Comparison of Biogeography Patterns of Two Freshwater Snails - Physa acuta and Helisoma cf. trivolvis by

KELLY ROSE MARTIN

B.S., University of Wisconsin Madison, 2017

A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirement for the degree of Master of Science Department of Museum and Field Studies 2019

This thesis entitled: Comparison of Biogeography Patterns of Two Freshwater Snails - Physa acuta and Helisoma cf. trivolvis written by Kelly Rose Martin has been approved for the Department of Museum and Field Studies

______Dr. Jingchun Li

______Dr. Pieter Johnson

______Dr. J. Patrick Kociolek

Date______

The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standard of scholarly work in the above mentioned discipline.

Martin, Kelly Martin (M.S., Museum and Field Studies) Comparison of Biogeography Patterns of Two Freshwater Snails - Physa acuta and Helisoma cf. trivolvis Thesis directed by Assistant Professor Dr. Jingchun Li

Despite the important roles freshwater gastropods play in aquatic ecosystems, much of their basic biology and ecology are understudied. Particularly, information regarding distribution, dispersal patterns and population structure is incomplete. This study addressed the biogeography of two native North American freshwater snails, Physa acuta (: Physidae) and Helisoma trivolvis (Gastropoda: ), in the western United States and globally. We amplified two genetic markers (COI, 16S) from individuals belonging to multiple populations along the West Coast and downloaded existing genetic data from Genbank for the two species. We utilized minimum spanning networks to compare the population genetic patterns between the species and preformed Analysis of Molecular Variance (AMOVA) and linear regression analyses to determine whether watersheds, geographic distance, or other biotic factors contributed to the observed genetic structuring. We found that P. acuta was more genetically diverse and showed less overall population structuring than H. cf. trivolvis in the West Coast. Overall, we did not find a strong geographical partitioning for either species in the West Coast. Geographic distance and watersheds do not appear to be a predominate factor in shaping the snails’ genetic structure. The North-South genetic similarity indicates that a lack of watershed connectedness does not restrict gene flow. Thus, among watershed dispersal vectors are likely maintaining the snails’ population connectivity. The observed genetic patterns reflect the Pacific Flyway, a major migratory route in the western United States. This suggests that waterfowl are possible vectors in promoting ongoing gene flow over large geographic ranges and impacting the snails’ population structure along the West Coast. An analysis of the population genetics across North America revealed a certain level of East-West genetic structuring in both species. In addition, we found a previously identified P. acuta clade from west of the Rocky Mountains to occur in populations along the eastern slope, suggesting that the clade is not limited to west of the Rockies. The genetic distances among East-West populations in H. cf. trivolvis are relatively high, indicating the presence of a cryptic species. This paper provides an initial framework for continued biogeographical analysis of Physa and Helisoma in their native range.

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Acknowledgments First and foremost, I would like to thank my primary thesis advisor, Dr. Jingchun Li. Her dedication to my success was unparalleled. It was a privilege and an honor to work with someone who cares deeply about the work they do and for her students. She provided critical guidance and expertise throughout this thesis when I needed it most, but she also gave me the space to learn and grow on my own. Thank you for continuously pushing me to be the best scientist I can be. I could not have wished for a better advisor, mentor or advocate than you. I would also like to extend my gratitude to my other committee members, Dr. Pieter Johnson and Dr. Patrick Kociolek. Without their guidance, this thesis would not have been possible. Their willingness to share their expertise and provide meaningful input was essential to the success of this thesis. I would also like to acknowledge the other members of the Li Lab. Your dedication to reading multiple iterations of this thesis was instrumental. Know that your comments and suggestions were greatly appreciated. Your friendship and encouragement mean a lot to me. Additionally, I would like to thank all of the individuals in the Johnson Lab and Jay Bowerman, who facilitated the collection of the specimens used in this study. The scale of this research was only possible with your help. Most importantly, I would like to thank my parents for their constant love and support. You both have taught me to never set a limit on what is possible and to embrace opportunity with an open heart and unwavering mind. You supported me through the highs and lows of this journey and for that I am profoundly grateful. This is for you. I love you. Finally, I am incredibly grateful to all the individuals in the Museum & Field Studies Graduate Program that have contributed to my growth and continued passion for museum collections. I would like to thank the University of Colorado Museum of Natural History for providing financial support of this thesis.

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Table of Contents 1. Introduction………………………………………………………………………………1 2. Methods 2.1 Sampling……………………………………………………………………….……...5 2.2 DNA amplification……………………………………………………………………5 2.3 Population Structure…………………………………………………………………..7 3. Results 3.1 West Coast…………………………………………………………………………….8 3.2 Global………………………………………………………………………………..10 4. Discussion 4.1 West Coast…………………………………………………………………...……....12 4.2 Global………………………………………………………………………………...15 4.3 Broader Implications & Ecological Interactions……………………………………..18 4.3.1 Host-Parasite Interactions………………………………………………….19 4.3.2 Conservation……………………………………………………………….20 4.3.3 Invasive Species……………………………………………………………21 5. Conclusions……………………………………………………………………………...21

Literature Cited………………………………………………………………………………23

Appendix……………………………………………………………………………………..39

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Tables 1. Analysis of molecular variance……………………………………………………………….30 2. Population statistics and molecular diversity indexes, by watershed…………………………30 3. Overall molecular diversity indexes, West Coast……………………………………………..31

4. Population ΦST values between watersheds…………………………………………………...31

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Figures 1. Museum examples of Physa acuta and Helisoma cf. trivolvis……………………………...32 2. Map of sampling locations………………………………………………………………….32 3. COI haplotype network in the West Coast, Physa acuta…………………………………...33 4. 16S haplotype network in the West Coast, Physa acuta…………………………………....33 5. COI haplotype network in the West Coast, Helisoma cf. trivolvis………………………….34 6. 16S haplotype network in the West Coast, Helisoma cf. trivolvis…………………………..34 7. Global COI haplotype network, Physa acuta………………………………………….……35 8. Global 16S haplotype network, Physa acuta……………………………………….……….35 9. Global COI haplotype network, Helisoma cf. trivolvis…………………………….….……36 10. Effect of geographic distance on pairwise genetic distance, 16S………………….….…...36 11. Effect of geographic distance on pairwise genetic distance, COI………………….….…..37 12. Effect of longitudinal distance on pairwise genetic distance, 16S………………………...37 13. Effect of longitudinal distance on pairwise genetic distance, COI………………………..38

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1. Introduction Freshwater snails play important roles in complex ecological interactions. For example, many freshwater snails are intermediate hosts of diverse parasites that ultimately infect vertebrate hosts, including humans (Adema et al. 2012). Some freshwater snails are invasive species, which disrupt ecosystem function, negatively impact agriculture, and threaten native species through predation and competition (Carlsson et al. 2004; Robert 2002; Burlakova et al. 2009). Additionally, freshwater snails can be excellent bioindicators of water quality because of their sensitivity to small-scale disturbances. Unfortunately, freshwater mollusks also represent one of the most threatened groups on Earth (Lydeard et al. 2004). Compared to other gastropods, freshwater snails face a disproportionately large risk of extinction (Strong et al. 2007; Lydeard et al. 2004). Their biodiversity losses are a result of anthropogenic impacts, such as habitat loss, degradation and manipulation (Strong et al. 2007). Despite the important roles freshwater gastropods play in aquatic ecology, climatology, conservation, and epidemiology, their basic biology and ecology are still understudied (Adema et al. 2012). Particularly, the understanding of their systematics is largely incomplete (Strong et al. 2007). The taxonomical status of many taxa is uncertain, which is further impaired by a lack of information on species distribution, dispersal patterns and population structure (Strong et al. 2007). If fundamental questions regarding freshwater gastropod ecosystem functions are to be answered, a thorough understanding of their biogeography is essential (Ebbs et al. 2018; Morgan et al. 2002). One of the major factors that determines biogeographical distribution of freshwater snails is their dispersal mode and ability (Brown & Lydeard 2010). Aquatic snails can move through the environment by both active and passive dispersal mechanisms. Active dispersal, such as crawling, is considered important only at a local scale (Kappes & Haase 2012). Freshwater snails mostly employ passive dispersal mechanisms for large-scale, rapid dispersal. Within water bodies, many biotic and abiotic factors, such as transport via fish and through currents, influence their passive dispersal ability (Kappes & Haase 2012 and references therein). For example, freshwater fish in the Salmonidae family have been known to successfully transport several freshwater gastropod taxa including Valvatidae, Lymnaeidae and Tateidae (Brown 2007; Haynes et al. 1985; Bondesen & Kaiser 1949). Passive dispersal downstream via currents is considered the most common dispersal mechanism in freshwater mollusks (Kappes & Haase 2012). Freshwater snails of the families Tateidae and Hydrobiidae often drift downstream in the water

1 column (Liu & Hershler 2009; Ribi 1986). However, the above mechanisms only apply to hydrologically connected water bodies. Dispersal of freshwater gastropods across non-connected habitats requires assistance from other vectors. In order to disperse across watersheds, snails require extra-aquatic vectors to transcend the lack of physical connectedness. Extra-aquatic dispersal can occur via abiotic vectors such as flooding events or tornadoes and biotic vectors such as mammals (including humans), insects, birds (Kappes & Haase 2012 and reference therein). Migratory waterfowl have long been proposed as long-distance dispersers of freshwater snails (Green & Figuerola 2005; Figuerola & Green 2002; Boag 1986; Malone 1965a; Malone 1965b; Roscoe 1955; Darwin 1859). Gastropod taxa in the families Physidae, Planorbidae and Lymnaeidae have been found attached to the feathers of the White-Faced Glossy Ibis (Plegadis mexicana) and experimentally on the Whistling Tundra Swan (Cygnus columbianus) (Roscoe 1955; Boag 1986). Studies examining the population structure of other freshwater organisms support the role of waterfowl-mediated dispersal. Several aquatic invertebrates, such as Daphnia and the bryzoan Cristatella mucedo, show genetic structuring following major flyways (Freeland et al. 2000; Taylor, Finston & Hebert 1998). This results in more genetic similarities between populations running North-South than East-West, corresponding with dominant flyway structures. This suggests that waterfowl- mediated dispersal promotes ongoing gene flow over large geographic ranges and could strongly influence freshwater gastropod distribution as well (Saito et al. 2018; Van Leeuwan et al. 2013). Physidae and Planorbidae are two of the most abundant and widespread freshwater gastropod families in the world (Burch 1982). Native to North America, these snails are ubiquitous in freshwater environments around the world (Wethington & Lydeard 2007; Dillon et al. 2002). Among the species that co-occur in North America, two abundant species are Physa acuta (Gastropoda: Physidae) and Helisoma trivolvis (Gastropoda: Planorbidae) (Fig. 1). While P. acuta and H. trivolvis exist sympatrically in freshwater ecosystems, their life history characteristics vary. P. acuta is known to have higher fecundity, a shorter hatching time, and significant reproductive plasticity compared to many Planorbidae species (Zukowski & Walker 2009; Gittenberger et al. 2004; Monsutti & Perrin 1999; Brackenbury & Appleton 1991; Clampitt 1970). Both species are hermaphroditic snails that are able to reproduce via self- fertilization or outcrossing. However, studies have shown that there are differences in the frequencies in which the snails employ each mating system. P. acuta successfully reproduces via

2 self-fertilization (Bousset et al. 2004; Tsitrone et al. 2003; Jarne et al. 2000; Wethington & Dillon 1997), while Helisoma sp. reproduce primarily through outcrossing (Norton et al. 2018; Escobar et al. 2011; Jarne et al. 1993; Paraense & Correa 1988; DeWitt & Sloan 1959). The ability to self-fertilize promotes rapid spreading and increases the likelihood of successfully inhabiting new environments (Bousset et al. 2004). In addition, P. acuta are considered super- generalists because they are capable of tolerating a variety of environmental conditions, such as temperature and pollution (Turner & Montgomery 2009; Kefford & Nugegoda 2005). Studies have found that some Helisoma have moderate resistance to desiccation, starvation and infection, but their distribution and abundance has been negatively impacted by environmental changes (Fernandez et al. 2010). Facilitated by its opportunistic life history characteristics, P. acuta is also considered an effective disperser (Albrecht et al. 2009; Van de Meutter, Stoks & De Meester 2006). Its potential to exploit a variety of dispersal vectors has allowed P. acuta to become globally invasive (Van Leeuwen et al. 2013). Both Physa sp. and Helisoma sp. have been found attached to feathers of waterfowl (Boag 1986; Roscoe 1955). Experimental flight simulations have shown that larger snails are more likely to survive long periods attached to feathers, but are less likely to remain attached to a feather for any length of time (Boag 1986). While external attachment is an effective mode of dispersal, internal transport is also possible (Malone 1965b). Results have shown that Physa sp. can be transported via the intestinal tract of birds, but this method of dispersal was not found for Helisoma sp. (Malone 1965a; Malone 1965b). Cumulatively, the results indicate that Helisoma sp. is less likely to disperse long distances compared to Physa sp. Studies investigating the genetic structuring of Physidae and Planorbidae snails have been conducted at global and regional scales (Ebbs et al. 2018; Bousset et al. 2014; Van Leeuwen et al. 2013; Escobar et al. 2007; Dillon & Wethington 2006; Bousset et al. 2004). To date, efforts to examine each family’s genetic diversity focus predominately on their invasive range, with considerably less sampling within their native range (Ebbs et al. 2018). Within North America, the genetic variation of P. acuta has predominately been studied in the context of Physid and invasion history (Ebbs et al. 2018; Bousset et al. 2014; Bousset et al. 2004). Recent phylogenetic work on P. acuta examined their genetic diversity across eight North American freshwater ecological complexes (FWECs), which delineate freshwater habitats into ecoregions and are categorized by habitat and biological diversity (Abell 2000). Results

3 indicated that FWECs are not able to explain the observed patterns of diversity (Ebbs et al. 2018). However, a significant East-West genetic structuring was identified, with the appearance of two distinct clades. Clade A was recovered from across the entire range, including invasive populations, while clade B was found only west of the Rocky Mountains (Ebbs et al. 2018). The factors leadings to the structuring remain unclear (Ebbs et al. 2018). Comparatively, the genetic diversity of H. trivovlis is largely unknown. The majority of molecular research in the family Planorbidae has concentrated on two genera, Biomphalaria and Bulinus, with little focus on the others, including Helisoma (Morgan et al. 2002). The relationships within and among the genera remain unclear (Albrecht et al. 2007). The limited molecular research on Helisoma has found that while traditionally the Planorbarius has been grouped with Helisoma due to their morphological similarities, a close phylogenetic relationship has not been supported (Morgan et al. 2002). Rather, the closest relative of Helisoma is Biomphalaria, a genus widespread in Africa that originated in the Americas (Campbell et al. 2002). In addition to a lack of phylogenetic studies, there has also been little study examining the population genetic structuring of H. trivovlis. A lack of information on the population structuring of these species in their native range prevents meaningful comparisons to be drawn and limits our understanding of the family-level global genetic diversity. Given the differences in the life history characteristics and dispersal ability between P. acuta and H. trivolvis, the genetic variation partitioning across their native ranges may exhibit different patterns. In particular, we expected that P. acuta’s propensity for dispersal leads to less overall genetic structuring compared to H. trivolvis. Therefore, the objective of this study was to address the biogeography of two freshwater snails, P. acuta and H. trivolvis, in the western United States, a less sampled native range of the two species. We used minimum spanning networks and genetic diversity indexes to compare the population genetic patterns between the species. We also conducted Analysis of Molecular Variance (AMOVA) and linear regression analyses to determine if watersheds, geographic distance, or other biotic factors, such as waterfowl, contribute to the observed genetic structuring. Comparison between the species offers insight into how biogeographical factors in the native range impact genetic variation, how dispersal mechanisms maintain this structure and will inform the understanding of their role in ecosystem function and aquatic community interactions.

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2. Materials and Methods 2.1 Sampling Physa acuta and Helisoma trivolvis populations were collected by hand or net from 28 ponds and reservoirs in California, Oregon, and Washington States. Localities (Fig. 2) include one site in Washington, 15 sites in California and 12 sites in Oregon. To assess the genetic structuring, the 28 localities were grouped according to watershed (Fig. 2). Watershed identities were determined using United States Geological Survey delineation maps (https://water.usgs.gov/wsc/watersheds.html). The localities spanned seven different watersheds along the West Coast of the United States (Lower Columbia, Middle Columbia, Middle Snake, Oregon-Washington Coastal, Willamette, Sacramento River, and San Francisco). Detailed locality information can be found in appendix 1 (Physa acuta) and appendix 2 (Helisoma cf. trivolvis).

A total of 76 specimens were collected, among which 16 were identified by the collectors as Physa acuta and 60 specimens were Helisoma cf. trivolvis. Species identities were later confirmed with genetic data (see below). Upon collection, some of the specimens were euthanized and stored in 95% EtOH. Others were kept alive, temporarily stored in the refrigerator and subsequently transferred to 95% EtOH for DNA extraction.

2.2 DNA amplification

Genomic DNA was extracted from the snail foot tissue utilizing the E.Z.N.A Mollusc DNA Kit (Omega Biotek). Two mitochondrial DNA loci were amplified and sequenced (16S and COI) as both have been used successfully to recover population genetic variation in freshwater snails (Ebbs et al. 2018; Wethington & Lydeard 2007; Wethington & Guralnick 2004).

The 16S mitochondrial gene was amplified using primer sets 16Sar 5′- CGCCTGTTTATCAAAAACAT‐3′ and 16Sbr 5′‐CCGGTCTGAACTCAGATCACGT‐3′ (Palumbi 1996). The COI mitochondrial gene was amplified using primers sets LCOI490 5′- GGTCAACAAATCATAAAGATATTGG‐3′ and HCO2198 5′- TAAACTTCAGGGTGACCAAAAAATC‐3′ (Folmer et al. 1994). PCR amplifications were performed in a total reaction volume of 12.5 µL with 6.25 µL GoTaq Green Master Mix

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(Promega), 3.75 µL of nuclease-free water, 1 µL of each primer and 0.5 µL of the DNA template.

The PCR protocol for 16S included an initial denaturation at 94 °C for 60 s, 35 cycles of 94 °C for 30 s, 55 °C for 50 s and 72 °C for 60 s, and a final extension at 72 °C for 10 minutes (Collado & Mendez 2012). The PCR protocol for COI included an initial denaturation at 96 °C for 2 minutes, 9 cycles of 96 °C for 40 s, 55 °C for 60 s and 72 °C for 60 s, 30 cycles of 96 °C for 40 s, 46 °C for 60 s and 72 °C for 60 s, and a final extension at 72 °C for 7 minutes. PCR products were assessed through gel electrophoresis.

Amplified products were sequenced by Sanger sequencing at Quintara Biosciences (California). Raw sequences were processed in CodonCode Aligner 3.1.7 to remove primer sequences and manually correct for low quality read. The final size of COI gene segment was 621 bp for P. acuta and 606 bp for H. cf. trivolvis. The final size of the 16S gene segment was 591 bp for P. acuta and 456 bp for H. cf. trivolvis. All Physa sp. and Helisoma sp. specimens sampled were confirmed to be Physa acuta and Helisoma cf. trivolvis based on BLAST match with existing sequences in Genbank (>97% similarity with known P. acuta and H. cf. trivolvis sequences).

In addition, all available 16S and COI sequences for P. acuta and COI sequences for H. cf. trivolvis ( and Helisoma ( trivolvis)) were downloaded from Genbank, resulting in 73 16S and 167 COI sequences for P. acuta and 48 COI sequences for H. cf. trivolvis. 21 of the 167 COI sequences for P. acuta represent West Coast samples, 68 are other North American samples, 38 samples are from Europe, 15 are from the Middle East, 12 are from South and Central America, 6 are from Asia and 4 are from Africa. 14 of the 73 16S sequences from P. acuta represent West Coast samples, 51 are other North American samples, 4 samples are from Europe, 3 from Central and South America and 1 from Africa. 23 of the 48 COI sequences for H. cf. trivolvis are West Coast samples and the remaining 25 are from other North American samples. Sequences downloaded from Genbank were also assigned to three watersheds (San Francisco, Tulare Lake, and San Joaquin River). Genbank accession numbers and associated studies can be found in appendix 1-2.

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2.3 Population Structure

Newly amplified sequences and the ones downloaded from Genbank were aligned together using the ClustalW algorithm (Larkin et al. 2007) implemented in CodonCode Aligner 3.1.7 and corrected by eye. Low quality regions at the beginning and end of the alignments were removed and not included in the final analysis. The final alignment size of the COI segment was 509 bp for P. acuta and 400 bp for H. cf. trivolvis. The final size of the 16S gene segment was and 465 bp for P. acuta and 454 bp for H. cf. trivolvis.

Minimum Spanning Networks (MSN) were constructed with PopARt v. 1.7 (Leigh et al. 2015) (http://popart.otago.ac.nz) for both gene fragments to view the connections among haplotypes. A MSN is an analytical approach using molecular data that enables the visualization of the relationships among individuals and the population genetic structuring. First, we constructed MSN networks for P. acuta and H. cf. trivolvis in their native range along the West Coast of the United States (Fig. 3-6). In addition, MSN networks were created to assess the global structuring of P. acuta (Fig. 7-8). Populations of Helisoma spp. are scattered globally (Cianfanelli et al. 2007; Frandsen & Madsen 1979), but lack of genetic information meant we were unable to access the global genetic structure of H. cf. trivolvis outside of North America (Fig. 9).

To compare the genetic structure of the two species in the West Coast, we conducted an Analysis of Molecular Variance (AMOVA) (Excoffier et al. 1992) using Arlequin v. 3.5 (Excoffier & Lischer 2010) on the 16S and COI datasets for both species to evaluate degrees of genetic variation within populations, among populations within a watershed and among the nine West Coast watersheds. An AMOVA is a statistical method of estimating population differentiation directly from molecular data. Φ statistics and the variance components were calculated for within populations (ΦST), among populations within watersheds (ΦSC), and among watersheds (ΦCT). A hierarchical analysis of genetic variation was utilized to assess how the genetic differentiation is partitioned among the three Φ statistics components. In order to further assess the population structure of the species, pairwise ΦST between the watersheds were also estimated from the 16S and COI dataset in Arlequin v. 3.5. Significance of all Φ statistics were determined through 10,000 random permutations (p < 0.05).

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Genetic diversity statistics were calculated for each species and gene segment in Arlequin v. 3.5. Summary statistics were estimated for each watershed and across the entire sampling range. The molecular diversity indexes for each watershed included number of haplotypes (h), haplotype diversity (Hd), and nucleotide diversity. The indexes estimated for across the entire sampling range include gene diversity, mean number of pairwise differences and nucleotide diversity.

To further assess whether geographic distance is associated with genetic distance, linear regressions models were produced in R version 3.4.2 (R Core Team 2019). Pairwise genetic distances between West Coast localities were obtained in Arlequin v. 3.5. Very small negative values known to be statistical noise were rounded to 0. Geographic distance was calculated as the shortest distance between two localities on the surface of a sphere, known as the orthodromic distance. Similar linear regression models were also done with latitudinal and longitudinal data. The results of the latitudinal analyses were the same as in the geographic distance analyses and therefore not included in the study. In addition, generalized linear mixed models (GLMM) were used to test how geographic distance, snail species (as a factor) and their interaction affected pairwise genetic distance.

3. Results

3.1 West Coast

We first report the population genetics statistics for P. acuta and H. cf. trivolvis in the West Coast only. Overall, we did not find a strong geographical partitioning for either species. Geographic distance and watersheds do not appear to be a predominate factor in shaping the genetic structure of these two freshwater snails. Thus, long-distance dispersal vectors are maintaining the connectivity of populations along the West Coast.

A total of 14 out of 16 P. acuta individuals were successfully amplified for the 16S gene fragment. Resulting in 12 unique haplotypes, including 11 singleton haplotypes (Fig. 4). The most common haplotype was shared by two individuals from the same California locality (PRNTHIDK). Within population variance accounts for 45.12% of the total genetic variation (Table 1). A moderate amount of heterogeneity was detected among populations within watersheds (34.34%) and among watersheds (20.54%). A total of 21 P. acuta individuals were

8 amplified for the COI gene fragment. 16 unique haplotypes were obtained, including 13 singleton haplotypes (Fig. 1). Within population genetic variation accounts for the overwhelming majority (95.27%) of the total genetic variation (Table 1). For both genes, most of the genetic variation was found within populations.

A total of 57 out of 60 H. cf. trivolvis individuals were successfully amplified for the 16S gene fragment. The genotyped individuals yielded 16 unique haplotypes, and 7 singleton haplotypes are present in the populations (Fig. 6). Two haplotypes were identified as the most common (H1 and H9). H1 was shared by 14 individuals from California and Oregon representing two watersheds, San Francisco and Willamette. H9 was shared by 16 individuals from California and Oregon representing three watersheds, San Francisco, Willamette and Sacramento River. Results from the AMOVA are shown in Table 1. Within population variance

(ΦST) accounts for 71.84% of the total genetic variation (p < 0.05), among populations within a watershed and among watershed account for 12.31% and 15.85% (p <0.05) respectively. For the COI gene fragment, a total of 23 H. cf. trivolvis individuals were successfully amplified. 20 of the individuals were also analyzed for the 16S gene fragment. Seven unique haplotypes were obtained as a result, and three singleton haplotypes are present in the populations (Fig. 5). The

AMOVA suggest a significant ΦSC and ΦST (p < 0.001 and p < 0.001, respectively). Among populations within a watershed accounts for 77.59% of the genetic variation, within population and among watershed account for 1.5% and 20.91% respectively (Table 1).

Tables 2 and 3 summarize the molecular diversity indexes for each watershed and across the entire West Coast sampling range for the two species. Haplotype diversity was high for almost every sampled watershed, even reaching 100% (Hd = 1) in some watersheds. Low haplotype diversity (Hd = 0) within certain watersheds are mostly attributed to low sample sizes. The haplotype diversity in P. acuta was greater in both genes compared to H. cf. trivolvis (COI:

Hdphysa = 0.962, Hdhelisoma = 0.85; 16S: Hdphysa = 0.884, Hdhelisoma = 0.839). Gene diversity and nucleotide diversity across the sampling area was comparable between the two species. P. acuta had greater overall gene diversity (COI: Physa = 0.9619, Helisoma = 0.8498; 16S: Physa =

0.9670, Helisoma = 0.8509) and greater nucleotide diversity in the 16S dataset (Physa = 0.067, Helisoma = 0.041). Overall, for both COI and 16S, haplotype diversity and gene diversity was

9 higher in P. acuta than in H. cf. trivolvis. Nucleotide diversity was higher in P. acuta within the 16S dataset and in H. cf. trivolvis within the COI dataset.

In order to further assess the population structuring of P. acuta and H. cf. trivolvis, pairwise ΦST values were calculated between watersheds for each species and gene segment (Table 4). No significant population genetic divergence was found among most of the 9 watersheds sampled for either species (p > 0.05). The San Francisco watershed shows significant (p < 0.05) genetic differentiation from the Willamette watershed for H. cf. trivolvis in the 16S dataset. This finding supports the AMOVA results which showed that only marginal amounts of variation can be attributed to the among watershed level in both species.

Within the COI dataset, geographic distance did not predict genetic distance for either species (Helisoma: DF= 1 and 53, F=0.6703, p=0.417; Physa: DF= 1 and 19, F=1.159, p=0.2952) (Fig. 10). The results show that, on average, H. cf. trivolvis has a larger pairwise genetic distance (more diversity) at a local scale than P. acuta. Within the 16S dataset, a similar result was found for H. cf. trivolvis, where geographic distance and genetic distance were unrelated (DF= 1 and 229, F=1.465, p=0.2273) (Fig. 11). However, for P. acuta as geographic distance got larger, genetic diversity decreased (DF= 1 and 26, F=4.821, p=0.0372). The latitudinal data yielded the same result. GLMM supported a significant geographic distance-by- species interaction for P. acuta only in the 16S dataset (p <0.0125). Within the 16S dataset, longitudinal distance did not predict genetic distance for P. acuta, but was marginally significant for H. cf. trivolvis (Helisoma: DF= 1 and 229, F=3.751, p= 0.054; Physa: DF= 1 and 26, F=0.69, p=0.414) (Fig. 12). For H. cf. trivolvis, as longitudinal distance increased, genetic distance decreased. Within the COI dataset, longitudinal distance and genetic distance were unrelated for P. acuta, but the results were marginally significant for H. cf. trivolvis (Helisoma: DF= 1 and 53, F=3.786, p=0.057; Physa: DF= 1 and 19, F=0.076, p=0.7851) (Fig. 13). For H. cf. trivolvis, as longitudinal distance increased, genetic distance increased.

3.2 Global

Genbank sequences were included in this study in order to assess the overall genetic diversity of P. acuta and H. cf. trivolvis across their entire range (native and invasive). Within their native range, both species show large-scale East-West genetic structuring. These results are consistent with previous studies that show a higher similarity between populations in a North-

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South than an East-West direction (Taylor, Finston, Hebert 1998). The cladistic structuring in H. cf. trivolivs appears to be more dependent on locality than in P. acuta.

Considerable existing genetic data does exist across the global range of P. acuta. Haplotype relationships across P. acuta’s entire range were assessed by constructing Minimum Spanning Networks (MSN) for the COI and 16S datasets. Both genes revealed two distinct clades, corroborating the results of Ebbs et al. (2018). The larger of the two clades in both genes (referred to as clade A) contains haplotypes from the native and invasive range. The smaller clade (referred to as clade B) contains haplotypes recovered from only the Western or South Western United States. Specimens with clade A and B haplotypes can coexist in the same locality. For example, individuals collected from a California locality (PRNTHIDK) exhibit both clade A and B haplotypes.

Previous research detected a similar pattern within the CO1 dataset (Ebbs et al. 2018). This finding suggests that the clades are not partitioned by locality. Additionally, our results contradict those found by previous investigators that found clade B restricted to only west of the Rocky Mountains (Ebbs et al. 2018). P. acuta individuals from Montana and Colorado, where the localities are geographically east of the Rocky Mountains, were also found in clade B.

A lack of available genetic data meant we were unable to access the global genetic structure of H. cf. trivolvis outside of North America, despite its scattered global appearance (Appleton 2003). In addition, there is limited existing genetic data from other parts of the United States. When the 25 additional Genbank COI sequences were added to create a range-wide network, the resulting network suggested increased East-West structuring across North America. The added Genbank samples from Canada, Maryland and New Mexico did not fall into the existing haplogroups and were more similar to each other than to haplogroups recovered from the West Coast. The average genetic distances among the Canada haplotypes and Maryland haplotypes to the West Coast populations are 5.1% and 12.2%, respectively. The average genetic distance between the Canada haplotypes and Maryland haplotypes is 17.3%.

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4. Discussion

4.1 West Coast

One of the objectives of this study was to address the biogeography of two freshwater snails, P. acuta and H. cf. trivolvis, in the Western United States, and compare the population genetic patterns between the species. Previous studies have shown that P. acuta and H. cf. trivolvis exhibit markedly different life history characteristics and dispersal potential. We hypothesized that given these differences, the two species would show differences in their population genetic structuring. The results of the AMOVA provided insight into the population structuring of each species. Within the 16S dataset, both species showed that most of the genetic variation comes from within population variation. Little variations were detected within or among watersheds, indicating overall panmixia. This suggests that a lack of physical watershed connectedness does not restrict gene flow. Within the COI dataset, genetic diversity of P. acuta is still attributed to within population variation, but the variation of H. cf. trivolvis is attributed to among populations within a watershed, indicating that H. cf. trivolvis may exhibit more population segregation within each watershed. The inconsistences in the results between the two genes could be a consequence of small sample sizes in the P. acuta dataset or from the differences between the two mitochondrial genes. The COI gene used in this study is considered a universal marker because of its ability to successfully recover gene fragments and rapid rate of evolution in diverse invertebrates (Knowlton & Weigt 1998; Zhang & Hewitt 1997; Folmer et al. 1994). COI has been shown to evolve at a rate that is three times faster than that of 16S in certain groups (Knowlton & Weigt 1998). The differences in the source of variation in the COI gene segment between P. acuta and H. cf. trivolvis may result from the faster evolution of the COI gene that is not detected by the 16S gene. Additionally, 16S has been used to assess intrafamilial differentiation within mollusks (Bonnaud et al. 1994). Therefore, it may not exhibit enough variations to reveal fine scale genetic structuring compared to COI If the higher COI population differentiation within watershed for H. cf. trivolvis is valid, it could be a result of its relatively lower local dispersal capabilities. P. acuta may be more effective at dispersing within watersheds via a variety of active and passive dispersal mechanisms (Van Leeuwen et al. 2013), allowing a larger diversity of genotypes to be present in

12 one locality. Comparatively, H. cf. trivolvis may lack the ability or the necessary vectors to facilitate movement within watersheds. This implies that scale is important to consider when evaluating dispersal ability of these two species. One possible explanation for the greater population differentiation for H. cf. trivolvis relates to their potential rate of spread. Previous studies have used the COI haplotype diversity to nucleotide diversity ratio to examine the rates of spread for species in their native and invasive range (Ebbs et al. 2018). Applying this concept to this comparative study, P. acuta has a larger haplotype diversity to nucleotide diversity ratio than H. cf. trivolvis, which could suggest that P. acuta is spreading rapidly in the West Coast. The proposed larger rate of spread in P. acuta may explain its more homogenous population genetic structuring. The species showed other noticeable differences in their population genetic patterns. P. acuta showed overall greater genetic diversity relative to H. cf. trivolvis. The West Coast haplotype diversity for the COI P. acuta dataset (Hdphysa = 0.962) is consistent with the haplotype diversity (Hdnative =0.97) found across their entire native range by previous investigators (Ebbs et al. 2018). Collectively, these results indicate that P. acuta is genetically more diverse when compared to its native co-inhabitant. One possible explanation is the relative abundance and distribution of P. acuta in the United States. P. acuta is often considered the most common and widely distributed freshwater gastropod in North America (Dillion et al. 2002). Large, ubiquitous populations coupled with rapid maturation rates and high fecundity could lead to overall higher genetic diversity. Alternatively, the global haplogroup networks suggest ongoing secondary contact with invasive populations which could also promote gene flow and increase genetic diversity. Continued sampling of P. acuta and H. cf. trivolvis will reveal if these observed population structuring differences exist across their native range. Another goal of this study was to determine whether watersheds, geographic distance, or other biotic factors contributed to the observed genetic structuring. In this study, we found that watersheds were not a significant factor contributing to the observed population structure of either species. While the species differed in the COI dataset for their source of variation, neither species’ variation was attributed to among watersheds. Similarly, we discovered that geographic distance did not relate to genetic distance, except for the P. acuta 16S dataset. A larger number of data points with high pairwise genetic distance at a local scale likely influenced the significant effect. The overall effect of longitudinal distance on genetic distance of these two species is

13 unclear, as the findings of this study show no obvious trend. This result corroborates prior research that shows that geographic distance is not strongly correlated to the genetic distance between populations of freshwater organisms (Freeland et al. 2000; Vanoverbeke & DeMeester 1997; Hebert & Finston 1996). One explanation for the lack of association between watersheds, geographic distance and genetic distance is that dispersal via waterfowl is promoting gene flow across the watersheds. The Pacific Flyway, the westernmost of the four primary bird corridors in North America (Wilson 2010), is a pathway for migratory birds between the Pacific Coast and the Rocky Mountains from Alaska down to Mexico. As the waterfowl travel North to South along this flyway, they are likely passively transporting the snails between sites. Other studies examining the biogeographic patterns of freshwater snails in the family Hydrobiidae have found similar genetic patterns mapping along the Pacific Flyway (Liu, Hershler & Clift, 2003). Experimental studies on different aquatic species have discovered latitudinal clines in genetic diversity (Hewitt 1996), which could identity source populations and predict the direction of dispersal. However, relatively little is known about the directionality of dispersal in many freshwater organisms (Figuerola & Green 2002). Within the COI dataset, both species show the greatest nucleotide diversity in a southern (California) watershed. The opposite is true for the 16S dataset. Both species show the greatest nucleotide diversity in a northern (Oregon) watershed. Individually, these results suggest a directionality to dispersal, albeit competing directions. Together, they show that dispersal is stochastic, occurring in both directions along the flyway. The stochastic movement across watersheds implies that dispersal is not unidirectional along the Pacific Flyway and that the random, multi-directional dispersal along this migration route could promote increased genetic mixing. The occasional dispersal by waterfowl may explain the North-South genetic similarity and the overall weak geographic structuring in these species. Overall, these results indicate that waterfowl are potential vectors for long-distance regional dispersal in P. acuta and H. cf. trivolvis and that dispersal occurs frequently enough to impact their population genetic structuring. These findings parallel other genetically-based biogeographic assessments of freshwater gastropods which demonstrate population structuring that correlates with dispersal (Liu & Hershler 2009; Liu, Hershler & Clift 2003).

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Despite the proposed differences, these results suggest that there is not a significant difference in the dispersal ability between P. acuta and H. cf. trivolvis at large scales. At the regional scale, watersheds are not a major barrier to the distribution of either species. Waterfowl appear to be transporting the snails across watersheds with generally the same frequency or success. These results contradict the historical view that Helisoma sp. would be a less effective long-distance disperser. Their generally larger size relative to Physa sp. suggests that they would be less likely to remain attached the waterfowl feathers for any length of time (Boag 1986). This is relevant if dispersal occurs over a smaller number of long-distance dispersal events as opposed to a larger number of short distance movements. This suggests that dispersal along the Pacific Flyway occurs as many small, gradual dispersal events as opposed to single, long-distance events. Additionally, the final size of the adult small may not dictate their dispersal ability. First, younger snails, whether as egg-masses, newly hatched individuals or sub-adults, tend to be dispersed most often by waterfowl (Boss 1978). Second, research has shown that each species has a size at which they disperse that maximizes their success (Boag 1986). The final relative size of each species can then be considered irrelevant to their dispersal ability. While neither species is limited in their dispersal by watershed, the mechanisms by which the waterfowl are transporting the snails, either internal or external, remain unclear. Although rare, previous research showed that internal transport is more likely for Physa sp. compared to Helisoma sp. (Malone 1965b). Whether the species are utilizing the same mechanism and with similar frequencies should be investigated. Similarly, the waterfowl species that transport the snails is unknown. Future studies should examine which waterfowl species are responsible for transporting the snails. The emphasis should be on smaller, more abundant waterfowl, such as ducks and waders, that are likely important disperses, but have received less scientific attention relative to larger waterfowl, such as geese and swans (Figuerola & Green 2002). 4.2 Global The global haplotype networks P. acuta show the appearance of two distinct clades. Clade A contains individuals from the native and invasive range and clade B contains individuals from only the western United States. The co-existence of the two clades within the same western locality implies that members of clade A were subsequently re-introduced into the western United States, leading to the current distributional overlap. Our results confirm the observed patterns from previous studies (Ebbs et al. 2018). Some of the possible explanations for the

15 presence of clade A in western populations were presented in the study. Clade A may have been re-introduced from outside of the native range, from eastern United States populations or a combination of both (Ebbs et al. 2018). Evidence supporting the re-introduction of clade A into the western United States from the invasive range comes from the connections among haplotypes in the MSN networks. Western populations in clade A are most closely connected to invasive range haplotypes than to eastern United States haplotypes, suggesting that clade A members were re-introduced into the western United States from the invasive range. Continued sampling in the native range is needed to explain the origin of clade in the western United States. Additionally, in the COI network, no Pacific Northwest samples were found in clade B. Their absence in clade B is likely a factor of small sampling sizes in Oregon and Washington. It is unlikely that biogeographical factors have prevented clade B from spreading into those areas as individuals from Oregon appear in clade B of the 16S network.

The appearance of two distinct clades in P. acuta suggests a period of possible historical isolation between eastern and western populations in the United States. Previous studies support the observed East-West genetic differentiation with phylogenetic analyses (Ebbs et al. 2018). No consensus has been reached on what mechanisms drove the divergence across North America, however, geographic barriers have been proposed. Historical barriers promote diversification via allopatric speciation. The Rocky Mountains have been suggested to play a role in restricting gene-flow and in structuring populations of freshwater organisms (Ebbs et al. 2018; Finn et al. 2006). Our results indicate that clade B is not geographically isolated to localities west of the Rocky Mountains. In the COI network, haplogroups containing samples from Colorado and Montana were recovered in clade B. Similarly, in the 16S network, haplogroups containing samples from Colorado were found in clade B. These results indicate that clade B can be considered a western United States clade, as it is not found east of Colorado. However, it is not exclusive to west of the Rocky Mountains. This finding differs with previous research which found clade B only west of the Rocky Mountains (Ebbs et al. 2018). The contradictory result is likely an outcome of previous under-sampling from the West Coast. The addition of new samples increased the number of potential connections between haplotypes resulting in their appearance in clade B. The presence of these samples in clade B suggests that either clade B is not geographically restricted but has yet to be recovered in other North American localities or that dispersal vectors have transported clade B to east of the Rocky Mountains. Additional

16 sampling is needed to determine if the Rocky Mountains served as a historical barrier or if other barriers, geographic (e.g., waterways, elevation) or environmental (e.g., temperature, lack of suitable habitat, salinity), promote the divergence of the clades.

Genetic sequences of H. cf. trivolvis is grossly under sampled. Efforts to understand the population structuring are complicated by lack of available genetic data, unawareness of their global occurrence and the historic methods of identification based on morphology. Prior to this study only 27 sequences were published on Genbank for H. trivolvis and H. anceps COI gene segment and 9 sequences for the 16S gene segment. This study adds 23 COI sequences (an 85% increase) and 57 16S sequences (a 633% increase) for their native range. It also represents the first effort to characterize the population genetic structure of H. cf. trivolvis.

The COI range-wide haplogroup network suggests that H. cf. trivolvis displays East-West genetic structuring in their native range, similar to the pattern seen in P. acuta. These results imply that both species experience significant genetic isolation across their native range. The isolation could be caused by insurmountable biogeographical factors, or a lack of dispersal vectors that traverse longitudinally across the continent. Similar factors could maintain the pattern between the species across their native range. Alternatively, the significant structuring across the United States may indicate the presence of a cryptic species, as the COI genetic distances among these populations are relatively high. Further genetic sampling and morphological studies are needed in order to fill in the geographic gaps and determine the true taxonomic identity of Helisoma species in their native range.

The current global distribution of H. cf. trivolvis is arguably unknown. Small, scattered populations have been identified outside North America, but their general abundance and distribution levels have yet to be elucidated. One area of study relevant to their global distribution is their dispersal ability. As evidenced by its current proposed distribution, the ability of H. cf. trivolvis to disperse effectively at a regional scale does not translate to the global scale. A possible explanation is that cross-continental dispersal, primarily mediated by humans, may be a less effective dispersal mechanism for H. cf. trivolvis. Anthropogenic transport may occur infrequently or the snails are not able to survive the journey. Additionally, waterfowl species that effectively disperse H. cf. trivolvis in their native range may not be present in other parts of the globe, limiting H. cf. trivolvis ability to successfully colonize new habitats. Alternatively, the life

17 history traits of Helisoma sp. could play a significant role in their limited global distribution. For example, once H. cf. trivolvis reaches a new environment, it may be less likely to successfully establish a population. This could be a result of the species preferential outcrossing mating system, where more than just one specimen is required to start a population. Ecological factors in the new environments also may prevent H. cf. trivolvis from successfully establishing a population. The aquatic environments may lack suitable habitat or resources, support effective predators and competitors, or have physiochemical properties or pollution levels that H. cf. trivolvis can not tolerate. In contrast, the life history characteristics of P. acuta facilitate its invasiveness. P. acuta is not only tolerant to a variety of ecological conditions, but also has reportedly high fecundity, significant reproductive plasticity, and readily reproduces via self- fertilization (Turner & Montgomery 2009; Gittenberger et al. 2004; Bousset et al. 2004). These characteristics, as opposed to strictly dispersal ability, likely contribute to the overall greater invasive success of P. acuta relative to H. cf. trivolvis. Additional studies that focus on the dispersal ability of H. cf. trivolvis will enhance our understanding of their current distribution and global population structuring. The lack of knowledge about the global distribution of Helisoma is compounded by their phenotypic plasticity. There is a large gap in the understanding of freshwater snails due to the traditional methods in snail identification, which rely almost exclusively on shell morphology and internal anatomy. Like many freshwater gastropod families, Planorbidae and Physidae snails exhibit considerable diversity in shell morphology within species and share extremely homogenous anatomical traits between species (Fiorentino et al. 2008; Pfenninger et al. 2006), often leading to unclear species identifications (Minton et al. 2008; Perez & Minton 2008). The morphological similarity of Helisoma sp. to other planorbid snail species has impeded the overall understanding of their distribution (Jelnes 1982). Genetic identification based on mitochondrial markers is necessary to advance our understanding of taxonomy (Wethington & Lydeard 2007; Pfenninger et al. 2006). Improving their systematics will resolve current species distribution confusion and inform the study of their ecology.

4.3 Broader Implications & Ecological Interactions

A lack of information on the population structuring of P. acuta and H. cf. trivolvis in their native range has prevented an understanding of their role in freshwater ecological interactions.

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The results of this study indicate that long-distance dispersal mechanisms, such as bird-mediated dispersal, help shape and maintain the structure of freshwater snail assemblages, which can influence their ecosystem interactions both in their native and global range. Their effective dispersal ability at a large-scale has important consequences for the spread of parasites, as invasive species and to the conservation of freshwater gastropods.

4.3.1 Host-Parasite Interactions

The effective dispersal capabilities of P. acuta and H. cf. trivolvis at regional scales is of critical importance to their role as intermediate hosts for trematode parasites. In this study, we found that the population structuring of these freshwater snails in the West Coast of their native range does not depend on geographic distance or hydrologic connectivity. Moreover, the overall North-South genetic similarity is maintained by long-distance dispersal vectors, most likely waterfowl, and that their dispersal ability does not impede their distribution. Determining the spatial and temporal patterns of dispersal between freshwater habitats of these freshwater snails allows for the identification of dispersal pathways of the parasites that they carry.

This study indicates that both P. acuta and H. cf. trivolvis are capable of transporting trematodes across long distances. Trematode parasites can be transported within the intermediate host snails via molluscan dispersal vectors or within the final hosts themselves, including waterfowl. Thus, the parasites also undergo long-distance dispersal similar to that of their intermediate or final host. Previous research found that migratory waterfowl serve as long- distance vectors for the parasites and the parasites are commonly dispersed along migration routes (Jourdain et al. 2007). Furthermore, the waterbodies that harbor infected snails are often distributed along major flyways (Horak et al. 2002). The frequency of dispersal that occurs in order to maintain the population structure of P. acuta and H. cf. trivolvis suggests that the parasites may also experience similar, if not more, levels of dispersal and gene flow. Additionally, mechanical barriers are often proposed as a way to stop the spread of parasites in freshwater habitats by preventing the spread of their gastropod hosts (Clennon et al. 2006). The results of this study indicate that mechanical barriers would mitigate only local dispersal. The snails would still overcome mechanical barriers via long-distance dispersal.

Studies looking to mitigate the spread of trematode parasites have emphasized the need to understand the patterns of dispersal in freshwater snails (Clennon et al. 2006). Specifically, the

19 directionality of dispersal, which is important in identifying source populations and optimizing prevention strategies. This study found that P. acuta and H. cf. trivolvis are not dispersed consistently in one direction, but rather back-and-forth along the West Coast. If this pattern is valid and holds true for invasive populations, then concentrated efforts, such as the application of localized molluscides, may be ineffective at preventing the spread of parasites.

Introductions of P. acuta and H. cf. trivolvis outside of their native range, as they already have been in many places, could promote an increase in the distribution of their associated parasites. Future research should consider the role of long-distance dispersal, particularly via waterfowl, in the transmission of trematode parasites infecting freshwater snails.

4.3.2 Conservation

This study indicates that the population structure of P. acuta and H. cf. trivolvis in the West Coast depends on long-distance dispersal, likely via migratory waterfowl. Thus, their continued success, as indicated by their relatively high levels of gene flow, relies on, in part, the frequency and abundance of contact with the birds. These important interactions with waterfowl depend on the availability of suitable habitat. Many freshwater habitats have experienced significant anthropogenic changes that threaten both aquatic and avian biodiversity. One example is habitat modification which has significantly impaired the ability of freshwater snails to disperse locally by reducing, altering or fragmenting suitable habitat and impeded contact with waterfowl that are required to disperse (Liu & Hershler 2009; Strong et al. 2007). Habitat loss has already been seen to decrease local dispersal and is expected to have major implications for the long-distance dispersal of freshwater organisms (Amezaga et al. 2002).

In the West Coast, P. acuta and H. cf. trivolvis readily utilize long-distance dispersal vectors, such as migratory waterfowl, to maintain population connectivity. Over time, a decrease in migratory waterfowl may decrease regional variation, result in isolated populations and reduce the geographic ranges of P. acuta, H. cf. trivolvis and other freshwater gastropods. Overall, the conservation of migratory waterfowl has important implications for maintaining connectivity and diversity of P. acuta and H. cf. trivolvis populations in North America.

However, it is unlikely that all freshwater snails are capable of traversing larger geographic distances. For example, the Bliss Rapids snail (Taylorconcha serpenticola), appears

20 to lack the same long-distance dispersal capabilities as P. acuta and H. cf. trivolvis, given its small range in the Snake River, Idaho (Liu & Hershler 2009). Therefore, reliance on waterfowl conservation is not sufficient to ensure continued survival of all freshwater gastropod species. In order to conserve freshwater gastropod diversity, information regarding their dispersal methods, distribution and population structure are necessary.

4.3.3 Invasive Species

Invasive species are one of the world’s largest threats to biodiversity. Of all ecosystems, invasive species have had the biggest impact on freshwater aquatic environments (Mooney & Cleland, 2001). Many freshwater gastropods, including P. acuta, are considered global invaders. Human-mediated dispersal, primarily as a result of aquaculture and the aquarium trade, has moved P. acuta between continents (Anderson 2003). Once established, these species use their own methods of dispersal to continue spreading. Studies considering the role of waterfowl dispersal in the spread of invasive snails have been neglected (Green & Figuerola 2005). This study provided an opportunity to assess the role of waterfowl in the spread of a global invader, P. acuta, in its native range. The results could then be used to assess its ability to spread globally in future studies.

Given the population genetic structure, P. acuta is likely using waterfowl to travel along the West Coast of the United States. This suggests that P. acuta can utilize waterfowl to promote its rapid, global spread. Overall, its ability to use waterfowl could explain its high invasiveness. However, the answer to freshwater gastropod invasiveness does not lie solely on the exploitation of waterfowl. H. cf. trivolvis exhibited a similar ability to utilize waterfowl to disperse along the West Coast, yet it is not considered a globally invasive species. Life history characteristics play an equally important role in determining invasiveness. Additional studies on the classification and distribution of H. cf. trivolvis will help clarify the relative role of bird-mediated dispersal on its global occurrence.

5. Conclusions

This study addressed the biogeography of two freshwater snails, P. acuta and H. cf. trivolvis, in the Western United States and was the first of its kind to compare their population genetic structuring. We found that P. acuta was more diverse and showed less overall genetic

21 structuring than H. cf. trivolvis. However, the observed genetic patterns were not explained by watersheds for either species. Additionally, genetic distance was unrelated to geographic distance. The North-South genetic similarity reflects the Pacific Flyway, which suggests that long-distance dispersal vectors, such as waterfowl, are promoting ongoing gene flow over large geographic ranges and that the population structure of the two species may be strongly influenced by long-distance dispersal. Additionally, the dispersal of the snails is not directional, which could promote increased mixing and supports the observed North-South genetic similarly. Cumulatively, these results indicate that, at a regional scale, the species do not differ in their long-distance dispersal ability, nor are their distributions impeded by dispersal. Other factors, such as the life history characteristics of H. cf. trivolvis, could explain the observed differences in their population structure within their native range.

The efforts to compare the range-wide structuring of the species was complicated by lack of available information on H. cf. trivolvis. This study is the first to examine the population structure of H. cf. trivolvis. Our genetic sampling significantly increased the available genetic information on the species. Our results showed that both species exhibit significant East-West genetic structuring across their native range. This study corroborated the results of other global population structure studies in P. acuta where two distinct clades were recovered. Our results suggest that clade B, which was thought to be found only west of the Rocky Mountains, has additional occurrences along the eastern slope. Overall, this paper provides an initial framework for continued biogeographical analysis of Physa and Helisoma in their native range.

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Tables

Table 1 Analysis of Molecular Variance for Physa acuta and Helisoma cf. trivolvis for each gene

16S Physa acuta (N=14) Source of Variation d.f. % Variation Fixation Indices p value Among Watersheds 4 20.54 ΦCT = 0.2054 0.2126 Among Populations within Watersheds 3 34.34 ΦSC = 0.4321 0.0852 Within Populations 6 45.12 ΦST = 0.5488 0.0612 COI Physa acuta (N=21) Source of Variation d.f. % Variation Fixation Indices p value Among Watersheds 3 0.26 ΦCT = 0.0026 0.6748 Among Populations within Watersheds 3 4.47 ΦSC = 0.0448 0.0032 Within Populations 14 95.27 ΦST = 0.0.473 0.3278 16S Helisoma cf. trivolvis (N=57) Source of Variation d.f. % Variation Fixation Indices p value Among Watersheds 5 15.85 ΦCT = 0.1585 0.0301 Among Populations within Watersheds 16 12.31 ΦSC = 0.1463 0.1605 Within Populations 35 71.84 ΦST = 0.2816 0.0111 COI Helisoma cf. trivolvis (N=23) Source of Variation d.f. % Variation Fixation Indices p value Among Watersheds 1 20.91 ΦCT = 0.2091 <0.001 Among Populations within Watersheds 9 77.59 ΦSC = 0.9811 0.0949 Within Populations 12 1.5 ΦST = 0.9850 <0.001

Table 2 Population statistics and molecular diversity indexes for each watershed for each species by gene

Gene Species Watershed N H HD Nucleotide Diversity Physa acuta Tulare Lake 8 8 1 0.0435 +/- 0.0245 San Joaquin River 1 1 1 0 San Francisco 9 6 0.839 0.0178 +/- 0.0103 Willamette 3 2 0.663 0.0013 +/- 0.0016 COI Total 21 16 0.962 Helisoma cf. San Francisco 21 6 0.824 0.0339 +/- 0.0177 trivolvis Willamette 2 1 0 0 Total 23 7 0.85 Physa acuta San Francisco 9 8 0.8669 0.0425 +/- 0.0236 Willamette 2 2 1 0.1011 +/- 0.1022 Middle Snake 1 1 1 0 Middle Columbia 1 1 1 0 Tulare Lake 1 1 1 0 Total 14 12 0.884 16S Helisoma cf. Lower Columbia 2 1 0 0 trivolvis San Francisco 42 11 0.716 0.0362 +/- 0.0183 Middle Columbia 2 2 1 0.0515 +/- 0.0526 Willamette 8 7 0.988 0.0564 +/- 0.0316 Coastal 1 1 1 0 Sacramento 2 1 0 0 Total 57 16 0.839 Population statistic abbreviations: N, number of individuals sampled, H, number of haplotypes, Hd, haplotype diversity and nucleotide diversity.

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Table 3 Overall molecular diversity indexes for each species by gene

COI 16S Gene diversity: 0.9619 +/- 0.0302; Gene diversity: 0.9670 +/- 0.0437;

Mean number of pairwise differences: Mean number of pairwise differences: Physa acuta 13.738 +/- 6.427; 30.989 +/- 14.411;

Nucleotide diversity (average over loci): Nucleotide diversity (average over loci): 0.02699 +/- 0.0141 0.0666 +/- 0.0348 Gene diversity: 0.8498 +/- 0.0364; Gene diversity: 0.8509 +/- 0.0300;

Mean number of pairwise differences: Mean number of pairwise differences: Helisoma cf. trivolvis 14.4032 +/- 6.698; 18.7851 +/- 8.446;

Nucleotide diversity (average over loci): Nucleotide diversity (average over loci): 0.0360 +/- 0.0187 0.0414 +/- 0.0206

Table 4 Population ΦST values for each species between watersheds, separated by gene

Species Gene Watersheds 1 2 3 4 5 6 7 8 9 Physa COI 1) San Francisco 0 0.142 - - - 0.030 0.272 - - acuta 2) Willamette 0.142 0 - - - 0.0086 0.913 - - 6) Tulare Lake 0.030 0.0086 - - - 0 -0.440 - - 7) San Joaquin 0.272 0.913 - - - -0.440 0 - - River Physa 16S 1) San Francisco 0 0.2897 - - -0.162 -0.169 - 0.714 - acuta 2) Willamette 0.2897 0 - - -0.081 -0.033 - 0.309 - 5) Middle Columbia -0.162 -0.081 - - 0 1 - 1 - 6) Tulare Lake -0.169 -0.033 - - 1 0 - 1 - 8) Middle Snake 0.714 0.309 - - 1 1 - 0 - Helisoma COI 1) San Francisco 0 0.406 ------cf. trivolvis 2) Willamette 0.406 0 ------Helisoma 16S 1) San Francisco 0 0.1354 0.021 0.187 0.021 - - - 0.544 cf. 2) Willamette 0.1354 0 0.065 0.222 -0.200 - - - 0.457 trivolvis 3) Sacramento 0.021 0.065 0 1 0.410 - - - 1 4) Lower Columbia 0.187 0.222 1 0 0.115 - - - 1 5) Middle Columbia 0.021 -0.200 0.410 0.115 0 - - - 0.452

9) Coastal 0.544 0.457 1 1 0.452 - - - - Values with a p-value <0.05 are bolded.

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Figures

Fig. 1 Exemplar specimens of freshwater gastropods in the family Physidae

and Planorbidae from the University of Colorado Boulder Museum of Natural History Invertebrate Zoology Collections: Physa acuta (left) (UCM37022) and Helisoma cf. trivolvis (right) (UCM30881).

Fig. 2 Map of the West Coast of the United States (Washington, Oregon, California) showing the 28 study sampling locations. The locality site colors correspond to each specific sampling location. The localities are grouped according to state and watershed (designated by the USGS). Detailed locality information can be found in Appendix 1-2.

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Fig. 3 COI haplotype network for Physa acuta in the West Coast. Each circle represents a unique haplotype. The size of the circle is proportionate to the frequency of that haplotype in the population. Each hashmark represents one base pair change. Snail haplotypes are color-coded according to their locality.

Fig. 4 16S haplotype network for Physa acuta in the West Coast. Each circle represents a unique

haplotype. The size of the circle is proportionate to the frequency of that haplotype in the population. Each hashmark represents one base pair change. Snail haplotypes are color-coded according to their locality.

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Fig. 5 COI haplotype network for Helisoma cf. trivolvis in the

West Coast. Each circle represents a unique haplotype. The size of the circle is proportionate to the frequency of that haplotype in the population. Each hashmark represents one base pair change. Snail haplotypes are color-coded according to their locality.

Fig. 6 16S haplotype network for Helisoma cf. trivolvis in the West Coast. Each circle represents a unique haplotype. The size of the circle is proportionate to the frequency of that haplotype in the population. Each hashmark represents one base pair change. Snail haplotypes are color-coded

according to their locality.

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Fig. 7 COI haplotype network for Physa acuta in the West Coast. Each circle represents a unique haplotype. The size of the circle is proportionate to the frequency of that haplotype in the population. Each hashmark represents one base pair change. Snail haplotypes are color-coded according to their locality. Clade A and clade B are shaded and correspond to the two clades designated in Ebbs et al. 2018.

Fig. 8 16S haplotype network for Physa acuta in the West Coast. Each circle represents a unique haplotype. The size of the circle is proportionate to the frequency of that haplotype in the population. Each hashmark represents one base pair change. Snail haplotypes are color-coded according to their locality. Clade A and clade B are shaded and correspond to the two clades designated in Ebbs et al. 2018.

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Fig. 9 COI haplotype network for Helisoma cf. trivolvis in the West Coast. Each circle represents a unique haplotype. The size of the circle is proportionate to the frequency of that haplotype in the population. Each hashmark represents one base pair change. Snail haplotypes are color-coded according to their locality.

Fig. 10 Effect of geographic distance (km) on pairwise genetic distance in Helisoma cf. trivolvis (red) and Physa acuta (blue) in the COI dataset. The two graphs show the data with (right) and without (left) data points. For both species, geographic distance did not determine the pairwise genetic distance. The results show that Helisoma cf. trivolvis has a larger pairwise genetic distance at a local scale than Physa acuta.

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Fig. 11 Effect of geographic distance (km) on pairwise genetic distance in Helisoma cf. trivolvis (red) and Physa acuta (blue) in the 16S dataset. The two graphs show the data with (right) and without (left) data points. For Helisoma cf. trivolvis, geographic distance did not determine the pairwise genetic distance. For Physa acuta, geographic distance did determine pairwise genetic distance, where as distance increased, genetic distance decreased. A large number of data points with high pairwise genetic distance at a local scale likely influenced the significant effect.

Fig. 12 Effect of longitudinal distance (km) on pairwise genetic distance in Helisoma cf. trivolvis (red) and Physa acuta (blue) in the 16S dataset. The two graphs show the data with (right) and without (left) data points. For both species, longitudinal distance did not determine the pairwise genetic distance (p > 0.05).

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Fig. 13 Effect of longitudinal distance (km) on pairwise genetic distance in Helisoma cf. trivolvis (red) and Physa acuta (blue) in the COI dataset. The two graphs show the data with (right) and without (left) data points. For Physa acuta, longitudinal distance did not determine the pairwise genetic distance. For Helisoma cf. trivolvis, longitudinal distance did determine pairwise genetic distance, where as distance increased, genetic distance increased (p = 0.05). A large number of data points with high pairwise genetic distance at a local scale likely influenced the significant result.

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Appendix 1: Locality information, NCBI Genbank accession numbers and associated scientific studies (Physa acuta)

Specimen ID/Genbank Date number Locality Watershed Lat/Long Collected Gene Citation AY651170 Gila River, - 32.94354, -113.85435 2004 COI Wethington Arizona, USA & Guralnick 2004 AY651209 Gila River, - 32.94354, -113.85435 2004 16S Wethington Arizona, USA & Guralnick 2004 AY651171 Gila River, - 32.94354, -113.85435 2004 COI Wethington Arizona, USA & Guralnick 2004 AY651210 Gila River, - 32.94354, -113.85435 2004 16S Wethington Arizona, USA & Guralnick 2004 EU038355 Gila River, - 32.94354, -113.85435 2007 COI Wethington Arizona, USA & Lydeard 2007 EU038308 Gila River, - 32.94354, -113.85435 2007 16S Wethington Arizona, USA & Lydeard 2007 UCM48174 Oregon (OR- Willamette 45.50164758, -122.837913 COI This Study THNP) UCM48173 Oregon (OR- Willamette 45.472681, -122.45655 COI This Study BCE11) UCM48316 Oregon (OR- Willamette 45.472681, -122.45655 COI This Study BCE11) UCM48313 Oregon (OR- Willamette 45.501323, -122.837844 16S This Study THNPW) UCM48320 Oregon (OR- Willamette 45.501323, -122.837844 16S This Study THNPW) UCM48326 Oregon (Baker Middle 44.757422, -117.127758 16S This Study county, Snake Richland, Hewitt Park, reservoir): OR3 UCM48329 Oregon Middle 43.866879, -121.450102 16S This Study (Sunriver, Columbia Stables Pond): OR4 UCM48333 California San 37.34675, -121.68752 COI This Study (NORTHGCP) Francisco UCM48310 California San 37.661759, -121.964781 16S This Study (PRNTHCHR) Francisco UCM48304 California San 37.607681, -121.890551 16S This Study (PRPND002) Francisco

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UCM48162 California San 37.65212, -121.95194 16S This Study (PRNTHIDK) Francisco UCM48161 California San 37.65212, -121.95194 COI This Study (PRNTHIDK) Francisco UCM48161 California San 37.65212, -121.95194 16S This Study (PRNTHIDK) Francisco UCM48166 California San 37.65212, -121.95194 COI This Study (PRNTHIDK) Francisco UCM48166 California San 37.65212, -121.95194 16S This Study (PRNTHIDK) Francisco UCM48164 California San 37.65212, -121.95194 COI This Study (PRNTHIDK) Francisco UCM48164 California San 37.65212, -121.95194 16S This Study (PRNTHIDK) Francisco UCM48163 California San 37.65212, -121.95194 16S This Study (PRNTHIDK) Francisco UCM48160 California San 37.65212, -121.95194 COI This Study (PRNTHIDK) Francisco UCM48160 California San 37.65212, -121.95194 16S This Study (PRNTHIDK) Francisco MF694471 Woodward Tulare Lake 36.8745288,-119.8021703 2012 COI Ebbs et al. Park, Fresno, 2018 California, USA MF694426 Woodward Tulare Lake 36.8745288,-119.8021703 2012 COI Ebbs et al. Park, Fresno, 2018 California, USA MF694431 Woodward Tulare Lake 36.8745288,-119.8021703 2012 COI Ebbs et al. Park, Fresno, 2018 California, USA MF694432 Woodward Tulare Lake 36.8745288,-119.8021703 2012 COI Ebbs et al. Park, Fresno, 2018 California, USA MF694445 Woodward Tulare Lake 36.8745288,-119.8021703 2012 COI Ebbs et al. Park, Fresno, 2018 California, USA MF694446 Woodward Tulare Lake 36.8745288,-119.8021703 2012 COI Ebbs et al. Park, Fresno, 2018 California, USA MF694413 Woodward Tulare Lake 36.8745288,-119.8021703 2012 16S Ebbs et al. Park, Fresno, 2018 California, USA MF694447 Woodward Tulare Lake 36.8745288,-119.8021703 2012 COI Ebbs et al. Park, Fresno, 2018 California, USA MF694448 Woodward Tulare Lake 36.8745288,-119.8021703 2012 COI Ebbs et al. Park, Fresno, 2018

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California, USA MF694444 Waterfowl San Joaquin 37.1178648,-120.8024595 2012 COI Ebbs et al. Managment River 2018 Area, Los Banos, California, USA MF694403 Harvey Bear, San 37.0756481,-121.5183384 2013 16S Ebbs et al. Gilroy, Francisco 2018 California, USA MF694433 Harvey Bear, San 37.0756481,-121.5183384 2013 COI Ebbs et al. Gilroy, Francisco 2018 California, USA MF694434 Harvey Bear, San 37.0756481,-121.5183384 2013 COI Ebbs et al. Gilroy, Francisco 2018 California, USA MF694435 Harvey Bear, San 37.0756481,-121.5183384 2013 COI Ebbs et al. Gilroy, Francisco 2018 California, USA MF694457 Harvey Bear, San 37.0756481,-121.5183384 2013 COI Ebbs et al. Gilroy, Francisco 2018 California, USA AY651181 Chaffe - 38.733068, -106.1712732 2004 COI Wethington County, & Colorado, Guralnick USA 2004 AY651219 Chaffe - 38.733068, -106.1712732 2004 16S Wethington County, & Colorado, Guralnick USA 2004 AY651183 Fremont Co., - 38.4777498, -105.6166603 2004 COI Wethington Colorado, & USA Guralnick 2004 AY651221 Fremont Co., - 38.4777498, -105.6166603 2004 16S Wethington Colorado, & USA Guralnick 2004 AY651184 Fremont Co., - 38.4777498, -105.6166603 2004 COI Wethington Colorado, & USA Guralnick 2004 AY651222 Fremont Co., - 38.4777498, -105.6166603 2004 16S Wethington Colorado, & USA Guralnick 2004 AY651176 Garfield Co., - 39.7288899, -108.3217724 2004 COI Wethington Colorado, & USA

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Guralnick 2004 AY651215 Garfield Co., - 39.7288899, -108.3217724 2004 16S Wethington Colorado, & USA Guralnick 2004 AY651174 Mesa Co. - 38.9335488, -108.4990457 2004 COI Wethington Colorado, & USA Guralnick 2004 AY651213 Mesa Co. - 38.9335488, -108.4990457 2004 16S Wethington Colorado, & USA Guralnick 2004 AY651177 Rio Blanco - 39.9380767, -108.3244424 2004 COI Wethington Co., Colorado, & USA Guralnick 2004 AY651216 Rio Blanco - 39.9380767, -108.3244424 2004 16S Wethington Co., Colorado, & USA Guralnick 2004 AY651175 Yuma Co., - 40.1221719, -102.7276757 2004 COI Wethington Colorado, & USA Guralnick 2004 AY651214 Yuma Co., - 40.1221719, -102.7276757 2004 16S Wethington Colorado, & USA Guralnick 2004 MF694410 Denver area, - 39.7645187, -104.9951942 2014 16S Ebbs et al. Colorado, 2018 USA MF694430 Denver area, - 39.7645187, -104.9951942 2014 COI Ebbs et al. Colorado, 2018 USA MF694405 Denver area, - 39.7645187, -104.9951942 2014 16S Ebbs et al. Colorado, 2018 USA EU038365 Santiago de - 20.0245035, -75.8611161 - COI Wethington Cuba, Cuba & Lydeard 2007 EU038318 Santiago de - 20.0245035, -75.8611161 - 16S Wethington Cuba, Cuba & Lydeard 2007 EU038366 Santiago de - 20.0245035, -75.8611161 - COI Wethington Cuba, Cuba & Lydeard 2007 EU038319 Santiago de - 20.0245035, -75.8611161 - 16S Wethington Cuba, Cuba & Lydeard 2007 EU038367 Santiago de - 20.0245035, -75.8611161 - COI Wethington Cuba, Cuba & Lydeard 2007

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EU038320 Santiago de - 20.0245035, -75.8611161 - 16S Wethington Cuba, Cuba & Lydeard 2007 EU038368 France - 43.7953547, 3.6768801 2004 COI Wethington & Lydeard 2007 EU038321 France - 43.7953547, 3.6768801 2004 16S Wethington & Lydeard 2007 AY651185 France - 43.7953547, 3.6768801 2004 COI Wethington & Guralnick 2004 AY651223 France - 43.7953547, 3.6768801 2004 16S Wethington & Guralnick 2004 AY651186 France - 43.7953547, 3.6768801 2004 COI Wethington & Guralnick 2004 AY651224 France - 43.7953547, 3.6768801 2004 16S Wethington & Guralnick 2004 MG001332 France - 49.4759314, 4.0208555 2014 COI Ebbs et al. 2018 MF694473 France - 49.4759314, 4.0208555 2014 COI Ebbs et al. 2018 KF737948 Epirus, Greece - 39.6602599, 20.1588988 - COI Albrecht et al. 2013 KF737947 Epirus, Greece - 39.6602599, 20.1588988 - COI Albrecht et al. 2013 KF737950 Western - 38.2734877, 20.9889862 - COI Albrecht et Greece al. 2013 KF737949 Western - 38.2734877, 20.9889862 - COI Albrecht et Greece al. 2013 KF737951 Western - 40.3857488, 20.9250748 - COI Albrecht et Macedonia al. 2013 KF737952 Western - 40.3857488, 20.9250748 - COI Albrecht et Macedonia al. 2013 KF737922 Central - 40.6517371, 21.8964561 - COI Albrecht et Macedonia al. 2013 KF737923 Central - 41.2099965, 23.1124904 - COI Albrecht et Macedonia al. 2013 KF737924 Lake Kerkini, - 41.2099965, 23.1124904 - COI Albrecht et Central al. 2013 Macedonia KF737925 Lake Kerkini, - 41.2099965, 23.1124904 - COI Albrecht et Central al. 2013 Macedonia KF737926 Nov Dojran, - 38.5611075, 21.3519344 - COI Albrecht et Lake Dojran, al. 2013 Macedonia

43

KF737927 Lake - 41.0371518, 20.5788244 - COI Albrecht et Lysimachia, al. 2013 Greece KF737928 Lake Ohrid - 38.5611075, 21.3519344 - COI Albrecht et al. 2013 KF737929 West Greece, - 39.6666827, 20.8745568 - COI Albrecht et Lake al. 2013 Trichonis KF737930 Lake - 39.6666827, 20.8745568 - COI Albrecht et Pamvotis, al. 2013 Greece KF737938 Lake - 41.2051644, 22.8066313 - COI Albrecht et Pamvotis, al. 2013 Greece KF737931 Lake - 41.6983378, 22.7104176 - COI Albrecht et Pamvotis, al. 2013 Greece KF737932 River - 41.6983378, 22.7104176 - COI Albrecht et Strymonas, al. 2013 Central Macedonia KF737933 River - 41.6983378, 22.7104176 - COI Albrecht et Strymonas, al. 2013 Central Macedonia KF737934 River - 41.6983378, 22.7104176 - COI Albrecht et Strymonas, al. 2013 Central Macedonia KF737935 River - 40.79233, 22.84481 - COI Albrecht et Strymonas, al. 2013 Central Macedonia KF737936 River - 40.79233, 22.84481 - COI Albrecht et Gallikos, al. 2013 Central Macedonia KF737937 River - 39.6666827, 20.8745568 - COI Albrecht et Gallikos, al. 2013 Central Macedonia KF737939 Star Dojran, - 38.2734877, 20.9889862 - COI Albrecht et Lake Dojran, al. 2013 Macedonia KF737940 Western - 38.2734877, 20.9889862 - COI Albrecht et Greece al. 2013 KF737941 Western - 38.5611075, 21.3519344 - COI Albrecht et Greece al. 2013 KF737942 West Greece, - 40.7146271, 21.7129633 - COI Albrecht et Lake al. 2013 Lysimachia KF737921 Lake - 40.6517371, 21.8964561 - COI Albrecht et Vegoritis, al. 2013 Central Macedonia

44

KF737943 Lake - 40.7146271, 21.7129633 - COI Albrecht et Vegoritis, al. 2013 Central Macedonia KF737944 Lake - 40.7146271, 21.7129633 - COI Albrecht et Vegoritis, al. 2013 Central Macedonia KF737945 Lake - 40.7146271, 21.7129633 - COI Albrecht et Vegoritis, al. 2013 Central Macedonia KF737946 Lake - 39.6602599, 20.1588988 - COI Albrecht et Vegoritis, al. 2013 Central Macedonia MF694429 Okoboji Lake, - 43.379834, -95.147231 - COI Ebbs et al. Iowa 2018 EU038371 New - 38.1281824, -87.9366731 - COI Wethington Harmony, IN & Lydeard 2007 EU038324 New - 38.1281824, -87.9366731 - 16S Wethington Harmony, IN & Lydeard 2007 EU038372 New - 38.1281824, -87.9366731 - COI Wethington Harmony, IN & Lydeard 2007 EU038325 New - 38.1281824, -87.9366731 - 16S Wethington Harmony, IN & Lydeard 2007 MF694458 Lake Victoria - -0.040238, 34.6559036 2013 COI Ebbs et al. - Kisumu 2018 MF694459 Lake Victoria - -0.040238, 34.6559036 2013 COI Ebbs et al. - Kisumu 2018 MF694460 Lake Victoria - -0.040238, 34.6559036 2013 COI Ebbs et al. - Kisumu 2018 MF694472 Lake Victoria - -0.040238, 34.6559036 2013 COI Ebbs et al. - Kisumu 2018 MF694402 Lake Victoria - -0.040238, 34.6559036 2013 16S Ebbs et al. - Kisumu 2018 MF694409 Medicine Lake - 44.9980332, -93.4250503 2009 16S Ebbs et al. 2018 AY651188 Reynolds - 37.3273429, -91.1647628 2004 COI Wethington County, & Missouri Guralnick 2004 AY651226 Reynolds - 37.3273429, -91.1647628 2004 16S Wethington County, & Missouri Guralnick 2004 MF694452 Bench Creek - 45.6136067, -111.0012205 2012 COI Ebbs et al. Pond 2018 MG001331 Bench Creek - 45.6136067, -111.0012205 2012 COI Ebbs et al. Pond 2018

45

MF694453 Bench Creek - 45.6136067, -111.0012205 2012 COI Ebbs et al. Pond 2018 MF694454 Bench Creek - 45.6136067, -111.0012205 2012 COI Ebbs et al. Pond 2018 MF694455 Bench Creek - 45.6136067, -111.0012205 2012 COI Ebbs et al. Pond 2018 MF694456 Bench Creek - 45.6136067, -111.0012205 2012 COI Ebbs et al. Pond 2018 MF694439 Mormon Lake - 40.8254407, -98.3727372 2012 COI Ebbs et al. 2018 MF694426 Mormon Lake - 40.8254407, -98.3727372 2012 COI Ebbs et al. 2018 MF694440 Mormon Lake - 40.8254407, -98.3727372 2012 COI Ebbs et al. 2018 MF694441 Mormon Lake - 40.8254407, -98.3727372 2012 COI Ebbs et al. 2018 MF694442 Mormon Lake - 40.8254407, -98.3727372 2012 COI Ebbs et al. 2018 MF694443 Mormon Lake - 40.8254407, -98.3727372 2012 COI Ebbs et al. 2018 MF694467 Mormon Lake - 40.8254407, -98.3727372 2012 COI Ebbs et al. 2018 MF694428 Mormon Lake - 40.8254407, -98.3727372 2012 COI Ebbs et al. 2018 MF694468 Mormon Lake - 40.8254407, -98.3727372 2012 COI Ebbs et al. 2018 MF694424 Mormon Lake - 40.8254407, -98.3727372 2012 16S Ebbs et al. 2018 MF694449 Stillwater - 39.4647101, -118.764105 2006 COI Ebbs et al. NWR 2018 MF694417 Stillwater - 39.4647101, -118.764105 2006 16S Ebbs et al. NWR 2018 MF694450 Stillwater - 39.4647101, -118.764105 2006 COI Ebbs et al. NWR 2018 MF694414 Stillwater - 39.4647101, -118.764105 2006 16S Ebbs et al. NWR 2018 MF694451 Bremmer Bay - -44.6763636, 169.117716 2010 COI Ebbs et al. 2018 MF694464 Bosque del - 33.8044978, -106.8932092 2009 COI Ebbs et al. Apache NWR 2018 MF694408 Bosque del - 33.8044978, -106.8932092 2009 16S Ebbs et al. Apache NWR 2018 MF694407 Bosque del - 33.8044978, -106.8932092 2009 16S Ebbs et al. Apache NWR 2018 MF694406 Bosque del - 33.8044978, -106.8932092 2009 16S Ebbs et al. Apache NWR 2018 MF694416 Bosque del - 33.8044978, -106.8932092 2009 16S Ebbs et al. Apache NWR 2018 MF694463 Bosque del - 33.8044978, -106.8932092 2008 COI Ebbs et al. Apache NWR 2018 MF694412 Bosque del - 33.8044978, -106.8932092 2008 16S Ebbs et al. Apache NWR 2018 MF694462 Bosque del - 33.8044978, -106.8932092 2008 COI Ebbs et al. Apache NWR 2018

46

MF694422 Bosque del - 33.8044978, -106.8932092 2008 16S Ebbs et al. Apache NWR 2018 MF694470 Bosque del - 33.8044978, -106.8932092 2012 COI Ebbs et al. Apache NWR 2018 GU247996 Bosque del - 33.8044978, -106.8932092 2009 COI Devkota et Apache NWR al. 2009 GU247995 Bosque del - 33.8044978, -106.8932092 2009 COI Devkota et Apache NWR al. 2009 GU247993 Bosque del - 33.8044978, -106.8932092 2009 COI Devkota et Apache NWR al. 2009 MF694465 Bosque del - 33.8044978, -106.8932092 2012 COI Ebbs et al. Apache NWR 2018 MF694466 Bosque del - 33.8044978, -106.8932092 2012 COI Ebbs et al. Apache NWR 2018 MF694404 Bosque del - 33.8044978, -106.8932092 2012 16S Ebbs et al. Apache NWR 2018 MF694415 Charette - 36.200339, -104.863894 2005 16S Ebbs et al. Lakes, 2018 Southwest of Springer MF694411 Charette - 36.200339, -104.863894 2005 16S Ebbs et al. Lakes, 2018 Southwest of Springer MF694418 Bitter Lake - 33.4560573, -104.4038836 - 16S Ebbs et al. NWR 2018 NC_023253 Bitter Lake - 33.4560573, -104.4038836 - COI Ebbs et al. NWR 2018 JQ390526 Bitter Lake - 33.4560573, -104.4038836 - COI Ebbs et al. NWR 2018 MF694421 Bitter Lake - 33.698314, -106.987490 - 16S Ebbs et al. NWR 2018 MF694461 Rio Grande - 35.1310354, -106.6847939 2008 COI Ebbs et al. Nature Center, 2018 Albuquerque MF694423 Rio Grande - 35.1310354, -106.6847939 2008 16S Ebbs et al. Nature Center, 2018 Albuquerque MF694420 Stubblefield - 36.5771811, -104.6719177 2008 16S Ebbs et al. Resivior, 2018 Maxwell NWR MF694436 Carson Nat. - 36.5114158, -106.0190758 2012 COI Ebbs et al. Forest, La 2018 Sombra Campground MF694437 Carson Nat. - 36.5114158, -106.0190758 2012 COI Ebbs et al. Forest, La 2018 Sombra Campground MF694438 Carson Nat. - 36.5114158, -106.0190758 2012 COI Ebbs et al. Forest, La 2018 Sombra Campground

47

MF694469 Monzano - 34.7140441, -106.4142393 2012 COI Ebbs et al. Mtns. NM 2018 EU038389 Netherlands - 52.2093396, 4.155728 - COI Wethington & Lydeard 2007 EU038342 Netherlands - 52.2093396, 4.155728 - 16S Wethington & Lydeard 2007 EU038356 Canada, - 43.0830438, -79.0802524 - COI Wethington Niagra River & Lydeard 2007 EU038309 Canada, - 43.0830438, -79.0802524 - 16S Wethington Niagra River & Lydeard 2007 EU038357 Canada, - 43.0830438, -79.0802524 - COI Wethington Niagra River & Lydeard 2007 EU038310 Canada, - 43.0830438, -79.0802524 - 16S Wethington Niagra River & Lydeard 2007 KM612022 Point Pelee NP - 41.9633942, -82.5205965 2010 COI Dewaard et al 2014 KM611969 Point Pelee NP - 41.9633942, -82.5205965 2010 COI Dewaard et al 2014 EU038361 Ontario - 43.0830438, -79.0802524 - COI Wethington & Lydeard 2007 EU038314 Ontario - 43.0830438, -79.0802524 - 16S Wethington & Lydeard 2007 AY651192 Philadelphia, - 40.0048631, -75.1882441 - COI Wethington Pennsylvania & Guralnick 2004 AY651230 Philadelphia, - 40.0048631, -75.1882441 - 16S Wethington Pennsylvania & Guralnick 2004 AY651193 Philadelphia, - 40.0048631, -75.1882441 - COI Wethington Pennsylvania & Guralnick 2004 AY651231 Philadelphia, - 40.0048631, -75.1882441 - 16S Wethington Pennsylvania & Guralnick 2004 AY651203 Big Horn - 40.0048631, -75.1882441 - COI Wethington River near & Lower Kane Guralnick Cave - WY 2004 AY651241 Big Horn - 40.0048631, -75.1882441 - 16S Wethington River near & Lower Kane Guralnick Cave - WY 2004

48

KP182986 Jurong Central - 1.3381482,103.7056151 2014 COI Ng et al. - Singapore 2015 KP182983 CleanTech - 1.3554556,103.6906506 2014 COI Ng et al. Park - 2015 Singapore KP182985 Perak - - 4.8030231,99.9325246 2014 COI Ng et al. Maylasia 2015 KP182984 Negeri - 2.8410847,101.9216455 2014 COI Ng et al. Sembilan - 2015 Maylasia KM067686 Thailand - 13.7248944, 99.5121514 2014 COI Bolotov et al. 2014 KF966541 South Korea - 36.79127,126.999976 2013 COI Park and Oh 2013 KJ769126 Stillwater, - 36.141072,-97.1526456 2013 COI Gustafson Oklahoma et al. 2014 KJ769127 Stillwater, - 36.141072,-97.1526456 2013 COI Gustafson Oklahoma et al. 2014 KJ769128 Stillwater, - 36.141072,-97.1526456 2013 COI Gustafson Oklahoma et al. 2014 KJ769123 Stillwater, - 36.141072,-97.1526456 2013 COI Gustafson Oklahoma et al. 2014 KJ769124 Stillwater, - 36.141072,-97.1526456 2013 COI Gustafson Oklahoma et al. 2014 JX680971 North Avoca, - -33.4560759,151.427242 2011 COI Colgan and Australia de Costa 2012 JX680972 North Avoca, - -33.4560759,151.427242 2011 COI Colgan and Australia de Costa 2012 GQ415039 Charles - 32.8103123,-79.9951763 - COI Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415016 Charles - 32.8103123,-79.9951763 - 16S Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415033 Charles - 32.8103123,-79.9951763 - COI Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415012 Charles - 32.8103123,-79.9951763 - 16S Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415036 Charles - 32.8103123,-79.9951763 - COI Wethington Towne & Landing State Guralnick Park, South 2004 Carolina

49

GQ415035 Charles - 32.8103123,-79.9951763 - COI Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415014 Charles - 32.8103123,-79.9951763 - 16S Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415034 Charles - 32.8103123,-79.9951763 - COI Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415013 Charles - 32.8103123,-79.9951763 - 16S Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415037 Charles - 32.8103123,-79.9951763 - COI Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415015 Charles - 32.8103123,-79.9951763 - 16S Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415038 Charles - 32.8103123,-79.9951763 - COI Wethington Towne & Landing State Guralnick Park, South 2004 Carolina GQ415009 Charles - 32.8103123,-79.9951763 - 16S Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415010 Charles - 32.8103123,-79.9951763 - 16S Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415011 Charles - 32.8103123,-79.9951763 - 16S Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415017 Charles - 32.8103123,-79.9951763 - 16S Wethington Towne et al. 2009 Landing State Park, South Carolina

50

GQ415018 Charles - 32.8103123,-79.9951763 - 16S Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415019 Charles - 32.8103123,-79.9951763 - 16S Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415020 Charles - 32.8103123,-79.9951763 - 16S Wethington Towne et al. 2009 Landing State Park, South Carolina GQ415021 Charles - 32.8103123,-79.9951763 - 16S Wethington Towne et al. 2009 Landing State Park, South Carolina KM206699 Babil, Al- - 32.6547996,43.9701469 2012 COI Al-Bdairi et Wardia, Iraq al. 2014 KM206698 Missan, Qilat - 31.4573931,47.369734 2012 COI Al-Bdairi et Salih, Iraq al. 2015 KT280445 Kahnooj, Iran - 27.9793487,57.6657535 2014 COI Nasibi et al. 2015 KT280444 Bam, Iran - 29.1023289,58.3112664 2014 COI Nasibi et al. 2015 KT280443 Gilan, Iran - 37.525346,48.4380484 2015 COI Nasibi et al. 2015 KT280442 Jiroft, Iran - 28.6919628,57.6818485 2013 COI Nasibi et al. 2015 KT280441 Kerman, Iran - 36.7221827,-120.0806515 2014 COI Nasibi et al. 2015 KT280440 Baft, Iran - 29.2308726,56.5791625 2013 COI Nasibi et al. 2015 KT280439 Rafsanjan, - 30.367196,55.9211545 2014 COI Nasibi et al. Iran 2015 KT280438 Bam, Iran - 29.1023289,58.3112664 2013 COI Nasibi et al. 2015 KT280437 Golbaf, Iran - 29.8849282,57.7144431 2014 COI Nasibi et al. 2015 KT280436 Bardsir, Iran - 29.8153853,56.277431 2014 COI Nasibi et al. 2015 KT280435 Kerman, Iran - 36.7221827,-120.0806515 2014 COI Nasibi et al. 2015 KT280434 Kerman, Iran - 36.7221827,-120.0806515 2013 COI Nasibi et al. 2015 KT280433 Jiroft, Iran - 28.6919628,57.6818485 2013 COI Nasibi et al. 2015 KX108852 Parque - -33.2806, -70.393831 - COI Collado O’Higgins 2016 Spring, Maipo River basin, Chile

51

KX108851 Consuelo - -31.464861, -70.573733 - COI Collado stream, 2016 Choapa River basin, Chile KX108850 Consuelo - -31.464861, -70.573733 - COI Collado stream, 2016 Choapa River basin, Chile KX108849 Choapa River, - -31.464861, -70.573733 - COI Collado Chile 2016 KX108848 Choapa River, - -31.464861, -70.573733 - COI Collado Chile 2016 KX108847 El Salto, - -33.2806, -70.393831 - COI Collado Marga Marga 2016 River basin, Chile KX108846 El Salto, - -33.2806, -70.393831 - COI Collado Marga Marga 2016 River basin, Chile KX108845 El Salto, - -33.2806, -70.393831 - COI Collado Marga Marga 2016 River basin, Chile KX108844 El Salto, - -33.2806, -70.393831 - COI Collado Marga Marga 2016 River basin, Chile

52

Appendix 2: Locality information, NCBI Genbank accession numbers and associated scientific studies (Helisoma cf. trivolvis)

Specimen ID/Genbank Date number Locality Watershed Lat/Long Collected Gene Citation UCM48193 California San Francisco 37.94566, 8/9/2017 16S This study (BNPND002) -122.133961 UCM48191 California San Francisco 37.94566, 8/9/2017 16S This study (BNPND002) -122.133961 UCM48195 California San Francisco 37.94566, 8/9/2017 16S This study (BNPND002) -122.133961 UCM48179 California San Francisco 37.9435248, 8/9/2017 16S This study (CA -BN004) -122.1410001 UCM48177 California San Francisco 37.9435248, 8/9/2017 16S This study (CA -BN004) -122.1410001 UCM48171 California Sacramento 40.50534821, 8/16/2017 16S This study (CA -LVBW1) -121.4378281 UCM48289 California Sacramento 40.50534821, 7/23/2018 16S This study (CA -LVBW1) -121.4378281 UCM48182 California (MUDLG) San Francisco 37.9605, 8/7/2017 16S This study -122.22242 UCM48332 California San Francisco 37.34675, 6/30/2018 16S This study (NORTHGCP) -121.68752 UCM48148 California (PRNTH1) San Francisco 37.670918, 8/12/2017 16S This study -121.956277 UCM48149 California (PRNTH1) San Francisco 37.670918, 8/12/2017 16S This study -121.956277 UCM48152 California (PRNTH1) San Francisco 37.670918, 8/12/2017 16S This study -121.956277 UCM48151 California (PRNTH1) San Francisco 37.670918, 8/12/2017 16S This study -121.956277 UCM48153 California San Francisco 37.65212, 8/2/2017 16S This study (PRNTHIDK) -121.95194 UCM48154 California San Francisco 37.65212, 8/2/2017 16S This study (PRNTHIDK) -121.95194 UCM48155 California San Francisco 37.65212, 8/2/2017 16S This study (PRNTHIDK) -121.95194 UCM48185 California (SF26) San Francisco 37.27889, 7/29/2017 16S This study -121.69814 UCM48187 California (SF26) San Francisco 37.27889, 7/29/2017 16S This study -121.69814 UCM48190 California (SF26) San Francisco 37.27889, 7/29/2017 16S This study -121.69814 UCM48186 California (SF26) San Francisco 37.27889, 7/29/2017 16S This study -121.69814 UCM48189 California (SF26) San Francisco 37.27889, 7/29/2017 16S This study -121.69814 UCM48202 California (SF34) San Francisco 37.30361, 7/25/2017 16S This study -121.69577 UCM48200 California (SF34) San Francisco 37.30361, 7/25/2017 16S This study -121.69577 UCM48206 California San Francisco 37.63934572, 8/1/2017 16S This study (WESTWING) -121.9419164

53

UCM48207 California San Francisco 37.63934572, 8/1/2017 16S This study (WESTWING) -121.9419164 UCM48208 California San Francisco 37.63934572, 8/1/2017 16S This study (WESTWING) -121.9419164 UCM48172 Oregon Willamette 44.101023, 8/6/2017 16S This study (BOWERMAN) -123.017077 UCM48308 Oregon (OR -FANNO) Willamette 45.467163, 7/22/2018 16S This study -122.790306 UCM48317 Oregon (OR -THNPE) Willamette 45.50194, 6/18/2018 16S This study -122.837872 UCM48315 Oregon (OR -THNPW) Willamette 45.501323, 6/18/2018 16S This study -122.837844 UCM48318 Oregon (OR -THNPW) Willamette 45.501323, 6/18/2018 16S This study -122.837844 UCM48312 Oregon (OR -THNPW) Willamette 45.501323, 6/18/2018 16S This study -122.837844 UCM48311 Oregon (OR -TOMP) Willamette 42.4287, 7/22/2018 16S This study -123.2816 UCM48323 Oregon (Private Middle 44.654356, 18 -Aug 16S This study stock/irrigation pond Columbia -120.250064 adjacent to entrance at Painted Hills Monument): OR2 UCM48330 Oregon (Sunriver Middle 43.883344, 18 -Aug 16S This study Ditch, Nature Center Columbia -121.433561 Ditch): OR1 UCM48168 Washington (WA - Lower 45.7404444, 8/21/2017 16S This study SWAMP) Columbia -122.677611 UCM48167 Washington (WA - Lower 45.7404444, 8/21/2017 16S This study SWAMP) Columbia -122.677611 UCM48192 California San Francisco 37.94566, 8/9/2017 16S This study (BNPND002) -122.133961 UCM48192 California San Francisco 37.94566, 8/9/2017 COI This study (BNPND002) -122.133961 UCM48178 California (CA - San Francisco 37.9435248, 8/9/2017 16S This study BN004) -122.1410001 UCM48178 California (CA - San Francisco 37.9435248, 8/9/2017 COI This study BN004) -122.1410001 UCM48176 California (CA - San Francisco 37.9435248, 8/9/2017 COI This study BN004) -122.1410001 UCM48303 California (CA - San Francisco 37.939884, 7/18/2018 COI This study BN018) -122.167815 UCM48303 California (CA - San Francisco 37.939884, 7/18/2018 16S This study BN018) -122.167815 UCM48307 California (MUD41) San Francisco 37.940023, 7/2/2018 COI This study -122.236489 UCM48307 California (MUD41) San Francisco 37.940023, 7/2/2018 16S This study -122.236489 UCM48183 California (MUDLG) San Francisco 37.9605, 8/7/2017 COI This study -122.22242 UCM48183 California (MUDLG) San Francisco 37.9605, 8/7/2017 16S This study -122.22242 UCM48181 California (MUDLG) San Francisco 37.9605, 8/7/2017 16S This study -122.22242 UCM48181 California (MUDLG) San Francisco 37.9605, 8/7/2017 COI This study

54

-122.22242 UCM48147 California (PRNTH1) San Francisco 37.670918, 8/12/2017 16S This study -121.956277 UCM48147 California (PRNTH1) San Francisco 37.670918, 8/12/2017 COI This study -121.956277 UCM48150 California (PRNTH1) San Francisco 37.670918, 8/12/2017 16S This study -121.956277 UCM48150 California (PRNTH1) San Francisco 37.670918, 8/12/2017 COI This study -121.956277 UCM48157 California San Francisco 37.65212, 8/2/2017 16S This study (PRNTHIDK) -121.95194 UCM48157 California San Francisco 37.65212, 8/2/2017 COI This study (PRNTHIDK) -121.95194 UCM48159 California San Francisco 37.65212, 8/2/2017 16S This study (PRNTHIDK) -121.95194 UCM48159 California San Francisco 37.65212, 8/2/2017 COI This study (PRNTHIDK) -121.95194 UCM48158 California San Francisco 37.65212, 8/2/2017 COI This study (PRNTHIDK) -121.95194 UCM48156 California San Francisco 37.65212, 8/2/2017 16S This study (PRNTHIDK) -121.95194 UCM48156 California San Francisco 37.65212, 8/2/2017 COI This study (PRNTHIDK) -121.95194 UCM48184 California (SF26) San Francisco 37.27889, 7/29/2017 16S This study -121.69814 UCM48184 California (SF26) San Francisco 37.27889, 7/29/2017 COI This study -121.69814 UCM48188 California (SF26) San Francisco 37.27889, 7/29/2017 16S This study -121.69814 UCM48188 California (SF26) San Francisco 37.27889, 7/29/2017 COI This study -121.69814 UCM48204 California (SF34) San Francisco 37.30361, 7/25/2017 16S This study -121.69577 UCM48188 California (SF26) San Francisco 37.27889, 7/29/2017 COI This study -121.69814 UCM48203 California (SF34) San Francisco 37.30361, 7/25/2017 16S This study -121.69577 UCM48203 California (SF34) San Francisco 37.30361, 7/25/2017 COI This study -121.69577 UCM48198 California (SF34) San Francisco 37.30361, 7/25/2017 16S This study -121.69577 UCM48198 California (SF34) San Francisco 37.30361, 7/25/2017 COI This study -121.69577 UCM48201 California (SF34) San Francisco 37.30361, 7/25/2017 16S This study -121.69577 UCM48201 California (SF34) San Francisco 37.30361, 7/25/2017 COI This study -121.69577 UCM48199 California (SF34) San Francisco 37.30361, 7/25/2017 COI This study -121.69577 UCM48305 California (YBBA) San Francisco 37.335943, 6/30/2018 16S This study -121.689529 UCM48305 California (YBBA) San Francisco 37.335943, 6/30/2018 COI This study -121.689529 UCM48196 Oregon (OR -FLOR) Willamette 45.456098, 5/19/2017 16S This study -122.75353

55

UCM48196 Oregon (OR -FLOR) Willamette 45.456098, 5/19/2017 COI This study -122.75353 UCM48197 Oregon (OR -FLOR) Willamette 45.456098, 5/20/2017 16S This study -122.75353 UCM48197 Oregon (OR -FLOR) Willamette 45.456098, 5/20/2017 COI This study -122.75353 HQ660034 California - - - COI Dayrat et al. 2011 KM612145 Canada, Point Pelee NP - 41.968, - 6/27/2010 COI Barcoding 82.531 Canada Data Release KM612048 Canada, Point Pelee NP - 41.968, - 6/27/2010 COI Barcoding 82.531 Canada Data Release KM611952 Canada, Point Pelee NP - 41.968, - 6/27/2010 COI Barcoding 82.531 Canada Data Release KM611923 Canada, Point Pelee NP - 41.968, - 6/27/2010 COI Barcoding 82.531 Canada Data Release KM611836 Canada, Point Pelee NP - 41.968, - 6/27/2010 COI Barcoding 82.531 Canada Data Release MG514686 Canada, Point Pelee NP - 41.968, - 6/27/2010 COI Barcoding 82.531 Canada Data Release KM612043 Canada, Point Pelee NP - 41.968, - 6/27/2010 COI Barcoding 82.531 Canada Data Release MF544974 Canada, Rouge Park - 43.8444, - 9/15/2013 COI Canadian 79.197 National Parks Data Release MF544141 Canada, Rouge Park - 43.8444, - 9/15/2013 COI Canadian 79.197 National Parks Data Release KM612167 Canada: Ontario, - 48.589, 86.275 6/30/2008 COI Barcoding Pukaskwa NP, Hattie Canada Data Cove Release KM612028 Canada: Saskatchewan, - 53.906, 106.03 6/20/2010 COI Barcoding Prince Albert NP, Canada Data Boundary Bog Trail Release KM612007 Canada: Alberta, - 49.11, 113.84 7/26/2008 COI Barcoding Waterton Lakes NP, Canada Data Maskinonge Lake Release KM611993 Canada: Alberta, Banff - 51.178, 7/31/2008 COI Barcoding NP, Vermillion 115.606 Canada Data Lakes/Bow River Release KM611980 Canada: Alberta, Banff - 51.178, 7/31/2008 COI Barcoding NP, Vermillion 115.606 Canada Data Lakes/Bow River Release KM611909 Canada: Ontario, - 48.589, 86.275 6/30/2008 COI Barcoding Pukaskwa NP, Hattie Canada Data Cove Release

56

KM611834 Canada: Alberta, Banff - 51.178, 7/31/2008 COI Barcoding NP, Vermillion 115.606 Canada Data Lakes/Bow River Release MG422921 Canada: Ontario, - 43.9807, 5/24/2014 COI BIOUG Happy Valley 79.593 Archive GGBN Data Release MH087675 USA: Maryland, Anne - 38.888, 76.554 9/29/2015 COI Chesapeake Arundel County, Bay Barcode SERC, Mathias Initiative: Wetland Invertebrates FY14 MH087626 USA: Maryland, Anne - 38.888, 76.554 9/29/2015 COI Chesapeake Arundel County, Bay Barcode SERC, Mathias Initiative: Wetland Invertebrates FY14 MH087568 USA: Maryland, Anne - 38.888, 76.554 9/29/2015 COI Chesapeake Arundel County, Bay Barcode SERC, Mathias Initiative: Wetland Invertebrates FY14 AY227371 Canada - - - COI Remigio & Hebert 2003 KP101090 USA: Albuquerque, - 35.216, 106.6 8/20/2014 COI Adema 2014 NM MF544894 Canada: Alberta, Jasper - 53.2, 117.913 6/12/2012 COI Canadian NP, Range Road 275a, National wetland Parks Data Release HQ926924 Canada - 58.6631, 8/9/2010 COI International 94.1662 Barcode of Life (iBOL)

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