenology. Finally, I examined the population genetic structure of two mussels in the genus Quadrula, whose distributions remain relatively intact. Using mtDNA, I analyzed the relationships between populations ranging from Louisiana to Ontario and Manitoba. Quadrula pustulosa exhibited isolation-by-distance, while the relationship between populations of Q. quadrula could not be predicted by the distance between them. Whereas Q. pustulosa had more haplotypes, Q. quadrula had more substitutions between haplotypes, which may indicate that Q. quadrula is an older taxon. Both species had lower genetic diversities in glaciated regions and contained unique variability in the Central Highlands.

CONSEQUENCES OF LIFE HISTORY VARIATION IN FRESHWATER MUSSELS: DEMOGRAPHY AND HOST RELATIONSHIPS

A DISSERTATION

Submitted to the Faculty of

Miami University in partial

fulfillment of the requirements

for the degree of

Doctor of Philosophy

Department of Zoology

Todd D. Levine

Miami University

Oxford, Ohio

2009

Advisor: David J. Berg

iii

Table of Contents

Chapter 1: General Introduction ...... 1

Chapter 2: Analysis of 10 years of mark-and-recapture data from a critically endangered species of freshwater mussel, Popenaias popeii ...... 7

Introduction ...... 7

Methods ...... 9

Habitat Assessment...... 9

Mark-and-Recapture ...... 9

Population Size Structure ...... 11

Results ...... 12

Mussel Habitat ...... 12

Population Dynamics ...... 13

Size Structure ...... 14

Discussion ...... 15

Habitat use and survival ...... 15

Age structure ...... 16

Conservation Implications ...... 18

Literature Cited ...... 20

Chapter 3: Fundamental and realized niche breadth in mussel-host relationships, field studies of the infestation of fishes by Popenaias popeii ...... 32

Introduction ...... 32

Methods ...... 34

Results ...... 35

Fish Abundance ...... 35

Infestation Prevalence ...... 36

Infestation Intensity ...... 36

Relative Host Suitability ...... 37

Discussion ...... 37

Literature Cited ...... 43

Chapter 4: Comparative phylogeography of two closely related, common mussel species ...... 54

Introduction ...... 54

Methods ...... 57

Collection Sites and Methods ...... 57

Sequencing COI mtDNA ...... 57

Results ...... 59

Discussion ...... 61

Phylogeography and Glacial History ...... 63

Conservation Implications ...... 64

Literature Cited ...... 66

Chapter 5: General Conclusion and Synthesis ...... 82

Literature Cited ...... 86

Appendices ...... 91

iii

List of Tables Table 2-1. All two-population (sites 1 and 2) models ...... 30

Table 2-2. Model results for four-population models ...... 31

Table 3-1. Fishes studied for determination of host status...... 47

Table 4-1. Partitioned genetic variation from Analysis of Molecular Variance...... 71

Table 4-2.Summary of differences in genetic structure observed between the two species of Quadrula...... 72

List of Figures Figure 1-1. Conceptual diagram of mussel life stages and dispersal ...... 5

Figure 1-2. Map showing the location of the Black River...... 6

Figure 2-1. Velocities at which P. popeii was found in the initial surveys during 1997-98...... 24

Figure 2-2. Model-averaged apparent survival estimates ...... 27

Figure 2-3. Length-frequency distributions...... 28

Figure 2-4. Observed growth increments are plotted against the length of the mussel...... 29

Figure 3-1. Conceptual diagram of ecological and physiological host relationships ...... 49

Figure 3-2. Total number of individuals caught and number of individuals carrying cysts consistent with infestation...... 51

Figure 3-3. Number of glochidia encysted on all preserved fishes...... 52

Figure 3-4. Three-dimensional diagram of the major factors influencing the demographic importance of host fishes...... 53

Figure 4-1. Sites from which freshwater mussels were sampled ...... 73

Figure 4-2. Haplotype accumulation curves for both Quadrula species...... 75

Figure 4-3. Mismatch distributions for both Quadrula species,...... 76

Figure 4-4. Intra-specific relationships between pairwise geographic and genetic distances...... 77

Figure 4-5. Pairwise Fst comparisons between Quadrula pustulosa and Q. quadrula ...... 78

Figure 4-6. Intrapopulation measures of haplotype richness...... 79

Figure 4-7. Genetic landscape interpolation...... 80

Figure 4-8. Genetic distances versus latitude...... 81

Figure 5-1. Strip plots of demographic data...... 90

ACKNOWLEDGEMENTS My thanks to Chris Barnhart, who introduced me to unionid mussels as an undergraduate. Without the help of Brian Lang, I would neither have had the opportunity nor ability to study mussels in New Mexico. For his assistance, friendship and ongoing support, I am very grateful. Stephanie Carman also provided feedback, camaraderie and important ideas, while in New Mexico. Chuck Hayes and Nik Zymonas provided substantial assistance in my research in New Mexico. The staff of Dexter National Fish Hatchery was kind, helpful and improved both my project and my time in New Mexico. Many thanks to the many private landholders who allowed me access and to the Davis family, who also gave me many stories and thoughts about the river. In addition, summer interns Theresa Hyde, Colleen Heenan, Adam Barkalow, Brianna McGuire and Stephani Clark worked alongside of me to collect the data that we hope will help to conserve P. popeii. In addition, I have had extensive help and support from many colleagues who have provided varied and invaluable assistance. For friendship, professional advice and unwavering support, Makiri Sei and Chad Hoefler have my enduring thanks. Rick Seidel has been a good friend and valuable colleague through many hard times in the field and lab alike. Curt Elderkin provided a detailed introduction to genetic techniques along with Jan Trybula. My thanks to John Bailer, Ben Bolker, Mark Miller, Hank Stevens and Allan Strand for their patience and expertise as they passed on statistical knowledge. Thanks to the colleagues who helped me to collect specimens from a nearly untenable geographic extent Joe Carney, Alan Christian, Jacob Culp, Kevin Cummings, Mike Davis, Scott Faiman, Ed Hartowitcz, Jessica Hoisington, Dan Hornbach, Monte McGregor (who also taught me blood collection techniques), Steve McMurray, Emy Monroe, Todd Morris, Dan Sallee, and Jeremy Tiemann. And my apologies to those whose names I may have unavoidably, but accidentally excluded here. Others colleagues, although under my mentorship, have provided invaluable and irreplaceable assistance, including (in chronological order): Nancy Benight, Meghan Saxen, Phil Iffland and Chris Webber. I also have many friends and colleagues who, although some have been outsiders to professional science, have provided invaluable help along the way. David Laughlin helped me to edit my work. Andrew Benz helped with analytical programming. Both provided moral support when it was needed most. My parents, in addition to supporting and encouraging my career choice, have helped in field work. Their generosity and insight were critical to all successes that I have had. Beth Dickman (Mette), Gary Gerald, Lesley Knoll, Molly Steinwald, Jim Stoeckel, Lisette Torres, Shawn Wilder, Kerri Wrinn, and other graduate students have provided me with friendship and professional support without which I could not have completed this dissertation. For financial support, I thank the National Science Foundation (two years of assistantships under the LABS program), New Mexico Department of Game and Fish and Miami University, especially the Department of Zoology. My thanks to my committee, for patiently suffering through my dissertation, inviting me to participate in their lab functions personal and professional and supporting me throughout. Thanks also to my advisor for providing me freedom to pursue many different avenues and providing guideposts to help me to grow as a scientist.

Chapter 1: General Introduction

The evolutionary potential of species resides at the population level. Demographic features (Orzack and Tuljapurkar 1989), selective pressures (Huey et al. 2000), and linkages between populations (Dieckmann et al. 1999) all influence the evolutionary potential and trajectory experienced at both the population and species levels. These population features determine the changes in genotypic and phenotypic traits of species over time. Because parasite population dynamics are obligately linked to those of their hosts (Arneberg et al. 1998), parasite evolutionary potential may be dictated by such interactions. Thus, evolutionary change in parasite populations is often shaped by the movement of hosts and further modified by demographic and epidemiological parameters (Gandon and Michalakis 2002). Most freshwater mussels in the superfamily Unionoidea undergo a period of obligate, larval attachment to a vertebrate host (Bauer 1998). This attachment to hosts has often been assumed to be parasitic in nature. When compared to other parasites possessing free-living stages (e.g. botflies, Catts 1982), freshwater mussels exhibit extraordinarily long free-living stages and often have longevities exceeding those of their hosts. Estimated maximum longevities of freshwater mussels range from just under a decade (Hanlon and Levine 2004) to nearly two centuries (Ziuganov et al. 2000). One of the most comprehensive reviews of freshwater fish life history, in contrast, reported mean longevities ranging from just 7 years (s.d. = 4.3) to 24.4 years (s.d. = 15.1) (Winemiller and Rose 1992). Such extreme longevity may allow freshwater mussel populations to survive for periods when hosts are absent, increase the timescale on which evolution occurs, and allow them to take advantage of relatively unpredictable host populations. Because of these distinct life-history features, unionid mussels are an excellent taxon to contrast with stereotypical parasites and with which to examine the interplay between demography, genetics and, ultimately, evolution. Moreover, mussels are unique amongst animals possessing parasitic habits in that they are objects of conservation concern. Within the last few decades, mussels have been identified as being among the most endangered species in North America (Ricciardi and Rasmussen 1999). Populations of adult freshwater mussels have been exploited for both pearl buttons and seed pearls, making them

` economically important (Anthony and Downing 2001); this exploitation, along with habitat alteration and, potentially, declines in available hosts, has caused a marked decline in freshwater mussel populations. Conservation activities, such as ex situ culturing and reintroductions of mussels have become more widespread, bringing with them research opportunities, funding and public interest. While captive populations provide opportunities for intensive study, they also increase the potential for anthropogenic alteration of ecological associations, such as those with fish hosts and therefore, the evolutionary trajectories of these species. To avoid the pitfalls associated with intensive conservation action, study of both endangered and intact mussel populations is required. Understanding both short-term demography and long-term evolutionary potential of mussel populations is critically important. Because research on the basic ecology of these species has been secondary to research dealing with commercial exploitation and immediate conservation concerns, a great deal of work remains to be done to understand mussel ecology. Of particular importance is the identification of general patterns in the demography, dispersal and genetic features of mussel populations (see Figure 1-1). Without this information, it will be difficult or impossible to take advantage of the unique ecology of mussels to provide insight into basic research questions, or to mount effective conservation efforts to protect these animals. I propose to increase the understanding of intrinsic demography and autecology of mussels, of patterns in host exploitation and its demographic consequences, and of how broad-scale, host-mediated dispersal shapes intra- and inter- population genetic structure. For the first two of these goals, I have used case-studies of a critically endangered New Mexican mussel, Popenaias popeii. This species is of conservation concern and has been monitored for over a decade. Taken together, the need for conservation and history of intensive monitoring of P. popeii make ecological patterns both critically important and possible to describe (Chapters 1 & 2). Because it is the only mussel in the locations where I studied it, clear and unambiguous identification of glochidial cysts on the fishes of the Black River is possible (Chapter 2). For the final goal, I have used two congeneric, broadly distributed mussels that remain common, Quadrula pustulosa and Q. quadrula. I did not use P. popeii to address the final goal for two reasons - 1) the only large population of P. popeii occupies a 14-kilometer stretch of one river, with <30 individuals found elsewhere in its range (Figure1- 2) and 2) the patterns of demographically intact mussels can be more informative

` in predicting and understanding the genetic patterns of mussels than studying endangered species’ patterns, which may already be altered by population declines.

Literature Cited

Anthony J. L., and J. A. Downing. 2001. Exploitation trajectory of a declining fauna: a century of freshwater mussel fisheries in North America. Canadian Journal of Fisheries and Aquatic Sciences 58:2071-2090.

Arneberg P., A. Skorping, B. Grenfell, and A. F. Read. 1998. Host densities as determinants of abundance in parasite communities. Proceedings of the Royal Society of London Series B- Biological Sciences 265:1283-1289.

Bauer, G. 1998. Characterization of the Unionoidea (=naiads). pp. 3-4 in Bauer, G. and K. Wachtler (editors). Ecology and evolution of the freshwater mussels: Unionoidea. Springer, Berlin, Germany.

Catts, E. P. 1982. Biology of the New World bot flies: Cuterebridae. Annual Reviews in Entomology. 27: 313-338.

Dieckmann U., B. O'Hara, and W. Weisser. 1999. The evolutionary ecology of dispersal. Trends in Ecology & Evolution 14:88-90.

Gandon S., and Y. Michalakis. 2002. Local adaptation, evolutionary potential and host-parasite coevolution: interactions between migration, mutation, population size and generation time. Journal of Evolutionary Biology 15:451-462.

Hanlon S. D., and J. F. Levine. 2004. Notes on the life history and demographics of the savannah lilliput (Toxolasma pullus) (Bivalvia : Unionidae) in University Lake, NC. Southeastern Naturalist 3:289-296.

Huey R. B., G. W. Gilchrist, M. L. Carlson, D. Berrigan, and L. Serra. 2000. Rapid evolution of a geographic cline in size in an introduced fly. Science 287:308-309.

Orzack S. H., and S. Tuljapurkar. 1989. Population-dynamics in variable environments. 7. The demography and evolution of iteroparity. American Naturalist 133:901-923.

Ricciardi A., and J. B. Rasmussen. 1999. Extinction rates of North American freshwater fauna. Conservation Biology 13:1220-1222.

Winemiller K. O., and K. A. Rose. 1992. Patterns of life-history diversification in North American fishes - Implications for population regulation. Canadian Journal of Fisheries and Aquatic Sciences 49:2196-2218.

Ziuganov V., E. San Miguel, R. J. Neves, A. Longa, C. Fernandez, R. Amaro, V. Beletsky, E. Popkovitch, S. Kaliuzhin, and T. Johnson. 2000. Life span variation of the freshwater pearl shell: A model species for testing longevity mechanisms in animals. Ambio 29:102-105.

Chapter 4

Chapter 3

Population 1

Chapter 2

Population 2

Figure 1-1. Conceptual diagram of mussel life stages and dispersal. Boxes indicate the portions of the mussel life cycle on which my dissertation focuses.

Black River

Pecos River

Rio Grande

Figure 1-2. Map showing the location of the Black River. The solid black circle indicates the approximate location of the sites at which I studied P. popeii: a 14 km stretch of the Black River in southeastern New Mexico. The shaded area represents the approximate historical range of P. popeii. The red circle indicates the approximate location of the only other extant population of P. popeii known in the United States.

Chapter 2: Analysis of 10 years of mark-and-recapture data from a critically endangered species of freshwater mussel, Popenaias popeii

Introduction Desert rivers are hydrologically unique ecosystems that challenge their inhabitants to adapt to a harsh and highly variable environment. The flow regime in these systems drives the population biology and life-history evolution of invertebrate inhabitants (Lytle 2002). In permanently inundated streams, flooding is the primary hydrological disturbance threatening inhabitants. High flow events may have devastating impacts on benthic invertebrates, reducing their biomass by as much as 98% (Fisher et al. 1982). Alternatively, the drying of ephemeral streams also leads to loss of aquatic communities. Some invertebrates, such as insects, respond to these disturbances by emigrating and returning once the disturbance has ended, whereas other organisms move to safer habitats within the same stretch of river, such as deep pools (Lytle and Poff 2004). The relationships with these extraordinary flow events are less well-known for long- lived, less vagile organisms. Understanding the demography of endangered taxa is a critical step in the formulation of effective conservation actions. Freshwater mussels (superfamily: Unionoidea) are amongst the most endangered freshwater animal taxa in North America (Ricciardi and Rasmussen 1999). Many methods have been developed to elucidate life history patterns and to provide estimates of demographic parameters for mussels. Measurements taken at a single point in time (e.g. Slade and Blair 2000), or repeated measurements taken without reference to individuals (e.g. repeated population surveys, Payne and Miller 2000) may be informative to assess age structure or current abundance. Length-frequency histograms have often been employed in studies of both mussels (Payne and Miller 2000) and fishes (Devries and Frei 1996) to identify cohorts of similarly aged individuals. However, better estimates of population parameters such as survival can be obtained using mark-and-recapture techniques, even when the organisms are sessile (Alexander et al. 1997). These parameters include vital rates of populations and the influence of “nuisance” parameters, such as recapture probability; thus mark-and-recapture studies may be a critical component of monitoring programs that seek to estimate so-called vital rates. Mark-and- recapture methods offer substantially improved estimates for the study of mussels because unionids are not completely sessile; juveniles and adults exhibit epi- and endobenthic 7

` movements (Amyot and Downing 1991, Amyot and Downing 1997, Amyot and Downing 1998), which may affect the number of individuals recovered in any given survey. Thus, methods that do not account for recapture probability may lead to underestimates of population size or inaccurate descriptions of other demographic parameters. While mark-and-recapture techniques have long been used for surveying mussels (Isley 1914), only a few contemporary studies have employed these methods (Hart et al. 2001, Villella et al. 2004). Combined with other standard methods, such as estimation of length-frequency relationships, these methods can provide informative assessments of many populations. In the United States, the Texas hornshell mussel, Popenaias popeii (Lea, 1857) historically occurred throughout the Rio Grande Basin from Roswell, New Mexico in the Pecos River basin, downstream to Brownsville, Texas. It is now relegated to two isolated populations in the U.S., occupying approximately 5% of its historic range there: the Black River in Eddy County, NM (Lang 2001), and a short reach of the lower Rio Grande in Webb County, Texas (Strenth et al. 2004). Its range is currently unknown in Mexico. Due to this restricted distribution, P. popeii is listed as endangered in New Mexico and is a federal candidate (priority 2) for listing under the Endangered Species Act (Federal Register). Many commonly used methods for studying mussel populations (see Strayer and Smith 2003) are intractable for use with this species because P. popeii colonize flow-protected areas (e.g., under bedrock shelves, beneath undercut banks, behind boulders) rendering their detection difficult (personal observation Brian K. Lang and TDL). When compared to stream systems in the eastern United States that contain most of the continent’s freshwater mussel diversity, desert streams are characterized by more extreme hydrological variability, i.e. “flashiness” (Grimm et al. 1997). Because mussels in desert systems experience more variable conditions, their reactions to extreme flows are likely to be distinct from those of other mussels. For example, a typical response of mussels to flow is to alter burrowing behaviors (Schwalb and Pusch 2007); however in high-scour situations such responses may be limited due to reduced benthic material into which animals can burrow. Initial surveys, conducted in 1997 and 1998, located extant populations and characterized habitat affinities of P. popeii. Three areas of dense mussel aggregation identified in these surveys were subsequently monitored for 10 years, except a population that was destroyed by a

` flood and replaced. In this paper, I first examine microhabitat use in P. popeii, particularly as it relates to flow. Second, I present results from the first decade of mark-and-recapture analysis of this system. Because P. popeii primarily inhabit flow-protected areas (“flow refuges”, sensu Strayer 1999), I predicted that flow would be an important variable describing survival, and that models of survival would be more parsimonious when they contained some measure of flow or discharge. By using a mark-and-recapture framework, I was also able to separate the effects of recapture probability from the estimates of survival. Finally, I used data derived from length- frequency analysis to examine population structure of P. popeii in the Black River. Results of this study were then used to evaluate the status of this species in New Mexico. Methods Habitat Assessment In 1997 and 1998, aquatic habitats were sampled in the Black River using timed-searches and tactile sampling to determine the habitats mussels occupied. Habitat parameters that were recorded when mussels were recovered included: riverine macrohabitat type (pool, run, travertine terrace), location within the river channel (midstream, undercut bank, crevice, under boulder), and substrate type as described by Strayer (1999): i.e. the general index of dominant grain size (such as, sand, gravel and cobble). Each time a mussel was captured during this survey, water velocity (m/sec) and depth (±1 cm) were measured with a Marsh-McBirney Flowmate7™ and metric topset rod. Six physicochemical parameters were measured with a Hydrolab (Loveland, CO): temperature, dissolved oxygen (DO), specific conductance, salinity, total dissolved solids (TDS), and pH. Mark-and-Recapture During the habitat survey in 1997, a mark-and-recapture study was also initiated at three sites (designated 1, 2, and 3), where large aggregations of P. popeii (>>1 mussel/m2) occurred in flow refuges. After several large floods in 2000 destroyed Site 3, a fourth study site upstream of the others was established in 2003. Disturbance to mussel aggregations was minimized by limiting surveys to one annually in September or October 1997, 1998, 2000, 2002-2006. This sampling period occurred after the May-August breeding period (Smith et al. 2003). We carried out three additional surveys during May and June 2005 and 2006, referred to collectively as “spring surveys.” The very short

` interval (1 week) between spring surveys allowed us to assume that populations were closed, i.e. there was no immigration, emigration, recruitment or death, and therefore, we could estimate recapture probability. For the purposes of analysis, I collapsed encounters from the spring surveys into a single event, i.e. if animals were seen once during the spring surveys, they were recorded as being present. During each survey, we searched all sites thoroughly for mussels. Starting in 1997, unique numbers were assigned to individuals by embedding an oval (4 x 10 mm) Floy laminated flex tag in Super Glue Gel along the valve hinge posterior to the umbo. Standard valve measurements were recorded (± 0.1 mm) for all mussels (new captures, recaptures), except during the spring surveys, prior to returning mussels in a natural orientation to their points of capture. I determined maximum and minimum discharge during each interval between surveys using discharge data from the USGS stream gauge downstream of the study sites on the Black River above Malaga (gauge number 08405500). . I also determined the median, 75th and 90th percentile discharge for the entire period of record (USGS data collection begun 1 January 1947) and the number of days that fell above each of these values for each interval between censuses. Using likelihood-based comparisons between models, I evaluated the relative power of temporal stochasticity, stream flow (maximum discharge, minimum discharge), days above discharge levels, and location to describe survival (see model structures Table 2-1). Using an implementation of Cormack-Jolly-Seber models for open population captures in Program MARK (White et al. 2006, White 2008), I created models of survival and recapture probability for the two locations for which we have census data between 1997 and 2006 (sites 1 and 2). I estimated survival by site, by year, and with hydrologic variables, separately and in combination. The hydrologic variables included maximum discharge, minimum discharge, days greater than 90% of flows in the period of record, days greater than 75% of flows in the period of record, and days greater than the median discharge. I used differences in the Akaike Information Criterion (AIC) between competing models to determine the most parsimonious models (i.e., those that best described the dataset while minimizing the number of parameters), where smaller AIC values indicate greater parsimony (Anderson and Burnham 1999). I modeled measures of discharge as covariates of survival, and incorporated stochastic variability between time intervals and among sites. To ensure that the patterns in the data were adequately represented, a general

` model with survival estimated for all site-by-year combinations was produced and a measure of overdispersion (greater observed variance in the dataset than expected), c-hat, was estimated using the bootstrapping method employed by program MARK. A c-hat value of one indicates perfect fit to the model, while larger values of c-hat indicate overdispersion in the data, given the model. I employed Akaike Information Criteria coefficients (AICc), to evaluate the best model. If overdispersion was detected in the most parameterized model, I used the c-hat adjustment to convert AIC to quasi-AIC (QAIC) in program MARK (Anderson and Burnham 1999). Because I do not assume that a “true” model is included in the dataset, we used AICc to identify the best- fitting model(s) within the set of models. I tested the hypothesis that some models estimated apparent survival and recapture probability “significantly” more parsimoniously than the other models in the set. I estimated only apparent survival and recapture probability, because we did not have a sufficiently large sample size to confidently estimate other demographic parameters from this dataset. The models were ranked from lowest to highest AIC (or QAIC, if a correction for overdispersion was required), which is the difference between the “best” or lowest AIC and the AIC of the model in question. Thus, the lowest AIC represents the most parsimonious model in the set. The “rules of thumb” proposed by Burnham and Anderson (2002) were used to evaluate competing models. These rules state that those models with a AIC of less than 2 are the most parsimonious, while models with a AIC greater than 2 are less likely. Models between 2 and 7 are less likely but have considerable support and models with AIC > 7 may be discarded as being far less likely than those with lower AIC. Also, this method does not consider models to be mutually exclusive, but rather identifies the factors most likely to be important to survival. To describe survival using the dataset for sites 1 and 2, I created a total of 30 competing models. I created another set of 30 candidate models describing survival based on the same predictors, but using data from all four locations. In this latter set, I set survival to zero for the Site 3 population destroyed in 2000 and set the recapture rate to zero prior to the onset of sampling of Site 4 in 2002. Population Size Structure For each year, I examined the length-frequency distribution of mussels at each site separately and by pooling all sites. I analyzed the shape of the length-frequency distributions using chi-squared tests. To conduct these comparisons, I compared the frequency of individuals

` captured in length categories (10 mm intervals) between sites and between years. I calculated expected values for the chi- square test as the average of the proportion of mussels in each size class from all sites or all years. This test determined whether the frequency of individuals in these size classes differed among sites or years. To avoid the problems associated with low expected values in chi-square tests, I used a permutation test to assess significance (see Hope 1968). I also used a linear mixed effects model to describe individual growth rates. I regressed growth increments onto mussel length at the beginning of the increment. By using a linear mixed effects model, I also accounted for variation among individuals, which was accomplished using the NLME package in the R Project (R Core Development Team). Results Mussel Habitat Within the initial 48 km stretch of river surveyed in 1997 and 1998, live P. popeii were found at 41 sites in a 14-km reach of the Black River between two low-head dams. The 92 mussels captured in this initial survey occurred most frequently in lotic macrohabitats consisting of bedrock (56.6% of captures) or shallow runs (19.7% of captures). Deep pools, common in the Black River, were the habitat for 23.7% of captures. Within the river channel, P. popeii were occasionally found singly in mid-stream (3.7% of captures), but were most often aggregated and associated with some form of structural cover, with undercut banks and boulders representing 55.5% and 34.1%, respectively, of all captures. Difficult-to-sample areas (riverbed crevices, fissures and shelves) accounted for 6.7% of all channel occurrences. Microhabitat conditions at mussel occurrences consisted of shallow (mean depth = 0.73 ±0.23 m (SD), range = 0.23-1.38 m), low velocity (0.03 ± 0.04 m/sec., 0.00-0.23 m/sec., Fig. 2-1) waters where clay (61.6% of P. popeii occurrences) and mixed substrata (35.5%) provided suitable media for embedded mussels. Live P. popeii occurred less frequently within the thalweg of the Black River where more coarse- grained substrata predominated (total captures: gravel, 2.6%; cobble, 1.0%). Physically unstable aqueous silt was seldom colonized by P. popeii (0.3 % of captures). No mussels were found in shifting sand. Physicochemical conditions (mean ± SD, range) observed at the 41 sites where mussels were recovered were: temperature = 22.6 ±5.8ºC, 5.5 − 32.3; pH = 8.0 ± 5.8, 7.3 − 8.5; specific conductance = 1647 ± 661µS/cm, 686 − 4820; salinity = 0.9 ± 0.4ppt, 0.3 − 2.7; TDS = 1054 ± 439 mg/l, 393 − 3085; DO = 6.9 ± 2.3mg/l, 3.4 − 13.8.

Population Dynamics A total of 295 unique individuals were marked over the ten-year period. Over 60 percent of marked mussels were recaptured at least once during the study period. Thirteen individuals captured in 1997 or 1998 were re-captured in the 2006 surveys; therefore, these animals were a minimum of 9 years old at the end of the study. Mussels marked in this study ranged in size from 44.7 to 123.4 mm, with only three smaller than 60 mm. The most parameterized model describing only sites 1 and 2 from 1997-2006 exhibited some evidence of overdispersion, with a c-hat value of 2.39. Therefore, I used QAIC to correct for this overdispersion (Table 2-1). Models that incorporated both random variation between years and hydrologic variables were most parsimonious. Within this group of models, all of the hydrologic variables used were equivalently parsimonious. A model with variation in survival between years, without a hydrologic variable, was also included in the set of models with QAIC < 2. Location was not as important in predicting survival, because it was not included in any of the most parsimonious models (those with a QAIC of 0). Although it was also included in models with QAIC of < 2, these had higher QIAC values than other models in this group. Thus, while location may affect survival among these populations, models including site are not among the most parsimonious models, i.e. those models that best describe the data using the least variables to do so. No overdispersion was detected for the model set that described survival in all four populations (c-hat = 1.18). Of the 30 survival models produced, 12 comprised the set of likely models (AIC < 7). Three of these models were included amongst the most parsimonious (see Table 2-2, AIC < 2). These models all incorporated some time dependency, i.e. survival estimates could not be grouped among years because of inter-annual variation that was not explained by hydrologic covariates. The most parsimonious set of models (AIC < 2, Burnham and Anderson 1999) contained both hydrologic parameters and time. Estimates of annual apparent survival, the probability of surviving and remaining within the sampled area, from the 4 population models ranged from 60 to over 90 % and declined with increasing flow (Fig. 2-2). Overall, recapture probability was high, averaging 72%, and varying between 6 and greater than 98 %. The hydrologic variable that exhibited the steepest slope when plotted against survival and fitted with a best-fit line was maximum discharge, which exhibited a negative

` relationship with survival. A slight positive relationship was observed between apparent survival and the number of days with discharge greater than 75% of maximum discharge and minimum discharge. Apparent survival varied between years, but did not exhibit any discernable trend (e.g. decreasing over time) over the course of the study. This mark-and-recapture monitoring also allowed the observation of substantial changes in habitat throughout the study. The destruction of Site 3 after high-flows in the 1998-2000 sampling interval is consistent with the incorporation of models including maximum flow in the set of most parsimonious models. The high flow recorded at the USGS gauging station upstream of Malaga, although above the 99th percentile of all flows, was just above the 50th percentile of peak flows, as recorded by the USGS gauging station. After the destruction of the habitat, despite many return sampling trips, mussels could not be located in Site 3. I have observed large changes at other locations, where banks or shelves collapse, drastically changing the number of mussels that can be recovered there after high-flow events. In many surveys, I have observed dead animals, both marked and unmarked, broken or with valves disarticulated. These animals were typically recovered downstream of the survey sites. Size Structure Length-frequency histograms (Figure 2-3) indicated that most mussels in the population were 90-120 mm long (85.3% of individuals, on average, in each year). A unimodal distribution of sizes with a right skewed mode and long left tail was observed in all years, and shape was similar among years when all sites were considered together (p > 0.1, in all cases). Only site 2 differed significantly in length frequency with other locations. It was consistently different from the other two sites after 2003 (2004, χ2 = 19.5, p < 0.005; 2005, χ2 =23.0, p < 0.005; 2006, χ2 = 24.9, p <- 0.005).The difference between this population and the others is attributable to a shift in the mode to one size class smaller. Amongst 183 mussels found and measured in consecutive surveys, annual growth increments ranged between 0.1 and 12.4 mm. Of these mussels, 24% grew more than 1 mm per year. Five mussels were observed to have grown more than 5 mm in a year. Annual growth rate declined with increasing length of mussels in the linear mixed effects model regressing observed growth increments on length of mussel at the beginning of the growth increment (see Fig. 2-4, n= 182, r2=0.72, p < 0.001).

Discussion Habitat use and survival These results show that P. popeii occupy small spaces below boulders and undercut banks, which are low-flow, low-scour habitats in an otherwise high-scour bedrock riverbed. Midwestern mussels infrequently colonize bedrock, and areas under rocks and boulders (Sietman et al. 1999), whereas P. popeii used these areas almost exclusively. Flow often plays a key role in the life histories and habitat use of benthic organisms, especially in flashy desert streams (Lytle and Poff 2004, Meffe and Minckley 1987). Current and historical stability in the benthic substrate may determine suitable sites for colonization (Effenberger et al. 2006). Inherently high hydrologic variability of desert rivers, such as those occupied by P. popeii, makes them particularly sensitive to environmental changes (Grimm et al. 1997). My models indicated that flow plays an important role in the survival of mussels. Among these models, increased maximum flows decreased survival. Moreover, mussels disappeared from refuges and the physical protection associated with refuges was destroyed as the result of high flows in 2000. After high flow events, dislodged mussels were encountered downstream, sometimes broken, indicating that they had been dislodged from their refuge. Damage and displacement of this type is consistent with dislodgement and subsequent breaking on downstream rocks as observed in other systems (Tucker 1996). Because I measured apparent survival, individuals must have both survived and remained within the sampling area for us to consider them to be “survivors.” Thus, my estimates should be sensitive to dislodgement, because those animals removed from an area by high flows would not be included with other survivors My results, however, likely underestimate the effect of flow on P. popeii in the Black River, because surveyed areas represent a subset of the available habitat. Most P. popeii found in initial surveys were in areas with low flow and some form of physical protection. This pattern may reflect the increased protection afforded by relatively sheltered and stable areas, ameliorating the effects of high flows. High flows still decrease survival, but the effect may be less pronounced in these protected habitats. In many cases, the surveyed areas are fairly stable and have remained much the same across the entire period of observation. However, substantial differences may exist between local habitats, e.g. stability of the riverbed around refuges.

Preliminary surveys presented here revealed that while most P. popeii were found in protected areas, a portion of the population existed in areas with higher flows and less physical protection. These differences may reflect differences in the location of hydrodynamic force or the quality of habitats. By including marginal habitats and those with less permanent protection (e.g. undercut banks in addition to bedrock shelves) in future monitoring of these populations, greater effects of flow may be detected and larger differences in survival may be observed among years in which flow differs. Adaptation to hydrodynamic stress has been a common theme in the evolution of bivalves (e.g. Stanley 1981). Extreme flooding (greater than 100 year return interval) has been observed to impact another freshwater mussel population in Scotland (Hastie et al. 2001). The Scottish mussel population exhibited similar effects to those experienced by the mussels in this study in response to high-flow events. In particular, some mussel beds in both studies were completely destroyed, while others only suffered some mortality. One proposed mechanism for increased bivalve mortality in floods is buoyancy and subsequent stranding (Tucker 1996), which may explain other bivalve strategies that may reduce dislodgement and passive movement. For example, shell ornamentation (Stanley 1981) and aggregation patterns (van de Koppel et al. 2005) have been observed to reduce dislodgement and other consequences of hydrologic stress amongst other bivalves. Despite potential increases in competition for food among individuals in aggregations, marine bivalves form dense beds, which disperse water currents, to ameliorate seasonal hydrologic stress (Gascoigne et al. 2005). Similarly, despite any deleterious effects of crowding, P. popeii are densely aggregated under boulders and bedrock shelves. In many ways, the use of these habitats and effect of flow on survival of P. popeii is consistent with a common issue in the evolution and ecology of bivalves: resisting dislodgement. Age structure This dataset did not reveal obviously separated cohorts (i.e. distinct modes in length- frequency distributions), such as those seen in other studies (e.g. Fusconaia ebena, Payne and Miller 2000). Patterns of length in these populations of P. popeii did not change between years, despite observations of individuals advancing to larger size classes. This indicates that the population is continuing in a state that approximates stability, with recruitment of small individuals taking place. Size-structured population models, which could be based upon these

` length-frequency distributions, are often important in ecology and may reflect functional or physiological interactions (van de Wolfshaar 2006) or may act as proxies for age structure (Pasteris et al. 1996). Length-frequency data, such as those collected in this study, have often been employed in examining the population structure of freshwater mussels and may reveal cohorts of individuals growing between years (Payne and Miller 2000). In contrast, this study revealed length-frequencies that are invariant between years and that do not exhibit modes at increasing sizes across years, which would indicate a group of mussels growing as a unit between years. The data from our study indicate that P. popeii has a stable population size structure. My detection limit, i.e. the minimum size of mussel that I could locate, was large (between 40 and 60 mm), as compared to newly excysted juveniles (~ 180 m, Smith et al. 2003). Small individuals, even within the sizes observed in this study, grow very quickly and are unlikely to remain in their size classes between sampling intervals. Substantially decreased growth rates were observed at large sizes, which can cause an accumulation of individuals in the maximum size classes. Many large individuals remained in their respective size class for long periods of time; when small individuals were captured, they often grew substantially in the intervals between captures. Therefore, new captures in small size classes are likely new recruits to the population rather than having previously been undetected. Mussels in other open canopy rivers, such as grassland rivers, tend to exhibit similar growth patterns; individuals enter maximum size classes early in life and survive for a long time relative to the period spent growing (Morris and Corkum 1999). The proposed mechanism responsible for this difference between closed-canopy and open-canopy rivers is that open- canopy rivers are flashier and larger mussels are less likely to become dislodged. This scenario is consistent with what is observed in the Black River, which is flashy with little riparian vegetation and seldom has overhead cover from that vegetation. Moreover, both species composition and growth rates (for mussels that specialize in neither grassland nor forested rivers) are related to canopy cover and, hence, flashiness. If flashiness is a factor in determining growth rates of mussels or favors mussels that grow quickly, then the open-canopy, desert environs of the Black River would favor rapid growth, similar to mussels in grassland rivers. Few small individuals were detected in the in-channel habitats included in this study; more, smaller mussels

` were discovered along cut banks, under washed-out root mats and under bedrock shelves associated with those banks. New individuals continued to enter my surveyed population in all size classes (pers. obs). This suggests that individuals may recruit to in-channel refuge habitats at an older age, i.e. as adults, having grown to a relatively large size elsewhere. As with other mussel species, P. popeii lives for a long time. All of the mussels that were observed in either 1997 or 1998 and were recaptured in the final year of the study were already large when they were first encountered. Because large individuals are expected to be relatively old, these individuals are likely to be substantially older than the minimum age of 9 years observed in this study. Likewise, other studies of freshwater mussels have shown extreme longevity for a variety of species (up to 190 years, Ziuganov et al. 2000; at least 30 years, San Miguel et al. 2004; minimum 55 years, Anthony et al. 2001). Conservation Implications High apparent survival and longevity, reflected in individuals with minimum ages approaching a decade, suggest that the population is relatively stable, but this conclusion may only be made with several caveats. The overall status of the species is tenuous, because P. popeii inhabits a substantially reduced range. Also, decreased survival with high discharge events may be more frequent if global climate change leads to extraordinary local rainfall within the basin. Climate change may already have affected the vegetation of the Chihuahuan Desert in this area (Swetnam and Betancourt 1990; Curtin and Brown 2001); in other parts of the Chihuahuan Desert, this change is likely driven by increased precipitation (Brown et al. 1997). Numerous low-water crossings of the Black River upstream of this population, oil and gas exploration, and increasing ground and surface water use may further threaten this population. The restricted geographic range of P. popeii means that both stochastic events and those driven by anthropogenic activity pose a significant threat to the existence of this species. This study confirms the conclusions of other investigations (Alexander et al. 1997, Villella et al. 2004) that mark-and-recapture can be a useful technique for monitoring animals with limited vagility. The recapture estimates are substantially higher than those reported for other mussels (1-19%, Villella et al 2004) and other sessile, but hard to detect, organisms, such as prairie plants (~25%, Alexander et al. 1997). The substantially increased recapture probabilities in P. popeii are likely a result of the very clearly delimited flow refuges used by this

` species. Because the area in which these mussels can be found is obvious, it is easy to thoroughly search each habitat and find the majority of the mussels living there. My survival estimates and the relative parsimony of models with hydraulic variables in them underscore the importance of hydrologic regimes in desert stream ecosystems, particularly for freshwater mussels. While large floods and habitat stability do have some effects on mussel species in other systems, the increased hydrologic variability of desert systems may drive the nearly exclusive use of flow refuges by P. popeii; these are areas only occasionally used by mussels in other systems. Hydrology clearly plays an important role in defining the available habit for various mussels (Strayer 1999, Morales et al. 2006) and other aquatic organisms (Effenberger et al. 2006). Because changes in hydrology are likely to exacerbate the plight of P. popeii in the Black River, a prudent approach to managing this population is to attempt to maintain the status quo in the river itself and to act to prevent anthropogenic catastrophes. For example, increases in impervious surfaces within the watershed should be restricted to avoid increases in flashiness. In addition, accidental release of harmful substances in the watershed must be avoided at all cost; risk could be minimized by building bridges to replace low water crossings. At the same time, representative individuals should be removed from the wild for maintenance in captivity. This would allow repatriation of the Black River from local individuals in the case of a natural or anthropogenic catastrophe.

Literature Cited

Alexander H. M., N. A. Slade, and W. D. Kettle. 1997. Application of mark-recapture models to estimation of the population size of plants. Ecology 78:1230-1237.

Amyot J. P., and J. A. Downing. 1998. Locomotion in Elliptio complanata (Mollusca : Unionidae): a reproductive function? Freshwater Biology 39:351-358.

Amyot J. P., and J. A. Downing. 1997. Seasonal variation in vertical and horizontal movement of the freshwater bivalve Elliptio complanata (Mollusca: Unionidae). Freshwater Biology 37:345-354.

Amyot J. P., and J. A. Downing. 1991. Endobenthic and epibenthic distribution of the unionid mollusk Elliptio complanata. Journal of the North American Benthological Society 10:280-285.

Anderson D. R., and K. P. Burnham. 1999. Understanding information criteria for selection among capture-recapture or ring recovery models. Bird Study 46:14-21.

Anthony J. L., D. H. Kesler, W. L. Downing, and J. A. Downing. 2001. Length-specific growth rates in freshwater mussels (Bivalvia : Unionidae): extreme longevity or generalized growth cessation? Freshwater Biology 46:1349-1359.

Brown, J.H., T.J. Valone, and C.G. Curtin. 1997. Reorganization of an arid ecosystem in response to recent climate change. Proceedings of the National Academy of Sciences of the United States of America 94: 9729–9733.

Burnham, K. P. and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information theoretic approach. 2nd Edition. Springer-Verlag, New York, New York, USA. 488p.

Curtin, C.G. and J.H. Brown. 2001. Climate and herbivory in structuring the vegetation of the Malpai borderlands. Pg. 84-94. In C.J. Bahre and G. Webster, Eds. Changing plant life of La Frontera: observations on vegetation in the United States/Mexico borderlands. Univ. New Mexico Press, Albuquerque.

DeVries, D. R., and R. V. Frie. 1996. Determination of age and growth. Pages 483–512 in B. R. Murphy and D. W. Willis, editors. Fisheries Techniques, 2nd edition. American Fisheries Society, Bethesda, Maryland.

Effenberger M., G. Sailer, C. R. Townsend, and C. D. Matthaei. 2006. Local disturbance history and habitat parameters influence the microdistribution of stream invertebrates. Freshwater Biology 51:312-332.

Fisher S. G., L. J. Gray, N. B. Grimm, and D. E. Busch. 1982. Temporal succession in a desert stream ecosystem following flash flooding. Ecological Monographs 52:93-110.

Gascoigne J. C., H. A. Beadman, C. Saurel, and M. J. Kaiser. 2005. Density dependence, spatial scale and patterning in sessile biota. Oecologia 145:371-381.

Grimm N. B., A. Chacon, C. N. Dahm, S. W. Hostetler, O. T. Lind, P. L. Starkweather, and W. W. Wurtsbaugh. 1997. Sensitivity of aquatic ecosystems to climatic and anthropogenic changes: The Basin and Range, American Southwest and Mexico. Hydrological Processes 11:1023-1041.

Hart R. A., J. W. Grier, A. C. Miller, and M. Davis. 2001. Empirically derived survival rates of a native mussel, Amblema plicata, in the Mississippi and Otter Tail Rivers, Minnesota. American Midland Naturalist 146:254-263.

Hastie L. C., P. J. Boon, M. R. Young, and S. Way. 2001. The effects of a major flood on an endangered freshwater mussel population. Biological Conservation 98:107-115.

Hope, A. C. A. 1968. A simplified Monte Carlo significance test procedure. Journal of the Royal Statistical Society B. 30: 582-598.

Isley, F.B. 1914. Experimental study of the growth and migration of fresh-water mussels. U.S. Bureau of Fisheries Document 792:1-24.

Lang, B. K. 2001.Status of the Texas hornshell and native freshwater mussels (Unionoidea) in the Rio Grande and Pecos River of New Mexico and Texas. New Mexico Department of Game and Fish, Completion Report, E-38, submitted to the Division of Federal Aid, U.S. Fish and Wildlife Service, Albuquerque, New Mexico.

Lytle D. A. 2002. Flash floods and aquatic insect life history evolution: Evaluation of multiple models. Ecology 83:370-385.

Lytle D. A., and N. L. Poff. 2004. Adaptation to natural flow regimes. Trends in Ecology & Evolution 19:94-100.

Meffe G. K., and W. L. Minckley. 1987. Persistence and stability of fish and invertebrate assemblages in a repeatedly disturbed Sonoran Desert stream. American Midland Naturalist 117:177-191.

Morales Y., L. J. Weber, A. E. Mynett, and T. J. Newton. 2006. Effects of substrate and hydrodynamic conditions on the formation of mussel beds in a large river. Journal of the North American Benthological Society 25:664-676.

Morris T. J., L. D. Corkum. 1999. Unionid growth patterns in rivers of differing riparian vegetation. Freshwater Biology 42:59-68.

Pasteris A., G. Bonomi, and C. Bonacina. 1996. Age, stage and size structure as population state variables for Tubifex tubifex (Oligochaeta, Tubificidae). Hydrobiologia 334:125-132.

Payne B. S., and A. C. Miller. 2000. Recruitment of Fusconaia ebena (Bivalvia: unionidae) in relation to discharge of the lower Ohio River. American Midland Naturalist 144:328-341.

R Development Core Team. 2007. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

Ricciardi A., J. B. Rasmussen. 1999. Extinction rates of North American freshwater fauna. Conservation Biology 13:1220-1222.

San Miguel E., S. Monserrat, C. Fernandez, R. Amaro, M. Hermida, P. Ondina, and C. R. Altaba. 2004. Growth models and longevity of freshwater pearl mussels (Margaritifera margaritifera) in Spain. Canadian Journal of Zoology-Revue Canadienne De Zoologie 82:1370-1379.

Schwalb A. N., M. T. Pusch. 2007. Horizontal and vertical movements of unionid mussels in a lowland river. Journal of the North American Benthological Society 26:261-272.

Sietman B. E., M. A. Furman, and F. A. Pursell. 1999. Colonization of bedrock by freshwater mussels (Bivalvia: Unionidae). American Midland Naturalist 141:209-211.

Slade N. A., S. M. Blair. 2000. An empirical test of using counts of individuals captured as indices of population size. Journal of Mammalogy 81:1035-1045.

Smith D. G., B. K. Lang, and M. E. Gordon. 2003. Gametogenetic cycle, reproductive anatomy, and larval morphology of Popenaias popeii (Unionoida) from the Black River, New Mexico. Southwestern Naturalist 48:333-340.

Stanley S. M. 1981. Infaunal survival - Alternative functions of shell ornamentation in the Bivalvia (Mollusca). Paleobiology 7:384-393.

Strayer D. L. 1999. Use of flow refuges by unionid mussels in rivers. Journal of the North American Benthological Society 18:468-476.

Strayer DL, and D. R. Smith. 2003 A guide for sampling freshwater mussel populations. Maryland: American Fisheries Society.

Strenth N. E., R. G. Howells, and A. Correa-Sandoval. 2004. New records of the Texas hornshell Popenaias popeii (Bivalvia: Unionidae) from Texas and northern Mexico. Texas Journal of Science 56:223-230.

Swetnam, T.W. and J.L. Betancourt. 1990. Fire-southern oscillation relations in the southwestern United States. Science 249:1017-1020.

Tucker J. K. 1996. Post-flood strandings of unionid mussels. Journal of Freshwater Ecology 11:433-438.

` van de Koppel J., M. Rietkerk, N. Dankers, and P. M. J. Herman. 2005. Scale-dependent feedback and regular spatial patterns in young mussel beds. American Naturalist. 165:E66- E77. van de Wolfshaar K. E., A. M. de Roos, and L. Persson. 2006. Size-dependent interactions inhibit coexistence in intraguild predation systems with life-history omnivory. American Naturalist 168:62-75.

Villella R. F., D. R. Smith, and D. P. Lemarie. 2004. Estimating survival and recruitment in a freshwater mussel population using mark-recapture techniques. American Midland Naturalist 151:114-133.

White G. C. 2008. Closed population estimation models and their extensions in Program MARK. Environmental and Ecological Statistics 15:89-99.

White G. C., W. L. Kendall, and R. J. Barker. 2006. Multistate survival models and their extensions in Program MARK. Journal of Wildlife Management 70:1521-1529.

Figure 2-1. Velocities at which P. popeii was found in the initial surveys during 1997-98.

Figure 2-2. Model-averaged apparent survival estimates plotted against flow metrics or time models from four-population models. Symbols represent survival estimates at each site 1-4, with squares, diamonds, triangles and circles, respectively. Note that population 3 and 4 were not monitored for the entire length of the study. Lines are best-fit lines to show general trends in the entire dataset, except in panel E where lines connect consecutive surveys at each site.

Figure 2-3. Length-frequency distributions of sampled animals in each year. The majority of observations occur between 90 and 105 mm.

Figure 2-4. Observed growth increments are plotted against the length of the mussel at the beginning of the increment.

Table 2-1. All two-population (sites 1 and 2) models determined to be most parsimonious. Apparent survival is indicated by Φ and recapture probability is indicated by p. Survival and recapture parameters were allowed to vary by site (g) and by year (t). Predictors of survival and recapture were either assessed additively (+) or interactively (*). All other modifications to the model parameters are indicated by full words. Thus, g*t indicates a parameter that varies by group and by time with an interaction term between them.

Model ΔQAIC Number of Deviance Parameters

Φ(t) p(g*t) 0.0000 20 297.96 Φ(MaxFlow + t) p(g*t) 0.0000 20 297.96 Φ(MinFlow + t) p(g*t) 0.0000 20 297.96 Φ(Days >75 + t) p(g*t) 0.0000 20 297.96 Φ(g + t) p(g*t) 0.3098 21 296.22 Φ(MaxFlow + g + t) p(g*t) 0.3098 21 296.22 Φ(MinFlow + g + t) p(g*t) 0.3098 21 296.22 Φ(Days >75 + g + t) p(g*t) 0.3098 21 296.22

Table 2-2. Model results for four-population models. Only the most parsimonious models are included. Apparent survival is indicated by Φ and recapture probability is indicated by p. Survival and recapture parameters were allowed to vary by site (g) and by year (t). Predictors of survival and recapture were either assessed additively (+) or interactively (*). All other modifications to the model parameters are indicated by full words. Thus, g*t indicates a parameter that varies by group and by time with an interaction term between them.

Model ΔAIC Number of Deviance Parameters

Φ(g * t + MaxFlow) p(g*t) 0.0000 33 245.26 Φ(g * t + MinFlow) p(g*t) 0.0000 33 245.26 Φ(g * t + Days >75) p(g*t) 0.0000 33 245.26 Φ(g * t + Days >90) p(g*t) 0.0000 33 245.26 Φ(g + t) p(g*t) 3.8649 27 262.12 Φ(g + t + MinFlow) p(g*t) 3.8649 27 262.12 Φ(g + t + Days >75) p(g*t) 3.8649 27 262.12 Φ(g + t + Days >90) p(g*t) 3.8649 27 262.12 Φ(t) p(g*t) 3.9103 27 266.45 Φ(t + MaxFlow) p(g*t) 3.9103 25 266.45 Φ(t + MinFlow) p(g*t) 3.9103 25 266.45 Φ( t + Days >90) p(g*t) 3.9103 25 266.45

Chapter 3: Fundamental and realized niche breadth in mussel-host relationships, field studies of the infestation of fishes by Popenaias popeii

Introduction Niche requirements vary across life-stages, often changing dramatically in species with complex life histories. Movements between environments and ontogenetic changes in niche requirements for survival may affect overall niche dimensions used by a species (Wilbur 1980). Amphibians, for example, undergo a major shift in niche use when they move from aquatic to terrestrial habitats (Whitfield and Donnelly 2006). Similarly, ontogenetic shifts in diet composition are common in fish (Osenberg et al. 1988). To fully describe the dimensions of the Hutchinsonian niche (Hutchinson 1957), the relevant dimensions exploited during each life stage must be understood. Niche dimensions are not always simple quantities. Niche breadth may be divided into fundamental and realized niches, which reflect the set of conditions under which a species can persist and the conditions under which a species does exist, respectively (Hutchinson 1957). Differences between fundamental and realized niches are caused by a variety of biotic factors, including competition and dispersal limitation (Pianka 1974, Pulliam 2000). Parasites may experience a disjunction between the resources that they could potentially exploit and those that they are able to actually exploit under natural conditions. Hosts are resources for parasites and therefore, should be included in the definition and dimensions of the parasite niche. Combes (2001) described “filters” as features of the ecology or life history of a parasite or its host that prevent the host from being exploited by the parasite. Knowledge of such filters is critical to distinguish between the large set of laboratory-identified hosts (the fundamental niche, in a sense) and the smaller set of ecologically relevant hosts (i.e., the realized niche) used by parasites in natural settings. Unionoid mussels are an example of organisms with disparate life stages with differing niche requirements. These mussels live as parasitic larvae (called glochidia) attached to vertebrate hosts before excysting and becoming filter feeding juveniles and adults.

Requirements for survival differ between these parasitic and free-living life stages. For example, adults may require particular substrates and food resources (Bronmark and Malmqvist 1982), while glochidia require appropriate host fishes. Depending on the mussel species, the number of fish species used may be quite broad (so called host generalists) or rather narrow (host specialists). For example, Strophitus subvexus infests ten species of fishes in five families (Haag and Warren 1997), while Alasmidonta minor infested only one of 24 fish species tested (Neves et al. 1985). Similarly, some mussels may specialize on an ecologically similar group of fishes, such as obligate benthic species including darters and sculpins (Rogers et al. 2001). Thus, use of host resources can be quite variable among freshwater mussels. The glochidial stage of freshwater mussels requires both physiological compatibility (infesting glochidia must be able to resist the fish’s immune system) and ecological compatibility (the fish must be in proximity to a female adult) with potential host fishes. These aspects of the mussel-host relationship are similar to the fundamental and realized niches, respectively. In a sense, all fish species that are physiologically capable, under any circumstances, of supporting recruitment through the glochidial phase constitute the fundamental niche. In contrast, the realized niche is the set of fish species that come into contact with gravid mussels, allowing glochidia to attach to them. While many laboratory studies have demonstrated the ability of mussels to successfully attach and transform into juveniles (see Hoggarth 1992) on various fishes (i.e. physiological compatibility), information regarding mussels’ ability to access and infest those fishes in natural settings is much more limited. The Unionoidea possess a wide array of morphological (e.g, lures, “nets”) and behavioral adaptations that may increase exposure of a subset of physiologically compatible host fishes to glochidia (Barnhart et al. 2008). In addition, fish behavior helps determine the extent to which they are vulnerable to these infestation strategies, resulting in infection of only those fish species that are both physiologically compatible and exhibit behaviors that bring them into contact with mussel larvae (Fig. 3-1). I compared differences between physiological and ecological compatibility of fishes exposed to glochidia of Popenaias popeii (Bivalvia: Unionidae) in the Black River, Eddy County, New Mexico. Popenaias popeii is the only extant unionid in the Black River, in contrast to many better studied species, which exist in complex communities. Diverse mussel communities present logistical and methodological complications, including identification of

` encysted glochidia (Kennedy and Haag 2005), although molecular markers may be used to distinguish them from one another (Kneeland and Rhymer 2007), and acquired immunity to infestation due to previous exposure to glochidia (Rogers and Dimock 2003, Dodd et al. 2005). Laboratory trials identified P. popeii as a host generalist, based on a broad set of native and nonnative fishes that were successfully infested ( n = 33 species tested; Lang 2001, 2004). As a putative generalist in a one-mussel system, P. popeii simplifies the difficulties that have precluded the application niche theory relative to host specificity in wild populations of mussels and fishes. My study focuses on differentiating hosts infested in the wild (ecological hosts) from those that possess the physiological and immunological compatibility required for successful infestation (physiological or fundamental hosts). I predict that only a subset of physiologically compatible fishes will be used and that significant differences exist in prevalence and intensity of infestations between the species of fish that are naturally infested. Such differences could arise from differences in host behaviors (e.g., habitat use, feeding patterns) that place some fishes into closer contact with P. popeii than others. Methods I sampled fishes in the Black River of New Mexico from May to July (the period of peak reproductive activity; Smith et al. 2003) during 2005-2008 using multiple gear types (seines, trammel nets, electroshocking). The 14-km reach of the Black River that I sampled represents the entirety of habitat occupied by P. popeii in New Mexico (approximately 12% of its historic range; Carman 2007). I anesthetized all fishes using MS-222 and recorded standard and total lengths and weight for all large-bodied fishes (ca. > 80 mm total length). I used an 8X magnification jeweler’s lens to inspect each individual for the presence of attached glochidia to determine the prevalence of infestations (proportion of the population infested) and released these individuals live at point of capture. Small-bodied fishes (ca. < 80 mm TL) and representative,subsamples of infested larger-bodied fishes were preserved in 10% formalin for laboratory examination. In the laboratory, opercular flaps were removed from each fish and all external surfaces, e.g. skin, fins and gills, inspected for the presence of encysted glochidia using a dissecting scope to enumerate intensity (number of glochidia attached) of infestation for each individual.

I tested for differences in prevalence,, between fish species and between years using chi- square contingency tables. Expected values were adjusted for differences in the abundances of each fish species by multiplying the total proportion of fishes infested by the number of each species captured. To account for low expected values, I used a Monte Carlo simulation, as implemented in the R-project (Hope 1968, Daalgard 2004), to assess significance of the chi- square test. I used an ANOVA to test for differences in infestation intensity among fish species. I fitted a multiple regression model to the relationship between infestation intensity and fish length for each species to test the hypothesis that larger fishes carried more glochidia. By fitting species separately in this model, I was able to simultaneously test whether the relationship between fish length and infestation intensity differed among hosts. All analyses and graphics were accomplished using R (R Core Development Team). To assess the relative value of hosts to recruitment of mussels, I plotted fish abundance against prevalence and intensity of glochidial infestations for all species for which I was able to collect all three measures. To facilitate comparisons of host suitability among fish species, I collapsed the dataset into a single dimension. I multiplied the values on all three axes and divided by the largest product obtained for a species (i.e. the species that was capable of carrying the greatest number of glochidia, when considering abundance, prevalence and intensity). This number, multiplied by 100, creates an index that serves as a basis for assessing relative host suitability ranging from 0 (not an ecological host) to 100 (confirmed ecological host). This index is the percent of glochidia carried by a given fish species of the fish species carrying the most glochidia. Hereafter, I refer to this measure as “relative host suitability.” Results Fish Abundance I observed a total of 2658 fishes representing 21 species (of which 23 individuals identified to genus only) during surveys from 2005 to 2008 (Table 3-1); this total included 2115 individuals from species previously identified as physiological hosts (Lang 2004). Of these fishes, 249 individuals (9.4%, comprising 16 species) exhibited cysts consistent with glochidial infestations (see Table 3-1, Fig. 3-2; note that the figure includes fishes which could not be identified to species). Half of all fishes with cysts were Cyprinella lutrensis, while Carpiodes carpio, Moxostoma congestum and Lepomis. megalotis; each carried 10 percent of total observed

` infestations. Overall, 1866 fishes belonged to species that form cysts and produce transformed juveniles under laboratory conditions, but were not infested under field conditions. Infestation Prevalence River carpsucker (Carpiodes carpio) and blue sucker (Cycleptus elongatus) were the most frequently infested (60%) large-bodied fishes. Gray redhorse (Moxostoma congestum) was infested at lower prevalence (10%) than other catostmids, but represented the most frequently captured large-bodied fish (292 captures). All infestations of these benthic feeding catostomids were detected on dorsal and lateral surfaces of the head region, including opercula. Among small-bodied fishes, Cyprinella lutrensis was the only species infested consistently and exhibited the highest prevalence (30%). Other taxa were either infested at very low prevalence or intensity (e.g. Pimephales promelas, Lepomis macrochirus and Lepomis megalotis) or were very infrequently encountered (e.g. Cyprinus carpio, one of three fishes was infested). Invariably, small-bodied species were infested on the gills, with occasional attachments on pectoral, dorsal or caudal fins. Chi-square tests indicated the proportion of individuals infested differed among fish species (2=184.18, p<0.001), but not between years (2=2.505, df=3, p=0.474;. Fig. 3-2). Cysts were confirmed as glochidial in origin by examining preserved fish under a dissecting microscope. In catfishes and shad, some apparent cysts may have been caused by glochidia that had been excysted prior to inspection, but were more likely sensory structures. Infestation Intensity In fishes for which infestation intensity could be enumerated, differences among species were significant (F=42.329, df=8, p<0.001, Fig. 3-3), with the highest intensities being found in Carpiodes carpio (150-1337 encysted glochidia per individual) and Moxostoma congestum (115 encysted glochidia). Cyprinella lutrensis was infested at relatively low intensities (1-50 encysted glochidia); however, intensities of 50 glochidia per individual are much higher than any of the remaining species (<12 glochidia/fish). Multiple regression revealed a strong relationship between fish length, species and infestation intensity (r2=0.715, p<0.001), indicating that both length and species were important in determining how many mussel glochidia were attached to wild fish. Larger-bodied fishes were infested at higher intensities than smaller-bodied individuals after accounting for differences among species.

Relative Host Suitability I plotted prevalence, intensity and abundance of host infestations in three-dimensional space, with the values increasing as they move away from the lower, left, front of the graph (Fig. 3-4). The species in the highest positions relative to other species were C. carpio, C. lutrensis and M. congestum. These species also had the highest ecological host indices (Table 3-1). The few Cycleptus elongatus and Cyprinus carpio that I encountered in the field appeared to exhibit qualitatively high infestation intensities. However, I did not enumerate these infestations in the lab. Therefore, they could not be quantitatively evaluated in this context. Discussion Overall, the list of species captured was similar to that obtained in a previous study of the fishes of the Black River (Cowley and Sublette 1987a), suggesting that the community was well represented in my study and has remained constant across the past two decades. In the Black River, fishes with the highest infestation intensity and prevalence were the catostomids, Carpiodes carpio and Moxostoma congestum, and a cyprinid, Cyprinella lutrensis. The latter was the most abundant host species, but it had moderate infestation intensity. These three species had the highest ecological host indices as well. Of an estimated 2.9 million glochidia infesting fishes in this study (excluding species for which glochidia could not be counted) 86.5 % were attached to C. carpio, 10.5% to M. congestum and 2.1% to C. lutrensis. Thus, more than 99% of the glochidia attached to fishes were attached to fishes of these three species. Because these fish species carried the largest number of glochidia of P. popeii, they represent ecological hosts, a distinct subset of fishes that are exploited more intensely than other physiological hosts and contribute the most to mussel recruitment. Cycleptus elongatus may be, or have once been, another important host. I have observed qualitatively high infestation intensities but overall abundance of this fish in the river is low. However I have no information on historical population sizes in the Black River, thus the relative historical contribution of this species cannot be known.. The current contribution of this species is also difficult to quantify, because I was not able to kill and collect infestation data on these fishes because they are protected by the State of New Mexico. Similarly, the invasive Cyprinus carpio could represent an important contemporary host, but a total of only three were observed and the species was only seen in 2008. I was also unable to obtain an estimate of infestation intensity. Therefore, it is difficult to

` assess the potential resource provided by this species, as well. Because this species is nonnative, it may be important to consider as introductions may augment the population or it may naturally establish itself in the Black River, increasing its population size dramatically. All other fish species likely represent relatively insignificant contributions to the recruitment of P. popeii. The set of ecological hosts was much smaller than the set of physiological hosts predicted by successful laboratory infestations. Although many species became infested in both the lab and the field, only three species were naturally infested at high prevalence or intensity and were abundant enough to carry many glochidia. While C. lutrensis was the most abundant (494 individuals captured), the other ecological host species, M. congestum and C. carpio were less abundant (5th and 10th most abundant, respectively). Although these two species had relatively low abundances in our surveys, the three species represent the largest potential contribution to the recruitment of P. popeii of all the fishes observed. If the number of glochidia infested on these fishes is proportional to the number that will ultimately recruit to the juvenile stage, then our results demonstrate that P. popeii is really a host specialist, functionally using only these three species. The Catostomidae are benthic feeding and often process large quantities of material from the bottoms of rivers (Pflieger 1975). Cycleptus elongatus and M. congestum both feed on benthic invertebrates living in firm substrates (Cowley and Sublette 1987b), which may include substrates that provide a suitable place for mussels to anchor. Similarly, C. carpio feeds on benthic alga and invertebrates associated with them (Pfleiger 1975). It is likely that catostomids’ use of these environments predisposes them to incidental contact with mussels and their glochidia. The pattern of facial infestation suggests that a passive mechanism may be responsible. The feeding habits of these fishes is likely to increase their exposure to the net-like masses of glochidia that P. popeii release. This pattern is consistent with infestation in P. popeii’s habitats, which consists of low-flow areas under boulders and undercut shelves, because fish feeding in benthic areas would have to swim through these nets of glochidia. Smaller bodied fishes, e.g. Lepomis spp. and Cyprinella lutrensis, were all infested on the gills. This discrepancy in the location of attached glochidia suggests that a different mechanism of infestation exists for these fishes. Cyprinella lutrensis lives and feeds in the water column and sunfish often do so as well (Pfleiger 1975). The location of infestations on the gills of these fish

` species may indicate that they are infested by ingesting drifting glochidia, rather than the intact strands that are initially produced by P. popeii. Glochidia may break loose and some become entrained in the water column, becoming part of the “drift” (pers. obs.), Use of large, vagile hosts may have important ecological and evolutionary consequences for P. popeii, because these hosts offer opportunities for parasite species to ameliorate environmental uncertainty. Large-bodied hosts represent a more valuable resource than small- bodied hosts because of the larger surface area for glochidial attachment; this is consistent with the significant, positive relationship between size and infestation intensity. This may be especially important in mussel species that have few opportunities to deliver their offspring to appropriate hosts. Because the ecological hosts of P. popeii are not the most abundant fishes in the river, the mussel may experience a tradeoff between large numbers of host individuals and large body sizes of individuals among the fish species that they parasitize. Desert rivers are unpredictable with both short, high-intensity, scouring floods and periods of low-flow or complete drying (Lytle 2002). Use of vagile, large-bodied hosts might allow mussels to exploit smaller tributaries, or reestablish mainstem sites after catastrophic events, by carrying large numbers of mussels relatively long distances. Physiological compatibility is not the exclusive determinant of the host niche of freshwater mussels; differences in behavior of both mussels and fish may also determine the realized niche of mussels. Data from this study portray a very different ecology governing the parasitic phase of P. popeii than those data collected in laboratory studies. Laboratory data described P. popeii as a host generalist, while field data suggest that, with occasional infestations across a wide variety of species, most infested fishes (> 75%) are restricted to a small number of species from only 2 families. Access to suitable fish hosts is a crucial step in the ecology of freshwater mussels and it is likely to be strongly related to their evolutionary history. Popenaias popeii has recently been placed rather unambiguously into the unionid tribe Amblemini (Chapman et al. 2008); species in this tribe share a common set of infestation strategies. While other groups use various lures to draw in their hosts, the amblemines’ infestation strategy has been described as “broadcast,” because it involves a relatively passive release of glochidia into the water column with subsequent infestation of hosts (Barnhart et al. 2008). However, many amblemines possess mechanisms for adhering glochidia together, which may prevent dilution

(Barnhart et al. 2008). When P. popeii releases glochidia, they tend to be organized into thin strands, similar to what is seen in other amblemines. These net-like conglutinates break up when disturbed, and likely provide a physical matrix that increases the incidental exposure of fishes by suspending the glochidia at appropriate locations in the water column. Structures such as these tend to infest fishes on the skin or fins (Barnhart et al. 2008). Similar strategies are used by members of the tribe Unionini, which produce a more complex conglutinate that acts as a “leg- hold trap” for passing fish and exhibits movements affected by osmotic gradients between the ambient water and that contained within the conglutinate (Watters 2002). To understand how these infestation strategies have been involved in the evolution of these lineages, it will be important to study the realized host use of other mussel species that use these types of conglutinates, and determine whether they infest ecologically similar fishes. Host specificity, therefore, is a complicated phenomenon amongst mussels, as it is with many parasites. The most basic requirement is that the potential host fishes are immunologically and physiologically compatible with infestation by glochidia; this defines the fundamental niche for a mussel species. Both tissue and humoral defenses are involved in determining physiological host specificity in a wide range of parasites (Fustish and Millemann 1978, Meyers et al. 1980). However, even when mussels attach to an individual of a suitable host species, immunological responses by the fish may be produced (presumably antigens, O'Connell and Neves 1999) that prevent re-infestation by others of the same (Rogers and Dimock 2003, Rogers- Lowery et al. 2007) and different species (Dodd et al. 2005). Ecological features of mussels and hosts may affect their relationship, further complicating mussel-host associations. For example, some mussels may even take advantage of environmental stressors, such as temperature, which depress the immune system of hosts (Roberts and Barnhart 1999). In this example, Anodonta suborbiculata release their infective larvae in the winter, to take advantage of the depressed state of host fish immune systems. Thus, a variety of features of the biology of both hosts and parasites must match to allow for successful infestation. Restriction to particular hosts because of ecological factors, is common amongst many parasites (Rodhe 1979). For example, shared geography or microhabitat use may define the subset of potential hosts to which parasites are exposed. The mechanism of infestation and the shared ecology of hosts and parasites, therefore influences host specificity (Simkova et al. 2006).

Host behavior may also be critical to the ability of parasites to access hosts (Dobson 1988, Barber et al. 2000). Acanthocephalan parasites alter various features of their intermediate hosts, including coloration, odor and movement to make the intermediate hosts more palatable and easier prey for final hosts (Baldauf et al. 2007). Mussels, likewise alter the behavior of and direct their larvae to specific fishes through delivery mechanisms, often referred to as lures (Barnhart et al. 2008). Many different delivery mechanisms are used to infest hosts including packaging of infective larvae to mimic prey items (O'Brien and Box 1999), modification of the maternal mantle tissue along with increasing the release of glochidia in the presence of host species (Haag et al. 1999, Haag and Warren 1999) and shaping of released glochidial masses into net-like structures (Watters 2002). These mechanisms likely serve to both increase the number of glochidia delivered to appropriate hosts and to target a subset of the fishes within the geographical range of the mussel. Lures that induce fishes to attack mussel glochidia are well known and studied, but nets and other mechanisms are not as well understood. The effectiveness of these mechanisms, including both lures and nets, is unknown and their numerical effects on infestation parameters is virtually unexplored. However, no specifying mechanism is absolute, leading to indistinct boundaries on the use of the host dimension of niche space. Indistinct delineation in niche breadth is common and methods are being developed to explicitly incorporate such uncertainty into ecology. For example, fuzzy sets have been used to re-examine niche relationships, using uncertainty in the use of niche dimensions by plant and animal species (Yimin et al. 2006). Even individual variability in niche exploitation has been suggested as a necessary component of understanding niche relationships (Bolnick et al. 2003). It will be critical to incorporate such information into the conservation of mussels, while the study of mussels may, in turn, inform the continued development of the niche concept. Strong differences among fish species, in terms of their potential value as hosts to mussels, are common and have even been observed between localized populations of the same fish species (Kobayashi and Kondo 2005, O'Brien and Box 1999, respectively). While glochidia of P. popeii do successfully infest a large number of fish species, they do so at higher intensity and prevalence on a small subset of fish species and, amongst those fish species, some occur at substantially higher abundances than others. Clear boundaries of the dimensions of niches, fundamental or realized, are not common; therefore, caution must be exercised in describing and using

` descriptions of niches. For mussels, it will be important to incorporate these indistinct niche boundaries into efforts to understand their ecology, and better inform conservation actions such as population viability modeling and captive rearing.

Literature Cited

Baldauf S. A., T. Thuenken, J. G. Frommen, T. C. M. Bakker, O. Heupel, and H. Kullmann. 2007. Infection with an acanthocephalan manipulates an amphipod's reaction to a fish predator's odours. International journal for parasitology 37:61-65.

Barber I., D. Hoare, and J. Krause. 2000. Effects of parasites on fish behaviour: a review and evolutionary perspective. Reviews in Fish Biology and Fisheries 10:131-165.

Barnhart M. C., W. R. Haag, and W. N. Roston. 2008. Adaptations to host infection and larval parasitism in Unionoida. Journal of the North American Benthological Society 27:370- 394.

Bolnick D. I., R. Svanback, J. A. Fordyce, L. H. Yang, J. M. Davis, C. D. Hulsey, and M. L. Forister. 2003. The ecology of individuals: Incidence and implications of individual specialization. American Naturalist 161:1-28.

Bronmark C., and B. Malmqvist. 1982. Resource partitioning between unionid mussels in a Swedish lake outlet. Holarctic Ecology 5:389-395.

Chapman E. G., M. E. Gordon, J. M. Walker, B. K. Lang, D. C. Campbell, G. T. Watters, J. P. Curole, H. Piontkivska, and W. R. Hoeh. 2008. Evolutionary relationships of Popenaias popeii and the early evolution of lampsiline bivalves (unionidae): Phylogenetic analyses of DNA and amino acid sequences from F and M mitochondrial genomes. Malacologia 50:303-318.

Carman, S.M. 2007. Texas hornshell Popenaias popeii recovery plan. New Mexico Department of Game and Fish, Conservation Services Division, Santa Fe, New Mexico.

Combes C. 2001. Parasitism :the ecology and evolution of intimate interactions. University of Chicago Press, Chicago.

Cowley D. E., and J. E. Sublette. 1987. Distribution of fishes in the Black River Drainage, Eddy County, New-Mexico. Southwestern Naturalist 32:213-221.

Daalgard, P. 2004. Introductory Statistics with R 1st edition. Springer: New York.

Dobson A. P. 1988. The population biology of parasite-induced changes in host behavior. Quarterly Review of Biology 63:139-165.

Dodd B. J., M. C. Barnhart, C. L. Rogers-Lowery, T. B. Fobian, and R. V. Dimock. 2005. Cross- resistance of largemouth bass to glochidia of unionid mussels. Journal of Parasitology 91:1064-1072.

Fustish C. A., and R. E. Millemann. 1978. Glochidiosis of salmonid fishes .2. Comparison of tissue-response of Coho and Chinook Salmon to experimental-infection with

Margaritifera margaritifera (Pelecypoda Margaritanidae). Journal of Parasitology 64:155-157.

Haag W. R., and M. L. Warren. 1999. Mantle displays of freshwater mussels elicit attacks from fish. Freshwater Biology 42:35-40.

Haag W. R., and M. L. Warren. 1997. Host fishes and reproductive biology of 6 freshwater mussel species from the Mobile Basin, USA. Journal of the North American Benthological Society 16:576-585.

Haag W. R., M. L. Warren, and M. Shillingsford. 1999. Host fishes and host-attracting behavior of Lampsilis altilis and Villosa vibex (Bivalvia : Unionidae). American Midland Naturalist 141:149-157.

Hoggarth M. A. 1992. An examination of the glochidia-host relationships reported in the literature for North American species Unionacea (Mollusca: Bivalvia). Malacology Data Net 3:1-30.

Hope, A. C. A. 1968 A simplified Monte Carlo significance test procedure. Journal of the Royal Statistical Society. B 30, 582–598.

Hutchinson, G. E. 1957. Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology. 22(2): 415-427.

Kennedy T. B., and W. R. Haag. 2005. Using morphometrics to identify glochidia from a diverse freshwater mussel community. Journal of the North American Benthological Society 24:880-889.

Kneeland S. C., and J. M. Rhymer. 2007. A molecular identification key for freshwater mussel glochidia encysted on naturally parasitized fish hosts in Maine, USA. Journal of Molluscan Studies 73:279-282.

Kobayashi O., and T. Kondo. 2005. Difference in host preference between two populations of the freshwater pearl mussel Margaritifera laevis (Bivalvia: Margaritifera) in the Shinano River System, Japan. Venus 64:63-70.

Lang, B.K. 2001. Status of the Texas hornshell and native freshwater mussels (Unionoidea) in the Rio Grande and Pecos River of New Mexico and Texas. New Mexico Department of Game and Fish, Completion Report, E-38, submitted to the Division of Federal Aid, U. S. Fish and Wildlife Service, Albuquerque, New Mexico.

Lang, B.K. 2004. Artificial culture and captive rearing of the Texas hornshell. New Mexico Department of Game and Fish, completion report, E-51, submitted to the Division of Federal Aid, U.S. Fish and Wildlife Service, Albuquerque, New Mexico.

Lytle D. A. 2002. Flash floods and aquatic insect life-history evolution: Evaluation of multiple models. Ecology 83:370-385. 44

Meyers T. R., R. E. Millemann, and C. A. Fustish. 1980. Glochidiosis of Salmonid Fishes .4. humoral and tissue responses of Coho and Chinook Salmon to experimental-infection with Margaritifera margaritifera (L) (Pelecypoda, Margaritanidae). Journal of Parasitology 66:274-281.

Neves R. J., L. R. Weaver, and A. V. Zale. 1985. An evaluation of host fish suitability for glochidia of Villosa vanuxemi and Villosa nebulosa (Pelecypoda, Unionidae). American Midland Naturalist 113:13-19.

O'Brien C. A., J. B. Box. 1999. Reproductive biology and juvenile recruitment of the shinyrayed pocketbook, Lampsilis subangulata (Bivalvia : Unionidae) in the Gulf coastal plain. American Midland Naturalist 142:129-140.

O'Connell M. T., R. J. Neves. 1999. Evidence of immunological responses by a host fish (Ambloplites rupestris) and two non-host fishes (Cyprinus carpio and Carassius auratus) to glochidia of a freshwater mussel (Villosa iris). Journal of Freshwater Ecology 14:71-78.

Osenberg C. W., E. E. Werner, G. G. Mittelbach, and D. J. Hall. 1988. Growth-patterns in Bluegill (Lepomis macrochirus) and Pumpkinseed (Lepomis gibbosus) Sunfish - environmental variation and the importance of ontogenetic niche shifts. Canadian Journal of Fisheries and Aquatic Sciences 45:17-26.

Pflieger W. L., Missouri. 1975. The fishes of Missouri. Missouri Department of Conservation, Jefferson City.

Pianka E. R. 1974. Niche overlap and diffuse competition. Proceedings of the National Academy of Sciences of the United States of America 71:2141-2145.

Pulliam H. R. 2000. On the relationship between niche and distribution. Ecology Letters 3:349- 361.

Roberts A. D., M. C. Barnhart. 1999. Effects of temperature, pH, and CO2 on transformation of the glochidia of Anodonta suborbiculata on fish hosts and in vitro. Journal of the North American Benthological Society 18:477-487.

Rodhe, K. 1979. A critical evaluation of intrinsic and extrinsic factors responsible for niche restriction in parasites. American Naturalist. 114:648-671.

Rogers C. L., R. V. Dimock. 2003. Acquired resistance of bluegill sunfish Lepomis macrochirus to glochidia larvae of the freshwater mussel Utterbackia imbecillis (Bivalvia: Unionidae) after multiple infections. Journal of Parasitology 89:51-56.

Rogers S. O., B. T. Watson, and R. J. Neves. 2001. Life history and population biology of the endangered tan riffleshell (Epioblasma florentina walkeri) (Bivalvia : Unionidae). Journal of the North American Benthological Society 20:582-594.

Rogers-Lowery C. L., R. V. Dimock, and R. E. Kuhn. 2007. Antibody response of bluegill sunfish during development of acquired resistance against the larvae of the freshwater mussel Utterbackia imbecillis. Developmental and Comparative Immunology 31:143-155.

Simkova A., O. Verneau, M. Gelnar, and S. Morand. 2006. Specificity and specialization of congeneric monogeneans parasitizing Cyprinid fish. Evolution 60:1023-1037.

Watters G. T. 2002. The kinetic conglutinate of the creeper freshwater mussel, Strophitus undulatus (Say, 1817). Journal of Molluscan Studies 68:155-158.

Whitfield S. M., M. A. Donnelly. 2006. Ontogenetic and seasonal variation in the diets of a Costa Rican leaf-litter herpetofauna. Journal of Tropical Ecology 22:409-417.

Wilbur H. M. 1980. Complex life-cycles. Annual Review of Ecology and Systematics 11:67-93.

Yimin L., S. Xihao, and G. Chongying. 2006. Niche width and niche overlap: a method based on type-2 fuzzy sets. Ecological Research 21:713-722.

Table 3-1. Fishes reported from the Black River by Cowley and Sublette (1987) and Sublette et al. (1990). All values that are not applicable due to missing data are blank. Resident status (N = native, NN = nonnative), conservation status (FE = federally endangered, FT = federally threatened, SE=state endangered, ST = state threatened). Lab host fish, + = produced transformed juveniles, N = no glochidia produced, U = uncertain because of mortality or other factors obscuring definite identification of host or non-host for Popenaias popeii from lab studies (Lang 2001; unpub. data). Fishes encountered in the field were given “Species Codes” to identify them in subsequent figures. Field host indicates that + = cysts observed in field study and confirmed by examination under microscope, - = no cysts observed in field studies and ? = cysts observed but not confirmed. Relative host suitability is an index of relative value of each fish species infested in the wild by multiplying abundance, prevalence and intensity, dividing by the highest value obtained for any species and multiplying by 100.

Family Species Species Resident Conservation Lab Field Relative Host Abundance Prevalence Symbol Status1 Status Host2 Host2 Suitability Esocidae Leposs Lepisosteus osseus N + - - 9 0 Salmonidae Oncorhynchus mykiss I - Clupeidae Dorcep Dorosoma cepedianum N ? ? 124 0.1 Characidae Astyanax mexicanus N ST + Cyprinidae Carassius auratus I - Cyprinidae Cyplut Cyprinella lutrensis N + + 2.4 494 0.3 Cyprinidae Cypcar Cyprinus carpio I + ? ? 3 0.3 Cyprinidae Dionda episcopa N + Cyprinidae Hybognathus placitus I + Cyprinidae Macrhybopsis aestivalis I + Cyprinidae Notropis jemezanus N FT, SE + Cyprinidae Pimpro Pimephales promelas N + + <0.1 275 0.02 Cyprinidae Camano Campostoma anomalum I + Catostomidae Carcar Carpiodes carpio N + + 100 42 0.6 Catostomidae Cycelo Cycleptus elongatus N SE + + ? 7 0.6 Catostomidae Ictbub Ictiobus bubalus N 1 0 Catostomidae Moxcon Moxostoma congestum N ST + + 12.2 292 0.1 Ictaluridae Ameiurus melas I - Ictaluridae Ameiurus natalis I + Ictaluridae Ictpun Ictalurus punctatus I + ? ? 24 0.1 Ictaluridae Ictlup Ictalurus lupus N + ? 1 1 Ictaluridae Pyloli Pylodictis olivaris N + - - 2 0 Cyprinodontidae Cyprinodon pecosensis I ST U

Fundulidae Fundulus zebrinus N + Fundulidae Lucania parva N + Poeciliidae Gamaff Gambusia affinis I + + <0.01 66 0 Poeciliidae Gambusia nobilis N FE, SE Atherinopsidae Menidia beryllina I U Centrarchidae Ambloplites rupestris I U Centrarchidae Lepcya Lepomis cyanellus N + - - 12 0.1 Centrarchidae Lepgul Lepomis gulosus I - - - 13 0 Centrarchidae Lepmac Lepomis macrochirus N + + <0.1 447 0 Centrarchidae Lepmeg Lepomis megalotis N + + 0.4 392 0.1 Centrarchidae Micpun Micropterus punctulatus I U + <0.1 364 0 Centrarchidae Micsal Micropterus salmoides N + - - 50 0 Centrarchidae Pomann Pomoxis annularis I U - - 9 0 Percidae Etheostoma lepidum N ST + Percidae Permac Percina macrolepida N ST - - - 9 0 Percidae Stizostedion vitreum I -

Figure 3-1. Conceptual diagram of ecological and physiological host relationships. Black arrows represent the necessary steps for a glochidium to take to achieve infestation on host fishes. Grey arrows indicate the path followed by laboratory infestation trials. Ecological hosts, indicated by the bracket on the right, must both be accessed by glochidia and must be physiologically compatible. Ecological hosts are the portion of physiological hosts that experience infestations under natural conditions, hence are equivalent to realized niches for the mussel in the glochidial stage. Physiological hosts, measured by laboratory infestations, represent the fundamental niche, given that the mussels are able to access these fishes.

Figure 3-2. Total number of individuals caught (full bar) and number of individuals carrying cysts consistent with infestation (black bar) for all surveys conducted between May 2005 and June 2008. For species abbreviations see table 3-1; additional abbreviations are constructed by the first three letters of the genus followed by “spp.”

1337

Figure 3-3. Number of glochidia encysted (infestation intensity) on all preserved fishes.

Cyprinella lutrensis

Carpiodes carpio

Figure 3-4. Three-dimensional diagram of the major factors influencing the demographic importance of host fishes infested by P. popeii. Species with zero values or for which intensity could not be estimated were omitted from this figure.

Chapter 4: Comparative phylogeography of two closely related, common mussel species

Introduction Dispersal of individuals among populations is critical for maintaining the evolutionary potential of populations and species (Dieckmann et al. 1999) and has important consequences for conservation biology (Smith and Green 2005). The genetic structure of populations reveals how they are connected. Many factors including vagility and distance between populations, may control the dispersal of individuals in a landscape, (Wright 1943). Landscape connectivity between sites containing populations is also an important factor; for example, the genetic structures of obligate aquatic species are influenced more by how locations are connected by watercourses than by overland proximity (Fetzner and Crandall 2003). Ecological factors such as niche specialization and habitat patchiness have also been observed to influence population genetic structure (Futuyma and Moreno 1988, Roderick 1996). For example, increased niche breadth has been linked to increased colonization potential in insects, presumably because wider niche breadth allows species to take advantage of more resources (Navajas 1998). Finally, the dispersal of some species is dependent on other species (e.g. seed dispersal in some species, Howe and Smallwood 1982). Parasite dispersal is partially or wholly dependent upon host dispersal, and as a result, dispersal behaviors of hosts are vital in structuring the genetic relationships among populations of parasites. Host population structure has been observed to influence parasite population genetic structure in the nematode parasites of salmon (Criscione and Blouin 2007) and botfly parasites of mice (Nieberding et al. 2006). In some cases, parasites even develop genetic population subdivisions at smaller spatial scales and within the observable spatial structure of their hosts (Criscione et al. 2006). However, such relationships have been studied only in parasites with short life spans, which allow for rapid development of genetic structure amongst their populations. The population genetic structure of parasites that spend long periods in free- living stages has not been studied.

One such group is the freshwater mussels (families: Unionidae and Margaritiferidae), which possess a parasitic larval phase (glochidia) that typically attaches to fishes. Fish hosts are believed to play an important role in determining both large- and small-scale patterns in freshwater mussel population and community structure. For instance, fish abundance and diversity are reliable predictors of mussel abundance and diversity (Vaughn 1997, Vaughn and Taylor 2000, Watters 1992); presumably, movement of glochidia while attached to fish hosts is a much more important method of dispersal than the limited movement of adult mussels. For example, short distance dispersal of darters, coinciding with release of glochidia by female mussels and the subsequent attachment of larvae to fish, results in the patchy and localized distributions of the mussel Alasmidonta heterodon (McLain and Ross 2005). The extent to which particular fish hosts structure mussel populations, via shared dispersal during larval attachment, remains unclear. In particular, no studies have examined such relationships for mussel species that parasitize larger, more vagile fishes. Such studies are difficult to accomplish because population structure may occur at scales of hundreds of kilometers. Connectivity between populations may be studied via phylogeography, the analysis of the geographic distribution of genetic variation, typically using mtDNA (Avise 2000). Phylogeographic analysis may be able to indicate the extent to which mussel population structure has been shaped by dispersal of host fishes. Such data would greatly improve our understanding of mussel-host interactions by providing insight into patterns of colonization, dispersal, and isolation of mussels and hosts. Furthermore, such knowledge is important for understanding the historical movement of mussels and the long-term importance of host- structure on mussel ecology. Historical connections between the Central Highlands, i.e. the Appalachian and Ozark Highlands, and glaciated areas to the north of them shaped the biogeography and phylogeography of fishes in eastern North America (Strange and Burr 1997, Mayden 1988). Studies of these regions have found that patterns exist not only amongst groups of species, but within species as well (Strange and Burr 1997, Berendzen et al. 2003). Thus, this region should prove fruitful for examining the phylogeographic patterns of mussels that are obligately dispersed by fishes. Indeed, these areas yield genetically subdivided mussel populations that reflect these distinct regions, including reduced genetic diversity in formerly glaciated regions

(Zanatta and Murphy 2008). Studies of closely related mussel species across these regions are needed to account for the effects of distinct evolutionary histories on the present-day distribution of genetic variation. These comparisons allow testing of hypotheses about the interplay between life-history traits and population structure.

I examined two congeneric species of freshwater mussels, Quadrula pustulosa (Lea, 1831) and Q. quadrula (Rafinesque, 1820), that differ in the host species they use. Studies have indicated that Q. pustulosa infests a variety of catfishes, including black bullheads, Ameiurus melas; brown bullheads, Ameiurus nebulosus; channel catfish, Ictalurus punctatus; and flathead catfish, Pylodictis olivaris (Coker et al. 1921; Howard 1913, 1914). Ameiurid catfishes often have home ranges of less than 1 km (Sakaris et al. 2005), while channel and flathead catfish have been observed to move further (home ranges of up to 8.5 km for channel catfish, Wendel and Kelsch 1999; 160 km within 3 months, Travnichek 2004), as do flathead catfish (see below). Quadrula quadrula has been repeatedly linked exclusively to flathead catfish (Howard and Anson 1922, Romano et al. 1990, 1991). These often disperse short distances (<1 km, Jackson 1999), but are capable of maintaining large home ranges and dispersing long distances (Jackson and Jackson 1999). Thus, these congeneric mussel species exhibit differing host specificity and likely differences in dispersal abilities as mediated by their hosts, which may affect how their populations are linked to one another. While Q. pustulosa uses a variety of host species, Q. quadrula has only been linked to a single host species. Although the host species used by these mussels overlap, the total number of hosts and the resulting opportunities for phoresic dispersal, i.e. transportation of one organism via attachment to another, are likely to differ between these species. For example, increased host specialization is correlated with increased population differentiation in lice (Johnson et al. 2002). The selection of closely related species (Serb et al. 2003) also minimizes taxonomic distinctiveness, which limits the influence of evolutionary history on the comparison between species. To describe differences between these species, I analyzed several characteristics of their population genetic and phylogeographic structure. To determine general patterns of population differentiation, I partitioned genetic variation across the ranges of both species and examined genetic versus geographic relatedness between populations of both species. I predicted that Q.

quadrula would exhibit more differentiation (e.g. higher Fsts) at smaller geographic scales, based on the characteristics of its single host species. In addition, I examined north-south gradients in the population genetics of both species to test for the common pattern of reduced genetic variation in glaciated regions. I predicted that Q. quadrula would exhibit a stronger decline in genetic diversity (number of haplotypes and number of substitutions between haplotypes found at a location) than Q. pustulosa, also based on the frequency of host-mediated dispersal opportunities. Methods Collection Sites and Methods A total of 414 individual mussels were collected from 38 sites throughout the Mississippi River (divided into the Upper Mississippi, Lower Mississippi and Ohio rivers), Great Lakes, and Hudson Bay basins (Fig. 4-1). Sampling was designed to represent major watershed divisions at a broad scale among regions, and opportunistic access determined smaller-scale sampling within regions and rivers. In most instances, mussels were retrieved via hand and tissue samples were taken via non-destructive mantle biopsy (Berg et al. 1995). In larger rivers, SCUBA was used to access river beds, from which mussels were collected. Samples were stored in ethanol or flash frozen in liquid nitrogen and stored at -20˚ C (if fixed in ethanol) or -80˚ C (if flash frozen), until DNA could be extracted. Additional tissue samples, fixed in ethanol, were acquired from the collection at the Illinois Natural History Survey. Sequencing COI mtDNA Total genomic DNA was extracted using Qiagen DNeasy kits (Valencia, CA). I amplified an approximately 600 base pair region of the COI mitochondrial gene by modifying the universal invertebrate primers developed by Folmer et al. (1994): LCO1490: 5’-GGTCAACAAATCATAAAGATATTGG-3’ HCO2198 (shortened by six nucleotides): 5’-TCAGGGTGACCAAAAAATCA-3’ Amplification of DNA was achieved using polymerase chain reaction (PCR) conditions of 94˚C initial denaturation for 2 min, followed by cycles of 95˚C denaturation for 30 sec, 42˚C annealing for 30 sec, 72˚C extension for 90 sec, 42˚C annealing for 30 sec, 72˚C extension for 90 sec for 35 cycles, and final extension at 72˚C for 3:30 min. PCR products were purified using Qiagen QIAquick kits (Valencia, CA) and sequenced on an ABI 3100 automated

` sequencer using ET-dye (MEGABace). Forward and backward sequences were obtained and used to check for accuracy. Sequences were examined, edited and manually aligned using BioEdit (Hall 1999). I conducted a rarefaction of all haplotypes detected by the number of sites sampled, per the methods described in Gotelli and Colwell (2001) performed in “vegan” for R (R Core Development Team). Descriptive population genetic structure statistics were generated using Arlequin (Version 3.11). I created mismatch distributions, which compare the frequency of the number of nucleotide substitutions between all pairs of sequences, for both species. I also used the equation  = 2µt to estimate the relative age of divergence amongst lineages detected (where  = the mean of the mismatch distribution, µ = mutation rate and t = generations to common ancestor, as described by Hartl 2004). If one assumes that µ, mutation rate, is the same in these species, this equation can be solved so that the relative difference in age of these two species, based on their mismatch distribution and expressed in numbers of generations, is the ratio Q. pustulosa/2 : Q. quadrula /2. Arlequin was used to conduct an analysis of molecular variance (AMOVA), where genetic variation was assessed at three levels: within populations, among populations and among regions. Regions were defined as the Upper Mississippi River, Lower Mississippi River, Ohio River and the Great Lakes. In addition, the Hudson Bay drainage was sampled for Q. quadrula, but Q. pustulosa was not found at these sites. Arlequin was also used to generate Fst values for comparison between all populations from which I was able to obtain a minimum of five individuals. I employed Mantel tests (Legendre and Legendre 1998) as implemented in the vegan package in the R (R Core Development Team) to evaluate the significance of the correlation between genetic and geographic distances. To directly compare population differentiation between species, I compared populations of both species that had been collected at, or near the same sites (<120 river kilometers apart, see Fig. 4-1). Mantel tests were used to compare the pairwise population genetic distance for populations sampled at the same locations for both species. Finally, to test whether the species exhibited declining genetic diversity in northern parts of their ranges, I regressed both the number of haplotypes recovered per sample per site and the genetic distance between haplotypes (π) against latitude for both species.

I compared these species by employing a landscape genetic approach; this provides a measure of differences in genetic distances experienced by a species as an interpolated plane across a geographic range (Miller 2005). This approach is useful because there are many specimens available from locations at which both species could not be collected or for which too few are available to consider them a representative sample of the whole population (<5 individuals sampled at a site). It also takes into account all data and forms an overall picture of the genetic distances throughout the sampled range of the species. I used Alleles In Space to produce an estimated plane of genetic distances experienced by each species across the sampled region (Miller 2005). Options used in Alleles in Space were set to use all pairwise distances between haplotypes and raw genetic distances; the latter are calculated as the proportions of pairwise nucleotide substitutions that differed between all pairs of sequences. To compare landscape genetics between the species, I used the interpolation plane from the shared portion of the sampled ranges of Q. pustulosa and Q. quadrula. To test whether a north-south cline in genetic distance existed for these species, I tested the hypothesis that genetic distance declined with increasing latitude, using the same raw, pairwise distances that were used to create the genetic landscape plane in a Mantel test, because pairwise distances are not independent of one another. Results I identified a total of 116 and 45 distinct haplotypes for Quadrula pustulosa and Q. quadrula from 263 and 147 total sequenced individuals, respectively. Thus, I observed 0.44 and 0.30 unique haplotypes per sampled individual for Q. pustulosa and Q. quadrula, indicating greater haplotypic diversity in Q. pustulosa. This observation was confirmed by rarefied haplotype accumulation curves (Fig. 4-2). This rarefaction showed that not all haplotypes had been detected for either species, but that for the number of sites sampled there was a substantial difference in the number of haplotypes recovered. Inter-haplotype nucleotide differences within populations, denoted by π, were greater in Q. quadrula (mean = 9.74, range = 0-38.93) than in Q. pustulosa (mean = 5.06, range 2-8.6). The values for Q. quadrula declined significantly with increasing latitude (r2 = 0.27, p < 0.05), but no significant relationship between π and latitude was observed for Q. pustulosa. The mismatch distributions for these species revealed differences between these species. Quadrula pustulosa exhibited a bi-modal

` mismatch distribution, while Q. quadrula exhibited a tri-modal mismatch distribution (Fig. 4- 3), indicating that these species had at least two and three detectable lineages, respectively. The maximum number of intra-specific nucleotide substitutions was 38 in Q. pustulosa, whereas the maximum number of nucleotide differences between sequences was 81 in Q. quadrula. An AMOVA revealed significant population genetic structure (p < 0.05) for both species at each level of sub-division: within populations, among populations, and among regions, except for the population level for Q. quadrula. Quadrula pustulosa contained most of the genetic variation within populations, while Q. quadrula had approximately equal variation contained within and among populations (Table 4-1). Variation among regions was a small, but significant, proportion of the total genetic variability in both species. The range in variation of population pairwise Fst was smaller in Q. pustulosa (0-0.4) than in Q. quadrula (0-1, see Fig. 4-

4). Quadrula pustulosa exhibited isolation by distance with Fst increasing with increased geographic separation between populations (r = 0.3122, p < 0.05; Fig. 4-4). Residuals between a best-fit line describing this relationship also increased with geographic separation between 2 populations (r = 0.27, p < 0.001). Comparisons between Fst values and geographic distance did not yield a significant relationship for Q. quadrula (p>0.1; Fig. 4-4). A comparison of pairwise population Fst values from locations from which we recovered a minimum of 5 individuals for both species did not yield a significant correlation between species (p > 0.10, Fig. 4-5). Only Q. quadrula exhibited a north-south decline in population-level diversity measures. Both the number of haplotypes and pi declined significantly with increasing latitude (r2=0.234, p<0.05; r2=0.342, p<0.01, respectively; Fig. 4-6). Landscape genetic interpolations revealed modestly different patterns between the two species, but with common features (Fig. 4-7). Both experienced high genetic distances near the confluence of three major rivers – the Mississippi, Missouri and Ohio – and in the northeastern corner of the Ozark Plateau. The landscape genetic interpolation plane height was lower in Q. pustulosa than in Q. quadrula, indicating greater overall genetic distances experienced by Q. quadrula. However, there were more peaks and more variability in the plane in Q. pustulosa. High points extend up the Ohio River Valley, indicating that Q. pustulosa experiences divergence at several points along this river. A slight rise in the plane constructed for Q. pustulosa between the Upper Mississippi and the Great Lakes was observed in contrast to the

` relatively flat plane observed for Q. quadrula, indicating that more difference exists amongst glaciated regions in different watersheds (Upper Mississippi and Great Lakes) in Q. pustulosa. Both species exhibited significantly declining genetic distances among populations with increasing latitude; genetic distances were negatively correlated with geographic midpoints of the vectors connecting each pair of populations (Q. pustulosa r2=0.65, Q. quadrula r2=0.21, respectively, p <0.001 for each species; Fig.4-8). Discussion The goal of this study was to compare and contrast the population genetic structure of two freshwater mussel species that are phylogenetically related, but differ in host use. The prediction that Q. pustulosa would contain less evidence of isolation-by-distance among populations because of its greater diversity of host species was supported by these data. As predicted, Q. quadrula has a wider range of variability in genetic distance among its populations than does Q. pustulosa. These more variable values also include much greater genetic distances and higher landscape interpolation height than Q. pustulosa. The scarce pre-existing data on Q. pustulosa suggest that this species possesses a higher allozyme diversity and heterozygosity than Q. quadrula (Johnson et al. 1998a). This observation is consistent with my study, in that Q. quadrula also had fewer unique haplotypes. Thus, mitochondrial diversity, like allozyme diversity, was lower in Q. quadrula. In contrast to the data generated by my study, Q. quadrula has been shown to exhibit low levels of allozyme differentiation across very long river distances (>1000 km), with small, but significant, isolation-by-distance structuring genetic relationships among the populations (Berg et al. 1998). These results conflict with a previous study of Q. quadrula which revealed relatively high genetic distances experienced by this mussel at a much smaller spatial scale (Johnson et al. 1998a). There are several potential explanations for the conflict between my study and the previous studies of Q. quadrula, which include sampling of harvested populations, difference in both the scale and location of sampled regions, and inherent differences in nuclear and mitochondrial markers. Harvesting of mussels in the Ozarks has been hypothesized as a cause for low genetic diversity and high population differentiation over the relatively small geographic distance in Q. quadrula from the Cache and White rivers in Arkansas (Johnson et al. 1998b). Conversely, other freshwater mussels have also exhibited less differentiated

` population structure than expected based on their life histories and local geography (e.g. Hughes et al. 2004). There are also other cases where mussels exhibit little evidence of isolation-by-distance and differ in whether genetic markers can detect these differences. Amblema plicata, for example, did show a small, but significant effect of isolation-by-distance based on nuclear markers, while mitochondrial markers did not reveal this effect (Elderkin et al. 2007). There is no predictable relationship between the populations of these species, in terms of pairwise Fst values. This is not surprising because there is no evidence of isolation-by- distance in Q. quadrula, though isolation-by-distance was found for populations of Q. pustulosa. Although the comparisons between Fst values and geographic distance did not yield a significant relationship for Q. quadrula (p>0.1), both species qualitatively fit descriptions of genetic by geographic relationships as described in Hutchison and Templeton (1999). Quadrula pustulosa fits case I, which is equilibrium between gene flow and drift. In this case, both genetic distance and the variation around the genetic-by-geographic distance relationships increase with increasing geographic distance. Quadrula quadrula exhibits a pattern similar to that of case III, where drift is much more influential than gene flow. Thus, Q. pustulosa experiences population subdivision that is related to distance between population pairs, whereas Q. quadrula experiences population subdivision that is determined by something other than distance alone and pairwise population distances cannot be predicted by geographic distance. Based on an AMOVA, geographic regions contained similar genetic variants in Q. pustulosa, accounting for only 5.53% of the genetic variation observed. In contrast, differences in genetic variation at the regional and inter-population levels contain much more genetic diversity in Q. quadrula. Assuming that mutation rates are equivalent between these species, Quadrula quadrula appears to be an older species, based on the amount of sequence divergence within the species as revealed by its mismatch distribution (Hartl 2004). This would explain the greater genetic distances experienced between populations of Q. quadrula., Older lineages, particularly from glacial refugia, where populations have likely remained for a long period of time, may developed very different mitochondrial sequences over time, which is consistent with the pattern in Q. quadrula. Quadrula pustulosa is a much younger species, having been in

` existence approximately 7 times fewer generations than Q. quadrula, based on a calculation of species age from the mismatch distribution (as described in Hartl 2004). Thus, although Q. pustulosa exhibits more extant haplotypes, Q. quadrula has more diversity contained within haplotypes reflecting a longer period for accruing mutations. A portion of the inter-region genetic structure that Q. quadrula exhibits may be attributable to a distinct lineage contained in several populations near the Mississippi River. This lineage is restricted to centrally located populations, in or near the Mississippi River between the Wisconsin populations and those in Arkansas. Because it is very different than other haplotype sequences and is restricted to a subset of locations, it increases the genetic differentiation between those regions that contain it and those that do not. Quadrula pustulosa uses several host fish species which are likely to disperse short distances, therefore the number of individual hosts available to Q. pustulosa is likely higher than the number of individual hosts available to Q. quadrula. This would allow Q. pustulosa females to infest a relatively large number of fishes and take advantage of their collective dispersal abilities, i.e. the number of individuals dispersing to various distances from a point of origin. In general Q. quadrula’s host, the flathead catfish, is quite sedentary but capable of dispersing very long distances (Jackson and Jackson 1999). Thus, although most individuals remain in a small portion of their range, occasional individuals move long distances in a short amount of time. This scenario would produce a pattern of genetic distances that are not structured by geography. Instead, because infrequent dispersal events may occur as a random sampling of individuals from nearly any source population in the landscape, genetic patterns across the geographic range will be dominated by drift. Ultimately, Q. pustulosa has the opportunity to move short distances frequently, while Q. quadrula has fewer host individuals and fewer opportunities to move. However, when the latter are able to attach to a fish host, they may move a longer distance, unpredictably homogenizing distant populations. Phylogeography and Glacial History Results from this study, when compared to other studies of freshwater mussel population genetic patterns, reveal some general features shared between species. As with many other aquatic taxa, the overall patterns of genetic relatedness among populations across the ranges of these two species bear an imprint of glacial history. The landscape genetic

` interpolations for both species indicated relatively high genetic distances within the unglaciated portions of their ranges, with genetic distances declining with increasing latitude. North-south gradients in genetic diversity and genetic distance likely resulting from the glacial history of North America are common and appear in terrestrial (Hewitt 1996) and other aquatic species (Bernatchez and Wilson 2002). This pattern occurs amongst freshwater fishes (e.g. northern hogsucker, Hypentelium nigricans, Berendzen et al. 2003) partially because watercourses such as the ancient Teays River were obliterated by glaciers and reformed after the glaciers retreated. Many mussel species share this pattern of declining genetic diversity in glaciated regions (Amblema plicata, Elderkin et al. 2007; Actinonaias ligamentina, Elliptio dilatata, Elderkin et al. 2008). This pattern of declining genetic diversity and inter-population distances at the northern edge of species’ ranges may be driven by the mechanisms described in the “leading edge” hypothesis (Soltis et al. 1997). This hypothesis suggests that as a species colonizes a new area, the “leading edge” of the colonization carries with it only a subset of the genetic variants contained in the previously occupied area. This creates a genetically depauperate forward edge in invasions that may exhibit low diversity and low inter-population genetic distances. This pattern is consistent with my observations of both species in this study. Both Q. pustulosa and Q. quadrula exhibit declining landscape genetic heights in more northern areas. Although the slopes differ, both species exhibit inter-population distances that decrease as latitude increases. Conservation Implications Knowledge of mussel-host relationships is especially important because freshwater mussels are one of the most endangered faunal groups in North America (Bogan 1993). Both population abundance and species diversity have been drastically reduced as mussels continue to face increasing anthropogenic changes in lotic ecosystems (Ricciardi and Rasmussen 1999). Many factors have been implicated in their decline, such as impoundments, loss of fish hosts and invasive species (Bogan 1993, Ricciardi et al. 1998). Rapid population declines require active intervention by humans. It is imperative that such conservation activities be grounded in population genetics, which can indicate both historical population structure, e.g. vicariant events and historical barriers to dispersal, and current factors influencing contemporary structure, such as the number of migrants and population size. Without the incorporation of

` population genetics, conservation efforts, including reintroduction and augmentation, may inappropriately mix populations, altering their structure and diversity. Moreover, gene flow from genetically distinct captive populations may disrupt the genetic structure and composition of wild populations, even if few individuals are released (Ford 2002). This may result in inbreeding or outbreeding depression (Hoftyzer et al. 2008). To avoid unintended, deleterious effects of conservation actions, we must develop an understanding of population genetics of threatened and endangered populations and the underlying natural history that shapes them. Conservation efforts for these species should be balanced between maintaining core and fringe populations to maintain maximum evolutionary flexibility and geographic range for these species. Quadrula pustulosa exhibited a pattern of isolation by distance, while Q. quadrula exhibited a pattern of inter-population distances that were largely random and dominated by drift. These samples were taken from a very large geographic range, which should have revealed geographic structuring, if any existed. These patterns may be related to fish host use, because Q. pustulosa is likely able to exploit many hosts and move in a stepping-stone fashion throughout its range. Quadrula quadrula, on the other hand, uses a single host that is largely sessile, but is capable of long-distance dispersal. Both species exhibited a legacy of glacial patterns, with declining genetic distances among populations with increasing latitude likely due to post-glacial dispersal reflected in homogenization of populations at higher latitudes. These populations at the edges of the species range are likely to become more important with global climate change as populations become imperiled. Individuals in these populations are likely to be immigrants into new habitat and will be necessary, if range shifts occur. The core of genetic variability was concentrated in the Central Highlands for both species, with the highest points of genetic landscapes in the north-eastern Ozarks. These populations need to be protected to maintain their substantial evolutionary legacy. Conservation of these species needs to address this core evolutionary heritage, while remaining sensitive to maintaining what genetic diversity exists at the edge of the range. Because these species remain relatively common, the range- wide genetic diversity can and should be maintained. At the same time, the spatial-genetic patterns observed here contribute to a general understanding of patterns common to the rich mussel fauna of North America and confirm the common imprint of glacial history on this faunal group.

Literature Cited

Avise J. C. 2000. Phylogeography: the history and formation of species. Harvard University Press, Cambridge, Mass.

Berendzen P. B., A. M. Simons, and R. M. Wood. 2003. Phylogeography of the northern hogsucker, Hypentelium nigricans (Teleostei : Cypriniformes): genetic evidence for the existence of the ancient Teays River. Journal of Biogeography 30:1139-1152.

Berg D. J., E. G. Cantonwine, W. R. Hoeh, and S. I. Guttman. 1998. Genetic structure of Quadrula quadrula (Bivalvia : Unionidae): Little variation across large distances. Journal of Shellfish Research 17:1365-1373.

Berg D. J., W. R. Haag, S. I. Guttman, and J. B. Sickel. 1995. Mantle biopsy: A technique for nondestructive tissue-sampling of freshwater mussels. Journal of the North American Benthological Society 14:577-581.

Bernatchez, L. and C. C. Wilson. 1998. Comparative phylogeography of Nearctic and Palearctitc fishes. Molecular Ecology. 7: 431-452.

Bogan A. E. 1993. Fresh-water bivalve extinctions (Mollusca, Unionoida) - a search for causes. American Zoologist 33:599-609.

Coker, R.E., A.F., Shira, H.W, Clark,., and A.D. Howard. 1921. Natural history and propagation of fresh-water mussels. Bulletin of the Bureau of Fisheries. 37:75-182.

Criscione C. D., and M. S. Blouin. 2007. Parasite phylogeographical congruence with salmon host evolutionarily significant units: implications for salmon conservation. Molecular ecology 16:993-1005.

Criscione C. D., B. Cooper, and M. S. Blouin. 2006. Parasite genotypes identify source populations of migratory fish more accurately than fish genotypes. Ecology 87:823-828.

Dieckmann U., B. O'Hara, and W. Weisser. 1999. The evolutionary ecology of dispersal. Trends in Ecology & Evolution 14:88-90.

Elderkin C. L., A. D. Christian, C. C. Vaughn, J. L. Metcalfe-Smith, and D. J. Berg. 2007. Population genetics of the freshwater mussel, Amblema plicata (Say 1817) (Bivalvia : Unionidae): Evidence of high dispersal and post-glacial colonization. Conservation Genetics 8:355-372.

Elderkin C. L., A. D. Christian, J. L. Metcalfe-Smith, and D. J. Berg. 2008. Population genetics and phylogeography of freshwater mussels in North America, Elliptio dilatata and Actinonaias ligamentina (Bivalvia : Unionidae). Molecular Ecology 17:2149-2163.

Fetzner J. W., and K. A. Crandall. 2003. Linear habitats and the nested clade analysis: An empirical evaluation of geographic versus river distances using an ozark crayfish (Decapoda : Cambaridae). Evolution 57:2101-2118.

Folmer, O., M. Black, W. Hoeh, , R. Lutz,, R. Vrijenhoek. 1994. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology and Biotechnology 3: 294-299.

Ford, M. J. 2002. Selection in captivity during supportive breeding may reduce fitness in the wild. Conservation Biology 16:815-825.

Futuyma D. J., and G. Moreno. 1988. The evolution of ecological specialization. Annual Review of Ecology and Systematics 19:207-233.

Gotelli, N. J. and R. K.Colwell. 2001. Quantifying biodiversity: Procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters. 4: 379-391.

Hall, T.A. 1999. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symposium Series 41:95-98.

Hartl D. L. 2004. The origin of malaria: Mixed messages from genetic diversity. Nature Reviews Microbiology 2:15-22.

Hewitt G. M. 1996. Some genetic consequences of ice ages, and their role in divergence and speciation. Biological Journal of the Linnean Society 58:247-276.

Hoftyzer E., J. D. Ackerman, T. J. Morris, and G. L. Mackie. 2008. Genetic and environmental implications of reintroducing laboratory-raised unionid mussels to the wild. Canadian Journal of Fisheries and Aquatic Sciences 65:1217-1229.

Howard, A.D. 1913. The catfish as a host for fresh-water mussels. Transactions of the American Fisheries Society. 42: 65-70.

Howard, A.D. 1914. Experiments in propagation of fresh-water mussels of the Quadrula group. Appendix 4, Rept. U.S. Comm. Fish. for 1913:1-52.

Howard, A.D. and B.J. Anson. 1922. Phases in the parasitism of the Unionidae. Journal of Parasitology, 9(2): 68-82.

Howe, H. F. and J. Smallwood. 1982. Ecology of seed dispersal. Annual Review of Ecology and Systematics. 13: 201-228.

Hughes J., A. M. Baker, C. Bartlett, S. Bunn, K. Goudkamp, and J. Somerville. 2004. Past and present patterns of connectivity among populations of four cryptic species of freshwater mussels Velesunio spp. (Hyriidae) in central Australia. Molecular Ecology 13:3197-3212.

Hutchison, D. W. and A. R. Templeton. 1999. Correlation of genetic and geographic distance measures: Inferring the relative influences of gene flow and drift on the distribution of genetic variability. 53(6): 1898-1914.

Jackson, J. R. and D. C. Jackson. 1999. Flathead catfish: biology and management. Pages 23– 36 in E. R. Irwin, W. A. Hubert, C. F. Rabeni, H. L. Schramm, Jr., and T. Coon, editors. Catfish 2000: proceedings of the international ictalurid symposium. American Fisheries Society, Symposium 24, Bethesda, Maryland.

Johnson K. P., B. L. Williams, D. M. Drown, R. J. Adams, and D. H. Clayton. 2002. The population genetics of host specificity: genetic differentiation in dove lice (Insecta : Phthiraptera). Molecular Ecology 11:25-38.

Johnson R. L., F. Q. Liang, and J. L. Farris. 1998a. Genetic diversity among four amblemini species (Bivalvia : Unionidae) in the Cache and White rivers, Arkansas. Southwestern Naturalist 43:321-332.

Johnson R. L., F. Q. Liang, and J. L. Farris. 1998b. Genetic relationships of several amblemini species (Bivalvia: Unionidae) in Arkansas. Journal of Shellfish Research 17:1237-1242.

Legendre, P. and L. Legendre 1998. Numerical Ecology. 2nd English Edition. Elsevier.

Mayden 1988. Vicariance biogeography, parsimony, and evolution in North American freshwater fishes.. Systematic Zoology. 37(4): 329-355.

McLain D. C., and M. R. Ross. 2005. Reproduction based on local patch size of Alasmidonta heterodon and dispersal by its darter host in the Mill River, Massachusetts, USA. Journal of the North American Benthological Society 24:139-147.

Miller M. P. 2005. Alleles In Space (AIS): Computer software for the joint analysis of interindividual spatial and genetic information. Journal of Heredity 96:722-724.

Navajas M. 1998. Host plant associations in the spider mite Tetranychus urticae (Acari : Tetranychidae): insights from molecular phylogeography. Experimental and Applied Acarology 22:201-214.

Nieberding C., S. Morand, R. Libois, and J. R. Michaux. 2006. Parasites and the island syndrome: the colonization of the western Mediterranean islands by Heligmosomoides polygyrus (Dujardin, 1845). Journal of Biogeography 33:1212-1222.

Ricciardi A., and J. B. Rasmussen. 1999. Extinction rates of North American freshwater fauna. Conservation Biology 13:1220-1222.

Ricciardi A., R. J. Neves, and J. B. Rasmussen. 1998. Impending extinctions of North American freshwater mussels (Unionoida) following the zebra mussel (Dreissena polymorpha) invasion. Journal of Animal Ecology 67:613-619.

Roderick G. K. 1996. Geographic structure insect populations: Gene flow, phylogeography, and their uses. Annual Review of Entomology 41:325-352.

Romano, M. A., D. B. Markillie, and R. V. Anderson. 1990. What’s in the soup: Electrophoretic analysis of the host-parasite relationship between the flathead catfish and the mapleleaf mussel. Proceedings of the Mississippi River Research Consortium 22: 17.

Romano, M. A., D. B. Markillie, and R. V. Anderson. 1991. Electrophoretic analysis of the host - parasite relationship between the flathead catfish (Pylodictus olivaris) and the mapleleaf mussel (Quadrula quadrula). Proceedings of the Mississippi River Research Consortium 23: 66.

Sakaris P. C., R. V. Jesien, and A. E. Pinkney. 2005. Brown bullhead as an indicator species: Seasonal movement patterns and home ranges within the Anacostia River, Washington, DC. Transactions of the American Fisheries Society 134:1262-1270.

Serb J. M., J. E. Buhay, and C. Lydeard. 2003. Molecular systematics of the North American freshwater bivalve genus Quadrula (Unionidae : Ambleminae) based on mitochondrial ND1 sequences. Molecular phylogenetics and evolution 28:1-11.

Smith M. A., and D. M. Green. 2005. Dispersal and the metapopulation paradigm in amphibian ecology and conservation: are all amphibian populations metapopulations? Ecography 28:110-128.

Soltis D. E., M. A. Gitzendanner, D. D. Strenge, and P. S. Soltis. 1997. Chloroplast DNA intraspecific phylogeography of plants from the Pacific Northwest of North America. Plant Systematics and Evolution 206:353-373.

Strange, R. M. and B. M. Burr. 1997. Intraspecific phylogeography of North American Highland fishes: A test of the Pleistocene vicariance hypothesis. Evolution. 51(3): 885- 897.

Travnichek, V. H. 2004. Movement of flathead catfish in the Missouri River: Examining opportunities for managing river segments for different fishery goals. Fisheries Management and Ecology. 11: 89-96.

Vaughn C. C. 1997. Regional patterns of mussel species distributions in North American rivers. Ecography 20:107-115.

Vaughn C. C., and C. M. Taylor. 2000. Macroecology of a host-parasite relationship. Ecography 23:11-20.

Watters G. T. 1992. Unionids, Fishes, and the Species-Area Curve. Journal of Biogeography 19:481-490.

Wendel, J. L. and S. W. Kelsch 1999. Summer range and movement of channel catfish in the Red River of the North. Pages 203-214. in E. R. Irwin, W. A. Hubert, C. F. Rabeni, H. 69

L. Schramm, Jr., and T. Coon, editors. Catfish 2000: proceedings of the international ictalurid symposium. American Fisheries Society, Symposium 24, Bethesda, Maryland.

Wright, S. 1943. Isolation by distance. Genetics. 28:114–138.

Zanatta, D. T. and R. W. Murphy. 2008. The phylogeographical and management implications of genetic population structure in the imperiled snuffbox mussel, Epioblasma triquetra (Bivalvia: Unionidae). Biological Journal of the Linnean Society 93: 371-384.

Table 4-1. Partitioned genetic variation among regions, among populations and within populations as revealed by Analysis of Molecular Variance.

Quadrula pustulosa Source of Variation d.f. Percent Variation P-value F-statistics Among regions 3 5.53 < 0.005 FCT= 0.06

Among populations 18 4.51 <0.005 FSC=0.05

Within populations 242 89.97 <0.005 Fst=0.10

Quadrula quadrula Source of Variation d.f. Percent Variation P-value F-statistics Among regions 4 10.05 <0.005 FCT=0.10

Among populations 13 47.57 <0.005 FSC=0.53

Within populations 129 42.37 0.08 Fst=0.58

Table 4-2.Summary of differences in genetic structure observed between the two species of Quadrula.

Quadrula pustulosa Quadrula quadrula Number of individuals sampled 263 147

Unique haplotypes recovered 116 45

Haplotypes per sample 0.44 0.30

Average nucleotide differences within populations 5.06 9.74

Hutchison and Templeton pattern observed Case I: Isolation by distance Case III: Genetic Drift

Figure 4-1. Sites from which freshwater mussels were sampled. Quadrula pustulosa sites are labeled with downward facing arrows (▼), while Q. quadrula sites are marked

with upwards arrows (▲). Circled points indicate sites from which 5 or more individuals were sampled from each species indicated.

Figure 4-2. Haplotype accumulation curves for both Quadrula species produced by rarefaction resampling. Vertical lines indicate standard deviation for the number of sites sampled, while the horizontal line represents the estimated mean.

Figure 4-3. Mismatch distributions for both Quadrula species, showing pairwise nucleotide differences between sequences.

Figure 4-4. Intra-specific relationships between pairwise geographic and genetic distances for Quadrula pustulosa and Q. quadrula. Quadrula pustulosa exhibited evidence of isolation-by-distance (Mantel r=0.312 , p=0.02). Quadrula quadrula did not (Mantel r=0.042, p=0.35).

Figure 4-5. Pairwise Fst comparisons between Quadrula pustulosa and Q. quadrula using locations from which both species were sampled (Mantel r=-0.03, p>0.10)

1.0

0.8

0.6

0.4

0.2 Haplotypes per sampleHaplotypes per

35 40 45 50

Latitude

10 0

35 40 45 50

Latitude

Figure 4-6. Intrapopulation measures of haplotype richness. The upper panel shows haplotypes per sample versus latitude. The lower panel shows pi, the average number of nucleotide substitutions between haplotypes within populations plotted against latitude. Open circles are Q. pustulosa and solid circles are Q. quadrula; the relationship is only significant for Q. quadrula (haplotype richness, r2=0.234, p<0.05; Pi, r2=0.342, p<0.01).

Figure 4-7. Genetic landscape interpolation for Quadrula pustulosa (left panel) and Q. quadrula (right panel). Dark colors represent lower landscape genetic height for that species. Colors are scaled so that the highest points for that species are white, while the lowest points for that species are black. Therefore, the same shade in both panels does not represent the same height, but rather the relative proportion of landscape genetic interpolation height within that species.

0.15

0.10

0.05

RawPairwiseDistance Genetic 0.00

35 40 45 50

Latitude (decimal degrees)

Figure 4-8. Genetic distances versus latitude. Lines represent significant relationships between raw pairwise genetic distances and latitude (Q. pustulosa r2=0.65, Q. quadrula r2=0.21, p < 0.001 for both species). Open circles and dashed line represent Q. pustulosa and the closed circles and solid line represent Q. quadrula.

Chapter 5: General Conclusion and Synthesis

Because evolutionary potential resides in populations, population-level processes must be understood to assess the evolutionary patterns of species. Freshwater mussels are particularly ripe for study, because they are both parasites with atypical life histories and are of critical conservation concern. Therefore, a basic understanding of the demographic patterns of mussel populations must be developed to effect their conservation. At the same time, to improve our understanding of the patterns exhibited by linked populations, mussels may be contrasted with other parasites, many of which develop population structure within the population structure of their hosts. Individual populations must be understood to take stock of their status and to understand the population dynamics, i.e. demography, that they undergo (see Chapter 1). To build an understanding of mussel populations, I studied a priority 2 candidate for endangered species listing, Popenaias popeii, in the Black River of New Mexico. These mussels exhibited a high annual survival rate similar to what has been observed across mussel taxa (Figure 5-1A). Several individuals in the populations studied here were, at minimum, 9 years old and likely substantially older than that. Therefore, it is likely that, as with many other mussel species (Figure 5-1B), P. popeii exhibits extreme longevity. To thoroughly understand the population dynamics of P. popeii more field research is required, because to-date very few juvenile mussels have been recovered (n=5, Lang pers. comm.). Although Chapter 2 presents some evidence that the population is stable, additional information will be important in confirming this determination. Without more information on the juvenile life stage, few hard-and-fast conclusions can be drawn and population models of this species will be difficult to parameterize. Few studies of juvenile mussels, of any species, have been completed under natural conditions (Berg et al. 2008). However, some population models have indicated that long-lived species may depend more on adult than juvenile stages for stability and long-term persistence (Gotelli 1991). This pattern of survival amongst adult stages more strongly affecting populations is likely to hold true for mussel species. Additional demographic data are

` required, but they will be insufficient without modeling them to bridge the gap between theoretical ecology and on-the-ground mussel research. Geographically separated populations of mussels are linked by fish hosts. The nature of this relationship is unclear at present, especially in terms of the demographic parameters governing that interaction. Many studies of mussel-host relationships have focused on laboratory research (e.g. Yeager and Saylor 1995, van Snik Gray and Stauffer 1999, Gray et al. 2002, Watters and O'Dee 1998, Haag and Warren 2003, Khym and Layzer 2000), which exclusively reveals physiological barriers to infestation. These studies are important as there is substantial variability amongst mussel “species” in terms of their physiological compatibility with host fishes. Within nominal species of mussels, host affinities vary by population; local populations of mussels may even specialize on local populations of fish (Riusech and Barnhart 1999, Kobayashi and Kondo 2005). Field studies are required to quantify differences between hosts that may be generated by ecological barriers to infestation such as behavior (Haag and Warren 2000) and phenology (Riusech and Barnhart 2000). Some studies have reported differences in the demographic parameters between fish species and have recognized that host suitability may be based on such measures (e.g. Trdan 1981, Chapter 2). These differences in host suitability have been described as “primary” and “secondary” hosts (O'Brien and Box 1999) or were hypothesized to be a strategic compromise using some hosts to disperse and some for local recruitment (Martel and Lauzon-Guay 2005). The latest thorough review of host associations throughout the Unionidae was completed in 1992 (Hoggarth 1992), although a synthesis of evolutionary patterns in host relationships has just been published (Barnhart et al. 2008). More synthetic studies are needed to describe the expansion of mussel-host data in the last decade and a half. Substantial field work is also required to understand the relationship between mussels and their hosts, both ecologically and evolutionarily. In particular, the demographic impacts of host species on the mussels that use them must be assessed, before the demographic relevance of hosts to mussel populations is truly known. With a set of well- established hosts, hypotheses describing the barriers to infestation that mussels must overcome will naturally follow and will substantially improve the study of the dependence of freshwater mussels on their fish hosts.

To date, only a single published study has attempted to link mussel populations by directly observing fish movement (McLain and Ross 2005), but the connectivity between populations of mussels has been studied by indirect methods, primarily using genetic relatedness between populations. Many studies have been conducted that use molecular markers, which evidence the connectivity between populations via dispersal (Bermingham and Moritz 1998). While several studies have been conducted, they provide a relatively sparse representation of the 300 unionid species in North America. A few common themes can be derived from the data that do exist, however. The Central Highlands has played a major role in the historical biogeography of many aquatic organisms (Mayden 1988). Mussel populations in this region may contain unique genetic variants (Zanatta and Murphy 2008; Chapter 4 here). Many mussel species also share the imprint of glacial history by exhibiting reduced genetic diversity in the northern parts of their ranges, where recolonization has occurred after these populations were wiped out by glaciers (Zanatta and Murphy 2008), as seen in fishes (Bernatchez and Wilson 1998). Some evidence supports the hypothesis that population genetic structure is increased by either having less vagile hosts or by using smaller river systems (Berg et al. 2007). Until more general patterns have been deciphered, however, it will be hard to make general predictions about how genetic variation is partitioned amongst populations in mussel species. In particular, three hypotheses need to be addressed, preferably using mussels for which host specificity is well-known. The first hypothesis is that host vagility is correlated with a reduction in inter-population genetic differentiation. The second hypothesis is that habitat preference, especially associations with river order, are correlated with inter-population genetic differentiation. The third hypothesis is that different groups of mussels exhibit predictable patterns in population genetic structuring at both inter-population and inter-specific levels. Methodological issues, unfortunately, hamper the study of freshwater mussels. First, in terms of studying populations of mussels, few data are available to describe the basic demographic parameters needed to understand general patterns in mussel population dynamics. This may be due to a strong preference for studies that are easy to carry out relative to mark- and-recapture, such as quadrat sampling (see Strayer and Smith 2003). Because mussels show surprisingly high movement and seasonal variation in their location (endobenthic vs epibenthic,

(Perles et al. 2003, Amyot and Downing 1997, Amyot and Downing 1998) studies of mussel population dynamics must either be interpreted with extreme caution or must use an explicit framework for addressing sampling inefficiencies, using methods such as mark-and-recapture. As with other mark-and-recapture studies (e.g. Villella et al. 2004), my data reveal important demographic parameters. Advances in statistical mark-and-recapture techniques and their incorporation into a common framework (program MARK) have made the analysis of such datasets more tractable. However, estimation of many parameters that determine the demographic patterns of populations require special techniques, such as employing the “robust design” that allows estimation of not only survival, but population size, trend and migration (Kendall et al. 1995). Similarly, glochidial morphometrics have been extensively studied and may be identifiable to useful taxonomic levels, such as subfamily or genus (Hoggarth 1999). However, identification to species or population is preferable. Population-level identifications could provide heretofore unobtainable measurements of glochidial dispersal. Molecular markers offer some solutions to this problem and species-level molecular keys have been developed (Kneeland and Rhymer 2007, Kneeland and Rhymer 2008), but such approaches will at least require genotypic data for many, if not all, of the mussel species in the study area. Thus, genetic studies are critical to improving our understanding of dispersal in freshwater mussels because they provide both indicators of migration between populations and because the information produced in these studies will be needed to adequately identify species or population-level origins of glochidia attached to fishes. Despite the difficulties in studying freshwater mussels, they are a taxon in critical need of the attention of the research community. As their numbers dwindle and efforts to recover them increase, both funding opportunities and opportunities to permanently damage these animals will be plentiful. Research intended to further conservation must employ basic science and place the evolution and ecology of these animals into a theoretical context. Patterns within and amongst species must be described and explained, especially in terms of the demography of mussel populations and how that demography interacts with mussel-host relationships.

Literature Cited

Aldridge D. C. 1999. The morphology, growth and reproduction of Unionidae (Bivalvia) in a fenland waterway. Journal of Molluscan Studies 65:47-60.

Allen D. C., B. E. Sietman, D. E. Kelner, M. C. Hove, J. E. Kurth, J. M. Davis, J. L. Weiss, and D. J. Hornbach. 2007. Early life-history and conservation status of Venustaconcha ellipsiformis (Bivalvia, Unionidae) in Minnesota. American Midland Naturalist 157:74- 91.

Amyot J. P., J. A. Downing. 1998. Locomotion in Elliptio complanata (Mollusca: Unionidae): a reproductive function? Freshwater Biology 39:351-358.

Amyot J. P., J. A. Downing. 1997. Seasonal variation in vertical and horizontal movement of the freshwater bivalve Elliptio complanata (Mollusca: Unionidae). Freshwater Biology 37:345-354.

Barnhart M. C., W. R. Haag, and W. N. Roston. 2008. Adaptations to host infection and larval parasitism in Unionoida. Journal of the North American Benthological Society 27:370- 394.

Bauer G. 1992. Variation in the life-span and size of the fresh-water pearl mussel. Journal of Animal Ecology 61:425-436.

Berg D. J., A. D. Christian, and S. I. Guttman. 2007. Population genetic structure of three freshwater mussel (Unionidae) species within a small stream system: significant variation at local spatial scales. Freshwater Biology 52:1427-1439.

Berg D. J., T. D. Levine, J. A. Stoeckel, and B. K. Lang. 2008. A conceptual model linking demography and population genetics of freshwater mussels. Journal of the North American Benthological Society 27:395-408.

Bermingham E., C. Moritz. 1998. Comparative phylogeography: concepts and applications. Molecular Ecology 7:367-369.

Bernatchez L., C. C. Wilson. 1998. Comparative phylogeography of nearctic and palearctic fishes. Molecular Ecology 7:431-452.

Christian A. D., D. L. Davidson, W. R. Posey, P. J. Rust, J. L. Farris, J. L. Harris, and G. L. Harp. 2000. Growth curves of four species of commercially valuable freshwater mussels (Bivalvia: Unionidae). Arkansas Journal of Sciences 54:41-50.

Eads C. B., A. E. Bogan, and J. F. Levine. 2006. Status and life-history aspects of Villosa constricta (Conrad 1838) (Notched Rainbow), in the upper Neuse River Basin, North Carolina. Southeastern Naturalist 5:649-660.

Gotelli N. J. 1991. Demographic models for Leptogorgia virgulata, a Shallow-Water Gorgonian. Ecology 72:457-467.

Gray E. V., W. A. Lellis, J. C. Cole, and C. S. Johnson. 2002. Host identification for Strophitus undulatus (Bivalvia : Unionidae), the creeper, in the upper susquehanna river basin, Pennsylvania. American Midland Naturalist 147:153-161.

Haag W. R., M. L. Warren. 2000. Effects of light and presence of fish on lure display and larval release behaviours in two species of freshwater mussels. Animal Behaviour 60:879-886.

Haag W. R., M. L. Warren. 2003. Host fishes and infection strategies of freshwater mussels in large Mobile Basin streams, USA. Journal of the North American Benthological Society 22:78-91.

Hanlon S. D., J. F. Levine. 2004. Notes on the life history and demographics of the savannah lilliput (Toxolasma pullus) (Bivalvia: Unionidae) in University Lake, NC. Southeastern Naturalist 3:289-296.

Harmon J. L., J. E. Joy. 1990. Growth-rates of the fresh-water mussel, Anodonta imbecillis Say 1829, in 5 West-Virginia Wildlife Station Ponds. American Midland Naturalist 124:372- 378.

Hoggarth M. A. 1992. An examination of the glochidia-host relationships reported in the literature for North American species Unionacea (Mollusca: Bivalvia). Malacology Data Net 3:1-30.

Hoggarth M. A. 1999. Descriptions of some of the glochidia of the unionidae (Mollusca : Bivalvia). Malacologia 41:1-118.

Kendall, W. L., W. H. Pollock, and C. Brownie. 1995. A likelihood based approach to capture- recapture estimation of demographic parameters under robust design. Biometrics. 51: 293-308.

Kesler D. H., and R. C. Bailey. 1993. Density and ecomorphology of a fresh-water mussel (Elliptiocomplanata, Bivalvia, Unionidae) in a Rhode-Island Lake. Journal of the North American Benthological Society 12:259-264.

Khym J. R., and J. B. Layzer. 2000. Host fish suitability for glochidia of Ligumia recta. American Midland Naturalist 143:178-184. 87

Kneeland S. C., and J. M. Rhymer. 2008. Determination of fish host use by wild populations of rare freshwater mussels using a molecular identification key to identify-glochidia. Journal of the North American Benthological Society 27:150-160.

Martel A. L., J. S. Lauzon-Guay. 2005. Distribution and density of glochidia of the freshwater mussel Anodonta kennerlyi on fish hosts in lakes of the temperate rain forest of Vancouver Island. Canadian Journal of Zoology-Revue Canadienne De Zoologie 83:419- 431.

Mayden R. L. 1988. Vicariance biogeography, parsimony, and evolution in North-American fresh-water fishes. Systematic Zoology 37:329-355.

McLain D. C., M. R. Ross. 2005. Reproduction based on local patch size of Alasmidonta heterodon and dispersal by its darter host in the Mill River, Massachusetts, USA. Journal of the North American Benthological Society 24:139-147.

Michaelson D. L., R. J. Neves. 1995. Life-History and Habitat of the Endangered Dwarf Wedgemussel Alasmidonta heterodon (Bivalvia, Unionidae). Journal of the North American Benthological Society 14:324-340.

Negus C. L.1966 A quantitative study of growth and production in unionid mussels of the River Thames at Reading. Journal of Animal Ecology 35:513-532.

Payne B. S., A. C. Miller. 2000. Recruitment of Fusconaia ebena (Bivalvia : unionidae) in relation to discharge of the lower Ohio River. American Midland Naturalist 144:328-341.

Perles S. J., A. D. Christian, and D. J. Berg. 2003. Vertical migration, orientation, aggregation, and fecundity of the freshwater mussel Lampsilis siliquoidea. Ohio Journal of Science 103:73-78.

Ravera O., A. R. Spracoti. 1997. Population dynamics, reproduction, assimilation and respiration of two species of fresh water mussel: Unio mancus Zhadin and Anodonta cygnea (Lam.). Memorie dell’Istituto Italiano di Idrobiologia 56:113-130.

Riusech, F. A. and M. C. Barnhart. 1999. Host suitability and utilization in Venustaconcha ellipsiformis and Venustaconcha pleasii.Tankersley, R. A., T. Watters, B. Armitage, D. Warmolts, editors. Proceedings of the Captive Care, Propagation, and Conservation of Freshwater Mussels Symposium. March 6-8, 1998, Columbus, Ohio; 1998; Columbus, OH: Ohio University Press; 2000. 83 p.

Strayer DL, Smith DR. 2003 A guide for sampling freshwater mussel populations. Maryland: American Fisheries Society.

Trdan R. J. 1981. Reproductive-biology of Lampsilis radiata siliquoidea (Pelecypoda, Unionidae). American Midland Naturalist 106:243-248. van Snik Gray E., J. R. Stauffer. 1999. Comparative microhabitat use of ecologically similar benthic fishes. Environmental Biology of Fishes 56:443-453.

Villella R. F., D. R. Smith, and D. P. Lemarie. 2004. Estimating survival and recruitment in a freshwater mussel population using mark-recapture techniques. American Midland Naturalist 151:114-133.

Watters G. T., S. H. O'Dee. 1998. Metamorphosis of freshwater mussel glochidia (Bivalvia : Unionidae) on amphibians and exotic fishes. American Midland Naturalist 139:49-57.

Yeager B. L., C. F. Saylor. 1995. Fish Hosts for 4 species of fresh-water mussels (Pelecypoda, Unionidae) in the Upper Tennessee River Drainage. American Midland Naturalist 133:1- 6.

Zanatta D. T., R. W. Murphy. 2008. The phylogeographical and management implications of genetic population structure in the imperiled snuffbox mussel, Epioblasma triquetra (Bivalvia : Unionidae). Biological Journal of the Linnean Society 93:371-384.

Figure 5-1. Strip plots of demographic data, for reference, points are jittered vertically; the y-axis contains no information. A – Reported annual survival for several species of unionoid mussels. Filled symbols are review data, open symbols are from chapter 1. B – Estimated longevities for a variety of unionoid mussels. Data for this figure are derived from my dissertation and Negus 1966, Harmon and Joy 1990, Bauer 1992,Kesler and Bailey 1993, Michaelson and Neves 1995, Ravera and Spracoti 1997, Aldridge 1999, Christian et al. 2000, Payne and Miller 2000, Ziuganov et al. 2000,Anthony et al. 2001, Hart et al. 2001, Rogers et al. 2001, Hanlon and Levine 2004, Villella et al. 2004, Eads et al. 2006, and Allen et al. 2007.

Appendices

Appendix 1. List of glochidial intensities on fishes infested by Popenaias popeii, described in Chapter 3. For species codes, refer to Chapter 3.

Species Intensity Species Intensity Species Intensity Carcar 1337 Cyplut 1 Lepmac 4 Carcar 150 Cyplut 1 Lepmeg 9 Cyplut 2 Cyplut 1 Lepmeg 1 Cyplut 2 Cyplut 1 Lepmeg 1 Cyplut 12 Cyplut 1 Lepmeg 3 Cyplut 11 Cyplut 1 Lepmeg 1 Cyplut 42 Cyplut 1 Lepmeg 1 Cyplut 11 Cyplut 12 Lepmeg 8 Cyplut 2 Cyplut 13 Lepmeg 4 Cyplut 6 Cyplut 1 Lepmeg 8 Cyplut 1 Cyplut 3 Lepmeg 1 Cyplut 1 Cyplut 8 Lepmeg 2 Cyplut 1 Cyplut 4 Lepmeg 3 Cyplut 1 Cyplut 2 Lepmeg 1 Cyplut 1 Cyplut 1 Lepmeg 3 Cyplut 5 Cyplut 1 Lepmeg 2 Cyplut 1 Cyplut 1 Lepmeg 2 Cyplut 1 Cyplut 1 Lepmeg 1 Cyplut 3 Cyplut 1 Lepmeg 2 Cyplut 13 Cyplut 3 Lepmeg 1 Cyplut 2 Cyplut 4 Lepmeg 4 Cyplut 1 Cyplut 1 Lepmeg 2 Cyplut 22 Cyplut 1 Lepmeg 11 Cyplut 2 Cyplut 2 Lepmeg 1 Cyplut 51 Cyplut 1 Lepmeg 1 Cyplut 2 Cyplut 1 Lepmeg 4 Cyplut 25 Cyplut 3 Lepmeg 4 Cyplut 21 Cyplut 1 Lepmeg 2 Cyplut 1 Cyplut 1 Lepmeg 2 Cyplut 2 Cyplut 1 Lepmeg 2 Cyplut 2 Cyplut 1 Micpun 1 Cyplut 1 Cyplut 3 Moxcon 115 Cyplut 11 Cyplut 1 Pimpro 1 Cyplut 1 Cyplut 1 Pimpro 2 Cyplut 1 Cyplut 2 Pimpro 1 Cyplut 2 Cyplut 9 Pimpro 1 Cyplut 2 Cyplut 1 Cyplut 4 Cyplut 1 Cyplut 2 Cyplut 2 Cyplut 16 Cyplut 3 Cyplut 1 Cyplut 2 Cyplut 1 Cyplut 2 Cyplut 4 Cyplut 29 Cyplut 1 Cyplut 1 Cyplut 1 Gamaff 1 Cyplut 1 Ictspp 1 Cyplut 4 Lepmac 1 Cyplut 1 Lepmac 3

Appendix 2. Number of haplotypes identified, by location, from Quadrula pustulosa which were used in AMOVA.

Appendix 3. Number of haplotypes identified, by location, from Quadrula quadrula which were used in AMOVA.

Appendix 4. Nucleotide substitutions observed from Quadrula pustulosa which were used in AMOVA. The first sequence is used as a reference and nucleotides that are the same in subsequent sequences are denoted with a “.”. Unknown nucleotides are denoted “?”. Hap1 GGTTAGGCGA GATGAGGGTA TGGGTTAGTG GGCGGGAGCT TAAGCTGTGC CATAAAAGTA ATGGGTCGTA GCAACGGAGA GGCGTGCAAT AACACGAAAC GTAGGTGAGA Hap2 ...... A...... G...... Hap3 ...... G...... G...... Hap4 ...... A...... G...... Hap5 ...... A ...... A...... Hap6 ...... A...... GG...... Hap7 ...... T...... C...... G...... G...... Hap8 ...... G...... Hap9 A...G...... C.....G...... A...... G...... G...A... Hap10 ...... Hap11 ....G...... C...... A...... T...... G...... G...... Hap12 ...... T...... G...... G...... Hap13 ...... G...... G...... T...... A...... A.... .G...... G...... Hap14 ...... A...... G...... G...... Hap15 A...G...... G...... A...... T...... G...... G...... Hap16 A...G...... A...... G...... G...... Hap17 ...... G...... G...... Hap18 A...G..T.. .G...... A...... A...... G.G...... G...... Hap19 ....G...... C...... A...... G...... G...... Hap20 A...G...... A...... G...... G...... G...... Hap21 ...... A...... Hap22 ...... G...... G...... G...... Hap23 ...... G...... Hap24 ...... C...... G...... G...... Hap25 ...... C...... G. .G...... Hap26 A...G...... A...... A...... G...... G...... Hap27 A...G...... A...... A...... G...... G...... Hap28 ...... A...... Hap29 ...... C...... Hap30 ...... A...... A...... G...... Hap31 A...G..T...... A...... ATC ...... C...... G...... T...... Hap32 A...GA...... A...... G....G... ..G...... Hap33 ...... C...... G...... Hap34 .A...... Hap35 .....A...... T...... G...... T ..G....G.. Hap36 ...... A...... A...... T...... G. .G...A...... G...... Hap37 ...... T...... T...... A. ....G...... G...... G...... Hap38 ...... C...... A...... G...... Hap39 .....A...... T...... G...... G...... Hap40 ...... T...... A. ....G...... G...... G...... Hap41 A...G..T.. A.....A... .A...... A.. C...... A...... T...... G...... G.....G.. .CG...... Hap42 ...... G.G...... Hap43 A...G...... A...... A..A...... G...... G...... Hap44 ...... T...... G ...... G...... Hap45 A...G..T.. .G...... A...... A...... G ...... G.G...... G...... Hap46 A...G...... A.A...... T ...... A...... T...... G...... G...... 99

Hap1 TGTTAGGGTA TGA Hap2 ...... Hap3 ...... Hap4 ...... Hap5 ...... Hap6 ...... Hap7 ...... Hap8 ...... Hap9 C..C...... Hap10 ...... A...... Hap11 ...... Hap12 ...... Hap13 ...... Hap14 ...... Hap15 ...C...... Hap16 ...C...... Hap17 ...... Hap18 ...C...... Hap19 ...... Hap20 ...C...... Hap21 ...... Hap22 ...... Hap23 ...... Hap24 ...... Hap25 ...... Hap26 ...... Hap27 ...C...... Hap28 ...... Hap29 ...... Hap30 ...... Hap31 ...C...... Hap32 ...C...... Hap33 ...... Hap34 ...... Hap35 ...... A.G ... Hap36 ...... Hap37 ...... Hap38 ...... Hap39 ...... Hap40 ...... Hap41 ...C...... Hap42 ...... Hap43 ...... Hap44 ...... Hap45 ...C.....G ... Hap46 ...C......

100

Appendix 5. Continued Hap47 ...... G...... A...... G...... Hap48 ...... T...... Hap49 A...GA.T.. ..A...... A...... G...... G...... Hap50 A...G..T...... A.C ...... A...... C...... G...... T...... Hap51 ?????????? ...... G. .G.G...... G...... Hap52 A...G...... G....A...... A...A...... GT...... G...... Hap53 ...... G...... G. .G...... G...... Hap54 ....G...... C...... A...... T...... G...... G...... Hap55 A...GA.T...... A...... G...... G...... Hap56 A...G..... AG....A...... A...A...... G...... G...... Hap57 A...G...... G...... A...... G.....G.. ..G...... Hap58 A...G...... A...... A...... A...... T...... G...... G...... Hap59 ....G...... A ...... G...... G. .G...... G ..G...... Hap60 A...G...... AT...... C...... A...... C...... G...... G...... Hap61 ...... G ...... Hap62 A...G..T.. A.....A... .A...... A.. C.G.....A...... G...... G...... G...... Hap63 ...... G...... Hap64 ....G...... A ...... G...... C...... G. .G...... G...... Hap65 ..GA...... T...... Hap66 ...... T...... G...... Hap67 ...... G...... Hap68 ...... A...... T...... G...... G...... G Hap69 ...... T ...... G...... Hap70 ....G...... C...... A...... G...... G...... G...... Hap71 A...G..T.G ...... A...... A.. A...... A...... G.....G.. ..G...... G Hap72 ....G...... C...... A...... T...... G...... G...... Hap73 .A...... A...... G...... G...... G...... Hap74 ...... G...... G...... Hap75 ...... A..... Hap76 ...... T...... G...... G...... G...... Hap77 ....G...... A...... G...... T...... G...... G.....A. Hap78 ...... G ...... G...... G...... Hap79 A...G...... A...... A...... G..C .G...... G...... Hap80 A...G..T...... A.C ...... A...... C. .T...... G...... T...... Hap81 ...... A...... G...... G...... Hap82 ...... A...... G...... Hap83 A...G...... C...... A.A ...... G...... G...... G...... Hap84 ...... C...... G...... G...... Hap85 A...G...... G...... A...... T...... A...... G...... G...... Hap86 A...GA.T...... A...... A...... G...... G...... Hap87 ...... T...... G...... G...... Hap88 ...... G...... G. .G.....G.. ..G...... Hap89 A...G...... G....A...... A...A...... G...... G...... G...... Hap90 A...G...... G...... A...A.. .G...... G...... G...... Hap91 A...G..T...... A.. .G...... G...... G.....G.. ..G...... Hap92 A...G...... A...... G.....G.. ..G...... Hap93 A...G...... A...... T ...... G...... G...... G...... Hap94 ...... T...... G...... G...... 101

Appendix 5. Continued Hap47 ...... Hap48 ...... Hap49 ...C...... Hap50 ...C...... Hap51 ...... GAT Hap52 ...C...... Hap53 ...... Hap54 ....T...... Hap55 ...C...... Hap56 ...C...... Hap57 ...C...... Hap58 ...... Hap59 ...... Hap60 ...C...... Hap61 ...... Hap62 ...C...... Hap63 ...... Hap64 ...... Hap65 ...... Hap66 ...... Hap67 ...... Hap68 ...... Hap69 ...... Hap70 ...... Hap71 ...C...... Hap72 ...... Hap73 ...... Hap74 ...... Hap75 ...... Hap76 ...... Hap77 ...... Hap78 ...... Hap79 ...C...... Hap80 ...C...... Hap81 ...... Hap82 ...... Hap83 ...C...... Hap84 ...... Hap85 ...C...... Hap86 ...C...... Hap87 ...... Hap88 ...... Hap89 ...C...... Hap90 ...C...... Hap91 ...C...... Hap92 ...C...... Hap93 ...C...... Hap94 ...... 102

Appendix 5. Continued

Hap95 A...G...... G....A...... A...A...... G...... G...... Hap96 A...G...... AT...... T ...... G...... G...... G...... Hap97 A...G...... A...... A...... G...... G...... Hap98 A...G...... A.. .G...... G...... G...... Hap99 ...... G..T...... Hap100 A...G...... AT...... T ...... G...... G...... Hap101 A...G...... G....A...... A...A...... G...... G...... G...... Hap102 .....A...... Hap103 A...G...... GA...... A...... G...... A.G...... Hap104 ...... C...... Hap105 A...G...... A...... G...... G...... Hap106 A...GA.T...... A...... T ...... G...... G...... Hap107 ....G...... G...... G...... Hap108 ....G...... AT...... G...... G...... G...... Hap109 A...G...... AT...... G...... G...... Hap110 A...GA.T.G ...... A...... C..A.. .A.....AT. .G.....C.T ..CGG...... A.....G .T...T..A...... G..G...... AC.... Hap111 A...G..T.. .G...... A...... A...... G...... G...... Hap112 ...... A...... Hap113 ...... G.G...... G...... Hap114 .A...... G...... G...... Hap115 ...... A...... G...... G...... Hap116 A...G..T...... A...... AT...... A...... G...... G......

103

Appendix 5. Continued

Hap95 ...C...... Hap96 ...C...... Hap97 ...C...... Hap98 ...C...... Hap99 ...... Hap100 ...C...... Hap101 ...C...... Hap102 ...... Hap103 ...C...... Hap104 ...... Hap105 ...C.A...... Hap106 ...C...... Hap107 ...... Hap108 ...C...... Hap109 ...... Hap110 .ACC....C. ... Hap111 ...C...... Hap112 ...... Hap113 ...... Hap114 ...... Hap115 ...... Hap116 ...C......

104

Appendix 5. Nucleotide substitutions observed from Quadrula quadrula which were used in AMOVA. The first sequence is used as a reference and nucleotides that are the same in subsequent sequences are denoted with a “.”. Hap1 TATTGTGCGG TGTTAGGGTG GCGTGCATAA AGGTTGTGGT TGGCTGAGAT TTAACATGGG CCGTTGTATT CTGTGGTGAG CTGTATAAAG GAGTGTTGGT GTGTGATCTC Hap2 ...... Hap3 ...... G...... Hap4 ...... A...... Hap5 ...... A...... A..... Hap6 ...... A...... A..... Hap7 ...... A...... A..... Hap8 ...... A...... AA..A.. Hap9 CG...... A ...... A...... T...... C...... AC.... A....G.... Hap10 ...... A..... Hap11 CG...... A...... T...... C...... AC.... A....G.... Hap12 CG...... A...... T...... C...... A.AC.... A....G.... Hap13 .G...... A...... Hap14 CG...... A...... A...T...... C...... AC.... A....G.... Hap15 .G...... A...A...... Hap16 .G...C..A...... A...GC.G CA...A..AC ..ATC...G. ...G.G.AA. ..A.G.C... TA.C.A...... G..G.A ..T....AAA A.A.T..... Hap17 .GC.A.AT...... T.AC. A...A...G. .AACCA...A C....TT... .C..TG.AA. ..T....G.. TG.GA...G. TAACG.GCTA .GT...G... A....GC... Hap18 .G...... A...... Hap19 .G....AT.. ...CGAA... A.....G..G GA...... A. ..A....A.. ..G.TG.A...... CG.. .A...A.A.. ..A.GC.... .G....C..A AC..T...C. Hap20 ...... Hap21 .G..A.AT.. ...CGAA... A.....G..G GA...... A. ..A....AGC ..G.TG.A...... CG.. .A...A.A.. ..A.G...... G....C..A .C..T...C. Hap22 .G..A.AT.. ...CGAA... A.....G..G GA...... A. ..A....AGC ..G.TG.A.. .T....CG.. .A...A.A.. ..A.G...... G....C..A .C..T...C. Hap23 .G..A.AT.. ...CGAA... A.....G..G G...... A. ..A....AGC ..G.TG.A...... CG.. .A...A.A.. ..A.G...... G....C..A .C..T...C. Hap24 .T..A.AT.. .A.CGAA... A.....G..G GA...... A. ..A....AGC ..G.TG.A...... CG.. .G...A.A.. ..A.G...... G....C..A .C..T...C. Hap25 .G..A.AT.C A.GCGAA... AT....G..G GA...... A. ..A....AGC ..G.TG.A...... CG.. .A...A.A.. ..A.G...... G....C..A .C..T...C. Hap26 .G...... AC.... A....G.... Hap27 .G...... A...... T...... C...... AC.... A....G.... Hap28 ...... GA...... Hap29 ...... Hap30 CG...... A...... T...... C...... A ..A.AC.... A....G.... Hap31 CG...... A...... T...... C...... AC.... A...... Hap32 CG...... A...... T...... C...... C.. ..A.AC.... A....G.... Hap33 .G.C.C..A...... A...GC.G CA...A..AC ..ATC...G...... G.A.. ..A.G.C... TA.C.A...... G..G.A ..T....AAA A.A.T..... Hap34 CG...... A...... TT.AT...... C...... AC.... A....G.... Hap35 .G...C.TA...... A...GC.G CA...A..AC ..ATC...G...... G.A.. ..A.G.C... TA.C.A...... G..G.A ..T....AAA A.A.T..... Hap36 .G...C.TA...... A...GC.G CA...A..AC ..ATC...G...... G.A.T ..A.G.C... TA.C.A...... G..G.A ..T....AAA A.A.T..... Hap37 .G...C.TA...... AC..GC.G CA...A..AC ..ATC...G. ....TG.A.. ...AG..... TA.C.AG.G. ....G..G.A ..T....AAA A.A.T..... Hap38 .G.C.C..A...... A...GC.G CA...A..AC ..ATC...G. ....TG.A...... G..... TA.C.A...... G..G.A ..T....AAA A.A.T..... Hap39 CG...C...... A...... A...... T...... C...... AC.... A....G.... Hap40 .G...C.TA...... AC..GC.G CA...A..AC ..ATC...G. ....TG.A...... G..... TA.C.A..G. ....G..G.A ..T....AAA A.A.T..... Hap41 .G...C.TA...... A...GC.G CA...A..AC ..ATC...G. ....TG.A.. ..A.G.C..C TA.C.A...... G..G.A ..T....AAA A.A.T..... Hap42 .G...C.TA...... AC..GC.G CA...A..AC ..ATC...G. ....TG.A...... G..... TA.C.A...... G..G.A ..T....AAA A.A.T..... Hap43 .G...... A...... A...... Hap44 .G...C..A...... A...GC.G CA...A..AC ..ATC...G...... G.A...... G..... TA.C.A...... G..G.A ..T....AAA A.A.T..... Hap45 .G...C.TA. ....G...... TGC.G CA...T..AC ..ATC...G...... G.A.. ..A.G.C... TA.C.A...... G..G.A ..T....AAA A.A.T....T Hap46 .G...C.T...... C...... C ...... CA...... 105

Appendix 5. Continued Hap1 TTACGATGAG ATTTTATCGG TCGGGGCGCT TTTTATAGTA AAGTTATG Hap2 ...... T...... Hap3 ...... Hap4 ...... Hap5 ...... Hap6 .....T...... Hap7 ...... A...... Hap8 .....T.A.A ...... C..C C...... AA...... CAC ..TA...T Hap9 ...T...A...... G.... Hap10 ...... A...... Hap11 ...T...A...... G.... Hap12 ...T...A...... G.... Hap13 ...... T...... Hap14 ...T...A...... G.... Hap15 ...... T... ..C....C Hap16 ...T...AG. T...... T.. ...A.A...... GAT..T T..A.G.. Hap17 .AGT..CTG. T.CCCG.T.A ....T....G ....G.G...... G.. Hap18 ...... G...... T...... Hap19 G.GTA...G. GC.....TAT ...A.A.A...... GAG... TG.G.G.. Hap20 ...... C...... Hap21 G.GTA...G. G...... TAT ...AAA.A...... GAG... T..G.G.. Hap22 G.GTA...G. G...... TAT ...A.A.A...... GAG... TG.G.G.. Hap23 G.GTA...G. G...... TAT ...A.A.A...... GAG... T..G.G.. Hap24 G.GTA...G. G...... TAT ...A.A.A...... GAG... T..G.G.. Hap25 G.GTA...G. G...... TAT ...AAA.A...... GAG... T..G.G.. Hap26 ...T...A...... G.... Hap27 ...T...A...... G.... Hap28 ...... Hap29 ...... A...... Hap30 ...T...A...... G.... Hap31 ...... A...... G.G.. Hap32 ...T...A...... G.... Hap33 ...T...AG. T...... T.. ...A.A...... GAT..T T..A.G.. Hap34 ...T...A...... A...T... C...... G.... Hap35 ...T...AG. T...... T.. ...A.A...... GAT..T T..A.G.. Hap36 ...T...AG. T...... T.. ...A.A...... GAT..T T..A.G.. Hap37 ...T...AG. T...... T.. ...A.A.A...... GAT..T T...GGA. Hap38 ...T...AG. T...... T.. ...A.A.A...... GAT..T T..A.G.. Hap39 ...T...A...... T..G.... Hap40 ...T...AG. T...... T.. ...A.A.A...... GAT..T T..A.G.. Hap41 ...T...AG. T...... T.. ...A.A...... GAT..T T..A.G.. Hap42 ...T...AG. T...... T.. ...A.A.A...... GAT..T T..A.G.. Hap43 ...... T...... Hap44 ...T...AG. T...... T.. ...A.A.A...... GAT..T T..A.G.. Hap45 ...T...AG. T...... T.A ...A.A...... GAT..T T..A.G.. Hap46 ...... C...... CC..T......

106